From dd0496068e4d57601f27a880712cc6ae5c9845e3 Mon Sep 17 00:00:00 2001 From: "Birmiwal, Rahul R" Date: Fri, 12 Apr 2024 21:30:53 -0700 Subject: [PATCH 1/6] adjoint method update to torchsde + lighting (vis in progress) --- examples/SDEs/HistoricalData_SPX.csv | 2534 +++++++++++++++++ examples/SDEs/README.md | 7 + examples/SDEs/basic_sde.ipynb | 828 ++++++ examples/SDEs/epoch=1-step=2.ckpt | Bin 0 -> 446866 bytes examples/SDEs/epoch=19-step=20.ckpt | Bin 0 -> 446866 bytes examples/SDEs/epoch=2-step=3.ckpt | Bin 0 -> 446866 bytes examples/SDEs/latent_sde_lorenz_system.ipynb | 537 ++++ .../SDEs/latent_sde_stock_forecasting.ipynb | 668 +++++ .../lightning_logs/version_0/hparams.yaml | 1 + .../lightning_logs/version_1/hparams.yaml | 1 + .../lightning_logs/version_2/hparams.yaml | 1 + .../lightning_logs/version_3/hparams.yaml | 1 + .../SDEs/lightning_logs/version_3/metrics.csv | 11 + .../lightning_logs/version_4/hparams.yaml | 1 + .../SDEs/lightning_logs/version_4/metrics.csv | 21 + .../lightning_logs/version_5/hparams.yaml | 1 + .../SDEs/lightning_logs/version_5/metrics.csv | 3 + examples/SDEs/sde_test.py | 0 src/neuromancer/dynamics/integrators.py | 58 + src/neuromancer/modules/blocks.py | 253 +- 20 files changed, 4908 insertions(+), 18 deletions(-) create mode 100644 examples/SDEs/HistoricalData_SPX.csv create mode 100644 examples/SDEs/README.md create mode 100644 examples/SDEs/basic_sde.ipynb create mode 100644 examples/SDEs/epoch=1-step=2.ckpt create mode 100644 examples/SDEs/epoch=19-step=20.ckpt create mode 100644 examples/SDEs/epoch=2-step=3.ckpt create mode 100644 examples/SDEs/latent_sde_lorenz_system.ipynb create mode 100644 examples/SDEs/latent_sde_stock_forecasting.ipynb create mode 100644 examples/SDEs/lightning_logs/version_0/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_1/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_2/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_3/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_3/metrics.csv create mode 100644 examples/SDEs/lightning_logs/version_4/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_4/metrics.csv create mode 100644 examples/SDEs/lightning_logs/version_5/hparams.yaml create mode 100644 examples/SDEs/lightning_logs/version_5/metrics.csv delete mode 100644 examples/SDEs/sde_test.py diff --git a/examples/SDEs/HistoricalData_SPX.csv b/examples/SDEs/HistoricalData_SPX.csv new file mode 100644 index 00000000..955a754a --- /dev/null +++ b/examples/SDEs/HistoricalData_SPX.csv @@ -0,0 +1,2534 @@ +Date,Close/Last,Open,High,Low +02/29/2024,5096.27,5085.36,5104.99,5061.89 +02/28/2024,5069.76,5067.20,5077.37,5058.35 +02/27/2024,5078.18,5074.60,5080.69,5057.29 +02/26/2024,5069.53,5093.00,5097.66,5068.91 +02/23/2024,5088.80,5100.92,5111.06,5081.46 +02/22/2024,5087.03,5038.83,5094.39,5038.83 +02/21/2024,4981.80,4963.03,4983.21,4946.00 +02/20/2024,4975.51,4989.32,4993.71,4955.02 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Neuromancer. We achieve this technically by integrating Neuromancer framework to work with the TorchSDE library which is a library for SDE solvers (i.e. integrators). The examples in this notebook are: + +1. Basic_SDE: This is the first case of SDE problem -- where the user knows explicitly the drift and diffusion processes of the stochastic differential equation. If these are known, we can perform integration (e.g. Euler-Murayama integration which is a special case of Euler integration for stochastic case ) to compute output data at future time steps. This is similar to the torch DiffEqIntegrator examples in our library. Note that this example is based off https://github.com/google-research/torchsde/blob/master/examples/demo.ipynb +2. Latent_SDE_Lorenz_System. This is the second case of SDE problem -- where the user does *not* know the drift/diffusion terms and instead seeks to learn these processes. This is the System ID equivalent for the stochastic case. The output of this notebook will be a LatentSDE neural network that can generate new time-series samples that exhibit the stochastic behavior of the original input data. +3. Latent_SDE_Stock_Forecasting: This is the same as the above lorenz system notebook but applied to a different use case. That financial markets often exhibit stochastic/brownian motion behavior over certain intervals is a core tenet in quantitative finance. Here we attempt to learn underlying stochastic process of the S&P 500 index. *Note that this notebook is still in development as there is an exploding gradient issue, but the core outline is correct* \ No newline at end of file diff --git a/examples/SDEs/basic_sde.ipynb b/examples/SDEs/basic_sde.ipynb new file mode 100644 index 00000000..9bb1040c --- /dev/null +++ b/examples/SDEs/basic_sde.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TorchSDE + Neuromancer: Basic (Explicit) Integration of Stochastic Differential Equations\n", + "\n", + "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/demo.ipynb\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "If necessary, install torchsde library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install torchsde" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "import matplotlib.pyplot as plt\n", + "from torch.utils.data import DataLoader\n", + "\n", + "from neuromancer.system import Node, System\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.loggers import BasicLogger\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "\n", + "torch.manual_seed(0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset: \n", + "Below we generate some data. We assume the process has a state size of 1, batch size of 5 and 100 timesteps. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size, state_size, t_size = 5, 1, 100\n", + "\n", + "ts = torch.linspace(0, 1, t_size)\n", + "y0 = torch.full(size=(batch_size, state_size), fill_value=0.1)\n", + "y0.shape\n", + "my_data = {'y': y0, 't':ts}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Strongly recommended to refer to TorchSDE documentation. For the basic/explicit SDE case, TorchSDE requires user to define the drift (f) and diffusion (g) functions. We define an example f and g below: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def f(t, y):\n", + " return torch.sin(t) + 0.1 * y\n", + " \n", + "def g(t, y):\n", + " return 0.3 * torch.sigmoid(torch.cos(t) * torch.exp(-y))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neuromancer Integration: \n", + "Now define Neuromancer variables and components" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "t = variable('t')\n", + "y = variable('y')\n", + "block = blocks.BasicSDE(f, g, t, y) #We use this block for the basic/explicit SDE case where f and g are defined \n", + "integrator = integrators.BasicSDEIntegrator(block) #instantiate integrator for the basic/explicit case \n", + "model = Node(integrator, input_keys=['y','t'], output_keys=['ys']) #define Neuromancer Node to wrap integrator. Output of the " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ys': tensor([[[ 0.1000],\n", + " [ 0.1000],\n", + " [ 0.1000],\n", + " [ 0.1000],\n", + " [ 0.1000]],\n", + " \n", + " [[ 0.1080],\n", + " [ 0.1097],\n", + " [ 0.1213],\n", + " [ 0.1378],\n", + " [ 0.0652]],\n", + " \n", + " [[ 0.1081],\n", + " [ 0.1088],\n", + " [ 0.1044],\n", + " [ 0.1320],\n", + " [ 0.0313]],\n", + " \n", + " [[ 0.0973],\n", + " [ 0.1383],\n", + " [ 0.0928],\n", + " [ 0.1080],\n", + " [ 0.0450]],\n", + " \n", + " [[ 0.0552],\n", + " [ 0.1100],\n", + " [ 0.0815],\n", + " [ 0.0922],\n", + " [ 0.0247]],\n", + " \n", + " [[ 0.0356],\n", + " [ 0.0934],\n", + " [ 0.1135],\n", + " [ 0.0769],\n", + " [ 0.0626]],\n", + " \n", + " [[ 0.0318],\n", + " [ 0.0799],\n", + " [ 0.0993],\n", + " [ 0.0476],\n", + " [ 0.0710]],\n", + " \n", + " [[ 0.0126],\n", + " [ 0.0761],\n", + " [ 0.1008],\n", + " [ 0.0193],\n", + " [ 0.0683]],\n", + " 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0.6805],\n", + " [ 0.4233]],\n", + " \n", + " [[ 0.4467],\n", + " [ 0.8226],\n", + " [ 0.5802],\n", + " [ 0.6768],\n", + " [ 0.4196]],\n", + " \n", + " [[ 0.4647],\n", + " [ 0.8051],\n", + " [ 0.5922],\n", + " [ 0.6889],\n", + " [ 0.4088]],\n", + " \n", + " [[ 0.4688],\n", + " [ 0.8008],\n", + " [ 0.6010],\n", + " [ 0.6935],\n", + " [ 0.4659]],\n", + " \n", + " [[ 0.4703],\n", + " [ 0.8253],\n", + " [ 0.6151],\n", + " [ 0.7034],\n", + " [ 0.4780]],\n", + " \n", + " [[ 0.4930],\n", + " [ 0.8461],\n", + " [ 0.5914],\n", + " [ 0.7034],\n", + " [ 0.4777]],\n", + " \n", + " [[ 0.4653],\n", + " [ 0.8341],\n", + " [ 0.6041],\n", + " [ 0.7258],\n", + " [ 0.4975]],\n", + " \n", + " [[ 0.4510],\n", + " [ 0.8403],\n", + " [ 0.6094],\n", + " [ 0.7492],\n", + " [ 0.4904]]])}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output = model(my_data)\n", + "output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization \n", + "Visualize ys which represents ts number of samples from a stochasic process parameterized by f and g for the provided batch_size" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting helper function\n", + "def plot(ts, samples, xlabel, ylabel, title=''):\n", + " ts = ts.cpu()\n", + " samples = samples.squeeze().t().cpu()\n", + " plt.figure()\n", + " for i, sample in enumerate(samples):\n", + " plt.plot(ts, sample, marker='x', label=f'sample {i}')\n", + " plt.title(title)\n", + " plt.xlabel(xlabel)\n", + " plt.ylabel(ylabel)\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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Stochastic Process)\n", + "\n", + "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "import matplotlib.pyplot as plt\n", + "from torch.utils.data import DataLoader\n", + "\n", + "from neuromancer.system import Node\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer, LitTrainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.loggers import BasicLogger\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "\n", + "torch.manual_seed(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import os\n", + "from typing import Sequence\n", + "\n", + "import matplotlib.gridspec as gridspec\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import tqdm\n", + "from torch import nn\n", + "from torch import optim\n", + "from torch.distributions import Normal\n", + "\n", + "import torchsde" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions to generate data from a Lorenz attractor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class LinearScheduler(object):\n", + " def __init__(self, iters, maxval=1.0):\n", + " self._iters = max(1, iters)\n", + " self._val = maxval / self._iters\n", + " self._maxval = maxval\n", + "\n", + " def step(self):\n", + " self._val = min(self._maxval, self._val + self._maxval / self._iters)\n", + "\n", + " @property\n", + " def val(self):\n", + " return self._val\n", + "\n", + "\n", + "class StochasticLorenz(object):\n", + " \"\"\"Stochastic Lorenz attractor.\n", + "\n", + " Used for simulating ground truth and obtaining noisy data.\n", + " Details described in Section 7.2 https://arxiv.org/pdf/2001.01328.pdf\n", + " Default a, b from https://openreview.net/pdf?id=HkzRQhR9YX\n", + " \"\"\"\n", + " noise_type = \"diagonal\"\n", + " sde_type = \"ito\"\n", + "\n", + " def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)):\n", + " super(StochasticLorenz, self).__init__()\n", + " self.a = a\n", + " self.b = b\n", + "\n", + " def f(self, t, y):\n", + " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", + " a1, a2, a3 = self.a\n", + "\n", + " f1 = a1 * (x2 - x1)\n", + " f2 = a2 * x1 - x2 - x1 * x3\n", + " f3 = x1 * x2 - a3 * x3\n", + " return torch.cat([f1, f2, f3], dim=1)\n", + "\n", + " def g(self, t, y):\n", + " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", + " b1, b2, b3 = self.b\n", + "\n", + " g1 = x1 * b1\n", + " g2 = x2 * b2\n", + " g3 = x3 * b3\n", + " return torch.cat([g1, g2, g3], dim=1)\n", + "\n", + " @torch.no_grad()\n", + " def sample(self, x0, ts, noise_std, normalize):\n", + " \"\"\"Sample data for training. Store data normalization constants if necessary.\"\"\"\n", + " xs = torchsde.sdeint(self, x0, ts)\n", + " if normalize:\n", + " mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1))\n", + " xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std)\n", + " return xs\n", + "\n", + "\n", + "\n", + "\n", + "def vis(xs, ts, latent_sde, bm_vis, img_path, num_samples=10):\n", + " fig = plt.figure(figsize=(20, 9))\n", + " gs = gridspec.GridSpec(1, 2)\n", + " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", + " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", + "\n", + " # Left plot: data.\n", + " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", + " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax00.set_yticklabels([])\n", + " ax00.set_xticklabels([])\n", + " ax00.set_zticklabels([])\n", + " ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax00.set_title('Data', fontsize=20)\n", + " xlim = ax00.get_xlim()\n", + " ylim = ax00.get_ylim()\n", + " zlim = ax00.get_zlim()\n", + "\n", + " # Right plot: samples from learned model.\n", + " xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", + " z1, z2, z3 = np.split(xs, indices_or_sections=3, axis=-1)\n", + "\n", + " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax01.set_yticklabels([])\n", + " ax01.set_xticklabels([])\n", + " ax01.set_zticklabels([])\n", + " ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax01.set_title('Samples', fontsize=20)\n", + " ax01.set_xlim(xlim)\n", + " ax01.set_ylim(ylim)\n", + " ax01.set_zlim(zlim)\n", + "\n", + " plt.savefig(img_path)\n", + " plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size=1024\n", + "latent_size=4\n", + "context_size=64\n", + "hidden_size=128\n", + "lr_init=1e-2\n", + "t0=0.\n", + "t1=2.\n", + "lr_gamma=0.997\n", + "num_iters=1\n", + "kl_anneal_iters=1000\n", + "pause_every=50\n", + "noise_std=0.01\n", + "adjoint=False\n", + "train_dir='./dump/lorenz/'\n", + "method=\"euler\"\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neuromancer Integration\n", + "\n", + "Generate the data and create Neuromancer DictDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def make_dataset(t0, t1, batch_size, noise_std):\n", + " _y0 = torch.randn(batch_size, 3)\n", + " ts = torch.linspace(t0, t1, steps=100)\n", + " xs = StochasticLorenz().sample(_y0, ts, noise_std, normalize=True)\n", + " train_data = DictDataset({'xs':xs},name='train')\n", + " dev_data = DictDataset({'xs':xs},name='dev')\n", + " test_data = DictDataset({'xs':xs},name='test')\n", + " return train_data, None, None, batch_size\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define Neuromancer components, variables, and problem to train the LatentSDE. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "device='cpu'\n", + "torch.manual_seed(0)\n", + "batch_size=1024\n", + "latent_size=4\n", + "context_size=64\n", + "hidden_size=128\n", + "lr_init=1e-2\n", + "t0=0.\n", + "t1=2.\n", + "lr_gamma=0.997\n", + "num_iters=1\n", + "kl_anneal_iters=1000\n", + "pause_every=50\n", + "noise_std=0.01\n", + "adjoint=False\n", + "train_dir='./dump/lorenz/'\n", + "method=\"euler\"\n", + "ts = torch.linspace(t0, t1, steps=100)\n", + "\n", + "sde_block_encoder = blocks.LatentSDE_Encoder(3, latent_size, context_size, hidden_size, ts=ts, adjoint=True) \n", + "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=True)\n", + "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", + "sde_block_decoder = blocks.LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", + "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", + "\n", + "xs = variable('xs')\n", + "zs = variable('zs')\n", + "z0 = variable('z0')\n", + "xs_hat = variable('xs_hat')\n", + "\n", + "\n", + "log_ratio = variable('log_ratio')\n", + "qz0_mean = variable('qz0_mean')\n", + "qz0_logstd = variable('qz0_logstd')\n", + "log_pxs = variable('log_pxs')\n", + "sum_term = variable('sum_term')\n", + "\n", + "\n", + "\n", + "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", + "\n", + "\n", + "# aggregate list of objective terms and constraints\n", + "objectives = [loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([model_1, model_2], loss)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the same Brownian motion for visualization.\n", + "bm_vis = torchsde.BrownianInterval(\n", + " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", + "\n", + "def vis(xs, ts, problem, bm_vis, img_path, num_samples=10):\n", + " fig = plt.figure(figsize=(20, 9))\n", + " gs = gridspec.GridSpec(1, 2)\n", + " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", + " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", + "\n", + " # Left plot: data.\n", + " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", + " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax00.set_yticklabels([])\n", + " ax00.set_xticklabels([])\n", + " ax00.set_zticklabels([])\n", + " ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax00.set_title('Data', fontsize=20)\n", + " xlim = ax00.get_xlim()\n", + " ylim = ax00.get_ylim()\n", + " zlim = ax00.get_zlim()\n", + "\n", + " # Right plot: samples from learned model.\n", + " xs = problem\n", + " xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", + " z1, z2, z3 = np.split(xs, indices_or_sections=3, axis=-1)\n", + "\n", + " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax01.set_yticklabels([])\n", + " ax01.set_xticklabels([])\n", + " ax01.set_zticklabels([])\n", + " ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax01.set_title('Samples', fontsize=20)\n", + " ax01.set_xlim(xlim)\n", + " ax01.set_ylim(ylim)\n", + " ax01.set_zlim(zlim)\n", + "\n", + " plt.savefig(img_path)\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neuromancer training the problem to learn the stochastic process" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/SDEs exists and is not empty.\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------\n", + "0 | problem | Problem | 104 K \n", + "------------------------------------\n", + "104 K Trainable params\n", + "0 Non-trainable params\n", + "104 K Total params\n", + "0.420 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USING BATCH SIZE 1024\n", + "USING LEARNING RATE 0.001\n", + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:104: Total length of `DataLoader` across ranks is zero. Please make sure this was your intention.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "import matplotlib.pyplot as plt\n", + "from torch.utils.data import DataLoader\n", + "\n", + "from neuromancer.system import Node\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.loggers import BasicLogger\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "\n", + "torch.manual_seed(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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DateClose/LastOpenHighLow
002/29/20245096.275085.365104.995061.89
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\n", + "
" + ], + "text/plain": [ + " Date Close/Last Open High Low\n", + "0 02/29/2024 5096.27 5085.36 5104.99 5061.89\n", + "1 02/28/2024 5069.76 5067.20 5077.37 5058.35\n", + "2 02/27/2024 5078.18 5074.60 5080.69 5057.29\n", + "3 02/26/2024 5069.53 5093.00 5097.66 5068.91\n", + "4 02/23/2024 5088.80 5100.92 5111.06 5081.46" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd \n", + "df = pd.read_csv('HistoricalData_SPX.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Assuming df is your DataFrame containing the stock data\n", + "# Convert 'Date' column to datetime format\n", + "df['Date'] = pd.to_datetime(df['Date'])\n", + "\n", + "# Plot Close/Last price over time\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(df['Date'][::-1], df['Close/Last'][::-1], marker='o', linestyle='-')\n", + "plt.title('SPX Closing Prices')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Close')\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Assuming your DataFrame is named df\n", + "# Selecting only the numerical columns to standardize\n", + "numerical_columns = ['Close/Last']\n", + "\n", + "# Instantiate the StandardScaler\n", + "scaler = MinMaxScaler()\n", + "\n", + "# Fit and transform the numerical columns\n", + "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateClose/LastOpenHighLow
02024-02-291.0000005085.365104.995061.89
12024-02-280.9919195067.205077.375058.35
22024-02-270.9944865074.605080.695057.29
32024-02-260.9918495093.005097.665068.91
42024-02-230.9977235100.925111.065081.46
..................
25282014-03-060.0186981874.181881.941874.18
25292014-03-050.0177161874.051876.531871.11
25302014-03-040.0177471849.231876.231849.23
25312014-03-030.0091571857.681857.681834.44
25322014-02-280.0133391855.121867.921847.67
\n", + "

2533 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Date Close/Last Open High Low\n", + "0 2024-02-29 1.000000 5085.36 5104.99 5061.89\n", + "1 2024-02-28 0.991919 5067.20 5077.37 5058.35\n", + "2 2024-02-27 0.994486 5074.60 5080.69 5057.29\n", + "3 2024-02-26 0.991849 5093.00 5097.66 5068.91\n", + "4 2024-02-23 0.997723 5100.92 5111.06 5081.46\n", + "... ... ... ... ... ...\n", + "2528 2014-03-06 0.018698 1874.18 1881.94 1874.18\n", + "2529 2014-03-05 0.017716 1874.05 1876.53 1871.11\n", + "2530 2014-03-04 0.017747 1849.23 1876.23 1849.23\n", + "2531 2014-03-03 0.009157 1857.68 1857.68 1834.44\n", + "2532 2014-02-28 0.013339 1855.12 1867.92 1847.67\n", + "\n", + "[2533 rows x 5 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.reset_index()\n", + "ts = (df['index'].values)[::-1]\n", + "ts = ts[::-1]\n", + "ts = torch.tensor(ts, dtype=torch.float32)\n", + "xs = (df['Close/Last'].values)\n", + "xs = torch.tensor(xs, dtype=torch.float32).flip(0)\n", + "\n", + "#xs needs to be of shape [t, batch_size, data_dimensions=1]\n", + "xs = xs.unsqueeze(1).unsqueeze(2)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2533" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts = 0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "device='cpu'\n", + "batch_size = 1\n", + "train_data = DictDataset({'xs':xs},name='train')\n", + "train_data_loader = DataLoader(train_data, batch_size=2533, collate_fn=train_data.collate_fn, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2533, 1, 3])" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "input_dim = 1\n", + "\n", + "latent_size=2\n", + "context_size=4\n", + "hidden_size=4\n", + "lr_init=1e-2\n", + "t0=0.\n", + "t1=2532.\n", + "lr_gamma=0.997\n", + "num_iters=1\n", + "kl_anneal_iters=1000\n", + "pause_every=50\n", + "noise_std=0.01\n", + "adjoint=False\n", + "train_dir='./dump/stock_forecasting/'\n", + "method=\"euler\"\n", + "\n", + "sde_block_encoder = blocks.LatentSDE_Encoder(input_dim, latent_size, context_size, hidden_size, ts=ts) \n", + "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, dt=1.)\n", + "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", + "sde_block_decoder = blocks.LatentSDE_Decoder(input_dim, latent_size, noise_std=noise_std)\n", + "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", + "\n", + "xs = variable('xs')\n", + "zs = variable('zs')\n", + "z0 = variable('z0')\n", + "\n", + "\n", + "log_ratio = variable('log_ratio')\n", + "qz0_mean = variable('qz0_mean')\n", + "qz0_logstd = variable('qz0_logstd')\n", + "log_pxs = variable('log_pxs')\n", + "sum_term = variable('sum_term')\n", + "\n", + "\n", + "\n", + "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", + "\n", + "\n", + "# aggregate list of objective terms and constraints\n", + "objectives = [loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([model_1, model_2], loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'zs': tensor([[[-1.0134e+00, 4.9340e-01]],\n", + " \n", + " [[-1.3344e+00, -1.6515e-01]],\n", + " \n", + " [[-1.9389e+00, -9.6584e-01]],\n", + " \n", + " ...,\n", + " \n", + " [[ 2.3176e+07, -6.6517e+06]],\n", + " \n", + " [[ 2.3289e+07, -6.6841e+06]],\n", + " \n", + " [[ 2.3402e+07, -6.7167e+06]]], grad_fn=),\n", + " 'z0': tensor([[-1.0134, 0.4934]], grad_fn=),\n", + " 'log_ratio': tensor([[2.0297],\n", + " [1.8233],\n", + " [1.5270],\n", + " ...,\n", + " [ nan],\n", + " [ nan],\n", + " [ nan]], grad_fn=),\n", + " 'xs': tensor([[[0.0133]],\n", + " \n", + " [[0.0092]],\n", + " \n", + " [[0.0177]],\n", + " \n", + " ...,\n", + " \n", + " [[0.9945]],\n", + " \n", + " [[0.9919]],\n", + " \n", + " [[1.0000]]]),\n", + " 'qz0_mean': tensor([[-0.0452, 0.4826]], grad_fn=),\n", + " 'qz0_logstd': tensor([[-0.3319, -0.4354]], grad_fn=)}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "baz = next(iter(train_data_loader))\n", + "baz['xs'].shape\n", + "foo = model_1(baz)\n", + "foo" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'zs': tensor([[[0.9840, 0.7725]],\n", + " \n", + " [[2.7798, 1.1453]],\n", + " \n", + " [[4.1100, 1.9832]],\n", + " \n", + " ...,\n", + " \n", + " [[ nan, nan]],\n", + " \n", + " [[ nan, nan]],\n", + " \n", + " [[ nan, nan]]], grad_fn=),\n", + " 'z0': tensor([[0.9840, 0.7725]], grad_fn=),\n", + " 'log_ratio': tensor([[3.6686],\n", + " [5.0823],\n", + " [6.7811],\n", + " ...,\n", + " [ nan],\n", + " [ nan],\n", + " [ nan]], grad_fn=),\n", + " 'xs': tensor([[[-1.2923]],\n", + " \n", + " [[-1.3073]],\n", + " \n", + " [[-1.2766]],\n", + " \n", + " ...,\n", + " \n", + " [[ 2.2042]],\n", + " \n", + " [[ 2.1950]],\n", + " \n", + " [[ 2.2238]]]),\n", + " 'qz0_mean': tensor([[0.0863, 0.4421]], grad_fn=),\n", + " 'qz0_logstd': tensor([[-0.4505, -0.4765]], grad_fn=)}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Expected parameter loc (Tensor of shape (2533, 1, 1)) of distribution Normal(loc: torch.Size([2533, 1, 1]), scale: torch.Size([2533, 1, 1])) to satisfy the constraint Real(), but found invalid values:\ntensor([[[nan]],\n\n [[nan]],\n\n [[nan]],\n\n ...,\n\n [[nan]],\n\n [[nan]],\n\n [[nan]]], grad_fn=)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[36], line 20\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# define neuromancer trainer\u001b[39;00m\n\u001b[1;32m 5\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 6\u001b[0m problem,\n\u001b[1;32m 7\u001b[0m train_data_loader,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 18\u001b[0m test_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_loss\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 19\u001b[0m )\n\u001b[0;32m---> 20\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/trainer.py:105\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 103\u001b[0m t_batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m i\n\u001b[1;32m 104\u001b[0m t_batch \u001b[38;5;241m=\u001b[39m move_batch_to_device(t_batch, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 105\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 107\u001b[0m output[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_metric]\u001b[38;5;241m.\u001b[39mbackward()\n", + "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/problem.py:68\u001b[0m, in \u001b[0;36mProblem.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m---> 68\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(output_dict)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output_dict, torch\u001b[38;5;241m.\u001b[39mTensor):\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/problem.py:76\u001b[0m, in \u001b[0;36mProblem.step\u001b[0;34m(self, input_dict)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_dict: Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnodes:\n\u001b[0;32m---> 76\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output_dict, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[1;32m 78\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m {node\u001b[38;5;241m.\u001b[39mname: output_dict}\n", + "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", + "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/modules/blocks.py:47\u001b[0m, in \u001b[0;36mBlock.forward\u001b[0;34m(self, *inputs)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_eval(x)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/neuromancer/src/neuromancer/modules/blocks.py:899\u001b[0m, in \u001b[0;36mLatentSDE_Decoder.block_eval\u001b[0;34m(self, xs, zs, log_ratio, qz0_mean, qz0_logstd)\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mblock_eval\u001b[39m(\u001b[38;5;28mself\u001b[39m, xs, zs, log_ratio, qz0_mean, qz0_logstd): \n\u001b[1;32m 898\u001b[0m _xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprojector(zs)\n\u001b[0;32m--> 899\u001b[0m xs_dist \u001b[38;5;241m=\u001b[39m \u001b[43mNormal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_xs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnoise_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 900\u001b[0m log_pxs \u001b[38;5;241m=\u001b[39m xs_dist\u001b[38;5;241m.\u001b[39mlog_prob(xs)\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m))\u001b[38;5;241m.\u001b[39mmean(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 902\u001b[0m qz0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdistributions\u001b[38;5;241m.\u001b[39mNormal(loc\u001b[38;5;241m=\u001b[39mqz0_mean, scale\u001b[38;5;241m=\u001b[39mqz0_logstd\u001b[38;5;241m.\u001b[39mexp())\n", + "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/distributions/normal.py:56\u001b[0m, in \u001b[0;36mNormal.__init__\u001b[0;34m(self, loc, scale, validate_args)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 55\u001b[0m batch_shape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_args\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/distributions/distribution.py:62\u001b[0m, in \u001b[0;36mDistribution.__init__\u001b[0;34m(self, batch_shape, event_shape, validate_args)\u001b[0m\n\u001b[1;32m 60\u001b[0m valid \u001b[38;5;241m=\u001b[39m constraint\u001b[38;5;241m.\u001b[39mcheck(value)\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof distribution \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto satisfy the constraint \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(constraint)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 68\u001b[0m )\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n", + "\u001b[0;31mValueError\u001b[0m: Expected parameter loc (Tensor of shape (2533, 1, 1)) of distribution Normal(loc: torch.Size([2533, 1, 1]), scale: torch.Size([2533, 1, 1])) to satisfy the constraint Real(), but found invalid values:\ntensor([[[nan]],\n\n [[nan]],\n\n [[nan]],\n\n ...,\n\n [[nan]],\n\n [[nan]],\n\n [[nan]]], grad_fn=)" + ] + } + ], + "source": [ + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "\n", + "\n", + "# define neuromancer trainer\n", + "trainer = Trainer(\n", + " problem,\n", + " train_data_loader,\n", + " train_data_loader,\n", + " train_data_loader,\n", + " optimizer,\n", + " patience=0,\n", + " clip=100,\n", + " warmup=0,\n", + " epochs=15,\n", + " eval_metric=\"train_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"train_loss\",\n", + " test_metric=\"train_loss\"\n", + ")\n", + "trainer.train()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neuromancer3", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/SDEs/lightning_logs/version_0/hparams.yaml b/examples/SDEs/lightning_logs/version_0/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_0/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_1/hparams.yaml b/examples/SDEs/lightning_logs/version_1/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_1/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_2/hparams.yaml b/examples/SDEs/lightning_logs/version_2/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_2/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_3/hparams.yaml b/examples/SDEs/lightning_logs/version_3/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_3/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_3/metrics.csv b/examples/SDEs/lightning_logs/version_3/metrics.csv new file mode 100644 index 00000000..cfc9d0e4 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_3/metrics.csv @@ -0,0 +1,11 @@ +epoch,step,train_loss_epoch,training_epoch_average +0,0,1.9077290296554565,1.9077290296554565 +1,1,0.3584807515144348,0.3584807515144348 +2,2,0.2566610276699066,0.2566610276699066 +3,3,0.37344491481781006,0.37344491481781006 +4,4,0.6258323192596436,0.6258323192596436 +5,5,0.6383430361747742,0.6383430361747742 +6,6,0.3977561593055725,0.3977561593055725 +7,7,0.2955189645290375,0.2955189645290375 +8,8,0.484247624874115,0.484247624874115 +9,9,0.521581768989563,0.521581768989563 diff --git a/examples/SDEs/lightning_logs/version_4/hparams.yaml b/examples/SDEs/lightning_logs/version_4/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_4/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_4/metrics.csv b/examples/SDEs/lightning_logs/version_4/metrics.csv new file mode 100644 index 00000000..9c8d7c97 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_4/metrics.csv @@ -0,0 +1,21 @@ +epoch,step,train_loss_epoch,training_epoch_average +0,0,1.9001942873001099,1.9001942873001099 +1,1,0.35951119661331177,0.3595111668109894 +2,2,0.26133501529693604,0.26133501529693604 +3,3,0.38079988956451416,0.38079988956451416 +4,4,0.6300961971282959,0.6300961971282959 +5,5,0.6331126689910889,0.6331126689910889 +6,6,0.4030741751194,0.4030742049217224 +7,7,0.321748286485672,0.3217482566833496 +8,8,0.5364363193511963,0.5364363193511963 +9,9,0.5001047849655151,0.5001047849655151 +10,10,0.46357452869415283,0.46357452869415283 +11,11,0.2364509403705597,0.2364509403705597 +12,12,0.20944221317768097,0.20944221317768097 +13,13,0.18236657977104187,0.18236657977104187 +14,14,0.16440057754516602,0.16440057754516602 +15,15,0.12057995796203613,0.12057995796203613 +16,16,0.09402063488960266,0.09402063488960266 +17,17,0.06944441795349121,0.06944441795349121 +18,18,0.06000132858753204,0.06000132858753204 +19,19,0.05472342669963837,0.05472342297434807 diff --git a/examples/SDEs/lightning_logs/version_5/hparams.yaml b/examples/SDEs/lightning_logs/version_5/hparams.yaml new file mode 100644 index 00000000..0967ef42 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_5/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/examples/SDEs/lightning_logs/version_5/metrics.csv b/examples/SDEs/lightning_logs/version_5/metrics.csv new file mode 100644 index 00000000..8cba8339 --- /dev/null +++ b/examples/SDEs/lightning_logs/version_5/metrics.csv @@ -0,0 +1,3 @@ +epoch,step,train_loss_epoch,training_epoch_average +0,0,1.9097636938095093,1.9097635746002197 +1,1,0.35968565940856934,0.35968565940856934 diff --git a/examples/SDEs/sde_test.py b/examples/SDEs/sde_test.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/neuromancer/dynamics/integrators.py b/src/neuromancer/dynamics/integrators.py index c22c7268..9a5bf9d9 100644 --- a/src/neuromancer/dynamics/integrators.py +++ b/src/neuromancer/dynamics/integrators.py @@ -7,6 +7,7 @@ from torchdiffeq import odeint_adjoint as odeint import torchdiffeq +import torchsde from abc import ABC, abstractmethod @@ -81,6 +82,63 @@ def integrate(self, x, *args): adjoint_options=dict(norm=make_norm(x))) x_t = solution[-1] return x_t + +class BasicSDEIntegrator(Integrator): + """ + Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined + Returns a single tensor of size (t, batch_size, state_size). + + Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py + Currently only supports Euler integration. Choice of integration method is dependent + on integral type (Ito/Stratanovich) and drift/diffusion terms + """ + def __init__(self, block): + """ + :param block: (nn.Module) The BasicSDE block + """ + super().__init__(block) + + def integrate(self, x, t): + """ + x is the initial datastate of size (batch_size, state_size) + t is the time-step vector over which to integrate + """ + ys = torchsde.sdeint(self.block, x, t, method='euler') + return ys + +class LatentSDEIntegrator(Integrator): + """ + Integrator (from TorchSDE) for LatentSDE case. Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py for more + information. Integration here takes place in the latent space produced by the first-stage (encoding process) of the LatentSDE_Encoder block + Note that torchsde.sdeint() is called, like in BasicSDEIntegrator, and thus the output of integrate() is a single tensor of size (t, batch_size, latent_size) + In this case we also set logqp to True such that log ratio penalty is also returned. + PLease see: https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py + """ + def __init__(self, block, dt=1e-2, method='euler', adjoint=False): + """ + :param block:(nn.Module) The LatentSDE_Encoder block + :param dt: (float, optional): The constant step size or initial step size for + adaptive time-stepping. + :param method: method (str, optional): Numerical integration method to use. Must be + compatible with the SDE type (Ito/Stratonovich) and the noise type + (scalar/additive/diagonal/general). Defaults to a sensible choice + depending on the SDE type and noise type of the supplied SDE. + """ + super().__init__(block) + self.method = method + self.dt = dt + self.adjoint = adjoint + if self.adjoint: + assert self.block.adjoint == True, "LatentSDE_Encoder block must have adjoint=True if using adjoint method here" + + def integrate(self, x): + if not self.adjoint: + z0, xs, ts, qz0_mean, qz0_logstd = self.block(x) + zs, log_ratio = torchsde.sdeint(self.block, z0, ts, dt=self.dt, logqp=True, method=self.method) + else: + z0, xs, ts, qz0_mean, qz0_logstd, adjoint_params = self.block(x) + zs, log_ratio = torchsde.sdeint_adjoint(self.block, z0, ts, adjoint_params=adjoint_params, dt=self.dt, logqp=True, method=self.method) + return zs, z0, log_ratio, xs, qz0_mean, qz0_logstd class Euler(Integrator): diff --git a/src/neuromancer/modules/blocks.py b/src/neuromancer/modules/blocks.py index 081e09bc..44493722 100644 --- a/src/neuromancer/modules/blocks.py +++ b/src/neuromancer/modules/blocks.py @@ -12,13 +12,19 @@ import neuromancer.modules.rnn as rnn from neuromancer.modules.activations import soft_exp, SoftExponential, SmoothedReLU +from torch.distributions import Normal + +import torchsde + + class Block(nn.Module, ABC): """ Canonical abstract class of the block function approximator """ - def __init__(self): + def __init__(self, concat=True): super().__init__() + self.concat = concat @abstractmethod def block_eval(self, x): @@ -31,12 +37,15 @@ def forward(self, *inputs): :param inputs: (list(torch.Tensor, shape=[batchsize, insize]) or torch.Tensor, shape=[batchsize, insize]) :return: (torch.Tensor, shape=[batchsize, outsize]) """ - if len(inputs) > 1: - x = torch.cat(inputs, dim=-1) - else: - x = inputs[0] - return self.block_eval(x) - + if self.concat: + if len(inputs) > 1: + x = torch.cat(inputs, dim=-1) + else: + x = inputs[0] + return self.block_eval(x) + else: + return self.block_eval(*inputs) + class Linear(Block): """ @@ -202,8 +211,8 @@ def __init__( :param dropout: (float) Dropout probability """ super().__init__(insize=insize, outsize=outsize, bias=bias, - linear_map=linear_map, nonlin=nonlin, - hsizes=hsizes, linargs=linargs) + linear_map=linear_map, nonlin=nonlin, + hsizes=hsizes, linargs=linargs) self.min = min self.max = max self.method = self._set_method(method) @@ -413,14 +422,14 @@ class InputConvexNN(MLP): """ def __init__(self, - insize, - outsize, - bias=True, - linear_map=slim.Linear, - nonlin=nn.ReLU, - hsizes=[64], - linargs=dict() - ): + insize, + outsize, + bias=True, + linear_map=slim.Linear, + nonlin=nn.ReLU, + hsizes=[64], + linargs=dict() + ): super().__init__( insize, outsize, @@ -707,7 +716,215 @@ def block_eval(self, x): :return: (torch.Tensor, shape=[batchsize, outsize]) """ return self.linear(self.expand(x)) + +""" +class Encoder(Block): + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.gru = torch.nn.GRU(input_size=input_size, hidden_size=hidden_size) + self.lin = Linear(hidden_size, output_size) + def block_eval(self, inp): + out = self.gru(inp) + out = self.lin(out) + return out +""" + +class Encoder(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super(Encoder, self).__init__() + self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) + self.lin = nn.Linear(hidden_size, output_size) + + def forward(self, inp): + out, _ = self.gru(inp) + out = self.lin(out) + return out + +class BasicSDE(Block): + """ + Wrapper class for torchsde explicit SDE case. See https://github.com/google-research/torchsde + """ + def __init__(self, f, g, t, y): + """ + :param f: Drift function + :param g: Diffusion function + :param t: Timesteps + :param y: Initial value of dimension (batch size, state size) + """ + super().__init__() + self.f = f + self.g = g + self.y = y + self.t = t + self.theta = nn.Parameter(torch.tensor(0.1), requires_grad=False) # Scalar parameter + self.noise_type = "diagonal" + self.sde_type = "ito" + self.in_features = 0 + self.out_features = 0 + + def f(self, t,y): + return self.f(t,y) + + def g(self, t, y): + return self.g(t,y) + + def block_eval(self): + """This is unused by torchsde integrator""" + pass + + +class Encoder(nn.Module): + """ + Encoder module to handle time-series data (as in the case of stochastic data and SDE) + GRU is used to handle mapping to latent space in this case + This class is used only in LatentSDE_Encoder + """ + def __init__(self, input_size, hidden_size, output_size): + super(Encoder, self).__init__() + self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) + self.lin = Linear(hidden_size, output_size) + + def forward(self, inp): + out, _ = self.gru(inp) + out = self.lin(out) + return out + + + +class LatentSDE_Encoder(Block): + """ + Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in a full stochastic process dataset + and encodes it into a latent space. The output of this block feeds into LatentSDEIntegrator class. + Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py + Note that the adjoint method is not currently supported (see https://arxiv.org/pdf/2001.01328.pdf and TorchSDE documentation) + """ + sde_type = "ito" + noise_type = "diagonal" + + def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False): + super().__init__() + + self.adjoint = adjoint + + # Encoder. + self.encoder = Encoder(input_size=data_size, hidden_size=hidden_size, output_size=context_size) + self.qz0_net = Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space + + + # Decoder. + self.f_net = nn.Sequential( + Linear(latent_size + context_size, hidden_size), + nn.Softplus(), + Linear(hidden_size, hidden_size), + nn.Softplus(), + Linear(hidden_size, latent_size), + ) + self.h_net = nn.Sequential( + Linear(latent_size, hidden_size), + nn.Softplus(), + Linear(hidden_size, hidden_size), + nn.Softplus(), + Linear(hidden_size, latent_size), + ) + # This needs to be an element-wise function for the SDE to satisfy diagonal noise. + self.g_nets = nn.ModuleList( + [ + nn.Sequential( + Linear(1, hidden_size), + nn.Softplus(), + Linear(hidden_size, 1), + nn.Sigmoid() + ) + for _ in range(latent_size) + ] + ) + self.projector = Linear(latent_size, data_size) + + self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) + self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) + + self._ctx = None + self.in_features = 0 #unused + self.out_features = 0 #unused + + self.ts = ts + + def contextualize(self, ctx): + self._ctx = ctx # A tuple of tensors of sizes (T,), (T, batch_size, d). + + def f(self, t, y): + ts, ctx = self._ctx + + i = min(torch.searchsorted(ts, t, right=True), len(ts) - 1) + + return self.f_net(torch.cat((y, ctx[i]), dim=1)) + + def h(self, t, y): + return self.h_net(y) + + def g(self, t, y): # Diagonal diffusion. + y = torch.split(y, split_size_or_sections=1, dim=1) + out = [g_net_i(y_i) for (g_net_i, y_i) in zip(self.g_nets, y)] + return torch.cat(out, dim=1) + + def block_eval(self, xs): + # Contextualization is only needed for posterior inference. + ctx = self.encoder(torch.flip(xs, dims=(0,))) + ctx = torch.flip(ctx, dims=(0,)) + self.contextualize((self.ts, ctx)) + + qz0_mean, qz0_logstd = self.qz0_net(ctx[0]).chunk(chunks=2, dim=1) + z0 = qz0_mean + qz0_logstd.exp() * torch.randn_like(qz0_mean) + if not self.adjoint: + return z0, xs, self.ts, qz0_mean, qz0_logstd + else: + adjoint_params = ( + (ctx,) + + tuple(self.f_net.parameters()) + tuple(self.g_nets.parameters()) + tuple(self.h_net.parameters()) + ) + return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params + + @torch.no_grad() + def sample(self, batch_size, ts, bm=None): + eps = torch.randn(size=(batch_size, *self.pz0_mean.shape[1:]), device=self.pz0_mean.device) + z0 = self.pz0_mean + self.pz0_logstd.exp() * eps + zs = torchsde.sdeint(self, z0, ts, names={'drift': 'h'}, dt=1e-3, bm=bm) + # Most of the times in ML, we don't sample the observation noise for visualization purposes. + _xs = self.projector(zs) + return _xs + +class LatentSDE_Decoder(Block): + """ + Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of + LatentSDEIntegrator and decodes it back into the "real" data space and also outputs associated Gaussian distributions + to be used in the final loss function. + Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py + """ + sde_type = "ito" + noise_type = "diagonal" + + def __init__(self, data_size, latent_size, noise_std): + super().__init__(concat=False) + self.in_features = 0 + self.out_features = 0 + self.noise_std = noise_std + self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) + self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) + self.projector = nn.Linear(latent_size, data_size) + + def block_eval(self, xs, zs, log_ratio, qz0_mean, qz0_logstd): + _xs = self.projector(zs) + xs_dist = Normal(loc=_xs, scale=self.noise_std) + log_pxs = xs_dist.log_prob(xs).sum(dim=(0, 2)).mean(dim=0) + + qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp()) + pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp()) + logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1).mean(dim=0) + logqp_path = log_ratio.sum(dim=0).mean(dim=0) + return _xs, log_pxs, logqp0 + logqp_path, log_ratio + + class InterpolateAddMultiply(nn.Module): """ @@ -735,4 +952,4 @@ def forward(self, p, q): "bilinear": BilinearTorch, "icnn": InputConvexNN, "pos_def": PosDef -} +} \ No newline at end of file From 4608994bb9f78babed5429dbbe42ab08453da8b2 Mon Sep 17 00:00:00 2001 From: "Birmiwal, Rahul R" Date: Tue, 16 Apr 2024 14:01:40 -0700 Subject: [PATCH 2/6] torchsde integration --- examples/SDEs/.DS_Store | Bin 0 -> 6148 bytes examples/SDEs/HistoricalData_SPX.csv | 2534 ----------------- examples/SDEs/README.md | 3 +- examples/SDEs/basic_sde.ipynb | 664 +---- examples/SDEs/epoch=1-step=2.ckpt | Bin 446866 -> 0 bytes examples/SDEs/epoch=19-step=20.ckpt | Bin 446866 -> 0 bytes examples/SDEs/epoch=2-step=3.ckpt | Bin 446866 -> 0 bytes examples/SDEs/latent_sde_lorenz_system.ipynb | 337 +-- .../SDEs/latent_sde_stock_forecasting.ipynb | 668 ----- .../lightning_logs/version_0/hparams.yaml | 1 - .../lightning_logs/version_1/hparams.yaml | 1 - .../lightning_logs/version_2/hparams.yaml | 1 - .../lightning_logs/version_3/hparams.yaml | 1 - .../SDEs/lightning_logs/version_3/metrics.csv | 11 - .../lightning_logs/version_4/hparams.yaml | 1 - .../SDEs/lightning_logs/version_4/metrics.csv | 21 - .../lightning_logs/version_5/hparams.yaml | 1 - .../SDEs/lightning_logs/version_5/metrics.csv | 3 - pyproject.toml | 3 +- src/neuromancer/modules/blocks.py | 29 +- tests/test_sdes.py | 188 ++ 21 files changed, 295 insertions(+), 4172 deletions(-) create mode 100644 examples/SDEs/.DS_Store delete mode 100644 examples/SDEs/HistoricalData_SPX.csv delete mode 100644 examples/SDEs/epoch=1-step=2.ckpt delete mode 100644 examples/SDEs/epoch=19-step=20.ckpt delete mode 100644 examples/SDEs/epoch=2-step=3.ckpt delete mode 100644 examples/SDEs/latent_sde_stock_forecasting.ipynb delete mode 100644 examples/SDEs/lightning_logs/version_0/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_1/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_2/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_3/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_3/metrics.csv delete mode 100644 examples/SDEs/lightning_logs/version_4/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_4/metrics.csv delete mode 100644 examples/SDEs/lightning_logs/version_5/hparams.yaml delete mode 100644 examples/SDEs/lightning_logs/version_5/metrics.csv create mode 100644 tests/test_sdes.py diff --git a/examples/SDEs/.DS_Store b/examples/SDEs/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0" + "" ] }, "execution_count": 2, @@ -77,17 +70,18 @@ "metadata": {}, "source": [ "### Dataset: \n", - "Below we generate some data. We assume the process has a state size of 1, batch size of 5 and 100 timesteps. " + "Below we generate some data. We assume the process has a state size of 1, batch size of 5 and 100 timesteps. \n", + "\n", + "***Note: For visualization purposes, we recommend ONLY to use a state size of 1***" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "batch_size, state_size, t_size = 5, 1, 100\n", - "\n", "ts = torch.linspace(0, 1, t_size)\n", "y0 = torch.full(size=(batch_size, state_size), fill_value=0.1)\n", "y0.shape\n", @@ -103,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -135,625 +129,6 @@ "model = Node(integrator, input_keys=['y','t'], output_keys=['ys']) #define Neuromancer Node to wrap integrator. 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"cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -781,27 +156,6 @@ " plt.legend()\n", " plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. \n", + "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. We train and utilize the Lightning framework to support custom functionality within the training loop.\n", "\n", "\n" ] @@ -20,27 +20,35 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import numpy as np\n", + "import torch\n", + "import tqdm\n", "from neuromancer.psl import plot\n", "from neuromancer import psl\n", - "import matplotlib.pyplot as plt\n", - "from torch.utils.data import DataLoader\n", + "import torchsde\n", + "import torchsde\n", "\n", + "from torch.utils.data import DataLoader\n", "from neuromancer.system import Node\n", "from neuromancer.dynamics import integrators, ode\n", "from neuromancer.trainer import Trainer, LitTrainer\n", @@ -50,30 +58,12 @@ "from neuromancer.constraint import variable\n", "from neuromancer.loss import PenaltyLoss\n", "from neuromancer.modules import blocks\n", - "\n", - "torch.manual_seed(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "from typing import Sequence\n", - "\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import tqdm\n", "from torch import nn\n", "from torch import optim\n", "from torch.distributions import Normal\n", + "from typing import Sequence\n", "\n", - "import torchsde" + "torch.manual_seed(0)\n" ] }, { @@ -85,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -143,105 +133,40 @@ " if normalize:\n", " mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1))\n", " xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std)\n", - " return xs\n", - "\n", - "\n", - "\n", - "\n", - "def vis(xs, ts, latent_sde, bm_vis, img_path, num_samples=10):\n", - " fig = plt.figure(figsize=(20, 9))\n", - " gs = gridspec.GridSpec(1, 2)\n", - " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", - " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", - "\n", - " # Left plot: data.\n", - " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", - " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", - " ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", - " ax00.set_yticklabels([])\n", - " ax00.set_xticklabels([])\n", - " ax00.set_zticklabels([])\n", - " ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", - " ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", - " ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", - " ax00.set_title('Data', fontsize=20)\n", - " xlim = ax00.get_xlim()\n", - " ylim = ax00.get_ylim()\n", - " zlim = ax00.get_zlim()\n", - "\n", - " # Right plot: samples from learned model.\n", - " xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", - " z1, z2, z3 = np.split(xs, indices_or_sections=3, axis=-1)\n", - "\n", - " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", - " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", - " ax01.set_yticklabels([])\n", - " ax01.set_xticklabels([])\n", - " ax01.set_zticklabels([])\n", - " ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", - " ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", - " ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", - " ax01.set_title('Samples', fontsize=20)\n", - " ax01.set_xlim(xlim)\n", - " ax01.set_ylim(ylim)\n", - " ax01.set_zlim(zlim)\n", - "\n", - " plt.savefig(img_path)\n", - " plt.close()" + " return xs\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "batch_size=1024\n", - "latent_size=4\n", - "context_size=64\n", - "hidden_size=128\n", - "lr_init=1e-2\n", - "t0=0.\n", - "t1=2.\n", - "lr_gamma=0.997\n", - "num_iters=1\n", - "kl_anneal_iters=1000\n", - "pause_every=50\n", - "noise_std=0.01\n", - "adjoint=False\n", - "train_dir='./dump/lorenz/'\n", - "method=\"euler\"\n", + "\n", "\n", "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neuromancer Integration\n", "\n", - "Generate the data and create Neuromancer DictDataset" + "As per the NeuroMANCER x Lightning workflow, generate the data_setup_function and return the DictDatasets. Note that we only have a train dataset here, so we return `None` for dev/test datasets" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "def make_dataset(t0, t1, batch_size, noise_std):\n", + "def make_dataset(t0, t1, batch_size, noise_std, steps=100):\n", " _y0 = torch.randn(batch_size, 3)\n", - " ts = torch.linspace(t0, t1, steps=100)\n", + " ts = torch.linspace(t0, t1, steps=steps)\n", " xs = StochasticLorenz().sample(_y0, ts, noise_std, normalize=True)\n", " train_data = DictDataset({'xs':xs},name='train')\n", - " dev_data = DictDataset({'xs':xs},name='dev')\n", - " test_data = DictDataset({'xs':xs},name='test')\n", " return train_data, None, None, batch_size\n", " " ] @@ -250,18 +175,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Define Neuromancer components, variables, and problem to train the LatentSDE. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data" + "Define some experimental parameters" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "device='cpu'\n", - "torch.manual_seed(0)\n", - "batch_size=1024\n", + "batch_size=256\n", "latent_size=4\n", "context_size=64\n", "hidden_size=128\n", @@ -273,13 +196,29 @@ "kl_anneal_iters=1000\n", "pause_every=50\n", "noise_std=0.01\n", - "adjoint=False\n", - "train_dir='./dump/lorenz/'\n", "method=\"euler\"\n", - "ts = torch.linspace(t0, t1, steps=100)\n", + "steps = 100 # number of time steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define Neuromancer components, variables, and problem to train the LatentSDE. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data. For this example, we set `adjoint` to `False` (do not use the adjoint sensitivity method). This is because this method seems to be significantly slower. \n", + "\n", + "Also note that we need to pass in the timestep tensor to our `LatentSDE_Encoder`, and as a result need to also define it outside the `make_dataset()` function. We note that this is not the cleanest code and breaks the data abstraction. Additional features will be added to mitigate this." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ts = torch.linspace(t0, t1, steps=steps)\n", "\n", - "sde_block_encoder = blocks.LatentSDE_Encoder(3, latent_size, context_size, hidden_size, ts=ts, adjoint=True) \n", - "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=True)\n", + "sde_block_encoder = blocks.LatentSDE_Encoder(3, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n", + "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False)\n", "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", "sde_block_decoder = blocks.LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", @@ -296,11 +235,9 @@ "log_pxs = variable('log_pxs')\n", "sum_term = variable('sum_term')\n", "\n", - "\n", - "\n", + "# NeuroMANCER loss function format\n", "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", "\n", - "\n", "# aggregate list of objective terms and constraints\n", "objectives = [loss]\n", "constraints = []\n", @@ -310,9 +247,16 @@ "problem = Problem([model_1, model_2], loss)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define helper visualization function (again see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py) that will fire every N epochs in our training loop. This visualization will allow us to see the learned Lorenz attractor samples " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -320,12 +264,16 @@ "bm_vis = torchsde.BrownianInterval(\n", " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", "\n", - "def vis(xs, ts, problem, bm_vis, img_path, num_samples=10):\n", + "# \n", + "def vis(data_dict, problem, bm_vis, img_path, num_samples=10):\n", + " encoder, decoder = problem.nodes[0], problem.nodes[1] #extract the encoder and decoder from our problem\n", + "\n", " fig = plt.figure(figsize=(20, 9))\n", " gs = gridspec.GridSpec(1, 2)\n", " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", "\n", + " xs = data_dict['xs'] #pull out data sample from the DictDataset\n", " # Left plot: data.\n", " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", @@ -342,9 +290,11 @@ " zlim = ax00.get_zlim()\n", "\n", " # Right plot: samples from learned model.\n", - " xs = problem\n", - " xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", - " z1, z2, z3 = np.split(xs, indices_or_sections=3, axis=-1)\n", + " mydata = data_dict\n", + " output = decoder(encoder(mydata))\n", + " xs_hat = output['xs_hat'].detach().cpu().numpy() \n", + " #xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", + " z1, z2, z3 = np.split(xs_hat, indices_or_sections=3, axis=-1)\n", "\n", " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", @@ -367,150 +317,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Neuromancer training the problem to learn the stochastic process" + "### Neuromancer training the problem to learn the stochastic process\n", + "\n", + "We now train and visualize results using Lightning workflow" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/examples/SDEs exists and is not empty.\n", - "\n", - " | Name | Type | Params\n", - "------------------------------------\n", - "0 | problem | Problem | 104 K \n", - "------------------------------------\n", - "104 K Trainable params\n", - "0 Non-trainable params\n", - "104 K Total params\n", - "0.420 Total estimated model params size (MB)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "USING BATCH SIZE 1024\n", - "USING LEARNING RATE 0.001\n", - " " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", - "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:104: Total length of `DataLoader` across ranks is zero. Please make sure this was your intention.\n", - "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", - "/Users/birm560/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0: 0%| | 0/1 [00:00" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "import matplotlib.pyplot as plt\n", - "from torch.utils.data import DataLoader\n", - "\n", - "from neuromancer.system import Node\n", - "from neuromancer.dynamics import integrators, ode\n", - "from neuromancer.trainer import Trainer\n", - "from neuromancer.problem import Problem\n", - "from neuromancer.loggers import BasicLogger\n", - "from neuromancer.dataset import DictDataset\n", - "from neuromancer.constraint import variable\n", - "from neuromancer.loss import PenaltyLoss\n", - "from neuromancer.modules import blocks\n", - "\n", - "torch.manual_seed(0)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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" - ], - "text/plain": [ - " Date Close/Last Open High Low\n", - "0 02/29/2024 5096.27 5085.36 5104.99 5061.89\n", - "1 02/28/2024 5069.76 5067.20 5077.37 5058.35\n", - "2 02/27/2024 5078.18 5074.60 5080.69 5057.29\n", - "3 02/26/2024 5069.53 5093.00 5097.66 5068.91\n", - "4 02/23/2024 5088.80 5100.92 5111.06 5081.46" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd \n", - "df = pd.read_csv('HistoricalData_SPX.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming df is your DataFrame containing the stock data\n", - "# Convert 'Date' column to datetime format\n", - "df['Date'] = pd.to_datetime(df['Date'])\n", - "\n", - "# Plot Close/Last price over time\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(df['Date'][::-1], df['Close/Last'][::-1], marker='o', linestyle='-')\n", - "plt.title('SPX Closing Prices')\n", - "plt.xlabel('Date')\n", - "plt.ylabel('Close')\n", - "plt.grid(True)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "# Assuming your DataFrame is named df\n", - "# Selecting only the numerical columns to standardize\n", - "numerical_columns = ['Close/Last']\n", - "\n", - "# Instantiate the StandardScaler\n", - "scaler = MinMaxScaler()\n", - "\n", - "# Fit and transform the numerical columns\n", - "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DateClose/LastOpenHighLow
02024-02-291.0000005085.365104.995061.89
12024-02-280.9919195067.205077.375058.35
22024-02-270.9944865074.605080.695057.29
32024-02-260.9918495093.005097.665068.91
42024-02-230.9977235100.925111.065081.46
..................
25282014-03-060.0186981874.181881.941874.18
25292014-03-050.0177161874.051876.531871.11
25302014-03-040.0177471849.231876.231849.23
25312014-03-030.0091571857.681857.681834.44
25322014-02-280.0133391855.121867.921847.67
\n", - "

2533 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Date Close/Last Open High Low\n", - "0 2024-02-29 1.000000 5085.36 5104.99 5061.89\n", - "1 2024-02-28 0.991919 5067.20 5077.37 5058.35\n", - "2 2024-02-27 0.994486 5074.60 5080.69 5057.29\n", - "3 2024-02-26 0.991849 5093.00 5097.66 5068.91\n", - "4 2024-02-23 0.997723 5100.92 5111.06 5081.46\n", - "... ... ... ... ... ...\n", - "2528 2014-03-06 0.018698 1874.18 1881.94 1874.18\n", - "2529 2014-03-05 0.017716 1874.05 1876.53 1871.11\n", - "2530 2014-03-04 0.017747 1849.23 1876.23 1849.23\n", - "2531 2014-03-03 0.009157 1857.68 1857.68 1834.44\n", - "2532 2014-02-28 0.013339 1855.12 1867.92 1847.67\n", - "\n", - "[2533 rows x 5 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "df = df.reset_index()\n", - "ts = (df['index'].values)[::-1]\n", - "ts = ts[::-1]\n", - "ts = torch.tensor(ts, dtype=torch.float32)\n", - "xs = (df['Close/Last'].values)\n", - "xs = torch.tensor(xs, dtype=torch.float32).flip(0)\n", - "\n", - "#xs needs to be of shape [t, batch_size, data_dimensions=1]\n", - "xs = xs.unsqueeze(1).unsqueeze(2)\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2533" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts = 0.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "device='cpu'\n", - "batch_size = 1\n", - "train_data = DictDataset({'xs':xs},name='train')\n", - "train_data_loader = DataLoader(train_data, batch_size=2533, collate_fn=train_data.collate_fn, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2533, 1, 3])" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "input_dim = 1\n", - "\n", - "latent_size=2\n", - "context_size=4\n", - "hidden_size=4\n", - "lr_init=1e-2\n", - "t0=0.\n", - "t1=2532.\n", - "lr_gamma=0.997\n", - "num_iters=1\n", - "kl_anneal_iters=1000\n", - "pause_every=50\n", - "noise_std=0.01\n", - "adjoint=False\n", - "train_dir='./dump/stock_forecasting/'\n", - "method=\"euler\"\n", - "\n", - "sde_block_encoder = blocks.LatentSDE_Encoder(input_dim, latent_size, context_size, hidden_size, ts=ts) \n", - "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, dt=1.)\n", - "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", - "sde_block_decoder = blocks.LatentSDE_Decoder(input_dim, latent_size, noise_std=noise_std)\n", - "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", - "\n", - "xs = variable('xs')\n", - "zs = variable('zs')\n", - "z0 = variable('z0')\n", - "\n", - "\n", - "log_ratio = variable('log_ratio')\n", - "qz0_mean = variable('qz0_mean')\n", - "qz0_logstd = variable('qz0_logstd')\n", - "log_pxs = variable('log_pxs')\n", - "sum_term = variable('sum_term')\n", - "\n", - "\n", - "\n", - "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", - "\n", - "\n", - "# aggregate list of objective terms and constraints\n", - "objectives = [loss]\n", - "constraints = []\n", - "# create constrained optimization loss\n", - "loss = PenaltyLoss(objectives, constraints)\n", - "# construct constrained optimization problem\n", - "problem = Problem([model_1, model_2], loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'zs': tensor([[[-1.0134e+00, 4.9340e-01]],\n", - " \n", - " [[-1.3344e+00, -1.6515e-01]],\n", - " \n", - " [[-1.9389e+00, -9.6584e-01]],\n", - " \n", - " ...,\n", - " \n", - " [[ 2.3176e+07, -6.6517e+06]],\n", - " \n", - " [[ 2.3289e+07, -6.6841e+06]],\n", - " \n", - " [[ 2.3402e+07, -6.7167e+06]]], grad_fn=),\n", - " 'z0': tensor([[-1.0134, 0.4934]], grad_fn=),\n", - " 'log_ratio': tensor([[2.0297],\n", - " [1.8233],\n", - " [1.5270],\n", - " ...,\n", - " [ nan],\n", - " [ nan],\n", - " [ nan]], grad_fn=),\n", - " 'xs': tensor([[[0.0133]],\n", - " \n", - " [[0.0092]],\n", - " \n", - " [[0.0177]],\n", - " \n", - " ...,\n", - " \n", - " [[0.9945]],\n", - " \n", - " [[0.9919]],\n", - " \n", - " [[1.0000]]]),\n", - " 'qz0_mean': tensor([[-0.0452, 0.4826]], grad_fn=),\n", - " 'qz0_logstd': tensor([[-0.3319, -0.4354]], grad_fn=)}" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "baz = next(iter(train_data_loader))\n", - "baz['xs'].shape\n", - "foo = model_1(baz)\n", - "foo" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'zs': tensor([[[0.9840, 0.7725]],\n", - " \n", - " [[2.7798, 1.1453]],\n", - " \n", - " [[4.1100, 1.9832]],\n", - " \n", - " ...,\n", - " \n", - " [[ nan, nan]],\n", - " \n", - " [[ nan, nan]],\n", - " \n", - " [[ nan, nan]]], grad_fn=),\n", - " 'z0': tensor([[0.9840, 0.7725]], grad_fn=),\n", - " 'log_ratio': tensor([[3.6686],\n", - " [5.0823],\n", - " [6.7811],\n", - " ...,\n", - " [ nan],\n", - " [ nan],\n", - " [ nan]], grad_fn=),\n", - " 'xs': tensor([[[-1.2923]],\n", - " \n", - " [[-1.3073]],\n", - " \n", - " [[-1.2766]],\n", - " \n", - " ...,\n", - " \n", - " [[ 2.2042]],\n", - " \n", - " [[ 2.1950]],\n", - " \n", - " [[ 2.2238]]]),\n", - " 'qz0_mean': tensor([[0.0863, 0.4421]], grad_fn=),\n", - " 'qz0_logstd': tensor([[-0.4505, -0.4765]], grad_fn=)}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Expected parameter loc (Tensor of shape (2533, 1, 1)) of distribution Normal(loc: torch.Size([2533, 1, 1]), scale: torch.Size([2533, 1, 1])) to satisfy the constraint Real(), but found invalid values:\ntensor([[[nan]],\n\n [[nan]],\n\n [[nan]],\n\n ...,\n\n [[nan]],\n\n [[nan]],\n\n [[nan]]], grad_fn=)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[36], line 20\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# define neuromancer trainer\u001b[39;00m\n\u001b[1;32m 5\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 6\u001b[0m problem,\n\u001b[1;32m 7\u001b[0m train_data_loader,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 18\u001b[0m test_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_loss\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 19\u001b[0m )\n\u001b[0;32m---> 20\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/trainer.py:105\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 103\u001b[0m t_batch[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m i\n\u001b[1;32m 104\u001b[0m t_batch \u001b[38;5;241m=\u001b[39m move_batch_to_device(t_batch, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 105\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 107\u001b[0m output[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_metric]\u001b[38;5;241m.\u001b[39mbackward()\n", - "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/problem.py:68\u001b[0m, in \u001b[0;36mProblem.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m---> 68\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(output_dict)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output_dict, torch\u001b[38;5;241m.\u001b[39mTensor):\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/problem.py:76\u001b[0m, in \u001b[0;36mProblem.step\u001b[0;34m(self, input_dict)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_dict: Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict[\u001b[38;5;28mstr\u001b[39m, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnodes:\n\u001b[0;32m---> 76\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output_dict, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[1;32m 78\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m {node\u001b[38;5;241m.\u001b[39mname: output_dict}\n", - "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", - "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/modules/blocks.py:47\u001b[0m, in \u001b[0;36mBlock.forward\u001b[0;34m(self, *inputs)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock_eval(x)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/neuromancer/src/neuromancer/modules/blocks.py:899\u001b[0m, in \u001b[0;36mLatentSDE_Decoder.block_eval\u001b[0;34m(self, xs, zs, log_ratio, qz0_mean, qz0_logstd)\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mblock_eval\u001b[39m(\u001b[38;5;28mself\u001b[39m, xs, zs, log_ratio, qz0_mean, qz0_logstd): \n\u001b[1;32m 898\u001b[0m _xs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprojector(zs)\n\u001b[0;32m--> 899\u001b[0m xs_dist \u001b[38;5;241m=\u001b[39m \u001b[43mNormal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_xs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnoise_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 900\u001b[0m log_pxs \u001b[38;5;241m=\u001b[39m xs_dist\u001b[38;5;241m.\u001b[39mlog_prob(xs)\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m))\u001b[38;5;241m.\u001b[39mmean(dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 902\u001b[0m qz0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdistributions\u001b[38;5;241m.\u001b[39mNormal(loc\u001b[38;5;241m=\u001b[39mqz0_mean, scale\u001b[38;5;241m=\u001b[39mqz0_logstd\u001b[38;5;241m.\u001b[39mexp())\n", - "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/distributions/normal.py:56\u001b[0m, in \u001b[0;36mNormal.__init__\u001b[0;34m(self, loc, scale, validate_args)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 55\u001b[0m batch_shape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_args\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/neuromancer3/lib/python3.10/site-packages/torch/distributions/distribution.py:62\u001b[0m, in \u001b[0;36mDistribution.__init__\u001b[0;34m(self, batch_shape, event_shape, validate_args)\u001b[0m\n\u001b[1;32m 60\u001b[0m valid \u001b[38;5;241m=\u001b[39m constraint\u001b[38;5;241m.\u001b[39mcheck(value)\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m---> 62\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 63\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof distribution \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto satisfy the constraint \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(constraint)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 68\u001b[0m )\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n", - "\u001b[0;31mValueError\u001b[0m: Expected parameter loc (Tensor of shape (2533, 1, 1)) of distribution Normal(loc: torch.Size([2533, 1, 1]), scale: torch.Size([2533, 1, 1])) to satisfy the constraint Real(), but found invalid values:\ntensor([[[nan]],\n\n [[nan]],\n\n [[nan]],\n\n ...,\n\n [[nan]],\n\n [[nan]],\n\n [[nan]]], grad_fn=)" - ] - } - ], - "source": [ - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "\n", - "\n", - "# define neuromancer trainer\n", - "trainer = Trainer(\n", - " problem,\n", - " train_data_loader,\n", - " train_data_loader,\n", - " train_data_loader,\n", - " optimizer,\n", - " patience=0,\n", - " clip=100,\n", - " warmup=0,\n", - " epochs=15,\n", - " eval_metric=\"train_loss\",\n", - " train_metric=\"train_loss\",\n", - " dev_metric=\"train_loss\",\n", - " test_metric=\"train_loss\"\n", - ")\n", - "trainer.train()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "neuromancer3", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/SDEs/lightning_logs/version_0/hparams.yaml b/examples/SDEs/lightning_logs/version_0/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_0/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_1/hparams.yaml b/examples/SDEs/lightning_logs/version_1/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_1/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_2/hparams.yaml b/examples/SDEs/lightning_logs/version_2/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_2/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_3/hparams.yaml b/examples/SDEs/lightning_logs/version_3/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_3/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_3/metrics.csv b/examples/SDEs/lightning_logs/version_3/metrics.csv deleted file mode 100644 index cfc9d0e4..00000000 --- a/examples/SDEs/lightning_logs/version_3/metrics.csv +++ /dev/null @@ -1,11 +0,0 @@ -epoch,step,train_loss_epoch,training_epoch_average -0,0,1.9077290296554565,1.9077290296554565 -1,1,0.3584807515144348,0.3584807515144348 -2,2,0.2566610276699066,0.2566610276699066 -3,3,0.37344491481781006,0.37344491481781006 -4,4,0.6258323192596436,0.6258323192596436 -5,5,0.6383430361747742,0.6383430361747742 -6,6,0.3977561593055725,0.3977561593055725 -7,7,0.2955189645290375,0.2955189645290375 -8,8,0.484247624874115,0.484247624874115 -9,9,0.521581768989563,0.521581768989563 diff --git a/examples/SDEs/lightning_logs/version_4/hparams.yaml b/examples/SDEs/lightning_logs/version_4/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_4/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_4/metrics.csv b/examples/SDEs/lightning_logs/version_4/metrics.csv deleted file mode 100644 index 9c8d7c97..00000000 --- a/examples/SDEs/lightning_logs/version_4/metrics.csv +++ /dev/null @@ -1,21 +0,0 @@ -epoch,step,train_loss_epoch,training_epoch_average -0,0,1.9001942873001099,1.9001942873001099 -1,1,0.35951119661331177,0.3595111668109894 -2,2,0.26133501529693604,0.26133501529693604 -3,3,0.38079988956451416,0.38079988956451416 -4,4,0.6300961971282959,0.6300961971282959 -5,5,0.6331126689910889,0.6331126689910889 -6,6,0.4030741751194,0.4030742049217224 -7,7,0.321748286485672,0.3217482566833496 -8,8,0.5364363193511963,0.5364363193511963 -9,9,0.5001047849655151,0.5001047849655151 -10,10,0.46357452869415283,0.46357452869415283 -11,11,0.2364509403705597,0.2364509403705597 -12,12,0.20944221317768097,0.20944221317768097 -13,13,0.18236657977104187,0.18236657977104187 -14,14,0.16440057754516602,0.16440057754516602 -15,15,0.12057995796203613,0.12057995796203613 -16,16,0.09402063488960266,0.09402063488960266 -17,17,0.06944441795349121,0.06944441795349121 -18,18,0.06000132858753204,0.06000132858753204 -19,19,0.05472342669963837,0.05472342297434807 diff --git a/examples/SDEs/lightning_logs/version_5/hparams.yaml b/examples/SDEs/lightning_logs/version_5/hparams.yaml deleted file mode 100644 index 0967ef42..00000000 --- a/examples/SDEs/lightning_logs/version_5/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/examples/SDEs/lightning_logs/version_5/metrics.csv b/examples/SDEs/lightning_logs/version_5/metrics.csv deleted file mode 100644 index 8cba8339..00000000 --- a/examples/SDEs/lightning_logs/version_5/metrics.csv +++ /dev/null @@ -1,3 +0,0 @@ -epoch,step,train_loss_epoch,training_epoch_average -0,0,1.9097636938095093,1.9097635746002197 -1,1,0.35968565940856934,0.35968565940856934 diff --git a/pyproject.toml b/pyproject.toml index 477369ae..7f273d52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,7 +54,8 @@ dependencies = [ "cvxpy", "cvxpylayers", "casadi", - "wandb" + "wandb", + "torchsde" ] version = "1.5.0" diff --git a/src/neuromancer/modules/blocks.py b/src/neuromancer/modules/blocks.py index 44493722..d537af16 100644 --- a/src/neuromancer/modules/blocks.py +++ b/src/neuromancer/modules/blocks.py @@ -717,30 +717,8 @@ def block_eval(self, x): """ return self.linear(self.expand(x)) -""" -class Encoder(Block): - def __init__(self, input_size, hidden_size, output_size): - super().__init__() - self.gru = torch.nn.GRU(input_size=input_size, hidden_size=hidden_size) - self.lin = Linear(hidden_size, output_size) - def block_eval(self, inp): - out = self.gru(inp) - out = self.lin(out) - return out -""" - -class Encoder(nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super(Encoder, self).__init__() - self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) - self.lin = nn.Linear(hidden_size, output_size) - def forward(self, inp): - out, _ = self.gru(inp) - out = self.lin(out) - return out - class BasicSDE(Block): """ Wrapper class for torchsde explicit SDE case. See https://github.com/google-research/torchsde @@ -951,5 +929,10 @@ def forward(self, p, q): "poly2": Poly2, "bilinear": BilinearTorch, "icnn": InputConvexNN, - "pos_def": PosDef + "pos_def": PosDef, + "encoder": Encoder, + "basic_sde": BasicSDE, + "latent_sde_encoder": LatentSDE_Encoder, + "latent_sde_decoder": LatentSDE_Decoder + } \ No newline at end of file diff --git a/tests/test_sdes.py b/tests/test_sdes.py new file mode 100644 index 00000000..06fc3f29 --- /dev/null +++ b/tests/test_sdes.py @@ -0,0 +1,188 @@ +import os +import logging +from typing import Sequence + +import torch +import torch.nn as nn +import torch.optim as optim +import torchsde +from torch.distributions import Normal +from torch.utils.data import DataLoader +import matplotlib.pyplot as plt +import numpy as np +import tqdm + +from neuromancer.modules import blocks +from neuromancer.dynamics import integrators, ode +from neuromancer.trainer import Trainer, LitTrainer +from neuromancer.problem import Problem +from neuromancer.loggers import BasicLogger +from neuromancer.dataset import DictDataset +from neuromancer.constraint import variable +from neuromancer.loss import PenaltyLoss +from neuromancer.system import Node + +from neuromancer.psl import plot + +# Import specific modules from neuromancer +from neuromancer.modules.blocks import BasicSDE, LatentSDE_Encoder +from neuromancer.dynamics.integrators import BasicSDEIntegrator + +import pytest +torch.manual_seed(0) + + + +class TestBasicSDEBlockAndIntegrator: + """ Testing class for BasicSDE and BasicSDEIntegrator """ + + @pytest.fixture(params=[ + lambda t, y: torch.sin(t) + 0.1 * y, + lambda t, y: 1.0 * t**2 + 0.1 * y**2 #quadratic drift + ]) + def f_function(self, request): + return request.param + + @pytest.fixture(params=[ + lambda t, y: 0.3 * torch.sigmoid(torch.cos(t) * torch.exp(-y)), + lambda t, y: torch.exp(-1. * t) * torch.sqrt(t) + torch.exp(-1. * y) #exponential diffusion + ]) + def g_function(self, request): + return request.param + + @pytest.fixture(params=[1]) # state size = 1 + def state_size(self, request): + return request.param + + @pytest.fixture(params=[1,100]) # Different time sizes + def time_size(self, request): + return request.param + + @pytest.fixture(params=[1,5]) # Different batch sizes + def batch_size(self, request): + return request.param + + @pytest.fixture + def basic_sde(self, f_function, g_function): + t = variable('t') + y = variable('y') + return BasicSDE(f_function, g_function, t, y) + + + def test_g_output_shape(self, basic_sde, batch_size, state_size, time_size): + ts = torch.linspace(0, 1, time_size) + y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) + output = basic_sde.g(ts, y0) + + assert output.shape[0] == y0.shape[0], "Dimension 0 of g output not equal to state size" + assert output.shape[1] == time_size, "Dimension 1 of g output not equal to time size" + + def test_basic_sde_initialization(self, basic_sde): + assert hasattr(basic_sde, 'noise_type'), "BasicSDE does not have a noise_type attribute" + assert basic_sde.noise_type == "diagonal", "noise_type attribute does not equal 'diagonal'" + assert hasattr(basic_sde, 'sde_type'), "BasicSDE does not have a noise_type attribute" + assert basic_sde.sde_type == "ito", "sde_type attribute does not equal 'ito'" + assert basic_sde.in_features == 0 + assert basic_sde.out_features == 0 + + + def test_f_output_shape(self, basic_sde, batch_size, state_size, time_size): + ts = torch.linspace(0, 1, time_size) + y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) + output = basic_sde.f(ts, y0) + assert output.shape[0] == y0.shape[0], "Dimension 0 of f output not equal to state size" + assert output.shape[1] == time_size, "Dimension 1 of f output not equal to time size" + + + def test_integrate_shape(self, basic_sde,batch_size, state_size, time_size): + integrator = BasicSDEIntegrator(basic_sde) + model = Node(integrator, input_keys=['y','t'], output_keys=['ys']) + batch_size, state_size, t_size = 5, 1, 100 + + ts = torch.linspace(0, 1, time_size) + y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) + my_data = {'y': y0, 't': ts} + output = model(my_data)['ys'] + assert output.shape == torch.Size([time_size, batch_size, state_size]) + + +class StochasticLorenz(object): + """Stochastic Lorenz attractor. + + Used for simulating ground truth and obtaining noisy data. + Details described in Section 7.2 https://arxiv.org/pdf/2001.01328.pdf + Default a, b from https://openreview.net/pdf?id=HkzRQhR9YX + """ + noise_type = "diagonal" + sde_type = "ito" + + def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)): + super(StochasticLorenz, self).__init__() + self.a = a + self.b = b + + def f(self, t, y): + x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) + a1, a2, a3 = self.a + + f1 = a1 * (x2 - x1) + f2 = a2 * x1 - x2 - x1 * x3 + f3 = x1 * x2 - a3 * x3 + return torch.cat([f1, f2, f3], dim=1) + + def g(self, t, y): + x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) + b1, b2, b3 = self.b + + g1 = x1 * b1 + g2 = x2 * b2 + g3 = x3 * b3 + return torch.cat([g1, g2, g3], dim=1) + + @torch.no_grad() + def sample(self, x0, ts, noise_std, normalize): + """Sample data for training. Store data normalization constants if necessary.""" + xs = torchsde.sdeint(self, x0, ts) + if normalize: + mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1)) + xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std) + return xs + + +class TestLatentSDEBlockAndIntegrator: + """ Testing class for LatentSDE_Encoder and LatentSDEIntegrator """ + + def setup_method(self): + torch.manual_seed(0) + batch_size=1 + self.latent_size=4 + self.context_size=16 + self.hidden_size=16 + t0=0. + t1=2. + self.noise_std=0.01 + _y0 = torch.randn(batch_size, 3) + self.ts = torch.linspace(t0, t1, steps=5) + xs = StochasticLorenz().sample(_y0, self.ts, self.noise_std, normalize=True) + train_data = DictDataset({'xs':xs},name='train') + self.train_data_loader = DataLoader(train_data, batch_size=1024, collate_fn=train_data.collate_fn, shuffle=False) + + def test_latent_sde_initialization(self): + sde_block_encoder = blocks.LatentSDE_Encoder(3, self.latent_size, self.context_size, self.hidden_size, ts=self.ts, adjoint=False) + assert torch.allclose(sde_block_encoder.ts, self.ts) + assert sde_block_encoder.adjoint == False + + + def test_latent_sde_forward(self): + sde_block_encoder = blocks.LatentSDE_Encoder(3, self.latent_size, self.context_size, self.hidden_size, ts=self.ts, adjoint=False) + integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False) + model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1') + + sample = next(iter(self.train_data_loader)) + output_1 = model_1(sample) + assert isinstance(output_1, dict), "Output of LatentSDE_Encoder should be a dictionary" + assert sorted(list(output_1.keys())) == ['log_ratio', 'qz0_logstd', 'qz0_mean', 'xs', 'z0', 'zs'], "Keys of output of LatentSDE_Encoder are incorrect" + assert output_1['z0'].shape == torch.Size([1,4]) + assert output_1['zs'].shape == torch.Size([5,1,4]) + + From 5228b45e954b95c58b022800e98c9c2ade3b5d0d Mon Sep 17 00:00:00 2001 From: "Birmiwal, Rahul R" Date: Thu, 25 Apr 2024 11:33:36 -0700 Subject: [PATCH 3/6] adding notebook for prototyping ode into sde framework with ref tracking loss --- examples/SDEs/test.ipynb | 6416 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 6416 insertions(+) create mode 100644 examples/SDEs/test.ipynb diff --git a/examples/SDEs/test.ipynb b/examples/SDEs/test.ipynb new file mode 100644 index 00000000..dd1fd270 --- /dev/null +++ b/examples/SDEs/test.ipynb @@ -0,0 +1,6416 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 430, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from neuromancer.system import Node, System\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "\n", + "def get_data(sys, nsim, nsteps, ts, bs):\n", + " \"\"\"\n", + " :param nsteps: (int) Number of timesteps for each batch of training data\n", + " :param sys: (psl.system)\n", + " :param ts: (float) step size\n", + " :param bs: (int) batch size\n", + "\n", + " \"\"\"\n", + " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", + " nx = sys.nx\n", + " nbatch = nsim//nsteps #500\n", + " length = (nsim//nsteps) * nsteps #1000\n", + " ts = torch.linspace(0,1,nsteps)\n", + " print('train sim ', train_sim['X'].shape)\n", + "\n", + " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " trainX = torch.tensor(trainX, dtype=torch.float32)\n", + "\n", + " print(trainX.shape)# N x nsteps x state_size \n", + "\n", + " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", + " train_loader = DataLoader(train_data, batch_size=bs,\n", + " collate_fn=train_data.collate_fn, shuffle=True)\n", + "\n", + " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " devX = torch.tensor(devX, dtype=torch.float32)\n", + " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", + " dev_loader = DataLoader(dev_data, batch_size=bs,\n", + " collate_fn=dev_data.collate_fn, shuffle=True)\n", + "\n", + " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", + " testX = torch.tensor(testX, dtype=torch.float32)\n", + " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", + "\n", + " return train_loader, dev_loader, test_data, trainX" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1" + ] + }, + "execution_count": 376, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 366, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "torch.manual_seed(0)\n", + "\n", + "# %% ground truth system\n", + "system = psl.systems['LotkaVolterra']\n", + "modelSystem = system()\n", + "ts = modelSystem.ts\n", + "nx = modelSystem.nx\n", + "raw = modelSystem.simulate(nsim=1000, ts=ts)\n", + "plot.pltOL(Y=raw['X'])\n", + "plot.pltPhase(X=raw['Y'])" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 2)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw['Y'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraHybrid(ode.ODESystem):\n", + "\n", + " def __init__(self, block, insize=2, outsize=2):\n", + " \"\"\"\n", + "\n", + " :param block:\n", + " :param insize:\n", + " :param outsize:\n", + " \"\"\"\n", + " super().__init__(insize=insize, outsize=outsize)\n", + " self.block = block\n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " assert self.block.in_features == 2\n", + " assert self.block.out_features == 1\n", + "\n", + " def ode_equations(self, x):\n", + " x1 = x[:, [0]]\n", + " x2 = x[:, [-1]]\n", + " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", + " dx2 = self.delta*self.block(x) - self.gamma*x2\n", + " return torch.cat([dx1, dx2], dim=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 431, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train sim (1000, 2)\n", + "torch.Size([500, 2, 2])\n" + ] + } + ], + "source": [ + "nsim = 1000\n", + "nsteps = 2\n", + "bs = 10\n", + "train_loader, dev_loader, test_data, trainX = \\\n", + " get_data(modelSystem, nsim, nsteps, ts, bs)\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "fx = LotkaVolterraHybrid(net)\n", + "# integrate UDE model\n", + "#fxRK4 = integrators.RK4(fx, h=ts)\n", + "# create symbolic UDE model\n", + "#ude = Node(fxRK4, ['xn'], ['xn'], name='UDE')\n", + "#dynamics_model = System([ude])" + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1000, 2])" + ] + }, + "execution_count": 433, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data['X'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 434, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 1, 2])" + ] + }, + "execution_count": 434, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo = next(iter(train_loader))['xn'].shape\n", + "foo" + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train sim (2000, 2)\n", + "torch.Size([1000, 2, 2])\n" + ] + }, + { + "ename": "ValueError", + "evalue": "too many values to unpack (expected 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[429], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_loader, dev_loader, test_data \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 2\u001b[0m get_data(modelSystem, nsim, nsteps, ts, bs)\n\u001b[1;32m 4\u001b[0m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mshape\n", + "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)" + ] + } + ], + "source": [ + "train_loader, dev_loader, test_data = \\\n", + " get_data(modelSystem, nsim, nsteps, ts, bs)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1000, 2])" + ] + }, + "execution_count": 236, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data['X'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 2, 2])" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo['X'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 2, 2])" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo = next(iter(train_loader))\n", + "baz = dynamics_model(foo)\n", + "crow = baz['xn'][:, :-1, :]\n", + "zee = foo['X']\n", + "crow.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0.15577884018421173\n", + "epoch: 102 train_loss: 0.3836634159088135\n", + "epoch: 103 train_loss: 0.3684500455856323\n", + "epoch: 104 train_loss: 0.28714412450790405\n", + "epoch: 105 train_loss: 0.4594331383705139\n", + "epoch: 106 train_loss: 0.17900754511356354\n", + "epoch: 107 train_loss: 0.2360505312681198\n", + "epoch: 108 train_loss: 0.3810018301010132\n", + "epoch: 109 train_loss: 0.4112996757030487\n", + "epoch: 110 train_loss: 0.25561970472335815\n", + "epoch: 111 train_loss: 0.1432962864637375\n", + "epoch: 112 train_loss: 1.3921493291854858\n", + "epoch: 113 train_loss: 0.5470081567764282\n", + "epoch: 114 train_loss: 0.4255885183811188\n", + "epoch: 115 train_loss: 0.14385442435741425\n", + "epoch: 116 train_loss: 0.08258526772260666\n", + "epoch: 117 train_loss: 0.1857418566942215\n", + "epoch: 118 train_loss: 0.25271618366241455\n", + "epoch: 119 train_loss: 0.4440329372882843\n", + "epoch: 120 train_loss: 0.4315054714679718\n", + "epoch: 121 train_loss: 0.16495734453201294\n", + "epoch: 122 train_loss: 0.1989777684211731\n", + "epoch: 123 train_loss: 0.06788838654756546\n", + "epoch: 124 train_loss: 0.10921105742454529\n", + "epoch: 125 train_loss: 0.197842538356781\n", + "epoch: 126 train_loss: 0.6866052746772766\n", + "epoch: 127 train_loss: 0.36010658740997314\n", + "epoch: 128 train_loss: 0.12890280783176422\n", + "epoch: 129 train_loss: 0.0686657503247261\n", + "epoch: 130 train_loss: 0.14874637126922607\n", + "epoch: 131 train_loss: 0.7163857817649841\n", + "epoch: 132 train_loss: 0.17970648407936096\n", + "epoch: 133 train_loss: 0.04967895522713661\n", + "epoch: 134 train_loss: 0.1680101603269577\n", + "epoch: 135 train_loss: 0.11648871749639511\n", + "epoch: 136 train_loss: 0.1996196210384369\n", + "epoch: 137 train_loss: 0.11213837563991547\n", + "epoch: 138 train_loss: 0.1378120332956314\n", + "epoch: 139 train_loss: 0.11473117023706436\n", + "epoch: 140 train_loss: 0.10234798491001129\n", + "epoch: 141 train_loss: 0.07809299975633621\n", + "epoch: 142 train_loss: 0.4205518960952759\n", + "epoch: 143 train_loss: 0.2789265811443329\n", + "epoch: 144 train_loss: 0.1455356925725937\n", + "epoch: 145 train_loss: 0.20582439005374908\n", + "epoch: 146 train_loss: 0.15785151720046997\n", + "epoch: 147 train_loss: 0.21461555361747742\n", + "epoch: 148 train_loss: 0.23502787947654724\n", + "epoch: 149 train_loss: 0.9507098197937012\n", + "epoch: 150 train_loss: 0.4213985502719879\n", + "epoch: 151 train_loss: 0.1036115363240242\n", + "epoch: 152 train_loss: 0.1722414642572403\n", + "epoch: 153 train_loss: 0.12554770708084106\n", + "epoch: 154 train_loss: 0.17391566932201385\n", + "epoch: 155 train_loss: 0.46148595213890076\n", + "epoch: 156 train_loss: 0.25193721055984497\n", + "epoch: 157 train_loss: 0.3315165042877197\n", + "epoch: 158 train_loss: 0.11715126782655716\n", + "epoch: 159 train_loss: 0.5376226305961609\n", + "epoch: 160 train_loss: 0.2285894751548767\n", + "epoch: 161 train_loss: 0.4176816940307617\n", + "epoch: 162 train_loss: 0.1202869787812233\n", + "epoch: 163 train_loss: 0.09944035857915878\n", + "epoch: 164 train_loss: 0.3274291753768921\n", + "epoch: 165 train_loss: 0.09140706807374954\n", + "epoch: 166 train_loss: 0.22308826446533203\n", + "epoch: 167 train_loss: 0.16436980664730072\n", + "epoch: 168 train_loss: 0.05272998288273811\n", + "epoch: 169 train_loss: 0.09057249873876572\n", + "epoch: 170 train_loss: 0.38802826404571533\n", + "epoch: 171 train_loss: 0.44728174805641174\n", + "epoch: 172 train_loss: 0.13979801535606384\n", + "epoch: 173 train_loss: 0.4556901454925537\n", + "epoch: 174 train_loss: 0.14013370871543884\n", + "epoch: 175 train_loss: 0.13612936437129974\n", + "epoch: 176 train_loss: 0.11906873434782028\n", + "epoch: 177 train_loss: 0.06214645877480507\n", + "epoch: 178 train_loss: 0.2867833077907562\n", + "epoch: 179 train_loss: 0.30405566096305847\n", + "epoch: 180 train_loss: 0.0545855276286602\n", + "epoch: 181 train_loss: 0.28040555119514465\n", + "epoch: 182 train_loss: 0.1463025063276291\n", + "epoch: 183 train_loss: 0.08449428528547287\n", + "epoch: 184 train_loss: 0.34794628620147705\n", + "epoch: 185 train_loss: 0.6925814151763916\n", + "epoch: 186 train_loss: 0.15413250029087067\n", + "epoch: 187 train_loss: 0.07249744981527328\n", + "epoch: 188 train_loss: 0.09451506286859512\n", + "epoch: 189 train_loss: 0.39824146032333374\n", + "epoch: 190 train_loss: 0.178904190659523\n", + "epoch: 191 train_loss: 0.07271047681570053\n", + "epoch: 192 train_loss: 0.10113447159528732\n", + "epoch: 193 train_loss: 0.20800787210464478\n", + "epoch: 194 train_loss: 0.2562291622161865\n", + "epoch: 195 train_loss: 0.0981765016913414\n", + "Interrupted training loop.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = variable(\"X\")\n", + "xhat = variable('xn')[:, :-1, :]\n", + "\n", + "# trajectory tracking loss\n", + "reference_loss = (xhat == x)^2\n", + "reference_loss.name = \"ref_loss\"\n", + "\n", + "# finite difference variables\n", + "xFD = (x[:, 1:, :] - x[:, :-1, :])\n", + "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n", + "\n", + "# finite difference loss\n", + "fd_loss = 2.0*((xFD == xhatFD)^2)\n", + "fd_loss.name = 'FD_loss'\n", + "\n", + "# %%\n", + "objectives = [reference_loss, fd_loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([dynamics_model], loss)\n", + "# plot computational graph\n", + "problem.show()\n", + "\n", + "# %%\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "\n", + "trainer = Trainer(\n", + " problem,\n", + " train_loader,\n", + " dev_loader,\n", + " test_data,\n", + " optimizer,\n", + " patience=5000,\n", + " warmup=5000,\n", + " epochs=500,\n", + " eval_metric=\"dev_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"dev_loss\",\n", + " test_metric=\"dev_loss\",\n", + " device='cpu'\n", + ")\n", + "# %%\n", + "best_model = trainer.train()\n", + "problem.load_state_dict(best_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 361, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learned parameter a= 0.10000000149011612\n", + "Learned parameter b= 0.10000000149011612\n", + "Learned parameter c= 0.10000000149011612\n", + "Learned parameter d= 0.10000000149011612\n", + "True parameter a= 1.0\n", + "True parameter b= 0.10000000149011612\n", + "True parameter c= 1.5\n", + "True parameter d= 0.75\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Batch sizes not consistent.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[361], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend(fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Test set results\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m pred_traj \u001b[38;5;241m=\u001b[39m test_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m][:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, :]\n\u001b[1;32m 27\u001b[0m true_traj \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", + "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." + ] + }, + { + "data": { + "image/png": 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PPw8A2LFjB5KTkzFnzhxERUWhtrYWaWlp+OCDD1BWVobbbrsN58+fx+XLl7tcblRUFD799FPcf//90Gq1OHToEIYMGYL7778fd9xxByIiIiCXy6FSqZCXl4fjx49j165duj2kQs9kfURkWy42NGDe+fN621K8vbGw3dEYuyR6AIDe2yeffKLr09DQIJ544gnh7e0tXF1dxT333CNKSko6LCc/P19MmDBBuLi4CD8/P7F48WLR0tLSoc++ffvEiBEjhEwmE4MGDeqwjjarV68W4eHhQiaTiVGjRonDhw/35OUIlUolAAiVStWj57XX0NAgsrKyRENDQ6+XQdYtLy/P4Pu9vYiICF2fffv29WgdTU1N4uabbzb4O9Z2i4uLE2VlZT1a17fffis8PT27Xfa1t7Nnz/boNZB+/IwgS2nWaMSoY8cE9u3rdPP58UdR1Nho6RJ7zdjv7x7tyRFG/GXn7OyMNWvWYM2aNQb7RERE4Ntvv+1yObfddhtOnjzZZZ/58+dj/vz53dZEZO1kMhm+++47LF68GB999FGnw1VyuRx//OMf8fbbb8O9h7uWJ0yYgJycHKxcuRKffPJJl1dwBQUFYcKECXj44Yc7jGRORLZnaX4+fqmp0dv2QUwMBlw9emLPJMKY5GKn1Go1PD09oVKpen0ScmNjI/Ly8hAZGQlnZ2cTV0j9UXl5Ofbs2YPCwkI4ODggPDwct99+O3x9fa972VqtFsePH0dmZiauXLmCpqYmKBQKhIaGIj4+nsGmD/AzgixhT1UVfn/6tN6rqR4NCsK/YmPNXpMpGfv9zQk6iayMv78/pk6d2ifLlkql+O1vf4vf/va3fbJ8IrK88uZmPHT2rN6AE+PigneHDDF7TZZi+1OMEhEREYBfTyuZmZ2NkqvTJLUnk0jwZXw83Oz0cnF9GHKIiIjsxOrLl7GzslJv298HDcIN7UZY7w8YcoiIiOzA6dpaPG1gdvE7fXywwN4vF9eDIYeIiMjG1Wk0mJqVhWY91xIFyWT4JDbWrmYXNxZDDhERkY1bmJuLbD1Tt0gAfB4biwA7m13cWAw5RERENmxzWRk+KinR2/Z0WBhS7HB2cWMx5BAREdmoS42NmG1gnrnfenjgr9fMMdnfMOQQERHZoFatFg9mZUGlZ0JfDwcHfBkfD5m0f3/N9+9XT0REZKNeuXQJh9RqvW1ro6Mx2MXFzBVZH4YcIiIiG3OguhqvXrqkt+2hwEBMDww0c0XWiSGHiIjIhpQ3N+PBrCy90zZEubhgTT+atqE7DDlEREQ2QisEHs7ORrGeaRscJRJ8GRcHD0dOS9mGIYeIiMhGrCosxC4D0zasiIzEjV3MyN0fMeQQERHZgEMqFV64eFFv23gfHywKCzNzRdaPIYeIiMjKVbS0YGpWFjpfLA6EyGT4LDYW0n44bUN3GHKIiIismBACM7OzUdjU1KlNCmBDfDz8++m0Dd1hyCEiIrJibxcV4ZuKCr1tywcOxK1eXuYtyIYw5BAREVmpX9RqPGvgPJyxXl54PiLCzBXZFoYcIiIiK1Td0oIpWVloEZ1HxAl0csK/4+LgwPNwusSQQ0REZGWEEJiVk4P8xsZObRIA/46LQ5Bcbv7CbAxDDhERkZVZc/kytly5orfthYgIpPj4mLki28SQQ0REZEVO1NRg8YULettu8fTEMp6HYzSGHCKidgYOHAiJRAKJRIL9+/dbuhzqZ9Strfi/zEw06zkPx8/JCRvi4+Eo5Ve3sbiliK565JFHdF9ut912m6XLIaJ+RgiBP507hwt6zsMBgM9jYzGA5+H0CEMOERGRFVhXXIyNZWV6254NC8N4X18zV2T7GHKIiIgs7JhajYW5uXrbblIo8NfISDNXZB8YcoiIiCyosqUF9xs4D8fb0RFfxsfDiefh9Aq3GhERkYVohcCM7Gxc0jMvFQB8GhuLcGdnM1dlPxhyiIiILGRVYSF2GJiX6rnwcEzy8zNzRfaFIYeoD125cgVvvfUWxo0bh4iICLi4uMDLywvx8fGYN28eDh8+bPSyGhoa8PXXX2PBggW4+eabERQUBLlcDjc3N4SHh+MPf/gD3n33XdTW1hq1vPZXky1fvhwAoNVqsX37djzwwAMYMmQI3N3dO7QD+i+xbmpqwieffII77rgDAwYMgFwuR0hICO6++25s2bLF6NfYRgiBbdu2YebMmYiNjYW3tzdcXFwQHh6Ou+66Cx9//DFaWlp6tEylUolly5ZhxIgR8PT0hEKh0P0/pKen97hGouu1v6oKzxuYl+pWT0/8deBA8xZkj0Q/plKpBAChUql6vYyGhgaRlZUlGhoaTFgZWcKMGTMEAAFA3Hrrrde9vLfeekt4enrqlmnoNn36dFFfX9/lsjZs2CA8PDy6XRYA4ePjI77++usevd5ly5YJpVIpxo4dq3eZy5Yt0z0vIiJC9/i+ffvE+fPnxYgRI7qs6Z577hFNTU1Gbbdjx46JG264odvXOWTIEHHs2DGjlrl161bh4+NjcFkODg5i5cqVel/f9eJnBOlT0tgoAn/6SWDfvk63wJ9+EsWNjZYu0aoZ+/3taNLERETQarWYM2cOPvzwQ91jEokEQ4YMQUhICBobG5GRkaHb4/LFF18gPz8fe/bsgdzAGBgXLlxATU2N7ueAgAAMHDgQHh4eaGhowLlz53Dl6hDwlZWVmDx5MrZt24ZJkyYZVXNTUxPuvPNOnDhxAgAQGBiIIUOGoLW1FTk5OQafp1Qq8cc//hGXL18GAERHR2PAgAGorq5Geno6NBoNAGDr1q1YtGgR3nvvvS7r2LVrF+6//37U1dXpHvPz88OQIUMgl8uRl5eHS5cuAQDOnz+P22+/Hd9//z2Sk5MNLnPHjh144IEH0NraqnssMDAQ0dHRaGhowJkzZ9DU1IRnnnkGbm5u3WwpouvXqtVi2tmzKNWzN1IKYGN8PII5Ho5pmCl0WSXuyaH2TLUnZ8WKFbrlSCQSsWDBAlFUVNShT1NTk1i7dq1wdXXV9V24cKHBZb766qvid7/7nfjggw/E5cuX9fb58ccfRVJSkm55fn5+Qq1WG1xm+9fbtpdo8ODB4vvvvxdarVbXr7m5WRQUFOh+br+nw9fXVwAQd911lzh//nyH5RcWForbb79d11cqlYpz584ZrOf8+fPC3d1d13/UqFFi//79HWoRQogjR4502NMTEREhqqur9S6zrKyswx4cX19fsWnTJqHRaHR9qqqqxKJFiwQA4ezs3KEG7smhvrDkwgW9e3Cwb594LT/f0uXZBO7JsQEnkk+YdHkBUwMQuiC0yz5F7xShbKP+waZ66zdpv+myvbmsGRl3Z5hkWdbu3LlzWLp0KYBf99588cUXmDZtWqd+MpkMc+bMQVxcHFJSUtDa2orVq1djwYIFGKjnOPzChQvxwgsvdLnuMWPGYP/+/bj99ttx+PBhXLlyBZ999hnmzZvXbd01NTUICwvDTz/9hKCgoA5tTk5OCAsL0/u8iooKPPjgg/j3v/8NiUTSoS00NBRff/01YmJiUFxcDK1Wi88//xyvvPKK3mXNmjVLt3dr0qRJ+O9//wsnJ6dO/UaNGoUff/wRY8aMwalTp3Dp0iW8++67eOmllzr1ffnll1FZWQkAcHZ2xvfff4+RI0d26OPl5YU33ngDrq6uePXVVw1sISLT2FlRgRUFBXrbJvr44NnwcDNXZN8YcixIfVht0uUpkhXd9mm81Gjy9XZH26w1+zot5a233tKdEDtz5ky9Aae9W2+9FbNnz8batWuh0Wjw/vvvY8WKFZ36GXsYxdnZGX/7298wduxYAMD27duNCjkA8Oabb3YKON3x8vLC2rVrOwWcNu7u7njsscd0webnn3/W2+/IkSM4ePAgAMDX1xefffaZ3oDTxs3NDevWrcPo0aMBAOvWrcOLL77YoY76+np8/vnnup+ffPLJTgGnvWXLlmHTpk04d+6cwT5E1yO/oQEPnT2rty1CLsdncXGQGvhdot7h1VVEJqLVarFhwwbdzwsXLjTqedOnT9fd37t373XXkZSUpLt/7Ngxo57j7++PyZMn93hdU6dOhULRdbgeM2aM7n52drbePu3DyIwZM+Dl5dXtupOSkhAVFQUAKC4u7rTsffv2Qa3+NVxLJBI88cQTXS7P0dERf/rTn7pdL1FvNGm1+L+sLFS1OzesjUwiweahQ+HTRbCn3uGeHCITOX36tO5L1c/PD4mJiUY9LyEhQXf/1KlTEEIY3DMCAHl5edizZw/S09NRXl6OmpqaDifVtldVVYX6+nq4urp2WcPo0aPh6Njzj4OuTvhtM2DAAN396upqvX1+/PFH3f077rjD6PUnJCQg9+pQ+CdOnEBcXJyu7ejRo7r78fHxCA3t+lAuAEyYMAGLFy82ev1Exlqcm4uj7S4eaO+tqCj8tps/Fqh3GHKITCQj4/+fd9TU1ITx48f3eBnNzc1Qq9Xw9PTs1JadnY0FCxZg9+7dEHqGfzdEpVJ1G3IGDx7c41oBGHV4q/266+vrO7ULIZCZman7+bXXXsPq1auNWv+ZM2d099uuLmuT224eoPZBsivR0dFwcnLq8Rg8RF3ZWFqKNcXFetumBQRgbkiImSvqPxhyLEgx2rTJ3Tmi+6G/nSOcTb7e7khlUrOv0xIq2o1aWlNTg++//75Xy1GpVJ1CzsGDBzFhwgS9IaE7TQaGi2/Pw8Ojx8sFfj2B+nqpVCrdpeYAcOjQoV4vp72qqirdfV8jZ292cHCAp6dnp8BE1FsZtbWYZWAYhlhXV3wQHd3lnlu6Pgw5FmSJK4lCF4R2ewWWqckCZDZ/1ZQx2o/tcj20Wm2Hn9VqNR544AFdwPHw8MCjjz6KcePGITo6GkFBQXBxcYGDg4PuOT390JRacPK/vtpuzc3Nuvs9CWOGxioi6ilVayvuzcxE/TXvTQBwlUrxn6FD4d6Lw8RkPG5dIhNpv/dl6NChHQ5fXY+PP/4YZWW/Xvbv7e2NI0eOYMiQIQb71xg47m+trt1rdfToUdx4443Xvdz2J0T3ZJvY2vYj66QVAg+fPYvzDQ162z+IicFQDj7Z53h1FZGJBAYG6u63hRJT2L17t+7+ggULugw4AHSjD9sKd3f3DuftmGrbBQQE6O7n5+cb9ZzKykrdyeNE12NFQQG+NjDx5tyQEExv93lBfYchh8hE2sZsAYDy8nJcNDDxXk8VtBs4zJg9HGlpaSZZrzm133Y9mbS0KzfccIPu/okTJzqc92PIL7/8YpJ1U//2fWUlXsrL09s2WqHA21eHPqC+x5BDZCJhYWEdLmH+9NNPTbLcnl7ps379epOs15xSU1N197/44gujAkl3br75Zt39qqqqDnvEDNm4ceN1r5f6t/yGBjyYlQV91z8GODnhP0OHQmbBc+D6G25pIhN68skndffffPNNnDUwumlPBAcH6+4bGjG4zebNm3UjB9uSxx57DO7u7gCAixcv6h31uafi4uI6jHC8dOnSLsNTVlYWvvjii+teL/VfDRoN7s3MRKWecascAGwaOhQDeGK7WTHkEJnQjBkzMHz4cABAbW0tUlJSOgx0Z0hGRgZmz56NTz75pFPbrbfeqrv/3nvvdRgbpr3vv/8ejzzySO8KtzAfHx+8+OKLup+XLl2Kl19+udu9WNXV1Xj33XcxZcoUve3PPfec7v7Ro0fxxBNP6B04saioCJMnTzY4qCJRd4QQmHvuHE5enX/tWisHD8atRozkTabFq6uI9Dh48CCcnbsfd6i9nJwcREREYMuWLRg1ahQqKipQXFyMW265BXfccQf+8Ic/IDY2Fh4eHqitrUVJSQlOnjyJH374QbfHR9/cSo8//jhWrlyJhoYG1NTU4KabbsLcuXNx++23w83NDQUFBdi6dSu2bNkC4Ne9Ih999NH1bwQze+aZZ/DLL79gy5YtEEJg+fLl+Ne//oVp06YhKSkJfn5+aG1tRWVlJTIyMpCWloZ9+/ahpaWlw1QW7d1///2YNGkSvvnmGwDABx98gF9++QWzZ89GXFwcGhoa8NNPP2Ht2rWorq7GTTfdhIKCAhQVFZnzpZMdWFdcjE9LS/W2TfH3x5NGjLhNpseQQ6SHEMKoQfSufQ4ADBo0CEeOHMFdd92FrKwsAL/OSdXbealCQkLw/vvvY8aMGRBCoLa2FqtWrcKqVas69b355puxevVqmww5EokEX331FRYuXIg1a9YAAAoLC7Fy5crrWu6GDRvw+9//XndC86lTp/ROWhoaGooNGzZ02HNGZIw0lQoL2o2w3d5QV1d8FBPDAf8shIeriPrA4MGDceLECbz77rsYNGhQl33d3d0xadIkfPnllwYPNz300EPYvn07IiMj9bZ7e3vjhRdewN69e3u8B8qaODo64r333sOPP/6IcePGdRjg8FoSiQQ33HAD/vrXv2Lz5s0G+7m7u2Pfvn149tln4eLi0qndwcEBkydPxvHjxxEREWGS10H9R2lzM+7PzESLnqlWFA4O2JKQwAH/LEgiejIJjp1pmyNIpVJ1O5OyIY2NjcjLy0NkZKRNf7lQ3zp//jyOHTuGsrIy1NTUwM3NDYGBgYiNjUViYiKcjJx9uLW1FWlpabrJQP38/DBw4EDcdtttJpliwdqoVCr89NNPKCwsRGVlJRwdHeHl5YWoqCgMGzYMfn5+PVpeTU0NfvjhB+Tl5UEIgdDQUIwZM6bDJKKmxs8I+9Wq1SLl9GkcuGZKkTbbEhJwdw/fo2QcY7+/GXIYcoioD/Ezwn4tzs3FmwbO33o+PBx/62YvLvWesd/fPFxFRETUQ1+WlhoMOOO8vfGKgUPLZF4MOURERD1wsqbG4MziEXI5NsTHw4EnGlsFhhwiIiIjlTc3Y3JGBhr0zCwul0iwJSEBvkaeY0d9jyGHiIjICC1aLR7IzESBgeEl1kZH4zceHmauirrCkENERGSERRcuGLyS6s8DBmBmuylYyDow5BAREXXj45ISvHf5st6227y88MbgwWauiIzBkENERNSFwyoV5p47p7ctQi7Hpvh4OHFmcavE/xUiIiIDipuacG9mJpr1DCnnIpViW0IC/O1wIE57wZBDRESkR5NWi/syM1HS3Ky3/eOYGIzgicZWjSGHiIjoGkIIPHHuHA6r1XrbnwkLw9TAQDNXRT3FkENERHSNfxYX42OlUm/beB8fvMYpG2xCj0POwYMHMWnSJISEhEAikWDbtm0d2h955BFIJJIOt/Hjx3foU1lZienTp0OhUMDLywuzZs1CbW1thz7p6em4+eab4ezsjLCwMKxcubJTLZs3b0ZsbCycnZ2RmJiIb7/9tqcvx2T68RRgRNQFfjbYngPV1ViYm6u3LcrFBRvi4jiisY3occipq6vD8OHDsWbNGoN9xo8fj5KSEt3tyy+/7NA+ffp0ZGZmYvfu3dixYwcOHjyIxx9/XNeuVqsxbtw4RERE4Pjx41i1ahWWL1+ODz74QNfn0KFDmDZtGmbNmoWTJ09i8uTJmDx5MjIyMnr6kq6L9OoZ9Vo9o18SEbV9Nkh59Y1NKGhsxP2ZmWjVE07dHRywLSEB3hzR2GZc1yzkEokEW7duxeTJk3WPPfLII6iuru60h6fN2bNnER8fj6NHj+LGG28EAOzatQt33nknioqKEBISgrVr1+KFF16AUqmE7OpZ68899xy2bduG7OxsAMCUKVNQV1eHHTt26JY9evRojBgxAuvWrTOqflPMQq7VanHu3Dn4+/vD19e3V8sgIvtVUVGB8vJyREdHM+hYuTqNBmNOnsSpa44stNmWkIC7/fzMXBXpY9FZyPfv34+AgADExMRg7ty5qKio0LWlpaXBy8tLF3AAICUlBVKpFEeOHNH1ueWWW3QBBwBSU1ORk5ODqqoqXZ+UlJQO601NTUVaWprBupqamqBWqzvcrpdUKoW7u7tJlkVE9ketVsPd3Z0Bx8pphcDDZ88aDDjLBw5kwLFBJv+tGz9+PD777DPs2bMHf//733HgwAFMmDABGo0GAKBUKhEQENDhOY6OjvDx8YHy6kleSqUSgdectd72c3d9lAZOFAOAFStWwNPTU3cLCwu7vhd7lUKhQGNjI+rq6kyyPCKyD3V1dWhsbOz1nmIyn2X5+dhy5Yretsl+fngpIsLMFZEpOJp6gVOnTtXdT0xMxLBhwzB48GDs378fY8eONfXqemTJkiVYtGiR7me1Wm2SoOPu7g43NzcUFhYiLCwMbm5u171MIrJtdXV1KCwshJubG9zd3S1dDnXhy9JSvHrpkt62eFdXfBYbCylPNLZJJg851xo0aBD8/PyQm5uLsWPHIigoCGVlZR36tLa2orKyEkFBQQCAoKAglJaWdujT9nN3fdra9ZHL5ZDL5df9mq4llUoRGhqKoqIiFBQUwNnZGQqFAs7OzpBKpZDwl4PI7gkhoNVq0djYCLVajcbGRri5uSE0NJSHqqzYL2o1Hs3J0dvm6+iIbxIT4eHY51+V1Ef6/H+uqKgIFRUVCL46O2tycjKqq6tx/PhxjBw5EgCwd+9eaLVaJCUl6fq88MILaGlpgdPVs9h3796NmJgYeHt76/rs2bMHCxcu1K1r9+7dSE5O7uuXpFdb0KmtrYVarUZ5eTkvHSXqhyQSCdzd3eHr68tzcazc5aYmTM7IQKOeq2MdJRL8NyEBg1xcLFAZmUqPQ05tbS1y240fkJeXh1OnTsHHxwc+Pj54+eWXcd999yEoKAgXLlzAM888g6ioKKSmpgIA4uLiMH78eMyePRvr1q1DS0sL5s+fj6lTpyIkJAQA8OCDD+Lll1/GrFmz8OyzzyIjIwPvvPMO3nrrLd16FyxYgFtvvRVvvPEGJk6ciI0bN+LYsWMdLjM3N6lUCoVCAYVCAa1Wi9bWVl5aTtSPSKVSODo6MtjYgHqNBnefOWNwyoa1Q4bgVi8v8xZFpid6aN++fQJAp9uMGTNEfX29GDdunPD39xdOTk4iIiJCzJ49WyiVyg7LqKioENOmTRPu7u5CoVCImTNnipqamg59Tp8+LcaMGSPkcrkYMGCAeP311zvVsmnTJhEdHS1kMpkYOnSo2LlzZ49ei0qlEgCESqXq6WYgIiIbpdVqxf9lZAjs26f3tuDcOUuXSN0w9vv7usbJsXWmGCeHiIhsyyv5+ViWn6+3LdXbGzsSE+HIvXFWzaLj5BAREVmjzWVlBgNOrKsrvho6lAHHjvB/koiI+oXjNTWYcXXU/Gt5Ozrim4QEePJKKrvCkENERHavpKkJd585gwYDV1L9Z+hQRLm6WqAy6ksMOUREZNcaNBpMzsjAZQNXUq2OisIdV4cnIfvCkENERHZLKwQeyc7GLzU1etvnDxiAOQMGmLkqMheGHCIislsv5eVhU3m53rYUb2+8NXiwmSsic2LIISIiu/RJSQleKyjQ2xbt4oJN8fG8ksrO8X+XiIjszt6qKjx+7pzeNu+rc1J5X502iOwXQw4REdmV7Lo63JeZiVY9Y906SSTYmpCAaF5J1S8w5BARkd0ob27GnWfOoLq1VW/7hzExnJOqH2HIISIiu9B49VLxvMZGve0vhIdjRlCQmasiS2LIISIim6cVAjNzcnBIrdbbPsXfH69ERpq5KrI0hhwiIrJ5y/LzsbGsTG9bskKB9bGxkEokZq6KLI0hh4iIbNqnSiVevXRJb1ukszO2JyTA2cHBzFWRNWDIISIim7W/qgqzc3L0tnk6OGBnYiL8ZTIzV0XWgiGHiIhsUk59Pe7NzESLnkvFHSUSbElIQJybmwUqI2vBkENERDanrLkZd6ano8rApeLvR0dz0k1iyCEiIttSp9HgD2fO4KKBS8WfCw/Ho8HBZq6KrBFDDhER2YxWrRZTs7Jw1MCs4g/4++NvvFScrmLIISIimyCEwPzz57GjokJve5KHBz7lpeLUDkMOERHZhNcLCvB+SYnetigXF3yTmAgXXipO7TDkEBGR1fu3Uonn8/L0tvk5OeE7XipOejDkEBGRVdtTVYVHDYyF4yKVYkdiIqI4qzjpwZBDRERWK722FvdmZOgdC0cKYGN8PJIUCvMXRjaBIYeIiKxSYWMj7kxPh1qj0dv+3pAhuMvPz8xVkS1hyCEiIqtT3dKCO8+cweXmZr3tz4WHY+6AAWauimwNQw4REVmVZq0W92ZmIqOuTm/79IAAjoVDRmHIISIiq6EVAo9mZ2NfdbXe9ju8vPAxx8IhIzHkEBGR1Xju4kV8UVamty3BzQ1bEhIgk/Kri4zDdwoREVmFNwoLsaqwUG/bAJkM3yYmwtPR0cxVkS1jyCEiIov7TKnEUxcu6G1TODjgu2HDEObsbOaqyNYx5BARkUV9W1GBR7Oz9bY5SSTYkpCARHd3M1dF9oAhh4iILCZNpcL9mZnQNxKOBMDncXEY6+1t7rLITjDkEBGRRWTV1WHimTNo0Gr1tr8TFYUpAQFmrorsCUMOERGZXWFjI1LT01HV2qq3/YXwcPw5NNTMVZG9YcghIiKzqmhpwbj0dBQ1Neltnx0cjL9ysD8yAYYcIiIymzqNBhPT05FdX6+3fbKfH/45ZAgkHOyPTIAhh4iIzKJFq8UDmZk4UlOjt/0WT098GRcHRw72RybCdxIREfU5rRB4NCcH31VW6m0f5uaG7QkJcHZwMHNlZM8YcoiIqE8JIfD0hQv4d2mp3vZIZ2fsGjYMXk5OZq6M7B1DDhER9am/XbqEN4uK9Lb5Oznh+2HDECyXm7kq6g8YcoiIqM+sLirCS/n5etvcr07XMMTV1bxFUb/BkENERH3iU6USf8nN1dvmJJFgW0ICRnp4mLkq6k8YcoiIyOS2lJcbnI9KCuALTtdAZsCQQ0REJrW7shLTsrKgf7IG4IOYGDzA6RrIDBhyiIjIZA6pVJickYFmIfS2vzl4MGYFB5u5KuqvGHKIiMgkTtXU4M70dNQbmHBzaUQEngwLM3NV1J8x5BAR0XU7V1+PcenpUGk0etsXDBiA5QMHmrco6vcYcoiI6LoUNDYi5fRplLe06G1/JCgIb0ZFcT4qMjuGHCIi6rXS5maknD6NQgMzit/n54cPo6MhZcAhC2DIISKiXqlqaUHq6dM439Cgt32ctze+iI/nhJtkMXznERFRj6lbWzE+PR2n6+r0tv9OocCWhATIGXDIgvjuIyKiHqltbcWE9HT8UlOjt32Euzt2JCbCjTOKk4Ux5BARkdHqNRpMysjAIbVab3uMiwu+54ziZCUYcoiIyCiNGg0mZ2Rgf3W13vYIuRy7hw9HgExm3sKIDGDIISKibjVrtbg/MxO7q6r0tofK5dg7YgTCnJ3NXBmRYQw5RETUpRatFlOysrCzslJve5BMhj3Dh2OQi4uZKyPqGkMOEREZ1KrV4o9nz2LblSt62/2dnLBn+HBEu7qauTKi7jHkEBGRXhohMDMnB5vKy/W2+zg64ofhwxHv5mbmyoiMw5BDRESdaIXAn3Jy8O/SUr3tng4O2D18OIa5u5u5MiLjMeQQEVEHQgjMP38e/1Iq9ba7Ozjg++HD8RsPDzNXRtQzDDlERKQjhMCiCxewtrhYb7urVIrvEhORpFCYuTKinmPIISIiAP8/4LxdVKS33VkqxTeJiRjj5WXewoh6iSGHiIi6DTgyiQTbEhJwh7e3mSsj6r0eh5yDBw9i0qRJCAkJgUQiwbZt2zq0CyGwdOlSBAcHw8XFBSkpKTh//nyHPpWVlZg+fToUCgW8vLwwa9Ys1NbWduiTnp6Om2++Gc7OzggLC8PKlSs71bJ582bExsbC2dkZiYmJ+Pbbb3v6coiI+j0hBJ7MzTUYcBwlEvx36FCk+viYuTKi69PjkFNXV4fhw4djzZo1ettXrlyJd999F+vWrcORI0fg5uaG1NRUNDY26vpMnz4dmZmZ2L17N3bs2IGDBw/i8ccf17Wr1WqMGzcOEREROH78OFatWoXly5fjgw8+0PU5dOgQpk2bhlmzZuHkyZOYPHkyJk+ejIyMjJ6+JCKifqst4Lxz+bLedkeJBF/Fx+MPfn5mrozo+kmEEKLXT5ZIsHXrVkyePBnAr78sISEhWLx4MZ566ikAgEqlQmBgINavX4+pU6fi7NmziI+Px9GjR3HjjTcCAHbt2oU777wTRUVFCAkJwdq1a/HCCy9AqVRCdnUOlOeeew7btm1DdnY2AGDKlCmoq6vDjh07dPWMHj0aI0aMwLp164yqX61Ww9PTEyqVCgqeREdE/YwQAgtzc/FuNwHnXn9/M1dG1DVjv79Nek5OXl4elEolUlJSdI95enoiKSkJaWlpAIC0tDR4eXnpAg4ApKSkQCqV4siRI7o+t9xyiy7gAEBqaipycnJQdXXelLS0tA7raevTth59mpqaoFarO9yIiPojIQQWMOCQnTNpyFFeHVMhMDCww+OBgYG6NqVSiYCAgA7tjo6O8PHx6dBH3zLar8NQH6WBcR0AYMWKFfD09NTdwsLCevoSiYhsXlvAWd1FwNnEgEN2oF9dXbVkyRKoVCrdrbCw0NIlERGZlRACfzEi4NzDgEN2wNGUCwsKCgIAlJaWIjg4WPd4aWkpRowYoetTVlbW4Xmtra2orKzUPT8oKAil1wwl3vZzd33a2vWRy+WQy+W9eGVERLZPKwT+cv481hgY6M9RIsHm+HhMZsAhO2HSPTmRkZEICgrCnj17dI+p1WocOXIEycnJAIDk5GRUV1fj+PHjuj579+6FVqtFUlKSrs/BgwfR0tKi67N7927ExMTA++oYDcnJyR3W09anbT1ERPT/aYTAn86d6zLg/GfoUAYcsis9Djm1tbU4deoUTp06BeDXk41PnTqFgoICSCQSLFy4EK+++iq+/vprnDlzBg8//DBCQkJ0V2DFxcVh/PjxmD17Nn755Rf8/PPPmD9/PqZOnYqQkBAAwIMPPgiZTIZZs2YhMzMTX331Fd555x0sWrRIV8eCBQuwa9cuvPHGG8jOzsby5ctx7NgxzJ8///q3ChGRHWnVajEzOxsflZTobXe6GnDu5mXiZG9ED+3bt08A6HSbMWOGEEIIrVYrXnrpJREYGCjkcrkYO3asyMnJ6bCMiooKMW3aNOHu7i4UCoWYOXOmqKmp6dDn9OnTYsyYMUIul4sBAwaI119/vVMtmzZtEtHR0UImk4mhQ4eKnTt39ui1qFQqAUCoVKqebQQiIhvRrNGIBzIyBPbt03tz2r9fbC8vt3SZRD1i7Pf3dY2TY+s4Tg4R2bMmrRZTMjOxvaJCb7tMIsHmoUNxF/fgkI0x9vvbpCceExGRdWjQaHBvZiZ2VVbqbXeWSrF16FCM9/U1c2VE5sOQQ0RkZ2pbW3FXRgb2VVfrbXe9Ops4J9ske8eQQ0RkR1StrZiYno6fDYzo7uHggG8TEzHGy8u8hRFZAEMOEZGdqGppQWp6Oo7W1Oht93J0xPfDhmEUz0GkfoIhh4jIDpQ1NyM1PR2namv1tvs6OmL38OG4wcPDzJURWQ5DDhGRjStobMTvT5/GuYYGve2BTk7YM2IEhrq5mbkyIstiyCEismHn6uuRcvo0Cpua9LYPkMmwd8QIRLu6mrkyIstjyCEislGnamqQmp6OsnZT4LQXIZdj74gRGOTiYubKiKwDQw4RkQ06pFLhzvR0qDQave1DXFzww/DhCHd2NnNlRNaDIYeIyMbsrqzE5IwM1Gu1etuHu7nh++HDESiTmbkyIuvCkENEZEP+W16OaVlZaDEwI0+yQoGdiYnwdnIyc2VE1ochh4jIRnxSUoLHcnKgf/8NMM7bG1sSEuDm4GDWuoisldTSBRARUffeKSrCo10EnPv8/PB1YiIDDlE73JNDRGTFhBB4MS8PrxUUGOzzSFAQPoyOhqOUf7cStceQQ0RkpVq1Wsw5dw7/UioN9lkwYADejIqCVCIxY2VEtoEhh4jICjVoNJialYWvKyoM9lk+cCCWRkRAwoBDpBdDDhGRlalqacFdGRn4SaUy2OetwYOxMCzMjFUR2R6GHCIiK3K5qQnj09ORUVent91RIsHHMTF4KCjIzJUR2R6GHCIiK5FdV4fU9HQUGJiHylUqxX+GDsUEX18zV0ZkmxhyiIiswBG1GhPT01HR2qq33dfRETuHDUOSQmHmyohsF0MOEZGF7aqowH2ZmQanaQiTy/G/YcMQ6+Zm5sqIbBtDDhGRBX1SUoLHz51Dq4FpGoa6umLXsGEI5USbRD3GkENEZAFCCLycn4+XL10y2Od3CgW+4TxURL3GkENEZGYtWi3+dO4cPulikL+7fH2xMT4eLpymgajXGHKIiMxI3dqK+zMzsbuqymCfR4OC8D6naSC6bgw5RERmcrmpCRPT03HawBg4APBiRAReGTiQoxgTmQBDDhGRGZyprcWdZ86gyMAYOA4A3o+JwazgYPMWRmTHGHKIiPrY3qoq3JORAbVGo7fd3cEBm+PjMZ6D/BGZFEMOEVEf+lypxKycHLQYuEQ8SCbDt4mJuMHDw8yVEdk/hhwioj4ghMDy/Hy80sUl4vGurvh22DBEcAwcoj7BkENEZGKNGg1m5uRgY1mZwT63enpia0ICx8Ah6kMMOUREJlTa3IzJGRk4rFYb7DMtIACfxMZCzkvEifoUQw4RkYlk1NbiD2fO4JKBK6gA4LnwcPwtMhJSXiJO1OcYcoiITGBXRQX+LysLNQauoHKUSPDPIUMwOyTEzJUR9V8MOURE1+m9oiIsyM2F/jnEAU8HB/w3IQFjvb3NWhdRf8eQQ0TUS61aLZ68cAHvXb5ssM8gZ2fsTExErJubGSsjIoAhh4ioV1StrZiWlYXvKisN9hnj6YmtQ4fCTyYzY2VE1IYhh4ioh87V1+OuM2eQ09BgsM/DgYH4ICaGV1ARWRBDDhFRD3xfWYkpmZlQGTjBGAD+FhmJJeHhnGSTyMIYcoiIjCCEwFtFRXj6wgWDJxg7S6X4LDYWDwQEmLU2ItKPIYeIqBuNGg3+dO4cPistNdgn0MkJXycmYpRCYcbKiKgrDDlERF0obmrCvRkZOFJTY7DPSHd3bEtIQCjnoCKyKgw5REQGHFWrMTkjA8XNzQb7PBgQgI9iYuDi4GDGyojIGAw5RER6/FupxGM5OWgSQm+7BMDrgwbh6bAwnmBMZKUYcoiI2mnRavHMxYt4u6jIYB+FgwM2xMdjoq+vGSsjop5iyCEiukrZ1IQpWVk4qFIZ7BPl4oKvExIQxxGMiaweQw4REYA0lQr3Z2Z2ef7NOG9vbIyPh7eTkxkrI6LeYsghon5NCIF1xcVYkJuLFgPn3wDA4tBQvD5oEBw5gjGRzWDIIaJ+q0GjwRPnz2O9Ummwj7NUig+io/FQUJAZKyMiU2DIIaJ+Kb+hAfdlZuJEba3BPgOdnbFl6FDc4OFhxsqIyFQYcoio39ldWYmpWVmobG012Ge8jw++iIuDD8+/IbJZPLhMRP2GRgi8kp+P1PT0LgPOSxER2JGYyIBDZOO4J4eI+oWy5mb88exZ7K6qMthH4eCAz+PicJefnxkrI6K+wpBDRHbvx+pqTM3K6vLy8KGurtiSkIBoV1czVkZEfYkhh4jsllYI/KOwEM9fvAhNF/3+z98f/4qJgbsjPxKJ7Al/o4nILlW2tGBGdjZ2VFQY7OMokeDvgwbhydBQzj9FZIcYcojI7hxRq/F/mZkoaGoy2CdULsdX8fG4ydPTjJURkTkx5BCR3RBCYPXly3jqwoUuRy8e7+ODz2Nj4SeTmbE6IjI3hhwisgtXmpvxaE4Ovuni8JQUwF8jI/FceDikPDxFZPcYcojI5u2rqsIfz57t8uqpIJkMX8bF4TZvbzNWRkSWxJBDRDarVavFy5cu4W+XLsHwwSngdi8vbIiLQ5BcbrbaiMjyGHKIyCZdamzEg1lZOKRWG+wjAfBiRASWDRwIBx6eIup3GHKIyOZsLivD7JwcqDSGR78JdHLCZ3FxGOfjY8bKiMiaMOQQkc2o12iwMDcXH5aUdNkv1dsbn8bFIZBXTxH1ayafoHP58uWQSCQdbrGxsbr2xsZGzJs3D76+vnB3d8d9992H0tLSDssoKCjAxIkT4erqioCAADz99NNovWYyvf379+M3v/kN5HI5oqKisH79elO/FCKyIidranDj8eNdBhwniQT/GDwY3w4bxoBDRH0zC/nQoUNRUlKiu/3000+6tieffBLffPMNNm/ejAMHDqC4uBj33nuvrl2j0WDixIlobm7GoUOH8Omnn2L9+vVYunSprk9eXh4mTpyI22+/HadOncLChQvx2GOP4fvvv++Ll0NEFqQRAisuXULSiRM4W19vsF+UiwsO3XADFoeF8fJwIgIASIToYsSsXli+fDm2bduGU6dOdWpTqVTw9/fHhg0bcP/99wMAsrOzERcXh7S0NIwePRrfffcd/vCHP6C4uBiBgYEAgHXr1uHZZ59FeXk5ZDIZnn32WezcuRMZGRm6ZU+dOhXV1dXYtWuX0bWq1Wp4enpCpVJBoVBc3wsnIpPLa2jAQ2fP4ucuTi4GgIcDA/HekCHw4NxTRP2Csd/ffbIn5/z58wgJCcGgQYMwffp0FBQUAACOHz+OlpYWpKSk6PrGxsYiPDwcaWlpAIC0tDQkJibqAg4ApKamQq1WIzMzU9en/TLa+rQtw5Cmpiao1eoONyKyPkIIfFJSgmHHjnUZcNwdHPB5bCw+jYtjwCGiTkwecpKSkrB+/Xrs2rULa9euRV5eHm6++WbU1NRAqVRCJpPBy8urw3MCAwOhVCoBAEqlskPAaWtva+uqj1qtRkNDg8HaVqxYAU9PT90tLCzsel8uEZnYleZm3JeZiUdzclDbxdVTN3p44OTIkfhjUJAZqyMiW2LyP30mTJiguz9s2DAkJSUhIiICmzZtgouLi6lX1yNLlizBokWLdD+r1WoGHSIr8m1FBR7NzkZpS4vBPlIAS8LDsXTgQMikfbIzmojsRJ/v3/Xy8kJ0dDRyc3Px+9//Hs3Nzaiuru6wN6e0tBRBV/8aCwoKwi+//NJhGW1XX7Xvc+0VWaWlpVAoFF0GKblcDjlHPCWyOrWtrXjm4kWsLS7ust8gZ2d8HhfHmcOJyCh9/mdQbW0tLly4gODgYIwcORJOTk7Ys2ePrj0nJwcFBQVITk4GACQnJ+PMmTMoKyvT9dm9ezcUCgXi4+N1fdovo61P2zKIyHYcqK7GsGPHug04jwUH49SNNzLgEJHRTL4n56mnnsKkSZMQERGB4uJiLFu2DA4ODpg2bRo8PT0xa9YsLFq0CD4+PlAoFPjzn/+M5ORkjB49GgAwbtw4xMfH46GHHsLKlSuhVCrx4osvYt68ebq9MHPmzMF7772HZ555Bo8++ij27t2LTZs2YefOnaZ+OUTUR+o0Gjx38SLeu3y5y35+Tk74KCYGd/v5makyIrIXJg85RUVFmDZtGioqKuDv748xY8bg8OHD8Pf3BwC89dZbkEqluO+++9DU1ITU1FT885//1D3fwcEBO3bswNy5c5GcnAw3NzfMmDEDr7zyiq5PZGQkdu7ciSeffBLvvPMOQkND8dFHHyE1NdXUL4eI+sCB6mo8mp2Ni42NXfab6OODf8XGcmA/IuoVk4+TY0s4Tg6RedVpNFhy8SJWd7P3xlUqxVtRUZgdHAwJB/YjomsY+/3NgSWIyCwOXt17c6GbvTe/UyjwSWwshri6mqkyIrJXDDlE1KdqW1vxQl4eVl++jK52G7tIpXgtMhJ/Dg2FA/feEJEJMOQQUZ/ZVVGBOefO4VJTU5f9uPeGiPoCQw4RmVx5czOezM3FF+2GgtDH+erem79w7w0R9QGGHCIyGSEEPi8txaLcXFS0tnbZ96are2+iufeGiPoIQw4RmcTFhgbMOXcOu6uquuznLJXib5GRWMC9N0TUxxhyiOi6tGq1eLuoCEvz89Gg1XbZ92ZPT3wYE4MY7r0hIjNgyCGiXjuqVmPOuXM4UVvbZT9PBwesHDwYjwUHQ8q9N0RkJgw5RNRjVS0teCEvD+uKi7u8LBwA7vPzw+ohQxDMyXGJyMwYcojIaEIIfFZaiqcvXEB5S0uXfUNkMqwZMgSTr07pQkRkbgw5RGSUjNpaPHH+PH5UqbrtOzckBCsGDYKnIz9iiMhy+AlERF2qaW3Fy/n5eLuoCJpu+sa6uuLD6GiM8fIyR2lERF1iyCEivYQQ+G95ORbm5uJyc3OXfZ2lUrwQHo6nw8Mhl0rNVCERUdcYcoiokzO1tViYm4u91dXd9p3o44PVQ4Yg0sWl7wsjIuoBhhwi0rnS3Iyl+fl4v7gYXY94A4TL5Xh3yBDc5esLCS8LJyIrxJBDRGjRarG2uBjL8vNR3c10DE4SCZ4KC8MLERFwc3AwU4VERD3HkEPUz/2vshILc3Nxtr6+2763e3lhzZAhiHNzM0NlRETXhyGHqJ/Kra/H4gsX8HVFRbd9g2UyrBo8GA8GBPDQFBHZDIYcon6moqUFr166hDWXL6NFdD1esVwiweKwMCwJD4c7x7whIhvDTy2ifqJBo8Hqy5fx2qVLUGm6G/EGuNfPD6sGD8YgXjVFRDaKIYfIzmmFwBelpXghLw+FTU3d9k9wc8M7UVG4w9vbDNUREfUdhhwiO7a7shLPXLyIU93MEg4APo6OeDUyErODg+HIAf2IyA4w5BDZodO1tXj2wgV8X1XVbV8HAPMGDMCygQPh4+TU98UREZkJQw6RHTlfX4/l+fn4sqwMXZ9S/KvJfn54fdAgxLi69nltRETmxpBDZAcKGhvxSn4+1iuV3U6iCQCjFQqsGjSIE2kSkV1jyCGyYcqmJrxWUID3i4vR3M3l4AAQ5eKCFZGRuM/fn+PdEJHdY8ghskEVLS1YWVCA1Zcvo0Hb3SxTgJ+TE5ZFRODxkBDIeFIxEfUTDDlENqS6pQVvFxXhzaIi1Bgx1o2zVIpFoaF4JjwcnhzMj4j6GX7qEdmAipYWvFVYiNWXL0NtRLhxkkjweHAwXoiIQLBcboYKiYisD0MOkRUrbW7GG4WF+Ofly6gz4rCUA4AZQUFYOnAgIpyd+75AIiIrxpBDZIWKm5qwsqAAH5SUGHXOjQTA1IAALB84ENG8HJyICABDDpFVKWhsxN8LCvBRSYlRV0sBwN2+vvhrZCQS3d37uDoiItvCkENkBTJqa/GPwkJ8UVaGViPDTaq3N16JjMQohaKPqyMisk0MOUQWIoTA/upqrCosxHeVlUY/b5KvL16MiGC4ISLqBkMOkZm1arXYcuUKVhUW4lhNjdHPu8/PDy9GRGCEh0cfVkdEZD8YcojMpF6jwSdKJd4sLMTFxkajniMFMCUgAC9ERGCom1vfFkhEZGcYcoj6WFFjI9YWF+P94mJUtLYa9RwHAH8MDMTzERG8WoqIqJcYcoj6gBACP6tUePfyZWwpLzdq0kwAcJVKMSs4GE+GhiLSxaVPayQisncMOUQm1KjR4MuyMqy+fBkna2uNfp6/kxP+MmAA5g4YAF8npz6skIio/2DIITKBtkNSH5SU4EpLi9HPG+LigsVhYXg4MBAuDg59WCERUf/DkEPUSxoh8L/KSnxQUoJvrlwx+pAUACR5eOCZ8HDc7ecHB4mkz2okIurPGHKIeuhyUxM+LinBRyUlKGhqMvp5DgDu8ffHXwYMwBhPT0gYboiI+hRDDpERNEJgV2UlPiguxo6KCnQ/m9T/5+voiMdDQjA3JARhnDSTiMhsGHKIupDX0IBPlUp8rFSisAd7bQBguJsbFoSGYmpAAM+3ISKyAIYcomuoW1vxn/JyfKpU4qBK1aPn8pAUEZH1YMghwq+Ho36oqsKnSiW2XrmCRm1PDkgBoXI5ZgUFYVZwMA9JERFZCYYc6tcyamvxWWkp/l1aipLm5h49Vwpgoq8vHg8OxngfHzhKpX1TJBER9QpDDvU75+rr8VVZGTaVlyOjrq7Hzw+Ty/FYcDAeDQpCKPfaEBFZLYYc6hcuNjTogs2pHoxE3MZRIsFEHx88HhKCVB8fjm1DRGQDGHLIbhU0NmJTWRm+Ki/HsZqaXi3jN+7umBEUhKkBAQiQyUxcIRER9SWGHLIbQgik19Vh+5Ur2H7lCk70Yo8NAATLZPhjYCAeDgxEgru7iaskIiJzYcghm9ai1eJHlQrbr1zB1xUVyG9s7NVyXKRS3OPnh4eDgpDi7c3DUUREdoAhh2xOdUsLfqiqwvaKCuysqEBVa2uvluMkkWCctzemBATgbj8/KBz560BEZE/4qU5WTysETtTUYFdlJXZVVuKwWt2jyTDbcwAw9mqwucfPD95OTqYslYiIrAhDDlklZVMT/ldVhe8rK/G/qipcaWnp9bKkAG7z8sL/BQTgXj8/+PMEYiKifoEhh6xCVUsLflSpsL+6Gvuqq3t1mXd7MokEY729cbefH+729UWQXG6iSomIyFYw5JBFVLYLNfurq3G6thbiOpfp7eiIib6+uNvXF6k+PvDgOTZERP0avwWozwkhUNDUhMNqNQ6pVDhQXY30urrrDjUAECGXY7KfH+7288MYT084cWoFIiK6iiGHTK5Oo8HxmhocVqt1t57OC2WIXCLBLV5eSPXxQaq3N4a6uXGmbyIi0oshh65Lo0aDjLo6nKqtxYnaWhxRq3G6trbXVz/pE+PiglQfH4z38cGtXl5wdXAw4dKJiMheMeSQ0SpbWnC6thYna2tx6uq/Z+vqTBpoAMDPyQm3eHpi3NW9NQNdXEy8BiIi6g8YcqiTqpYWnK2vR1Zd3a//Xr1f0NTUJ+vzc3LCbV5euNXTE7d5eSHezQ1SHoIiIqLrxJDTT9W2tuJiYyMuNDT8emtsRE59Pc7W10NpovNnDAlwcsItXl647eot3tWV59UQEZHJMeTYISEEKltbUdTUhMLGRhQ2Nf16v6lJF2pKr2NwvZ5wlEhwg7s7RisUSFYoMFqhwEBnZ4YaIiLqczYfctasWYNVq1ZBqVRi+PDhWL16NUaNGmXpskyuRauFqrUVVa2tKG9pQVlzM8paWlDa/t/mZpQ0N6OwqQkNWq1F6gyTyzH6apgZrVDgBnd3uPBEYSIisgCbDjlfffUVFi1ahHXr1iEpKQlvv/02UlNTkZOTg4CAALPXc7q2FsVNTdAIAecJFwABCLTdfh0VRgAQAtBCQKvnXw0EtEKgVQAaIdAqBDRC4Ifbgf/e3/X67/sP8MA+076m+Wv0Py4FEOvqiuQmV0xeUAM3Bwe4OkjhJJECaAJQDqAcZ3uxzgF/GYDAaYFd9rn4wkVU763uxdL1c/J3QuLXiV32qT1Ti3OPnzPZOgEg8tVIeI/17rJPzuwc1GXUmWydbgluiPkwpss+VXuqkPdinsnWCQDRH0TDPdG9yz5n7jqDlnLT7WX0usMLg/42qMs+pV+W4vK7l022TgBI2J4AWUDX04ecSD5h0nUGTA1A6ILQLvsUvVOEso1lJl3vb9J+02V7c1kzMu7OMOk6+RnRkTV/RnT3/uhrNh1y3nzzTcyePRszZ84EAKxbtw47d+7Exx9/jOeee87s9fzt0iVsLi8HAOw7Ztpln4nvvk9gKTA0y7TrBX49MTje1RXxbm4Y4e6OG9zdkejmBhcHBzQWNeLwycMAgIart+vl/4B/t30azjVAfVhtgrX9Sjag+/msNLUak64TAFoqu/9Cr8uoM/l6u9NS2WLydWpqu78Or+ZEDZovm+6cMHlo99N5NJc0m/y1apu735Nq6nUqkhXd9mm81Gj295K2WWvydfIzoiN7+YzoCzYbcpqbm3H8+HEsWbJE95hUKkVKSgrS0tL0PqepqQlN7a4QUqtN+x/kYIfnmZTfdBP8OKElERHZIJsdA//KlSvQaDQIDOy4yzIwMBBKpVLvc1asWAFPT0/dLSwszKQ12eOZJww4RERkq2w25PTGkiVLoFKpdLfCwkKTLt/RQntyJPj1kJKPk83umCMiIjI5m/1W9PPzg4ODA0pLSzs8XlpaiqCgIL3PkcvlkMu7P0bfW+0PV2UacQ5NexIAUgkghQRSya+ByRESOEgkcJRIEBvjipcHesPT0RF+Tk4IdHJCgEyGACcn+Dk5wVEqRdGxIpTlmfakwu5IZVIoRnd/LkBPyIK733vkEu1i0vU6+Tt128fB3cHkr9XJp/v1uiW4mXSdxizPycfJ5K/Vwb37fZ0ev/FAS5jpTjx2ie5+tGxZsMzkr1Uq6/7vR1Ov0znC2ag+pl5vd/gZcZ3r7UefEX1BIoQwxWTQFpGUlIRRo0Zh9erVAACtVovw8HDMnz/fqBOP1Wo1PD09oVKpoFBc/3/WmdpalDQ3w0EigQN+DT1tIeXax5ylUrhKpXB1cICLVMrZs4mIiIxk7Pe3ze7JAYBFixZhxowZuPHGGzFq1Ci8/fbbqKur011tZW6J7u7o+gJDIiIiMhebDjlTpkxBeXk5li5dCqVSiREjRmDXrl2dTkYmIiKi/semD1ddL1MfriIiIqK+Z+z3N08EISIiIrvEkENERER2iSGHiIiI7BJDDhEREdklhhwiIiKySww5REREZJcYcoiIiMguMeQQERGRXWLIISIiIrtk09M6XK+2wZ7VarWFKyEiIiJjtX1vdzdpQ78OOTU1NQCAsLAwC1dCREREPVVTUwNPT0+D7f167iqtVovi4mJ4eHhAIpGYbLlqtRphYWEoLCzknFhG4PYyHreV8biteobby3jcVj3TF9tLCIGamhqEhIRAKjV85k2/3pMjlUoRGhraZ8tXKBT8BegBbi/jcVsZj9uqZ7i9jMdt1TOm3l5d7cFpwxOPiYiIyC4x5BAREZFdYsjpA3K5HMuWLYNcLrd0KTaB28t43FbG47bqGW4v43Fb9Ywlt1e/PvGYiIiI7Bf35BAREZFdYsghIiIiu8SQQ0RERHaJIYeIiIjsEkNOH1izZg0GDhwIZ2dnJCUl4ZdffrF0SRa3fPlySCSSDrfY2Fhde2NjI+bNmwdfX1+4u7vjvvvuQ2lpqQUrNp+DBw9i0qRJCAkJgUQiwbZt2zq0CyGwdOlSBAcHw8XFBSkpKTh//nyHPpWVlZg+fToUCgW8vLwwa9Ys1NbWmvFVmE932+uRRx7p9F4bP358hz79ZXutWLECv/3tb+Hh4YGAgABMnjwZOTk5HfoY87tXUFCAiRMnwtXVFQEBAXj66afR2tpqzpfS54zZVrfddlun99acOXM69OkP22rt2rUYNmyYbnC/5ORkfPfdd7p2a3pPMeSY2FdffYVFixZh2bJlOHHiBIYPH47U1FSUlZVZujSLGzp0KEpKSnS3n376Sdf25JNP4ptvvsHmzZtx4MABFBcX495777VgteZTV1eH4cOHY82aNXrbV65ciXfffRfr1q3DkSNH4ObmhtTUVDQ2Nur6TJ8+HZmZmdi9ezd27NiBgwcP4vHHHzfXSzCr7rYXAIwfP77De+3LL7/s0N5ftteBAwcwb948HD58GLt370ZLSwvGjRuHuro6XZ/ufvc0Gg0mTpyI5uZmHDp0CJ9++inWr1+PpUuXWuIl9RljthUAzJ49u8N7a+XKlbq2/rKtQkND8frrr+P48eM4duwY7rjjDtx9993IzMwEYGXvKUEmNWrUKDFv3jzdzxqNRoSEhIgVK1ZYsCrLW7ZsmRg+fLjeturqauHk5CQ2b96se+zs2bMCgEhLSzNThdYBgNi6davuZ61WK4KCgsSqVat0j1VXVwu5XC6+/PJLIYQQWVlZAoA4evSors93330nJBKJuHz5stlqt4Rrt5cQQsyYMUPcfffdBp/Tn7dXWVmZACAOHDgghDDud+/bb78VUqlUKJVKXZ+1a9cKhUIhmpqazPsCzOjabSWEELfeeqtYsGCBwef0120lhBDe3t7io48+srr3FPfkmFBzczOOHz+OlJQU3WNSqRQpKSlIS0uzYGXW4fz58wgJCcGgQYMwffp0FBQUAACOHz+OlpaWDtstNjYW4eHh/X675eXlQalUdtg2np6eSEpK0m2btLQ0eHl54cYbb9T1SUlJgVQqxZEjR8xeszXYv38/AgICEBMTg7lz56KiokLX1p+3l0qlAgD4+PgAMO53Ly0tDYmJiQgMDNT1SU1NhVqt1v3lbo+u3VZtvvjiC/j5+SEhIQFLlixBfX29rq0/biuNRoONGzeirq4OycnJVvee6tcTdJralStXoNFoOvzHAUBgYCCys7MtVJV1SEpKwvr16xETE4OSkhK8/PLLuPnmm5GRkQGlUgmZTAYvL68OzwkMDIRSqbRMwVai7fXre0+1tSmVSgQEBHRod3R0hI+PT7/cfuPHj8e9996LyMhIXLhwAc8//zwmTJiAtLQ0ODg49NvtpdVqsXDhQvzud79DQkICABj1u6dUKvW+/9ra7JG+bQUADz74ICIiIhASEoL09HQ8++yzyMnJwZYtWwD0r2115swZJCcno7GxEe7u7ti6dSvi4+Nx6tQpq3pPMeSQWUyYMEF3f9iwYUhKSkJERAQ2bdoEFxcXC1ZG9mbq1Km6+4mJiRg2bBgGDx6M/fv3Y+zYsRaszLLmzZuHjIyMDufCkX6GtlX787YSExMRHByMsWPH4sKFCxg8eLC5y7SomJgYnDp1CiqVCv/5z38wY8YMHDhwwNJldcLDVSbk5+cHBweHTmeRl5aWIigoyEJVWScvLy9ER0cjNzcXQUFBaG5uRnV1dYc+3G7Qvf6u3lNBQUGdTmxvbW1FZWVlv99+ADBo0CD4+fkhNzcXQP/cXvPnz8eOHTuwb98+hIaG6h435ncvKChI7/uvrc3eGNpW+iQlJQFAh/dWf9lWMpkMUVFRGDlyJFasWIHhw4fjnXfesbr3FEOOCclkMowcORJ79uzRPabVarFnzx4kJydbsDLrU1tbiwsXLiA4OBgjR46Ek5NTh+2Wk5ODgoKCfr/dIiMjERQU1GHbqNVqHDlyRLdtkpOTUV1djePHj+v67N27F1qtVvch3J8VFRWhoqICwcHBAPrX9hJCYP78+di6dSv27t2LyMjIDu3G/O4lJyfjzJkzHYLh7t27oVAoEB8fb54XYgbdbSt9Tp06BQAd3lv9YVvpo9Vq0dTUZH3vKZOexkxi48aNQi6Xi/Xr14usrCzx+OOPCy8vrw5nkfdHixcvFvv37xd5eXni559/FikpKcLPz0+UlZUJIYSYM2eOCA8PF3v37hXHjh0TycnJIjk52cJVm0dNTY04efKkOHnypAAg3nzzTXHy5Elx6dIlIYQQr7/+uvDy8hLbt28X6enp4u677xaRkZGioaFBt4zx48eLG264QRw5ckT89NNPYsiQIWLatGmWekl9qqvtVVNTI5566imRlpYm8vLyxA8//CB+85vfiCFDhojGxkbdMvrL9po7d67w9PQU+/fvFyUlJbpbfX29rk93v3utra0iISFBjBs3Tpw6dUrs2rVL+Pv7iyVLlljiJfWZ7rZVbm6ueOWVV8SxY8dEXl6e2L59uxg0aJC45ZZbdMvoL9vqueeeEwcOHBB5eXkiPT1dPPfcc0IikYj//e9/Qgjrek8x5PSB1atXi/DwcCGTycSoUaPE4cOHLV2SxU2ZMkUEBwcLmUwmBgwYIKZMmSJyc3N17Q0NDeKJJ54Q3t7ewtXVVdxzzz2ipKTEghWbz759+wSATrcZM2YIIX69jPyll14SgYGBQi6Xi7Fjx4qcnJwOy6ioqBDTpk0T7u7uQqFQiJkzZ4qamhoLvJq+19X2qq+vF+PGjRP+/v7CyclJREREiNmzZ3f6I6O/bC992wmA+OSTT3R9jPndy8/PFxMmTBAuLi7Cz89PLF68WLS0tJj51fSt7rZVQUGBuOWWW4SPj4+Qy+UiKipKPP3000KlUnVYTn/YVo8++qiIiIgQMplM+Pv7i7Fjx+oCjhDW9Z6SCCGEafcNEREREVkez8khIiIiu8SQQ0RERHaJIYeIiIjsEkMOERER2SWGHCIiIrJLDDlERERklxhyiIiIyC4x5BAREZFdYsghIiIiu8SQQ0RERHaJIYeIiIjsEkMOERER2aX/BwASqq2RodlkAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('Learned parameter a=', float(fx.alpha))\n", + "print('Learned parameter b=', float(fx.beta))\n", + "print('Learned parameter c=', float(fx.gamma))\n", + "print('Learned parameter d=', float(fx.delta))\n", + "\n", + "print('True parameter a=', float(modelSystem.a))\n", + "print('True parameter b=', float(modelSystem.b))\n", + "print('True parameter c=', float(modelSystem.c))\n", + "print('True parameter d=', float(modelSystem.d))\n", + "\n", + "# evaluate learned black box block\n", + "x1 = torch.arange(0., 150., 0.5)\n", + "x2 = torch.arange(0., 150., 0.5)\n", + "true_block = x1*x2\n", + "learned_block = net(torch.stack([x1, x2]).T).squeeze()\n", + "plt.figure()\n", + "plt.plot(true_block.detach().numpy(), 'c',\n", + " linewidth=4.0, label='True')\n", + "plt.plot(learned_block.detach().numpy(), 'm--',\n", + " linewidth=4.0, label='Learned')\n", + "plt.legend(fontsize=25)\n", + "\n", + "# Test set results\n", + "test_outputs = dynamics_model(test_data)\n", + "\n", + "pred_traj = test_outputs['xn'][:, :-1, :]\n", + "true_traj = test_data['X']\n", + "pred_traj = pred_traj.detach().numpy().reshape(-1, nx)\n", + "true_traj = true_traj.detach().numpy().reshape(-1, nx)\n", + "pred_traj, true_traj = pred_traj.transpose(1, 0), true_traj.transpose(1, 0)\n", + "\n", + "figsize = 25\n", + "fig, ax = plt.subplots(nx, figsize=(figsize, figsize))\n", + "labels = [f'$y_{k}$' for k in range(len(true_traj))]\n", + "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, labels)):\n", + " if nx > 1:\n", + " axe = ax[row]\n", + " else:\n", + " axe = ax\n", + " axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n", + " axe.plot(t1, 'c', linewidth=4.0, label='True')\n", + " axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n", + " axe.tick_params(labelbottom=False, labelsize=figsize)\n", + "axe.tick_params(labelbottom=True, labelsize=figsize)\n", + "axe.legend(fontsize=figsize)\n", + "axe.set_xlabel('$time$', fontsize=figsize)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'X': tensor([[[ 2.1548, 88.2591],\n", + " [ 1.0439, 76.8354],\n", + " [ 0.5640, 66.5173],\n", + " ...,\n", + " [ 2.5745, 90.9051],\n", + " [ 1.2156, 79.3024],\n", + " [ 0.6416, 68.7135]]]),\n", + " 'xn': tensor([[[ 2.1548, 88.2591]]])}" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraHybrid(ode.ODESystem):\n", + "\n", + " def __init__(self, block, insize=2, outsize=2):\n", + " \"\"\"\n", + "\n", + " :param block:\n", + " :param insize:\n", + " :param outsize:\n", + " \"\"\"\n", + " super().__init__(insize=insize, outsize=outsize)\n", + " self.block = block\n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " assert self.block.in_features == 2\n", + " assert self.block.out_features == 1\n", + "\n", + " def ode_equations(self, x):\n", + " x1 = x[:, [0]]\n", + " x2 = x[:, [-1]]\n", + " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", + " dx2 = self.delta*self.block(x) - self.gamma*x2\n", + " return torch.cat([dx1, dx2], dim=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "import torchsde\n", + "\n", + "class LotkaVolterraSDE(nn.Module):\n", + " def __init__(self, block, insize=2, outside=2, noise_type=\"diagonal\"):\n", + " super().__init__()\n", + " self.block = block \n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.in_features = 0\n", + " self.out_features = 0\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + "\n", + " def f(self, t, y):\n", + "\n", + " x1 = y[:, [0]]\n", + " x2 = y[:, [-1]]\n", + "\n", + " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", + " dx2 = self.delta*self.block(y) - self.gamma*x2\n", + "\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, y):\n", + " return torch.zeros(10, 2)\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "fx = LotkaVolterraSDE(block=net)\n", + "\n", + "\n", + "class BasicSDEIntegrator(integrators.Integrator): \n", + " \"\"\"\n", + " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", + " Returns a single tensor of size (t, batch_size, state_size).\n", + "\n", + " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", + " Currently only supports Euler integration. Choice of integration method is dependent \n", + " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", + " \"\"\"\n", + " def __init__(self, block, numsteps ): \n", + " \"\"\"\n", + " :param block: (nn.Module) The BasicSDE block\n", + " \"\"\"\n", + " super().__init__(block) \n", + " self.numsteps = numsteps \n", + "\n", + " def integrate(self, x): \n", + " \"\"\"\n", + " x is the initial datastate of size (batch_size, state_size)\n", + " t is the time-step vector over which to integrate\n", + " \"\"\"\n", + " t = torch.tensor([0.,0.1, 0.2], dtype=torch.float32)\n", + " x = x.squeeze(1) #remove time step \n", + " \n", + " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", + " ys = ys.permute(1, 0, 2)\n", + " return ys \n", + "\n", + "integrator = BasicSDEIntegrator(fx, numsteps=2) \n", + "# integrate UDE model\n", + "# create symbolic UDE model\n", + "model = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", + "dynamics_model = model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1, 2])" + ] + }, + "execution_count": 238, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data['xn'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 1, 2])" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo['xn'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 3, 2])" + ] + }, + "execution_count": 226, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo = next(iter(train_loader))\n", + "\n", + "baz = dynamics_model(foo)\n", + "baz['xn'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rt7Bz1wGctBo8PP4YDRseFgRAtcnM8ZR0zmbnkp2dz+HDxzCbLfzpvrkM6BeHTudESHAATz71dz5YvBQXFz2BAb4dfxAcWd0r++7UoLZ5j4WjdYEIQtsQYwaE5mllF/rFI8BBrVIh22wUFhVTUlKGVqM5lwVORqGQCAkOqJnXXlpOfkERKqWq3kJYLBbKyirw8fZk/OUj8PYy1BT53DQ3pVLJM3/9E599sYzPv1zO+o07ePLxBQwfOuCPUeg9XUcOnGzX7VzwYFsnzmxqOqC41SR0UaJnQGiedqzg3N1ccXN1Yd+BJCorq+w7s1ituLq6kNAnFme9jtT0kxSXlJ73t7Iso1Gr8fLyYP+BJA4cTLanmS03VrBh004qK6uw2mw885f7efapBygtLeOb736hpLSs7oba7wV2F02eA808hs0+l5rYXr3bacGJ2para4pAQOiiRDAgdAhjRSU2m0x1tQmoqX8tVisWixU3N1euGD+S0jIjew8kYbXWrCG/a/dBxl8+gjGjhpA4MIHTp8/y089rKS83YrFYSUs/hcVqpazcSHxcNCqVkpdefo/PvviBX39bz3Mvvk1YryCKikt5971PcXd3Y/qV4xkzeph9H3YiAUwbaM9lKtuReOsFQdwmENrfocPH+PW3DVRVVvHO+//lrnmz2bBpBzt27sfDoybV6+yZV1JYVMKLL73LoUPHKCouwWQy89xTD+Dq6sw10ydw9Fg6iz74jBU/ryE2JhyFJGGzWvnifyuYdd1UHrjvNl5+/QMeefz/cHN14YmF9xAeFsLpM2dZvXYLCx54huFD+lNZWcWsGVPx8vTo7EMjCILgEEQ64p6uA9IRW61WqqpNWC1WNBo1Go0ai8VKtcmEhIRWq0GlUmI2WygtKycj4wyRkb1wctLipNXYkwaZTGZOnc7CaKwkIiKErLO5uLu54mlwR61WY7VaKSwq5tSpLPz9ffDz9UatVmG1WikoKKaisgqTyUxggC96vRMKhaLx1LVtcf+3p6Qj7m7a8vVfuC2RjljoQGJtAqF5HGFtgjrqno71NdT1na51n9eM0/nc30DDq/E0/KtL1lWDAYfRRm+GIwU3IhgQOpBYm0DokppaZKa1v29Wq+AoGey6jdYczLZaYKC5TxRvvNAziQGEQgdr7lVoe12tNrOir/s0R75w7hK6UuPalcoqCG1HBANCG2uq5WxuZducK/zG9tWGLbi9KD05KmjqeHeGziiTox0DQWgbIhgQWu+8+rGjrqwkkP9Y0bDe3zdXs+v3HnDV2OCxqHO8HYXcyFrL7dZmO9gxEIQ2IsYMCK3XWcv+ttVytaJ+/0NDx6LcDGUmcWHcEIMWnJRiUKDQZYlgQOgYLa4ju8qArq5SzhYqroaTZSIYaEhvQ00wIAhdlAgGBAfn4A2sPQaop5zdqeG0yGC2ga2zC+KgbHK3jweF7k2MGRC6l2Y3wA088VIb8MYq/27XMNT3grpTxNPGxKERuhARDAjdS7Mb4AaeeOHDokJvQreLeNpObb6KWuJcEhyYCAYEoTGNtXWicu8G2vlNlOpsX8RNggMTwYDQiDaqKJvaTFdtVLtz5d5V35NL1t5vYnc+SYTuRAQDQiM6KBWsw9WX8nnfeiSHe08a05PfKEFoGyIYEIS6ZLC3hF2qQezJHPSNEjGK0IWIYEBoQ1249qstuoO2K0IXJCZfCF2ICAaEc9qilqqt/bpSjXeurCIIEDqCOM8EByWCAeGctqylulKN15XKKnQZXSkeFgREBkKhliw3mlfdalNgsWg6sECOTVLYUCnNKCRR6wv1EDGm0MWIYECo0cQCK2aLluJyrw4qjOPTqKtx0xehUFo6uyido7NS77Z2vyJlsCDUSwQDQv0uqDRlWUKmkSVjexjZ0Zbz7Wid9fLFCpWC0C7EmAGhfg5daYqueUEQhLYkegaES1Z3eEETQw2au0UuLfq4+LltU45WamUXdGVlJSaTCVmuCXZcXFyorq7GbDajVqvR6XRYrVZKSkowGAxYLBZKSkrQarW4ubkhdfoBaB2rzUqJ0Uh5VSU2m4y3mzsuOl29z5VlmdKKCgrLS/FwdsHg4tr4xsXtAUFolAgGhEtWt81pm/an9RtxiHawlWU4ffo0GzduJC0tjaCgIG699Vb279/P9u3bGTduHOHh4ezZs4dly5axcOFCjh07xvr163F1dWXevHmEhoa2zevoBDabjQPpaRzPPE169ll2HEtifL+BPHDNTBTSxR2YFdXV/LJ7O2/98DX3TruGeROvbHwHjnB+CIIDE7cJhDYm0zHd+N3vVkFoaCienp58/fXXfPfdd1itVjIyMigoKCAkJASj0ciyZctYunQp27ZtIzMzE19fX5YuXcqXX37Z2cVvldySYt7/aRnh/gH8edZNTE4cyqGTJ7DZ6n+fTWYzHs4uHMpIx2q1dXBpBaH7ET0DQhvrqEswB7zUa2VXtJOTE9deey0FBQU8+eSTLFq0CLVazUMPPURgYCAAo0ePZvny5URGRjJixAjKy8vZvHkz+/fvb5OX0FmST5/kRE4Weq0TKqWSu6deRV5JMSqlst7nG1xdGRoT18GlbKVOuFUhyzJlZWXs3r2bzMxM3N3dGT58OL6+vvU+t7Kykj179pCTk8OIESMICgrq2AILnUYEA0K312HjCdpgHwqFgptvvpldu3bx0Ucf8fe///28ClmlUiFJEr169UKSJBQKBd7e3uTn57d+521AlmVO5+Vy9MwpBkfHcigjnSAvHyIDAqk2m9l5LJmTuTkEenkxqk8/FJLEqbxcNh0+SG5xMfvTUik1GhkR14dAT+9G96VUXHzArTYbp/NyOXgiFZVKxYDwKAI8vezjKUqNRvalpVBaWYFOo2FEXALOTk7YbDaOnMzgaOYpPJxdCPP1JzoouG0PTifEryaTiddee41p06ah0+lYvHgxmZmZzJ8/H4Xi/I5hWZbJzMxkxYoVHDp0iLCwMBEM9CDiNoHQ7TnEeIJLoDs3aM7Pz49vv/2W4uJi++/qGySoVCrtgw47kyzL/LxrO7e98TIvLF3CF+t/5y+fLObrTes4kXOWt374mrzSYnzc3Xn166U88Z9/YayuoryygoLSEqrMJgrLS8ktKW7RTSCrzcZPO7by086tBHh6k1WQzy1/f4lNhw8CcLawgG+3rEdSSGhUKv65/Ftyi4uQZZnFv/5Iek4Wkf6BbDy0nx93bGnbg9NJNm/ezPLlyxk8eDBXXnklCxcuZMCAAfWeR5Ik4efnR3R0NKWlpQ5xTgkdRwQDQis5SoXhKOVovY8++oiJEyfy5ptvsnv3bhYvXtwmFXNxcTF79uwhPz+/3Sr6UfF9iQwMIrMgn8iAQBY/uJCbxl7BVxvX4u7szBX9BzGu30BmjRrHd1s2sGbfHuJCe9E3PAIPZxcui+/L5EFDULQggtudcpSfd24jPjSMAZFR3DjmCjxd3bj7nb+TU1zEydwcNhw6QExQMGP7DmBMn344aTRYrFbeXvEtsUEh9AuPZMrgYYT4+LXD0el427ZtQ5ZlNBoNOp2O0aNHM3jw4AaDAWdnZwwGQyeUVOhs4jaB0EqOctldd5GklpapDW/qtmBTsiyzbds2UlNTeemll7BYLMyZM4elS5cyZMgQxo0bR3l5OVarlaqqKvvfmc1mTCYTZrPZfhuhPjk5OTz11FPs2rWLAQMGMHH8SMaPTWTggDhUqvOrgpZMU5QkCRedDi9XNwwuLlzePxGtSk1phZENB/cT7ONLTnExEpBfWkKEfwDFxnI0KjUalRqFpMBJo0GvdbrkfVttNo6cPEF2cSEBnl6olSrUOhXzr7ya6c8/yXeb1zM0No7NRw7y8lef8+dZNzNv0pV4OLugUioxmS3c9uYrvD3/QQZGRpPQK/ySy9CgVp5WsixTXl7O2rVrCQkJwWw2s3v3bm677TacnZ3Jycnhhx9+oKCggLFjxzJ8+HDKysrYtm0bGzZsoLy8nG+++QaDwcCECRNQNjAOo9aF770sy5hMJrZs2cKWLVsIDw/niiuuwN/fH0mSkGWZw4cP8+uvv+Lp6YmTkxO33HILsiyTl5fHqlWrKCoqwtnZmVmzZuHm5tbygyG0G9EzIHSq2gvUtrtQbU1j3oaBTQs2tX37dh544AEKCwuRJAlJklCr1aSnp/PCCy/w0Ucf8csvv2A2m3n77bdJS0vj+++/Z+vWrZw5c4YffviBysrKBrev0WjQ6/UUFRWxdu1ann7uFYaPmYlnwGCuvu4e3nnvv+w/kIzRWEF1tQmLxYIsy5fci1BbdqVCgSRJ5JWUUFpZwZzLJ/LszXN5bs7tvHvvQ6x77R3umHQlbdGrU2o0cvT0aaw223m9CnHBodhkmdSzWUT6B3Hr+Ml8tmYVlz/5EGsP7MVitQLw1j1/4lRuDlOfeZyFH79PeVVV2/WetPK0ysvL48UXX+S2227jgw8+4B//+AfPPPMMO3bsYP/+/TzxxBPEx8fj7+/Pvffey5IlS7Bareh0Ovu4Er1ej1arbdH+jUYjt9xyC/n5+dxzzz1kZGQwc+ZMdu7ciSzLfP755+zYsYM77rgDg8HA008/jSzLpKen8/HHH9OnTx8GDRrEhx9+SEFBQesOhtBuRM+A8IdOGO1cW2874n39moGHHXdQRowYwZ49e+w/azQann/+eZ5//nn7Y/fcc895fxMVFcXcuXObtX2NRoO7u7v9Z+u5hrC83MjPK9fx66oN2Gwy/n7ejB0zjMvHDmf4sIH4+XrhbpLQ0rIjoVGpUCqU7E05zojefdCqa65BzBYLFdVVTScMag4JFJLEydwccoqLiA0OBWQ8XFxQSApigoJx1et57LobGBmfwGvffMH8d99Ao1Jz1dARjO+fyM5/fshr33zBN5vWY7ZY+L/b78HX3aP1ZWslX19f5syZw86dO1Gr1bz33ntUVVXh5+fHtGnTeOCBB+jfvz8BAQFs3ryZFStWMG7cOMaPH88XX3xBTk4O06ZNa9G+ZVnm+eefx8PDgxkzZqBWq7nzzjvZsmULb7/9Nm+//TYbNmxgxIgRqNVqZsyYwdGjRwHIyspi37593HjjjfTt25d77rnnvPNPcCyiZ0D4QwtqekcZY9Qe5agJUJo4KI3tt5OPTe3thNLSUgoKCiguLrZ3615IlsFqtSHLMmez8/jmu1+494FnGD95Do8+/n8cSUlr9kGu7U2w2Wrm//t6GAjx9uHjVT+x83gyBaUl5BQXseNYEodPngBqkg5ZbNZLeiOtcs32bbKMi5OOuNBelBjLOZWbc+6KXyI9+yw+7u6MjEsg7WwWv+3dycSBg/n0sb/SLzyS9Yf2U1Jh5O3l3xLk5c3rdy7grzfcQurZLE7lZDe7LO1NqVSiVqtJTEzE29ub0NBQzp49y+bNm0lPT2fp0qWsXLkSPz8/Bg4ciEbTNiuMlpSUsG7dOsLCwuy3Dzw8PJgyZQpJSUkcOXKE0NBQXn75Zd5++20OHz7MnXfeiSRJuLm5kZaWxpNPPsn69euZNGmSuEXgwETPgNCExq+MHeWK/uJytN0VfaPplxvbRTsdG5vNRnV1NVVVVVRWVtq/X/hlNBopKCigqKiI4uJiSkpK2Lt3b5PblyQJN1cXeveOZPzY4cyeNY0+zgFIp4yNNtayLJOZn8epvFzKKyvZn55Kv/BIdBott14xmZSsM9z1z9eYMngYEhJebm48PvMmCkpLSDubSXZhIUmnTuLt7oGfh6HRcQtVJhP701Kx2Wyknc2kpMLIZX36MilxCOsP7SfExxdnJx2/7NrOY9fdQJ9e4Ww8fIBPf19JsLcPHs6uRAcGMSgyBoUk8a9fljM4OpZw/0A8XV0ZGBmNjwP0CjTm7NmzKJVKrr32WsLD/xjjYLVa2yw19cmTJykrK6OkpMT+mFqtJjAwEJPJRFVVFbfccgslJSV8/fXXfPnllzz66KPcdNNNRERE8Mgjj/Dxxx9z//33c/3113PvvfcSGBjY5VNnd0ciGBCacCkf2u6ZAL7Z6Zdr28lWHAKLxYLRaKSsrIyqqirKysou+iotLaW0tJSSkhJKS0vPW9PAZrNhs9kwmUyo1WqcnZ1xdXXF3d2dXr16UVxczIEDB+rdt0Ih4eHhzvChAxg9aghTJ40hrncUGo0aMsqaVf4iYzn9wiOJCgyiqLwMs8WCTqNl4sBByLLMqr27KCwrJdw/kJvHTUCjUpFTXEGwtw83Xz4Bs8VCsbEcXw9Do4fRbLVQVlnBg9fMItTXj7KKCiL9A3l85o38unsHR05m4KbXE+4fwI1jxqOQJII8vekXHsmB9DQCPL2YOngYEwYMxkmj4aZxEzh4Io2K6mrc9M7MHT+JEN/mzSiw2qycPZuDu5M3Lq7OHdbQeXt7Y7PZ+OGHH3jooYdQKpWYzWYyMjLw8fHBw8Oj1ftwdXVFq9WyZcsWrFarfYCqUqnE29sbX19fFAoFTz31FJMmTeLLL7/kiSeeIDQ0lOHDhzNq1CgGDBjAV199xaeffkpQUBB33XUXarW69QdAaFMiGBBaoKFGv+lKsLkJgOo+z2SqJjX1ONu2beSKK6YQFhbZwvJd7MSJFEwmEzEx8a2vxBv5c4vFQkVFBUajkZKSEvvVenFxMUVFRZSWllJcXExpaSnl5eUYjUb7jAG1Wo1KpUKhUNi7i93c3HB3dycoKAg3Nzd0Oh0uLi72r9qfnZ2d7Y9JksQnn3zCihUrsFgsNUU+d9vA28vA1MljueLykQwd2p/I8NCaIOBSXr4kMSAiigERURf9TqVUceWQ4Yzvn0hZZQUGF1d7dsEwvwDmX3nNJe3LVafn2hGjuXbE6PMe7x3Si+igEEorjDhrnVCfa7xkWSYqMIjn58yjymRCkiT0Tk4ozv3u/267C6vVRkV1FW565wYzH9Ynr7iY//vsCyrVVgID/AgNDaRXaBC9QoMICQ7AxUXf6nOr9tZLdXU1VqsVhUJBSEgIw4YN44MPPkCv15OYmMipU6fQaDT2DIO1vUSXsh+z2YzVasVqtRIUFMTo0aP5+uuvSUlJoU+fPlRWVnLq1ClGjhxJaGgoH3/8MdOnT2fSpEn069ePtWvXcuLECTw9Pdm8eTMPPvggCxcuZO/evZw+fdp++0hwLCIYEFqg5RVbc+vEus8rLy9n06Y1/Pe/i+ndO74ZwUDTOzl16gS//rqc335bwZQp1xATE3/uN5feu2E2mykqLiW/oIiCgmLyCwopKCimsLCYotJq8guM5OfnYzQa7V2rsizbG20nJydcXV1xcXHBYDAQFRWFl5cXBoMBJycnnJyc0Ol0aLVa+8+1jzk5OV3SVZZer0ev11NWVoYsywQF+nHDrGlMnjSa2JgIggL9mpx61rjGj5+TRoNTG93PbohSobhoUGLtDAeFQoG6nmmUaqUKtZIWlc1Vr2f6lMtJyj7NyVOZrF2/jeLiUsqNFXi4uxESHEBkRCjh4SHERocT28cdjZNLs8+y0tJSNm/ezIkTJ1i9ejWJiYkMHjwYrVbLq6++yuOPP87zzz9PcHAw/fr148EHH0SlUrF69Wp27tzJ2bNnWbJkCaNGjSIq6uJArZbNZuPkyZOsW7eOzMxMtmzZQkxMDI899hjl5eU8/vjjzJ49m9zcXKqrq7n77rtxdXXl8OHD7Nmzh+uvvx6LxcLIkSOZPHkyqampfPrppwAEBwcTFBTE1VdffdE0VsExSHIz5s+Ulpbi7u5OSUmJGADS3cgymNKbfFpltZ5ioxftcxug8QbEYjGzdu1vvPLK07z44huMHn1Fq/dYXl5Kaupx5sy5ittvv5eFC5+9pKu3gwe2s+STNzl58iQVFZWYzRZMZjMKpQJ3V1cMBjcMHu54evvhYfDD09MTLy8v/Pz88PLyQqfToVar7Vf9Go3G/nPt/xvLGdBSP/74I/fddx9OTk7cdssspl85ipCQAAweTSyBnFFW89XAwkFtKbe4iBeWLqkZUNiACQMGM2Pk6Eu6gm9P1ngPqlwkKiqrqKiopKKyioKCIo6lpJN8NI2UlAxS0k5iNptxdfMgMjKavn37kpCQQFxcHOHh4Tg51Z9fwWKxkJeXR35+PlqtFj8/P1xdXZEkCZvNRkZGBmlpaVRXVxMXF0doaChKpZLi4mKysrKwWq34+vri4eGBXq9v8DXIskxVVRVnz57FaDTi6emJr68vKpWKnJwcMjMzKSsrIzg4GBcXF/z8/JBlmbS0NPvtqoCAADQaDUFBQZSWlpKSkoLVasXFxQVXV1cCAwPb5bwWGtbc9luEaIIDaLxiUKnUuLi4ola33RWls7Mr8fF9Ualadu/S2VlP/35xDB0cT4C/L36+3vj7eWPwdK/pzlcoUSoVKDSeKDXe9i7+2u+dVRlGRUXx1ltvMXDgQAJ9nXBSVzXSW9M5Y0A8Xd145ubbGp3n7+ykQ6nozMlQdY+NjFKhwFnvhLNLTWNb260/ZEg/zKaaQLG6uprMzBySU/M4eOg4W7du5eOPP6aiogIPDw/i4uIYPHgwgwYNYtCgQfakPiqVioCAAAICAi4qhVKpJCIigrCwMGRZPu/c8vLywsvLq9mvSJIkdDodERERF/3O398fPz8/LBbLRY15VFQUsixjtVrP27+7uzuDBg3CZrPZe2Xa87y3ZmdTtWoV1ry8dttHV6Dw8sJp4kRUl7iuhAgGhDZVm61s376dFBUVMnToSNavX0V5eTlXX309Hh4G+/Py83PZvHkdOTlnGThwKP37J+LkpLNvq6SkmFWrfqKsrBStVnte4yDLMsXFhfz88w9kZZ1h4MDBjB49Aa1W26wKp6aSVdf73OaMa+gdG83g/nejVpkBCUn6I3ObJEl/tBVKV1A5N1mejhIbG0tMTExNxWwtAms1Dc+B7JyARalQ4Ofh6ClxG59SUntbQqvRoD1360GWZfz9fBg4xIebbnbGZrNRVVXF8ePH2bt3L4cOHWLVqlW8+eabmM1mPD09SUhIYMiQIQwbNozExER8fX3t2657vjV1a6f2s9PU/frGGuzaJFgXPlb7/cKFjxp7vD3INhu2ykrk8vIO2Z+jknU6sDbcq9YQEQwIbcpsNvHZZx/x9tuvkJDQn5SUoxw9epjNm9dx9OhhXnjhDdRqFampR3n33b9z++3zCQ3txd///iJDhozknnsewtXVjTNnTvHUUw8xbdp1jB49nnfffZ3jx5Pt+zl2LIm3336FuXPvxs8vgNdee5a9e3fy4INPotM13BVa13mN93mPN/53siyfCyYUDVfCDtoL2lEVc2u029XjeR0dHd/rUfu6lAoFKJSgUqFWq+09AbXKy8tJSkpiz5497Nmzh1WrVvHBBx8gyzIhISHEx8fTu3dvEhMTGTRoEM7OziiVSlQqFUqlst6eJ5vNxs6dO3nttdcaLJ9Wq2XevHlMmTKlfQ5AF3F+n0/DZ0lzn9dViGBAaFMajZYZM25g9+5tlJeXMWPGDYSEPMFzzy1kzZpfsVotmM0mXn31OYYNu4xBg4YDcO21N/Dmmy8xePBwRo4cy+uvP0evXuHccENNdr2pU69m9+6tAFitFl588UluuOFWYmL6YDSWcfnlk9m2bRMTJ15FYuLQdn2N4n5nFyU1+EP7qG0hGmkp6juXXF1dGTZsGMOGDQNqGvKSkhKSk5PZt28f+/bt48cff+TTTz+1j/jv3bs3MTExxMfHExERgaurq32AqU6nQ6lUMmzYML7++utGi9y6waPdQ0vOjO5QI4hgQGhzkqRAq9Wi1zsTFBQKQHBwKCUlxciyTE7OWfbs2c6sWTfb/2b48NF4eHjy66/LCQoKYeXK5fzjHx/af+/h4YWbmwcA2dln2bt3O1dcMZmVK5djs9lwdnblssvGotc7Tpe80MNJF3xvIYVCgcFgYOTIkYwcORKoCRByc3NJS0sjOTmZI0eOsGHDBr799ltsNhu+vr6EhIQQEhJCXFwc/v7+uLi44O7ujpubG25ubmIgXzPUd3RqY7vuduREMCA0rBV9XxdWMhqNFputJt3t8eNJmEwmysr+SGTj7e2Lk5MOo7Gc9PQULBYr2jqr19XdXEFBHjabjTFjJhAd3dv+eE3mtZaVt/W6Q0dhV9Kzj7dCocDf3x9/f38uu+wyAEwmE1lZWZw4cYLTp0+TlpbG8eM1AxWrq6vR6XR4enri7+9PREQEgYGBeHp64unpibe3N97e3qJnoBkc7axrq0+CCAaEhtV7hv1x6tU30K7piaoSXl4+mM0mDh3aZ78NUDMgCuLj++Hi4obNZiMp6RBTplxdZ9sysgyenl4olUq+//4LFi58DqVSicViISvrzLmpVwF1ytfwR+Xi0eqNf6waH1joaFVECzT08h1k/YnzOfjx7oRYRaPREBYWRlhYmP2x6upqsrOzOXPmDJmZmZw9e5asrCx27NhBRUUFFosFi8ViX+8gMDDQnhMgMDAQg8HQ5fICXHjoHTFsbGmy0vpeS1u9tq71Lgsdq95P0R8P1NcwShLIso3q6iq0Wp09W5rNZjuX1cxCVFRvEhOHcuTIAbKzs/D3DyQ9PQU/vwBGjx5PSEgYUVGx/PrrMiZPnk5cXAIlJcUYjeWUl5fh6enNyJFj+fLLJTg7uzB8+Bhyc7Opqqpi7NgJF5Sv4Y9K7ap9JlP1Ra+tPo33OjhilXOJGiq+Q74sBz/eDlI0rVZLr1696NWrF/BHLoHCwkJyc3PJy8sjNzeXs2fPkpmZac8ZUFFRYc8lEB4eTmRkJJGRkURERDj8yoPt1Vi2pZaWqbHbFq0lggGhYS04wywWMzt3buHQof0YDJ7s2LEZNzd3tm3bQFVVBV9//V9mzpzDk0++yKJFf+e1156jT59+nDlzihtuuI2YmHg0Gg3PPvsqL7zwBAsXzqdv34H2UdIbNqwmLi6Bxx9/nhdeeJz33vsH3333JZGR0dx990MYDJ7NKmdm5mk2bFhNRYWRbds2snbtb4wbN7EV3aSOWOV0Z+J4t0RtLoGgoCCCzs1Drw0QysvL7Wte5Ofnk5qaap/2+Ntvv1FQUICrqyt+fn5ER0cTHx9Pnz59iI2Nxdm5bddkqKys5PTp08TExLTZNtuKo4WhbVUWkYGwp2vjDISyLFNWVkphYT5KpRKDwQuFQkFxcSGVlVW4u3tgMNTMIc/LyyUvL8f+PE9PLzQaLcC5bv/TnDqVgZubO87OLlRUGPHy8sHb2xe1Wk1eXg6nTp2grKyUyMgYAgKC7H/fFJPJRHl5KcXFRahUKjw8PHF1bSILXx0aVRXuzoWolJbGn6g0gKp5AUqHsxSCtZhm3QfowAyEXVKCJ3g7gaLO+dPQbAKVLyhcmp+buxPUXRmzqqqK6upqCgoK7MsWJycnk5qaSllZGW5ubkRFRREXF0dsbCz9+/enT58+rVpGee/evdx4443MnTuX+++/315nNMaSlUXljz9iy267paebO7XQkSh8fNBddRWq0JrB2yIDodApatYxd8fN7fyuxPpG+fv5BeDr62//u7oNsVqtJjQ0nODgXvbxBDX37P94nq+vPz4+NSlRa5OlNHchJI1Gg6enN56e3pf8GpsRPwuOqKPzDLTRbILOoFAo0Ol06HR/JAELDQ2lX79+WK1WLBYLJpOJnJwcDhw4wJ49e9i1axcff/wx1dXVuLi40KdPH4YMGUJiYiIDBw4kNDT0ovEHDQXf+/btIyMjg1dffZXvv/+eF154galTp3ZI9s6WnBmOGhhcChEMCJ3mwgCgvt/X7ba/8KkX/n1tStTGsqzVbrOx5DtNBRTNqoy6Q+3Q3XR0noFuRqFQ1Cz0dC4LoSzLGAwGevfuzezZs5FlGYvFwtGjRzl8+DAHDhxgy5YtLF68GKPRiMFgoH///gwaNMiedrk2o2Lttms/W/v27UOWZYxGI/v37+eWW27hmmuu4S9/+csfGTTbKShoPLfkpT+vqxDBgNAumnuF3pbbragw8uWXn7Bjx5YG/z48PIobb7yNiIjoBp/TJuXuDrVDHbIkYZNtmMwmtBoNCgfu4u4UPfBw1G2M7dkVlUr69+9P//79mTNnDlCTUTE5OZndu3dz6NAhNm/ezEcffYTFYsFgMBAXF8eAAQNITEykX79+eHh4sGPHDvsy27IsU1paymeffcamTZt44oknmDFjBt7e3u2+3kFPIsYM9HRdYNXCZm/lXM+ALDfeM6BQNN4z0Bw9ZcyAzWajtMxIYVo2J/ankZ57lpuvmopzAyvs9Vj+zuCsal4k2QXGDLQnWZYpKiriyJEjHD58mMOHD5OcnEx2djY2mw0fHx927tyJyWS66G9rP7fjx49n4cKFDBw4EB8fHyRJapcxA63VGR2EYsyA4ICa+1Fo7sel8e3VrvDW6VpYA5SUlGA0Gu1rH3h7e1NeXk5lZSU6nQ53d3fMZjNZWVmEhITY55A7Ozvj5+fXZusO1HTPVpCZlUN6+mm2bt/Lb6s3cjwlg0X/fA7nBP822U/X0VPvIrcPSZLw9PRk9OjRjB49GqgZMJyTk8Px48f55Zdf2L17d71/W3sLcO3atWzZsoVbb72VW265hb59++LSYa+gfu2ZA6Ch7bclB6g5ha6n5Y18c28f1P+8tgka2oLNZsNqs6GqbyZiC3ddWFjIr7/+yt69e4mJieG+++7j4MGDrF69mhkzZuDv78+aNWv48ccfefrppzl48CA7duxAoVBw7733Eh8f36rXZLVaOXMmm4OHj7F332G279zP9p37KSkpQ5Zlbr35Wq6feWWr9nHJOrxNbasqXQQCl0KlUtmnO6anp6NWq6mqqmrw+TabjcrKSj7++GN+++03br31VqYOHkyM2UzddRU78vRp7/209/ZFMCC0QMtPy+b2jLauB7X9P/5JycfYteMnBvSLYtiQAbi46Ft97zIoKIj4+Hjef/99Dh06xG233cbRo0dxc3MjLCwMs9lsX+b26quvxs3NjWHDhvHaa6/h5eXF888/36L95uYVsHffEXbs3M+hI8c5cCiZjIwzWK22czM1JCIjevHMX/+EVtvy6WIt0tghbZeaXjTinW3//v2YzebzHqv9bNXe1dZoNHh7exMUFISvry/5+fnk5uYSabOdFwy09t20yTIni4rYfuoUoR4ejOzVq9uOURDBgNCErtrd2XblrttLUVMX1VRIR4+msuLHlYQEBzB50miunDIOL0+PFlcWGo2GMWPG8NJLL3HnnXfy4osv0qtXL+666y68vLwAGDhwIHq9noSEBAYOHEhZWRnLli0jKSnpkvZVXl7Onp1bWbXqV/buO8zpM2dJSz9FVVX1uSmaf4wjUKlUPP/Mg0RF9rq0F9TkW9DK96i9cs521VO+G5BlmYMHD1JdXZMVVKvVEhgYSHh4uH3hpeDgYHx8fHBzc8PDwwM3NzdcXV1xLi+HNWvOGwHT2jwB5dXVHMrO5o1Nm5gzYAAje13iZ6CdteWpKoIBoQlNJRlyjHFQF5ej7QpVd7u1+Q5ioiN5+q8PcfJkOqvXbObd9/7LO+99yhXjRnLzjdPp17c3UgvKoFAomDp1KnfeeSf//e9/WbRokT0QAOwrzfn6+tqfbzAYyM3NvaT9ZGZm8n+v/pNt27ZTXm48LwCoGwhIksRNs6dz1dTxl/xamn75bXjitOU56ADnc09lMpm4+uqrmTlzJuHh4fj7+6PT6dDr9fa8B3q9HrVafVHQbcnKolKSzgsGmjs1sCE6tZoBgYFUmM1YHTC/SFueqiIYEFrFEQIBaKtyNC/OliQJjVqDj8GfkGBPEgf24e55N/Db75v5+tufWfq/5QwfNpA7b5vF5VdcidMljmxSq9Xk5uYSHh7O4sWLGT9+PJ6eDc9IaMnAwfDwcGZdN51dO3cB9SdSkiSJwAA/HnnwDlxdXVrY49HVL7O7evm7Fo1Gw4IFC+z5DDoiyVBj1EolnjodSkep6NqRCAYc0MaNG8nOzkaWZby8vIiNjeXo0aMUFRUxePBgevXqRUZGBl9//TWPPvoo+/btY8eOHYwcOZLExMRm59e3NwDnur5rvsmYTGYys3I4dvwEx1NOkJqawaxZs4mJn9zmjX/tFb1j9DBcegGUCgWuLs64OOu547aZzLlxOpu37OHDj7/g1jsX0rfvZ9x33/1MmTIFF5c/ooKGKjhZlnnnnXeYOXMmQUFBzJgxg3fffZdnn322yUqxdhZCc2g0Gu64/SaSkw7yr8VLqaqurvd5jz96F9HRYa14bzr4TW3ztrvTT8oeRZIknJ0vzlbamS7MqVGzeqpMVlkZG9LTKamqYkRoKH38/VGfC8xNViubTpzgSE4OBr2e4SEhxPj4IMsy+7Ky+D01FX9XV7z0eqb17l3fbjucCAYcUEJCAp9++inff/89jz32GEOHDmXVqlXodDomTJjATz/9xKOPPsrZs2cJCwvjxx9/JC0tje+++4633nqLQYMGXbRNWZax2WxYLBZsNlvN/PHSUtLT00lJ3kpqagbHjqdzOCmFU6czqaioGcmrVqt4cuECBg8aSFnDg3tbrPZz1vmBQOvUTmtUKpVMmjiK8ZeP4OixNP718dc8/PDDeHh4cPvtt3Pbbbfh7u6OWq2+6IrearWyfv16Tp8+zQMPPEB1dTXz5s3jf//7H0OGDGHChAkUFRVhsVgoKyuzv5+1ueOrqqpwcnJqdkCgUql46flHWbNuC0nJqfZVHKGmt2HiFSO5cso4dE7a+rfZ3Ia32Q10E09szna6+Hl0oT9yZzTcRa1QKOwNVF21GTovNTFP7bZqB/HVd652Za2NF2VgT1YWX+zfz22JiRRWVvLIzz8zu18/7hw8GEmSeGX9ekaHhXF1fDxvbt6MQpKI9vbmnS1b8Hdz497hw1l25AjvbdvGtN69W7ykcVsSwYADMhgMvPnmm5w+fZrVq1cTHR1NWFgY1113HZ6enlxzzTXs2bOHN954g5CQEL744gvWrFnDk08+yYkTJ+oNBgCSk5P517/+xcGDB0lJSSE3NxebzYYkgUqlviiVr07nxN133MATC+9B0WBvg6N0ozpGOWorXbVaRd+EWBa98wYPPVLAl19+yZIlS1i8eDFTp07llltuISIiAjc3N/v9z7179/Lyyy/Tt2/fc++LhF6vp6qqig8//JD8/Hx27NiBh4cHn376KfPnz2fXrl2kpKSgVqtZtWoVkyZNOi+ffGOMxgq++2El+QVFBAT4kJWVa3//PTzcuPXmGYSHBde8pkZn3DVx7Jv9tjQx1Ku+XzvG235O2xfGZDKxY8cO+6JArq6uaLV/LOZlNBoZOHAgFouFlJQUSktL0ev16PV6nJ2d0ev19OrVCy8vLwwGQ7PycNhsNo4fP87777+Pk5MTCxcuxM/Pr01fV2dq7TuUbzTy3tatXBEdzYDAQAAOnT3LG5s2MTwkhGhvb34+epRr4+MJcnPj5v790apUWGWZX48f55FRo1AqFMzo0wfTuQDcEU7h7hPudSOSJOHq6spLL71EXl4en3zyCX379iUgIMD+HJ1Oh1KpZPjw4QC4u7uj0+morKxscJuyLJOUlMSmTZvs2b6gpovebDafFwg4O+u55aZr+OsT96LXN9y4yHJLT+OGrnRaNkin5eVoX5IkERMTw3PPPcemTZt4+umnSUlJ4Y477uDBBx/k22+/5fjx45SWljJo0CDWrFnDP//5T9RqNTqdjj//+c+cOHGC5cuXM3fuXD755BNSUlJ49dVXCQ8PZ/bs2Wzfvp1NmzZxzTXXNDsQKCgo4L0P/sNrb3zIw3+6na8/f5eQ4JpkQhq1muuumcTlY4f/0Xg0engvHNbf1toi0OgIbV8YjUbDwIEDyczM5IUXXiA7O5vg4GACAwPR6XSsXbuWdevWMWDAAHJycnjmmWfYvXs3er0eo9HIhg0buPnmm5k7dy6///57g/VDXWazGaPRSHp6Orm5uV1uYa72LK0MZJaWcjA7myBXV/vjk2JicFIq+e7QIdQKBV56PQ/9+CPLkpLwc3Wlt48PKoWCSE9PHv35Z/6zaxdnSkq4ppW5QdqS6BlwUJIkERERwciRIzly5AhlZWVYLBZ75Xxht13t4juNfXCjo6OZPn06e/fupaSkpMHnubg4c/W0K3j80bvx9fFqYjGhS3xhf/zlJT7exNYa6112iPEI4Onpydy5c7npppvYunUrv/76K0uWLOHLL79k+PDhDB8+nISEBHx8fNq1WzYrK4sPP/yQtWtW8eTj87lp9lUAvPziQh7/62t4ebpz/cwrCQxsydWgAxzobqT2wiAqKgpnZ2f69OnD2LFjgZru/PHjx/PLL7/g6upKXFwcHh4eTJgwgeuuu87+nNmzZ/PEE0+wYMEC3n77baZPn97o+eXk5ERCQgJhYWGUl5d3yOtsS+3ZgWSz2TiWl4fJaqWiTi6EAFdX1EolJdXVqJRKnhk/nve3b+f1DRsYEBjIfcOH08/fnwcvuwylQsGSPXv45tAh/jRyJNN690avVjey144hggEHZbVa2bt3L5GRkVgsFj799FOio6OJjo5u8ehaJycnJk6cyM8//8zatWvrDRyc9TqmTh7Dk48vICryjwQbKqUZZ6fSVr2m7kSlsCBJF6yB0IwaR5IkNBoN48aN47LLLiM1NZX169ezbds2Nm3aRFhYGIMHD2bw4MEkJCS0eXrls2fP8v7777Nv3z4ee+Repk4cat/H1VdNoKi4lMqqKkaNrP9Wk9AKrWiRFArFRQODc3Jy8PX1Zfr06fbnXNjIS5JEv379eO6557jhhhv46KOPGDx4MEFBQY3ur3aswXnFPzeOICMjg9zcXPz8/AgJCcGpzjoVZ86cIT09HVdXV1xcXIiOrlkQrLCwkNTUVJRKpX0xo46cJdBWe5IkCU+djvLqao7m5XFVXBwASqlmInH/gAAsViueej1vXnUVPycns3T/fj7ft4+FY8YgAS9PnsymjAy+OXiQ19avp5eHB0NDQuz7uJTTROQZ6OZkWebQoUPs3r2befPmcfToUV544QW+/PJLHnzwQQwGAxUVFUBNl55KpbpogGB9kX9lZSV5eXkNdhU6OWkZO2YYTy5cQJ/4qPM+rGqVGbWq4d6EdtUWZ3xLugcudb+XuHm1Wk1cXBwxMTFMnz6dgwcPsm3bNn744Qf7oMHx48czfPhwXOt0SbZUQUEB//nPf9i3bx/z589n0vjBaNVV1HasOjvrmDd3JuXGikZvDXVJjjC2oA33b7VaWbJkCY899hgGg6HR5yoUCuLj4xk8eDDJyckcOHCgWcHAhUwmE++//z6RkZEoFAp+//13goKCmDVrFgaDgZUrV2IymXB3d2fLli1kZGTwj3/8g+TkZI4fP46bmxtnz57liy++YMWKFV0mk5/ZakWmZoaAJEn09vUlxseHIzk5FFRU4KXXk5SXR5jBwOUREVSazSzaupW3r76amwcOpLiqiuTcXEqrqvh41y7+On48U2JiCDMYuPPbb8m5oPflUpJuijwD3dzWrVt5+umnGTVqFH5+fpSUlODs7MzixYtRKpWMHDmSdevWYTKZ+Oqrrxg7diwbN27kxIkTbNq0iVGjRhEZGWm/krBarRw7dowlS5awdetWevXqRW5uLmlpaXXSe6oZnNiXvz33MAP6xzXdTV1f5Vr7WFtXvG2xrZZUPB1UVymVSoKDgwkODuayyy7j5MmTHD58mLVr1/Lss88SGBjI1KlTmTZtGv7+/i2qRCsrK1m+fDm///47CxYsYMKECThpqsD6xxSRmgGLutYFAo7Q6NanJWnoHOy1lJWVsWTJEjZv3kxWVhbr16/n4YcfRt2MLubawHP79u1kZWW1aP9vv/02R48eZc6cOej1egoLC1m8eDFeXl5ceeWV/POf/+Tpp59m2LBh9kRYNpuNtWvXYrVamTt3Ln369OGXX35p0f47Q7nJxMrjxzGaTGzNyGBvZib9AwL489ixfLxzJ//YuJFwg4ETRUU8eNllhBkMFFdWsio1lRfXrGFwUBCVZjMToqLwdXZmT2YmT/32G9NiYymsrGRkr14MDQ5udnna83QUwYADSkhIsGeekySJsLAwFi1aRFlZGQaDARcXFz766CNMJhM+Pj64u7tzww03MHHiRPR6vX0FO1mW7feHly1bRlBQEH/6058YPXo0X3zxBa+//jr5+fmoVEqiI8N495/P0rdPbxRSM+5X13dWSo38rjEdXem2x/7aaJvu7u707duXuLg4xo8fT1paGsuXL+fDDz/k3//+N1dddRW33HILISEhzR5XYLVa2b17N//617+YPXs206dPR6/Xg6WRuaItfT2d3Xi2ZW9Om06bbD29Xs+UKVOYMGECRqMRo9F4SYFh7a2G5gQPF6qqqmLRokUsXLjQnv1y1KhRLFu2jJ9++okRI0ZQUlLCU089xSuvvMLgwYOZN28ekiRRXV3N0qVLMRgMzJo1i6eeeqrL9AroVCrGR0Xxy7x5qBQK/F1dUSkUjAkLI8rTk1yjEb1azZTYWPxdXVEqFLjrdHx9883266KBgYEYdDp0ajXvXXMNFpsNo9lMX39/psTE4OMgeRVEMOCA3N3dcXd3t//s5OREeHj4ec+5sGtQp9OdN9uguLiYDz/8kPfeew8vLy8ee+wxrrrqKtzc3FCpVMybN4+lS5dSUFBAgL8PX3/xLr1jIzpnPnFH1wvtsb+2zKwrSajVavz8/PD19WXgwIEsWLCAlStX8sknn/DBBx9w+eWX88ADDzBkyJBGK1ZZljlz5gwPP/wwQ4cOZcGCBTWBQAe+ng7V0Q14Bx4npVKJv78/kZGRyLLMn/70p2YnGLNarZw8eRJXV1f8/S99Cep9+/aRl5d3XkIgHx8fAgICOHz4MEajkb/85S/Mnz+f6dOnM23aNF5++WUkSWLixImsXr2aBx98kE8++YS33377kvffWZQKBT7Ozhc12BqVil4GAyEeHkj8kdNBlmVUCgUDAgKQqfn8Ker8Ls7XF5magYjKS8z/0N7E1MJuonbMQGlpKR9//DFDhw7l3Xff5U9/+hMbN27klltuwdPT0z6n3cvLi/nz5xMaGsovy/9DXO/Itg0E2mt+T3vPcnKgWVS1g7j0ej3h4eHMnz+f33//nb///e+kp6dz+eWXM378eL766isqKiqw2WwXDQqtqqrizjvvxNXVlb/97W84Ozvbk8p0sRljbaej6t92PL6SJDF+/PhmBQOyLFNSUsKmTZsIDQ2lX79+l7w/k8mELMvs2rXL/ljtOgF+fn44OTlx5ZVXsm/fPm688Ua+//575s6dS2ZmJn369OGLL77g6aefJjk5mXHjxpGcnNxgWS0Wi/3LarXa85/Ufv1x/l6caKkjSZKE8tygzdpGvTYokCQJxbnfX/g7hSSh6uQ0y/URPQNdXG0QUF5ezoYNG3jjjTc4deoUt99+Ow899JC9h+HCE0+SJObOncv48eOJ6dUOJ2Z7neft/flxrM+nnSRJKJVK3NzcuPHGG7nxxhvZv38/ixYt4sEHH+SVV17hzjvv5JprrsHHxwetVovZbOaxxx4jPT2dn3/+2b7g0c6dOykqKmL44FjcXRxj2mWH6qiu/TbaR22Dd2GwVzd4b6hhlGWZsrIy3njjDQBuv/32ZvUMXLhoVUJCAu7u7mzduhWz2YxarcZoNFJZWUnfvn3RaDR8+umn3HrrrbzzzjtMmDCBxx57jC1btuDv74+Pjw+PPvooEydOZMyYMXz//ffE1zPHfteuXcyfP5/y8nJ8fX0JCAjAx8cHb29v+5evry8+Pj54eXmh1+vtsyiUlZVcSoTrYENCOp0IBrqo2g9/YWEhSUlJfPLJJ+zYsYNx48axePFioqOjm7zSd3Z2JjYmBkzpjjMZv6N1sdddN2gbOHAgH330ESdOnGDp0qUsWbKEJUuWMGHCBGbOnMn27dv54Ycf+PTTT4k7NwXKZDLxyy+/8M477/Dnx+7jrtuvxdvb4HBXKe2qC71Um81GWVkZubm5lJSUkJeXR3l5ub0RrPuczMxMSktLyc3N5fTp00iShMlk4u2332bDhg289NJLzJo1q8n32mazUVJSQmlpKcXFxZSUlBAdHc3jjz/O22+/zU8//cTIkSM5evQo7u7uTJs2DWdnZxYtWkRiYiK9evUiMjKS+Ph4fH192bhxI66urnh6ehIYGEifPn2IiIiod9/x8fFYLBZSU1NJTU21B8GSJGGz2c5LmQ01SZkMBgNxcXFcP24cswwGtM08tl3oNOgQIhjogmRZJi8vj8OHD/Ptt9+yc+dO+vXrxwcffMDw4cPt6UovSU9qDOrq4q9boVAQGRnJM888w913381PP/3Et99+y+eff05+fj7Tpk0jMDCQ0tJS3NzcSElJYceOHZSWlvLsC69TVJjLfQtuoVdoUMONRIUZbD31nsIlUCrASVlzTrXRZafZbGbv3r0cO3aMIUOGkJ6ezuHDh0lMTESj0QA1aYn3799PUlISgwcPZtu2bWRmZuLk5ITZbCYyMpKHHnqowQb4QhaLhbS0NJycnHB2dubo0aOEh4fzyCOPoNVq+eabb0hPT6e6uppJkybRv39/zGYzCQkJvPPOO1x++eWYTCbmzJnDyJEjSU9PZ9WqVfYEStdddx3XX399vft2cXEhMTGRY8eOYTab7bcN6iNJElarFX9/f6677jrmTJmCYu1abHStq35HKaskN+OmS2lpKe7u7pSUlODm5tYR5RLqIcsyZ8+eZdeuXaxZs4a9e/cSGxvLjBkzGD9+fPMGhl28UTCdxqFulncnSndQeXTY7ioqKvjyyy9ZtGgRsbGx5OTkoNPpGD58OMOGDSMpKYmXX36Z/Px8oKZCvWn2VTz28F0kDuxT/0YPFkB1/RWyUIe7FqLdmw4wVb6g6Jr3Z2RZprKyktLSUnx8fOxjFmoTElmtVvLy8vDz80Or1doXWrJYLBQWFuLu7o5er2+0d2Lx4sU89thjTWY/DAwMZOLEidxzzz2MHDkSS1YWlT/+iC07u01fc1ej8PFBd9VVqEJDgea336JnoIsoLCxk/fr1/PLLL6SlpdG/f3+eeOIJxowZc97MgxZRebZNIYWLSZoO21VlZSU//fQTS5cuZdasWSxYsICTJ0+yfft2duzYwaZNmzhz5ow9EICaSvyLr37kbHYuTyxcwMQrLrv49pLRDJVWhCZolI5zmddOahfPuvDCozazJkDouUao9nGVSoVKpSLw3KI+dcmyTFVVFSdPniQtLY20tDS2bt1KdQNLatfeNhg9ejRz5sxh1qxZjdZ/3fztaFMiGHBw5eXlrF+/nu+++47Tp0+TmJjI7NmzGTp0KO7u7q2/1ytJoHRpm8IKnaayspIVK1bw8ccfM2nSJO666y48PT3x8vJiwIABXHXVVXzzzTf85S9/qffvN2zaRUlpOQWFxcyeObXN0yD3SA7YEplMJrZt28a2bdsafI7BYGDChAlERka2Sxmqq6tJT08nOTmZpKQkjh07Rk5ODkqlEk9PT8LCwoiJiSEpKemiQZHe3t7cfPPN3HrrrfTv37/J2RQOdvgvOiUc6RQRn3gHZbVa2b59O4sXLyYpKYmRI0fy1FNP0bdvXzw9PbvV+uJC61RVVfHzzz+zaNEipk2bxl133WVPWAU14woMBgOVlZUXDcCqZbPZOHDwKH97eRElJWXMmzsTJ6cWjD3pstqhWnbAWl+pVBIZGXlevoALOTk54e3t3Sb7q53WmJyczMGDBzlw4ABHjx6lsLAQJycnIiMjiYmJ4brrriM0NBSDwYCHhwdKpZKUlBRMJhMAKpWKvn378vDDDzNlyhS8vb27ZB144SngAKeEnQgGHNDBgwf55z//ybp16xg6dCivvfYaAwcOxM3NrdlJRoSewWw2s3LlSl566SWuu+465s+fj4eHx0U9RkVFRXz99deNzsu2Wq2kpJ7g1X/8C4vFwp23X9/91ihoUDtXyw5S6ysUCoKCgppcm6ClZFmmtLSUPXv2sGPHDvbs2UNSUhJlZWX4+vrSt29fJk+eTP/+/endu7c9V0Htkuy15+3UqVN57bXXgJrMizNmzOCJJ54gOjoarVbbs2a/dBARDDiA2go6IyODN998k6VLl9K/f38+/PBDRo0ahVarFUGAcBGbzcaaNWt45JFHmD17No899li9g7MsFgs7d+7kyJEjTSZpsVptnDlzlhdffhdJkpg3dxZ6HKYt67ocpGegtY3oheePyWQiLS2N3bt3s2vXLnbv3k1ycjKyLBMaGspll13Gn//8Z4YNG0ZgYCBqtRqVSmVfvbAhQ4cOxcXFBYVCwcsvv8wtt9xy3nRKoe2JYKAT1SYMysvL4/PPP+edd97BYDDw/vvvM2vWrPMiZUGoy2az8dtvvzF37lzmzZvHSy+9hEqlqvd8qa3A77rrLoqLiykoKKCoqIji4kLKSkspKzdelGSmpKScp597E4VCwe3Ro9DJ4lxskdogoD0W8GpntblM6mb+Ky4uZteuXezcuZNdu3Zx4MABiouL8fb2ZsCAAUybNo3nn3+ewYMH23uo6p43zT2H1Go1Tz75JNdeey0xMTEXbafVr42OTUTZFd52EQx0gtq5s/n5+axcuZJ//etfmEwm/vznPzNv3rwmp94IPZvFYmHNmjXMmzePefPm8corrzR6laVWq5k1axazZs26YEOFYC1Glm2UlRkpKyunpLSc0tJySkrLKCkpxVhRibGyEp1TQ4NMu0pV1wnac73ZNlZbJ5nNZkwmE2azmeLiYo4dO8ahQ4c4cuQIycnJnDx5Eq1WS1hYGIMHD2bOnDkMGTKEiIiIFi2A1BBJknjyySfbbHsXbb/dtty5+2oNEQx0MIvFQlZWFnv37uWzzz7jxIkTTJ8+nbvuuovg4GARBAiNMplM9qVrb7zxxiYDgeaQJAk3Nxfc3Fyo91bytuxGphaK87VBDnpoanMCVFRUYDQaqaiooKyszD7CPz09nfT0dLKzs1EqlQQEBBAbG8u8efPo27cvffr0abMBhu2lK4WojlJWEQx0EIvFwqlTp9i5cyfff/89p0+fZsyYMTzzzDP07dtXjAm4QG2q5YqKivMe9/LyQqfTUVFRQWFhof1xvV5vz73fXVVXV/P777/zt7/9jWnTpvG3v/1N3EMVGlXb8JeUlFBUVERRURGFhYVkZ2eTnp5OVlYW2dnZ5ObmotPp8PX1JTw8nCFDhhAVFUV8fDz+/v5dbqqpIzSuzeUoZW3RO2y2yRRWWSk329q6PF1OgF6FXt1whWy1WklPT2fTpk2sXbuWzMxMhg0bZl9Stqt9yDqKLMtkZ2ezdetW9u7dS1VVFSNGjODaa69Fp9NRWVnJ7t27+emnnxg2bBijR4/u1sGAyWRi3bp1vPXWW1x++eU88cQTODk5tVFPkqNcmwitYbVaKS8vJy8vj/z8fPLy8sjNzSU/P5+srCyKi4sxGo1UV1fbVxuMjY3liiuuIDAwkLCwMIKCgtq0u78jSVotqpAQbI1Mm+wJFB4eSE5Ol/x3LWqJqiw2DhZUkV5mbsmfdytTQ1wIrScYsNlsnD17ll9//ZWVK1diNBoZM2YM9913H4MHD7Zn6xLqJ0kSERERVFRUsHPnTr7++msiIiLs4ym8vLwoLCyksLCQQYMGtVuCFEdgsVjYsmULixYtYsiQIfbVKNvullKd7Yi4oEswmUzk5uaSmZlJVlaW/Xt2djaFhYXYbDY0Gg1qtRpPT09CQkJITEzEz88Pf39//Pz88Pb2bnDQaVekcHdHO3IkcgNrGfQUkkqF1IKAqEXBgE0Go8VGUXVnpiht61qrZdszX7CAS22SjWXLlrFs2TLKysqYOHEi48ePp2/fvm14Nde9SZKETqdj0KBBPPDAA5w5c4ZVq1Zx7bXXkpCQQFJSEjt37uSVV16hd+/enV3cdmO1Wtm7dy9vvfUWMTEx3Hvvvfj6+rb9OVR31LvgMGrXAjh16hQZGRmcOHGCjIwM0tPTKSwsxGg04uLigsFgwNPTk169enH55Zfj5+eHh4cHHh4e9kQ+XfWKv7kklQrJw6Ozi9FldeE+6rautVo//9ZqtfLLL7/w/vvvk5+fz/Tp07n66quJiorCxcVFBAEtoFAoiIuLY968eTz//PMsWbKEBQsW8Nprr/Hoo48SGxvb2UVsNzabjaNHj/Lss88SHBzMAw88QHBwcPuME2jVqdne3Qk9o7uidnBxamoqKSkppKSkcPz4cbKysqisrMTJyYng4GBCQkIYNGgQ0dHRhISE4OzsbE/cU7tugJiWLFyqLhwMOA5ZltmwYQMvv/wyhw8f5qqrruL1118nMjISnU4nBnm1klqtZtq0aWzfvp2lS5eya9cunn76afr27dttKzxZljl9+jQPPPAAnp6ePPXUU/Tq1ctBz6X2fg+613ssA8XFRaSlpdtz8x87dozk5GQKCwuxWq34+PgQFRVFdHQ006ZNIyEhgdDQUHQ6HWq12v7Vnbr5hc7VBYKBzroqaHq/siyz/8B+Fr71CmvXrmXKlCmsXr2a6Oho1Gq1+JC2Ib1ez3333cfWrVvJzMykf//+Dtowtl7t4MnZs2fj6urKW2+91SWmnTZjNfRmae7rrLs/GRmJtk1Mc6lk5Jp/bed+kmWKS8o4djydI0dSSDqaQnJyGsnHT5CfX4gkSXh6etK7d2/i4+OZOHEi8fHx9O7d2559T5IkFAqF/f+Ofg4IXZfjBwOy1EkXBk3vNC8jlU9eWUjvAC/WrFlDYmKi+MC2k9oG0tvbmwMHDvDSSy/x5ptvdrv7oFarlaSkJG644QY8PT3573//S0BAQMefU03Gwhc/Ydfxo6RknaGiugqlQonm3EwZWZaxWK1EBwWjUipJO5tFRXUVCkmBRqVCr3XC3dmZcP8AfD0M6DVae+PX1H4rqqv5csPvrNyzk3umTGdS4pBWvvCWO5iSys7dazmakkZKSgbHjp/g7NlcZFnGy8tAbHQ4vXtHMmPmTHrHDSA2NhY/P78GtyfqEaEjOX4w0KGfh0vrhfAJi2LxJ58yIjoErbYnrfDWsWRZJiMjg3Xr1vH888/j4uLC4sWLmTp1KlOnTu02lWZFRQUbN27kkUceIT4+nsWLF3fedMkmD+nFTxgS05tKUzUT/vool8Un8NcbbsEmy5QYjfy2dxd701J4+ba7qayuZtLTC4kODObBq2cCsC89hac+/Ri1SsnC625i8qAhOGk09by35/+cV1JMfkkJ+1JTGlyRsaMs/XUlK3ZtJTDAj96xkUwYfxlRkb3oEx9NYIAfavW56lblCwqXmuXDBcFBOH4wUFe73zG4tI1LkkRISCharZgm2F5kWSY/P59ffvmFgQMH0r9/f1544QUOHDjAI488QmxsbJefVmi1WsnOzubbb79lyZIl9oRCOl3XWjFQkiSGxcaj1zrhb/BiUuJQ+++mDh7G15vWoVAouKxPPwwurkQHBbNg2jX259w+YSrPfPZvbvnH33h7wYPMm9B4oCdJEqG+fkwYMJiPVv7Urq+tOZ69+y5e+fBZFMqGejUEwXF1rZuuDvP5apt7o0Ljaqdp/vjjjyiVSkaNGoVarSY2NpannnqKM2fO8Oyzz5Kbm9vZRW2xyspKduzYwfPPP8+KFSuYP38+L730Ek4tSBriKNQXJNIyVlWiVWuYMWI0unP5NVSKizNuRgUG8fyc2+nl68eT//mQEznZTe5LApTK+vN85BYXcfBEGilZZzBWVZ33+7KKCo6ePsnxzNOkZmXaxx+YLRZO5eWQdCqD1Hr+rjEuOh3KBm9vCIJja4eegZ4wDai7v77OZ7PZ2LNnDz/99BPr169n3rx59kRNNpuNqKgo1Go1y5Ytw9/fnxtuuIH+/ft3mds1VquVU6dO8csvv7By5Up8fX154YUXGDp0aLdKSCXLMtuPJhPq60tUQFBNQ2lrOHNpbHAoEwYO5v2flrF82yYeve6GS96n1WZj1d5dVJlMKBUKTuZmU1ZZyS2XTyTU14+MnGyOnTmFxWqloKyUVXt38d/H/kq12cyWpENUmUxUVFex81gyN18+kYGR0a05BILQJbRNMHBe+++ADWVPiE+6IZ1OR+/evYmOjiY+Pt5+xSVJEu7u7rz11ltYLBb7HOuucEVWe9vj22+/ZfXq1ciyzOTJk7nqqqsIDQ3tvBkSbfgZsdlsHD19kr9/+yXllZVsPHyAJ2fPISqgvlWQLjYgIgpJktifntqi/W84uJ/vt2zgnqnT6RsWSdKpDB744G1yi4v429w7WXdwL8XGcuZeMZnyyiqWb9uMDBSVl7F45Y88P2cevh4GUrLOYOnkcQiC0FHaJhiwVyKO1uqeK48jFUloFoVCQUJCAgkJCRf9TpIk4uLiiIuL64SStVxOTg4rVqzgq6++oqqqiquvvpqpU6cSGRmJXq/v3MLV/Yy08mMsSRK+HgbG9RtAldlMeVUl+kvosdGoamaIKOu5ldAUi9XK99s2olVriAoMxkmjoW9YBNddNobFv67gmhGjKKuo4PstGwn28uGqYSP56w23oJAkZFnmQHoq/13zG/dNu5abxk3A4Ox6yWUQhK6ojW8TOFqr62jlEXoaWZY5evQoX3zxBStWrECWZWbMmMHNN99MUFAQzs7OjtejIdGqgECSJDxd3RgSE4csy7jp9Hi7ezT77zNyskGGmKDgS953VkE+qVmZxIeGoZBqelmUCgWDo2N5/VsjKZlnGJPQn1927+CRxYv4Yv0a/nbrHQC4Oztzw5jx/OuXFWw4tJ8Hpl/H1cNHXXIZBKEr6qDZBHI75wtwtB4JoaczmUzs37+fxYsX89tvvxEcHMz8+fOZMWMGHh4ejrtGRRuvUSBJEgm9wpv9Ws1WC6v37UKpUDBt6IhL3p/JYsZssXD09EmqzSbA2d5ToVWp8XJ1pW9YBJ888iSfrP6Vd1d8x9Rn/szvr7xJTFAIC2feyLDYeF756nMe+te7WG02rh99OdqW5LMQ1ZLQhXTQDcrWBgJNjd4Xnzih88iyjM1mw2KxcPr0af7xj38wevRoJk6cyIkTJ3jnnXf47bffWLBgAf7+/o4bCEDbfJRk+bzsgEql8qIkQvIFn2lZlrHabHz6+0oOpKdx//QZ9A2LaO4O7f/zN3jhZzBwMCONSlM18rmyFJaV4uHiQnRQKLtTj1NiNPLE9Tfz4/OvUlZVwdcb15FfWsLv+/YwZfBQlj7xLKMS+rFyz05KjOUtOw4O+hYLQn26SM9AU38oQnChY8myjMViobq6msLCQtasWcPy5cvZtGkTHh4eXHnllbzxxhsMHTq0a6WmbsVHyWyxUFJRTnlVFcXGciqrq9Go1SjrDIo0WywUlpVSUFqK2WKhuLwchULCYrXy1cZ1/HP5Nyy48hqeuWlu00WVZaw2K4WlZVisFkorK3DSaJh12Th2Hz/Gqr27mD16PFablU2HD3Lj2CvoHRLCWz98g5vemSAvb+JDehEX0otAL2+KjeUs+vF7xiT0w9vNjb5h4VisVlTKFlaToloSupAOCgb+CARkWcZUUU55UQFKlRoP/6ZHGNdeZTRcobb+E9f0PoSeTpZlqqurKSsro7CwkAMHDrBy5Uq2bduGQqFg7NixLF26lLFjx3a5hEF2tad/CxqywyfT+WzNKiIDAqkymfhm83omDhxMgOcfWRSTTmWwZPVKooOCySsp5k8fvIWXqzs2WcZVp+c/Dz/B0Ji4i3IVNCSnqIjf9u7Ex92DfWkpDI2N49qRo6gyVbPk95WcLSygsroavdaJJ2bPQSFJuOr0fLt5PSXGclx1eiYOHMyt4yeRWZhPeWUlT336MQMio9Gq1Nw4ZjwGF5dLOxC1RFUidCGS3IzVRUpLS3F3d6ekpAQ3NzdKqq2syzJytNh0wTObsbiPzcbJ/Tv4bdH/4eLly5y//6fJQlaUFCHbZPQehktsrJtfo1nNZkpysjAEhV7SPq6PcCPSvfvMCxfOZ7PZKCoqIjc3l9zcXI4dO8aBAwc4duwYlZWVxMTEMHbsWKZMmYKfn1/XCiYthWAt5qLbcBd+bLZlQ2XXm2Jnspg5k5dHsLcPmnP3/Gt7E8xWK3klxRicXXE5F7jJsozJYsZYXY3ZbMHb3R2V8hJmNHhpoa8XKJo4B0Q6YqEDXdh+N6SN8ww0fXJLCgX+MQl4hoRjqjA2+Xyr2Uzqjg0olCr6jJ92iQWr7Y5ovGiyLFOYdYpd3/+XqQ89Jz6kPZgsy5hMJrKzs8nIyCA9PZ3Dhw9z6tQpSkpKcHV1JTY2lvnz5zNo0CBCQkK6x2JJdT8j3eT016jURAQEnveYJEmolCpUShWhPn4X/c5Jo8VJ0zUSVwlCW2rjPAPQUMtrs1nJy0ilNOcszl7eF11BVZWXkX8qjeryMtz8AvAOjcRmMXPy4G52/fA5AbEJeIWE4x0agdpJh7G4kLyMFGSrFc/gMNz9/vjQl+bnkHfiOGonHUqVmqC4/jVlsFopzc0i/9QJlGo1gbEJaPQuFJ7OYNNn75OVfICzKUm4evvi6uXbJodGcHw2m42cnBzS0tI4duwYKSkpZGRkUFZWhpOTExEREUyZMoXo6Giio6Px8/PrfssnO1gAIMsyeSXF/Lhja6PPGxzTm/7hXXttCkFwBO0wZuDiWsViqmb38i8wV1cRHD+AzKQDnNy/E//oeABOH97D8a3r6NV/KBUlhWxY8g6jbr2XsAHDKcw8SUlOFu5+gRSfPYMhIISjm1ZxJvkACeOnkXn8CLtXfMmoOQsIiOlDxv6dlBfm4ebtS/6pdA6s/J557/4Pi9nEmcP7yDtxHL2HJ8e3rePAyu+ZdP9fKc7JJO/EcUyVFRSfPY1aq204GBCDgro8m81Gbm4uhw4d4siRIxw5coSsrCwqKipwd3cnODiYyy67jKioKHr16kVISAguLi5d6xZAN6BRqQny9rH/XJv+oO7Pbrq2TNYkPtxCz9UhAwiT1q8kfc9WJsx/HK/gcFw8vdnm9McAq+Nb15F/Kp1B19yEVj+A1e+/yt4fvyZq6FgCYvrg7h+EZ3AYvQYOQ6PXs+WLxYT2G0xgbF+UKg3bv/mEE3u34RfVm8NrfiS072ACYvviGRLOid1bACjOOs2xLb/Te9QE/KP7ICPz4+t/xScsigFTZ+EXFUdm8gHCBg5H7dTI4C9RV3QptYP+jh07xpEjRzh8+LC98QcICwsjKiqK8ePHExERgYeHBx4eHri7u6PVakUA0IncnZ2ZNHBwo89p2/dHvNdCz9XuwUB1hZH9v3yLb2QshsBQlGo1rt5++EfHYT63Iljc2MmAhIunDwWn0lFpNJQX5KBQKtHqXVCq1Ki1Tuhc3ZEkiXF3PIxveAySQkFZfg7IMlVlJUiSAqvZxPpP3kap0RA3ehLj7ngYm9VKXkYKKdvXo3bScfLALqrKS3H3DaDo7Bm0zi6oNFoUShU6Nw/RAHRhZrOZkydPcuDAAfbv38+BAwdITk6mqqoKV1dXevfuTb9+/bjzzjuJi4vDzc0NnU6Hk5NT15oC2M3VXYdCEIT21+7BQElOJqV5Z4kcNhqp9j6rJKFUqTFTEwz4R/fhzJG9/PbuS/hHx+PmG4C5uuGlQ2NGjufYltVs+uw9/KLicPb0RpZtSJJE4rTZnD60h6/+uoDQvoOY8dQ/sFrMFJ09jSEwlOGz70DjpEOWZcbd8XBNmUSF47Aam+xSVlbG0aNHSUpKsnf3JycnU1BQgFarJS4ujsTERK677joSExMJCwtDpVKhVCpRqVQXJcIR6miyx7yzutS7YFd+Fyyy0PO0ezBgqa7CZrVgLMhDPm/p0ppPhyzLJK3/hcNrfuKKexbiHRJB8sbfGgwGZJuNtR+/SU5qMrNfeh9jcQFJ63+1bysofgB3fvAtW//3MVu++JAP776aeYu+QqFUUVlaREVxAbqQCKRzC5NUlZc2flugETUNVU2GM9GotFxtg2+z2ewZ42qz+tlsNkpKSkhPTyc1NZXk5GSSkpI4duwYp0+fRpIkXF1diYuLo0+fPlxzzTUkJibSp08fnJycgPOvLsX71ExNHqbOOo5ttd8ObKHFKSd0Ae0eDLj5+KNz8yBt9xZG3HQ3Ko0WZBmb1Yws2ygvyOXgqmV4hUTg6uWL1WLGarEgW6013201V/w2qwWrqZqCzJNs/+rfXPOX11FptFjNNc+3Wa2Yq6tI2bqWkL6DGH/3Y/QePZGP7r6GtJ2b8A2PIe9EKskbfmPwNXNQqtWU5edw6tAe+k64GklSgM2GxVSFQqlGoVQ22XDYrFYyMzPRlKrtV5q1qVfr/v/C7z2lQarbqFut1ga/zGYzBQUFnDx5kpMnT5KZmUlmZiZnzpwhMzOT0tJSFAoFLi4uhIWFERcXx8SJE+nduzexsbEEBQWhvJT54IIgWmhBOE+7BwMuXr70uXwaGz59l82f/4sBV87CUlVJ0dkzKBRKygrycHJx4+T+HZwcMAxkGWNhLhUlxZw9dhiVRoOzwYvs1GRSdmzAEBCCzt3Agd++xxAYQk76MYxF+RRmniL/ZCoHVy3HXF1FxODLcPH0wS8qDq/QCHzDY4gYMopt//uY3BMpePgHUZR5kqkPP4+kUODhH8SxLSUc3bQa/+h4vELCkaTGG5iK4gL+8sKzFKQewdnZGTc3N1xdXc/7cnNzw2AwYDAY8PT0xGAwoNVqUalU9i7rul91H6sNKi4MKCRJsjd+7RlY1F6ZW61W+//rNuy1KXlrv6xW63k/m81mjEYjhYWF5Ofnk5eXR0lJCYWFhZSWllJcXExxcTGlpaXIsoxWq8XFxYXAwECCg4NJTEwkODiYkJAQgoOD8fPzQ3sJS+EKHaW9r7JFP7sgtLcOmU0w+JqbsVotpO7YyObPP8C7VyRhA4ZTWVZCVVkJCeOv4sBvP7D/12/pO/EaRt1yH4dWL6c0P5uYEeOJGzuFw2t+wliYT/igkYyZez/JG39j5w+f0efyaQydcSsFZ04g22SC4vtzYs9WLCYTKrWaQdNvInbkFUiSxNjb/sRONw/yThynsrSYYTPn1gwYVCiIGXk5mUcPcPb4EQJj+zZrHrnOzYOnnn0OfUVN41ZSUkJxcTElJSX2n0+dOkVFRQXV1dX2L7VajVarRavVolar0Wg0aDQa+//rBgs6nQ6tVotOp0Oj0aDX61Eqlej1ensvg0qlQpIke7BQn9oAovZ11TbqtV30ZrMZOJeh7Vzjb7FYMJlMGI1GzGYzlZWVVFVVUV1dTWVlJVarFZPJdN5rq/tlMtVkqHRyckKr1eLs7IyHhwdeXl5ERkbi6emJl5cXnp6e+Pj44Ofnh6enp7jK73Lao6HuhlmQBMGBtXE64sbVpBW2oXPzoNpYjkanR3kue5u5ugqFUoni3KIgluoq+718WbZhs9pqGr9zjZmpwohK64RCqcRmtWKzWVGq1NisVmSblYqSIvRuBpQazXlXzxaziaqyEjQ6Z9ROOvvvZFlGtlkBCcUlNEZNpSOWZZnKykrKy8vP+zIajVRUVFBVVUVlZeVFXyaTyf5V2yjXfrdarVRXV9v3IUnSeaOvL+wtqG3ga3scoCYYqPt727nbMbVd+wAKhQKVSoVWq0WpVNqDl9rH1Go1Op0OZ2dnnJ2d0ev16PV6+8+1Xy4uLvZek26XrKcrqy8dcX0X4V00HXGHE+mIBQfUsemIG3R+zaJ3N9j/r3NzP++Zaq3T+T/XGdQnSQqUqvMbEY3e2f7/miBCCcgoVSpAhZuPf70lUqk1uHj6XPS4JIHU0tXJGiFJkr2R9PW9tKyGtalxGwsGap9T2+BbLJZ6t2U2m88LACRJOm8qneZc0FTbg1A7BqJuMFDbe1H7JXRDon0ShB6pnYOBjq5ZWrOIUWOLF7Rg821AkiT77QRnZ+em/0AQBEEQWqCDljB2RJfQsourJUEQBKEb6wI3cJsc0tAOu+rAfQpClyY+Kw0Sh0boQhynZ6DB2UMdeFkuXfQfQRC8tFBd/wBC+dw/sixTYiwn7Uwmg+N6d2jxHIar9uI7j3XrNTFDUnBgjhMMdGTq09ZsTnyghZ4m2uO8H2VqZqOYzGZKS8rZtHU3X371Ixs2bufHHz6GBK9OKaZDkupUGKLeEBxYBwYDrWlF2/hT1JrNiQ+00BOcN81fsk8/LS+voKi4hCNJKSz78Xd++XU9Z7NzUSgkFj58F8OG9G9gylwrPv9d+uq6SxVW6ME6MBho6kPR2k95nb/vchWGIDiYc58fi8VKUXEJubkFnMg4zbqNO1i1ehNJyanUrssBEoMS+/LIQ3c0khGzFR/IZk76EQSh5TrvNsFFDXYzP+XNGVvQIRWGiDiE7slms1FQUExK6gmOp2awb38SW7buZt+BZHtCqtrvEhAQ4MPzTz+Il6ehe6y70aIVG+t5TFQRQhfSxsHAJZz9HXLHoD0/jeJTLnRPsiyzY9d+Xn9jMbv2HKK62oQkgc128fB4rVbDvNtmMXJ4IipVTeKvDvtstPWuarfXohUb63msobslguCA2jgYcJQGstmfakEQLqBQKAgPC8HFxdm+fkVDScvHjB7KTbOn4+bmcu6Rzpj90xodPMBPVEmCg+oCeQZaQnziBKGlJEmid2wECx+5i6jIXg0+J6xXEPPmziI6Kqxzbg9cylV2g8+VmnrC+b9q7j5FD4DQxXTTYKCZxAdWEOqlVCoZNXIwd95+fZ2r/j+oVEqmThnHpAmj0Gg6aZ2KS4k/WtT1X8+vmrtPcT0idDGdHAx0cmssNbT/pstVd3U/QehO5HNJhHbvPcQ336+0L1xV14D+8cy7dSYGg3sDWxEEoSvp5KRDnR0+t3QalIxstdr/LwhdksIZJA11z2EZqKys5H9ffccrr/6DYUMH899PP+HmW+7gwIFDWCwWvL29mDlzJoOHT+j0T3CXJDk1/RxB6GAtCgbUSoleLho0PXRt+ipjOT999SG52VlMfOxhQntHoVKpuse0KqHnUGgBLYA9qVBmZiYffPAB3333HXfccRcLFizAYDDwxRf/Y/To0eTn55OYOIj5Cx5EUrp2bvkFQWgzktyMvu7S0lLc3d0pKSnBzc2tI8rl0EwmE2vWrOHNN9/EbDbzpz/9idGjR+Pr6ysCAqHLkWUZo9HI/v37eeONN8jPz+fxxx9nypQpaDQa+3OWLVvGc889xzvvvMO4ceM6t9CCIDRLc9tvEQy0wqlTp/j3v//Nr7/+ysCBA5k7dy79+vXD1VVcMQldg81mIzU1lRUrVvDVV18xYMAAHnroIRISEi56rtVqZdu2bVx22WUi6BWELkIEAx3EYrGwfv16PvvsM1JTU5kyZQqTJk1i4MCB9qsqQXBEJSUlrF27liVLllBUVMTcuXO5/vrrcXcXgwIFobsQwUAHy8/P55dffmHFihUUFxdz1VVXMX36dCIiIsRVlOBQbDYbycnJfP7552zevJmRI0cyc+ZMhgwZIs5VQehmRDDQwWqnGqampvL777+zfPly9Ho9c+fOZeLEibi4XDxXWxA6WnFxMStWrOB///sfOp2OuXPnMm7cONzc3EQgIAjdkAgGOoksy1RWVpKUlMQ333zD2rVrGT58OA899BAREREoeugMDKFzWa1Wjh49yptvvklSUhLXXXcdV199NZGRkSiVShEICEI3JYKBTlaz9ns5W7Zs4a233uL06dPceeed3Hvvvej1elH5Ch1ClmVMJhNLlizh3XffJTo6mkceeYRBgwaJ81AQegARDDiA2rnbZWVlfPnll7z22mvo9XqefvppZs+eLa7IGmG1WtmxYwenTp067/HLLruMoKAgTp06xY4dO5BlGUmSCAkJYeTIkZ1UWsdT+7FOS0vjwQcfZN++fTzwwAPce++9uLm5oVQqO7mEgiB0hOa236LPuh1JkoRSqcTd3Z0FCxawY8cOxo8fz/3338+1117Lzp07MZlMIq1xPRQKBbGxsRQUFPDkk09y++23k5mZibe3N5Ik0atXLzw9Pbn//vvJzs6mb9++nV1khyDLMlarlcLCQl588UWGDRuG2Wxm9erV/OUvf8HDw0MEAoIgXEQEAx1AkiQkScLPz493332XNWvWoFarmTNnDs8//zxHjx6loqJCBAV1SJKEl5cX99xzD6+++iru7u6cOXMGo9EIQHV1NZs3b+aFF17goYceErkdqOlNKS4u5vvvv+eqq67if//7H6+++iorV64kISHBfh4KgiBcqJPXJuh5JEkiMTGRpUuX8tNPP/Hvf/+bzZs3c+2113LFFVcQFRUl7uXWoVarmTRpEvPmzePrr79mzJgxTJo0ieXLl2M2m7n//vs7u4idTpZlysrK2L9/P59//jl79uxh4sSJPPjggwQEBIhzSRCEJokxA51IlmXy8vJYsWIFP/zwA5WVlUybNo2xY8fSp08fdDpdZxfRYRw4cIDHH38cm83GDTfcwJEjR3jppZd69JRNWZapqqri0KFD/P7776xdu5bg4GDmzZvH8OHD0Wq1nV1EQRA6mRhA2IXIskx6ejorV65k9erVVFVVMWrUKMaOHcvAgQN7dINXy2q18s033/DXv/4VjUbDsmXLiI2N7bFXvWazmT179rBmzRq2bt2Ki4sLV199NZMnT8bb27uziycIgoNobvstbhM4AEmSiIyM5N5772XixIls2bKFVatWsWHDBvr06cPUqVMZMWJEjw7ElEol8fHxhIaGcvDgQfbs2UNMTEyPCwasVit79uxh2bJlbN++HR8fH2bNmsXo0aNFHgtBEFpM9Aw4IKvVysmTJzl48CC//vorSUlJREREMHv2bMaNG4ezs3NnF7HDFRUVsXTpUpydnVm0aBFlZWX8+OOPxMbGdnbROoTNZuPo0aN8/vnnrF+/nqCgIGbNmsWgQYMICwtDpRJxvSAIFxO3Cbq42iliBQUFHDlyhG+//ZYtW7YQFhbG7bffzpVXXolGo+kRV8Ymk4nvvvuOiooKZs6cyebNm7n55psZN24c3377bbdeEMpms3HixAmWLFnCzz//jL+/P7fffjvDhw8nICAAtVrd2UUUBMGBidsEXZwkSahUKnx9ffH29mbIkCEkJSXxn//8h/vvvx9fX1/uvPNObr75Zry8vDq7uO3GZrOxY8cOTpw4wS233IK7uzuTJ09m3rx5fPTRR7z77rs89thjnV3MNifLMllZWbz33nt8/fXX+Pr68sADDzB58mR8fHxQqVQ9IhAUBKFjiJ6BLsRms2G1Wjl79ixLlixh8eLFWK1WbrzxRu644w7i4+Pt94y7ekNRm70xOTmZt99+m5kzZzJ58mQkSUKWZYqLi4mOjkapVPL1118zevToLj+Pvnaxq5ycHBYvXswHH3yAm5sb999/P3PmzMFgMKBQKLr0axQEoWOJDITdkEKhQK1WExISwjPPPMPRo0d58cUX2bp1K1OmTOGOO+5g06ZNFBcXU11dbW9cuiKr1cpHH33EnDlz2LhxI4cOHaKgoACoCYqWLVuGVqvFbDZz33338a9//Yvy8vJOLvWlk2UZi8VCeXk527Zt47777qN///58//33vPrqq+zfv58HH3wQLy8vkb5aEIR2I3oGugGj0cjatWtZsmQJBw4cIDIyksmTJzNp0iS8vb1xc3NDp9OJhsSByLJMRUUFubm57Nixg6+++orDhw8TGxvLrbfeyowZM7r1WAhBEDqGGEDYA5lMJpKSkli1ahWrV68mLy+PiIgILr/8ckaMGEFAQABeXl5otVoRGHSSqqoqcnJyOHPmDFu3bmXdunUUFRUxaNAgbrjhBoYOHSqSBQmC0GZEMNDDFRUVsX37dnsXu9lsplevXiQkJBAbG0t4eDghISE4OTmJwKCdVVdXc+bMGVJTUzly5Ah79+4lOzsbg8HA6NGjmTRpElFRUWJ6oCAIbU4EAwJQ0x196tQp9u7dy6FDh0hJSaGkpAQnJyciIiKIj48nLi6OqKgoDAZDZxe327BYLGRlZXHw4EH27dvHkSNHKC8vx8PDg4SEBAYMGMDAgQPx8/Pr7KIKgtCNiWBAuIjJZOLs2bNkZGSQmprKgQMHOH36NMXFxXh7exMVFUW/fv3o168fERERYm2ES2Sz2Th27Bh79+5l7969HD9+HLPZTHBwMImJifagKzAwUCwjLAhChxDBgNAos9lMUVEReXl5ZGdnc+DAAQ4fPsyJEycoKysjICCA/v37M3ToUIYOHYqfn5+4nVAPo9HIoUOH2LhxI9u2bePUqVO4uLgQHx/P4MGDSUhIICgoCB8fnx6TJEoQBMchggGhWWrf/srKSoxGI+Xl5WRlZbFr1y62bdvG/v37sVgshIeHM2bMGIYPH86QIUN67C2F2uWCd+/ezerVq1m3bh1ZWVmEhIQwYsQIRo0axYABA3B1dcXZ2VkM1hQEoVOJYEBokdrcBGazGbPZTFlZGbt27WLDhg1s3ryZo0eP4ubmxtChQxk3bhxjxoyhd+/eF02D68oN4IUfidrkR2vXrmXVqlXs3r0bhUJBYmIiEyZMYMKECQQHB6PValGr1WIgoCAIDkMEA0KbqD09ajMC5uTksHbtWn7//Xe2bNnC2bNn8fT0ZNiwYYwcOZKRI0eSkJCARqOxZ8tz9KyItQFQ7Wu0WCycPHmSjRs3sn79ejZt2kRBQQG+vr5MnDiRKVOmMHr0aLy8vM7Leuior08QhJ5LBANCuzObzaSkpLB161a2bNnC9u3bycvLQ6vVkpCQwMCBA0lMTGTAgAEYDAY0Gg0qlQqVSoVare6U5XZrG3yz2YzFYsFsNmMymUhLS2P37t3s37+fffv2kZmZibu7O0OGDGH06NGMGTOGuLg4sUSwIAhdiggGhA5ntVpJT0/nwIED9q+MjAyqqqrw9vamV69ehISEEBUVRXR0ND4+Pmi1WrRaLU5OTmg0GjQajb2rvaVX2rUpfk0m03lfVVVVGI1GsrKySElJISMjgxMnTpCamorJZMJgMNCnTx8GDhzIsGHD6NOnT49cLloQhO5DBANCp7PZbBQUFHD8+HFSU1NJS0sjPT2dnJwcysrKUCqVeHh44OHhgbu7O15eXnh7e2MwGPDw8ECv16PRaFAqlahUKvv32m3X3Y/JZLJf5ZtMJoqLi8nJySE3N5fS0lJKS0spKiqiuLgYAHd3d4KCgoiMjKR379707t2biIgIkQJYEIRuRQQDgkOqHY1/5swZe2Nd+72goICysjIqKyupqKhAqVSiVCpRKBT2rwt7C2rv9VutVvuXzWbDyckJV1dX3Nzc8PT0xMvLCz8/PwIDAwkODiYgIEBkXxQEodtrbvsthj0LHUqSJNzc3IiPjyc+Pv6835lMJoxGo/2rvLycyspKzGYz1dXV9q5/m82GWq22N+QKhQInJyd0Op39y9nZGTc3N1xdXUWCH0EQhCaIYEBwGLVjBnpqDgNBEITOIoZGC4IgCEIPJ4IBQRAEQejhmnWboHaMYWlpabsWRhAEQRCEtlPbbjc1V6BZwUBZWRkAISEhrSyWIAiCIAgdraysDHd39wZ/36yphTabjaysLFxdXcVULEEQBEHoImqncwcGBjaaQbVZwYAgCIIgCN2XGEAoCIIgCD2cCAYEQRAEoYcTwYAgCIIg9HAiGBAEQRCEHk4EA4IgCILQw4lgQBAEQRB6OBEMCIIgCEIP9/+GgB8N9TTRnwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0 train_loss: 133.12298583984375\n", + "epoch: 1 train_loss: 132.64720153808594\n", + "epoch: 2 train_loss: 130.9735565185547\n", + "epoch: 3 train_loss: 126.02873229980469\n", + "epoch: 4 train_loss: 118.0386734008789\n", + "epoch: 5 train_loss: 113.14652252197266\n", + "epoch: 6 train_loss: 110.42718505859375\n", + "epoch: 7 train_loss: 104.79252624511719\n", + "epoch: 8 train_loss: 100.4343032836914\n", + "epoch: 9 train_loss: 96.34988403320312\n", + "epoch: 10 train_loss: 87.50508880615234\n", + "epoch: 11 train_loss: 81.53534698486328\n", + "epoch: 12 train_loss: 73.87696838378906\n", + "epoch: 13 train_loss: 64.91850280761719\n", + "epoch: 14 train_loss: 57.168968200683594\n", + "epoch: 15 train_loss: 49.983306884765625\n", + "epoch: 16 train_loss: 41.7336311340332\n", + "epoch: 17 train_loss: 35.65283203125\n", + "epoch: 18 train_loss: 29.61258316040039\n", + "epoch: 19 train_loss: 22.141714096069336\n", + "epoch: 20 train_loss: 15.57487678527832\n", + "epoch: 21 train_loss: 10.8868989944458\n", + "epoch: 22 train_loss: 8.614974975585938\n", + "epoch: 23 train_loss: 8.053958892822266\n", + "epoch: 24 train_loss: 7.4709153175354\n", + "epoch: 25 train_loss: 6.822552680969238\n", + "epoch: 26 train_loss: 6.289606094360352\n", + "epoch: 27 train_loss: 5.890585422515869\n", + "epoch: 28 train_loss: 5.6151933670043945\n", + "epoch: 29 train_loss: 5.645315647125244\n", + "epoch: 30 train_loss: 5.033112525939941\n", + "epoch: 31 train_loss: 4.737390041351318\n", + "epoch: 32 train_loss: 4.485416412353516\n", + "epoch: 33 train_loss: 4.714532375335693\n", + "epoch: 34 train_loss: 4.0359320640563965\n", + "epoch: 35 train_loss: 3.7806291580200195\n", + "epoch: 36 train_loss: 3.9372406005859375\n", + "epoch: 37 train_loss: 3.717404365539551\n", + "epoch: 38 train_loss: 3.304453134536743\n", + "epoch: 39 train_loss: 2.779893159866333\n", + "epoch: 40 train_loss: 2.547934055328369\n", + "epoch: 41 train_loss: 2.3251867294311523\n", + "epoch: 42 train_loss: 2.286116123199463\n", + "epoch: 43 train_loss: 2.093520402908325\n", + "epoch: 44 train_loss: 2.443225622177124\n", + "epoch: 45 train_loss: 1.875758409500122\n", + "epoch: 46 train_loss: 2.1676034927368164\n", + "epoch: 47 train_loss: 1.836241602897644\n", + "epoch: 48 train_loss: 1.4220737218856812\n", + "epoch: 49 train_loss: 1.3957267999649048\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = variable(\"X\")\n", + "xhat = variable('xn')[:, :-1, :]\n", + "\n", + "# trajectory tracking loss\n", + "reference_loss = (xhat == x)^2\n", + "reference_loss.name = \"ref_loss\"\n", + "\n", + "\n", + "\n", + "# finite difference loss\n", + "fd_loss = 2.0*((xFD == xhatFD)^2)\n", + "fd_loss.name = 'FD_loss'\n", + "\n", + "# %%\n", + "objectives = [reference_loss, fd_loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([dynamics_model], loss)\n", + "# plot computational graph\n", + "problem.show()\n", + "\n", + "# %%\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "trainer = Trainer(\n", + " problem,\n", + " train_loader,\n", + " dev_loader,\n", + " test_data,\n", + " optimizer,\n", + " patience=50,\n", + " warmup=0,\n", + " epochs=50,\n", + " eval_metric=\"dev_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"dev_loss\",\n", + " test_metric=\"dev_loss\",\n", + " device='cpu', \n", + " epoch_verbose=1\n", + ")\n", + "# %%\n", + "best_model = trainer.train()\n", + "problem.load_state_dict(best_model)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learned parameter a= 0.7175129055976868\n", + "Learned parameter b= 0.33722737431526184\n", + "Learned parameter c= 1.1291733980178833\n", + "Learned parameter d= 0.24657443165779114\n", + "True parameter a= 1.0\n", + "True parameter b= 0.10000000149011612\n", + "True parameter c= 1.5\n", + "True parameter d= 0.75\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Batch sizes not consistent.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[243], line 26\u001b[0m\n\u001b[1;32m 24\u001b[0m new_test_data \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 25\u001b[0m new_test_data_dict \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m: new_test_data}\n\u001b[0;32m---> 26\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_test_data_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHI\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 29\u001b[0m pred_traj \u001b[38;5;241m=\u001b[39m test_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m][:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, :]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", + "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print('Learned parameter a=', float(fx.alpha))\n", + "print('Learned parameter b=', float(fx.beta))\n", + "print('Learned parameter c=', float(fx.gamma))\n", + "print('Learned parameter d=', float(fx.delta))\n", + "\n", + "print('True parameter a=', float(modelSystem.a))\n", + "print('True parameter b=', float(modelSystem.b))\n", + "print('True parameter c=', float(modelSystem.c))\n", + "print('True parameter d=', float(modelSystem.d))\n", + "\n", + "# evaluate learned black box block\n", + "x1 = torch.arange(0., 150., 0.5)\n", + "x2 = torch.arange(0., 150., 0.5)\n", + "true_block = x1*x2\n", + "learned_block = net(torch.stack([x1, x2]).T).squeeze()\n", + "plt.figure()\n", + "plt.plot(true_block.detach().numpy(), 'c',\n", + " linewidth=4.0, label='True')\n", + "plt.plot(learned_block.detach().numpy(), 'm--',\n", + " linewidth=4.0, label='Learned')\n", + "plt.legend(fontsize=25)\n", + "\n", + "# Test set results\n", + "new_test_data = test_data['xn']\n", + "new_test_data_dict = {'xn': new_test_data}\n", + "test_outputs = dynamics_model(new_test_data_dict)\n", + "print(\"HI\")\n", + "\n", + "pred_traj = test_outputs['xn'][:, :-1, :]\n", + "true_traj = test_data['X']\n", + "pred_traj = pred_traj.detach().numpy().reshape(-1, nx)\n", + "true_traj = true_traj.detach().numpy().reshape(-1, nx)\n", + "pred_traj, true_traj = pred_traj.transpose(1, 0), true_traj.transpose(1, 0)\n", + "\n", + "figsize = 25\n", + "fig, ax = plt.subplots(nx, figsize=(figsize, figsize))\n", + "labels = [f'$y_{k}$' for k in range(len(true_traj))]\n", + "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, labels)):\n", + " if nx > 1:\n", + " axe = ax[row]\n", + " else:\n", + " axe = ax\n", + " axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n", + " axe.plot(t1, 'c', linewidth=4.0, label='True')\n", + " axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n", + " axe.tick_params(labelbottom=False, labelsize=figsize)\n", + "axe.tick_params(labelbottom=True, labelsize=figsize)\n", + "axe.legend(fontsize=figsize)\n", + "axe.set_xlabel('$time$', fontsize=figsize)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['train_X', 'train_xn', 'train_name', 'train_140361679634384', 'train_140361679634384_eq_X', 'train_140361679634384_eq_X_value', 'train_140361679634384_eq_X_violation', 'train_objective_loss', 'train_penalty_loss', 'train_loss'])" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "www = problem(foo)\n", + "www.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data(sys, nsim, nsteps, ts, bs):\n", + " \"\"\"\n", + " :param nsteps: (int) Number of timesteps for each batch of training data\n", + " :param sys: (psl.system)\n", + " :param ts: (float) step size\n", + " :param bs: (int) batch size\n", + "\n", + " \"\"\"\n", + " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", + " nx = sys.nx\n", + " nbatch = nsim//nsteps\n", + " length = (nsim//nsteps) * nsteps\n", + " ts = torch.linspace(0,1,nsteps)\n", + "\n", + " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " trainX = torch.tensor(trainX, dtype=torch.float32)\n", + "\n", + "\n", + "\n", + " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", + " train_loader = DataLoader(train_data, batch_size=bs,\n", + " collate_fn=train_data.collate_fn, shuffle=True)\n", + "\n", + " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " devX = torch.tensor(devX, dtype=torch.float32)\n", + " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", + " dev_loader = DataLoader(dev_data, batch_size=bs,\n", + " collate_fn=dev_data.collate_fn, shuffle=True)\n", + "\n", + " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", + " testX = torch.tensor(testX, dtype=torch.float32)\n", + " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", + "\n", + " return train_data, dev_data, test_data, bs" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[229], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneuromancer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrainer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer, LitTrainer\n\u001b[1;32m 2\u001b[0m lit_trainer \u001b[38;5;241m=\u001b[39m LitTrainer(epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m, accelerator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m, train_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m 3\u001b[0m dev_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, eval_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, test_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, custom_optimizer\u001b[38;5;241m=\u001b[39moptimizer)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mlit_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproblem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_setup_function\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodelSystem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsteps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/trainer.py:178\u001b[0m, in \u001b[0;36mLitTrainer.fit\u001b[0;34m(self, problem, data_setup_function, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, problem, data_setup_function, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 170\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124;03m Fits (trains) a base neuromancer Problem to a data defined by a data setup function). \u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;124;03m This function will also instantiate a Lightning version of the provided Problem \u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;124;03m :param data_setup_function: A function that returns train/dev/test Neuromancer DictDatasets as well as batch_size to use\u001b[39;00m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 178\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproblem_copy \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_setup_function \u001b[38;5;241m=\u001b[39m data_setup_function\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlit_problem \u001b[38;5;241m=\u001b[39m LitProblem(problem,\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_metric, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdev_metric, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtest_metric, custom_training_step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcustom_training_step, hparam_config\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhparam_config )\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (1 times)]\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", + " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (1 times)]\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (8 times), _deepcopy_dict at line 231 (4 times), _reconstruct at line 271 (4 times), deepcopy at line 146 (4 times), _reconstruct at line 297 (3 times)]\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:153\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 151\u001b[0m copier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__deepcopy__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 155\u001b[0m reductor \u001b[38;5;241m=\u001b[39m dispatch_table\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/_tensor.py:86\u001b[0m, in \u001b[0;36mTensor.__deepcopy__\u001b[0;34m(self, memo)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__deepcopy__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, memo)\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_leaf:\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 87\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly Tensors created explicitly by the user \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(graph leaves) support the deepcopy protocol at the moment. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you were attempting to deepcopy a module, this may be because \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof a torch.nn.utils.weight_norm usage, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msee https://github.com/pytorch/pytorch/pull/103001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 92\u001b[0m )\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m memo:\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m memo[\u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m)]\n", + "\u001b[0;31mRuntimeError\u001b[0m: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001" + ] + } + ], + "source": [ + "from neuromancer.trainer import Trainer, LitTrainer\n", + "lit_trainer = LitTrainer(epochs=50, accelerator='cpu', train_metric='train_loss', \n", + " dev_metric='dev_loss', eval_metric='dev_loss', test_metric='dev_loss', custom_optimizer=optimizer)\n", + "\n", + "\n", + "\n", + "lit_trainer.fit(problem=problem, data_setup_function=get_data, sys=modelSystem, nsim=nsim, nsteps=nsteps, ts=ts, bs=bs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Stochastic Lotka Volterra System Data" + ] + }, + { + "cell_type": "code", + "execution_count": 420, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + 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"KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[420], line 47\u001b[0m\n\u001b[1;32m 43\u001b[0m x0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m10.0\u001b[39m, \u001b[38;5;241m10.0\u001b[39m])\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m) \u001b[38;5;66;03m#[1x2]\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# Integrate the SDE model\u001b[39;00m\n\u001b[0;32m---> 47\u001b[0m sol_train \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_span\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m sol_dev \u001b[38;5;241m=\u001b[39m torchsde\u001b[38;5;241m.\u001b[39msdeint(sde, x0, t_span, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meuler\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 49\u001b[0m sol_test \u001b[38;5;241m=\u001b[39m torchsde\u001b[38;5;241m.\u001b[39msdeint(sde, x0, t_span, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meuler\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:110\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extra_solver_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 109\u001b[0m extra_solver_state \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39minit_extra_solver_state(ts[\u001b[38;5;241m0\u001b[39m], y0)\n\u001b[0;32m--> 110\u001b[0m ys, extra_solver_state \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_solver_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parse_return(y0, ys, extra_solver_state, extra, logqp)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/base_solver.py:145\u001b[0m, in \u001b[0;36mBaseSDESolver.integrate\u001b[0;34m(self, y0, ts, extra0)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m prev_t, prev_y \u001b[38;5;241m=\u001b[39m curr_t, curr_y\n\u001b[0;32m--> 145\u001b[0m curr_y, curr_extra \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurr_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnext_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurr_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurr_extra\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 146\u001b[0m curr_t \u001b[38;5;241m=\u001b[39m next_t\n\u001b[1;32m 147\u001b[0m ys\u001b[38;5;241m.\u001b[39mappend(interp\u001b[38;5;241m.\u001b[39mlinear_interp(t0\u001b[38;5;241m=\u001b[39mprev_t, y0\u001b[38;5;241m=\u001b[39mprev_y, t1\u001b[38;5;241m=\u001b[39mcurr_t, y1\u001b[38;5;241m=\u001b[39mcurr_y, t\u001b[38;5;241m=\u001b[39mout_t))\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/methods/euler.py:32\u001b[0m, in \u001b[0;36mEuler.step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m extra0\n\u001b[1;32m 31\u001b[0m dt \u001b[38;5;241m=\u001b[39m t1 \u001b[38;5;241m-\u001b[39m t0\n\u001b[0;32m---> 32\u001b[0m I_k \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m f, g_prod \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msde\u001b[38;5;241m.\u001b[39mf_and_g_prod(t0, y0, I_k)\n\u001b[1;32m 36\u001b[0m y1 \u001b[38;5;241m=\u001b[39m y0 \u001b[38;5;241m+\u001b[39m f \u001b[38;5;241m*\u001b[39m dt \u001b[38;5;241m+\u001b[39m g_prod\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Define the Lotka-Volterra SDE\n", + "class LotkaVolterraSDE(nn.Module):\n", + " def __init__(self, a, b, c, d, sigma1, sigma2):\n", + " super().__init__()\n", + " self.a = a\n", + " self.b = b\n", + " self.c = c\n", + " self.d = d\n", + " self.sigma1 = sigma1\n", + " self.sigma2 = sigma2\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + "\n", + " def f(self, t, x):\n", + " x1 = x[:,[0]]\n", + " x2 = x[:,[1]]\n", + " dx1 = self.a * x1 - self.b * x1*x2\n", + " dx2 = self.c * x1*x2 - self.d * x2\n", + " foo = torch.cat([dx1, dx2], dim=-1)\n", + " print(\"FOO SHAPE \", foo.shape)\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, x):\n", + " sigma_diag = torch.tensor([[self.sigma1, self.sigma2]])\n", + " return sigma_diag #[batch_size x state size ]\n", + "\n", + "# Define parameters\n", + "a = 1.1 # Prey growth rate\n", + "b = 0.4 # Predation rate\n", + "c = 0.1 # Predator growth rate\n", + "d = 0.4 # Predator death rate\n", + "sigma1 = 1\n", + "sigma2 = 0\n", + "\n", + "# Create the SDE model\n", + "sde = LotkaVolterraSDE(a, b, c, d, sigma1, sigma2)\n", + "\n", + "\n", + "# Define time span\n", + "t_span = torch.linspace(0, 20, 2000)\n", + "\n", + "# Initial condition\n", + "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", + "\n", + "\n", + "# Integrate the SDE model\n", + "sol_train = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "sol_dev = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "sol_test = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 378, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.0100)" + ] + }, + "execution_count": 378, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t_span[1] - t_span[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 369, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2000, 1, 2])" + ] + }, + "execution_count": 369, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sol.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 421, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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99wOUGHZZ2uM6lQ8//JCYmBhiY2Pp3Lkzv/32G2PHjuXee+8ttt/IkSOLPZY1a9awdetWhg0bxtGjR11tlZmZyWWXXcaiRYtcQ9BGjBhBbm5usWGT06dPp6CgoNh8qNPp1asXkZGRTJs2DcMwmDZtGtddd12p+5a13X755Rfy8vK46667iv0P+efjh7K/PkSkcmi4nUgt16lTJ2bMmEFeXh5r167lm2++4bXXXuPqq69mzZo1pX6QdDpy5AhZWVmlDqlp2bIlDoeDPXv20Lp1a7Zv385VV11VppgaNGhQ7HpERARAsURn9+7dPP7448yaNatEApSamgqYlcZeeOEF7r//furUqcP555/PFVdcwYgRI4iLiytTLCdyDptyV6nfXbt2uYYnnsg51GzXrl1lOveOHTsYPnw4Q4YMKVHK3Wny5Mm88sorbN68mfz8fNf2Ro0auS4fOXKEwsJC1/Xg4GCCg4Pp27cvkZGRTJ061TU37fPPP6ddu3a0bt3aFWtCQgIhISEnfSzltXXrVgC6d+9e6u2hoaHFrnt7e1OvXr1S9z3xcZ5o9uzZPP3006xZs6bY3JGyJsH+/v589913gPn6a9SoUakxlHb+rVu3sm7dOtcwt39yFlPZtWsX8fHxBAcHF7u9tL/D5ORkxo8fz7Rp00oUY3H+nZzKrl27sNvtJYa/xcXFER4eXuJ5/OfjSk5OLjZsLCAggLCwMNf1gQMHcuedd2Kz2QgJCaF169alFtf453Gdr4VTVQ1MTU0lIiKCFi1a0KlTJ6ZMmcJNN90EmEPtzj///BKP61R8fHwYMmQIU6dO5bzzzmPPnj0MGzas1H3L2m7O382aNSu2X0xMjOv/3omPuSyvDxGpHEqSRAQAX19fOnXqRKdOnTjrrLMYPXo0X375JU888USVx1LaJGjANeG/sLCQnj17kpyczP/93//RokULgoKC2LdvH6NGjSo2ifnee++lf//+zJw5kzlz5vCf//yH5557jl9//ZUOHTpUyeOpavHx8cTHx/PDDz+wYsUKzj333GK3f/bZZ4waNYpBgwbx4IMPEhsbi5eXF88995wrEQQzgT7xQ/ATTzzBuHHj8PHxYejQobz//vscOnSI3bt3s3XrVl588UW3Pi7n8/rpp5+WmuR6exd/S/Pz8ztpBbTSKq4tXryYAQMGcPHFF/P2228THx+Pj48PkyZNOmXZ8xN5eXnRo0eP0+5X2vkdDgc9e/bkoYceKvU+Z511VpliONHQoUNZunQpDz74IO3btyc4OBiHw0GfPn3KNdm/rEniPx/X4MGDWbhwoev6yJEjixUZqVevXoXayxn7Sy+9dNI5dCcmkSNGjOCee+5h79695ObmsmzZMv773/+e9rz/NGzYMN555x3GjRtHu3btTvklElROD7OTO14fInJySpJEpATnh+oDBw64tpX2Zh8TE0NgYCBbtmwpcdvmzZux2+3Ur18fMAs9bNiwoVLiW79+PX/99ReTJ09mxIgRru1z584tdf8mTZpw//33c//997N161bat2/PK6+84locsqwfZJzFKjZs2FCmD3bllZiYeNK2dN4Op4/X39+f2bNn0717d/r06cPChQtdPTwAX331FY0bN2bGjBnFjvXPhHjKlCnFqgWeOKTy+uuv55133mH69Ons2LEDm81WbOhRYmIiv/zyC+np6cV6k/75WEpzssfnbP/Y2Fi3tP/XX3+Nv78/c+bMKVbefNKkSZV+rtI0adKEjIyM0z62xMRE5s2bR0ZGRrFE4J+vnWPHjjFv3jzGjx/P448/7tru7IU50cnaPDExEYfDwdatW129gGAWGUlJSTnl8wjwyiuvFOvpLa0EekU4XwuhoaFlei1ce+21jB07ls8//5zs7Gx8fHy45ppryn3eCy+8kAYNGrBgwQJeeOGFk+5X1nZz/t66dWuxv68jR46U6CEv6+tDRCqH5iSJ1GLz588vtRyzczz9icN3goKCSpQY9vLyolevXnz77bfFFv48dOgQU6dO5cILL3QNgbrqqqtcw/n+qbQYTsXZ03Ti/QzDKFGGOysri5ycnGLbmjRpQkhISLGhVKU9ttKcc845NGrUiNdff73E/uV9DKXp168ff/zxR7GSxJmZmbz33ns0bNjQ9a21czjSqWIOCwtjzpw5xMbG0rNnz2I9RKW13/Lly0uUQu7atSs9evRw/Zz4Ia5r1640bNiQzz77jOnTp3PJJZcUG1bWr18/CgsLS3xb/9prr2Gz2U652OzJHl/v3r0JDQ3l2WefLTZE0OnIkSMnPWZZeHl5YbPZig0x3LlzZ5Utojx06FB+//135syZU+K2lJQUCgoKALNtCwoKmDhxouv2wsLCEkMrS3uegVKroJ2szfv161fqfV599VWA01b869ixY7HX0Ol6XsqqY8eONGnShJdffpmMjIwSt//ztRAdHU3fvn357LPPmDJlCn369ClWUbOsbDYbEyZM4IknnuCGG2446X5lbbcePXrg4+PDm2++Wex5Ku05KuvrQ0Qqh3qSRGqxu+66i6ysLK688kpatGhBXl4eS5cuZfr06TRs2JDRo0e79u3YsSO//PKLa0HMRo0a0blzZ55++mnmzp3LhRdeyO233463tzfvvvsuubm5xYZfPfjgg3z11VcMGTKEG2+8kY4dO5KcnMysWbN45513aNeuXZnjbtGiBU2aNOGBBx5g3759hIaG8vXXX5f45vWvv/7isssuY+jQobRq1Qpvb2+++eYbDh06xLXXXlvssU2cOJGnn36apk2bEhsbW+q8F7vdzsSJE+nfvz/t27dn9OjRxMfHs3nzZjZu3Fjqh5d/+vrrr129KScaOXIkDz/8MJ9//jl9+/bl7rvvJjIyksmTJ7Njxw6+/vpr19CxJk2aEB4ezjvvvENISAhBQUF07ty5xLyN6Oho13PTo0cPlixZQt26dbniiiuYMWMGV155JZdffjk7duzgnXfeoVWrVqV+4CyNzWZj2LBhPPvsswA8+eSTxW7v378/3bp149///jc7d+6kXbt2/Pzzz3z77bfce++9rp6A0rRv3x4vLy9eeOEFUlNT8fPzo3v37sTGxjJx4kRuuOEGzjnnHK699lpiYmLYvXs333//PV27dq3QECqnyy+/nFdffZU+ffowbNgwDh8+zFtvvUXTpk1LLfFc2R588EFmzZrFFVdcwahRo+jYsSOZmZmsX7+er776ip07dxIdHU3//v3p2rUrDz/8MDt37qRVq1bMmDGjxByj0NBQLr74Yl588UXy8/OpW7cuP//8s2sNohN17NgRgH//+99ce+21+Pj40L9/f9q1a8fIkSN57733SElJ4ZJLLuGPP/5g8uTJDBo0iG7durm9XUpjt9v54IMP6Nu3L61bt2b06NHUrVuXffv2MX/+fEJDQ11zw5xGjBjB1VdfDZglxStq4MCBDBw48JT7lLXdYmJieOCBB3juuee44oor6NevH6tXr+bHH38skcSV9fUhIpXEmqJ6IuIJfvzxR+PGG280WrRoYQQHBxu+vr5G06ZNjbvuuss4dOhQsX03b95sXHzxxUZAQECJ0rSrVq0yevfubQQHBxuBgYFGt27djKVLl5Y439GjR40777zTqFu3ruHr62vUq1fPGDlypJGUlGQYxsnLZO/YsaNEaeNNmzYZPXr0MIKDg43o6Gjj5ptvNtauXVtsv6SkJOOOO+4wWrRoYQQFBRlhYWFG586djS+++KLY8Q8ePGhcfvnlRkhIiAG4yoH/swS405IlS4yePXsaISEhRlBQkHH22Wcbb7755inb2nmsk/04y35v377duPrqq43w8HDD39/fOO+884zZs2eXON63335rtGrVyvD29i72mP9ZitgwDGPbtm1GfHy80bJlS+PIkSOGw+Ewnn32WSMxMdHw8/MzOnToYMyePdsYOXKkkZiYeMrHcaKNGzcagOHn51eiJLphmCW777vvPiMhIcHw8fExmjVrZrz00kslyqX/swS4YRjG+++/bzRu3Njw8vIq8RzMnz/f6N27txEWFmb4+/sbTZo0MUaNGmWsWLHCtY+zHHdpAOOOO+4o9bYPP/zQaNasmeHn52e0aNHCmDRpkvHEE08YZXm7PNU5//l4L7/88lJvS09PNx555BGjadOmhq+vrxEdHW1ccMEFxssvv1ysNP/Ro0eNG264wQgNDTXCwsKMG264wVi9enWJv5O9e/caV155pREeHm6EhYUZQ4YMMfbv319qieinnnrKqFu3rmG324uVA8/PzzfGjx9vNGrUyPDx8THq169vPPLII0ZOTk6ZH1dpTvU8OJ2udP7q1auNwYMHG1FRUYafn5+RmJhoDB061Jg3b16JfXNzc42IiAgjLCzMyM7OLlOMZSndbxil/92Vtd0KCwuN8ePHG/Hx8UZAQIBx6aWXGhs2bCj176Ksr4/Snl8RKR+bYVTCGBERERERD1ZQUEBCQgL9+/fnww8/tDocEfFwmpMkIiIiNd7MmTM5cuRIsWIvIiIno54kERERqbGWL1/OunXreOqpp4iOji7zwsAiUrupJ0lERERqrIkTJ3LbbbcRGxvLJ598YnU4IlJNqCdJRERERETkBOpJEhEREREROYGSJBERERERkRPU+MVkHQ4H+/fvJyQkBJvNZnU4IiIiIiJiEcMwSE9PJyEhwbVIe2lqfJK0f/9+6tevb3UYIiIiIiLiIfbs2UO9evVOenuNT5JCQkIAsyFCQ0MtjSU/P5+ff/6ZXr164ePjY2ksNZHa173Uvu6l9nUvta97qX3dT23sXmpf9/Kk9k1LS6N+/fquHOFkanyS5BxiFxoa6hFJUmBgIKGhoZa/QGoita97qX3dS+3rXmpf91L7up/a2L3Uvu7lie17umk4KtwgIiIiIiJyAiVJIiIiIiIiJ1CSJCIiIiIicoIaPydJRERERKo3wzAoKCigsLDQLcfPz8/H29ubnJwct52jNqvK9vXy8sLb2/uMl/5RkiQiIiIiHisvL48DBw6QlZXltnMYhkFcXBx79uzRuppuUNXtGxgYSHx8PL6+vhU+hpIkEREREfFIDoeDHTt24OXlRUJCAr6+vm75kO1wOMjIyCA4OPiUC4xKxVRV+xqGQV5eHkeOHGHHjh00a9aswudTkiQiIiIiHikvLw+Hw0H9+vUJDAx023kcDgd5eXn4+/srSXKDqmzfgIAAfHx82LVrl+ucFaFXgYiIiIh4NCUuUh6V8XrRK05EREREROQESpJEREREREROoCRJRERERKSGmzdvHi1btqzUEtw//fQT7du3x+FwVNoxPYWSJBERERGRSjZq1ChsNhs2mw1fX1+aNm3Kk08+SUFBgSXxPPTQQzz22GN4eXmV+T533303HTt2xM/Pj/bt25e4vU+fPvj4+DBlypRKjNQzKEkSEREREXGDPn36cODAAbZu3cr999/PuHHjeOmll0rdNy8vz21xLFmyhO3bt3PVVVeV+7433ngj11xzzUlvHzVqFBMmTDiT8DySkiQRERERqTYMwyArr6DSf7LzCk+7j2EY5YrVz8+PuLg4EhMTue222+jRowezZs0CzORi0KBBPPPMMyQkJNC8eXMA9uzZw9ChQwkPDycyMpKBAweyc+dOABYtWoSPjw8HDx4sdp57772Xiy666KRxTJs2jZ49e7rKYRuGQY8ePejdu7frMSUnJ1OvXj0ef/xx1/0mTJjAHXfcQePGjU967P79+7NixQq2b99errbxdFonSURERESqjez8Qlo9PseSc296sjeBvhX/+BwQEMDRo0dd1+fNm0doaChz584FID8/n969e9OlSxcWL16Mt7c3Tz/9NH369GHdunVcfPHFNG7cmE8//ZQHH3zQdZ8pU6bw4osvnvS8ixcvZtiwYa7rNpuNyZMn07ZtWyZMmMA999zDrbfeSt26dYslSWXRoEED6tSpw+LFi2nSpEm57uvJlCSJiIiIiLiRYRjMmzePOXPmcNddd7m2BwUF8cEHH+Dr6wvAZ599hsPh4IMPPsBmswEwadIkwsPDWbBgAb169eKmm25i0qRJriTpu+++Iycnh6FDh570/Lt27SIhIaHYtrp16/Luu+8yYsQIDh48yA8//MDq1avx9i5/epCQkMCuXbvKfT9PpiRJRESkmkrOzCM5M4+mscFWhyJSZQJ8vNj0ZO9KPabD4SA9LZ2Q0JBTLkQa4FP2ogcAs2fPJjg4mPz8fBwOB8OGDWPcuHGu29u2betKkADWrl3Ltm3bCAkJKXacnJwc13C2UaNG8dhjj7Fs2TLOP/98Pv74Y4YOHUpQUNBJ48jOznYNtTvRkCFD+Oabb3j++eeZOHEizZo1K9fjcwoICCArK6tC9/VUSpJERESqoZ82HOD2KasAmDLmfLo0ibI4IpGqYbPZzmjIW2kcDgcFvl4E+nqfMkkqr27dujFx4kR8fX1JSEgo0Uvzz8QmIyODjh07llotLiYmBoDY2Fj69+/PpEmTaNSoET/++CMLFiw4ZRzR0dEcO3asxPasrCxWrlyJl5cXW7duLeejOy45OdkVX02hJElERKQa+nrVPhxFc8h/WH9ASZKIBwoKCqJp06Zl3v+cc85h+vTpxMbGEhoaetL9xowZw3XXXUe9evVo0qQJXbt2PeVxO3TowKZNm0psv//++7Hb7fz444/069ePyy+/nO7du5c5Xjjey9WhQ4dy3c/TqbqdiIhINWMYBpv2p7muH0jNtjAaEaks119/PdHR0QwcOJDFixezY8cOFixYwN13383evXtd+/Xu3ZvQ0FCefvppRo8efdrj9u7dmyVLlhTb9v333/PRRx8xZcoUevbsyYMPPsjIkSOL9Tht27aNNWvWcPDgQbKzs1mzZg1r1qwpVq582bJl+Pn50aVLl0poAc+hJElERKQa2ZOcRaNHfmBfyvHE6EBqjoURiUhlCQwMZNGiRTRo0IDBgwfTsmVLbrrpJnJycor1LNntdkaNGkVhYSEjRow47XGvv/56Nm7cyJYtWwA4cuQIN910E+PGjeOcc84BYPz48dSpU4dbb73Vdb8xY8bQoUMH3n33Xf766y86dOhAhw4d2L9/v2ufzz//nOuvv57AwMDKagaPoOF2IiIi1ciHS3a4Ll93XgM+/2M3Ww9lcCgthzqhJSdmi4g1Pv744wrdHhcXx+TJk097/H379tGvXz/i4+NPu29kZCR33nknr776Ku+++y4xMTEl1lry8fFhxYoVxbadbq5TUlISX331VYn71QTqSRIREalGdh7NdF1+cmBrOjWMIK/QQedn55GUkWthZCJSFVJTU1myZAlTp04tVk78dP7973+TmJiIw+GotFh27tzJ22+/TaNGjSrtmJ5CSZKIiEg1Me/PQyzYcgSAWXd2xcfLzpBz67tu/2LFHqtCE5EqMnDgQHr16sWtt95Kz549y3y/8PBwHn300Uqt3nfuuedyzTXXVNrxPImG24mIiFQDP6w/XvIboGW8OT+hQ/1w1zZ70eKTIlJznW4InFQO9SSJiIh4uPlbDhdLkIaeWw8fL/MtvFmdEKKDzcUoM3IKLIlPRKSmUZIkIiLi4X5cf8B1+Y1r2zNuQOtit4/o0hBAc5JERCqJhtuJiIh4sIzcAr5YYa6P8to17RjYvm6JfeLCzKp2u5OzqjQ2EZGaSj1JIiIiHmzVruMLO7avH1HqPq2K5if9eSANwzCqJC4RkZpMSZKIiIgHO5xuDqEL8vWiUXRQqfs0jQ0G4FhWPqnZ+VUWm4hITaUkSURExIMdKUqSereJO+k+/j5eRAf7AbD3WHaVxCUiUpMpSRIREfFg36/fD0BMiN8p96sbEQAoSRKpjUaNGsWgQYOsDqOEefPm0bJlSwoLCyvtmO+88w79+/evtOOdjJIkERERD7Xs76Ns2JcGQL2IwFPuWy/cTJL2pShJEvEEo0aNwmazYbPZ8PX1pWnTpjz55JMUFFhfqn/nzp3YbDbWrFnj1vM89NBDPPbYY3h5eZVp/7Vr13LddddRv359AgICaNmyJW+88UaxfW688UZWrVrF4sWL3RGyi6rbiYiIeKhFfx1xXXYmQSfj7Enap54kEY/Rp08fJk2aRG5uLj/88AN33HEHPj4+PPLIIyX2zcvLw9fX14Ioz0x+fj4+Pj4lti9ZsoTt27dz1VVXlflYK1euJDY2ls8++4z69euzdOlSbrnlFry8vLjzzjsB8PX1ZdiwYUyYMIGLLrqo0h7HP6knSURExEPZbMcvn9Og9Mp2TnXDncPtVAZcajjDgLzMyv/Jzzr9PuWsHunn50dcXByJiYncdttt9OjRg1mzZgHHh8g988wzJCQk0Lx5cwD27NnD0KFDCQ8PJzIykoEDB7Jz507XMQsLCxk7dizh4eFERUXx0EMPlahq+dNPP3HhhRe69rniiivYvn276/ZGjRoB0KFDB2w2G5deeikADoeDJ598knr16uHn50f79u356aefXPdz9kBNnz6dSy65BH9/f6ZMmVLqY582bRo9e/bE39+/6Gkz6NWrF71793bFm5ycTL169Xj88ccBs5fojTfe4JJLLqFx48YMHz6c0aNHM2PGjGLH7t+/P7NmzSI7231fCqknSURExEPtTDITntsvbUJYYMlvak9UL0LD7aSWyM+CZxMq9ZB2ILwsOz66H3xLrzJZFgEBARw9etR1fd68eYSGhjJ37lzA7JXp3bs3Xbp0YfHixXh7e/P000/Tp08f1q1bh6+vL6+88goff/wxH330ES1btuSVV17hm2++oXv37q7jZmZmMnbsWM4++2wyMjJ4/PHHufLKK1mzZg12u50//viD8847j19++YXWrVu7erDeeOMNXnnlFd599106dOjARx99xIABA9i4cSPNmjVzHf/hhx/mlVdeoUOHDq4k6J8WL17MsGHDXNdtNhuTJk2iXbt2TJgwgXvuuYdbb72VunXrupKk0qSmphIZGVls27nnnktBQQHLly93JXiVTUmSiIiIB8orcLBoqznc7rKWdU67f10lSSIeyzAM5s2bx5w5c7jrrrtc24OCgvjggw9cScpnn32Gw+Hggw8+wFbUlTxp0iTCw8NZsGABvXr14vXXX+eRRx5h8ODBgFnIYM6cOcXO988hbh999BExMTFs2rSJNm3aEBMTA0BUVBRxcccrZ7788sv83//9H9deey0AL7zwAvPnz+f111/nrbfecu137733us5/Mrt27SIhoXgyW7duXd59911GjBjBwYMH+eGHH1i9ejXe3qWnJEuXLmX69Ol8//33xbYHBgYSFhbGrl27ThnDmVCSJCIi4oHm/XmI9JwCooP96FA//LT7O4fbpWTlk5lbQJCf3uKlhvIJNHt0KpHD4SAtPZ3QkBDs9lPMRvE5dQGVf5o9ezbBwcHk5+fjcDgYNmwY48aNc93etm3bYvOQ1q5dy7Zt2wgJCSl2nJycHLZv305qaioHDhygc+fOrtu8vb0599xziw2527p1K48//jjLly8nKSkJh8MBwO7du2nTpk2psaalpbF//366du1abHvXrl1Zu3ZtsW3nnnvuaR97dnZ2qb1MQ4YM4ZtvvuH5559n4sSJxXqoTrRhwwYGDhzIE088Qa9evUrcHhAQQFaW+4YX6z+oiIiIh3nl5y28+es2ADomhmO3205zDwjx9yHAx4vs/EKSMnKVJEnNZbOd0ZC3Ujkc4FNoHvdUSVI5devWjYkTJ+Lr60tCQkKJHpOgoOKPIyMjg44dO5Y6z8fZ+1MW/fv3JzExkffff5+EhAQcDgdt2rQhLy+vYg/kH/4Zd2mio6M5duxYie1ZWVmsXLkSLy8vtm7dWup9N23axGWXXcYtt9zCY489Vuo+ycnJ5WqT8lLhBhEREQ+SmpXvSpAAvMqQIDlFBZvfSCdlVM4HIRE5M0FBQTRt2pQGDRqcdEjZic455xy2bt1KbGwsTZs2LfYTFhZGWFgY8fHxLF++3HWfgoICVq5c6bp+9OhRtmzZwmOPPcZll11Gy5YtSyQrzt6rE9cvCg0NJSEhgd9++63Yvr/99hutWrUq92Pv0KEDmzZtKrH9/vvvx2638+OPPzJhwgR+/fXXYrdv3LiRbt26MXLkSJ555plSj719+3ZycnLo0KFDueMqKyVJIiIiHuT5n/4sdv2Obk3LfN+oYHPB2aMZuZUak4hUjeuvv57o6GgGDhzI4sWL2bFjBwsWLODuu+9m7969ANxzzz08//zzzJw5k82bN3P77beTkpLiOkZERARRUVG89957bNu2jV9//ZWxY8cWO09sbCwBAQH89NNPHDp0iNTUVAAefPBBXnjhBaZPn86WLVt4+OGHWbNmDffcc0+5H0vv3r1ZsmRJsW3ff/89H330EVOmTKFnz548+OCDjBw50pXEbdiwgW7dutGrVy/Gjh3LwYMHOXjwIEeOHCl2nMWLF9O4cWOaNGlS7rjKSkmSiIiIB1n0V5Lr8h//vozWCWFlvm90kPntcHKmepJEqqPAwEAWLVpEgwYNGDx4MC1btuSmm24iJyeH0NBQwOyJueGGGxg5ciRdunQhJCSEK6+80nUMu93OtGnTWLlyJW3atOG+++7jpZdeKnYeb29vJkyYwLvvvktCQgIDBw4E4O6772bs2LHcf//9tG3blp9++olZs2addN7QqVx//fVs3LiRLVu2AJCUlMTNN9/MuHHjOOeccwAYP348derU4dZbbwXgq6++4siRI3z22WfEx8e7fjp16lTs2J9//jk333xzuWMqDw1YFhER8SAh/sffmqOC/Mp138iiJOmokiQRy3388ccVuj0uLo7Jkyef9H7e3t68/vrrvP766yfdp0ePHiWGuv1zLaUxY8YwZsyYYtvsdjtPPPEETzzxRKnHbdiwYYnjnExkZCR33nknr776KhMnTiQ6Opr9+/cXK4zh4+PDihUrXNfHjRtXrLBFaTZu3MiaNWv44osvyhRHRaknSURExIPEhZnVoM5rFFmu+UhwfLhdkobbiYgH+Pe//01iYqKrul5lOHDgAJ988glhYWXvZa8IJUkiIiIewuEwWP53MgA3X9S43PePLirc8NXKvZUal4hIRYSHh/Poo4+euqx6OfXo0YPevXtX2vFORkmSiIiIh5i4cDvZ+Wa1qRZxIafZu6TwQDNJSs8pYL8WlRURqTAlSSIiIh7gl02HeGmOOcHZy26jfmT5Fq0E6Nwo0nV5y6H0SotNRKS2UZIkIiLiAW7+9Pjk5TWP96zQMepHBtKzVR0A/j6SWSlxiXiCshYLEIHKeb1YmiQ999xzdOrUiZCQEGJjYxk0aJCrTKBTTk4Od9xxB1FRUQQHB3PVVVdx6NAhiyIWERGpfIZh4HxP79smjhB/nwofq0FRD9ShtJzKCE3EUj4+5t9CVlaWxZFIdeJ8vThfPxVhaQnwhQsXcscdd9CpUycKCgp49NFH6dWrF5s2bSIoKAiA++67j++//54vv/ySsLAw7rzzTgYPHlxiNWAREZHqKjU733X5tWvan9GxolXhTmoQLy8vwsPDOXz4MGCuI2Szla/qY1k4HA7y8vLIycmp1CIDYqqq9jUMg6ysLA4fPkx4eDheXl4VPpalSdJPP/1U7PrHH39MbGwsK1eu5OKLLyY1NZUPP/yQqVOn0r17dwAmTZpEy5YtWbZsGeeff74VYYuIiFSqpduPAmaC4+9T8Td18xhm8YZN+9POOC4RTxAXFwfgSpTcwTAMsrOzCQgIcEsSVttVdfuGh4e7XjcV5VGLyaampgLm4lMAK1euJD8/nx49erj2adGiBQ0aNOD3338vNUnKzc0lN/f4t2dpaeabRH5+Pvn5+SX2r0rO81sdR02l9nUvta97qX3dy9Pb96f1BwCoE+p7xjFGBppv7ZsPpnPgWIarZ8mdPL19a4La3sbR0dFERERQUFDglvlJBQUFLF26lAsuuABvb4/6eFwjVFX72mw2vL298fLyoqCgoNR9yvo3ZDM8ZCacw+FgwIABpKSksGTJEgCmTp3K6NGjiyU9AOeddx7dunXjhRdeKHGccePGMX78+BLbp06dSmBg+SsFiYiIuNt/N9rZmmZnUGIh3RLO7G05rxAe/MP8EHJXqwKaune9RRGRaiUrK4thw4aRmppKaGjoSffzmFT5jjvuYMOGDa4EqaIeeeQRxo4d67qelpZG/fr16dWr1ykboirk5+czd+5cevbseUYTyaR0al/3Uvu6l9rXvTy9fSds+w3IZHD38+jSOOqMjzf90B+s2JVCs7bn0LfNmQ05KQtPb9+aQG3sXmpf9/Kk9nWOMjsdj0iS7rzzTmbPns2iRYuoV6+ea3tcXBx5eXmkpKQQHh7u2n7o0KGTjjP08/PDz6/k0AIfHx/LnxQnT4qlJlL7upfa173Uvu7lqe2blJEHQEJEUKXE51M0WXnsl+sZ0KH+GR+vzOf10PatSdTG7qX2dS9PaN+ynt/S8h2GYXDnnXfyzTff8Ouvv9KoUaNit3fs2BEfHx/mzZvn2rZlyxZ2795Nly5dqjpcERGRSpeTX+iqbhcT7F8pxzxSVNmuwGFQ6PCIUfUiItWKpUnSHXfcwWeffcbUqVMJCQnh4MGDHDx4kOzsbADCwsK46aabGDt2LPPnz2flypWMHj2aLl26qLKdiIjUCM5S3b5edkIDKmeAxwtXne26vCdZ68uIiJSXpUnSxIkTSU1N5dJLLyU+Pt71M336dNc+r732GldccQVXXXUVF198MXFxccyYMcPCqEVERCrPkXQzSYoJ8au00rgdEyNoERcCwI6kzEo5pohIbWLpnKSyFNbz9/fnrbfe4q233qqCiERERKrWoTQzSYoOqdxS3fUiAth8MJ39qdmVelwRkdpASwqLiIhYaOFf5gKZTWKCKvW4CeEBAOxPUZIkIlJeSpJEREQskpaTz4xV+wC4qFl0pR67QaS5NuDOJM1JEhEpLyVJIiIiFlm3J5XcAgfBft4MbFe3Uo/drI45J+m37Uk4VOFORKRclCSJiIhYZM2eYwB0axGL3V45RRuc2tULAyAlK58l25Iq9dgiIjWdkiQRERGLrNmTAkD7+uGVfuzwQF86N4oE4FBaTqUfX0SkJlOSJCIiYgHDME5IksLcco74MHNx2pSsfLccX0SkplKSJCIiYoFDabkkZeThZbfROsE9SVJ4oC8AKdl5bjm+iEhNpSRJRETEAruTzapzdcMD8Pfxcss5wgJ8APUkiYiUl5IkERERC2w5mAZA/cgAt50jOtjsSXIuWCsiImWjJElERMQCs9buB6BFXKjbztE4JhiAv49kuO0cIiI1kZIkERGRKmYYBpsPpgNwZYfKXR/pRE2KkqRdyVkUaq0kEZEyU5IkIiJSxVKy8knPKQCgaWyw284THeyLzQaFDoNjWSreICJSVkqSREREqtjOo5kAxIX6u61oA4C3l52Iogp3RzOUJImIlJWSJBERkSrmrGzXICrQ7eeKDHImSSreICJSVkqSREREqtiuo2aSlBjp/iQpqihJOpyuJElEpKyUJImIiFQxV5JUBT1JzeqYc5427k91+7lERGoKJUkiIiJVbHeyOSepQVSQ2891dr1wADbuT3P7uUREagolSSIiIlWsKofbNYkxE7GdSZluP5eISE2hJElERKQKZecVuuYHVcVwu4ZFvVX7U3PIyS90+/lERGoCJUkiIiJVyFnZLtTfm/Ci8tzuFBnkS4i/N3C8B0tERE5NSZKIiEgV+vOAOTfInYvInshms7l6k5zrM4mIyKkpSRIREalCM9fsA44XVKgKzvWY9h7LrrJziohUZ0qSREREqohhGCzddhSA/u3iq+y8McF+ACRpQVkRkTJRkiQiIlJFkjPzyCt0ANC2bniVnTcmpChJ0oKyIiJloiRJRESkihxKM5OUqCBffL2r7i04OtgsELHlUHqVnVNEpDpTkiQiIlJFDqXlAFAn1L9Kz9s6IQyAdXtTOVwUg4iInJySJBERkSpysChBiQur2iSpTd0wooJ8i8UgIiInpyRJRESkihxMtaYn6cRzHs3Mq/Jzi4hUN0qSREREqsjxJMmvys8dVTQvKTlDSZKIyOkoSRIREakiG/anAlW3kOyJIouG2yWrJ0lE5LSUJImIiFSBbYcz2Lg/DYCOiRFVfv6wAB8A0nLyq/zcIiLVjZIkERGRKrBgy2EALmwaTXxYQJWfP8TfG4D0nIIqP7eISHWjJElERKQKrN9nDrXr3CjSkvMH+5k9SUqSREROT0mSiIhIFVi/10ySzq4fbsn5j/ckabidiMjpKEkSERFxs+y8Qv5OygSgTUKoJTFouJ2ISNkpSRIREXGzI+m5APj72F1V5qpaqL8KN4iIlJWSJBERETc7kmEmSTEhfthsNkticK6T5EzYRETk5JQkiYiIuNmRdHMR2Zjgql9E1qluuFlR73B6LrkFhZbFISJSHShJEhERcbPfth0FoG5EoGUxRAb54u9jvu0fTM2xLA4RkepASZKIiIibzS9aI2lQ+wTLYrDZbCQU9SbtO5ZtWRwiItWBkiQRERE3WvjXEfYWJSXnNIiwNBbnkLt9KUqSRERORUmSiIiIGy3dlgRAo+ggIiyqbOekJElEpGyUJImIiLjR9iPm+kijuza0NhDM6noARzPyLI5ERMSzKUkSERFxoz3JWQA0iLSuaINTeKDZk3UsS0mSiMipKEkSERFxI+caSXFh/hZHAhGB5oKyKVlaUFZE5FSUJImIiLhJXoGD5Eyz18bKNZKcItSTJCJSJkqSRERE3CSpqBfJ225zJShWCldPkohImShJEhERcZOfNx4EoEFUIHa7zeJo1JMkIlJWSpJERETc5LPluwHoUN/a9ZGcnElSVl4huQWFFkcjIuK5lCSJiIi4yYGi9YiGnlvP4khMIf7eODu0NOROROTklCSJiIi4QWZuAZl5Zm9Nm7phFkdjstttrjLgzoISIiJSkpIkERERNzicbhZtCPL1IsjP2+JojosMUpIkInI6SpJERETcYNfRTMAz1kc6UZ1QsxT54fQciyMREfFcSpJERETcYMO+VABaJ3jGUDunOiFm0nYoLdfiSEREPJeSJBERETdYtDUJgI6JnlHZzik21EySDqaqJ0lE5GSUJImIiFQywzBYv9fsSeraNNriaIprGBUIwMw1+yyORETEcylJEhERqWRp2QVk55uV7epFBFgcTXHN40IAswS4c0igiIgUpyRJRESkkh1IM9dHigzyxd/Hy+Joiju7Xrjr8nolSSIipVKSJCIiUsmcRRFiQ/wsjqQkL7uNG85PBGDb4QyLoxER8UxKkkRERCpZSpa5BpFzTSJP0zA6CIAf1h/AMAyLoxER8TxKkkRERCpZSlY+AOGBPhZHUrp+beMAOJCaQ26Bw+JoREQ8j5IkERGRSuZMksICPLMnKTbk+AK3GbkFFkYiIuKZlCSJiIhUspRsc7idp/YkedltBBQVlMhUkiQiUoKSJBERkUqWlFE0JynQM3uSAIL9vQH1JImIlEZJkoiISCXbk5wFQP1Iz1oj6UTBfmaSlJlbaHEkIiKeR0mSiIhIJTueJAVaHMnJBflpuJ2IyMkoSRIREalE+1KyOZqZh5fdRqOiUtueKMjX7ElKV5IkIlKCkiQREZFKtG5PCgCt4kMJLEpEPFF0sLnQ7dGMXIsjERHxPEqSREREKtGB1BwAGnjwUDuAmBAzSTqUpiRJROSflCSJiIhUosPpZtIRG+pncSSnVifUXCvpcHqOxZGIiHgeJUkiIiKV6FCamXScuGCrJ3L2JDnLlYuIyHGWJkmLFi2if//+JCQkYLPZmDlzZrHbR40ahc1mK/bTp08fa4IVERE5DcMw+H37UQDOqhNscTSnFh5gLnSbmp1vcSQiIp7H0iQpMzOTdu3a8dZbb510nz59+nDgwAHXz+eff16FEYqIiJTd/tQcDqbl4G23cWGzaKvDOaWwwKIkKUs9SSIi/2Rp2Z2+ffvSt2/fU+7j5+dHXFxcFUUkIiJScdsOZwDQMDoIP28vi6M5NWdPUop6kkRESvDc2qRFFixYQGxsLBEREXTv3p2nn36aqKiok+6fm5tLbu7xSj1paWkA5Ofnk59v7RuB8/xWx1FTqX3dS+3rXmpf96qq9t1x2HzPSYwM8PjnMsjHBkBadj65uXnY7bYKH0uvX/dTG7uX2te9PKl9yxqDzTAMw82xlInNZuObb75h0KBBrm3Tpk0jMDCQRo0asX37dh599FGCg4P5/fff8fIq/Ru6cePGMX78+BLbp06dSmCgZ5djFRGR6u373XZ+3mfnwjoOhjR2WB3OKRU44P7l5nelz5xbQLCPxQGJiFSBrKwshg0bRmpqKqGhoSfdz6OTpH/6+++/adKkCb/88guXXXZZqfuU1pNUv359kpKSTtkQVSE/P5+5c+fSs2dPfHz0blTZ1L7upfZ1L7Wve1VV+z78zQa+XrWf+y5ryu2XNnbbeSrLBS8s4EhGHt/cej5t6lb8PVKvX/dTG7uX2te9PKl909LSiI6OPm2S5PHD7U7UuHFjoqOj2bZt20mTJD8/P/z8Sq5N4ePjY/mT4uRJsdREal/3Uvu6l9rXvdzdvofTzSII8RGB1eJ5rBsRyJGMPA5l5NGhEuLV69f91MbupfZ1L09o37Kev1qtk7R3716OHj1KfHy81aGIiIgUYxgGm/abc5KaxXp2+W+nxtFBAGzYl2ZxJCIinsXSJCkjI4M1a9awZs0aAHbs2MGaNWvYvXs3GRkZPPjggyxbtoydO3cyb948Bg4cSNOmTendu7eVYYuIiJRwOD2Xo5l52G3QMt7a4d1ldX4TsxDSHzuSLY5ERMSzWDrcbsWKFXTr1s11fezYsQCMHDmSiRMnsm7dOiZPnkxKSgoJCQn06tWLp556qtThdCIiIlbadMDsjakbEYC/j2eX/3ZqGWcmc38nZVgciYiIZ7E0Sbr00ks5Vd2IOXPmVGE0IiIiFTd60v8ACParPvMZGkabVV+TMvJIy8kn1L/6xC4i4k7Vak6SiIiIp2sYVX2Wmwjx9yGsaFHZQ6k5FkcjIuI5lCSJiIicoZz8QtflcQNaWxhJ+cWEmEPYj6TnnmZPEZHaQ0mSiIjIGXp34d+uy9HB1WvebHSwLwBHMpQkiYg4KUkSERE5Q0czjycYXnabhZGUX0yIP6CeJBGREylJEhEROUNJRb0w4/q3sjiS8lNPkohISUqSREREzpCzFyY6pHoNtYPjc5KS0vMsjkRExHMoSRIRETlD+1PMynAx1Ww+EhyfQ6WeJBGR45QkiYiInIE7pq5iX0o2AC3iQy2OpvyO9yQpSRIRcVKSJCIiUkE5+YV8v+6A67pzzaHqJLwo5tTsfIsjERHxHEqSREREKmjb4QzX5ccub2lhJBUXWpQkpeUoSRIRcVKSJCIiUkGb9qcBEBnky00XNrI4mooJ9TeTpIzcAhwOw+JoREQ8g5IkERGRClq24ygAgzvUxWarXusjOYUGeANgGJCeW2BxNCIinkFJkoiISAVk5hYwe605H6lnqzoWR1Nxft5erssfLtlhYSQiIp5DSZKIiEgF/LEjmbxCBw0iAzmvUaTV4VSKA0VV+kREajslSSIiIhWwYV8qAB0TI6rtUDun5wa3BeBgWo7FkYiIeAYlSSIiIuVkGAbzNh8GoFU1XBvpnxIjAwFc6z2JiNR2SpJERETKae+xbNbsScHbbmNA+wSrwzljzgVlj2bkWRyJiIhnUJIkIiJSTv/bmQxAm7ph1An1tziaMxcZ5AuYC8oWFDosjkZExHpKkkRERMrpjx1mktS5hhRsCAvwwTmtKiVbi8qKiChJEhERKSdnklRTqtp5e9kJCzAXlT2WqSF3IiJKkkRERMohLSefv5MyATg3sWYkSQCRgeaQu2QlSSIiSpJERETKw9nTEuTrRVigj8XRVJ6IonlJx7KUJImIKEkSEREph/ScAgBC/GtOggTHizccVU+SiIiSJBERkfJIKypsEOLvbXEklcs53E5zkkRElCSJiIiUS1qOmSSFBtSsniTncLvkTFW3ExFRkiQiIlIOP244CNS8nqTQAPPxOJNAEZHaTEmSiIhIOazYeQwAP++a9RbqnGOVUTTnSkSkNqtZ/+FFRETcyOEwSMrIBeBflzSxOJrKFeJn9iSl56onSURESZKIiEgZHUzLIbfAgbfdxtl1w6wOp1I5hw+qJ0lEREmSiIhIme0oWkS2QVQg3l416y3UOdwuXUmSiIiSJBERkbL6uyhJahQVZHEklS/Yz1m4QUmSiIiSJBERkTLaWZQkNYyueUmSczHZY1l5OByGxdGIiFhLSZKIiEgZ7Tpac5Ok6GBf7DYodBgkZeZaHY6IiKWUJImIiJSRc05Sw6hAiyOpfN5edqKD/QA4lKokSURqNyVJIiIiZVDoMNiTnA1Awxo4JwmgbkQAADuKesxERGorJUkiIiJl8NehdPIKHfh62UkID7A6HLdoW1TWfO2eFGsDERGxmJIkERGRMvhu7X4A2tYLw8tuszga92hfPxyANUqSRKSWU5IkIiJSBruSswDo2ybO4kjcp11RkrRhXyqGoQp3IlJ7KUkSEREpg91HzSQpPqxmDrUDqFs0jDC3wEFattZLEpHaS0mSiIjIaexLyWb9vlQA4sL8LI7Gffx9vAgpWlT2qMqAi0gtpiRJRETkNNbvTXVdbp0QZmEk7hcVbC4qezQzz+JIRESsoyRJRETkNA6l5QDQp3Uc/j5eFkfjXpFBRUlShnqSRKT2UpIkIiJyGs4kqU5ozR1q5xQW4ANAWo7mJIlI7aUkSURE5DQOOpOkMH+LI3G/YH8zScpQkiQitZiSJBERkdM4nGYOPYsLrQVJUlHhhoxcJUkiUnspSRIRETkNV09SrUiSzDlXSpJEpDZTkiQiInIKDofBgZRsoLYkSUXD7ZQkiUgtpiRJRETkFDbuTyMzr5BgP28SowKtDsftgv2LhttpTpKI1GJKkkRERE5hw35zjaRzEiPw8ar5b5uhRUlSSna+xZGIiFin5v+3FxEROQPbDmcA0Cw22OJIqkZs0ZDCw0XzsEREaiMlSSIiIqew/YiZJDWJqR1JknMtqENKkkSkFlOSJCIicgrOnqSmtaQnyVnm/FhWPtl5hRZHIyJiDSVJIiIiJ5FX4GBfUWW7xjFBFkdTNcICfIgONnuTNhbNxxIRqW2UJImIiJxEcmYehgHedhuRgb5Wh1MlbDYbZ9cLA+DPg+kWRyMiYg0lSSIiIieRlJELQGSQL3a7zeJoqk7d8ABAxRtEpPaqUJJ06NAhbrjhBhISEvD29sbLy6vYj4iISE0wZ+NBAIL8vC2OpGqpeIOI1HYV+q8/atQodu/ezX/+8x/i4+Ox2WrPt2siIlJ7zFi1D4Da9i7nLAN+MC3X4khERKxRoSRpyZIlLF68mPbt21dyOCIiIp4jt8Cs7jaiS6LFkVSt6GBz/lVyppIkEamdKjTcrn79+hiGUdmxiIiIeIzM3AKOZuYB0K9tvMXRVK3woiIVxzLzLY5ERMQaFepJev3113n44Yd59913adiwYSWHVMvlZcKf38HOJXB0GxQUjQePaQFNe0CrQeBVu8bGi4hUtfxCB9e+twzDgPgwf1dJ7NoioihJSs1WkiQitVOFPm1fc801ZGVl0aRJEwIDA/Hx8Sl2e3JycqUEV6sYBqz4CH59GrJLab/9q2Ht57DwBRj8HiR0qPoYRURqiZ1JmazfZ64R9MJVZ9eqynYAEYHm+3pGbgF5BQ58vVUMV0Rqlwr3JEklchTCt3fC2qnm9fBEaDMY6rQB32Bw5MO+VbDqE0j6Cz7oAT2fhPNvBxXNEBGpdEfSzbk4TWODufisGIujqXqh/j7YbeAw4FhWHnWKCjmIiNQWFUqSRo4cWdlx1G6/PGEmSDYvM/npfGvJIXUt+8MFd8F398Cfs2DOo3B4E1z+GnjXjgUORUSqypGi9ZFiatkwOye73UadUH8OpOZwIDVHSZKI1DoVntxSWFjIzJkz+fPPPwFo3bo1AwYM0DpJ5fXnbFj6pnn5qvehzVUn3zcwEoZ+AsvfMZOk1Z9Byh4Y9gX46A1MRKSyOHuSYkJqZ5IEkBAewIHUHPanZNO+frjV4YiIVKkKDTLetm0bLVu2ZMSIEcyYMYMZM2YwfPhwWrduzfbt2ys7xporM8nsGQKzl+hUCZKTzQbn3wbDvjSH4u1YCDNuNofsiYhIpVCSZCZJAPtTsi2ORESk6lUoSbr77rtp0qQJe/bsYdWqVaxatYrdu3fTqFEj7r777sqOseaa8yhkJUFsa+j+n/Ldt1kPuHYqePmaw+9+eMAs/iAiImdMSRIkhJsjFPYpSRKRWqhCSdLChQt58cUXiYyMdG2Liori+eefZ+HChZUWXI12YB2sm25eHvhf8K7AG3HjS2Dw+4DNrIy3dEKlhigiUls55yTVttLfJ6qrniQRqcUqlCT5+fmRnp5eYntGRga+vioiUCbznzV/t7ka6p5T8eO0HgR9XzAv/zIOts0708hERGq9w2nOJKn2vqclhJlJknqSRKQ2qlCSdMUVV3DLLbewfPlyDMPAMAyWLVvGrbfeyoABAyo7xponaSv89SNgg0sfOfPjnXcLdLgBDAd8dSMk/33mxxQRqaXyCx3sSMoEoHF0sMXRWCe+aLjdwdQciyMREal6FUqSJkyYQJMmTejSpQv+/v74+/vTtWtXmjZtyhtvvFHZMdY49j8mmhea94Popmd+QJsNLn8F6nWCnBSYdj3kZpz5cUVEaqEVO4+RV+gg2M+behEBVodjmfiinqSkjDxyC1QcSERqlwqVAA8PD+fbb79l69atbN68GYCWLVvStGklfOCv4Xzz07Bv/sK8csGdlXdgbz8Y+im8d4m5ftLM28xy4VpsVkSkXN5esA2A/u3isdtr7//QiEAf/Lzt5BY4OJSaS4OoQKtDEhGpMhXqSXJq1qwZ/fv3p3///hVKkBYtWkT//v1JSEjAZrMxc+bMYrcbhsHjjz9OfHw8AQEB9OjRg61bt55JyJZreHQBtoIcSOgADbpU7sFD4+Gaz8DuY1a8W/xK5R5fRKSGMwyDFTuPATC6ayOLo7GWzWYjPswccrc/VfOSRKR2KXNP0tixY3nqqacICgpi7Nixp9z31VdfLdMxMzMzadeuHTfeeCODBw8ucfuLL77IhAkTmDx5Mo0aNeI///kPvXv3ZtOmTfj7V8PFUw0HDY4WVf8771/u6eWpf5459O67u+HXpyGuLZzVu/LPIyJSAx3JyCU7vxCbDRpGBVkdjuXiwwLYeTRL85JEpNYpc5K0evVq8vPzXZcrQ9++fenbt2+ptxmGweuvv85jjz3GwIEDAfjkk0+oU6cOM2fO5Nprr62UGKqSbecSgvKOYPiFYGs10H0n6jgSDqyFFR+aC83+axFENHTf+UREagDDMLj23WWAWdnN1/uMBlvUCM6epANKkkSklilzkjR//vxSL7vLjh07OHjwID169HBtCwsLo3Pnzvz+++8nTZJyc3PJzc11XU9LSwMgPz/fleRZZcPs/9IByGg6EH+bD7gznh5P4bV/Dfb9K3FMH0HhyO/Buxr2vpWD8/m1+nmuqdS+7qX2da+ytO/vfx/l76KqdjecX1/PBVAnxCyBvu9Y5inbQ69f91Mbu5fa1708qX3LGoPNMAyjvAe/8cYbeeONNwgJCSm2PTMzk7vuuouPPvqovIfEZrPxzTffMGjQIACWLl1K165d2b9/P/Hx8a79hg4dis1mY/r06aUeZ9y4cYwfP77E9qlTpxIYaN2kU5+CDC5bdw9+tny+aPAkflEN3X7OgLwkLtn8OH6FGeyI7s66+qPcfk4Rkepq8l92Vh210ybCwc0tHFaH4xGWHLTx5Q4v2kY4GKM2EZEaICsri2HDhpGamkpoaOhJ96tQdbvJkyfz/PPPl0iSsrOz+eSTTyqUJFWWRx55pNicqbS0NOrXr0+vXr1O2RBuZxg8uDWZZhl/0Pr8q+naLLZKTmvbXhdj2rU0SvqV+hdcjdF2aJWc1wr5+fnMnTuXnj174uPjY3U4NY7a173Uvu5VlvadvO8POJrCLb3a07dNXBVH6Jn8txzhyx2rcQSE0a/fyYsN6fXrfmpj91L7upcnta9zlNnplCtJSktLcy0em56eXqx4QmFhIT/88AOxsZXz4T8uznyDOnToULGepEOHDtG+ffuT3s/Pzw8/P78S2318fCx/UnaGdGBmSmPeyjeqLpYWfeCSh2DhC3j/+AAktIO4NlVzbot4wnNdk6l93Uvt616nat/D6eZQ7bqRwXoOijSJNb9c3HY4k0Ls+Pt4nXJ/vX7dT23sXmpf9/KE9i3r+cs1KzU8PJzIyEhsNhtnnXUWERERrp/o6GhuvPFG7rjjjgoF/E+NGjUiLi6OefPmubalpaWxfPlyunSp5NLZVSTM33xSUrMLqvbEl/wfNO4G+VkwbRhkJVft+UVEPNwT325g7zGzzHWd0JJftNVWTWKCiAnxI7fAwerdKVaHIyJSZcrVkzR//nwMw6B79+58/fXXREZGum7z9fUlMTGRhISEMh8vIyODbdu2ua7v2LGDNWvWEBkZSYMGDbj33nt5+umnadasmasEeEJCgmveUnUTGmA2d1pOFU9as3vB1R/B+93g2E74ciQM/wa8KjTaUkSkRjmakcvk33e5rtcJrdlFbsrDZrNxQZMovl2zn2V/H6VLkyirQxIRqRLl+pR8ySWXAGYyU79+fez2MyuPumLFCrp16+a67pxLNHLkSD7++GMeeughMjMzueWWW0hJSeHCCy/kp59+qp5rJAHhAc6eJAsqewRGwrWfwwc9YMcimPsf6PNc1cchIuJhUv7xP9nHS6W/T3RWHXP+8f4ULSgrIrVHhboSEhMTAbM6xO7du8nLyyt2+9lnn12m41x66aWcqriezWbjySef5Mknn6xImB4ntChJSqvq4XZOdVrB4Hdh+nBY9ra50Gz7YdbEIiLiIU784qpdvTALI/FMEYFmGfBjWdaX7hURqSoVSpKOHDnC6NGj+fHHH0u9vbCw8IyCqqnCrOxJcmrZ35yjtPAF+O5eqNsRYppbF4+IiMVO/J/8/shzLYzEM0UEmu9dKVl5p9lTRKTmqNCYgnvvvZeUlBSWL19OQEAAP/30E5MnT6ZZs2bMmjWrsmOsMUL9LZqT9E+XPAxNLoPCXJh5GxRa1LMlIuIB0oqSpC6No4gNqZ7Dud0prChJOqYkSURqkQolSb/++iuvvvoq5557Lna7ncTERIYPH86LL77Ic89pnsvJHO9JsjgpsdthwJvgFwb7VsLvb1obj4iIhY5lmh/+nf+jpTjncDtLR0GIiFSxCiVJmZmZrvWQIiIiOHLkCABt27Zl1apVlRddDeOsbucRbzRhdaHv8+bl+c/C4T+tjUdExCI7j2YB0CAq0OJIPJMzSUrJyj/lPGIRkZqkQklS8+bN2bJlCwDt2rXj3XffZd++fbzzzjvFFn6V4hLCAgDYl5JNXoHD4miAdtfBWX2gMM+cn+TwgJhERKrY30mZADSODrI4Es8UXjTcrsBhkJ6r4dkiUjtUKEm65557OHDgAABPPPEEP/74Iw0aNGDChAk8++yzlRpgTVI33J8AL4P8QoPtRzKsDgdsNrj8VfAJgj3LYO3nVkckIlLldiSZ/48bKUkqlb+PF/4+5seFlEwPGAkhIlIFKlTdbvjw4a7LHTt2ZNeuXWzevJkGDRoQHR1dacHVNDabjSh/2JsJB1KzaRkfanVI5rC7S/8P5j5u/jTva66pJCJSC+TkF7L3mLn+T+OYYIuj8VwRgb4cSM0hJTuPBmhYoojUfJWyYl5gYCDnnHOOEqQyCPUxx3MfSc+1OJITnH87xLSArCT49SmroxERqTJ7krMwDAjx8yY62NfqcDyWc17S0UxVuBOR2qHMPUljx44t80FfffXVCgVTG4QUFU9KyvCgNxovH7j8Ffj4clgxCToMN9dPEhGp4fYcM4s21IsMxGazWRyN54oN9WPTATiS5kFf8ImIuFGZk6TVq1eXaT+9yZxaSNEXlftTsq0N5J8aXghnXwPrpsPssXDzr2D3sjoqERG32pNs/i9uEBlgcSSeLTbED4DD6TkWRyIiUjXKnCTNnz/fnXHUGnUDzeF2a/akWBtIaXo+BVt+ggNrYOUk6DTG6ohERNxqT7LZk1Q/QvNsTqVOqLnI7sE0JUkiUjtUypwkKbuGIWaStOVgOgWFHlZyO6QOdH/MvDzvScg4Ym08IiJuttuZJEUqSTqVqCBzGMQxVbcTkVqiQtXtunXrdsphdb/++muFA6rpwn3B19tOXoGDA6k5nvfG3OkmWP0pHFxnVru7cqLVEYmIuM2uo84kScPtTiXcuaBstgfNpxURcaMK9SS1b9+edu3auX5atWpFXl4eq1atom3btpUdY41it0G9cPPN2Pnm7FHsXnDFa4AN1k6FXb9bHZGIiFuk5+Sz9XA6AK0TwiyOxrOFFS0om5KlniQRqR0q1JP02muvlbp93LhxZGR4wCKpHi4xKoC/kzLZeTSTC5t5YNn0eufCOSNg1WT4/n741yLwqtBLRUTEY207nIHDgDqhfq45N1K68AAlSSJSu1TqnKThw4fz0UcfVeYha6QGRUPsnGPhPVKPcRAQCYc3wnINuRORmudgqlmEoG64htqdjmu4XdYphtsZBhzeBP/7ABa8AH+8D38vhDwPfq8TETmJSu0e+P333/H317dxp1MvwnxD3nfMw8qAnygwEnqOh1l3wfxnoWV/iGhodVQiIpXmQFGSFBem963TiSpaaDczr5CsvAICfU/4+FCYj/1/79Fz06v4rEkqeWe7D7ToBxfcbY5UEBGpBiqUJA0ePLjYdcMwOHDgACtWrOA///lPpQRWk0UHOVcu9/BF+doPh7XTYdcSc+2k4V+D1sESkRpi5a5jgHqSyiLEz5sgXy8y8wo5mJpD45hg84aj2+Hrm/Dav5pAwPD2x5Z4AYTVg6xk2LcK0vfDpm/Nn1aDoM/zEBpv5cMRETmtCiVJYWHFJ7ja7XaaN2/Ok08+Sa9evSolsJos0pkkZXh4lSC7Hfq/ARMvgO3zYP2XcPZQq6MSETlj+YUOfvnzEAD92yVYHI3ns9ls1Anz5+8jmRxMK0qS9q+BT6+E7GQM/3DWxgyi9XVP4RMYevyOhgGHNsKyt2HtNNg0E3YshAFvmiMUREQ8VIWSpEmTJlV2HLVKlKsnycOTJIDopnDJQ/DrU/DTw9DkMgiKsjoqEZEzsnZPCrkFDsIDfWijynZlUj8ikL+PZPLXwXQuCD4EkwdAbiokdKDgqsnsWrKG1j7/6JWz2SCuDQx6GzrfCrPuhANrYfpw6DgKej8LvkGWPB4RkVM5o8INK1as4NNPP+XTTz9l5cqVlRVTjecc230sK49Ch2FxNGXQ9R6IbQ1ZR2HOo1ZHIyJyxn7eZPYiXXJWDHa7hhGXxXmNIgHYun0rTBliJkj1z4cRsyC0DL1x8WfDTb+Y7ykAKz+G9y6FA+vcFrOISEVVKEnau3cvF110Eeeddx733HMP99xzD506deLCCy9k7969lR1jjeMspWoYZqLk8bx8zKER2GDdNNj6i9URiYhUyKYDafx5IM01H+mSs2Isjqj6aBAZiBeFDN8zDtL2QfRZMGwa+Iee9r4u3r7Q80m4YSYEx0HSX/DBZfD7W+BwuCt0EZFyq1CSNGbMGPLz8/nzzz9JTk4mOTmZP//8E4fDwZgxYyo7xhrH28tORNHCfB4/L8mpXkc4/zbz8rd3mBNyRUSqkZxCGPj2Mvq+sdiVJLWvH25tUNVIVJAvt3nNomX+RvANgeumQUBExQ7WpBvcthSa94PCPHOUwpSrIeNI5QYtIlJBFUqSFi5cyMSJE2nevLlrW/PmzXnzzTdZtGhRpQVXk0UF+wFwNMPDK9ydqPt/zG8OMw7C7HvNrjARkWriyD9WXagfGUCjaM2HKau4vJ3c4z3DvHL5yxDV5MwOGBQF106Fy18Bb3+zQNAHl0HS1jMPVkTkDFUoSapfvz75+SVX3S4sLCQhQVWCysJZvOFIdUqSfANh8Htg9zZLua6bbnVEIiJldii7+Nyjy1rUwaZlDcrGMKi3bBw+tkJ+LuxIYZtKqnRqs0GnMXDLAohoBCm74MOesOv3yjm+iEgFVShJeumll7jrrrtYsWKFa9uKFSu45557ePnllystuJosoWhdjv0pORZHUk4JHeDSh83L3z9grpEhIlINHM75R5LUMtaiSKqhzbPx3b2YHMOHJwtuIDWnoHKPH9sSbpoLdc+F7GPwyUDY+E3lnkNEpBwqlCSNGjWKNWvW0LlzZ/z8/PDz86Nz586sWrWKG2+8kcjISNePlK5ehJkk7T2WZXEkFdD1PmjQBfLS4avRUFCNesNEpNY6/I/hdp0baTmDMjEMWPA8AJ/Y+rPXiCXZHYuhB8fAyO+g+eVQmAtfjoL/fVj55xERKYMKrZP0+uuvV3IYtY+zJ+lAajXrSQLw8oarPoR3LjTXu5j7BPR93uqoREROyjAMdmUc70kadUFDfL3PaBWM2uOvn+DQBvANZrbPYMgxiw41dUdHnG8gXPMp/Ph/8L/34fuxYLPDuaPdcDIRkZOrUJI0cuTIyo6j1nEtKFud5iSdKKwuDJoIn18DyydCwwuh5RVWRyUiUqo9x7JJzrXh42Xjt4e7ExXkZ3VI1YNhwMIXzcvn3YzP1ig4eoxkdy6GbveCfi+Btx/8/l+zUJDdC84Z4b5zioj8Q4WSJDCLNMycOZM///wTgNatWzNgwAC8vLwqLbiazLmg7FF3vtG4W/M+0OVO803s2zsgvh2E17c6KhGREjYfTAfgrDrBxIb4WxxNNbL9V9i/CrwD4Pw7qHNoNwB73D1U3GaDXk+bSdqyt2DW3eAbBG2ucu95RUSKVGiswbZt22jZsiUjRoxgxowZzJgxg+HDh9O6dWu2b9dE/rJwfovp1m/jqsJlT0DCOZCTAl/fBIUlqx6KiFhty8EMAJrXCbE4kmpm6Zvm746jIDiGs+uFA7B6d4r7z22zQe9n4NwbAQNm3AJbfnL/eUVEqGCSdPfdd9OkSRP27NnDqlWrWLVqFbt376ZRo0bcfffdlR1jjRRZ1JOUlVdITn6hxdGcAW9fGDIJ/MJgz3KY/4zVEYmIlLD5kNmT1CJOSVKZHdkCf8835wQVLSbevKj9diRlVk0MNhv0ewXaDgVHAXwxAnZoPUYRcb8KLyb74osvFqteFxUVxfPPP8/ChQsrLbiaLMTPGx8vcxJxtR5yBxDREAZMMC8veQ3++tnScERE/mnDvjQAmtcJtjiSauSP98zfzftBRCIACWEWFB2y22HQ28er3k29Fvb8r+rOLyK1UoWSJD8/P9LT00tsz8jIwNfX94yDqg1sNhuRRcUbkjOqeZIE0HoQnHuTefnrm+DwZkvDERFx2peSzf7UHOwYtKsXZnU41UNOKqz53Lx83i2uzQnh5nyu1Ox8MnMrea2kU/Hygas/gsaXQn4mTLkKDm6ouvOLSK1ToSTpiiuu4JZbbmH58uUYhoFhGCxbtoxbb72VAQMGVHaMNVZk0byko+5Yb8IKfZ6DBhdAbhpMHQqZSVZHJCLCa3P/AqBeEAT5VbheUe2y7gszGYlpCY0udm0O8fchpKgND6Rmn+ze7uHjD9dOhfqdzSTu00GQtK1qYxCRWqNCSdKECRNo2rQpF1xwAf7+/vj7+9O1a1eaNm3KG2+8Udkx1ljOMuDVvniDk7cfXPMZhCdCyi6YPlwLzYqIpQoKHczZeBCAxqGGxdFUI2ummL87jjTnBZ0gvqg3aX+KBev8+QbBsC8gri1kHoFPBkLK7qqPQ0RqvHIlSQ6HgxdeeIHLL7+cffv2MWjQIL788ku++uortmzZwjfffENYmIYylFV4oA8AKVk1qCJcUJT5BuYXCrt/h+/uNUu4iohYYM2eFNJzzGFh/eo7LI6mmji0CfavBrs3tB1S4ub4onlJ+1OquCfJKSAcbpgJ0WdB2l4zUUo/ZE0sIlJjlStJeuaZZ3j00UcJDg6mbt26/PDDD8ycOZP+/fvTtGlTd8VYY4X4m0mS8w28xohtYVa8s9lh7VRY8qrVEYlILXX1O78D0KtVLH5axq9s1k41f5/VB4KiS9wcF2r2JB1Ot3CkQFC0mSiFNYDkv82hd1nJ1sUjIjVOuZKkTz75hLfffps5c+Ywc+ZMvvvuO6ZMmYLDoW/nKiI0wBzXnZ5Tg3qSnJr2gD4vmJfnPQmrPrE2HhGpdfad0NMxuH2ChZFUI4X5sHa6ebn99aXuEh1iDhVPyrB4OHVYXRj5LQTHweFN8NlVkFuyqJSISEWUK0navXs3/fr1c13v0aMHNpuN/fv3V3pgtUFoUU9SWk1MkgA63wIXFK2b9d098Od31sYjIrXK2j0pALStG8ZlLWOtDaa62DYPMg9DUAw061nqLtHBZtEhy5MkgMjGMGImBETA/lVmefB8i4YBikiNUq4kqaCgAH9//2LbfHx8yM+voR/y3SzE39mTVMOG252o55PQYTgYDvjqJi0CKCJVZvvhDACaaW2kstvwtfm7zdVm2e1SOJOkI1YOtztRbEsYPgN8Q2DXEnPB2YIaUhBJRCxTrlqohmEwatQo/Pz8XNtycnK49dZbCQoKcm2bMWNG5UVYgzl7kmpU4YZ/stngijcgOwU2z4bPh8Go7yChg9WRiUgNVlDo4JWi0t9NYpQklUl+Nmz5wbzc5qqT7tY4xny/37Q/jbwCB77eFSqUW7nqngPXfwGfDoatP8PXN8LVk06a6ImInE65/rONHDmS2NhYwsLCXD/Dhw8nISGh2DYpm8SoQAA2H0zDqMkV4Ly84aoPoeFFkJdujhtP2mp1VCJSg/286Xi1s/MbR1oYSTWybR7kZUBYfah37kl3axkXSoifN5l5hexIyqzCAE8j8QK49jPw8jWHd389Bgpr8EgNEXGrcvUkTZo0yV1x1EqtE8Lw8bJxLCuffSnZ1IsItDok93EuAji5PxxYA58MgpvmQFg9qyMTkRpoTdF8JID29SNw6MPy6W38xvzdamCJtZFOZLfbiAn1I/1IQdFi6CFVE19ZNO0BQz811+nbNBPsXnDle+aXdSIi5eABfeS1l6+3nYRw53oTFizKV9X8Q2H41xDVzFzb4tMrIfOo1VGJSA30+3bzf8sLV7XFy37yD/xSJD8btvxoXm49+LS7RweZw+49cjH05n1g6CfmOk8bvoaZt6pHSUTKTUmSxRKKFuXbl5JlcSRVJCgabvgGQutC0l8wdQjkZlgdlYjUIIfTc1i/LxWbDS5rWcfqcKqHrXMhP9Ncd6juOafdPTLILAPukUkSQIt+MORjM1Fa/yV8c4sSJREpFyVJFmsYbQ6x+21bLepRCa9vJkoBkbBvpTksosBDqiSJSLV2NCOXj3/bCUCz2GBXJTY5DedQu9anHmrn5Fwr6UCqB4+CaNnfLN7g7FGaMcZcB0pEpAyUJFmsT5t4AP7YUctWCo9pDtd/BT5B8Pd8mHELOAqtjkpEqrlh7y/n7QXbAWgVH2pxNNVEXhb89ZN5ufWVZbpL6wSzSNPKXcfcFVXlaDWgaOidj5kIfjlKX8qJSJkoSbJY42izlOrB1Bwcjhpc4a409TqalYjsPuYE2+/uBofD6qhEpBpJy8nnoa/WsvCvIxiGwZZD6a7bYkLUi1Qm2+dBfhaEN4CE0w+1A7igSRRgJkkesajsqbS4HK6dAl5+5lIU067XgrMiclpKkiwWF+aPzQZ5hQ6OeurYbndq0h2u/hBsdlj9Gcx5BGpyOXQRqVSPzljPFyv2MvKjPziYVnzoV2JU0EnuJcU4e5FaXFGmoXZgtm3j6CAKHQZbDqaf/g5WO6s3DJsO3gGwbS5MHQp5HlS+XEQ8jpIki/l42Ykt+rbzQGot/War1UAY+JZ5efk7MO9JJUoiUiZzT1gPadvh40VgLm8bzzWd6lsRUvXicMBfP5uXz+pdrrvWjTALD3n0vKQTNelmVlj1DYYdi8w1+3LSrI5KRDyUkiQPcLwMeC1NkgDaD4N+L5uXl7wKvz6tRElETik7r5DcguNDdBdvTQKgV6s6vHX9Ofh46S3utPavgszD4BcKDS4o113jw/wBOFidvuBr2BVumAl+YbD7d/h0EGR7+LwqEbGE3kE8gLMMeK1YK+lUzrsZej9nXl78shIlETmleZsPFbv+3qK/AWgaG2xFONWTc6hdk+7g7Vuuu8aFVbOeJKf6nWDkLAiIMCusTr3WLF4hInICJUkewPltXK3uSXLqcrsSJREpk51Jpc8pUZJUDluKkqTmfct9V+d7V7VLkgAS2sPI2eAfBnuWmVXvVB5cRE6gJMkDOIfbVcs3GndQoiQiZbAjyfz2v+U/Sn03jwuxIpzqJ3UvHFpvFs5p2rPcd4+rzkkSQFwbGPYFePvD1jnw7Z2qsCoiLkqSPEBCeFFPUnUa1+1uSpRE5BQOpeXw9aq9AFx1Tt1it2l9pDJyDrWrdx4ERZX77seHilfj964G55vrKNm8YN00+OVxqyMSEQ+hJMkD1Ak1k6RD1fXbOHf5Z6L082P6lk9EALjslYWuy71axfH4Fa0AeGVIO2xlLGNd67mG2vWp0N3rR5pJUmp2PilZ1Xio2lm9j1dYXfomLP2vtfGIiEfwtjoAgfBAc7JsWk6BxZF4oC63m0NBfvo/+P2/kJUMA94EL710RWorh8MgI9f8f+nnbadBVCA3XtiIqzrWIyzAx+Loqom8TLMMNsBZFUuSAn29qRPqx6G0XHYereZrDrW/DjIOwS9PwM//huA6cPYQq6MSEQupJ8kDON/UM3ILKChUT0kJ598KgyaawyHWToUvbtBq6SK12L4Thnd9d9eFrstKkMrh7wVQmAvhiRDTosKHaVi0YO+u5BrwP7nrPdD5NvPyzNtg+6/WxiMillKS5AFC/Y/3iqg36STaD4NrPgMvP9jyA3wy0OxVEpFaZ0dRVbumscGcVUdFGipky4/m77P6wBkMT3QlSdW9JwnMduj9LLS5Chz5MP0G2Pmb1VGJiEWUJHkAby87wX5mopSaXY3Hdbtbi34wYmZRydbl8FFvSNltdVQiUoV+2XSIxVuPAJAYGWhxNNWUwwFbfzYvV3A+klPDaGeSVAN6kgDsdnPkQuNukJcBn10F236xOioRsYCSJA/hHCaiJOk0Ei+AG+dAaF1I+gs+6AkH1lkdlYhUgRmr9jLmkxW8v3gHAA2ilCRVyIHV5vwb32BIvPD0+5+Cs3hDjarO6u0H102DZr2hINtcbPbP76yOSkSqmJIkDxGqJKnsYlvCTXMhthVkHIRJ/czx9SJSo037Y0+x6+pJqqC/5pi/m3QHb98zOlS9CPM52F0T5iSdyMffHOLdapA59O6LkbDmc6ujEpEqpCTJQzjnJSlJKqOwujD6R/Nb0Lx0+OxqbBu/tjoqEXGjzLziczYTi+bDSDk55yM173vGh2oUHYSvl53D6bnsTD/jw3kWb1+4+iNofz0YhTDzVpj/nNbsE6kllCR5COdwuzQlSWUXEA7Dv3Z90+c98180OfSj1VGJiBss+/soG/enAeb8+nMahHNeo0iLo6qGUvfBwXWADZr2POPDhQX4uJ6Hg9k1cH0quxcM+C9ceJ95feHzMOMWKMi1Ni4RcTslSR5Cc5IqyMcfrp7kKtvaZv/n2Odq0VmRmuTXzYe49r1lAJzXKJK/n+3HjNu7EuSn9dLKbWvRULt6nSA4plIOGRviB0BmTX37stuhxzhzjT67N6z/Aj4ZpAqrIjWckiQPoSTpDNjt0Oc5Ci8bB4DXH+/A1zfqmz6RGmLigu2uy0/0b4XtDEpW13pbfjJ/n2FVuxNFBpnzmtLza/jzcs4IuP4r8AuF3Uvhgx5wdPvp7yci1ZKSJA8RF+YPwM6kGrDWhBVsNhzn38mKxFsx7D6w8RuzdGt2itWRicgZ2p2cBcDXt3WhdUKYxdFUY3lZsGOhefmsykuSooLNnqSM2rDMX5NucNPPENYAkrfDB5fBrqVWRyUibqAkyUM43/g3HUizOJLqbV/kBRReOx18Q2DnYrPyXdp+q8MSkQrKyS/kUJrZK9woOtjiaKq5HQuhIMf8gB/bqtIOG1/0JV9yTg3vSXKKbQk3z4O6HSH7mLm4+bovrY5KRCqZkiQP4Vxr4nC6hoidKaPRxTD6BwiuA4c3mmspHf7T6rBEpALmbz4MQFyoPxGBPhZHU805q9qd1dusflFJmtUxk9cD2WDUlspvwbEwcja07A+FeTBjDCx8UZXvRGoQJUkewjknKa/AQU5+ocXR1ADxZ5trKUU1g7S98FFv2L3M6qhEpJz+2GlOju/TJk5zkc6EYRxfH6kS5yMBNIkJxm6DrAIbRzLyKvXYHs03EIZ8AhfcZV6f/wzMvB0KalEbiNRgSpI8RLCfN1528wOAijdUkohEc+x4/c6Qk2pWI9r+q9VRiUg5bCoq+906IdTiSKq5A2vMxbd9gqDhRZV6aH8fL9fCvlsPZ1TqsT2e3Q69nobLXwWbF6ydCp8NNt9zRKRa8+gkady4cdhstmI/LVq0sDost7DZbIQULSh7MDXH4mhqkMBIuGGmuR5IQTZMvQb+nG11VCJSRtuPmMVszqoTYnEk1Zyzql2TbuDtV+mHbxRtLuy762hWpR+7Wuh0E1z/xfH5sJ9eqcJBItWcRydJAK1bt+bAgQOunyVLllgdktukZJk9SE/O3mRxJDWMbyBcOxVaDjDHjn8xAtZ9YXVUInIaGbkFJGWY8zQbFn0Ilwr6y1n6u69bDh8baiZezuerVmraA0Z/DwGRsG+lWdBBaymJVFsenyR5e3sTFxfn+omOjrY6JLc7ouINlc/b11x0tt0wMArNFdNXfGR1VCJyCs4lESKDfF3zNqUC0g6Yw+2wQbNebjlFbFEZ8MPptXw+Tnw7GPkdBEaZbf7JACVKItWUxy9XvnXrVhISEvD396dLly4899xzNGjQ4KT75+bmkpt7PMlISzPHs+fn55Ofb+1cH+f5TxbH+zd04OZPV+Pvbbc81urodO0LwOWvY/cOwGvlhzD7Pgqz03Ccf0cVRVi9lal9pcLUviVtO2T+/06MDDjjdqnN7Wvb/APegCPhHAr9IsANbRAR6AXA4bScWtnGxUQ1h+Hf4j3lSmwH12N8fDkFw2ZA0Jl9yVubX8NVQe3rXp7UvmWNwWZ4cL3OH3/8kYyMDJo3b86BAwcYP348+/btY8OGDYSElD4+fdy4cYwfP77E9qlTpxIYGOjukM/IgSx4fq03/l4Gz3cqrMwKrXIiw6DlgS8565A5N2lT/BC2xvW3OCgR+ad3/7SzKcVOpxgHw5s6rA6n2jpv+2vEp63mz/ir+CtuoFvOsT7ZxgdbvEgMNhjbVhVaAYJz9tF16/P4F6SS5l+XpU0fJtdHiyGLWC0rK4thw4aRmppKaOjJiwJ5dJL0TykpKSQmJvLqq69y0003lbpPaT1J9evXJykp6ZQNURXy8/OZO3cuPXv2xMen5NCRnPxCzn9hAZm5hXx247l0bhRpQZTV1+na95/si1/Ca9ELABRe8iiOC8e6O8RqrbztK+Wj9i2u0GHQ4el5ZOc7+GjEOVzU7My/ha+V7ZufjferZ2EryCZ/zAKo08Ytp1m58yjXfriSuFA/Fj94iVvOUS0d3Yb3Z4OwZRzEiGpGwfXfQEhchQ5Va1/DVUTt616e1L5paWlER0efNkny+OF2JwoPD+ess85i27ZtJ93Hz88PP7+SlXt8fHwsf1KcThaLj48Pl54Vy/frD7D5UCYXnlXHguiqvzI/190fBW8f+PVpvBY+i5cNuPT/3B5fdedJf0s1kdrXtG5XMtn5Dvx97FzSIs61RMKZqnXt+/c8s7JnaD186rav1EVkTxQfbo7UOJiWy99Hc2gep2qEAMS1NBc3nzwA29Gt+EwZZM5ZCk2o8CFr3Wu4iql93csT2res5/f4wg0nysjIYPv27cTHx1sdits0jjErODnL3oqbXfwgXPaEeXnBszD/Wa2YLuIBvlyxF4AeLetUWoJUK235wfzdvK/bEiSAqODjX05+tGSH285TLUU1MavehTWAo9tgUj9I3Wt1VCJyGh6dJD3wwAMsXLiQnTt3snTpUq688kq8vLy47rrrrA7NbRoULci391gtXWvCCheNhZ5PmZcXvgC/Pq1EScQCfx5IY1vRYqQrdh0DYGD7ulaGVL05HG4v/e3k520nzNf8v5lToDlJJUQ0NBOl8EQ4tgM+vgLS9lsdlYicgkcnSXv37uW6666jefPmDB06lKioKJYtW0ZMTIzVoblNTIhzrYlaXka1qnW9G3o/a15e/DLMG69ESaQK7UnOou8bi+k3YTEPfLnWlSx1TIywOLJqbP9qyDhkLnDa8EK3n25wQ7O4xqK/jpCZW+D281U74Q3MoXcRDY8nSukHrY5KRE7Co+ckTZs2zeoQqlx00ZCFPw+kYRgGNpW4qzpd7gCbHX56GJa8Bo5C6PmkW4eoiIjplz8PAZBX4OCrleZQpCYxQUQG+VoZVvXmHGrX9DLwLjlXt7K1DDeoG+7PvpQcZq3dz3XnnXy5jlorrJ45J2nS5ZC8HSb3h1HfQ3Cs1ZGJyD94dE9SbeTsSQL4cYO+Yapy598GfV8yLy+dAD8/ph4lkSpwKK3kItp92lSsCpgUcQ2161clp/Pzgr5Fz9mWg+lVcs5qKbwBjJwFoXUh6S/4+HJI2WN1VCLyD0qSPEzMCZNff9uWZGEktVjnW+DyV8zLv/8XfnpEiZKImyVlFE+SWsWHcvulTS2KpgY4tgsObTB7x5v1rLLTxof5A3AoLafKzlktRTYqqnJXlCh92AsObbI6KhE5gZIkD2O323jzug4ArNmTYm0wtVmnMXDF6+bl5RPhx4eUKIm40T+TpCljOhPk59Ejwj2bsxepQRcIrLo19+oUjYY4qCTp9KKawE0/Q0wLSN8PH/WBHYutjkpEiihJ8kDnNjQnKm8+mE52nqoEWebc0TDgTcAGf7wHs+815ymJSKU7mHr8Q3X3FrFEaC7SmTmx9HcVqhNqJkmHUpUklUlYPRj9o5nM5qbCp4NgxUdWRyUiKEnySPFhAUQE+lDoMNiRpPWSLHXOCBj4FmCDlR/DlyMhX2/+IpXJ4TDYedT8X/feDR15Z3hHiyOq5nJSYecS83IVzUdyqhNqDrc7nJ6Lw6He9zIJjIQbvoE2V4GjAGbfB7PHQmG+1ZGJ1GpKkjxUgyhzUdlPft/J9iMZFkdTy3W4HoZMAi9f+PM7+Owq80OIiFSKg2k55OQ78Lbb6N4iFl9vvTWdka1zzQ/b0WeZQ7qqUEywL3YbFDgMkjJLFuOQk/AJgKs+hMseB2yw4kP4ZBBkam6yiFX0TuShGkebSdK0/+3hrqmrLY5GaH0lDP/aXG9k1xL4qK+qEYlUEmePeYPIQLy99LZ0xjbNNH+37F/lp/b2sruWsjioIXflY7PBRffDdZ8ff695rxscXG91ZCK1kt6NPNSQc+u5Lm86kGZhJOLS6GJzxfSgWDi8Ed7vDntXWB2VSLWVmp3PgdRsNu03/8c1KvpySM5AXiZs/cW83GqgJSHUiwgAYO+xbEvOX+017wtjfoHIxpC626x8t+lbq6MSqXWUJHmoC5pE06NlHdf1Qo3t9gzx7eDmX6FOG8g8DJP6wfqvrI5KpNrZdjiDduN/pstzv/LMD38C0LROsMVR1QBb50JBNoQnQtzZloTQIDIQgN3JWZacv0aIbQFj5kHjSyE/C74YgX3RC2A4rI5MpNZQkuTB3r7+HNfl9BxN4PQY4fXhxp/grD5QmAtf3wQLnleJcJEyMgyDHq8uLLH9/MZRFkRTwzh7HFoNNIdvWaB+UZK0R0nSmQmMhOu/hvNvB8Br8Ut02vEm5Gh0iUhVUJLkwXy97QQXrRNyLEtJkkfxC4Frp0KXO83rC56Dr8dAvoaXiJxOykn+n13SLKaKI6lh8rNh68/m5VaDLAujvnqSKo+XN/R5Dga+jeHlS0LqSrw/7Ab7VlodmUiNpyTJw0UFm2uF7NPYbs9j94Lez0D/CWD3hg1fwcdXQPpBqyMT8WilzbMM9PXCbrem56PG2P4r5GVAaD2oe87p93eT+hFmkrRxfxp5BRoeVik6XE/hDbPJ9I3GlrLLnKe09E1wqH1F3EVJkofrUD8cgOU7jlobiJxcx5HmGhf+4bBvBbx3qb7lEzmJnPxCrv9guev6fT3Owt/HzitD2lkYVQ2xaZb5u9UAy4baAbStF4a/j53kzDz+tzPZsjhqGqPuOSxo/hSOFgPMEu8/PwafXwOZ+nwg4g5KkjzcBU2iAVi6Xf8EPVqji82CDtHNIf2AWdBh3ZdWRyXicTbuP96L9OTA1tzToxkbx/ehb9t4C6OqAQryYMuP5uWWAywNJdjPm46JEQAcSlMZ8MpU4B1E4eAP4fJXwcvPHF75TlfYscjq0ERqHCVJHq5LE3Mi89o9KWTkFlgcjZxSVBOzbOtZfaAgB2aMgblPgKPQ6shEPMYfO8yehe4tYhnRpSEAXhpmd+b+XgC5qRAcB/U7Wx2Na62kpAwtKFvpbDbodFPRF3NnmV/MTe4Ps8dCbrrV0YnUGEqSPFz9yEDqRQRQ4DBYszvF6nDkdPxDzYIOF441r//2Onx+naoRiRT5ds0+AC5rGWtxJDXMhq/N3y37g936t3ZnkrR+XxqGKn+6R1wbuGUBdBxtXl/xIbzdBbb9YmlYIjWF9f9J5bRaxIUAsCMpw+JIpEzsXtDjCbjqQ/D2h61z4IMecHS71ZGJWCYlK49HZqxn88F0vO02rmibYHVINUdeFmyebV5uO8TaWIo0iTHXvPpu7X5e+2WrxdHUYL5B0P91GDHLXBsrdQ98dhXMvAOyj1kdnUi1piSpGmgYZa5CvyNJ5VSrlbZXw+gfISQBkrbA+93M6lMitdCdU1fz+R+7AagbEUBYoI/FEdUgf/1oVrULbwD1z7M6GgCu6liXprFmovSHCg+5X+NL4PbfofNtgA3WfAZvnQ+bv7c6MpFqS0lSNdAw2kySdh3NtDgSKbe658At86FeJ8hJNb/h+22CyrZKrbNkW5LrcnJmnoWR1EDrvzJ/tx1iaVW7E/l5e/H84LYA7NUSFlXDNwj6Pm8udh7VFDIOwrRh8NWNkJl0+vuLSDFKkqoBV0+SkqTqKSQORs6GdsPAcMDc/8CUq7SektQaOfnFi5f4eumtp9JkJcPWueZlDxlq51Q3IgCAA6k55Bfqi6Eq0+B8uHUJdL0XbHZzvtp/O8HqKaD5YSJlpneqaqBhtLkw357kLAr0RlM9+fjDoLfNsq3eAeawu7e7aCiE1AozVu1zXY4K8uW/w6xb6LTG2fQtOPKhTluIbWl1NMXUCfEnLMCHQofB5gOqulalfAKg53iz4mpsa8hOhm9vNxc8P/KX1dGJVAtKkqqBhLAAfL3t5BcaHEjVmhPVlrNs678WQlxb801r2jD47l5z4rVIDfTFij08+s16AP6vTwtW/qena2kDqQTri9Zja3u1tXGUwm63cU6DcAB+3XzY2mBqq7odzfecHuPNL+h2LYGJF5jLU6hcuMgpKUmqBux2Gw0izd6kHUkaclftxTSHMfPggrvM6ysnwXuXwO7l1sYlUklmrNrLf2Zu4K9D6Tz01TrX9psubGRhVDXQ0e2w6zfA5pFJEkCv1nEALFfxBut4+cCF98Idy6FZb7Pn8bfX4c1zYc3nmiMrchJKkqoJ57yknZqXVDN4+0Gvp+GGmRASD0l/wUe9YNZd5hwDkWrq2zX7GPvFWj5dtotery0qdpuvt95yKtXqT83fTS+DsHrWxnIS8WH+AKRk5VsciRCRCMOmw3XTIKKRWdhh5q3wYU/Yu9Lq6EQ8jt6xqolGRfOSdqoMeM3SpBvcthQ6DDevr/oE3uwIKz8GR+Ep7yriif63s2SSHxXkyzvDNQ+pUhXmmxPxAc4ZaW0spxAWYJZ6T81WkuQRbDZo3tfsVeoxDnyDYd8K+KA7fHMrJO+wOkIRj6EkqZpwDrfbc0xJUo0TGAkD34LRP0FsK3Ou0nf3mEPwdi6xOjqRMtmZlMmCLYc5lJZb4rZlj15GnzbxFkRVg/31E2QehqBY80OvhwoP9AWUJHkcbz+48D64ayW0u87ctvZz80u6b+9QsiQCeFsdgJRNVLAfYK5aLzVUYhf41yL44z1Y8AIcXA8fXw6tBkLPp8yhEiIe6OeNB7nl0+LDdZ4f3JbzGkWSEB6Aj0p+V76Vk83f7YeZc048lLMnKSO3gPxCh14LniYkDq58BzrdDAuehW2/wOrPYO00M3m6+AGIaGh1lCKW0H+raiK8aHX6YxrXXbN5+UCXO+DuVXDujeYaF5u+Nde4mPekFgQUj3T3tNUltjWKDqJxTDD+Pl4WRFTDJf9tfpgFOGeEtbGcRqi/N952c4Hb8d9ttDgaOal6HWH413DTL9C0BzgKzDlvE86BGbfAwQ1WRyhS5ZQkVRMRRUMW1JNUSwRFwxWvwb8WQ8OLoDAXFr8Cr7U2h+Id2WJ1hCIA/LYtiZz84tWxWieE0qFBhEUR1QLL3wUMaNoToppYHc0peXvZ6dYiFoAvV+wlM7fA4ojklOp3KkqW5kKT7mAUwrrp8E5X+HQw/L1AC9JKraEkqZpwJklJGXnsT8m2OBqpMnFtYOR3cM0UiG8PBTlmUYe3zoMpQ/SGJZa7/oPipeubxgbz/d0XqZKdu2SnmMOhALrcbmkoZfXmdR0AyC1wMGfjQYujkTKpfx7c8A3csgBaDzZHNWyfB58MNOfLrv8KCpXwSs2md7FqIirYF38f8+l6/Retll2r2GzQ8grzzWr0j9DiCsAGW38237DeuRDWTIWCkhPmRdxl/d5UZq7e57rep3Ucs++6kK9u7WJhVLXAqk8gL8Ms8tK4m9XRlIm/jxejLmgIwNbDGdYGI+WT0AGGTIK7V8N5t5gL0h5YC1/fBG92gKX/1bIVUmMpSaomfLzsvHh1OwC+WrmXoxn6QFzr2GyQeAFcO8WsSHTeLeATCIc2wMzb4PW2sPAlyDhidaRSC/T/7xLunb7Gdf3NYR1oUzfMVc1M3KAw3yzsAnD+7eb/hGri+JBxzautliIaQr+X4L6NcOmjEBgFKbvh53/DKy3MeUu7ftfIBqlRlCRVIwPaJRDi743DgP0pOVaHI1aKanL8DavHOHNB2oxDMP9peK0VzPgX7NPigOIeDkfxD0Jt6oaqallVWPcFpO6BoBhoO8TqaMrFWXwoNVvzaqu1oCi49P/g3g1wxesQd7Y5Z3bddJjUB97uYs6Zy06xOlKRM6Z3tWompqgU+NQ/dlkciXiEwEhzrYt71sHg96FuRyjMg3XT4P3u8EEPWPclFOiDiVSepMziPdl1QvwtiqQWKSyARS+Zly+4G3yqV5u7KrRmqiepRvANhHNHm8tWjPnVXBDdOwCO/Ak/PmT2Ls28A/auUO+SVFtKkqqZfUVFGz7/Yw9r9qRYG4x4Dm9fOHso3Pyr+YZ19jVg94G9/4MZY8zepXlPQepeqyOVGmD+5sPFrkcXfXkjbrT+Czi2AwKjodNNVkdTbs5hmEc0VLxmsdnM8uED34L7N0Pfl8z5cgXZsOYz+OAyePci+N+HkJtudbQi5aIkqZp5on9r1+VBb/3GjqRMC6MRj1SvIwx+D8Zugm7/NofiZR6BxS/D62fD9OHw90J9uycVNmfjIddlX287t3fz7DLU1V5hASx80bzc9W7wDbI2ngpoFR+Kl93GtsMZfLD4b+6bvkZLWtQ0AeHQ+Ra4bSnc+DOcfS14+ZkLo38/1uxd+u4e2L/G6khFykRJUjUzrHMDJo3q5Lq+YMvhU+wttVpwLFzyENy7HoZMhsQLzTUv/vwOPhkAb58P//sAclVtSsru/75ax69FPUkTruvAqv/0JDGq+n1or1ZWTS7qRYqCTmOsjqZCYkL8uPSsGACe/v5Pvlm9j/ZPzqWg0HGae0q1Y7NBg84w+F2zd6n3sxDVzKzKuPJjs4T4e92KKjXqi17xXEqSqqFuLWK5u3tTQOVUpQy8fKD1IBj9Pdz2O5x7I/gEwZHN8P398GpL+PFhSNpmdaTi4f46lM70FXtc1+tFBBDs521hRLVATirMf9a8fMn/VcteJKerOtYrsW31nhQ27k/lWKZ6lWqkwEjocgfc+T8YORvaXGUOBd+/CmbdZfYuzbobdi4BhxJm8Sx6d6umYkLNSbtJ6RrfLeVQpxVc8ZpZEW/NVPjjfUjeDssnmj9NukOnm+Gs3mD3sjpa8TBrdqcUu968Tog1gdQmi16GrCSIPsv8gqMau7ioJ+lEQ975HYDzG0cy7RatsVVj2WzQ6CLzJ+MIrJli9iod22H2lK6aDKH1oO3V5vzaOq1Pe0gRd1NPUjUVE2xOgk3SJFipCP8wOP82uHMFXP81NOsF2GD7rzDtOnijPSx+FTKTrI5UPMiJk+5n3tGVIPUiudfR7bBsonm51zNmr3A1dqpex2V/J/Pp7zurLhixTnAMXHgv3LUKRswyK+P5hULaXvjtdZh4Abx9ASx5TcWGxFJKkqopZzWppAwNUZAzYLdDsx5w/ZfmiuoX3A0BEZC6G+aNN4fizfiXyrgKGbkFvDRnCwB3dGtC+/rh1gZU0xkGzL4XHPnQ5DJo1tPqiCrFhOs6YLPBA73OKnnbr9sw9H+m9rDbofElZmW8B/4y5862uMIcjnd4I/wyDl5rDR/2gt/fgpQ9pz2kSGXS14DVVEyImSQd0XA7qSyRjaDXU9DtUdgwA/73Puxfba65tG4aXnHtaODTEXIvAp9Iq6OtFvIKHKzYlUynhpHVfrHV79bud12OUclv91szBXYsAm9/uPxlc7hSDTCgXQI9WsYS6OvN9+sP8ueBNCKDfEnOzONIei5bD2dwloZx1j4+Aebc2daDIPsYbPrWXDx512+wZ7n5M+dRqHsutBoILS43F1UXcaPq/a5dizl7krLzC0nWhFepTD4B0OF6uGWBueZSu2Hg5Yf94Fo67PkI7zfawLd3qnfpNBwOgzGfrGDY+8v5v6/WWRbH/3Ym89jM9WTlFQBwOD2HjNyCMt//hZ82c/mExWzan+badlnLOpUep5wg4zDM+bd5udujENnY2ngqWaCv+f3sR6PO5brzGvD6Ne3p1DACgCsmLGGt1gCs3QIioOMoGP0DjP0T+r4IiV0BG+xbAXP/A2+eAxM6mEWHts2D/Byro5YaSElSNXXiXIBzn55LbkGhhdFIjVWvI1w5Ecb+SeFl48jwi8OWnwmrPzUXCZx4ASx7B7KSrY7U46zfl8qiv44AMGP1PgodVZtQZuUV0O+NxQx553c+W7abSb/t5FBaDhe/OJ+h7/xe5mFNExdsZ+P+ND5dtguAf/drSf3IQHeGXrs5HDDzdshJgbiz4fw7rI7IbeLDAnhucFsuPiuGJjHBAOQVOhj41m8WRyYeIzQBOv/LTJju3wL9XoZGF4PdG5L/NgsOfTYYXmwEn18HKz7SPCapNEqSagCHAY99s8HqMKQmC4rCcf6dzGv5AgU3fGcuEujtD4c3wU//Z5Zx/XqMOTxIZVwBOJCaXez6x0t3Vun5F/11hE0Hjvf+bDucwapdx8jJd7DpQBordx077TF2HS25hokSJDdbPhG2zTX/vq58B7xqx6j4prHBxa6v35tqUSTisULqwHk3w8jv4KEdMPRT6HADBMdBfhZs+QFm32fOY5rYFeY9BXv+p/ckqTAlSdXYQ32auy5/uXIvOfmF/9/efcc1db1/AP/cQEjYe09BGSLTgUjde9ta6+hQ29qhdtndb9X2172X1k5Hh1ZtbbXVuvdERHEhCrL3DpuQ3N8fhyySMCwQxvN+vfKC3NwbTq7Xm/vcc87z4ExKMaRUnI90FI4D7xXdWCSw8a6ecwggqwOubAc2TWfDIE58ClTkGbq1BlXQZL7gzkvZnfr349I0g6A/L2bjyV/jlc/Xn0ptdvut5zMw8qOjGsvcrMUYHaidxpm0k5yLwIHV7PeJ7/SqNMg+TQoSb43LMFBLSLcgtgL6zwBmrmEFax8/AYx5HfCMAjgBkH8VOPEx8OM44BN/4M8ngWt/AbWSFt+aEAUKkrqxpaP6ajwPXLkX878/i68O3TJQi0ivYmrD7uo9cQJYcgQYuBgwsWR1Lw69CXzaH9iyALi5D5C1fg5MT8DzPL4+ktJkWee24WZjoel5gz11vr73ap5ynlJTWaXVePmPK1rLn58QAJEx1c/qENUlwPbFLJtd4DRg0COGblGnCvGw1ngen15mmIaQ7ofjANdQYMSLwCP7gReSgbu/A4LvZqnFqwqBhM3A9oXAh77AphnAma9Zin1CmkFBUje3eUmU1rIvDycboCWk1+I4wD0SmP45u6M3cy27m8fLgKTdwOb7gM9DgEP/B+Rd6RXJHuIzypAnYROJZ0d6AABKqzsvwYqkVoq4NDZPbG6TIGnZaD+YGAsg56GRjEFh79U83PXBEZ3vOyPcrf0bSwCZFNj2ELvBYOMFzPiqx2Szay1nKzF2LY/BpoeHAABSCis7fR4f6SHM7YGwucCcjcBLt1ktpujlgH1fdhMi9Riw71U26uGrgSxJyu1jgIySYBFNvWOwcw8W6WVr6CYQoiKyYIUBIx4AChKB+J+BhC1ARQ5w4hP2sO3DhkkEzWTBVQ+8GMwsqVb+vjjGB3/EZ6Ggog5yOQ+BoOM/78WMMlTXy+BpZ4pwTxv8tSwGW89n4uVJAbAxM0FSXgUOJhbgSnY5BvlopnN/4pcLGs+vvTkRm89lwNPOtNunMe+SFPWQ0k4AJhbA/K2AWe9MsR/qYQO5nIdYKECtVI5Xd1zGqunBzRahJaRZRkJWi8l3JBvCWpzCRjfc3AuknwaKk4Eza4Aza2AsssQg0yBwlyuAwEmAuYOhW08MjM483ZxYaISB3rZak7ClMrnWBU2DTI5Vu67Bw9ZUa6geIe3OKQiY9C4wbjVw4x/gyh9AyiF2t/zUF+xh5QEETWdBk2cUIOgZQ7mKKtl8pBlhbgh0sYTIWIC6BjnSiqvg62jRwtb/XVIe6yEKdbcBx3EI97TRKP4a4m6Dg4kFuJrd/Pj8oy+MgrnIGEtG9KwU1F0GzwP7Xwcu/sLmUcz+AXDub+hWGZRAwKGvkwWuZkuwLS4L7jZmeGZcP0M3i/QU9n5A9FL2qJUAt4+woOnWfnBVhXCviwX+jgX+5gCPQYD/RMB/EuA8oEfe0CPNoyCpB/jhoUG4lFWGxRvOK5fFp5ciytde+bykqh5/XMjC5nNsMuwDQ71hJRZ2eltJL2QsAgbMZo+6SuDWfiBxF3BzPyDJYtm8zq0DzJ2AoGlA0AzA5y52B7CbKmwMkhwsRDA2EiDI1QqXMstwNUfSoUFSZV0DPtmfhD1XcgEAAS66i3L6OrJJ8uo9XgA05igdf3E0vOwpk12H4Xng6PvsLjbAhtgFTDZsm7qIYFdrZQB/OKmAgiTSMcRWrDBt/5mAXI6GjPNI2bsW/kgFl3cZyDrPHoffBqzcgT4jAZ8Y9v1k401BUy9AQVIPYGtugtEBTjj9yhgs+SkO13IkePffG9i5LAYAcCmzDLOa1J24lFGGEf6UpYp0MpEFMOAe9pDWACmHgeu7gKR/gaoCVuMibj0rJug/CfAdzWpiWLkauuVtUihhQZKjJSv6HOJuzYKk7HLMCGv9vJ76Bjm2xmXCz9Ecw/w0h35U1TXgTEoxhvs7KJMp/O/PK9h5KUe5jr4gycPWFACQWaoKkvIltXhvTyIAwEJkTAFSR5LLgf3/A85+zZ5PfI8NUSUAgBcmBmD/9TyUVkuRVlSlc2QEIe1KIADvHokbrrPhO2UKhDWF7Ibezf2st0mSzZI/JGxm61t5sIDJuzFosvOloKkHoiCpB3GzMcVH94ZhypcncD2nHHI5D46DVoAEAFmlNTregZBOJDQFAqeyR0M9q7GUuBO4sRuoLmZzmRK2sHUdAtiY8j4j2ReSqY1Bm96SrDL2/8vNRgwAGOBuBQC4mt222i+fHriJb46xDExbHxuKKF97NDSm+F+0IRbn00rx+tQgPDqcDYdTD5AAFpzpoqh1lCepRX2DHCbGAjz2UxwSGmvTVNb1rmyEnUpaC+x6CriyjT2f+B4b+kOUHC1FiHt9PIa8cxDFVfU4e7sYw/vRTT3SiazcgIGL2ENaC6SfYo+0k0B2PBsFcXkrewCApWtjwBQDeN8FOPSjoKkHoCCph+nnbAGOA6QyHsVV9eDVMolN6O+MA4n54Hkgr5yCJNKFGJsA/caxx9TPgIwzrKDm7WNAbgJQlMQesd+xuRuu4aqgyWsoC7i6kOzGmxCKHpsBjcHK1exy8DwProUvzwaZHN+fSFUGSAArRptfUYent1yEg4VIOe9p79U8PDrcF5Jaqdb7uNno3i/25ibKyfE5ZTVwsRYrAyQAeGGCfxs+LWm18ixg6wOsHhJnxDJBhs83dKu6JCMBh4kDXLD5XAb2XMmjIIkYjlAM9B3LHgBQXw1kxQJpiqApDqjIBa7+zh4AGz6u3tPkGEhBUzdEQVIPIzQSwMFChMKKOgx+5yDuiXAHALhai/HdQ4Ow5vAtfLz/Jq608Y42IZ3GyBjoM5w9AFY/Ju0kS9t6+xhQfAvIiWePk58BRiI2wdZjsOqnpYvBml9TL1Om//a0ZT02/ZwsIeAASW0DCivq4GQl1rv9zfwKTF1zRmv5sZuF+PcqK9CrCJAAICmvAvUNcuSV1yqXOViIsPvpu/T+DY7j4GFrhuSCSpxKKUKou43ytcUxPlg+huaAtLub+4G/ngSqi9hw0ns3AH6jDd2qLm1iMAuSjt8sxNGkAuy6lIPV04NhbdZ95yuSHsDEDPAdxR4AGzqeFafqaco6z4aPX/uTPQDAzF4VMHnHAE79AQENIe3qKEjqgcI8rHEwsQAAsONiNgDVnexAFzbs50hSIS5llmlkvCKkSzKzY9nv+s9gz8uz2dA8RdBUkaMaCqFg7ckCJvfGoMk1tNN6m67llEMm5+FkKVIGQybGArhYiZFTXousshqdQdK1HAmulnJ47ftYjeUPRXtj9+VcFFfpruFRUdeAhKwy5RC5QBdL7H12RIvtDHSxRHJBJVbtvIaXJwUAAGL62mP19OA2fV7SgroKVoclfhN77hICzP0VsPU2bLu6gX5OLMlJdlkNFjUmJiqsrMPPj2jXB1RXIKmF2MSIkhORziE01byx11AHZF9o7Gk6AWTGsiHkibvYA2A3SryGsaDJJ4Zlz+sh2V17EgqSeqBpoW7KIElBMVl8qJ8q411CNw2S6hpkKK+RwslS/9140oNZu7MhSuHzWYaw4mQg42xjJqI4oOA6UJ7JHoq7eJyAFRJ0CWEP58afls7t3rxbBZUAgCBXK43l7ramyCmvRXZpjc76ZrPWnQVgBECmsfzxkX7o52SBlTuv6f2b17LLkVJYBQDwsTdvVTtfnBiAfy7nQibn8e6eGwBUN1FIO+B54OofwIFVbNI3AAxdBoxd2eWGh3ZVisQn6k7cKkLE/+3HhdfH66w5llVajYmfHYeTlRgzw90wLdQVnnZmWHM4GRFeNhgT2P7/5wnRYCwCvIexx8gX2ZzbnItA+knW05RxDqgpZcXWk3azbUTWLFjq01jTiYbndQkUJPVALtaawcP8IZ6YFsqyg1mIjDElxAV7ruTpvTPdXg7fyMcXh5Lxxdxw+Di07sKtNV7cfhn/Xs3FTw9HIVot6CO9EMexCbIO/YDIB9myugr2haQImrLi2NCHopvscfUP1fbmjtqBk31fNuTvDuSV1+JiBqtZ5tMkO5yHrRnOp5Uiu0x7PmB5tfZ8IgDwtjeDs6UIC6K8cTa1BLsv52J6mBuO3yxEeY0UowMccSSpEHuu5qGmngVX08JalwnQ294c798Tgld2XFEui/al/0//Gc+zXs3Db7O5dQBLFzxzrepOM2kVfRntSqulemuO/X4hC1X1MqQWVeHzg7fwW2wmFkR54avDyQCAHxcOwphApxbnBRLSboxNAK8o9hj+PCCTsrm2aYqg6SxQVw4k7WEPALBwZpldFUGTjZdhP0MvRUFSDxTuaQN3G1Nkl9UgzNMGK6f11/hC6OtkCSAPxWrzGtpbgaQWD2+MAwCM+vgoUt+b0i5fSgmZZdiVwDJ4rdp5FQdWjPzP70l6GJFl45dL45Azngcq84G8K5qP4mSgqpClIU85rNreWMwK4aoHTk5BzWbUq2+QY+mvFzR6cBUZ5BTcG5MovP/vDTwx0k/jtYwm9YoAIH7leJgYC2DceKG4Zn4EFg3zQZiHDUyM2bK0oiocu3kUsaklyu18HVpfh2lGuJtGkBTTlyrM3zG5nBVLPvGJKjgyNmUXRcOWU+/RHXp8hC++PX4bALBx8WDlsLtrORK8suMKYlNL8NqUQDwU7YP3/72BjafTNLbPk9Tieo6qaPIjm+Iwvr8zvr4/sl3SihdU1MLWzIRSlHeyAkktKusaOqU4d7szEjbOnx0E3PUsIGsA8hLY8PHUYyxoqswHrmxnD4DdaPFpHNLnM5yNqCAdjoKkHkgsNMLxl0ZDMRKhaXDiYGECQHPyd3s7fqtI4/mNvAqt4Uf61DXIYCwQwEjHUIqZaunM6UYgaRWOY4kcLF2AfuNVy+urgIJEzcAp/xogrWI9UTkXNd/HzB6w82M9Tfa+jb/7AXZ++OFMntYQ1/5Njnc7cxPl75vPZWBBlOrOYEphpca6HramGuuzj8FhsI+dxjIfB3OMCXTGwcR85TJ329ZfjJuZGGPZaD+sPZKC/00JgqkJjYlvs6oi4NKvQNwGoDSVLTMyASIeBIavAKw9DNu+bu7VKUF4Zlw/FFfWw9PODHf1dcDJ5CI8tUX1//PdPTeUQ0Z1yZXUajw/cD0fv5xNx+KYPq1uR2ZJNR77+QK87czwwexQmBgL8OqOy/jrUg4ejumDVdP7t/3DkTsil/OY9MUJlFXX4/QrY7VGz3Q7RsaA+0D2GL6CpRzPilUFTdnxQFk6cCkduPQL28bOV5UEwn0g+z6iRBDtjoKkHkpXgKHgas0uonQN+2kvZdWaQ/myS2sQ5GoFnufx9u5EVNc34J1ZIVpjyncl5OC5rZcwM8wNn84NVy7PK6/Fi78naKybWlQFSa2UJueSO2NirrqbpyCXswvdvMtA3lVV8FSRwybeVhezL68m5nK2GGziiHzeDvm8LfJ5G0SUlwOpHqx+hoUzJgU74//+uQ4AeO3PK3C2EmFsEJsfkZjL7nRHOcoxf3Q4wr3stP6GPsP7OeBgYj5MjAVYMMQL1qZt+/+wYnwA7o/y1psunOhQXwXc3Atc3cEKTsoaz3ciKxYcDXuq2xVA7srMTIxhZscuV7zszYDk1m8rFgqUKfn/Xn4Xfj6bhm1xWXjz7+sIcLHUKtKsz7fHU5CYK0FirgR7r+VpvLb+VGqvD5Jkch7/XM7BUF97ODeTvbM9nE8rQUnjdIHzaSWYNMAF+ZJa3MitwJnbxSirluKZsf26b0FsoVhtNMRKNoQ84yxLWJR2Esi9BJTcZo/4n9g2YmvALVIVbLkP7JA5t70NBUm9kGKuxNVsCd7+5zpen9b+J/fyGs05Fo/+FIctS4ZCLBTgx5Psbuu8wV4Ia5I4Yv3JVMjkPHZczMbHc8IgEHDgeR5D3zuk9TekMh5jPj4KEyMB1twfiRBXVbf7s79dRL6kDj8/MkQ5XKknuJVfAS97M4iM6Y5/hxAIWO+QvR8QfLdqea2k8UspBShW/ExhP6uLYc+Xwl5Qqvlef2/ReOomNMcpM2tkNlijgLfB7V9tMXZKDGDpAqNMCTw5oI+5FaaGuEAobH2gM3+IF7zszTDYxw4Woraf0o0EHAVILeF59u99+wiQcoT9lKoNkXSLAAY9DAyYzYJv0mGeHtMPm89lAGDJUUyFAsRnlGms88mcMPR3s8LkL06gVipHrZSNmujjaK4xPGvB9+eQ+H+TWtWDmpRX0ezrvfGGXUlVPR744Rz8nS3wl1oh60HetnhlciAG+bT+Zk9b/HQ2Xfn77cIqvPz7ZWUmX4Wy6nr8uGhwh/z9TieyZKMgFCMhasuB9DMsc17WeTa/qbacnZduH1FtZ+UBuEeyh2s44BrGssWSVqMgqRdSnyvxw8lULB/TFzZmJs1s0XZlOiaiz//+rMbzbXGZyiBJMXHdWK1nKbmwEv7Oliio0D8ssKiS3U265+vTOPkim4NSWdegPGFfzi7XmUmsOzpwPR9LforDwmhvvDlzgNbr5TVSyOS81jAt0g7EVoBbOHs0kZGdg+VrfoevsAifTXYBV5kHVOSx4oIVeYAkF6ivAKRVcEcV3AWqiwnsZ5N0XwLwkghAHsB/+gJg6caGB1q5sp4oMwc23M/cnv1UPBeKYWIswOgAp87YC71HXQWQc4nV4sqOZ8k/JFma69j6AMH3sGDaNdQQreyVXKzFSHt/KuRyHgIBh7LqelzPkWDBD+cAAC9NCsDsgWyIo62ZEKVq30UWImMMbZKcZNbaU9j3XMsp8xW9Ufpcy5b0ukRCL/2egOu5ElzPlWgsj0svxYM/xiLxrUn/6f0V/8YKueU1ePmPKzh+s1C5LClfgj1X8rS2PXSjALVSGcTCHnhDUWwNBExiD4Algii4ztKOZ19g56yCRHbOkmSp0o4DLAGEa1jjIxxwDmbfMTR/QScKknohsdAI3z44EI//fAEAkFJYhYHe7RwkNfYkvTgxAB/tS9K5zq/nMvDixACIhUaI+eCwss6LwoTPjuPL+REw13GX748no7H013jkS1QB1F0fHcdLoZqT4NOLq3pMkPT2bjZUa9OZdLwxI1hjrplczmPmmpOQ1Dbg6Iujet0dTUO6UCDHZd4PcIoEF62ngGtdJZuIW5GHyqIsfP7nMThxZXDhStDfvArCmnw4owRiTgquppSlhy3Qn/JbSWiuHTjpCqbMG3+KbWjcuoJc1hjEZrMkHoVJLPthYRLrNQSvub6RCeAZxQrA+o1lFxl0YWEwiotnGzMTDOvrgB1Lh2F7XBbuHaiaA+bnaIG4dNbDO72xDEaYhzWWj+6LNUfYmL2k/AqUVtXDtpmbS1KZXFkgOsjVCiP8HfDChABU18vw6o7L2HMlD/O/P4uUd6c0O9S9J7mYUao1D1NdjVT2n4KUpLwK3LvuNO4f6o1XJgcCALadz9IIkADg3O0SXZuz9eMy8VC0zx39/W7FSKgKfAY9zJbVVbAepqw4Nr82N4ENJS/LYI/Ev1Xbi6xZciKnQFbk1rHxp4WjYT5PF0JBUi81MdgFQ33tcPZ2CTJLqjHQ+78HEqVV9biUVYZoX3uUNo4XdrUWQ8ABcl73NhtOpeGLQ7f0vufTWzQnz994a5LypLv76eEY9PZBjdc/vGyMkOLryufHbxbh7oieMXG6Vqqqn1NWLVV+qR9JKkBNvQxpxSw4PH6zENNC3QzSxt4oIbMcADCkuaElIgv2sPeDhQ/ww+9qGZnK2A8jAfBJeDmmxgyAsKawsSeqsTequpglCKguaZwbVQTIG1iSibIq9qXXWiaWrGdMZMnm0IiaPBdb63it8aFYz1jc9QIEuYwNOakpBWrK2M/axp81pRBUFWFQ6kUYbVoDSHLYvuVl+t/PygNwj2Bj+90i2dw1GkrXZUV62WrdEPOwNVUGSYqCyRzH4YWJAVg+pi8CV+4FwEYtDDbX/f83o7gaIz5SDWHa/dRdygDN2lSAsYHOyp6MxFyJsnB7T9Ygk+Od3Yk6X1MPQL8/fhtPje2n930OXs+HSChAjJ8D4tJL4eNgpqx/+MxvF1FR14BvjqUog6S4dM2AyNFShEK1kSZDfOxwKbMMRgIONVIZVu281juCJF1Elo2FatVu3NWUsfm2uQmspzzvMhtGXFcOZJ5lD3Vm9oCDPyuxYd9YasPBn2Xau8MyGd1N7/iURCcfe3OcvV2CGy2MtW6tB348h2s5EqwY74+CCnbXzdFShKMvjEZqcRUWrmcT3r3tzWBjZoKEzDK9AZKDhUgr+97jI3017ko5WIh09lRdyVZ1/Z+4VQSe57t9TYyEzDKNXrOErDK4WptCJuexuDElrsKhxAIKkjpIRnE1zqeV4HBSAZaO8kOwmzWySllw2pZaYAujvbHpTLrGMn8nS3CiBnZHT9jC8C2eB+okaoFTkSqxhM5lxeyLEGBD/+r/4/95gbBJoGXFgkCBMburKRA2/m7MfgqEjcuNtF/j5ewhl6t+Vz5k7GdDHZsDVF/d+LMKkNaofq8tY/PGmvb+qDEC4A4og1IAAGcEWLmxoXMO/uwOqqM/4BhEk557APXzvnuTeXdioRECXSxxI68Cy36NR+z/xmm8fiG9BE9vuaSV4KhpsqFZEe54fjtLKpSUV9GtgySe51FYUQcnKzE2n8vAa39ewSdzwpTDFwHgWk45pn55Uvl886NREAmNMHvdaQDse1oRJJ24VaQVJGWX1WDd0WTMG+yFR3+K03gt0ssGO5bGAGA9fOqSCypxQi1r7rcPDsThxAJsjcsEwK4Htj0RjfoGOfZcycWzWy8pP1N3//5vN6Y2muUxAHZuLboFFN5gQ/YKbgCFiUBJKvvuyDijKmmgIBACdn3YOdO+ryqIsvNloxZ60P6mIKkXG9LHDr+dz8SZlKKWV25BdX0DrjXWovj0wE2YNgYzTpZieNmbwcveDIuG+WBLbAY2LBqsM12rjZkQ9w3yxCh/R9RIZXhkk+YJtOlYcgBYNrovlo3ui/t/OItTycVarxdV1iG7rAYett00y02jpoHgoiaBkbo/L2ZDJufx5fyIjm5Wj9Ugk8NIwGl8uW6Py8SLv19WPt99ORd/PDkM2WXshkBbUm+/Pq2/VpD0xIg+QGapni2a4DjW4yO2ZkkmWqOhnvW01EnYo1bChmRo/F7OftY2rqP8vUL1HDwgl6oCsK7GxAIwtWUXBKa2bIihqS1kIitczyhGUNQ4GNt5s+DIwpkFbqRHWjbaD7uv5OLhmD46L5T7ObMgqaCiDlKZXKPW0V8Xc7QCJEX5DHVGAg6Lhvlg4+k0PL89AdPCXLttYp1Np9Pwxt/X8dG9oXjtT1Y/7fntCahrkGNBlBdKq+o1AiRfB3NEettCZCzAnIEecLMxhaVYqOxNsjXXHvb9zJaLiEsvxS9ntXu/4zPKkFpUhTMpxeDV7neU10gx7asTyuf/PHUXBrhbo7xaqgySFDdVTYwFGN9fdYOjRiqDmQld6uplLAJcBrCHuvpqNvy4OLmxEPst9ihOBhpqVMXZmzKxZAGUna/2Q9z95uzRkdOLDfJmwwsS8yq0viCaUyuVwVjAaWSNW/prvMY6NY1Dw5wsRcplq6b1x8pp/WEk4HRmE1o5tb/yjpV6ccxlo/0Q6mGDUf76x8d+dG8Yvj5yC7+cy1Qu87E3Q1pxNS5llnX7IOlaDusFsBQZo6LJ3C1ddiXk4H9Tg+BsJcbxm4XYEpuB1dODu389iU6QXFCBed+dQ6iHNdYvGgye57F443kcTSrUWnfzuQykFLAaR33sW9+TJDQS4MobE3AzvxIAjz4OFrA04bAns8VN75yxCRtj/l/GmcvlQH2ljgCqnM27kjewh0za+LuUFUrU+buU9RJxHMAJ1B5GTZ5z7ItcaMaGuwnNGn83Uy0T27CgSGzDPqeupkuluL1nDwKDpgBtyB5Iuq++TpZIaiZ5wMqpQfi7sTj58ZuFypT8AJBbrqqtNLyfA+7q64CxQboTpLjZqM6rp1OKu20ilTf+ZkPV1W8GAaxkwT2R7nhbbYjdA0O98Mb0YOV1wEdzwpSvDXBnNeL2X8/XurZQDH/U57fYDJy5rXnzJezN/RrPFTXoRgaozmWKIXkAYGZiBCMBB5mch6SmgYKkO2FipjtZkVzO5nEqA6hbjb+nsOX1FY0lNC5rvaWx0AyDzIMBTOmMT9Au6MjpxTxsTZUX3fHppYjS0VPTVIGkFuM+PYZoP3t8+yCrL1NV14CTt7R7oyK8bGBjproYUR+m8NLEAMSnl8LGTIghfezgai3G3RGqCtK+jqoLzhcmBLTYXe5mY4rV04I0gqS7+jkgrTgDyzdfxOQBrt12Qm1FrVSZoamfs4VWult1I/0dkVFSjdSiKryzOxGf3BeGhxqHOdqam+CeCHcs+SkOTpZirFkQgX7Olp3xEQxu37U8uFiJtVLO6/LL2QwUVdbh8I0C/HgyFd8cS9EY967uj3iW8czf2aJNw+0AwFIs1JgLKJVqZ4TscgQCNsxO3LrC0IQYWnPfHU5WYozwd8Txm4V4ZFMc0t6fCoDNr1UUaP5x4SCN4EmXyQNclaMjEnMl3TJI+qtJCu2mRnx4RJlpNszTBm/PCtG7rrsNuynJ88CPJ1PxxEjW211TL9Oaozwp2AXxGaUwMRYgq7QGl7PKcTmrXOf73tXXAW/NGqC8lnC2EuPUK2Mgk/HwtFP15HMcB2tTIUqq6lFeI4WTpQi5klqtIZfkDggEgI0ne/Qdq/matJYVvVXUcFJ/lGWCk1aju12FUZDUiwkEHKaEuGJrXCa+PpqCy1nlmBLq2uyJZFdCDiS1Ddh3LR8z15zEh/eG4UhSARrkPLzszBDta6/s/n5r5gC9X1CedmY4+fJova87WIhw4LkRsBQL2zSeeJ6vDL/dNsKCKC+EqI0ND39zP7Y9EY0g1+53cZfTOJzLxkyI/5s5ANO+Oqm1jomxAEYch5cmBeBUchHe3XMDuxJykKM2XGRLbAa2ns+ETM6jtFqK8Z8dR+p7U3r8eO0jNwrw+M8XIOCA2+9NbXF99X321j/Xm1lT5a6+lAWIkO5ofH9nZca0mnoZTE2M8NF+1fBm9bpK+njamWHpKD98fTQFtwurOqytHUkxh0cf9VIcvz8R3ey6Qa6qm2/v/3tDGSRdyynXSuK0ekZ/uFqb4mJGKe7++rRWL5JCgLMlfnk0Smu5vusVRZC05Kc4jYy3i4b54I0Zwc22v6nTKUV4659EPDHSFzPD3VveoLcSigHHAPZoSiaFtOg2rh89gpGd37I7Rrlge7mH7+oDADh2sxDv7EnEfd+c0bleYUUd7l13WqO7PSGrHBM/P473/2V30O4d6AF7tTHb3i1Uu27p4ryfs2Wbh4dFO/P468mh+N+UII27eRV1DXo/W1dXWs0yBdqZm2CAuzVuvDUJSW9Pwmi1oQbfPTgQV9+ciGA3a40CfupDG3ieVUVXJ6lpeehed/fTmTQA7O5lfYO8xfUzW6iHcnHleDwU7a2xrL9b9wu+CSHAg0O9YdlYhDm1iAU4R26w1NZPj+2HPq3sIQ5wYYFBRkk1yqrr8eL2BI1h410Zz+tPePLXshiN55/PDW9xaL6xkQBLR7HAyKuxLuO1nHJsjmXzkDxsTSEyFsDX0RwuVuw73r/JqIZpoa54frw/AODhmD7Y+HDbCsMq6gWqB0gAsPF0mjKxVGsUSGpZ0eFcCZ757RLkch51Dc1kxSS6GQkBO19UiV0N3ZI26RZB0tq1a+Hj4wOxWIyoqCjExsYaukk9hr+zBTzUJpxnl9UgPkN7zHDM+4dbHEv8ULQ3FkR5wd3GFNG+9rA0UK2eYDcrmIuM4WQlxjt3qyYjVtQ1IDFXgqe3XMTOS80PLehKFIV5bUzZ/hQLjSAyNsIH94bi3oEemBnuhmg/e+VwwkgvW/R1avnuJwAUVekv1NsT5JbX4IjaXKLCyuY/b3ZZDW42yaoEsMBosI8tnhvnD1tzEzw+UjNZQh+H7j3njZDeTDG8O7O0GrVSmXI+0sMxPq1+D0UwdTW7HC9sT8D2C1m479vOvTHH8zzOpBTj66PJGP/pMdzScS5rKrWoSqOUxkf3qjJrfjY3DOGeNhrlDWZFtK4nZXEMuwGbWVqNIzcKMPXLk9gRz753Z4W749DzI7H98WjlzVJzkWpg01BfO3w+NxzLx/TFyZdHY+W0ILhat22onLOVSO9r4z45plFSozmHbmjWgnp3TyICXt+LIzcKkFxQiYc3nsfplCL8czkHUe8eREJmWZvaSbq2Lh8kbd26FStWrMDq1asRHx+PsLAwTJw4EQUF+ouYkdbjOA5Pj9FM0bn5HLvbk1xQgWM3C5FZUo16meYd+J8eHqIxb2hsoBNszEzgYWuGYy+OwuYl2t3ihtD0LuDkL05gV0IOnvntEmrqu8fdIEljYV4bM80J6U6WYnw8JwxfzIvQyqb09izNTDUiY93/1Yv0zLXpCW7mV2DW2lMay3LLmu8leun3BK3etvH9nWFrboLtTwzDM+PY/xV3G1N8MS9cuU7Tu6CEkO5DcQH+wvYEZW0+kbEA1qatv9E3wM0afRzMUV0va7bIake4lFmGixml2BKbifnfn8WHe5Nwq6AS4z87jrSi5of/7b6cg+LGuoYAO9/ZmZsg3NMGM8JYQPTCxAAEuVrhtSmB+t5Gi4OFCWzNhOB5YPFGzWysQa5W8LA1g72FZiCz79kRWHd/JH57LBrGRgJwHAcPW7M7GhKuuLmocO3Nicqsd5LaBmWNrOZIZXJ8eyxFY9kPJ1MBsOQWU788gcM3CrDg+3NYvvki8iV1mLn2FC6kd48eRNKyLh8kffrpp1iyZAkWL16M/v3745tvvoGZmRnWr19v6Kb1GPcN9sTmJVGI6sPuFv1+IQt/J+Rg5ppTWLg+VlkDQmFckBNG+Dvi8POjsGt5DB65qw8+nRuufF1xcusKIr1sMdRXd5HAXQndozeprIZ9gdm04Qt7iI8dHor2hp25Cf43JQiLhvkAAELcrZH63hQM9mEJA1oaWtZdXc4qw4TPjmvUlgKAf6/m6d0mvbgKp5KLYSTg8L8pQcrln94XpnP9aaFuWD29P/556i6D9ZoSQv67ysaMoRW1Ddh/nSVscLEWt+l7TCDglN+h6uZ9dwY/nLjdPg3V4Wp2OWatPYW7vz6tTNutTjEcXvEZvzmWgrA39yMurQQf70vCx/tVaZx9HcxhY2aCM6+OwbbHo5WjE4b0scO/zwzHYyNaWW4A7AZsPyfdN48U2e+aCnCxxOSQ9hmONW+IFwBW0H7zo1EwFxnD0VIzKCuQND/s7uStIqQVV8PO3AT3RGr2oBVV1qFOz/Dt2evOIKmd6k8Sw+rSiRvq6+tx4cIFvPrqq8plAoEA48aNw5kzurux6+rqUFenujCSSFjtHqlUavDsUYq/b+h26DLYyxpvzwzC+M/ZnfenGu+mAap03JFeNvC2M8Vz4/opP0OQszmCJrK764b+XLr2rxGAnxcPwvFbRXjkJ8005etPpuKe8K4/Praw8URuJTZq0z5eOSUAK6ewCZSVdQ2I8LRCjJ89GhoaMNDLBufTSvFPQjZmhjafuammXoZjt4owyJN94Rn637k5DTI5Xt5xDbsu52ostzc3QXFVPU4lF+lt//nbLENjuIc17o10xbfHU+BmI4bYSP9nfmAIS1nfHvukK58fegLavx2rO+/f2RGuOJmsmaHVzVrc5s8yKdgJv53XzON/9nYJzt4uwQBXC41slm1V1yDHlcwSnM7nYJGUjxEB7Ly982JWs9vtvZaHlX9exs/nMmFjKkRZ48iETw8k4XSKZo/HqmmBkEqlyrvn0lYOSdPHz9EMsWmqv7Ew2gvD/OzhZmXS4cfJpCAH7FoaDX9nCxgJOEilUkwd4KQcKQMACZklytIiUpkcj/0cD1QKMF4qhVQmx6cHWAKPScFOeDjGRzlcsDWOJeXD155KbqjrSueI1raB45ubsWdgOTk5cHd3x+nTpxEdrcqm8tJLL+HYsWM4d+6c1jZvvPEG3nzzTa3lmzdvhpkZzRtojpwHnjurP25+sK8Mgxy77OHSotI64I141efjwOODITKIunDdv3oZ8MFlIxTVcrjbR4ZRru2z/3OqgQ8S2L54qJ8MAx30v+83iQIklgkwwkWO2X1aTnxgCDzPyulcKeHwQ5LmP6ipEY8ngmT47Cr7vPP9ZBjqpPq8O9MFuFnOwUnMI75YgGgnOeb5yVEvA4w4oJXlwwgh3RTPA1dLNc8dY9zkmOnd9vPdM2d0f4eKjdj3TUv2ZXEoqOGwoK8cRmodWX+nC3AwR3UyWh3ZAFsT4J1LRiis1e7xCrGV40pp605ermY8Xgpl6bnb09USDj8kCWAnAu7xkWOAneGvH26UcViXyP6d7/GRYaQrj0vFHDbcVP3br4xowOZkI6RUsB2yIqQB3ha6/20F4CHXkdja3YyHqTGPES48wuwN/7mJpurqaixYsADl5eWwstKfeKlL9yTdiVdffRUrVqxQPpdIJPD09MSECROa3RGdQSqV4sCBAxg/fjyEXbSYYWh0NcZ+pp1iGgDum3gXAl267tyL1uzf37JP40Y+K/7Jg0Of8GEIb0XtnI52PVeCr4/exjNj+qKfM0u6wPM85v1wHkW1ZQCACcMGYpyeYoZtxfM81iUdgaS2AT/dMsKlGisUSOoQ7GaFb+6P0Fj3mTOskF9KrRmAyi53/P4Rn40P9t3Em9ODcD4zHQCrsfHZnBBMC1X1FG7LOo7sslpsSTHCC3NHw8pUiOyyGjxzhlVyz6piX3SjBwZhyjBvrb/T0brD+aE7o/3bsbr7/p0KIHfrZexuHJI7865wTAlxafP79B1YgYuZ5ejnZIG536uSTNXKOESPGgdbM93Fjnmex4f7b2FPZhoA4MkpgzCinwN4nsehG4U4eOaSxvo2fhFwsBSh8GyccpmTpQgzwlwRm1qCNfPDMee7c1pDjnXZ+cxove36L6YAeKJGCguRsUadREOaAkC67yZ+OJmGHWlGyOBtEJdeprHOWxc1L42fmDMZHMehzjUHL+24qvHa+P7O2HedzUGbEeqqHMWQXc0B4JAsAW69NaGjPk630pXOEYpRZi3p0kGSg4MDjIyMkJ+fr7E8Pz8fLi66T14ikQgikXZWE6FQaPB/FIWu1JamfJ1UgeSy0X5Ye0Q1abGfizWEwi7c7dKouf37x9IYnLhViDVHknE1W4KSGlmX+Ld4Z89NxKaVYN/1AsyO9ECYpzUivWw1Csey/d9+bf1yfgQWbWATai9nsRNGnqQQOy7lYu5gNp67ul6VIlzQOD6/M45fqUyOhMwyRHrZany58jyP6noZZDwPoUAAUxMjrDueitJqKZ7eyip8cxxw7rWxcLLUHOrwxKi+WPkX+4Lbl1iEm/kVOuerzRnkZdBjoiufH3oC2r8dqzvv39UzgpVBUqSP/R19jmAPOwR7sPPK4yN9kVtWi3OpxciX1CGlqBbRftopxVOLqjD646Mayz7cdwtj+7ti9+VcLNt8SWub57ZfUc4Zmhrqii/nRWgVTLcUCzWCJDtzE6ya1l+rJpKj1Z0lR2gN+y54LAS5qmooNg2Qmvpx4SCYmLAA8r4h3gjzskNqURXe/zcRq2cEw9fBHEn5lVgywhfTQt1wl78jXvr9ssZ7ND2O3vz7GgQch5XT+rfPB+pmusI5orV/v0sHSSYmJhg4cCAOHTqEWbNmAQDkcjkOHTqE5cuXG7ZxPRTHcdjz9HD8ci4dC4f5wM/RAiu2JWD19P4Qd4MAqSXmImNMGuCKP+KzcTVboiyQdymzDH6O5gaZgM/zvMa47T/is7DzUrayhpWCn6P2l+t/MdLfER62pshqkrzh7d2JmB3pgV0JOVixLUHP1h1r3dEUfHrgJh4b4YvXpgRBJuexIz4L6cXVWHMkGQCr+v7nk8OQXqxZByPA2VIrQAJYPZTTyUX492qecoLzpsYaSgpmJkawNW//O6qEkK7PyUqM9+8JQa1UBk+7/z48/9XJLAHMQ+tjkS8pREZJFaL97LXW+7+/r2ktS8qvwM38Cvx6Ll3v+ysycT43zl8rQAKAylrNOngbFw9GqIcNskqrcSmzHCmFlQj1sO4yiZY6y/QwN3xzLAW3Cir1rrN6en88MNRbqyZUgIslAlwsMWmA6kb90RdHK3+/b5An1hxO1qjP1CCT40J6KV74PQEPx/TBhlNpAFgdrrZkUCSdr0sHSQCwYsUKLFy4EIMGDcKQIUPw+eefo6qqCosXLzZ003qs/m5WePfuEADAPZEeuCfSw8Atan9OjVluVv51FU6WIjz+8wXc1ddBZ0XvjlRcWYcD1/O1ljfIeWXCDHcbU7w/O6Tdv8g4jsP2J6JRUy9DYUUdquob8PDGOFTUNqDv//7VWj+ztAbl9TreqJ3xPI9PD7CMS98dv43lY/riyI0CvNjk7lxCZhkWNUktC7AMfvrMGeShkeGu6YxMBwv9tTUIIT2fIitae/KxN8NxAGlNbugoZDeWJrA1E+K7hwZh0fpYVNXLMOGz4xrrfXJvCP48kYAakR0uqI0y0FcX777Bnvjy0C30dbLA53PDMaDx3Li8SdmP3sbEWIAtjw3FK39cwfj+Tpg72AunU4oQl1oMz8obmDZ1yn/q6Vg6yg9/X87BqeRiAEBueS3WHElGZkkN3vz7unK9sup6CpK6uC4/JXnu3Ln4+OOPsWrVKoSHh+PSpUvYu3cvnJ2bz8hFSHNCPVQX0oq6GCeTi7D7ci7C3tyPQ4nagYs6mZzH2dvFKKn6b1HDim0JeGWHdtpWALiRx4bArZreH8P7Of6nv6OPq7UpfB0tEOVrjzGBzi1mX3rnkhHq9aQ9bY5czuNGnkSrBpEu878/q/H8swM3cSWrXOe6x28Wai0b7q9/X40JdMbOZTF659Y9MLT9L5AIIb2btz0bBZBerLtmUVEl+x7ZvGQoBvvYwdlKuyf8+v9NxIwwV8zxleO3JUNwT2NRV/V6bU09OrwPPpkThr+X36UMkAjjYCHCDwsHKYeWD/NzwJMjfdslecW8IV749dGh8HVQFSlO0dFr9V+vH0jH6/JBEgAsX74c6enpqKurw7lz5xAV1TUKlZLua85AT+Xv6rUOlm2OR3mNFI9sitO1GWqlMlxIL8GL2xMw77uziHzrABZ8fxZXs3VfxDenpl6GY2oX+YO8bbFstKoORa2UtcvBovOGf43QE4yNCmDL62Scshq9PrVSGX46k6bxBbD+VComfX4CP55svl5IalEVzt7WTEubUlilNQSu6Vyi16YEYlSAI6aGuGJC/+ZvoIR52mD7E9EayzYsHozP54bj4Zg+erYihJA742PPhu6lFWn3JNU3yJXnSsUIhwnBmnOu5wz0gJmJ5sCfN2cGY9fyGMwM16zfo85KLMTsgR4wNen+Q+W7I3dbVqR4wffnkKPje7O0moKkrq5bBEmEtDeBgMOS4c1fEKcUat/5mf/9WcxedwY7LqrqJZxOKca0r05CKmtbD8s/l3M0nvs4mOPFiYEYE6iZwc7evPOGgN03WPfQyrULIpVzov69mofmKgd8f/w2Vu28hgmfHVN++b+9OxEA8O6eG/j5TBpGfHgEt3Xs3z1XVPWNHmmck3X8ZqFy6GE/JwukvjcFW5YM1egNnDfECxsXD8Ha+yNbNXfOUizEXX0dALB6KKMDnDArwh3GlO+bENLO/BzZcLjkgkqU16jqs1zLKceoj44AAIRGnDLD3JOj/PDmjGDMCHODo6UIT4/VHh5nKRYi1MOm4xtP7phXC/PaiispSOrq6IqA9Fq+jrrHcSuM/eQYiipVmYGyy2pwUW0ceFO6hn7posgYl1mieVfRu/GEaiVW3TH0dTBX3o3qDK7Wpjj+4mh8NT8Cn80Ng725CWaEucFcZAwPG9aOTw4mo8+rezSK8qn7PZ4VNyyqrEfkWwcwY41mSvmVO68ho6Qa7+5J1NpWEQytnNYfT45S9aopetymhbqB4zhwHIc18yMR5mmDt2cNgNUdJNz4dG4Ynh3XDzuWxrR5W0IIaS1vezMEOFuiXibXuBH02p9XlT0Mfo4Wykye1qZCLBzmgy/nRyD2tbHtkkSCdL6WgqTmEkeQroGCJNJrRXqp5t/MCnfTuc77/97A6ZQiFEhqEfP+YY3X7JsMAXtkU5zOQCmrtBqLN8Ti8I18bInNQP9V+7DnSq5W/QqvxiEZjw73BQBYio2x7Ylorew6Hc3L3gzTw9xwd4QHYv83Dl/OZ3WT3Gw0x8mv3HkVBRLNIQQ8z0NSo1nJ+rKe+UQHEwuweqdmzYnUIjZmf4CbFRwsRPC21/ySsTVXBUNe9mbYuSwGDwy9s5pGTpZiPDvOHy7WVBWdENJxOI7D3ZFsWNy2uExlT3xijqpWi745Q70t81xP0sdBOyOt0IhTLt90Og2FFS3XsSKGQ0ES6bX8nS0wKdgFJkYCLNYzF+X3C1lY8P05jVTYit6VtfdHaq3/0PpYVNU1oLCiDl8euoXyaikWfH8OR5IK8cL2y3i1MUnDR/uSkNckwHBszKw2wN0aqe9NwZU3Jho825p6Wll3G80eLZmcx3fHb6NWqqoin1Vag9JqzSCpOZvOpCsvGOob5MgqZb1rii+Rnx4eorF+VB/t9LmEENLVzQp3h7GAw8WMMpxMLkJJVT3qG4doPzuuH1aM9zdwC0l7i2kc0q3w3Dh/3HhrMr5qvPFY1yDH3O/OGKJppJW6fApwQjoKx3FY90AkaqXyFie2nkwuUv5+/1Bv5Rfae/eEID69FPkVdcpepGM3C/HnxWwcuJ6vTGUNaGaycbESI79JkBSsdiexK949VK/T5GQpQkFFHX44mYofTqZi6Sg/vDQpEJcyyzS2uT/KC7/qGZansGxzPMYFOSPUwwZyHjA3MYJj4wRmb3tzLBrmg42n0wCwwJYQQrobF2sxpoW64q9LOTh5S/V94mFrimfHUYDUE5mLjLFreQyKq+ohqZFiQn8XGAk49FP7HrtdWIXbhZUtDv8nhkE9SaRX4zhOK0Dyd2bJAT66N1Sr5s7sSA88OVI1V2b+EC98NCcM6xcOUs4l+v7EbZ21j9RJZXJlkLT1saGIfW1sl6+XMMrfAff2kWHdgnCtnrevj6agsKJOmeVvVrgbdj99F54Zp5pwbCFS3ZOZGKzKQLfnSh5WbEtQDrXzcTDXCBKfG+ePWeFu+PXRqC4ZPBJCSGsM7sOycn57/DYe/DEWADDUl3rHe7JQDxuMDnDCzHB35bWGyNgIC6NVw8RX77oGn1d2Y/GGWEM1k+hBQRIhjTYuHgxfR3N8PCcMHMdhziBP7Foeg6mhrgCAN6b3xyf3hensdTI2EuCtWQMAABczyiAWNv9fKy69VDksLcDFEk466mJ0NcZGAgx34TEuyAmLY3zw2AhfjddvFVQgKb8CALsYCHazVg4hBNgQuidG+mH19P749sFBWu+/5CeWdj3AWbOGkbWZEJ/Pi9AaukAIId3JLB3puhXlFUjvsmp6MCYPYKneTzT2LB5JKkQZpQXvUihIIqTRqAAnHH5+lEZaVY7j8MXccOxYOgwPRfs0u/3UEFfl74oaRy3xsDXt8j1IuoiFRliqln0OADaeSsPRJDbk0L8x0OE4DuMb6xbNHeyJVyYH6p3/pTCimWKwhBDSXZmLjLH/uREay0bS+a5XMhJwOnsRdZUeIYZDQRIhLTA2EiDSy1aZnrW59aaFuja7DsDm6VibCuFgYYI1CyK77RAyGzPN7H771YYY+jupeoPWLojEr49GYe5gT431v39oEDzttNObzwjTnWmQEEK6O3+1nnI7cxNY3kH5AtIzqA9BV0gpqDJAS4g+FCQR0o6C3VRzmPo56Z6IOTXEFedeG4uTL49BuKdNJ7Wsc1mbqb74TYwFiOnroJXKfHx/Z5x4aQy+mBeuXDbMz77FYJQQQrqzD2eHwkpsjG8eGGjophADUi8/MSmYDb1Lpp6kLoWCJELa0fB+qnkzr0/rjw9mh2BsoBMivWyUy23MTCAWGkEsbD6jXnfw+xPRuCdCc5z9Mzqqwzdnpto4fepFIoT0dPcN9kTC6gkY0pjIgfROw/zs8frUIGx7PBoxjdcOKVRgtkuhFOCEtKMB7taIfW0s7MxNYNzYczJ3sBc+O3AT8RllAABnK8PWPmpPg3zsMMjHDoP72GFLbAamh7phSZOEDq1x6PmRuJhRhtmR2hObCSGkp+muw6xJ++E4Tlk8vkHO5jFTT1LXQkESIe1MV6a6+UO8cDAxH1NDXWFv4AKxHWH+EC/MH+J1x9v7OVrAj+pEEEII6YX6Ng7PzyypRq1U1iNGmvQEFCQR0glcrMXY/fRwQzeDEEIIIV2Mo4UIlmJjVNQ2IK24CoEuVoZuEgHNSSKEEEIIIcRgOI5T9ibdSYa7hMwy7LmS297N6vUoSCKEEEIIIcSAFEPOk9uYvEEu5zFz7Sks/TUeJxsL0x68no991/LavY29DQVJhBBCCCGEGJCiJ+mzgzdb1SuUXFCJH07cRmKeRLnsgR/P4WhSAR79KQ6P/3wB5TXSDmtvb0BzkgghhBBCCDEg9eRFS3+Nx5U3JjRbbHjRhlhkldbAsklR2kUbzit/zyuvhbUpFSy+U9STRAghhBBCiAH1bVKA/vCNAr3rSmVyZJXWAAAq6hr0rldQUatzW/Xfvzx0C98eS4Fczre1yT0e9SQRQgghhBBiQJ62prA3N0FxVT0AYP+1fI1i6wq1UhkCV+5t1XvmS+o0nq8/mYq3d1/Hk6P88OLEQDz44zmcvV0CALAUCzHU1w4lVfUY5EOFjgHqSSKEEEIIIcSgjI0E2PZENN6aNQAAEJdeonO9c6m6l6e+NwU/LhyksSxfotmT9P6/NyDngbVHUpCUV6EMkADWc/XwxvO495szlPShEQVJhBBCCCGEGJifowWmhbgCYL1Am06naa2TkFmmc1uO4zA2yBnrFw1CmKcNAKBALUhqkMlRrzbUbltcpsb2BxPzkVZcDQD4YO8N8DwNv6MgiRBCCCGEkC7AxkyVaGH1rmv48WSqxus5ZWwu0nPj/PHG9P6wEBlj/SJVD9KYQGfcE8GG6akPtytrkumu6fuqu11YhWs5Er2v9xY0J4kQQgghhJAugOM4LI7xwYZTaQCAt/65jsRcCZwsRRAZGyGnnPUOudqIcd8gTzwU7QOBgNN4D2crMQAgV60nqaxadzrw/00Jwjt7ErWW/3I2HTPD3THU1w4cx+nYsuejIIkQQgghhJAuYvX0YEwLdcXsdWcAAL9fyNJax9WaBUJNAyQA8LIzA8CG5kllcgiNBDiVXKTcbs2CCGSV1iDYzQp9nSwhlcux/1o+LqkN5fvtfCZ+O8+G5Pk6mmPT4iGwtzBBSVU9PGzN2vXzdlUUJBFCCCGEENKFRHrZYqC3LS6kl+p8PcDFUu+2Pg6qIGbT6TRkl9Uoe6Zyy2sx0NsOA71V6y8d1RdLR/VFckEl3t2TqJV+/HZhFR748RwsxcZIzK3AwRUj0cfB/M4/XDdBc5IIIYQQQgjpQjiOw//NDNaqnwQA1qZCOFmK9W5rZqLqA/n9QpYyQGpJXycLZS9UU+nF1biaLYFMzusN3HoaCpIIIYQQQgjpYoLdrHFwxUj88eQwjeUT+ju3uO1H94YCAG7kVWgsf/+ekGa3a00P0ZbYDPi8shs74rWHAfYkFCQRQgghhBDSRQ30tsXpV8Zg1/IYLBrmg6fH9mtxGz8dPVAAMCtCu0CtuvujvFp8b0VP0optCUgrqmpx/e6K5iQRQgghhBDShbnZmMLNxhShHjatWj/YzUrj+ZyBHvCyM4NYaNTsdsZGmv0ni2N8kC+pxZ4rugvMHkzMx6PDfVvVpu6GgiRCCCGEEEJ6EJGxEd67JwSv7riCD2aHYO7glnuIFNYuiMThGwV45+4BEAuNUFXXgEkDXJFTVoP3/72hsW5acRWkMjlqpDJYiYV63rF7oiCJEEIIIYSQHmb+EC/MH9L64Ehhaqgrpoa6Kp+bi4wxI8wNZdX1WkHSL2czEJtagoySamxYNATRfvb/ud1dBc1JIoQQQgghhDTLxswESW9PwmtTAvHjwkHK5TfzK1ErlePxn+NQVFlnwBa2LwqSCCGEEEIIIS0SGRvhsRF+GBvkjBXj/TVek9Q2YNDbB7FwfSwq6xoM1ML2Q0ESIYQQQgghpE30Zdk7drMQ3x2/3cmtaX8UJBFCCCGEEELabGIwq9nUNHX4l4duobSq3hBNajcUJBFCCCGEEELa7It5EfhkThhemRyo9drPZ9MN0KL2Q0ESIYQQQgghpM3EQiPMHugBS7EQ/ZoUsP30wE0UVNQaqGX/HQVJhBBCCCGEkP/k10ejsGZBBLY9Hg1jAQcAiEsrNXCr7hwFSYQQQgghhJD/xMlKjGmhbhjSxw73DvQAACTmSgzcqjtHQRIhhBBCCCGk3QS5WgEAvjqcDJmcN3Br7gwFSYQQQgghhJB2E+xmpfz9TEqxAVty5yhIIoQQQgghhLSbSC9b5e838ysM2JI7R0ESIYQQQgghpN0IBByWjfYDAFzL6Z7zkihIIoQQQgghhLSrGD8HAMDRpALwfPebl0RBEiGEEEIIIaRdRXrbQsABxVX1KKqsN3Rz2oyCJEIIIYQQQki7EguN4GlnBgBIKawycGvajoIkQgghhBBCSLvr62gBAPg1NhPdbcQdBUmEEEIIIYSQdudtbw4A2HstHwklnIFb0zYUJBFCCCGEEELa3dggJ+XvsYUUJBFCCCGEEEJ6uZi+Dvhz6TC4WovhboZuleWOgiRCCCGEEEJIh4jwssXRFcMx1UsOjus+vUkUJBFCCCGEEEI6jEDQfYIjBQqSCCGEEEIIIUQNBUmEEEIIIYQQooaCJEIIIYQQQghRQ0ESIYQQQgghhKihIIkQQgghhBBC1FCQRAghhBBCCCFqKEgihBBCCCGEEDUUJBFCCCGEEEKIGgqSCCGEEEIIIUQNBUmEEEIIIYQQooaCJEIIIYQQQghRQ0ESIYQQQgghhKihIIkQQgghhBBC1FCQRAghhBBCCCFqKEgihBBCCCGEEDUUJBFCCCGEEEKIGgqSCCGEEEIIIUSNsaEb0NF4ngcASCQSA7cEkEqlqK6uhkQigVAoNHRzehzavx2L9m/Hov3bsWj/dizavx2P9nHHov3bsbrS/lXEBIoYQZ8eHyRVVFQAADw9PQ3cEkIIIYQQQkhXUFFRAWtra72vc3xLYVQ3J5fLkZOTA0tLS3AcZ9C2SCQSeHp6IjMzE1ZWVgZtS09E+7dj0f7tWLR/Oxbt345F+7fj0T7uWLR/O1ZX2r88z6OiogJubm4QCPTPPOrxPUkCgQAeHh6GboYGKysrgx8gPRnt345F+7dj0f7tWLR/Oxbt345H+7hj0f7tWF1l/zbXg6RAiRsIIYQQQgghRA0FSYQQQgghhBCihoKkTiQSibB69WqIRCJDN6VHov3bsWj/dizavx2L9m/Hov3b8Wgfdyzavx2rO+7fHp+4gRBCCCGEEELagnqSCCGEEEIIIUQNBUmEEEIIIYQQooaCJEIIIYQQQghRQ0ESIYQQQgghhKihIKmdrV27Fj4+PhCLxYiKikJsbGyz62/fvh2BgYEQi8UICQnBnj17Oqml3ct7772HwYMHw9LSEk5OTpg1axaSkpKa3Wbjxo3gOE7jIRaLO6nF3csbb7yhta8CAwOb3YaO3bbx8fHR2sccx2HZsmU616fjt3nHjx/H9OnT4ebmBo7j8Ndff2m8zvM8Vq1aBVdXV5iammLcuHG4detWi+/b1nN4T9Xc/pVKpXj55ZcREhICc3NzuLm54aGHHkJOTk6z73kn55meqqXjd9GiRVr7atKkSS2+Lx2/TEv7V9e5mOM4fPTRR3rfk45fpjXXY7W1tVi2bBns7e1hYWGB2bNnIz8/v9n3vdNzdkeiIKkdbd26FStWrMDq1asRHx+PsLAwTJw4EQUFBTrXP336NObPn49HHnkEFy9exKxZszBr1ixcvXq1k1ve9R07dgzLli3D2bNnceDAAUilUkyYMAFVVVXNbmdlZYXc3FzlIz09vZNa3P0EBwdr7KuTJ0/qXZeO3bY7f/68xv49cOAAAGDOnDl6t6HjV7+qqiqEhYVh7dq1Ol//8MMP8eWXX+Kbb77BuXPnYG5ujokTJ6K2tlbve7b1HN6TNbd/q6urER8fj5UrVyI+Ph47duxAUlISZsyY0eL7tuU805O1dPwCwKRJkzT21ZYtW5p9Tzp+VVrav+r7NTc3F+vXrwfHcZg9e3az70vHb+uux5577jn8/fff2L59O44dO4acnBzcc889zb7vnZyzOxxP2s2QIUP4ZcuWKZ/LZDLezc2Nf++993Suf9999/FTp07VWBYVFcU//vjjHdrOnqCgoIAHwB87dkzvOhs2bOCtra07r1Hd2OrVq/mwsLBWr0/H7n/3zDPP8H5+frxcLtf5Oh2/rQeA//PPP5XP5XI57+Liwn/00UfKZWVlZbxIJOK3bNmi933aeg7vLZruX11iY2N5AHx6erreddp6nuktdO3fhQsX8jNnzmzT+9Dxq1trjt+ZM2fyY8aMaXYdOn51a3o9VlZWxguFQn779u3KdRITE3kA/JkzZ3S+x52eszsa9SS1k/r6ely4cAHjxo1TLhMIBBg3bhzOnDmjc5szZ85orA8AEydO1Ls+USkvLwcA2NnZNbteZWUlvL294enpiZkzZ+LatWud0bxu6datW3Bzc4Ovry/uv/9+ZGRk6F2Xjt3/pr6+Hr/88gsefvhhcByndz06fu9Mamoq8vLyNI5Ra2trREVF6T1G7+QcTlTKy8vBcRxsbGyaXa8t55ne7ujRo3ByckJAQACefPJJFBcX612Xjt87l5+fj927d+ORRx5pcV06frU1vR67cOECpFKpxrEYGBgILy8vvcfinZyzOwMFSe2kqKgIMpkMzs7OGsudnZ2Rl5enc5u8vLw2rU8YuVyOZ599FjExMRgwYIDe9QICArB+/Xrs3LkTv/zyC+RyOYYNG4asrKxObG33EBUVhY0bN2Lv3r1Yt24dUlNTMXz4cFRUVOhcn47d/+avv/5CWVkZFi1apHcdOn7vnOI4bMsxeifncMLU1tbi5Zdfxvz582FlZaV3vbaeZ3qzSZMm4aeffsKhQ4fwwQcf4NixY5g8eTJkMpnO9en4vXObNm2CpaVli8PB6PjVput6LC8vDyYmJlo3TFq6Hlas09ptOoOxwf4yIXdo2bJluHr1aotjgaOjoxEdHa18PmzYMAQFBeHbb7/FW2+91dHN7FYmT56s/D00NBRRUVHw9vbGtm3bWnV3jbTNjz/+iMmTJ8PNzU3vOnT8ku5AKpXivvvuA8/zWLduXbPr0nmm9ebNm6f8PSQkBKGhofDz88PRo0cxduxYA7as51m/fj3uv//+FhPj0PGrrbXXY90V9SS1EwcHBxgZGWll78jPz4eLi4vObVxcXNq0PgGWL1+Of/75B0eOHIGHh0ebthUKhYiIiEBycnIHta7nsLGxgb+/v959RcfunUtPT8fBgwfx6KOPtmk7On5bT3EctuUYvZNzeG+nCJDS09Nx4MCBZnuRdGnpPENUfH194eDgoHdf0fF7Z06cOIGkpKQ2n48BOn71XY+5uLigvr4eZWVlGuu3dD2sWKe123QGCpLaiYmJCQYOHIhDhw4pl8nlchw6dEjjbrC66OhojfUB4MCBA3rX7814nsfy5cvx559/4vDhw+jTp0+b30Mmk+HKlStwdXXtgBb2LJWVlUhJSdG7r+jYvXMbNmyAk5MTpk6d2qbt6PhtvT59+sDFxUXjGJVIJDh37pzeY/ROzuG9mSJAunXrFg4ePAh7e/s2v0dL5xmikpWVheLiYr37io7fO/Pjjz9i4MCBCAsLa/O2vfX4bel6bODAgRAKhRrHYlJSEjIyMvQei3dyzu4UBksZ0QP99ttvvEgk4jdu3Mhfv36df+yxx3gbGxs+Ly+P53mef/DBB/lXXnlFuf6pU6d4Y2Nj/uOPP+YTExP51atX80KhkL9y5YqhPkKX9eSTT/LW1tb80aNH+dzcXOWjurpauU7T/fvmm2/y+/bt41NSUvgLFy7w8+bN48ViMX/t2jVDfIQu7fnnn+ePHj3Kp6am8qdOneLHjRvHOzg48AUFBTzP07HbXmQyGe/l5cW//PLLWq/R8ds2FRUV/MWLF/mLFy/yAPhPP/2Uv3jxojK72vvvv8/b2NjwO3fu5C9fvszPnDmT79OnD19TU6N8jzFjxvBfffWV8nlL5/DepLn9W19fz8+YMYP38PDgL126pHFOrqurU75H0/3b0nmmN2lu/1ZUVPAvvPACf+bMGT41NZU/ePAgHxkZyffr14+vra1Vvgcdv/q1dH7geZ4vLy/nzczM+HXr1ul8Dzp+dWvN9dgTTzzBe3l58YcPH+bj4uL46OhoPjo6WuN9AgIC+B07diift+ac3dkoSGpnX331Fe/l5cWbmJjwQ4YM4c+ePat8beTIkfzChQs11t+2bRvv7+/Pm5iY8MHBwfzu3bs7ucXdAwCdjw0bNijXabp/n332WeW/hbOzMz9lyhQ+Pj6+8xvfDcydO5d3dXXlTUxMeHd3d37u3Ll8cnKy8nU6dtvHvn37eAB8UlKS1mt0/LbNkSNHdJ4TFPtQLpfzK1eu5J2dnXmRSMSPHTtWa797e3vzq1ev1ljW3Dm8N2lu/6ampuo9Jx85ckT5Hk33b0vnmd6kuf1bXV3NT5gwgXd0dOSFQiHv7e3NL1myRCvYoeNXv5bODzzP899++y1vamrKl5WV6XwPOn51a831WE1NDb906VLe1taWNzMz4++++24+NzdX633Ut2nNObuzcTzP8x3TR0UIIYQQQggh3Q/NSSKEEEIIIYQQNRQkEUIIIYQQQogaCpIIIYQQQgghRA0FSYQQQgghhBCihoIkQgghhBBCCFFDQRIhhBBCCCGEqKEgiRBCCCGEEELUUJBECCGEEEIIIWooSCKEENIjLFq0CLNmzTJ0MwghhPQAxoZuACGEENISjuOafX316tX44osvwPN8J7WIEEJIT0ZBEiGEkC4vNzdX+fvWrVuxatUqJCUlKZdZWFjAwsLCEE0jhBDSA9FwO0IIIV2ei4uL8mFtbQ2O4zSWWVhYaA23GzVqFJ566ik8++yzsLW1hbOzM77//ntUVVVh8eLFsLS0RN++ffHvv/9q/K2rV69i8uTJsLCwgLOzMx588EEUFRV18icmhBBiSBQkEUII6bE2bdoEBwcHxMbG4qmnnsKTTz6JOXPmYNiwYYiPj8eECRPw4IMPorq6GgBQVlaGMWPGICIiAnFxcdi7dy/y8/Nx3333GfiTEEII6UwUJBFCCOmxwsLC8Prrr6Nfv3549dVXIRaL4eDggCVLlqBfv35YtWoViouLcfnyZQDAmjVrEBERgXfffReBgYGIiIjA+vXrceTIEdy8edPAn4YQQkhnoTlJhBBCeqzQ0FDl70ZGRrC3t0dISIhymbOzMwCgoKAAAJCQkIAjR47onN+UkpICf3//Dm4xIYSQroCCJEIIIT2WUCjUeM5xnMYyRdY8uVwOAKisrMT06dPxwQcfaL2Xq6trB7aUEEJIV0JBEiGEENIoMjISf/zxB3x8fGBsTF+RhBDSW9GcJEIIIaTRsmXLUFJSgvnz5+P8+fNISUnBvn37sHjxYshkMkM3jxBCSCehIIkQQghp5ObmhlOnTkEmk2HChAkICQnBs88+CxsbGwgE9JVJCCG9BcdTeXJCCCGEEEIIUaLbYoQQQgghhBCihoIkQgghhBBCCFFDQRIhhBBCCCGEqKEgiRBCCCGEEELUUJBECCGEEEIIIWooSCKEEEIIIYQQNRQkEUIIIYQQQogaCpIIIYQQQgghRA0FSYQQQgghhBCihoIkQgghhBBCCFFDQRIhhBBCCCGEqPl/pl2xATfWnu8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, sol_train[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, sol_train[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, sol_dev[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, sol_dev[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 422, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, sol_test[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, sol_test[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 439, + "metadata": {}, + "outputs": [], + "source": [ + "nx = 2\n", + "nsim = 2000\n", + "nsteps = 2\n", + "nbatch = nsim//nsteps\n", + "length = (nsim//nsteps) * nsteps\n", + "bs = 10\n", + "x_train_lvs = sol_train.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", + "train_data_lvs = DictDataset({'X': x_train_lvs, 'xn': x_train_lvs[:, 0:1, :]}, name='train')\n", + "train_loader_lvs = DataLoader(train_data_lvs, batch_size=bs,\n", + " collate_fn=train_data_lvs.collate_fn, shuffle=True)\n", + "\n", + "x_dev_lvs = sol_test.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", + "dev_data_lvs = DictDataset({'X': x_dev_lvs, 'xn': x_dev_lvs[:, 0:1, :]}, name='dev')\n", + "dev_loader_lvs = DataLoader(dev_data_lvs, batch_size=bs,\n", + " collate_fn=dev_data_lvs.collate_fn, shuffle=True)\n", + "\n", + "x_test_lvs = sol_test.squeeze(1)[:length].reshape(1, nsim, nx)\n", + "test_data_lvs = DictDataset({'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}, name='test')\n", + "test_data_lvs_2 = {'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}" + ] + }, + { + "cell_type": "code", + "execution_count": 444, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 1, 2])" + ] + }, + "execution_count": 444, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data_lvs_2['xn'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train SDE on this system" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraSDE(nn.Module):\n", + " def __init__(self, block, insize=2, outside=2, noise_type=\"diagonal\"):\n", + " super().__init__()\n", + " self.block = block \n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.g_params = nn.Parameter(torch.randn(10, 2), requires_grad=True) # Learnable parameters\n", + " self.in_features = 0\n", + " self.out_features = 0\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + "\n", + " def f(self, t, y):\n", + "\n", + " x1 = y[:, [0]]\n", + " x2 = y[:, [-1]]\n", + "\n", + " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", + " dx2 = self.delta*self.block(y) - self.gamma*x2\n", + "\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, y):\n", + " return self.g_params\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "fx = LotkaVolterraSDE(block=net)\n", + "\n", + "\n", + "class BasicSDEIntegrator(integrators.Integrator): \n", + " \"\"\"\n", + " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", + " Returns a single tensor of size (t, batch_size, state_size).\n", + "\n", + " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", + " Currently only supports Euler integration. Choice of integration method is dependent \n", + " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", + " \"\"\"\n", + " def __init__(self, block ): \n", + " \"\"\"\n", + " :param block: (nn.Module) The BasicSDE block\n", + " \"\"\"\n", + " super().__init__(block) \n", + "\n", + "\n", + " def integrate(self, x): \n", + " \"\"\"\n", + " x is the initial datastate of size (batch_size, state_size)\n", + " t is the time-step vector over which to integrate\n", + " \"\"\"\n", + " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", + " x = x.squeeze(1) #remove time step \n", + " \n", + " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", + " ys = ys.permute(1, 0, 2)\n", + " return ys \n", + "\n", + "integrator = BasicSDEIntegrator(fx) \n", + "# integrate UDE model\n", + "# create symbolic UDE model\n", + "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", + "dynamics_model_sde = model" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0 train_loss: 0.023017268627882004\n", + "epoch: 1 train_loss: 0.018558792769908905\n", + "epoch: 2 train_loss: 0.016840744763612747\n", + "epoch: 3 train_loss: 0.0161198228597641\n", + "epoch: 4 train_loss: 0.015907132998108864\n", + "epoch: 5 train_loss: 0.01590757444500923\n", + "epoch: 6 train_loss: 0.01598076894879341\n", + "epoch: 7 train_loss: 0.015865400433540344\n", + "epoch: 8 train_loss: 0.015806077048182487\n", + "epoch: 9 train_loss: 0.0158010795712471\n", + "epoch: 10 train_loss: 0.015765946358442307\n", + "epoch: 11 train_loss: 0.0157708078622818\n", + "epoch: 12 train_loss: 0.01589883863925934\n", + "epoch: 13 train_loss: 0.0157649964094162\n", + "epoch: 14 train_loss: 0.015724308788776398\n", + "epoch: 15 train_loss: 0.015662459656596184\n", + "epoch: 16 train_loss: 0.015650611370801926\n", + "epoch: 17 train_loss: 0.015621047466993332\n", + "epoch: 18 train_loss: 0.015543315559625626\n", + "epoch: 19 train_loss: 0.015556756407022476\n", + "epoch: 20 train_loss: 0.015441193245351315\n", + "epoch: 21 train_loss: 0.015387657098472118\n", + "epoch: 22 train_loss: 0.015203121118247509\n", + "epoch: 23 train_loss: 0.014927961863577366\n", + "epoch: 24 train_loss: 0.014727238565683365\n", + "epoch: 25 train_loss: 0.014125929214060307\n", + "epoch: 26 train_loss: 0.013562526553869247\n", + "epoch: 27 train_loss: 0.013236057944595814\n", + "epoch: 28 train_loss: 0.013077814131975174\n", + "epoch: 29 train_loss: 0.013006513938307762\n", + "Interrupted training loop.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 398, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = variable(\"X\")\n", + "xhat = variable('xn')[:, :-1, :]\n", + "\n", + "# trajectory tracking loss\n", + "reference_loss = (xhat == x)^2\n", + "reference_loss.name = \"ref_loss\"\n", + "\n", + "\n", + "\n", + "# finite difference loss\n", + "fd_loss = 2.0*((xFD == xhatFD)^2)\n", + "fd_loss.name = 'FD_loss'\n", + "\n", + "# %%\n", + "objectives = [reference_loss, fd_loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([dynamics_model_sde], loss)\n", + "# plot computational graph\n", + "problem.show()\n", + "\n", + "# %%\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "trainer = Trainer(\n", + " problem,\n", + " train_loader_lvs,\n", + " dev_loader_lvs,\n", + " test_data_lvs,\n", + " optimizer,\n", + " patience=50,\n", + " warmup=0,\n", + " epochs=50,\n", + " eval_metric=\"dev_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"dev_loss\",\n", + " test_metric=\"dev_loss\",\n", + " device='cpu', \n", + " epoch_verbose=1\n", + ")\n", + "# %%\n", + "best_model = trainer.train()\n", + "problem.load_state_dict(best_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 426, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 1, 2])" + ] + }, + "execution_count": 426, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo = next(iter(train_loader_lvs))\n", + "foo['xn'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 425, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1000, 1, 2])" + ] + }, + "execution_count": 425, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data_lvs_2['xn'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Batch sizes not consistent.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[427], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model_sde\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data_lvs_2\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", + "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." + ] + } + ], + "source": [ + "test_outputs = dynamics_model_sde(test_data_lvs_2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": {}, + "outputs": [], + "source": [ + "true_traj = test_data_lvs_2['X'].detach().numpy().reshape(-1, 2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neuromancer8", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5ed251d0abe2f5832ef54df7a9c504ba6955f1e5 Mon Sep 17 00:00:00 2001 From: "Birmiwal, Rahul R" Date: Mon, 13 May 2024 09:40:42 -0700 Subject: [PATCH 4/6] refactoring torchsde integration and rewrote sde example notebook to make it more motivating --- examples/SDEs/basic_sde.ipynb | 182 --- examples/SDEs/latent_sde_lorenz_system.ipynb | 378 ----- examples/SDEs/sde_walkthrough.ipynb | 1318 ++++++++++++++++++ examples/SDEs/sde_walkthrough_draft.ipynb | 545 ++++++++ src/neuromancer/dynamics/sde.py | 255 ++++ src/neuromancer/modules/blocks.py | 204 +-- 6 files changed, 2125 insertions(+), 757 deletions(-) delete mode 100644 examples/SDEs/basic_sde.ipynb delete mode 100644 examples/SDEs/latent_sde_lorenz_system.ipynb create mode 100644 examples/SDEs/sde_walkthrough.ipynb create mode 100644 examples/SDEs/sde_walkthrough_draft.ipynb create mode 100644 src/neuromancer/dynamics/sde.py diff --git a/examples/SDEs/basic_sde.ipynb b/examples/SDEs/basic_sde.ipynb deleted file mode 100644 index 7dd6e497..00000000 --- a/examples/SDEs/basic_sde.ipynb +++ /dev/null @@ -1,182 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TorchSDE + Neuromancer: Basic (Explicit) Integration of Stochastic Differential Equations\n", - "\n", - "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/demo.ipynb\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports\n", - "\n", - "If necessary, install torchsde library" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install torchsde" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "import matplotlib.pyplot as plt\n", - "from torch.utils.data import DataLoader\n", - "\n", - "from neuromancer.system import Node, System\n", - "from neuromancer.dynamics import integrators, ode\n", - "from neuromancer.trainer import Trainer\n", - "from neuromancer.problem import Problem\n", - "from neuromancer.loggers import BasicLogger\n", - "from neuromancer.dataset import DictDataset\n", - "from neuromancer.constraint import variable\n", - "from neuromancer.loss import PenaltyLoss\n", - "from neuromancer.modules import blocks\n", - "\n", - "torch.manual_seed(0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset: \n", - "Below we generate some data. We assume the process has a state size of 1, batch size of 5 and 100 timesteps. \n", - "\n", - "***Note: For visualization purposes, we recommend ONLY to use a state size of 1***" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size, state_size, t_size = 5, 1, 100\n", - "ts = torch.linspace(0, 1, t_size)\n", - "y0 = torch.full(size=(batch_size, state_size), fill_value=0.1)\n", - "y0.shape\n", - "my_data = {'y': y0, 't':ts}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Strongly recommended to refer to TorchSDE documentation. For the basic/explicit SDE case, TorchSDE requires user to define the drift (f) and diffusion (g) functions. We define an example f and g below: " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def f(t, y):\n", - " return torch.sin(t) + 0.1 * y\n", - " \n", - "def g(t, y):\n", - " return 0.3 * torch.sigmoid(torch.cos(t) * torch.exp(-y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neuromancer Integration: \n", - "Now define Neuromancer variables and components" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "t = variable('t')\n", - "y = variable('y')\n", - "block = blocks.BasicSDE(f, g, t, y) #We use this block for the basic/explicit SDE case where f and g are defined \n", - "integrator = integrators.BasicSDEIntegrator(block) #instantiate integrator for the basic/explicit case \n", - "model = Node(integrator, input_keys=['y','t'], output_keys=['ys']) #define Neuromancer Node to wrap integrator. Output of the " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualization \n", - "Visualize ys which represents ts number of samples from a stochasic process parameterized by f and g for the provided batch_size" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Plotting helper function\n", - "def plot(ts, samples, xlabel, ylabel, title=''):\n", - " ts = ts.cpu()\n", - " samples = samples.squeeze().t().cpu()\n", - " plt.figure()\n", - " for i, sample in enumerate(samples):\n", - " plt.plot(ts, sample, marker='x', label=f'sample {i}')\n", - " plt.title(title)\n", - " plt.xlabel(xlabel)\n", - " plt.ylabel(ylabel)\n", - " plt.legend()\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "neuromancer3", - "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": "3.1.undefined" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/SDEs/latent_sde_lorenz_system.ipynb b/examples/SDEs/latent_sde_lorenz_system.ipynb deleted file mode 100644 index 38143888..00000000 --- a/examples/SDEs/latent_sde_lorenz_system.ipynb +++ /dev/null @@ -1,378 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TorchSDE + Neuromancer: Latent Stochastic Differential Equations (System ID of Stochastic Process)\n", - "\n", - "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. We train and utilize the Lightning framework to support custom functionality within the training loop.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "import numpy as np\n", - "import torch\n", - "import tqdm\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "import torchsde\n", - "import torchsde\n", - "\n", - "from torch.utils.data import DataLoader\n", - "from neuromancer.system import Node\n", - "from neuromancer.dynamics import integrators, ode\n", - "from neuromancer.trainer import Trainer, LitTrainer\n", - "from neuromancer.problem import Problem\n", - "from neuromancer.loggers import BasicLogger\n", - "from neuromancer.dataset import DictDataset\n", - "from neuromancer.constraint import variable\n", - "from neuromancer.loss import PenaltyLoss\n", - "from neuromancer.modules import blocks\n", - "from torch import nn\n", - "from torch import optim\n", - "from torch.distributions import Normal\n", - "from typing import Sequence\n", - "\n", - "torch.manual_seed(0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functions to generate data from a Lorenz attractor" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class LinearScheduler(object):\n", - " def __init__(self, iters, maxval=1.0):\n", - " self._iters = max(1, iters)\n", - " self._val = maxval / self._iters\n", - " self._maxval = maxval\n", - "\n", - " def step(self):\n", - " self._val = min(self._maxval, self._val + self._maxval / self._iters)\n", - "\n", - " @property\n", - " def val(self):\n", - " return self._val\n", - "\n", - "\n", - "class StochasticLorenz(object):\n", - " \"\"\"Stochastic Lorenz attractor.\n", - "\n", - " Used for simulating ground truth and obtaining noisy data.\n", - " Details described in Section 7.2 https://arxiv.org/pdf/2001.01328.pdf\n", - " Default a, b from https://openreview.net/pdf?id=HkzRQhR9YX\n", - " \"\"\"\n", - " noise_type = \"diagonal\"\n", - " sde_type = \"ito\"\n", - "\n", - " def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)):\n", - " super(StochasticLorenz, self).__init__()\n", - " self.a = a\n", - " self.b = b\n", - "\n", - " def f(self, t, y):\n", - " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", - " a1, a2, a3 = self.a\n", - "\n", - " f1 = a1 * (x2 - x1)\n", - " f2 = a2 * x1 - x2 - x1 * x3\n", - " f3 = x1 * x2 - a3 * x3\n", - " return torch.cat([f1, f2, f3], dim=1)\n", - "\n", - " def g(self, t, y):\n", - " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", - " b1, b2, b3 = self.b\n", - "\n", - " g1 = x1 * b1\n", - " g2 = x2 * b2\n", - " g3 = x3 * b3\n", - " return torch.cat([g1, g2, g3], dim=1)\n", - "\n", - " @torch.no_grad()\n", - " def sample(self, x0, ts, noise_std, normalize):\n", - " \"\"\"Sample data for training. Store data normalization constants if necessary.\"\"\"\n", - " xs = torchsde.sdeint(self, x0, ts)\n", - " if normalize:\n", - " mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1))\n", - " xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std)\n", - " return xs\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Neuromancer Integration\n", - "\n", - "As per the NeuroMANCER x Lightning workflow, generate the data_setup_function and return the DictDatasets. Note that we only have a train dataset here, so we return `None` for dev/test datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def make_dataset(t0, t1, batch_size, noise_std, steps=100):\n", - " _y0 = torch.randn(batch_size, 3)\n", - " ts = torch.linspace(t0, t1, steps=steps)\n", - " xs = StochasticLorenz().sample(_y0, ts, noise_std, normalize=True)\n", - " train_data = DictDataset({'xs':xs},name='train')\n", - " return train_data, None, None, batch_size\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define some experimental parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size=256\n", - "latent_size=4\n", - "context_size=64\n", - "hidden_size=128\n", - "lr_init=1e-2\n", - "t0=0.\n", - "t1=2.\n", - "lr_gamma=0.997\n", - "num_iters=1\n", - "kl_anneal_iters=1000\n", - "pause_every=50\n", - "noise_std=0.01\n", - "method=\"euler\"\n", - "steps = 100 # number of time steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define Neuromancer components, variables, and problem to train the LatentSDE. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data. For this example, we set `adjoint` to `False` (do not use the adjoint sensitivity method). This is because this method seems to be significantly slower. \n", - "\n", - "Also note that we need to pass in the timestep tensor to our `LatentSDE_Encoder`, and as a result need to also define it outside the `make_dataset()` function. We note that this is not the cleanest code and breaks the data abstraction. Additional features will be added to mitigate this." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "ts = torch.linspace(t0, t1, steps=steps)\n", - "\n", - "sde_block_encoder = blocks.LatentSDE_Encoder(3, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n", - "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False)\n", - "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", - "sde_block_decoder = blocks.LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", - "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", - "\n", - "xs = variable('xs')\n", - "zs = variable('zs')\n", - "z0 = variable('z0')\n", - "xs_hat = variable('xs_hat')\n", - "\n", - "\n", - "log_ratio = variable('log_ratio')\n", - "qz0_mean = variable('qz0_mean')\n", - "qz0_logstd = variable('qz0_logstd')\n", - "log_pxs = variable('log_pxs')\n", - "sum_term = variable('sum_term')\n", - "\n", - "# NeuroMANCER loss function format\n", - "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", - "\n", - "# aggregate list of objective terms and constraints\n", - "objectives = [loss]\n", - "constraints = []\n", - "# create constrained optimization loss\n", - "loss = PenaltyLoss(objectives, constraints)\n", - "# construct constrained optimization problem\n", - "problem = Problem([model_1, model_2], loss)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now define helper visualization function (again see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py) that will fire every N epochs in our training loop. This visualization will allow us to see the learned Lorenz attractor samples " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Fix the same Brownian motion for visualization.\n", - "bm_vis = torchsde.BrownianInterval(\n", - " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", - "\n", - "# \n", - "def vis(data_dict, problem, bm_vis, img_path, num_samples=10):\n", - " encoder, decoder = problem.nodes[0], problem.nodes[1] #extract the encoder and decoder from our problem\n", - "\n", - " fig = plt.figure(figsize=(20, 9))\n", - " gs = gridspec.GridSpec(1, 2)\n", - " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", - " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", - "\n", - " xs = data_dict['xs'] #pull out data sample from the DictDataset\n", - " # Left plot: data.\n", - " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", - " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", - " ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", - " ax00.set_yticklabels([])\n", - " ax00.set_xticklabels([])\n", - " ax00.set_zticklabels([])\n", - " ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", - " ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", - " ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", - " ax00.set_title('Data', fontsize=20)\n", - " xlim = ax00.get_xlim()\n", - " ylim = ax00.get_ylim()\n", - " zlim = ax00.get_zlim()\n", - "\n", - " # Right plot: samples from learned model.\n", - " mydata = data_dict\n", - " output = decoder(encoder(mydata))\n", - " xs_hat = output['xs_hat'].detach().cpu().numpy() \n", - " #xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", - " z1, z2, z3 = np.split(xs_hat, indices_or_sections=3, axis=-1)\n", - "\n", - " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", - " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", - " ax01.set_yticklabels([])\n", - " ax01.set_xticklabels([])\n", - " ax01.set_zticklabels([])\n", - " ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", - " ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", - " ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", - " ax01.set_title('Samples', fontsize=20)\n", - " ax01.set_xlim(xlim)\n", - " ax01.set_ylim(ylim)\n", - " ax01.set_zlim(zlim)\n", - "\n", - " plt.savefig(img_path)\n", - " plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Neuromancer training the problem to learn the stochastic process\n", - "\n", - "We now train and visualize results using Lightning workflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Fix the same Brownian motion for visualization.\n", - "bm_vis = torchsde.BrownianInterval(\n", - " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", - "\n", - "# Define the custom_training_step to support visualization. \n", - "def custom_training_step(model, batch): \n", - " output = model.problem(batch)\n", - " loss = output[model.train_metric]\n", - " img_path = os.path.join('', f'current_epoch_{model.current_epoch:06d}.pdf')\n", - " if model.current_epoch % 50 == 0: \n", - " vis(batch, model.problem, bm_vis, img_path, num_samples=10)\n", - " return loss\n", - "\n", - "\n", - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "lit_trainer = LitTrainer(epochs=300, accelerator='cpu', train_metric='train_loss', \n", - " dev_metric='train_loss', eval_metric='train_loss', test_metric='train_loss',\n", - " custom_optimizer=optimizer, custom_training_step=custom_training_step)\n", - "\n", - "\n", - "\n", - "lit_trainer.fit(problem=problem, data_setup_function=make_dataset, t0=t0, t1=t1, batch_size=batch_size, noise_std=noise_std)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "neuromancer3", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/SDEs/sde_walkthrough.ipynb b/examples/SDEs/sde_walkthrough.ipynb new file mode 100644 index 00000000..529f4ba8 --- /dev/null +++ b/examples/SDEs/sde_walkthrough.ipynb @@ -0,0 +1,1318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TorchSDE + Neuromancer: Moving from Neural ODEs to Neural SDEs and Motivating the Latent SDE Architecture\n", + "\n", + "This notebook goes over how to utilize torchsde's functionality within Neuromancer framework. This notebook is based off: https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py. In this example, we generate data according to a 3-dimensional stochastic Lorenz attractor. We then perform a \"system identification\" on this data -- seek to model a stochastic differential equation on this data. Upon performant training, this LatentSDE will then be able to reproduce samples that exhibit the same behavior as the provided Lorenz system. We train and utilize the Lightning framework to support custom functionality within the training loop.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From Neural ODEs to Neural SDEs\n", + "\n", + "#### 1. Neural ODEs\n", + "\n", + "The general form of an ordinary differential equation (ODE) is:\n", + "\n", + "$$ \\frac{{dx}}{{dt}} = f(t, x) $$\n", + "\n", + "Neural ODEs parameterize the evolution of the system, in continuous-time, in terms of a neural network: $ \\frac{{dx}}{{dt}} = f_{\\theta}(x_k)$ where $\\theta$ are network weights\n", + "\n", + "\n", + "Next we need to solve the continuous-time NODE model with suitable ODE solver, e.g., [Runge–Kutta integrator](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods). \n", + "\n", + "$$ x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k)) $$\n", + "\n", + "\n", + "\n", + "\n", + "For training we need to obtain accurate reverse-mode gradients of the integrated NODE system. This can be done in two ways, either by unrolling the operations of the ODE solver and using the [backpropagation through time](https://en.wikipedia.org/wiki/Backpropagation_through_time) (BPTT) algorithm, or via [Adjoint state method](https://en.wikipedia.org/wiki/Adjoint_state_method). Assuming we have a suitable ODE solver (integrator) then we can train $f_{\\theta}(x_k)$ using our standard reference tracking loss (MSE) between predicted and ground-truth trajectory as they unroll.\n", + "\n", + "\n", + "Neuromancer provides a set of ODE solvers implemented in [integrators.py](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/dynamics/integrators.py).\n", + "For adjoint method we provide the interface to the [open-source implementation](https://github.com/rtqichen/torchdiffeq) via DiffEqIntegrator class.\n", + "\n", + "#### 2. Latent ODEs\n", + "\n", + "We can reformulate the above reference-tracking method for training Neural ODEs via a generative / variational inference approach. That is \n", + "$$ z_{t_0} ∼ p(z_{t_0}) $$\n", + "\n", + "$$ z_{t_1}, z_{t_2}, ..., z_{t_N} = ODESolve(z_{t_0}, f, θ_f, t_0, ..., t_N) $$\n", + "\n", + "where each $$ x_{t_i} ∼ p(x|z_{t_i}, θ_x) $$\n", + "\n", + "In this way the evolution of the latent states follow an ordinary differential equation and we observe observations emitted from the latent system dynamics. Since we never actually observe the \"point estimates\" of $z$ we have to integrate over $z$ to learn p(x), which can be done by treating this as a standard variational autoencoder network and training thusly.\n", + "\n", + "#### 3. Neural ODEs with Added Stochasticicity\n", + "\n", + "Stochastic differential equations are an extension of ordinary differential equations in that they are ODEs with instantaneous noise dynamics added to their determinisitic system dynamics. We denote the former as the diffusion process; the latter the drift process. They are often used to model phenomena governed by many small and unobserved interactions in chemistry and microbiology such as molecular motion in liquid; allele frequencies in genetics. They are also known to model price fluctations in financial markets for short-medium time horizons. \n", + "\n", + "The general form of a stochastic differential equation is, in differential form:\n", + "\n", + "$$ dx = f(t, x) \\, dt + g(t, x) \\, dW $$\n", + "\n", + "Where $W$ denotes a Weiner process (e.g. Brownian motion). We can see that if $g()$ is zero then the SDE simplifies to an ODE. Thus for training SDEs, we need an equivalent BPTT or adjoint state method to compute tractable and scalable gradients. Li, et al recently developed such an analogous backpropagation algorithm in https://arxiv.org/pdf/2001.01328. In our codebase we leverage such SDE solvers via the torchSDE library (https://github.com/google-research/torchsde/tree/master/torchsde). For reference, the main reason we can use numerical solvers for this stochastic calculus case is that the above differential equation's dynamics can be interepreted as: \n", + "\n", + "$$ X_{t+\\Delta t} \\approx \\text{ODESolve}\\left(X_t, f(\\cdot) + g(\\cdot) \\frac{\\Delta W}{\\Delta t}, [t, t+\\Delta t]\\right) $$\n", + "\n", + "(See the Euler-Murayama method)\n", + "\n", + "\n", + " Given that these solvers exist, we now move on to the question of **training these non-deterministic dynamics. What loss do we optimize**? \n", + "\n", + "Fitting an SDE in the same vein as an ODE (e.g. via maximal likelihood -- our reference tracking (MSE) loss since we assume Gaussian distribution) **will not work**: it will overfit and send the diffusion term to zero. Thus the inclusion of the diffusion term into our differential equation is a roadblock formulating the SDE in a rollout-based manner as we do with Neural ODEs. Devising loss functions to support rollout and physical constraints on the system is an open research question. \n", + "\n", + "#### 4. Latent SDEs\n", + "\n", + "It is natural to view training a Neural SDE from a probabilistic perspective: given training samples from a stochastic process, we wish to devise neural network(s) parameterizing the drift and diffusion terms (and perhaps other terms as well as shown below) that has the ability generate new data samples governed by the (learned) stochastic process. Thus we can employ the variational autoencoder approach similar to latent ODEs, but now the evolution of the latent states is modeled by an SDE and not an ODE. Other approaches exist as well, such as treating the SDE as Generative Adversarial Network (GAN). We only show the variational autoencoder form here.\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "______" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Imports\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import numpy as np\n", + "import torch\n", + "import tqdm\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "import torchsde\n", + "import torchsde\n", + "\n", + "from torch.utils.data import DataLoader\n", + "from neuromancer.system import Node\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer, LitTrainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.loggers import BasicLogger\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from neuromancer.system import Node, System\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "\n", + "from torch import nn\n", + "from torch import optim\n", + "from torch.distributions import Normal\n", + "from typing import Sequence\n", + "\n", + "torch.manual_seed(0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Neural ODE\n", + "\n", + "To motivate this walkthrough, we first construct a Neural ODE to solve a (deterministic) Lotka-Volterra System" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "def get_data(sys, nsim, nsteps, ts, bs):\n", + " \"\"\"\n", + " :param nsteps: (int) Number of timesteps for each batch of training data\n", + " :param sys: (psl.system)\n", + " :param ts: (float) step size\n", + " :param bs: (int) batch size\n", + "\n", + " \"\"\"\n", + " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", + " nx = sys.nx\n", + " nbatch = nsim//nsteps #500\n", + " length = (nsim//nsteps) * nsteps #1000\n", + " ts = torch.linspace(0,1,nsteps)\n", + " print('train sim ', train_sim['X'].shape)\n", + "\n", + " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " trainX = torch.tensor(trainX, dtype=torch.float32)\n", + "\n", + " print(trainX.shape)# N x nsteps x state_size \n", + "\n", + " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", + " train_loader = DataLoader(train_data, batch_size=bs,\n", + " collate_fn=train_data.collate_fn, shuffle=True)\n", + "\n", + " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " devX = torch.tensor(devX, dtype=torch.float32)\n", + " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", + " dev_loader = DataLoader(dev_data, batch_size=bs,\n", + " collate_fn=dev_data.collate_fn, shuffle=True)\n", + "\n", + " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", + " testX = torch.tensor(testX, dtype=torch.float32)\n", + " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", + "\n", + " return train_loader, dev_loader, test_data, trainX\n", + "\n", + "torch.manual_seed(0)\n", + "\n", + "# %% ground truth system\n", + "system = psl.systems['LotkaVolterra']\n", + "modelSystem = system()\n", + "ts = modelSystem.ts\n", + "nx = modelSystem.nx\n", + "raw = modelSystem.simulate(nsim=1000, ts=ts)\n", + "plt.figure(figsize=(5,1))\n", + "plot.pltOL(Y=raw['X'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We construct the NODE for the Lotka-Volterra system, creating a class inheriting from the neuromancer.ode ODESystem base class with learnable parameters\n", + "\n", + "The standard Lotka-Volterra system, also known as the predator-prey model, describes the dynamics of two interacting species in a biological community. The system consists of two coupled ordinary differential equations (ODEs), typically represented as follows:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt \\\\\n", + "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt\n", + "\\end{align*}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraHybrid(ode.ODESystem):\n", + "\n", + " def __init__(self, block, insize=2, outsize=2):\n", + " super().__init__(insize=insize, outsize=outsize)\n", + "\n", + " self.block = block\n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " assert self.block.in_features == 2\n", + " assert self.block.out_features == 1\n", + "\n", + " def ode_equations(self, x):\n", + " #Lotka Volterra equations\n", + " x1 = x[:, [0]]\n", + " x2 = x[:, [-1]]\n", + " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", + " dx2 = self.delta*self.block(x) - self.gamma*x2\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the Neuromancer System to train this model:" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train sim (1000, 2)\n", + "torch.Size([500, 2, 2])\n" + ] + } + ], + "source": [ + "nsim = 1000\n", + "nsteps = 2\n", + "bs = 10\n", + "train_loader, dev_loader, test_data, trainX = \\\n", + " get_data(modelSystem, nsim, nsteps, ts, bs)\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "fx = LotkaVolterraHybrid(net)\n", + "\n", + "# integrate UDE model\n", + "fxRK4 = integrators.RK4(fx, h=ts)\n", + "# create symbolic UDE model\n", + "ude = Node(fxRK4, ['xn'], ['xn'], name='UDE')\n", + "dynamics_model = System([ude])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can train the above model using standard Neuromancer training set-up with reference and finite-difference loss functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Neural ODEs with Added Stochasticity\n", + "\n", + "We now attempt to add stochasticiity to the Neural ODE case and train likewise. We will show that this model **cannot** be trained using our standard Neural ODE training setup and thus it will necessitate a different approach -- the Latent SDE shown in section 3. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The stochastic Lotka-Volterra system is given by:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt + \\sigma_1 dW_1 \\\\\n", + "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt + \\sigma_2 dW_2\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where the additional terms are the diffusion coefficients.\n", + "How can we represent this in NeuroMANCER?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SDE Base Class\n", + "\n", + "We define a base class for SDEs here (as well as in sde.py). Note that the noise_type and sde_type are hardcoded to ensure correct functionality with torchSDE. Note that the two main functions required to be implemenented are the $f$ and $g$ functions as discussed earlier. The $f$ function is equivalent to the ODESystem's `ode_equations()` but with a different signature to be compatible with torchSDE. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import abc\n", + "class BaseSDESystem(abc.ABC, nn.Module):\n", + " \"\"\"\n", + " Base class for Neural SDEs\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + " self.in_features = 0\n", + " self.out_features = 0\n", + "\n", + " @abc.abstractmethod\n", + " def f(self, t, y):\n", + " \"\"\"\n", + " Define the ordinary differential equations (ODEs) for the system.\n", + "\n", + " Args:\n", + " t (Tensor): The state variables of the system.\n", + " y (Tensor): \n", + "\n", + " Returns:\n", + " Tensor: The derivatives of the state variables with respect to time.\n", + " The output should be of shape [batch size x state size]\n", + " \"\"\"\n", + " pass\n", + "\n", + " @abc.abstractmethod\n", + " def g(self, t,y):\n", + " \"\"\"\n", + " Define the diffusion equations for the system.\n", + "\n", + " Args:\n", + " t (Tensor): The state variables of the system.\n", + " y (Tensor): \n", + "\n", + " Returns:\n", + " Tensor: The diffusion coefficients per batch item (output is of size \n", + " [batch size x state size]) for noise_type 'diagonal'\n", + " \"\"\"\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We implement the stochastic Lotka-Volterra system with dummy parameters using this base class. We initially set the diffusion coefficients to zero to show that this simplifies to an ODE" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraSDE(BaseSDESystem):\n", + " def __init__(self, a, b, c, d, g_params):\n", + " super().__init__()\n", + " self.a = a\n", + " self.b = b\n", + " self.c = c\n", + " self.d = d\n", + " self.g_params = g_params\n", + "\n", + "\n", + " def f(self, t, x):\n", + " x1 = x[:,[0]]\n", + " x2 = x[:,[1]]\n", + " dx1 = self.a * x1 - self.b * x1*x2\n", + " dx2 = self.c * x1*x2 - self.d * x2\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, x):\n", + " return self.g_params\n", + "\n", + "# Define parameters\n", + "a = 1.1 # Prey growth rate\n", + "b = 0.4 # Predation rate\n", + "c = 0.1 # Predator growth rate\n", + "d = 0.4 # Predator death rate\n", + "sigma1 = 0\n", + "sigma2 = 0\n", + "g_params = torch.tensor([[sigma1, sigma2]])\n", + "# Create the SDE model\n", + "sde = LotkaVolterraSDE(a, b, c, d, g_params)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's generate data from this ODE: " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Define time span\n", + "import torchsde \n", + "\n", + "t_span = torch.linspace(0, 20, 2000)\n", + "\n", + "# Initial condition\n", + "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", + "\n", + "# Integrate the SDE model\n", + "data = torchsde.sdeint(sde, x0, t_span, method='euler')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vX15iSJzLzp07efLJJ/n222/d7eni+l3k5uaW+r0kJycDZgGHRx55hIULF3LRRRcxdepUcnJy3B/sTvZcAFq1asWPP/5YqWIFmzZtYtWqVSdNfP5aROSvv8fT3ZeWlsaoUaOYNGlSqWOV9Roty5133snAgQMJCgoiPj7ePb/sdOfftGkTcHxOzl/FxsYCFX8tfv/994wePZoVK1aUmKtTnqGDhw4dIicn56S/R6fTya5du0q8xk58XgUFBaSlpZV4XGJiYokk6quvviI2NpaQkBDq1avnHuZ3orp165YquLFp0yb+/PPPcr0WbrnlFj7//HPmz59Ply5dmDVrFgcOHODmm28+TQuUdMMNN/DII48wffp0JkyYwBVXXEFMTEyp/SrSbjt27Cg1ZBFK/+2U9/UhIp6lJElETio0NJSOHTvSsWNHmjdvzq233srkyZN56qmnTvm4+++/n3HjxvHQQw/RqVMn4uLisNls3HDDDWVOUj7Z1WijEqWBn3vuOf79738zfPhwnnnmGWrWrElQUBAPPfRQmecuaz6ALxkwYACTJk3ihRde4OOPPy7RM1VUVESvXr1IS0vj//7v/2jZsiVRUVHs2bOHYcOGudvj888/L3X13tX2N954I4899hgTJ07koosuYuLEidSoUYN+/fp59Hk5nU569erFY489Vub9zZs3L/HzqX6PZd03aNAgFi5cyKOPPkrbtm2Jjo7G6XTSt2/fk06k/6tmzZrRs2fP0+731/O7jv/JJ5+4k9ETBQdX/F/z/Pnz6d+/P126dOGdd96hTp06hISEMG7cuDKLb1SFE5/XwoUL6d69e4n7t23bVmLuTJcuXdzV7cpzTBen08nZZ5/Nq6++WuZjUlNT3bf79OlD7dq1+fTTT+nSpQuffvopycnJ5fo9nahOnTp069aNV155hV9//bVaK9p54vUhIhWnvzQRKZfzzjsPMIcAuZzsCvWXX37J0KFDeeWVV9zb8vLySlRuq4gGDRoA5hXWE4e0HDp0qFTviKvq2gcffFBie3p6+mk/oP31XH+1YcOGCsd+qnNs2LChxPMpKChg27ZtJT7Qna4X4KqrrqJ3794MGzaMmJgYxo4d675v9erVbNy4kY8++ohbbrnFvX3mzJkljtGnT59S21xSUlLo3r07kydP5t///jczZ85k2LBh7qv9Jz6Xv1q/fj0JCQmn7EU62fNr0qQJWVlZFf5wWx5Hjx5l9uzZjBo1iieffNK9vazfuSe4elCSkpJO+fwq8lr86quvCA8P58cffyzRmzVu3LhSjy2rzRMTE4mMjDzp7zEoKKhEMvJX5557bqnXUFkf8CujSZMmrFy5kh49epz278FutzN48GDGjx/Piy++yNSpUys8LNBl8ODB3H777cTHx5/0okBF2q1Bgwbl+l2W9/UhIp6lOUkiUsKcOXPK7MFxzS05cWhIVFRUmYmP3W4vdYw333yzxNC3iujZsychISG8+eabJY5b1iKrZZ178uTJ7Nmzp1znqlOnDm3btuWjjz4qMexq5syZrFu3rlLx/1XPnj0JDQ3ljTfeKBHrBx98QEZGhrsSGZhtfLrhX7fccgtvvPEG7777Lv/3f//n3u76YHjiOQzDKLUocJ06dejZs2eJrxMNGTKEgwcP8re//Q2Hw1FiDsWJ7XXia2HNmjX89NNPp+1xOtlraNCgQfz222/8+OOPpe5LT0+nsLDwlMc9lbLaBcp+PXlCnz59iI2N5bnnnsPhcJS631X+viKvRbvdjs1mK/E3tn379jIXjS2rze12O7179+abb74pUar/wIEDTJw4kYsvvviUw7xq1KhR6jVUVQuvDho0iD179vC///2v1H25ubmlKuvdfPPNHD16lL/97W9kZWVVesHj6667jqeeeop33nmnzDW3oGLt1q9fPxYtWlRiodpDhw4xYcKEEscs7+tDRDxLPUkiUsL9999PTk4OV199NS1btqSgoICFCxfy+eef07BhwxLDsjp06MCsWbN49dVXSUlJoVGjRlxwwQVcccUVfPLJJ8TFxXHWWWfx22+/MWvWrDLnBJVHYmIijzzyCM8//zxXXHEF/fr1Y/ny5UyfPr1U79AVV1zB008/za233spFF13E6tWrmTBhQokem9N5/vnnufzyy7n44osZPnw4aWlpvPnmm7Ru3ZqsrKxyHePQoUOMHj261PZGjRoxZMgQHn/8cUaNGkXfvn3p378/GzZs4J133qFjx44lPtR16NCBzz//nBEjRtCxY0eio6O58sorSx33vvvuIzMzk3/961/ExcXxz3/+k5YtW9KkSRMeeeQR9uzZQ2xsLF999VWp3rfTufbaa7nnnnv45ptvSE1NpUuXLiXu/89//sNll11Gp06duO2229wlwOPi4hg5cuQpj92hQwfGjh3L6NGjadq0KUlJSVx66aU8+uij7gIRw4YNo0OHDmRnZ7N69Wq+/PJLtm/fXq6ewbLExsbSpUsXXnrpJRwOB3Xr1uWnn35yr9fjabGxsYwdO5abb76Z9u3bc8MNN5CYmMjOnTuZNm0anTt35q233gLK/1q8/PLLefXVV+nbty+DBw/m4MGDvP322zRt2pRVq1aVOP/J/m5Hjx7NzJkzufjii7nnnnsIDg7mvffeIz8/n5deeqla2qYsN998M1988QV33XUXc+bMoXPnzhQVFbF+/Xq++OILfvzxR3dPN0C7du1o06aNu+BD+/btK3Xe8rx+gXK322OPPcYnn3xC3759efDBB90lwBs0aFDid1SR14eIeJAlNfVExGtNnz7dGD58uNGyZUsjOjraCA0NNZo2bWrcf//9xoEDB0rsu379eqNLly5GREREiVLcR48eNW699VYjISHBiI6ONvr06WOsX7/eaNCgQYnSw65Swn/88UeJ47rK7M6ZM8e9raioyBg1apRRp04dIyIiwujWrZuxZs2aUsfMy8sz/v73v7v369y5s/Hbb78ZXbt2LVFquqxSvif66quvjFatWhlhYWHGWWedZXz99dfG0KFDy10CHCjzq0ePHu793nrrLaNly5ZGSEiIUbt2bePuu+82jh49WuJYWVlZxuDBg434+PgSZZ9PFv9jjz1mAMZbb71lGIZhrFu3zujZs6cRHR1tJCQkGHfccYexcuVKAzDGjRt32ufiMnDgQAMwHnvssTLvnzVrltG5c2cjIiLCiI2NNa688kpj3bp1JfYpqwT4/v37jcsvv9yIiYkxgBK/o2PHjhmPP/640bRpUyM0NNRISEgwLrroIuPll192l6R3leP+z3/+UyomV3nkQ4cOlbpv9+7dxtVXX23Ex8cbcXFxxsCBA429e/cagPHUU0+dsi1Odc6ynu9fX98uc+bMMfr06WPExcUZ4eHhRpMmTYxhw4YZS5YsKbFfeV+LH3zwgdGsWTMjLCzMaNmypTFu3LgyS0Sf7O/WMAxj2bJlRp8+fYzo6GgjMjLS6N69u7Fw4cIKPa+/OtXv4USnKp1fUFBgvPjii0br1q2NsLAwo0aNGkaHDh2MUaNGuUvRn+ill14yAOO5554rV4ynO7/Lyf7uytNuhmEYq1atMrp27WqEh4cbdevWNZ555hnjgw8+KPV34TrX6V4fKgEu4jk2w6jEzGgRERERL/X666/z8MMPs3379lLVM0VEykNJkoiIiPgNwzA499xzqVWrltYUEpFK05wkERER8XmuhY7nzJnD6tWr+eabb6wOSUR8mHqSRERExOdt376dRo0aER8fzz333MOzzz5rdUgi4sOUJImIiIiIiJxA6ySJiIiIiIicQEmSiIiIiIjICfy+cIPT6WTv3r3ExMRgs9msDkdERERERCxiGAbHjh0jJSWFoKCT9xf5fZK0d+9eUlNTrQ5DRERERES8xK5du6hXr95J7/f7JCkmJgYwGyI2NtbSWBwOBz/99BO9e/cmJCTE0lj8kdrXs9S+nqX29Sy1r2epfT1PbexZal/P8qb2zczMJDU11Z0jnIzfJ0muIXaxsbFekSRFRkYSGxtr+QvEH6l9PUvt61lqX89S+3qW2tfz1Maepfb1LG9s39NNw1HhBhERERERkRMoSRIRERERETmBpUnS2LFjOeecc9xD4Tp16sT06dPd9+fl5XHvvfdSq1YtoqOjufbaazlw4ICFEYuIiIiIiL+zdE5SvXr1eOGFF2jWrBmGYfDRRx8xYMAAli9fTuvWrXn44YeZNm0akydPJi4ujvvuu49rrrmGX3/91cqwRURERKQaGYZBYWEhRUVFHjm+w+EgODiYvLw8j50jkFVn+9rtdoKDg8946R9Lk6Qrr7yyxM/PPvssY8eOZdGiRdSrV48PPviAiRMncumllwIwbtw4WrVqxaJFi7jwwgvLPGZ+fj75+fnunzMzMwHzl+NwODz0TMrHdX6r4/BXal/PUvt6ltrXs9S+nqX29bxAbmOHw8GBAwfIzc312DkMwyA5OZmdO3dqXU0PqO72jYiIoHbt2mUWiSjv35DNMAyjqgOrjKKiIiZPnszQoUNZvnw5+/fvp0ePHhw9epT4+Hj3fg0aNOChhx7i4YcfLvM4I0eOZNSoUaW2T5w4kcjISE+FLyIiIiIeULt2baKjo6lZsybBwX5fmFnOUGFhIWlpaWRlZZU5TScnJ4fBgweTkZFxysrXlr/SVq9eTadOncjLyyM6OpopU6Zw1llnsWLFCkJDQ0skSGD+oezfv/+kx3v88ccZMWKE+2dXLfTevXt7RQnwmTNn0qtXL68pf+hP1L6epfb1LLWvZ6l9PUvt63mB2sb5+fns3LmT+vXre/Rit2EYHDt2jJiYGPUkeUB1t29sbCw7d+6kTZs2hIWFlbjPNcrsdCxPklq0aMGKFSvIyMjgyy+/ZOjQofzyyy+VPl5YWFipxgAICQnxmjcVb4rFH6l9PUvt61lqX89S+3qW2tfzAq2Ni4qKsNlsBAcHExTkuXpjTqcTMNfO8eR5AlV1t69rTlJwcHCpv5fy/v1YniSFhobStGlTADp06MAff/zB66+/zvXXX09BQQHp6eklepMOHDhAcnKyRdGKiIiIiIi/87pU2el0kp+fT4cOHQgJCWH27Nnu+zZs2MDOnTvp1KmThRGKiIiIiIg/szRJevzxx5k3bx7bt29n9erVPP7448ydO5chQ4YQFxfHbbfdxogRI5gzZw5Lly7l1ltvpVOnTietbCciIiIiIqXNnj2bVq1aVWkJ7hkzZtC2bVv3cDp/YmmSdPDgQW655RZatGhBjx49+OOPP/jxxx/p1asXAK+99hpXXHEF1157LV26dCE5OZmvv/7aypBFRERERE5r2LBh2Gw2bDabe3rJ008/TWFhoSXxPPbYYzzxxBPY7fZyP+aBBx6gQ4cOhIWF0bZt21L39+3bl5CQECZMmFCFkXoHS+ckffDBB6e8Pzw8nLfffpu33367miISEREREakaffv2Zdy4ceTn5/PDDz9w7733EhISwuOPP15q34KCAkJDQz0Sx4IFC9iyZQvXXntthR87fPhwFi9ezKpVq8q8f9iwYbzxxhvcfPPNZxqmV/G6OUkiIiIiIidjGAY5BYVV/pVbUHTafSq6vGhYWBjJyck0aNCAu+++m549e/Ltt98CZnJx1VVX8eyzz5KSkkKLFi0A2LVrF4MGDSI+Pp6aNWsyYMAAtm/fDsC8efMICQkptRzOQw89xCWXXHLSOCZNmkSvXr0IDw93t2HPnj3p06eP+zmlpaVRr149nnzySffj3njjDe69914aN2580mNfeeWVLFmyhC1btlSobbyd5dXtRERERETKK9dRxFlP/mjJudc93YfI0Mp/fI6IiODIkSPun2fPnk1sbCwzZ84EzPWw+vTpQ6dOnZg/fz7BwcGMHj2avn37smrVKrp06ULjxo355JNPePTRR92PmTBhAi+99NJJzzt//nwGDx7s/tlms/HRRx9x9tln88Ybb/Dggw9y1113Ubdu3RJJUnnUr1+f2rVrM3/+fJo0aVKhx3ozJUkiIiIiIh5kGAazZ8/mxx9/5P7773dvj4qK4v3333cPs/v0009xOp28//777kVXx40bR3x8PHPnzqV3797cdtttjBs3zp0kfffdd+Tl5TFo0KCTnn/Hjh2kpKSU2Fa3bl3ee+89brnlFvbv388PP/zA8uXLCQ6ueHqQkpLCjh07Kvw4b6YkSURERPyWYRis2p1BQZGTDvVrEBRkszokOUMRIXbWPd2nSo/pdDo5lnmMmNiYUy52GhFS/qIHAN9//z3R0dE4HA6cTieDBw9m5MiR7vvPPvvsEvOQVq5cyebNm4mJiSlxnLy8PPdwtmHDhvHEE0+waNEiLrzwQsaPH8+gQYOIioo6aRy5ubnuoXYnGjhwIFOmTOGFF15g7NixNGvWrELPzyUiIoKcnJxKPdZbKUkSERERv5TnKOLeCcuYvf4gAD1aJvH2kPaEV/CDrngXm812RkPeyuJ0OikMtRMZGnzKJKmiunfvztixYwkNDSUlJaVUL81fE5usrCw6dOhQZrW4xMREAJKSkrjyyisZN24cjRo1Yvr06cydO/eUcSQkJHD06NFS23Nycli6dCl2u51NmzZV8Nkdl5aW5o7PXyhJEhEREb/0wvT1zF5/EFfn0ez1B3nz50082qeltYFJwIiKiqJp06bl3r99+/Z8/vnnJCUlERsbe9L9br/9dm688Ubq1atHkyZN6Ny58ymP265dO9atW1dq+9///neCgoKYPn06/fr14/LLL+fSSy8td7xwvJerXbt2FXqct1N1OxEREfE7Ww9l8dFv2wH4YFhH3h7cHoD/ztvKwWN5FkYmcnJDhgwhISGBAQMGMH/+fLZt28bcuXN54IEH2L17t3u/Pn36EBsby+jRo7n11ltPe9w+ffqwYMGCEtumTZvGhx9+yIQJE+jVqxePPvooQ4cOLdHjtHnzZlasWMH+/fvJzc1lxYoVrFixgoKCAvc+ixYtIiwsjE6dOlVBC3gPJUkiIiLid979ZQuGAT1bJdG9RRKXnV2H9vXjcRQZTFi00+rwRMoUGRnJvHnzqF+/Ptdccw2tWrXitttuIy8vr0TPUlBQEMOGDaOoqIhbbrnltMcdMmQIa9euZcOGDQAcOnSI2267jZEjR9K+vXkBYdSoUdSuXZu77rrL/bjbb7+ddu3a8d5777Fx40batWtHu3bt2Lt3r3ufzz77jCFDhhAZGVlVzeAVNNxORERE/EpadgFTlu8B4O5ux4c6Db2oIct2ruCbFXt4qGczd/UwEU8YP358pe5PTk7mo48+Ou3x9+zZQ79+/ahTp85p961Zsyb33Xcfr776Ku+99x6JiYml1loKCQlhyZIlJbadbq7T4cOH+fLLL0s9zh+oJ0lERET8yvQ1+3AUGZxVJ5YODWq4t/dsVZuw4CC2H8nhz33HLIxQpPIyMjJYsGABEydOLFFO/HT+9a9/0aBBA5xOZ5XFsn37dt555x0aNWpUZcf0FkqSRERExK98u8IcCjSgbcl1YaLCgrmkmVmBa86Gg9Uel0hVGDBgAL179+auu+6iV69e5X5cfHw8//znP6u0et95553H9ddfX2XH8yYabiciIiJ+4+CxPH7fngbAFeemlLr/kmYJzPrzAAu3HObe7uWvOibiLU43BE6qhnqSRERExG/M23gYw4Cz68ZRNz6i1P2dm9YC4I/tR8lzFFV3eCLiI5QkiYiIiN+Yt/EQAN1alL2wZZPEaGpFhVJQ6OTPfZnVGZqI+BAlSSIiIuIXipwG8zeZSVKX5mUnSTabjbPrxQGwandGtcUmIr5FSZKIiIj4hT/3ZXI0x0FMWDBtU+NPut859cz7lCSJyMkoSRIRERG/8EdxwYbzGtYgxH7yjzjnunuS0qsjLBHxQUqSRERExC8s2X4UgPMa1jzlfq7hdpsPZZGVX+jxuETE9yhJEhEREZ9nGMbxnqQTFpAtS1JMOEkxYRgGbDygRWXF9w0bNoyrrrrK6jBKmT17Nq1ataKoqOoqSb777rtceeWVVXa8k1GSJCIiIj5vV1ouB4/lE2K3ce4p5iO5NK8dA8DmA1kejkwC1bBhw7DZbNhsNkJDQ2natClPP/00hYXW915u374dm83GihUrPHqexx57jCeeeAK73V6u/VeuXMmNN95IamoqERERtGrVitdff73EPsOHD2fZsmXMnz/fEyG7KUkSERERn+fqRTq7bhzhIaf/QNY0KRqATQfVkySe07dvX/bt28emTZv4+9//zsiRI/nPf/5T5r4FBQXVHF3VcDgcZW5fsGABW7Zs4dprry33sZYuXUpSUhKffvopa9eu5V//+hePP/44b731lnuf0NBQBg8ezBtvvHHGsZ+KkiQRERHxeUt3lm8+kkuz2q4kST1JPscwoCC76r8cOaffxzAqFGpYWBjJyck0aNCAu+++m549e/Ltt98Cx4fIPfvss6SkpNCiRQsAdu3axaBBg4iPj6dmzZoMGDCA7du3u49ZVFTEiBEjiI+Pp1atWjz22GMYf4lrxowZXHzxxe59rrjiCrZs2eK+v1GjRgC0a9cOm81Gt27dAHA6nTz99NPUq1ePsLAw2rZty4wZM9yPc/VAff7553Tt2pXw8HAmTJhQ5nOfNGkSvXr1Ijw8vPjXZtC7d2/69OnjjjctLY169erx5JNPAmYv0euvv07Xrl1p3LgxN910E7feeitff/11iWNfeeWVfPvtt+Tm5lbo91ERwR47soiIiEg1WV1czvuc4qIMp9MsyRxut0nD7XyPIweeS6nSQwYB8eXZ8Z97ITSq0ueJiIjgyJEj7p9nz55NbGwsM2fOBMxemT59+tCpUyfmz59PcHAwo0ePpm/fvqxatYrQ0FBeeeUVxo8fz4cffkirVq145ZVXmDJlCpdeeqn7uNnZ2YwYMYJzzjmHrKwsnnzySa6++mpWrFhBUFAQv//+O+effz6zZs2idevWhIaGAvD666/zyiuv8N5779GuXTs+/PBD+vfvz9q1a2nWrJn7+P/4xz945ZVXaNeunTsJ+qv58+czePBg9882m41x48Zx7rnn8sYbb/Dggw9y1113UbduXXeSVJaMjAxq1ix58eO8886jsLCQxYsXuxO8qqYkSURERHxaQaGTDfvNYXNn1y1vkmT2JO1JzyU7v5CoMH0kEs8xDIPZs2fz448/cv/997u3R0VF8f7777uTlE8//RSn08n777+PzWYDYNy4ccTHxzN37lx69+7NmDFjePzxx7nmmmsAs5DBjz/+WOJ8fx3i9uGHH5KYmMi6deto06YNiYnmYsu1atUiOTnZvd/LL7/M//3f/3HDDTcA8OKLLzJnzhzGjBnD22+/7d7voYcecp//ZHbs2EFKSslktm7durz33nvccsst7N+/nx9++IHly5cTHFz239/ChQv5/PPPmTZtWontkZGRxMXFsWPHjlPGcCb0jiAiIiI+beOBYxQUOYkND6Z+zchyPaZGVCi1okI5kl3A1kPZ7rLg4gNCIs0enSrkdDrJPHaM2JgYgoJOMRslpHyvL5fvv/+e6OhoHA4HTqeTwYMHM3LkSPf9Z599tjtBArNwwebNm4mJiSlxnLy8PLZs2UJGRgb79u3jggsucN8XHBzMeeedV2LI3aZNm3jyySdZvHgxhw8fxul0ArBz507atGlTZqyZmZns3buXzp07l9jeuXNnVq5cWWLbeeedd9rnnpubW2Yv08CBA5kyZQovvPACY8eOLdFDdaI1a9YwYMAAnnrqKXr37l3q/oiICHJyck4bR2UpSRIRERGftmaPOdSuTd0499X38mhQK5Ij2QXsSFOS5FNstjMa8lYmpxNCiszjnipJqqDu3bszduxYQkNDSUlJKdVjEhVV8nlkZWXRoUOHMuf5uHp/yuPKK6+kQYMG/O9//yMlJQWn00mbNm2qrDjEX+MuS0JCAkePHi21PScnh6VLl2K329m0aVOZj123bh09evTgzjvv5Iknnihzn7S0tAq1SUWpcIOIiIj4tNXFSVJ5h9q5NKhlftDbccRzV6MlsEVFRdG0aVPq169/0iFlJ2rfvj2bNm0iKSmJpk2blviKi4sjLi6OOnXqsHjxYvdjCgsLWbp0qfvnI0eOsGHDBp544gl69OhBq1atSiUrrt6rE9cvio2NJSUlhV9//bXEvr/++itnnXVWhZ97u3btWLduXantf//73wkKCmL69Om88cYb/PzzzyXuX7t2Ld27d2fo0KE8++yzZR57y5Yt5OXl0a5duwrHVV5KkkRERMSnrdmbCZg9SRXhGpq3U0mSeIkhQ4aQkJDAgAEDmD9/Ptu2bWPu3Lk88MAD7N69G4AHH3yQF154galTp7J+/Xruuece0tPT3ceoUaMGtWrV4r///S+bN2/m559/ZsSIESXOk5SUREREBDNmzODAgQNkZJgXGh599FFefPFFPv/8czZs2MA//vEPVqxYwYMPPljh59KnTx8WLFhQYtu0adP48MMPmTBhAr169eLRRx9l6NCh7iRuzZo1dO/end69ezNixAj279/P/v37OXToUInjzJ8/n8aNG9OkSZMKx1VeSpJERETEZzmKnPy5z0ySKt6TZCZJO9KyqzwukcqIjIxk3rx51K9fn2uuuYZWrVpx2223kZeXR2xsLGD2xNx8880MHTqUTp06ERMTw9VXX+0+RlBQEJMmTWLp0qW0adOGhx9+uNTaTMHBwbzxxhu89957pKSkMGDAAAAeeOABRowYwd///nfOPvtsZsyYwbfffnvSeUOnMmTIENauXcuGDRsAOHz4MHfccQcjR46kffv2AIwaNYratWtz1113AfDll19y6NAhPv30U+rUqeP+6tixY4ljf/bZZ9xxxx0VjqkiNCdJREREfNamA1kUFDqJCQ92Jz3l5dpfPUniCePHj6/U/cnJyXz00UcnfVxwcDBjxoxhzJgxJ92nZ8+epYa6/XUtpdtvv53bb7+9xLagoCCeeuopnnrqqTKP27Bhw1LHOZmaNWty33338eqrrzJ27FgSEhLYu3dvicIYISEhLFmyxP3zyJEjSxS2KMvatWtZsWIFX3zxRbniqCz1JImIiIjPWrvXHCbUOiW2QkUbAOrXNOck7cvMI7+w6DR7i0hF/etf/6JBgwbu6npVYd++fXz88cfExXm22Ip6kkRERMRnbTxgro/UMjm2wo9NiA4lMtROTkERu4/m0iQxuqrDEwlo8fHx/POf/6zSJKlnz55VdqxTUU+SiIiI+KwNB7IAaJEcc5o9S7PZbMeLN6RpyJ2IHKckSURERHzWhv1m0YbmtSueJAHUiTMXu9yXnldlMYmI71OSJCIiIj4pPaeAA5n5ADSvXbmhcnXiIwDYl5FbZXFJ1StvsQARqJrXi5IkERER8Ukb9pvzkerGRxATHlKpY9QtTpL2qifJK4WEmL/XnBwNh5Tyc71eXK+fylDhBhEREfFJrqINlZmP5OIebqeeJK9kt9uJj4/n4MGDgLmOUEWrGJaH0+mkoKCAvLy8EiWqpWpUV/sahkFOTg4HDx4kPj4eu91e6WMpSRIRERGftH5/VSRJruF26knyVsnJyQDuRMkTDMMgNzeXiIgIjyRhga662zc+Pt79uqksJUkiIiLik9w9SZUs2gCQEm/2JO1Nz8UwDH1A9kI2m406deqQlJSEw+HwyDkcDgfz5s2jS5cuZzRES8pWne0bEhJyRj1ILkqSRERExOcYhuGek1TZynYAycXD7fILnRzNcVAzKrRK4pOqZ7fbq+TD78mOXVhYSHh4uJIkD/DF9tWgSxEREfE5+zPzyMwrxB5ko0lSVKWPExZsJyHaTIz2pmtekoiYlCSJiIiIz3H1IjVKiCIs+Mx6F1LiNS9JREpSkiQiIiI+xzUfqbLrI51IFe5E5K+UJImIiIjP2XIwG4CmSZWfj+TiqnCntZJExEVJkoiIiPicLYeyAGiSWPn5SC6uCnfqSRIRFyVJIiIi4nOOJ0lnPtyudqyZJB3IVE+SiJiUJImIiIhPScsu4GiOuV5O4yroSUqMCQPg0LH8Mz6WiPgHJUkiIiLiU7YW9yLVjY8gMvTMl3xMijF7kg4qSRKRYkqSRERExKe4htpVRS8SHO9JOpZXSJ6jqEqOKSK+TUmSiIiI+JQth8zKdlUxHwkgNjyYsGDzI5GG3IkIKEkSERERH7PlYNVVtgOw2WwkxZq9SQePqXiDiChJEhERER+z9XDV9iQBJEareIOIHKckSURERHxGfmERO9NyAGiSVHVJkoo3iMiJlCSJiIiIz9h5JIcip0F0WDBJxQUXqoJ7uF2mkiQRUZIkIiIiPuT4IrJR2Gy2KjuuhtuJyImUJImIiIjPqOrKdi4q3CAiJ1KSJCIiIj7D3ZNUhfORQHOSRKQkJUkiIiLiM473JFVN+W8X14KyGm4nIqAkSURERHyEYRhsda+RVNU9SWaSdDgrnyKnUaXHFhHfoyRJREREfMKhY/kcyy8kyAb1a0VW6bFrRYdhs4HTgLTsgio9toj4HiVJIiIi4hM2F89HSq0ZSViwvUqPbQ+yUSMyFFCSJCIWJ0nPP/88HTt2JCYmhqSkJK666io2bNhQYp9u3bphs9lKfN11110WRSwiIiJW2X7YXES2cULVzkdyqRllJklHsjQvSSTQWZok/fLLL9x7770sWrSImTNn4nA46N27N9nZ2SX2u+OOO9i3b5/766WXXrIoYhEREbHKjiPm54MGtTycJKknSSTgBVt58hkzZpT4efz48SQlJbF06VK6dOni3h4ZGUlycnJ1hyciIiJeZNthM0lq5KGepIRo9SSJiMnSJOmvMjIyAKhZs2aJ7RMmTODTTz8lOTmZK6+8kn//+99ERpY9YTM/P5/8/ONvbpmZmQA4HA4cDoeHIi8f1/mtjsNfqX09S+3rWWpfz1L7elZ1te/24iQpNT7MI+eKjzA/Fh06lud1rxW9hj1L7etZ3tS+5Y3BZhiGV9S5dDqd9O/fn/T0dBYsWODe/t///pcGDRqQkpLCqlWr+L//+z/OP/98vv766zKPM3LkSEaNGlVq+8SJE0+aWImIiIh3cxrw2GI7DsPGv9sVkhBe9eeYviuIGbuD6FzbyaDGzqo/gYhYLicnh8GDB5ORkUFsbOxJ9/OaJOnuu+9m+vTpLFiwgHr16p10v59//pkePXqwefNmmjRpUur+snqSUlNTOXz48Ckbojo4HA5mzpxJr169CAkJsTQWf6T29Sy1r2epfT1L7etZ1dG++zLy6PLyPIKDbKx+sgfB9qqfVv3p4p2M+n49vc9K4u0b21b58c+EXsOepfb1LG9q38zMTBISEk6bJHnFcLv77ruP77//nnnz5p0yQQK44IILAE6aJIWFhREWFlZqe0hIiOW/FBdvisUfqX09S+3rWWpfz1L7epYn23d38ZD81JqRRISX/j9fFRJjIwBIzyn02teJXsOepfb1LG9o3/Ke39IkyTAM7r//fqZMmcLcuXNp1KjRaR+zYsUKAOrUqePh6ERERMRbuMp/N6ziRWRPVCvKTL4OZ6twg0igszRJuvfee5k4cSLffPMNMTEx7N+/H4C4uDgiIiLYsmULEydOpF+/ftSqVYtVq1bx8MMP06VLF8455xwrQxcREZFq5Ony3wC1orWYrIiYLE2Sxo4dC5gLxp5o3LhxDBs2jNDQUGbNmsWYMWPIzs4mNTWVa6+9lieeeMKCaEVERMQqni7/DcfXSUrPcVBY5PTIvCcR8Q2WD7c7ldTUVH755ZdqikZERES81fbinqSGHkySakSGYrOBYUBaTgFJMR4ooSciPkGXSERERMSrOZ0GO454fk6SPchGjUgNuRMRJUkiIiLi5fZn5pFf6CQ4yEbd+AiPnqtW8ZC7I1lKkkQCmZIkERER8WquoXapNSM9Pk/INS/piHqSRAKakiQRERHxatVR/tvFXeEuS2XARQKZkiQRERHxatVRtMHFtVaSepJEApuSJBEREfFq24vLfzf04BpJLhpuJyKgJElERES8XLX2JEW7CjdouJ1IIFOSJCIiIl6rusp/u7iG26kEuEhgU5IkIiIiXqs6y3+DhtuJiElJkoiIiHgt13yk6ij/DVAjKgSA9ByHx88lIt5LSZKIiIh4re3VONQOoEak2ZOUnlOA02lUyzlFxPsoSRIRERGvVZ1FGwDiIsyeJKcBx/ILq+WcIuJ9lCSJiIiI16rO8t8A4SF2IkLsgNmbJCKBSUmSiIiIeK3q7kkCqBFp9iYd1bwkkYClJElERES8UnWX/3aJL56XdFQ9SSIBS0mSiIiIeKXqLv/tcrzCnZIkkUClJElERES8kms+Uv1qKv/tEu+ucKfhdiKBSkmSiIiIeCVX+e8G1TjUDiA+QnOSRAKdkiQRERHxSlYUbYCSayWJSGBSkiQiIiJeqbrLf7vEq7qdSMBTkiQiIiJeST1JImIVJUkiIiLidawq/w0nVrdTT5JIoFKSJCIiIl7HqvLfoHWSRERJkoiIiHghq8p/w/HqdupJEglcSpJERETE61hV/huOz0nKyi+koNBZ7ecXEespSRIRERGvY1XRBoDYiBBsNvN2eq6G3IkEIiVJIiIi4nW2WVT+G8AeZCOueMhdhobciQQkJUkiIiLidXZY2JMEx4fcaa0kkcCkJElERES8yonlvxtZ0JMEuHuSVOFOJDApSRIRERGvcmL575T4cEtiqBHpqnCnJEkkEClJEhEREa9iZflvFw23EwlsSpJERETEq2wrno9kRflvF9eCslorSSQwKUkSERERr+Kaj2RV0QbQcDuRQKckSURERLyKleW/XeIjVbhBJJApSRIRERGvYnX5bzg+3E5zkkQCk5IkERER8RreUP4bjhdu0HA7kcCkJElERES8hjeU/4bjw+1UuEEkMClJEhEREa/hDeW/oWSSZBiGZXGIiDWUJImIiIjX2F481M7K8t8AcRFmklRQ5CTP4bQ0FhGpfkqSRERExGts94KiDQDRYcHYg2wAZORqyJ1IoFGSJCIiIl5juxeU/waw2Wzu3iQlSSKBR0mSiIiIeA1v6UkClCSJBDAlSSIiIuIVTiz/3dDiOUkAsUqSRAKWkiQRERHxCieW/64bH2F1OOpJEglgSpJERETEK7jmI6VaXP7bxZUkaUFZkcBj/TuQiIiICMfLf3vDUDuAuIhgADLVkyQScJQkiYiIiFdwFW1oYHFlOxcNtxMJXEqSRERExCu4hts18oLKdgDxEaGAkiSRQKQkSURERLzC8Z4kbxlup54kkUClJElEREQsd2L5b2/pSVIJcJHApSRJRERELOdt5b9BPUkigUxJkoiIiFjONdTOW8p/w4lJUqHFkYhIdfOOdyEREREJaNsPm0PtvGU+EkBcpCtJKsAwDIujEZHqpCRJRERELLejuCepoZeU/4bjPUmOIoNcR5HF0YhIdVKSJCIiIpbbdtiVJHlPT1JUqJ3gIBugeUkigUZJkoiIiFjOVdmuoZdUtgOw2Wwq3iASoJQkiYiIiKWcTsNduMGbhtvBCcUbcpQkiQQSJUkiIiJiqQPHjpf/rlfDO8p/u2itJJHApCRJRERELOWaj1SvRoTXlP920XA7kcDkXe9EIiIiEnC8cT6Si5IkkcCkJElEREQstf2wd85HAiVJIoHK0iTp+eefp2PHjsTExJCUlMRVV13Fhg0bSuyTl5fHvffeS61atYiOjubaa6/lwIEDFkUsIiIiVe140QbvKf/toiRJJDBZmiT98ssv3HvvvSxatIiZM2ficDjo3bs32dnZ7n0efvhhvvvuOyZPnswvv/zC3r17ueaaayyMWkRERKrS9sPeO9wuPlJJkkggCrby5DNmzCjx8/jx40lKSmLp0qV06dKFjIwMPvjgAyZOnMill14KwLhx42jVqhWLFi3iwgsvtCJsERERqSJOp8GONO8dbqfqdiKBydIk6a8yMjIAqFmzJgBLly7F4XDQs2dP9z4tW7akfv36/Pbbb2UmSfn5+eTn57t/zszMBMDhcOBwWPsG5zq/1XH4K7WvZ6l9PUvt61lqX886k/bdl5FHnsMs/107OtjrfkfRIeagm/ScAktj02vYs9S+nuVN7VveGGyGYRgejqVcnE4n/fv3Jz09nQULFgAwceJEbr311hJJD8D5559P9+7defHFF0sdZ+TIkYwaNarU9okTJxIZ6X1jnUVERALZpgwbb62zkxBu8O92RVaHU8rmDHhzXTBJ4Qb/8sL4RKRicnJyGDx4MBkZGcTGxp50P6/pSbr33ntZs2aNO0GqrMcff5wRI0a4f87MzCQ1NZXevXufsiGqg8PhYObMmfTq1YuQkBBLY/FHal/PUvt6ltrXs9S+nnUm7fv5kt2wbh2tUxPp16+9hyKsvPX7j/Hmut8osofRr183y+LQa9iz1L6e5U3t6xpldjpekSTdd999fP/998ybN4969eq5tycnJ1NQUEB6ejrx8fHu7QcOHCA5ObnMY4WFhREWFlZqe0hIiOW/FBdvisUfqX09S+3rWWpfz1L7elZl2nfX0TwAGiVGe+XvplZMBGDOSQoODsZms1kaj17DnqX29SxvaN/ynt/S6naGYXDfffcxZcoUfv75Zxo1alTi/g4dOhASEsLs2bPd2zZs2MDOnTvp1KlTdYcrIiIiVcyby3/D8RLghU6DnAINtxMJFJb2JN17771MnDiRb775hpiYGPbv3w9AXFwcERERxMXFcdtttzFixAhq1qxJbGws999/P506dVJlOxERET/gKv/dwAvLfwNEhtoJDrJR6DTIyHUQFeYVg3BExMMs/UsfO3YsAN26dSuxfdy4cQwbNgyA1157jaCgIK699lry8/Pp06cP77zzTjVHKiIiIlXN6TTcPUmNvLD8N4DNZiM+MoTDWQVk5DpIiY+wOiQRqQaWJknlKawXHh7O22+/zdtvv10NEYmIiEh12ZeZR36hkxC7jXo1vDf5iI04niSJSGCwdE6SiIiIBK6th7IAqF8zkmC7934kidOCsiIBx3vfkURERMSvbTtcPNQuIdriSE5NSZJI4FGSJCIiIpbYeshMkhoneud8JBdXkpSpJEkkYChJEhEREUu4epIae2llOxdXkpSeoyRJJFAoSRIRERFLbD1szklq5CNJkobbiQQOJUkiIiJS7fILi9h9NBeARj4y3E5JkkjgUJIkIiIi1W7nkRwMA2LCgkmMDrM6nFNSkiQSeJQkiYiISLXbUly0oVFiFDabzeJoTk1JkkjgUZIkIiIi1e54+W/vHmoHqm4nEoiUJImIiEi121ZctKGxl6+RBBAXqZ4kkUCjJElERESq3dYThtt5uxOH2xmGYXE0IlIdlCSJiIhItfOVNZIAYsPNJKnQaZBTUGRxNCJSHZQkiYiISLXKyHFwJLsA8I05SZGhdoKDzOISGnInEhiUJImIiEi12nbE7EWqHRtGVFiwxdGcns1mU4U7kQCjJElERESq1dZDZtEGX+hFclGSJBJYlCSJiIhItTpe/tv7K9u5xKoMuEhAUZIkIiIi1WprcZLUxAcq27moJ0kksChJEhERkWrlLv+t4XYi4qWUJImIiEi1cToNth/23SRJw+1EAoOSJBEREak2B47lkesoIjjIRmrNSKvDKTf1JIkEFiVJIiIiUm22FQ+1q18zkhC773wMUZIkElh8591JREREfN4WHxxqB0qSRAKNkiQRERGpNq6epMY+VNkOjpcAV5IkEhiUJImIiEi12XbYtZCs76yRBOpJEgk0SpJERESk2mzxwfLfALERwQBk5BZaHImIVAclSSIiIlIt8hxF7DqaA0DTJN/sScrMdWAYhsXRiIinKUkSERGRarH1UDaGYSYcCdGhVodTIa4kqaDISZ7DaXE0IuJpSpJERESkWmw5ZM5HapoUjc1msziaiokOC8YeZMaseUki/k9JkoiIiFSLzQfNJKmJj1W2A7DZbMSGm/OSMvOUJIn4OyVJIiIiUi1O7EnyRapwJxI4lCSJiIhItTjek+TjSVKOkiQRf6ckSURERDyuyGmw9bBZ/ttXe5K0oKxI4FCSJCIiIh6352guBYVOQoODqFcj0upwKkXD7UQCh5IkERER8bjNh44B0Dghyl0lztcoSRIJHEqSRERExOPc85F8dKgdKEkSCSRKkkRERMTjthw05yP5atEGOJ4kZSpJEvF7SpJERETE4zb7ePlvUOEGkUCiJElEREQ8yjAM93C7pn7Qk6QkScT/KUkSERERjzqSXUBGrgObDRonRlkdTqUpSRIJHJVKkg4cOMDNN99MSkoKwcHB2O32El8iIiIiLq5epHo1IggP8d3PCUqSRAJHcGUeNGzYMHbu3Mm///1v6tSpg83mm6U8RURExPO2FM9H8uWiDaAkSSSQVCpJWrBgAfPnz6dt27ZVHI6IiIj4G3+YjwTHCzfkFzrJcxT5dK+YiJxapYbbpaamYhhGVcciIiIifsidJPlwZTuAmLBgXINnMvPUmyTizyqVJI0ZM4Z//OMfbN++vYrDEREREX+z9VDxGkk+niQFBdmIDddaSSKBoFLD7a6//npycnJo0qQJkZGRhISElLg/LS2tSoITERER35adX8ie9FzA94fbgTkvKSPXoXlJIn6uUknSmDFjqjgMERER8UeuXqSaUaHUiAq1OJozp+INIoGhUknS0KFDqzoOERER8UObDx0D/KMXCZQkiQSKSiVJAEVFRUydOpU///wTgNatW9O/f3+tkyQiIiJuG/abRRua1fazJClHSZKIP6tUkrR582b69evHnj17aNGiBQDPP/88qampTJs2jSZNmlRpkCIiIuKbNh0we5JaJMdYHEnViHX3JBVaHImIeFKlqts98MADNGnShF27drFs2TKWLVvGzp07adSoEQ888EBVxygiIiI+auNBM0lqluQvSZJ5fVnD7UT8W6V6kn755RcWLVpEzZo13dtq1arFCy+8QOfOnassOBEREfFdOQWF7EozK9s197fhdkqSRPxapXqSwsLCOHbsWKntWVlZhIb6fuUaEREROXOuRWQTokOpFR1mcTRVQ0mSSGCoVJJ0xRVXcOedd7J48WIMw8AwDBYtWsRdd91F//79qzpGERER8UEb9vvXUDs4niRpMVkR/1apJOmNN96gSZMmdOrUifDwcMLDw+ncuTNNmzbl9ddfr+oYRURExAdtKu5J8pehdnBCkpSnJEnEn1VqTlJ8fDzffPMNmzZtYv369QC0atWKpk2bVmlwIiIi4rs2Fle2a1bb/3qSNNxOxL9Vep0kgGbNmtGsWbOqikVERET8yKYDZk+Sv5T/BiVJIoGi3EnSiBEjeOaZZ4iKimLEiBGn3PfVV18948BERETEdx3Lc7AnvbiynR/OScopKMJR5CTEXqmZCyLi5cqdJC1fvhyHw+G+LSIiInIyrvlISTFhxEWGWBxN1YkJP/5cMnIdJPhJ1T4RKancSdKcOXPKvC0iIiLyV5uK5yP501A7AHuQjZjwYI7lFSpJEvFjleojHj58eJnrJGVnZzN8+PAzDkpERER828bi+Uj+VP7bRfOSRPxfpZKkjz76iNzc3FLbc3Nz+fjjj884KBEREfFtrsp2/lT+20VJkoj/q1CSlJmZSUZGBoZhcOzYMTIzM91fR48e5YcffiApKancx5s3bx5XXnklKSkp2Gw2pk6dWuL+YcOGYbPZSnz17du3IiGLiIiIBfyx/LeLFpQV8X8VKgEeHx/vTlaaN29e6n6bzcaoUaPKfbzs7GzOPfdchg8fzjXXXFPmPn379mXcuHHun8PCNPZXRETEm2XkOjiQmQ/4Z09SbLh6kkT8XYWSpDlz5mAYBpdeeilfffUVNWvWdN8XGhpKgwYNSElJKffxLrvsMi677LJT7hMWFkZycnJFwhQRERELuYo2pMSFl6gG5y/cw+1ylCSJ+KsKJUldu3YFYNu2baSmphIU5Pm1AebOnUtSUhI1atTg0ksvZfTo0dSqVeuk++fn55Ofn+/+OTMzEwCHw+EuYW4V1/mtjsNfqX09S+3rWWpfz1L7etZf23fd3nQAmiZF+WWbR4eZn3+OZudX2/PTa9iz1L6e5U3tW94YbIZhGJU9SU5ODjt37qSgoKDE9nPOOafCx7LZbEyZMoWrrrrKvW3SpElERkbSqFEjtmzZwj//+U+io6P57bffsNvtZR5n5MiRZQ75mzhxIpGRkRWOS0RERCrmy61BzD8QxKV1nAxo6LQ6nCr3024b03bZuSDRyeCm/vf8RPxZTk4OgwcPJiMjg9jY2JPuV6kk6dChQ9x6661Mnz69zPuLiooqesgyk6S/2rp1K02aNGHWrFn06NGjzH3K6klKTU3l8OHDp2yI6uBwOJg5cya9evUiJMT/hh9YTe3rWWpfz1L7epba17P+2r43vv87S3ak8/K1bRjQtvzD8H3FhN93MfK7P+nVKol3BretlnPqNexZal/P8qb2zczMJCEh4bRJUoWG27k89NBDpKens3jxYrp168aUKVM4cOAAo0eP5pVXXql00KfTuHFjEhIS2Lx580mTpLCwsDKLO4SEhFj+S3Hxplj8kdrXs9S+nqX29Sy1r2eFhIQQHBzMhuI1klrXq+GX7V0zOhyAY/mF1f789Br2LLWvZ3lD+5b3/JVKkn7++We++eYbzjvvPIKCgmjQoAG9evUiNjaW559/nssvv7wyhz2t3bt3c+TIEerUqeOR44uIiMiZ2ZuRx7G8QkLsNpok+l9lOzhxnaRCiyMREU+pVOWF7Oxs93pINWrU4NChQwCcffbZLFu2rNzHycrKYsWKFaxYsQIwC0KsWLGCnTt3kpWVxaOPPsqiRYvYvn07s2fPZsCAATRt2pQ+ffpUJmwRERHxsD/3mgWTmiRGExrs+QJPVtA6SSL+r1LvXi1atGDDhg0AnHvuubz33nvs2bOHd999t0K9PEuWLKFdu3a0a9cOgBEjRtCuXTuefPJJ7HY7q1aton///jRv3pzbbruNDh06MH/+fK2VJCIi4qXW7zeTpFZ1rJ0H7EnHe5KUJIn4q0oNt3vwwQfZt28fAE899RR9+/ZlwoQJhIaGMn78+HIfp1u3bpyqbsSPP/5YmfBERETEIn/uN9dIapkcY3EknuNKkrLyCykschJs988eM5FAVqkk6aabbnLf7tChAzt27GD9+vXUr1+fhISEKgtOREREfMuf+8yepJZ+3JMUG37841NmXiE1o0ItjEZEPKFSSdJfRUZG0r59+6o4lIiIiPio3IIith/OBqCVH/ckBduDiA4LJiu/kIxch5IkET9U7iRpxIgR5T7oq6++WqlgRERExHdtPpSF04BaUaEkxvj3/OG4iBB3kiQi/qfcSdLy5cvLtZ/NZqt0MCIiIuK71u8310dqWSfG7z8PxBQPuVOSJOKfyp0kzZkzx5NxiIiIiI9b7y7a4L/zkVxU4U7Ev6kci4iIiFSJDQf8v7Kdi5IkEf9WqcIN3bt3P2U3+s8//1zpgERERMT3GAZsKB5u589rJLloQVkR/1apJKlt27YlfnY4HKxYsYI1a9YwdOjQqohLREREfEhGAaTnOrAH2WiaFG11OB6nJEnEv1UqSXrttdfK3D5y5EiysrLOKCARERHxPXtyzBEmjROiCA+xWxyN52m4nYh/q9I5STfddBMffvhhVR5SREREfMDeHPO7Py8ie6K4SCVJIv6sSpOk3377jfDw8Ko8pIiIiPiAvdlmT1IgFG0A9SSJ+LtKDbe75pprSvxsGAb79u1jyZIl/Pvf/66SwERERMR37C5OklqnBEZPUqySJBG/VqkkKS4ursTPQUFBtGjRgqeffprevXtXSWAiIiLiG7LyCzmUZ95unRJ36p39hHqSRPxbpZKkcePGVXUcIiIi4qPW7z+GgY3aMWEkxoRZHU61UJIk4t8qlSS5LFmyhD///BOAs846iw4dOlRJUCIiIuI71u0zF5E9KyUw5iPB8STpWF4hRU4De9DJ148UEd9TqSRp9+7d3Hjjjfz666/Ex8cDkJ6ezkUXXcSkSZOoV69eVcYoIiIiXmzt3kwAWgdIZTuA2PAQ9+3MXAc1okItjEZEqlqlqtvdfvvtOBwO/vzzT9LS0khLS+PPP//E6XRy++23V3WMIiIi4sXWFSdJZwVQkhQaHER0mHmtOV1D7kT8TqV6kn755RcWLlxIixYt3NtatGjBm2++ySWXXFJlwYmIiIh3y3MUsflQNgCtA2i4HZhD7rLyCzmaU0AjoqwOR0SqUKV6klJTU3E4Sl81KSoqIiUl5YyDEhEREd+w8cAxCp0GUcEGdeICa63EGlHmkLv0nAKLIxGRqlapJOk///kP999/P0uWLHFvW7JkCQ8++CAvv/xylQUnIiIi3m3NHnOoXb0oA5stsIoX1Ig05yEdzdZwOxF/U6nhdsOGDSMnJ4cLLriA4GDzEIWFhQQHBzN8+HCGDx/u3jctLa1qIhURERGvs3ZvBgD1AnC0WbwrSVJPkojfqVSSNGbMmCoOQ0RERHzRmr3He5ICTY1IrZUk4q8qlSQNHTq0quMQERERH1NY5GT9vsBNktSTJOK/Kr2YbFFREVOnTnUvJtu6dWv69++P3W6vsuBERETEe205lE1+oZOoMDsJ4YVWh1PtXD1JR3PUkyTibyqVJG3evJl+/fqxZ88edxnw559/ntTUVKZNm0aTJk2qNEgRERHxPmv2mPORWiXHEGTLtzia6hcfqep2Iv6qUtXtHnjgAZo0acKuXbtYtmwZy5YtY+fOnTRq1IgHHnigqmMUERERL7S2eD5S65TAWUT2RPGqbifityq9mOyiRYuoWbOme1utWrV44YUX6Ny5c5UFJyIiIt5rTXFlu7PqxMA+i4OxgKsEuHqSRPxPpXqSwsLCOHbsWKntWVlZhIaGnnFQIiIi4t2KnIZ7uF2bAO1J0pwkEf9VqSTpiiuu4M4772Tx4sUYhoFhGCxatIi77rqL/v37V3WMIiIi4mW2HMoip6CIyFA7TRKjrQ7HEq7hdrmOIvIcRRZHIyJVqVJJ0htvvEHTpk256KKLCA8PJzw8nM6dO9O0aVNef/31qo5RREREvMyKXekAtKkbhz3IZm0wFokND3Y/d62VJOJfKjQnyel08p///Idvv/2WgoICrrrqKoYOHYrNZqNVq1Y0bdrUU3GKiIiIF1m1Ox2AtqnxlsZhJZvNRlxECGnZBRzNKaB2bLjVIYlIFalQkvTss88ycuRIevbsSUREBD/88ANxcXF8+OGHnopPREREvNDKXeZ8pHPrxVsbiMXiI4uTJFW4E/ErFRpu9/HHH/POO+/w448/MnXqVL777jsmTJiA0+n0VHwiIiLiZfIcRazfb5b/PqdenMXRWEsV7kT8U4WSpJ07d9KvXz/3zz179sRms7F3794qD0xERES805/7MnEUGdSKCqVejQirw7GUKtyJ+KcKJUmFhYWEh5ccbxsSEoLDoTcGERGRQLFqtznU7px6cdhsgVm0wcW9oKx6kkT8SoXmJBmGwbBhwwgLC3Nvy8vL46677iIqKsq97euvv666CEVERMSrrCyubHduABdtcHH1JGm4nYh/qVCSNHTo0FLbbrrppioLRkRERLzfiuLKdoFetAGO9ySla7idiF+pUJI0btw4T8UhIiIiPiAzz8HWQ9mAijaAWd0ONCdJxN9UajFZERERCUyri+cj1asRQa3osNPs7f9U3U7EPylJEhERkXJb6Rpqp/lIwIk9SUqSRPyJkiQREREpN1fRhraajwSc2JOk4XYi/kRJkoiIiJTbieW/5YQkKdeBYRgWRyMiVUVJkoiIiJTLvoxc9mXkEWSDNnWVJMHx4XZFToPMvEKLoxGRqqIkSURERMpl2Y50AFrViSUqrEIFcv1WeIidiBA7ABkacifiN5QkiYiISLks3XEUgA4NalgciXdR8QYR/6MkSURERMpl2U4lSWVxLSirJEnEfyhJEhERkdPKcxSxdq9ZtKF9fSVJJ6pR3JOkCnci/kNJkoiIiJzW6j0ZOIoMEmPCqFcjwupwvEoN9SSJ+B0lSSIiInJay1zzkerXwGazWRyNdzk+J0k9SSL+QkmSiIiInJaraEP7BvHWBuKFakYV9yRlqydJxF8oSRIREZFTMgxDRRtOwZUkpSlJEvEbWuRARERETmlXWi6HswoItQfROkWLyP6VK0k6nJV/+p0NA7IPg1EEUYkQZPdwdCJSGUqSvEleBqz7Bnb/AbnpEBEPdTtAsz4QW8fq6EREJEAt3ZkGQOu6sYSH6EP9XyVEhwGn6Uk6sgV+HQN/fg+5ZnsSGg2p50PbIdCqPwSHej5YESkXJUnewFkEi96BuS9AQVbJ+5Z9DDY7tLkGuj0OtZpYE6OIiASspScUbZDSTjnczjBg4ZswexQ4C4s3Fhe+KMiCLT+bXzUbQ5/noMVl1RO0iJySkiSrFWTDl8Nh4wzz58SW0PJyiKkDx/bD1jmwZymsngx/fgdd/w8uegDs+tWJiEj1WLojHYD2mo9UplpRx0uAO50GQUHFSZBhwA+Pwh//M39u2hM6PwipF0BQMBxaD+u+hSUfQNpW+OwGaH8L9H0BQqMsejYiAkqSrFXkgM9vMq8gBYfDZS+Zb44nllbt8W/YuwJmPQVb55pXorbOhevGQVQtiwIXEZFAkZHrYP3+TADOa6gkqSw1ipMkpwHpuQ53zxK/vFScINnM//Hn31Hyf3zt1ubXRfeZ+y580xxBsnsJDJkMcfWq/8mICKDqdtb68V9mghQSCbd8Ax2GlnzzdElpCzdPhavGQkgUbPsF/tsNDm+q5oBFRCTQLN2RhmFA44QokmLCrQ7HK4XYg4gNN687p2UXF2/Y+gvMfd68fcVrcMGdZf+PBwiLgd7PwNDvILo2HFwH7/eEfauqIXoRKYuSJKts+Rl+f8+8fe0HUP/CU+9vs0HbwXDHbHPccsZO+LCP2cskIiLiIYu3mUUGzm9U0+JIvJureMPhrALIPwZf3wkY0O5mOO/W8h2k0SVw+2xIbAXH9sH4K2DPMs8FLSInpSTJCgXZ8M395u2Od0DLfuV/bFIruG0m1DkXco6Yb6C7l3omThERCXi/K0kqlxLFG+a9DFn7zYual71UsQPFp8LwGVC/E+RnwCdX6YKoiAWUJFkg6Pd3IXM3xDeAXqMqfoCoBBj6PTToDAXH4NNrYP+aqg9UREQCWk5BIat3ZwBKkk7HlSTlH9piVqwF6PM8hEZW/GAR8eacpNQLzeVBPh4AB9ZWXbAiclpKkqpZqCOToN/eNH/o8WTlq9eEx8LgL6BeR8hLN680Hd5cVWGKiIiwfGc6hU6DuvER1KtRiQ/7AaRWtJkkNd/wHhQVQJMe0LxP5Q8YFmMmSsX/54M/v4HwgiNVFK2InI6lSdK8efO48sorSUlJwWazMXXq1BL3G4bBk08+SZ06dYiIiKBnz55s2uTbxQqaHpyOrSAL6rSF1tec2cHCos030OSzIfuQ2aOUdahK4hQREdF8pPKrGRVKMkdocfAHc0O3x09eqKG8wmPN//MJLbAd20enLS+bPUsi4nGWJknZ2dmce+65vP3222Xe/9JLL/HGG2/w7rvvsnjxYqKioujTpw95eXnVHGkVKcii4ZE55u2u/wdBVdD8ETXgpilQoyGk74BJN4Ij98yPKyIiAe/3bWbPhZKk06sZFcbtwT8QbBRCw0sgtWPVHDiiBtz0JUZ0bWLz9mD/8hYoLGPRWhGpUpYmSZdddhmjR4/m6quvLnWfYRiMGTOGJ554ggEDBnDOOefw8ccfs3fv3lI9Tr7i8ILxhBTl4KzRGJr3rboDRyfCkC8hPB52/wFT7gKns+qOLyIiASe/sIjlO9MBJUnlUTu8iEH2X8wfOj9UtQePr0/h9ZMoDAonaMev8MMj5kK1IuIxXruY7LZt29i/fz89e/Z0b4uLi+OCCy7gt99+44Ybbijzcfn5+eTn57t/zsw0F8BzOBw4HA7PBn0qziIcC8eCDQ60HEpCUREUFVXd8eMaYrvuI+wTr8O2bipFsxri7P5E1R3fB7h+v5b+nv2Y2tez1L6epfatuOU7jpJf6KRWVCipcaGnbDu1LzQ5OJNYWw77bLVJaHAJVHFbOGq1ZGXDe7lw66vYln1EUVJrnB2GV+k5Aplew57lTe1b3hi8Nknav38/ALVr1y6xvXbt2u77yvL8888zalTpinE//fQTkZHWTToNKTxGBKlEGTlM3p9Cgx9+8Mh56qUOp8OO97AvHMOyvQ721rjAI+fxZjNnzrQ6BL+m9vUsta9nqX3Lb+YeG2AnNSyP6dOnl+8xAdy+Hf/8AIAvirrScPoMz5wk7lzWpQyi9d7Psc14nMWb0zkS09Iz5wpQgfwarg7e0L45OTnl2s9rk6TKevzxxxkxYoT758zMTFJTU+nduzexsbEWRgaD9jVg0+79jG7TkX7n1PXQWfpR9HMY9t/e4Lw94yjsOQhqt/bQubyLw+Fg5syZ9OrVi5CQEKvD8TtqX89S+3qW2rfivhi/FDjClZ3Oot+F9U+5b8C375FNhCzfRKERxKTCrsztexlBQWdYtOEvXG3caMhrOH9wELT2azrvfY/CW2dC/Kl/P3J6Af8a9jBval/XKLPT8dokKTk5GYADBw5Qp04d9/YDBw7Qtm3bkz4uLCyMsLCwUttDQkIs/6UkxISznEjS85yejaXXSDi4BtuWnwn58ha4cy5EBs54cm/4Xfszta9nqX09S+1bPvmFRSzdeRSAS5onlbvNArZ9138HwHzn2exz1iC3COLDPNMOIaGhBA14G9K2YNu3kpAvh8JtP1VuPSYpJWBfw9XEG9q3vOf32nWSGjVqRHJyMrNnz3Zvy8zMZPHixXTq1MnCyCrPtYbCkaz80+x5hoLscO0H5mK16Tvgq9vAWYXzn0RExK8t35lOnsNJQnQYzZKirQ7H+639GoBZ9s4AHMn2cPW50Ei4YSJEJcKB1TDt7yrkIFLFLE2SsrKyWLFiBStWrADMYg0rVqxg586d2Gw2HnroIUaPHs23337L6tWrueWWW0hJSeGqq66yMuxKSyhejftwVjWU7oysab6BhkTClp9h9tOeP6eIiPiFhZsPA3BRk1rYznStH393YB0cWg/2UFZGXgTAker4Px9XD64bB7YgWDkRln3k+XOKBBBLk6QlS5bQrl072rVrB8CIESNo164dTz75JACPPfYY999/P3feeScdO3YkKyuLGTNmEB4ebmXYlZYQXY1JEkByGxhQvAbVr2Ng3bfVc14REfFpC7eY6yN1blrL4kh8wNop5vcmPQiNNoe2p2V7eMSIS6NLoIf5mYkfHoW9y6vnvCIBwNIkqVu3bhiGUepr/PjxANhsNp5++mn2799PXl4es2bNonnz5laGfEZqFvckebwb/kRtroFO95m3v7kXjmypvnOLiIjPyc4vZMWudAAuapJgbTC+YGNx5b+z+lMzypwTXa3/5zs/BC36QVEBfHEL5KRV37lF/JjXzknyRwnRxW+e1dWT5NJzJNTvBPmZ5huoI7d6zy8iIj7j921pFDoNUmtGkFpTxQBOKXMv7F8N2KBZb2oVXwxNq87/8zYbXDUWajSE9J0w5W9aUF6kCihJqkbu4XbV1Q3vYg8xxy1HJcKBNTDtkeo9v4iI+IyFW4rnIzVWL9JpbfrJ/F63A0QlUDPaghEjABHxMOgTCA43Y1rwSvWeX8QPKUmqRq4kKTu/iDxHNVebi60D131oTvBc8Sks+6R6zy8iIj7h183mfKSLNB/p9DYWJ0nN+wC4e5KqPUkCqHMO9HvZvD3nOdgyp/pjEPEjSpKqUXRYMHabWaLz0LFq7k0CaNQFLn3CvP3DI7BvVfXHICIiXistu4B1+8yFFjUf6TQK82HrXPN2s94AJMaYw+oPW/E/HqD9zdDuZjCc5vIfGXusiUPEDyhJqkY2m42Y4vWrLLnKBND5YWjeFwrz4IubITfdmjhERMTrLNpq9iI1rx3t/sAvJ7HjV3BkQ3Qy1DkXOJ4kHfL0eoin0u8/kHw25ByBycOg0KLPGyI+TklSNXMlSZZdZQoKMid4xteHo9th6j1agE5ERACYv+kQAJ2bqhfptFy9SE17mMUTgCRXkmTV/3iAkAhzflJYHOz+HWY+aV0sIj5MSVI1iwkxE5LDVl5liqwJAz8CeyhsmAYL37AuFhER8QqGYfDLBjNJ6to80eJofMC2eeb3Rl3dmxKjzXUcM3Id5BdW89zjE9VsBFe/a95ePBbWfGVdLCI+SklSNXP3JFmZJAHUbQ+XvWjenjUKtv9qbTwiImKpLYey2JuRR2hwEBc0UtGGU8pNh30rzduNLnFvjo0IJtRufrSqtoXjT6ZlP7j4YfP2tw/AoY3WxiPiY5QkVbPjSZIXjBHucCuccz0YRfDlrXDsgNURiYiIReYW9yJd0KgmEaF2i6PxcjsWmsURajWF2BT3ZpvN5q5ka+mQO5fuT0DDS6Agy5yHnJ9ldUQiPkNJUjWLCfWC4XYuNhtc8RoktoKsA2YlnKJCq6MSEREL/LJRQ+3Kbft883ujLqXuSvSGeUku9mBz+Y/oZDi0Hr57UPOQRcpJSVI185rhdi6hUXD9JxAabb7p//yM1RGJiEg1yy0oYvG2NAC6tVCSdFqu+UgNLyl1l1clSQDRSTBwPNjssOZL+ON9qyMS8QlKkqpZtDcNt3NJaAYD3jJv/zoG/vzO0nBERKR6Ld52hIJCJylx4TRJjLY6HO+WfQQOrDFv+0KSBNCgE/Quvgg643HYvcTaeER8gJKkauYV1e3K0vpq6HSfeXvK3ZrgKSISQNxD7VokYisuZy0nsWuR+T2xJUSX7nVLjHatlZRXnVGd3oX3wFkDwOmAL4aayZ6InJSSpGoWV9yTlJ5jcXnQsvQcCQ06Q8Ex+PwmTfAUEQkQmo9UATuLk6TUC8q82yt7ksCch9z/LbPYROZu+Pp2cHrZ5xARL6IkqZpFBkOI3bxK53VvoPYQc9xyTB04vAG+uVcTPEVE/NyutBy2HsrGHmTjIi0ie3q7Fpvf619Y5t2uJMmrhtW7hMeaC82GRMKWn+GXl6yOSMRrKUmqZjbb8RW5D2R6WZIE5gTPQR9DUAismwq/vWV1RCIi4kFzNhwEoEP9GsSGh1gcjZcrzIe9y83bvtaT5FL7LLhijHn7lxdh0yxLwxHxVkqSLJDkfgP1svHKLqnnQ9/nzdszn4Jt862NR0REPGbmOnONvB6tkiyOxAfsXQFFBRCZADUbl7lLQvTxJMnw1tEY514P5w0HDHPYXfpOqyMS8TpKkiyQ6M09SS4db4dzbji+0GzGHqsjEhGRKpaVX8jirWbp7x6talscjQ9wFW2of6E5NKQMriQp11FEdoEXz/np+wKktIPco/DFLeDw0gu3IhZRkmSB2u4kyYvfkFwLzdY+G7IPweSh5jADERHxG/M3HqKgyEmjhCiaJEZZHY7321k8H+kkQ+0AosKCiQq1A1485A4gOMwcXh9RwxxC+P3DmocscgIlSRZwDbc76M1vngChkXD9xxAeB7v/MNdWEBERvzHzz+Khdi2TVPr7dAzjtEUbXLx+XpJLfH24bhzYgmDlRFj8ntURiXgNJUkWSPSFniSXmo3hmuLVuZd8AEvHWxqOiIhUjSKnwZz1ZtEGDbUrhyNbIOcw2MOgzrmn3NVnkiSAJt2h92jz9o//hK2/WBuPiJdQkmSBpFgfevMEaN4buj9h3p72COxYaG08IiJyxpbtPMrRHAdxESGc17CG1eF4vz1LzO8pbc2haqeQ6B4x4gMXQ8FcaNY1D3nyMDi63eqIRCynJMkCSdE+1JPk0uUROOsqc6Xuz29WJRwRER83q3ioXbcWiYTY9XHgtPYsM7+ntD/trrVjwwHY7yv/5202uHJMcSGHNJg0BAqyrY5KxFJ6V7SAqyfpaI6D/EIvrnxzIpsNrnoHks82hxt8NlhvoCIiPmz2n+ZQu54aalc+rvWR6p4+SUouTpIOZPhIkgQQEgHXT4CoJDiwBqbeo0IOEtCUJFkgPiKE0OKrdj4z5A4gNApu+MxcH+LAaph6t95ARUR80NZDWWw+mEVwkI0uzROtDsf7FTlg/yrzdjl6kpLjzCRpny8lSQBxdeH6T44vKL/gVasjErGMkiQL2Gw231grqSzxqXD9p8VvoN/AvP9YHZGIiFTQ9DX7AbioaQJxESEWR+MDDv4JhXkQFnfSRWRP5Bpu51PD6l3qXwj9iv+3z34GNky3Nh4RiyhJskjt4iF3B33xDbRBJ7ii+OrSnGfhz++sjUdERCpk+pp9AFzWJtniSHzEXtd8pLYQdPqPTsknzEkyfHHExXm3wnnDAQO+vA32rbI6IpFqpyTJIkkx5huo16+VdDLtb4EL7jJvf30n7F1haTgiIlI+O4/ksGZPJkE26H2W5iOVi7toQ7ty7e4abpfncJKZW+ipqDzrspegcTdwZMPE6yFzn9URiVQrJUkWcfUk+WRXvEvvZ6Fxd3DkmG+gGbutjkhERE5jxlrzw+6FjWtRK/rUpaylWAWKNgCEh9iJjzSHMfpMhbu/sofAwI8goTkc2wuf3aCCTRJQlCRZJCnWx3uSAOzBMOgjSGwFWfthwiDIy7Q6KhEROYUfVpvzkTTUrpwceXBwnXm7HEUbXJJ9rQx4WSLiYfAXEFkL9q0wR444nVZHJVItlCRZJKm4cMN+X6t881fhcTDkC7Nk6MG18OWtUOSjQwtERPzc3vRcVuxKx2aDPq2VJJXL/tXgLISoRIirV+6H1fbFMuBlqdkIbpgI9lBY/z3MHml1RCLVQkmSRVLiIwDYm5FrcSRVIL4+DJ4EwRGweRb88IhKg4uIeKEZxVXtzmtQwz2iQU5j7wmLyNps5X6YX/QkudS/EAa8bd7+9XVY+pG18YhUAyVJFnElSfvSfbTyzV/V7QDXvg/YYOk4WPim1RGJiMhfHK9qV8fiSHyIq2hDOecjudT21bWSTuacQdD1/8zb00bAplnWxiPiYUqSLFKn+M0z11FERq7D4miqSKsroM+z5u2Z/4a1Uy0NR0REjtubnssf248C0Ffzkcpvb8Uq27kk+/JaSSfT7XE4e5A5/PCLW2DPUqsjEvEYJUkWCQ+xUzMqFIC96X70BnrhPdDxDvP213fAtvnWxiMiIgB8t3IvAOc3qukezSCnkZcJhzeZtytQtAGOXwz1+bnHJ7LZzGF3jbubpcEnDIIjW6yOSsQjlCRZqI67K94P5iW52Gxw2YvQ8gooKoBJg81JryIiYqmpK8wk6aq2dS2OxIfsWwkYEJcK0YkVemhtf+xJAggOhes/geRzIOcwfHotZB2yOiqRKqckyUJ14lzFG/zsDTTIDtd+AA06Q36m+Qaats3qqEREAtbGA8f4c18mIXabSn9XRCWH2sHxBWWPZBeQX1hUlVFZLywGhnwJ8Q3g6DaYOBDys6yOSqRKKUmyUN344p6kdD/qSXIJCTdLhtZuA1kH4NNrdKVJRMQi3xb3InVtnkiN4qHeUg6VLNoAUCMyhNBg82PWwUwfXhPxZGJqw01fm2so7V1uzlEq8pM51iIoSbJUHVcZcH9MksBchO6mr8wS4WlbYcJ1kH/M6qhERAKKYRh8s3IPAP011K5iTiz/XUE2m809rN5v/88nNDUXmw2JhC2z4Zt7tdis+A0lSRZyv3n623C7E8Ukw81TITLBXK170hAo9MMraiIiXmrZznR2peUSGWqnV6vaVofjO7IPQ/pO83adcyt1iLrFF0P3+GuSBFDvPBg4Hmx2WPW51koUv6EkyULutZL8qXBDWWo1gSGTITQatv0CXw5Xl7yISDX5ZoXZi9SndTIRoXaLo/Ehe1eY32s1NUdGVII7STrq5//nm/eBa/4L2GDJBzDrKSVK4vOUJFnIlSTtz8jD6fTzN5O67eGGCWAPg/Xfw5S/gdPPJrKKiHiZ/MIivi0u/T2gbYrF0fiYMxhq51K3RgD0JLmcfR1cOca8/evrMP9lS8MROVNKkixUOyaMIBs4igwOZwfAELTG3eD6TyEoBNZ8Bd/er7HLIiIeNGvdQdJzHCTHhnNJs4qVsA54Z1C0wSUghtudqMMw6F28qPzPo2HRu5aGI3ImlCRZKNgeRFKMq8KdH89LOlHz3nDdB+bY5RUTNHZZRMSDJi/dBcC1HepiD7JZHI0PMYyq7Uny9+F2J7roPuj2uHl7xv/B8k+tjUekkpQkWaxOvB8uKHs6Zw2Aq9/FPXb5pyeUKImIVLF9GbnM22guvXBdh1SLo/ExmXvN5Stsdkg+u9KHqRcfCZg9SUYg/Z/r+n/Q6T7z9rf3w6rJ1sYjUglKkiyWEufqig+QniSXcwZB/zfM27+9ZXbLB9I/EBERD/t62R6cBpzfsCaNEqKsDse3uHqRklpBaGSlD5McF47NBvmFTg5nFVRRcD7AZoPeo83hd4YTptwJq76wOiqRClGSZLF6Nc0kaffRHIsjsUD7W+Cy/5i3578MPz+jRElEpAoYhsHkJeZQu4Hn1bM4Gh+0d7n5PaXdGR0mNDiI2sXD6gNmXpKLzQaXvwbthxYnSn+DlZ9bHZVIuSlJslhqDfMK1a60AEySAC64E/o8b96e/wrMfFKJkojIGfpj+1G2H8khMtROv7PrWB2O76mCog0uATkvySUoCK4Yc7xHaepdSpTEZyhJslj9mmaStDNQkySATvcc71Fa+Ab8+E8lSiIiZ2DSH+YiqJefXYeosGCLo/ExhnFCT1IVJEnuCncB+n8+KMjsUXIPvfsbrJxkdVQip6UkyWKuJGlXWoBN6vyrC+6EK14zby96B6Y/pkRJRKQS0rIL+H7VPgAGX1Df4mh8UNpWyEs31/Wr3fqMD+fqSdodiD1JLu5E6VbAgCl3wYqJVkclckpKkiyWEh+BzQa5jqLAmtRZlvOGQ/83ARv8/l/4/mGtoyQiUkGTl+yioNBJm7qxtE2Ntzoc3+MaalfnHLCHnPHh3D1JgZwkQXGi9Kr5vx4Dpt4Dv//P6qhETkpJksVCg4PcFe4CesidS/tb4Kp3ABssHQff3AtFhVZHJSLiE5xOgwmLzaF2N1/YAJtNayNVWBWsj3Qi95ykQCvcUJagIOj3Cpz/N8Aw10qc/4rVUYmUSUmSF6hXI4Ar3JWl7WC45r/m+hQrJ8IXt4AjwEqki4hUwrxNh9iZlkNMeDBXnptidTi+ac9S83sVFG0ASD1huF1AD6t3CQqCy16ELo+aP89+GmY+pSH24nWUJHkBd/GGI0qS3M4ZBNd/ao4J3zANPr0W8jKtjkpExKt9umgHANd1qEdkqAo2VFiRA/atMm/X7VAlh6xXIxKbDbLyC0nLDvBh9S42G1z6BPR6xvz51zEwbYSG2ItXUZLkBVTh7iRa9oObv4bQGNixAD66ArIOWR2ViIhX2pOey8/rDwJw04UNLI7GRx38EwpzISwWajapkkOGh9ipE2uulbRdF0NL6vwAXPk6YIMlH5qLzhY5rI5KBFCS5BVSlSSdXMOLYdj3EJkA+1bCuL6QvtPqqEREvM7HC7fjNOCiJrVokhhtdTi+yT0fqZ05LKyKNKgVBcCOI9lVdky/0WEYXPcBBAXD6snw2Q2Qn2V1VCJKkryBK0kK6PKgp5LSFob/CHGpcGQzfNAHDqy1OioREa+RlV/IxN/NC0i3XdzI4mh8WBXPR3JpmGD+n1dP0km0uRZu+AyCI2DzLBjfD44dsDoqCXBKkryAa7jd3oxcCgo1HrdMCU3NRCmhBRzbCx/2hS1zrI5KRMQrfPHHLo7lFdI4IYruLZKsDsd37am6RWRPpJ6kcmjeG4ZNOz5y5IOecHiT1VFJAFOS5AUSokOJCLFjGCoRekpxdWH4DGjQGfIzYcJ1sPxTq6MSEbFUkdPgw1+3ATD84kYEBansd6UU5MDBdebtKira4NKwlnqSyqVeB7jtJ6jZ2Bxa/0Ev2LnI6qgkQClJ8gI2m83dm7RdV5lOLbIm3DwFzh4IzkJzHaWfn1XpUBEJWD+u3c/uo7nUiAzh2vb1rA7Hd+1fBUYRRNeG2Kotn66epAqo1QRumwl1z4Pco/BRf1j3jdVRSQBSkuQlGieab6BbD+kN9LSCw+Dq/8Ilj5g/z3sJpvwNilRaVUQCz/vztwJmRbuIULvF0fgw13yklPZmieoq1KC4Jyk9x0F6jv5XnVZUAgz9Dlr0g6J8+GIoLHhNF0SlWnl1kjRy5EhsNluJr5YtW1odlkccT5JU0aVcgoKgx7/hyjfMRWdXfY594rWEOrSWkogEjiXb01i2M51QexA3d1LZ7zOyp7iyXRUPtQOIDA0mKSYM0JC7cguNNNdL7HgHYMCskTDlLi0uL9XGq5MkgNatW7Nv3z7314IFC6wOySMaJ5jlWtWTVEEdhsKQyRAaQ9DO3+i6caQq34lIwHjz580AXNO+Lkkx4RZH4+Pcle3aeeTwDTXkruKC7HD5y9Dv5eILopPMNRNV+U6qgdcnScHBwSQnJ7u/EhISrA7JI9w9SYfVk1RhTXvA7bMwajQisuAwwR/1g3XfWh2ViIhHrd6dwS8bDxFkg7u7Vc3CpwErJw2OmsUvqrqynYtryN32w+pJqrDz74CbvoLwONj9B/zvUti3yuqoxM8FWx3A6WzatImUlBTCw8Pp1KkTzz//PPXr1z/p/vn5+eTn57t/zsw0h185HA4cDmtXcXadv6w46seb3fAHMvNJO5ZLTLjX/2q8S40mOG6aRtb4gSQdWwtf3EzRJY/ivORRsHn9tQCfcKrXr5w5ta9n+WP7vjF7IwBXnlOHlNhQS5+br7evbftvBANGzSYUhsSAB55Hag2zp2/roWOVaidfb+MzVv9iGPYjwV8MwZa2BePDPhT1fxuj5ZVVcviAb18P86b2LW8MNsPw3llw06dPJysrixYtWrBv3z5GjRrFnj17WLNmDTExMWU+ZuTIkYwaNarU9okTJxIZGenpkM/Iv5bYyXLY+PvZhdTXYumVYjOKaL1nEk0O/QjA3rjzWNbgTorsGoYiIv5jbw68uDIYGwb/OLeIZO/+9+b1Wu2dTPMD37Gz5iUsb3CHR86x4oiNcRvt1I8y+Ps5RR45RyAIKczmvO1vk3RsDQCbki7nz5TrMGwqWiLlk5OTw+DBg8nIyCA2Nvak+3l1kvRX6enpNGjQgFdffZXbbrutzH3K6klKTU3l8OHDp2yI6uBwOJg5cya9evUiJCSk1P03vv87S3ak8/J1ZzPg3DoWROjbTmzf0HWTsU9/BFtRAUZCcwqvGQeJLawO0aed7vUrZ0bt61n+1r4Pf7GK71fvp2/r2rx5w7lWh+Pz7Wv/dABBO36lsN9rGO1u9sg5Nh/M4rI3FxIZamfFE5diq2AFPV9v4yrlLCRo9kjsv79r/tjgYoqu/h9EJVb6kGpfz/Km9s3MzCQhIeG0SZJPjemKj4+nefPmbN68+aT7hIWFERYWVmp7SEiI5b8Ul5PF0jQphiU70tmZlus1sfqikJAQgs8bCrVbwRe3YDu8kZBxvcxKeOcMtDo8n+dNf0v+SO3rWf7QvpsOHOOHNfsBuO/SZl71fHyyfYscsHc5AMENLwIPxd80OY7gIBs5BUUcyimibnxEpY7jk21c5UKg34tQ/wL45j6Cdiwg6IMeMOhjSO14ZkdW+3qUN7Rvec/vU5M1srKy2LJlC3Xq+Gcvi6t4w5bDqnxTJVLPh7/Nh0ZdwZEDX98O0/4Ohfmnf6yIiJd65aeNOA3o07o2berGWR2O7zuwxvwfER4HCc09dpoQexCNEsz/85sOHPPYeQJKm2vgjp+hVjM4thfGXQZ/vK/1lKRKeHWS9Mgjj/DLL7+wfft2Fi5cyNVXX43dbufGG2+0OjSPUBlwD4hOhJunQJdHzZ//eB8+7AvpO62NS0SkElbuSmfG2v3YbPD33hpCXCV2Lja/1zvfXIPPg5rVNv/Pbz6oSrZVJqmlmSi16g9Oh3kx9KvbIU/rJsqZ8eokaffu3dx44420aNGCQYMGUatWLRYtWkRiYuXHnHqzJknmm+e2w1k4nboKUmWC7HDpEzDkS4ioAXuXwbuXqEy4iPicl3/aAMDVbevSvHbZBYykgnYVJ0mpF3j8VE2TzN/ZpgNKkqpUeKw51K7XM+Z6Smu+hPcuOb72lUgleHWSNGnSJPbu3Ut+fj67d+9m0qRJNGniv2tBpNaIICw4iDyHk51pWkehyjXrBX+bZ66BkZcOX9wM3z0IBWprEfF+v205wvxNhwkOsvFQT88NCws4u343v6ee7/FTNSu+GLrpoIbbVTmbDTo/AMNnQFx9OLodPugNv74BTqfV0YkP8uokKdAE24PcVwbX71c3sUfE14fhP0LnhwAbLB0P/+0K+1dbHJiIyMkZhsFLP64H4IbzU6lfSzW/q0TGbsjcba6nV7eDx0/nGm636WAWPlRc2Lekng93zYezrgJnIcz8N0y4DrIOWh2Z+BglSV6mRbKZJP25T1eZPCY4FHqNglumQnQyHN5ort69aKwme4qIV/pu1T6W70wnIsTO/Zc2szoc/+HqRardBsI8v0Bho4QogmxwLK+Qg8dURMhjIuJh4Hi4YgwEh8OW2TC2M2yaaXFg4kuUJHmZlsVJ0ob9SpI8rnE3uHshtOgHRQUw4x/m1abMfVZHJiLiluco4sXpZi/S3d2aUDtWi2NXmZ2LzO/VMB8JICzYTsNargp3mpfkUTYbnHcr3DEHEltB9kHzf/x3D0K+2l5OT0mSl2lVx1zUSsPtqklULbhhIlz+inm1afMseOcCWPm5epVExCu8P38re9JzqRMXzh2XNLY6HP+yfYH5vWHnajtl0+J5SRtVBrx61D4L7pwDF9xt/rx0PIy9CHYstDQs8X5KkryMa7jdjrQccgoKLY4mQNhs0PF2uPOX4qIOGTDlTpg0RGOYRcRSBzPzeGfuFgD+cVlLIkLtFkfkR7KPwMG15u0GF1fbaVsWXwxdt08XQ6tNSARc9gIM/Q7iUiF9B4zrBz89AY48q6MTL6UkycskRIeREB2GYcBGdcVXr6SWcNtMuPTfEBQCG6bB2xfAmq+sjkxEAtSLMzaQU1BE29R4+p+bYnU4/mVHcS9SYitzTb1q0jrFTJLW7lWSVO0adTGH2be7CTBg4Ztm8SaVCpcyKEnyQq3qFFe401Wm6mcPhi6PwJ1zIflsyE2DL4fDF7fAsQNWRyciAeS3LUf4atlubDZ46sqzsNlsVofkX1xD7RpdUq2nbVM3DoBNB46RX1hUrecWzDWVBrwNN06CqCQ4tB7e70nQzCewF6mYhhynJMkLtXCXAdd4ZcsktzEne3b9BwQFw7pv4K2OsORDrbcgIh5XUOjkianm0gSDz69Pu/o1LI7ID22bb35vWH1D7QBS4sKJjwyh0GmoeIOVWlwG9yyCc64Hw4n993fpvv5xbFt+tjoy8RJKkryQe7yyuuKtZQ+B7o/DHT9DnbaQnwHfPwzjLoODf1odnYj4sf/N38qWQ9kkRIfyWJ+WVofjf7IOwaHi9/FqnI8EYLPZ3EPu1uzJqNZzy19E1YJr/gtDvsKISyWq4DDBkwbB138z56xJQFOS5IXOqWd2xa/Zm0GRUxXWLFfnXDNR6vsChETBrkXw7iXw82hN+BSRKrfzSA5vzN4EwBOXn0VcZIjFEfkh13ykpNbmB+Vq1jrF/D+veUleollPCu+cz5bEPhjYYNUkeLsjLP9Uo0cCmJIkL9QkMZrIUDs5BUVsPqiueK8QZIcL74Z7F0Pzy8DpgHn/gbGdYOOPVkcnIn7CMAz+NXU1+YVOLmpSiwFtVazBIywaaudyvHiDepK8Rmg0a+oNoWjYDDN5zjkC39wLH/aBvSusjk4soCTJC9mDbJxdPLFz5a50a4ORkuJT4cbPYNDHEJ0MaVth4iCYMAiObLE6OhHxcZ/9vov5mw4TGhzEM1e1UbEGT3HNO2nc1ZLTu5KkP/cd04gRL2PU7WAWb+r1DIRGw+7f4b/d4PsRkJNmdXhSjZQkeam2qfEArNydbmkcUgabDc4aAPf9ARfdbxZ22PQjvHMhzBqplbxFpFJ2peXw7LR1ADzWpwVNEqMtjshPHdkCR7eZ792NulgSQqOEaCJC7OQ6ith2WP8zvE5wKHR+wPw/3+Y6wIAlH8CbHczFaDUELyAoSfJS5ypJ8n7hsdB7NNz9GzTpAUUFsOA1eOs8WPUFGLo6KCLl43QaPDJ5JdkFRZzfsCa3dm5kdUj+y9WLVL8ThMVYEsKJI0aW7Uy3JAYph9gUuO4DGPq9uZ5Wbhp89yD8r/vxEvLit5QkeSlX8Yb1+46R59A6Cl4tsTnc9BXc8BnUaAjH9sHXd8D7PWD7r1ZHJyI+4KPftrN4WxoRIXb+M/Ac7EEaZucxm2eZ35tcamkY7RuYZd2X7ThqaRxSDo0ugbvmQ5/nISwW9q2A8ZfDZzfC4U1WRyceoiTJS9WNjyAhOpRCp8E6LSrr/Ww2aNkP7lkMlz5hVsHbsxTG9zPfRA9tsDpCEfFSG/Yf44Xp6wH4Z7+WNKgVZXFEfqwwH7bNM2837WlpKB2Kk6SlSpJ8gz0EOt0D9y+DjreDzQ4bfoC3L4Bpj0D2YasjlCqmJMlL2Ww2zq0XD8AKdcX7jpBw6PIoPLAczht+/E30nQvNLvpj+62OUES8SE5BIfdOXEZ+oZOuzRMZckEDq0Pybzt/A0cORNeG5LMtDaV9/XgANh3MIiPHYWksUgHRiXD5K+ZCtM0vA6MI/vgfvNEO5r8KBTlWRyhVREmSF2tX/Aa6ZIeqqficmNpwxWvmm2jLK8BwmpM932gHs5+BXF05FBF46pu1bD6YRe3YMF4ddC5BGmbnWRtmmN+b9DBHAFioVnQYjRLMXsNlu/Q/weckNofBk2Dod+Z6ivmZMHsUvH4uLH7P7LUUn6YkyYtd2Nhc4G7x1jQMFQHwTYnN4YYJcOsMqNfRvII5/2UYcw7MfQHytEaGSKD6etluJi/dTZANXr+hHbWiw6wOyb8ZBqyfZt5uebm1sRRrX1/zknxeoy5wx1y4+j2Irw/ZB2H6Y/BGe1j6ERSpl9BXKUnyYufUiyc8JIgj2QVaVNbXNegEt82E6z81F6nLz4S5z8OYs81FafOPWR2hiFSjjQeO8cTUNQA82KO5+6KYeND+1ZCxE4IjLC/a4KJ5SX4iKAjOvQHuW2qOIolJgczd8N0D8FZHWPk5OFWEy9coSfJiocFB7qtMi7YesTgaOWM2G7S6Eu5aANeNg4QWZk/Sz6PNnqUFY7TGkkgASM8p4I6Pl5BTUMRFTWpx36VNrQ4pMLh6kZr2gNBIa2Mp5kqSVuxKp6BQa+/4vOBQcz7yA8vNSnhRieaaXFPuNAs8rJioniUfoiTJy7muLi7apnlJfiMoCNpcA/f8Bte8D7WammsvzHoKXmsNc56DbCXFIv6osMjJfROXs+NIDqk1I3h7cHuV+64uXjbUDqBZUjS1okLJKShixa50q8ORqhISblbCe2AF9HgKwuPhyCaYerc5DO/3/4Ejz+oo5TSUJHm5CxrVBDQvyS8F2eGcgWbZ8KvehZpNIC8dfnkRxrSB6f+AjN1WRykiVej56etZsPkwkaF2/nfLedSICrU6pMCQtg0OrDYrjjbva3U0bkFBNi5qmgDAgs0qIe13wqLhkhHw0GroOQqikswhnz88Aq+fA7++oeH2XkxJkpc7NzWesOAgDmfls+VQttXhiCfYg6HtjXDfHzDwI7NKjiMHFo+F19vC1Hvh0EaroxSRMzRx8U4+WLANgFcGnkvL5FiLIwoga740vze6BCJrWhvLX1zc1BwxsmDTIYsjEY8Jj4WLH4KHVkG/lyEuFbIOwMx/m3OTZz+jJUK8kJIkLxceYnePWZ63UW+gfi3IDq2vgjt/gZu+hoaXgNMBKz6FtzvChIGw5WezQpOI+JRZ6w7wxNTVADzUsxmXnV3H4ogCiGHAqsnm7bMHWRtLGS5ulgjAyt0ZZOZpvopfC4mA8+8wF6Qd8HbxcPujZtXb19rAlLtg3yqro5RiSpJ8QPcWSQDM2XDQ4kikWths5sTiYd/DbbPMdZawwaaf4JOrzYVpl47XgnUiPmLZzqPc99kynAYMOq8eD/ZoZnVIgWX/Kji8Aexh0OoKq6MppW58BI0SoihyGizaovmoASE4FNrdBPf+DoM+gfqdzIuiKz+D9y6B8VfAhungVDEPKylJ8gHdW5pJ0uKtaWTnF1ocjVSr1I7mOkv3L4UL7oLQaDi0Hr57EF47C2aNgow9VkcpIiex5VAWt43/gzyHk24tEnn26rOxWbyIacBZXdyL1KIvhMdZG8tJXKx5SYEpyA5n9YfhM+D2n6HNtea8ue3z4bMbzFEki96F3HSrIw1ISpJ8QJPEKOrXjKSgyKk30EBVqwlc9iKMWGeWFY1vYHbRL3jVHM88aQhsmqV1GES8yPbD2Qz+3yKO5jg4p14cbw9uT4hd/3arVVEhrC6ej3T2QGtjOYVLmplJ0uw/D6pIU6Cq1wGu+9Cct3TRAxAWB0c2w4z/g1dawjf3wp5lVkcZUPRu7QNsNhuXFvcmzVmvIXcBLTyuuKzocrh+AjS4GIwiWP89TLjWLPQw7z+aACpisV1pOQz+3yIOZObTLCmaccM6EhUWbHVYgWfTT3BsH0TWgma9rY7mpC5plkh4SBB70nNZuzfT6nDESnH1oPcz5kXRfi9D0llQmAvLP4X/dYf/doNln2jIfTVQkuQjXEPufl5/EKdTV5kCXpDdHFt/6zS4Z5E5FC88ziwt+vNoc72lz2+CzbM1plmkmu1Nz2Xw+4vYm5FH48QoJtxxAbWiw6wOKzAtHWd+bzsYgr33dxARaqdrc7OAw09rdZFLMMuHn38H3L0Qhv9oFh2xh8Le5fDtffBqS5j2iNm7pN5Hj1CS5CMuaFSTqFA7B4/ls2znUavDEW+S1Mocivf3DeZ6S6kXgLMQ/vwOPr2muLzo03B4k9WRivi97YezGfTeb+xKy6VhrUg+u+NCkmLCrQ4rMKXvhE0zzdsdbrU2lnLo2yYZgBlKkuRENhvUvxCu/R+M+NNcb6lGQ8jLgD/+Z/Yujb0IFr4JWRptVJWUJPmI8BA7vVubb6DfrdxrcTTilUIizPWWbvvJvPJ0/p3mmObM3TD/FXjrPPhfD/jjfchJszpaEb/z575Mrnv3N3YfNROkiXdcSO1YJUiWWfYxYECjrua8Ti93aYvaBAfZ2Hggi62HsqwOR7xRVIK53tL9y+HmKdDmOggOh4Pr4KcnzLlLE28wL5IWFlgdrc9TkuRD+p+bAsC01fsoLNIQKjmF2q2h33/gkY1w3ThzLL7NDnuWwLS/wyst4PObYf00KMy3OloRn7d0x1Guf+83Dmfl06pOLJPvuoiU+AirwwpcBTnwxwfm7fOGWxtLOcVFhtCpibmw7PQ16k2SUwgKgiaXwnUfmKNIrngN6nU05yhvnG4Ot3+luVkJd9t8FXWqJCVJPuTiZgnUiAzhcFYBi7aqJ0DKISQc2lwDQyab3fS9n4XabaCoAP78FiYNhv80NRew2/iTrjyJVMK0VfsY/L9FZOYV0qFBDSbdeSGJMd47/yUgrJgAuWnmsKSW3rc20slceY55MfSrZbtV5U7KJyLevBBw+yxz3aXOD0F0slkBd+l4+OgKc57yjMdh91LNX6oAJUk+JMQe5F6l/ZsVWhtHKiimNlx0H9z9K9y1AC68F2LqQH6muYDdxIHwcjP45j7Y8rNZOldETsowDN76eRP3TlxGfqGTS1sm8clt5xMXEWJ1aIGtqNCcnwHQ6T6w+05VwX7n1CEixM7WQ9ks35VudTjiaxJbQK9RZmW8W76BdjebRZ2O7YNF78D7l8Ibbc15yvtWKWE6DSVJPmbACUPujuU5LI5GfFby2dD3OXh4Hdw6HTreAVFJkJcOyz+BT642u+q/fQA2/giOPKsjFvEq+YVF/H3ySl7+aSMAt3ZuyP9uOY/IUN/5QO631k6B9B1m2e+2Q6yOpkKiw4K5rLiAw5dLd1scjfisIDs07gYD3oJHNsONk8z5SyGRcHS7OU/5vUvg9XNhxj9hx0INySuD3s19zPmNatIkMYoth7KZunwPN3dqaHVI4suCgqDBRebXZS/C9gWw9mtY9y3kHIFlH5lfIVHYm1xKvdwUyO0EIUlWRy5imV1pOdwzYRmr92RgD7Ixsn9rbr6wgdVhCUCRA+Y+Z96+4G4IjbQ2nkq4rkM9vl6+h+9W7uXJK84iPMRudUjiy4JDocVl5ldBNmycwf+3d+fxUZX3/sA/Z9Zksi9kgyzskVUWicG6FJBVhEpFuZaKVXq14JXSey96bxWt90ot/XmtlAv+WgH7s7VI3aqilF3BsIZ9CRBCSMiekHWyzMx5fn88M5PJvshkJpPP+/V6PNtzzjx5fDjnfM/yHJz9CLiyS15MOLRepoB+Mk/yXGDQvV7dZX5PYZDUyyiKgh/dmYhXPjuP9w5dx4/uTISiKJ4uFvkCjVbuGAfdKz9gl/U1kLEduLgdqMqD5uJnmABA/M8fZVCV/AAwfKZ85p+oj9h9oRArPziFiloLQk16vPXoONxj/74NeYET7wFlVwFTJHDn054uTbfcOSgC/UP9caO8Fn8/lYeFE+M9XSTyFYYAYNQCmRpq5LcUL34uA6eaYtkjZPqfAEMQMGSq7PRpyDT5uH4fxCCpF3po/AD85qsMZBRW4Vj2TdyRFO7pIpGv0erlDnLIVBkw5Z2A7fznqDm+FcF1ucC1b2T6ahUQMUTuRIfcDyTdJbsiJ/IxDVYVb+y8hI37MwEAY+ND8b+PjUd/9mDnPRrMwP7X5fg9/wYYgzxbnm7SaBQsTk3Er7+8iE0HsvDwhAG8GEq3niEAGPGgTDaLPKZf+Fz2eltdAJz/RCYAiB0rA6ah04H+E+RF1T6AQVIvFOKvx4Nj47D1WA7++M1VBknkXooC9B8PNWo09prHYnbqCOgz/yHvMF1PA0qvyHR4o/xeQ+Jd9qBpGhA5VK5P1ItlFFRhxdaTuJBfCQBYMjkJ/zH7Nhh0fK3Xq3zzf+QL6iEJwETv/3hsexbdkYC3dl/GxYIqHLhSgruH8m4luZFWL7sUHzzFfmE0Xb6PfGUnkHcCyD8l09drAf8wYPBUYOj98r2noBhPl95tGCT1Uk/dPRAfHM/BjnOFyCiowvCY3nnFjHqhsCQgdZlMdRXysbzLO+Vt+8pcIHO3TDtekCcrg+4Bku4BBt4NBMd5uvREnWZTBTYdyMLaHRlosKkIM+mx5qHRmDkq1tNFo+aKLwEHfyfHZ77W69+nCDHpsXBiPLZ8ew1/+CaLQRL1HI0GGDBRpin/CVQXyfeXLv8DuLJHdi1+9m8yAUDkcPmY/sB7gaTvyS7JfQSDpF5qaHQQZo2KwfYzBfj93itYt2icp4tEfZFfCHDbXJmEAIoz5M70yi4g+yBQcV2+I3DiPZk/Yggw8B6Zku6WXw8n8kJnb1TgPz8+g1O5FQCAKclR+PWC0YgK8vNwyagFVQW+WAmoFmDojF71XaT2/OSugfhT2jV8fakYp3LKMSImwNNFor4oMAq4/Z9kslmB3KMyYMrcLbsRL8mQ6cj/BRQNEHu7PWi6B0hI7dWP4DNI6sWWf38otp8pwOen8/Dc1CEYEsW7SeRBigJEJcs0ebl8KTT7W3mn6do38la949G8Y5vkOlEj5ZWnhBQg/k4gpL9n/wbq82rqrXhj5yVsPpgFVQBBRh3+Y85tePSOeL4X4q0Ob5T7GJ0/MPs3PvOIb0KECT8YNwAfpudi7Y4MbH58vKeLRH2dVgckpso0bTVgLpP/9q7ul8f60svyUb28dODA/wBag3yHKXEylP4p0NlqPf0XdAmDpF5sRFww7h8RjZ3nC/Ha9ovYtOQOTxeJqJEhQD6zPPR+OV1b3jRoKjwLFJ2T6cjbMk9Igj1gSgES7gSiRvSZF0TJs1RV4O+n8vD6VxeRXyG/CzZnTCxeemAEooN598hrFZ4Ddr0sx2f8l8/1trli2lD8/dQNHLhSgm8zSz1dHKKmTOHAiHkyAUDFDXmMz9ovA6eqPPnu8vU06AB8zy8ewAJPlrhLGCT1cs/PSsbei0XYc7EI+zKKcN9wfr+GvJR/KJA8WyYAqCmRwVJ2GpBzCCg4Ix/PO3MdOLNN5jEGy+ei4+8E+o8H4sYDAREe+xPINx26Wor//uICztyQj9bFh/vj1XmjuD/1dnUVwLYlgK0eGDYTmPikp0t0y8WHm/BYSiK2fHsNr++4hKf4OS7yZiH9gdsXySQEcDMLuHYQyP4WIvsgynSD0Zu+XMYgqZcb3C8QSyYn4Y8HsvDq5+eROjgCRh2vvFMvEBAJjPyBTABQXwXkHgNyDgPXD8nx+kogc49MDqGJjQFT//Gya9Je2tUvedbZGxV4c9cl7LpQBAAINOrwzH2D8eT3BvIDnt5OtQEfPgWUXAKC+wMP/t5nHrNrbvmUIfj4xA2cz6/C1wYFcz1dIKLOUBQgfJBM4xfDarHgzOd/xwBPl6sLGCT5gGenDsUnJ28gs7gGv9t1Gf8+M9nTRSLqOmMQMPj7MgHyJKjwnAyaco7IZ5xLr8gvhJdnA+c+tq+oAP2Gy6ApbhwQMxqIHgn4BXvsTyHvdjq3HG/tvuwMjrQaBYsmxWPFtGGIDOzdvaL1CUIAX66SL4/r/IBH/wwE+m7vb5GBRrwwKxnPf3QG23M0+Hl5LRL76T1dLKIuE5reFXb0rtJSq0L89fiv+aPw9Hvp2Lg/E1Nvi8aExDBPF4vou9FogdgxMk1aKufVlgP5J4Eb9hdDb5yQ3Y4XX5Tp1F8a1w9LAqJH2YMm+zA0wWevNlP7hBC4XKHgqf+Xjv2XSgAAGgWYOzYOz04ZiiFRgR4uIXWKEMDuXwFH/wBAAeZvkBdHfNzCifH42/EcHMsux6qPzuLPS1Oh1XBfRuRODJJ8xMxRsfjBuP74+MQNrNh6Ap8t/x5CTQZPF4vo1vIPlR+vG3Rf47yqQvmxuxvHgYLT8t2myhvAzWsyXfy8Ma8xRN5lihkF9EtuTHzPyWfVWWz47FQe3jmQhYsFWgAl0CjA/HH9sez7QzC4H4OjXkNVgX/8Eji0Xk4/8AYw6iHPlqmHaDQKXps/Eg+sO4BDWTfx+z1X8Ny0oZ4uFpFPY5DkQ16eOxLHssuQU1aLZ98/gS1PTOKVJvJ9QdHA8JkyOZjLZO95BWeAAvuw+CJQXwFc/1YmV6ZIe8A03CUlA4HRvPPUS10trsa247nYdiwHJdUNAACDRuCHE+Px03uGICmS35zpVaz1wKfLGjt1mbEGmPgTz5aphw2MDMDDg1T8+YoWv9t9CbcnhOLeYb77mCGRpzFI8iEhJj3e/tFELNjwLb65XIJffXYOLz84kt/2oL7HFN740VoHa4N8ybvgjAygSi7JwKn8OmAuAbIPyOTKL0R+TTxiMBA+GIgYZB8OZmcRXsjcYMX2MwX44GgOjlwrc86PDfHDj1LiEVZ2AQ/PHQG9nu9z9Co3r8le7PJOABodMO9/gbGPeLpUHjGpn0BdcBw+TM/Dz947jg+eTsXIuBBPF4vIJzFI8jEj4oLx24fHYtlf0vFuWjYC/XT4txnsyIEIOoN8zC5mVNP5DTX2gCnD/m6TfXjzmuxiOPeITM0FRDUGT+EDm44zgOoxdRYb9l8qxhen87H7QiFqGmwA5PtG9w7rh4UT4zFtRDSg2rB9+wUPl5a6RAjgzN+A7b+Q/xb9w4AF7wBDpnq6ZB71q7kjkFdej7SrpXhi81G8/9M7+dgokRswSPJBc8bEosw8Ci9+chbr92bCahNYNTMZGj56R9SSIUC++N385W9LrexNr+QSUHoVKMsEyq4CpZnyzlNNkUzX01pu0z9cdhLhTIku4/EMor6jmnorDlwpwZdn8rHrQhGq663OZQnhJiycOAALJgxAbIi/c75FtXmiqNRdZVnAl/8ue7ADgP4TgYe3yH8/fZxBp8HGxROwcGMaMgqr8Mjbh/CXpSkYFs39CtGtxCDJRy2+MxF1DTb89/YLePvrqyisrMOvF4zhtz+IOkvvL3vEixndclldRWPA5DosywTMpUBtmUz5J1vfdvMgKjgOSkA0wmquARW5QNgAQMtHwhyEEMgqqbF/NLsYR7LK0GBTnctjQ/wwZ3Qs5oyJxe3xoXzEuDerLgL2/wY4vhlQrYDWANzz78Bdz8m7wQRA9mr7l6Up+NE7R3AhvxILNnyLdYvG8QPIRLcQgyQftvSeQQgLMGDVh6fxyck8XCyowvrHxvO2PNF35RfS+t0nQH4UtzzH/j2n6/bkMl57s9UgSgfgHgC49CoARXYaERwrP5QZFAsEx8nx4FggKA4IjJJ3pHw0IMgrr8XhrFIcvlqGbzNLcb3M3GR5QrgJU2+LwgNj4jAuPpR3ynu7ogvAoQ3A6a2AtU7OGzwFmPk60G+YZ8vmpSICjXh/aQqeevcYjmXfxE+2HMUvpg/H0/cOZqdNRLcAgyQf98MJAxAX4od/+esJXCyowtx1B/CL6cPxeGoidFqNp4tH5HuMQUD0CJlaU1cJVOS4BFDXgap8qBU3UFt4BSZrBRTVAlQXyJR3ou3f0vnLj2gGRst3pAJdUkCUnO9YbvDe3txsqkBmcTVO5ZTj6LUyHLpa1iIo0msVpAyMwH3D++H7yVEYFBnAO0a9nbkMOP+pfO/ItdOUuPHAtJeBQfd6rGi9RajJgD8vTcHqT8/hr0dzsHZHBvZcLMLaH47BIF4QJfpOGCT1AZOHRGL7v9yN5/56EmlXS/Hq5+fxUXounp+VjO8NieSJBlFP8gsG/EbK7zW5sFks2LV9O2bPmgl9Q6X81lNVvhxW5gGV+S7z8oCGasBa2xhodUQfIAMmUyRgipA9ALoO/R3T9uQfBmhv/SFCCIHcm7U4c6MCp3LKcTKnHGdvVDg7XHDQKMCo/iG4c1AEUgaGI2VQBAKNPGT1akLId/wy9wBXdgFX9wOqRS5TNEDyHODOnwEJqT57h9QdjDot1jw0GuMTwvCrz8/jePZNzHzzGzw+ORHLvj+E30wk6iYecfqIqGA//PmpFGw9loM12y/gXF4lFr9zBJOSwrFsyhDcPSSSj6sQeQNFY7/70w/A7W3nazDLjiOqi4DqQvuwyGWey3xrLWCpAW7WyF77OssvpGkA5R8q5/mFAH6u4y7JPxQwBgMaLUqr65FRWIVLBVXIKKxGRkElLhdWo8qlowUHf70Wo/uHYFxCKO4cFIGJSWEI8uN7Wb1agxnIPwXkpcuPPV8/DFTmNs0TPRoY/UNg1AJ2yvAdKIqChXfEY/KQCPzHx2fx9aVi/OGbLGw9moMlk5Pwo9RERAX5ebqYRL0Kg6Q+RKNRsGhSAu4fEY31e6/gz4ev48i1MhzZdARJESb8U0oCHhgTh7hQ/443RkSeZTABhiQgLKn9fELIu06OwKm2THYu4Uw3G8cdy2pvynXrKmTC1S4Xrxom1AoTwoQJt8GEOBGAO+CPGuEHs94fpsAQhIWFIzoyEnFRkYjpFwmtHwCjFTDUADYNYAkEdEbeVfBmqg2oKYZSchXxpd9As+eY7MCk5JLszEQ061VQawQSU4HBU4Gh04EofqLiVhoQZsK7T9yB/ZeKsWb7RWQUVuGtPVewcf9VzB4dg/nj+uN7QyL5uD1RJzBI6oMiA41YPXckfnrPILy9/yo+PJ6La6VmvLb9Il7bfhHjEkIxc2QM7hoSiRGxwbzDRNSbKYp8T8oYJL/l1Bk2K1BXDkt1CcqK81FZWojqm0WoqyqDpaYMNnM5lPoKaOor4a9WIxhmhCg1CIYZJqUeABAIMwIVM9DW7qPWnvI6KItGJ9+nMgQBxkA5rjfJ3gd1fo3jzmGzefY8ikaP0JpMoOg84B8k3+fSGWXvaTqj/B0GYzKottS6dDByU7475JiuKW18BLQqXybVCh2A8QDQ/MnPwBig/wSg/3g5jE+RAT65jaIouG94FO4e2g9fnS3AOweuIv16OT45mYdPTuYhMtCA+0dE456h/TB5SCRC/HnHlqg1DJL6sNgQf7z84Ej824zh+PupPHycfgNHs8tw4no5TlwvBwCEmvRIGRiOMQNCMap/CEbFBSMi0OjZghNRt9VZbCitaUBpdT1KqxtQUl2PspoGlNbI8dLqBpTW1KOwsh4l1fUQAgCC7Kn1ICsqyIikiAAkRpgwMEyPwcEqkgKsSAxogJ+tGqgtl3ek6qvkXa36ajl0HW++zGLvuEG1utzR6j4dgHsB4NIrbeRQGgMmrcE+bpB3PlzHdQaX5fZlGh2g0doDLftQo2k2rZWpybROPl6p0cqhg6x0F6L9ZTarfLfHZrEPXaetMjmWWevlY3CWGvvQLD+obDE3Tje/+9MRRQMRGIMSEYrw4anQRiUDkUOBfsNlr4zkEVqNgjljZNf4J3PK8VF6Lj4/nY+S6ga8fyQH7x/JgVajYFRcMG6PD8XtCaEYMyAUSREB7B2PCL0kSFq/fj3Wrl2LgoICjB07FuvWrcOkSZM8XSyfEWDUYdGkBCyalIDCyjrsOFeA/RnFOJxVhnKzBTvOFWLHuUJn/qggI5IiAzAwIkAOI02IC/VHVJAfIgMNvI1P5AZCCNRbVZgbbKipt6LWIofmBhsqay2orLOgstaKCue4BZV19mmX5bWWrp0AG3QaxIb4ISbYD7EhfogN9XdOx4ebkBBuQoA7OlRQbS5BUw3QUNU4bq2VdzuaJLMcWpvPqwMsZgiLGbWVZfDXK1Acy4Tq8oMCsNXLRDKA8w+XHXj4h8kOPhxDZ7f0/WUQFBgNqyrw7fbtmD1zNrR63pnwNrfHh+L2+FC8+MAIHLxSgn0Zxfj6cjGuFtfgVG4FTuVW4N20bADy3/ygyAAMjgrE4H6BiA/zR5z9331siD/8DfzeIvUNXh8kbd26FStXrsTGjRuRkpKCN998EzNmzEBGRgaiovjRtFstOtgPP05Nwo9Tk2CxqTidW4Hj2WU4e6MSZ29U4GpJDYqq6lFUVY8jWWUt1lcU+ThfVJAR/YKMCPXXI8SegpuNmwxa+Ou18NNr5bhBCz+dlo/3kVdSVQGrKmBVVVhVAZtNwKKqsKkCVptAvdWGequKequKhiZDG+otKhpsKuotNvvQvtxlXm29FVdzNPhb8XHUWmQwZG6w2odyXG1+E6GbDFoNIgINiAg0IDzAiMgAg33aiIgAAyID5b/f2BA/hAcYPNMDpkbb2BnELWC1WLBz+3bMnj0besdJvGqTd1Zs9YC1AbDZk7W+2Xi9vBPTYr59qFplwKXa7OP2oao2m7bJ1GTaPmyuRZ0rbS/T6OTHhzV62SOhRm+f1rVcpjXKx930AfahqfERRsfQGAgYArv2+KGjlzryanqtBvcNj3J+dDb3phnp18tx8no5TubcxLm8StRbVVwsqMLFgqpWtxFq0iMqyIgwk0GmAAPCTHqEBxgQ4q9HoFEHk1GHAIMWJoMOAcbGob9eyx51qdfw+iDpjTfewNKlS/HEE08AADZu3IgvvvgCmzZtwvPPP+/h0vk2vVaDCYlhmJAY5pxXVWdBZnENrpXUIKukBtdKa3Ct1IzCijoUV9fDpgoUV9WjuKr7V2ONOg387QGUXquBXqvYhxronOON8xzjGgUouKFB2t/PQ6/VQqPIzio0igKtRoGiAFpFTre3TFHkYwoaRYFGozhPTRQFcEw59vFKk/HGmY3rKC3zueRvfqxQlPZ/z/GkjbA/fiNE44M4wuUxHNd8zvFW8grnf5rlhes6cspqteFsgYKbR3Kg1Wic+Rq3L5qs17hcTqhCwKbKoRACqpDjqpB5bGrjuOqyXAjYl7WyXG2ZV7Uvt6ktt2W1NQY5clzAapOBjkVV7YGPLIvF1hgAWVX1lgUo7dMAZaUd5vLTa2Ay6GAyaBFg0CHYX4dgP3nxIdhP57wgIec1LnNcoAj20/FEBZCBmMEEgO/IUN80IMyEAWEmPDhWPhZpUwVu3KzFleIqXCmqRmZRDfIqapFXXov8ijqYG2woN1tQbu5eUKwo8hiv12pg1Glg0Gpg0Mmkd4zbh458Wo08Tmvtx2StokCrtQ/tx2qd1nE8h32+BloNnPkdx3bHMVZRHMdlxTluU1WcK1BQcTQHOq3OJY/9WOy6jmO+0vRY3nT7TfexAi2P0Y3Lmk23eKy2+fLm64s2l3f1txznBQKOY27j+YHjmC5E4zHfcdx1/JYjr+oyDshziNISBbPb/cu8i1cHSQ0NDTh+/DheeOEF5zyNRoNp06YhLS2t1XXq6+tRX994gl5ZWQkAsFgssFg8e6XL8fueLsd34acFRsYEYGRMyw9T2lSBspoG552mkup6VNRamz72U2dBRa0VVXXysZ86i80+bHzsxXE1vhzdqScN0opyO85G3aTFtqwLni6E11AUQKdRoNMoMOq0zoO9wX4CYNQ3jjsO+o5xg04Lg7ZxPa0ikJ15CRPGjEKgv8F+FVbeYQ0w6ODvmNZrv/P7AlZryy64fZ0v7H+9GevX/XqqjmOD9YgNDsfdg8ObzBdCoLLOivyKOpRUN6Dc3IDyWgtummUqNzegotZifyTY1uRuuOM7aEIAdRYVdRYVrd+n8jQe49zptlDFK/YRnS2DVwdJJSUlsNlsiI6ObjI/OjoaFy9ebHWdNWvW4JVXWr6Y+49//AMmk3dcLdy5c6eni9AjAuwpDgC0AALtqRWqAKwq0OBINsCiAlYB2FTAJhQ57kiqy7h9Xce4vHqhyCsZsF/ZcB1H02nX+Wqz5a53D9q7GtP8DkqL5a2s29Fyx/2o5ssabwC45mq+zOVuVhvL21qmdLDcdZnSLGN762rQePVO0+QKXfvzNY7fUkTL5Y48zaabb8vx21p73pZD0c6ylkPH+Hd+MlQF0GBPABJiABSdgQqgyp7o1uor+19PYf26n7fUsQZAuD1BQeNBvxWqkMf0epdju9VlaBNKk+nG+bA/CWBPgPMuhQrFOd/1mC0EYGuSr+mxH3A5Ztv/43pMbi1Pa09ZOJe1up48B2l5w77pAb0zh5CObvq382Dud9qm41jqOu0cbzYNND0uw3XcZVmMSXhF+zWbzZ3K59VBUne88MILWLlypXO6srIS8fHxmD59OoKDgz1YMhm57ty5E/fff3/jM/F0y7B+3Yv1616sX/di/boX69f9WMfuxfp1L2+qX8dTZh3x6iApMjISWq0WhYWFTeYXFhYiJiam1XWMRiOMxpZdVOv1eo//T3HwprL4Itave7F+3Yv1616sX/di/bof69i9WL/u5Q3129nf9+q+mg0GAyZMmIDdu3c756mqit27dyM1NdWDJSMiIiIiIl/l1XeSAGDlypV4/PHHMXHiREyaNAlvvvkmampqnL3dERERERER3UpeHyQ98sgjKC4uxksvvYSCggLcfvvt+Oqrr1p05kBERERERHQreH2QBADLly/H8uXLPV0MIiIiIiLqA7z6nSQiIiIiIqKexiCJiIiIiIjIBYMkIiIiIiIiFwySiIiIiIiIXDBIIiIiIiIicsEgiYiIiIiIyAWDJCIiIiIiIhcMkoiIiIiIiFwwSCIiIiIiInLBIImIiIiIiMgFgyQiIiIiIiIXDJKIiIiIiIhcMEgiIiIiIiJyofN0AdxNCAEAqKys9HBJAIvFArPZjMrKSuj1ek8Xx+ewft2L9eterF/3Yv26F+vX/VjH7sX6dS9vql9HTOCIEdri80FSVVUVACA+Pt7DJSEiIiIiIm9QVVWFkJCQNpcroqMwqpdTVRV5eXkICgqCoigeLUtlZSXi4+ORk5OD4OBgj5bFF7F+3Yv1616sX/di/boX69f9WMfuxfp1L2+qXyEEqqqqEBcXB42m7TePfP5OkkajwYABAzxdjCaCg4M93kB8GevXvVi/7sX6dS/Wr3uxft2PdexerF/38pb6be8OkgM7biAiIiIiInLBIImIiIiIiMgFg6QeZDQasXr1ahiNRk8XxSexft2L9eterF/3Yv26F+vX/VjH7sX6da/eWL8+33EDERERERFRV/BOEhERERERkQsGSURERERERC4YJBEREREREblgkEREREREROSCQdIttn79eiQlJcHPzw8pKSk4cuRIu/m3bduG5ORk+Pn5YfTo0di+fXsPlbR3WbNmDe644w4EBQUhKioK8+fPR0ZGRrvrbNmyBYqiNEl+fn49VOLe5eWXX25RV8nJye2uw7bbNUlJSS3qWFEULFu2rNX8bL/t+/rrrzF37lzExcVBURR88sknTZYLIfDSSy8hNjYW/v7+mDZtGi5fvtzhdru6D/dV7dWvxWLBqlWrMHr0aAQEBCAuLg4//vGPkZeX1+42u7Of8VUdtd8lS5a0qKuZM2d2uF22X6mj+m1tX6woCtauXdvmNtl+pc6cj9XV1WHZsmWIiIhAYGAgFixYgMLCwna32919tjsxSLqFtm7dipUrV2L16tVIT0/H2LFjMWPGDBQVFbWa/9tvv8WiRYvw5JNP4sSJE5g/fz7mz5+Ps2fP9nDJvd/+/fuxbNkyHDp0CDt37oTFYsH06dNRU1PT7nrBwcHIz893puzs7B4qce8zcuTIJnV14MCBNvOy7Xbd0aNHm9Tvzp07AQAPP/xwm+uw/batpqYGY8eOxfr161td/pvf/AZvvfUWNm7ciMOHDyMgIAAzZsxAXV1dm9vs6j7cl7VXv2azGenp6XjxxReRnp6Ojz76CBkZGXjwwQc73G5X9jO+rKP2CwAzZ85sUlfvv/9+u9tk+23UUf261mt+fj42bdoERVGwYMGCdrfL9tu587Gf//zn+Oyzz7Bt2zbs378feXl5eOihh9rdbnf22W4n6JaZNGmSWLZsmXPaZrOJuLg4sWbNmlbzL1y4UMyZM6fJvJSUFPHP//zPbi2nLygqKhIAxP79+9vMs3nzZhESEtJzherFVq9eLcaOHdvp/Gy7391zzz0nBg8eLFRVbXU522/nARAff/yxc1pVVRETEyPWrl3rnFdeXi6MRqN4//3329xOV/fhfUXz+m3NkSNHBACRnZ3dZp6u7mf6itbq9/HHHxfz5s3r0nbYflvXmfY7b948MWXKlHbzsP22rvn5WHl5udDr9WLbtm3OPBcuXBAARFpaWqvb6O4+2914J+kWaWhowPHjxzFt2jTnPI1Gg2nTpiEtLa3VddLS0prkB4AZM2a0mZ8aVVRUAADCw8PbzVddXY3ExETEx8dj3rx5OHfuXE8Ur1e6fPky4uLiMGjQIDz22GO4fv16m3nZdr+bhoYGvPfee/jJT34CRVHazMf22z1ZWVkoKCho0kZDQkKQkpLSZhvtzj6cGlVUVEBRFISGhrabryv7mb5u3759iIqKwvDhw/HMM8+gtLS0zbxsv91XWFiIL774Ak8++WSHedl+W2p+Pnb8+HFYLJYmbTE5ORkJCQlttsXu7LN7AoOkW6SkpAQ2mw3R0dFN5kdHR6OgoKDVdQoKCrqUnyRVVbFixQrcddddGDVqVJv5hg8fjk2bNuHTTz/Fe++9B1VVMXnyZOTm5vZgaXuHlJQUbNmyBV999RU2bNiArKws3H333aiqqmo1P9vud/PJJ5+gvLwcS5YsaTMP22/3OdphV9pod/bhJNXV1WHVqlVYtGgRgoOD28zX1f1MXzZz5kz86U9/wu7du/H6669j//79mDVrFmw2W6v52X67791330VQUFCHj4Ox/bbU2vlYQUEBDAZDiwsmHZ0PO/J0dp2eoPPYLxN107Jly3D27NkOnwVOTU1Famqqc3ry5Mm47bbb8Pbbb+PVV191dzF7lVmzZjnHx4wZg5SUFCQmJuKDDz7o1NU16pp33nkHs2bNQlxcXJt52H6pN7BYLFi4cCGEENiwYUO7ebmf6bxHH33UOT569GiMGTMGgwcPxr59+zB16lQPlsz3bNq0CY899liHHeOw/bbU2fOx3op3km6RyMhIaLXaFr13FBYWIiYmptV1YmJiupSfgOXLl+Pzzz/H3r17MWDAgC6tq9frMW7cOFy5csVNpfMdoaGhGDZsWJt1xbbbfdnZ2di1axeeeuqpLq3H9tt5jnbYlTbanX14X+cIkLKzs7Fz58527yK1pqP9DDUaNGgQIiMj26wrtt/u+eabb5CRkdHl/THA9tvW+VhMTAwaGhpQXl7eJH9H58OOPJ1dpycwSLpFDAYDJkyYgN27dzvnqaqK3bt3N7ka7Co1NbVJfgDYuXNnm/n7MiEEli9fjo8//hh79uzBwIEDu7wNm82GM2fOIDY21g0l9C3V1dXIzMxss67Ydrtv8+bNiIqKwpw5c7q0Httv5w0cOBAxMTFN2mhlZSUOHz7cZhvtzj68L3MESJcvX8auXbsQERHR5W10tJ+hRrm5uSgtLW2zrth+u+edd97BhAkTMHbs2C6v21fbb0fnYxMmTIBer2/SFjMyMnD9+vU222J39tk9wmNdRvigv/71r8JoNIotW7aI8+fPi5/+9KciNDRUFBQUCCGEWLx4sXj++eed+Q8ePCh0Op347W9/Ky5cuCBWr14t9Hq9OHPmjKf+BK/1zDPPiJCQELFv3z6Rn5/vTGaz2Zmnef2+8sorYseOHSIzM1McP35cPProo8LPz0+cO3fOE3+CV/vFL34h9u3bJ7KyssTBgwfFtGnTRGRkpCgqKhJCsO3eKjabTSQkJIhVq1a1WMb22zVVVVXixIkT4sSJEwKAeOONN8SJEyecvav9+te/FqGhoeLTTz8Vp0+fFvPmzRMDBw4UtbW1zm1MmTJFrFu3zjnd0T68L2mvfhsaGsSDDz4oBgwYIE6ePNlkn1xfX+/cRvP67Wg/05e0V79VVVXiX//1X0VaWprIysoSu3btEuPHjxdDhw4VdXV1zm2w/bato/2DEEJUVFQIk8kkNmzY0Oo22H5b15nzsaefflokJCSIPXv2iGPHjonU1FSRmpraZDvDhw8XH330kXO6M/vsnsYg6RZbt26dSEhIEAaDQUyaNEkcOnTIuezee+8Vjz/+eJP8H3zwgRg2bJgwGAxi5MiR4osvvujhEvcOAFpNmzdvduZpXr8rVqxw/r+Ijo4Ws2fPFunp6T1f+F7gkUceEbGxscJgMIj+/fuLRx55RFy5csW5nG331tixY4cAIDIyMlosY/vtmr1797a6T3DUoaqq4sUXXxTR0dHCaDSKqVOntqj3xMREsXr16ibz2tuH9yXt1W9WVlab++S9e/c6t9G8fjvaz/Ql7dWv2WwW06dPF/369RN6vV4kJiaKpUuXtgh22H7b1tH+QQgh3n77beHv7y/Ky8tb3Qbbb+s6cz5WW1srfvazn4mwsDBhMpnED37wA5Gfn99iO67rdGaf3dMUIYRwzz0qIiIiIiKi3ofvJBEREREREblgkEREREREROSCQRIREREREZELBklEREREREQuGCQRERERERG5YJBERERERETkgkESERERERGRCwZJRERERERELhgkERGRT1iyZAnmz5/v6WIQEZEP0Hm6AERERB1RFKXd5atXr8bvfvc7CCF6qEREROTLGCQREZHXy8/Pd45v3boVL730EjIyMpzzAgMDERgY6ImiERGRD+LjdkRE5PViYmKcKSQkBIqiNJkXGBjY4nG7++67D88++yxWrFiBsLAwREdH4w9/+ANqamrwxBNPICgoCEOGDMGXX37Z5LfOnj2LWbNmITAwENHR0Vi8eDFKSkp6+C8mIiJPYpBEREQ+691330VkZCSOHDmCZ599Fs888wwefvhhTJ48Genp6Zg+fToWL14Ms9kMACgvL8eUKVMwbtw4HDt2DF999RUKCwuxcOFCD/8lRETUkxgkERGRzxo7dix++ctfYujQoXjhhRfg5+eHyMhILF26FEOHDsVLL72E0tJSnD59GgDw+9//HuPGjcNrr72G5ORkjBs3Dps2bcLevXtx6dIlD/81RETUU/hOEhER+awxY8Y4x7VaLSIiIjB69GjnvOjoaABAUVERAODUqVPYu3dvq+83ZWZmYtiwYW4uMREReQMGSURE5LP0en2TaUVRmsxz9JqnqioAoLq6GnPnzsXrr7/eYluxsbFuLCkREXkTBklERER248ePx4cffoikpCTodDxEEhH1VXwniYiIyG7ZsmUoKyvDokWLcPToUWRmZmLHjh144oknYLPZPF08IiLqIQySiIiI7OLi4nDw4EHYbDZMnz4do0ePxooVKxAaGgqNhodMIqK+QhH8PDkREREREZETL4sRERERERG5YJBERERERETkgkESERERERGRCwZJRERERERELhgkERERERERuWCQRERERERE5IJBEhERERERkQsGSURERERERC4YJBEREREREblgkEREREREROSCQRIREREREZGL/w/yaLGTQvSwdAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, data[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, data[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Standard Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's add some stochasticity: " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sigma1 = 1\n", + "sigma2 = 0\n", + "g_params = torch.tensor([[sigma1, sigma2]])\n", + "# Create the SDE model\n", + "sde = LotkaVolterraSDE(a, b, c, d, g_params)\n", + "\n", + "\n", + "t_span = torch.linspace(0, 20, 2000)\n", + "\n", + "# Initial condition\n", + "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", + "\n", + "# Integrate the SDE model\n", + "data_stochastic = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "\n", + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, data_stochastic[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, data_stochastic[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Training a Neural SDE Using Maximal Likelihood methods (reference tracking loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now show how might attempt to train a Neural SDE to fit this data using our standard Neuromancer training procedure for Neural ODEs. First we construct a Neural SDE with learnable parameters. Note that to ensure the output size condition of $g$ we pass in batch size" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraSDELearnable(BaseSDESystem):\n", + " def __init__(self, block, batch_size):\n", + " super().__init__()\n", + " self.block = block \n", + " self.batch_size = batch_size\n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + "\n", + "\n", + " def f(self, t, y):\n", + "\n", + " x1 = y[:, [0]]\n", + " x2 = y[:, [-1]]\n", + "\n", + " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", + " dx2 = self.delta*self.block(y) - self.gamma*x2\n", + "\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + " \n", + " def g(self, t, y):\n", + " return torch.rand(self.batch_size, 2)\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "sde_learnable = LotkaVolterraSDELearnable(block=net, batch_size=10)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We transform the shape of the Lotka-Volterra stochastic dataset from earlier to have a \"rollout\" of 2 steps (to mimic how training is done for the Neural ODE case)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "nx = 2\n", + "nsim = 2000\n", + "nsteps = 2\n", + "nbatch = nsim//nsteps\n", + "length = (nsim//nsteps) * nsteps\n", + "bs = 10\n", + "x_train_lvs = data_stochastic.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", + "train_data_lvs = DictDataset({'X': x_train_lvs, 'xn': x_train_lvs[:, 0:1, :]}, name='train')\n", + "train_loader_lvs = DataLoader(train_data_lvs, batch_size=bs,\n", + " collate_fn=train_data_lvs.collate_fn, shuffle=True)\n", + "\n", + "x_dev_lvs = data_stochastic.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", + "dev_data_lvs = DictDataset({'X': x_dev_lvs, 'xn': x_dev_lvs[:, 0:1, :]}, name='dev')\n", + "dev_loader_lvs = DataLoader(dev_data_lvs, batch_size=bs,\n", + " collate_fn=dev_data_lvs.collate_fn, shuffle=True)\n", + "\n", + "x_test_lvs = data_stochastic.squeeze(1)[:length].reshape(1, nsim, nx)\n", + "test_data_lvs = DictDataset({'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}, name='test')\n", + "test_data_lvs_2 = {'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define our integrator which is a NeuroMANCER wrapper for torchSDE's sdeint. To support rollout of two time steps we give it a time step tensor of three values." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "class BasicSDEIntegrator(integrators.Integrator): \n", + " \"\"\"\n", + " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", + " Returns a single tensor of size (t, batch_size, state_size).\n", + "\n", + " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", + " Currently only supports Euler integration. Choice of integration method is dependent \n", + " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", + " \"\"\"\n", + " def __init__(self, block ): \n", + " \"\"\"\n", + " :param block: (nn.Module) The BasicSDE block\n", + " \"\"\"\n", + " super().__init__(block) \n", + "\n", + "\n", + " def integrate(self, x): \n", + " \"\"\"\n", + " x is the initial datastate of size (batch_size, state_size)\n", + " t is the time-step vector over which to integrate\n", + " \"\"\"\n", + " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", + " x = x.squeeze(1) #remove time step \n", + " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", + " ys = ys.permute(1, 0, 2)\n", + " return ys " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We **attempt** to train the model using reference tracking and finite-difference loss workflow. Note that this model **will not** train, and this is meant to serve a motivating reason to utilize the **Latent SDE** framework." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch: 0 train_loss: 0.022880392149090767\n", + "epoch: 1 train_loss: 0.02143489383161068\n", + "epoch: 2 train_loss: 0.021883254870772362\n", + "epoch: 3 train_loss: 0.021374505013227463\n", + "epoch: 4 train_loss: 0.021629080176353455\n", + "epoch: 5 train_loss: 0.020452190190553665\n", + "epoch: 6 train_loss: 0.02012060210108757\n", + "epoch: 7 train_loss: 0.02023428864777088\n", + "epoch: 8 train_loss: 0.019538771361112595\n", + "epoch: 9 train_loss: 0.020421745255589485\n", + "epoch: 10 train_loss: 0.020114833489060402\n", + "epoch: 11 train_loss: 0.020311430096626282\n", + "epoch: 12 train_loss: 0.01905309595167637\n", + "epoch: 13 train_loss: 0.019500289112329483\n", + "epoch: 14 train_loss: 0.019971054047346115\n", + "epoch: 15 train_loss: 0.01975109986960888\n", + "Interrupted training loop.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrator = BasicSDEIntegrator(sde_learnable) \n", + "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", + "x = variable(\"X\")\n", + "xhat = variable('xn')[:, :-1, :]\n", + "\n", + "# trajectory tracking loss\n", + "reference_loss = (xhat == x)^2\n", + "reference_loss.name = \"ref_loss\"\n", + "\n", + "# finite difference variables\n", + "xFD = (x[:, 1:, :] - x[:, :-1, :])\n", + "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n", + "\n", + "\n", + "# finite difference loss\n", + "fd_loss = 2.0*((xFD == xhatFD)^2)\n", + "fd_loss.name = 'FD_loss'\n", + "\n", + "# %%\n", + "objectives = [reference_loss, fd_loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([model_sde], loss)\n", + "# plot computational graph\n", + "problem.show()\n", + "\n", + "# %%\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "trainer = Trainer(\n", + " problem,\n", + " train_loader_lvs,\n", + " dev_loader_lvs,\n", + " test_data_lvs_2,\n", + " optimizer,\n", + " patience=50,\n", + " warmup=0,\n", + " epochs=50,\n", + " eval_metric=\"dev_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"dev_loss\",\n", + " test_metric=\"dev_loss\",\n", + " device='cpu', \n", + " epoch_verbose=1\n", + ")\n", + "# %%\n", + "best_model = trainer.train()\n", + "problem.load_state_dict(best_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "with torch.no_grad(): \n", + "\n", + " # Initial condition\n", + " x0 = torch.ones([10,2])*10. #[1x2]\n", + " # Integrate the SDE model\n", + " data_stochastic_hat = torchsde.sdeint(sde_learnable, x0, t_span, method='euler')" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, data_stochastic_hat[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, data_stochastic_hat[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Learned Stochastic Lotka-Volterra Trajectories')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear using our rollout-based loss functions on observed stochastic data, similar to that in the Neural ODE case, does not work as it cannot capture the inherent randomness. Another way of putting it is the undeterministic nature of the system can cause the learned system to be unstable and blow up. Developing appropriate loss functions to ensure correctness and stability using this framework is an open research question. Thus we necessitate the need to reformulate the above procedure in a latent space and use a variational inference method to learn the stochastic data-generating distributions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Latent SDEs\n", + "\n", + "Using mean squared error (MSE) to train a neural SDE on time pairs might encounter challenges due to the stochastic nature of SDEs. While MSE is a common loss function used for deterministic systems, it may not be directly applicable to stochastic systems like SDEs. As discussed above, this is because these rollout-MSE -based losses do not capture the underlying uncertainty: \n", + "\n", + "Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n", + "\n", + "Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n", + "\n", + "SDEs inherently involve randomness or uncertainty, typically represented by the stochastic terms in the differential equations. Instead of using refernence tracking + finite difference loss through our rollout mechanism, we can train the model using variational inference. \n", + "\n", + "#### Variaional Inference \n", + "Variational inference allows us to capture this uncertainty by providing a probabilistic characterization of the latent variables' distribution. Instead of obtaining a single point estimate, variational inference provides a full probabilistic description, including measures of uncertainty such as confidence intervals or predictive distributions. Variational inference offers a solution to this problem by approximating the true posterior distribution with a simpler, parameterized distribution, often chosen from a family of distributions such as Gaussian distributions. This is done via an encoder network. The decoder network draws from samples of this learned, approximate posterior to reconstruct the data distribution. Using the KL divergence, between these distributions, we learn the latent space's parameters known as the variational parameters. \n", + "\n", + "The Latent SDE is essentially a variational \"autoencoder\" where instead of seeking resynthesize samples corresponding to the \"same time\" instance, it tries to reconstruct future samples given the current dynamics of the system, where the dynamics are known to be modeled via a SDE. To do this, the latent space is **itself** going to be governed via an SDE and we perform integration on the latent space to synthesize forward-looking samples. \n", + "\n", + "#### Latent SDE Architecture\n", + "\n", + "We now define the architecture for the Latent SDE and train on a 3D stochastic Lorenz attractor dataset. Code adapted from https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. **Encoder**:\n", + " - The encoder maps each input data point $x$ to a distribution over latent variables $z$. This distribution is typically Gaussian with mean $\\mu$ and standard deviation $\\sigma$.\n", + " $$\n", + " q_{\\phi}(z | x) = \\mathcal{N}(\\mu_{\\phi}(x), \\sigma_{\\phi}(x))\n", + " $$\n", + " - Here, $\\mu_{\\phi}(x)$ and $\\sigma_{\\phi}(x)$ are the mean and standard deviation parameters of the Gaussian distribution, which are output by the encoder neural network parameterized by $\\phi$.\n", + "\n", + "2. **Latent Dynamics**:\n", + " - The latent variables $z$ evolve over time according to a stochastic differential equation (SDE). The dynamics of $z$ are governed by drift and diffusion functions, similar to the SDE for the observed data $x$.\n", + " $$\n", + " dz_t = f(z_t, t) \\, dt + G(z_t, t) \\, dW_t\n", + " $$\n", + " - $f(z_t, t)$ represents the drift component, determining the deterministic evolution of the latent variables.\n", + " - $G(z_t, t)$ represents the diffusion component, introducing stochasticity into the latent dynamics.\n", + " - $dW_t$ is the increment of a Wiener process (Brownian motion), representing random noise.\n", + "\n", + "3. **Decoder**:\n", + " - The decoder takes samples from the latent space $z$ and maps them back to the data space $x$. It models the conditional distribution of $x$ given $z$.\n", + " $$\n", + " p_{\\theta}(x | z)\n", + " $$\n", + " - The decoder neural network, parameterized by $\\theta$, outputs the parameters of the conditional distribution $p_{\\theta}(x | z)$, such as the mean and variance of a Gaussian distribution or the parameters of a Bernoulli distribution for binary data.\n", + "\n", + "4. **Latent Variable Prior**:\n", + " - We assume a prior distribution over the latent variables $z$. This distribution is typically chosen to be a standard Gaussian.\n", + " $$\n", + " p(z) = \\mathcal{N}(0, I)\n", + " $$\n", + "\n", + " though in TorchSDE's framework (and as shown in the code below), these are learnable parameters pz0_mean and pz0_logstd\n", + "\n", + "5. **Objective Function**:\n", + " - The objective function for training the Latent SDE model is similar to the ELBO in VAEs but now includes the evolution of latent variables governed by the SDE.\n", + " $$\n", + " \\text{ELBO}(\\theta, \\phi; x) = \\mathbb{E}_{q_{\\phi}(z | x)} [\\log p_{\\theta}(x | z)] - \\text{KL}[q_{\\phi}(z | x) || p(z)]\n", + " $$\n", + " - The first term represents the reconstruction loss, measuring how well the decoder reconstructs the input data $x$ from the latent variable samples $z$.\n", + " - The second term is the KL divergence between the approximate posterior $q_{\\phi}(z | x)$ and the prior $p(z)$, which encourages the approximate posterior to match the prior.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Functions to generate data from a Lorenz attractor\n", + "\n", + "We define the 3D stochastic Lorenz attractor model. This model as appropriate $f$ and $g$ functions defined. To produce data samples we integrate this using the Euler-Murayama integrator from TorchSDE library" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "from neuromancer.dynamics.sde import StochasticLorenzAttractor\n", + "class StochasticLorenzAttractor(BaseSDESystem):\n", + " def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)):\n", + " super(BaseSDESystem).__init__()\n", + " self.a = a\n", + " self.b = b\n", + "\n", + " def f(self, t, y):\n", + " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", + " a1, a2, a3 = self.a\n", + "\n", + " f1 = a1 * (x2 - x1)\n", + " f2 = a2 * x1 - x2 - x1 * x3\n", + " f3 = x1 * x2 - a3 * x3\n", + " return torch.cat([f1, f2, f3], dim=1)\n", + "\n", + " def g(self, t, y):\n", + " x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1)\n", + " b1, b2, b3 = self.b\n", + "\n", + " g1 = x1 * b1\n", + " g2 = x2 * b2\n", + " g3 = x3 * b3\n", + " return torch.cat([g1, g2, g3], dim=1)\n", + "stochastic_lorenz_model = StochasticLorenzAttractor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neuromancer Integration\n", + "\n", + "As per the NeuroMANCER x Lightning workflow, generate the data_setup_function and return the DictDatasets. Note that we only have a train dataset here, so we return `None` for dev/test datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def make_dataset(t0, t1, batch_size, noise_std, steps=100):\n", + " _y0 = torch.randn(batch_size, 3)\n", + " ts = torch.linspace(t0, t1, steps=steps)\n", + " xs = stochastic_lorenz_model.sample(_y0, ts, noise_std, normalize=True)\n", + " train_data = DictDataset({'xs':xs},name='train')\n", + " return train_data, None, None, batch_size\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define some experimental parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "data_size = 3\n", + "batch_size=256\n", + "latent_size=4\n", + "context_size=64\n", + "hidden_size=128\n", + "lr_init=1e-2\n", + "t0=0.\n", + "t1=2.\n", + "lr_gamma=0.997\n", + "num_iters=1\n", + "kl_anneal_iters=1000\n", + "pause_every=50\n", + "noise_std=0.01\n", + "method=\"euler\"\n", + "steps = 100 # number of time steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the aforementioned discussion as well as equations above for the latent SDE, we now define the model architecture consisting of a LatentSDE Encoder and Decoder. For integration in the latent space we use a LatentSDE Integrator." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "class Encoder(nn.Module):\n", + " \"\"\"\n", + " Encoder module to handle time-series data (as in the case of stochastic data and SDE)\n", + " GRU is used to handle mapping to latent space in this case\n", + " This class is used only in LatentSDE_Encoder\n", + " \"\"\"\n", + " def __init__(self, input_size, hidden_size, output_size):\n", + " super(Encoder, self).__init__()\n", + " self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size)\n", + " self.lin = nn.Linear(hidden_size, output_size)\n", + "\n", + " def forward(self, inp):\n", + " out, _ = self.gru(inp)\n", + " out = self.lin(out)\n", + " return out\n", + " \n", + "class LatentSDE_Encoder(BaseSDESystem):\n", + " def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False):\n", + " super(BaseSDESystem).init__()\n", + "\n", + "\n", + " self.adjoint = False\n", + "\n", + " # Encoder.\n", + " self.encoder = Encoder(input_size=latent_size, hidden_size=hidden_size, output_size=context_size)\n", + " self.qz0_net = nn.Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space\n", + "\n", + " # Decoder.\n", + " self.f_net = nn.Sequential(\n", + " nn.Linear(latent_size + context_size, hidden_size),\n", + " nn.Softplus(),\n", + " nn.Linear(hidden_size, hidden_size),\n", + " nn.Softplus(),\n", + " nn.Linear(hidden_size, latent_size),\n", + " )\n", + " self.h_net = nn.Sequential(\n", + " nn.Linear(latent_size, hidden_size),\n", + " nn.Softplus(),\n", + " nn.Linear(hidden_size, hidden_size),\n", + " nn.Softplus(),\n", + " nn.Linear(hidden_size, latent_size),\n", + " )\n", + " # This needs to be an element-wise function for the SDE to satisfy diagonal noise.\n", + " self.g_nets = nn.ModuleList(\n", + " [\n", + " nn.Sequential(\n", + " nn.Linear(1, hidden_size),\n", + " nn.Softplus(),\n", + " nn.Linear(hidden_size, 1),\n", + " nn.Sigmoid()\n", + " )\n", + " for _ in range(latent_size)\n", + " ]\n", + " )\n", + " self.projector = nn.Linear(latent_size, data_size)\n", + "\n", + " self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))\n", + " self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))\n", + "\n", + " self._ctx = None\n", + " self.ts = ts\n", + "\n", + " def contextualize(self, ctx):\n", + " self._ctx = ctx # A tuple of tensors of sizes (T,), (T, batch_size, d).\n", + "\n", + " def f(self, t, y):\n", + " ts, ctx = self._ctx\n", + "\n", + " i = min(torch.searchsorted(ts, t, right=True), len(ts) - 1)\n", + "\n", + " return self.f_net(torch.cat((y, ctx[i]), dim=1))\n", + "\n", + " def h(self, t, y):\n", + " return self.h_net(y)\n", + "\n", + " def g(self, t, y): # Diagonal diffusion.\n", + " y = torch.split(y, split_size_or_sections=1, dim=1)\n", + " out = [g_net_i(y_i) for (g_net_i, y_i) in zip(self.g_nets, y)]\n", + " return torch.cat(out, dim=1)\n", + "\n", + " def forward(self, xs):\n", + " # Contextualization is only needed for posterior inference.\n", + " ctx = self.encoder(torch.flip(xs, dims=(0,)))\n", + " ctx = torch.flip(ctx, dims=(0,))\n", + " self.contextualize((self.ts, ctx))\n", + "\n", + " qz0_mean, qz0_logstd = self.qz0_net(ctx[0]).chunk(chunks=2, dim=1)\n", + " z0 = qz0_mean + qz0_logstd.exp() * torch.randn_like(qz0_mean)\n", + " if not self.adjoint:\n", + " return z0, xs, self.ts, qz0_mean, qz0_logstd\n", + " else:\n", + " adjoint_params = (\n", + " (ctx,) +\n", + " tuple(self.f_net.parameters()) + tuple(self.g_nets.parameters()) + tuple(self.h_net.parameters())\n", + " )\n", + " return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params\n", + "\n", + "class LatentSDE_Decoder(nn.Module):\n", + " \"\"\"\n", + " Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of\n", + " LatentSDEIntegrator and decodes it back into the \"real\" data space and also outputs associated Gaussian distributions\n", + " to be used in the final loss function.\n", + " Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py\n", + " \"\"\"\n", + " def __init__(self, data_size, latent_size, noise_std):\n", + " super().__init__()\n", + " self.noise_std = noise_std\n", + " self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))\n", + " self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))\n", + " self.projector = nn.Linear(latent_size, data_size)\n", + "\n", + " def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd):\n", + " _xs = self.projector(zs)\n", + " xs_dist = Normal(loc=_xs, scale=self.noise_std)\n", + " log_pxs = xs_dist.log_prob(xs).sum(dim=(0, 2)).mean(dim=0)\n", + "\n", + " qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp())\n", + " pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp())\n", + " logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1).mean(dim=0)\n", + " logqp_path = log_ratio.sum(dim=0).mean(dim=0)\n", + " return _xs, log_pxs, logqp0 + logqp_path, log_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define Neuromancer components, variables, and problem to train the LatentSDE. We define two nodes -- one for encoding and integrating, one for decoding. Upon training, this LatentSDE will generate new samples that exhibit the behavior of the Lorenz attractor training data. For this example, we set `adjoint` to `False` (do not use the adjoint sensitivity method). This is because this method seems to be significantly slower. \n", + "\n", + "Also note that we need to pass in the timestep tensor to our `LatentSDE_Encoder`, and as a result need to also define it outside the `make_dataset()` function. We note that this is not the cleanest code and breaks the data abstraction. Additional features will be added to mitigate this." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "ts = torch.linspace(t0, t1, steps=steps)\n", + "\n", + "sde_block_encoder = LatentSDE_Encoder(data_size, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n", + "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False)\n", + "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", + "sde_block_decoder = blocks.LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", + "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", + "\n", + "\n", + "\n", + "log_ratio = variable('log_ratio')\n", + "log_pxs = variable('log_pxs')\n", + "\n", + "# NeuroMANCER loss function format\n", + "loss = (-1.0*log_pxs + log_ratio) == 0.0\n", + "\n", + "# aggregate list of objective terms and constraints\n", + "objectives = [loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([model_1, model_2], loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define helper visualization function (again see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py) that will fire every N epochs in our training loop. This visualization will allow us to see the learned Lorenz attractor samples " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the same Brownian motion for visualization.\n", + "bm_vis = torchsde.BrownianInterval(\n", + " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", + "\n", + "# \n", + "def vis(data_dict, problem, bm_vis, img_path, num_samples=10):\n", + " encoder, decoder = problem.nodes[0], problem.nodes[1] #extract the encoder and decoder from our problem\n", + "\n", + " fig = plt.figure(figsize=(20, 9))\n", + " gs = gridspec.GridSpec(1, 2)\n", + " ax00 = fig.add_subplot(gs[0, 0], projection='3d')\n", + " ax01 = fig.add_subplot(gs[0, 1], projection='3d')\n", + "\n", + " xs = data_dict['xs'] #pull out data sample from the DictDataset\n", + " # Left plot: data.\n", + " z1, z2, z3 = np.split(xs.cpu().numpy(), indices_or_sections=3, axis=-1)\n", + " [ax00.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax00.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax00.set_yticklabels([])\n", + " ax00.set_xticklabels([])\n", + " ax00.set_zticklabels([])\n", + " ax00.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax00.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax00.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax00.set_title('Data', fontsize=20)\n", + " xlim = ax00.get_xlim()\n", + " ylim = ax00.get_ylim()\n", + " zlim = ax00.get_zlim()\n", + "\n", + " # Right plot: samples from learned model.\n", + " mydata = data_dict\n", + " output = decoder(encoder(mydata))\n", + " xs_hat = output['xs_hat'].detach().cpu().numpy() \n", + " #xs = latent_sde.sample(batch_size=xs.size(1), ts=ts, bm=bm_vis).cpu().numpy()\n", + " z1, z2, z3 = np.split(xs_hat, indices_or_sections=3, axis=-1)\n", + "\n", + " [ax01.plot(z1[:, i, 0], z2[:, i, 0], z3[:, i, 0]) for i in range(num_samples)]\n", + " ax01.scatter(z1[0, :num_samples, 0], z2[0, :num_samples, 0], z3[0, :10, 0], marker='x')\n", + " ax01.set_yticklabels([])\n", + " ax01.set_xticklabels([])\n", + " ax01.set_zticklabels([])\n", + " ax01.set_xlabel('$z_1$', labelpad=0., fontsize=16)\n", + " ax01.set_ylabel('$z_2$', labelpad=.5, fontsize=16)\n", + " ax01.set_zlabel('$z_3$', labelpad=0., horizontalalignment='center', fontsize=16)\n", + " ax01.set_title('Samples', fontsize=20)\n", + " ax01.set_xlim(xlim)\n", + " ax01.set_ylim(ylim)\n", + " ax01.set_zlim(zlim)\n", + "\n", + " plt.savefig(img_path)\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neuromancer training the problem to learn the stochastic process\n", + "\n", + "We now train and visualize results using Lightning workflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Fix the same Brownian motion for visualization.\n", + "bm_vis = torchsde.BrownianInterval(\n", + " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", + "\n", + "# Define the custom_training_step to support visualization. \n", + "def custom_training_step(model, batch): \n", + " output = model.problem(batch)\n", + " loss = output[model.train_metric]\n", + " img_path = os.path.join('', f'current_epoch_{model.current_epoch:06d}.pdf')\n", + " if model.current_epoch % 50 == 0: \n", + " vis(batch, model.problem, bm_vis, img_path, num_samples=10)\n", + " return loss\n", + "\n", + "\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "lit_trainer = LitTrainer(epochs=300, accelerator='cpu', train_metric='train_loss', \n", + " dev_metric='train_loss', eval_metric='train_loss', test_metric='train_loss',\n", + " custom_optimizer=optimizer, custom_training_step=custom_training_step)\n", + "\n", + "\n", + "\n", + "lit_trainer.fit(problem=problem, data_setup_function=make_dataset, t0=t0, t1=t1, batch_size=batch_size, noise_std=noise_std)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neuromancer3", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/SDEs/sde_walkthrough_draft.ipynb b/examples/SDEs/sde_walkthrough_draft.ipynb new file mode 100644 index 00000000..bc90997e --- /dev/null +++ b/examples/SDEs/sde_walkthrough_draft.ipynb @@ -0,0 +1,545 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from neuromancer.system import Node, System\n", + "from neuromancer.dynamics import integrators, ode\n", + "from neuromancer.trainer import Trainer\n", + "from neuromancer.problem import Problem\n", + "from neuromancer.dataset import DictDataset\n", + "from neuromancer.constraint import variable\n", + "from neuromancer.loss import PenaltyLoss\n", + "from neuromancer.modules import blocks\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "\n", + "\n", + "from typing import Sequence\n", + "import abc\n", + "import torchsde\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "An ordinary differential equation is given by \n", + "\n", + "The general form of an ordinary differential equation (ODE) is:\n", + "\n", + "$$ \\frac{{dx}}{{dt}} = f(t, x) $$\n", + "\n", + "A neural ordinary differential equation replaces the RHS with a neural network. That is, the evolution of a system over time is represented by a continuous flow governed by an ODE, where the dynamics are parameterized by a neural network, as shown below: \n", + "\n", + "a continuous-time NODE model: $\\dot{x} = f_{\\theta}(x)$ with trainable parameters $\\theta$.\n", + "\n", + "Given training data consisting of several time-series \"episode\" (e.g. the system dynamics at t, t+1, t+2 -- which in Neuromancer terminology would be a rollout of nsteps=2), we can train such Neural ODE: \n", + "\n", + "Next we need to solve the continuous-time NODE model with suitable ODE solver, e.g., [Runge–Kutta integrator](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods). \n", + "$x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k))$ \n", + "\n", + "For training we need to obtain accurate reverse-mode gradients of the integrated NODE system. This can be done in two ways, either by unrolling the operations of the ODE solver and using the [backpropagation through time](https://en.wikipedia.org/wiki/Backpropagation_through_time) (BPTT) algorithm, or via [Adjoint state method](https://en.wikipedia.org/wiki/Adjoint_state_method).\n", + "\n", + "Schematics illustrating the adjoing method used in the [Neural Ordinary Differential Equations](https://arxiv.org/abs/1806.07366) paper:\n", + " \n", + "\n", + "Neuromancer provides a set of ODE solvers implemented in [integrators.py](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/dynamics/integrators.py).\n", + "For adjoint method we provide the interface to the [open-source implementation](https://github.com/rtqichen/torchdiffeq) via DiffEqIntegrator class.\n", + "\n", + "We give a quick example here to motivate how one might include (and not include) stochasticity (randomness) into the system dynamics. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The ordinary Lotka-Volterra system, also known as the predator-prey model, describes the dynamics of two interacting species in a biological community. The system consists of two coupled ordinary differential equations (ODEs), typically represented as follows:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt \\\\\n", + "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where:\n", + "- \\(X\\) and \\(Y\\) represent the population sizes of the prey and predator species, respectively.\n", + "- \\(\\alpha\\), \\(\\beta\\), \\(\\gamma\\), and \\(\\delta\\) are parameters governing the growth and interaction rates of the species.\n", + "- \\(dW_1\\) and \\(dW_2\\) are independent Wiener processes representing white noise in the population dynamics.\n", + "- \\(\\sigma_1\\) and \\(\\sigma_2\\) are the volatility parameters associated with the noise processes.\n", + "\n", + "This system captures the stochastic fluctuations in population sizes due to random environmental factors, which can influence the dynamics of predator-prey interactions over time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You have a neural network, denoted as $f_{\\theta}$ which represents the right-hand side (RHS) of the ordinary differential equation (ODE). This neural network takes the current state of the system as input and outputs the rate of change (derivative) of the state variables. \n", + "\n", + "We then use ODE solver to generate data states at future time, e.g. at $t+1$ given by $x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k))$ \n", + "\n", + "\n", + "You train the entire model, including the neural network dynamics and the ODE solver, end-to-end using pairs of consecutive time points (for the case of a rollout of 1 step, though this can be scaled up to predict longer horizons) from your dataset. We reshape our full system trajectory dataset into these bunched time \"episodes\" to achieve this rollout-based training. \n", + "\n", + "​" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraHybrid(ode.ODESystem):\n", + "\n", + " def __init__(self, block, insize=2, outsize=2):\n", + " \"\"\"\n", + "\n", + " :param block:\n", + " :param insize:\n", + " :param outsize:\n", + " \"\"\"\n", + " super().__init__(insize=insize, outsize=outsize)\n", + " self.block = block\n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " assert self.block.in_features == 2\n", + " assert self.block.out_features == 1\n", + "\n", + " def ode_equations(self, x):\n", + " x1 = x[:, [0]]\n", + " x2 = x[:, [-1]]\n", + " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", + " dx2 = self.delta*self.block(x) - self.gamma*x2\n", + " return torch.cat([dx1, dx2], dim=-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\dot{x} = f_{\\vec{\\theta}}(x)$ \n", + "\n", + "Here, $ \\vec{\\theta} $ is a vector with parameters $ [\\alpha, \\beta, \\gamma, \\delta, \\theta'] $ and $\\theta'$ parameterizes the multi-layer perception block" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def get_data(sys, nsim, nsteps, ts, bs):\n", + " \"\"\"\n", + " :param nsteps: (int) Number of timesteps for each batch of training data\n", + " :param sys: (psl.system)\n", + " :param ts: (float) step size\n", + " :param bs: (int) batch size\n", + "\n", + " \"\"\"\n", + " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", + " nx = sys.nx\n", + " nbatch = nsim//nsteps #500\n", + " length = (nsim//nsteps) * nsteps #1000\n", + " ts = torch.linspace(0,1,nsteps)\n", + " print('train sim ', train_sim['X'].shape)\n", + "\n", + " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " trainX = torch.tensor(trainX, dtype=torch.float32)\n", + "\n", + " print(trainX.shape)# N x nsteps x state_size \n", + "\n", + " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", + " train_loader = DataLoader(train_data, batch_size=bs,\n", + " collate_fn=train_data.collate_fn, shuffle=True)\n", + "\n", + " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", + " devX = torch.tensor(devX, dtype=torch.float32)\n", + " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", + " dev_loader = DataLoader(dev_data, batch_size=bs,\n", + " collate_fn=dev_data.collate_fn, shuffle=True)\n", + "\n", + " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", + " testX = torch.tensor(testX, dtype=torch.float32)\n", + " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", + "\n", + " return train_loader, dev_loader, test_data, trainX\n", + "\n", + "torch.manual_seed(0)\n", + "\n", + "# %% ground truth system\n", + "system = psl.systems['LotkaVolterra']\n", + "modelSystem = system()\n", + "ts = modelSystem.ts\n", + "nx = modelSystem.nx\n", + "raw = modelSystem.simulate(nsim=1000, ts=ts)\n", + "plot.pltOL(Y=raw['X'])\n", + "plot.pltPhase(X=raw['Y'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural ODEs with Added Stochasticity: \n", + "\n", + "A stochastic differential equation is given by :\n", + "\n", + "$$ dx = f(t, x) \\, dt + g(t, x) \\, dW $$\n", + "\n", + "THe $f$ term is known as the drift process; the $g$ term is known as the diffusion process. Note that if the diffusion process is zero then an SDE simplifies to an ODE and can be solved with via backpropagating ODE solver and doing the reverse-time ODE as we have shown previously. \n", + "\n", + "For simplicity we can assume there exists reverse-time integration/backpropagating through SDE solvers. This paper, https://arxiv.org/pdf/2001.01328, describes it in detail. \n", + "\n", + "A natural question therefore is how to train such Neural ODEs with Stochastic Terms. Can we do it using standard Neuromancer training procedure for Neural ODEs -- our reference tracking and finite difference losses. We attempt to do this below. Note that it will **not** work and the purpose of demonstrating this is to motivate the need for a variational inference approach to train the SDE -- the Latent SDE. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can formulate neural networks to parameterize not only the drift process, but also the diffusion process, e.g: \n", + "\n", + "$$ \\dot{x} = f_{\\vec{\\theta_f}}(x) + g_{\\vec{\\theta_g}}(x) $$\n", + "\n", + "Where $g$ is a neural network to model the stochastic process. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To support this framework and integrate it with TorchSDE solvers, we define a base class: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class BaseSDESystem(abc.ABC, nn.Module):\n", + " \"\"\"\n", + " Base class for SDEs for integration with TorchSDE library\n", + " \"\"\"\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + " self.in_features = 0\n", + " self.out_features = 0\n", + "\n", + " @abc.abstractmethod\n", + " def f(self, t, y):\n", + " \"\"\"\n", + " Define the ordinary differential equations (ODEs) for the system.\n", + "\n", + " Args:\n", + " t (Tensor): The current time (often unused)\n", + " y (Tensor): The current state variables of the system.\n", + "\n", + " Returns:\n", + " Tensor: The derivatives of the state variables with respect to time.\n", + " The output should be of shape [batch size x state size]\n", + " \"\"\"\n", + " pass\n", + "\n", + " @abc.abstractmethod\n", + " def g(self, t,y):\n", + " \"\"\"\n", + " Define the diffusion equations for the system.\n", + "\n", + " Args:\n", + " t (Tensor): The current time (often unused)\n", + " y (Tensor): The current state variables of the system.\n", + "\n", + " Returns:\n", + " Tensor: The diffusion coefficients per batch item (output is of size \n", + " [batch size x state size]) for noise_type 'diagonal'\n", + " \"\"\"\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use a stochastic Lotka-Volterra model (for forward passes only) with user-defined parameters to generate ground truth data: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Lotka-Volterra SDE\n", + "class LotkaVolterraSDE(nn.Module):\n", + " def __init__(self, a, b, c, d, sigma1, sigma2):\n", + " super().__init__()\n", + " self.a = a\n", + " self.b = b\n", + " self.c = c\n", + " self.d = d\n", + " self.sigma1 = sigma1\n", + " self.sigma2 = sigma2\n", + " self.noise_type = \"diagonal\"\n", + " self.sde_type = \"ito\"\n", + "\n", + " def f(self, t, x):\n", + " x1 = x[:,[0]]\n", + " x2 = x[:,[1]]\n", + " dx1 = self.a * x1 - self.b * x1*x2\n", + " dx2 = self.c * x1*x2 - self.d * x2\n", + " foo = torch.cat([dx1, dx2], dim=-1)\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, x):\n", + " sigma_diag = torch.tensor([[self.sigma1, self.sigma2]])\n", + " return sigma_diag #[batch_size x state size ]\n", + "\n", + "# Define parameters\n", + "a = 1.1 # Prey growth rate\n", + "b = 0.4 # Predation rate\n", + "c = 0.1 # Predator growth rate\n", + "d = 0.4 # Predator death rate\n", + "sigma1 = 1\n", + "sigma2 = 0\n", + "\n", + "# Create the SDE model\n", + "sde = LotkaVolterraSDE(a, b, c, d, sigma1, sigma2)\n", + "\n", + "\n", + "# Define time span\n", + "t_span = torch.linspace(0, 20, 2000)\n", + "\n", + "# Initial condition\n", + "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", + "\n", + "\n", + "# Integrate the SDE model\n", + "sol_train = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "sol_dev = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "sol_test = torchsde.sdeint(sde, x0, t_span, method='euler')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(t_span, sol_train[:, 0,0], label='Prey (x1)')\n", + "plt.plot(t_span, sol_train[:,0, 1], label='Predator (x2)')\n", + "plt.xlabel('Time')\n", + "plt.ylabel('Population')\n", + "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class LotkaVolterraSDELearnable(BaseSDESystem):\n", + " def __init__(self, block, batch_size):\n", + " super().__init__()\n", + " self.block = block \n", + " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.g_params = nn.Parameter(torch.randn(batch_size, 2), requires_grad=True) # Learnable parameters\n", + " def f(self, t, y):\n", + "\n", + " x1 = y[:, [0]]\n", + " x2 = y[:, [-1]]\n", + "\n", + " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", + " dx2 = self.delta*self.block(y) - self.gamma*x2\n", + "\n", + " return torch.cat([dx1, dx2], dim=-1)\n", + "\n", + " def g(self, t, y):\n", + " return self.g_params\n", + "\n", + "# construct UDE model in Neuromancer\n", + "net = blocks.MLP(2, 1, bias=True,\n", + " linear_map=torch.nn.Linear,\n", + " nonlin=torch.nn.GELU,\n", + " hsizes=4*[20])\n", + "fx = LotkaVolterraSDELearnable(block=net, batch_size = 2)\n", + "\n", + "\n", + "class BasicSDEIntegrator(integrators.Integrator): \n", + " \"\"\"\n", + " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", + " Returns a single tensor of size (t, batch_size, state_size).\n", + "\n", + " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", + " Currently only supports Euler integration. Choice of integration method is dependent \n", + " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", + " \"\"\"\n", + " def __init__(self, block ): \n", + " \"\"\"\n", + " :param block: (nn.Module) The BasicSDE block\n", + " \"\"\"\n", + " super().__init__(block) \n", + "\n", + "\n", + " def integrate(self, x): \n", + " \"\"\"\n", + " x is the initial datastate of size (batch_size, state_size)\n", + " t is the time-step vector over which to integrate\n", + " \"\"\"\n", + " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", + " x = x.squeeze(1) #remove time step \n", + " \n", + " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", + " ys = ys.permute(1, 0, 2)\n", + " return ys \n", + "\n", + "integrator = BasicSDEIntegrator(fx) \n", + "# integrate UDE model\n", + "# create symbolic UDE model\n", + "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", + "dynamics_model_sde = model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using mean squared error (MSE) to train a neural SDE on time pairs might encounter challenges due to the stochastic nature of SDEs. While MSE is a common loss function used for deterministic systems, it may not be directly applicable to stochastic systems like SDEs.\n", + "\n", + "Here are some considerations when using MSE for training neural SDEs:\n", + "\n", + "Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n", + "\n", + "Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n", + "\n", + "SDEs inherently involve randomness or uncertainty, typically represented by the stochastic terms in the differential equations. Variational inference allows us to capture this uncertainty by providing a probabilistic characterization of the latent variables' distribution. Instead of obtaining a single point estimate, variational inference provides a full probabilistic description, including measures of uncertainty such as confidence intervals or predictive distributions. \n", + "\n", + "The Latent SDE is essentially a variational \"autoencoder\" where instead of seeking resynthesize samples corresponding to the \"same time\" instance, it tries to reconstruct future samples given the current dynamics of the system, where the dynamics are known to be modeled via a SDE. To do this, the latent space is **itself** going to be governed via an SDE and we perform integration on the latent space to synthesize forward-looking samples. \n", + "\n", + "Variational inference is a powerful method used to approximate complex posterior distributions in probabilistic models. In the context of latent stochastic differential equations (SDEs), variational inference plays a crucial role in estimating the posterior distribution of the latent variables given the observed data.\n", + "\n", + "In latent SDEs, the goal is to infer the hidden or latent variables that govern the dynamics of the system. These latent variables capture unobserved factors that influence the observed data, such as underlying trends, patterns, or noise sources. However, directly computing the posterior distribution of the latent variables given the data is often analytically intractable due to the complex and nonlinear nature of the model.\n", + "\n", + "Variational inference offers a solution to this problem by approximating the true posterior distribution with a simpler, parameterized distribution, often chosen from a family of distributions such as Gaussian distributions. This is done via an encoder network. The decoder network draws from samples of this learned, approximate posterior to reconstruct the data distribution. Using the KL divergence, between these distributions, we learn the latent space's parameters known as the variational parameters. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Latent SDE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. **Encoder**:\n", + " - The encoder maps each input data point $x$ to a distribution over latent variables $z$. This distribution is typically Gaussian with mean $\\mu$ and standard deviation $\\sigma$.\n", + " $$\n", + " q_{\\phi}(z | x) = \\mathcal{N}(\\mu_{\\phi}(x), \\sigma_{\\phi}(x))\n", + " $$\n", + " - Here, $\\mu_{\\phi}(x)$ and $\\sigma_{\\phi}(x)$ are the mean and standard deviation parameters of the Gaussian distribution, which are output by the encoder neural network parameterized by $\\phi$.\n", + "\n", + "2. **Latent Dynamics**:\n", + " - The latent variables $z$ evolve over time according to a stochastic differential equation (SDE). The dynamics of $z$ are governed by drift and diffusion functions, similar to the SDE for the observed data $x$.\n", + " $$\n", + " dz_t = f(z_t, t) \\, dt + G(z_t, t) \\, dW_t\n", + " $$\n", + " - $f(z_t, t)$ represents the drift component, determining the deterministic evolution of the latent variables.\n", + " - $G(z_t, t)$ represents the diffusion component, introducing stochasticity into the latent dynamics.\n", + " - $dW_t$ is the increment of a Wiener process (Brownian motion), representing random noise.\n", + "\n", + "3. **Decoder**:\n", + " - The decoder takes samples from the latent space $z$ and maps them back to the data space $x$. It models the conditional distribution of $x$ given $z$.\n", + " $$\n", + " p_{\\theta}(x | z)\n", + " $$\n", + " - The decoder neural network, parameterized by $\\theta$, outputs the parameters of the conditional distribution $p_{\\theta}(x | z)$, such as the mean and variance of a Gaussian distribution or the parameters of a Bernoulli distribution for binary data.\n", + "\n", + "4. **Latent Variable Prior**:\n", + " - We assume a prior distribution over the latent variables $z$. This distribution is typically chosen to be a standard Gaussian.\n", + " $$\n", + " p(z) = \\mathcal{N}(0, I)\n", + " $$\n", + "\n", + " though in TorchSDE's framework (and as shown in the code below), these are learnable parameters qz0_mean and qz0\n", + "\n", + "5. **Objective Function**:\n", + " - The objective function for training the Latent SDE model is similar to the ELBO in VAEs but now includes the evolution of latent variables governed by the SDE.\n", + " $$\n", + " \\text{ELBO}(\\theta, \\phi; x) = \\mathbb{E}_{q_{\\phi}(z | x)} [\\log p_{\\theta}(x | z)] - \\text{KL}[q_{\\phi}(z | x) || p(z)]\n", + " $$\n", + " - The first term represents the reconstruction loss, measuring how well the decoder reconstructs the input data $x$ from the latent variable samples $z$.\n", + " - The second term is the KL divergence between the approximate posterior $q_{\\phi}(z | x)$ and the prior $p(z)$, which encourages the approximate posterior to match the prior.\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/neuromancer/dynamics/sde.py b/src/neuromancer/dynamics/sde.py new file mode 100644 index 00000000..b328687b --- /dev/null +++ b/src/neuromancer/dynamics/sde.py @@ -0,0 +1,255 @@ + +from typing import Sequence +import abc +import torch +from torch import nn +import torchsde + +import abc +class BaseSDESystem(abc.ABC, nn.Module): + """ + Base class for SDEs for integration with TorchSDE library + """ + def __init__(self): + super().__init__() + self.noise_type = "diagonal" + self.sde_type = "ito" + self.in_features = 0 + self.out_features = 0 + + @abc.abstractmethod + def f(self, t, y): + """ + Define the ordinary differential equations (ODEs) for the system. + + Args: + t (Tensor): The current time (often unused) + y (Tensor): The current state variables of the system. + + Returns: + Tensor: The derivatives of the state variables with respect to time. + The output should be of shape [batch size x state size] + """ + pass + + @abc.abstractmethod + def g(self, t,y): + """ + Define the diffusion equations for the system. + + Args: + t (Tensor): The current time (often unused) + y (Tensor): The current state variables of the system. + + Returns: + Tensor: The diffusion coefficients per batch item (output is of size + [batch size x state size]) for noise_type 'diagonal' + """ + pass + + +class Encoder(nn.Module): + """ + Encoder module to handle time-series data (as in the case of stochastic data and SDE) + GRU is used to handle mapping to latent space in this case + This class is used only in LatentSDE_Encoder + """ + def __init__(self, input_size, hidden_size, output_size): + super(Encoder, self).__init__() + self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) + self.lin = nn.Linear(hidden_size, output_size) + + def forward(self, inp): + out, _ = self.gru(inp) + out = self.lin(out) + return out + +class LatentSDE_Encoder(BaseSDESystem): + def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False): + super(BaseSDESystem).init__() + + self.adjoint = adjoint + + # Encoder. + self.encoder = Encoder(input_size=latent_size, hidden_size=hidden_size, output_size=context_size) + self.qz0_net = nn.Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space + + # Decoder. + self.f_net = nn.Sequential( + nn.Linear(latent_size + context_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, latent_size), + ) + self.h_net = nn.Sequential( + nn.Linear(latent_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, latent_size), + ) + # This needs to be an element-wise function for the SDE to satisfy diagonal noise. + self.g_nets = nn.ModuleList( + [ + nn.Sequential( + nn.Linear(1, hidden_size), + nn.Softplus(), + nn.Linear(hidden_size, 1), + nn.Sigmoid() + ) + for _ in range(latent_size) + ] + ) + self.projector = nn.Linear(latent_size, data_size) + + self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) + self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) + + self._ctx = None + self.ts = ts + + def contextualize(self, ctx): + self._ctx = ctx # A tuple of tensors of sizes (T,), (T, batch_size, d). + + def f(self, t, y): + ts, ctx = self._ctx + + i = min(torch.searchsorted(ts, t, right=True), len(ts) - 1) + + return self.f_net(torch.cat((y, ctx[i]), dim=1)) + + def h(self, t, y): + return self.h_net(y) + + def g(self, t, y): # Diagonal diffusion. + y = torch.split(y, split_size_or_sections=1, dim=1) + out = [g_net_i(y_i) for (g_net_i, y_i) in zip(self.g_nets, y)] + return torch.cat(out, dim=1) + + def forward(self, xs): + # Contextualization is only needed for posterior inference. + ctx = self.encoder(torch.flip(xs, dims=(0,))) + ctx = torch.flip(ctx, dims=(0,)) + self.contextualize((self.ts, ctx)) + + qz0_mean, qz0_logstd = self.qz0_net(ctx[0]).chunk(chunks=2, dim=1) + z0 = qz0_mean + qz0_logstd.exp() * torch.randn_like(qz0_mean) + if not self.adjoint: + return z0, xs, self.ts, qz0_mean, qz0_logstd + else: + adjoint_params = ( + (ctx,) + + tuple(self.f_net.parameters()) + tuple(self.g_nets.parameters()) + tuple(self.h_net.parameters()) + ) + return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params + +class LatentSDE_Decoder(nn.Module): + """ + Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of + LatentSDEIntegrator and decodes it back into the "real" data space and also outputs associated Gaussian distributions + to be used in the final loss function. + Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py + """ + def __init__(self, data_size, latent_size, noise_std): + super().__init__() + self.noise_std = noise_std + self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) + self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) + self.projector = nn.Linear(latent_size, data_size) + + def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd): + _xs = self.projector(zs) + xs_dist = Normal(loc=_xs, scale=self.noise_std) + log_pxs = xs_dist.log_prob(xs).sum(dim=(0, 2)).mean(dim=0) + + qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp()) + pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp()) + logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1).mean(dim=0) + logqp_path = log_ratio.sum(dim=0).mean(dim=0) + return _xs, log_pxs, logqp0 + logqp_path, log_ratio + + + +def StochasticLorenzAttractor(BaseSDESystem): + def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)): + super(BaseSDESystem).__init__() + self.a = a + self.b = b + + def f(self, t, y): + x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) + a1, a2, a3 = self.a + + f1 = a1 * (x2 - x1) + f2 = a2 * x1 - x2 - x1 * x3 + f3 = x1 * x2 - a3 * x3 + return torch.cat([f1, f2, f3], dim=1) + + def g(self, t, y): + x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) + b1, b2, b3 = self.b + + g1 = x1 * b1 + g2 = x2 * b2 + g3 = x3 * b3 + return torch.cat([g1, g2, g3], dim=1) + + @torch.no_grad() + def sample(self, x0, ts, noise_std, normalize): + """Sample data for training. Store data normalization constants if necessary.""" + xs = torchsde.sdeint(self, x0, ts) + if normalize: + mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1)) + xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std) + return xs + +def SDECoxIngersollRand(BaseSDESystem): + def __init__(self, alpha: float=0.1, + beta: float=0.05, + sigma: float=0.02): + super(BaseSDESystem).__init__() + self.alpha = alpha + self.beta = beta + self.sigma = sigma + + def f(self, t, y): + r = y + return self.alpha * (self.beta - r) + + def g(self, t, y): + r = y + return self.sigma * torch.sqrt(torch.abs(r)) + + +def SDEOrnsteinUhlenbeck(BaseSDESystem): + def __init__(self, theta: float = 0.1, sigma: float = 0.2): + super(BaseSDESystem).__init__() + self.theta = theta + self.sigma = sigma + + def f(self, t, y): + return -self.theta * y + + def g(self, t, y): + return self.sigma + +class LotkaVolterraSDE(BaseSDESystem): + def __init__(self, a, b, c, d, g_params): + super().__init__() + self.a = a + self.b = b + self.c = c + self.d = d + self.g_params = g_params + + + def f(self, t, x): + x1 = x[:,[0]] + x2 = x[:,[1]] + dx1 = self.a * x1 - self.b * x1*x2 + dx2 = self.c * x1*x2 - self.d * x2 + return torch.cat([dx1, dx2], dim=-1) + + def g(self, t, x): + return self.g_params \ No newline at end of file diff --git a/src/neuromancer/modules/blocks.py b/src/neuromancer/modules/blocks.py index d537af16..da2f0604 100644 --- a/src/neuromancer/modules/blocks.py +++ b/src/neuromancer/modules/blocks.py @@ -12,9 +12,7 @@ import neuromancer.modules.rnn as rnn from neuromancer.modules.activations import soft_exp, SoftExponential, SmoothedReLU -from torch.distributions import Normal -import torchsde @@ -22,9 +20,8 @@ class Block(nn.Module, ABC): """ Canonical abstract class of the block function approximator """ - def __init__(self, concat=True): + def __init__(self): super().__init__() - self.concat = concat @abstractmethod def block_eval(self, x): @@ -37,14 +34,12 @@ def forward(self, *inputs): :param inputs: (list(torch.Tensor, shape=[batchsize, insize]) or torch.Tensor, shape=[batchsize, insize]) :return: (torch.Tensor, shape=[batchsize, outsize]) """ - if self.concat: - if len(inputs) > 1: - x = torch.cat(inputs, dim=-1) - else: - x = inputs[0] - return self.block_eval(x) - else: - return self.block_eval(*inputs) + if len(inputs) > 1: + x = torch.cat(inputs, dim=-1) + else: + x = inputs[0] + return self.block_eval(x) + class Linear(Block): @@ -719,191 +714,6 @@ def block_eval(self, x): -class BasicSDE(Block): - """ - Wrapper class for torchsde explicit SDE case. See https://github.com/google-research/torchsde - """ - def __init__(self, f, g, t, y): - """ - :param f: Drift function - :param g: Diffusion function - :param t: Timesteps - :param y: Initial value of dimension (batch size, state size) - """ - super().__init__() - self.f = f - self.g = g - self.y = y - self.t = t - self.theta = nn.Parameter(torch.tensor(0.1), requires_grad=False) # Scalar parameter - self.noise_type = "diagonal" - self.sde_type = "ito" - self.in_features = 0 - self.out_features = 0 - - def f(self, t,y): - return self.f(t,y) - - def g(self, t, y): - return self.g(t,y) - - def block_eval(self): - """This is unused by torchsde integrator""" - pass - - -class Encoder(nn.Module): - """ - Encoder module to handle time-series data (as in the case of stochastic data and SDE) - GRU is used to handle mapping to latent space in this case - This class is used only in LatentSDE_Encoder - """ - def __init__(self, input_size, hidden_size, output_size): - super(Encoder, self).__init__() - self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) - self.lin = Linear(hidden_size, output_size) - - def forward(self, inp): - out, _ = self.gru(inp) - out = self.lin(out) - return out - - - -class LatentSDE_Encoder(Block): - """ - Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in a full stochastic process dataset - and encodes it into a latent space. The output of this block feeds into LatentSDEIntegrator class. - Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py - Note that the adjoint method is not currently supported (see https://arxiv.org/pdf/2001.01328.pdf and TorchSDE documentation) - """ - sde_type = "ito" - noise_type = "diagonal" - - def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False): - super().__init__() - - self.adjoint = adjoint - - # Encoder. - self.encoder = Encoder(input_size=data_size, hidden_size=hidden_size, output_size=context_size) - self.qz0_net = Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space - - - # Decoder. - self.f_net = nn.Sequential( - Linear(latent_size + context_size, hidden_size), - nn.Softplus(), - Linear(hidden_size, hidden_size), - nn.Softplus(), - Linear(hidden_size, latent_size), - ) - self.h_net = nn.Sequential( - Linear(latent_size, hidden_size), - nn.Softplus(), - Linear(hidden_size, hidden_size), - nn.Softplus(), - Linear(hidden_size, latent_size), - ) - # This needs to be an element-wise function for the SDE to satisfy diagonal noise. - self.g_nets = nn.ModuleList( - [ - nn.Sequential( - Linear(1, hidden_size), - nn.Softplus(), - Linear(hidden_size, 1), - nn.Sigmoid() - ) - for _ in range(latent_size) - ] - ) - self.projector = Linear(latent_size, data_size) - - self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) - self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) - - self._ctx = None - self.in_features = 0 #unused - self.out_features = 0 #unused - - self.ts = ts - - def contextualize(self, ctx): - self._ctx = ctx # A tuple of tensors of sizes (T,), (T, batch_size, d). - - def f(self, t, y): - ts, ctx = self._ctx - - i = min(torch.searchsorted(ts, t, right=True), len(ts) - 1) - - return self.f_net(torch.cat((y, ctx[i]), dim=1)) - - def h(self, t, y): - return self.h_net(y) - - def g(self, t, y): # Diagonal diffusion. - y = torch.split(y, split_size_or_sections=1, dim=1) - out = [g_net_i(y_i) for (g_net_i, y_i) in zip(self.g_nets, y)] - return torch.cat(out, dim=1) - - def block_eval(self, xs): - # Contextualization is only needed for posterior inference. - ctx = self.encoder(torch.flip(xs, dims=(0,))) - ctx = torch.flip(ctx, dims=(0,)) - self.contextualize((self.ts, ctx)) - - qz0_mean, qz0_logstd = self.qz0_net(ctx[0]).chunk(chunks=2, dim=1) - z0 = qz0_mean + qz0_logstd.exp() * torch.randn_like(qz0_mean) - if not self.adjoint: - return z0, xs, self.ts, qz0_mean, qz0_logstd - else: - adjoint_params = ( - (ctx,) + - tuple(self.f_net.parameters()) + tuple(self.g_nets.parameters()) + tuple(self.h_net.parameters()) - ) - return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params - - @torch.no_grad() - def sample(self, batch_size, ts, bm=None): - eps = torch.randn(size=(batch_size, *self.pz0_mean.shape[1:]), device=self.pz0_mean.device) - z0 = self.pz0_mean + self.pz0_logstd.exp() * eps - zs = torchsde.sdeint(self, z0, ts, names={'drift': 'h'}, dt=1e-3, bm=bm) - # Most of the times in ML, we don't sample the observation noise for visualization purposes. - _xs = self.projector(zs) - return _xs - -class LatentSDE_Decoder(Block): - """ - Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of - LatentSDEIntegrator and decodes it back into the "real" data space and also outputs associated Gaussian distributions - to be used in the final loss function. - Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py - """ - sde_type = "ito" - noise_type = "diagonal" - - def __init__(self, data_size, latent_size, noise_std): - super().__init__(concat=False) - self.in_features = 0 - self.out_features = 0 - self.noise_std = noise_std - self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) - self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) - self.projector = nn.Linear(latent_size, data_size) - - def block_eval(self, xs, zs, log_ratio, qz0_mean, qz0_logstd): - _xs = self.projector(zs) - xs_dist = Normal(loc=_xs, scale=self.noise_std) - log_pxs = xs_dist.log_prob(xs).sum(dim=(0, 2)).mean(dim=0) - - qz0 = torch.distributions.Normal(loc=qz0_mean, scale=qz0_logstd.exp()) - pz0 = torch.distributions.Normal(loc=self.pz0_mean, scale=self.pz0_logstd.exp()) - logqp0 = torch.distributions.kl_divergence(qz0, pz0).sum(dim=1).mean(dim=0) - logqp_path = log_ratio.sum(dim=0).mean(dim=0) - return _xs, log_pxs, logqp0 + logqp_path, log_ratio - - - class InterpolateAddMultiply(nn.Module): """ Implementation of smooth interpolation between addition and multiplication From cc3774b913b329e20631565af0147fe5a8f4bc6e Mon Sep 17 00:00:00 2001 From: "Birmiwal, Rahul R" Date: Fri, 17 May 2024 16:28:31 -0700 Subject: [PATCH 5/6] cleaned up code, README and fixed minor bug in blocks.py --- examples/SDEs/README.md | 12 +- examples/SDEs/sde_walkthrough.ipynb | 4711 ++++++++++++++- examples/SDEs/sde_walkthrough_draft.ipynb | 545 -- examples/SDEs/test.ipynb | 6416 --------------------- src/neuromancer/dynamics/sde.py | 71 +- src/neuromancer/modules/blocks.py | 6 +- 6 files changed, 4647 insertions(+), 7114 deletions(-) delete mode 100644 examples/SDEs/sde_walkthrough_draft.ipynb delete mode 100644 examples/SDEs/test.ipynb diff --git a/examples/SDEs/README.md b/examples/SDEs/README.md index d891283d..82a3901a 100644 --- a/examples/SDEs/README.md +++ b/examples/SDEs/README.md @@ -1,6 +1,12 @@ # TorchSDE x NeuroMANCER -These folders illustrate how one might solve two cases of stochastic differential equations in Neuromancer. We achieve this technically by integrating Neuromancer framework to work with the TorchSDE library which is a library for SDE solvers (i.e. integrators). The examples in this notebook are: +The example in this folder, sde_walkthrough.ipynb, demonstrates how functionality from TorchSDE can be, and is, integrated into the Neuromancer workflow. https://github.com/google-research/torchsde/tree/master -1. Basic_SDE: This is the first case of SDE problem -- where the user knows explicitly the drift and diffusion processes of the stochastic differential equation. If these are known, we can perform integration (e.g. Euler-Murayama integration which is a special case of Euler integration for stochastic case ) to compute output data at future time steps. This is similar to the torch DiffEqIntegrator examples in our library. Note that this example is based off https://github.com/google-research/torchsde/blob/master/examples/demo.ipynb -2. Latent_SDE_Lorenz_System. This is the second case of SDE problem -- where the user does *not* know the drift/diffusion terms and instead seeks to learn these processes. This is the System ID equivalent for the stochastic case. The output of this notebook will be a LatentSDE neural network that can generate new time-series samples that exhibit the stochastic behavior of the original input data. \ No newline at end of file +TorchSDE provides stochastic differential equation solvers with GPU spport and efficient backpropagation. They are based off this paper: http://proceedings.mlr.press/v108/li20i.html + +Neuromancer already has robust and extensive library for Neural ODEs and ODE solvers. We extend that functionality to the stochastic case by incorporating TorchSDE solvers. To motivate and teach the user how one progresses from neural ODEs to "neural SDEs" we have written a lengthy notebook -- sde_walkthrough.ipynb + +Please ensure torchsde is installed: +``` +pip install torchsde +``` diff --git a/examples/SDEs/sde_walkthrough.ipynb b/examples/SDEs/sde_walkthrough.ipynb index 529f4ba8..3e744ece 100644 --- a/examples/SDEs/sde_walkthrough.ipynb +++ b/examples/SDEs/sde_walkthrough.ipynb @@ -95,25 +95,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import torch\n", "import os\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", "import numpy as np\n", "import torch\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "from torch import nn, optim\n", + "from torch.utils.data import DataLoader\n", + "from torch.distributions import Normal\n", + "\n", "import tqdm\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "import torchsde\n", "import torchsde\n", "\n", - "from torch.utils.data import DataLoader\n", - "from neuromancer.system import Node\n", + "from neuromancer.psl import plot\n", + "from neuromancer import psl\n", + "from neuromancer.system import Node, System\n", "from neuromancer.dynamics import integrators, ode\n", "from neuromancer.trainer import Trainer, LitTrainer\n", "from neuromancer.problem import Problem\n", @@ -123,25 +134,6 @@ "from neuromancer.loss import PenaltyLoss\n", "from neuromancer.modules import blocks\n", "\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import DataLoader\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuromancer.system import Node, System\n", - "from neuromancer.dynamics import integrators, ode\n", - "from neuromancer.trainer import Trainer\n", - "from neuromancer.problem import Problem\n", - "from neuromancer.dataset import DictDataset\n", - "from neuromancer.constraint import variable\n", - "from neuromancer.loss import PenaltyLoss\n", - "from neuromancer.modules import blocks\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "\n", - "from torch import nn\n", - "from torch import optim\n", - "from torch.distributions import Normal\n", "from typing import Sequence\n", "\n", "torch.manual_seed(0)\n" @@ -158,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -249,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -323,7 +315,58 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can train the above model using standard Neuromancer training set-up with reference and finite-difference loss functions" + "We can train the above model using standard Neuromancer training set-up with reference and finite-difference loss functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %% Constraints + losses:\n", + "x = variable(\"X\")\n", + "xhat = variable('xn')[:, :-1, :]\n", + "# finite difference variables\n", + "xFD = (x[:, 1:, :] - x[:, :-1, :])\n", + "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n", + "\n", + "# trajectory tracking loss\n", + "reference_loss = (xhat == x)^2\n", + "reference_loss.name = \"ref_loss\"\n", + "\n", + "# finite difference loss\n", + "fd_loss = 2.*(xFD == xhatFD)^2\n", + "fd_loss.name = 'FD_loss'\n", + "\n", + "# aggregate list of objective terms and constraints\n", + "objectives = [reference_loss, fd_loss]\n", + "constraints = []\n", + "# create constrained optimization loss\n", + "loss = PenaltyLoss(objectives, constraints)\n", + "# construct constrained optimization problem\n", + "problem = Problem([dynamics_model], loss)\n", + "\n", + "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", + "logger = BasicLogger(args=None, savedir='test', verbosity=1,\n", + " stdout=['dev_loss', 'train_loss'])\n", + "# define neuromancer trainer\n", + "trainer = Trainer(\n", + " problem,\n", + " train_loader,\n", + " dev_loader,\n", + " test_data,\n", + " optimizer,\n", + " patience=50,\n", + " warmup=100,\n", + " epochs=500,\n", + " eval_metric=\"dev_loss\",\n", + " train_metric=\"train_loss\",\n", + " dev_metric=\"dev_loss\",\n", + " test_metric=\"dev_loss\",\n", + " logger=logger,\n", + ")\n", + "trainer.train()" ] }, { @@ -363,20 +406,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import abc\n", "class BaseSDESystem(abc.ABC, nn.Module):\n", " \"\"\"\n", - " Base class for Neural SDEs\n", + " Base class for SDEs for integration with TorchSDE library\n", " \"\"\"\n", " def __init__(self):\n", " super().__init__()\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - " self.in_features = 0\n", + " self.noise_type = \"diagonal\" #only supports diagonal diffusion right now\n", + " self.sde_type = \"ito\" #only supports Ito integrals right now\n", + " self.in_features = 0 #for compatibility with Neuromancer integrators; unused\n", " self.out_features = 0\n", "\n", " @abc.abstractmethod\n", @@ -385,8 +428,8 @@ " Define the ordinary differential equations (ODEs) for the system.\n", "\n", " Args:\n", - " t (Tensor): The state variables of the system.\n", - " y (Tensor): \n", + " t (Tensor): The current time (often unused)\n", + " y (Tensor): The current state variables of the system.\n", "\n", " Returns:\n", " Tensor: The derivatives of the state variables with respect to time.\n", @@ -400,8 +443,8 @@ " Define the diffusion equations for the system.\n", "\n", " Args:\n", - " t (Tensor): The state variables of the system.\n", - " y (Tensor): \n", + " t (Tensor): The current time (often unused)\n", + " y (Tensor): The current state variables of the system.\n", "\n", " Returns:\n", " Tensor: The diffusion coefficients per batch item (output is of size \n", @@ -419,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -464,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -482,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -518,12 +561,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -565,7 +608,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Training a Neural SDE Using Maximal Likelihood methods (reference tracking loss)" + "#### Training a Neural SDE Using Reference Tracking + FD Loss" ] }, { @@ -577,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -590,6 +633,7 @@ " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", + " self.g_params = nn.Parameter(torch.rand(self.batch_size, 2), requires_grad=True)\n", "\n", "\n", " def f(self, t, y):\n", @@ -603,7 +647,7 @@ " return torch.cat([dx1, dx2], dim=-1)\n", " \n", " def g(self, t, y):\n", - " return torch.rand(self.batch_size, 2)\n", + " return self.g_params\n", "\n", "# construct UDE model in Neuromancer\n", "net = blocks.MLP(2, 1, bias=True,\n", @@ -623,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -657,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -679,11 +723,11 @@ "\n", " def integrate(self, x): \n", " \"\"\"\n", - " x is the initial datastate of size (batch_size, state_size)\n", - " t is the time-step vector over which to integrate\n", + " x (xn) is the initial datastate of size (batch_size, 1, state_size)\n", " \"\"\"\n", - " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", - " x = x.squeeze(1) #remove time step \n", + " \n", + " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32) # t is the time-step vector over which to integrate. Hard coded right now to support rollout=2\n", + " x = x.squeeze(1) #remove rollout dimension\n", " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", " ys = ys.permute(1, 0, 2)\n", " return ys " @@ -698,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -715,22 +759,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0 train_loss: 0.022880392149090767\n", - "epoch: 1 train_loss: 0.02143489383161068\n", - "epoch: 2 train_loss: 0.021883254870772362\n", - "epoch: 3 train_loss: 0.021374505013227463\n", - "epoch: 4 train_loss: 0.021629080176353455\n", - "epoch: 5 train_loss: 0.020452190190553665\n", - "epoch: 6 train_loss: 0.02012060210108757\n", - "epoch: 7 train_loss: 0.02023428864777088\n", - "epoch: 8 train_loss: 0.019538771361112595\n", - "epoch: 9 train_loss: 0.020421745255589485\n", - "epoch: 10 train_loss: 0.020114833489060402\n", - "epoch: 11 train_loss: 0.020311430096626282\n", - "epoch: 12 train_loss: 0.01905309595167637\n", - "epoch: 13 train_loss: 0.019500289112329483\n", - "epoch: 14 train_loss: 0.019971054047346115\n", - "epoch: 15 train_loss: 0.01975109986960888\n", + "epoch: 0 train_loss: 280370720.0\n", + "epoch: 1 train_loss: 1259269.5\n", + "epoch: 2 train_loss: 3173.81787109375\n", + "epoch: 3 train_loss: 4029.0517578125\n", + "epoch: 4 train_loss: 2160.29150390625\n", + "epoch: 5 train_loss: 3204.1767578125\n", + "epoch: 6 train_loss: 2170.798095703125\n", + "epoch: 7 train_loss: 6954.16552734375\n", + "epoch: 8 train_loss: 4428.49951171875\n", + "epoch: 9 train_loss: 8502.1796875\n", + "epoch: 10 train_loss: 6236.666015625\n", + "epoch: 11 train_loss: 3221.912109375\n", + "epoch: 12 train_loss: 9576.5546875\n", + "epoch: 13 train_loss: 7514.75146484375\n", + "epoch: 14 train_loss: 2029.9180908203125\n", + "epoch: 15 train_loss: 3116.38720703125\n", + "epoch: 16 train_loss: 3704.189697265625\n", + "epoch: 17 train_loss: 6373.3974609375\n", + "epoch: 18 train_loss: 3277.477294921875\n", + "epoch: 19 train_loss: 2923.333984375\n", + "epoch: 20 train_loss: 2974.04150390625\n", + "epoch: 21 train_loss: 2931.277587890625\n", + "epoch: 22 train_loss: 3578.0654296875\n", + "epoch: 23 train_loss: 5337.3037109375\n", + "epoch: 24 train_loss: 2434.29296875\n", + "epoch: 25 train_loss: 6157.87646484375\n", + "epoch: 26 train_loss: 3036.404296875\n", + "epoch: 27 train_loss: 4701.6611328125\n", + "epoch: 28 train_loss: 8118.04638671875\n", + "epoch: 29 train_loss: 2118.330810546875\n", + "epoch: 30 train_loss: 5187.4013671875\n", + "epoch: 31 train_loss: 2097.62451171875\n", + "epoch: 32 train_loss: 2507.549560546875\n", "Interrupted training loop.\n" ] }, @@ -740,7 +801,7 @@ "" ] }, - "execution_count": 94, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -799,26 +860,26 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad(): \n", "\n", " # Initial condition\n", - " x0 = torch.ones([10,2])*10. #[1x2]\n", + " x0 = torch.ones([10,2])*10. #[batch_size x2] set of initial conditions\n", " # Integrate the SDE model\n", " data_stochastic_hat = torchsde.sdeint(sde_learnable, x0, t_span, method='euler')" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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gKvx9lc7+PSxuXWJioqZMmaLPP/+8yLZO74uyKs/X3b59uyTp8ssvL3Z9UFCQJLn+sFHWSzmU9Hte4Gz9XlhJ38/y/h4D+BvhDECFUXDC+a233qrhw4cX26Zg6uuPP/5YI0aM0ODBgzVu3DiFh4fLZrPpxRdfLHYiiMJ/CT/dmWZbM06df+J0OmWxWLRgwYJi257p/KLzFRkZqaFDh+qGG25Qy5Yt9Z///Edz5swpURApj3NxCpyrf6SSvx/h4eFau3atfvjhBy1YsEALFizQBx98oNtvv11z584tt5ormkGDBunzzz/XSy+9pA8//NDtSFxeXp769eunxMREPf7442revLkCAgJ08OBBjRgxwvW9+OKLL3THHXe4bbfgPRg2bJjGjx+vTz/9VN26ddOnn36q0NBQXXXVVR7dL6fTqX79+mn8+PHFrr/ooovcHp/te1jcuhtvvFHLli3TuHHj1K5dO9WoUUNOp1MDBgwoMkFFWZXn6xas++ijj1zBubDy/iNCSb/nZ+v3wkr6flbX7zFwIRDOAFQYtWvXVmBgoPLy8tS3b9+ztv3qq6/UqFEjffPNN26/oDzzzDPlXlfjxo1lGIZiYmKK/LJZWMGEHtu3b3f7y7nD4dDu3bvVtm3bMr2+3W5XmzZttH37dtdwyjP9UhYdHS2n06nt27e7TaJx5MgRJSUluWosmChj48aNRf76XhaleT+8vb11zTXX6JprrpHT6dT999+v9957T08//bSaNGlSqmDZuHFjLV++XA6Hw3UEs7wU9NW2bdvcjnzm5ORo9+7dbp/Rc9U8ePBg9e/fXyNGjFBgYKDeffdd17oNGzbor7/+0ty5c3X77be7lp8+ZCw2NvaMw8jq1q2rPn366Msvv9TTTz+tuLg4jRgxwjXUtPC+nG7r1q0KCws761GzM+1f48aNlZaWds7va1mcPHlSixcv1pQpUzRp0iTX8oKjU55yPq9b8L0KDw8/a58UfJ42btx41u2d7/e8tErzfp7rewygbDjnDECFYbPZdMMNN+jrr78u9peWY8eOubWV3I/eLF++XPHx8eVe1/XXXy+bzaYpU6a4vV7B6xdMr92pUyfVrl1bM2fOVE5OjqvNnDlzSnS+2Pbt27Vv374iy5OSkhQfH6/Q0FDXcKOCX6RP327BkZLTZ8l7/fXXJUkDBw6UJPXv31+BgYF68cUXlZWVVWSfSquk78fpU5FbrVbX0dCCKcDPtG/FueGGG3T8+HG99dZbRdaVZT8K69u3r7y9vTVjxgy3bf373/9WcnKyqy8Laj7XMLvbb79dM2bM0MyZM/X444+7lhfXd4ZhFJmWPDIyUn379nX7KeyWW27R0aNHdc8998jhcLiGNBY8t127dpo7d65bv27cuFGLFi065xG2gICAYt+PG2+8UfHx8frhhx+KrEtKSlJubu5Zt3s2xfWLVPSzXd7O53VjY2MVFBSkf/7zn25DewsU/BtWu3Zt9erVS7Nnzy7ynS/8uuf7PS+tkr6fJfkeAygbjpwBuOBmz56thQsXFln+8MMP66WXXtLPP/+sLl266O6779bFF1+sxMRErV69Wj/++KMSExMlSVdffbW++eYbXXfddRo4cKB2796tmTNn6uKLL1ZaWlq51tu4cWM9//zzmjBhgvbs2aPBgwcrMDBQu3fv1n//+1+NGjVKjz32mOx2u55//nndc889uvzyy3XTTTdp9+7d+uCDD0p0ztm6det0880368orr1TPnj1Vs2ZNHTx4UHPnztWhQ4c0bdo01y+OHTt2lCQ99dRTGjp0qOx2u6655hq1bdtWw4cP16xZs5SUlKTevXtrxYoVmjt3rgYPHqw+ffpIyj/35Y033tBdd92lzp07u67ztG7dOmVkZJR6aFJJ34+77rpLiYmJuvzyy1W/fn3t3btXb775ptq1a+c6AtCuXTvZbDZNnTpVycnJ8vHx0eWXX67w8PAir3v77bfrww8/1NixY7VixQr17NlT6enp+vHHH3X//fdr0KBBZ6372LFjev7554ssj4mJ0S233KIJEyZoypQpGjBggK699lpt27ZN77zzjjp37uw2SUrHjh31xRdfaOzYsercubNq1Kiha665psh2H3zwQaWkpOipp55ScHCwnnzySTVv3lyNGzfWY489poMHDyooKEhff/11kXPPzuWGG27Q/fffr//973+KiopSr1693Na/8soruvLKK9W1a1eNHDnSNZV+cHCwJk+efNZtd+zYUe+++66ef/55NWnSROHh4br88ss1btw418QjI0aMUMeOHZWenq4NGzboq6++0p49e1xTwJdWUFCQevXqpZdfflkOh0P16tXTokWLtHv37jJt70K8blBQkN59913ddttt6tChg4YOHaratWtr3759+v7779W9e3fXHxJmzJihHj16qEOHDho1apRiYmK0Z88eff/991q7dq2k8/+el1ZJ38+SfI8BlNGFnRwSQHVWMO30mX72799vGIZhHDlyxHjggQeMqKgow263GxEREcYVV1xhzJo1y7Utp9Np/POf/zSio6MNHx8fo3379sZ3331nDB8+3G1K84IpvF955ZUi9RRMpX3s2LFi6zx9+uqvv/7a6NGjhxEQEGAEBAQYzZs3Nx544AFj27Ztbu3eeecdIyYmxvDx8TE6depkLFmypMgU8MU5cuSI8dJLLxm9e/c2IiMjDS8vLyM0NNS4/PLLja+++qpI++eee86oV6+eYbVa3ep1OBzGlClTjJiYGMNutxtRUVHGhAkTjKysrCLb+Pbbb41u3boZfn5+RlBQkHHJJZcYn332mWv9maabP72fS/p+fPXVV0b//v2N8PBww9vb22jQoIFxzz33GIcPH3bb/r/+9S+jUaNGhs1mc5sCvbh+zMjIMJ566inX/kZERBj/+Mc/jJ07d561vwsuE1DczxVXXOFq99ZbbxnNmzc37Ha7UadOHeO+++4zTp486battLQ04+abbzZCQkIMSa59LjyVfmHjx483JBlvvfWWYRiGsXnzZqNv375GjRo1jLCwMOPuu+821q1bZ0gyPvjgg7PuR2FDhgwxJBnjx48vdv2PP/5odO/e3fV+X3PNNcbmzZvd2hT3+U9ISDAGDhxoBAYGFrksRGpqqjFhwgSjSZMmhre3txEWFmZ069bNePXVV42cnBzDMMr2PTQMwzhw4IBx3XXXGSEhIUZwcLAxZMgQ49ChQ4Yk45lnnilxv5xtKv3zed0z/Vvx888/G7GxsUZwcLDh6+trNG7c2BgxYoSxcuVKt3YbN250vY6vr6/RrFkz4+mnn3Zrc77f8+joaGPgwIHF9svpU+kbRsnez5J+jwGUnsUwznPcBwAAQDX073//W3fddZf279+v+vXrm10OgCqAc84AAADK4PDhw7JYLG7XjgOA88E5ZwAAAKVw5MgRffXVV5o5c6a6du0qf39/s0sCUEVw5AwAAKAUtmzZonHjxqlJkyaaM2eO2eUAqEI45wwAAAAAKgCOnAEAAABABUA4AwAAAIAKgAlBPMDpdOrQoUMKDAyUxWIxuxwAAAAAJjEMQ6mpqapbt66s1rMfGyOcecChQ4cUFRVldhkAAAAAKoiSXBORcOYBgYGBkvLfgKCgIFNrcTgcWrRokfr37y+73W5qLVUR/etZ9K9n0b+eRx97Fv3rWfSvZ9G/nlWR+jclJUVRUVGujHA2hDMPKBjKGBQUVCHCmb+/v4KCgkz/YFZF9K9n0b+eRf96Hn3sWfSvZ9G/nkX/elZF7N+SnO7EhCAAAAAAUAEQzgAAAACgAiCcAQAAAEAFQDgDAAAAgAqAcAYAAAAAFQDhDAAAAAAqAMIZAAAAAFQAhDMAAAAAqAAIZwAAAABQARDOAAAAAKACIJwBAAAAQAVAOAMAAACACoBwBgAAAAAVAOEMAAAAACoAwhkAAAAAVACEMwAAAACoALzMLgAAAAAAzltOupR+TEo/LkvyYYUnr5N0ldlVlQrhDAAAAEDF4nRK2clSRqKUcUJKP55/m3FCyjjuvvxUIJMj3fV0L0ktfetJmmDaLpQF4QwAAACAZxhG/hGtrCQp82R+qMpMLHT/5JmXG3mlfz2bj1QjXE7/WkrO8pNfue+QZxHOAAAAABTPMKTcLCkz6VTASsoPTgX3s5Kl7JT828I/rmUpZQtZBbwDpYBakv/pPzX/vh8QLtWoLQXUlrxrSBaL8hwOrZ4/v5INaiScAQAAAFVHQZjKycgf5ldwm50mZadKOaduXffT/l5W8Lhwu5w0yZl7/nVZvSS/mvmhyq+m5Bcq+YcWul+z0PrQv+97+Zz/a1cihDMAAADA05x5Um52fnDKzZay0hSYeVA6vE5S3t/LXbeZpwWsjPzhgY6M4pcXrHNkSIaz/Ou3WCXfEMkv5NRt6Kn7wfk/PkF/3y9umd1PsljKv64qhnB2Bm+//bZeeeUVJSQkqG3btnrzzTd1ySWXmF0WAAAAysLpPBV8Tg9BZ7stTdtz3DodbuXYJV0uSVs9uM82H8nbX7IHSD6Bkk+N/Fvvwrc1zv3YJzB/eKGVq3B5GuGsGF988YXGjh2rmTNnqkuXLpo2bZpiY2O1bds2hYeHm10eAACA5xiG6yiPzZmdP7Qt15p/NMaZl3/+kNutM3/YmzPv1G1uoWWFfvJypbycUz+O/LCS5zi1ruBxbqHlBY9PLSvYxunPy8s5bRuO4sNRXo7ZPfs3q5cMLx/lOK3y9guUxe4refnmD+EruC0crLz9Jbu/5B1w6rbQcu+A4tvY/SUbv+pXNrxjxXj99dd1991364477pAkzZw5U99//71mz56tJ554wuTqSmfV/96SddcGrUpcJi+bVVaLRVarRVaL8u+7HltksVhktVrz11mtf9+3WGUpWGaRLMpvl7/MIoss+fcLtbMU2r7FIlksBX9psRQ6pG1xP7xd7HJP3C+/17Lk5alO8lpZdtglL3uhNirzNvPvWwrVea77KmX7stzXeTz3PF4rN09euen5JxPn2T33WhWJYZyrQaE2xd03it+O2/6eup+bK6vz1C82FsP9c3im51gqeP+hdIxCnxvDedpPoWUFn6+CZa72RqF1hdq5rdfZ2xZ7X6dtq/hbS65DYambZdlTQ7LZiml3xh0/rSanzr5vZ6rD+XcgKRxcTr9fEHZcfVu4jfH3Mlfgyc0/yuO6f9prOHNPC0anB6bTt3e25c7TXjdPBR1nl3S1JK0rt09cxWGxSl5+7mGoVLdlfI7dNz902byU63Bo4fz5uuqqq2S3289dM6oFwtlpcnJytGrVKk2Y8Pc1EaxWq/r27av4+Phin5Odna3s7GzX45SUFEmSw+GQw+Eo9jkXSs0tn+jSvO3SXlPLqLK8JF0qSbtMLqSKsksaKEkbTC6kirJLukYql1+8jDMFOosl/5cgi/Xvx7K4NTnDgzO+UmkWn7n9mZ5Qvu29JF1jOGVZZz3V0rP1WM7cEVWSl6TukrTD5EKqGcNilSw2yWo7dWvNn+zB6vX3cqtXofX5yw0vb8nmLdnsktWev9xW+Db/vlFw3+Z1apnt7/ZWW/42rHYZBett9tO26e0KQUaRkHTqvtXEX4GdhuT8+3dEs39XrKoqUv+WpgbC2WmOHz+uvLw81alTx215nTp1tHVr8YOCX3zxRU2ZMqXI8kWLFsnf398jdZaUxaed9jvqy9CpP84VvpVknPoPvmCZZPz9h85TK9xuC7g9NmTI4vql4O9bnfbYKPSrl+HWrvj7JWlvuL1O4V9Mztzm1H1LSdsXfV2VoE1p2pe6DkvZ6i7JNgvaWFx1n/t90hm2qWLaWAvdR9Xheo9PDw283fnfhfOZRrqCMgr/a2GRDFldywuO6P+9/rR/hSx//wsjWfJ/2S/8r4elmH9tLO7/eunUsmL/hT/t+cXWb7EU85xCy07ftsX9X8aCZYasMiynfk7dl6yF1uXfqlBb17+WBfctVtfjwtsxZMt/vsVW7OuUZpksVvd6y7ANw+2PLB7kPPVTpgkCc0/9ZJRnRR4VFxdndglVWkXo34yMkn8eCWflYMKECRo7dqzrcUpKiqKiotS/f38FBQWZWJnk6NdPcXFx6tevn8cPmTudhvIMQ06noVynIadx6tb1WMp1OvPPx3UW3y7PMJTnPPVT6H7+c5xyGsp/fIbnOA3JaRQ8J/9xQU1u6wzDta28QnW7bp35z9Op5xS0N4y/t2MYUp7TqePHjyu0Zi3JYim2XUFbp5G/HwWhOL+WQtt3GspztSuo5VTNxe6DR9/OC6Q8Q/ff27FZJZvVIrtV8rJaZLNIdqtFXjaLbFaLvKySl9Uqu80iL1v+rbfV6rrvZbPKy2aRl9WS/9ha8Pjv9d6n2tptFnnbTj3XapHdyypvL6t8vKzysVll97LK126Tt80qH7tVPjbJ7uUlb1v+UOLineMXn3MN1Tx9eOIZgpPDkaOff/5ZfS67LP/fh8JDJIu0P31ZaR4XDNtyFr/NYmrLX3eGfihtv5nYPjfXoSVLlqpXr57yKjz0uRSbvyD7VXB005IfIooe8Sy0roy/nJ/+qSwPDofjgv0fVx3Rv55F/3pWRerfglF1JUE4O01YWJhsNpuOHDnitvzIkSOKiIgo9jk+Pj7y8Sl6DQa73W76h6FARaqlKnE4HJo/f76uuqrzBe9foyAgFgqcrrBZKNz9HThPD6Fyhdw8p1O5efltHIUe5zr/Xu84tT7XaSgvz1lonSFHntO1LvfUumIf5xnKdTpPPaegzemP859TsN209AzZfXyLbK/gp1gFf3UteW9Kyjv1c2FYLJLPqeDm42WVj5dNvnb3W7f1rlurfL1sbrfFPbe4bfh6599arad+PXY4lGvzlz0wjH8fPMXhUJb3JnnVjKaPPYj/4zyL/vUs+tezKkL/lub1CWen8fb2VseOHbV48WINHjxYkuR0OrV48WI9+OCD5hYHFGI5NdmKVRbZbWZX4xl/h9/exf7DZhiFAuPp4c9pKO/U49xT6wrCYO6p4OfIM+TIdcqR51ROweM8p3JOLct1nlp/KhQWPCcnzylHbsFznMrJdV+Wk+tUliNP2blOZefmKcvx9+3ftUtZDqfbsgvFbrPI18smby+rjFybpm//TT52L/narfKz21TDx0uBvnYF+nqpho+Xavh6ue4H+uavq+Hz9+MaPl7ysjG9MgAA54twVoyxY8dq+PDh6tSpky655BJNmzZN6enprtkbAVQMFkv+UEWvShJODSM/xGUXhDdH4fDmVPapQHemYFf4NtvhVNap52Sd9lz3cJh/v/BRxvwQmitlS5JFidnnf26Gn92WH+IKAltBsPOxnwp0Xq7QV9DOLfT52BXgYyPkAQCqNcJZMW666SYdO3ZMkyZNUkJCgtq1a6eFCxcWmSQEAErDYrGcGmpoU5DvhR1ikXsqFBYOfemZ2frp16Xq2OVS5RpWZTvylJGTp7TsXKVl5yo1y6G0rFylZufm32blFlqXvz47N//IX6YjT5mOPB1LzT5HJWfn7237+2idT6GjdKdCXJAr+NldIfDvoGfPX+/jdZbz+QAAqLgIZ2fw4IMPMowRQJXhdWrSkoBCp8c6HN7aGShd0rBmmcfj5+Q6lX4qsKWcCnNprse5px47XOGuIOi5wt+poFcQ8jJy8gPi0fMIeXabRSH+3qrp763QALtqBngXeuytmgF21+OaAd4Kq+EjP+9KcvgVAFClEc4AAGXm7WWVt1d+6DkfObnO/FCXlavUQmEuLbvwkTuHq01KQegrdFQvNSvXde7gsdTsUh3F87PbVKuGt2rV8FGtAG/VCvBWzRreCgvwUVigt+oE+io8yEfhQb4K5MgcAMBDCGcAANN5e1lV0yv/SNb5yMzJ08mMnPyfdIcSM3KUlJGjxPQcnUzPUWKGw/U4MT1HJ9JzlJPrVKYjTwdOZurAycxzvoav3arwQF/VCfJReKCvagf6qE6Qr8IDfRQe9Pf9YD9mXwMAlA7hDABQZfh52+Tn7ae6IX4lam8YhtJz8pSYlqPj6dk6kZajxPRsHU/L0Ym0HJ1Izz8CdzQ1W0dSspSalassh1P7EjO0L/HsE6l4e1kVXsNbXrk2fZ+8VpHBfqoT7Kv6of6qH+qn+qF+ql3Dh6NwAAAXwhkAoNqyWCyuywI0qOV/zvaZOfmTnhxJzdLRlGwdTc3SkVO3x04FuKOp2UrKcCgn16kDSVmSLNqz+Wix2/PxsqpeqJ9bYIty3fdXWA1vwhsAVCOEMwAASsjP26YGtfzPGeSyTs1cefhkuhb8Gq+opi11IsOhw0lZOpCUqYMnM3U4OVPZuU7tOpauXcfSi92Oj5fVFdQKbqNq/v24VgDhDQCqEsIZAADlzNduU1RNf0UE2nW4lqGrLm1QZEZMR54zP6ydzDh1vluG67y3AyczdDglS9m5Tu08lq6dZwhvvnar21G3+qH+hY68+akm4Q0AKhXCGQAAJrDbrGc9CpeT61RCcpb2n8woEtwOnMxUQkqWshxO7Tiaph1H04rdhp/dpuha/mpcu4Ya1Q5w3TaqXUM1fPgVAAAqGv5lBgCgAvL2Ond4O5ycqf2JmcUefTuSmqVMR562JqRqa0JqkedHBPmqSXiNIj8MlQQA8xDOAACohLy9rIquFaDoWgHFrs/OzdOhpCztPp6mXaeGRu48ln//eFq2ElKylJCSpd92HHd7Xqi/XU3Ca6hZRKBaRAapeUSQmkcEKoAjbQDgcfxLCwBAFeTjZVNMWIBiwgJ0eXP3dcmZDu08luYaElnws/9khk5mOPTnnpP6c89Jt+dE1/JX80KBrUVkoKJC/WW1cpQNAMoL4QwAgGom2M+uDg1C1aFBqNvyLEeedh5L0/YjadqakKoth1O0NSFFR1KytfdEhvaeyNAPm4642gd429QiMkit6werTf1gta4XokZhAQQ2ACgjwhkAAJCUP8tky7rBalk32G15YnqOth5O0ebDKafOYUvRX0fSlJ6Tp5V7T2rl3r+PstXw8VKrekFqWz8kP7TVC1FUTT/OYwOAEiCcAQCAs6oZ4K1uTcLUrUmYa1lunlO7jqdr06FkrdufrA0Hk7XpULLSsnP1x65E/bEr0dU2xN+u1vXyj661qR+iNvWDFRnsZ8auAECFRjgDAACl5mWz6qI6gbqoTqCua19fUn5g2340TRsOJGvdgSRtOJisLYdTlJTh0NLtx7V0+9+Tj9QL8VOnhqHq3LCmOjesqabhNRgOCaDaI5wBAIBy4WWzqkVkkFpEBunGzlGS8meN3JaQqvUHkl2hbfvRNB1MytTBtZn639pDkvLPg+sUHarOMTXVuWGoWtcLkbeX1czdAYALjnAGAAA8xsfLdmooY4hrWXp2rtbsS9KfexK1cm+iVu9NUnKmQ4u3HtXirUdPPc+qtlEhuqRhTXVqGKqO0aEK9LWbtBcAcGEQzgAAwAUV4OOlHk3D1KNp/jlsjjynNh9K0Z97EvMD256TOpGeoxW7E7Vid/65a1aL1DwiSJfE1FTXxrV0aaNa8ue3GABVDP+sAQAAU9lt+UfJ2kaF6K6ejWQYhnYdT9fKPYlasfukVu5N1N4TGdp8asbIOcv2yGqRWtUNUrhhVfDOE+rapLZ8vGxm7woAnBfCGQAAqFAsFosa166hxrVr6KbODSRJR1KytHLPSf2x64R+33lcu46la/3BFElW/Thnlfy9berZNExXNK+jy5rXVnigr7k7AQBlQDgDAAAVXp0gXw1sE6mBbSIlSYeTM7V021F9uXS99mb56Whqtn7YdMR1key29YN1RYs6urx5uFrWDeI6awAqBcIZAACodCKD/XRd+7ryObxWAwb00l/HMrV46xH9tPWo1h9I1rpTP6/H/aWIIF/1aR6uvi3C1a1xmPy8Gf4IoGIinAEAgErNarWodf1gta4frDF9L9LRlCz9vO2oftxyVL9tP66ElCx9tmKfPluxTz5eVnVvEqbLm4frihbhXAwbQIVCOAMAAFVKeJCvburcQDd1bqAsR57+2HVCi7cc1U9bj+pgUqZ+2pp/f+I86eLIIF3RIlwDWkXo4kiGPwIwF+EMAABUWb52my5rFq7LmoXrWcPQtiOpWrzlqBZvOaI1+5NcM0C++dMOxYQF6KrWERrYuq5aRAYS1ABccIQzAABQLVgsFjWPCFLziCA90KeJTqRl65dtx7Roc4J+2XZMu4+n6+2fd+rtn3cqJixAA1tHanD7umoSHmh26QCqCcIZAAColmrV8NENHevrho71lZadq8Vbjmj+hsP6+VRQe+vnHXrr5x1qUz9Y17evp2va1lWtGj5mlw2gCiOcAQCAaq+Gj5cGtaunQe3quYLa/607pF+2HdP6A8lafyBZz3+/RZc1q63rO9TX5c3D5Wtn1kcA5YtwBgAAUEjhoHYiLVv/t+6QvllzUOsPJOvHLfmzQAb5eunqtnV1Q4d66tAglPPTAJQLwhkAAMAZ1KrhoxHdYzSie4x2HE3VN6sP6r9rDupwcpY+Xb5Pny7fp+ha/rqufT1d176eomsFmF0ygEqMcAYAAFACTcIDNX5Acz3Wv5n+2HVCX68+qIUbD2vviQxN+3G7pv24XZ0bhmrYJQ10VetIhj0CKDXCGQAAQClYrRZ1axKmbk3C9Nzgllq06Yi+Xn1Av+84rj/3nNSfe05qyv9t1g0d6uvmLg3UJLyG2SUDqCQIZwAAAGXk7+2lwe3raXD7ekpIztKXK/fr8z/362BSpmb/vluzf9+tHk3CdHvXaF3Roo5sVs5NA3BmhDMAAIByEBHsq4euaKr7+zTRku3H9Mkf+/TT1iP6bcdx/bbjuOqF+OnWS6N1U+co1QzwNrtcABUQ4QwAAKAc2awW9WkWrj7NwnXgZIY+Wb5Pn6/Yp4NJmZq6cKve+PEvDWpbV6N6NVLTOlzgGsDfrGYXAAAAUFXVD/XX4wOaK37CFXp1SFu1rhesnFynvlx1QP3eWKKRc/7Uit2JMgzD7FIBVAAcOQMAAPAwX7tN/+hYXzd0qKfV+5L0ryW79MPmBC3eelSLtx5Vu6gQ3du7kfpdHMF5aUA1RjgDAAC4QCwWizpGh6rjbR2161ia3v9tt75adUBr9yfp3o9XKyYsQHf3bKTrO9RjKn6gGmJYIwAAgAka1a6hf17XWr8/frke7NNEQb5e2n08XU/+d4N6TP1Jb/20XUkZOWaXCeACIpwBAACYqHagjx6Lbab4CVdo0tUXq16In46n5ejVRX+p20s/aerCrUpMJ6QB1QHhDAAAoAII8PHSnT1i9Mu4yzTtpnZqERmkjJw8vfvLTvWc+pNeXrhVJwlpQJVGOAMAAKhA7DarBrevp/mje+hft3dSy7pBSs/J0zu/7FSPqT/plR8IaUBVRTgDAACogCwWi/pdXEffPdRDs27rqIsj80Pa2z/vVM+Xf9abi7crIyfX7DIBlCPCGQAAQAVmsVjUv2WEvh/9d0hLy87Va3F/qfcrv+jjP/bKkec0u0wA5YBwBgAAUAkUhLTvHuqhN4e1V4Oa/jqWmq2J8zaq/xtLNH/DYS5mDVRyhDMAAIBKxGq16Jq2dfXj2N56dlBL1Qrw1u7j6br/k9Ua/M4yxe88YXaJAMqIcAYAAFAJeXtZdXvXhvp1fB+N6dtU/t42rdufpGH/+kMjPlihv46kml0igFIinAEAAFRiNXy8NKbvRfp1XB8N7xotL6tFv2w7piunL9Uz/9vIzI5AJUI4AwAAqAJqB/poyqBW+nFsb8W2rKM8p6G58Xt12au/aM7vu5k0BKgECGcAAABVSMOwAL13Wyd9elcXNY8IVHKmQ5P/b7OunL5Uv+84bnZ5AM6CcAYAAFAFdWsSpu8e6qHnB7dSzQBv7TiaplveX64xn6/R8bRss8sDUAzCGQAAQBXlZbPq1kuj9fNjl2l412hZLNK8tYd0+au/6NPl++R0MvU+UJEQzgAAAKq4YD+7pgxqpXn3d1fLukFKycrVk//doCHvxWtrQorZ5QE4hXAGAABQTbSNCtH/Huiup6++WAHeNq3ae1JXz/hNLy/cqixHntnlAdUe4QwAAKAa8bJZNbJHjH58NH9Wx1ynoXd+2amBM5Zq1d5Es8sDqjXCGQAAQDUUGeyn927rpJm3dlTtQB/tPJauf8yM1+RvNykjJ9fs8oBqiXAGAABQjQ1oFaEfH+mtf3SsL8OQ5izbo9hpS7Rs5wmzSwOqHcIZAABANRfsb9erQ9pq7p2XqF6In/YnZmr4nFX6zy4rR9GAC4hwBgAAAElS74tq64dHeum2S6MlSb8fsWrQO39o7f4kcwsDqgnCGQAAAFxq+HjpucGtNGdERwV7G9pzIkM3vLtM03/crtw8p9nlAVUa4QwAAABFdG9cS4+3ydPAVhHKcxp648e/9I+Z8dp9PN3s0oAqi3AGAACAYgXYpWk3tdH0oe0U6OultfuTdNX0pfp0+T4ZhmF2eUCVQzgDAADAWQ1qV08Lx/RS10a1lOnI05P/3aC75q7UsdRss0sDqhTCGQAAAM6pXoifPrmriyYObCFvm1WLtx7VgGlL9Otfx8wuDagyCGcAAAAoEavVort6NtK3D3VX84hAnUjP0YgPVuj1uL+U52SYI3C+CGcAAAAoleYRQfrfg911S5cGMgxpxuLtGvHBCp1IY5gjcD4IZwAAACg1Hy+bXriutV6/sa387DYt3X5cV7/5m1btPWl2aUClRTgDAABAmV3fob7mPdBdjWoH6HBylm56L14f/L6b2RyBMqg04eyFF15Qt27d5O/vr5CQkGLb7Nu3TwMHDpS/v7/Cw8M1btw45ebmurX55Zdf1KFDB/n4+KhJkyaaM2dOke28/fbbatiwoXx9fdWlSxetWLHCA3sEAABQNTSLCNS3D/bQwDaRynUamvJ/m/XgZ2uUlp177icDcKk04SwnJ0dDhgzRfffdV+z6vLw8DRw4UDk5OVq2bJnmzp2rOXPmaNKkSa42u3fv1sCBA9WnTx+tXbtWY8aM0V133aUffvjB1eaLL77Q2LFj9cwzz2j16tVq27atYmNjdfToUY/vIwAAQGVVw8dLbw1rr2euuVheVou+X39YN7yzTPsTM8wuDag0Kk04mzJlih555BG1bt262PWLFi3S5s2b9fHHH6tdu3a68sor9dxzz+ntt99WTk6OJGnmzJmKiYnRa6+9phYtWujBBx/UP/7xD73xxhuu7bz++uu6++67dccdd+jiiy/WzJkz5e/vr9mzZ1+Q/QQAAKisLBaL7ugeoy/u6arwQB9tO5Kqa9/6TfE7T5hdGlApeJldQHmJj49X69atVadOHdey2NhY3Xfffdq0aZPat2+v+Ph49e3b1+15sbGxGjNmjKT8o3OrVq3ShAkTXOutVqv69u2r+Pj4M752dna2srP/np0oJSVFkuRwOORwOMpj98qs4PXNrqOqon89i/71LPrX8+hjz6J/Pet8+rdN3Rr6+t4uuv/TtdpwMEW3/Xu5Jg5srlsuiSrvMistPr+eVZH6tzQ1VJlwlpCQ4BbMJLkeJyQknLVNSkqKMjMzdfLkSeXl5RXbZuvWrWd87RdffFFTpkwpsnzRokXy9/cv0/6Ut7i4OLNLqNLoX8+ifz2L/vU8+tiz6F/POp/+vb2e9Hm2VauOWzX5/7YobsUm3dDQKVulGbvleXx+Pasi9G9GRsmH9poazp544glNnTr1rG22bNmi5s2bX6CKymbChAkaO3as63FKSoqioqLUv39/BQUFmVhZflKPi4tTv379ZLfbTa2lKqJ/PYv+9Sz61/PoY8+ifz2rvPp3kGFo1tI9eu3H7fr9iFXOgDC9NbStgvyq93vG59ezKlL/FoyqKwlTw9mjjz6qESNGnLVNo0aNSrStiIiIIrMqHjlyxLWu4LZgWeE2QUFB8vPzk81mk81mK7ZNwTaK4+PjIx8fnyLL7Xa76R+GAhWplqqI/vUs+tez6F/Po489i/71rPLo3wevuEjNI4P18OdrFL8rUUPf/1OzR3RWVM2KMcLITHx+Pasi9G9pXt/Ug8q1a9dW8+bNz/rj7e1dom117dpVGzZscJtVMS4uTkFBQbr44otdbRYvXuz2vLi4OHXt2lWS5O3trY4dO7q1cTqdWrx4sasNAAAASq/vxXX05b3dFBHkq+1H03TdO8u0/kCS2WUBFUqlGfG7b98+rV27Vvv27VNeXp7Wrl2rtWvXKi0tTZLUv39/XXzxxbrtttu0bt06/fDDD5o4caIeeOAB11Gte++9V7t27dL48eO1detWvfPOO/rPf/6jRx55xPU6Y8eO1b/+9S/NnTtXW7Zs0X333af09HTdcccdpuw3AABAVXFx3SD994Fuah4RqONp2brpvT+0aFOC2WUBFUalCWeTJk1S+/bt9cwzzygtLU3t27dX+/bttXLlSkmSzWbTd999J5vNpq5du+rWW2/V7bffrmeffda1jZiYGH3//feKi4tT27Zt9dprr+n9999XbGysq81NN92kV199VZMmTVK7du20du1aLVy4sMgkIQAAACi9yGA/fXlvV/W+qLYyHXm65+NVmrtsj9llARVCpZmtcc6cOZozZ85Z20RHR2v+/PlnbXPZZZdpzZo1Z23z4IMP6sEHHyxtiQAAACiBQF+7/j28kyZ9u0mfLt+nZ77dpMT0HI3p21QWi8Xs8gDTVJojZwAAAKg6vGxWvTC4lcb2u0iSNH3xdj3z7SY5nYbJlQHmIZwBAADAFBaLRaOvaKrnBrWUxSJ9GL9XY75Yq5xcp9mlAaYgnAEAAMBUt3VtqOlD28vLatG36w7p7g9XKiMn1+yygAuOcAYAAADTXdu2rt4f3kl+dpt+/euYbn1/uZIycswuC7igCGcAAACoEC5rFq6P7+qiYD+7Vu9L0tBZf+hEWrbZZQEXDOEMAAAAFUbH6FD9556uqh3oo60JqRo66w8dTckyuyzggiCcAQAAoEJpFhGo/9zTVZHBvtp+NE03zfpDh5IyzS4L8DjCGQAAACqcmLAA/eeerqof6qfdx9N143vx2p+YYXZZgEcRzgAAAFAhRdX01xf3dFXDWv46cDJTN70Xr93H080uC/AYwhkAAAAqrHohfvrinq5qXDtAh5KzNHRWvPaeIKChaiKcAQAAoEKrE+SrL+7pqovq1NCRlGzd/K/lnIOGKolwBgAAgAovrIaPPr6rixrW8tfBpEzd+v5yHUtlmn1ULYQzAAAAVArhgb765O5LVS/ET7uOp+u2f3OhalQthDMAAABUGvVC/PTJXV1c10EbPnuFUrMcZpcFlAvCGQAAACqVhmEB+uSuLgr1t2vdgWTdOedPZeTkml0WcN4IZwAAAKh0LqoTqI9GdlGgj5f+3HNS93y0Stm5eWaXBZwXwhkAAAAqpVb1gjXnzs7y97Zp6fbjevDTNXLkOc0uCygzwhkAAAAqrY7RNfX+7Z3k7WVV3OYjGv/VejmdhtllAWVCOAMAAECl1q1JmGbe2kFeVov+u+agXlm0zeySgDIhnAEAAKDSu7x5Hb14fWtJ0ru/7NRH8XvMLQgoA8IZAAAAqoQhnaI0tt9FkqRJ327SD5sSTK4IKB3CGQAAAKqMhy5vomGXRMkwpNGfrdGqvSfNLgkoMcIZAAAAqgyLxaLnBrXS5c3DlZ3r1F1z/9SuY2lmlwWUCOEMAAAAVYqXzaq3bm6vtvWDdTLDoeEfrNCx1GyzywLOiXAGAACAKsff20v/HtFZDWr6a39ipkbO/VOZOVykGhUb4QwAAABVUlgNH8298xLVDPDW+gPJGvuftVwDDRUa4QwAAABVVkxYgN67raO8bVYt2Jig1+K4BhoqLsIZAAAAqrTODWvqpRvyr4H29s879fWqAyZXBBSPcAYAAIAq7/oO9XX/ZY0lSRO+2aA/9ySaXBFQFOEMAAAA1cJj/ZtpQMsI5eQ5dc9Hq7Q/McPskgA3hDMAAABUC1arRa/f1Fat6gUpMT1Hoz5axQyOqFAIZwAAAKg2/L29NOu2Tgqr4a0th1M0/uv1MgxmcETFQDgDAABAtVI3xE/v3NJRXlaL/m/dIc1assvskgBJhDMAAABUQ5fE1NQz11wsSZq6cKt+/euYyRUBhDMAAABUU7deGq2bOkXJaUgPfbpae46nm10SqjnCGQAAAKoli8WiZwe3VLuoEKVk5WrURyuVnp1rdlmoxghnAAAAqLZ8vGx677aOqh3oo7+OpOmp/25gghCYhnAGAACAaq1OkK/evrmDbFaL5q09pE9X7DO7JFRThDMAAABUe5fE1NS42GaSpCnfbtbGg8kmV4TqiHAGAAAASBrVs5H6tghXTp5T932ySsmZDrNLQjVDOAMAAAAkWa0WvTqkreqF+Gl/YqbGfbmO889wQRHOAAAAgFNC/L31zi0d5G2zatHmI3p/6W6zS0I1QjgDAAAACmkbFaKJV7eQlH+B6vUHkswtCNUG4QwAAAA4zW2XRmtAywjlOg2N/myN0rj+GS4AwhkAAABwGovFopduaK3IYF/tOZGhZ/63yeySUA0QzgAAAIBihPh7a9pN7WS1SF+vPqD/rT1odkmo4ghnAAAAwBl0aVRLD/ZpIkma+N+N2p+YYXJFqMoIZwAAAMBZjL6iqTpGhyo1O1cPf75GuXlOs0tCFUU4AwAAAM7Cy2bVtJvaKdDXS6v3JWnG4u1ml4QqinAGAAAAnENUTX/987rWkqS3f9mptfuTzC0IVRLhDAAAACiBa9rW1bVt6yrPaejR/6xVliPP7JJQxRDOAAAAgBJ6dlBL1Q700c5j6Xpt0Tazy0EVQzgDAAAASijE31svXZ8/vPH933brzz2JJleEqoRwBgAAAJTCFS3qaEjH+jIM6bEv1ykjJ9fsklBFEM4AAACAUnr6motVN9hXe09k6KUFW80uB1UE4QwAAAAopSBfu17+R1tJ0ofxe/X7juMmV4SqgHAGAAAAlEGPpmG69dIGkqTxX61XapbD5IpQ2RHOAAAAgDKacGULNajpr4NJmXr+uy1ml4NKjnAGAAAAlFGAj5deHdJWFov0xcr9+mnrEbNLQiVGOAMAAADOwyUxNTWye4wk6YmvNyg5g+GNKBvCGQAAAHCeHottpka1A3Q0NVsvLWR4I8qGcAYAAACcJ1+7TS9d30aS9NmK/fpj1wmTK0JlRDgDAAAAysElMTU17JL82Ruf/O8GZTnyTK4IlQ3hDAAAACgnT1zZXLUDfbTrWLre+XmH2eWgkiGcAQAAAOUk2M+uKde2lCS9++tO/XUk1eSKUJkQzgAAAIBydGWrCPVtES5HnqEJ32yQ02mYXRIqCcIZAAAAUI4sFoueHdRKAd42rdp7Up+s2Gd2SagkKkU427Nnj0aOHKmYmBj5+fmpcePGeuaZZ5STk+PWbv369erZs6d8fX0VFRWll19+uci2vvzySzVv3ly+vr5q3bq15s+f77beMAxNmjRJkZGR8vPzU9++fbV9+3aP7h8AAACqlrohfhoX20ySNHXBViUkZ5lcESqDShHOtm7dKqfTqffee0+bNm3SG2+8oZkzZ+rJJ590tUlJSVH//v0VHR2tVatW6ZVXXtHkyZM1a9YsV5tly5Zp2LBhGjlypNasWaPBgwdr8ODB2rhxo6vNyy+/rBkzZmjmzJlavny5AgICFBsbq6wsvlAAAAAoudu6NlS7qBClZefqmW83nvsJqPYqRTgbMGCAPvjgA/Xv31+NGjXStddeq8cee0zffPONq80nn3yinJwczZ49Wy1bttTQoUM1evRovf76664206dP14ABAzRu3Di1aNFCzz33nDp06KC33npLUv5Rs2nTpmnixIkaNGiQ2rRpow8//FCHDh3SvHnzLvRuAwAAoBKzWS166YbWslkt+mHTEf287ajZJaGC8zK7gLJKTk5WzZo1XY/j4+PVq1cveXt7u5bFxsZq6tSpOnnypEJDQxUfH6+xY8e6bSc2NtYVvHbv3q2EhAT17dvXtT44OFhdunRRfHy8hg4dWmwt2dnZys7Odj1OSUmRJDkcDjkcjvPe1/NR8Ppm11FV0b+eRf96Fv3refSxZ9G/nkX/lo/Gtfw0/NIGmr1sr57530Z1frCbfOw2+tfDKlL/lqaGShnOduzYoTfffFOvvvqqa1lCQoJiYmLc2tWpU8e1LjQ0VAkJCa5lhdskJCS42hV+XnFtivPiiy9qypQpRZYvWrRI/v7+pdgzz4mLizO7hCqN/vUs+tez6F/Po489i/71LPr3/DXLlYLsNu1LzNT4DxYptv7fszfSv55VEfo3IyOjxG1NDWdPPPGEpk6detY2W7ZsUfPmzV2PDx48qAEDBmjIkCG6++67PV1iiUyYMMHtiFxKSoqioqLUv39/BQUFmVhZflKPi4tTv379ZLfbTa2lKqJ/PYv+9Sz61/PoY8+ifz2L/i1f9oaHNfbLDVp82K5xQ7qrTg0v+teDKtLnt2BUXUmYGs4effRRjRgx4qxtGjVq5Lp/6NAh9enTR926dXOb6EOSIiIidOTIEbdlBY8jIiLO2qbw+oJlkZGRbm3atWt3xhp9fHzk4+NTZLndbjf9w1CgItVSFdG/nkX/ehb963n0sWfRv55F/5aP6zpE6T+rDuqPXYn658K/9M6wtpLoX0+rCP1bmtc3dUKQ2rVrq3nz5mf9KTiH7ODBg7rsssvUsWNHffDBB7Ja3Uvv2rWrlixZ4jamMy4uTs2aNVNoaKirzeLFi92eFxcXp65du0qSYmJiFBER4dYmJSVFy5cvd7UBAAAASqvg2mdeVoviNh/Rz9uOmV0SKqBKMVtjQTBr0KCBXn31VR07dkwJCQlu54HdfPPN8vb21siRI7Vp0yZ98cUXmj59uttww4cfflgLFy7Ua6+9pq1bt2ry5MlauXKlHnzwQUn5X5oxY8bo+eef17fffqsNGzbo9ttvV926dTV48OALvdsAAACoQi6qE6g7e+TPkfDc91vlcJpcECqcSjEhSFxcnHbs2KEdO3aofv36busMI/+EyuDgYC1atEgPPPCAOnbsqLCwME2aNEmjRo1yte3WrZs+/fRTTZw4UU8++aSaNm2qefPmqVWrVq4248ePV3p6ukaNGqWkpCT16NFDCxculK+v74XZWQAAAFRZo69oqv+tPaj9JzP1y2GLBpldECqUShHORowYcc5z0ySpTZs2Wrp06VnbDBkyREOGDDnjeovFomeffVbPPvtsacsEAAAAzqqGj5ceH9BcY/+zTnEHrDqamq16NTnnDPkqxbBGAAAAoKoY3K6e2tYPVrbTotfitptdDioQwhkAAABwAVmtFk28qpkk6Zs1h7T+QJK5BaHCIJwBAAAAF1i7qBB1DsufEWTK/212zaOA6o1wBgAAAJjg6gZO+dmtWrX3pL5dd8jsclABEM4AAAAAE4T4SPf2aiRJemnBVmXm5JlcEcxGOAMAAABMcmf3aNUP9dPh5CzN/n232eXAZIQzAAAAwCS+dpvGxeZPDjLzl51KTM8xuSKYqUzh7MiRI7rttttUt25deXl5yWazuf0AAAAAKJlr2tTVxZFBSs3O1ds/7zC7HJioTBehHjFihPbt26enn35akZGRslgs5V0XAAAAUC1YrRY9cWVz3T57hT6K36sR3Roqqqa/2WXBBGUKZ7/99puWLl2qdu3alXM5AAAAQPXTs2mYujeppd93nNAbcX/p9ZvamV0STFCmYY1RUVFciwEAAAAoJxaLRU8MaCFJ+u/ag9p8KMXkimCGMoWzadOm6YknntCePXvKuRwAAACgempdP1jXtK0rw5CmLtxqdjkwQZmGNd50003KyMhQ48aN5e/vL7vd7rY+MTGxXIoDAAAAqpPH+l+kBRsO69e/jmnZjuPq1iTM7JJwAZUpnE2bNq2cywAAAAAQXStAt3RpoLnxe/XSwq363wPdmXyvGilTOBs+fHh51wEAAABA0kNXNNVXqw5o/YFkzd+QoIFtIs0uCRdImcKZJOXl5WnevHnasmWLJKlly5a69tpruc4ZAAAAcB7Cavjo7l6NNO3H7Xrlh63q37KO7LYyTRWBSqZM7/KOHTvUokUL3X777frmm2/0zTff6NZbb1XLli21c+fO8q4RAAAAqFbu7tlIYTW8tedEhj5fsc/scnCBlCmcjR49Wo0bN9b+/fu1evVqrV69Wvv27VNMTIxGjx5d3jUCAAAA1UqAj5cevqKpJGn64u1Kz841uSJcCGUKZ7/++qtefvll1axZ07WsVq1aeumll/Trr7+WW3EAAABAdTX0kgZqWMtfx9NyNGfZHrPLwQVQpnDm4+Oj1NTUIsvT0tLk7e193kUBAAAA1Z3dZtWYvhdJkmYt2aXULIfJFcHTyhTOrr76ao0aNUrLly+XYRgyDEN//PGH7r33Xl177bXlXSMAAABQLV3Ttq6ahNdQcqZDs3/bY3Y58LAyhbMZM2aocePG6tq1q3x9feXr66vu3burSZMmmj59ennXCAAAAFRLNqtFY/rmn3v2/m+7lJzB0bOqrExT6YeEhOh///uftm/frq1bt0qSWrRooSZNmpRrcQAAAEB1d1WrSDWrs0PbjqTq37/t0tj+zcwuCR5S5uucSVLTpk3VtGnT8qoFAAAAwGmsVose6ddU9368WrN/36M7uscoNIB5HqqiEoezsWPH6rnnnlNAQIDGjh171ravv/76eRcGAAAAIF//iyN0cWSQNh9O0b+W7tL4Ac3NLgkeUOJwtmbNGjkcDtd9AAAAABdG/tGzi3T3hys1Z9kejewRo1o1fMwuC+WsxOHs559/LvY+AAAAAM/r2yJcbeoHa/2BZL23ZJeevKqF2SWhnJVptsY777yz2Oucpaen68477zzvogAAAAC4s1jyj55J0ofxe3QsNdvkilDeyhTO5s6dq8zMzCLLMzMz9eGHH553UQAAAACKuuyi2moXFaIsh1Pv/7bL7HJQzkoVzlJSUpScnCzDMJSamqqUlBTXz8mTJzV//nyFh4d7qlYAAACgWrNYLHro8vzLV30cv5frnlUxpZpKPyQkRBaLRRaLRRdddFGR9RaLRVOmTCm34gAAAAC4u7x5uJpHBGprQqrmxu/R6Cu4tFVVUapw9vPPP8swDF1++eX6+uuvVbNmTdc6b29vRUdHq27duuVeJAAAAIB8FotF9/dpotGfrdHs33drZI8YBfic1+WLUUGU6l3s3bu3JGn37t2KioqS1VqmU9YAAAAAnIeBrSP1+qJt2nMiQ5+t2Ke7ejYyuySUgzJF7OjoaElSRkaG9u3bp5ycHLf1bdq0Of/KAAAAABTLZrXovssa6/GvN2jWkl269dJo+dptZpeF81SmcHbs2DHdcccdWrBgQbHr8/LyzqsoAAAAAGd3Xfv6mvbjdh1OztLXqw/oli7RZpeE81SmcYljxoxRUlKSli9fLj8/Py1cuFBz585V06ZN9e2335Z3jQAAAABO4+1l1ahe+cMZZ/66U7l5TpMrwvkqUzj76aef9Prrr6tTp06yWq2Kjo7WrbfeqpdfflkvvvhiedcIAAAAoBhDOzdQrQBv7U/M1P+tP2R2OThPZQpn6enpruuZhYaG6tixY5Kk1q1ba/Xq1eVXHQAAAIAz8vO26c4eMZKkd37eKafTMLkinI8yhbNmzZpp27ZtkqS2bdvqvffe08GDBzVz5kxFRkaWa4EAAAAAzuy2rtEK9PXS9qNpWrT5iNnl4DyUKZw9/PDDOnz4sCTpmWee0YIFC9SgQQPNmDFD//znP8u1QAAAAABnFuRr1/CuDSVJb/+8Q4bB0bPKqkyzNd56662u+x07dtTevXu1detWNWjQQGFhYeVWHAAAAIBzu6N7Q73/2y5tOJis33YcV8+mtc0uCWVQLleR9vf3V4cOHQhmAAAAgAlq1fDR0M4NJEn/Wrrb5GpQViU+cjZ27NgSb/T1118vUzEAAAAAyubO7jH6MH6Plvx1TFsTUtQ8IsjsklBKJQ5na9asKVE7i8VS5mIAAAAAlE2DWv4a0CpC8zck6P2lu/XqkLZml4RSKnE4+/nnnz1ZBwAAAIDzdHfPRpq/IUH/W3tQ42KbqU6Qr9kloRTK5ZwzAAAAAOZr3yBUnRuGypFnaM6yPWaXg1Iq02yNffr0OevwxZ9++qnMBQEAAAAou7t6NtKfe1bpkz/26oE+TVTDp0y/8sMEZXqn2rVr5/bY4XBo7dq12rhxo4YPH14edQEAAAAog74t6igmLEC7j6frP3/u1509YswuCSVUpnD2xhtvFLt88uTJSktLO6+CAAAAAJSdzWrRyB4xmjhvo2b/vlu3d42Wl42zmSqDcn2Xbr31Vs2ePbs8NwkAAACglG7oUF81A7x14GSmFm5KMLsclFC5hrP4+Hj5+jIjDAAAAGAmP2+bbr00WpL0ryW7ZBiGyRWhJMo0rPH66693e2wYhg4fPqyVK1fq6aefLpfCAAAAAJTd7V2jNfPXnVp3IFkrdieqS6NaZpeEcyjTkbPg4GC3n5o1a+qyyy7T/Pnz9cwzz5R3jQAAAABKKayGj27oUF+SNPv33SZXg5Io05GzDz74oLzrAAAAAFDO7ujeUJ+t2Ke4zUd04GSG6of6m10SzuK8zjlbuXKlPvroI3300UdatWpVedUEAAAAoBxcVCdQ3ZvUktOQPvpjr9nl4BzKdOTswIEDGjZsmH7//XeFhIRIkpKSktStWzd9/vnnql+/fnnWCAAAAKCMhndtqN93nNAXf+7XmCsukp+3zeyScAZlOnJ21113yeFwaMuWLUpMTFRiYqK2bNkip9Opu+66q7xrBAAAAFBGV7Soo/qhfkrKcOh/aw+aXQ7Ookzh7Ndff9W7776rZs2auZY1a9ZMb775ppYsWVJuxQEAAAA4PzarRbd3zZ9Wf86yPUyrX4GVKZxFRUXJ4XAUWZ6Xl6e6deued1EAAAAAys9NnRrIz27T1oRUrdidaHY5OIMyhbNXXnlFDz30kFauXOlatnLlSj388MN69dVXy604AAAAAOcv2N+uwe3rSco/eoaKqUwTgowYMUIZGRnq0qWLvLzyN5GbmysvLy/deeeduvPOO11tExNJ5gAAAIDZRnTLn1Z/0eYjOpiUqXohfmaXhNOUKZxNmzatnMsAAAAA4EnNIgLVtVEtxe86oY//2KvHBzQ3uyScpkzhbPjw4eVdBwAAAAAPG9G9oeJ3ndDnK/bp4SuaytfOtPoVSZnCmZQ/+ce8efO0ZcsWSVLLli117bXXymbjDQYAAAAqor4t6qheiJ8OJmXq27WHdGPnKLNLQiFlmhBkx44datGihW6//XZ98803+uabb3TrrbeqZcuW2rlzZ3nXCAAAAKAcFJ5Wf2480+pXNGUKZ6NHj1bjxo21f/9+rV69WqtXr9a+ffsUExOj0aNHl3eNAAAAAMrJjZ2i5O1l1aZDKVp/INnsclBImS9C/fLLL6tmzZquZbVq1dJLL72kX3/9tdyKAwAAAFC+QgO8dXXrSEnSJ8v3mlwNCitTOPPx8VFqamqR5WlpafL29j7voopz7bXXqkGDBvL19VVkZKRuu+02HTp0yK3N+vXr1bNnT/n6+ioqKkovv/xyke18+eWXat68uXx9fdW6dWvNnz/fbb1hGJo0aZIiIyPl5+envn37avv27R7ZJwAAAMAMN3dpIEn6dt0hJWc6TK4GBcoUzq6++mqNGjVKy5cvl2EYMgxDf/zxh+69915de+215V2jJKlPnz76z3/+o23btunrr7/Wzp079Y9//MO1PiUlRf3791d0dLRWrVqlV155RZMnT9asWbNcbZYtW6Zhw4Zp5MiRWrNmjQYPHqzBgwdr48aNrjYvv/yyZsyYoZkzZ2r58uUKCAhQbGyssrKyPLJfAAAAwIXWMTpUzeoEKsvh1Lw1B80uB6eUKZzNmDFDTZo0Ubdu3eTr6ytfX191795dTZo00fTp08u7RknSI488oksvvVTR0dHq1q2bnnjiCf3xxx9yOPKT/ieffKKcnBzNnj1bLVu21NChQzV69Gi9/vrrrm1Mnz5dAwYM0Lhx49SiRQs999xz6tChg9566y1J+UfNpk2bpokTJ2rQoEFq06aNPvzwQx06dEjz5s3zyH4BAAAAF5rFYnEdPftk+V4mBqkgSjWVvtPp1CuvvKJvv/1WOTk5Gjx4sIYPHy6LxaIWLVqoSZMmnqrTTWJioj755BN169ZNdrtdkhQfH69evXq5DauMjY3V1KlTdfLkSYWGhio+Pl5jx45121ZsbKwreO3evVsJCQnq27eva31wcLC6dOmi+Ph4DR06tNh6srOzlZ2d7XqckpIiSXI4HK7waJaC1ze7jqqK/vUs+tez6F/Po489i/71LPrXsypC/17TOlwvLdiiv46k6Y+dx9QpOtS0WspbRejfAqWpoVTh7IUXXtDkyZPVt29f+fn5af78+QoODtbs2bNLXWRZPP7443rrrbeUkZGhSy+9VN99951rXUJCgmJiYtza16lTx7UuNDRUCQkJrmWF2yQkJLjaFX5ecW2K8+KLL2rKlClFli9atEj+/v6l2EPPiYuLM7uEKo3+9Sz617PoX8+jjz2L/vUs+tezzO7ftqFW/XHUqlfnLdftTZ2m1uIJZvevJGVkZJS4banC2Ycffqh33nlH99xzjyTpxx9/1MCBA/X+++/Lai39CMknnnhCU6dOPWubLVu2qHnz5pKkcePGaeTIkdq7d6+mTJmi22+/Xd99950sFkupX7s8TZgwwe2IXEpKiqKiotS/f38FBQWZWFl+Uo+Li1O/fv1cRxlRfuhfz6J/PYv+9Tz62LPoX8+ifz2rovRv/QPJuuG95dqQ5KVLe/dSzQDPTO53oVWU/pX+HlVXEqUKZ/v27dNVV13lety3b19ZLBYdOnRI9evXL82mJEmPPvqoRowYcdY2jRo1ct0PCwtTWFiYLrroIrVo0UJRUVH6448/1LVrV0VEROjIkSNuzy14HBER4botrk3h9QXLIiMj3dq0a9fujDX6+PjIx8enyHK73W76h6FARaqlKqJ/PYv+9Sz61/PoY8+ifz2L/vUss/u3Q8NaalUvSBsPpujb9Ud0d69G535SJWJ2/xbUUFKlOtyVm5srX1/fIi9W1rGctWvXVvPmzc/6c6ap+Z3O/MOuBed6de3aVUuWLHGrJS4uTs2aNVNoaKirzeLFi922ExcXp65du0qSYmJiFBER4dYmJSVFy5cvd7UBAAAAqgqLxaJbukRLkj5dsY+JQUxWqiNnhmFoxIgRbkeJsrKydO+99yogIMC17Jtvvim/CiUtX75cf/75p3r06KHQ0FDt3LlTTz/9tBo3buwKTTfffLOmTJmikSNH6vHHH9fGjRs1ffp0vfHGG67tPPzww+rdu7dee+01DRw4UJ9//rlWrlzpmm7fYrFozJgxev7559W0aVPFxMTo6aefVt26dTV48OBy3ScAAACgIri2bV298P0W7T6ervidJ9StSZjZJVVbpQpnw4cPL7Ls1ltvLbdizsTf31/ffPONnnnmGaWnpysyMlIDBgzQxIkTXUExODhYixYt0gMPPKCOHTsqLCxMkyZN0qhRo1zb6datmz799FNNnDhRTz75pJo2bap58+apVatWrjbjx49Xenq6Ro0apaSkJPXo0UMLFy4scsQQAAAAqAoCfLw0qF1dfbJ8nz7/cz/hzESlCmcffPCBp+o4q9atW+unn346Z7s2bdpo6dKlZ20zZMgQDRky5IzrLRaLnn32WT377LOlrhMAAACojG7qHKVPlu/Twk0JSsrIUYh/1ZgYpLIp00WoAQAAAFQdresFq0VkkHJynZq35qDZ5VRbhDMAAACgmrNYLLqpU/7s61+sPMDEICYhnAEAAADQ4Pb15O1l1ZbDKdp4sOTX5kL5IZwBAAAAUIi/t2Jb5l/394uV+0yupnoinAEAAACQJN3UKUqS9L+1h5TlyDO5muqHcAYAAABAktStcS3VD/VTalauFmw8bHY51Q7hDAAAAIAkyWq16MZTR88+X7Hf5GqqH8IZAAAAAJd/dKwvi0VavjtRe46nm11OtUI4AwAAAOBSN8RPvZrWliR9vfqAydVUL4QzAAAAAG5u6Jh/zbNvVh+U08k1zy4UwhkAAAAAN/0vrqNAXy8dTMrUH7tPmF1OtUE4AwAAAODG127T1W0iJUlfrzpocjXVB+EMAAAAQBE3dMgf2rhg42GlZ+eaXE31QDgDAAAAUETH6FA1rOWvjJw8LdyYYHY51QLhDAAAAEARFovFdfSMWRsvDMIZAAAAgGJd16GeJGnZzhM6cDLD5GqqPsIZAAAAgGLVD/VX10a1JEn/Xc3EIJ5GOAMAAABwRq5rnq05KMPgmmeeRDgDAAAAcEZXtoqQv7dNu4+na83+JLPLqdIIZwAAAADOKMDHS7EtIyRJ/1vD0EZPIpwBAAAAOKtB7epKkv5v/WE58pwmV1N1Ec4AAAAAnFWPJmEKq+GtxPQc/bb9uNnlVFmEMwAAAABn5WWz6uo2+UfP5q1laKOnEM4AAAAAnFPB0MZFm44oPTvX5GqqJsIZAAAAgHNqFxWi6Fr+ynTkKW7zEbPLqZIIZwAAAADOyWKxaFC7epIY2ugphDMAAAAAJTL41NDGpduP63hatsnVVD2EMwAAAAAl0qh2DbWpH6w8p6Hv1x82u5wqh3AGAAAAoMQY2ug5hDMAAAAAJXZN20hZLdKafUnaeyLd7HKqFMIZAAAAgBILD/RV9yZhkqT/rT1kcjVVC+EMAAAAQKm4hjauOSjDMEyupuognAEAAAAolQGtIuTjZdWu4+nadCjF7HKqDMIZAAAAgFKp4eOly5uHS5K+Y9bGckM4AwAAAFBqV7fJv+bZd+sPMbSxnBDOAAAAAJTa5c3D5e9t04GTmVp3INnscqoEwhkAAACAUvPztumKFnUkSd+tY9bG8kA4AwAAAFAmV7eJlCR9v+GwnE6GNp4vwhkAAACAMul9UW3V8PHS4eQsrd530uxyKj3CGQAAAIAy8bXb1O/iU0MbmbXxvBHOAAAAAJRZwdDG+RsOK4+hjeeFcAYAAACgzHo2ra0gXy8dTc3Wn3sSzS6nUiOcAQAAACgzby+rYltGSMq/5hnKjnAGAAAA4Lxc3Tb/gtQLNiQoN89pcjWVF+EMAAAAwHnp1riWQv3tOpGeoz92MbSxrAhnAAAAAM6L3fb30MaFm5i1sawIZwAAAADO24BW+eHsh01HuCB1GRHOAAAAAJy3bo3DFOjrpWOp2VrFBanLhHAGAAAA4Lx5e1nVr0X+BakXbEgwuZrKiXAGAAAAoFz8PbQxQYbB0MbSIpwBAAAAKBe9Lqotf2+bDiZlav2BZLPLqXQIZwAAAADKha/dpj7NwiVJCzYytLG0CGcAAAAAyk3B0MaFGw8ztLGUCGcAAAAAyk2f5uHy9rJqz4kMbU1INbucSoVwBgAAAKDc1PDxUq+mtSUxtLG0CGcAAAAAytWVhYY2ouQIZwAAAADKVd8WdeRlteivI2naeSzN7HIqDcIZAAAAgHIV7G9XtyZhkqSFDG0sMcIZAAAAgHIX27KOJGnR5iMmV1J5EM4AAAAAlLu+LfLD2br9STqSkmVyNZUD4QwAAABAuasT5Kt2USGSpB+3cPSsJAhnAAAAADyi38X5R8/iGNpYIoQzAAAAAB7R/1Q4W7bjhNKyc02upuIjnAEAAADwiCbhNdSwlr9y8pz6ddsxs8up8AhnAAAAADzCYrEUGtrIlPrnQjgDAAAA4DH9W0ZIkn7aelSOPKfJ1VRslS6cZWdnq127drJYLFq7dq3buvXr16tnz57y9fVVVFSUXn755SLP//LLL9W8eXP5+vqqdevWmj9/vtt6wzA0adIkRUZGys/PT3379tX27ds9uUsAAABAldWhQahqBXgrJStXf+5ONLucCq3ShbPx48erbt26RZanpKSof//+io6O1qpVq/TKK69o8uTJmjVrlqvNsmXLNGzYMI0cOVJr1qzR4MGDNXjwYG3cuNHV5uWXX9aMGTM0c+ZMLV++XAEBAYqNjVVWFtdmAAAAAErLZrXo8ubhkrgg9blUqnC2YMECLVq0SK+++mqRdZ988olycnI0e/ZstWzZUkOHDtXo0aP1+uuvu9pMnz5dAwYM0Lhx49SiRQs999xz6tChg9566y1J+UfNpk2bpokTJ2rQoEFq06aNPvzwQx06dEjz5s27ULsJAAAAVCmFp9Q3DMPkaiouL7MLKKkjR47o7rvv1rx58+Tv719kfXx8vHr16iVvb2/XstjYWE2dOlUnT55UaGio4uPjNXbsWLfnxcbGuoLX7t27lZCQoL59+7rWBwcHq0uXLoqPj9fQoUOLrS07O1vZ2dmuxykpKZIkh8Mhh8NR5n0uDwWvb3YdVRX961n0r2fRv55HH3sW/etZ9K9nVbf+vbRhiHztVh1MytT6/Ym6ODLIo69Xkfq3NDVUinBmGIZGjBihe++9V506ddKePXuKtElISFBMTIzbsjp16rjWhYaGKiEhwbWscJuEhARXu8LPK65NcV588UVNmTKlyPJFixYVGyTNEBcXZ3YJVRr961n0r2fRv55HH3sW/etZ9K9nVaf+bVrDqg0nrXr32991ZdSFOXpWEfo3IyOjxG1NDWdPPPGEpk6detY2W7Zs0aJFi5SamqoJEyZcoMpKZ8KECW5H5FJSUhQVFaX+/fsrKMizfxU4F4fDobi4OPXr1092u93UWqoi+tez6F/Pon89jz72LPrXs+hfz6qO/ZsRcVAT/rtJ+/JCdNVVXT36WhWpfwtG1ZWEqeHs0Ucf1YgRI87aplGjRvrpp58UHx8vHx8ft3WdOnXSLbfcorlz5yoiIkJHjrifYFjwOCIiwnVbXJvC6wuWRUZGurVp167dGWv08fEpUpsk2e120z8MBSpSLVUR/etZ9K9n0b+eRx97Fv3rWfSvZ1Wn/u3fMlJPzdukzYdTdTQ9V/VC/Dz+mhWhf0vz+qaGs9q1a6t27drnbDdjxgw9//zzrseHDh1SbGysvvjiC3Xp0kWS1LVrVz311FNyOByuDoiLi1OzZs0UGhrqarN48WKNGTPGta24uDh17Zqf3GNiYhQREaHFixe7wlhKSoqWL1+u++67rzx2GQAAAKiWatXwUfsGoVq196R+3npUt14abXZJFU6lmK2xQYMGatWqlevnoosukiQ1btxY9evXlyTdfPPN8vb21siRI7Vp0yZ98cUXmj59uttww4cfflgLFy7Ua6+9pq1bt2ry5MlauXKlHnzwQUn5VzAfM2aMnn/+eX377bfasGGDbr/9dtWtW1eDBw++4PsNAAAAVCUFU+r/tPWoyZVUTJUinJVEcHCwFi1apN27d6tjx4569NFHNWnSJI0aNcrVplu3bvr00081a9YstW3bVl999ZXmzZunVq1audqMHz9eDz30kEaNGqXOnTsrLS1NCxculK+vrxm7BQAAAFQZBeHs9x3HlZmTZ3I1FU+lmK3xdA0bNiz2+ght2rTR0qVLz/rcIUOGaMiQIWdcb7FY9Oyzz+rZZ5897zoBAAAA/K15RKDqBvvqUHKW4ncd1+XN65z7SdVIlTlyBgAAAKBis1gs6sPQxjMinAEAAAC4YK5okR/Oft56rNjRcNUZ4QwAAADABdO1UZh8vKw6mJSpbUdSzS6nQiGcAQAAALhg/Lxt6ta4liSGNp6OcAYAAADggrq8Rf5EID9tIZwVRjgDAAAAcEEVTKm/et9JnUzPMbmaioNwBgAAAOCCqhfip+YRgXIa0q9/HTO7nAqDcAYAAADggmNK/aIIZwAAAAAuuCtOhbNfth1Vbp7T5GoqBsIZAAAAgAuufYNQhfjblZKVq9X7kswup0IgnAEAAAC44GxWiy67qLYkafHWIyZXUzEQzgAAAACYouC8s1+3MSmIRDgDAAAAYJKeTWvLYpG2JqTqSEqW2eWYjnAGAAAAwBQ1A7zVpl6wJGkJU+oTzgAAAACYp9ep88643hnhDAAAAICJep8KZ7/tOK48p2FyNeYinAEAAAAwTbuoEAX6eikpw6H1B5LMLsdUhDMAAAAApvGyWdW9cZgkaclfx02uxlyEMwAAAACm6t2s4LyzoyZXYi7CGQAAAABTFUwKsnZ/kpIzHCZXYx7CGQAAAABT1QvxU5PwGnIa0u87q+/QRsIZAAAAANP1anpqaOO26julPuEMAAAAgOkKzjtbsv2YDKN6TqlPOAMAAABgui4xNeXjZdXh5CxtP5pmdjmmIJwBAAAAMJ2v3aZLYmpKkpb8VT2HNhLOAAAAAFQIvS8qmFKfcAYAAAAApikIZ8t3JyozJ8/kai48whkAAACACqFJeA3VDfZVTq5Ty3efMLucC45wBgAAAKBCsFgsrgtSV8ehjYQzAAAAABVGQTirjpOCEM4AAAAAVBjdm4TJapF2HkvXoaRMs8u5oAhnAAAAACqMYD+72tQPkST9vuO4ucVcYIQzAAAAABVKjyZhkghnAAAAAGCq7qfC2W87TsgwDJOruXAIZwAAAAAqlA7RIfK1W3U8LVvbjqSaXc4FQzgDAAAAUKH4eNl0SUwtSdJv26vP0EbCGQAAAIAKp0eT/HBWnc47I5wBAAAAqHAKzjtbvjtROblOk6u5MAhnAAAAACqcFhFBqhngrYycPK3dn2R2ORcE4QwAAABAhWO1WtSt8anzzqrJ0EbCGQAAAIAKqWfT6nW9M8IZAAAAgAqp4LyztfuTlJrlMLkazyOcAQAAAKiQ6of6q2Etf+U5DS3flWh2OR5HOAMAAABQYRUcPasO550RzgAAAABUWD2aVJ/zzghnAAAAACqsro1ryWKRth9NU0JyltnleBThDAAAAECFFeLvrVZ1gyVJf+w6YXI1nkU4AwAAAFChdT11vbP4nYQzAAAAADDNpY1qSpLiOXIGAAAAAObp3LCmbFaL9iVm6GBSptnleAzhDAAAAECFFuhrV6t6p847q8JDGwlnAAAAACq86jC0kXAGAAAAoMLr2ih/UpCqPGMj4QwAAABAhdfp1HlnB05man9ihtnleAThDAAAAECFV8PHS23qV+3rnRHOAAAAAFQKBUMbq+p5Z4QzAAAAAJXCpQXnne08IcMwTK6m/BHOAAAAAFQKnRqGym6z6FBylvYnVr3rnRHOAAAAAFQK/t5eals/RJIUv+u4ucV4AOEMAAAAQKXhGtq4K9HkSsof4QwAAABApdG18alJQargeWdeZhdQneXl5cnhcHj0NRwOh7y8vJSVlaW8vDyPvlZ1dCH61263y2azeWTbAAAAlU2HBqHytlmVkJKlPScyFBMWYHZJ5YZwZgLDMJSQkKCkpKQL8loRERHav3+/LBaLx1+vurlQ/RsSEqKIiAjeQwAAUO35edvULipEK/Yk6o9dJwhnOD8FwSw8PFz+/v4e/YXb6XQqLS1NNWrUkNXKKNby5un+NQxDGRkZOnr0qCQpMjKy3F8DAACgsrm0cS2t2JOo+J0nNOySBmaXU24IZxdYXl6eK5jVqlXL46/ndDqVk5MjX19fwpkHXIj+9fPzkyQdPXpU4eHhDHEEAADV3qWNamrGYumPXfnnnVWV0UX8tn6BFZxj5u/vb3IlqEwKPi+ePkcRAACgMujQIFTeXlYdTc3WruPpZpdTbghnJqkq6R4XBp8XAACAv/naberQIERS/qyNVUWlCWcNGzaUxWJx+3nppZfc2qxfv149e/aUr6+voqKi9PLLLxfZzpdffqnmzZvL19dXrVu31vz5893WG4ahSZMmKTIyUn5+furbt6+2b9/u0X0DAAAAUDoF1ztbsbvqXO+s0oQzSXr22Wd1+PBh189DDz3kWpeSkqL+/fsrOjpaq1at0iuvvKLJkydr1qxZrjbLli3TsGHDNHLkSK1Zs0aDBw/W4MGDtXHjRlebl19+WTNmzNDMmTO1fPlyBQQEKDY2VllZWRd0X3F2ixcvVosWLcp1+vqFCxeqXbt2cjqd5bZNAAAAeMYlDWtKyg9nVeV6Z5UqnAUGBioiIsL1ExDw97SZn3zyiXJycjR79my1bNlSQ4cO1ejRo/X666+72kyfPl0DBgzQuHHj1KJFCz333HPq0KGD3nrrLUn5R82mTZumiRMnatCgQWrTpo0+/PBDHTp0SPPmzbvQu1uhjBgxwnXE0tvbW02aNNGzzz6r3NxcU+oZP368Jk6cWKrJMUaPHq2OHTvKx8dH7dq1K7J+wIABstvt+uSTT8qxUgAAAHhC+wah8rJalJCSpQMnM80up1xUqtkaX3rpJT333HNq0KCBbr75Zj3yyCPy8srfhfj4ePXq1Uve3t6u9rGxsZo6dapOnjyp0NBQxcfHa+zYsW7bjI2NdQWv3bt3KyEhQX379nWtDw4OVpcuXRQfH6+hQ4cWW1d2drays7Ndj1NSUiTlT95w+gQODodDhmHI6XRekCM0BX9FKHjN89lObGysZs+erezsbM2fP18PPfSQvLy89MQTTxRpn5OT4/ZelKfffvtNO3fu1HXXXVeqfTIMQ3fccYeWL1+uDRs2FPvc4cOHa8aMGbrllltKvM2CW0++n06nU4ZhyOFwVKvZGgu+P0yE4hn0r+fRx55F/3oW/etZ9O/587JIreoFae3+ZMXvOKaI9nVd6ypS/5amhkoTzkaPHq0OHTqoZs2aWrZsmSZMmKDDhw+7jowlJCQoJibG7Tl16tRxrQsNDVVCQoJrWeE2CQkJrnaFn1dcm+K8+OKLmjJlSpHlixYtKjIro5eXlyIiIpSWlqacnBxJ+b/YZzk8G9QyTyQVWeZrt5Z4oomCUODv7y9/f3/dcsst+vrrrzVv3jzdf//9uv/++5WcnKz27dvr3//+t7y9vbVu3TodOHBATz/9tH766SdZrVZ17dpVL730kho0aKDff//dNay0cJ9PmDBBa9eu1YIFC4qt5aOPPtJll12mnJwc5eTkyDAMXXfddbLZbPrqq69ksVh08uRJ9ejRQ7fccouefPJJSdJzzz0nSTpw4IDWrl3rCtGF9e7dWw899JDWrVtX5PN0NqmpqSVuWxY5OTnKzMzUkiVLTDtaaaa4uDizS6jS6F/Po489i/71LPrXs+jf81MrzyrJqm9+Wy+fw2uLrK8I/ZuRkVHitqaGsyeeeEJTp049a5stW7aoefPmbke82rRpI29vb91zzz168cUX5ePj4+lSz2rChAlu9aWkpCgqKkr9+/dXUFCQW9usrCzt379fNWrUkK+vryQpIydX7ade+A/Oxsn95O9dso+A3W6Xl5eX2/7UqFFDycnJCgoKkt1u15IlS1SzZk0tWrRIUv71uW688UZdeumlWrJkiby8vPTCCy/oxhtv1Nq1a3XllVeqUaNG+t///qfHHntMUn4I/Oqrr/TSSy8V6bsCK1as0LBhw9zWf/TRR2rbtq3mzp2r0aNH6+6771b9+vX1/PPPu46uFvDx8ZHNZit2+y1btlSdOnW0Zs0atW3b9pz9YhiGUlNTFRgY6NEZFbOysuTn56devXq5PjfVgcPhUFxcnPr16ye73W52OVUO/et59LFn0b+eRf96Fv1bPny3HdPij9foSF4NXXVVD9fyitS/xR0QOBNTw9mjjz6qESNGnLVNo0aNil3epUsX5ebmas+ePWrWrJkiIiJ05MgRtzYFjyMiIly3xbUpvL5gWWRkpFub4s5RKuDj41NsQLTb7UU+DHl5ebJYLLJara6LFpt1cejCNZxLwflmVqtVhmFo8eLFWrRokR566CFZrflH4AICAlxHzSTp448/ltPp1L///W9XcJkzZ45CQkK0ZMkS9e/fXyNHjtScOXM0fvx4SdL333+vrKwsDR069Iy17d27V/Xq1XNbHxUVpffee0+33367jhw5ogULFmjNmjXFDq0sqOVM269bt672799for4pGMpY0DeeUtDHxX2mqoPqut8XCv3refSxZ9G/nkX/ehb9e34ubVRbFou0+0SGTmblKTzQ/Y/YFaF/S/P6poaz2rVrq3bt2mV67tq1a2W1WhUeHi5J6tq1q5566ik5HA5XB8TFxalZs2YKDQ11tVm8eLHGjBnj2k5cXJy6du0qSYqJiVFERIQWL17sCmMpKSlavny57rvvvjLu5bn52W3a/GysR7btdDqVmpKqwKDAIuHBz166c5e+++471ahRQw6HQ06nUzfffLMmT57sWt+6dWu3MLRu3Trt2LFDgYGBbtvJysrSzp07JeVPNDJx4kT98ccfuvTSSzVnzhzdeOONbpO9nC4zM7PYo0dDhgzRf//7X7300kt699131bRp01LtXwE/P79SHX4GAACAOYL97WpWJ1BbE1L15+6TGtgm8txPqsAqxTln8fHxWr58ufr06aPAwEDFx8frkUce0a233uoKXjfffLOmTJmikSNH6vHHH9fGjRs1ffp0vfHGG67tPPzww+rdu7dee+01DRw4UJ9//rlWrlzpmm7fYrFozJgxev7559W0aVPFxMTo6aefVt26dTV48GCP7Z/FYinx8MLScjqdyvW2yd/b67yP7PTp00fvvvuuvL29Vbdu3SLDBU8PVGlpaerYsWOxsx8WhPLw8HBdc801+uCDDxQTE6MFCxbol19+OWsdYWFhOnnyZJHlGRkZWrVqlWw223ldmy4xMbHMfzQAAADAhdUlpmZ+ONuTSDi7EHx8fPT5559r8uTJys7OVkxMjB555BG387yCg4O1aNEiPfDAA+rYsaPCwsI0adIkjRo1ytWmW7du+vTTTzVx4kQ9+eSTatq0qebNm6dWrVq52owfP17p6ekaNWqUkpKS1KNHDy1cuLBanedzJgEBAWrSpEmJ23fo0EFffPGFwsPDz3j+mCTdddddGjZsmOrXr6/GjRure/fuZ91u+/bttXnz5iLLH330UVmtVi1YsEBXXXWVBg4cqMsvv7zE9Up/H9Vr3759qZ4HAAAAc3SOqam58Xu1vApcjLpShLMOHTrojz/+OGe7Nm3aaOnSpWdtM2TIEA0ZMuSM6y0Wi5599lk9++yzpa4T7m655Ra98sorGjRokJ599lnVr19fe/fu1TfffKPx48erfv36kvIvZxAUFKTnn3++RP0eGxuruXPnui37/vvvNXv2bMXHx6tDhw4aN26chg8frvXr17uOru7YsUNpaWlKSEhQZmam1q5dK0m6+OKLXcMx//jjD/n4+LiGugIAAKBiK7gY9daEFCVnOhTsV3nP4atUF6FG5eLv768lS5aoQYMGuv7669WiRQuNHDlSWVlZbkfSrFarRowYoby8PN1+++3n3O4tt9yiTZs2adu2bZKkY8eOaeTIkZo8ebI6dOggSZoyZYrq1Kmje++91/W8u+66S+3bt9d7772nv/76S+3bt1f79u116NAhV5vPPvtMt9xyS5FLIAAAAKBiCg/yVUxYgAxDWrW3ch89qxRHzmC+OXPmlGl9REREkaNcxTl48KCuuuoqt1kyz6RmzZp68MEH9frrr+u9995T7dq1i1yHzm63a+XKlW7LznUu2/Hjx/XVV18VeR4AAAAqts4NQ7X7eLpW7D6py5vXOfcTKiiOnMFUycnJ+u233/Tpp5/qoYceKvHznnrqKUVHR7umsi8Pe/bs0TvvvFOqi08DAADAfJfE1JIkrdh9wuRKzg9HzmCqQYMGacWKFbr33nvVr1+/Ej8vJCRETz75ZLnW0qlTJ3Xq1KlctwkAAADPKzjvbMPBZGXm5MnLYnJBZUQ4g6nONdQQAAAAOJeomn6KCPJVQkqW1uw/qc4Ngs0uqUwY1ggAAACgUrNYLOock3/0bOWeotfDrSwIZwAAAAAqvc4N8y+f9OeeyjtjI+EMAAAAQKXXMTo/nK3Zl6Q8p2FyNWVDOAMAAABQ6TWPCFINHy+lZedq25FUs8spE8IZAAAAgErPZrWow6mjZ6v3JZlbTBkRzgAAAABUCZ1OhbOVe5PMLaSMCGeocEaMGKHBgwebXUYRixcvVosWLZSXl1du25w5c6auueaactseAABAddbp1KQgq/ZWzhkbCWcokREjRshischiscjb21tNmjTRs88+q9zcXLNL0549e2SxWLR27VqPvs748eM1ceJE2Wy2ErVft26dhg0bpqioKPn5+alFixaaPn26W5s777xTq1ev1tKlSz1RMgAAQLXSLipEXlaLElKylZhtdjWlx0WoUWIDBgzQBx98oOzsbM2fP18PPPCA7Ha7JkyYUKRtTk6OvL29Tajy/DgcDtnt9iLLf/vtN+3cuVM33HBDibe1atUqhYeH6+OPP1ZUVJSWLVumUaNGyWaz6cEHH5QkeXt76+abb9aMGTPUs2fPctsPAACA6sjf20st6wZp3YFk7UqxmF1OqXHkrCIwDCkn3XM/jozilxulm2LUx8dHERERio6O1n333ae+ffvq22+/lfT3UMQXXnhBdevWVbNmzSRJ+/fv14033qiQkBDVrFlTgwYN0p49e1zbzMvL09ixYxUSEqJatWpp/PjxMk6ra+HCherRo4erzdVXX62dO3e61sfExEiS2rdvL4vFossuu0yS5HQ69eyzz6p+/fry8fFRu3bttHDhQtfzCo64ffHFF+rdu7d8fX31ySefFLvvn3/+ufr16ydfX99Tb5mhvn37asCAAa56ExMTVb9+fU2aNElS/lGx6dOnq3fv3mrUqJFuvfVW3XHHHfrmm2/ctn3NNdfo22+/VWZmZqneDwAAABTVqWH+xah3p1a+cMaRs4rAkSH9s65HNm2VFHKmlU8ekrwDyrxtPz8/nThxwvV48eLFCgoKUlxcnKT8o1CxsbHq2rWrli5dKi8vLz3//PMaMGCA1q9fL29vb7322muaM2eOZs+erRYtWui1117Tf//7X11++eWu7aanp2vs2LFq06aN0tLSNGnSJF133XVau3atrFarVqxYoUsuuUQ//vijWrZs6TpiN336dL322mt677331L59e82ePVvXXnutNm3apKZNm7q2/8QTT+i1115T+/btXeHrdEuXLtXNN9/semyxWDR37ly1bt1a7733nsaPH697771X9erVc4Wz4iQnJ6tmzZpuyzp16qTc3FwtX77cFSwBAABQNp2iQ/Xv33ZrF+EM1YFhGFq8eLF++OEHPfTQQ67lAQEBev/9913h6OOPP5bT6dT7778viyX/y/HBBx8oJCREv/zyi/r3769p06ZpwoQJuv766yXlT5Dxww8/uL3e6UMJZ8+erdq1a2vz5s1q1aqVateuLUmqVauWIiIiXO1effVVPf744xo6dKgkaerUqfr55581bdo0vf322652Y8aMcb3+mezdu1d167oH6Hr16undd9/ViBEjlJSUpPnz52vNmjXy8ir+a7Vs2TJ98cUX+v77792W+/v7Kzg4WHv37j1rDQAAADi3jg1D1aCmnyJs6XJWsotRE84qArt//lEsD3A6nUpJTVVQYKCs1tNGsdr9S7Wt7777TjVq1JDD4ZDT6dTNN9+syZMnu9a3bt3a7TyzdevWaceOHQoMDHTbTlZWlnbu3Knk5GQdPnxYXbp0ca3z8vJSp06d3IY2bt++XZMmTdLy5ct1/PhxOZ1OSdK+ffvUqlWrYmtNSUnRoUOH1L17d7fl3bt317p169yWderU6Zz7npmZWexRtSFDhujLL7/U1KlT9e6777odkSts48aNGjRokJ555hn179+/yHo/Pz9lZGScsw4AAACcXXigrxY/0lPz58+X1Vq5jp4RzioCi+W8hheeldMp2fPyt396OCulPn366N1335W3t7fq1q1b5AhRQID7PqSlpaljx47FnsdVcLSrJK655hpFR0frX//6l+rWrSun06lWrVopJyenbDtymtPrLk5YWJhOniw6JWtGRobWrVsnm82m7du3F/vczZs364orrtCoUaM0ceLEYtskJiaWqk8AAABQ9TAhCEosICBATZo0UYMGDc44dK+wDh06aPv27QoPD1eTJk3cfoKDgxUcHKzIyEgtX77c9Zzc3FytWrXK9fjEiRPatm2bJk6cqCuuuEItWrQoEpIKjtYVvv5YUFCQ6tatq99//92t7e+//66LL7641Pvevn17bd68ucjyxx57TFarVd9//71mzJihn376yW39pk2b1KdPHw0fPlwvvPBCsdveuXOnsrKy1L59+1LXBQAAgKqDcAaPueWWWxQWFqZBgwZp6dKl2r17t3755ReNHj1aBw4ckCQ9/PDDeumllzRv3jxt3bpV999/v5KSklzbCA0NVa1atTRr1izt2LFDP/30k8aOHev2OuHh4fLz89PChQt15MgRJScnS5LGjRunqVOn6osvvtC2bdv0xBNPaO3atXr44YdLvS+xsbH67bff3JZ9//33+uCDDzRr1iz169dP48aN0/Dhw13hcePGjerTp4/69++vsWPHKiEhQQkJCTp27JjbdpYuXapGjRqpcePGpa4LAAAAVQfhDB7j7++vJUuWqEGDBrr++uvVokULjRw5UllZWQoKCpIkPfroo7rttts0fPhwde3aVYGBgbruuutc27Barfr888+1atUqtWrVSo888oheeeUVt9fx8vLSjBkz9N5776lu3boaNGiQJGn06NEaO3asHn30UbVu3VoLFy7Ut99+e8bzws7mlltu0aZNm7Rt2zZJ0rFjxzRy5Eg988wzatu2rSRpypQpqlOnju69915J0ldffaVjx47p448/VmRkpOunc+fObtv+7LPPdPfdd5e6JgAAAFQtFuP0i0rhvKWkpCg4OFjJycmuEFIgKytLu3fvVkxMzBmnbS9PTqdTKSkpCgoKKjohCEpl3LhxSklJ0Xvvvedadr79u2nTJl1++eX666+/FBwcfMZ2F/pzU1E4HA7Nnz9fV111VbEXB8f5oX89jz72LPrXs+hfz6J/Pasi9e/ZssHp+G0dKKGnnnpK0dHRrtkiy8Phw4f14YcfnjWYAQAAoHpgtkaghEJCQvTkk0+W6zb79u1brtsDAABA5cWRMwAAAACoAAhnAAAAAFABEM5MwjwsKA0+LwAAAFUf4ewCK5gtJiMjw+RKUJkUfF7Mnm0IAAAAnsOEIBeYzWZTSEiIjh49Kin/WmAWi8Vjr+d0OpWTk6OsrCym0vcAT/evYRjKyMjQ0aNHFRISIpvNVu6vAQAAgIqBcGaCiIgISXIFNE8yDEOZmZny8/PzaAisri5U/4aEhLg+NwAAAKiaCGcmsFgsioyMVHh4uBwOh0dfy+FwaMmSJerVqxdD4jzgQvSv3W7niBkAAEA1QDgzkc1m8/gv3TabTbm5ufL19SWceQD9CwAAgPLCSUgAAAAAUAEQzgAAAACgAiCcAQAAAEAFwDlnHlBwweCUlBSTK8mfsCIjI0MpKSmcE+UB9K9n0b+eRf96Hn3sWfSvZ9G/nkX/elZF6t+CTFCQEc6GcOYBqampkqSoqCiTKwEAAABQEaSmpio4OPisbSxGSSIcSsXpdOrQoUMKDAw0/dpiKSkpioqK0v79+xUUFGRqLVUR/etZ9K9n0b+eRx97Fv3rWfSvZ9G/nlWR+tcwDKWmpqpu3bqyWs9+VhlHzjzAarWqfv36ZpfhJigoyPQPZlVG/3oW/etZ9K/n0ceeRf96Fv3rWfSvZ1WU/j3XEbMCTAgCAAAAABUA4QwAAAAAKgDCWRXn4+OjZ555Rj4+PmaXUiXRv55F/3oW/et59LFn0b+eRf96Fv3rWZW1f5kQBAAAAAAqAI6cAQAAAEAFQDgDAAAAgAqAcAYAAAAAFQDhDAAAAAAqAMJZFfD222+rYcOG8vX1VZcuXbRixYqztv/yyy/VvHlz+fr6qnXr1po/f/4FqrRyefHFF9W5c2cFBgYqPDxcgwcP1rZt2876nDlz5shisbj9+Pr6/n87dx8TdR3HAfx96oEPKBjkHZeBaDw0A4ZsMmithoyHnEI5UeZIzKzodNHDRm0JtbbKbG1ljVwTtLmZunxYuWSAQEX4BJRijpAxnJOHSTviQYLdffrDcXbe8yX3AO/Xdtvd7/f5fv3e188+/D735KYV+553333XbL9iYmJsjmH+Om7JkiVm+6tQKKDVai3GM39t++mnn7BmzRpoNBooFAqcOHHC5LyIoKSkBKGhoZgzZw7S0tLQ3t5ud15na/hUZWt/x8fHUVxcjNjYWMybNw8ajQbPPfccbt68aXNOV2rMVGUvfwsKCsz2KjMz0+68zN877O2vpVqsUCiwe/duq3Myf+9w5HpsdHQUWq0WwcHBCAgIwLp169Db22tzXldr9mRjc+bjDh8+jNdffx2lpaVobm5GfHw8MjIy0NfXZzH+119/RV5eHrZu3YqWlhbk5OQgJycHra2tbl6596uvr4dWq8XZs2dRVVWF8fFxpKenY3h42Oa4BQsWoLu723jr6upy04p90/Lly03265dffrEay/x1zoULF0z2tqqqCgCwfv16q2OYv9YNDw8jPj4eX375pcXzH3/8MT7//HN89dVXOHfuHObNm4eMjAyMjo5andPZGj6V2drfkZERNDc3Y+fOnWhubsaxY8fQ1taGtWvX2p3XmRozldnLXwDIzMw02atDhw7ZnJP5e5e9/f3vvnZ3d6O8vBwKhQLr1q2zOS/z17Hrsddeew3ff/89jh49ivr6ety8eRPPPvuszXldqdluIeTTVq5cKVqt1vhYr9eLRqORDz/80GJ8bm6urF692uRYUlKSvPTSS5O6zqmgr69PAEh9fb3VmIqKCgkMDHTfonxcaWmpxMfHOxzP/P1/Xn31VVm2bJkYDAaL55m/jgMgx48fNz42GAyiVqtl9+7dxmM6nU78/f3l0KFDVudxtoZPF/furyXnz58XANLV1WU1xtkaM11Y2t/NmzdLdna2U/Mwfy1zJH+zs7MlNTXVZgzz17J7r8d0Op0olUo5evSoMebq1asCQBobGy3O4WrNdge+c+bDxsbG0NTUhLS0NOOxGTNmIC0tDY2NjRbHNDY2msQDQEZGhtV4umtgYAAA8MADD9iMGxoaQnh4OB5++GFkZ2fjypUr7liez2pvb4dGo8HSpUuxadMmXL9+3Wos89d1Y2NjOHjwIJ5//nkoFAqrccxf13R2dqKnp8ckPwMDA5GUlGQ1P12p4XTXwMAAFAoFgoKCbMY5U2Omu7q6OixatAjR0dEoLCxEf3+/1Vjmr+t6e3tx6tQpbN261W4s89fcvddjTU1NGB8fN8nFmJgYhIWFWc1FV2q2u7A582G3bt2CXq+HSqUyOa5SqdDT02NxTE9Pj1PxdIfBYEBRUREef/xxPPbYY1bjoqOjUV5ejpMnT+LgwYMwGAxISUnBjRs33Lha35GUlIT9+/fj9OnTKCsrQ2dnJ5544gkMDg5ajGf+uu7EiRPQ6XQoKCiwGsP8dd1EDjqTn67UcLpjdHQUxcXFyMvLw4IFC6zGOVtjprPMzEx88803qKmpwa5du1BfX4+srCzo9XqL8cxf1x04cADz58+3+7E75q85S9djPT098PPzM3uhxt718ESMo2PcZZZH/3UiH6HVatHa2mr3s97JyclITk42Pk5JScGjjz6KvXv34v3335/sZfqcrKws4/24uDgkJSUhPDwcR44ccegVRXLcvn37kJWVBY1GYzWG+Uu+YHx8HLm5uRARlJWV2YxljXHcxo0bjfdjY2MRFxeHZcuWoa6uDqtWrfLgyqae8vJybNq0ye4PLjF/zTl6PebL+M6ZDwsJCcHMmTPNfo2mt7cXarXa4hi1Wu1UPAHbt2/HDz/8gNraWixevNipsUqlEgkJCbh27dokrW5qCQoKQlRUlNX9Yv66pqurC9XV1XjhhRecGsf8ddxEDjqTn67U8OluojHr6upCVVWVzXfNLLFXY+iupUuXIiQkxOpeMX9d8/PPP6Otrc3pegwwf61dj6nVaoyNjUGn05nE27senohxdIy7sDnzYX5+fkhMTERNTY3xmMFgQE1Njcmr3/+VnJxsEg8AVVVVVuOnMxHB9u3bcfz4cZw5cwYRERFOz6HX63H58mWEhoZOwgqnnqGhIXR0dFjdL+avayoqKrBo0SKsXr3aqXHMX8dFRERArVab5Offf/+Nc+fOWc1PV2r4dDbRmLW3t6O6uhrBwcFOz2GvxtBdN27cQH9/v9W9Yv66Zt++fUhMTER8fLzTY6dr/tq7HktMTIRSqTTJxba2Nly/ft1qLrpSs93Goz9HQv/bt99+K/7+/rJ//375448/5MUXX5SgoCDp6ekREZH8/Hx56623jPENDQ0ya9Ys+eSTT+Tq1atSWloqSqVSLl++7Kmn4LUKCwslMDBQ6urqpLu723gbGRkxxty7v++9955UVlZKR0eHNDU1ycaNG2X27Nly5coVTzwFr/fGG29IXV2ddHZ2SkNDg6SlpUlISIj09fWJCPP3ftDr9RIWFibFxcVm55i/zhkcHJSWlhZpaWkRAPLpp59KS0uL8dcCP/roIwkKCpKTJ0/KpUuXJDs7WyIiIuT27dvGOVJTU2XPnj3Gx/Zq+HRia3/HxsZk7dq1snjxYvntt99MavI///xjnOPe/bVXY6YTW/s7ODgob775pjQ2NkpnZ6dUV1fLihUrJDIyUkZHR41zMH+ts1cfREQGBgZk7ty5UlZWZnEO5q9ljlyPvfzyyxIWFiZnzpyRixcvSnJysiQnJ5vMEx0dLceOHTM+dqRmewKbsylgz549EhYWJn5+frJy5Uo5e/as8dyTTz4pmzdvNok/cuSIREVFiZ+fnyxfvlxOnTrl5hX7BgAWbxUVFcaYe/e3qKjI+H+hUqnk6aeflubmZvcv3kds2LBBQkNDxc/PTx566CHZsGGDXLt2zXie+fv/VVZWCgBpa2szO8f8dU5tba3FmjCxhwaDQXbu3CkqlUr8/f1l1apVZvseHh4upaWlJsds1fDpxNb+dnZ2Wq3JtbW1xjnu3V97NWY6sbW/IyMjkp6eLg8++KAolUoJDw+Xbdu2mTVZzF/r7NUHEZG9e/fKnDlzRKfTWZyD+WuZI9djt2/flldeeUUWLlwoc+fOlWeeeUa6u7vN5vnvGEdqticoREQm5z05IiIiIiIichS/c0ZEREREROQF2JwRERERERF5ATZnREREREREXoDNGRERERERkRdgc0ZEREREROQF2JwRERERERF5ATZnREREREREXoDNGRERERERkRdgc0ZERPQ/FBQUICcnx9PLICKiKWCWpxdARETkrRQKhc3zpaWl+OyzzyAibloRERFNZWzOiIiIrOju7jbeP3z4MEpKStDW1mY8FhAQgICAAE8sjYiIpiB+rJGIiMgKtVptvAUGBkKhUJgcCwgIMPtY41NPPYUdO3agqKgICxcuhEqlwtdff43h4WFs2bIF8+fPxyOPPIIff/zR5N9qbW1FVlYWAgICoFKpkJ+fj1u3brn5GRMRkSexOSMiIrrPDhw4gJCQEJw/fx47duxAYWEh1q9fj5SUFDQ3NyM9PR35+fkYGRkBAOh0OqSmpiIhIQEXL17E6dOn0dvbi9zcXA8/EyIicic2Z0RERPdZfHw83nnnHURGRuLtt9/G7NmzERISgm3btiEyMhIlJSXo7+/HpUuXAABffPEFEhIS8MEHHyAmJgYJCQkoLy9HbW0t/vzzTw8/GyIichd+54yIiOg+i4uLM96fOXMmgoODERsbazymUqkAAH19fQCA33//HbW1tRa/v9bR0YGoqKhJXjEREXkDNmdERET3mVKpNHmsUChMjk38CqTBYAAADA0NYc2aNdi1a5fZXKGhoZO4UiIi8iZszoiIiDxsxYoV+O6777BkyRLMmsU/zURE0xW/c0ZERORhWq0Wf/31F/Ly8nDhwgV0dHSgsrISW7ZsgV6v9/TyiIjITdicEREReZhGo0FDQwP0ej3S09MRGxuLoqIiBAUFYcYM/qkmIpouFCIinl4EERERERHRdMeX44iIiIiIiLwAmzMiIiIiIiIvwOaMiIiIiIjIC7A5IyIiIiIi8gJszoiIiIiIiLwAmzMiIiIiIiIvwOaMiIiIiIjIC7A5IyIiIiIi8gJszoiIiIiIiLwAmzMiIiIiIiIvwOaMiIiIiIjIC/wLNX95WhpxyuwAAAAASUVORK5CYII=", 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", 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" ] @@ -844,7 +905,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It is clear using our rollout-based loss functions on observed stochastic data, similar to that in the Neural ODE case, does not work as it cannot capture the inherent randomness. Another way of putting it is the undeterministic nature of the system can cause the learned system to be unstable and blow up. Developing appropriate loss functions to ensure correctness and stability using this framework is an open research question. Thus we necessitate the need to reformulate the above procedure in a latent space and use a variational inference method to learn the stochastic data-generating distributions." + "It is clear using our rollout-based loss functions on observed stochastic data, similar to that in the Neural ODE case, does not work as it cannot capture the inherent randomness. Another way of putting it is the undeterministic nature of the system can cause the learned system to be unstable and blow up. Developing appropriate loss functions to ensure correctness and stability using this rollout framework is an open research question. Thus we necessitate the need to reformulate the above procedure + loss in a different way. We do so in a latent space and use a variational inference method to learn the stochastic data-generating distributions." ] }, { @@ -855,9 +916,9 @@ "\n", "Using mean squared error (MSE) to train a neural SDE on time pairs might encounter challenges due to the stochastic nature of SDEs. While MSE is a common loss function used for deterministic systems, it may not be directly applicable to stochastic systems like SDEs. As discussed above, this is because these rollout-MSE -based losses do not capture the underlying uncertainty: \n", "\n", - "Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n", + "* Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n", "\n", - "Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n", + "* Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n", "\n", "SDEs inherently involve randomness or uncertainty, typically represented by the stochastic terms in the differential equations. Instead of using refernence tracking + finite difference loss through our rollout mechanism, we can train the model using variational inference. \n", "\n", @@ -926,14 +987,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "from neuromancer.dynamics.sde import StochasticLorenzAttractor\n", + "\n", "class StochasticLorenzAttractor(BaseSDESystem):\n", " def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)):\n", - " super(BaseSDESystem).__init__()\n", + " super().__init__()\n", " self.a = a\n", " self.b = b\n", "\n", @@ -954,6 +1015,15 @@ " g2 = x2 * b2\n", " g3 = x3 * b3\n", " return torch.cat([g1, g2, g3], dim=1)\n", + " \n", + " @torch.no_grad()\n", + " def sample(self, x0, ts, noise_std, normalize):\n", + " \"\"\"Sample data for training. Store data normalization constants if necessary.\"\"\"\n", + " xs = torchsde.sdeint(self, x0, ts)\n", + " if normalize:\n", + " mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1))\n", + " xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std)\n", + " return xs\n", "stochastic_lorenz_model = StochasticLorenzAttractor()" ] }, @@ -968,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -990,12 +1060,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "data_size = 3\n", - "batch_size=256\n", + "batch_size=1024\n", "latent_size=4\n", "context_size=64\n", "hidden_size=128\n", @@ -1020,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -1031,7 +1101,7 @@ " This class is used only in LatentSDE_Encoder\n", " \"\"\"\n", " def __init__(self, input_size, hidden_size, output_size):\n", - " super(Encoder, self).__init__()\n", + " super().__init__()\n", " self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size)\n", " self.lin = nn.Linear(hidden_size, output_size)\n", "\n", @@ -1042,13 +1112,30 @@ " \n", "class LatentSDE_Encoder(BaseSDESystem):\n", " def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False):\n", - " super(BaseSDESystem).init__()\n", + " \"\"\"\n", + " LatentSDE_Encoder is a neural network module designed for encoding time-series data into a latent space representation,\n", + " which is then used to model the system dynamics using Stochastic Differential Equations (SDEs).\n", + "\n", + " The primary purpose of this class is to transform high-dimensional time-series data into a lower-dimensional latent space\n", + " while capturing the underlying stochastic dynamics. This transformation facilitates efficient modeling, prediction, and\n", + " inference of complex temporal processes. \n", + "\n", + " Taken from https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py and modified to support \n", + " NeuroMANCER library\n", "\n", + " :param data_size: (int) state size of the data \n", + " :param latent_size: (int) input latent size for the encoder \n", + " :param context_size: (int) size of context vector (output of encoder)\n", + " :param hidden_size: (int) size of the hidden layer of encoder \n", + " :param ts: (tensor) tensor of timesteps over which data should be predicted\n", "\n", - " self.adjoint = False\n", + " \"\"\"\n", + " super().__init__()\n", + "\n", + " self.adjoint = adjoint\n", "\n", " # Encoder.\n", - " self.encoder = Encoder(input_size=latent_size, hidden_size=hidden_size, output_size=context_size)\n", + " self.encoder = Encoder(input_size=data_size, hidden_size=hidden_size, output_size=context_size)\n", " self.qz0_net = nn.Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space\n", "\n", " # Decoder.\n", @@ -1121,12 +1208,16 @@ " )\n", " return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params\n", "\n", - "class LatentSDE_Decoder(nn.Module):\n", + "class LatentSDE_Decoder(BaseSDESystem):\n", " \"\"\"\n", " Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of\n", " LatentSDEIntegrator and decodes it back into the \"real\" data space and also outputs associated Gaussian distributions\n", " to be used in the final loss function.\n", " Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py\n", + "\n", + " :param data_size: (int) state size of the data \n", + " :param latent_size: (int) input latent size for the encoder \n", + " :param noise_std: (float) standard deviation of the Gaussian noise applied during decoding\n", " \"\"\"\n", " def __init__(self, data_size, latent_size, noise_std):\n", " super().__init__()\n", @@ -1134,6 +1225,12 @@ " self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))\n", " self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))\n", " self.projector = nn.Linear(latent_size, data_size)\n", + " \n", + " def f(self, t, y): \n", + " pass #unused \n", + " \n", + " def g(self, t, y): \n", + " pass #unused\n", "\n", " def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd):\n", " _xs = self.projector(zs)\n", @@ -1158,17 +1255,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "ts = torch.linspace(t0, t1, steps=steps)\n", "\n", - "sde_block_encoder = LatentSDE_Encoder(data_size, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n", - "integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False)\n", + "sde_encoder = LatentSDE_Encoder(data_size, latent_size, context_size, hidden_size, ts=ts, adjoint=False) \n", + "integrator = integrators.LatentSDEIntegrator(sde_encoder, adjoint=False)\n", "model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1')\n", - "sde_block_decoder = blocks.LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", - "model_2 = Node(sde_block_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", + "sde_decoder = LatentSDE_Decoder(3, latent_size, noise_std=noise_std)\n", + "model_2 = Node(sde_decoder, input_keys=['xs', 'zs', 'log_ratio', 'qz0_mean', 'qz0_logstd'], output_keys=['xs_hat', 'log_pxs', 'sum_term', 'log_ratio'], name='m2' )\n", "\n", "\n", "\n", @@ -1196,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -1264,33 +1361,4395 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Fix the same Brownian motion for visualization.\n", - "bm_vis = torchsde.BrownianInterval(\n", - " t0=t0, t1=t1, size=(batch_size, latent_size,), device='cpu', levy_area_approximation=\"space-time\")\n", - "\n", - "# Define the custom_training_step to support visualization. \n", - "def custom_training_step(model, batch): \n", - " output = model.problem(batch)\n", - " loss = output[model.train_metric]\n", - " img_path = os.path.join('', f'current_epoch_{model.current_epoch:06d}.pdf')\n", - " if model.current_epoch % 50 == 0: \n", - " vis(batch, model.problem, bm_vis, img_path, num_samples=10)\n", - " return loss\n", - "\n", - "\n", - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "lit_trainer = LitTrainer(epochs=300, accelerator='cpu', train_metric='train_loss', \n", - " dev_metric='train_loss', eval_metric='train_loss', test_metric='train_loss',\n", - " custom_optimizer=optimizer, custom_training_step=custom_training_step)\n", - "\n", - "\n", - "\n", - "lit_trainer.fit(problem=problem, data_setup_function=make_dataset, t0=t0, t1=t1, batch_size=batch_size, noise_std=noise_std)\n" + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:653: Checkpoint directory /Users/birm560/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/neuromancer/examples/SDEs exists and is not empty.\n", + "\n", + " | Name | Type | Params\n", + "------------------------------------\n", + "0 | problem | Problem | 104 K \n", + "------------------------------------\n", + "104 K Trainable params\n", + "0 Non-trainable params\n", + "104 K Total params\n", + "0.420 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "USING BATCH SIZE 1024\n", + "USING LEARNING RATE 0.001\n", + " " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/utilities/data.py:104: Total length of `DataLoader` across ranks is zero. Please make sure this was your intention.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n", + "/Users/birm560/opt/anaconda3/envs/neuromancer9/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: 0%| | 0/1 [00:00 \n", - "\n", - "Neuromancer provides a set of ODE solvers implemented in [integrators.py](https://github.com/pnnl/neuromancer/blob/master/src/neuromancer/dynamics/integrators.py).\n", - "For adjoint method we provide the interface to the [open-source implementation](https://github.com/rtqichen/torchdiffeq) via DiffEqIntegrator class.\n", - "\n", - "We give a quick example here to motivate how one might include (and not include) stochasticity (randomness) into the system dynamics. \n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ordinary Lotka-Volterra system, also known as the predator-prey model, describes the dynamics of two interacting species in a biological community. The system consists of two coupled ordinary differential equations (ODEs), typically represented as follows:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - "\\frac{dX}{dt} &= (\\alpha X - \\beta XY) dt \\\\\n", - "\\frac{dY}{dt} &= (\\delta XY - \\gamma Y) dt\n", - "\\end{align*}\n", - "$$\n", - "\n", - "where:\n", - "- \\(X\\) and \\(Y\\) represent the population sizes of the prey and predator species, respectively.\n", - "- \\(\\alpha\\), \\(\\beta\\), \\(\\gamma\\), and \\(\\delta\\) are parameters governing the growth and interaction rates of the species.\n", - "- \\(dW_1\\) and \\(dW_2\\) are independent Wiener processes representing white noise in the population dynamics.\n", - "- \\(\\sigma_1\\) and \\(\\sigma_2\\) are the volatility parameters associated with the noise processes.\n", - "\n", - "This system captures the stochastic fluctuations in population sizes due to random environmental factors, which can influence the dynamics of predator-prey interactions over time.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have a neural network, denoted as $f_{\\theta}$ which represents the right-hand side (RHS) of the ordinary differential equation (ODE). This neural network takes the current state of the system as input and outputs the rate of change (derivative) of the state variables. \n", - "\n", - "We then use ODE solver to generate data states at future time, e.g. at $t+1$ given by $x_{k+1} = \\text{ODESolve}(f_{\\theta}(x_k))$ \n", - "\n", - "\n", - "You train the entire model, including the neural network dynamics and the ODE solver, end-to-end using pairs of consecutive time points (for the case of a rollout of 1 step, though this can be scaled up to predict longer horizons) from your dataset. We reshape our full system trajectory dataset into these bunched time \"episodes\" to achieve this rollout-based training. \n", - "\n", - "​" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class LotkaVolterraHybrid(ode.ODESystem):\n", - "\n", - " def __init__(self, block, insize=2, outsize=2):\n", - " \"\"\"\n", - "\n", - " :param block:\n", - " :param insize:\n", - " :param outsize:\n", - " \"\"\"\n", - " super().__init__(insize=insize, outsize=outsize)\n", - " self.block = block\n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " assert self.block.in_features == 2\n", - " assert self.block.out_features == 1\n", - "\n", - " def ode_equations(self, x):\n", - " x1 = x[:, [0]]\n", - " x2 = x[:, [-1]]\n", - " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", - " dx2 = self.delta*self.block(x) - self.gamma*x2\n", - " return torch.cat([dx1, dx2], dim=-1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$\\dot{x} = f_{\\vec{\\theta}}(x)$ \n", - "\n", - "Here, $ \\vec{\\theta} $ is a vector with parameters $ [\\alpha, \\beta, \\gamma, \\delta, \\theta'] $ and $\\theta'$ parameterizes the multi-layer perception block" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "def get_data(sys, nsim, nsteps, ts, bs):\n", - " \"\"\"\n", - " :param nsteps: (int) Number of timesteps for each batch of training data\n", - " :param sys: (psl.system)\n", - " :param ts: (float) step size\n", - " :param bs: (int) batch size\n", - "\n", - " \"\"\"\n", - " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", - " nx = sys.nx\n", - " nbatch = nsim//nsteps #500\n", - " length = (nsim//nsteps) * nsteps #1000\n", - " ts = torch.linspace(0,1,nsteps)\n", - " print('train sim ', train_sim['X'].shape)\n", - "\n", - " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " trainX = torch.tensor(trainX, dtype=torch.float32)\n", - "\n", - " print(trainX.shape)# N x nsteps x state_size \n", - "\n", - " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", - " train_loader = DataLoader(train_data, batch_size=bs,\n", - " collate_fn=train_data.collate_fn, shuffle=True)\n", - "\n", - " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " devX = torch.tensor(devX, dtype=torch.float32)\n", - " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", - " dev_loader = DataLoader(dev_data, batch_size=bs,\n", - " collate_fn=dev_data.collate_fn, shuffle=True)\n", - "\n", - " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", - " testX = torch.tensor(testX, dtype=torch.float32)\n", - " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", - "\n", - " return train_loader, dev_loader, test_data, trainX\n", - "\n", - "torch.manual_seed(0)\n", - "\n", - "# %% ground truth system\n", - "system = psl.systems['LotkaVolterra']\n", - "modelSystem = system()\n", - "ts = modelSystem.ts\n", - "nx = modelSystem.nx\n", - "raw = modelSystem.simulate(nsim=1000, ts=ts)\n", - "plot.pltOL(Y=raw['X'])\n", - "plot.pltPhase(X=raw['Y'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Neural ODEs with Added Stochasticity: \n", - "\n", - "A stochastic differential equation is given by :\n", - "\n", - "$$ dx = f(t, x) \\, dt + g(t, x) \\, dW $$\n", - "\n", - "THe $f$ term is known as the drift process; the $g$ term is known as the diffusion process. Note that if the diffusion process is zero then an SDE simplifies to an ODE and can be solved with via backpropagating ODE solver and doing the reverse-time ODE as we have shown previously. \n", - "\n", - "For simplicity we can assume there exists reverse-time integration/backpropagating through SDE solvers. This paper, https://arxiv.org/pdf/2001.01328, describes it in detail. \n", - "\n", - "A natural question therefore is how to train such Neural ODEs with Stochastic Terms. Can we do it using standard Neuromancer training procedure for Neural ODEs -- our reference tracking and finite difference losses. We attempt to do this below. Note that it will **not** work and the purpose of demonstrating this is to motivate the need for a variational inference approach to train the SDE -- the Latent SDE. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can formulate neural networks to parameterize not only the drift process, but also the diffusion process, e.g: \n", - "\n", - "$$ \\dot{x} = f_{\\vec{\\theta_f}}(x) + g_{\\vec{\\theta_g}}(x) $$\n", - "\n", - "Where $g$ is a neural network to model the stochastic process. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To support this framework and integrate it with TorchSDE solvers, we define a base class: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BaseSDESystem(abc.ABC, nn.Module):\n", - " \"\"\"\n", - " Base class for SDEs for integration with TorchSDE library\n", - " \"\"\"\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - " self.in_features = 0\n", - " self.out_features = 0\n", - "\n", - " @abc.abstractmethod\n", - " def f(self, t, y):\n", - " \"\"\"\n", - " Define the ordinary differential equations (ODEs) for the system.\n", - "\n", - " Args:\n", - " t (Tensor): The current time (often unused)\n", - " y (Tensor): The current state variables of the system.\n", - "\n", - " Returns:\n", - " Tensor: The derivatives of the state variables with respect to time.\n", - " The output should be of shape [batch size x state size]\n", - " \"\"\"\n", - " pass\n", - "\n", - " @abc.abstractmethod\n", - " def g(self, t,y):\n", - " \"\"\"\n", - " Define the diffusion equations for the system.\n", - "\n", - " Args:\n", - " t (Tensor): The current time (often unused)\n", - " y (Tensor): The current state variables of the system.\n", - "\n", - " Returns:\n", - " Tensor: The diffusion coefficients per batch item (output is of size \n", - " [batch size x state size]) for noise_type 'diagonal'\n", - " \"\"\"\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use a stochastic Lotka-Volterra model (for forward passes only) with user-defined parameters to generate ground truth data: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Lotka-Volterra SDE\n", - "class LotkaVolterraSDE(nn.Module):\n", - " def __init__(self, a, b, c, d, sigma1, sigma2):\n", - " super().__init__()\n", - " self.a = a\n", - " self.b = b\n", - " self.c = c\n", - " self.d = d\n", - " self.sigma1 = sigma1\n", - " self.sigma2 = sigma2\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - "\n", - " def f(self, t, x):\n", - " x1 = x[:,[0]]\n", - " x2 = x[:,[1]]\n", - " dx1 = self.a * x1 - self.b * x1*x2\n", - " dx2 = self.c * x1*x2 - self.d * x2\n", - " foo = torch.cat([dx1, dx2], dim=-1)\n", - " return torch.cat([dx1, dx2], dim=-1)\n", - "\n", - " def g(self, t, x):\n", - " sigma_diag = torch.tensor([[self.sigma1, self.sigma2]])\n", - " return sigma_diag #[batch_size x state size ]\n", - "\n", - "# Define parameters\n", - "a = 1.1 # Prey growth rate\n", - "b = 0.4 # Predation rate\n", - "c = 0.1 # Predator growth rate\n", - "d = 0.4 # Predator death rate\n", - "sigma1 = 1\n", - "sigma2 = 0\n", - "\n", - "# Create the SDE model\n", - "sde = LotkaVolterraSDE(a, b, c, d, sigma1, sigma2)\n", - "\n", - "\n", - "# Define time span\n", - "t_span = torch.linspace(0, 20, 2000)\n", - "\n", - "# Initial condition\n", - "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", - "\n", - "\n", - "# Integrate the SDE model\n", - "sol_train = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "sol_dev = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "sol_test = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the results\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(t_span, sol_train[:, 0,0], label='Prey (x1)')\n", - "plt.plot(t_span, sol_train[:,0, 1], label='Predator (x2)')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Population')\n", - "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class LotkaVolterraSDELearnable(BaseSDESystem):\n", - " def __init__(self, block, batch_size):\n", - " super().__init__()\n", - " self.block = block \n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.g_params = nn.Parameter(torch.randn(batch_size, 2), requires_grad=True) # Learnable parameters\n", - " def f(self, t, y):\n", - "\n", - " x1 = y[:, [0]]\n", - " x2 = y[:, [-1]]\n", - "\n", - " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", - " dx2 = self.delta*self.block(y) - self.gamma*x2\n", - "\n", - " return torch.cat([dx1, dx2], dim=-1)\n", - "\n", - " def g(self, t, y):\n", - " return self.g_params\n", - "\n", - "# construct UDE model in Neuromancer\n", - "net = blocks.MLP(2, 1, bias=True,\n", - " linear_map=torch.nn.Linear,\n", - " nonlin=torch.nn.GELU,\n", - " hsizes=4*[20])\n", - "fx = LotkaVolterraSDELearnable(block=net, batch_size = 2)\n", - "\n", - "\n", - "class BasicSDEIntegrator(integrators.Integrator): \n", - " \"\"\"\n", - " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", - " Returns a single tensor of size (t, batch_size, state_size).\n", - "\n", - " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", - " Currently only supports Euler integration. Choice of integration method is dependent \n", - " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", - " \"\"\"\n", - " def __init__(self, block ): \n", - " \"\"\"\n", - " :param block: (nn.Module) The BasicSDE block\n", - " \"\"\"\n", - " super().__init__(block) \n", - "\n", - "\n", - " def integrate(self, x): \n", - " \"\"\"\n", - " x is the initial datastate of size (batch_size, state_size)\n", - " t is the time-step vector over which to integrate\n", - " \"\"\"\n", - " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", - " x = x.squeeze(1) #remove time step \n", - " \n", - " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", - " ys = ys.permute(1, 0, 2)\n", - " return ys \n", - "\n", - "integrator = BasicSDEIntegrator(fx) \n", - "# integrate UDE model\n", - "# create symbolic UDE model\n", - "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", - "dynamics_model_sde = model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using mean squared error (MSE) to train a neural SDE on time pairs might encounter challenges due to the stochastic nature of SDEs. While MSE is a common loss function used for deterministic systems, it may not be directly applicable to stochastic systems like SDEs.\n", - "\n", - "Here are some considerations when using MSE for training neural SDEs:\n", - "\n", - "Ignoring Stochasticity: MSE only considers the deterministic part of the model and ignores the stochastic component represented by the diffusion term in the SDE. This can lead to suboptimal results as the model does not capture the inherent randomness in the system.\n", - "\n", - "Overfitting the Drift Term: MSE optimization might focus excessively on minimizing the errors in the drift term while neglecting the diffusion term. This can result in overfitting of the deterministic part of the model and underfitting of the stochastic part.\n", - "\n", - "SDEs inherently involve randomness or uncertainty, typically represented by the stochastic terms in the differential equations. Variational inference allows us to capture this uncertainty by providing a probabilistic characterization of the latent variables' distribution. Instead of obtaining a single point estimate, variational inference provides a full probabilistic description, including measures of uncertainty such as confidence intervals or predictive distributions. \n", - "\n", - "The Latent SDE is essentially a variational \"autoencoder\" where instead of seeking resynthesize samples corresponding to the \"same time\" instance, it tries to reconstruct future samples given the current dynamics of the system, where the dynamics are known to be modeled via a SDE. To do this, the latent space is **itself** going to be governed via an SDE and we perform integration on the latent space to synthesize forward-looking samples. \n", - "\n", - "Variational inference is a powerful method used to approximate complex posterior distributions in probabilistic models. In the context of latent stochastic differential equations (SDEs), variational inference plays a crucial role in estimating the posterior distribution of the latent variables given the observed data.\n", - "\n", - "In latent SDEs, the goal is to infer the hidden or latent variables that govern the dynamics of the system. These latent variables capture unobserved factors that influence the observed data, such as underlying trends, patterns, or noise sources. However, directly computing the posterior distribution of the latent variables given the data is often analytically intractable due to the complex and nonlinear nature of the model.\n", - "\n", - "Variational inference offers a solution to this problem by approximating the true posterior distribution with a simpler, parameterized distribution, often chosen from a family of distributions such as Gaussian distributions. This is done via an encoder network. The decoder network draws from samples of this learned, approximate posterior to reconstruct the data distribution. Using the KL divergence, between these distributions, we learn the latent space's parameters known as the variational parameters. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Latent SDE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. **Encoder**:\n", - " - The encoder maps each input data point $x$ to a distribution over latent variables $z$. This distribution is typically Gaussian with mean $\\mu$ and standard deviation $\\sigma$.\n", - " $$\n", - " q_{\\phi}(z | x) = \\mathcal{N}(\\mu_{\\phi}(x), \\sigma_{\\phi}(x))\n", - " $$\n", - " - Here, $\\mu_{\\phi}(x)$ and $\\sigma_{\\phi}(x)$ are the mean and standard deviation parameters of the Gaussian distribution, which are output by the encoder neural network parameterized by $\\phi$.\n", - "\n", - "2. **Latent Dynamics**:\n", - " - The latent variables $z$ evolve over time according to a stochastic differential equation (SDE). The dynamics of $z$ are governed by drift and diffusion functions, similar to the SDE for the observed data $x$.\n", - " $$\n", - " dz_t = f(z_t, t) \\, dt + G(z_t, t) \\, dW_t\n", - " $$\n", - " - $f(z_t, t)$ represents the drift component, determining the deterministic evolution of the latent variables.\n", - " - $G(z_t, t)$ represents the diffusion component, introducing stochasticity into the latent dynamics.\n", - " - $dW_t$ is the increment of a Wiener process (Brownian motion), representing random noise.\n", - "\n", - "3. **Decoder**:\n", - " - The decoder takes samples from the latent space $z$ and maps them back to the data space $x$. It models the conditional distribution of $x$ given $z$.\n", - " $$\n", - " p_{\\theta}(x | z)\n", - " $$\n", - " - The decoder neural network, parameterized by $\\theta$, outputs the parameters of the conditional distribution $p_{\\theta}(x | z)$, such as the mean and variance of a Gaussian distribution or the parameters of a Bernoulli distribution for binary data.\n", - "\n", - "4. **Latent Variable Prior**:\n", - " - We assume a prior distribution over the latent variables $z$. This distribution is typically chosen to be a standard Gaussian.\n", - " $$\n", - " p(z) = \\mathcal{N}(0, I)\n", - " $$\n", - "\n", - " though in TorchSDE's framework (and as shown in the code below), these are learnable parameters qz0_mean and qz0\n", - "\n", - "5. **Objective Function**:\n", - " - The objective function for training the Latent SDE model is similar to the ELBO in VAEs but now includes the evolution of latent variables governed by the SDE.\n", - " $$\n", - " \\text{ELBO}(\\theta, \\phi; x) = \\mathbb{E}_{q_{\\phi}(z | x)} [\\log p_{\\theta}(x | z)] - \\text{KL}[q_{\\phi}(z | x) || p(z)]\n", - " $$\n", - " - The first term represents the reconstruction loss, measuring how well the decoder reconstructs the input data $x$ from the latent variable samples $z$.\n", - " - The second term is the KL divergence between the approximate posterior $q_{\\phi}(z | x)$ and the prior $p(z)$, which encourages the approximate posterior to match the prior.\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/SDEs/test.ipynb b/examples/SDEs/test.ipynb deleted file mode 100644 index dd1fd270..00000000 --- a/examples/SDEs/test.ipynb +++ /dev/null @@ -1,6416 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 430, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import DataLoader\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from neuromancer.system import Node, System\n", - "from neuromancer.dynamics import integrators, ode\n", - "from neuromancer.trainer import Trainer\n", - "from neuromancer.problem import Problem\n", - "from neuromancer.dataset import DictDataset\n", - "from neuromancer.constraint import variable\n", - "from neuromancer.loss import PenaltyLoss\n", - "from neuromancer.modules import blocks\n", - "from neuromancer.psl import plot\n", - "from neuromancer import psl\n", - "\n", - "def get_data(sys, nsim, nsteps, ts, bs):\n", - " \"\"\"\n", - " :param nsteps: (int) Number of timesteps for each batch of training data\n", - " :param sys: (psl.system)\n", - " :param ts: (float) step size\n", - " :param bs: (int) batch size\n", - "\n", - " \"\"\"\n", - " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", - " nx = sys.nx\n", - " nbatch = nsim//nsteps #500\n", - " length = (nsim//nsteps) * nsteps #1000\n", - " ts = torch.linspace(0,1,nsteps)\n", - " print('train sim ', train_sim['X'].shape)\n", - "\n", - " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " trainX = torch.tensor(trainX, dtype=torch.float32)\n", - "\n", - " print(trainX.shape)# N x nsteps x state_size \n", - "\n", - " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", - " train_loader = DataLoader(train_data, batch_size=bs,\n", - " collate_fn=train_data.collate_fn, shuffle=True)\n", - "\n", - " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " devX = torch.tensor(devX, dtype=torch.float32)\n", - " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", - " dev_loader = DataLoader(dev_data, batch_size=bs,\n", - " collate_fn=dev_data.collate_fn, shuffle=True)\n", - "\n", - " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", - " testX = torch.tensor(testX, dtype=torch.float32)\n", - " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", - "\n", - " return train_loader, dev_loader, test_data, trainX" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1" - ] - }, - "execution_count": 376, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 366, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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QGB+Wg8A4wAHVh8LTcVkxfi2fCI7jYtlW7qzMnvcNQG7lJcEDiksC89YYgmlL1azseYagXPvXnTr3MCxl5dTpiYpxhuNia1KIvZ/8ysrw6xvvvCrGya/tbse+3z8C4wAHVGcQX3e6nDHukoAhaFaOFPPWrsXDwBFVawPjLgnIr71i/Bo8CBxZWSF1pp0uAzO53CnqupPxomOCC2KGoNElzTsvA9KoGL9uee5RNcsQtI0MsPb7R2Ac4ICmZSv1y5cEZowzBOUlQZk9b/mT3bqWim1zqCCbaUvllEsChqBROVUER4wSYAjq9X96Mlp0SbP2GYL2MQLhpZf0qsb4sKKVur2fASgTQ+q9fzZ33983AuMAB9TWSl32PEOw7pLA+ie7RsV4cUkgOMgQtF0SaKXOEJQtFZ17GJr6nffsZNklzcpnCNa/816Lp4Hjmczak8F1SWMIqjXnfnc+/SIwDnAg8/l8cVg8OzVjnGFptpUrKsavxcPAETVnjBcV4y4JGIDyMqBuKapqkCGYrJ0xfi2eBo5rUnRJ887LkDS7pJWt1H0DkFt55n9gMT5s0jJ7GbIpE0PKMRoSwvtFYBzgQKrZfHEhcDYeFTPG/aAkv4vrZoxb/yRXZsk3Z4xb++TXVjXrgoAhKJOirjtRMc6wLAPjY++8DMrF8txzJimK4SjP/I133pbZy5BN1dIlLSLC8u8XgXGAAykvyM5OxnH5jkD2PIMwWdNayPonu0YrdZcEDMx0dr5qtpIUwgA02umeCYwzLHWApDFj3NpnABrdQk688zIczfFJksEZlnUjlCSE94vAOMCBlBWzpyejGI8vt5W7Vg8ER3RxWi0+lxfEKsbJbl0rdZcEDEGdGDIeRZxePve4IGAIyj2+UTGubJABuNioGNdKneFotFIv33nd+pBcVSTDPvC6spW6ZHDya0sGjzBjvG8ExgEOZPWCTPY8Q9KsGF++KDknkl35kvSAMnve4mcA6r3/dDyOkzowbu0zAOsuyCx/hqB+7z0bj+Py1i8wziBM1o4PuxZPA8fTSAYXGGRgGq3Uiy6B1n+/nF7rBwDIarJSMV63UhcYZwjK7PnygtjqJ7vGvDXZ8wxMfRlwMh4tAuPOPQxBmRDYDIxb/+S3mDF+Olr8nrXPEDQD47qkMRzTNeeeicAgA9AoBDqRGNJXKsYBDqQMDJZt5YyZZQguuiRgoBozxs1bY2Amlw85pycjFeMMynTNuUdwkOzm8/micvB0rEsaw3JhTTK4ow/ZlRXjZyfjxQilqWRwBqBZMe7c31cC4wAHsnpQHI82fDEks7Zi3DmR5CZrXpK0UmcIqkVwZFQERyRFkV+5x19nhAwDMlkZH7aYMX6tHgiOqDk+zDsvw1HOGD8dj+L0pA6MW/zkV3YLuU7FeG8JjAMcSPmD8uxkJHueQVm0VDwZxclomRUyd01GcuUlwQPPyhnjsufJr74MOz1ZzhiPEBwkv9XgYM25n+zK883pyagxY1xSFNlNpu0zxu39ZFeee05ORnE2vnT2mXjnZQDWVYwLjPeLwDjAgTRnjI+jjg16SWII6vV/XbH2IwRHyK8xY7ysGJc9zwDUAZLTcTMpSmII2TVaqZctFR18SG4ybXZJK5ukee0lu3XjwyC7MgCoYpyhWe0QW3Pf3y9+agMcyGrlSF0x7uckQ1C3Uj8tWipGWP/kV74kPaCoGJ+Yt8YA1JdkJ+NRjIs3TXFxsivHaDQrxq/F08DxlNWBZZe0CBfE5DdZExjXLYHspo3A+DhOL599zBhnCBrJ4OMyGdze3ycC4wAHUh4IT8ejomL8Gj0QHFGdGLJaOeKCjOwagfGipaK2WgxBvf7PVlqpV/Z+kpuuCY4495Bdc3zYuBEYt/rJ7uJ0TbcQi5/kVu87zy6f+ycWPwNQd0Y4WemSplNUvwiMAxxI2Vbr7LSsGPeDkvyqRXBk1KgYh+yqYu+/rgiOuCRgCMpLgjI4IjGE7MqkqOtOBUcYjrJt7um5EUq+AchtNTGkNpcWQnLluefkZLSoGHfmZwgqyeApCIwDHEh5SXBmxjgDMy3b6RYXZBJDyK7RVu5kFGeLeWvaypFf2VbudCx7nuFY107X2ie7RjL4yagRGHfsJ7s6ODIaRSM4YusnuzIAfjYeL879xocxBI37zrFk8L4SGAc4kEb2cFE55cckQ1AGR5qVI9fogeBImvPWRnE6ruetWfzkV6/z05OR7HkGpdzjr9NKnQFpJIOPx2aMMyiLETLjcYyKAWKWPtmV950n41GcLpLBLX7ya8wYb7RSv1ZPxJUQGAc4kEl5SXBaVIyLDDIA1SI4Mm60UndJQHZVIzC+zJ6feksiufl8XmTPrwRHnH1IrtzjrzvRSp3haCSDnzbHaDj3k11jhEzjht3iJ7dqpUvaIhncOy8DsC4Z3PrvF4FxgAMpLwlOx8v8YRcEDEEdHCnXfoTKEfJrZM+fFNnzoiMkVy7xs7GKcYZl0qgYP1l8NkKG7JrvvGaMMyyNLmmhapDhmK4kg9fjwybV3NmH9Jb3neOVMRrWfp8IjAMcSKNy5HRZOeUHJUNQr/+T8ahZMX6tHgiOpFptpX6ilTrDsNpSsZy3Zv2T3XTNjHGzBslusjJGYDQyZ5nhqPf405NRFMeemHvrJbnybF++80Y4+5Bfufc3ksElRfWKwDjAgUymzQxKM8YZkkUG5cm4cUmgZQLZncue10qdgVhtqXhiziwDMpk1g4M1d8NkN13pktYIDtr7SW6yaKW+2i3hGj0QHEk1W5kx3mgn7RuA3OqE8JNxc4SMpJB+ERgHOJCL5by1k9FyxrgLApKbzeaL+Pe5tnKWP8k1LohPRnFSt1JXMUtyzcqR8Ur2vPVPbuXe3wyMW/vk1nznHZsxzqBU5fgwa58Bma4kxJ4VFeMTZbMkV+79xdJ37u8ZgXGAA5muXBKMFq3Ur9UTwXFMVrKHtZVjSMpLgpPxKM7Gl47bLgjIruyKcLqSPe+SgOym1bqKcWuf3Mq1f7bSTtr6J7tp0U632STN2ic3CbEMWXPGuDECfSUwDnAgk8YlQdFO2s9JkludsRzayjEg5fo/G4/jtK4Yt/hJbrWV+ql5awxI2Ur9eoFxBmSykgweOkUxIHVS4LmK8Wv1QHAk55LBT5brf6JTGsnVhXCr48MExvtFYBzgQMqq2dOTZeWUCzKya7bVGjdaqVv+ZFdeBFyat3bpuC0wTnaTWbNyZKxyhAEpO0Vdb8Y4AzJZOfebMc6QVIsZ4yNrn0EpZ4yfnSzfeSOaXaQgm9lsvjjfn6y0UvfO2y8C4wAHMpkWLRVPxmaMMxjNtlpaqTMs1UpS1KJiXMksyVUrSSHmrTEkjVbqJyeLz4IjZDeZLs831500x2hY/WRXJ76erSSDi42Q3blk8KJifKpinMSmKx0CG8ngzv29IjAOcCDTleCIGeMMxXRlxnijrZz1T3LTlVECdTvp2fxSdjFktXruKdvK6ZhAdmWnqHLGuMoRsmvu/eMYS4piQOr1f+mdd/n7Vj7ZVSudos6KjNiJhHASq1bGCJTvvO57+kVgHOBALq7MW9Nai6FozFg+0VaOYWl2TBjHaXlJoK0cia0mhWilzpBMi3a6J2NVgwzHxao896uaZVjqs8/pSmBcUgjZNcfnLZPBV/8MsjmXDK5ivLcExgEOZFqtBgdVzTIM00ZbrfHKJcE1eCA4omrlkuDsRHCQYVjd+xvZ8w4+JFdXR62OkLH2yW5aNefMNs79zj0kNpvNF/c6pyfjRpc0JeNkV+79p+NRMxlcxTiJrY6OPJEM3lsC4wAHMlmpGC9ek1ySkdpq1WCEpBCGo5FBPB7Fybi8JPANQF7l2j9bzZ53SUBy5ZxZybAMyeo7b7n+IbPJyvgwSVEMyXSlnfSZGeMMRHPtj73z9pjAOMCBTFbayjUuya7FA8GRVCuBwUYrdauf5JpVs6M4K9vKyZ4nsdULsrKVuqpBsqv399OVLlEuyMhuslI5JTjIUFQryeAjyeAMSHN8XjM4qJU6mZ0fHenc31cC4wAHMtnUVs6bEok1LshORo22cpY+2TXnrY3j9MQlAcOwekF2at4aA1KffU7H4ygahTjzk17jnfe02U7asYfMVs/8ksEZkulKx4RTVbMMRHnuOVlppe7c3y8C4wAH0swgbs5Z9rOSzKrVqsHG2rf4yW21euS0iJAIjJPZ6iWB7HmGpL4gXq0ccewhu7JTztnKO68LYjJbnTMbjbV/DR4Ijmh1/TfPPr4ByGv1vuek8c57LZ6IKyUwDnAg51qKjmSRMQzT1aSQUDnCcKwGBxsV496USOzcJYHseQakviBebaVu7ZNdOWd5df1b/mS2WjFr7TMk1UrHBIkhDMW0ce5ZmTFu8+8VgXGAAykDIC4JGJLG2h83xwhoK0d2q+2kx1qKMhDNypGVWYOVxU9udVLU2bjZTle3BLKryorxE52iGI7VObPF0rf2SW+ykhBbfge48yGz6YZk8EohRK8IjAMcyGrFuLZyDEVj7Z+MjBFgUMr1v9JV0SUZqTVnbaqaZVjq9X96MorxWEIUw9F859UpiuEok/5OxuMYlYUQ1+KB4IiqlY4Jo8ZL7/GfB46lkQy+cu6XC94vp9f6AQCyamQQr7wouSQgs3Nr3wUZA1K31rrULaEZGbf8yay8IFvtFiJ5nuwWrdTHY3M2GZTpyt4/LspvJEWR2WrVoG4JDMlqcFBcnKFYHR1ZzhifufDsFYFxgANZrZodOykyEKszlseN/jQWP7mVc2YjwhgNBmPSqJwaNf7MvDWyq+csr7aStvbJrlrpFjJy7mEgziUESgZnQFaDg955GYrVbgknxX2nc3+/CIwDHMjqnGUtRRmKasO8qZmqQZJbtNO9nBGilTpDsbr3l6td9jyZVbP54hL49ESXKIalUTW4UjXrnZfMVkfIjIrgiJVPdufWv72fgZistlIvFn/l4N8rAuMAB7I6Y9wlAUPRfEkaN2eMuyYgufplqK6YHWmlzkCs7v1lIohLAjJb7ZRTdkyQEEV2m2aMW/5kNl3plCMZliE53zFhyeons9Vk8LpT4Oqfcf8nMA5wIKtzlkNrLQZiU+WIOwKymxbtdCPCBTGDsdopp/hRoK0cqZWBwdVW6pJhyW7TnGXrn8y0kmbIzo1QGkkKZBg27f0C4/0iMA5wIPUPy9EoYrwaHJRDSWLTlZk7kkIYkjoxpL1i3DcAea12yolidIZLAjJrJoW4IGNYGlWDqzPGr8UDwZGcq5iVFMKArBYCqRhnKFaTwctOUfb+fhEYBziQ+ofl6eUfkjKIGYpqQ+WUwCDZnZsxbu9nIMpLgrOTccyLyLjgIJmVVVNnK7MGLX2yW20n3Vz/vgHIq1ExezLSJYpBOV8IZH4Yw9BIBj8ZxYmE2N4aX+sHAMhq05xZlwRk1rwgGwsMMij13l/PmrL3MxSrFeMngiMMxHS2WjG+/DPtRMmuWmkpOrL+GYjVObO6RDEkq4VA3nkZitVuCePi4G98WL8IjAMcSH1BfHa5alD1CEOxOmuw0VbLQZHkJpcvCRZJUcWfWf5kttotpGwrJ3uezMqEwFMV4wzMZCUpqjlj/Bo8EBzJ6pzZkbXPgFSrXdKKP/POS2aTqjk68rRspW7z7xWBcYADWVSMt1QNCg6S2eqswbFZgwzI8pLg/N4PmTUrxley510SkNhkZYxAue9b+2S3OmfZ+DCGYnXOrFbSDMl0wzuv5U9mjW4hJ6PGO+/Uub9XBMYBDmRixjgDNVmZNSh7niE5N2PcvEEGolE1q5U6A3K+ne6yalYyLNmtdkwY2fsZiNU5s2UurLVPdnViyLIQqHzntf7Jq/nOO26+87rw7BWBcYADMWOcoTJrkCFbzFs7qZOiln9m3iCZrXYLabZSvxZPBMcxaQQGmyOU3I+R3aZzv3deMludMzvSJY0BOZcMrmKcgVgdHXlixnhvCYwDHMjqQVHFOEMxna2vHLH2yWw2my+CIMukKLNmGYbVObPlJYHgCJlNZ2Ur9WanKGuf7Mq9fzxaSQi0/Elsdc7sWFIIA3JufFioGGcYNo2OlAzeLwLjAAeyOCi2zBj3okRmq/PWyrZyKmbJrMwQPhufP2a7JCCzRuXUyThOim+BsuUcZDNZaakYsTz3mzFOdvUF8XKMgIRYhmF1zqxkcIakPvu0dci0/slsdXRkIxncub9XBMYBDqTOID5pyaD0s5LMpitVgy7IGIrpyktShLZyDMf5yilt5RiGMiGwrhivfwZY+mRXn31OW+bMSgYns+lsfVKUlU92dVJUfe5pFkNAXqsjZMrA+NRlf6/cLwPjL3vZy+Krv/qr4zM/8zPj+uuvj9FoFDfffHPr137rt35rjEajtf936623tv59P/dzPxdPf/rT4yEPeUg89KEPjc///M+PX/iFXzjc/yhgcFZbCzXbyvlhSV6rVYO6JTAU05W2WhGrbeWO/khwNNWGeWuy58lsdYRMhFbqDEe1Ome2+DPrn8ymVfPcE7Fc/+57yG66UjGuGIKhWD33Gx/WX6fX+gHavOhFL4rbbrstbrjhhnj0ox8dt91229a/5yu+4iviCU94wrnff/jDH37u9172spfFc5/73HjUox4Vz3ve8yIi4uUvf3k885nPjP/4H/9jfOmXfulV/i8A2DJj/Jo8ERzHZNasGtRWi6FYDQxGNJOi7P5ktlo5daJinIGYNEbINKsGXZCRXb33C44wNNXKO2/EpfU/m8+tfdJbve9UDMFQrI6ObLzzSgbvlftlYPwlL3lJPOlJT4rHP/7x8Z3f+Z3xwhe+cOvf87znPS9uvPHGrV/3oQ99KL7+678+brjhhnjDG94Qj33sYyMi4n/73/63eMpTnhJf8zVfE1/0RV8UD3nIQ672fwYwcKszxstxsw6KZFatZM8bI8BQNOdNtV0SHPuJ4HgalwQnoxirGGcg2qoGlxXj1+SR4GimxYzxiJUuaRICSaytW4ikKIZi9b6zZPWT2bnRkcVdv2TwfrlftlJ/xjOeEY9//OMP8s/+T//pP8WHP/zh+Pqv//pFUDwi4rGPfWx83dd9Xbz//e+P//yf//NB/t3AcMzn88VBcdlWpbwgvgYPBUfSvCRotlL3mkRmbRXjI5VTDMTqJUHZVk72PJmtnnsilud/wRGyq6rVZHDvvAxDMymqHiVwaf3b+sluspIU1Xzn9Q1AXuX4vLOTsfFhPXa/DIxfiV/6pV+K7/qu74rv/u7vjp/8yZ+Mu+66q/XrXvOa10RExLOe9axzf/ZFX/RFERHx2te+9mDPCQzDdEs7XdnzZNaYszweNVoqOieSWfuM8SWXBGRWXhCfjZuXBLLnyaxM/Dg7aZ77BcbJbrWdbsn6J7PVhMCIZcW4lU9ms9lyXMBi7V/D54Fjkgyex/2ylfqV+D/+j/+j8dcPf/jD41/9q38VX/7lX974/be85S0REfGkJz3p3D+j/r36a9a5cOFCXLhwYfHXd9xxR0RETCaTmEwmuz88rer/lv6b0kf3TarF55PR5XVcXAxMJlNrm2vqkHvsZLpc//NZFdPp8t9RzWbWPmndd2G5tsdx6ftrVgTLJ1N7/5AM7Szb3PunMa+Wfz2dVoP578DwXCjX9vzSOae+IququbV/IEPbY++vJpfHaIwvv/POnXsYiIuT6eLzKC7v/XVSVJJ3XvssbS5Ol/v8yXh0ae+fl3u/cz95TYr7/tF8FrPinXdS7bb27bH7t8t/y94Hxj/t0z4tfviHfzhuvPHGePSjHx3vfve742d+5mfin/7TfxrPe97z4uEPf3j8zb/5Nxdf/5GPfCQiIh72sIed+2c99KEPbXzNOt/xHd8R3/Zt33bu91/5ylfGgx70oKv5n0OLV73qVdf6EWBn91UR9Rb7oQ9+IG655ZZ4223jqBt1/PLrXhe3fdQ1ezxYOMQee2ux1n/ldb8cD70uov5+eO973xu33HLL3v+dcH/wnnsj6rX+7ne9I2655e3x1uL74dd+7dfjA38ki3hohnKWfce7lmv9ta95ddxxMaL+fvivt94Wt9zy1mv2bHBIv/2+UUScRETEm//oD+OWD/1BXLx4EhGjuOfee517Dmwoe+z91X0XLq31C/feE7fcckv8yTuW3w+//dtviOmtzj3k9EdvX5573vhbvxX3/dk8ZtWl74c77rgz1d5vn6V0objv/PDl+84/fNdy7/+d3/mdOHvHG6/Z88Eh/elbl3v/r//qr8SfPSBicQf0niu777TH7s8999zT+Wt7Hxj/4i/+4sZfP+EJT4iv+7qviyc/+cnxzGc+M170ohc1AuP78MIXvjBe8IIXLP76jjvuiMc97nHxrGc9axFc5+pNJpN41ateFc985jPj7OzsWj8O7OQj904ifuPVERHxcR/zMXHTTZ8Rv/WKN8cvv+dtERHxOZ/7tPi0x55P0IFjOeQe+4s/9qaI970rIiK+4POfHo980HXxwt+89P1www2Piptu+st7/ffB/cWfvOfOiN/51YiIeMInPC5uuukvxR+96i3x8++8FBD8K5/1V+Jz/txHX8tH5IiGdpb9yQ++IeKD74+IiGc98xnx3jsuxPe86dL3w2Mfd+n7ATK6+7ffEfGnfxAREZ/2KZ8SNz31sfGdf/hL8ZGL98X11z8gbrrp6df4CXMa2h57f/WPf/sXIqoqHvbQj4qbbnpavOv1t8ZPve1PIiLi05/ylPjrn/xx1/gJ4TDe/PNvibj90hn/sz/7r8TTnvjR8Y9/+xfi4sUqHvxRl74f+s4+S5s771ved37sx1y63/ngr78tfvzWN0dExKd+6qfFTU/5+Gv5iHAwv/7Tfxjx7tsjIuKv/dXPi0945APjH//Wpe+Hj97xvtMeu391Z+8ueh8YX+cLv/AL44lPfGK86U1vijvuuGMRsK4rxT/ykY/ER39082Ky/g/XVk1euv766+P6668/9/tnZ2cW8QH470ovXVi2ETo7HcfZ2Vmcniznrp2cnFjX3C8cYo+dFROmHnDddXF2XfHPH42sfdKaj04Wn89OL+3zJ429/9T6H6ChnGVn8+Xe/8Drrovrr1ueheZh7yex0XKfv/66S/v8+HI/3VmEtX9gQ9lj768WM8Yvv9+enizPQmPvvCQ2j+Xe/4DrLu1Do7qXerJ3XvsspdHFZSeQ607t/QzLvLzvvP4srr/uuuLPruzcb4/dn13+O463f0l/3XDDDRHRLKHfNEd80/xxgF1Us+VB8WR86YdmfUEWETHTUY7EqmK24OnJqLH259Y+iZV7/+nlvX8U1j/D0Fj/J6MockKimrX8DZBE49xTn/svr/+ZQz/J1Xv/4tzjnZeBmFbn9/7FjHGHfhKbzJozxiNiufgjYm79k9ikat75LL4Hovk+zP1f2sD43XffHX/wB38QD37wgxcB8oiIpz/9UhuzV77ylef+np/7uZ9rfA3AlZo2LocvbbWj4s8dFMmsPCiejEfNtR/WPnm17v3FN4D1T2aTqnlJ1kwItPbJa/XcExFxUleMW/skNp/PF2efZTJ4888hq2lLMcRi+Vv6JNaWDN7Y+4/9QHBEzfU/bgTGZ5LBe6XXgfE777wz/uRP/uTc7997773xP/1P/1Pceeed8Xf+zt+J09Nlx/i/83f+TjzsYQ+Lf/2v/3Xcfvvti9+//fbb4/u+7/vihhtuODe3HGBXVdVyUCx+WDooktnqQVHFOEPRXjli/TMMq5dksucZitVzT8SyU5SlT2btwRHnHoahXP9nlxNi6zsfS5/MplVbIZCKcYZhNRn8pDj3TEXGe+V+OWP8JS95Sbzuda+LiIg3velNi997zWteExERn/d5nxfPf/7z4wMf+EB80id9Ujz1qU+NJz/5yfFxH/dx8Z73vCd+/ud/Pm6//fb4lE/5lPju7/7uxj/7EY94RHzf931fPPe5z43P+IzPiL/7d/9uRES8/OUvjw984APx8pe/PB7ykIcc738skFJba6GyalBbRTKbrrTTbax9L0kk1jZGo+yYYP2T2WRl/TcC49Y+ia2eeyK002UYNq39COuf3KZtdz6X/9raJ7Np2/iwRreQYz8RHM/q+LBx4533WjwRV+p+GRh/3eteFy996Usbv/f6178+Xv/61y/++vnPf3488pGPjK/92q+N3/iN34hbbrklPvShD8UDH/jAePKTnxz/y//yv8TXfd3XxQMf+MBz//x/8A/+Qdxwww3x7d/+7fHv//2/j9FoFH/5L//leNGLXhTPeMYzDv6/D8hvW/a8uDiZtVXN1rwkkdn2VuqQVz1n+XQ8itFoJTDuloDEWmeM1xXjDv0k1tYtwYxxhmLa1iXw8vr3zktm1YakkAjvvOQ2bTn7nIxHUc3mzv09c78MjN98881x8803b/26hz70ofF93/d9V/TvePaznx3Pfvazr+jvBdimvbXQktZCZLY6b63ZVutaPBEcx7QlOFKuf7cEZFaffeqqwbKtnIpxMmudMT7WSp38pi1r34xxhqLakBCrYpzMpo0xAirGGZa2QqCT0SiqmBsf1jO9njEOcH+1dd7a0Z8Ijqcxb208XqmYtfrJq+2C2PpnKOpLssWM5SI6InuezDZVzQqOkFlbQqAZ4wxFWzvpuhzC2iez9nfe8r7TNwB5NQqBLieGXD7+O/f3jMA4wAG0zRgvs+f9sCSzOoNyNLoUGHFBxlBUbdnzxZ9b/2RWr/+TsYpxhqVtznJ97rf0yWx1zmZE89zjnZfM2maMr0wRg5TaWkk39/4jPxAc0WohUMTy+2Bq8feKwDjAAbRVjId5awzEsmrQBRnDMmmMEWipmrX8Say+ID5bZM4XgXGLn8Q2zRiXFEJmbcGRsXdeBqI5Pq/ZKco7L5m1nXtGeqkzEJvGyOiS1i8C4wAH0DZjXMU4Q1GttNNttpKGvNouCUpmbZJZffZZnbEc4dxDbo2WiuNmYoi1T2ZGyDBkbWM06sQQWz+ZTcq9v61L2pGfB46pbYxMfQaSENsvAuMAB7BtxriTIplNLrdSb8selkBJZpsqRyJs/eS2OmO80Urd5k9ijb1/3EyInc8lRZHXthnjtn4ym7QkRdWrX1IUmbXddyoYZyjqd97xaJkIuwiMO/j0isA4wAGYMc6QLebMniwX/eJFydonsWnbJUGRP2/5k9m0Too6aakYn7X+LZBC+4xxwUHya50x3giOWPzkVXaKOjtpJoRb+WS2bYyGvZ/M6oTYeu1HLNe/Vur9IjAOcABV1ZZB6YKMYVitGowo2spdkyeC45i2zBgvL4h9B5BZvf7bWqlPRcZJrH3G+PLPJcSSVdu5p5EU4qWXxDaNEhAYJLM6GTaivWLc1k9mi9GRRSHQqVbqvSQwDnAAzcqRljnLfliS2DKDsqgYv/yry2Eyq6rzlSPjxt5/7CeC46kvCc5WWklHRFTWPom1zhhvJMT6BiCnaWsy+PLPrXwya6uaXQbGr8UTwXE0zj0no3N/bvmTWd0htkwCH2ul3ksC4wAHsG3GuJ+VZLZaNRhRVIxb+yTWFhwpW6nb+8msDpCcFJ1y6h8DqgbJrH3GuDEa5DdtHR/m3MMw1Of+0ej8+rf0yay87zxbJIVopc4wLCrGi/tOM8b7SWAc4ADaLgnKPEoHRTKrW4qeldnDdXDE0iex1hnjjcop3wDkVZ99yrZyLgkYgqqlcqqYJmP9k1YjOGLGOANTv/PqksbQTKrN952Q2aJD5snysH8y8s7bRwLjAAfQ1lZO9jxD0VYxXn9yQUZmVUtLxZLlT1az2Xxxtjlt6RbigpjMyoTYM63UGZCtM8atfRJb7ZQToUsaw9B45z05f+6x/sls2pIUVbdSd9ffLwLjAAdQbZsxrmqQxJYzxpfHjPJFCbJqZM8vKqeKS4KjPxEcR9uczQgV4wxD1TJGQ0IsQ9CeDL78c8ERMpvOzr/zLrukWfzk1XbuL697rH8yW7RSL7ukqRjvJYFxgANomzPrgoyhaDsojlwSMABVSyv15gWx9U9ObZUjEcUlgbVPYpPWGePLP7f3k1Xr+DDvvAxE6ztv/cHaJ7G2pKjG6MgjPw8c06SlEEgyeD8JjAMcQHlJ0Dpn1gUZibW2FtJWjgFozZ4v/tz6J6tJS3AkYtk5wSUBmbXOGC8O/tY/WbXOGC/+XEIsmdWdooyQYWiq1qSo5Z9b/mRWtYyOXATGLf5eERgHOIAyg7KtYtzPSrIq58y2zRh3SUBm06KV+mlrK3Xrn5yqlsqRCG3lGIa2hNjxWNUs+W2bMS4ZnMzagiP18rfyyWza0inKOy9DsWnGuHfefhEYBziAZvb8+ZaKgoNk1XxJOj9vzcons7YxGrLnGYJ1e399STBzSUBixmgwVK1rvzj+W/pk1jZjXJc0hmDaMkJGlzSGol7/5RgN5/5+EhgHOIC24EiYt8YAtF2QRRTVI9Y+iTWSolwSMCBtFbMRZowzDJMtnaKc+8lqUpkxznDVnaLK4EhNIQSZtSeD6xZCfvP5fLH+T4qkKHc+/SQwDnAAjXa6LZUjXpTIat2c2fo9ydons7bgSLOtHOTUqBwpKsYX89Zm5/4WSKNsp1vv+Y0Z484+JFW1tdMt/ty5n8ymLa3UFxXj1+SJ4DjKGeNnLXu/rZ+smoUQ5X2nO58+EhgHOIC2DMpxo5/usZ8IjqOcM3t20tJW7uhPBMdTXhIs560t/9wFMVlN13QLWbL2yWvZTvf8rMEIowTIq7n3mzHOsNQBkrOyarBukmbtk1h7Mvjyz61+smrtDhuriSG+A/pCYBzgAMwYZ6i2HRStfTJrCw6Owi0B+VVbuoXY+smsXv/NETLLP7f+yap1xriqKQai7pbj3MPQVFuToo7+SHAUbXf9ERJD+kpgHOAAWmfuFMERhSNktW7OrEsChqDRTrqeMd54SfINQE7TxiVBy95/7AeCI2oLjmilzhBMW2aMSwZnKOr33nLGuC5pDMG0ZYxGeOdlAKYt3RIimvf9jj79ITAOcABtM8YFRxiCdXNm65k7Dolktm3WpvVPVtsuCbSUI7NFK/U1lSOCg2S1LTgiGZysZrP5Yn2f6pLGwLTedxZ/bu8nq3WFQBJD+klgHOAAts0Yd1Akq7aWihHLc6LgCJm1vShpKcoQtM2ZjVAxzjBULTPGT8xZZgC2tdMVHCSr9eceyeDk19ohs1EJ5BuAnNoKISIUQ/SVwDjAAbTOGC92XBdkZDVdM2dWWzmGoLVqVtUgA9BWORJRJkUd+YHgiKatM8YlxJLftmRwB3+yqlrWfsRKl0CHH5Jqu+9sBAaP/DxwLJMtyeD0i8A4wAFsnTHuhoykts2ZFRgks2ZL0ZZLAsufpBrnnsber5U6+dUXxOXaLxNiK+d+kmpLijJjnCFodIlqmTEe4dxPXm3FENY+Q1BV6yrGrf8+EhgHOICqpZ2u5HmGYP2c2UscEsmsbZTASCt1BqBROVJmz1/+1dons0lVt1Jfrn3tpBmCtqSoUSMwfuwnguMo33nbOuVE2PvJq239N+87rX1ymqzpkGn995PAOMABtAUHtVRkCLbOWzv6E8HxTKrzL0qNrlouyEiqbe1HhMg4g9A2Y1zlFEPQlhQ1khTCADQ7BLa307X6yara0iXNfSdZtRVCRKyO0TjmE3E1BMYBDqAtOGjeFEPQ1i0hYrn+rX0ya5u3VrbTtfrJau0lweVfrX0yq1uKnjQC48s/Fxwkq20zxi19sqrWjg+z/slv0nbuFxhkABrdEk7KLmm6BPaRwDjAATQzKF0SMByNbgkt89asfTIrL4iXdwRF5ZT0eZKatlSORJgxzjAsKsbXBEfMGCerxozxxTvv8s/t/WTVNmM5Qit1hqFqWf/NwKC1T07TLYVAEc4+fSIwDnAAbT8sVY4wBNMtrYWsfTKr9/7T8WgRFNFSkSHYNmvT2ier+Xy+mDFettMtAyXi4mTVVjHeSAi09klq3bmnLIaArJpVs+fvOx38yart3LPK8u8PgXGAA2ibMT4yY5wBWDtj/PKvlj6Z1Xt/WTVYkhdCVmsrpxZjNI79RHAc5Zn+dE0rdZUjZNU+Y3z55xJiyWp9p5zl11j/ZNU+OlIrafJrG50XYYxGXwmMAxxA60Gx+HMvSWS1bsa4VuoMwaKd7njNS9LRnwiOo0wIbMzavHz60VKRrNa1VBxLiGUAts4YP/oTwXFUW7qkRXjvJa/WbiFlUoiDD0lNqu1jNBx++kNgHOAAts0Yh6wma2aM1ydFSSFkVl8SnKgaZGCqxgXZ+copS5+s2s78EWaMMwxVWzvd4pbRuYes1gVHmklR1j85lcUQdUKsTuoMQbNTzpqkKN8BvSEwDnAAbS1Fy0sCGZRk1dZSMaK4JLD0Saze+9sqZiMEB8lruiU4aO2TVSMhsDFjfPk1goNkNWl5523MGJ+d+1sghXXtdEt2frKatI6OXP65Yw9ZTdec+xuJIdZ/bwiMAxxA+cPytO2SwA9KkmprqxWxPCjKnCezeu9vm7EcIXuYvNa1k64/Wftkta6drlbqDEHb+i875Tj3k9W6d96xObMMQLV1xrjFT07bksEjJEX1icA4wAFsnbnjLYmkpkVbueZB8dKvVj6ZTdtmjBd/busnq2lL5UiEVurktzYppGyl7huApJoXxG3BEcip8c67dsa47wByqvf+0ajsFrJk6ZNVtSUZPMLe3ycC4wAHUC2CI6PF5cDYJQEDMG3JHo5Yrn9nRDJb7P0tSSER9n7yWtdSVFIU2a2bMa5qliGoWrqkWfsMwfq9X8U4+dWJIesSAgUGyaptjECEO5++EhgHOIA6OLi+rZYfleS0rqVozQUZmU0uXxKcNNa+CzLya5szG7EcI+PcQ1brZg2eOPczANO2GeMCgwxAs0Nge6co771kVbXcdzYqZo/8PHAs6+87nX36SGAc4ADaMyiXf+4liazKtnJtiSFWPpmV3UJqKqcYgrJq8MwYDQZkum7GePG5iB1CKm3r37mHIVg/RmP5NVY/WdV7/9n4fJeoCIFB8po0RkeuWf92/94QGAc4gNYMSgdFBmC6pq3ccs6sxU9erTPGy80fkppsqZyy9ZPV2lmDxdZvxjhZVS0zxrWSZgjKbiHNd17rn/zqYoiTdWMEBAZJal3FeOPGx/LvDYFxgANYZFCenJ+xHBEx84OSpKo1M8aXgfFjPxEcz6JbyEn7S5LEELJaFxwMiSEkty4hUCt1hmDaMmNclzSGoEtwxN5PVtOWLmkl951kNW1JCIxYTQyhLwTGAQ5g68wdL0kkNWnM2tRKneGYzeaLSwDdQhiade2knX3Ibrrl3BPhgpi8zBhnqNZ1yhEcYQiq1i5pyz+395NVOTrS6NT+ExgHOIBJy4zxxqxBPyhJam1L0cu/WvtkVbbKXTtv7ZgPBEe0vqXo8mts/2Q03dIpJ8LZh7zMGGeouozRsP7Jqi6GaBYCNdJhj/xEcBzTWXtCrHfefhIYBziARcV4Y+bO8s/9oCSr9TPGL1eMW/skta5qUOUUQ1BtmTEe4YqMnNYFR8qqwUrJOEmVXdLq844Z4wzBuoRA658hqM8+jbVfRJisfbIqz/RnJ+2JIZZ/fwiMAxzAYsZ4o3JES0Xya84aXNdayzcA+ZTtRNfNGFc5QlaTtW3lzFkmt0ZSVDljfCw4Qn7TtqpBFbMMwLoRMmH9MwBtM8YbgUFLn6Sma5LBw31nLwmMAxzAthnjXpLIam1roeJrLH8yqtbNWDZrkAGo1nULKb7G+iejdcER7aQZgjopcF1wRDI4Wa0LjnjnZQjqpCgjZBiaZiGQ+86+ExgHOIC6cupkTUtFyKpsKXq2rq3cUZ8IjmNSbb8g85ZEVl3mLFv+ZLQ2OKJTFAMwbUkGH6uaYgCqavs7L2S1rRDIzk9Wa7sE2vt7SWAc4ADqg+K6eVMyKMlqsnbO8vJrrH8yWl8xvvwaK5+sputaqTfmrfkOIJ9GQuCahNiZyDhJ1Wefs5Pl1aJkWIZgbZc077wMQB0cbMxYlgzLAOiQmYvAOMCezefzYuaO1kIMS7W2atDMKXJblz08tvYZgHWXBOGSjOTWzxhffo1zP1m1zRiXDM4QrB+j4dxPbrPZfNEJ52Tt+DCLn5zK+84yKbBZDGH994XAOMCelUUh66oGFY6Q1bTDnFmXZGTUZd6UtU9Way8JrsXDwBGt7xailTr5LbqkrUmIsvbJam0yePE1zv1ktHZ8UvlFlj5JTYouaSrG+09gHGDP1v2glD3MEKxtpys6QnLr5sw2KmaP+DxwTF3GaDj7kNFkzd5fnvsri5+k6m45ZowzNOvufJz7ya5aVwihWwgD0CUh1urvD4FxgD1bd1BsBsb9qCSnak07XYkhZNesmF0zY9naJ6nGnOV16981AQmVa/9UcJCBmW6bMW7pk1S3O5+jPhIcxWTWoWL2iM8Dx9SlY4Jzf38IjAPs2doflGaMMwDTDjN3rH8yWttWy7wpBmDdjHEV42TXGKPRmDFeVE7pJ01SlRnjDJTgCENVVdvveyx9smp0yCzO/bqF9JPAOMCerWutMnZQZACmazKIx1oLkdz6vd9bEvk1goNrkgItfzIyY5whm7bMGJcMyxCsC4545yW7dcmw1j5DMF137tclsJcExgH2bLq2atAFGfk1gyPtg8VdkpFR4yVJtwQGZu28wcYlgfVPPutnjC+/xt5PVm0zxpvnnmM/ERxHl0459n4ymq4ZIVNy5ierLsngUkP6Q2AcYM+mZowzYNXa4KAMSnJrVI6sm7dm7ZNUY97gqP2C2PIno2rN3n+inTQD0FYxrlMOQ1C+856NvfMyHM0RMlqpMyzl3n9y4s6n7wTGAfas6jBvygUZWU3WttYqvsjyJ6Gqy4zlYz4QHFG9/sejiPHa6pFjPhEcx7qEWJ2iyG42my/29TI4YsY4QzCpVIwzTF1GyMy99ZJU2THhzJ1P7wmMA+zZupkjZu4wBNWa1loSQ8huXSv1MG+KAairR5prv3lJ5vBDRusuiLVSJ7u1raSLr7H2yarxzqtqkAEpA4Pr9n5rn6ym65Ki3Pn0ksA4wJ5V6w6K5q0xAM3WWhJDGI5189bGjbig1U9O9fpfnTXYbBZi/ZPPdO2M8aJq1sGfhNade7zzMgRdiiEgo3Ltn60dHXnUR4Kjma7rEOvOp5cExgH2bLImMKitFkOwdpSA9U9ya7OHXRIwAG1zZiPMGyS/RkJgOWO8+Cw4SEZrK8ZHo8Xeb+mTlXdehmr9O+/yawQGyapaO0Jp+TW2/v4QGAfYs3UvSc0MSj8pyamcMd6MjwgOklu1Jnu+2VbO4ienta3Ui89WPxmtbacrOEJyVVWee5p7f/3e69xDVo3goKpZBmRtxWzxNRICyWpStXeIdd/ZTwLjAHu2LnveSxJDUF8Qn52MGpWyjXbSvgFIaLKmna4RywxBnRhycq5iXFIguXU597sgJqN1az9ieT0sKYSsylECZyvrv2b9k1G1ZoxGqJhlAJrFEFqp953AOMCerTsojlWOMAB19vz54Mjys9VPRuv2/pHsYQagviBevRxWMU526yqnzBgnu3UzxiOW67/4EkilWjtKYPk1dn4yWtctYeTUzwCs65DZ7BJ4vOfh6giMA+zZZM1BMRqB8SM+EBzRcs5se0vFCAdFcmrMmV03b8olAUktkqJONiRFWf4ktG7WZnkMkhBLRuvWfkSYMU565Z2P8XkMSTMhcF2HwGM+ERxPXQxxOm52yByN1v0d3J8JjAPsWbX2oCgwSH71+j/dEBxxQUxG6y4JRpKiGIB1SVGNVupCJCRUdgs5WzNn1t5PRuvaiUaYMU5+jTufRtXskuVPRmtnjDfO/JBTnRR47r5Tl8BeEhgH2LMuLRVdEpDVtMigLDUOikd9IjiO6boZ416SGIBptW7vL1j/JNRlxrhzPxltnDF++S8lw5JVOUqg2S3EOy+5rR8ftmTvJ6v1yeDLz5LB+0NgHGDPHBQZsi4zxs3aJKOqaq8aHIkMMgD1JcHGvd/yJ6Hp2na6y6+pLH4S6jRj3NInqebev+bOxzcACa0bHWl8EkNQrXvnLT5b//0hMA6wZ+sOiloqMgRd2ulCRusqp1wSMAT1JcFqO91mtxDfAOSztmJ87NxPbp1mjDv4kNT6c7+KcXJrjNFY1yXtqE8ExzO5XAxxdm50pPXfRwLjAHu2bsb4qNhx/aAkq3UzxseCgyS3bsZ4s53uUR8JjmI+n3eqGLf+yajbjHGLn3y6zRg/6iPB0SzeecejRkCk2SnHNwD5rE0KKe87rX2SWlcxXrL++0NgHGDPzBhnyOoMyk2thVwSkFG1Zu+39sluXUJgxOq8Nchn/Yzx5dfY+8nIjHGGrMs7r4MPGU2L8WFlMYT+gAxBtxnj9IXAOMCeNWaMrzkouiQgqzJ7vqS1ENlNikuCtfPWjvlAcCSNhMDVtnIhKZDc1iVFqRgnu01JUWaMk926d96xd16SW1cINHLuYQDqxJDN77xHfSSugsA4wJ5N1sxb006XIeiSQelFiYzWXxDb+8lt3QVZRDSyAq1/Mupy7hccJKOyanC1arb+y7nQIEktx4d552VYplX7O2/5U8DSJ6su48OkRfWHwDjAnq2dMe4liQGQQclQrc+eX36NC2IyqsoLsnN7P+S2dsa4WZskN904RuNyxfgsIKXJ5cW9sUuarZ+EynOPQiCGpr7vP1tJBm+OUDrmE3E1BMYB9qzLjHE/KMloNpsv1va6ypFLfAOQT6d5a5Y+CU3KETIuiBmY9TPGl58rB38SaiSDr1bNXv5VUghZ1UmBm2aMK4Ygo3UjlCSDMwTTdXu/d95eEhgH2LNqXXCk0U7UT0ryqYp1vZpBOZJBSXJdgiMuyMioWrP2I1baKrokI6FuM8aP+khwFOvOPRFmjJNfvf7PTlarBs0YJ7dmK/X2sJJXXrKaXk4IP9vQJc19f38IjAPsWZfgiJ+TZLQpOGL9k125/s/WtlKHfKabqgbNGCe56doZ48uvkRRFRo1OOWaMMzBd5swKjpDRuvtOZ36y29QhM9z59JLAOMCerZu35oKM7CZruiVErFaMW//kM1kTHBmFpBBy2xQcaVaMQz7TNaMEJASS3aaK8ZGKcZKrzz4bzz3WPwmVM8bPTta88zr1k9C6sakR7nz6SmAcYM/WtVQcaalIctWapJBLHBTJrbwkWD9vDfLpEhyJUDlFTvXZZzyKGJsxzoA0OuWs6RZi3yerev2vJoOXPwcsfzJalwxevgJY+2TUuO/cUAgkMaQ/BMYB9qzRUnHND0s/JsmoGRxZnbe2/OygSEbruoWUXBCTUXnuOTuXPb9k9ZNRvfevVo6Uf6lTDhmZMc6QTRat1Ndfq9v7yahLIZCVT0aTohDiXDJ4+Re+AXpDYBxgz9a1VIxYXhIIjpBRGRw511ZOBjHJNdf/8ojdqBw56hPBcZTnntWEwLD3k1y9968LDEYIjpBTpxnj1j5JLSrGN+z9Vj8ZTddUzTbHCFj95FN1ve881gNx1QTGAfZsc/b8pV9dkJHRdE0r6QizNslvXWstlwRk12iney57Xs04udVnHwmBDI2KcYZqPp+vbaXe3Pt9A5DPuqSocu3b+8momRRixngGAuMAe7Zx3trlH5ZF/BDS2DRjvPwriSFktK5biOAI2TVnDbbPmY2w/slpbXDEBRnJbXrnDcngJLZpfFIzIfZIDwRHtC4pSit1stvUHdaM8X4SGAfYs8aM8TU/LP2YJKPNwREvSuS2bu8XHCG7dd0SIswYJ7/pmjmzLsjIrkvFuHMPGVUd1n6Eqlly2lgIVC9/mz8JNUbnnSuCW7L8+0NgHGDPzBhnqDZWjGsrR3LrWmsJjpBd5+x5y5+E6ksyVYMMjRnjDNW0S2AwnPvJqTz3nysEuvyrlU9Gm7qFhEKgXhIYB9izTdnz9c9KbeXIaNOM8bJqVvY8Ga1LDBEYJLtG9vyGGeMuiMloWTG+ekG2/Gjlk5EZ4wxVmRRy/r5Hpyhy23juX+z9Fj/5VB2SQiIkBfaJwDjAnlXV+gxilwRktuklqfmXvgHIZ7LmkkxSCNlVa7olREgMIb/6kuzMjHEGZuOM8csER8io64xx65+MNp77L/9q6ZNRs1vIphnj9IXAOMCedakYl0FGRs21vz44IjhIRusuiEeSQkhuXVJIhMA4+a2rGLf3k50Z4wxVs0vUaiHEsZ8GjmvSYXyevZ+MykKg8xXjIuN9JDAOsGfdZowf9ZHgKKqNGZQqp8itvCAut35zZslu3RiBS7RSJ7fljPH2qqkIez85VZveeS9/O9j3yaiRFLLhnVfFOBltbCdd33ce9YngOKYbkqKaFeO+A/pCYBxgz8wYZ6g2z1tbftYxgYzqpKjT8ahxKdZICjn6U8HhTbVSZ8CqtRXj9n5y21Q5ZXwYmZXvvOsqZiOce8ipMT7v3BiZS9z3kNGmhEAJsf0kMA6wZ11mjPs5SUab562Zs0xui6rBk9XL4eVnlwRktKlTjo6iZDddO2N8yd5PRhuToi7/KhmcjDZXDeqSRm5dqmatfTKaVJu6hSw/W//9ITAOsGeb561d+tUlARlVGy7IGsFBqSEkVK//8+10JYWQ28Z5ay4JSGw2my/29Y1r/4jPBMeyaYzGyPgwEtu49ovP7nzIaGOHzKgLgax98mmMjtyUFHW0J+JqCYwD7Nmm9ir1q1LxJZBGlzECES7JyGm6pp1uCI6QXOOS4FzVrBnj5LWxaipUDZLbdMOcWd1yyGxSjg871ylKcITcyvvOc+d+FeMkNtkwOrLk3NMfAuMAe9alYtwPSjLaOG/NBTHJrW2n63KY5CaNc48Z4wxHteHMb44A2W2aM1sGB3XLIZtm1aBzP8My2dApyuhIMtvcKWf52frvD4FxgD2bmjHOQG2aNaiVOtnVe//5lnKQW1VtqBwpPtv5yaasmF0NDJasfTKadrwg1k6abKYbEgLHEgJJrtrYLecSSSFktOm+UyFQPwmMA+zZpuoRM8bJbFMGZagcIbnpmhnjjZaK1j4JbR6jUa5/3wDk0rlyxNonoY3Bkca53/onl03dEsrgiHdeMqrP/aPR+m45tn0yauz9G879UmL7Q2AcYM8a89ZG7RfEXpLIaNPMHbMGya6+ID53QaZqiuQ2VQ2WrH6y2VQ1qFsI2XUZHxYhQEI+jW4hG8Zo6JJGRvX4vLYz/6Ji/IjPA8fSuOvf0CXQuac/BMYB9qy+JBiPIsZrssj8oCSjxry1Ddnz1j8Z1Ykh51+SrH1y21Q1qGMCmW2uHLH2yW1arR8lYO8ns03dQqx9sqvX/7lq8ViefRRCkFFzbKoZ4xkIjAPsWf3DcvVyOKKYMe6gSEIbK6dkz5PcomJ8Uztda5+EGt1CNlwSuCYgm0blyLmEwCV7PxltHqOx/KxbDtmUwZFN3ULc+ZBRvfeftd53XvrVyiejamOnKElRfSQwDrBn69rpRpgxTm6NyhEtFRmYdTPGS9Y+GW2cs1x8tv7JptEpZ+OM8WM9ERxPc/2v7xbivZdsGiNkVrslFN8KVj4Z1Xc+qwmBEWXF+FEfCY5i096vGKKfBMYB9qyuHtnUWsiMcTLaXDlSXpAd7ZHgaBbz1ja1Ez3qE8FxTBrtpDd1C4FcNs8YVzlCbo31f+6C2NmHvKoNM8bLvX/mpZeEpmu6pEUsE2IlRJHRdMPeX7L8+0NgHGDPNh4UFzPG/aQkn40zxhuVU9Y/ucxm80XCx6Z2otY+GTUuiDcER1wQk83mGePLzypHyKhzp6hZQCqTalMy+PKznZ+Mqg1d0pb3ncd8IjiO6ca9X0JgHwmMA+xZ/cNytXIkopwxftRHgqPoWjklNkI2VbGpr7YT1Uqa7KZdW6kf6XngWDbNGC/Z+8loY6eo4rPKQbLZOEKmDI5Y+iQ0Wdx3ru+QCRk1C4E23fnY/PtCYBxgz+oflqsVsxFaC5Fb18op4RGykT3MkE03tFKPRseEIz0QHEnnGePHeiA4IjPGGarmnNn1wRFrn4zqTlGrXaIilutfYJCMJmVC7Mb7TvpCYBxgz+oXpbYMykXF+FGfCI5jUzvdseAIiU03tZIuPrskIKPNVYNlYoj1Ty5dO+VY+mRUr//RKGIsKZAB2TxGQHSE3DqNjjzmA8GRVFVZMb5+75cU1R8C4wB7VgdINh0U/aAko0nH4IhW6mSzuaXi8rOlT0ZlUtTqJUHjftg3AMk0uiVsWPuSQsioDg62vfOWv+W9l2w2JgRa+yRXn31aZ4xfvvOx9slosikhViFQLwmMA+xZtWHmTp1FJjBIRs3g4IaDogtikplUm16SJIWQ28ZRAsVny59spptaKhafXZCRUbWhS5oLYjLbNGdWlzSy67L3W/tk1OiQ6dyfgsA4wJ5NFzPGWzIoFwdFPynJZ3PllOAgeW2qGI8oLojt/SQ03XBBLDhCZptnjGslTW6Ld96WqkEtRclsUq1Pigpd0kiuTgpc7RIVYXQkuU033fk49/eSwDjAnm3KoFwcFP2kJKHuGZS+Achl04zxiOX6t/LJaHPVrBnj5LV5xviScw8ZLd55NwRHIrz3kk/3EUoWP7nMZvNFwkfbfWfNvk9GGwuBis/O/f0hMA6wZ5MNM8br35I5T0abZoxveG+C3mu8JLVWjEuKIq9N61/FOJl1nzEO+Uw3vPOas0xmjarBc63UJYWQ13TD6LyIcu+3+Mmn6jpj/FgPxFUTGAfYo9lsvngBas2gNGOcxKrigvh8O10tFclrU9VgRFkxbu2TT7XhgtiMcTKrNnVLEBwhuToxpH3OrPVPXhsTAovPqgbJpnnmX58U5b6TjKYbO2SKjPeRwDjAHm3KHo5oVs16USKbaceKcUufbBpzZje0FC3epSCNTd1CGvPWbP4ks3HWYMHKJ6N6/bdVDY5VjJPYpqSo8tvB0iebyYa1H7EMDjrzk1H3TlHWf18IjAPs0aZ5UxFaa5HbpgzKaFSMH+uJ4Dgm1eZLgjqB2NIno2pj9vyS9U822879i6OPQz8J1eu/rWrQOy+ZTTdUzZZVg955yabaMj5s7J2XxDaNEmh2CznSA3HVBMYB9mhbBqXseTLb1DFBWzky2xocufyrtU9GXbPn3ZKRzaRY+yctnaKWYzQgn+nlpMD2VurLz955yWbjnGVVgyS2rUNmPUbDtk9G02pDMrhO6r0kMA6wR9syKGUQk9mm9d+oHDnaE8FxbL8kOObTwHFtzp4v9367P7ls6pYQ4YKY3Jat1L3zMixlQuD58WHWPnlt7BAYksHJbdPoyMY7r+XfGwLjAHu0S3DEBTHZTDd0TGisfSdFktmUPRxRzls72iPB0dRnn9Fo295/zKeCw9s2Y3xZMW7xk0+9/k+2zBh37iebMinq7Fwr9YK1TzKbkkIiYjk+zNInobJL4Nnqfb+7/l4SGAfYIzPGGbJNF8RjwRESqzZkD0eU89YsfvKpE0M2BQYj7P3kU22YMxuxTAyx9smoXv+rgcEInaLIbVPVoLVPZhsDg2GEDLlNNiSGeOftJ4FxgD2aVJtnjJu3RmbVxhnj2sqRV9d5a9Y+GVWLqsHN5x7Ln2yalVNtF8S6hZDTfD7fuPeX3w7eeclm2hgftvLO676HxDZ1CIxYJoboFEJGm0YojSRF9ZLAOMAe7VIxLkBCNpNN7aS1FiIx89YYsjox5KwtMNjolGP9k8u2vX/RUvRIzwPHsm2MQHnwL75NIIXphm4hRsiQ2dYRMs49JLZx7y//wubfGwLjAHu004xxPyxJZlM7aWMEyKzzvLUjPQ8cU91K/aSlnW7J+iebzjPGHXxIpusImQhVs+SzsWpQlzQSa3RLaBsho1MOiXXtFmL594fAOMAeqRhnyDZdEDdn7lj85NKct7ZhzrKlT0L13r96QRChcorcqm0XxJtzRaC3plvmzI4tfhKbbEgMaQZHHHzIpXnfs/7cLyGKjCrdQtIRGAfYo20zd1SMk1mdQXkyHjXa50Y0Zw1a+WTTvCBruSC+/PPA2iejeu9vr5htpEUd6YngOKZb9n6VU2RVbemUo2KczMr1v5oYMlY2SGLVlvvOmqVPRpNy/Y/Wdwtx198fAuMAe9RsrbK5YtzPSrKpL4hbk0IaswYtfnLZ1FIxYlkx7nKYjDbu/bLnSWzbjPHlrE2Ln1wmW9e+LmnkNe1YMe7cTzbbWqmPDRknsbpifDxaFj7U5ET1k8A4wB5tmzEue57M6uDg2bbgyLEeCI5k67y1kapB8lrs/ZvGCIS9n3w2BUciyhnjR3ogOJJtM8YFB8lsU1LUWEIgiW0anRchIZDcFl3SWu76S/b+/hAYB9ijbTPGZc+TWdlKfdVItwQS23pJcPlXlwRktHnvX36295NNs53uhqSooz0RHMf2ZHDnfvKqNq5/9z3ktXWETB0Yt/ZJqE6K2nbXb/n3h8A4wB5tnTFefDZ3hGzqF6W2C7Jy7ascIRuXBAzZYu/fMGM5QmII+WyfMX6JMz/ZVFvHhy0/W/9ks2l8XmPtO/eQTDk+rLVL4OWTj/seMto8OnLJuac/BMYB9minGeNHeSI4nmoRHNm89iGbqiouCbRSZ2AWe3/r2l9+tv7JZtuM8fqWzNInm63J4Lqkkdim9a9LGplNivvOkw3nfkufjOr7/rO2QiDXnb0kMA6wR5vbakWUxSSyKMlmc2uh5Wdrn2y6z5m19slnsmHvL1n9ZLN1znL9weInma5zZiOc+8ln0/pXNUhmXUdHWvpkVG2sGJcU1UcC4wB7tD04InuevBZzZtuyh4vPDopk0/WC2NInm9lsvtjTW8doNCqnfAeQy9SMcQaq0SXNjHEGpgyOjFbKBHUIJLPmO+/m8XmQTV0I1DpGwBiNXhIYB9ijaktLxWZLUT8syaV+UTprnbEsKYS8qq2XBLLnyWmypZ3uliJy6LVq24zxOinK5k8y26oGzRgns7qddPsYgeVn3RLIZlqMD9s+Qsn6JxeFQPkIjAPsUWPmzrYZ435YkszG1kIyKElsUlwSbJ63Zu2Ty9aWisVn5x6ymXRc/5Y+2WydMa5LGolVHceHOfeQzS4V4/Z+sqnXf+vab9x30hcC4wB7tEv2vAxisqmDg5JCGJrte7+KcXJqXJBta6XumoBkqm3BQXs/SW0bIeOdl8yWwRFJIQxL13feCBXj5FNt2PvDjPFeEhgH2KNdLoi9KJHN4qC4tbWQxU8u27Lna/Z9smnMmVU5xcA05yxvqhi3+Mll2uiSti0pCnKp13/7fU/5V1Y/uZSt1Ld3CYRcNhUC6ZDZTwLjAHtkxjhDNZ/Pu7cWsvRJpvu8NYufXLa3012y95NNta2l6GLG+LGeCI6jXPtnLeeecSMZ3DcAuWwaH9ZY+7Nzfwy91iwE2twxwdZPNvXef9aWFFV8tvb7Q2AcYI92mTGucpBMdmmrZe2TTXlJsCmD2EsS2ZRVg23BkRAcIbHJlr2/viaz9Mlma1KUZHASq9f/mapBBmZbQmAZHXTuJ5OyEGjT+CT6RWAcYI+qrRmUSy4JyGRbYDBcEpBYo3KqrWqwDo4c7YngOKrG3r8le/4IzwPHtEunKMhk2xiNxoxxVbMkU6//k9ZuCcvPrnvIZlshkGMPWW0vBFp+dtffHwLjAHu0bc7s2Lw1kmq2VNyy9i1+ktneLeTSr16SyGbaSArZEhi0/Elm24xxyGq6LSnKOy+JbRofVoYGdUkjmzIhcNsYDa+9ZLJ1jECjW8Ixnoh9EBgH2KNtlSPlu5PWQmQy3SF7WHCQbBp7f+uL0qXf85JENtNq24zxMjjiG4Bcts4Yv/yrcw/ZmDHOkFWLwPiWqkHnHpLpOj4swvonl21FcI13Xku/NwTGAfZoa2uh8pJAWzkSme7QTtQ5kWymW1prCY6Q1S7Z85Y/2ZTrv3WKTN0t5EjPA8dixjhDNrmcFLjtvsfSJ5vmGI22biHLz9Y/mVRb7vqNjuwngXGAPdplxrjseTLZtvZVjpDZtkuCEBwhqa4VsxHWP/nUwcHT8agRDKnV1SOOPWSz04xx659kFhXjW+57JIWQzdaE2HDnQ07TLWMEmnv/ER6IvRAYB9ijXWaMQyaTXYIjDook0zkpytonmcm2VuoqR0isDg6umy++rBi3+Mml2jJj3JxZsprP5xtnjDfW/tGeCo5j2+hIXQLJavsYAXt/HwmMA+zR1hnjjex5Py7JY1trIQdFMts2SqC+JLP2yWbbnFkzxsms2hAciSjHaBzpgeBIto/RUDVITtW28Um6JZDYLqMjbf1k0jz3bC4Esvj7Q2AcYI+mu8wY97OSRBqBwS1zZl2Qkc32vf/Sr9Y+2TQvyNaPEYhwR0A+dYCkddZgLM/9lj7ZbK0aLD47+5DJ9qrB5Wet1MmmmRC7LTh4+OeBY5lWuiVkJDAOsEfbs+eXn10SkMm27PmxkyKJbc8gNmeWnLZWThWfLX+ymRQzxjex95PNtqrBsaQoktoaGFQxS2I7JYY4+ZPI1rUf9v4+EhgH2KNqhxnjfliSybaqQUkhZNa1raILArLZ3i1EdIS86jEy6yvG60/WPrlsfectviecfchkW3BkLDBIYlurZovPOmSSSSMpast9p72/PwTGAfaovCDenj3vhyV5bJ8zu2Tpk82k2rz316x9sinHCKgYZ2jqAElb1WBEkRRl8ZPMTjPGZ+f+GHpre2DQ2ievaluXtEYhkMMPeTTue9x3piEwDrBHjQvibZcEfliSyLakkMZL0lGeCI6na1tFa59smpVTW7LnfQOQzNYZ42HvJyczxhmqaofReaoGyWa6dXze8rPVTybbOgSGtd9LAuMAe7TtoDhSMU5Su6x9F2RkU67/tvek8aJq0Nonl63dQpx7SKyuHlk3Y3xk7yep7e2kjQ8jp+mWMQISAslsWzFEmLNMUlv3fmu/lwTGAfZolxnjKsbJpNktYfPad1Akm/qS4HQ8as5Uvkw7XbLa2i2kvCQ4yhPB8WyvGL/E2ieb5hiNtnP/8rOqWTIp1/62c4/7HrLZ3iFz+dneTybWfk4C4wB7tD17fvlZ9QiZbGst1Jy5Y+2TS/2i1PaSFKGdLnltS4pSOUVm9bm/be1HFGM0rH2S2aViXHCQTMqEwLZzf/PbweInl22FQOYsk9V0hxEytv7+EBgH2KNptflFSQYxWU22zhhffrb0yaa+JGi7IIjQTpe8tl0SlKx+slnu/Vsqxu39JFPOGG8bo1HeEBuhRCZbk8ElhZDYZEtSlIRYsmp2ytm891v6/SEwDrBHO1WM+3FJItWWg6JW6mQ21U6Xgdp27mlcEtj8SWQ+n2/d++vN38onGxXjDNWk0Up9W8WsxU8u25KiGnc+Tj8kUs227P26w/aSwDjAHpU/LM9af1i6JCCn8oJsXUvRmsoRsqmrZlurpkI7XfJqnHtaO+VATtuqBiOK9W/vJ5ldZow7/JDJtnPPWNUgiU2rbQmxy8+2fjJp3nduGx15hAdiLwTGAfaokT3f9sNSWzmS2tZO1yUBmdUdE9ZWjMsgJqmtlVMuyEhq2wVZRJEUdZQnguNRMc5QTbeMD2uOETjCA8ERTbfOGHfnQ06NsalGR6YhMA6wR9UOwUE/Lcmk2mnelMVPLpNtM8aLz5Y/mTRaKrbt/Y0LMoufPKotl8MRZoyT17Z2uuW5vxIdJJHplm4hY++8JFav/9GoQ2KIvZ9EtnfINDqyjwTGAfZosqW10FjFOEk1Wiq2HBRVDZJZfem7rWowQk4UuWw799j7yWpbO9GI5fq39MlmW8U4ZDXd2ilHcIS86qrZrSNkIJltHTKbNXA2/74QGAfYIzPGGaqdWqlb+yQzuXxJsDY4UnxWPUIm1S7z1o7wPHAs2849EcuOCbZ9stk2Y1xCIFltnzG+/Cw4Qjb1+l/3zuvOh6y2JcTqENhPAuMAe1S2FhpvySJTMU4m2ypHyt+x9slmUTHe5ZLgKE8Ex7F11qCKcZLalhQSUVaMW/zk0hihtC0pyuZPIpMtM8bLETLFl0IK9bm/rQgoQtUseW1LiioTAukPgXGAParnrXUKjjgnksguB0VLn2ymW2aMh6QokqpbKkasO/uYMU5O25JCSrZ9sik7JpxtSQaHTKpGtwTtdBmW+tzflhAVoWqWvCaNQqCWZPDis4TA/hAYB9ijur3K+tZCy89+WJLJ9nlry88Cg2SzmLfmkoCBqbZ1C1ExTlLdZoxfbqV+lCeC49m694dkcHJqJEWd6JTDsEy3dElrjo70DUAe1ZZk8GZSFH0hMA6wR1tbC5Wttfy0JJFtszYbv2Ptk8hsNl/s5+uDI0d8IDiirRfEx3wYOKJuM8Yvc+4hmcnWGePLz6pmyWT7O6+kEPKqtnRJExwkq+mWEUr2/n4SGAfYo/qguLa1kEsCkmq2FD2//s1YJquqePPpkhTlRYlMtrVSb46QsfjJw4xxhmy3GeNHeCA4km3dEsbue0hssqVDpndestp23+muv58ExgH2aLplxnjJQZFMGvPWWmeMLz9rq0UmXdrplvFyL0pksjV7XktRkppumTUYUQTGrX2S2Vo1q2qQpKaNd962bgk6BJJXNdsyPkybQJJqJMSuOffXnPv7Q2AcYI+2zRgfqZolqcmWC+Jm1eBRHgmOonE5vHbGuEsycpru1E4X8qi2VI5ELPd+a59stq9/535y2pYU0qgYt/hJZuuM8eKz5U8mk6JLWmunHO+8vSQwDrBH020zd4rPXpTIpCouCc62dExQMU4mnYIjLslIarqlpaiWimTVuCDbsvfb98lm696vpShJbV/7kmHJq06I7TJj3Ponk213Pt55+0lgHGCP6h+W3VoLQR67XZBBHpNqc7eEVdY/mTSSotrOPoIjJNWtYvwSK59syi5po5YXXFWDZFXu/a3nnoKlTzb1+l87PqzRIdN3AHlMt7RSN0agnwTGAfZoerl6ZG3lSPHZJQGZTLfMGB+LjJNUt4pxGcTkNNlaMb5k7ZNJ44KsZc5sRCxuyax9stnaTlc2OEl1SYhdfFvY/EmmHiWwLimk2SXtGE8ExzGtNo/Ps/b7SWAcYI92uSSQQUkm1ZYZ4822WtY+eXSZMd74kWD5k0i1dcZ4mRRi8ZPHLhXjkE3dLaTbnFl7P3lUW2aMRyzPPlpJk8lsNl+s6XWFQKGdNElt65A5bozRsPj7QmAcYI+mi9ZC22fu+FlJJtMtlwRjFbMk1eiW0OGC2IsSmTSrZrdUjB/heeBYdpkxHiE4SC7TLe10vfOS1bRDUlT92wohyGRbK+mI1fF51j95NMZotCWDF5+de/pDYBxgj+oflmtbCxWf/bAkk22t1AUGyWq6pVtCxGq3EMhjW1KU4AhZ7Voxbv2TSX3uXzdGQCd1sqq2vPNGRIwu7/7FEQl6r9qSDBvh3ENezTEaW955j/FA7IXAOMCezOfzxWFxbWshwRGS2po976BIUo3s4U6XBL4DyKPaMmd5VLZUtPuTSJcZ45KiyGrrO2/B2ieTSYeE2FhUjEMek9n2Tjm6BJJVOUaj/c7H2u8jgXGAPenSVktwhKyqLa21vCSR1c7tdA/9QHBE5fpXMc6Q7F4x7huAPOpuIWdr175zPzk1giPbWqlb/CRSdRkfppU6SU13qhi39vtCYBxgT6rZ5h+UEYIj5LUtOOhymKw6BUckhpDUtrOPGeNkJSmKIVtUjK/plBMuiElq2uXO5/I3gDM/mXTqlFN8tv7JZLqlEKjx08Da7w2BcYA9mTba6a47KLohI6dtM6fG2omS1O6XBL4DyGOnMRqWPol0m7UpKYqc6lmbbZfDEYIj5DXtMGN8UTHurZdEprPNXaIijJAhr23nfmu/nwTGAfak2tJaJUJ7FfLamkFZrP2ZGzISmW5pJR2hapC86gvik/GocSFQM2OcrLadeyJC1SxpbZsx7oKYrLaND4tYrv+ZxU8i21pJr3LnQyZbx4cVnxVC9IfAOMCeTDpkUEJW29pJq5oiq05jNKx/kqqDg12SQkRHyGT3GeMHfiA4orpysMvat/jJpKya3VYMIThCJlWHDplj48NIauv4MIUQvSQwDrAnnbKHi88OimTSmLXZ1lqo+Jaw9MmkUyt1VYMkVXdM6BQYPMLzwLFMd5wxDplsrxhffrb3k0mXVur177rvIZNdkkIu8Q1AHtvufBRC9JPAOMCebAsMRrgkIK9GBnFbK/Xis+x5Mukyb032PFnt1E7X4ieR6Y4zxiGL+Xy+nDG+LiHQBTFJdRmjUZ99LH0ymeqUw4Btu/Nx199PAuMAe9IMDGqny7BMt7QWEhgkq07z1orfNm+NTOq9f11LxcYlgaVPIl06RZWsf7Io5yavfefVKYekuozRqH9bQiCZdOqWIDhIUl3Wf83e3x8C4wB7MmkER9Zsry4JSKpsKbotg1JgkEya89ZkzzMs9d6/tmK8+Gzpk8m2hMAIwUFymnQZI1B8du4hk07r//LmP7P2SWSXbgkRETPfACSybf1LCukngXGAPREcYcjq9T8eRYzbAuO6JZDUZLY9KWpk0CxJ1ZcEa1sqqhgnqW0JgRHWPzk133m7JINDHlWHMRqLinGrn0SqHWeMW/1kUgbG25b/yOLvJYFxgD3ZJXs4ws9KclkGRzq007X6SaTqMGNcUhRZ1RfE6+bMlqvf3k8mjYrxDjPGrX6y6DJCRkIsWXWpmq3PPsUrAvRep1bq9n6Squ98zk5GrUUPzS5pFn9fCIwD7EmX7PnGj08nRRKpX5S6ZA/rqkUmu89b8w1AHnVSoIpZhqZx7u+SFOgbgCSmRbRvbZc05x6S2rVbCGTRTAqx9zMsu9x3OvL3h8A4wJ5MtRZiwOr1v+4laWzxk1SXS4Jy/UsMIZNlxfj2bgmQSZcZ4yVbP1l0Wfv2frLq0i2k/raYiY6QyLTL+LDis+VPJvX6X5sMq1tCLwmMA+zJzlWDfliSyC7BEZcEZLL7JYH1Tx6TWZ09v65itrwksPbJo8uc2eb6P/gjwVE0EgLXjtEoWPsk0qlbyOWTv32fTMrxYWeqZhmYulvI2vFJuiX0ksA4wJ50ai0ULojJaVLtEBw5yhPBcVTV9paioWECSS2SojpUDVr7ZDKpOnSKKv/CNwBJdGsl7dxPTmUxxLq9v/5twREymZRrf223hHLvt/7JY7rLO6+l3xsC4wB70gyMd5g1eOgHgiPaFhwZN7KHrX7y6NZSVNUg+czn8+3dQlSOkNTOM8ad/Eli53demz+JlOPztiWGGJ9EJlWnQqAlWz+ZLO8714RS3fX3ksA4wJ50yZ4vOSiSyXRrcMSMZXLq1C1E2SAJ7dwpx9onkV3nLDv3k0VjfJi1z8DUwZHxKGK85dxv7ZNJl6So0C2EpJYdMhVCZCIwDrAnXeataStHVnX2/KakkMUlwTEeCI6k6nBJMFY1S0Jd1r6KcbKqyuBglxnjB38iOI5GxWynWZuQRx0cWRsYjDIwbvWTR6MQaN3eX3yeWf8kUtX3nR3OPU4+/SEwDrAnu2bPQybVlgzKiOX6d0lAJo05s2svCXRMIJ9JhwuykqVPJpPZbjPGnX3Ioss7b6icIqk6KXDzO++lP7P0yaRLp5yxrCiSMmM8J4FxgD3pkj1fckFGJvVB8WxNt4SIZeWUpU8mneatmTNLQlWXVtIqxklq970fcujWJW352bmHTKZbqgYjlp2i3PeQSXnuOevSKcreTyLTLd1CdInqJ4FxgD3pVDGuZJyk6kuCTdnzi0sCR0US6TJvTXCQjDqt/Wb+/IGfCI6nS3BQ1SwZNdrpqpxiYLZVDUYsAyS6RJFJufd365Rz4AeCI6oTQ7qMEZAU1R8C4wB7UnXJnndBRlKdLgkur/+iuQL0Xpd5a4IjZLRrQqC1TybVrutfYghJNN9516192eDkNF2MDzNjnGGZdtr7l58tfzKpRyjpEpXL/TIw/rKXvSy++qu/Oj7zMz8zrr/++hiNRnHzzTef+7rJZBI//uM/Hl/xFV8RT37yk+OjPuqj4iEPeUh81md9VvzAD/xAVFV17u+59dZbYzQarf2/b/3Wbz38/0AgpU6zBl2QkdBsNl+8+KyvmiouCY7wTHAs3eatLT/b+8miHCFzsuaCrJw16IKMTKY7zhi39ZPFpHHuWZcMviQ4SCbVYnzYpmTwSyx9Mqk6dUlbfl/MfAOQRHnfuf7M7523j06v9QO0edGLXhS33XZb3HDDDfHoRz86brvtttav+7M/+7P40i/90vioj/qo+MIv/ML4m3/zb8ZHPvKR+Omf/un42q/92rjlllvip37qp1qzVT/t0z4tnvOc55z7/RtvvHHP/2uAoWjM3OnUXuXADwRH0iUwGCF7npx2nrdm+ZNEc+1LCGRYpmaMM1BVkRTSbe+HPLqND7v0Z9Y+mUyq7Xc+9n4y6jQ+ydrvpftlYPwlL3lJPOlJT4rHP/7x8Z3f+Z3xwhe+sPXrHvKQh8T3f//3x1d8xVfEgx/84MXv/4t/8S/ixhtvjJ/5mZ+JH/uxH4u//bf/9rm/99M//dNVhwN71TwodgiOHPqB4EjKqqkurdQFBsmk0yWBDGIS6nTuKT5b+2RihBJD1dj71yaDW/vk1G3G+KVfVcySSdXhzsfeT0Zd7jubhRAWf1/cL1upP+MZz4jHP/7xW7/uMY95THzt135tIygeEfHgBz84XvCCF0RExGtf+9qDPCPAqi7Z8+bMklGnDMpYtpNWNUgmjUuCLvPWrH+S6NQpR0IgSU3L4OCaecr2fjLauVOOtU8i1eW9f+P4MMngJLTrjHEnf7Lo0iFzfaoU92f3y4rxfTg7O4uIiNPT9v+J73znO+P7v//74yMf+Uh87Md+bNx4443xxCc+8ZiPCCSze2shB0VyqIq1vzl7/tKfzSx9EunUTrf47JKMLCbV9hnLEgLJarprUpT1TxLd9v4la59MdqkYd99DJtPGnY9OUQxHed95tq5L1Mg7bx+lDYz/8A//cEREPOtZz2r981e96lXxqle9avHXo9Eo/of/4X+If/tv/+25CvRVFy5ciAsXLiz++o477oiIiMlkEpPJ5Gofncvq/5b+m9IXFyfTxedRzFrX7qyqFp+raWV9c83sc4+998LFxedxzNf+Mxdt5Wbrvwb65uJkua/PZ+37etlOazKdWv8Dkf0se+Hi8n/Xur2/mi7PRrM13x/QRxeny+DgvJrGZHY+SDIvEqcu3RWkvX65JrLvsfdX5TvveM0777Sx97d/DfRRnRQ1Hm3fe2bz/u9P9llqF4t9PebtZ/pZkTTonZcsutx3lu+81Q7nHnvs/u3y3zLlm9kP/uAPxs/+7M/GF3zBF8RNN93U+LMHPehB8U/+yT+J5zznOfHEJz4xZrNZvOENb4j//X//3+NlL3tZ3HPPPfHjP/7jG//53/Ed3xHf9m3fdu73X/nKV8aDHvSgvf5vIRoJDHB/9ua3jaOeUPGG3/qtuOdPz6eJvemDo4g4ufT1f/zHccvdbz7iE8J5+9hjP3whoj5SvO+974lbbrml9eumk5OIGMVdd9219mugb25/53Lv/+XXviYecf35r7nt1uXXvP71r493POR4z8e1l/Us+9Y7I+q9/+1vuy1uueWt577mvfcuv+b2298Rt9zy9qM9HxzSBz906Uwzjnn87M/+bOvXvPvdy73/F1/96nhky88Hrl7WPfb+6g3vXb7P/tEf/kHc8oHfP/c1t98dUe/9t73tbXHLLbce7fngkKbVpb3/7jvvWPs+e9edl76mqqo077z2Wd5SvM/+5q//Wnzgj85/zR+/c/nz4Q1veGPM36Z0lv4r7zvfv+a+82JVfM3737/z3m+P3Z977rmn89emC4z/zM/8THzd131dPP7xj4+Xvexl5/78Yz7mY+LFL35x4/e+8Au/MD7ncz4nPuMzPiN+4id+It7whjfEZ3zGZ6z9d7zwhS9czDCPuFQx/rjHPS6e9axnxUMf+tD9/Y8ZuMlkEq961avimc985qI1Ptyf/dGr3hLxjkuXwp/72Z8Vn/3nHnnua65/83vjJX/8OxER8ef//F+Im278c8d8RFjY5x57+4fujXjDL0dExGMf8/Fx002f2vp1//R3fjHuvXcaD3rwg+Ommz7vqv6dcH/xMx/+nYgPvDciIp75jC+Mj3nI+cjHG295c7z23W+LiIjP+ZzPjad8wsOP+IRcK9nPsr9x6wcjfv+3IiLiE5/45+KmL/rz577m1g/cHf/8d14fERGP/vjHxE03fcpRnxEO5Qf+669E3H1XnJ2exE03fVHr17zyzt+LN37g3RERceONnx+PfcQDj/mI6WXfY++vPvKbb4/4s0sRkU//tE+Nmz7jMee+5o/edWd89+/9akREPO5xnxA33fQXj/qMcAiz2Tzmv3opePHRj3xE3HTTX2n9uh+87Vfj9rvvjNFovPbnQ1/YZ6n99iveHPGuS++zn/e0z41Pf9zDz33Ne37ltvjJ2/44IiI+/SlPiZs+5eOO+YhwEG//0D0Rb3hdREQ8Zs19532TKv7Rb/xCREQ88qM/Om666amd/tn22P2rO3t3kSowfsstt8SXfumXxsd+7MfGL/7iL8ajH/3ozn/vgx70oHjuc58bL3rRi+L1r3/9xsD49ddfH9dff/7S8+zszCI+AP9d6YtZMVHn+uva1+3Z6XLbHY/H1jbX3F722PGytdB1pydr/3njy73U55f/vZBB0Sk3HrBm7z85OVl+3vA9Qk5Zz7Kj0XJdr9v7z06Xvzcaj1L+d2CY6nGDZyfrz/PjYg7h6emp9X8gWffY+63Rcl1ff9a+rs/Oyndeez85XJguxyedbtr7L3+PZHrntc8yL+47H3DddVvfeccn3nnJYTRenmnWvfNWUc4e3/3cY4/dn13+O7ZPjO+hV7ziFfG3/tbfihtuuCFe/epXx5/7c7tXYd5www0REXH33Xfv+/GAAZhWy+jI6cn5OYMRyxnLEZdelCCDqpgldTJuX/sREaM6MG7xk8h0Vu797Ufr8rvC+ieLTmu/PPdY+yRSr/+N557is/VPFpPK3s8wVcW552zNfU/EpfnjERFzi59Emuf+NfedxWfrnyzK+87TNed+d/39lCIw/opXvCK+5Eu+JB75yEfGq1/96vjET/zEK/rn/Pqv/3pERDzhCU/Y49MBQzHt8sMy1r9AQV9Nd70kcFQkkfKSbN3ePy5+3+oni13PPdY+mdTrf9O5p3lJ5juAHDpdEJd7v6VPEuU778l4w3X65c1/Zu2TyLTaMTho/ZPEpEsRXIiM91HvA+M/+7M/G1/yJV8Sj3jEI+LVr351POlJT9r49W984xtbs5Z+4id+Il760pfGIx7xiPjrf/2vH+pxgcQaGZSbXpQuc1Aki7JbwqbKqTqHuLhPg96bVNs7JpS/O3NLRhI7d8px8CGRev2rGGdoGhfEnSqnLH5ymHZY+xHLZPAIZx/yqGbb73zGozIh1tonhy5r37mnn+6XM8Zf8pKXxOted2mo/Zve9KbF773mNa+JiIjP+7zPi+c///nx5je/Ob74i784Lly4EDfeeGP86I/+6Ll/1hOe8IR43vOet/jr//V//V/jz/7sz+JzPudz4rGPfWxUVRVveMMb4nWve11cf/31cfPNN8fDHvawg/9vBPJpZFCuqx7xw5KEuiaFjDbFzKGnmm0V16x/CcQkNO3QLaFk7ZNJHRzcfO7RMYF8qp3b6R74geBIunTKiTi//r0Dk8G0wzuvinEy6nLf6dzTT/fLwPjrXve6eOlLX9r4vde//vXx+te/fvHXz3/+8+Pd7353XLhwISIi/sN/+A+t/6ynP/3pjcD4P/gH/yB+/Md/PH7t134t3v/+98dsNovHPOYx8fznPz++6Zu+KT7pkz5p//+DgEHockHshyUZdWmpGLHMnp9Z/CRS7v3rlr+WomTUpaVo4zLY2ieR+uyzNhk2zNokp2Y73Q7BkUM/EBxJl6SQiNWqWcihTAzp0iXNsYcsuo0RsO/30f0yMH7zzTfHzTffvPXrbrzxxp1fMJ///OfH85///Ct8MoD1mq211l0S+GFJPmVLxZONF8SX/sxLEpnUlwSn41Fjjy9prUVGZVLUujnLIy0VSWq6qBjfUAooOEhC3bqFSAgkn+b4sG5d0mbzeZzEhp8T0BOdRgm47yShxrlnXbeE4rNk2P7o/YxxgPuLXdvKuSUgi6pjO936jwRHyGQRHNlYOVL8heVPEo2kKJUjDEx9SbZ2hEboFkJOnS6IJQSSUNcRMvZ+Mqp2DA7qEkgWXZJCdMrpJ4FxgD2ZdGqvsvzshyVZdJ8xfukbYGbxk0h9SbBx7RfXBNY/WZQXZGdd2ula+yRSdwtZlxQSsTpX1jcAOUx3TIqy9Mmi6/gwiSFkNJlt3/vHLjxJqNMYgZGEqD4SGAfYk24ZlH5Yks+ulwTWPpnUiSFdgyMuyMiinLe2PjiilTr5zOfzRceEdWf+CB0TyGm68xgNyKHslLOpU5SkQDLqNkJp+dm5nywayeAb9v6ald8fAuMAe9Ilg9JBkYw6zxhfBMatffKoL4g3vSQJjpDRtMsIGZfDJFR2/uheNQg5TLu88xafnfvJouqw9iOaVbOWP1ns2i3E2ieL5viwTR0yL3+w+HtDYBxgTzplUBaf/awkiy7tdCOWlYOWPplU1faK8VA5RUJVhzEajXPPgZ8HjqXL+KQInaLIqewWctZhxjhk0X182PKzOctk0WX9Swgko64V44u4+IGfh/0RGAfYk0mHDMpwUCShLpUjERFjFeMkNOkwY3zcqJq1/slh53OPpU8SVYduCRE6RZFT13N/zconi2nHpKixhFgSqvf+0ajbCCVJIWTRZcZ4xHKMjKXfHwLjAHvSpWpW5QgZNS4JNl4QX/qzmbVPIvXev3Ht2/tJqOyUs+6CeKRmnITKdqJdqwbt/WRRrn/vvAxJIymkw5zZCMFB8qjP/Z1HyFj6JNE893epGLf4+0JgHGBP6uDgaBQxNmOcAdm1rZyKWTKpW+puzh5efrb3k4UZ4wzVtMP4pEsEB8mn6hAc1E6XjDqPDzNjnISmHcaHjXRLIKFmp6gu952HfiL2RWAcYE/qC+LNM5YLfliSRHPObIcMSmufRBYV4x3WfoT1Tx5dqmbVi5NR11bSkqLIaFLOGO/SSt3BhyTKtd9lfFhEOPyQxu73nRY/OUy6tlIPrdT7RmAcYE9kUDJU046XBIuZOwd/IjieaYcZ46pmyai8JFhfMV5WTVn85DBpjJDpdkFs+ZNF1SExRMU4Ge2aDB6hlTp51Ot/0xgBez8Zdd37683f2u8PgXGAPanbKm6esXysp4Hj6dJON2KZPS84QiZ1YsjmvV9SFPlUHeatKZoio87BEed+Epp0aCk6Eh0hoWnHdrpj534SqpMCN517yrU/m1n95DCpuu39yw6Z1n5fCIwD7Mm0Qzvdkh+WZFF1nTF++ajoHYksZrP5Yj13baercoQsulwQ65ZARpMOYwQgq6rsFtIpKcrmTw7TDgmBEc795FTt2iXt0A8ER1KeezaNkJEQ2z/e4gD2pH5R0lKRoSkviLsEB12QkUVVbOSb562V7aQP+khwNI120muDI6qmyKd7O117P/k0K6c6tFK39kli2nHObNj7SahOiN289pesfbLofN9pxnjvCIwD7MmilXrHDDI/K8miS+VIxLKtoopxspjumBRyiW8Acqi6jNFoBEesfXJozhjveu63/smhS6coSSFkVK79sw7jwyLs/eRhfBhDVd75nG0qhFMI1DsC4wB7smgttOGgKHuYjLrOGF/8ibVPEmXlSKe1H/Z+8ujSTlpLOTJqBkd0imJY6uDIaLQ+KVBSCBk1E2I7tpO2/Emiy+jI5rnH4ieHasc7H0u/PwTGAfakviDuPnPHT0tymHactVn/kbVPFp3b6eoWQkJlYsi6yimBQTLq2k5X5RQZ7R4cOfADwZFMO577xyPFEOTTZca4tU9Gk1mZFLX93G/p94fAOMCeVC4JGKhp14NiaKVOLpOOlSMuCcioy97fDAxa/OTQZcbyKpVTZDHtkAweEgJJqHPVYPFHM3s/SdR7f9fxYc79ZDGtymTw7Z2inPn7Q2AcYE/qeYNdK0cgi6pD1WDEct6agyJZdK0YL7kgI4sulwSNOZuWPknoFsKQ1R0TNieDSwgkn87J4LqFkFC992+671EIREZdu4XU3wCWfn8IjAPsSX1J1n3WoB+X5ND1kqC+IVYxThadZ4y7ICOhLsFBwREymhRJIRtHKFn/JLRopd6xYhay6Do+rFz+My++JDCbzRd3ON0rxiGHnfd+i783BMYB9mA+ny8uCRwUGZoruSSADJprv2v2vN2fHCYd9n4tFcmoTArZWDnV+CPrnxyW7XS7Xida++TQqBrsmBALGTQrZjft/RICyWfXYghLvz8ExgH2oPMFmYMiCXW9JGi21PUNQP81uyVsSApxP0ZCXS8JarZ9siiTQjbu/cVn658stNNlqBozxjckxBojQzbVFdz3GB9GFmUxRJeEWHed/SEwDrAH3edNLT+rnCKLrpcEZfa8rnJk0DUpajySFEU+jY4Ja9a/TjlkdCUV49Y/WVSduqQZIUM+zaSobokhgoNkUCbDdt37IYuuxRBjoyN7R2AcYA+mjQuyblurdySymHa8JFAxTjblnNmuSVEuyMiiS1vFslOO6AhZXMkFsa2fLOrg4KZ3XiNkyKjqeOczlhhCMsaHMWTlnU+X9a8Irj8ExgH2YHoFwRE/Ksmia2JIGSCRRUkGjbZynS8JDvhAcET12Wc0Wn/20SmHjCaNbgmCgwxLt4rx5Wcrnyy6dgkMCbEk0xyd1218mKVPFl1HCSxbqR/6idgXgXGAPehSNRWxUjkFSVRXcEkgQEIGXS8JQuUICdXr/8yMZQam6wgZDRPIqK6c2pwQqFsC+Uw7Vw1a/+TSORm8kRRyyCeC42kkxG54760P/vb9/hAYB9iDrq2FSn5YkkXX1kJjGcQk0/2CbEnVIFnUZ5+uraRVTZHFpHNLUcER8qkDJJuqpiSFkFHXhFjjw8im+/iwMhnc2ieHcoTSWYeKcfpDYBxgD8oflF1aq1zioEgOXSvGXRCTTde1b9YgGU0un302nnuKz9Y+WXSdM2uUANnM5/NFcHBjlzSBQRK6kqpZq58MOp97is+2frLoft95iXNPfwiMA+xB14pxM3fIqHP2fPFHLojJYHolF2Q2f5JYVA069zAwnSunyr+w/kmgc2DwGA8DR1YWQ3ROiLX3k8C0a2BQMjgJlef+Lgmx1n5/CIwD7EHXwKCKWTKadpy1Wa5/M6fIoNktRPY8w1InBW5c+y7ISGjaqJxSNchwNN95OwZHLH6SuJJiCGNkyKD7fU/B2ieJznu/GeO9IzAOsAedD4paKpJQeVDcnEG8/Kxqlgwac2YFRxiYOnv+bMO+32DfJ4lmS0UJsQxHs1NOx4RAJx+SqLoWQ0gMIZnpFbzzKoQgi+4dEy796tzTHwLjAHvQ+aBYfPaSRBbdZ22qHCSXcu8/ExxhYOq9/2TDuSdCWznyKVsqdk+K8h1A/02vZO1b+iQxuYJRAirGyaBrUlRjjIBzD0nUhXCn41HjTnPVcsb4ER6KvRAYB9iDztnzqgZJqLwk2FQ42EgMma39MuiNrrMGBUfIaFkxvvmV0iUB2VzJnGXrnwy6V4xLCCSf6grO/ZBBIynKuYeBWY4P25YMfrmV+sGfiH0RGAfYg6pjK/VwSUBCVccMyrHgIMk0KsY7ztrUVo4s6gBJ90sCi58cGmM0NiWG6JRDMlcyY9neTxbduwS68yGX5viwTeee5UdLnywW77xbksFr9v3+EBgH2IPyoLippahLAjLaNYMyQnCQHMqK8Y2zBsu/8KZEEvUlwaYZyxEqxsmnTIjdmBRVfJ77BiCB5rlHWSzDcmVdAu399N+087lHUgj51B0Ttt931p8s/r4QGAfYg8aM5Y1t5Qp+VpJE1TGD0gUx2ZQXZN1bqUMO9SXBpguyiGLGuMVPEo2EWHs/A9K1Yrxk7yeLzuf+4rP1TwZX9M5r8ZNEfe7fet/pnbd3BMYB9mBSdZ03paUi+SyrBrtXjFv/ZNC9lfrysxclMpjN5ovOH9uCI3X1iKVPFs3KqW5zln0DkEGjYnbT2rf0SahztxDvvCTTfOddv/ePrX0SWhYCeefNRmAcYA8aFeNaKjIw9QVx16rBiIiZ9U8CZVLUpgzixiWBtU8CXduJRsTi8GPtk0V1Rd1CrH/6r9FKfWPFrMg4+XTtFlJy9iGDadVx71cxTkL12adrK3Vrvz8ExgH2oPmSJHueYamqbhXjY1WzJDPtmBRVmln7JLDLnFlTaMlm0rVbSPHZuYcMGq3Uu3bK8dZLEtUVzRiH/uvcLaT47NxDFvW5f1O3hIjl+rf0+0NgHGAPuleMl1WDB30kOJpJ5xnj1j+5NKsGN12QaStHLpOq2wVZhHlr5NN9719+tv7JoGu3EMERMqrX/2i0bca4d15y6dwtRFIICVU7jo607/eHwDjAHpQHxa5ttSCLxcydHVqpQwaNVurGaDAgzaqprvPWrH1ymHRuKSopilyqzsERa5986nbSW889jT/2HUD/TTp3C1n+mdF5ZDHpuvdf/tV9T38IjAPswbRj5ZQMSjKqLwl2SQoRICGDcu8/61g1CBl0nTUYoWKcfJqdorpdqbgkI4PG+DAJgQxM56rB4rPlTwad33mLz9Y+WdTdQrae+et33gM/D/sjMA6wB11bC5VcEpDFdNFKvXv2vOVPBpOOc5bHZeWUtU8Ckx0Cg+atkU0jOKilKAPSSAoxY5mBWQRHto0Ps/5JpurYIXMkG5xk5vP57klRNv7eEBgH2INpx5aiXpLIaLo4KO4wY/ygTwTHUVUd9/7is7ZyZFB1DAxGlPPWrH1yKBNizzZWzUqKIpdyjEDX4Ii1TxaLLmlbx4dZ/+TSuZV68dm5nwymjWTwju+8B30i9klgHGAPplcwc8dPS7KoFq2FtmQINyrGfQPQf42kKGM0GJCu3RIiVIyTT1k127Vi3HcAGVQ7XBDXrHyy6NwlrfjsnZcMmgmB3nkZjqpRBNexS5p9vzcExgH2YNrxh2XjJclRkQSuqLVQyJ4nh0nnOcsqR8ilazvdiBAZJ51y7994QVx8tveTQXOMQMd20hY/SVSLwHjHcw8kMenYJa0cH6ZLGhk07nu2Voxf+tXK7w+BcYA9mHYMjpixTDZdxwhEmDlFPmW3kO7Z8zZ/+q/RTlfFOANTGaHEQO1SMW7vJ5tpx2TwkvVPBo1zT9duIRY/CUw7JoVELEcoWfv9ITAOsAed2+mqGiSZru1EI1ROkc+0azvd4rO1TwaNpBAzxhmYZtWsGeMMR9lOd+u5f+SCmFzqYojtI2Ts/eTSLATSSp3h6HrXH1FWjFv9fSEwDrAHXbPIVA2STdd2ohHWP/k05611aysnOEgGV3ZJADlUxZzZTd1wnHvIpvHOu23vP/TDwJF1njFu7yeZSceKcUkhZFPe92zb+2vWfn8IjAPsQTXrNndE1SDZ7NZaaMn6J4OuF8TGaJBN1xEyEUU7XWufJOqkwJ3a6Vr/JHBFF8QCgyRRn/u3zRhvfGdY/iQw7VgMMbL4SWanhMC6U85Bn4h9EhgH2INJ13bSWguRzKSRFNLtoBhh/ZND1wtibeXIZrrDrMHx4pLA6ieHev1v75Tj3EMujb2/Y9WspBCyqM/9W1upO/eTTNfxYWWXtOI1GXqrXPtbx4fVH2z8vSEwDrAH5ZzljRmUWguRTGPO7NZ5a0vaSZNB5zEa9n6SaV6QdWylbu2TRH3u3zpjufjs3EMGzcqpbnOWLX0ymM/nMVlUjJsxzrA07nw6zxi3+Om/slvC1nO/GeO9IzAOsAeTjj8stRYim2qH4IiOCWQz6Tpn2SUByTRaKm5tpys4Qi71uX9rQqAhyyTTrBjvdu639ZNBsfQ7dElbfnbuJ4Op0ZEM1GSnVuqXfrX2+0NgHGAPGhXjmzIoi89+WJLBZKfgCOTSnLfWra2cvZ8MruSSALKoz/27zJm195NBee7ZXjV7iW4JZDC5grUfYe8nh0nHLoHGCJBNszts1/Fh9IXAOMAelAfFzRXjbofJZZc5s9rKkU3XeWva6ZJNtcuc2cu/WvtkUZ/7t7VUhGymXTvlhKQocpl2HJ0XEY3F7+RDBt27BLrvIZfJbIdW6pd/nVn8vSEwDrAH1axb1WDJj0oyaGTP73RB5juA/mu2k+46bw36r2tLxYhy3hrkUK//7a3Uy+CI7wD6rzFjvOOcZffDZNDolrA1GXxJUiAZdO2YUP6RtU8G02r3pChLvz8ExgH2YNq1Yrz47KBIBo2DorZyDExdPTIeRYw37v2y58llp1bqgiMkU3WsGG8kRVn/JFBdUVKUxU//Nc4928ZoSIglma7BwZFuCSQzvYKKcfpDYBxgD7q21vKSRDbNqkGXBAxLfUm229q3+um/RnCkY3DQ2ieLyaJi3IxxhmXScYRMRDlG44APBEey2wgZL73kYnwYQ7VTIZCOCb0jMA6wB12zyFQNks2VtFSMsP7JoQ4Odg0MRlj75DDZae+/xNonizpAsnXGuMopkql2mLNcVw5a+2Qw2aGVOmQz7Tg6UiEE2exUCFR89t7bDwLjAHvQCA5uelFyUCSZMnu4a0vFCJWD5FDv/bskhcwsfRIoZ20KjjAk8/m8e7eQlb8P+q4MDnavGLf26b+uHQIjvPOSz7TjCCWFEGTTdWxqhFECfSQwDrAHjeDghplTWquQTSN7fpd5a5Y/CXRup+uCjGS6tlQsOfeQQZnctFNLxQM9DxxTo2J8y7m/jo9Y+2QwrXYYIVN8dvQhg0nH9V+ee2YWPwk0k6J22fut/z4QGAfYg65Vs5pukU1j5s7WtnIyiMmlWlQNdn9JckNMBjtdEtTBEWufBHarmBUZJ5fJLpVT9QdrnwQmHStmIySDk0/VKATSSp3huOJCoEM9EHslMA6wB10ziButVfykJIGdZu6omiWZyaxupd6tlXSElyRymF7BJYG1Twba6TJkVcc5sxHNsw/0XeOdVztdBmbSsVPUSGSQZKpdRkcqBOodgXGAPejcSr347IKMDBrZ89rKMTB1cHDbS9K4UTli8dN/jVbqHS8JrH0yqK6kYjace8hhl1mbNUufDLp2CFzl7EMG9Tvv2cloY9KT+06ymVbd7vojIkJCbO8IjAPsQfeK8eVn70hk0LWtVkRz/UMG9YvSLmt/Zu8ngcYYDRXjDMhkp4rZ5WfrnwyupGOCwCAZNMeHaafLsCzfeY0RYFgmjQ6ZEmKzERgH2IM6ODgeRYw3Bca1ViGZxsydbZcE1j/JTDu2Ug9rn2Su5JLA2ieDqmM70QjnHvIp20l37Zhg6ZNB10KICHs/+dR7/7a1Py4i4zOLnwQUAuUmMA6wB5MryaB0TUACzcoRJ0GGZXFJsFPVoL2f/qt2GaMx0kqdPHZJCNRSkWwaLUU7zhi39ZPBpNFKvfudj9QQMlgkg6uYZWAaoyMVAqUjMA6wB12DIyU/KMmgmT0vMYThmM/ny6SorS9J5d93wIeCI5nuckF8+VdLnwzKypEzM8YZmMbev+3cf/lXZ34yKN957f0MzbTjO28zIRD6b6e9331n7wiMA+zBlcyZ9WOSDCa7VI4Un10S0He7BEfG+mqRTNlOd9vZR2ScTMpzz8nWhMCicuRgTwTH0+yY0O2915mfDMqkkJOdOkVB/9V7//akEBWz5DLdZYRSufdb/70gMA6wB/WszbNtGZSQTLVL5YgLYhK50pck89bIYKd2upd/tfLJoEwK2TZCptlN13cA/Vfu/Wdbzv31d4ClTwa7rH3BQbKp73y2JYU0X4ktfvqvsffv0kr9YE/EPongAOzBsrVQ9wxKPynJYDLrXjlSMmuWvisD4zu9JFn6JNDoFtIxKcq+TwbT6gorRw71QHBE0x3O/ZrlkIm1z5AtK8a7F0LMHHxI4Er3fu+9/SAwDrAHizmzZiwzMM0MShfEDMf0CtqJRtj7yaG6gqpZK58MdkqK0lKRZJpJUR33foufBJrjw7pfpTv3k0F99tltdJ61T//t0iWwZPX3g8A4wB7UF8RmLDM0jeCgtnIMyC4VsyVrnwwmVzBKwNongzIpZHtgsDz3+Aag/+rKqZPxqFEZ2Gax9x/6oeAIynfebXOWS7Z+MpheUSEQ9F9j79+aEOu+s28ExgH2YHlQ3HZBYOYIuZTBkd3ayvkOoN+qWfeqqbG9n2R2uiSo58xa/SRQJkVtm7Xpgphsur7zRhR7v8VPArslBDr3k8u0cyGQwCC5XEmnnIiw+feEwDjAHtRzlrdfDi+pHCGD3YIjS5Y/fTe50lbqFj8JVCrGGahy7W+btVmy/smgbim67cwfUVaMW/z0X3XF77zWP/02m80X88K3BgaLb42ZtU8CzWIIo1OzERgH2INF9rzKEQZml5k71j+ZmDPLkJXZ812Dg5Y+GZRJUaoGGZo6IXbbO29EOWP8gA8ERzK94i5p0G+TcoTMDkkhkMF0tkMxRPHZ2acfBMYBrtJ8Pl+8KG3PINNaiFymOwRHrH8yKefMbg2OaCtHMjtdEhg0SyKNc89OF2S+Aei/Scc5sxHLvd/KJ4NmO11d0hiOXcaHue8hm53O/RJie0dgHOAqNasGu+dI+kFJBleeQek7gH6b7PSStPysrRYZlJcE2xNDLrH2yaDZKad7txDIYLoYH2ZxMyzN8WG7BEecfei3RlLILmMErH0S2OncX3x239kPAuMAV2m6Q/ZwRHFJ5gclCewSHIxGcBD6bZe9v4wb2vrJ4EpGCVj7ZFAmBG6vGFc5RS5dx4eVrH0ymMx2CA4695NIIylkl9F51j4JlCOUtndMWH62/PtBYBzgKk12qJiNKCunoP+mjYOiYwXDsdvev/zzmc2fBOq9fzTqMmf50q+WPhns1lJ0+VnlFBnUF8TbxidFlOvf2qf/yhFKW/f+4rPgIH03bSSFbF7741H5zmvx03/VDsngISG2d9xgA1yl5syRHeat+UFJApNGayGVUwyH4AhDVl+SbVv7Ecu9X0s5Mig75Zzs0lLU8ieBxd7fJRlctxASaXaK2tYlzZxZ8mgExncohLD3k8Fkl/Fh7nx6R2Ac4CpNd2itEmHWJrlUOySGOCiSSaOt1g7BEUufDBbtdHeoGrT0yeCKW4oe6oHgiHba++ukqIM+ERzHFc9ZFh2k5xr3ndtGyDj3kMxuI5QKvgF6QWAc4Crt0looQvY8uUx3aCftoEgmjW4hW4MjKkfIpR4l0O3co1MOeUx36JSjpSLZ1Hv/tsvhiPKd1+Kn/3YKjggOkkgjKWRLUlTZIdDiJ4NGl8CdCoHoA4FxgKs03eGgGLFyWISemzSCgw6KDEczKUTlCMNS7dRKHfJoVIzrlMOAVLP5IsFje1JI2SUN+u+Kg4PQc1eeFGL3p/8mO3SINTqyfwTGAa7SZIeK2ZIflGSwW8W4gyJ5THeYNzUubglm1j4JLNrpbgkMRqxcktn86bldKsbNGCeTXUbIROgWQi7NpKgdAt/WPz3nnZchaxbCSQzJRmAc4CpNd6iYjYjFLZkfk2TQnLfmoMhwlMERLRUZmjpAsm2MQITgILnstverGiSPaoe1H1FUjNv4SaDaJSnKOy+JNM89uqQxLLskxEoM6R+BcYCr1Myed0nAsEwbrYV2eVE60APBkUx2WPslez8Z1BfEJzvMGI+QGEL/leeek53OPVY//bbr+DDJ4GQyueLg4IEeCI5kuksracngJFN3yDwdj7YnvOqS1jsC4wBXaZcMyojmYRH6rg6OjEYd5g0KjpBIo1uIinEGZlkx3uHcU3x2SUDfXXGnHEufnpvsMGc2otj7rX0SmO5QDOHcTybNc8+WpJDyvsfiJ4Hl+DBd0jISGAe4SrtkUEYs5yz7QUkG9YuS4AhD02yp2P2SwA0ZGdRJgZ0uCSx/EpmWwcGte//ys7VP3+1aMb6YMX6wJ4Ljmeyw/kchOEge5Ttvl/vOmqVPBot33h3OPfSHwDjAVdolgzJieUlm3hQZLFoLCY4wMGXl1Lb1P26sfauf/qsvCbYlhUS4ICaX6S4V49Y+iRgfxpBNd+mY4NxPIru880Ys33vt/WRQF8KpGM9JYBzgKu30khTlJcGBHgiOaNFaaIduCREhMk7v7dRKvVj7xY8M6K36kqDLuccFMZlMdtj7rX0yudLxYVY+GTQ7RW079y+586Hvmu+8O3QLsfZJYFLtUjG+/Ozc3w8C4wBXaTrr3lYrQls5cqkziHe5IItwUKT/pju1Ul9+tvbpu9lsHvXy75YUteSSjL4rE2K3t9NdsvbpuysdHwYZlB0Ttr33NuYsH+yJ4Diq2a57/yXeecmgmu1SCLTk3N8PAuMAV2mXlooR2sqRS1XV7XRdfjEs5QXxmcoRBmT3hMBDPg0c12SXVuqCIySy6/iwmnMPGTTufHY49/sGoO/Kvb/Lnc+iW4ilTwK7jY507u8bgXGAq7Rr9nz9puQHJRlMLgdIOlWMF5+9KNF3jeDgtvVvigCJTHecNdgYJWDzp+emu1QNln9h7dNzO48PW7zzWvv032SXVurO/STS3Pu73PlopU4edWLI7vedvgH6QGAc4CpNdgmORPHD0s9JEqgviLtlUC4/OyfSd2VLxW3rf1xmD1v79NxOSSFh7yeXZscEwRGGo1Ex3qFbSM2+Twb1O+/ZyahRFdjGuYdMdumUE7Fc/5JhyaBupd6pQ6Zzf+8IjANcpWblSPf2Kn5QkkHdVq7TzB2thUik2iU4UnyWPUzf7dJONEJwkFx2mjMbkqLIY7pDQmCEd15yWb7zdq+YjXDup/92eeeNMEKJXOpzf7cZ4879fSMwDnCVpjtmzy9n7vhJSf9NZnX2/G5HCuufvtulckpSCJnsOkLGBTGZTHesnIIsdumWEKFLGrks5swKDDIwzXO/VuoMy3SX0ZHNGUqHeSD2SmAc4CpNdp61CXnUF8RdWgupGiSTXbqFqBgnk2YrdXs/w1LO2tyeFLX8bM4yfXelYzSsfTKo1/+uCVFWP313pa3U7f303Xw+36mVevPO50APxV4JjANcpbJypEsr9Zqfk/TdfD4vLgl2bSt3sMeCo9jlglhgkEx27ZRTsvfTd5Mdzv0uyMikkRC4Q0KstU8Gi1bqO3dJO8TTwPGUCYFdqmbHIxXj5FDe93Qbm7r8bPn3g8A4wFWa7NpayEGRJMp5U7tckF3iG4B+a1YNbgmOjCSFkMfOnXLcEpBIY+/fdkFs6ZNIs2pwh3a6B3siOJ76zqfbO68RSuRRBgd3qZqdeeml53Yem6oQqHcExgGu0s5ZZJd/1VqIvtu5nW7x2UGRvttlzmwZF3RJQN9VVzpnNpx96L9GcHBbUpQLMhJpVg3uUjFu8dN/u3VJW7L+6budO2QuWqlDv013TgZffvbO2w8C4wBXqXlBvMO8NT8n6blJtVtbLUWDZDLZIYN4t2mEcP/W6JSz695v86fnpjucfVyQkcl0h6SQiDIZHPqvPvt0WvsO/iQy3bVDZv3B5k/P7Xzu8c7bOwLjAFepeUHc5S1IK3Vy2P2CTOUUeVRaqTNQjcqRnSvGod/qqsHxaHtLURNkyGTXpKgwPoxEqkXFuHdehmWya5fAeu8/2BPBcUx2GZ8UEWHv7x2BcYCr1GwttFvlFPRZeVA82aFbQoTKKfpvl0sCraTJpDlrsMveX14SWP/0W90tpFu3BHNmyeNKx4dBBvWdzy4dAiOc++m/XTtk1jmDzvz03c7jw+z9vSMwDnCVmsHBHdrKOSjSc9WOF2Qly5++26Wd7ljFOIk0176KcYalXv+duiU0Wipa/fTbzu10rX8Sqe98dj73WPr03K4dMuukwJm1T881OmR2SYgtPtv7+0FgHOAqNSvGu1+S+TlJ3+16UIRMdhklUF4Oz7wl0XPTnVsqLj9b/vTddLZDxXjx2dqn7ybVjnt/8dn6p8+q2Xyxhrt1C1l+tvTpu+YIpe5nHxWz9F0jKWTHhFj6wS02wFXaOXvejHGSKA+K3SqntBQlj+mObeVq9n76brLjuacxb83uT8/V63+XZNgI5x76bzrr3ikHMtk1OGLOLJlMZ7tWjF/61dqn73YfIWPv7xunWYCrtMuc2YiyYtxPSvpt56rB4rOWivTdLpcEgiNkcqWdciLCNwC9t8ucWcERMikrxjuND5MQSxLV1XTKsfrpuV26pF2iEIgcmmMEdu0W4hugDwTGAa7SLnNmI8oZ4wd6IDiSqzkoQt/t0lK0zB72jkTfNZNCdmwnfYDngWOq17/gCEOzc1JU8VlCLH3WDAzudu6BvmsWQ2xf/51i59ADzTECRshkJDAOcJV2zaCss+f9nKTvGtnzDooMTCMpassl2bgtOOKbgJ66uPO5Z/nZsqfv6qSoXZJhI6x9+q+a7Tg+TE4gSUwaYwR2i/rZ++m7K52zPJsX77y+EeihnZPBdcrpHYFxgKs0acwdMWeW4Zjsmj3fOCj6BqDf6sSQ0ShivOWSoFz7s3lEfOT2iO/7zIgf/PyIi/cc8jFh73bvlGPvJ496/e+SDAsZ7Dw+zCgBkti5YtzeTyI7jxIoW6nf95GIH3haxPc9NeKu9x7qEeEgdukQuEqnnH4QGAe4StUOc2Yjyux5Pyjpt2ZwRNUgw7KoGtyxpeJ8Po94xTdFfOBPI975hojX/6sDPSEcRrOd7o5Vg/Z+eq4ODu48RsDip+d2TYqKtm450EPN8WHGCDAsuxdDXPp1HvOI13xXxHv/IOIDb4n42W851CPCQTRbqeuUk5HAOMBVutKDIvTddOfs4aX5bB7xU18f8X8/K+KDbz3A08FhXfmc2Yh45+8sf+P9f7LX54JDm1xxQqBLAvqvDg7unBB4qAeCI5nsOkaj+Cw2SJ9Ndx0ftpoQePtvR/zGD0VcuPMATweHNd1xlED9FfN5XAqI1971e/t9MDiwnd952zrl3HdHRPHP4f5FYBzgKu1aNVtzQUDfNedNdakcWX5/fMz7fiXiDf9PxNt/PeI/fvkhHg8Oqs4g3rWd7nweEePT5R/Opvt+NDioZsX4rpcE84i3/0bEv/28iFd/+0GeDw5lNptHHR/pFhjUSpo8msERyeAMR7XznNnl59Pp3REv+YKIW7454pUvOsTjwUE1RgnsMGd5HuGdl16bTK/m3DOP+LNfjPjuT4z4d38tYlbt/wG5agLjAFepWTXbfdam+zH6bvfgyNKD7nn78i/eLXuY/pnu0E43omwrFxHjk+UfeEmiZ3ZNijpXOfVrPxDx7jdF/NJ3R9z74f0/IBzI5CqCI1pJ03fTHWdtSgwhi8lVJAR+9N1/uvyD3755n48FR1EmRe3SMWE+n0eMirOSd156ZuduIcXn+Twi/uAnI6oLEe95U8R7/3Dvz8fVExgHuErNC+IdD4rQY+VB8WTH4Mj05AGHeCQ4mrpbSJd9P6JsKzdfCYzLnqdfyr2/Wzvpla+594OXfp3PIi7etc9Hg4PaNSGw5NhP313N+DCJIfTZdMe1X0ZHJuPrD/BEcDzTXcdoLO47Q8U4vTaprrxifB4RMbm3+Ifdt78HY28ExgGu0s4Hxcu/uh6g73afN7X8mmosME6/TS4HB7u8JEUUbeVWLwnmsufpl+muFePF5/k8mhcD0wv7ezA4sF2DI2aMk8nO5/7VbiHQU7vPmV2ajs4O8ERwPJNGMUT3Ox+t1Om7XRNix8XBZzabR0yLwHjlnff+SGAc4CrtfFAsgyPQY7smhTT+Xtnz9Fx1ee/vsu9HlElRc5cE9Npk13a6q1WD0yIwXl3c56PBQU2uIiHQuZ++23nObLn+D/JEcBzN4Ej3GcsREaP5bMNXwv1fnRB7djI63wWqRaNDpndeemy64wilWE2ILRPAJYPfLwmMA1ylnQ+Kl3/VSp2+a4wR2Lm10G6BdLi/qdd/l8BgxEpbuZEZ4/RXs2rwCirGpyrG6aerqRgXGqTvGi1Fd2inG+G9l34rzz3dKmaLz4KB9NyuyeB11ez5inHvvPTLZOfusCsJsWUrdcng90sC4wBXqb4k6zRvKmLxpuR6gL7bec5s+Vn7aHqu3vvPOu79i7Zy84go/x6XBPTM7rMGV6oGXRLQU82EQK2kGZbpjl3SSpY/fdaoGN8xKWQUzvn0W3326f7Oe8lsNo8Yl8ngkkTol+lVzRifSwbvAYFxgKtUt1XsXDVYf5hHxPvfEvGr/ybirvce5NngkMqD4smOlVPaytF30133/stfNtNWjp6b7NxOd2k+d0lAfzUTAndsJV3/re9/S8Qv/4uID79t348HB7V7pyjdochh13a65d4fs5V33sq5n36pzz5d33kbhUDlO6/CCHqmPPd3Sogt/2K1S1o12dtzsT+n278EgE3q1kJdLsgiihnjERE/+vciPvCnEW9+RcRXvuJATwiHsXvFeHlJ4FKAfltcEnSdMd5omVBkz7skoGca7XS7XJKtzlubmDFOP5UJgd26JSw/z+ua2f/7mRH3fijij/+/iOe/at+PCAdT7XjuL40+8KcRb/kvEZ/8JRE3PGnfjwYH1UwI3G3vH6+e8y/eGfHAR+zr0eDgFh0yu9531h/moWKcXptcxQil8++8ksHvj1SMA1ylZSv13SrG5/P5paB4RMRtrzvAk8Fh7TprMxoV4yuXBKoG6ZFqNl9U/3W/JChbqZu3Rn81Kqe6XBKsVs1OtVKnn66uW0JcqhS890OXfuP239jvw8GB7XruL9f/A1/x9RGv+Y5LiSHQM81W6rtdo59rpX7hzn08EhzNokta52TwohConCOjYyA9M90xGfz8O29xx+m+835JYBzgKi1m7nSuGL/063x12tp9d+zzseDgJrMdZ22Wn1cD4xfu2tNTweFN/v/s/XvQbdld1/9+5lzr2btzMcFIxGBCghyECMEQLoIghAABG0vlH6usUqGoIJaFORJLKapAoFKKqHUiwh+KrcTC+lX5K0X4HU9rLkDARMItBJJwi4QkYK4kMZ3u9H7WZc7zx1pzrTHmmvcx5prf0f1+Vel+9u61dzbJ6PGMMb63kVWDUm3v97LnCYwjLW5w8M56XOWUip0fDOeRAAlxk0IGPZDVK0ce+WD8vxRwJduA9b9+zy8evnj0I9Lmkdh/NWBW7t6/GtItxP26fs4nMI7EnCvGh915q39FirKUCtpHI13jR8icvz7MGCcZ3DoC4wAQaOzMnSqLLK9nTH74d6L+vYC5ednzgx7I3JNiQ1s5IBHuJenOelxbuZK2ckic3056XNXgRSCcRwIkZGxLRdUrRx5+X/y/FHAl1bl/lWeD5oe3fuIDvxnvLwVcwdg7b2eXNJLBkZhTIdDAbglel7Q991yka+uOkBmZFFWW8lupkwxuEoFxAAhUHRTHVg1mqgXGP0RgHGkJCY5cZs/zSIB0+O1Eh2bPu23lnP2fwDgS4z4SjJ21qd09/x/ySICEjG6pWK8c+RiBcaRr/J235XMfeFusvxJwFd4ImUFjNNzNv/bmQ8U4ErMdWTHudUmjYhwJ8879Q4ohnHNPKfn3XpLBTSIwDgCBzjPGx22p+UVg/H/F+isBV7ErxgUHs67s+Q2BcaRjO/KBTNIpM6QoSz97vv7vAmCcHxwc+UDstpSTpD2BcaRjNzYpxP1JKQLjSFq1/gePD2v7B+8nMI60bEcmxLp33rysJcDeMj4PaakSQ4bu/ZVDxTiBcaRr9N7vfF3ud35iCMngJhEYB4BA1UFxaAZlJatnDxMYR2L8B+JxM3cu1j/Z80jI6JaKci5KpfxLEi3mkJixHRP8ivF6K3UezJCO7dhOOfXKkYffP8PfCriOKilqbNXgBQLjSMz4hEDna+68SFhZlk7F+MCkKLdLGhXjSJjbLWTQ3u++dxb1Oy+BcYsIjANAgJCD4mUrdQLjSIv3QDyocsr5TL19NI8ESMhu9JzZ2iOB20qLSxISsw1JitrSSh3pGpsU5c8aLKWPvdf/QFG7CwCGbUd3STv/G1DcefL5l9//tmMpIZCG0d1CnIPP5Yxx7rxIx37kjGWplgxeTwCvj9MDDPPefEa+d64uxofRSt0iAuMAECDkoLi6qBj/HR4JkBTvgXhQ5ZTzNa3UkbDNyKQQyZm3Vm+lXg8UAsaNnbPsBkeyfb2VOo8ESMfoObPejHFJH6tVjFNJhYSc2+mOrxjP3GDIox+mewKSEjY+rBYY5M6LhIxNCpF6ZoxvH738DYBR25HFEN7eXy9+oBjCJALjABAg5KB4UTF++5D0yAdj/dWA2fkPxGNHCdSz53kkQDrctX9nYLeQvK2tXH3mMmDc2I4JVIzjsWL8rEGnlXop6eHajHESQ5CQ3alL2siqQeky+Zt26kjIbuwYDffremcQKsaRkM3IMQKSmwyuy5FJ9SpawDC/lfq4TlFZfa0zPswkAuMAEMANjI89KF7Mm5Jop46kbCfMWa4wbw0pG9tWSzpflIqy9C9G+w3tdJGUbcgjwb7+SEBgHOkImTV4qBivB8Z5JEM6qhFKw0fIOD+pJ8QSGEdCtmPb6boJgaongz8U668FzM4fITNw75ebDF7rmEDFOBLiv/mMO/fnF63UufNaRGAcAAL42cNDgyPVjPGGtul/8PYofy/gGtxRAqtBVYPuvLV6WzkC40jHdmTliFTLnq8/EpA9j4RU63+VZ96+3qa7YpyKWaRjbHDEU+ykhz/g/xoV40hIlRA+9s4rSboYIUYyONIxNinKGyFDxTgSNum90xsfRsU40uW++QwZneq9d9JKPQkExgEgwHZkBpl0Pijm9cx5Sfro78X4awFXsR15UfLbytFKHenyu4UMDY4cs+dpK4fEndrpTgiOXM4Y55EA6fAqp0aOEXjS/qOXVbNUjCMhp8D4yDuv1DBCiXMPErIbO0ajs2KcwDjSsZ3SIfP448X4MIm9H0nx3jsHrv9KXu+SRjK4SQTGASDA2Jkj0vmg2FgxTnsVJGRsa63OB7INgXGkY8olySusrT8S0FYOCanW/9gRMhLz1pA299w/rJ3u+TNP3X7o8gNUjCMhu9PeP65qUE13XvZ+JGR0K3Xn64tiCJLBkRCvYnxoMnjmJoPXW6kTGEc6xhZDeHdeKsaTQGAcAAL42cMDt9Tjd8tcDTNleSRAQkY/ELtVg8wYR8K2k+atHRzaytFKHemqHgnGJgRKDYFxEgKRkLGdoty1/5T9H1x+gHM/ElEUpar34aHdQiqrxjsvSSFIx74YP0KpcpEMzp0XCdlOeO/0vkNcVIyTDI50eK3UB537z6s/r99xqRg3icA4AATYTsigrD7VGBivHxwBw7YBLUUvZoyTPY+E7EbOm5KceWsSbeWQtFMr9cEV405S1EXFOIFxpGPs3u+ee56y+/DlBwgOIhHbYkKnHFXJ4A0V48Xu8tcAo9x20mO7hZAMjpS575131mO7hUhlPQGQinEkJGSMxorxYUkgMA4AAdzWKoNnbVYzxqkYR+LcivHVyLZyF48EGx4JkI6xVYMXeCRAwk6t1EdWDUpSxrw1JMw794+sHGlupc65H2nwxycNbad7+IE7L1K3G101eJZfJIM/FOlvBcxvSodMKsbxWOEmRa2GBMbdr+uBcO68JhEYB4AAk+bMHn9sbivHIwHSEZJBedlWjopxpMNNChneTvo4b60oqRhH0qrg4PCKcefrels5sueRkCmdoipP3TNjHOkKCY7QSh2pC7vz1pPBHz4OXwbs244cnSf5HROYMY6UVUlRN6vMX9ct3I/kzBhPAoFxAAjgZc8Prhg/fC5rbCtHYBzpcCunhs1ZdtvKMW8N6Rr7QCa53UIa2oeSPY+EVMHB4SNknHlrVIwjYbuAETJPLBqqBAkOIhHbKQmBWdVKvWl8GK3UkY7tyDtv1nXnLXYkxCIZfreQCSEkKsaRsNP4sMEJge6dtx4Y58xvEYFxAAiwmzRv7YCKcaSuyqDMsoGthboqxjdUjCMdm5BuIfW1L5E9j6RUjwRDAoNSLXueGeNI2NjKKX+ETENCLOd+JCKkYpxW6kjd3h0fFnrnlUgIRzLcMQKDk8Hdn9T3+nrnKMCw6tw/vFvC+WuSwdNAYBwAAniPBCNbKuaNFeNkzyMdmyo4MjIwKDU8Emw/LhUNDweAQe7ef2dwO+nDvwGrxopxAuNIx27sI4H7df2RgOx5JGT0nGU3ONJYNUtwEGmYMkbg3CmnKSmEvR/p2OymV81etFKXCIwjGZOSwd1vEfX3zS0V40jHbuR7p+siME4yuEkExgEggNdKemTlVHP2PI8ESMdp5s7IMQJSyyMBVeNIxG7CvLVKY8U4gXEkoixLbffTZ4xfVIyTPY+E+JVTY9vpEhxEusaPT+rpkkYyOBKyGzlKwDv3qKlivGG0BmCQnww+foTSZcU4d16k4zQ+bMJ7Z14/43PnNYnAOAAEmJQ9r6pqkLZySFv1SDY0OOLKmx7EyJ5HIrZet5BxwcF1U8U42fNIxN5LCJyQFEUrdSTMnTM7qJV6X8U4534kwk0KGdJKWnJnjDNGAGnbjTz3d84Yl6RbksGRhimjI71WUfXOONx5kZBtQIfMFRXjSSAwDgAB/JaK4w6KtFJH6qrEkEHtRFWbN9XYVo5HAqTBfSAeHhw8/LguaaWOdG0njJBxP8W8NaTM2/sHBUecr6kYR8K2Y8cIqGfGOGMEkJDtyDnL/oxxWqkjXd65f+SM8UzF5frnzouEVIkhg8893ozxWiB8v5Ga7gJYFIFxAAjgZVCOPCjSSh2pOwfGJ7TTbQoO0kodidgVEyrGT91CaKWOdG2LcYFBSV508PKRgOx5pGM38oHY65bAuR8Jc7uFDBkjIKk7GZyKcSRkOzYpyrvz0kod6dpNOPdX6/+m6c5LxTgSshs7Psz5+uLOK3HuN4jAOAAECGmn2xwY55EA6TgfFMdnUDY+EJM9j0RsduNmDUrn9b9qSgrZEhhHGqZ0ysm7WqlTMY6EbEfOWfYrxjn3I13bImB8WMbaR9qqhNg8GzZKoLeVOgmxSMS0TlGHz61JBkfiQmaMX7RSl6QdCeHWEBgHgAB+BuXYGeNNrdR5JEA6ThXjAytHvEeCoumixEERaZiUPX/8cdU0Y5xHAiRiN7KdqNTTSp3MeSTEW/+jZ4zTSh3pmpIUVa3/xmRY7rxIyHZk1aCrsWKcxBAkYmy3BMkZH0ZgHImrkqKGJ4M7XxdUjKeAwDgABHAzKIdkD0tOxXhj9jwzxpGOav1PaaebNV2UeCRDIsa205XOGcTMGEfKxlbMSvV5a/XA+C3z1pAMf9bmkIpxWqnjsSEkKWpFlzQkrgoO3plw7mlODOHNB2nwk6LGvXc2BsbpkoZElGV5GiMztFuCvIrxhsA4hUDmEBgHgADeQXFo1WxnK3UeyJCOqmp2eFutM7LnkbJJYzSqzzdVjPNIgESMrZiV/OBg87w19n6kYXSnKDc40pQAQkIsEuEmRY2tmmXGOFJXnX248+LxZuslRY3rEthcMc6McaRhO+Wt3/maGeNpIDAOAAF2IfPWaCuHhJVlObqtnDtzp3HWJtnzSMSUMRrVTWnFIwESNrZiVqpVjDd1R2h6OAAM2o1MiuqtGuSBDIlwk6JuxnZJ486LxJ3uvBPOPY0zxln/SMSumF4xfkMyOBI26a3f+VjjjHHO/eYQGAeAAGMfyCT3kYDKEaRr716SJsyZbXwkIHseiZiWPX/8fNPap60WEjElKaQ3e37HIwHSsB3ZTto79zBjHAmb1inn8G9A4523LKSi4TwEGFSdfQYnw7qdcpqSwXnzQSJ2E+68p89nJIMjXSHnHklaNc0Y583HHALjABDAeyAeGBys0EodKfNaC02ZM0v2PBLmrv8763EzxleN2fM8EiANfkLguHlrK+2Vlw3rn4pxJMKvnBpSMd7TKYeEQCRiUlLUqVNOw9qXWP9IRnXun3LnzZo6RXHnRSI27pvPelyXQGaMI2XbgE45Eq3UU0FgHAACTMoiyzqy57kkIRHbgDECEhXjSNuU7PlzxXhDYLCpvTRgUEi3hPvU8hhA9jwSUe39WSatqBjH44iXFBWjlbrEvRfJ2DJjHI9Tk8ZoVJ9vHB/GnRdpmJIM7n6KivE0EBgHgAAh3ywbs+dpq4VE7GapGGf9Iw3bYsLe3zVjnIpxJCJk1mBrYJwHYiTiVDU4Zc4snaKQMC8pauC5X12t1CX2fiRjN3rv72mlzp0XidgV00dHrpu6pBEYRyK8ivEJ752rxopxAuPWEBgHgAB+K/Vx3ywbK0fInEci/IPilBnjtBRFunZTLkrHfwOas+e5JCEN29344Ei19tsD46x/pKE690/rlNNUMc65B2nwgiMjK8ZppY7UVffem6Hjk9yvqRhHwvykqAgV47RSRyLGjk+ShswYJyHWGgLjABBgO2XW5lFzxfhGano4A4yZVjly1vhIQGIIEjGlpWilccb4jopxpMHtljB23tp9WVsrdR4JkIZq7x8bGJSoGEfadhPO/dXyp5U6UlaW5SlAMniEjNslrWn9s/aRCK9L4MiOCY0zxrnzIhH+6LyB7z1Ot4TmMRokg1tDYBwAAuwCqmZbs+eLhm+ggDH+JWncQVGS8qaLEqMEkIjNlAfirrZyZM8jESHBkfvU8hDMIwESsT1WjA+tHHHRKQopCxmj0RoYJzEECZi09r1uIVSMI11TKsZPn89aRufx5oME+EVw4+68d9vuvCSDm0NgHAACTGqvcnwlaKwckXgkQxLcMQJj2+lKVIwjbW5iyJ2RAZJ1ybw1pMt9JBg7b+1uWyt1RgkgEaeK8ZGBQYkRMkib3yln3Lm/fcY4wRHYN2ntu8ngjXde1j7SMOncX32+KRlcomocSfDGpg4+9zM+LDUExgEggFc1OPiR4KB93hpZZLBvs5seHJGknAdiJMxPDBl3UWpuK0dgHGkIeiRoa6XOuQeJ2O5HttN1EgIbg4OsfSRiM6VLWjVjPCMZHOny1v56/BN6Y8cE7rxIxLRz/+HHxjuvRKc0JGE7KSHwoLVinHO/OQTGASDApFbqVVu5jOx5pGvSJcn5mux5pGw7YcZ49anGR4Lto1LZ8j0BMGQ3Ye1XvEeCO092/lCy55GG3amVOjPG8fiyC6gabBwjILH+kQTvvWfkuUdqu/MSGEcadhPaSVe7/01bYJyKcSQg5K3fSwbPb5w/lHOPNQTGASDAlNZCOrWVI3se6ZqUQem8EDNvDSmrgiPrPPPWdZfOGeMqWf9IwnbKjPHqkcBtK3ffU89fExxBIs6t1McFBiUpq5KfVnfPv8i+j0RsvQficef+9i5pJMTCPnd03pQxGs0V46x9pGE7ITGkt2KchFgkIGTvb7/zsvatITAOAAH8B+KRbeVaHwl4JIN9IRmUEtnzSNt2N27OrOQ8EjStfYl26kiC+0gwvFtIw7y1u085f01gHImozv2DO4W4CYHVuX915/wB1j4SsZ0wQqbSOmOccz8SsNlNTwqRWpLB6ZKGRPjBwXFJgc3J4GL9IwnTxqYeVr/XJe0+585Lxbg5BMYBIEBIW7nWRwIeyZCAKd0SvMqppsQQLklIxPbUTnf4Ubq6KK3cR4LM+f2sfyRgN+WRoKmtnJs9T+UIElE9EE879xzPTXl+Do5z5kci/DvvyPFhJIMjYX5C4Pi9n2RwpGwbUAxxk7Ukg7P3IwEh5x7vzuslg3PntYbAOAAEmNZW7vCj90iQr89fExxBAqZUjnizNhtbqbP2kYbqojQqMN7UVm79hPPXPBIgAd4YjcEV4wc3blLIXWfGOMFBJKAsS+2Lcd1CGtvpZm5gnH0faZhy5634d15n1ibrHwnwEwJjtVJn7SMNk947jyd/785788Tz1ySGIAG7gHOPXzFOMrhlBMYBIMB2ytyR04xxp2J8fd/5ay5KSMCUbglu/vw5e97594ZLEhKxG9lOV3LbyrmPBE5gnKQoJGBXhCQEtpx7eCRAArxOOYO7JbjtdI+/P8ul1TE4yJkfifCSoka2FPXGh7l7P+d+JMBPCBy39iUpK52kqApnfiRi5+39ATPGvfdO1j/s204ZI5D1tFInGdwcAuMAEGDrzJy6M/KB2H8kuHv+mm+WSEBIWy3JyZ6/oWIW6dmObKcr6fQvgDdvjex5JGY75YGsKSHQ2/sJjMO+XcCMZclppU7FOBLknvvvrMed+zMvKcq987L+YZ+39ifceVdVcHDF2kd63ODgamRg/Kb1zktgHPbtprx3Hn/0OoW4a59kcHMIjANAAO+RbORB0Zux7F6UOCgiAdspc2adr08V42vWPtJzqhgfERzprRgnex4JCBkhQ3AEKZtSNSi5HROOe3/GjHGkZxdw7m+tGGf9IwF+UtT4O+9pfFi+Po/PIxkWiaj2/ptV5nXB6dLcSt3tksb6h327KZ1y+rqkce4xh8A4AARwH8kGZ1A2tpXjgRhp8VupT6icOgXGqRhHeqq9f1Qr9eNH73jZ87QURVp2E2aMV/zAuLP3kz2PBHiVIxPGaPgV41UrdR7IkIZJ48Mau6TdOX9NQiwSsJ1y7mmqGM9zKa/2ftY+0rA9jQ8b0yXt8MONFxinlTrSsp3QKercJa0lIZA7rzkExgEgQHVQvLPKB2dQ6vRI0JJFRnAECZiUPe/8O9JcMc7aRxq2p+z5EVWDxx/97HnayiEt20kzxhseCW7InkdadhMCg5Kz/t0Z4zkzxpGWaePDDms/a62cYv3DPr+VesCM8Xx9TorizotEVAmxYwohGu+8ayrGkZYphUDnLmkO7rymrZf+CwBAyqpvltNmDVIxjnRtvIPihLZyzBhHwnYTZoxXD8TrrCUwzvpHAiY9EtR+lET2PJLjjZCZkBR1OvfQSh0J8hNDaKWOx4+QdrqSkxSYraT8+Gdx5kciqoTYaXdet0uaGxgnGRz2hYyOzDj3JIPAOAAEOLcWGp9B2f5IwEUJ9nktRUdnUBbnuTvMGEdiyrLUvhifFFV98kY8EiBdk+bMNs5bcxMCCYzDPi8pZMIYjXMr9cxvpV6WfhQFMGg74dyvvr2fcw8S4CdFjUsIlJwuafnqvNez9pGIKYVA5zsvyeBIl5sQOPa9s3XGOMng5tBKHQACVBmUd9bjMyjbHwk4KMK+Sdnzx2sSSSFI2dYLjozZ+w8/+q3U6ZiAtEyaM3v60d373RnjZM/DvikjZKSGeYNuxbhKqdg3/0bAED8wPu7cn2d0SUO6pgVHnPFhclqpM0YDianuvWNmjPfeeUkMQQLcETLD13/fCBnuvNYQGAeAALspB8Xjj/4jAcFBpGUTUDHuBcaz1eGhQCIpBEnwgyNjsuePbeV4JEDCdlOCI30JgVSMIwHbCWMEJJ0O/ueK8fxcMS5x9kES/ITYSJVTPBAjAVOSQlx56SZFcedFWqr1HzxjnGRwJMZNBr8ZWAh3OvdkLXdeKsbNITAOAAFOB8X1+JaK/iPBnfPXHBSRgN2EGeMVLzCer5zseQKDsG+7m7j2j3t/a1s5HsmQgJDgiJc9zwMZEjOlU47UM2NcIjiIJFR33iyTVkP3/uOPfqcoWqkjLW5S1NBuIV4r9erc71WMs/aRht0+YMY4yeBImJcMPvLc49153XM/yeDmEBgHgADVRWlUO93jj63tpAmOIAFTqmYb22rlq3PlFGsfCdgWE+ZsOtZtM8Z5JEMCvOz5kQ/EzFtDyrZTu4VUCbGlWzFOQizS4t553TbRXc7J4HRJQ7omBUecj3kzxrnzIjHVuX/MCJlKe2Cc9Q/7dsWEpKisoZV6lkurY1Ig48PMITAOAAGqi9K4B7LarEGJeWtIzjZgxri39vP1uZU6ax8JCK0avMmcR4I1jwRIi/tAPPjsU517sraKcR4JYJ87a/DOhBnjra3UWf9IQJUQO2WETPudl7UP+7beuWfcnVdyiiHc8WHceZGIXUAr9dY7L8ngSMB2wp23MRncPfdTMW4OgXEACHDKnh/1QHbQPm+NixLscw+KdwaOEuifMc4lCfZNuSRJLR0TbtxuIax/2DctKarCvDWky68cmTJCyQmOEBhHYibdeZvGh61opY60bL3xYUMTAp0vS6eVulsxXpaXvw8wpChKVUefoeOTJJ3Wf2uXNJLBkQBvdOTQO2/TuSfLzvdeKsbNITAOABOVZXlqqziqtVBTcJBW6kiMVzU48qB4MWP8lEHJ2od9uwmtpKVz9chN9UjgttWSyJ5HEnYTRgnkp045ziOB1y2EwDjs27jtdCckxPoV47RSR1q2E6oGK60zxln7SIB/7hl355WcpKg8P88Yl6RiL8Ayf4TM+Duvnwz+xPPXJEUhASGjI9tnjBMYt4bAOABMtC/KU6Lv0HlT0pC2cjwSwL7thMqpau1fBMZPFeOsfdjnVYyP2fuPH72pHglWd6TV+vwB1j8SMKmlaNOcWWXOIwFrH/Z5lSOjqmarVupVxXhGxTiScw6MBySFSLUuaax92Od3yhnXTldygoP1biGc+2Gcu/ZHjZCp33ml2gglAuOwb0q3kPYZ41W3ENa+NQTGAWCiyVWDTQ/EtFJHYnYTKqdOFePuvCm3rRyXJCTADQzerMfv/acHsvzGrxxh70cCQh6IvU9nOWM0kBRv759QNZu3VowTHIR9VWLImDEC1cGHLmlI2ZRz/0VClOR3ypE498O83dTxYdXvaQuMs/cjAZM6ZB5/vEgG585rFoFxAJho8pzZ44/+vDXngYyDIhKwnVg5JTXNGHfmrQHG+fOmxncLOc1bW9UeyLgoIQEhSVF5PXv+1EqdvR/2bSc8kElDWqmz98O+U8X4hLXv7f1r1j7SMmnO7PHH1vFhEud+mOcnw06pGG+ZMc65Hwnw1v/oinH3F50xGqx9cwiMA8BEoa2FWrPn+WaJBExpJ92YPZyvz+2kWftIwG7qvLXqkSBzKsZ5IENiqm45eSatBu/9DZVTbjtp1j4S4CUEjugWovq5322pKFExjiRU6z9ulzTWPuzbBsyZXV1UjNMpCulw77xjOuU0zxh3K8Y598O+7ZRk8OOPef3Om68OX7P2zSEwDgATTW8t1DNjnG+WSMCUWZuNVYN57leMl+XlbwQMmZI97Do9EqxuaKmI5GxP7XTHBwZbK8Y59yABXreEUd1CJF3MGqSVOtJSBUim3HlXrXdezj2wb7ubMGe2ae1n+TkZXGL9w7ydd+edMD7MHZ9388Tz15z7kYApo1Obu6Q5yeDlnvdOYwiMA8BEm31Y1aDfVo7seaRl2qzNhuzhrN5Wbi/AsiktFaVza63zjPF1be3zQAb7dqd2uuNHyGS0UkfCplSOSIe9/yIpxAuMs/5hW1mWYRXjWVvFOMER2OdXzY4Ljvhd0lYkxCIpm6kJgadW6s76d5OiWPtIwJTRqecuafU7L91CrCIwDgAT7Sa2Uq+sspbseb5RIgHbYnwGcWNLxXpbOTKIYdzk4Ej1e04zxmsV46x9JKDKnh+XEFh1yqFiHOma2i0ky+otFfNacISEWNjmV02NT4pq75LGnRf2TZ2zLHHnRdqmdAg8qJLBj2v8Yu2z98O+KcUQ1XvnZWB8df45e78pBMYBYCJvzuyEDErvmyWPBEjMbkLFePOM8RVt5ZCUKdnDUkP1SH5Tyx7mkgT7qvU/JTjinXvEjHGkxd37xyTEZmpICqGVOhKymxgYpEsaHgum3Hkrfiv12p2XYggYF/XOuyIpBGnx3vtHvndenvtJDLGKwDgATLTZTZu5o755a1ySkIApj2RV1eCqK3ue9Q/jQiunzjPG1ySFIDnV3j8lOJLV561Vez/7PhKwm9ApRzqcfS7WPq3UkRCvne56fLeQ9jsvwRHYN6VTVHXu8e+8K6pmkZQpM5Ylt0tadeclGRzp8buFDHzzaUoIVFbb+xkdaQmBcQCYyM0guzMhg9JvK+dmz3NJgn2bgIrxVb2lKBnESIiXPT96xnipu1nVSv0OSSFITnX2mVI5ctlK/dhWjn0fCdjsplUNHirGO849VM3CuN3EObMVLzFkRZc0pMUfHzZuzuxFYHxFcBDp2O3DOmSeK8ZJBkd6TnfePDsl+vVpnzFOtxCrCIwDwER+a6HxGZStgXEOikjApINilT2f1SvGOSgiHf68tXHBEf+BrD5jnOxh2Fdlz4+rHKlmjLvBQaeVermXyrLhdwJ2uAmxo9Z/1tNKnXM/jNtNCAxKXVWzx7MPSSFIwC6kYjyrtVKnYhwJ2Ux+7zzOGM/cinHee5CWU5e0Ceeei8A4iSFmERgHgIm2+4mthU6PBC0zxskeRgImHRSbgiP17HkOijBuclJU5mTOS4d1zyUJianW/5TKEe93ZHntgZizD2ybeu6Xsp4Z4+z9sM3vljAlKcpd/05wkDsvErCb0E733CXNOffXq2bZ+2Gcu/andMi80XGPz29oJY3kVHfemzEdAo8/XiSDe8UQnH0sITAOABNNrxpsCA56rdTJnod9VQbxlKSQdf2RgJlTSMh24rw1KTs/EEgNa58HMth3Tooaf43M6u2kq1bqEusf5vlJUeMeiC/WPq3UkZDJc2abxoe5iSEkBCIB3viw9dCK8ZZW6lSMIyFup5wpHTJPbz6rNYUQSM52UsV41Urd/cWc907DCIwDwETT58wefvQD48xbQ1p2E9rpVvwHshVVs0iK31JxXHDkomI8Z+0jHWVZant8JBtXOdJQNaiMRzIkxT333xn5QNxdMU5gHLZtp557jj/6naKclqKsfSTAK4YY+OZzrhhnxjjS5XbKmRIcvDnNGKeVOtJTvfmM7RAoSXlWHx/Gm49VJgPj/+E//Ad9y7d8iz7/8z9fd+/eVZZleuUrX9n6+Yceekgve9nL9OxnP1t3797Vc57zHP39v//39fDDDzd+vigK/eAP/qCe97zn6QlPeIKe/vSn66/+1b+qd7zjHTP9XwTgsch7JFhPeSRwHslWtFJHWnYB7XTXXdnzXJRgnN9ScVxw5MbrlnBTCwzSVg627YvyNAp8XDvdg4vgoJcYwvqHbbvJD8RNgXHOPUjH1BEyldVFtxBaqSMdftXswFbqp9F59RnjBEeQjilJIa5zxXg9GZy9H/ZVXQLHJsMefqwlg7P+zTIZGP/O7/xO/fAP/7De9a536RnPeEbnZx955BF9+Zd/uV7xilfoMz/zM/Vt3/Zt+ozP+Az983/+z/WiF71I9+7du/g93/It36KXvvSlKstSL33pS/W1X/u1+rEf+zF9wRd8gd7+9rfP9X8WgMcYr63cqIrxhtZaaypHkJbqoDilYrwze55HAhi3nfBAdvq8VzG+JnseSQltp5tdBMYJDiIdXjvdkXOWuwPjnPthmx8cGZ8QezFjvFr/nPmRgM2UGeNtrdRJikJCpiSFuNanGeP1VuoEBmHfacb4yGRYiTtvSkwGxh944AG9853v1Ac/+EH9rb/1tzo/+0//6T/Vm9/8Zn37t3+7XvWqV+mf/JN/ole96lX69m//dv3iL/6iXvGKV3if/+mf/mk98MAD+rIv+zK96U1v0vd///frR3/0R/XjP/7j+vCHP6xv/dZvnfP/NACPIVNnDVb8tnJOgIRHAiRg2kGxeiRwgoNZvWKcixJs8x6Ix7ZSz9wZ4/WKcfZ+2LaZOmP5mD+fZ/XgIJVTSMfUyqnLGeMZrdSRlG1AUojUNGO8qhhn7cO+nXPnre6yQ3l33nxdmzHOnRe2bXYT9/5MkkrdyVoqxgkMIgHTRkc2jA+rJ8TSJc0Uk4Hxr/qqr9Kzn/3s3s+VZakHHnhAT37yk/Vd3/Vd3j/7ru/6Lj35yU/WAw884P36v/k3/0aS9PKXv1x37pwvpH/+z/95vfCFL9SrX/1qvfvd747wfwWAx7rtPqxy6rKlKG3lkI7qoDhq5s7xx1U9KYTgCBIS8kB80zVjnL0fxrmBwVFt5Rqz52krh7SEjFDqnjHOuQe2+XNmA++87gglzj1IwOnOO7KVdJY1tFKnYhwJ8TtFjUuIvXjvcc/9vPcgAZuQGeMXd97V+eesf1NMBsaHevvb3673vOc9+pIv+RI96UlP8v7Zk570JH3Jl3yJ3vGOd+j3fu/3Tr/+ute97vTP6r7ma75GkvQzP/Mz8/7FATwm+MGR8ZVTq6x+UTo+kvGNEgnYBswYv2grR2shJGQ7dcZ4Jt3IrRincgRpmdopp3HeWpbRLQRJmb73N7RSp3IKCXHb6d6Z0CXNv/PmtFJHUqoRSmM7BGaqV4zXZ4xz7oFtO/fcP/LO640Pq877p8A4FbOwr7r3jjn3NN95aaVu2br/I3ZV88A//dM/vfGff/qnf7pe9apX6e1vf7ue9axn6ZFHHtF73/teffZnf7ZWq1Xj590/t83t7a1ub29PP3/ooYckSdvtVtstCzyW6r9L/juFVbeb89rMVQ5eq2V5+AbrfrPc7gutV2tlksrdRjvWPWYWusdWGcTrPBv8Z+x2hwcANzB+eGfOVX1X3m3uqWT9w7DN9vyQlZX7EXt/6a995SoKqbomFfuN9qz9x5TH2ln24/fObW/X2fC9f78/PIDltXPPSvkpS3u7uSc9Rv57wmPTdnd+yB2z90ul10q6kLQvs/Pev7tl75/osbbHWvXo7fm/32zEnbcomu+8q3ytXFK5584L+6p20jer4eeeyspZ+3tlKpWfHuH323sqElj/7LOPX/fcO6+K4XfeovCSwYt8pf12q3V+fO9k74dx+6JUedy+VyPeO6s7r3vu2e2LYxeF48+3t957J3tsfGP+u0w6MP7Rj35UkvTUpz618Z8/5SlP8T439vNtvu/7vk/f+73fe/Hrr371q/XEJz5xwN8cY7zmNa9Z+q8ANPrV9x7ygCXprb/2q7rznjcP+n2/+85ch0Dg+ZHswf/+Kn3NZq/7JD36yEN6zYMPRv/7Ak2m7LFFKe2LwxHi4Yc+qgcHrtePbiRprVV2flx+y9t+Q3d2H9OfOv78l3/xjXrfbzNzEHb9r9897OGS9Atv/Dm9/23Dft/73pd7l6R3/97v6W2v/Un9hePPP/SB9+l/svc/Jj1WzrIfeFSqro8feN979OCDvz/o9/3qhw7nJTc4+NM//Tr9yfe/V885/vz1P/PTeuiJvxPzrwtE9d4PnPf+n3rta3T3Ms++0aOPrvTE7Lz3v+e979Nvvf4N+srjz3//3e/Sr7D3B3ms7LFW/dqHz3fe3/lfv60HH/2tQb/vN453ZffO+7qf+Vl93kMP62mSsrLQg/+//3qopgKMeviRlaRM+81m8J1XklSulOfntf8773inPvaejT7v+PO3veVX9bvvS2fvZ599/Hnre857/1t+9c1a/f6vDPp9v/vO3KsY/+CHPqI3Pvig/vy+1B1Jj3zso/pJzj0wbLOXqjvvQ//nw4P3/nc9fPh9bjL4z73xjfrEh39Xzz3+/Jd+/uf0/t989OL3ssfG8/GPf3zwZ5MOjC/lO77jO/Syl73s9POHHnpIz3rWs/TiF7/4FFxHuO12q9e85jX66q/+at3c3PT/BuDK/vfrf1d656HDxOd/3gv0tZ/1SYN+36/+t9/S6977Lu+B+P77v07rd3yn9ND/0RPurHX//ffP8ncGKiF77O2ukN74WknS0z/xabr//i8Y9Ps+8LFb/cNf/hnvgeyzP+f5yh79kPTew88/73P/tMrPZP3Drjf+P78uve8QEPyyP/el+qxPHnb2e9XHflXv/vA7Tz//lGd/qp751V8n/erh53/kDz+Vvf8x5rF2ln37+x+W3vw/JUnP/pRn6f77P2vQ77v59Q/o3/32m+U2ovuKF32l8p/7delDPy1J+nN/9s+o/OTPjf1XBqL5D+/9RemjH5Ekfd2f/1rdWQ8L5v2z3/wfyjfnB7JP/uPP1Cf9ua+UfuPw82d+8ifpGez9kzzW9liz3vI+6bd+TZL0vD/1XN3/Jc8Z9Ns++HPv0n9552955/4Xvugrtfp//rP0yP+SJN3/NV8tre9G/ysDsbz8La+TNhs9+UlP0P33f9ng3/f3fv41XnDw0z79T6p8+mdK7/rXkqTP+sw/qef+Gft7P/vs49e7fuYd0rsOe/UXfv7n6aue+0cH/b5f+++/pbe899dOP3/6Jz1D999/v9a//STpkUf0pPvucOeFaR+7t5N+4ackSX/sjz5d99//eT2/4+At//uj+v+85ee9t/4v/rNfouzdufTeH5Mkff4Lnq/yM87rnz02vqqz9xBJB8aryu+2Cu/qv4jqc2M/3+bu3bu6e/fy8H5zc8MingH/vcKqUucHsSfcGb5OV6vD7zt/s8x0c+eOtD7MGM+KLWseVzNlj904M9HurPPBv//m5vA44D6QrW/uSLv7zj9XIbH+YZgzZlZPuHtn8PrP89zLHl6tVlrdPXcaysu9ctb+Y9Jj5SxbOvMF796sBv/ftF4fqk0yZ++/uTmfe6TDWA72flhWjZCRpCfed0dZNmzmYJ5l3gNZnq+V333C+efs/cEeK3usVWXm7v3r4Xv/qm3vP7+l3eQlez9M2x4P/nfWw8890mHO8qo8r/3V+o50c77zrlRoldDaZ599/CmclNb77g7/33+9Wnld0vLVzeGcc5wxnhV71hJscxJax+z9N+vD59wbwnpdu/Nmzece9th4xvz3mHTPor6Z4PUZ5E960pP0jGc8Q7/7u7976vvf9XkA6FLNm5Kk9WrY45ik00PaKTiYH3sx5sfNe79r+F2AHdu988C1Gn6UyFRb+9LhgpQ7B5eC9Q/bdk5k/Gbk3u8+EijLD69m2fF7wJ65UrBt6639EXv/8dyTe+s/k1ZOjnbB+odt1d6/zrPBQXHpsNTz+t6fu2ufcw9sc8/96wl7/8pb/yv/3M/ZB8btjut/nQ/f96XDvddNilK2klbunZe1D9u8O28+InyU+TOWT6pzP2sfxvnvnePO/JJqe39e2/s591uSfGD8kz/5k/WGN7xBjzzyiPfPHnnkEb3hDW/Qp37qp+pZz3rW6de//Mu//PTP6l71qldJkr7sy4a3xwHw+LUrpgYHD06PZFUWfvXNkoMijHODI+sRl6TqoLi6OCg6D8Q8kMG4bTExOKhacKT6bnDa+7kkwbbJjwTHHy+DgzwSIB3V+h+TDCsd1v9FUpQXGGTtwzb33H9nVGD88KP3QJzXg4Osf9hWnfvHJIVIkjJ5rdSV15NCWPuwbVtMLARSQzK45BQC8d4D26YmBFb89S8SYg1LOjCeZZle8pKX6OGHH9bLX/5y75+9/OUv18MPP6xv/uZv9n79b/7NvylJ+q7v+i5tNpvTr/+3//bf9LrXvU4vfvGL9exnP3v+vzyA5O0mVk6p/khQVQtW3yw5KMI4PylkfHBk5T0S1CvGWf+wbbef2i2kITgisfcjGZO7hRz/NbkMDpIUhXRU63/UmV+HN4vubgk8kMG23eTgyEFn5RR7P4zbnfb+8UlR3t6fr+iUg6RM75LW0CVKIhkcyQhNCOTOmw6TM8YfeOABvf71r5ckveUtbzn92ute9zpJ0pd+6ZfqJS95iSTpH/yDf6Cf+Imf0Pd///frV37lV/SCF7xAb3rTm/TqV79aX/AFX6C/+3f/rvdnf8VXfIVe8pKX6IEHHtALXvACfd3XfZ3e+9736j/+x/+opz3tafrBH/zBq/3fCSBtm6nBEdVailat1FfHuSPlXioKaUy7IuCK3EvStJaKHZUjZM/DuMkdE1S/JFVZUlVbOdY+bNtO3vuPP9YfCagaREKq9T86MK6GwGBOcATpcMeHjUsGbxqjUe+YsBFg1b4oVTWKGp8UVa8YryWDExyBcV4yeNCdt5YMzpkfxu0md0lrOfcwRsMsk4Hx17/+9fr3//7fe7/2hje8wWt/XgXGn/SkJ+lnfuZn9D3f8z36z//5P+unf/qn9YxnPEN/7+/9PX33d3+3nvCEJ1z8+f/6X/9rPe95z9MP//AP6wd+4Af05Cc/WV//9V+vf/SP/pE+7dM+bd7/4wA8ZuwCs8hOwcF6K3Xp8M0yvxv6VwRmsQlsp7uqz1vjgRgJmdwxoT5jvMIYDSRi6wRH7oQ+Eig7JwZKPJLBvKlVg2qcMU5CINKxKyZWDR5/vByhRFIU0uC1040+Y5y1D9s2EztkZpmUZw3jw+iShkRsJrZSP4+Qqd953ffOvWCHycD4K1/5Sr3yla8c/PmnPvWpesUrXqFXvOIVgz6f57le+tKX6qUvfenEvyEA1OeOjH8kyOozxuvtVdYExmGT11ZrQmcDv2J8zSMBkjJ15tRFS8WLeWusfdjmt9MdP0LmomMClVNISPVAPKZqSmrZ+5k1iIRMrRqsZPV20rQURSL8pJDxFeMXXdJY+0hIWNVsw523evMp91JZnqOIgDG0Un/8oE8vAEw0uZ1ua8X4nfOHaCsHwyYnhVRrP3PbytUrpzgowrbtxHlrys6JUYef11ups/Zh2+TKkeOP3W3lCA7Ctiox5M56/Izx7KKVutstgb0ftnl7/4j1f3HnlY57P3depGG7mxYYlA5nn7V3561XjLP3wzY3MWRs1WzjjHHefJCI3cRuIVWXtO7xYax9SwiMA8BEbuXUqCwy1eYsn2aM80CMNGz302YNXqx9iYpxJMdb/6PmrWXKsqbseeatIQ3uI8GoVuqnObNucKTeVo71D9uqAMn4droNSSFuxwTWPozzqgYnPBD765920kjHdmqnHB3OPhdrnzEaSEjI+LzmGeOMUEIavLU/ISHwIjGEO69ZBMYBYKLQqtlTgCQ7HhBpr4JETJ01WJUNrrtmjLP2Ydzu1E43Uz7mgTirPRKolj3PAxmMmzpGoPFfE9rKITHb49lnSjvd5jEa1axN9n7Y5p37pzwQZ/WOCez9SMNuapcoHU75F63UqRpEQvyE2DEjlLLLGcsS6x/J2E3sklbdeS9bqZMUZRWBcQCYyGulPmHG+GUrdQ6KSMPkGcunDMquinHWPmyr1v/o4Ihasuer9c/ah3HTW6k3VYwzZxlpOe/9Y4Mj2eXal9j7kYzNbmpL0YP8IjhIK3WkYTdxdF5lJbeV+pqkECTFf+8ce+9tSggkOIg0bCd2yqlOPnm9SyDdEsxa938EANAkJINSch4JTq3U3UcCLkqwy8ugnPBAtlZt3lrp/PvDJQnGVa21xiREVZrnrR2/B7Dvw7jd1JaKTW3llNFOF8nYF6XK4/Id3063/kBc2/tZ+zDOHR82KimqdcY4CbFIw2bi+DBJh2NOvUsaax8J2UZrpU7FONKyDWylnrl7/8Wdl7VvCYFxAJgoJINScgLjpwcyMoiRhskzxpvmzLrZkxIHRZhXrf9RCVFqCo7UsufLvVSW5+8JgDGT9/7aj4ef0E4X6Zj6OFxpbqXOGA2kYbsL7RZSnn7lMFuAqkGkwU8KmdJKvZYMztpHQqa/+bSND6NqFmnw3vonFAL5d976uYc7ryW0UgeAiUIyKCXnkaCaMU4WGRIxNSmksWI8qz8SsPZhW9UxYXwr9SHz1ngkgF3bia3UT+9hXXNmWfswbOrjsHRICmwMjNNKHYnYFu4IpRHBwfoIpabxYbRSh2G7iaPzpMPefzlj3D33sPfDtqnn/os776kQiDcfpME9998ZVTE+ZHzYXrCDwDgATOTPHQloK3d6JKCVOtIwNXueGeN4LNhObKXeXjFO1SzS4J57xqz/qmrwYv2z9yMRk5NCdIgNXiSFSLRSRzL8EUrjE2JXF+PD2PuRhpBW6lkmrbxzD8ngSEt17s8zaTWmarZthAzJ4EjEbnJSyMFFYghJUWYRGAeAiXbF4Ztdnkn5qPYqLTPGCY4gEbvJrYUOn/UeCWgrh8RsdrFaqTeM0eCiBMO87PkJc2YvHgncvZ/seRi2c5NCRpx7pAFjNDjzwzh/1uaY4EitlXp97Uuc+2Ha1OCI1NRKvZ4MztqHbdWdd1JCIMngSNhm4rm/+c6b1+687P2WEBgHgIm2U9vp1r9Z0kodifGy50e1Fjr8uM5qrdTJoERCqqSosXv/ITWkL3ue4CDsmp4UVf1YfyRwZg3yQAbDpp57pGMOSGcrdfZ92ObP2pxSOVV1SauSwZkzizSEJUU1tFKnYhwJqe68wcngakoGZ++HXZNbqZ9OPvU7r5sUwtq3hMA4AExUfbOckkEpNbRSJ4sMiZjaUrGS80iAhIW0Um+cMc76RyKmJ0XVqgYPv0hCIJLhn3tG7v3KLmcNSudHMtY+jIvWLYRW6kjMtnBnjAeO0chXUp6fvwew9mHc9DvvOTx4+IVaQqDE+odpu6kJgW0V46x9swiMA8BEu1NgfHxwRHIfCZpaCxEYh13ujPFRc2arinGvrdyKtnJIRlmW07uFqKWdLh0TkAj3kWBKcCQ7BQepHEFatoFzZjtbipIQBeNCz/3ninESApGW7c5NChn/5uPdeU8dE6oxGpx7YNt2Yit1yT3zq2XvZ/3DLi8ZfOTeLzUUQ9ApxywC4wAwURUcGZ09fKqcqlWMExxBIja76Q/EUr1ifM28KSRjV0wLDEpNwZGG4CDrH4a5wcFRwZHaj8yZRWq8VtKxkqKqvb/cS2V58fsAK/z1P2bvr995q8Cge+dllADs8pNCxo9QWrl7f71jAu89MG47cXzYoZW6+wu8dyItXqeoCaMjL8eHkRBoFYFxAJjo1Ep9ZEvFSvcjAQ/EsMsNDo7JoKweyNZeBnG9YpyDIuzyqgbX49vpXsybkmpjNHgghl2biVWzl1WDPJAhLX4r6ZHn/qzeSv34++mWg0R4Z58JI5Quxoex9yMRXlLI6Bnj0srrkrb2fyQ4AuOqvX/MjGWpYYQMnaKQmKnnnubxYfVW6qx9SwiMA8BEVXBwTAaZVL2HlVplx2+WjZVTXJRg186tGpw0c6deMU7VINKw3U2bNyV1zBjngRiJ8Ocsj1n/tUeCpm4JPBLAsLCqwZ5W6hLnfphW7f2rPFM+Ijh42uqrO29eayUtsfZhmpcUNTo46CSFSOdiCCrGkYiqlfqUpJDGinHefJCI7cRW6ucuabWEWO68ZhEYB4CJJh8UlfkPZPW2WhLfLGHa9JaKx99TnzGeExhEGqZWzEpd7XR5IEYapnZMOLeVq36BBzKkZbNzO+WM3/t7A+Oc+2HYqUvahDmbUkO3EFqpIxG7fWhCrJsMXusSyLkHxlVvPtPOPU2dcnjzQRqmjlC6uPNKx1bqJMNaRWAcACbaFtUjQeAl6dRKfXX+NR7IYJjfUnR8a6FV/ZHAXfscFGGYWzV4Z2wr9SzrnzHOIwEMcx8JRrVSP/3YFRzh3AO73L1/bHDw8txftZMmIRZpOI8PG3vnrXcLqZLBOfcgDVuvW8j4xJCLZHDpnBTI2odhZVme3zsndch0f4FkcKRl8nvnqUtaLTGEM79ZBMYBYKKQDMpV0wMZB0Ukwq8aHB8cuWgrl2U8EiAJIa3UpVrF+KmVOjPGkYZ489aoHEFatoHdQqgYR8qq8WFjA4MX5/6mOy9rH4ZVHQKlccERqWHOcj0xhPceGLYvSpXHo8vNpA6ZxeU/4NyDRHgJsRO6pF2Mz2Ptm0VgHAAmKMtS+6mPBFktOJJTOYK0TK4arGIhWW3GuHRe/7SVg2FBrdTrez9zZpGYya3Ujz+eqwZJCERapo6QkapuIU0Jsez9SEMVHJxy7pGcs0/O2kdapt55pcP6X6vhzntKBufOC7tC137jjHHeO5GIzcRiiHOXtNqbD+ceswiMA8AEQQdFZS0V42SRIQ1+5dSYDMqqlTpt5ZCmoFbqymqXpKaKcdY/7NoFJkVlF4Fx5swiDVNbKkqHR7LGpCj2fiRiW0ztklYboURwBInZhozRUD0ZvKoYr5LB2fdhl7f2R7ZSl+qdchrGh7H+Ydjkc3/9zisdzj5Zdu4awrnHFALjADDB1MBg5aKVtMRBEckIeSCWWtY/beWQgJBW6q3Z897ez0UJdrkdE9Yj2ipWwZFzYJykEKTFTQoZs/alasZ4Xyt1EkNg1+6490/pkiY5szZPd97V+UM8EMMw99w/fs5yT7cQzj0wzB8jML5TTpaREIh0ucUQY84+5xnjHcUQnHtMITAOABP4D2SR2ulyUEQiQltrnR8JMqetYnVQ5HEYdgW1Upda5q1ROYU0VI8EN6vs1AFkiMvgSEPlCGsfhnl7/9jgyMWc2Wr9M0oAaajO/VPOPRJjNJCu0GTwtdclrTY+rCykouFeABiwDXjvlGrvneLcj7S4rdSDuqRJl2cfzj2mEBgHgAn8tlqBFbN5Q8U4wUEY5lUNTmgrd5q35laMrGilDvt2E2csS13z1ty9n/UPu6rKqSkPZJKz/pmxjMR4YwTGrv9Myhsrp3ggRhqq4OCUbgmSc++tt5KWOPfAtO3ETjlSPRlcl+PDJNY/zNoGJQT2BAYluqTBNG983pjA+PHHxnN/Tit1iwiMA8AEIa3UD2213G+UtFJHWvzWWuPbyp0fyJw1n9NKHfa52fNTKkfyjKpBpKs6+4w/9xx+zJkzi0RtQ5Ki1PZATFIU0lCt/zuj58xWLUVrez/J4EhEULeQTFq5e/+pYpw3H9gX9t7Z1kqacw/SsJ1YCFR1VGvsmEAhkEkExgFgAq+Vemg73cZZmzwQw65tQDtpSVpVbeWypopx1j7s8itHxieF+L/Q9EDM+oddVbecscGR07y1U/Y8LRWRlrC9v/ZA3FQ1SOUUjCqKUsVx+U6tGO8MjBMYhGG7gITYTNn5zis5xRC8+cC+bUCnnIuEQJEMjrRsp7ZSP/7ov/czOtIyAuMAMMEmOIOyr5U6B0XYtSucg+KE1lrNFePM3IF9m8Cqwbwxe5jgINIwtZX6xTjy6oEgy84Pxez9MMx7IJ4QHGl+IHOSA9n7YZQ7PmxKMrjknH1opY7EhCSDHyrGG958KIZAAoI65Vx0yKRbCNLij04dUzF+/NH7xdr6585rCoFxAJggZNagFxiUmLmD5Gx2YYkh58C48+9OFRzkgQyGhVSOKBsyb431D7uqeWvjH8gOP15UDUp0C0ESdlFbijaNEmDvh00hI2SqTjnrjFbqSFNQMYSktVsx3lQMwbkfRm0Ck0IyWqkjYVViyDrPLrv+dcjqI2Sky/XP2jeFwDgATBCcQenNma0uSVSOIA3e+h+dGNI2Y9wJjpTl5W8EDPDb6Y59IKtnzzeN0eCiBLuqpKgp+77kBAfdB4bq+wDnHhgWWjVIUhRStZs4Z1OqKqbctd80RoC1D7uCuoVk9W4hdExAOsLGCHDuQdqq9T96bOQpGbzhzed05yUh0BIC4wAwQdRZg1SOIDHVI8Eqz5SPDA4qk1ZZx4xxiQAJzPJbqccKjriVI6x92LWd+EiQ1R8J3Ipx2sohAVtnhMz44GBbK3WqZmFfSGBQaumSxggZJGK7m54UJUkr99xf7fkEB5GAbUBSlLKsZcY4FeNIQ/XmM/rMf/p4052X0ZEWERgHgAm8GcsTsucb503RSh2JqC5KY9e+1DVjnEcy2LcLmjOrAY8ErH3YNbmV+ulHKsaRJjc4Mr6ddFtCLA/EsG8bc4xAUytp1j4MC1r/cpLBpZYZ4yRFwaagVupqa6XO2kcaqjefKd0SpJZkcFqpm0RgHAAmcB/I1qPbq9QyKLOmRwIeiGHXOTA+/hjROmOceWtIQOgDcWPFON1CkICyLE+Vg1M65UhuYJwZ40jLzqsYH3/2aW6pSKcc2OcmBE7rktbULYFkcKTB7RYytlOUsnrHhKbxeZz7YVNQMvhFQmBDMjjvPTBsO7livDY+TC3J4IyONIPAOABM4F6SpmSR+ZekKoOS7HmkYTsxg1KqzRinlToSEzRntm3GOA/ESMA2YNZg9ShwDpC4jwS0lYN9m6CkqExZXyt11j+MCqsaHDJGgHMP7ArqFiL3zSc7J4TTSh0JCOuWUC8EIhkcaZlaCHTZJa2hlbpExwRDCIwDwATeJWnCnNnmtnJckpCGqRmUUlUxfjwI0kodiQmZtTloxjiPBDCqaqMuTZ+3dvpd3iPB8QzEvg/DdoEtRZtbqZMQCPvcvX/0CKWLitnq3JOfv+bcA8OCEmKzzLnztiWDExyBTWFrf8D4MJLBYdjUQqBT3UNjYJz3TosIjAPABO5BcZ2Pz6BsfiRwv1FySYJdQa3U5bZSdx4JOCgiAe7eP6VyxAuOVFasfdi33QUkhRx/PFUO0kodiYnbUrQ697vtdFn/sCl0729MBpecbiGsfdhVnfvzTFqNfvORVqfgSNudl8QQ2LTZhSUEev+2NI3R4NwDw6YWAuXHyPi5Ytz5/XSINYnAOABMENJWTqrPWztelGgthERUF6VJrdSzrKVqkJaisM9LihpdOdXSVo5uIUjAtph+7ul8JMgJjMO+TVBCbFtgnL0f9gW1Us9aWqlL/qxNwKjNMSlqPenOO6BLGns/jNqFjI7M6u+djI5EWqqE2Clv/dKAVurs/WYQGAeACbx2upNaqTsHxVMrdS5JSEN1UZpaMd44Z5aWokhA2KzNeiv16pGAtQ/7gmYNDmmlzrkHhrkV4+NHKLXMGGfvRwK8vX8dmhTitpOuAuPs/bCrGqMxKRnc7RLY2kqd9Q+bQpLBDzPG3V8gIRDpKMvy9OYzNilqeCt1OsRaQWAcACbw2+lOaavV10qdBzLYdWqlPvKBTJKUucERt2qQ9Q/7QtvpNs9bo50u7Atrp3tY692t1LdS2TBqADAgbIRSW8U45x7YFzRCpm3GuOS0Uic4ArvO48PG33m9YgiCI0hMSCt11QuBmmaMc+6BUX63hPFJIVJbIRAdEywiMA4AE2yD2srVqwapHEE6yrI8dUyY2lpIje106ZgA+4KqZpXRThfJclupj64cqWfPq2XvL91HNMCObRHaKaonMM7eD6OC7ryqjZDJm1qpExiEXaF33t6qQfZ+GOUGB6PNGOe9EwkILYRo/YWc9W8RgXEAmCAkg9JrqyVROYKkeGME8ilt5QYER1j/MCpuUlRTK3UeyGBTSNVgpfmBmMQQ2Ld1z/2jzz6Z8qxh1ibnHiRgE9ItpKti3O0WAhi1OVWMT5kx7iSGNK19ifUPs9xzz52xYzQuRsg0nHs488OojTdGYHxSiDSgWwjr3wwC4wAwQUgGpTIpz9zs+WMb3Sw7z17jGyWMCpk1KA19JOCBGDZtQzKI1ZI97wUGWfuwKU72fEO3ENrKIQG7IqBbCJ2ikLCwTjnSyksKcUbHVA/E3HlhWLX+74zsFCLVksEZH4bE+CNkpq3/80849yAdu5CxqVnVSr3pzsv6t4jAOABMEJRBqdrMncbseb5RwqaQ4IhUaynKIwESE/RAnMnPnmfGOBLiZ8+HPhK0zdpk/cOmjXP2Wc0xY5zgIIxyk0JGBwfr556mvZ9W6jCsevNZj9z3peO5P2vqkkZCLOzbBCaD+zPGjzj3IAFuIcTYpJBzxXjTnZc3H4sIjAPABGHtdOut1Buy5/lGCaM2AWtfOl6UGh8JuCjBvtC9vz97nrUPm7yEwOC2cjwQIy1V9cidVX5K9BjqkBDICCWkaRvSSr0+Psx9FKaVOhKwLcJmjJ94hRB0yoF9XtXs6FbqbV3SOPfAPr9D5rQuaaekKMaHmUdgHAAmCM+gbGilLtFWDuaFzpltvCRJtBZCEkJaqUv14EhVMU5gEPa5I2TGV4wffzz9AtnzSEt19hm79qVDcLCxYpykKCQgNCHWX/skgyMdZVme9v6xwRGplhRFlzQkJqSVepZJedZw580yZ+/n3AObwjoEHj7P6Mh0EBgHgAnCqgaHtFLnoAibtgHtdKX6AxmPBEjLLIkhBEeQgJDgyKFXQnnuFtL6SMD6h03VGJmpI2QaZ4y7SVG0k4ZRoSNkWu+8JIPDuH1Rqjxu3WPnzEqHs09zQiBVg7AvrBCo/u9LQ6coksFhlFcIMTIppHJ+8+S90zoC4wAwQUhwZNAjAQ9kMCokKURy26hLHBSRmpDEkIvseWaMIyG7kAeyemCwddYmD8SwqUoMGRsYlKpzf1Ng3Nn7WfswKujOq3qXtKakqJJ7L0wK7RLlv/k43zuoGkQCQlupN557JLqFwDy/lfq0c39jxTijI00iMA4AE4R8s7yYt9aUQcw3Shi1CZg1KNUyiDkoIjGhszb9ivGmVuqsfdgUVDWojjEaXlIUwRHYVCWGjG0nKlWt1BvO/XRLQAJCgoNZ1nXnJSEWtm28ZNhpXaLOFeNuQiAJsbAvtBgia+sSuKKVOmwLGSMg1ZICaaVuHoFxAJjADQ5OmbmTtc0Y56AI43aFWzkyoZV607wpiYMikrAtwlqKZk0zxln7SEDQA1nnCBk3OMLZBzad58yOP/eotWKcwCDs2+zcZPApXdJ6ZoxLJAXCJK9idsKdV1nmVA3SKQdpcZOixiaGZFnWUTFOK3XY5q79OyPPPdJh/Z/efNxvHZz7TSIwDgAThM6Z7c2ep2oKRgW3Und/QuUIElOt/3WeKcsiVc3yOIwEBD2Qqf5A1vJAzN4Po06B8YmVI42VU15whLUPm0K7hbQnRdExAbYFt1KXu/eTDI60bII7RbWMUKrWP/s+jNoFrH2p3i2EDpnWERgHgAmCWqlftJVzs+dppQ7bvG4JU5JCmmYsSxwUkYSqlXr4rEHptP6z7Px9gAcyGLUNqJy66JTT+kjA+odNVYBkauVIcyt1EgJhX0hC7MW5J2+pGCchHAYFt5J2zj4lrdSRmF1AIVD3uZ87L2zbhLZSd88+FAKZR2AcACaIOnOnsZU63yhhU0hwRKrPGG8JjLP+YVTVSn1a9nB9xnhD5RRrH0btQoIj6po1SNUg7DtVjE+sGuxvpc7ah01hVbMDZ4yTEAuDNsHvPc7Zp6mVtMTah1mhnaJaz/0UAsG4XWgrda9TGnde6wiMA8AEm4BHgkxDWqlvpbIUYE3UpBCCI0hMUHDkYtYmjwRIx2a2WYNUTsG2oii1K6puIROSolpnjNNKHfYFtdPtqhrk3A/jvGTwkR0CpfrZhzsv0jL73s+ZH0ZtvYrx8Xu/siFJUax/KwiMA8AE211Ya6H2B2L3okRbOdjjVY5MyKBcZS3zpmipiASEtFI/aHskcJKiAIOCWqmrZYyAROUUzKs6hUghVYMNCbF0ykECgu68GpAMLrH+YdLWHR82pZ2uBgRHuPPCKK8YYuT6v+iU03Tu58wPo+brFkIyuEUExgFggl0RkEGpTHnW8kjgZRDzzRL2xK0Yp6Ui0nKqGA+uHJEaE0PY92HULmDe2qFyxP0Fzj1Ixza0pWJbx4QVrdRhX9iM8Uy5mxDrzRincgq2BSdFeVWzLTPGufPCqKqd9DrPlI+smr2sGG9Y/5z5YdTO6w4b2CmKDpnmERgHgAmqlqJZJq0mHBS97HnvkcDNIuObJezZBrTVkiTvXxdmjCMxoXNm2xNDqux51j5s2gR0CznMGmyrGiR7Hra5FbNxZ4zTLQH2hXSKuugW0tQpR2Lvh0ne3j8lIVbZORxOcASJqe686wnvPVI9IbBh/Zd7RkfCpPBCoGxAtxDOPVYQGAeACaqL0s0qV5aNrRgf2EqdRzIYFHpQbK2Y5ZEACageiMe2lJN6ZozTSh3G7UKSorrWPuceGBeaEKhMftVsYyt12unCppD1f5EMnrnJ4HRMgG1et5CJI5Sq4EjZeu4hOAKbNiHJ4JnUOj6Mcz+M2xZuxXjgm09rh0z2fisIjAPABNUjwZRL0uEb5ZBHAh7JYI9XNThp/Q+4JLH2YVRQK3XVKsaZt4aEhLXTHThGg+x5GHQbXDHe0jGBhEAkwN37x88YbxkjIBEchHmhyeB+UiBd0pCWqp30pPdOdRVDkBQF29xuIVM6Jvjrn7VvHYFxAJjg3E53amuhllbqfLOEcUFV/YJqzgABAABJREFUg5JWWc+8KYngIEwqy1K7YwbxtKSQrD84SFIIjNoGJEV1dsphxjiMCwkMVho7JmTZOTmWtQ+jQvZ+SR13XvZ+2LYJbqfrJAU2dYmSeO+BWSGt1AfdeSX2fpgUeu4/rP+i+sn5H7D2TSIwDgATnNrpTqwYX7XO2qS1EGwLPii2/YwHMhjnPQ5PbKXuPxI4/7Ba/zyQwaiwdrpZe+WI11aO9Q97QgODl2M0Gh6IqZiFUSHBwcsuae7ad4LknH1g0M7b+6cEB91zP+89SMs2oJW6NHSEEmcf2LNzWqmvJydFVT+hU451BMYBYIKgmTsa8EAmERyEScGVI62t1Fn7sM0LDMZopd4YHOGBDDYFtVJXfe2TPY90xNj7W4ODJEXBuJCkKIlkcKQrtJX6YYxGQ8U4o/OQgM0uYHSkasUQrZ2i2Pthz2YXdu6RmxRItxDzCIwDwAQhrdQPlVM9D2QSD8QwaRM4c2dVDske5qAIe3aBSSHqqpqt9v5yL7n/jgBGhKz/zhnjPJDBuOB2ul3rP6eVOmxz77xZNrZbSG3tu1XiBAdhXNS9n3MPEhM2PkznVtKHXzl/SacoGLcrwjtknsZHUghkHoFxAJhguwurGF+1zVvjoAjjwmfuuD9pa6nIQRH2uA9k6ymt1DWgYlxi/cMkf/2PDI4o6+iU4z4QExyBPdtd6APZgPXPmR9GbXcBwRFl7RXjVE7BuNBuCe4YDS/llfceJCBsxnhHxTh3Xhjndsic2kr9/BM65VhHYBwAJgidMU4rdaTKbyk6fv2v1FINS/Y8jPOSQia005W65q3xSAbb/PUfOmfWXftOUhRrHwbNOmP81EqdMz9sCpkze7n23WRwHohhm5cUNeHO67VSb6uYZe+HQWVZhr13qt4hs6FLmsT6h0mhrdRbR0euSAa3iMA4AIxUluV5xviUS9LFA7HzSMBBEcaFPhArGzJnloMi7AmeNZhJop00EuW2Uh9bMS51dAth7cO4GHt/6wil6uzDmR9GbYID4z1rX2L9w6QYSVGnow+j85CQqo26NK1TjuoV47RSR0LcVupT9v5V2+g8r0Mma98KAuMAMJJ/UIxQNdg6b42LEuwJbis3pFsClyQYFPxAVm+n612U3Mop9n7YswmsGM8IjiBR3pzZSd1Csv6zD+ceGOXOGB/r4tyTkwyOdGyL6SNkKlXlYEmXKCRkG3juySTlWdsYDRJiYVs1Qkaa9uaTtyWD0ynHJALjADBScOWIMq2ytpaiXJRgW+j6bw8MEhyBbTFmDTJvDany28qNDIyTFIKEeWME5hqhRKccGBU6PmzVOkaDOy9s84Ijk1qpt/wsy84dAznzwyB37a/zKXt/VrvzOl9z54VxIePDJGnV1kqdtW8SgXEAGCm8lXS9cqote55HAtgTuv79DMq2eVOsfdgTnhTVMWd55V6UWP+wJ2T9H5JC2sYIsPZhW+gDWffeX80YZ+3DpmrO8rSK8XrVIF3SkI7QpKjWhCjJ2ftZ+7BnE+HO294pioRY2LYJLIbw9/62907WvhUExgFgpBjBkdWQgyLVIzAo7kGRtY90RJk1SGstJKpa/6s802pkS9E8y/orZiUeCWBSaEvFQRXj7PswKnzGeFtSFAmxsC24S1rW0ilHOp/72fthkDtj+c6UVupdM8ZJiIVxMTpFNf6ETjkmERgHgJH8ypEpB8VMq9YZ487XfLOEQbuY2fNeO13WPmxz9/71xFmbXva8yCBGOqr1P+mBQPWKcVqpIx3hlVMDZoyz78Oo094/oVuClPndErw7LwmxsC00Gbz13COd/11g74dBwa3U63s/yeBISMj4MKmrCI5kcIsIjAPASMGVI6KVOtIV3Erd/Ulr5QgHRdgTmj2sixnjLYkhrH8YdK4anFY50tpWjkcCGLcNDY5k9VbqTWefkuAgzNkXpYrj1h1nxrj7QExCLGzbBd95z2u/rFeMM0YDhgUnBHbeed1zP+ce2OO9d4bOGKcQwjwC4wAwUoyDIllkSFVo1azXVo7gCBIS+kDWOWeW7HkYF1I1mGUdFbO0VIRxwS0VNXSMDGcf2BKcFKL6uYdkcKQjfHye8+/MRcV41UqdfR/2BLdSV71jAud+pCO8W4j7E8781hEYB4CRYlyS8tZW6rQUhW2hrYWy1lbqHBRh2ya0lXqWtV+UeCCGcdXeP2XflwYGBkkKgUGhnXKyLKslBVI1izQE33mzrGPvJyEWtoUGR/zxYbV/f1aM0YBdoR0yD13SBrz5cO6BQaEJsSvO/EkhMA4AI/nfKGO0VHTbq5BBCdvCH8ncn3BQRDriVA22zBgnKQrGVcHByYHxrG3t01IRtnkJgZPmLHdVTpEUBbvcpJBJI2RU65KWtyVFce6BPcHjw5xzT3lRMV4Fxtn3YY+XDD5xxjjnHqRq64wPy+p79wCt48NY+yYRGAeAkfxW0jFaKroV42TPw7bdcdjgKs+0yiMeFLOMRwKYFiMppL21FjPGYdt2d34kmML7N4aWikjIrDPGSQyBYeFd0upJIW4rdfZ+2Lbdue2kA1up19FKHYZ5e/+UVupZx5sP5x4YV3VMmJ4M7vyEM795BMYBYKRNhNZC7TPGaS0E2zbBwZGWS5LkBMY5KMKe8Ha6UtbaLYQMYti2Oc0YX/V8sll7WznGaMC28G4hA9tJc+6HMaHdErLOOy/J4LAtPCG2Zd+Xzud+zvwwaBfYLaRzxjjnHhi32YeND8t4608KgXEAGCm4lboyv3LErRRc8UAM206thSa01ZLkz9msZ9Kfsuc5KMKebeiM8YvaEVqpIx3V+p9y7pEGVo6w98OgGElRK5KikKDgbgn1pBD3zsu5B8ZtC3fvH7/+3X2/vLjzHv9d4L0HBm1DW6lntb1fnHuQjnMh0MQRMm3vnXl+DpSz95tBYBwARorRTre9lTrtdGHbac7sxDmbfjtdHgmQDq+lYpS9n3bSSMO+KFW9D09vKzdk1iB7P+zZRGwnXdarBqmahWGhM8Y7xwhw7oFx7rl/6t5//kl9768qxndSWQqwZBPYSl3qqhinaha2zZYMLjE60iAC4wAwkj9zZ9olaZXRXgVpOlWMTzwoqu2SJNFWDqbtisAxGqo/ErTNWyM4Als2gY/DUseMcRICYdzGmzMbNmO8rD+/UDULw0KTwaV6YNxNBufcA9tC139rxaxEUiBMCx4hk9XvvM4/ZM4yjDsFxqMUArWc+znzm0FgHABG2gS2VJRqc0daW6lzUIQ9p4rx2K2FJGaMw7RNaCv1LCN7HknaBCYESrXgSOsYAdY+7AnvFOW0FL1ICCQ4CLvCzz0kgyNd1d6fZ9IqH7/+3U45Zf23kxgCw9wZ4+sJa79zxjjdQmBc6Htn3nbukZz3TvZ9KwiMA8BIu9AZ41nWPmvQq5zioAh7zq2FIlfMSswYh2l+1WBYO93zrxxROQLDtoHnHqljjAZrH8YFB8blVA62tVSUOPfDnPARMrWEQHdWLcngMG4TGBzxt/uOMRrce2GMmxR1Z73q+GSzi/FhJMQiIcEzxrveO1e0UreGwDgAjDTvjHEOirDt3Ep9amDc/UlL5RTBERgU2lZO6pq3xgMZ7IrSTre1apCqKdi22QV2inJaipZZ7YGZVuowbBvYJS2TasngbWM0OPfAnl1oMnjZUgghkRQI0/wRSlMSYutd0kiIRRrKsjwlhkztkuZt97RSN4/AOACMFNpK/VA50vJIwEERxlUBkiktFSU/KaRsbaXO2oc9wQ/EF0lRzBhHGrZOYHDqvDUvHHgRHDn+u0BSCAwKnrWp7DxjvK2losTeD3NIBsfj2TYwOOL+rtY7r8TeD3O8c8+ULmkXe39bUhRrH7bsCufOO/G9c9W29iXeOw0iMA4AI7lt5aZclLKslj3vHg65JMGwsiwjzNwZ0EqdtQ+DwlupD82e54EYtmyiV4yTFIV0eMHB9bQ5y+cH4paWihJ7P8zZhK59Jynk8AskgyMd1Z13yoxlqV412BEYJzEExoQnBEqilToSFKNLWpa1rH2JVuoGERgHgJHCZ21mHdnzXJJgl1sxG6WV9EX2/PHfBdY+DPKDg6HBEan9kYAHYtjit1QMr5y6HKNBUhTsijFj/NxKvaWlosT6hznBwZH6uYdkcCRkEzg+zE0KuagYJyEWhoV3Sau/d7YlRbH2YUuMLmmta1+iEMggAuMAMFKctnLM2kR6dkVY5YhUayvXNm+t2EplKcASt1vI1Oz51hnjVA3CsPCEwHq3kLZ5a6x92OOOUJpSOeid+7taqZMUBWNiJIW0zxgnGRy2Vet/anDE/27RNUZjP+nPB+ZyG9ohU7X1n5EMjjRE6ZI2pJU6a98MAuMAMFKcGeMt2fNkUMIwN4MyykGx65GgLARYEmPeWmsrdZKiYFiMtnKt3RKk8zmItQ+DqqSoO6tcWT2hb4DM6RR1WTVIUhTsCq0a1EUyOBXjSEe190/pEiXVgyMt48MkEkNgzjZKl7S2pChmjMOuTWCnHKk+Y5xW6tYRGAeAkdyD4nrSQTGrZc+TQYk0uAfFdR4+Z/aiYpxHAhgWnkFcnzHuPhKw98Muf85shHlrtFJHQoKrBt120l0V46x/GBMaHJHqwRHnz2DGOIzbFod9e3JCoHPuISkKKXG7pN2dVDGe1eKBbXs/ax+2bHfh5x7/zlt/76wC45x7rFj3fwRYwC/9iFa/9d/1Re9/j/SR50p/9E8u/TcCTmZtp0sGJQzzK2YnHhS9h4GWqkGJ9Q9zNoEdEzpnjPNABsPcqsE42fPMW0M6tvuwqsFM50eyzhnjJATCGO/OOzE44iWDezPGWfuwqyxLZ++f2kp9QHBE4uwDc0KTwQ9d0toqxkkGh11xuqQ52s79ZSEVhTSx2AjxEBiHTR/4deVv/+/6JEm7Rz+y9N8G8MSYMZ61BkfIoIRdMQ6K7tpvnTEusf5hTnArddXnrbU9ErD2YUtocETqayl6DJaw9mFQlRgy9dyjLHOqZrvOPTwQw5bQVuoXCYFeK/Vch38fStY+zNkXpcrj0p3eLeHsomKc4CAMi/Pm4/6kbXwY537YEqNL2srpkNl97t9K+d1J/xmIh9QE2OR9s9wv9/cAGoTPGK+306WVOtIQPGtQtezhi0cC9n7YtQntFpJl3igBZowjFZsI7XS9WHhbUhQPZDBoE1w1eA4OXlaM0ykHdgVXDapjzqzE3g+z4tx5WwohJDpFwTS3S9qkZPB6IVDW1iWNcw9sidElzcO53zwC47CJLDIYFtpO+vKg6FYNclCEXfO3FnLWP5WDMCZ01ubF73DXP1WDMCzG3r/qCo6c5q2REAV7Ys4Yp5U6UhJ87smyWsV4y/onGRzGhCaFSP6c2c69n3M/jHHX/7TRkVn7+DDOPTAstEOgNHB8mMT6N4LAOGxaUTULu+IEB5kzi/SEPpBJtUcCWooiIdX6zzJplU9LisrbOiaQFALD3G4Jk889XsV4W3CEtQ97qlEC02eMZ063HM49SMc2ODgi5VnXA3GVFMXahy27GO89pXvnrf9Dzv2waxt47u8sBOLcA8P8O+/UMRod48O89U9CuAUExmETVbMwbBdh3lr7zB0OirArflJI/R+SGAK7qjEad1a5svolZ4DLvb/tkYC1D1titJVrTQqRzkmBrH0YFDpjvLtinDsv7PLaScdupyux98Ms79wzoUOgVB8h0zJGQGLvhznh3UI6goMkhcCwGN1C/NF5Xed+1r8FBMZhU84DMeyK3VqrvWqQSxJsCZ03JfkPZGX9GJKTQQm7NrvDmpwaGDy0lWPGONITo63coDEaZSEVhQAryrKMOmP84vmFB2IY5lZOrad0ylEmDWkpypkfxrjnnnU+de/vSAhk74dhm8Bzf+fez50XhoV2S5DqSSGc+60jMA6bVgQHYVd4W7lsYGshvlHCljgZlB2VI/nq/DUHRRhzqhqcGBjU0G4hnHtgTIxuIa3nHoluOTBrV4R3S3DHaJQX5x4eiGFX8N5frxqsq9Y/Z34Yc7uLkRDYUgghsffDtCgjlFpHR3Lmh11ut5C7cyaDS6x/IwiMwyZ3syjZLGCL90gwobVWZ1u5LJOyY3CQb5Qwxs2gvDN55s4ZM8aRkmrvn14xfq4euVz7tNWCXbcR5q2thiZFsffDkNAzvyRl2Tkh9qKVOgmxMCy0Wwit1JGqOAmBZxdJUdx5YVhwIVDXjHEvGZy9H7ZEGR2ZdY0PY++3hsA4bOKbJQzb7mLMGB+QQUzVIIwJbasl9TyQMXMHhlXZ82HBkYOLwDjnHhjmBwdnqBgnOAijQs/8lfZW6nQLgV3ejPFJXdLqnXJaKqd4HIYxbsXs9KpBd3wYrdSRjmrvX+eZ8kljNLpmjLvJsKx92BJlbKr3E/Z+6wiMw6YV7SVg12YfPm+t85GgeiDmoAhjYrfT7Q4OsvfDlhhzZs9Vg11JIczahC2hlSNS37w19n7YFOWBzGknfbn30y0Bdvnrf0qXtOw0RuD4K/4Hqr2ffR/GbAPXviRlGa3UkaZTMnjAuac1ITbLSIqCWZsYXdKGzhhn/ZtAYBw2sVnAMLedblZ/4BrAnTV4+gVXTit12LSJMW/NXe4XB0UeiGFXcCt1b9ZmV1stkqJgS2jVoFR7JOCBGInYREgKyXQODpb15xf2fhjmj1CKUTFOK3WkIUaXNK9inE45SMj2lAw+LTB4OPl0nftJioJNoSNkJCnPOgqB2PvNITAOm5zKkYz2EjCm+ma5npo9rL72KtVBkbUPW2JUTuXl0Jk7rH/YUiWGTL0kSU7F+EU7Xdpqwa44SVED5sxK7P0wZRuhckRyu4V0VY7QLQS27Irw8WEaUjlFQhSM8asGI8wYp0saElK9+UwenXdRMd7y5sOZH8bE7pDZ2SWNc78JBMZhkxcc4aAIW6rKqRithYr6JUlyDoqsfdiyjTBvzb0XlfV/SNUgjNoXpar34emXpOzcTrczKYS1D1titBT1A+NdjwSsf9gR5YHM7RbSlgwrkRQFc7z1H6FqtrVqsCykohBgRYxOOdnlTfeMLmkwLLhLmvpGKB3XP+ceGOPu/ZPHh2Ud3ULcvZ/1bwKBcdjEQRGGhc7c8VsLNTwwkz0Po6LM2uxqK0f2PIyKM2tQ0qAZ46x92LIJbKcrSVk5cN4aez8M2UQIDB4eiNtaqdMtAXaFztrM6u1026oGJdY/TNlESAZ3R+fRSh0pOb13Ti6EqLdSr6nefKiYhTHeuWfq3l92rH32fnMIjMOmnM0Cdp0zKKcHR7K2qkHp/EBMBhmMiZE97z4SXFaOkBQFm269VtKrjk+287PnOwLj7P0wJkbVLK3UkaIYlSNZJq2yIa3UOffAFm/vz6e1UvfHh3VUTrH+YUisbiHOz/x/SLcQGBbcIVM9FeO0UodRsbukce63j8A4bFpRNQi7qm+WIZUjnYFxWqnDqBhzZjvnrZFBCaPcS9LUpCi5SVH1wGCWOd1CWPuwxUuKijJjnEcCpCFKtxCvcIQ5s0hHtfev80x5PvGBuDMhluAgbIpx53V/1+WMcffcQ9UsbNmEtlLvmzFOIRCMitElzR8hQ4dM6wiMwyZmDcKw8BnjWfusQYlW6jBrE+OB2PtJxwMZjwQwJErlSNeMcem8/rkkwZgYe3/3rEGCI7Bpu4vRLaGrnS5JIbDrlAweFByp/YJrxbkfNsUYH+YlhdApB4koyzK8lfrFGI2WhFjOPTAmzpuPWzFe+4fs/eYQGIdNtNWCYcGPBDo/EjROHyGDEkZtY89b62qlzvqHIdud00538rw1t1tIw5/BIwGMch8J7q6mjRLws+e7giOsf9gRIziisuvcwwMZ7Nqc7rxTk2EzaUhwRGL9w5Qo3ULc4AjdEpCIXeGOkAkfHXn8Ff8DdMiEUZsIoyP9vZ8uadYRGIdNtNOFUWVZhj8S9AVHOCjCqBgPxF4r9c7KEdY/7Njsz5VMYUlRLTPGpXMGMeceGOM9EK/DH4iZM4tUeDPGJ48ROH99OWuQ4Ajsqvb+kITAzqQo94GY9Q9DNhGSwX1dSVGce2CHNz4sYO0P6hTFvg9jYqz/Vef4MFqpW0NgHDbxSACj9kWp8vh9burMEalnxvjpolTSVg6mxGgt5M2ZpXIKidjsImQPZ+e2chdJIRKt1GHWJkI76RWt1JEg74Fs6rnHqxjvqhzhzA9bqm456zxGQqB6Wqlz9oEdUe68Xjtd1j7SEOPM3z9Gg2Rw2BTl3N/ZLYGkKGsIjMMmHglg1CZCBtnFzJ06Hohh1O0uxvrvai3ErEHY5LWSjlA51ZgUdeoWwr4PW9y2cut8WsV43hUcoXIKRsVop+slBDJnFgnZBFeM1+fMsvcjDZvod166JSANUToEZpnyrHB+oSUpsCykohBghZcYMrFLWmdSFIVA5hAYh008EsCobZSqwfNFqagfEiWyyGCW11I0wsydywcyZozDpk2UWYN93UKO6599H8Zsj48Ed1a5sqZuB0N0tZWjcgpG+Q9kE889nRXjJMPCrmrvDzn30EodKYozZ7YjMMi5B0bFee/p+ZWc9Q+bYnQLyQa3UufcYwGBcdjkbBYZmwUMuXXmzMaZudPVSl0khsCUbYTs+e7WQu4libUPO7aR2sqdKwe7Wqmz9mFL9UgwNTgijagYZ/3DkG2E4MjwyhE65cCW21PF+Krnk83cZPDTL7ioGIdRMSrG3d9V1psFsvZhVIz3nsu9v54YwnsnbIqRFJWX7rmfEUrWERiHTe5mUbJZwI4oGZSZWzXYwMsi46IEO6K01nK+7n4gZu3Djhhr3139F5ckyWmlztqHLdvAdrrSiKpB1j8MiTJrMHPb6XZVDfI4DDvKsjzv/VMrxuvn/M6qWdY/7Iix9/t33o7gCAmBMCROl7SsJyGWqlnY5CWGxDj31x/8SQoxh8A4bFpxUIRNUeZNOQfDiwcyyW8nzQMxDPEeCSavf/eg2HVJYu3Djjh7v5Qf2yp2JkVx7oExVVLg9KSQesU4LUWRhigtFbtaqbv/LrD2YciuKE8PumFJUe78WM79SMMmQqeozrVPUgiMirH2pZ6KcapmYVSM9051rn3uvNYQGIdNbBYwahtpzuw5ONLwZ3BRglG3u/D177cW6pgxzt4PQ2LNW8u8n9UwYxxG3Z7mzAYERzrnrVExDpuiVE5lHeeeLCMpCibFSAqRaoWCnPuRiOjJ4PVvH5x7YFSctd8zQon3ThhVrf88k1b5xPdODWylzrnfBALjsImDIoyKVTXY+S2W1kIwynsky8MfCcieRyqiJEVlmZMU1dFKvdw39N0ClhO9lTqPBEjEdufMGpw6Z7bsaKUuMUYDJsW480q1qkHO/UhEnHbSZxd7P90SYNQmQivpQzL4Ye8vGpPBOffDpijJ4IPPPez9FhAYh00rAoOwKc6M5cw5KDa1Uqe1EGyqgiM3q0z5xAxK94HsomMCSVEwauNlz686PtnOrRjvbKUucfaBKe7ePxWVI0hRnDmzTiv1+tqXzmcfzj0wZBNh7Uu1J+GulqIEB2FIjMSQvOvOy5xZGOV2SZscHMzcNx86ZCIdp2TwSIFxOmTaR2AcNhEcgVHx5swev1k2vTFzUYJRmwgZlFk5cOYOD2QwZBNhjEDmPBJctNWS2Pth1jkwHnJ1ZNYg0hOjnbQfHOlIiCUhCoZ4555YM8YvHoh584FNMZKiWtJgD1j7MCrGuccvBOqpGOfcD0OqxJCQc4+bEHsRdqUQwhwC47CJgyKMilM5cm4n3ThjnG+WMKo6KAa1VHSWfHf2PHs/7Igyb815JGje+1n/sKcsyyh7P63UkaIY7XQ7K0ckp5U6j8Owww2M341VMU7VLBIRp0vgGa3UkYpN9BnjtFJHOqJ0Set876SVujUExmETrVVgVIyZO/K+UdJKHemIUTHuVo5c5NHnPJDBpjiVI+oJjPNIBnuitFTUmHlr7P2wI07llHvu6Tj3s/ZhSKy9P+tMiuKBGDZtd+d1Oz0htqNbAuceGBWlS5p67rysfxh1aqUekgxedrVSpxDCGgLjsInHYRgVpZW6slNwsLHBFgdFGLWJMHNncPY8B0UY4j8ShGfP80iAVESbM5sNbaXO3g874syZdQPjHZVTVE3BkBhrX6p3C+l4IObNB4bcOmefdT41ONgxY9w9B3HugSFRuqRl2fnO29Qph/d+GBVldGTmnPvpkmYegXHYlOfnwyMHRRgSpa1Wdg4O0k4XKYmRQZnxQIYEbdzKqQjrv/mRgIsS7NlGqByR+oIjPJDBphhVs5lXOdJVMc7ahx0x7rxSLSmKVupIRHX2ubPOlTWd2QfIOwPj2fnsw5kfhsQaHSnGhyFBMQqBvDtvSSt16wiMw67jhpGxWcCQOBXjfe10CY7ApnMGZZzgyEXHBB7IYNQ2wpzZUfPWOPvAiBitpKW+wPjq/DVrH4Zs5q4alJwZ46x92BGrYtxb8bQURSK2UbqkdZx7JPZ+mHSdLmm8+cCmKiE2bITMGa3U7SMwDruqDEq+UcIQt3IkThZZXztdvlnCjugV4xet1Dkowib3keBuwBiNKh2kaDqCs/fDIK9qMKidrjtrs/bnMEYARsXZ+/tmjFM1CHtitNOV6nt/V7cQ1j/s2ES585417/10C4E9MbqkZcp6CoHY+2HPvii1L6rAeEghUMedlyI4cwiMw66qeoSDIgzZ7Panr0Nm7nQfFKmcgj1FUUbKoHRbitJOF2mIUTXrV4w34KIEg9yEwLuRsucvHwl4IINNMYKDXqecxlbq3Hlhj1cxHvBAXP3OgmRwJGQbpUuamxTV9IHjuZ9zDwyJ0UpdvRXj7P2wJ1aXtM5OURQCmUNgHHZV3ywJjsCQKLMGJZ2qBhvnzPJADHu2RYRLkuoHRSrGkYZYgfFzUlRfxTh7P2yYp5V6196/F2BFnBFKA1upl3up7EieAq4oVsU4yeBIUVUxHq2dLmM0kAj/3DN9fBh7P1ITr1NOx3sn7z3mEBiHXafWQmwWsGMT4Ztl75xZvlnCoFlmDdYxbwpGbXbOGI2AtnLn7PkGJIbAIG/W4MQHMqmncoq9H0b5VbNTK8adtd9YMU7lFOzZRE6Kop0uUlLt/dFaqTfu/bRShz1RksHlBMb7zj3s/TAi1thUOmSmhcA47MrJoIQ93gPxxNZa/TN3qJyCPbEOin5wpKO1EJckGLKJ0FbOy57vfSTg7AMbYgVH3HeBy24hBAZhU/VAnGXSKp927vcqR8qeyinOPjAidkJsfztd1j7sOM0YDwqOOHfepoxYWqnDoBjnfn90ZAP2fhjkv/UHvHdm7qqv33nplmANgXHYRQYlDIpRMa7MfSRoQHAQBkWbuZMNzKAkKQSGbCNclLzs+cYHYqpmYc82QsWsJOVlR9Wsd+7h3A87bnfn4EjWNP5oADchsHGEEg/EMChaUhTJ4EhQlRAeY4yA1JIQe2qlzr4PO7ZRuqS53UJIBkcavPfOkFbqZcd7J2d+cwiMw64VGZSwJ8YD8aGV+uHPYc4sUuG30w14JCg7Zm2y9mFUjJlT/fPWaCsHe9xuIdGSour/kL0fRp2qBgPOPeqaNSgRHIRJ7rn/bsgDcTYwMM65B0bsi1L74rBuY4wRaP9AFRhn34cdm/15PUbpktb0AapmYZCfEDh9fJh75y3qnaIoBDKHwDjsopU6DIoyY1wjKsZZ/zAiRitpqT5vrXZQzDIpO16UeCCDIXEuSlnPrE2CI7AnRlKI5FdO6aJinAcy2LSN0E4376saJCkKBkXrFDW4nS57P2zYxgqOqCMZXKKVOkxyK8and0nLnPdOCoGQBnfvD0oI7LzzkhBoDYFx2EUrdRgU45Hg0IpxaNUg6x82+LMGIz0SNM3aXJEUBXtizJzqrRinlToMitVOd9VVNUtgEEZVe39QUog7RqB37+fsAxs2kcZoZJ3tdFn7sMcvhFh1fLKbt9s3jtGo3js598COaAmxnd1COPfDnhhJIVI9KarGSwZn7VtAYBxmlWRQwqDbXeyKcYIjSMM2UsV4nvU8ELP3w6AY6z+TtOKRAInxk0JiVU7VUDUIo6IExjXw3CNx7ocZG2eMRsj6zzvb6XLugT1+UkjIuadv7z+u/7KQiuLynwMLuI3QMaE/GZxzP+yJlQyedXWKyjKKQI0hMA67jt8sM5W0FIUZ7qzNODPG+x7I+GYJG2K1VHRfxi5aqUscFGFSdVFa55nyfOIjgfN1/yMBD8SwIVblSN5ZMc65BzZV5/6gObNl34xxOkXBnhidcg6Gdsph7cOGOUbINHZJ4+wDg7aRuoV0jw9jhBLsiXXuyYcmRXHmN4HAOOzimyUM2uzOSRrTK8az7opxHshgUIxuCZKUZ15k/PIDp1bqBAZhR9VaK172MDPGkYbYc2YPP2HeGtJwqhgPWvvOA1nv3s+5HzbECw4eFHRLQCJiBUe8hNj6uUeiSyBMijM6sqdinG4hMMjvEDi9W4g/Y5z3TusIjMMuvlnCoBjz1sbNmeWBDDa43RKCHgnKvuAgGZSwp7ooxWsl3fNAzLkHRmxi7f1dbeUIDMKgsixP3ULCuiWc7w5F0/MLwREYFGvG+PmBmGRwpCHW+DC/lXoD3jth0DbCGI1MWXfFOK3UYVC8ZPCzzo4JrH0TBv8v/bKXvUybzWbOvwvgy2kpCnu84GCUeWtkzyMNm0gV41nWUTUonR+IWfsw5BwcWfV8sh2t1JGiWDPG864HYh7IYFCM8UlST1KIRHAEJsVuJ007XaRis4vz3tNbMU6nKBgUo2OCVwjU1ymHcw+M8ALjQXu/e+ftOPeTEGjC4P+l/8W/+Bd6wQteoF/5lV+Z8+8DnHkXJQ6KsCFW9nznvDUeyGBQrOx5b9Zm47y16qDI2ocd53a6IRXjA+dNSax/mOGee+7GeiDumjHO2ocRmxnmzDZWjBMcgUGxkqLOgfEGJATCoE20ivGeOy/rHwZt9uF7f6a+pCg6RcGe2+idcnqKIVj7Jgz+X/q7v/u79fa3v11f9EVfpJe//OUqiqL/NwEhVjwQw54oB8XemTs8kMGebYS1L/kV440ZxBwUYdCplXpAcETliFbq7P0wIlbVoPdIUN/7s0zKaCsHW2J1ysnLnqQoWqnDoHiJIQfNSSG898Ce2N0Sjj+5RFIgDKrW/51VrqzprWaALMtIikJyonXI7Jsxfmqlztq3YFRg/I1vfKM+4zM+Q9/zPd+jL/7iL9Zv//Zvz/l3w+Md7aRh0LlqMOCg2Dtzh7UPe7wMypB20kODgwRHYEjVUjferEH2fqTB75Qzfe93W6k3vhCTFAVjoiUEepUjPcFB1j+MiDdjvKOghoRAGBS7W4LUtvdTNQt7qvUftvbPCbH9SVGsfdgwS7eQzlbqvPdYMOp/6c/93M/VL//yL+vbv/3b9aY3vUmf+7mfq3/5L//lXH83PN5xUIRBp6rBoIrZnrZyZM/DoHkeiDsC46x9GHJ+JIh1SaKVOtIQq2pQXRXj0nn9c+aHEZtYCYFOYLDo6xbC3g8jYleMkxCIVPjBkTgJgf1Vs5x9YEOMLmnue2cj9n4YFK1TlLv3N955q0IgEgItGP2/9M3Njf7xP/7Hev3rX69P+ZRP0bd927fpj//xP64/8Sf+xMX/+7RP+7Q5/s54vFiRRQZ7qotS6APB+ZGA7GGkIV5robPOmTvl3ms9DSylLMvT3h/0SOD+mV1rX2LvhxnxHgn6suePj88EBmFErFmDvUlR7P0wKFY7aZ2qBkkIRBq8ivF1WNVspXeEEusfRlRd0oKSwbOsp2Kc907YE22MRtlz5z11SWPft2Dd/5FmT3/60/W0pz1NZVnqve99b8y/E3BAK3UYtI1RNZhlyo9zlsumuxYPZDBoG6m1kF812PCP649k6zsB/1lAuF1xXrN3YnVL6Jo3JbH3w4zbGVqKNm7+PBLAGP+BbPra9ytHOpJCJPZ+mOG3kw5PiuoNDFI5BSNi3Xndc09vtxD2fhhx64yODNO191MEB3tijZDJMjpkpmTS/9L/6l/9Kz3/+c/XG9/4Rv2dv/N39PGPf1xFUTT+P2CqkoMiDIpSMe5mkDU9DpM9DIOq7GEp9IHMbSvXlEFJUhRsidctwVn7jXs/lVOwx30gvktbOTyORHsgK/uCI+z9sGdzPPdnmbTOw5Oi+rslsPZhQ7RuCV7nM9Y/0rCN8N4p9SRFsfZh0Bxd0jqTouiQacKoivH3vOc9+qZv+ia95jWv0TOf+Uz9+I//uF70ohfN9XfD4x1ZZDBoEyGD0s0gK7oehyWSQmDG7Qyt1BvxQAxj3AeyoG4hzr2nsa0cjwQwyA8OhsxZPmtupX5c/+z7MCLWjGX1JgTSKQr2bJwuaVnTfXWgzsB4vtLhu0PJ2ocZsboluP/aNHcL4b0T9pxmjAd0iZL69n4KgWDP7Qyt1PuTotj7lzb4f+kf/dEf1Wd/9mfr1a9+tf76X//restb3kJQHPOiahAGXaVinG+UMChacNB7ICYxBPZt5lj7vWMEWPuwIV72fE876ercz5kfRmxjtZIuz38O7XSRiurcfzewnW5ncESipSjM2ezdEUohbz59d17GaMCe2BXjnHuQinhJURTCpWRwxfg3fMM36OlPf7p+7Md+TH/5L//lGf9KwFFOcBD2VO2kQ75RejOWaamIRMRrJ31G5RRS4I4RCFv7fXs/D2SwJ1bVrL/+G9BKHcZEqxzx1n5T1SCVU7DnVDEeGBypTjutzUJXN4eEKM49MMILjsx57qFTFIwpijLSe6e//i/w3gODYo0P8wrhhpz7V6OaeSOywf9L/6W/9Jf01re+laA4rodHAhizL0rti8M3uVjzpgiOIBXevLWgqtm+9c/eD1tizZlVX7cEHshgkJ89H95OV+ppKcq+DyPmmDVIpxyk4lQ1GCk40jhCRnKSolj7sGGeOy+t1GHftohTMSu5FeMkBCINc3RJo5W6fYPTEv7Lf/kvc/49gEs8EsCYaMGRvrZaBEdgkJ89Hyk40vQB1j+M8ccIhKz9s+ZHAh7IYM8cFePNbRWPSYGc+WFEtOBI2bP2eSCDQeeK8bA5s9UDcWvtIK3UYYwfHJnzzst7J2xxu6SFVMwedIzR4L0HBsV67+/tkEmsy5TQnQ6YD48EMMabMxt0UOxrJ+qufVqKwoYN2fN4nIo2b8rLHm7AIwEMivVIkA9Niiq2XmcdYCnxRsg4CbF9swYJDsKITbSK8cuvPKe9nzM/bPATYq/UJY1zPwyIdeeV3DEafWuf907YEC8ZnHN/SgiMwy63nTSbBQyIVzFOK2mkx80gjvZI0HRQXPFIAFtuYwVHnFhfc8U4YzRgT3X2ubPKlTXt2QP5swZ7kqJ4JIMB8SpHzn9OUVI5gjSc9v71queTfap2ui3fP2ilDmPmqRpsevNxk8G582J5sbqkSW63ENY+0hArMaR3hBJFoKYQGIddOZVTsMVrqRjSVq7syyDLpWr+JmsfRmx250BFSGstZowjNfGqBkd0C2Htw4hT1WBgS0U/e57gIOyLVTUoNzDemBDInRe2lGXpVIyHBkc62ulKtFKHObG6BHrnnt7gCAmBWJ5/5w1LiqpWfOf4JIlzD8zYOIVA8d58+jomcOddGoFx2EU7XRgTrWLc/UZZkj2PNMxSMd70AaoGYYz7QHZ3zpaKZA/DoNOc2UjBEUnNQwXoFgJj4nUL6Xsg49wDW/ZFeWpwFp4UVQXGW7hjNAADttHGh501jw8jOAhbbqO9d0p51pEUlWUkRcGc6xUCUQxhCYFx2EVLURgTrXKkdB+H2wLjx2+WJIXAiGhVs+7s2KbEECqnYMx2jjmzdEtAIrbRKsbdpMC+VuqcfbC8eLMG+x7IGB8GWzbRuiW4VYMtf84pGZykENgQ787rnvsbEByBMbHWfu/oSOm8/tn7YcQcI5SaO8QS67KEwDjMKgmOwJhYlSNeK/W2g2JVOcXahxGxHsn8GeM97XR5JIAB0YIjbk4IbbWQiPOc2YiB8d69n/WP5W13TkvFkHNP3wMxd14Y4639SGM0WivGT8ngrH3YEK1LWsbej7R4XdKivXe2oFsIjKnW/zrPlOfTO6W5v7MxKZC93xQC47CLDEoYE2/WYE8raYlW6jDHX/8hB0UeCZCWaGM0sp5uIbRSh0GnwHho1WBvcJBW6rBlsz9XMQUlRfUFR0iKgjG3ztoPrxjvmTG+4s4LW+JVjJ+/7t/7qZrF8uaoGO/tFsJbP4w4jw+LkxB4+ElfK3XOPksjMA67vPYSHBSxPPegGJZB6T6QtR0UaaUOW9zgSNZ0wBso65szy0ERxviPBKuOT/Yo3RXPvCmk4fbUSj1g7UvKx4wSIEACA6IFR5yv+2cNsvaxPLdiNrxi/KBsGp8knff+cu/dkYGlbKIlgzNCCWmJlgw+pGKcQiAYE6tLWu6+9zedfWilbgqBcdhF1SCMidVWyw+Mt6C1EIypKsZDHgikesV4U2shqgZhy+0Mc2YbK8a9SxJrH8sry9JJigrb+z292fOsfyzPfSAOaqfrPhA3Hfw598AYLxk8UuVU75xZib0fJvjrf3pSoHvub1z+vHfCmFidctTXIVBy3jsJDMKG6r0/NDDurf+mOy97vykExmEXlyQYc9WDYhUg4aAII2aZM9v0AaoGYUy8loo9lSNZRvY8TNkV8aoG3Yrx/nlrrH8sb+NWzYYExp2v6ZaAFMQbH+ZUjPe1UpdY/zDBW//rOEmBjece9n4YE6+Vunvm7+kWwls/jLiNND4sV88ogZw7ryUExmEXrdRhzGYXv2K8/aBIK3XYUmVQhj6Q9SaGkBQFY2K1lesNjkjs/TAl2gOZhiRF0VYOtsRb/+cVv6eVOhIwx97f205XonIKJriB8bBzf18rdfZ+2HIbqVOO3x6nr2KcfR82bHaHuFPQ2FSNHaPB3r80AuOwK6e9BGzZRGqnq76qQYmDIszZ7GeoGO9tLcRBEcvzWioGtVJn70daos0a1IAxGiRFwZjNtYIjnHtgzCZqxfhh7+9NBpd4IIYJ1dkny6RVHjBj3IkNNu/9JIXAlkUqximCgxGb0+jImHde9n7rCIzDLnez4IEMBmy9B+KQtloDZozTSh3GbCK1FsqcDOKy7MugZO/H8mKN0eidMS7RVg6mREsIVD0pqq+lKI9kWN421hiNjp9JqnVLYO/H8uapGO8590jce2HCxumSljUlcQ80qmqQtQ8D3HP/3aA3n573Hsnpksa5BzbMMTqy8c2Hvd8UAuOwi80CxsSrGB8yY5yDImypDooxMyjVFBxZ0S0EtsSbMT5k7yd7HnZsYrVUlD9jvGzKCmTvhzGxzv1Z3wgluiXAmE20ZPABgXEqp2DMqZ1u1KrBBnRLgDGzVIy3JZdUez/7PgzYF6WK40YdGhjPS7qkpYTAOOzKaSsHW2Y5KLZlUJ4eiEupKJo/A1xRvFbqZzwSIAWxgoODKsZppQ5DbqNWDZ41jtGgWwiM8ff+WFWDDeiWAGO2EbuFVIa1Umfvx/JO7XQjnnuKpqd3kkJgTLz3zhEV42XBeycWF3d8WM+5nxFKphAYh108kMGYbbR5ayOqBiUuSljcvii1P6ZQhgdHemZO0VIUxsSaMzvskYBW6rDDPffcDd37j0mB+zJrrhjn3ANjolWMO1837v10S4AxMbuFrI7n/vbxYSSGwJZo48PUE+yjQyaMiRYcHDNjXGL9Y3FxR8icNVeMO++dvPksjsA4zCr5RgljbqMdFN1W0gMOinyzxMLiZlD2BAfJoIQxsapms8ydsUzFOOybY+8/VE01hEi8vZ/gCJYXb/073UKatn7uvDAm2vgwR2tCIFWzMCbWnNneYgi6JcCYWfZ+xmggAbf7890zaiFQY5c0EmItITAOu8iehzFexXjQN0vngayvalDikQyLi5tB2RMc5JEAxrjrP6RqNhtTMU5gEAbEfCDLnKrB3opx9n4YUO39N6tMWVsy0wCZUzlVlk2VI6x92DJHO11aqSMVsQLj/gilplbqJIPDljlGR7Z3yGTvhx2ztVJvLAS6c/6a0ZGLIzAOu7xHAjYLLM8LjkRqLdSeQclFCXbEzaDsyZ5n7cOYeIkhbtVgzyMBDwQwIGY73WrFl8oHzFtj/WN5VUJs6ANZb9Ug5x4Ys92f12zQ3j8oOEIyOGzZRNr7/YTYhg9448NY+1herC5p3vgw3juRAPfcE6tbgiSVWVNSFN0SLCEwDrv4RgljolWMe9nzLagegSGxKmYl9V+UWPswJtaMcb9qsKetHJckGBC3W8jhzyrUNmOcyhHYcgqOBK793NnuG8/9BAZhzGZ3TogNOvcTHEFiiqI8BUju3tAlDY8v20h3XmaMIzUxK8ZzLymw6QPs/ZYQGIddOVk0sGWOWYNkzyMFc7VSbxwlQNUgjIm1/t3V3ttStCykojV1CriKmK3U82OApFSmsm/GOI8EMODcSn3mivEsk7Jj5SBrHwZEqxjXkFbq3HlhR6xkWIkuaUhPvGKIsuGrGoohYMhs7529ez9rf2kExmEXrdRhzCZaW7kBjwR8s4Qh7iNBaMW4e1BsXP5eBiV7P5a3jRQczPqyhyUeiGFKzOz5atW3V4yTEAtbtpEqxt12uo0JgdL53M++DwOiBQfHnnsIjmBhMRMC+7ukERiHLdHWv/fe2fLn8N4JQzYzjY5sDLvy3mkKgXHYxUERxkTLIvPmTfVUDUp8s8TiYgZH3BXfuP4JDMKYav1nmbTOW/bsAbJsSEtRgoOwI+YYjawvME7FOIypZm3GfCDrDQ5y7oEB7t4fa3zYsFbq7P1YVtRzjzdGo+nO68wY59wDA+K9d7rjw1o+w3snDLl1zz0Ru4U0V4zz3mMJgXHYReUIjIk2c2dQK3XnosQjGRY2x5xZSSqyhj+LgyKMOQVHVrmyphmBA2VDuoUwcwqGuJUjoY8E+Wnvb2mlTkIsjNk4e3+YAZVT1b2XfR8GxGsnPaBbgvfms2/+DHAlUe+8XscE2unCvjlGRxZtd2feO2HIVfd+3ntMITAOu1a01YIts2RQtn2GixIMuZ1p5k5zxTgHRdiyidROd1BSFPMGYUjMR4LeVupUjMOYeK3UR5z72fdhgL/3T08IVN/jsEQrdZgSt0ta645/QMUsjInXSt2tGKdbCOzbOmNTQ7uFqO+9k/ceUwiMwy4qR2DM1qucCnkkGFIxzkUJdviPBKuOT/Zzq2Yb70kcFGFMtf6DL0ne3t9TNSix/rG4bcRZm9Xe39otgQcyGLLbFyqOW3Z4xfhZ+wglWqnDDr9LWsC5v/THCJRNWVHs/TAk6oxxR2O3EM78MCZaMQTvnUhMzKSo3OuY0LP3kxC4OALjsIuDIoyJd1Ea0k6X9Q87YlaM91bNclCEMdHa6Q6pGmT9w5DbqI8Ehz+rUN5cMc4DGQyJOUYgG9RS9Lj+2fdhQLxuIf4Ygea9n1bqsON2O1c73QZ5LlVBE5JCYEC04KCz9kmIRQo2+/P5I/i9kzEaSSEwDruy7JxZySMBDIh2URp0UGTOMuyImT2f9QXGOSjCmDlaqfNIgBREnbd2XP+l1DxjnHMPDNnuzms0anCktWL8WJVLMiwMcPf+sC5phf/Tps+QEAhDvOBIaJe0WmJIo1NSFHs/lucmg2dtiXyDlA1f1TBjHIb4556ICbG9M8ZZ+0sjMA7TyoxHAthxu4+VQVk2feljlAAMmSM4Ig1oK8dBEQacHgliZg+3Bke4KMGOTaxzjySdAuNDKsYJjmBZtzErRxzMGEcKbqPNmfWTYWmlDutidknLNKJTFHs/DIiWDF6OSAqROPdjcXO9d1IIZB+BcZhGYByWuMGRWBmUjTNHJIKDMMU9KN4Nbifdk0FMUgiMiRcYH9JOl+x52BHzkSB3Zow37v08EsCQ+RICe5Ki2PdhgHfuX4dUzfprv7dinPWPhcXc+12tCbFVtxzOPTCgWv9BnUKk/vceqXbuZ+/Hsjb7iJ2i3MB405sPnXJMITAO04oqMM5mAQM2u0P1SHhg0K0abPkMLUVhSLX2pbgHRTVmULL2YUdRlNoVhzUb2lZrUMU4wUEYso06RuPwZ7VWDdItAYZs3Qey0JaKfbMGpXNSFHdeGBAtOHhRMd7wGfZ+GOInhURMimpNiK1aqbP3Y3lzdEkrWrukERyEHd65J+a5v2n9895jCoFxmHYKjLNZwIA5Wgu1P5CRPQ875pox3j9zh70fy5qjlbQ0oGpQ4oEYi4tbOdVXMU5SFOyI+kA2ZO8/tVLfdmTNAtdx6yTEBgUH64Hxpt1/xZ0Xdrjn/uDA+KARSlXF+L75nwNXFO29s6+VtMR7J0yZKymqcf3z3mMKgXGYdm6lzkERy7vdXvOgSHAQdsQ8KPrrvwFttWBIzFmDGlI1SAYxDImbPV/NGB9SNcjax7LmaqdbtMW83Qdi53sFsIR4e79/5m/e+0mKgh1z7f2NXdIkPykKWNipYjxil7TehECJ9Y/FbfbxO2QWZdYyY5xzjyUExmFaSSt1GFJlUAYHBkcfFAkOYllRZ232Zc/TVguGRE0KcR6E2yvGmTEOO27naKVeZmpMi+LcA0PcqsHQMRrV2pdIiEUaqvV/s8qU5wGzZr2EwJZ/j1j7MGSubiGNc2al872XtQ8Dzq3UVz2f7DGoQyZVs7DD3fuDz/1lNT5MzZ1yOPeYQmAcptFKHZbEm7nT+KWP4AgMuY3YTtq9GhVNx5Ask9j7YUTMMQL+A3ELLkowJO4D8UHRWjFOUhTsmGOMwOGrtoRYqkdgR7yqwQEV415SFF0Csayo48O8ZPCWD9FKHUaUZRlxdOSQQiDOPbBjuz9v0vHGh+X95x7uvIsjMA7TTpnFZJDBgGiBcXfWYOu8Kb5Zwo5qjIAUd8Z4bztpHgmwsJiBQX/vb/mzqJqFIdsZKsbbZ4zTUhF2xA2ODJgxzrkfhsxx522dMe4lg7P2say5kqJ6uwSy9rEw99xzN+KdlxnjSMHtDN1CWu+8OXdeSwiMw7QiqzIo2SywrH1RanccDBhz5g5zZpGCqFWzKhq+qqkOizwOY2FRH8gGVYxTNQs74o7ROGfPF02DlgkMwpCt90AW0EpakvdA3Lb5kxQFQ26jdUnzA4PN3ULY+2HHHMERqWuUAK3UYcNcd14KgZCCeUZHZiqbDj55LmVVEShrf2kExmEaM8ZhhT9nNubMnRZeBiVVs1hWzDnLmTdKoKe1FkkhWFjMitn6A3EjsudhSNxW6j2JIQQGYcjVK8a9toqboP88IFS88WF+Mnhz5RTnHtgxR0Kg1JUMXt15WftYVtzA+IgOgRLrH4vzOiYEd0k7aD3zS+fEENb+4giMw7TTjPFy35FiD8zv6m21qBqEIX5wJCwxJPOeBnoyiBmjgYXdRlz7dAtBaqpHgjyT1pHaKrbOW+PcA0PmGqPRnhBL5RTsqNZ/cDL4kLVPcASG3M705tN/7i8phsCivBnLETtk8t6JFGx25/33JlIyeGunHOm897P2F0dgHKadWqlLbBhY1O3+/I0yamshgiNIQNTseeatISGzJUUNaivHAzGWVa3/0AcCyW2l3jJnlopZGOJWjtxEa6koFWXLn0VwEIbcHtd/eHDEbyXd3FKU9x7YEbVL2uhzP+sfy5nrzjusQybnHixrjlbqZdudV+K90xAC4zDt1EpdYsPAomZrLdR6SeKgCDvizhh3ZD3rnwcCLCzq2ncrxts6a63cvZ/1j2VV6z/Gvl9lz5fKml/JcgKDsCPunNmzQQ/EnH2woLIsZ2ql3rL+ufPCkI1bDBHaKcoNDrbdeVesf9iwiVoIdP5yT0IgEuB1TIj03tlZMU6HTDMIjMM0v2Kc6hEsx30guxuxpWJ7ayGqBmHHHBmUUseEDOatwYiYlSMkRSE153a6MQLjh/XfOmd2RdUU7PD2/pt4wZHWObN0ioIRcZNh/VbSjed+giMwZK4Z4713Xom9H4uKOkaglhTViIRAGBJzhJKbDN7bSp19f3EExmFa4VaMExzEgvwHsiscFMkehiFxD4qHVb8vW4IjknNQZO1jWVHnzHpjNFr+LFoqwpBT1WDsVupNm3+WSdW5n0cCLGyOc4/U0UqdhFgYMVdCYNHaLYTgCOyYa3xY64xx9n4YEfXOO2Z0nsS5H4u7dUcordpa+w1zvvPm/YkhnHsWR2AcptFKHVbEDY4MuSSRPQw7Yh4U5VYN9rYWYu1jWVHbyo2uGuSBDMuaq5V677w1HoexsNvdee8PT4gdMGvT65hAlzQsJ25g3E0Gb9n76ZQDQ9yOCeHdcoYEB3nzgQ1z7v2N2PthiJsMnrWNvhjofOeVirYHTyrGzSAwDtP8inE2DCxnrrZyRWs7XbKHYYc7azD4oOhWDbYGR6pW6uz7WFbMypHRSVGce7Cwav3fRKgYr84+nfPWVncOP7L3Y2GbiCOUstFJUax/LCdqO13HsFbqrH0sa7bxYQQHYVzU985BYwR474Qdm2NCbJRzj1Mx3poRy4xxMx4TgfFXvvKVyrKs8/995Vd+5enz3/M939P52Xe+853L/R8Dj18xzoaB5cQNjnBJQlqqyqkYc2ZHVYyXhVS0PiMDs1u2Wwh7P5a1jVkx7iVFtaCtHIyIeu73AuOM0YBtc557Gvd+d+0X+6ZPAFcTMzHEPekXg4KD7P1Yznzjw+iWAPu2+8MmHadLmvPeSSGQeev+j9j3/Oc/X9/93d/d+M/+03/6T3rb296mr/mar7n4Z9/wDd+g5zznORe//gmf8AmR/4aYyq8Yp60cluO1VFyvOj45QDmkcoSDIuyoLkoxAuPn7PkBM8alw/rP7wb/5wJTuA9kN9foFkIrdRhRFGXUR4L81FYuV9nbVo61j2Xdei1Fw879XtUgez+M81tJh955a63Um/b+nA6BsGOuOctl1vJnsffDiPkSAod0yGTvx7LcVuqhqnM/oyPT8JgJjD//+c+/+PXNZqMf+qEf0nq91jd8wzdc/PNv/MZv1Atf+ML5/4KYrMhoKQobOCji8SzmQbFSKGvvrVV/JFsTGMcyvLZyUbPnW9BKHUbEXfv+jOX2inEeCWDDfOf+Fuz9MGKutV+WLXs/rdRhyFzn/vZOUXTIhA1xW6kPqBj3uqTRLQTL2kTskuaND2v7yOnsU7L+F/aYaKXe5sd//Mf1oQ99SH/hL/wFfdInfdLSfx1M4LdS56KE5dzO1VaudcY4B0XYEfOg6LcWapGTPQ8bvDmzEeettbbT5YEYRsw1a7Bz3hpt5WCE3ykqclJgE/Z+GOGu/dh7f2M+LHdeGFKd+29WmfK8Zb8eKBvSKYpiCBjhdUmL+t7Z8hnOPTDE3ftDZW5gfMgYDdb/oh4TFeNtHnjgAUnSS17yksZ//rM/+7P6+Z//eeV5rk//9E/XV33VV+nJT35y7597e3ur29vb088feughSdJ2u9V2y4KOZbvdeq3Ud5t7KvnvFwt59Pa89lZZGfTverbbnjbfUmr+s0qp+lZZ7G61Z+0jsmrdDVnL7kEx+PvcMYO4UKbdbt/4562y1SlsuL19VFo/Kew/E5jo3uacmJGriLf3l83/7mXF+XC+321UsPcnbcw+a83HHz3fddZ54P8Nxe58plGm7W7X+Oet87UySeV+q12C/53hsePe9hygC937y/35+0hRNP+7lGul6ta72zzKnXeglPdYqz5+7/zf5ToL/O92u5FTE3V8L6sFXAr3zrvhzotFVXv/nVUevK+UxTnQuC+av4/kmbv323zvZJ99fJjrvbNQy79LBe+dsKNKCryJsPfLaaW+2zffeVe5895579HDj/w7EM2Y/y4fs4Hxd73rXfrJn/xJPfOZz9TXfu3XNn6mPpf8Ez7hE/QDP/AD+ht/4290/tnf933fp+/93u+9+PVXv/rVeuITnzj9L40Lf8pppf7GN7xeH/pDf7Dg3waPZ296fyYdry2/9etv1YN/8JbJf9YnffTN+qLj10UpPfjggxefecLmD/Ti49fv/d+/p19q+AwQw2te85rez9zbriRlevSRhxvX6xhffDz4lcr0tre9TQ9++K0Xn/mCD35In3z8+qde+yrdu/nDQf+ZwFS/+c5cVYOlX/7Fn9f/+a3pf9anfOhX9bnHr+9tdo3/Lv3hR/6Xvuz49Tt/5+166y17/2PBkH3Wmo/cStVV8cMf/EDQ3p8VO/3F49elMv3iL/2SHv2dyxT6r3jknp4iab+9F/y9Bgjx++857/0/+7qf0h+66f58l895z//Wpx6//vBHPtK4tp/zB7+lP338+td+5U36vXeTEDhGinusVb/xkfOd912/+zt68MG3T/6z/tCjv6cXHb8ulem1P/mTeuqd2ofKUn/p+OVHP/xB/Sx7Pxb0fx463HlVNJ/Tx/ic//MRPe349Tve+S49+OAHLj7zWb//bv2/jl//3Bv+hz785PcH/WfOiX32se3N7z3v/b/+ll/Tg+/71cl/1tMfeqv+7PHrQs3vnfdtPqyvOX79vv/9+/pF9n4spCyl7f5w53304YeC9/6v2G4Of64yveUtb9VTP3gZO/iiD31EVV/r1/3Ua6T1k9ljI/r4xz8++LOP2cD4j/zIj6goCn3jN36jVquV98/+9J/+0/p3/+7f6YUvfKGe8Yxn6H3ve5/+63/9r/qH//Af6hu/8Rv1CZ/wCfqLf/EvtvzJ0nd8x3foZS972ennDz30kJ71rGfpxS9+sZ7ylKfM9n/T4812u9Xv/fv/dPr5F33h56n81C9f8G+Ex7MP/Ny7pHccIiKf/4Ln6/7PecbkPyt7+0p6x+HrQrnuv//+yw997L3S2w5fPuOTnt78GSDAdrvVa17zGn31V3+1bm7aX3yLotT/++cOh7SnP+0TdP/9fyboP/fRt3+39PChcuS5f+pP6f4vfvbFZ1Y/9p+lj/6yJOlFL/wy6anPCvrPBKb6xf/6G9J7f0+S9OVf+iX6nGc+dfKflb35w9K7D1+v79xp3tff+2bptw9fPudTnqlP+Vr2/pQN3WcteteHPy696fWSpE955ifr/vs/Z/oftruVju9rpTJ93ud9nr7yM//oxcfW7/ln0r3f10ol5x4s6v/+wC9LH/mQJOn+r3mx/tB9059Nyh///0ofOXz91D/8tMa1nb35w9LhW40+57Ofq+d9Lut/iJT3WKvu/MYHpN98syTps5/7Gbr/yz61+zd0ef/bpN88fFkq04te9CJ90lPuu/hY+WtrZcVOn/CHnsTej0X947f9jHR7qyc/4T7df3/Y2+PD7/th6ZAPrmc/5zm6//7Pu/hM/lO/JH3wv0uSvvjPfL7KZ39p0H/mHNhnHx9+/3/8rvTOQyLUF37+C/S1nzV9HG32O/dJv3P4umx773z4A6f3zj/2Rz+RvR+L2ewK6Y2vlSQ9/Y/8Yd1//xcG/Xn7t/1daXMYofFZn/3Zuv8LL98yV//3/yU99GuSpBd+2ZfqNf/zzeyxEVWdvYd4TAbGi6LQj/zIjyjLMn3TN33TxT//+q//eu/nz3nOc/St3/qteu5zn6uv/uqv1nd+53d2Bsbv3r2ru3fvXvz6zc0NiziywqkYX6uQ+O8XC9k7c6GeeDfw3/X83EKuVNb8Z909d5/Iy71y1j5m0ve9y20nevdmFfx97taZMZ7nLX/e+vw99iYr2fuxGGfcmp5435359/475wfjVbnXirX/mJDiHaHUeb3ed7MO/Ps7raTLTKtVy5+3PpQSZsVWN+u1lIXPeQOm2BbnjgZPesId3axXHZ/utnPWcam8ee3fnPd+7rzjpbjHWrVz7rz33Qnc+1eX557GP291Ryp2yso9/ztiUdv9Ye+/c9OyV4/gjijP2u68N+c779r4nZd99rEt6nunU5xYSlqv18rqZ/q7Tzh9yXsnlnRbnO+p4XdeqXDeO1d5y/eS9bl9zs3xqMQeG8+Y/x7z/o+k57Wvfa3e/e5360UvepE+9VOHZ7h+5Vd+pT7t0z5Nb3nLW0ZlF2A+pTNjXAXzFrCcjRMdubMO3TrLhq9qcidvac/ax3JuvbU//WH4pDwfFFu56985qALXFnXvL89/VlG2rP/cOcQX++bPAFfg7v13b+Kt/VKZyrLl9MP6hxHe3r8KW/+Zc9ov2j60ctc+5x4sx137dyPfedu2/tPez50XC6vWf+i+L0mZs+Dbz/3unZdzD5Yz35235c/yzj3s/VhO3Ld+qTr7FN4NoIb1b8ZjMjD+wAMPSJJe8pKXjP69n/iJnyhpXD96zKdwA+NclLAg/4EsMDjoXJL2bcFBvlHCiJiPw9L5gbjsPCgSGIcNm33M9T82OMLej+XcznTu4ZEAKajW/511flnlNNbY4Ah3XizIO/cEB0fcwHimsm33r8797PtY2CkwHiMZ3Dv3t/y7xN4PI+a687YmRbnJsHvee7Cc253TITNCYDw7JoYUyvsTAiX2/oU95gLjH/rQh/QTP/ETetrTnnbRMr3PI488ore97W160pOedAqQY1l+xTjfLLEc75tlzMqptgey1bm1Ct8osST3khTjoOhlUA6pGmT9Y0FRK6dqVbONeCCDEbOde5R1PBKw/mHD5rj+70ZICPQqp1oTYt1z/yb8PxOYyD/3hCZF+Wu/de+v1j/7PhZUluXp3hszOCJ1dAkkIRBGzFYxrrx5/dMhEEbErhh3u4W0vndSCGTGYy4w/qM/+qPabDb6a3/trzXOAf/Yxz6m3/7t37749UcffVTf/M3frI997GP6K3/lr2i9fkyOX08OFeOwIm7VLK3UkY5bZ8Z4lNZCQ1qp01IURsxVOdXaLcR7JGDvx3KinntqD2Stpx/2fhhRVYwHJ4VI8tZ7a2CQtQ8b3KSouOPDOrqF0EodBkQ980sa1CmK4CCMuI0aGK9XjDfs/jljU2HDbdQRMtK5EKglKUSqFQKRELukx1z099/+238rqb2N+oc+9CF95md+pr7gC75Az33uc/XH/tgf0/vf/3699rWv1e///u/rec97nv7ZP/tn1/wro4NXMc5mgQXNFRw5tFcpL9s0ZtnholTsOChiUXHbaknZ8WmAqkGkIG72vJs9PGSMBrMGsZz5ZowPmDMrsfdjUVHnzDpfF21t2Tn3wIi4SVHulx2domilDgOidomS5CWGtM1Zpp00jNjG7BLonftbgoNZdlj/xZZzDxZ1u41cMa6qlXpXp5zz3p+x9y/qMRUY/4Vf+AW99a1v1Rd+4Rfqec97XuNnnva0p+lv/+2/rV/4hV/Qgw8+qI985CN6whOeoOc+97l66Utfqm/91m/VE57whCv/zdGmyKicgg1xMygvH4gb38lWdw6BcQ6KWFDs1kLVzah71iBt5WDDXN1CWtvpEhyBEXHX/lln9vyKcz9sqJIC45x7nI4Jg5KiWPtYTtxzf61ivC8pisdhLCj2ucdrp9uWFMW5B0b46z9wjMaQGePS4exTbOmWgEVt9u6M8dC1L69D5qCKcfb+RT2mAuNf+IVf2J6FevSUpzxFP/RDP3SlvxFC+a3U+WaJ5cTNIHb3qY520rSVgwGxs+er4yFVg0hBFRzJM2kdsZ30sIpx1j6W41eMX2nOLG3lYERVPRLjgSwb1E6XqkHYcBu1S5qbDN41QqmaMc6+j+XM2Uq9Vc4YDdgw34zxjmKIKiGctY8FuRXjUd47S7dD5pAZ47z5LOkxN2Mcjy0lFeMwYq6ZO0VXFhlt5WBA7Irxc2uhrpk7zFuDDad2ulGqBkfOGiQ4ggVFTYoq3RU/sFsI6x8LilsxXjZ96XMfyAgOYkFRH4gv5sy2fI47LwyIfud1z/1tCbF0ioIRc42OHDQ+j7WPBUVNCJR0njE+oAhO4s67MALjMM2vGOebJZbjPRCHtha6OCi2ZVDSVg7Li35QLAdUjK8IjMOGmHNm69nzjagcgRG3O7etXOSEwL4HMokACRaz2xfaF4dFGnvObNH2/FJVzEqsfSwqbtWsv/bbqwaPZ5+ykIp982eAmd1GHyEz4Ny/4twPG+Yao9EZHKzWP2sfC/ITAiN0iirPgfH2907O/VYQGIdppRsY55slFjTrvLW2j9FWDgZEnzF+yqAc8EAmkRSFRd3GrBiv7f2Ncvfcw9rHcuaqGO/ulMPej+XFbqfrVg22hvyoHIERc+393QmxzgMxez8WEr9i/Px1+7mfinHYsImZGFLvFEXFOAyLP0bDaaXe2iWNvd8KAuMwza8YJziI5czVWqg7i4y2clhe1EuS/Afi9rVP9jxsOLXTjVIxPiB7PsucbiHs/VhO3BEy/pzZ9nlrbvY8ez+WMVdCoDT03MPej+X45/7IXdLaPsesTRgQ9dwjqQqOFOWAGcsS5x4syu0SeLPqqPIe4mLvZ8Y47LrdRuySpoEV43QJNIPAOEwrMrJoYEP1SLDOM63y0INi7YG4r2qWyhEsKGrliKTqgbhsm7UmkT0PM+LOGHf2/iHrn3aiWJC/94e2lRvYUpG9HwZEP/d4SVEtfx5rH0Z4YzRuIndJ6xsfJrH+sZjoSVGDxocRHIEN7p03y+IFxrsLgapW6uz7WE70TlGnivF8WJc01v+iCIzDNFqpw4rqkSB25Yg0oK0c3RKwoLkOikVn1SAHRdhQ7f333YTPm3L3/s6QN48EMMANjsSvGG/5HHs/DPCrBiPs/V5SVMtnWPswYq52ut0V4wQHsTz3zns3Rpc0b3xYC5KiYMTmeO6PsfYHj46kEAgG+DPG43WH7UyK8vZ+1v+SCIzDNL+VOgdFLCdu1eCAyhGJVuowYb7seaoGYVtZlvNVjA9Z/6x9LOh2tjmzAzrlSKx/LOY28ggZv2NCC9Y+jJhrfNjgqlkSwrGQRSrGaaUOI6qzT3inEHnn/s5iiJz3TizPS4qKOjY175gxTkKsFQTGYZpfMc5mgeWcgiNR5sy6D8RdF6Wb8+dpqYuFeFWDobMGpVM4cNDal1j7WMyuKFUc12j0drpdrdRPFeM8kGE5UR+ILx7IWj5H1SAMiNtKWrW9v+XPWxEYhw1xg4MDk8FJDIEBcUfInCvGD8mwBEdg2ykwHqVTzsCK8RUzxrE8v2I8cP0PvfM6556Mc8+iCIzDNGaMw4oqiyzKA9lFa6G2i9Kd89esfywkfvZ81Uq9a+aOmz3P2scybiM/kNUfCVrlPBJgeVHXfz17flBbOfZ+LCNqK2nJfySjlTqMq9Z/lknrPHTObD0ZfEhwkLMPlrHZRxwhI0lOYHxQMjjtdLGgObukDSsEau2pA8wq6t4/tEMg5x4zCIzDNFqpw4oqiyzOA5n7QNxVOUVwEMuLHhh3E0MGPRKw9rGMOdf+vqtinFbqMCBuxXitnW7b5wgOwgCvajByQmyRtez9BEdgxLlqMFfWtl6HGlw1SCt1LC/2uT87Bkg6A4O898CIqltOlC5ptUKg1s2fcz8MiDpjvF4E1zdGQGLtL4zAOEwrMzYL2HC7rzIoY1QN1mdttiA4CANu93ErpzKvYpy2crDLa6cbPXu+w6mVOmsfy4m7/t1HglxF6yMBwUEsz+uWEDshlsdhGBd1fNjFA3HLx7jzwoDo3UKO67/o6hDI2ocBZVl6SVHhf2CtnXTr+ndHp3LuxzJu5xofVg4cH8add1EExmEaFeOwoCzLuK2FagfD9rZytFLH8uJXzR50zhhn1iYMiJs9LG/Bd1eMH9c/lyQsaK5HgrKrZJzKKRgw1wgZqaOtYpZJ1b2Xcw8WdLuLmQzudonqGh9GS1EsL+q5Rzqt/+52urz3YHm7ojxt1zHXvtQ3Pow3HyxvE3V82IQiOO68iyIwDtPKjAwyLG+7P387ix0cKUrmLMM2/6AYoWLcnbfW9iEuSTBgs58zONKBGeMw4Dbm3n/RTpfKKdjl7v3BD2SSvFbqXUlRVXCQtY8FVes/9p23u500rdSxvOjn/iEzxnnvgQG3MQOD0mXF+KBuOdx7sYyoXdLq48OG7P2c+xdFYBymUTEOC/wHsvjBEdrKwbLYgXGdWqln7YvfzZ7nkQAL8SvG4wZH9l1H8OqixNrHguK2FHUCg4MfyFj/WIb7QBa7cmrflRVVnX1Y+1jQ7XaeObOFsoFjNFj/WIZ77o8zY3xAMjgV4zCg2vel+Hv/5c8cOcFBLC9qUpSXFNIxOjInKcQKAuMwzasYJ3sYC3EPinHmTZ11t9bimyWWN1f2vDofCXggw/IWmzFeXZTKQiqKrk8Cs7l1RshkWcdZZYgpbeUYJYCFxE8I9IODraoHYs49WFB17p9jjABJUbDMK4aI8uZz+PMohIB13tq/iTxjvMzbR0e6gXHeO7GQuOPz/E45Bece8wiMw7Qi4xsllhe/nW69cmrAvDUSQ7CQ2LM2s7J6JOh4ICN7GAbEnzPrj9FoxUUJBmyOiSFRHocHt9OlpSiWF33OrPtI1lkxftz7ufNiQZtdzMC431K0FUlRMGCuc3/nCBnO/DDA65YQ/dzfsf+z/mHAXBXjZVeHTM49ZhAYh2m0UocFc7WSlvqCg2QQY3mb2DOnjorORwK3rRxJIVjGvPPWOuTOfxYBEiykWv+xK0eYMQ7r5kyK2ndWjDNjHMva7YtTdVOcLmnuXs+McdgWPRn8uP4P48NaPsTahwHx77xuIVA+8L2TOy+WEXV83uCkEJLBrSAwDtuyXGV2XKZsFlhI9Aey+iNB28dopQ4DoldOnbLnOyqnWPswwGulHiM46M7aLAcERyQCJFhMdfaJkxBVeyRg74dhcyZFlV17f/VIxp0XC4l/5ncTAqmahW3Rk8GHzBgnMAgDvLUf+c57CA627f0EB7G8W6di/GYVOD7MMTQpJOO9Z1EExmHfKXuegyKWMVdgUKKVOuyLPWc5O9bKFsoHzhhn7WMZ3t4fpa2c/0DciuAgDLidqZ1u597vjdFg78cyZm2l3vUxKsaxsPhd0gbO2iQ4CANi33nlVIzz3gPL3LUf+85bKm8//DBjHAbcbo/jw9a5siwwMO6t/a6kEBICrSAwDvvInsfC/OBI5MqRwRnErH8so1r/eSat8wgZlE7FeCvWPgy4jZ097z4Qdw2a9YKDrH8s41wxHvuBTO0l4yv2fixvEz0pymml3lkxfhwjw9rHQqLO2ZR02S2E4CDsin3uz05nn673npVEh0wsbNY7rzrefUiKggHV2Sd2MnjZWTHOe48VBMZh3yl7nksSljFnK/VSUjHkkYCLEhZSzdy5u16FZ1DKfRromjXozhhn7WMZc7VUlKR91xGc7HkYUFWPxD/3DEwIZO1jIdFbirqJIV3nKJLBsTD/zjvDnNm2z3HnhQHRx2jIaaXelRF+Sori3INlRL/zqrb3DxqhxN6PZbjvncEuKsZb0CHQDALjsI+DIhbmZs/HbitXdLUWonIKBlTBkdjZw92zBpk3heXN2k6384GMRwIsa7cvTi1v45x73C+7kqI492B50VuKDt37q8SQYtfzQWAe87bT7VjWVA3CgLlGCRRdCYEShUBYnLf3z9ApqvXNh6pZGFC998ccoXH4quPOS4dMMwiMw76c7HksK3rF+NAsMr5ZwoC47XT9g2J7UggV41he9FmD9aSoNjwQY2HRk0LqI2TaXglykqKwPPfcf1/EpMCi7EgIlKgeweKit9P11nvXnGX2fiwvfnDQrRjv2vt578SybmdKCpH6goN0ScPy3BnjwQYXAlEIYQWBcdhXbRgER7CQ6MGRi0eClo/xzRIGVBel2MGRzux5kkJgQPzKkdqc5TY8EGNhc7ZU7GylTlIUDPDmLK/irf9CmYqi42NUTmFhXjJ4lIrx+gNxC2/vp2oWy6juvHkmrfN448M6CyEkp0Mm+z6WEX2MwNA3HxICYcBc753dhUCsfSsIjMM+KsaxsPgV4wNnjFM1CANudxFn7ly0FiKDEnbN2Ur9MG9tSNUsez+u73bu4AiPBDCsmjUoxX0kK9UTaPFGCRAcxPXN1UpaGtNSlL0fy3DnzGZZeGD8nBTV8+9STiEQljXnnbezWwgJgVhYWZYztlKnO2wKCIzDvlPFOJckLGPOqsFC+bAMSh7IsJDTjPFrPpBl2fmixNrHQtzgSOzseWZtwrJN7Ha69VbqbZ/jgQwGuBXjMc8+h32/q52uUzVLYggWMGdwpDshlk45WN7pzhtljIC8s09nyfiKGeNYVtVKWprjvZMOmbBruy9P6zPOe4977ukohCAZ3AwC47Av56CIZXktFefIIqNqFkYVRant/rA+47eS7giOSM7ez0ERy9js55sxXg5NimLvxwK8OZsxKsY59yAh0efMntrpduz7EokhWNycFeOFMhWtwRFaqWN55y5psZ7JjxXjZcecWel89iE4goXMlRAo9XTLoWoWC4v+1j+4EIIzvxUExmFeeToobjt2FWA+/ry1GFlk7pe0lYNd/iUpciv1smPtS868NR7IsAy/YnyOixJt5WDT7awV4x1/HuceGDBXcLDzgUwiMQSLm/eBuCM4SHAEBmyijg+TN0Zj2J2XtY9leHfem7hVs4xQgmXRuyXUO+W0fczpkJlx5l8UgXHY516Uin3754CZ3MZ+INOEgyLBQSwg/pzN+sydruz5Y3CQgyIW4u/9cRNDusdoUDmFZd1GTwisV4y3fI7AIAyogiN5Jq1jdEw4BcZ7qgYJDmJhfjJ47Dmzlz89ITgCA6qzT5xOIVJ2XO+9gXHGh2FhtzPu/Z1nH947sbD4CYED3/ql87mfc8+iCIzDPmZOYWFeFlnkyikNPSiy9rEAt53oVedNSU72PAdFLGMTe9bm0NZazJnFwqLPGK8nBLZWDdItAcuLHRw5tdPtPfcQHMSyvHN/9KrBgSNkCI5gIacZ43Ps/UNaqZd7qSjaPwfMJPq5v9YtpBXJ4FjYvB0CB+793HkXRWAc9pE9j4VFrxqszdyhlTqs8tvpRm6l3nVJkpwZ41ySsIz4iSH1B+K2pCg3OMj6x/VFnzFefySgahCGzdpOt+tzOXs/lhW9S9rgWZu892BZ+6LUdn9YoNEC42NbqUsUQ2ARc955ee+EZdErxh39FeNVh0zW/pIIjMM+qkewsNlbqbd9jOx5LGzOivHDJYlW6rAr/pzloe2kyZ7HsuJXjgyct0ZwBAZErxh3W6kPDY6w/rEAv3IqfkJs0fYvAElRWNgm+vgkyV//HXjvxMLmTIrivROWRT/31LrDdqJi3AQC47CPDEoszG8rFz84OOyRgLWP64t/SRpRMX5qpc7axzLmnLXZuf7JnsfConfKqScEtlaOOP9ZnHuwkFNgPMq+L1Xr/7DvD2ipKBEcxCLm7ZTTlRRFYBDLiv7eI53Wf6F8RFIUwUFcX/zEkHrFeNt7J2/9WNZm73RJm6VbQsfmf5oxztpfEoFx2LeiegTLip9FNrBqkMopLOw29ozl+iWp66M5GZRYVrX+V3mm9VLtpHkgwwI2sff+WvZ86xiBLGPvx+I21ZzZaMGRqp2uVAxpqSix/rGI+J1yBnaKIjCIhW2idwiUt/eTFAXL5uwSOLxinHMPri/6jHE3Gbzsee9c0UrdAgLjsM99JCCTBguYt5V6rtaLEpckLGzOpJDemTsrZoxjWdUjQbwHsnrlFNnzsCl+txD/y0F7P+ceLCR6xbjXSn1ocIS9H9c3Z7eQy585uPNiYX4yeJxW6lVIpL9inOAglhW9GGLojHHWPhZ2G3vG+NBCCIlkcCMIjMO+FS1Fsaw5MyilrjmzVA1iWXO2VCy7AoOSs/5Lqdi3fw6YySb6nNkpFeNclHB9m9h7v5c93/Pn8UiABZVlqc3xkezuTdw5s51VUxJjNLC42+2MrdTLvD0xhG4JWFj0O6/kJEX1zRjnzQfLit5KvXbnbf03gLWPhc3eHXbIeycJgYsiMA77qBjHwvy2crG/WXbNWyM4gmVFb6mo2kFx8Lw11j+ur1r/0R7I6nOW2z5GYBwLi18x7j6Qqadqtmorx9rH9e2K8nQ2uRutYrxqp9vXKccNDvJAjOubt5V6VzI4rdSxrHvR2+lK53P/mG4hBEhwfbOOzyu7ksF578Gy3KSo2Gu/t0NmFeti7S+KwDjMK7koYWFzzx0pWuetcUnCsqK3VPQeyPKe7HkeiLGsc2A8UtXg0FmbZM9jYfFnjA9sqSg5FeOce3B98R+HNaKVOmM0sKz4rdTPBnfK4c6LBcyy9stzt5BOdAnEwqrg4DrPtMp71usQg2eMUwSHZW1mTAZXX6eo496fFdueiiHMicA47KOtHBZWHRTz7HBYDDapnS6XJFxf/IPimIpxHsmwrPit1N0F3/VIQPY8ljVnUlRvO+lq7+fcgwVEP/dIqhJiO7tESXSKwuLmHKHUufez9rGwTfQuafK7hXR9jvWPhW1id0kb+uZDERwWFr9L2oRkcEmZio4PYk4ExmEfWWRYmFs1mGUxAuMDD4pckrCw6K2FvOBIrs6Ja1yUsLBq/cd7JHDWf2dbOc49WNacbeU0tK0cax8LiN4tQfIqxjujIyQEYmHRu6TVksFbu6TluZQd//PY+7EA79wTa4zGKSmK8WGw7fTeGWNsZE2hvH3Osrf2Offg+uJ3SfOTwbvfO53AeLlv/xxmRWAc9hEcxMLOB8X4W2b3QZEHMixrzjECvY8EXit19n5cV1GU2u4PC3SOGeNlV1tFkkKwsHm7hWTt5x7JqRjn3IPri14xKzmB8e64OOceLM2fMR4jQDIiKao6+7D2sYDbWSrGq1bqPePDSIjFwqo3n2hJIaVf/dpeCMToPCwr/hiNMRXj5/Wfl9x7l0JgHPblHBSxrNvtfFWDUldrIVqpY1nx2+mOOCgSHMSCNvv5qgalYwbxoLZyXJJwfdHnLNdbqQ9pK8eZHwuYZ8by0BnjbkIs6x/XN2cr9dL5/xtVez+BcSxgnhnjVSt19ez9VIxjWdW9N3ZSiNQ3OpL3HixrzmTw3juvc+7PqRhfDIFx2LeiYhzLclupRzH0oEi3BCzMeyCLcVG6mDM79IGY4CCuy++WEPeBTOqpmiV7HgvbzJo931M1W1VOce7BAty9/77Ic2Y7ZyxLnPuxuOrOm2fSOo8wPkzuA3He80DMGA0spyqEkOIVQ2SndJCeP4+9HwubsxCo882H9x4sbN6EwJ73zpxW6has+z8CLIzAOBZ2DozHb6fLQRGWRc+gHNVSkY4JWM4s7XQdg7PneSDGAuJXjNeDIwMqp8r94fdlMYIzwDD3vITAuAmx/Z1yuPNiWVViyN31SlmMvddLCOxLiqKVOpZzO+sImb4Z43TIxLKiFwINbSfNew8WFn3GeO2k07n3/5FPkz75BSrydX8CFWZDYBz25bSVw7KqAEm81kK1qkEqxmFU/EeCevZwB9Y/FhQ9MCg1VIy3IDiChc259/c/ENdaiq7vtH8WiMzvFhI3IbZUpoJW6jAs/p3XD450rn9aqWNBXjJ4jKSoMVWDtJPGgvZFqV1xWJ9z3HkLdSRZMUYAC5tzdGShvPu988UvlyTtt1ttHnww/D8bk5CSAPu84AhVs7iufVFquz98O5urlXrrI0GeS9nxP5NLEhbgV83GfyToRMcELGieWYP1mVMDuoWw92MB7t4fP3t+xBgN1j+ubJZuIVXFeDkmIZBzD65v9i5ptFKHUV5C7Cruuad37ZMMjgXF7xAoRkciGdErxsckg8MEAuOwj9ZCWNAsB8WLVuodqgdiAoNYQPzKqTFt5QiOYDlecGSWbiEdLUV5JMDCop99LpJCOj6bs/djOfMkRR3+zPL0/7XgzouFRW+n65x7NLRTFOceLCD6uX9oh0CJTlFY1DwJgfUuaUNGR7L2cX3x1389KYTIuHUExmFeyQMxFjT3QbF3zjKPBFjQ/K2FBrRUlFj/uLpN9MoRyU+KyofNGGftYwHRK6cuWop2YP1jQbMkRTmt1Dn3wLLb7bGV+ixVg+p+IGbGOBY074zxEZ1yCA7iymYZH1YLDrbKMik/JgWSDIsFbPaxK8ZHFMHBBALjsI8MSixo7na6pfP/N6rWPwdFLCB+9nxt7Y+ZMwtckbf3z1YxTvY8bKoSQ+6sc2VZz9iLQQa2VJRY/1jULDPGjwu+v5U03RKwrFPFeOSkEKknIVCilToW5e/9Md58xpx7uPNiOZsZO+VIQ84+JEVhOdHP/RfdQgiNW0dgHPbxQIYFed8oZ3okKIY8ktFKHQuIXzVbnzPbgZaiWND8SVF5e2IIwREsrEqKmqNqsGDGOAy7tz0nBN53E7uV+ohuCZz7cWW7faHd8VI6R3CkMyFQOndMKHYM5cTVRe8S6K79sifBkG4hWNA8HTJHdIqiQyYWFL9jwoj3TphAYBz2cVDEguafudNz9+egiAVFr5p1s4fLngxKL3ue4AiuK/qMZWn4jHFv7RMcwfVVbeXmmjVItxBYFb2driSvlXrXuSd3EgJZ+7gyt51ovL3f/ZKqWdjlnvvvi9wlrbedLsngWNC97Qyt1Ovrv/PNhw6ZWE70jgljuiXABALjsC/noIjlzF81yEERdi06Z9ZLiiI4iOuaJSlqaEvRnIpZLKvqlhPt3FPb7bsfiFn/WM485/7qh77gCF3SsJxZxgioHhzp+CjBQSzIv/PGDY6MGx/GuQfX5SdFxT/3D+6WQ6ccLMB987lZRRgfVhsdSVzcPgLjsG9FcATLmT84MvCRjIMiFlCt/3WeaR0lMD5m3hQPxFjOPHNmB7YUzXMpOz5M8ECGBZzmzM6w9g9JIUO7hbD347rm7BTVWzFLQiAWNE9SSOH8JFPR2TGBLoFYjrf3RxmfVw8MDl377P24rllGR17MWe74bJUUxZ0XC6jOPvfd5MqyCIHxi0IIQuPWERiHfTnBESxnnoOif1Ea9EjAAwEWcK4ajJ8UMqpyiosSrszNnp+nrVxHxbhEUhQWVc1ZvhttxnIte35wcJCzD65rlnP/qZX68Wdt/wJQMYsFxQ8MalynKJKisKDoYzQuOgR2fJY7Lxbk7v1ROgRKF+u/s2622vvZ97GAczJ4/IRAKsbTQGAc9nFQxILmbqV++HnHZ2mljgWd5szOFhyhcgQ23W7dqsF59v5BD8Ts/biysizjV4yTFIVEzHnuL45PL4zRgEXRA4OS6l3Suu+8JIZgOX6nqNit1BmjAbu8GcszVIz3dgmkEAgLqpLB75spIZDIuH0ExmEfrdSxoKvMme36aLX+y31PiRUQX/VIEC972H0kGLj2JS5KuLr5H4j72knzSIBl+LMGF3ggc6sGeSDGld3bztlKXd6PF7yKWe68uK7ogUGpIThIQixs8qpmo+z99YpxWqnDplkSAqckxJIQiAVErxgfM0YDJhAYh3llTvYwljNLcKTeXoWWojCqeiSI3U709BUzxmGUmz0fr5X6hL2ffR9Xds8Jjtw3S7eQnkcC2uliQe65P9r6P633w+zC9lbqnHuwnFmSwceM0fDWP8FBXFeVFHizyrTKI8yZ9cYn0SkHds393tmfEFuND9tSCISrq7oEznLuKXvWPkwgMA77CAxiQW72/J0Z2un2zhinrRwWFL2dbn3G8uDgCI8EuK65x2iU6nl0q9Y/+z6ubO5OOf2zNp1zD3s/rmzO4GBRBcbbPucmg3PnxZXdztFOt9Ypp2DOMoyK3yVt4oxxzv24snmSwd0F39MxwesURVIUrute9d45Uyt1AuP2ERiHfWQPY0FXeSDu+ijBQSwoemuhyd0S2PtxXd7ev8S8tSo4yL6PK/Pa6UarGHf2/nJgUojE+sfVzZMUVbVSryrGWz7HnRcL8u+88fd+SbRSh1mnO2/0TiHHO2/XR+mUgwXNkxDonvv71j97P5ax2xfaHzP27puhlXpBK/UkEBiHfTmVI1jOLNnzF8ERZk7BHvegOFdSSCey57EgL3s+VvXIqPV/fCTjgQBX5j6Q3TdbO92h3UI49+C6/MSQuHv/ecZ4Wyt1kkKwHH/GOFWzeHw5jQ+bZXxS3p0NTrcQLGjuGeOF8hHFEJx9cD1zv/VTMZ4GAuOwjwxKLGjudrrqrRinlTqWUc1ak+ZpqzXqgYxLEq5snotSfe8fkBTFuQdXdm+WwODZYYxGB/Z+LMhNDIndUrc4Pr20nn3ylZQd/zPZ+3Fls8yZdYMj5Zguaax/XNec48PGVYxz7sF1bWaZMV5f/0MTYtn7cT3XGJ1HXNw+AuOwj7ZyWNDtduZW6n03JS5KWMhVKkcGt1Rk78d13c5RMX7MIC7Knna60vnsw76PK5uzlbQ0dowG6x/Xdc+ZM5vnPZ09hjq1Uh+AbiFYiBccmWOMRm+XNKpmsZxN7PFhYypmee/EguZupV4o73nvpBAIy7jnvPXfR8X44xaBcdhHYBALmiV7vvQvSgWt1GHQ3G21qBiHZV476cjzBotqzmzXR09nn1Iq9l2fBKKaJSFwTFIUlSNYUPR2upLOu/2ApKicpCgsY57gSH2MRsdnaaWOBVX33nhd0vyEwE45M5axHLcYItr6d9AxAVbN/97p/xw2ERiHfQRHsKDb2bPn+x4JyKDEMrwHshkyKIveMQI8kGE592bpmFBVDVbBka7gIGcfLMM990RLChmTPc/ejwWd2ulGW/t+UsjhxwF7P/s+rmyeB+Kz/opxksGxjN2+0L44rM1lEgI582M585/7+zomuIFx9n5cz9wJgQUV40kgMA77yKDEgmb5Zlmvmu36KBmUWMjc3RL6gyNUDWI5s1SMn+bMDqkY5+yDZSx/7uGBGMuZc85sMaRinFbqWMhVzv1dn2Xvx0L8Qoj4557e4Ait1LGgedpJj5gx7o3RYO/H9biFELMlg8f5UzEjAuOwj0sSFjT3nOWC7HkYtZm5tdDhgZhZg7Bp3r1/RDtdifWPq5qnWwJJUUhDNUpgjuBIVTHeOULpFBjnzovr8sZozDRrs6BbCAyapVuCs/Y16tzD3o/rmnv9V0mBrdz1z96PK5plfNjF6EhC49YRGId9WX4OkHBQxJXN00rd/ebIIwFscqsG55i3xiMBLLt3XP/rPNN6FfeidH4goK0i7JmnW0L9gYy1D5vOFeNztFL3f2xUrX/O/Liy+WdtkgwOm+Zup9s7YzlfqUqaJSEQ13bvKsHBjo/SJQ0LWbxTDkwgMI400FYOC7lGS1GqZmHR3N0Seh8JaCuHBVXrP96+r4YZ4x2fJSkKC5m7pSgV47CqLMv4rdS9Tjn58T+n4+PcebGQuR+I+6sGSYrCMjazrH03ITDvrxqs9n7O/LiyeQqB/IRYxmjAIi8pZK5W6kTGzSMwjjRU3yz5Rokru8YjAVWzsOgabbW6K0dICsFyqorxaBWz0sgZ4wQHsQy/cmSOqtmecw9jBLCQec78RcOvdXyeOy8W4iWDz9RKnYRAWOTu/dG6pKls+KrDae9n7eO6ZqkYr3XIHN4thPWP65nl3O/oLQSCCQTGkQay57EQv2p2jiwyqmZh0ywPZPWqwa6PkhSCBZmqGOfsgytyzz33zRIckUpaqcOguccnnfb+zvXvzBinzARXNMudtxYc7Fz7BEewkHnee/xuIb3b+Wl0JGsf1zV3UmB/xTjJ4FjG3Of+Qd1CsDgC40gDgXEspAoOZpl0s+ppATeY/0hWUDULg64yc2dw5QhJIbiuKns+asX4ce8fFBzJCQ5iGdeZM9vxUfeBjKpBXNHc45OKckhSlLv+OfvgeuY/9+cqSAiEQdeZMT6wlTprH1dW3XnvrnNl2TzvnYM65Uic+3FV83RL8JPBYR+BcaSBtnJYiDtrMNpBkVbqSMAsbeXGZA8TGMSCqvUfr6WinIrx408JDsKguVsqUjkCq/xuCfPMGjz82GHlJsRy9sH1zBMcrHULITgCg/yqwfhJURoyZ7Za/6x9XNnmuP7jjg+rv/nQKQr2uHt/vPU/slsIFkdgHGkggxILOQfG5zkoqveBmKpZLGPuqkGpJ9Ekz6Xs+J/L3o8rKsvytP7nmTGeuz9tRuUUFjLL3j+qWwiBQSxj/orZ6pcGtFKXWP+4qllail6MUKKVOuzxk0JmaKdb9rz3SBQCYTFuxXg0o879JMRiGXMnBEoDuoVgcQTGkQZ33hpwRbdzHBQvWorSSh323DpVg/HmzNYeCfpSKEmKwgJmCY5IlxXjZM/DIPeRINrePyY4QmAQC5m9lXqVFNX1cR6IsRB/zvJM3UIYoQSD3LU/R5e03vce6ZwYsmft47rmTAaXBgTGee/EQuY+9/SufZhAYBxpqL5Z8kCGKztVjEd7HBat1JEEv53uPFWDvWgrhwXM0k5XUhUOGVQxTuUUFnJvO0fFeK2d9OBzDw/EuJ651/7pl+gWAoOqxJA8k9Z5rPFhI4KDJARiIffchMAZkqLKvg6B0vnsw50XVzZLxfiY9c97Jxbi7f2ztFInMJ4CAuNIQ/XNstxLxb77s0BEc7dS76+cInsey/Bn7szxSNDzOCw5beV4JMD1zFM1qIuK8U5UDWIh3vqfoVtI/wMZwREs4xpr//BLdEyAPdW5/846V5ZFCoxfdEnr+CjnHixkloTYWiFErxWFQFjGPBXjtRnjQ5OiSAzBFc1TMT7irR8mEBhHGrgoYSGbU2B8rgzKMZUjXJRwPfe2M2RQepekvP+gmBMYx/Xdm6tivKx+GDljnEcCXJGXFBUtKdA/9xSdI2Q492AZ84zRuAyO0EodFs2TDH7+shhVNcjax/V4XdKiJUW5wRH1Z8VWZ59iNyBzHIhjty+0Kw7rbdYZ412fZe/HQvxz/zwdMtnO7SMwjjQQHMQCiqLUZj9DYNzLIM7JnodJ92afuTOiYpzAIK7oWhXjzBiHRXM/EHPugVW3s7RSb6oY7/g8ez8WcjtHO93aKAHWPiy6N3tCYD68lbrE2QdX43cInLFDJiNkYNCtVwg0T0IsgXH7CIwjDRwUsYAqKC7Fzp73vzt2Vk6x9rGQ25ln7pR9wRHJaaXOAxmuZ7aK8eP6Lwe1VGTOMpbhPpLdWc2UFNX12XwlVf+OkBSFK/LPPTO2Uu9MiqKVOpZxqhiP9jgs1VupD7/zsvZxPfO0UvcrxjtbSUvnVuoSZx9cjZcMO1OHzEO3HDpFwR6vYnyGvV/qOfPDBALjSAMtRbEAr3JkxkcCZm3ConkqxmuPBINbqRMYxPXMVzF+WO/FkKrB3HkgY+/HFVVnnzvrXHk+15zZjsWfZSRFYRGztFRsaqVOxwQYNE8r9VrlVNdnWftYyL05zv1jWklLJIZgEfNVjLsrvq9inL0fy5jlzYdW6skhMI40cFDEAuYOjkjHR7LBB0XWPq5nlorxsQfFav2z9nFFc7eVK0fPmWX943qqvX+udrqjHoh5IMMV3c5ROeVVjgzY+0mKwkKusfd3HvxJBsdC/GKIeZKieu+8XtUsCeG4jtkqxo97f1EOufPSLQHLmKdLoLP3lwPuvFgcgXGkgSwyLGCeyhE1PBDTSh32eAfFaLM23TmzQ4Ijx4sSlyRc0dxt5c5zZgc+EBc8kOF6qr1/rqrBsi8hUKJiHIvwWyrOM0bg8EsDz/2cfXAl+6LUdn9Yl3O10+3vkkZCIJZxb5YxGiPeeyQ6ZGIRs7SSlnS+8x5/RiEQDLpOxTihcesIjCMNZBBjAe43yjtzPhJ0HhRZ+1iGFxyMNkpgbHDkeFEqC6nYd38WiGT2ivFB2fPs/VjGuZ3uTLMGyyEPxFXFOEkhuJ75W6kf/p2ipSis2cyWDD6iapa1j4X4CbGxksHdL4d0SePcj+ubvWK8OvcMnjHOuR/XU53780xaxxofNmaEDEwgMI40kEWGBfitVeZrpV4MfiS4jfd3AHr4D8TzVE4NnjEu8UiGq5knKUTjZozzQIyFVEmBcc89tcqpoS1FOfPjiuZupT7ocYzgCBbgVU3NtPdLmQpaqcMgPyF2nmRwWqnDorlnjHPugWXVm899NytlWazA+Mg7LxZHYBxpWBEcwfW5wZForaSli0cCWqnDomr9313nMx0U8wHZ88ycwvXNkhQiXcwY73wuICkEC7mdvZV6T1KIRCt1LGL+hMCxSVGsf1zHLOOTJHnBwbKvUw53XizDTYqKFhysBUd6ERzEAuarGK8C4yM75fDegyuau0uaqBhPAoFxpIGDIhbgPhJEzZ6vfXuklTosmuegeDYsg5hHMlzfvTkeyCRVq35YxTh7P66vKEpt9se9f7ZOOfnwVuoFVVO4nnkqpxpmjHcmxJIUheubu1OONKByKufcg2V4bz4zJEUVQ+bMMmMcC5itYrw+Y7zro5x7sJC5k8ELZQOywbE0AuNIA9nzWMB8FeOHb477QXNmWftYxq3TWiiai+BID6pmsYBrVYwPfiTggQxXUgXFpfmqBqUhFePVjHHOPbieWdpJe4HBw585fIQS6x/XcW83f5e0oq9LWp5L+bFTFGsfV+Tu/XNVjPfeeUkGxwKuNmOcMRowqDr7zDVCZtDej8URGEcaOChiAffmuCRJl8GRzoMiax/LuLebo2rQn7U5KnueixKuxK+cirn3Vz8wYxw2Xa1qsO/zK4IjuD6/anCO4Mjxx8HnftY/rsNrpT5Tl7RBszZJisICqvW/yjPdrOaYMT4gITB3xodx7seVeMngM5z7BwUFKYTAQqqK8dlGyIiC8RQQGEcaCI5gAbczPxIMC46w9rGMKkAy30FxSHCEqllc39Uqxrs2fx7IsIC5175UtRTt+XwVHCkLqdh3fxaIxKsYj7b+G2aMd32clqJYwCwzlqWGVuoDE2K58+KKqjvvHDOWpXO3kE7MWcYCbmfrkFmvGO/4LMngWEBZlqdz/6Ljw7A4AuNIA98ssYB7u7mqButzZrsqR+6ev2bt44qqAMkc3RKkkZUjEusfV3O1GeNdH6VqEAvwEwLnS4rqrSFh78cCbq8wZ7b2S5cIjGMB97yqwZh7/xkV47Bq/juv+oMjFENgAbNVjF/MGB/YSp2kEFzJrihPo43mSganYjwNBMaRBh7IsIB7czyQSc53x7GVI1yScB3bfaH98aQ4X/b8gIpxqmaxgOtVjHd81qsc2cX7OwAd7s1SMauGqsGez3P2wQJu5wgO1hICq69akRSFBfgJgQvO2jwFxjnz43rOXdKWHCNAUhSu797MFeN0yIRVcxdCHAzY+7E4AuNIA98ssYC5v1lW3yOLrm+W+UrKjv/ZrH1cifs4PNdBsVDWn0LJAzEWMNvef5q3ViVFdQVH3KQQ1j6u43aOGcvSZSv1vs8zbxALmKeV+tn4B2LWPq7DPffE3fv9WZsFrdRh0KmV+kxjBAade7jzYgHzzxgfmQy+Jxkc1zFfIcTIvR+LIzCONHBQxALmnrc2aM6sRPY8rs5c5YjE+sfVXKtivPNfAM49WMB8gcF69jwtRWFPtfev8kw3q/it1IfNGGfvx/X5YzTm2fuLUa3UOfPjeqq9f752uowPg03zJYNXM8YHnHtyksFxff57z5yjIwmNW0dgHGngoIgFeFWzM1yUilNLxR7MW8OVXadyhHa6sGm2pKgxM8a9ilmy53Ed9+aaMV6rGhyVFMW8QVzJqWpwxuCINKZyinMPrsMdozFHO13JHSXQgTsvrqwsy3lmjNe7pDFjHAbNlgyuEYVAWXa+97L2cSXee+eMCYGwj8A40kAWGRYwfyv1AQ9kEm3lcHWztdWqV470PhLwQIzrm79i/PhT5q3BmNkqxr1W6jmzNmFSlRgyV7eEat13j9Fg7eP6bq+SFDWiWwjnHlzJ3Gf+gyHJ4HfPX7P+cSUmKsal895fkAyO65hvfNjIQiAsjsA40kBwBAtwK6fmnLnTP2+NtnK4rrkvSaef9h0U13QLwfXN/UBcHI/fg4MjVMziSq6RFDWsYpzEEFxfdfaZKzB42vupGIcxs41QUu2BuO/j1fovC6nYd38WiGC+M7//JePDYNF8iSHVFwPGh0kkReHq5ksGr995iYxbR2AcaeCgiAXM1lauXjHe93EOirgyLylktoPi2HlrrH9cR7X355m0ziO2wBpVMc65B9c3X1KUHxzsrxpk78f1zRMYd1tJH3QmxLL3YwHenXe29Z8NTwaX2PtxFf7an6lLWjnk3OMkBO5uI/49gHZzF0Oc3zspBIIts40P8xICB3RJw+IIjCMNVI5gAfMfFI8/HRocZO3jSm6v8EBWlCMqRyTWP66mqh6572alLIs5G6pa8QP+zJx2uri++SpH/Oz5Xt7eT1tFXMe93cyt1IeMUHLHh9EtBFdynYTYEePDJM79uIr52un6SVHceWHR3DPGi6GjI08zxjn34DquMT5s0N6PxREYRxo4KGIB7kExagZxrZU6GZSw5nauBzJHOSQ4yAMZFlBVj8z1ODxo3lqeS9nxgY61jyu53bqPBHNlzw/oFuIGB1n/uIKiKLXZnZOioikvA+OduPNiAbMlg7tVs8ppJw1zZqsYD+qSxtrHdVyvYrwHHTJxZbOND7vY+wmNW0dgHGlwD4oFlSO4jrkfCQZnUHJQxJVdp53ugIPi6u75a9Y/rsStGI+mITgyuJ00D2S4knuzJQT67XQHJwRK7P24ivmSYf21LzFGA/ZcZ/1TMQ57rpMUMuTOy9rH9bl7/51V/ODg6PdOOuXgSu5dIRm80IAOmVgcgXGkgYMiFuC2lYt7UKxlUA4NjhQ7qSi6PwtE4D2QzdZaaGzlFBclXMcsFeMaWTEu8UiAq5uvpWhA5RTrH1dwjeDIaYRS1+7PnRcLmO2BeGzlFAmxuLL5Rsi4bzYDzj1r1j6ur3rvvLPOlefxx4fRIRNWXS0hlsi4eQTGkQYqR7CA6pEg+kGx3kqdB2IY4z2QzfRAPOiRwH0g3t1G/HsA7eapGG9ICqFbCIyZbd5aLTg4rmqQcw/m57XTnSkwWByfXoZXjLP34zrcZPC4iSFntJOGRVfrktb3efZ+LOB2lmRwnQuByoFvqNUIJfZ9XImfFDVjQmC8PxkzITCONHBJwgKqb5ZRK2YlXWZQ9qB6BFc2XwZl/ZGAdrqwpSzL2WeMD86ez6vAOCNkcB1ucCTuvLXzn3uYM0tLUdjiBwbnayXt/tiIOy8WcDvbnOWxYzTY+3FdsyWF1Nc+rdRh0CzJ4JJTCOT9tN2pQ+Z2wIeBcLfbaySDM2M8BQTGkQYOilhA9c1yvoPiIThSDG2lLvFIhqvwsuejZlDWHwl6Ps/ax5Vt9+VpXUbtljB2zqzktJXj3IPr8CvG58qeH9kph70fV3CdVuoDRihx58UCrtdKvefzJMTiyq7VKadg7cOguSvGT51y+j7vdcgkIRzz8wuBZnrzKakYTwGBcaSBRwIs4N7OSgYl6x/XNVvVYL2dbt/H1zwS4LruXeGBrCiHBsaZMY7rmq1qtp493/dxWqnjyuZrp+t+OaBTVJY53UI49+A6qr0/z6SbVfw5s9KhU9S44CB7P+bn33nnG6Mxrksaax/XcW+uinH57529l97V+vw1Zx9cwWwV4/UOmUTGzSMwjjRwUMQCqkey6BmUxyPipAxKDoq4gtvZZm367XTHdUtg7WN+t1dqqXj4sUcVHNyx9nEd92arGPez53sXf05CIK7rGmMExncLoWoK11Gd+++7WSnLIgbGaaUO4/wuafON0aBiHBaZrBhn/eMK7l2jYlwDRudhcQTGkQYuSbiysixPF6X4FeOHb5aDMyjXd89fEyDBFcz3QOxXjPeeE7kk4cruXSl7+PBLAx+IqRjHldzOVjVbrxgnKQq23JsrIbChlXrv4aeqnGLt40pmqxrk3A/j3Ha6USvGvcU+ICHQXfu89+AK9kWp7f6wMOMHxms/pVsIjHl049555wmNDhohg8URGEca+EaJK9vuy1Nmb/RvlLUZ4+NainJRwvxmqxqsPRD3V4yz9nFdV5k3NXjvPyZF7TcDXhSAcG5S1BPuzLf3jxshw7kf87tOUsjB4MpBzj24klMy+Exd0g5fDRmjwZsPrmu+ivH6GAHuvLDF6xA4UyHQqWJ8TJfA3W3cvwvQYL4RSiPvvFgcgXGkgexhXNmsB0X5gXHaScOaa7WTpq0WrLm9wozx0e10JR6IcRXXaClajN376ZiAK7jnnXvm2fvPD8Q9v+UUGGft4zrm7pJ2kA3vlCNx7sdVXK9TTg/uvLgyr0PgTElRpfezDqx/XJnXSn2u8WFDzj1YHIFxpCFfSdlxufJIgCvwD4oztVIvCY7ApntzBQfLsRXjrH1c170rzhjvb6fLAzGu69HjA/E6z7ReLbn3s/ZxXbMlxNZmDUojxmiw9nEl1QPxnejtdA9rvSirZPCezxMcwZV5rdRnSogtBiWFcOfFdV23Yrzn86x/XJlfMT5fMQRhcfsIjCMdZM/jirw5s7O1Upf3YyseiHFl81WM1x8Jej7PAxmubLaK8cYZ4z2/Z333/DXrH1cwX9UgrdRh22wV47W1f/ixB3deXFFZlvr/s/fnwdKud103+r3v7l5rPdPeT/bOJgNTTAiIQoFiAEVIICTqc3wVpZTCEbTU0rIsy7LKoo5H0MKDlnVUXj3vgcJjMQjoqfdYWOpGCArCYQgOASKEZAcSyLjHZ1pTr57OH91339fVfXf3PVxTr/58qsjTT6+1enXC77n6d/2G7/dq7MljvPHWIGc/hMWfnK49ELt7KIR6D4TF68Z403pn36z5IKUO/rHOfpf2YU3rnRAdGuOwP+C3BgGxJihdb4wvUsN2E5TEP/jHniD2OEHZJPbHxD74J8RQSOPmiMTZD0EoimRuz32pm88ssQ/+sW0EXOb9HWw0iH0IgLkx6/zsn63eebEPg7Sw499z3r8t/rOMsx+CEmJjfNbq7GcoCvzjLe9fs46kM546NMZhf0BWDgLiz2tQRqK4+CuJIiSGpZjgNFFcbYwjpwtpEcJjvPbGOPEPgSni372FjHn2a3fwk/dAYELaaOwsknHnhYB425iV1u+8u76fxjgExr7z+hsGlxiKgrQI6TG+E85+CEwR/3kmDXrZju9ugn3nZWM8fWiMw/6ArBwExO8EZZEo1t0apDkCYSmm550nimsTlDvgkgSBCdEcUe3miCGljmICBKCI/xsuJeUkK/6ndc7+HDldCMvl2JOF0upQiPVgA0XuM5tI0+n27wXoiJX3eFJJq7UxKyGlDsEJOxS1A4aiICDDAENR9YfBTZVApNTBP6Z9WJa5rHeaeX/OvvgeQGMc9oecRBHC4XVjvHGRgOYghMVborgyQTndFfuWxzIFMvCPt82RNh7jDEVBYC6WZ79nKXViHxLD39asefbnK89swMz7p+Q+4Bc79l2rpBV/tGiOcPZDALzZh1UMRdWu+XDnhQBcjkN4jNcdBkcpCsJSDMT6GgqR2BjfF2iMw/7ABCUExJuUtLSeKDYqEpAogn+KwRBflyRpXiDeGft5v3zM2Q8BMM9+p1uzFZsjO7EGQ4h/8MtoMtVkOj+UnW8NrtpoNCmQ0RiEAHjbml0pkEk18n5yHwjI0GqO+JJSpzEOaWLLSfsdiprujP9F3k/sQwCseqc3G42aZ3/fPPvZGAf/LFXSXMf+2tlPZzx1aIzD/sAEJQTEr8e43RjffUlicwrCMgw1Qbnr+7PMOPu5JIF/LgIUyPDahBQJ4TMrtdkYJ+8H/wx9bc2uDIXM/2RzCtLB68b4qkraztjn7IewWDYaTpWiKoai6sY/OT8EwBoG99QcrG8jwJ0XwlLEv1P7JImN8T2ExjjsDySKEBCvBeJFajhtVSAj/sE/3jbG1+R0a2SKDEVBQLwViCul1JsUiDn7wS8hLGTmj5BSh/S4HPuXUmcgFlLE71CUfefdHfv4zEJYhovc56ifK899+cw2VEwYc+6Df7yd/V1iX6LmA0FYWkd6V0mD1KExDvtD8WE5HTF2A94xZeXcf1h2kZUjUQT/mB7jTmm6NSgxFAVBMZsjTqfn20ip9wwpdQrE4Bm/koq2YkKzzRHyHvCPPRjiqUA8qzsUxUAshMPymY0tp8vZD4Ep8n739mHreX/toUDOfQiAmffcOPI8DN5kEYg7L3hmMp1pNFnYh3kcBp/WXQSCqNAYh/3B8hscx3sfcBDYBWJfUuo1YXMEAjKbzZaDIX6bI5mmtTbGC781CmTgn8urcFuDFIghJYa+hkIkq0A8Vd6wMUjsg39CqIW0kxQl/sEvYaTU7b9vhDsvBKbYGPelECgZQ1F1m4MsAkEArLPfm31Y3WFwBgIhHCGUciQ2xvcFGuOwP3BRgoDYflO+PHfmR/DO5iCJIgTEVEvwKaU+ndVMFJeyckwPg3/8bQ1WbI5QIIaEuLgKJaVeAyv2OfvBPyEKxMXm1M5/BJz9EBB/sS9jGLzNQCCxD/4phgKd5z0VW7M7rQT6hlIUQ1HgmQtvUurmMGzNs7/PQCCEI1RjvOKvkCA0xmF/4KIEAfHqtbmUlVv8tVGBjEQR/GLZCHiWUp/urBAIWTkIij8p9SqP8R0/YxXIiH/wi+Wx7NFvbb4xvuP72ZiFwBR5/1HPtc9sm6Eo4h/CYQ3EervzopYAaVKc/c4XISp9lhmIhXQIYiHTamOcgVjwi2kh41wlTat3XjrjqUNjHPYHLkoQkGGAKbJ2BTIuSeAXK/ad+6019JmVyvjn3IcAXFz5ktPt4DUocfaDd4JOz+86/XNiH8Ky9Jn1uDVYfyCWOy+EY+hzY1z2nXe3ShpnP4SlyH1CKOXsnAen5gMB8WchU3XnZSAQ0sGs9/gaCJQa1DshKjTGYX/gogQB8bo1u/h4rL01yCUJAmJODzv3GDeYKq+XKRbSWsQ+BOBy7D/+p7U3p9gYh3B4VcpZ8Rvcmffkedkcx0YDAhDEZ3ZRetmd9/fLx5z94BlvW4PSMti580KKjCdTjRfdap9KOcXZv9tGg/iHcPgbiDVif9bi7CfvB8+E9BinM54+NMZhf6AxDgHxNkEprcnKNZseZoIS/GJLSfuboJRqbI5IZfxPR5j0gHcur3xJqZux32J6fkzeA37xWySw/QZrneSFlQA5PwTA29bgyuaI+edGaI5AQELeeZFSh5QIYSMgmRvjKCZAOphnv687b/1hcPIeCMfQq31YizsvRIXGOOwPXJQgIGai6Mtzal1gawMkihAQW0ras89snZ8h/iEgxWBInkmDnkufWSP2Z3WLBBTIIBwhp+dr+a0V8U/sQwCWjfEkfGa580I4zIFYX4oJpY0AjUFIhwtfjUFJqz6z9jMbsJSiOPvBLxe+8n4sZCBxwqqk0RpPHRrjsD9wUYKAhPmwbCMrR6IIfvE2PSytTc/X2xjn7IdwFPF/Y9BTlrlsjK/7re2skPXNAhmycuAXr43x1SJBnR8pCsSoJUAAChsN51uDVnOEoShIj6HPO++s+KOujQDDsBCOUEo5DIZAinird1obs8VQyI7Y75tnP3de8IvfeqdtJUBbPH1ojMP+wEUJAnLpS15lVlEg21kk4JIE4TCnh917LDf0mZUYDIGgXCzldH1ujhTNEbYGIR28DgSuyio2OfvJe8Azo8lUE28+s+tDUTQHISXMO697lbRCSn3xV/IeSIigPrNqaJ+HzzJ4JoTH+PIZ8h5ICPvO6zfvZ2E8fWiMw/7ARQkC4i1RrLwkNSkScEkCv5iJos8JymmrxjgXJfBLEf++L0kSRQJICyvv8Sgnrdoe40VjnLwH/BKuOVI8RXMQ0iGESlrtvCfvqVBUI+8B33i98zIQC4lT5D5ZJh33fW+M74DYh4DYNgKepdTZGU8eGuOwP1hbs3xYgl+G4zKhc5ooar0xTqIIKXHpM1FcnaCskyjSHISAXF4VG+OuY7/j2c/mCHgmhM/s8m+1bDSKxjh5D/gllNeg2kipT4l/8IuZ97vfGG/YGM8yzn4IhtfmiHHnrW+fh0oghGM5DN53bR+2PhC4M/i580JALn0qZLZZBIKo0BiH/YHmCASkSBSPerny3E+iiJQ6pIhXz52VAnHjjXG8ZsEzRXMwvY1xCsTgF2tz6shPgXhStzEolfFPgQw8E7JAtvJUNdx5ISAhJEWndVXSJGw0IBjBfGbXn6qmf1w+Jv7BM+Wd198wOBvjkCJ+laJWF4EgdWiMw/7AxjgEZLj4sDz2vDE7/7OJrBaXJPCLOT1/48hvgbhegYzBEAjDeDLVaLLwmU1BUrGPjQaEI6mtQaksEM8m0nSy/XsBOjAch7ERaJf3c+cFv1jx71tKvc6P9GmMQxgurjwORamqOcjZD+lwceVpGFxthkKod0I4THXYE6fqsDaz5X9AytAYh/2B5iAEpCgQ+5YT3fSUBZckCIi5OeK+OdJigpKzHwJxOfboNcjGOCTOhc/p+eVpX7MxKHH2QzBCSanX91nul4+JffDM0OvG+EqwN1GKIu8Bz1x43Rg38/4500YqgQzEgl+Keqff2G8zFELsg18uAy0CzZTjMb4H0BiH/SFnaxDCUTRIvEoLzfLFnzs+LPOeimIysQ++8boxvrI122hrUKJIBl659Oo1WLUxvgPUEiAgQ5/NwRU53eY2GhTJwB9+JRXbnP0MhUA4CjndPJP6Lu3DJBXRPp01GYpa5D7EPnhmaFnIuD77y4fLjfEmPsvEP3imGAr0aSFTe2OcYXAIiKkW4ldKveadF6JCYxz2B6TUISBhJijtPzeSZfitQTCGZoHYtbRQ5dbsriIBzUEIQyif2SL2d9IzhkJoDIJn/DYHiz9aNsbJ+8EjXjfGW+U95lAIeQ/4xbzzZpnjxvgi/lud/eT84JkLrwOxFTUfmoOQCJPpTFcTX4tAVdaRO8j7Wi4CcecFz1z6tFBaUYqiL54+NMZhf0BeBQIxm82WFyXnjfE2kooSsnIQDL8e4+sPmxUJOPvBH5c+JRUrbTTwGoR0sIoEnoYCaw+FSCt+g5z94A9rKMRjgaw2eG1CQJZ3XucqUVrmOcXZv1MlTeLOC8EIl/e3sVDi7Ad/DMdhYr+2UpS1CMTZD34JNRA7V8ikNZ46NMZhf0BOFwIxHE+XyVsSkooSsnIQDK/SQhWDITuLZDQHIRDhLkmF39oOaI5AQKz4d60WIrs5UqtIQIEYAuF3KMTM+/PVp6phIBACcnFVbA16aIwXUurceSFBLgIpRZXxj5Q6pEE4KekGNhpFvZ/YB8+EslBiY3w/oDEO+4Mpp4u8CnjE7wfleqLI9DykxOXY8FsLclHaAVLqEIgLn1uDnf3WiH3wS5H7DHqZ+j3XgyErjfE6P2NZCRD/4I9wUurFn7uaIwyDQziKs99LY7yLlPp0hDkneMU8+0Pceac7836z3kneA/4w6z3u8571oZBaLIeiqPWDX+y837OUOmlM8tAYh/3BKhKQKII/vF6SKuV0a/wYfmsQCL8TxG02xjn7IQyXPm0EVLU1yOYIpEMxGOJeSlotmyMMRUEYLn1uDVYp5Uw3fe8CUy2EYXDwzKU3+zCt3XHrDUWhFAVh8LoMUWmfR94PaRBqEWjpG451JCTE0FKK8jkQ20ApDaJBYxz2hz7NEQiDX4/liuZInUwRWTkIRKhEsbbnFNPzEAiv08OVW4M7yHtStngfNEfAM8NF/LtvDEprUupNJBUlch/wSihJxdpy0gwEQiBGk6nGizVWL43xpZR6zYFAiaEoCIY5DO5+Y3z97N+9Mc5QCIQhlHVeMxsNFoEgDOEWgdaeggShMQ77Q4/peQhDOM+dxZ9MUEJChPYYR04aUsHyGnTtsWxdkmr6zEqc/RCMUk7Xw/VwKaVu/XU75P0QiKEpKer87G+xOYJ9GATCspBxrpQjQy1k8demZz95P3jkMvDW4M72YJ/YhzCYixA+h0IabcwWZz+LEOCZ4uzv5ZkGru3DqlQCHf8GcAuNcdgfuCRBILxekgwaeYz3maCEMBTx3/eRKFZuTiErB2ngVUq9zca4xNkPwQjjM9ugQMDZD4EIJae7+ZkVUEuAQFyaw7Cuh0IkI+9voBbC1iwEwuvGeEWsN9sY5+wHf9gqaQkoBEpsjEMwivj3k/dULcLRGk8ZGuOwPzBBCYG4DCyr1ThR5IMVPFIUCfx4Da57Tu0M5z4FMgjD0GyOOPdZrjr7GxSIJ2wNgl8uF1uzPuV0GxUIaA5CIIJJqc8WpZedzRFiH8JgNkecDwRKWkqpZ02UcpBShzBcGmohzm1kzObgrGb80xiHQFgKgc7vvCWzJlLqfe68EAa/w+AVignufws4hMY47A9mkQBZOfCI5THuVUq9heeOJE3Hbt8TgMFlAJ9ZqcwZdyomUCSAQFwE2xjPtnzjCkipQwBGk6kmi1Umv1LqTQYCaY5AGEJtTpVqIQ3kdLnzgke83nmllYHYGhuzEnk/BCPUMkTts5+8BwJhKmSGuPM2WgSaTaXpZPv3AnRguTHupd5Z0ij+IRo0xmF/YHMEAuG1OWJdiCgQQ3oUE5Q3jnxKKjbw3KFABoHw2xxxoBYC4IkLr1LSMqTUmwwEMhALYTDj/6aXrdk5tQtk5D0QCP9n/+pQVB2lHPPOy1Ag+MP2mW0wtFqL9bx/Ot30vQs4+yEQ5sa4e7WEqo3ZpjYaxD/4o9wY9y2l3iD+IRo0xmF/MC9JFMjAI2ZzxGei2EpOVyJRBK8sE0UfslpVm1M7iwQMhUAYLnxKqbe9JBVn/5jYB3+YUtLHXiQVaY5AunhtDlbl/bt+hpwfAmHJ6XpUipq2UcqRiH/wShH/J/1cWea4Md6mOWjZaJD3gD9MGwHnPsuWjUCLYXCJej94JZSUeqNlCIgGjXHYH/Bbg0AkL6XORQk8MZvNlvHv02tw/qhukYACGYTB8pn1qBbSSlaO2AePDH2qJUhlsGdNvAbNvJ8CGfjDr5xuC0nRLDOGooh98Iclp+tVSr1lc4Q7L3jE6523qjG+Uy2EYXAIwzCQQmYr+zCJsx+8MZ5MNV7ah/leBHKtRAI+oDEO+wNS6hAIrwUyK1Fc/Nl4c4r4Bz+MJrOl/1+oCcqdfoNMz0MgLCl15xvjxtk/a9IcLBrjQ8aNwRuXgeR0W9kISCgmgFeCWyjVUgtZ5D7k/OAR687r0UKpmVIOd14IQ5H3+1TKmT9qYaPBUBR4xFIL8aiS1kghs88yBPjHUkvwMQzedhkCokFjHPYHEkUIhF0gC+AzW+fn2JqFAITyGpSkWeExvitTpEAGgbCbg4l5jEvSdOz2PQEsMIdCvGwNLjMdLGQgPfzaaFQNxNb4uSL34c4LHvGqkiZp1UZj5zCstHL2E//gj0uvG+MtLJTYmIVAmGohPi1kZit/boWzHwJw6Tvvqaz30xlPGRrjsD9QIINAXAb2ma1XJDCag2xOgSfsRNHH5kiFx/iun2EoCgJx6XNrsKvHuETuA9648DkUIi3jv9FAIFLqEIhic+q4nyvPXfvMmptTNQcCpTL+OffBI+bZf+xRUnTWaCjKOPu584JHSp9ZjxYyKnOe3SppDINDGKyBWOeLQOadt8h7avwcgyEQgMtAec8cNsb3ARrjsD+QKEIgLgL5zDbbnKJADP7xLqer9QnK6c6NcS5JEAavQ1EVsd9cTpqzH/wQSkp96bVGgQwSwuvWYJWFUp0fK+KfOy94xL9ayJxZ1sJCRuLOC94YGT6zfrYGWwxF9bEPgzBYQ1Ee77xl3tNwGJw7L3jCb71Hqsp06IunDY1x2B+yrGwOMj0MHvEqr1IhrVKLPtPz4B/vkooVRYKdmSJDURAIr1YCFdPDtaA5CAEINxTV0kKGAhl4pDj7fUsqNvIa7HPnBf+EuvM2G4oy77yc/eCHkMPgtTfGc+68EIbgKmkMxEIimAOBPtVCpka7tdYiHESDxjjsF0tZOS5J4I9CUlHyUSRwkSgS/+AHO1H07bc2Z2eRwJqep0gA/jDj/7gfwmO8jpwuZz/453LsMfaldSl1PMYhIYq836fHstQy/jn3wSPmnTfEQOBOlShpJe/h7Ac/eB2GlVYGQ/Llo63kuZT35485+8EjQ581n0qP5RqwDAEB8K+SVljIGE+5/y3gEBrjsF8URQKmh8EjfpuDVdPDDbwGJeIfvOG1QCZVFgl2SmsxPQyBKC5Kvn1mZ2yMQ2JcGmf/zaO++18wa7ExTnMEAlHk/b4HAlspJoyHmBOCNyylKOc+s21tBLjzgn8urzzbCFgqaS2WIcj5wSPWYIjHYfBWSjkSgyHgDe8KmVqxDxNpfOrQGIf9Ar81CIDXD0trgjJffWozbE5BAC7HZmPc39ag1GBj3Joe5pIE/iga46EkFeud/UzPg3+8NkckLYsEWVulHGIf/DCZznQ1mecmXjzGq5qDjQrEM2k62fqtAG3xuzllBnrb5ghnP/gh7J23UEyo8XNF3k/sg0e8SqlrvTFeayyKYXAIgKUO6zHvn5ntVhrjSUNjHPaLPo1x8I81QRlker5hosj0PHji0qeNgCRbUrQYDGFjHNKg2Br0vzlSqCXUgM0pCID36fmlrFyxMVsn7zFjn7wf/ODVY1lSZd5T58ewUIIABB8G584LieDVOk9SexuNwjqSvAf8YW+M+7eOrAUDsRAA/3fe9XO+Vu4D0aAxDvtFkShSIAOPDBcflnkmHfVcH5NMz0O6mNPzfiYo1zfGd8Z/zsYshOFiuTHuY3Ok7cY4gyHgn1A2Gs0kFWkMgn/8+8xW5T00ByENvG6MV3mMT6u/1YKhEAhAKJ9ZqenGOFLq4J9iGPyo58M+rGoopMbPkfdAAPyqJUhFtj/NynoSUuppQ2Mc9ovlxjgflOCPC0NON8s8JoqzFl6DEokieOPC8FtzPj0stbMSyPOyOU5jHDwSTkq9weYUPssQgHDT8y3zHmIfPGEPhfgYijIfNrhTMBALAfC7NWvkPU2u02bewzIEeML/UFT5cNpILWdx56XeAx4Zeh0GXx8KYRgcUsG7Wsgy/g2Pcfe/BRxCYxz2C9NjnLEb8ERxUfItpztrIqtlFci4KIEfrOl5716bTYoEi7OfAhl4YjqdaTien88+N2alLhvjnP3gB7NIcPOo7+E3LKbniyJBrdhHSh38E1ZKvW2BmPgHPxRbg5KHzalZxUBgEylpibwHvGHGfqiN8UZnP41B8MhFoGHwRkMhKEVBAC68n/2FSpr5FL2rlKExDvuFdVEiWQQ/FFuz/rcG20oLUSADP9h+U2EmiOvJyrExDn4pmuKSfyn19j6z5D3gB2tj/Mhn/LfYmpI4+8EbF74lFavkpJsOxJL3gyfM+D92nfdX2QjU+TliHwJgD0UluDVL3gMe8aqSVrUIwUAgJIL3vF9FYzxfeQZShcY47BdMkUEACmkhPwWyqgnKGrAxDgEI5bkjmZtTDTbGaQyCJ7xvDVYUiGtVCbDRgACE8lmeZU08xpGSBv/4l1SsUsqpgTUYwtkPfrg05HSd24dZkd62OULsgx+85z1dt2ZRyASPFIoJwRQy6/wcw+AQgIur8fLxTY8DsaZ9Ekd52tAYh/0CWUUIwEUwz53Fn01l5Yh98MRloOaI1PCiVDRIaI6AJ8IWyNpujBP/4IdL383B5fR8g3M/Z2Mc/BOlOdLYSoDmIPjh0qt92LqUemO1BM5+8IT3YfDWKmlF3j+TpuOt3wrQhtlspsuxx3qnE4VM8h7ww4X3ZYhFsBuzhrWGoiAaNMZhv2B6Hjwzmkw1XtxafHsNqsklCbUECECwRFFNN8YLKXViH/wQVk538VTjs5/pefCD//hfkVKvE/x5XjbHKZCBJ2I0R+ptDWIfBv658NoYN2I/a7k1yDA4eMK2EUjpzktzEPwyHE+X4el7EaKRWgIb4xCAwjZV8jsQa0qp0xdPGxrjsF8wQQye8S8n2lZSkc0R8E8hqyX5vyg1k1VcxD8FMvBESDndcmO8aZGAsx/8YOU+XgrE87O/kYWMhNcmeCfkQOBs/anNcPZDAIrc58SzfVKj2Mc+DAJg3nm9D0XNsJGBdDDvvH6kpNfrnbUS/z4qaeCfUAOxZsjTF08bGuOwXyAnDZ7x7jNrSioWXm61ZOVIFME/9mCIPyuBqaEt1EgxgQIZeMJ/c6TKRqPGzzE9DwEoimTH/Vx57tpnVlpGfROPccn22gTwgLk54j3vb1J6YWsWAlA0B/0MRJkHfRH72IdBGniv+bRVC2FjHDwTUiWt0UAsA4EQgHADsXiM7ws0xmG/QE4aPHPpW1qlamO2zs+xMQ4BGAYaDDGlheoVCRbxPx1L03VJaoCueJ+ed+ExztkPnljK6XqJfS0rAjM1bLqjFgKesQYCg9loNB2I5ewH90ymM11N5vHp10KjoccysQ8BMPN+3z7L0yYqaSgmgGfOrzwrZFZYRza2EWAYHDzhXSWwqHdmDeudEA0a47BfMD0PngkpqVh6jDf1GiT2wQ/+rQTWC8S1+twUCcAzIZsj7TfGOfvBD0WR4KaXAoEMWTlzer5BkYzYB0/43xqssFBqYiEjEf/ghZAqaUs13VrnPhvj4J8YG+O1BkOIf/CMGfu+pdSns5Yb4wyDgydCKWTO2BjfG2iMw35BgRg8491zRG0LZCSK4B//HuMtJyit+L90/a4AgnqMt5ZUZHoePFEUCfz4zEpl7tOwSICNBngm1OaI1KVAzJ0X3ON/GNaBUg5nP3gibPw32Jrtc+cFv5zHuPMyDA6JUOT9NwY9ZZkH+7BZ8Ue2+hQkCo1x2C/YGgTPhLwkNUoU2RiHABSJ4lEvV8+Hz2yFnG7j+KdADB648D49v745UuuWhKQoBKAYCvTTGFT19Hydn1tujDMUAn6wvTb9bY5ITfN+zn7wi3cp6Sr7sDqxn+dSPpg/ZhgcPBFqGFwqLcQab4xT8wEP2HlP3/0vcOIxTt4PfvBvH9ZSJQ2iQWMc9gukhcAz/qVVTFk5pIUgLS7Hi61BL15rai8txFAUeOYioN9a661BCmTggdFkqtFkHo3eGuMLZllLKXXyHvBE2IHYObUslKw7L/EP7hmOw0up14p9qcz7yXvAE5eB5HQlWy9wJ33OfvBLSKWcWVu1BOo94IkL38PgbVXSIBo0xmG/6A3Kx3xYggcuk00UaQyCf4qLkp/GoFTEf3MpdYaiwC8XUXxmG8Y+0/PgAe8WMlact9wYn02k6WT79wK0wL/PcsmsyeZUn6Eo8MvFVdm48332F1LqtfVEGYoCz1z6HooyB2JbqwQS/+Cei9F4+fhGoKEQNsYhFS6vfC8CFfVOD+qb4AUa47BfMEEJnrkc+y4Qt5RUpDEIASiag16kpKUVWcU5tWTlmCAGz5jT8ze9yMq1LJCZA4HkPeAB/xuzFTYCwkYG0sD/YEiVjUYDtQSJ2Acv+B8IrJBSr/uzbIyDZwqf5UEv06AXxkajsZQ6NR/wgDkU5fvOO2syFEXeAwEIJaVuDoPXVsuBKNAYh/2CKTLwjJko+pZULKXU6xTI+lKxZUtjEDxRFAm8+E1Jhse4sTGOpCgkQAyf2VqQ94BnvEsqVqglzB83bA5y9oMHYsR/vc0pmiPgFzPvOfYtpV48U7c4zMY4eMa7nG6lzzJy0hAfayDW+0Bg8Sc5P8RnNJlqvJhQujnwVO8sFDKRUt8baIzDfoG0EHjG++aU1icoa39QFkUyCmTggcl0pqvx/CLjRVZLKj3GDSn1ehvjNMbBL+cBPcYbbU5RIAPPmHmPH7WQDY3xxtsjDIaAe8Lm/U3kdDn7wS/ebQTMoZBF3s/GOKTCxZXvrcH1mg8b45ACF1emlHoYG4FakPODZ7wPhUiVUur0xdOGxjjsFySK4Bn/RYIqKemaH5VFkYwiAXjAbo742hhfj/9aqWKPAjH45dJ3/FdtjiArBwlw4XsoxNwcyRpePWkOgmcuRvP4zDLpuO9DLaTCRqNW3sNAIPjFvvOGkpKuuzFeDIMT++CH80VzMMSdd7k1Wyf+zbxnfOn2PQEowEBsa+tIwz6MnB88YKtE+V0EkjUMTms8ZWiMw37BhyV4Juz0fEO/taJIQOyDB7w3RyQtpYUyU0q9xo/1T8rHDEWBB+LI6TZtjhD74J4L7x7L62oJa09vgsEQ8MzlVSmnm2UNNpvq0rpAzOYU+MXKezyrhahJ7EvGMPgQDVLwwuViKMr3nVeSpk1UAnsoZIJfQlpHNhoGzzIUMsEr3us9kiql1D39JnADjXHYLywpdT4swT12kcDHEVkhKVq7SECiCP4wY9+PnK5KKXUjUawnpc7WIPjl3PdQVIWsHHK6kALeBwKtvKek1uYggyHgGe8+sxXxj40GpIB3G4HKgcCaWM1BBkPALePJVFeT+Z3U951XauoxTt4DfrkYeZZStxQCG8S+ZNhokPeAe7wPg0vVUup0xpOGxjjsFxTIwDPmh+VxP5C0UN2fLRQTSBTBA/59ZmVkhQ2lhZAUBc9c+t6cqjj7a0HeA54xN0d8F8gstZA6P2spRRH/4J4i9/G2NVjhM9t4a5CzHzxQbMxKvuLfCPSs6TA4gyHgj7B33oZqIX02xsEvQZYhFjQaBpdKtRxsBMAD3gcCpUopdXbG04bGOOwXXJLAMyGn59Xab40CGbin8FqT/BeIzeZI841x4h/cU5z9vTzToOdDTreqQNZ0c4QiAbjHynuCSqk3jH/OfvDAUko9wNZguTHe0GeWOy944CKkfVjjOy9WAuCPIPZhlfFf4+cYigLPhLRQarwIVNjnEfvggcsgQyGFlHpD60iIBo1x2C/MSxIfluCBS+8TxKbHeIPNEcn2WwNwTCwp9eY+yzQHwT3FYEgYn1n7z61kWZn7cPaDBy6uPEsqboh0NsYhBcJKqTfYmu0xEAh+uYwwDF5fJc2s+ZD7gFvCbIxXqATWGghkKAr8ch5QJW3aJPYl6p3gFe8DgZKxCGQ85ec3gSNojMN+QZEAPHPuuznYxXOnaA5Ox9J0uv17ARoSJFEsYj1rmCnitwaeKSRFvW0NWh7jDYeilmohFAnAPf63BjdIqTfenCL+wS2jyVTjxQqf7wKZVBaIa23NsjUInjGVovwPgzdtjiAnDf6wGoMBhqIayUmT94BnLr3XfFraCEjcecEr50HVQvAY3xdojMN+wSUJPON/grLiqdob48Q/+MN77EuVG+P1pNSJffCL963BtnK6Uhn/FAnAA6bHuJ+BwIqN2flfdoOUOnjEHAo5Hngqi1RuDdb4ObYGwTMhtwabb4wzGAL+CHnnlYzGeFMbDfJ+8ECR+/TzTIOeh9ynKu+p+7PcecEj3m0EJFVKqbMznjQ0xmG/4JIEninkpHt5piMfiWIXKXVk5cAjQRLFZbA3TBQ5+8EzppS6F4wwb7wxXhQJaAyCBy68y+muN0ekumc/Uurgj8vAW4ONCsTkPeAZ7xZKs6qN8Zo/y2AIeMT/xqzaD0X1GAYHvxSDIf7rPWXs11LKkco772wiTcbbvxegIUHP/oyN8X2BxjjsF1aBjEQR3GNuDfr3me0iK0eRDNwS1GM8a7oxToEM/DGdzvxLqbf1GJeM6flLl+8IQNJKkcDb9PycWdMiAZKi4JEwA4Hlw2ZyugyFgF8s+7BB38NvqFYLqXXvZTAEPOLdOk9aOeiL5mCNn8M+DDxTDAWGGAqZNol9CZVA8MpFiIHYwmMcKfW9gcY47BfWB+Uo3vuAa8tFwAnKYoqsvqwcG+PgjyAe48uLUpcCGc1BcMtwXF7gw2wNtvUYp0AG7vFeJKgoDkt1t2bN5iB5P7glbN5jPFUn+rOszPspDoMHLLWQI79yuo29NhmIBY94V8qRKu3D6kmp0xgEvywXgTxLSc8fNVwEYiAWPBJWSr3h2Q/RoDEO+wWNQfBMIafrbXq4MlGs+aNsjINHbL81H5sj0jJRzAwpdabnITLFuS8F3hivrRZCcwT84b05WKGUI9WMfwrE4BFzKMRbc6RL3s9QFHjEu31YxTC41MZKgLMf3HJh5P03A955a23NopYAnjmPsDFOvRNSIN4ikJ9fBW6gMQ77BQUy8EzIRFFLr8G6E5QMhoA/zCJB0ESxls8smyPgjzByumWcN/cYP1n84FiaTrZ/L0BDzKEoL1uDZmOwcXPEPPvZGAe3FBYaUqizv6FFE0NR4JHlMLgv+7AKpRypzVAUzRFwS0j7MPPOWyvxRy0BPDKdzpZKaUl7jEuoBIJzLq8C1ny85FXgAxrjsF9YjUEuSeCW0Ili8WGJ5w6kgNkc9O231thntmgMSpz94JzLwHK6RcjXLhIwFAUe8R7/m6TUa21OEfvgj4tRyIHANpKii/gn7wEP+LcPq5ZSr7c1y9kP/jgPKaWOWgIkxOU45FBIB6UcidwHnHMewmO8SkqdjfGkoTEO+0Xe1/JixfQwOCZIY1AdtgZJFMEj5yEkRZeN8YayckzPg0dCXpKk8uxnKApSIKyUurE1iNcmROY8xNZglZR63R9deoyT84N7zn37zM42qYWwMQ5xuQyyMV48MO68dRL/Po1x8EfoO+9s1nRjnJoP+OMi4FCUmuY9EA0a47BfZFmZLHJJAsfYiaInvylren75ZL2fJVEEj1wG2Riv8typI6VOkQD8EVZS0Xiq9tlvKiYQ/+CWIv6Pern6PnxmVb0xXiv8KRCDR0IOBEodvDa584IHLnzbh1UMhUiohUB8zoPI6bbcGM97UrZ4T9R7wDEXgfOe2cqfO+HOCx65DGifZ1vI+PlV4AYa47B/FA0SPijBMWGaI+WnYpaxMQ7pEHJzykwU622M0xwBf4SZHjaaI7OmZz8FYvBHUSQ4GXi6Flpbg8bTdX6WoSjwSPihqIabU9x5wRMTwz4sROw39jBHLQQ8ch7CQmmZ5TS880pl/FPvAceEtM6TTIVM7MMgPmEVYhsORUE0aIzD/tEbzP/kkgSO8S4nKsmans+QFoJ0CDNBvC4tVCtV7BH74I+LgJsjUguPcXN6ns1BcMyFdzldszHYcHqezRHwSHAp9dnqMzsw77ysm4BD7OKwL5U086HpMd5UKYq8B9xyGWtjvHbeX6iFkPeAWy5CSKlX3nlr/qy1DHHp7C0BSCv1zr4P+7ANw+Dk8ElzbRrjb3jDG5RlWeX/ve1tb1v7/uFwqL/39/6e3vzmN+vk5ESvf/3r9Rf/4l/UCy+8EP7NQzOYoARPnF+Nl4/DNEeaeg2yOQX+KIpkeSYd931vDpoFsho/16dABv4IOz1snP21iwTm9DxFAnDLsjEecCBw/mwdr02GosAfF1be76s52OXsN3Kf6Xjz9wE0xLzzehuG3XT218r7OfvBH0GGoirtw2r+bI96J/jBUkkLcudtuDFuqYUQ/+CWi9H8XD7u58rzhko2dZitx/78MaSMpxtgHJ588kn99b/+19eef8Mb3mD9fTqd6g//4T+sH/mRH9GXfumX6mu/9mv13HPP6V/8i3+h//yf/7N+7ud+Ts8880yYNw3NKTYH+aAEx4SWUleXAhnxD445N7wGG0se1qViY7xW/Od9KcvnP0+BDBwTXEq9rZyuRJEMnFPkPt6VQiQ1LhCzMQ4eCb0xPl3Gf0tJ0WKDHKAjsWwEpDY2GuQ94JYgKoFFkpOZzZGGKoHcecEx1tk/8DUQ2GFjnEUg8MhlUJW0FkNREIVr1Ri/e/euvuVbvmXn933P93yPfuRHfkRf//Vfr+///u9fNgC+4zu+Q3/5L/9l/e2//bf1nd/5nZ7fLbRmKS3EJQncEqZAVlJMz+O5AylQXJS8bU1JhqxcwyJBls0vSuMLCmTgnDCyco62BtkYB4eYPrP+igTrsT9/XANiHzwSxGe2SimKgViITGifWbtAjFoIxCWkhdKMjXFICGso5Mi3QmBZ82m1MU69ExxTqOWEUElrbB0J0bg2UupN+K7v+i5J0rd927dZW3F/6S/9Jb3xjW/U93//9+vi4iLW24NdFM1BPijBMWG2BtenyCiQQQqUPrM+U4PZ4j/L31Hfc4rpefBDkM2pKin12psj5tlP/IM7LkM0BjcUCWoVydgcAY+EVopq3BhnIBY8cR6iMaj15ohUM+/vcecFfxR33l6e6ajn695bbIw3tA+TDOtIBgLBLfZQiP+NcTW1juTOCx65uPJsH7ZpGJy+eNJcq43x4XCo7/7u79bHP/5xPfHEE3rLW96iL/mSL7G+5/LyUu9+97v1OZ/zOfrMz/xM62tZlukd73iHvvM7v1P//b//d335l3/5xt8zHJaH9KNHjyRJo9FIo9HI8X+rw6X433L1f9NePpi3VKYjja6GljwRQBceX5QX7+Ne5uXfczYeLQ/e4sNyqlmt35Wpt/zZydW5ppw30IHVM7bw2rzR73n7LOvPploVaR+Px7V+X793rEzSbHSpMbEPDjkblvE0yOudx03JJ2MVV7Di7B9PprV+V571lz87Hp5rRvzvDZty2VR4dF7eZ076uZ/3ObpSIQA9m5WfAKNRnbO/t/zZ6ehSk0T/d4T95PTS/9nfm06Xo4DF2T+Z1jv7e3l/+bOj4blE/K+R+hmbKo+Ns9/bnXd0VVlsHI1GGu2oQtp33kvuvOCUs+H8znsyyDUej738jv50fuc1h8Enk0nNs39R75wMNbq6Wtk8DA/n7PXh8WVZ7zwKcOctYrdu3pOpX579wwvOfnBKMRR17OvOOx6Wd16j6jnaUe/kjHVPk/8tr1Vj/JOf/KS+8Ru/0XruLW95i37wB39Qb3rTmyRJv/Zrv6bpdKo3v/nNla9RPP/cc89tbIx/27d9m/7u3/27a8//6I/+qG7evNnlvwJU8K53vcv6+5c9OtWrF4//03/895rm+K2BG/7HJzJpkcZ94Ffeq2df/CXnv+PTXvlFfdHicTFgMx6N9eyzz+782dc+eK+KUZ/3//J79dzLu38GYBfvete7NJ1JF4sq1fD8ca14bMMfWoxLnl+WE/Dvec8vqPfR9+z82XdcTXRz8f5+xNP7g8PkVz6UqxBRes9/e7fu/6r73/H5H/2Q3rh4XIyHfOITn6z1b+1Nz39In7d4/D9//mf1iecmW78f0mM1l02Fly6l4jr44KXnvZz9ty4/oa9ePDbP/h//8R/X0yfVP1PQmwz1BxePX37+4/oZzn5wyEc+Xp79/7+f+C+66aEy8sXPf0KvWzwuimQf+chH9Oyzv7HzZ3/HJ1/UZywe/+R/eZdOT1639fsPmVTP2FR57yvlnfcjH/qgnn32Oee/4+nTX9XvXTw+Oy/VGH/0R9+lWzvKN0+c/4a+cvH4Nz/0nH6Jsx8ccv9RT1Km3rReDaYNX31+qluaN0QK3verv6pnT9+382d/76MzPb14/MP/8d9rlqdRtuec3X/e8/Hy7H/f//olPfuJX3T+Oz7r+ffpty8eTxabsg8ePKr1b+31939Zb1k8ft//eo9+7QXOfnDDZCqNJvOz9PKsXjw2JZ9e6X9bPL4w7rw/+7M/qxd+effPc8a64/z8vPb3pvEJ64Bv/MZv1Jd/+Zfr8z7v83T79m194AMf0D/+x/9Y3/d936e3v/3teu9736s7d+7o4cOHkqQnn3yy8nWeeOIJSVp+XxXf9E3fpL/xN/7G8u+PHj3Sp3/6p+ud73zn8uehO6PRSO9617v0jne8Q4NBeXvq/cC/lE7fL0n6/e/4Kun4Tqy3CNeMj/zkh6QPzwsDv/stX6R3/LZPcf47sl96JC1qYccnN6RLqdfv696937f7Zz94JH3o2yVJn/NZb9Cbv+Ke8/cHh4N5xo5nufRz/1mS9LpnntK9e2/Z8dPtyN4zvx3duHFTejx/7gu+4At07wtfv/Nn+7/xLdIrL+u4L927R+yDO376h35Z+uTHJElvf+uX63Ne6z6vyP/Tf5VenD8umiOvec1rdO/e79j9s//tY9LH/7Uk6Xd+wW/X7POI/31hUy6bCu//5GPpPT8rSXrTGz5d9+799h0/0YKXn5MWdeAbN25Kp/PHb33b2/QZT+0YKJ6OpcWM4tN3b3P2g1O+92M/Lz18IEn6w/+X36+BB0nd3v/n+6VFWaFQUvzUT/s03bv3eRt/Zvmz//Fd0is/LUn6ii/7Uuk1Hv597jmpn7GpMv2lT0jvf68k6Qs+77fp3u/5zB0/0ZzsN56UFv32mzdvSWfzx1/9jq/Wq24ebf5BSXrpA9L7/2+SpM98/Wv1aZz94JBv+cUfl65GevL2Td27V70M1ZX+r/9fpSupf3QkLerzn/3Zn6N7b3vj9h+U1Hvw/5Y+9AFJ0h94x1dGr3dyzl4ffv3Hf036jV+TJH3Zl/wuve2zn3H+O/KfeU76+PxxlvekiXTniSd0797v3vmz2fslffj/kCR97pvfpM/5Ms5+cMOji5H07h+XJH3aa16te/e+aMdPtODqTFrMmpwYd94v+dIv1Re/4amNP8YZ655C2bsO16Yx/s3f/M3W37/wC79Q3/u93ytJ+r7v+z5913d9l9XM7sLx8bGOj4/Xnh8MBgSxB9b+dx2U6yWDbCrxvzk44mpSmn/cvnHk599zbhTdFjYA09ms3u86LgvIvdlYPWIfHDAYDHQxLL2gbh57+iwzzXXy0tcny3v1fl//xvz7x1d81oJThsbZf+fmsaez3/AYLKS1sqzx2d+fTch79pBU7whXhrT5LV9nf8/wcTPkQPv9fo3fN5CynjSbKJ9cKU/wf0PYXy5G89xn0Mt082T9bu+EbP3sz7K83r81687L2b+NVM/YVLkyLGDv+Lrzmme/cf/t92v8/8rIe/LZiLMfnHK+8Jm9dVwnD2nJ4t6bGVLqeV7z7O+bZ/8smbOfc3b/GRqiY7dP/N95M0NOut6d99byIfVOcMnovAx+b3feqdFiNfL/Xq/eZw1nrDua/O947c2Z/9Jf+kuSpJ/+6fm0dbEpvmkjvJgq2LRRDgnQMwJ8PNz8fQANKS5JknTzqLflO7swW39m/alq+kbRbny1+fsAGlL47UjSDV+xPzOqcEb6UTf81V9sl0w498Et5tl/YxAi/ucXpWnd4O+ZZ//l5u8DaMj50CwS+Ip9I9CzvPLprRQFYnJ+cEyR+3g79yXr7C/UQmZ1Mx/yfvBEkDtvRd4jzQfCd9IzNso5+8Eh0+lMw/E8Nk98nv2Lc35mDkfVznuM+OfeCw65DFzzmRmLQLUwY587Lzjk/Kq0tQhT629x54UoXPvG+KtfPXejPjubaze98Y1vVJ7neu65ah+l4vlNHuSQAMYEJYkiuOQ8SKJYfioWiWLtz8kelyTww0XoxmBmPF03Uyyag7OpNBlv/16ABoQpEpRxXmwN1o59q0BGcwTcYRcJfAmJGXmPcfjXH4panP0UyMAxRe7jL/Zl5/3Ls7/mz3LnBU+YjXF/zUEz0I2zv078m0Mh5D3gEHMY3F9zROW91xwIrJv5WAOxnP3gjosgQ1Hlwyzrkvdw9oM7rEWIEAOBmXnnpTOeMte+Mf7ud79bkvSGN7xBknTjxg198Rd/sd7//vfrN37jN6zvnc1mete73qVbt27pd/2u3xX6rUJdmJ4HT9iJoqciWdX0fKuNcS5J4I4wmyNmcbjN1iATxOCHixAF4oqtwfqbI0aRgLMfHGKphQQZijKbIw23ZimQgWOKwRCvzZGKwZDGsS9x9oNTwjRH1ofBpZoFYjbGwRNB8h7JyH3abIwzGAJ+OA+c9xeKCbU3xjn7wRNmvfPWsa9af4eBQIjGtWiM/+qv/qrOz88rn/9bf+tvSZL+xJ/4E8vn/+Jf/IuSpG/6pm+yLqbf+Z3fqV//9V/Xn/yTf1I3btzw/K6hNUiKgieCbM3aI5SSmiSKXJLAD8Gl1NtMUBL/4IniojToZRr0fKXG6xvjrc5+igTgkPBDUWyMQzospdRDbA3KlFKvSZ87L/ghyNbsBqWoWv8AaAyCJy5CbA1KZe5jDIXU7o70GAYHPwQZBq9QC2lnHcmdF9xhqqSFqPXP2tiHQRQ86oaF41//63+tf/yP/7G+4iu+Qp/5mZ+pW7du6QMf+ICeffZZjUYjfdM3fZO+4iu+Yvn9f/bP/ln9m3/zb/SDP/iD+tCHPqS3vvWt+uAHP6h/+2//rX7Lb/kt+tZv/daI/21gJ2xOgSeCS6mroZR6nwlK8EP4oZAyUWw1PU/8g0Ni+czWBq9B8ITVGPc2PV/tM1u7SFAMhqASBQ4ZTaYaTeZB6FdO18z7157aDnde8IQlKTrwb6Nh7uPUyvvZGgRPBN8YzzoMBErkPuCUi5HRHAzoMd4q9rnzgkNCK+VYd16k1JPmWjTGv/Irv1Lve9/79J73vEc/9VM/pfPzc7361a/WvXv39Ff+yl/RO9/5Tuv78zzXv/t3/07/4B/8A33f932f/sk/+Sd66qmn9Of//J/Xt37rt+qZZ56J9N8EasH0PHjiwvLaDDc939hjWWJ6HpwSfHOklZQ6FyXwQymn69NntnzYeGPcao5w9oM7zodG3hN4er52mYyNcfCA7TXosyRiqoV0KBAT/+AQ887rrzliPG6qFJVl8+b45IqcH5wSRClHWt57Z5aUeouNceIfHGLFvzcp9fXmICppEJvQKmnmHgQb42lzLRrjb33rW/XWt7610c8cHx/rm7/5m/XN3/zNnt4VeIPpefBEaDldNZZUZHoe/HAeYmPcmh42nm4jpU5zEBxyPpzH/83jMBvjBdP1p6pBUhE8cR5kKKqj31rRHJyO5v9o8mvhBAaRuQhRHJZW1EIWf7YaiiLvB3eEKRBXK+U0UguZXJHzg1MuQg9FtZHT5ewHTxTxf2PQU543VDCri7UItBgIRCEQImNJqXs7+9fVYe1nIUWoLMD+wfQ8eKLYmvXnt6OVKbKGiSIb4+AJ22Pcl5xuVyl1pufBPbPZbNkc9Lo5Urk12KI5QuyDQ4J4bW7wma0/FEj8g3vOQ6hESStS6g0HYpGTBk9chLAPsyyU2shJL+Kfcx8cYklJB5ZSb3fnpeYD7jhbqqSFufM23hhHIRA8YQ4E3gpy5zUHAmmNpwyNcdg/+jQHwQ9FgThUgazxhyWxD56wJBUDbIy30hZCWgs8MBxPNZkWPrM+pdTXtwZbFciIfXCI3RwMPD3fanuEgVhww3mIoZAVlluzrbYGiX1wx0UQpagNHuN1k58i72djHBwSTkp98ae5Md5KJY28H9yx3BgPYCMgaVnvbLUIxNkPDgmS92/0GIeUoTEO+wcFMvDE+bIxHsZrsLGkaN6TssWHOJckcEiYIoHDjXHiHxxxEaxAVrExzlAIRCaMnK7xOGvjtUmRDNxzEcJGQKqUk66vFsLZD34IYh+2YXOqNmyMgwfMvP8kiMd4x4FAliHAIWcL+7BbXofB1xeB6t95B1rWSKn1g0PsvN+XQqZpHWnkVnTGk4bGOOwfTM+DJ0zPHW/MNmxO1f354qLEJQkcYiaK3qwENkipt2oOUiQDR5gey36LBFU+szV/1pKS5uwHd4T2me3kMS6R94MzzkP5zFZJqbMxDpEp8n6vd94uw+ASG+PgBas5ElhKvf6d1xwG5+wHN0yns/LsDzQQmC1qPrUXIbKMeid4IYyFUnWg1x6IhSjQGIf9g+l58MB4MtXVZJ7EhZMWMp5uelEi9sEhQbZmNzRH6m+MmwViLkrghvOhYSMQyG9t1thvjQIZ+CG4lHobSVE2p8ADF6E8xmWqhTQ9+xmKAj+cL31mAw2FWB7jDXMfhmHBIRfBbDQWcZ51HQgk/sEN5lDIreNE77ySMRRF7IM7zodhpdStOy998aShMQ77B9Pz4IFgkopWMaCNnDSJIrgntOeO3RypidkcpEgGjgjnNWhsjM+KIkHNn0VKGjxRFIizTDoZ+JLT7bg1yMY4eCDK2d94Y5yhKPBDaR8Wamuwzdm/qPlMrqgqgzOC3HklI/5bDIObG+MMRYEjrNgfBJZSb/Lz1DvBA2b8e1MJ7KqSBlGgMQ77R58CMbjHmh4OJKWuVtPzhbQQiSK4w4z/W8f+E8WslawcZz+45yzIxqy6FQlMvzXOfnDIsjky6FnnslM2KOXUhrMfPHAeIe+frfy5E2sYnLMf3HG5GAj3Zp8kaZOUeivFBOIfHHERzEKp2Bhvo5RD7IN7TJUorxvjFfZ5tes9EvVO8IJpn+dvKGpTrR9ShsY47B89NkfAPTE2R6ZqIa9SXJS4JIFDrOagtyLZJo/xmj/O5hR4IIiNgKSq+K9dJDD91jj7wSFF7uPVY3mDUg4b4xAT++wPE//NN8aJfXDPaDLVaDIPQr93XsNGoJVSFPEP7jkbBrLRWHqMd8x72BgHR8Sod0oNVdIkrCPBC0EslKxhWPPspzWeMjTGYf9gghI8cBFkgkwrm1MtjuBlc4QCAbjD9Ny56WuC2JQTbaOWYA5FMUEMjjiLWiTAbw3iUvrMpqyUg4USuCdcgbhqa5atQYhHeClpqZ2NBvEP7gneHGylkmYOgxP74IZgUupdhsElFoHAC0X8D3qZBj1f9mHVKmm0xdOGxjjsH0wPgwfCee5Uy0k3lpWbXEnT6fbvBajJ+WjeHOnnmY5CJIpt/Naw0QAPXESRUs/XntoJsnLggdA+s7NWG+N4bYJ7irxHCtMcnLVpDNIcAQ/EUMrJLB+NhvZhEjUfcIYtJx2gOdhVLYG8BxwRTkp9vd7ZaGO8yPsnQ8yZwRlLlbRgFjIt7rwQBRrjsH8wPQweCFYk2LQ122Z6ngYJOKLYGL9x5NNntquUOhvj4B5zKCqc31qbIgEb4+CWyXSm4Xiek3htDHb1W2NjHDwQvDnYOfY5+8EN51EGAsuH9QdiiX9wT3jFhBaLED3sw8A94WLftNHooJI2m0rT8fbvBahJkft4HYjaoJLGznja0BiH/YPmCHjALBKEKhBbm1N1f5zpefBA4TF+y2uBzJQWalMkYGMc3GOrhQQ6+7PCZ7aJrByNcXCLaSET7OxXG0lRzn5wT3Ap9ayF1yA5P3jAPPtPfOY9ptdm1qt6ejvEP3jA8hj3Ff8uh8HJ+8ER1sa4z7zfVAsp4r/NMLhE/IMzlhvjwe2T2BhPHRrjsH/wQQkesDzGAxUJ2hXJmJ4H9yzldH1uzFb4TTXCktMl9sENwSQVLVm5FlLqeIyDY4INBM7Wh0IkBgIhLhfW5lSIs79F7GcZZz84J4aUuh3/3HkhHsWd97ifq+/NPqz6zltbLaGHlDq4J7xaggyVtBbD4BLxD864CGEfZi1CtFiCgyjQGIf9gwIZeCCcnK4ppd7iokT8g2Nms1lwn1nbc6fN1iAFMnDD2TC8rFxRJGtWJDD81gAccD6M3BxpZaNBgQzcYMlJh1ALaWOfJJXNQc5+cEQ4tQSzOdJ1a5Y7L7gh+J23jZwui0DgATPv96sUte6z3Mg+DCsBcMzVeKrxIghvDmKopPn7ldAdGuOwfzA9DB6wZLWCSYqaz9f8eeIfHHM1nmpSJIqBLklZK69B85JE7IMbgm1OVVyUmjXGT8rXmeC3Bt2J0xzpWiCmQAZuCLc5tfiz7eZIkfsQ++CIcB7jm4bB2RiHeBTx7zX2LZU0BgIhDWLm/bWVQiTOfnBODJU06+xnZzxpaIzD/tGjQAbuibExbhfJKBBDHM4tn9mUvQaNSxKbU+CI81A+yxVFskZXJKbnwTEXo1DNEfMvbbYGzQIZsQ9uKCyU8mwuqeuNKin1NkNRFIfBEaZSTrg7b8fmIGc/OKKo+QSLfePsrz0U0mMYHNxzPgrUHKywz2u0Mc5gCDgmWK2/61AURIHGOOwfeV4miySK4IizYNPzmzzGa/48E5TgGHt6OJBagrUx3kZKnUsSuOF8GGqC2PQYXzTGKRJARIJtjmwqEtT9catATOyDG0o53f7yTPZDVyn1wmOcxiC4IcbGuNr8G2MoChwznc6ss98bnes9SKmDe8JJqVcMhiClDhGxVKJiSKn7+43gABrjsJ8UDRISRXCEKacbKlGcqY2sHNPz4JYoPrPmxnjdH+9zSQL3nAc7+w3FBLXxGKdIBm4JJyXddWuQ5gi4p8j7/W5NqQz0tpKKbIyDY+y8J5BSVCu1EPIecMvFKIaUtFnvqfnzqKSBB4JbyEjKsjZ3XjPvZyAWuhPOOm/Txjit8ZShMQ77CdPz4BhTVu5mDFm5uj/Pxjg45jxCkcDaG6k9FHKjfEzsgyOKzaksk04GIeR0W26MYyMDjrG2BgehmiMtigSoJYAHSp9Z343xKin1Bj/fN4bBKayBA87MAvFxqI3xNvZhDEWBW+KopLUYisqyMu8fEfvgBjPvD2UlMFvEf7PGuLEMwWAIOMBWhw2zCDSj3bo38P8p2E+YngfHWIlihI1x/NYgFlZzJEKBrP70PLEP7llKKg56YeR0Jc0Wigmtp+dpDoIDghWIu0qpc/aDB4r4v+FzKERSZyn15VDUTJqMnL0rOFxMCxm/G+Mu835qPtCdYI1BI8PJ2kipS0a9k7wH3GDl/T7lpCviv9FYH8Pg4JiL2ENRzLUmDY1x2E/YGAfHnMWQFM1bTBCzMQ6Osf2mQkkLdd0cIfbBDcvmiNfGoKz4L+5JzQpkppUA8Q/dsYoEoZRy2gwEWgUyhkKgO5PpTMPxPC79b4wXjfEWeY/EYAg45yxCgbidWggb4+CWs1D2YZt8ZttYKJHzgyPMwZBQ9U7zzttKKYq8HxxwHkNKXS0tlCA4NMZhPykuSmxNgSPM6XnvRbICo0hW+7OSAhk4xpRS99oc3DBBWXtzpDfQMsEk9sERRZHA7+aIKgdDGhXIemxOgVtiFAlmbSRFyXvAMabP7C2fSjmSAyl1hgLBLeHkdDcNxNaEjXFwzMXIrPf4vPN2jH1JGrAxDm4p8v5BL9NR36d9WLWcdCuFTKTUwQHBhkI2qaTRF08aGuOwnxSbU+NLThlwQjE9f9TPNeiF8Zk1j+D6snKmzzIXJeiO2RzxujGuDUWCurGfZdJgEf/EPjgimJxuhcd47XNfokgAzjHP/hs+JRU3bE7VHwikMQhuCTsMWyGl3uTHGQwBx9hbs2HO/qxNgZiNcXDMWTCVNFMhsEW9R0JKHZxzEfHOKzXIfRgGB8eYA7HB1EJojO8NNMZhPykSxdlUmo63fy9ADZZbg94lFQ1Zuc6bUySK0B1razCYx3hXWTmKBNCdoHK6WpfWauYxztkPbjGn5/3Gf1ePccNGgKEQcMCp5bEcyEajjUqUZDcHUUoDB8TwWZ5ZA7FYKEEc7K3BQBvjnaXUufOCG86WKmme8x4r7y8/Y2rfe7nzgmPC2WgYD9uqhUBwaIzDfsL0PDim+LD0OjkvMT0PyWE1xn1OEFseyy0TxeX0PJck6I5dHA539i+l1Jv8fA+PcXCLpRYSQ06XjXGIhD0QGGogtqXXoDkYQt4PDjA9xk/6ETan6v489R5wTLC8x4zyvEXeI9nWkdPp9u8FqMFSJS3gIpApFNWqMc5AIDjg4iqUjUa1SlqjoSgIDo1x2E8okoFjwvnMmhvj+K1BfKytwSjNkSYFYmTlwB22lHQ4j/EszxZPtYh9ia1ZcMKFGf8xigR1Mx8kFcExZyE3xiuk1FvJ6UrkPuCEwkrg5lFPeZ7t+O4OWM2RMsdiKApiYQ6FRGmONBqKIu8Hd8xms+W9N5hSjtRuILbHQCC4xRqITVklDaJAYxz2EyaIwSHTaZko+t8YNz8WDc+pulUyCmTgmAvLYzy8lHqrAvGI2Ifu2JsjAWXlFmd/a49xCsTgAGsoyutgyIYiQd34z3MpH8wfk/eAA85DNUekMvxbDwRy9oNbwt15N9iHtZLT5eyH7pwPA1nIbFJJazsUNbpw8KbgkLmaTDVZXDxDbox3V8gk74HunI/MYfAYZz+t8ZShMQ77Cdsj4JCLUShZLVXK6TaCRBEccxZlgrJtkQC/NXCHuTUYskiQt9oYJ+8Bt1iKCYGKBLO20/NF/COpCA44C+axrA1S6g1gIBYcE0MlLTOHwWmOQCSCbQ1uqPfUlpKWyPvBKedDcxHC89m/wUqgvpQ69mHglovYaiH0xZOGxjjsJySK4BBbTjfk9Hwbr02m58EtttdmmEQxk7kx3kJWbjaRJuPt3wuwA2soyntjvJDTzZUvmoONNsYZCATHFGd/nknHfY9XQnN6vq3fGkNR4BCrQBxILaTV1pS0cvYzGALdOYugkmYPRbXZGGdjFrpzbg1FhVdJa70xTu4DHTE3ZkOe/Vkr60hiH9xyFkotBCn1vYTGOOwnfFiCQ86jbI5Yn5UNigRMz4NbbCn1QB7jecv0Y2DGP0Uy6MZ5KI9lyd4aXJz9jYZCBuQ94Jaz5dZg32raucdsjrQokEll7kPeAw44DVYgk5H7dFRLkDj7oTOjyVRX43k+4n8gcINKGhvjEIkzaxkivEpao4HYAfEP7jgPqpK2Kf7r1jtvlI+JfXBAMPu8DbapbIynDY1x2E/YGAeHnA0DTlBaF6UyKa0vK2fEPn5T4ABTUtSvnK4pLdRWVo4iAbjDLBIEk5UzNsYbdUeIfXBMsTV7K+DmiNkcbBT/vYWsIrEPDrAGYkPl/VleDsSS90AkgqlESSvNkRaDIT1TTpehEOjORbDmSLXHcqPEp88wOLjjPNQihLRZTnq6/q2VoBYCjrHqnT6Hoix12BZKORAFGuOwn7AxDg6xC2QBJyjbSIpSIAPHFBelfp7pqOdTTteFrBybU+COYF6DUhn/Waa8zcY4BTJwTCErF1Ypp2WRgI1xcMiZ1RwMd/Yv++JNfp68BxwS9s5r5v3l76qd+mQZZz845SzUQOwGtYT2UurEP3QjqEqakeW0yvsHbIyDW4ph8BuDnnp5GJU061n64klDYxz2EzNRnOC3Bt04Czo9v8FjvO7PUyADxxTT8zeOesHkdLPcjH02pyAO55Zagu+zf/Fnli99lhtJKpqxP+Lsh27MZjNLSt3zbzMetS0QLzYHJ5z70B1bLSTU1my2zLFojkAsgqqkbdgabDYUuLj3cucFBwRTTOgqJS2xCAROibYI1MZKAIVMcMzpMNCd14p9YyDQ72+FjtAYh/2kj7QWuCOonG7XCWIKZOCYokjgvzi8qUDW4DWs5iAXJehGHFm5TKWSOh7jEIfL0XR59gY9+00l9TZn/3QsTSfbvxdgB2eWnG4oGw1zY7xJc8S48zIYAh2xmiMB1ULUZhhcYmMcnHIeQU43a2MjIDEQC06xN8YjKUXhMQ6ROL8Kr5Imy0WD1njK0BiH/YTmIDgk1sZ4q0SxN9DyU5bmCDigiH//cqLV08NsTkEsghYJKjzGWw+FcPZDR86CNkccTM9bXrOc/dANszkSbGs2M4aiOPshEkE3xs1Tvs2dV2JjHJxS5P0ng9yznK5Bm41ZCZVAcIo9FBXy7O+4MY59GDigyH3C5j0tBwIhODTGYT8hUQSHxJIWmrWZIMZvDRwym0kXoyJRDDg9bEqptymQSZz90Jn4zREkFSEO59GaI+VDJEUhFqdDUy0kdSl1M+8h74duJOEx3uQ1uPOCQ5YWMkGVctreeRkGB3dYNgJBz/4WQ1F4jINDrsZTXU3mMXk76Ma4Gft+fy10g8Y47CckiuAQe3o+4IeljCJBmyIZxWHoyHgmTRbju/4bg2aQt0wUaY6AQ4LK6c7WN8Zbxz6SitCR02HAzRFzKKq1xzjNQXCHaaHkXS3HUAspMh+GQiAWZ5HkdNkYhxS4WMR/PCnpBq+BhRI4xFJJ82kjIFmBnrXZms17Uj6YP8Y6DzoSdhGifJipZd4DwaExDvsJBTJwSJStQUlZbm6MtyiSEfvQEWMmxP/miBnjuSmrhc8yxOHMaI7cDtYczFQc/cQ+xCKaUo4lXdpSLQSfZehI0Rw86uca9DyXQyrUQhph3XmvnLwlOFwuQsrpbtyabfAaxZ13ciVNp9u/F2AHxTJEOKUQrcjpMhQFcTiNcue1652thgKpd0JHzIFA77FvnPEzpNT3BhrjsJ/0kNMFd5gb4/63BstEcWbIyjW66zM9D464MuIu6FBIGxsBiSIBOMVsjAeTk86y5eYUHuMQC7NIcDNgkSBzohZCkQy6cb6U0/U9ECgj/LNl/DeKfe684JBoKmltt2bNs5+hKOjAdDor7cMC1ntab4xjHwYOOQ+pFGVVd1p4jEvlQDge49ARSyUqqDosUur7Ao1x2E/YGAeHXIzibIxbRQI2xiEC5sa4/0Sx+pKEnC7EwiwQB5uez8qNcamBtFbvSMsLFgUy6IitlhCpOdLkNSgQg0OKs9//QJRkSalnxTP4zEIcbLWQWFuzDWAoEBxRNMWlAHdeVcd+exsNzn7oxqm1CBTy7G9ro0G9E9wQ1j5sQ+z7/a3QERrjsJ9wSQKHWBvjQYpkBR0niIl96IglpR5UVstsjDe5JN0oH+M5BR05WxSI80w6GfiW0133GDef3kmWlbkPHuPQkaBqCU6Gooy8n/iHjiw3xr37i8uWUi+eYmsQImGrhQQciG3dHGQgFtxwFsk6r7ONgMTZD505s5qDkTzG28Q/OT905PwqoDrshqEoPMbThsY47CdMUIJDLI/xoNJaLX9XEf/TkTSdbP9egC1cTcsG3Y1YkopNXoMCGTikKBLcOupbUodeMDzGzd/UymecAhl05Cyk16BJV6UcifiHTkyns2WRLMjG+PKMz5afM+03Zsl7oBuWnC7NQTggzq1FiH2QUmcgENxhDoaEXIYw8/52HuPEPnTjNOgweHXsQ9rQGIf9xGyO4DcFHQm6MW5+WOZtE0Wag+AGS0p9sG+yclyUoBvF2e/fa00yPcbz1oMhFAnADdbW4D74rQ0MtRDiHzpwPgq5OSJZUurFM21zfu680JGzkJtTGxrjjTIfBkPAEebW4I2QSjmmSlqj2EctBNxxFnIoyojzLC8/Z5rl/YuzfzKUptPt3wuwBXMJzr91nhH7eIzvDTTGYT+hMQgOKT4ssyByumZix/Q8xCWalLqTzRHOfuhGUSTwrhQiWVLq5gAx0/MQg/OgmyNljM/ytj6zRt6PjQZ0IOjGrGRJqWvpMd4A7rzgkPNoctId7cMkch/ohJX3hNwYdzIQyNkP3SiGwU8GuXp5KJU0db/zSgwFQifMJTjvw+CbpNRxGU8aGuOwn3BJAocU0/M3B72AcrpCThqiMzTCMegEZdayOTIwG+M0R6A9s9lsKSsXREraktOteLoO+K2BIyylnJBDUVaBuEmBjI1xcIO9MRv47F8+1+Dne0flY2IfOhJLJa113s9ALDjidBhwINDamG2rkka9E9wR584rSWUjctraSoCaD7TnLOTZv9FGwO+vhW7QGIf9hEsSOOR8uTUYNlGcWVuzyElDeGJtjHf2m5I4+6ETl6Pp8pISZGvQkNPN225O4TEOjrAlFUNOz7ccPhyQ94AbziyvwZBS6pnyvIXHeJYZaiHkPdANc2v2RqTNKZqDEANzKCTkMLicDIUQ+9CNIvcJMxBoDkWZeT/WkRCeoAOxm85+GuNJQ2Mc9hMuSeCQ4sPSf3FYTM9DUlxOystKNK9BJBUhAvbmSMCzf8VjvNVgyGwiTUYO3xwcGqbXpvehQCvG28rpGhvjKCZAB86Db4yXZ38R/Y3OfanMfch7oCNF/B/1ch31Q9qHtT37ufOCG2JtDWbWUEiD1+DOCw5ZKmSGHAaXlOct49+yEmBjHNoTdBh8w1BI47wfgkJjHPYTLkngkGJ6PkiiuKE52GxjnIsSuMHcGA8irbWgvawcZz+4IajHsuTWY1zi7IdOmIMht4P6zLb0W8NGAxwRfGPcstHI7Kfq0isa41fO3hYcJkVj/GbIgUBJysvf194+jLwH2hN0IHZla3CZ97e2kOHOC+0ZTaa6Gs/P49tBz/6sw52XjXFwg1nz8V/vN60jW9qHQXBojMN+Yvmt8UEJ7bkaTzWazD+ogm4NSrbHONPzEAGzMe49UdzoM9vgNfCbAkecDkNekrRSJDDiv8lrWPFPgRjaYxUJvOc+ZpGgpawcsQ+OODOHooLaaJSZT6OhEMmQUif2oRtLOd3gw+AtC8TcecER9tZgwIFAyTj7G8BQCDjiLPid1xwGd6EURc0H2nOagI0GHuNpQ2Mc9pMsM6bnuSRBe2yvtbAe42rdHOGiBG64DLkxbsY+G+MQGVNON8j0vOkza0zPr9TOtsPWLDiiKBIc9XMNer7ldKvznmaSoqglgBvOh6aNQEgbjXJrsPHiCFLq4Igi9/HvLy4r0NsPxHLnBTecBlWKspsjhYVSozuvWe9kIBA6cHYVsDEo2RYybIxDZM6HAYfBN9X6aYwnDY1x2F+W0/MUh6E9lpxoyOaIpCwzZOXYGIcImBvjIT3Gra3BJq9BgQwccRrSa1Cq9JmVGAyBOBRDgd691qQtXptNpNRNSVHOfmhP+I3xgkzF3mDzxjgb49Cd2Wy2jP8wZ785EGtKqWMhA+E5s2o+YS1k2g9FcfZDd85C2ghIKofBc3sYHI9xiICZ9/sfDKlWScNjPG1ojMP+UmxOURyGDpwZncHQsnKz3NycwmMcwjOclDHovTmIjQAkRFBJRcmSlcudSKlTJID2FLlPEElFs0iQO5DTJfahA6ZaiP+8x94cMTenGlHceSdX0nSy/XsBNnB+NVmGZNCBQKlD3s/WILjBqvkEHAafn/3FxnjD16HeCQ4Ibx9WPMjsOy9nP0SgOPvzTDruh1NJw2N8f6AxDvtLUSSjQAYdiLY1KElquTFuTVCSKEJ7hotw7OeZ/0Rxo89sg+DvDUq/HqaHoQPnw4DNEWmjxzgb4xCDYjAkjKTipun5Bq/B1iA4wh6KCimpmJc+s00LZMQ/OOAs5p03d6GSRuxDe8JujLs6+7HRgO6ch/RYlmwLGePpZndePMbBDaVSTt+qwXhho0qa318L3aAxDvtL0RwkUYQOBL0kSSsflubWIBvjEJ7CY/zWcbxEsVGemGWGrByNQWjPaURZudZ+a3iMgwMm05kuRouN8SAey5s8xlvGPgUy6MCZ5TUYUClH5cZ44/qYORCL1yy05DTinVdyoZJG3g/tOQvpMb7p7EdKHSIQfBFoeefNVuqdDeDsB0cUeX/Q2Jfa33khODTGYX9hYxwcYDXGT8IWCXLLdKfBazA9D44oBoiDbw22tRGQmJ4HJ8STUs8sv7X2Zz9FAmhH0RSXQnksu9gYRykH3HBmSql791lekVLv6jEuMRQFrTmLtTUoKctbDsRy5wVHnC7iP8ukG4OQZ39podRoEULizgtOCO4xbmyM560HYvEYBzcUigmhh8HNvIeN8bShMQ77S/FhOZtIk1Hc9wJ7S3gpdWNr0Jqeb/AaTFCCI4bLjfGAlyRJZvrRvEC8OPvZmoIOnIX0mZU2eoy3lpNmKBBaEr5A5sBjHLUEcMT5VciN8RU53eXGeMPEh41xcEBc+zCj7MhAIESgyH1uDnr2coIPLI9xU0q94esUd97JlTSdbv9egA2YeU/QYXBDLWH+NPZhEJbZbLZUC4mqDsvGeNLQGIf9hQIxOMCWUg87QWklio2k1Jmeh+5MpjNdTedBGN5r0JygZHoewhNten6lSIDHOIQmvFpCdZFg2mQqxMr5OfuhPebWrH+PcVtOd7k12LQ+xuYUOOA09J13g6Roa/sw6j3QgaByuisWMsXZ3/rOK0kT8n5ox6mZ9wStd9oqaY3Cn1o/OOByNF0uIdz0rhIlbVZJozGeMjTGYX+xpuf5sIR2nMYqEBvTw1LTRJGNceiOOT0cZIJyQ6LYGDzGwQFBvQYl2R7jLf3W2JoFB5iNwSCyclZzpPx9jdQS8p6UD+aPiX3ogOUx7j3v3+Q12PBl+myMQ3fOIm6MW1LqbIxDBIqaT2j7MGW5tFQLaQjNQXBA8LPf8Bh3opLG2Q8tOQuulmCrhRQgpZ42NMZhf8FvDRxwGtxvzUFzhNgHB5ixH2SC0pTTtaSFGr4OG+PgAFstJORQ1EqRoO3WLEUCaEnwoZBNG+NND/9iIJbYhw4UzZGjfq6jvudSyMrWYBn+TWOfvB+6E80+TFKmlkNR1jA4eT+0YzabBd4Yt9VCWkupD8j7oTvx8v5MaquSRt4DDgg/EFhd72RjPG1ojMP+gt8aOCDe9HwXOV02xqE7URPFLlLqxdk/m0iTkYt3BgfIWazBkCzroBbC5gh0J57XoKyhkOZDUYv4J+eHDsTyGjTz/tY+sxLxD62JNhAoaZa3lVLHPgy6Y8rpBpGSXlELKTzNG/vMEv/ggPAWSuUiUOu8n2FwcMBZaBuBDQqZ9MXThsY47C9szYIDrCLBSWA5XWVrT9eC5gg44PwqtFqCuTXYIVFkewQcEE9aa8VvrXWBmCIBtMP2Ggxro6EufmvF9gg5P3Tg9DJgY3ylOVLk/Y3rY2xOgQNiSqmba4Pt7cPI+aEdpxGHQkz7vOZS6ixDQHfOrlLxGGcoCsJiDoP7t0+SO5U0CAqNcdhf2BgHB0S7KGW5tTFOcwRCE81jWVrx2uwyPU/8QzuKAvHNo95yk8MvDvzWaI6AA86tzZGABTJJWd7FZ5mNcehOsT0SWinHzPubbw1y54XuRLMPk5Tn5WdNo+in3gMOiDoUYuT93e685P3QjuBqIRsWgVp7jHP2Q0uiWsjQGN8baIzD/kKiCA6I5rmT5SsTlA1ewywSEPvQkrPgBTKzOWJsjDd9HbZHwAFF/AeZHpass7+13xpDIeAAc3PkZvAigekz27JAzLkPLRmOJ7qazM/i2yG3piRZPrNNX4ehKHBATCl1WXK6Df4F9AZSvnivxD60JHy9Z9NQVMPXIe8HB5hnf5it2SLQO6ikDdgYh+6YCplBhsENsty88wb91dAQGuOwvwyYIoPumNPzNwdhpYWytluDSKmDA84ibg1a6QdemxCB0mc20CXJKhI48Fvj7IeW2M2R0H5rLZsjUjkUOB1J08n27wWoIPhA4KqUelb4zDZ8GfIecIC9ORU478+7WCgt4p/Yh5bEHAafT8O2PftpDkJ3ivg/6uU66gdoARke407qncQ+tCT8xni1lHrjOy8EhcY47C99tmahO0WB+FYoOV2jMW493eTDElk5cMDpVWCfWUtSsYPPLBvj4IBSSj3QxrghK+fGb43NEWhH+M2RTX5rDV+HwRDoSOEvLkXaGlw+3XQohI1x6E5Mn+VMhpR62/jn3IeW2HlP4KEQI+9vbqNB3g/dKYbBgwxESSuLQOXT7VXSqPdAO86CDwRukFKfVn0zpAKNcdhf2BgHBxRFsjAey1qZoDSebvIa2AiAA5KRVGz6OhQJoCNX46lGk3nkhfbZVJa191ujOQIOCN8cMYoEXYaiLBsZ8n5ojhn7d04Cn/3KljYazfMeBmKhO1F9lvMuef8i/sl7oCXB8x6tDEW1PvuNYXAGQ6AlcYfBWxY88RgHB9j1zoH/X7jBOhKP8bShMQ77Cxvj4IDiwzLMJUkrHuMt5XSzDFk56IwpKxdmMMTHxjhnPzQn5vSwstxUFG3mt0aRABwQvDlokGXm2d/wh1ELgY5YPrNBCsT2UFTetjvCUBQ4oDj7j/q5Br0QcrqmWkiHs3/AnRe6EX4opPrs7zYQyDA4tKOo+QSvd1qj4A3jv9eX8sX7JeeHljwOXfNZyfsL8BhPGxrjsL+wMQ4dmc1mpc9sqOKwuTG+8l4asZSVO3fytuDwsArEwT13TEnFhq/D1iB0xGwM3ozgNdhaWgtZOXCAKScdXE63i98aW7PQESv2Q2+MG3l/84FAYh+6s7zzhmqOmGSmxzh3XghLeJ/ZTTYaDV+HgUDoyGQ608Vo3hi/GUxK3dgYzx1YKBH70BJzKOpOkI1xUyWtg4UMBIXGOOwvg5vlY6bnoQUXo8kyQQuzOSJrY7y1lLpUxj+JIrTk3PIYD+23VnpONW+OmEUCpuehOWbs3w6+NZi3n56nMQ4OeByxQGxvDeKzDGGJ6bE8H4paPN30dYh9cEC8rUFZUuqNKQZDZhNpMur2nuAgMVXSbge/8+bLocDWjUGJOy+0wlyECK+QKbve2bjmQ2McuhF+ILY8+3NrY5zGeMrQGIf9BUlR6Ejw6WFpRVqopZS6VMY/flPQEktWLkRz0LLaNKbnm74OzUHoiL0xHn4oxPJbawKbI+CA4uwf9DId9/dITpetWehI+LzfllQs8v7WOb9E7ENrivgPf+eVMjlSiuLeCy0IbqMxs+WgyrS/ZWNQYigKWnFuWudF8Bg3652tB0PIe6AlwQdiZQ6DI6W+L9AYh/0FOV3oSPDpYUlWomhtjLf0nKJAAC2x4z+enDQFYgjNWfCtwRWPcePsbzRBnGUUCaAzRZHg9nHfurT7w5SV6zA9z2AIdCTq2S8ZG+NdfGbJ+6E5V+OprsbzPDzYnddsjOeGlHqn+Ofsh+bEHYoqaz6dNsbJ+6EFwYfBJSP3yVbEQloqRXHuQ0vCK0XZZ38BG+NpQ2Mc9hcrUaRIAM05i7kxbshqSR2m5yfDhia1AHOCe4yrujnYXE6XAjF0I3xzZHVzBL81iEchKxcu7zEb43nV0/WgOQIdiSqlnnWIfZoj0JGod145ynskfMahFedRz/5SKaqxlDR3XuhI8DuvZNU7c+68EJEi7+/lmU4G8VTS6IunDY1x2F8okEFHwkuryEgUs/Y+sxJy0tCZ08XG+KCX6SiwnK47SVGKBNAc02M5iN/Uhs0RCb81CI+5MR6ETX5rTStknP3QkeBeg6tS6kVzpOnLcOeFjsSxD6venOompU78Q3NOTTnpCFuDre3DsBGAjphn/50geY9UKmRm1p23db1zfElnEVpR5P17p5IGQaExDvsLBTLoiFUgi7Ix3uF1uChBR84iNkfMi1LzjfGb5WMKZNCC4Gf/Fo/xxtPzyMpBB67GUw0XcrrBC2TSiqxcw5ehOQgdieozazRHGndH2JiFjpixfzuUz+yGOy9WAhAaWzEhgJz0ytlf5P2NBwKp90BHHlt33kGYX1rUdlwpZErSeNj9fcHBUQxFhat3mh7jbIzvCzTGYX+hQAYdCS8lrY0b490SRS5K0Jwi/m8dhfabUrfBkAEFYuhG8On5DZsj8y+1nJ5nKARaEEdSsYzxPO/gt4ZSDnTELBCHOfvN5kivvcd4lnH2Qydi24flXZojLENAR4o7b55JNwaBG+PKpOXZ3xBiHzpyGlwlTRs9xpvn/cflY/J+aMHpcCQpVmOcjfF9gcY47C8kitCROFLq5QRlbmSKjYtkfWTloBtniwnKGF6D84vSYnq+y8Y4lyRogX32B5ieX4196+xvCFLq0IE4crrVPrOth0Ik8h5oRfDm4OrGeNEcaVMfW5793HmhOaaUdLjmiLExbj7dxWeZmg+0oMh9bh2Fl9O1pNQbL0KYKmkMg0NzTi9Hy8d39m0RiIFY6MBoMtXlaB6LwfIeVW+MN1ZJg6DQGIf9hY1x6Eic6XlDWsh4ejqt/O7NmFuzFMmgIeNJKacbLPadFQnMAhlFAmjO4+A2GpvVElrLKs4m0mS0/XsBVojqNSgpy8tNrdY2AhJ5D7TizPKZDbE1uJr3tPQYl8qzn6EQaIGtFhJYKWpVSh0bDQhMEf9RhsENC6VOQyHEPrQgyiKQXC0C0RiH9sRRSSvPflMlrfHZD0GhMQ77S28gZYuLHdPD0ILT0AUyyZqeN6sEjT8qrQli4h+acXZVxv7NYFLqq5tTLQvEfTZHoBtxpdQzmfPzjZuDyMpBB6Iq5aijrBxKOdCRx4v4P+rlOu7H8JmdP2wlqcjGOHTg9DKmWki24jHeEPJ+6EipkhbXPqx5vYfYh248jiKlbp79Rt7fZRGIvB8astd3XggKjXHYb4pkkeIwtCDqFJkrn1mJixI05nFMWS1p3hxsWyCmSAAdMWXlwsjp2gWy3CoQ0xyEcMRpjhhFgi5DIWyMQ0fKrcE4A4Fabg22eC02xqEDsbcGsy4e4wPuvNCe6XS29Bi/fRLAPkmqyPtb2of1juafHRKxD604Da6Sps0KmV3qndT7oSFR8x51VEmDoNAYh/2m+LAkUYQWxJFSLyco804b40hrQXuiyOmuSYoWzzd8HRrj0BHbZznw1uDq2c/GOAQkepGg10FWziqQDbu+KThAivgPvzWlNa/Nxpgb42yeQEOsO+9R3GHwTmoh5D3QkLOr8fLIjDIMrg45f5aV8c+dF1oQp+ZTnP3qVu+kMQ4dsIZCIuT9bIzvDzTGYb9hYxw6EH963ni208Y4PsvQjKjTw5KkbOk51WljnLMfWlB4jB/1A8npWh7jtqQoigkQkqgFMklZZjbGG74OSjnQkSL+gzcGpe55v5X7MBgCzTi9ijgMvrox3vR1yHugA1HynrW834FaCEo50IIoNR/TY9xV3sPZDw1JSUqdvnja0BiH/Wa5MU5zBJoT58OyKBJkK1LqDV9ngJwutOdx5K1Bc3uk0/QwQyHQgkJSMY6NQFdJUbM5QpEAmhFbSj3vMj3PUBR04Go81dV4fhbHGApZt1Bq+Fp9rASgPXHtw1bUEmiOQEDiDIPbaiFFc7CxfZJk2GgQ+9Cc+B7j5dPNLZQ4+6E9sVXScktKnc54ytAYh/2m8JyiOQItiCqlvro50vR1aI5ABx5HlhYym4ONE0VLVo7mCDSnKJKFi307xvNOG+M3y8cUCaAhsYeisry8ejYukLExDh2IkvNPJ+Vjp1uz5D7QjLNhGYvB8/4VpZxucrqc/dCMR1HuvCvD4Nn607WhMQ4dKO68R71QKmmyPcY7DYObd17q/dAMpNShLjTGYb8pmiPTkV18AKjB6aJIcNTLddQPdBxukpWjQAwBsafnQ12SzMZ42RzsViTgkgTNmM1mpc9sNLUEV2c/8Q/NOEtKSh0paQhHVJUoScp7KxvjHSyUaA5CQx5baiHhmyO5s+YIQyHQDEtKPZJSVN52GFwyFDI596E5yztvMBsBqZRSt9VCsA+DkMSXUu8wDA5BoTEO+82A5iC053Q4khQ4UdwgK8fWIISkiH0pksd4lkty4LdG7ENDhuOpRpN50MWWVJRayCpSJIAO2ENRgzC/1JRSzztMz/ePy8c0BqEhsQtkTpWiaA5CQ+JKqecd77wMhUB7zLznzkmovMfI+41x2Fa9kaLmMx1Jk/H27wVYIfgw+EreYw9FUe+EcMSWUs/yDrEPQaExDvtNH79BaE8xPR9ua2q1QNZFUtEsEhD70IzY0kKS4bfWJlEsCsQUyKAhp1E2ZleKBGZzcFrx/dugSAAdOB1G2Bo0iwSZ6bfW8GX6NAahPVHtk6TuaiFYKEEHHi8GYnt5phuD8Bvj6iSlzkAgtMcaBg+2DLEyDJ45GAaXOPuhMUv7sBh5jzIZDkrdbDRQSYOGxK535myM7w00xmG/YXMKWjKbzSIkiqtyuhu+VgeKBNCB2D6zlt9am5fqszEO7bA3ZuMUCUyQlYOQ2JKi4Tensi4b472+lC/+zVIchoZEyXtWGuPqohZiFYgZDIFmnBrD4OZgtlesjfEuQyEoBEJ7HsfO+1eVosj7IRDD8URXk3kshmsMbrYPmzbtDprD4CwCQUPOriJLqXe580JQaIzDfjNgYxzacTmaarxIzmL4bM63Bo0vNX0tLknQgfhFgo5+a0X8T66k6cTBm4NDwZLVirQ5kndSC2FjHNoTZWPclFK3PMZbvFYxFIXHODQkqpS0VNEcafhabA1CBx6HHgaXrMZ4NwsZmiPQnsdRtgY3L0M03hy0aj5szUJ9LBuBSHmPOwsZYh+aEafeaaukFfHPxnja0BiH/abPBDG047HlsRzBb6rzBCVDIdAe228t0kWpeLqVrBxnP7TjcRSPZV+bIxQJoBnF2X8yyNXvhboGmtPzpqxci8O/8Bnn3IeGnMUYinIppc7GOHSgUEwI5rEsGfGf2fZhnWKfsx+aYQ4EPhHLQqmLzzI2MtCS1IbBuw2FcPZDM2J7jM9rPoWNBp3xlKExDvsNzUFoidkcCXdJsuV0O01QUiSADkRJFFen54tEsc1rsT0CLUnBY9w8+5sXCdgYh/YU8R9sKERa8VvrKCtX5P2c+9AQM++P5jHeaWuWOy+0Yzie6Go8j8VgW4OSlhn+auw3bgxy54X2nMYeiO1a82EgFloSXSFwNfYbD4Obd15iH5pxGiXvXx0MmT9ESj1taIzDfsNFCVoSZWN2ZYLM+grNEQhIsTmSaaabg/ByuuZFqdUEJUUCaMmppRYS4ZKkbGVzpOFrWWoJxD4043RY+swGw/JbKz9vWsnKFWc/eQ80xB4IDJX3bGmMszULgYhz55UhpZ5ZUtKNYz/Ppd5CLYShEGiIpRIYaWs26zIUaOb9xD80IMrG+Kx6Y3b1S7VgYxw6UMT/cT/XUT9Q63NtMGRhHTmt/nZIAxrjsN8wPQ8tieM3tVog6+Iza16SSBShGY8v50WC456U59mO73ZF9dZsJ59ZiYsSNOI0xvS80wIZsQ/tmM1mxsZ4yK3BkqyLnKhUDgUyFAINsZuDobYGJ+XjVSn1pq/FnRdaEuXOK1ke453uvBJDUdCa6FuzmboNhrA1Cy2J4jG+JiVd/pU7L4Qkzp3XjH+xMb4n0BiH/YbpeWiJuTUYrkBmN8Y7+cziNwUdWPrMBlqaklThs7yYoOy8Mc7ZD/V5HN1n1t6cal4kMG0EiH2oz+VoqsliTftWqI1ZaYuUeovXKuJ/OpYmo+3fC2DwOMbW7NaNceSkIQxRLGSklcZ4+XSnvJ/Yh4akYR9W/aVaDKj5QDvixL6d95hjIXiMQ0iWjfFIKmnzen+29jSkB41x2G+YnoeWPIo+PbyyOdL0w7LXl/JFQ5/pYWhIkSjGa4zny+jvtDkicVGCRkSZnl+TlevwWsQ+tMQukIX0GC/jP7ek1LHRgHA8jjIQa579dsLFxjiE4tGlaSET8ux3JKUulYMhDARCQ4q8/9ZRT71QKmlryxCmYkKXZQjiH+pjDoNH8ViWfecl9iEUs9lMZzE2xlek1DstAkEwaIzDfsP0PLQkit/a6tZgl0RRKotkFMigAZPpTOdXc3nPsI3xas+paZu1QatAzNkP9Ynjt7ZyScq7SKmbkorEPtQn+tagpKzXdWOcIhm0I42N8Q4Dsdx5oSVRYl8qpz9WYr8VSxsN7rzQjCL+o9gISJr7zBpf6rQ1y0Ag1CeFemcnj/FeX+odzR8T+9CA4Xiq0WQecPGk1Muzn8Z42tAYh/2G6XloSZwC2Yqs1oYv1aYoklEkgAaYl6STXsAkbaVIUPwDYGMcQpKEx7jxlel07Zu3Q4EMWmLGflApdSP+s6y8enbyGJeIf2iEpRR1FN9Go3Hyw50XWhKlOSLZUurm063OfmNjnAIzNCC+z2y+YiPTwWeZsx8aYFpHhlML2Wwj0GkZgnoPNCCKjYC0sd5P2pI2NMZhv2F6HloSx2N8NVE0ZbVasEwUKQ5DfUw50ZD1sY3NwVZDITTGoR2Po/utZd3OfmIfWvI4SoFMK1Lq5dWzu5Q68Q/1ebyQk7593LdUO7yyzWO8saQod15ox+NL884bpzGed73z9mkOQnOm09myQRKs3iOt5f3Wl5q+FgOx0BJrGDzGIpCy7mf/Ui2EvAfqYw+DR5RSz5FS3wdojMN+MzCKBFySoAGPU/AYNycouxSIiX1ogDlBeSOax3hHzx2aI9CS6EWCrpsjvb6UL4p7FMigAfHkdMuzPzc2xhurJUj2xvgV8Q/1Kc7+WLHfWSmKrUFoib05FcNj3MWdl8EQaM7ZVfy8Z20ZomnuYw3EcvZDfeIMg2/ZGG9z9i8VMsn5oT7R7rwb1EJoi6cNjXHYb9icgpY8juG16bJAJhmJIrEP9TEbg2HVdKsvSkipQ0iKAnGeSTcGgf4BbPUa7CAnTexDA8wiwRORigRZ3mEoRJKOkFKHdjxOoDHeaSiKjXFoSfShqNWN2TaJP4Mh0IJU5HTzLmohbIxDS1LwGM+svKfF6xV3Xs59aICtlBNPLaQ4+9kYTxsa47Df0ByBllhFglDT8ysjwlnWUcqxSBRnE2ky2v69AAvMoZBoHuNGgbjzxviYsx/qY3oNdj6Da2MWyDK7QNalQMzmCDQgXpGgWkq9c3OEvB9qMppMdTGaSIpZIFuVUm+IqZZA7EMDomwNSkb8Z91iX2IZAloRRSFQWpOT7qYWgkImtCPOYMjmoZBWib9pHUlzEWryyBwGvxHy7Df/Ug6GtFJJg2DQGIf9xiwS0ByBBpwaBeJgcrpbEsV2W4Pm9ggTxFAPs0hwEktK3SgRtFNLoEAG7Si3BmM2R7pOzxtFAoCaRN8a1IqUehe1BIn4h9pE2ZqS1s7+TrkPagnQElstJGDuU9x7Vz3GW+U9KCZAcx7HsE+StElOV2qR+5D3QEuKxniWSTePIqmkydGdV2IwBGoTbRh8Q72/Va0fgkFjHPYb84MSr0FoQHFRunnUUy8PtDW41WO8xevhOQUtOI3VGDcxPXfYGoSAnBkb48FYnR42/tapOUjsQwMeXcQvEpgb453VQoh/qIk9FBIw9qfbNsbbSKkvXoDmCDQgzjC4NnqMd7KQkWiOQG1Oh5HOfqdqIeQ90I6i5hNUJc2yEbBV0roPxBL/UI9o9mFrUuqFQma4twDNoTEO+83RrfIxH5TQgOKiFHNzJOu6NYucNLTgdFgWyEIqC617Ti2ebiOqyCUJWjA25HRT2RxpdU8qzv7xBdpcUJt4G+OGx7i1Md7itdicghY8MhuDUaSkJeU9ayiq8eGfZQxFQSviyUkbjfHOSlGopEFzLLWQWGd/tvwPSS2agyxCQEsKG414sW97jHe680qc/VCbFO68UtbNOhKCQWMc9hvrg/Is3vuAvePxZYytwRVJxS6bIxKyctCKaBvjM7s5uGyMd5ZU5JIE9UhBSlpZJmNptuXmFLJy0JzHQ3NjPFKRwNge6Rz7nP1QE3NrMObmSGef5SL+r7jzQn2K+D/u5zrqByr/bcj5pbZ3XpqD0JzH0dQStvksN3wt8h5oSRH/MaWkMzbGIQJpSKmXeT8b42lDYxz2mz5S6tCc6XRmbIzHktXKuvut4bMMLXhkNcYDZmkrgyGdJihpDEILzK3BoD6bqwVic3OkzU0JWUVogSUrdyOm31oHWTkKZNCCNIaiXChFsTEOzSniP+6d1/hS141xVNKgJuZQVLRlCGXd7PO480ILrsZTXY7mcRgt75Fd72w1EcgyBLTg0WWsYfDqej8e42lDYxz2mzwvm4MUCaAmp1cJSKtkuSWpyNYghMIsEoTdGLcln4v4b5UmMhQCLUiiOSK52xqUKBJAbYqhqCyTbh/FL5J13xwh9qEe0TZHtvrMtoj/o6IxTuxDfYr4D6uWYN95bSnpFq9nnf3ceaEe0fL+rRZKDf8B9AZSvnjvnP1QEzPvCToMu0UtoXvez9kP9bDP/kjxr3IoECn1tKExDvvPskiArBzU4zQFz5G1AlkLaI5AC6JJqa9JaxUTlC1eio1ZaMGji0hFgi0Fss4FYoaioCZFkez2UV+5pevpmRUp9U6ycpz90IIkhqJW8/4u8T86b/kCcGjMZqVKWlgpacdDIWwNQgvMYfC9PvtZBIKGpJH3dFRLkKh3QitSG4pCSj1taIzD/oOsHDTE/KCM6THeWVLRlJVjghJqYm2MB80Tt3htNv0HQHMEWvAowSIBPssQilJON+TBL20qErSLfTbGoTmWz2wqeX+b1xvcKl93ctXprcFhcH41WRZk49oImF9r8XooRUEL7LM/npWAZaHUJe+n3gM1iWYftmERQmIZAsJRnP1RVdKsYXA64ylDYxz2n6JIhsc41OR0mIKk4orfWqvpebw2oTlmongUMgtYk9bqcFHqHS2kGYXXINTmcSIe451iX+Lsh1YU8R8075EqpNTnjzoVhyViH2rzeBhLUnHz1uC0zfqIGf9XKKXBbtIYBs/s5kib1ztiKAqaY3mMp6IS2MVnmdiHmsTbGF+Vku46FMLZD80p4v/2cUyVNHMYPNxbgObQGIf9B1k5aMijFIoEyky7NaSFIBhF/N857itknrhte6TxRSnLUKsEn3kAAQAASURBVAuBxiSxMa7Mgd8aZz8042o81eVoHofBN8ZXpNQ7ycoxFAItSENS1B6KasUR8Q/NSGIYfCXv6a6UQ+xDPR5dlGf/ExGbg923ZhdnP/ZJUJNo9mGrQyHWlzj7IQxFzSesWoIqcp/izkufKmVojMP+c1TIyk2QlYNaRPMYX5UW6iqlXsS+RHMEalNsDQYtEEhriaLtN9iCPtPz0IxoG+MrZ78sScUWL0eRABpixn5cKfWOsnJszEILzMZ4XLWQ8q9sTkEIkhgGX7nztst7uPNCc0yVtFsh5XTXLGTMr3SRUmcRCOqRhseyvTHert7DnReaU6qk7fGdF4JBYxz2HzanoCGpbI5knS9JRoGMAjHUYDabLafnb4eeoDRZ8ZzqVCDGbw1qYm2O3IgnKdq5QEaRABpi5z2xpufngV9I2uE1CKGINhgym5SPVzbGJ62k1GmMQzPsoZCIw+Bdh2GPuPNCcyyVtKByuptV0lrlPkXeP5tKk9H27wWQ7TGejIVM14FY7POgBsPxRMNxCippeTeVNAgGjXHYf6zmIEUC2E0SsnKrkoqtNsYpkEEzhuOpribzOIy6MZ5l3YsES781LklQj8fRigSuPcYZCIRmxNscURnki7jvJCuX9wy1EM5+qIflsxxTSj13qBbCnRdqcJpI7FtfQi0BAlHISQeVkpbW77ydh8HJ+6EZj2INRa3ZCJR/w0IJQhB3GNyO/zwvnqYznjI0xmH/4cMSGvI4FVm5zhOUhqwcBTKowSNLSjqNCcrVL9WmKBIwPQw1SUJOd7VI0HlrEMUE2I1lIxC6QFxM/i2aI3lXWTlTUhSgBkX8nwxyDXoByx9rA7HlX1vFPxZK0JBkBgLzjnK6VuyT98NuZrPZ8t4b3me2Wk5Xahn/qOVAQ+Kd/WbeI4bBIThRh8E3WAmwMZ42NMZh/7G2ZpHWgt2kIaXuYmOWRBGaYUpJ34k6Pe/ASqCQlZuOkZWDWjyK5rO8ZSikzctx9kNDHkXdGLel1IvNqel0w/fvYmmjQXEY6lFszcazEdDa2c/WIITgdJjIMLj5pa6xj5Q61GA4nmo0mcdaUPskyY5/ZTKrPp3jn4FwqIFZ83kyaPxvW4TomvcQ+7CbaPZJ0tpQYNZFJQ2CQWMc9h82xqEhhayWFHhzyloadOy3RoEMamA1BkMWyKS1IoHbCWLOfthNMRTVyzPdPOqF+8VrkorlX9s1R8h7oBnRNkekMsFZ2RhvLSvHxjg05PGyMR7TQmZFSr3NYAhnPzQk3p13c97TLudHLQGaYcZ+8LxnrTlofKWVx/hJ+ZizH2qQxMb4mpQ6Nhrgn2gKgVJFvXPx9Aw59ZShMQ77DxPE0BBbTjqex7jUtTGIlDo0wyqQRZYW4qIEoSnO/jsnfcvvzztrkooOfWaJfajB41heg1KZ+6x5jLd8vWVjnOIw7GY6nen0KpWNceN9kfdAAJLwmc3ylY3ZFq/X60u9o/lj7rxQg2j1HmmHSloLGIqChsSrdzoeChkwFALNiDoMvkFKXWoZ/xAEGuOw/5AoQkMsOeloUuouvAaxEYBmWM2RqFLqdlMSOWkIQRH/qUwPz7+GrBz4Jw2/tcz4zy4e44vcZzKUppNO7wyuP6dX4+UxG20oRJKyXD2XUuo0B6EG5kDskxE3xq3mSLus37DRIPZhN9ZQSEwp9ZXmSOeaD4tAUIMi7+/nmU4GAds+a/WersPg1PqhGXHtw1Yb4+VfkVNPFxrjsP8wPQ8NKSYoj/u5TgYx5XQ7biySKEJD4nksq0wUl3K6xgRlG0lRq0jA2Q/bmc1mywJxvMagKgpkLV6Oxjg05FFUKXX77M9cbYxL5P2wE3MoJKjHsrTRa1BqGf9HyElDM6ytwWiN8dzsjbQ/+4v4J/ahBrZKWqS8R1rfGMdKAAIQTSVtq40Aw+Dgn6jD4GtWAh3zfggCjXHYf/BZhoYUF6WoG7OWqFzLRDHvSb3j+WMag1ADSy0hlsf4sjlifKnN9ghFAmjA5Wiq8eJGEttn1jz7O28NEvtQg8dRh6JWpNQXt8/2HuPm2U+RDLaTROxLiwJxefpP2lTIOPuhIamopHWWUpfK+OfOCzWIuzVo13zsrVk2xsE/S5W0yPVOaxGizev1aYxDMx7HtNFYGQzpXPOBINAYh/1nwNYgNKO4KAWVlJO09kHZ1W9KKi9KSKlDDezNkVhFAttnVmq7OUWRAOoT9ZJkXYTs6eHOXoPjy7bvCg4Ie3o+Vu5jn/2t8x6ag9CAqLFvSv1nmXpG5aXd5hRDIdCMIu8/GeQ67kdSSVO24qDUVUqdnB92Y22MB6/5GDjxWWYRCOozm82WuU/4oRDj8Uq9s1VjsNeXekfzx8Q+1CDaQKC01UaDvni60BiH/YdEERownkx1Oix8ZtPZHGk/Pb8okjEUAjVIYoKy2Bg3v9KqQMzZD/WJKiW9RVaOjXEIQVxZuWobjfYe42yPQH2ieSxLdt6f99xujDMQCDVYqqQFHwjcrJTT+s5bSKlPx9L4qu07gwPBzHuix3/Xxrhpo0HNB3ZwfjVZ5hhxY99RY7DIfcj5oQaPU7APkyRlS5U0qaVCJgSBxjjsPxSIoQFFU1yKLC2U2cdv6wLxcmOc2IfdxJ2gLJoj2eKPjluzR7fLxxQJYAempGI8tQRJ2crGeJvgR1YOGlIUCbJMun0US0pdy/cgSdO2ZmsMRUEDHlo+s5FtNFwq5XD2Qw0exrIPWxsI7JjzS5z90Ig0VNK03hxsZR9mqqSddnhjcAhEHYbdopDZut7ZpzEO9Ukp/jsrZEIQaIzD/nOErBzUx2wMJjU93PY1lxOUFAhgN4+iem2ubIx3vSgdUSSA+phbg1Gnh9ckFZGVA/8URYLbx33lebbju12zQUq96+aIxFAU7MTaGL8ZN+/vLqVOYxDqM55MdXY1l/MPPxTioTnCMgQ0wJJSj5z3d1ZMsOqdxD5s51FU+7BVGw2XG+PEPuzm8TCijYZ19mcrA7F0xlOFxjjsP8jKQQNSmh7O5FBKHVk5qIE1QXkc12O8s90gBWJogC2pGPHs1+olqeVrIisHDSgUE8JbaKhCSn3+1/bNEbZmoT6PEpLTtaTUsdEAz1h5T2SVNCd3XuSkoQHJnP1S9+YIsQ8NSEZKOsvsYfC2q0BF3k/ODzUocp88k24d9cL+8lUpdTP+p2vfDYlAYxz2Hwpk0IC408PG4yy3PEda74xbsooMhsB2ivi/ddRTvxc4BVjK6do+s1JbSVGKBFCfqNPzJs42pxZnP7EPNSiKZOEl5WSc/fbGeOuhkCOGoqA+D1PxGHchqWjJ6RL7sJ1ktgaz3JqGdSOlzp0XtvM45jLEFjnpVvFP7EMD4lrnbbYRmLZtDBZ5//iiw4vAoWCqpJlDSWHYJqXOxniq0BiH/YetQWiAvTGezvR86xyPIhk0oIj/8F6D0jJRrJBS7+y3RpEAdvA4IY/x3ImsXDE9T+zDdi5HEw3H8xiMMxRiS6lny8a4CzldBmJhO9ZAbFRJRQcFst5Ayhf/Hbjzwg4s+7CoeY+6W8hI2OdBI8yz/3Y0lTRVKCZ0tQ/j7IftRK13rg6FGF9xoxRF/MN2irM/uFqCVFHzKf9KYzxdaIzD/sPmCDTgYdSN8RUpdRfSQnhOQQOKIlncrcH5H2yMQ0jSkZVz4DEulfFP7MMOojYGJUNKvdgYt59uDAUyaEBKW7NWgaytZMIRkqJQj7ixv7ox63AgUMI+D3byyNgajKaSJq3LSbdSC6HeA/UxbQSiboyveoy3fc2j2+Vjzn7Ywmw2W9b7g6tEzd+A8RdH9nngHRrjsP+wMQsNSGd63p6gbF8kwG8Q6jGaTHUxmkhKw2dWXZuDbIxDA5KRlZNstZCuXpvTkTS+avkicAiYzZE4RYJiKGru85azMQ4BSUdKPVOeOzj7l2oh5PywnUfJxP7Knbe1zyx3XqhPuTUYYxjcsZT6EUMhUJ/HCQ1FuRkGp+YD9Ti/mmi8SLCj3HnXpNSNr7Axniw0xmH/QVYOGpDM5oirCUoGQ6AmtpR0TGmhzPjPxZdabYwT+1CfqEWCrV6DLtRCKBLAZiylnOA+myrP/rxojM//6kZSkcY4bKcYihr0Mp0MYm4N2lLqk7adcRrjUJOk7MO6bsxKKEVBI4p7b5xh8M01n1ZqISjlQAOsmk9kj3EnG7PW2c+dFzaTzDC4tGafx8Z4utAYh+sBRQKoSVxJ0c3NkdYFYpojUBMr9qNKqc9Tj84+y6asFmc/7CAZWTlXlyQkRaEmUTdmJWk6Vyopzv7SY7zl61lbg8Q+bMeUVDQLtEGYTcrHrjZHirOfxiDswFJJi24j4FhKnbwftnA1NlTSogwEOt4Yz3tS/2T+mLMfdpBMvVOrNgJt8x6GoqAe0e+81tm/WvOhM54qNMbheoDfGtTkUTITlLasXOuVcTbGoSaPYnosS1oG+bI5Yn6l4/Q8jUHYgVkkiO0xnjmRlaNIAPUwmyNxpdSLoSjjS51tNMj7YTtF7hN9azDrqedCSr24806G5dAJQAX2xnhcn1mT9sPgNMahHnFVoqS1ZQg5aI4sF4G488J24g6Dr955HeY9knR12vJF4BB4eB5bJW11GLz8Eo3xdKExDteDYnuE5gjsIOoE5ZqsnGMpdQrEsAVbSj3i9Pwi7jtvzQ5uaFls4+yHHRQTxDePejrqR5TTVeZmc8pqjFMkgM1YUupRmoN2kaD72U9zBOoxnc50OlzI6UaXVLTz/knr5gg+y1CPRzHPftcbsxLD4FCbqI1BqUIpyvxay9cslNKIfdhB1K3ZlY1Z9wqZxD9sJv7GeDEMXtiHOaj5gHdojMP1YMDGONTD3pqNuzHuRFrImp6nOQibiVogk4zG+GKC0vpSi/jPMmw0oDamnG547AJx7qJIQHMQapJMkSBfLxK0in+zMUiBGLbw+HK8TD1SaIz3XEqpS9x7YSuWSlps+zA56IwPuPNCPayN8ehyuo4GQ46480I9HsZUSVu78zqwsEFKHWpi5j1x7MM2q6SxMZ4uNMbhejBAVg7qUUiKngxyHfd7YX/56gSlHEgLMT0PNXkUvUhQFIjnce9EMeEIr02oR9TG+MrmiD093/I1i80RCcUE2Io5FPXkzfhFgs7bI2bsUyCGLVh5T5StwZUCce5QTlfi7Iet2AOxke3DutonSStbgwyFwGZMC5noNhparfl0PPuvzlg9hK0UZ/+dk75l4RKENZW08m9ONsZRSYMtPIypDivtGAYP/3agHjTG4XqArBzUJK7XoI/pYaSFoB6PY8vKbfMYx28NPHI5mmg4nl9U0tgccSGlTnME6hFfSt2xrBwFMqhJMmoJ0rqU+rTi++tg3XlpDsJmbJW0mPZhjixkGAqBmtjD4GlJqXe3UJpx9sNW4g6Db1YLmbbNe45QSYN6xG+Mr3qMOxiKAu/QGIfrwRHyKlCPYoIyBUlF60tsjoBn0mmOVEmpt3zN4uzn3IctPEqpObLmMY7fGvglfnNwxWPcSH/aS6kv/g2R98AWHkUvkNnNkZ6LAhlnP9Sk2Jq9MejpqB+47Lc6FGJ8qX3s0xyBejyKKiUtlSsP2XwS3MXZj4US1GA2myWkkiY3aiGWlDp5P2wmes2nUC9eDoOXX2pd8wHv0BiH6wEb41CD8WSqs6v5h1UcScWV6eHcgZQ0XoNQk/jNkaIxPv/DibRQEf/jiw5jyHDdiR77JlnmpkBMkQBqEj3+l7Jyhd9ax7M/y4yhKGIfNmNuDcYvEOe2pGjbxIc7L9RkqZKWwMasE5U07MOgJsnkPYvAz52oBLIMAbs5u5possgv4tx5NytktrcP484L9YjeGF87+5FS3wdojMP1gAlKqIEpJR1/cyRf2Zh1MT1PogibMYsEd2P4zM62SKk78Rvk7IdqHqRySZIWzREHQ1EUCaAmjxa5z1Ev18kgwtVvVS3E5dYssQ9bSEYpR1o7+9sPBOKzDPUo4j8N+zDHUurk/LAF684bU056qZLmQClqwJ0XdpPMUIikVZW01pdehkKgJvHjf7ExXniMd1VJgyDQGIfrAVuzUAPLbyp2gUwr0/NO/Na4JMFmHpzHThSL+J8HvtUcae05RZEAdvMweuybB3zmaHqeIgHUo7SQ6VvnbjCmK1LqZu7T9ewn9mELhZS0FGlrtoh9ad4Yz13I6Rob48Q/bGA0meq8UEmL3RxZGYh1o5JGzg+beZCKjUZV3uMk7yf+oZqk7rxrA4Fth2Fvl485+2ELyQzEVg2DI26ZLDTG4XpAgRhqEL1AtiotJAfT8zQGoSYPU/Fbc7kxPuDsh91Enx62zv7VxjhFAvDLcmswWoGsOPsLvzUXzUEa47CbpKXUnShFMQwO1ZgqaSnEvrR0Umq/MZvnUn8xGEJjELYQPe9fxngxDF7xpaYMUAmE3cSP/RUbDeNL7XN+s95z2u414CAo8v4bg56O+hHanUX3u8JjnI3xdKExDtcDJoihBmaBLEpjcFVK3UljkM0RqMdya/Ckr14eYWvQ+/Q88Q/VpFYkcCIrR5EAajCeTHU6nDdI4sf++tnfWUp9MpQmo+3fCwdLMpsjkpT3rNyrvcc4Nhqwm0dW7Mf2GLc3pzrVhou8n8YgbMHcmo1iH7Y2DO7QQkZiMAQ28jC2WsIWj3E39mHEPmymiP84ixCqqHc6WIQD79AYh+sBXptQA1NKOo7f1ObGeGs53b7RGGdzBLZQyMo9GaVAIMNvbTE970IxAb81qEH8xrgPWTkkFWE35tZgCo1BabVA3PJ1yfuhBo8SO/vdxz5DUVBN9ObIioVM+Z8dhsGlciiQvAe2UMR/L890+zjiYEhWsTHe9jUtlUDyHqgmft6zah1ZKqU5yXuo98AW4jfGVzzGXdR8wDs0xuF6QIEMavDg4mr5OMr08Gpj3EVjMM/LIgGJImxgNpslkCh6kFJnYxxqEL9AvFokKP+GlDr4JPpQyIrHsrSqFuJic4qzH6pJ6ux3JqVO7MNuTI/lFIbBpbJA3Kk2vLzzMgwOmylqPk+c9K2BpGCs3nmtmo8LOWnyfqgmet6/MhAoObDR6A2kfPHfhYFA2MBwPNHlaJ57xLFN1dpQlAmN8XShMQ7XA7NATJEANmAnikfh34CVKGYrn5cupueJfajmdDjWZDGmezdG7EtGkWwe+LmLzSlsNKAG0afnt8nKIaUOHolfINvcHJHYHgG/PDIUE+4kICfdc6IWwp0XdmOd/Tdj5P3rzRF13RqUbCl1isywgUJK/W6U2Jcn+zDyHthN/LzfrndKjoaiivhnKAQ28OiizPmjLQIVA+FZ1cZ4jDcEdaAxDtcDigRQg+h+U6uSihu+1JgjNsZhO9EvSZJW/dasr+C3Bh6JHv9bPMadDIUQ+7CBR5fmxmyMxmDVxrhrr00GQ6CaYijq1lFPg16EssfK2W9uLk6mFd9fB2IfavDw3FBJSyDvkRxsDUqlhdJsKo2H7V8Hri2T6UyPh/MGSRyPZa0NgzuRUh+gkga7MRUyUxgGl8q8v9PG7LIxTuxDNdFVoqStQ1GdFuHAKzTG4XpAkQBqYHqMx2+O2D6znT4mkZWDHaSZKLreGuSiBNXEb4xvG4pqGfymjQZFAthA/Niv8hgvn0JOGnxSxH/0vEdCSh2CktqdV6pUFm3O4Eb5mIFwqODx5WiZdkcZCpFUDoMXQyEuBgJpjMNuHsbeml2xDzP/6KYWssh9OPdhA9HvvFIZ/4XHeM7G+D5AYxyuBzTGoQbmh2V8j3FHPrOS3RxBVg4qiK6WIBmN8cUfLiYo2ZqFGhRn/41BT0f9yFuDytwNRS2LBBTIoJroRYJKj3HTa7Pl66IUBTVYNsZP0miM93Iz9lsG//Gd8jGxDxt4kNSd1/ZZdqKSJtEggUqi5z3Sms+sGwslpNRhN9Hjv1JKvfiSi3rnKfVOqMS0zoue91cMBE7pjCcLjXG4HlAggxqY0kJRfJZXigTm9LATzx3NpPFlhxeC60r0S5JUBrk3vzXOfqimiP/oNgLSmlqIs6EogApMv7UoRQKrQFb4rZVPIaUOvrgcTTQcz/Pu6AOB0trZ70ZKnbMfqkltGFwymiNdRgIHxD9sJ7pagrR2582cWMiwMQ67ia4SWHn2O/QYx0YDNmDah+ExDk2gMQ7XAwpkUIPionTUz3UyiL81aOrpOpFSl7goQSXW5kjs6fml3xo+yxCG6I3x1el5qzHY4XWLoUBiHzYQfShqp8d4y9fl7IcdPEhJKUeaN8ZzB82R3pGU9+ePufPCBsz4j9McWfeZzZY+sx1el8EQ2EH0oRBph89sSwaoJcBuiq3ZOyd9S6UmHOYBnxn/6chjXCL+oZLod15p69nfSTEBvEJjHK4HXJKgBmZzJHNidNaU1a1B4ytdPiiPTcUEimSwThKJolam582vuNga5JIEFZhbg2k0xh2e/cX2yPjClqwGWJDU5sjSY9zF5hRKUbCd6CpR0kpjvOfm7M+yMvch54cNPDTiP7rP7Erez50XfPIghTvvMsbtxqD1paZY9U7uvFBNWsPg9kCsu0Ugzn5Y52ESaiGLekzucBgcvENjHK4HFMigBkWiGH9jVnMpdRc+mxKDIbCTJBrja547rrcGuSTBOlbsx94ckbTmMe7q7GcwBCqILitneYzbcrqSo6Eozn6owNoYv5XA2b8qpd5pKKpQCyHnh2qK+L951NNxvxf+DVQ0xovuYKfaMHde2EESd97VYXAnA4HYh8F2ZrNZAo3xdSn1Ivy7bYyb9X7uvLCOeeeNMgwu7ah30hlPFRrjcD3o9aX+yfwxlySoYDie6PxqXqCNLqslLTzGjS91KRMc3SkfDykQwzqW31oUr8EVKenyj/mX28b/EXK6sJ2kCmSSlGVW7HfzGGd7BLbzMLqcrr0xKzmanqc5AjuwGuNJbIzn7jZHlhvjxD5UE38YfPPWYKfOOHde2MHD88hqCZLRHKkaCGz5mljIwA7OriaaLJKLNO68dnOw2zA41pGwneg1n9nMOPuLO2/5ZRrj6UJjHK4PyMrBFuwPyjQKZJmLS5LE5hTs5FEKiWJBld9a6yIBG7OwneiXJGltet7dxjiKCbCdQk66l2d64qQf/g1UeYwbt08nm1MUyKACU0o6jYHYzC6QdemMm3deCm2wwmw2W8pJR9+akrSUk3ayNcidF7aTRt5vb4zLxdZg3isXgbjzQgVpxP762V/kPp1sNMzBEBQToIL7xkDsq6IvAq1LqZOupwuNcbg+FBclpoehguiNQWlta9bZByV+a7CD6BelqgKZ2BoE/6ThN2VflJxtjCOlDju4f1ZuDZpybsHY5TE+Xf2BmiApCjuwN8ZjF4ized6fO5JULCRFZ1NpfNn+deBacjma6mo8j71UhkIkw2O8y+ty54UdmHfeuzcjL0NUDIM78VnmzgsVpHjnlcq8v5tSDtapsJ2H0RUyjWHwnI3xfYLGOFwf8FuDLVgFsmhFgvVEscDd9DzxD+uYW4O3jyNsDVbIauUupNQHN7QstdEYhAqiD4VIa4Mh5lBIpzvSgLMftrP0GoyV90zXfWadFAnIe2AH92MXyKSK5ohjKXWJ+Ic1HphqCbFU0rzJ6dIcge08SKI5aAxFSSt5v4OaD3deqCC5O++KlUA360ik1GE79xc2GjePejru98K/gQr7sMxV3g9eoTEO14ciURxfSNPJ9u+FgyOtzRGt+cx2+pzEbw12sGyOpLA1WCkr1/J1s4zpedhKEkUCraqFmF9hKAr8cDWe6nQ4liS9KvbWlFTpMd66PkxzBHbwMIXm4JatwUknKXW2ZmEz9sZsCndeO/6d5T3Dx+1fB64tScR/EeOFWoJr+zzyHqjAjP1oNhomqxvjbVWiJO68sJPCQiZard/sQS2HQhwpRYFXaIzD9YEPS9jCgxQuSase43LUGcdvDXZQDIakMhQiyYx+N9PznPtQwYMUGuMrZ7+7rUHTb43tEbCxisPRYn/dY9w8+1sXCQZsjsB20lCKshvjvdzx1qBE/MMaaW3MylBJc9EcYSgKtlPkPkf9XCeDCFuDknH22xuzUtdliOLOe9rxHxJcR8yBwCTOftk1n24e49iHwWZms5keLDbGo1toSJVS6p3iH7xCYxyuD1yUYAtJTFCuNUeML3W5JuG3BluYTGd6fDnfGowX+/bGrORoa1Aq45/NEaiguCRJidhoKHPoMU7eA5uxYz+FIoG9OSJ1iP/+8XIDnbwHqkiiMT71JaVunP0oRcEKD1KyEZCMrUEHr8swOOzAVEmLRpHbLAcCHW0Nmmf/iLwfbMyzP55SVJV93sJGo8vrIqUOWzi/mmg0mUdYvHrP+jC4s7wfvEJjHK4PFIhhCw9TKxArc+c5QuzDFh5fJrY5ogpZuU6yiov4vzrt2GGH68j9FIoEK16bmauhELZmYQtJKOVMPRUJssw4+9kcgXWK+D/q5boRfWtwvTE4cbYxTnMQbJKyEZDWlKI6bU0dG/Zh5D1QQXSVNGnr2d9tGBz7PNiMfedNoOazopjQbSgEpRzYzH2j1p+SfZizZQjwCo1xuD5YH5ZsDoLNgxQkRVebI+ZXXE0Pc0mCFZLYmlLV9LDxVRdFgulYmlxt/144OB4kcVGqUkwovkSRAPxw/8yM/RQKZOuycp32R7DRgC0UA7FP3hxYw0hBKeJ/IamIlDqEIAmP5YqtweKfoRMpaQmlKFjjajzVxWg+kJfExvhyGNzV2c8yBGwmCaUord95lx7jnYbBkVKHzSShlDNdV8phY3w/oDEO1weKBLCFJJqDqx7jrv2mJDZHYI2HyXksrxcJkBQFXxRnf55Jd076cd5EZYHYQZHgiCIBbMYcCHwy2lBIxcZ47urspzEOmyniP42twXULmUmX4OfOC1tI1WM8X+Y9XXxmb2q5e07swwpJ3HklLSs7VcPgXV7Wss9jMARsrK3ZWwmc/SsqgZ0WIah3whYepKaWgMf4XkFjHK4PTFDCFpK4KK01xh17LEskirCGeUmKViDe4TXYTVaRIgFspoj/uzePrIZcUCqKBMuNcRcbsxJnP6zxMIkigRHfeYWsXKfm4MJKABsNWGE4nuj8aj6UEc9CQ2tyuuZHkDsLJc5+sHmQxJ13s8/sZFr1AzWxbDSIfbAxN2ajbQ1Ka0NRzqTUGQaHLZhS6vFsNLZ4jHdSSzDtwxgGB5sHSVjI7BoG566aKjTG4fpAgRi2UBQJsky6c5JOkaCcoERWC/xgqyWkc0nKxMY4+CcJr8EKK4HlxniXAjEe47AFeygq0tnvy2NcKs/+2UQaDzu8EFw3rGHYJJojVbHPxjj44WGCKmlSaSXQuThcDMQS+7CC7bGczlCUfed1VfPhzgs2xWDIcT/XjaNenDdRqRI4/6u72OfsB5v7KUipVy4CGWd/l5oPeIXGOFwfKBLAFgqvwSdOBpbHX1CqEsXiS11etzeQesfzxzQGYQWzOfLUrQQa41qfnu/mM0uRAKq5Gk91OhxLilgcllbOfuuPbkWC4zvlY/IeWOFBEj6zVXK6xpddNQexEgADqzGYhJR6RWO8y1TIMQVi2ExyKmkLivt3JxsBybDRIOcHmyTuvFJ5761USevwuscMg8NmiuZg1KGQimHw5cZ4l5el3gNbeGjaCMSK/8phcOPLbIwnC41xuD4wRQZbKIoEcZsj64kiRQLwzf0UNkcqL0nlU53CnyIBbMCU1Yq7ObK5SNAJ1BJgC6akaLzGuM+NcZSioJo08h6tNUd6uQelHO68sEKR+/TyTLeP+3HexJahqE5DIVIZ/0NsNMAmibxHMuK/GAb3sTGOfRiUzGaz5VBgGrEvrS5DdDr7+8dSvvg8Gz5q/zpwLUki76/0GHdknQpeoTEO1wd8lmED0+ls2RiPNjkvVW6Ml35rrmTliH2wuX+WQHOwUi3BUaJIkQA28DAFGwFpq8d4t41xszFOkQBsHqQgKVpRJHBWIB6gFAXV2M2RBM7+iq1Bd1Lq5D1gY1rIZC6G8FqxZRi8a3UYGw3YwCtnCeQ9kpbxn9k5f2cYBocNnF9NdDWZ5xypDYNnFV9qTJbZQ1EABvadN6Vh8PIpNsbThcY4XB/MIgEflmDw6HK03MxIokAmufdbO8JvDaq5n4K0UKXnjvHlLuJaFAlgA/dTuCRJ2uox3uXoH9xcvh5DUbBKEf+DXqabSXgNOi4SYKEEG3iQgpS0hMc4RKEoEEfz2ZQ2nP2Oh8Elch+wSG5j3PkwuGmhROxDiVXvuZWAUo60tgjUuS14/MT8T2IfVjDP/idvJGAdmS2GweVIKQq8QmMcrg8UCWADZnPkqRQuSdJakaDzB2XRGB+d2/4mcPCYE5R3Y12UKi5JmTM5XYoEUI1dJEhlKMqWlevksZxlZfwzFAIrFH5rd28exdsatPzWHMvKIaUOG3iYgqSiVG6PVEmpr1sw1wcpddjA1Xiq0+FYkvTUtR0G5+yHatLxGPekFnLM2Q/VPEhFJc1qf9uN8c5n/zEb41CNORAbLe+v8Bh3dvaDV2iMw/WBIgFs4JWzBJsjq3K6rjzGJeIfLIoiQT/PdCcFr0Gty8p1ag6yOQIbSGdzZLPHeOc7EjYasIH7hpxuNCpjv3zK3dbsefvXgWtH4bEsSXdjbY5IW5sjneSkyflhAw+SHAj0NAwu0SABi/spWMhIZe5Tkfd0Cn8r9rHRgBJbITCtvH/pMe5KIXN8IU3G3V4LrhVF/N8+7mvQi9TmtOzD1pWiaIunC41xuD4wPQwbSMJjWZJXvzWag7CBIv6jbg1WSUmbX3XlMU6BDAzSKZCtD4Y4LxJQIAODy9FEF6P55Hrc2Dem5ys9xju8Nnk/bOBBMhvjm6XUOw0E9k+w0YBKXkmmObKulJMXd97Ow+AsQ0A1Zs0n6tm/vPdmK3+6HAjk7IeSJO+8KyqBzobBJemKey+UFEpRSahESWXeb3RcO+X94BUa43B9YHoeNpCmnO5KY5wiAXiiuCilViDLrAlKNsbBPebZH3VrtmIwxJ3fmrEx3kmbF64TD02P5cTOfnc+y2xOQTVmYzwlj/Geq6GQLCvjn5wfDO6fGc2RqHde4/Ey/sunOiml0RyBDRR5/62jno77vThvYodSTqfeyLFhH8YwOBjYHsup3HkXNZ/iK50b48Q/rDObzZZS6kkMw0qV9mGdFWLBGzTG4fpAYxA2kI600BZZOZeNcQrEsCCdrcEdcrquvDa5JIHBw1T81mbrRYLc1ca4WSQYkfvAnAfW5kjEvKfSY7x8qpuNhhH7DEWBQao+s2aBrPtA7GIgnDsvGFixn8zWoD0MLmElAH54sNwaTCv2M1dqIUcMg0M1D1LcGC/s8xZdp+4qaeT9sM7j4XiZU0eN/UqPcUcDseAVGuNwfegNpN7x/DEflGBgSgvFLRJ4lFJHWgsqSE5OVCrlP1157lAkgA3YaiFp+a0VxYLOS94MhkAFllpCKgWyiuZgpyKBtTnCQCCUvLKQ0z3q57p5FGlrUFr3mTUqL85sNMh7wOCVVOzDdqiFdBoMIe+BCqbTcmswbs5foZJmfrnL0d8/lvL+/DF5DxikfOd1rpImEf+w5GFqKlHS0j7MWgRCSj1ZaIzD9WI5Pc8lCUpsv6k0iwSTrs2RYxQTYJ3ktqYkLaeHnW0NckmCapL0W6uI/07QHIQK0hmKqvIYL5/qVCQg9mEDRe7z1M0ja1sjOFs9xju+dnHnHZ46eDG4LjzYA/swyaGNBndeWPD4MpGtwarGoOkz2+W1LRsN6p1Q8iAZlbTNgyHOBgIl8n5YYqvDphL7HvJ+8AaNcbhe4LcGFZjT83Gbg5s3xt0milyUYE4yQyEmhbSQHCWKA9QSoJqiQHwyyHUyiLg1WOG3trTRcHn247UJCx5eGGf/jXSLBO42xjn7Yc5sNlv6LEdtDEqepdQXZ/9sIo2H3V4Lrg2vGB7jT6WyNbvI9y0pdTzGwTFJNkdUNAYdDYVIZe5DvRMMkol/rdc7iwFFpx7j1HxgQTrD4FUe4+VTbIynC41xuF7gtwYVpPlhaRcJnBXIJBJFWHI/FZ/Zqulhc2O8y/x8npfxT3MEDIr4j1sg0IrnlB3/Tj3GiX9YYDZH4nqMby8SOPMYHz5q/zpwrTi7muhqIcMUtTEorTfGnUqp47MM6zzYFxuNLkppxD5UYDcGY57968Ow1p23s1oId15Yp7jzZllCctJeVQKJf5hjnv1RY7+y3oPH+D5AYxyuF8VFaXRuH0xw0Lyy+LC8edSLuzVYWSSY/3XauTHO1iysk8z0cKXfVPlU50QRWTlYYTabLQvE0dUSTDnpZXPQ1fQ8Q1GwTpI2GsvmoKPNKaTUoYL7qXgsS+VdtMJj2W1jnPiHOa+YZ39iW4PupNQZCIR10pSStjdmpY6NQcmod551nDCB60Rx533iZGCdtcGpqPk4aw4esTEO65jqsE/fTuTsz+16j8TGeMrQGIfrBQViqKBIFKMXyLb4rU1cbg0yPQ8L7M2RRKaHs3VZOWfNQc59WHB+NdFoMg+suzGnhyV7UG/FZ7n7UAjNQVjn5dNUGuNm7FcUyDptDZLzwzrJ2CdJW6XUO5/9qIVABcVgSJ5JT6SyNVgxENvp3svGOFSQzMb4Fo9lqaPHuES9Eyopzv64agmqjH+UosAn9628/zjeG7EWIdbzns5DUeANGuNwvaBIACvMZrNSTjcVSUVJpbSQKyl1o0hAcwQWWFLqqWwNFn5rrqTUJVtWjqQTtFIgS+nsz+zGeOdLkiUrx9kPc+z4T+Tsr1QL6WKj0ZMGi9yH2IcFr6SilCOV8Z978BpEMQEqKPL+J2/E3hrcJaWOxzi4xRyKipv3VKmkORwGZygQVphMZ3p0OZYkPZlK3iMZd15HQ4FIqUMFL58lopRT6TGOlPo+QGMcrhfHT5SPKRKApEeX42XTOXqBbIusXHdJRfOSxPQ8zElHUtSjrJZUxv9sIo0vO74YXAeSkVSUtkprdd8cQVYO1imKBFkWWTFhutlGQHKhFrKIf3J+WHA/lY3x2UzLE97Ie5ZqIV0TH+68UMFyazC6WsJ2KXU2xsE1Zt4f1z5s+zC426Eo8n6QHl6YsZ+gSpr55U5nP0MhsI5lHxZTSn26vjHu9OwHb9AYh+sF0/OwQjqNQW2dnu/eGMRjHNZJWVbOWmRxujVL/IM9Pfx07ALxluag06Eo8h5YUOQ+T94YqN+LeN3zuTEulWc/5z4sSHlrUJJ6rvJ+JEVhhdFkqsfD+dZgXH9xVef9ZmO8yz+AI3J+WOd+MmohVcPglV9txxGKCWDzytlw+Tj+2V8lJ+1IvYShEKjAsg+LOhRlnO6VHuOh3xDUhcY4XC+s6fmH8d4HJIMpqRjfa7BCWqvwGO+8OYLHOKxjSqk/GdVrcHuRwNnGuESRACTZRYL4Q1EVPsuLv3bfGqRIAOsUjfH4eU/V9LwHn+XhI2w0QNLK5kgqW4NGY9zZUBTD4LCCGfvxlXIqNsYtKfUOr90bSL2Fjyh3XlhgK0UldueVmfc4HAYn/kErjcHYeX/FxnhudJ06xT95D1RQ5D4ng1w3jnrx3siOoRA8xtOFxjhcL/iwhBUeJDM9rMoiWc9sDnaansdjHNYp4v+Jk35yW4NmkcCtzzLNQbCLBE/HlNWSKjfGlx7jXV8bWTlYYTieJLQ1uGt63lVzcEaBGCRJr5wZkqK3UlHKKfOvIvydDsSyMQ6S7hux/1TM2JcqN8adSalL5b2XYVhY8EoyNhrrsW8tzLocBufOC0pISlpaaQ4WUuqOBmJZhIAKirz/6VvHcd+Ib5U08AaNcbhe0BiHFZIpkEl+iwSDm2XhjeYILCg2xuN7De7yW+v4+jQHYYX7SamFVEzPFx7jTuV0yXtgxWczduxX+K2ZRYLOQ1FHploOZz+k5DFe3Rgv8n7OfnBNOlLS2mofJjkcDCH2YUER/0e9XDejbg1WKASaW4NdX587L6yQln2Ycfbn9jC41DHvP7qlpeYaQyGg+WJZcfZHr/VXeowjpb4P0BiH6wVFAlghrY3x7RelTkWCLCsLxMQ+aO41+PBi0RiPHfuVfmsOiwTIScMKyWyOSHaRYGVj3KmkImc/aEUtIXbs72iOuPVZJv7BtlCKmvtUDMNKZfx33pg9ebJ8TOyD7KGQ6ENRVSppruR0pdI+j9iHBUVz5O7NgXXHDM6uYfDOQyHk/WDziiWlHntrdn1j3Fnen2XlYAhDISDp8eV4WUOPH/vrQyFsjO8HNMbhemF5jJMogt0cid4c3LEx7kxSlNgH2Zsjr44uq2U2xucx73ZrkOl5sLGbg7EvSh43xol9WMHaGozeHKmani+fcuuzjJw0lM3Bm0c9nQxibg1ul1LHYxxcYw6FxLfRWG8OOrXROFnUfCZX0njY7bVg75nNZsuaz9O3I+f82nHn7fry5P2wwsvWMHhCW7OuN8Yl6p1g8fJZ+fn/1M3Y6rAVKmnG4U9fPF1ojMP1giIBrGAXiGN/WFZNzzuUlSuKBJcUh8FuDEbfmK1QS7A9xju+/jFFArCxhqJin/2VHuPz+O9cHM57cysNCbUEkLSilpBSc6TSY7zj6x/jtQk2S0nF6LG/XiCTfEmpk/eDbaNxN3qBuEItxOWd14x/7r0Hz+PhWKPJPKZSVMqRXC5CkPeAja2SltAweJWVgKu8n9gHrVrnxY79dYXA3KVaCHiDxjhcLygSwAr3TY/x6EWyHZKi09UfaEihmDC+kCaj7d8L1560LklVsW98uev8/BFFArAp4v/2cV/H/Yhbg9LKxngxGLL4kos7UpH7MBQCSs1GYFeBjK1ZcMfca3Ce/0aP/YoCmWRIqbtsDBL7oMTO/goLpZ6PO69EzQcSGwZfVwi0VdI6vv6RcfaT94MSO/urNsbNL7tSCbw6ZQUXVs7+2IsQ6/VOPMb3AxrjcL2gSAArJJUoVsjKWRvjFIjBIaasVnwp9fUKmC2n2/H1kZWDFQpJ0ejnvlQWCczmyCID7zwUIpXxT3EYlHDeUzU978pnViLvActrML6NwHpjUCob451jf3BLy3IzsQ+yPcbvJjgM7u/OS+5z6LxiyukmePZnTgcCufOCTZH3D3qZnjjpx30zFR7jTpuDy/ifSVdnHV8M9p1kN8YrVdLojKcKjXG4XpxQIAObwnfkVmyvQUlV0/Pmh6UzKXWJIgHoldOEigQVsS9fUuqc/QfPeDJdSorGj32VRYJ8fWvQyfSwKSvHpevgSbcx7mF6nqEoMLA9ltOTkpbKwZDOR3We47UJFmkNxIa88xL/h465NRg/9tcXIcyNWace48Q+qMz7X3XzyMqxozCtag6WT3UeCGcgFgxeMdRho2+MW0MhVWoh1GhShcY4XC+O8JsCm6JI8PTtyBNkUmWRoGecwt03p4h/KEm3ObKeKDqNfS5JB899w2czutegVBYJjI3xIvydTA8Xuc9sIo0vu78e7DVWczB2/FdOz5dPuT37yXsOHTPvib8xvqkx7khKXaIxDhbFMHg/z/TESewCcdXGePkUd15wSVL2YTsWIboPg3PnhZLZbLaM/+g5v7RhY9z4MgOx4BBTLSQt29TCRgAp9X1g7xvjH/vYx/RP/+k/1Tvf+U59xmd8ho6OjvTa175WX/u1X6t3v/vda9//Ld/yLcqybOP/ffjDHw7/XwLc0etLg5vzxySKB8/I2Bp8Ovb0sLRbVq6z3yAb41DyklEkeDp2kWCHrFxniH0wSKo5IlVujBfx79RjXJpvjcNBcz+l+J9WTc9TIAY/mLH/VFIFsjLmi7zfyVAUjXEwKLZmX3XrSHkeeWuwaiDW252X+D90Xk52GLy485ZPObWQYSjk4DkdjnU1mcdc9NiXKj3G3eb9KCZAibkxHr3eb915PZz94I3IBhTd+Wf/7J/pH/7Df6g3velNeuc736lnnnlGzz33nH7oh35IP/RDP6Qf+IEf0Nd93det/dyf/bN/Vm94wxvWnr97967/Nw1+Ob4jjc75oATLcySJrcGKi5JTWTmKBGDwiiErFz1RrIx948tdE8WTJ8vHFAkOnpeN6eEkzv4qj/GV+O80KGIVCR5Jt59p/1qw9xSDIUf9XLeOIlvIVHmMm1uDnfMeM/YZCjl0zKGou7HP/g0b48VR77QxfnU6/5zJY1tGQSxms1mpkhY79qWVgdh50PdMGw2GwcEhLyd1512PfWtjtuvr9/rS4JY0OiP2IS2FQGllY3y93ulULYSN8YMn2Y3xqqGQ0O8HarP3jfEv/uIv1k/8xE/orW99q/X8T/3UT+ntb3+7/vJf/sv6mq/5Gh0f29ty3/AN36C3ve1tAd8pBOP4jnT6PI1BsC9JsTdmpcrpeXNjvHOieMIEMZQkdVGq8luzJig7vj5yumCQVOxLxsa42RyxpbV6XZa7kJUDg6WkYgpegxUFMqce4wwEgoHlsRz77N8hpe5EUnFVMeHGXQcvCvvI6XCsq/E85l6dhH3YdpW0zvF/QmMcSl5JaSC2SiVN5sasg8P/5Il5Y5x6z8Hz8llii0AVG+NyuTV7xMY4lLyyUIfNMulu9MZ4xVCIMQyOx3i67L2U+h/9o390rSkuSV/+5V+ur/zKr9T9+/f13ve+N8I7g2gsZeUeOdIohX0lqelhqfKilDstENMchJJia/bWUU8ng9hbRNul1Dsf1b1BaaNBkeDguZ9aY7zGxngn2JqFBbPZbKmWk0Ts75ied+sxToHs0Hnp1GiOxG4ObmiMu5VSZzAE5ph33uTO/oq8f8LZDw55OSn7sO3D4G4slBZnP/Weg8e+86Y2FOVDSh37MCgphqLu3hhYw3dRsGr9FXfe6eoPQCrs/cb4NgaDgSSp31//r/mTP/mTeve73608z/XmN79ZX/3VX63bt2+vfV8Vw+FQw2F58X70aJ6QjEYjjUajTT8GDSn+t2z6v2nv6PZi4mOm0fkDe6oMDooXHp0vH9+90Y/+77M3nSynkUbj8eJR+QE6vOp2hmT9W8tDfXL+QFPOo4PG9Bqsiqu2Z2wbstGojM3pVNPRSNNJOVU5now7v4/+8R1lo3PNLh9qTOwfNC8+ulw+fvKkF/3s708nyiTNsrwyNodXI8367WdV8/4tFS338fl9zYj/ZAh5zkrS48uRRpN5XnH3Zvy8Jx+PyticTDVbO/sn3d5jfqLB4uF0+EgTYv+gMc/+uyd53Pi/GpaxKS1jM1vk/ZPprPP76w1ulfeKs/vSrdd2er19JPQZmyovPCzvvK9K4Oy37ryTqTQaWQ2Tq651s97N5b8v7rzw0uN5bbafZ7rR7362dmJ0VZ79s/nZP52Ml1+eTKfdz/7jO/N/X1enGg0vvdtocM6mywuPLpaPn7wR/87bm4y8nv1Z7wb1TlhSqKS96mZ1vTMk+fiqvPNO53feybg8+8fTzXdezlj3NPnf8to2xn/zN39TP/ZjP6bXve51+vzP//y1r3/zN3+z9fe7d+/q27/92/Vn/syf2fna3/Zt36a/+3f/7trzP/qjP6qbN2+2f9NQybve9a5G3//F98/1usXj//LD/06Xg1e5f1OwF/zUJzJp8fH0ked+Rc8++OWo7+crHtzXqyTNlOnZZ5+VJH38o7kK8Y6f+K//Ve/vcIS8+vGv6MsWj3/tfb+g9z18ttP7hf1lMpMeXMw/4nuj82W8VdH0jG3Dpzz6Jf3uxePnPvhrev/Zs3rfg/Lf5wee+6CevfxAp9/xVaNcdySNz17Z+t8Xrj/v+fXyXH3fL/w3Xfxa3Pfzzosz3ZB0eTXSjy5i8/4rPRWbJD/8n/6TBh00nN74wkdUZLq/8O6f0seem2z9fghPiHNWkl68kIrr3eWDl6Kfhb/9o7+mz1o8/pmf+3nd/1/39X7j7H/uuQ/q2WGHs3820x9SpkwzPXzho/pJzv6D5v2/UZ79/+Nn/qveF7HScWv4vL568fhjH/+k/uciNs/P5mf/1WjU+d/nb//4S8t/Xz/7Ez+q+7c/1On19plQZ2yqvPeV8lx96aMf0rPP/nrU9/OWT3xcr188/i//5cd1efSUfu2j5Xv8+Z//7zr/YPvVwZvDF/SOxeNPfPj9+h+c/QfNxxc59c3eVD/8wz8c9b3cufiovmrx+CMf+5h+4dln9YlzqcjNfvM3P6Jnn/2NTr/jSx8N9ZrF43f9h3+rUf9Wp9ery6Gfsynysx8rz9UPv/9/6dmX4qrl/t5XXtbTi8c//CM/qlnW08c/XuZmP/7jP6FnbrR//dc9eE5fvHj8/l/6b3ruhcMbCIQ546l0Nlwk+sPT6HfeN77wv5b1mPf84i/p4795U88b9/KPfOSjevbZ39z6Gpyx7jg/P9/9TQuuZWN8NBrpT//pP63hcKh/+A//oXq9coLuC77gC/Qv/+W/1Nve9ja97nWv0yc/+Un9h//wH/R3/s7f0Td8wzfo7t27+kN/6A9tff1v+qZv0t/4G39j+fdHjx7p0z/90/XOd75TTzzxxJafhCaMRiO9613v0jve8Y7l9n8dev/+WemX/ock6au+7IulV7/Z11uExPnVH3tO+vC8SPRVX/bF+r2f9fSOn/BL7xP/D+lcUt7TvXv3JEk/8+9+We9+8WOSpC/7vV+u3/raO1teYQefeL30wX8gSXrTpz6j3/IH7nV9y7CnvHQ6lH7uv0qS3vipz+jevd+59j1tz9g2ZB8cSIvm5Js/+7P1pi+/pyc++LK+433zs/qzPuuzdO/tn7XlFXbTe/7bpY9/QoPppe79/t/nfXoe0uVH/s0vSs8/L0n6g+94mz7jqbhDi/0P/E1pJJ3cuLU8+3/wk/9NH3x0X5L0+37f7+tkd5D94kPpY98vSfodn/tGfcEXcfanQshzVpL+528+kH7h5yVJn/fmN+jevd/q/XduI//Rn5ZenD/+PV/2ZZp96u/Sq379Zf0fi7P/jW96k+69o2Oe/r470vCR7t4ocys4TP5fH/pZ6eFjDXqZvvZ/+wOWdHNwXv6g9Cvzh5/6aZ+u1y5i8//5az+jT16cqtfr696939fpV+Q/+V7pxR+RJP2eL/o8zd709k6vt4+EPmNT5fF//6j0/nnA/e7f+fm697s+Ler76f2f/0Z6MH/8VW9/u3TndfrIT35I//Ejz0mSfsfv/CK947d9SvtfcP6K9Ct/U5L0+qdu6zWc/QfLbDbT3/z5H5M00+ufuqN7935P3Df0wq9Ivzp/+Omf/ul6/b17+uALp/oHv/gzkqRP/bRP0717n9fpV/T+7f9Xet+8AfqOr/gS6e5ndHq9XXDOpssv/af3S785H7T46t/7JfqS3/JU1PfTe/7bpbP54z9w7w9KWaYf/z/fq//x0ickSW9921v1hqfbD3JkH74tfeh/lyR9zm95vd78lZz9h8onHl5K7/5JSdKbPu01unfvC6O+n/znPiTNy/r6Hb/zi/SFn3tPH375TP/3X/hpSdLrX/+pundvfWlX4oz1QaHsXYdr1xifTqf6hm/4Bv3kT/6k/sJf+Av603/6T1tf/yN/5I9Yf3/DG96gv/pX/6o+93M/V+94xzv0t//2397ZGD8+Ptbx8bp/x2AwIIg90Ph/15Mny5+dXEj8/+RgeXBRSpd8ypM3Evj3OZ+Mz5SVVg/G4E6W97q9x1tlItwbnakX/b8vxOLRsJQTffXtk61xFeSzKzf8NXsD9QYDDQybkyzLu7+HG8bZP72Uju92ez3YW+6fl2f/a+7e0mAQOd1dSMiZZ3yvZ/yb6Pe7vcdbpTJOb3TK2Z8goe4IDy5LtYDXpJD3ZOVGYH9wJA0GGvSN9+Ti7D9+Qho+UjY8jf/fF6JSSCo+fetYR0eRfZaN/D7v9ZUvYjNfeCBOpjMHec/d5cP++Pyg77yHXod5aJz9n/JECmd/OZQyGBwvzn7zztvx7L9d3nnz0dny3xccHo8MC5mn7xzHj/2Ks996T1nm9s47CXf2H/o5myIPLoyz/8mbCfz/p5BNzzRY5GG9lX8T3eqd5aJT7+oxd94D5uFluRH8KU9sr3cGwfA47/fnd96jhmc/Z6w7mvzv2EG4MT2m06n+3J/7c/qBH/gB/ak/9af0Hd/xHbV/9u1vf7ve9KY36b3vfW+jyQJIkGNj43bI/y8PmZcWHsuS9Orb68Ms4VkUiDOjIWJ8gE5n7SXlJM2LwwXE/kHz8tlw+fip25GLw5KWsS8ti2VG6GumjrEvEf+w5P75/Ow/6ue6dZSAcsBsUbQwVAwymWd/x9c3BgJ1SewfMi+dlmf/07cSOPsNX8Ei97HO/q55jyQd357/OXzc/bVgb5lOZ2VjPIW8x4r9MuiLvN9F6Nt3XuL/kLHO/hTuvBVnv3nnnXT9B9AbSP2FHi95z0HzilHveepWYrG/yPUt8RInZ79x5yX+D5pXzJpPCnn/tOLO6zLvPyH2Yc5LRuwnUeufGlZ2yzuvw3oPeOPaNMan06m+8Ru/Ud/zPd+jr//6r9d3f/d3K8+b/dd79atfLamZFj0kCI1xWFAUyCTpVTcTSBRn641x88Ny0vXTkkQRFrxsDoWkViQoYt64JDlJFIl/WPDy4ux/6uZRXCndguki/jNPRQKGQmDBy6kNBFpFgnn850ZzxEmNoMj7R2f274OD4tHlSONFMpFqY1Aq8/7OjUGJxjgsMc/+dIeiHN55pTL+if2D5uWz/Yp9J3mPORBL3n/QvHI+kjS/V969kcCm6Wz9zmsPxHZ8/WNiH+a89NhsjCd29i8GQzKr3klnPFWuRWO8aIp/7/d+r77u675O3/d932fJddTh7OxMv/zLv6xbt24tG+Swp1AkgAUvL6bnnzjp66ifwHG3TBQ9bYz3j6XeIikg9g8acygkielhM7arigSup+e5KB0s5tbgq+8kEPtS5ca40wlia2P8YccXg33G3hpMIP53bIxPXTZHJOnqtPvrwV5iqUQlkfdsaozP/3RSILPyHvL+Q8ZUikrv7J8HvdM7r1QOxA7Jew6Z5O68Wr/zmiO6nP3gkqI5ePfGQP1eAvXOio1xt3decxGCs/+QSU4ddtdQFH3xZEng5OxGIZ/+vd/7vfpjf+yP6V/9q3+1sSn++PFjfeADH1h7/uLiQn/hL/wFPX78WH/8j/9x9fvXznr9sCBRhAXF9HwSH5TSzsb4ZLr6Ay04pkgA9vR8ElLqVbJy5pedFMiQk4a5jHqxiZTM2V8UCXxtjBP7sMBsjCcR/zNjg3s5Pe9YVs5sjFMkO1heNmP/Tgqxv6Exbkipd1cLQSUN5hR33qN+rtvHCdSyKhrj9lCUg99hboxTcT5YXk56ILCIfcfNEZqDoHkOUeT9SeT8Upn3b7jzdh4M6Q2kwc35Y+68B83LSVvILFTSMscDgeCFBLLmbvy9v/f39D3f8z26ffu2PvuzP1vf+q3fuvY9X/M1X6Mv/MIv1Msvv6zf+lt/q97ylrfocz/3c/Xa175Wzz//vH7sx35MH/3oR/X5n//5+kf/6B9F+G8BTmFjHCQNxxM9Ho4lJXJJkozGePkB6UVW7vwlYv/AMf2mkpCVq5qedy0rx8Y4KMHpYcnYGK+20eguK0djEOYkF/871ELcbA0yGAJ27CeR9+yQUpfmgyG9Lm4fNMZhQTEQ+/StRCxkqs5+lx7jUpn3z6bS1Zl0fLv7a8LekZ6UuvF4eectn3K/Mc7Zf6icDscajue5RhI5v2RsjJd5T+ZDJXB0zp33wLGHwRM4+y37sIqBQBrjybL3jfEPf/jDkqTT01P9/b//9yu/5w1veIO+8Au/UE899ZT+yl/5K/r5n/95Pfvss7p//75u3Lihz/3cz9Vf+2t/TX/1r/5V3bhxI+C7By9QJAClKKslw2O8/IQ0FY+cyspdPpr/vhSKIxAc02swifjfIafrZmOc6XlIcGNWqtwYd3pR6g2kwa25xzJ5z0FTxP+to55uHDWzlfJClce487Mfv0FYlZJO4Ozf0BjvrQyG9NQhT2cYHGRbyCQ3DC4t49+Kfdc2GsPHNMYPFLvmk+bZn/uyEZAYCDxgrGHYFJRypOqNcePLzgZiTz9Jzn/gWENRqeX9vlTSwAt73xj/7u/+bn33d393re994okn9M//+T/3+4YgPidIqYPdGEzig1KqllJ3vjFeTM9PpNGFdHSz+2vC3pFcc7BCVs6ennfwO5ieByU4PSxVeoybZQIn8X/yxLwxToHsoFlayCRTINsuKeom9s2NcYaiDhVrYzyFs39DY9zp5iB3XpD06HK0vEM+nUJjUNrZHHSyMb42FPW67q8Je8dLKUupL3L9vhH74wkb4+CGJO+8hU/GBo9xp1YCV6fSZCz19r6tBS148fE8/o96uZ44SSAGTPswX4tA4IW99xgHWIPpeZA9QfbqFDZmpcrGuDdZOYmL0gFTFIjvnPR1Mkhga7BCUjFzPRRCgRhUXpIk6ZkUmoOmkeaGjfGZCzOBokBMY/BguRpP9fBiJCkROVFpg8d4+ZQbSVEa47DiMZ5Cc7COlHpXn+UjY0OWvOdgSc5GQIqzMQ4HidkcTCLvt+zD5jHfY2McPPDS48QWIaTKjXFvd16JeucB87KhlJOGhcwuj/HQbwjqQmMcrh/HNEfALpAlISUtVTfGrQlKigTghqI5+EwylyTLcE2SPT3v3G+NIsHBkp7H8npjUPIwPV/E/+hsPj0PB4cpJ5pE7EsbbDTYGAf3vLwnG+NOGyR5r2yOk/ccLC+ntjErVeb9Zuw7VUmTOPsPmOXWYD/XneMUtgbXlXLMoZCxk9inMQgpDoXI8Bg3pNRd5/0sAh08poVMMnfeadXGuON6J3iBxjhcP8zGIJekg8XyWE7lw3L5YWh6jJtFAge/gwnig+fiaqLT4bwxloycrtY3xp0XyE64JEGCNgIVHsuSh61Z4v/gseVEE4h9aYPHuOOBQBrjINtjPImB2BBS6lJZIObcP1iS81iWakipO/gdDIODyoHYZ24fJ7I1WHHn7bkeCjHrnZz9h8qLRr0znWWICo9x13LS1DsPnvvnV8uzNBkbATO2C5U0o+PKxni60BiH60dvIA0Wvsp8UB4s1gRlKoli0RzcsDHufHp+SIH4EEky9qum551vjpjNEc7+QyU5vzVrY3yTWoiD30Nz8OCxz/4EYl/asDFePuVmKITYh3IgNh0LmQBS6hI2GqAXU9warBqIdS2lzkDgwTOaTMutwVRif4eNgJM7b/9I6p/MHxP7B0tyw+CSsTG+Ie/hzgsOMG1TkxkGr/QYdzwMDl6gMQ7Xk5O78z8vH8R8FxCR5HxmpUop9Z41RYaUOnQnyQJZRWO877wxbsY+RYJDpSgS9PJMr7qZQHMw1MY4snIHj+Uzm0yRwDj7lx7jPgtkDxy8IOwjxdmfTHE4hJS6VMb/6FwaX23/XriWJH3nlQylqPIp7rzgAlMtIclhcF82AlKZ9zMMfrBYHuN3ErjzStUb4+aXufOCA6zYT/Hs9zUMDl6gMQ7XE6bnD56km4M+N8aRFjp47EQxlUvS+uaI89g3p+eJ/YPlpcfzItlTt44s2c5obPAYz9gYB8ckuTkSfGOcs/8QuRpP9ehybiHzdAoy6tKWjfHy6YmL+L9xt3zM2X+QWI3xJM/+bPGHKaWOUhR0J8mhkBD2YVJZ86ExeLBYFkqp2GgUUjjGnde8jzuJfu68B89LxlBUMvXOXR7jLlSiwAs0xuF6wvT8wVNclI76uZ446Ud+NwsqN8Ydb45YE5RMzx8iSQ+FSCrmhvuGxJaTApmE1+aBM5vNlj6zyTQGzVuQMT1vNkfcNMYZijp0Xk7NRkDaWSRwH/sUyA6R5PzFpXpS6lgJgANeSjrvz0oLJddS6myMHzwvJm8hsx777u+8j+m4HCiFUtSTNwY66ifS2tnhMe7GRoOhqEMn+Y3xpUpa+RQb4+mSyOkJ4Bim5w+eojH+zO1ja0I9KsvGePmUe59lpIUOnWJjVkooUayYnjf64hq7np7nknSQPLwYaTSZx1IyjcENG+POmyPHNAcPneSl1Kum513E/tHtsvFI7B8k5tbgpzyRbuxLPgZDaIwfOkX851mCgyFGvNt3Xge/w/IYJ/YPkSQ3xqtU0vJs+U/B+ca4ZtIVgyGHSGkhk8i5Lxke46aUumMLJeqdB4+llpBK/Fd4jJuxT188XWiMw/WEIsFBM5pM9cr5vECczCVJKj8Ng0mpE/uHyIunl8vHycR/RYHY2hifeNgYZ3r+4LC2plJpDFZszEq235qbrcG75WOKBAdJkvFfMRhiT887+B1ZhoXSgfPCIzP2TyK+E4OK5ohkDwW6yfvvlo8v73d/Pdg7iubg07ePreZzVKruvLnjrVmz3nPxoPvrwd6RpoVM9dnfX8S/c49xiYHwA+T8aqzzq3mOnUzsS8bGeLWFzMyFmDr1zoPn5dMEF4Fm6yqBeIzvBzTG4XpCY/ygeeXsanknSaYxKIWRUjcLZBQJDpIkN8arZOVcF8iklen5UzevCXvDi2bsp3L21/EYd/F7kFI/eIqN8UEv0xM3ErOQkazNqQJnRQIa4wdNkhYyG4aikFIHl8xms2X8JzMQJVXfeV1LqVtDIcT+IZLmxvi6fZhUnv3uN8aFlcAB8lKKd16pcmMcpRxwTZIb49MAKmngBRrjcD1hev6gSfKSJO0sEjiRlcNG4OB5McVEsaI54txGQEJa68B5KXmP5U0e40ipQ3eK+H/6VkIWMtPt0/NOYl+yG+MUHg4OS0o9lbzf8hpESh38YFrIpHXnrdoYL7/sZCB2cEPqLXK9ywfdXw/2jpdS3BqssA+TfGyMG2c/d96D48UkVaJmWsb/Jo9x13deYv8gKe68WSY9dTORms8Oj3Fup+lCYxyuJxQJDhqrMZ5KoiiFl5WjSHCQFInikzcGOu73dnx3IMzmSD7fZPTSGGdr9qBJU1Jx08Z4+bST8DfPfrw2D47JdKaXF/GfjMeytNtj3JXjRRH/s4l0deboRWFfeOHxfljISPZgiJPcxxyIRSnq4NjXYXAnG+OmjcYFec8h8mLyZ3/5MHfdGOfOe9AkPwy+SSWNgUBwwAuFhcytI/V7ibQ1LY/xbPFHtqz5uDr6wT2JRBCAY/CcOmiSLxIYtyTzc9xJkWBwQ+ot/jsT+wdJEf9Jxf50XD5eXJT8bIwzPX/IJNkYr9iYlXxsDbIxfsi8fDpcXriT2ZiVaniMO94Ylzj7DxBrYzyVwZBNjXHnFkoUiA+Z/brz+hiIvTv/k2Hwg6TYGL8x6OnWcYIWMkb8LzfGXeU9bM0eNEneeWe7VdKc5D1Ht7X8t8VQyMExnc6MeudJ5HdjUOExLpU1H2cqaeAcGuNwPUFO+qB5McVEUVIpLVQtqeisSFDEP0WCg+NsONb51fxSksz0sGQ3xheJYt9LgcwsEHNROjQsv7VUzv4aHuPumyPE/qHxQvLNEW3wW3P0e45pDh4yZvw/fSuR+N+4MU5jHNyR7J23YmM883H2F3fe4SN7WxEOgqI5mFTeY23Nls36YjBkPPGxMf7AzWvC3pDknXfTxrgcD4PneTkYQt5zcNw/v9J4kUQkNQxu2eetK0XhMZ4uNMbhekKR4KBJfnreZ4FMKqfn2Rg/OMzp4bQmKNeLBF6HQiSKBAeI5beWytm/4ZLkfGv26Hb5+myOHBy2lHRCZ/8y/rNl0NtqCT6Gosj7D40i73/q1pGO+omUNzY0xnuum4NYKB00yd55K4bBe67VEqTyzitx9h8YV+OpHpyPJCU2DF5x55XK+HcW+zdeVT6m5nNwvHiaoo1AdWPc3BifuXJaLnIf7rwHh1nvSaoxXuExLpVDgc7sw8A5idwcARxDkeCgsSQVU/yw9F0kKJqDozNpMnLzmrAXJOk3JVVKqZsb42NXmaJZILu47+Y1YW94/tG8SNDLMz19K5H4r1MkcHH0Z5l0fGf+mOLwwfHCo/3Je5xLKko0xg+Y2Wy23Bh/JpWtKWmLlHr5tJuN8bvlY2L/4LAa4ynGvzEI0mMgFhzy8lmiQyGbNsYX8T92bSMgcec9QKy8PxULmWm1lLo1DO6qOVgoJqCSdnAkGfvSFqWo+Z9sjKcLjXG4nlAkOGjMIkEy0kJSZZHA3pp19Husi9IDRy8K+0CymyMVRYI8z5b/FFypylkFMmL/4DCbI6aPa1Q2FAmce4xLZXOQIsHB8UKyA4GL+N9oI+Do99AYP1geXY51NZ4n0GkVyIzg3iAn7aQ5ePyESq9NYv/QSDbvrxqKMiqPznyWzbOfvP+gSLbeUzEMLkm9XrE1yFAIdOf5RfxnWULxv2NjVnLYHCyk1CdDaXS5/XvhWmHfeRNSSdvpMR76DUFdaIzD9YQC2UFTyKvcOe7rxlFvx3cHpGp63ufGuET8HxgvniboNyVt9JwqpucnrsaHTVk5igQHxXgyXSompNUc2eS3VuKuSFA0xh9y+zowTCn1T3kipSLBupwuG+PgkhdNG4GU8p4aUurOvTZpDB4cSVrISLtV0nxszZL3HxS2SlpCsb/hzttfTIY42xhHSv2geXGhkvb0rSMNeom0deoMg7v6XWbej5z6QWHdeVPKezZ6jDu20QDnJHKCAjjGnJ4nUTw4Xiq2BlP6oJQqC8RmHutMVg4rgYPlhUdlovialJoj5vS8cVEqimR+1BKQlTskXj67Wh6xSU0PT6unhzOfRYLpSBozPX9IJCulXhQJQqklSOQ9B4a5OfJMqkNRllJU+bS7gVhjKAoOimJr9qif64mT/o7vDkgMKXVqPgfF80bek+yd15BSX8rpepFSf+DmNWEvmE5LC5mk7rybhsEt+zBX8f9E+Zjc56DYCyn13FSKmv9JYzxdaIzD9STPDd8RPigPiYuriR4P5xeSV6dUHJY2eG16KBJwUTpYnrca4wnF/2yD31ruemP8bvmY2D8o0r0kbfIY96wWQvwfFC+mujm1I+9xtzFuFsjYHDkkkvdYlvxKqUuGjQZqIYfGi4aFjBlb0SnC0JJSN2Lf2dl/t3zMUNRBkeydd8MwuPON8cENqXc0f0zsHxT3z6+WcZTUnXfjxmz5tLMUBcWEg+XFZKXUt2+Mk56nC41xuL4siwQPor4NCMtLyUrKzbSsEmySlfMipf7AzWvCXmBNz6eUKG6SUl82ximQQTfMAlmSG7PSSnOkfNrZ9LzVGEcx4ZAoBkOeunWko35C17uKxnjmY2MWKfWDxSqQpbQ1uMFr0M77Hf2uIveZjqTRuaMXhdQZTaZ65XxuoZTUnVeqllI3h6LYGIeOJLsxbm0NGhvjru+8WVY2B4n9gyLZek+tYXBHv8tqjHPnPSRMKfWkcp+NHuPzP9kYT5eEKicAjimKBEzPHxRmcyStzREjBjcUCfxsjJMoHhJF/B/1ct29OYj8bgw2yMo5b4z3j6TBrfljYv+gMOV00yqQbSoSGN/iujkiEf8HxGw2WzYHkxoKkcr4zzdtjDv6PTTGD5Z92xj3IqVO/B8kL5+WFjJJFYelnR7jEx95D7F/UJj2YWltzZp3XnNjfHHndVmXLOKfnP+gsDyWk4r9ao9x61ucDYPTGD9UiprPEyd9nQyq4ywKOz3GQ78hqAuNcbi+LL02x0zPHxDmBOVrn0ypOWJKRRtegz5k5dgYP1iKRPGZO4lJKm7YGC+KBM5k5aQy/pmePyj2Y2Oc6Xlwz8OLka4m8xxjH5ojuQ+1BBqDB8sLj1O10djQGM89bM3SHDxIzLzntSkNBEqR1EIeuHlN2AueXzQH80x6+lZCZ/+Gxri5Me5cKWp0Jk1Gbl4Tkse2D0vo7LfUEqrvvM4qPgyDHySz2WwZ/0nFvmRvOhjxny0b43TGU4XGOFxfzIsSDZKD4ZOp+k2ZaeAmKXU8xqEDV+OpXjmbSyqmFfva6LdWxL+z2JcMWTkuSYfEXmyMG2e/fBSIzcY4BeKDwYz95Brj06I54nko5OiOlv+oiP2D4sVU43/jxrhnxQTy/oMh3TuvjPgv493LnRcp9YOlWIZ45s6xFVvRsYbBS5W0vg8bDXyWDxJrYzylvGfjMHj5tLuhEIbBD5HT4VgXo3mcJRX70haP8cWX6YsnC41xuL5YW7NMzx8KL1hFgpSaI7sLZBNzqbwLbE4dJC+eJtoYlFbkpA0p9czDxngxGDIZSqMLd68LSfNCshvjNabn2RiHDlibIyl5DUrhtgbzvMx9yHsOiqJAfNzPdee4v+O7A7LBQsmLlDp33oMk2TuvZJz9RmPct30YQ1EHw3gy1UuLe29ysb/JPsyI//HUUdGHrdmDxPIYTyn+rXqPmfebQyEMg0N7LJWolOo90haPcTbGU4fGOFxfkJU7SD6ZapHA+qDcMD2PlDp04PlUY1/aKKXe63lIFNkeOUiKi1KeSU8n5TPL9Dz4JdnNEcnwGLcl5Yo0yKnfWhH/5684fFFInU8+nMf/a544SctCZsNAbM9HgZiB2IMk2TuvpKVS2iYbAVexf3ynzK3I+Q+Gl06vlrNHyQ0EbtiatRUTHP0umoMHSbJ5f62NcUe/izvvQZKsjYC0Ev+GderyzktjPFVojMP1Bc+pgyTZ5mCNApmX6XmKBAeDtTGbmqTijul5Lx7jEhelA6IoErz6dsqSisb0vMwCsaPfRZHgIEnWY1mq3BiXyul5Z0MhUhn/lw/tf3dwbbm4mujR5Ty/SNZjWbIKZJlvKXXuvAdDsluDUuXZ7+XOm2WGWsgDN68JyWM2BtOzEdgwDG7cTZxtjDMMfpAka6G0Ifa95D3ceQ+SZIdCJDvvqcj7nQ6Dg1NojMP1hY3xg6QoEtw+7ut2UpKK1QUyo0+iiasC8dGtsvlIkeBgsApkKU/PVxQJnBXIJGQVD5DJdLb0mU2vMVhjel5sjEN7kpZSL87+tcb44ssuG+M3n1o8mJH3HwjWxuyTicV+HY9xL3LSxP6hYA6DJzsYsmFjfOKyQIyNxsGR9FDIpmFwHxvjSKkfJEXe//StIw16CbV0NmyMe7FQMgcCif2D4cVUh0Kksuazeudd/NXpMDg4JaFTFMAx1oflg2hvA8Ixm82WRYL0miObvAZNn1mX0/N3548vKBIcCsmqJUh2kaBCVs5pY5zp+YPj5bPhcgo3ucbgrNpjnOl5cMXz+zI9b7CcnndVHJaI/wOkkFGXpNemlvdbBWJja9b4p+CnQPzAzWtC8hR5/3E/1xM3EhoGl8p7rxX7HoZCpDLvv3zo+EMFUsW+86Z29puNcd8b40ipHxqz2Wy5NZteY7D6zmvWO13NgqvXl44XuQ85/8Fgnv3J1nyMWqdkeoyHfkNQFxrjcH3BZ/ngOB2OdX41L0QlOzkvbSwSeGkOEvsHgz09n9pFydwYN6fn5/8W3Eqp0xw5NF5IOfZDTs9bXpvE/qHwvNkcTG5rdt1jXPK0MX7jqfIxPuMHQdIDgTWGopzl/VjIHCTFYMhrnzyx4ioJlgXi8n15kVKXymHw2VS6euzudSFZbPuwxM7+6aY7r6mY4OPsf+DmNSFp7p+PNFpIbiSX99TwGHeb99+d/0neczB80qj5vC61O+90u30YHuPpQmMcri9mc4QC2UGwNwWyDRvjE5dD7kWRYPgIr80DwfbcSSz+NxQJ+nmxNYiUOrTHjP1nUov9kNPzWVbmPhQJDoZPLJojr7o50Mmgt+O7A1OxNShJmTLry05gKOrgMKXU0xsK2T0Q604txBgKIfYPgouriR5dzjdTk7NPkirPfi8DgRLNwQNkf+zDjMa4j8EQpNQPjrQ9ls3YN85+ech7JKMx/sDxhQJS5ZMPL5aPk837V4bBi6Pfab0TnEJjHK4vVpGAxvghkLTf1AYpdbtA5rI5aMgq4rl2ECQtqbhBVq7wG3S7MX63fEyB7CB43vJYTqxIsEFO1//0/AN3rwnJMp2WFjLJ5T2S4TEeYGP8Js3BQ8OWUk8s/jcOxBrf4mxr0BwK4c57CFjD4KkVh6VKGw1/d9675WMGYg8C00ImPaUo0z7MiP+eD7UQpNQPDevOm1zs71ZJmzmbBlcZ/7OJNEQt5BAoBmLvJjkMXu0xXoQ/sxvpQmMcri83kVQ8NMwCWXKXJLNApgCyclgJHBzFRek1TyQoqWhNz5dJbN+H3yBbgwfHJ4yz//V3EysQz6pj34vHuFTG//CRNBlv/17Ye14+u1oOFiUnKSdt9Bj3IitHc/DgSFspqnog1lKKchX//SPp6M78MXfeg8CK/dQGAqXAUurGMDhDgQdBcecd9DK96uZR5Hezwgb7sL4P+zyGwQ8Oc2P2dU/eiPhOKoh155Wo+RwAs9lMzz+cn/3JDcNKYe+84BQa43B9OXlSywYkBbKDwJweTu/DckOBzDiFnRXIpBVprQfuXheS5HI00cOLkaQEh0Kklen58qJkFsmcbY2zOXJwWLJaTyRWJKgzPe+rOYhayLXH2phNrUAmGR7jK9Pzi/hHSh268MmkG+O7LZScFohvFjYa3HkPgaRtBKTKAnFueSw7/F0Mgx8chcf4p9w5seIqCSyVNKTUwS3mMHhyA7E1PMa93XmJ/2vPK2dXulp4jyaZ90yrN8bLxnjoNwR1oTEO15e8V16UmJ4/CJ43EsVPSbpAZkzP+9iYlVYmiEkUrzsvWFLSicW+tOKzbBQJfMgqckk6OJIuEmyYnvfWHCH+DwqrOZJa3iNtnp5fnP1OawQ3UIo6NIq8/+lbRzrqJ1bW2JD3+ysQL+L/4r40nW7/Xth7rLw/5bNfdtOyyPvd3nnJew6Jq/FUL59dSZKeSVEtYYNKWs/Hxnj/SBrcmj9mKOQgsAdiEzv7rXpPdeyPXU5FcfYfFJ9I2T5J2ukx7tRGAJyS2A0SwDFmkQCuPabnTtKJouk35UtW7ubT5WPi/9rzcUtWK7HYlzZPz+c+NsaRVDw0iiLBcT/X3ZuDyO9mhRrT8/7kpDn7rzufTP7s3+Qx7kNK/W75mNi/9kynM73wuLSQSY5NG+M+miNSaSE2m0pD1EKuO5/cGyl1u9xY3Hu93XkZirr2PJ/6QOC0Wkrdaoz7yH248x4E9jB4YkpRG+68fUM1yq11JHfeQyLpoRCphpR66DcEdaExDtebokgwfCRNRnHfC3jHLBI8czuxIkGNApnTArFVJHjZ3etCkiSfKFqN8Q3T864miPOedLxojjM9fxAU8f+6J08sH7Mk2Dg9T5EAumM1R1I7+2czLXfC14oE8z/d5j3Gxjixf+156Wy4HKhLMu8JLaWOYsJB8XzqUuqbzv7FX7nzQlusxuDdBGO/xp3X6dZsIadO3nMQFHfeo36uV6U2DG6ppJVnf7/nYRFC4s57YOyPStrKMPjin4JTlShwCo1xuN7coEh2SBR+U/skqehtY5wC2UFhboy//m5i08OS7bljxHzf2/T8ojHOuX/teXw50uPhvAiVZHHYmp43igSWWoJD2VuKBAdF2jYC1UMhkpYDLE4Vn4+fLP+N4bN87Xn+4f+fvfMOk6O60v7bcXLOkkYa5RwQSEgCkXM0DhhsA8YYex3Wee1dZ3t3vbufc7ZxxNiYYEzOGQSSUM5Zmpxznk71/XG7um7NTKeZrqp7q8/veXi4I1rTF+lO9T3nvOc9mkuUmB3jkz/7dYVxIzrGAXr2pwF8YVy488/f583oGKd8T1rRwse8onXMAvrCuGPywrghTlHBMcA/Evu1hPSoOR8hxeBRO8Z5UQjFvMTUEL8RiGaMy4pglSOCSDHZVBxMF4IhBW1hS0Uh1cNRkgQ6S8VUfliSej6taOkVuDgCaEmCCQpKg4uDI736nz3CdvDJYSETZFFmjOvU8zRvjZgifJJAvOLI5B2zgNYxnlL1vNNJnVNphDSdI4B+hBL3o5Da4gjFvOmEKooqyPIg0+OK82qTifXsDz/8UyqGpZg3rWjuFbxjXIlvpZ7artlCbU13H1szOBbAwGhYDC7kvWfymNews6/e+QFyCUwD+Hu/cGMEAG7GuP7e4zBifBiRUqgwTtgbnYKYkgR2pn1gNKI+F/qDEhiXIDOqc4QrjlCSwPYIPW8K4C6Kbt0vu3XnP4Xvpz77lSAbpUHYlhZZ1MNAdPU82coRU0RNEmR7XcjPdMd5tclEOfuAgep59fwP09m3O7rCeIFg45OAcfd+7fw7jLJSz6aYN10IhpSIKFBIl6iYoih2/lOaH+bvPSQKsT2tXMe4mGLwKIVxM9xC6PzbmlaRXaKAqPd+D6cIpPFhxFTRdYyLLAyJKgYnO3VRocI4YW+yKVBKF3j18AwRL4rRCuNGWanz6nlKkNke1VbO7XSgLE/ABLHaMT6uMG5Yxzh1j6QNQltJAzHU89rngGGFcVLP2xpFUSJJgkoRLRWjjJAB+MJ4ihMEaoJ4rA8IBmK/lpCaNpHdEoDoTlH8vZ86xokp0Dk4Bn/YaUb8mFf/7FcF4SmNeV1uIDM8Qonu/LanWXQxeLQZ40bNWaaYN23QW0kLePajjFDiG4H8ho0P603d9yWERBXEZnlcyM8STAwORJ8xzt2DqC4uJlQYJ+wNdYynDc29nHpYSPX85AkyPl+Q0gRZRr5WhKQgyfaoxcGK/Exd8CEMkcK4/tphWMe4LklAz347I3ySIDR516CHS5AFad4aMQUGxgIY9jHhhdDKeWCSGePs34Z1jAMkDLE5wruFJGClntLOEV4MTjGvrdHHvCKe/egzxp1GzBgHtJwPxby2R733Ox1AuZBi8CiCWDOaIej825oW4d0SOFEI9+zXx7w0RoCYGkKLwQEt5xOlYxwgO3VRocI4YW+oOJI28BdF4W3loH068kXMlCbIHA4uSUAXRTsz6g+ie8gHQNDkMKAlCcZ1jPNJAuoYJ6aCvB3jJsxboySBrWmVtDAI8Ha6qS6OkDAkXeCLgzNFv/dH6RhPqZ0udYynDfy9R/iYd0LHOPt36t1Cwvf+UXILsTtqzqc8LxNul4DpbF3HuBb38mJwKowTU0H4e380UYhRLmmeLMAd/gykO7+tGRj1Y3CMPVuFFIMD3OjI8WJwg0YoESlDwJsEQaQQmreWNghvpY7J1fOGqYcBLVCiIMnWCD9vCtACpXHWQi7DkgR8gpjOv53hZw0KnyTgnv1uo5IEqp0oQMURmyP8rLWYM8bDL0l5YZy/91OSzM40h5/9eRlu5GV6LN7NJEQpDjp0VuopfD+KedMGXhQyQ0SnnBiiKJdRHeOR86+QW4iNGQsE0TnIxOBCuiUAUe8+TlMK4/TstzMt/YLnfJTJz77bqNF5gCaIpbNva9r6BReFAHFnjAPUMS4qVBgn7A2p59MGnbWQ8Op5bakLklL9OakmCQIjgG84xd+cEIVm0d0SgKgzxt28tVZKLUW5JMFQZ+q+LyEcaueU1+VEcbbX4t1MQpSOccM6R1xurWucRCG2RnhLxQTsdA21Uqd7v20JhRS0hAWxwt57olmpOwxyiqKYN23gxeBiPvtjuIU41We/QWJwgM6/jWnrG4ushRSFAOPu/WZ3jFPMa2f0zRACnv8oHeO6fI+RjUBUdLQt/L2nQkQxOEAzxiWGCuOEvcmmzpF0Qf2wdDqACiHnTfG2WvrOFrVrNqWWigB1j6QJwncNAlqSYFxhnL8oBlKpDMkp1dZUHLQ1rWEFcUVBhk5oJAxROkdcXJLAn8oZ44B2/uns25qmHs5KukjABFlCM8aNvPfQvd+udA354As/N2eI2jUY1Upd++WUJogz8rQ7Ft35bY2s48MALeY1rDgC0N3HxjSLLggExuV8Ju8YT6lTFLmkpQ2qW4jH5UBJjohicP7eE6VjPNWdQDnhZ3/ID4z1p/Z7E8KgG58kYswLcA6Z+nuPboQSVcaFhArjhL0h9XzaIPy8qaBfW0eZs0xJAmIq6GcNCp4kcOp/NvlAKaUXRTr7acGIL4jeYfZsrcoXNEiKUhz0cD8LqX/2hwvjY/1AYCz2awlpaeqVac5stBnjKX5P3YxxuvfbFZ2VtIhnH4heGNfde1L4fg6HFvcOkyjEzjSH7/0Oh6iWotHdQgyLeenZnxa0iD4+CdBiXodTVyChmJeYLhExeH6m+GJwM2aMA1rMC5BLoI1p4u79s0S/90cRgwNUGBcVAatHBJFCPJmAJ5utKUiyLfy8KXELg5PbagFardDQWZskDLEtfIJYSFstQDv/E86+Uep5slRMB3SdI8I++ydXz7ucfMd4qtXz5JiQDujU8yImCXRuCdGs1A0sjtCz37ZIURiPcv4N7RxROwcp5rU16vkvy82AR0QxeIxnv3r8U54bpuJgWqAXgwv+7I/lkpZSt5B8zY2Qzr5tGfYFImJw4R0CAd2z321UvgegmDdNaJLh3h91xrhBglgiZQh4kyaIFBNRz1OSwK7o5u2I+kHJ22q5qGOcSB36eVOCBkoRayG9glKnnk/l+c8iW7l0oLFH8MIgEH3GuG7eWoqt1MlWMS1QkwR5mW7kZXrivNoCYnSMm2OlTvd+u9IkuigE0Ff+eDtdvnMk5V2z4fPvHwb8o7FfS0iJLxBC5yBzgpEj5p18fFjQyGc/5XxsS0svxbwTcDi4Oct09u0KLwicJbqVNGBizEv5znSAHx8mZCMcf6eZMGOcfxlVxkWECuOE/ckOd4+MdBsgTyZEQKcgEzZIim6l7jQjSUCzNm2LaqnocTlQmpth8W6iELFSN6lj3O1lCnqAgiQbI/yMZSBq55Sh6nmylbM9oZASsRQVtzAYfca41jGe4vfUnf2OFH9zQhSaRR8jAJhvpQ6QMCQNaOsfjaQzZoqYHAbGzVievGuWxODEVGjpk8ElbfKY1+Uy0k5aLYx3Ub7TpjTKEPPqOsZNcknjn/0U89oW1SUwX1QxeAIuaQB1jIsKFcYJ+6Oq50MBYGzA2r0QhqBXDwt6UdQlCSZXz6e8c4SSBGmBqiAWdt4UoAVKzujqecOSZMMUJNmVpt7hyFq24qCbn7dGVupEknQMjkWSS+Ke/RgzxsNfplw5z5/9ITr7dkVvpS5ocTBaYdxIK3UaJWB7pBifFIwuBo/EvEaODyNRiG1RRVFupwNleYKKwaPEvC6HQR3jgCaKCowyxxDCduidcrIt3EkMonSMe7iYN+X5Hl3MSzkfOxIMKZF8/8wiQc9+DDG4w8h7P5ESqDBO2B9Sz9seXj0sbOdIMLp6PmKlnvKOcSqM253+UT/6RlgCSlhbLYBTz0dPEhhWGB/p1f/8EbaB7xgX9vyHJlfP87Zy1DFOJIscs9Zi2coZ1DHuyQK8uWxNCTLbonaOOBxMFCgkusK49rzXWanTKAEiSZp1HbOCnn3+3hPNSt1QMTidfbvS2MOKvlWFmbouVKGIEvMa6xRFOR+7I4VLmu7ew3WMGxrzUse43ekYGIucG2GdcmKJwY289xMpgQrjhP3JoplTdqe5j7dUFPTDUtcxPi5BHOkYT/F7UueI7dEXBkVVUCraZXFC54iBCuJIoKQAo72p/d6EEMhRHIzWMc4lCYI0b41IDr5rUNgEWQxbOYfDoK5BQDv/ZKVuW9SuwYq8THhcgqYzoiTJXIZ2jPMxLz377Qg/RkBYtxDd+LDooqiUOoZQzGt7+kf96B9l+ZRZonbMAtrdJ8b4MEObIag4aEv0HeOiPvutiHn5jnF69tsRKRwCE7RSp7q4mAgaSRJECiFLUdvDz9wRtjjCF8bHq+dp3hoxRWTumAX0XbN0/olkUc9/cY4X2V53nFdbRCiKet7IMQI5/NmnBJkdaZLh3sMXBp2Tq+eVVBdHACCnjP17pIfcQmzIqD+IzsExAAKLYYGohXGHziknxe+pnn2AiiM2hS+OVIn67E9gfBiQYscQtxfIyGdruvPbEiliXiBqYVw3PsxQQSwVB+2IrmNc1Ge/MnlxUDc+jKzUiSRp6uWb4EQ9+/HHhwHUMS4qVBgn7I8uSUDdI3ZEtdXK8rhQkuO1eDdRCMWft5Zy9XBmgVaIIUtFW6KefUDgICkUfYwAr6BMvbUWdU7ZGX8whNb+8LwpUc8+ELVjnO9yJCt1IlmaZegciZIgAwxWz5Mg1ta09kmQIAOid4w7+bNvkCgEoGe/TeHF4NWiFgdjzRg3dIRS+N5PMa8taZTBJQ3Q4t7xI2R0HeMpfk8Sg9se9fyX5HiR5XXFebVFJNAxnvLnflYRgPD3p3uPLZFjjECiM8bN2hCRDFQYJ+wPFcZtjaIokQ/LWUVZug8eodBdFPXqeXXLoVR/UjocWpKA1MO2RIokQYyLottpoKUoJQlsTWvfaCS4ELYwCES11nLpZg2muHOECoO2p0kGO90oswYBo+csU/eInZFCFAJELYwbevb5Zz/FvLaEF4MXCysGjz5j3NDOKXWUwEiPfg+ELdCJwUUtjgAJzRgPpvreTzGvrfEFQmgbCIvBRT77yuQugU6nI5LvTLkY3OnSRmnQnd+WNEsxOo871+PHh3HrlOf7iZRAhXHC/vCF8cF26/ZBGELHwBjGAiy4qC4WtDAIjFPP6wMltUBi6KxNCpJsCW+pKKytnK5jPLp63lBrLVIQ244mGWYsAwnOGE/x2fdkAZ4ctqZnvy1Rz7/H5UB5XobFu4lCAjPGAQPU8/TstzUNshRHLLdSp8K43QiFlIggtrpYZDF4/BnjgIGCWCUEjPSm9nsTltMoi5W6EmXGuJHPfnJJszWtfaORupvQgsAoHeMA4AmrolI+YxzQ7v3UCGRLdPlOUc9/jNGRNGNcfKgwTtgfspWzNQ2yBEkx7KQNmzEOaJ1T/mHAN5T6709YipokcDkdqCoQdNamLkiKMW+N1PNEEkgxaw2IGii5OSt1Y5794fNP9x5boqrnKwsydQIjodDNGDexY5y6Zm2N3kpaYEFsAlbqxp59evbbjc7BMfjCYnBhXaKAhGeMp/zuQ8IQWyPfjHHqGCdSQ2OvBKPzgJhOUZHRkUbmO32DgH809msJ6VBjXq/LidJcQcXgsWaMGxnzEimBCuOE/aEEma3hbbXEDpK4JMEEWzm1Y9yA96Xzb2vU81+Zn6krtAlFLAWl00j1PJ8kIAWx3ZCnY3zy4iCfHPYb8fDPCZ//kW4g1Qk4wlIGxwLoG2EdeTMKJDn7Zs4Yz6ZRAnamoVuSe3+UBDGfIEv5jHFPFuDNY2u689uOBlli3hgzxnUd4yl3TKCY186oxUGX04HKfEHF4ABnpT6uEcK0mJfuPXZDOlEIEFUYknKHQGCcYwKJAu0EPza1qlBkMTh/9mPEvGbth0gKQbPoBJFCMgu1iykFSbZDihnLgHUd47nl2nqQzr+dGBoLoGeYJZ+ELgzGsFIn9TwxVWTvGPe4DDz7gFYcVEJs3iZhG/jCoNAjZGLOGDeya5acouxMgyz3ft351867zk7XiM4RtThIMa/tkMYtIcaMcV1xMNXnn495h2h8nt1Qz7/QYnAgase4y7SYl8TgdkMvBhf42a9EH6HkDse9hlqpA5TzsRl9I34MjLFcotD5nhhicIeRMS+REgS+URBEinA6tQQxJchsB98xLnaSIHphXFW+GZMgoySBXZFivjgwTkFplXqenv12gz//Qj/7o8wY13WMp3rGODAuSUDn307Uc4Xx2SIXxnWiEL3C32GolTr37KfioO1QhSGluRnI8rrivNpCoiTJ9KIQA95XFYaM9gIBnwFvQFiFNDOWE5wxnnordRKD25XBsQB6w2Jwoc8+oOV8olhJAwbkfLzZgCd8H6Q7v+2QUgw+QRgSnjFupJU6QPl+m9HQrZ19eWLe6OPDUu4URaQEKowT6YGaJBjqMMCzkbASaZIEMWzlVNFzyJCOcZq3Zld0YwSEDpKid4y7HAaq5zMLtYspnX3boZ7/3Aw38rPccV5tIaHJ1fNup0kzxgFSz9uMBlkK4zFnjBtYHOQ7xilBbCtG/UG0D4wBAKqLBb73ANEL41z2xZB7v+7807PfTujHCAj87I85Y5x7WcpFUWSlbleaZHEKCYUQMcuNIQY3pDionv9BaoSwG/KMD4teHHQbOmOcYl67Ui+lS1p0K3VDBLHEtKHCOJEeqBfFkB8Y7bN2L0RKUZMEOV4XCrM9cV5tITFmjEes1A3pGOcSZKSetxXyjBGI3jHudhnYOeJ0auefzr6tCIaUyPmvLs7WWVQJR4yOcXXbxqjn+a5ZKg7aCb2VuiwJsvFJAu5lqb77UOeIbZHGShqImiRzGW2pSMVB26I7/yI/+2OKwQ2895OVum3hxeDSFAZjnX1DnKLC53+kW/8zSEiPWhwsyPKgIEvkfGd0Qaya8zHcJY3u/bZCGpe0WGJwIwWBREqgwjiRHtC8QVsSCikRBaXwxZEErNQVxYAEMVmp25YmWdwSYhTGeQWlIcVB1TFhqF0frBFS09w7Ejkvs0VODgMxrbVU9bzx89bo3mMnSD0fB08m4M1la7rz24qGHklEIUDU8+8wy0odoMK4zeCdcsQujkSfMU5W6sRUkMYhMJZLmpFW6gCQW6Gt6dlvG3yBEJrD+U6hC4NAgh3jBsS81DFuW/h7v9DnP8GYN2CEMISYNlQYJ9IDShLYkvaBsYjqUOggCUjCTjrV6nk6+3alXkZLxXEXRbeRnSOAliQIBdi8TcIW8B2zc0pyLNxJAsRQELsMtZXj1fOUJLAT6rM/0+NEWW6GxbuJQQxRiMPIew+gCUNIFGIrGnlRiMj3HkA/uks3Y1z7ZcOt1EkYYhuCnBh8VlGW4GLwxGaMp7w2mF0CIPz9Kea1FfoxAgLnfKzK9wD6nA/ZqduG5t6RiIhO6MIgEGfGeFgMbrhLGj377USDLGLwGDGvh5shY8j5J6YNFcaJ9ICKg7ZEN2NZ9ARZjHlrTiMVxGSlblvqutj5dzsdmFGYafFuYhDDVs7Qsw+M6x6hJIFdkKZjFogdKIW9tYxxSyC3EDsS4scIFAnulBOlMAgAHhfvFmJE90i4MD7SQ5aiNqJBZyUt+LM/mpW6k6zUieRpHxjlxOCCn/2YM8YNvPe73EB2MVvTvcdW1MkiiI3hkmboGAFgnEsgPfvtgs5KukTwZ3+MEUpqcdCQjllyS7AtamE8N8ONIpHHpuru/PrYnI95/Ua4BBLThgrjRHpAHeO2pKFHEvUwEHveGm+rmOrPSm8O4AkHkJQksA2KokQCpZlFWXC7BP44j6Gedxs9b40vDg62pf77E5ZQJ8u8KSDqjHEAcLkMtFKns29LOgbHMBZg50Xms89/ZhmSJNONEuhO/fcnLKFBpo7x0OQJYkPHCAAU89qUhm5JrKSBmDGvoVbqgFYcHOwwoCWdsIq6riEAgNflRGW+wGLwWOOTXEZ3jNO9345IFfMm0DFunEtU+OeLzr5tCPJicNHHpiYY81JhXEwEzqQTRAqhJIEtqe2U9KLoMlE9D3Bzluns24XuIR8Gx1jBWaqzH0s9b8jZJ/W8HeHV83OkOf+OCQpit5Ed4+SWYEukcktIUD3vMyJJQF2ztkQVxDodQJXITjlAjBnj2i8b45RDVup2RC0MApLd+yfEvNzLjHRMCIwAvqHYryWkgBeDzyrO0sWOwqETg0dvhDC+ME73frvQIFPMm8CMcUNcolwezU6dzr5taOkbieRIqkUXBMaYMe7hPrNoxriYUGGcSA8oQWZL+ARxTanAtlpAzHlruiSZkQUSshS1DXpLOcGDJFFs5UhBbBvqu7TiyIxC0QOl8Pkf99wHuCSBEUGSJxPILGBrOvu2QZpZa0BCYwQAg85/Nt377YjaNVtVkKWb2SckfJLMObmVumJ4YZzOvl3Qx7yiP/ujx7yG3/tpjIztaB8Yw6ifPU+lKgyaffZJEGtLeFGU+Pd+/t4zeddsSGFjoVKOaqc+2EZuITaBd8qRShAYc8Y4dYyLiOARJUGkCEoS2JJaqdTzMRTERifJqGvWdqiFQQCYUyy6KIQ7++MUlKYmyChJYBvUBHFVQRa8bsGvsmoA5JhYGHdF1PMGBfCRJAGdfbtQL5OlYiz1vNvgeWv8vEE6/7agb8SPvhFWcKsuFlwQBWjnf9zZN9xKOrsYEUtRuvPbhlr+3i/yjGUgZsxrvJU6l/MZpPNvB+qkPfsxCuOG53vo3mMX6sPFQbfTgaoC0Z1yohcH+fNvSNyrnv+gDxjtTf33J0yHF4PPFr0RiH+mj7v381bqvgCJNkRE8GwiQaQIXecI2crZBTVQqszPRKZnYtFBKHSB0jhbOcOTBNQ5ZTf4JIH4F8UYHeNkK0ckSd+wVhwR3i0BiNkxrtpJB41SD6vFQd8gMDZozHsQpiJXYTzGvDWnwfPWaNam7eC7puaK7hIFJFQYN0QT5XRplqIU89oG9fw7HTLMGI8R8zr582+0YwLd++2AXGMEEmuEMLQwCFDMaxMURYkUB2cVZekKbEISY8a42/BmCBLE2g3d+LAiwZ/9MWJefnwYdYyLieBPVoJIEd5swJvL1lQYtAX9o350D/kASFIcCcZQzxutINZZa9H5twN13VqSQPjzH0M973YZnCTIIfW83ZCqMAhoSYJYHeNGzZui7hHb0cjZyglfHInVMW70sz+vUltTYdwWnOnk7z0yF8a5lxhl96kWB4c6yFLUJqiC2KqCLGS4ZRKDm9wxTi5ptqNeqvFhvJV09MK4IVbS3lzAE/7zocKgLege8mFwjD1PhbdRB8Z1jEfvmvUbURwkQaztaOiRaHxYzJjX4PFhxLShwjiRPqhds3RRtAW8lXSNDAkyPkngit41a4iIjIojtoM//8IXB2PMGDc8QZZVpL0nPfttQb1MM5YBrmN84pVb7Zo13EodoPNvE86EO6dKczOQk+GO82qL4S80MeatGdMxzhXGB1pT//0J06mT7d6vFqTNtlIHtJg3MMIcQwip6R32RZxyhJ8vDuhnjI+Pec3sGCcxuC3QW6kLfv5148NMtpJ2ODhRFN357YC0YnAgdse4EcVBinltR21YEOuQwSknFEsUop19nxExLzFtqDBOpA/qh+VoLxAYs3QrxPTRzRcXPUgC9EmCGApiYzrG+SQBXRTtQF04UCrLy0C2V/TiSGJ2uoYkiJ1O7fzT2bcFUnWOADE7xt0RK3UTOsapOCg9g2MBdAyw++tcGYojMdTzus4RIxJkeXyCjDpH7EAt1zEuRXEwWse402ArdUDvmDBA5192pJovDsTpGOdeZsT5J6co26HGvKw4IvizP9aMcYfBHeOAdu8f6QECPmPegzAN6WJeJbog1nBhCDlF2QpFUSJOUTMKssQfmxqrY9xJHeOiQ4VxIn3Io+4ROyFd50isrlmjrbV089ZIPS87wz6tODJHCvVwYup5Q0QhgJYkGOowyJKBMBOpZg0CMWeMq+p5QyzlAFLP2wy+MCjHjGX+3jM+ScAlyIxQz2fkAZ7wnxHd+W0BL4idUyzD+Y9vpW5IxywwLuZtMeY9CNPg7z01MhRHYswYdxo+Z5ZiXrtRHz7/lfmZEhRHoud7eDG4KU5RdP6lp04mh0AgZjOE4XOWSQxuK3qG/egfZXcJ+WLecWffTTPGRYcK40T6kEsqMjvBJwmkUFAG+Y5xfZKAuyfSvDUiLjpbLRnOfowkgctoWy1A6x5RgsBItzHvQZjGaV3XoASBkhoATdoxzq7himKQKIrmrdmKM7Kd/VjqebfBVuqA1jVOZ98WqF2zlfmZyPIKXhwBtLtPDCt1wwrjFPPaCn1xRIJnf4yOccO7ZslK3Vb0j/rRM8xyKHIUBqN3jPP6QMOe/bpmCBLEys4ZnSA218KdJIhuxvj4ZgiDu2ZJDG4rzsjqEgVMdEnjzr4vQIVxEaHCOJE+kHreVkg1bwqIEygZ3DVLxRFboTv70iXILOwYByhQsgFqoFSam4H8TE+cVwtAlOIIoD//hnSN55KdtJ3QdYzL5pTjiD5r0BArdUArDo71A77h2K8lhKZvxI/uIWYLK0WCDNAEsTEEgYY1jlDMayt4twQpzn+CM8YNufd7soCMfLYepK5B2amXLt+TYMe4YfceKg7aidPcjGX5zn+MGeMkBifiIJ0oJBR9jIDeLYGs1EWECuNE+pBXpa3JXkV61OJgSY4XeTIUR/jioGt8x7jB6vmMfMATvkzT2Zce6RSUMZIELqODJIACJRsxMOqPjBGYJ0PHLKCd/xhW6oBRSQJKkNmJM1326Rj3unlLUaNGCdCz3y7oraQlOPuAVhh3eXW/7OBdogyzUqeY107IZ6cba8a4Cfd+9fwPtDJLHkJa5HOJil4Y5DvGjROD844JdO+XGUVRcKZjEIAkM5YBrmPcob/sQB/zGnLvzyzU7lt09qVHPz5MgntPLJc0Fy+Koo5xEaHCOJE+5HEJYkoSSM2oP4jW/lEAkqgngdi2ckar5x0OrXuEOkek53Q4SAKA+WUyKCgTUw8bpqDMoVECdoFPDksxbwqI2THudhk8bzC7RFMtU2FQevgkgRTFwRjz1vjOKX/AhDnLdP6lppZ79ktRHAG0rtlxhXFeDKuYMmOcYl7ZUe8+FfkZyPa647xaAGLMGNc5Jhh9/v3DzDGEkBY+5p0nRdcgd/YnOOVo9x7DRCG6mJeKgzLTPeSLzFieVybLvSeGGNzorlmHQxOE051fes7IJojVxbzjrNS5fI/PKLcQYlpQYZxIH0g9bxvOyJYcBsYVxvVJAqcZXbPq+R/tI0tRyTndwSsoJTj/MZIETqfBbgkAdQ3aCL5zZK4dkgS8MMSIQMnp0uYNknpeetS7T1WBLDOWuTM9ft6ay+AxAoDeMYHu/VKjF4VIIogNMuv38VbS+hnjBr03nX3b0D/qR+cgc8qZI2XMG2OEkmGjBLicTz8JwmWGj3nny3Dvj+mWoK2Nc0njn/0U88qM3kpagrMPaF2zjokxisvomBfQcj7DXZprDyEl6r3f5XSgWgannFgd47qzTx3jIkKFcSJ90FmKUpJAZk7xHbPlEqiHAe5y5pigItNbqRv0/jphCCUJZEYtDlbkZyAnQ4LOESXWvDWDbbUA6pyyEWdkE4UAiScJjLaTHmo38AOGMJq+YT96htk9QhpBoBJ93prXxXeMm/DsJ1GU1NTKNkYA0LpmY1mpG1UcycgFvHlsTTGv1EhXGARijg8zxUo9n2Jeu6AWB50OYLYMoqgYMa/D4Yjc+41rhOBjXjr7MnNaxsJ4TDG4wS5pAJfvV4ChTmPegzAcRVEiz/7qoiydFbmw8A6Z42eMu004+8S0kOCEEUSKyCoCXBlsTcURqeGTBPLMmQ0nCZwTC5mGW6kDFCjZhN5hH7qHWBeSFJZygGCdI80GvQlhBmc6eUtFWZ790ZMEHpcJCWI1SRAKACM9xrwHYTjSzRcHxiUJxs0aNNpSEaCuWRvBd4xLMWMZ0DrGx7lEORyOSOegYVbqADdCic6+zEg3PgnQxggAMbtmjRslQC6BdkBRlMj5n1WUjQy3BE45McaHAVozhOEOgQDleyRHzo5xdXxYbJe0oNFicIAEsRLTMTCGYR87S9LEvLwYPMboSJ9RYnBiWlBhnEgfaM6ybZCyY1wNlMYp5wGTukcoSWALePWwdPOmgAkJMpfDhCApf4a2pme/1KhJAocDcthqATGTBC5ePW+YrRy55diBWl2CTJazHz1JwKv//UadfeoYtwWKouBkO7v3zyzMkmPGMsDNGJ9471e7Zg1tHFHPv28QGBsw8I0II+FjXjnv/dFnjJsjBidBrKy0D4xhKFwckefsRxeDAzC+Y9ztBbJL2ZrGCEjNGV0jkGT5TufEMpPLZYaVOh/z0r1fVqQcm8q7hYy3UnfxHeNUGBcRKowT6YUaKI30AIExa/dCTBm1Y9zpAObIYKsFaAmyyTrGueKgYep5spWzBdLNFwdiquf5IMmoGAmeLCCzkK37mwx6E8JoFEWJCENmFmYh0yNB5wigFQcnSRLoZk4Z1jXLqefp2S8tp22XJOBmjBtlF0IJMlvQOehD/ygrNshTHAlqz/4YhXHDiiPAuOIgnX9Z0VupS1Ic4We7jrv3m2KlnscLYkkQKCunZS4MApPmfNTOQUOLI2rOZ6BFvx9CKtTioMflwMyiLIt3kyAxxOAeM6zUySHTFvDjk6TJd/I5/PFW6i4TGiGIaUGFcSK9oFmz0sPbalUXS2KrBSRupW5GxzgpiKVFTktFTj0/7qJoSsc4AOTPZP8eaNVfXAlp6BryYSBcHJEmSAK0pFS8GeNGFQd5xwR69kvLqXYJnXJizBjXzRo06uxnFWt3LioMSsspGe89fGFw3IxxQNNJhcywUgcoQSwx6vn3upyYVSSLGDz6jHH+3mPY+aezbwtOc+OT5kojiooe8wKA02mGW0j43q8EgaEOA9+IMIpQSImMUJpTkqN7bgqNmsuJ4ZYAGFgYV/M9AI3Pk5iT7ZI75YwTg/Pjw3yGzY4kpgMVxon0IpdsFWWnrZ+z1ZKpOBKMXhh30oxxIkHOyGilrsSwUneaYKsFaOr5oA8Y7jLufQjD0J19mZ79SvQZ46bMWaYkgS1QkwQelwNzZBkjECNJoLNSN+rsO51a1ziNEZAWvjC+QBZRiDpfHJj83h+xUjfw3kMxr/QEQwpqO4cBADWl2RIVR3g76XEzxnVicIPen3cLIUGgtOjcEmS59wvRMc4LYuneLyPNfSORWcRSicGtnjFOZ98W8IVxae79uvFh42JeM0bnEdOCCuNEekHFQemRsnMEiN0xznXNGhYn5ZGVuh1QkwQelwMzCyWx1Yoxb82UzhFgnGMCBUoyckqnHpbk2R+K3jEL6LtmDXML0SUJaJSAjASCoYgwpKYkB26XJOEb/0yfMGOcs1IPGJggVgskQ51AwBf7tYSQ8Akyae79cTrGXWbOGAfo3i8pjT3Dke4iaaykgXH3/nEd47qY18A5yzllbE0OgdKiF4NLcv6V6OPDAK5j3MimQf7eT89+KTnVIakYPBRLDM4JYo0qDpIY3BacDOf7czPcqMzPtHg3CRJrfJibb4SgjnERkSSzQhApQlccpEBJRngraWmCJEBLErgs6hjn5yxTkCQl4221pCmOxFDP8wkywzpmAUoS2IATXHFkoTTq4dgJMl4YYticZUoSSE9Dz0ikOCKNch6IM2PchFmDAPfsV6hrXFL4BPH8ckkSxCG+MD5xxrh69THPSp3OvozoxOCynH0g5oxxPnQxLOYFtPM/2GpwFZIwCjXnk+11oSI/w+LdJEgMMThgUsc4icGl50TbQGQt5b0/bse4Qc/+rCLAHS6k0tmXkhFfEI09IwCA+WU5cDgkccpJcHyYL0Ad4yIiSVadIFJEHmetRUkCKdElyGSxkga0JFncjnETumb7W2jOsoQ09AxHbLWkVA8DE5IETqcDapxkytkHqGtWUvjC+IIKSZIEOitpi5IE2SVaxyIlCaRESks5YFySwKJ5awWztHUfPftlRHULyct0oyxXkuIIb6U+SWFcFTYaaqlIYnDpOa3rGpTo2c+7pI1LajsdJtx7AG3OcigADHca9z6EIYz6g6jvZmME5pZKVByJMUYA0M6/oWNm88klUHb4e//CijwLd5IkkY7xiWUmU2aMOxyaIJZiXik51TEYSVMvKJfw7AOTiMGpY1x0qDBOpBeUJJCeU9J2jKsXxYkJMp163tDOKXXO8hgw0mPc+xCGcLxNO/uLpAqSuCTBJMVBV0Q9b+TZ57tmKUkgIyfD6vmCLI88xRFdx/jEK7fbjK5Zp5MTRVFhUEakLYwnOGM8YGSGWPfsp/MvGyO+IJp6WefIgvJceYojuo7Zifd+NUlmmFMIoJ+zTMURKdF3jMv07I8xPsy0EUo0SkBmTncMRUZNyBXzxpkx7lIL40Z2jNOcZdk5Ieu9P0bHuK44aMa9f6wPGBuI/VpCOPh7j1xnn58xPn58GM0YFx0qjBPpBXUNSo/aOZKf6UZp7sS5fcISjN4xboqVOkDCEMk50a5d7hfK0jELjCsORk+SmSIKAYABShLIxsCoH819owCYjbo0xZEkOsYNDZTUJMFoL+AbivlSQjyknLEMiJEkKOAK432Nxr0PYQi6wqBMZz/OjHGvm51/1QXIEDJygcwCtqaYV0r4Z/88qVzS1ML4RFGI02yXNIAEsRIibcwb597vcpgc81JhXDoURYlYqc8oyERuxsTcibCogo9J8z0mjVCiZ7/USCsGF8EljZgyVBgn0ovMAsAbVp1Sgkw6+rniyKKKPHmKIwCXJIgeJAEmJgmoOCgdJ6TtGI8zZ9mMJIFOPU9BkmzwIzSkSpDFmTHuNstaq4AcE2TmZLg46HBIVhyMMWOcF4UYmiTI56zUqTgoHdIWxnUzxicmiL0uEwrjgHb++5pozrJkKIoScYqqzM9EfubEIrOwRMTgE+89ejG4gXsgO2mp0cW8Utnpxp4xbooYPLMQcGexNZ196egYGEP/KDtHC2TK9wDavX+ymNeM8WGAZqUO0L1fQuxRGB8nBnea5JJGTBkqjBPphcOhzRvsb6I5y5KhqicBYFGlRBdFRdGSZJPMGnSZ1jHO2cpRcUQ6jofPv9PB5q1JQ5x5a6YkCbKLAVfYfpvU89LBP/vlmjcVPUgCzOwYpySBrCiKEnHKmVmYhSzvxHMkLLGSBGZ1jOeTpajMnNK5JUh079HNGJ/YMa6ef8M7R9SYN+QHhjqMfS8ipbQPjKFvhMWOUsW8gHbvnyzmtUQMTjGvbBxvk7RjPFGXNCPzPbo5y3T2ZYO3UV8oU2EQ0JohJot5zbZSB+jeLyFqYdzrcqK6KMvi3SSBzi1E37zndDrMGR1JTBkqjBPph9o5FRgFhrus3QuRFLoZyzJdFHV2opNYqTusUFDSRVEmgiElclGsKclBpkei4ki8jnGzkgSqMITcEqTjpKxJgjgd46bZylGSQFra+scwOBbuHJHp7APjhCHjZ4yb5JaQV6W9NzlFScfRVq04slim4mCQFwROLA5muLXCuGLk3aeAc0yg8y8VfGFwsUyFQSDOjHFtbei9n495+0gQKBtqcTDT40R1UbbFu0kCETrGAe38+wZozrJk8GJwqWJegOsYn1hm4huBjI15Kd8pK4FgCLVdzCVwbmkO3C6JypUxxocBWjOE4U5RxJSQ6KQRRIrQJQkarNsHkTTHWiXtGA/FTpDxF8WQoQmyam3dV2/c+xApp7FnGGPhi5RUynlAf/4nm7cWDp4M7RoEuDnLfYBv2Nj3IlKKTj0v0/kPRbeSBvTFQbKVIyaDn7O5QCYraSBmkoBPdviMfPa73EBuWBRFZ186joUTxFkel1zFEV3H+MR7vzpjXFEMfvZTzCstfMy7UDY73RiFcdPE4BTzSsuoP4i6cHFkQXmuzn5feEIJdowbXRinOcvSIm3MC8TuGOeK5RTzEpNR2zUMfzgmlE4MHmN8GKA5RVHHuJhQYZxIP0g9Ly18gnixTEmCIDdrcLJ5a1y8Z6irYiGXJOilBJlMHJd1vjgQ10pdVVAaKgoBaN6gxKjP/twMNyrzMy3eTRLE7Rg3qWuW1PPSckzWjllgXJJAn9j2ukyct6Y6RQ11AIExY9+LSBnDvgDqu5mIbVGFbMURfsZ4dCt1wGA7dYp5pUXfMS7Zsz8yYzx6YRAw2Eo9swDwhv/c6OxLxamOQahHQ6r54kDChfGQAmPdQviYl4qDUsEXxheUSXT+FQVA+ExP1jHLicH9ho5QIpc0WdE1wcl274kxPgzQmiFoxriYUGGcSD90CmIKlGTiWCu7KJbkeFGSm2HxbpIgTmHQaVbHeGYh4A2r7+jsS8Vx3Yxl2RSUsUcJmDZzR2erSMIQWRgaC6CxZwQAML88Fw6HTMURvjBo5YxxShLIypEW7dm/pDLfwp1MgRhJAn2CzOAkge78U4JYFk60DUK9EksnCuEFsTE6xgGDbRX5wjidfangBbHSdg3GiXkNH6GkCsL7GvWjPQihOaE7+5I9+3UuaZPYSZs2Po9EUTKiKErESr0sLwMF2RPvD8KSRMwbNPJ5nFOmffbQ+DypONbaH1kvqZLt2R+7Y1x1SjNUFEJMGSqME+kHnyCji6I0dA/50DnIOn2kU5DxQdIkCTI+SDJUPe9waMIQShJIBT9jWerzH6Nr1tCzDwCFc7Q1OSZIw7G2gUhxZKlsxZE4HeO8nbShHeN8koCKI1JxrI0lCZwOiYsjwIQkAZ8gMzxJoCsOUpJMFo61Sdw5Eqcwbk3HON17ZIEvjlQXZyHbO7HALDSqY4KVMS+gxbxBH3MMIaTguB1mLANxHRMMFYQXztbW9OyXhvaBMfQMs+fnEpvFvKadfacTyAs3Q9CdXyqOtPJicNnOP3emJzn/nvD5N1wMTkwJKowT6QclCaTkuC5BJlmQlETHuKHqeUA7/8ExShJIhGot5HI6MK8sx+LdJEkosUDJ8I5x3i2kl+YNysLRFomDpFBsWy3zkgQubd4gFcalIRAMRboGa0pzkOmZeIaERtcxrg85HQ6HZitntEhP5xZC518WjrVK7JbAzxh3WtgxnlcFIPw5Q2JwaWjqHcGQj92dpbNRB7gZ45OND+Nd0gzeB+V8pOS4zKKoeOPDXCa5BBZSzCsjR1q0jtmlVZLde+J0zHp0I5RMcgkc7gL8I8a+F5Ey1Ht/tteF6qJsi3eTJPFmjLtpxrjIUGGcSD/yZ0BLElCCTBZ0QZJ0xZHYQZKp6vlCGiUgG75AKDJjeV5pDjLckhVHdLZy0YuDhlrKAaSel5SjOlstyZIEcdTzHpdJloqA5pYz3AX4ho19LyIl1HYNR4pm0olCgHFdsxPnLLudYVu5gElnHwD66d4jC7pZg5USC2InOfsZLpMK4y6PJoqimFcadB2zshUGAW7G+ERRiJPLPhouBqfioJSoI2RyvC7MKsqyeDdJEkcMzgtDDC2Q6MTgFPPKwlGpO2YF6RgHxomi6N4vA4NjAdR3s/zEooo8XeOYFOjE4BP3rjql+Y288xNThgrjRPrhzgByK9iaPiilgb8oSqce5pPDcWy1DC+O8IFSHyUJZOBUx2DEalY69TCQsDDE+MI4JchkRKeel61rMI563sVliA23k+aFIXT+pUDqjlmAOdOouCcWB1VhiN/ojnFdgoyKg7KgWqkX53hRlpth8W6ShO8Yd0289/CdU6aNEhhqB/yjxr4XkRL0Ma9sopAQgPCZtloMXkBicNnoG/ajqZd1eC6pypevOBKKbaXOj5Ex9Pxn5gOZhWxNd35psE/HuIUzxgGgiBuf11Nn7HsRKYEXBEonCgHG3fsni3nDYnAaZSokVBgn0pOCcPfIYCsQGIv9WkIIDjdzXYOyfVjyF8VJ5q3prdQN3gspiKVD6iAJ0CsoYwhDDC+MZ+QBWUVsTWdfChRFiVipzyjIREH2xOen0MSbMW5VkoCSZFLAuyUslu3eA+jv166Jhc1IksDoeWu6jnEqjMtA95APHQPs/CyqyIVjku4LoYmTIDPNSh3QC0Po/EsBH/MuqyqwcCdTIMQ7hUwyY9wyMTjd+2XgSCt/9iWMeXVicIu7ZlVBbH8TEAzEfi0hBGrM63E5ML9MMlGULt8zScxrpiBQJwanwrgMSD06DwAC/L0/esxr+BgBYkpQYZxIT3RJgmbr9kEkRDCkRBLEc0qykZcpWXGETxJMFiRZZqVOSQIZ4Avjy2bYL0mgzlsz3FIRoCSBZDT1jmBgjP09SWejDiSlnjctQQZQkkASpLZUBPTFQffEJIH67Dc8SZBboRUnSRQiBUdbeDGshM9+/n4Rb8a40cKQAk4YQl2zUnA4fP69bifml+VYvJskiXPn14vBaXwYoUd6MXiC48MAM3I+4Xu/EgQGKN8pOmOBIE51DAIA5pfl6u4JUpBUxzjFvISeYzoxuITP/jguaZGYN6RAMSPnSSSFZE9bgkgRZK0lFWc6hzDqZ4kj+dXDk3XMamvDkwR09qXjCKegXFolYXEkwXlrQTMuiur5pySBFByRXT0cr2OcmzFueHGwkO8YpySBDKiCwGyvC9VF2RbvZgroOsYnFge1jnGDz77TqSXJeuoASkgIz6FmybsGdR3j0c8+YEbHOHfvp45x4Rn2BXCmcwgAu/fwXXZSoBsfNknHuJli8NwKLe4mpygp0LklyCgGV2JbqVvSMQ7Q+ZeAk+2DkTMhpShEpBnjheSSJhvSi8HjuaSZOT6PSBrJbtoEkSJ08wapOCg6hzn18HIZg6Q4SQKnw0QFZV4lJQkkQlGUyPkvzfWiPC/T4h1NgTjqed28NaPviRQoScVR6TtHuILHpJ0j2jXc1AQZzVsTnv5RPxq62ZzNxZV58s3ZBMYVBy20Uge0Z79/CBjqNP79iGlxsLkvspayOBLHTtrcjnEu5qV7v/AcaRmIaHekFIXEmbHsMrNj3OnSRmmQS5oUqFbqTgewuELC4kjc8689+00dJUAxr/BIbyWt6xifWGby6MTgZtx7wu9HMa/wKIoScQupyM9AUc7EjmvhieOS5nHzwhCaMy4aVBgn0hOyV5GKQ9InyBJPEhiunne6gPwZbN1HQZLotA+MoXuIXbSkLAwCcc+/02niRbGQkgQywauHpXRLiKOe9zhNThKoiQo6+8LDd02tmCHZjFmVuF2zqpW6CQmCohptTfd+4VE7xj0uBxbJWBzhBbGTzRjnEsR+ozvGeUFgT62x70VMm8PSj0+KLQrRi8FN2I9aHBztBcYGYr6UsJZAMITjbcxKuqY0B1neifdm4Yk3Y5zTOJpqJ03CEOE52iq5GDxux7iJohB3hpbvpJhXeBq6R9A/yp6d0sa8uo7xSazU+Y7xAHWMiwYVxon0hE+QdZ+xbBtEYuhstaok/LDkgyRX7MKgKXOWC8KB0mgfMNof+7WEpRyWvWMWiGsrZ93MKUoSiI7aNZjhdqKmRLI5m0Bc9bzLzLPv8midU1QYFJ6DTZogcMVMSZ/9gXBh3JUBOCZ2vKtJAr/hViEAiqg4KAsjviBOh+dsLqrIk2/OJjDOKWrivcfUjnE6+1LBx7xSuqTFKwyaKQYHaM64RJzuHIqMlpDSLQGIOz7MXKcoXgxO937ROdikPfuXyCgGT2LGuOFnH9ByPsOdwNig8e9HTBneJWr5TAlz/UD8jnFeEEsd48IhYaRJECmAL4xTkkBoFEWJJAmKc7yoyJ/4QSM8odgJMlPnrQHjkgRUHBSZw7LP2QSSSpKRrRyh0jfsR13XMADWNSXdnE0ggRnjJibIAK1zcKSHRFGCwxfGl8uqng+G1fOTJAgALUlgipU63ful4Uhrf2SsipSFQWCcW8JkHeMmzhjPyANyyti6h8TgoqMKYh0OYHGlhOc/3oxx7ipnyr2Hv/eTpa7Q8DGvtGJwXcw7mUsg91KjmyFIDC4NiqJEioOV+Zlyjs5TuLvMpDGvyVbSvFsO5TuF5gAX866UtTAep2Pcw+d8aMa4cEiYZSSIFODNAXIr2JqSBELTMTCGrrCV9LKqfDgm6ToSnrhBkskd4+SYIA18cURKS0VAUxA7nJN2DZpaGNfZylFhXGR49bC0QVIy6nlT5izzSTI6/yJzMJwg9rqcclpJA1qSYJIEAaAlCRTFjGc/dc3KwiFdx6ysz37eKWqSMQJmdowD2r1/oAXwjxj/fsSUCARDOBoujNeU5CA3Y2LMKDxxxidluLW7kClnv3iutqacj9AcsEXMyz37J7n36zrGjS6OZBYC3vD9ke78QlPXNYwB1UrapjGvSxfzmtgxDpAoSnBs4ZIWp2Ocb4YwRRBOJAUVxon0RU0SDLYBvmFLt0JEhy+OSDljFgCCfGF8YoJMZ6lodOcIABTP09bdp41/P2LK7G9k5z/b68L8slyLdzNF1CTBJEESYLKVelYhkBEOOClJIDQHdEGSpEmCuPPWTLaV4y11yVZRWIZ9AZwKW0kvrpTUShrQkgRROsb57hHDkwQ0Y1waDtktQTZJYdzUjnEAKOKLg3T+ReV05xDGpLeSjj0+LMOjnf0xf3DCf085FPNKw4FG7dm/Stp7P981a3HHuMOhFQf7GvWFS0Io9tuhY1YX806MW9xmzhgHxsW8lPMRFUVRIoLYkhwvKvMldEsA4neMO02MeYmkkTTTQhApQJckqLVsG0Rs9jVwQdKsQus2Mh3iWElncEnvMbML46SeF5auwTE09bLOnhUzC3RFNKlQA/FJEgQA4HSYWBgHgCIuScBbPhJCwRfGV82SNEnAW8VNIgzhbbVMOfvUMS4FR1r6oeZLpS0MAlzH+MTCIKA//4YnCbIKgczwc4Tu/EKjJsgcDmCJjFbSQFw7aV7sQqMECJV9Db2RtbSCwDjjw/iOcVNiXj7fQ4VxYQmGNCvpmYVZKMmVcHQeEDfn4zZzxjigFQdDfqC/2fj3I6YE3zG7cpak9554Lmkuk8XgupiXBIGi0tI3iu6wO+yKmQVyusMC2vgwh2vSZ7/H7PF5RFJQYZxIX8haSwr2N/ZG1qvtUBifJEGc6eGSBH5KEhAMXj0srXIe0BTEUQrjfKBkyigBVRgSClBxUGDUJEGmx4kFsrolxFHPu3TqYRNnjAPUNSgwfNeUtFbSgNY164o2Y9zkeWvq+e9rIlGUoIwFgjjWOgAAmFuagxwZraQB/fmyesY4MK4wTjGvqOznnv2rqyV99odiu6TxYvBRMzrGc8sBTw5b0/gwYTndMYhhHzsP0p59IG5h3HQxuM4x4ZTx70dMCf7eL60oKo5Lmm58mNkzxqkwLiwH7OASBQCBxF3STLn3E0lBhXEifSH1vPAoihJJEhRle1BdnGXxjqZIXPU8lyQImJAkyC7W7KSpMC4sfJC0UtaOWUBLEkxSGAT0SQJTiiPF87U1nX8h6Rv2o66LjThZWpWvm8skFUnMGA+akiTg563VGv9+xJQ4yM1YljZBBnBW6pPPGHebbSun3vuVIHMMIYTjaMtAZO7wGlnFsID+3j+ZlbrpI5TIJU0G9oXF4A6HxHa6uvFhsWNeUzrGHQ7t/PfW6fdHCMM+PuadWWjdRqYLf++f5PzrxOBmFMZLuJi3iwrjIhIKKRExeGV+JsrzJLWSFm3GeP5MbR907xEW3fgkqcXgqkva5DEvdYyLjaSZRoJIAbquWVIQi0hjzwi6wtYqK2cVymutEidI0tnKmdExzicJ+ho1hRshFHzniLRjBIC4VuqmzhgHaN6gBKh2ioDEyWEgvnqen7FsxtnPn6l179LZFxbVTtftdGBJZZ61m5kqihK/Y5y3kzZ93iB1j4jIPt4lqrrQsn1Mm3gzxq20UqeYV0jGAkEcaWGiqHmlOcjLnHwEhfDEmTHucDgi59+UwjigxbyhANBPoigROcA9+6UdnwTEzflY2zFO934RqesexsAYe25KLYZVuOf5ZFbSZs8Yd7m1e3/XacAMV0IiafbawS0BiNsx7uFHCdCMceGgwjiRvlDHuPDwhcE1MgdJwcQ7xsfM6BgHtCSBEiI7aUE50NQLAMjLdKOmJNvazUwHNUkwiXoYAJx8YdyMoIXU88Kz3y5BUtyOcS5JYIZ63unUzn/3af3+CCHoH/XjZMcgAGDZjHzdqBWp4AuD0ZIETpOTBHTvF5699b2R9RqpC+OxrdT5zpExM85+biXgDneh0dkXkqMtA5GRKtKODgPiuqQBWtxrWsxLI8SEZ79dugZ5YUhcpyhySSP0YyOlFoPHiXmdTgdUXYgpYlgAKFkQfsMhYKDVnPckEiYUUiJi8NJcL2YVSeoOC8QVg/Puhz4qjAsHFcaJ9IWfOUXz1oRkv049XGjZPqZNKLatHJ/4HjWjYxzQK4jp/AtHW/8o2vqZJc+qWQXyuiUAnJW6KB3jfJKACuMisqe+J7I+S+biSBz1PN8xbpqtlvrsD/lJFCUg+xv6Ik0NUhcGA2PaOoqtHJ8kMKdrliuOkChKSPaG7/1elxNLqiR1SwDGCWIFsFJ3OrV5mz21gBmjO4iksI1bQpwZ44AW95rikgZQ16zg+IMhHA6PkKkpyUZBtqRuCUBcpyiX2WJwcooSnj2cIHBVtcSFcd3Zn7zMpOZ8TBkfBuhzPl0nzXlPImHOdA2hb4Tdl9dUF8md71St1KOMD/OYPUqASAoqjBPpi8OhdY/01lPnlIDsDSvIAMkvijpbudgJMvM6xilJIDJ8kCT1rDVAC5SiFMZdZltr5ZYD3ly2prMvHIqiYE/42Z+X6cb8slxrNzQdkpgxHjArSaCq5wEShgiIThQyu9C6jUyXRDrGdYVxM9xCuLNPCTLh6Bvx43THEABg6Yx83Zgh6YhnpW62KATQnKKCY8AgdU6Jxr4GfnySxDFvnBnjAN8xbvLZB2iUgIAcax2InIWVMjdCAFrOx+EEJiny6ArjZtz7nU7t/HefIVGUgOzh8p1rq4us28h0iRPzAppTmmmFwRIqjIsM7xIldcwLaFbq0caH6WaM03NYNKgwTqQ3amE86AP6myzdCqEnGFJwMGyrVVWQifK8TIt3NA3idIy7nI7I3BHTkgRkKyc0fHFkrewXxUjH+ORXDu6eaE5h3OHQkgQ9dfrOLsJyGntG0DHAVLdrqgt1VvvSEUc97zLbLQEYVxykwrho8ILANTInyHQd45N3f/Hz1kwpDhZUa3bSnSeMfz8iKXiXKKnHJwFJCWJN6RgHyDFBcNSOcbfTgaVV+dZuZjrEmTEOWGClrhODU2FcNHbbMuaNJgbn7/1mbAja+Q+OUb5TMEb9QRxuZvnO+WU5tnZLADRBuGkuaSQGF5o9DTZxCATidozrrNQD1DEuGlQYJ9KbUu7DkpJkQnGsdQBDPnbBktpOFIhbGAeAzHBnjHnqeUoSiMyuOi5JMEfi4gigKYijWqmb3DEOaNZaSpDspAVDp5yfLfnZD4xqa/dEcZdOPWyaep66ZkWFd0sozPagpiTb2g1NhyBfGBekY9zp1J79PWdIFCUY+3hRiOzFEV3H+MQkmdfssw/oY94uinlFonfYh5PtgwCAZTPydSO2pCOhGeMmW6nnz9Rs3Wl8mHDwMe/Z0se84TOdUGHcipwPFQdF4mBTX+QOIH3My+c7o3SMu1yqlboVHeN09kVDFYM7HMAqmfP9wYA2Pi9qzGuBSyCRMFQYJ9Kb0kXamgrjQrGrrjuylj5ICiaQJPCwx/Go3yT1fF4l4M5iawqShGIsEMT+sFtCTUk2SnMnv2BJg1oYjxIkOR38RdGCQIkcE4Rid51NrKQBfdfsJIVxl9VW6lQYF4qG7hF0D7GC2prqQrlnrQXiW6m7+SSBWa1TanEwFGCOIYQw6OZsym6nG7n3OybtnPJY0TFOMa+w8B2z0se8CYjB1ZjXFwwhZMa93+nSXAK7T5OdtGCohfFMj1NutwSAs1KP3TELmNgxTsVBYdmjs5KW/Nmvi3mj3PtVK3WznsH5s7RCJcW8QjHiC+JIywAAYFF5HnIzJr8vSAEvBo82Y9yKZggiYagwTqQ3uiTBMev2QUxgJ1ccOaem2MKdpIBEkgRmd4w7HFqg1FNLnVMCcai5P5Iolb5bHIhrK8cXR0zvGAcoSSAYfMf4WTJbSQOAf0RbT1IY188YN+ns55QCGWGbYjr7QqG3lJP87MfpmAUAj9OCrtmShdqaumaFIRRSIvf+4hwv5pXmWLyjaaLeqaOcfb5j3LR7vy7mPW7OexIJsbPWRoVx3YzxyW2BM3hhiFnVQVUUGBgF+hrMeU8iLm39o2jsYXfl1bMKdcUDKYnEvImIwa3oGCcxuEjoxgjMKbRuI6mAd0nzZE36koiVull3fqdTy3d2n9HPQScs5WBzXyTvZ6tGiCgd43y+07R7D5Ewkt88CGKalJCVuqioSYJMjxPLZ9hEPQxEnbUZmbdmVsc4oCXJQgEKlASC75iV3lYL0GZOJZAkMK8wTrZyImKrWWtAXCt1h8MR6Ro3LUngcAAl4fPfW68P5ghL2VGrOeVInyQIxu8Y11mpm5UgLuUK43TvF4aTHYPoG2HF5HPmFMntlgBodtJR7vz8jHG/WQmy3AogIxxPUWFcKHRi8Dl2EoNPfu9XxeCAiXbqZYu1NZ1/Ydhtp9FhABfzxheDhxQLxOCU7xEGRVEihfHcDDcWludZvKNp4o8d8wKaU5ppYnBAy/mE/DQ+TyDSLuZ1Use4yFBhnEhvsouBnDK2piBJGFr7RtHUa0P1MBA9SeAxuWMcAMqWaOuOo+a9LxETW1kqAnHV83pbOQvmLFNxRBgO2GnWGjBOPS9QkiBy/hWmoCeEYMcZ9ux3OmyQINap5yfvmuUTxH6z7j66jnGyVRQFPkG2TnaXKEBLkkUrjLsssFJ3ODRhSG8D4Bs2532JmPgCIewLO+XMLMxCZcHkdwVp4GeMRzn/mR7eMcEkQThfGKeYVxh0Ma8d7v2hxMXgphVH8mdq4/M6yCFTFJp6R9DWz+7Kq6sLdOO1pCSOGBzQ5iyblu8Bxo0Qo2YIUXjnjI3u/QnEvB43zRgXGcmrTQSRAkrDgdJgGzDSa+lWCMYunY26HYKkxG3lxgIhKGYpiMs4W8UOEoaIgKIokfOfm+HGogrJ1cOKAijhy18U9bwlc5ZzSoGs8CWcEmTCsP10V2S9bq7kQRKQkHre41STBCYGSTRnXDh6h3041sZmra2YWSD3rDVAP28tATtp04QhpXT2RYS3krbFvV+1k45y5/daYSUNcHbqCrnlCMKh5r6IKNoWZz+J8WGAmaME+MI4FQdFYafdOsbjjA+z5NnvdGo5n54z+tiEsAy+MCi9UwiQlBjcNKccgGJeAQmGFOwK3/tLc72YK/34pPgd426nBYJYImGoME4QpdQ9Iho762x2UeTnd0dNElgxb5C3laMkgQjUdQ1H1MNnzS6UXz3Mz3JyTK6e11uKmmgnXb6UrQdaSBQlCNu5JMG5diiMJ6CeN91KHRjnmEDPfhHgC4PSK+cBIJBAkoDvGDcrSZZZAOSUszW5hQiDmiBm45MKLN5NCoh0jE8uCnE5HVCvd6YmiHWjBEgQKwI6MbgdCoNJzhgfNWuEGJ194Rj2BXCgkY1Pmleag+KcyZ+XUhGnYzzLo/36qFljBADNJVAJAV109xGB7ae5mHeeDe79/hFt7Y42Y5w9+y3rGKdnvxAcaenHwBi7K6yfWyz/+KREOsZdfCMQWamLBhXGCaKU65qlD0sh4BWUtrDT5YuDUQrjmR4L1PMl87ViJXXNCsE2rmN2w7wSC3eSIhLoHOGTBMO+wKSvMYQy6h4RCX8wFEkQV+ZnYnZxtsU7SgE69XyUJEG4a9bUIIkfo9FOz34ReMd2VtKJWKlbIIoCtALJUDsw2mfe+xKT0tw7EhmfdFZ1kU4sJy2RGePRnR/U/09TO0d0glgqjogAH/OebQcxeELjwywQg2fmM0tpgN35zXJnI6Kyq64ncvc91w4xL6Cd/yhicH1h3CRRCDBufB7FvCKg3vs9LgfOqrZBvpMvDsYRxJoa85bzMe8R896XiIr9xifFP/v8aNiAmYJYIiFsEHkSxDThC+N0UbSc3mEfDrf0AwCWVuWjIHtytblU6OatJdAxblag5M4AiueydecJfQGfsAS+Y3aDHdTDCi8KiZIk8FqVJFiqrTsoULKag019GPaxv39bqIeBcR3j0ay1LJi3VrpQS9pRkkAI9LPW7JAgi98x7uXV81Z1zdIYGcvRJ8hscPYBzSkqiigE0EYJWGOlDhKDC0AopETu/QVZHiyplHx8EpDQjHG9lbqJ9371/I/2AoPt5r0vMSl8x6wtYl6A6xiPIgbnYt4Rn0WFcbr3W057/yjOdA4BAFbPKtSdC2kJcB3j0cTgEZc0E+89WUVAXhVbtx8mUZQA2Gq+OKCPeRMQg/vMFIMTCUGFcYLQWWuRet5qtp/pjtxXNtpNPQxEt1K3omMc0LpHAqNAb71570tMQFGUSMd4lseFlTMLrd1QKkigc4RXz5ubJOA6p6hr1nL4IMkWlnLAuBnjsZMEptrpujM0a7nOY3rrU8J0hn0BHGxincsLynNRkjt5IVkqggkkCbh5a35THRM4UVT7IfPel5gU3ilnnR1GaABaYTyKlTRgUcd48VwtDqHCuOUcbulH3wg7KxvmFcMp+/gkIMEZ47wY3Ew7aRohJhK2c0kDNEF4Ag6BI2aKwfmuWXIJtJztto95Y48PCylMGGYa6vi80V5goNW89yUmoChKRBCbl+HG0qp8i3eUAhJwSfM4LRKDEwlBhXGCKKjWEtZ0UbScrae0IGnjfJsESaHk5q2Zqp7XJQkoSWYl9d3DaOljQcU5NXaxE01ujMCImQmycr5jnJ79VmO7+eJAQh3jLpcFHeOAdv6DPqDnjLnvTejYWavZidpCOQ8kZCunmzFuZnGwYpm2bjts3vsSk/LWSXbv97qcOMcOVtIAN2M8RmHcZUFh3OUBisgpShT4wqBtxOAJzBi3ZHwYQCOUBGLYF8C+xl4AbL54Rf7khTTpUHM+iYjBzSyMF87RipUU81rO9jPas3/9XJs8+wPxC+N812zQzM7tcu7eTy6BlnKyfRCdg+yOfHZNUUQsITUJuKR5uLwuzRgXDxtk3QlimjidWqDUfRrwDVu7nzRHTRI4HcxO1xYE46vnM7l5a6NWqecpSWApvKWcbQqDCRTGdbZyZiYJcsqArPCfMyUJLCUQDGFHuDBekuPF/LJci3eUIhJIEnjCXbOmdowD+iRBOxUHreStk52R9XkL7JIg4zvGo1mp80kCM0VRy7U1nX1LaegeRn03i7vOmm0TO9FQSOsajFUYd1v07FeFIYFRoOuUue9N6NCLwUst3EkKSWTGuFVi8FISg4vC7rpe+IM2my8OxC+M8+PDzHRJc7q0UQLdp/XzoAnT2RbO+bicDpw9xyYjZPiY1xOlMK7rmrWgYxygUQIWo4t57XLvScglTTv7pgpiiYSgwjhBAEDlivBCoQ9LC+kaHMPR1gEAwPIZBSjIssF8cSDBJIHF89YAKg5azFY7WsrxZ98x+ZWDV8+bOmPc4dBmrg20ACO95r03oWNfYx8GxthZ2Ti/xB7zxQEtSeB0A67JhSEuK2aMA3pbRbr3WMoWLkmwyTZJAt5WbvK7HN854jczQZZTAuRWsnXbQZo3aCF8YfC8BTY5+7oZy9FnjHus6BgHgIoV2rrtgLnvTUQIBEORETIlOV4sqrCJIDChGeN8YdxMMTjde0RBb6NuEzE4EH/GuFUd44B2/pUQjY+0kJa+EZxsHwQArJpVgNyMyc+KdCQgBue7g80VxPKFcRLEWsmWkza89yfgkuaxSgxOJAQVxgkCoCSBIPBWuraxUQeSTxKY2jG+RCtYttLZtwpFUfDmCVYcyfa6sGpWobUbShVK/I7xbE49P+wzedaxbuYaOSZYxZsnOiLrzQttEiQB2ry1KPPFAa04aLqtFnWMC0H3kA+HW/oBAMtn5KM4J3ohTSoSsZXjrdSt6pod6aF5gxby9ileFGKTe7+ucySBGeOmn30+5j1k7nsTEQ4190cEgRvm2UgQmIBTVIZVglheFNV6gERRFsLf+20zRiAUAhA+Uw7BrNQBmjMuCFtOaPeezQvLLNxJiklgxjhfHDRVEE6iKCEIBEMRUVRJjhdLKvMs3lGKCMTvGNcVxs0UgxMJQYVxggAoSSAIb3IXRdsESUDSc5ZNVc97s4GShWzdcVT/wU6YxpGWAXQOMrXhxnkl9pgvDiTklqCbMe4zOUGsC5To2W8VfJLgfDslCVT1fJTCIKBZa5leGC+aq1lcU5LAMrae6ork5s+3i3IeGNcxLmCSQCcMoWe/FSiKgrfDHePZXhdWVxdau6FUEeTEsFFmLAPa+fcHFShmFugquZi39aB570vo4J1CbCUGT+D8W9YxDgCVK9m/R3uBvkZz35sAAPQM+bC/qQ8AsKQyD+V2my8ORM/3eLWzP2KmlToAlHFds5TvtIw3dYVxG937dVbqkwvC+Y5xU52ivDlA4Ry2bj8aFrEQZrOvsQ+DYUHgpgWlcNphvjiQUMe422XR2ScSwiaZd4KYJhXcvEFKEliCoih44zhTD3vdTpxrJ1utJJMEpqrnAS1JEPTRzDWLsH3HLBC1a1Y3b82qsw8ALfvNfW8CADAw6seehl4AwLzSHMwsjN5dLR1qkiBKggDQW6mbWhxxuYGy8CiNrlP6n1XCNHQ26nYqjAcSSBI4rewY5+79beSYYAUn2gfRPsDOybqaYp1QQmqCiVmp8wJIU7vGC6qBjAK2puKIZbx+TLv3X2AnQWCy48PMdEkD9Pd+ckqzhC0nOyOCwAsWpdfZ97qcUK8+lsa8rRTzWkEopERmLOdmuLHGLoJAQF8Yd8W/95s/QiwsiPUPAX315r43AUA/X/z8BTYSBOo6xqO4pDn58WEkzBANm0SgBDFNsouB/Jls3XaIrLUs4FTHIJp6RwAA584tRrbXJvN2gASTBAKo5wFKEljEG1xh3FZJAv+QtvZmT/oSS23leLcQShJYwrbT3ZHg+Hw7iUKAhDrGeTtpy5IESpBsFS1AURRsORkWBLqcWFdTZPGOUoiuOBglSeC2aMY4oC+M0ygBS3j1aHtkbat7j258UvRYJsOq8+9waOe/vxEY7o79eiLl9I/6sau+BwATBM4umfx+LCVJzxi3sjhIMa8V2FcMPqKtowhiHQ5HJO41PeYtmAVkhe+ZJAa3hCOt/egaYkW0DfNsJAgENIG1KwNwTv7/5bZyzjI/Z5wa4SyBdwi0zXxxYFzHeBSXNLeW76EZ4+JhoycxQUwTtUAy1gf0korMbF7jlPMX2ilBBugL49GSBDordUoSpBMjviB2nGEJsllFWZhbmmPxjlKIb1hbR0kS6ArjZtvKZeYDxfPZuu0QEDR5xjmBLboEmc2e/QnMGOdt5Uy3U69cpa1b9pr73gROdQyioZslUs+pKbKXIDCRJIGV6vnSxdoMUOqatQT+3n/xYhs9+4PxZw0C+lECPrMFsSQMsZS3T3ZGhHC2EoUAYo8PA/T3HhLEmg5zCGTFkQy3E+tqbOQQ6Odj3uhiF9UpzfTCuMOhnf+hdmCg1dz3J+w7XxzgXNKij0bgO8ZNH6FUtVpbU8xrOn3DmiBwbmkOZhXZSBDIu6RFdUuwUAxOxIUK4wShUklzxq3k9eNpUhiPmiQQpWOckgRms+1MV8RGc/PCMjgcNpm3A4xTz09e8Oc7R0xPEgBAVThJEBilUQImoygKXjnGugbdTgc22GmEhqJoxcGYHeMW2ekCwIw12rp5r7nvTeDVo9q955Il5RbuxAASsZWzUj3vyQRKFrB1+xF9UoMwnIFRP3bUsk7l2cXZ9hIE8gK7GDPGvVYWxmnOuKW8flwrjlxoJ1EIMG582OQxb4aVMW/xXC0eaaOzbzYn2gfR2s8KaOfOK9GJJKSHL4x7o3+mqf/PIz4LugZ1xUHK+ZjNK5xTjq3cEgDOJS16YTzTSpdAinkt5Y0THRFB4MWLbRbz8oLYaGJwl4Xjw4i4UGGcIFR08wYpUDKTUX8Q75xhCbIZBZlYUJ5r8Y5STAKFcX7emukzp3LLgdxKtm49QKMETOaVI1qQdOEimwVJvJV6lI5xp5OzlTO7YxwYlyTYZ/77pzEn27WO2XPnFSMvM3oRQTr4WWsxZoxne7Vn//CY2W4hfMc4nX2z4RNkF9utMM53jEdxyrFcPT/jLPbvkJ+6Zk3mrZNdEYeMixfbTBCo6xiPURh3W+iYwI+RaSOnKDNhHbPhERpuJzbMtdGcTSDBmJcrjJsd8zpdWs6npxYY7TP3/dMc/t5zgd0Kg774MS+gOaWZnu8BKOa1kL5hP3bWaR2z88pslu9UmyFiFMZz+JjXZ7JLX+EcILOQrVv2Ur7TZPjxSfYTg8fvGOcbIUx3SyDiQoVxglDhE8SkIjOVLSc6I4rxCxbZLEEGcOp5R2Izxv0WqMjUrvHRXqCv0fz3T1MURcHLR9oAsO6h8+1mq8V3jEeZMQ5YaCsH6JME5JhgKi9xopBLl1RYuBMD4AvjMTrGczK0xPGQ2UmCzHyta7btkL7TizCUfq5jdk5JNubZqWMW0CcJopx/y9XzamEcAJp2m//+acxrx7Rn/0V26xxJYMYyoE+Smd41W75MGyVAMa+pHGsbQFNvWBA4tzhy/7UNCc0Y58XgFsa8ALkEmsxLh9si60uX2uzePwUrdcXs4pxulAAVxs3ktePtkY7ZS+1WGAS0e3+Mwng2F/MOmi0Gdzi0rvGhDmCgxdz3T2OCIQWvhQWBOV4X1s0tsnhHKUbXMR7FSt3qmJeICRXGCUKleD6QUcDWTTtJRWYiL3JB0mV2C5IATT0fRTkP6JMEpifIAH2SgBTEpnG4pR/NfayAtnF+CXIzbDRjFhinno9e+Il0jFtRGK8k9bxVvHKUT5DZLEng5wvjsdTzXGF8zIIZ91Vr2L+DY8xSmjCFLSc6uY7ZchsKAhOwUrdaPT9zrbZu3mP++6cpoZCCV8OFca/biQ3zbNYxywuMYswY5zvGTbdS92az4jjA3BL4uxphKC8csnvMG3/GuE4MHrDi3k8xrxV0DY5FZswuKM+11wgNYJwYPL6VejCkmO+WUzJfi8fJSt1UeLeES+wW8wJAIHz+Y8wYz83gXdIsjHkBEgWayL7GXnQPsbjwvAWlury3LdB1jEexUnda6BJFxIUK4wSh4nQCM8PdI4NtQH+ztftJE4IhBS+FO2azPC6cbzdbLUBLEsToHOFnjFtircXP3WnaZf77pykvHdaCpMuW2TBBplPPR7eVU8//qBVW6jklQP4stm49AJg96zZN6RnyYVfYUm5+WQ7mlNgsQRZIrDCezSUJhsxWzwP6Z3/LXvPfP01R7z2ADW3UgXEd45MnCSxXz1es4LpmqTBuFnsbe9HWz87H5gWl9uuYTWDGMqAvDlpy/lVhiBKi4qCJ8GLwy+1470/ESt3KGePAOLcQinnN4pWj7ZG+E1uKQnRi8Bgd41bOWXa6gMrwKI3eOmCkx9z3T1MCwRBeO8Y6ZvMy3VhXU2zxjlJMMKA9+92xxofxLmkU86YL/NhI29moA/rxYVE6xp1OB1xOFveqwnhCHKgwThA8M8/R1k07rdtHGrG7vgddYQXZBYtKIypaW6HayoncMT5rnbams28afHHkMjuqhxNUz1tqpQ5odupj/UD3KWv2kGa8eqwdITsnyHQzxmOo57kkgenz1gBSz1uALxCK2InmZrixYZ7NEmTAuK7ZyZMEXpfFhUFvNlC+lK3bjwC+4divJ1LC84daI+srl1dauBOD0LklRO8Y50cJ+CwpjJ+trak4aArNvSM40MRmWq+YmY8ZhdELCNKSgDAk0+qYt2K5Jlhs3GH++6cpfMx7+TI7xryJicH5wrjlc8ZJFGgKO2p70DfCno0XLirTOSbZgoTHh/FicIp504VnDzLbeofDpoXxQGL3fne4MG66UwgRF5s9kQlimlCSwHT0ynkbJsgAzko9etFfr563IEjKnwHkz2Trpt16KzzCEFr69AmyqgIbJsh06vn4SYJASLGmQDKLe/Y3vGP++6chzxzQiiO2dEtIuGOcn7dmRZKAmzdI6nlT2Ha6C/2j7O/6kiXl9rOUA/Tq+ShJAjdvpW6Vel7tHFSCzDGEMBRFUSJW0k6HDUdoAPqO2RhOUbyVut+K4iDFvKbDx7xX2D3mBaLPGLc65nV5tGd/Ty0w2GH+HtKMUX8QbxzvBACU5HixptpmM2YBfcybgBgcAEas6JrlmyEaSBhiBs8d1OZZ29IpRCcGT7Rj3IKYt6gGyCxk65a9NDrVBE62D+BUB3s2njOnCOX50XMi0pJAxzigjRAjK3XxoMI4QfDw8wabdlu3jzRBUZRI54jTAVxqRwUZwOyFAMAZw0rdavU8AMwKOyb4BoGOo9bsIY3gC4OXL7Vpgkynno9uK5dppa0cAMxar60bqTBuNAOjfrxxgiUiy/MycPZsGybIEpwxrpu3ZkWCLLMAKFnI1i379S4PhCE8e1B79l+9wqbPftVK3elho4omQVXOAxbMWFbhLXWpc8pwTrQP4kwnS5CtqylGSW70BJK06DrGo9/7+Y6xMSuSZGVLtHsZFcZN4YXD3L3fjsURICErdd4tZMxvccwLkFOaCbx2rD0S3126tDxiKWsr+Puz0DEvVxinmNdwQiElcu/3up027ZhNNOblXNKsGB/mcGj3/sE2oLfe/D2kGc9y+c6rVlRZuBMDSbBjXHWKClBhXDioME4QPHmV2qzZ5j3UNWswB5v6UdfFCmfnzi1BUU70DxKpiXSMx7BSt3rGODAuUCIFsdE8tb85sr52lU0viglaqWdbrZ6fuVabNUvqecN55Wh7pBB29YpKOO2YIEu0Y5xXz1vRMQ4As89l/w75qThoMMGQghfDxZFMjxMXLi6zeEcGoRYHYyYIBOgY1wliqThiNM8dtLmNOqAvjMcQxPId45YIQ1xuzVa0t566Zg2mY2AMW091AQBmF2djSWWexTsyiAQK426XMyKMGrWiYxzQj89rpGe/0Ty1X+uYvXbVDAt3YiC8GDxWx7jVhfGiGiAnfPds3AGEqEhjJLvqe9A+wMSiFywsQ15m9HuBtCQoBufzPZa4pAHA7A3aumG7NXtII3gx+FV2FYMnOELJHekYJ6cC0aDCOEGMR7XU9Q0CHces3YvN4QuD16+2aZAEaIFSjDmzGW7eVs4q9TwVxs2isWcYe+p7AQBLKvOwoDzX2g0ZRZJW6oBFhXFvDlC5gq3bDwOjfebvIY145oCWILt6pU1FIQnOGNfPW7MoQVzNJQnqt1mzhzRhR203OgdZAH3RonKdMMJWqB3j7ugJApfTAVUTY5l6vmKF1tlFZ99QFEXBE/u0e/+Vtk2Q8VbS0c8/3zVrma2iThhCXeNG8tzBFqj6n+tXV8HhsKEgENBmjDtcrDsvCmrca13HOMW8ZjHiC+LlI+0AgKJsDzbNL7F4RwaRaMzr5ZohrIh5HQ6gOiyIHe0DOo+bv4c04mmdKMSm954A75YQK+blOsatsFIHtLMP0L3fYOq6hnC4pR8AsHpWAWYW2nBsJKAvjMeyUo/MGCcxkmhQYZwgxsMriElFZhihkBJRD7udDvsqyBSFiSwAwBu9+MnPGLUsSVC1WlP3N1KCzEh4W6Hr7NotDoyzlUtw3ppljgmqnbpCCWIDGRwL4LVjrDOtNDcD62qKLd6RQSTYMZ7jFSBJQOp503h8r1YYvHqlTe89ANcxHtsqW+0at0wQ6PJolrp9DUBvgzX7SAOOtAzgZDu7D6+rKUqPBJkruvDF8o5xQG8n3UAJYiN5cp9WHLG1GFwtDsbomAWAjLAg1rJnf8FMIC/899C0m1wCDeRVzkb9qhWVOrcYW5Hg+DDLO8YBslM3iVBIiTjleFwOXLrUpiM0AvyM5VgzxjkxuBWiEIDdexzhZxDFvIbyhC7mtXG+kz//MeJedYyGZe6wRFRseishiGkwZ5O2rnvLun3YnD0NPWjqZUWz8xaUotiuNuqBUc1WLiM/6sv0HeMWfVh6slj3FMBmjI/0WrOPNIB3S7jGzhdFXZIgeqBk+bw1AKjm5ow3UJLAKJ490BJJhF69otKecwaBhG3lePW8ZbZyJQuA7HAHT/02slU0iLFAEE+Hn/3ZXpd9Z8wCCXWMA9q8QcvOPgDM5u799Vut24fN4bvFb7BzYTDk19aid4zzZ7+WYl6jaOkbwTu13QCAheW5WFxhUxt1ICExOMB1jFsV8wKaMMQ3ALQfsW4fNkc3OmyljZ/9CY4PEyPm5bpmqThoGNtOd6G1n8WDFywsQ74dbdQB/dmP0TGrnzFu0b0/I0/Ld7YdIpdAg1AUBY/ubYp8bWtBYDAc8zrdgDN6iTUvU4t5FYXs1EWCCuMEMZ6q1VpnY+1brOOXSDm8gszWH5Rjg9o6I3qSwOl0RJJko1Z1jAPA7I3hhQLUvW3dPmzMyfZB7Gtkl/DlM/Ixr8ymNuoAZyvnSNhK3RJbOYAK4ybx6B4tSHrXWTMt3InBJNoxrrOVs+js62wVe8lW0SBeO9aB/lGWCLpyeaV9bdQBzU43Tse4miQYGLWyMM45JtC9xxBCIQVPhgvjLqfD3oLAYIKFcRE6xvMqmDAKAJp3662AiZTx1LhucdvaqANa3JsRu/ifaXXHOMDFvABqt1i3DxvTN+zHS4eZjXpJjhcb5tnUJQoYZ6Ueo2Pca/H4MACYsUZzCaSY1zD+ycW8N621c8zLdczGyPfwcY9l48MAThii0CgNgzjQ1IfTHeyZeO7cYvu6RAFAIDGXtLywMCakWOiYQEwKFcYJYjwuj1YgGWgGes5Yux8bMhYI4vFwgizD7cQVy23cNeUb0NYyqOdrztfWtW9atw8b88juxsj63WtnWbgTE1AVxJ7smLMGhbCVK5wD5IatjRu265PbREpo6RvB1tNdAICakmysnV1o7YaMRDdjPHowmMMlyKztmuXt1MlS1wgeSxdRCKCp52N0jgBAfhZLEliqnp+1js3DBahj3CB21mkuUZsXlqIkN/a5kBr+7uCMLn7h7YQtLQ7OOY/9OxSgBLEBKIqCh3dpIxpsPT4pFNLi3hhicECAGeMAMHeztqaY1xCe2N8MX9gR48Y1M+G2q406oHdJi9ExrhODWxXzerKAqjVs3XkcGGy3Zh82ZsQXxLMHmCgqL8ONy+xqow7oZ4zHuPd73U54XCwfNGTV+DBAH/PWk2OCETy2R2uCS5+YN7ZLmioGB4CBUcozioSNbyYEMQ1qztPWZC2Xcl4+0o7eYfZhcNWKSvvaCgEJd4wDAsxbA8JnP1zAPENJglQTDCl4dDcrjridDty4xsZuCQDgD6vnYxQGAb163tKuWTVJ5htkMweJlPLYnuaICctNZ82yd9eUrmM8epJA3zFuYZKgmksS0L0n5fSN+PHyUZZ4LM314rz5JRbvyEAUReseccW+36lJgmBIse7Zn5HL3KIANkZmuNuafdiYh3ZqhUHb33t0M8YT6xj3By10J9MJYunZn2r2N/bheBuLBc+eU2Rvlyg/1zGbhBjcMlFU+XIgq4it696iMTIG8MguTQz+nrNtXhzxJTY+TAgxOADMvUBbn3nDun3YlBcOt0a6Qq9dVaWz0LcduvFhsXM+atxr2Z0fGOcURfeeVOMPhiLjk7wuJ65ZYWNBIJBExzhfGLcw50NMgArjBDEZc7gkAX1YppyHuQTZ+86utnAnJuDjCuMJJgkstVLPKgIqV7J120FKEKeYt052RmZNXbS4DKV27poCtI5xb3RLOWCcrZylSYILtfWZ163bhw1RFAX/4LqmbrK7ejjBJAGfIBu00lZuxlnaGJkzr9MYmRTz+L6WiF3yDatt3jUVCgAIn594SYIMrXDeb6V6fg43a5nu/SllYNSPp/eHu6Yy3bja7gky3Yzx6MIQfsa4ZVbqgNYxDtDZNwC+W/x9Z9vcJWqMc0mLY6We4WZ3n5BioTDE6dTO/0gPi3uJlHGyfRB7G3oBAEur8rF8RoG1GzKaSMe4I+YIpUydlbqFz/55FPMayT84UYjtY16dS1r0sw8AOV5tzrJlFMwCiuaydcM7+kYmYtq8fKQdnYNMIH3p0nIUZNu4CQ5IomNc+3OgjnGxsHFWhiCmwcy12oWW1PMppa1/FK8f7wAAzCzMwiY7d00B45IE+TFfmuERwEod4BTECiXJUgzfNfVeuyfIAE09H2PWGiCIrRygV8+fpiRBKnnnTDdOhWdNrZ9bjNklsc+E9CTYMe50OpAdTpINW5kkcHs1t5zBNqD9iHV7sRmKAjy4Q0uQ3bre5oJAftZgUrZyFp5/vmv21KvW7cOGPLW/JSJ4u3HNDHt3TQHjZozHKIzzM8aDFt57CmYCRTVs3bhTL+oipsWoP4gn9rKuqSyPC9fa2UYdGOeSFqcw7uFHCVg5Qoy3U6c546mEb4R4j53nK6uohXFvjvjjwwA2Z1kVL1LMm1Lquobw5olOAMDs4mysqym2eEcGo4t5YxfGhYh5AWD+xezfIT9Q97a1e7EZD+yoj6zfv87mMS8wpY7x/hHqGBcJKowTxGS4M7Q54331QNcpa/djIx7a0YBQWBj+nrUz4XTa2EoXGFcYj9cxLoCVOqBPEpCdespoHxjFcwdbAQAlOV5cvKTc4h0ZjKJoSYIkCuMjVlprFc3hEsTv6G3xiGlx/ztakPTBc2dbuBOTSHDGOCCIrRwAzLtYW5+m4mCqqBsEjrdrVroLK2IXDKRHZyUdL0kgiHq+ZjPgDO/l1CvW7cOGPLhDK47csi4Nnv26GeOJFcYttVIHNGFIcAyopwRxqnjmQAv6w4Kfq1dW6p53tsSXTMc4XxinOeN2Y9QfjIjBPS6H/WfMAvKJwT1ZWr6ztw7oqbVuLzbj7+9o955b18+2f77Tz88Yj1MYV2NefxChkIV3n3kXaevTr1m1C9vR1Duia4LbvLDM4h2ZQKRjPPGY11KXNGICVBgniGjMv0Rbn3jRun3YiEAwhL9tZ8URpwO4OR0UZElYqWeG1fO+QMi6eWsAMGcj4Ah/PNBFMWU8tKMBgXAAcPO66ogQwrYERhGx0/XmxHxppihW6oBmpx70AQ3brN2LTege8uHZA0wUUpTtwVUrKi3ekQkk2DEOADnh82+prRygqecBevankLfbtHArPZTzfMd4Eup5KzvGM3JZ9xQA9JwBuk9btxcbcbCpL2Klu6wqHytm2txKF0h4xrhHFCt1AJh/qbY+8ZJ1+7AZ926ti6xvXZ8GohBeDB5vfBhXHLS0MF62FMgOu9fVbtELW4gp8+zBFvQMsz/Lq1dU2X90GAD4mStWPDGsbnyY5YJY3k6d5oynAl8gFBkd5nE58L5z0sAhkL/3xzn/uRns/CuK1ePzLuDynSQGTxUP7WiITGN7/7pquOwuClEU7fzHuPMDArmkEROgwjhBRGPB5dr6JBXGU8GLh9si85UvWVKBWUU2t9IFxtnKJTZjHLA4SZBZAMxax9adx0hBnAKCISWiHnY4gA+kQ4KM77aOlyQQxVYO0CcJyFI3JTy8swG+IHumvffsWfYXhQAJzxgH+I5xi4OksiVAbli0UPuWZg1GTJnuIR92d7KkQF6GG9fZ3UoX0JTzgFxJggWcIPbky9btw0b8+e3ayPpDG+ZYtxEzCSR2/nMzeFGIxcW4+RdrCWKKeVPC/sZe7OPmK58zp8jaDZlBMlbqfMxr5b3f6dSaIcb6gfqt1u3FRtzHiUJu25gmz361azaOGFyomHcuH/OSW04qeP5QKzoHWfx0xfLK9BCFBPiO8dj/v9le7e4zZGXcm1UEzDiLrdsPAwNt1u3FJowFghGHQKcD6SEKCQUQaQSKc/bzRYp5CR1UGCeIaFQsB/JmsHXtFr1FDDEl/sIFSXdsSpMgSdcxHi9JwKnn/RZ3jyy8Qlsff8G6fdiEl460oamXPUMuWlSG6uI0EIWoynlAHit1AJh7kZYgPv68lTuxBYFgCPdyxZG06JoCkuwYZ4GSP6hYO2vT4dCs5fxDQMN26/ZiEx7c2Qi/wgrjN6+r1iWEbAvfcRcvSZAliJU6oO+apQTxtOkcHIvMV87PdONdZ82weEcmMdavrTOjd8gXZmtnv2/Y4rOfVQTMClvqdh4Heupiv56ICx/z3r5xDhwxZg7bhrFkrNQF6RgHgIVXamu690+bg0192F3fCwBYXJGXHqKQUCjh8WGZIhXGZ6wFMgvZ+uQr5JiQAv741pnIOi1GhwHjnKLiiME5x4ThMaubIS7S1nTvnzZP729BxwA7C1etqERVQeyzYAsSFMMCAo0PIyZAhXGCiIbDASy8jK0Do6w4TkyZw8392Hq6CwAwrzQH580vtXhHJpFEkkC1UgdgbXEEABZdpa2PP2fdPmzC79/UbFlv31hj3UbMhBcTxUkSZItkK5dTolnqdh4Duk5Zux/JefZgK5r7WJH40iXlmFcW2znDNiQ1Y1ygJMECrjhIz/5p4Q+G8LftzCnE6QA+vKnG2g2ZhS5JEHumLq+e7x+xWD1fuQrIDt9Nz7xBjgnT5IF36iNOIbesn50eohAAGOnV1rEK45wopGdYgLOmxrwAcJLs1KdDx8AYntjHRCF5mW7cuCZNRCFJjA8TxiUNYPceVRB7gsTg0+V3XMx7W7qIQviOWW8cMTgX81o6YxwAXG6tGWKsD6h729r9SM6uuh7sCYtCllTmYeO8Ems3ZBa6nE/sGeM5GYJ0jAPAAu7ec/xZ6/ZhAxRFwR+2aKKQj5w318LdmAg/PimJ8WHUMS4WVBgniFjwduo0Z3xa/PYNrbh0x6YaOO0+b0RFVxiPlyTgAyWLkwQVy4H8mWxd+6beHo9Iit31PdhR2wMAWFieiwsXlVm8I5PwcR3jcZIEQqnnARKGpAhFUXSikLs2p0mQBCTVMZ4tUpJg4eWAI/zzePQpRAaFEUnzzIEWtIWV85cuKU8PpxBgnJV6vCSBQOp5p1MThvgGgVqatzlVRv1B/Plt1jHrcAC3pYuNOgCM9mrrrMKoL3O7nMgLP/t7RwToHNGNEKPC+HT489tnInPjb0kXpxBA75YQr2OcE4NbXhzMLtY7JnSfif16IipNvSN4an8LAKA4x4v3rE0DK11g3PiwODEvJwqxXAwOAIu5mPcYFQenwx+2aDHvRzfPSw9RCDAu5k2iMG61GHzWeiA7LF448ZJ+DBqRFO+c6cahZnYHWDmzAGeng1MIQB3jNoEK4wQRi3kXAc7wh/exZyhBPEUauod1QdLN51RbvCMTmbJ63uKLosMBLApbywV9wJnXrd2PxPzuDS1IuvuCeekjCkmiY1wo9TwALL5GW1OSYMq8c6Yb+xr7AADLqvLTRzkPJDdjnDv/licJsoqAmvPZuqeWzV0jkiYUUvCrVzlB4MY0sVME9J3Wsqnnl1ynrY88ad0+JOfhXY3oHGTJoqtXVKaPKATQOsY9OXEdEwpz2H/vtdpKHWCOCbkVbH3qFRLETpHBsUBkvrLH5cBd58+zeEcmopsxnrgY3PKOcQBYxI0Qo67xKfPHLWcQDLF82W0b5ujiO1vjT7ww7nY54XWxnI8QYvAFl1G+MwXUdQ3huYOtAICyvAxcv7rK4h2ZSBKFcd4l0HIxuMsNLLqarf1DlO+cBr96TYt57zyvJn1EIXzHeNzCuGAxLxGBCuMEEYvMfGDuhWzd1wA07bJ2P5LyBy5IumNjTfoEScC4JEHi6nkhkgT8zLUjT1m3D4k52T6A5w5pQVLa2CkCSSUJskTrGC9dCBSHk5l1bwMjPdbuR1J+/srJyPqjm+emT5AEaEkCh4sF3jEQylYO0BcHjz5t3T4k5qUjbTjWxhxjanIVrK9JE+U8MK5jXDL1/IJLNSHL0aeBkACfR5IRCIZwD+cS9cmLFli4GwsYZWKwWN3iKoVZ7Oejd9iHUMjiYoTTCSy5lq0Do1QcnCJ/316P/nDC811rZqKyIHaRwFb4koh5eTG4CPd+3imKRFFTonvIh7+/Uw+A/f3etjGNnEL4mDeOSxqgjc8TIubNLADmbGLr3jqg46i1+5GUX756EurH+B0b5+jEP7bHn0THOOegYvn4MEC79wDMKY1Imv2NvXj9eAcAYGZhFq5fnUb5TrJStwVUGCeIeCx/l7Y+/JhVu5CW1r5R3B8OkrI8LtyeTkESkFTHeKZOPS/ARXHeRUBGPlsffUpvFUMkxM9ePhkRXt91/tz0CpKmaqUugq2cw6EpiJUgcPQZa/cjIbvqurHlZCcAYHZxNm5IpyAJ0ArjceaLA/okwdCYAIHSEs4xgZIESaMoCn7xqiYKuWJWKM1EIRInCbw5mp36UAfQsN3a/UjIk/ub0dDNHGM2LyzFipnR52zbEtVKPcZ8cZXCbCYMCSnAgAjP/mXv0tYU8ybNiC+I33IuUR+/MI26xQH9+DBv7MK4cCOUypcBxfPZunYLMNBq7X4k5J43TmM4HMPdfE41SnNjf/7bCp2Vek7cl6tNIqMixLyA3int8OPW7UNSGrqH8c/dTQCA/Ew3bt9UY+2GzCYwxRnjItx75l2kCWKPPUuC2CnwSy7m/cRF8+FxpVGZMQkr9Qy3C96wKLBfBDE4ESGNTixBTJEl12nzNg89TvZCSfLLV09G5qzdtnEOinJif2DYDjVJ4PTETRDzF8X+EQEuip5MLVAa66eZg0lyom0AT+5vBsBGCKSdKCQJK3WX0xG5KI74BXBLAPSiqIP/sGwbsvKzl7Ug6dMXL4A7nYIkQCuMx3nuA4LNWwOAgllA1Rq2btkHdJ+O+XJCz6vH2rE/PEJgaWUelhWm2b0xqY5xwQrjALD0Bm1NnYNJ4Q+G8OMXT0S+Trtucf+o9uzPLIz78sJs7eejTwQ79TnnAdmlbH38Bb3AkYjLX7bWRkYIXLOyEgvKYxeHbQdfGI9jpV6UrbmF9Az5YrzSJBwOYMW7w18oVBxMkq7BMfxlay0AwOty4pMXz7d2Q2bj556VCQhiVac0IUQhQPjeExZwHvgH5TuT5FevnUIg3C7+kfPnIj8z9hgV28EXB+OND8sQyEodYM0bvCC27i1r9yMZh5r78PyhNgBARX4G3nv2LIt3ZDJ8zJtAzic/HPcKE/MSAKgwThDxyS4G5ql26vVA825r9yMRjT3DeGAH6xbP9rrw8QvSTDkPaEmCjFwWdMegNFdLkHUMCtKdveI92vrgP63bh4T85OUTkbjy4xfMQ7Y3tp2y7dAlCeLbykWSBCIESQAwax1QGJ4LfPo1YLDd0u3IxPbTXTpLrZvWzrR4Rxag2srFSRAA45IEIqjnAWD5Tdp6/0PW7UMyQiEF/++5Y5GvP3nRvHgf/fbDl/gYjQy3K2KpK4x6ftGVTMwIAIcepe6RJHhoZwPqu9nf/6b5Jdg4v8TiHZmMaqMOJGSlzhcHe0cEKA663MDS8CiNwAjZqSfB4FgAv3mdjRBwOIDPXbbI4h1ZgK4wHlsUUMJ1E3eJUBgHKOadBr/lusVvWV+NqoL4d19bwYvBvfE7xjNFK4wXzGTCKADoOsFEsURCnO4YxMM7GwAAeRlu3LlprsU7soDI+XcArtiiAD4fNiyKY4Iu5n3Qun1IyPef12Lej18wX+cGkxboxgjEL4yrI8SEGB9GRKDCOEEkAm8tt/9hy7YhGz968Tj8QVYZvPO8Gl0QnDaoVupxLOUA6CzHugYFSRLMu0jrejn2rD7hTURld30Pnt7fAgAoyfGm15w1FV9y89ayvYIlCRwOYOX72FoJsQIJERdFUfC9Z7X5dJ+9bGF6WWqpJNExrk8SCFIYX/k+RLpH9j1A3SMJ8vi+JhxtZcWBVbMKcOWycot3ZAHDndo6O35hVEsSCHL2swqBhZez9UALcPpVS7cjC6P+IH72stYt/qUrF1u4G4tQbdSBxKzUs7iuWRE6xgGKeafIPW+cjvwd3rh6BhZVpFm3OKAfHxbHTrqEc5DrFEUMXr4UKFvK1g3bgN4Ga/cjCQ3dw/jz27UAAK/bmX5OIYDeXSMRMbhqpe4PIRQS5H698r3a+gA9+xPl+88fi3SLf3TzPBRkp1m3OKB1jHuy4jYC5Xi1wumgKGLwxddoudrDT+iFLkRUtp3uwmvHtEaID26YbfGOLGC4S1tnFcd9ueqUNjgWgEK5FWFIw0wlQUyBZTcArnBye/+DNGs5AfY39kZm7RRkeXD35jTsFgeAsXCSII6lHACU5mkFFGGSBG4vsPR6tvYPka1oAiiKgu89fSTy9ecuW5h+3eJAUlbqAN8xLkhhHNAK4wB1zSbIMwdasa+hFwCwuCIP71mbZpZaKknMGM/V2coJcv4LZgJzN7N1zxmgcYe1+5GAUX8QP3zheOTrr1y1JL1mi6sMcYXxnPiFcdVWTpiOcQBY8wFtvfd+6/YhEfe8cRpt/ezuetnSCqydXWTxjixgpFdbJ2ml3jssiCC2ZjOQV8XWx58DBtqs3Y8EtPSN4J43WLe42+nAZ9OxWxzQYl5vHuCMnWYs08W8gpx9gLNTB3UOJsgPXjgWGZt353k1qCyIPWPYlviTE4NncV2VYwFBRogtu1Fzyzn4T3LLSYDd9T149mArANbg8tHNadgtDmgzxt3xf/b58WHDohTGvdks3w+w8ZHHnrV2PxIQCin4H64R4vOXL0KGO826xYEpiMHZ+Q8pAuV8CCqME0RCZBVpxcGRbuDYM9buR3AURcF/PaUVBj976UJd8idtCAa0i6I3fmFcSPU8oE8Q777Xun1IwrMHW7GzrgcAMK8sB7esT0P1JJC0lbpqvTQqyoxxgHWPVKxk66adQPuR2K9Pc0Z8QXzvGe3P6N+vXgKXMw0Lg4oy5Y5xYazUAWDVLdp63wPW7UMS7nnjNBp72Gf+5oWlOG9BqcU7sghdkiD+nwGvnhemc2rhlZry/+jT+oInMYGWvhH8+jVWGHQ5HfjKVWnYLQ4kbaVeyFupi9Ix7nIDq29layUI7Kdnfzz+33PHInfX2zfWYG5pfCtlW8KPD4tDMRfzdokU8656PyJuObv/AoQEikkEZG9DLx7f2wyAjYZIy25xIKkRMoC+MC6MU1p2MbDgMrYeaAZOvWLtfgQnFFLwnScPR77+3GULdUXftCIyPiyRwriAYnAg/OwPQzFvXB7Z3RhphFhUkYubzkrDsXkAMMR1jOckEPNmaPd+slMXByqME0SinPUhbb3nr9btQwKe2NeMd2q7AQBzS3PwoQ1paCMN6C3l4sxaAwTtGAeA2RuB0nD3Q91bQMfx2K9PY4bGAvjPp7Qg6d+vWpKeNtJA0h3jqpW6LxjCWECgQIl/9u/8o3X7kIBfv3YSTb3s7/28BSW4aHGZxTuyCN5VJqEZ43xhXKCzv+wGbf8HHtbPDyV0NPYM41evnQTACoPfuG6ZxTuykOFubZ1IkiBspa4owJAoowTcXs0xJDAKHKJ5s7H432ePRpL7t22Yg4XpaCMNJG2lXqTrGBcoQTY+5iW7x6jsqO3Go3uYQ1phtgefvXShxTuyEDXuTSDm9bicEWGIUB3jRXOA+ZewdW8dcOY1S7cjMsGQgq8/diDy9WcuXYiCrDS0kQb0HeMJxLy5mdq9Xxi3EABYe5u2ppg3Jg/tbIgUBheU5+L966qt3ZCVRFzS4hfGhRwfBjC3nPxwcffkizRKIwb9o37833Nat/i3rl+eno0QgN5KPQkxOCDQCDGCCuMEkTBzLwQKw52fJ18Geuut3Y+g9A37dYXBr12zFF53mj5q+CJCAur5vAx35M9KmBnjAJsVtPYO7WvqGo/KT146jpY+FhxcsKgMly+rsHhHFpLkjHHeVrG9XyBhyOpbtOLgvgf0c+SICLWdQ/jNG6cBAB6XA9+5YUV62kgDmlMIkFDHuE49L1LHeEaeNnNwrJ9sRaOgKKxrRO0YvGNjTXrOl1UZmpqtHAD0i5Qk4N1y3vkdFQej8MbxDl3H4OfT1UYaSNpKnZ9F2iNScaRkPjDnPLbuPA7Ub7N2P4LiC4Tw1X9qhcHPX7YoPefLAuz5qMa9CbikAZpTmlAd4wBw9oe19a4/W7UL4blvay0ONvUDYKOT0rYRAkjaSr0iXysgtvaPGrGjqbHwSq04ePw5oK/R2v0ISs+QT1cY/O6Ny9O3EQLgXNKSE4MPiiQGdzq1Z78SImFIDH70wvGIoO3qFZXp65AG6F3SkhCDA9QxLhJp/PQmiCRxOoGzVBWlAmz/raXbEZX/fe5I5IPyyuUVuCytC4Ncx7g3fpLc4XCgLJcVUYTqGAeYraIr3Nmy93590ZMAABxs6sMf36oFAHjdTnz3huXpWxgEkrZS52fSCZUkyCoEVr6Hrcf6gQP/sHQ7IhIKKfjyI/sjMwY/cv5cLChPLDFqS3jxRAIzxnN4K3WR1PMAsP5ubf3O76k4OAlPH2jBi4fZHN7SXC8+d3kadwwCWpLAm5eQMCRf1CTBjDXArHVs3X4YOPOGpdsRkWFfAF99VCsMfuWqJelbGASStlLnO8b7RgQ6+4BeELvtV9btQ2DueeMUTrSzWG/VrIL0Lgz6hgCE7wcJiMEBNpMXYHa6IyJZ6i6+GsgpZ+ujTwP9zdbuR0Cae0fwgxc0B7n/vmlFehcGdff++KMUKrnCeJtIMa/LrT37lRCwi5ohJuM7Tx5CT9jl5YbVM7BpfhoXBpMeH6aJwYWZMa6y9g7AGb7D7r5Xs4gnIuys7ca9W2sBAJkeJ7527VJrN2Q1dhGDpzlpfHshiClw9p2AK/yBv/svwGi/tfsRjNePd+Dv7zDbmRyvC9++YbnFO7KYMd5KPUH1fC5LknUP+RAUZdYmAOSUAMvexdYj3cDev1m6HdEYCwTxhYf2Rv7OPnXRAtSk64xBlSSt1PkkQWufYIHIOXdp6+2/oZmD47hvWx3eOcPsk2cVZeEzl6R5YXCwTVvnxheH8er5YZGSwwBQtRqo3sDWHUeA2jet3Y9gdA/58K3HD0W+/u6NK3SF3rREtZXLiZ8gAAS3lTv3X7T19t9Ytw9B+f7zx9DYwz7rN8wrTm8rUWCclXph3JcXZgnaMQ4Ay28CcivZ+uhTQPcZa/cjGEda+vGzl7XxGd+7aWX6WokC48aH5Sf0W9TCOCCYINzlAdbeztahALDt19buRzAURcGX/7Efg+Gi1vvPqcY5NcUW78pidFbq8QWxOjF4n0BnH2B26o5w8XLnH6kZYhwvHGrFY2GXnPxMNxUGh7uZiAJIqGPW43JGHDKFmjEOAHkVwLIb2Xq4Czj0qLX7EYxRfxBffmR/RCP/pSsWY1ZR/ByfrVHF4E5PQiOUhI550xgqjBNEMuSWAavfz9Zj/cCe+6zdj0D0Dvvw5X/si3z9lauXoKogfmBga3yclXqCtnJqkiCksIS7UGz6V2299RdAkD7MVX784gkcb2NJoWVV+fjERfMt3pEA8Or5BGzl9EkCwQrjM9cCs9azdfth4MTz1u5HIE53DOJ/n9Xs5P7fe1bpCr1pyUCrts6rivtyXj0/KJp6HtB3jW/5iWXbEA1FUfDvj+xH15DmknP1ikqLd2UxwQAw0sPWCSjnAcFt5ZbdCOTNYOtjzwJdp6zdj0C8cbwDf+Jccr5308r0dskBxlmpx0+Q5Wd5oP6RCTVjHADcXu3Zr4RIGMIxFgji8w/uhS/IigEf3TwXK2bG//u2NWNTiXk1xwShCuMAsP5jWjPEzj/p3SDSnL9ur8eWk6wYUJmfia+me2EQ0IvBvfGF8RWidowDQP4MrjjYCez5q7X7EYjOwTF87bGDka+/df1y3d9lWjLAOWrkJRYD5YTj3sExwe49gD7mfeun1AzB8b/PHsXpDpbfW1NdiDvPm2vxjgRgKCwGzy4BEoiBhHVJS3OoME4QybLhU9p666+AgGCBnAUoioL/+OcBtIXnAm9eWIrb0tlOTkXXMZ7YvFE+SdA1JNjZqloFzL+ErXtqgSOPW7odUdhyohO/fYMlyz0uB3548+qIEjat4ZMECcycqhR13prK5i9o6zd/SJbSYMnhT9+/ByN+pvj+wLmzsSmd50ypDLRo6wSSBBluZ6Q43jEg2HMfYAmywtlsfeploHGXtfsRhPvfqccLYQv1omwP/vPGFVQYHOnW1tmJPQuEVs+7PMD6j4a/UIA3fmDpdkShe8iHLz2siWH//aolmFeWxuMzVPiO8QSs1F1ORyRJ1itaxzgAnPMR7f62+z69ZWQa8/3njuFoKysEL6nMwxcuX2TxjgSAL4wnGPOWcB3jXYOCnf+8CmDNrWztG2DFcQJHW/vxX08djnz9/967CgVZae6SA4yzUk9ODN7SNxLjlRZx/ue19ds/A4JUwAmFFHzp4X2ROO2SJeV499qZFu9KAJIUgwNAeR47/219YwiJ5JAJANXnas0QHUeYYw6BV4624c9v1wJgeYvvv3dVervkACwXqHaMJ+CWAAge86YxlLkniGQpXwIsvJKt+xtp9g6AP71Vi2cPsktRfqYb33/vakoOA+Ns5ZLrGAeAzgHBkgQAcN5ntfXr3wdCglkgmUx7/yg+9+CeSI30C5cvxtKqxCwEbY9qK+fOApzxrxu8w4RwHeMAe+6Xh8dDNO6gebMA/ueZozjcwkaKzCvLwdepa4QxwFmpJ5AkcDgcqA5bkTX1jIiXJHB59EmyN75v3V4E4WBTH777pJYc/v57V6M83btGAH3hbApJAiHnra37qNb9u//BtO8aD4YUfPaBPWgf0MSwH95UY+2mRIHvKk3ASh1gohoA6BVtxjgAZBczW10A8A8BW35s7X4E4LmDrfj9FmYr73E58KOb1yDD7Yrzu9IAXWE8ufFhgIBicADY+K8AwvmMt3+u/39MQ4bGAvjU33ZjLMA6KG/bMAcXLCqzeFeCwFupJ+CSVp6XEWkubO0X8OxXrQIWXM7WfQ1wHPyHtfsRgN+9eRqvHesAwPJ1//eeVZTvBJIWgwNs7BoA+IKhyF1SGBwO4MIva1+/8f/SvhmiqXcEX3p4f+Trr1+7FAsrEhPA2ZqxASAYztfbwSUtjaHCOEFMhYu/qq3f/EFaz97ZWduN7z1zJPL1D29eo1PBpjU6W7nk1fPC2coBwNwLgVnr2LrjCEsSpyljgSA++bfd6Ax3OVy4qAwfv2CexbsSiFFWME3EUg4AyvO1sy9kx7jTqe8af+nbaW2v9Y9djRHlsNftxC8/sBbZ3jS3UFfRJQnizxgHgOpiLUnQNiDg+V/zQSA/3Blx/Nm07hrvGfLhX/66S5ccvmxZYn/PtkedLw5MKUnQL2JxMLMgXCABoATTXhjyk5eO480TTABRmuvFD9+3Gs507xpRUa3UnZ6E5swCQEE2Kw72jfgRFE0UBQDnfwFwh+O6Hb8H+ptjv97GnO4YxL9xTglfv3YZls0gMSyAcWLwRF3S+JhXQDF46QJg+U1sPdwJbP2ltfuxkFCIzRU/FbbRXVaVT7OVeZLsGPe4nCjJYee/TUQxOABs/mJk6Xrj/+AMCfgzahJbTnTi/57Txob9+P2rUZaXEeN3pBF8x3huYoXx6mLtZ6ShR8A8+oLLgBlnsXXrAeDIE9bux0JG/UH8y327IiM+L1tajg+RMyxjeHpicOoYFwcqjBPEVJixBlh6A1sPtgHbf23pdqyivmsYH7tvFwLhRM7HL5yHyyk5rDEF9bzQ89YApqK87Nva169+D/ALGtAZiKIo+PqjB7Gzjs1SrczPxI9upuRwhKBfmzlVMCuh35LpcaE4h51/ITvGAZYgU7vGm3cDh/5p7X4sYnd9D7766IHI19+5YTk5JfBMwVZuVhGXJOgW0FbRnaHvGn/+q2mpoPcFQvjU/bvR2MP+jtZUF+Lr11FyOAKfJEiwMJ6fxXWMi1gYB4BzPw5kFbH1vgeAln2xX29THt/bhJ+/chIAswH/+a1rySmBR+0YzypMaNYgABSGbYgVRdDukfwq5poAAIFR4PX/s3Y/FtEz5MNH/rwDA2MskXntqircvpGSwxH48WEJisGFj3kB4JKvA87wZ9TbPwcGO6zdj0X87JUTePoAE33mZrjxyw+uRaaHnBIi9Dexf2cVsftyAlQWsNd1DI6JKYqas5EVCAE4+hsxr+NFizdkDbWdQ/jU/buh/hV9+uIF2LyQnBIiTKNjHAAaRSyMOxzAhV/Rvn7xm2k5PlVRFHzlkf040MTutrOLs/HD960hpwSVIV4MnlhhnB89oooNCOuhwjhBTJWLvwY4wj9Cb/wQ6Guydj8m0zvsw1337og80M9fUIp/u2KxxbsSDF49702sMF4munoeAGrO19lrYesvrN2PBfzy1ZN4eFcjADZn557bz9Z1+6c9/U2AEu6mVmcTJ0BFOMHe1j8qnp00ADhdwOXf1b5++TtpJww53TGIj967E75wt+yHNszGresT/ztOC9TCuNMDZBUn9Ft06vluAZMEAHD2h4GShWzdsA049Kil2zEbRVHw74/sx9unWCBcmuvFrz+0lmx0eaZgpT6rUDv7dV2Cnv3MfNY5CwBQgGe+nHbCkHfOdOPfOCvFf79qCTbOT0z8kDaoM8YTtFEHNCt1AOgZFrAwDrCzr8Yxu+4FmvdYux+TGfUH8S9/3YXa8PNpcUUe2eiOZ6xfW09lfJioMW/JfGDt7WztGwRe+U9r92MBj+xqxE9eOgGA1Yt+dusazC1NzA0sLQgGtDxgEjFvZTjmDYYUcYUhl383ku9c1Pak/o6XBnQMjOGOP72DvrBo87Kl5fjC5Yss3pVgTEEMro95BRSDA8Ciq4CazWzdUwts/62l27GC7z9/DI/vZY0uWR4X7rn9bBRwd9a0Zwod41WFmVD7qGq7hmK/mDANKowTQtLQPYxnD7bimQanmMURgM0aP+cjbO0fYt1TacLQWAAf/tMOnGhnhd/5ZTn45QfXwu2iR4qOsSnYyuUJbqWuctm3OWHI94HuM5Zux0zu316PH7xwPPL199+3GqtmFVq3IRHprdfWSSQJqsJjGAIhBV2iqigXXArMu4ite+vZOI00oa1/FHf86Z2IIGrDvGJ887rlFu9KQAbDSYLcCmbBnwDVnHpeSFs5gM0av/K/ta9f/GbazNxUFAX/8+xR/HMPS35muJ347W3noKogMbvktGG4W1snqJ6fWZQFr5v9nJzqGIzzags591+AkgVs3bANOJA+MzcPNvXhrnt3wBdkgqhb11fjo5vnWrwrwQgFteKgOpM+AQqzta7Z3mFB7z05JdzMTQV4+ktpM0rGHwzh0/fvwfYz7NlWmpuBP3z4HORm0OgYHVOwUucFxV0ix7wXfkUThuy+F6jfbu1+TOTlI2348iN6QdQlS8gdUEd/ExuzAiRXGOdGD7aI6pRWsRxY8wEAgCc4DNfL37R4Q+bRP+rHR/68IyLYXFSRix+/fw25A45H7Rh3OIGcxDrp+Y5xYcXgDgdw5fcAhP++3/h+Wo2S+eOWM/jVa6cAsD+Kn9yyBksqyR1Qx1DyLmkZbldEGHK6YwhKmomsRYWqWISQfOfJQ/jMg/vxfKMTjb2CqsgAZq+lJv4OPwYcf97S7ZjB0FgAd927A3sbegGwBMEfP7xOZwtChBnhEsQZiV0kSnIksJUDgMoVLEkMMGvFZ76UFt1TD+1swNce0yyk/+PqJbhh9QwLdyQovQ3aegod44DAduoOB3DV/7JuYADY8mOg7ZC1ezKB9v5R3HrPtoiye0llHu65/ZxIQYsIE/QDQ2GrzQQt5QBgdokE6nkAWHgFMP8Stu5rAF76tqXbMQNFUfDDF47jnjdOA2CPgJ/echbOnlNk8c4EZArqeZfTgbklrPustmsIgaCgBTe3lz37VZ77SlrY6h5rHcDtf3wnMgtv88JSfPfGFdQtOx7VRh1gVuoJUpgtia3ihk8CZUvYumknsPMP1u7HBALBEL7w0D68dKQNAJDtdeH3d5yjG31ChOHPf4IuaTleFzI97A4pdMybV8lyPipPfZ7d9WzOa8fa8Ym/7Y7YfN+xcQ4+dsE8i3clIDoxeOLjFSpliHkB4JJvQgmLvZwHHgJOvmTxhoxnYNSPO/74TsRCuqogE3++cz3yMinfOQG1YzynHHAlJhgTfsa4StUqYO1tbD3Wz579aZDv/Ou2Onz3qcORr79zw3JcuTzxnEbaMIWYFwDml7E70rAviNZ+gZ/9aQRlMwkhWcbNKj3SInA3UlaR3lb38U/b2mJIvSRuO80KvvmZbtx313rMKSE7rUnpYnMY4XAChdUJ/ZaibC9cYSWq0EkCALj4q0D+TLY++RKw60/W7sdg7t9ej688sj9yH/7YBfPw8QvnW7spUZlmxzgAsS+K5UuBzWFb3VAAeOyTQEDghPY0aewZxi33bMPpTmb5VF2chT/fuR75lCCYyGCbtk6iMF5dJEmSwOEArv0R4Anvd8fvgTNvWrsnA1E7xX/x6snIr/33u1biqhWUIJgUnXo+sTECADC/nN0j/UEFDT0iC0MuB5Zez9bDXcBTn7N1kuxAYx/ef8/WSMH2nDlF+O1tZ8NDDlETUW3UgaSs1GdzCWLViUtIXB7gmu9rX7/wDaDzhHX7MRhfIIR//fsePLmPdYh5XU787vZzsKa60NqNiQrvHJbgvd/hcKAkh3WNd4lqpa6y7m6gchVbtx8CXv3v2K+XnJcOt+Fjf9kVGZt0/eoZ+Nb1y0kQNRlTjHl5MXibyDFvXgWCl35H+/rJzwMjvZZtx2h6h3247Q/vYE99LwA27uTej6zHjEJyiJpAKKjFvUnEvPmZnkhTVaPId34AuOw7zAEOAI4/B+x/0Nr9GMwftpzB1x87GPn6M5cuxO0ba6zbkMjoYt7EC+PzuFEkpzvITl0EKKolhGTZDM2C7rDIhXGA2QstvIKth9qBJz5jyyRZa98obv7tNuys6wHAiuJ/uetcLK0iS5VJURSgM5xIL5wDuBObP+10OlAc7hoXPkmQkQdc+0Pt6+f+A2g/Yt1+DEJRFPz0pRP46qMHIj/ad55Xg/+4eom1GxOZKSYJ9Op5wQOlzV8EShezdcte4KVvWbodozjWOoD3/nprpCg+qygLf797g84CkOAY4Avjic1aA4CcDHfk2S+srZxK8Vw2TkPln3fbsnPWHwzhK4/sj3SKA8C3r1+GD5yb+DMt7RieapJA6zA8LbKdOgBc+2PNMu/oU8Duv1i7H4PYcqITH/jdNvSG516vnlWAP965DtlespCeFL5QkETH+FKdGLw/xisFYO4FwLqPsnVghD37bSgK7B/1484/v4NnD7JOOK/LiV9/aC3OW5D4My3tUEUSTk9SXbOlueze0z3si3QmC4nLDdzwc8AZfv5t+Qlw+nVLt2QUD+6ox8f/uisyOuPalVX40c2ryUI6GlPtGJdFDA5AWf1BdOQuZV/01QNPfNqW+c7m3hG87zdbI86Yhdke/O2jG7CoIrHxEGnHUAeghF2ekoh5ASayB9gYAWGdogAm8r32R9rXT3/RlqLAUEjB958/iv/kOsX/5cL5+PxlCy3cleAMd2nrJDrG55VJFPOmCVQYJ4Rk+QwuSdAqeJLA4QBu+IWWJDv2NLDlR7F/j2Tsqe/BTb96K5KwKcz24P67N5BqPhb9zWz2PACUJnehKAvPXOsYGMOoP5jqnaWWxVdzSbJR4KE79HZ6kjPsC+BzD+7Fj1/SZop/7IJ5+OZ1y0g1Hws+SVCQmFsCIFeSAO4M4D2/A1zh8QfbfgUcfsLaPaWYV4624T2/fjvydzGvNAcPfGwD2YjGQp21BgB5yc1hVOeMt/aPYiwg+LN/3d2sSAKw/+dH7mKdAzahd9iHO/74Dh7a2QiAXfW+d9NKfPg8mqscE3XGuMub8JxZQOsYBwSfMw4AuWXAdT/Rvn7mS0DjLsu2k2oURcHfttfhw396BwNjzD59fU0x/vrRc8klJBa6jvHEZ4zPL8uFx8Xuk4ebBY95AeDy/wRKFrB18x7bjVI63TGI9/16K946yRKeGW4nfn/HObh0Kc1VjkooqLmkFc9L2E4XAMrDglhFkUAUOGMNcMk3wl8owCMfBfoardxRSvEHQ/ivpw7jK48ciIgUblg9Az+9ZQ25hMSit05bT1kMLnjM63Bgz5y7oahuKEeeBLb/xtItpZo99T248ZdvRZxbSnMzcP9HN2DZDGoCioou5k3OSWtWIcslBEMKWkQ//0uvA1bdwta+QeCh2wGf4J9XSTDiC+JfH9iDX756KvJrn7tsIb5y1WLKd8Ziih3j88v4mJc6xkWAbjiEkMwqykJuBguqhLZSV8mrAG78lfb1y/8JHHvWuv2kCEVR8Ke3zuDm326NXFhmFWXhkU9swoqZiSd90pIuTklYuiip36p2jwRCCg41S1BkvuK/gPLlbN15DHj4TiAYsHZPKeBk+yBu/MVbeHxvc+TXvnbNUnz1mqV0SYyHWhjPKgIyEw8o+cK48EESAFStBq78nvb1ox8HmnZbt58U4Q+G8KMXjuGue3diMFwYWTWrAA//y0YqisdDlyRITj0/K2ypqyhAc6/g59/pBN79eyA3nAg58zrw7FdsUSDZWduNa3+2BW+fYoURr9uJn996FnWKJ4KaJMguZWqCBJnPqedPtUuQJFh2A7D+Y2wd9AEP3Qb0NVm7pxQwMOrHZx/Yi689ehCBcGHk8mUVuPcjNFszLnwHkTpmKAG8bicWljMRyenOIfEFsd5s4D2/B1xhJ6zd97KRGjbgqf3NuOEXb+FYG8s9FIWF4BcsKrN4Z4LTWw8Ew+O/khSDr+TyCWqXptBs+gww7yK2HmoH/n4L4JPgMysObf2j+MDvtuH3WzRL/LvOn4ufvH8N3FQUj42uYzxxMXhFgUSFcQAj3lIEr/+59gvPfxU4/rx1G0oRiqLgz2+dwfvv2YaOAfYcm1OSjUc+sZGK4vFQ54sDU+4YBwQfIaZy7Q+BsrBbZPth5phjA0H4sdYB3PjLLXh6P8tfOBzAt65fhs9dtojynfFQO8YdTpbzTBC+Y1x4MXiaQLccQkgcDgeWVrEkQWv/WGS2ndAsvgq4+OvhLxRWHKx9y9ItTYf+UT8++bfd+M6Th+EPsuTY2XOK8M9PbtIlMIko8AkytbMiQdbOKYysd9f1pmY/RuLJAm75q3YhOPUy8NRngZDAtkgxUBQFj+9two2/2BJRDedmuPHrD67F3RfMs3h3EhAMAP3hAkES3eKAvjBe1yVBkAQwx4SV72Nr/zBw//uBrlOxf4/A1HUN4X2/2YqfvXIyUuO8ekUlHvjYBpTkJjYSIq3RJQmSU8/r5oyL3jkFMFHge/+oWYvu+B2w5cfW7mkaBENsbMbNv92Kpl42yqE014u/370B162aYfHuJEBRtCSB6qKUIHNLJeoYV7niv4HqDWzd3wT89d1ax7yEHGzqw/U/34In9mliwLvOn4vffOhsZHldFu5MElr2a+uq1Un9VlUQGwwpON4mgSB8xlnMVlrlmX8DDj5i3X6myag/iG88dhCfvn9PRAy4oDwX//zkeTh7TuLJzrRF7RYHkhaDr52t/fnuru9J1Y6Mw+kE3vNHoKiGfd16gHUPBsYs3dZ02HKiE9f+7E3sqGV//h6XA/9543J847plZJ+eCBExeHFSTjl5Ge5II9DpzkEoEghLlUVXA+d/PvxFiOU7JXbM6RgYw51/3oFvP3kYvgDLW507txiPfvI8zCnJifO7iel0jFcXazFvY7fg4/MAICMXuPkvgDecBz/6FLv7SPBzOxmKouC+bXW44RdbcLyNxV05Xhd+d9s5uJPc0RJDHR+WVczuBglSmutFXmb42U8d40JAhXFCWJZWahdL4WeuqVzwJWD5u9k6MMIKJA3vWLunJFEUBU/tb8ZlP3w9MlsNYPbRD3xsA8rzaK5sQuiSBMmp5/kkjBRJAoBZ573/r2y2HADs+SvwzBelK4439gzjrnt34rMP7MWQj6lAF1fk4YlPn4erVyanhE1bBpoBJaygTcJSDgDyMz2oKWGB0v7GXgyNSeA84HAAN/4SmL2RfT3UDtx7vXTF8WBIwf3b63HNT9+MdO24nA7825WL8csPrKW5sokymEbqeQCoOU9fIHn5O8BbP7NuP1PkWOsAbrlnK3780nGoY07Xzy3GE58+nwojiTLYDoTYPOpkZq0BQF6mBxX5THhzulOSJIHbC7z/PqAonEDqOArcd5N0xfERXxA/fOEY3v2rt1EbFqTlZbjxyw+sxTeuWwYXFUYSo3Uf+7fDCZQvS+q38l1p0sS8q98PnPe58BcK8M+PAUeesnJHU2LLiU5c/dM3cd82zQ753WfNxOOfOk8n2CFi0KmNm0q2ML6quiBiLrKnvjd1ezKSnBLg1geBjPDP7cmX2CixgASNHBzdQz588aF9+NAftqNzkO19RkEmHvr4Rty2scbazclC0K+JwZOMeR0OB84QcVBzAABJ70lEQVSaXQgAaOsfwxlZ7j6XfBNYdiNb+4fYvUey4riiKHj2QAuu+skbeO1YR+TX7zyvBvfddS6Kc7wW7k4iptMxzovBZYl5yxaz4rgqCN/5B+C5/5CuON7YM4y7/7IT33jsIMbCgpAllXl47FPn4bJlNDYmIYIBFvcCSce8Docj0mjY1DuCEZ/8zgOyQ4VxQljUjnFAkplrACuQ3PRbYMFl7GvfAHDvDcCJF63dV4LUdw3jw3/agU/fvwftYSuh/Ew3fnf7OfjqNUtpvlQy6DrGkyuMLyzPiyiId9f3SKEgBgDUnM9mLjvC52TnH4FHPyaFit4XCOH3b57GFT9+A68cbY/8+k1nzcSjn9qks7wh4qCzlJuT9G8/bwG7XPqDCt6plaTA4M4AbrlfS4b3NwF/ugZo3mvpthJlZ203bvzlFnz10QMRQQizkduET128gDpGkoFPEuQmp56v4boT9jdIMEZDZc0HgEu/qX394jeAV/5bikRB/6gf333yMK7huqWcDuALly/C3+/egBmFWXG+AxGhYbu2TrJjFtDs1LuHfHI4RQFAbjlw26NAbjiR1LIX+NPVQH9zzN8mCi8dbsPlP34dP3/lJHxBlhxbNasAT39mM65dRWLAhAn4gPajbF26iNmNJ4GUMS8AXPZtYO3tbB0KsJECu++zdEuJ0jEwhs89sAcf+sP2SEEqw+3E/757JX5482rkZJAYMGF0hfHkYt78TA8WlrNn/5GWfnkSxOVLgA88CHjCP+vHnwX+9l5gVPy7Wyik4JFdjbjsR6/jkd3ajPTNC0vx1Gc246zZJAZMmL5G1jkNAEXJx7yb5msFlbfC43uEx+lk+c4557Ovx/qA+94FnHrF0m0lyrHWAXzw99vxib/tRlf4rlmam4E/37kO37p+ObxuyncmzDQ6xms44Zk0oigAWHApa4hQ2f5r4Il/ZSIZwRn1B/Gzl0/g0h++jpeOaPnOOzbOwWOfOg8LKxJ3vEh7WvcDgfAIjCTFsAAwj5szLo0oysbQU58QFr5jXIo5yypuL3DzfcDcC9nXauf41l8KmyRu6RvBNx47iMt+9DpeP66pJi9dUo5nP3cBLiflWPKohfGMApY4TQKX04E11YUAmIJYtXSVguU3sWBJLY4feJgpiQfarN1XFIIhBf/Y1YhLf/Qa/uvpIxgOJ2TK8zLwmw+txY9uXk2dssmiK4wnP5NXLYwDwFsnOlOxI3PILgZuf0K7HA+2sgLJ4Ses3VcMTncM4rMP7MF7f7MVB5u0ZPz7zp6FZz6zOfIcIpKgOzyf0eVlZyIJzp5ThCwPsyx++WgbgiEx7wyTsvmLwCVf175+4/8B/7gT8InZBTDqD+LPb53BJT94HX9860zkz3p2cTYe/PhGfObShdQpmyz127S16qCRBHyS4KgsXbMAUDwXuP1xTQjTcRS45yKgfnvM32Ylu+q6cdsftuOjf9mJxh52x3Q7HfjkRfPxj3/ZhNklyRV2056OI5pbQuWqpH/7siq+Y1wCK3UVhwO47ifAqvezr5UQ8MSngRe+wbppBKR32IfvP38UF33/VTy2VxOwnDOnCE/+6/m4Zf1smquZLNMYHwZoduqBkIIDTRLlfOZsAm59AHCHBXRnXgf+eBXQeTL277MIRVHw4uE2XPOzN/HFh/dFBGh5mW5876aVuPfO9dQpmyzTjnm1sTNvn5Qo5vVkAR98iCuO9wN/fS+w4/fC5jtb+0bxzccP4pqfvYm3ORHCZUvL8fznNuOixcnl6wgAPZrTSrId4zUl2ZhVxJ6d2053oW9E/MJyhNW3ADf+CoBqd3Kf0OOU/MEQHtnViCt+/AZ+9OLxSJd4aW4Gfnf7OfjOjSuQ6aGRSUnBx7xzNiX92/nRtIdlinltCmX7CWFZUJ4Lp0NBSHHgoEzqeYB1CnzwYWYrd/gxZiv8/FfZA/S6nzALLgFo6B7GH7acwf3v1Efm6gBAVUEmvn3DclyxrIKSA1PBPwL0NbB16QJgCn+Ga2cXYks4QNpd34tZRRIlKVfdDHhzgH/cxYQhdW8BvzkfuOk3TGUpAKP+IJ7e34JfvXYSp8bNdvnQhtn48lVLkJ/psWh3kjPNJMHGeSVwOFhcLY16XiW3DLjjKeDvtwCN77CZ4w/dBqz/OHD5d1giQQAONffhV6+dwjMHWnT5iyWVefjW9cuxcb4Yn1HS0VMHdIct9GesTfrZn+lx4YJFpXj+UBs6B33YU9+Dc2qSK65bygX/xmavPfcfABTg0KNA2yHgPb+fUgexEQz7Avjbtnr89o3T6BzU3EwyPU586qIFuPuCeZQcmCr1W7V19fqkf/vZc4rw123s8+OpAy3YtCA5azpLKV8KfORZ4C/vAnrrgME24M/XAhd/FTjvs4DT+jOlKAp21PbgZy+fiNwvVTbNL8F3b1yOBeXULTIldPPFky+MF2Z7MaMgE819ozjS0o9AMAS3LC5dThfwrt+wGYvbf81+7e2fAU27gXf9akpdlEbQMTCG+7bW4o9v1UbmiANAQZYH/3H1Etx8TjW540wVtWM8twLIKkz6t581uxAP7GBx8+76HqyfK9G9Z96FwB1PsCaIkW6g/TATRl33I2Dl+6aUA0g1gWAILx9tx69ePYl9jXrhwbWrqvCt65ahPJ9G5U2JabqkLZ9RgPxMN/pHA9h6uguhkCLPc8ibw4rjj9wNHHua5Tuf/iJQ+xZw3Y+n9CwwgtrOIfzm9VN4ZHcj/EEt6K0uzsLXrlmGK5dTvnNK+IY1p6i8GVOyk758WQX+9FYtAiEFrx1rx41rZhqwUYM464PMMfCxTwBBH3DmDeDX57F857wLrd4dAGAsEMQju5rw69dPooGb4+5yOnDnphp85rKFlO+cKvVva+vZG5L+7fyYtqf2N+PGVdSIaCVUGCeEJcPtxMxsoGEIONk+iEPNfVg+o8DqbSWOOwN47x+BV+YBW37Efu3IEyxxeOX/ACvfa0mwFAopeP1EB/66tQ6vHGvXFUVyvC58+LwafOKiBRErb2IKdJ0CEP6DTdJGXeUs7sNyV203blg9IwUbM5El1wIffgp48EPMZmmonSkpz/oQcNl3LROHNPeO4G/b6/DAOw0R+yyVzQtL8cUrFlOX7HTh7XSL5yb924tyvFgxowAHmvpwpKUfnYNjKM3NSOEGDSanBLjjSeDxTwEH/8F+7Z3fAidfZMIoi4KlUX8Qzx9qxQPvNGDrab3goDDbgy9esRi3rquWJxkvIie5sSkLL5vSt7hiWSWeP8QcNl443CZXYRwANnyCzVx+5C7AN8gS5r+7BNj0mXDh3BqR1+Hmfjywox6P7mnCwKi+k/HqFZX42rVL5RKgiYZvCGgJz1guW5q0WwLAzn6W5yBG/EE8ta8Z37xumVwiheJ5wN2vAA9/GKh9k3UQv/wd4NizwLU/nFLBNBUMjPrx2N5m/G1bHY626ruRZxVl4d+uXIwbVs+gxPB0aOUK41PoGAeANbML0XygFQNjAbx0pA1XrZDIyt7pBK76H/Yz8Px/MFv1ui3ArzexMRvn3AW4zI8rFUXBzroe3Le1Ds8ebNEVRTwuB96/rhqfv2wRSmS6Y4rGSA8wFHabS3K+uMpazrp7Z203cOH8VOzMPKrXA3e9CDzwAaDzGBul98+7mTjw6v+bkkg4FXQNjuHBnQ3427b6Ce5zq6sL8W9XLMb5CyUSoIlIEzdbewqFcZfTgQ3zSvDC4Tb0DvtxuKUfK2ZKlO/05gDvvw948ZvA1l+wXzv0T6DubXb2l91oSb4zEAzh1WMdeHBHA1452gbegCvL48KnLp6Pj24mIey0qN2iWUkvvHxKf89qYRxgMa9UhXGA5fMLqoEHP8g+Bweagb/cAJz9YTZqJsuasRRnOofwwI56/GNn44R853kLSvCt65djEdmmTx1FAerCYvCMgilZqa+vKcbMwiw09Y7gzROdOrE+YT5U+SKEZkN5CA1n2IXlT2/V4gfvE6PjKGGcLuCybwEzz2bWcmrw+M+PAjt+B1z4FWD+JYZfGBVFwf7GPjy1vxlP729Bc9+o7r9neVy4Y1MNPnbBPLLQSgXNu7V1afKWcgCwtroILqcDwZCCR/c04QtXLEZBlmSKvlnnAB9/k80ZV+dO7fkrcPhJYPPngXM+AmQaH/z1DPnw7MFWPLGvCdvPdE9w+FpXU4QvXrEYG+ZRl+y0GWwHTr/G1oWzgbIlU/o25y0ojdgpvn6sA+85e1aKNmgSnkzWJTtnE+ueDY4B3adZsLT4GuDirwGVKwzfRiAYwju13XjuYCse39s8waasNNeLu86fhw9tmI08UgxPnxNcYXzB5VP6FpcsKY88+58/1Ir/uHqJfAWrxVcBH3sdeOQjrFgaCjCB4P4HgYv+HVh9K+Ay/rw19gzjuYOteHJ/C/Y19Or+m8MBXLOiCp+8eL5coktRadzJuoWAKSnnASAnw42rV1Tin3ua0D8awKtH23H1SomKgwDrmLntUeDV7wFbfgxAYe4h91wIrLoF2PyFpGfwToVRfxBvHO/AMwda8MLhtsiYGJXZxdn49MULcNPamfCQGGr68B3jlSun9C1uWTcbzxxoBcBiXqkK4wB7qJ77MSYAeeSjzDnLNwg8+2Vg55+AC7/MiiQmuCccbxvAU/tb8NT+Zpwe5wrldjrwvnOq8elLFmBmoRguPlKjCqKAKdmoA8xStDjHi+4hH1491oG6riHMKcmJ/xtFonQBE0Y99XngwEPs1449w+LfDZ8ANnwy6dFqU2FwLIAXD7fiib3NePNEJwLjRvIsqczDF69YjMuWlst3txSNgI85QwJs1vwU7HQBFvO+cJgJYl852i5XYRxgz/Qr/xuoPhd4/NNs5vhgK/DwHcDssDhq9gbD852hkIJ9jb147mArHt3ThPYBfaEpL8ON2zfNwZ3nzZVLcC8qJ17Q1guvmNK3WF9TjIIsD/pG/Hj9WAfGAkFkuCUTK8w+F/j4G8CjH2dd4wCw68/A4cfZmLF1HzXFMbBjYAzPH2rFU/ubse30REv3CxaV4dMXL5DLkUVUuk4Bw2HnrdnnTule63Q6cOOaGfjVa6cQDCl4cn8rqGfcOqgwTgjNujIFz7cwe6En9jbj369eIudFZul1wKx1wNNfAI4+xX6tYTvroK1cxWwWl70rpWr6obEAtp7qwhsnOvDqsXadfYrKjIJMfODc2bhl/Ww5/1xFZeeftPXcqXWHFmR78O6zZuLhXY3oHw3gT2+dwecum5oS31Jyy4APPgLsvpfNHPQNsIDppW8Db/wQOPsOljAoSF3hU1EUHG0dwOvHO/DasXbsrO2ZkBhwOx24emUV7tg4B2fPKaLkQKo49BibMQkAK6buinHR4jL85nVmSf3Tl0/g2lVV8qm6HQ5g3V1AzfnAE58BGsKziI49w/6ZeyGw8VOsgOpMXWGie8iHt0914vVjHXjpSBt6hifO7JpbmoMPb6rB+9dVy/fnKir+US0gzq2YctdgUY4X62uKsfV0F+q6hrH1VJdcltIqpQuAu14C3vwBKxAGfUB/E/DEvwKv/g+w7iPA2g+zz4gUEQiGsK+xF1tOdOHlo23YP84yFGBCwOtXV+HuzfOwkNTyqWOa88VV3r12Fv65pwkA8MjuRvkK4wATfVz2LZYofOLTQNdJ9rm4735g399ZcfC8zwIzzkppori5dwRbTnTijRMdeO1Yh84uWmXt7EJ8aMMcXL96BhXEU4VvCGg9wNYFs6fklgAw16L5ZTk41TGE7We6caSlH0u52ePSMHsD8Im3gRe+Buz+C/u1jiPAP+5kHeWb/pWJo1KYKPaH2Oidt0/34NWj7TjRPjjhNcU5Xtx8TjU+tGE2uYOkkl1/1tZTLAw6nQ58eFMNfvTicQRDCn7+ykn5miEAICMXeM/vWN7n6S8xt7TAKLsDbf0Vm0u78dNAWerieUVRUNs1jNePteO14x3YeqorMj9WxeEALl5cjjs21WDzglJ5rLpF59QrrOkFYKLnjNzYr4/ChYu0e/Dv3jyND547W04Xi2U3ADPXsrN//Fn2a/VvA3+6io2X2vgpdv9JoTC2f9SPd053440THXjhUBta+0cnvKYyPxMf2jAbt22ska/JRFQUBTjxPFs7PVN2w3O7nLh0STn+uacJg2Ms3/++c6pTuFGTyJ8B3PY4cwh85b+YKHCkB3jh68CbPwTW3sEK5IWp+38LhRQcbunHWyc78fLRduyondj843E5cMXySty9eR45YqaSadqoq7x77Uz86jWW73x8XzM+JsbkobTEoSjjf3zShx07duBb3/oW3n77bfj9fqxcuRJf+MIXcPPNNyf1ffr7+1FQUIC+vj7k50sYwAqK3+/HM888gwOuBfj9lloAwB0b5+A7NxrfZWcoJ15k88bVeVwq2aXA8ncBK94DVG9IulDSPjCKPfW92F3Xg111PdjX2KuzjFNxOx04f2Epbl0/G5cuKSfb3FTTuAv4/SVsXbmKKQinmPis7xrGJT98DYGQgrwMN9748sUokrmjf6CVXRb3/BURq3kAcLiAeRcBy29iFuxJJhWHfQHsb+xj57++B3vqe9A56Jv0tXNLc3Djmhm4df1sVKT5PDX1GXvNNdfA40lRoPiHKzQr9U+8DVQsn9K3URQFH/jd9ojl95euWIRPX2J8l51hhMJFkVf+i40W4Cmay87+shvZHOYknheKoqChewR7Gnqwt6EX206zZPpkZLiduHZlFd6/rhrr5xaTGCTVnHyZid0AYM0H2WzVKfLgjnp85RFWaJlVlIXnPneB3ONNOo4zm0U1Uabi8gJLr2f/LLg86aTi4FgA+xt7I3efd850Y2CSYiAALKvKx63nzsaNa2aYOk/NkOesaIRCwO8u0joHP7t/ynOFgyEFm/73ZbT1s06f33xorXydszyBMWDbr4AtPwFGe/X/rXQxsOp9bA5tUU1S3zYUUnC6cxC769i9Z0dtN06N64xVyc904/rVM/DBc+dg2QyKU1POmz9ilvkAK/je9Jspf6v7ttbiG48fAgBctrQC99x2ttxFrIYdwHNf0dsNA4A3jxUPV7yXJdSTLJT0Dvuwt4E993fUdmNnbRf8oYl/Tg4HsK6mGLeur8bVKyQUWIpOXxPwk5XMLSSnDPj8ITZKbgr0j/px/v++gv7RAFxOB174/AWYXza1QqMQjPQyYeC237CxGjyzN7J7/9IbgPzkPt/GAkEcau7H7roe7GnoxZ66nglOgCpVBZm4YfUMfODc2fJ14MvAP+7SRmbd+iBzS5oi//bwPjy8q5F9q/XV+J93WzN6JRpJ3WUVBTj6NPDSt5gwkCevijUDLbuRdZgnme9s6RvB3vpe7G3oxfYz3TjQ1IdgaPJ856VLy3HLutm4YFEZXDJ/jopIx3Hgl+vYeu6FwB1PTPlbvXmiA7f94R0A7L76wucvRGWBxDm6/mbWALT/IejznU5g4ZXs7C+6Mul856g/iINNLN+5p6EHW091Tdr8ALB8563rq/GetbPkFNmIzv23aDmNO58D5kxdEH7DL7ZEhPz/sTqAj7zXxvkCk0mmTpu2hfFXX30VV155JTIzM3HLLbcgLy8PjzzyCOrq6vCDH/wAX/ziFxP+XlQYNwb1ArZm08W49MdbIh2fX792KT66eZ7Fu5smoSBw5EngrZ8AzXsm/vfcCqBmMzD3AmDuZlY4cTigKAr6Rvyo7RrG8dYBHG0dwLG2fhxtGZgwP4THHZ5fdN2qKly5vFLu4qro/PPjwP4H2PrGX7KZ2tPg3x/Zjwd2NAAAVszMx18+cq78dvcdx4FtvwT2/p1ZTPM43SxhUHM++2fmOcyWGoA/GEJd1xBOtg/iRNsgTnYM4njbII63DUwaFKnMLs7G1Ssqcf3qGVg+I58KgmFSXrBpPwL8KqyaLFsKfGpb7NfH4VjrAK752ZsIhhRkuJ342a1n4crlldPfp5X4hoE99wHbfg30nJn43wtnA/MvBeacxzpvCtisLUVR0D3kw/G2QZxsH8DxtkGcaB/AsdaBqEERAGR7Xbh4cTmuWF6Bi5eUm1oQTCuCAeDe6zUF8Xv/BKx499S/XUjBrfdswzu1zArt2pVV+OHNq+VP6tdvA976GXNMwLhntiuDCaTmbmafAZWrADf7rBv1B1HXNYzjbQPcP4Oo6xpCjEc/llXl45qVlbhqRRUWlFuTYE+LwviOPzBHJIDNWfvE29PqhOaLgzleFx7+l03yF3THBtif09Zfsi7C8ZQtYcnFeRex7oNwwiwUUtA+MIaT7YM41jbA7v5tAzjRNjDBHp0nP9ONK5ZX4tqVVThvQSm8bhLBGsJIL/DT1Uz04HACn9w+rW7QobEANv3vK5GxJx/eVINvXb9M7nurogCnX2XikDOvT/zvmYXsvj/3Qhb3li6KFEsGRv2o72bP/qOtAzjawu49k3UFqjgcbGb1tSurcO2qqrQXwRrKy99l3XAAGw938Ven9e1+/vIJ/PBF1jgwqygLf797A6qLJe/u72sEtv8G2Pln5pqmw8HG7c3dzH4GqjdEBILBkILGnmGcaBvEifZBFvu2s58BXzA04W1UKvMzcfmyCtywZgbOnl0kt7BGZAY7gJ+uAvzDQFYx8KXj0+qEbh8YxSU/eB2DYwE4HMB3b1yB2zaI0z44pbts0A8ceJi5JbQdmPjfcyvCMe8m9k/xvMjdsW/YjxPtAzjRzvI8J9sHcax1YII9Oo/X5cTmhaW4cnklLltWIX++TFQUBfjn3ezvFgCu/B5zA5gGn31gDx7f2wwAOHduMe657RwUZEseM7UeBN7+GXDwnxPFUQ4XUHMeu/fM3sicFsIuOr5ACPXdwzgRjnXVuPdM59AEB0yeeWU5uGZFFa5aUUn5TiM5/jxwf7iRNrsU+MLhKQsCAeBPb53Bd586jI1zi7Expx3/crON8wUmQ4XxOAQCASxZsgSNjY3Ytm0b1qxZAwDo6+vD+vXrUVtbi+PHj2POnMQuI1QYNwb+AvbgrmZ8/bGDkf921fJKfP7yRVhUkSv3Q19RmPXqzj9AOf48HIHJA/1BZz5OOmuw1z8bhwIzUB+qQL1SjjYUIYTJk101Jdk4f2EpLlhYho3zS2h+rBnsvg948jPMMjOrCPjCkWlbBbb0jeC6n22JCB9mFWXh85ctwvWrZ8if6BzsAHb8Hsrev8HR1zDpSwJwo9E9G0dCs7HbNwsnQ1VoVMrQqJRiBJMnu/Iz3Th7ThEuXFSGCxeXY24pqeQnI6UFm4FW4A+XA7317OtLv8nmKk2T7zx5CH96qzby9R0b5+Cjm+fJnygLBdlsru2/gXLmDTiUyZNc3a4ynHDMwX7/TBzwz0K9UoFGpQydyAcweXfU8hn5OG9+KTYtKMW5c4vlL6bKwEvfDs8TBpA/C/j0O4B3es+d+q5hXPXTNyLFryWVefjyVYtx4aJy+TsfeurYs3/PfXCoNpTj8DkycNo1D0eCM7HPNwMnlJloVMrQopTAh8mfVyU5XmxaUIrzF5TgvAWlQtjl2r4w3nUKuOdiNiIFAO54iiX5p4GiKPjsA3vxxD6WJPO6nPjkxfPxwXPnoCxP8s4H/wiw7wFg/4NA/daoL+twVeAI5mG3bxZOBytQp5SjTqlAL3Ix2bPf5XRgTXUhNi8sxeaFpVg9q5AcoYwm4GN3/n1/Z1+v/gBw06+n/W1fOdqGu/+yKyL43DCvGP925RKsnV0od8wLAE272bP/yJNwjE3ubjPizMZp51zsD8zGPv9M1CvlaAg/+wNRpgAWehVcumImLlpcgfMXlJIA3AyOvwA8dDsQGGGi5s8fAvKmJ14dHAvghp9vwelO5n5RmuvFpy5egPevq0a2V2LXHAAY7QN23ctc0zqPTfqSEJxods/CMWUO9vhm4XiwEg3h8z+Iye8z2V4X1lQX4oJFZbhocRkWV+TJ/5wQnbEB4M/XAS172dfn3AVc96Npf9vfv3ka//X0kcjX71ozA5+8eAEWCTD2Z1p3WUX5/+3deZQU9b338U9VbzPTPQswA8wM+6IYl7gALqCgJoNRr9GbGJe4QBS9Jnlyc683anIS0ZionEu85knyPBo5CsaLwWj0ieYaQQ0QCaAoxgVUEBn2nVl6hunp7vo9f1R3T/fsI7PRvF+HPl1TVd1dzfnWt3+/37cWacsbbp/3k1dkNS8SJtR4irTJGq33Y+V6r3GYtiT6vHtVJNPGeOf4wSFNGVesc8YOYryzt7w1X/pzYozHny99960uX/miuar6RlX814rUgQ/lRbm68ysTNOPEIUffPcebq90jvb1A5u0nZDW/amBCTF5VekdrgzNM7zaW6RNnmLabEu0wxYqo9fZMfo5X54wdpKnj3DGfMcVBcn9PC++VHrtASo5bX/Goe4uUI1DTEFVdJKbiPG92jxf0AQrjHViyZIlmzJihWbNm6fHHH89YtnDhQs2cOVP33nuv7r777k69H4XxntG8AfbLVzfqv17NvPz4mOKgTh85QMcNCWlIQY5K8gManB9QMOBVjtejHJ9HAa99xEfLGmMUd4zixshxpJjjyHGkSDyuSNRRJBZXQ9RRJOZOR2JOan4k6qiuMabahphqG6KJ55hqUtNR1TTEFKmr1vlaq0s9q3WO/aGCVttHRCY1Go+2mxLt85QonjNIvoLBKiop09DSYcofMMQ96tifn3gONf3djfcyP+ZFD7tn/b/zZNPgmCRd8GPpvB90y0ds2lurax9bk3GUbJ7fo8mjB2psSUijBuVpxKCgBub5lRfwKBTwKs/vUZ7f2y0FlObx3xhzUrHfGHdSz42J+HefHdU3xlUXiSkccWM+OR2OxBRuiKk2EtWhuqj2hxs0Ib5RF3vW6Cv2mxph7+vUdu0zBdphShT2D5avoET5A4eqZEiZBg0ul503IC3mg27c+4PugQo0GiV1Q8HGGOngZumTv0h//7VU6xYxNOQk6Vt/kQJH3pGPxOK649n3UkcRJ00Ymq/TRgzQiIF5GjYgV2VFuSrI8SrX71HQ7z4HvPYRdxCMMXKMe+aGY4xijlFjrCnWI82m03N/fWM8EeduvIcj0dS+EI7EVHM4qgN1jfLU71eFZ62+Yr+ps+318lltnwWY1GB87mCBZ7CcvBLlFg1WUXGpSkvLlVc0RMopTMR9Mv6Dki9I7u9OkbB7BvTq37j3GZTcweFZL0vDJ3fLR/zlg136/uJ31RBtOnBiUNCviaMG6Pgh+SorylVpUa7b7vF7leO3lef3Ktfn6Zbc7yTyfjL+o3GTkeNT7ZzE38l9oSHqKNyQiPdkvm9Iz/0xVdc3qqquXqc7H2qG/ZYqPGs1xKrqeJuMpX0q1C6VqDYwVL6CEhUkc3/JUNl5A6VAQVPc+0OSP0/y5vRJ7s/KwrjjuAP76/+fexZo7LA7/whvIZCuvjGmbzy6Sh/saCqeJYu/pwwr1IiBeRpakKMhhTkamOdXjs+jXJ9HOX5bfk/35P644+Z8JzHdfvwn+wOODkfjqm2IKtwQU01Dsg3UlP9rG2I6WNeo/Iadusz+u77keVunWJvltdo+CzCpxuRqmxmsWu8gOXnFyh0wVAMHl6u0bLj8wSL3d9cfavYcpN3TneoPujl/9f9pukS47ZX+19tdviR+W55Zu013PPtexryywhxNHDVQE0rzNbQgR4Pz3X5vKMerHK+tXL9HOV5Pt/d548YoHjdpfd6Wfd2GtH2hPr2dk9H3jSbaPjGF68I6x6zTP3lW6Vz7fRVZrd8GIF3M2NppBmm3PVixnEHy5A9WUfFQlQwp16YtO3T62dPkzStKa/snHl6K5N0m1uie+fneM25xxEncuuSMmdI//bJbPmJvTYO+OX9Nxn3i/V5bE0cO0HFD8jViYJ5GFedpUDCgYMCjYMCrPL9XQb+nWw4GatHnbda/bavfezja1OavS2vr1KX1ew/WN+pAuFHl0Upd6lmti+w3dby9vVPbVWWC2mZKVOMbLG9+iYIDhqp4SJlKhpTJEyxO6++m5X1fHrm/uxgj1exwb5v09//ddInwgnLp5teOuDAouW3uB//ykX67YnPG/DElQZ0xYoBGDsrTsAF5Kh+Qq4IcX2Ksx90HurPPm2zzxBN93kjMUV1DRK++vkxnnjNVcdmZbaFE3k8f40lv/yfbQAfCjYrWH9IF1jpd7Fmj8+z3lGO1fdWzpIjxaqcZpD32YMXyBiunsESFg0o1tLTcHe/MKUzL+Wlt/268l/kxL3pY2v6WtPZx6cPnm+ZfudC9FWg3ePOzg7rld2tVlXYlvIIcryaOGqgTSvNVWpir8qJcDSnIUTDgtvlz/e5zd+T+5n3emGPSxjpb9nXT20N1kab2flPMN7V7qg9HdTB8WCfEPtYMz1pd1MXxzt2mWNWBIbLzh6hg4GCVDHZzvx0clNnn9QUZ7+xuyfHOj192b4+SPKB/zHTp+he67f85K8cL+hiF8Q786Ec/0gMPPKCnn35aV1+deYTH7t27VVpaqgsuuECvvfZap96PwnjPaJ4cjDF69u3tmvuXj7U/3HHROJ3fa8trW7Ik2ZYluf9kWZZsy322pNSPYfLhpH4ce+Ibts2rmE6xNutse70m2R/rRHuLSqzq7vsAj9+9bKnH5176w+NLzPOnTQfcIorlcS8PmPGw3Ge7tWVpy5OPVs5uaf1HpI/WM457BqeJp00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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "torch.manual_seed(0)\n", - "\n", - "# %% ground truth system\n", - "system = psl.systems['LotkaVolterra']\n", - "modelSystem = system()\n", - "ts = modelSystem.ts\n", - "nx = modelSystem.nx\n", - "raw = modelSystem.simulate(nsim=1000, ts=ts)\n", - "plot.pltOL(Y=raw['X'])\n", - "plot.pltPhase(X=raw['Y'])" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1000, 2)" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw['Y'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.1" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "class LotkaVolterraHybrid(ode.ODESystem):\n", - "\n", - " def __init__(self, block, insize=2, outsize=2):\n", - " \"\"\"\n", - "\n", - " :param block:\n", - " :param insize:\n", - " :param outsize:\n", - " \"\"\"\n", - " super().__init__(insize=insize, outsize=outsize)\n", - " self.block = block\n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " assert self.block.in_features == 2\n", - " assert self.block.out_features == 1\n", - "\n", - " def ode_equations(self, x):\n", - " x1 = x[:, [0]]\n", - " x2 = x[:, [-1]]\n", - " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", - " dx2 = self.delta*self.block(x) - self.gamma*x2\n", - " return torch.cat([dx1, dx2], dim=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 431, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train sim (1000, 2)\n", - "torch.Size([500, 2, 2])\n" - ] - } - ], - "source": [ - "nsim = 1000\n", - "nsteps = 2\n", - "bs = 10\n", - "train_loader, dev_loader, test_data, trainX = \\\n", - " get_data(modelSystem, nsim, nsteps, ts, bs)\n", - "\n", - "# construct UDE model in Neuromancer\n", - "net = blocks.MLP(2, 1, bias=True,\n", - " linear_map=torch.nn.Linear,\n", - " nonlin=torch.nn.GELU,\n", - " hsizes=4*[20])\n", - "fx = LotkaVolterraHybrid(net)\n", - "# integrate UDE model\n", - "#fxRK4 = integrators.RK4(fx, h=ts)\n", - "# create symbolic UDE model\n", - "#ude = Node(fxRK4, ['xn'], ['xn'], name='UDE')\n", - "#dynamics_model = System([ude])" - ] - }, - { - "cell_type": "code", - "execution_count": 433, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1000, 2])" - ] - }, - "execution_count": 433, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data['X'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 434, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 1, 2])" - ] - }, - "execution_count": 434, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo = next(iter(train_loader))['xn'].shape\n", - "foo" - ] - }, - { - "cell_type": "code", - "execution_count": 429, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train sim (2000, 2)\n", - "torch.Size([1000, 2, 2])\n" - ] - }, - { - "ename": "ValueError", - "evalue": "too many values to unpack (expected 3)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[429], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m train_loader, dev_loader, test_data \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 2\u001b[0m get_data(modelSystem, nsim, nsteps, ts, bs)\n\u001b[1;32m 4\u001b[0m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mshape\n", - "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)" - ] - } - ], - "source": [ - "train_loader, dev_loader, test_data = \\\n", - " get_data(modelSystem, nsim, nsteps, ts, bs)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 236, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1000, 2])" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data['X'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 235, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 2, 2])" - ] - }, - "execution_count": 235, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo['X'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 2, 2])" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo = next(iter(train_loader))\n", - "baz = dynamics_model(foo)\n", - "crow = baz['xn'][:, :-1, :]\n", - "zee = foo['X']\n", - "crow.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 203, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0.15577884018421173\n", - "epoch: 102 train_loss: 0.3836634159088135\n", - "epoch: 103 train_loss: 0.3684500455856323\n", - "epoch: 104 train_loss: 0.28714412450790405\n", - "epoch: 105 train_loss: 0.4594331383705139\n", - "epoch: 106 train_loss: 0.17900754511356354\n", - "epoch: 107 train_loss: 0.2360505312681198\n", - "epoch: 108 train_loss: 0.3810018301010132\n", - "epoch: 109 train_loss: 0.4112996757030487\n", - "epoch: 110 train_loss: 0.25561970472335815\n", - "epoch: 111 train_loss: 0.1432962864637375\n", - "epoch: 112 train_loss: 1.3921493291854858\n", - "epoch: 113 train_loss: 0.5470081567764282\n", - "epoch: 114 train_loss: 0.4255885183811188\n", - "epoch: 115 train_loss: 0.14385442435741425\n", - "epoch: 116 train_loss: 0.08258526772260666\n", - "epoch: 117 train_loss: 0.1857418566942215\n", - "epoch: 118 train_loss: 0.25271618366241455\n", - "epoch: 119 train_loss: 0.4440329372882843\n", - "epoch: 120 train_loss: 0.4315054714679718\n", - "epoch: 121 train_loss: 0.16495734453201294\n", - "epoch: 122 train_loss: 0.1989777684211731\n", - "epoch: 123 train_loss: 0.06788838654756546\n", - "epoch: 124 train_loss: 0.10921105742454529\n", - "epoch: 125 train_loss: 0.197842538356781\n", - "epoch: 126 train_loss: 0.6866052746772766\n", - "epoch: 127 train_loss: 0.36010658740997314\n", - "epoch: 128 train_loss: 0.12890280783176422\n", - "epoch: 129 train_loss: 0.0686657503247261\n", - "epoch: 130 train_loss: 0.14874637126922607\n", - "epoch: 131 train_loss: 0.7163857817649841\n", - "epoch: 132 train_loss: 0.17970648407936096\n", - "epoch: 133 train_loss: 0.04967895522713661\n", - "epoch: 134 train_loss: 0.1680101603269577\n", - "epoch: 135 train_loss: 0.11648871749639511\n", - "epoch: 136 train_loss: 0.1996196210384369\n", - "epoch: 137 train_loss: 0.11213837563991547\n", - "epoch: 138 train_loss: 0.1378120332956314\n", - "epoch: 139 train_loss: 0.11473117023706436\n", - "epoch: 140 train_loss: 0.10234798491001129\n", - "epoch: 141 train_loss: 0.07809299975633621\n", - "epoch: 142 train_loss: 0.4205518960952759\n", - "epoch: 143 train_loss: 0.2789265811443329\n", - "epoch: 144 train_loss: 0.1455356925725937\n", - "epoch: 145 train_loss: 0.20582439005374908\n", - "epoch: 146 train_loss: 0.15785151720046997\n", - "epoch: 147 train_loss: 0.21461555361747742\n", - "epoch: 148 train_loss: 0.23502787947654724\n", - "epoch: 149 train_loss: 0.9507098197937012\n", - "epoch: 150 train_loss: 0.4213985502719879\n", - "epoch: 151 train_loss: 0.1036115363240242\n", - "epoch: 152 train_loss: 0.1722414642572403\n", - "epoch: 153 train_loss: 0.12554770708084106\n", - "epoch: 154 train_loss: 0.17391566932201385\n", - "epoch: 155 train_loss: 0.46148595213890076\n", - "epoch: 156 train_loss: 0.25193721055984497\n", - "epoch: 157 train_loss: 0.3315165042877197\n", - "epoch: 158 train_loss: 0.11715126782655716\n", - "epoch: 159 train_loss: 0.5376226305961609\n", - "epoch: 160 train_loss: 0.2285894751548767\n", - "epoch: 161 train_loss: 0.4176816940307617\n", - "epoch: 162 train_loss: 0.1202869787812233\n", - "epoch: 163 train_loss: 0.09944035857915878\n", - "epoch: 164 train_loss: 0.3274291753768921\n", - "epoch: 165 train_loss: 0.09140706807374954\n", - "epoch: 166 train_loss: 0.22308826446533203\n", - "epoch: 167 train_loss: 0.16436980664730072\n", - "epoch: 168 train_loss: 0.05272998288273811\n", - "epoch: 169 train_loss: 0.09057249873876572\n", - "epoch: 170 train_loss: 0.38802826404571533\n", - "epoch: 171 train_loss: 0.44728174805641174\n", - "epoch: 172 train_loss: 0.13979801535606384\n", - "epoch: 173 train_loss: 0.4556901454925537\n", - "epoch: 174 train_loss: 0.14013370871543884\n", - "epoch: 175 train_loss: 0.13612936437129974\n", - "epoch: 176 train_loss: 0.11906873434782028\n", - "epoch: 177 train_loss: 0.06214645877480507\n", - "epoch: 178 train_loss: 0.2867833077907562\n", - "epoch: 179 train_loss: 0.30405566096305847\n", - "epoch: 180 train_loss: 0.0545855276286602\n", - "epoch: 181 train_loss: 0.28040555119514465\n", - "epoch: 182 train_loss: 0.1463025063276291\n", - "epoch: 183 train_loss: 0.08449428528547287\n", - "epoch: 184 train_loss: 0.34794628620147705\n", - "epoch: 185 train_loss: 0.6925814151763916\n", - "epoch: 186 train_loss: 0.15413250029087067\n", - "epoch: 187 train_loss: 0.07249744981527328\n", - "epoch: 188 train_loss: 0.09451506286859512\n", - "epoch: 189 train_loss: 0.39824146032333374\n", - "epoch: 190 train_loss: 0.178904190659523\n", - "epoch: 191 train_loss: 0.07271047681570053\n", - "epoch: 192 train_loss: 0.10113447159528732\n", - "epoch: 193 train_loss: 0.20800787210464478\n", - "epoch: 194 train_loss: 0.2562291622161865\n", - "epoch: 195 train_loss: 0.0981765016913414\n", - "Interrupted training loop.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 203, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = variable(\"X\")\n", - "xhat = variable('xn')[:, :-1, :]\n", - "\n", - "# trajectory tracking loss\n", - "reference_loss = (xhat == x)^2\n", - "reference_loss.name = \"ref_loss\"\n", - "\n", - "# finite difference variables\n", - "xFD = (x[:, 1:, :] - x[:, :-1, :])\n", - "xhatFD = (xhat[:, 1:, :] - xhat[:, :-1, :])\n", - "\n", - "# finite difference loss\n", - "fd_loss = 2.0*((xFD == xhatFD)^2)\n", - "fd_loss.name = 'FD_loss'\n", - "\n", - "# %%\n", - "objectives = [reference_loss, fd_loss]\n", - "constraints = []\n", - "# create constrained optimization loss\n", - "loss = PenaltyLoss(objectives, constraints)\n", - "# construct constrained optimization problem\n", - "problem = Problem([dynamics_model], loss)\n", - "# plot computational graph\n", - "problem.show()\n", - "\n", - "# %%\n", - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "\n", - "trainer = Trainer(\n", - " problem,\n", - " train_loader,\n", - " dev_loader,\n", - " test_data,\n", - " optimizer,\n", - " patience=5000,\n", - " warmup=5000,\n", - " epochs=500,\n", - " eval_metric=\"dev_loss\",\n", - " train_metric=\"train_loss\",\n", - " dev_metric=\"dev_loss\",\n", - " test_metric=\"dev_loss\",\n", - " device='cpu'\n", - ")\n", - "# %%\n", - "best_model = trainer.train()\n", - "problem.load_state_dict(best_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 361, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learned parameter a= 0.10000000149011612\n", - "Learned parameter b= 0.10000000149011612\n", - "Learned parameter c= 0.10000000149011612\n", - "Learned parameter d= 0.10000000149011612\n", - "True parameter a= 1.0\n", - "True parameter b= 0.10000000149011612\n", - "True parameter c= 1.5\n", - "True parameter d= 0.75\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Batch sizes not consistent.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[361], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend(fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Test set results\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m pred_traj \u001b[38;5;241m=\u001b[39m test_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m][:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, :]\n\u001b[1;32m 27\u001b[0m true_traj \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", - "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print('Learned parameter a=', float(fx.alpha))\n", - "print('Learned parameter b=', float(fx.beta))\n", - "print('Learned parameter c=', float(fx.gamma))\n", - "print('Learned parameter d=', float(fx.delta))\n", - "\n", - "print('True parameter a=', float(modelSystem.a))\n", - "print('True parameter b=', float(modelSystem.b))\n", - "print('True parameter c=', float(modelSystem.c))\n", - "print('True parameter d=', float(modelSystem.d))\n", - "\n", - "# evaluate learned black box block\n", - "x1 = torch.arange(0., 150., 0.5)\n", - "x2 = torch.arange(0., 150., 0.5)\n", - "true_block = x1*x2\n", - "learned_block = net(torch.stack([x1, x2]).T).squeeze()\n", - "plt.figure()\n", - "plt.plot(true_block.detach().numpy(), 'c',\n", - " linewidth=4.0, label='True')\n", - "plt.plot(learned_block.detach().numpy(), 'm--',\n", - " linewidth=4.0, label='Learned')\n", - "plt.legend(fontsize=25)\n", - "\n", - "# Test set results\n", - "test_outputs = dynamics_model(test_data)\n", - "\n", - "pred_traj = test_outputs['xn'][:, :-1, :]\n", - "true_traj = test_data['X']\n", - "pred_traj = pred_traj.detach().numpy().reshape(-1, nx)\n", - "true_traj = true_traj.detach().numpy().reshape(-1, nx)\n", - "pred_traj, true_traj = pred_traj.transpose(1, 0), true_traj.transpose(1, 0)\n", - "\n", - "figsize = 25\n", - "fig, ax = plt.subplots(nx, figsize=(figsize, figsize))\n", - "labels = [f'$y_{k}$' for k in range(len(true_traj))]\n", - "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, labels)):\n", - " if nx > 1:\n", - " axe = ax[row]\n", - " else:\n", - " axe = ax\n", - " axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n", - " axe.plot(t1, 'c', linewidth=4.0, label='True')\n", - " axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n", - " axe.tick_params(labelbottom=False, labelsize=figsize)\n", - "axe.tick_params(labelbottom=True, labelsize=figsize)\n", - "axe.legend(fontsize=figsize)\n", - "axe.set_xlabel('$time$', fontsize=figsize)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 233, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'X': tensor([[[ 2.1548, 88.2591],\n", - " [ 1.0439, 76.8354],\n", - " [ 0.5640, 66.5173],\n", - " ...,\n", - " [ 2.5745, 90.9051],\n", - " [ 1.2156, 79.3024],\n", - " [ 0.6416, 68.7135]]]),\n", - " 'xn': tensor([[[ 2.1548, 88.2591]]])}" - ] - }, - "execution_count": 233, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data" - ] - }, - { - "cell_type": "code", - "execution_count": 213, - "metadata": {}, - "outputs": [], - "source": [ - "class LotkaVolterraHybrid(ode.ODESystem):\n", - "\n", - " def __init__(self, block, insize=2, outsize=2):\n", - " \"\"\"\n", - "\n", - " :param block:\n", - " :param insize:\n", - " :param outsize:\n", - " \"\"\"\n", - " super().__init__(insize=insize, outsize=outsize)\n", - " self.block = block\n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " assert self.block.in_features == 2\n", - " assert self.block.out_features == 1\n", - "\n", - " def ode_equations(self, x):\n", - " x1 = x[:, [0]]\n", - " x2 = x[:, [-1]]\n", - " dx1 = self.alpha*x1 - self.beta*self.block(x)\n", - " dx2 = self.delta*self.block(x) - self.gamma*x2\n", - " return torch.cat([dx1, dx2], dim=-1)" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "metadata": {}, - "outputs": [], - "source": [ - "import torchsde\n", - "\n", - "class LotkaVolterraSDE(nn.Module):\n", - " def __init__(self, block, insize=2, outside=2, noise_type=\"diagonal\"):\n", - " super().__init__()\n", - " self.block = block \n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.in_features = 0\n", - " self.out_features = 0\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - "\n", - " def f(self, t, y):\n", - "\n", - " x1 = y[:, [0]]\n", - " x2 = y[:, [-1]]\n", - "\n", - " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", - " dx2 = self.delta*self.block(y) - self.gamma*x2\n", - "\n", - " return torch.cat([dx1, dx2], dim=-1)\n", - "\n", - " def g(self, t, y):\n", - " return torch.zeros(10, 2)\n", - "\n", - "# construct UDE model in Neuromancer\n", - "net = blocks.MLP(2, 1, bias=True,\n", - " linear_map=torch.nn.Linear,\n", - " nonlin=torch.nn.GELU,\n", - " hsizes=4*[20])\n", - "fx = LotkaVolterraSDE(block=net)\n", - "\n", - "\n", - "class BasicSDEIntegrator(integrators.Integrator): \n", - " \"\"\"\n", - " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", - " Returns a single tensor of size (t, batch_size, state_size).\n", - "\n", - " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", - " Currently only supports Euler integration. Choice of integration method is dependent \n", - " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", - " \"\"\"\n", - " def __init__(self, block, numsteps ): \n", - " \"\"\"\n", - " :param block: (nn.Module) The BasicSDE block\n", - " \"\"\"\n", - " super().__init__(block) \n", - " self.numsteps = numsteps \n", - "\n", - " def integrate(self, x): \n", - " \"\"\"\n", - " x is the initial datastate of size (batch_size, state_size)\n", - " t is the time-step vector over which to integrate\n", - " \"\"\"\n", - " t = torch.tensor([0.,0.1, 0.2], dtype=torch.float32)\n", - " x = x.squeeze(1) #remove time step \n", - " \n", - " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", - " ys = ys.permute(1, 0, 2)\n", - " return ys \n", - "\n", - "integrator = BasicSDEIntegrator(fx, numsteps=2) \n", - "# integrate UDE model\n", - "# create symbolic UDE model\n", - "model = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", - "dynamics_model = model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1, 2])" - ] - }, - "execution_count": 238, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data['xn'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 1, 2])" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo['xn'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 226, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 3, 2])" - ] - }, - "execution_count": 226, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo = next(iter(train_loader))\n", - "\n", - "baz = dynamics_model(foo)\n", - "baz['xn'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 230, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epoch: 0 train_loss: 133.12298583984375\n", - "epoch: 1 train_loss: 132.64720153808594\n", - "epoch: 2 train_loss: 130.9735565185547\n", - "epoch: 3 train_loss: 126.02873229980469\n", - "epoch: 4 train_loss: 118.0386734008789\n", - "epoch: 5 train_loss: 113.14652252197266\n", - "epoch: 6 train_loss: 110.42718505859375\n", - "epoch: 7 train_loss: 104.79252624511719\n", - "epoch: 8 train_loss: 100.4343032836914\n", - "epoch: 9 train_loss: 96.34988403320312\n", - "epoch: 10 train_loss: 87.50508880615234\n", - "epoch: 11 train_loss: 81.53534698486328\n", - "epoch: 12 train_loss: 73.87696838378906\n", - "epoch: 13 train_loss: 64.91850280761719\n", - "epoch: 14 train_loss: 57.168968200683594\n", - "epoch: 15 train_loss: 49.983306884765625\n", - "epoch: 16 train_loss: 41.7336311340332\n", - "epoch: 17 train_loss: 35.65283203125\n", - "epoch: 18 train_loss: 29.61258316040039\n", - "epoch: 19 train_loss: 22.141714096069336\n", - "epoch: 20 train_loss: 15.57487678527832\n", - "epoch: 21 train_loss: 10.8868989944458\n", - "epoch: 22 train_loss: 8.614974975585938\n", - "epoch: 23 train_loss: 8.053958892822266\n", - "epoch: 24 train_loss: 7.4709153175354\n", - "epoch: 25 train_loss: 6.822552680969238\n", - "epoch: 26 train_loss: 6.289606094360352\n", - "epoch: 27 train_loss: 5.890585422515869\n", - "epoch: 28 train_loss: 5.6151933670043945\n", - "epoch: 29 train_loss: 5.645315647125244\n", - "epoch: 30 train_loss: 5.033112525939941\n", - "epoch: 31 train_loss: 4.737390041351318\n", - "epoch: 32 train_loss: 4.485416412353516\n", - "epoch: 33 train_loss: 4.714532375335693\n", - "epoch: 34 train_loss: 4.0359320640563965\n", - "epoch: 35 train_loss: 3.7806291580200195\n", - "epoch: 36 train_loss: 3.9372406005859375\n", - "epoch: 37 train_loss: 3.717404365539551\n", - "epoch: 38 train_loss: 3.304453134536743\n", - "epoch: 39 train_loss: 2.779893159866333\n", - "epoch: 40 train_loss: 2.547934055328369\n", - "epoch: 41 train_loss: 2.3251867294311523\n", - "epoch: 42 train_loss: 2.286116123199463\n", - "epoch: 43 train_loss: 2.093520402908325\n", - "epoch: 44 train_loss: 2.443225622177124\n", - "epoch: 45 train_loss: 1.875758409500122\n", - "epoch: 46 train_loss: 2.1676034927368164\n", - "epoch: 47 train_loss: 1.836241602897644\n", - "epoch: 48 train_loss: 1.4220737218856812\n", - "epoch: 49 train_loss: 1.3957267999649048\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 230, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = variable(\"X\")\n", - "xhat = variable('xn')[:, :-1, :]\n", - "\n", - "# trajectory tracking loss\n", - "reference_loss = (xhat == x)^2\n", - "reference_loss.name = \"ref_loss\"\n", - "\n", - "\n", - "\n", - "# finite difference loss\n", - "fd_loss = 2.0*((xFD == xhatFD)^2)\n", - "fd_loss.name = 'FD_loss'\n", - "\n", - "# %%\n", - "objectives = [reference_loss, fd_loss]\n", - "constraints = []\n", - "# create constrained optimization loss\n", - "loss = PenaltyLoss(objectives, constraints)\n", - "# construct constrained optimization problem\n", - "problem = Problem([dynamics_model], loss)\n", - "# plot computational graph\n", - "problem.show()\n", - "\n", - "# %%\n", - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "trainer = Trainer(\n", - " problem,\n", - " train_loader,\n", - " dev_loader,\n", - " test_data,\n", - " optimizer,\n", - " patience=50,\n", - " warmup=0,\n", - " epochs=50,\n", - " eval_metric=\"dev_loss\",\n", - " train_metric=\"train_loss\",\n", - " dev_metric=\"dev_loss\",\n", - " test_metric=\"dev_loss\",\n", - " device='cpu', \n", - " epoch_verbose=1\n", - ")\n", - "# %%\n", - "best_model = trainer.train()\n", - "problem.load_state_dict(best_model)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 243, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learned parameter a= 0.7175129055976868\n", - "Learned parameter b= 0.33722737431526184\n", - "Learned parameter c= 1.1291733980178833\n", - "Learned parameter d= 0.24657443165779114\n", - "True parameter a= 1.0\n", - "True parameter b= 0.10000000149011612\n", - "True parameter c= 1.5\n", - "True parameter d= 0.75\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Batch sizes not consistent.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[243], line 26\u001b[0m\n\u001b[1;32m 24\u001b[0m new_test_data \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 25\u001b[0m new_test_data_dict \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m: new_test_data}\n\u001b[0;32m---> 26\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_test_data_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHI\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 29\u001b[0m pred_traj \u001b[38;5;241m=\u001b[39m test_outputs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxn\u001b[39m\u001b[38;5;124m'\u001b[39m][:, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, :]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", - "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print('Learned parameter a=', float(fx.alpha))\n", - "print('Learned parameter b=', float(fx.beta))\n", - "print('Learned parameter c=', float(fx.gamma))\n", - "print('Learned parameter d=', float(fx.delta))\n", - "\n", - "print('True parameter a=', float(modelSystem.a))\n", - "print('True parameter b=', float(modelSystem.b))\n", - "print('True parameter c=', float(modelSystem.c))\n", - "print('True parameter d=', float(modelSystem.d))\n", - "\n", - "# evaluate learned black box block\n", - "x1 = torch.arange(0., 150., 0.5)\n", - "x2 = torch.arange(0., 150., 0.5)\n", - "true_block = x1*x2\n", - "learned_block = net(torch.stack([x1, x2]).T).squeeze()\n", - "plt.figure()\n", - "plt.plot(true_block.detach().numpy(), 'c',\n", - " linewidth=4.0, label='True')\n", - "plt.plot(learned_block.detach().numpy(), 'm--',\n", - " linewidth=4.0, label='Learned')\n", - "plt.legend(fontsize=25)\n", - "\n", - "# Test set results\n", - "new_test_data = test_data['xn']\n", - "new_test_data_dict = {'xn': new_test_data}\n", - "test_outputs = dynamics_model(new_test_data_dict)\n", - "print(\"HI\")\n", - "\n", - "pred_traj = test_outputs['xn'][:, :-1, :]\n", - "true_traj = test_data['X']\n", - "pred_traj = pred_traj.detach().numpy().reshape(-1, nx)\n", - "true_traj = true_traj.detach().numpy().reshape(-1, nx)\n", - "pred_traj, true_traj = pred_traj.transpose(1, 0), true_traj.transpose(1, 0)\n", - "\n", - "figsize = 25\n", - "fig, ax = plt.subplots(nx, figsize=(figsize, figsize))\n", - "labels = [f'$y_{k}$' for k in range(len(true_traj))]\n", - "for row, (t1, t2, label) in enumerate(zip(true_traj, pred_traj, labels)):\n", - " if nx > 1:\n", - " axe = ax[row]\n", - " else:\n", - " axe = ax\n", - " axe.set_ylabel(label, rotation=0, labelpad=20, fontsize=figsize)\n", - " axe.plot(t1, 'c', linewidth=4.0, label='True')\n", - " axe.plot(t2, 'm--', linewidth=4.0, label='Pred')\n", - " axe.tick_params(labelbottom=False, labelsize=figsize)\n", - "axe.tick_params(labelbottom=True, labelsize=figsize)\n", - "axe.legend(fontsize=figsize)\n", - "axe.set_xlabel('$time$', fontsize=figsize)\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['train_X', 'train_xn', 'train_name', 'train_140361679634384', 'train_140361679634384_eq_X', 'train_140361679634384_eq_X_value', 'train_140361679634384_eq_X_violation', 'train_objective_loss', 'train_penalty_loss', 'train_loss'])" - ] - }, - "execution_count": 174, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "www = problem(foo)\n", - "www.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "metadata": {}, - "outputs": [], - "source": [ - "def get_data(sys, nsim, nsteps, ts, bs):\n", - " \"\"\"\n", - " :param nsteps: (int) Number of timesteps for each batch of training data\n", - " :param sys: (psl.system)\n", - " :param ts: (float) step size\n", - " :param bs: (int) batch size\n", - "\n", - " \"\"\"\n", - " train_sim, dev_sim, test_sim = [sys.simulate(nsim=nsim, ts=ts) for i in range(3)]\n", - " nx = sys.nx\n", - " nbatch = nsim//nsteps\n", - " length = (nsim//nsteps) * nsteps\n", - " ts = torch.linspace(0,1,nsteps)\n", - "\n", - " trainX = train_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " trainX = torch.tensor(trainX, dtype=torch.float32)\n", - "\n", - "\n", - "\n", - " train_data = DictDataset({'X': trainX, 'xn': trainX[:, 0:1, :]}, name='train')\n", - " train_loader = DataLoader(train_data, batch_size=bs,\n", - " collate_fn=train_data.collate_fn, shuffle=True)\n", - "\n", - " devX = dev_sim['X'][:length].reshape(nbatch, nsteps, nx)\n", - " devX = torch.tensor(devX, dtype=torch.float32)\n", - " dev_data = DictDataset({'X': devX, 'xn': devX[:, 0:1, :]}, name='dev')\n", - " dev_loader = DataLoader(dev_data, batch_size=bs,\n", - " collate_fn=dev_data.collate_fn, shuffle=True)\n", - "\n", - " testX = test_sim['X'][:length].reshape(1, nsim, nx)\n", - " testX = torch.tensor(testX, dtype=torch.float32)\n", - " test_data = {'X': testX, 'xn': testX[:, 0:1, :]}\n", - "\n", - " return train_data, dev_data, test_data, bs" - ] - }, - { - "cell_type": "code", - "execution_count": 229, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[229], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneuromancer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrainer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer, LitTrainer\n\u001b[1;32m 2\u001b[0m lit_trainer \u001b[38;5;241m=\u001b[39m LitTrainer(epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m, accelerator\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m, train_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m 3\u001b[0m dev_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, eval_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, test_metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdev_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, custom_optimizer\u001b[38;5;241m=\u001b[39moptimizer)\n\u001b[0;32m----> 7\u001b[0m \u001b[43mlit_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproblem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_setup_function\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodelSystem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnsteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnsteps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/trainer.py:178\u001b[0m, in \u001b[0;36mLitTrainer.fit\u001b[0;34m(self, problem, data_setup_function, **kwargs)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, problem, data_setup_function, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 170\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124;03m Fits (trains) a base neuromancer Problem to a data defined by a data setup function). \u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;124;03m This function will also instantiate a Lightning version of the provided Problem \u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;124;03m :param data_setup_function: A function that returns train/dev/test Neuromancer DictDatasets as well as batch_size to use\u001b[39;00m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 178\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproblem_copy \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproblem\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_setup_function \u001b[38;5;241m=\u001b[39m data_setup_function\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlit_problem \u001b[38;5;241m=\u001b[39m LitProblem(problem,\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_metric, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdev_metric, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtest_metric, custom_training_step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcustom_training_step, hparam_config\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhparam_config )\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (1 times)]\u001b[0m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (1 times)]\u001b[0m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - " \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (8 times), _deepcopy_dict at line 231 (4 times), _reconstruct at line 271 (4 times), deepcopy at line 146 (4 times), _reconstruct at line 297 (3 times)]\u001b[0m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:297\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m dictiter:\n\u001b[1;32m 296\u001b[0m key \u001b[38;5;241m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 297\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m y[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_reconstruct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrv\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m x:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:271\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m deep:\n\u001b[0;32m--> 271\u001b[0m state \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(y, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__setstate__\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 273\u001b[0m y\u001b[38;5;241m.\u001b[39m__setstate__(state)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[38;5;241m=\u001b[39m _deepcopy_dispatch\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;28mtype\u001b[39m):\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:231\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 229\u001b[0m memo[\u001b[38;5;28mid\u001b[39m(x)] \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 231\u001b[0m y[deepcopy(key, memo)] \u001b[38;5;241m=\u001b[39m \u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/copy.py:153\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 151\u001b[0m copier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(x, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__deepcopy__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copier \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mcopier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 155\u001b[0m reductor \u001b[38;5;241m=\u001b[39m dispatch_table\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mcls\u001b[39m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/_tensor.py:86\u001b[0m, in \u001b[0;36mTensor.__deepcopy__\u001b[0;34m(self, memo)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(Tensor\u001b[38;5;241m.\u001b[39m__deepcopy__, (\u001b[38;5;28mself\u001b[39m,), \u001b[38;5;28mself\u001b[39m, memo)\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_leaf:\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 87\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOnly Tensors created explicitly by the user \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(graph leaves) support the deepcopy protocol at the moment. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you were attempting to deepcopy a module, this may be because \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof a torch.nn.utils.weight_norm usage, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msee https://github.com/pytorch/pytorch/pull/103001\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 92\u001b[0m )\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m memo:\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m memo[\u001b[38;5;28mid\u001b[39m(\u001b[38;5;28mself\u001b[39m)]\n", - "\u001b[0;31mRuntimeError\u001b[0m: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see https://github.com/pytorch/pytorch/pull/103001" - ] - } - ], - "source": [ - "from neuromancer.trainer import Trainer, LitTrainer\n", - "lit_trainer = LitTrainer(epochs=50, accelerator='cpu', train_metric='train_loss', \n", - " dev_metric='dev_loss', eval_metric='dev_loss', test_metric='dev_loss', custom_optimizer=optimizer)\n", - "\n", - "\n", - "\n", - "lit_trainer.fit(problem=problem, data_setup_function=get_data, sys=modelSystem, nsim=nsim, nsteps=nsteps, ts=ts, bs=bs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate Stochastic Lotka Volterra System Data" - ] - }, - { - "cell_type": "code", - "execution_count": 420, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE torch.Size([1, 2])\n", - "FOO SHAPE 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"KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[420], line 47\u001b[0m\n\u001b[1;32m 43\u001b[0m x0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m10.0\u001b[39m, \u001b[38;5;241m10.0\u001b[39m])\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m) \u001b[38;5;66;03m#[1x2]\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# Integrate the SDE model\u001b[39;00m\n\u001b[0;32m---> 47\u001b[0m sol_train \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_span\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m sol_dev \u001b[38;5;241m=\u001b[39m torchsde\u001b[38;5;241m.\u001b[39msdeint(sde, x0, t_span, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meuler\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 49\u001b[0m sol_test \u001b[38;5;241m=\u001b[39m torchsde\u001b[38;5;241m.\u001b[39msdeint(sde, x0, t_span, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124meuler\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:110\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extra_solver_state \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 109\u001b[0m extra_solver_state \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39minit_extra_solver_state(ts[\u001b[38;5;241m0\u001b[39m], y0)\n\u001b[0;32m--> 110\u001b[0m ys, extra_solver_state \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_solver_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parse_return(y0, ys, extra_solver_state, extra, logqp)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/base_solver.py:145\u001b[0m, in \u001b[0;36mBaseSDESolver.integrate\u001b[0;34m(self, y0, ts, extra0)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 144\u001b[0m prev_t, prev_y \u001b[38;5;241m=\u001b[39m curr_t, curr_y\n\u001b[0;32m--> 145\u001b[0m curr_y, curr_extra \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurr_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnext_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurr_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurr_extra\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 146\u001b[0m curr_t \u001b[38;5;241m=\u001b[39m next_t\n\u001b[1;32m 147\u001b[0m ys\u001b[38;5;241m.\u001b[39mappend(interp\u001b[38;5;241m.\u001b[39mlinear_interp(t0\u001b[38;5;241m=\u001b[39mprev_t, y0\u001b[38;5;241m=\u001b[39mprev_y, t1\u001b[38;5;241m=\u001b[39mcurr_t, y1\u001b[38;5;241m=\u001b[39mcurr_y, t\u001b[38;5;241m=\u001b[39mout_t))\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/methods/euler.py:32\u001b[0m, in \u001b[0;36mEuler.step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m extra0\n\u001b[1;32m 31\u001b[0m dt \u001b[38;5;241m=\u001b[39m t1 \u001b[38;5;241m-\u001b[39m t0\n\u001b[0;32m---> 32\u001b[0m I_k \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m f, g_prod \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msde\u001b[38;5;241m.\u001b[39mf_and_g_prod(t0, y0, I_k)\n\u001b[1;32m 36\u001b[0m y1 \u001b[38;5;241m=\u001b[39m y0 \u001b[38;5;241m+\u001b[39m f \u001b[38;5;241m*\u001b[39m dt \u001b[38;5;241m+\u001b[39m g_prod\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# Define the Lotka-Volterra SDE\n", - "class LotkaVolterraSDE(nn.Module):\n", - " def __init__(self, a, b, c, d, sigma1, sigma2):\n", - " super().__init__()\n", - " self.a = a\n", - " self.b = b\n", - " self.c = c\n", - " self.d = d\n", - " self.sigma1 = sigma1\n", - " self.sigma2 = sigma2\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - "\n", - " def f(self, t, x):\n", - " x1 = x[:,[0]]\n", - " x2 = x[:,[1]]\n", - " dx1 = self.a * x1 - self.b * x1*x2\n", - " dx2 = self.c * x1*x2 - self.d * x2\n", - " foo = torch.cat([dx1, dx2], dim=-1)\n", - " print(\"FOO SHAPE \", foo.shape)\n", - " return torch.cat([dx1, dx2], dim=-1)\n", - "\n", - " def g(self, t, x):\n", - " sigma_diag = torch.tensor([[self.sigma1, self.sigma2]])\n", - " return sigma_diag #[batch_size x state size ]\n", - "\n", - "# Define parameters\n", - "a = 1.1 # Prey growth rate\n", - "b = 0.4 # Predation rate\n", - "c = 0.1 # Predator growth rate\n", - "d = 0.4 # Predator death rate\n", - "sigma1 = 1\n", - "sigma2 = 0\n", - "\n", - "# Create the SDE model\n", - "sde = LotkaVolterraSDE(a, b, c, d, sigma1, sigma2)\n", - "\n", - "\n", - "# Define time span\n", - "t_span = torch.linspace(0, 20, 2000)\n", - "\n", - "# Initial condition\n", - "x0 = torch.tensor([10.0, 10.0]).unsqueeze(0) #[1x2]\n", - "\n", - "\n", - "# Integrate the SDE model\n", - "sol_train = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "sol_dev = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "sol_test = torchsde.sdeint(sde, x0, t_span, method='euler')\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 378, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(0.0100)" - ] - }, - "execution_count": 378, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_span[1] - t_span[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([2000, 1, 2])" - ] - }, - "execution_count": 369, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sol.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 421, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(t_span, sol_train[:, 0,0], label='Prey (x1)')\n", - "plt.plot(t_span, sol_train[:,0, 1], label='Predator (x2)')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Population')\n", - "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 385, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(t_span, sol_dev[:, 0,0], label='Prey (x1)')\n", - "plt.plot(t_span, sol_dev[:,0, 1], label='Predator (x2)')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Population')\n", - "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 422, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the results\n", - "plt.figure(figsize=(10, 6))\n", - "plt.plot(t_span, sol_test[:, 0,0], label='Prey (x1)')\n", - "plt.plot(t_span, sol_test[:,0, 1], label='Predator (x2)')\n", - "plt.xlabel('Time')\n", - "plt.ylabel('Population')\n", - "plt.title('Stochastic Lotka-Volterra Predator-Prey Model')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 439, - "metadata": {}, - "outputs": [], - "source": [ - "nx = 2\n", - "nsim = 2000\n", - "nsteps = 2\n", - "nbatch = nsim//nsteps\n", - "length = (nsim//nsteps) * nsteps\n", - "bs = 10\n", - "x_train_lvs = sol_train.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", - "train_data_lvs = DictDataset({'X': x_train_lvs, 'xn': x_train_lvs[:, 0:1, :]}, name='train')\n", - "train_loader_lvs = DataLoader(train_data_lvs, batch_size=bs,\n", - " collate_fn=train_data_lvs.collate_fn, shuffle=True)\n", - "\n", - "x_dev_lvs = sol_test.squeeze(1)[:length].reshape(nbatch, nsteps, nx)\n", - "dev_data_lvs = DictDataset({'X': x_dev_lvs, 'xn': x_dev_lvs[:, 0:1, :]}, name='dev')\n", - "dev_loader_lvs = DataLoader(dev_data_lvs, batch_size=bs,\n", - " collate_fn=dev_data_lvs.collate_fn, shuffle=True)\n", - "\n", - "x_test_lvs = sol_test.squeeze(1)[:length].reshape(1, nsim, nx)\n", - "test_data_lvs = DictDataset({'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}, name='test')\n", - "test_data_lvs_2 = {'X': x_test_lvs, 'xn': x_test_lvs[:, 0:1, :]}" - ] - }, - { - "cell_type": "code", - "execution_count": 444, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1, 2])" - ] - }, - "execution_count": 444, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data_lvs_2['xn'].shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train SDE on this system" - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "metadata": {}, - "outputs": [], - "source": [ - "class LotkaVolterraSDE(nn.Module):\n", - " def __init__(self, block, insize=2, outside=2, noise_type=\"diagonal\"):\n", - " super().__init__()\n", - " self.block = block \n", - " self.alpha = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.beta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.delta = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.gamma = nn.Parameter(torch.tensor([.10]), requires_grad=True)\n", - " self.g_params = nn.Parameter(torch.randn(10, 2), requires_grad=True) # Learnable parameters\n", - " self.in_features = 0\n", - " self.out_features = 0\n", - " self.noise_type = \"diagonal\"\n", - " self.sde_type = \"ito\"\n", - "\n", - " def f(self, t, y):\n", - "\n", - " x1 = y[:, [0]]\n", - " x2 = y[:, [-1]]\n", - "\n", - " dx1 = self.alpha*x1 - self.beta*self.block(y)\n", - " dx2 = self.delta*self.block(y) - self.gamma*x2\n", - "\n", - " return torch.cat([dx1, dx2], dim=-1)\n", - "\n", - " def g(self, t, y):\n", - " return self.g_params\n", - "\n", - "# construct UDE model in Neuromancer\n", - "net = blocks.MLP(2, 1, bias=True,\n", - " linear_map=torch.nn.Linear,\n", - " nonlin=torch.nn.GELU,\n", - " hsizes=4*[20])\n", - "fx = LotkaVolterraSDE(block=net)\n", - "\n", - "\n", - "class BasicSDEIntegrator(integrators.Integrator): \n", - " \"\"\"\n", - " Integrator (from TorchSDE) for basic/explicit SDE case where drift (f) and diffusion (g) terms are defined \n", - " Returns a single tensor of size (t, batch_size, state_size).\n", - "\n", - " Please see https://github.com/google-research/torchsde/blob/master/torchsde/_core/sdeint.py\n", - " Currently only supports Euler integration. Choice of integration method is dependent \n", - " on integral type (Ito/Stratanovich) and drift/diffusion terms\n", - " \"\"\"\n", - " def __init__(self, block ): \n", - " \"\"\"\n", - " :param block: (nn.Module) The BasicSDE block\n", - " \"\"\"\n", - " super().__init__(block) \n", - "\n", - "\n", - " def integrate(self, x): \n", - " \"\"\"\n", - " x is the initial datastate of size (batch_size, state_size)\n", - " t is the time-step vector over which to integrate\n", - " \"\"\"\n", - " t = torch.tensor([0.,0.01, 0.02], dtype=torch.float32)\n", - " x = x.squeeze(1) #remove time step \n", - " \n", - " ys = torchsde.sdeint(self.block, x, t, method='euler')\n", - " ys = ys.permute(1, 0, 2)\n", - " return ys \n", - "\n", - "integrator = BasicSDEIntegrator(fx) \n", - "# integrate UDE model\n", - "# create symbolic UDE model\n", - "model_sde = Node(integrator, input_keys=['xn'], output_keys=['xn'])\n", - "dynamics_model_sde = model" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epoch: 0 train_loss: 0.023017268627882004\n", - "epoch: 1 train_loss: 0.018558792769908905\n", - "epoch: 2 train_loss: 0.016840744763612747\n", - "epoch: 3 train_loss: 0.0161198228597641\n", - "epoch: 4 train_loss: 0.015907132998108864\n", - "epoch: 5 train_loss: 0.01590757444500923\n", - "epoch: 6 train_loss: 0.01598076894879341\n", - "epoch: 7 train_loss: 0.015865400433540344\n", - "epoch: 8 train_loss: 0.015806077048182487\n", - "epoch: 9 train_loss: 0.0158010795712471\n", - "epoch: 10 train_loss: 0.015765946358442307\n", - "epoch: 11 train_loss: 0.0157708078622818\n", - "epoch: 12 train_loss: 0.01589883863925934\n", - "epoch: 13 train_loss: 0.0157649964094162\n", - "epoch: 14 train_loss: 0.015724308788776398\n", - "epoch: 15 train_loss: 0.015662459656596184\n", - "epoch: 16 train_loss: 0.015650611370801926\n", - "epoch: 17 train_loss: 0.015621047466993332\n", - "epoch: 18 train_loss: 0.015543315559625626\n", - "epoch: 19 train_loss: 0.015556756407022476\n", - "epoch: 20 train_loss: 0.015441193245351315\n", - "epoch: 21 train_loss: 0.015387657098472118\n", - "epoch: 22 train_loss: 0.015203121118247509\n", - "epoch: 23 train_loss: 0.014927961863577366\n", - "epoch: 24 train_loss: 0.014727238565683365\n", - "epoch: 25 train_loss: 0.014125929214060307\n", - "epoch: 26 train_loss: 0.013562526553869247\n", - "epoch: 27 train_loss: 0.013236057944595814\n", - "epoch: 28 train_loss: 0.013077814131975174\n", - "epoch: 29 train_loss: 0.013006513938307762\n", - "Interrupted training loop.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 398, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = variable(\"X\")\n", - "xhat = variable('xn')[:, :-1, :]\n", - "\n", - "# trajectory tracking loss\n", - "reference_loss = (xhat == x)^2\n", - "reference_loss.name = \"ref_loss\"\n", - "\n", - "\n", - "\n", - "# finite difference loss\n", - "fd_loss = 2.0*((xFD == xhatFD)^2)\n", - "fd_loss.name = 'FD_loss'\n", - "\n", - "# %%\n", - "objectives = [reference_loss, fd_loss]\n", - "constraints = []\n", - "# create constrained optimization loss\n", - "loss = PenaltyLoss(objectives, constraints)\n", - "# construct constrained optimization problem\n", - "problem = Problem([dynamics_model_sde], loss)\n", - "# plot computational graph\n", - "problem.show()\n", - "\n", - "# %%\n", - "optimizer = torch.optim.Adam(problem.parameters(), lr=0.001)\n", - "trainer = Trainer(\n", - " problem,\n", - " train_loader_lvs,\n", - " dev_loader_lvs,\n", - " test_data_lvs,\n", - " optimizer,\n", - " patience=50,\n", - " warmup=0,\n", - " epochs=50,\n", - " eval_metric=\"dev_loss\",\n", - " train_metric=\"train_loss\",\n", - " dev_metric=\"dev_loss\",\n", - " test_metric=\"dev_loss\",\n", - " device='cpu', \n", - " epoch_verbose=1\n", - ")\n", - "# %%\n", - "best_model = trainer.train()\n", - "problem.load_state_dict(best_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 426, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10, 1, 2])" - ] - }, - "execution_count": 426, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "foo = next(iter(train_loader_lvs))\n", - "foo['xn'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 425, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1000, 1, 2])" - ] - }, - "execution_count": 425, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_data_lvs_2['xn'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 427, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Batch sizes not consistent.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[427], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m test_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mdynamics_model_sde\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data_lvs_2\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/system.py:50\u001b[0m, in \u001b[0;36mNode.forward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;124;03mThis call function wraps the callable to receive/send dictionaries of Tensors\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \n\u001b[1;32m 46\u001b[0m \u001b[38;5;124;03m:param datadict: (dict {str: Tensor}) input to callable with associated input_keys\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m:return: (dict {str: Tensor}) Output of callable with associated output_keys\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 49\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [data[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_keys]\n\u001b[0;32m---> 50\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 52\u001b[0m output \u001b[38;5;241m=\u001b[39m [output]\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Library/CloudStorage/OneDrive-PNNL/Documents/neuromancer/neuromancer/src/neuromancer/dynamics/integrators.py:40\u001b[0m, in \u001b[0;36mIntegrator.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;124;03m This function needs x only for autonomous systems. x is 2D.\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;124;03m It needs x and u for nonautonomous system w/ online interpolation. x and u are 2D tensors.\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintegrate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[228], line 61\u001b[0m, in \u001b[0;36mBasicSDEIntegrator.integrate\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 58\u001b[0m t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0.\u001b[39m,\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0.2\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 59\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m#remove time step \u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m ys \u001b[38;5;241m=\u001b[39m \u001b[43mtorchsde\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msdeint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43meuler\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m ys \u001b[38;5;241m=\u001b[39m ys\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ys\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:93\u001b[0m, in \u001b[0;36msdeint\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 90\u001b[0m misc\u001b[38;5;241m.\u001b[39mhandle_unused_kwargs(unused_kwargs, msg\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sdeint`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m unused_kwargs\n\u001b[0;32m---> 93\u001b[0m sde, y0, ts, bm, method, options \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_contract\u001b[49m\u001b[43m(\u001b[49m\u001b[43msde\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madaptive\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogqp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 94\u001b[0m misc\u001b[38;5;241m.\u001b[39massert_no_grad([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124matol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt_min\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 95\u001b[0m [ts, dt, rtol, atol, dt_min])\n\u001b[1;32m 97\u001b[0m solver_fn \u001b[38;5;241m=\u001b[39m methods\u001b[38;5;241m.\u001b[39mselect(method\u001b[38;5;241m=\u001b[39mmethod, sde_type\u001b[38;5;241m=\u001b[39msde\u001b[38;5;241m.\u001b[39msde_type)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/neuromancer8/lib/python3.10/site-packages/torchsde/_core/sdeint.py:247\u001b[0m, in \u001b[0;36mcheck_contract\u001b[0;34m(sde, y0, ts, bm, method, adaptive, options, names, logqp)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_size \u001b[38;5;129;01min\u001b[39;00m batch_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_size \u001b[38;5;241m!=\u001b[39m batch_sizes[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 247\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBatch sizes not consistent.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m state_size \u001b[38;5;129;01min\u001b[39;00m state_sizes[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_size \u001b[38;5;241m!=\u001b[39m state_sizes[\u001b[38;5;241m0\u001b[39m]:\n", - "\u001b[0;31mValueError\u001b[0m: Batch sizes not consistent." - ] - } - ], - "source": [ - "test_outputs = dynamics_model_sde(test_data_lvs_2)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 415, - "metadata": {}, - "outputs": [], - "source": [ - "true_traj = test_data_lvs_2['X'].detach().numpy().reshape(-1, 2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "neuromancer8", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/neuromancer/dynamics/sde.py b/src/neuromancer/dynamics/sde.py index b328687b..30b07f79 100644 --- a/src/neuromancer/dynamics/sde.py +++ b/src/neuromancer/dynamics/sde.py @@ -1,20 +1,24 @@ -from typing import Sequence + import abc import torch from torch import nn import torchsde - import abc + +from torch.distributions import Normal +from typing import Sequence + + class BaseSDESystem(abc.ABC, nn.Module): """ Base class for SDEs for integration with TorchSDE library """ def __init__(self): super().__init__() - self.noise_type = "diagonal" - self.sde_type = "ito" - self.in_features = 0 + self.noise_type = "diagonal" #only supports diagonal diffusion right now + self.sde_type = "ito" #only supports Ito integrals right now + self.in_features = 0 #for compatibility with Neuromancer integrators; unused self.out_features = 0 @abc.abstractmethod @@ -47,7 +51,6 @@ def g(self, t,y): """ pass - class Encoder(nn.Module): """ Encoder module to handle time-series data (as in the case of stochastic data and SDE) @@ -55,7 +58,7 @@ class Encoder(nn.Module): This class is used only in LatentSDE_Encoder """ def __init__(self, input_size, hidden_size, output_size): - super(Encoder, self).__init__() + super().__init__() self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size) self.lin = nn.Linear(hidden_size, output_size) @@ -66,12 +69,30 @@ def forward(self, inp): class LatentSDE_Encoder(BaseSDESystem): def __init__(self, data_size, latent_size, context_size, hidden_size, ts, adjoint=False): - super(BaseSDESystem).init__() + """ + LatentSDE_Encoder is a neural network module designed for encoding time-series data into a latent space representation, + which is then used to model the system dynamics using Stochastic Differential Equations (SDEs). + + The primary purpose of this class is to transform high-dimensional time-series data into a lower-dimensional latent space + while capturing the underlying stochastic dynamics. This transformation facilitates efficient modeling, prediction, and + inference of complex temporal processes. + + Taken from https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py and modified to support + NeuroMANCER library + + :param data_size: (int) state size of the data + :param latent_size: (int) input latent size for the encoder + :param context_size: (int) size of context vector (output of encoder) + :param hidden_size: (int) size of the hidden layer of encoder + :param ts: (tensor) tensor of timesteps over which data should be predicted + + """ + super().__init__() self.adjoint = adjoint # Encoder. - self.encoder = Encoder(input_size=latent_size, hidden_size=hidden_size, output_size=context_size) + self.encoder = Encoder(input_size=data_size, hidden_size=hidden_size, output_size=context_size) self.qz0_net = nn.Linear(context_size, latent_size + latent_size) #Layer to return mean and variance of the parameterized latent space # Decoder. @@ -144,12 +165,16 @@ def forward(self, xs): ) return z0, xs, self.ts, qz0_mean, qz0_logstd, adjoint_params -class LatentSDE_Decoder(nn.Module): +class LatentSDE_Decoder(BaseSDESystem): """ Second part of Wrapper for torchsde's Latent SDE class to integrate with Neuromancer. This takes in output of LatentSDEIntegrator and decodes it back into the "real" data space and also outputs associated Gaussian distributions to be used in the final loss function. Please see https://github.com/google-research/torchsde/blob/master/examples/latent_sde_lorenz.py + + :param data_size: (int) state size of the data + :param latent_size: (int) input latent size for the encoder + :param noise_std: (float) standard deviation of the Gaussian noise applied during decoding """ def __init__(self, data_size, latent_size, noise_std): super().__init__() @@ -157,6 +182,12 @@ def __init__(self, data_size, latent_size, noise_std): self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size)) self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size)) self.projector = nn.Linear(latent_size, data_size) + + def f(self, t, y): + pass #unused + + def g(self, t, y): + pass #unused def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd): _xs = self.projector(zs) @@ -169,11 +200,12 @@ def forward(self, xs, zs, log_ratio, qz0_mean, qz0_logstd): logqp_path = log_ratio.sum(dim=0).mean(dim=0) return _xs, log_pxs, logqp0 + logqp_path, log_ratio - - -def StochasticLorenzAttractor(BaseSDESystem): +""" +---------------------------------- Data Generation Classes, for forward pass only ------------------------------------------- +""" +class StochasticLorenzAttractor(BaseSDESystem): def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)): - super(BaseSDESystem).__init__() + super().__init__() self.a = a self.b = b @@ -203,12 +235,13 @@ def sample(self, x0, ts, noise_std, normalize): mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1)) xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std) return xs + -def SDECoxIngersollRand(BaseSDESystem): +class SDECoxIngersollRand(BaseSDESystem): def __init__(self, alpha: float=0.1, beta: float=0.05, sigma: float=0.02): - super(BaseSDESystem).__init__() + super().__init__() self.alpha = alpha self.beta = beta self.sigma = sigma @@ -222,7 +255,7 @@ def g(self, t, y): return self.sigma * torch.sqrt(torch.abs(r)) -def SDEOrnsteinUhlenbeck(BaseSDESystem): +class SDEOrnsteinUhlenbeck(BaseSDESystem): def __init__(self, theta: float = 0.1, sigma: float = 0.2): super(BaseSDESystem).__init__() self.theta = theta @@ -233,7 +266,8 @@ def f(self, t, y): def g(self, t, y): return self.sigma - + + class LotkaVolterraSDE(BaseSDESystem): def __init__(self, a, b, c, d, g_params): super().__init__() @@ -243,7 +277,6 @@ def __init__(self, a, b, c, d, g_params): self.d = d self.g_params = g_params - def f(self, t, x): x1 = x[:,[0]] x2 = x[:,[1]] diff --git a/src/neuromancer/modules/blocks.py b/src/neuromancer/modules/blocks.py index da2f0604..151c624f 100644 --- a/src/neuromancer/modules/blocks.py +++ b/src/neuromancer/modules/blocks.py @@ -739,10 +739,6 @@ def forward(self, p, q): "poly2": Poly2, "bilinear": BilinearTorch, "icnn": InputConvexNN, - "pos_def": PosDef, - "encoder": Encoder, - "basic_sde": BasicSDE, - "latent_sde_encoder": LatentSDE_Encoder, - "latent_sde_decoder": LatentSDE_Decoder + "pos_def": PosDef } \ No newline at end of file From 7624a3ea226f44350159f57084939b422fc62d2a Mon Sep 17 00:00:00 2001 From: RBirmiwal <122936393+RBirmiwal@users.noreply.github.com> Date: Tue, 2 Jul 2024 16:19:38 -0700 Subject: [PATCH 6/6] Delete test_sdes.py, need to re-write to support new abstract class implemntation --- tests/test_sdes.py | 188 --------------------------------------------- 1 file changed, 188 deletions(-) delete mode 100644 tests/test_sdes.py diff --git a/tests/test_sdes.py b/tests/test_sdes.py deleted file mode 100644 index 06fc3f29..00000000 --- a/tests/test_sdes.py +++ /dev/null @@ -1,188 +0,0 @@ -import os -import logging -from typing import Sequence - -import torch -import torch.nn as nn -import torch.optim as optim -import torchsde -from torch.distributions import Normal -from torch.utils.data import DataLoader -import matplotlib.pyplot as plt -import numpy as np -import tqdm - -from neuromancer.modules import blocks -from neuromancer.dynamics import integrators, ode -from neuromancer.trainer import Trainer, LitTrainer -from neuromancer.problem import Problem -from neuromancer.loggers import BasicLogger -from neuromancer.dataset import DictDataset -from neuromancer.constraint import variable -from neuromancer.loss import PenaltyLoss -from neuromancer.system import Node - -from neuromancer.psl import plot - -# Import specific modules from neuromancer -from neuromancer.modules.blocks import BasicSDE, LatentSDE_Encoder -from neuromancer.dynamics.integrators import BasicSDEIntegrator - -import pytest -torch.manual_seed(0) - - - -class TestBasicSDEBlockAndIntegrator: - """ Testing class for BasicSDE and BasicSDEIntegrator """ - - @pytest.fixture(params=[ - lambda t, y: torch.sin(t) + 0.1 * y, - lambda t, y: 1.0 * t**2 + 0.1 * y**2 #quadratic drift - ]) - def f_function(self, request): - return request.param - - @pytest.fixture(params=[ - lambda t, y: 0.3 * torch.sigmoid(torch.cos(t) * torch.exp(-y)), - lambda t, y: torch.exp(-1. * t) * torch.sqrt(t) + torch.exp(-1. * y) #exponential diffusion - ]) - def g_function(self, request): - return request.param - - @pytest.fixture(params=[1]) # state size = 1 - def state_size(self, request): - return request.param - - @pytest.fixture(params=[1,100]) # Different time sizes - def time_size(self, request): - return request.param - - @pytest.fixture(params=[1,5]) # Different batch sizes - def batch_size(self, request): - return request.param - - @pytest.fixture - def basic_sde(self, f_function, g_function): - t = variable('t') - y = variable('y') - return BasicSDE(f_function, g_function, t, y) - - - def test_g_output_shape(self, basic_sde, batch_size, state_size, time_size): - ts = torch.linspace(0, 1, time_size) - y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) - output = basic_sde.g(ts, y0) - - assert output.shape[0] == y0.shape[0], "Dimension 0 of g output not equal to state size" - assert output.shape[1] == time_size, "Dimension 1 of g output not equal to time size" - - def test_basic_sde_initialization(self, basic_sde): - assert hasattr(basic_sde, 'noise_type'), "BasicSDE does not have a noise_type attribute" - assert basic_sde.noise_type == "diagonal", "noise_type attribute does not equal 'diagonal'" - assert hasattr(basic_sde, 'sde_type'), "BasicSDE does not have a noise_type attribute" - assert basic_sde.sde_type == "ito", "sde_type attribute does not equal 'ito'" - assert basic_sde.in_features == 0 - assert basic_sde.out_features == 0 - - - def test_f_output_shape(self, basic_sde, batch_size, state_size, time_size): - ts = torch.linspace(0, 1, time_size) - y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) - output = basic_sde.f(ts, y0) - assert output.shape[0] == y0.shape[0], "Dimension 0 of f output not equal to state size" - assert output.shape[1] == time_size, "Dimension 1 of f output not equal to time size" - - - def test_integrate_shape(self, basic_sde,batch_size, state_size, time_size): - integrator = BasicSDEIntegrator(basic_sde) - model = Node(integrator, input_keys=['y','t'], output_keys=['ys']) - batch_size, state_size, t_size = 5, 1, 100 - - ts = torch.linspace(0, 1, time_size) - y0 = torch.full(size=(batch_size, state_size), fill_value=0.1) - my_data = {'y': y0, 't': ts} - output = model(my_data)['ys'] - assert output.shape == torch.Size([time_size, batch_size, state_size]) - - -class StochasticLorenz(object): - """Stochastic Lorenz attractor. - - Used for simulating ground truth and obtaining noisy data. - Details described in Section 7.2 https://arxiv.org/pdf/2001.01328.pdf - Default a, b from https://openreview.net/pdf?id=HkzRQhR9YX - """ - noise_type = "diagonal" - sde_type = "ito" - - def __init__(self, a: Sequence = (10., 28., 8 / 3), b: Sequence = (.1, .28, .3)): - super(StochasticLorenz, self).__init__() - self.a = a - self.b = b - - def f(self, t, y): - x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) - a1, a2, a3 = self.a - - f1 = a1 * (x2 - x1) - f2 = a2 * x1 - x2 - x1 * x3 - f3 = x1 * x2 - a3 * x3 - return torch.cat([f1, f2, f3], dim=1) - - def g(self, t, y): - x1, x2, x3 = torch.split(y, split_size_or_sections=(1, 1, 1), dim=1) - b1, b2, b3 = self.b - - g1 = x1 * b1 - g2 = x2 * b2 - g3 = x3 * b3 - return torch.cat([g1, g2, g3], dim=1) - - @torch.no_grad() - def sample(self, x0, ts, noise_std, normalize): - """Sample data for training. Store data normalization constants if necessary.""" - xs = torchsde.sdeint(self, x0, ts) - if normalize: - mean, std = torch.mean(xs, dim=(0, 1)), torch.std(xs, dim=(0, 1)) - xs.sub_(mean).div_(std).add_(torch.randn_like(xs) * noise_std) - return xs - - -class TestLatentSDEBlockAndIntegrator: - """ Testing class for LatentSDE_Encoder and LatentSDEIntegrator """ - - def setup_method(self): - torch.manual_seed(0) - batch_size=1 - self.latent_size=4 - self.context_size=16 - self.hidden_size=16 - t0=0. - t1=2. - self.noise_std=0.01 - _y0 = torch.randn(batch_size, 3) - self.ts = torch.linspace(t0, t1, steps=5) - xs = StochasticLorenz().sample(_y0, self.ts, self.noise_std, normalize=True) - train_data = DictDataset({'xs':xs},name='train') - self.train_data_loader = DataLoader(train_data, batch_size=1024, collate_fn=train_data.collate_fn, shuffle=False) - - def test_latent_sde_initialization(self): - sde_block_encoder = blocks.LatentSDE_Encoder(3, self.latent_size, self.context_size, self.hidden_size, ts=self.ts, adjoint=False) - assert torch.allclose(sde_block_encoder.ts, self.ts) - assert sde_block_encoder.adjoint == False - - - def test_latent_sde_forward(self): - sde_block_encoder = blocks.LatentSDE_Encoder(3, self.latent_size, self.context_size, self.hidden_size, ts=self.ts, adjoint=False) - integrator = integrators.LatentSDEIntegrator(sde_block_encoder, adjoint=False) - model_1 = Node(integrator, input_keys=['xs'], output_keys=['zs', 'z0', 'log_ratio', 'xs', 'qz0_mean', 'qz0_logstd'], name='m1') - - sample = next(iter(self.train_data_loader)) - output_1 = model_1(sample) - assert isinstance(output_1, dict), "Output of LatentSDE_Encoder should be a dictionary" - assert sorted(list(output_1.keys())) == ['log_ratio', 'qz0_logstd', 'qz0_mean', 'xs', 'z0', 'zs'], "Keys of output of LatentSDE_Encoder are incorrect" - assert output_1['z0'].shape == torch.Size([1,4]) - assert output_1['zs'].shape == torch.Size([5,1,4]) - -

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