{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Feed-forward Diffractive Optical Neural Network for MNIST task\n",
    "\n",
    "In that example notebook we will make some experiments^ based on a n opticel network architecture proposed in [the article](https://www.science.org/doi/10.1126/science.aat8084)."
   ],
   "id": "d369bcec1062bad"
  },
  {
   "cell_type": "markdown",
   "id": "35c58e4e-9226-4d24-ae10-b84c5cc05ce5",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d15a31da-c6c3-427d-a070-afdc48a01305",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f0f6378-5bc3-477d-bfd3-a08a9123b091",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f309cc84-f338-409f-b1a3-6ac36b945622",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c39328b9-9fa8-4937-9a53-905ea59276c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59041be7-8d30-4981-a3da-e5d090a32339",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ba00109-7b62-4f32-b685-17f437630230",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a39c467c-b066-4afb-b470-4265088d5301",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import InterpolationMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "902804f6-c104-4b4b-9b9f-f43ac9b4c1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our library\n",
    "from svetlanna import SimulationParameters\n",
    "from svetlanna.parameters import BoundedParameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b7826e8-9de7-4539-b16a-60827a25959f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our library\n",
    "from svetlanna import Wavefront\n",
    "from svetlanna import elements\n",
    "from svetlanna.setup import LinearOpticalSetup\n",
    "from svetlanna.detector import Detector, DetectorProcessorClf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5a64cdd-5ed6-4fe1-ab77-d95120a45f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from svetlanna.transforms import ToWavefront"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1cbc8319-2f82-4979-889a-4efcc8466039",
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets of wavefronts\n",
    "from src.wf_datasets import DatasetOfWavefronts\n",
    "from src.wf_datasets import IlluminatedApertureDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8603ed7b-af59-4d79-b6a7-dcb599e28e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0aec829e-f605-4786-a7be-da53dc878043",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3c66403-21ec-44bf-a439-b8eb1e5356eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02414ffa-fc8b-4cd7-a6fa-1f744a9a68a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.path import Path\n",
    "\n",
    "plt.style.use('dark_background')\n",
    "%matplotlib inline\n",
    "# %config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92e2ca5-59c9-49cb-9442-4008446ad4eb",
   "metadata": {},
   "source": "## 0. Experiment parameters"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7e34ebb-3e36-402b-ad52-9cb62be6670f",
   "metadata": {},
   "outputs": [],
   "source": [
    "working_frequency = 0.4 * 1e12 # [Hz]\n",
    "C_CONST = 299_792_458  # [m / s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9cfa892b-ac25-4baf-af99-9d9ed67eb9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXP_NUMBER = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aee213df-c564-496d-8702-967b792d846d",
   "metadata": {},
   "outputs": [],
   "source": [
    "EXP_CONDITIONS = {\n",
    "    # SIMULATION PARAMS\n",
    "    'wavelength'  : C_CONST / working_frequency,  # [m]\n",
    "    'layer_size_m': 8 * 1e-2 / 2 * 3,  # [m] - x and y sizes are equal!\n",
    "    'layer_nodes' : int(100 / 2 * 3),  # 100,\n",
    "    # TOOLS\n",
    "    'tensorboard' : True,  # to use tensorboard or not!\n",
    "    # DATASET\n",
    "    'digit_resize' : 17,  # the actual digit size after resize (in nodes)\n",
    "    'ds_apertures': True,  # if dataset is created with diigit-shaped apertures (True) or with direct modulation (False)\n",
    "        # must be specified if 'ds_apertures' == False, values: 'amp', 'phase' or 'both'\n",
    "    'ds_modulation': None, \n",
    "        # must be specified if 'ds_apertures' == True\n",
    "    'gauss_waist_radius': 2e-2,  # [m] - gaussian beam for dataset creation\n",
    "    'distance_to_aperture': 3e-2,  # [m]\n",
    "    # SETUP\n",
    "    'propagator': 'AS',  # FreeSpase propagation method: 'AS' or 'fresnel' (also needed for dataset with apertures)\n",
    "        # diffractive layers\n",
    "    'n_diff_layers': 5,  # number of diffractive layers\n",
    "    'diff_layer_max_phase': torch.pi,  # maximal phase for each DiffractiveLayer\n",
    "    'diff_layer_mask_init': 'const',  # initialization of DiffractiveLayer masks: 'const' or 'random'\n",
    "    'diff_layers_seeds': 123,  # if 'random': seed to generate seeds to init all masks!\n",
    "        # free space\n",
    "    'layers_distance': 3e-2,  # [m], distance between layers\n",
    "        # apertures\n",
    "    'add_apertures': True,  # if True - adds square apertures (in the middle) before each diffractive layer\n",
    "    'apertures_size': (50, 50),  # size of additional apertures in a setup\n",
    "        # detector\n",
    "    'detector_zones': 'segments',  # form of a detector zones: 'squares' or 'circles' or 'strips'\n",
    "    'detector_transpose': False,  # transpose detector or not (makes 'strips' horizontal instead of vertical)\n",
    "    # TRAINING PROCESS\n",
    "    'train_bs': 8,\n",
    "    'val_bs': 20,  # batch sizes\n",
    "    'train_split_seed': 178,  # seed for a data split on train/validation\n",
    "    'epochs': 10,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f113c10a-446e-4cf3-9e6b-36a65961dd76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import SummaryWriter from tensorboard\n",
    "\n",
    "if 'tensorboard' in EXP_CONDITIONS.keys():\n",
    "    if EXP_CONDITIONS['tensorboard']:\n",
    "\n",
    "        from torch.utils.tensorboard import SummaryWriter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96955aff-11d2-4df7-ba03-4ce06008efa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'models/03_mnist_experiments/13-12-2024_experiment_01'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date = datetime.today().strftime('%d-%m-%Y')\n",
    "\n",
    "RESULTS_FOLDER = (\n",
    "    f'models/03_mnist_experiments/{today_date}_experiment_{EXP_NUMBER:02d}'\n",
    ")\n",
    "\n",
    "RESULTS_FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c2eba601-a1f5-4c12-871f-a4c7854974cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(RESULTS_FOLDER):\n",
    "    os.makedirs(RESULTS_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f381dc5f-d4d3-4c23-8f28-86d84f0aeef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save experiment conditions\n",
    "json.dump(EXP_CONDITIONS, open(f'{RESULTS_FOLDER}/conditions.json', 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd0dd89c-abfb-4399-9871-1b4f48686849",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OR read conditions from file:\n",
    "# EXP_CONDITIONS = json.load(open(f'{RESULTS_FOLDER}/conditions.json))\n",
    "# print(EXP_CONDITIONS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "098d998f-0ef1-4bb0-b01e-21562d5e002d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e8a46e5e-6053-4e74-93eb-fb8e2c8f45a0",
   "metadata": {},
   "source": "## 1. Simulation parameters"
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e389f46c-f2ca-4204-a30f-c270e5987163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda = 749.481 um\n"
     ]
    }
   ],
   "source": [
    "working_wavelength = EXP_CONDITIONS['wavelength']  # [m]\n",
    "print(f'lambda = {working_wavelength * 1e6:.3f} um')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d74aeced-a6b6-4335-bb08-db138373441b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# physical size of each layer (from the article) - (8 x 8) [cm]\n",
    "x_layer_size_m = EXP_CONDITIONS['layer_size_m']  # [m]\n",
    "y_layer_size_m = x_layer_size_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "37057e7b-8ba8-49d4-a9e3-c4b9ece0c7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of neurons in simulation\n",
    "x_layer_nodes = EXP_CONDITIONS['layer_nodes']\n",
    "y_layer_nodes = x_layer_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85db7726-46ae-448d-847f-f821e4d41509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layer size (neurons): 150 x 150 = 22500\n"
     ]
    }
   ],
   "source": [
    "print(f'Layer size (neurons): {x_layer_nodes} x {y_layer_nodes} = {x_layer_nodes * y_layer_nodes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87befd1d-a9fa-4403-8d13-416964fbe6a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neuron size = 800.000 um\n"
     ]
    }
   ],
   "source": [
    "neuron_size = x_layer_size_m / x_layer_nodes  # [m]  increase two times!\n",
    "print(f'Neuron size = {neuron_size * 1e6:.3f} um')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c3d95cc-633a-481a-bfa6-fe25a1d5b1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulation parameters for the rest of the notebook\n",
    "\n",
    "SIM_PARAMS = SimulationParameters(\n",
    "    axes={\n",
    "        'W': torch.linspace(-x_layer_size_m / 2, x_layer_size_m / 2, x_layer_nodes),\n",
    "        'H': torch.linspace(-y_layer_size_m / 2, y_layer_size_m / 2, y_layer_nodes),\n",
    "        'wavelength': working_wavelength,  # only one wavelength!\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d6f07c-1cb7-4b52-87c8-7c5306df2a75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ddbc9f3f-308d-4c58-b79c-de72e1eeff7b",
   "metadata": {},
   "source": "## 2. Dataset preparation (Data Engineer)"
  },
  {
   "cell_type": "markdown",
   "id": "f18398b5-9ee7-470c-b2ef-bcd15b6f4768",
   "metadata": {},
   "source": "### 2.1. [MNIST Dataset](https://www.kaggle.com/datasets/hojjatk/mnist-dataset)"
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0de278d4-1f7a-4651-8fce-74c41567c08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize a directory for a dataset\n",
    "MNIST_DATA_FOLDER = './data'  # folder to store data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab77fa7-dba0-4fbd-bb42-57560fbcac5c",
   "metadata": {},
   "source": [
    "### 2.1.1. Train/Test datasets of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1d740b2-dfd1-4bf3-a86c-6fb13a831df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN (images)\n",
    "mnist_train_ds = torchvision.datasets.MNIST(\n",
    "    root=MNIST_DATA_FOLDER,\n",
    "    train=True,  # for train dataset\n",
    "    download=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "14fb6dc4-3849-467c-957d-26617d8c5214",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST (images)\n",
    "mnist_test_ds = torchvision.datasets.MNIST(\n",
    "    root=MNIST_DATA_FOLDER,\n",
    "    train=False,  # for test dataset\n",
    "    download=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "72f00e8c-0624-499f-9176-8820274bf05f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data: 60000\n",
      "Test data : 10000\n"
     ]
    }
   ],
   "source": [
    "print(f'Train data: {len(mnist_train_ds)}')\n",
    "print(f'Test data : {len(mnist_test_ds)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208ca22f-13b4-4066-a850-1bd35776637b",
   "metadata": {},
   "source": [
    "### 2.1.2. Train/Test datasets of wavefronts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1f649151-abcf-4bd4-b1c4-0a9039eb4657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DS_WITH_APERTURES = EXP_CONDITIONS['ds_apertures'] \n",
    "# if True we use IlluminatedApertureDataset to create datasets of Wavefronts\n",
    "# else - DatasetOfWavefronts\n",
    "DS_WITH_APERTURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8c37007c-0731-4b58-9059-e2f2bb792b52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select modulation type for DatasetOfWavefronts if DS_WITH_APERTURES == False\n",
    "MODULATION_TYPE = EXP_CONDITIONS['ds_modulation']  # 'phase', 'amp', 'amp&phase'\n",
    "\n",
    "# select method and distance for a FreeSpace in IlluminatedApertureDataset\n",
    "DS_METHOD = EXP_CONDITIONS['propagator']\n",
    "DS_DISTANCE = EXP_CONDITIONS['distance_to_aperture']  # [m]\n",
    "\n",
    "DS_BEAM = Wavefront.gaussian_beam(\n",
    "    simulation_parameters=SIM_PARAMS,\n",
    "    waist_radius=EXP_CONDITIONS['gauss_waist_radius'],  # [m]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd13987a-b54c-4413-a759-492bf9118eb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "47e20e55-53f7-4f92-95eb-3ef75d5dddeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# image resize to match SimulationParameters\n",
    "resize_y = EXP_CONDITIONS['digit_resize']\n",
    "resize_x = resize_y  # shape for transforms.Resize\n",
    "\n",
    "pad_top = int((y_layer_nodes - resize_y) / 2)\n",
    "pad_bottom = y_layer_nodes - pad_top - resize_y\n",
    "pad_left = int((x_layer_nodes - resize_x) / 2)\n",
    "pad_right = x_layer_nodes - pad_left - resize_x  # params for transforms.Pad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9da80a7-99cc-4a4a-b0fb-bbf628ec0baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compose all transforms for DatasetOfWavefronts\n",
    "image_transform_for_ds = transforms.Compose(\n",
    "  [\n",
    "      transforms.ToTensor(),\n",
    "      transforms.Resize(\n",
    "          size=(resize_y, resize_x),\n",
    "          interpolation=InterpolationMode.NEAREST,\n",
    "      ),\n",
    "      transforms.Pad(\n",
    "          padding=(\n",
    "              pad_left,  # left padding\n",
    "              pad_top,  # top padding\n",
    "              pad_right,  # right padding\n",
    "              pad_bottom  # bottom padding\n",
    "          ),\n",
    "          fill=0,\n",
    "      ),  # padding to match sizes!\n",
    "      ToWavefront(modulation_type=MODULATION_TYPE)  # <- select modulation type!!!\n",
    "  ]  \n",
    ")\n",
    "\n",
    "# compose all transforms for IlluminatedApertureDataset\n",
    "image_to_aperture = transforms.Compose(\n",
    "  [\n",
    "      transforms.ToTensor(),\n",
    "      transforms.Resize(\n",
    "          size=(resize_y, resize_x),\n",
    "          interpolation=InterpolationMode.NEAREST,\n",
    "      ),\n",
    "      transforms.Pad(\n",
    "          padding=(\n",
    "              pad_left,  # left padding\n",
    "              pad_top,  # top padding\n",
    "              pad_right,  # right padding\n",
    "              pad_bottom  # bottom padding\n",
    "          ),\n",
    "          fill=0,\n",
    "      ),  # padding to match sizes!\n",
    "  ]  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7309365a-2986-45e8-a259-97f378fcccef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TRAIN dataset of WAVEFRONTS\n",
    "if not DS_WITH_APERTURES:\n",
    "    mnist_wf_train_ds = DatasetOfWavefronts(\n",
    "        init_ds=mnist_train_ds,  # dataset of images\n",
    "        transformations=image_transform_for_ds,  # image transformation\n",
    "        sim_params=SIM_PARAMS,  # simulation parameters\n",
    "    )\n",
    "else:\n",
    "    mnist_wf_train_ds = IlluminatedApertureDataset(\n",
    "        init_ds=mnist_train_ds,  # dataset of images\n",
    "        transformations=image_to_aperture,  # image transformation\n",
    "        sim_params=SIM_PARAMS,  # simulation parameters\n",
    "        beam_field=DS_BEAM,\n",
    "        distance=DS_DISTANCE,\n",
    "        method=DS_METHOD,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "873dfbe6-81a1-47ab-9dfd-2f1513044fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST dataset of WAVEFRONTS\n",
    "if not DS_WITH_APERTURES:\n",
    "    mnist_wf_test_ds = DatasetOfWavefronts(\n",
    "        init_ds=mnist_test_ds,  # dataset of images\n",
    "        transformations=image_transform_for_ds,  # image transformation\n",
    "        sim_params=SIM_PARAMS,  # simulation parameters\n",
    "    )\n",
    "else:\n",
    "    mnist_wf_test_ds = IlluminatedApertureDataset(\n",
    "        init_ds=mnist_test_ds,  # dataset of images\n",
    "        transformations=image_to_aperture,  # image transformation\n",
    "        sim_params=SIM_PARAMS,  # simulation parameters\n",
    "        beam_field=DS_BEAM,\n",
    "        distance=DS_DISTANCE,\n",
    "        method=DS_METHOD,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "038b5a54-175a-433b-98b6-8e37565d19b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data: 60000\n",
      "Test data : 10000\n"
     ]
    }
   ],
   "source": [
    "print(f'Train data: {len(mnist_wf_train_ds)}')\n",
    "print(f'Test data : {len(mnist_wf_test_ds)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "849494fd-0a2e-436b-a0ea-61838cdcc874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x960 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot several EXAMPLES from TRAIN dataset\n",
    "n_examples= 4  # number of examples to plot\n",
    "# choosing indecies of images (from train) to plot\n",
    "random.seed(78)\n",
    "train_examples_ids = random.sample(range(len(mnist_train_ds)), n_examples)\n",
    "\n",
    "all_examples_wavefronts = []\n",
    "cmap = 'hot'\n",
    "\n",
    "n_lines = 3\n",
    "fig, axs = plt.subplots(n_lines, n_examples, figsize=(n_examples * 3, n_lines * 3.2))\n",
    "for ind_ex, ind_train in enumerate(train_examples_ids):\n",
    "    image, label = mnist_train_ds[ind_train]\n",
    "    \n",
    "    axs[0][ind_ex].set_title(f'id={ind_train} [{label}]')\n",
    "    axs[0][ind_ex].imshow(image, cmap='gray')\n",
    "\n",
    "    wavefront, wf_label = mnist_wf_train_ds[ind_train]\n",
    "    assert isinstance(wavefront, Wavefront)\n",
    "\n",
    "    all_examples_wavefronts.append(wavefront)\n",
    "\n",
    "    axs[1][ind_ex].set_title(f'$|WF|^2$')\n",
    "    # here we can plot intensity for a wavefront\n",
    "    axs[1][ind_ex].imshow(\n",
    "        wavefront.intensity[0], cmap=cmap,\n",
    "        vmin=0, vmax=1\n",
    "    )\n",
    "    \n",
    "    axs[2][ind_ex].set_title(f'phase of $WF$')\n",
    "    axs[2][ind_ex].imshow(\n",
    "        wavefront.phase[0], cmap=cmap,\n",
    "        vmin=0, vmax= 2 * torch.pi\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecce2e47-6bc9-4288-8195-2e3143eb8e8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73ae7308-f611-4948-a9fd-afa66856dc09",
   "metadata": {},
   "source": "## 3. Optical network"
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20220f66-4f82-453c-9af4-13af3125a28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_OF_DIFF_LAYERS = EXP_CONDITIONS['n_diff_layers']  # number of diffractive layers\n",
    "FREE_SPACE_DISTANCE = EXP_CONDITIONS['layers_distance']  # [m]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "628a0c01-5dc2-46cb-9ee3-b6d1c0acaa26",
   "metadata": {},
   "source": "### 3.1. Architecture"
  },
  {
   "cell_type": "markdown",
   "id": "c017b60c-cb2c-4707-800e-5dfd2770389c",
   "metadata": {},
   "source": [
    "### 3.1.1. List of Elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40a16b2-43b8-4f87-8ff1-4baf87313031",
   "metadata": {},
   "source": [
    "> To help with the 3D-printing and fabrication of the $D^2NN$ design, a sigmoid function was used to limit the phase value of each neuron to $0-2π$ and $0-π$, for imaging and classifier networks, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "572433d3-ddab-40ec-9fbf-974e0150f721",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_PHASE = EXP_CONDITIONS['diff_layer_max_phase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8a4f8a54-fe64-417e-b58e-ab9b5a964106",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.for_setup import get_const_free_space, get_random_diffractive_layer\n",
    "from torch.nn import functional"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b103a40-895b-4915-bdb8-01cab2838c1d",
   "metadata": {},
   "source": [
    "Function to construct a list of elements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "30dc7d99-a888-47ec-bcac-7fab8e4ae148",
   "metadata": {},
   "outputs": [],
   "source": [
    "# WE WILL ADD APERTURES BEFORE EACH DIFFRACTIVE LAYER OF THE SIZE:\n",
    "ADD_APERTURES = EXP_CONDITIONS['add_apertures']\n",
    "APERTURE_SZ = EXP_CONDITIONS['apertures_size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f4e3cf49-5e7f-4fe1-81c9-dd90021c9406",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_elements_list(\n",
    "    num_layers,\n",
    "    simulation_parameters: SimulationParameters,\n",
    "    freespace_method,\n",
    "    masks_seeds,\n",
    "    apertures=False,\n",
    "    aperture_size=(100, 100)\n",
    "):\n",
    "    \"\"\"\n",
    "    Composes a list of elements for setup.\n",
    "        Optical system: FS|DL|FS|...|FS|DL|FS|Detector\n",
    "    ...\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    num_layers : int\n",
    "        Number of layers in the system.\n",
    "    simulation_parameters : SimulationParameters()\n",
    "        A simulation parameters for a task.\n",
    "    freespace_method : str\n",
    "        Propagation method for free spaces in a setup.\n",
    "    masks_seeds : torch.Tensor()\n",
    "        Torch tensor of random seeds to generate masks for diffractive layers.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    elements_list : list(Element)\n",
    "        List of Elements for an optical setup.\n",
    "    \"\"\"\n",
    "    elements_list = []  # list of elements\n",
    "\n",
    "    if apertures:  # equal masks for all apertures (select a part in the middle)\n",
    "        aperture_mask = torch.ones(size=aperture_size)\n",
    "\n",
    "        y_nodes, x_nodes = simulation_parameters.axes_size(axs=('H', 'W'))\n",
    "        y_mask, x_mask = aperture_mask.size()\n",
    "        pad_top = int((y_nodes - y_mask) / 2)\n",
    "        pad_bottom = y_nodes - pad_top - y_mask\n",
    "        pad_left = int((x_nodes - x_mask) / 2)\n",
    "        pad_right = x_nodes - pad_left - x_mask  # params for transforms.Pad\n",
    "        \n",
    "        # padding transform to match aperture size with simulation parameters     \n",
    "        aperture_mask = functional.pad(\n",
    "            input=aperture_mask,\n",
    "            pad=(pad_left, pad_right, pad_top, pad_bottom),\n",
    "            mode='constant',\n",
    "            value=0\n",
    "        )\n",
    "\n",
    "    # compose architecture\n",
    "    for ind_layer in range(num_layers):\n",
    "        if ind_layer == 0:\n",
    "            # first FreeSpace layer before first DiffractiveLayer\n",
    "            elements_list.append(\n",
    "                get_const_free_space(\n",
    "                    simulation_parameters,  # simulation parameters for the notebook\n",
    "                    FREE_SPACE_DISTANCE,  # in [m]\n",
    "                    freespace_method=freespace_method,\n",
    "                )\n",
    "            )\n",
    "    \n",
    "        # add aperture before each diffractive layer\n",
    "        if apertures:\n",
    "            elements_list.append(\n",
    "                elements.Aperture(\n",
    "                    simulation_parameters=simulation_parameters,\n",
    "                    mask=nn.Parameter(aperture_mask, requires_grad=False)\n",
    "                )\n",
    "            )\n",
    "            \n",
    "        # add DiffractiveLayer\n",
    "        elements_list.append(\n",
    "            get_random_diffractive_layer(\n",
    "                simulation_parameters,  # simulation parameters for the notebook\n",
    "                mask_seed=masks_seeds[ind_layer].item(),\n",
    "                max_phase=MAX_PHASE\n",
    "            )\n",
    "        )\n",
    "        # add FreeSpace\n",
    "        elements_list.append(\n",
    "            get_const_free_space(\n",
    "                simulation_parameters,  # simulation parameters for the notebook\n",
    "                FREE_SPACE_DISTANCE,  # in [m]\n",
    "                freespace_method=freespace_method,\n",
    "            )\n",
    "        )\n",
    "    \n",
    "    # add Detector in the end of the system!\n",
    "    elements_list.append(\n",
    "        Detector(\n",
    "            simulation_parameters=simulation_parameters,\n",
    "            func='intensity'  # detector that returns intensity\n",
    "        )\n",
    "    )\n",
    "\n",
    "    return elements_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0867fe-6de7-4891-8d33-7fd12e69a060",
   "metadata": {},
   "source": [
    "Constants for a setup initialization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9273d16b-671d-4a07-bedc-f8111b3bb64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.5708, 1.5708, 1.5708, 1.5708, 1.5708])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FREESPACE_METHOD = EXP_CONDITIONS['propagator'] # TODO: 'AS' returns nan's?\n",
    "\n",
    "if EXP_CONDITIONS['diff_layer_mask_init'] == 'random':\n",
    "    MASKS_SEEDS = torch.randint(\n",
    "        low=0, high=100,\n",
    "        size=(NUM_OF_DIFF_LAYERS,),\n",
    "        generator=torch.Generator().manual_seed(EXP_CONDITIONS['diff_layers_seeds'])\n",
    "        # to generate the same set of initial masks\n",
    "    )  # for the same random generation\n",
    "\n",
    "if EXP_CONDITIONS['diff_layer_mask_init'] == 'const':\n",
    "    MASKS_SEEDS = torch.ones(size=(NUM_OF_DIFF_LAYERS,)) * torch.pi / 2  # constant masks init\n",
    "\n",
    "MASKS_SEEDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f234601-8f80-46d2-b52d-ea0fe94ddf0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "61958d45-f06e-4292-be5e-e0025dd1b2db",
   "metadata": {},
   "source": [
    "### 3.1.2. Compose `LinearOpticalSetup`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "727a9d29-8b31-47e9-b0b0-fdae9d44c813",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_setup(\n",
    "    simulation_parameters, \n",
    "    num_layers, \n",
    "    apertures=False, \n",
    "    aperture_size=(100,100)\n",
    "):\n",
    "    \"\"\"\n",
    "    Returns an optical setup. Recreates all elements.\n",
    "    \"\"\"\n",
    "    elements_list = get_elements_list(\n",
    "        num_layers,\n",
    "        simulation_parameters,\n",
    "        FREESPACE_METHOD,\n",
    "        MASKS_SEEDS,\n",
    "        apertures=apertures,\n",
    "        aperture_size=aperture_size\n",
    "    )  # recreate a list of elements\n",
    "\n",
    "    return LinearOpticalSetup(elements=elements_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "762206a1-1bc1-4725-b7dc-53dcb252d66e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_optical_setup = get_setup(\n",
    "    SIM_PARAMS,\n",
    "    NUM_OF_DIFF_LAYERS,\n",
    "    apertures=ADD_APERTURES, \n",
    "    aperture_size=APERTURE_SZ\n",
    ")\n",
    "# Comment: Lin - a surname of the first author of the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "deb9f4e3-584b-427b-add2-76b084d9e770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): FreeSpace()\n",
       "  (1): Aperture()\n",
       "  (2): DiffractiveLayer()\n",
       "  (3): FreeSpace()\n",
       "  (4): Aperture()\n",
       "  (5): DiffractiveLayer()\n",
       "  (6): FreeSpace()\n",
       "  (7): Aperture()\n",
       "  (8): DiffractiveLayer()\n",
       "  (9): FreeSpace()\n",
       "  (10): Aperture()\n",
       "  (11): DiffractiveLayer()\n",
       "  (12): FreeSpace()\n",
       "  (13): Aperture()\n",
       "  (14): DiffractiveLayer()\n",
       "  (15): FreeSpace()\n",
       "  (16): Detector()\n",
       ")"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_optical_setup.net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953f8419-7c00-4893-99a3-b777a49e15f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0176fa87-f51c-4ef1-bf22-64855a370695",
   "metadata": {},
   "source": [
    "#### Example of a wavefrnt propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "84bcc0dd-14cb-4220-82d2-6b987c48e8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_wf = mnist_wf_train_ds[128][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8538e8b8-911d-475a-bc33-7ed4a161c9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_wf_train_ds[128][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "defeaf77-da7c-44a4-a284-16f581b699ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "setup_scheme, wavefronts = lin_optical_setup.stepwise_forward(example_wf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "59816b90-2e18-4cc6-907f-6265fe5f515d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-(0)-> [1. FreeSpace] -(1)-> [2. Aperture] -(2)-> [3. DiffractiveLayer] -(3)-> [4. FreeSpace] -(4)-> [5. Aperture] -(5)-> [6. DiffractiveLayer] -(6)-> [7. FreeSpace] -(7)-> [8. Aperture] -(8)-> [9. DiffractiveLayer] -(9)-> [10. FreeSpace] -(10)-> [11. Aperture] -(11)-> [12. DiffractiveLayer] -(12)-> [13. FreeSpace] -(13)-> [14. Aperture] -(14)-> [15. DiffractiveLayer] -(15)-> [16. FreeSpace] -(16)-> [17. Detector] -(17)->\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1280 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(setup_scheme)  # prints propagation scheme\n",
    "\n",
    "n_cols = 5  # number of columns to plot all wavefronts during propagation\n",
    "n_rows = (len(lin_optical_setup.net) // n_cols) + 1\n",
    "\n",
    "to_plot = 'amp'  # <--- chose what to plot\n",
    "cmap = 'grey'  # choose colormaps\n",
    "detector_cmap = 'hot'\n",
    "\n",
    "# create a figure with subplots\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 3, n_rows * 3.2))\n",
    "\n",
    "# turn off unecessary axes\n",
    "for ind_row in range(n_rows):\n",
    "    for ind_col in range(n_cols):\n",
    "        ax_this = axs[ind_row][ind_col]\n",
    "        if ind_row * n_cols + ind_col >= len(wavefronts):\n",
    "            ax_this.axis('off')\n",
    "\n",
    "# plot wavefronts\n",
    "for ind_wf, wavefront in enumerate(wavefronts):\n",
    "    ax_this = axs[ind_wf // n_cols][ind_wf % n_cols]\n",
    "\n",
    "    if to_plot == 'phase':\n",
    "        # plot angle for each wavefront, because intensities pictures are indistinguishable from each other\n",
    "        if ind_wf < len(wavefronts) - 1:\n",
    "            ax_this.set_title('Phase for $WF_{' + f'{ind_wf}' + '}$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].phase.detach().numpy(), cmap=cmap,\n",
    "                vmin=0, vmax=2 * torch.pi\n",
    "            )\n",
    "        else:  # (not a wavefront!)\n",
    "            ax_this.set_title('Detector phase ($WF_{' + f'{ind_wf}' + '})$')\n",
    "            # Detector has no phase!\n",
    "\n",
    "    if to_plot == 'amp':\n",
    "        # plot angle for each wavefront, because intensities pictures are indistinguishable from each other\n",
    "        if ind_wf < len(wavefronts) - 1:\n",
    "            ax_this.set_title('Intensity for $WF_{' + f'{ind_wf}' + '}$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].intensity.detach().numpy(), cmap=cmap,\n",
    "                # vmin=0, vmax=max_intensity  # uncomment to make the same limits\n",
    "            )\n",
    "        else:  # Detector output (not a wavefront!)\n",
    "            ax_this.set_title('Detector Intensity ($WF_{' + f'{ind_wf}' + '})$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].detach().numpy(), cmap=detector_cmap,\n",
    "                # vmin=0, vmax=max_intensity  # uncomment to make the same limits\n",
    "            )\n",
    "            \n",
    "    # Comment: Detector output is Tensor! It has no methods of Wavefront (like .phase or .intensity)!\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65f114a8-d0be-4f76-83ab-89bcee6e5d87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "450c6c03-bd60-4a40-a557-ce3fb0ccd0c6",
   "metadata": {},
   "source": [
    "### 3.1.3 Detector processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "91d1e9b5-8703-4201-8743-b54eac91da99",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dfe31fa5-8279-4d9f-96a8-eb1d760ce4bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import src.detector_segmentation as detector_segmentation\n",
    "# Functions to segment detector: squares_mnist, circles, angular_segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ef52887c-0289-427c-a85e-123aa80659be",
   "metadata": {},
   "outputs": [],
   "source": [
    "if ADD_APERTURES or APERTURE_SZ:\n",
    "    y_detector_nodes, x_detector_nodes = APERTURE_SZ\n",
    "else:\n",
    "    y_detector_nodes, x_detector_nodes = SIM_PARAMS.axes_size(axs=('H', 'W'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "15755220-768f-4b30-84c5-0a0e13dfee84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ADD_APERTURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e2ff0a3-1548-4ca7-b0a3-c85b87045beb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8d1dabde-317c-45c3-a9ee-4bffd403b5cb",
   "metadata": {},
   "source": [
    "#### Detector mask (square zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7803b2ba-3aaf-46cc-9431-f546a19bfe3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_squares_mask = detector_segmentation.squares_mnist(\n",
    "    y_detector_nodes, x_detector_nodes,  # size of a detector or an aperture (in the middle of detector)\n",
    "    SIM_PARAMS\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7510a1d-03c1-4fdf-ba85-ccc85190204b",
   "metadata": {},
   "source": [
    "#### Detector mask (circular zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fa08d293-cfae-4cd4-afe3-f7913637e12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_circles_mask = detector_segmentation.circles(\n",
    "    y_detector_nodes, x_detector_nodes,  # size of a detector or an aperture (in the middle of detector)\n",
    "    number_of_classes,\n",
    "    SIM_PARAMS\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12646c2-1740-4bed-9c6a-63ef5add19c1",
   "metadata": {},
   "source": [
    "#### Detector mask (angular segments zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3af0abca-7fbe-4148-81b5-154a846280df",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_angles_mask = detector_segmentation.angular_segments(\n",
    "    y_detector_nodes, x_detector_nodes,  # size of a detector or an aperture (in the middle of detector)\n",
    "    number_of_classes,\n",
    "    SIM_PARAMS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "782bfd55-58ad-481b-b677-a4b6e40929c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9b79963c-18c5-4620-82cb-2485a986a1fd",
   "metadata": {},
   "source": [
    "#### Detector processor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3b918ae1-2c6f-4ec2-a200-c19e8f4559cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CIRCLES_ZONES = EXP_CONDITIONS['detector_zones'] == 'circles'\n",
    "CIRCLES_ZONES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "49c444c1-c2bd-4e2d-9a69-4830029bcc46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angular segments selected!\n"
     ]
    }
   ],
   "source": [
    "if EXP_CONDITIONS['detector_zones'] == 'circles':\n",
    "    selected_mask = detector_circles_mask\n",
    "    print('circles selected!')\n",
    "\n",
    "if EXP_CONDITIONS['detector_zones'] == 'squares':\n",
    "    selected_mask = detector_squares_mask\n",
    "    print('squares selected!')\n",
    "\n",
    "if EXP_CONDITIONS['detector_zones'] == 'segments':\n",
    "    selected_mask = detector_angles_mask\n",
    "    print('angular segments selected!')\n",
    "\n",
    "if EXP_CONDITIONS['detector_zones'] == 'strips':\n",
    "    selected_mask = None\n",
    "    print('strips selected!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cb2adcfd-788b-4939-8471-b87b95c31b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "detector_processor = DetectorProcessorClf(\n",
    "    num_classes=number_of_classes,\n",
    "    simulation_parameters=SIM_PARAMS,\n",
    "    segmented_detector=selected_mask,  # choose a mask!\n",
    "    segments_zone_size=APERTURE_SZ\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4d8e0ed4-03b5-421e-becc-913a40084884",
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'detector_transpose' in EXP_CONDITIONS.keys():\n",
    "    if EXP_CONDITIONS['detector_transpose']:\n",
    "        detector_processor.segmented_detector = detector_processor.segmented_detector.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f8a5ac14-69bc-44fa-b1bf-dc82c438873f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax0 = plt.subplots(1, 1, figsize=(3, 3))\n",
    "\n",
    "ax0.set_title(f'Detector segments')\n",
    "ax0.imshow(detector_processor.segmented_detector, cmap='grey')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb74ba3-f0eb-4282-a902-849d90c041f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dd14a5ad-133e-49c9-b26d-1476deb22573",
   "metadata": {},
   "source": [
    "#### Zones visualization. To draw zones on a detector..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7b6bc396-cfbb-40a0-9bfe-e12987f9e808",
   "metadata": {},
   "outputs": [],
   "source": [
    "ZONES_HIGHLIGHT_COLOR = 'w'\n",
    "ZONES_LW = 0.5\n",
    "selected_detector_mask = detector_processor.segmented_detector.clone().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bd154079-4d24-4787-9304-df43fd2b7d3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_zones_patches(detector_mask):\n",
    "    \"\"\"\n",
    "    Returns a list of patches to draw zones in final visualisation\n",
    "    \"\"\"\n",
    "    zones_patches = []\n",
    "    \n",
    "    if EXP_CONDITIONS['detector_zones'] == 'circles':\n",
    "        for ind_class in range(number_of_classes):\n",
    "            # use `circles_radiuses`, `x_layer_size_m`, `x_layer_nodes`\n",
    "            rad_this = (circles_radiuses[ind_class] / x_layer_size_m * x_layer_nodes)\n",
    "                    \n",
    "            zone_circ = patches.Circle(\n",
    "                (x_layer_nodes / 2, y_layer_nodes / 2), \n",
    "                rad_this, \n",
    "                linewidth=ZONES_LW, \n",
    "                edgecolor=ZONES_HIGHLIGHT_COLOR,\n",
    "                facecolor='none'\n",
    "            )\n",
    "            \n",
    "            zones_patches.append(zone_circ)\n",
    "    else:\n",
    "        if EXP_CONDITIONS['detector_zones'] == 'segments':\n",
    "            class_segment_angle = 2 * torch.pi / number_of_classes\n",
    "            len_lines_nodes = int(x_layer_nodes / 2)\n",
    "\n",
    "            delta = 0.5\n",
    "            idx_y, idx_x = (detector_mask > -1).nonzero(as_tuple=True)\n",
    "            zone_rect = patches.Rectangle(\n",
    "                (idx_x[0] - delta, idx_y[0] - delta), \n",
    "                idx_x[-1] - idx_x[0] + 2 * delta, idx_y[-1] - idx_y[0] + 2 * delta, \n",
    "                linewidth=ZONES_LW, \n",
    "                edgecolor=ZONES_HIGHLIGHT_COLOR,\n",
    "                facecolor='none'\n",
    "            )\n",
    "            zones_patches.append(zone_rect)\n",
    "            \n",
    "            ang = torch.pi\n",
    "            x_center, y_center = int(x_layer_nodes / 2), int(y_layer_nodes / 2)\n",
    "            for ind_class in range(number_of_classes):\n",
    "                path_line = Path(\n",
    "                    [\n",
    "                        (x_center, y_center), \n",
    "                        (\n",
    "                            x_center + len_lines_nodes * np.cos(ang), \n",
    "                            y_center + len_lines_nodes * np.sin(ang)\n",
    "                        ),\n",
    "                    ],\n",
    "                    [\n",
    "                        Path.MOVETO,\n",
    "                        Path.LINETO\n",
    "                    ]\n",
    "                )\n",
    "                bound_line = patches.PathPatch(\n",
    "                    path_line, \n",
    "                    facecolor='none', \n",
    "                    lw=ZONES_LW,\n",
    "                    edgecolor=ZONES_HIGHLIGHT_COLOR,\n",
    "                )\n",
    "\n",
    "                zones_patches.append(bound_line)\n",
    "                \n",
    "                ang += class_segment_angle\n",
    "        else:\n",
    "            delta = 0.5\n",
    "            \n",
    "            for ind_class in range(number_of_classes):\n",
    "                idx_y, idx_x = (detector_mask == ind_class).nonzero(as_tuple=True)\n",
    "                \n",
    "                zone_rect = patches.Rectangle(\n",
    "                    (idx_x[0] - delta, idx_y[0] - delta), \n",
    "                    idx_x[-1] - idx_x[0] + 2 * delta, idx_y[-1] - idx_y[0] + 2 * delta, \n",
    "                    linewidth=ZONES_LW, \n",
    "                    edgecolor=ZONES_HIGHLIGHT_COLOR,\n",
    "                    facecolor='none'\n",
    "                )\n",
    "                \n",
    "                zones_patches.append(zone_rect)\n",
    "\n",
    "    return zones_patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aee6a5ec-6c7a-42c1-af40-5f5f8189925c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b9bf3718-ed29-4fcc-9474-c5d7909b9970",
   "metadata": {},
   "source": [
    "## 4. Training of the network\n",
    "\n",
    "Variables at the moment\n",
    "- `lin_optical_setup` : `LinearOpticalSetup` – a linear optical network composed of Elements\n",
    "- `detector_processor` : `DetectorProcessorClf` – this layer process an image from the detector and calculates probabilities of belonging to classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9c37298b-6f84-4586-81d2-80268e84c566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# if DEVICE == torch.device('cpu'):\n",
    "#     DEVICE = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')\n",
    "    \n",
    "DEVICE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78553183-6043-4f46-8910-e33f0dc3a1a3",
   "metadata": {},
   "source": [
    "## 4.1. Prepare some stuff for training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec8cc8c-1876-4790-a012-48f2474e7e74",
   "metadata": {},
   "source": [
    "### 4.1.1. `DataLoader`'s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d3dd9a8b-3e30-44df-8335-099fb140cd62",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_bs = EXP_CONDITIONS['train_bs']  # a batch size for training set\n",
    "val_bs = EXP_CONDITIONS['val_bs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d742c8-28c6-428e-971d-7523dc565b0d",
   "metadata": {},
   "source": [
    "> Forthis task, phase-only transmission masks weredesigned by training a five-layer $D^2 NN$ with $55000$ images ($5000$ validation images) from theMNIST (Modified National Institute of Stan-dards and Technology) handwritten digit data-base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1eaacee1-7361-4d4c-83d6-815ccebaebef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mnist_wf_train_ds\n",
    "train_wf_ds, val_wf_ds = torch.utils.data.random_split(\n",
    "    dataset=mnist_wf_train_ds,\n",
    "    lengths=[55000, 5000],  # sizes from the article\n",
    "    generator=torch.Generator().manual_seed(EXP_CONDITIONS['train_split_seed'])  # for reproducibility\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "43c3fbef-5a00-4784-a5c8-85c47231890e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_wf_loader = torch.utils.data.DataLoader(\n",
    "    train_wf_ds,\n",
    "    batch_size=train_bs,\n",
    "    shuffle=True,\n",
    "    # num_workers=2,\n",
    "    drop_last=False,\n",
    ")\n",
    "\n",
    "val_wf_loader = torch.utils.data.DataLoader(\n",
    "    val_wf_ds,\n",
    "    batch_size=val_bs,\n",
    "    shuffle=False,\n",
    "    # num_workers=2,\n",
    "    drop_last=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff84f0d-925b-407c-a506-4d02c35fd4ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ffb8ff99-0e12-4502-9219-40718ef15852",
   "metadata": {},
   "source": [
    "### 4.1.2. Optimizer and loss function\n",
    "\n",
    "Info from [a supplementary material](https://www.science.org/doi/suppl/10.1126/science.aat8084/suppl_file/aat8084-lin-sm-rev-3.pdf) for MNIST classification:\n",
    "\n",
    "> We used the stochastic gradient descent algorithm, Adam, to back-propagate the errors and update the\n",
    "layers of the network to minimize the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8fbf825c-e9ca-4f94-b1d6-da4921748cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_clf = torch.optim.Adam(\n",
    "    params=lin_optical_setup.net.parameters()  # NETWORK PARAMETERS!\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "390a425d-cff3-44ba-81f6-2fe467c52a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_func_clf = nn.CrossEntropyLoss()\n",
    "loss_func_name = 'CE loss'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09af0a6d-09b9-4235-b10d-86cd5af1b178",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a7381166-44cd-4136-a67b-e703fd1d8509",
   "metadata": {},
   "source": [
    "### 4.1.3. Training and evaluation loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f30d9f5b-57d5-4b53-8e71-637536583b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.clf_loops import onn_train_clf, onn_validate_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea484d71-20a9-4590-be00-32eb052cedbc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5916b72f-dacb-47b0-a41c-853b430f4f89",
   "metadata": {},
   "source": [
    "## 4.2. Training of the optical network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d19488d-031b-43ed-909c-562fbeb76c59",
   "metadata": {},
   "source": [
    "### 4.2.1. Before training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224a3df0-4bc1-41e0-aaa0-fc75c04f3f8c",
   "metadata": {},
   "source": [
    "#### Diffractive layers masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "23349d93-c9ea-46bc-a833-a0af29a1275b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x320 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = NUM_OF_DIFF_LAYERS  # number of columns for DiffractiveLayer's masks visualization\n",
    "n_rows = 1\n",
    "\n",
    "lin_architecture_elements_list = get_elements_list(\n",
    "    NUM_OF_DIFF_LAYERS,\n",
    "    SIM_PARAMS,\n",
    "    FREESPACE_METHOD,\n",
    "    MASKS_SEEDS,\n",
    "    apertures=ADD_APERTURES,\n",
    "    aperture_size=APERTURE_SZ\n",
    ")\n",
    "\n",
    "cmap = 'gist_stern'\n",
    "\n",
    "# plot wavefronts phase\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 3, n_rows * 3.2))\n",
    "ind_diff_layer = 0\n",
    "\n",
    "for ind_layer, layer in enumerate(lin_architecture_elements_list):\n",
    "    if isinstance(layer, elements.DiffractiveLayer):  # plot masks for Diffractive layers\n",
    "        if n_rows > 1:\n",
    "            ax_this = axs[ind_diff_layer // n_cols][ind_diff_layer % n_cols]\n",
    "        else:\n",
    "            ax_this = axs[ind_diff_layer % n_cols]\n",
    "\n",
    "        ax_this.set_title(f'{ind_layer}. DiffractiveLayer')\n",
    "\n",
    "        im = ax_this.imshow(\n",
    "            layer.mask.detach().numpy(), cmap=cmap,\n",
    "            vmin=0, vmax=MAX_PHASE\n",
    "        )\n",
    "        \n",
    "        ind_diff_layer += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "993952bc-6a92-40f0-877d-4c55cc765be9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9261ceec-dae5-4be3-950c-390991f8879d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "76c5c93e-de70-415c-a940-4bf1cb03d5e6",
   "metadata": {},
   "source": [
    "#### Metrics for test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "99c7df68-46cc-4217-a9c1-1a332f9b9764",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_optical_setup = get_setup(\n",
    "    SIM_PARAMS.to(DEVICE),\n",
    "    NUM_OF_DIFF_LAYERS,\n",
    "    apertures=ADD_APERTURES, \n",
    "    aperture_size=APERTURE_SZ\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "779d1c65-4ad8-47b3-85b3-7a9ee0427a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_optical_setup.net = lin_optical_setup.net.to(DEVICE)\n",
    "\n",
    "SIM_PARAMS = SIM_PARAMS.to(DEVICE)  # IMPORTANT!\n",
    "detector_processor = detector_processor.to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74746528-f628-4a98-bae6-9a66450774c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0523bf4f-a304-4240-aae7-f12ff846565e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_wf_loader = torch.utils.data.DataLoader(\n",
    "    mnist_wf_test_ds,\n",
    "    batch_size=10,\n",
    "    shuffle=False,\n",
    "    # num_workers=2,\n",
    "    drop_last=False,\n",
    ")  # data loader for a test MNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9f6932db-44c5-439a-a14f-c06b5e645f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:19<00:00, 50.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results before training on TEST set:\n",
      "\tCE loss : 2.304648\n",
      "\tAccuracy : 9.7 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_losses_0, _, test_accuracy_0 = onn_validate_clf(\n",
    "    lin_optical_setup.net,  # optical network composed in 3.\n",
    "    test_wf_loader,  # dataloader of training set\n",
    "    detector_processor,  # detector processor\n",
    "    loss_func_clf,\n",
    "    device=DEVICE,\n",
    "    show_process=True,\n",
    ")  # evaluate the model\n",
    "\n",
    "print(\n",
    "    'Results before training on TEST set:\\n' + \n",
    "    f'\\t{loss_func_name} : {np.mean(test_losses_0):.6f}\\n' +\n",
    "    f'\\tAccuracy : {(test_accuracy_0*100):>0.1f} %'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6142a33b-e47f-4283-bc30-13165b96fedf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "de9a5938-095b-4e9a-8994-1e82ccdc2382",
   "metadata": {},
   "source": [
    "### 4.2.2. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2ce8ef8d-dd2a-4bb6-8b6d-ca3792dbc6c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_epochs = EXP_CONDITIONS['epochs']\n",
    "print_each = 1  # print each n'th epoch info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6adb8840-df52-425c-bacf-0cfea6e8c1da",
   "metadata": {},
   "outputs": [],
   "source": [
    "scheduler = None  # sheduler for a lr tuning during training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c58a2fc5-fed3-4730-ae75-bb814ab0daf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recreate a system to restart training!\n",
    "\n",
    "lin_optical_setup = get_setup(\n",
    "    SIM_PARAMS.to(DEVICE),\n",
    "    NUM_OF_DIFF_LAYERS,\n",
    "    apertures=ADD_APERTURES, \n",
    "    aperture_size=APERTURE_SZ\n",
    ")\n",
    "\n",
    "# Linc optimizer to a recreated net!\n",
    "optimizer_clf = torch.optim.Adam(\n",
    "    params=lin_optical_setup.net.parameters()  # NETWORK PARAMETERS!\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "823e10fb-b4ca-4824-a88b-e4947ca37335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): FreeSpace()\n",
       "  (1): Aperture()\n",
       "  (2): DiffractiveLayer()\n",
       "  (3): FreeSpace()\n",
       "  (4): Aperture()\n",
       "  (5): DiffractiveLayer()\n",
       "  (6): FreeSpace()\n",
       "  (7): Aperture()\n",
       "  (8): DiffractiveLayer()\n",
       "  (9): FreeSpace()\n",
       "  (10): Aperture()\n",
       "  (11): DiffractiveLayer()\n",
       "  (12): FreeSpace()\n",
       "  (13): Aperture()\n",
       "  (14): DiffractiveLayer()\n",
       "  (15): FreeSpace()\n",
       "  (16): Detector()\n",
       ")"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_optical_setup.net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b19fb2f0-0f23-4df4-8f83-8f9acb1d550c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_optical_setup.net = lin_optical_setup.net.to(DEVICE)\n",
    "\n",
    "SIM_PARAMS = SIM_PARAMS.to(DEVICE)  # IMPORTANT!\n",
    "detector_processor = detector_processor.to(DEVICE)  # detector processor also must be on device!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "af7cc0dd-9f44-45ee-9dbb-d2a0e3fcfca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorboard writer created!\n"
     ]
    }
   ],
   "source": [
    "# tensorboard writer\n",
    "if EXP_CONDITIONS['tensorboard']:\n",
    "    # TODO: A custom name for a run?\n",
    "    tensorboard_writer = SummaryWriter()\n",
    "    print('Tensorboard writer created!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f4517be-7929-43e4-b2cf-bd2ab1e0069c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "158d9843-5ca6-42bc-84b2-eb67f25fcc6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d0327fee-335a-4597-b8fc-586f8902d524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [05:09<00:00, 22.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 2.026149\n",
      "\tAccuracy : 69.7 %\n",
      "\t------------   309.92 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:16<00:00, 15.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.976295\n",
      "\tAccuracy : 76.0 %\n",
      "\t------------   16.36 s\n",
      "\t-> tensorboarded\n",
      "Epoch #2: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [05:25<00:00, 21.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.954368\n",
      "\tAccuracy : 78.8 %\n",
      "\t------------   325.61 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:17<00:00, 14.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.944904\n",
      "\tAccuracy : 78.9 %\n",
      "\t------------   17.38 s\n",
      "\t-> tensorboarded\n",
      "Epoch #3: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [05:15<00:00, 21.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.932177\n",
      "\tAccuracy : 80.4 %\n",
      "\t------------   315.66 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:18<00:00, 13.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.927474\n",
      "\tAccuracy : 80.7 %\n",
      "\t------------   18.08 s\n",
      "\t-> tensorboarded\n",
      "Epoch #4: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:05<00:00, 18.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.919771\n",
      "\tAccuracy : 81.1 %\n",
      "\t------------   365.97 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:19<00:00, 12.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.918410\n",
      "\tAccuracy : 81.6 %\n",
      "\t------------   19.74 s\n",
      "\t-> tensorboarded\n",
      "Epoch #5: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:09<00:00, 18.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.911985\n",
      "\tAccuracy : 81.5 %\n",
      "\t------------   369.08 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:21<00:00, 11.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.911945\n",
      "\tAccuracy : 81.1 %\n",
      "\t------------   21.25 s\n",
      "\t-> tensorboarded\n",
      "Epoch #6: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:21<00:00, 18.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.906370\n",
      "\tAccuracy : 81.9 %\n",
      "\t------------   381.45 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:19<00:00, 12.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.906746\n",
      "\tAccuracy : 82.1 %\n",
      "\t------------   19.66 s\n",
      "\t-> tensorboarded\n",
      "Epoch #7: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:52<00:00, 16.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.902148\n",
      "\tAccuracy : 82.1 %\n",
      "\t------------   412.09 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:20<00:00, 12.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.901731\n",
      "\tAccuracy : 81.5 %\n",
      "\t------------   20.67 s\n",
      "\t-> tensorboarded\n",
      "Epoch #8: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:38<00:00, 17.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.899022\n",
      "\tAccuracy : 82.0 %\n",
      "\t------------   398.52 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:19<00:00, 12.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.900397\n",
      "\tAccuracy : 80.5 %\n",
      "\t------------   19.51 s\n",
      "\t-> tensorboarded\n",
      "Epoch #9: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [07:19<00:00, 15.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.896814\n",
      "\tAccuracy : 82.2 %\n",
      "\t------------   439.46 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:20<00:00, 12.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.898544\n",
      "\tAccuracy : 82.6 %\n",
      "\t------------   20.02 s\n",
      "\t-> tensorboarded\n",
      "Epoch #10: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train: 100%|█████████████████████████████████████████████████████████████████████████████| 6875/6875 [06:51<00:00, 16.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training results\n",
      "\tCE loss : 1.894883\n",
      "\tAccuracy : 82.3 %\n",
      "\t------------   411.80 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|██████████████████████████████████████████████████████████████████████████| 250/250 [00:17<00:00, 14.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation results\n",
      "\tCE loss : 1.896155\n",
      "\tAccuracy : 81.4 %\n",
      "\t------------   17.83 s\n",
      "\t-> tensorboarded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_epochs_losses = []\n",
    "val_epochs_losses = []  # to store losses of each epoch\n",
    "\n",
    "train_epochs_acc = []\n",
    "val_epochs_acc = []  # to store accuracies\n",
    "\n",
    "torch.manual_seed(98)  # for reproducability?\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    if (epoch == 0) or ((epoch + 1) % print_each == 0):\n",
    "        print(f'Epoch #{epoch + 1}: ', end='')\n",
    "        show_progress = True\n",
    "    else:\n",
    "        show_progress = False\n",
    "\n",
    "    # TRAIN\n",
    "    start_train_time = time.time()  # start time of the epoch (train)\n",
    "    train_losses, _, train_accuracy = onn_train_clf(\n",
    "        lin_optical_setup.net,  # optical network composed in 3.\n",
    "        train_wf_loader,  # dataloader of training set\n",
    "        detector_processor,  # detector processor\n",
    "        loss_func_clf,\n",
    "        optimizer_clf,\n",
    "        device=DEVICE,\n",
    "        show_process=show_progress,\n",
    "    )  # train the model\n",
    "    mean_train_loss = np.mean(train_losses)\n",
    "    \n",
    "    if (epoch == 0) or ((epoch + 1) % print_each == 0):  # train info\n",
    "        print('Training results')\n",
    "        print(f'\\t{loss_func_name} : {mean_train_loss:.6f}')\n",
    "        print(f'\\tAccuracy : {(train_accuracy*100):>0.1f} %')\n",
    "        print(f'\\t------------   {time.time() - start_train_time:.2f} s')\n",
    "\n",
    "    # VALIDATION\n",
    "    start_val_time = time.time()  # start time of the epoch (validation)\n",
    "    val_losses, _, val_accuracy = onn_validate_clf(\n",
    "        lin_optical_setup.net,  # optical network composed in 3.\n",
    "        val_wf_loader,  # dataloader of validation set\n",
    "        detector_processor,  # detector processor\n",
    "        loss_func_clf,\n",
    "        device=DEVICE,\n",
    "        show_process=show_progress,\n",
    "    )  # evaluate the model\n",
    "    mean_val_loss = np.mean(val_losses)\n",
    "    \n",
    "    if (epoch == 0) or ((epoch + 1) % print_each == 0):  # validation info\n",
    "        print('Validation results')\n",
    "        print(f'\\t{loss_func_name} : {mean_val_loss:.6f}')\n",
    "        print(f'\\tAccuracy : {(val_accuracy*100):>0.1f} %')\n",
    "        print(f'\\t------------   {time.time() - start_val_time:.2f} s')\n",
    "            \n",
    "    if scheduler:\n",
    "        scheduler.step(mean_val_loss)\n",
    "\n",
    "    # ---------------------------------------------------- TENSORBOARD SECTION\n",
    "    if EXP_CONDITIONS['tensorboard']:\n",
    "        #exprimentation tracking section: tensorboard\n",
    "        tensorboard_writer.add_scalars(\n",
    "            main_tag=\"Loss\",\n",
    "            tag_scalar_dict={\n",
    "                \"train_loss\": mean_train_loss,\n",
    "                \"val_loss\": mean_val_loss,\n",
    "                \"train_accuracy\": train_accuracy,\n",
    "                \"val_accuracy\": val_accuracy,\n",
    "            },\n",
    "            global_step=epoch\n",
    "        )\n",
    "    \n",
    "        # image of SLM at each epoch\n",
    "        diff_layer_number = 1\n",
    "        for layer in lin_optical_setup.net:\n",
    "            # save masks for Diffractive layers after each epoch\n",
    "            if isinstance(layer, elements.DiffractiveLayer):\n",
    "                mask_np = layer.mask.detach().unsqueeze(0).numpy()\n",
    "\n",
    "                # TODO: Figure to add?\n",
    "                # fig_this, ax_this =  plt.subplots(1, 1, figsize=(5, 4))\n",
    "                # im_this = ax_this.imshow(\n",
    "                #     layer.mask.detach().numpy(), cmap=cmap,\n",
    "                #     vmin=0, vmax=MAX_PHASE\n",
    "                # )\n",
    "                # cbar_this = fig.colorbar(im_this)\n",
    "                # im_this.set_clim(0, MAX_PHASE)\n",
    "                \n",
    "                # WRITE\n",
    "                tensorboard_writer.add_image(\n",
    "                    f'DiffractiveLayer_{diff_layer_number}',\n",
    "                    mask_np,\n",
    "                    global_step=epoch\n",
    "                )\n",
    "                diff_layer_number += 1\n",
    "        print(f'\\t-> tensorboarded')\n",
    "    # ---------------------------------------------------- TENSORBOARD SECTION\n",
    "    \n",
    "    # save losses\n",
    "    train_epochs_losses.append(mean_train_loss)\n",
    "    val_epochs_losses.append(mean_val_loss)\n",
    "    # seve accuracies\n",
    "    train_epochs_acc.append(train_accuracy)\n",
    "    val_epochs_acc.append(val_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8685b519-d6ff-430f-a899-a8c2d2326a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "if EXP_CONDITIONS['tensorboard']:\n",
    "    tensorboard_writer.flush()\n",
    "    tensorboard_writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a5f857-6d03-423c-96af-315fa786afad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# run tensorboard\n",
    "# !tensorboard --logdir=runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bade9ab1-0da9-4e8c-8a73-6e46e90a574a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "16f1f83d-5d45-47b5-9186-41eef968ef93",
   "metadata": {},
   "source": [
    "### Learning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "540a4c28-76ca-4821-ada5-c23bd04cbf96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 3))\n",
    "\n",
    "axs[0].plot(range(1, n_epochs + 1), train_epochs_losses, label='train')\n",
    "axs[0].plot(range(1, n_epochs + 1), val_epochs_losses, linestyle='dashed', label='validation')\n",
    "\n",
    "axs[1].plot(range(1, n_epochs + 1), train_epochs_acc, label='train')\n",
    "axs[1].plot(range(1, n_epochs + 1), val_epochs_acc, linestyle='dashed', label='validation')\n",
    "\n",
    "axs[0].set_ylabel(loss_func_name)\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].legend()\n",
    "\n",
    "axs[1].set_ylabel('Accuracy')\n",
    "axs[1].set_xlabel('Epoch')\n",
    "axs[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "612833fc-dcbb-4de6-9eea-aaf6cc9d4862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# array with all losses\n",
    "# TODO: make with PANDAS?\n",
    "all_lasses_header = ','.join([\n",
    "    f'{loss_func_name.split()[0]}_train', f'{loss_func_name.split()[0]}_val',\n",
    "    'accuracy_train', 'accuracy_val'\n",
    "])\n",
    "all_losses_array = np.array(\n",
    "    [train_epochs_losses, val_epochs_losses, train_epochs_acc, val_epochs_acc]\n",
    ").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22333179-61de-4bb4-8061-da15cf85a258",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a934366-eddf-46ec-b526-14d3323854ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "262e94d6-9692-44b1-ba4a-b35b4358abb0",
   "metadata": {},
   "source": [
    "### Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "a14343ae-bec9-487d-b624-3284e2b830ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'models/03_mnist_experiments/13-12-2024_experiment_01'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RESULTS_FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5d598843-5494-4c0c-b9fe-3b4d98239684",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(RESULTS_FOLDER):\n",
    "    os.makedirs(RESULTS_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2ddecfd6-3d27-414b-b6a0-9bdcafc84c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath to save the model\n",
    "model_filepath = f'{RESULTS_FOLDER}/optical_setup_net.pth'\n",
    "# filepath to save losses\n",
    "losses_filepath = f'{RESULTS_FOLDER}/training_curves.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "51b40f46-241a-4728-9518-05153cee6475",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving model\n",
    "torch.save(lin_optical_setup.net.state_dict(), model_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "daacb87d-27b5-4bc5-9071-861e8915b35c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving losses\n",
    "np.savetxt(\n",
    "    losses_filepath, all_losses_array,\n",
    "    delimiter=',', header=all_lasses_header, comments=\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87de01af-3d6e-4802-b1a0-6321be4da8c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "791c3907-0626-4acb-b44e-19d43c5d872a",
   "metadata": {},
   "source": [
    "### 4.2.3. Trained masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6d6f7924-8e90-4eb9-8158-ec01ea559ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33nl4+umncfbZZ3c6/m9/+xsaGhrwu9/9DgUFBd2uN9FzXnvtNVx11VV48MEHMX/+fDQ1NeGYY47Bj3/8Y9x11119smpL8kvySww9HnnkEZx88sk48cQT8eWXX3b6/vXXX8fcuXPxf//3f3jppZewaNEiALb8xGIx3HHHHfjXv/6F//73v0gkElnHPv744/jyyy9xzTXXeLHIuvOcA0BVVRXeffddFBcX47777sOaNWswfPhwnHPOOQiFQnj99ddx55134rLLLsMf/vAHfPHFFwDg/fVTX1+P5cuX47zzzsPvfve7rO9mz54NwzDw+OOPA7AtLpYvX47hw4fj3nvvRU1NDY455hjcdNNNqKqqwi9+8Ysete+8efOQSCRw2223IZFI4Ktf/SpuvPFGFBYW4te//rX3G8yfPx+zZ8/G3Xff7R0rSRLOOeccPPHEE57nyNy5c/Hwww/jxRdfxJVXXolQKIQf//jHWLlyJQ499NCs+E6iKOLFF1/EypUr8atf/YqswfqJI444AhdeeCGOO+64PrdstiwLixcvxu9//3scd9xx+O9//9tpnyeffDKnrNbV1WH79u249tprceedd+K9997L6vO6en7OPfdchEIh3HPPPWhqasKRRx6JSy+9FCNGjMB5553nHX/QQQdhxYoV0HUd9913HzZv3oxx48bhtNNOw3XXXYcnn3wSEydOxPnnn4/LL78cjY2NAOyxQUdWrVqFDRs24LzzzvPeQS6zZ89Gc3MzXnzxRQA9n3/ujh//+Mf4/PPP8cwzz8AwDJx22mm45557wPM8/va3vwGwZfjCCy/EySefjOeee847dtiwYfjqV7+K3/72t962nszdS0tL8fzzz+PRRx/F//3f/2X9Pv3FgGuf86XMmTOHMcY6aVy7W3ZnEQaAffjhh6ypqcn73NGaCuh69SuXRt3VBsdisU4rMTzPd4ovUFRUxHbs2MEeeOABb5u7QnvHHXfs8v66ihHW8R4ikQhLpVLs1ltvzdrvV7/6FTNNk40cOZIBtgWXruvs6quvztpvypQpTNO0rO3dsQjLpck/6qijGGOMzZ0719v2gx/8gDHG2KRJk7xtoiiy+vr6rPu7//772fbt2zut8v3rX/9iLS0t3vXc3339+vU560Clb8r06dPZ448/zi666CJ22mmnsSuvvJI1NDSwZDLJDjnkkD065+4sSgCwlpYWtmrVKu9zR2sMoOvVr02bNrGlS5dmbdvV8yPLcqcV3NGjR7NUKsWuu+46b9u8efMYY7ktHd2yqxhDHe9hwoQJjLHsFWYA7K9//Stra2vz6nnssccyxhj7zne+k7Wfu7rl394di5Jc8jN79mzGGGPHHXect+0Pf/gDS6VSWauHZWVlTNO0rPt7+eWX2ccff8xkWc4658qVK9natWs7/e6vv/561io3lb4t1157LWtvb89aSd2bWD0kv/Znkt+Bf7aHahmMFmEHH3wwY4yxv/zlL942vzWVWxjrbP3UlUWFK1P//Oc/O12vu8/5Qw89xAzD2OX7aFcxwjregzt27RjH+LPPPmOvvPKK9/naa69l8XicjR8/Pmu/P/7xj0zX9SyrjVxt0p37veeee1gikciSzTfeeIO99dZbWfudccYZWfdXUFDAmpub2b333pu1X0VFBWtpacna7lr7/PGPfxzw534ole7I8Ntvv+09+70Rh2p3/ce3vvUtxhhjl156qbfNb021q3p0Nffe1fOT65m+8sors+aoANhrr73GWltbs7Z1LLuKEdbxHv7whz+wdDqdZXEtSRJrbm7Ompd3d/7Z3d8m1/0+//zzbP369d5njuNYTU0NW7x4cdZ+l19+OTNNk40ZM4YBPZu7L1u2jDHG2CWXXDJgzztljewmkyZNwt13340333wTDz/8cJ9dJ5FIIBKJ9Pp5n3jiCU8T7WJZlhdjgOM4RKNRiKKI999/H4cddpi339lnnw3LsrK0vXtDPB7H888/n6VVB2yN99tvv42tW7cCAM466yzwPI/HHnsMpaWlXtm5cye+/PJLnHDCCT26rhvzBbBXAUpKSrB+/Xq0tLRk3e9jjz2GVCqFOXPmeNtOPvlklJeX4//+7/+8bWeffTaWLl0KjuOy6vfiiy+iuLg465yAnc3EXweib3nrrbdw7rnnYuHChVi6dCluueUWHH300WCM4aabbuqz6/aVDOd6fvxxhnieR0lJCRKJBNauXdtJhhsaGnot+9OXX36JDz/8ELNnz/a28TyPc845B0uXLvXqee655yIWi+Hll1/OkpFVq1YhHo/vlQwHAgGUlpZ61iH++120aBEURcE555zjbZs9ezYkSfJkOBqN4qtf/Soee+wxRCKRTjI8ceJEVFdXZ13//vvvh2VZPaozseds3rwZr7/+On7wgx/grLPOwoMPPohrrrkGP/3pT/vsmiS/JL/E0MK14uoLuf773//eaVt3nnOO43DGGWdg6dKlvWah/uSTT0LX9Sy5njJlCqZMmYIlS5Z4284991ysWLECLS0tWXLzyiuvQBRFHH/88T26rv9+w+EwSktLsWLFChQUFGD//ff3vlu0aBGOPvpojB071ts2Z84c1NTUYPny5QBsa7poNIrFixdn1c00Tbzzzjs53zn33HNPj+pL7B3z5s3DQQcdhCuvvLLfrtmXMpzr+fE/06FQCKWlpXjzzTfB8zwOPfRQAEBZWRlmzJiBf/zjH968dW9ZsmQJZFnGWWed5W076aSTEI1Gs2S4p/PP3eG/38LCQpSWlmL58uUYN24cCgsLAQCMMfzzn//E6aefnpVhe86cOXjzzTexefNmAD2fu6uqioULF/aovr0JKcK6wbBhw/Dcc8+htbUV55xzTp8OpMLhMOLxeK+fd9OmTTm3f/e738XHH38MVVXR3NyMxsZGnHrqqVlpXseNG4fa2lq0tLT0Wn2WLFniuUICwNixY3H44YdnCfqECRPA8zzWr1+PxsbGrDJ58mQvAGJ3URQFv/3tb1FTU4N0Oo2mpiY0NjYiGo1m3W9rayuWLl2K888/39s2Z84cbNu2Da+++ioAOyhjNBrFD3/4w051e+ihhwCgU/26+g2I/mPDhg34z3/+gxNOOAE83zevv/6UYY7jcPnll2PdunVZz/TBBx/cSYbXrl3bqwGzlyxZgmOPPdabbM6cORPDhg3rJMPFxcVoaGjoJCeRSKTHMhyNRnHHHXdg586dUFUVjY2NXufrv9+1a9fi3XffzVJmz5kzB2+99Zbnsj1+/HjwPI/f//73nermupaQDA8cs2fPxn333YeLL74YDzzwAJ566ilcfPHFePjhh3HLLbegpKSkT65L8kvySwwt3Elbf8l1d57z8vJyFBUV4bPPPuu1ujQ1NeF///tf1iLz7Nmzoeu65y4N2HJ9yimndJKb//3vfwA6y83umDx5sueaFo/H0djY6AU998v1kiVLoKqqJ9eFhYU49dRTswKkT5gwAQCwbNmyTvU7+eSTO9VN13Vs27atR/Ul9pxIJIKbbroJt956a7+2e1/JcFfPz8iRI7Fw4UI0NTWhvb0djY2NeP311wFknmlXodubMvzJJ5/giy++yFJmz549Gw0NDXs1/9wdxxxzDF5++WUkEgm0traisbHRMxjwy/CiRYsQCoVw5plnAgAmTpyIww8/HI888oi3T0/n7tu3bx/QxB8UI2w3FBYW4vnnn0dxcTG+8pWveDE7+gJRFDFx4sReFSqXXBkY5syZg4cffhhPPfUUbr31VtTX18M0TVx99dUYN25cr9fBz9KlS714Hm+99RbOO+88mKbpxTAA7BVqy7Jwyimn5JwAdIzVsDsWLFiAiy66CHfccQfeeusttLa2gjGGRx99tJNSZNGiRTjvvPMwffp0fPrppzj99NPxt7/9LWv1HrB9pruyEOwYn6W/s2AQudm6dSsCgQAKCgp6vVMdPnw4iouLsX79+l49L5D7+bnmmmvw+9//Hg8++CCuv/56NDc3w7Is3HHHHX2m6HNZsmQJbr75Zpx77rm48847cd555yEWi+GFF17w9uF5HnV1dVkTWj+54iTsisceewzHHHMMbr31Vnz00UdIJBLgeR4vvvhiThm+8847MXz4cAQCAUyfPj3Lksjd/9Zbb/XiLnSk4+9IMtx//OQnP8GHH36I7du3Z21/5plncNFFF+HQQw/1Jm69BckvyS8x9DjwwAMBdH4eeoNcz1RPnvPe5tFHH8VDDz2Egw8+GB9//DHOO+88/O9//0NTU5O3D8/zeOmll/CnP/0p5znWrVvX7esVFRVh+fLlaGtrw29+8xts2LABqqrisMMOw5/+9Kes+43FYnj22WcxZ84c3HjjjTjnnHOgKEqWp4W7/9y5c7Fz585O1+sYkzCdTlP29X7kV7/6FWRZxpIlSzB69GgAwIgRIwDYCuDRo0ejtra21xUbfSXDuZ4fnufx8ssvo6SkBLfccgvWrFmD9vZ2DB8+HA8//HC/9M3XXnstSktLEY/Hcfrpp2Px4sXePHhP5p+7YuzYsfjf//6HNWvW4Je//CW2bt0KTdPwjW98A7/85S+z7veLL77A+++/j7lz5+KRRx7B3LlzkU6n8dhjj3n79HTuPtD9MinCdkEgEMDSpUsxceJEzJo1K2eAyt7EDZDZ1aCuL663YcOGLBNMAJ1cIDds2ICTTz4Z0Wh0l1ZhPemMkskknn32WZx77rn45S9/idmzZ2PFihVZisYNGzaA53ls2rQpZ4DTnnLOOefg4YcfzgrGHwgEUFxc3GnfF154AfX19ZgzZw7eeecdFBQUZGm8Gxoa0NbWBkEQen0yRvQtY8eORSqV6rEitTtccMEFANCvMvzqq696iTNciouLs1yhN2zYgKOOOgqiKHYZ3Lqng8nNmzfjnXfewezZs/HXv/4VZ511Fp5++umsJBAbNmzArFmz8MYbb+y1W3BxcTFmzZqF3/zmN1mpusePH59z/0cffRS33XYbvvOd7yAYDELTtCxrl40bNwKwVwRJhgcfw4YNy9nfSJIEwF446m1Ifkl+iaHHBRdcAMuy8PLLL/f5tbr7nDc0NKC1tdWb4HdFT+X66aefRjqd9ixKJk2a1CkUxIYNGxAOh3tFbmbOnImysjKcddZZWLFihbd9v/32y7n/okWL8Mwzz+Dwww/HnDlz8MEHH2D16tVZdQPs4P8k14OPUaNGoaSkJOs3c7n22mtx7bXX4pBDDslKtra38DyP888/H+3t7Vi5cmWvnbcrDjroIEyaNAnf/e53s+Z9s2bNytrP7YN6W4bdZDZnn3026urqUFRUlJXAprfnn6eddhoURcHpp5+e5eLZVeiDRYsW4bbbbkNlZSXOP/98PPfcc4jFYt73vT1372vINbILeJ7HkiVLMH36dJx77rmef38uKisrMWnSpL0amE+dOhV33HEHmpubszKq9CWuptbNdAMARx55pOeu6PLEE0+A53nMnz9/l+drb2/PqVTqiiVLlmD48OG4+OKLccghh2QNcgE73oFhGF1et6euMaZpZt0rAFx66aU5fzfTNLF48WKcd955mDdvHj755BN8+umn3veWZeGJJ57A2WefjSlTpnQ6vqysrEd1I3qfXL/B1KlTcfrpp+Oll17K6pxGjhyJSZMm7dX1TjjhBFx//fXYuHFjlql/X5LrmT7nnHO8FTqXJ554AuXl5fjZz37W5bncbDk9leHp06fje9/7HsrLyzvJ8GOPPQZRFHH99dd3OlYQhCyT692R630FAJdffnnO/ZuamvD8889j7ty5mDNnDl544YWsVfGGhgYsW7YMP/zhD1FZWdnpeJLhgWXdunU49NBDPVcZl+985zswTTNrxZPkl+S3IyS/+c+kSZMwcuTIvTrHlVdeiZNPPhlLlizpE4uwjnT3OWeM4emnn8Zpp52GadOmdXm+9vZ2AN2X69bWVrz44os477zz8O1vfxvpdLpT9lTXYu2kk07qdHxRUREEQejWtYDc9ytJEn7yk5/k3P/5559HQ0MDrrzySsyYMSPLGgywFyFaW1txzTXX5Bybk1wPLHfddRfOOOOMrHLJJZcAsDO0n3HGGZ67sCiKmDRpUs73c3fheR533XUXJk+ejLvuuqtP3Js70pUMX3bZZVmfGxsbsXz5cnzve9/b5XuqpzK8Zs0afPLJJ5g9ezZmz56N2tpazy0T6P35Z677LSwsxEUXXZRz/8WLF4MxhjvvvBPjxo3rJMO9PXfva8girAv+8pe/4Fvf+haeeeYZlJSUdHIN8A+Ub7rpJsybNw9jxozJSuvbFV/5ylegKAoEQUBpaSmOPfZYnH766WhtbcWZZ57Zb6lDn332WZx99tl46qmn8Nxzz2G//fbDj370I6xevTorEN5rr72GRYsW4bLLLsOECRPwwgsvgOd5fOUrX8GyZcs8xd2qVaswa9Ys/OIXv0BtbS02bdqEd999t8vr//e//0VbWxv+/Oc/wzAMPPHEE1nfb9y4Eddddx1uvvlmjBkzBk8//TTi8Tj2228/nHnmmbjvvvu8NPWA3flee+21na7T3NyMe+65B88++ywuuOACtLa2YvXq1Zg+fTpmzZrVKYmAi3vPX/3qV70U0H6uuuoqnHDCCXjnnXdw//33Y/Xq1SgpKcFhhx2GWbNmobS0dNc/ANGnLFmyBKlUCm+++Sbq6+sxefJkXHLJJUgmk7jqqquy9l20aBFmzpzZqePrilNOOQX7778/RFH0UgefeOKJ2LJlC04//XQvDXhf8+yzz2L+/Pn4xz/+gTfffBMHHXQQ5syZ462quixatAjf/e53cfvtt+PII4/0AtnOmjULf/vb3/DMM89AVVV8/vnnmD17NtatW4fm5mZ89tln+Pzzz7u8/mOPPYY///nP+POf/4ympia88sorWd+//vrr+Pvf/45rrrkGhxxyCF566SXouo4JEybg3HPPxWWXXZYl9+Xl5TlleNOmTfjXv/6F5cuX49e//jUkScL27dtx0kkndbny7N63e/5ck/mf/vSnWLlyJT799FPcf//92LhxI4YNG4bp06djxIgROOSQQ7o8N9G33HrrrTjllFOwYsUK/PWvf0VTUxNOPfVUfOMb38D999+fZT1M8kvyS/I7sPz0pz9FcXGxF3PutNNO8xS6CxYsQFtbGwDbosS1vDz88MMBwHtmtmzZkjWpWrNmDV577bVuJWUQRdEbpyuKgtGjR+P000/HwQcfjFdffdWbrPc18Xi828/5Nddcg5NOOgnLly/Hfffdhy+++AJVVVU499xzcdxxx6G1tRUfffQRDMPAlVdeiaKiIqTTabz66qu7dEtesmQJ/vnPf+InP/mJp1jyc+utt+L000/Hs88+i4ceegirVq1CQUEBDjroIJxzzjkYM2ZMltL58MMPzynXr732Gt588000Nzfj4Ycfxl133QXGGC644IIu38WGYeDRRx/FpZdeCsMwsHjx4k7t9+Mf/xiPPPIIPvjgAzz66KNoaGjAqFGj8M1vfhNvvPEGLr300l3+BsSe0R0Z/vDDD/Hhhx9mHee6SH7++ef4z3/+420fPnw41qxZg4ceeqhLpYqfoqIiT4ZDoRDGjx+Ps846C+PHj8fixYtz9gF9wZo1a7B+/Xr8+c9/xvDhw9HW1oazzz4b0Wi0074///nPsXLlSnzwwQe47777sGnTJowZMwbf/OY3vaD6bjKMP/zhD3j00Ueh6zqWLl3qLV7lYsmSJfjd734HVVXx4IMPdrIq6+n882tf+xoURel0naeffhovvfQS0uk0li5dinvvvRfhcBg/+MEPUF9f3ynhDGArAF944QWcd955aGlpwXPPPZf1fU/n7oOBAUtZOZiLm9KzK/z7uilYc6VG9Rc3hatLOp1mdXV17LXXXmNXX301Kysr63SMm/bbf+6uUkXnSj+7u9SpV111Fdu0aRNLpVJs1apV7Bvf+AZbuHAh27RpU9Z+PM+zK664gq1evZqpqsrq6urYc889xw499FBvn4kTJ7LXXnvNS3fvpoTNdQ9ueeSRRxhjjL300ktdttuZZ57JXn/9dRaPx1k8HmerV69mCxYsYBMmTOj0G+Tiyy+/ZABYUVERe/DBB1l9fT1ra2tjzz//PJs4cWKn9LX+8umnnzLDMFh1dXXO78vLy9mCBQvYli1bWDqdZrW1tezll19mF198caffvWPqXip9Wy699FL29ttvs8bGRqZpGtu+fTtbtGgRGzduXKd9XXnf3TndZ9lFVVVWW1vLXnzxRXbppZeycDjc6Rg3zXrH6+VKFb1p0ya2dOnSrG27en5kWWa33nor2759O2tvb2crVqxgRx11VM7U8IqisBtvvJFt2LDBe1Yfe+wxtt9++3n7HH300ey9995jqqoyxhibP39+l/fglhUrVjDGGLvvvvu6bLeLL76Yvffee6y9vZ21trayjz/+mN18882ssrKy02+Qi5dffpkBYNXV1eyJJ55gzc3NrKWlhS1ZsoRVVlZm1dVfJEliTU1NrKWlhQUCgZx122+//dhDDz3EamtrWTqdZlu3bmXPPPMMO+usszr97rtKc0+l98sRRxzBnnvuOe+3WbNmDbv66quZIAhZ+5H82vuQ/JL8DlTZtGlTl7+/f+zXcRzsp+Mzn2tbrtJx/JdIJNjGjRvZ448/zs466yzGcVynY3LJGGOMLViwIGtbV2NoV6ZKS0s7nbsnz/nIkSPZQw89xOrq6lgqlWLr169nCxYsYJIkeft8//vfZ+vXr2e6rjPGGJsxY0aX9wCAhcNhbxx+/vnn52yzgoIC9oc//IGtW7eOqarK6uvr2cqVK9kvf/lLJopiVpt0xbXXXssAsOnTp7M333yTtbe3s23btrGbb76ZnXjiiVl19ZfDDz+cMcbYCy+80OVvOmPGDPb888+zlpYWlkwm2Zdffsn+8Y9/sMMOOyzrd881F6LStzLcsXQlI+72ruZX/tKx/2hra2Nr165lixYtYrNmzeqyvv5zd1WPrvrgXT0/+++/P3vppZdYW1sbq6+vZ/feey876KCDGGOMXXjhhVn7Tp482ZP3ZDLJvvjiC/bb3/42a59rr72Wbd26lRmGkdWeXc0/x40b57XFMccck7OO3Zl/um3SFXPmzGEA2Kmnnso++ugjlkwm2caNG9n/+3//j82bN6/L3/6cc85hjDH297//vcvftDtz967GUv1cBl74qFAZrOWDDz5gr7zyyoDXgwoVKj0vgiCwuro69sADDwx4XahQodKzQvJLhcrQK1OnTmWMMTZ37twBrwsVKlR6Xk4//XTGGGPHHXfcgNdlbwvFCCOILpg2bRoOPfRQLFq0aKCrQhDEHnDGGWegoqKCZJgg8hCSX4IYevzgBz9APB7Hk08+OdBVIQhiD/jBD36ADRs29Evygr6GYoQRRAemTJmCadOm4YorrkBtbW2nAMIEQQxujjzySEydOhXXX389Pvjgg6xAowRBDG5Ifgli6HHqqad6sVr/+te/7jJGEkEQg4/Zs2dj6tSpOPXUU/Hzn/98oKvTawy4WRoVKoOpzJ8/n5mmyVavXs2OP/74Aa8PFSpUelYWLlzIdF1n7733HpsyZcqA14cKFSrdLyS/VKgMvbJp0yaWTCbZU089lTMeIxUqVAZ3ceO33X///Z1iteZr4Zx/CIIgCIIgCIIgCIIgCGJIM6Axwn7yk59g06ZNSKVSePvtt3HEEUcMZHUIgugBJL8Ekd+QDBNE/kLySxD5DckwQQw8A2KKdt555zFVVdm8efPYAQccwO69917W3NzMysvLB9xMjgoVKrsuJL9UqOR3IRmmQiV/C8kvFSr5XUiGqVAZ+DJgrpFvv/023nvvPVx66aUAAI7jsHXrVixYsAC33HLLbo+vrq5GPB7v62oSRN4SiURQW1vbJ+feW/kFSIYJYlf0pfwC1AcTRF9DfTBB5C/UBxNEftMdGR6QrJGSJGHatGm46aabvG2MMbzyyiuYPn16p/1lWUYgEPA+V1VVYe3atf1SV4LIZ4YPH97rHXlP5RcgGSaIPaEv5BegPpgg+gvqgwkif6E+mCDym93J8IAowsrKyiCKIurq6rK219XVYf/99++0/9VXX40bbrih0/a//MWEpvVVLQkif5Fl4IorhD5ZLeqp/AKDWYZ5AFYf7+/Sk+MGAmqLDAPbFn0pvwD1wQTR11AfTBD5C/XBBJHfdFeGB0QR1lNuuukm3Hbbbd7nSCSC7du3Q9NALwCCyAMGrwz3VCHT1/sPJNQWGagt/Axe+SUIojuQDBNE/kLySxB9w4AowhobG2EYBoYNG5a1fdiwYdi5c2en/TVNg0aSThCDgp7KL9A9GWaMB8f1TEGwJ8f0Nt2tQ0/qSm2xZ/vuzTG9TV+0RW9BfTBB5C991QcTBNE/UB9MEIMDfve79D66rmPVqlX42te+5m3jOA5f+9rX8NZbbw1ElQiC6CZ9Jb+7UwYw1vl15T8m1/e5tg0UPVF2UFt0f999qS16C+qDCSJ/IfkliPyGZJggBgcD5hp522234eGHH8b777+Pd999F5dffjkKCgqwcOHCgaoSQRDdZCDkd1cKg64UG3uqZOiJlc5AKDKoLbp3zX2tLXoC9cEEkb+Q/BJEfkMyTBADz4Apwh577DGUl5fjd7/7HSorK/HRRx/h61//Ourr6weqSgRBdJPBJr+9rXTorfMNhNsbtUUGaouuGWwyTBBE9yH5JYj8hmSYIAaeAQ2Wf/fdd+Puu+8eyCoQBLGH9If8DgaFwd7Qm3WntshAbdE7UB9MEPkLyS9B5DckwwQxsAyeQCkEQRA+Oio7+iquU1/HkOp4rj05N7VF9jHUFgRBEARBEARB7Ck08iYIYlDS0Wpmd7Gg9lSRkOu8e2ux469LT+6jK6gtuj5mX24LgiAIgiAIgiB6DinCCILIezjOyqlIGCgrm4FUalBbZF+b2oIgCIIgCIIgCD+kCCMIYtDQ2+5i+ax4oLbIQG1BEARBEARBEERvQYowgiAGDUPdXawnrnrUFhmoLQiCIAiCIAiC6C1o5E0QxD5FXysddnXurlz1BgpqiwzUFgRBEARBEASxb0CKMIIg9il2pXTYnSKkO4qSrjIa9nUWwj2B2iLDYGoL6poJgiAIgiAIou+g0TZBEITD7qxyemq149+/L7IQ9iXUFhn6uy2AwdsWBEEQBEEQBJHvkCKMIIhBQW9aBA20ddHeQm2RgdqCIAiCIAiCIIjehGYFBEEMCnrTImgwWxe5ypjdxYzqLagt+uZcvU132oIgCIIgCIIgiL2HRtwEQQwqOioChpJigDHeU8Z0RylDbZG9/64+5zM9bQuCIAiCIAiCIPacoTOTIAgiD8lf5caeZBnsqOTYVdB0aovc3w12eqMtqGsmCIIgCIIgiL6DRtsEQQwg2QqAXJn7+sJCprcUK3uaZTDX8bnum9oi83lfagsKlk8QBEEQBEEQfQcpwgiCGEAG5hXUG0qUXZ1jT84/UFZP1BYZBmNbEARBEARBEATRu5AijCCIAWTvFAO7U5gMtEtdT9zk9lZJQm2Rfa29+b6v2RP3SYIgiH0Fej9moLbIQG1BEERvQm8UgiDylt0pTHqiUOmLAZbfpa+vB3DUFtnX2pvv/eR7WxAEQeQbZD2bgdoiA7UFQRC9CY3ACYIYNHSlFOipsmBPlAu9OcDKFey9p+entuj6HLvb3tPz7IrB1hYEQRAEQRAEQewdpAgjCGLQ0JVSoKfKgoFQLviVHP7r76myg9oiA7UFQRAEQRAEQRC9BSnCCILYp3AVE3vqkuY/rislh5/B7AJHbZGB2oIgCIIgCIIg9g1oBE4QxD6Fq4DYU2sc/3G7OkdHBcdgtP6htshAbUEQBEEQBEEQ+wakCCMIgugDSMGRgdoiA7UFQRAEQRAEQQwspAgjCGIA6fkraE9cyfLB/ay/7ovaYu+OIQiCIAiCIAgiv+n1WcBVV12Fd999F21tbairq8NTTz2FiRMnZu2zbNkyMMayyj333NPbVSEIoof0v/z2rnVMV4qNwWCFszulS2/Xkdpi99fLh7boKdQHE0T+QvJLEPkNyTBB5A+9rgibMWMG7r77bhx99NE48cQTIUkSXnrpJYRCoaz97rvvPlRWVnrl17/+dW9XhSCIHpIP8rsr5UVX3w0Gy5++ULpQW3TvnPtSW+SDDBMEkRuSX4LIb0iGCSJ/EHv7hKecckrW53nz5qGhoQHTpk3DihUrvO3JZBJ1dXW9fXmCIPaCgZBfxvg+t8wZDJY/3YHaIgO1xZ5BfTBB5C8kvwSR35AME0T+0OfL4UVFRQCA5ubmrO1z5sxBQ0MDPv30U/zxj39EMBjs8hyyLCMSiWQVgiD6nt6QX2BXMrxrZcdgsNjpKYzxXvFv685x1BaZfagtegfqgwkif+n7PpggiL6E+mCCGLz0ukWYH47jcMcdd2DlypX4/PPPve3/+te/sGXLFtTW1mLq1Km45ZZbMGnSJJx99tk5z3P11Vfjhhtu6MuqEgTRgd6SX2BXMrxri5x8tNjJVefu3Mfu9qG26Nk5Bht72hZ7d03qgwkiX+mfPjg/6Q+L4XyB2iLDYGsL6oMJYnDDAWB9dfK//e1vOOWUU3Dcccdh+/btXe53wgkn4NVXX8W4ceOwcePGTt/LsoxAIOB9jkQi2L59O266yYSm9UnVCSKvkWXg6qsFFBYWIh6P79E5ekt+7fr0vwz39YCoO1ZLg2VARm2RIR/aojfkF6A+mCAGCuqDCSJ/oT6YIPKb7spwn1mELViwAKeeeiqOP/74XQo/ALzzzjsAgPHjx+d8AWiaBo0knSD6jd6UX6D3Zbg7yoSBji/VX4ofaosM1BYZqA8miPxlsPfBBEHsGuqDCWLw0yeKsAULFuDMM8/EzJkzsXnz5t3uf8ghhwAAduzY0RfVIQiiB+SD/A4W66K9pTesk6gtMgyVttjb8J35IMMEQeSG5Jcg8huSYYLID3pdEXb33Xfj/PPPx7e+9S3E43EMGzYMANDa2gpVVTF27Ficf/75+O9//4umpiZMnToVt99+O5YvX45PP/20t6tDEEQPGAzy61eI7Esudbu7JrUFtUV3GAwyTBDEnjFU5HewvRcHEmqLDPtCWwwVGSaIfYFeV4T95Cc/AQAsX748a/u8efPw8MMPQ9M0zJo1C5dffjkKCgqwdetWPPHEE/j973/f21UhCKKHDAb59Q+SBtOAqS+UMD1xo6O22Hfawk4iIezRkYNBhgmC2DOGivwOpnf0QENtkWFfaIuhIsMEsS/Q64owjuN2+f22bdswc+bM3r4sQRC9AMnv7unOQG6wWi/1NtQWGQZLW5AME0T+QvJLEPkNyTBB5A97F4iEIAiC6MRgtV4aCKgtMlBbEARBEARBEMTAQ4owgiCI3cAYvSpdqC0yUFsQBEEQBEEQRP5Bo3iCIIjdQNY7GagtMlBbEARBEARBEET+QYowgiAGlD2xqslnS5xd1Z3aonvf9eYxg4V8rjtBEARBEARB5BM08iYIYkDZE6uanh4zmJQqu6o7tUX3vuutY/KlLQiCIAiCIAiC6D1IEUYQxJCnP5Qq+QK1RQZqC4IgiIGHLGIzUFtkoLYgCKIvoTcMQRBDAsb4vRo0DaUBF7VFBmoLgiCIwQ0tMGSgtshAbUEQRF9CI3yCIIYMXQ2auqPMGGoDLmqLDNQWBEEQBEEQBEG4kCKMIIhBwd5a7vR2vKneYE/vh9oi+zhqC6Ivod8jA7VFBmoLgiAIghi6iANdAYIgCGBoWt7s6T1RW+z9cYOZoXhP+QpjPP0eDtQWGagtCIIgCGJoQ8tdBEEMaQZyVX+wWRRQW2SgtiAAUkr6obbIQG1BEARBEEMbGo0TBDGkGYgJjavoGGyTKWqLDNQWBEEQBEEQBLFvQoowgiDyit62pukL65z+UnRQW2SgtiAIgiAIgiAIojuQIowgiEFNR4XErpQJHffdW2VGT4/v6fX39vzUFhmGUltQ10wQBEEQBEEQfQeNtgmCGNT0xIqm477+z3uiiOmpBc+urt+d/Xt6/j2tC7XF4G4LgCzHCIIgCIIgCKKvIEUYQRADSi5Fw95aD/VEeeFu31O3Nfda/mvuqn49/Y7aonv77+n3+dAWBEEQBEEQBEH0HjTyJghi0OEqH7pSDvREObE3CobuHJtLYbKr+vnvrSfnp7bYd9qCumaCIAiCIAiC6DtotE0QxACy61fQnlrj+JUKuzvHrpQavR3c3H8tjrN65KJHbbH3dcmXtiDXSIIgCIIgCILoO0gRRhDEAGLt1sonF93Z16/s2FWw8v7M5Mdx1i6tmagtMt9RWxAEQRAEQRAE0RfQyJsgiEFBdxQPrrKg4767i/3U82DlvUtPFSzUFujRPvtKWxAEQRAEQRAEsfeQIowgiEFLTy18+rouPbXa8StocgVN70trp76E2iL7Or3dFtQ1EwRBEARBEETfIQ50BQiCILqiOxY7/WVJsyfXyWV95FfQ9OSc1BZdX3+otYUdI0zohdoRBEEQBEEQBNGRXl92nj9/PhhjWeWLL77wvg8EAvjrX/+KxsZGxONx/Pvf/0ZFRUVvV4MgiD0gH+V3sMdW6li/vlTQUFt0fa3BRl+1RT7KMEEQNiS/BJHfkAwTRP7QJzOFzz77DJWVlV457rjjvO9uv/12nHbaaTj33HMxY8YMVFdX48knn+yLahAEsQfkg/zmiq3UW4qPPXF1y3WOPflub69FbUFtAeSHDBMEkRuSX4LIb0iGCSI/6BPXSMMwUFdX12l7YWEhvv/97+P888/HsmXLAAAXXXQR1qxZg6OOOgrvvPNOX1RnSGNPzlzXot7Xa/pj2LjXyXyX2c5xvLevZRnO5871cffteL7erDsFnd478kF++9IVrjfO05OA7L0VMD071pQFICOT/ndEx7hUHGfB8l2W5/3/azBNEf41E7+CybIM8Hx2N7Kr+8313e4C2veEofRc7A35IMNDhT1VYPr7P1fmdtfXdvzO3W5ZhrddEGSYpgaO4z3Z7PgeyHXe3oT64L2D5Ld/6Qvr3M4Zia2s/13ZtP/vPJbvzXE9yWP/QzLcv3Cc0eNjGMutArEsQBAMcJwBxkRYlgye17xrMCZ633V1jp7Wzz2P/xq5ju3OebpbJ8KmT1prwoQJ2L59O1RVxVtvvYWrr74aW7duxbRp0yDLMl555RVv37Vr12LLli2YPn16ly8AWZYRCAS8z5FIpC+qnZcwZmHr1rcgCBJEUUEgUAhJCnmdqGWZEEW77XheBM+LYMwCYxYsy4BhpAEwSFLIG0ALggxdTwIAiooUNDfHoaox6Ho7VDUG09QRCpUiGCyBJBUgmWzAsGFTEY2+gg0bxmLNmqdRVXUYxoyZiWSyEQUFFTAMFclkExKJHQiFyqBpCbS310PXU4hEqiAIAUhSELIcBs+LEATZ++sfCLgDfrv+9r1xnD0h1/UkFKW4v3+CIUdvyy9AMrw7NK0dslwAQUhAkurB8ypEsQWCEAfPqwB0MBaEaUagaVUwTbv9TFOBphVBFC2YJg9dTyKdbsPOnR9jzJiZKC1djS1bokgk6iBJQRQUVGDUqC9RX38EkslGMGahsjKANWvWwrIMBIMlGDVqIgQhjkjkHUhSPd54YzwKC0d4da2oGIuCgo+xdWs51q17DmPGzEQ4PAyiGIQkBcFxAM+rvg5bByB5x9uDiJTzSYKuh6HrKViWDtPUUFBALgJ7C/XB/Ut9/ecAAJ4XvAmuX0Hs9sdu32WaOixLh2Go0LQEDjqoEppejlSyCO3t9VDVGAC7v0smGzFy5DHQtASSyUaoaivi8e0oLZ2IaHQs0uk2rF//Alp2fIhxWhzhr1wD4bUb8EX14TjggLMQDJZg9Oj/YOfOC/Dll897/awoKl7/6o4NOE6AKAZyKtv8ijV/P2yaOgDmfS4uHtO3jb0PQH1w/5NOt0EQZGfsaXj9V+d+TIcg2P0bx+kAAMYkCEIclqXAMCMwjSgMA9D1JDTNHjdrmj2OVtUY4vFaHHzwd6GqMaTTbRg7thB1dUHE49uhaQlUVx+OurpPIIoKgsESBAKFEEUFAAeeFzz5s2XT3ubScRHalmsDPK9CEOIQhLh3X/5Jt2lGYFoKLDMCy1L6uLWHPtQH9x8cZ2D06OsgCC0QBBUAwPOpTkojV0lkGFGkUpOg6+UwjCh0vRymGYEs70AqNREff/wezjjjUQSD69DWdhzeeecnOOaYW1BY+Bp43kBd3fdQXX0n6uu/i4aG70AUW2CaERhGFKYZga6Xg+NSkKQWiGILAoGtUJQN4DgDlhUEAIhiiyd3otiCtrZjwXEGgsF1KCp6Ddu3XwHLUhAMrkMk8g4CgRqIon0+no+D5+17syx3nCGCMQmmGcYnn6wkZVgP6PWWeueddzBv3jysXbsWVVVVmD9/PlasWIEDDzwQlZWVSKfTaG1tzTqmrq4OlZWVXZ7z6quvxg033NDbVR0yyHIBeF6EKCqQpBACgUJYlgnGTFiWAVkOd1pdchVhbmcaCBRCEHgkk5r3vYuiFMOydDBmoqRkPFKpZm/AoCjFUNUWaFoCqdQkhMOlMIw0dD0FjuMRDJYAsFepCwrKUVBQjlSqGZZlIBQqg2GozrXtAbkoKlmKOv/g293u3ocoihCETMa5vlrd3pfoC/kF+keG3WDju8sW2PH7/sguuLtrSFIQgpD0FF/2wDWVNXh1lV8cVw5BiNsrVcxWbEtSAzguCsYUWJYBSQoCYDCMKEQxCEUpcuSIg6ZVIRAIwbKKYZoaZHkLZDkMw1BtOeRT4HlbUSWKLTjyyDbs3DkG8XgtdD0Jno9DEBLg+WoIguQp4e17MMDzcXC8Ad65B3flzK67/b97j/b2UeC4QhiG2qdtn4/PxZ5AfXD/IwiSM+HkwfMSeF7ImpD6+y7LMiFJDJZleP2zaUacCajhKJc0r1+0j+NgWSYADgUF5TCMFHjeVi4HAoUAAAkMABAMliDGCTig9n08oCVw2TH/D19++TWkUp9BlgsgikpWseufUYh17Edd6xT7fuwJt23BYmUtSlH/2zvkcx+cz3BcRmZtJZHdB3LOhJPnDACZSbZ/sce1EOE4u99jvAqOCznjcnuR1zBS3rhZEAId/k+A58NZ37mKavd/UVQc+bRgWXLWWNi1KOM4y6mT5atTEhyXgigmfPXWOykJOM4AbymweBXp9Mj+afQhCvXB/Y8gtEAU457St+Mz7o5BLSvojav92+xzxL2xryAknHO2OPNN+/yAX/5TWQplVznOcQZEMeGcI9HF+0J3jtGzlFaWpUDTqpxjUs71UxCEuE8RlnIWyDPKPcYksgbbQ3q9xV544QXv/08//RTvvPMOtmzZgvPOOw+pVGoXR3bNTTfdhNtuu837HIlEsH379r2u61CA43jIchgcxztWVSFIkuyZWVuWgUBAhWUpjpBkBuaudRjH8ZAkDbwQB8cFAHC+AbwIRRGhqhI4jkcoVAZBCCCVakJb23ZYlgHT1GCaGgwjCkGQUVIyDopS7Azm4zDNKETRtnQBAE0rgSyHEQjYk19RVHwDkM6Z0iyro1Y/415pf5/tGkLsOX0hv0D/yHB3sw/ujbJjT/bfHfbgFeCdTtjfqbqdodtpc1wkq6NjlgjT1CBJDbCYCFEshyCUIBKpdibREhSl2FOKAwy6EYUs74BpDoNhqOB5FYFAuSeDohB3Vq7sd0ZFxSLEYrMAVMOyTDQ1NSEUUiAIkmdJar93EuD4FASng+Y4HTyvgudTYEzytjEmOZ27/X9m9bnQmez3Lvn6XOwp1Af3P65FlWvJnOnPeE+R1LF/cvtnUVRgGEGYZgiMtXnfiaLi9aOuxYcgSAgGS5BOtznWlzwkKWT3h5Fq1IoKRirFaBQVlBkqjuJF8LyAeLzWeU+EcirC/BZhvhoC4ADYSgLX2s1W1CHrvtz/O/bVRM/J5z44n3Gfb7s/TnmWJUBH16RMv+Zut6ygM7FVnG161juhY7FlV/D+5/k2Z38pa2E7Y6HmnivpjAtEr990J/4Zsi2w3T5ZEFwlQarD/XRWiBF7B/XB/Y9rxdnZkjOjBHPHoe73rjxkxtcpR7ZF75y2vPA55cK9Zkc3Rncx2VaK6Z3kraOyyrKUrHeJrld0qJ/qfXbl3T+u9kPy23P6XHXY2tqKdevWYfz48Xj55ZcRCARQVFSUpQ0fNmwYdu7c2eU5NE2Dpml9XdW8xXVJEoSA04kmvc6Y4w0E5B2wLMUz3fT7NwMhAAAvNEHgVfB8geMaYTor1gpCodWIxaLQ9SQEQUY0GkUstgk1NSshigqqqg5zFGIl0LQYJk48DZalI51uQ1HRNiSTEQSDaxGJvIN4/CjwfAUkKYRgqBW6NgqGocKyTMfk24QgyAAyrhimqXkTZEGQnP/tz4YBAAymqUHXU+Qa2cv0hvwCg1eGe2qN01f7C0LCkb/OSiR/x+Yqp1wTa9OMQtPqEQjU2N9ZIsLhTyCK0yAIDeD5FAoLG2FZRVDVmC3TZgkKIu/ANBUwZluZBYMl3iRWFFug6xUwjCg0rRqqOhaAjsrK51FU9AYWLhyGsvKLPetTVwnmunS6SrRMJ57IGrQD7qA8BcA25bbRwVi0R+3bVwyW56I3oD6473GVSBmFEu8pj1zcPqzjNlthnW3hwZgFWQ4jGFRgGMXOeQVvMSqZbPQG64qyAxzHY/To41FVNQ1KsAVJQcL2qkNxyP5nOvvYCjUAPssSwZtkA5xn1dZRaefWx70vW3kX8NwiXas114Kb6F2Geh88WLBlzLUE6dz3umNmjjM8pVLGwjnls8Cyx90cZzkKK8Oz6nKtrgUhAFEMwLJ05x0QdxazbWtSQbAV3H7luh2jyJ4UW861bPQsqzWOM2A6/a1rle0fS/iVBfZ96c79w+u7id6F+uD+JzuOl+Rty3gn6Fly6yqcbHkRve95XnX6xIwyzbKCtkeGJWZdz09HOet4ff8xfoWWaSowjKhzjlTmneIp+LL/Z1nvAmJP6PNRS0FBAcaNG4cdO3Zg1apV0DQNX/va17zvJ06ciNGjR+Ott97q66oMYRgEQXbcHtrsOAVGISzLnuS6wmcxEaYZ8rYBgCDwEEXLNudmdqcbiaxBMKhAEAKOZZnoxRlraFgNjjOw//5lOP74XyISqcZXv/pvVFbuxM6dnzluj7ptWSLaLwtNs6CmRyGdHoVUaiwKC+MoK3sL5WWPIxz+CIGAhUAgCFnmfbHOLCjBFijKDsgy76yaCY5STIdppmGadnwzAN4gguhd+lN+98RKpi8ta3q7Drvaz40HJghxSFK9Z/7sH2y7nS8ARwkWgWla3vGA7cYRibwNRdmIgoKPUVi4EoWFKxEO2wNrV7kdCNRAkuxj2tsnO/WzHJfl1TCMMFLqWKRSE5FOj4JlRaDr1bAsBaeffgxCwXWorn4OJ5xQg2CwBMHgWijKRkhSg2da3rHDd+vsxmUwzShMM+yYdNuyaw/Gu04m0F2GynPRG1Af3Pf4FUCuFZjfPdKe0Eqesinj8hQAz9suDXbfZnjnCAQKEQ6vQihU5m2zrbja0LLyZkReuBz3r7gJweA67/qCICEg7wAAfO3L57Fu+W89qzG3yHLY+VvgKe5E0XXVMiAIfFYdMzHE7OQZGRdQ+zhbsSb5lGpEb0Ly2z/Yz7gdj9OvMMo1oQUyblCd3Z6kTuf1x7y1ZTjgs/Ky+z9Xvm0LT8NRlrmyxTvuXPZ1BF/IBI63P4uORTnPpyAKcYjO97ZiL/M321JF73RP2dZlRG9AMtz3dBUTzP9/tiLML9e6506Z2d8f19YNaxCEaYZhmmFHYSx6Y1t3fJ5RbmW7LGa8srL3t8fxYd9222DFlsXO75+MC6SU5Q6ZOZ4U2T2l10ctt956K5YuXYotW7aguroav/3tb2GaJhYvXoy2tjY8+OCDuO2229Dc3Iy2tjYsWLAAb775JmXK2Av8A1BXCOxJrQwYUWgAmCPA9upUJtuFLWwZv2jAmZSLLRCEKExTQzo9CjxvxyzRtARMMwxNK4ckNeDYY8sRCNQ4yjIZw4cDzc0WeF5CMMjBshQnCGk5jGDUW7lqbZ2B+vojEI1u9q5rr6xl7su/msVYGJaVCdqbGfjLzt5c3zTuPsZAyu/urGRyuZ/tzp2tP+I8dZee1KNjPAG3w7Q7Twmm06m6mKbixTmwlVy2Mk2SGiBJDWhu/jo0LQFNs+MeuJ2m7RoZBcc1w42RYpuJB2FoITBLREHBx9B1QHUUYwCgaVUAAFGMI6A0QZZ3gONSYCwINwaYfc+dJxBusFKOsxX1hhGFYUSdgYIr411nV+x8vn3nuegO1AcPBNkBq93wAv5A8x2VZS52whcNHCdm7WtbhTRAkhLQ9bB3XlmuhXzo97DZ1HBuuBKK8jYOOeR70HXNOV7H8Klz8ZuNr+CM2GYAbj/Jef9nYoFx3oTcVnKpzrig89CwY3Dt/sxCuS9B8tv/uLG1bAVUJr5QVwow+2/HOFuKpxwDXAvobPn3W45m91HZVqS2BZfgKcpcDw8AnRaaOCZlKQH8FiK5rMp3B8UY2ntIhvsfWymU+znPKKIySil3u43kO4ffeszd3/COsxVXEU+ZZZoRcJzuU3RJTjxB+I7PVly558ksEPs/K55lppvYIruu2fit3uzrkPz2lF5vsREjRmDx4sUoLS1FQ0MDVq5ciaOPPhqNjY0AgF/84hewLAtPPPEEAoEAXnzxRfzkJz/p7Wrsc7ixujLaaFeDLYNzssBYlhtfK2P+7E5YMxNX3jYRdfykdT0FwwiC49ygvhpM0wLHRSCKCVRV3e0cZ0BRKhCJvIymppPtlenARmhaNdLpekhSCKYZ8V4QqppAa+tWFBUp3oDDTlFrwbIsu75MhOD6WzMRPK/ANHX445hkr0CzPm3jfYHBLL+7C3Te02N2RU8UJb2lyHA7RXfQapqZlR3XXNrN7MSc1SR3gO2uIvkD0buru5LUAMaCCAQYZDkMWW7wdb4a0uk2z4LFdm8yYJohaFo7GItC1yu8GICaVu39BWyluSw1eK4ipin5JgP+NsoMDPwDdgB2pipLsRX3e8BQfy56ymCW4aFOLoWXXawOSl732ej8fLquiW7mWNtV2VaE8bwASWrA8OFHwrJMKIoEUXwew4b9A7HYiWhtjaK+fhxKSjR8rXEN9NYa57hMP+lapmTX0VEE8CnA9w7qjO4pyRizrcXsugqwLNZJMUb0HJLfgcQNYG106sfcvitXoPnMMX63JQMcJ/veAbbi2bUKdY5y+m83aYXkvT/8sQZ5Pum4W+aO7dWVC6dfodexT7bvR/f2t7fRJLo3IBkeGHK5Hbr/24orEUC2d0VHCy5RbEFFxYE+BVNGEeYqwbJL2Bn7RjrVJaOAy1hq2QvBrreW5CxiRzxFmN8K1VVsdbYyy1i1ZW8PdqoHsXt6/a33ne98Z5ffp9Np/OxnP8PPfvaz3r70PothqE6sDskLsOl2tHaGGdfSwg2Q7//Z7cCa/o7S7SBtVwjbHdE12/ZnoASAVGoiBMGODxSJTHYE14IoiggEatDcXIl0us3JSheFKLYgnXatSQLQ9Sg4PgWOibAsO2i/rvPOhD4CJsS94Nt2rYPwx1IRBAOmSZ13b5Gv8tvbSofeU251T3FiGGEIQsa0medVx2LLVYqFvY6XWRkza8C28LBjedmTW8sSoWnVnnUHYyJ0XUZV1SoUFq5EPH60c6wExlS0t9cjFCqDZRnQ9SQ4ToemtUPT4jDNtBMjLJNiPa1VIZ3mwdgoSFKD59bp7gNEsyYD/r92fXX4zc55zoAbIr9jIoy9Jd+fiz0hX2U438m2+vD/1bKef1dZnRkku7KlZyV9cTNFufGD3GtIUoPTx1vg+VYvph9jIlKpZtz/0UO4cPI5AICkF/Q3Y23mHx9kskHadbRlMRNzqCO8o+y2zwln8Ur06kyKsL2H5HdgsBXB/oDUqazv/NYmuWNt+ZLccAYszgDHiZ58uOE77PEqB9v6TIJppuG6RmYsNTPB9m1Z1b1FZCC3wipXEO5MXTMKvsw5JOQKtk3sPSTD/Y+9cKyA57PHnBmLrIzrYibGV7Y7o2WJCIffx9FH3wlRbHEWaG3PCcOwF5t1vbxDqYAd3zbou2620tn2gHDfJ25c3GzrMMOIwmKiJ+f2mDrjBml5C8aZ+LudQ6WESRG2B5AGYQjgBrG3g2x2XIGyB6Y8nwmyyPvSxgqCCsMIOsJld8KuhluUGiAIIlS1AKoag6q2gjHT6ZwTzoS5Gp98MguyHEFxMaDr1QiHyxAMrgMAtLRsxCGHfArD2IbW1pkIBtcina5COJJCcTGgaUGIfAOYJYLnQwDsetoWbiEYjhUZzxngxRYAUZ823L2nzgF+iaHBYHJh2xO6W3fLioCxIOw05/Y2v4uD7dYcdDp7ybb05FzZlpBKTYSqxpz3gIG0VgVZrvVWqgwjjbQTp08U62FZEchyLUKhiYjHa8HzIlQ1hkRiJwQhAU2LOy7NMlR1LFS1BYJgd/xptRTxeA1CoTIEAuUIBLZ6g3DLsgMHZwfHh/e/Pw5Dtpm4AsaiYN006txXngsif/D3u25MLVfB5J+kugNljstYjfhx+zFBULPSubuIYrNnieUqrFwltaJYuOGb56ChoRLjx58CYdLpWZYlmbplFGF2JrqUpwRws+W5E2hXTr1tjtUYYCvDmLOI1dEajiDyma6CXbuTUH8srVyWKJnjxSy54HnRyRppy767OO23EgNMX2wxzaegc90j1U7X9L9nsuObdWXBpnfaRhD5imlGnb/2547WUu4is1/5m7HEjDtjV8NRfEW9Y9PpkVDVGHTd9oLQ9WqkUpOQTE50xt0yOE7x9a/utW1rL443shau3XrYIUgkr+/W9fKsMb/rueXGJeuozHY9RhgLOt4itrFJOj2qr5p4yEKKsCGALEcQCEQgSSEIAu9ZkdhZa/yxejqaRtsB9IFM+mfGLOh6BXg+BUlsgaaVQ5Z5JJNwLEQ0yIEGAIBhRhAI1GTFMWht/QqAjKtlPF6LRGKa10FLUgNkuQG64bpchSCKHQcRvtgJlghOzAw4eCHuuYXZLzcZpqlmraQTQ4OOyo6+Un70dQyp3d0HYzxMkwfP21miDAPgeMUJfpsduJbjDTDLVmy7rpE8L0DXbfNt+/godG2YFzPMDsRpeq6VxcWvoKXl6wAAQTDA8xI4TsCwYVNRWXkoOO4DqGoM6XQbJCmEVKoMhpHyXK9VNeZlrTPMCALIjrXQ0ZS744DcvSdXCcbzKjjeAM9UMBba6/bsLQb6uSDyh+z4YK5SLKMEsxVftvU1ACfzmwEe/kxSnd2JM8XyndfwLEzsJDeSE7NEQSq1DR9+uAVlZQoCgUKIotKhTq4iTMu+Dm94K9FdBR3Ojn9kK/Iy32Vcvwgin9mVO2G2XGS+cxXCfmsy03QXl2XffpkYYf7PriLMzZ7O8QYEIeJ8r3qy2ZVyzj9O8Mcw8++Tyzrb3U4ukUS+4waYd+n4nLuuh27xhxCxscfLlqVA18thOZ4XdnifNqTTIx3F2CikUiOhafbCsqYlvEzMrlKb41wlmwJYcOasdt/vZnR1LbcsJsIyI55nE8fJMM2Io5wLehZjtpeI6oVPceXWNCPQtCroehSGUY50ehTJcw+h1hoCyHIBAoFCCEISAJxVJjfgtr2a7Hd1YD7/aHt/f/B8HoYRhiynvAmqwLd4HbeuJyFLDU6coCrEYrMAwHsBJJONCIcLoevlTmDtV5FITEYwuNXLKseYCNOIwjDsiZ+rjLPjqPg7bHfwnxmYCELci5NkmREAMizL9GKkEUOHXQVA70iuIOt7ep09PU/H+mSe393fh2GkIQgWABlABDxUMF4FY0bWAJYHYPEAx9wYAvZKlJttDgB0vRzh8EcQxRbPulOSQgCSsCwFklTvWyGTnfg+OhSlGMHgephmBMlko5MYQ0cq1Yx0ug2KUgzLCiKZbISiFNtx/4xC3+BC8lawXCUckB2w1+9yYq/A2fIt8CqY75hdsS89F0T+sCuLKFuB7Sq7HNdC1w3R6X/d4NiZ8+mdlFJu7DA/mdglduZJTYvDn30yu06cs933XnFigvpXnHNNljvfD0BDSGKo4Lo05nIlzB4vZyysbAuS7Hg93n68AViG771uxwOzF40FT8HtZjv3Z5q1z51tSeZXbHW2MO14L9n131WoAoIYCtjKomx3ZpfseF2uRZjqk2MVbrwuIOjF22UsCE2rhqbFnOzpQcdDIgZVHesowuq9RaCMpbX7l3fq1jlAf/aCsAzLcpPd8L5xdGY8ne0aqTvjf9tazHXR1LQqL5QJ0X1oFDMEsC063OD4CgQBEIQWiGLC6xAtKwiLiV58oY4DYVcZJYoqLCsCoAFuGmZdj0KWAwgGSxzXKVsZ1d7ejC++SCIaTSIYLIFlyTAMFaHQNtTXj8COHStRVrY/UqlmRCItGDbsH+A4Hcnkz6GqMXAcj3CYdyYIdp0tK+KLZ2K7afBOqmhRbAHHGV6GuUywwnKYZhqGQWmf91W6UiIMlJVNT69pGClkkj3InSa7vKMUY0wExyRHZtzYfpkMqjxvr1gddNBM1NV9Dy0tX0dBwccoLPzMU36l06O8WAfpdBsMQ0Uq1QxVjaG9vRAFBWMRj9dC15MIBjUADJqWgKIUQ9dHoK3tCxxxRBK6Xo7GxoMQCmUy6GQyQHZWarnuJO5gheMkmCZ828K9/lvl+3NB5Af+LIzZk9eMJXZGGQavv3X3sRXEclZcIFsuVMeFIjtDo53UgrdXky3FkeckJCmE448vxObNEUdp1nGIxxxrslSWFVjHIN8d3TH98ZEE1y1LMGCaAMeJYEx22iHbMo4g8ge/JVhnazA/GauP7k+hXIWX7Y7sKr8cObaCcK01XTq6Vnd2d+ycIc5fZ9fixZ+BOvtes+8n1/8EkQ/4Lb46L+RkvBRcJVjGuKKjjEswTXuM7MbntLOlv4JU6mvQ9QqkUhMRj29CMjkFqRTzPCcEIeCc242XyXueSqapQdddgdOc8UJG0c2YltW/+y3XshViGYswAL6+vwLp9EjHWm1SH7Tw0IbeeEOATLwBA7K8w1MY2Wad5e5ezl8dPB+Hf1Wa4wwvE51lKQgEtnoxCHQ9CsuKQNdj0PUkeF6yXawsBaFQGaZPD6G4+P8Qidhpf5cvX4hE4hCUlGxBVVUQwA6UlNzn1ScYtLNY2UF/QwBUiGLCs/CyV7rsl4NlydD1chhGFLwQhyS2QJIavPs2nf0jkY1gzA4Qblk00cxnettdLF8UD+l0m9MRchBF0euEBcc90h+rx7IUz9rLMCMAotB1OBldNcTjR6Gp6Uw0Np6L1tajIAgJJJOTwXEGRLEFzc2nIZUai2TS7kxnzrwZr79+HRKJnRBFBcXRmTjooChkeQcUZSMKCt5HPH4UUmoBDF3EYYcJSKUmIhYbgVhsMwrCVZCkehhGFMnklIzZOZ/qNNDIzlJr35dlxZ17Cjqx0jp3S/vqc0HkB56romPV5Y+75be2YrwE01Iy2wTAstyV4XIn9p/gKMJS4Pk4JKkBdtIb1yVShxsjzFWsqepIz505E4vE9IUMcBV0nHOtIGClAMHIUlD7lWEds1NlBt9BJ1CvGzRf7eAeSRD5SLZbfy46Wlp1tAjzF7/FZ7aSi0EUFTsbOgCeL3TOpQGwY3/ynAFBSADQIQhqp37Uf72cFpudFNy5lXrZlmWdA+oTRL7Qley6Y+Zc212yAtY7rpHuglAicQg2tv8Lh7eVQRSHI5VqRlvbVrS1zUIisRnJZCOCwRJIUoF3PluuZDBmwTBUpNNxWJYrX/bcVxSDXpbYjELMchRnllcfQUg4f+NZ57fn+SkIgoqiouXePZlmBB9++BEptHsAtdQQwLIMWBbAcSEIQhiaZmdldFeRRTGTPa6jpYkbzI9ZrvYZUNXRCAZ1KMpGlJS8gKamMxEMlqCgoAIHHFCPkpK/oanpTLSky5FKTYIs7wBgD4irqgQUFv4TodDn4HkVq1f/BsXFr0BRNgIAZLnWezkAgCgW2pN9zgDjUmAsCElyXRwzro6WGYFmBaEbUW9FOpN2VvQG/KJIcXbymaHuLpbLVY8xC6oa81aE7Ml0NGvyyTvzS3eba4FlGlEkk0lUV7+JcHgiUqnxSCQ0tLUdi4KCjyHLNWhvn4bi4lfQ2joTra0F0PWjIMs7YFm2C+T69XejsXEZiovHoKLiQDQ1NgJogKKUekE44/EjkE63wbJiEISpiMc5CAKH6upRMA17VSoSeQfV1XdBlmuxfv29SKXGwxIi3kDcDf7tZo/NBP2MOJ1+GJYld2qf7nzOd/bGhZMYfPjjbmW7NOkQeGQNagE3U7OdZc7ej3cG4oanoDJNzennJLju0HZa9iA0rQglJRJCoa2Oi4hrmcV1uI7pjBcUCIIB1z0kV2yVzLsn2zrM/9mNdWZfLQxahyLyFTdmroDOVlG5Y+ZlW4TtauLpz3TuBcH3FGXud4YXaNsNA+K/dq74YLu6dva+ErpShrn7dsygRxD5hGWJ4Dg7E6o/o2LGqioTxxbQ4QtN7bhFhj2FmH0+W3nG8xpOmHyOk0E9hmSywYl9vROJxE60t9ejoGCYLx5nwLHWNLzFacNIeYvd/iyyAPOsyRmzYFkmAAbLMiEIYXB8CqIZhq5X+BbFM3LN84oXBsG9TzsjJdET6I03BDAM1Qlk7QpXdofLcSFP08xYCBxnZUym+c4DYNPUYJgRMCZBkhqQTBrgOIbCwu0YPvwvCAS2IpGYBsYspFIjoaoXIhhcD0XZCFmu9QQWADQtgXR6pLPNDv4nSRpkOey4cqm+VSnJW0UH4K12uyt1phkGzDCsDr7V/mD5/uDABJGLvlY67MpaqavtmpbwVoZsuS0Gx2UyxfhXfTsGo7csw1Ew2xacbW0ppFKTUFKyFKGQipaW0xzFlwJA8+L0SVIIklSAZDKAoqJRCIerIEm2IsqydBiGClUNI5GYgPb2dTBNDYIgIxoNAIggEAgiHF6O+vojIct2R11c/ArCYQ5ffil5WSc5zg4aynFFEMVymGYEotjiWaLYgb6DsLNhGjncufqHwfhcEPnD7pK1dJzQ+mXbn8mqK4LBEmSyOruxR0KwLAXpdBtEMQxBiEPTqjtZZ9lxAN3+XffcL92J/67qmqlj55hEPJ8CxyQYluuSQhZhRH5iWUpWRvWOdA4qn61c8rszZRJRuVZmmfg/HV0gO8b74riUY8mVya6cXQ/dJ5euu2VnqzB/vezj3C27dvnszruIIAYbjAVhmrZM+pNAWJYd66tjH2YYfvmVfKF2Ip5lle2SKKO8fApSqSakUs1IJHYiHt/hKcHa2+uhqi2QpKAzxzbBcQIABsNQYRgqdD0JVY15IQ3sOvAQRcUZI/O++7DHEYYhgeOiMIS4ZxWWK3an6y1iZ42MeNkzie5DirAhgO0SEYdhpKDrSYiigoKCCsiyPWDWtHa48Yd4XoSiaPBbaGReGEG4LwbLjCAePwqqOhabN78GWQ5DVSehvHwmyssXwzQV6HoSgiBj9ep/IxKpRmnpZFiWgUBgEiQpBFnmMXLkO0gkDkd7+8EQxRakUqtRVvY4TFMBY0Ev1Wtaq4KmhVBQsNmLZ8bxhpe50k5B7WbJCHkvC1FMQlEaYFoK0mpp/zc+kXfsLrj63nzf8fz+/bvKQghYXsy8bAV2KVwX5uzJp+pYd6YgyzvAceVobDwThlHqddRunC6eVyGKLVi9OoDy8iRGjHgVI0bcjA8+eBPR6IeYMOFx8LyKr371bqxatRafffYKjjlmB5LJybCsBNLpUfjss9e8SXQoVIaJE+ejqelMxONHIRY7AbHY5+D5cQgEjkMwuA6m+Rra26uQSGz24pYBHARBgijaZueiWAVBSHjvH9sabGCV2IPpuQBIoZCP2Cu6gBcXLIe1letykcutyXWv7AhjIsaM+TeKipajsfFcCEIShmH37XZckGbU1KyEridxyCHTAJhwA3NnFHScM0CHs/qsgONSWTFH/C5V/s8Zd8ugV2930pwdX4gUukT+4S6CMEsE4yUvHk+uwPkd/7cXcOwg3P54PsyxzjDNjFuzi18R5no3uB4bgpAJH9AxdleuOvjjIuVyofQvmtn7dN7utwYjixIiH9G0Kl8mSN3XT2WSOPkVYe5CLGDLgJtV3VWAueFGDKMNmpbwFF+trVsRat2C1tYatLbWYEvrVgSDpeB5EbqegiwX+BaqNE8Jlkw2QteTjkeUHdPadpFUbFdpTvCSZZim7jNcqfbCo9gWXxEvq6R7r6YZ8cII6XoFWXX2EGqtIYAkhVBWHoASqAEQdILl6ZCkFijKRthuFba/saaXg1lBJwi9bT6aEagGSJKCWOxgyLLtg5xOj8SYMVGIogIl2ALTjODxx//sKNzeRChUhurqw6EoxQiFylBe/l/Icq1ngfLOOydCEHhnwFyGQOBUCILsFQAIBAohSRpCoe3geRW6HgXHG5AdyxVBUGGaABCEplnQ9SR0PQVdb/etrqVgWZtRUjJ+4H4IIu/piTKjp+frKguhZVlobl7vmU27ijDT1FFWNgnTplnIzsioQNcrvEG2piWgqnFUVbWgouIdFBfPhGHYAwNF2Yj99rsC9fV/AmMWYrEZiMVmYMKEXyKdHomWllOgaVVIp0dClndg/PhZaGpqQTzOOVlYk5gy5SRwXArJpIJksgHr19+L5uZKaFoCur4OTU1rIQgSeH46eD4Fw4hi0qTLvLhDjEl4990/oKlpEyQp5GWcFEUFklQOUfSncB+cE+n+fi7sdqDA4/mCaWYmoIahguftzK+uWiw7/lb2ZJUx0Y6RaSmwLM2nTMvA8yoMI4J0eqSjpFZgWSkwZiKVmoRkcgdGjjwGFRXb0N4eBdAIQQhAlg0noK7pnEeAZZnOINteKTctezCdifXlul92nlQLjsWMq2S3LDsdvO2iKYMxSlhD5CeuqzFjmVAifvyWUm42Z9fSJDurW9AZbxc6bsgG7NifQbiuydnn5cGYE+eXT8GfYdmvbLbH6XoHL4pclmq562x/Fjt9l1GAuQoAsigh8o9UapItP77FJiBj5WjHwbatNG10T+lsmgpMM2r3Z6YGy9Kgqq0wzTR0PQVVbUE8XovW1hpYloFxh/8IEybwiMW+gera9/Hkhpfw2xnlaGo6HFu2vO4puv2KsNbWGnxgatA5HjOcbZIUhCDIjuFI2KcU451rJ2EYYTA20ZvDu6GIGBO9OGGBQA0KCj6GINgJ8lpavk7KsB5ALTUEEEUFpiEj5Qi5qsqwLAGiOMZTKvH+lSSno3UzUrkZ3lz/4tLSN6BpVdC0ahiGvZLV1PQlRDGACeNfQSRyAjQtDlFUUFg4AsFgCcLh1QiHX0Qk8jY2b/69Z80VDkcgywVwXTN53nbJcjXmlmXAMFRwXAi8oDhCHwQsOzskAM96zPbfTgBwM/AUwDU/tV0w2xCNjgWXHRaFyBN6M5PfQGUF3BN4XsSkSad7FmFu/B9dT0KWw4jHSzwTbXdgnU63IZ2uhygqGD58BxobJyGdtgfh8XgxLEuHLE+Drlfgyy8PQXW1Bl0HkkkFqhpDVVUVUqlJSCSmIZnkwXGnZJleux1+IFAIy6pAOm0hEAiiuFhFLDYCBQU8olEVgpBEMHg8Wltr0Na2DZJ0HDjOgKJshCQ1eHFO0uk2qGrMUa7BN0EAGFOc94HhvSP87KvPBZFfZAe97dq9ysWdgHZ0afJbhPmDb5tm1M746sUw0Z1AvCORTq+FYaQBAOm0PQGwA/HGwVgImYy0AMBgmrq3iORawTBmZFmH5apzpk6dLcb8Mk0Q+YarHLaYCN5T9qZ2O6HM5RJpW4i5MT9FL2SH268D8GLcutfl+TgEJ1NsRsaULIWX36LUVcDZliKKVxe/As3vRtmxzvbfTIIAywp6Sm2CyCcY46GqMng+5POs0Hzf2/GvMyGCLNjZ2YsAuHJpuy7a1limo4RSoWntSCYbEY/XoqltG0KmjmSyEcnkTLS374RhqPjWmJkwjAgKCjZj9OjjIUma02eHoesakslGNDevx0hmQZJCKCiowObNryGVaoIgyJDlsBOqxC6BQCF4PmPNbSvGRnpZ2Q0jCklqgCg2e54VotjihRwhegYpwoYAdrBtDUDAMbtsgx18zwTP2+6CPK95mdz8Waz8AastKwhdL0dh4RuOtUkYhpGAHQusyTl3FGVlkxCP18I0NUQiIRQUvOPFBwMMJBICGOMQDO6PaDQJUWwBYKeM57k0AoEvvGum01VIp53BgLOq5mKaiqext++TB8cJEATeG1z4FWwUnyS/6U0FxWBWdrjKGPcvx1kYVhmBKMTB80m4GVZ1oxyWGYGuK9D1FCwr6Zhqq0ilmpFMNqKgoAKRyDtIpSZB03ioahEYsy0+kskpiMePwObN/8D48WVoba2AqsYgCBJ0vRyqOhbptIJAQEQmDTMgy0FPYWV3wHb6Z1luRTi8Cps3GygIl6OgYBUKClYjFJqI1aunQdPakUoxiOLBsKyg0zE3g+MMyHIYoVAZRFFxVr6CkKQgRFHxFF/+GId+9sXnYrBaxhFdwbIGrbkmn+7kNpdFB+9Ncu2+rbh4PyjK0973rsWGrkfBnIUr273RTpRTUFABSQoirVXBMFI+q2sVuu53jcyWLzc2Z1dKucw+2f+7VmOA7U5mK+lV771BEPlKRjGcUTT5Lav8iieXjOuV5CWeymSSBQShCQBgmlEYhuD9zxgPy9Ls8SufcenK/HUytXZw6eoYb7CjjGZkWen0vf+z3+LMVcx1lbCGIAYz6XQb/OFF/FmSATfRTEYRZsfKtGXRskzoersXa9tN6OZXhH2R2In9DRU6x6OlZSNiscmIxTYjlWrGlCnnQZL+C1FsgSQ1QJZrnIyy9pxaUQ6GZY0Bx/GO95SIdesS+DixE62CjGOceL2BQATBYAlkOeyrm195LsM0x8IwI5CNKHg+5SnC/FnmyRqsZ1BrDQHi8VrP/FqWCxAMliAUEh2N8casFR8AyATXy14NMowoUikLO3bMgSwnIQgJpFLNCIcrUVw8Bqoaw/vv/x6jRq2Aqh6E1tYaVFUFUVi4Ei0tp2Dr1tMQjqQQCoWcl8F7EMUWuFk8RLEFslyLUGg1LEuErlcjkZjmWZ+l08Mhik2eBVu2ibjqmICqjo90JvaCLIdRWDjCuTeaROY7Ha12hpIVj/9e/DGiEvEgZLkckpSALNciEKhBKPQxeN52a04mJyOVGo9Uqhm6nkQq1YxUqhk8b8c2KC5+BfX1p8OyDIwa9Rw4zsDWrWejqekDjBt3EhoaKhGLbYZhqDj44JWO23MVGLNwzDEh7NjxE2zddiUSiTRkOYzKykOcOGV2Yo2KipcBSGhtnYn6+hWQpCNhmadBklowfvwP0db2OdraysDzAkxTQTo90kn5XA6eT2H8+A+yBvD2fWfeO7bSPQpNq4Y/E1dXbZfrcz6T67kg8gdbAcR5Sl3XzUlwrK7deCTuX45LwVVA2dYj9kSU56MQBBnHHbc/TDOC9vapcONiuv23e35BsNOzS1IChx/+MuLxo9HcfBQsq9lROjd5yjXX3cLNTuVfOHKtWDhmW4W557e/c2MUZgJ3Z1m/WEHYmZs1LyhwMFjST61OEL2HLROdXXv9LoTuX7+lmJtpzu7Lwo48uNZggCC0QJYb4Loi87zgLF4nnIVdyfbOcKzBurLoyFiDqb5tOjjOH1oATp0y7wpXicZ3UKp59+fETrLfMQYMox2yXNCzxiOIAaa9vQEAHHkSO1hoW97CkfvXr/RyM7e7YQkEQYJpak7YkRjead2Ki5u+xHvDj0Bx4UjEtr6B+uIxaG5eD8sycMAB5yAafQGbNv0ZGzeeh0DgCAiCvYjsjtfr6z+HIEgIhcqQTg/D5tgWfDOxE0lBQo1UgPdCpTgGdlKcQKAQilLs9de24k7z1b0UujYMPK9lKcUBPSsBHtE9SBE2BAiFyjw/44DSBGbZWTPs4HlhyHKDpy12s0u4A1vXcoPj7JhgpaUfoyU2y07tbEZgWQaSySaUlhajuPh9FBcvwKef3oJwOIxRo+oRCLyP9et/AAAIBsMoK/2XF2NAEFTIci0KCj6GYUSRTE7Bjh0/BS/EYZkRmGYIhYVvgeMMBAI1kOVaxx0zCsalvHgkprP6JooJ547d1TLRGeBbsCx4rmREPpGtuHSD1uYDe5JlMHewfCCZbEQ6HUcgEIFh7A8ACAQAoAWSVI/CwhaEQqsdq61yaNrBXkyD+noeqhpDff070PV28Py3EAzyUNUWKEoxRo9ejVDo3wgGz0VLSzE2bz4fLS0bkUp9DMYsLFvWiIqK98AsEYLAEIttRkFBBRSlGIFACIFADVpaTkNbWwKtrTWoqDjQOb4ZqdRcBAI1GDHi96iruwQbNoRQUjLOkf0djotkve/+XTeviJOuWuzwXQq2O0hnpVe+0BvPBQXLzx9si+lmiGIQlqU7iqYSSJwB+JRfmYWdlLNa7CqVJEcZHPUUSm4K90ww30xMr2SSRzBoQJJCjtVXA5qbT4OqjnWstCsgCG0QBNWxQNMAyFl1zmSQBDhOBM+XO5P2BCwr6MUNtetgy6R9XNCRWzuhjWVGoKo6NC3uuGu3IRKpBoUnIPIN09TAcSEIAry4eR0Xbvz/Z0KKKDDMCCwzAsNQYJoaTNMOhO0Gv7etvIOO5XUbdD2JdDoOWS5wwnygkyKL51NeEGwAMB2FummGfe+FTOxQF1vmU/AnsmCWCIv3Z2J3sfcxDAW63u65W5MijMg3iopGelZgHRPOZILX67BDA2hwg9brejsSiZ1IJHZC0+w5ZiBQiFSqGSvUGA5rb8DP49txaf1n+D0v4DNRwbhD5mHcuGFoaTkS8fgO7NhxMNrbD8a2bd/D1q0vQ1GKHWWaDlkuwOGHv4xnnon6+kceJx91Kb744kksXHUfFpVORFKQYCrFnrLLTnYT8BLe+BVh/izzGas33nkfcAgGh84icX9AirAhgC0wsh2U3hKh61HwvDuxBDStHKKYMfHW9XLPtJvjDOh6uWdKrenlaE+EATBYlgFRDKKkREEwuBaMiYjFToGuJyFJdoC+9vaDEQgUQhRVKMpq6HoFBCGOZHIykskpCAbXIZmcAstSHKsvHjxf6mTNaARjR0GSEuCcVTB3MMHzCjQNMIxSaFq74+4V8NxPAPuFZVkGKiu3IBDYinR6JBKJaQPwCxB7TvbLuquA8r1Nb1kTdXWO7p7fdYWzLNvCw12xcgNjutaQtkUlwDkKYp5XkdYM6NowpFLNGDnyZYTDs5BICDBNDa2tGlKpJnCc4MQNOwqBQA0KC6fgk09ewogR01FcPAayHEZV1T+RSk1EOh1BOr3ZSV4RclI723EHdD2KYLAEPC96deV5EaapYefOHyIQqIGmVaO4uAiKokGSGpxBuwLGqn2rVrpv4h9xslsqWROLXO26rz0XFCw/f+B5YOzYcsdNoQGCsClL6eUqpPy4VtJAJhOj/b/pKMOiWS5Y2e6WHGS5FoYx1ln8sZPT2IqysXaiGie4tiDEYZgR8F4wb9m7jh03jIMbm8+yeFiWnb2V58NeWAK/UsxFEOIQAHBSA2TZDRBeCdOkZDVEfmIYrnJJgQjA6pDxFchOdJEVZ8uTr4yliW2VYrs0ZUIPuFYmrU78TcOxJuWdUCAi3Ax2kqQ7Xhqq099WdKpzVmwvS3SsQZQs6y83QDizpKzjMsfLME0Vppn2FPEEkW/4Y0/b/aVrYZ+RY1c5bFkhz/3RPVaWw54F2HvxHRinxXGuGsNhyUZ8p3k9FlQciE+GH4mykgloa9uGtrbJKCiQEQ7vh7q6kVDVseB5ESNGHAVZDjsJr+zkN4lEC449diQ2b05i06ZXUVIyHqFQOaqqDsPXJnwTB1QdisNKJ6KoaBQikQrU1a12xtkBL6mcO/b2v2P8Vt4Zl840ghTmr0eQImwIIAgBxzJK89wUOE4Bz7uD6wgMQ4JrNmmbcmdia1mWm85ZhJ5isCzdm5ArShEUZT04zoCqjkV7+4EAtgMADCOKRGIqRFH14oDF40chGFznBOJWYZqHQFXHAnA7XdN5WdkxVZLJRkhSAQQh6tXHMFJOdqsoNK3NWSHXnJeK4Zm9ui8GUWxBIFADAKQIyzsGxpW1L5Ude3J+09R82SI1T0kEdE6j7sYQYVbQSxQRDG5EIPAIwuGDsXPnoV7naVm651oZCq2GKLYgmWxEVVULJGkdeD6FYcP+gU2b/ox02k4TXVg4wlGEWRDFBAQhAVlugCgmEFAUqKkoJCnkKaVbW6OQ5ZGQpBYUF29DKjXRU67zfOYevHp7irAwDDOSGaj7MnUNlNvjYHsuiMEPxxmoqrobQCZYtZuqvXPsHtsiu/M5XOsT5gx0w1nn8V9LFAOQpAYAY2FZBiSpAaYZAc+rCAS2OuEIMoow0YjC8gW1twfO/kDCljeodi3PBCHqrEobWa5VGSsx1VPIZxRlOgAJTU1n9Gr7EkR/4I9vx3EhcFzKczPiOyjFcgebR9YE1bbEtBVnhhmBroeRTjdCVVuhqjEUFY2EmywKCMFOCBV0MsRGIYot0LQQkskaZ2JdllVf17LashRYZsSWbce1GU6SGn+WvOz+VYSdrdKCnSQjM8GmhBdEvsFxFkTRgjuXyHgXON874XbcvsyVC44Le+F1KioORCBQiHi8Fge21uCrDZ/jsGQjxqbbELIMLKs+ApVlByAcHobGxrVobl6PyspDUFi4Ci0to5BOj4QoqohEQpDlbRCEhDfGtSwFBxxwFizreWzdqqC5eb3jcVGIQ8bMQFXVYSgv51BU9D8oykbU1n4TpqlDkuwkN4KQBMcp4DjZm0O7c3S33wbczLeds04Tu4YUYXkOx1moqHjXyU5RAVUdjUAgCY5rgzswBdwBsA6BVxEo+MQ3CbUH2qLY4K1QBwJbvcwUQAr19SOceEECUqkaFBRUQFVl1NdfANO0kEjUwR7Ah5FM7kA4PBGhkIyCAgWCWIeAvMMbLPPCZFhmxJkQKmhvtyfrqhqDrrdDUYphmrrn5w3Ay6LBcTx0PekJvas4aGiohGkeDcNQUVBAJqH5xd79VrtTmAx0HKnuuMkxZqGtbRtkOYxAoNB5vothmmMhCC0wzYjn1uzGI7HjaYnQtDak03G8/vpRGD/+aESjSyFJIUye/H2sW/cw6uq+AMcloetAQ8MoMGZh2rRLcNBBFdiy5ffYunUeCgo+hpoeBV1PQtdTvvgKtuJKFFtgWQrS6QgMvRyBQALJpOCkehZhGAaCwa0IhT6HKLbggw/WIByuRDBYgmCwEoFAIWQ5Yx3jujwDgMCrMKwwDKPQUQDqkGV5r3+zofBcEPmDf7GnY7weF1sJZmdly7bIUHwKLzeratBRhIlZMYEAHZKkOUouA6KoIBCowYcfHo3i4hEYNuxTL4OUfR47xbppGo7FWhyGUd7BaiXjhmtPiE3YGed4ADJ4c5j3vZt0J3NPIvwZ6/xWnQSxN/T3O1rTEhCEAADXoiQKntkLOKaziMxzmYQSblw/wO2XNU+hZHtgGADsWLymGfbieiaTDUgkdqCy8mDPAovjZBhGIXhBgcBEJJMCBKEcqVQzWltroDguU7IchiyPB2OSk/Fdga5HnXcIAGT6blcJ584D/Aq7TMbmzAQ6c9+D2y1/oPtuYnAiOH0ekFFc+5O7AIDgZmMVMm7+PC9BlsM47rg5UNWxaG2diaamyzHjnln4X9M6TJfDGHPwhThrwimIx3cgFtuMiRO/iQ0bXnLGrNMwYcJiBINrsXnzH7F582tQlGKIogLTTCIQEPGNb5yI7duvQCj0OQ466Hz89p6puHzkMfiyfAqGDTvI6YfTsCzRkekkDCMFwwh43hf2eFx2XCVNTwGWiXtmW3kPdvkdjJAibAhgr+DaroKGEQZjQUhSvZdNQtfL7ZUjpyN0V6QFXoUJIBxa7TtPHOn0KNjBeOPQ9HIUFZuIt1lIp+OoqkpCFD+Eomx0Am6PQkWFHfjXshRIUgO+/LIS8XgtAoFCVFTsgK6Xg+N0yPIOBOQdUFX/KpXlWHylPSWXPTCwM0RKUtCXAUv2+UmbnuabMQua1g5VjSEUKgPFJ9l32N2AqCcDpr4YYHUnuDvPAwceeIA3sLYzSxkwDAOWFQVjQW9i6//eTvNswTBSmDo1gvZ2ERs3Hofm5jexbt2ZKCn5DEVFo9DQABQVNaCkZKPjtjwFK1dug6YlkE7X4n//OwgjR0pQlCACgUJPGWeaIeh6FInENIhiCwQhjkDAnVBHvDhfdtBuEe3tB8M0I9h//0KIYhs4PgWeS0MQNkI3onacEiYCTvwj123DdX+2rde0XolPMhSeCyI/sEMGzPK5/2YPwjPB5TMxe+xnP+OumE63wTAanQC97Xj11RswfjxQWXm34yJpOAklKpBMGigsBAoKPkYgEIWuR1FcPAY8LyGRmIZweBVCoc+d+EJjPSsTv5ulm63VDbhvGKo3KbYzxfpdOdNw3ShF0bY+t8cZIjgu4rll+RV8BLG39Oc70Y3zZ8eYZc71w2AsBI6zbOWvZcAecbqyIXny6yq03Ng+dkB8HqYZcmQ6hni8Fu3t9YjHa/F6aw1GqK0wzTQsy0BJSTVSqWYAw8AsEfH4dvD8KLS1rUZLy0aEQmWwLMMJW1AAyxoOWU56IUXce3AV2ZYVylJqZYJsp8CYmRUo3FXeuVnyTFNDcfGYQTuOpr6SyIXphBLhOAMWn7J9TVxrZmefjn20bdEs+TwoXGWxgru/cg2amtbhdVPDGeNOQnPzehQXj0F5+WSMGPEfiOLZ2L79XaxadR8WLduG75/0OxQUfIbJkysRDq+CZQWxdu2B+OyzR7F69Q0YPfp4JBI7sXzbzfh/loEyQ8UHZhonOd5ObqIN24U62SEOWNgxDHH7aMEOheRzibQtRHWyCNsDaNQyBGhvnwwAYCwIXZcdxdAo2LHDeE9JBgAWn4KpRyGKCce9Koz25GSfCbUOwyj1JrJptRSCwANoAwDE42NRXb0YbW3HorW1CsEgh1SKIRKJoajoNUSjzyMaPReplC2c8fhUyIEGx1VyGhRlI3Q9CklqQTj8CQoKRGhalWOdVuYo7sZ6gwtJyvbxNowKb9CRTsehqjGkUk1Q1RjS6TaMHDm9n1uf6E26Ugr0VFmwJ8qF3hxg9TTDoRvgVhDi0A0DjA2DprU7GWcMTJq0CG4qZlUdi7a26d5gVlGKUV9fAkHQIYoBGIaKwsIRGD58DIqLn0dj42zwfCNUdaxnSVZf/wl4XvQmw5qW8DLLmabmZYy0s1w1QJLqnWPLkU5XIRBwXKL4FBob0ggGS7zjg8EtjqK8HqIYRyCwFVu2/BbpdBskqQiSpIEX4nZmWCnupI0vd9y3u9ee3W3Xvd3f/m0G7rkgBj+M8UilMrGx/Nlg/QNVv6Ips5KrwjTbnBVg1ZtMa1oCNTXVaGu7BxUVT0AULU/+bSWW7T6lqmMRCq32XJUNQ0YyNRGS1OBZaNkKdneAHYauJyAIsmPR2eRZafqzWtr11OEPxu0q9Fwln3d+K+iz6NR6xaKTIPob/8TT3zfaE1DF5xqpeMdYluzJtn2MBJ4XHAUzPAWTPU5tRmHhCJSVHYCiolEwzTQKC0cgFCqDpmlIJhvtsytRtLd/AUkqQEFBBcaMmYlIpBqFhZsRCGwGxxnYtu1ICEIJBOhOLL8WMFbqvGMMTyHntw6xv2Neff2Bw01T8yzCTTPdTy1OEL0Hz6vO4qvheTNwvsUnF/980rWyAoDVq5eisHAleF6FaYpO7GsFpmkv0JaV7Q9dT6K9vR6jRt0AywpCVY+BYaj46rCpiETegSTVwna5NFBXdypM80tUVR0GxiyUl0+GpiVQpSfxrUnfwjgtjiJP7pIwjFEwjCh4Xs1ShLmeUa6yDuhswel/b2XiaBPdhRRheQ5jPNrbXesqDa5rhT2gNsHzghfkmucBzglM7Zp5m2YURtoN2AnYGdvaYFkh8Hwh0ulmSFIIAMDzAhKJndD1csRiFair+xDFxfuhvb0OwCREIrY5eEHBx7Cso5BMxtDaWoPCwhFOx5tEQYEt3LLcgHD4fUhSAzSt2ntZ8XwKul4BXS93Bv0tXowkOxNmBVR1JAShFBzHwzBS8GfUIPKbriZQA6m86C5+pUZPlR2aVg1JsrO7BuQdTrDb4dC0BAxDRSi0OiupRTC4HqpaBtPUEA5XYefOj5x4BTJaWytQWjoJkcgbCAR2QBSbEAhsRSIxDe3tGhizO1rXMkRRiqHrdlBfV9bdeluW4q2cAXa8wURiJziuGqFQCxS5FqmUBFFUAHAIBGxr1ECgxssEGwqthiDY5xcE3gukb8cNs2Mj2RYlpR0sUTLsq88FkR/Y1hw2fkWXi79v6pjBKltJJkCSbOvneLwWyWQjIpEpzv4KgChEUfQSWKRSbTDNCERR9OJ+ptVSJMXJnsLKtcY0jEInM5ybcTIJQVAhSQ0IBGo8q08+y8okk9jCNMNOTKKgpxyzQyjYGedsq+4UZW4m8hI7OYzsLAJluxfZruxdT5f8VpYcZ3kTbcuyPEVYMtmIiooDUVGxDYHAYair+wTBYAnKyp7Fli1Hwo374y5KaVoc4XAlioraUVy8BOHwKgQCNbAsBTt2HAdBSDgxPOPgedvlOpMMw/Am0G79Mhn1RLixhVy3KleRJ4qBXm9XgugPvPA7TqIaAGDMXbSR4IYHcnH/d2V948ZPMGzYcSgsjEPXM/PKT/UkhutJRCLVnuW0mw1dlsMIhcowfPiBUJTF4DgdhlGCVGoiWltrADBEo2Mhy2EUFjahvj4EiVkoH3kMWje9ijIjo/SyF77LwXEGNK3GG1O79ev4XnLl2/7LgePc5EqsT9t5KEKKsCFAa2uNt8JrB9UMQJJ4z+TZTQkL2ANyWQ4jnS5wYho0OqbW9otDUYq943Q96XWo7uoYx/H45JMSCMJ6hEJlMIwUSksnIRRSkUpNxPoNd0PXhqG1tQax2GZwnIBYbDOqqg7DfvstQmHhSsTjR0MQ4pDlWi/LpJsmmuMinisnYPtxuwoCAN5KtyCoCIXiKC2NQ9MmwDCO9Fw/CGJXuAqIPVVE+I/rSsnhJ9e+Hc+XSlkAyiEIcSjKWkQib3sKYE0rx8ZNf0YiHkR7ez3a2rZhxoxHUFd3CRKJnZgwYQWSyWMxbNgbKC9fjGHDDkZd3Wh8+mkh2tpGYNw4CZMn34CmpjPB8d8Bs4IIBo9Hc/N6xGKbvXdHOFyJSGEaQeVDxGJHIJ1ucwbME+3sjp4ViIF4vBaSVI5w+G2EwzNQUqJDlleD51UvVpg7oda0Kowf/0NP6WVZQWhaFTSt2gmsXwHTjDoDgfYBS90+2J4LIn9oa9vmTTT98Lzg9Z9+NyV/gHpJCiEcroQk10GWGiBJdkzNtvhRaE+E0diYhmk2IxQqg+wsCodCn6O55etQ1TgMI+rE9bRdK1RVR1vbGADw+vGiIgmSXAdRtF0YOS7pLCyVe1ambjId183RznSXGQe4wbQzg3EGy6r3uV3ZMZIKC4f3S5sTRG/B88CkSWFn0myAsRSAlN3feZkX/WQsTezxKMDzSU+RzHEGdCMKS7Uz0cXjtd6YWJYPRGvrem+bLM9EIrERBx64EbHYLMRiSRx66Of4+OOD0dpaA9OshMW+Dk2r8sbCY8f+w3PhMowIVHUSOC4Fng/BfQf5LcJcaxHX0tu1gvEHzXfbwQ7y3R+tThC9h8VEiM4CqztftDOS+62aO3odZBRjpqlh9ep/g+dFTJp0OjQtgTe0BKosHaFQGda88SdMOf46HHLIbXjjDR11dV9g8+aHsK1lI6ZjLjjuXPBCHKYRRTKZxH77fYotW0Zi/foXUFFxIGIxEVu3volvttZgeaQax6gtCDET9ek2pFLNUNUWyPJ4x9LsE69ObsgQOylewGd5KnYaW7jhUoieQYqwPIcxKyuAPMfxUJRib7XHMFRPc5y9Em1nm7SD02tQFMkJNlgHUYjbq75OR9+eSPs04cUIhyuhKMWe0kxVY2httcBYGSxLR1PTq+B5EYpSjMLCEdD1JNLpODZsOB+MfRtVVV9A06oQj49AOFyHtjY7G44kBb0VKtdU216hOxRu5+6andsddgSGUYi2tu1IpdY7rpHHgKdYgcQu2J2CorvH7+4cHRUcu9o3kdgJVQ1ClkdAUQ6EG7NEVWNIJOpw6KHvADgKllWCVKoZphnG6NGfI5WahPb2EEpKqhGPl6C1dSba2xMYN+5fKCg4GO3tJ4HjU/hizWMoKnwDwyoWoaXlFAhCFNXVEoYPHwNdr0B7u22NkkoqiLWMg2nWAbDlLR6PobKyFgCQsoJQlGJoWsIZZATR3LwBHDcBRUWTUFT0GnheRTi8CrHYLKxf/xsUFa1HQcHHUJSNkOVaABlLGXcFz864lYamJVBQ0DlNfH8wGJ8LYvDDmIWWlo1w41oKguRYXMnO5DMAQbCy9s9MTAOQ5QgEIQnLjCBlRJFKTbRXnMUWFBXFoesVAEQUFz8Dnk/ho48mobh4Fgy9HEANWlsnZynZ7IzLRlZWKVFsgSTVO24fdhYrUWyB7CSycRXXuTLU2tlfU8jEVUkBMMDzmVhorsWYrlegtnZq//4ABLGXMMajpWWM84mDIEjoaBXm4pc1v6LJzbDKMQk8r0IU4igIA4oyApFINSorD4FhqGhqWodYbDOmTj3VicNp4cgjl6Kp6Uw0N7ehpWUjwuEL0dj4KHheRDodB88fAGYdDVmu9ZRhPB+HG6fMlll7ki8I9t9M0pvOizt+l07AgmGkYVm6d0+KQoszRH4h8KrnZeB6Ttj9md1nmWbHuJ2Ss+BjL/a4c04ATvzcNoxKx7FDLsABB7RCEH6G0jIJ7e0Ho7FxPWpqViASqcaxo49HWdn+kOUalJQ8C0mqx+uvfwOTJz8Fxs4Fx30DY8asgWFEUVb2Pawtn4zVW17HD1PNKDLSeCBYh0h7A4LBnY7MVuErXzkDweA61NcfifXrn4dlGQgGS5wkchIUpcgbX7jzXcZ4L+Yn0TNIETYE4HkRshyGJIXA8yJSqWZvoO2aa3dOicw5QQINSFI7RDHhWXBYlh2c2w6+FwbPF0EUFViWgfb2BgSDJZDlMDjOQjptxw4zTR08L6C0TEJR0UykUm3QtAQUpdjrkEXRzoYRCNSgvX0yGhpWo7k5AJ5POfFKFG/w7mIHxc+kiHWVepbFQ9dl6HozeF5w6uOugNGLgBh4ejKQDIerIMsFEIQkeKEOWrrcG6hKUhAVFYuQSExDPN7oKMhGeRnaUqmJSCaTKC5uhSi2IBYrx9atlyAUaoCibERaq4KWLkcsNgOBwERnUmtbXLorZKKowE1CYVupBLOU66nUWJhmFKqacOKPCHBTVI8cOR2trTUwjArIci22b78cra0zYVkKZJmHomxEXd13YJoaAgELslzrDFgyk2hRVBEMlmS5Zg5VaIIxtOA4HiNGHO3L7pSJMeS6SglCCzjecddwslVZvsfAMABdT8Gyks7n8V7g62CQAy/E0dp2LHRtGETxc2haNQA4LlQJZ+U4k0DG/c52rwCAdidLlu3aaDrZZzWtGhynQ5IakB03RXdkNAWez7ibuIowV37dOGKu26RryU0Q+UZ7e70jrwJEMeCNJ/1uhYC9OJRLScYcyzHGZTLBWmYEpinCNO0QB8lkIyzLwMiRx8CyFLS31zuuUVVoadkPLS2rEIttdmKJ7e8E198ORSkCx41AIDARciDqZWJ3r2t7Q+jgeQ0c5yoBEjnr6NXVZ+FtW3caXrB8RSnuo1YmiL7BtBTP3THbvT/jGun+7+5vmiEYRltWoggAnlJslN6OcVocGzeOhKIoqK9rx6aNk/CftQ/jICmIsrL9UVg4Ak1NX6KtLQhdvwBFRW+gpGQ8Nm36M7Ztex91dS+hsnImEolRaGtbh2SyEUcl6vBw6UQUmRqOSjagNrEDilLseWcoSgo1NRMRi33oS8AhOt8HOlh3uv22e6/93PBDAFKE5TmuBZiiFHuTSNdNQ5JCnoIIyCirAHiZMni+DcHgRk+YeF6Fqo71VontgbI9CHBjchUUlEMUVcfNyYAkFXiDhaLCVyHLtWhtnYmWljGQpARMU4YkyQgEtkJVRztpnyNQ1RgYsxAOV3ruFf7Jtxsc0B8k0L2Om+lK0xKQ5bDnFkrkGz1XXO6JK9lgdj/jOAulpau9pBaWpcA0olkuycHgOhhGFKnUBiSTjVDVA51g2aORTrc5GaVqnNgFp6G+/jOUl09GUVELmCVCEAyoagFUdTxKSj70FFAAnHhDI71BgCgGIYqBLFcoVRWRTjdC0xIIhcqcibcMQMLw4X9DLPYNJ9urgVhsM1paChEMliBSaMcdisU2I5m03bBDoVIoygjIMo9AoAYcZziWYoBhRmDocr/9xoP5uSDyA46zUFm501k8cuOOZC88uXHxAHhhAOz4WlFoWjU0TYRl6d6ClR0zJAHGLAQCI8ALcbQnCpBMbkZBQTnSaTt2n6IUI5HY6Q3c3cDXgUAhgsFSBAKFzgQ37GV3dK1IdCchjV2/UT7rr7i3MCYIcS/7tBuc3w2s77o6u/dkWQoMUoQReYhrge26HWVckOz4O66FmO1p4VeOARl36CAYi4IxFRynOBngeOh6AqlUMxKJnTAMFYpSjGHDNqG9vdDJKJlAS8sp0LS4lzRD15MYMWIs6upC2L79Xceros257jDHa0Px1d+1ArMtOnlHSeZ9b7leIR3vXIRfyed6YxBEvmHo5WBW0IuZ58a1BVxlcdDXB9rxbt2EVG6MLjdAvdv/jk7HMU6L4/X1L2DSpNNRV/cJ3t/6Jq6q/xwvjJkBWbazOTY1rYWup6DrB8MwToeiqNi+/QPU1KwEmtYhmfweYjHbHTqVasYRagtWVB6CGjWGM2Kb8VKqGUerLQgEIk7csVHYuPFfUNUYQqEyz9rc7wrpZbNFxnLbhtQ6PaXXW2zTpk0YM2ZMp+133303fvazn2HZsmWYOXNm1nd///vf8eMf/7i3q7JPwHEWqqstMBaHYTDoehSBQCFMUwfH8U7QP9PTKruB9ERRhCC0eT7UsrwDklQPSWrwguba8Yo2IhbbH6oaAwAMq4ygNdaGUCiN4uJXUFhY7qwsV0FVx2L9+sNgGJNRUFCBcJgHx7fDsngwZnfamtaOLVsuAGMqhg8/EhUV70FVI+A4HYLQ6E0W3BeXYUQB+AbclgKBb4JpKdC1YU4b8M49UYyw3qB/Zbh3lRBdKTYGg7Kj67oZKC19yrPQSCYnQ1U1Lx5YY+NafPjhR2htrYXr8uzuo6rbYBgqjjjiLscybApKS+tRVibDNOuQTlfBMEoxbNjD0LQqpNOjkE6PQjxum1YXFq7DgQeeiI8+eh/xeDEsS0coFEIgsBm6HoWuRz2ls22dFsKkSTfjrbfOgaYlEAgchcrK+yAIASSTjaiv/y6mTPkHVq2aiVhsM2KxEtTXTffcpGXZgKJshKK8C1muhSzvAGMi2tsPtu8pFYUg9L5yKh+fiz2F+uD+hTEe8fhUb3AKwFkkkp3/7VVbQWyB4Axc3dhDAMALcUQiLV5iGFdBZhhRWJYCnk+A5wwUF29DcTGQTlegoWEnysvLUFa2Cjt3BiHLYWhaO5LJBjQ1rcOECd+AIEgwDBWpVDM0LYRgsARKsAXMsmMB8XwckmxA6uAi6cqlG+8oENgKUWzBjh0/wZdfliAQiEAUS7IWpTKumGkUFZFyeW8hGe5/2tq2eQtP/r+uNUZH6zDXMiwz/gxCkoLOvgFYVtJbrE0mG9HSshH77fdVVFYm0NBQiUMO+S6SySloajoT27frmDTpFZSVzUQsdjRGjHgVlZVXoKjop5CkEyBJQUhSCJIkQ5TqvPGwa+HixgfMBMtXvMmxHei/a3jegiwXQJLssAfkWrX3kPz2L4zxaG2tgSSFIIpFkKQqSFKLk1HVjh9mOYs1lgUvdrabzbWtbRsSiR0oaNsGyTLR2lqDd9NtONpIoUprx/JkI0arMZimholFo7D5sIsxyQlLtGPHB9D1FNbvWAXL0mEYKrZtexuHH/4jhMNViMU2o6joHfD8Uc68uBKN295GcWwLZEHCsnAVJjlzV3dByzB4+MMb2H/NTh5TQGbRLWOxDZgmGYX0hF5XhB1xxBEQBMH7fOCBB+KVV17B448/7m2777778Jvf/Mb7nEwme7sa+wyM8ait5SHLQUhSAIEAUFBQAU1rB2MmTDPdKVCvm35dEOJO3JAUOC4FQUiA51UEg2uh6+VZ7heybAfXb2sVEYkoMIwQmlu+jvKyx6GqYxEI2P7YyWQ1AoFCxzrNgiw1QCyK25YeRhSFhY0wjCja24F4vBaxWAWKi0udmCoGZHkHFGUjRLEeohgHYxLsTBwRO8OlEfUEPyDvAM9Phq5n4p4Re89gl+FdTbK6+m4wWP50dX3LkrF27VUQRcWn6NrpxcirqJiCSZPO9ywy2tsPhmF8DMamQdMSSCR2OsHnywEApaVPYdu2K9He3gxNs603Ro2qgaZVob19ImKxzTDNbWhr2wbDSIOxZUilhkFRLAQCO+z4JmILJKkBhhFFU1MFGhq+8JThxcVXYcKEjRDFHZDlVSgpWYpw+Hokk41IJnk0NZ2JkaMUCHzUySD5kadkd2OcuJN+jtNhWUGEw6s8ZXpt7WVZcYr2tn139d1gfi72lMEuv0MNxizU1Kz0ss75s7Vlw3kW2ZZlekFwTVOHYaSgaQkEgyWYOjWOtFYFjk9B4FMw9HKoqozm5hYkEjshik2wLB2aloAkjUQyuRYVFQeisLAJssyjre0MVFY+gOLiVyDLO/DFmsccC1MVPGdADDSgvf1AL9Nymi+FIEz0XCE53kDAWRhz+2NJqkVb23GIx7cjmXTv0bUiyR6gFxaOyGF5QvQEkuH+hTEL9fWfeQowNzB1Z/dIIGMBlsn4apoajjnmm+D5Zuh6FIZR6sXnlaQQFKUYkUg1TFPD5s0mYrGVKC29DsnkZDQ2rsf27SuhKN9CW9s2xGKfgeNmwTTDKCj4GPvvX4MPPzwewWApFKUIjA0Ds0SIYsb1keMN8Mgo2e0A+G4csIxsun8ty4S7KO5afrvuYYahYtgwivO3N5D89j+BQKHPmhNgLAjTzLhEGgYPw8hWTicSO9HeXo9XWmvw44bV+P/t/XuYXFd554t/933Xtbv63i2pJbVlSZZtyUi25RtgG9sxOVwMJEwIJBMyz+THhIRhDjkBzswc4AfzI2TOhPklTCYQhpCEy5DAgTgTUMABO/FFii3ZsrCQZVuXlvp+qeq67trX88faa+1d3S2pu9Wt7up+P8+zn+6u2rVr1ep691rrXe/7fV/o3Q89uwUTEyfxofGf4ssdu/H6vlvxb697CKOjL6BUGoZtl9HS0o89X/95/M3tv4WbbnoYf/e/PoaOVBfuvvtW3HX3O/HUk4+ip+d30dLyOADg0KGvYnLy25icPIWt06/iVLIDjiRjUjVxnZ5BJtOL++/X4DgGzp1LwnUt7N37vjBN8jzS6cM4ebIXMzODMIwsDCPbIJ7P4fadSKz+vLaZWHZH2OTkZMPfH/vYx/Dqq6/iiSeeEI9Vq1WMjY0t91tvWJLJjtAwqpCkMhwnLTzTvu+KBTZfZLO0piTbkfYT8P0EHKcPQZCApo0jCDTYdi8cpwu+bzIh31AUn4ngb2O6XS5zlnEBXrZQL8Tyl+uQ5ZpY1DLNoGG4bk5UyjKMLFKpn4rdq6h8u8Y0FnwVQEZcQ1Xz4bUSQiTU82rwfXKELRfX2oavhTNirQ8KbDLthGKYKjKZPiSTHaJqK7dFgFV14k5qrgOoKCUYxggAJuZZLk+iUpmA59WhKAbK5QPhQnYEiqJjauo0fN9DMtkOAFBVP0zfslCv96Nc3oNM5kVks0/C896MiQkACCDLKqpVBbZ9E7LZnyKTOYzR0d9AMjmEREKFYZxAS8vjGBr6CCwHUNUtqNUGoKplqGq+IX0s+uxa+JOla0Y6JvS9WAo0Bl97eLVmVTWuKFjLtC59uC7ChbIEoAWua0FVEyiXt6Na5bu8MjyvIFImDSOLTIbZPh/fN226DV1dzyKX+wF0fQTV6k6wSllME6xWbYHrVsM5QCtMs7NBd0RVLchKKRQbrgnhe983w7TNXuj6CACgr+/WBqkC/nniUWGk03n1kA1fWyRJRkfH7tAJFh3zRX7N/glA2CerTJcQFVTjFWLr9RKy2U1obc1ClhVMT/fBMGS0te1ArTaN6elXAQCtrdvQ2jqOXO5QKIewC11dN2Fi4iQcp4JksgOJRDsk2YUSbibJkgoPDmQZ4TyBL+2iNs53T6Lor5WB7PfaEgQ+CoVzQhdTlrVYwQtbZDXw1GNWhGoU5fIofN/F/9Z1E45KMkwtCSBAR8du/HdrBlVFC6VIZjA6+gKCwEdLSz8sq4A/7LoJu8xWBIGK7QMPYGbmAvL5bTh75k1w3Bw2bfpDBEGAkZEPYnT0BZwY/yn2VyfR4tmYDCPAJlUTN+gpGEYWra1/j3p9AIbxr6HraZjmebEZlUicQRDcAMepihRJNt468268JRJt1/Yf0OSsaDKppml43/vehz/4gz9oePy9730v3ve+92F0dBR/+7d/i09/+tOo1WqXuAqg6zoMwxB/ZzKkQ8GRJB/JpApdfxWS5MK2+0TOM09nAiSoKus/fkNgzrEcPLCQSqZV0gtN6xQpiY7TCc9LQtPHAChi95oL5MuyCssagGm+zCbctW5UKs9B1zNIJNrEjhUTEU0IRxdzrskwzBract+FopSEI02I+YYLYp4iyauAcIFQ182hXu+H40TlZT3PIZHPZWblbfjyzo61ELGzWOKpCLztV/ocrKiELcKbDWNQfP8BYGrqHcJRVLd74djdkKSoiisAYRuFwgOwrHOiCpTr1lAs3oNisQzLyqO1dRtc10Im04f29p2QpItCw8j3TdTrJiqVYZhmJwzjAtLpozCMbQAAw8gACFAqDSOZzEHXR3DhwsfR3v4oNG0cilJGLncI5859BOXyaOgQlyFJKUhSBrI8ACBaOLA+ksHKQbMdVFXlCw36XlwtNAavPJIko6WlXyye55uYxiOyudObRWRIYbGX6LvhODLqdbbhwypAMgF8NjluRWtrFzyPFcVxXQttbQ4ymcNIJk9ClmtwnByq1T3g1R6rVb4oY1pHluU0plqFUghy6AhjaZ2mEL+3nU5o9X6oah59fc9BUcpzxmZ+vuPm4DrNZZdrHbLhlSde8ILZsCYiwvjzPMWZH4DDIrHCDAU/nOeyDdpGnS3Pc+A4FRhGFm1tj8H370WpNIxEwgyjRh7AuXOPI5vdjK6uzchkvgtZLsFxdqJa3YNc7gkMD6fgOFWhh6v6CQRhwQpJciHJrkh7jvTDVEgSKy4Vh53jgRW9iSLeuFg3sXyQ/V4byuVRoRfNo7PjzmruCOMpkUCksb15851IJjuQz5+B41TR03MLHgAwMXEShwvnkZv8GU7PDOLmrpvQ0bEbExMnsXPX25BMdqBYnEZ//z04dep7ePXVQxgf/5fQ9Qkk7rseoyNdeOml/y8GRz6JZBgBBgAzsgYNAQ4KTcIEfJ/9P02zFYYxBE0bB8CKYbGINks4v5iOH4tM5ZtPl6pyS1yZFXWEPfLII2htbcVXv/pV8dg3vvENnD9/HsPDw9i7dy8+97nPYdeuXXjXu951yet8/OMfxyc/+cmVbGrTEgQypqbGIcuJcNJcFEJ/PJqEGwj3kAdhbrPrKuFieQuCQA4NakBEjrGjAlVV0dZWRi6XQ6nUikplDFyMv1y+BZnMYRQKWzA5+TIcp4qOjt1IJuvQtPGw9DvLWa7Xq7DtPpTLRahqArruYnz8VxpSMgp5Sew4c6FuXj0rCDy4rh4buMcbdEpod2v5WXkbvvz/rNmcHcD8bb5S2l5r62NC6Nb3EyiX96JS0cIKkQV4XiqMuqjA815Cvf4MXve6m7Bp0+dg2/0YG/t12HYfPC8J264gkWhDS0s/fN/D1NTLKBbZxDyT6UM2O4W775YBjMKykjhzxkdPTy9k2YLvmzCMPJJJCZ5n4sKFfw/XldHaWkU2W0Jr62Ow7VEYxiCy2aeQTh9FOv0CstknMT39VgwObkV39zvQ2fkYurvZ9Vw3h5o1AN9jxTxM8wwSidPQ9WEYxiA0bQK+r4ZVKQcwOvobS+rjtc5qaJTRGLzySJKP7u6zIt2XC87zaouybAlHc1TeXQujrhKhDpgVvr6EIEjAcTqF47tavRH1+hbMzPSiWp3A+PgZDA8/h82b70Bb2w48++zXsGfPvwPw70JB7WnI8gGMjm5FqTSM3t4+ZLNPIZl8CUGg4oknuqAoOnQ9g2SyPUy/6BGLf12vhm1hizLXzaEe6hdyZzuPGnNdF7XaNOp1Nu9wnEls2dIJmebkywbZ8Pws5yaCJPnYvPkCNG1CZCXwyqgc7iRubEPkEGYbs6wIhqrl4HsZ8IisVEpHKtWJUmkE4+M9mJ7+X3jd6w6gVlMxMnI7du78T/D9j6Kl5SRSqb/GqVOvx0svXcCNNz6EvXu/hSNHHkQ63SVkR1RVFfeHqFJrY6S17+tz5sVxoe3IqeeFadhRaqVtN9/4yllrm2RkvyuPJMno778ndIQVIck1qLGCEUGgwXH6UK/LwhF2zz2/E8qF7MPMTAmathe6nkahcA6nTv0NbrrpX0DX00hMv4ofnP0JHtnzC9i2fSsCX8VNf/oOvPivHsP4+BmcOvU9yLKK49Ov4v++eBjpiZPw23fix31P4uTJw/jYj++FWTiH3v//7+HcuV/G4A8+hGTgoSopUBQdmpaArqdw7NiHkEx2IpmshnIEE7hw4V68+OLXQueYLRx9/HV84y2uYXhpaQbiUkjgieIrwKFDh2DbNt72trdd8pz77rsPP/7xj3HdddfhzJkz854znyd8aGgIn/2sB3uDFzjxfWB4+DnIshpGf9QBIDQSA7qegq6nw9QN5kSy7YqoUBX3JvOKOYaREI4xvnvNUj5kKEo5XNyyAT6dfhXp9FFMTLwHY2Mn4boWtm/vharmRRUpLUxn5MKe4+MqMpletLcfQql8AC3Zx5FInBHC2Y7TKRYI9Xq/GOw9Lw1eBtfzTLhuOwqFcygWh1CtTqBancTrXvfrNAkHoOvAxz+uIJvNolQqXfkFl6CZbXilJ0RXuv5C3z8IZBFlGR/IeHqF47AFKdMFqojqUjfd1IXu7q9gZuZeFilW3wLfTyCZfAlTU/eLCjWua0HX02Eache6u3+Izs5voFi8BzMz9yIIVHR2fhPj47+CsTEThpHFzMwgLGsGqmrgllteRr3eLzS88vlB3Hrrf4LvZzA5+YvwvAw6O7+BfP7NGBzcit7eKtrbvwvDOANVLcGyBmDbfSLik+1u5UQUaKnUKjSSLKuAnp5bVvX/ttLXX8j7k/02D2wz6nS4QDXFEYcVgGiMJuHi1vy7cMMN70QmcwTPPnsekuRCVkoIfBXVqgnLyofXZYLcnseKaZRKwyiXR9HXdyssawaeV8f27e04f35GVI/s6W2B72XghY7o9vZHYdu9OH8+h8HBJ3HvvVvBitWUoarTMM0zQiBflmvQtAm88MJzKBQmIMvKHE0SHt0WBD4kSQ41wtbOQnS1IBtuHvg8Oi6U3yiSr4iN5NmLzsbfo2IZkVi9CsexMTMzGBaQOYfhiZM4sPd9KJfHYFkF7Nz5v+Hs2R8jk+lDZ+cetLUN4sSJUSSTHejo2A3D8NHS8jgmJx/C8PBzSKd7kEx2hIviePqjLOywMWXZExHikU6YG5vj2zHZFBe9vfs3vA2T/TYPkuRi165fhq6PxCKceeE1DZ5nwvNYlpPjdMGyWGZCrbYTljUQSodMi3TJ6enXUKtN4YxTxfVmK2666ZeQz59BqTQCz6ujvX0n+vpuxeTkKRQK57B16xtQLF7EyMgx1OtFvPe9J/Daa1/E+fPP4OLFZ9A6cRKnzFY4YRTXjGpAC3xcp6eRTHagrW0H2tp2IJnsRCpdRiZ9FJo2gd7e/4Zt2zrxzDPvFjq7XGs30tllUaGAC1lm0dzHjr0kskk2Mgu14RXrqf7+fjzwwAN45zvfednzjhw5AgDYsWPHJW8Atm3D3uiWfhk8z4ampaDrKQBscspDvDUtFVayiapLqKoB12WLbtetw/OqMZFQH6pWhqqUxGBer/fC8xLhIKuG19HDyhudmJ5+KyyrLJxxAAvNdpwcJJkJ4HNR7HL5ABSlLKLUPDeHIEiEWiR9UNV86PxKM82xej9cp1NU+og7/BxnHJZVgO+zCpmGkV2V/l+vrGUbXogzYbX1pRbz/o5TFXpfqmpC08fEDi2A0HnUBdtWUalMoFplO9fcqVSp7IPjpKFpLDWxXu+HrvehWm3H6OjzACQEgQ/bLqNQeACqmkcQqDDNM1CUEmy7F5LkwjRbhSaCYWSQzW6G7w9CUUrQdTapKJdHMTLybyFJLixrAKnU8VCTyBT3hGr1RnheBopSCqvfcb0/iNTnKP26HIasVxpSJpfCevteXA1r2X7XG3HxeOa8rjboZvEFpmm2oq/vuYb0f/YaE7XaTvi+ySIkAxWBr8L3M5Bl9n1xXXY/0PWUGK81LYlkshOalgzfR0Mi8TISiRth2yUoio6W7FMYHn4IljUEWVag6Qfh2N1w3Z9CljWMj98OVTWETqBhDIYLiumwiuQIdH0E2ezWUIe0JlLCWOVLVZSkBzR43sZeQC8nZMOrgTSPk4ul7Tfqg0nid3aO3RAxFl+IcntVVROalsSIlgw3p6oIAg+GkYWup1EsXkS1OgnXvR2plI9Eoh2apiObfQxTUw+jUDgjhO2jrI9IVgDgQvhAXMx/9sHOizTM4qmRkhRdi7g6yH6vHTx4gqcF+358wyYh7FGSHMhyDZY1IOamLCLbDINGMjDNVjxdGcN1roVXrAJyQ/+Mx0ePQ0OAm5MdSKd7cPHiYXiezaoxm62YmPiZyGJynD6MjLyIYvECZFnFTNdNeK1exN1hUMqxmUFcVy/iNQB7jKyYJ/C4JN5Wx+lCuXwS6fRRobHLdcMiOQO3wWZZcItLjrBFsGI99f73vx/j4+P4u7/7u8ued8sttwAARkZGVqop6x5NSyKb3QTDyDYMbIoiQ9UmIEsFeJ4JgEVTqaoKVdXg+ynwsqxcK0CWVRj6oPA4a9qE8Jpb1gAcR4eiyHDdGjyvDiCFcnkUAKsuydK4THgec4TpxgRUdRyKYoXeeD1c7BfDxQCLAKvX+2FZA9D14TB6JAvbrsBxKnCcQTFocyHi+GSA53nTAL68rGUbXi+7lVw3Kq7PoeljSCZOi50tSXLFjla93g9Z3hlGaB4TkVWlkgvHGUE63Y1U6jgMYxDJ5HFkMjkUCn0wjIxIOS4Wy5CVh5FJH4VpnoHnZZDPvxlBoCKRkDE5OQlNS6C1dRva28+gUHgjTPMCDGMw1PhrwcWLSSQSbUgmTWjacCxNw4Ztt6BYOgittlO0P75A4BGf7J7E7l/8nuV5iavqz/XyvYiLHC+VtWy/6w0WScLGH6bfwXSBWDq/FaYNMsH6TObIHBvw/cgG+dhr232o1wFNY8VluNAv1zoBmCguj/aUZaYj4nkZtLZ2olw24DhV6PowLGtGOMbGxwDH+Slsu4x0uhsjI0dhGFmxSNf1XdC010HTdChKHp6XE6mVnpeBLLMK00A0YY/rhRWLd1/r7l+3kA1fGyRJRjLZAUUxRMoR19RrrBzJUgpl2cJsjTA+xvGMBYCNZWyxqiKRaBOVWvfbZbS2boOup2FZM2hvP4R8ficuXjyMiYmTACT09NyCVKoTisIiP4aG/jm05zQAHjHuQVG0OQUs4k6uxse9mNNMEp+LtZM7CiilYrkg+712VCp74TidkOUSFMWKRYSpQoqAZxfxyDCuo8k2fDbBdS2YpgXTbMHtRhYvezZ2WwW0nvobfCv/Gv6kcw/KmT5Uq5OYnDyFTKYP6XQPSqURVKsTQsfrzJl/j6Ghr+Jw4TySgYf9nXvwltZtUBQDQIBknjk7q5ICVTXDcb3O7g9+QmQ+5fMPo1i8G5o20aDFyd5HFZ8zPr/mWmPEwlkRR5gkSXj/+9+PP//zP4fneeLxgYEB/PIv/zK+//3vY2pqCnv37sXnP/95PPHEEzhx4sRKNGXdI8vAnj2TaGn5NsrlW3Hy5C60tV2HdHpQVHryvAxMky9ImY5AEGiQ5RoSidOhJkAnfD8jDMrzMrDtBBKJ00gmX4KuD0PXRzA8fDNUNQHXrYnqG/V6UaSFKIoOwxhBrbYTlco4JKkHgAbbzqFavRFB4COdqSGZOA3DGER39zeh68Mole5AtSpD1wHAgaoWoaqA6zKNMb6YYBooMsvdDicW/CbCI8OoYtXVs5o2HI/qWUt6DyvZFl4h0nXrsIsG8tPXN+T+84mtbZdRLD6Du+/+R1SrN2J07NdRLiVgmmk4DhPGHh7+t7hwYS9830Uy2YG77vp3ePXV/46urq+jre1vUSzeA8MYhCS5qNV24uWX3xjqCzqw7bOYmRnEgQN7EATjmJzchlOnvof+/nuQzT4E06zguh0uNDUPSboIJuw/Al0fhO8nUCq9BlU10SL1QxYLBohFMgDU6/1wXeYAkGVA111oWhtc14Vtly/ZRxvxe7FUaAy+dvi+i5Mnv92QGskjtLlzSQsjQHzfxfHj7xX2HI8YixxnedxzzxmY5hnUartQLh9AMtkKRWG7xdPTk6hUmJCuLKviugCLLH3mmQu4/vqbEQQ+arVpPP/8G5DJGMhkeqEbEwh8FYVCMtQJS0PTkjDN1tChZkHTxmEYx6Hrw5DlGn7ykzbcf/84bLsPL72kQVFSkCQ2LkdpVew7piga+vpA8gTLQLPb8Fq8L16OVKobmpYQVVTjWRF87tyY3hzBMxh8PxFGSaKhEquilCAr/VDVbWLjp739WaRSA3Cc69HZ+XvI5/8cllWAoujo7LwBnZ3AyMggBgefDKtI60gk2sS9Je6wkiQFQeDF7idOQ6ENRhBmUzji3sN/8vk1d9jv2fMLkKSV6edm+14slWa332aDpwnKclTB1fNMsCquabFpzAuyMUeZI+aorpuG67LNXKbJlcWkXYYjyUgGHoqKjlNmK/p8Fzv+6f+Hnf/q9wEAFy+qeMtfvQs/eM+j2LnzLXCcKiyrgEplHB8bfQG/UDiLz8xcwJF0N7rTPUgk2nBdvYjem34Jt3XvRSqVxnPP/QWSyQ7oegqGsRmumxObyEDkZPf9SJOQPx/XHgXchug3YmGsSG898MAD2Lp1K77yla80PG7bNh544AF8+MMfRiqVwoULF/Cd73wHn/nMZ1aiGRuGmZl7w1QkEy0tGmRZhePkmBfcNxH4CVHRjadTeV4mNKISPC8H12XnS5IDTZtAvb4FjsMed90cVDUP181hcvJltLRsgWm2IpFog++7yGZPw3VTsO1etLZuRiLx1yiVdqNUGkZ7u4dqdQ9UNY90+ija2pjTi6VOdsJxOkW+dnv7zxAEGhIJ5i2PO+X43/F0sHq9H9UqSzfh1TCJ5WE1bTg+SVpLE6aVcMLw62j6BAx9JLTTPGTZbUgfrNf7cf31v47W1sdQLh9oqK6qyAOo19MIAg9TU6/h1Klh7NzJbMd1LVSrN2LXrl+B56UxM3MvXn75rejsBFKp49C0CfT1MaFuZo9bcN11VZbe5eYgyxqSyU7hlNP1CaEppqp5SJKDlpYncPz4YUxPl5DNbkYi0QZFYbbKRPWfxMmTH0O1OglNS0GWi5DlqtiNNs1WqOoUFAXQ9QR8f/6+3UjfC+bMX3qEK43B1w4WTdIZphdyIVu2WOVOMF4dkp/PnV7sbwVAAM+z4Tg1OE4FLS2PQ1HKqNePIpU6jrGxX0G9nhEL1SDwsWnTfnR1/SXK5QPw/YuQZQuKUsKOHSoKhTqqVebgqteLSCTakEqdFoUqhocfgONUYZqtoo2qqsYiUNNiXN6z5yBKpRcRBCr6+3fC9+dGn0SpHTyiZO3YZ7PS7Da8lu7RV0KSfGQyBej6yVArj0djz+/84gUvuAaYFxaT4AUw2PzaiUWPAapSgqp2ik3cEyfK6OpKYdOmQ8jlfhAWnumD77vI5QZw8uQP4boWstnNSKd7Q2d1StgqL8wB8IjMqCothzm6Lp0qye3WdS2Rar3S8+hm+l5cDc1uv82E68r49rfboKq9YgyOpzbzDZsbbngnurq+A4DbJpfcsaAoFhQlI9KeFUXH7aGz2ZFkfPv1/yd21YtwnCqGHvq/UTk1Dte1UKtN4w+690J79RCuv/7n0d19FocPn8WePb+AZ9p24K8vPI10pg8D6R7oehqSJOMf0j14u10Rjm9ebKZeL8KyCtD1LQDY2pfLmLDotQw8jznWmcSQIxyA/F4QFc8gFsqKOMJ+9KMfQZpnO+HixYu49957V+ItNyxBIMNxfDhOJwBAkuzQ6HPCM8yqx6iQpBo0LR9OdLlzyYHr5kTpdD64sypXbIFsWVtg273wfR2bN29CPn9mVvqHGjqp8lDVMur1AWiajfb2XUilnkA+/zCSyZNIpY6HwoRMDFvVJuB7GSH+ZxgXAECEtPo+Cy1nYaCmCA1l57BKXKbJKl7y3GxieSAbvjQLFcBfqIMkCGSMjsxA0zphmjtgGMkwpdkVJdlZytE9cN0MLGsnCoUHYZpnoOvDqNe3oF6vol4vwvddtLZug6qaQnfLtvsQBCosawDV6o3I5XKwLGZHuj4M30/AthOo17fAdV2oahckyYGhj8Bsd2CaD8LzVKiqJXaihkd+E4psQdeHUa3eCMdJQ1VtqKoBw8jCNE8hmTyJZPIl5PNvhmFkYdtllEpDSCY7RCoXT2fm4dx8kd2Mk+Xl/l5cDWS/15ZUqrOhbHu8jLuqJhqcYPFKyADCyA22INU0C7ZtYGTkg+FzNXR2fhOmeS+ALZCkFtRq09D1NDzPR622E7Jcw/R0PwDANNPYtOm/wDQHYNs3QZbV0NGVh2EMIpE4jUzmMDTtbQgCvyGKTVGmRPRKfNGfyRyJfc7jePVVA5s2bUMqdRyS5Ia6LAnxs15PXptOX+eQDV87gkDG0FAJitIORekTTm3uwI6nEEaviZxJnmdj06bD4eOayLxgf6siPQuAyGiwrAKKxYtQ1YeQTH4EilJGMtkOz6sjlfqpkAJJJjuQSnXGokynoKrlhnmy6+YApGM6SNEYM9cxxqNLoogwvtHF5RnImX31kP1eOyRJRk/PLcJu49kUAMB1pQ1zCoAWPnZ5Pdog8PHPgY/7fBcdroUg0wdNS6JeLyIIfBQK52BZM3DdGn6a7MBbOvcglWqDJL2C1tZt2L37OHp6HkRHx24UCudg22UEgQ9VNfGW7r2w7RJmZgbheTZOVcaRSnVB05jkyNatv4/JyV+E6+bANc0AnmbN18IJEQ3Gn2ftpmiwxUI9tg5wXUsMdlxryPN8+L4Zm3TrAHR4Xg6KkofvM6FeXqbdNM9AlmsiB5mJY4+E+iIDKJdT8P0Ktm//U+TzPy8G0VptCrK8TVTZkWQX9fIBJBKn0d5+FLJcQ7VagKp1QlFKmJ5+AIXCGciyCtNsDSvedEJV8wAcaFo+1CFKwHUjwUPfZyXlbbs39IDXoKplqNoE3NAJSBBrhcVEL/m+iwsXnhZinaaZg2m2iEW1ogRIp7MYHv4tsXtbKJzH1q17kckcCfX1JmDbZWhaEjt23IFqlQt2e6hU9kLTxlEuH4DjdGHTpv+Cc+c+DsvqDfUS0mE493DoPOtAJhPANM8gnT6KTZv+C8bGfp2Ji4b3hsHzQ6FQaDuSyV9GMgmkUm3wPB+qOhUuul+GaZ7Bq6/+7+jsfAay/HpMTp4KI8aYs0CWAVmuwvOS4T3FnqN3sp5Yq1FtxNWRTHbEIqtYaqSqGmEBjCIAC1KYsqGEkRw8hYqL4vt+Gp7Hvv/nzslhxNYA2tvVMLqSSR2oagKqaqBWm0a9vgttbdOYmbkAx6lA01Job78Ouj4MTdsJQAp1xMbFDjJbcHeEMgbZ0BE2BU3LC0FeIKp8x6tcKgrTHzt/fhhdXTeFm2W1MAKGVXRmlZ63rMJ/gCCWju+7eO21H0KSFKERxhfUkUA+i9yMvyae2tzRsV9ESXOnFxBpFLluGr4fOcKSyQ5Uq5Mol0fh+x9CT88TME0bjtMKWZ5CItEGrl3GUiJtaNp5Ea0Wz+6Ivxdraw1cEzi+MGZRJaZwss12hMmyBt+PnHgE0QzIMvC6102JSsdRqiBEcAdPi5zPJgDAD9QwsMMHEEDX07hLS2JSNeFAgusyQf0g8DEzM4hSaRjPhlIeB5Md6O/fCV0/jWr1RnR2KhgYeBeq1T3IZD6C8+dvw6lT3wMApNPdaG/fiXPnHhdRYPsrE6hWJ6HraWQyfbhux49QKDyAam1n+PnimmDMEcZ1Ojnx9G1icZAjbB1g2+WGijSaZoNXdgKAINyt9TwVllWGqmZCB5ktRHdrtQ4AHaGYZytkWUUq1Ybdu9+NUukgOjtfhu8n8PzzD6CjYwAdHT+ELFt4/vlJdHbuCdM6qjDNHAYGEmEk2QBGR69HqXQRwGaoyjtQrxcxMzMoFgxdXccxPr4PtdoOJJMDkOSaaG883JsLBDvOIAC+s64AcAEMw3Ut1OtF9PbuX5X/AbG+WcnoHUmS0dm5R4Rwu66FUmk4TINiRSkefLAVgIMgYLbR0jKAanUAY2MJTE8/jet33gBFGQgn5H+Nf/7n7Whv34lUqgsnTz6O3t5fgmFkoWllzMzci9bWZ0XI9fT0A2IyDwA9PS+hULgbY2N3Y2zsLvi+i66unyGVOgpAQ6HwAPq31lAuJTAzMwgmdM/DywFJdsPCGl0olw9g27Y/Qzp9FIpSwuhon4gE07QyksmXROVKgIV1T0y8Z0X6eSXYKHonxKXh6b3MqZSApiWgaWUoypgQso2nVsXFbXk0tdAv8U3oegaa1od6vYh6vYgnnvjXUBQdljUJ3x/Fpk23I5lUUav5YeW4BA4e/BJGR38Dw8MyJiffiS1bPhsW1uhAa2sNhjGIiYlHMDFxEsnkr0BV/VCTRIai5qFrE7Gy80xsmDvNeCl6RSmH95578Mor38drryUBJENnQFVEmtxwA2mEEc0FK2LR1xDVKTZ3LxEN5rp1BIEn5r5PP30Mu3c/gu7uo6FUSTK8duP4wCQGMpBlFclkB5trp1k6pmmeQbm8DU89dRodHbuQTvcglepCIjEGwxgMF/olqGpJpEZymRM+nnPYHFprWOiz6q7RY+ynHKZ0ulBVmyRGiKakUtnXIAEUOYQc8O983CZ4xhHANP4CX0UQeOGm8AzuuGM7ZmbeicnJl3F2+hUUh56FbZeg62nceusHsP25PwE234EdOx4OBfJPwPcduO45VCrjyOV+gFdftXH27I/x0Y/+PJ54+rfwSOEc/q6lHycuPINet4Y6gCFFx2C6B6/zXVSrkygWL2KmUECxdBC1aovICFHVPHy/JMbpiMa0bYoIWzzUY01OEPgolYahaalwJ8sAgPD3NExTixapag3Z7GCoZ8DE8XU9DceBqEZVqUzAsgqQZQWFgoqxsU+hq+tGmOZ5pjcWjMFxqsjnH4YsW9izx0Jraw0zMylUKuMYGBiDpv0AlrULpdJeuO4INC0Jw8gikTgMTZtAa+t2WNZWWFYBpdJBaFqYh+3IUBRTpEoyL7gJx81B8zIwjA4AgKLkIcmu2EnnDjPHqV2ynwjialhJZwebhG+C7zth1cVop6e7ey/27ftrZLP/TyzFQoVt94W7TbfCMPbj3NlTyOUGkEp14cSJW7F9+/UoFMYxNXUaN9/chosXC2wn2qiJEsznz78bExMnsXPnaSSTKjo6yqFQNptwsx20NFSVpVU5Theq1T2Yni7BtktIpTT09fXjzJkXUat1iYgYTWuBLLcDYPenZPJGuG4OhjGIvXujiBMAYmebT+pZKHjzlH4mJxgBIJYKaUBRXEhhJap46kLc+RUn7iBT/ARcyYXr9gpRbb4JxBfpppmGJOWRSrtIQYLr5jA9/VY4ThdM00WtNo1Tp/4VPM8BwNIvWbVHhONwW5gG5c5xgvl+Ak8/beP22/vR3v5dAMDhw++DJCmigl4iMSZ+B3hkjIcg8BrSsAiimYiiI7nGkAZVNRqcYXGH2GytrSDwMTDwFbS1/S2OHv0bBEERkqSI1EpZVhEEfiwF0Qh/r8LQT4c6vaz4RTrdLarBGkYJibC4FI/Y5PeVuFwIfz2PRuNRmlF71dihNUSlyjKL6mSVI1dIJZ8gVpCWlseFbcyuVs4dX1w033XbQrkfHiSSgWWlUK0Oo1qdDNfAWWQypyDLN8E0W5HNbsbhE9/At058A9Xnv4L/Y/Md2LblLuRy1+G55/47XNfCKaeKXaqJvXvfh5ERHbZ9BslkB7785f8HwI/x3zv34JbMJvSZLcjnz0CSFPQm27Ej1SX0OnU9jaefPh9uTHuo103Y9u4wYtMWkkWNm2xR9Bu/DxALh3prHeA4VXieA9dljiAW0q1AUQyxU8QXnCx0m4vQW/ADFbKcgKJkw5RKB4qiCcdYtTqBQuEcOrsSSKeOI5e7G5lMAZaVRrXKUjLr9U1Ip4toa/ORyTCdBMMYQSp1HI7zC9C0XgDA5ORDUFUfsswq1FSrk5AktoDmEwwlHOj54tj3zTAEXBMDPsBubC4ykAKW9uk4ldBbn7qWXU8sA0uJqmnmSJzZbWfO7CExWZYkGZqWFDvSxeI9mJm5F76fCCutfkXo8UiSg0SCTd4tqwDXtaDrKXR3fwVB8BuYmRmBokygVvPDyK1uyNIBGMYgXNeCJMmo1/uRyx2Cpo1DklxUKvuEUL/nmzACDY7TBcvagkpFQ7l8WojrmuY2IUzK226aNgqFAnzfg2FkxO4VL3bB06mCQEW93i80ThynE3aYqj1fPy2lb5uJZm77RidKLVIhSVXI4QSVO8G4YDYnSmGKnGDRtUxoWhlBkAyjNEwRbcUWxxfgummooV2NTTqQpD1g2p46LKsAyyqI+4jnZUX6VCSKXxbzATU25vp+Cd3dt2Fyshvl8gHIsoVq9TkRxakoWmjTuhizufA/ieUTzUy88nlccDueHgkAs51h8Z+scEVkrx0dLyOROI2LFx8Rr2XjPADIUJSiEOVnVSdN8EqwPOuBVWwfhmmegaaxarFDQx8Rc3k+x4/DI03Z75FmGd9kCgL2U+IOMbkWlmZJQ1WN5e1YgrgGFIt3N0Qwz63smhGR147Tid7e/xbq43bCtvvhOJ3CEWUYGZgmK9rGxsc90PUd6EiwDd7tlXHcVxrGi6VhtLT0C83b3aGm4MzMIMrlUVQq4/jH2jTucy10tm7DFrMVmUwfUqmucD6uIJXqRCp0hHFHPADkcudgGIPQ9WGoah6jo78eFsFLQwrnC1FBDvYZmd1TavNiIUfYOoDtSDmo1+vwfVZFkU2CE5CkMrRwx5cvQCXJhaZNQFFKTChfm4CmsYW1rnfC9zfBsgqo1aZRq02jXB5Fb28HcrlDcN0cMpkjGB//1XCx68J169i0aRjd3V+CZbGc5tbWx2CaZzA19Ra0tPwDpqbehrGxF8P0rKRwtNXrRWhaMtQKsqCH7eI53pIUVbLiYeCsul0OnpcLI8HKqNWmUSoNI5XqwkqVfSZWhqUs/q+Fg2SlHBOzrxkEPiYnT4lojUSiDYaRFTvNr7zSBtetwXXLaGm5BZnMHhSL98C2++D7JgxjELncACYnT8H3XVx/fQdaWx9DrbYLnvd61OsubPu8qEpXq2XQ2noQmlZFJrMJlYqNzs5pyLKFer0X09MH4XkOPK8eCnjvCB3eRVSrF1AujyKZ7IDj1FAsXkQq1QXDyIoFRDL5LMbG2uF5TlgOelDca6rVG4U9+74pqr+Wy1a4gB9Cb283JMnf8N8LonmIIkb8hp3oOJeKBOOLVn5E2h9dCIKk0CXhQruGcRauuy98rYZC4TwymTuhaWXougnLAnzfExWxHCcNSWY6mqqamNcJxucHmmZh376v47nn3oFSaVjoFvLPF2n7aaIAANct5It/gmhG4tFg/Od8kWDzOcL478PDuyBJN4iFaW/vH6O9/bu4ePHtIhqMXc8G195jc1stjE5h6Vq6ngYA+L4Dx82JTS9Z5jp9FnS9E7KsQddT6Or6WcNniUeGsb+j3+MR13zRLAPw4IRRZlTsgmgugkDG5GQWitIhnNcAs1Vuo55nw/PsUBOvip07j4h5abl8IIwauy28no90+jFIkgPDGISmDcP334GWli34i7brcbA6gfdMv4o/HD2Ot2Y2obPzRjhOJVzTljAycgwjxYvYX53Ev/RdVLa+Qejy6Xoa2exm2HY5lCDqEnN+TSuLDen29u8ikzmCVOo4ZLmGiYlfRqVSDotZ5BCEEgZyg1OMNMKWAjnCmhxJktHS0g9VTUDXU2LSyifOtp2EJPeGIpnMYAzjgoi0SiROh4L50a5RrbYLpgmk0y2YmlKRy/WgpeUvUK/34+jRMdx6K4so6enJobPzO9D1EZTLB3DhwieRzxvIZiUMDX0EtRrTOAHuRiLxKq67TkUi8SxqtRvR3f0V9PR8CY8/PgZdn4GisB1ztptmIl4S1nZ2wnNz8Dw1TH/kZaCL8Dxb3HyoaiRxKa6FU2WpSJIs9LxYhSgITS3uMLLtPljWDbDtFrz00odRqUxgz56X0dPzRdh2H1Kph5FIvBW+b6K//1/i3LnfwyuvTKJa/Xs88MAx5PMfxNTUaUxO/gwzM4P4xV/0MDLyQRQKeWzd+hIGB38JxeJFVCrjcJwfo7d3P0qlYRQK55BO94SaJgqCwIdlFXDffa+iWt2D114bgGFkRdVWJiR6ENu3H0E+fwDnz/8jPO8WpNMPQFHKYVqnGmqwVCGHn9E0W0XKyLVkLX8viOaBOYbUhkloPG0peoxXjosiNHikpO+XAGgiVYNdqxOe1yqc4qpqIpF4Gfn8nZDkDFLJk9izJ4dazQrbUYKuZ1Auj8Lz6pAkVlHZc9mmEYsUL4BViFZFeXa+W85TSLZvB2xnH+x6p6hGy3aw5bAAgIl4RBhP6+Y6gwSxWFY7IpanBc6OAONEovlRJbr4Qnt2qqQsq/D92cLc/DV8oa7B800gtD0WsWJD05JwnBrOnXsc+fwZtLZuQybzNphmK0yzFa2taZFJIUk+bLuvYRHc6OxqTBHjj8X/ZmjwvCRsu7KmMitW+3tBrH0kyUd7ew2KMi6yn2anRfJxta3tb3H77Yfxne/8NXbs+DC6uv4Cuj4CRSlD10eQTO5ES8tu9PR8CfX6FlQq+6Drw3jiCQnl8ii+3LEbx2qd6HAtPLjlTmzdeieCQIZtV+B5dZHt1Fo4h8nKOHzfw/4td8F1LQwOPomjp76HjkQ7duz4uVDOJA3TPINE4h9FFffHH8+gs/MXkU7/NmRZw3d/8n9hUv1j3BwW8uAZI7OjVvmxe/de0ulcBOQIa3IkycemTYAs5yFJE6HGwLBIxeDh1vEJN3OKlSDLLnxfDc+1hPZQOn0YtdqNqNe3IJEwUCiMo1B4M1KpTjz00Cfx3HO/ikplHKbpo7eXhWSPj78XIyPHcPPN5/Daa7eiXh+FLCtIpbrg+2nYNkuFyue3oaVlFFNT70Ch8AAymULD4kFV89D1ETGQ+77ZoFfkODkAQBAk4HnJcPLtQNfTqNeLlJaxgQkCPlFd2v9/tSZcrIpURmiUBIEb26Ey4bpuuBh14PtjAAIYRgbDww9hfPxtYNVbp1GtnoFtl3Hu3L/GgQNH0df3LkxOvoyTJw+gtVWFad6Orq6bkUqXMT2dh+uaaG3djmz2r0Nb7EM63QNNS6Kj43G89toAZmYG0da2A7KshNFhSdx22324cKE7rJxzUVTM44NxJvMsLl7cAtcdRFfXTWjNBTCNoyLShTvhud3X6/2Q5E7IcueKOLOb9XtBNA+eVwfboGFRJZ5vglWPi/R45hO0nf0YP59XtwIgIqjr9WIo0K1BUWQYYVVnx+mEYQzCtjvheTkYBm8T2yTSNB1MUFdmaZaBCvgJkZIVJ+68U2QLmlaGJLU2OLi4M4ydz+0iAEFcDat5j40cvMasNOfZ+mBSLBLSA//ez3aKcV577Ys4d+73QueYJx5n9sNsTQrUhoJWnlcFK2blgFeYTCTaYJqtInJE12U255ddyJIrHNuNKZGRIP7sKJHZOmFRgSofiqJhLUFjL3ElfB84fvw4JEkJbViHJEWR1L5fg+cV4ftDkOUb8PTTd2FgII+Rkd/E0NBHUKn04uab3w1NG4dpnoHjdGJs7P04d+4zGBubAgCo6ilkMn24w2yFZbZgHMBN3XvR1fWXACDkPRynE+XyTTCMLEqlYfi+i+7uH8JxOlGt7sbmcHM5ne5FJqMik3kcicRpmOYZTEy8B6+9lkQqlUcuN4BM5gwMYxDvf8e/hq6PYHLyTrzyyveFI4zfo+IRcNG9iuxmoZAjrMkJAhkzM73Q9RRUtRhzdMVDJrleSSSwyRxJkbecn8sG1EQsRYOlQzhOFaXSMM6f/wyKxR+L0uvl8q1CA8g0W0PnWRskSUEQeDDNVlhWLVzEVyHLKhynSwzMjW3igoYZ8BLU3IvPNZFYmXk9dIBFouJ8kk9sbC41aVqIM2N1J1xBWAHOheOY4WAWQJZt+L4nBrv45Nz3mYOMT7LZDrSHcnkUtdqNIqKrWp1AS8vtSCbPQNMm0N7+XUxO/qLYMRoa+hDy+UFwTSHHqWJs7G4EwRB6em6BaebgujX4vgff92BZvbCsaeEY0/W0iOr0AxVjY9tRKLwMAEilfFTKfbDrByDL0cJfi6VAB4EKRbYQKHnoevuK9G7zfi+IZoBHgLCJN1tYumDOpHga0uxIDc5sEes48dQsVpWKbQZxUWCAFZ2Q5Bx8h53f1rYDlco4LKuAIJDh+yr4xDjwE0ITKF5ZS5Ia5wqsRLsKwIEsZ+KfFoAdazdzEMQn5ATRbEQLS2XOWMsdYHFbjG/aeJ7TECnGHWOWVQagYfYGD3OYyQCiiDFe8IlVbveF85mnbHINM1U1oahjUIR8iNtwX4l+zp8OObuSZPwc7qAjiGajXi9BUTT4vjNno4YVdHFF1HKxeBGZTC9aWnZA01jV1/Hx9yCbPYJM5jBsuy/Usm5BufwSAIgIzHgxjWx2M1paHgfQ6AhTlFsRBEzg3vNspNNfx+DgJ+C6J5HJ9IVHL1xXxtjY3Ugm74BhDML3TfT09OPixReRyx1FS8vj0LSJUGboHVDV+5DNbm7QEJ497kqScm07fh1Ad7wmJwh8XLz4jNAWSiY7ALQAkKCqBgxzSlSFYuezfzkXDeQOJ0VRhW5Ivd4PSXKg68MolVi6lmUVMDMziN//yVfxtt79aG/fiba2DhQKb4SqlpFMnoJpJjA6uhW9vSdQswZQKbcgk3kRw8OdsKwCgAA9PTth2zI0TYVhDKJa2wlDKUcpYE6nqBwnSQ5cLyN2q3xfD3fGx8VkgRUG4JVv6OvczFxt5M7lXrdazoyFOFr4pLdSGQfX4WHVorJQlDQSCR9BkIbv6w2TbI7n2Ugk2lCvp8KCF3VMTPRgdPRpTE2dRmvrNnheEopSDkOwTyOdPgrb7kOppODChRfhOFVRqr1eL2Jy8hS6um7CwMCtqNV81GqsIpzrWjh//h/R27sfqRQrdGGYUzBjlSZPDI6JgZoJhk6E1fR0cMFtTUtB13tgmq0wjKFQn4jtcLtO55w+BDbe94K4dlzt/6MxLQqQJBMyANdXRdo/j4icXcntUqXPJcmFJEcC9Pz6TMpAbkh5kmULsuSKe0lf3w8xPv42VCrjcF1LXIM50E0oszbJGt4zFPnnKEpJFNYR7fRV8DROSVIhywo8L4p4WS+QnW4cuCNMUbSYzhATnI07wZiDjDmyorEpsk8A8+jlRY40oNFZxje12OEIHaO4I4ynQXFJAXWeqnjxtOvZzvT5KkU2tE6uAb4abo6ZoGgSotlIpTrnpArGIzHj6fuua2F4+DmkUt3I5Y5BkvZhcPBNGBgoIZ0+CgBIJk8ikWBzWQDC/pgzmkkRpVKsmit7vhRmWllwnC4Yxk44TjaUNMhjePgF1GrTyGT6oOsZaFoehYKDixcPi/n+9u053HjjmzE09J+RTh9FJsN0zLLZp/Dyy99AsXgxlEIyhU7nbOc8SQQtHvIcrAMMgxlbrTYN2y4jmeyArqfZpNnuhl3vBNcS4KjahBhMuSHzSDLTfBmu2wXXzSGZbIUkscgT267g/Tf9kohQsW0V/f1/iWLxbhjGBQSBipMnk9i6tYiJiRtx8eLTuP32/4CZmScBBFAUHW1t/wul0kEUCl24ePE2dHerKBZvCj3tltAHk5USNLUGTc3D9y0xgGs6kEgCgAZZcqGqTFjQ89KheDgN4M3KelxwLOQzSZIsxPEB5tBNJjtgGEkYxgWxU+Q4XXDddPgqTUSRKIoMXb8AVe2ELHdDkmScOPEN3Hbbb+LBB4/j1Kl70d7+KC5evAfj40Xkcl/AzTf/KVKp46jVHkIuNwBZVtHR0YbW1h+gUjmAlpaHMDV1Gs8//13YdgnXXfcQ0umeMPTcxI4d/w3V6o0YGbkfCfNEqLFQgqKcxpvexFKwNW0CklTDU0/9dpjOkYaqJsIiHjJYKegpcS4v5lGc5QjbqN8L4tqwHM6O+MKVTbwTCIJ0KG7dmGrUGKnhYHYqU1znR1VK8JUMZFkT+nmybEHTx0Txifhr2O8ybLsXrlsPF9kedD0lPqOiTkENdVQiPRUnTMk0oSiYo20WSRUwMW/IoRNPrkGSMmCL5/UFOcE2FnxTNa6907jI9EPt2hnhoGYO4Uiqg4/hTJsvSjuevWBlr4kcZ3zBzh1i3KEdVZA2wqgwHZp2AYpSFjYbRZOqsfeLqtByhJj2LJ1C9v4aPJgI1p8vm9ggpFJdkGUNqmrMa2dcu487w+6//4vw/Z+gXu8PI6sPYnDwPXj11bfAcap485tvRzL5EkxzAABEQRhe9ELX0xgd1TE+/nXxvG1X4DgVVCoTKBT+DC+UhtDi1lGr/SY0jWVNOU4N1ac+h3/quQUtLf1Ip9mGsKLoGBmpYWTki1AUC88//wZI0n1Ip7vxznfeh5aWx+H7D4tiNzxYJD5f4FSre65dx68DyBHW5PBFNN+p4oM4G0gdYbTMOy6LXSjfy4jUDdvug64Px3asE2FpZifU5NLQ3a2ho+NmjI+Pi/fiEVhDQ31o7+hHW+4QstmdGBzsgO9X0dNzC0ZHfyNM0ZiBpiUwOfnOcPAvs1BT2YWq+pAkWwhnxyfo7DOySbgcqHC9DHwvI/TB6nUDrmuFu2gX0d6+kyavRAOruaBZ6HurqhnbFXZRKJwHEIQDtwHLymP79tehre2fwHV8fD8hFqfPPfcMNm26HR0dPdD1bcjlroPrWiiVDiKb1ZHJHEY6/QCq1R60tPThqafejNbWbWhtnUFHRz8cByiVyiiVHsTAwF9BUe6AZXWhUDiHXG4AjlOF59lIJjuQTHbg1Vc/CE1LIpWeEvoGrLS7ixde+HQslUNFLleDaWph9dpRyHItTHdOwPNNVKu7Ua/3oV4vwXVraGu7Nv+vZvheECvPcvwf4rpCXGMnithwhC4XH8v4JHZuW9w5UgGuayIIyohStGoi5dLz0nOuoapqmHrM2hEEHhSlCoA5uAJfhaRG6VS8kA5vk++7kKRawwRblqNNqriOmAwgkGqQZTN8fP1UrCL73FjMTofkkV+8wiOL0KyJlEQACGRmY57faE+SpIuIFE5jirM75zmAO8R9MYcHpDCCOhE66qxQTiRydPFoLx7ppczSA5sdcTqfqL5ou1SDJDU61QhirSNJspDkuZQjjDmmA1E9/dixz2LbtifQ2vpYrFjMPgAZSJKM5547g2pVEdlUPHITYPcKXimdpVIrcF0LqmrCto0wcruCvb4DWdbw8z//II4dewmnT/8zHjv7Y/SbregSm8MmAAk/OPZl7Mj0YceOh2NzCeaQf+65T8G2+5BInBaZW7MdYPE5A7E46G63DjDNVrGbNVvgM6r0xAZzJogZ7fjKMe0AwAHAJ8/hoKpYUNUR1Ou9cJw0kklWNY7vTl+8+CHoehGemwwX3VuEsDYAFAoPQlVNmCYb0HlIeaTp5UGWS0L00wObYPBFPv+dCXqq8P0MXFeG67IKHXEvPYWEEvOxGgsa7uhY6HvrerpBxwCAcPDW60VYViHchRoOdfTS8H1L2LGuZ8L0ShvV6jDS6W7UatMYGbkdtdokVPX/A9tWw6qUL6JcHkU63QNVrWNsrIK2tg54ngHbLmF6+q0oFjOw7XPQ9QwSibZYekYVup6GbZehKDKSidOYnPxFbNv2Mfh+AoXCA0LrjN0DsshmJ8MS1BPCue04nWLy4bm5cLfOEWlc14Jm+F4QzUmUAqVCkoDA18TYBkCkS7Jz3XkXnjwKmo+lbIxTIMsWVDUv9DPZ9UrhAlmFLFfDv+evfsc0ixpTInk7+E+WImU1LJIBpmN0+UWydIWeIYi1SdyZzZ1gvHo54ECS3QZdrobXSiq8mM4e0Ggn8civ+Rxkcbg8QrxdUVRaYxrzfMSjw+az8/jr4/pislwLHfaX7SaCWJNEulnKnDRk3/egqp7Q8pQkGaXSMMbH3wXPyyGbfQqaNhHOrwcQBB6KRVsEknANXg6P0OSBJ2xOx9OcPWhaMpT/SEOWVdTr/TAMpsO72yrgWLIDfWoiZtsBtiXawnWAB0XRhB6gqpoolw9g8+bfQxBoyOcfhiyXxBxCOOXDiO5L2TtxacgRtg5gqZAzUNUZEcUlyS4Cn4niB4EKRc0zR5NnwvNyYAK6rIJUXFovmvBGk+xE4jSmp/sxM3MSW7bkYNsaNI2Fgb/yyvexb98+1OtZTE52oKPjAjRtApXKPpTLPsrlbiSTZnijcGGap8MURvb6wHegqiwSzQsX9X6gIvBM+H5GtIlHs/m+C9etCh0Frqc0X8UeYn2y3NE0KxGds9jrmWar2Lly3brQM2BV35LhDtQQVDUP140c2b6fgOPmsGPHz6FcHsXk5CkMDx/Fddc9iHJ5FCMjx1AsDiGf34W2tgCtrdug64dFFSpgBmfP/hhdXQ+ipaWGanUAZ88ew+TkT2CarUKYM5XqgmFkIStj0LQcDCNANnsIpnkGx4976O83Ua3eiLNnb0IymQWrwGXCMKfC8PIzIpzbDyvquW4m1B/KwPO2hA77pVesWo/fC6I5YBNlv2EMii+E46mGLF0SYvOHFauJokzYayM74ClW0aZWDaqah233hjqfLIJLUcrhhldNOLO4gH+cKCIt0gKbLy2TCejHnWO1WYt7rjekheOzC6oeSTQrPAIjigirMlsK0yDlWCrS7Egqf57N5Nl6YHG4bhGvQDlfW+YjXhlydkSILLmiHUGgifvJfBUjIycYP2eurhhBNBOqmohVfbVjz8gIAh2e5wtHGMCcWRcvHkGhMIB9+46ydbJSZrJBYnOWR4BBaGAyR7XWoNnHbCyNINChaQnYth5GcSbg+w5eeukHMIxBJBJtOGHmcEeiDbqeihXY8XH99T8v7hlxPTJN06GqeVx//WHY9hhKpYOx1MjIhoNAgyybcOYGmhNXgO58TY4k+dD1KpLJ01DVfDhQ7mLPKQ5keQRc+4trCrhuGV6YIhH4qjAqgA2SrpsTO2Gel0Gh8ABSKcA092B8/GIYhdWCdHoMd96p4tSpEtrbXXR3n8WJExVUq5Po6LiAvr4tMIxXcfYsG9jT6R543k6oqgVNs6EoabiuDN/PiPf3fRMKWA60rk/M2S0PAhWOk4PntcHzfBFGDqChxDuxfpjtkLicM2H2uVcvgr241y/2/fnzLLVwQtipL8qpZ+B5OXhePyQpj3z+Yfh+ApVKO0qlYZRKIyiXT+HGG98pHGaKosHzHCSTHdC0FCRJRirVhUSiDa5r4fBhH0ARrmvBddNobd2GlpbHkUy+BNfNQVH+LQAIRxjfIdf0MRj6CDylhETiNDKZIzDNM2htfQteeul3oaom0mkdmgZ4HhvgrVoOlfIb4Hl3COc1T/doLHJRBit+YSy5r9fT94IlnRHNCJ9AswW1DklSEZeuDIIkVNVF4AO+DCBQIYei83y8YxGfCfheBo5Tbpgw87Qo30/AcTrD17B0CDZm5+B5Zvi3LNrC0ibY+7OCNI5YAEsSH4Pjs2jtslFjkuQCtHgm1glRNBjA0iGj9EM5Fj0FzI2KZNkM89tC3CEW/8lTl/lj8XTJKEosEHbPoqyTIluisQIts1ueFhmXFeHziag9l44QU2QL/iUiVAliLaPrqdApVYUk15gEgBx91xWFz6nT4fkWPK+OYvEifvjDXSiVzmL37n3o6xuB5+bguo0RZbPTmQGIAA9JrkEOVMhyJNavKMwZBiSgaeNIJF5CMnk3DqQ6kU73iGixuB5gPB0ykjoqQ5ZrePbZ/9ggGxTfxGL3gvnlFogrQ3e7JicIZDhOGpXKPkhSDapahuumxcSYD2iybAl9Ay+2Ox0/z/czYsAUA2z4mrExCYXCC2hp6Y8JX7+KROI0WlruQak0jEKhBblcd3izCGBZOrLZQWQyd4iy0ADELpsk1eA4OchySbSFtzNaEGTgeRmxExZN5vnEIPLSU0TY+mQxDofZ5y7FKbXU977S+1/ufC6AqSh5qCqPnEqICE4/1BeKtEH2wXVZldjbb/8ZpqYn4PssLYmFUxtCZNc0W6HraSQSZqjTFS2SJdlFNrsZr7wyAEm6F0HgoVr9KUyzNawiOQVdH0atNgBNzYc6YICq5jEx8R5MTXVB130YRhaaBihqHro2gXq9F3xn3PPS8DxT7K6xwb0xLYNr/jlOdcl9vZhz1/r3guWnUBnsZiEIvNiC1RNRJew5Hi0VRXUxvS0g8Nm45zekXJkx20/CdSdFBDYQpVJG+l6WiLKUpBqCIAkepTUbviMeVZHTwp3kWvhYuiEajb1ftOMcf//ommZs7KXUSKI5iWzWjxy+YdTm5TT94j8bH2/UCeNzVcbcyEkmi+CIe0T88egAPI/r8Zmx+4A2b/t4Wxo1xSKx/Pl0hmSlNKdYBkGsZSTJh6pNXDJ1GQB83wqLTWVEtJWiGGH1RTWM8tKEXuZsojRGqcE+gahoRlxfkF3TgCwrMIwLOHnyJuTzx6Draeh6RqRWxiWNmAZo9FpVVWNOL4fpgyolUSwDwJzxOl7xmVgY5AhbB7DJdzixDiwoCku94GmRLA1SZ4K2wki4EUWh1ACECLeYGPsqPJhw3Rk4ThW+76K1NQlJHoNlbYFl/QpMM41abRqWVcCmTbcjnz8jBn/Py4hJvNgpF3pgcVE/TUzqo8+lhYuBNOJC/1zEgAuMRmKItljkE83DfI6GpTgnrvT8pc7njy81Qoi/V/w9L9e++ds2d/CWJAeq6kCWXfi+ClmOJq26Pgxdfx2YKGdXOFlnIqGqakLX09A05lzi2gmGMQjDuABVZXbHIinrMM1WWFZB6JP5vgtdT4dVK08gm30yjBRj1R19PwFFKcG2O1EqDYfplqxEtKrmYRiDzIHtm5DFZ0qHaScuZDmqesPTStjEfnZVLfpeEM1DVJ3KQxAo4u/4Yjj6yXV8Giu+8XGYRXRpUNUienurmJ7OxhxroXxBOLZH76/F2hFFkDEc8OleEPgxmzOF072xXRE8Ui3+3o2fO0q/vFSqF0GsdXhl9Ss5wa6swcNsjd/7+bUBDzzCK67h63lOzG6C2H0kcqzH/wbitq7Na5NRWxvTI7lYPn9sPgeefMXPRxBrDy0UkG9M+Y1/79nvrqtBUdLC+aWqRoNDimdDMa2xyLYkSWtwdLHfGzX7IieYFs7DU+GcfAS1mgLHqUJVE2H0WmPkV6MDTQ/bVRb6n4pihcUyeMGMKCKs0SEebTATC4N6ah3ADKcYM5gyFMUMK06l4brtYNpDMmQ5G+qIcC0wHfESzKxyBp8wq3DddkiSD01zkEp1wXXr6On5IgqFBzE62oOxsRexa9cBGEYWtl1Ba+tP4HkOWDVLP4xUY+KfTN/LElpljpMOB3oe8cVuLBLiDrpE6Mhzw+tUwwm8LkJV4+WmifXBlRwHV5OWthgW8tr5HCaXe038szX+zaIyHYenOdUgy2xQUxS3IYVZ10eg6wMolSScONGH/n5T6BZoWjKsoMMm2qqaCB1hryCZfAmadpfYCQPqMIwsEok2OE4VjlOF61phFNkIdH0QudwhFAoPQlHK0PVhOE4nZNmCpuWZbpisQtPGxP2HRZDtBBBGnsk1EdskyTWxuBB9LApjXLngxUb6XhDNRVwnbHakMn+ML3iDQIbnJcX/3A/UWcmwbExOJl/CTTc9iCefHIdlFSBJSrQI9hPh5k+0qdWoqxk54hSlBs/TwBxvPoIw9Tqu+3U5ZxcnPulmbZjtNKPvLtGcxAXpeVokX1QvSJg+3KgK/LjjeLZuYKNDnEWAeZAkTcxl48UxeIGM+d53dgTY7PRHthjWxKJ4trbZbEfYpaLbCKIZmF1JcbbmJv/uKwqTHpjteFIUI3RIOZDkGlS1Ha6LUCzfbYjcipxhVfBCGoALRVERBMwB5jgV8TrDOAbD2IV6vQjPs2NRaLrQ041fn7VFbkiD5NVieVQYrzLL1/J8HU/RnIuHHGHrAL4DzHVB+IDoByxHWlWLYsDmWiWRJpcLz0uLCXGttpMZnVIKF6ws5DSZ7AAgwXVr0PURvPaajO+f+K/45df/n0gmX4JlvQ6qakBV85DlVmQyfejo+GuMjLwXbIGrAAgdYWHoqePkwr9ZxTzHKcM0Wf52EOqVsEHbh6r6kGV20/G8driuBcephjeV6OZENBsLc3wsloVE4HAud85yR+jE36vRQeKKnSjfN+E4nXCcLjhOJ2q1AJZVwM03fwmdnd9Avd6PSmUfFKUEw9gHRbkBgAVJUsJILuac4o7hdLo73GEah64PQ9OSSCY7oKoGAt9CKjUCWa7FHOf9yGafCavoTIRaYC9DUayw8iMLy2bRX9fDNG2Y5hkALKJU10diJaddKAA8j4tz1zBbGNx1c2C76DkEgS766XJsjO8FRdc0C65rQVF0uG4QTmzZ43L4NWaOsaBBC4Q5ycIoL19lemH8d9+E7+tw3Rzq9f7YYjoQcgGum4XjFGAYmtAUZONlo0g3m2xPh6+zRWokS7+cK7o9m/jucjwCJQiF8n2fp29Fjj6CaDaY0ymKCIunWcUdSJyGzZxAaxCrn03kZIsc47xaHF8se54jHGGSJIvo7NmV4OPveSX4OBtVv4zJngi752MxVZwjmhddH0GkcakhXrgiLhHEM48iRxTfQE6EDrE8NDWPQFehaQkwXW0zdEyVY9dlhd542jErglOCJOUgSe0NY72uDyORaEO9XgwLYCWE4yteHZLbdzwaTFHK0LRxkXHBH4unO/PPyLOoiMVBjrB1gONUmUErbBBjGgIJzN7ZjXaJ1Ya/XS8TGjjgeT4kSYUWGl1kbC2QZQWpVBdsuxebt3TjHcYHQsE/C5qmQ9fTsKwBKEol1BDLh9ElOmQ5Aa6jwh11LJUzSnOUJAWKkhcRYoA5J9+ZCQHbopxttKCYW5mHaAZ8SKGszGIidBbryLicWPm1TEebnSoXPe6KwY5p9pTEQBgEOwG04MKFf4+hoY+gXjdhWQVUKuMAJPT0aAgCCTyiSlVNMQjzVGEWNZmA7yeQSLQhlWqDqk3AdTpx7NghqKqBzZvvxObNp+G6OSSTL0GSXLhuDidO/ATp9FEkEqehaRPg1XUUpRxWtbkYlp1m4t08hZK9b+SUDzwTkmxCiYV0e2F1WM9TRWomL0fN2cjfC6J5aBzLZjtyG1OeGhe44XfRnz1e+8KmuFgve1wNdffs8Hq6mATz79B8ESm+b8J1i1BVE76fFKXWZ2sFza5MN9sJFjnRTKEPxtK3PPg+VY0kmpMG2Q05Hlky1zkUtxP298KqLsbtn//N7gUAd4rFK9vNTYn0waNFGx1xc997dmELXixrbpuWXqmZINYKjdpefI7JZEUkidko2/A1wSormyI9MkqLVKGqJbGRy8c6Wc6wKLEwOotFU1tic5e9f3yDVxPzcOZcK4lKkL7vQtOSDc7t2Y5uWVZZ9kQYCcajwqIN8/mr1wYBpUQuBeqxdQCr4piOxQ9o4Y4y3+WSxc40Jz6Ie24OntDs8TG7GkWtthP1+iQcp4pUqguel0FL9imoyl5MTU2jo6MUplG1IAhUmGYrAMCyBlCtTsI0+5jDzbWgaSl4nhRrRzTIMy94IRTt54thFnbKFglRWWpZVuD7TJOEO8Jcl0QCm5mFOAGihd6VtaMul5J2rR0OV3KwOE4nADTk/vMBUNdH4HlbUKuV4ThV2PY46vUiLKsARTHCCo0yfL8cvk6F61rQ9bQYeNluExuwVdVEIsGqzNYlFy0tWyBJClKpTuj6k2Gk1wURjVKp7EN7+3dhGIMAMCf0mg/W7LPxctMlyHIGjSK+GgJfQ4NkcJAI07NdsSM+m438vSCaj3jFt7hA9uwFbaModuNilG9SMQmBTvEa5tBW4YbRXSzq04Tr5sIiOWxjYWTkTSiXBxuuySLMLDCx/iRkOQHPdyFL3OkVOcQanWHRRJs5vrQw8oU7xlwgFP+ez34JohmZT3x+drXGOc/H5qnRa+LagVH6I/+dbVrxDA1P3Bfm0wnjbYi3J2pv3Gk9O2XZafh5qWvMpxFIEM3A7Aip6HHA9+MOYT5HVUTEFtuUUsAqxrrhBrQabkpHDi8ejcVSK13EnczRulkLi1u1xnTArIb3mh3lGTnBJPCxVI6NydKc3+dGclNq89KhO946wHUtphXiJwBhwKxSlevWoSgaWBl3PsA2Dn627UOWc1DVMpimUJ7tRLs5BH4ChcJFVKuT4nzH6RJRH5OTo7juumEkkyZc9x4oSgnp9PXwPAfT029BPv84stnNsO0yLKuAbDYtIr6ilAo3vGEwI+b6X5IkQ9ctEW4eiembkCQdsuyHNxRfXItYX1xtCttytwVYmg7VpUTT+TUrlZ2o13sAALqeRjr9KjRtIoy2HA+1+5KxxXAkuMuF7uMRkbZdDqMyp+A4OaRSP4Wm5cPdMAXJ5EnIcg2JxGncc88wAMD3nw0d6GzHyfPScN0c0umjMIwLUJSSSMvyvHR4Lp9URM47JqZvwfdLQoybp3txDUBFnu20TkKWXbju/JOZS/Xr1Z6zHKzE9+JKKcPE2mK2wHU8Oow/H//JK0vGqzhG14rGRs/LoVbbhXhEme8n4DqdsO3x0LGVhG13htVlXUiSjhde+CoMI4P29p0N94V4QQzPS7L3C6s4y/OI4vMFwKUWzrP7gCDWG3Hn0mwnWDxa0p/1OLMlrsUbVX7kerZxp1iU+RA03CuicT0eUTq7wEajM6yxDfNXkpz/M4TRnuQII5qMuGNo/mjJuVGP8erOPLiCR1zxg1d75o4oPiefbVeybIW6XXz8NqGq3fC8erhGjadUz3V+cSdZY2XZ+T6jE3OIRVUjebTnbG00YmHQHW8dUKtNw/dd2HYWhtEdGpUPRZEhSQlwkfn4rq4sl1ikVdCJWq0IlkppwbbLMM0zqNV2wrG7oeljqNWmsWXLVrS2PoFjx2p47bUkXFdGEIwglepCKnUcqpqH43RB0yZgmreBaRywAb9anRSaXq5rhpP1KEScOcGYnpHr5mDbzCHHqtpBCPtGjjCEEWB1KIoPTeMFA/RV+x8QK8NCInauVSTNUt5nvuij2VFA8WIQAMLwbFZ9kS1CtXCXaQ9cVwPbNYoW2Z5nh1p7mnjMsmZgmjkYJhPalyQXr732C5iZGURbm4dE4mXIsoVS6SCefrpXaAex1xYA7EVn5x709e1ALncIR458Gr29LgYGPoxKZR90fRCO0yl2sz0vIyYgtt0r/uaaDIpiMb3CMMItfi9ynC74/hbwYhpL+V+st+8FS9EhvaVmIS6Oz4vF8L+B+aLBPHgeoChaaPtRlCXfxGLOKhWVyj6x2SVJchj9xWyF24vj5ISDC2C73XzBzRbdGrZs+Sz27DmCp576A6gqT2nUoSgmFJThzRN9DQCqylO/NLFQ5mOy70O8x2xnPEE0E3HZkOixeNrR/GmFPMU/COZWXuWO57iN8HGOV4xkUZ1OQ4TYfJFj/HxFSSGq8qrBCyO0+dbJ7IiRqJpcZMONjrRIR8n3E3NStAmimZjfKeaAi8rHmW+8ms+xzAtVyXIJqsocXnxNyuERadEaO4o0iyIy5XDja/6NMoQ6or7P9Aa59mfE3Igw4upZ9Lbz61//ejz66KMYGhpCEAR4+9vfPuecT33qUxgeHka1WsWPfvQj7Nixo+H5XC6Hr33ta5iZmUE+n8eXv/xlpFKppX+KDQw3INe1YFkFlMtjsO0yIlF8OVwom3DdLFzXhOeFlRr9BHjJV7aYNVGrTUOSnLC8ug9DH2HRJ7IrPN5M/C+NZLID6XQPzp37HEqlO5BMvgQASCTGkEhcQCIxhmx2MyqVcXiejWSyA5XKOCRJhqbZMAwrFAmcgiS5sCwHtp0UEwFJksObgRlGiQGua6Jer6JeL8K2S7DtcuiUowiKhbAe7Xet/+9nt28+xwlLb+a2yKNG4gNyvHJVEFab4pWnbNh2Bb4fpRayKDFHVGi0rAEEgR9qEyh44YV3YWzs1yHLVuj4itKVFUVHtTqFer2IatXF0aMdKBaHcPGij6NHvxaK9++C43QBAOp2b5g+3Q/XzcGyBlCvbwkdXGYohg9MTbo4cuQwnn12BseOeTh+3MNLL9l49dURXLz4HCYmTqJUGl62xfR6+F7Mx3q04WaGLVSdWLRVJHrNJ7WuWxcO62ix64l0YLZpxA8brlsXC+ZqdY8oDMOOdJgiXYHjsAIyllUIx/1o4Ry1gekXDQ5+FM8884fh2FkRFWKZUy3L9DfdHJhmXzqcL2RZAQ0vA9dNw/My8L1MbEx2Gz4rVW6+MmS/a5PZqYe+nwBPc+IOJ7YwjR73fFPoXAaxuWpky433Ama3ZVSrU+HjjrCZyIYccKmPuIOZV5hk52SETQZ+Iqwga8IPVNHOQCykE6GmILfb6HPEi2z4vgnPjxx6xKUhG15bzE7pnRsxOdsBrIGnIHKYPeqY7SjmqZJMtmRuSmK8gEz8cfa7PKeADE9zjirEBuLcIPDhODVm874653qXi12iSLCls+iVQiqVwvHjx/HBD35w3ud/93d/Fx/60IfwgQ98AAcPHkSlUsHf//3fwzAMcc7Xv/513HjjjXjwwQfxlre8BW94wxvwpS99aemfYoMT30ly3VoYfVUTk+ooFcKG49TCAZYL7qphioYJz8uhXi8CYPnJHEXRUSmnMZ1/GJqWCJ1XCVGhrlz24TidQiCbl3xl6ZNJVKuTQtCfLbpDr71cC6NQuKaZA9eth8+zsFG286yLHXW+mGCfj3+eOk3CF8h6sd+4EyEeUbNc177aa13u9fM9x52/HDZBzcB1M2F0VeNgG4dPqOMTZ9suhY41VoXSspgNMfH8AFNTr6BW2wLAgeNUIcuKcKYz3bE6HKeKer2EsbETcJwKyuVRTE6egm2XYFlb4DhhtVe7G/V6P2y7F47TCdvug+N0wXVzYrLNIsVKODr+U4yP/xQTEycxOXkK09OvoFA4h2LxIiqVcXF/WI5+Xw/fi/lYLza8nohHfcyO/uDOsfhzfMziTrPI8cXHsrqIoq7VdgqnFXtNInRg1eA4VQRBAvV6EY5TBa/eGHdQsffOYGZmEBcuPB06zUoxR5gVc4jJ4dwg7kzjzq9MOGdIIipuETkA+dhMXB6y37UJm2PKbBHsh5GP4eH5pniMO5s8z4zZhSrshtu369bBCki4wjZc1wplQvINNsqyMyJnF4CG8Zw5w7gTjMkWcKc1m7unEQSJ0CnGC1pEDq5GxxeP5ow7xULn2ZwIFGI+yIbXHrOjHecvFhd/TmootsbO8ee8drY2V+P1NGFnly6sIYvHuR0DEGNnpOMdanZ7dfDiVnPTn+cv3EFcHYuOgT106BAOHTp0yec//OEP4zOf+QweffRRAMCv/uqvYmxsDI888gi+9a1vYffu3Xjzm9+MW2+9FUePHgUA/PZv/za+//3v43d+53cwMjKyxI+y0ZGEQUcl0pnBxbWz4ukLrGQzcx55ngrHqcKyCvC8TCgM6MJxOmEYHiYnf4bRUQstLf2oVifBK9Lx8rPxKjqsykYNXPC3Wp2ELKtIp3vCCK46NE0FQo83242OHAEsxVEK25VuaD8PKQe4B92D69bEgkLX0yvbzU3OerHflUyFW47rLFaQ3XXrUFU2SQoCWURR8XBr5lTSAUTO3mjgjBzd/O9qdUo4lR2H/Q0EYcU4NyzfDAAabJvZM69E2dd3q7iPeF4d0Y6VIpzZlrUNnlcOo9HKADZBlu1wMlBDECSi9MdADSPTZMyE4qHxCjk8Em45Kr+ut+/FfKwXG15P8AWqpiVEaqTYzIlViputAaIo7Dk2Fnsxu4s2dioVWSyKWaRIDq47HnOOJVGvF8Nqq4qQCPB9T6ReeV4atn0B9XoRmpaEpiVFG/i9h0/G45Ny9tl08Kkis0++KcUW7fHdbdqMujJkv2sT7kBmKVQaAh/wxBIp3Ijy+YKWO5l4JBfXtpxbHdbznJg0CHOEcTmTeAVJHh3C/26M+o4iy9imVySDELYeTDuXtd/zojTOKIIl0hQSUW9igW02pDvHI9OJuZANrz08zxTjVtw55PtRkRd+AEyWIAg8MJF85hjjGpw84pNLk0hSuWF9yx3HzAGtxTT+GOz1buxeEI2ffIxkju1AzBNU1YLjyCL6OwjSs94zEUqNlISzmtt0lCqtit+JhbOsyeDbt29Hb28vHnvsMfFYsVjEkSNHcOedd+Jb3/oW7rzzTuTzeWH8APDYY4/B930cPHgQ3/ve9+ZcV9f1Bk96JpNZzmY3NZIko6fnliW/nkWIMHQ9je3b70epFD1v271oaZHR0tIvPNctLf1zrlOp7EWlsnfO421tMnK5AdHWdJoJgvs+G7h1nQ3miqIjkWibp4V+w6ScV6Qklp+Vsl+AbPhKZLObGv6u17egXt8y5zxdT0PX08hmN1/yWkHgY8uWuyBJMup1H6oqz7n+tm33AgCmph7BrbdG1eu4jW/d+gYxQN966wfmaB4B7N5hGNnY+/KdtOie4nmRY7q9XcaH3vh/ib8bJ/KY9ThNwpcCjcHXHkmSsWXLXeL3lSCZ7BCLYt8HWlu3NYzD3d2NY+/tt/+WaA+3p66uG9HZeUNDuxdL/DXc4Ra/BxBXB43Bq4eu87Q0v2HcuhxMf1dfsD5tfKNHkmQkkx0AANfx0dHR3bDJe9tt/0aMx/yxy9MYzbLQzzCblbqHbRRoDF4dpqZ+cVHnKwqzXcPIIpvdhO7uvZAkGRMT71nS+8/MvLHhb1mWxdh4+vRfIJXShb3PZ2O+70PXZXR13QSA3SssawCWNYBC4YFFRX5R5dfFsax3vJ4e5uQYGxtreHxsbEw819PTg/Hx8YbnPc/D9PS0OGc2H//4x1EsFsUxNDS0nM1uanjlseU65Hm+EfHnLvW6y7VPljHntQtt/1I+I7E0Vsp+gWtjwzy17EopZrOfvxZaUpd7j4V+hxdjw3xHN16dcD47DwIZiiILG2UCnxCPSZLf8PzV2F10LXXWezYey23Dzfq9WAo0Bl97LjXGrcTYzO32Su/JJvlygz3FX7PSbSWWRrOPwc3KUr+7S7Xh+ebDs206Pg4vxF6Xuy+IpUFjcHMw/7x5+e2IzcPVK47Zs18zm9mpnZc7iMXRFK7/z372s8hms+LYtGnTlV9EEMSa4VrYcHwwWch5wOxKfVdmsY6L5dCVWgrUFxHUF1cPjcEE0dyQDRNE80L2SxArw7LOxEdHRwEA3d3dDY93d3eL50ZHR9HV1dXwvKIoaGtrE+fMxrZtlEqlhoMgiOVlpewXWLs2vJQd4MWe3yw7rNQXEc3aFzQGE0TzshHHYIJYT9AYTBDNxbI6ws6ePYuRkRG86U1vEo9lMhkcPHgQzzzzDADgmWeeQS6Xw/79+8U5999/P2RZxpEjR5azOQRBLILVtt+lRMishaiahbZhMW2lvljauVfzmuVmJfriSqy2DRMEsXTIfgmiuSEbJojmYtHJpKlUCjt27BB/b9++Hfv27cP09DQuXLiA//pf/yv+w3/4D3jllVdw9uxZfPrTn8bw8LAQ/zt16hR+8IMf4E//9E/xgQ98AJqm4Qtf+AL+5//8n1QpgyBWmLVsv1eKjpkvXe1K6WyLTXFbSRbTDuqLhZ+7kfoCWNs2TBDE5SH7JYjmhmyYINYPi3aE3XrrrXj88cfF35///OcBAF/96lfx/ve/H7//+7+PVCqFL33pS2htbcWTTz6Jhx9+GPV6Xbzmve99L77whS/gH/7hH+D7Pr7zne/gQx/60NV/GoIgLksz2+/lHAaXiqpZqrNjMY6S1XCoUF8s7D3XY180sw0TxEaH7JcgmhuyYYJYP0gAgtVuxGLJZDIoFov47Gc92PZqt4Yg1h66Dnz84wqy2eya1BLYKDa8liKPVhvqi4gr9QXZL0E0N2TDBNG8kP0SRHOzUBtefSEVgiCIS7AWtJ6uhuV0/FBfRFBfEARBEARBEASxVJp7NUEQxLpldtTMSjk/5rvucr7X7GstVfyd+iJ6DfUFQRAEQRAEQRBLhWbeBEGsSS4ngD6bIJCX7EiY77pXG7ETb8tiPseloL649Gs2cl8QBEEQBEEQBLF4yBFGEETTI0n+vI6E1YqyWU2nBvVF43tTXxAEQRAEQRAEEYccYQRBrBmWO12smR0P1BcR1BcEQRAEQRAEQSwX5AgjCGLNsN7TxRaTqkd9EUF9QRAEQRAEQRDEckEzb4IgNhQr7XS43LUvlaq3WlBfRFBfEARBEARBEMTGgBxhBEFsKC7ndLiSI2QhjpJLVTRc6SqES4H6ImIt9QUNzQRBEARBEASxctBsmyAIIuRKUTmLjdqJn78SVQhXEuqLiGvdF8Da7QuCIAiCIAiCaHbIEUYQxJpgOSOCVju66GqhvoigviAIgiAIgiAIYjmhVQFBEGuC5YwIWsvRRdwZcyXNqOWC+mJlrrXcLKQvCIIgCIIgCIK4emjGTRDEmmK2I2A9OQaCQBbOmIU4ZagvGs+/3N/NzGL7giAIgiAIgiCIpbN+VhIEQTQhzevcWEqVwdlOjsuJplNfzP/cWmc5+oKGZoIgCIIgCIJYOWi2TRDEKtLoAJivct9KRMgsl2NlqVUG53v9fJ+b+iL6eyP1BYnlEwRBEARBEMTKQY4wgiBWkdW5BS2HE+Vy11jK9Vcr6on6ImIt9gVBEARBEARBEMsLOcIIglhFrs4xcCWHyWqn1C0mTe5qnSTUF43vdTXPrzRLSZ8kCILYKND9MYL6IoL6giCI5YTuKARBNC1XcpgsxqGyEhOseErfSk/gqC8a3+tqno/T7H1BEATRbFD0bAT1RQT1BUEQywnNwAmCWDNcyimwWGfBUpwLyznBmk/sfbHXp7649DWu9Phir3M51lpfEARBEARBEARxdZAjjCCINcOlnAKLdRashnMh7uSIv/9SnR3UFxHUFwRBEARBEARBLBfkCCMIYkPBHRNLTUmLv+5STo44azkFjvoigvqCIAiCIAiCIDYGNAMnCGJDwR0QS43Gib/ucteY7eBYi9E/1BcR1BcEQRAEQRAEsTEgRxhBEMQKQA6OCOqLCOoLgiAIgiAIglhdyBFGEMQqsvhb0FJSyZoh/exafS7qi6t7DUEQBEEQBEEQzc2iVwGvf/3r8eijj2JoaAhBEODtb3+7eE5VVfze7/0eXnzxRZTLZQwNDeHP//zP0dvb23CNs2fPIgiChuOjH/3o1X8agiAuy9qz3+WNjrmUY2MtROFcyemy3G2kvrjy+zVDX8xm7dkwQRALheyXIJobsmGCWD8s2hGWSqVw/PhxfPCDH5zzXDKZxP79+/HpT38a+/fvxzvf+U7s2rULjz766Jxz/+N//I/o6ekRxx/90R8t7RMQBLFg1oP9Xs55cann1kLkz0o4XagvFnbN9dQX68GGCWKjQvZLEM0N2TBBrB/Uxb7g0KFDOHTo0LzPFYtFPPTQQw2P/dZv/RaeffZZbNmyBRcuXBCPl0oljI2NLfbtCYK4Ctai/QaBvOKROWsh8mchUF9EUF/Mz1q0YYIgFgbZL0E0N2TDBLF+WPHt8JaWFvi+j0Kh0PD4xz72MUxOTuLYsWP4nd/5HSiKcslr6LqOTCbTcBAEsfIsh/0Cl7Phyzs71kLEzmIJAlkc8ccW8jrqi+gc6ovlgcZggmheVn4MJghiJaExmCDWLouOCFsMhmHgc5/7HL75zW+iVCqJx//wD/8Qx44dw/T0NO666y589rOfRW9vLz7ykY/Me52Pf/zj+OQnP7mSTSUIYhbLZb/A5Wz48hE5zRixM1+bF/I5rnQO9cXirrHWWGpfXA00BhNE83JtxuDm5FpEDDcL1BcRa60vaAwmiLWNBCBY6ouDIMAjjzyCv/mbv5nznKqq+M53voPNmzfj3nvvbbgBzOb9738/vvjFLyKdTsO27TnP67oOwzDE35lMBkNDQ/jsZz3MczpBbHh0Hfj4xxVks9lL2t61sl/Wnmtvwys9IVpI1NJamZBRX0Q0Q18sxH7ZtWgMJoi1CI3BBNG80BhMEM3NQm14RSLCVFXFX/3VX2Hr1q24//77L9sAADhy5Ag0TcO2bdtw+vTpOc/btn3JwZ0giOVlue0XWH4bXogzYbX1pa6V44f6IoL6gkFjMEE0L80wBhMEcWloDCaI5mDZHWHc+K+//nrcd999mJ6evuJrbrnlFnieh/Hx8eVuDkEQi6BZ7HetRBddLcsRnUR9EbFe+uJq5DubxYYJgpgL2S9BNDdkwwTRPCzaEZZKpbBjxw7x9/bt27Fv3z5MT09jZGQE3/72t7F//3685S1vgaIo6O7uBgBMT0/DcRzccccdOHjwIH7yk5+gVCrhzjvvxOc//3l87WtfmyMkSBDE8tIM9ht3iGyklLorvSf1BfUF0Bw2TBDE/GwU+11L9+jVhvoiYj30xUaxYYLYCCxaI+yNb3wjHn/88TmPf/WrX8UnP/lJnDt3bt7X3XvvvXjiiSfwute9Dn/8x3+M3bt3wzAMnD17Fn/5l3+JP/iDP1hw2Gcmk0GxWKTcaIK4BJfKjV4L9gs0tw2vh4ncckF9EbGcfXE5bYO1YMPNbL8EcS2gMZggmhcagwmiuVkxjbAnnngCkiRd8vnLPQcAzz//PO68887Fvi1BEMsA2e/VsxBnx1qNXlpuqC8irlVfkA0TRPNC9ksQzQ3ZMEGsH5YuREIQBEHMS9zBsV4dPwuF+iKC+oIgCIIgCIIgVh9yhBEEQVyBIKBbJYf6IoL6giAIgiAIgiCaD5rFEwRBXAGK3omgvoigviAIgiAIgiCI5oMcYQRBrCpLiapp5kicy7Wd+mJhzy3na9YKzdx2giAIgiAIgmgmaOZNEMSqspSomsW+Zi05VS7XduqLhT23XK9plr4gCIIgCIIgCGL5IEcYQRDrnmvhVGkWqC8iqC8IgiBWH4qIjaC+iKC+IAhiJaE7DEEQ64IgkK9q0rSeJlzUFxHUFwRBEGsb2mCIoL6IoL4gCGIloRk+QRDrhktNmhbizFhvEy7qiwjqC4IgCIIgCIIgOOQIIwhiTXC1kTvLrTe1HCz181BfNL6O+oJYSej/EUF9EUF9QRAEQRDrF3W1G0AQBAGsz8ibpX4m6ourf91aZj1+pmYlCGT6f4RQX0RQXxAEQRDE+oa2uwiCWNes5q7+WosooL6IoL4gAHJKxqG+iKC+IAiCIIj1Dc3GCYJY16zGgoY7OtbaYor6IoL6giAIgiAIgiA2JuQIIwiiqVjuaJqViM65Vo4O6osI6guCIAiCIAiCIBYCOcIIgljTzHZIXM6ZMPvcq3VmLPb1i33/q70+9UXEeuoLGpoJgiAIgiAIYuWg2TZBEGuaxUTRzD43/vdSHDGLjeC53Psv5PzFXn+pbaG+WNt9AVDkGEEQBEEQBEGsFOQIIwhiVZnP0XC10UOLcV7wx5eatsbfK/6el2vfYp+jvljY+Ut9vhn6giAIgiAIgiCI5YNm3gRBrDm48+FSzoHFOCeuxsGwkNfO5zC5XPvin20x16e+2Dh9QUMzQRAEQRAEQawcNNsmCGIVufwtaKnROHGnwpWucTmnxnKLm8ffS5L8RaXoUV9cfVuapS8oNZIgCIIgCIIgVg5yhBEEsYr4V4zymY+FnBt3dlxOrPxaVvKTJP+y0UzUF9Fz1BcEQRAEQRAEQawENPMmCGJNsBDHA3cWzD73StpPixcrX14W62ChvsCiztkofUEQBEEQBEEQxNVDjjCCINYsi43wWem2LDZqJ+6gmU80fSWjnVYS6ovG91nuvqChmSAIgiAIgiBWDnW1G0AQBHEpFhKxc60iaZbyPvNFH8UdNIu5JvXFpd9/vfUF0whTlqF1BEEQBEEQBEHMhradCYJoata6ttLs9q2kg4b64tLvtda4ln1BEARBEARBEETE2l4pEARBzMN82krL5fhYSqrbfNdYynNX+17UF9QXBEEQBEEQZNnkkgAACQBJREFUBEFcnqZOjdT11W4BQaxNmsU2lt7O+aJnliuiZjmu41/i9+W6/pWuR32x/O9x7fpi/dsvQaxvmsU2mqWdBHEtaRa7aJZ2EsS1ZqG2IQEIVrQlK0BfXx+GhoZWuxkEsebZtGkThoeHV7sZc9i6dSvOnTu32s0giDXNWrVfGoMJYmGsVRumMZggrsxatV8agwliYVzJhpvSEQYAO3fuxMsvv4xNmzahVCqtdnNWlUwmg6GhoQ3fF9QPjWQymTU5gAOsbcVikf5XoO9tHOqLiLVsvwCNwXHoe8ugfmhkLdswjcER9L2NoL6IWMv2C9AYHIe+twzqh0YWYsNNmxo5MjICACiVSvTPDqG+YFA/MJqhD+h/FUF9EUF9sfbtl8bguVBfMKgfGM3QB/S/iqC+iKC+WPv2S2PwXKgvGNQPjIX0AanzEgRBEARBEARBEARBEBsCcoQRBEEQBEEQBEEQBEEQG4KmdYTV63V88pOfRL1eX+2mrDrUFwzqh+aB/lcR1BcR1BfNA/2vIqgvGNQPzQP9ryKoLyKoL5oH+l9FUF8wqB8WT9OK5RMEQRAEQRAEQRAEQRDEYmjaiDCCIAiCIAiCIAiCIAiCWAzkCCMIgiAIgiAIgiAIgiA2BOQIIwiCIAiCIAiCIAiCIDYE5AgjCIIgCIIgCIIgCIIgNgRN6Qj7zd/8TZw9exa1Wg2HDx/GbbfdttpNWnE+8YlPIAiChuNnP/uZeN4wDHzhC1/A5OQkSqUSvv3tb6Orq2sVW7x8vP71r8ejjz6KoaEhBEGAt7/97XPO+dSnPoXh4WFUq1X86Ec/wo4dOxqez+Vy+NrXvoaZmRnk83l8+ctfRiqVulYfgZjFRrNhsl+y3/XERrNfgGyYbHh9sdFsmOyX7Hc9sdHsFyAbJhteOYJmOt797ncHlmUFv/ZrvxbccMMNwRe/+MVgeno66OzsXPW2reTxiU98Ijhx4kTQ3d0tjvb2dvH8H//xHwfnz58P7rvvvmD//v3B008/HTz55JOr3u7lOB5++OHg05/+dPDII48EQRAEb3/72xue/93f/d0gn88Hb3vb24Kbb745+N73vhe89tprgWEY4pzvf//7wfPPPx/cfvvtwd133x2cPn06+PrXv77qn20jHhvRhsl+yX7Xy7ER7RcgGyYbXj/HRrRhsl+y3/VybET7BciGyYZX7Fj1BizqOHz4cPBHf/RH4m9JkoKLFy8GH/3oR1e9bSt5fOITnwief/75eZ/LZrNBvV4P3vWud4nHdu3aFQRBEBw8eHDV276cx3w3gOHh4eAjH/lIQ3/UarXgX/yLfxEACHbv3h0EQRAcOHBAnPNzP/dzged5QW9v76p/po12bEQbJvtlB9lv8x8b0X4BsmF+kA03/7ERbZjslx1kv81/bET7BciG+UE2vLxHU6VGapqGAwcO4LHHHhOPBUGAxx57DHfeeecqtuzacP3112NoaAivvfYavva1r2HLli0AgAMHDkDX9YZ+efnll3H+/Pl13y/bt29Hb29vw2cvFos4cuSI+Ox33nkn8vk8jh49Ks557LHH4Ps+Dh48eM3bvJHZyDZM9jsXst/mYiPbL0A2PB9kw83FRrZhst+5kP02FxvZfgGy4fkgG746msoR1tHRAVVVMTY21vD42NgYenp6VqlV14YjR47g137t1/Dwww/j3/ybf4Pt27fjn/7pn5BOp9HT04N6vY6ZmZmG12yEfuGf73LfiZ6eHoyPjzc873kepqen133/rDU2qg2T/c4P2W9zsVHtFyAbvhRkw83FRrVhst/5IfttLjaq/QJkw5eCbPjqUFe7AcTCOHTokPj9xIkTOHLkCM6fP493v/vdqNVqq9gygiCuBNkvQTQ3ZMME0byQ/RJEc0M2TKwETRURNjk5Cdd10d3d3fB4d3c3RkdHV6lVq8PMzAxOnz6NHTt2YHR0FIZhoKWlpeGcjdAv/PNd7jsxOjo6p3KIoihoa2tb9/2z1iAbZpD9Msh+mwuy3wiyYQbZcHNBNswg+2WQ/TYXZL8RZMMMsuGro6kcYY7j4OjRo3jTm94kHpMkCW9605vwzDPPrGLLrj2pVArXXXcdRkZGcPToUdi23dAvO3fuxNatW9d9v5w9exYjIyMNnz2TyeDgwYPisz/zzDPI5XLYv3+/OOf++++HLMs4cuTINW/zRoZsmEH2yyD7bS7IfiPIhhlkw80F2TCD7JdB9ttckP1GkA0zyIavnlVX7F/M8e53vzuo1WrBr/7qrwa7d+8O/uRP/iSYnp4Ourq6Vr1tK3n85//8n4M3vOENwdatW4M777wz+OEPfxiMj48HHR0dAcDKxp47dy649957g/379wdPPfVU8NRTT616u5fjSKVSwb59+4J9+/YFQRAEH/7wh4N9+/YFW7ZsCQBWNnZ6ejp461vfGtx0003Bd7/73XnLxh49ejS47bbbgrvuuit4+eWXqWzsKh0b0YbJfsl+18uxEe0XIBsmG14/x0a0YbJfst/1cmxE+wXIhsmGV+xY9QYs+vjgBz8YnDt3LrAsKzh8+HBw++23r3qbVvr45je/GQwNDQWWZQUXLlwIvvnNbwYDAwPiecMwgi984QvB1NRUUC6Xg+985ztBd3f3qrd7OY43vvGNwXz82Z/9mTjnU5/6VDAyMhLUarXgRz/6UXD99dc3XCOXywVf//rXg2KxGBQKheB//I//EaRSqVX/bBv12Gg2TPZL9ruejo1mvwDZMNnw+jo2mg2T/ZL9rqdjo9kvQDZMNrwyhxT+QhAEQRAEQRAEQRAEQRDrmqbSCCMIgiAIgiAIgiAIgiCIpUKOMIIgCIIgCIIgCIIgCGJDQI4wgiAIgiAIgiAIgiAIYkNAjjCCIAiCIAiCIAiCIAhiQ0COMIIgCIIgCIIgCIIgCGJDQI4wgiAIgiAIgiAIgiAIYkNAjjCCIAiCIAiCIAiCIAhiQ0COMIIgCIIgCIIgCIIgCGJDQI4wgiAIgiAIgiAIgiAIYkNAjjCCIAiCIAiCIAiCIAhiQ0COMIIgCIIgCIIgCIIgCGJDQI4wgiAIgiAIgiAIgiAIYkPw/wLi0e3buu678AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x320 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = NUM_OF_DIFF_LAYERS  # number of columns for DiffractiveLayer's masks visualization\n",
    "n_rows = 1\n",
    "\n",
    "# plot wavefronts phase\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 3, n_rows * 3.2))\n",
    "ind_diff_layer = 0\n",
    "\n",
    "cmap = 'gist_stern'\n",
    "\n",
    "for ind_layer, layer in enumerate(lin_optical_setup.net):\n",
    "    if isinstance(layer, elements.DiffractiveLayer):  # plot masks for Diffractive layers\n",
    "        if n_rows > 1:\n",
    "            ax_this = axs[ind_diff_layer // n_cols][ind_diff_layer % n_cols]\n",
    "        else:\n",
    "            ax_this = axs[ind_diff_layer % n_cols]\n",
    "\n",
    "        ax_this.set_title(f'{ind_layer}. DiffractiveLayer')\n",
    "\n",
    "        trained_mask = layer.mask.detach()\n",
    "\n",
    "        # mask_seed = MASKS_SEEDS[ind_diff_layer].item()\n",
    "        # random_mask = torch.rand(\n",
    "        #     size=(sim_params.y_nodes, sim_params.x_nodes),\n",
    "        #     generator=torch.Generator().manual_seed(mask_seed)\n",
    "        # ) * (MAX_PHASE)\n",
    "        \n",
    "        ax_this.imshow(         \n",
    "            trained_mask, cmap=cmap,\n",
    "            vmin=0, vmax=MAX_PHASE\n",
    "        )\n",
    "        ind_diff_layer += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7958b394-2713-428c-88e1-886a2948b827",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2bf2bee5-a666-417a-b1f2-4d8eb4bfb709",
   "metadata": {},
   "source": [
    "### 4.2.4. Applying the model to an unknown data (test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "744e9868-c412-40b0-ab6a-0ec168643e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".DS_Store\n",
      "09-12-2024_experiment_01\n",
      "13-12-2024_experiment_01\n",
      "22-11-2024_experiment_01\n",
      "22-11-2024_experiment_02\n",
      "22-11-2024_experiment_03\n",
      "27-11-2024_experiment_01\n"
     ]
    }
   ],
   "source": [
    "# list of all saved models\n",
    "dir_models = 'models/03_mnist_experiments'\n",
    "\n",
    "filepathes = []\n",
    "\n",
    "for file in os.listdir(dir_models):      \n",
    "    filename = os.fsdecode(file)\n",
    "    if not filename.endswith(\".pth\"):\n",
    "        filepathes.append(filename)\n",
    "\n",
    "print(*sorted(filepathes), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e0242e0e-3cb4-4a47-acfc-7712a30b350e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'models/03_mnist_experiments/13-12-2024_experiment_01/optical_setup_net.pth'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filepath to save the model\n",
    "load_model_subfolder = f'13-12-2024_experiment_{EXP_NUMBER:02d}'\n",
    "load_model_filepath = f'{dir_models}/{load_model_subfolder}/optical_setup_net.pth'\n",
    "\n",
    "load_model_filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "54a769f0-1057-485f-883d-143160f4155a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wavelength': 0.000749481145,\n",
       " 'layer_size_m': 0.12,\n",
       " 'layer_nodes': 150,\n",
       " 'tensorboard': True,\n",
       " 'digit_resize': 17,\n",
       " 'ds_apertures': True,\n",
       " 'ds_modulation': None,\n",
       " 'gauss_waist_radius': 0.02,\n",
       " 'distance_to_aperture': 0.03,\n",
       " 'propagator': 'AS',\n",
       " 'n_diff_layers': 5,\n",
       " 'diff_layer_max_phase': 3.141592653589793,\n",
       " 'diff_layer_mask_init': 'const',\n",
       " 'diff_layers_seeds': 123,\n",
       " 'layers_distance': 0.03,\n",
       " 'add_apertures': True,\n",
       " 'apertures_size': [50, 50],\n",
       " 'detector_zones': 'segments',\n",
       " 'detector_transpose': False,\n",
       " 'train_bs': 8,\n",
       " 'val_bs': 20,\n",
       " 'train_split_seed': 178,\n",
       " 'epochs': 10}"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# experiment conditions\n",
    "json.load(open(f'{RESULTS_FOLDER}/conditions.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "cf47597e-786d-4ee6-b8bd-912385d30fbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mt/0w6nmsr119bb2g4h4xrv9p6m0000gn/T/ipykernel_98211/3316892598.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  optical_setup_loaded.net.load_state_dict(torch.load(load_model_filepath))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setup to load weights\n",
    "optical_setup_loaded = get_setup(\n",
    "    SIM_PARAMS,\n",
    "    NUM_OF_DIFF_LAYERS,\n",
    "    apertures=ADD_APERTURES, \n",
    "    aperture_size=APERTURE_SZ\n",
    ")\n",
    "\n",
    "# LOAD WEIGHTS\n",
    "optical_setup_loaded.net.load_state_dict(torch.load(load_model_filepath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46db3f51-f72f-4b9a-8960-f7c5b2a51594",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "8e271845-0665-4fb4-9545-d4e3ef8c38d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation: 100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results after training on TEST set:\n",
      "\tCE loss : 1.889739\n",
      "\tAccuracy : 82.3 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "test_losses_1, _, test_accuracy_1 = onn_validate_clf(\n",
    "    optical_setup_loaded.net,  # optical network with loaded weights\n",
    "    test_wf_loader,  # dataloader of training set\n",
    "    detector_processor,  # detector processor\n",
    "    loss_func_clf,\n",
    "    device=DEVICE,\n",
    "    show_process=True,\n",
    ")  # evaluate the model\n",
    "\n",
    "print(\n",
    "    'Results after training on TEST set:\\n' + \n",
    "    f'\\t{loss_func_name} : {np.mean(test_losses_1):.6f}\\n' +\n",
    "    f'\\tAccuracy : {(test_accuracy_1 * 100):>0.1f} %'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e1ba17b-2d77-4248-b4f4-6eed19be6c7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "62f69f0d-ab16-485c-bd13-9a11295bb119",
   "metadata": {},
   "source": [
    "### 4.2.5. Example of classification for a random wavefront (propagation through)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c02dd214-3ff5-46c8-87ac-0d18fd2071ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot an image\n",
    "# '1' - 3214, good (1318, )\n",
    "# '4' - 6152, good (1985, )\n",
    "# '5' - (5134, )\n",
    "# '6' - 123, good\n",
    "# '8' - 128, good (1124, 8105)\n",
    "# '0' - 3, good\n",
    "ind_test = 6152\n",
    "cmap = 'hot'\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(2 * 3, 3))\n",
    "\n",
    "test_wavefront, test_target = mnist_wf_test_ds[ind_test]\n",
    "\n",
    "axs[0].set_title(f'intensity (id={ind_test})')\n",
    "axs[0].imshow(test_wavefront.intensity[0], cmap=cmap)\n",
    "\n",
    "axs[1].set_title(f'phase')\n",
    "axs[1].imshow(\n",
    "    test_wavefront.phase[0], cmap=cmap,\n",
    "    vmin=0, vmax=2 * torch.pi\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fa0432db-8e6d-4bb1-8723-b1c2988d3d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7e9e7f51-3662-49a4-9b2d-04fba703dcdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# propagation of the example through the trained network\n",
    "setup_scheme, test_wavefronts = optical_setup_loaded.stepwise_forward(test_wavefront)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2581250c-82e1-4cf2-91c5-d23bbb73a892",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ef9ccfe5-bbf0-452b-84f4-073c0ea02e6d",
   "metadata": {},
   "source": [
    "#### Amplitude profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "51379bd7-7a6e-4d25-add1-ad429b50d4cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-(0)-> [1. FreeSpace] -(1)-> [2. Aperture] -(2)-> [3. DiffractiveLayer] -(3)-> [4. FreeSpace] -(4)-> [5. Aperture] -(5)-> [6. DiffractiveLayer] -(6)-> [7. FreeSpace] -(7)-> [8. Aperture] -(8)-> [9. DiffractiveLayer] -(9)-> [10. FreeSpace] -(10)-> [11. Aperture] -(11)-> [12. DiffractiveLayer] -(12)-> [13. FreeSpace] -(13)-> [14. Aperture] -(14)-> [15. DiffractiveLayer] -(15)-> [16. FreeSpace] -(16)-> [17. Detector] -(17)->\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1280 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(setup_scheme)  # prints propagation scheme\n",
    "\n",
    "n_cols = 5  # number of columns to plot all wavefronts during propagation\n",
    "n_rows = (len(optical_setup_loaded.net) // n_cols) + 1\n",
    "\n",
    "to_plot = 'amp'  # <--- chose what to plot\n",
    "cmap = 'grey'  # choose colormaps\n",
    "detector_cmap = 'hot'\n",
    "\n",
    "# create a figure with subplots\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 3, n_rows * 3.2))\n",
    "\n",
    "# turn off unecessary axes\n",
    "for ind_row in range(n_rows):\n",
    "    for ind_col in range(n_cols):\n",
    "        ax_this = axs[ind_row][ind_col]\n",
    "        if ind_row * n_cols + ind_col >= len(wavefronts):\n",
    "            ax_this.axis('off')\n",
    "\n",
    "# plot wavefronts\n",
    "for ind_wf, wavefront in enumerate(test_wavefronts):\n",
    "    ax_this = axs[ind_wf // n_cols][ind_wf % n_cols]\n",
    "\n",
    "    if to_plot == 'phase':\n",
    "        # plot angle for each wavefront, because intensities pictures are indistinguishable from each other\n",
    "        if ind_wf < len(wavefronts) - 1:\n",
    "            ax_this.set_title('Phase for $WF_{' + f'{ind_wf}' + '}$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].phase.detach().numpy(), cmap=cmap,\n",
    "                vmin=0, vmax=2 * torch.pi\n",
    "            )\n",
    "        else:  # (not a wavefront!)\n",
    "            ax_this.set_title('Detector phase ($WF_{' + f'{ind_wf}' + '})$')\n",
    "            # Detector has no phase!\n",
    "\n",
    "    if to_plot == 'amp':\n",
    "        # plot angle for each wavefront, because intensities pictures are indistinguishable from each other\n",
    "        if ind_wf < len(wavefronts) - 1:\n",
    "            ax_this.set_title('Intensity for $WF_{' + f'{ind_wf}' + '}$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].intensity.detach().numpy(), cmap=cmap,\n",
    "                # vmin=0, vmax=max_intensity  # uncomment to make the same limits\n",
    "            )\n",
    "        else:  # Detector output (not a wavefront!)\n",
    "            ax_this.set_title('Detector Intensity ($WF_{' + f'{ind_wf}' + '})$')\n",
    "            ax_this.imshow(\n",
    "                wavefront[0].detach().numpy(), cmap=detector_cmap,\n",
    "                # vmin=0, vmax=max_intensity  # uncomment to make the same limits\n",
    "            )\n",
    "            \n",
    "    # Comment: Detector output is Tensor! It has no methods of Wavefront (like .phase or .intensity)!\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3d3525ce-ba73-46f1-970a-0251c85ea4b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure with subplots\n",
    "fig, ax_this = plt.subplots(1, 1, figsize=(3, 3.2))\n",
    "\n",
    "# Detector output (not a wavefront!)\n",
    "ax_this.set_title('Detector Intensity ($WF_{' + f'{ind_wf}' + '})$')\n",
    "ax_this.imshow(\n",
    "    test_wavefronts[-1][0].detach().numpy(), cmap='hot',\n",
    "    # vmin=0, vmax=1  # uncomment to make the same limits\n",
    ")\n",
    "\n",
    "for zone in get_zones_patches(selected_detector_mask):\n",
    "    # add zone's patches to the axis\n",
    "    # zone_copy = copy(zone)\n",
    "    ax_this.add_patch(zone)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4879418-beff-4767-ad30-1cf89fe96ab6",
   "metadata": {},
   "source": [
    "#### Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8d6382b9-d81d-49f5-91d1-57f36d13cbe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : 0.85%\n",
      "1 : 4.22%\n",
      "2 : 5.14%\n",
      "3 : 1.11%\n",
      "4 : 64.29%\n",
      "5 : 2.39%\n",
      "6 : 1.11%\n",
      "7 : 1.32%\n",
      "8 : 0.70%\n",
      "9 : 18.87%\n"
     ]
    }
   ],
   "source": [
    "# get probabilities of an example classification\n",
    "test_probas = detector_processor.forward(test_wavefronts[-1])\n",
    "\n",
    "for label, prob in enumerate(test_probas[0]):\n",
    "    print(f'{label} : {prob * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b0b1fd2-d8c9-4da8-84f8-9b8b788d8566",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1f6171a-5a94-476e-87d1-539cad868200",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38ef083b-2c76-4b19-8e8a-c593d3839928",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}