The Annotated Transformer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b_O4xXx6zpk2"
   },
   "source": [
    "# Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WrT6N-dwzpk4"
   },
   "source": [
    "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention.\n",
    "\n",
    "Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations. End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and\n",
    "language modeling tasks.\n",
    "\n",
    "To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence aligned RNNs or convolution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J-Xt01o85FOI"
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/harvardnlp/annotated-transformer/master/images/aiayn.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sL1D2eTUzpkn"
   },
   "source": [
    "The Transformer from [\"Attention is All You Need\"](https://arxiv.org/abs/1706.03762) has been on a lot of people's minds over the last year. Besides producing major improvements in translation quality, it provides a new architecture for many other NLP tasks. The paper itself is very clearly written, but the conventional wisdom has been that it is quite difficult to implement correctly. \n",
    "\n",
    "In this notebook we present an \"annotated\" version of the paper in the form of a line-by-line implementation. I have reordered and deleted some sections from the original paper and added comments throughout. This document itself is a working notebook, and should be a completely usable implementation. In total there are 400 lines of library code which can process 27,000 tokens per second on 4 GPUs. \n",
    "\n",
    "To follow along you will first need to install [PyTorch](http://pytorch.org/). The complete notebook is also available on [github](https://github.com/harvardnlp/annotated-transformer) or on Google [Colab](https://drive.google.com/file/d/1xQXSv6mtAOLXxEMi8RvaW8TW-7bvYBDF/view?usp=sharing). \n",
    "\n",
    "Note this is merely a starting point for researchers and interested developers. The code here is based heavily on our [OpenNMT](http://opennmt.net) packages. (If helpful feel free to [cite](#conclusion).) For other full-sevice implementations of the model check-out [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) (tensorflow) and [Sockeye](https://github.com/awslabs/sockeye) (mxnet).\n",
    "\n",
    "- Alexander Rush ([@harvardnlp](https://twitter.com/harvardnlp) or srush@seas.harvard.edu)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3dT98rw4ld9h"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_bleHZ2-QGHM"
   },
   "source": [
    "We, [SkoltechNLP](https://sites.skoltech.ru/nlp/) group, revisited this notebook in April 2021 and made some adjusments to make it runnable like \"Run All Cells\" with updated required packages. Aslo, we add some clarifing images from the amazing resources about Transformers that emerged during these years: \n",
    "* [Seq2Seq and Attention by Lena Voita](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)\n",
    "* [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)\n",
    "\n",
    "We believe that will add some additional understanding of the main idea and code. Enjoy your Transformer!\n",
    "\n",
    "*   Daryna Dementieva ([@dementyeva_ds](https:///dementyeva_ds) in any social network or daryna.dementieva@skoltech.ru)\n",
    "*   Anton Razzhigaev (anton.razzhigaev@skoltech.ru)\n",
    "*   Alexander Panchenko (a.pnachenko@skoltech.ru)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZQ_akN8zpks"
   },
   "source": [
    "# Prelims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "azfvsrU7zpkv"
   },
   "outputs": [],
   "source": [
    "# !pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl numpy matplotlib spacy torchtext seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "ecfAee9tzpkx"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import math, copy, time\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set_context(context=\"talk\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pk5ZpgijC7iH"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "emJ4iOIUzpk1"
   },
   "source": [
    "> My comments are blockquoted. The main text is all from the paper itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R9dpyClQzpk9"
   },
   "source": [
    "# Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GvsVttLx5jy8"
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/harvardnlp/annotated-transformer/master/images/ModalNet-21.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSTCMF97zplC"
   },
   "source": [
    "Most competitive neural sequence transduction models have an encoder-decoder structure [(cite)](https://arxiv.org/abs/1409.0473). Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\\mathbf{z} = (z_1, ..., z_n)$. Given $\\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive [(cite)](https://arxiv.org/abs/1308.0850), consuming the previously generated symbols as additional input when generating the next. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mlZMyHWgFH1"
   },
   "source": [
    "![alt text](https://lena-voita.github.io/resources/lectures/seq2seq/transformer/encdec_intuitive_words-min.png)\n",
    "Source: [Lena Voita's Lecture about Seq2Seq](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "b7UiLrmZzplK"
   },
   "outputs": [],
   "source": [
    "class EncoderDecoder(nn.Module):\n",
    "    \"\"\"\n",
    "    A standard Encoder-Decoder architecture. Base for this and many \n",
    "    other models.\n",
    "    \"\"\"\n",
    "    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):\n",
    "        super(EncoderDecoder, self).__init__()\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_embed = src_embed\n",
    "        self.tgt_embed = tgt_embed\n",
    "        self.generator = generator\n",
    "        \n",
    "    def forward(self, src, tgt, src_mask, tgt_mask):\n",
    "        \"Take in and process masked src and target sequences.\"\n",
    "        return self.decode(self.encode(src, src_mask), src_mask,\n",
    "                            tgt, tgt_mask)\n",
    "    \n",
    "    def encode(self, src, src_mask):\n",
    "        return self.encoder(self.src_embed(src), src_mask)\n",
    "    \n",
    "    def decode(self, memory, src_mask, tgt, tgt_mask):\n",
    "        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "QL87i9HbzplL"
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \"Define standard linear + softmax generation step.\"\n",
    "    def __init__(self, d_model, vocab):\n",
    "        super(Generator, self).__init__()\n",
    "        self.proj = nn.Linear(d_model, vocab)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return F.log_softmax(self.proj(x), dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "POidg7UBzplM"
   },
   "source": [
    "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AGJ7gD9lzplW"
   },
   "source": [
    "## Encoder and Decoder Stacks   \n",
    "\n",
    "### Encoder\n",
    "\n",
    "The encoder is composed of a stack of $N=6$ identical layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "ceddwOl7zplX"
   },
   "outputs": [],
   "source": [
    "def clones(module, N):\n",
    "    \"Produce N identical layers.\"\n",
    "    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "UvfoxnmkzplY"
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"Core encoder is a stack of N layers\"\n",
    "    def __init__(self, layer, N):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.layers = clones(layer, N)\n",
    "        self.norm = LayerNorm(layer.size)\n",
    "        \n",
    "    def forward(self, x, mask):\n",
    "        \"Pass the input (and mask) through each layer in turn.\"\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bvRoL2ZGzplY"
   },
   "source": [
    "We employ a residual connection [(cite)](https://arxiv.org/abs/1512.03385) around each of the two sub-layers, followed by layer normalization [(cite)](https://arxiv.org/abs/1607.06450).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "qBKcw5K4zplZ"
   },
   "outputs": [],
   "source": [
    "class LayerNorm(nn.Module):\n",
    "    \"Construct a layernorm module (See citation for details).\"\n",
    "    def __init__(self, features, eps=1e-6):\n",
    "        super(LayerNorm, self).__init__()\n",
    "        self.a_2 = nn.Parameter(torch.ones(features))\n",
    "        self.b_2 = nn.Parameter(torch.zeros(features))\n",
    "        self.eps = eps\n",
    "\n",
    "    def forward(self, x):\n",
    "        mean = x.mean(-1, keepdim=True)\n",
    "        std = x.std(-1, keepdim=True)\n",
    "        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rOKUvU_czpla"
   },
   "source": [
    "That is, the output of each sub-layer is $\\mathrm{LayerNorm}(x + \\mathrm{Sublayer}(x))$, where $\\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  We apply dropout [(cite)](http://jmlr.org/papers/v15/srivastava14a.html) to the output of each sub-layer, before it is added to the sub-layer input and normalized.  \n",
    "\n",
    "To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $d_{\\text{model}}=512$.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "_Dq8_Lsyzplv"
   },
   "outputs": [],
   "source": [
    "class SublayerConnection(nn.Module):\n",
    "    \"\"\"\n",
    "    A residual connection followed by a layer norm.\n",
    "    Note for code simplicity the norm is first as opposed to last.\n",
    "    \"\"\"\n",
    "    def __init__(self, size, dropout):\n",
    "        super(SublayerConnection, self).__init__()\n",
    "        self.norm = LayerNorm(size)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x, sublayer):\n",
    "        \"Apply residual connection to any sublayer with the same size.\"\n",
    "        return x + self.dropout(sublayer(self.norm(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nzp5_8rfzplw"
   },
   "source": [
    "Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "8lQqyQ1gzplx"
   },
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    \"Encoder is made up of self-attn and feed forward (defined below)\"\n",
    "    def __init__(self, size, self_attn, feed_forward, dropout):\n",
    "        super(EncoderLayer, self).__init__()\n",
    "        self.self_attn = self_attn\n",
    "        self.feed_forward = feed_forward\n",
    "        self.sublayer = clones(SublayerConnection(size, dropout), 2)\n",
    "        self.size = size\n",
    "\n",
    "    def forward(self, x, mask):\n",
    "        \"Follow Figure 1 (left) for connections.\"\n",
    "        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))\n",
    "        return self.sublayer[1](x, self.feed_forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2AykNKlMzply"
   },
   "source": [
    "### Decoder\n",
    "\n",
    "The decoder is also composed of a stack of $N=6$ identical layers.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "8F4FYZv2zplz"
   },
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    \"Generic N layer decoder with masking.\"\n",
    "    def __init__(self, layer, N):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.layers = clones(layer, N)\n",
    "        self.norm = LayerNorm(layer.size)\n",
    "        \n",
    "    def forward(self, x, memory, src_mask, tgt_mask):\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, memory, src_mask, tgt_mask)\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SzXK1fcfzpl0"
   },
   "source": [
    "In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "YIYXxGoZzpl0"
   },
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    \"Decoder is made of self-attn, src-attn, and feed forward (defined below)\"\n",
    "    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):\n",
    "        super(DecoderLayer, self).__init__()\n",
    "        self.size = size\n",
    "        self.self_attn = self_attn\n",
    "        self.src_attn = src_attn\n",
    "        self.feed_forward = feed_forward\n",
    "        self.sublayer = clones(SublayerConnection(size, dropout), 3)\n",
    " \n",
    "    def forward(self, x, memory, src_mask, tgt_mask):\n",
    "        \"Follow Figure 1 (right) for connections.\"\n",
    "        m = memory\n",
    "        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))\n",
    "        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))\n",
    "        return self.sublayer[2](x, self.feed_forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "NQh3julnzpl1"
   },
   "source": [
    "We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "QeeFH9gmzpl2"
   },
   "outputs": [],
   "source": [
    "def subsequent_mask(size):\n",
    "    \"Mask out subsequent positions.\"\n",
    "    attn_shape = (1, size, size)\n",
    "    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')\n",
    "    return torch.from_numpy(subsequent_mask) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ksVfKXvzpl2"
   },
   "source": [
    "> Below the attention mask shows the position each tgt word (row) is allowed to look at (column). Words are blocked for attending to future words during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "AV1GL7P1zpl2",
    "outputId": "882dbafb-45e9-44a4-9b3b-cc57aa86813d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.imshow(subsequent_mask(20)[0])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nY0wTytkzpl5"
   },
   "source": [
    "### Attention                                                                                                                                                                                                                                                                             \n",
    "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.                                                                                                                                                                                                                                                                                   \n",
    "\n",
    "We call our particular attention \"Scaled Dot-Product Attention\".   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.                                                                                                                                                                                                                                  \n",
    "                                                                                                                                                                     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zXvrh1SQ6MjU"
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/harvardnlp/annotated-transformer/master/images/ModalNet-19.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ShBYC0ePzpmB"
   },
   "source": [
    "\n",
    "In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:                      \n",
    "                                                                 \n",
    "$$                                                                         \n",
    "   \\mathrm{Attention}(Q, K, V) = \\mathrm{softmax}(\\frac{QK^T}{\\sqrt{d_k}})V               \n",
    "$$   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vIZMj5AJPvgG"
   },
   "source": [
    "![alt text](https://lena-voita.github.io/resources/lectures/seq2seq/transformer/qkv_explained-min.png)\n",
    "Source: [Lena Voita's Lecture about Seq2Seq](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "4Oznj4J8zpmC"
   },
   "outputs": [],
   "source": [
    "def attention(query, key, value, mask=None, dropout=None):\n",
    "    \"Compute 'Scaled Dot Product Attention'\"\n",
    "    d_k = query.size(-1)\n",
    "    scores = torch.matmul(query, key.transpose(-2, -1)) \\\n",
    "             / math.sqrt(d_k)\n",
    "    if mask is not None:\n",
    "        scores = scores.masked_fill(mask == 0, -1e9)\n",
    "    p_attn = F.softmax(scores, dim = -1)\n",
    "    if dropout is not None:\n",
    "        p_attn = dropout(p_attn)\n",
    "    return torch.matmul(p_attn, value), p_attn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGsI-tFzpmC"
   },
   "source": [
    "The two most commonly used attention functions are additive attention [(cite)](https://arxiv.org/abs/1409.0473), and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\\frac{1}{\\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.                                                                                             \n",
    "\n",
    "                                                                        \n",
    "While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ [(cite)](https://arxiv.org/abs/1703.03906). We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  (To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.). To counteract this effect, we scale the dot products by $\\frac{1}{\\sqrt{d_k}}$.          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hPVORhQb6aEX"
   },
   "source": [
    "![alt text](https://raw.githubusercontent.com/harvardnlp/annotated-transformer/master/images/ModalNet-20.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8L6VPfczpmK"
   },
   "source": [
    "Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.                                            \n",
    "$$    \n",
    "\\mathrm{MultiHead}(Q, K, V) = \\mathrm{Concat}(\\mathrm{head_1}, ..., \\mathrm{head_h})W^O    \\\\                                           \n",
    "    \\text{where}~\\mathrm{head_i} = \\mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)                                \n",
    "$$                                                                                                                 \n",
    "\n",
    "Where the projections are parameter matrices $W^Q_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_k}$, $W^K_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_k}$, $W^V_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_v}$ and $W^O \\in \\mathbb{R}^{hd_v \\times d_{\\text{model}}}$.                                                                                                                                                                                             In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=d_{\\text{model}}/h=64$. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JF8CBPsjhSTK"
   },
   "source": [
    "![alt text](https://jalammar.github.io/images/t/transformer_multi-headed_self-attention-recap.png)\n",
    "Source: [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "_Log79IyzpmL"
   },
   "outputs": [],
   "source": [
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, h, d_model, dropout=0.1):\n",
    "        \"Take in model size and number of heads.\"\n",
    "        super(MultiHeadedAttention, self).__init__()\n",
    "        assert d_model % h == 0\n",
    "        # We assume d_v always equals d_k\n",
    "        self.d_k = d_model // h\n",
    "        self.h = h\n",
    "        self.linears = clones(nn.Linear(d_model, d_model), 4)\n",
    "        self.attn = None\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "    def forward(self, query, key, value, mask=None):\n",
    "        \"Implements Figure 2\"\n",
    "        if mask is not None:\n",
    "            # Same mask applied to all h heads.\n",
    "            mask = mask.unsqueeze(1)\n",
    "        nbatches = query.size(0)\n",
    "        \n",
    "        # 1) Do all the linear projections in batch from d_model => h x d_k \n",
    "        query, key, value = \\\n",
    "            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n",
    "             for l, x in zip(self.linears, (query, key, value))]\n",
    "        \n",
    "        # 2) Apply attention on all the projected vectors in batch. \n",
    "        x, self.attn = attention(query, key, value, mask=mask, \n",
    "                                 dropout=self.dropout)\n",
    "        \n",
    "        # 3) \"Concat\" using a view and apply a final linear. \n",
    "        x = x.transpose(1, 2).contiguous() \\\n",
    "             .view(nbatches, -1, self.h * self.d_k)\n",
    "        return self.linears[-1](x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zRKnsJPszpmL"
   },
   "source": [
    "### Applications of Attention in our Model                                                                                                                                                      \n",
    "The Transformer uses multi-head attention in three different ways:                                                        \n",
    "1) In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [(cite)](https://arxiv.org/abs/1609.08144).    \n",
    "\n",
    "\n",
    "2) The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.                                                   \n",
    "\n",
    "\n",
    "3) Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\\infty$) all values in the input of the softmax which correspond to illegal connections.                                                                                                                                                                                                                                                      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFP1Ryw7zpmN"
   },
   "source": [
    "## Position-wise Feed-Forward Networks                                                                                                                                                                                                                                                                                                                                                             \n",
    "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
    "In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.\n",
    "\n",
    "$$\\mathrm{FFN}(x)=\\max(0, xW_1 + b_1) W_2 + b_2$$                                                                                                                                                                                                                                                         \n",
    "                                                                                                                                                                                                                                                        \n",
    "While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $d_{\\text{model}}=512$, and the inner-layer has dimensionality $d_{ff}=2048$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "nM00nyxSzpmP"
   },
   "outputs": [],
   "source": [
    "class PositionwiseFeedForward(nn.Module):\n",
    "    \"Implements FFN equation.\"\n",
    "    def __init__(self, d_model, d_ff, dropout=0.1):\n",
    "        super(PositionwiseFeedForward, self).__init__()\n",
    "        self.w_1 = nn.Linear(d_model, d_ff)\n",
    "        self.w_2 = nn.Linear(d_ff, d_model)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.w_2(self.dropout(F.relu(self.w_1(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3j_EAhaQzpmT"
   },
   "source": [
    "## Embeddings and Softmax                                                                                                                                                                                                                                                                                           \n",
    "Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $d_{\\text{model}}$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [(cite)](https://arxiv.org/abs/1608.05859). In the embedding layers, we multiply those weights by $\\sqrt{d_{\\text{model}}}$.                                                                                                                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "yDy0y_S2zpmW"
   },
   "outputs": [],
   "source": [
    "class Embeddings(nn.Module):\n",
    "    def __init__(self, d_model, vocab):\n",
    "        super(Embeddings, self).__init__()\n",
    "        self.lut = nn.Embedding(vocab, d_model)\n",
    "        self.d_model = d_model\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.lut(x) * math.sqrt(self.d_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EEQUHQujzpmY"
   },
   "source": [
    "## Positional Encoding                                                                                                                             \n",
    "Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $d_{\\text{model}}$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed [(cite)](https://arxiv.org/pdf/1705.03122.pdf). \n",
    "\n",
    "In this work, we use sine and cosine functions of different frequencies:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
    "$$PE_{(pos,2i)} = sin(pos / 10000^{2i/d_{\\text{model}}})$$\n",
    "\n",
    "$$PE_{(pos,2i+1)} = cos(pos / 10000^{2i/d_{\\text{model}}})$$                                                                                                                                                                                                                                                        \n",
    "where $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\\pi$ to $10000 \\cdot 2\\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$. \n",
    "\n",
    "In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks.  For the base model, we use a rate of $P_{drop}=0.1$. \n",
    "                                                                                                                                                                                                                                                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "DENE0D2fzpmZ"
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    \"Implement the PE function.\"\n",
    "    def __init__(self, d_model, dropout, max_len=5000):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "        # Compute the positional encodings once in log space.\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2) *\n",
    "                             -(math.log(10000.0) / d_model))\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0)\n",
    "        self.register_buffer('pe', pe)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x + Variable(self.pe[:, :x.size(1)], \n",
    "                         requires_grad=False)\n",
    "        return self.dropout(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RUCsMEFFzpma"
   },
   "source": [
    "> Below the positional encoding will add in a sine wave based on position. The frequency and offset of the wave is different for each dimension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "id": "lhY1ktFdzpma",
    "outputId": "b0076398-dbc3-4219-96d0-cbd7df1ca354"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "pe = PositionalEncoding(20, 0)\n",
    "y = pe.forward(Variable(torch.zeros(1, 100, 20)))\n",
    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uCxzwdCIzpmc"
   },
   "source": [
    "We also experimented with using learned positional embeddings [(cite)](https://arxiv.org/pdf/1705.03122.pdf) instead, and found that the two versions produced nearly identical results.  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GA9FRDeezpmc"
   },
   "source": [
    "## Full Model\n",
    "\n",
    "> Here we define a function from hyperparameters to a full model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0vv3eHKZerll"
   },
   "source": [
    "![alt text](https://lena-voita.github.io/resources/lectures/seq2seq/transformer/model-min.png)\n",
    "Source: [Lena Voita's Lecture about Seq2Seq](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "nSt-jTVWzpmd"
   },
   "outputs": [],
   "source": [
    "def make_model(src_vocab, tgt_vocab, N=6, \n",
    "               d_model=512, d_ff=2048, h=8, dropout=0.1):\n",
    "    \"Helper: Construct a model from hyperparameters.\"\n",
    "    c = copy.deepcopy\n",
    "    attn = MultiHeadedAttention(h, d_model)\n",
    "    ff = PositionwiseFeedForward(d_model, d_ff, dropout)\n",
    "    position = PositionalEncoding(d_model, dropout)\n",
    "    model = EncoderDecoder(\n",
    "        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),\n",
    "        Decoder(DecoderLayer(d_model, c(attn), c(attn), \n",
    "                             c(ff), dropout), N),\n",
    "        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),\n",
    "        nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),\n",
    "        Generator(d_model, tgt_vocab))\n",
    "    \n",
    "    # This was important from their code. \n",
    "    # Initialize parameters with Glorot / fan_avg.\n",
    "    for p in model.parameters():\n",
    "        if p.dim() > 1:\n",
    "            nn.init.xavier_uniform(p)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "p064kMV5zpme",
    "outputId": "59210e75-f33c-4fb3-c6ed-07b252ff75f5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n"
     ]
    }
   ],
   "source": [
    "# Small example model.\n",
    "tmp_model = make_model(10, 10, 2)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HYwSCtfBzpmf"
   },
   "source": [
    "# Training\n",
    "\n",
    "This section describes the training regime for our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GlQ05nOAzpmf"
   },
   "source": [
    "> We stop for a quick interlude to introduce some of the tools \n",
    "needed to train a standard encoder decoder model. First we define a batch object that holds the src and target sentences for training, as well as constructing the masks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-fjGHykczpmf"
   },
   "source": [
    "## Batches and Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "vBd6U8jIzpmg"
   },
   "outputs": [],
   "source": [
    "class Batch:\n",
    "    \"Object for holding a batch of data with mask during training.\"\n",
    "    def __init__(self, src, trg=None, pad=0):\n",
    "        self.src = src\n",
    "        self.src_mask = (src != pad).unsqueeze(-2)\n",
    "        if trg is not None:\n",
    "            self.trg = trg[:, :-1]\n",
    "            self.trg_y = trg[:, 1:]\n",
    "            self.trg_mask = \\\n",
    "                self.make_std_mask(self.trg, pad)\n",
    "            self.ntokens = (self.trg_y != pad).data.sum()\n",
    "    \n",
    "    @staticmethod\n",
    "    def make_std_mask(tgt, pad):\n",
    "        \"Create a mask to hide padding and future words.\"\n",
    "        tgt_mask = (tgt != pad).unsqueeze(-2)\n",
    "        tgt_mask = tgt_mask & Variable(\n",
    "            subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))\n",
    "        return tgt_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hntATWqVzpmh"
   },
   "source": [
    "> Next we create a generic training and scoring function to keep track of loss. We pass in a generic loss compute function that also handles parameter updates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oYk__CHMzpmh"
   },
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "dQ-zUhRizpmi"
   },
   "outputs": [],
   "source": [
    "def run_epoch(data_iter, model, loss_compute):\n",
    "    \"Standard Training and Logging Function\"\n",
    "    start = time.time()\n",
    "    total_tokens = 0\n",
    "    total_loss = 0\n",
    "    tokens = 0\n",
    "    for i, batch in enumerate(data_iter):\n",
    "        out = model.forward(batch.src, batch.trg, \n",
    "                            batch.src_mask, batch.trg_mask)\n",
    "        loss = loss_compute(out, batch.trg_y, batch.ntokens)\n",
    "        total_loss += loss\n",
    "        total_tokens += batch.ntokens\n",
    "        tokens += batch.ntokens\n",
    "        if i % 50 == 1:\n",
    "            elapsed = time.time() - start\n",
    "            print(\"Epoch Step: %d Loss: %f Tokens per Sec: %f\" %\n",
    "                    (i, loss / batch.ntokens, tokens / elapsed))\n",
    "            start = time.time()\n",
    "            tokens = 0\n",
    "    return total_loss / total_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mc15CmRczpmj"
   },
   "source": [
    "## Training Data and Batching\n",
    "\n",
    "We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs.  Sentences were encoded using byte-pair encoding, which has a shared source-target vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary.\n",
    "\n",
    "\n",
    "Sentence pairs were batched together by approximate sequence length.  Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dUUAQ9Cizpmp"
   },
   "source": [
    "> We will use torch text for batching. This is discussed in more detail below. Here we create batches in a torchtext function that ensures our batch size padded to the maximum batchsize does not surpass a threshold (25000 if we have 8 gpus)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "9Dg79p3Bzpmr"
   },
   "outputs": [],
   "source": [
    "global max_src_in_batch, max_tgt_in_batch\n",
    "def batch_size_fn(new, count, sofar):\n",
    "    \"Keep augmenting batch and calculate total number of tokens + padding.\"\n",
    "    global max_src_in_batch, max_tgt_in_batch\n",
    "    if count == 1:\n",
    "        max_src_in_batch = 0\n",
    "        max_tgt_in_batch = 0\n",
    "    max_src_in_batch = max(max_src_in_batch,  len(new.src))\n",
    "    max_tgt_in_batch = max(max_tgt_in_batch,  len(new.trg) + 2)\n",
    "    src_elements = count * max_src_in_batch\n",
    "    tgt_elements = count * max_tgt_in_batch\n",
    "    return max(src_elements, tgt_elements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7NOqljpzpms"
   },
   "source": [
    "## Hardware and Schedule                                                                                                                                                                                                   \n",
    "We trained our models on one machine with 8 NVIDIA P100 GPUs.  For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds.  We trained the base models for a total of 100,000 steps or 12 hours. For our big models, step time was 1.0 seconds.  The big models were trained for 300,000 steps (3.5 days)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_A7FVAswzpmt"
   },
   "source": [
    "## Optimizer\n",
    "\n",
    "We used the Adam optimizer [(cite)](https://arxiv.org/abs/1412.6980) with $\\beta_1=0.9$, $\\beta_2=0.98$ and $\\epsilon=10^{-9}$.  We varied the learning rate over the course of training, according to the formula:                                                                                            \n",
    "$$                                                                                                                                                                                                                                                                                         \n",
    "lrate = d_{\\text{model}}^{-0.5} \\cdot                                                                                                                                                                                                                                                                                                \n",
    "  \\min({step\\_num}^{-0.5},                                                                                                                                                                                                                                                                                                  \n",
    "    {step\\_num} \\cdot {warmup\\_steps}^{-1.5})                                                                                                                                                                                                                                                                               \n",
    "$$                                                                                                                                                                                             \n",
    "This corresponds to increasing the learning rate linearly for the first $warmup\\_steps$ training steps, and decreasing it thereafter proportionally to the inverse square root of the step number.  We used $warmup\\_steps=4000$.                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u6Z_tL7Tzpmu"
   },
   "source": [
    "> Note: This part is very important. Need to train with this setup of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "JEki3jFbzpmu"
   },
   "outputs": [],
   "source": [
    "\n",
    "class NoamOpt:\n",
    "    \"Optim wrapper that implements rate.\"\n",
    "    def __init__(self, model_size, factor, warmup, optimizer):\n",
    "        self.optimizer = optimizer\n",
    "        self._step = 0\n",
    "        self.warmup = warmup\n",
    "        self.factor = factor\n",
    "        self.model_size = model_size\n",
    "        self._rate = 0\n",
    "        \n",
    "    def step(self):\n",
    "        \"Update parameters and rate\"\n",
    "        self._step += 1\n",
    "        rate = self.rate()\n",
    "        for p in self.optimizer.param_groups:\n",
    "            p['lr'] = rate\n",
    "        self._rate = rate\n",
    "        self.optimizer.step()\n",
    "        \n",
    "    def rate(self, step = None):\n",
    "        \"Implement `lrate` above\"\n",
    "        if step is None:\n",
    "            step = self._step\n",
    "        return self.factor * \\\n",
    "            (self.model_size ** (-0.5) *\n",
    "            min(step ** (-0.5), step * self.warmup ** (-1.5)))\n",
    "        \n",
    "def get_std_opt(model):\n",
    "    return NoamOpt(model.src_embed[0].d_model, 2, 4000,\n",
    "            torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CQXE_EqLzpmu"
   },
   "source": [
    "\n",
    "> Example of the curves of this model for different model sizes and for optimization hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "uxD-GDLnzpmv",
    "outputId": "d08db09c-b244-4850-aff9-31b7585f76db"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Three settings of the lrate hyperparameters.\n",
    "opts = [NoamOpt(512, 1, 4000, None), \n",
    "        NoamOpt(512, 1, 8000, None),\n",
    "        NoamOpt(256, 1, 4000, None)]\n",
    "plt.plot(np.arange(1, 20000), [[opt.rate(i) for opt in opts] for i in range(1, 20000)])\n",
    "plt.legend([\"512:4000\", \"512:8000\", \"256:4000\"])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PQ6rwqGAzpmw"
   },
   "source": [
    "## Regularization                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
    "                                                                                                                                                                                                                                                                                                                      \n",
    "### Label Smoothing\n",
    "\n",
    "During training, we employed label smoothing of value $\\epsilon_{ls}=0.1$ [(cite)](https://arxiv.org/abs/1512.00567).  This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CzNB96RHH26D"
   },
   "source": [
    "![](https://i2.wp.com/img-blog.csdnimg.cn/20200506164053747.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0d1b19QeXRob24=,size_16,color_FFFFFF,t_70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZAibyEOdzpm0"
   },
   "source": [
    "> We implement label smoothing using the KL div loss. Instead of using a one-hot target distribution, we create a distribution that has `confidence` of the correct word and the rest of the `smoothing` mass distributed throughout the vocabulary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "Mt80kgahzpm1"
   },
   "outputs": [],
   "source": [
    "class LabelSmoothing(nn.Module):\n",
    "    \"Implement label smoothing.\"\n",
    "    def __init__(self, size, padding_idx, smoothing=0.0):\n",
    "        super(LabelSmoothing, self).__init__()\n",
    "        self.criterion = nn.KLDivLoss(size_average=False)\n",
    "        self.padding_idx = padding_idx\n",
    "        self.confidence = 1.0 - smoothing\n",
    "        self.smoothing = smoothing\n",
    "        self.size = size\n",
    "        self.true_dist = None\n",
    "        \n",
    "    def forward(self, x, target):\n",
    "        assert x.size(1) == self.size\n",
    "        true_dist = x.data.clone()\n",
    "        true_dist.fill_(self.smoothing / (self.size - 2))\n",
    "        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)\n",
    "        true_dist[:, self.padding_idx] = 0\n",
    "        mask = torch.nonzero(target.data == self.padding_idx)\n",
    "        if mask.dim() > 0:\n",
    "            true_dist.index_fill_(0, mask.squeeze(), 0.0)\n",
    "        self.true_dist = true_dist\n",
    "        return self.criterion(x, Variable(true_dist, requires_grad=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YtbzMqwWzpm1"
   },
   "source": [
    "> Here we can see an example of how the mass is distributed to the words based on confidence. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tvQ4jC98zpm3"
   },
   "source": [
    "> Label smoothing actually starts to penalize the model if it gets very confident about a given choice. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aIfF9X6MzpnC"
   },
   "source": [
    "# A First  Example\n",
    "\n",
    "> We can begin by trying out a simple copy-task. Given a random set of input symbols from a small vocabulary, the goal is to generate back those same symbols. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IaVpObFEzpnD"
   },
   "source": [
    "## Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "b5tjZmF7zpnE"
   },
   "outputs": [],
   "source": [
    "def data_gen(V, batch, nbatches):\n",
    "    \"Generate random data for a src-tgt copy task.\"\n",
    "    for i in range(nbatches):\n",
    "        data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))\n",
    "        data[:, 0] = 1\n",
    "        src = Variable(data, requires_grad=False)\n",
    "        tgt = Variable(data, requires_grad=False)\n",
    "        yield Batch(src, tgt, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6H_4TnTCzpnF"
   },
   "source": [
    "## Loss Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "id": "juK-hXtGzpnF"
   },
   "outputs": [],
   "source": [
    "class SimpleLossCompute:\n",
    "    \"A simple loss compute and train function.\"\n",
    "    def __init__(self, generator, criterion, opt=None):\n",
    "        self.generator = generator\n",
    "        self.criterion = criterion\n",
    "        self.opt = opt\n",
    "        \n",
    "    def __call__(self, x, y, norm):\n",
    "        x = self.generator(x)\n",
    "        loss = self.criterion(x.contiguous().view(-1, x.size(-1)), \n",
    "                              y.contiguous().view(-1)) / norm\n",
    "        loss.backward()\n",
    "        if self.opt is not None:\n",
    "            self.opt.step()\n",
    "            self.opt.optimizer.zero_grad()\n",
    "        return loss.item() * norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5m7j-96wzpnG"
   },
   "source": [
    "## Greedy Decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ALq4rd4zpnG",
    "outputId": "1c50d4ae-aaf0-4046-d43b-43d7fed6ab13"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch Step: 1 Loss: 3.141402 Tokens per Sec: 366.091766\n",
      "Epoch Step: 1 Loss: 2.003330 Tokens per Sec: 569.114868\n",
      "tensor(2.0107)\n",
      "Epoch Step: 1 Loss: 2.005640 Tokens per Sec: 462.402679\n",
      "Epoch Step: 1 Loss: 1.702494 Tokens per Sec: 561.515442\n",
      "tensor(1.7636)\n",
      "Epoch Step: 1 Loss: 1.877485 Tokens per Sec: 460.141144\n",
      "Epoch Step: 1 Loss: 1.578294 Tokens per Sec: 561.941895\n",
      "tensor(1.5469)\n",
      "Epoch Step: 1 Loss: 1.730473 Tokens per Sec: 463.742737\n",
      "Epoch Step: 1 Loss: 1.237015 Tokens per Sec: 551.932556\n",
      "tensor(1.2932)\n",
      "Epoch Step: 1 Loss: 1.341792 Tokens per Sec: 464.056763\n",
      "Epoch Step: 1 Loss: 0.937989 Tokens per Sec: 562.530029\n",
      "tensor(0.9542)\n",
      "Epoch Step: 1 Loss: 1.254783 Tokens per Sec: 461.936035\n",
      "Epoch Step: 1 Loss: 0.687648 Tokens per Sec: 567.693848\n",
      "tensor(0.6864)\n",
      "Epoch Step: 1 Loss: 0.678843 Tokens per Sec: 469.833252\n",
      "Epoch Step: 1 Loss: 0.401347 Tokens per Sec: 579.331909\n",
      "tensor(0.4222)\n",
      "Epoch Step: 1 Loss: 1.208522 Tokens per Sec: 458.967010\n",
      "Epoch Step: 1 Loss: 0.520356 Tokens per Sec: 551.022400\n",
      "tensor(0.5877)\n",
      "Epoch Step: 1 Loss: 0.793398 Tokens per Sec: 467.611328\n",
      "Epoch Step: 1 Loss: 0.318018 Tokens per Sec: 582.787292\n",
      "tensor(0.3629)\n",
      "Epoch Step: 1 Loss: 0.453723 Tokens per Sec: 482.395020\n",
      "Epoch Step: 1 Loss: 0.366663 Tokens per Sec: 575.152344\n",
      "tensor(0.3871)\n"
     ]
    }
   ],
   "source": [
    "# Train the simple copy task.\n",
    "V = 11\n",
    "criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)\n",
    "model = make_model(V, V, N=2)\n",
    "model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,\n",
    "        torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))\n",
    "\n",
    "for epoch in range(5):\n",
    "    model.train()\n",
    "    run_epoch(data_gen(V, 20, 30), model, \n",
    "              SimpleLossCompute(model.generator, criterion, model_opt))\n",
    "    model.eval()\n",
    "    print(run_epoch(data_gen(V, 20, 5), model, \n",
    "                    SimpleLossCompute(model.generator, criterion, None)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4p_mlAeWzpnH"
   },
   "source": [
    "> This code predicts a translation using greedy decoding for simplicity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XWNDnosZzpnI",
    "outputId": "c14041d5-e151-4f01-db75-143624345701"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1,  2,  3,  4,  5,  6,  7,  8, 10, 10]])\n"
     ]
    }
   ],
   "source": [
    "def greedy_decode(model, src, src_mask, max_len, start_symbol):\n",
    "    memory = model.encode(src.to(device), src_mask.to(device))\n",
    "    ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)\n",
    "    for i in range(max_len-1):\n",
    "        out = model.decode(memory, src_mask.to(device), \n",
    "                           Variable(ys).to(device), \n",
    "                           Variable(subsequent_mask(ys.size(1))\n",
    "                                    .type_as(src.data).to(device)))\n",
    "        prob = model.generator(out[:, -1])\n",
    "        _, next_word = torch.max(prob, dim = 1)\n",
    "        next_word = next_word.data[0]\n",
    "        ys = torch.cat([ys, \n",
    "                        torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)\n",
    "    return ys\n",
    "\n",
    "model.eval()\n",
    "src = Variable(torch.LongTensor([[1,2,3,4,5,6,7,8,9,10]]) )\n",
    "src_mask = Variable(torch.ones(1, 1, 10) )\n",
    "print(greedy_decode(model.to(device), src, src_mask, max_len=10, start_symbol=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zSAtvphnAt79"
   },
   "source": [
    "# Memory Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QXCe4wSDAyrx"
   },
   "outputs": [],
   "source": [
    "def loss_backprop(generator, criterion, out, targets, normalize):\n",
    "    \"\"\"\n",
    "    Memory optmization. Compute each timestep separately and sum grads.\n",
    "    \"\"\"\n",
    "    assert out.size(1) == targets.size(1)\n",
    "    total = 0.0\n",
    "    out_grad = []\n",
    "    for i in range(out.size(1)):\n",
    "        out_column = Variable(out[:, i].data, requires_grad=True)\n",
    "        gen = generator(out_column)\n",
    "        loss = criterion(gen, targets[:, i]) / normalize\n",
    "        total += loss.item() #.data[0]\n",
    "        loss.backward()\n",
    "        out_grad.append(out_column.grad.data.clone())\n",
    "    out_grad = torch.stack(out_grad, dim=1)\n",
    "    out.backward(gradient=out_grad)\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8rBYbyfAA04D"
   },
   "outputs": [],
   "source": [
    "def make_std_mask(src, tgt, pad):\n",
    "    src_mask = (src != pad).unsqueeze(-2)\n",
    "    tgt_mask = (tgt != pad).unsqueeze(-2)\n",
    "    tgt_mask = tgt_mask & Variable(subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))\n",
    "    return src_mask, tgt_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "luVJLd5LA28B"
   },
   "outputs": [],
   "source": [
    "def train_epoch(train_iter, model, criterion, opt, transpose=False):\n",
    "    model.train()\n",
    "    for i, batch in enumerate(train_iter):\n",
    "        src, trg, src_mask, trg_mask = \\\n",
    "                batch.src.to(device), batch.trg.to(device), batch.src_mask.to(device), batch.trg_mask.to(device)\n",
    "        out = model.forward(src, trg[:, :-1], src_mask, trg_mask[:, :-1, :-1])\n",
    "        loss = loss_backprop(model.generator, criterion, out, trg[:, 1:], batch.ntokens) \n",
    "                        \n",
    "        model_opt.step()\n",
    "        model_opt.optimizer.zero_grad()\n",
    "        if i % 10 == 1:\n",
    "            print(i, loss, model_opt._rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MWnv7_0yA3_G"
   },
   "outputs": [],
   "source": [
    "def valid_epoch(valid_iter, model, criterion, transpose=False):\n",
    "    model.eval()\n",
    "    total = 0\n",
    "    for batch in valid_iter:\n",
    "        src, trg, src_mask, trg_mask = \\\n",
    "            batch.src.to(device), batch.trg.to(device), batch.src_mask.to(device), batch.trg_mask.to(device)\n",
    "        out = model.forward(src, trg[:, :-1], src_mask, trg_mask[:, :-1, :-1])\n",
    "        loss = loss_backprop(model.generator, criterion, out, trg[:, 1:], batch.ntokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_RihaYgEA844"
   },
   "outputs": [],
   "source": [
    "class Batch:\n",
    "    def __init__(self, src, trg, src_mask, trg_mask, ntokens):\n",
    "        self.src = src\n",
    "        self.trg = trg\n",
    "        self.src_mask = src_mask\n",
    "        self.trg_mask = trg_mask\n",
    "        self.ntokens = ntokens\n",
    "    \n",
    "def data_gen(V, batch, nbatches):\n",
    "    for i in range(nbatches):\n",
    "        data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))\n",
    "        src = Variable(data, requires_grad=False)\n",
    "        tgt = Variable(data, requires_grad=False)\n",
    "        src_mask, tgt_mask = make_std_mask(src, tgt, 0)\n",
    "        yield Batch(src, tgt, src_mask, tgt_mask, (tgt[1:] != 0).data.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8s2YaO5EA9ei",
    "outputId": "5cea812e-b2b4-4a25-802d-be80fb765ec4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n",
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: nn.init.xavier_uniform is now deprecated in favor of nn.init.xavier_uniform_.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 3.3115475475788116 6.987712429686844e-07\n",
      "11 2.8431503772735596 4.192627457812107e-06\n",
      "1 2.515135943889618 7.686483672655528e-06\n",
      "11 2.4407246112823486 1.118033988749895e-05\n"
     ]
    }
   ],
   "source": [
    "V = 11\n",
    "criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)\n",
    "model = make_model(V, V, N=2).to(device)\n",
    "model_opt = get_std_opt(model)\n",
    "for epoch in range(2):\n",
    "    train_epoch(data_gen(V, 10, 10), model, criterion, model_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gE-RgRjNzpnL"
   },
   "source": [
    "# A Real World Example\n",
    "\n",
    "> Now we consider a real-world example using the IWSLT German-English Translation task. This task is much smaller than the WMT task considered in the paper, but it illustrates the whole system. We also show how to use multi-gpu processing to make it really fast."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JmojRkrjzpnM"
   },
   "source": [
    "## Data Loading\n",
    "> We will load the dataset using torchtext and spacy for tokenization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aVwmn3Zs9-mn",
    "outputId": "1cc91baf-80ce-4308-ed95-8ea93aaaca9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchtext in /usr/local/lib/python3.7/dist-packages (0.9.1)\n",
      "Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchtext) (2.23.0)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torchtext) (4.41.1)\n",
      "Requirement already satisfied: torch==1.8.1 in /usr/local/lib/python3.7/dist-packages (from torchtext) (1.8.1+cu101)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchtext) (1.19.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.0)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.5)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.1)\n",
      "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (7.4.0)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.5)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (54.2.0)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.1.3)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (2020.12.5)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.8.1->torchtext) (3.7.4.3)\n",
      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (3.10.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.4.1)\n",
      "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.7/dist-packages (2.2.5)\n",
      "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.2)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.19.5)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.5)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.5)\n",
      "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (54.2.0)\n",
      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.10.1)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.12.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.4.1)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.7.4.3)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('en_core_web_sm')\n",
      "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
      "/usr/local/lib/python3.7/dist-packages/en_core_web_sm -->\n",
      "/usr/local/lib/python3.7/dist-packages/spacy/data/en\n",
      "You can now load the model via spacy.load('en')\n",
      "Requirement already satisfied: de_core_news_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz#egg=de_core_news_sm==2.2.5 in /usr/local/lib/python3.7/dist-packages (2.2.5)\n",
      "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from de_core_news_sm==2.2.5) (2.2.4)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.4.1)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.8.2)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (54.2.0)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.5)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.23.0)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.5)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.0)\n",
      "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (7.4.0)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.1.3)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.0.5)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (4.41.1)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.5)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.19.5)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (1.24.3)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2020.12.5)\n",
      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.10.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.4.1)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.7.4.3)\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('de_core_news_sm')\n",
      "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
      "/usr/local/lib/python3.7/dist-packages/de_core_news_sm -->\n",
      "/usr/local/lib/python3.7/dist-packages/spacy/data/de\n",
      "You can now load the model via spacy.load('de')\n"
     ]
    }
   ],
   "source": [
    "!pip install torchtext spacy\n",
    "!python -m spacy download en\n",
    "!python -m spacy download de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xLVr0t3r-Wgz"
   },
   "outputs": [],
   "source": [
    "from torchtext.legacy import data, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jKP2f4of-Rjz",
    "outputId": "c492b185-18ea-4f78-80d8-aa3d937a1ee9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading training.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "training.tar.gz: 100%|██████████| 1.21M/1.21M [00:02<00:00, 588kB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading validation.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation.tar.gz: 100%|██████████| 46.3k/46.3k [00:00<00:00, 172kB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading mmt_task1_test2016.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mmt_task1_test2016.tar.gz: 100%|██████████| 66.2k/66.2k [00:00<00:00, 164kB/s]\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "spacy_de = spacy.load('de')\n",
    "spacy_en = spacy.load('en')\n",
    "\n",
    "def tokenize_de(text):\n",
    "    return [tok.text for tok in spacy_de.tokenizer(text)]\n",
    "\n",
    "def tokenize_en(text):\n",
    "    return [tok.text for tok in spacy_en.tokenizer(text)]\n",
    "\n",
    "BOS_WORD = '<s>'\n",
    "EOS_WORD = '</s>'\n",
    "BLANK_WORD = \"<blank>\"\n",
    "SRC = data.Field(tokenize=tokenize_de, pad_token=BLANK_WORD)\n",
    "TGT = data.Field(tokenize=tokenize_en, init_token = BOS_WORD, \n",
    "                 eos_token = EOS_WORD, pad_token=BLANK_WORD)\n",
    "\n",
    "MAX_LEN = 100\n",
    "train, val, test = datasets.Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TGT), \n",
    "                                         filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and \n",
    "                                         len(vars(x)['trg']) <= MAX_LEN)\n",
    "MIN_FREQ = 1\n",
    "SRC.build_vocab(train.src, min_freq=MIN_FREQ)\n",
    "TGT.build_vocab(train.trg, min_freq=MIN_FREQ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aa3yHaGEB9Ed"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WQ5j-MogzpnN",
    "outputId": "7fa9e1fc-09b4-4326-ea83-e50066acb4e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:torchtext.legacy.data.iterator:The `device` argument should be set by using `torch.device` or passing a string as an argument. This behavior will be deprecated soon and currently defaults to cpu.\n",
      "WARNING:torchtext.legacy.data.iterator:The `device` argument should be set by using `torch.device` or passing a string as an argument. This behavior will be deprecated soon and currently defaults to cpu.\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 4096\n",
    "global max_src_in_batch, max_tgt_in_batch\n",
    "def batch_size_fn(new, count, sofar):\n",
    "    \"Keep augmenting batch and calculate total number of tokens + padding.\"\n",
    "    global max_src_in_batch, max_tgt_in_batch\n",
    "    if count == 1:\n",
    "        max_src_in_batch = 0\n",
    "        max_tgt_in_batch = 0\n",
    "    max_src_in_batch = max(max_src_in_batch,  len(new.src))\n",
    "    max_tgt_in_batch = max(max_tgt_in_batch,  len(new.trg) + 2)\n",
    "    src_elements = count * max_src_in_batch\n",
    "    tgt_elements = count * max_tgt_in_batch\n",
    "    return max(src_elements, tgt_elements)\n",
    "\n",
    "class MyIterator(data.Iterator):\n",
    "    def create_batches(self):\n",
    "        if self.train:\n",
    "            def pool(d, random_shuffler):\n",
    "                for p in data.batch(d, self.batch_size * 100):\n",
    "                    p_batch = data.batch(\n",
    "                        sorted(p, key=self.sort_key),\n",
    "                        self.batch_size, self.batch_size_fn)\n",
    "                    for b in random_shuffler(list(p_batch)):\n",
    "                        yield b\n",
    "            self.batches = pool(self.data(), self.random_shuffler)\n",
    "            \n",
    "        else:\n",
    "            self.batches = []\n",
    "            for b in data.batch(self.data(), self.batch_size,\n",
    "                                          self.batch_size_fn):\n",
    "                self.batches.append(sorted(b, key=self.sort_key))\n",
    "\n",
    "def rebatch(pad_idx, batch):\n",
    "    \"Fix order in torchtext to match ours\"\n",
    "    src, trg = batch.src.transpose(0, 1), batch.trg.transpose(0, 1)\n",
    "    src_mask, trg_mask = make_std_mask(src, trg, pad_idx)\n",
    "    return Batch(src, trg, src_mask, trg_mask, (trg[1:] != pad_idx).data.sum())\n",
    "\n",
    "train_iter = MyIterator(train, batch_size=BATCH_SIZE, device=0,\n",
    "                        repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),\n",
    "                        batch_size_fn=batch_size_fn, train=True)\n",
    "valid_iter = MyIterator(val, batch_size=BATCH_SIZE, device=0,\n",
    "                        repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),\n",
    "                        batch_size_fn=batch_size_fn, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gPxy5G-W-e4S"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xP5Kz1DvBbeC"
   },
   "outputs": [],
   "source": [
    "# Create the model an load it onto our GPU.\n",
    "pad_idx = TGT.vocab.stoi[\"<blank>\"]\n",
    "model = make_model(len(SRC.vocab), len(TGT.vocab), N=6)\n",
    "model_opt = get_std_opt(model)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BJZenW0rBa_n",
    "outputId": "411242f9-50a4-4681-8160-19840f7181af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 65,082,455 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model: nn.Module):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0eMIHah2-k0c",
    "outputId": "f6a2e835-e442-467b-b613-e6d20b41d2f2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "11 0.9874321413371945 0.0005446921838940895\n",
      "21 1.0242825765453745 0.0005481860401089329\n",
      "31 1.1918642440123222 0.0005516798963237763\n",
      "41 1.2357005751691759 0.0005551737525386198\n",
      "51 0.910615436732769 0.0005586676087534632\n",
      "61 0.8482562080025673 0.0005621614649683065\n",
      "71 1.3833208223629754 0.00056565532118315\n",
      "81 0.9309256882406771 0.0005691491773979935\n",
      "91 1.167970525133569 0.0005726430336128369\n",
      "101 1.3127744505518422 0.0005761368898276803\n",
      "111 0.9478237924631685 0.0005796307460425237\n",
      "1 0.7979159941896796 0.0005827752166358827\n",
      "11 0.8164822906255722 0.0005862690728507262\n",
      "21 0.7262998009100556 0.0005897629290655697\n",
      "31 0.840700250955706 0.0005932567852804131\n",
      "41 0.930657397955656 0.0005967506414952564\n",
      "51 1.034325017651554 0.0006002444977100999\n",
      "61 0.771095750737004 0.0006037383539249433\n",
      "71 0.9387424848391674 0.0006072322101397867\n",
      "81 1.0414447961229598 0.0006107260663546301\n",
      "91 0.8077587024308741 0.0006142199225694736\n",
      "101 0.7002947409637272 0.000617713778784317\n",
      "111 1.0447583426721394 0.0006212076349991604\n"
     ]
    }
   ],
   "source": [
    "criterion = LabelSmoothing(size=len(TGT.vocab), padding_idx=pad_idx, smoothing=0.1)\n",
    "criterion.cuda()\n",
    "for epoch in range(100):\n",
    "    train_epoch((rebatch(pad_idx, b) for b in train_iter), model, criterion, model_opt)\n",
    "    valid_epoch((rebatch(pad_idx, b) for b in valid_iter), model, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KVGjHU2N-pVE"
   },
   "source": [
    "Example of usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "70KZ7QlB-pDg",
    "outputId": "55289022-eb45-4092-cdcd-67240da716df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Translation:\t<s> <unk> <unk> <unk> <unk> <unk> - <unk> - lined up <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> <unk> \n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "sent = \"Hallo , wie gehen Sie ?\".split()\n",
    "src = torch.LongTensor([[SRC.vocab.stoi[w] for w in sent]])\n",
    "src = Variable(src)\n",
    "src_mask = (src != SRC.vocab.stoi[\"<blank>\"]).unsqueeze(-2)\n",
    "out = greedy_decode(model, src.to(device), src_mask.to(device), \n",
    "                    max_len=60, start_symbol=TGT.vocab.stoi[\"<s>\"])\n",
    "print(\"Translation:\", end=\"\\t\")\n",
    "trans = \"<s> \"\n",
    "for i in range(1, out.size(1)):\n",
    "    sym = TGT.vocab.itos[out[0, i]]\n",
    "    if sym == \"</s>\": break\n",
    "    trans += sym + \" \"\n",
    "print(trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EXFZrtfQzpnh"
   },
   "source": [
    "# Results\n",
    "\n",
    "On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big)\n",
    "in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0\n",
    "BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is\n",
    "listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model\n",
    "surpasses all previously published models and ensembles, at a fraction of the training cost of any of\n",
    "the competitive models.\n",
    "\n",
    "On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0,\n",
    "outperforming all of the previously published single models, at less than 1/4 the training cost of the\n",
    "previous state-of-the-art model. The Transformer (big) model trained for English-to-French used\n",
    "dropout rate Pdrop = 0.1, instead of 0.3.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a4zyFHiJb5ZC"
   },
   "source": [
    "![alt text](https://nlp.seas.harvard.edu/images/the-annotated-transformer_113_0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I7ZuDnbnzpnj"
   },
   "source": [
    "> The code we have written here is a version of the base model. There are fully trained version of this system available here  [(Example Models)](http://opennmt.net/Models-py/).\n",
    ">\n",
    "> With the addtional extensions in the last section, the OpenNMT-py replication gets to 26.9 on EN-DE WMT. Here I have loaded in those parameters to our reimplemenation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "sWVUYv45zpnk",
    "outputId": "205d808e-07ac-4b18-bb37-b04cb7ca6edd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-04-22 07:44:52--  https://s3.amazonaws.com/opennmt-models/en-de-model.pt\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.65.246\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.65.246|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 393031182 (375M) [application/x-www-form-urlencoded]\n",
      "Saving to: ‘en-de-model.pt’\n",
      "\n",
      "en-de-model.pt      100%[===================>] 374.82M  8.44MB/s    in 30s     \n",
      "\n",
      "2021-04-22 07:45:23 (12.4 MB/s) - ‘en-de-model.pt’ saved [393031182/393031182]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://s3.amazonaws.com/opennmt-models/en-de-model.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "3xOHbINyzpnk",
    "outputId": "8b206135-1cf6-47a6-bd27-a4af60a0c77e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/serialization.py:656: SourceChangeWarning: source code of class 'torch.nn.modules.container.ModuleList' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/torch/serialization.py:656: SourceChangeWarning: source code of class 'torch.nn.modules.linear.Linear' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/torch/serialization.py:656: SourceChangeWarning: source code of class 'torch.nn.modules.dropout.Dropout' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/torch/serialization.py:656: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/torch/serialization.py:656: SourceChangeWarning: source code of class 'torch.nn.modules.sparse.Embedding' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n"
     ]
    }
   ],
   "source": [
    "model, SRC, TGT = torch.load(\"en-de-model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PTjRYgFKzpnl",
    "outputId": "a9c930b1-8579-41f2-da44-6bd0412a3397"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Translation:\t<s> ▁Die ▁Protokoll datei ▁kann ▁ heimlich ▁per ▁E - Mail ▁oder ▁FTP ▁an ▁einen ▁bestimmte n ▁Empfänger ▁gesendet ▁werden . \n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "sent = \"▁The ▁log ▁file ▁can ▁be ▁sent ▁secret ly ▁with ▁email ▁or ▁FTP ▁to ▁a ▁specified ▁receiver\".split()\n",
    "src = torch.LongTensor([[SRC.stoi[w] for w in sent]])\n",
    "src = Variable(src)\n",
    "src_mask = (src != SRC.stoi[\"<blank>\"]).unsqueeze(-2)\n",
    "out = greedy_decode(model.to(device), src.to(device), src_mask.to(device), \n",
    "                    max_len=60, start_symbol=TGT.stoi[\"<s>\"])\n",
    "print(\"Translation:\", end=\"\\t\")\n",
    "trans = \"<s> \"\n",
    "for i in range(1, out.size(1)):\n",
    "    sym = TGT.itos[out[0, i]]\n",
    "    if sym == \"</s>\": break\n",
    "    trans += sym + \" \"\n",
    "print(trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VGuVkeT4zpnn"
   },
   "source": [
    "## Attention Visualization\n",
    "\n",
    "> Even with a greedy decoder the translation looks pretty good. We can further visualize it to see what is happening at each layer of the attention "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "YbT14lKtzpnq",
    "outputId": "c27dd7e3-48b4-4045-8ce9-1797f1a77dce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 2\n"
     ]
    },
    {
     "data": {
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ICt9h+7bftMNgR5xJKlalaCZ1go7/Ni9JkiSpkp2qcBYRKyMiJ3lZP8F9r4mIy6b4IUiSJEmSJGmG2KkKZ5IkSZIkSVKr7Gyram4ADp1kzMBUJCJJkiRJkqTOtlMVzuqrX65rdx6SJEmSJEnqfJ6qOUkRcURE/DwieiPi2og4fpQ2PRHxrxFxfUT0RcSGiHhvROxUhUpJkiRJkqSdmYWcydkD+DbweeALwEuBz0TEtsz8MkBEdAHfBB4LnAFcDRwOvAPYD3hV804jYvN4HT9o14e06CFIkiRJkiRpIiycTc6+wBsy83SAiPg48CvglIg4JzMTOBY4EnhiZv5iJDAirq+3+2BmejqpJEmSJEnSDOepmpPTB3xy5EZmbgc+BTwYGBkSdjRwBXBdRCwbuQAX1u8/snmnmbl0vMtUPihJkiRJkiQ9kCPOJmdjZvY2bbumfn0gtYUJVlFb2fP2Mfax59SkJkmSJEmSpFaycNZ6XcClwD+Ncf/105iLJEmSJEmSClk4m5wVEdHTNOpsVf16Q/36OuBhmXnB9KYmSZIkSZKkVnKOs8mZD/zNyI2ImA+8GlhP7TRNgHOBAyLilc3BEbE4IhZMQ56SJEmSJEmqyBFnk7MReFdEHExtZNnLqM1n9rL6ipoAZwEvAT4TEU8Dfg50A4cBLwYOB66d7sQlSZIkSZI0ORbOJucO4G+B04HXUiukvTozvzDSIDOHIuK5wJuBlwMvArZQK5adUo+RJEmSJEnSDBd/HCilmezR+zyp+IW67r5bivvdPthfHFvlvbVkwaLiWIAt/duLYxd3l59N+4alf1Yceyn3FceuuevK4tgt/c0LxU5cV1Q723tu15zi2IHhweLYdh73+vtujrZ13gL7LD2s+Mm7a3v5e3xoeLg4top5c6r9/9Ltr3tkceyDP7Fu/EZj6B8q//uo8lz3VfnMKI6s6YryP63F3T3Fsff2bSuOrXIwqPp8DfZv7Ohj0dzuFbPuC2zvprXFsT3LV7cwE6l1Ov1YBPCovZ9YdDyq8hvtSXs8tDgW4MLbflMcW+X7e5XvGE950MOLY6/pvbU4dtPWO4tjAfZeuHtx7N195d+d+4YGimOHK/xW6q7w3blK7QFgwdzu4th7tly3w2ORc5xJkiRJkiRJo5h1hbOIWBkROcnL+gr9PS4iTo6IpS18GJIkSZIkSZpis65w1gaPA94DWDiTJEmSJEnqILNxcYAN1FbCnIzyE4QlSZIkSZLUkWbViLOIWAV8EVgDXAZcAJwM3JKZ6zJzHXA18Gzgq8DlwI+AEyNicdO+MiI+FBEvjIjfRkRf/foZDW1OBv69fvOGhlM/D5zChylJkiRJkqQWmDUjziKiGzgfGAJOA+4E9qNWJFsK3FNv+ingZcCngQ8Bq4B/AA6LiKfm/Zfj+3PgRcBHgC3AG4DzImL/zLwT+ApwMPBy4CTgjnrc7VP0MCVJkiRJktQis6ZwBhwGPBh4TGb+smH7ySP/iIjVwPHA0Zl5XsP2X1IbqfZ04HsNsYcCh2bmDfV2P6I2Su2lwIcz89cRcQm1wtnXMnP9aIlFxObxkn/U3k8c/xFKkiRJkiSpZWbTqZojI8qeUx99NpqjgbuAiyJi2cgF+DG1kWpHNrU/f6RoBpCZvwbuBQ5qaeaSJEmSJEmadrOmcFYvcJ0GvAu4MyK+FRGviYhdGpqtAnandipl42UTMAfYs2m3N47S1d3AbpPMbel4l8nsT5IkSZIkSdXNplM1ycw3RcRngOdRO+3yI8A7IuLxmbmRWiHxFuAVY+xiU9PtoTHaRSvylSRJkiRJUvvMqsIZQGZeAVwBvC8iHgtcDLyW2ki064CjgLWZ2deqLlu0H0mSJEmSJE2jWXOqZkTsGhHNhcIrgEFgQf32uUA38LZR4udHxK4FXW+tX3u6pSRJkiRJUgeZTSPOngJ8OCLOBa6iNmfZy6mNCDsPIDN/FBGfBE6OiMOBC4Fh4BDgxcCxwAWT7PeS+vX7IuKLwADwzczcuoMYSZIkSZIktdlsKpxdDpwPPBt4DbCtvu2vMvPihnYnUCt2nQCcAvQBNwCfAC6bbKeZeWlEvB14HfAMaqP8HswfR6JJkiRJkiRpBopMp+DqBLstXln8QvUO9hf3O5zDFWLb997qivL1GeZ0zSmOfeiSfYtjv7DrkuLYE7aWT8n389vXFcd2z51XHAvQPzhQHBsVXuMqx72urmpnuPdtv6mjFw+Z273CD41psqh7wfiNxnDbpZ8t7/dhLyqOVecY7N/osWgW6d20tji2Z/nqFmYi3V+nH4sA9lry0KLj0ea+8nEUVX/D79FTMgNRzfYKvy2fscfDi2OP217+vejl2y4Zv9EYtg5sL46tqsrrXOW30vBweQ2gSr9VYqsa7zfarJnjTJIkSZIkSZqMjiycRcTKiMhJXta3KdeHRsTJEXFgO/qXJEmSJElSmY4snHWYhwLvAQ5scx6SJEmSJEmahE5dHGADcOgkY8onU2oQEXOAuZlZPqmUJEmSJEmSZryOG3EWEauALwJrqK1yeQFwMnBLZq7LzHXA1dRWz/wqtZUzfwScGBGLR9nfKyPifyNiW0TcFREXRsTqhvszIj4UEa+IiHXUVtl8fP2+/SLirIi4LSL6IuLXEfHShtjj6zkA/KjhtNEjW/usSJIkSZIkqdU6asRZRHQD5wNDwGnAncB+1IpkS4F76k0/BbwM+DTwIWAV8A/AYRHx1KwvTxER/xd4J7C2fp3UimKr69tG/CXwEuC/gM3ALRGxD3Ax0A/8B3AX8Dzg8xExPzPPBH5c7/9E4P3AlfX9XYkkSZIkSZJmtI4qnAGHAQ8GHpOZv2zYfvLIP+qjxY4Hjs7M8xq2/5LaSLWnA9+rj1x7O3AOcExmjqy5+u/xwHVQDwEOy8yrG/b3KWAYeFRmbq5v/mhEfBd4f0SclZnXR8RF1ApnP8jMNaM9qIjYPNr2RksXHTxeE0mSJEmSJLVQp52qOTKi7Dn10WejOZra6K+LImLZyIXa6K8h4Mh6u+dTe/zvbSiaATAyIq3BD5uKZgG8APgGMLepn+8B+1ArtkmSJEmSJKlDddSIs8y8ISJOA94FnFQfzfVN4POZeV+92Spgd+D2MXazZ/36IGqFtHUT6PqGUfaxFPj7+mWsfiaybzJz6Xhtdlu8srmYJ0mSJEmSpCnUUYUzgMx8U0R8htp8Yk8HPgK8IyIen5kbqY0iuwV4xRi72FTQbW/T7ZGRemcCnxsj5oqCfiRJkiRJkjRDdFzhDCAzr6BWmHpfRDyW2iT9r6U2Eu064ChgbWb27WA31wFzgIcy+SLX7cB9QGTmBeOlO8l9S5IkSZIkaQboqDnOImLXiGgu9l0BDAIL6rfPBbqBt40SPz8idq3f/Dq1yf3fExFdTe2aFwe4n8wcAr4KvCQiHjJKP3s23Nxavx73dExJkiRJkiTNHJ024uwpwIcj4lzgKmojxl5ObVTXeQCZ+aOI+CRwckQcDlxIrUB2CPBi4Fjggsy8JiI+CPwzsCYivkatAPd44DfA+8fJ5W3URrb9b0R8nNp8ZsuAI4BHAwfW211e3+9bI2IJ0EdtsYHbKj4XkiRJkiRJmkKdVji7HDgfeDbwGmBbfdtfZebFDe1OAC6pX59CrVh1A/AJ4LKRRpn59oi4AXgdtULZVuBS4KLxEsnMWyLiMcB7qBXk9gLuAH4NvLOh3e0RcUJ926eoFfuOAiycSZIkSZIkzWCR6RRcneARez+++IW6pfeu4n639G8vjh0YGiyO3eG5slNsnDN1d2iPnl3HbzSG9+zy6OLY8+Oe4tiL7p7Q4q+j6q/wGgNsH+wvjp3TVX6m+dyuOcWxQ8PDxbEA27ff2M63d2V7Lz20+Fh057Z7i/vt1E+qKu/TKp/P79z7z4tjlw2Xv0W/zp3FsRfe+uvi2E5V5f0xXPFYNNC/saOPRXO7V3TqYaHj9G5aWxzbs3x1CzPRzmiww49FAPMKj0ftPIhVedKr5F3lc+++dV8tjl14yPOKYzU7jHcs6qg5zmaqiDg5IvwCJ0mSJEmStBNpW+EsIlZGRE7ysr5d+UqSJEmSJGl2ccSZJEmSJEmSNIp2Lg6wATh0kjEDU5GIJEmSJEmS1KxthbPMHADKZyWfgaI2q/wNwOWZ+bym+3YFbgVOz8x/akd+kiRJkiRJmjhP1WyhrC2BdjbwjIjYrenuFwAL6vdLkiRJkiRphrNw1nqfBbqBo5u2Hwv8JjN/3RwQEZvHu0xH4pIkSZIkSfojC2ctlplXAf9DrVAGQETsAzwFR5tJkiRJkiR1DAtnU+OzwOqIWFG/fQwQwBdGa5yZS8e7TFfikiRJkiRJqrFwNjW+CAwBL63fPha4KDNval9KkiRJkiRJmgwLZ1MgM+8AvgscGxEPAQ7H0zQlSZIkSZI6ioWzqXMW8Ejg/wJ9wLntTUeSJEmSJEmTYeFs6nwLuBt4EfCtzLynzflIkiRJkiRpEiycTZHM7AO+XL/paZqSJEmSJEkdJjKz3TnstCLidGoLA+ydmf1V9tU9f9/iF6pdr/FsfGd1RRTHHrRkn+LYvqHyt9evX7K8OHbvM68sjgXo7ppbHLulv7c4Niq8TlX/ngb6N5Z3PgMsXvjg8mNRhaNC/+BAcexsPBZVceLyJxfHPnxgTnHsRyqun3Nr393FsRvvu7M4tsofdJVjUVX9fTd39LFobvcK/7Q7QO+mtcWxPctXtzATzVSDHf69CGC3xSuLjke9g5V+HlZy+B4ri2Ov3rKxOLbKd///mv+nxbHHbP5JcezSBYuKYwF2696lOPbazeXPdZUPySq/aYcr/FaqejCo8pjHOxY54mwCIuLIiMiIOHISbV8MvAz4UtWimSRJkiRJkqafhbNCEXFMRJw4yl1L69dvqv/79OnLSpIkSZIkSa1SPlZydvkx0AM0jhw7htqqmR9qantg/foQ4HWZ+bspz06SJEmSJEktZ+FsAjJzGNg+weaX1a9flZlfm6KUJEmSJEmSNMVm1amaEfGPETEYEbs0bHt3fU6ydzds27Xe7vX12/eb4ywi1gDPAw6ob8+IWN/UXVdEvCsiNkbE9oi4MCLKZ2GUJEmSJEnStJptI85+AswBngCcX9+2GhgGntTQ7gn1dmMtv/E+YDFwAHBSfduWpjbvBIaA/w/YDXgL8DngsZUegSRJkiRJkqbFbCuc/QrYSq1Ydn5EzAEeB5wH/FVEzMnMofr99wKXj7aTzPxBRLwOWJaZZ4/R1zzgsZk5ABARdwH/EREPz8wrGhtGxObxEp/XvWJCD1CSJEmSJEmtMatO1czMQeAX1ApjAI+iNnLsA/XrR9a3rwZ+Xp/brNSnR4pmdWvr1wdV2KckSZIkSZKmyawqnNWtBR4TEd3UCmTXZOalwPXA6oiYDzyGsU/TnKgbm27fXb/erblhZi4d71IxF0mSJEmSJE3SbCyc/QRYABxBbV6zkZFga+u3jwDmN2wvNTTG9qi4X0mSJEmSJE2D2Vg4u5haUevJ3L9w9pP67dVAP/A/4+wnpypBSZIkSZIktd+sK5xl5hbgUuB44EHcf8TZXvXtl2Rm7zi72gosmZosJUmSJEmS1G6zrnBW9xPgEOCWzLwOIDOvAm6vb5/I/GaXAEsj4rSIeGlEPGfKspUkSZIkSdK0m62Fs8bTMxv9ZIztozkDOJvaCLXPA6e3JDNJkiRJkiTNCJHpVF2dYMnig4tfqMHhsdYpGN/Q8HBx7HBWie3M92WVlR+6584rjt1jwS7Fsad3/0lxLMDH5t09fqMx/GbrTcWx9/ZtK46NKH+l+ocGi2MBens3dPQCIfO6VxT/cXbmX/Xs01Xh7+P3zzy4OHavb19bHAvwrn2OLI597y1rKvXdDlUPJAP9Gzv6WDS3wrFInaF3U/k6XT3LV7cwE02lwQ4/FgEcsMcjio5Hd2/fUtznnKg2/mXbYF9xbJX6QZXfeLt09xTH9g72F8ceunS/4liA1Qv2LY79/rbri2N3nbuwOPbXd91QHDu/wm/a3oHy9yXArvPLH/Md9169w2PRbB1xNqUi4viIyIg4sGHb+og4s21JSVOgStFMkiRJkqSZrmMLZxGxsl6cmsxlfbvzliRJkiRJUmfo2MLZDKFhMjIAACAASURBVPdZoAfY0O5EJEmSJEmSVGZuuxOoYANw6CRjBqYikWaZOQSUTywmSZIkSZKktuvYEWeZOZCZ60YuwO7UVrb8H+AS4MPAoqY274qIzRHx4Ij4bkRsjYgNEXE8QEQ8NiJ+FhG9EXF1RDytsc+IOCAiPhIRV9Xb3BkR5zTOZVZv94A5ziRJkiRJktRZOrZw1igingqsAeYD7wbeBewL/DgiDmtqPg/4LnA98BbgduDTEfEy4KvABcDbgAXAORGxuCH2COAJwBeBNwAfA/4CWBMR5Us4SJIkSZIkacbp5FM1AYiILuCjwPmZ+ZyG7Z8C1lErpB3TELIQ+FRm/lu93deAG4Gzgadl5oX17VcC5wP/h9qcZQDfzsxzm/r/JvBz4IUN7Sb7GDaP12bXRQeV7FqSJEmSJEmFdoYRZ38KrAS+EBHLRi7URpatBY4cJeaTI//IzE3ATcCNI0Wzul/Urx/c0LZ35N8RMS8i9gCuBTYDj27Nw5EkSZIkSdJM0PEjzoBV9evPjXH/cNPtLZl5d9O2e4D7Gjdk5j0RAbDbyLaI6AH+GXgVsAKIhpAlk0v7fn0tHa/NksUHZ+n+JUmSJEmSNHk7Q+FsZNTcScAVE2g/1mqXY21vLI6dTq1o9iFqp2feAyS1Oc92htF7kiRJkiRJqtsZCmfX1a83Z+YFU9zX0cB/Z+abRjZExAJg3BFjkiRJkiRJ6iw7wyipX1FbIfPNo61sGRF7trCvIe4/Ag3g9cCcFvYhSZIkSZKkGaDjR5xl5lBEnAB8G/hNRPw3cAuwL/CX1CbvP65F3X0LOC4i7gF+BzweeCpwZ4v2L0mSJEmSpBmi4wtnAJl5YUQ8AXg38EZgEbAJ+BnwsRZ29UZqo86OBRYAP6VWODu/hX1IkiRJkiRpBohMF2vsBPO6V8yqF6qrq9pZxEPDzYupznzN5wBPRpXna8+FxQvCAnDULocUx76JweLY5953Q3HsXdu3FMf2DfYXxwIM9G+s8lK3XZVj0aw6iM1ScyocixbOnV+p71suOrU4dpfHv65S36Xa+Tcx2OHHormz7HuRJqd309pK8T3LV7coE42n049FAMt2PaToeLR5+9ZWpzJh797nyOLYb/TfWBx72Z3XF8d2RflbZU5X+cxKr97rccWxAJ/4/c+KYx+22wHFsb+5s/y3UtXf4qXa+Rt+vGPRzjDHmaQ2qVI0kyRJkiRppptRhbOIWBkROcnL+nbnLUmSJEmSpJ3PjCqcSZIkSZIkSTPFTFscYANw6CRjBqYikakUEQszc1u785AkSZIkSdLYZlThLDMHgHXtzmOiIuIlwNuoFfvuBb4JvDUz72hoswZYCpwAnAYcDnwQOHma05UkSZIkSdIkeKpmoYg4HvgisB34J+C/gWOBH0XEgqbmewLfAn4JvBG4ePoylSRJkiRJUokZNeKsU0TEPGqjxi4HjszMvvr2S4AvAH8LnN4Qshx4dWZ+eoz9bR6vz7nzlldNW5IkSZIkSZPgiLMyfwY8CPivkaJZ3ZeBjcCzmtpvAz47TblJkiRJkiSpBRxxVuaA+vVVjRszczgirmm4f8TN9fnbRpWZS8frcF73ipx0lpIkSZIkSSrmiLPp0dvuBCRJkiRJkjQ5Fs7KbKhfP6RxY0QEsKrhfkmSJEmSJHUoC2dl/he4Dfi7iOhu2H40sAL4dluykiRJkiRJUss4x1mBzByIiLcCnwHWRMQXgP2ANwBXAJ9oZ36SJEmSJEmqzhFnhTLzTOClQA9wKvAq4HPAUzJzextTkyRJkiRJUgtEpos1doIliw8ufqEGh4eK+x0YGiyOrfreqk0ZN/2xw8PDxbFVHnF5xtV1z51XHBsVMn/znk8ojr1o6Lbi2MPnLiuO/cQd/1McC3DPluva+VJX1q4Vfv2k0ni2XnpWcezSP/vr4tgqn3VDFT6fodrfxWD/xo4+Fs11tXFNod6b1xTHLtz3yOLY2fim7vRjEbTvu1Gl3zsdWAOo8kap+mir9L24u6di72U2feLY4tjdXvWZ4tjhLP8tXYsvf7XmzSk/obK3d8MOX2ZHnGlGqvJBoMlpV9FMklSu837ySJJaxd9K08dnevrM5ILurC+cRcTKiMhJXta3O29JkiRJkiRNrVlfOCt0wFhFNYCIOL5pe29EXBkRp0TEknYnL0mSJEmSpPG5qiZsAA4FPgA8HXjPKG0uAQ5vuD0EvBHYBrxvB/t+B3AjsAh4KvBW4MiIeHw6uZwkSZIkSdKMNusLZ5k5AKyLiHuA/sz8tzGa/rDxRkS8AticmWfvYPffyczL6v8+IyLOAY4GHg/8rGLqkiRJkiRJmkKeqjm91tSvD2xjDpIkSZIkSZqAWT/irElExLKmbf2ZeW+L9n9Q/frOFu1PkiRJkiRJU8TC2f3tCtzetO3bwLML97e0XohbCDwN+HvgNmBtY6OI2DxuYosOGq+JJEmSJEmSWsjC2f1tBZ7ftK25kDYZP2q6vQ54ZWZuq7BPSZIkSZIkTQMLZ/c3mJkXtHB/rwWuAwaBTZl59WiNMnPpeDtasvhgV+GUJEmSJEmaRhbOptYvGlbVlCRJkiRJUgdxVU1JkiRJkiRpFBbOJEmSJEmSpFFYOJMkSZIkSZJGYeFMkiRJkiRJGkVkulhjJ5jXvaL4herqak99dGh4uDi2K6JS311R/pgHh4cq9V2qyiOOCs9XlWPAwu4FxbEAS+cvKo597eJHFMdeE9uLY+8cLo8F+PqN36r25m6zBQv2L37DDFX42+rUT6oqL3anPuZ2OWDXvYpjb7z31uLYu096bHHsUZ+9ozj213fdUBwL0Lf9po4+Fs2t8L1Imkq9m9YWx/YsX93CTDrDYP/Gjj4WQfl3o+Es/600b061Nf76BweKY5csKP/+fulD9iuOPfjXVxXHDlf4vVP1d+nqPQ8rjv3ZneWPuUreVd4fVSzu7qkUv32oPO/e3g07fMIccSZJkiRJkiSNYqcrnEXEyojISV7WF/RzTEScOAUPQZIkSZIkSTPATlc4m0bHABbOJEmSJEmSdlLVTo6emTYAh04ypj0n8UqSJEmSJGnG2ukKZ5k5AKybyj4iYg3w5/V/j8w0uCEzD6xvexBwCvAcYBfgd8D7M/PcqcxLkiRJkiRJrbPTFc6myfuAxcABwEn1bVsAIqIHWAMcBJwO3AS8FDgnIo7LzLOnPVtJkiRJkiRNmoWzApn5g4h4HbBslELYCdROFT0mM78EEBFnAL8ATo2IL9VHxf1BRGwer8+585a3JnlJkiRJkiRNiIsDtN4zgY3Al0c2ZGYf8FFgL+DwNuUlSZIkSZKkSXDEWesdAFydmdm0fV3D/Rc33pGZS8fb6bzuFc37kyRJkiRJ0hRyxJkkSZIkSZI0Cgtn5cYaAbYBWBUR0bT9IQ33S5IkSZIkaYazcFZuK7BklO3fAfYFjh7ZEBHdwN8BtwKXTEt2kiRJkiRJqsQ5zspdAhwbEacBvwS2ZOY3gY8DrwE+GxFHADcBLwUeCRzXvKKmJEmSJEmSZiYLZ+XOAB4NHA+cRO0UzG9mZm9EHAWcAvw1sAvwO+DFmXlOm3KVJEmSJEnSJMUDF3/UTLRgwf7FL1SOOR3b1BoaHm5LvwDNE8xNW78PmNpuemKH2/Rcz+maUyn+4CX7FMcunbuoOPYdgw8qjv1/8+4qjgW48Obvt+vt2RJzK6zwW+WBd+on1Wx8zO3Srud62cJdi2NvOPeNxbHLn39qcSzA3VuunbXHImmm6t20tji2Z/nqFmYyfQb7N3b0sQhg/oL9pv14tGBud6X47YP9LcpkcubPmVcc2zvQVxxb5XfWonkLimOh2mv1sF32K469vve24tjnL37I+I3G8F+//2lx7GFL9y+OBbhle/nvtN9vvnKHbxLnOJMkSZIkSZJGMesLZxGxMiJykpf17c5bkiRJkiRJU2vWF84kSZIkSZKk0bg4QG1S/0MnGePKmJIkSZIkSTu5WV84y8wBYF0r9xkRq4E3AI8F9gJuA84F3p6Zva3sS5IkSZIkSVNj1hfOpsiLgIXAR4E7gccArwf2rd8nSZIkSZKkGc7C2dR4a9PIso9HxLXA+yNi/8y8sbFxRGweb4fz55cvRStJkiRJkqTJc3GAKdBYNIuIRRGxDPgZEMCj2paYJEmSJEmSJswRZ1MgIvYH3gs8F9it6e4lze0zc+l4+1ywYP9sTXaSJEmSJEmaCAtnLRYRc4AfALsDH6S28MBWYAVwJo7ykyRJkiRJ6ggWzlrvT4BDgFdm5lkjGyPiae1LSZIkSZIkSZPl6KfWG6pfx8iGiAjgje1JR5IkSZIkSSUccdZ664DrgFMjYgVwL/BCHjjXmSRJkiRJkmYwR5y1WGYOAM8BLgP+GXgPcA3winbmJUmSJEmSpMlxxNkUyMwrgdHmNItRtk1Iz9zu4nyW9TxgIc8Ji/KUWX/v74tjM6stItrVVV4TrvKYly5YVBzbNzhQHDswPDR+ozFsH+wvjq1q/X23Fsfut3jP4tgTBm8rjn3L/EcWx+4MVuyyR3HstsG+4tjNvVuKY6scTcqPBu1V5Vi0pX97cexghWNRVe16nYdyuDh28Ctfb0u/kmamnuWri2N7N61tS78q/+0wVOEzc2uFz2po3/ebbcPl3wXnz51XHDsnyn8bbunvLY6tGv/bCv1W+d69dNGc4th37/Xk4tiP3HtZcSxU+x0/nlk14iwiTo6IbNo2LyL+X0TcHBEZEWdGxIH1fx8/lX1LkiRJkiRp5nLEGbwa+EfgVOByavOTjXhQQbFrQ2Ye2KLcJEmSJEmS1CazrXD2r8ApTduOBNZn5ltGNtRXwewB9gc+OG3ZSZIkSZIkacaYVYWzzBwEBps2Pwi4p6ldAtsj4gbg0El2Uz5RlSRJkiRJkmaMKZnjLCJWRcR5EfH7iNhenz/sixGxpH7/yBxiJ0bEmyPipojYFhEXRMQho+zvYRHxlYi4KyJ6I+LiiHjA5PsRsV9EfCYibqn3e21E/EfD/X+YZ2wkB+Ao4E/r+WREHNlw37GZuW7kAswB3g/8DLgUOBPYr7FNZl4XEU+KiF/Wc7guIl7T+mdZkiRJkiRJU6nlI84iohs4HxgCTgPuBPYDng0s5f6ju/4aWAj8J7AIOBH4UUQ8PDPvru/vT4CfABuADwDbgWOB70bE0zPzwnq7FcD/ALsAZwBXAQcALwHeOEqqtwPHAe+o5/CO+vYrqZ2m2fy4JprHnwDfB24D3gPMA/4FKF8+UJIkSZIkSdNuKk7VPAx4MPCYzPxlw/aTR2l7ELAqM28BiIgfAhcBb6BWbAL4EHAt8LjMHKi3+yjwK+B9wIX1dqcAewJ/lpl/WMc0It45WpKZuRU4OyL+BliamWc3xBw4SshE83gvkMATM3Njvd15wG9Gy6N+/+ax7huxdNHB4zWRJEmSJElSC03FqZojI8qeUx99tiNfGSmaAWTmj4ErgL8CiIjdqZ1KeQ6wJCKWRcQyaiPXvg8cERELI6ILeB7wtcaiWX2fk10V8wEmkccc4On1x7WxIYcrqY3CkyRJkiRJUodo+YizzLwhIk4D3gWcFBEXAd8EPp+Z9zU1v2aUXVwDPKH+75VAUDs18gNjdLkH0E/tFM0rKqY/lonmMUDtNM/RHtdVwDNHC8zMpeMlsNvilZULgJIkSZIkSZq4KVlVMzPfFBGfoTYK7OnAR4B3RMTjG0diTcDIiLgPAheM0eZ2YElxsq3NY9wCmCRJkiRJkjrDlBTOADLzCmojwN4XEY8FLgZeS20k2ohVo4SuojYBP8D19evtmTlWwYqI6AfuAx5eNe8xTDSP24FeRn9cD5mKxCRJkiRJkjQ1Wj7HWUTsGhHNBbkrgEFgQdP2F0TEPg2xT6ZW/PouQGbeBvwY+LuI2HOUvvastxsGvg48PyIe1dQmqj2iSeUxRG0usxfUV/kcuf9QaiPvJEmSJEmS1CGmYsTZU4APR8S51Ob1mgO8nNpKk+c1tb0eWBsRHwMWAScBtwD/2dDmdcBa4IqI+CRwA7APsJpaIe7J9XZvB/6yYX9XAfsBxwCHtOBxTTSP9wDPAH5aX3VzLvB64LfAI1qQhyRJkiRJkqbBVBTOLqc26urZwGuAbfVtf5WZFze1/XQ9hzcCy4CfAa/LzLtGGmTmFRFxBHAy8DfU5hG7FbgE+I+GdjfVTwn9V+CV1BYLuAn4Vise1CTy+HVEPB04DXgvcDO1Yto+WDiTJEmSJEnqGJE5/Ys1RsSB1EZsnZSZH5r2BDrQEcufXPxCbeq9s7jf3bp3KY694b7fF8cODA0WxwLMm1NeEx4cHiqOrfL3tGTBouLYLsrPSL63v7c4FmDBnHnFsduHBopjd1+wuDi2yvv6YQv2Lo4F+NKGr1U+fbyd5navKH6TV3ngLis8OXO6ymdiGBoeLo7tqjA7wnDF7yPtesxVVPmb2HL52ZX6nn/oUbP2WCTp/no3rS2O7Vm+ulLfg/0bO/pYBLBgwf5Fx6Mqn1vLF+1RHAtwwz3lv9PapV3fI6u+QQ/Zbd/i2PX33Voc+5Rl5dO/X9Vb/v64q+/e4tiq3wX/bo8jimPft/7zO3ypWz7HWaeJiIyIk9udh9SJqhTNJEmSJEma6XZYOIuIlfXC0mQu66cpd0mSJEmSJGnKTMUcZ52mh9qKn5IkSZIkSdIfjFc42wAcOsl9jjtpUWaup+F04YhYmJnbJtlPS2Tm9unus52PV5IkSZIkSROzw1M1M3MgM9dN8nLdjvYZEWsi4rKIeExE/CQieoF/qt/XExH/GhHXR0RfRGyIiPdGxNymfXRFxD9GxBURsT0ibouIb0bEw5ravDkirqzv65aIOD0iFjft6w9znEXEi+q3nzhK3m+NiOGI2K9h2xMi4gcRcW9EbI2ICyLi8Ka4MyNic0SsiojvRcQW4CM7eo4kSZIkSZLUfu06VXNP4FvA54CzgBsjogv4JvBY4AzgauBw4B3AfsCrGuLPBI6r7+MMYAFwVL39b+ttPgW8DPg08CFgFfAPwGER8dQcffnDbwNbgRcDP22678XAzzPzJoCIeCrwHeBi4N3UipAnAD+OiCMy83cNsfOA84EfAv8I3D2B50iSJEmSJElt1K7C2XLg1Zn56ZENEXEccCTwxMz8RcP264FTIuKDmbkuIv6CWtHstMx8U8M+/y0ioh6zGjgeODozz2vY1y+BLwJPB77XnFRmbouIbwFHR8SJI8W1iDgYeDTwxvrtLuCjwPmZ+ZyG/X8KWEetkHZMw64XAmdn5rtHezIiYvOOniyAP9un2lLPkiRJkiRJmpwdnqo5hbYBn23adjRwBXBdRCwbuQAX1u8/sn79AmAIeG/zThtGkR0N3AVc1LSvH9djj2yObfBlaoW9JzVsezEwDJxTv/2nwErgC037nwesHWP/H9tBn5IkSZIkSZph2jXi7ObMbF5EYBW1hQhuHyNmz/r1QfX4e3aw/1XA7hPY12i+A9wHvIRaEQxqhbOfZOYtDfuH2qmmoxluut2XmZvG6jAzl+4gHwCOWP7k0U4tlSRJkiRJ0hRpV+Gsd5RtXcCl1BcKGMX1k9h/F3AL8Iox7t9REWt7RHwTeGFEvIFaoe6RwOua9g9wErVRcuOZ9pU7JUmSJEmSVE27CmejuQ54WGZeMIF2T4uIpZk51txg11FbLGBtZvYV5PJlagsLPBl4ArXTO89t2j/A5gnkK0mSJEmSpA7UrjnORnMucEBEvLL5johYHBEL6je/CswB3jVKu2jYVzfwtlHazI+IXcfJ5XvAvdRO13wxsCYzb2u4/1fURsC9OSIWjtLHjk4FlSRJkiRJUgeYSSPOzqJWqPpMRDwN+Dm14tdh1IpXhwPXZuaFEfEF4B8j4hDg+9Qm5T+K2kixz2bmjyLik8DJEXE4tQUGhoFD6vs6FhhzpFhm9kXE14GXA4uBE5ruH4qIE4BvA7+JiP+mdmrovsBfAtdSW/lTkiRJkiRJHWrGFM7qxajnAm+mVrB6EbCFWhHqFGBjQ/PjgMuBv6ZWqNoM/AK4pKHNCfXbJ9Tj+4AbgE8Al00gpS/V+xkEvjJKvhdGxBOAdwNvBBZRmzvtZ7iCpiRJkiRJUseb9sJZZh65g/v6gffXLzvaxxDwwfplrDZJrYC1wyJWZsYY278NjHpfQ5tfAc8fp83xO7pfkiRJkiRJM1PU6kvqZBGxGSAzlxo7c/uebbHt7LsTY3cGnfi8+x7vjNh29j3bYncGnfq8d2LenRjbzr6NnV069XnvxLw7Mbadfc+22FbEFy8OEBErIyIneVlf2p8kSZIkSZI0nWbSqpqSJEmS9P+3d+fhklTlHce/7yAw7AiDgoCAMAyQGIyARgUEQdwAFyAqKmAQ3IjgCiYExiVBQATUGCGCoyCILAYksogIQkBBNkE2YRYWUXZkH2bmzR/vuXP79lT17aW6u6rr93mefma6qk7V6b7dv6o+daqOiIhIafRyj7N5wKYdlnmhh+2JiIiIiIiIiIgMTNcNZ+7+AnB7gXUREREREREREREpDV2qKSIiIiIiIiIikkENZyIiIiIiIiIiIhnUcCYiIiIiIiIiIpJBDWciIiIiIiIiIiIZzN2HXQcREREREREREZHSUY8zERERERERERGRDGo4ExERERERERERyaCGMxERERERERERkQxqOJORZWYvN7PlWsxfzsxe3mL+tma2Rov508xs217rWQbpvTjMzHbqouw/mNn7zOzNZja1H/Urm/R+nWxmuw27LlINveSRsqij8rXKI2WRdEpZ1D4dG7VPWSSdUha1T1nUmX7l0YuKXJkMTvribAesARzj7reb2UrAVsCN7v5oRpnJAsSB54D73P2BjPJrAJ9u2O5e7n61ma0OHAj82N1v7f5V5TOzjRu2+yN3n2tmywJrA/e7+/MZxeYAHwJOy1ntrmneUjnzfzVJ+R0mKd8TM9sMeAXwYsCa57v7D83s5C5W7e6+b9OEZ83sEOBTHdRvZeBC4LUNk/9iZru4+3Vd1AszWwrYAlgvTZoHXOfuC9souw7w98AqZJwUcPcfdlOnLOn92gO4qqh1VlXdsihtf9B5pCyavI6F5pGyqHqURcqiIrMoTRz6sZGyqHqURcqise/WKP1Oq0oWpfX1J4/cXY8KPYAVgF8AC4EX0r9vSvOWAf4MfCmn7KK0fDuP24H3NpTdEPgT8HT6EC7ebpp/M/BfOds14KPANcDDOdtbkFN2KeCktMwiJr7eFYDHgS+0eL17tngvPwi80GL+ZOX3Aub34W+8IXB1w2vOeixMy84ldjydPGbnbPdKYgffbj2PSHX5CfAJ4BjgGeCWLl/3nukz1vi6F6Zp729RbipwBrCgoUxj+YUN71cn34EJZTO2eyFwYj+/72V+ULMsSuWHkkdtlK11FqUyheURyqJKPVAWKYv6lEVpfUM7NkJZVKkHFcyiNL9yv9PaKDv0LErLz2UEfqdRsSxK6ys8j9TjrHqOALYG3kd8ge4fm+Hu883sTGBn4PCMsm8FjgSWBf4buCtNnw58BHgW+CrRkvxR4DQzW+DuZwNHEx/QzYhgfrBp3ecBed0hjwI+A9wInAo81v7L5TBgb+CLwKVEqAPg7k+n1/vutI2x1vVVG8qvntPNd1XiPZxw1iYtu37DpE1yzgKtSrxH8/IqbmZfA05x9z/kLZPjBOCVwEHAFbR4v9x9/bx5Xfg08DMz+z1wqk9+BuGdwJnu/t6xCWZ2K3CimW3k7nflF50odaU9FbgJmEkcFABsAnwcONXMnnf3czKK/wfwHuBfiZ3ZZcRn5gHiPXwZsQMF+DJx1q7Ru4G/AS4C7mjY7k7ALcD/5FT7AOAiMzscOMHd/9zeqx0ZdcsiGGAeFZlFaX3d5FFVsggKyiNlUSUpi5RFixWcRTCkYyNlUSVVMYugIr/TqpZFMPRjozpnEfQjj4puidWjvw+iZfeo9P/VidbZxrMKBwCP5pT9BvGhXSZj3lTgN8CRDc9vAa5JzxefMcjZ7n7AUznbfZD44nbzeueSzpLkbPcg4KGG54fTfiv1IuDgpu0dTnst3ouA+cDeLer+TFr2RuCzwNptvuZngcOG8Nm6Hrgv1flJ4LY0rfFxXdPr269pHeum92bbDrd9HfB/OZ/NpYkzaNfllL2HdEYh5zNyKfln/fdP36kZGfM2JYJ9v5yyjwFPNXwmngEebXo8Mui/4wA/L7XKolR+LgPKIwrMorS+jvOIimRRw+vrOY9QFlXugbJIWdTfz9dQjo1QFlXuQQWzKM2vxO80lEXKovF5LbMoLVN4HqnHWfWsBvyxxfwpxNmKLB8Avuru85tnuPtzZvYjokX44Ibnh6ZFXkR8SfOsTnRLzrIc0XW5G2sSX9g884EVG55fSOxAjNgJnZpR3okzMte5+41N835MBKgB5wDHAZfnlL/J3R9uUbeXEmd49iTOIh1pZpcDpwBnu3ve+/kIbZ7tGTtL4+73ND6fzNjyTf4KPEHrz1ejqcT70OiZ9G+n2bIZ8Pmcz+YLZnYacUYty0sYP8P1bPp3hYb5ZxNnxD6eUfbzwLfd/Y7mGe5+m5l9G/gCcfav2bkseWakTuqWRTDYPCoyi6C7PKpKFkFxeaQsqh5l0ZKURcVkEQzv2EhZVD1VzCKozu+0SmURDP3YqM5ZBH3IIzWcVc9sYPMW83cgWqCzrECERJ41mRhujxEttBDdM3cC/qu5ULpZ4Htp6J7b5FfAlsCJLbad536im3Ke1wF3jz1x998Cv031WoUIvlva3Vj6ct6Ryn8Y+LW7z+mi3qTAnQXMMrM1gfcT4Xwy8B0zO58I5wvcfUFD0ROAD5rZd3zybrhzATez5VKgzaW9kFjiZpnuvl0b5Zqta2Z/1/B8rPv1K8xsiR2Wu/8+Zz1PAuu02g75BwV/IQ4KcPdnzOwxYAbwszR/ZWLnkWUdYqee54W8ern7Pi3K1UHdmJyaLwAAIABJREFUsggGmEdFZlFaXzd5VKUsgmLySFlUPcqiJSmLCsiiVN/t2ijbTFlUT1XMIqjI77QKZhEM/9iollmUtrdPi7Ld6aR7mh7DfwCfI1ps38V4l8ftia6ShxEhun9O2fOID/ZbM+a9jejOeF7DtO8CN6f/75zWfRzwhrTdfwS2Jc4eLAC2z9nuOsCtRKvwqh2+3q8SretbNr7eNG+vtN1DBvC+b5xe9yoFresMxruO/hU4n2ht3zX9ba8jdoSfIq7v3rX5kda1D3GtuDU9b/ko6D3J6y6dNX3CzTIz1nUycRZk94x5u6V5J+WU/Qnws4bns4ig/gAx4s6DwEU5Za8lDnTWzJi3FnGTzmv6/fmq4oOaZVEqP/Q8osAsalhfXh5VIovS9grJI5RFlXugLFIWKYua5yuLhvCgglmUylf6dxolzaK0rrH8qezvNJRFix9jf0SpCDMz4gO8N9FddHXi+t/Vie6/33P3/XPKvpw4q7A+ccPEsTMAGxI3m5xHhN08M5tKdHG8wN2PS+X3AY4lWoeN8RbzJ4ED3P3UnO0+Ruw0lkuTxq43buTuvnpG2alEWG1LXM+/OXAD0R16PeBiYBef2GOrsfyLiTMIeUMGu2cMQd5Qfi/ixoZrpUlvdvdLzWwacfbmX9399LzyTetaK9XlA8SQvI8RgfKxxvqkxxJDG2fUu/Ahls1sGeJeCO9g4nDD5xOfrfkNy+7d6frd/Qc5212D6G49gzh7dWeaNZ3Yod8ObOfuD2WU3RrYg7i/w/Nmti5wCeNnwO4GdvaMrr6p7EXEDuNsJt6M9T3EGZ+d3P3KnHpvQhwMbQdMIw54xj4fxwDfdferc9+QCqtbFqXyQ8ujIrMorS8rj1ZjYv60c4Aw9CxKyxeSR8qi6lEWKYsa6lx4FqU6DvzYSFlUPVXMolS2kr/TlEXKIlpkUVpHoXmkhrOKMrNtiFbe6cQ183cDZ7n7ZZOUW55oqHkLE79wFxEjTjRfC91cfgWiO/BGDdu92N3/2qLMLNoIGnf/cE55I4YkXuL1Aj9090U55d6SllmBOFuQdU26u/srcsq/DziNOFtzEbFD2tHdL03zzwaWc/e3572m1A15dyKEtyG+/BcAPwTO9xhl543EDvXfiJsdthr5prHizdf198TMVidu0vhKIhjHrqGfDqwN/B7Ywd0fKXK7DdufSoyC8zYmfjZ/TtxY8rkO1jWFeB0Lgdsbd9hmNof4O+zo7nPM7G+BrxCf67GDhmeJv/nh7n5zzjZeTexIniRG1tmdtNNO868C7nL3vdqtdxXVKYvSOgaeR0VkUVquZR4Rl1RAh3mkLGq5LmXRgCiLlEVFZ1Gq69DySFlUTVXKolRuFhX7naYsUhbRIovSugrPIzWcycgys1uIoHtPqy9Wi/I3Afe7+9tTWD3ExFA+hDiLk3l9tZn9lBheelnizMcpwI/zAs3M9iSGF57SaV2b1jOVCPVXA6sQO7BGmWdvzOx7xI5jH+An3hAOZrYHsRM5ZexsmZktBD7YydmcMjCzRcQBwqbufmfD9CnAGunpQ3k7+oblf0HsqF5L/I0fZOLnYybx/mxU+IuQyuklj3rNorTMwPNoUFmUplcuj5RFMgzKovazKJUd+WMjZZEMQx2zKK1Hv9NyFJVFqUzheaTBAWrI4kaRWzDeYjwXuN4bbnJobY760czzRyXqmZltTHRhBpjnGd06m2xEjALScaNZMgP4Tov5DxPdPvP8PfB1IsTubLHcmF8A25vZbOAgdz8vayEz2xn4Zs4ZmPUY7+r9OBHIjxI3g1wq1fmpnO3vktZ7RvMMdz/TzLYiup+P/Vg1mPSS0rb08poLtAyRiW0FMnEW6lB3f9Ki63Sz+4CXFVnBUVPVLIKB51GvWQTd5dGfzWzXCmQRFJRHyqJ6Uha1rW5ZBEM6NlIW1ZOyqG2Vy6I0v3K/0yqaRdCHPFLDWcWk7rD7A/syfj14M3f3zL9tai3/OjFyS+P12n8xs882tErPpbshXHOv6TazdYiQympdx91/mFNud+BrwAZjk4gRSuYCX3T3n+Rs8i4mjkDTqSeIa9vzbELc4DCTu6/fycY8rg2/3MzWp3W9V2R8h9rsaOL9/QfihooPEqPp/B9xE8sDiC7gWVYG7m2x3XvSMv2wPt2/5p6Y2fbAvwNbEZ/LNwNj17+fBRzp7hdkFB27mWaetVhyGOiRUccsSmWHkUc9ZRF0l0cWo0wpiyZSFpWMskhZRP+yCIaXR+ujLKqUKmdR2n6VfqdVMYugmsdG61O9LII+5JEazqrnKOAzwI3AqWRfD57JzHZLZW4CZhI384MIl48Dp5rZ8+5+DvBPdBfKWdudCvyA6JY6hfybLC4RyBY3Nvw+MXzz5xm/nntjYsd0usUQv1k3nT8MON7MftTlWZaLgI+Y2bcz6rUxsXM8pWHaWPfSTuTtQFutZyvy/+5vAr7j7teY2dgOxdz9eeBoM9uUGHXnHRllbwPeZ2YnuPsLjTPM7EXEDTNv7aCener2NXfNzHYk7mVwK/G+fHZxZdwfNrPngY+kZZpdQ9yc8psZ612OOOtzRdF1LpFaZVEqP6w86iiL0vSi8qgqWTRZXTuhLKoWZZGyqF9ZBMM9NlIWVUvlsihtu4q/06qYRVDd32lVyyLoQx7pHmcVY2YPApe7+x5dlL0OeI4YlaV5RLKliRvoLevuWxRS2fF1fwP4Z+BQ4GrgMuLD+gBwENFNci93vyWj7B+J1vg3pVBpnLdMqvM0d5+eUfYbxCgamxA3j7yX7FFiPkuGdOblt8RQyucBnyCG0TXi7MBDwGvc/cG0/Ey6CCh3/5KZHQgcmCatR3TVzWoFX4Xoznuquy8xWoqZPQP8s7uflEL0eWCPtKPFzPYDvu7uq2SUfS9wOjEazn8yPmrKDGKn/ffA+8fOHKUd0G1MckZn4kv1HRq2V8hr7lTDjnNT4kDBga2JM1fN178fCnwk66yUmb2e6G59AfBj4ialBxJnwT5HdEF/vbvf2Gudy6huWZTKDyWPOs2iVGYm3R0wPU7FsiiV6TqPlEXVpixSFtGnLErzB3ZspCyqtipmUVp/5X6nVTGLUh0q8Tut6lmU5heeR+pxVj3LEddYd2Mz4lry+c0z3P0FMzuN6EJatN2B77v7kRY3cIS4oeOlwCVmdinwSeJL32xt4PjmME51nm9mPyLO8GQ5qOH/78pZxmlowW5a/31mtgXRRfQDRBh/mBid40zgkMZAdveZOdtox5+Js0wQXWLvIa69bq7r08B1wAk567mHGBoYd19gZvcT3YHPSfM3I3bMS3D3M1IL/NeA7zG+czEirP7Jl+xuvQLZXdHbUdRr7sXmxHdikZll7UwfILrML8HdrzKzdxD3WDgtTT4+/TubGF55JA8Ok7plEQwpjzrNolRmZs42WkoHZlXMIug+j5RF1aYsmlhvZdFEXWdRKjPIYyNlUbVVMYuggr/TKppFUJ3faZXOIuhPHqnhrHp+BWwJnNhF2SdJX9Yc66ZlMLOTiS/E/u6+MD2fjHv2qEQvIbpLQgwhC/FFHnM20V03K5BvJO4TkGdDxr/YzZXpZQSmZYggeyC9pn3NbA2iC3MnNyZsi8eNHs9I2/4V8FV3/2UXq7oUeCfwpfR8FvBFM3sxUfcPkXPpR6rHLDM7lfiMjV2vPg/4nTcMF5wYcZCwvbvP6bSivbxmaxquuNNtN3gWmNpi/gbEmaZM7n6Jmc0gwr1xCO7ri/6MlFDdsgiGkEfKosmzKOWBAce6+/F0SFlUecqiJSmLxvWURakuAzk2UhZVXhWzCCr2O63CWQQV+Z02ClkEfcgjd9ejQg8iVG8FvgCs2mHZk4FngN0z5u2W5p2Uns8lWmOXbng+Z5LH7JztzgMObnj+CPC5hudfBJ7IKbsl0er9CaKL8tj0ZYkbKP4Z2KIP7/MUYD7wiWH/zTus98vT33LZ9HwqcVbiMaKb7Sxg5YK25UQwbjyE17mI6M7d8bYbyxJneG5Mf+/V07w3peWmpc/XD3PWs/aw/95D/qzVKovS/IHnkbKorW2Nde3/zBBep7Jo+J81ZdH4dGXRkvUeWBal9Q/l2EhZNPxHFbMola/U77SqZlGq+8j/TitDFqVlCs8j3eOs5MzsMZa8HnsZogUZYsjarOvBV2+aRmqNv5y4Fvp+xq+Nnk6E/e3Adh6jOxbGzH4CLOfuu6Tns4C3ETfQnAIcA9zg7m8xs+szVjGN6Ar8fKo36fmywJ+AB73FNf9mthNxDf0awDHufruZrUTczPBGd380p9xtxBDF/9HZK+6MmW0L4O6/bnw+mbHlhyV1m3VgE29vGOcit734GvhOt910/fyLgKuI78JZwBHAkUQ36Y8T37Wt3H12xnoWEqPgnAGc5e7t3kOgkuqWRWl+KfJIWdRa+k4b8Fl3/8YQtq0sGiBl0WLKoibDziIY3rGRsmjwRiGL0rYr9ztNWTS5OmdRWlfheaSGs5JL4dXxH8ndP5yzvqnAR4lAXC9Nngf8HDjR3Z9Ly80GDnL389Lzw4BzPOdGtZO8hq2BPYAvuPvzZrYucAmxM4DoMrmzu99hZpfR3evdPmO7KwD/Q4xgsog0jK27X5q6+d4DnODuh+fU+0PEdeTbuvvdndapXQ0hsZzH/QAmG/XFiB1vy2Gl+20UGs7c/U4zeyUxWst2sHgUIYBfA5909z/krOdg4nP9auLA6Eri5pNnu/vDnb2i8qtbFqXlL6MEeaQsam0UGs6URe1TFrVPWTR4Vf+xqixq3yhkUSpfud9pyqLJ1TmL0roKzyM1nEkmM5sP7Ovup6Tni4APuvtprUu2vf4pwCuJD/LtvuR12d2udw7pumrg08B+wF7El+V+Jo7G8S1iNI3MsyAWo728hdhx/JLoCv1s02LuOaNydlDnN6YVXd74fDJjy2esb3ng3cS13y9mYtCkor3VOW2n62Ac5rbzylrcX2AjYsc9u92zemb2CmIEnz2AVxEj/FxGnOE4x90LH6K5TqqaRWndheSRsqi1hu/09oM+y6ssqg9lkbKoHcM6NlIW1Ue/syits9S/06qaRWkdI/07rUxZlMoWl0degut99Zj0et/ZwK4D3uYdxLXF6xBDzS4C9gVWbvUowXvVeG30n4Cj0vQJ10anaQcAj06yrskeC4f9mpvqvCPw6CDqTNO188BKxJDQrxzk33mQZdtY90bAvxBDRS8Enhv2Z6Lg16cs6qzuheSRsqit99lpuMfZoPJIWTS0z5eyqLO6K4sGVGeGdGykLBra50tZ1Fnda5tFqd4j/zutrFmU1t9THmlUzWpYH1ix00IWwwd3yt19B+I64u8RI39AfPlOZPKRYpbommpm+wJvd/fdcup5FnC+u8/qor6trAb8scX8KcT195m8h1E5e2FmnwSucPffd1H8P4lRd94L/Nbd/1po5VpbBtiZ6FJbSma2F3HT1p8C89LzSbl7yxG3Gpa7y8yuJA5mNmTiqESjYH2URd3qOo+URV0pdR4pi3q2PsqibimLBktZpCxaQlmyKNWlcr/TKppFoN9pufqdRWnZnvJIDWejbQqdX4dusHi4298C2wIvBWYSH+RuguKTxM398txPnFWY1cW6W5lNDD+bZwfgtoK3WYTjgClm9lfgaqL78hXANe7+/CRl1yVGxvlFn+tYVbOI78Q33X1Buj/FZJxJhqo3szcQO8HdgDWJneJPSUM5S+2zCKqZR8qi/pmFsmgYlEXKIploFsqiYShLFoF+p3WilywC5VErs+hDFkFxeaSGsxHm7tv1uIqfM34d+oeBH3i6EeVkmq5hnw58t8XifwD26amm2U4GvmJmlxChBuBmtjQxtPLOxKgcmdKXbBt3/1rO/EOAy9396mKrzWrAG4CtgW2AfyPOuDxvZtcRIX2lu5+fUfaGVF6ybQDg7vMbn3fDzF4H/COwO/AyYvSknwE/AS5s2EbtKYuAHvJIWTSSlEVDoCwClEUykbJoCMqSRe4+hwr+TqtoFoHyqJXCsgj6k0dqOKuO/c1sxzaXdXfft4Btrke05C7t7p1+eBeXJW7C95IWy760u+pN6hjgb4j7ADySpp1CXEe/LPA9d2/VrflLQKtRN/6OGAlmp96rOs7dnwQuTA8sRpbZigjofYDPp0dWl+vPAOeY2eXuflmR9epAmUccORw4gRilCOJzept3N7z3/wFPA+cTIfzzNs82VZ2yqDu95JGyqD1Z2VPWPFIW9U5Z1B1l0XAoi0ZXlbMIqvk7rYpZBOXIozpkEfQhjzSqZgWkESaeAV5os4i7e8+t2UWNigEcT5zNeJW7P9W03MpE6/tsd39zP+psZtsQXTOnE12j7wbOmiywzOwR4MvufnzO/H8GDnP3NXqtd876jRj9YxvGz2y8FHiIOJuRdy+C9xM7nnuAe4mbHzYau0dCr/UbC48niDNXRtyk9CliJ9zM3X31Xrebtt3VZ7N55CEzWwh8yLsYicjMdgf+192bR/EZWcqi3uvdTR4piyat31j+PA2MnUEcSB4pi4ZDWdR7vZVFExSSRWlbQzk2UhYNR9WzyN3vNLMLqNjvtKpmUSo70r/TypBFqXzheaQeZ9Wxf7cfnBL4MjHs6/VmdizR5Rfgb4mhiNcB9u7Xxt39Csa7AHdiKksOEdzoRfThJqdmdigRwq8jRkCZTdT/UOKGlLkhZGZ7AycxfjZh5aLr18SJYaAHfbPdbt1PnIUa+y4ZXZ55cfeziqpUxSiLetBlHimLJufARSiL6kRZ1ANlUV9V6dhIWdS7KmcRVPN3WuWyKJXX77R8hWUR9CeP1OOsAppbYAe83aLOZrwV+C/GuwdDfCHmAZ9w9wuKrjPwALCqu9/bMH8t4GNE4J7t7te0WNe1wFPuvn3GPCN2Miu5+6uLqHvTa1gInA0c4e43dVB2LvAg8E53f6DIemVsq5fPR/P9FQaybTM7hjgImEOcgXkV8Rl8rEUxd/ctzOywtM1/d/dF6flk3N2/0m79yk5Z1H296SGPlEWTbqvrz0cq33UeKYuGQ1nUfb1RFvXVsI6NlEXDMQpZlKZV6ndaFbMolZ9LBY6NqpZFqXzf80g9zmQg3P1CM9sQeDXwijT5buAGd1/UuGyvjSoNTiSGmn1NWu+KwG+IEU0WAAea2VtbdAX+NvB9MzudOBtzR5q+CXEd9tbA/j3UL8/MtO63A3uY2R+JsxlXEGczWr0n04CvDeLgkOhSDePXonei+f4Kg9r2wcTQ128k7ufgwLNEQE9mZlr+SOJysJltlHFgZA4QR8GQsgh6yyNlUWu9ZBH0lkfKIumKsqgjM6lGFsHwjo2URdK1Cv5Oq2IWQXWOjaqWRTCIPHJ3PUr+IMJpzy7LziGCb4Mut7sQ2LiKZYnrxg9rmLc/EcRbEd13fwNcMsn6vpLKLExfwvnp/wuIFu1+/t2nAFsSre9nAX9O274POD2nzAXAt4b1WR3E37gP9ejqu1XHh7Ko+/K95pGyqK+vb+h5pCwa3PulLFIWlfWhLKreo25ZVOS265ZFqVwl8khZlP1Qj7Nq2B64rcuyvfbsqbJpRCiP2YW4YeO1AGZ2CpO0Rrv7v5nZD4B3w+Jhce8GznX3uwqv8cRtLwJ+Z2Y3EjuP3xEjtmxMDK/7/oxiHwd+bmafAU5y93Zb6etqA+JGntIeZVH3esojZdHIUxZ1RlnUPWWRtKIs6oyyqHt1yyJQHnWidFmkhrMKcPfLh12HinqcGNIYM1ua6Pp5RMP8hcDyk60khe/R/ahgFjNbAXg9MULLNkQX5qlEy/sNwHHk30TzBmIHfDRwtJk9RfZoLYWMbll17t7tpV2LmdlOwHbAGsAx7n67ma1EnDG70d0f7XUbZaEs6knPeaQsGl3Kos4oi3qiLFIW5VIWdUZZ1JO6ZREoj9pWRBZBsXmkhjMZNY3XVV8F7GdmlwLvJLr9ntuw7AxiBI+WhnAA8DjRBfgZ4LfAUcCVwG/c/elJyp5LDyOQjLp0w8pFwPLuPr/hBpatuLsvkZVp5/k/wJvSOqcApwO3A88To8KcQNxrQeqp0DxSFo0OZZEMmLJIMimLZMDqnEWgPMpVZBal9RWeR2o4k5Hi7neP/d/MDgYuJrrPQgTqrWneUsDuwIV56xriAcAXiBC+3t2bz0Jk1bPxJp37FFyXUfNlIoQXND3vxhHEDULfR/y9Fu/cU+CfCeyMDhBrq6g8UhaNJGWRDIyySFpQFsnA1DyL5iiPWioyi6APeaSGMxlZ7n6Xmc0ANgOecPe5DbOXBw4AFg8jnDFKzFAOANz92A6L1P0eCW1z95mtnndod+IGn2eaWVaX6juAD/SwfhkhveQRcfNZZdEIURbJsCiLpJGySIZFWSSNCs4i6EMeTemxQiKl5u4vuPtNTWGMuz/p7uc2TV8PWJ/xcFv8hSNGaml2B+M3oywNM9vEzE4zsz+Z2Xwze1OaPs3MfmBmrxt2HUfEasSwyXmmAMsOqC5SAT3kkbJIWlEWSUeURcqiPlEWSUfqlkWgPBqgwvNIDWfSynRidJDZAy5bFpU7ADCzVwPXEtf7XwEsNTbP3R8m/i4fH0rlSsjMjjOzO1vMv8PMvp4zezaweYvV70D3Iy3JRMoiZdFIUxZVhrJIWTTSlEWVoSyqWBaB8qgTPWYR9CGP1HAmudz97vRYMPnSxZUtkSoeABxJDO08A/gkYE3zLyZGg5HwDuCMFvPPIIbHznIysK+ZvathmpvZ0mZ2GNFF/MRiqllvyiJlUQ0oiypAWaQsqgFlUQUoiyqZRaA86kQvWQR9yCPd40wk38nAV8zsEsaHFnaLIZO/SHzhynZW4HXAoe7+pJktkzH/PuBlA65Tma1DjOyT5x5g3Zx5xwB/A5wDPJKmnUIMrb0s8D131wGiFEFZNPqURVIFyqLRpyySKqhiFoHyqBO9ZBH0IY/UcCaSr4oHAAuJG2fmWQtoZ7jkuniM6K6eZwbwZNYMd3fgw2Z2MrAb0b16CnA3cJa7X1ZsVaXGlEWjT1kkVaAsGn3KIqmCKmYRKI860XUWQX/ySA1nIjkqegBwDfAe4JvNM8xsOWBvxs/MDNP09G+rMwmDcBHwMTM7xd1vbpxhZpsDHwN+2moF7n4F5XhPZUQpi/quDHmkLJLSUxb1nbJIpA0VzSKoTh6NRBZBsXlk8bmTUWVmi4hhcDd199wb7JWJmW2Y/jtvkNffV/G9gon1BqYBvwIuAH4MnAYcCDwBfA7YCHi9u984nNqWi5mtQ9ykc3UifP+QZv0t8C7iLNZr3f2ejLKbAlu4+6k56/4AcJ27396PuldNFb9fw8qitO0qvl/Koi4piwanot8tZVEHlEXdUxYNThW/W6DfaZ1orrOZvR7lUVt6yaJUvvA8UsPZiBvmwVbVZITbSsCq7n5vwzJrES3cU4Gz3f2a4dR2XEa9dwS+Q4Rvo9nA/u5+6aDrWGZm9jLiZp27AiulyU8C5wL/4u735ZQ7D5ji7ju3mL/I3d+VNb9ulEWdafrh9wDKopGnLBoMZVFnlEX1oywaDGVRZ0Yhi9I05VGbus2iVLbwPFLDmUiScaB1OrChu78mzV+RaO1eF1hAXKP+1mF3B84JZSNGm2nsuny9uy9qKjuHeB07uvucgVa8ZNJ7tkZ6+pBPEo5m9hfgKHc/Jmf+p4EvuPtaxdZU6qDpAPFLKItqQ1kkZaIsqi9lkZTJqGRRmq486kCnWZTKFJ5HuseZSL6tgf9ueL4nsDbwWuBW4JfAocBlA6/ZJFKg3JgeraxHBPrSfa9U+S1LvA9tBTKwMvBci/kvAKsWUTGpPWVRvSiLpKyURfWiLJKyqmwWgfKoC51mEfQhj6Z0srBIzUwD7m14vgtwpbtf6+5PE6O3bD6UmklhzGx7M7uK6Pp7D7EzxsymmdllZva2nKJ3AW9useqdgLlF1lVqS1lUA8oiqQBlUQ0oi6QClEU10EMWQR/ySA1nIvkeJ25IiJktDbyRGOFjzEJg+SHUq9l0Yrje2cOuSNWk+wxcDKwAHAfY2Dx3fxh4HvhITvHvA7ua2VHpfnhj61zZzI4G3gGc3K+6S60oi0acskgqQlk04pRFUhHKohHXYxZBH/JIl2qKjGseevcqYD8zuxR4J/HFPbdh+RnA/YOrXjZ3v3vYdaiwrxAjtmwNrAZ8tmn+FeSH8rHAq4hRcA4ys7EbVK5DZOvpwNeLrrDURmMeKYtGn7JIykpZVC/KIikrZVG99JJF0Ic8UsOZSJIRbgcTLd2/S8+PcfdbAcxsKWB34MLB1VD6YHPg8+6+yMyyrpl/AHhpVsF0jf1eZvYD4D3ABmnW/wI/1ag40ovGPDIzZdHoUxZJKSmLakdZJKWkLKqdrrMI+pNHajgTyeHud5nZDGAz4Al3n9swe3ngAOCmsQka+aSSniWGrc6zAdEdPJe7/5K4CalIXyiLakFZJKWnLKoFZZGUnrKoFnrOIig2j9RwJtKCu79AQ/A2TH+SiV2CQSOfVNHlwIfM7NjmGWY2jegCfHGrFZjZxsB2xDDJP3L3uWa2LDG6z/3u/nzhtZbaURaNPGWRVIKyaOQpi6QSlEUjr+csSssWlkcaHEBE6uxQYH3gN8C+xE51RzM7DLiZ2MHOzCpoZkuZ2UnAbcB3gS8Dr0izXwRcDxzYx7qLyOhQFolIGSiLRKQMus4i6E8eqeFMRGor3Q9hG2KY4yOIEVsOIYL4DmBbd88bCecwYG/gi8BrmDjay9PAmcC7+1V3ERkdyiIRKQNlkYiUQY9ZBH3II12qKSK15u43AzuY2YuBjYgTCrPd/aFJiu4N/Le7H2Vmq2fM/wPwrmJrKyKjSlkkImWgLBKRMughi6APeaSGMxERwN0fI4Y9bteawHUt5s8HVuypUiJSO8oiESkDZZGIlEEXWQR9yCM1nIlIrZnZKsCngLcR19IDzAN+DnzL3fNGbLkfmN5i1a8D7m4xX0Sb1YThAAAE4UlEQVRkMWWRiJSBskhEyqCHLII+5JHucSZSb9OBjYFW14iPLDObDtwCfIk463B1eqyQpt2cRmPJcjrwMTPbsmGap/XuBbwfOLVPVRcZNcoiZZFIGSiLlEUiZVHbPOoxi6APeWTu3snyIpLDzBYRX8hN3f3OYddHJmdmvwC2BHZ19yua5m1LDGd9rbvvlFF2KnA+sC0R7JsDNwCrEcNeXwzs4u4L+voiRJooi6pHWSSjSFlUPcoiGUXKourpJYvSMoXnkXqciUidvR44tjmQAdz918BxwBuyCrr7c8CbiSGS7wFuJ86C3JqmvUMHhyLSJmWRiJSBskhEyqDrLErLFJ5HuseZiNTZI8DTLeY/BTzcPNHMlgM+D/zG3U8BTulP9USkJpRFIlIGyiIRKYOusgj6l0fqcSYidfZN4KNm9pLmGWa2JvAx4Pjmee7+LHAI8PK+11BE6kBZJCJloCwSkTLoKougf3mkHmciUmfzgWeBu8zsbOCuNH068B7iZpwLzOxTDWXc3b8FXA9sOsjKisjIUhaJSBkoi0SkDHrJIuhDHmlwAJGCmNmG6b/zdA+Hakg3C+2Uu/tSZrYV8DPgYOBUd19YbO1EuqMsqh5lkYwiZVH1KItkFCmLqqeXLErlC88jNZyJSG2Z2XrdlHP3eWZ2PfASYC3gGeA+4sxI06K+RW+1FJFRpywSkTJQFolIGfSSRal84XmkSzVFpLbGwrVLfwWeAP5YUHVEpKaURSJSBsoiESmDHrMI+pBH6nEmIrVlZisBq7r7vQ3T1iJuODkVONvdryloW3OARcCO7j6niHWKyGhQFolIGSiLRKQMBplFad2T5pEazkSktszsdGBDd39Ner4i8AdgXWABEaBvdffLCtjWIsCBTd39zl7XJyKjQ1kkImWgLBKRMhhkFqX1T5pHU4rYkIhIRW0NnN/wfE9gbeC1wIuBG4FDh1AvEakXZZGIlIGySETKoHRZpIYzEamzacC9Dc93Aa5092vd/WngFGDzodRMROpEWSQiZaAsEpEyKF0WqeFMROrscWB1ADNbGngjcFHD/IXA8kOol4jUi7JIRMpAWSQiZVC6LNKomiJSZ1cB+5nZpcA7gRWAcxvmzwDuH0bFRKRWlEUiUgbKIhEpg9JlkRrORKTODgYuBn6Xnh/j7rcCmNlSwO7AhUOqm4jUh7JIRMpAWSQiZVC6LFLDmYjUlrvfZWYzgM2AJ9x9bsPs5YEDgJvGJmjodBHpB2WRiJSBskhEyqCMWWTu3o/1ioiMnF6GTtew6yJSFGWRiJSBskhEyqDXPGmnvAYHEBERERERERERyaCGMxERERERERERkQxqOBMREREREREREcmghjMREREREREREZEMGlVTRGQwpqd/5w21FiJSd8oiESkDZZGIlMWkeaSGMxGRAXD3u4ddBxERZZGIlIGySETKop080qWaIiIiIiIiIiIiGdRwJiIiIiIiIiIikkENZyIiIiIiIiIiIhnUcCYiIiIiIiIiIpJBDWciIiIiIiIiIiIZNKqmiEj7NHS6iJSBskhEykBZJCJl0PcsMnfv17pFREREREREREQqS5dqioiIiIiIiIiIZFDDmYiIiIiIiIiISAY1nImIiIiIiIiIiGRQw5mIiIiIiIiIiEgGNZyJiIiIiIiIiIhk+H/cTYCtrc3THgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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5Engz8B/ADcDJtC5h/Z+OIU8CDgBeRauo9i3gG1W1JsmjgdfSuiPq0cAqWvvKHc/vVtlJkiRJkiRplknVeNuSaWuz/vufbPzN3utxr2k879qN6xvHdmuX7XZoHLt6/ZoeZiL1zqaN1876y9LXffZtjc5Hi5/1vsZzbh4ebhwLcLcdd2scOzzuFqATu+HOVY1jpX7aKs5F5y9rdi46/PVdzdvN+aibD33/XfZuHHvlbTd0MbPUP1vFuejTb278h8JBx5zReN7frLujcewuC7dvHHvnUPN/H27YNNQ4VuqnyZyL3ANOkiRJkiRJ6iMLcBNIcl6SHw86D0mSJEmSJM1OFuAkSZIkSZKkPrIAJ0mSJEmSJPWRBThJkiRJkiSpjyzANZDkSUk2JPnvJHOS7JbkP5L8LMmdSW5P8uUk9++IW5KkkjwlyeuTXJtkfZKvJzmoo+95SX6c5H7tr9e2+79qet+tJEmSJEmSujFv0AnMNkmeCpwB/Dfwj1VVSQ4EjgDOBK4E9gJeCHwryX2q6rqOYV4HbAb+DdgV+GfgY8BDOvrtDnylPe4ngacBb0/ys6r6ckdeqyfKfd33PjGVtypJkiRJkqQesAA3BUmeCXwUeF9VvXzUSz8D7l1Vw6P6fhRYDhwN/GvHUPOBh1TVULvvrcC7k9yvqn4+qt9+wLOr6ox2v1OBq9pjfhlJkiRJkiTNeF6COklJ/hY4HXhXR/GNqtowUnxLMjfJ7sCdwKXAA8YY7n9Gim9tF7QfD+zodxvw22VrVbUR+L8x+lFViyc6pvaOJUmSJEmS1AsW4CbnYGAZ8NGqussebO194I5LchmwAbgFuBn4Y2CXMca7uuP5qvbjrh3t11RVjdG3s58kSZIkSZJmKAtwk3MtcCFwRJI/HuP11wDvBM4HjgIeDxwG/IKxP+PN48yThv0kSZIkSZI0Q7kH3OSsBf4K+Bbw1SSPrKrLR71+JPDNqjp6dFCSxbRWw0mSJEmSJGkb5Qq4Saqq1cDhwO3AuUn2G/XyZjpWpSV5GrDv9GUoSZIkSZKkmcgC3BRU1Y3A42h9bl9Lskf7pS8CS5KcluSYJO8BTgGuGFCqkiRJkiRJmiG8BHWKqurqJIfR2u/tnCSPBd4C7AA8G3gGcDHw18DbBpboGDaeemrj2LUb1/cwk8lbNH9hV/H32/kejWNvWXRn49jlq65pHCttC4a+8s1BpzBlqzesaRx71J4Pahz7wTu/0zhW0pbd/E8fbRS3cO78ruZdN7yhcey8uc3/fH/O9n/YOPY9629vHHv7hrWNY6VtQe55aOPYm9fe1jj2rvf7m7zbuvi53mnhosaxGzcNNY5t/m6l3rAAN4GqWjJG26XAXh3Nr2wfo/1ebFWdxxg3UKiqlZ3tY83bbl+6pXwlSZIkSZI0s3gJqiRJkiRJktRH20wBLslBSWqKx8pB5y1JkiRJkqTZbZspwEmSJEmSJEmDsC3tAXcVMNXdLZvv8ChJkiRJkiSxDRXgqmoIWD7oPCRJkiRJkrRt8RLUrUSS1RMdg85RkiRJkiRpW2QBTpIkSZIkSeojC3BbiapaPNEx6BwlSZIkSZK2RRbgJEmSJEmSpD6yACdJkiRJkiT1kQU4SZIkSZIkqY8swEmSJEmSJEl9ZAFOkiRJkiRJ6qN5g05A0+eXZ+/YODZJ49jhqsaxa4c2NI4F+P4tv2ocu2Bu8x+PL+76qMaxT1x1QeNYabaYu8fOjeKqi/PJnC7OYwAbN29qHHv2Hcsbxy7ebofGsXsv2q1x7PJV1zSOlWaL86/bp1HcuqHmP9MAzc9ksKmLc9FfbGj+d9WbN6xtHHvQ4rs1jr189XWNY6XZ4mdHnN44tqu/jeY0X4+zeXhz49jbuzifLJy3oHHs6Ts/rHHskbd+q3GsNMIVcJIkSZIkSVIfzaoCXJITk1SSxT0ab2WSZb0Yqz3eknZ+S0a1nZfkvIbjHNGr3CRJkiRJkjQYXoI6QyQ5CLiso/mzE1z6eVVV7d+3pCRJkiRJktS1bb0Adwgw3Oc5Du/z+JIkSZIkSZrBtukCXFV1t8P/5ObYOMmuVwGHtr/+M+DDwD8CX99CzFAXqUmSJEmSJGkazKo94EbZNclHktzWPk5Lsv3oDkmel+TiJOuS3JLkw0n26ujze3vAJVna3nvt4UlOTvKbJKuSvDPJnCR7JTkrye1JbkzyyokSHWsPuCSLkrwpyWVJNiS5DvgocEdVLQeubne9HngarSLcj4H3AZuqann7WDHlT06SJEmSJEnTarYW4M4CFgGvBj4FLAVOGHkxyQnAh4BfAsfRKlw9GfhmkkWTGP/9wN2B1wNfa4/xL8A5wK3A8cCvgH9P8qipJJ5kLvCl9tgXtsd+L7AfcFBH99cBTwL+DXgb8FDgY+OMu3qiYyp5SpIkSZIkqTdm6yWoP6iqF448SbI7cDRwfJL9aRW3XlVV7xjV58vAd4HnAqdMMP6vq+qJ7a/fn+SXwL8CJ1XV69vjnQFcR6v4d8EUcl8KLAFeWlXvHdX+1tz1jgvzgYdU1VB7zluBdye5X1X9fApzSpIkSZIkaUBm6wq4zgLaBcDuSXYG/h8Q4DNJ9hg5gMtpXdK5ZBLjn9rx/ML2mL9tr6rVwKXAAVPM/SnAjbRW2f2eqqqOpv8ZKb61jRT6DhwjdvFExxTzlCRJkiRJUg/M1hVwV3c8X9V+3BU4mFZh8YpxYvdsMP5t7cdrxmjfdRLjjXYgsLyqNjfIY/T7lCRJkiRJ0iwwWwtw4xWvQqv4thn4S6BzRRn8rog15fHHKZp1XjbaS1t6n5IkSZIkSZoFZmsBbktWAHOBy6pq5YBzGcsK4EFJ5lXVpkEnI0mSJEmSpP6arXvAbclngWHgDZ0vJJmTZLfpT+n3fBbYC3hx5wtj3IRBkiRJkiRJs9xWtwKuqi5P8gbgzUnuBXwBWAPcC3gqcBLwoQGm+GFad2J9T5I/o3Vn1p2AvwJOAL41wNwkSZIkSZLUY1tdAQ6gqk5KchnwcuCNtFbEXQ18DvjagHPblOQJwOuBZwLPAG4Gzqd1p9a+ecB3X9M4dvjeT+5hJtNn0/Bk7nXR+9in3vH9xrHStuApH7mzUdxdbxY9febObb5ofNX6Zu8XoJvF0d971mTuOzS2PU65tnHs5uHhxrHSdHraBf/YKO55h36zx5lMjz+/9XsDmXfl7Tc2ju328pDB/daQJu/ef35749j6ZPN5589pXg5YP7yxcexwF38nbKyhxrGnL2z+99jeO3Z3H8Qb7pzMVvTa2s2qAlxVnQicOEb7MmBZR9ungE9NMN7+E43Tbn85rWJeZ/uSjufn0fF3Qmefdtta4F/ax1h53WWcdvvKsdolSZIkSZI0c22Ne8BJkiRJkiRJM4YFOEmSJEmSJKmPLMBJkiRJkiRJfWQBbpZKsiDJrNrDT5IkSZIkaVtkAa4PkpyX5MdJHpzke0nWJbk8ydIx+i5K8uYkVyTZkOSqJG8aXVxLsn+SSnJcklckuRJYB+w3jW9LkiRJkiRJDbiCqn92B84GPg6cATwLOC3J2vYdWkkyB/gC8BDgA8CvgAcCrwXuDjyvY8znA/OB9wPDwG/vo5xk9UQJbbjyB929I0mSJEmSJE2ZBbj+2Q94aVW9FyDJB4GLgbclObOqCngOsAR4RFVdOBKY5Ip2v7dX1fJRY94NOKiqfjNdb0KSJEmSJEnd8RLU/tkAfGjkSVWtB04FDgAOaTcfCfwcWJFkj5ED+Hr79SUdY545XvGtqhZPdPTwvUmSJEmSJGmSXAHXP9dW1bqOtsvaj/sDy4GDgUOBm8cZY8+O51f2LDtJkiRJkiRNCwtwgzUH+BHwqnFev6LjeWdBT5IkSZIkSTOcBbj+2TfJoo5VcAe3H69qP64A7ltV505vapIkSZIkSZou7gHXPwtp3bUUgCQLgaOBlbQuPwX4NHDPJM/tDE6yY5LtpiFPSZIkSZIk9ZEr4PrnWuD1Se5Fa6Xbs2nt9/bs9h1QAT4CPAM4LclhwPeABcB9gKcDDwQun+7EJUmSJEmS1DsW4PrnFuAY4L3Ai2gV5I6uqjNGOlTV5iRPAl4JHAU8DbiTVtHtbe2Ynnnao0/s5XDagqHNmxrH3v6OJzeO3eUV/9s4tibuIvXMmzYvahT3nXkLepzJ5G0a3tw4duPwUOPYBfPmN47d85SfNI79tz94TOPY4286v3EsdPdZS1ORRTs1ihvk78zZ+Pt6kD/T6SJ2Nn7Wmp0Wvf09zYM/eXjj0PWbNjaftwvd/Gz9bi3L1H3u+ou6mLk7c9L8bDTcxXvWzGIBro+q6gfAQyfosxF4S/sYr89Kuvv7QZIkSZIkSQPiHnCSJEmSJElSH1mA24IkByWpKR4rezT3si3McUsv5pAkSZIkSVL/eQnqzLYWeOEY7eunOxFJkiRJkiQ1YwFuy66idefSqRiqqhU9mn+oqk7v0ViSJEmSJEkaAAtwW1BVQ8DyQechSZIkSZKk2csC3MyWJHuM0b6hqu7o6Lh6osGedPe/7llikiRJkiRJmhwLcDPbzsDNY7SfBRw5zblIkiRJkiSpAQtwM9sa4Igx2m/sbKiqxRMN9uR7PLF6kZQkSZIkSZImzwLczLapqs4ddBKSJEmSJElqbs6gE5AkSZIkSZK2ZhbgJEmSJEmSpD6yACdJkiRJkiT1kXvAzWzzkxw1zmufrKqhac1GkiRJkiRJU2YBbmbbHvjoOK99EVg9jbloChbMnd84tu5c0zy2cSTMSbqIhuHyJruavHWb5zaK2zS8ufGcwzXcOBYgXfyMdBO7aN6CxrHz5zT/Nf/93Nk49pDF+zWOBbhk1dWNYz0XaUqGNgw6g1mjm78SuvmpnDunuwt2hoe7O/dL02Hzry4cdAqahG7+vbTfTns2jr3m9psax/pX0cziJagzVFUtraps4bD4JkmSJElSn3W7WEECC3CSJEmSJElSX1mA67MkByWpKR4rB523JEmSJEmSesMCnCRJkiRJktRH3oSh/64CDp1ijHc3lSRJkiRJ2kpYgOuzqhoClg86D0mSJEmSJA2Gl6BuJZKsnugYdI6SJEmSJEnbIgtwkiRJkiRJUh9ZgNtKVNXiiY5B5yhJkiRJkrQtsgAnSZIkSZIk9ZE3YZglkiwAdutovrmqNg8iH0mSJEmSJE2OK+Bmj4cD13ccdx9oRpIkSZIkSZqQK+Bmj58Ah3W03TCIRCRJkiRJkjR5FuBmiapaBZw76DwkSZIkSZI0NV6CKkmSJEmSJPWRK+C2If93+xWDTmGbcbcddm8ePFyNQ9N8VpJuomGv7XdpHHvjmtVdza3Z59dzFzaKG67hxnNWNf/ZApiT5v/Pau8dd20ce/2dtzaOPWHvxzSOffst32scu25oQ+NYgG6+Uwfssnfj2Ctvc2eHbc3bH/2ORnHz5sztat5Nw83voTWni9/XF+71wMaxD77hh41jD168b+PYK26/vnEsdHc+6UY336fhLn9fafZ5znM/N5B5B/Vvh25ih4eb/y343r0e2zj2jXdc3DgWYNHcBY1jF85rHvvg3Q5qHHvBTb9sHKuxuQJuhkqyf5JKsnRU27IkKweXlSRJkiRJkqbKAlwXkixtF8kqyYPGeH1Bklvbry8bQIqSJEmSJEkaMC9B7Y0NwLOBzrX5fwnsAgw1GPMqYFHDWEmSJEmSJM0QroDrjS8Bz0juskHQs4DzgLVTHbBa1ldV801CJEmSJEmSNHAW4HrjDOBuwG93u06yI/A37dcY1b4gyZuSXJTktiRrklyQ5LEd/e6yB5wkSZIkSZJmHwtwvXEZrctPnzWq7cm0LvE9q6PvzsDzaa2MOx44EdgTOCfJn/Q7UUmSJEmSJE0v94DrnTOA1yY5tqqGaO0J95WqWtVxm+VVwP5VtXGkIcl/A8uBlwBHN5k8yeqJ+uy9y6FNhpYkSZIkSVIXXAHXO58EFgNPSLI7cBgdl58CVNXmkeJbkjlJdqNVCP0h8IBpzFeSJEmSJEnTwBVwPVJV1ya5gNbKt32BjcDnx+qb5LnAK4A/BOaPeunKLuZfPFGffRbfp5qOL0mSJEmSpGZcAddbZwBPAv4e+HxV3eXup0mOApYBK2hdbvoEWqvlvoHfD0mSJEmSpK2OBbFFle0AACAASURBVJ/eOpPWirYHAx8fp8+RwBXAU6rqo1V1TlWdC2w3TTlKkiRJkiRpGnkJag9V1a1JjgXuBpwzTrfN7ccABZDkIcDDgKv7nqQkSZIkSZKmlQW4Hquq/56gyxeBpwCfTXI2cADwIuCXwI59Tk+SJEmSJEnTzALc9FsG7A28EHg8rcLbUcDTgCUDy0qSJEmSJEl9kSpvjLmtWPPmoxp/s3d50zd7mcpWb/dFOzWOPWTHfRvHfvfm5Y1jB2n7+Qsbx64d2tDDTGaHTRuvzaBz6NYO2+/f6Hy0YdNQ4zm7/dAG9duym7y7ybmbeXfffucuomHT8OaJO43jmnc9qXHs3Y8b8+blk7J6/ZrGsbPV1nAu2nXHgxr9mNyxcV1X8w7qg5s7Z27j2G5+Lt+4z5LGse9ZfVHjWIDbNtzlnmiTNlzDjWM/t/iRjWP/fsNPGsfesvb2xrGz1dZwLnrUvn/R+Ff297v4+39Qf9vMSfNv2XAX9Ys/2f3AxrHf+Kvutmzf7aO/7Cq+qW5+ODKg79NsNZlzkTdhkCRJkiRJkvrIAtw0SnJekvMGnYckSZIkSZKmjwU4SZIkSZIkqY8swEmSJEmSJEl9ZAFulkqyIIl3sZUkSZIkSZrhLMBNUZJnJPlRkvVJbkpyapI9xuj3giQrkqxL8n9JHjXOeIuSvDnJFUk2JLkqyZtGF9eS7J+kkhyX5BVJrgTWAfv1751KkiRJkiSpF1xBNQVJlgKnAd8HXgXcHXgJ8GdJHlxV69v9jgY+AHwb+E/gQODzwK3ANaPGmwN8AXhIu/+vgAcCr22P/byOFJ4PzAfeDwwDd44aa/VE+d/5r8+Z4juWJEmSJElStyzATVKS+cDbgZ8AS6pqQ7v9IuAM4Bjgve1+bwEuAv68qoba/X4JfJBRBTjgOcAS4BFVdeGoua4A3pbk7VW1fFT/uwEHVdVv+vMuJUmSJEmS1Gtegjp5DwL+AHjfSPGt7VPAtcBfd/Q7eaT41rYMuK1jzCOBnwMrkuwxcgBfb7++pKP/meMV36pq8UTH1N6uJEmSJEmSesEVcJN3z/bjpaMbq2o4yWWjXh95vKyj31B7ZdtoBwOHAjePM+eeHc+vnFLGkiRJkiRJGjgLcIM1B/gRrf3kxtJZsFvX33QkSZIkSZLUaxbgJu+q9uMhwPkjjUlCayXbzzv6HdzRbz5wAK095EasAO5bVef2KWdJkiRJkiQNmHvATd4PgZuAFydZMKr9SGBf4OxR/W4GXtQuuo1YCnTuw/Zp4J5Jnts5WZIdk2zXo9wlSZIkSZI0IK6Am6T2Hm7HA6cB5yU5A7g78FJaq9/+e1S/1wEfAL6R5FO0Vr49j7teUvoR4BnAaUkOA74HLADuAzwdeCBweb/fmyRJkiRJkvrHAtwUVNWyJOuB44H/AG4HPga8uqrWj+r3wSRzgX8G/h34GfAk4F87xtuc5EnAK4GjgKcBd9Iqur2N1t1Ve2a3N3+rl8PNCukitrqInT+3+Y/W+trUxcyD0817fteuD28cu6qLdbyvvuGbzYPVlcfucd9Gcefc8OPGc3bzMw3dnU/+/m7N/xv/n+u+2zj2jfssaRz79lu+1zj2lrW3N47t1uJjP9k4dt8d9+hhJpO3ev2agcwr2H7+wkZxm2u4q3nXDW1oHDtnTvNffJccckjj2IMv+WXzebvYxnj1hu5+PjYPd/e9auqotRc1jr1zY/PPa96cuY1jNw1vbhyr7vzw1uZrLrr9+6apbv4uGq7BZH3+K5qfA3d73Vd7mMn06eZ3xg7zm1+Qd88d/qBx7M9uXdk4dqazADdFVfUJ4BOT6HcycHJH85Ix+m0E3tI+xhtrJd2d4yRJkiRJkjQg7gEnSZIkSZIk9dE2WYBLclCSmuKxchLjLttC/EMnO1d7rKUd7euSXJLkbUl26fuHJEmSJEmSpJ7wEtTeWwu8cIz264G/7Wg7qd3/pC2M91rgamAH4HG09p9bkuRhVQO6eF6SJEmSJEmTtq0W4K4CDp1izNBk+1XV6VuY97eSvBJYvYX+AF+qqpFdxz+Q5EzgSOBhQPOduSVJkiRJkjQttskCXFUNAcsHnUdD59EqwO2PBThJkiRJkqQZb5sswPVZkuzR0baxqm7v0fgHth9/0zHp6okCFyzcr0cpSJIkSZIkabIswPXezsDNHW1nA09sON7idkFve+Aw4B+Am4ALGmcoSZIkSZKkaWMBrvfWAEd0tHUW5Kbimx3PlwPPraq1oxuravFEAy3c7u7etEGSJEmSJGmaWYDrvU1VdW4Px3sRsALYBFxXVb/q4diSJEmSJEnqMwtwM9+Fo+6CKkmSJEmSpFlmzqATkCRJkiRJkrZmFuAkSZIkSZKkPrIAJ0mSJEmSJPWRe8BtQ/5jz8c0jv2nGztvxjp5c+fMbRy7eXhz41iAOXOa15g3Dw83jt1twU6NY3+x+qrGsdvNW9A4dsOmjY1jAQ7e5W6NY4+95fzGsY//gz9uHLv3jrs2jr3hzlWNYwU7ZX6juEGeT3Zb1Pzn+qt3DOb+OZ/acGXj2KEuP69B2X7ewsaxC+c0++8S4O92e0Dj2Pff8N3GsZtm6fdppnjGLn/UKO59N3ynq3m7uS39/DnN/3x/xepFXczc3E83XN84dk66Wy8wTPO/57r5PnXzs7lgbvNzUXWR9RP3+pPGsZ+7/qLGsYIv7vzQxrF/dVvz3yHd/Hunm7/JFnXxb5Y7Nq5rHPvod1zaODZJ49hB6uYcuq6Lfx/eOnRH49gle92vcSzAeTf+vKv4frIA10NVtXSK/cf9LVdVy4Bl3WUkSZIkSZKkQfMSVEmSJEmSJKmPLMCNIclBSWqKx8pB5y1JkiRJkqSZxwKcJEmSJEmS1EfuATe2q4BDpxgz1I9EJEmSJEmSNLtZgBtDVQ0By3s9bpJHAS8FHgLsBdwEfBp4TVWtG9VvGXAE8EfA+4A/B9YBHwaOrypveSZJkiRJkjRLeAnq9HoasD1wMvAS4Jz240fG6Dsf+CqtIt0rgfOBVwAvGGvgJKsnOvrwfiRJkiRJkjQBV8BNr+NHr3QDPpjkcuAtSe5RVVePem174CNV9db281OSXAwcTauAJ0mSJEmSpFnAFXDTqOMy0x2S7AF8Fwjwp2OEfKDj+QXAgeOMvXiio1fvQ5IkSZIkSZNnAW4aJblHkmVJbgXuBG4GvtV+eZeO7ndW1a0dbauAXfucpiRJkiRJknrIS1CnSZK5wNeA3YC307rJwxpgX2AZdy2GeqMFSZIkSZKkrYAFuOnzR8C9gedW1W9vupDksMGlJEmSJEmSpH7zEtTpM7KiLSMNSQK8bDDpSJIkSZIkaTq4Am76LAdWAP+RZF/gduCpuKebJEmSJEnSVi1VNegcthlJDgXeAzwUWA98Fvgv4CfA86pqWbvfMuCIzjuXJjkROKGqQgNDN13W+Ju9aL8lTUMl9dimjdc2OgfMJPfa4wGNzkdX335j4zm7/W03f27z/2c1XMONYzcPN4+9+057NI79zfo7GseuHdrQOLZbC+fNbxz7uD3u1zj2T9N5L6XJ+/bwLc1jb76kcSzApuHmW85uDeeiO176xEanhl1P+VGvU9mqzUnz/1SGZ+m/Vbr54RjUO95z++bnMYBH7nJw49jPXv/DxrHb8rkIujsfDeqDy4DOCXvtsHjiTuO4Zd3tjWOhu7/nutHN+XfunLmNY3dcsF3j2G5rVPO6yPuWtc2/z5M5F7kCbhpV1SXAWHu+paPf0nHiTwRO7HVekiRJktRUN8U3SdpWuAfcFCVZlmTloPOQJEmSJEnS7OAKuGbmJZnqusirqmr/fiQjSZIkSZKkmcsVcFN3DHD4oJOQJEmSJEnS7OAKuCmqqqEklwGHTjF0qB/5SJIkSZIkaWZzBdwoSR6e5GtJbk+yJsm5SR7Y0WcZcFlVLa+q5bTuZnoJ8ARgCXA2rbuafhTYaaRfVa1ox983yWeS3JpkXZLvJzmsY44Tk1SSA5J8JMlt7eO0JNv3/YOQJEmSJElSz7gCri3J44AvAd8H3kCrOPkC4PwkD66qX04wxN8BOwAfoHX38FcBn0lyYFUNtef4I+DbwFXAW2kV754DfDnJ46vq6x1jngWsAF4NPAB4PnATcPwY+a+e6D1uvPFXE3WRJEmSJElSj1mAA5LMAU4GzqmqvxnVfiqwnFZB7pkTDLMfcHBV3daOvRT4X+DxwBfbff4TuBx46Kii3MnAxcBJQGcB7gdV9cJR+ewOHM0YBThJkiRJkiTNTF6C2nJ/4CDgjCR7jBzAfOACWpeWTuSMkeJb2wXtxwMBkuwGPBY4E9hl1ByLga8CDx7j8tJTOp5fAOyeZOfOyatq8UTHJN6DJEmSJEmSeswVcC0Htx8/Ns7rw5MY4+rRT6pqVRKAXdtNBwGhdenpW8cZY3dg7XhjAqvaj7sCt08iJ0mSJEmSJA2YBbiWkZWAxwE/bzjG5nHa0zHH24Fzx+l78xTHlCRJkiRJ0gxnAa5lRftxdVWNVxzr1hXtx/V9nEOSJEmSJEkzjHvAtVxMq0D2yjH2YSPJnt1OUFU3AecDLx5rvF7MIUmSJEmSpJnHFXBAVW1O8gLgbOBnST4MXE/rzqaH07pz6d/2YKpjad1I4edJPgRcCewDPArYDnh0D+aQJEmSJEnSDGIBrq2qvp7k4cAbgJcBOwDXAd/lrncjbTrHz5M8GDgReD6tO6DeCFwEvLsXc2zRnLl9n0KSJuOnx92vUdwub7yxx5lM3tDmTQObu6lr7rilcey8Wfo7Y/PwZO6bNLZL1t3QOPacO37aOHbHBds1jp07p7uLGYar+ee1Ndj1lB8NOoVtwnDVoFOYdrPxHd+6/o7GsZ9ffzF3XPGVxvHb739449itwT4f+sVA5h3Uf6c1oHPCjWtWdxU/Gzdi7+b8O9zF3763rV/TOHaH+c3/LgK45ouvaRy7+PDXdzX3RCzAjVJVFwNHTNBnacfzlYzzs1hVd2mvqsuBoyaY40RaRbrO9mXAsi3FSpIkSdJ06qb4Js0Gs7H4ppmnr3vAJTkvyY/7OcdUJKkkJw46D0mSJEmSJG07trqbMCQ5sl1ou8vR7nLCGK+tHFCueyc5McmfDGJ+SZIkSZIk9d9WV4ADZlMxa2/gBGZXzpIkSZIkSZqCrXEPuFvbj48Drp1kzFCfcpEkSZIkSdI2blpWwCV5cJLvJVmX5PIkS8fosyjJm5NckWRDkquSvCnJvI5+z0xyUZI7ktye5GdJXtZ+7UTgHe2u5wKXtI/1VbW8/fUzq2p5+/kz221J8on2eDcmOb493r2TfDXJmiRXJzmqI5el7UtYH57k5CS/SbIqyTuTzEmyV5KzRo37ylGxS4CRW2+dNupy2KWj+jw8ydfa8WuSnJvkgU2/D5IkSZIkSZp+01GA2x04G7gQOB64mVbB6ekjHZLMAb4AvAz4DPAS4CvAa4H/HtXvMOAMYCXwSuBfgPOBR7S7fAY4vf31ccDfto+bJ8jx08Cmdn4/Bd6W5IXAV4GfA68CbgOWJdl/jPj3A3cHXg98rT33vwDn0FqRdzzwK+DfkzyqHXNJ+/0BfHBUrue33+vjgPOAhcAb2mPvB5yf5D6dCSRZPdExwWcgSZIkSZKkPpiOS1D3A15aVe8FSPJB4GJaRa4zq6qA5wBLgEdU1YUjgUmuaPd7e3vF2l8Dv6iqp441UVX9NMlFwFHA56pq5SRz/E5VHdue8zTgOuBk4JiqOrXdfi6wnFaR7F874n9dVU9sf/3+JL9s9zmpql7fjj+jPe5S4IKqujHJl4CTgO9V1UjhcKQgeTJwTlX9zaj2U9s5vIHW6j1JkiRJkiTNcNOxAm4D8KGRJ1W1HjgVOAA4pN18JK2VZiuS7DFyAF9vv76k/bgauHuSh/Q4x878fgJsBj4yqv3S9vwHjBF/asfzC4GMbq+q1cCl48R3uj9wEHBGx+cxH7iA330ev1VViyc6JjGvJEmSJEmSemw6VsBdW1XrOtouaz/uT2tF18HAoYx/qeie7cf3A08Hvp/kSlqXe55ZVed2mePVHc9vA26oqs6bM9wG7DrJeIBrJhnf6eD248fGeX14EmNIkiRJkiRpBpgpd0GdQ+uGBK8a5/UrAKrqpiR/AhwO/GX7eEGS/6mqo7uYf/Mk26C1sm1SfatqrPax4juNrEw8jtbKQEmSJEmSJM1S01GA2zfJoo5VcCMrvK5qP64A7juZlWxVtRH4IvDFJAHeCxyb5C1VtQKoHubeb+PluqL9uLoHq/skSZIkSZI0QNOxB9xC4PkjT5IsBI6mdSfT5e3mTwP3TPLczuAkOybZrv317qNfa9/A4aftp9u1H9e0H2fDnmfj5XoxrVV/r0yyfWdQkj072yRJkiRJkjQzTcsecMDrk9yL1squZ9Pa7+3Z7QIatG528AzgtCSHAd8DFgD3obXn2wOBy4EPJdkN+Abwa+AewEuAHwOXtMe6qP14UpJPAEPAF6pqpNg1k6wEbgVelOQOWgW5C6vqyiQvAM4Gfpbkw8D1tO4oezitz+JvB5OyJEmSJEmSpmI6CnC3AMfQulT0RbQKckdX1RkjHapqc5InAa8EjgKeBtxJq9D0tnYMwOnAC4B/oLVq7AbgU8CJVTXcHutHSV4DHAs8gdYqvwP43WqzGaOqNiX5O+DtwCm0vh/PA66sqq8neTjwBuBlwA7AdcB3230ladZ62Hsvm7jTGCazieaWdLNHQbdzN9VNznPnNF/ovuOC7SbutAWr18+4X7sTeswOk7lR+djOHe68b9PkPWmnQxvHAnz0NxdN3Gkcu2+3U1dzz3Zrf/W/jWO3v/eTe5iJNHibh4eZk2a/7XY84PGkYazgRX/w0Max/3nd+Y1ju/k7YXh4MPcF7Ha/qUG9525+Orp5z/PmzG0cO1zdfY932W6HxrH32+kejWPv8TdvbRx7zN4Pbxw7GX0twFXVklFPt3hWae/t9pb2MV6fs4CzJjHvW4G7fOpVlY7nJwInjtHviHHG3b/j+TJg2Rj9Xg68fIz2JWO0nU1rpdtY810MjJmLJGlqZtMGobPdbCy+zVbdFN/UnMU3bY2aFt8Ai2+aFWZjwXG2GlTxbaabjj3gJEmSJEmSpG3WpApwSQ5KUlM8VvY5d0mSJEmSJGnGcwWcJEmSJEmS1EeT3QPuKlp3Lp2K5rsRS5IkSZIkSVuJSRXgqmoIWN7nXDRKkhOBE4ADgTcCI7v9fgY4tqrWDig1SZIkSZIkTYGXoM58ZwGLgFcDnwKW0irMSZIkSZIkaRaY7CWoGpwfVNULR54k2R04Gjh+dKckqycaaOPNK3qfnSRJkiRJkrbIFXAz3ykdzy8Adk+y8yCSkSRJkiRJ0tS4Am7mu7rj+ar2467A7SONVbV4ooGGbrmiepiXJEmSJEmSJsEVcDPf5nHaM61ZSJIkSZIkqRELcJIkSZIkSVIfWYCTJEmSJEmS+sgCnCRJkiRJktRHFuAkSZIkSZKkPkqVN8bcVixYuF/jb/aw/51Mm27urrEtfpcWzpvfOPaduz2ycezem4Ybxz779u82jgVYs3blrL8JyzPueUSj/1zPuv4HvU5lWvxwnwc2jn3Q9Rc1jn3MH9y3cey3bvpF49hBGtQPx26Ldmocu2r9nY1jB/n7edPGa2f9uejgPR/Y6AO88rYbep3KtLjjnDc2jt3p8Sf0MBOpd7aGc9G993xQ45P5Fbdd38tUJm02/pvlj3bbv3Hsz29d2bM8pqqbz2vfnXZvHLtmaH3j2L/c9X6NY28cXts4FuDCVZc1jl2/aWPj2A3rr5nwx8IVcJIkSZIkSVIf/f/27jxOjqpe//jnSVjCvqsoSBTC5oJXBBUBQRDlijtcF5Tlh6IoAl4V9IqAuAGKoHgRUDAsirIpirKILMIFQVllh4QECCg7hEDW+f7+OGdIpdPd0+t09fTzfr3mlXRVnarTPd1P15w6dc5ANcBJ2kPSPZLmSZpWWP60pMn5/9Mk/a5XdTQzMzMzMzMzs7FliW7sNDdm7V5j9RMRsXo3jluPpI2Bk4ELgO8AzxRW7w08mP9/APDU6NbOzMzMzMzMzMzGqq40wGXPA5+psrz1G4nbszWpx99+ETG9uCIizir8373fzMzMzMzMzMysY7rZADcvIs7o4v6b9ZL87zN1t+oTkpaNiPZGJzQzMzMzMzMzs67r6RhweUy2kLSFpJ9KekLSU5J+KGmcpJdKOlfSs5L+LenLFeW3yeV3lnRE3ua5XOZlhe2mAcPTPj2VyxyW171f0h8lPSxpjqQpkg6RNL7iWFdIulnSa/P/n5c0Q9KBVZ7XOpJ+L2mWpEclHSPpXfm421Rsu4WkP+fnOEvSpZI2rdhmch6nbpKkiyQ9Bxzf+itvZmZmZmZmZmajpZs94CSp2lhvcyJiZsWy44GHgG8A2wBfBJ4AdgH+DhwEfBz4vqTrIuKqivKHAPOB7wKvAPYH1pW0WUTMI43rthvwQdJtsc8Dt+ayewCzgGOAmcA7SI11KwBfqTjOasBFwNnAb3L9jpT0z4i4MD/p5YDLgJcBPwb+leu+bZUXaHvgT8Df8nMYRxqP7q+57ncUNl8SuDjv+7+pGKdO0tOV+6+05FKvGGkTMzMzMzMzMzPrsG42wK0IPFZl+bnAzhXLHoqInfL/j5d0B/At4DsR8Q0ASWcCD5MazCob4FYCXhMRz+VtbwNOJTV8nRoRv5P0BlID3FkRUWys2rXiVs4TJJ0AfE7SwRExp7BuLeDjEXFmPs7JwHRgL+DCvM1ngFcDO0XEH/N2JwI3FSssaRzwU+DiiHhvYfnJwF2kBrmPFoosC5wREYdgZmZmZmZmZmZ9o5sNcLOAD1RZ/u8qy06ueHwdsFFxeUQ8Lelu4FVVyp863PiW/Qo4FtiR1BBXU7HxTdIKwNKkBr7PABsCtxQ2fwb4daHsXEnXkxrchr0bmD7c+Ja3my3pZ8DRhe02AdYDDq3SU/AqUk/ASifUeR4r11o3bKml14qRtjEzMzMzMzMzs87qZgPc/Ii4tMFtH6h4PDxRwoNVlq9Spfy9xQcRMT+P+zZxpANLeg3wbdKtpytWrF6p4vGDEVHZiPUU8PrC43WAKVUOdV/F40n531/WqNpQxeM5EfFwjW3NzMzMzMzMzKykutkA14wF1RZGRLXl6tRBJa0MXAk8S7rlcwowG3gjcCSLT1JRtZ4t1ml4318Ebmtg+9ktHMPMzMzMzMzMzHqsLA1w7ZpUfCBpCVLvtz+PUG4b0sQKH4qIvxbKV7vNtVHTgfWrLF+v4vFwL7mnm+gpaGZmZmZmZmZmfaayh1e/2l3S8oXHHyfdqnphje2HDfdoe7EHm6SlgM+1UZeLgXUkvaewzwnApyu2uxGYCnxZ0rKVO5G0Rht1MDMzMzMzMzOzkuhmD7glJX2ixrrfRMS8Dh7rGeCvkk4FXgHsT7qts9b4asOuIY3hdqqkHwMBfJL2bnM9EdgXOEvSj4B/Abuy8BbSgHR7raS9gT8C/8x1f4Q00+oOpDHjPtlGPczMzMzMzMzMrAS62QC3LHB6jXUXAE938FiHA5sBXweWIzVq7TtSI19EPCFpJ9LspN8mNcadAfyF1JOtaRHxnKR3AMcBBwDPAaeRGvvOpTCWW0T8RdIWpPHn9s91fzhvW3PG01atuPRiHe0a9vTsWR2sydg3YYmlWi47e/7cDtZk9Cy31ISWy86a2/oQh0uNaz3G9n308pbLtsPTEcNELdPrKoyql7xiZuuFH2m96DVP3N164T7Vzuernatv84ZqDRM7sqHF5ney0fLIrCdbKtfugMTt/MbHqY2j/6tyfrPR0c7r1e6nY4lx41suO7+Nz3WvdGywbBtVmy67VstlH5j5aMtlF7TxHl9ifOvn4ENROd9g4xYMtV52XtUh5hszblx7Nw+2U+92/HtW680u7fye7pr3eMtl73l2RstlAdZabvXWj/3UQ20deyRdaYCLiD2APRrYbjIwucryA0iNV5XLt6mxq/kRcRBwUJ1jHQYcVmX5NcBbqxRRxXZVj52fa+Wy+4GdFtmZNPx8HqrY9kbgA7XqXesYZmZmZmZmZmbWH8bKGHClIi3atSOPAfcZYD7wnZ5UyszMzMzMzMzMeqJuDzhJ6wH3NrnP6RExseUajQ3nS7ofuBlYmTQG3IZAS/0wJb0M+Czwu4i4uWO1NDMzMzMzMzOzrnMPuO64CNgS+D5pfLfZwEeAVgdSexlwKPCGjtTOzMzMzMzMzMxGzUhjwE0HNmpyn52c3bSuiLiCEo41GhE/BH5YuVzSUT2ojpmZmZmZmZmZ9VDdHnARMS8i7mryZ0o7FZL0eUlTJb0g6XpJW0m6QtIVFdstI+nbeds5kqZLOlzSEhXbfVTSDZJmSnpW0j8l7V+xzdqSTpP0aN7XrZI+VrHNHpJC0lskHSvpMUmzJP1W0hoV20rSwZIekvS8pMslvabG811V0nF52zmS7pa0X2H9NsBN+eEvch1C0h5NvrRmZmZmZmZmZtYDXZkFtVWS9gF+AlwJHANMBH4HPEVh9lBJ44A/AG8GTgTuATYFvg6sDeyZt3sncCZwHnAS6fluDLwN+FHeZk3gb8DcvOxJ4P3AryQtnWdqLToeeII0o+qrSLO1/oR0i+mww4GDgQtIt6O+EbgEWKri+S6Xn+tLgROAGcC2wI8krRIR3wTuzM/rO/k5XJWLX1OxrxHnF15thUkjbWJmZmZmZmZmZh1WmgY4SUsB3yI1hm0fEfPz8luByRQa4EiTGmwDvC0irivsYypwhKQjI+Iu4D3A7RHx4TqH/jYwBPxHRAw3Yv1U0oXAdyWdFhFDhe0fBXaMiMjHHAfsJ2mliHgm94Y7EDgf+GBhu+8A/1Nx7P8G1gE2iYj787ITJT0FfFXSjyPi35L+RGqAuzYizqjzXMzMzMzMzMzMrGTKNAnDm4DVgJOGG9+yX5J6wBXtGfi0SAAAIABJREFUDNwGTJG0+vAP8Je8fpv879PA2pLeXO2AkgR8CPg9sETFvi4C1gTWryh24nCjWnYVMJ7UkAawPamn23EV2x1bpQo7k3rAzaw49iXABFIPv4ZExMoj/TS6LzMzMzMzMzMz65zS9IBjYQPWfcWFETFf0rSKbSeRJod4rMa+hsdkOx74L+Bvku4H/gycHRGXFrZbGfhc/qm1r7sKjx+oWD/cOLhKxfO4t+J5PJZ7tlU+j9c38DzMzMzMzMzMzKxPlakBrhnjSBMTHFhj/VSAiHhU0huAHYAd88/ekk6JiL1Y2ANwMqmnXTW3VTxeUGO7VmZjHUfqaXd0jfW3t7BPMzMzMzMzMzMrkTI1wE3P/67HwokGyLOaTgRuLWw7BXhNoSdbTRExlzQZwgX5ltPjgM9L+i4wDZgJqJF9NWj4eUyi0Fsujw23SsW2U4BlGjh2jLDezMzMzMzMzMxKqkxjwP2DNLvo3rnRbdiuLN5wdQ6wjqTdK3ciaXlJE/L/Vyuuy2OyDTfkTYiIBcBvgY9I2qDKvlq5BfRSYB7whYrlB1TZ9hxga0nbVDn26rnBEGBW/tfjuJmZmZmZmZmZ9ZnS9ICLiLmSDiP1ULtU0jmknm97kHqKFXuBnQZ8BPiFpHcC15ImPtiYNObbpqSx5H4uaVXgMtIsqq8kNYzdDNyZ9/VVYFvgH5JOIo33tjqwGfDGXIdmnsdjkn4AfE3SH4AL8352BB6v2Pwo4P3AxZJOyfVaEdgE+DCwAjCf1FPvSeCzkmaSGuSuK8ycamZmZmZmZmZmJVWaBjiAiPhJ7vX1JeAHwC3A+4AfA7ML2y2Q9D7gy8AngF2A50iNbkcAM/KmZwB7kyZYWBn4F3AWcFhEDOV9PSJpc+BQUuPdS0kNZbcCB7f4VA7O9f0ssB1wHWkcuj9WPN9ZkrYGvk6aEXUv0qQOdwEHkcebyxNR7AYcCZxA+r3tCTTVAPf07Fkjb2QdMXv+3F5XYdTNmjt75I26YO7Q/JE3qqGde7vHqZVhH5PWS44dhx6wYkvljq418mfJfWDaUE+OOxS9OW6/aicTZs3rTQZae5YcN76lcr38bQ9F6+/UFXf/eQdr0rhllly65bLPz5vT1rHnD9Uavrm7JiyxVMtl57RxHrn8Usu0XFZtnNu0m4HRxvt6LPj4nNZ/b+e08R7v1bnwUI9+3bPmv9By2aGh/jynijZ+y0uOb725aNqsf7dcds78eS2XBXh89jMtl11pwnJtHXskpWqAA4iI40i94ACQNA54FWnSheJ2c4Hv5p9a+zoXOLeBY/4L2Cf/1NpmMmmyhsrlV1Dx93Ru3Ds8/xRNrFL+WVJj20Ej1PGPVDTgmZmZmZmZmZlZ+ZVpDDiGx26rsBuwKnDF6NamfZIOkzTYl3PMzMzMzMzMzAZcV3rASVoPuLfJYtOBT0k6ktRr7QnS2Gl7AbcBZ3e0kmZmZmZmZmZmZqOgbLegTgUeBvYj9Xp7EjgV+Fq+5dTMzMzMzMzMzKyvdKsBbjqwUZNl5kXEVOC9XahPV0haChiKiNZHgTczMzMzMzMzszGtK2PARcS8iLiryZ8p7RxT0n9Lmi9phcKyQySFpEMKy1bM232hsGwZSd+WNFXSHEnTJR0uaYnCNhPzvr4o6UuS7gdeANbK67eU9HdJsyVNkfSZOnXdU9KNkl6Q9LikUyW9tGKbaZJ+J+nthf1OzbOhmpmZmZmZmZlZnyjbLajtuBoYD2wBXJyXbQUMAVsWttsib3c1vDjL6h+ANwMnAvcAmwJfB9YG9qw4zqeAJYHj876fk/Q64BLgUeDQvP6bwGJz70o6FDgEOBM4CVgT2B/YTNKmEVGcG3l94CzgZ6QZWPcCJku6ISJur9jv0yO8Poxf8uUjbWJmZmZmZmZmZh02lhrgbgRmkRrdLpY0HngLaUKHHSWNj4gFef2zwC253K7ANsDbIuK64Z1JmgocIenIiLircJyXA+tFxBOFbX8GRN7HjLzsXOCfxQpKmgh8AzgwIo4uLL8QuAbYHTihUGSjvM9r8nZnAQ+SGgW/3OTrY2ZmZmZmZmZmPdCVW1B7IY/Ddh2pgQ3gP4Dlge/lf9+Ql28FXBsRQ/nxzqRZVqdIWn34B/hLXr9NxaHOrmh8Gw+8CzhvuPEt1+dOFvbEG/ZBQMB5Fce6D3ikyrFuHW58y/t8DLgbeHWV57/ySD+LvWhmZmZmZmZmZtZ1Y6YBLrsK2DxPjrAVcG9E3ESaXXUrSUsDm5NvP80mAZsAj1X8/D2vX6PiGPdXPF4DWAa4t0p97q54PIn0mk+tcryXVznWA1X2+RSwSpXlZmZmZmZmZmZWQmPpFlRIDWuHApuRxn27Ki+/Kj/+B7B0YTmkBrGbgANr7HNqxeMXqm7VmHHAAmBH0i2rlZ6qeLygxn7URh3MzMzMzMzMzGwUjbUGuL+RGq22JjW4HZSXXw18G7gBmAtcXygzBXhNRFza4jEfIzXKTaqyboOKx1NIE0DcGxHTWjyemZmZmZmZmZn1kTF1C2pEPEfqzbYH8BIW7QH30rz8hoqZRs8B1pG0e+X+JC0vacIIx1xAGuvtQ5JeUSi7EWlsuKLfkmZOPaTKscZJWrXesczMzMzMzMzMrP+MqQa47GpgfeCRiJgCEBF3k3qqrc+i478BnEZqQPuFpDMkfV7SF/PMpjOAtRo45qGk1/L/JB0k6evA5cDtxY0i4j5S49uekq6U9GVJ+0j6Aal33IdafM5mZmZmZmZmZlZSY+0WVEi93Q5g8Ya2q0mzkC6yPCIWSHof8GXgE8AuwHOkmUmPIDXC1RURt0p6F/BD4HDgIVKj3JrA6yu2/Y6ke3Mdv0nqEfcA8Dvgz8080WbNuuWMlssut8knOliTsa+dQfqqDQ5otc2ZP6/lsuPUm+EUh8K/5Znn39XrKjRt/LjWr1nd+mTl/D2Na+ddOk5tXGdro+iCoaGRNyqhJcaNb7nsULT+nD2wa+88fNr/a6ncCh/93w7XZGx7ft6cXldh1M2eP7cnx505t53hqq1X3vK6h1suu+r1K7Rc9uk5s1ouu/oyK7Zctp3Px5MvzGy57Iznnmi5bLtn7736rm/nnKyd86JnZrf+3oo2/1Zq5z2y1BJLtnXskYy5BriIOI8q7++IqNm7LCLmAt/NP7W2mVZtv4X1fwXeVGXVYVW2PQs4q9a+8jYTayzfpl45MzMzMzMzMzMrl7F4C6qZmZmZmZmZmVlplLIBTtJ6kqLJn2m9rreZmZmZmZmZmVmlUjbAmZmZmZmZmZmZjRVlHQNuOrBRk2VaH4ndzMzMzMzMzMysS0rZABcR84D+myLPzMzMzMzMzMysQikb4Kx5kp4eaZvZd1w2GlUxMzMzMzMzM7MCjwFnZmZmZmZmZmbWRe4BN0ZExMojbTPnzstjNOpiZmZmZmZmZmYLuQecmZmZmZmZmZlZF7kBzszMzMzMzMzMrIvcAGdmZmZmZmZmZtZFboDrI5KWlbShpNV7XRczMzMzMzMzM2uMG+D6y+bAncC+va6ImZmZmZmZmZk1xg1wZmZmZmZmZmZmXaSI6HUdrAQkPQ0QESv3S9leHttl++PYg1Z2LOjX170f692PZXt57EEr24ny/axfX3eX7Y9jD1rZXh+7n/Xr6z5oZXt57EEr28tjdyKL3APOzMzMzMzMzMysi9wAZ2ZmZmZmZmZm1kVugDMzMzMzMzMzM+siN8CZmZmZmZmZmZl1kRvgzMzMzMzMzMzMusgNcGZmZmZmZmZmZl3kBjgzMzMzMzMzM7MucgOcmZmZmZmZmZlZFykiel0HMzMzMzMzMzOzMcs94MzMzMzMzMzMzLrIDXBmZmZmZmZmZmZd5AY4MzMzMzMzMzOzLnIDnJmZmZmZmZmZWRe5Ac4GiqT1Je0t6euSJuZlS0t6taSla5R5paRl6uxzGUmvrLFua0lr1Cm7uqStm3sW5ZVfi0Mk7dBC2bdI+qikd0qa0EYdxkvaXNIu+WdzSeNb3V+35NfqFEkf7nVdbPQ5i7rLWdQc59HgchZ1l7OoOc6iweUs6i5nUXO6mUVLdHqH1j/yB3AbYA3g6Ii4S9IKwGbAzRHxZJUyIwVRALOBhyLikSrl1wC+WDjubhFxraTVgP2BX0fEHVXKCdgb2At4NbBKtWNHRNX3dP5wnwTsASjX81pgGulzcCPwXeCoKsXvBz4J/KrGc35fXlctQC4foex2dcq2TdLGLHy9VLk+Ik6TdEoLu46I2KvKwhckfRXYr4k6rghcBLy5sPjfkt4bETc0UylJHwd+ALyUhc838v6+FBFnjlB+LeA/gJWocoEiIk5rpj715NdqF+CaTu2zX/VTFuWyLeeRs6h2FuXtOpJHzqLmOI+SXmRR3seonxsNahbB6J4bOYua4yxKWsmiXK7v/k5zFjmLBi2L3AA3gCQtB/wOeAcwRHoTnwncBcwhhc2JwKFVil9B+rA0cpx7gUMj4jf58brAVaQPzi2kwFkGICKekPRBUtjvU2V3RwH/DdwMnAE81UgdCg4Bdge+BlwGXD+8IiJmSTob+CDVw32xQKywJOl1rGaksksDC+ptIOkI4PSIuH2EfRXLrEt6nTavU4cATiO9Dxr6nVaUreVGYKMm9vU14C3AOaT317qk98CpwGsb3Um+QnEG6b11GOn9DLBh3t8ZkuZExHlVyk7Ix/sw6fMQLPrlMOw0SUM0/3oREdW+wP+P9Dv6ebP7Gwv6NIugvTxyFi1uOIugs3nkLKqiRhbBAOdRr7IoL+vVudFAZVEu16tzozGdRXl7nxt1QJtZBP35d5qzqDpnEWMzi9wAN5i+B2wJfBS4GpgxvCIi5uag24nq4f5u4EhSKP0MuC8vnwR8CngB+DawDvAZ4FeS5kfEucD3SUG2MTALeLRi378nfbiq2R04LyJ2aeqZLlr+ZxFxVL6KU+l24APDD3KL/8qF9aupehfmlUmv44tXkfJ2EwvbbFjjitTKpNdo+gh13w/4iqR/AqeTrj7NGKHMicDrgANIX6Y1vwgjYmKtdS36IvAHSbcCZ0RE3S8v4P3A2RHxkeEFku4ATpK0XkTcV7voIv6HdMVs24iYW1j+V0m/AK4Evg4sFu6kK2sfyuuvJX3J7E76vR4AvBzYLW97OIsH+weB1wAXA3fnZRsCOwC3kU6mqtkXuFjSocCJEfGvRp7oGNKPWQTt5ZGzqI4O55GzKGkki2Cw86hXWQS9OzcatCyC3p0bjfUsAp8bdUo7WQT9+Xeas8hZNFhZFBH+GbAf4GHgqPz/1UhXBt5RWL8v8GSNsj8kvfmXqrJuAvA34MjC49uA6/Pjp4ED6xz308BzNY47E9i7jec8G/hUnWN/Dnih8PhQ0pdQIz9DwEEVZYcaLDcX2H2Euq9A6pZ9CTA///wlL1uhRpkXgEN69P66EXgoP8eZwJ15WfHnhsL2zwOfrtjH2vn12bqJ474A7Ftn/b7F33HFugeAk+q8Py4Dflqj7N75M7VBlXUbkb4gPl2j7FPAc4X3xPPAkxU/T/Ti9zhK75W+y6K8vuU8wlk0mu8vZ9HCdXWzKG8zsHlEj7IoL+vJuREDlkW5XE/yiAHLorze50atvVdazqK8vu/+TsNZNJrvL2fRout6kkXuATeYVgXurbN+HOnKSTW7At+ORVuvAYiI2ZJ+SWqhPqjw+OC8yRKkD3stqwHzaqy7HHgTaYyAVswgXf2p5a3AlMLji0hfRCJ9mZ0BVN7rHqQrRDdExM2F5b8mdcEWqSX/WFLLfrWyt0TE4/UqHhEzgcnAZEkvAz4GfBw4BThe0gWkqy4XRsT8XOwJGuz+PXzVKCIeKD4eyfD2VTwLPEP991jRBNJrUfR8/reZjJoJrFVn/drUfv+9hIVd3l/I/y5XWH8uqYt8tW73XwF+EhF3V66IiDsl/QQ4kHQlstL5tNBNegzpxyyC9vLIWVRHh/PIWZQ1kEUw2HnUqyyC3p0bDVoWQe/OjQYti8DnRq1qJ4ugP/9OcxbV4Swae1nkBrjBNBXYpM767Ugt4tUsRxo8sZaXAcsXHj/FwvvnbyF19fxpZSGlATg/QuG+/wqfAy6RdCCpFfzpOnWo5kzgC7nr9v15WeRj70YKzBdPiCPiOuC6vH4l4NyIuK2RA+UP+N257J7AXyPi/vqlGhOp6+sxwDGS1ge+BexM6hL+lKRfAz8hdW3+hKTjY+TuxdOAkLRM/sKeRmNhU3UcoYjYpoGyldaW9PrC4+Gu5a+WtNiXX0TcWmUfFwD7SfpHRJxTXJHHHvgC6X1Qzb9JJxZExPOSngI2AP6Q169I+hKqZi3SVbJa5lHjSyci9qhTbhD0YxZBe3nkLKpvGh3KI2fRYmpmUT7eHnXKjnW9yiLo3bnRQGVRRNxFj86NBjCLwOdGrWoni6A//05zFtU3DWfRmMoi5e51NkAkfZkUCh8j3Xf+GCnQryYNtngosE9ELHYVQ9LvgW2BXSLioop1OwJnA5dFxPvyshOAt0XE6yTtRGpJPi5vdxXp3vx/ke4N3x54Z0RcXuW4T5EG0hyeanq4O2hRRES1sQOGB3C8ANia1N16E+Am0pWmdUhdh99bcXWio3IYrwHcFhHPtLGfNUm/u11Js8E8RfoiWgC8HViWFOxvITWynww8SJWBRCPi95L2IA/0GRFReFxXRJza6nMoUu3BMlVludKhFx8oU2nmpitJoTwDuCevmkQK1ruAbSLisSplzwKWiYj35seTgR1JA8qOA44GboqId1Up+3fSF8MWUTE2QP5dXQM8FhGbV3v+g6wfsyjvq+U8chbVzqK8zz3oUR45iwZXr7IoP+7JudEAZNFZpM/bcBadAFwKfIOSnxv1exbl7Z1HLWgni3L5vvs7zVnkLGLAssgNcANIkkhdY3cndYFdjXRv9Gqkbs0/j4i9a5R9Jamb8UTSwJTDXYLXJYXkdNIAi9NzoJ5P6nZ7bC6/B+nqwIos+uGdSbo3/Iwax51MY2Gz5wjP+xOkKxGTSB/aKaSZXU6LiFqz5CBpFVKg1poqOqJiuudC2d1IA0iumRe9MyIuk7Q66UrS12PkqZdXIl1F2RXYinT/+4WkGV8uII2fELle1WaGqaZqSHaCpKVIY0W8h/S+gPTeuID0/ppb2Hb3Zvdf60slv+c+Qwrm4nH/RLoiN7tGuS2BXUhjX8yRtDbpy3G4S/wUYKeo0n05l72Y9Ds5l0UHvP0Q6QrUDhFxdY1jb0jqOr0NsDrw7sL742jghIi4tsZL0df6MYty2cm0kUfOosU4ixaW61kW5X0MZB71MovyPvagB+dGYzmLIg1YP/zH47hcbKhKPSt1JY8GKYsK5X1u1KR2siiX78u/05xFVTmLGJtZ5Aa4ASZpK6oEXURcMUK5ZYHPAu9i0Q/QxaQZQirvFa8svxypi/N6heNeEhHPtvxkukjSu0hfAMuR7p2vds9+RMSrq5T9KGnK8ItIr88xwPYRcVlefy6pVf8/6xz/t6RZjZYmfRkMz7LzRGGbtxeKbEfqqr3tSM8tIirHPWib0gxGl5Fm95nBwnEGJgGvAG4FtivWv4wkjSM9hwXAXcUrb5LuJwX59hFxv6TXkq5Y7sDCq38vkH7nh0bEP2sc442kK0IzSVcadyZ/+ef11wD3RcRu1cqPFc6ixjiLmuMsajyL8r4GPo96lUV5H32TR/2QRXm74TxqOItyxTuaR4OYRXmZz41a1GoW5bIDc27kLGqOs6gcWeQGOBs4St2MJ+aH02u1mBe2v40UrB+q98dLjbK3ADMi4j9z6D3GouH+VdIVpZrj8kiaRgr00yPinlrbFbZfA9i4ndDOVyk+DLwRWImFV2yG1bua9HPSVaA9gLOiEDKSdiFdETp9+AqepAXAJ0a6wtRAnacCB0S+la3K+p2AH1f7Em7hWMNXsjYq/k7yF8Ia+eFj9a7Y5e3/TPrCezPpPfYoi74/DiO9Nuu1W2crH2dRw3VvKY+cRY1nUS7jPBpQzqKG6+4sqn2sqlmU1/ncyBriLGq47s6i2scqfRZ5EgZridJgnJuy8MrKNODGKAwkqQZnaakUtWfXRNJapHvqq4UNEXFanbI7A0cArxpeRBrUchrwtYg4q0bR9YCvNBvs2QbA8XXWP07qzlpTRExs5oAR8ZikX0hqKegkrcPC7utPk17rJ0mDbo7PdX6uThXem/f9myp1O1vSZqSu9cNd6Iu3qrVjIosOLFtpeRa+X7tlKVKuNvQHL2lmp4MjYqZSl/BKDwEv72QFx5peZVHeb0t55CxaaKSTrjbzyFnUeBaB86gtjWRR3q4050aDlEXAle38EegsaovPjUZZv/2d5ixalLOoa0qRRW6AG0CSRPpg7cXC++UrRURUfX9I+jjwA9IsO8Uxfv4t6UuFVvJpNDZLS6VqgzdOAE4ltfaPY+EYQ8PHHlYr2HcHfkGaOegrLOxyuz7pdThTaXaZavet30f90KjnGdIgorVsSJrdpVjXWgNe1lP5+5pI60H3fVKYv4U0G9OjpJmP/g/YD9iX1K29lhVJg4nW8kDephvqvW6b0eCU382StC3wnXyMccA7geHxAc4BjoyIC6sUXUDqJl3Lmiw+/feY0Y9ZlI/bch45ixYz0klXO3nkLGo8i2CA82gUswhKcm40gFkEvTs3GrgsAp8btaLdLMr76Ku/05xFVTmLOqhsWeQGuMF0FGnmkJuBM2jiDa80VfAZpKmqDyPNWgIppPYBzpA0JyLOA/4frQV7Nd8lDZT4deBa4ApSC/0jwAGk1ud6918fnMu9IyLmFFdIOo50f/fBpC+PSocAP5L0y5F6xFRxMfApST+pXJG7We9N6rpcdDided1aDbp3AMdHxPWShr+YlF+370vaCDiWNHhnNXcCH5V0YkTMK66QtARpoNQ7mqhrTZL2B/Yv7ONYSd+psulKpCtDNQfWb5Wk7UmDrd5Bel2+NLwuIh6XNAf4VN6m0vWk9/WPq+x3GdJ7/KpO17lE+jGLoL08chYtaqSTrnbyyFk0XKGRswgGO49GK4ugPOdGg5hFjLCfbp0bDVQW5Xr43Kg1LWcR9O3fac6ixTmLOqSMWeQx4AaQpEeBKyNilxbK3kCa5W7bKMySktctSQrJpSNi045UduG+HwAuioi9Vf0+/cuAuyNinxrlnyfNnrJYyOb1+wJHRcSyVdb9kDTzyYakgTqrTRUdEfGlimXDXbGvA+YDvwc+B0wmXRX6SH4em0fEo3VfgAZUBN06pC7I1VrlXwy6iFhsdpv8Wn0hIk7OYTyHNJ35eXn9p4EfRMRKNerxEeBM0hTi/8vCqaY3IH35/wfwseHu5Plq0p1UXGWqIyJiu8KxPpqXvx+4gdQdeJHtSa/DDaTBZ19o8Dg1Fa6AbUQ6IQhgS9KVtMrxAQ4GPlWtm7qkLUjdyC8Efk0aDHZ/0lW5L5O61m8RETe3W+cy6scsyvtvOY+cRYuom0V5Xy3nkbOo8SzK6wc2j5xFVffd91mUj9fzc6NBy6KIuEfStfjcqGntZFEu33d/pzmLFuMsakM/ZJF7wA2mZYA/t1h2Y9K99nMrV0TEPEm/InWN7bSXkFqhIc1aAmnGm2Hnkq6CVG2AI11Jqjew47qkq0XVHFD4/wdqbBMUWtRfXBjxkKRNSd1edyWF+p6k2VTOBr7aqWAH/sXC5zCR1I24btDV2M8DwFq5/vMlzSB1cx6+er8x6cu9qoj4Tb4qcATwcxZeOREp9P5fLD6Ww3JU72ZfV6QxDH4DIOly4NsR8ZdGyqrKLDkt2oT0mRiSVO2KxiOk2wAWExHXSHoPaQyKX+XFP8r/TiVNqz3mTjAL+jGLoL08chYtrOdIWQRt5JGzaDE1swgGPo+cRYsbC1kEJTg3GsAsAp8btaqdLIL+/DvNWbRoXZ1FYzyL3AA3mC4H3gSc1ELZmeQPfQ1r522QdArpg713RCzIj0cSUX12zX8Dq+UNnpf0FKm1/g95/YrAhDr73Q+4QNJ9wMmRuzhLWhr4NKnLbdVbKiNisUFEG6E0WONbgEfyc9pLafabcTQ3MHZD2gm6CpeRrlR8Mz+eDHxN0iqkun+SGmPtFeoyWdIZpPfZ8BgG04F/RGGq6EykE45t2wnZiGhoSu+CdUjvzyVbPWb2AvXfe68iDZJaVURcKmkD0hdEcbr5Gzv9HimhfswiaC+PnEXNaSuPnEWLqJtFMNB5NCpZBKU6NxrzWQTlOTcasCwCnxu1qp0sgv78O81Z1BxnUXPKl0UR4Z8B+yEF8x3AgcDKTZY9BXge2LnKug/ndSfnx9NIrcNLFh7fP8LP1BrHPQv4Q+HxZFLY70oKmkeBiwvrb6zy8wCpW/LzpAE+783/X0DqsnxDh1/nccBc4HO9/p03We9X5t/l0vnxBNJVkqdI3aUnAyt28HhBusqx/ig/z6H8u2/6uMWypCtON+ff92p53TvydquTrnidVmM/r+j177vH77W+y6JcvuE8cha1XfdRy6NBzqK8zcDm0WhlUV7WsTxyFo3qe8RZ1ETZdvLIWdRaFuXypf87zVnUdt2dRU2ULWMWeQy4AZCvQlT+opcitWhDmqq42v3yq1XZ1xqk8QM2AGaw8N7xSaQvjbuAbSJNs9wxkrYEdiGNETBH0trApfm4kFqid4qIu/P2V9DCoJFRp4Ve0g6kcQbWAI6OiLskrUAaKPPmiHiySpk7gdMj4rvN1qUZkrYGiIi/Fh+PZHj7XsrdgQPYMCLuGWn7Dh53kTECWi1L6kl8DemzcA7wPeBIUvfvfUiftc0iYmqV/SwgzVr0G+CciGh0jIW+NBayKB+74TxyFjmLGjhuz7Mo72tg8shZ1Lh+zaJ8rL7Mo37PokjjLm2Mz41G1Mksyvsr/d9pziJnUQPHHdNZ5Aa4ASBpMq0F3Z419jcB+AywI4t2Xf0TcFJEzM7bTQUOiIjf58eHAOdFxG3N1qVGPcYBryN9Md0Vi3eqUlbRAAAYHElEQVSbbWff95PvPSddtfkdadaZIfL0xRFxWe7C/ABp4MhDq+znk6T77LeOiCmdql+V4wyHzTIRMVcjT5Mt0hf4YlOJj7YxEu6vI82ssw28OO06wF+Bz0fE7TX2cxDphOWNpPfx1aRBPs+NiMebe0blN1azKO+zK3nkLBo9g5xFeV8Dk0e9yqK8bV+eG/VbFuVj9WUejYUsyst8bjSCTmdR3mcpzo2cRYscy1nU3HHHdBa5Ac66RtJcYK+IOD0/HgI+ERG/ql+y9yp6FexLGoNgN9IHbwaLzp5yHGkGlMVmFFKaneddpCtAfyF1766c4SWiyuw8Tdb37XlHVxYfj2R4+yr7Wxb4IOm++FVYNKxy0fbqXDhWyyHbq+PWKqs0/sJ6pBOAqY1eYZT0atKMS7sAbyDNyHQF6YrLeRHR1DT0tihnUf9mUd7HqOSRs+jFss6jLurXPOq3LMrH6stzo7GURXmdz41KyFnkLBqJs+jFcp3NoijBvcz+6f4P6R7/943yMe8m3Xe9FmlK5SFgL9JAnDV/auxrL1Jrc61jnQPs0cG6F8fVeZg0/TVU3Duel+0LPFlnPyP9LOj1+6OiztsDT45WnakYXwBYgTQd+Ou6/Dw7Nr5Ah+u1HvA/pCnCFwCze/2e6PDz6+ssyvsbtTwa5CzK9R61PHIWVd3/mM2jXmRRPm5fnhs5i5xF3Srb4P6dRZ0/rrPIWTTSsZxFi++/7SzyLKiDYyKwfLOFJF3WwrEiIrYj3WP9c9JMLZA+xCcx8sw+1brbfp50/3YtM0ghO7mpmjZmVdJgoLWMA5autiJanJ2nXZI+D1wVEbe2UPx/STMkfQS4LiKe7WjlRrYUsBOpq3DpSNqNNDjub4Hp+fGIIqLuzLGF7e6TdDXphGhdFp3GfSyYSH9nEfQujwYti6C3eTTQWZS3Hct5NJHRzyIYG+dGfZdF0NfnRqXOouzA/O9UeDGfRuRzI6DFLIJSnRs5i5rgLOqq0meRG+BsJONofmwCAUSa5vg6YGvgpcBhpD8UWgmbScAJddbfDuzRwn4bMZU09XAt2wF3dunYrToWGCfpWeBaUrfsq4DrI0/vXcfawEER8ecu17FfTSZ9Jn4cEfPz+B0jCepMCQ4g6W2kL9MPAy8jfbn+ljxluZUmi6B3eTRoWQTOo3om04UsAufRCFrOIhgz50b9mEXgc6NuOoqcR/nx5AbK+NyofWU5N3IWNcdZ1D2lzyI3wFldEbFNm7v4EwsHytwTODXyYJ8jqRhkcz7wkjqbv7TNetZzCvAtSZeSwhEgJC0JfI10JWCfagXzh3WriDiixvqvAldGxLUdrvOqwNuALYGtgG+QrgDNkXQDKeivjogLqpS9KZe36l4FEBFzi49bIemtwH8BOwMvJ8129QfSdO4XFY4x8MqSRRFxP73Lo0HLInAe1dOxLALnUaPazaLKPJHUj+dG/ZhF4HOjbvK5UQ+U5dwIZ1GznEXdU/os8iQMAyIPSPhXcnfMBkRE7NWh47Y9iCLwI9LVlTdExHMV261ICqOpEfHOdutc5dj3kgJ+d+AJ0hgDD+d/lwZ+HhF719jPpcDjEfHRGut/BaweETt0ot61KM0EtBkp5PcgTwseVWbXkfRm0pgQu0bEFd2sVz7eIjPsSFoNeAzYLiIu7+JxW3pvSjqFNKPSdfnx1sCd0cKU7rkOs4ALSGH+pwZ7BfWtfs+i/B69kFHKo0HOorz9qOXRIGdRoR4Dk0f9mEWV5RnFc6OxlkX5WKU8N+q3LMplfW7Uol5lUeHYffV3mrPIWTRC2dJnkRvgBkR+Az0PzGuwSERE263rHQz21UizjUwHjiF1ZQZ4LfBF4JWkMLi63TrXqrekrUjdTieRunxPAc6pF36SngAOj4gf1Vj/BeCQiFijE/Wusn+RZmvZioVXWV5KCs+rI+LDNcp9DDidNH33g6RBJouGx4/oRB2HQ+gZ0pU0kQaDfY50Ra1SRMRqHThuq3/0DlGYJUrSAuCT0cKsUZJ2Bv4YEZWzLo1Z/Z5F+QTkrYxSHg16FuWyo5JHg5xFufxA5VE/ZlFleUbx3GisZFE+RqnPjfotiwplfW7Ugl5lUeHYffV3mrPIWdRA2VJnkW9BHSx7t3pi3msRca2k9wM/JQ0+ORwIIoX9BzrV+FanDlexsHtzoybAYlNDFy1BFwaSlXQwKcjfSpqxZiqp7geTBv2sGWaSdgdOZuFrvGKn61dFkKYAH+0JH1oxA3g9MPxZEs2PvwFARJzTqUr1mb7NIuh9Hg1KFuXyo51HA5lFMLB55Cxq7/h9k0XQd+dG/ZRF4HOjdjmL2ju+s6h7nEUd5h5wA6KyNXiUj9uRXid52TjgjcCr82ZTgJsiYqiibOWYTS3XG3gEWDkiHiysXxP4LCm8z42I62vs5+/AcxGxbZV1Il0tWiEi3thsHRuo/wLgXOB7EXFLE2WnAY8C74+IRzpZrxrHa+c90vLvWdK6+b/TI6LaVZxa5Y4mXc27n3RF6A2kk4un6hSLiNhU0iGk5/qdiBjKj0cSEfGtRutXdmMli/LyrufRIGdRLj+NUcqjQcqiXH6g86gfs6hWeWdRU8+h9OdG/ZZFuazPjVrUqywqHLuv/k5zFjmLRihb+ixyDzjrKznA/5F/6lmH9AFasgOHPYk0zfDmAJKWB/5GmoVmPrC/pHfX6Ob8E+AXks4EDgfuzss3BA4lXf2oOjZBmw7L+/5PYBdJ95KurFxFurJSLwhXB44Yjca3bFL+d3oLZVv+PUfElBaOB3AQacyJt5MGnA3gBVLIj+SwvP2RwNz8eCQBjImTzLGmB3k0aFkEo5tHg5RF4DwaM5xFDTuM/jg36rcsAp8bGc6iJhyGs6imsZ5FboCzEbXbm2wM2BL4WeHxx4FXAG8G7iB1yz2YdKVkERFxqqT1SDPx/BcL79EfT/rAHhERJ3e6whFxOCxyJWor0mw7RwFrSHqEFPAfq1L8KuA1na5Tnbq2E7KjLl+JOSH/DF8d+nYjVy4jYly9x1afs2jgsghGMY8GKYtyeedRi5xF/ZdF+dh9cW7Ub1kEPjfqFWeRs6ibnEXdySI3wA2ObYE7Wyzbyd5k/Wh10gCXw95LGhjz7wCSTqdOC3lEfEPSqcAH4cWpkKcA50fEfV2p8cJjDwH/kHQz6YrQP0iz66xP+rKpFuz7AH+S9N/AyRHRaG+KQfUq0mCp1hhnUesGLYvAedQMZ1FznEWt69ssysf3uVH3OY8a5yxqnbPIWTSS0mWRG+AGRERc2es69Jlil9unSbP7IGlJUpfW7xW2XQAsW29nOcS/3/lqVidpOWAL0hWVrUhdsyeQrpLdBBxL7cFKbyJ9kX8f+L6k56g+u07bs9yMBRHRSrfsRUjaAdgGWAM4OiLukrQCaUrymyPiyXaPURbOoqYNchaB86hhncgiGJw8chY1ra+zCHxuNJp8btQ4Z1HTnEXOooaVMYvcAGdWRbHLraRrgE9Lugx4P2lGnPMLm29AmnGlph6cRDxNmob7eeA6Upfmq4G/RcSsEcqeT4uzxQyC3JV5CFg2IuYWBiitJyJisbzNX8C/A96R9zkOOBO4C5hDmsHnRNJYFDaABjyLwHlUUyezKO/PeWQ1jYEsAp8bdY3PjWy0OIucRfX0Qxa5Ac5sZAcBl7BwQNGjI+IOAEnjgZ2Bi6oV7OFJxIGkIL8xIiqvilSrZ3EMiT06XJex5nBSkM+veNyK75HGr/go6ff14klC/tI4G9gJn2RaMmhZdL/zqK5OZhE4j6xx/ZhF4HOjbvK5kfWCs8gqlT6L3ABnNoKIuE/SBsDGwDMRMa2wellgX+DF6aOLIUmaBnnUTyIi4pgmiwz6GBINi4jD6j1u0s7AcRFxtqRqXcXvBnZtY/82hjiLrKjDWQTOI2tQP2ZR3r/zqEt8bmS94CyySv2QRZ5lxqwBETEvIm6pCHYiYmZEnF+xfB1gIikkX/zgkqYzrnQ3Cwf9LA1JG0r6laSHJc2V9I68fHVJp0p6a6/rOEasSpoqu5ZxwNKjVBfrA4OWReA8GkXOI2uYs8hZ1EXOImuYs8hZ1EVdySI3wFm3TSLN5DJ1lMuWRd+dREh6I/B30ngIV5Gm4wYgIh4n/V726UnlSkjSsZLuqbP+bkk/qLF6KrBJnd1vR+szY9minEV9lkXgPGpGm1kEzqPR0m6e9HseOYsGgM+N+sYgnxs5iwZAGbPIDXDWVRExJf/MH3nrzpUtkX48iTiSNKX3BsDnAVWsv4Q0c48l7wF+U2f9b0jToldzCrCXpA8UloWkJSUdQur6flJnqjnYnEV9mUXgPGpGO1kEzqNR0W6ejIE8chYNBp8b9YEBPzdyFg2G0mWRx4Az665TgG9JupSF00mH0lTZXyN9cMt2leKtwMERMVPSUlXWPwS8fJTrVGZrkaZCr+UBYO0a644GXgOcBzyRl51OmlJ9aeDnEeGTTOuEfswicB41o50sAueRjQ5n0WDwuZGVnbNoMJQui9wAZ9Zd/XgSsYA0QGktawIjTZE9SJ4idcGvZQNgZrUVERHAnpJOAT5M6jY+DpgCnBMRV3S2qjbA+jGLwHnUjJazCJxHNmqcRYPB50ZWds6iwVC6LHIDnI1Vk/K/9Vq8u65PTyKuBz4E/LhyhaRlgN1ZeKWo18rwe74Y+Kyk0yPin8UVkjYBPgv8tt4OIuIqyvOaWuf1/H3ap1kE/ZNHPf8d04EsAufRGNfz96mzqOt6/jvOfG5k9fT8feos6rqe/46z0mWR0nvPrDZJQ6SpjzeKiJqDGFrSj69Xsc7A6sDlwIXAr4FfAfsDzwBfBtYDtoiIm3tT23KRtBZpMNTVSAF+e171WuADpKtqb46IB6qU3QjYNCLOqLHvXYEbIuKubtS93/TjZ6uX+vH1qqyzpC1wHjWknSzK5Z1HDerHz1Yv9evr5XOj1vncaHT062erV/r19XIWta6MWeQGOBuRpHXzf6f38UCbo6YiJB8BVo6IBwvr1yS1tk8Azo2I63tS0YIqf/RuDxxPCvGiqcDeEXHZaNexzCS9nDQo6vuAFfLimcD5wP9ExEM1yv0eGBcRO9VZPxQRH6i2ftA4i5ozFrIoL3MeNajVLMplnUcNchY1px+zCHxu1C6fG3Wfs6g5zqLBVLYscgOcWYdVhPs3gXUjYvO8bnlSy/vawHzSPfzv7nU35xp/9Io0O1CxS/aNETFUUfZ+0vPYPiLuH9WKl0x+zdbIDx+LEQJW0r+BoyLi6BrrvwgcGBFrdramNgjGShbl5c6jJjSbRbmM88i6oh+zCHxu1Ck+N7KycBY5iyhBFnkMOLPu2hL4WeHxx4FXAG8G7gD+AhwMXDHqNRtBDqWb808965C+FJbseqXKb2nS69DQH7zAisDsOuvnASt3omI28Po2i8B51IJmswicRzY6nEWDx+dGVkbOosFTiiwa12wBM2vK6sCDhcfvBa6OiL9HxCzSbDub9KRm1jGStpV0Dak78wOkL3UkrS7pCkk71ih6H/DOOrveAZjWybrawHIWDYA2sgicRzY6nEUDwudGVnLOogFRtixyA5xZdz1NGvQRSUsCbyfNxjJsAbBsD+pVaRJpiuapva5Iv8njMFwCLAccC2h4XUQ8DswBPlWj+C+A90k6StLwmARIWlHS94H3AKd0q+42UJxFY1ybWQTOIxsd/ZJF4Dxqmc+NrA84iwZAGbPIt6CadV5x2uVrgE9Lugx4P+nDf35h2w2AGaNbvcVFxJRe16GPfYs0u86WwKrAlyrWX0XtYD8GeANp1qIDJA0PAroWKZ/PBH7Q6QrbwHAWDZZ2sgicR9Y9fZdF4Dxqk8+NrIycRYOndFnkBjizDiuGpKSDSK3u/8iLjo6IO/K68cDOwEWjXknrpE2Ar0TEkKRq4wk8Ary0WsE8/sBukk4FPgS8Kq/6I/Bbz2Jk7XAWDZyWswicR9Y9zqKB5HMjKx1n0UAqXRa5Ac6siyLiPkkbABsDz0TEtMLqZYF9gVuGF3immr70Amm68lpeRermXlNE/IU02KtZVziLBkLbWQTOI+suZ9HA8LmRlZqzaGCULovcAGfWZRExj0KAF5bPZNGuzuCZavrRlcAnJR1TuULS6qRuzZfU24Gk9YFtSFNj/zIipklamjQb04yImNPxWtvAcRaNeW1nUd7WeWRd5SwaCD43stJzFg2E0mWRJ2EwM2vPwcBE4G/AXqQv5+0lHQL8k/RFfVi1gpLGSzoZuBM4ATgceHVevQRwI7B/F+tuZmNHy1kEziMz6yifG5lZGZQui9wAZ2bWhjxexFakqa2/R5pd56ukML8b2Doias1adAiwO/A1YHMWnZlnFnA28MFu1d3Mxo42swicR2bWIT43MrMyKGMW+RZUM7M2RcQ/ge0krQKsR7q4MTUiHhuh6O7AzyLiKEmrVVl/O/CBztbWzMaqNrIInEdm1kE+NzKzMihbFrkBzsysQyLiKdJU1416GXBDnfVzgeXbqpSZDZwWsgicR2bWBT43MrMyKEsWuQHOzKxNklYC9gN2JI0zADAd+BNwXETUml1nBjCpzq7fCkyps97M7EVtZBE4j8ysg3xuZGZlULYs8hhwZtauScD6QL2xhcYsSZOA24Bvkq6CXJt/lsvL/plnz6nmTOCzkt5UWBZ5v7sBHwPO6FLVzcaigc2jNrMInEdmnTSwWQQ+NzIrEWdRybJIEdFsGTPrEklDpA/2RhFxT6/rYyOT9GfgTcD7IuKqinVbk6Yx/3tE7FCl7ATgAmBr0pfDJsBNwKqk6c4vAd4bEfO7+iTMKjiL+k87WZS3cR5Z6TiL+pPPjWyscRb1pzJmkXvAmZm1ZwvgmMpQB4iIvwLHAm+rVjAiZgPvJE2L/QBwF+mKzB152Xt8gmlmDWo5i/I2ziMz6xSfG5lZGZQuizwGnJlZe54AZtVZ/xzweOVCScsAXwH+FhGnA6d3p3pmNiBayiJwHplZx/ncyMzKoHRZ5B5wZmbt+THwGUkvqVwh6WXAZ4EfVa6LiBeArwKv7HoNzWwQtJRF4Dwys47zuZGZlUHpssg94MzM2jMXeAG4T9K5wH15+STgQ6RBT+dL2q9QJiLiOOBGYKPRrKyZjVntZBE4j8ysc3xuZGZlULos8iQMZiUiad383+ke36I/5EFZmxURMV7SZsAfgIOAMyJiQWdrZ9YaZ1H/aSeLcnnnkZWOs6g/+dzIxhpnUX8qYxa5Ac7MrA2S1mmlXERMl3Qj8BJgTeB54CHSVZqKTWPT9mppZmNdO1mUyzuPzKwjfG5kZmVQxizyLahmZm0Y/uO1Rc8CzwD3dqg6Zjag2swicB6ZWYf43MjMyqCMWeQecGZmbZC0ArByRDxYWLYmaVDPCcC5EXF9h451PzAEbB8R93din2Y2NoxmFuV9O4/MrCqfG5lZGZQxi9wAZ2bWBklnAutGxOb58fLA7cDawHxSEL87Iq7owLGGgAA2ioh72t2fmY0do5lFef/OIzOryudGZlYGZcyice0eyMxswG0JXFB4/HHgFcCbgVWAm4GDe1AvMxssziIzKwvnkZmVQemyyA1wZmbtWR14sPD4vcDVEfH3iJgFnA5s0pOamdkgcRaZWVk4j8ysDEqXRW6AMzNrz9PAagCSlgTeDlxcWL8AWLYH9TKzweIsMrOycB6ZWRmULos8C6qZWXuuAT4t6TLg/cBywPmF9RsAM3pRMTMbKM4iMysL55GZlUHpssgNcGZm7TkIuAT4R358dETcASBpPLAzcFGP6mZmg8NZZGZl4TwyszIoXRa5Ac7MrA0RcZ+kDYCNgWciYlph9bLAvsAtwwsanaLazKwZziIzKwvnkZmVQRmzSBHRjf2amVkVjU5R3emyZmZF7eaJ88jMOsXnRmZWBqORRZ6EwczMzMzMzMzMrIvcAGdmZmZmZmZmZtZFboAzMzMzMzMzMzPrIjfAmZmZmZmZmZmZdZFnQTUz6x+T8r/Te1oLMzPnkZmVg7PIzMqgoSxyA5yZWZ+IiCm9roOZGTiPzKwcnEVmVgaNZpFvQTUzMzMzMzMzM+siN8CZmZmZmZmZmZl1kRvgzMzMzMzMzMzMusgNcGZmZmZmZmZmZl3kBjgzMzMzMzMzM7Mu8iyoZmajq6Epqs3MusxZZGZl4TwyszLoehYpIrq1bzMzMzMzMzMzs4HnW1DNzMzMzMzMzMy6yA1wZmZmZmZmZmZmXeQGODMzMzMzMzMzsy5yA5yZmZmZmZmZmVkX/X+8BFKIqDds0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tgt_sent = trans.split()\n",
    "def draw(data, x, y, ax):\n",
    "    seaborn.heatmap(data, \n",
    "                    xticklabels=x, square=True, yticklabels=y, vmin=0.0, vmax=1.0, \n",
    "                    cbar=False, ax=ax)\n",
    "    \n",
    "for layer in range(1, 6, 2):\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    print(\"Encoder Layer\", layer+1)\n",
    "    for h in range(4):\n",
    "        draw(model.encoder.layers[layer].self_attn.attn[0, h].data.cpu(), \n",
    "            sent, sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n",
    "    \n",
    "for layer in range(1, 6, 2):\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    print(\"Decoder Self Layer\", layer+1)\n",
    "    for h in range(4):\n",
    "        draw(model.decoder.layers[layer].self_attn.attn[0, h].data[:len(tgt_sent), :len(tgt_sent)].cpu(), \n",
    "            tgt_sent, tgt_sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n",
    "    print(\"Decoder Src Layer\", layer+1)\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    for h in range(4):\n",
    "        draw(model.decoder.layers[layer].self_attn.attn[0, h].data[:len(tgt_sent), :len(sent)].cpu(), \n",
    "            sent, tgt_sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0cxUVOZlzpnv"
   },
   "source": [
    "# Conclusion\n",
    "\n",
    "> Hopefully this code is useful for future research. Please reach out if you have any issues. If you find this code helpful, also check out our other OpenNMT tools.\n",
    "\n",
    "```\n",
    "@inproceedings{opennmt,\n",
    "  author    = {Guillaume Klein and\n",
    "               Yoon Kim and\n",
    "               Yuntian Deng and\n",
    "               Jean Senellart and\n",
    "               Alexander M. Rush},\n",
    "  title     = {OpenNMT: Open-Source Toolkit for Neural Machine Translation},\n",
    "  booktitle = {Proc. ACL},\n",
    "  year      = {2017},\n",
    "  url       = {https://doi.org/10.18653/v1/P17-4012},\n",
    "  doi       = {10.18653/v1/P17-4012}\n",
    "}\n",
    "```"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
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    "q7NOqljpzpms"
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   "name": "Seminar 4 - Transformer.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
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}