Attention-is-All-You-Need Paper Implementation(PyTorch) .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paper link : https://arxiv.org/abs/1706.03762  <br>\n",
    "Special thanks to BenTrevett and <a href = \"https://jalammar.github.io/\"> Jay Alammar </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Translating german to english </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to define the models\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "#to load,iterate and process the dataset\n",
    "import torchtext\n",
    "from torchtext.datasets import Multi30k\n",
    "from torchtext.data.metrics import bleu_score\n",
    "from torchtext.data import Field,BucketIterator\n",
    "\n",
    "#to visualize loss plots on localhost while training\n",
    "# See https://pytorch.org/docs/stable/tensorboard.html for more details\n",
    "from torch.utils.tensorboard import SummaryWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#miscallaneous imports\n",
    "\n",
    "import math\n",
    "import spacy\n",
    "import random\n",
    "import numpy as np\n",
    "from time import time\n",
    "\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Device: cuda:0\n",
      "NVIDIA GeForce RTX 2060\n",
      "Total GPU Memory 6442450944 B , Cached GPU Memory 0 B, Allocated GPU Memory 0 B\n"
     ]
    }
   ],
   "source": [
    "# get GPU details if available\n",
    "\n",
    "def get_gpu_details():\n",
    "    t = torch.cuda.get_device_properties(0).total_memory\n",
    "    c = torch.cuda.memory_cached(0)\n",
    "    a = torch.cuda.memory_allocated(0)\n",
    "    print(torch.cuda.get_device_name())\n",
    "    print(f'Total GPU Memory {t} B , Cached GPU Memory {c} B, Allocated GPU Memory {a} B')\n",
    "    \n",
    "    \n",
    "    \n",
    "if torch.cuda.is_available():\n",
    "    device='cuda:0'\n",
    "else:\n",
    "    device='cpu'\n",
    "print(f'Current Device: {device}')\n",
    "if device=='cuda:0':\n",
    "    torch.cuda.empty_cache()\n",
    "    get_gpu_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "\n",
    "SEED = 1234\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spacy_english = spacy.load('en')\n",
    "spacy_german = spacy.load('de_core_news_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_english(eng_text):\n",
    "    return [token.text for token in spacy_english.tokenizer(eng_text)]\n",
    "\n",
    "def tokenize_german(german_text):\n",
    "    return [token.text for token in spacy_german.tokenizer(german_text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lets', 'play', 'a', 'game', '!', '!']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_english('Lets play a game!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lassen', 'Sie', 'uns', 'ein', 'Spiel', 'spielen']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenize_german('Lassen Sie uns ein Spiel spielen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create text processing pipelines, first channel of the input to the model will be batch, so set batch_first = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_process_pipeline = Field(tokenize = tokenize_german,\n",
    "                       init_token = '<sos>',\n",
    "                       eos_token = '<eos>',\n",
    "                       lower = True,\n",
    "                       batch_first = True)\n",
    "\n",
    "\n",
    "target_process_pipeline = Field(tokenize = tokenize_english,\n",
    "                       init_token = '<sos>',\n",
    "                       eos_token = '<eos>',\n",
    "                       lower = True,\n",
    "                       batch_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset and split into train,test and validation <br>\n",
    "Refer the below link for more details <br>\n",
    "https://torchtext.readthedocs.io/en/latest/datasets.html#multi30k <br>\n",
    "https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/multi30k.html \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data,validation_data,test_data = Multi30k.splits(exts = ('.de','.en'),\n",
    "                                                       fields = (source_process_pipeline,\n",
    "                                                                target_process_pipeline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'src': <torchtext.data.field.Field at 0x1f1b09469c8>,\n",
       " 'trg': <torchtext.data.field.Field at 0x1f1b0946d08>}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_process_pipeline.build_vocab(train_data,min_freq=2)\n",
    "target_process_pipeline.build_vocab(train_data,min_freq=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7854"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(source_process_pipeline.vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5893"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(target_process_pipeline.vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Token indices for both source and target begin from 0 for each token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('<unk>', 0),\n",
       " ('<pad>', 1),\n",
       " ('<sos>', 2),\n",
       " ('<eos>', 3),\n",
       " ('.', 4),\n",
       " ('ein', 5),\n",
       " ('einem', 6),\n",
       " ('in', 7),\n",
       " ('eine', 8),\n",
       " (',', 9),\n",
       " ('und', 10),\n",
       " ('mit', 11),\n",
       " ('auf', 12),\n",
       " ('mann', 13),\n",
       " ('einer', 14),\n",
       " ('der', 15),\n",
       " ('frau', 16),\n",
       " ('die', 17),\n",
       " ('zwei', 18),\n",
       " ('einen', 19),\n",
       " ('im', 20),\n",
       " ('an', 21),\n",
       " ('von', 22),\n",
       " ('sich', 23),\n",
       " ('dem', 24),\n",
       " ('mädchen', 25),\n",
       " ('junge', 26),\n",
       " ('vor', 27),\n",
       " ('zu', 28),\n",
       " ('steht', 29),\n",
       " ('männer', 30),\n",
       " ('sitzt', 31),\n",
       " ('hund', 32),\n",
       " ('den', 33),\n",
       " ('straße', 34),\n",
       " ('während', 35),\n",
       " ('gruppe', 36),\n",
       " ('hält', 37),\n",
       " ('spielt', 38),\n",
       " ('das', 39),\n",
       " ('hemd', 40),\n",
       " ('personen', 41),\n",
       " ('über', 42),\n",
       " ('drei', 43),\n",
       " ('eines', 44),\n",
       " ('frauen', 45),\n",
       " ('blauen', 46),\n",
       " ('neben', 47),\n",
       " ('ist', 48),\n",
       " ('kind', 49)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(source_process_pipeline.vocab.stoi.items())[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('<unk>', 0),\n",
       " ('<pad>', 1),\n",
       " ('<sos>', 2),\n",
       " ('<eos>', 3),\n",
       " ('a', 4),\n",
       " ('.', 5),\n",
       " ('in', 6),\n",
       " ('the', 7),\n",
       " ('on', 8),\n",
       " ('man', 9),\n",
       " ('is', 10),\n",
       " ('and', 11),\n",
       " ('of', 12),\n",
       " ('with', 13),\n",
       " ('woman', 14),\n",
       " (',', 15),\n",
       " ('two', 16),\n",
       " ('are', 17),\n",
       " ('to', 18),\n",
       " ('people', 19),\n",
       " ('at', 20),\n",
       " ('an', 21),\n",
       " ('wearing', 22),\n",
       " ('shirt', 23),\n",
       " ('young', 24),\n",
       " ('white', 25),\n",
       " ('black', 26),\n",
       " ('his', 27),\n",
       " ('while', 28),\n",
       " ('blue', 29),\n",
       " ('men', 30),\n",
       " ('red', 31),\n",
       " ('sitting', 32),\n",
       " ('girl', 33),\n",
       " ('boy', 34),\n",
       " ('dog', 35),\n",
       " ('standing', 36),\n",
       " ('playing', 37),\n",
       " ('group', 38),\n",
       " ('street', 39),\n",
       " ('down', 40),\n",
       " ('walking', 41),\n",
       " ('-', 42),\n",
       " ('front', 43),\n",
       " ('her', 44),\n",
       " ('holding', 45),\n",
       " ('one', 46),\n",
       " ('water', 47),\n",
       " ('three', 48),\n",
       " ('by', 49)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(target_process_pipeline.vocab.stoi.items())[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Define the iterator</h1>\n",
    "\n",
    "<b>BucketIterator</b>: <br>\n",
    "Defines an iterator that batches examples of similar lengths together.\n",
    "\n",
    "Minimizes amount of padding needed while producing freshly shuffled batches for each new epoch. See pool for the bucketing procedure used.\n",
    "<br><br>\n",
    "refer:\n",
    "https://torchtext.readthedocs.io/en/latest/data.html#bucketiterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iterator, validation_iterator, test_iterator = BucketIterator.splits(\n",
    "    (train_data, validation_data, test_data), \n",
    "     batch_size = batch_size,\n",
    "     device = device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[torchtext.data.batch.Batch of size 128 from MULTI30K]\n",
      "\t[.src]:[torch.cuda.LongTensor of size 128x23 (GPU 0)]\n",
      "\t[.trg]:[torch.cuda.LongTensor of size 128x21 (GPU 0)]\n"
     ]
    }
   ],
   "source": [
    "print(next(iter(train_iterator)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the transformer model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Transformer's encoder does not attempt to compress the entire source sentence, $X = (x_1, ... ,x_n)$, into a single context vector, $z$. Instead it produces a sequence of context vectors, $Z = (z_1, ... , z_n)$. So, if our input sequence was 5 tokens long we would have $Z = (z_1, z_2, z_3, z_4, z_5)$. Why do we call this a sequence of context vectors and not a sequence of hidden states? A hidden state at time $t$ in an RNN has only seen tokens $x_t$ and all the tokens before it. However, each context vector here has seen all tokens at all positions within the input sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/encoder.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Dimensional Notations followed </h2>\n",
    "<h3> =================================================== </h3>\n",
    "<h3>bsiz = batch_size</h3>\n",
    "<h3>slen = source_length</h3>\n",
    "<h3>hdim = hidden_dimension size</h3>\n",
    "<h3>qlen = query length</h3>\n",
    "<h3>vlen = value length</h3>\n",
    "<h3>klen = key length</h3>\n",
    "<h3>tlen = target length</h3>\n",
    "<h3>nheads = number of heads in multihead attention</h3>\n",
    "<h3> =================================================== </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>The Encoder</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Token Embeddings and Positional Encodings </h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refer : https://jalammar.github.io/illustrated-transformer/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src= './images/positional_encoding_vectors.png' width = 500, height = 400>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input sequence of tokens are passed through an embedding layer, this embeddin layer can be of any type, either use pretrained embeddings(Word2Vec,GLoVE,fasttext) etc. or can be learned on their own.\n",
    "<br><br>\n",
    "However since we are not using RNNs, the model has no idea about the positional sequence of input tokens,i.e the model doesn't know which word comes after what word and the relative distances between any pair of words. Hence another embedding is added to the original input embedding, which is called as positional embedding.\n",
    "<br><br>\n",
    "The positional encoding has a fixed vocab size 'x' which means the model can accept sequences which are 'x' tokens long."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the token and positional embeddings are elementwise summed together to get a vector which contains information about the token and also its position with in the sequence. However, before they are summed, the token embeddings are multiplied by a scaling factor which is $\\sqrt{d_{model}}$, where $d_{model}$ is the hidden dimension size, hid_dim. This supposedly reduces variance in the embeddings and the model is difficult to train reliably without this scaling factor. Dropout is then applied to the combined embeddings.(mentioned in section 3.4 of the <a href = \"https://arxiv.org/abs/1706.03762\">paper</a>)\n",
    "\n",
    "\n",
    "\n",
    "The combined embeddings are then passed through $N$ encoder layers to get $Z$, which is then output and can be used by the decoder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WE'LL BE LEARNING THE POSITONAL ENCODINGS RATHER THAN USING STATIC PE(AS USED IN THE PAPER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input sequence is the masked with a source_mask which masks the input sequence wherever there is padding. It marks the token if it is not a pad token as 1 and 0 when it is a pad token.This makes sense because the pad tokens do not serve any useful purpose apart from making the lengths similar.So the model should not apply attention over these pad tokens and hence they are masked with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self,input_dimension,hidden_dimension,\n",
    "                 number_encoder_layers,num_attention_heads,pointwise_ff_dim,\n",
    "                 dropout,device,max_length = 100):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.device = device\n",
    "        \n",
    "        self.token_embeddings = nn.Embedding(input_dimension,hidden_dimension)\n",
    "        self.positional_embeddings = nn.Embedding(max_length,hidden_dimension)\n",
    "        \n",
    "        \n",
    "        self.encoder_blocks = nn.ModuleList([EncoderBlock(hidden_dimension,num_attention_heads,\n",
    "                                                  pointwise_ff_dim,dropout,device)\n",
    "                                     for _ in range(number_encoder_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        # sqrt(d_model)\n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hidden_dimension])).to(device)\n",
    "        \n",
    "        \n",
    "    def forward(self,source,source_mask):\n",
    "        \n",
    "        #src = (bsiz,slen)\n",
    "        #src_mask = (bsiz,1,1,slen)\n",
    "        \n",
    "        batch_size = source.shape[0]\n",
    "        source_length = source.shape[1]\n",
    "        \n",
    "        \n",
    "        positions = torch.arange(0,source_length).unsqueeze(0)\n",
    "        #positions = (source_length,1)\n",
    "        \n",
    "        positions = positions.repeat(batch_size,1).to(self.device)\n",
    "        #positions = (batch_size,source_length)\n",
    "        #see posmask_example1 below after this cell for clarification\n",
    "        \n",
    "        #add the token_embeddings to positional_encodings for letting \n",
    "        #the model know about the sequence of occurance of tokens\n",
    "        \n",
    "        #get the embeddings for the token and multiply with sqrt(d_model) scale\n",
    "        source = self.token_embeddings(source)*self.scale\n",
    "        \n",
    "        #add the positional embeddings to the source\n",
    "        source = source + self.positional_embeddings(positions)\n",
    "        \n",
    "        #apply dropout\n",
    "        source = self.dropout(source)\n",
    "        #source = (bsiz,slen,hdim) see the image below for tensor shape clarification\n",
    "        \n",
    "        for encoder_block in self.encoder_blocks:\n",
    "            source = encoder_block(source,source_mask)\n",
    "        \n",
    "        return source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Few visualizations to clarify the above shapes and tensors</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Below image shows the shape of a tensor after passing through the token and positional embeddings </b> <br><br>\n",
    "The shape remains same after passing through the encoder layers as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/embedding_op_tensor.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posmask_example1 = torch.arange(0,5).unsqueeze(0)\n",
    "posmask_example1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posmask_example1.repeat(4,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4, 0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4, 0, 1, 2, 3, 4],\n",
       "        [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posmask_example1.repeat(4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 16])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the embedding layer\n",
    "\n",
    "# let m = input dim (number of unique words/items)\n",
    "# let n = hidden_dim i.e we want each word to be represented by a\n",
    "#         size n numerical vector\n",
    "\n",
    "m=8\n",
    "n=16\n",
    "inp = torch.LongTensor([[1,2,3,4],[4,2,1,7]])\n",
    "example_embedding_layer = nn.Embedding(m,n)\n",
    "op = example_embedding_layer(inp)\n",
    "op.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.7988, -0.0923, -0.7049, -1.6024,  0.2891,  0.4899, -0.3853,\n",
       "          -0.7120, -0.1706, -1.4594,  0.2207,  0.2463, -1.3248,  0.6970,\n",
       "          -0.6631,  1.2158],\n",
       "         [-1.4949,  0.8810, -1.1786, -0.9340, -0.5675, -0.2772, -2.1834,\n",
       "           0.3668,  0.9380,  0.0078, -0.3139, -1.1567,  1.8409, -1.0174,\n",
       "           1.2192,  0.1601],\n",
       "         [ 1.5985, -0.0469, -1.5270, -2.0143, -1.5173,  0.3877, -1.1849,\n",
       "           0.6897,  1.3232,  1.8169,  0.6808,  0.7244,  0.0323, -1.6593,\n",
       "          -1.8773,  0.7372],\n",
       "         [ 0.9257,  0.9247,  0.1825, -0.0737,  0.3147, -1.0369,  0.2100,\n",
       "           0.6144,  0.0628, -0.3297, -1.7970,  0.8728,  0.7670, -0.1138,\n",
       "          -0.9428,  0.7540]],\n",
       "\n",
       "        [[ 0.9257,  0.9247,  0.1825, -0.0737,  0.3147, -1.0369,  0.2100,\n",
       "           0.6144,  0.0628, -0.3297, -1.7970,  0.8728,  0.7670, -0.1138,\n",
       "          -0.9428,  0.7540],\n",
       "         [-1.4949,  0.8810, -1.1786, -0.9340, -0.5675, -0.2772, -2.1834,\n",
       "           0.3668,  0.9380,  0.0078, -0.3139, -1.1567,  1.8409, -1.0174,\n",
       "           1.2192,  0.1601],\n",
       "         [ 0.7988, -0.0923, -0.7049, -1.6024,  0.2891,  0.4899, -0.3853,\n",
       "          -0.7120, -0.1706, -1.4594,  0.2207,  0.2463, -1.3248,  0.6970,\n",
       "          -0.6631,  1.2158],\n",
       "         [ 0.9570, -0.5510,  2.6617,  2.1479, -0.8555, -0.7208,  1.3755,\n",
       "           0.9988,  0.1469, -0.9053, -0.3587,  1.6374,  0.6897, -0.5844,\n",
       "           0.9078,  0.4848]]], grad_fn=<EmbeddingBackward>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example each token in the input is represented by a vector of dimension n.The embedding vector is created for m unique items, where each unique item has a vector of size n. <br>\n",
    "\n",
    "We see the shape of embedding op is (x,y,z) where x is number of input sequences,y is the sequence length and z is the size of embedding (which is same as n). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details go to https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>The Encoder Block</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='./images/EncoderBlock.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is the most important part of the Encoder, it contains the main network components repeated and split across several blocks. The steps are as follows:\n",
    "<ul>\n",
    "    <li>For first encoder block after passing through the embedding layers <br> it is passed through the multi head self attention layer and dropout is applied</li>\n",
    "    <li>Then the input is also passed as a residual connection and through a \"Layer normalization\" layer</li>\n",
    "    <li>It is then passed through a feed forward layer then again dropout,residual and layer normalization layer, the output of this layer is passed to the next encoder block</li>    \n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The multi head self attention layer is called to find and apply attention to the sentence and it applies and returns the attention scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Layer Normalization: </b>  normalizes the values of the features, i.e. across the hidden dimension, so each feature has a mean of 0 and a standard deviation of 1. This allows neural networks with a larger number of layers, like the Transformer, to be trained easier.<br>\n",
    "See https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html and \n",
    "https://arxiv.org/abs/1607.06450 for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderBlock(nn.Module):\n",
    "    def __init__(self,hidden_dimension,num_attention_heads,\n",
    "                      pointwise_ff_dim,dropout,device):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.attention_layer_norm = nn.LayerNorm(hidden_dimension)\n",
    "        self.feedForward_layer_norm = nn.LayerNorm(hidden_dimension)\n",
    "        \n",
    "        self.selfAttention = MultiHeadSelfAttention(hidden_dimension,\n",
    "                                                    num_attention_heads,\n",
    "                                                   dropout,device)\n",
    "        \n",
    "        self.pointWise_feedForward = PointWiseFeedForwardLayer(hidden_dimension,\n",
    "                                                              pointwise_ff_dim,\n",
    "                                                              dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        \n",
    "    def forward(self,source,source_mask):\n",
    "        \n",
    "        #source(x) = (bsiz,slen,hdim)\n",
    "        #sourcemask = (bsize,1,1,slen)\n",
    "        \n",
    "        #self attention(z)\n",
    "        #MHSA accepts input in (query,key,value,mask) format\n",
    "        attention_contexts,attention_scores = self.selfAttention(source,source,source,source_mask)\n",
    "        \n",
    "        #dropout,residual skip connection and attn_layer norm(x+z)\n",
    "        attention_contexts = self.dropout(attention_contexts)\n",
    "        \n",
    "        source = self.attention_layer_norm(source + attention_contexts)\n",
    "        \n",
    "        \n",
    "        #repeat the above for feedforward layer norm\n",
    "        #source = (bsiz,slen,hdim)\n",
    "        \n",
    "        #pointwise feedforward\n",
    "        attention_contexts = self.pointWise_feedForward(source)\n",
    "        \n",
    "        \n",
    "        #dropout,residual skip connection and ff_layer norm(x+z)\n",
    "        attention_contexts = self.dropout(attention_contexts)\n",
    "        source = self.feedForward_layer_norm(source + attention_contexts)\n",
    "        \n",
    "        #source = (bsiz,slen,hdim)\n",
    "        return source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Multi Head Self Attention </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Self attention can completely replace recurrence.Each element in the sentence attends to other elements.Each word is a query as well as a key.We want to score each query-key pair.In this way everything is fully contextualized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/scaled_dot_product_attention.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Example</b> <br>\n",
    "$X_i$ = {$x_{i1}$,$x_{i2}$,$x_{i3}$,...$x_{in}$} <br>\n",
    "Each sample will have its own query key and value vectors.These projections(Q,K,V) are just linear layers on top original word embeddings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to calculate attention, take a query vector k of <b>$x_{i}$</b>, lets call it <b>$x_{ik}$</b>, and apply dot product with each of the key vectors of all <b>$x_{i}$</b> i.e apply dot product of query vector of <b>$x_{ik}$</b> with all key vectors <b>$x_{ij}$</b> for each j."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then get attention scores after applying softmax, we do this for every word and get an attention distribution for all the words.After which we get a representation of the word which is purely context dependent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below images show the above in visual form."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/selfattention.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"./images/selfattentionoutput.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since all of the above is done in batches of tensors, the above calculations can be easily done using Matrix multiplications.\n",
    "Below images show how to multiply inputs <b>X</b> with weight matrices <b> $W_q$, $W_v$,$W_k$ </b> to obtain the query, key and value vectors and then apply softmax on scaled dot product and multiplication with value vectors to obtain the context vector <b>Z</b>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/self-attention-matrix-calculation.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/self-attention-matrix-calculation-2.png'> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above images Wk,Wq,Wv are nothing but the weights of the linear layers which will be used to define query,key and values.These weights are nothing but matrices for linear layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Multiple Attention Heads <b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above parts we see that the representations learnt are fully contextual in nature, and hence provide multiple subspaces of the learnt representations by each head.\n",
    "<br>\n",
    "As the <a href = \"https://arxiv.org/abs/1706.03762\">paper</a> says, in section 3.2.2 <br>\n",
    " \n",
    "<i>\"Instead of performing a single attention function with dmodel-dimensional keys, values and queries,\n",
    "we found it beneficial to linearly project the queries, keys and values h times with different, learned\n",
    "linear projections to dk, dk and dv dimensions, respectively.\n",
    "    <br><br>On each of these projected versions of\n",
    "queries, keys and values we then perform the attention function in parallel, yielding dv-dimensional\n",
    "output values. These are concatenated and once again projected, resulting in the final values.<br>\n",
    "Multi-head attention allows the model to jointly attend to information from different representation\n",
    "subspaces at different positions. With a single attention head, averaging inhibits this.\n",
    "\"</i>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"./images/mha_1.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above might look very intimidating at first, but understanding it visually helps alot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"./images/transformer_multi-headed_self-attention-recap.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets implement the above,and then understand the details of implementation after it. Dont worry, the code isnt that long, most of them are comments so that there is no confusion while manipulating the tensors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll be using the above step numbers as an indicator for the corresponding section of the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadSelfAttention(nn.Module):\n",
    "    \n",
    "    def __init__(self,hidden_dimension,num_attention_heads,\n",
    "                dropout,device):\n",
    "        super().__init__()\n",
    "        \n",
    "        assert hidden_dimension % num_attention_heads == 0\n",
    "        \n",
    "        self.hidden_dimension = hidden_dimension\n",
    "        self.num_attention_heads = num_attention_heads\n",
    "        self.head_dimension = hidden_dimension // num_attention_heads\n",
    "        \n",
    "        self.W_q = nn.Linear(hidden_dimension,hidden_dimension)\n",
    "        self.W_k = nn.Linear(hidden_dimension,hidden_dimension)\n",
    "        self.W_v = nn.Linear(hidden_dimension,hidden_dimension)\n",
    "        \n",
    "        self.W_o = nn.Linear(hidden_dimension,hidden_dimension)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dimension])).to(device)\n",
    "       \n",
    "    \n",
    "    def split_heads(self,item,batch_size):\n",
    "        item = item.view(batch_size,-1,self.num_attention_heads,self.head_dimension)\n",
    "        item = item.permute(0,2,1,3)\n",
    "        return item\n",
    "    \n",
    "    def forward(self,query,key,value,mask = None):\n",
    "        \n",
    "        batch_size = query.shape[0]\n",
    "        # Step 3 from the image\n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "        \n",
    "        Q = self.W_q(query) #Q,query shape is (bsiz,qlen,hdim)\n",
    "        K = self.W_k(key)  #K,key shape is (bsiz,klen,hdim)\n",
    "        V = self.W_v(value)  #V,value shape is (bsiz,vlen,hdim)\n",
    "        \n",
    "        Q = self.split_heads(Q,batch_size) #Q shape(bsiz,n_attn_heads,qlen,head_dim)\n",
    "        K = self.split_heads(K,batch_size) #K shape(bsiz,n_attn_heads,klen,head_dim)\n",
    "        V = self.split_heads(V,batch_size) #V shape(bsiz,n_attn_heads,vlen,head_dim)\n",
    "        \n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        # Step 4 from the image\n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "             \n",
    "        # permute because Q = (bsize,n_attn_heads,qlen,head_dim)\n",
    "        #             and K = (bsize,n_attn_heads,klen,head_dim)\n",
    "        # we need to multiply across batches and corresponding attention heads,\n",
    "        # while following matrix multiplication compatibility\n",
    "        #Q(bsize,n_attn_heads,qlen,hdim) x K(bsize,n_attn_heads,hdim,klen)\n",
    "        #mainly we have to ensure that m1 = (qlen,hdim)  x m2 = (hdim,klen)\n",
    "        #                       gives  m3   = (qlen,klen)\n",
    "        \n",
    "        # Q x K.T / sqrt(d_k)\n",
    "        energy = torch.matmul(Q,K.permute(0,1,3,2)) / self.scale\n",
    "        #energy = (bsize,n_attn_heads,qlen,klen)\n",
    "        \n",
    "        #the below masking is done so that after softmax,useless and very low values go to 0.\n",
    "        if mask is not None:\n",
    "            energy = energy.masked_fill(mask == 0, -1e15)\n",
    "        \n",
    "        # softmax is applied over last dimension, across all batches, across all heads\n",
    "        # softmax(Q x K.T / sqrt(d_k))\n",
    "        attention = torch.softmax(energy,dim = -1)\n",
    "        \n",
    "        #apply attention_score x value\n",
    "        #attention = (bsiz,n_attn_heads,qlen,klen)\n",
    "        #V shape(bsiz,n_attn_heads,vlen,hdim)\n",
    "        \n",
    "        # softmax(Q x K.T / sqrt(d_k)) * V\n",
    "        # equivalent to attention_scores * V\n",
    "        \n",
    "        attention_scored_value = torch.matmul(self.dropout(attention),V)\n",
    "        #as klen = vlen, attention_scored_value(bsiz,n_attn_heads,qlen,hdim)\n",
    "        \n",
    "        attention_scored_value = attention_scored_value.permute(0,2,1,3).contiguous()\n",
    "        \n",
    "        #attention_scored_value(bsiz,qlen,n_attn_heads,hdim)\n",
    "        # contiguous makes a copy of the tensor such that the order of its elements in memory\n",
    "        #is the same as if it had been created from scratch with the same data.\n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "\n",
    "        \n",
    "        \n",
    "        #Step 5 from the image\n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "\n",
    "        attention_scored_value = attention_scored_value.view(batch_size,-1,self.hidden_dimension)\n",
    "        #attention_scored_value = (bsiz,qlen,h_dim)\n",
    "        \n",
    "        attention_contexts_Z = self.W_o(attention_scored_value)\n",
    "        #==========================================================#\n",
    "        #==========================================================#\n",
    "\n",
    "        return attention_contexts_Z,attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we calculate $QW^Q$, $KW^K$ and $VW^V$ with the linear layers, $W^q$, $W^k$ and $W^v$, to give us Q, K and V. Next, we split the hid_dim of the query, key and value into n_heads using .view and correctly permute them so they can be multiplied together. We then calculate the energy (the un-normalized attention) by multiplying Q and K together and scaling it by the square root of head_dim, which is calulated as hid_dim // n_heads. We then mask the energy so we do not pay attention over any elements of the sequeuence we shouldn't, then apply the softmax and dropout. We then apply the attention to the value heads, V, before combining the n_heads together. Finally, we multiply this $W^O$, represented in the code by W_o.\n",
    "\n",
    "Note that in our implementation the lengths of the keys and values are always the same, thus when matrix multiplying the output of the softmax, attention, with V we will always have valid dimension sizes for matrix multiplication. This multiplication is carried out using torch.matmul which, when both tensors are greater than 2-dimensions, does a batched matrix multiplication over the last two dimensions of each tensor. <br>This will be a <b>[query len, key len] x [value len, head dim]</b> batched matrix multiplication over the batch size and each head which provides the<br> <b>[batch size, n heads, query len, head dim]</b> result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <h3> Example 2 </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bsiz = 4\n",
    "q_len = 6\n",
    "v_len = 6\n",
    "k_len = 6\n",
    "\n",
    "\n",
    "hdim = 8\n",
    "nheads = 2\n",
    "head_dim = hdim // nheads\n",
    "\n",
    "Q_ = torch.rand(bsiz, q_len,hdim)\n",
    "K_ = torch.rand(bsiz, k_len,hdim)\n",
    "V_ = torch.rand(bsiz, v_len,hdim)\n",
    "\n",
    "scale = torch.sqrt(torch.FloatTensor([head_dim]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(torch.Size([4, 6, 8]), torch.Size([4, 6, 8]), torch.Size([4, 6, 8]))\n"
     ]
    }
   ],
   "source": [
    "print((Q_.shape,K_.shape,V_.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[8.7327e-01, 3.2295e-01, 7.9166e-01, 5.4470e-01, 6.2199e-01,\n",
       "          1.6677e-01, 3.9321e-01, 8.2983e-01],\n",
       "         [7.2933e-01, 3.5544e-01, 5.6151e-01, 8.8281e-01, 6.1901e-02,\n",
       "          8.2117e-01, 3.4973e-01, 3.9828e-01],\n",
       "         [7.3793e-01, 8.4621e-02, 4.2447e-01, 9.7781e-01, 6.7995e-01,\n",
       "          3.1512e-01, 3.9109e-01, 8.9431e-01],\n",
       "         [6.8890e-01, 8.3894e-01, 1.7802e-01, 6.4415e-01, 5.8938e-01,\n",
       "          5.8716e-01, 9.6428e-01, 7.7679e-01],\n",
       "         [8.4465e-01, 7.1955e-01, 7.1014e-01, 4.1303e-01, 5.8708e-02,\n",
       "          7.7635e-02, 1.8859e-01, 1.7628e-01],\n",
       "         [7.3538e-01, 6.5478e-01, 3.5743e-01, 1.1912e-01, 9.9223e-01,\n",
       "          8.7573e-01, 7.3779e-01, 9.9492e-01]],\n",
       "\n",
       "        [[2.3380e-01, 2.1526e-01, 2.0733e-01, 4.7579e-01, 5.8631e-02,\n",
       "          8.9582e-01, 5.1286e-01, 7.4900e-01],\n",
       "         [2.2541e-01, 4.4849e-01, 5.6576e-01, 3.6315e-01, 9.7195e-01,\n",
       "          2.7156e-01, 6.5525e-01, 1.6075e-01],\n",
       "         [4.4082e-01, 3.6735e-01, 7.1573e-01, 8.4927e-01, 6.2160e-01,\n",
       "          4.5460e-01, 3.7197e-01, 8.9201e-01],\n",
       "         [3.8186e-01, 8.6096e-01, 2.7753e-01, 9.1208e-01, 9.9800e-01,\n",
       "          8.4274e-01, 7.8677e-01, 6.8610e-01],\n",
       "         [2.5415e-01, 5.0597e-01, 6.7217e-01, 3.6935e-01, 5.8963e-01,\n",
       "          7.9284e-01, 3.6734e-02, 4.4899e-02],\n",
       "         [2.0082e-01, 5.6099e-01, 4.0732e-01, 6.0775e-01, 3.8110e-01,\n",
       "          1.7268e-01, 5.6638e-01, 8.3864e-01]],\n",
       "\n",
       "        [[1.9978e-01, 9.3829e-01, 6.2306e-01, 9.9302e-01, 4.1706e-02,\n",
       "          2.1394e-02, 6.6963e-01, 3.3555e-02],\n",
       "         [9.0252e-02, 7.5424e-01, 7.4271e-01, 4.4390e-02, 8.5979e-01,\n",
       "          4.0278e-01, 8.7121e-01, 2.0658e-01],\n",
       "         [2.4565e-02, 1.6854e-01, 4.2249e-01, 6.3777e-04, 4.6593e-01,\n",
       "          2.5046e-01, 3.8362e-01, 4.9993e-01],\n",
       "         [5.2806e-01, 1.0909e-02, 7.5719e-01, 1.9355e-01, 8.0471e-01,\n",
       "          9.5816e-01, 3.3325e-01, 5.5612e-01],\n",
       "         [7.7569e-01, 4.6039e-01, 8.1637e-02, 7.2460e-01, 7.3787e-01,\n",
       "          2.2607e-01, 1.6334e-01, 4.1274e-01],\n",
       "         [5.9803e-01, 2.4714e-01, 4.8798e-01, 2.8393e-01, 3.0521e-01,\n",
       "          4.3157e-01, 3.0943e-01, 8.8407e-01]],\n",
       "\n",
       "        [[6.3138e-01, 9.4747e-01, 9.5146e-01, 5.8172e-01, 2.9199e-01,\n",
       "          8.0405e-01, 4.8260e-01, 8.7014e-01],\n",
       "         [3.9324e-01, 9.4216e-01, 5.6605e-01, 3.8891e-01, 1.7381e-01,\n",
       "          8.7004e-01, 5.6504e-01, 6.7296e-01],\n",
       "         [7.0210e-01, 4.7418e-01, 2.4863e-01, 3.5077e-01, 4.7774e-01,\n",
       "          8.1048e-01, 4.6346e-01, 3.7324e-01],\n",
       "         [4.3205e-01, 5.8209e-01, 8.9907e-01, 8.7732e-01, 4.7076e-01,\n",
       "          5.6628e-01, 9.5345e-01, 7.1967e-01],\n",
       "         [4.4724e-01, 7.9641e-01, 7.2774e-01, 3.6572e-01, 7.0227e-01,\n",
       "          5.3963e-01, 3.1437e-01, 4.6078e-01],\n",
       "         [9.7060e-01, 6.5019e-01, 8.7197e-02, 8.1864e-01, 6.0743e-01,\n",
       "          7.4596e-01, 5.0210e-01, 6.9886e-01]]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_ #[batch size, query len, hid dim]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After applying .view() we get 4 batches each of qlen size, having nheads with each having dimension head_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 6, 2, 4])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_.view(bsiz,-1,nheads,head_dim).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[[0.8733, 0.3230, 0.7917, 0.5447],\n",
      "         [0.6220, 0.1668, 0.3932, 0.8298]],\n",
      "\n",
      "        [[0.7293, 0.3554, 0.5615, 0.8828],\n",
      "         [0.0619, 0.8212, 0.3497, 0.3983]],\n",
      "\n",
      "        [[0.7379, 0.0846, 0.4245, 0.9778],\n",
      "         [0.6800, 0.3151, 0.3911, 0.8943]],\n",
      "\n",
      "        [[0.6889, 0.8389, 0.1780, 0.6442],\n",
      "         [0.5894, 0.5872, 0.9643, 0.7768]],\n",
      "\n",
      "        [[0.8447, 0.7195, 0.7101, 0.4130],\n",
      "         [0.0587, 0.0776, 0.1886, 0.1763]],\n",
      "\n",
      "        [[0.7354, 0.6548, 0.3574, 0.1191],\n",
      "         [0.9922, 0.8757, 0.7378, 0.9949]]]), torch.Size([6, 2, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[0.2338, 0.2153, 0.2073, 0.4758],\n",
      "         [0.0586, 0.8958, 0.5129, 0.7490]],\n",
      "\n",
      "        [[0.2254, 0.4485, 0.5658, 0.3631],\n",
      "         [0.9719, 0.2716, 0.6552, 0.1607]],\n",
      "\n",
      "        [[0.4408, 0.3674, 0.7157, 0.8493],\n",
      "         [0.6216, 0.4546, 0.3720, 0.8920]],\n",
      "\n",
      "        [[0.3819, 0.8610, 0.2775, 0.9121],\n",
      "         [0.9980, 0.8427, 0.7868, 0.6861]],\n",
      "\n",
      "        [[0.2542, 0.5060, 0.6722, 0.3694],\n",
      "         [0.5896, 0.7928, 0.0367, 0.0449]],\n",
      "\n",
      "        [[0.2008, 0.5610, 0.4073, 0.6077],\n",
      "         [0.3811, 0.1727, 0.5664, 0.8386]]]), torch.Size([6, 2, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[1.9978e-01, 9.3829e-01, 6.2306e-01, 9.9302e-01],\n",
      "         [4.1706e-02, 2.1394e-02, 6.6963e-01, 3.3555e-02]],\n",
      "\n",
      "        [[9.0252e-02, 7.5424e-01, 7.4271e-01, 4.4390e-02],\n",
      "         [8.5979e-01, 4.0278e-01, 8.7121e-01, 2.0658e-01]],\n",
      "\n",
      "        [[2.4565e-02, 1.6854e-01, 4.2249e-01, 6.3777e-04],\n",
      "         [4.6593e-01, 2.5046e-01, 3.8362e-01, 4.9993e-01]],\n",
      "\n",
      "        [[5.2806e-01, 1.0909e-02, 7.5719e-01, 1.9355e-01],\n",
      "         [8.0471e-01, 9.5816e-01, 3.3325e-01, 5.5612e-01]],\n",
      "\n",
      "        [[7.7569e-01, 4.6039e-01, 8.1637e-02, 7.2460e-01],\n",
      "         [7.3787e-01, 2.2607e-01, 1.6334e-01, 4.1274e-01]],\n",
      "\n",
      "        [[5.9803e-01, 2.4714e-01, 4.8798e-01, 2.8393e-01],\n",
      "         [3.0521e-01, 4.3157e-01, 3.0943e-01, 8.8407e-01]]]), torch.Size([6, 2, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[0.6314, 0.9475, 0.9515, 0.5817],\n",
      "         [0.2920, 0.8040, 0.4826, 0.8701]],\n",
      "\n",
      "        [[0.3932, 0.9422, 0.5660, 0.3889],\n",
      "         [0.1738, 0.8700, 0.5650, 0.6730]],\n",
      "\n",
      "        [[0.7021, 0.4742, 0.2486, 0.3508],\n",
      "         [0.4777, 0.8105, 0.4635, 0.3732]],\n",
      "\n",
      "        [[0.4320, 0.5821, 0.8991, 0.8773],\n",
      "         [0.4708, 0.5663, 0.9534, 0.7197]],\n",
      "\n",
      "        [[0.4472, 0.7964, 0.7277, 0.3657],\n",
      "         [0.7023, 0.5396, 0.3144, 0.4608]],\n",
      "\n",
      "        [[0.9706, 0.6502, 0.0872, 0.8186],\n",
      "         [0.6074, 0.7460, 0.5021, 0.6989]]]), torch.Size([6, 2, 4]))\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in Q_.view(bsiz,-1,nheads,head_dim):\n",
    "    print((i,i.shape))\n",
    "    print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q_split = Q_.view(bsiz,-1,nheads,head_dim).permute(0,2,1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 2, 6, 4])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_split.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[[0.8733, 0.3230, 0.7917, 0.5447],\n",
      "         [0.7293, 0.3554, 0.5615, 0.8828],\n",
      "         [0.7379, 0.0846, 0.4245, 0.9778],\n",
      "         [0.6889, 0.8389, 0.1780, 0.6442],\n",
      "         [0.8447, 0.7195, 0.7101, 0.4130],\n",
      "         [0.7354, 0.6548, 0.3574, 0.1191]],\n",
      "\n",
      "        [[0.6220, 0.1668, 0.3932, 0.8298],\n",
      "         [0.0619, 0.8212, 0.3497, 0.3983],\n",
      "         [0.6800, 0.3151, 0.3911, 0.8943],\n",
      "         [0.5894, 0.5872, 0.9643, 0.7768],\n",
      "         [0.0587, 0.0776, 0.1886, 0.1763],\n",
      "         [0.9922, 0.8757, 0.7378, 0.9949]]]), torch.Size([2, 6, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[0.2338, 0.2153, 0.2073, 0.4758],\n",
      "         [0.2254, 0.4485, 0.5658, 0.3631],\n",
      "         [0.4408, 0.3674, 0.7157, 0.8493],\n",
      "         [0.3819, 0.8610, 0.2775, 0.9121],\n",
      "         [0.2542, 0.5060, 0.6722, 0.3694],\n",
      "         [0.2008, 0.5610, 0.4073, 0.6077]],\n",
      "\n",
      "        [[0.0586, 0.8958, 0.5129, 0.7490],\n",
      "         [0.9719, 0.2716, 0.6552, 0.1607],\n",
      "         [0.6216, 0.4546, 0.3720, 0.8920],\n",
      "         [0.9980, 0.8427, 0.7868, 0.6861],\n",
      "         [0.5896, 0.7928, 0.0367, 0.0449],\n",
      "         [0.3811, 0.1727, 0.5664, 0.8386]]]), torch.Size([2, 6, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[1.9978e-01, 9.3829e-01, 6.2306e-01, 9.9302e-01],\n",
      "         [9.0252e-02, 7.5424e-01, 7.4271e-01, 4.4390e-02],\n",
      "         [2.4565e-02, 1.6854e-01, 4.2249e-01, 6.3777e-04],\n",
      "         [5.2806e-01, 1.0909e-02, 7.5719e-01, 1.9355e-01],\n",
      "         [7.7569e-01, 4.6039e-01, 8.1637e-02, 7.2460e-01],\n",
      "         [5.9803e-01, 2.4714e-01, 4.8798e-01, 2.8393e-01]],\n",
      "\n",
      "        [[4.1706e-02, 2.1394e-02, 6.6963e-01, 3.3555e-02],\n",
      "         [8.5979e-01, 4.0278e-01, 8.7121e-01, 2.0658e-01],\n",
      "         [4.6593e-01, 2.5046e-01, 3.8362e-01, 4.9993e-01],\n",
      "         [8.0471e-01, 9.5816e-01, 3.3325e-01, 5.5612e-01],\n",
      "         [7.3787e-01, 2.2607e-01, 1.6334e-01, 4.1274e-01],\n",
      "         [3.0521e-01, 4.3157e-01, 3.0943e-01, 8.8407e-01]]]), torch.Size([2, 6, 4]))\n",
      "--------------------------------------------------\n",
      "(tensor([[[0.6314, 0.9475, 0.9515, 0.5817],\n",
      "         [0.3932, 0.9422, 0.5660, 0.3889],\n",
      "         [0.7021, 0.4742, 0.2486, 0.3508],\n",
      "         [0.4320, 0.5821, 0.8991, 0.8773],\n",
      "         [0.4472, 0.7964, 0.7277, 0.3657],\n",
      "         [0.9706, 0.6502, 0.0872, 0.8186]],\n",
      "\n",
      "        [[0.2920, 0.8040, 0.4826, 0.8701],\n",
      "         [0.1738, 0.8700, 0.5650, 0.6730],\n",
      "         [0.4777, 0.8105, 0.4635, 0.3732],\n",
      "         [0.4708, 0.5663, 0.9534, 0.7197],\n",
      "         [0.7023, 0.5396, 0.3144, 0.4608],\n",
      "         [0.6074, 0.7460, 0.5021, 0.6989]]]), torch.Size([2, 6, 4]))\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for splithead in Q_split:\n",
    "    print((splithead,splithead.shape))\n",
    "    print('-'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q_split has shape <b>(bsize,nheads,hdim,headdim)</b>\n",
    "So there are bszie number of batches in Q_split and each batch is split into nheads , where each item is hdim x headdim.\n",
    "<br><br>\n",
    "\n",
    "The same happens with all the three matrices -> Q,K and V.\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q_split = Q_.view(bsiz,-1,nheads,head_dim).permute(0,2,1,3)\n",
    "K_split = K_.view(bsiz,-1,nheads,head_dim).permute(0,2,1,3)\n",
    "V_split = V_.view(bsiz,-1,nheads,head_dim).permute(0,2,1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Q x K.T / sqrt(d_k)\n",
    "energy = torch.matmul(Q_split,K_split.permute(0,1,3,2)) / scale\n",
    "#energy = (bsize,n_attn_heads,qlen,klen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 2, 6, 6])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 2, 6, 6])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attention = torch.softmax(energy,dim = -1)\n",
    "attention.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "         [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]],\n",
       "\n",
       "        [[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "         [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]],\n",
       "\n",
       "        [[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "         [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]],\n",
       "\n",
       "        [[1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "         [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sum of attentions should be 1 across all heads\n",
    "torch.sum(attention,dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Point Wise Feed Forward Layer </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mentioned in section 3.3 of the <a href = 'https://arxiv.org/abs/1706.03762'> paper</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i> In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully\n",
    "connected feed-forward network, which is applied to each position separately and identically. This\n",
    "consists of two linear transformations with a ReLU activation in between\n",
    "    </i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>FFN(x) = max(0, $W_1$*<i>x</i> + b1)*$W_2$ + $b_2$ </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is mainly used to add additional learnable parameters and give appropriate weightage to the attention outputs, while learning the other non-linearities present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PointWiseFeedForwardLayer(nn.Module):\n",
    "    \n",
    "    def __init__(self,hidden_dimension,pointwise_ff_dim,dropout):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.fully_connected_1 = nn.Linear(hidden_dimension,pointwise_ff_dim)\n",
    "        \n",
    "        self.fully_connected_2 = nn.Linear(pointwise_ff_dim,hidden_dimension)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    \n",
    "    def forward(self,inp):\n",
    "        \n",
    "        #inp  = (batch_size,seqlen,hid_dim)\n",
    "        \n",
    "        fc1_op = self.fully_connected_1(inp)\n",
    "        fc1_op = torch.relu(fc1_op)\n",
    "        # fc1_op = (batch_size,seq_len,pf_dim) \n",
    "        \n",
    "        fc2_op = self.fully_connected_2(fc1_op)\n",
    "        # fc2_op = (batch_size,seq_len,hid_dim) \n",
    "        \n",
    "        return  fc2_op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> The Decoder </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decoder is used to use the encoded representations of the source <b>Z</b> and convert it to target tokens. We need to compare these predictions to find the loss and learn to improve our predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/transformer-decoder.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/transformer_decoding_1.gif\" width=\"950\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The encoder outputsare transformed to a set of attention Key and Value vectors, they will be used by the decoder in its encoder decoder attention layer,which will help in decoding the appropriate positions to pay attention to while decoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Until a end/special token is reached, the decoder continues to decode.The output of each step is fed to the bottom decoder in the next time step, and the decoders bubble up their decoding results just like the encoders did. And just like we did with the encoder inputs, we embed and add positional encoding to those decoder inputs to indicate the position of each word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/transformer_decoding_2.gif\" width=\"950\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each decoder block has 2 multi-head-self-attention(MHSA) layers.\n",
    "<br>\n",
    "These MHSA layers are used as follows:\n",
    "<ul>\n",
    "    <li>The first MHSA layer uses masked self attention mechanism, so that the model doesnt \"<i>cheat</i>\" and looks the actual word ahead of its actual decoding.\n",
    "        <br><br>\n",
    "    According to the paper, <br>\n",
    "        <i>\"We also modify the self-attention\n",
    "sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This\n",
    "masking, combined with fact that the output embeddings are offset by one position, ensures that the\n",
    "predictions for position i can depend only on the known outputs at positions less than i.\"</i></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>     <li> The second MHSA layer uses the decoder representation of query from the previous MHSA layer and encoder representations of key and value from the encoder. This is also called as <i>cross-attention</i> </li>\n",
    "    \n",
    "</ul>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul><li>Similar to the encoder the decoder also uses Positional Embeddings which is then combined with the scaled token embeddings by a pointwise sum.Here also the decoder can accept a sequence of max length previously given.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul><li>The number of encoders dont need to be the same as the number of decoders.The input positional + token embedings are passed to all the stacked decoder layers. Each layer gets the encoded source sentence, along with the source and target masks. Please note that the encoder masks are used to mask attention over pad tokens and the target masks by the decoder is used to prevent the model from <i>cheating</i> by looking ahead the correct word before it even decodes it.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul><li>The final decoder layer has a output linear layer which applies softmax over the output to predict the most probable next word.</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets implement the decoder class which implements the above, it will also use a DecoderBlock class to create a stack of decoder layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    \n",
    "    \n",
    "    def __init__(self,output_dimension,hidden_dimension,\n",
    "                num_decoder_layers,num_attn_heads,\n",
    "                pointwise_ff_dim,dropout,\n",
    "                 device,max_length = 100):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.device = device\n",
    "        \n",
    "        self.token_embeddings = nn.Embedding(output_dimension,hidden_dimension)\n",
    "        self.positional_embeddings = nn.Embedding(max_length,hidden_dimension)\n",
    "        \n",
    "        \n",
    "        self.decoder_blocks = nn.ModuleList([DecoderBlock(hidden_dimension,\n",
    "                                                         num_attn_heads,\n",
    "                                                         pointwise_ff_dim,\n",
    "                                                         dropout,device)\n",
    "                                           for _ in range(num_decoder_layers)])\n",
    "        \n",
    "        \n",
    "        self.fully_connected_op = nn.Linear(hidden_dimension,output_dimension)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hidden_dimension])).to(device)\n",
    "    \n",
    "    \n",
    "    \n",
    "    def forward(self,target,encoder_src,source_mask,target_mask):\n",
    "        \n",
    "        batch_size = target.shape[0]\n",
    "        target_len = target.shape[1]\n",
    "        \n",
    "        #target = (bsiz,tlen)\n",
    "        #encoder_src = (bsiz,slen,hdim)\n",
    "        #target_mask = (bsiz,1,tlen,tlen)\n",
    "        #src_mask = (bsiz,1,1,slen)\n",
    "        \n",
    "        #positions same as the encoder\n",
    "        #if tlen = 4 and bsiz = 3, positions.repeat(batch_size,1) will give\n",
    "        #tensor([[0, 1, 2, 3, 4],\n",
    "        #        [0, 1, 2, 3, 4],\n",
    "        #        [0, 1, 2, 3, 4]])\n",
    "\n",
    "\n",
    "        positions = torch.arange(0,target_len).unsqueeze(0)\n",
    "        positions = positions.repeat(batch_size,1).to(self.device)\n",
    "        #positions = (bsiz,tlen)\n",
    "        \n",
    "        #get the embeddings for the token and multiply with sqrt(d_model) scale\n",
    "        target = self.token_embeddings(target)*self.scale\n",
    "        \n",
    "        #add the positional embeddings to the target\n",
    "        target = target + self.positional_embeddings(positions)\n",
    "        #target = (bsiz,tlen,hid_dim)\n",
    "        \n",
    "        \n",
    "        #apply dropout\n",
    "        target = self.dropout(target)\n",
    "        #target = (bsiz,tlen,hid_dim)\n",
    "        \n",
    "        for decoder_block in self.decoder_blocks:\n",
    "            target,attention = decoder_block(target,encoder_src,\n",
    "                                             source_mask,target_mask)\n",
    "            \n",
    "        #target = (bsize,tlen,hdim)\n",
    "        #attention = (bsize,num_att_heads,tlen,slen)\n",
    "        \n",
    "        output = self.fully_connected_op(target)\n",
    "        #output = (bsize,tlen,output_dim)\n",
    "        \n",
    "        return output,attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Decoder Block </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decoder block is almost same as the encoder block,and uses the same concepts only in a different way. As seen in the previous part and decoder image, it uses cross attention to use encoder representations of different key and value vectors and uses decoder representations for query vectors in the cross attention section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = './images/transformer-decoder.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the encoder_block, the decoder_block also uses layer normalizations and residual connections as a part of its architecture,shown in the diagram above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps used in DecoderBlock are as follows : <br> <br>\n",
    "\n",
    "<ul> \n",
    "    <li>Step 1 : The decoderblock gets input as embeddings of size hidden_dimensions for each of the tokens in the sequence.</li><br>\n",
    "    <li>Step 2 :Apply decoder MHSA to get the key,query and value representations of the target using decoder attention.</li><br>\n",
    "    <li>Step 3 :Add a residual connection from the decoder MHSA input and add it to the decoder MHSA output, and apply decoder's Self attention Layer Normalization.</li><br>\n",
    "    <li>Step 4 :Now, pass the output of the previous layer to the encoder cross attention, using Key and Value from the encoder source and query from the target</li><br>\n",
    "    <li>Step 5 :Add a residual connection and apply encoder's cross attention layer normalization</li><br>\n",
    "    <li>Step 6 :Pass the outputs through a point wise feed forward network</li><br>\n",
    "    <li>Step 7 :Add another residual connection from the encoder cross attention outputs and apply feed forward layer normalization</li>\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderBlock(nn.Module):\n",
    "    \n",
    "    def __init__(self,hidden_dimension,num_attn_heads,\n",
    "                pointwise_ff_dim,dropout,device):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.decoder_self_attn_layer_norm = nn.LayerNorm(hidden_dimension)\n",
    "        self.encoder_cross_attn_layer_norm = nn.LayerNorm(hidden_dimension)\n",
    "        self.feedForward_layer_norm = nn.LayerNorm(hidden_dimension)\n",
    "        \n",
    "        self.decoder_self_attention = MultiHeadSelfAttention(hidden_dimension,\n",
    "                                                            num_attn_heads,\n",
    "                                                            dropout,device)\n",
    "        \n",
    "        self.encoder_cross_attention = MultiHeadSelfAttention(hidden_dimension,\n",
    "                                                            num_attn_heads,\n",
    "                                                            dropout,device)\n",
    "        \n",
    "        self.pointWise_feedForward = PointWiseFeedForwardLayer(hidden_dimension,\n",
    "                                                              pointwise_ff_dim,\n",
    "                                                              dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self,target,encoder_source,source_mask,target_mask):\n",
    "\n",
    "        #target = (bsiz,tlen,hid_dim)\n",
    "        #encoder_src = (bsiz,slen,hdim)\n",
    "        #target_mask = (bsiz,1,tlen,tlen)\n",
    "        #src_mask = (bsiz,1,1,slen)\n",
    "        \n",
    "        \n",
    "        #STEP2 \n",
    "        #=====================================================#\n",
    "        #decoder self attention\n",
    "        #MHSA accepts input in (query,key,value,mask) format\n",
    "        tar_self_attention_contexts,tar_self_attention_scores = self.decoder_self_attention(target,target,target,\n",
    "                                                                          target_mask)\n",
    "        #=====================================================#\n",
    "        \n",
    "        \n",
    "        #STEP3\n",
    "        #=====================================================#\n",
    "        #dropout,residual skip connection and attn_layer norm(x+z)\n",
    "        tar_self_attention_contexts = self.dropout(tar_self_attention_contexts)\n",
    "        \n",
    "\n",
    "        target = self.decoder_self_attn_layer_norm(target + tar_self_attention_contexts)\n",
    "        #target = (bsiz,tlen,hdim)\n",
    "        #=====================================================#\n",
    "\n",
    "        \n",
    "        \n",
    "        #STEP4\n",
    "        #=====================================================#\n",
    "        #Encoder Cross Attention\n",
    "        #MHSA accepts input in (query,key,value,mask) format\n",
    "        tar_cross_attention_contexts,tar_cross_attention_scores = self.encoder_cross_attention(target,\n",
    "                                                                                            encoder_source,encoder_source,\n",
    "                                                                                            source_mask)\n",
    "        #=====================================================#\n",
    "        \n",
    "        \n",
    "        #STEP5\n",
    "        #=====================================================#\n",
    "        #encoder cross attn layer norm\n",
    "        tar_cross_attention_contexts = self.dropout(tar_cross_attention_contexts)\n",
    "        \n",
    "        target = self.encoder_cross_attn_layer_norm(target + tar_cross_attention_contexts)\n",
    "        #target = (bsiz,tlen,hdim)\n",
    "        #=====================================================#\n",
    "        \n",
    "        \n",
    "        #STEP6\n",
    "        #=====================================================#\n",
    "        #pointwise feedforward\n",
    "        tar_cross_attention_contexts = self.pointWise_feedForward(target)\n",
    "        #=====================================================#\n",
    "        \n",
    "        \n",
    "        #STEP7\n",
    "        #=====================================================#\n",
    "        #dropout,residual skip connection and ff_layer norm(x+z)\n",
    "        \n",
    "        tar_cross_attention_contexts = self.dropout(tar_cross_attention_contexts)\n",
    "        target = self.feedForward_layer_norm(target + tar_cross_attention_contexts)\n",
    "        \n",
    "        #=====================================================#\n",
    "        \n",
    "        #target = (bsiz,tlen,hdim)\n",
    "        #tar_cross_attention_scores = (bsiz,nheads,tlen,slen)\n",
    "        \n",
    "        return target,tar_cross_attention_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Understanding the Source and Target Masks </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Source Mask </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The source mask is used to create masks wherever a pad token is present.\n",
    "We apply a <i>unsqueeze</i> operation to correctly broadcast the correct mask value to the right dimension.<br>\n",
    "It is applied on energy which is of shape \n",
    "<b><i>[batch_size,num_attn_heads,seq_len,seq_len]</i></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_list = np.array(['<sos>','w1','w2','w3','w4','w5','<eos>','<pad>','<pad>','<pad>','<pad>','<pad>'])\n",
    "s_mask = np.where(1 ,t_list !='<pad>',0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<sos>' 'w1' 'w2' 'w3' 'w4' 'w5' '<eos>' '<pad>' '<pad>' '<pad>' '<pad>'\n",
      " '<pad>']\n",
      "[1 1 1 1 1 1 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(t_list)\n",
    "print(s_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that places where the token is not pad, the corresponding mask is 1, and 0 wherever it is a pad token."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Target Mask</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target mask is created in the way that it is not allowed to look at future words.<br>\n",
    "There are two masks created for the target mask,\n",
    "<br>\n",
    "<ul>\n",
    "    <li>Pad mask</li>\n",
    "    <li>Subsequent mask</li>\n",
    "</ul>\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pad mask is created in same way as done for the source mask."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To incorporate the time steps and avoid cheating, it is then logically anded with a <i>subsequent mask</i>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 0, 0, 0, 0],\n",
       "        [1, 1, 0, 0, 0],\n",
       "        [1, 1, 1, 0, 0],\n",
       "        [1, 1, 1, 1, 0],\n",
       "        [1, 1, 1, 1, 1]], dtype=torch.int32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tril creates a lower triangular matrix\n",
    "torch.tril(torch.ones((5,5),dtype=torch.int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows what each target token (row) is allowed to look at (column). The first target token has a mask of <b>[1, 0, 0, 0, 0]</b> which means it can only look at the first target token. The second target token has a mask of <b>[1, 1, 0, 0, 0]</b> which it means it can look at both the first and second target tokens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the and operation, the mask might look as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src ='./images/math.svg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 0's indicate both the pad tokens and the future time steps that have been masked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After applying attention, the masking is done on the result of tensor product of Query and Key."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"./images/masked_lm_demo.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Creating a Seq2Seq Model </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating the Encoder and Decoder stacks and understanding how to mask the source and target inputs. We can now connect all of them and create a sequence to sequence model which generates one word per time step till the end special token is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Seq2SeqModel(nn.Module):\n",
    "    \n",
    "    def __init__(self,encoder_stack,decoder_stack,\n",
    "                source_pad_idx,target_pad_idx,device):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder_stack = encoder_stack\n",
    "        self.decoder_stack = decoder_stack\n",
    "        self.source_pad_index = source_pad_idx\n",
    "        self.target_pad_index = target_pad_idx\n",
    "        self.device = device        \n",
    "        \n",
    "    def get_source_mask(self,source):\n",
    "        \n",
    "        #source  = (bsize,source_len)\n",
    "        #for all indexes which are not pad indexes, make them 1 else 0\n",
    "        \n",
    "        source_mask = (source != self.source_pad_index)\n",
    "        \n",
    "        #unsqueeze(i) operation adds an i_th dimension to the item\n",
    "        \n",
    "        source_mask = source_mask.unsqueeze(1)\n",
    "        #source_mask = (bsize,1,source_len)\n",
    "        \n",
    "        source_mask = source_mask.unsqueeze(2)\n",
    "        #source_mask = (bsize,1,1,source_len)\n",
    "        \n",
    "        #this shape is reqd because it will be applied after calculating- \n",
    "        #the attention which has 4 dimensions (bsize,n_attn_heads,qlen,klen)\n",
    "        \n",
    "        return source_mask\n",
    "\n",
    "    def get_target_mask(self,target):\n",
    "        \n",
    "        #target = (bsize,target_len)\n",
    "        \n",
    "        #we'll be creating pad mask and subsequent mask for target\n",
    "        # as discussed in the above section\n",
    "        \n",
    "        target_pad_mask = (target != self.target_pad_index).unsqueeze(1).unsqueeze(2)\n",
    "        #target_pad_mask = (bsize,1,1,target_len)\n",
    "        \n",
    "        target_length = target.shape[1]\n",
    "        \n",
    "        target_subsequent_mask = torch.tril(torch.ones((target_length,target_length),\n",
    "                                                      device = self.device)).bool()\n",
    "        \n",
    "        #target_subsequent_mask = (target_len,target_len)\n",
    "\n",
    "        final_target_mask = target_subsequent_mask & target_pad_mask\n",
    "        # final_target_mask = (bsize,1,tlen,tlen)\n",
    "        \n",
    "        return final_target_mask\n",
    "    \n",
    "    \n",
    "    def forward(self,source,target):\n",
    "        \n",
    "        #source = (bsize,slen)\n",
    "        #target = (bsize,tlen)\n",
    "        \n",
    "        source_mask = self.get_source_mask(source)\n",
    "        target_mask = self.get_target_mask(target)\n",
    "        \n",
    "        #source_mask = (bsize,1,1,slen)\n",
    "        #target_mask = (bsize,1,tlen,tlen)\n",
    "        \n",
    "        encoder_output = self.encoder_stack(source,source_mask)\n",
    "        #encoder_op = (bsize,slen,hdim)\n",
    "        \n",
    "        decoder_output,decoder_attention = self.decoder_stack(target,\n",
    "                                                             encoder_output,\n",
    "                                                             source_mask,target_mask)\n",
    "        \n",
    "        #decoder_output = (bsiz,tlen,output_dim)\n",
    "        #decoder_attention = (bsize,num_attn_heads,tlen,slen)\n",
    "        \n",
    "        return decoder_output,decoder_attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the index of source and target pad tokens, they will help in identifying the indices which are paddings and will be used to mask those indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<pad>'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_pad_token = source_process_pipeline.pad_token\n",
    "s_pad_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<pad>'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_pad_token = target_process_pipeline.pad_token\n",
    "t_pad_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_process_pipeline.vocab.stoi[s_pad_token]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_pad_index = source_process_pipeline.vocab.stoi[s_pad_token]\n",
    "target_pad_index = target_process_pipeline.vocab.stoi[t_pad_token]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below class is used to configure model by applying weight initializations and getting the model details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConfigureModel(nn.Module):\n",
    "    \n",
    "    def __init__(self,model):\n",
    "        \n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "                   \n",
    "    def get_model_details(self):\n",
    "        \n",
    "        total_param = sum(p.numel() for p in self.model.parameters())\n",
    "        trainable_param = sum(p.numel() for p in self.model.parameters() if p.requires_grad)\n",
    "        \n",
    "        return total_param,trainable_param\n",
    "    \n",
    "    def init_xavier_uniform(self,m):\n",
    "        \n",
    "        if hasattr(m, 'weight') and m.weight.dim() > 1:\n",
    "            nn.init.xavier_uniform_(m.weight.data)\n",
    "            \n",
    "    def initialize_model_weights(self):\n",
    "        \n",
    "        self.model.apply(self.init_xavier_uniform)\n",
    "        return self.model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_VOCAB_SIZE = len(source_process_pipeline.vocab)\n",
    "OUTPUT_VOCAB_SIZE = len(target_process_pipeline.vocab)\n",
    "\n",
    "HIDDEN_DIMENSION = 256\n",
    "ENCODER_POINTWISE_FF_DIM = 512\n",
    "DECODER_POINTWISE_FF_DIM = 512\n",
    "\n",
    "NUM_ENCODER_LAYERS = 4\n",
    "NUM_DECODER_LAYERS = 4\n",
    "\n",
    "NUM_ENCODER_ATTN_HEADS = 8\n",
    "NUM_DECODER_ATTN_HEADS = 8\n",
    "\n",
    "ENCODER_DROPOUT = 0.1\n",
    "DECODER_DROPOUT = 0.1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Encoder and Decoder stack with the above configurations, these stacks will then be used in combination for our sequence to sequence model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_stack = Encoder(INPUT_VOCAB_SIZE,HIDDEN_DIMENSION,\n",
    "                 NUM_ENCODER_LAYERS,NUM_ENCODER_ATTN_HEADS,ENCODER_POINTWISE_FF_DIM,\n",
    "                 ENCODER_DROPOUT,device)\n",
    "\n",
    "decoder_stack = Decoder(OUTPUT_VOCAB_SIZE,HIDDEN_DIMENSION,\n",
    "                 NUM_DECODER_LAYERS,NUM_DECODER_ATTN_HEADS,DECODER_POINTWISE_FF_DIM,\n",
    "                 DECODER_DROPOUT,device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final transformer model combines the encoder and decoder stack and uses the pad indices of source and target to create the corresponding masks before passing through the encoders and decoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer_model = Seq2SeqModel(encoder_stack,decoder_stack,\n",
    "                                source_pad_index,target_pad_index,device).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = ConfigureModel(transformer_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of trainable parameters in model are 10,356,485\n"
     ]
    }
   ],
   "source": [
    "params = cm.get_model_details()\n",
    "print(f'The number of trainable parameters in model are {params[0]:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer_model = cm.initialize_model_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use Adam Optimizer with fixed learning rate as used in BERT and other transformer models, in the  <a href = \"https://arxiv.org/abs/1706.03762\">paper</a> they have used a variable learning rate which has a warmup and cool down period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to keep the learning rate low so that learning is stable and doesn't show a large stochastic behaviour.\n",
    "<br><br>\n",
    "Since we are predicting the next word from a set of words, it is a classification type problem, and hence the loss function we'll be using will be CrossEntropyLoss.\n",
    "<br>\n",
    "We'll be setting ignore index to be the index of the pad token. <i>( Ignore Index Specifies a target value that is ignored and does not contribute to the input gradient. When size_average is True, the loss is averaged over non-ignored targets.)</i><br>\n",
    "For more details see <br>\n",
    "https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html <br>\n",
    "This is done so that we can ignore the loss incurred over pad tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 5 * 1e-4\n",
    "\n",
    "optimizer = torch.optim.Adam(transformer_model.parameters(),lr = LR)\n",
    "\n",
    "loss_criterion = nn.CrossEntropyLoss(ignore_index = target_pad_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Training</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want our model to keep predicting till the <i>eos</i> token is reached,but we dont want our model to see this before hand,and want the model to learn when to generate it on its own. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So while feeding it to the network, we'll slice it (the last index) off. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"./images/target_1.svg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above list x_i's denote what the model is supposed to predict, and in the below list y_i's are what the model predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While calculating the loss we use the original target tensor with <i>sos</i> tag removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src =\"./images/target_2.svg\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loss(output,target,loss_criterion):\n",
    "    \n",
    "    output_dim = output.shape[-1]\n",
    "    \n",
    "    output = output.contiguous().view(-1,output_dim)\n",
    "    target = target[:,1:].contiguous().view(-1)\n",
    "    \n",
    "    #output = [bsize * tlen - 1, output dim]\n",
    "    #target = [bsize * tlen - 1]\n",
    "    \n",
    "    loss = loss_criterion(output,target)\n",
    "\n",
    "    return loss\n",
    "\n",
    "\n",
    "def train_one_epoch(transformer_model,train_iterator,\n",
    "                    optimizer,loss_criterion,clip = 1):\n",
    "    \n",
    "    #set the model in training mode\n",
    "    transformer_model.train()\n",
    "    single_epoch_loss = 0\n",
    "    \n",
    "    for it_idx,batch in enumerate(train_iterator):\n",
    "        \n",
    "        source = batch.src\n",
    "        target = batch.trg\n",
    "        \n",
    "        \n",
    "        #see https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        decoder_output , _ = transformer_model(source,target[:,:-1])\n",
    "        #decoder_output = (bsize,tlen-1,op_dim)\n",
    "        #target = (bsize,tlen)\n",
    "        \n",
    "        \n",
    "        loss = get_loss(decoder_output,target,loss_criterion)\n",
    "        \n",
    "        #backpropagate the gradients\n",
    "        loss.backward()\n",
    "        \n",
    "        #clip the gradients so that they dont explode(their norm remains <= 1)\n",
    "        nn.utils.clip_grad_norm_(transformer_model.parameters(),clip)\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        single_epoch_loss += loss.item()\n",
    "        \n",
    "    return single_epoch_loss / len(train_iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_validation(transformer_model,validation_iterator,loss_criterion):\n",
    "    \n",
    "    #set the model to evaluation mode\n",
    "    #model.eval() is a kind of switch for some specific layers/parts of the model\n",
    "    #that behave differently during training and inference (evaluating) time.\n",
    "    \n",
    "    transformer_model.eval()\n",
    "    validation_loss = 0 \n",
    "    \n",
    "    \n",
    "    #see https://pytorch.org/docs/stable/generated/torch.no_grad.html\n",
    "    with torch.no_grad():\n",
    "          for it_idx,batch in enumerate(validation_iterator):\n",
    "        \n",
    "                source = batch.src\n",
    "                target = batch.trg\n",
    "\n",
    "                decoder_output , _ = transformer_model(source,target[:,:-1])\n",
    "                #decoder_output = (bsize,tlen-1,op_dim)\n",
    "                #target = (bsize,tlen)\n",
    "\n",
    "                loss = get_loss(decoder_output,target,loss_criterion)\n",
    "\n",
    "                validation_loss += loss.item()\n",
    "\n",
    "    return validation_loss / len(validation_iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Translating a sentence </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should be able translate any source sentence, so we create a function which translates a sentence using the transformer model. This is also useful in understanding the learning stages of the model which can prove to be useful while model understanding and debugging should any such need arises.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence(source_sentence,source_field,target_field,source_tokenizer,\n",
    "                       transformer_model,device,max_length = 50,verbose = True):\n",
    "    \n",
    "    \n",
    "    start = time()\n",
    "    \n",
    "    transformer_model.eval()\n",
    "    \n",
    "    \n",
    "    #step 1 tokenize the source sentence if it has not been tokenized (is a string)\n",
    "    \n",
    "    if isinstance(source_sentence,str) and source_tokenizer is not None:\n",
    "        source_tokens = [token.text.lower() for token in source_tokenizer(source_sentence)]\n",
    "    else:\n",
    "        source_tokens = [token.lower() for token in source_sentence]\n",
    "        \n",
    "    #step 2  append the sos and eos tokens\n",
    "    source_tokens = [source_field.init_token] + source_tokens + [source_field.eos_token]\n",
    "    \n",
    "    #step 3 numericalize the source sentence\n",
    "    source_to_idx = [source_field.vocab.stoi[token] for token in source_tokens]\n",
    "    \n",
    "    \n",
    "    #step 4 convert it to a tensor and add a batch dimension\n",
    "    source_tensor = torch.LongTensor(source_to_idx).unsqueeze(0).to(device)\n",
    "    \n",
    "    #step 5 create the source sentence mask\n",
    "    source_mask = transformer_model.get_source_mask(source_tensor)\n",
    "    \n",
    "    #step 6 feed the source sentence and mask into the encoder\n",
    "    with torch.no_grad():\n",
    "        encoder_output = transformer_model.encoder_stack(source_tensor,source_mask)\n",
    "    \n",
    "    #step 7 create a list to hold the output sentence, initialized with an sos token\n",
    "    target_to_idx = [target_field.vocab.stoi[target_field.init_token]]\n",
    "    #as of now target_to_idx only holds the index of sos token \n",
    "    \n",
    "    #step 8 while we have not hit a maximum length\n",
    "    i = 0\n",
    "    eos_token_index = target_field.vocab.stoi[target_field.eos_token]\n",
    "    \n",
    "    while i < max_length:\n",
    "        \n",
    "        #step 8.1 convert the current output sentence prediction into a tensor with a batch dimension\n",
    "        target_tensor = torch.LongTensor(target_to_idx).unsqueeze(0).to(device)\n",
    "        \n",
    "        #step 8.2 create a target sentence mask\n",
    "        target_mask = transformer_model.get_target_mask(target_tensor)\n",
    "        \n",
    "        #step 8.3 place the current output, encoder output and both masks into the decoder\n",
    "        with torch.no_grad():\n",
    "            decoder_output,decoder_attention = transformer_model.decoder_stack(target_tensor,\n",
    "                                                                              encoder_output,\n",
    "                                                                              source_mask,target_mask)\n",
    "        \n",
    "        #step 8.4 get next output token prediction from decoder along with attention\n",
    "        next_predicted_token = decoder_output.argmax(2)[:,-1].item()\n",
    "        \n",
    "        #step 8.5  add prediction to current output sentence prediction\n",
    "        target_to_idx.append(next_predicted_token)\n",
    "        \n",
    "        #step 8.6 break if the prediction was an <eos> token\n",
    "        if next_predicted_token == eos_token_index:\n",
    "            break\n",
    "        i+=1 \n",
    "    \n",
    "    end = time()\n",
    "    \n",
    "    #step 9 convert the output sentence from indexes to tokens\n",
    "    target_tokens = [target_field.vocab.itos[idx] for idx in target_to_idx]\n",
    "    \n",
    "    if verbose is True:\n",
    "        print(f'Time taken to translate {end - start} seconds')\n",
    "    \n",
    "    # step 10 return the output sentence (with the <sos> token removed) and the attention from the last layer\n",
    "    return target_tokens[1:-1],decoder_attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3> Training loop </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(transformer_model,num_epochs,\n",
    "          train_iterator,validation_iterator,\n",
    "          loss_criterion,clip,\n",
    "          source_field,target_field,\n",
    "          source_tokenizer,\n",
    "          actual_sentence,target_sentence,\n",
    "         device):\n",
    "    \n",
    "    \n",
    "        \n",
    "    train_losses = []\n",
    "    validation_losses = []\n",
    "    perplexity_train = []\n",
    "    perplexity_validation = []\n",
    "\n",
    "    best_validation_loss = float('inf')\n",
    "    \n",
    "    print(NUM_EPOCHS)\n",
    "    \n",
    "    writer = SummaryWriter(f'runs/loss_plot')\n",
    "    step = 0\n",
    "\n",
    "    for epoch in tqdm(range(num_epochs)):\n",
    "        start = time()\n",
    "\n",
    "        train_loss = train_one_epoch(transformer_model,train_iterator,\n",
    "                                    optimizer,loss_criterion,clip)\n",
    "\n",
    "\n",
    "        validation_loss = perform_validation(transformer_model,validation_iterator,\n",
    "                                            loss_criterion)\n",
    "        \n",
    "        \n",
    "        end = time()\n",
    "\n",
    "        if validation_loss < best_validation_loss:\n",
    "            best_validation_loss = validation_loss\n",
    "            torch.save(transformer_model.state_dict(),'transformer_model(ge-eng).pt')\n",
    "\n",
    "        \n",
    "        model_translation,_ = translate_sentence(actual_sentence,\n",
    "                                               source_field,target_field,\n",
    "                                               source_tokenizer,\n",
    "                                               transformer_model,device,\n",
    "                                               max_length = 50,verbose = True)\n",
    "\n",
    "        \n",
    "        train_perplexity = math.exp(train_loss)\n",
    "        val_perplexity = math.exp(validation_loss)\n",
    "        \n",
    "        \n",
    "        print('*'*50)\n",
    "        print(f'Actual Sentence : \\n{actual_sentence.split()}')\n",
    "        print(f'Actual Translation : \\n{target_sentence.split()}')\n",
    "        print(f'Model Prediction : \\n{model_translation}')\n",
    "        print('*'*50)\n",
    "        \n",
    "        print('='*50)\n",
    "        print(f'Epoch: {epoch+1:02}')\n",
    "        print(f'\\tTrain Loss: {train_loss:.3f} | Train perplexity: {train_perplexity:7.3f}')\n",
    "        print(f'\\t Validation Loss: {validation_loss:.3f} |  validation perplexity: {val_perplexity:7.3f}')\n",
    "        print(f'Time for epoch: {end - start} seconds')\n",
    "        print('='*50)\n",
    "\n",
    "                \n",
    "        train_losses.append(train_loss)\n",
    "        validation_losses.append(validation_loss)\n",
    "        perplexity_train.append(train_perplexity)\n",
    "        perplexity_validation.append(val_perplexity)\n",
    "        \n",
    "        writer.add_scalar('Training_loss',train_loss,global_step = step)\n",
    "        step+=1\n",
    "\n",
    "    return train_losses,validation_losses,perplexity_train,perplexity_validation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During each epoch a sample test sentence is translated just to visualize how well the model has learnt to translate a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14146b9eb45a4ca194eac96d6534f857",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to translate 0.31977152824401855 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'person', 'and', 'a', 'child', 'are', 'sitting', 'on', 'a', 'sidewalk', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 01\n",
      "\tTrain Loss: 4.412 | Train perplexity:  82.456\n",
      "\t Validation Loss: 3.248 |  validation perplexity:  25.743\n",
      "Time for epoch: 36.68290901184082 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.11999678611755371 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'small', 'couple', 'and', 'a', 'child', 'are', 'walking', 'down', 'a', 'street', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 02\n",
      "\tTrain Loss: 3.110 | Train perplexity:  22.422\n",
      "\t Validation Loss: 2.746 |  validation perplexity:  15.577\n",
      "Time for epoch: 34.30966877937317 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.12996482849121094 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'little', 'girl', 'and', 'mother', 'are', 'walking', 'down', 'a', 'path', 'in', 'the', 'woods', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 03\n",
      "\tTrain Loss: 2.666 | Train perplexity:  14.380\n",
      "\t Validation Loss: 2.475 |  validation perplexity:  11.884\n",
      "Time for epoch: 34.160839557647705 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.12699103355407715 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'a', 'mother', 'walk', 'down', 'the', 'beautiful', 'day', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 04\n",
      "\tTrain Loss: 2.360 | Train perplexity:  10.590\n",
      "\t Validation Loss: 2.269 |  validation perplexity:   9.667\n",
      "Time for epoch: 35.279236793518066 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.16499733924865723 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'mother', 'walk', 'down', 'a', 'sunny', 'day', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 05\n",
      "\tTrain Loss: 2.076 | Train perplexity:   7.975\n",
      "\t Validation Loss: 2.069 |  validation perplexity:   7.920\n",
      "Time for epoch: 36.509071350097656 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.09896254539489746 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'mother', 'enjoying', 'a', 'beautiful', 'day', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 06\n",
      "\tTrain Loss: 1.822 | Train perplexity:   6.183\n",
      "\t Validation Loss: 1.906 |  validation perplexity:   6.726\n",
      "Time for epoch: 37.55660700798035 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.10199832916259766 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outdoors', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 07\n",
      "\tTrain Loss: 1.597 | Train perplexity:   4.939\n",
      "\t Validation Loss: 1.821 |  validation perplexity:   6.175\n",
      "Time for epoch: 38.32285690307617 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.12996339797973633 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outside', 'on', 'the', 'day', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 08\n",
      "\tTrain Loss: 1.417 | Train perplexity:   4.126\n",
      "\t Validation Loss: 1.755 |  validation perplexity:   5.784\n",
      "Time for epoch: 39.052929401397705 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.10598015785217285 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outdoors', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 09\n",
      "\tTrain Loss: 1.272 | Train perplexity:   3.569\n",
      "\t Validation Loss: 1.710 |  validation perplexity:   5.531\n",
      "Time for epoch: 38.497931718826294 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.10899758338928223 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'son', 'enjoy', 'a', 'beautiful', 'day', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 10\n",
      "\tTrain Loss: 1.153 | Train perplexity:   3.168\n",
      "\t Validation Loss: 1.708 |  validation perplexity:   5.515\n",
      "Time for epoch: 39.81792235374451 seconds\n",
      "==================================================\n",
      "Time taken to translate 0.10596585273742676 seconds\n",
      "**************************************************\n",
      "Actual Sentence : \n",
      "['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien.']\n",
      "Actual Translation : \n",
      "['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside.']\n",
      "Model Prediction : \n",
      "['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.']\n",
      "**************************************************\n",
      "==================================================\n",
      "Epoch: 11\n",
      "\tTrain Loss: 1.051 | Train perplexity:   2.861\n",
      "\t Validation Loss: 1.723 |  validation perplexity:   5.599\n",
      "Time for epoch: 39.90203404426575 seconds\n",
      "==================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "NUM_EPOCHS = 11\n",
    "GRADIENT_CLIP = 1\n",
    "\n",
    "\n",
    "source_test_sentence = 'eine mutter und ihr kleiner sohn genießen einen schönen tag im freien.' \n",
    "target_translation = 'a mother and her young song enjoying a beautiful day outside.'\n",
    "\n",
    "history = train(transformer_model,NUM_EPOCHS,\n",
    "          train_iterator,validation_iterator,\n",
    "          loss_criterion,GRADIENT_CLIP,\n",
    "          source_process_pipeline,target_process_pipeline,\n",
    "          spacy_german,\n",
    "          source_test_sentence,target_translation,device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(history[0])\n",
    "plt.plot(history[1])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Perplexity </h4>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In information theory, perplexity is a measurement of how well a probability distribution or probability model predicts a sample. It may be used to compare probability models. A low perplexity indicates the probability distribution is good at predicting the sample.\n",
    "\n",
    "<br><br>\n",
    "See the link for more details: \n",
    "https://towardsdatascience.com/perplexity-in-language-models-87a196019a94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for perplexity\n",
    "plt.plot(history[2])\n",
    "plt.plot(history[3])\n",
    "plt.title('model perplexity')\n",
    "plt.ylabel('perplexity')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Attention Visualization </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the attention is the best part of working with transformers,it helps us in understanding what part of the input the model is paying more attention to, it improves interpretability of Neural Networks significantly and also helps in debugging should any problem occur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
    "    \n",
    "    assert n_rows * n_cols == n_heads\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,25))\n",
    "    \n",
    "    for i in range(n_heads):\n",
    "        \n",
    "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
    "        \n",
    "        _attention = attention.squeeze(0)[i].cpu().detach().numpy()\n",
    "\n",
    "        cax = ax.matshow(_attention, cmap='bone')\n",
    "\n",
    "        ax.tick_params(labelsize=12)\n",
    "        ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], \n",
    "                           rotation=45)\n",
    "        ax.set_yticklabels(['']+translation)\n",
    "\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the attention from each head below. Each is certainly different, but it's difficult (perhaps impossible) to reason about what head has actually learned to pay attention to. Some heads pay full attention to \"eine\" when translating \"a\", some don't at all, and some do a little. They all seem to follow the similar \"downward staircase\" pattern and the attention when outputting the last two tokens is equally spread over the final two tokens in the input sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to translate 0.10600018501281738 seconds\n",
      "source = ['eine', 'frau', 'mit', 'einer', 'großen', 'geldbörse', 'geht', 'an', 'einem', 'tor', 'vorbei', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "target = ['a', 'woman', 'with', 'a', 'large', 'purse', 'is', 'walking', 'by', 'a', 'gate', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "translation = ['a', 'woman', 'with', 'a', 'large', 'purse', 'walking', 'past', 'a', 'gate', '.']\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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kZGzatAlubm7o378/BgwYgDfeeAOjR49GTk4OCgoK0KZNG602JDMzM1y6dAlfffUVpk+fji5dusDc3BwbN27EgAED4Ofnh5s3b+KLL76An5+fXn68lhUZhUKBixcv4uTJk/Dx8YG/vz+8vb0xbdo0+Pj4wMrKCgUFBUZ36QLw4s84OTkZnTp1grm5uVH9+NdoNPjll1/Kf2iUPSGsV69ecHBwwMmTJ9GtWzd88skn6NWrFwYNGoQGDRrI3e3nunbtGrKzs1GzZk307dsXtra2+Oijj+Dv74+bN28iLCwMb775JmxsbIzqsyDDM5UaWZXrI2C8NZL1UXdYI42D3utjhe40M0E7d+4US5YsEdOmTRPJyckiLy9PaDQakZaWJn777TcxePBgcevWLZ3lKywsFLNnzxb5+fli/fr1Yty4cSIvL0+MGzdOREREVDhu2Q2cZbKyssS2bdvEoEGDxK5duyrb7X/Efl7uynja+snNzRXjxo0T165dE7du3RIZGRmS465fv1707dtXHDt2TAghxLVr18SRI0dEjx49RGRk5HOXTUpKEj/99FP5zbE7duwQkydPLv/3+/fvi88++0wMHTpUJCUlidzcXMn901ZSUpJYuXKlEEKIkydPiu7du4tx48aJ9u3bl4/t0KFDon379qJr167i0qVLeutLRenrM9a3J29WX7hwofjss8/E6NGjy/8uIyND/Pvf/xZbtmyRo3sVEhISIt5++23x8ccfi9dff13s2LFDCCHEV199JWbMmCEGDBggbt68KXMvSU6mUCNNpT4KYXw1kvVR91gjjYMh6iPfM4bHN+MdPHgQI0eORFpaGoYPH46lS5fi0aNH+Pnnn6FWq7Fo0SJ4enpWOIf425EfpVKJe/fuYcSIEahbty5+/PFHKBQKqNXqSl0vW5bj3Llz6Ny5MxwcHMofD7pmzRrY2NigX79+FY5fJjMzE0FBQRgxYgSsrKzQpEmTSh2V0Xb9lJSUlF+Lrm3Mq1ev4vbt23B0dMSIESPg4OCAefPmwdraGhkZGbh27RpmzZoFX1/f58bLy8vD2rVrUVRUhM8++wzu7u7YsWMHIiMj4efnB1dXV3h7e6O0tBQqlQpubm4VXh8vkpeXh3Xr1kGlUiE3NxfBwcF46aWXsGrVKgQGBsLMzAz9+vUr748xvEBRH59xRXNXJk7Zzeq7d++GtbU1WrRoUf6upS5dusDFxQXu7u7IysrSaW59OXr0KHbv3o2NGzeiqKgIMTEx+Oabb+Dq6opBgwZBpVLh448/1vpyJzI9plIjq2p9BIy/RrI+Vh5rpPExVH2s9pcppqWlYc2aNVi+fDnatm2Lvn37Ijc3FytXrsSsWbPQr18/DB48GHXr1q1wjie/aNevX0dGRgZcXV1hZmZW/rLD5s2bIzQ0FCdPnsSwYcMknzJ/ModKpcK0adPw559/wt/fH1ZWVqhXrx6uX7+OkJAQuLm5wcvLq8LjKSwshIODA1JSUrB06VKcPXsWb775ZoXjSVk/p06d0nr9KBQKHD9+HHPmzEFhYSEuXLiAnTt3Yvz48XB2dkZgYCBeffVVvPHGG2jatOlzdwgajQa1a9dG165dERwcjEePHqFNmzZISEhAYmIiUlNToVarsX79ekyfPh2tW7eu8Pp4kbK+dOvWDatWrUJ6ejoGDBiA2rVrw9fXF4WFhVi6dCkaN26Mdu3a6fTlnxWlr89Yau7i4uLym4kroizOqVOn8OOPP2Lp0qXo0KEDEhISEB0djYsXLyIlJQW7du3ChAkTULNmTaMtMmVOnToFR0dH9O7dG+bm5mjcuDESEhJQUlKCwYMHw9vbG46OjnJ3k2RiCjWyKtfHv/ffGGsk62PlsUYaJ0PVx2o/GcvNzcUvv/yC9u3bl7+00MfHBxcuXEDv3r1hZ2dXqevTn3wD99q1axEUFIRff/0VBQUF6NKlCywsLLBhwwb88ccfCA8Px9KlSyXPsJ/ckAoKCuDg4IAWLVogLCwMkZGR6N27N6ytrXHv3j00b94cPXr0qPCX59atW1iyZAn8/f2Rnp6O3377DXXr1kXz5s1Ru3bt8jFru3Hpc/0kJyfju+++w4IFC/DOO+/Az88P9+/fR1hYGCZMmIC8vDy4u7vDw8MDwLMf1lF2pKe0tBR169ZF165dERQUBFtbW7z88svIzs5GWFgYoqKi8MEHH6BLly5a9a8inuyLq6srevbsib179wIAWrVqBUtLS/j6+qK4uBju7u5o2LCh3vqiLUNsA89T9rmGhIRgy5Yt8Pf3r9TOPzExEUuWLIFarUaXLl3g6OgIX19fJCYm4vfff4elpSVmzJiBJk2a6GoIenX79m3cvHkTnTt3Ro0aNaBQKBAeHg4LCwt07NjRqI9akv5V9RpZletjWXtjrpGsj5XHGmm8DFYfK3WRYxUWHR0t7t69KwoLC8W8efPEypUry69337lzpxg2bJhOXzoXExMjJk6cKHJzc8WVK1dEv379xE8//SSysrLE/fv3xc2bN0V6errkuE9eh75hwwbx6aefigULFgghhLh8+bJ4//33xdixY8XPP/8sXn31VXH//v1KjSMzM1NkZGSICxcuiGvXron79++Ln376SYwZM0acO3euwnF1tX7K1odKpRKZmZni3//+t3j48KFQq9VCo9GIQ4cOiTFjxjx1mefFCw8PF3PmzBHLli0TZ86cEQkJCcLf31+sWrVK5OTkiNLS0vJr5XV9b8CL+pKYmCheeeUVsWzZMpGdna312KSIiIgQ169fF2lpaRWOoa9tQBv79+8Xw4YNq1D/n7YO//zzT/Hxxx+L5cuXi8TERCGEEEVFReKnn36q9DZmCHv27BHbtm0TmzZtEnFxcSIgIEAsXbpUHDx4UISGhop+/fqJ+Ph4ubtJMjKFGmkq9VEI46yRrI+P6aI+CsEaaSzkqI/V8sxYSEgI1q9fj6tXr6Jnz57lp4E3b96Mq1evYu/evVi0aBHq169f4RzXr1/HhQsX4O3tjfDwcEyZMgVdunRBjx49UKdOHbRt2xbBwcFIS0tD7969UadOHdSoUUNynrIZ+ebNm7F3716MGzcOs2bNQk5ODgYPHox27dohOjoaycnJ+Oabbyr82NCyo3k2NjZQq9XYsmULNmzYgIEDB6Jdu3ZISUnB0aNHcfXqVSQmJqJNmzbPjaev9aNQKHDixAn873//Q8OGDbFnzx74+PjA3d0dCoUCjx49QkREBHr27Alra+vypy29KN7ChQsxbNgw7Nu3DxcvXsSoUaPQtWtXfPfdd8jLy0O3bt3K34Gjr7MIL+rL/PnzUVBQgI4dO5YfZdNFX0JCQrBs2TIcO3YMmZmZqFWrFmrVqvXC5Qy1DTyNeOJoVX5+Pvbs2YOwsDC89dZbqFmzZvmjl6XE2bFjB3bu3ImtW7eie/fucHNzw9mzZ5GdnQ1nZ2fUqlULvr6+RvtUrjIhISHYvXs3evXqhSlTpqB79+4ICAjAuXPncP36ddy+fRv/+c9/KnWpFlVtplIjq2p9BKpGjWR9rHh9BFgjjZFs9VGnUzsjV1paKg4ePCjeffddIYQQUVFR4uzZsyI0NFTs379fnDlzRhw8eFAkJydXOs/evXtFRkaGSE1NFUIIMXXqVDF48GCRmJhY/qSZqKgoERAQIDIzMyXnePJpNQkJCeLdd98VmZmZIjo6WkyZMkV069ZNzJw5s/zIZVFRUaXGVGbbtm1i8uTJYsWKFSIwMFAMGzZM3Lt3Tzx69EisXr1avP322yIuLu65MfSxfsqOzNy6dUuMHDmy/ChkSEiI6NSpkwgODhbr1q0TAwYMEEeOHNE6Xl5envj000/FnTt3xIULF8Tbb78t7t+/LzZt2iTu3bsnLl++XOkjnsbcl4iICPHRRx+JkpISceHCBTF16lSxaNEiERsb+9zlDLENPMuTR+kKCwtFaWmpyMjIELNnzxaDBg0SCQkJ5X3UNs66devEsGHDxKFDh0RgYKDo0qWLOHPmjDh//rwYNWqUWLdunSguLtbbUV9dKCwsFLdv3xajR48WpaWlIiQkRIwfP17cu3dPHDhwoLxdXl6ejL0kOZlKjazK9VEI46+RrI+PVbQ+CsEaaWzkro/V5szYn3/+iUePHiEuLg5KpRIZGRnYt28fjh8/jpiYGNStWxcDBw6El5dXpZ5mqNFooFQq4e3tDZVKhdmzZyMvLw+TJk3C+fPncejQIfj6+sLe3h716tVDQECA5KMEhYWF5dfoX716Fenp6bh69So6dOiAvXv34sMPP0T79u2xYMEClJSUoGPHjuVHpaQSTxz1OHz4MH788Ue8/PLLWL58OVq1agUXFxf8+uuv6NGjB3r16oXXXnsNrq6uBl8/CoUCERERWLNmDfLy8lCjRg20bdsW7dq1Q926dZGamoqsrCx88MEH+Ne//vXC63wVCgUuX74Ma2tr5OXl4eDBg9i7dy+WLFmC+vXrY86cOejRoweaN2+Ohg0b6vW+Grn6EhkZiZCQENjb26N///5o0KABHBwcEBERgbi4ONSvX/+pNz8bYht4lifHvmnTJmzevBmbNm2CpaUlOnToAHNzc6xfvx6+vr5wdnZ+bqyyOCkpKQgNDcWCBQvQunVrdO/eHZaWlpg/fz6mT58OBwcHdO/eHQ4ODkZ7b9XFixeRlpYGpVKJy5cv4/bt24iIiMDy5cvx8OFDfPvttxg6dCjMzc11+p5DqjpMpUZW5fqoz/WjyxrJ+ljx+giwRhobY6iPLz4HaSIOHjyIS5cuoUmTJkhMTMSuXbswaNAgbNy4Eb1790ZiYiKEEBBCVDiHeOKRnpcvX8atW7fQsGFDXLp0Cbt378YPP/wAFxcXTJo0CSkpKQAg+YM9fvw41q5dCwAIDQ1FYGAgunbtikmTJkGlUkGj0cDNzQ3p6ekYOHAgRo4cqZNCc+PGDeTm5mLJkiUYM2YMfvzxR+zZswdWVlZwd3fHt99+i5KSElhbWz83nq7XT9nndfv2baxevRoXL15Ew4YNkZmZicOHDwMAXn/9dUydOhVff/11+c3Dz3tYR1m8+fPnY+TIkbh48SISEhLw9ddfw83NDXFxcSgqKvrLKXx97GDk7Mvly5eRk5MDe3t7FBUV4ffffwcAdOrUCYMHD0ZxcfFTd9SG2Aaep2zsv/76K3bv3o3PP/8cPXv2xPXr13Hy5EkMHDgQnp6emD17NkpKSl64vZ8/fx7BwcGIj49HVFRU+d8PHToUrVq1QkFBAfr27fvCH1hyO3DgAGJjY8u3jUOHDmHFihWwsLBAeHg46tevr9UlKWS6TKFGVuX6WBbTmGsk6+NjFa2PZf1mjTQuxlAfq8WZsRMnTmDr1q2YOnUqfHx8MGDAAAwdOhQpKSm4dOkStm3bhilTpsDZ2blSG+qTRxvWrl0LpVKJ9evXw9PTE/Hx8SgsLMSECRMQHR2Nzp07w97eXlK+e/fuYerUqZgxYwaOHTuGrVu34oMPPoCnpydq166NX375BcePH0fNmjURHByMuXPnlj8FSaonC80vv/yCefPm4fz58ygsLES7du3g5eWFFi1aYPbs2ejZsyemTJnywvHoY/0oFAocPXoUCxYsQKtWrWBhYYGoqCjY2tqitLQUmZmZ8Pb21nrcT8bz8/MDAERFRaGkpAT5+fnYvn07tm/fjgkTJpT/u77I1ZcNGzZg/vz5KCkpwb179+Do6Ijs7Gzk5+fD09MT7u7u6NKly1OPjut7G3iW2NhYnDp1Cs2bN0d+fj62bNmC8ePHo0WLFvD19UVRURG2bduGQYMG4eWXX0afPn20yn3p0iWoVCr07t0bycnJsLGxQZ06dbB//36cO3cOr732GmxsbCrdf30q2/9NnDgRLi4uKC4uRmFhIXbs2IHk5GTs3bsXs2fPLn/aG1U/plAjq3p91Nf60WWNZH2sXH0s6zfAGmksjKU+mvRkrOym2j///BPt2rWDr68voqOj8fvvv+PKlSs4evQorly5glmzZunspX9lH+yaNWtw/fp1ODk54fPPP0dKSgquXLkCtVqNTz75BPb29pJjl5SUYOPGjUhISEBoaCju378PFxcXtGvXDhYWFvDx8UF8fDyuXLmC7777rlI3GJZtgEePHsXJk++tyq0AACAASURBVCcRGBgIBwcHpKamQqVSoUmTJvD09ESHDh3g6+uLBg0aaBVX1+tHpVLh+++/x7fffouAgAC8/PLLKCkpwalTp2BmZobCwkJ4e3trfYr/yXhDhgzByy+/DDMzM8TExKB///7o0aMH+vXrh27duun9kd9y9GXv3r04cOAA1q5di6tXryItLQ05OTmwsrJCSkoK1Go1mjRpAnNz82fm0+c28Cw5OTn49NNPER4ejtjYWFy/fh1NmzYt/5Hh5eWFsLAwvPTSS/Dw8ICtre1z4xUUFMDCwgJNmjTBTz/9hNjYWNjZ2WHjxo2Ijo7GgQMHsHjxYqN5NPLT/H3/17FjR9y4cQMxMTHlR7+dnZ3x0Ucf8WEd1ZQp1UhTqI+AcddI1sfK10eANdIYGFt9NOnrUszMzPDo0SPs3bsXZ8+exaZNm/Dll19CrVajadOmWLZsGYKDgyWdOXmR/Px8DBo0CKGhoTh//jwWLVqEixcvIjIyEp07d37uG+xfxNnZGf3798f+/fsxcuRIbNmyBQcOHMCuXbtQUlICZ2dnzJ8/H0uWLIGPj0+lx5KQkICgoCBYW1vDzc0NI0aMgJeXFy5fvowtW7YgLy8PnTp1QuPGjbWOqev1Y25ujoKCAuTm5gIAbGxs0LlzZzg5OSEpKQl9+vSRdHr8afHat28PW1tb/P7772jTpg1eeuklg7x7ydB90Wg0iIyMREBAAIqLi1FaWor3338fNjY2iIyMRHFxMdq1awfg+Zd66HMbeJbGjRvj448/RlRUFBo0aIBJkyYhKCgIFy9eBPD4MoT79+9r9V0ICwtDUFAQjh49CnNzc0yYMAHdunXDO++8gxkzZqBfv35Ys2aNzn6c6svT9n+ff/45lEolatWqhUmTJuG9996Dp6en3F0lmZhSjTSF+ggYd41kfax8fQRYI42BsdVHkz4zJoRAZGQktmzZAjc3N7Rs2RJDhw7FgAED4ObmBgAvPIIhVWZmJmbOnIn09HRs3LgRSqUSR48eRdOmTTFq1KhK34RZ9lLFWbNmwdvbG0OHDsXSpUuhVqvRsmVLmJubw9zcXCdjsba2hhAC27dvR4MGDeDj44O2bdvi1q1bSElJQefOnV94Dfzf6Xr9WFhYIDExEQ8ePEDt2rXh4uKCe/fuISUlBfb29qhVqxZ8fHy03iE/LV5qaioyMjKgVqsBAM2bNzfITaiG7otCoUBeXh4cHR0RFRWFFi1aoG/fvjhx4gRcXV3x6aefavUoa31vA89Sr149dOrUCf/5z39Qp04d9OrVC9OmTUNCQgIOHDig9YsyNRoNUlNTsWLFCqSnpyMhIQHnzp1D586d0b59ezRu3FinRyz15Wn7v2HDhuG1114rf3kvVW+mViOren0EjLtGsj5Wvj4CrJHGwNjqo0lPxhQKBerXrw9fX1988MEH8Pb2/sesX9c7DRcXFzx69AhCCKjVakRFRWH//v34+OOPn/lkHanxPT090alTJ0ycOBF+fn4YOHAg1qxZg0GDBlVo5/8s5ubmaNmyJezt7bF27Vo4OTmhWbNm8PX1ha+vL5ycnCrUf12uH4VCgQYNGuD48ePYtWsXEhISsHLlSsyYMQMPHz5EjRo10LJlS60/52fFmz59OrKysmBra4uWLVtK7mdFyNEXLy8v1K5dG7NmzULfvn2RmJiIffv2YeHChVpfaqPvbeBZHBwc0LhxY3Tr1g3ffvst+vXrh/bt22Po0KF48803tSoywOMj7H5+fujVqxcePHiA9PR0hIWFQa1Wl79zyRifCPV32uz/qHoztRpZ1etj2RiMtUayPla+PgKskcbA2OqjQlTm0UhVjFqthlKp1HuejIwMHDp0CIcPH0b9+vUxZswYnV7mUeby5csYNmwYFi1aBH9/f50WmicVFxcjNDQUwcHBmDlzJl555ZVKxdPH+lGpVIiOjsb9+/fh5uYGZ2dnTJ8+HUFBQZIvE9FHvMowdF8KCwsxc+ZMFBUV4fbt2xW6rMdQ28CzREVFYfLkyVAoFNi8eXOFj3SVHS3+7bff0LVr10q95FZXcnNzIYSQfOTRUPs/qrpMqUZW1foIGH+NZH2sXH0EWCP1qSI1Uu76WK0mY4ZWWloKADq7LOJprly5gho1auj9utbi4mLs3bsXHTt2LL98pbL0sX6uXr2K0NBQXLx4EfPmzav0vQG6jldV+pKWlobExETUrVu3Up+3IbaBZ1GpVADwwnelPI8h7n2Qori4GBMnTsT3339f4SPvRMZC3/uHqlwfAeOvkayPlauPAGukrlXVGsnJGGnN2Da6pykpKUFqaiqsrKx0cspZ1/FMpS8kn4KCAqN+VDBRdVQV6iOg2zpiTDXJmPpC8qqKNZKTMSIiIiIiIhmY9KPtiYiIiIiIjBUnY0RERERERDLgZIyIiIiIiEgGnIz9TXZ2NoKDg5GdnV0l45tKDo6h+uQwhTEYIocpjIGqNlP5DnJblT++qeQwhTEYIgfH8AKC/iI5OVl4e3uL5OTkKhnfVHJwDNUnhymMwRA5TGEMVLWZyneQ26r88U0lhymMwRA5OIbn45kxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGTAydjfKJVKNGjQAEqlskrGN5UcHEP1yWEKYzBEDlMYA1VtpvId5LYqf3xTyWEKYzBEDo7h+RRCCKHzqERERERERPRc5nJ3wBB69+6DlJR7Wre/ceM6mjVrrnX7V/qNktSf4P9Ow78nLNC6fW332pLiA8C3U8Zg9uJ1Wrdft3y25BwJCQlo1KiR5OX0Fb9GDQfJOa5du4wWLdpo3T4/X9ojTfW9jkwlhymMwRA5pMZXKqXv4m/fvgVPTy+t2jZo0AAnT56QnIOMy6BBw5Cael+rthERJ9GhQ09J8Vu16iGp/fr18zB69Ayt29dpWFdSfABYMOffmDYzWOv2WzculRQ/IeEOGjVqLLFX0o6NS90f2No6Sop/9Wo0WrZsK2mZvLwsSe2r4365uuYwxjFYWdlKin/jxlU0a9ZS6/YNGtTHsWNHXtiuWkzGUlLuITExSdIyUtqnZzyU2iVJy5g7WEmODwAPH2k/cUhMTKxQjooup4/4UgtNmaQk7T9rqYUG0P86MpUcpjAGQ+SQEr8ikzGpOajqS029j7t3U7RuL6UtANSpkym1S0hL034ZZQ0byfEBIFOl/f68ItuE9GWkX6gkJYedXU3J8ZOSkiW1z82V/nuouu2Xq3MOYxuDtbWd5PhSfjNqi/eMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikgEnY0RERERERDKoEu8Z02g0mD9/PqKjo5GXlwchBObOnQtfX1+5u0ZERCQb1kcioqqtSpwZi46ORlpaGrZu3YoDBw4gICAAq1evlrtbREREsmJ9JCKq2hRCCOmvfJdBfHw8zp07h+TkZJw/fx62trbYuHGj3N0iIiKSFesjEVHVVSUuUzx+/DjmzZuH0aNHo0+fPmjSpAn27Nmj9fLNmjVHYmKS1u0LC/NgbW2rdfshb/xb67YAsOWXQAwf8ZXW7es1qSspPgD8MPcLTPomSOv2QfMmSc4hhIBCoZC8nL7i29o6Ss6Rm/sIdnZOWrfPy8uSFF/f68hUcpjCGAyRQ2p8pVL6Lr60tATm5hZatfXw8MDt27ck5yDdqWx9BIAOHXri7t0Urdqmpt5GvXqekuK3b+8vqf3+/Svx2mvjtW5fr1FDSfEBYM3/vsFHn83Vuv3aFd9Kii+EBgqF1IuPpB0bl7o/sLOrKSl+To4K9vbOkpbJzX0oqX113C9X1xzGOAZraztJ8QsKcmBjY691e3d3d9y4cfWF7arEZCw8PBy9evXCiBEjUFhYiNWrV0OtVsvdLSIiIlmxPhIRVW1V4p6x4cOH48KFCxg4cCACAgLg5uaGu3fvQqPRyN01IiIi2bA+EhFVbVXizJinpydCQ0P/8nfffPONTL0hIiIyDqyPRERVW5U4M0ZERERERGRqOBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZcDJGREREREQkgyrxnrHKEkIDIaS9AFNK+zOn90jsUaCkZb58c6HE+I95tvOq0HJVVb16FRuvlOVu3fqzQjmI5FDRF/9quxxfLGwaCgtykJ+XpXV7KW0B4HL0H1K7JGmZUd/+LDk+APR731/rtmtXzKxABlGBZfSnuLjAIMsQVRVqdYlel9FoSrVqxzNjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZcDJGREREREQkA07GiIiIiIiIZMDJGBERERERkQw4GSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBuYvajB48GB89dVX6NKlC/bt24fp06cjIiIC1tbWmDFjBry9vXHlyhXExsZCoVCgR48emDx5MszNzdG6dWuMHj0aZ86cQX5+Pj7//HMcOnQIN2/eRJ06dbBy5UrUqFEDv/32G7Zu3YqSkhJkZWVh7NixGDFiBHbu3IkjR47AzMwMiYmJsLa2xoIFC+Dp6WmIdUNERPRcrJFERFQZLzwz5u/vj5MnTwIATp06BUdHR0RGRkIIgRMnTuDkyZNwcnLC3r17sWPHDty4cQPr1q0DABQXF6NWrVr47bffMGTIEHzzzTeYMWMGDhw4gNzcXISFhSEvLw/bt2/HqlWrEBoaih9++AGLFi0qzx8REYGZM2di3759aNu2LVatWqWnVUFERCQNayQREVXGC8+M+fv7Y/Lkyfjyyy8RGRmJUaNGITw8HLa2tnB3d8e1a9cwc+ZMKBQKWFpaYvjw4diwYQM+/vhjAEC/fv0AAO7u7vD29oarqysAoGHDhsjKyoKtrS1WrlyJEydOICEhAbGxscjPzy/P37JlS9StWxcA0KJFCxw5ckTyIG/evCF5maKiAsnLSJGUdE2v8QHg8zdf176tEBXKISq4nLHEB4C4uEi9xjfEGEwhhymMwRA5DDEGjUat9xymwhRq5JWYKEnts7IzJOeQKvmu9Lot1dBOnbRuW5HtzhT2N/r+LQSYxnoyhTEYIocpjKG4uFDnMV84GWvWrBlKSkoQFhaGRo0aoVevXpg0aRLMzc3Rr18/rFixAgqFory9RqNBaWlp+Z8tLCye+v9l7t+/j7fffhvDhg2Dr68v+vfvjz/++KP8362trcv/X6FQVGgle3s3Q2Jiktbti4oKYGVlo3V7V9fGkvqTlHQN7u4ttG7/5Q8LJcUHHk/Elu/Yp3X7f781UHIOIcRfPntdkxrfy8tXco64uEg0beqndftbt/6UFF/f68hUcpjCGAyRQ2p8hUL6bcEajRpmZkqt2np4eODOnXjJOUyJKdTI1q3aISkpWau2WdkZcHSoJSm+g4OLpPbJd2/ArWEzrdsv3fGzpPjA44nY9vPntW4/rHNnSfGNcX9jaWn94kZPkPpbCJD+Q9UY15OxxTeVHMY4BgsLK0nxi4sLJW1HHh4eiIt78YElrSp13759sWTJEnTr1g2enp7Izc3F3r178corr6B79+7YtGkThBAoLi7Gtm3b0LVrV607GhMTA2dnZ3z66afo3r17eZFRq3lkloiIjB9rJBERVZRWkzF/f3/Ex8eXF5CuXbuidu3aqFevHr755huoVCoMHDgQAwcOROPGjTF+/HitO9CtWze4urqif//+GDBgAFJTU+Hs7IzExMSKjYiIiMiAWCOJiKiiFMIQF3DKjJcpvhgvU9QOL1OsmvFNJQcvUyR94GWKL8bLFLXDyxSZQ674FclRpS5TJCIiIiIiIt3iZIyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGRgLncHDKFGDUfY2dWUtIyU9o8ePZDaJUnLTB/1vuT4n7+pkrTcvJ82S84hdbkFUydKju8g4RHKt29HSY4vdTmpjwaWuozUxwJTVVeRx/xqv4yzc90KxNd+OSen2hWKT8ZFrVFDrSmV0F77tgCQlq79q2UqsszbXbR/iXaZoRq1pOWCf9srOYfUZf47/TvJOaS8miUuTtqrWQCguLhI4hL63acBJv82JvoL/X6frK1tJUeXsoy2r4bgmTEiIiIiIiIZcDJGREREREQkA07GiIiIiIiIZMDJGBERERERkQw4GSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhkYzWRs2bJlCA0NBQAsX74cR48eBQB89dVXWLt2rZxdIyIikg3rIxGR6TKXuwNlJk6cWP7/58+fh5eXl4y9ISIiMg6sj0REpstgZ8YGDx6Ms2fPAgD27duH1q1bo7CwEAAwY8YMdO3aFWvXrsXmzZsRExODhQsX4siRIwCAqKgoDB8+HH379sUnn3yC/Px8Q3WbiIhIr1gfiYiqL4OdGfP398fJkyfRpUsXnDp1Co6OjoiMjES3bt1w4sQJNG/eHAAwcuRIHDp0CCNHjoS/vz/CwsLw4MED/Pzzz7C0tMTQoUNx+PBhDBkyROvcly5dkNzfzMx7kpeRIjs7U6/xASAnR6X3HF9/PEIvbctkZaVLXkYqjUat1/hFRQV6jQ8AQogqn8MUxmCIHEJo9BofADIyUvSeg/4fOesjAFy7dllS+9zcR5LaV4Qh9pv63vd//ubrem0PADdvRkheRgpD7G8Mk4P1yxhyGGYM+v0+6eP3u0EnY5MnT8aXX36JyMhIjBo1CuHh4bC1tYW7uztq1679zGX79u0LGxsbAEDTpk2hUkmbZLz0UkckJ9/Vun1m5j24uNTXun1JSZGk/mRnZ8LBwUXr9hX58ubkqGBv76x1++lLlkvO8fXHIzB/1S9at18wdeKLGz0hKysdjo7P/l78XUUmnxqNGmZmSq3bW1hYSopfVFQAKysbrdsXFxdKig88/n4oFArJyxlTDlMYQ8VySOuPEBooFNpf0ODiUk9SfODxRKxWrQZatXVza4ioqPOSc9BfyVkfAaBFizZISkrSqm1u7iPY2TlJii+1Rkrdb5aUFEuKD0jf9/93+25J8T9/83Us37FP0jL/nf6dpPY3b0bA27uD1u3j4v6UFF/q/qYipOeQ/nuI9cs4clQsvn5rpL19TUnxpf5+d3d3Q0zMpRe2M9hlis2aNUNJSQnCwsLQqFEj9OrVC+Hh4Th27Bj69ev33GXNzf/fnFGhUBhkZk1ERGQIrI9ERNWXQZ+m2LdvXyxZsgTdunWDp6cncnNzsXfvXrzyyit/aadUKlFaWmrIrhEREcmG9ZGIqHoy6GTM398f8fHx6Nq1KwCga9euqF27NurV++ulNL1798bSpUuxa9cuQ3aPiIhIFqyPRETVk0Efbd+uXTvcuHGj/M9z584t///AwMDy/3///ffx/vvvAwACAgL+EuPJdkRERKaA9ZGIqHoympc+ExERERERVSecjBEREREREcmAkzEiIiIiIiIZcDJGREREREQkA07GiIiIiIiIZMDJGBERERERkQwUQgghdyf0rVGjRkhMTNS6vRACCoVC6/YKhbQ5rUajhpmZUlJ/pBJCI7lf+s5hYWEpKX5xcSEsLa21bn9flSEpPgA429lBlZurdXsXe3tJ8aV+lyrCFHKYwhgMkcPYxuDh4YGEhAS99of0T0qNNLbvoOFySOuPMdZgtUYtKb6ZQgGNxN8fSjNpYzbOz9q44ptKjuo4Bm1rJM+MERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikgEnY0RERERERDLgZIyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGZjL3QFtaDQazJ8/H9HR0cjLy4MQAnPnzoWvr6/cXSMiIpIN6yMRUdVWJc6MRUdHIy0tDVu3bsWBAwcQEBCA1atXy90tIiIiWbE+EhFVbQohhJC7E9qIj4/HuXPnkJycjPPnz8PW1hYbN26Uu1tERESyYn0kIqq6qsRlisePH8e8efMwevRo9OnTB02aNMGePXu0Xr5Ro0ZITEzUur0QAgqFQuv2CoW0E4wajRpmZkpJ/ZFKCI3kfuk7h4WFpaT4xcWFsLS01rr9fVWGpPgA4GxnB1VurtbtXeztJcWX+l2qCFPIYQpjMEQOYxuDh4cHEhIS9Nofer7K1kdAWo00tu+g4XJI648x1mC1Ri0pvplCAY3E3x9KM2ljNs7P2rjim0qO6jgGbWtklZiMhYeHo1evXhgxYgQKCwuxevVqqNXSdipERESmhvWRiKhqqxL3jA0fPhwXLlzAwIEDERAQADc3N9y9excajUburhEREcmG9ZGIqGqrMveMVQYvU9QPXqb4YqZwWt4QOUxhDIbIYWxj4GWKpoGXKWqDlylqg5cpModc8Q2RQ1+XKVaJM2NERERERESmhpMxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSASdjREREREREMjCXuwOmQAjpL9esyDLSGeIVctrnKCkpkhxdyjLOdnaS41dmOSIiqi4qUk+NqwabVeD9SxVZhoik4ZkxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREclAL5Ox8+fP4/XXX9dHaCIioiqNNZKIiMrwzBgREREREZEMzPUZ/M6dO5g9ezby8vKQnp4OHx8fBAUFwcrKCq1atUKfPn0QGxuLxYsXQ6VSYfHixTAzM0Pz5s1x5swZ/PLLL2jYsCG2b9+OX3/9FRqNBk5OTpg5cyY8PT312XUiIiK9Yo0kIiIIPTh37px47bXXRGBgoAgNDRVCCFFcXCxef/11cejQISGEEN7e3mLXrl1CCCFUKpXo2LGjuH79uhBCiJ07dwpvb2+RnJwszp8/L0aMGCHy8/OFEEKcOnVK9O/fX1J/PDw8BACt/xNCSGov9T99xzeVHFLjG4KxrSNTyWEKY6iO68nDw8Mg252pqco1Ugjj+g4aaw5jHIMhmMJ6Mrb4ppKjOo5B2xqp1zNjU6dORXh4OFavXo2EhASkpaUhPz+//N/9/PwAAJGRkfD09ISPjw8AICAgAHPnzgUAHD9+HImJiRg+fHj5ctnZ2Xj06BGcnJy06kdCQoLkvj9e3/qj7/imksMQY5CiIv0xhc/BEDlMYQyGyGEKY6DHqmqNNJXvILdV3auuNdIUxmCIHBzD0+l1MjZ58mSo1WoMGDAAL7/8MlJTU/8yiBo1agAAlErlPwZnZvb4djaNRoPBgwdj6tSp5X9OS0uDo6Oj1v1o1KgREhMTtW4vhIBCodC6vVT6jm8qOaTGN8RGLnW8pvA5GCKHKYzBEDmMbQweHh4VOthFj1XFGmls30FjzWGMY2CNrJrxTSVHdRyDtjVSrw/wOH36ND777DO8+uqrAIDo6Gio1ep/tGvfvj0SEhIQGxsLAPj999+RnZ0NhUKB7t27Y//+/UhLSwMA/Prrr/jggw/02W0iIiK9Y40kIiK9nhmbNGkSPvvsM9SoUQN2dnbo0KEDkpKS/tHOyckJS5cuxbRp02BmZoZWrVrB3NwcNjY26N69O8aOHYsxY8ZAoVDAzs4Oy5cv1/vsmoiISJ9YI4mIyDB3dL5ATk6OWLBgQfkNyDExMaJbt25Co9HoJD4f4FE1c0iNbwjGto5MJYcpjKE6ric+wMMwjKlGCmFc30FjzWGMYzAEU1hPxhbfVHJUxzEYxQM8tGVnZwcLCwu89dZbMDc3h7m5OYKCgnhkj4iIqj3WSCIi06X4/2d5Jo0P8KiaOaTGN8RXuTrenGyIHKYwBkPkMLYx8AEepoEP8Kh68SuSgzWyasY3lRzVcQxG8QAPIiIiIiIiejpOxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSgVE8TVHfFAozKBTS5p1S2lfsplhDPAVL+xwuLvUqlMHFpb7WbWvWlJ7Dy8tX67Z9+74vOf7Roz9LWm74iK8k55CyzJZfAiXHr54quv1IWU7/N7ubmSkNsoy+chiiL0TGwNzcUu/LODnVkZyjVq2GWrdt166PpNhRUWGSl1m2dbek9lKXmfj2YMnxqyfTqJEVG4exjeHFeGaMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikgEnY0RERERERDLgZIyIiIiIiEgGnIwRERERERHJwFxXgc6fP4/Fixejfv36iI+Ph7W1NQIDA7F69Wo0bdoUH374IQDgq6++Kv9z79690aZNG9y4cQOTJ09Geno6tmzZAgsLC1hZWWH27Nnw8vLCgwcPMHv2bKSmpqKkpASvvfYaxo8fr6uuExER6Q3rIxERPYvOJmMAEBMTg2nTpsHPzw+//vorpk6dCm9v7+cu07RpUwQFBUGtVqNt27Y4duwY6tSpg9DQUPz555/w8vLC1KlTMWrUKPTu3RtFRUUYO3Ys3N3d8eqrr+qy+0RERHrB+khERE+j08mYj48P/Pz8AABvvvkmZs+ejTp16jx3mbL2SqUS/fv3x/Dhw/Hyyy+je/fu+Ne//oX8/HxEREQgKysLy5YtAwDk5+cjNjZW62Jz50685LFoNGrJy0ghhEav8Q2VIyMjRa/x4+Ii9RofAI4e/Vmv8X/d/L1e2j5JCFGh5Ywph2HGYIjtTr/jUKtL9RrfUDmqG2OtjwCQkJAgaSymsL8xRI6SkiK9xgeA9PRkvcaPigrTa3wAmDBskIS2FfvMTKN+Vf0ayTE8nU4nY0ql8h9/V7Nmzb90vKSk5C//XqNGjfL/X7x4MW7evIkzZ85g1apV2L17N+bN2bJ0twAAIABJREFUmwchBLZs2QIbGxsAgEqlgpWVldb9aty4CRITE7Vur9GoYWb2z7E8i9QPRggNFAr93q4nNYeLSz3JOTIyUlCrVgOt29esKS1HXFwkmjb107q9h0cLSfGBxxOxvn3f17p9bVftxws8nly9M3K61u23/BIoKT7w+PunUCgkL2dMOaTHl94X6dud9B2u1HFI2c8AjydJSqVOd9uVyuHh4YH4+Nt67Y+pMNb6CACNGjXSukaawv6mIjnMzS0lxS8pKYKFhbTPwcnp+ZPzv0tPT0bt2m5at2/Y8PlnYv8uKioM7dr1kbTM6OkTJbWfMGwQ/rttj9btJ749WFJ8wBjrlyFyGF+NrNh6ktbe2Mbg4eGh1cEunc4IYmNjERsbCwDYunUr2rVrh5o1ayImJgYA8ODBA1y4cOGpy6pUKvzrX/+Ck5MTRo0ahS+++AJXrlyBnZ0dXnrpJaxfvx4AkJ2djXfeeQdhYfo/WkNERKQLrI9ERPQ0Oj3EWqtWLQQFBSElJQXOzs5YuHAhlEolpkyZgn79+qFhw4bo3LnzU5d1dnbGJ598glGjRsHa2hpKpRJz584F8PiI4Jw5czBw4EAUFxfj9ddfx6BB2p/WJiIikhPrIxERPY1OJ2N2dnZYuXLlP/5+8+bNT21/7Nixv/x5+PDhGD58+D/aNWzYED/99JNuOklERGRgrI9ERPQ0fM8YERERERGRDHQ2GevUqRP27dunq3BEREQmgfWRiIiehWfGiIiIiIiIZMDJGBERERERkQw4GSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBgohhJC7E/rWqFEjJCYmat1eCAGFQqG3/ug7vqnkMMYxWFnVkBS/sDAP1ta2WrdPe5ghKT4AONjYILugQOv2jjWkjQGonp+1YXJI648QGigUUo6hSd+9SxmDh4cHEhISJOcg4yKlRhrndmR8OarrGKTtnwCNRg0zM6XW7e+kPZAUHwA8atVCYob2tbVp/YaS4hcXF8LS0lrSMiUlxZLaS9/3Syc1h1Kp/ecGAKWlJTA3t5DaLb3mUKtLJcWXuk1oWyN5ZoyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSASdjREREREREMtDbZKxZs2ZQqVTYuXMnxo0b949/DwsLw9y5c/WVnoiIyGixRhIREQCYy5W4T58+6NOnj1zpiYiIjBZrJBFR9fDcM2ODBw/G2bNnAQD79u1D69atUVhYCACYMWMGNmzYgNGjR2PYsGHo1asXPvnkExQVFT0z3qFDh9C3b1/Ex8f/5Wjge++9hyVLlmDkyJHo3bs3ZsyYAY1GAwDYuXMn+vfvjyFDhiAwMBAtWrTQycCJiIgqgzWSiIgq67mTMX9/f5w8eRIAcOrUKTg6OiIyMhJCCJw4cQJxcXEYMmQItm3bhsOHD+Pu3bs4fvz4U2Pt27cPy5cvx8aNG9GkSZN//HtSUhI2btyIPXv24OTJk7hw4QJu3bqFxYsXIyQkBKGhobCzs4Nara78qImIiCqJNZKIiCrruZcp+vv7Y/L/x96dB8Z07//jf04m+0pCFiRBSGMviSW2otTSoqlL76ULunBxKR+t6upSar9646pa46K1C2qprbYgpIgEsWcRISFE9kwm7+8ffsnPbZXznmRyZibPx1/36uu83u/3mTnnldecM2cmTMAnn3yCmJgYDB06FFFRUXBycoKfnx+mTp2KqKgoLF26FImJiUhPT0deXt4f8sTFxeHo0aP47LPP4OPj89SxunbtCisrKzg7O8Pf3x9ZWVlISEhAhw4d4O3tDQB46623EB4eLr3IxMRE6W2EENLbmFJ+SxnDEtZQUJBr1PwA4OrgoDjW0PXytTaNMYQoMWr+x2MYfz9ZgqpaIy3hOKqMMbgGZUpKjP8Bgn+NGopji4oKpPMbso2syjn3G3eM4mKdUfNXxhjGOCae2Yy98MIL0Ol0OHDgAOrWrYuuXbti/PjxsLa2Rs+ePTFhwgTo9Xr07t0bXbp0QVpa2lMn6eLignnz5uGjjz5Cly5dUKdOnT/E2Nvbl/1vjUYDIQS0Wu3/5NNqtQYtsm7dukhKSlIcL4SARqMxaCxTyG8pY5jiGuzsHKXyFxTkwt7eSXF8+oN7UvmBx43Yo/x8xfFujnJrAKrma105Y8jNR4gSaDQyz12SLxoya/D39zfowy5LURVrpGkeR6Y3RlVdg9z56XEjZmWl/H17M/2uVH7gcSOWdE95bW1Y64/H37MUFRXA1tb++YFP0OmKpOLlz/3yZMeQPd8UF+tgbW0jOy2jjqHXF0vllz0mlNbI5+717t27Y968eejQoQMCAgKQk5ODHTt24JVXXsGxY8cwevRo9OnTBwAQGxv71Fsk6tati9DQULz99tuYNGlS2b3uz9OxY0ecOHECd+8+Pvg2btyoaDsiIqLKwBpJRETl8dynKfbo0QPLly9H+/btAQDt27fH5cuX4ePjg/Hjx2P06NFwdHSEs7MzWrdujeTk5D/NNXLkSBw8eBDLli1DDQWXjOvVq4fJkyfjvffeg62tLRo1agQHiduxiIiIjIk1koiIykMjTPjLASkpKdi2bRtGjRoFKysr7N27F0uXLpX+9I+3KZrnGKa4Bt6maBym+FpXzhi8TZEMp0aNNM3jyPTGqKpr4G2KyvA2ReMw19sUVfudMSW8vb2Rnp6Ovn37QqvVwsXFBTNmzFB7WkRERKpjjSQiMn8m3YzZ2Nhg6tSpak+DiIjI5LBGEhGZP+Ne8yQiIiIiIqKnYjNGRERERESkAjZjREREREREKmAzRkREREREpAI2Y0RERERERCow6acpEpmawsI8o25j6G+AyWzXrNlL0mPIbhcXd8SAEWR+z8b4P4+4OzbW6Nv1btHCgBFM9qchiaiKE6LEqNvUrVnTgPxCart/hq+SHuOL+Uuk4pfOnC49Rp3aDRXH6kv00vkBwMenvuJYFxd36fwBAS2l4q9cOS09huxvh5kCXhkjIiIiIiJSAZsxIiIiIiIiFbAZIyIiIiIiUgGbMSIiIiIiIhWwGSMiIiIiIlIBmzEiIiIiIiIVsBkjIiIiIiJSAZsxIiIiIiIiFZhdM7Zw4ULs379f7WkQERGZHNZIIiLzYnbNWHR0NIqLze/XtYmIiIyNNZKIyLxYV8Yg0dHRmDt3LmrVqoUbN27A3t4eM2fOhJWVFaZOnYrc3FxkZGQgKCgICxYsgJ2dHf79739j3759sLGxQfXq1fHtt99i3759iI+Px+zZs6HVatGjR4/KmD4REZHRsEYSEVVdGiGEMPYg0dHRGDp0KFavXo2QkBD89NNP2LhxI9q2bYugoCD0798fOp0Ob7zxBsaMGYPmzZvjtddew4kTJ2Bra4sVK1bAz88P3bt3x9tvv40hQ4agV69exp42ERGR0bFGEhFVXZVyZQwAgoKCEBISAgAYMGAApk6diuXLlyM+Ph5Lly5FYmIi0tPTkZeXBy8vLwQFBSEsLAydO3dG586dERoaavDYdevWRVJSkuJ4IQQ0Go3B46md31LG4BqMM0azZi9Jj3H+/CE0b95FcXxc3BGp/EKUQKORuWta/jMk2f20OzZWeoxezZtjz/nziuN7t2ghld/U3k/+/v5ITEw06nyqCnOpkab2HjTVMbgG8x3jn+GrpPJ/NeYdTF34X6ltls6cLhWfcusyfOu8oDheX6KXyg8At29fQ61aDRTHu7i4S+W/fPkUXnihjdQ2V66cloo3tWNCaY2stGZMq9X+4d8mTpwIR0dH9O7dG126dEFaWhqEELCyssKaNWsQFxeHEydOYMaMGejUqRM++eSTypouERFRpWGNJCKqmirtAR4JCQlISEgAAKxfvx4tW7ZEbGwsRo8ejT59+gAAYmNjodfrkZCQgNdeew0BAQEYMWIEhg4diri4OACPCxa/nExERJaENZKIqGqqtCtjNWrUwIIFC5Camgp3d3fMnj0bhw8fxujRo+Ho6AhnZ2e0bt0aycnJGDhwIHr37o0BAwbA0dER9vb2+OKLLwAA3bp1w/z586HT6RAWFlZZ0yciIjIa1kgioqqp0poxZ2dnLF68+H/+bciQIRgyZMhT48eMGYMxY8b84d/feecdvPPOO0aZIxERkRpYI4mIqiaz+50xIiIiIiIiS1ApzVjbtm3x888/V8ZQREREZoU1koio6uKVMSIiIiIiIhWwGSMiIiIiIlIBmzEiIiIiIiIVsBkjIiIiIiJSQaU92p6IlNAYfbvs7EyDRpDZbuzkedL5ZbY5e+yEdH4A6NRpoOLYwV16SufPzEwzaDsiIkug0ch/xi+zjRBCOv//N4riyCUzv5HK/NWYd6S3adK4o1Q8ADRu3EFxbGDLJtL5AWDA239XHLvyP9Ok86emXpHepirglTEiIiIiIiIVsBkjIiIiIiJSAZsxIiIiIiIiFbAZIyIiIiIiUgGbMSIiIiIiIhWwGSMiIiIiIlIBmzEiIiIiIiIVsBkjIiIiIiJSAZsxIiIiIiIiFbAZIyIiIiIiUoG12hNQoqSkBDNmzEBsbCxyc3MhhMA333yD4OBgtadGRESkGtZHIiLzZhZXxmJjY5Geno7169dj165dCAsLw9KlS9WeFhERkapYH4mIzJtGCCHUnoQSN27cwMmTJ5GSkoLo6Gg4OTlh9erVak+LiIhIVayPRETmyyxuUzx06BCmT5+OYcOG4eWXX0b9+vWxfft2xdvXrVsXSUlJiuOFENBoNIZM1STyW8oYVXMN8nMRogQajfKL3HXrNpUe4+bN86hXr7ni+H5/GyaV/7sZ4zHus38pjj977IRUfgA4cmQDOncepDg+Pv6o9BiZmWlwd/dRHP/gwR2p/KZ2TPj7+yMxMdGo86FnK299BORqpKm9B011jKq6BplaBAAlJXpYWWml5iRLtkbWrt1AKv+tW1dQp06g1DZNGneUiv9l7wr0fGW44vjAlk2k8gNA+Kz/wz8mzVMcv/I/06Ty5+Q8hLNzNaltcnOzpOJN7ZhQWiPNohmLiopC165dMXjwYBQUFGDp0qXQ6/VqT4uIiEhVrI9ERObNLL4z9te//hWnTp1C3759ERYWBl9fX9y6dQslJSVqT42IiEg1rI9ERObNLK6MBQQEIDIy8n/+7YsvvlBpNkRERKaB9ZGIyLyZxZUxIiIiIiIiS8NmjIiIiIiISAVsxoiIiIiIiFTAZoyIiIiIiEgFbMaIiIiIiIhUwGaMiIiIiIhIBWzGiIiIiIiIVKARQgi1J2FsdevWRVJSkuJ4IQQ0Go3R5mPs/JYyBtdgvmM4O1eXyp+dnQkXF3fF8UlpyVL5AcDd2RmZOTmK4z1cXKTHqGrvJ39/fyQmJhp1PmR8MjXS1N6DpjoG12C+Y2i1cj/BW1ysg7W1jdQ27tV9pOLTM5LhWdNPcfzR89FS+QHgBR8fXE5LUxwfVKuWVH5TfK2NnV9pjeSVMSIiIiIiIhWwGSMiIiIiIlIBmzEiIiIiIiIVsBkjIiIiIiJSAZsxIiIiIiIiFbAZIyIiIiIiUgGbMSIiIiIiIhWwGSMiIiIiIlKBSTZjCxcuxP79+9WeBhERkUlhfSQisiwm2YxFR0ejuLhY7WkQERGZFNZHIiLLYl0ZgyxZsgSbNm2Ck5MTQkJCcODAASxfvhxTp05Fbm4uMjIyEBQUhAULFmDTpk2Ij4/H7NmzodVq8dJLL2Hu3Lk4ffo09Ho9GjdujC+++ALOzs6VMXUiIiKjYX0kIqrajH5l7OjRo9iyZQs2bdqELVu2IDc3FwCwYcMGvP7669iwYQP27t2LW7du4dChQxgyZAiaNm2KTz75BD169MCSJUug1WqxZcsWbN++HZ6enpg7d66xp01ERGRUrI9ERGT0K2OHDx9Gr1694OrqCgAYMmQITp48iY8//hhRUVFYunQpEhMTkZ6ejry8vD9sf+jQIWRnZ+P48eMAAJ1OBw8PD6k5JCYmSs9bCCG9jSnlt5QxuIaqM0Z2dqZR8wOAu8QVA0PXy/cTKWUK9RGQr5GW8h7ksap+fksZo7hYZ9T8AJCekWz0MV7w8VEca8g+tYTX2hj5jd6MWVtb/8/EtVotAGDChAnQ6/Xo3bs3unTpgrS0tKcusKSkBJ999hleeuklAEBubi4KCwul5lC3bl0kJSUpjhdCQKPRSI0hw9j5LWUMrsF8x3B2ri6VPzs7Ey4u7orjk9Lki5K7szMyc3IUx3u4uEiPUdXeT/7+/gZ92EWPmUJ9BORqpKm9B011DK7BfMfQauX+NC4u1sHa2kZqG/fqypse4HEj5lnTT3H80fPRUvmBx43Y5bQ0xfFBtWpJ5TfF19rY+ZXWSKPfpvjSSy9h7969yM7OBgBs2rQJAHDs2DGMHj0affr0AQDExsZCr9cDeFyQSr+g3LFjR6xduxZFRUUoKSnBl19+ifnz5xt72kREREbF+khEREa/MhYaGopBgwbhzTffhL29PRo2bAgHBwe89957GD16NBwdHeHs7IzWrVsjOfnxp93dunXD/PnzodPpMGrUKMyaNQthYWHQ6/Vo1KgRPv30U2NPm4iIyKhYH4mIyOjNWFxcHLRaLXbt2gUAWLlyJQoLCzFkyBAMGTLkqdu88847eOedd8r+/9dff23saRIREVUq1kciIjJ6M1avXj0sXboUGzZsgEajgY+PD6ZNm2bsYYmIiEwa6yMRERm9GXN2dsa///1vYw9DRERkVlgfiYjI6A/wICIiIiIioj9iM0ZERERERKQCNmNEREREREQqYDNGRERERESkAjZjREREREREKjD60xRNga2tHWxt7SW3UR4vhJCdEmxs7BTHFhfrpPMDgEajvNe2lZjPk+xsHRTH6kv00vmtrW0Vxxq6nwCNgduZSn65Mbp2HWzQCF27Pv13j54mOztTOn9QUDvFsY0bNJPOf+fOTantfH0bSY8hu11WVoZ0flfXGopjCwtypfMDyo9rO8nzKhGRqQsJ6WX0bVp17CQ9xl/eHaM49r2BymNLHTu2WWo7mb8xDd1GiBLpMcwRr4wRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREamAzRgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREalAI4QQak+CiIiIiIioqrFWewKVITDwBSQlJSuOLyzMh52dg+J42X62qKgAtrb2iuOLi3VS+QGgpEQPKyut4nhbGzvpMQoK82Bv56g4Xl+il8qv0xXCRmJehuwnIUqg0RjvArGx8xsyRteug6XHOHhwDbp1e0txfHZ2plT+06d3oXXrPorjU1IuSeUHgDt3bsLbu57ieFtb5eeAUsnJF+Hn11hxfFZWhlT+rKwMuLnVVBxfWJArlR+QO679/f1w+UqC9BhkWurWrYukpCRFsUIIaDQao87HEsbgGsx3jLZtX5PKf/LkDrRr11dqm1YdO0nFL5r7CUZNnK04/vzJaKn8AHDs2GZ07DhAcfzx45FS+WX/LgUe/30jF29ax4S/vz8SExOfG8fbFImIiIiIiFRgls3YBx98gAMHDqg9DSIiIpPC+khEZF7M8jbFpUuXqj0FIiIik8P6SERkXszyyhgREREREZG5YzNGRERERESkAjZjREREREREKmAzRkREREREpAI2Y0RERERERCpgM0ZERERERKQCNmNEREREREQqMMvfGZNlY2MPW1sHqW1k4j09/WSnBF/fIMWx2dkPpPMDQI0adRTHPnp0z6AxoNEoDrW2tpVOL7ONECXS+QFAq9UqjtXr9QaNYUqysjKMvl1BQa50/ry8R4pjHzy4I51fdjsnp2oGjZGTo/x4lYk1ZBvZ814pjZXCY0Kj/Ngh02VrawdbW3uJeOWxj+Pl34fOztUVxxYW5knnBwAbGzvFsVYa+c+u7STX7VbNU3oMT09/xbGZmWnS+WXrtlbpueMJMvtJa20jnR8AHB1dFcfeu5cqnV92mx+XzZWKXzT3E6ltsrMzpfKXOnFim+LY1179u3R+2W327l0hPYbM+0lASOeXO1cqO8fwyhgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREamAzRgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREalAI4QQak+CiIiIiIioqrFWewKVoUmTFkhOTlEcn52dCRcXd8Xxnp5+UvO5fv0cAgJelJjPA6n8AJCengRPT3/F8Y8e3ZMeo6AgF/b2TorjNRq5C7H5+dlwcHBRHK/TFUjlB4DiYh2srW0Ux+v1eqn8QpRIr1uW7BitWvWQHuO3335BcHBPxfEFBblS+S9cOIYmTToqjr927Tep/ABQWJgPOzsHxfFOTtWkx8jMTIO7u4/i+KysDKn8en0xtFrlp21bW+XrLSVz3Pn5+eHy5QvSY5BpCQx8AUlJyYpiZY8jQP59KFuDCwvzpPIDQFFRAWxt7RXHW0mexwsK82Bv5yi1jVs1T6n4u3cT4eVVV3F8ZmaaVH6drhA2NnZS22ittFLxsvtJK1GvS+XmZsHJyU1xvI9PgFT+a9fOoEGDVlLb3Lt3Syr+4cN0VJN4f2RnZ0rlB+Try6t9Rkrl375jIfr1HSO1zd69K6TiZd9PAnLXo2TPf/7+frhy5fJz43ibIhERERERkQrYjBEREREREamAzRgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREamAzRgREREREZEKlP/UthmrVauW9DZ+fr6KYz085PPXrq18m9xcJ+n8AODrW0dxbE6O8l8sf5K/v59EtHzv7+enPH9xcaF0fgDw9/dXHKvX642a31AyY/j4eBk0hsx2hYV50vlr1fJWHKvTybzv/n8y71cHB1eDxpA57qpVkz/uZF5rGxt76fyA8uNO5jxGpqt27dpS8XLnfcPehzI1uKioQDo/IHcsWWk0BuSX208urjWkx5A537i42Ernl61fWiv5Oi+zn6y0NtL5Abm/JTw9faTz164tt42jo/z7SeaYyMlxkc4PyL3enp7u0vllt5E9hmS3ERBGza/03KoRQsjPhIiIiIiIiMqFtyn+TlpaGrp164a0tDSzzG8pY3ANVWcMS1hDZYxhCWsg82Yp70Eeq+rnt5QxLGENlTEG1/BsbMZ+R6/XIzU11aDb0Uwhv6WMwTVUnTEsYQ2VMYYlrIHMm6W8B3msqp/fUsawhDVUxhhcw7OxGSMiIiIiIlIBmzEiIiIiIiIVsBkjIiIiIiJSgXbKlClT1J6EqbGzs0Pbtm1hZ2dnlvktZQyuoeqMYQlrqIwxLGENZN4s5T3IY1X9/JYyhiWsoTLG4Br+HB9tT0REREREpALepkhkBviZCRER0R+xPpK545UxIhMihIBGo0FMTAwyMzORkZGBIUOGqD0tIiIiVbE+kqXilTEiE6LRaHDw4EFMnz4dhYWFmDZtGr777ruy/87PToiIqCpifSRLxWaMyITcvn0by5Ytw7Jly2Bra4uQkBC88cYb2LlzJ4DHxYiIiKiqYX0kS8VmjMiEFBYWonbt2ti3bx+WL1+Ob7/9Fvb29li0aBFyc3PVnh4REZEqWB/JUrEZI1JR6W0VWVlZAAAfHx9kZWVh5cqV+Pbbb+Hr64vY2Fi4uLjwFgwiIqoyWB+pquDvjBGppPTLyIcPH8aiRYsQGxuLjh07Ijc3F8XFxThz5gwyMzOxePFijBs3DoGBgWpPmYiIyOhYH6kq4dMUiVR07NgxzJ8/H9OmTUNaWhratWuHe/fu4erVq4iPjwcAtGnTBh06dCgrTkRERJaO9ZGqCjZjRJXsyaLx1VdfoWnTpmjfvj3WrVuHX3/9FXZ2dpgwYQI6duwIvV4PrVar8oyJiIiMj/WRqiJ+Z4yoEpUWml9//RURERHw9/fH+vXrMWzYMLi5uWHFihVo2bIlbt68CQCwsuIhSkRElo/1kaoqa7UnQFSVaDQanDt3DuHh4Zg1axY8PDzQrl07+Pj4wNraGo8ePcK5c+fQp0+fsngiIiJLx/pIVRWbMaKnKP2ELjk5GXl5eQgKCjI4V1ZWFpycnGBtbY3MzEysXbsW+fn5aNiwIQDA3d0du3btwrZt25CSkoIJEyYgODi4opZCRERUoSqqRrI+EvFpikRPpdFocODAAXz66aeIjIzE9evXERwcDHt7e6k8hYWFmDBhAkJCQuDq6gobGxs4ODggPj4e8fHx6Ny5MzQaDYQQ6Ny5M1555RW0bt2aX0a2IHwticjSVESNZH0kgDUS4AM8iJ4qJSUFCxYswAcffIBq1aph5MiRaNWqFT766CO4uroqynH9+nXcvXsX7du3x7Vr13Ds2DE8evQIvXr1wo0bN3Dw4EG4u7tj0qRJVf5EZMnS09Ph6emp9jSIiCpMeWsk6yOVYo3kAzyI/uDKlSv44IMPUL16dQQFBcHb2xv/+c9/cO7cOcycObPsByifJysrC3q9HidPnsS4ceNgb2+P6OhorFy5EgUFBejduzeuX7+O6dOnG3lF5skSPidKS0vDN998g7y8PLWnUiFKSkr+5/9bwmtERHIqokayPpafJZx/LalGlqc+8sqYhJKSElhZWaG4uBjW1vy6nSWbOnUqjh49in//+98IDAyEVqtFSkoKRo4ciQULFpTdz/48RUVFeO+99zBq1CiEhoZCCIF//etfSEhIwL/+9S+cOnUKnp6eaNKkiZFXZF6evG0hNTUVWq0W3t7eKs9KXkFBAYqLixEXF4e7d+/i9ddfV3tK5ZaTk4ObN2+iWbNmak+FTAxrZNVRETWYzC0RAAAgAElEQVSS9dFwrJGmydD6yO+MScjPz0dGRgbCw8Ph7OwMHx8ftadEFaD0pHbu3DnExMQgNTUVQ4cORXJyMjZu3IgWLVqgevXqqFatGv7yl78oupxemvPRo0fYsmUL3nzzTbi6ukKj0aBVq1ZYuXIlevbsiWbNmlX5y/NPU1pkli9fjtmzZ2P37t3QarVmU5RLX39ra2vY2tri4MGDWLFiBTw8PBQ38qYmJiYGFy9exLRp03D8+HE0bdoUHh4eak+LTAhrpGWq6BrJ+lh+rJGmpbz1kc2YQhs3bsTu3buxZ88ebN26FXXq1EHr1q3VnlaVYqwveWo0Ghw8eBBz5syBl5cXFi1ahOzsbEycOBGXLl3CihUrEBISgho1akCr1SqaQ3Z2Nuzs7ODg4ICEhAT8+uuv6Ny5M2xsbBAfH48jR46gb9++cHZ2rvD1mLMnX+ObN29i9+7d+Pzzz+Hr64u5c+fC1dXV5IvNk2vIyMiAXq9Ho0aNUKNGDSxZsgTVqlUzq2Jz+vRpbNmyBevWrYOXlxeuXr0KLy8vDBkyRO2pkQlhjVSfudRI1kfDsUaaloqqj7yP4DlOnjyJw4cP49SpU5g4cSLu3r0LV1dXvPvuu2pPrUp58uC9f/9+hX4if/PmTaxYsQIRERGIiopCrVq10L9/f5w4cQJffvklPvnkk7L7mf+syGRmZuLBgwcICAjA4cOHsXbtWtja2qJBgwZo3bo1YmNjERYWhkGDBmHz5s2YMGECvLy8KmwNluDJ13j//v2IioqCr68vGjZsiIYNG0Kj0WD27NkoKioy6UagdA0rV65ETEwM7t+/j+DgYPTt2xdvv/02lixZgqKiIrO5HePKlSvIysrCnDlz4Ofnh+Li4rJiX3pbGlVdrJGmwZRrJOtjxWCNND0VVR95Zew5HB0d4eHhgbfffhuBgYG4evUqsrOz0b59e2g0Gj7lp5KU7uc1a9YgMjISnTp1qrDvJOTk5CAjIwPJyclYv3495syZU3Zw/eUvf0GPHj2eebtNUVERZsyYUfbe+P777zF27Fi4ubnh/v37OHfuHN5//324uLjAzc0N/fr1Q6dOnfg4198p3Rfr169HREQEioqKEBMTg9atW8PDwwMvvPAC3NzcsHz5crz++uuwtbU12f23bt06HDhwAEuXLsUvv/wCrVaL/v37o169erCyssLWrVvRq1cv2NjYmOwaSjVv3hydO3eGm5sbTp8+jYiICPTv3x/u7u4mP3cyPtZI02CqNZL1seKwRpqeCquPgv7UjRs3RGpq6v/8/759+4qjR4+qOKuq69ixY+L1118X2dnZQggh9Hq9dI4bN26IjRs3isjIyLJ/u3r1qnjrrbdEWFiYuHnzphBCiF9//VW89dZbIisr65njpKWliZSUFHHx4kUxYcIEMWLECDF37tyy/x4bGytGjBghzp8/Lz1XWXfv3hVRUVFCCCGKioqMPp4xxMTEiA8++EDk5eUJIYSYNGmSGDZsmIiPjxclJSVCCFH2+puyBQsWiFu3bonly5eL4cOHi5ycHDFu3Dixb98+odfrRVZWltpTfK5NmzaJ8PBwsX//fiGEEDk5OeKzzz4TGzZsUHlmZCpYI02LqdVI1seKxxppGiq6PvL+kj+xevVqfPXVV1i4cCHu3r0L4PG9rd26dUPHjh0r5JGiFZHDkj25fzIzM7F3717cv38fv/32GwDAyspKah/euHEDo0aNQnx8PNavX49Tp04BABo0aICXX34ZQgjs3LkTK1aswLfffovhw4fD1dX1Ty8z63Q6bN68GWPHjoWzszNGjhwJIQQuXLiAmzdvAnj8qYmtrS3S0tIM3Q2KpaWlYfTo0Zg2bRoWLVqE4uJio49ZXqWvnxACmZmZ2LFjBy5evIhDhw4BAGbOnAkvLy98/fXXuHz5MgCY3PcIfv8425KSEly/fh3Dhw/HhQsXsGTJEjg5OaG4uBharRZWVlaKf6tOLREREYiMjISXlxdq166N/Px8ODo6ol27dvxCPQFgjTQFplwjWR8rBmuk6TFGfeRtik+xefNmbN++HeHh4WjQoAEyMzNx7NgxhISEwMrKCnXr1i33ZVPxxCX4oqIiFBQUwNbWtiKm/4cxhBle7v/9/nF2dkaLFi3w4MEDxMfHw9XVFXXq1FG8Lr1ej+nTp6NTp074v//7P8TExMDb2xuenp54+PAhmjVrhpo1ayItLQ2ZmZkYPnz4c2+V0Gq1CAgIwJ07d7Bhwwb07t0b7dq1Q2xsLLKzswE8frrY2rVrMWDAAKP/Eevt7Y3i4mIsX74czZs3N/lbPZ6cm06ng7OzM5o3b44HDx7gxo0bcHBwgK+vL7p3744LFy6gQ4cOcHFxUXnW/0uv10Or1QIAzp8/j/v378POzg7NmzfHzp07ERoairZt22Lz5s3Yt29f2R8vpiwtLQ0rVqzAqlWrUFhYiGPHjmHu3LnYu3cvJk6ciLp166o9RVKZJdRIc66PgOnXSNbH8mONND3Gqo9sxp7i6NGjCA0NRUJCAn755RccPnwYq1atQu3atdG3b98KGaP0AFu1ahU2bNiAJUuWQK/XIzAwsMLu8y4dIzExEdWrV6+QnJXlyf2zbt06LFq0CNWrV0fr1q1x+/ZtxMbGws7ODn5+foryWVlZISYmBjVq1ECLFi3KngQVFRWFPXv2YObMmXjvvfcQFhaGTp06leX9sxO1Xq+HlZUVzp49i1OnTiExMRHR0dF4+eWX8eKLL+Lnn3/Ghg0bcPHiRXzwwQdo27ZtxeyYpygpKSmbZ35+PgIDA7Fo0SJ4eXmZ9FOVnvUa37p1C3FxcdBqtahbty66du1qckUmISEBJ06cQFBQENauXYvFixfj+PHjOH78OIqKijB48GB89913OHbsGM6cOYO5c+eaRSOTnZ2N8PBwnD59Gnv37oW/vz/eeOMNXL16FS+++CJcXFxM/o8YMi5LqJHmXB8B066RrI8VgzXS9BirPrIZe4rExERs3LgR58+fx5tvvomPPvoI3t7eyMrKQqtWrSrsj5D//ve/OHDgAGbOnIn9+/cjOzsbXbt2hY2NTYXkB1B2KdjNzQ2NGjWqsLyVoXT/zJo1C3v37sWDBw/w7rvvokGDBoiJiUFSUhLatm2reH+lpKRgzZo18PX1Ra9evdC7d2+88cYbGDZsGLKzs5Gfn4/mzZsrOpCsrKxw7tw5fPrpp5g8eTJCQ0NhbW2N1atXo3fv3ggJCcGVK1fw0UcfISQkxCh/vJYWGY1GgzNnzuDIkSMICgpCjx49EBgYiEmTJiEoKAh2dnbIz883uVsXgOe/xikpKWjbti2sra1N6o//kpIS/Pjjj2V/aJQ+Iaxr165wdXXFkSNH0KFDB/z9739H165d0a9fP9SuXVvtaT/TxYsX8ejRI1SvXh3du3eHk5MT3n//ffTo0QNXrlzBgQMHMGDAADg4OJjUa0GVz1JqpDnXR8B0ayTrY8VhjTQNRq+PBn3TzAJt2bJFzJs3T0yaNEmkpKSI3NxcUVJSItLT08WmTZtE//79xbVr1ypsvIKCAjF16lSRl5cnVq5cKUaMGCFyc3PFiBEjxOnTpw3OW/oFzlJZWVliw4YNol+/fmLr1q3lnfYfcj9r7PJ42v7JyckRI0aMEBcvXhTXrl0T9+7dk867cuVK0b17d3Hw4EEhhBAXL14U+/btE506dRIxMTHP3DY5OVn88MMPZV+O3bx5s5gwYULZf79z544YPXq0GDhwoEhOThY5OTnS81MqOTlZLF68WAghxJEjR0THjh3FiBEjRKtWrcrWtmfPHtGqVSvRvn17ce7cOaPNxVDGeo2N7ckvq8+ePVuMHj1aDBs2rOzf7t27J/7xj3+IdevWqTE9g0RERIg333xTfPjhh+K1114TmzdvFkII8emnn4rPP/9c9O7dW1y5ckXlWZKaLKFGWkp9FML0aiTrY8VjjTQNlVEf+TtjePxlvN27d2PIkCFIT0/HX//6V8yfPx8PHz7Ef//7X+j1esyZMwcBAQEGjyF+98mPVqvF7du3MXjwYHh7e+P777+HRqOBXq8v1/2ypWOcPHkS7dq1g6ura9njQZctWwYHBwf07NnT4Pyl7t+/jwULFmDw4MGws7ND/fr1y/WpjNL9o9Ppyu5FV5rzwoULuH79Otzc3DB48GC4urpi+vTpsLe3x71793Dx4kVMmTIFwcHBz8yXm5uL5cuXo7CwEKNHj4afnx82b96MmJgYhISEwMvLC4GBgSguLkZmZiZ8fX0N3h/Pk5ubixUrViAzMxM5OTkIDw/Hiy++iCVLlmDmzJmwsrJCz549y+ZjCj+gaIzX2NCxy5On9Mvq27Ztg729PRo3blz2W0uhoaHw8PCAn58fsrKyKnRsY9m/fz+2bduG1atXo7CwEPHx8fjiiy/g5eWFfv36ITMzEx9++KHi253I8lhKjTTX+giYfo1kfSw/1kjTU1n1scrfppieno5ly5Zh4cKFaNGiBbp3746cnBwsXrwYU6ZMQc+ePdG/f394e3sbPMaTb7RLly7h3r178PLygpWVVdmPHTZq1AiRkZE4cuQIBg0aJH3J/MkxMjMzMWnSJPz222/o0aMH7Ozs4OPjg0uXLiEiIgK+vr5o0KCBwespKCiAq6srUlNTMX/+fJw4cQIDBgwwOJ/M/jl69Kji/aPRaHDo0CFMmzYNBQUFOHXqFLZs2YKRI0fC3d0dM2fORJ8+ffDGG2+gYcOGzzwhlJSUoGbNmmjfvj3Cw8Px8OFDNG/eHImJiUhKSkJaWhr0ej1WrlyJyZMno1mzZgbvj+cpnUuHDh2wZMkSZGRkoHfv3qhZsyaCg4NRUFCA+fPno169emjZsmWF/vinoYz1GsuOXVRUVPZlYkOU5jl69Ci+//57zJ8/H61bt0ZiYiJiY2Nx5swZpKamYuvWrRg7diyqV69uskWm1NGjR+Hm5oZu3brB2toa9erVQ2JiInQ6Hfr374/AwEC4ubmpPU1SiSXUSHOuj7+fvynWSNbH8mONNE2VVR+rfDOWk5ODH3/8Ea1atSr70cKgoCCcOnUK3bp1g7Ozc7nuT3/yF7iXL1+OBQsW4KeffkJ+fj5CQ0NhY2ODVatW4ddff0VUVBTmz58v3WE/eSDl5+fD1dUVjRs3xoEDBxATE4Nu3brB3t4et2/fRqNGjdCpUyeD3zzXrl3DvHnz0KNHD2RkZGDTpk3w9vZGo0aNULNmzbI1Kz24jLl/UlJS8M9//hOzZs3C3/72N4SEhODOnTs4cOAAxo4di9zcXPj5+cHf3x/Anz+so/STnuLiYnh7e6N9+/ZYsGABnJyc0KVLFzx69AgHDhzA2bNn8e677yI0NFTR/Azx5Fy8vLzQuXNn7NixAwDQtGlT2NraIjg4GEVFRfDz80OdOnWMNhelKuMYeJbS1zUiIgLr1q1Djx49ynXyT0pKwrx586DX6xEaGgo3NzcEBwcjKSkJv/zyC2xtbfH555+jfv36FbUEo7p+/TquXLmCdu3awdHRERqNBlFRUbCxsUGbNm1M+lNLMj5zr5HmXB9L4025RrI+lh9rpOmqtPpYrpsczVhsbKy4deuWKCgoENOnTxeLFy8uu999y5YtYtCgQRX6o3Px8fFi3LhxIicnR8TFxYmePXuKH374QWRlZYk7d+6IK1euiIyMDOm8T96HvmrVKjFq1Cgxa9YsIYQQ58+fF++884744IMPxH//+1/Rp08fcefOnXKt4/79++LevXvi1KlT4uLFi+LOnTvihx9+EMOHDxcnT540OG9F7Z/S/ZGZmSnu378v/vGPf4gHDx4IvV4vSkpKxJ49e8Tw4cOfus2z8kVFRYlp06aJ7777Thw/flwkJiaKHj16iCVLlojs7GxRXFxcdq98RX834HlzSUpKEq+88or47rvvxKNHjxSvTcbp06fFpUuXRHp6usE5jHUMKLFz504xaNAgg+b/tH3422+/iQ8//FAsXLhQJCUlCSGEKCwsFD/88EO5j7HKsH37drFhwwaxZs0acfXqVREWFibmz58vdu/eLSIjI0XPnj3FjRs31J4mqcgSaqSl1EchTLNGsj4+VhH1UQjWSFOhRn2sklfGIiIisHLlSly4cAGdO3cuuwy8du1aXLhwATt27MCcOXNQq1Ytg8e4dOkSTp06hcDAQERFRWHixIkIDQ1Fp06d4OnpiRYtWiA8PBzp6eno1q0bPD094ejoKD1OaUe+du1a7NixAyNGjMCUKVOQnZ2N/v37o2XLloiNjUVKSgq++OILgx8bWvppnoODA/R6PdatW4dVq1ahb9++aNmyJVJTU7F//35cuHABSUlJaN68+TPzGWv/aDQaHD58GP/5z39Qp04dbN++HUFBQfDz84NGo8HDhw9x+vRpdO7cGfb29mVPW3pevtmzZ2PQoEH4+eefcebMGQwdOhTt27fHP//5T+Tm5qJDhw5lv4FjrKsIz5vLjBkzkJ+fjzZt2pR9ylYRc4mIiMB3332HgwcP4v79+6hRowZq1Kjx3O0q6xh4GvHEp1V5eXnYvn07Dhw4gL/85S+oXr162aOXZfJs3rwZW7Zswfr169GxY0f4+vrixIkTePToEdzd3VGjRg0EBweb7FO5SkVERGDbtm3o2rUrJk6ciI4dOyIsLAwnT57EpUuXcP36dXz99dflulWLzJul1EhzrY+AedRI1kfD6yPAGmmKVKuPFdrambji4mKxe/du8dZbbwkhhDh79qw4ceKEiIyMFDt37hTHjx8Xu3fvFikpKeUeZ8eOHeLevXsiLS1NCCHExx9/LPr37y+SkpLKnjRz9uxZERYWJu7fvy89xpNPq0lMTBRvvfWWuH//voiNjRUTJ04UHTp0EF9++WXZJ5eFhYXlWlOpDRs2iAkTJohFixaJmTNnikGDBonbt2+Lhw8fiqVLl4o333xTXL169Zk5jLF/Sj+ZuXbtmhgyZEjZp5ARERGibdu2Ijw8XKxYsUL07t1b7Nu3T3G+3NxcMWrUKHHz5k1x6tQp8eabb4o7d+6INWvWiNu3b4vz58+X+xNPU57L6dOnxfvvvy90Op04deqU+Pjjj8WcOXNEQkLCM7erjGPgzzz5KV1BQYEoLi4W9+7dE1OnThX9+vUTiYmJZXNUmmfFihVi0KBBYs+ePWLmzJkiNDRUHD9+XERHR4uhQ4eKFStWiKKiIqN96lsRCgoKxPXr18WwYcNEcXGxiIiIECNHjhS3b98Wu3btKovLzc1VcZakJkupkeZcH4Uw/RrJ+viYofVRCNZIU6N2fawyV8Z+++03PHz4EFevXoVWq8W9e/fw888/49ChQ4iPj4e3tzf69u2LBg0alOtphiUlJdBqtQgMDERmZiamTp2K3NxcjB8/HtHR0dizZw+Cg4Ph4uICHx8fhIWFSX9KUFBQUHaP/oULF5CRkYELFy6gdevW2LFjB9577z20atUKs2bNgk6nQ5s2bco+lZIlnvjUY+/evfj+++/RpUsXLFy4EE2bNoWHhwd++ukndOrUCV27dsWrr74KLy+vSt8/Go0Gp0+fxrJly5CbmwtHR0e0aNECLVu2hLe3N9LS0pCVlYV3330XL7300nPv89VoNDh//jzs7e2Rm5uL3bt3Y8eOHZg3bx5q1aqFadOmoVOnTmjUqBHq1Klj1O/VqDWXmJgYREREwMXFBb169ULt2rXh6uqK06dP4+rVq6hVq9ZTv/xcGcfAn3ly7WvWrMHatWuxZs0a2NraonXr1rC2tsbKlSsRHBwMd3f3Z+YqzZOamorIyEjMmjULzZo1Q8eOHWFra4sZM2Zg8uTJcHV1RceOHeHq6mqy3606c+YM0tPTodVqcf78eVy/fh2nT5/GwoUL8eDBA3z11VcYOHAgrK2tK/R3Dsl8WEqNNOf6aMz9U5E1kvXR8PoIsEaaGlOoj8+/Bmkhdu/ejXPnzqF+/fpISkrC1q1b0a9fP6xevRrdunVDUlIShBAQQhg8hnjikZ7nz5/HtWvXUKdOHZw7dw7btm3Dv/71L3h4eGD8+PFITU0FAOkX9tChQ1i+fDkAIDIyEjNnzkT79u0xfvx4ZGZmoqSkBL6+vsjIyEDfvn0xZMiQCik0ly9fRk5ODubNm4fhw4fj+++/x/bt22FnZwc/Pz989dVX0Ol0sLe3f2a+it4/pa/X9evXsXTpUpw5cwZ16tTB/fv3sXfvXgDAa6+9ho8//hifffZZ2ZeHn/WwjtJ8M2bMwJAhQ3DmzBkkJibis88+g6+vL65evYrCwsL/uYRvjBOMmnM5f/48srOz4eLigsLCQvzyyy8AgLZt26J///4oKip66om6Mo6BZyld+08//YRt27ZhzJgx6Ny5My5duoQjR46gb9++CAgIwNSpU6HT6Z57vEdHRyM8PBw3btzA2bNny/594MCBaNq0KfLz89G9e/fn/oGltl27diEhIaHs2NizZw8WLVoEGxsbREVFoVatWopuSSHLZQk10pzrY2lOU66RrI+PGVofS+fNGmlaTKE+VokrY4cPH8b69evx8ccfIygoCL1798bAgQORmpqKc+fOYcOGDZg4cSLc3d3LdaA++WnD8uXLodVqsXLlSgQEBODGjRsoKCjA2LFjERsbi3bt2sHFxUVqvNu3b+Pjjz/G559/joMHD2L9+vV49913ERAQgJo1a+LHH3/EoUOHUL16dYSHh+Obb74pewqSrCcLzY8//ojp06cjOjoaBQUFaNmyJRo0aIDGjRtj6tSp6Ny5MyZOnPjc9Rhj/2g0Guzfvx+zZs1C06ZNYWNjg7Nnz8LJyQnFxcW4f/8+AgMDFa/7yXwhISEAgLNnz0Kn0yEvLw8bN27Exo0bMXbs2LL/bixqzWXVqlWYMWMGdDodbt++DTc3Nzx69Ah5eXkICAiAn58fQkNDn/rpuLGPgT+TkJCAo0ePolGjRsjLy8O6deswcuRING7cGMHBwSgsLMSGDRvQr18/dOnSBS+//LKisc+dO4fMzEx069YNKSkpcHBwgKenJ3bu3ImTJ0/i1VdfhYODQ7nnb0yl579x48bBw8MDRUVFKCgowObNm5GSkoIdO3Zg6tSpZU97o6rHEmqkuddHY+2fiqyRrI/lq4+l8wZYI02FqdRHi27GSr9U+9tvv6Fly5YIDg5GbGwsfvnlF8TFxWH//v2Ii4vDlClTKuxH/0pf2GXLluHSpUuoVq0axowZg9TUVMTFxUGv1+Pvf/87XFxcpHPrdDqsXr0aiYmJiIyMxJ07d+Dh4YGWLVvCxsYGQUFBuHHjBuLi4vDPf/6zXF8wLD0A9+/fjyNHjmDmzJlwdXVFWloaMjMzUb9+fQQEBKB169YIDg5G7dq1FeWt6P2TmZmJb7/9Fl999RXCwsLQpUsX6HQ6HD16FFZWVigoKEBgYKDiS/xP5nv99dfRpUsXWFlZIT4+Hr169UKnTp3Qs2dPdOjQweiP/FZjLjt27MCuXbuwfPlyXLhwAenp6cjOzoadnR1SU1Oh1+tRv359WFtb/+l4xjwG/kx2djZGjRqFqKgoJCQk4NKlS2jYsGHZHxkNGjTAgQMH8OKLL8Lf3x9OTk7PzJefnw8bGxvUr18fP/zwAxISEuDs7IzVq1cjNjYWu3btwty5c03m0chP8/vzX5s2bXD58mXEx8eXffrt7u6O999/nw/rqKIsqUZaQn0ETLtGsj6Wvz4CrJGmwNTqo0Xfl2JlZYWHDx9ix44dOHHiBNasWYNPPvkEer0eDRs2xHfffYfw8HCpKyfPk5eXh379+iEyMhLR0dGYM2cOzpw5g5iYGLRr1+6Zv2D/PO7u7ujVqxd27tyJIUOGYN26ddi1axe2bt0KnU4Hd3d3zJgxA/PmzUNQUFC515KYmIgFCxbA3t4evr6+GDx4MBo0aIDz589j3bp1yM3NRdu2bVGvXj3FOSt6/1hbWyM/Px85OTkAAAcHB7Rr1w7VqlVDcnIyXn75ZanL40/L16pVKzg5OeGXX35B8+bN8eKLL1bKby9V9lxKSkoQExODsLAwFBUVobi4GO+88w4cHBwQExODoqIitGzZEsCzb/Uw5jHwZ+rVq4cPP/wQZ8+eRe3atTF+/HgsWLAAZ86cAfD4NoQ7d+4oei8cOHAACxYswP79+2FtbY2xY8eiQ4cO+Nvf/obPP/8cPXv2xLJlyyrsj1Njedr5b8yYMdBqtahRowbGjx+Pt99+GwEBAWpPlVRiSTXSEuojYNo1kvWx/PURYI00BaZWHy36ypgQAjExMVi3bh18fX3RpEkTDBw4EL1794avry8APPcTDFn379/Hl19+iYyMDKxevRparRb79+9Hw4YNMXTo0HJ/CbP0RxWnTJmCwMBADBw4EPPnz4der0eTJk1gbW0Na2vrClmLvb09hBDYuHEjateujaCgILRo0QLXrl1Damoq2rVr99x74H+vovePjY0NkpKScPfuXdSsWRMeHh64ffs2UlNT4eLigho1aiAoKEjxCflp+dLS0nDv3j3o9XoAQKNGjSrlS6iVPReNRoPc3Fy4ubnh7NmzaNy4Mbp3747Dhw/Dy8sLo0aNUvQoa2MfA3/Gx8cHbdu2xddffw1PT0907doVkyZNQmJiInbt2qX4hzJLSkqQlpaGRYsWISMjA4mJiTh58iTatWuHVq1aoV69ehX6iaWxPO38N2jQILz66qtlP95LVZul1Uhzr4+AaddI1sfy10eANdIUmFp9tOhmTKPRoFatWggODsa7776LwMDAP3T9FX3S8PDwwMOHDyGEgF6vx9mzZ7Fz5058+OGHf/pkHdn8AQEBaNu2LcaNG4eQkBD07dsXy5YtQ79+/Qw6+f8Za2trNGnSBC4uLli+fDmqVauGF154AcHBwQgODka1atUMmn9F7h+NRoPatWvj0KFD2Lp1KxITE7F48WJ8/vnnePDgARwdHdGkSRPFr/Of5Zs8eTKysrLg5OSEJuQXvo0AACAASURBVE2aSM/TEGrMpUGDBqhZsyamTJmC7t27IykpCT///DNmz56t+FYbYx8Df8bV1RX16tVDhw4d8NVXX6Fnz55o1aoVBg4ciAEDBigqMsDjT9hDQkLQtWtX3L17FxkZGThw4AD0en3Zby6Z4hOhfk/J+Y+qNkurkeZeH0vXYKo1kvWx/PURYI00BaZWHzWiPI9GMjN6vR5ardbo49y7dw979uzB3r17UatWLQwfPrxCb/Modf78eQwaNAhz5sxBjx49KrTQPKmoqAiRkZEIDw/Hl19+iVdeeaVc+YyxfzIzMxEbG4s7d+7A19cX7u7umDx5MhYsWCB9m4gx8pVHZc+loKAAX375JQoLC3H9+nWDbuuprGPgz5w9exYTJkyARqPB2rVrDf6kq/TT4k2bNqF9+/bl+pHbipKTkwMhhPQnj5V1/iPzZUk10lzrI2D6NZL1sXz1EWCNNCZDaqTa9bFKNWOVrbi4GAAq7LaIp4mLi4Ojo6PR72stKirCjh070KZNm7LbV8rLGPvnwoULiIyMxJkzZzB9+vRyfzegovOZy1zS09ORlJQEb2/vcr3elXEM/JnMzEwAeO5vpTxLZXz3QUZRURHGjRuHb7/91uBP3olMhbHPD+ZcHwHTr5Gsj+WrjwBrZEUz1xrJZowUM7WD7ml0Oh3S0tJgZ2dXIZecKzqfpcyF1JOfn2/SjwomqorMoT4CFVtHTKkmmdJcSF3mWCPZjBEREREREanAoh9tT0REREREZKrYjBEREREREamAzdjvPHr0COHh4Xj06JFZ5reUMbiGqjOGJayhMsawhDWQebOU9yCPVfXzW8oYlrCGyhiDa3gOQf8jJSVFBAYGipSUFLPMbyljcA1VZwxLWENljGEJayDzZinvQR6r6ue3lDEsYQ2VMQbX8Gy8MkZERERERKQCNmNEREREREQqYDNGRERERESkAjZjv6PValG7dm1otVqzzG8pY3ANVWcMS1hDZYxhCWsg82Yp70Eeq+rnt5QxLGENlTEG1/Bs/NFnIiIiIiIiFVirPYHKMGzoR0hPv6c4fueuNXi1z1uK4339gqTms3jxFxg58hvF8XFxh6XyA0BU1D506NBDcfzdu0nSY1y7dgUNGgQqjheiRCr/9evXEBDQQHG8tVb+7Xz5SgJeCFT++hUWFUjlT0xMRN26dSVnJUd2DI1G/oL4zZs3UK9efcXxsq+1Ke4nKyv5T79u3LiO+vUDFMdrJd+zV69eRsOGLyiO9/FRPpdSR47sQufOfRTFent7YsOGCOkxyLT07/8m0tLuKIo9deow2rR5SSr/1z8skIp/tWVL7Dx7VnH8hL8qr9elLl++gBdeaKI4vqgoXyq/7DkTAKys5M7NsjVSry+Wym+K52VTHMMS1lAZY5jiGpyc3KTyX7gQiyZNWiiOr1WrFvbt2/3cuCrRjKWn30Na2l2pbWTi7R28ZKeEjIwHimNv3botnV92u9u35ZsxAEhKUr6d7B/osvmttTbS+R+Pkaw4tlCyID/Ob9i+NdYYhjRjsmMY+7U2lMwYhjRjsmPINmOy+UtKHKTzA0BqqmHnHDJPaWl3cOtWquJ4mVgAyCsqkp2S1DbJycrP4YZuV1iYJ51f9pwm24zJjiHbjMnmN5QljGEJa6iMMUxtDc7O1aXzJyenSG/zPPzOGBERERERkQrYjBEREREREamAzRgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqYDNGBERERERkQrYjBEREREREanALH70uaSkBDNmzEBsbCxyc3MhhMA333yD4OBgtadGRESkGtZHIiLzZhZXxmJjY5Geno7169dj165dCAsLw9KlS9WeFhERkapYH4mIzJtGCCHUnoQSN27cwMmTJ5GSkoLo6Gg4OTlh9erVak+LiIhIVayPRETmyyxuUzx06BCmT5+OYcOG4eWXX0b9+vWxfft2xdu/2uctpKXdVRx/5uw+tGrZQ3F8vfrNFccCwObN8zBgwP8pjo+J2SOVHwCSki7A37+J4vjbt69Jj6HTFcLGxk5xvBAlUvmLi3WwtrZRHG+tVR5bqqAwD/Z2jorjC4vypfILIaDRaGSnZdQxNBr5C+IlJXpYWWkl5iT3WpvifpJZbym9vhharfLTqkwsABQVFcDW1l5xvK9vkFR+ALh+/RwCAl5UFFu7di0cObJLegyqOOWtjwDQps1LuHUrVVHs7dvXUKtWA6n8321dKxU/sG1bbIyOVhz/TpfuUvkBID8/Gw4OLorjCwvzpPLLnjMBwMpK7twsWyP1+mKp/KZ4XjbFMSxhDZUxhimuwdm5ulT+7OxMuLi4K4738/PFhQuxz40zi2YsKioKXbt2xeDBg1FQUIClS5dCr9erPS0iIiJVsT4SEZk3s/jO2F//+lecOnUKffv2RVhYGHx9fXHr1i2UlMh9+k5ERGRJWB+JiMybWVwZCwgIQGRk5P/82xdffKHSbIiIiEwD6yMRkXkziytjRERERERElobNGBERERERkQrYjBEREREREamAzRgREREREZEK2IwRERERERGpgM0YERERERGRCtiMERERERERqcAsfmesvG6nXUVKyi2pbZJTLiqOrVPnBdkpQVdUqDj25V6DpPPLbrdyyRSDxiguLlIcq9HI9/4yP1xqbW8rnR8ArG2Ub1dYlG/QGKZECMN+DNbQ7cxVSYne6NsZ8sO8Op3yYy49PVk6v8x29vbCoPxkWnS6AhRJnNtkYgFg2VdLpOIH/tJWapsPx0+Vym/Idv/+9v+k8wshd3wYcj6Q2cbOzlE6v+w2hYV50mMQqUWvLzbqNnq9sr8HeGWMiIiIiIhIBWzGiIiIiIiIVMBmjIiIiIiISAVsxoiIiIiIiFTAZoyIiIiIiEgFbMaIiIiIiIhUwGaMiIiIiIhIBWzGiIiIiIiIVMBmjIiIiIiISAXWzwvo378/Pv30U4SGhuLnn3/G5MmTcfr0adjb2+Pzzz9HYGAg4uLikJCQAI1Gg06dOmHChAmwtrZGs2bNMGzYMBw/fhx5eXkYM2YM9uzZgytXrsDT0xOLFy+Go6MjNm3ahPXr10On0yErKwsffPABBg8ejC1btmDfvn2wsrJCUlIS7O3tMWvWLAQEBFTGviEiInom1kgiIiqP514Z69GjB44cOQIAOHr0KNzc3BATEwMhBA4fPowj/4+9+w6Mqkr/P/6ZTHoChFADhMSliIhYAKkqvX1FBFdFWPsqKl9/Kiq6iu6KqIiA2FgEaSorIrAoLAgIgvRiCQSMLmBCCwQIBEjPzP39wcJXV5R7hkzuZPJ+/UU553nOmfbMM/fO3K++UlxcnBYsWKC5c+fqhx9+0NSpUyVJRUVFql69uubMmaMbb7xRw4cP17PPPqtFixbp1KlTWr58uXJzc/XJJ59o0qRJmj9/vl5//XW99tprZ/Nv3rxZzz33nBYuXKjLL79ckyZN8tNNAQCAGWokAOBCnPfIWLdu3TR06FANGzZMW7Zs0V133aW1a9cqJiZG9evX144dO/Tcc8/J5XIpPDxcAwYM0IwZM3T//fdLknr06CFJql+/vho3bqxatWpJkurVq6ecnBzFxMRo4sSJWrVqldLT05WWlqa8vLyz+S+99FLVrl1bktS0aVMtW7bMeJPffrvReM6RI/uN55j4bMHbfo0vSVPf/atfxv6cZVk+zbPL6/X4Nb4knTp13K/x/X0bBUuOYNhDWeSwLK9f40vSyZPZfs8RLCpijfR3fZSkJUum+D3HGy8/5pexZ5TFc9XfNbKgINev8aVgeV0u/3soixzBsIe8vBOlHvO8zdjFF1+s4uJiLV++XMnJyerUqZMee+wxhYaGqkePHpowYYJcLtfZ8V6vVyUlJWf/HhYWds4/n3Hw4EHdeuutuuWWW9SiRQv17NlTX3755dn/j4yMPPtnl8vl04185ZWttXfvPtvjjxzZr+rV69oe365tP6P1fLbgbd3Q539tj69ep4ZRfOl0c3XP4Bdsj5826W/GOSzL+sV9fz4ul9lXFL1ej0JC3LbHR0dXMoovnW7EYmPjbI/Pzc0xim96G/kiGHIEwx58y2G2HsvyGj2PTB7bZ5w8ma1KleJtja1fP1Hbt6cY5wgmFa1GmtZHSWrRoqfR+CVLpqhHj3ttj2/SoplRfOl0c/XIM6/bHv/mK48bxTd9rkoyfn0yrZHh4ZHnH/QzBQW5ioyMMZpTWJh3/kE/E5ivy4EVP1hyBOIeoqLM3jfm5Z1QdHRl2+Pr16+vtLTU846z9UrRtWtXjR07Vu3bt1eDBg106tQpLViwQN27d1eHDh304YcfyrIsFRUVafbs2WrXrp3thaampio+Pl4PPfSQOnTocLbIeDz+PyICAMCFokYCAHxlqxnr1q2bdu/efbaAtGvXTjVq1FBCQoKGDx+u7Oxs9enTR3369NFFF12kBx54wPYC2rdvr1q1aqlnz57q1auXMjMzFR8fr4yMDN92BABAGaJGAgB85bLK4gROh3Ga4vlxmqI9nKZYPuMHbg5OU4TzOE3x/DhN0R5OUySHU/F9yVGuTlMEAAAAAJQumjEAAAAAcADNGAAAAAA4gGYMAAAAABxAMwYAAAAADqAZAwAAAAAH0IwBAAAAgAMqxHXGmjdvoT179toef/x4luLiatoeX1CQa7Qe02t3lJQUGcU/PadYoaFhtscPvPMvxjnenzJCd9z7vO3xn875u1H8nJzDqlLF/jXWTpw4YhRfMr8mRWRkrFH8/PyTRtex8HiKjeJLUlFRgdH1Y4qLC41zVMTrj5heA+x0DrNrCzVrdo1R/G3bVumyy66zPf7IEfvXVzwjM3OXEhIa2Bpbr15dbd78lXEOBJamTZtrz549tsaaXptRktwhoUbjc04cUZXK1W2PP3nqmFF8yfwaXa+9P9so/uN/ukljP5xrNOet4X81Gp+enqrkZPvXWMvI2G4U35fXZZPbVJI8nhK53fYfH16vxyi+VFHrV+DlCMTHk91rap5h2h/Ur5+orVu/Pu84jowBAAAAgANoxgAAAADAATRjAAAAAOAAmjEAAAAAcADNGAAAAAA4gGYMAAAAABxAMwYAAAAADqAZAwAAAAAHBEwz9sYbb2j+/PmSpLfffltffPGFJOnpp5/WlClTnFwaAACOoT4CQPCyf5lqP3vkkUfO/nnjxo1q2LChg6sBACAwUB8BIHiV2ZGxvn37av369ZKkhQsX6rLLLlNBQYEk6dlnn1W7du00ZcoUzZw5U6mpqRo9erSWLVsmSfr22281YMAAde3aVQ8++KDy8vLKatkAAPgV9REAKq4ya8a6deumr776SpK0evVqValSRVu2bJFlWVq1apUuueQSSdKgQYPUrFkzDRs2TN26dZMkHTp0SNOmTdOSJUt06NAhLV26tKyWDQCAX1EfAaDiKrPTFLt166ahQ4dq2LBh2rJli+666y6tXbtWMTExql+/vmrUqPGbc7t27aqoqChJUqNGjZSdnW2Ue+vWr43Xe/x4lvEcEwUFuX6NL0klJcV+z/H+lBH2B5uM/Y+cnMPGc0xZluXX+Pn5J/0aX5KKigr8nsPft5O/45ddDq9f42/btsqv8SUpM3OX33Pg/zhZHyVpx46tRuNPnTpunMNUzokjfs/h9Xr8Gv/xP93k1/GSlJ6eajzHRFm8Zno8JX7PQf0KjBzB8HjyR39QZs3YxRdfrOLiYi1fvlzJycnq1KmTHnvsMYWGhqpHjx76/vvvf3uRof+3TJfLZXxnNm/eQnv27LU9/vjxLMXF1bQ93rSxKijIVWRkjO3xJSVFRvFPzylWaGiY7fED7/yLcY73p4zQHfc+b3v8p3P+bhQ/J+ewqlT57Tch/+2ED8Xbsiy5XC7b4yMjY43i5+efVFRUJdvjPR7zBrqoqEDh4ZG2xxcXFxrnML2dAi2+bznM12NZXrlc9k84aNbsGqP427at0mWXXWd7/JEj+4ziS6cbsYSEBrbG1qtXV5s3f2WcA7/kZH2UpKZNm2vPnj22xp46dVyxsXFG8d0hZm81ck4cUZXK1W2PP3nqmFF86XQjFhLitj3+tfdnG8V//E83aeyHc43mvDX8r0bj09NTlZzczPb4jIztRvF9eV02uU2l02+c3W77jw9fGuiKWb8CL0cgPp4qVYo3im/aH9Svn2jrgFCZ/ppi165dNXbsWLVv314NGjTQqVOntGDBAnXv3v0X49xut0pK/P9JCQAAgYD6CAAVU5k2Y926ddPu3bvVrl07SVK7du1Uo0YNJSQk/GJc586dNW7cOP3zn/8sy+UBAOAI6iMAVExl+tP2V155pX744Yezfx85cuTZP48aNersn++44w7dcccdkqR+/fr9IsbPxwEAEAyojwBQMQXMRZ8BAAAAoCKhGQMAAAAAB9CMAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOMBlWZbl9CL8LTk5WRkZGbbHW5Yll8vlt/X4O36w5DCNXxYPZdP9BsP9UBY5gmEPZZEj0PaQlJSk9PR0v64H/teo0cW2a2RRUYHCwyON4leuXM1o/JEj+1W9el3b448dO2QUX5I8nhK53fYvtRoTU8Uo/okTR433fUO/B4zGfzjjJf3pzmdtj88/VWAUf+7csbrppseN5syb97rReMvyyuUyOS5gXucr2utyoOYIxD2YvpYVFuYrIiLK9vikpPr68ccfzjuOI2MAAAAA4ACaMQAAAABwAM0YAAAAADiAZgwAAAAAHEAzBgAAAAAOoBkDAAAAAAfQjAEAAACAA2jGAAAAAMABNGMAAAAA4ACaMQAAAABwQKjTC7DD6/Xq5ZdfVkpKinJzc2VZlkaOHKkWLVo4vTQAABxDfQSA8s1lWZbl9CLO59tvv9W0adM0fvx4hYSEaNKkSfrmm280ceJEp5cGAIBjqI8AUL6ViyNjV155papUqaJZs2Zp79692rhxo2JiYmzPT05OVkZGhu3xlmXJ5XL5stSAiB8sOUzjl8XnCqb7DYb7oSxyBMMeyiJHoO0hKSlJ6enpfl0Pft+F1kdJatToYts1sqioQOHhkUbxK1euZjT+yJH9ql69ru3xx44dMoovSR5Pidxu+2+BYmKqGMU/ceKo8b5v6PeA0fgPZ7ykP935rO3x+acKjOLPnTtWN930uNGcefNeNxpvWV65XCbfmDGv8xXtdTlQcwTiHkxfywoL8xUREWV7fFJSff344w/nHVcuvjO2cuVKDR48WJLUpUsX3XbbbQ6vCAAA51EfAaB8KxdHxtauXatOnTpp4MCBKigo0OTJk+XxeJxeFgAAjqI+AkD5Vi6OjA0YMECbNm1Snz591K9fPyUmJmrfvn3yer1OLw0AAMdQHwGgfCsXR8YaNGig+fPn/+Lfhg8f7tBqAAAIDNRHACjfysWRMQAAAAAINjRjAAAAAOAAmjEAAAAAcADNGAAAAAA4gGYMAAAAABxAMwYAAAAADqAZAwAAAAAHuCzLspxehL8lJycrIyPD9njLsuRyufy2Hn/HD9wcZuuxLK9cLvufF5R4SoziS5I7JEQeg4ujhrrdRvED834IvBzBsIeyyBFoe0hKSlJ6erpf1wP/M6mRgfYYPM18Pab1RTJ7q+Tb7eTfGllYXGQUPzw0VEUlZnU1IizMaHxgPp4CK36w5KiIe7BbIzkyBgAAAAAOoBkDAAAAAAfQjAEAAACAA2jGAAAAAMABNGMAAAAA4ACaMQAAAABwAM0YAAAAADjAL83Yxo0bdf311/sjNAAA5Ro1EgBwBkfGAAAAAMABof4M/tNPP2nEiBHKzc3V4cOH1aRJE40fP14RERFq1qyZunTporS0NI0ZM0bZ2dkaM2aMQkJCdMkll2jdunX6xz/+oXr16umTTz7RRx99JK/Xq7i4OD333HNq0KCBP5cOAIBfUSMBAH49MjZ79mzdeOONmj17tpYuXap9+/Zp5cqVkqTi4mJ16tRJS5YsUb169TRs2DC99tpr+vTTT9W6dWsdOnRIkrRp0ybNnz9fM2fO1Pz58/XnP/9Z//u//+vPZQMA4HfUSACAX4+MPfnkk1q7dq0mT56s9PR0ZWVlKS8v7+z/t2zZUpK0ZcsWNWjQQE2aNJEk9evXTyNHjpQkrVy5UhkZGRowYMDZeSdOnNDx48cVFxdnax3p6enGa7csy3hOIMUPlhyW5fVrfElyh9j/TMKX/QbD/VAWOYJhD2WRIxj2gNPKa40Mlsegv+tLMOwhPNTsbWJFrZHBsIeyyMEezs2vzdjQoUPl8XjUq1cvdezYUZmZmb/YRHR0tCTJ7Xb/anMh/3mD7PV61bdvXz355JNn/56VlaUqVarYXkdycrIyMjJsj7csSy6Xy/Z4U/6OH7g5zNZjWV65XPYbpRJPiVF86XQj5vHaL2ahbrdR/MC8HwIvRzDsoSxyBNoekpKSfPqwC6eVxxoZaI/B08zXY1pfJLM3YL7dTv6tkYXFRUbxw0NDVVRiVlcjwsKMxgfm4ymw4gdLjoq4B7s10q+nKa5Zs0ZDhgxR7969JUkpKSnyeDy/GnfVVVcpPT1daWlpkqQlS5boxIkTcrlc6tChg/71r38pKytLkvTRRx/pzjvv9OeyAQDwO2okAMCvR8Yee+wxDRkyRNHR0YqNjVWrVq20Z8+eX42Li4vTuHHj9NRTTykkJETNmjVTaGiooqKi1KFDB913332655575HK5FBsbq7ffftvv3TUAAP5EjQQAuKwA+HLAqVOnNGHCBD388MOKiorS9u3bNXjwYK1evbpUCgqnKQZKDk5T9IdgyBEMeyiLHIG2B05TLBuBVCMD7TF4Gqcp2sFpiuUzfrDkqIh7sFsj/XpkzK7Y2FiFhYXpj3/8o0JDQxUaGqrx48fzyR4AoMKjRgJA8AqII2P+xpGxQMnBkTF/CIYcwbCHssgRaHvgyFhw4MiYrRmG8TkyZkdgPp4CK36w5KiIewiIH/AAAAAAAJwbzRgAAAAAOIBmDAAAAAAcQDMGAAAAAA6gGQMAAAAABwTET9vDeRHhUX6fF1upqnH8atUSbI+t7EP83Nwco3lfpaUZ5zCZc22TJsbxUZ758qtPJnOC/sdygTIRHV3Z73Pi4moa56hTp4HtsVUMa2R+/knjOf/cssVovOmcfi1bGsdHeebvGumL0v+lV46MAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOIBmDAAAAAAcQDMGAAAAAA6gGQMAAAAAB9CMAQAAAIADaMYAAAAAwAGhpRVo48aNGjNmjOrUqaPdu3crMjJSo0aN0uTJk9WoUSPde++9kqSnn3767N87d+6s5s2b64cfftDQoUN1+PBhzZo1S2FhYYqIiNCIESPUsGFDHTp0SCNGjFBmZqaKi4v1P//zP3rggQdKa+kAAPgN9REA8FtKrRmTpNTUVD311FNq2bKlPvroIz355JNq3Ljx785p1KiRxo8fL4/Ho8svv1wrVqxQzZo1NX/+fH399ddq2LChnnzySd11113q3LmzCgsLdd9996l+/frq3bt3aS4fAAC/oD4CAM6lVJuxJk2aqGXLlpKkm266SSNGjFDNmjV/d86Z8W63Wz179tSAAQPUsWNHdejQQdddd53y8vK0efNm5eTk6I033pAk5eXlKS0tzXaxSU9PN96LZVnGcwIpflnlKCjM82v8I0f2+zW+JOXm5vg1/jUXX2x7rK/3WTA8noJhD2WRw7K8fo1/Oof/b6eKJlDro2ReI4PheXQ6h3+fS/6uLZK0f/+//Ro/P/+kX+NL0o0tWtgeG6g1MnieE8FwO/n3ee2P+KXajLnd7l/9W9WqVX9x4xcXF//i/6Ojo8/+ecyYMfrxxx+1bt06TZo0SZ9++qleeuklWZalWbNmKSoqSpKUnZ2tiIgI2+tKTk5WRkaG7fGWZcnlctkeb8rf8X3JEREeZZyjoDBPkRHR5x/4H7GVqhrFP3Jkv6pXr2t7fH7+KaP40uliGRNTxfb4z7/ZZBT/mosv1uoffrA9/tomTYziS4H5eAq0+IGbw2w9luWVy2XyVV/zwmeyh6SkJJ8+7KqIArU+SmY1MhieR6dzmD2XoqMrGcU3rS2SFBf3+835f9u//9+qW7eR7fHZ2QeN4ufnn1RUlNm+P1qz0mj8jS1aaP7XX9se3+8/H1CYoH4FRg7f4vu7RpoxjX+6Rv503nGluuK0tDSlpaVJkj7++GNdeeWVqlq1qlJTUyVJhw4d0qZN534zm52dreuuu05xcXG666679Oijj2rbtm2KjY3VFVdcoWnTpkmSTpw4odtuu03Lly8vzaUDAOA31EcAwLmU6pGx6tWra/z48dq/f7/i4+M1evRoud1uPfHEE+rRo4fq1aunNm3anHNufHy8HnzwQd11112KjIyU2+3WyJEjJZ3+RPDFF19Unz59VFRUpOuvv1433HBDaS4dAAC/oT4CAM6lVJux2NhYTZw48Vf/PnPmzHOOX7FixS/+PmDAAA0YMOBX4+rVq6d33323dBYJAEAZoz4CAM6F64wBAAAAgANKrRlr3bq1Fi5cWFrhAAAICtRHAMBv4cgYAAAAADiAZgwAAAAAHEAzBgAAAAAOoBkDAAAAAAfQjAEAAACAA0r1OmMov4pLivw+7+jRA8bxfZljKi/vhO2xt3XpYxR7374fjeZ8snGjUXxf5t3curVPOVD6atas79c5WVkZxvGB8sfy+zyTOuHrHF9yHDiw03iOiYKCU0bjb2nb3mh8UVGB0ZycvDyj+L7MqxId7VOOisbtNm8hTOd4PCXGOXx/PfBHfHtjOTIGAAAAAA6gGQMAAAAAB9CMAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOIBmDAAAAAAc4Ldm7OKLL1Z2drbmzZunwYMH/+r/ly9frpEjR/orPQAAgro6MQAAIABJREFUAYsaCQCQJPPLZ5eSLl26qEuXLk6lBwAgYFEjAaBi+N0jY3379tX69eslSQsXLtRll12mgoICSdKzzz6rGTNm6O6779Ytt9yiTp066cEHH1RhYeFvxvv888/VtWtX7d69+xefBt5+++0aO3asBg0apM6dO+vZZ5+V1+uVJM2bN089e/bUjTfeqFGjRqlp06alsnEAAC4ENRIAcKF+98hYt27d9NVXX6lt27ZavXq1qlSpoi1btqh9+/ZatWqVXC6XbrzxRvXt21fFxcXq37+/Vq5cqR49evwq1sKFCzVx4kR98MEHSkhI0HffffeL/9+zZ48++OAD5eXlqVevXtq0aZOqV6+uMWPGaN68eapdu7befvtteTwe402mp6cbz7Esy3hOIMUvqxweT4lf4wfD7bRv349+jS9Jf7z6attjfd0vz4nAyHHoULpf40tlczsFg4paI4PheVQWOdiDPUVFBX7PUTkqyvZYX/YcDPd1WeQoKSn2a3ypfD4nztuMDR06VMOGDdOWLVt01113ae3atYqJiVH9+vU1YsQIrV27VpMnT1Z6erqysrKUl5f3qzjbtm3T6tWr9cwzzyghIeGcuTp16qSQkBDFxsYqKSlJOTk5SktLU/v27VW7dm1J0p/+9Ce99dZbxptMTk5WRkaG7fGWZcnlchnnCZT4vuQICXEb5/B4SuR22z/T1es1e5MQiLdT3bqNjOLv2/ej6tVrbHv8+HkfGsWXTjdiczZtsj3+5tatjXNUxOdEWeSoWTPJKP6hQ+mqVSvZ9visLPuve2eY7CEpKcmnD7uCRUWskYH4PArEHBV1D2FhEUbji4oKFB4eaXv8kZxjRvGl043Yifx82+OrREcbxQ+G+9qXHCbv/6TTjVhoaJjRHNMP/APtOWG3Rv7uLXnxxReruLhYy5cvV3Jysjp16qTHHntMoaGh6tGjh4YOHSqPx6NevXqpY8eOyszMPGfHWKlSJY0dO1aPPvqoOnbsqHr16v1qTGTk/z0ZXS6XLMuS2+3+RTy327xhAADAH6iRAIALdd5fU+zatavGjh2r9u3bq0GDBjp16pQWLFig7t27a82aNRoyZIh69+4tSUpJSTnnKRLJyclq27atbr/9dj311FNnz3U/nw4dOmj9+vU6dOiQJOmTTz4x2RsAAH5FjQQAXIjzHmPs1q2bpkyZonbt2kmS2rVrpx9++EEJCQl67LHHNGTIEEVHRys2NlatWrXSnj17fjPWAw88oBUrVui9995T9erVz7u4iy66SH/5y1907733Kjw8XJdccomiDM77BQDAn6iRAIAL4bIC+Jvae/fu1aeffqqHHnpIISEhWrp0qSZPnmz86R/fGTs/vjNmD98Z849AvK/LIgffGcOFcKJGBuLzKBBzVNQ98J0x/wjEHHxn7PxK5TtjTqtdu7aysrLUp08fud1uVapUSS+//LLTywIAwHHUSAAo/wK6GQsLC9OIESOcXgYAAAGHGgkA5d95f8ADAAAAAFD6aMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOCCgf00RZcf0GmAXOq+82r//336dc0ubtsbxvV6P0TxfrilnOi8YHhdFJWbXN/FlXnio+UuwL9cOA4CyUFxc6Nc5ptcAk05fG8qXefh9JSXFfp/j72urBQqOjAEAAACAA2jGAAAAAMABNGMAAAAA4ACaMQAAAABwAM0YAAAAADiAZgwAAAAAHEAzBgAAAAAOKHfN2Ntvv60vvvjC6WUAABBwqJEAUL6Uu2Zs48aNKvHxYqwAAAQzaiQAlC+hZZFk48aNGjNmjOrUqaPdu3crMjJSo0aNUkhIiEaMGKHc3FwdPnxYTZo00fjx4xUREaE333xTy5YtU1hYmKpWrapXXnlFy5YtU2pqqkaPHi23261u3bqVxfIBAPAbaiQAVFxl0oxJUmpqqp566im1bNlSH330kZ588km1bt1aN954o/r27avi4mL1799fK1euVPPmzTVjxgytX79e4eHhmjp1qrZu3apBgwbp888/16BBgygyAICgQY0EgIqpzJqxJk2aqGXLlpKkm266SSNGjNCUKVOUmpqqyZMnKz09XVlZWcrLy1OtWrXUpEkT9evXT9dee62uvfZatW3b1ufc6enpxnMsy/I5XyDED5Yc7MEer9fj9xwej39PfQqG+1qSwtxu22N9WU+w3E74pfJUI4PlMRgMr/3sITByBMMeyiqHiUCskf6IX2bNmPscb1CeeOIJRUdHq1evXurYsaMyMzNlWZZCQkL04Ycfatu2bVq/fr1efvllXXPNNRo2bJhPuZOTk5WRkWF7vGVZcrlcPuUKhPjBkqMi7sHlMv8ap9frUUiI/QbAl/16PCVyu+2/XJg2h4F4Xxf58L2bMLdbxR77ew8PNXsJDrTbKSkpyacPu/Br5aVGBtpjMFBzsIeKkyMY9uBLjrJo3Ez3HGj3td0aWWY/4JGWlqa0tDRJ0scff6wrr7xSKSkpGjJkiHr37i1JSklJkcfjUVpamq6//no1aNBAgwcP1l133aVt27ZJOl2w+HIyACCYUCMBoGIqsyNj1atX1/jx47V//37Fx8dr9OjRWrVqlYYMGaLo6GjFxsaqVatW2rNnj26++Wb16tVLN910k6KjoxUZGanhw4dLkjp37qxx48apuLhY/fr1K6vlAwDgN9RIAKiYXFYZHGfcuHGjXnzxRS1cuNDfqc6J0xTLZ46KuAdOU/QfTlMs/Rycplg6ylONDLTHYKDmYA8VJ0cw7MGXHJymeH4Bd5oiAAAAAOD/lEkz1rp1a8c+8QMAIJBRIwGg4uLIGAAAAAA4gGYMAAAAABxAMwYAAAAADqAZAwAAAAAH0IwBAAAAgAPK7KLPgP/5em0Jk3n+va6GZXn9Pq9q1QSfcsTF1bQ91us130dcXC3bY0NCfPscKT7e/t7nbt5kHH9Am7Y+zQMAnJ/JNTV9nde9+z3G8Xv2vM9ofHFxgXGOLl1utz12zeo5xvElKSI8yvbYHj3uNYq9ZMkU4zm+va8LnPd0dnFkDAAAAAAcQDMGAAAAAA6gGQMAAAAAB9CMAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOIBmDAAAAAAcQDMGAAAAAA4IdXoBdni9Xr388stKSUlRbm6uLMvSyJEj1aJFC6eXBgCAY6iPAFC+lYsjYykpKcrKytLHH3+sRYsWqV+/fpo8ebLTywIAwFHURwAo31yWZVlOL8KO3bt3a8OGDdq7d682btyomJgYffDBB04vCwAAR1EfAaD8KhenKa5cuVIvvfSS7r77bnXp0kV/+MMf9Nlnn9men5ycrIyMDNvjLcuSy+XyZakBET9YcpjHN1+LZXnlcpkcIDb77CIQ74f4+ATjHEePHlC1anVsj/d6vUbxjx07qKpVa9seHxJiflDfdA/v/GuucY4Bbdpq1ob1tsff1radUfxAezwlJSUpPT3dr+vB77vQ+iiZ1chAewwGag724J8cISFu4xweT4ncbvtvd7t3v8co/uLFk9Sr1/1Gc4qLC4zGf/HF++ra9Q7b49esnmMUX5IKCvMUGRFte/x1HW8zir9kyRT16HGv0ZylS6cZjQ+093R2a2S5aMbWrl2rTp06aeDAgSooKNDkyZPl8XicXhYAAI6iPgJA+VYuvjM2YMAAbdq0SX369FG/fv2UmJioffv2GX/6DgBAMKE+AkD5Vi6OjDVo0EDz58//xb8NHz7codUAABAYqI8AUL6ViyNjAAAAABBsaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOIBmDAAAAAAcQDMGAAAAAA4oF9cZA+yxynhe+ZSdnen3ebGxVY3jl5QU2R675+Be4/iStHPPv22PjY+NNY4/wLJ0W9t2xvMAAOfn9Xr8Pm/ZsmmG0ScZz2natL1hDikra4/tsZ9sWGMc33TeDVe1MIw+RUuXTjWc44vy956OI2MAAAAA4ACaMQAAAABwAM0YAAAAADiAZgwAAAAAHEAzBgAAAAAOoBkDAAAAAAfQjAEAAACAAwKyGXv77bf1xRdfOL0MAAACCvURAIJLQDZjGzduVElJidPLAAAgoFAfASC4hJZFkkmTJmnOnDmKiYlRy5YttXz5ck2ZMkUjRoxQbm6uDh8+rCZNmmj8+PGaM2eOUlNTNXr0aLndbl133XUaM2aMNm/eLI/Ho6ZNm2r48OGKjY0ti6UDAOA31EcAqNj8fmRs9erVmjdvnubMmaN58+YpNzdXkjR79mzdeOONmj17tpYuXap9+/Zp5cqVGjRokJo1a6Zhw4apW7dumjRpktxut+bNm6fPPvtMNWvW1JgxY/y9bAAA/Ir6CADw+5GxVatWqWfPnqpcubIkadCgQdqwYYOefPJJrV27VpMnT1Z6erqysrKUl5f3q/krV67UyZMntW7dOklScXGxqlWrZrSG9PR043VblmU8J5DiB0sO9lBxcpw8me3X+JJUNSbG9lhf98vjCXYFQn2UzGtksDwGea46Hz9YcpSUFPs1viRt3brS7zn6XHmV7bG+3KbBcF/7I77fm7HQ0NBfLNztdkuShg4dKo/Ho169eqljx47KzMw85wa9Xq+eeeYZXXfddZKk3NxcFRYWGq0hOTlZGRkZtsdbliWXy2WUw4S/4wdLDvZQfnPExlY1in/yZLYqVYq3PX7Pwb1G8aXTjdix/xx5sCPeh1O9KtrjKSkpyacPu3BaINRHyaxGBtpjMFBzsIfym8PtNntrXFJSrNDQMKM5TZu2Nxq/detKNW/e0fb4l2aMM4ovnW7EFnz7je3xN1zVwih+IN7X/o5vt0b6/TTF6667TkuXLtXJkyclSXPmzJEkrVmzRkOGDFHv3r0lSSkpKfJ4PJJOF6QzX1Du0KGDZs6cqaKiInm9Xj333HMaN878QQYAQCChPgIA/H5krG3btrrlllt06623KjIyUo0aNVJUVJTuvfdeDRkyRNHR0YqNjVWrVq20Z88eSVLnzp01btw4FRcX66GHHtKrr76qfv36yePx6JJLLtHTTz/t72UDAOBX1EcAgN+bsW3btsntdmvRokWSpGnTpqmwsFCDBg3SoEGDzjnnjjvu0B133HH273/961/9vUwAAMoU9REA4Pdm7KKLLtLkyZM1e/ZsuVwuJSQk6MUXX/R3WgAAAhr1EQDg92YsNjZWb775pr/TAABQrlAfAQB+/wEPAAAAAMCv0YwBAAAAgANoxgAAAADAATRjAAAAAOAAmjEAAAAAcADNGAAAAAA4wO8/bR8IXK4QuVxmfafJeJfLZbokhYS4jef4M4fX6/HjShBIevS41+/zqlarYRz/+hsG2x7bvePNxvE3b15kOM/8eX1h8wIlvkmOslgL/M20RprXU//WSK/Xaxz/NJN1WT7mQHlz6aUd/D7n0uZtjXM0u7yd7bF/f/Zd4/h9Fr1rOM+X13+zOb68dpi8PlmWL89rkzXZG8uRMQAAAABwAM0YAAAAADiAZgwAAAAAHEAzBgAAAAAOoBkDAAAAAAfQjAEAAACAA2jGAAAAAMABNGMAAAAA4ACaMQAAAABwAM0YAAAAADiAZgwAAAAAHOCyLMtyehEAAAAAUNGEOr2AsnDRRX9QRkaG7fFer0chIW7b410ul9F6PJ4Sud3+velNc3i9HuMclmUZ7z2Q4pdFjkDcQ48e9xrn+Pzz99Sz559tj69arYZR/I9mvqLbBv3F9vidP6YYxZekzZsXqVWr3rbHb9nyuXEOy/LK5fLfCQf+jm+aIykpSenpP/l1PfA/kxppWh8l/9dIr9drFF/y5blk9rl1IL72B1r8QM3RvHlHo/gpKV/q8ss7Gc25tHlbo/H/+OBlDbz9Gdvjjx89ahRfkhYtele9ew+2PX7x4slG8X2pX6aPDdPXJ9PjUaZ7sFsjOU0RAAAAABxQLpux++67T8uXL3d6GQAABBTqIwCUL+XyNMXJk80OjQIAUBFQHwGgfCmXR8YAAAAAoLyjGQMAAAAAB9CMAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOKBcXmfMVK1aSSouNttqnToNbI+tWTPZcEXS5Zd3tj02O/uAcXxJSkxsYnvswczdPuWICI+yPdYV4jaOHxkZa3usZXmN40tSRES07bGFhXk+5bDL5fLt8xGTeampX/mUw2ReTs4Rw+ivaOFn79oenZ9/0jD+ad9+u8z22MqVq/mUw2ReUVGBcfzIyBjbY2Nj44zjS1L16nVtjYuPr+1TfASW+Kq1lXuqxPb46tXsPT7OqFQ53nRJSk5uZnvsgQO7jONLZs+l4mLz56rbbfa+I8SHGhkWFmF7bHFxoXH8QFMWNbJqXC3j+KZzdmzbYJzDZE5a2kbj+NK7WrHiQ9ujw8PtP/Z8neP1mr+vM3nexcZWNY4fF1fT9tjKlavbGseRMQAAAABwAM0YAAAAADiAZgwAAAAAHEAzBgAAAAAOoBkDAAAAAAfQjAEAAACAA2jGAAAAAMABNGMAAAAA4ACaMQAAAABwAM0YAAAAADiAZgwAAAAAHOCyLMtyehEAAAAAUNGEOr2AstCmTSft27ff9vh9+35UvXqNbY+vWTPZaD3ffLNUV13V3fb47OwDRvElKT09VcnJzWyPP5i52zhHQWGeIiOibY93hbiN4ufnn1RUVCXb4y3LaxRfkgoKchUZGWN7fGFhnlF8y7Lkcrlsj3e5zA9We70ehRjctnXqNDDOYfqcyMk5YhT/5MlsVaoUb3t8fv5Jo/iSVFJSrNDQMNvjY2LijHPk5BxWlSo1bI8vKiowim/6nIiNNd/D4cN7VaNGoq2xiYn19M03641zILC0uKqd9u7dZ2ts1uE9qlmjvlH8SpXtP7cladeu79SgwRW2xx84sMsovmT+XCouNnuumr7eSDJ6HZdOv36Eh0faHl9cXGgU37R++SIQa+S119xsFH/lqlnqeN0AoznHc7KMxn/33QpdcUVn2+PT0jYaxZfM3w+ZvucqLMxXRESU0Ryv1yxHcXGhwsIibI+Pja1qFP/YsYOqWrW27fGJiYnaunXzecdxmiIAAAAAOIBmDAAAAAAcQDMGAAAAAA6gGQMAAAAAB9CMAQAAAIADaMYAAAAAwAE0YwAAAADgAJoxAAAAAHAAzRgAAAAAOIBmDAAAAAAcEOr0AspCQkIt4zn16tW1PbZaNfP4JmuKjjYOL0mqV6+O7bER4SU+5UhKqm9/sMttHL9+fYP48hrHl8z2UFiY70P8JNtjXS7fPh8xyVGrlv3H9s+ZPCeqVIkyjl+/fqLtsQUFucbxJbPbKTq6sk85TPZRVFToQ3z7j9eYGN/2kJhYz9a4hITaPsVHYDG9H+0+Ps6IiY0zGi9Jdevar1+hocXG8SWz51JJiflz1eT1RpJCQsxrpEmO4mL/78EXgVYja9euYRzfdE50jMs4R5069t835uebvHf6PybvhyzL/D2X0XtGSV6v5UMOkzpfxTh+YqL9Gl+nToKtcS7Lssx3CgAAAAC4IJym+F8yMzPVuXNnZWZmlsv4wZKDPVScHMGwh7LIEQx7QPkWLI9BnqvOxw+WHMGwh7LIwR5+H83Yf/F4PNq/f788Hk+5jB8sOdhDxckRDHsoixzBsAeUb8HyGOS56nz8YMkRDHsoixzs4ffRjAEAAACAA2jGAAAAAMABNGMAAAAA4AD33/72t785vYhAExERodatWysiIqJcxg+WHOyh4uQIhj2URY5g2APKt2B5DPJcdT5+sOQIhj2URQ728Nv4aXsAAAAAcACnKQLlAJ+ZAADwa9RHlHccGQMCiGVZcrlc2rJli7Kzs3X48GENGjTI6WUBAOAo6iOCFUfGgADicrm0YsUKvfTSSyosLNSLL76oN9544+z/89kJAKAioj4iWNGMAQHkwIEDeu+99/Tee+8pPDxcLVu2VP/+/fWvf/1L0uliBABARUN9RLCiGQMCSGFhoerWratly5ZpypQpeuWVVxQZGakJEyYoNzfX6eUBAOAI6iOCFc0Y4KAzp1Xk5ORIkhISEpSTk6Np06bplVdeUWJiolJSUlSpUiVOwQAAVBjUR1QUXGcMcMiZLyOvWrVKEyZMUEpKijp06KDc3FyVlJTom2++UXZ2tiZOnKhHHnlEjRs3dnrJAAD4HfURFQm/pgg4aM2aNRo3bpxefPFFZWZmqk2bNjpy5Ij+/e9/KzU1VZJ09dVXq3379meLEwAAwY76iIqCZgwoYz8vGs8//7yaNWumdu3aadasWfryyy8VERGhoUOHqkOHDvJ4PHK73Q6vGAAA/6M+oiLiO2NAGTpTaL788ktNnz5dSUlJ+vjjj3X33XerSpUqmjp1qq688kr99NNPkqSQEJ6iAIDgR31ERRXq9AKAisTlcum7777TW2+9pVdffVXVqlVTmzZtlJCQoNDQUJ04cULfffedevfufXY8AADBjvqIiopmDDiHM5/Q7dmzR3l5eWrSpInPsXJychQTE6PQ0FBlZ2dr5syZys/PV6NGjSRJ8fHxWrRokT799FPt3btXQ4cOVYsWLUprK3AY32UAEGxKq0ZSH0GN5NcUgXNyuVxavny5nn76ac2fP1+7du1SixYtFBkZaRSnsLBQQ4cOVcuWLVW5cmWFhYUpKipKqampSk1N1bXXXiuXyyXLsnTttdeqe/fuatWqFS9OQeTw4cOKiYlxehkAUGpKo0ZSHyFRIyV+wAM4p71792r8+PG67777FBcXpwceeEBXXXWVHn30UVWuXNlWjF27dunQoUNq166ddu7cqTVr1ujEiRPq2bOndu/erRUrVig+Pl5PPfUUheUcgqHgZmZm6pVXXtGoUaMUHR3t9HIumNfr/cX3NILhPgJg7kJrJPXxwgXD628w1cgLqY98+9GA1+uVJJWUlDi8EvjTjz/+qPvuu09Vq1ZVkyZNVLt2bb3zzjv67rvvNGrUqLMXoDyfnJwceTwebdiwQY888ogiIyO1ceNGTZs2TQUFBerVq5d27dqll156yc87Kn9+/iK2f/9+HTx40OEV+aZq1ap6+eWXlZKSovnz5zu9nAsWEhKiU6dOadu2bZL4zgZ+iRpZMZRGjaQ+XhhqZOC5kPrIaYoG8vPzdfjwYb311luKjY1VQkKC00uCH1SrVk27d+/WV199pVatWik+Pl5VqlRRu3btNGXKFF1zzTWqVq3aeeMkJCQoISFBw4cP1+OPP65evXqpf//+2rp1q9avX697771X8fHxatmypWrWrFkGOys/zryITZkyRaNHj9bixYvldrt16aWXOrwye84UytDQUIWHh2vFihWaOnWqqlWrdva7EOXNli1btGPHDr344otat26dmjVrZut5gIqDGlkxlEaNpD5eGGpkYLnQ+sgPeNj0ySefaOfOncrMzNTSpUtVrVo1XXXVVU4vq0Lx1yH5M3G/++477du3TzExMXr++ec1cuRIvfDCC3rhhRfUsGFDJSYmat68eYqIiLAdMzc3V/n5+UpMTJR0+gV0yJAhuvnmm3Xy5El16tSp1PdTnv38Pv7pp5+Ulpam8ePH6/vvv9eoUaMkSTfffLOTSzyvn+/h8OHDioiIUL9+/VSlShX9/e9/l6SzvwZWHmzevFkbNmzQmjVr1KtXL3k8HtWsWVONGzd2emkIINRI55WXGkl99B01MrCUVn3kyNh5bNiwQTNnztSSJUs0cOBA1axZU3FxcXr44YcVFhbm9PIqjJ8/eY8ePVqq5xa7XC6tWLFCr732mmrVqqUJEybo5MmTeuKJJ/T9999r6tSpatmypapXry63222r2J08eVIRERGKiopSWlqavvzyS1177bUKCwtTamqqvvrqK/Xp00exsbGlto/y7uf38RdffKGFCxeqVq1a6t27ty6++GLVrFlT48aNU0REhJo3b+7wan/bmT1MmzZN//jHPzR79mwdOHBA7du3V+3atTV9+nRFRkZe0C90lqWVK1fq4MGDevLJJ3Xdddfp+PHj6tSpkxITE+X1ejlVsYKjRgaG8lQjqY++oUYGntKqjxwZO4/GjRsrPDxcf/7zn1WtWjUtWLBAlStXVmho6K++rAf/OfOA/vDDD5WamqoXXnjB1hEqO3766SdNnTpV06dP19q1a1WnTh317dtX69ev13PPPadhw4YpLy/vF+v4b9nZ2Tp27JgaNGigVatWaebMmQoPD1fDhg3VqlUrpaSkqF+/frrllls0d+5cDR06VLVq1SqV9QeLM7ftxx9/rI8//lhVq1bV2rVr1b17d1188cXq06ePioqK9O677+qGG25QbGxswDYCs2bN0qpVqzR9+nTdd999On78uJKTk1WnTh0VFhZq1qxZ6tq1q2JiYgJ2D2cMGjTo7J83b96sxYsXq2PHjpK46CqokYEikGsk9bF0UCMDT2nVR35N8Xf89NNPioiIUJ06dc7+/ZFHHtGwYcPUoUMHh1dX8axdu1ZjxozRBx98oNjY2FIr9Hv37tVHH32kmjVrauHChXr99deVk5OjkSNHatasWeedX1RUpL/97W+KjY3VFVdcoffee09PP/200tPTlZ6ergMHDujRRx/VqlWrVKlSJdWrV09XX311UPwSUmn7+uuv9e677+qNN95QVFSUnn76aWVlZenxxx9X06ZN5XK5dOrUqYD/xPSNN97QH//4Ry1ZskRr167Vm2++qWeffVbXX3+9OnfurFOnTtn+VU6nzJ07V5mZmbrkkkvUpUsX5ebm6uWXX9YVV1wR8KfBoGxQIwNLINZI6mPpokYGhtKuj3xk9Rs++OADPf/883r77bd16NAhSafPbe3cubM6dOig0uhh6YN/389vn+zsbC1dulRHjx7V119/Len0pw6mt+FPP/2kOXPm6NNPPz37b4WFhdq2bZs+++wzjRkzRomJiTpy5IjCwsJ04sT0nFXGAAAgAElEQVSJs78Qdi4HDx5UVlaWbr/9dh09elSfffaZ2rdvr6uvvlq33HKLevbsqYKCAp08eVJ33nmn+vfvr6uvvlpS6f8SXVZWltatWydJKi4uLtXY/nLm/rMsS9nZ2VqwYIF27NihlStXSpJGjRqlWrVq6a9//at++OEHSQq4IvPfjw+v16tdu3bpnnvu0fbt2zVp0iTFxMSopKREbrdbISEhAV1kJGn69OmaP3++atWqpbp16yo/P1/R0dFq06YNX6aHJGpkIAj0Gkl9vHDUyMDjj/rId8bOYe7cufrss8/01ltvqWHDhsrOztaaNWvUsmVLhYSEKDk5+YJfKH7+qU9RUZEKCgoUHh5eGsv/VY7y+AnTf98+sbGxuvzyy3Xs2DGlpqaqcuXKqlevntG+du/erSFDhig8PFxfffWVEhMTVbduXcXHx+vEiRNKS0uTx+NRamqqJk6cqIcfflhNmjT5zRzFxcX68MMPNWHCBPXt21dXXHGF1q1bp4MHD6p58+aqWrWqatWqpaVLl6pOnTpq0KBBqdw2v2Xnzp26//77dfjwYW3duvXs4zVQ/fw+Li4uVmxsrJo3b65jx45p9+7dioqKUmJiorp27art27erffv2qlSpksOr/iWPxyO32y1J2rp1q44ePXr2fP1//etfatu2rVq3bq25c+dq2bJluueeewK6yEinr/sydepUzZgxQ4WFhVqzZo3GjBmjpUuX6oknnlBycrLTS4TDgqFGluf6KAV+jaQ+XjhqZODxV32kGTuH1atXq23btkpLS9OSJUu0atUqzZgxQ3Xr1lWfPn1KJceZJ9iMGTM0e/ZsTZo0SR6PR40bN1ZoaOl8le9MjvT0dFWtWrVUYpaVn98+s2bN0oQJE1S1alW1atVKBw4cUEpKiiIiIlS/fn1b8Twej1566SVdc801evzxx7VlyxbVrl1bNWvW1PHjx3XZZZepRo0ayszMVHZ2tu655x5dc801v1uo3W63GjRooIMHD2r27Nnq1auX2rRpo5SUFJ08eVLS6Z96njlzpm666Sa/H1GoXbu2SkpKNGXKFDVv3vy863fa793H+/bt07Zt2+R2u5WcnKxOnToFXJFJS0vT+vXr1aRJE82cOVMTJ07UunXrtG7dOhUVFWngwIF64403tGbNGn3zzTcaM2ZMuWhkTp48qbfeekubN2/W0qVLlZSUpP79++vf//63rrjiClWqVCmgH1fwv2CokeW5PkqBXyOpjxeOGhl4/FUf+QGPc6hWrZpmzZqlwsJCPfTQQ+rSpYvmzJmj7OzsUn3yvv/++1q+fLnefvttDRkyRN9++6369+9fKrHP2LVrl+6991498sgj6tevX6nG9ref3z4PPvig1qxZoxtvvFEJCQl65513tHz5crVo0UJRUVHnjeV2u1WtWrWzY6+++mo98sgjatq0qdxut3bs2KFZs2apd+/evzjP/rfu6zOf9qSlpenQoUM6ePCg/vKXv2jEiBF64IEH9M4772jOnDm66KKL9MQTT/j12h8/X+9ll12moUOHaty4cbrooosC/rs957uPV61apdatWysyMjKgiqbX69WSJUv0448/at++fdq4caNmz56tY8eO6euvv9bChQvVtm1bLViwQIWFhQoJCVFcXJzTy/5dO3bsUEREhGrUqKH3339fW7duVdu2bZWYmKgvvvhCe/fuVWRkpCQu9lzRBUuNLM/1UQrcGkl9LD3UyMDg9/powbIsy5o3b541duxY66mnnrL27t1r5ebmWl6v18rKyrLmzJlj9e3b19q5c2ep5SsoKLBGjBhh5eXlWdOmTbMGDx5s5ebmWoMHD7Y2b97sc1yv1/uLv+fk5FizZ8+2brjhBuuf//znhS77V7F/L/eFONftc+rUKWvw4MHWjh07rJ07d1pHjhwxivnhhx9aPXr0sFauXGlZlmXt3LnTOnjwoGVZlvXKK69YH3zwgWVZluXxeGzF+/bbb61OnTpZKSkp1po1a6x33nnHuv322629e/daO3futIYMGWL98MMPlmWV7m1zxs/X+fXXX1uzZs06+xhdsmSJ1aRJE2vZsmXWvn37zu4zkPjjPi4LP7/dR48ebQ0ZMsS6++67z/7bkSNHrIcfftiaNWuWE8vzyfTp061bb73Vuv/++63rr7/emjt3rmVZlvX0009bzz77rNWrVy/rxx9/dHiVcFIw1MhgqY+WFfg1kvp44aiRgaEs6iNHxnT6y3iLFy/WoEGDlJWVpQEDBmjcuHE6fvy43n//fXk8Hr322msXdE6z9V+fFrrdbh04cEADBw5U7dq19fe//10ul0sej+eCzpc9k2PDhg1q06aNKleurJ49eyosLEzvvfeeoqKi1KNHD5/jn3H06FGNHz9eAwcOVEREhP7whz9c0Kcydm+f4uLis6c/mBo0aJCKi4s1cuRIeb1ederUSd9//722bdumRYsW6fXXX5f02z9HunfvXi1evFgDBw5UbGysdu/erSuvvPLs9TwaNmyoHTt2aOjQoRo7dqxeffVVxcTESCr9Iwl79+7VokWLNHjwYK1evVrPPPOMLr30Uo0ePVpjxoxR9+7dNX78eD311FOKjIzUhAkTHP+p4LK4j+3mvpA4Zx4fn376qSIjI9W0aVNt2LBBq1atUtu2bVWtWjXVr19fOTk5pZrbX7744gt9+umn+uCDD1RYWKjU1FQNHz5ctWrV0g033KDs7Gzdf//9tk93QvAJlhpZXuujFPg1kvp44aiRgaes6mOFb8aysrK0atUqvfvuu4qLi9MNN9ygN998U8OHD9fSpUvVvn17uVyuC7qA4s8faN9//70k6ZJLLlGfPn00depUde/eXS6XS/Pnz1dmZqaqVKlyQTmys7M1evRoNWzYUKNHj1alSpXUqVMnrV27Vs8//7wsy1LPnj193k9BQYGqVaumxo0b64knnlBcXJxmzpzpczyT2+fQoUO2b58zcbdv365du3apSpUqGjhwoCpXrqyXXnpJkZGROnLkiHbs2KG//e1vatGixe/Gy83N1ZQpU1RYWKghQ4aofv36mjt3rrZs2aKWLVuqVq1aaty4sUpKSpSdna3ExESfb5Pzyc3N1dSpU5Wdna1Tp07prbfe0hVXXKFJkyZp1KhRCgkJUY8ePc6up1GjRn5bix3+uo9NcxcVFV3QjwCcibN69WpNnz5d//znPyWd/rWxzz//XOvWrdNFF12k5cuXa8KECb+YE6j279+vq6++WjExMQoPD9c111yjjh076vvvv9ef//xnp5cHhwVDjSzP9fG/1x+oNZL6eGGokYGprOpjhf8Bj1OnTukf//iHrrrqKiUkJEiSmjRpok2bNqlz586KjY1VWFiYz/F/fr7ylClTNH78eH300UfKz89X27ZtFRYWphkzZujLL7/U2rVrNW7cOOMO++dPpPz8fFWuXFlNmzbV8uXLtWXLFnXu3FmRkZE6cOCALrnkEl1zzTU+P5F37typsWPHqlu3bjp8+LDmzJmj2rVr65JLLlGNGjXO7tnuk8uft4/L5dLKlSv14osvqqCgQJs2bdK8efP0wAMPKD4+XqNGjVLv3r3Vv39/NWrU6Hc/nfF6vapRo4batWunt956S8ePH1fz5s2Vnp6ujIwMZWZmyuPxaNq0afrLX/6iyy67zNYafXFmLe3bt9ekSZN0+PBh9erVSzVq1FCLFi1UUFBw9pz4K6+8UtWqVfPbWuyu19/Pgd9z5j6dPn26Zs2apW7dul3Qi39GRobGjh0rj8ejtm3bqkqVKmrRooUyMjK0ZMkShYeH69lnn9Uf/vCH0tqCX+3atUs//vij2rRpo+joaLlcLq1du1ZhYWFc7wflvkaW5/p4Znyg10jq44WvmRoZmMqqPlbYZmzr1q0qKSlRfHy89u3bp2PHjqlq1aqKj4/X4sWL9fXXX6tPnz4XfAX7M3fS9u3btXjxYk2cOFHXXnut3nnnHUVGRurWW29Vnz59dPXVV2vAgAHGnxb9/IHw/vvva9q0adq+fbv69++vBg0a6PPPP9fixYt1/PhxzZ49W88888zZC3T6qn379tq+fbuqV6+u+++/X/n5+frkk09Uu3Zt45/S9efts3fvXr3wwgt69dVXddttt6lly5Y6ePCgli9frv/3//6fcnNzVb9+fSUlJf1iLf/tzGH3kpIS1a5dW+3atdP48eMVExOjjh076sSJE1q+fLm+/fZb3XnnnWrbtq3tNZr6+Vpq1aqla6+9VgsWLJAkNWvWTOHh4WrRooWKiopUv3591atXr1TybtmyRSdOnJBlWWdPLbHL388BOxYtWqQ5c+bopZdeMl7/f7/YxsXFKSkpSTt27NDx48dVu3ZtxcfH67LLLlNhYaFuv/12v37qWxoWLFigrVu36rvvvlOLFi00e/ZsHT16VDk5Odq+fbvmzZunxx57TFX/P3t3GhhVle1//1epTIQEQpjHBMIQEEUIg0xCGBRURFqxEa6I11m42vCAQ4OtIqAieOkWu9WIQiMqQ2ME5CoCl0GEQAQi0IIKnQAhEjAyJWSqqucFf7jaIpxdpHKqKt/PK4e919q7cuqsWnVOVdWoQSNWSQVDjQz0+ij5f42kPl5ZfZSokf7GjvpYKZuxuXPnXjgpX3/99QoJCVFmZqYWLFigPXv2aPny5XrllVeu6KT8zTffaOvWrWrZsqU2bdqk8ePHq2vXrurZs6fq1Kmjdu3a6bXXXlNeXp769OmjOnXqeHWbx/kDYcGCBVq+fLkeeughPffcczp9+rQGDx6s9u3bKzMzU4cOHdKkSZO8/trQ8+/mValSRS6XSx9++KHmzZunQYMGqX379srJydHq1au1Z88eZWdnX7hP/Lf46vE5f1L46aefFBYWpu3bt2vQoEEKDw9XTEyMCgoKtHXrVg0ePFidO3dWw4YNL/nOxvn/9+WXX2r+/PnauXOnYmNjNXz4cL344ouKi4vT8OHDdccdd6hv375q3bq1z64kXGwtMTExGj58uF599VUVFBSoTZs2ioiIUMeOHdWoUaNyWcvcuXP15z//WWvXrtWPP/6oWrVqqVatWpedV1HPgYv5+b4LCwu1bNkyrVmzRnfccYdq1Kghl8tl6Tdmfh7nH//4h5YuXaqFCxeqR48eaty4sTZv3qxTp04pLi5OtWrVUnJyst/94Oa/mzt3rj7++GOlpKRo/Pjx6tGjh4YMGaItW7bom2++0f79+/Xss8+qefPmdi8VNgmWGhmo9VEKjBpJffS+PkrUSH9kV32sVM2Yy+XSqlWr9PHHH+v9999X3bp1deTIEeXn5ys+Pl59+/ZVnTp1dP/996tp06ZXlGfr1q3q2rWrTp06pTZt2uif//ynvvrqK3Xr1k0xMTGqV6+e2rZtqw8++EADBgyw9NWzP/fzWx2ys7P11ltv6fXXX1dRUZGKioq0du1aHTx4UP369dOtt96qvn37XtEHVM/nWrx4sd577z01aNBAderU0TvvvKO+ffuqc+fOOnHihL788kuNHDlScXFxtjw+DodD69ev1+uvv65GjRpp2bJlSkpKUpMmTeRwOHTixAlt27ZN119//YWvgr3Uyfh8vOnTp+vOO+/UihUrtH37do0aNUrdunXT888/r4KCAnXv3v3Cfda+upJwubVMmzZNZ8+eVefOnS/71fxWZWRk6KOPPtL777+vVq1aKSMjQzk5OapZs+YlC05FPAd+y8+LQ3FxscLDw3XVVVfp7Nmzeuedd9StWzdLxebncd599119/PHHuuWWW3T27FlNnz5dt9xyi9q1a6ePPvpIHo9Hbdu2VUhIiN9eSSouLtbBgwc1f/58vfvuu1q/fr3CwsJ0xx136JtvvtFDDz2kAQMGqFevXqpXr57dy4UNgqVGBnJ99PXjU541kvroXX2UqJH+xu76WGmasa+++konTpzQd999J6fTqePHj2vFihVat26ddu/erXr16mnQoEFq3rz5FX2bodvtltPpVMuWLZWfn6/JkyeroKBAY8eOVXp6uj799FMlJycrJiZG9evX15AhQ4zfJSgqKrpwj/6ePXt07Ngx7dmzR506ddLy5ct13333qUOHDnr55ZdVWlqqzp07e/2BzJ8/2VatWqW//e1v6t27t2bPnq22bduqZs2a+uCDD9SzZ0+lpKTo5ptvvmRR89Xjc36d+/fv18yZM/XAAw+oS5cuKi0t1dSpU1VUVKQ9e/bo9ddf14MPPqirrrrqkieE8/EKCws1a9YsTZkyRWVlZRfu1/7444/VsmVLpaSkqH79+uV2u8OVrqVevXrldvk/IyNDc+fOVUxMjAYMGKCGDRuqWrVq2rZtm7777js1aNDgovfbV8Rz4Lf8/Hh97733tGDBAr333nsKDw9Xp06dFBoaqnfffVfJycmXfUF0Pk5OTo7S0tL08ssv6+qrr1aPHj0UHh6uadOm6emnn1a1atXUo0cPVatWzS+LjCRt375deXl5cjqd+vrrr7V//35t27ZNs2fP1k8//aQ//elPGjp0qEJDQ6/o8z8IXMFSIwO5Pvry8SnPGkl99L4+StRIf+MP9bHSNGPvvPOOiouLlZCQoPXr12v37t266667NG7cOJ04cUKHDh1Sr169JHn/bsnPv9Lz66+/Vk5OzoXYBQUFevzxx/XFF1/oww8/VPfu3VW9enXjdwnWrVunzz77TJ07d1ZaWppSU1P1+OOPq3nz5jp16pT279+vm266SRkZGapataoeeOCByz6hLrWf82vbt2+fcnJyNGLECN1www265ppr9Nprr6lt27aKiIjQ8uXLdfPNNys8PPyyn73yxePjcDi0bds2vf322yooKFBUVJTatWun9u3bq169esrNzdXJkyd1zz33qFevXpe9PcHhcOjrr79WZGSkCgoK9D//8z9avny5Zs6cqQYNGuiFF15Qz5491bp163K73cGf1vL111/rxIkTOnz4sM6ePauQkBA1b95cjRo1UlRUlPbt26c+ffr86paJingOXMr5OB988IHS0tI0ceJEuVwu/etf/1Jubq4GDx6srKwsrVixQjfffPNlc6enp2vevHn67rvv1KRJkwsfOG7VqpW2b9+ulJQUtW7d2q9vu5DOfSjc5XKpc+fOWrx4sXbv3q358+crLCxMn3zyiY4fP65BgwbJ6XTavVTYJBhqZCDXR18/PuVZI6mP3tVHiRrpj/yhPlaKZmz9+vVauHChJkyYoKSkJA0cOFBDhw5VTk6Odu7cqUWLFmn8+PGKi4u7ogP+5+82zJkzR06nU++++64SExN14MABFRUV6bHHHlNmZqauu+46xcTEGOU7cuSIJkyYoIkTJ2rt2rVauHCh7rnnHiUmJqp27dp6//33tW7dOtWoUUOvvfaapkyZcuGDt6Z+fsJ6//33NXXqVKWnp6uoqEjt27dX8+bN1aZNG02ePFnXX3+9xo8ff9n9+OLx+fm7fX/5y1+0a9cuXXXVVZLOfQtYixYt1LJlS3Xv3l09e/a88K7Y5d7x279/v55//nktWLBApaWlysnJ0dNPP62kpCR99913WrlypQYOHHihkPvyHviKXsu8efM0bdo0lZaW6siRI6pevbpOnTqlwsJCJSYmqkmTJuratetF3x339XPgt+zdu1cbN25U69atVVhYqA8//FAPP/yw2rRpo+TkZBUXF2vRokW69dZb1bt3b/Xt29dS7p07dyo/P199+vTRoUOHVKVKFdWpU0effPKJtmzZoptvvrncbh3xlfPnv8cff1w1a9ZUSUmJioqK9I9//EOHDh3S8uXLNXny5Avf9obKJxhqZKDXR189PuVZI6mPV1Yff56fGukf/KU+BnUzdv6+8a+++krt27dXcnKyMjMz9dlnn2nXrl1avXq1du3apeeee67cfmfi/B/27bff1jfffKPY2FiNGTNGOTk52rVrl1wulx555BHFxMQYxy4tLdX8+fOVlZWltLQ0/fDDD6pZs6bat2+vsLAwJSUl6cCBA9q1a5eef/75K/qA4fkn4OrVq7Vhwwa99NJLqlatmnJzc5Wfn69mzZopMTFRnTp1UnJysho2bGgpbnk/Pg6HQ6tXr9bLL7+stm3bKiwsTDt27FDVqlVVVlamH3/8US1btjTa9/l4HTt2lCTt2LFDpaWlKiws1OLFi7V48WI99thjF/6/r9ixluXLl2vlypWaM2eO9uzZo7y8PJ0+fVoRERHKycmRy+VSs2bNFBoa+psnaV8+B37L6dOn9eijj2rTpk3au3evvvnmmwsvMqRzPzi6Zs0aXXvttYqPj7/st0WdPXtWYWFhatasmd58803t3btX0dHRmj9/vjIzM7Vy5UrNmDHDp7ffXKl/P/917txZ+/bt0+7duy98o1lcXJzuv/9+vqyjkgqmGhkM9VHy7xpJfbzy+ihRI/2Bv9XHoP7R55CQEJ04cULLly9XrVq1dPbsWc2fP1933HGHWrdurZEjR6qwsLDcvp1GOvetNLfeeqvS0tKUnp6uN998U2lpacrIyNCQIUMu+8PClxIXF6cBAwbo73//uyZMmKCUlBTdd999atCgge68807FxcVp2rRpKioqUmRk5BXvJSsrS7NmzVK7du3UuHFjDR8+XPPnz9fXX3+toqIi3XXXXerSpYtRzPJ+fPLz8zV37lw9//zzatOmjc6cOaO5c+cqLS1NLpdLxcXF6tSpk+UPZ18s3oIFC7Rw4UK1adNGTZo0UVRUlK699lqf//5SRa/F7XZf+DuUlJSorKxMI0eO1KJFi5SRkaHk5GS1b99e0qXfXfTlc+C3NG3aVA8++KBef/119erVS3369NHzzz+vBg0aqEOHDlq5cqV++OEHS8fBmjVrtHXrVnXq1En9+vXTY489pu3bt2vAgAG64YYbVFBQoMcee+yKvwLb1y52/ps3b55+97vfqVatWho7dqzdS4TNgqlGBkN9lPy7RlIfr7w+StRIf+Bv9TGor4x5PB5lZGToww8/VOPGjXXVVVdp6NChGjhw4IVL8Zd7B8PUjz/+qGeeeUbHjh3T/Pnz5XQ6tXr1arVo0UKjRo264vtmz/+Ox3PPPaeWLVtq6NChevXVV+VyuXTVVVcpNDRUoaHl02NHRkbK4/Fo8eLFatiwoZKSktSuXTt9//33ysnJ0XXXXWdc1Mr78SktLdWSJUsufAWv0+lUaGioMjIylJ+fr1GjRqlVq1ZXFE+SNm/erNzcXN17771q0qRJhfwQbkWvxeFwqKCgQNWrV9eOHTvUpk0b9evXT+vXr1fdunX16KOPWjq5+vo58Fvq16+vLl266Nlnn1WdOnWUkpKiJ598UllZWVq5cqXlH8p0u93Kzc3VX//6Vx07dkxZWVnasmWLrrvuOnXo0EFNmzYt13csfeVi578777xTN99884Uf70XlFmw1MtDro+TfNZL6eOX1UaJG+gN/q49B3Yw5HA41aNBAycnJuueee9SyZctfdf3lfcKoWbOmTpw4IY/HI5fLpR07duiTTz7Rgw8+WC6/9F6zZk0lJiaqS5cuevzxx9WxY0cNGjRIb7/9tm699dZyecfvvNDQUF111VWKiYnRnDlzFBsbq1atWik5OVnJycmKjY31av3l+fiEhYUpOztbR48eVe3atVWzZk0dOXJEOTk5iomJUa1atZSUlGT5hHyxeLm5uTp+/LhcLpckqXXr1hXyjUB2rKV58+aqXbu2nnvuOfXr10/Z2dlasWKFpk+fbvlWG18/B35LtWrV1LRpU3Xv3l1/+tOfdOONN6pDhw4aOnSobr/9dktFRjr3DnvHjh2VkpKio0eP6tixY1qzZo1cLteF31zyx2+E+ndWzn+o3IKtRgZ6fTy/B3+tkdTHK6+PEjXSH/hbfQzqZkySnE6nGjRoIIfDYflH7K5EWFiYWrdurbNnz2rZsmUqKCjQhAkTjD63ZEWDBg3Us2dPPfzww+rRo4eeeuopn7yb4nQ61apVK0VERGjmzJlq1KiRWrRo4XVRK+/Hx+FwqGHDhlq3bp0++ugjZWVl6Y033tDEiRP1008/KSoq6rJfY28l3tNPP62TJ0+qatWqFz787Gt2rMXhcMjtdmv37t3auXOnli1bpldeecXog+4V9Rz4LfXq1VPXrl31/PPPa+fOnRo6dKhXJ9nY2Fhde+21F74S+fbbb1dsbKztRebMmTMqLi5WRETEZcdW9PkPgScYa2Sg1kfJv2sk9fHK66NEjfQ1qzXSn+qjw+PxeGzLHuTKysokqdxui7iYXbt2KSoqSomJiT7LIUklJSVavny5OnfuXG6/01Gej09+fr4yMzP1ww8/qHHjxoqLi9PTTz+tWbNmefXjpOUd70rYsZa8vDxlZ2df8e+yVMRz4Lfk5+dLktdfXS2pQm63MVFSUqLHH39cL774otfvvAP+wtfnh0Cuj5L/1kjqY/nUR4kaWd4CtUbSjMEyf3vSXcyePXuUlpam7du3a+rUqUpKSvKreMGyFtjn7Nmzfv1VwUBlFAj1USrfOuJPNcmf1gJ7BWKNpBlDUCktLVVubq4iIiLK5f7f8o4XLGsBAASe8qwj/lST/GktgCmaMQAAAACwAZ/mBgAAAAAb0IwBAAAAgA1oxv7NqVOn9Nprr+nUqVMBGT9YcrCHypMjGPZQETmCYQ8IbMFyDPJctT9+sOQIhj1URA72cBke/MKhQ4c8LVu29Bw6dCgg4wdLDvZQeXIEwx4qIkcw7AGBLViOQZ6r9scPlhzBsIeKyMEeLo0rYwAAAABgA5oxAAAAALABzRgAAAAA2IBm7N84nU41bNhQTqczIOMHSw72UHlyBMMeKiJHMOwBgS1YjkGeq/bHD5YcwbCHisjBHi6NH30GAAAAABuE2r2AijBkyF36Ifeo5fGbt6xV1+v6WB4/8I7/MFrPn8b/pybPeMfy+G3r1xvFl6Tly+dp0KB7LI/fs+cL4xwHDuxXs2aJxvN8FRw/oxAAACAASURBVN/tdhvnyMr6lxISmhrMMHvvIisrSwkJCUZzTAVDjmDYgzc5QkLM3mEzfU5Ur17LKL4k7diRrvbtu1gaW79+fa1cmWacA/7lpptuU25urqWxJsfHefc8NsFo/B/uvUOz3l1iefzfXpxkFF+S9n27V61aJlmf4DB7ru7bt0etWl1lNCc2trbR+PT0derSpbfl8cePHzaKv3//90pMbG40x+UqMxrvj+dlyWEY3/R1hBQSYnZjmvnrIZdRfMn8cQoLizCK/913+9SiRSujOab7MD1mo6KqGcXftWu7rr66g+XxDRrU12effXLZcZWiGfsh96gOH84xmmMy/qcT5r85YDIn16CR9HZedna2Vzm8neeL+N40Y6Y5TJsx8/jeCYYcwbAH0xymzZhp/Bo1iozjS9KhQ2Yv2hDYcnNzjf7mpsfHidNnTJdkNCc7+6BxfNN5Di+eqwcPmq3rzJkS4xwmr1WOHjU//5meM02bMW9yeMMsh1kzZh7fvBkzzeFNM2aaw7QZM40vebcPkxxVq8Yaxz948JDxnMvhM2MAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2oBkDAAAAABvQjAEAAACADQLid8bcbremTZumzMxMFRQUyOPxaMqUKUpOTrZ7aQAA2Ib6CACBLSCujGVmZiovL08LFy7UypUrNWTIEKWmptq9LAAAbEV9BIDA5vB4PB67F2HFgQMHtGXLFh06dEjp6emqWrWq5s+fb/eyAACwFfURAAJXQNymuG7dOk2dOlX33nuv+vbtq2bNmmnZsmWW53e9ro8OH86xPP7Q4X1q3KiV5fF3jHrE8lhJ+u8pf9DYSbMsj9/46adG8SUpI+NTdew4wPL4HTtWG+dwucrkdPruEDKN73a7jXN4PG45HCYXiM3eu/B4PHI4HGaLMhQMOYJhD97kCAlxGsU3fU7UqFHXKL4kHT+eo1q1Gloa27hxI+3YkW6cA+XnSuujJLVv30WHDh22NNbk+DhvzJ+mGo1/7rFReu4vcy2Pf2nCo0bxJamouFCREVGWxzsMn6tnz55WlSoxRnPi4uoZjc/J+U4NG7awPP7o0Syj+GVlpQoNDTOa43KVGY33x/OyZLYe89cRUkiI2Xjz10Muo/iS+eMUFhZhFL+kpEjh4ZFGc0z3YXrMVq0aaxT/5Mljql69tuXxTZo01q5d2y87LiCasU2bNiklJUXDhw9XUVGRUlNT5XKZH2gAAAQT6iMABLaA+MzYsGHDtHXrVg0aNEhDhgxR48aNdfjwYa+uhAAAECyojwAQ2ALiylhiYqLS0tJ+8d8mTZpk02oAAPAP1EcACGwBcWUMAAAAAIINzRgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAYB8TtjV+ps0RkVnj1tNMdk/L7t35guyWhO1779jeObzvvqq8+8yuF2uyyPdTjMe3+Px2N5bFhYuHF803mlpcVe5QAuxuT5482c4uKzxvFN5pWUFHkVH/6lrKxUpaUllsebjJWktUtWGo1/7rFRRnPuf3yyUXxv5v1t5lPG8UtKzJ5/eXkHjXOYzKlRo55xfNM5x48fNs7hf6y/7vB2jq/P/RXBm9dDFfEayuUqszzW13uweq7kyhgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgA5oxAAAAALABzRgAAAAA2IBmDAAAAABsEHq5AYMHD9ZTTz2lrl27asWKFXr66ae1bds2RUZGauLEiWrZsqV27dqlvXv3yuFwqGfPnho3bpxCQ0N19dVX695779WXX36pwsJCjRkzRp9++qm+/fZb1alTR2+88YaioqK0ZMkSLVy4UKWlpTp58qQeeOABDR8+XEuXLtXnn3+ukJAQZWdnKzIyUi+//LISExMr4rEBAOCSqJEAgCtx2Stj/fv314YNGyRJGzduVPXq1ZWRkSGPx6P169drw4YNio2N1fLly/WPf/xD+/bt0zvvvCNJKikpUa1atbRkyRLddtttmjRpkiZOnKiVK1fqzJkzWrNmjQoKCrR48WK99dZbSktL03//93/rlVdeuZB/27ZteuaZZ7RixQq1a9dOb731lo8eCgAAzFAjAQBX4rJXxvr3769x48bpiSeeUEZGhkaNGqVNmzapatWqatKkif75z3/qmWeekcPhUHh4uIYNG6Z58+bpwQcflCTdeOONkqQmTZqoZcuWqlu3riSpUaNGOnnypKpWrao33nhD69evV1ZWlvbu3avCwsIL+a+66irVq1dPktSmTRt9/vnnxpvcuXOr8ZwffzxiPMfEypVv+jS+JL328v/nk7E/5/F4vJpnldvt8ml8SSopKfJpfF8/RsGSIxj2UBE5KmIPp0/n+zxHsAiGGrlr13aj8SdPHjPOYWrDhkU+zzF7+nifjD3P5SoznmOqtLTYp/GPHTvk0/gS5+XKlCMY9lBYeKrcY162GWvVqpVKS0u1Zs0aJSQkKCUlRWPHjlVoaKhuvPFG/fWvf5XD4bgw3u12q6zs/05AYWFhF/3n83744Qf9/ve/15133qnk5GQNGDBA//u//3vh/0dGRl74Z4fD4dWDfO21nXXo0GHL43/88Yhq1mxgeXyXLoOM1rNy5Zu66aaHLI9PvLqlUXzpXHP1X0/OtDzem0Lj8Xh+8be/HIfD7COKbrdLISFOy+NDQ399fF1OSUmRwsMjLz/w/zEtfKaPkTeCIUcw7KEicpjGj46uYZzj9Ol8xcTEWRrbpElj7dmTaZwjmARDjbz66g46eNDai+6TJ4+pevXaRvHbtUsxGr9hwyJdf/2dlsdfc11no/jSuZo35okZlsf/beZTRvFdrjI5nZd9ifULJvVOOlePwsIiLI+Pja1jFP/YsUOqXbux0Zzjx62/1pIq53m5subwxz1UqRJjFL+w8JSioqpZHt+kSRPt3bv7suMsvTru16+fZs6cqe7duysxMVFnzpzR8uXLdcMNN6hHjx5677335PF4VFJSokWLFqlbt26WF7p7927FxcXp0UcfVY8ePS4UGZfL91dEAAC4UtRIAIC3LDVj/fv314EDBy4UkG7duql27dqqX7++Jk2apPz8fA0aNEiDBg1S06ZN9fDDD1teQPfu3VW3bl0NGDBAAwcOVG5uruLi4pSdne3djgAAqEDUSACAtxyeiriB02bcpnh53KZoDbcpBmb8YMnBbYrwBW5TvDxuU7SG2xTJYVd8b3IE1G2KAAAAAIDyRTMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgg0rx1fYJCQlGv8lSGb/eUzJfj8fjNvy6erNDzR8fp9DQcKP4pl89nJ33g1F8SWpQo4aO/PST5fGNatYyzmH6MwMej9sovj/+rU2/Zloy/zprX3+VdVlZiVF8yexxio+PV1ZWlnEO+JdmzRIt10hvvrLdlGkOt9vsfCOZ169mza4xir9//04lJl5rNOfEiTyj8aY/w5Ofn2sU35vzcocONxiN/+qrz5ScfKPl8Q0btDCKL0nLls/WrYPGWB7/yco3jOJ785xwu81+tN0fa6Svf65IkurWTTAan5u7X/XrJ1oeb/pTDKY1OD4+Xt9//+1lx3FlDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgA5oxAAAAALABzRgAAAAA2IBmDAAAAABs4DfN2J///GelpaVJkmbPnq3Vq1dLkp566inNmTPHzqUBAGAb6iMABK9Quxdw3uOPP37hn9PT09W8eXMbVwMAgH+gPgJA8KqwK2ODBw/W5s2bJUkrVqzQ1VdfraKiIknSxIkT1a1bN82ZM0cLFizQ7t27NX36dH3++eeSpB07dmjYsGHq16+fHnnkERUWFlbUsgEA8CnqIwBUXhV2Zax///7asGGDunbtqo0bN6p69erKyMhQ9+7dtX79erVu3VqSNGLECH366acaMWKE+vfvrzVr1ujo0aP6+9//rvDwcA0dOlSrVq3SbbfdZjl3VlaW8Xo9Ho/xHH+KX3E53D6OH/iPU2lpsU/jS1KDGjUsj3W7XV7l8HaeVcHwt5Ykl6vMp/Er4niqiMcJ/8fO+ihJBw7sNxrv62O8onL4un7t37/Tp/El6ccfj/g0fkWcC7766jOf51i2fLbBaJOx51TM8Rr4NdLXryMkKTfX7Hxmyhc1uEKbsXHjxumJJ55QRkaGRo0apU2bNqlq1apq0qSJateu/Ztz+/XrpypVqkiSWrRoofz8fKPcCQkJys7Otjze4/HI4XAY5TDh6/je5TBfj8fjlsNhcnHV7Enuj49TaGi4UfzS0mKFhUVYHp+d94NRfOlcI3bkp58sj29Us5ZxDrfbpZAQp+Xxpi9y/PFvbbLf81yuMjmd1k+rpjlMj6eyshKj+JLZ4xQfH+/Vm134JTvroyQ1a5ZouUaaHuPeMM3hdps3Vab1q1mza4zi79+/U4mJ1xrNOXEiz2j8jz8eUc2aDSyPz8/PNYrvzXm5Q4cbjMZ/9dVnSk6+0fL4hg1aGMWXzjVitw4aY3n8JyvfMIrvzXPCtCnxxxpp9vrP/HWEJNWtm2A0Pjd3v+rXT7Q8/vjxw0bxTWtwfHy8vv/+28uOq7DbFFu1aqXS0lKtWbNGCQkJSklJ0aZNm7R27VrdeOOln4ihof93kDscDt61BQAEDeojAFReFfptiv369dPMmTPVvXt3JSYm6syZM1q+fLluuOGX76Q4nU6Vlfn+ki8AAP6A+ggAlVOFNmP9+/fXgQMH1K1bN0lSt27dVLt2bdWvX/8X4/r06aNXX31VH330UUUuDwAAW1AfAaBycngqwT0NfGbMCj4zZgWfGbOGz4z5JgefGYMv8Jmxy+MzY9bwmTFr+MyYNXxmDAAAAADgMzRjAAAAAGADmjEAAAAAsAHNGAAAAADYgGYMAAAAAGxAMwYAAAAANuCr7S+Cr7a3msPsq4FN92z6Nahni4uM4ktSRFiYiktLLY+PDLf+laaS77/+/1wOfzye/Ct+sOTwtz3w1fbBwaRGencMmo33z/Omr/cgRYRHGo0vKi5UZESU5fE//HTcKH5sVJROFBYazalRtarReH87p/lj/GDJURn3YLVGcmUMAAAAAGxAMwYAAAAANqAZAwAAAAAb0IwBAAAAgA1oxgAAAADABjRjAAAAAGADmjEAAAAAsAHNGAAAAADYgGYMAAAAAGxAMwYAAAAANgi1ewFWuN1uTZs2TZmZmSooKJDH49GUKVOUnJxs99IAALAN9REAAltAXBnLzMxUXl6eFi5cqJUrV2rIkCFKTU21e1kAANiK+ggAgc3h8Xg8di/CigMHDmjLli06dOiQ0tPTVbVqVc2fP9/uZQEAYCvqIwAEroC4TXHdunWaOnWq7r33XvXt21fNmjXTsmXLLM9PSEhQdna25fEej0cOh8ObpfpFfO9ymK/H43HL4bB+cdV0z263SyEhTsvjzxYXGcWXpIiwMBWXlloeHxkeYRTf9DGSzN8b8c/jyb/iB0sOf9tDfHy8srKyfLoeXNqV1kfJrEZ6dwyajffP86av9yBFhEcajS8qLlRkRJTl8T/8dNwofmxUlE4UFhrNqVG1qtF4fzun+WP8YMlRGfdgtUYGRDO2adMmpaSkaPjw4SoqKlJqaqpcLpfdywIAwFbURwAIbAHxmbFhw4Zp69atGjRokIYMGaLGjRvr8OHDcrvddi8NAADbUB8BILAFzGfGrgS3KVrBbYpWcJtiYMYPlhz+tgduUwwO3KZoBbcpWsFtiuSwK35F5PDVbYoBcWUMAAAAAIINzRgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAahdi8AlUdIiHnvbzKnxOUyjh8RFmY0z5vfrzCZUwl+9g9ApePNec3fzoW+30NxyVnjDCZzYqOs/ybZlcwBYIYrYwAAAABgA5oxAAAAALABzRgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY+acbS09N1yy23+CI0AAABjRoJADiPK2MAAAAAYINQXwb/17/+pcmTJ6ugoEDHjh1TUlKSZs2apYiICLVt21Z9+/bV3r17NWPGDOXn52vGjBkKCQlR69at9eWXX+r9999Xo0aNtHjxYn3wwQdyu92KjY3VM888o8TERF8uHQAAn6JGAgB8emVs0aJFuu2227Ro0SKtWrVKhw8f1rp16yRJpaWlSklJ0WeffaZGjRrpiSee0CuvvKKPP/5YXbp00dGjRyVJW7duVVpamhYsWKC0tDTdf//9GjNmjC+XDQCAz1EjAQA+vTI2YcIEbdq0SampqcrKylJeXp4KCwsv/P+OHTtKkjIyMpSYmKikpCRJ0pAhQzRlyhRJ0rp165Sdna1hw4ZdmHfq1CmdOHFCsbGxltaRlZVlvHaPx2M8x5/iV1wOt0/jl5WV+jS+JMVERloe63a7jON7M8dUMBxPwbCHisgRDHvAOYFaI4PlGOS5Wv68WU8wPE7BsIeKyMEeLs6nzdi4cePkcrk0cOBA9e7dW7m5ub/YRFRUlCTJ6XT+anMhIecu2rndbg0ePFgTJky48O95eXmqXr265XUkJCQoOzvb8niPxyOHw2F5vClfx/cuh/l6PB63HA7rF1edTqdR/LKyUoWGhlke/9OZ00bxpXON2OmiIsvjq0dVNYrvdrsUEmJ93940t/55PPlX/GDJ4W97iI+P9+rNLpwTiDXS345Bf83hj3uoiBfCpnv2x8fJ3+IHS47KuAerNdKntyl+8cUXGj16tG666SZJUmZmplyuX18l6NChg7KysrR3715J0meffaZTp07J4XCoR48e+uSTT5SXlydJ+uCDD3TPPff4ctkAAPgcNRIA4NMrY2PHjtXo0aMVFRWl6OhoderUSQcPHvzVuNjYWL366qt68sknFRISorZt2yo0NFRVqlRRjx499MADD+g///M/5XA4FB0drdmzZ/u8uwYAwJeokQAAh8cPbjg+c+aM/vrXv+q//uu/VKVKFe3Zs0cPPfSQNm7cWC4FhdsUreA2RSu4TTEw4wdLDn/bA7cpVgx/qpH+dgz6aw5/3AO3KQZm/GDJURn3YLVG+vTKmFXR0dEKCwvTHXfcodDQUIWGhmrWrFm8swcAqPSokQAQvPziypivcWXMCq6MWcGVscCMHyw5/G0PXBkLDlwZC7z43uTgylhgxg+WHJVxD37xBR4AAAAAgIujGQMAAAAAG9CMAQAAAIANaMYAAAAAwAZ+8W2KsF+1ajV9Pq9N667G8Tt1HGh5bHyDpsbx8/NzjeY9/9pc4xwmc/40ZqRxfAQuky/A8WaO9x/Yt/oBZb7NLzg4ZPa3NP27B/73hMXF1ff5HLfb/AucYmPrWh5bq1ZDo9jHj+cYz5n65gKj8aZzJj18t3F8qSLOm2bPCdMvNDs3x/pL9ujoGsbxJal69dqWx548ecyrHCYiIqJ8Oqe4+KxxfF+cK7kyBgAAAAA2oBkDAAAAABvQjAEAAACADWjGAAAAAMAGNGMAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2oBkDAAAAABuElleg9PR0zZgxQw0aNNCBAwcUGRmpl156SampqWrRooXuu+8+SdJTTz114d/79Omja665Rvv27dO4ceN07NgxffjhhwoLC1NERIQmT56s5s2b6+jRo5o8ebJyc3NVWlqqm2++WQ8//HB5LR0AAJ+hPgIAfku5NWOStHv3bj355JPq2LGjPvjgA02YMEEtW7a85JwWLVpo1qxZcrlcateundauXas6deooLS1NX331lZo3b64JEyZo1KhR6tOnj4qLi/XAAw+oSZMmuummm8pz+QAA+AT1EQBwMeXajCUlJaljx46SpNtvv12TJ09WnTp1Ljnn/Hin06kBAwZo2LBh6t27t3r06KFevXqpsLBQ27Zt08mTJ/XnP/9ZklRYWKi9e/daLjZZWVnGe/F4PMZz/Cl+ReU4efKYT+Nv3rLMp/ElKT8/16fxnxl9t0/G/lwwHE/BsIeKyOF2u3waX5I8HrfPc1Q2/lofJSkr619Ge6mI4yMYnqs//njEp/El6aeffvBp/OPHc3waX5L++OBwn4z9OV+fNyviOVFWVurzHCdO5Pk0fkU8r4uKCnwa3xd/63JtxpxO56/+W40aNX7x4JeW/vJgioqKuvDPM2bM0Lfffqsvv/xSb731lj7++GNNnTpVHo9HH374oapUqSJJys/PV0REhOV1JSQkKDs72/J4j8cjh8NhebwpX8f3Jke1arWMc5w8eUzVq9e2PL5N665G8TdvWaau191qefy+b7cZxZfONWJxcfUtjx/7wnSj+M+MvlsvvD7f8vg/jRlpFF/yz+PJ3+L7aw6Hw+xju263SyEhvz7PXmo9pjwet+V1xcfHG7+Qr6z8tT5KUkJCU8s10uT4+Nkss9F++Fw1qRPSuUasZs0GRnPcbrMXeT/99INq1KhnefzFjsFLOX48R7VqNTSaM27aK0bj//jgcE17633L4yc9bP6Gpa/Pm948J0z/FmVlpQoNDbM8Pjq6hlF86VwjFht76TeIfs70zXhvntcREVGXH/QzRUUFioysanl8cfFZo/imf2urNbJcv8Bj79692rt3ryRp4cKFat++vWrUqKHdu3dLko4ePaqtW7dedG5+fr569eql2NhYjRo1Sn/4wx+0a9cuRUdH69prr9W7774rSTp16pTuuusurVmzpjyXDgCAz1AfAQAXU65XxmrVqqVZs2YpJydHcXFxmj59upxOp8aPH68bb7xRjRo10nXXXXfRuXFxcXrkkUc0atQoRUZGyul0asqUKZLOvSP4wgsvaNCgQSopKdEtt9yiW2+1fsUEAAA7UR8BABdTrs1YdHS03njjjV/99wULFlx0/Nq1a3/x78OGDdOwYcN+Na5Ro0Z68803y2eRAABUMOojAOBi+J0xAAAAALBBuTVjXbp00YoVK8orHAAAQYH6CAD4LVwZAwAAAAAb0IwBAAAAgA1oxgAAAADABjRjAAAAAGADmjEAAAAAsIHD4/F47F6EryUkJCg7O9vyeI/HI4fD4bP1+Dp+sOSojHuoiKejN/v1t8epsubwtz3Ex8crKyvLp+uB75nUSH87Bv01R2XdQ1hYhNH4kpIihYdHWh5/8swpo/iSVCU8XGdLSiyPj4ow20Mw/K0rIkdl3IPVGsmVMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2oBkDAAAAABvQjAEAAACADWjGAAAAAMAGNGMAAAAAYAOfNWOtWrVSfn6+li5dqoceeuhX/3/NmjWaMmWKr9IDAOC3qJEAAEkKtStx37591bdvX7vSAwDgt6iRAFA5XPLK2ODBg7V582ZJ0ooVK3T11VerqKhIkjRx4kTNmzdP9957r+68806lpKTokUceUXFx8W/G+/TTT9WvXz8dOHDgF+8G3n333Zo5c6ZGjBihPn36aOLEiXK73ZKkpUuXasCAAbrtttv00ksvqU2bNuWycQAArgQ1EgBwpS7ZjPXv318bNmyQJG3cuFHVq1dXRkaGPB6P1q9fr++++0633XabFi1apFWrVunw4cNat27dRWOtWLFCs2fP1vz589WsWbNf/f+DBw9q/vz5WrZsmTZs2KCtW7fq+++/14wZMzR37lylpaUpOjpaLpfryncNAMAVokYCAK7UJW9T7N+/v8aNG6cnnnhCGRkZGjVqlDZt2qSqVauqSZMmmjx5sjZt2qTU1FRlZWUpLy9PhYWFv4qza9cubdy4UX/84x9Vv379i+ZKSUlRSEiIoqOjFR8fr5MnT2rv3r3q3r276tWrJ0n6j//4D7322mvGm8zKyjKe4/F4jOf4U/xgycEeyp+36wmGxykYcgTDHoJFZa2RwXIM8ly1P74klZQU+TxHlfBwy2O92XMw/K0rIgd7uLhLNmOtWrVSaWmp1qxZo4SEBKWkpGjs2LEKDQ3VjTfeqHHjxsnlcmngwIHq3bu3cnNzL7rImJgYzZw5U3/4wx/Uu3dvNWrU6FdjIiMjL/yzw+GQx+OR0+n8RTyn0+nVJhMSEpSdnW15vMfjkcPh8CqXP8QPlhyVcQ8VcaLyZr/+9jhV1hz+tof4+Hiv3uwKFpWxRvrbMeivOSrrHsLCIozGl5QUKTw88vID/5+TZ04ZxZfONWJnS0osj4+KMNtDMPytKyJHZdyD1Rp52W9T7Nevn2bOnKnu3bsrMTFRZ86c0fLly3XDDTfoiy++0OjRo3XTTTdJkjIzMy96i0RCQoK6du2qu+++W08++eSFe90vp0ePHtq8ebOOHj0qSVq8eLGleQAAVARqJADgSlz22xT79++vOXPmqFu3bpKkbt26ad++fapfv77Gjh2r0aNHKyoqStHR0erUqZMOHjz4m7EefvhhrV27Vm+//bZq1ap12cU1bdpUTz/9tO677z6Fh4erdevWqlKlisH2AADwHWokAOBKODx+/OGAQ4cO6eOPP9ajjz6qkJAQrVq1Sqmpqcbv/nGbYmDmqIx74DZF3wmGHP62h8p+m6Ld7KiR/nYM+muOyroHblP0jWDIURn3YLVG2vY7Y1bUq1dPeXl5GjRokJxOp2JiYjRt2jS7lwUAgO2okQAQ+Py6GQsLC9PkyZPtXgYAAH6HGgkAge+yX+ABAAAAACh/NGMAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbODX36YIVDYV8RtgDod378GYzPN43F7l8Ccut3d7MJnnDOH9MADBo7S02KdzTH8DTDpXI03mjXrgOeMcpnPmvW3+LahmNdjb3yy1/lpi4itvR5D47AAAIABJREFUGUef+Eqq0fipEx4wzhGIeCUAAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgA5oxAAAAALABzRgAAAAA2IBmDAAAAABsEHDN2OzZs7V69Wq7lwEAgN+hRgJAYAm4Ziw9PV1lZWV2LwMAAL9DjQSAwBJaEUnS09M1Y8YMNWjQQAcOHFBkZKReeuklhYSEaPLkySooKNCxY8eUlJSkWbNmKSIiQn/5y1/0+eefKywsTDVq1NCLL76ozz//XLt379b06dPldDrVv3//ilg+AAA+Q40EgMrL4fF4PL5Okp6erlGjRmn+/Pnq2LGjPvjgAy1evFhdunRRUlKSBg8erNLSUv3ud7/TmDFjdM011+iWW27R5s2bFR4ernfeeUdNmjRRv379dPfdd2vEiBEaMGCAr5cNAIDPUSMBoPKqkCtjkpSUlKSOHTtKkm6//XZNnjxZc+bM0e7du5WamqqsrCzl5eWpsLBQdevWVVJSkoYMGaLrr79e119/vbp27ep17oSEBGVnZ1se7/F45HA4vM5nd/xgycEefJPD4TC/O9ntdikkxGmwJrdRfH98nFxusz1IUojDIbfB+1vOELO/hb89TvHx8crKyvLpeiqLQKmR/nYM+msO9hC4OUY98JxR/Hffelb3Pvi80Zx5b082Gm9eg82vs3g8bqPXBxNfecso/pTx92vSjLeN5kyd8IDReH97TlitkRXWjDmdvz6Ixo8fr6ioKA0cOFC9e/dWbm6uPB6PQkJC9N5772nXrl3avHmzpk2bpp49e+qJJ56oqOUCAFBhqJEAUDlV2Bd47N27V3v37pUkLVy4UO3bt1dmZqZGjx6tm266SZKUmZkpl8ulvXv36pZbblFiYqIeeughjRo1Srt27ZJ0rmDx4WQAQDChRgJA5VRhV8Zq1aqlWbNmKScnR3FxcZo+fbrWr1+v0aNHKyoqStHR0erUqZMOHjyooUOHauDAgbr99tsVFRWlyMhITZo0SZLUp08fvfrqqyotLdWQIUMqavkAAPgMNRIAKqcKa8aio6P1xhtv/OK/jRgxQiNGjLjo+DFjxmjMmDG/+u8jR47UyJEjfbJGAADsQI0EgMop4H5nDAAAAACCQYU0Y126dNGKFSsqIhUAAAGFGgkAlRdXxgAAAADABjRjAAAAAGADmjEAAAAAsAHNGAAAAADYoMK+2h6AFQ6fz6tVq5FXGUzmnT172jh+dHQNy2NdLu9+1LZKlRjLY1fu3Gkc/5b27Q3nefP3Npnj8SI+AFRe61cvNZzxrPGcZ2amGuYwmzNl/IPG8SUpJMT6NZp1af9jFnz8/eZzKgmujAEAAACADWjGAAAAAMAGNGMAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2oBkDAAAAABvQjAEAAACADWjGAAAAAMAGoXYvwAq3261p06YpMzNTBQUF8ng8mjJlipKTk+1eGgAAtqE+AkBgC4grY5mZmcrLy9PChQu1cuVKDRkyRKmpqXYvCwAAW1EfASCwOTwej8fuRVhx4MABbdmyRYcOHVJ6erqqVq2q+fPn270sAABsRX0EgMAVELcprlu3TlOnTtW9996rvn37qlmzZlq2bJnl+QkJCcrOzrY83uPxyOFweLNUv4gfLDkq5x7M1+LxuOVwWL/IXbt2Y+MceXnZqlMn3vL4s2dPG8U/fTpfMTFxlse7XGVG8SWpsPCUoqKqWR6/aNN64xy3tG+vFTt2WB4/qIPZrWSmf2vJ/L02k2M2Pj5eWVlZxjlQfq60PkpmNTIYzssVkYM9BG6Opk2vMYp/4ECmmjVrZzTn7v963Gj882P/U8/+9zuWx08Z/6BRfOlcXXU6rbcFXbsONor/xRf/UI8etxvN2bRpqdF4f3tOWK2RAdGMbdq0SSkpKRo+fLiKioqUmpoql8tl97IAALAV9REAAltAfGZs2LBh2rp1qwYNGqQhQ4aocePGOnz4sNxut91LAwDANtRHAAhsAXFlLDExUWlpab/4b5MmTbJpNQAA+AfqIwAEtoC4MgYAAAAAwYZmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgg4D4nTGg8vD4fN6xYwe9ymAyr06deOP4UVHVLI/9InOLcXxJyjzwreWxLevXN47v8Xg0qEMH43mGWXwcH0AwcjjM3383nePxBP6PjR858r3P5yxOfdto/PNj/9NozqItm43iezPvjs5djONv2vSR8ZzKgCtjAAAAAGADmjEAAAAAsAHNGAAAAADYgGYMAAAAAGxAMwYAAAAANqAZAwAAAAAb0IwBAAAAgA1oxgAAAADABn7ZjM2ePVurV6+2exkAAPgV6iMABBe/bMbS09NVVlZm9zIAAPAr1EcACC6hFZHkrbfe0pIlS1S1alV17NhRa9as0Zw5czR58mQVFBTo2LFjSkpK0qxZs7RkyRLt3r1b06dPl9PpVK9evTRjxgxt27ZNLpdLbdq00aRJkxQdHV0RSwcAwGeojwBQufn8ytjGjRu1dOlSLVmyREuXLlVBQYEkadGiRbrtttu0aNEirVq1SocPH9a6des0YsQItW3bVk888YT69++vt956S06nU0uXLtWyZctUp04dzZgxw9fLBgDAp6iPAACfXxlbv369BgwYoGrVqkmSRowYoS1btmjChAnatGmTUlNTlZWVpby8PBUWFv5q/rp163T69Gl9+eWXkqTS0lLVrFnTaA1ZWVnG6/Z4PMZz/Cl+sORgD5Unx9GjWT6NL0kt6tWzPNbb/XI8wSp/qI+SeY0MlmOQ5+rlud0un8aXguNxKioq8Gl8SfrnP7/0eY7bO3WyPNbjcRvH92aOeY7Ae177vBkLDQ39xcKdTqckady4cXK5XBo4cKB69+6t3Nzci27Q7Xbrj3/8o3r16iVJKigoUHFxsdEaEhISlJ2dbXm8x+ORw+EwymHC1/GDJQd7CNwcderEG8U/ejRLdesmWB7/ReYWo/jSuUbsux9+sDy+Zf36xjkq2/EUHx/v1ZtdOMcf6qNkViP97Rj01xz+uAeHw+xmKLfbpZAQp+GazF5s++PjFBERZRS/qKhAkZFVjeY0a9bOaPw///ml2rTpZnn8C/P+bBRfOteI/WPbNsvj7+jcxSi+x+M2PgYls8bH357XVmukz29T7NWrl1atWqXTp09LkpYsWSJJ+uKLLzR69GjddNNNkqTMzEy5XOfegXE6nRc+oNyjRw8tWLBAJSUlcrvdeuaZZ/Tqq6/6etkAAPgU9REA4PMrY127dtWdd96p3//+94qMjFSLFi1UpUoV3XfffRo9erSioqIUHR2tTp066eDBg5KkPn366NVXX1VpaakeffRRvfzyyxoyZIhcLpdat26tp556ytfLBgDAp6iPAACfN2O7du2S0+nUypUrJUnvvvuuiouLNWLECI0YMeKic0aOHKmRI0de+Pdnn33W18sEAKBCUR8BAD5vxpo2barU1FQtWrRIDodD9evX1wsvvODrtAAA+DXqIwDA581YdHS0/vKXv/g6DQAAAYX6CADw+Rd4AAAAAAB+jWYMAAAAAGxAMwYAAAAANqAZAwAAAAAb0IwBAAAAgA18/m2KAPzLkCFjfT4vokqEcfw+/e6yPHbErfcZx9+69ROv5gFAMPB43BUyJ9A9PukVn8/Z8Mkq4xzVq9exPPbZe8cZx79990ajeaGhYcY5TOeUlZUY5whEXBkDAAAAABvQjAEAAACADWjGAAAAAMAGNGMAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2oBkDAAAAABvQjAEAAACADWjGAAAAAMAGDo/H47F7EQAAAABQ2YTavYCKkJCQoOzsbMvjPR6PHA6Hz9bj6/jBkoM9+CbHkCFjjXMsXfqqfve7cZbHR1SJMIr/wYIXddeIpy2P3//d10bxJWnr1k/UufPNlsdv27bSOEdlO57i4+OVlZXl0/XA90xqpL8dg/6agz0Ebo4nXnjdKP7Lkx7Vk1P+ajRnwyerjMZv3pymrl1vszz+9OkfjeJL0u7dG9W2bU/L4/ft22oUv7S0WGFhZq8NyspKjMb723PCao3kNkUAAAAAsAHNGAAAAADYICCbsQceeEBr1qyxexkAAPgV6iMABJaA/MxYamqq3UsAAMDvUB8BILAE5JUxAAAAAAh0NGMAAAAAYAOaMQAAAACwAc0YAAAAANiAZgwAAAAAbEAzBgAAAAA2CMivtjdVo0ZdnTlTajSnZs0GlsfGVq9juiQlNrvW8tiCwlPG8SWpXr1mlsfm5x/xKkd4eKTlsR6Pxzh+WFiE5bGlpSXG8c9xGIw134O/OXbskM/n5R753jD6i9q25VPLow/nfGsY/5yvv15neazJseftPG+eE6Gh4ZbHRkVVM44vSdWq1bI0LiYmzqv48DcOmZ0HTcZ6y7/OyyEhTp/PcTjMH1en0/rLOJerzDi+KYfD/D1+kzkm+/05k/Pmzo1fGcc3nVOzZn3jHCZzdmxfZRxfkr7/zvo+2rTpZhzfdM4332w2zuH7143lf67kyhgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAG9CMAQAAAIANaMYAAAAAwAY0YwAAAABgA5oxAAAAALABzRgAAAAA2IBmDAAAAABsQDMGAAAAADagGQMAAAAAGzg8Ho/H7kUAAAAAQGUTavcCKkL79l106NBhy+OPH89RrVoNLY+PrV7HaD3f79+h5ontLY8vKDxlFF+ScnP3q379RMvj8/OPGOcoLj6riIgqlseb9v0lJUUKD4+0PL60tMQoviR5PG45HCYXiM324PF45HA4zBZlyDRHjx53GOfYuHGxevYcanl87pHvjeKbPicO53xrFF+SiooKFBlZ1fJ4t9tlnMP0mDV9TpSWFissLMLy+KioakbxJenkyWOqXr22pbFNmjTWrl3bjXPAvyQkNFV2dralsebnTHO+Pi+fy2F23gwJcRrFd7nK5HSavcQyrRVlZaUKDQ0zWpMJb+qX6bHhdruMHlvTx1QyP2/26fMfRvE/+2yObrzxPqM5YWHhRuNXrPibbrnlEcvjV38+zyi+JBUVFyoyIsry+FZJXYziZ2b+r9q1SzGa8803m43G+/p1o+m5KT4+XllZ/7rsOG5TBAAAAAAb0IwBAAAAgA1oxgAAAADABjRjAAAAAGADmjEAAAAAsAHNGAAAAADYgGYMAAAAAGxAMwYAAAAANqAZAwAAAAAbmP+UeQCqX7++8ZzGjRtZHlstpqZx/IYNra+p8GyMcXxJatSooeWxMTHeHQrx8U0sj/V4vIkfb3ms6S+pe5NDMt+EWXzvmOSoV6+2VzlM5oU4CozjmzwnnKFFxvEls+PV7XZ7mcP638LjxZPCJH6VKtHG8SWpSZPGlsY1aGB+boX/MakVkv+d07w5L5vmCAlx+jS+JDkcDp/mcLnKfBpfkhwO8/f4TXI4nd6+VrGeo25d89d0pnNCQ8OMc9SpE2d5rEmt83ZegwZ1jeObziksND/X+Pp1o0l8q+dWh8ebVwMAAAAAgCvCbYr/Jjc3V3369FFubm5Axg+WHOyh8uQIhj1URI5g2AMCW7AcgzxX7Y8fLDmCYQ8VkYM9XBrN2L9xuVzKycmRy+UKyPjBkoM9VJ4cwbCHisgRDHtAYAuWY5Dnqv3xgyVHMOyhInKwh0ujGQMAAAAAG9CMAQAAAIANaMYAAAAAwAb/P3t3HhdVvf8P/DUMOwiKC7gAKki4p7ijXjdSKxcqzbRyKdOsm8nVrMyu1y1c04vXzBWvWi6p5L5hbrhBKoqKG7KEIMgo+zrz+f3hF352c5kzzMyZGV7Px6PHI/Vz3u/P58w55z2fs41yxowZM+TuhKmxs7NDx44dYWdnZ5bxLSUHx1B1cljCGIyRwxLGQObNUrZB7qvyx7eUHJYwBmPk4Bieja+2JyIiIiIikgFvUyQyAzxnQkRE9Fesj2TueGWMyIQIIaBQKBATEwOVSoXMzEyMGDFC7m4RERHJivWRLBWvjBGZEIVCgaNHj2LOnDkoLi7GrFmzsHTp0op/57kTIiKqilgfyVJxMkZkQu7du4fVq1dj9erVsLW1Rbt27fDGG29g7969AB4XIyIioqqG9ZEsFSdjRCakuLgY9evXx+HDh7FmzRp89913sLe3x/Lly5Gfny9394iIiGTB+kiWipMxIhmV31aRnZ0NAKhbty6ys7Oxbt06fPfdd/D09ERsbCyqVavGWzCIiKjKYH2kqoK/M0Ykk/KHkY8fP47ly5cjNjYWXbt2RX5+PsrKynDhwgWoVCqsWLECEydOhJ+fn9xdJiIiMjjWR6pK+DZFIhmdOnUKixcvxqxZs5CWloZOnTrhwYMHuHXrFuLi4gAAHTp0QGBgYEVxIiIisnSsj1RVcDJGZGRPFo1vv/0WLVq0QJcuXbB582b89ttvsLOzQ0hICLp27Qq1Wg2lUilzj4mIiAyP9ZGqIj4zRmRE5YXmt99+Q3h4OLy9vbFlyxaMHj0arq6uWLt2Ldq0aYO7d+8CAKysuIsSEZHlY32kqspa7g4QVSUKhQKXLl1CWFgY5s2bh5o1a6JTp06oW7curK2tkZOTg0uXLuHVV1+taE9ERGTpWB+pquJkjOgpys/QJScno6CgAP7+/jrHys7OhpOTE6ytraFSqbBp0yYUFhaiSZMmAAA3Nzfs27cPv/76K1JSUhASEoKAgAB9DYWIiEiv9FUjWR+J+DZFoqdSKBSIjIzEl19+iYiICNy5cwcBAQGwt7eXFKe4uBghISFo164dXFxcYGNjAwcHB8TFxSEuLg7du3eHQqGAEALdu3fHK6+8gvbt2/NhZAvCz5KILI0+aiTrIwGskQBf4EH0VCkpKViyZAnGjh2L6tWrY/z48Wjbti0+//xzuLi4aBXjzp07uH//Prp06YLbt2/j1KlTyMnJQb9+/ZCQkICjR4/Czc0NU6dOrfIHIkuWkZGBOnXqyN0NIiK9qWyNZH2kcqyRfIEH0V/cvHkTY8eORY0aNeDv7w8PDw/85z//waVLlxAaGlrxA5Qvkp2dDbVajbNnz2LixImwt7fHuXPnsG7dOhQVFaF///64c+cO5syZY+ARmSdLOE+UlpaG2bNno6CgQO6u6IVGo/nTny3hMyIiafRRI1kfK88Sjr+WVCMrUx95ZUwCjUYDKysrlJWVwdqaj9tZspkzZ+LkyZP497//DT8/PyiVSqSkpGD8+PFYsmRJxf3sL1JSUoIPPvgAEyZMQOfOnSGEwPfff4/4+Hh8//33OH/+POrUqYPmzZsbeETm5cnbFlJTU6FUKuHh4SFzr6QrKipCWVkZrly5gvv372Pw4MFyd6nS8vLycPfuXbRs2VLurpCJYY2sOvRRI1kfdccaaZp0rY98ZkyCwsJCZGZmIiwsDM7Ozqhbt67cXSI9KD+oXbp0CTExMUhNTcWoUaOQnJyMbdu2oXXr1qhRowaqV6+Ot956S6vL6eUxc3JysGPHDrz99ttwcXGBQqFA27ZtsW7dOvTt2xctW7as8pfnn6a8yKxZswbz58/H/v37oVQqzaYol3/+1tbWsLW1xdGjR7F27VrUrFlT64m8qYmJicG1a9cwa9YsnD59Gi1atEDNmjXl7haZENZIy6TvGsn6WHmskaalsvWRkzEtbdu2Dfv378eBAwewc+dONGjQAO3bt5e7W1WKoR7yVCgUOHr0KBYsWAB3d3csX74cubm5mDx5Mq5fv461a9eiXbt2qFWrFpRKpVZ9yM3NhZ2dHRwcHBAfH4/ffvsN3bt3h42NDeLi4nDixAkMGDAAzs7Oeh+POXvyM7579y7279+PadOmwdPTEwsXLoSLi4vJF5snx5CZmQm1Wo2mTZuiVq1aWLlyJapXr25WxSY6Oho7duzA5s2b4e7ujlu3bsHd3R0jRoyQu2tkQlgj5WcuNZL1UXeskaZFX/WR9xG8wNmzZ3H8+HGcP38ekydPxv379+Hi4oKRI0fK3bUq5cmdNysrS69n5O/evYu1a9ciPDwcUVFRqFevHgYNGoQzZ85g+vTp+OKLLyruZ35WkVGpVHj48CF8fHxw/PhxbNq0Cba2tvD19UX79u0RGxuL4OBgDB06FNu3b0dISAjc3d31NgZL8ORnfOTIEURFRcHT0xNNmjRBkyZNoFAoMH/+fJSUlJj0RKB8DOvWrUNMTAyysrIQEBCAAQMG4L333sPKlStRUlJiNrdj3Lx5E9nZ2ViwYAG8vLxQVlZWUezLb0ujqos10jSYco1kfdQP1kjTo6/6yCtjL+Do6IiaNWvivffeg5+fH27duoXc3Fx06dIFCoWCb/kxkvL1vHHjRkRERKBbt256eyYhLy8PmZmZSE5OxpYtW7BgwYKKneutt95CUFDQc2+3KSkpwdy5cyu2jR9++AGfffYZXF1dkZWVhUuXLuHDDz9EtWrV4OrqioEDB6Jbt258nev/KF8XW7ZsQXh4OEpKShATE4P27dujZs2aeOmll+Dq6oo1a9Zg8ODBsLW1Ndn1t3nzZkRGRmLVqlU4ePAglEolBg0ahEaNGsHKygo7d+5Ev379YGNjY7JjKNeqVSt0794drq6uiI6ORnh4OAYNGgQ3NzeT7zsZHmukaTDVGsn6qD+skaZHb/VR0DMlJCSI1NTUP/15wIAB4uTJkzL2quo6deqUGDx4sMjNzRVCCKFWqyXHSEhIENu2bRMREREVf3fr1i3x7rvviuDgYHH37l0hhBC//fabePfdd0V2dvZz86SlpYmUlBRx7do1ERISIsaNGycWLlxY8e+xsbFi3Lhx4vLly5L7KtX9+/dFVFSUEEKIkpISg+czhJiYGDF27FhRUFAghBBi6tSpYvTo0SIuLk5oNBohhKj4/E3ZkiVLxB9//CHWrFkjxowZI/Ly8sTEiRPF4cOHhVqtFtnZ2XJ38YV++eUXERYWJo4cOSKEECIvL098/fXXYuvWrTL3jEwFa6RpMbUayfqof6yRpkHf9ZH3lzzDhg0b8O2332LZsmW4f/8+gMf3tvbq1Qtdu3bVyytF9RHDkj25flQqFQ4dOoSsrCz8/vvvAAArKytJ6zAhIQETJkxAXFwctmzZgvPnzwMAfH190bt3bwghsHfvXqxduxbfffcdxowZAxcXl2deZi4tLcX27dvx2WefwdnZGePHj4cQAlevXsXdu3cBPD5rYmtri7S0NF1Xg9bS0tLwySefYNasWVi+fDnKysoMnrOyyj8/IQRUKhV2796Na9eu4dixYwCA0NBQuLu745///Cdu3LgBACb3HMH/vs5Wo9Hgzp07GDNmDK5evYqVK1fCyckJZWVlUCqVsLKy0vq36uQSHh6OiIgIuLu7o379+igsLISjoyM6derEB+oJAGukKTDlGsn6qB+skabHEPWRtyk+xfbt27Fr1y6EhYXB19cXKpUKp06dQrt27WBlZYWGDRtW+rKpeOISfElJCYqKimBra6uP7v8lhzDDy/3/u36cnZ3RunVrPHz4EHFxcXBxcUGDBg20HpdarcacOXPQrVs3/OMf/0BMTAw8PDxQp04dPHr0CC1btkTt2rWRlpYGlUqFMWPGvPBWCaVSCR8fH6Snp2Pr1q3o378/OnXqhNjYWOTm5gJ4/HaxTZs24c033zT4l1gPDw+UlZVhzZo1aNWqlcnf6vFk30pLS+Hs7IxWrVrh4cOHSEhIgIODAzw9PdGnTx9cvXoVgYGBqFatmsy9/jO1Wg2lUgkAuHz5MrKysmBnZ4dWrVph79696Ny5Mzp27Ijt27fj8OHDFV9eTFlaWhrWrl2L9evXo7i4GKdOncLChQtx6NAhTJ48GQ0bNpS7iyQzS6iR5lwfAdOvkayPlccaaXoMVR85GXuKkydPonPnzoiPj8fBgwdx/PhxrF+/HvXr18eAAQP0kqN8B1u/fj22bt2KlStXQq1Ww8/PT2/3eZfnSExMRI0aNfQS01ieXD+bN2/G8uXLUaNGDbRv3x737t1DbGws7Ozs4OXlpVU8KysrxMTEoFatWmjdunXFm6CioqJw4MABhIaG4oMPPkBwcDC6detWEfdZB2q1Wg0rKytcvHgR58+fR2JiIs6dO4fevXvj5Zdfxp49e7B161Zcu3YNY8eORceOHfWzYp5Co9FU9LOwsBB+fn5Yvnw53N3dTfqtSs/7jP/44w9cuXIFSqUSDRs2RM+ePU2uyMTHx+PMmTPw9/fHpk2bsGLFCpw+fRqnT59GSUkJhg8fjqVLl+LUqVO4cOECFi5caBYTmdzcXISFhSE6OhqHDh2Ct7c33njjDdy6dQsvv/wyqlWrZvJfYsiwLKFGmnN9BEy7RrI+6gdrpOkxVH3kZOwpEhMTsW3bNly+fBlvv/02Pv/8c3h4eCA7Oxtt27bV25eQ//73v4iMjERoaCiOHDmC3Nxc9OzZEzY2NnqJD6DiUrCrqyuaNm2qt7jGUL5+5s2bh0OHDuHhw4cYOXIkfH19ERMTg6SkJHTs2FHr9ZWSkoKNGzfC09MT/fr1Q//+/fHGG29g9OjRyM3NRWFhIVq1aqXVjmRlZYVLly7hyy+/xFdffYXOnTvD2toaGzZsQP/+/dGuXTvcvHkTn3/+Odq1a2eQL6/lRUahUODChQs4ceIE/P39ERQUBD8/P0ydOhX+/v6ws7NDYWGhyd26ALz4M05JSUHHjh1hbW1tUl/+NRoNfvrpp4ovGuVvCOvZsydcXFxw4sQJBAYG4uOPP0bPnj0xcOBA1K9fX+5uP9e1a9eQk5ODGjVqoE+fPnBycsKHH36IoKAg3Lx5E5GRkXjzzTfh4OBgUp8FGZ+l1Ehzro+A6dZI1kf9YY00DQavjzo9aWaBduzYIRYtWiSmTp0qUlJSRH5+vtBoNCIjI0P88ssvYtCgQeL27dviMIgLAAAgAElEQVR6y1dUVCRmzpwpCgoKxLp168S4ceNEfn6+GDdunIiOjtY5bvkDnOWys7PF1q1bxcCBA8XOnTsr2+2/xH5e7sp42vrJy8sT48aNE9euXRO3b98WDx48kBx33bp1ok+fPuLo0aNCCCGuXbsmDh8+LLp16yZiYmKeu2xycrL48ccfKx6O3b59uwgJCan49/T0dPHJJ5+IIUOGiOTkZJGXlye5f9pKTk4WK1asEEIIceLECdG1a1cxbtw40bZt24qxHThwQLRt21Z06dJFXLp0yWB90ZWhPmNDe/Jh9fnz54tPPvlEjB49uuLvHjx4IP7+97+LzZs3y9E9nYSHh4u3335bfPTRR+L1118X27dvF0II8eWXX4pp06aJ/v37i5s3b8rcS5KTJdRIS6mPQphejWR91D/WSNNgjPrI3xnD44fx9u/fjxEjRiAjIwPDhg3D4sWL8ejRI/z3v/+FWq3GggUL4OPjo3MO8T9nfpRKJe7du4fhw4fDw8MDP/zwAxQKBdRqdaXuly3PcfbsWXTq1AkuLi4VrwddvXo1HBwc0LdvX53jl8vKysKSJUswfPhw2NnZoXHjxpU6K6Pt+iktLa24F13bmFevXsWdO3fg6uqK4cOHw8XFBXPmzIG9vT0ePHiAa9euYcaMGQgICHhuvPz8fKxZswbFxcX45JNP4OXlhe3btyMmJgbt2rWDu7s7/Pz8UFZWBpVKBU9PT53Xx4vk5+dj7dq1UKlUyMvLQ1hYGF5++WWsXLkSoaGhsLKyQt++fSv6Ywo/oGiIz1jX3JWJU/6w+q+//gp7e3s0a9as4reWOnfujJo1a8LLywvZ2dl6zW0oR44cwa+//ooNGzaguLgYcXFx+Oabb+Du7o6BAwdCpVLho48+0vp2J7I8llIjzbU+AqZfI1kfK4810vQYqz5W+dsUMzIysHr1aixbtgytW7dGnz59kJeXhxUrVmDGjBno27cvBg0aBA8PD51zPLmhXb9+HQ8ePIC7uzusrKwqfuywadOmiIiIwIkTJzB06FDJl8yfzKFSqTB16lT8/vvvCAoKgp2dHerWrYvr168jPDwcnp6e8PX11Xk8RUVFcHFxQWpqKhYvXowzZ87gzTff1DmelPVz8uRJrdePQqHAsWPHMGvWLBQVFeH8+fPYsWMHxo8fDzc3N4SGhuLVV1/FG2+8gSZNmjz3gKDRaFC7dm106dIFYWFhePToEVq1aoXExEQkJSUhLS0NarUa69atw1dffYWWLVvqvD5epLwvgYGBWLlyJTIzM9G/f3/Url0bAQEBKCoqwuLFi9GoUSO0adNGrz/+qStDfcZSc5eUlFQ8TKyL8jgnT57EDz/8gMWLF6N9+/ZITExEbGwsLly4gNTUVOzcuROfffYZatSoYbJFptzJkyfh6uqKXr16wdraGo0aNUJiYiJKS0sxaNAg+Pn5wdXVVe5ukkwsoUaac3383/6bYo1kfaw81kjTZKz6WOUnY3l5efjpp5/Qtm3bih8t9Pf3x/nz59GrVy84OztX6v70J3+Be82aNViyZAl+/vlnFBYWonPnzrCxscH69evx22+/ISoqCosXL5Y8w35yRyosLISLiwuaNWuGyMhIxMTEoFevXrC3t8e9e/fQtGlTdOvWTeeN5/bt21i0aBGCgoKQmZmJX375BR4eHmjatClq165dMWZtdy5Drp+UlBT861//wrx58/DOO++gXbt2SE9PR2RkJD777DPk5+fDy8sL3t7eAJ79so7yMz1lZWXw8PBAly5dsGTJEjg5OaFHjx7IyclBZGQkLl68iJEjR6Jz585a9U8XT/bF3d0d3bt3x+7duwEALVq0gK2tLQICAlBSUgIvLy80aNDAYH3RljH2gecp/1zDw8OxefNmBAUFVergn5SUhEWLFkGtVqNz585wdXVFQEAAkpKScPDgQdja2mLatGlo3LixvoZgUHfu3MHNmzfRqVMnODo6QqFQICoqCjY2NujQoYNJn7UkwzP3GmnO9bG8vSnXSNbHymONNF1Gq4+VusnRjMXGxoo//vhDFBUViTlz5ogVK1ZU3O++Y8cOMXToUL3+6FxcXJyYOHGiyMvLE1euXBF9+/YVP/74o8jOzhbp6eni5s2bIjMzU3LcJ+9DX79+vZgwYYKYN2+eEEKIy5cvi/fff1+MHTtW/Pe//xWvvvqqSE9Pr9Q4srKyxIMHD8T58+fFtWvXRHp6uvjxxx/FmDFjxNmzZ3WOq6/1U74+VCqVyMrKEn//+9/Fw4cPhVqtFhqNRhw4cECMGTPmqcs8L15UVJSYNWuWWLp0qTh9+rRITEwUQUFBYuXKlSI3N1eUlZVV3Cuv72cDXtSXpKQk8corr4ilS5eKnJwcrccmRXR0tLh+/brIyMjQOYah9gFt7N27VwwdOlSn/j9tHf7+++/io48+EsuWLRNJSUlCCCGKi4vFjz/+WOl9zBh27doltm7dKjZu3Chu3bolgoODxeLFi8X+/ftFRESE6Nu3r0hISJC7myQjS6iRllIfhTDNGsn6+Jg+6qMQrJGmQo76WCWvjIWHh2PdunW4evUqunfvXnEZeNOmTbh69Sp2796NBQsWoF69ejrnuH79Os6fPw8/Pz9ERUVh8uTJ6Ny5M7p164Y6deqgdevWCAsLQ0ZGBnr16oU6derA0dFRcp7yGfmmTZuwe/dujBs3DjNmzEBubi4GDRqENm3aIDY2FikpKfjmm290fm1o+dk8BwcHqNVqbN68GevXr8eAAQPQpk0bpKam4siRI7h69SqSkpLQqlWr58Yz1PpRKBQ4fvw4/vOf/6BBgwbYtWsX/P394eXlBYVCgUePHiE6Ohrdu3eHvb19xduWXhRv/vz5GDp0KPbs2YMLFy5g1KhR6NKlC/71r38hPz8fgYGBFb+BY6irCC/qy9y5c1FYWIgOHTpUnGXTR1/Cw8OxdOlSHD16FFlZWahVqxZq1ar1wuWMtQ88jXjibFVBQQF27dqFyMhIvPXWW6hRo0bFq5elxNm+fTt27NiBLVu2oGvXrvD09MSZM2eQk5MDNzc31KpVCwEBASb7Vq5y4eHh+PXXX9GzZ09MnjwZXbt2RXBwMM6ePYvr16/jzp07+Oc//1mpW7XIvFlKjTTX+giYR41kfdS9PgKskaZItvqo16mdiSsrKxP79+8X7777rhBCiIsXL4ozZ86IiIgIsXfvXnH69Gmxf/9+kZKSUuk8u3fvFg8ePBBpaWlCCCGmTJkiBg0aJJKSkireNHPx4kURHBwssrKyJOd48m01iYmJ4t133xVZWVkiNjZWTJ48WQQGBorp06dXnLksLi6u1JjKbd26VYSEhIjly5eL0NBQMXToUHHv3j3x6NEjsWrVKvH222+LW7duPTeGIdZP+ZmZ27dvixEjRlSchQwPDxcdO3YUYWFhYu3ataJ///7i8OHDWsfLz88XEyZMEHfv3hXnz58Xb7/9tkhPTxcbN24U9+7dE5cvX670GU9T7kt0dLT48MMPRWlpqTh//ryYMmWKWLBggYiPj3/ucsbYB57lybN0RUVFoqysTDx48EDMnDlTDBw4UCQmJlb0Uds4a9euFUOHDhUHDhwQoaGhonPnzuL06dPi3LlzYtSoUWLt2rWipKTEYGd99aGoqEjcuXNHjB49WpSVlYnw8HAxfvx4ce/ePbFv376Kdvn5+TL2kuRkKTXSnOujEKZfI1kfH9O1PgrBGmlq5K6PVebK2O+//45Hjx7h1q1bUCqVePDgAfbs2YNjx44hLi4OHh4eGDBgAHx9fSv1NkONRgOlUgk/Pz+oVCrMnDkT+fn5mDRpEs6dO4cDBw4gICAA1apVQ926dREcHCz5LEFRUVHFPfpXr15FZmYmrl69ivbt22P37t344IMP0LZtW8ybNw+lpaXo0KFDxVkpqcQTZz0OHTqEH374AT169MCyZcvQokUL1KxZEz///DO6deuGnj174rXXXoO7u7vR149CoUB0dDRWr16N/Px8ODo6onXr1mjTpg08PDyQlpaG7OxsjBw5En/7299eeJ+vQqHA5cuXYW9vj/z8fOzfvx+7d+/GokWLUK9ePcyaNQvdunVD06ZN0aBBA4M+VyNXX2JiYhAeHo5q1aqhX79+qF+/PlxcXBAdHY1bt26hXr16T3342Rj7wLM8OfaNGzdi06ZN2LhxI2xtbdG+fXtYW1tj3bp1CAgIgJub23NjlcdJTU1FREQE5s2bh5YtW6Jr166wtbXF3Llz8dVXX8HFxQVdu3aFi4uLyT5bdeHCBWRkZECpVOLy5cu4c+cOoqOjsWzZMjx8+BDffvsthgwZAmtra73+ziGZD0upkeZcHw25fvRZI1kfda+PAGukqTGF+vjia5AWYv/+/bh06RIaN26MpKQk7Ny5EwMHDsSGDRvQq1cvJCUlQQgBIYTOOcQTr/S8fPkybt++jQYNGuDSpUv49ddf8f3336NmzZqYNGkSUlNTAUDyB3vs2DGsWbMGABAREYHQ0FB06dIFkyZNgkqlgkajgaenJzIzMzFgwACMGDFCL4Xmxo0byMvLw6JFizBmzBj88MMP2LVrF+zs7ODl5YVvv/0WpaWlsLe3f248fa+f8s/rzp07WLVqFS5cuIAGDRogKysLhw4dAgC8/vrrmDJlCr7++uuKh4ef97KO8nhz587FiBEjcOHCBSQmJuLrr7+Gp6cnbt26heLi4j9dwjfEAUbOvly+fBm5ubmoVq0aiouLcfDgQQBAx44dMWjQIJSUlDz1QG2MfeB5ysf+888/49dff8Wnn36K7t274/r16zhx4gQGDBgAHx8fzJw5E6WlpS/c38+dO4ewsDAkJCTg4sWLFX8/ZMgQtGjRAoWFhejTp88Lv2DJbd++fYiPj6/YNw4cOIDly5fDxsYGUVFRqFevnla3pJDlsoQaac71sTymKddI1sfHdK2P5f1mjTQtplAfq8SVsePHj2PLli2YMmUK/P390b9/fwwZMgSpqam4dOkStm7dismTJ8PNza1SO+qTZxvWrFkDpVKJdevWwcfHBwkJCSgqKsJnn32G2NhYdOrUCdWqVZOU7969e5gyZQqmTZuGo0ePYsuWLRg5ciR8fHxQu3Zt/PTTTzh27Bhq1KiBsLAwzJ49u+ItSFI9WWh++uknzJkzB+fOnUNRURHatGkDX19fNGvWDDNnzkT37t0xefLkF47HEOtHoVDgyJEjmDdvHlq0aAEbGxtcvHgRTk5OKCsrQ1ZWFvz8/LQe95Px2rVrBwC4ePEiSktLUVBQgG3btmHbtm347LPPKv7dUOTqy/r16zF37lyUlpbi3r17cHV1RU5ODgoKCuDj4wMvLy907tz5qWfHDb0PPEt8fDxOnjyJpk2boqCgAJs3b8b48ePRrFkzBAQEoLi4GFu3bsXAgQPRo0cP9O7dW6vcly5dgkqlQq9evZCSkgIHBwfUqVMHe/fuxdmzZ/Haa6/BwcGh0v03pPLj38SJE1GzZk2UlJSgqKgI27dvR0pKCnbv3o2ZM2dWvO2Nqh5LqJHmXh8NtX70WSNZHytXH8v7DbBGmgpTqY8WPRkrf6j2999/R5s2bRAQEIDY2FgcPHgQV65cwZEjR3DlyhXMmDFDbz/6V/7Brl69GtevX0f16tXx6aefIjU1FVeuXIFarcbHH3+MatWqSY5dWlqKDRs2IDExEREREUhPT0fNmjXRpk0b2NjYwN/fHwkJCbhy5Qr+9a9/VeoBw/Id8MiRIzhx4gRCQ0Ph4uKCtLQ0qFQqNG7cGD4+Pmjfvj0CAgJQv359reLqe/2oVCp89913+PbbbxEcHIwePXqgtLQUJ0+ehJWVFYqKiuDn56f1Jf4n4w0ePBg9evSAlZUV4uLi0K9fP3Tr1g19+/ZFYGCgwV/5LUdfdu/ejX379mHNmjW4evUqMjIykJubCzs7O6SmpkKtVqNx48awtrZ+Zj5D7gPPkpubiwkTJiAqKgrx8fG4fv06mjRpUvElw9fXF5GRkXj55Zfh7e0NJyen58YrLCyEjY0NGjdujB9//BHx8fFwdnbGhg0bEBsbi3379mHhwoUm82rkp/nf41+HDh1w48YNxMXFVZz9dnNzw4cffsiXdVRRllQjLaE+AqZdI1kfK18fAdZIU2Bq9dGi70uxsrLCo0ePsHv3bpw5cwYbN27EF198AbVajSZNmmDp0qUICwuTdOXkRQoKCjBw4EBERETg3LlzWLBgAS5cuICYmBh06tTpub9g/yJubm7o168f9u7dixEjRmDz5s3Yt28fdu7cidLSUri5uWHu3LlYtGgR/P39Kz2WxMRELFmyBPb29vD09MTw4cPh6+uLy5cvY/PmzcjPz0fHjh3RqFEjrWPqe/1YW1ujsLAQeXl5AAAHBwd06tQJ1atXR3JyMnr37i3p8vjT4rVt2xZOTk44ePAgWrVqhZdfftkov71k7L5oNBrExMQgODgYJSUlKCsrw/vvvw8HBwfExMSgpKQEbdq0AfD8Wz0MuQ88S6NGjfDRRx/h4sWLqF+/PiZNmoQlS5bgwoULAB7fhpCenq7VthAZGYklS5bgyJEjsLa2xmeffYbAwEC88847mDZtGvr27YvVq1fr7cupoTzt+Pfpp59CqVSiVq1amDRpEt577z34+PjI3VWSiSXVSEuoj4Bp10jWx8rXR4A10hSYWn206CtjQgjExMRg8+bN8PT0RPPmzTFkyBD0798fnp6eAPDCMxhSZWVlYfr06cjMzMSGDRugVCpx5MgRNGnSBKNGjar0Q5jlP6o4Y8YM+Pn5YciQIVi8eDHUajWaN28Oa2trWFtb62Us9vb2EEJg27ZtqF+/Pvz9/dG6dWvcvn0bqamp6NSp0wvvgf9f+l4/NjY2SEpKwv3791G7dm3UrFkT9+7dQ2pqKqpVq4ZatWrB399f6wPy0+KlpaXhwYMHUKvVAICmTZsa5SFUY/dFoVAgPz8frq6uuHjxIpo1a4Y+ffrg+PHjcHd3x4QJE7R6lbWh94FnqVu3Ljp27Ih//vOfqFOnDnr27ImpU6ciMTER+/bt0/qHMjUaDdLS0rB8+XJkZmYiMTERZ8+eRadOndC2bVs0atRIr2csDeVpx7+hQ4fitddeq/jxXqraLK1Gmnt9BEy7RrI+Vr4+AqyRpsDU6qNFT8YUCgXq1auHgIAAjBw5En5+fn+Z9ev7oFGzZk08evQIQgio1WpcvHgRe/fuxUcfffTMN+tIje/j44OOHTti4sSJaNeuHQYMGIDVq1dj4MCBOh38n8Xa2hrNmzdHtWrVsGbNGlSvXh0vvfQSAgICEBAQgOrVq+vUf32uH4VCgfr16+PYsWPYuXMnEhMTsWLFCkybNg0PHz6Eo6MjmjdvrvXn/Kx4X331FbKzs+Hk5ITmzZtL7qcu5OiLr68vateujRkzZqBPnz5ISkrCnj17MH/+fK1vtTH0PvAsLi4uaNSoEQIDA/Htt9+ib9++aNu2LYYMGYI333xTqyIDPD7D3q5dO/Ts2RP3799HZmYmIiMjoVarK35zyRTfCPW/tDn+UdVmaTXS3Otj+RhMtUayPla+PgKskabA1OqjQlTm1UhmRq1WQ6lUGjzPgwcPcODAARw6dAj16tXDmDFj9HqbR7nLly9j6NChWLBgAYKCgvRaaJ5UUlKCiIgIhIWFYfr06XjllVcqFc8Q60elUiE2Nhbp6enw9PSEm5sbvvrqKyxZskTybSKGiFcZxu5LUVERpk+fjuLiYty5c0en23qMtQ88y8WLFxESEgKFQoFNmzbpfKar/GzxL7/8gi5dulTqR271JS8vD0IIyWcejXX8I/NlSTXSXOsjYPo1kvWxcvURYI00JF1qpNz1sUpNxoytrKwMAPR2W8TTXLlyBY6Ojga/r7WkpAS7d+9Ghw4dKm5fqSxDrJ+rV68iIiICFy5cwJw5cyr9bIC+45lLXzIyMpCUlAQPD49Kfd7G2AeeRaVSAcALfyvleYzx7IMUJSUlmDhxIr777judz7wTmQpDHx/MuT4Cpl8jWR8rVx8B1kh9M9cayckYac3UdrqnKS0tRVpaGuzs7PRyyVnf8SylLySfwsJCk35VMFFVZA71EdBvHTGlmmRKfSF5mWON5GSMiIiIiIhIBhb9ansiIiIiIiJTxckYERERERGRDDgZ+x85OTkICwtDTk6OWca3lBwcQ9XJYQljMEYOSxgDmTdL2Qa5r8of31JyWMIYjJGDY3gBQX+SkpIi/Pz8REpKilnGt5QcHEPVyWEJYzBGDksYA5k3S9kGua/KH99ScljCGIyRg2N4Pl4ZIyIiIiIikgEnY0RERERERDLgZIyIiIiIiEgGnIz9D6VSifr160OpVJplfEvJwTFUnRyWMAZj5LCEMZB5s5RtkPuq/PEtJYcljMEYOTiG5+OPPhMREREREcnAWu4OGMOQISORnp6hdfuTJ/ejW7f+Wrd/uW0PSf0JWzoVf584T+v2mff/kBQfADZvDsOwYX/Xun109H7JOe7cuQ0fH1+t20ud9yck3EHjxj5at7e1dZAUHwDi4+Pg799C6/ZFRXmS4icmJqJhw4YSeyWNJeSwhDHolkMhMf5dNGzYSOv2rq61JMUHgNjYaLRu3V6rtvXq1cX+/bsk5yDT8tprwUhLS9eq7YULZ9C2bWdJ8Rs1aiWp/fbtP+LNN8dp3b5D7+6S4gPA1AkjMG/5Jq3b/zB/mqT4pni8qeZcQ1L8K3EX0bJFG0nL5OY9lNTeFNeTqcW3lBymOAY7OydJ8W/cuIqXXmqudfv69evh6NHDL2xXJSZj6ekZSE29J2kZKe0beD2S2iU8eKD9MunpDyTHl7pcUlKSTjmkLKfLRVgp8e3sHCXHB4Dk5GSt2xYW5kqOr+u6rWo5LGEM0nNIm4xJjV+jRpHk+ACQkiL9BBCZr7S0dEmfudTtw8GhrtQuIT09U+u2D7OlH5elLqfLscPUjjcu1aSdTASA5OQUSe1zcrMk5zC19WSK8S0lh6mNwd7eWXJ8Kd8ZtcVnxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZcDJGREREREQkA7P40WeNRoO5c+ciNjYW+fn5EEJg9uzZCAgIkLtrREREsmF9JCIyb2ZxZSw2NhYZGRnYsmUL9u3bh+DgYKxatUrubhEREcmK9ZGIyLwphBBC7k5oIyEhAWfPnkVKSgrOnTsHJycnbNiwQe5uERERyYr1kYjIfJnFbYrHjh3DnDlzMHr0aPTu3RuNGzfGrl27tF6+W7f+SE29p3X7hIRYNG7cWuv2HTu/qnVbAPh503d4Z8RXWrdPS02UFB8Ajh37GT16vKN1+1OnfpGco6ysFNbWNlq3lzrvV6vLoFRqv4na2TlKig8ABQU5cHR00bp9YWGupPhCCCgUCqndqnI5LGEMuuWQ1h8hNFAotL+hoUYNd0nxAUClSoObW12t2np6NkBsbLTkHKQ/la2PANC2bWekpPyhVdvMzBTUru0pKb6fX3tJ7aOidiAw8A2t23d7/RVJ8QEg9Kvx+PK7FVq3n/f1x5Lim+LxxqVaTUnxs3MewNWllqRlcnKzJLU3xfVkavEtJYcpjsHe3llS/MLCXDg4VNO6vZeXF27cuPrCdmYxGYuKikLPnj0xfPhwFBUVYdWqVVCr1XJ3i4iISFasj0RE5s0snhkbNmwYzp8/jwEDBiA4OBienp74448/oNFo5O4aERGRbFgfiYjMm1lcGfPx8UFERMSf/u6bb76RqTdERESmgfWRiMi8mcWVMSIiIiIiIkvDyRgREREREZEMOBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZmMXvjFXWo4fpyHqQKmkZKe1Pn9olsUffSVrm62WLJcZ/7J1/jNK67fHjW3TKoVarJbQWkuNrNNrHt7d3khxf6nKFhbk65SB6Oun7hJRl8vMf6RBf++UKC110ik+mJS9PhZycB1q3l9IWAK5dOy21S5KWmblyluT4ABA0sJvWbed9rVMKk6K0tjHKMkTmoqSk0KDLlJYWadWOV8aIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikgEnY0RERERERDLgZIyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGRg/aIGgwYNwpdffonOnTtjz549+OqrrxAdHQ17e3tMmzYNfn5+uHLlCuLj46FQKNCtWzeEhITA2toaLVu2xOjRo3H69GkUFBTg008/xYEDB3Dz5k3UqVMHK1asgKOjI3755Rds2bIFpaWlyM7OxtixYzF8+HDs2LEDhw8fhpWVFZKSkmBvb4958+bBx8fHGOuGiIjouVgjiYioMl54ZSwoKAgnTpwAAJw8eRKurq6IiYmBEALHjx/HiRMnUL16dezevRvbt2/HjRs3sHbtWgBASUkJatWqhV9++QWDBw/GN998g2nTpmHfvn3Iy8tDZGQk8vPzsW3bNqxcuRIRERH4/vvvsWDBgor80dHRmD59Ovbs2YPWrVtj5cqVBloVRERE0rBGEhFRZbzwylhQUBBCQkLwxRdfICYmBqNGjUJUVBScnJzg5eWFa9euYfr06VAoFLC1tcWwYcOwfv16fPTRRwCAvn37AgC8vLzg5+cHd3d3AECDBg2QnZ0NJycnrFixAsePH0diYiLi4+NRUFBQkb958+bw8PAAADRr1gyHDx+WPMgrcRclL5Od80DyMlIkJV01aHwAGDegr/ZthUanHELH5bSPLwwaHwBUqjSDxjfGGCwhhyWMwRg5jDGG4uJCg+ewFJZQI2/evCGpvTG2j4cP0w2eo3fz5lq31WW/s4TjjaHrI2AZ68kSxmCMHJYwBrW6TO8xXzgZe+mll1BaWorIyEg0bKTA+iQAACAASURBVNgQPXv2xKRJk2BtbY2+ffti+fLlUCgUFe01Gg3Kyv5/R21sbJ76/+XS09Px9ttvY+jQoQgICEC/fv3w22+/Vfy7vb19xf8rFAqdVnLLFm2QnJyidfvsnAdwdamldfvqNdwl9Scp6Sq8vbUvAl8vWywpPvB4Ivbj7oNatx8/sL/kHEJooFBIeexQ2mcnhPjTtvUiNWp4SIoPPC40bm51tW4v9QuC1DHowhJyWMIYjJFDanxbW/sXN/ofxcWFsLNz0Kqtt7eX5C/ylsYSaqSf30tISkrWqq2U7aOco6OrpPYPH6ZLOp7/cipSUnzg8UQs8qr2J0X7tGghKb4pHm+k1kip9RGomjXSEsZgjBymOAYrK6Wk+Gp1GZTKF06dKnh7eyMh4c6L+6FNsD59+mDRokUIDAyEj48P8vLysHv3brzyyivo2rUrNm7cCCEESkpKsHXrVnTp0kXrjsbFxcHNzQ0TJkxA165dK4qMWq3WOgYREZFcWCOJiEhXWk3GgoKCkJCQUFFAunTpgtq1a6Nu3br45ptvoFKpMGDAAAwYMACNGjXC+PHjte5AYGAg3N3d0a9fP/Tv3x9paWlwc3NDUlKSbiMiIiIyItZIIiLSlUIY4wZOmfE2xRfjbYraqYq3YBgjhyWMwRg5eJsiGQJvU3wx3qaonapYIy1hDMbIYYpjMKvbFImIiIiIiEi/OBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZcDJGREREREQkA+1flm/G1Bo11Joyicto3/7evdtSuyRpmQmDX5Mcf5y6TNJyizb+IjmH1OXm/+MfkuO7uzfUuu39+4mS4wPSfhfF0dFFcnwpyxQU5EiOT+bLzs7RoMvosr0CgJNTda3aOTjoFp9Mi4ODi9afOaD99lGuuLhAapdQUlKoddvXAjpIjl9UlC9pufnrtkrOIXWZ+VNCJOeoVauB1m1VqjTJ8bOzMyW1l/bbo9KXEUIjOT6ZL6m/AyZ1mZpu9STHl7JMjera/Q4xr4wRERERERHJgJMxIiIiIiIiGXAyRkREREREJANOxoiIiIiIiGTAyRgREREREZEMOBkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAYmMxlbunQpIiIiAADLli3DkSNHAABffvkl1qxZI2fXiIiIZMP6SERkuazl7kC5iRMnVvz/uXPn4OvrK2NviIiITAPrIxGR5TLalbFBgwbhzJkzAIA9e/agZcuWKCoqAgBMmzYNXbp0wZo1a7Bp0ybExcVh/vz5OHz4MADg4sWLGDZsGPr06YOPP/4YBQUFxuo2ERGRQbE+EhFVXUabjAUFBeHEiRMAgJMnT8LV1RUxMTEQQuD48eNo2rQpAGDEiBFo0aIFvvjiCwQFBQEA7t+/j3Xr1uHgwYO4f/8+Dh06ZKxuExERGRTrIxFR1WW02xSDgoIQEhKCL774AjExMRg1ahSioqLg5OQELy8v1K5d+5nL9unTBw4ODgCAJk2aQKVSScp97dplyf3Ny3skeRkpSkuLDRofANTqMoPnCBnxhkHalktPvyt5GamEEAaNn5+fbdD4gOHHYIwcljAGY+QoKso3aHwAUKnSDJ6D/j856yMAxMZGS2pvjO3DGMdNQ+9LU0YNMWh7AMjMTJG8jBTG+B6h0agNnoP1yzRyGGMMht5mMzKT9R7TaJOxl156CaWlpYiMjETDhg3Rs2dPTJo0CdbW1ujbty+uX7/+7E5a//9uKhQKyR9ms2atkJys/crLy3sEZ+fqWrcvLi6U1J/S0mLY2Nhp3V6XA5VaXQalUvuPd8F/t0rOETLiDSzetEPr9vP/8Q9J8dPT78LDo5HW7e/fT5QUH3h8YFAoFFq3d3R0kRQ/Pz8bTk6uWrcvKMiRFB+QPgZdGDqHJYxBlxx2do6S4hcV5cPe3knr9lK3V+DxF203t7patfX0bCD5izz9lZz1EQBat26PlJQ/tGorZfsoV1ws7dZJqcdNXb58Sd2XZq0IlxR/yqghWBC+TdIy86eESGqfmZmC2rU9tW4vdRIt9XsEIP3LtkajhpWVUkJ8jaT4j5dh/TKFHLrEl7JtANK32Zpu9STFz8hMRp3aXlq39/RsgN8vnH5hO6O+TbFPnz5YtGgRAgMD4ePjg7y8POzevRuvvPLKn9oplUqUlRn+bAwREZEpYH0kIqqajDoZCwoKQkJCArp06QIA6NKlC2rXro26df98lq1Xr15YvHgxdu7caczuERERyYL1kYioajLqq+3btGmDGzduVPx59uzZFf8fGhpa8f/vv/8+3n//fQBAcHDwn2I82Y6IiMgSsD4SEVVNJvOjz0RERERERFUJJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkoBBCCLk7YWgNGzZEUlKS1u2FEFAoFAbrj6HjW0oOqfGNsSlLHa8lfA7GyGEJYzBGDlMbg7e3NxITEw3aHzI8KTXS1LbBx6T3RwgNFArDnY82dHxdchSXlkiKb2ttjZKyMknL2NnYSGpvmtuTacW3lBxVcQza1kheGSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikgEnY0RERERERDKwlrsD2tBoNJg7dy5iY2ORn58PIQRmz56NgIAAubtGREQkG9ZHIiLzZhZXxmJjY5GRkYEtW7Zg3759CA4OxqpVq+TuFhERkaxYH4mIzJtCCCHk7oQ2EhIScPbsWaSkpODcuXNwcnLChg0b5O4WERGRrFgfiYjMl1ncpnjs2DHMmTMHo0ePRu/evdG4cWPs2rVL6+UbNmyIpKQkrdsLIaBQKHTpqknEt5QcUuMb47yC1PFawudgjByWMAZj5DC1MXh7eyMxMdGg/aHnq2x9BKTVSFPbBh+T3h8hNFAoDHdzkKHj65KjuLREUnxba2uUlJVJWsbOxkZSe9PcnkwrvqXkqIpj0LZGmsVkLCoqCj179sTw4cNRVFSEVatWQa1Wy90tIiIiWbE+EhGZN7N4ZmzYsGE4f/48BgwYgODgYHh6euKPP/6ARqORu2tERESyYX0kIjJvZvPMWGXwNkXzzMHbFKtODksYgzFymNoYeJuiZeBtivrH2xS1Y5rbk2nFt5QcVXEM2tZIs7gyRkREREREZGk4GSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDa7k7QERERKQ7XX9j0tC/TWmMn3HVPoettfSvfLosQ0TS8MoYERERERGRDDgZIyIiIiIikgEnY0RERERERDLgZIyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGXAyRkREREREJAODTMbOnTuH119/3RChiYiIzBprJBERleOVMSIiIiIiIhkY9KfV7969i5kzZyI/Px+ZmZnw9/fHkiVLYGdnhxYtWqB3796Ij4/HwoULoVKpsHDhQlhZWaFp06Y4ffo0fvrpJzRo0ADbtm3Dzz//DI1Gg+rVq2P69Onw8fExZNeJiIgMijWSiIggDODs2bPitddeE6GhoSIiIkIIIURJSYl4/fXXxYEDB4QQQvj5+YmdO3cKIYRQqVSiQ4cO4vr160IIIXbs2CH8/PxESkqKOHfunBg+fLgoKCgQQghx8uRJ0a9fP0n98fb2FgC0/k8IIam91P8MHd9SckiNbwymto4sJYcljKEqridvb2+j7HeWxpxrpBCmtQ2aag5THIMxWMJ6MrX4lpKjKo5B2xpp0CtjU6ZMQVRUFFatWoXExERkZGSgoKCg4t/btWsHAIiJiYGPjw/8/f0BAMHBwZg9ezYA4NixY0hKSsKwYcMqlsvJycGjR49QvXp1rfqRmJgoue+P17fhGDq+peQwxhik0KU/lvA5GCOHJYzBGDksYQz0mLnWSEvZBrmv6l9VrZGWMAZj5OAYns6gk7GQkBCo1Wr0798fPXr0QFpa2p8G4ejoCABQKpV/GZyV1ePH2TQaDQYNGoQpU6ZU/DkjIwOurq5a96Nhw4ZISkrSur0QAgqFQuv2Uhk6vqXkkBrfGDu51PFawudgjByWMAZj5DC1MXh7e+t0soseM8caaWrboKnmMMUxsEaaZ3xLyVEVx6BtjTToCzxOnTqFTz75BK+++ioAIDY2Fmq1+i/t2rZti8TERMTHxwMADh48iJycHCgUCnTt2hV79+5FRkYGAODnn3/GyJEjDdltIiIig2ONJCIig14ZmzRpEj755BM4OjrC2dkZ7du3R3Jy8l/aVa9eHYsXL8bUqVNhZWWFFi1awNraGg4ODujatSvGjh2LMWPGQKFQwNnZGcuWLTP47JqIiMiQWCOJiMg4T3S+QG5urpg3b17FA8hxcXEiMDBQaDQavcTnCzzMM4fU+MZgauvIUnJYwhiq4nriCzyMw5RqpBCmtQ2aag5THIMxWMJ6MrX4lpKjKo7BJF7goS1nZ2fY2NjgrbfegrW1NaytrbFkyRKe2SMioiqPNZKIyHIp/m+WZ9H4Ag/zzCE1vjE25ar4cLIxcljCGIyRw9TGwBd4WAa+wMP84uuSgzXSPONbSo6qOAaTeIEHERERERERPR0nY0RERERERDLgZIyIiIiIiEgGnIwRERERERHJgJMxIiIiIiIiGZjEq+1JftbWtgZfTqmUvrnZ2Tlq3dbBoZrk+IWFuZKWu33/vuQcUpbxdXeXHJ+IqHIU//eflPZSGPotfrq+PU375erV85EcvV49X0ntbWzsJOfw9m6udVs3t7qSYqtUaZKXWRC+VVJ7qctMGTVUcnwyX7p8N5WyjFpdJjm+QqH9dSxt2/LKGBERERERkQw4GSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDDgZIyIiIiIikoG1vgKdO3cOCxcuRL169ZCQkAB7e3uEhoZi1apVaNKkCT744AMAwJdfflnx5169eqFVq1a4ceMGQkJCkJmZic2bN8PGxgZ2dnaYOXMmfH19cf/+fcycORNpaWkoLS3Fa6+9hvHjx+ur60RERAbD+khERM+it8kYAMTFxWHq1Klo164dfv75Z0yZMgV+fn7PXaZJkyZYsmQJ1Go1WrdujaNHj6JOnTqIiIjA77//Dl9fX0yZMgWjRo1Cr169UFxcjLFjx8LLywuvvvqqPrtPRERkEKyPRET0NHqdjPn7+6Ndu3YAgDfffBMzZ85EnTp1nrtMeXulUol+/fph2LBh6NGjB7p27Yq//e1vKCgoQHR0NLKzs7F06VIAQEFBAeLj47UuNomJiZLHIoSQvIwpxTdWjtLSYoPGLyrKN2h8ACgszDVofJ8X7ANP0vUzs4TtyRLGYIwcljCGqshU6yMAJCbelTQWITSS2uvCONu5YceRmnrLoPEBIDExzqDxVao0g8YHgMkjh0hoa5o10lKOy5awngz9vVSjUes9pl4nY0ql8i9/V6NGjT+t/NLS0j/9u6OjY8X/L1y4EDdv3sTp06excuVK/Prrr5gzZw6EENi8eTMcHBwAACqVCnZ2dlr3q2HDhkhKStK6vRACCoVC6/ZSGTq+LjmsrW0l5ygtLYaNjfafg1IpbXMrKsqHvb2T1u0VCumPQBYW5sLBoZrW7eOS7kiK71OnDu5kZGjd3tfdXVJ8wDS3J1OLbyk5TG0M3t7eOp3sqopMtT4CQMOGjbSukUJodDjWSvsCJn07l75PSB1HvXo+kuKnpt5C/fpNJC0jpZ4CjydiDRu20Lp9Tk6WpPgqVRrc3OpKWubr7/8tqf3kkUOwcP02rdtPGTVUUnyg6h2XTTWHLvGlfjeV+r1UrS6TFF+jUcPK6q/H8mfx9vbG3bsJL2yn1xd4xMfHIz4+HgCwZcsWtGnTBjVq1EBc3OMzN/fv38f58+efuqxKpcLf/vY3VK9eHaNGjcLnn3+OK1euwNnZGS+//DLWrVsHAMjJycE777yDyMhIfXadiIjIYFgfiYjoafR6ZaxWrVpYsmQJUlNT4ebmhvnz50OpVGLy5Mno27cvGjRogE6dOj11WTc3N3z88ccYNWoU7O3toVQqMXv2bACPzwjOmjULAwYMQElJCV5//XUMHDhQn10nIiIyGNZHIiJ6Gr1OxpydnbFixYq//P2mTZue2v7o0aN/+vOwYcMwbNiwv7Rr0KABfvzxR/10koiIyMhYH4mI6Gn4O2NEREREREQy0NtkrGPHjtizZ4++whEREVkE1kciInoWXhkjIiIiIiKSASdjREREREREMuBkjIiIiIiISAacjBEREREREcmAkzEiIiIiIiIZ6PV3xsh8qdVlBl+urKxEcvzi4gLJy0hVVJSnddue7XpIip2cfE3SMsevX5cUX5fl/ta0qU45SP/sbB0MukxxSaHk+FQVif/7T0p7U6Jrf7RfLi/3oeToUpfRpd6lpyVoH1+H48HDh+mS2v/879WS2k8eOUTSMp9+sVBSfF2WW7nkG8nxbW3tJbUvKSmWnANQ6LCMIXMY/jhQr56vQZdJTpb+nUsI7cetbVteGSMiIiIiIpIBJ2NEREREREQy4GSMiIiIiIhIBpyMERERERERyYCTMSIiIiIiIhlwMkZERERERCQDTsaIiIiIiIhkwMkYERERERGRDAw2GXvppZegUqmwY8cOjBs37i//HhkZidmzZxsqPRERkclijSQiIgCwlitx79690bt3b7nSExERmSzWSCKiquG5V8YGDRqEM2fOAAD27NmDli1boqioCAAwbdo0rF+/HqNHj8bQoUPRs2dPfPzxxyguLn5mvAMHDqBPnz5ISEj409nA9957D4sWLcKIESPQq1cvTJs2DRqNBgCwY8cO9OvXD4MHD0ZoaCiaNWuml4ETERFVBmskERFV1nOvjAUFBeHEiRPo3LkzTp48CVdXV8TExCAwMBDHjx+HQqHA4MGDMWjQIJSWluKNN97AsWPH0Ldv37/E2rNnD1asWIENGzagbt26uHTp0p/+PTk5GRs2bEBBQQH69++P8+fPo1atWli4cCF27NgBDw8PLFu2DGq1WvIgExMTJS8jhJC8jCnFN1YOjUb65yGFJayn5ORrBo0PAN39/bVuq+t4uU+YRo6i4gKDxgeMs54sQVWtkZawHxkjR3bOA4PGBwx/PDDG5/D77wcNniNs3j8M0rZccXGh5GWkEkJj9jmMsT0lJV01aHxDrKMXTsZCQkLwxRdfICYmBqNGjUJUVBScnJzg5eWFmTNnIioqCqtWrUJiYiIyMjJQUPDXA8OVK1dw8uRJfP3116hbt+5Tc/Xs2RNWVlZwdnaGt7c3srOzER8f///au/voqOo7j+OfO5OQEMKjISQIyUC0AnJsqbAc8CFARQqtp4DH2tOxqLWeuo27FavYWnxCRWUB8eH0uES0VluPyOF0qVrLQxcWKUbWuhG0YFdMCDSatLQ+BPI0c/ePrG5dUO53PHd+M5P36y/lfH/f7+83c+/93e/cSaKzzjpLZWVlkqRLLrlEDzzwgHmRsVhMjY2NgeN935fneeY6mZI/lRqeZ//xwWQyoUgkapiT7QDOxNdp5MixpvwHDryuiorgn1Q/sXG9Kb/U04j9x969geOrx9rWIPXOcyIdNQr69DXlb+84osKCosDxHZ32GwTLGiorK1P6sCtX9MY9MhPPo3TUGND/JFP+d9/7swYOKDGN6TA2VmFfD1J5H774xfNN8S+//BudeeaxH058kqnn2fJLPc3VP92wInD86lWLTfk7Oo6qoMB2Le/s/OQn5Mfj+8mU7tPCrWFrrFI5niz3T1JPI1ZZeXrg+AMH/mDKb32NevbIt04Y96nN2Gmnnaauri5t2bJFsVhM06dP18KFC5WXl6dZs2bp2muvVSKR0OzZszVt2jQ1Nzcft+vt37+/VqxYoWuuuUbTpk3TiBEjjokpLCz86L89z5Pv+4pGox/LF40Gv/EHACBM7JEAgM/qhO3deeedpxUrVuiss85SVVWVPvjgA/3qV7/S+eefrxdeeEE1NTWaM2eOJKm+vv64X5GIxWKaMmWKvvWtb+mGG2746LvuJ3L22Wdr586deueddyRJTz/9tGVtAACEij0SAPBZnPC3Kc6cOVNr1qzR1KlTJUlTp07Vvn37VF5eroULF6qmpkZFRUUqLi7WpEmTdODAgU/MddVVV+m3v/2tHn74YZWUnPjx/ahRo/SjH/1IV1xxhfr06aOxY8eqb1/bo2AAAMLCHgkA+CxO2IxNmDBB+/bt++j///7vnsTjccXj8eOO+3DM/PnzNX/+/J5ieXlav/7/fibmw39//PHHPzb2w/9vampSQ0ODNmzYoEgkoo0bN+qNN94ItDAAAMLGHgkA+Cyc/Z2xIMrKytTS0qILLrhA0WhU/fv319KlS11PCwAA59gjASD7ZXQzlp+fryVLlrieBgAAGYc9EgCyX7i/JxMAAAAAcFw0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIADGf3bFJE+vp9M67hs1dT0h1DHVI8dZ87v+0nTuPilPzbXsI77xc/uMuf3vOCfDfm+b87/v1UCR95875qUKtx87yOBY5cs/LY5f0fnUfMYAJ/NB21/C31MMmnfTzs6281jwrR797ZQx7y2Z7s5/wP3/EC1q24KHF/7/PPmGtYx35k1y1wjP79P4NhU98i8vPzAsWXDYub8I07+nCn+nbffMtewjUnldbKMCRbLkzEAAAAAcIBmDAAAAAAcoBkDAAAAAAdoxgAAAADAAZoxAAAAAHCAZgwAAAAAHKAZAwAAAAAHsq4Ze/DBB7V582bX0wAAIOOwRwJAdsm6Zqyurk7d3d2upwEAQMZhjwSA7JKXjiJ1dXVavny5hg8frv3796uwsFB33323IpGIlixZora2NrW2tmrMmDFatWqVCgoKdP/992vTpk3Kz8/X4MGDddddd2nTpk3as2ePli1bpmg0qpkzZ6Zj+gAAhIY9EgB6r7Q0Y5K0Z88e3XDDDZo4caKefPJJXX/99Zo8ebLmzp2rr33ta+rq6tL8+fO1detWnXHGGXrssce0c+dO9enTR4888oheffVVxeNxPf/884rH42wyAICcwR4JAL1T2pqxMWPGaOLEiZKkCy+8UEuWLNGaNWu0Z88e1dbWqqGhQS0tLTpy5IiGDRumMWPGaN68eTr33HN17rnnasqUKSnXbmhoMI/xfT/lepmQP1dqsIagNZKh13jip3eEEvuhZDJhHmOVjtfptmsuDyX2Q7lwTuBY2bRH5soxGHaNRCL8r4uGfU1Lx/vQ2dkeeo32jiOh5l8wvdoWn8Ka0/E6dXV1hJq/6eC+UPNL4b/XYZwTaWvGotHoMf923XXXqaioSLNnz9a0adPU3Nws3/cViUT0xBNPaPfu3dq5c6eWLl2qc845R4sWLUqpdiwWU2NjY+B43/fleV5KtTIhf67U6J1rsM/F95PyvOA//hm/9EZzjSd+eocuuWxx4Phf/OwuU/5kMqFI5NhrxCdJ5WJofZ1uvneNucZt11yuW1Y9Gjh+ycJvm/Jn2jlRWVmZ0oddOFa27JGZdgymq4bl+iT1NGLRqO0WK5m0NVbWa5pku26m8j7k5xeY4js729WnT2Hg+IhpvT3aO46osKAocPzq539tyr9gerV+9u/bTGO+M2uWKd76OqWyR3Z1dZjev7JhMVP+poP7NHLEaaYxra1Npnjre93RedSU33pOBN0j0/YLPPbu3au9e/dKkp566ilNmDBB9fX1qqmp0Zw5cyRJ9fX1SiQS2rt3r7761a+qqqpK3/3ud3XZZZdp9+7dkno2LH44GQCQS9gjAaB3StuTsZKSEq1atUqHDh3SkCFDtGzZMm3btk01NTUqKipScXGxJk2apAMHDuiiiy7S7NmzdeGFF6qoqEiFhYVavLjnU/kZM2Zo5cqV6urq0rx589I1fQAAQsMeCQC9U9qaseLiYj300EMf+7d4PK54PH7c+KuvvlpXX331Mf++YMECLViwIJQ5AgDgAnskAPROWfd3xgAAAAAgF6SlGZs8ebKeeeaZdJQCACCrsEcCQO/FkzEAAAAAcIBmDAAAAAAcoBkDAAAAAAdoxgAAAADAAZoxAAAAAHAgbX9nDAib56X22YJlnO8nU6oRnB/6uGf/7eEU8t9hGjd27BRzBcuYuZdeas4vSTfe86+BY5/8yU/M+W+75vKUxgHIbMlkIi1j7FLdM8LR1dUR6phU9/lOQ403/+tNW/Lp1eYxw4efYqthHFNQUGTOL0mjR38+cOx77/3FnL870W2K7+g8aq6RyhjXeDIGAAAAAA7QjAEAAACAAzRjAAAAAOAAzRgAAAAAOEAzBgAAAAAO0IwBAAAAgAM0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIADea4nEEQymdTSpUtVX1+vtrY2+b6vO+64Q2eeeabrqQEA4Az7IwBkt6x4MlZfX6+WlhY99dRTeu655zRv3jzV1ta6nhYAAE6xPwJAdvN83/ddTyKI/fv368UXX1RTU5Pq6urUr18/Pf74466nBQCAU+yPAJC9suJrilu3btWdd96pyy+/XF/60pc0evRobdiwIfD4WCymxsbGwPG+78vzvFSmmhH5c6WGNb/n2R/0JpMJRSJRw5ySpvyZ+D4MGjTMXOOvf31bgweXBY4fPvwUU/7XXntBp59+duD4uZdeasovSXcuulI/Xhb8icHTD68213jjjV363OcmBY7/4x//05Q/046nyspKNTQ0hDoffLrPuj9Ktj0y047BTK3BGsKpTQOYuQAADIdJREFUkY59/qYVtifLty38tm659xHTmMfuW2mKb2jYo1hsfOD4goIiU35J2rfvJZ122j8Ejn/vvb+Y8jc3v6ny8irTmLff3m+Kz7RzIugemRXN2I4dOzR9+nR985vfVHt7u2pra5VIJFxPCwAAp9gfASC7ZcXPjH3jG9/QSy+9pAsuuEDz5s3TyJEjdfDgQSWTtqcUAADkEvZHAMhuWfFkrKqqSr/85S8/9m+LFy92NBsAADID+yMAZLeseDIGAAAAALmGZgwAAAAAHKAZAwAAAAAHaMYAAAAAwAGaMQAAAABwgGYMAAAAABygGQMAAAAABzzf933XkwhbLBZTY2Nj4Hjf9+V5XmjzCTt/rtRgDdlbo6RkhCl/a2uThg4dGTh+1+u/N+WXpNjQoWpobQ0cP6q01Fyjtx1PlZWVamhoCHU+CJ9lj8y0YzBTa2TmGmzz8f2kPM/6mb3tljITX6dIJGrKn0h0Kxq1/dnegQOHmuIPH27WkCHlgeN37a035ZekqtJSvdnSEjj+lGFlpvy5cDxZ8wfdI3kyBgAAAAAO0IwBAAAAgAM0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIADNGMAAAAA4EBGNmMPPvigNm/e7HoaAABkFPZHAMgtGdmM1dXVqbu72/U0AADIKOyPAJBbbH8yPEWrV6/WunXr1K9fP02cOFFbtmzRmjVrtGTJErW1tam1tVVjxozRqlWrtG7dOu3Zs0fLli1TNBpVdXW1li9frl27dimRSGjcuHFavHixiouL0zF1AABCw/4IAL1b6E/Gtm/frvXr12vdunVav3692traJElr167V3LlztXbtWm3cuFEHDx7U1q1bFY/HNX78eC1atEgzZ87U6tWrFY1GtX79em3YsEGlpaVavnx52NMGACBU7I8AgNCfjG3btk1f/vKXNWDAAElSPB7Xiy++qOuvv147duxQbW2tGhoa1NLSoiNHjhwzfuvWrXr//ff1u9/9TpLU1dWlk046yTSHhoYG87x93zePyaT8uVKDNfSeGq2tTaHml6TY0KGBY1NdL8cTgsqE/VGy75G5cgxyrgbJnww1f0+N7H+dEonwvzp8+HBz6DWqSksDx6ZybOTC8RRG/tCbsby8vI9NPBqNSpKuvfZaJRIJzZ49W9OmTVNzc/NxF5hMJnXjjTequrpaktTW1qaOjg7THGKxmBobGwPH+74vz/NMNSzCzp8rNVhD9tYoKRlhyt/a2qShQ0cGjt/1+u9N+aWeRqyhtTVw/CjDpvSh3nY8VVZWpvRhF3pkwv4o2fbITDsGM7VGZq7BNh/fT8rzrF+gst2oZuLrFIlETfkTiW5Fo7bb6YEDg38wKPU0YkOGlAeO37W33pRf6mnE3mxpCRx/yrAyU/5cOJ6s+YPukaF/TbG6ulobN27U+++/L0lat26dJOmFF15QTU2N5syZI0mqr69XIpGQ1LMhffgDymeffbZ+/vOfq7OzU8lkUjfddJNWrlwZ9rQBAAgV+yMAIPQnY1OmTNHXv/51XXzxxSosLNSpp56qvn376oorrlBNTY2KiopUXFysSZMm6cCBA5KkGTNmaOXKlerq6tL3vvc93XPPPZo3b54SiYTGjh2rH/7wh2FPGwCAULE/AgBCb8Z2796taDSq5557TpL06KOPqqOjQ/F4XPF4/LhjFixYoAULFnz0/7fcckvY0wQAIK3YHwEAoTdjo0aNUm1trdauXSvP81ReXq7bb7897LIAAGQ09kcAQOjNWHFxse6///6wywAAkFXYHwEAof8CDwAAAADAsWjGAAAAAMABmjEAAAAAcIBmDAAAAAAcoBkDAAAAAAdoxgAAAADAgdB/tX0m8LyIPM/Wd1rifd+3TkmSZ4hNJT+yk+W4SG1cRcXYlCpUVIwLHBuLjTfnHzduauDY++7/hTn/vbd/3zQuGk3t8mgZZ70uSVJeXp/Asd3dXeb8PYIeT6kerwDSL5V7id53//GPP7g79DGbNzxtrlFaWhE49p8vv9mc/9lnHzKN8zz79d86JqXb6yy8v+bJGAAAAAA4QDMGAAAAAA7QjAEAAACAAzRjAAAAAOAAzRgAAAAAOEAzBgAAAAAO0IwBAAAAgAM0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIADnu/7vutJAAAAAEBvk+d6AukwatRoNTY2Bo5PJhOKRKKB4639rO8n5XmWh5L2ftn3fXmeZx6XSTV65xrsc7EeTxUVY801GhtfU2Xl6YHjY7Hxpvzbtj2l6uqLA8d/8dyppvySdO/t39fCm+4LHP/AXdeZa3R3dykvLz9wvO06IHV1dSg/v8A0HyvL8VRZWamGhrfMNZBZYrFY4D0yF67L6ajBGrK3Rs31/2LK/+Cy63T1ouWmMZs3PG2K37u3TmPGTA4cX1U1wZRfkp599iF95StXBY7/9a9rTfmt99ZS5t1fW4+lnj2y4YRxfE0RAAAAABzIymbsyiuv1JYtW1xPAwCAjML+CADZJSu/plhba3s0CgBAb8D+CADZJSufjAEAAABAtqMZAwAAAAAHaMYAAAAAwAGaMQAAAABwgGYMAAAAABygGQMAAAAAB2jGAAAAAMCBrPw7Y1YDBpykQYOOmsYMGlQaOLaj44h1Sioq6h841vd9c35J6ts3eI38/IKUagwYUBI4tr39A3P+Pn0KA8d2drab81t5nv3zC8uYsrJR5vySVF4+OnBsRcW4lGpYxtXVPWPObxmzffs6c/57b/++7rvz2sDxltf07w0bFgsce/So/Zzo339I4NhIJLXP2046qTxQ3ODBwa+TyFyRSFSRSNQUHzZLjVT3SMu1ObUani3as8X3jMmsNUSj9mMjGg1+Kzp4cJk5vySVlIwIHLvvld3m/NYx/foNMtewjNmxY705v/SQadz48eeYK1jHvP76DnMNyzGYTCbN+S3nXNBYnowBAAAAgAM0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIADNGMAAAAA4ADNGAAAAAA4QDMGAAAAAA7QjAEAAACAAzRjAAAAAOAAzRgAAAAAOOD5vu+7ngQAAAAA9DZ5rieQDp///CQ1NR0MHH/4cLOGDCkPHN/RccQ0n7a2d9Wv38DA8an0y0eOvKeiogGB4/PzC8w13n23VQMHDg0c397+gSl/R8dRFRT0DRzf2dluyi/1vLae5wWO9zzbw+RkMqFIJBo4vqxslCm/JP3pT/+t4cNPCRxfVTXBXGP79qd1zjkXBY7ftes5U/729jYVFvYLHJ/Ke219L8rLR5trHDr0R5188qmB448etZ0T1mtTJGL/8sOf/3xIJSUnB4odOXKEXnmlzlwDmWX06Co1NjYGik0kuhWNhnvrYK2Ryh5pvR5Ya/h+0rxfWPYiKTPXEI0Gn48kdXd3KS8vP3D84MFlpvyS1NrapKFDRwaO/8IXZpjyb9r0mGbOvNQ05vDht03xL7/8G5155qzA8W+++YopvyT97W8tGjSoNHB8RcU4U/5XX92qM86YZhrz+us7TPHW4ymZTJryW8+5yspKvfXW/hPG8TVFAAAAAHCAZgwAAAAAHKAZAwAAAAAHaMYAAAAAwAGaMQAAAABwgGYMAAAAABygGQMAAAAAB2jGAAAAAMABmjEAAAAAcIBmDAAAAAAcyHM9gXQYPrzcPGbkyBGBYzs72835KyoqAsf6vm/Ob62Rn98nxRojA8d2dLSZ81dWBl9DZ2eHOX9PjcrAsZ5n//zCkn/o0JPN+SVpxIjg48rKhqZUwzLO8r6lMiYd73VpafjvRXu7/ZywXJsikdQ+bwtao7zcfm1F5jn5ZNuxbjmPUmWpkeoeGXYN6+vkeV6oNdKxhmg0GmqNgQNT278s181hw0rM+a1jCgrs70V5+bDAsV1dwe/N/p7lnm748DJzfuuYtjb7tcZyPCWTyVDzB722en6qVzEAAAAAQMr4muL/09zcrBkzZqi5uTkr8+dKDdbQe2rkwhrSUSMX1oDslivHIOeq+/y5UiMX1pCOGqzh09GM/T+JREKHDh1SIpHIyvy5UoM19J4aubCGdNTIhTUgu+XKMci56j5/rtTIhTWkowZr+HQ0YwAAAADgAM0YAAAAADhAMwYAAAAADkRvvfXWW11PItMUFBRo8uTJKigoyMr8uVKDNfSeGrmwhnTUyIU1ILvlyjHIueo+f67UyIU1pKMGa/hk/Gp7AAAAAHCArykCAAAAgAM0YwAAAADgAM0YAAAAADhAMwYAAAAADtCMAQAAAIAD/wNJ2/eqVoCeYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "src = vars(train_data.examples[example_idx])['src']\n",
    "trg = vars(train_data.examples[example_idx])['trg']\n",
    "\n",
    "\n",
    "translation, attention =  translate_sentence(src,\n",
    "                                            source_process_pipeline,\n",
    "                                            target_process_pipeline,\n",
    "                                            spacy_german,\n",
    "                                            transformer_model,device,\n",
    "                                            max_length = 50,verbose = True)\n",
    "print(f'source = {src}')\n",
    "print('-'*100)\n",
    "print(f'target = {trg}')\n",
    "print('-'*100)\n",
    "print(f'translation = {translation}')\n",
    "print('-'*100)\n",
    "\n",
    "\n",
    "display_attention(src, translation, attention,n_heads = NUM_DECODER_ATTN_HEADS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to translate 0.10097122192382812 seconds\n",
      "source = ['eine', 'frau', 'spielt', 'ein', 'lied', 'auf', 'ihrer', 'geige', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "target = ['a', 'female', 'playing', 'a', 'song', 'on', 'her', 'violin', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "translation = ['a', 'woman', 'is', 'playing', 'a', 'song', 'on', 'her', 'side', '.']\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 14\n",
    "\n",
    "src = vars(validation_data.examples[example_idx])['src']\n",
    "trg = vars(validation_data.examples[example_idx])['trg']\n",
    "\n",
    "\n",
    "translation, attention =  translate_sentence(src,\n",
    "                                            source_process_pipeline,\n",
    "                                            target_process_pipeline,\n",
    "                                            spacy_german,\n",
    "                                            transformer_model,device,\n",
    "                                            max_length = 50,verbose = True)\n",
    "print(f'source = {src}')\n",
    "print('-'*100)\n",
    "print(f'target = {trg}')\n",
    "print('-'*100)\n",
    "print(f'translation = {translation}')\n",
    "print('-'*100)\n",
    "\n",
    "\n",
    "display_attention(src, translation, attention,n_heads = NUM_DECODER_ATTN_HEADS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to translate 0.0709984302520752 seconds\n",
      "source = ['ein', 'typ', 'arbeitet', 'an', 'einem', 'gebäude', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "target = ['a', 'guy', 'works', 'on', 'a', 'building', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "translation = ['a', 'guy', 'working', 'on', 'a', 'building', '.']\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "src = vars(test_data.examples[example_idx])['src']\n",
    "trg = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "\n",
    "translation, attention =  translate_sentence(src,\n",
    "                                            source_process_pipeline,\n",
    "                                            target_process_pipeline,\n",
    "                                            spacy_german,\n",
    "                                            transformer_model,device,\n",
    "                                            max_length = 50,verbose = True)\n",
    "print(f'source = {src}')\n",
    "print('-'*100)\n",
    "print(f'target = {trg}')\n",
    "print('-'*100)\n",
    "print(f'translation = {translation}')\n",
    "print('-'*100)\n",
    "\n",
    "display_attention(src, translation, attention,n_heads = NUM_DECODER_ATTN_HEADS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to translate 0.0989980697631836 seconds\n",
      "source = ['eine', 'teenagerin', 'spielt', 'bei', 'einem', 'spiel', 'trompete', 'auf', 'dem', 'feld', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "target = ['a', 'teenager', 'plays', 'her', 'trumpet', 'on', 'the', 'field', 'at', 'a', 'game', '.']\n",
      "----------------------------------------------------------------------------------------------------\n",
      "translation = ['a', 'teenage', 'girl', 'plays', 'a', 'trumpet', 'in', 'a', 'field', '.']\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 21\n",
    "\n",
    "src = vars(test_data.examples[example_idx])['src']\n",
    "trg = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "\n",
    "translation, attention =  translate_sentence(src,\n",
    "                                            source_process_pipeline,\n",
    "                                            target_process_pipeline,\n",
    "                                            spacy_german,\n",
    "                                            transformer_model,device,\n",
    "                                            max_length = 50,verbose = True)\n",
    "print(f'source = {src}')\n",
    "print('-'*100)\n",
    "print(f'target = {trg}')\n",
    "print('-'*100)\n",
    "print(f'translation = {translation}')\n",
    "print('-'*100)\n",
    "\n",
    "\n",
    "display_attention(src, translation, attention,n_heads = NUM_DECODER_ATTN_HEADS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> BLEU Score </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Paper : https://www.aclweb.org/anthology/P02-1040.pdf <br>\n",
    "Library : https://pytorch.org/text/stable/data_metrics.html <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Bilingual Evaluation Understudy Score, or BLEU for short, is a metric for evaluating a generated sentence to a reference sentence. A perfect match results in a score of 100.0, whereas a perfect mismatch results in a score of 0.0.<br><br>\n",
    "Lets create a function which calculates the BLEU score for every epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "def calculate_bleu(data,source_field, target_field,source_tokenizer,\n",
    "                       transformer_model,device, max_len = 50):\n",
    "    \n",
    "    transformer_model.eval()\n",
    "    target_tokens = []\n",
    "    predicted_tokens = []\n",
    "    \n",
    "    start = time()\n",
    "    with torch.no_grad():\n",
    "        \n",
    "        for item in tqdm(data):\n",
    "            \n",
    "            source = vars(item)['src']\n",
    "            target = vars(item)['trg']\n",
    "                \n",
    "            \n",
    "            predicted_sentence,attention = translate_sentence(source,\n",
    "                                                   source_field,target_field,\n",
    "                                                   source_tokenizer,\n",
    "                                                   transformer_model,device,\n",
    "                                                   max_length = 50,verbose = False)\n",
    "                \n",
    "            predicted_tokens.append(predicted_sentence)\n",
    "            target_tokens.append([target])\n",
    "            \n",
    "\n",
    "    return bleu_score(predicted_tokens, target_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "332ff8191f1a4a98928636fcbeebb86c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "BLEU score on Test Data = 34.714582562446594\n"
     ]
    }
   ],
   "source": [
    "bleu_score = calculate_bleu(test_data,source_process_pipeline,\n",
    "                            target_process_pipeline,spacy_german,transformer_model,device, max_len = 50)\n",
    "\n",
    "\n",
    "print(f'BLEU score on Test Data = {bleu_score*100}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We acheived a perplexity of ~35 , which quite good, if we had trained the model on a significantly larger corpus, the results would have been even better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Conclusion </h3>\n",
    "\n",
    "So, now we are done with the neccesary implementations, one can try out different ways of implementations some of which were skipped here like a scheduled learning rate with warmup and cooldown, and static positional encodings using a cosine function like discussed in the paper. Feel free to reach out and add any suggestions !!  "
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<h2> References and Additional Readings </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[1] Attention is All You Need, Paper https://arxiv.org/pdf/1706.03762.pdf\n",
    "\n",
    "[2] The Illustrated Transformer by Jay Alamar, https://jalammar.github.io/illustrated-transformer/\n",
    "\n",
    "[3] What Does BERT Look At? An Analysis of BERT’s Attention,\n",
    "https://arxiv.org/pdf/1906.04341.pdf\n",
    "\n",
    "[4] Bertology, https://huggingface.co/transformers/bertology.html\n",
    "\n",
    "[5] The Annotated Transformer (PyTorch Implementation (some portions of the code might be obsolete now), http://nlp.seas.harvard.edu/2018/04/03/attention.html\n",
    "\n",
    "[6] BenTrevett and jadore, https://github.com/jadore801120/attention-is-all-you-need-pytorch\n",
    "\n",
    "[7] Advanced NLP, https://people.cs.umass.edu/~miyyer/cs685/"
   ]
  },
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   "execution_count": null,
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   "outputs": [],
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