07_end_to_end_auto_encoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fc8ca6c",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/aldebaro/ai6g/blob/main/07_end_to_end_auto_encoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc42cc4f",
   "metadata": {},
   "source": [
    "**Inteligência Artificial e Aprendizado de Máquina Aplicados a Redes 5G e 6G**.\n",
    "*Aldebaro Klautau* (UFPA). Minicurso 5 do SBrT - 25 de setembro de 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a09d932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clone the repository if running in Colab and install all the dependencies\n",
    "if 'google.colab' in str(get_ipython()):\n",
    "    import sys\n",
    "    import os\n",
    "    try:\n",
    "      !git clone https://github.com/aldebaro/ai6g.git\n",
    "    except:\n",
    "      print(\"ai6g is already in the contents\")\n",
    "    %cd ai6g\n",
    "    !pip install -r requirements.txt "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64aa340f",
   "metadata": {},
   "source": [
    "The original version of this code can be found at https://github.com/NVlabs/sionna\n",
    "in the folder examples, with the name Autoencoder.ipynb.\n",
    "The main reference is:\n",
    "\"An Open-Source Library for Next-Generation Physical Layer Research\", 2022,\n",
    "by Jakob Hoydis, Sebastian Cammerer, Fayçal Ait Aoudia, Avinash Vem, Nikolaus Binder, Guillermo Marcus and Alexander Keller."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1af397-500c-4a88-8168-abc57a1aa513",
   "metadata": {},
   "source": [
    "# End-to-end Learning with Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79b1ed0-1dcc-4a62-a9dd-09c13eec0b80",
   "metadata": {},
   "source": [
    "In this notebook, you will learn how to implement an end-to-end communication system as an autoencoder [1].\n",
    "The implemented system is shown in the figure below.\n",
    "An additive white Gaussian noise (AWGN) channel is considered.\n",
    "On the transmitter side, joint training of the constellation geometry and bit-labeling is performed, as in [2].\n",
    "On the receiver side, a neural network-based demapper that computes log-likelihood ratios (LLRs) on the transmitted bits from the received samples is optimized.\n",
    "The considered autoencoder is benchmarked against a quadrature amplitude modulation (QAM) with Gray labeling and the optimal AWGN demapper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4694269-5aee-4623-9ab0-6a9f84d9956c",
   "metadata": {},
   "source": [
    "![System Model](data:image/png;base64,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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2920adb0-9dd0-484d-9496-cfbf87af5eaa",
   "metadata": {},
   "source": [
    "Two algorithms for training the autoencoder are implemented in this notebook:\n",
    "\n",
    "* Conventional stochastic gradient descent (SGD) with backpropagation, which assumes a differentiable channel model and therefore optimizes the end-to-end system by backpropagating the gradients through the channel (see, e.g., [1]).\n",
    "* The training algorithm from [3], which does not assume a differentiable channel model, and which trains the end-to-end system by alternating between conventional training of the receiver and reinforcement learning (RL)-based training of the transmitter. Compared to [3], an additional step of fine-tuning of the receiver is performed after alternating training.\n",
    "\n",
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/examples/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3287acf2-0dcd-419f-8cfc-27ea29ca3af6",
   "metadata": {},
   "source": [
    "* [Avoid using GPU and Imports](#Avoid-GPU-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Neural Demapper](#Neural-Demapper)\n",
    "* [Trainable End-to-end System: Conventional Training](#Trainable-End-to-end-System:-Conventional-Training)\n",
    "* [Evaluation](#Evaluation)\n",
    "* [Visualizing the Learned Constellations](#Visualizing-the-Learned-Constellations)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d35352-0937-4710-9f2c-7e50f819ea2c",
   "metadata": {},
   "source": [
    "## Avoid using GPU and Imports <a class=\"anchor\" id=\"Avoid-GPU-and-Imports\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "31621ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We want to disable the GPU and use the CPU\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"-1\" #make any GPU invisible / unavailable\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e83f5cd3-ec80-4f19-ac7a-36870a6d95e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Layer, Dense\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna\n",
    "except ImportError as e:\n",
    "    # Install Sionna if package is not already installed\n",
    "    import os\n",
    "    os.system(\"pip install sionna\")\n",
    "    import sionna\n",
    "    \n",
    "from sionna.channel import AWGN\n",
    "from sionna.utils import BinarySource, ebnodb2no, log10, expand_to_rank, insert_dims\n",
    "from sionna.fec.ldpc.encoding import LDPC5GEncoder\n",
    "from sionna.fec.ldpc.decoding import LDPC5GDecoder\n",
    "from sionna.mapping import Mapper, Demapper, Constellation\n",
    "from sionna.utils import sim_ber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca65b28-74a4-4cd9-8c66-748259d05e57",
   "metadata": {},
   "source": [
    "## Simulation Parameters <a class=\"anchor\" id=\"Simulation-Parameters\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ddfd420b-0f45-468a-b292-8aaea2ce280f",
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################################\n",
    "# SNR range for evaluation and training [dB]\n",
    "###############################################\n",
    "ebno_db_min = 2.5 #original value is 5.0 when using num_bits_per_symbol = 6\n",
    "ebno_db_max = 4.5 #original value is 8.0 when using num_bits_per_symbol = 6\n",
    "\n",
    "###############################################\n",
    "# Modulation and coding configuration\n",
    "###############################################\n",
    "num_bits_per_symbol = 4 # Must be a mutliple of 2. Original value is 6 because the baseline is 64-QAM\n",
    "modulation_order = 2**num_bits_per_symbol\n",
    "coderate = 0.5 # Coderate for the outer code\n",
    "n = 1500 # Codeword length [bit]. Must be a multiple of num_bits_per_symbol\n",
    "num_symbols_per_codeword = n//num_bits_per_symbol # Number of modulated baseband symbols per codeword\n",
    "k = int(n*coderate) # Number of information bits per codeword\n",
    "\n",
    "###############################################\n",
    "# Training configuration\n",
    "###############################################\n",
    "num_training_iterations_conventional = 300 # Number of training iterations for conventional training. Original value=10000\n",
    "training_batch_size = tf.constant(128, tf.int32) # Training batch size\n",
    "model_weights_path_conventional_training = \"./files_07_end/awgn_autoencoder_weights_conventional_training.weights\" # Filename to save the autoencoder weights once conventional training is done\n",
    "\n",
    "###############################################\n",
    "# Evaluation configuration\n",
    "###############################################\n",
    "results_filename = \"./files_07_end/awgn_autoencoder_results.pkl\" # Location to save the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d711f03c-93b9-4524-a7a6-63a35b825623",
   "metadata": {},
   "source": [
    "## Neural Demapper <a class=\"anchor\" id=\"Neural-Demapper\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caff5b7e-0ae4-4890-a45b-378afc5bf9a5",
   "metadata": {},
   "source": [
    "The neural network-based demapper shown in the figure above is made of three dense layers with ReLU activation.\n",
    "\n",
    "The input of the demapper consists of a received sample $y \\in \\mathbb{C}$ and the noise power spectral density $N_0$ in log-10 scale to handle different orders of magnitude for the SNR.\n",
    "\n",
    "As the neural network can only process real-valued inputs, these values are fed as a 3-dimensional vector\n",
    "\n",
    "$$\\left[ \\mathcal{R}(y), \\mathcal{I}(y), \\log_{10}(N_0) \\right]$$\n",
    "\n",
    "where $\\mathcal{R}(y)$ and $\\mathcal{I}(y)$ refer to the real and imaginary component of $y$, respectively.\n",
    "\n",
    "The output of the neural network-based demapper consists of LLRs on the `num_bits_per_symbol` bits mapped to a constellation point. Therefore, the last layer consists of ``num_bits_per_symbol`` units.\n",
    "\n",
    "**Note**: The neural network-based demapper processes the received samples $y$ forming a block individually. The [neural receiver notebook](https://nvlabs.github.io/sionna/examples/Neural_Receiver.html) provides an example of a more advanced neural network-based receiver that jointly processes a resource grid of received symbols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3e636efd-9a46-4fd1-8790-b4cb1b63f31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralDemapper(Layer):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self._dense_1 = Dense(28, 'relu') #original number of neurons is 128\n",
    "        self._dense_2 = Dense(28, 'relu') #original number of neurons is 128\n",
    "        self._dense_3 = Dense(num_bits_per_symbol, None) # The feature correspond to the LLRs for every bits carried by a symbol\n",
    "    \n",
    "    def call(self, inputs):\n",
    "        y,no = inputs\n",
    "        \n",
    "        # Using log10 scale helps with the performance\n",
    "        no_db = log10(no)\n",
    "        \n",
    "        # Stacking the real and imaginary components of the complex received samples\n",
    "        # and the noise variance\n",
    "        no_db = tf.tile(no_db, [1, num_symbols_per_codeword]) # [batch size, num_symbols_per_codeword]\n",
    "        z = tf.stack([tf.math.real(y),\n",
    "                      tf.math.imag(y),\n",
    "                      no_db], axis=2) # [batch size, num_symbols_per_codeword, 3]        \n",
    "        llr = self._dense_1(z)\n",
    "        llr = self._dense_2(llr)\n",
    "        llr = self._dense_3(llr) # [batch size, num_symbols_per_codeword, num_bits_per_symbol]\n",
    "        \n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d232019-1c8b-4d51-b125-a1f0a4189dac",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: Conventional Training <a class=\"anchor\" id=\"Trainable-End-to-end-System:-Conventional-Training\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cdeeb8-879c-4ae1-a734-6a04ffa48dd3",
   "metadata": {},
   "source": [
    "The following cell defines an end-to-end communication system that transmits bits modulated using a trainable constellation over an AWGN channel.\n",
    "\n",
    "The receiver uses the previously defined neural network-based demapper to compute LLRs on the transmitted (coded) bits.\n",
    "\n",
    "As in [1], the constellation and neural network-based demapper are jointly trained through SGD and backpropagation using the binary cross entropy (BCE) as loss function.\n",
    "\n",
    "Training on the BCE is known to be equivalent to maximizing an achievable information rate [2].\n",
    "\n",
    "The following model can be instantiated either for training (`training = True`) or evaluation (`training = False`).\n",
    "\n",
    "In the former case, the BCE is returned and no outer code is used to reduce computational complexity and as it does not impact the training of the constellation or demapper.\n",
    "\n",
    "When setting `training` to `False`, an LDPC outer code from 5G NR is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2709aae3-7d40-4f35-a56f-79bd09a81e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "class E2ESystemConventionalTraining(Model):\n",
    "    \n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._training = training\n",
    "            \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            self._encoder = LDPC5GEncoder(k, n)\n",
    "        # Trainable constellation\n",
    "        constellation = Constellation(\"qam\", num_bits_per_symbol, trainable=True)\n",
    "        self.constellation = constellation\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "            \n",
    "        #################\n",
    "        # Loss function\n",
    "        #################\n",
    "        if self._training:\n",
    "            self._bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "    \n",
    "    @tf.function(jit_compile=True)\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        \n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created as it is what is expected by some layers\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c) # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel([x, no]) # [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper([y, no])\n",
    "        llr = tf.reshape(llr, [batch_size, n])\n",
    "        # If training, outer decoding is not performed and the BCE is returned\n",
    "        if self._training:\n",
    "            loss = self._bce(c, llr)\n",
    "            return loss\n",
    "        else:\n",
    "            # Outer decoding\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b,b_hat # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f347823-c28d-4a00-8a02-f9b5e6d450f3",
   "metadata": {},
   "source": [
    "A simple training loop is defined in the next cell, which performs `num_training_iterations_conventional` training iterations of SGD. Training is done over a range of SNR, by randomly sampling a batch of SNR values at each iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95542ed-a6be-4c72-bae5-5154ded0340b",
   "metadata": {},
   "source": [
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/examples/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ab429a86-f606-420c-8ad1-e0efc6874584",
   "metadata": {},
   "outputs": [],
   "source": [
    "def conventional_training(model):\n",
    "    # Optimizer used to apply gradients\n",
    "    optimizer = tf.keras.optimizers.Adam()\n",
    "    \n",
    "    for i in range(num_training_iterations_conventional):\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = tf.random.uniform(shape=[training_batch_size], minval=ebno_db_min, maxval=ebno_db_max)\n",
    "        # Forward pass\n",
    "        with tf.GradientTape() as tape:\n",
    "            loss = model(training_batch_size, ebno_db) # The model is assumed to return the BMD rate\n",
    "        # Computing and applying gradients        \n",
    "        weights = model.trainable_weights\n",
    "        grads = tape.gradient(loss, weights)\n",
    "        optimizer.apply_gradients(zip(grads, weights))\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print('Iteration {}/{}  BCE: {:.4f}'.format(i, num_training_iterations_conventional, loss.numpy()), end='\\r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5959e24-1107-4271-be5c-944eacdc3033",
   "metadata": {},
   "source": [
    "The next cell defines a utility function for saving the weights using [pickle](https://docs.python.org/3/library/pickle.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f32c0d68-55f1-41e1-88da-28b96c3cac0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_weights(model, model_weights_path):\n",
    "    # Show the network model\n",
    "    print(model.summary())\n",
    "    # Save weights in file\n",
    "    weights = model.get_weights()\n",
    "    with open(model_weights_path, 'wb') as f:\n",
    "        pickle.dump(weights, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6ad9e4-7df4-4988-bbb4-dedd0ba50f47",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5971c6da-52bf-46ab-91f0-57baf560964d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"e2e_system_conventional_training_19\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " binary_source_25 (BinarySou  multiple                 0         \n",
      " rce)                                                            \n",
      "                                                                 \n",
      " constellation_25 (Constella  multiple                 32        \n",
      " tion)                                                           \n",
      "                                                                 \n",
      " mapper_24 (Mapper)          multiple                  32        \n",
      "                                                                 \n",
      " awgn_24 (AWGN)              multiple                  0         \n",
      "                                                                 \n",
      " neural_demapper_18 (NeuralD  multiple                 1040      \n",
      " emapper)                                                        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,072\n",
      "Trainable params: 1,072\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Fix the seed for reproducible trainings\n",
    "tf.random.set_seed(1)\n",
    "# Instantiate and train the end-to-end system\n",
    "model = E2ESystemConventionalTraining(training=True)\n",
    "conventional_training(model)\n",
    "# Save weights\n",
    "save_weights(model, model_weights_path_conventional_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0345da-9e49-4917-8197-0787254b5cf7",
   "metadata": {},
   "source": [
    "## Evaluation <a class=\"anchor\" id=\"Evaluation\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4344594-739d-485f-8ee0-fbb9c8cb9055",
   "metadata": {},
   "source": [
    "The following cell implements a baseline which uses QAM with Gray labeling and conventional demapping for AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f81f69a9-cc45-42ad-92a3-8ea469b1363e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Baseline(Model):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n)\n",
    "        constellation = Constellation(\"qam\", num_bits_per_symbol, trainable=False)\n",
    "        self.constellation = constellation\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        self._demapper = Demapper(\"app\", constellation=constellation)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "    \n",
    "    @tf.function(jit_compile=True)\n",
    "    def call(self, batch_size, ebno_db, perturbation_variance=tf.constant(0.0, tf.float32)):\n",
    "        \n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created as it is what is expected by some layers\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        b = self._binary_source([batch_size, k])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c) # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel([x, no]) # [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper([y, no]) \n",
    "        # Outer decoding\n",
    "        b_hat = self._decoder(llr)\n",
    "        return b,b_hat # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9bf294cb-a410-46cf-9844-0e80ed8e1b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Range of SNRs over which the systems are evaluated\n",
    "ebno_dbs = np.arange(ebno_db_min, # Min SNR for evaluation\n",
    "                     ebno_db_max, # Max SNR for evaluation\n",
    "                     0.5) # Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6e921887-3d86-4ef1-b550-69bf086a4972",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility function to load and set weights of a model\n",
    "def load_weights(model, model_weights_path):\n",
    "    model(1, tf.constant(10.0, tf.float32))\n",
    "    with open(model_weights_path, 'rb') as f:\n",
    "        weights = pickle.load(f)\n",
    "    model.set_weights(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125b4bd1-911f-4f19-b989-575c765e80d1",
   "metadata": {},
   "source": [
    "The next cell evaluates the baseline and the two autoencoder-based communication systems, trained with different method.\n",
    "The results are stored in the dictionary ``BLER``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ff0fe56c-6e7b-41f7-9b6d-a7919699ec7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      2.5 | 1.3428e-01 | 9.9316e-01 |      103127 |      768000 |         1017 |        1024 |         3.4 |reached target block errors\n",
      "      3.0 | 6.4558e-02 | 7.9531e-01 |       61976 |      960000 |         1018 |        1280 |         1.0 |reached target block errors\n",
      "      3.5 | 1.1156e-02 | 2.5580e-01 |       33200 |     2976000 |         1015 |        3968 |         3.7 |reached target block errors\n",
      "      4.0 | 6.3927e-04 | 2.0313e-02 |        6137 |     9600000 |          260 |       12800 |        10.3 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      2.5 | 1.6953e-01 | 1.0000e+00 |      130200 |      768000 |         1024 |        1024 |         1.8 |reached target block errors\n",
      "      3.0 | 1.1246e-01 | 9.7852e-01 |       86371 |      768000 |         1002 |        1024 |         0.8 |reached target block errors\n",
      "      3.5 | 4.0188e-02 | 6.7383e-01 |       46296 |     1152000 |         1035 |        1536 |         1.2 |reached target block errors\n",
      "      4.0 | 5.8055e-03 | 1.8278e-01 |       23965 |     4128000 |         1006 |        5504 |         4.2 |reached target block errors\n"
     ]
    }
   ],
   "source": [
    "# Dictionnary storing the results\n",
    "BLER = {}\n",
    "\n",
    "model_baseline = Baseline()\n",
    "_,bler = sim_ber(model_baseline, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=100)\n",
    "BLER['baseline'] = bler.numpy()\n",
    "\n",
    "model_conventional = E2ESystemConventionalTraining(training=False)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "_,bler = sim_ber(model_conventional, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=100)\n",
    "BLER['autoencoder-conv'] = bler.numpy()\n",
    "\n",
    "with open(results_filename, 'wb') as f:\n",
    "    pickle.dump((ebno_dbs, BLER), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "905f3b1a-f906-4eab-b081-2705c9e1f6ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# Baseline - Perfect CSI\n",
    "plt.semilogy(ebno_dbs, BLER['baseline'], 'o-', c=f'C0', label=f'Baseline')    \n",
    "# Autoencoder - conventional training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-conv'], 'x-.', c=f'C1', label=f'Autoencoder - conventional training')\n",
    "\n",
    "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "plt.ylim((1e-4, 1.0))\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35d47f5-226f-435d-bf65-d46cd0fad5ca",
   "metadata": {},
   "source": [
    "## Visualizing the Learned Constellations <a class=\"anchor\" id=\"Visualizing-the-Learned-Constellations\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "99fab7bb-d5fd-4173-9acc-688fb395c3a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KjY2VYRjXvO1169YpMTGxAqoEULuqCwBgPlu3btXIkSPVvXt3rVmzRp6envZl3bt319NPP63k5OQqrNBcPD091alTpyqtoX379vL395d06Xd4/PhxvfPOO9qyZYvuuuuuKq0NwP8w4wbAadOnT5fFYtGiRYscQlsRDw8P9enTx/65sLBQM2fO1K233ipPT081atRIjz76qI4cOeKwXlRUlFq1aqWdO3eqc+fO8vb21k033aQZM2aosLDQYXsvvPCCmjdvrjp16qh+/fpq06aNXn/9dUmXTv/9+c9/liSFhYXZTwNu3LjRvo2VK1cqIiJCdevWVb169dSjRw/t3r27XOORmJiojh07ys/PT76+vmrXrp2WLFniMFvVpEkT7du3T5s2bbLX06RJE0mlnyr96quv1K1bN/n4+Mjb21uRkZH65JNPHPoUncLcsGGDRo4cKX9/fzVs2FB9+/bV0aNHy3U8kuxB8tChQ1fs99e//lW33367vLy85OfnpwcffFDfffedffmQIUP05ptvSpLDKdnynHIFQHAD4KSCggJ98cUXat++vYKDg8u0zsiRIzVhwgR1795dH330kZ5//nklJycrMjJSx44dc+ibmZmpP/zhDxo0aJA++ugjxcbGauLEiVq+fLm9z8yZM5WQkKABAwbok08+0cqVKzV06FD7adFhw4Zp9OjRkqRVq1Zp69at2rp1q9q1ayfpUvAcMGCAWrZsqQ8++EDvvPOOTp8+rc6dO2v//v1Oj8nBgwf15JNP6oMPPtCqVavUt29fjR49Ws8//7y9z+rVq3XTTTcpPDzcXs/q1atL3eamTZt0zz33KCcnR0uWLNH7778vHx8f9e7dWytXrizWf9iwYXJ3d9d7772nmTNnauPGjRo0aJDTx1Lkxx9/lCTdcMMNpfZ56aWXNHToUN12221atWqVXn/9df373/9WRESEfvjhB0nSlClT9NBDD0mS/bi3bt2qwMDActcG1GgGADghMzPTkGT079+/TP2/++47Q5IxatQoh/bt27cbkoxJkybZ27p27WpIMrZv3+7Qt2XLlkaPHj3sn3v16mW0bdv2ivt95ZVXDElGWlqaQ3t6erpRu3ZtY/To0Q7tp0+fNmw2m/Hwww/b26ZNm2b89o/Jrl27Gl27di11vwUFBUZ+fr7x3HPPGQ0bNjQKCwvty2677bYS101LSzMkGUuXLrW3derUyWjUqJFx+vRpe9vFixeNVq1aGY0bN7Zvd+nSpSWO78yZMw1JRkZGRqm1Xn6MmZmZRn5+vpGdnW0sX77cqFOnjhEcHGycP3/eYT9F45mdnW3UqVPH6Nmzp8P20tPTDU9PT2PgwIH2tj/+8Y/FxhFA+TDjBsClNmzYIOnSKbPLdejQQS1atNDnn3/u0G6z2dShQweHtjZt2jicsuvQoYO+/fZbjRo1Sp9++qlOnTpV5no+/fRTXbx4UY8++qguXrxo//Hy8lLXrl0dTqeW1RdffKF7771XVqtVbm5ucnd319SpU3X8+HFlZWU5vb2zZ89q+/bteuihh1SvXj17u5ubm+Li4nTkyBEdOHDAYZ3LT01Ll8ZMuvqpziI2m03u7u5q0KCBBg0apHbt2ik5OVleXl4l9t+6davOnz9f7PcaHByse+65p9jvFUDF4OYEAE7x9/eXt7e30tLSytT/+PHjklTiqbGgoKBiwaJhw4bF+nl6eur8+fP2zxMnTlTdunW1fPlyLVy4UG5uburSpYtefvll3XHHHVes55dffpEk3XnnnSUur1XLuX/P7tixQ9HR0YqKitLixYvVuHFjeXh4aM2aNXrxxRcd6i6r7OxsGYZR6phJ/xvXIr8dt6JrD8u6/88++0xWq1Xu7u5q3Lhxib+Hy13t95qSklKm/QJwDsENgFPc3NzUrVs3/fOf/9SRI0fUuHHjK/YvCgAZGRnF+h49etR+J6MzateurXHjxmncuHE6efKkPvvsM02aNEk9evTQ4cOH5e3tXeq6Rfv7xz/+odDQUKf3/VsrVqyQu7u7Pv74Y4fZqTVr1pR7mw0aNFCtWrWUkZFRbFnRDQflGbcruf32253a5uW/198q7+8VwNVxqhSA0yZOnCjDMDR8+HDl5eUVW56fn6+1a9dKku655x5Jcri5QJJ27typ7777Tt26dbumWurXr6+HHnpIf/zjH3XixAn73YqlzTj16NFDtWvX1n//+1/dcccdJf44w2KxqHbt2nJzc7O3nT9/Xu+8806xvr+dOSxN3bp11bFjR61atcqhf2FhoZYvX67GjRvrlltucarOihYREaE6deoU+70eOXJEX3zxhcPv1dnZPwClY8YNgNMiIiK0YMECjRo1Su3bt9fIkSN12223KT8/X7t379aiRYvUqlUr9e7dW82bN9cTTzyhuXPnqlatWoqNjdXBgwc1ZcoUBQcHa+zYsU7vv3fv3mrVqpXuuOMO3XDDDTp06JDmzJmj0NBQNWvWTJLUunVrSdLrr7+uwYMHy93dXc2bN1eTJk303HPPafLkyfrpp58UExOjBg0a6JdfftGOHTtUt25dpx4We99992nWrFkaOHCgnnjiCR0/flyvvvpqiY9Jad26tVasWKGVK1fqpptukpeXl73O33rppZfUvXt33X333XrmmWfk4eGh+fPna+/evXr//feLvdGhstWvX19TpkzRpEmT9Oijj2rAgAE6fvy4EhMT5eXlpWnTptn7Fh3jyy+/rNjYWLm5ualNmzby8PCoqvIB86rquyMAmFdqaqoxePBgIyQkxPDw8DDq1q1rhIeHG1OnTjWysrLs/QoKCoyXX37ZuOWWWwx3d3fD39/fGDRokHH48GGH7XXt2tW47bbbiu1n8ODBRmhoqP3za6+9ZkRGRhr+/v6Gh4eHERISYgwdOtQ4ePCgw3oTJ040goKCjFq1ahmSjA0bNtiXrVmzxrj77rsNX19fw9PT0wgNDTUeeugh47PPPrP3KetdpX/961+N5s2bG56ensZNN91kvPTSS8aSJUuK3dV68OBBIzo62vDx8TEk2Y+ppLtKDcMwvvzyS+Oee+4x6tata9SpU8fo1KmTsXbtWoc+RXd77ty506F9w4YNxY65JEXH+Ouvv16x32/vKi3y9ttvG23atDE8PDwMq9Vq3H///ca+ffsc+uTm5hrDhg0zbrjhBsNisZS4HQBlYzGMCnifCQAAAFyOa9wAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACbBA3ivorCwUEePHpWPj0+VP/ASAABUP4Zh6PTp0woKCrrq+5IJbldx9OhRBQcHV3UZAACgmjt8+PBV3/9McLsKHx8fSZcG09fXt4qrMZf8/HytX79e0dHRcnd3r+pyahTGvmox/lWHsa86jH35nTp1SsHBwfbMcSUEt6soOj3q6+tLcHNSfn6+vL295evry//ElYyxr1qMf9Vh7KsOY3/tynJJFjcnAAAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkzBVcNu8ebN69+6toKAgWSwWrVmz5qrrbNq0Se3bt5eXl5duuukmLVy40PWFAgAAuICpgtvZs2d1++23a968eWXqn5aWpp49e6pz587avXu3Jk2apDFjxujDDz90caUAAAAVz1TBLTY2Vi+88IL69u1bpv4LFy5USEiI5syZoxYtWmjYsGF6/PHH9eqrr7q4UgCoXubPn6+wsDB5eXmpffv2+vLLL+3LDMNQQkKCgoKCVKdOHUVFRWnfvn0O6+fm5mr06NHy9/dX3bp11adPHx05csShz4svvqjIyEh5e3urfv36lXFYgOnUruoCXGnr1q2Kjo52aOvRo4eWLFmi/Px8ubu7F1snNzdXubm59s+nTp2SJOXn5ys/P9+1BVczRePFuFU+xr5qVbfx/+CDDxQfH6+5c+cqIiJCb7/9tmJjY/Xtt98qJCREr7zyimbNmqW3335bzZo100svvaTu3btr79698vHxkSSNGTNGn3zyiZYvXy4/Pz9NmDBB9913n7Zv3y43NzdJ0vnz59W3b1917NhRS5cuLdf4VbexNxPGvvycGbNqHdwyMzMVEBDg0BYQEKCLFy/q2LFjCgwMLLbOSy+9pMTExGLt69evl7e3t8tqrc5SUlKquoQai7GvWtVl/BMTE9WtWzfZbDalpaWpW7duWr16tSZMmKBBgwbp1Vdf1YMPPihPT0+lp6fr4Ycf1ieffKK//OUv6tGjh86ePau//vWvio+PV25urjIyMvToo49q2LBhmjFjhsLDwyVJd955pyQpPT1d+fn5WrduXblrri5jb0aMvfPOnTtX5r7VOrhJksVicfhsGEaJ7UUmTpyocePG2T+fOnVKwcHBio6Olq+vr+sKrYby8/OVkpKi7t27lzi7Cddh7KtWdRr/vLw8/fTTT3rxxRfVs2dPe/sDDzygb7/9Vi1atFB2drb++Mc/2gOYJN1zzz06ffq0evbsqQ0bNujixYv685//rAYNGtj7zJ49WxcvXnTYriQdO3ZM7u7uxdrLojqNvdkw9uVXdHavLKp1cLPZbMrMzHRoy8rKUu3atdWwYcMS1/H09JSnp2exdnd3d76I5cTYVR3GvmpVh/H/9ddfVVBQoBtvvNHhWAIDA7V+/XodP35cktS4ceNiyw8dOiR3d3cdO3ZMHh4eatSokcO2bTabfv3112JjVHTq9FrGrjqMvVkx9s5zZryqdXCLiIjQ2rVrHdrWr1+vO+64gy8VAJSioNDQjrQTyjp9QZZz2ZJKPntxedvVlpekLH0AODJVcDtz5ox+/PFH++e0tDSlpqbKz89PISEhmjhxon7++Wf97W9/kySNGDFC8+bN07hx4zR8+HBt3bpVS5Ys0fvvv19VhwAA17XkvRlKXLtfGTkXJElGQb5Uq5bW7fhOERER9n5ZWVkKCAiQzWaTdOma4suvGy5aLl2aWcvLy1N2drbDqdKsrCxFRkZWxmEB1YapHgfy9ddfKzw83H4dxbhx4xQeHq6pU6dKkjIyMpSenm7vHxYWpnXr1mnjxo1q27atnn/+eb3xxhv6/e9/XyX1A8D1LHlvhkYu/8Ye2iTJ4uYuj4Cmen3Zh0rem2FvT0lJUWRkpMLCwmSz2RwuSM/Ly9OmTZvsoax9+/Zyd3d36JORkaG9e/cS3AAnmWrGLSoqyn5zQUmSkpKKtXXt2lXffPONC6sCAPMrKDSUuHa/SvoT1vfOB3Ts41n647TX9P+ee1xL3l6s9PR0jRgxQhaLRfHx8Zo+fbqaNWumZs2aafr06fL29tbAgQMlSVarVUOHDtXTTz+thg0bys/PT88884xat26te++9176f9PR0nThxQunp6SooKFBqaqokqWnTpqpXr14ljAJw/TNVcAMAuMaOtBMOM22Xq9uiiwrPn9ahz95Ru7Vz1bp1K61bt06hoaGSpPHjx+v8+fMaNWqUsrOz1bFjR61fv97+DDfp0h2ktWvX1sMPP6zz58+rW7duSkpKst+IIElTp07VsmXL7J+Lzq5s2LBBUVFRLjhqwHwIbgAAZZ0uObQV8Wl3n3za3afX+7fV/W1vdFhmsViUkJCghISEUtf38vLS3LlzNXfu3FL7JCUllXjmBMD/mOoaNwCAazTy8arQfgBcg+AGAFCHMD8FWr1U2sM5LJICrV7qEOZXmWUB+A2CGwBAbrUsmta7pSQVC29Fn6f1bim3Wjx3DahKBDcAgCQpplWgFgxqJ5vV8XSozeqlBYPaKaZV8fc7A6hc3JwAALCLaRWo7i1t9jcnNPK5dHqUmTbg+kBwAwA4cKtlUcTNJb/PGUDV4lQpAACASRDcAABApZk/f77CwsLk5eWl9u3b68svv7QvMwxDCQkJCgoKUp06dRQVFaV9+/Y5rL9o0SJFRUXJ19dXFotFJ0+eLLaPF198UZGRkfL29lb9+vVdfESVi+AGAAAqxcqVKxUfH6/Jkydr9+7d6ty5s2JjY+3vGZ85c6ZmzZqlefPmaefOnbLZbOrevbtOnz5t38a5c+cUExOjSZMmlbqfvLw89evXTyNHjnT5MVU2ghsAAKgUs2bN0tChQzVs2DC1aNFCc+bMUXBwsBYsWCDDMDRnzhxNnjxZffv2VatWrbRs2TKdO3dO7733nn0b8fHxevbZZ9WpU6dS95OYmKixY8eqdevWlXFYlYrgBgAAXC4vL0+7du1SdHS0Q3t0dLS2bNmitLQ0ZWZmOiz39PRU165dtWXLlsou97pFcAMAAC537NgxFRQUKCAgwKE9ICBAmZmZyszMtH8uaTku4XEgAADAJQoKDfszAS3nsiVJFovjMwENw3Bou9rymo7gBgAAKlzy3gwlrt2vjJwLkiSjIF+qVUvrdnyniIgIe7+srCwFBATIZrNJkjIzMxUYGFhsOS7hVCkAAKhQyXszNHL5N/bQJkkWN3d5BDTV68s+VPLeDHt7SkqKIiMjFRYWJpvNppSUFPuyvLw8bdq0SZGRkZVa//WMGTcAAFBhCgoNJa7dL6OEZb53PqBjH8/SH6e9pv/33ONa8vZipaena8SIEbJYLIqPj9f06dPVrFkzNWvWTNOnT5e3t7cGDhxo30bR9XA//vijJGnPnj3y8fFRSEiI/Pz8JEnp6ek6ceKE0tPTVVBQoNTUVElS06ZNVa9ePVcPgUsR3AAAQIXZdSjbYabtcnVbdFHh+dM69Nk7ard2rlq3bqV169YpNDRUkjR+/HidP39eo0aNUnZ2tjp27Kj169fLx8fHvo2FCxcqMTHR/rlLly6SpKVLl2rIkCGSpKlTp2rZsmX2PuHh4ZKkDRs2KCoqqiIPt9IR3AAAQIU5dib3ist92t0nn3b36fX+bXV/2xsdllksFiUkJCghIaHU9a+2XJKSkpKUlJRUxorNhWvcAJTL5s2b1bt3bwUFBclisWjNmjUOy8vy6prc3FyNHj1a/v7+qlu3rvr06aMjR4449KnOr64BqiP/ep5l6tfIx8vFlVRPBDcA5XL27FndfvvtmjdvXonLy/Lqmvj4eK1evVorVqzQV199pTNnzqhXr14qKCiw96nOr64BqqP2oQ0UaPVSaQ/wsEgKtHqpQ5hfZZZVbXCqFEC5xMbGKjY2tsRlv311jSQtW7ZMAQEBeu+99/Tkk08qJydHS5Ys0TvvvKN7771XkrR8+XIFBwfrs88+U48ePSTJfi1LdT3tAVQ3brUsmta7pUYu/0YWyeEmhaIwN613S7nV4tls5cGMG4AKV5ZX1+zatUv5+fkOfYKCgtSqVStebwOYXEyrQC0Y1E42q+PpUJvVSwsGtVNMq8BS1sTVENzgtLJe2xQaGqqHH35Y9957b7FrmxYtWqSoqCj5+vrKYrHo5MmTxfbDtU3mVZZX12RmZsrDw0MNGjQotQ8A84ppFaivJtyj94d30uv92+r94Z301YR7CG3XiOAGp5X12qY5c+bolVdeUUBAQLFrm86dO6eYmBhNmjSp1P1wbdP1paDQ0Nb/Htf/S/1ZW/97XAWFJT2lyVF5Xl3D622A6sOtlkURNzfU/W1vVMTNDTk9WgG4xg1OK+u1TQ8++KDWrVunxx9/XI0bN7Zf2yRduihdkjZu3Fjqfri26frx21fXSJcuLp7Wu2WJ/3ouy6trbDab8vLylJ2d7TDrlpWVxVPSAaAUzLihQpXl2iaYS0mvrpGkzJwLGrn8G4dX1xQpy6tr2rdvL3d3d4c+GRkZ2rt3L8ENAErBjBvKpKDQ0I60E8o6fUGNfC7dxl3SlPeVrm06dOhQpdSKinOlV9cU5J3XxewMjX/r0nPX0tLSlJqaKj8/P4WEhFz11TVWq1VDhw7V008/rYYNG8rPz0/PPPOMWrdubb/LVKrer64BAGcR3HBVVzpNVpryXNuE68+OtBOlvromL/MH/fL+JBXNt40bN06SNHjwYCUlJZXp1TWzZ89W7dq19fDDD+v8+fPq1q2bkpKS5ObmZu9TnV9dAwDOIrjhiopOk/12xqXoNNlvXX5tk7+/v7398mubYB5Zp0sObZLkFdJGoRM+lqRyv7rGy8tLc+fO1dy5c0vtU51fXQMAzuIaN5TqSqfJLm8rvOzuwrJc2wTzKOsraXh1DQBUDmbcUKrSTpMV/t+1TUU27dqnm24KK3ZtU1hYmDIyMjR06FCHa5ukSzNymZmZ+vHHHyVJe/bskY+Pj0JCQuTnd+k1KFzbVPU6hPkp0OqlzJwLJQZ4iy49UJNX1wBA5SC4oVSlnSYrurapyBvTp+iN6VOKXds0ZswYHT9+XJ06dSp2bdPChQvtj/uQpC5dukiSli5dqiFDhkji2qbrAa+uAYDri8UwjKs/RbMGO3XqlKxWq3JycuTr61vV5VSqrf89rgGLt1213/vDOyni5obF2vPz87Vu3Tr17NlT7u7urigRpajosXf2OW41Hd/9qsPYVx3GvvycyRrMuKFUnCZDkZhWgere0lamR8IAAFyH4IZScZoMlyt6dQ0AoOpwVymuKKZVoBYMaieb1fGuQZvVSwsGteM0GQAAlYgZN1wVp8kAALg+ENxQJpwmAwCg6nGqFAAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJmC64zZ8/X2FhYfLy8lL79u315Zdfltp348aNslgsxX6+//77SqwYAACgYpgquK1cuVLx8fGaPHmydu/erc6dOys2Nlbp6elXXO/AgQPKyMiw/zRr1qySKgYAAKg4pgpus2bN0tChQzVs2DC1aNFCc+bMUXBwsBYsWHDF9Ro1aiSbzWb/cXNzq6SKAQDSlc+WGIahhIQEBQUFqU6dOoqKitK+ffsc1s/NzdXo0aPl7++vunXrqk+fPjpy5IhDn+zsbA0ZMkQDBw6Uv7+/4uLidPLkyco4PKDS1K7qAsoqLy9Pu3bt0rPPPuvQHh0drS1btlxx3fDwcF24cEEtW7bUX/7yF919992l9s3NzVVubq7986lTpyRJ+fn5ys/Pv4YjqHmKxotxq3yMfdVi/B198MEHio+P19y5cxUREaG3335bsbGx+vbbbxUSEqJXXnlFs2bN0ttvv61mzZrppZdeUvfu3bV37175+PhIksaMGaNPPvlEy5cvl5+fnyZMmKD77rtP27dvt/9jfMCAATp8+LCmTp2qDh06aPTo0frDH/6gNWvWVOHR1xx878vPmTGzGIZhuLCWCnP06FHdeOON+te//qXIyEh7+/Tp07Vs2TIdOHCg2DoHDhzQ5s2b1b59e+Xm5uqdd97RwoULtXHjRnXp0qXE/SQkJCgxMbFY+3vvvSdvb++KOyAAqCH+/Oc/6+abb9aIESPsbU899ZQ6duyoQYMG6fHHH1fv3r3Vt29fSZf+Ehs8eLAGDx6sHj166OzZsxo8eLDi4+P1u9/9TpJ04sQJDRs2TFOmTFF4eLgOHz6s0aNHa+bMmbrlllskXfo7YMKECXrzzTd14403Vv6BA2V07tw5DRw4UDk5OfL19b1iX9PMuBWxWCwOnw3DKNZWpHnz5mrevLn9c0REhA4fPqxXX3211OA2ceJEjRs3zv751KlTCg4OVnR09FUHE47y8/OVkpKi7t27y93dvarLqVEY+6rF+P9PXl6efvrpJ7344ovq2bOnvf2BBx7Qt99+qxYtWig7O1t//OMfFR4ebl9+zz336PTp0+rZs6c2bNigixcv6s9//rMaNGhg7zN79mxdvHhRPXv2VFJSkqxWq/74xz/ax75nz56aPn26PDw8HPYN1+B7X35FZ/fKwjTBzd/fX25ubsrMzHRoz8rKUkBAQJm306lTJy1fvrzU5Z6envL09CzW7u7uzhexnBi7qsPYV62aOv4FhYZ2pJ1Q1ukLspzLVkFBgW688UaHsQgMDNT69et1/PhxSVLjxo2LLT906JDc3d117NgxeXh4qFGjRg77sdls+vXXX+Xu7q5ff/1VjRo1sm+jaOwbNWpk74PKUVO/99fCmfEyTXDz8PBQ+/btlZKSogcffNDenpKSovvvv7/M29m9e7cCAwNdUSIA1HjJezOUuHa/MnIuSJIunr4UzLb/dFyXXeVS7GyJM2dTyrqNsm4HMBPTBDdJGjdunOLi4nTHHXcoIiJCixYtUnp6uv26iYkTJ+rnn3/W3/72N0nSnDlz1KRJE912223Ky8vT8uXL9eGHH+rDDz+sysMAgGopeW+GRi7/RpdfOO3m7StZaumlVdvUou0diml16R/ORWdLbDabJCkzM9PhH9WXn02x2WzKy8tTdna2w6nSrKws+zXPNptNv/zyS7Gafv31V6fOygDXO1M9DuSRRx7RnDlz9Nxzz6lt27bavHmz1q1bp9DQUElSRkaGwzPd8vLy9Mwzz6hNmzbq3LmzvvrqK33yySf2C2ABABWjoNBQ4tr9+u3dbhY3d3nYmurCwVQlrt2vgsJLPVJSUhQZGamwsDDZbDalpKTY18nLy9OmTZvsoax9+/Zyd3d36JORkaG9e/fa+0RERCgnJ0c7d+6099m+fbtycnIcbmhD1bnWR8IsWrRIUVFR8vX1lcViKfFRL9nZ2YqLi5PVapXVaq2Wj4Qx1YybJI0aNUqjRo0qcVlSUpLD5/Hjx2v8+PGVUBUA1Gw70k7YT4/+lu+dD+jYx7P0g62pPmjnrR3JH9jPllgsFsXHx2v69Olq1qyZmjVrpunTp8vb21sDBw6UJFmtVg0dOlRPP/20GjZsKD8/Pz3zzDNq3bq17r33XklSixYtFBMToxEjRmjQoEFq2LChRo0apV69ejncpIaqUfQA/fnz5+uuu+7SW2+9pdjYWO3fv18hISGaOXOmZs2apaSkJN1yyy164YUX1L17dx04cMD+SJhz584pJiZGMTExmjhxYon7GThwoI4cOaLk5GRJ0hNPPKG4uDitXbu20o7V5QxcUU5OjiHJyMnJqepSTCcvL89Ys2aNkZeXV9Wl1DiMfdWqieO/ZvcRI3TCx6X++HUfabj5NjJqu3sY7dq1MzZt2mRft7Cw0Jg2bZphs9kMT09Po0uXLsaePXsctn/+/HnjqaeeMvz8/Iw6deoYvXr1MtLT0x36HD9+3BgwYIBRp04dw8fHx/jDH/5gZGdnV8bhw7jy975Dhw7GiBEjHNpuvfVW49lnnzUKCwsNm81mzJgxw77swoULhtVqNRYuXFhsWxs2bDAkFfvd7t+/35BkbNu2zd62detWQ5Lx/fffX+PRuZYzWcNUp0oBANenRj5eV1zu0+4+NR75V23+7qh27drl8Egmi8WihIQEZWRk6MKFC9q0aZNatWrlsL6Xl5fmzp2r48eP69y5c1q7dq2Cg4Md+vj5+WnZsmV6//33dfz4cS1fvlz169evsGNE+RQ9QD86OtqhvegB+mlpacrMzHRY7unpqa5du171AfuX27p1q6xWqzp27Ghv69Spk6xWq1Pbud4R3AAA16xDmJ8CrV4q7f5Ni6RAq5c6hPlVZlm4Dhw7dkwFBQXFbhIJCAhQZmam/TFfpS0vq8zMzGKPjJEuvfbSme1c7whuAIBr5lbLomm9W0pSsfBW9Hla75Zyq8WjOWqKgkJDW/97XJ/uzZAkFf7mzhWjAh4J81s14ZEwBDcAQIWIaRWoBYPayWZ1PG1qs3ppwaB29keBoPpL3puh3738hQYs3qaElMOSpZaGLkhR8v+FOKnkR8JcztkH7NeUR8KY7q5SAMD1K6ZVoLq3tNnfnNDI59LpUWbaao7PvvtFo9771v5omKJHwmTs36GRy++wh/iiB+hf/kiYoteeFT0S5uWXXy7zfoseCbNjxw516NBBUvV8JAzBDQBQodxqWRRxc8OqLgNVZMY/vy/2PL+iR8J42Jrq2SW/6J/Gv516JIwk+/VwP/74oyRpz5498vHxUUhIiPz8/OyPhBk+fLjeeustSZceB1LdHglDcAMAABUm89QF/fZKx7otuqjw/Gmd/NcKHV8/XxdubenwAP3x48fr/PnzGjVqlLKzs9WxY0etX7/e/gw3SVq4cKESExPtn4vuTF66dKmGDBkiSXr33Xc1ZswY+x2qffr00bx581x4tJWP4AYAAFzOp9198ml3nyTp5f5t1aXtjfZlRY+ESUhIKHX9qy2XLj0SZvny5RVR7nWLmxMAVIjNmzerd+/eCgoKksVi0Zo1axyWG2V4pU1ubq5Gjx4tf39/1a1bV3369NGRI0cc+tSEV9oA1d3VnvuH0hHcAFSIs2fP6vbbby/1tETRK23mzZunnTt3ymazqXv37jp9+rS9T3x8vFavXq0VK1boq6++0pkzZ9SrVy8VFBTY+wwcOFCpqalKTk5WcnKyUlNTFRcX5/LjA1A2Nl+e5+dKnCoFUCFiY2MVGxtb4jLDMDRnzhxNnjxZffv2lSQtW7ZMAQEBeu+99/Tkk08qJydHS5Ys0TvvvGN//+Ty5csVHByszz77TD169NB3332n5ORkbdu2zf509MWLFysiIkIHDhyoVhcgA2b1bOytGvXet7JIDjcp8Dy/isGMGwCXK8srbXbt2qX8/HyHPkFBQWrVqpW9T015pQ1gZve2COB5fi7EjBsAl7vSK20OHTpk7+Ph4aEGDRoU61O0fk15pQ1gdjzPz3UIbgDKpaDQcPoP5fK80uZqr8Up63YAVC6e5+caBDcATkvem6HEtfuVkXPB3hZo9dK03i1LPA1y+SttAgP/t/zyV9rYbDbl5eUpOzvbYdYtKyvL/tTzmvJKGwAoDde4AXBK8t4MjVz+jUNok6TMnAsaufwbh3cRFrn8lTZFil5pUxTK2rdvL3d3d4c+GRkZ2rt3r73P5a+0KVIdX2kDAKVhxg1AmRUUGkpcu7/Y62wkqSDvvC5mZ2j8W5eeu5aWlqbU1FT5+fkpJCTkqq+0sVqtGjp0qJ5++mk1bNhQfn5+euaZZ9S6dWv7XaY15ZU2AFAaghuAMtuRdqLYTFuRvMwf9Mv7k1Q03zZu3DhJ0uDBg5WUlFSmV9rMnj1btWvX1sMPP6zz58+rW7duSkpKkpubm71PTXilDQCUhuAGoMyyTpcc2iTJK6SNQid8LEl6vX9b3X/Z62yksr3SxsvLS3PnztXcuXNL7VMTXmkDAKXhGjcAZVbW19TwOhsAcA2CG4Ay6xDmp0Arr7MBgKpCcANQZm61LJrWu6UkFQtvvM4GAFyP4AbAKTGtAnmdDQBUEW5OAOA0XmcDAFWDGTcUs3nzZvXu3VtBQUGyWCxas2aNw3LDMJSQkKCgoCDVqVNHUVFR2rdvn0OfRYsW6d5779WAAQPk4eGhkydPFttPdna24uLiZLVaZbVaFRcXV2I/XJ+KXmdzf9sbFXFzQ0IbAFQCghuKOXv2rG6//fZSn401c+ZMzZo1S/PmzdPOnTtls9nUvXt3nT592t7n3Llzio6O1kMPPVTqfgYOHKjU1FQlJycrOTlZqampiouLq/DjAQCguuBUKYqJjY1VbGxsicsMw9CcOXM0efJk9e3bV5K0bNkyBQQE6L333tOTTz4pSYqPj1d+fr5efvnlErfz3XffKTk5Wdu2bVPHjh0lSYsXL1ZERIQOHDjAU/ABACgBM25wSlpamjIzM+1PrZckT09Pde3aVVu2bCnzdrZu3Sqr1WoPbZLUqVMnWa1Wp7YDAEBNwowbJF16B2VZLjTPzMyUJAUEBDi0BwQE6NChQ2XeX2Zmpho1alSsvVGjRvZ9AAAARwQ3KHlvhhLX7nd4B2Wg1cv+vK6SWCyOoc4wjGJtV1NS//JsBwCAmoJTpTVc8t4MjVz+TbEXh2fmXNDI5d8U62+z2S4t/82sWFZWVrFZuCux2Wz65ZdfirX/+uuvTm0HAICahOBWgxUUGkpcu19GCcsubyss/N+nsLAw2Ww2paSk2Nvy8vK0adMmRUZGlnnfERERysnJ0Y4dO+xt27dvV05OjlPbAQCgJuFUaQ22I+1EsZk2SSrMO6+L2Rn2z5t27dNNN4XJz89PISEhio+P1/Tp09WsWTM1a9ZM06dPl7e3twYOHGhfJzMzU4cPH7bPzO3Zs0c+Pj4KCQmRn5+fWrRooZiYGA0fPlxvvfWWJOmJJ55Qr169uKMUAIBSMONWg2WdLh7aJCkv8wdlJI1RRtIYSdIb06coPDxcU6dOlSSNHz9e8fHxGjVqlO644w79/PPPWr9+vXx8fOzbWLhwoTp06KA333xTktSlSxeFh4fro48+svd599131bp1a0VHRys6Olpt2rTRO++846rDBQDA9Jhxq8Ea+XiV2O4V0kahEz62f35/eCdF3NzQ/tlisSghIUEJCQmlbjshIUGTJ0/WunXr1LNnT7m7uxfr4+fnp+XLl5f/AAAAqGGYcavBOoT5KdDqpdLu4bTo0t2lHcL8KrMsAABQCoJbDeZWy2J/5Mdvw1vR52m9W/IOSgAArhMEtxouplWgFgxqJ5vV8bSpzeqlBYPaKaZVYBVVBgAAfsvpa9zc3NyUkZFR7Kn3x48fV6NGjVRQUFBhxaFyxLQKVPeWtjK9OQEAAFQdp4ObYZT01C8pNzdXHh4e11wQqoZbLYvDDQgAAOD6U+bg9sYbb0i6dEfh22+/rXr16tmXFRQUaPPmzbr11lsrvkIAAABIciK4zZ49W9KlGbeFCxfKzc3NvszDw0NNmjTRwoULK75CAAAASHIiuKWlpUmS7r77bq1evVr169d3VU0AAAAogVN3lebn5+vQoUM6evSoq+oBAABAKZwKbu7u7srNzZXFwt2GAAAAlc3p57iNHj1aL7/8si5evOiKegAAAFAKpx8Hsn37dn3++edav369Wrdurbp16zosX7VqVYUVBwAAgP9xOrjVr19fv//9711RCwAAAK7A6eC2dOlSV9QBAACAq+BdpQAAACbh9IybJP3jH//QBx98oPT0dOXl5Tks++abbyqkMAAAADhyesbtjTfe0GOPPaZGjRpp9+7d6tChgxo2bKiffvpJsbGxrqgRAAAAKkdwmz9/vhYtWqR58+bJw8ND48ePV0pKisaMGaOcnBxX1AgAAACVI7ilp6crMjJSklSnTh2dPn1akhQXF6f333+/YqsDAACAndPBzWaz6fjx45Kk0NBQbdu2TdKld5kahlGx1QEAAMDO6eB2zz33aO3atZKkoUOHauzYserevbseeeQRPfjggxVeIAAAAC5x+q7SRYsWqbCwUJI0YsQI+fn56auvvlLv3r01YsSICi8QAAAAlzgV3LZv366PPvpI+fn5uvfeexUdHa2HH35YDz/8sKvqAwAAwP8pc3BbvXq1+vXrJy8vL9WuXVuvvfaaXnvtNcXHx7uwPAAAABQp8zVu06dP15AhQ3Ty5EmdPHlSiYmJeuGFF1xZGwAAAC5T5uB24MABjR8/XrVrX5qk+/Of/6yTJ0/q2LFjLisOAAAA/1Pm4HbmzBnVr1/f/tnT01N16tTRqVOnXFEXAAAAfsOpmxM+/fRTWa1W++fCwkJ9/vnn2rt3r72tT58+FVcdAAAA7JwKboMHDy7W9uSTT9r/22KxqKCg4NqrAgAAQDFlDm5Fz24DAABA1XD6zQkAAACoGgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACbhdHAbMmSINm/e7IpaAAAAcAVOB7fTp08rOjpazZo10/Tp0/Xzzz+7oi4AAAD8htPB7cMPP9TPP/+sp556Sn//+9/VpEkTxcbG6h//+Ify8/NdUSMAAABUzmvcGjZsqD/96U/avXu3duzYoaZNmyouLk5BQUEaO3asfvjhh4quEwAAoMa7ppsTMjIytH79eq1fv15ubm7q2bOn9u3bp5YtW2r27NkVVSMAAABUjuCWn5+vDz/8UL169VJoaKj+/ve/a+zYscrIyNCyZcu0fv16vfPOO3ruuedcUS8AAECN5dRL5iUpMDBQhYWFGjBggHbs2KG2bdsW69OjRw/Vr1+/AsoDAABAEaeD26xZs/Twww/Ly8ur1D4NGjRQWlraNRUGAAAAR06dKr148aIef/xx/fjjj66qBwAAAKVwKrjVrl1boaGhKigocFU9AAAAKIXTNyf85S9/0cSJE3XixAlX1AMAAIBSOH2N2xtvvKEff/xRQUFBCg0NVd26dR2Wf/PNNxVWHAAAAP7H6eD2wAMPuKAMAAAAXI3TwW3atGmuqKPM5s+fr1deeUUZGRm67bbbNGfOHHXu3LnU/ps2bdK4ceO0b98+BQUFafz48RoxYkQlVgwAAFAxrunNCZVt5cqVio+P1+TJk7V792517txZsbGxSk9PL7F/Wlqaevbsqc6dO2v37t2aNGmSxowZow8//LCSKwcAALh2Tge3goICvfrqq+rQoYNsNpv8/Pwcflxp1qxZGjp0qIYNG6YWLVpozpw5Cg4O1oIFC0rsv3DhQoWEhGjOnDlq0aKFhg0bpscff1yvvvqqS+sEAABwBadPlSYmJurtt9/WuHHjNGXKFE2ePFkHDx7UmjVrNHXqVFfUKEnKy8vTrl279Oyzzzq0R0dHa8uWLSWus3XrVkVHRzu09ejRQ0uWLFF+fr7c3d2LrZObm6vc3Fz751OnTkm69Kqv/Pz8az2MGqVovBi3ysfYVy3Gv+ow9lWHsS8/Z8bM6eD27rvvavHixbrvvvuUmJioAQMG6Oabb1abNm20bds2jRkzxtlNlsmxY8dUUFCggIAAh/aAgABlZmaWuE5mZmaJ/S9evKhjx44pMDCw2DovvfSSEhMTi7WvX79e3t7e13AENVdKSkpVl1BjMfZVi/GvOox91WHsnXfu3Lky93U6uGVmZqp169aSpHr16iknJ0eS1KtXL02ZMsXZzTnNYrE4fDYMo1jb1fqX1F5k4sSJGjdunP3zqVOnFBwcrOjoaPn6+pa37BopPz9fKSkp6t69e4mzm3Adxr5qMf5Vh7GvOox9+RWd3SsLp4Nb48aNlZGRoZCQEDVt2lTr169Xu3bttHPnTnl6ejq7uTLz9/eXm5tbsdm1rKysYrNqRWw2W4n9a9eurYYNG5a4jqenZ4nH4e7uzhexnBi7qsPYVy3Gv+ow9lWHsXeeM+Pl9M0JDz74oD7//HNJ0p/+9CdNmTJFzZo106OPPqrHH3/c2c2VmYeHh9q3b19sCjYlJUWRkZElrhMREVGs//r163XHHXfwpQIAAKbj9IzbjBkz7P/90EMPqXHjxtqyZYuaNm2qPn36VGhxvzVu3DjFxcXpjjvuUEREhBYtWqT09HT7c9kmTpyon3/+WX/7298kSSNGjNC8efM0btw4DR8+XFu3btWSJUv0/vvvu7ROAAAAV3A6uP1Wp06d1KlTp4qo5aoeeeQRHT9+XM8995wyMjLUqlUrrVu3TqGhoZKkjIwMh2e6hYWFad26dRo7dqzefPNNBQUF6Y033tDvf//7SqkXAACgIpUruP3nP//Rxo0blZWVpcLCQodlrnwkiCSNGjVKo0aNKnFZUlJSsbauXbvy/lQAAFAtOB3cFi9erJEjR8rf3182m83h7kyLxeLy4AYAAFBTOR3cXnjhBb344ouaMGGCK+oBAABAKZy+qzQ7O1v9+vVzRS0AAAC4AqeDW79+/bR+/XpX1AIAAIArcPpUadOmTTVlyhRt27ZNrVu3LvY8NFe98goAAKCmczq4LVq0SPXq1dOmTZu0adMmh2UWi4XgBgAA4CJOB7e0tDRX1AEAAICrcPoaNwAAAFSNMs24jRs3Ts8//7zq1q2rcePGXbHvrFmzKqQwAAAAOCpTcNu9e7fy8/Pt/12ayx/GCwAAgIpVpuC2YcOGEv8bAAAAlYdr3AAAAEzC6btKH3zwwRJPiVosFnl5ealp06YaOHCgmjdvXiEFAgAA4BKnZ9ysVqu++OILffPNN/YAt3v3bn3xxRe6ePGiVq5cqdtvv13/+te/KrxYAACAmszpGTebzaaBAwdq3rx5qlXrUu4rLCzUn/70J/n4+GjFihUaMWKEJkyYoK+++qrCCwYAAKipnJ5xW7JkieLj4+2hTZJq1aql0aNHa9GiRbJYLHrqqae0d+/eCi0UAACgpnM6uF28eFHff/99sfbvv/9eBQUFkiQvLy8eDQIAAFDBnD5VGhcXp6FDh2rSpEm68847ZbFYtGPHDk2fPl2PPvqoJGnTpk267bbbKrxYAACAmszp4DZ79mwFBARo5syZ+uWXXyRJAQEBGjt2rCZMmCBJio6OVkxMTMVWCgAAUMM5Hdzc3Nw0efJkTZ48WadOnZIk+fr6OvQJCQmpmOoAAABg53Rwu9xvAxsAAABcp1zB7R//+Ic++OADpaenKy8vz2HZN998UyGFAQAAwJHTd5W+8cYbeuyxx9SoUSPt3r1bHTp0UMOGDfXTTz8pNjbWFTUCAABA5Qhu8+fP16JFizRv3jx5eHho/PjxSklJ0ZgxY5STk+OKGgEAAKByBLf09HRFRkZKkurUqaPTp09LuvSYkPfff79iqwMAAICd08HNZrPp+PHjkqTQ0FBt27ZNkpSWlibDMCq2OgAAANg5HdzuuecerV27VpI0dOhQjR07Vt27d9cjjzyiBx98sMILBAAAwCVO31W6aNEiFRYWSpJGjBghPz8/ffXVV+rdu7dGjBhR4QUCAADgEqeDW61atRxeMP/www/r4YcfrtCiAAAAUFy5nuN24cIF/fvf/1ZWVpZ99q1Inz59KqQwAAAAOHI6uCUnJ+vRRx/VsWPHii2zWCwqKCiokMIAAADgyOmbE5566in169dPGRkZKiwsdPghtAEAALiO08EtKytL48aNU0BAgCvqAQAAQCmcDm4PPfSQNm7c6IJSAAAAcCVOX+M2b9489evXT19++aVat24td3d3h+VjxoypsOIAAADwP04Ht/fee0+ffvqp6tSpo40bN8pisdiXWSwWghsAAICLOB3c/vKXv+i5557Ts88+6/A8NwAAALiW08krLy9PjzzyCKENAACgkjmdvgYPHqyVK1e6ohYAAABcgdOnSgsKCjRz5kx9+umnatOmTbGbE2bNmlVhxQEAAOB/nA5ue/bsUXh4uCRp7969Dssuv1EBAAAAFcvp4LZhwwZX1AEAAICr4A4DAAAAkyjzjFvfvn3L1G/VqlXlLgYAAAClK3Nws1qtrqwDAAAAV1Hm4LZ06VJX1gEAAICr4Bo3AFVm8+bN6t27t4KCgmSxWLRmzRqH5atWrVKPHj3k7+8vi8Wi1NTUYtvIzc3V6NGj5e/vr7p166pPnz46cuSIQ5/s7GzFxcXJarXKarUqLi5OJ0+edN2BAYCLENwAVJmzZ8/q9ttv17x580pdftddd2nGjBmlbiM+Pl6rV6/WihUr9NVXX+nMmTPq1auXCgoK7H0GDhyo1NRUJScnKzk5WampqYqLi6vw4wEAV3P6cSAAUFFiY2MVGxtb6vKicHXw4MESl+fk5GjJkiV65513dO+990qSli9fruDgYH322Wfq0aOHvvvuOyUnJ2vbtm3q2LGjJGnx4sWKiIjQgQMH1Lx584o9KABwIWbcAJjWrl27lJ+fr+joaHtbUFCQWrVqpS1btkiStm7dKqvVag9tktSpUydZrVZ7HwAwC4IbgEpVUGho63+P6/+l/qyt/z2ugkKj3NvKzMyUh4eHGjRo4NAeEBCgzMxMe59GjRoVW7dRo0b2Pqhc8+fPV1hYmLy8vNS+fXt9+eWX9mVc1whcGcENQKVJ3puh3738hQYs3qY/rUjVgMXb9LuXv1Dy3owK3Y9hGA6v4CvpdXy/7YPKsXLlSsXHx2vy5MnavXu3OnfurNjYWKWnp0uquOsaH330Ua5rvM5ca2BftGiRoqKi5OvrK4vFUmIQrwmBneAGoFIk783QyOXfKCPngkN7Zs4FjVz+Tbm2abPZlJeXp+zsbIf2rKwsBQQE2Pv88ssvxdb99ddf7X1QeWbNmqWhQ4dq2LBhatGihebMmaPg4GAtWLBA0qXrGqdOnWq/ZvG3iq5rfO2113TvvfcqPDxcy5cv1549e/TZZ59Jkg4fPqxPP/1Ub7/9tiIiIhQREaHFixfr448/1oEDByrtWPE/FRHYz507p5iYGE2aNKnUPjXhRiSCGwCXKyg0lLh2v0o6KXp5W6GTp03bt28vd3d3paSk2NsyMjK0d+9eRUZGSpIiIiKUk5OjHTt22Pts375dOTk59j6oHHl5edq1a5fDNYmSFB0dXebrDctyXeOBAwe4rvE6c62BXbo00/rss8+qU6dOJS4vuhGpugd27ioF4HI70k4Um2mTpMK887qY/b/TpJt27dNNN4XJz89PISEhOnHihNLT03X06FFJsv/ha7PZZLPZZLVaNXToUD399NNq2LCh/Pz89Mwzz6h169b2vwBatGihmJgYDR8+XG+99ZYk6YknnlCvXr24o7SSHTt2TAUFBcVmOi+/JvFqynJdY3Z2tm644YZi63JdY9UoCuzPPvusQ7szgb0srnYjUnX5/50ZNwAul3W6eGiTpLzMH5SRNEYZSWMkSW9Mn6Lw8HBNnTpVkvTRRx8pPDxc9913nySpf//+Cg8P18KFC+3bmD17th544AE9/PDDuuuuu+Tt7a21a9fKzc3N3ufdd99V69atFR0drejoaLVp00bvvPOOqw4Xl7n8ZpSvD56QVPyaw4q43pDrGq9fFRHYy6Km3IjEjBsAl2vk41Viu1dIG4VO+Nj++f3hnRRxc0P75yFDhmjIkCFX3LaXl5fmzp2ruXPnltrHz89Py5cvd65oXLPkvRlKXLvfPttqFORLtWpp3Y7vFBERYe93+TWJV3P5dY2Xz7plZWXZT303aNBAWVlZxdblusbKU1BoaEfaCWWdviDLuUvXoLoisP9WTQjszLgBcLkOYX4KtHqptD86LZICrV7qEOZXmWXBhUq6GcXi5i6PgKZ6fdmHDncSp6SklPl6w7Jc19i8eXOua6xCn333i8Pd46NX/2gP7JdzJrCXRU25EYngBsDl3GpZNK13S0kqFt6KPk/r3VJutarPv4prsivdjOJ75wM6/e16/XHaa9q7b7/Gjh2r9PR0jRgxQpJ04sQJpaamav/+/ZIuXdeYmppqP9V1+XWNn3/+uXbv3q1BgwY5XNcYHBysHj16aPjw4dq2bZu2bdum4cOHc11jJRm7MrXCA3tZ1JQbkQhuACpFTKtALRjUTjar42lTm9VLCwa1U0yrwCqqDBWttJtRJKluiy7y6zZchz57R+3Cw7V582atW7dOoaGhkiruusZly5ZxXWMlK3qYtisCu3TpGrbU1FT9+OOPkqQ9e/YoNTVVJ05cunby8huRqnNg5xo3AJUmplWgure02a99aeRz6fQoM23VS2k3oxTxaXeffNrdp9f7t9X9bW90WMZ1jea161B2qcvqtuiiwvOnLwX2tXPVunWrYoH9scces/fv37+/JGnatGlKSEiQJC1cuFCJiYn2Pl26dJEkLV261P6deffddzVmzBj742L69OmjefPmVdgxXg8IbgAqlVsti8MNCKh+SrsZpbz9YA7HzuRecfm1BvaEhAR7iCtNTQjsnCoFAFQobkapmfzreZapH4H92hDcAAAViptRaqb2oZcez0Jgdy2CGwCgwnEzSs1zeRAnsLsO17gBAFyCm1FqptmPtNVznxxwuLPYZvXStN4tCewVgOAGAHAZbkapee5tEaDoVjcS2F2E4AYAACoUgd11uMYNAADAJAhuAAAAJkFwAwAAMAmCGwAAgEkQ3AAAAEyC4AYAAGASBDcAAACTILgBAACYBMENAADAJAhuAAAAJkFwAwAAMAmCG65rmzdvVu/evRUUFCSLxaI1a9Y4LF+1apV69Oghf39/WSwWpaamFtvGokWLFBUVJV9fX1ksFp08ebJYn+zsbMXFxclqtcpqtSouLq7EfgAAVCWCG65rZ8+e1e2336558+aVuvyuu+7SjBkzSt3GuXPnFBMTo0mTJpXaZ+DAgUpNTVVycrKSk5OVmpqquLi4a64fAICKVLuqCwCuJDY2VrGxsaUuLwpXBw8eLLVPfHy8JGnjxo0lLv/uu++UnJysbdu2qWPHjpKkxYsXKyIiQgcOHFDz5s3LVTsAABWNGTfUeFu3bpXVarWHNknq1KmTrFartmzZUoWVAQDgiBk3XHcKCg3tSDuhrNMX1MjHSx3C/ORWy+Ky/WVmZqpRo0bF2hs1aqTMzEyX7RcAAGeZZsatPBePDxkyRBaLxeGnU6dOlVMwyiV5b4Z+9/IXGrB4m/60IlUDFm/T717+Qsl7M1y6X4uleDA0DKPEdgAAqoppZtwGDhyoI0eOKDk5WZL0xBNPKC4uTmvXrr3iejExMVq6dKn9s4eHh0vrRPkl783QyOXfyPhNe2bOBY1c/o3L9muz2fTLL78Ua//1118VEBDgsv0CAOAsUwS3a7l43NPTUzabrbJKRTkVFBpKXLu/WGiTJENS0bxXYWFJPa5NRESEcnJytGPHDnXo0EGStH37duXk5CgyMrLC9wcAQHmZIrhd7eLxKwW3jRs3qlGjRqpfv766du2qF198scTrmYrk5uYqNzfX/vnUqVOSpPz8fOXn51fA0dQcReNVlnHbkXZCJ86cl6ebY3th3nnlZWfYA93Gr/coOLix/Pz8FBISohMnTig9PV0ZGZdOpe7bt0/5+fmy2Wz2wJ6ZmanMzEwdOHBAkrR7927Vq1dPISEh8vPzU9OmTdWjRw8NGzZM8+fPlySNHDlSPXv21E033WTK37szY4+Kx/hXHca+6jD25efMmFkMw6j4KYwKNn36dCUlJek///mPQ/stt9yixx57TBMnTixxvZUrV6pevXoKDQ1VWlqapkyZoosXL2rXrl3y9PQscZ2EhAQlJiYWa3/vvffk7e197QcDp+zZs0dTpkwp1n733XfrT3/6kz7//HPNnTu32PJHHnlEAwYMkCS9//77WrlyZbE+o0ePVrdu3SRJp0+f1ttvv60dO3ZIkjp06KDhw4erXr16FXk4AAAUc+7cOQ0cOFA5OTny9fW9Yt8qDW6lhaTL7dy5U+vXr9eyZcvsMyZFmjVrpqFDh+rZZ58t0/4yMjIUGhqqFStWqG/fviX2KWnGLTg4WMeOHbvqYMJRfn6+UlJS1L17d7m7u1+x7460E3p82c6rbvOvg+9UhzC/iiqx2nJm7FHxGP+qw9hXHca+/E6dOiV/f/8yBbcqPVX61FNPqX///lfs06RJE/373/+ukIvHAwMDFRoaqh9++KHUPp6eniXOxrm7u/NFLKeyjF2npo3kV6+OMnMulHidm0WSzeqlTk0bufTRINUN39uqxfhXHca+6jD2znNmvKo0uPn7+8vf3/+q/Srq4vHjx4/r8OHDCgwMLHfNcA23WhZN691SI5d/I4vkEN6KYtq03i0JbQCAGs0Uz3Fr0aKFYmJiNHz4cG3btk3btm3T8OHD1atXL4cbE2699VatXr1aknTmzBk988wz2rp1qw4ePKiNGzeqd+/e8vf314MPPlhVh4IriGkVqAWD2slm9XJot1m9tGBQO8W0InADAGo2U9xVKknvvvuuxowZo+joaElSnz59ir14/MCBA8rJyZEkubm5ac+ePfrb3/6mkydPKjAwUHfffbdWrlwpHx+fSq8fZRPTKlDdW9oq9c0JAACYhWmCm5+fn5YvX37FPpffZ1GnTh19+umnri4LLuBWy6KImxtWdRkAAFx3THGqFAAAAAQ3AAAA0yC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQAAmATBDQAAwCQIbgAAACZBcAMAADAJghsAAIBJENwAAABMguAGAABgEgQ3ANetzZs3q3fv3goKCpLFYtGaNWsclq9atUo9evSQv7+/LBaLUlNTi20jNzdXo0ePlr+/v+rWras+ffroyJEjDn1efPFFRUZGytvbW/Xr13fdAQHANSK4AbhunT17VrfffrvmzZtX6vK77rpLM2bMKHUb8fHxWr16tVasWKGvvvpKZ86cUa9evVRQUGDvk5eXp379+mnkyJEVfgwAUJFqV3UBAFCa2NhYxcbGlro8Li5OknTw4MESl+fk5GjJkiV65513dO+990qSli9fruDgYH322Wfq0aOHJCkxMVGSlJSUVHHFA4ALMOMGoNratWuX8vPzFR0dbW8LCgpSq1attGXLliqsDADKh+AGoNrKzMyUh4eHGjRo4NAeEBCgzMzMKqoKAMqPU6UArhsFhYZ2pJ1Q1ukLauTjpQ5hfnKrZanw/RiGIYul4rcLAK5GcANwXUjem6HEtfuVkXPB3hZo9dK03i0V0yqwXNu02WzKy8tTdna2w6xbVlaWIiMjr7lmAKhsnCoFUOWS92Zo5PJvHEKbJGXmXNDI5d8oeW9Gubbbvn17ubu7KyUlxd6WkZGhvXv3EtwAmBIzbgCqVEGhocS1+2WUtCzvvC5mZ2j8W5eeu5aWlqbU1FT5+fkpJCREJ06cUHp6uo4ePSpJOnDggKRLM202m01Wq1VDhw7V008/rYYNG8rPz0/PPPOMWrdubb/LVJLS09Pt2yooKLA/D65p06aqV6+eS48fAJxBcANQpXaknSg201YkL/MH/fL+JBXNt40bN06SNHjwYCUlJemjjz7SY489Zu/fv39/SdK0adOUkJAgSZo9e7Zq166thx9+WOfPn1e3bt2UlJQkNzc3+3pTp07VsmXL7J/Dw8MlSRs2bFBUVFQFHSkAXDuCG4AqlXW65NAmSV4hbRQ64WNJ0uv92+r+tjc6LB8yZIiGDBlyxe17eXlp7ty5mjt3bql9kpKSeIYbAFPgGjcAVaqRj1eF9gOA6ozgBqBKdQjzU6DVS6U9nMOiS3eXdgjzq8yyAOC6RHADUKXcalk0rXdLSSoW3oo+T+vd0iXPcwMAsyG4AahyMa0CtWBQO9msjqdDbVYvLRjUrtzPcQOA6oabEwBcF2JaBap7S1ulvDkBAMyK4AbguuFWy6KImxtWdRkAcN3iVCkAAIBJENwAAABMguAGAABgEgQ3AAAAkyC4AQBcav78+QoLC5OXl5fat2+vL7/80r5s1apV6tGjh/z9/WWxWJSamlps/dzcXI0ePVr+/v6qW7eu+vTpoyNHjjj0efHFFxUZGSmr1aqBAwe6+pCAKkNwAwC4zMqVKxUfH6/Jkydr9+7d6ty5s2JjY5Weni5JOnv2rO666y7NmDGj1G3Ex8dr9erVWrFihb766iudOXNGvXr1UkFBgb1PXl6e+vXrpyeffNLlxwRUJR4HAgBwmVmzZmno0KEaNmyYJGnOnDn69NNPtWDBAr300kuKi4uTJB08eLDE9XNycrRkyRK98847uvfeeyVJy5cvV3BwsD777DP16NFDkpSYmChJWrJkiYuPCKhazLgBAFwiLy9Pu3btUnR0tEN7dHS0tmzZUqZt7Nq1S/n5+Q7bCAoKUqtWrcq8DaA6IbgBAFzi2LFjKigoUEBAgEN7QECAMjMzy7SNzMxMeXh4qEGDBuXeBlCdcKoUAFBhCgoN+2vLLOeyJUkWi+NrywzDKNbmrIrYBmBGBDcAQIVI3puhxLX7lZFzQZJkFORLtWpp3Y7vFBERYe+XlZVVbBauNDabTXl5ecrOznaYdcvKylJkZGTFHgBgApwqBQBcs+S9GRq5/Bt7aJMki5u7PAKa6vVlHyp5b4a9PSUlpcyhq3379nJ3d1dKSoq9LSMjQ3v37iW4oUZixg0AcE0KCg0lrt0vo4Rlvnc+oGMfz9Ifp72m//fc41ry9mKlp6drxIgRkqQTJ04oPT1dR48elSQdOHBA0qWZNpvNJqvVqqFDh+rpp59Ww4YN5efnp2eeeUatW7e232UqSenp6fZtFRYWKjU1Ve7u7mratKnq1avn8jEAKgvBDQBwTXaknXCYabtc3RZdVHj+tA599o7arZ2r1q1bad26dQoNDZUkffTRR3rsscfs/fv37y9JmjZtmhISEiRJs2fPVu3atfXwww/r/Pnz6tatm5KSkuTm5mZfb+rUqVq2bJn9c4cOHSRJGzZsUFRUVEUeLlClCG4AgGuSdbrk0FbEp9198ml3n17v31b3t73RYdmQIUM0ZMiQK67v5eWluXPnau7cuaX2SUpKUlJSkvLz87Vu3Tr17NlT7u7uZT4GwCy4xg0AcE0a+XhVaD8ApSO4AQCuSYcwPwVavVTawzkskgKtXuoQ5leZZQHVEsENAHBN3GpZNK13S0kqFt6KPk/r3VJutXjuGnCtCG4AgGsW0ypQCwa1k83qeDrUZvXSgkHtFNMqsIoqgyvNnz9fYWFh8vLyUseOHbVv3z77slWrVqlHjx7y9/eXxWJRampqsfUXLVqkqKgo+fr6ymKx6OTJk8X6vPjii4qMjJS3t7fq16/vuoMxCYIbAKBCxLQK1FcT7tH7wzvp9f5t9f7wTvpqwj2Etmpq5cqVio+P1+TJk7V792797ne/0/PPP6/09HRJ0tmzZ3XXXXdpxowZpW7j3LlziomJ0aRJk0rtk5eXp379+mnkyJEVfgxmxF2lAIAK41bLooibG1Z1GagEs2bN0tChQzVs2DBJ0muvvabVq1frrbfe0syZMxUXFydJOnjwYKnbiI+PlyRt3Lix1D6JiYmSLt05DGbcAACAk/Ly8rRr1y5FR0c7tLdt21bbtm2roqpqBmbcAADAVRUUGtqRdkJZpy/Ici5bBQUFxd45a7Va7W+/gGsQ3AAAwBUl781Q4tr99jdkXDx9XJK0/afj+u0rYy0W7h52JU6VAgCAUiXvzdDI5d84vNbMzdtXstTSS6u2KXlvhr09Jyen2CwcKhbBDQAAlKig0FDi2v0yftNucXOXh62pLhxMVeLa/SoovNQjNTVVnTp1qvxCaxBOlQIAgBLtSDvhMNN2Od87H9Cxj2fpB1tTfdDOW9vWrdCxY8f0xBNPSJJOnDih9PR0HT16VJLs177ZbDbZbDZJUmZmpjIzM/Xjjz9Kkvbs2SMfHx+FhITIz+/SmzbS09Pt2yooKLA/D65p06aqV6+ey479ekVwAwAAJco6XXJok6S6Lbqo8PxpnfzXCj362UK1bnWbpkyZotDQUEnSRx99pMcee8zev3///pKkadOmKSEhQZK0cOFC++M+JKlLly6SpKVLl2rIkCGSpKlTp2rZsmX2PuHh4ZKkDRs2KCoq6pqP0Ww4VQoAAErUyMfrist92t2nxiP/qs3fHdX27dt122232ZcNGTJEhmEU+ykKbZKUkJBQYp+i0CZden5bSX1qYmiTCG4AAKAUHcL8FGj1KvYO2iIWSYFWL3UI86vMsmo0ghsAACiRWy2LpvVuKUnFwlvR52m9W8qtFo8AqSwENwAAUKqYVoFaMKidbFbH06Y2q5cWDGrHu2grGTcnAACAK4ppFajuLW32Nyc08rl0epSZtspHcAMAAFflVsuiiJsbVnUZNR6nSgEAAEyC4AYAAGASBDcAAACTILgBAACYBMENAADAJAhuAAAAJkFwAwAAMAmCG2qkzZs3q3fv3goKCpLFYtGaNWsclq9atUo9evSQv7+/LBaLUlNTi21j0aJFioqKkq+vrywWi06ePFmsz4svvqjIyEh5e3urfv36LjkWAEDNYZrgVp6/AA3DUEJCgoKCglSnTh1FRUVp3759ri0UpnD27FndfvvtmjdvXqnL77rrLs2YMaPUbZw7d04xMTGaNGlSqX3y8vLUr18/jRw58pprBgDANG9OKPoLMCIiQkuWLCnTOjNnztSsWbOUlJSkW265RS+88IK6d++uAwcOyMfHx8UV43oWGxur2NjYUpfHxcVJkg4ePFhqn/j4eEnSxo0bS+2TmJgoSUpKSnK2RAAAijHNjFtiYqLGjh2r1q1bl6m/YRiaM2eOJk+erL59+6pVq1ZatmyZzp07p/fee8/F1QIAAFQ80wQ3Z6WlpSkzM1PR0dH2Nk9PT3Xt2lVbtmypwsoAAADKxzSnSp2VmZkpSQoICHBoDwgI0KFDh0pdLzc3V7m5ufbPp06dkiTl5+crPz/fBZVWX0XjdT2MW0GhoV2HsnXsTK7863mqfWgDudWy2JdfvHixxDovP4bSjuPixYtX7VNQUOCwPVe7nsa+JmL8qw5jX3UY+/JzZsyqNLglJCTYrwEqzc6dO3XHHXeUex8Wi8Xhs2EYxdou99JLL5VY0/r16+Xt7V3uOmqylJSUqi7BwTFJn37n2LZr1y65u7sX6/vLL79Ikr766isdPXq0xO3t2bNH0qXvSL169Urs8+233yo/P1/r1q0rf+HlcL2NfU3D+Fcdxr7qMPbOO3fuXJn7Vmlwe+qpp9S/f/8r9mnSpEm5tm2z2SRdmnkLDAy0t2dlZRWbhbvcxIkTNW7cOPvnU6dOKTg4WNHR0fL19S1XLTVVfn6+UlJS1L179xJDUWX47LtfNHZlqozftBdF99mPtJUktW/fXj179iy2ftHNCb/73e/Utm3bEvdRt25dSVJ0dHSpdzwfO3ZM7u7uJe7DFa6Hsa/JGP+qw9hXHca+/IrO7pVFlQY3f39/+fv7u2TbYWFhstlsSklJUXh4uKRLd6Zu2rRJL7/8cqnreXp6ytPTs1i7u7s7X8RyqqqxKyg09NwnB3ShoPgMa2HeeV3MztCExT9Lkg4fPqx9+/bJz89PISEhOnHihNLT0+2zbD/99JPc3d1ls9kc/lGQmZlpD3fff/+9fHx8FBISIj8/P0lSenq6Tpw4oZ9//lkFBQX2x9E0bdq01Nm5isT3tmox/lWHsa86jL3znBkv09yckJ6ertTUVKWnp6ugoECpqalKTU3VmTNn7H1uvfVWrV69WtKlU6Tx8fGaPn26Vq9erb1792rIkCHy9vbWwIEDq+owUIl2pJ1QRs6FEpflZf6gjKQx2jNvhCRp3LhxCg8P19SpUyVJH330kcLDw3XfffdJkvr376/w8HAtXLjQvo2FCxcqPDxcw4cPlyR16dJF4eHh+uijj+x9pk6dqvDwcE2bNk1nzpxReHi4wsPD9fXXX7vkmAEA1Ztpbk6YOnWqli1bZv9cNIu2YcMGRUVFSZIOHDignJwce5/x48fr/PnzGjVqlLKzs9WxY0etX7+eZ7jVEFmnSw5tkuQV0kahEz6WJL3ev63ub3ujw/IhQ4ZoyJAhV9x+QkKCEhISrtgnKSmJZ7gBACqMaYJbWf4CNAzHK5ksFkuZ/nJF9dTIx6tC+wEAUNVMc6oUcFaHMD8FWr1U2j3EFkmBVi91CPOrzLIAACg3ghuqLbdaFk3r3VKSioW3os/Terd0eJ4bAADXM4IbqrWYVoFaMKidbFbH06E2q5cWDGqnmFaBpawJAMD1xzTXuAHlFdMqUN1b2rQj7YSyTl9QI59Lp0eZaQMAmA3BDTWCWy2LIm5uWNVlAABwTThVCgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJhE7aou4HpnGIYk6dSpU1Vcifnk5+fr3LlzOnXqlNzd3au6nBqFsa9ajH/VYeyrDmNffkUZoyhzXAnB7SpOnz4tSQoODq7iSgAAQHV2+vRpWa3WK/axGGWJdzVYYWGhjh49Kh8fH1kslqoux1ROnTql4OBgHT58WL6+vlVdTo3C2Fctxr/qMPZVh7EvP8MwdPr0aQUFBalWrStfxcaM21XUqlVLjRs3ruoyTM3X15f/iasIY1+1GP+qw9hXHca+fK4201aEmxMAAABMguAGAABgEgQ3uIynp6emTZsmT0/Pqi6lxmHsqxbjX3UY+6rD2FcObk4AAAAwCWbcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0V6sUXX1RkZKS8vb1Vv379Mq1jGIYSEhIUFBSkOnXqKCoqSvv27XNtodVQdna24uLiZLVaZbVaFRcXp5MnT15xnSFDhshisTj8dOrUqXIKNrH58+crLCxMXl5eat++vb788ssr9t+0aZPat28vLy8v3XTTTVq4cGElVVo9OTP+GzduLPYdt1gs+v777yuxYvPbvHmzevfuraCgIFksFq1Zs+aq6/C9dw2CGypUXl6e+vXrp5EjR5Z5nZkzZ2rWrFmaN2+edu7cKZvNpu7du9vfE4uyGThwoFJTU5WcnKzk5GSlpqYqLi7uquvFxMQoIyPD/rNu3bpKqNa8Vq5cqfj4eE2ePFm7d+9W586dFRsbq/T09BL7p6WlqWfPnurcubN2796tSZMmacyYMfrwww8rufLqwdnxL3LgwAGH73mzZs0qqeLq4ezZs7r99ts1b968MvXne+9CBuACS5cuNaxW61X7FRYWGjabzZgxY4a97cKFC4bVajUWLlzowgqrl/379xuSjG3bttnbtm7dakgyvv/++1LXGzx4sHH//fdXQoXVR4cOHYwRI0Y4tN16663Gs88+W2L/8ePHG7feeqtD25NPPml06tTJZTVWZ86O/4YNGwxJRnZ2diVUVzNIMlavXn3FPnzvXYcZN1SptLQ0ZWZmKjo62t7m6emprl27asuWLVVYmbls3bpVVqtVHTt2tLd16tRJVqv1quO4ceNGNWrUSLfccouGDx+urKwsV5drWnl5edq1a5fD91WSoqOjSx3nrVu3Fuvfo0cPff3118rPz3dZrdVReca/SHh4uAIDA9WtWzdt2LDBlWVCfO9dieCGKpWZmSlJCggIcGgPCAiwL8PVZWZmqlGjRsXaGzVqdMVxjI2N1bvvvqsvvvhCr732mnbu3Kl77rlHubm5rizXtI4dO6aCggKnvq+ZmZkl9r948aKOHTvmslqro/KMf2BgoBYtWqQPP/xQq1atUvPmzdWtWzdt3ry5Mkqusfjeu07tqi4A17+EhAQlJiZesc/OnTt1xx13lHsfFovF4bNhGMXaaqKyjr1UfAylq4/jI488Yv/vVq1a6Y477lBoaKg++eQT9e3bt5xVV3/Ofl9L6l9SO8rGmfFv3ry5mjdvbv8cERGhw4cP69VXX1WXLl1cWmdNx/feNQhuuKqnnnpK/fv3v2KfJk2alGvbNptN0qV/nQUGBtrbs7Kyiv1rrSYq69j/+9//1i+//FJs2a+//urUOAYGBio0NFQ//PCD07XWBP7+/nJzcys2u3Ol76vNZiuxf+3atdWwYUOX1VodlWf8S9KpUyctX768osvDZfjeuw7BDVfl7+8vf39/l2w7LCxMNptNKSkpCg8Pl3TpOpZNmzbp5Zdfdsk+zaSsYx8REaGcnBzt2LFDHTp0kCRt375dOTk5ioyMLPP+jh8/rsOHDzuEaPyPh4eH2rdvr5SUFD344IP29pSUFN1///0lrhMREaG1a9c6tK1fv1533HGH3N3dXVpvdVOe8S/J7t27+Y67GN97F6rKOyNQ/Rw6dMjYvXu3kZiYaNSrV8/YvXu3sXv3buP06dP2Ps2bNzdWrVpl/zxjxgzDarUaq1atMvbs2WMMGDDACAwMNE6dOlUVh2BaMTExRps2bYytW7caW7duNVq3bm306tXLoc/lY3/69Gnj6aefNrZs2WKkpaUZGzZsMCIiIowbb7yRsb+CFStWGO7u7saSJUuM/fv3G/Hx8UbdunWNgwcPGoZhGM8++6wRFxdn7//TTz8Z3t7extixY439+/cbS5YsMdzd3Y1//OMfVXUIpubs+M+ePdtYvXq18Z///MfYu3ev8eyzzxqSjA8//LCqDsGUTp8+bf/zXJIxa9YsY/fu3cahQ4cMw+B7X5kIbqhQgwcPNiQV+9mwYYO9jyRj6dKl9s+FhYXGtGnTDJvNZnh6ehpdunQx9uzZU/nFm9zx48eNP/zhD4aPj4/h4+Nj/OEPfyj2CITLx/7cuXNGdHS0ccMNNxju7u5GSEiIMXjwYCM9Pb3yizeZN9980wgNDTU8PDyMdu3aGZs2bbIvGzx4sNG1a1eH/hs3bjTCw8MNDw8Po0mTJsaCBQsqueLqxZnxf/nll42bb77Z8PLyMho0aGD87ne/Mz755JMqqNrcih6r8tufwYMHG4bB974yWQzj/64WBAAAwHWNx4EAAACYBMENAADAJAhuAAAAJkFwAwAAMAmCGwAAgEkQ3AAAAEyC4AYAAGASBDcAuEYJCQlq27ZtVZcBoAYguAGotoYMGSKLxSKLxaLatWsrJCREI0eOVHZ2dqXWcfDgQXsdFotFDRo0UJcuXbRp06Zr3rbFYtGaNWuuvUgApkBwA1CtxcTEKCMjQwcPHtTbb7+ttWvXatSoUVVSy2effaaMjAxt2rRJvr6+6tmzp9LS0sq1rby8vAquDoAZENwAVGuenp6y2Wxq3LixoqOj9cgjj2j9+vUOfZYuXaoWLVrIy8tLt956q+bPn++wfMKECbrlllvk7e2tm266SVOmTFF+fr7TtTRs2FA2m01t2rTRW2+9pXPnzmn9+vU6fvy4BgwYoMaNG8vb21utW7fW+++/77BuVFSUnnrqKY0bN07+/v7q3r27mjRpIkl68MEHZbFY7J8BVF+1q7oAAKgsP/30k5KTk+Xu7m5vW7x4saZNm6Z58+YpPDxcu3fv1vDhw1W3bl0NHjxYkuTj46OkpCQFBQVpz549Gj58uHx8fDR+/Phy1+Lt7S1Jys/P14ULF9S+fXtNmDBBvr6++uSTTxQXF6ebbrpJHTt2tK+zbNkyjRw5Uv/6179kGIYaNmyoRo0aaenSpYqJiZGbm1u56wFgDgQ3ANXaxx9/rHr16qmgoEAXLlyQJM2aNcu+/Pnnn9drr72mvn37SpLCwsK0f/9+vfXWW/bg9pe//MXev0mTJnr66ae1cuXKcge3s2fPauLEiXJzc1PXrl1144036plnnrEvHz16tJKTk/X3v//dIbg1bdpUM2fOLLa9+vXry2azlasWAOZCcANQrd19991asGCBzp07p7ffflv/+c9/NHr0aEnSr7/+qsOHD2vo0KEaPny4fZ2LFy/KarXaP//jH//QnDlz9OOPP+rMmTO6ePGifH19na4lMjJStWrV0rlz5xQYGKikpCS1bt1aBQUFmjFjhlauXKmff/5Zubm5ys3NVd26dR3Wv+OOO8o5CgCqC4IbgGqtbt26atq0qSTpjTfe0N13363ExEQ9//zzKiwslHTpdOnlM1uS7Kcdt23bpv79+ysxMVE9evSQ1WrVihUr9Nprrzldy8qVK9WyZUvVr19fDRs2tLe/9tprmj17tubMmaPWrVurbt26io+PL3YDwm+DHICah+AGoEaZNm2aYmNjNXLkSAUFBenGG2/UTz/9pD/84Q8l9v/Xv/6l0NBQTZ482d526NChcu07ODhYN998c7H2L7/8Uvfff78GDRokSSosLNQPP/ygFi1aXHWb7u7uKigoKFc9AMyHu0oB1ChRUVG67bbbNH36dEmXHp770ksv6fXXX9d//vMf7dmzR0uXLrVfB9e0aVOlp6drxYoV+u9//6s33nhDq1evrtCamjZtqpSUFG3ZskXfffednnzySWVmZpZp3SZNmujzzz9XZmZmpT+fDkDlI7gBqHHGjRunxYsX6/Dhwxo2bJjefvtt+/VmXbt2VVJSksLCwiRJ999/v8aOHaunnnpKbdu21ZYtWzRlypQKrWfKlClq166devTooaioKNlsNj3wwANlWve1115TSkqKgoODFR4eXqF1Abj+WAzDMKq6CAAAAFwdM24AAAAmQXADAAAwCYIbAACASRDcAAAATILgBgAAYBIENwAAAJMguAEAAJgEwQ0AAMAkCG4AAAAmQXADAAAwCYIbAACASRDcAAAATOL/AxooRT1twHOQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_conventional = E2ESystemConventionalTraining(training=True)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "fig = model_conventional.constellation.show()\n",
    "fig.suptitle('Conventional training');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2774433f-05e1-4421-9e9f-a24dd2f3fd64",
   "metadata": {},
   "source": [
    "## References <a class=\"anchor\" id=\"References\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67ab4d0-2aa8-453d-a810-3c5f105567dd",
   "metadata": {},
   "source": [
    "[1] T. O’Shea and J. Hoydis, \"An Introduction to Deep Learning for the Physical Layer,\" in IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017, doi: 10.1109/TCCN.2017.2758370.\n",
    "\n",
    "[2] S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. ten Brink, \"Trainable Communication Systems: Concepts and Prototype,\" in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489-5503, Sept. 2020, doi: 10.1109/TCOMM.2020.3002915.\n",
    "\n",
    "[3] F. Ait Aoudia and J. Hoydis, \"Model-Free Training of End-to-End Communication Systems,\" in IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2503-2516, Nov. 2019, doi: 10.1109/JSAC.2019.2933891."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e85753",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "- [Luan Gonçalves - LASSE/UFPA](https://github.com/LuanAGoncalves)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.14 ('ai6g-xssCGMI2')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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