unsupervised_spanish_eng_cluster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pip\n",
    "import torch\n",
    "from torch import nn\n",
    "import argparse\n",
    "from torch.autograd import Variable\n",
    "import io\n",
    "from scipy import linalg\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pretrained_embeddings(path):\n",
    "\n",
    "    word2id = {}\n",
    "    vectors = []\n",
    "    id2word={}\n",
    "    emb_path =path\n",
    "    _emb_dim_file = 300\n",
    "    with io.open(emb_path, 'r', encoding='utf-8', newline='\\n') as f:\n",
    "        for i, line in enumerate(f):\n",
    "            if i!=0:\n",
    "                word, vect = line.rstrip().split(' ', 1)\n",
    "                \n",
    "                word = word.lower()\n",
    "                vect = np.fromstring(vect, sep=' ')\n",
    "                             \n",
    "                word2id[word] = len(word2id)\n",
    "                id2word[len(word2id)]=word\n",
    "                vectors.append(vect[None])\n",
    "            if len(word2id) >= 200000:\n",
    "                break\n",
    "\n",
    "    \n",
    "    print(\"Done\")\n",
    "    embeddings = np.concatenate(vectors, 0)\n",
    "    embeddings = torch.from_numpy(embeddings).float()\n",
    "       \n",
    "    return embeddings,word2id,id2word\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "src_embedding,src_word2id,src_id2word=load_pretrained_embeddings(\"../MUSE/data/wiki.en.vec\")\n",
    "tgt_embedding,tgt_word2id,tgt_id2word=load_pretrained_embeddings(\"../MUSE/data/wiki.es.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embedding(200000, 300, sparse=True)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src_embeddings = nn.Embedding(200000, 300, sparse=True)\n",
    "src_embeddings.weight.data.copy_(src_embedding)\n",
    "\n",
    "tgt_embeddings = nn.Embedding(200000, 300, sparse=True)\n",
    "tgt_embeddings.weight.data.copy_(tgt_embedding)\n",
    "\n",
    "src_embeddings.cuda()\n",
    "tgt_embeddings.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_batch2(X,batch_size):\n",
    " \n",
    "\n",
    "    N=200000\n",
    "    batch_indices = torch.LongTensor( np.random.randint(0,N,size=batch_size) )\n",
    "\n",
    "    batch_xs = X(Variable(batch_indices).cuda())\n",
    "\n",
    "    return batch_xs.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, dis_hid_dim,dis_input_dropout):\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        self.emb_dim = 300\n",
    "        self.dis_layers = 2\n",
    "        self.hidden_dim = dis_hid_dim\n",
    "        self.dis_input_dropout = dis_input_dropout\n",
    "        \n",
    "        self.layers=nn.Sequential(nn.Dropout(self.dis_input_dropout),nn.Linear(self.emb_dim, self.hidden_dim),nn.LeakyReLU(0.2),nn.Linear(self.hidden_dim, self.hidden_dim),nn.LeakyReLU(0.2),nn.Linear(self.hidden_dim, 1),nn.Sigmoid())\n",
    "\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        assert x.dim() == 2 and x.size(1) == self.emb_dim\n",
    "        return self.layers(x).view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = nn.Linear(300, 300, bias=False)\n",
    "mapping.weight.data.copy_(torch.eye(300))\n",
    "modelDisc= Discriminator(2048,0.1)\n",
    "\n",
    "def Orthogonal(mapping,beta):\n",
    "    W=mapping.weight.data\n",
    "    mapping.weight.data.copy_((1 + beta) * W - beta * W.mm(W.transpose(0, 1).mm(W)))\n",
    "    return mapping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping.cuda()\n",
    "modelDisc.cuda()\n",
    "linoptimizer = torch.optim.SGD(mapping.parameters(), lr=0.1)\n",
    "discoptimizer= torch.optim.SGD(modelDisc.parameters(), lr=0.1)\n",
    "\n",
    "loss_fn=torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_xy():\n",
    "#get minibatch of spanish words\n",
    "    tgt_batch=get_batch2(tgt_embeddings,batch_size)\n",
    "    #getminibatch of english words\n",
    "    src_batch=get_batch2(src_embeddings,batch_size)\n",
    "\n",
    "    #generate fake spanish embeddings\n",
    "    fake_tgt_batch= mapping(src_batch)\n",
    "\n",
    "    #stack real and fake\n",
    "    x = torch.cat([fake_tgt_batch, tgt_batch], 0)\n",
    "    #         print(x.shape)\n",
    "    y = torch.FloatTensor(2 * batch_size).zero_()\n",
    "    \n",
    "    y[batch_size:] = 0.2\n",
    "    y[:batch_size]=1-0.2\n",
    "    y=Variable(y).cuda()\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_csls(src_emb, tgt_emb, knn=10):\n",
    " \n",
    "    bs = 1024\n",
    "    all_distances = []\n",
    "    emb=tgt_emb.transpose(0,1)\n",
    "    for i in range(0, src_emb.shape[0], bs):\n",
    "        distances = src_emb[i:i + bs].mm(emb)\n",
    "        best_distances, _ = distances.topk(knn, dim=1, largest=True, sorted=True)\n",
    "        all_distances.append(best_distances.mean(1).cpu())\n",
    "    all_distances = torch.cat(all_distances)\n",
    "    return all_distances.numpy()\n",
    "\n",
    "\n",
    "def get_neighbour(src_emb,tgt_emb):\n",
    "    bs = 128\n",
    "\n",
    "    all_sc = []\n",
    "    all_tgt = []\n",
    "\n",
    "    n_src =30000\n",
    "    knn = 10\n",
    "\n",
    " \n",
    "    avg_dis1 = torch.from_numpy(get_csls(src_emb, tgt_emb, knn)).type_as(src_emb)\n",
    "    avg_dis2 = torch.from_numpy(get_csls(tgt_emb, src_emb, knn)).type_as(tgt_emb)\n",
    "\n",
    "\n",
    "    for i in range(0, n_src, bs):\n",
    "        \n",
    "        scores = tgt_emb.mm(src_emb[i:min(n_src, i + bs)].transpose(0, 1)).transpose(0, 1)\n",
    "        scores.mul_(2)\n",
    "        scores.sub_(avg_dis1[i:min(n_src, i + bs)][:, None] + avg_dis2[None, :])\n",
    "        best_scores, best_tgt = scores.topk(2, dim=1, largest=True, sorted=True)\n",
    "\n",
    "     \n",
    "        all_sc.append(best_scores.cpu())\n",
    "        all_tgt.append(best_tgt.cpu())\n",
    "\n",
    "    all_sc = torch.cat(all_sc, 0)\n",
    "    all_tgt = torch.cat(all_tgt, 0)\n",
    "\n",
    "    all_pairs = torch.cat([torch.arange(0, all_tgt.size(0)).long().unsqueeze(1),all_tgt[:, 0].unsqueeze(1)], 1)\n",
    "\n",
    "\n",
    "    diff = all_sc[:, 0] - all_sc[:, 1]\n",
    "    reordered = diff.sort(0, descending=True)[1]\n",
    "    all_sc = all_sc[reordered]\n",
    "    all_pairs = all_pairs[reordered]\n",
    "    \n",
    "    \n",
    "    selected = all_pairs.max(1)[0] <= 30000\n",
    "    mask = selected.unsqueeze(1).expand_as(all_sc).clone()\n",
    "    all_sc = all_sc.masked_select(mask).view(-1, 2)\n",
    "    all_pairs = all_pairs.masked_select(mask).view(-1, 2)\n",
    "    return all_pairs\n",
    "\n",
    "\n",
    "def create_dictionary(src_embedding,tgt_embedding):\n",
    "    src2tgt = get_neighbour(src_embedding,tgt_embedding)\n",
    "    tgt2src = get_neighbour(tgt_embedding,src_embedding)\n",
    "    \n",
    "    tgt2src = torch.cat([tgt2src[:, 1:], tgt2src[:, :1]], 1)\n",
    "    \n",
    "    final_pairs = set([(a, b) for a, b in src2tgt]) & set([(a, b) for a, b in tgt2src])\n",
    "    dictionary = torch.LongTensor(list([[a, b] for (a, b) in final_pairs]))\n",
    "\n",
    "    print('Dictionary Length = %i.' % dictionary.size(0))\n",
    "    return dictionary.cuda()\n",
    "\n",
    "def evaluation_metric(src_embs, tgt_embs):\n",
    "     \n",
    "    indices= torch.LongTensor(np.arange(200000))\n",
    "    batch_xs = src_embs(Variable(indices).cuda())\n",
    "    src_emb = mapping(batch_xs.cuda())\n",
    "    src_emb=torch.FloatTensor(src_emb.cpu().data.numpy()).cuda()\n",
    "    tgt_emb = tgt_embs(Variable(indices).cuda())\n",
    "    tgt_emb=torch.FloatTensor(tgt_emb.cpu().data.numpy()).cuda()\n",
    "    src_emb = src_emb / src_emb.norm(2, 1, keepdim=True).expand_as(src_emb)\n",
    "    tgt_emb = tgt_emb / tgt_emb.norm(2, 1, keepdim=True).expand_as(tgt_emb)\n",
    "    dictionary = create_dictionary(src_emb, tgt_emb)\n",
    "    mean_cosine = (src_emb[dictionary[:, 0]] * tgt_emb[dictionary[:, 1]]).sum(1).mean()\n",
    "    print(\"Mean cosine\", mean_cosine)\n",
    "    return mean_cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.3204421997\n",
      "(0, ' Discriminator Loss :  ', \n",
      " 0.6786\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      ")\n",
      "('Generator Loss : ', \n",
      " 0.7156\n",
      "[torch.cuda.FloatTensor of size 1 (GPU 0)]\n",
      ")\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-76-5249a55f1eea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0;31m#         print(loss.data)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python2.7/site-packages/torch/autograd/variable.pyc\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, retain_variables)\u001b[0m\n\u001b[1;32m    165\u001b[0m                 \u001b[0mVariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \"\"\"\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_variables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python2.7/site-packages/torch/autograd/__init__.pyc\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(variables, grad_variables, retain_graph, create_graph, retain_variables)\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[0;32m---> 99\u001b[0;31m         variables, grad_variables, retain_graph)\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "iterations_per_epoch=1000000\n",
    "batch_size=32\n",
    "epoch=5\n",
    "val_score=-1\n",
    "\n",
    "for _ in range(epoch):\n",
    "      \n",
    "    for _n in range(0,iterations_per_epoch,batch_size):\n",
    "        modelDisc.train()\n",
    "        mapping.eval()\n",
    "        for __ in range(5):\n",
    "\n",
    "            x,y=get_xy()\n",
    "            \n",
    "            y_pred = modelDisc(Variable(x.data))\n",
    "\n",
    "            loss= loss_fn(y_pred, y)\n",
    "\n",
    "            discoptimizer.zero_grad()\n",
    "\n",
    "\n",
    "            loss.backward()\n",
    "    #         print(loss.data)\n",
    "  \n",
    "            discoptimizer.step()\n",
    "          \n",
    "        modelDisc.eval()\n",
    "        mapping.train()\n",
    "        for __ in range(1):\n",
    "            \n",
    "            x,y=get_xy()\n",
    "            \n",
    "            y=1-y\n",
    "            y_pred = modelDisc(x)\n",
    "            loss2= loss_fn(y_pred, y)\n",
    "            linoptimizer.zero_grad()\n",
    "            loss2.backward()\n",
    "            linoptimizer.step()\n",
    "\n",
    "            mapping=Orthogonal(mapping,0.001)\n",
    "        if _n%500==0:\n",
    "            print(torch.norm(mapping.weight.data,2))\n",
    "            print(_n,\" Discriminator Loss :  \",loss.data)\n",
    "            print(\"Generator Loss : \", loss2.data)\n",
    "        \n",
    "    val=evaluation_metric(src_embeddings,tgt_embeddings)\n",
    "    if val_score<val:\n",
    "        torch.save(mapping.state_dict(), './mapping.pth')\n",
    "        val_score=val\n",
    "    \n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary Length = 1193.\n"
     ]
    }
   ],
   "source": [
    "def build_dictionary(src_emb,tgt_emb):\n",
    "    src_emb = mapping(Variable(src_emb, requires_grad=False).cuda())\n",
    "    src_emb=torch.FloatTensor(src_emb.cpu().data.numpy()).cuda()\n",
    "    tgt_emb = tgt_emb.cuda()\n",
    "    src_emb = src_emb / src_emb.norm(2, 1, keepdim=True).expand_as(src_emb)\n",
    "    tgt_emb = tgt_emb / tgt_emb.norm(2, 1, keepdim=True).expand_as(tgt_emb)\n",
    "    dictionary= create_dictionary(src_emb, tgt_emb)\n",
    "    return dictionary\n",
    "\n",
    "dictionary=build_dictionary(src_embedding,tgt_embedding)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def procrustes(src_emb,tgt_emb):\n",
    "    A = src_emb.weight.data[list(dictionary[:, 0])]\n",
    "    B = tgt_emb.weight.data[list(dictionary[:, 1])]\n",
    "    M = B.transpose(0, 1).mm(A).cpu().numpy()\n",
    "    U, S, V_t = linalg.svd(M, full_matrices=True)\n",
    "    mapping.weight.data.copy_(torch.from_numpy(U.dot(V_t)).type_as(mapping.weight.data))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-e5061a40ee56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprocrustes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_embeddings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtgt_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevaluation_metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_embeddings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtgt_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mval_score\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./mapping.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-69f6a63ab70d>\u001b[0m in \u001b[0;36mevaluation_metric\u001b[0;34m(src_embs, tgt_embs)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0msrc_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msrc_emb\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msrc_emb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0mtgt_emb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtgt_emb\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mtgt_emb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgt_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mdictionary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_dictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtgt_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0mmean_cosine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msrc_emb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtgt_emb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Mean cosine\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmean_cosine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-69f6a63ab70d>\u001b[0m in \u001b[0;36mcreate_dictionary\u001b[0;34m(src_embedding, tgt_embedding)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_dictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_embedding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtgt_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     \u001b[0msrc2tgt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_neighbour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_embedding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtgt_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0mtgt2src\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_neighbour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgt_embedding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msrc_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-69f6a63ab70d>\u001b[0m in \u001b[0;36mget_neighbour\u001b[0;34m(src_emb, tgt_emb)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0mavg_dis1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_csls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtgt_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0mavg_dis2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_csls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgt_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc_emb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgt_emb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-69f6a63ab70d>\u001b[0m in \u001b[0;36mget_csls\u001b[0;34m(src_emb, tgt_emb, knn)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mdistances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msrc_emb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mbest_distances\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtopk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlargest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mall_distances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest_distances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mall_distances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_distances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mall_distances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python2.7/site-packages/torch/tensor.pyc\u001b[0m in \u001b[0;36mcpu\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;34mr\"\"\"Returns a CPU copy of this tensor if it's not already on the CPU\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdouble\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python2.7/site-packages/torch/cuda/__init__.pyc\u001b[0m in \u001b[0;36mtype\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    394\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_CudaBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0m__new__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lazy_new\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python2.7/site-packages/torch/_utils.pyc\u001b[0m in \u001b[0;36m_type\u001b[0;34m(self, new_type, async)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_sparse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot cast dense tensor to sparse tensor\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "procrustes(src_embeddings,tgt_embeddings)\n",
    "val=evaluation_metric(src_embeddings,tgt_embeddings)\n",
    "if val_score<val:\n",
    "    torch.save(mapping.state_dict(), './mapping.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance explained: 0.08\n"
     ]
    }
   ],
   "source": [
    "# fake_src_embeddings=(mapping(Variable(src_embedding, requires_grad=False).cuda())).cuda()\n",
    "# fake_src_embeddings=torch.FloatTensor(fake_src_embeddings.cpu().data.numpy())\n",
    "pca = PCA(n_components=2, whiten=True)  \n",
    "#pca.fit(np.vstack([fake_src_embeddings, tgt_embedding]))\n",
    "pca.fit(tgt_embedding)\n",
    "\n",
    "\n",
    "print('Variance explained: %.2f' % pca.explained_variance_ratio_.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_embedding_space(src_words, src_word2id, src_emb, tgt_words, tgt_word2id, tgt_emb, pca):\n",
    "\n",
    "    Y = []\n",
    "    word_labels = []\n",
    "    for sw in src_words:\n",
    "        Y.append(src_emb[src_word2id[sw]])\n",
    "        word_labels.append(sw)\n",
    "    for tw in tgt_words:\n",
    "        Y.append(tgt_emb[tgt_word2id[tw]])\n",
    "        word_labels.append(tw)\n",
    "\n",
    "    Y = pca.transform(Y)\n",
    "    x_coords = Y[:, 0]\n",
    "    y_coords = Y[:, 1]\n",
    "\n",
    "    # display scatter plot\n",
    "    plt.figure(figsize=(10, 8), dpi=80)\n",
    "    plt.scatter(x_coords, y_coords, marker='x')\n",
    "\n",
    "    for k, (label, x, y) in enumerate(zip(word_labels, x_coords, y_coords)):\n",
    "        color = 'blue' if k < len(src_words) else 'red'  # src words in blue / tgt words in red\n",
    "        plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points', fontsize=19,\n",
    "                     color=color, weight='bold')\n",
    "\n",
    "    plt.xlim(x_coords.min() - 0.2, x_coords.max() + 0.2)\n",
    "    plt.ylim(y_coords.min() - 0.2, y_coords.max() + 0.2)\n",
    "    plt.title('Visualization of the multilingual word embedding space')\n",
    "\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src_words = []\n",
    "tgt_words = ['leche','el','los','las', 'pato','pollo','avestruz','tortugas','cerdo','abuelo','abuela','hermana','hermano','huevo', u'tenis',u'fútbol','baloncesto',u'béisbol','grillo','']\n",
    "plot_embedding_space(src_words,src_word2id,src_embedding.cpu().numpy(),tgt_words,tgt_word2id,tgt_embedding.cpu().numpy(),pca)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance explained: 0.08\n"
     ]
    }
   ],
   "source": [
    "pca2 = PCA(n_components=2, whiten=True)  \n",
    "#pca.fit(np.vstack([fake_src_embeddings, tgt_embedding]))\n",
    "pca2.fit(src_embedding)\n",
    "\n",
    "\n",
    "print('Variance explained: %.2f' % pca.explained_variance_ratio_.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NrBB4wsxWhBAeqMlJQgg3ATellgsLC0Oe2ykiIiIi25gt3nMcQlgZQvg2el4M/AvPMQZYDOwU2717tE5EREREZLPb4sGxmXUxswbR81bA4cC70eYpwJFm1tnMDDgbmLyl2ygiIiIi26Z8l3K708yKgULgKTNbEK3/u5kdGe12LDDHzGYDrwHPABMBQgifAlcALwMLgOXAnflso4iIiIhILuZFIeq/wsLCUFy8KYUyRERERGRzMrMlIYTC2m5HZTRDnoiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEhEwbGIiIiISETBsYjk3ZIlcOmlMGAAbLcdNGkC3brBKafAO+9kP+bDD+Hoo6FtW2jZEvbZB6ZMgaIiMEs/xo6teOxnn8Fpp0GHDtCsmV/3r3+FEJLHDh++GW9aRES2Cg1ruwEisnV5/HE46SRYuTK5/rPP4N574Z//hJtugvPPT2977TU46CBYvTq97tVXYfRo+O//rvx6CxbA0KGwbFl63axZcNZZ8Prrm34/IiKybVHPsYjkzfvvw3HHpQPjhg09cD30UO8RBigvhwsvhOnTffn77+GEE5KBcadOHix37Ah33VX5NU87LRkYt23rx3btWvWxIiIimRQci0jeXHUVrFvnz1u3hvfegxdegCeegE8/hZ49fVsI8Lvf+fOHH/bUiZSBA2H+fHj6afj4Y+jXL/f1Xn0VXnklvdy9O3zwgR87fz789Kf5vDsREdkWKK1CRPKivByefDK93KwZXH55cp/169PP334bvvgCnnsuuc9vfgOtWvnzNm3gkks8TSObzGPPOw+6dPHnTZrAFVd4oCwiIlJdCo5FJC9WrEimRpSU+IC6yixaBMXFyXW7755c3mOP3MdvyrEiIiLZKK1CRGrNmjUV15lt/Pk25VgRERFQcCwiedKuHbRokV4eOtRziyt7DB/uJd7i5s5NLr//fu5rbsqxIiIi2Sg4FpGNNr9kFaVl5QAUFMDBB6e3vfQS3H13xWOWL/caxOed58sHHJDcfsMNsHatP//2W/jjH3NfP/PY//s/T+8Ar4Jx5ZU1uBkREREUHIvIRppfsoqjb3+FCybP2hAg/+aycgoa+fMQfMKOXr1g1Cgv59anD3Tu7DWI33vPz3P00dCjR/q8L78MvXt7oL3LLjB7du42DBrkk4VsaNN8v0bqWA3GExGRmlJwLCIbpXv7FgzbpQPT5izlgsmzWFdaxsSPZrH9Ee/QuFnZhv0++cRLuU2f7qXZyj12pmE0HLhxY58YJJ6S8fnnHth+8QWcfXbyuo0bJ5fvvtvrIqd8+aUfu3hx1ceKiIhkUnAsIhulUUEDJozpz6i+XZg2Zyl9Lp/OtDlLOe6YBsz72Pjd77xmcZs2nnLRqpVXkzjlFLjnHpg6NX2uwYPhjTfgZz/z6aabN/d1Dz/sk4rEde6cXO7VC95808/bvj00beq1kf/2N7j44sqPFRERyWQhhNpuQ14UFhaG4sy6TiKy2a0rLaPP5dM3LM+9+hCaNiqo0Tm+/NKD2njvMUBpKRx5ZHo2PYDnn/eBfCmrV8MPP8D22yePDcHTN/72t/S6iRM91UNERGqHmS0JIRTWdjsqozrHIrLRSsvKGfdAMil43AOzmTCmP40Kqv/F1PPPw6mnwogRsPPOPgX0smUeFH/2WXq//fdPBsbgFSqGDPFtu+7qvccrVvg549UreveGE0/ciJsUEZFtioJjEdkopWXlXDB5FtPmLGVU3y7ceHw/xj0wm2lzlgLUOED+7jvPTc5l8ODck4qsX++z5WXOmJfSpw88/rhyjkVEpGoKjkVkoxStWMPMecsZ1bfLhkB4wpj+AMyct5yiFWvo3alVtc41aJBPGz1zJhQVec9vQYEPtNtrLzj+eBg9GhpkibV79vSSbTNmwIIFXioOvAe5Xz+vhnHyyT6dtIiISFWUcywiG21+ySq6t2+R6CEuLSuvUWAsIiLbDuUci8hWLVsA3KiggQJjERGpt1TKTUREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJJLX4NjMbjWzIjMLZtY/xz4HmNkbZvahmX1gZn80swbRtu5mVmZms2KPnvlso4iIiIhILvnuOX4I2A9YVMk+XwNjQgg/AvYC9gFOjW1fFULoH3t8kuc2ioiIiIhk1TCfJwshvABgZpXt827s+TozmwV0z2c7REREREQ2Rq3mHJtZZ2A08HhsdQsze9PM3jGz35tZQY5jLzKz4tRj9erVW6TNIiIiIrL1qrXg2MxaA48BfwwhvBWtXgrsGEIYCIwEhgLjsh0fQrgphFCYerRs2XKLtFtEREREtl61EhybWStgOjA1hHBTan0I4fsQwhfR86+Au/AAWURERERks9viwbGZtcQD4+khhGsytnU0s0bR8ybAMcC7Fc8iIiIiIpJ/+S7ldqeZFQOFwFNmtiBa/3czOzLa7XzgJ8AxsXJtl0Xb9gPeNbPZwDvAMuDafLZRRERERCQXCyHUdhvyorCwMBQXF9d2M0REREQkBzNbEkIorO12VEYz5ImIiIiIRBQci4iIiIhEFByLiIiIiEQUHIuIiIiIRBQci4iIiIhEFByLiIiIiEQUHIuIiIiIRBQci4iIiIhEFByLiIiIiEQUHIuIiIiIRBQci4iIiIhEFByLiIiIiEQUHIuIiIiIRBQci4iIiIhEFByLiIiIiEQUHIuIiIiIRBQci0idNmMGmKUf48fXdosq9803cPHF0KcPNGuWbLuIiNR9DWu7ASIiW5PDD4eXX67tVoiIyMZScCwikidz5yYD4+bNYfhw70EWEZH6QWkVIiJ5UlKSXD7vPJg2DR56yB/bqvqWGiMi2zYFxyJS7xQXw2mnQefO3ivbvz/84x/Z9/3qK7j2Whg8GNq2hcaNYYcd4Nhj4bnncl+jtBQmTYKDD4ZOnfy47beHESPgrrtg/frk/mbeSxz3hz+kA8KxYzfhhkVEZItRWoWI1Csffwx77gnLl6fXzZ4NZ5wBc+bAhAnp9W+8AT/7GSxdmjzH0qXw8MP+GDcO/vSn5Pbly+GII+D115Prv/7ae0FnzIB77oHHHoOWLfN5dyIiUtvUcywi9crkyfDttzB0KOy1V3LbLbfA9On+vKQERo1KB8ZmMGgQHHaY9zin3Hgj3Hln8jzHHpsMjHfd1Qfa7bZbet2MGfCLXySP2X//5Hl2283XH3ssDBy4UbcrIiJbmIJjEalXGjSA//wHXngB3noLbropuf3mm/3fG2+EFSvSx8ycCa+95jnARUWwzz7pY664AsrK/PkTT8CLL6a3XXOND7R77DH48EPvaU6ZPNl7q8Fziq+8MtmW449P5xv/8pebfOuVypbX++mncPLJnhbStGnl6ScrVnj7Bw1Kp5906uRpJXfd5WkmmcaOTV6zqMhTUQYOhBYtoE2bqtv9ww/+ISZ+nksv3eiXQURkkymtQkTqlYMPhv32Sy+fey5cf306zeLFFz3Qffzx9D7Nm3uv8i23pNd9/XX6eUkJvP02/OQnyeMAXn0VRo9OL8fTOQCefBL69t20e9oc3n3XU0y+/Ta9Llf6ycsvw9FHV7y3L76Ap5/2x1//6h8QOnTIfc1LL4X7708vb7dd5W1cvx5OOME/kKRcfjlcdVXV9ycisrkoOBaReiWe2gDQsCH06pUO7L77zntBi4rS+6xeDVOmVH7eRYs8OI4fB97TXNVxddGjj3rv79ChsHatB/8pt9wChxzij88/hyOP9IGLKT17+uOdd9K976+/7j3hzz+f+5r33+/XHDAAWrXy/PBcysvh1FM97zvlmmvgsss27n5FRPJFaRUiIsCaNVv2uM2tJukn8cD4zDNh3jx46ikPbn/84/S2GTPg2WdzX7NDBx8E+dpr8MwzMH9+9v1C8B7sf/0rve6Pf1RgLCJ1g4JjEalXPvooubx+PSxYkF5u1gzatYOddkqv69bNA7LKHqlSa/HjAD75pPLjJk3aHHe56bKln8RTIlLpJ08+mV7XoAFcd53/C166LjP/N54CkWncOOjXL73cpEn2/W6/HSZOTC9PmOBTbouI1AUKjkWkTplfsorSsvKc2596Cl56Kb385z8nc2X3289TLQ47LL1u8WL/yr4847QrV8I//+mD1lLixwFceCGsWpVcV1rqPagnnOA1l+uiXOknKdnSTzp18g8WcbvvnlyuLI1k6NDqtS2VqgH+Gp5/fvWOExHZEpRzLCJ1xvySVRx9+ysM26UDE8b0p1FBA9aXlRP/HF9eDgce6JN6rF3rKQNxF17o/44b572TqYF3l1/uJdt23x0KCuCzz7wXev36ZG/x4YfDkCE+EA88d7ew0Gsrt27tg/fefz+dTnH99ZvntaiP4iXyquv++z3necyY/LdHRGRjqOdYROqM7u1bMGyXDkybs5QLJs9iXWkZtz23ILHPkUd6WbJULm3cr34Fhx7qz3fYwQfTxQO24mLveX7iCa/YkJrlrmGsm8AMHnnEB+elrFzp+baPPuoD0+J5xgUFm37fNZWtd319xvLGpJ+UlMCXXyaP++CD5HJm2klcg2r+j3LyydCxoz9PDcyrauCjiMiWouBYROqMRgUNmDCmP6P6dmHanKX0uXw6ry1MRmsDBngVhTFjoH17z2vt29dLjd16a/J8Q4Z4beIbbvCv/Nu182C2eXPYZRc47jjvTX7jjeRxnTp5ebN77vGe5C5dvApDkybQtavn8153nQ8469p1M78oGVK96xdMnrUhQC4tK6/wIaK66SepDxPggervfue51ADffOMD5eLi+2+snj0917lVK18uLfWfxQsvbPq5RUQ2lYXUX8F6rrCwMBTX1eQ/EamRdaVl9Ll8+obluVcfQtNGtdBFWweVlpVzweRZTJuzlFF9u3Dj8f0Y98Bspkz7npJ/DUns27hx7vSTJ57wQHfJEthjDw+EU3r1gp13TpZyA/+AEQ9gx46Fu+9OLy9cCN27V2zzjBkwYkR6+YorfJKSZ5/1CUB++MHXt24Nzz1XceZDEdl6mNmSEEJhbbejMuo5FpE6pbSsnHEPzE6sG/fA7EoH6W1LsvWuT5uzlME9kiPpxozxSTiqSj/ZcUeYOtV74VMWLPCJP+KB8d57+0x/+TRypFf7MPPllSu99vLcufm9johITSg4FpE6I7NXdO7Vh2wIAuNpBNu6RgUNuPH4fol154zomVjedVef+OPUUz2/t7L0k/3399zi3//eg+DWrT3lon17D2D/9jdPM0nlCefTCSckazCvWAEHHVR3J1cRka2f0ipEpM7IVq0iFTDPnLecR87Zh96dWtV2M2td/ENESv+C3ky9bpcNy6nUBRGRuqQ+pFWolJuI1Bm9O7XikXP2oXv7FjQq8C+2UmkERSvWKDCmspzjL6s+WEREqqS0ChGpU3p3arUhME5pVNBAgXGkaMUaZs5bzqi+XZgwpj9NGxUwYUz/CjnHIiKycRQci4jUI6ne9VTaCfiHh18d0KuKI0VEpDqUViEiUs9k60UfeWADtpIhJCIitUo9xyICwPDhXlIr9dhSx2YTP9fw4Zt+PhERkepScCwiIiIiElFahYhssmHDkpNIiIiI1FcKjkVkk115ZW23QEREJD+UViGylVuxAq65BvbZB9q1g8aNoVMnGDIELr8cSktzH/uvf8HgwdCiBbRtC8ccA/PnV9yvOjnHH30E557rs7S1bu0zthUW+nTBEydW/36uvTZ5rQMPhLVr09tLS31K4oMP9vts3Bi23x5GjIC77oL16yuec+zY5DmLiuDRR71HvFUrfxx0UMVpmEVEZOujGfJEtmLPPgtjxsCXlcwP8fXX0KaNB7gzZ6bXn3023HFHxf07dYI5c6BDh/S6zGMz/6z86U/wm99kD0wB+vWDWbPSy/EAe9gwmDHwXCBfAAAgAElEQVTDn990E4wbl9528MHwyCPQrJkvL18ORxwBr7+e42ajtj72GLRsmV43dizcfXd6ecwYmDy54rHNm/uUzH365D6/iIjkVh9myFPPschW6qOP4Gc/SwbGbdp4sHnIIdCxY+XH33EHdOkCI0d6z3FKSQn8+c/Vb8f/9//BxRcnA+NUj/GwYR5wVsfttycD48MPh6lT04ExwLHHJgPjXXf1/XbbLb1uxgz4xS8qv9bkyd7bPHKk/5uydi384Q/Va6+IiNRPCo5FtlJXXglr1qSXjzrK0wVmzIAnn4TPP/cgsHHj7McPHQoLFsAzz/gjLtWTW5Xycu8xjrvqKli0yNswYwYsWwaXXlr5eSZOhF/9Kr189NEwZYqnZqQ88QS8+GJ6+ZprYO5c7yX+8MNkYD15svd+57Lrrn7sM894KkXTpult1b13ERGpn/IaHJvZrWZWZGbBzPpXst/PzWy+mX1iZn8zs0bV2SYi1VNe7sFiSvPmnm+73XbpdQUF8F//lbvndvz49LYhQ7zXOWXp0uq14+23PQhPGTzY85wbxP7ynHsunHBCMt837r334Iwz0qkaxx8PDzxQMah//PHk8quvwujR6cebbya3P/lk7nZfckk6baRHj2QaRXXvXURE6qd8V6t4CPgj8FKuHcysB3A1sCdQAkwFzgT+XNm2PLdTZKuVbUDcj36UTA+ojgEDksstW8I33/jz77+v+vjx4ytWsdhvv5q1ATwnOqVbN7j3XmiY5S9XZlA9bVrl5120KPe2bPee8sMPlZ9XRETqt7z2HIcQXgghVDUqbjTwaAhhWfDRgHcAJ1Rjm4hsQW3bJpcLCmqnHXGLF8P55+fnXPGUk0x18d5FRGTLqI06x92AeJ9NUbSuqm0JZnYRcFFqebv498UikvDhh/DVVzXvPc63l1+u+TGDBkGjRvBS9H3UX/7iKR7XXZfcb6edksuffAI777xx7RQRkW1XvR2QF0K4KYRQmHq0jH/vKbKNmV+yimHDQs4aw2vXeo3jE0/0AWXxmr6XXZb9mNT2SZMqblu0yLeNHQvFxT54LfPYbBODvPpqxdziXOXdUpo29RJu8cD++ut9Rr6zz07XXT7ssORxF14Iq1Yl15WWenm7E07wdouIiGSqjZ7jxUDP2HL3aF1V20Qki/klqzj69lf4+ot9gFaV7vvQQ96LHFdWtvHXLiqCvff28m41ddppnq4QrzCRzdKlnqucmev75Zdw551en/iee7yM25AhHoCDT+JRWAh77umTjpSUwPvvp9Mprr++5m0WEZGtX20Ex1OAl8xsPD7o7mxgcjW2iUgW3du3YNguHbhv+6XsPHA9n76ZTpht3Gw9YX0BpaXepVxaCrNn5+/aqYk/mjRJDtI79lgPwj/6KPexL7xQvWvMm5d+3rSp9zTHe5vXrYPjjksv9+gBCxf685Urc5deKyiA7t0rH5gnIiLbnnyXcrvTzIqBQuApM1sQrf+7mR0JEEL4FLgCeBlYACwH7qxqm4hk16igARPG9Oekc1ZTdsAriW2DBxZQXGxceaXn7rZpkyyjBp7PuynOPtvPHffQQ15yLe6uu+Ccc7xyRsuWXopthx38UR3dusGnn3pQHZ/4I9Nll3lP8uGHJ2fxA7/Wddd5KkbXrtW7roiIbFvy2nMcQjgrx/ozMpb/Bvwtx745t4lIdo0KGnDj8f2YNidZhNfM6NgRfv97f4D3pI4Ykd4nVYkh1cM6aRKcfnrFaxQV+aNHj/S6du3gxhurN8tdjx7Zz5s5dTN4TeMvvvCpquPtPPdcf57KL545E1asSG8/6ijo2dOniD755IrtPeigipOSpOy0U8VycCma+ENEZNtRG2kVIpJnpWXljHugYr6EV0TMMUovD/bcs/rTP9dUZqC6cGE6XSKbsjKf8jqzt1hERKQm6m21ChFxpWXlXDB5FtPmLGVU3y6JbQu+WE1pWXmlx2cOyFu+vPrX7ty5+vtCxUoZ48f7wLq4X/4ydw9uVTp2zF5dY2Pcc4/3Rqfa+uMfe2+2iIhs3RQci9Rj80tWsaBkFTPnLWdU3y5MGJOctf2b70opWpGc7SJz2uX4DHQAr71W/etn5i/H5SorF/fSS8lprsGX99mn8ln47r7bUy9CgIkTq9fWmnjwQU8BKY8+VwwYAM8/78G3iIhs3RQci9RTqRJutz3/CQ+eNXhDYNywcbqnuGNBG3p3SpZ3y+ztnTYt3Vs8dao/8iFz0NySJRX3+c9/sgfYS5fCwQfnPvcVV6Sff/XVxrUvl0cfhZNOSveoDxwIzz3n+dUiIrL1U86xSD2VKuGWGoR34/H9GPfAbKxNT/jCZ4ws+rQBAwb4THFmPhhtr7288sPiqIJ4UZEvt23rQWm+7LJLcvl//scrVcQVFHgQPG1axeO/+85Lwj38sPcQxxUVwf77+0QeleUh19RTT3mVjdJSX95nH3jySa+TLCIi2wb1HIvUU6kSbqP6dmHanKX0uXw60+Ys5Sc/XZnYb9YsDzCnTEkHv+PHJ8+1bp1va9YMTjklP+376U+TZdpWrYLXX0/uM3Kkz3SXy+23+3TRmakg4JOH5DMwXr4cjj46nc6x//4eLCswFhHZtig4FqnHUiXc4p75xw7cfDPssYdPmpHN6afDvfdCv34eeG6/PYweDe+8AwcckJ+2NW/u6QjHHOMVJLKlT+y2W+7j27b1HN+zzoIPPoDBg5PbW7SA/v09iM2HtWu9txq8DvPDD/u/IiKybVFwLFKPZSvh9usHZ/PLc8uZM8eDvdTAtRB8YoyUk07yXuXvv/eKEQ8+CH36eN3h+DFjx6aP6d49ua2qyhC77uo91l984Tm8zz+f3L7ddn6O1Pl22im9Ld5j26tXxRzkxx+Hd9/NXjt5U61e7eeNz8QnIiLbBgXHIvVUZgm3uVcfsiHF4oLJs6os4SZJXbsmp6F+7LH0BwUREdl2KDgWqaeKVqxJlHBr2qhgQw7yzHnLK5Rwqw3zS1bVmyC9QQOvbRyfPfC++9Kz8omIyLZB1SpE6qnenVrxyDn70L19CxoV+Ofc1CC9ohVrKpRw29JSpeaG7dKBCWP606igAevLyqnLn8mbNIF//xuGDfOUE4A//9nTP669tnbbJiIiW0bd/V9KRKrUu1OrDYFxSqOCBrUeGEOy1NwFk2exrrSM255bUNvNqlLr1l6+rUeP9LrrroM//an22iQiIluOeo5FZLNI9WIDTJuzlGlzlrJu8fa13Krq6dzZy7jtu296gpSLL4Y2beCMM2q3bSIisnlZ2EpGmxQWFobi4uLaboaIZFhXWkafy6dvWJ579SE0bVRQiy0SEZHaYmZLQgiFtd2OyiitQkQ2m2yl5sY9MLveDNITEZFtj4JjEdksVGpORETqI+Uci8hmkVlqLp6DnCo1VxcGDoqIiMQp51hENpv5JasSpebAe5QVGIuIbJvqQ86xeo5FZLPJFgDXlVJzIiIi2SjnWEREREQkouBYRERERCSi4FhEREREJKLgWGQjTZoEZunHpEm115axY5NtKSqqvbaIiIjUZwqORUREREQiCo5FRERERCIKjkVEREREIgqORarw73/DPvtAixbQrh0ccwx8+GHlx8yYkcwBHj8+ub06+cqrV8Mtt8ABB0DHjtC4MbRvD3vuCb/+NXz1VfXa/9573u7Utdq0gddeq96xIiIi2xpNAiJSiQkT4MIL08tr18Ijj8D06XDSSZvvurNmwc9+BosWJdd/+aU/3n0XTj4Ztt++8vPMnQsHHZQOpLffHp5+Gvbaa/O0W0REpL5TcCySw5w5cPHFyXW77QZdusDrr8Pf/755rrt8ORxyCJSUpNe1aAH9+kHbtt4T/NlnVZ/nk0/gwAPhiy98uX17ePZZP4+IiIhkp+BYJIf/+z9Yvz69fN55nuYA3iM7aBCsXJn/6954YzIwHjIEpkzxoDzlqac82M1l0SI49VT4/HNf7tQJ/vMf2H33/LdXRERka6KcY5Ecnnsu/bxBg2TecJ8+cOKJm+e6jz2WXJ44MRkYAxx8MBQW5j7H6NGweLE/32EHz4FWYCwiIlI1BcciORQXp5937uwpDXG77bZ5rrtwYfp5+/aw6641P8eKFennEyd6MC8iIiJVU3AssgWUlSWXly/fctf+n/+BpUu33PVERETqMwXHIjnE0xZKSuDrr5Pb587NfWzjxsnlzGMrK6XWo0f6+YoV8PHHlbczm3Hj0s8//RR++tPql34TERHZlik4FonML1lFaVn5huURI9LbysrgqqvSy/PmwX335T5X587J5WnT0r3FU6f6I5fDD08u//d/V+z5fe65yitW/OpX8Nvfppfffx8OO8xrJ4uIiEhuCo5F8MD46Ntf4YLJszYEyGefU441CBv2mTDBB7WNHOkTcVRWqWLnnaFbt/RyUZEv77CD1y/OTLOIGzcOOnRIL7/yCvTuDfvtB0cc4ec+8ECvd1yZa6/1wDrl9dfhqKPg++8rP05ERGRbpuBYBOjevgXDdunAtDlLuWDyLNaVlnHXh7PYbv9k7sSHH3pJtB9+gOOPr/ycmbPirVvnPcDNmsEpp+Q+rmNHn2Ska9f0ujVr4OWX4fHHkwP2qnLnncme6Oeeg//6r2SJOhEREUlTcCwCNCpowIQx/RnVtwvT5iylz+XTmTZnKSee8R33P1jOoEEe1LZpA6NGwauvwqGHVn7O00+He+/1STcaN/bZ6UaPhnfe8SmhK7Pnnp4KcdNNMGyYT//csKGfo39/uOiiZM90Lg0bwv33e63klKlTvUc5hNzHiYiIbKssbCX/QxYWFobieO0tkY2wrrSMPpdP37A89+pDaNqooBZbJCIisvUwsyUhhEoq9dc+9RyLRErLyhn3wOzEunEPzE4M0hMREZGtm4JjETwwvmDyLKbNWcqovl2Ye/UhG1Is4oP0REREZOvWsLYbIFIXFK1Yw8x5yxnVtwsTxvTfkIMMMHPecopWrKF3p1a13EoRERHZ3JRzLBKZX7KK7u1b0Kgg/YVKaVm5AmMREZE8qQ85x+o5FolkC4AbFTRQYCwiIrINUc6xiIiIiEhEwbGIiIiISETBsYiIiIhIRMGxiIiIiEgkr8GxmfU2s1fMbJ6ZvWlmu2fZ53QzmxV7rDCzh6Nt3c2sLGN7z3y2UUREREQkl3xXq7gT+GsIYZKZjQYmAQPjO4QQJgITU8tm9j5wX2yXVSGE/nlul4iIiIhIlfLWc2xmHYG9gXujVVOArmbWq5JjBgEdgUfz1Q4RERERkY2Vz7SKrsDSEMJ6gOCziywGulVyzM+Be0IIpbF1LaKUjHfM7PdmVpDtQDO7yMyKU4/Vq1fn6z5EREREZBtVawPyzKwFMAb4R2z1UmDHEMJAYCQwFBiX7fgQwk0hhMLUo2XLlpu9zSIiIiKydctncPwZ0MXMGgKYmeG9xotz7H8c8EEI4cPUihDC9yGEL6LnXwF34QGyiIiIiMhml7fgOApq3wFOjlYdCxSHEBbkOOTnJHuNMbOOZtYoet4EOAZ4N19tFBERERGpTL7TKs4CzjKzecClwOkAZvZ3MzsytZOZ7Qr0B+7POH4/4F0zm40H2suAa/PcRhERERGRrMzHzdV/hYWFobi4uLabISIiIiI5mNmSEEJhbbejMpohT0REREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJKLgWEREREQkouBYRERERCSi4FhEREREJJLX4NjMepvZK2Y2z8zeNLPds+wz3My+M7NZsUez2Pafm9l8M/vEzP5mZo3y2UYRERERkVzy3XN8J/DXEMIuwA3ApBz7fRxC6B97fAdgZj2Aq4GhQC+gE3BmntsoIiIiIpJV3oJjM+sI7A3cG62aAnQ1s141OM1o4NEQwrIQQgDuAE7IVxtFRERERCqTz57jrsDSEMJ6gCi4XQx0y7JvTzN7J0q9OCe2vhuwKLZclON4zOwiMytOPVavXp2XmxAREakNM2aAWfoxfnxttyi/4vc2fPiWuebYscnrFhWlt02alNw2adKWaZPUfQ1r4ZrvAIUhhG/NrBB4wsxWhBAeqMlJQgg3ATellgsLC0Oe2ykiIiIi25h89hx/BnQxs4YAZmZ4r+/i+E4hhJUhhG+j58XAv/AcY6J9d4rt3j3zeBERERGRzSVvwXEI4Qu8V/jkaNWxQHEIYUF8PzPrYmYNouetgMOBd6PNU4AjzaxzFFyfDUzOVxtFRERERCqT72oVZwFnmdk84FLgdAAz+7uZHRntcywwx8xmA68BzwATAUIInwJXAC8DC4DleAUMERGRbU5xMZx2GnTuDM2aQf/+8I9/VNzvppvgpJPgxz+GLl2gSRNo3hx69IDjjoMnn8x+/m++8dzmvfeGNm2gYUNo1w769PHj/vQn+PLLisctWQKXXgoDBsB22/n1unWDU06Bd96p3r2tWweXXw49e0LTpt7W3/4W1q6tuO+//w1nnQWDBkHXrtCihV+zc2cYORLuvBPWr6/edUWqYj5urv4rLCwMxcXFtd0MERGRjTJjBowYkV4eMwb+8x9YvrzivuefDxMmpJcbNoSyssrPP26cB7spa9fCwIHw4YeVH/fii7Dffunlxx/3QHzlyuz7N2jgwfr55yfXm6WfDxwIBQXw2msVjx8yxO+7WbP0upEjfV1l9tsPnnnGA+2UsWPh7rvTywsXQvfu/nzSJDj99PS2iRN9f9m8zGxJCKGwtttRGc2QJyIiUgdNngzffgtDh8JeeyW33XILTJ+eXLf99h50HnwwHHkkDB4MjRunt994I7zxRnr5kUeSgXHPnnDEEV5JomdPD3Izvf++9yinAuOGDb19hx4Kbdv6uvJyuPDCiu2Le/NND4z79YNhw7wXOOXVV+Gqqyoe07Sp95wfcAAcdZR/kGjTJr39pZfg1ltzX1OkumqjWoWIiIhUoUED7y1N9drefDNcdFF6+803wyGH+PO33/aUinjvLMBHH8GPfpRefugh+MlP/Hn8y9Zdd4UPPvDe3JTlyz0dozDWx3fVVZ4OAdC6tQe4u+3my9984+kZn3wCIcDvfpduXzbXXutpFOBB+777plMjbr8drrgi3Qv8f//naRfxXmGA1as9wP700/T9XXJJ7muKVIeCYxERkTro4IOT6QznngvXX59Os3jxRU+lKCiADh080Hz2WQ9OV63KnoP78cfp571iU3QtXOj5vwMHeqDcq5ef89RT0/uUlydzl5s182Pi4td8+2344gvo2LFiO7bbDn796/TyT34Chx/uucXgPdPvvAP77OPL3bt7XvHUqR7wf/UV/PBD5fcnsrGUViFSy2qj8H9RUfKamXl23bunt6Xy86pr+PDkuePGj09umzFjI29AZBuQ6pFNadgwGdB+9x2sWAFz5sAee8Af/gBvvQVff517cFo8T/iII3xAHXigef31cMwxsPvu0KqV/y7fd196/xUrvKc2paQEpkxJPhbFp/Gi4nJKz57JlI9s9/vZZ/7vmjWeh3zBBfD887BsWfbAOPP+RDaWeo5FRETqsUsu8YA4pW1b74lt2dKXp0xJb4uPwW/c2D+g3n47PPYYzJ7tgSh48Dlzpj+++QZ++cuNa1vqfJvittu8bSmNG3s+dfv2/iF75kwP3EXyRcGxyDaoRQs49tj08sCBW+a6P/pR8rodOmyZ64rUNfNLVtG9fQsaFaS/wF1fVk78C92PPkoes349LIjNHNCsmZdde/nl9LoddvDjWrf25WXLksFxptatvSTbpZd64LxsmQeil1ziPdLgwekvf+nXatEiHfAOHQovvLARN4+nfvzwQ7L3eO7c5D5du/q/8ftLLe+9d3q5Tx8Fx5JfSqsQ2QZ16OADV1KPje0Vqqnjj09ed/fdt8x1ReqS+SWrOPr2V7hg8ixKy8oBKC0r57bnEnNm8dRTXoEh5c9/TpZ1228/T7WIp1A0bJgOONevh9/8Jnc73nkH7rorXcfYzGskH3KID3JLKSryfwsKPA865aWXkmXSUpYvh7/+Fc47L/e1v/02WVburbe8RFxK69aw557p+4hr0SL9/O9/V56x5J+CY5E6qDqF/6vKVZ40Kbl90qT0tqpyjquydi1cdll69HjPnj4wJzWKPZfKco6z3c+iRfDzn6cnNejTx0tY5fLYYz7ivUULL2t11FEwa1blr4XIlta9fQuG7dKBaXOWcsHkWawrLeOCybN4bWFyto3ycjjwQC91NnCg59zGXXih/xv/5mfxYh9Qd/jhnp+cLXhN+fRT//3q1Mlzlg87DH72M696ce+96f16904///3v02XXQvC/Hb16wahRXs6tTx//u3XWWfDee5W/Dpdd5jnPI0Z4L3RpaXrb2WenK1NkfrM1eLBfb8AA+MUvKo5tENlUSqsQqWM+/th7TOI9RLNnwxln+Nec8cL/teG77/w/7Hjx/k8/hWuu8ZHyVU1EUF2vv+73+u236XUff+wBwsqVFUfJ33abj+ZPWbsWHn3Ua60ed1x+2iSSD40KGjBhTH8Aps1ZyrQ5SwEY3KM3U2P7HXmkf2jMlrrwq195MApw3XUeYKaCy8WL/ZHaliqXlktZmZdx++CDitsaN4Ybbkgv9+sH99/vVSxSg98++cQfmRpWEmH06+fB/6xZFbf95Cdexi3lvPN8go7UAL2VK+GJJ/z5oYd6ZY54D7vIplLPsUgdU9PC/1va+PHJwLhJE+/Z6t/f17/5Zn6uM326j4zfZ5+K6Rc33JAcNf/BB8n6r+C9ZyNG+H/Q8RH3InVBo4IG3Hh8v8S6c0b0TCwPGOCpD2PG+OCzJk2gb19PWYhPdrHvvj4obeRI/9akRQsPMCdPrjytYtgwT9UYM8bHA7Rv76kTzZv7788ZZ3g5tlQQnnLUUZ7X/Lvfea9umzZ+XKtW/rt6yilwzz1edi2XNm08d/jXv/aKOI0b+/TT//u/8Nxz3oaUdu18YpCTT/bnTZrALrvA1Vf7NeK1mUXyQT3HInVMTQr/b2nr1sFf/pJebtjQe7VSkwpcd51/VZovjzzi5abKy/1r1NQHgzVrPEdx+HBfvvXWil/J3n67f936/vv+NWw+Rs2L5EtpWTnjHpidWDe1ZDY/rO+fGKQH8K9/VX2+IUN86uRs4hUq4jp0gHPO8UdN7bCDB6dXX139Y7K14//9P39UZccdPeDOprKSkJMm5U6jGjtW00VLduo5ls1mU/Nat1XZCv/HqzqkCv/Xhrfe8q8wU444Ih0YA4wb58X982HoUD8/+AeGzA8ES5emnz/3XPp5gwb+H3YqD3GPPeDEE/PTJpF8KC0r54LJs5g2Zymj+nZh7tWHMKpvlw05yKlBeiJSOxQcy0aLB76pHjzZdNUt/F8b4tPNQsW2NmkCO++cn2ulJidISdVsTfn+++zt6tTJvx6O22OP/LRJJB+KVqxh5rzljOrbhQlj+tO0UQETxvRnVN8uzJy3nKIV+ppDpDYprUJkK5HZmxwf0FcftW2bXK5uXqFGrktd17tTKx45Z59EnePUIL2iFWvo3alVLbdQZNumnmORLWh+yaoKX5muz1iubuH/zKlX4zNkQXLQXL4UFiaXM9v6ww+wcGH+r1uVbt3Sz0tKfEavuPff37LtEalK706tKuQWNypooMBYpA5QcCw1Nnx4xd65mTOrl1/87bfp0clNmnid3N//vmKR95SvvoJrr/UBVW3bekC4ww4+y1o8z7Q+yHfh/86dk+efNi2939SplY8U31h77+0j0lMef9zzkFNuvrliYLolHHBA+nlZGVx1VXr5/ffhn//c8m0SEZH6SWkVssUsWeLBVbwXtKjIB08tWVJxkos33vCC9PGBV+DLDz/sj3HjkrMs1WXxwv8ANx7fj3EPzOa1hd8n9ksV/h882Gv1xoNPSBf+33ln7zFN1TMtKvLltm0rvmb50rSpV4JIjS4vLfVgfcgQ/+Dz7rub57pVOfdcf/+kKlbcfLN/yOjc2XvQ166tnXaJiEj9o55jqbFhw7znNq59e1+XemTOaAQ+QcSCBV67N3P7xInJr+NLSrx0VyrIM4NBg3wGp3iP6Y03wp135ue+NrdUTmFqVHqfy6czbc5SBvdol9jvyCM9CH3hhYqBcbzwP1ScFW/dOn/NmjXzWqObw/jxyQoV33/vpZTefddrsPbrl+vIzedHP/L3QtyHH/q3C+XlPttgXGZKioiISIqCY6mxK6+Ehx5Krtt9d1+Xevzyl9mPvfVWD/jeeAPOPDO9PgRPzUi58cZ0RYYGDXzba6956kBRkU8MkXLFFbVX2qym8ln4H+D0032a1379PODbfnsYPdqPj6ca5FPz5h50Xnop7LSTX3ennbwX/+WXvbh/bTj3XJ8Rb8gQ/3DQtq1/0HjjjWROMlRMSREREUmxkKs6eD1TWFgYijPrTMlmFc87HjasYiH2oiLPKU7ZaSefZrhB9JFs6lRPm0i57rr0bE4/+lF6sFfLll77N+7DD5ODwV5/PdmbWVfF65umpMo5ZQ7OkZpZssSD3syqFiUl/m3FktSx8p0AACAASURBVCW+3Ly552bHZ+ASEZEtw8yWhBAKq96z9ijnWLaYfv3SgTFUXre2qCj9fPVqmDKl8nMvWlT3g+PMwv+pnONUoLw5AuSxY+Huu9PLCxf6YMit0fXXwwMPeI95YaH3aC9e7B/C4lNN//rXCoxFRCQ3BceyxWxs3drqqA9TA2cW/k/lIAMbCv+rjNOmWb4c7r8/9/Yzz/Q0HBERkVwUHEu1zC9ZlShYv7nttBPMnevPu3XznuH6ToX/N6+TTvLc81de8UGJX3/tucfdunke8umnJ3PVRUREslFwLFVK1ecdtkuHDT2epWXlNGwM63/wIO/zz/N7zcMOSwfHixfDNdfAb3+bTMtYudLr7D7xhA9Kqw+yBcAq/J8fQ4b4Q0REZFNoBJBUKV6f94LJs1hXWsYFk2dhbVZt2Gf+fK+ycOyxXi3h7bc37ZrjxiXTMC6/3HuTDznES7z9+Mc+S9xJJyUnzKjMY4/BvvtCixZe1eGoo2DWLJg0KTmByaRJ6WOKiipOblJc7P926eKpIRMm+L7PP+8VE/bbz/N6W7WCRo284sTQoV4bOFv6x/jxyWvMmOH3dNhhXvmheXPv8Xz66ez3tX6913ru08dLwHXt6u3InDEvU7b7njXLB0m2a+fXHTIE/v3viseuXesDKEeP9kolnTp5jm/LltC7N5x6Krz6auXXFxERqYvUcyxViufGTpuzdMMAsp/8tAMv37vdhv1mzfIHePDYrl3mmapvhx28bNsxx8CyZb6uuNgfmRpW4118220eMKasXetlv6ZPh+OOq367iop8IpOSkorb7ruv4kQmAF9+6cHuSy/59pdfrvy1+ctf4MEHvbxdyquverD8zDMwYkR6fQhw/PHwyCPpdcXFfr/Tp3ugWl3Tp8NZZ/kU0CmvvQZHH+2TalxwQXr9V1/BZZdVPEdpqdeyXrDAe/NvuSX5uouIiNR16jmWaslWn/eZf+zAzTfDHnt4j2W+DRniJdtuuMF7Xtu1857a5s1hl108qL3zTq9jW5kPPoCLLkqu23VXDzIbNvSgtrpmzvTAuLDQe7F33z1Z0q5hQ69JPHy490yPHAkdO6a3f/yx94JX5oEHvHf7wAO9dzqlrMxrTMfdcUcyMC4o8Ndt0CAvm/fkk9W/t/vv9/YfcICX0ou7+GKfhjlT585+rUMPhSOOgD33TA+0DMG/AVCFRRERqVdCCFvFY8cddwyy+fywviycc+/bYaf/fXzD45x73w4/rC+r7aZV6cwzQ/BQzR9nnx1CeblvmzMnhBYtktsnTkwfu3Bhclvq+PXr0/usW+f/zp8fwqpVFa//ww8h7Ltv+viOHZPbr7gief6OHUNYsMC3ffllCF26pLc1ahTC99+nj+3dO3nsww+nt913X8W2L1yY3j5xYnJb69YhfPRRevt55yW3n3lmett33yX3jXviieRxf/5z9v1ERGTbAxSHOhA3VvZQz7FUKbM+79yrD9kwBfIFk2dRWlZe202s1HPPpZ83aABXX53u7d1jDzjxxOqfq107n70vXoauSRP/t3t3T4c47DDvWW7WzK/TuLGnUqR88UXl+cBnnQU9o0nztt/ee2ZTSks9TQO8R3b+/PS2AQM8BSLlxBM9D7m6Tjopuf8VVyQHQD7/fPp506a+7bzzoH9/2G47f03M/P7jPv64+m0QERGpbco5lirV9/q88a/1O3XyAXJxe+xR/XPtuWf2CSTKy32gYK5Bc5lWrqxY9zllwIDkcq7JUjLTFXbbreK5dtstXfWjKpnHb7+9v15Lo8n8Pvssve3ZZz2NYt26qs+7cmX1ri8iIlIXKDiWKm1N9Xnj+cEbo3Pn7OsffDAZGDdo4DP2deniz996K1mrubJZ2zfnZCn5cu65ycC4SxfvQW7e3Ac7xnOdK7tXERGRukbBsVRLfarPmzlhSbduMG+ebyspgW++8RJpKdkGmuXSIEciUjxtAnxw2+jR6eVDDsn/RCaFGTPTf/RRxX2q22uc7fivv05W5eja1f/96qvkeQcM8KoWjRv78muv1WwgoIiISF2inGPZqqQmLInnQg8fke66LCuDq65K7//++/DPf276ddevTy63aJF+/tRTnoaQb4WF0KtXevndd2Hq1PTy/fdnD5hzue++ZNB79dWeLpIyfLj/m3mvTZp4PWfwXmNNzywiIvWZeo5lqxKfsATgxuP7sXLnj6FBHyj3z4I33+wBa+fO3su5du2mX3fgQK9PnPL/t3ffYVZVZ9/Hv2sKHaUM4OAIoxQLIkVRISDYIhFDFCwoRjH2V+ODYoxRiZUnagSxhMSC4qMmKKCxoNgQbKCAgEgRkDo4DiAqXZiZ9f5xn8M++0zhDBym8ftc177mrN3OOhsY7llzr3v16wcnnQSbNtl77Cs33gjXXRe0+/e3CXzew+efl+1eGzdaDecTTrAR4/nzg2NpaUG94qZNbUGW6Ej49OmWr9yqFXz5ZfE1oEVERKoKjRxLtRLNhY5W0zhi6CSmbVhO94vXhM5bsMCqWBQWwqWXhu8RTQ8oi4suCk+k277dcpCnTbNc3P799+DDJOCaa6Bv36BdUACffWbvm5kZjPYm4vLL7XlMnhwOjAHuv9/qN0c9+GD4+Dff2DLeeXk24iwiIlJVKTiWaqfYBUuebs7rr9sCGbVr26S3vn1tAZEWLcLXlzTprjQ1a1pQed11FpSmp9vo6k03wUcfhdMskiklBcaPt+C1bVsL7DMz4corbQnvli0Tv1f37jBjhpWDa9TIntMJJ8CECbaYR6zzz4c337TnWasWHHCABeLvvmsl4URERKoq56vJVPKsrCyfo6W4hHBd5qieB7fgiavbUatG+OfBvDw49lhYExlYrlMH1q0rvlxbdTNmDFx2WdB+9llb9ltERGRfcc6t8d5n7f7MiqOcY6lW4hcsGX5+B4a8PJfnR9Rn3B0FnNXb0eIQR40asGqVTWDbvDm4/uab94/AWERERIqn4FiqlZIWLPnoufV8/WU6414u+dqrrlKlBRERkf2dgmOpVkpasGTUXzP4Z+YO5s+pQW6u1fCtXdvyjbt2tfSCbt0quPMiIiJS4ZRzLCIiIiLloirkHKtahYiIiIhIhIJjkSpu0CBwLthWrKjoHomIiFRdCo5FRERERCIUHIuIiIiIRCg4FhERERGJUHAsldqYMeF82jFjSj//jTfgV7+y5ZobNYLf/Q7mzCn9PitWhI/F5/A6ByNH2rkffgh//KMttZydDfXr21LRGRnQowf8/e+wZUvRft11V/h+U6bAJ5/AmWdCgwa28Ei3brb8cnHy8+Ghh+CII2y55kMOsX78+GPZn98HH0DPnlCvHjRtCldcAT/8YOevXAkXXwxNmlifunYtvk9Ll8Jtt0Hv3tC6tT3r9HQ48EDo1MmWm165svS+iYiIVEaqcyzVxuOPW8AYtXUrvP46TJoE552X+H1Km9D24oswenTR/T/8YMHuJ5/Y8U8/hcaNS77PP/8J48ZBbCXFadMsWH7vPTj55GC/93D++fDqq8G+nBz7vJMmQZs2CX80Xn7Zrom+75Yt1t/PP4d//xtOOQXWrw/Onz69+D5Nnw5/+1vR+2/caD+MzJkDTz5pgXXXron3T0REpKIldeTYOdfGOfeZc26xc26Gc65dMeec4pz7wjm3wDk33zn3oHMuJXIs2zlX4JybE7O1SmYfpXqaPx9uuim87/DDLaBLS7OgNlFTp4bbderYqGtUWhq0bw+9etnI9Gmn2Qhs1DffwNChpb/Hyy/b6Papp0JmZrC/oADuvjt87r/+FQ6MU1Mt4DzhBFi2DN5+O/HP9vbbcMABcPrpNmId9fXXdr8NG2wE+/DDS+9TVKtWNoret6+NIscG6ps326h0NSmlLiIi+4lkp1U8ATzpvW8LPACMKeacH4EB3vujgGOBbsAlMcc3ee87xmzfJrmPUg09+ijs3Bm0r7kGFi6EyZNtVLRu3T2/d5cudj+AW2+1VIavvrIUi//+10ZVc3IsnSNqwoTS79m0qY2uvv++BaaxAfJnn8GOHUH74YfD144bZ+dMnw7PP1+2z9KgAXz5pY3ojhsXPrZtmz3HTz+FuXMtdaOkPp12GuTmWnrFxx/Da69Z4L14MfzpT8F5CxbYn4OIiEhVkbTg2DnXFDgOeCGyawJwiHOudex53vvZ3vtlkdfbgTlAdrL6IVXTf/9rI5Z161o6Qr9+FljtzogRMHBg0VzkiRMtFeHtt+Hoo+Gii4peu3Il/P730LlzeH9x6RA1a9rX7GwLKs88E7KybAlq56BGDQsqo9auLT0f+OqrbdQVLF/3hBOCYzt3BjnAOTmwZElwrFMnOOecoH3RRZaHnKgLLoDDDrPXxx8fPnbggXDllfa6Zs3w8dg+ARx0kI3WDxwIbdvan1tKij2Lv/89fN9vvkm8fyIiIhUtmTnHhwC53vt8AO+9d86tAloAS4u7wDl3EHAucFbM7rrOuRlAKvBfYJj3vqCYa28Cdv0i/cADD0zW55ByNnIk3Hhj0N661dIIJk2y4Ks0t9xiv/aPt3q1bePH2+Swo48OH//+exsRXreu6LV16oQDwajCQujTp+RJc/E2boSGDYs/1qlTuF2vXrj9yy/2NX5F9COPLHqvI4+ERYsS61Ps9fHvedhhFuTvrk8Aw4bBHXck9p4bNyZ2noiISGVQYdUqnHMHAG8AD3rvZ0Z25wIHe++7AKcBPYAhxV3vvR/hvc+KbvXi/yeXKmHevPCv4cECuFNOsZHIp5/e/T0aNQrnBKenh4O84cOLVk546qniA2OwoLo448aFA+OUFDjxRBvJ7d8fWrYMn19arm180JyaWvK5yRT7M2RK3L/+Aw5I7B7ffQd33hned+SRlnfcvz8cd1z4mHKORUSkKklmcLwayHTOpQE45xw2arwq/kTnXH1gEvCa935EdL/3/hfv/drI6w3AM1iALNXUY49ZmbKoG26wdIoPPoCZM3cfsM2aZdUVYieCFRbCRx+Fz3v//XB72bLgdXxQm5VV/HvFpk0AvPSSVZh45RUboS5LekOi4vtSXP5uoqPGyTJ9eni0/rrr7M/stdfsOfTvX779ERERSaakBceRoPZL4OLIrv5Ajvc+lFLhnKuHBcaTvPf3xR1r6pxLj7yuCfQDZierj1L5TJ4cvE5JsXrAUUccUXyuMMCSvE3sLCikSROrtxub31tQYCO6sebPL7kPl14abh9zTPHnxQbxEJ7k9847RQPwZMjKsjrCUbNnWxAa9dJL5T/hrbTnsHKllZgTERGpqpJd5/hqYIxz7jZgI3AZgHPuaeB17/3rwP8Ax2O5xf0i143z3g8DugP3OOcKIn2bDAxLch+lEonNqT3ooKLpBsXl2C7J28Q5oz7j6NqH8O6DR/Ljj67oSXGKy0uOat063I4tcRarSxerTxzVrx+cdBJs2mSjqfvKjTfa6GxU//42gc97q8RR3o47ztJYoukSDz5oFSvq1rWqFtu2lX+fREREkiWpOcfe+2+89129922998d57+dF9l8RCYzx3g/z3qfHlWsbFjn2ivf+aO99B+99O+/9H733v5T2nrL/yc6oS8+2TXjtyYxQYOzS8ku8plmzvX/fiy4KT6Tbvt1ykKdNg44d9106wTXXWD5vVEGBBaHTplkJuF699s37luSww+Daa8P7pk2zkfMaNYrmkIuIiFQlWj5aylU0HSIqNqc2L69o+bPi8mnTU1MYOaAjBbnhmms+P23XPaOl16I2bSq5TzffHG7//HNcn5fYCHbjxpaeUa+eLeGclmb5yjfdZDnO8bWUDz3USr9t3ly0wkVsTvTo0eFUCbBrolJSLJf3/vutbFqNGhYUX3ml5VzH50yXh8ces4mObdvaBMgmTaxM3MyZxY/2i4iIVBXOV5Op5FlZWT4nvu6VVCrRdIiebZswckBH0lNTuPwKzzOjg9HfwYODRS8WL7ZUhthSYM8+C4MG2es6dTzbthVNqTj5ZFvlrrCwyCEgnBIQ67nn4MILbQT4jTcS+0xXXAFPPBGu/JCdHVTHaNbMFtOYObPotS+8AF98YQtvxDv1VFtcxO0+Y0RERKTKcM6t8d6XMPW9ctDIsZSbaDrExHm5DB47h+07C9jcahG4IIodORLatbMV2Dp3LrlG7s6CQhq23FzssQ8/LDkwBguM69Qpuv/qq20ktLjA2DnLte3Z00aNo55+Gu69t+T3ysuzwLhjx6Ilzq680gLjZs3s88aOdn/wQdFlrEVERGTfU3As5SaaDtGnfSYT5+VyxNBJfP7zMroO+C50XrSU244dtspdvJ0FhQweO4eCY+eRklpKFFyCIUMsHSEjI7x/+3ZYsQIuvrjoNc8/DzNmwJQpRZejfvDBoqkYsW64wapMzJgB3bsH+7dtsxJ0CxfaKPGIEeHrpkwp4wcTERGRvabgWMpVemoKw8/vENo3+blMJkywCgy1a1u1iD59bJLXb35T9B4r1m9h6uJ19P9NLaZMgWOPDR+vWdPTpEnJfbjlFisTN2NG0drEQ4YUXf64bt3wSn3HHBNub91a+ijvX/4SvO7SJXzsiiuCCh0nnRQ+lptb8j1FRERk31BwLOVqZ0EhQ16eG9o35OW5/PZ3hUyfboHmjz/Cm29a0DtokKVBRLdBg6BNs/q8+v+6MXJAR3p0T+Ghh8Lvce21jrVrg2sOOSQ41rAhNG1qr7OzbRJZrLPOglVxy9bEVoqIatcu3I5fgS+qQQMrURcVv5BjbHBe2nLNIiIiUj4UHEu5iaZDTJyXS5/2mSy6t/euFIvBY+eEqljsTptm9UlPLf6vb+wSyRCeLJfoEsnJUlpfoPz7IyIiIqVL9iIgIiWKpkP0aZ+5q1rFyAEdAZi6eB0r1m+hTbP6FdzLoqXRFiwoek78insVUU5NREREkk/BsZSbaDpEdkbdXaO+0QC5LIHxkrxNoXskW7NmttjH7MjC5XPnwn/+Y2XeAL7+Gl58MTi/dm2rYiEiIiJVn9IqpFwVlw6RnppSpsD4nFGfhdIw8suQjpGoe+4JtwcOhOOPtxrKxx8PW7YEx26+uWj6hIiIiFRNCo6lSimuVvLjk5cm/X3OOgseecRWwQOb2Bct5bZtW3DeoEFw111Jf3sRSZIxY6xOeXQbM2bv7jdoUPh+K1bsfR9FpHJRWoVUKbF5yhPn5TJxXi7bVzXaJ+91ww1wyinw+OO2sEhODuTnW7WLE0+0RTx+/et98tYiIrvErpTZs6dqoIvsa1o+Wqqk7TsLOGLopF3tRff2plZ6agX2SEQqoylT7AfcqOuvh1699vx+gwbZUvNRy5dbWch9ScGxVCdVYflojRxLlVNSreRoBQwRkahevfYuGBaR/Y8iCalSklkrWUQqn/Xr4b77oFs3aNwYatSwCjJdu8LQobBzp53Xq1c497egwJZgP/poqFULOlr2VUI5x5s32xyDU06xtKkaNWx5+c6dbcLthg2J9f2rr6zP0fdq0ACmTw+fM2sW/OEPtnR8nTq2HXWUvc9334XPjX7GWFOnhj/PoEGJ9U1EEqeRY6lSqkqtZBEpu/ffhwED4IcfwvvXrrVt+nRb4r1Bg6LXDhwIL71U9vecMwfOPrvoKpc//GDb7Nlw8cXQaDdTGxYtgtNPDwLpRo3g3XfDy9vffbdt8dmMCxfa9uyz8OqrRZeSF5HypeBYqpRk1UoWkcpl4UILUmPLJDZoAB06WC3xL7+0ALkkL71ko7DHHWcjqoksv75uHfTuDXl5wb66de09Gza0keDVq3d/n2+/hVNPDfqXkWGBfocOwTmjR4cr2zRoACecYP385BOb7LthA5xzjtVSz8y0/OKMDJgwIbguIyNcV71Ll933T0TKRsGxVDnFBcBlqZUsIpXP3XeHA+Pf/c4mvkVriBcUwPjxlvJQnDZtbKQ2OjkukeB4+PBwYNy1qwWimZnBvnfesYC0JCtXwiWXBCkRzZrBBx9Au3bBOQUFcMcdQbtzZ5tUVz/yLWv2bAtyCwosQB4+HB56yJ4JhFMr2rWz5yAi+46CYxERqVCFhfDWW0G7Th145pnw4jqpqXDBBSXf4957w1Ujatbc/fu+8Ua4/eyz4cAY4IwzSr/HuedanjRA8+YWGB9xRPicWbPg+++D9o4dcNll4XNq1oStW+31W29ZcCwiFUPBsYiIVKj162HTpqB91FG7z/GN16NH2d93+fLgdUYGHH542e8RDYzBguv4wBiKLhTy9de2lSQ+/1lEypeqVYiISJV30EEV3QO49lrIzd37+0RHkEWkYig4FhGRcrUkb1Oo7GJGBtSrF5RwWLAg8fJpUSl78L/ZoYcGr9evh2++Kfs9hgwJXi9bZqtmxve9Zctwe+hQq1hR2iYiFUfBsYiIlJsleZs4Z9RnobrkBb6QZu1+2nXO1q1WC/jnn4PrvLeJaMkcVT3rrHD7D38oOvI7eXLpFSuuvx5uuy1of/01nHmm1U6OOu44q58c9Y9/2CS8eIsX2yS8f/0rvL927eB1fC1kEUk+BcciIlJusjPq0rNtk10L92zfWcDgsXPYevRXpNUs2HXea6/ZBLuTT7ZgMysLzjvPJrMly5Ah0KRJ0P7sM6t60b07/Pa3cNhhVqItvu5yvGHDLLCO+vxzq7YRrZiRmhpUngAbWT72WNv69oXTTrPPd/jhVu4tdvIeQNu2weslS6BTJ+jf3yYDzpq1Rx9dREqhCXkiIlJuYhfumTgvl4nzbKj2nJMzObOf45LfB2kJP/1kJc/2laZNYdIkq68cHR3esgU+/bTs93riCatz/Oab1p482aprjB8PaWlwzTU26jtsmFXn8N5qN3/5ZdF7pcX9z3zZZTB4cNCeM8c20Ap5IvuCRo5FRKRcpaemMPz8DqF9w8/vQJ8zU1i40EZZTzjBFspIS7PR3RNOgNtvt0U6kqlzZ0uFGDHCFtdo3Njes1EjW4L6ppugRYvd3yctzRYi6do12PfaazaiHM0hvucemDkTrrrKqlrUq2ejyo0aWZ3j66+3usqxaRoAN9wADz8cLI0tIvuW89Uk8z8rK8vn5ORUdDdERGQ3dhYUMnjsnF2jxkBoSXgRqb6cc2u891kV3Y/S6LuQiIiUm9jAuE/7TBbd25s+7TN35SDHVrEQEakIyjkWEZFys2L9FqYuXhcaKY7mIE9dvI4V67doKXgRqVBKqxARkXK1JG8T2Rl1QykUOwsKFRiL7AeqQlqFRo5FRKRcFRcAp6emKDAWkUpBOcciIiIiIhEKjkVEREREIhQci4iIiIhEKDgWEREREYlQcCwiIiIiEqHgWEREREQkQsGxiIiIiEiEgmMRERERkQgFxyIiIiIiEQqORUREREQiFByLiIiIiEQoOBYRERERiVBwLCIiIiISoeBYRERERCRCwbGIiIiISISCY5FKYtAgcC7YVqyo6B6JiIjsfxQci1RTd90VDranTKnoHomIiFR+acm8mXOuDfAckAH8DAzy3s8v5rzLgVux4Hwy8P+89zt3d0ykOuvSBTZvDtp161ZcX0RERPZXyR45fgJ40nvfFngAGBN/gnPuUOBeoAfQGmgGXLW7YyLV3XXXwfjxwdakSUX3SEREZP+TtODYOdcUOA54IbJrAnCIc6513KnnAq9777/33nvgX8CFCRwTqXJmzYLf/x5at4bataFmTWje3EaJr70Wxo0Lzt1dzvEHH8C550LLllCrlm2HHALdusHgwfDuu3belCl2/d13h68/+eTS7z95MgwYENy/fn3o1Mnu89NPRT/bmDHh+40ZA59+Cr17Q8OGtm/OnL15eiIiIuUvmWkVhwC53vt8AO+9d86tAloAS2POawGsjGmviOzb3bEQ59xNwE3R9oEHHrh3vRdJsvffhzPPhJ1xSUG5ubbNnAnvvQfnnbf7e40ZA5ddVnR/To5t06bB11/Dr39d9n4WFsLVV8PTT4f3//KLBbdz5sAzz8Dbb8NRR5V8n4kT4dVXoaCg7H0QERGpLJKac1yevPcjgBHRdlZWlq/A7ogU8cADQWCcmgonngiNGsH339uo7bp1id9r2LDgdc2a0LUr1KsHa9bA8uXhkd0mTaB/f1iwABYuDPafdFI4VSOa03znneHAuFkz6NwZfv7Zgm7vYdUq+O1vLQCvXbv4Po4fb1+PPtpGtL/6KvHPJyIiUlkkMzheDWQ659K89/nOOYeN+q6KO28V0CqmnR1zTmnHRKqUnJzg9d13w+23h4/PnQszZpT9XqNHw8CBQbuwED7/HFZGfufSrp0FqnfdFU6tuPtu6NUrfN8ffoCHHgraZ55po781alj7jTegb197vWyZvff115fczxdeCPrmPeTnJ/b5REREKoukBcfe+7XOuS+Bi7GJeP2BHO/90rhTJwCfOOfuAvKAa4CxCRwTqVJat4ZFi+z1Cy9AgwZwxBG2HXwwdOhgW6L3+vpre/3YY7B1Kxx+uN2raVMbSe7atex9/OAD2L49aK9fDxddFLR93O9j3nqr5OC4d+9w0O4cpKeXvU8iIiIVKdlpFVcDY5xztwEbgcsAnHNPYxPtXvfeL3PO3Ql8GrlmClblgtKOiVQ1t90GkybZ6OmiReGgsmlTCyb//OfS83ij/vpXOP98e/3557ZFZWXZ6O6tt1o6Q1nET8r74gvbSrJyZcnHevQo23uLyN7Jzg7+TbZsqYWDRJIlqaXcvPffeO+7eu/beu+P897Pi+y/wnv/esx5T3nvW0W2y2PrGJd2TKQq6drVJt1ddRW0aQMpMf/a1q6F//s/6N4dVq/e/b3OOw8++gguvtj+E3QuOJaTA6NGWcrExo1J/xghW7aUfOygg/bte4vsjdjKKvHpRSIisbRCnkiSLMnbxM6CwtC+o44u5OZ7NrF4MWzbZhPk/vEPm6AH8OOP8O9/J3b/Hj3g+edtdGjLFpvwFptTvGyZVZSIig2gS9KyZbg9erSlUpS0lTYylaLvMW7xcgAAIABJREFUJiLl6swzbfJt//72WkSSQ/+diSTBkrxNnDPqMwaPnbMrQH56dCHn3vEtZz8+jSV5m6hRw3KEBw4MV3xI5Fehjz4aVI4Au759+3COb/y94qtKrFlT9L6nnmrVL6KGDbPqF/HmzIGbb4b//nf3fRWR8jFqVLBo0KhRFd0bkepDwbFIEmRn1KVn2yZMnJfL4LFz2L6zgPse38Tr97dh6YhTuOTsevzudza607p1eJnoNm12f/8nn7TFPpo2tZJsZ59tNY07dQqfF3uvtm3Dx669Fvr0sYVE/vxn25eRYQuIRC1bZtd17Qq/+50tHNK0qb3P8OHFLwYiUpn16lX0tyhTp4bTLAYNCo5t2GA/JJ54oi1mU6OGLdzTv78tlFOc7OzgXtnZVsLx4YftB9hatayE4qWXQl7evr02VmGhlWg89VT7N5yebgv7HHYYnHGGzWOYPz+xZyiyv6mydY5FKpP01BRGDugIwMR5uUycl8vaTccBB7JjaxrTpxd/3VFHwZVXJv4+69fDxx8Xf6xXr6DsGljw3Lw5fPedtTdtsmoTAMceG5w3bJjVXH7mGWvn51Nif9P0HUOqsS++sB88c3PD+3Nz4ZVXbBsyJFz+MN727fZvb8qUYN8vv9gcg5kzbdXMWrWSf228yy+3xYNibd5s2/LltqJmjRpW+lFEwjRyLJIk6akpDD8/qM12YLcl3HZ7IaeeCoceaqM2qam2EEi3bnD//VZ1on793d/7kUfglltsAl+LFlCnjgWqTZva6O4//gHvvBMOXuvUsZGufv1s9KmknODUVMs1njLFJvy1amUpGWlpdl23bpZS8ckndlykKunZ00Z9Y2VkBLm6/fvbcu55efablWhg7ByccIL9tid2sunw4fBEKTWU8vLs39Khh9q/zWjNcLCFecaWUpx0b66NlZMTDowbN7bqOGecYT+QJxpgi+yvnI8vZFpFZWVl+ZzYlRJEytnOgkIGj53DxHnBsFOf9pmMHNCR9FT9HCpSkWJTK3r2DI/Ogv3w+fe/2+uUFDseLU/4yy9wyinw2WfWbtbMcvijE2tjS6qBlV3897/t+H/+E64dfuml4cA1WdfGlnL7/HNLC4nKybHa6lHbttkPzvXq2bMQKU/OuTXe+6yK7kdp9EtSkSSIDYz7tM9k+PkdGPLy3F2BsgJkkcrtzTeD13Xq2G9rHnkk2Pfjj8HrvDxLcTj++OLv9be/BYFzfBWJ+JSNZF4b1bp1uH3LLTYq3qaNTQquX9/aIlI8BcciSbBi/RamLl4XGimO5iBPXbyOFeu30KZZAvkTIlIhYiu9bN4MEyaUfv7KlcUHxwccYJPeourVCx//5ZeS77k318Zq3Biuu87SrcBGoqMlI52Do4+GAQNsMm6dOondU2R/ouBYJAnaNKvPq/+vG9kZdXeNEEcDZAXGItVPSQviNGwYbkdHgROxN9fGe/RRS634z39souH69bbfe5g3z7bZs2HcuD1/D5HqSr/nFUmSNs3qF0mdSE9NUWAsUo6KW4wnvl2c2AVxWrQofTEc78Pl3yqjlBSbQDtxolWj+fFHy5k+99zgnPHjiy8RJ7K/U3AsIiLVQnGL8UTnA7i0gl3nRcsbxorN7121Cu67z2oFx9q40dITKnvVls2b4YEHYOnSYF+DBla/vHfv8LmJLEIksr9RWoWIiFQLsYvxAKGJsY2aH8EPqyzBdskSW9jmsMMsB/cvf7H6xc8+G0y8GzrUSra1a2fpDatX2/Lv+flFl12vbLZvh1tvtS072ybo1atnI8iffx6cl5oaznEWEaORYxERqRaief592mcycV4uRwydtKuCzG2Dw8V958yxRT0mTLAqEM2bWwpCbE3jnByrH/7WW5ajm59v+6vSYjgrVsD779vS759+GnwGsB8KmjSpmH69+KKt9tmggaWARFf6i1+4pLIpbVXCfeWuu8IrOsaWIZwyJXzsrrvKp0/VXRX6Jy4iIlK66GI8sfXGh5/fgZppKaQ4W/Bm6VIbXY3XtasttvHUU1babcECWzK9Zk3IyoIOHeC008J5u5VRgwbwwgu2muaMGRb8//CDBU/NmtkKmX/4A5x1VsX076mn4KqrKua9S9Krly0rHlVNloCQPaTgWEREqo2dBYUMeXluaN+Ql+cyckBHBg9OYfDg0q9v2NDqAt9yS+Lvubu83dICrX1xbVoaDBxoW2U0enS4ffzxcMgh9rq8RmNFSqPgWEREqgUtxlM1xFbIyMwM50GLVAb6LiEiItVC/GI8tdJTd+UgRxfjkYozaJCldsSOeOfmhnNmozZvhhEjLC+5cWNIT7evPXvCyJEl15kGC7YvvdQmItapY1vr1vb+M2aEz43m88amVEC4T6WNZhcUWD+POgpq1bLc9euuszSWeB9+CH/8I3TvbvesX98+V0aGLVX+97+X/rmk/GjkWEREqgUtxlM9LFpky1svWxbev2EDfPSRbf/8p02gjF8q+7bb4P77i6ajfPutbf/3f3DnnbbtrYICOO88ePXVYF9uLowaBe+9ZxMgYyc8vvhi0ZQSsED6k09sGz3armvceO/7J3tOwbGIiFQbxQXAWoyncujSxUaE334btm61fXXqwG9+E5yzdavVnF6+PNh38MFWUu/rr4Ma1YsX24TCr76CGjVs37/+BX/7W3BdWprlM3tvI8b5+fb6rrts5PbSS23Et39/GzmOriIIti+qadPiP09Ojm1t21rO9Oef2+cDKxd4ww22QmGstDQ48kgLfg880EaKv/oK1q614998Y2UER41K5InKPuO9rxbbwQcf7EVERKRya9kyWGuwZcvwsUcfDa9F2KeP99u327Ft27w//fTw8aeesmP5+d43bRrsT031furU4L7vvee9c8HxrCzvCwuD4z17hu+bSN/B+6uvDu6zdKn3jRoFx1JSvF+9Orh2yRLvN20qes8dO7z/1a+C65o2DR+/887we374YXDsww/Dx+68s+S+VxZAjq8EcWNpm3KORUREpFJ4++1w+957rZQeWE7v3XeHj7/1ln2dNSsYfQVLyzjppKB92mlw+ulBOyfHalfvrbvvDnKlW7WCSy4JjhUWWgpIVHY2jBtnI+NZWVC7tl1bo4alUkStXRssRiMVQ2kVIiIiUinETtZzztIeYrVrF26vXFn0uuLOi+57993wtcccs6c9hUaNrG50rCOPDLdXr7avhYUWsMe+f2k2brSyglIxFByLiIiI7EPjxoUD45QUy4fOzLTXM2cGgT5oEZKKprQKERERSboleZvYWVAY2rezoJD8uH2xWrYMXnsPCxeGj8+fX/z5sdeBrW4Yr6RrIVxGLlEbNoRrNoNV2ogVXdwkNm0C4KWXYNo0W8J8/Hg44oiyv7/sOwqORUREJKmW5G3inFGfMXjsnF0BcnSRlrWbfinxutjKFQB//Svs2GGvt28vmnMcPf/YY8Nl095800qjRU2ebOXVog4+GNq3D9q1a4fvu2ZNaZ8ucOedwSjvsmXw3HPBsZSUIO85Pz98Xd26wet33oH330/s/aR8KDgWERGRpMrOqEvPtk2YOC+XwWPnsH1nwa7VC2ullxx6XH55eET3jTdsolvv3lbT+J13gmOtWwcT4NLSwrWLCwrglFNswY1f/QrOOCOcqnDPPeHR4rZtw/3o1g3OPhvOPddqI5fkiScsz/j006FjRxtNjjr3XJt4B1bGLla/ftanbt0swC8seTBdKoByjkVERCSpoouvAEycl7trCe8+7TOZWLcGm9YXf13dura4x1lnBZPsovWEY7VqZedFK1mArUy3cqWtNAewc2fRdAbn4Pbb4Q9/CO+/5BJ4/HELqgFWrbINSl4hLzPTJgx+8IHVJ47v36OPBu2LLoLHHoPZs629fXuQg9ypk50/fnzx7yPlTyPHIiIiknTpqSkMP79DaJ+1S0/wbdfOFsb4+99t1LdhQ0hNta/du8Pw4TBnTtHRXoAHH7SA+Pe/h0MPtfJvtWrZ60susTzfe+8tel3nzjZK3aOHLeuciBo1rJTcffdZX2rWhIMOgmuusfeJrWRRs6aldlx3nQXV6ek2Qn7TTVbuLTbNQiqe89VkSmRWVpbPif/RUkRERCpENMc4OmoMNnI8ckDHXct7y/7HObfGe59V0f0ojf52ioiISFLFBsZ92mey6N7ellIRyUGOr2IhUpko51hERESSasX6LUxdvC40UhzNQZ66eB0r1m+hTbME8xdEypnSKkRERCTpluRtIjujbiiFYmdBoQLj/ZzSKkRERGS/1KZZ/SK5xempKZU2MJ4yxapZRLe77qroHhVVmfvYq1e4b+XBOQY5h4/ZBsUcy447NibR+yo4FhEREZFiDRoUDnqjJfaqMwXHIiIiIiIRCo5FRERERCIUHIuIiIgUIycHLr3UFveoXduWiB49uuh5I0bAwIFwzDG2yEfNmlCnji0+ct558Pbbxd//p58sb/i446BBA1sGu3FjOOIIu+6hh+CHHxLr644d0KdPOAXi1lvD52zYAMOGwYkn2qIqNWpA8+bQv78tUhJrzBi7x3PPhfcfemjiucVjxsCxx9qzaNIELr4Y4LDU+POco7NzDHeOyc6xzDl+co6dzrHBOT53jnucIyOxJ7H3VMpNREREJM4339jKeevWBfvmzoUrroB582DkyGD/LbcES0/HWrHCtvHjYcgQC3ajtm61FQAXLAhfs2GDbd98Y9edeKKtDFia/Hy48EJbsS9q6FC4556g/cUXcPbZkJsbvjY3F155xbb4Pu6NG26wJbOjtm2DF18EmN7UOdp6z+KY088EbirmNg2B4yPbVc7R3XuWJqeHJVNwLCIiIhJn7FgbWe3RwwLZWbOCY488Ar172xbVqBG0amVfa9aEtWvhyy9tRBds2evzz4fjj7f2q6+GA+NWreCoo2DTJli9GpYvh8IE1kopLLSlsV95Jdh3331w++1BOy/PRpXXr7e2c9aPxo2tj99/H/SxTRu4+mrIzrYR5ZkzYeXK4F6/+Y2NBO/OY49BixZw+OH27DZsiB5pkgI8B3SN/yjAYmAt8CNQCzgKOCRyvBnwKBZI71MKjkVERETipKTABx8Eo7YPPww3xYxtPvxwEBzPmmUpFfFpBgsXWsAbNX58EBzHLs1w+OEwfz6kxiQcrFtn6RhZpVQE9t5Gsv/zn2Dfgw/Cn/4UPm/48CAwTkmxknA9elj7l1/glFPgs8+sfeedds9evWwbNCicWjFqlAXOu9Onj/0AkJ5uPyh06wbffrvr8InO0dV7pkXazwOjvGdD7D2cwwH/AS6I7Pq1c9T3nk2778GeU86xiIiISJwzzginM/zxj5Y3G/Xxx0EqRZMmcNtt0KWLjRynp1ugHBsYg6VKRLVuHbxevtzSIKKjyTt22D0vuaT0QHTUKHj22aA9cmTRwBjgzTeD13Xq2Mj3uefaNnAg/PhjcDwvLzxKvqf++ld7DgBNm9rzi3Ny9IX3rAR6OccrzrHcObY6h8dGky+IuSYVaM0+ppFjERERkThHHhlup6VZQBvNQd62zUZj166Fnj3DAWZJNm4MXv/2t9CpE8yebcHw3/4WHKtRA7p2hSuvtOC1JNHRYLCc4//5n+LPi61NvHkzTJhQej9XrgxGuPdU/POLbxOkS+AcTwOXJ3jrA/aiWwlRcCwiIiKyh265JRwYN2xogWW9etaODUS9D17XqGHpDaNGwRtv2GS/LVvs2I4dMHWqbT/9BNddt/t+vPQS9O0LAwbs9Ufa1Y/y4BwnUDQwng2sBAqwvOPY0Hqfr7+ntAoRERHZ7yzJ28TOgpJnvC1cGG7n58PSmDoJtWvbhLZPPw32NW9uo7STJll+8eOPl96HAw6wcmuffmoT8b77zvKM27cPzintHhdfbCkLEEzMmzix6HktWwavW7SwIL20bdCg4Pw9XQo6/vktWlTklNWRr7+K2/8n7+nsPed4z7nAR3vWgz2n4FhERET2K0vyNnHOqM8YPHbOrgA5Py5Qfucd+OSToP2Pf4TLunXvbqkW+fnBvrQ0GxEG2/+Xv5Tchy+/hGeeCeoYO2c1knv3hg4dgvNKW665VSsLpuvXt/bOnVYf+aO4cPLMmPoOq1ZZNYv4ShgbN8K//x2tRRyoXTvcXrOm5P7Euuce6w9Y+kcxQf6UyNf4LIZd49bO0REoJbFk31BahYiIiOxXsjPq0rNtEybOs6K/w8/vwOOTvwXa7jqnsBBOPdXqDG/daiXNYt14o33t0iUIRletssoT7dvD119buyTLlsHll8NVV9miHy1aWGC9bJnVUY5q06b0z9K5s5Vx69PH0jG2bbN85smTbQEOsPrFzz4bpH8MHQpPPAHt2lmFjNWrbaQ3Pz88ygzQtm24fc459kyiedFDhhTfr4kTLUf7iCNsgl/cYiZfeE+kPgYz4i59zDnOwwZwu1EBsaqCYxEREdmvpKemMHJARwAmzstl4rxctq9qFDqnb1/LCY4fhQW4/nqr9wvwv/8LJ58cjJKuWhUExf/7v1bFojQFBVbGbf78osdq1IAHHtj95zntNFuNbuBAS4vYuNFGoD/+2ILT5s0tWO3XL6hpnJMTLicXlRYXGZ53ngXTmzdbe906y5Hend//Hp5/vrgfEH4ohMaXRlve86FzvEVQvziVoJLFKuBt4Ordv2PyKK1CRERE9jvpqSkMP79Dicc7dbLUhwEDICPDFvZo3x6efBIefTQ471e/solzp50GdevadvzxtohIaWkVPXtaqsaAAVbyLSPDRnHr1LHR5yuusBHXaBC+OxdeaMtYR61fD6efHizg0bWrlYl74AGrcdy4cfB+bdtaEPzEE7aSXqyDD4b337dgu2HDxHOQn3vOPt8xx0CtWvZ+F10EcMJa74nPQO4HDMMm4e0EcoGngS7A94m9Y/I4Hzt1ck9v4lwK8AgW9XtgpPe+SHaJc64WMBabebgNWwXlWu/90sjxKUBL4OfIJc957x9OpA9ZWVk+p7gfgURERETi7CwoZPDYObtSKwD6tM9k5ICOpKdq7HBfcc6t8d6XsrRJxUvWn/7FWMDbFlv/+k/OuXYlnPskcLj3vgPwGvaTQawbvfcdI1tCgbGIiIhIomID4z7tM1l0b2/6tM9k4rzc0CQ92T8lKzi+AHjKe1/gvd8AvARcGH+S93679/4tHwxXTweyk9QHERERkd1asX4LUxev2zVSXCs9lZEDOtKnfSZTF69jxfpyLPQrlU6y0irmAVd576dF2v8PONF7f8lurnse2OC9/59IewpwEJZvsgD4i/d+WQnX3gTsWuX8wAMPPPinn37a688iIiIi1d+SvE1kZ9QNpVDsLChkxfottGlWvwJ7Vr1VhbSKhKpVOOemASUVE+m0J2/snLsNWx/71Jjdv/fer3bOOeA64E0sXaMI7/0IYFfqeVZW1t5H+SIiIrJfKC4ATk9NUWAsiQXH3vuupR13zq3CJtJNi+zKxspvlHT+zdjMxNO891tj3md15KsHHnfOPeSca+y9/6GEW4mIiIiIJE2yco7HAVc651Kdc42wHOSXijsxkg5xIXC69/6nmP1pzrlmMe3+QJ4CYxEREREpL8laBOR5rBbdEqyU2wjv/TwA51xfoK/3/grnXBYwHFgGfGjZE/zivT8BqAlMdM7VBAqB9UDfJPVPRERERGS3kjIhrzJQnWMRERGRyq0qTMhTlWsRERERkQgFxyIiIiIiEQqORUREREQiFByLiIiIiEQoOBYRERERiVBwLCIiIiISoeBYRERERCRCwbFUKVOmgHPBdtddFd2jsluxIvwZBg2q6B6JiIhIlIJjkSSKDXp79aro3oiIiEhZKTgWEREREYlQcCwiIiIiEqHgWKq8nBy49FI46CCoXRs6doTRo8PnFJfnm5NjXzMzITUVRo4Mzi8shHHjoG9faN4catSAAw+0e//5z3ZtrF697L6xpk5NPLf455/h5pshOxtq1oRDD4W//hXy84s/f8MGGDYMTjwRGja0/jVvDv37w+TJxV+TnR30JTsbtm2DoUOhTRu7/uyzS+6fiIjI/iKtojsgsje++QY6d4Z164J9c+fCFVfAvHnhgDfWihVw3HGQl1f02JYt0K8fvPtueP/OnXbvuXPhX/+Cf/8b+vTZ+8+wZo31ZenScP/uvdeOxQf6X3xhgWxubnh/bi688optQ4bAQw+V/J47dsAZZ8DHH+99/0VERKoTBcdSpY0da6OePXrA1q0wa1Zw7JFHoHdv2+JNnWpfs7Lg6KNh9epg5Pfyy8OB8QEHwPHHW/A5f77t27gRzjvP3u/II6FnT8jIgAkTgusyMmx/VJcuxX+G99+3r8ceCykpMGNGcOzZZ+GOO2wkGSyY79MH1q+3tnPWt8aN4csv4fvvbf/w4TYifPXVxb9nbq5tDRrY+27dCunpxZ8rIiKyX/HeV4vt4IMP9lL9ffih9xBsKSnef/xxcHzEiPDxX//a9i9fHt4P3l9zjff5+cG127d7/9VX4XOys73PyQnOueOO8PGBA8P9iz3Ws2fxn6G4vjz6aHD8qqvCx559Njj2pz+FP/tHH4X7361bcLxZs/Dna9kyfN8TT/R+/frw9SIiIvsSkOMrQdxY2qacY6nSzjgDuncP2n/8IzRpErQ//hgKCope17ixja6mpgb7ataEt98On3fDDXDwwUH79tttJDkq/vw90bIlXHdd0D7zzPDx2PSJN98MXtepY6Pj555r28CB8OOPwfG8vPBIeryRI+05RNWsuWf9FxERqU6UViFV2pFHhttpadC6dZCDvG1bkIIQq3NnCy7jrVgRbrdrF27XqgWtWsHs2dbesAE2bYL69feo+wB06GDpFFH16oWP//JL8f3bvDmcxlGclSst7SJezZrF7xcREdnfaeRY9ksHHVTRPQg0bBhux45m760tW4rf37Rp0eoaIiIiouBYqriFC8Pt/Pxw1YfatcOpA1EpJfzNb9ky3F6wINzevh2+/TZoN2y4d6PGZRXbvxYt4jOXi24llY8r6fOLiIjs7/RfpFRqS/I2sbOgsMTj77wDn3wStP/xj3BZt+7dLdUiUb/5Tbj96KPw3XdB+/77rVJFSefXrh28jr0uWWLzkVetgvvus5rMsTZutDJzF1+c/PcXERGp7pRzLJXWkrxNnDPqM3q2bcLIAR1JT00hv6CQ2J/pCgvh1FNtMYytW2HmzPA9bryxbO95zDE2uW38eGsvXw5HHWVl2L7/Hr7+Oji3Vi247bbw9W3bWh1kgCVLoFMnOOwwS2H4y1+sbNreGDLEyrtFJ94NHQpPPGG50ampVpJu4UIbQY8fBRcREZHdU3AslVZ2Rl16tm3CxHlWrmH4+R14fPK3QNtd5/TtC1OmwEcfFb3++uuLjuwm4plnLPj84ANr//xzUIs4ql49eOGFohP2LrsMBg8O2nPm2Aalr5CXqObNYeJEW6QkWtM4J6foin1QthFzERERMfrvUyqt9NQURg7oCMDEeblMnJfL9lWNQud06gQjRthCGe+/b5Uj2ra1km5XXLFn71u/vi0C8vLLFgDPmgU//GAVHg47DH79a7t/ixZFr73hBsv1HT3acp+3b9+zPpSma1fLhX7qKSvttmAB/PST9S8ry6pfnHaajYCLiIhI2Tirx1z1ZWVl+Zzihs+kytu+s4Ajhk7a1V50b29qpSexpIOIiIiUC+fcGu99VkX3ozSakCeV2s6CQoa8PDe0b8jLc0udpCciIiKypxQcS6W1s6CQwWPnMHFeLn3aZ7Lo3t70aZ/JxHm5DB47RwGyiIiIJJ1yjqXSWrF+C1MXr6NP+8xd1SqiOchTF69jxfottGlWjkWGRUREpNpTzrFUakvyNpGdUZf01OCXHDsLChUYi4iIVEFVIedYI8dSqRUXAKenpigwFhERkX1COcciIiIiIhEKjkVEREREIhQci4iIiIhEKDgWEREREYlQcCwiIiIiEqHgWEREREQkQsGxiIiIiEiEgmMRERERkQgFxyIiIiIiEQqORUREREQiFByLiIiIiEQoOBYRERERiVBwLCIiIiISoeBYRERERCRCwbGIiIiISISCYxERERGRCAXHIiIiIiIRCo5FRERERCIUHIuIiIiIRCQlOHbOpTjnHnPOfeucW+qcu76Uc1c4575xzs2JbBfEHGvjnPvMObfYOTfDOdcuGf0TEREREUlEWpLuczFwFNAWOBCY7Zz70Hs/v4TzL/Dezylm/xPAk977Mc65c4ExQJck9VFEREREpFTJSqu4AHjKe1/gvd8AvARcWJYbOOeaAscBL0R2TQAOcc61TlIfRURERERKlazguAWwMqa9IrKvJP/nnJvnnBvtnGsS2XcIkOu9zwfw3ntgVUn3cc7d5JzLiW6bN2/e6w8hIiIiIvu3hIJj59w059z6ErZDyvieJ3nvjwE6A+uB58raaQDv/QjvfVZ0q1ev3p7cRkRERERkl4Ryjr33XUs77pxbBbQEpkV2ZWOjvsXda1Xk607n3EhgceTQaiDTOZfmvc93zjls1LjY+4iIiIiIJFuy0irGAVc651Kdc42wHOSX4k9yztV1zjWI2XUhMBvAe78W+BKb3AfQH8jx3i9NUh9FREREREqVrGoVz2NVJZYAHhjhvZ8H4JzrC/T13l8BNAMmOOdSAQcsAy6Juc/VwBjn3G3ARuCyJPVPRERERGS3nM17q/qysrJ8Tk5ORXdDRERERErgnFvjvc+q6H6URivkiYiIiIhEKDgWEREREYlQcCwiIiIiEqHgWEREREQkQsGxiIiIiEiEgmMRERERkQgFxyIiIiIiEQqORUREREQiFByLiIiIiEQoOBYRERERiVBwLCIiIiIS4bz3Fd2HpHDO/QKsq+h+VFP1gM0V3Yn9hJ51+dGzLj961uVHz7p86XmXXRPvfc2K7kRpqk1wLPuOcy7He59V0f3YH+hZlx896/KjZ11+9KzLl54tYRIXAAAE7klEQVR39aS0ChERERGRCAXHIiIiIiIRCo4lESMqugP7ET3r8qNnXX70rMuPnnX50vOuhpRzLCIiIiISoZFjEREREZEIBcciIiIiIhEKjkVEREREIhQcS4hzLsU595hz7lvn3FLn3PWlnHumc+5L59wc59zXzrlLy7OvVV0Zn3VN59zjzrklzrl5zrkXyrOv1UFZnnfMNZc557xz7uzy6GN1keizds7Vcs791zm32Dk31zn3nnOudXn3t6pxzrVxzn0WeW4znHPtSjjv8sj3jG+dc08559LLu69VXSLP2jl3inPuC+fcAufcfOfcg845xVdVmP7wJN7FwFFAW+B44E8lfDNwwAvAIO99R+As4AnnXP3y7GwVl9Czjrgf8EBb73174Oby6WK1UpbnjXMuG7gSmF4enatmyvKsnwQO9953AF4Dni6fLlZpTwBPeu/bAg8AY+JPcM4dCtwL9ABaA82Aq8qxj9XFbp818CMwwHt/FHAs0A24pNx6KEmn4FjiXQA85b0v8N5vAF4CLizhXA80iLw+APgB+GXfd7HaSOhZO+fqApcDt/tIeRnv/ffl2tPqIeG/25FRn6eBP6K/03sioWftvd/uvX/LB2WTpgPZ5dfNqsc51xQ4DhucAJgAHFLMiPu5wOve++8jz/dflPy9XIqR6LP23s/23i+LvN4OzEF/j6s0BccSrwWwMqa9IrIvJPLN9gLgFefcSuAT4FLv/Y7y6GQ1kdCzBloBG4DbnHMznXMfO+dOLYf+VTeJPm+Am4BPvfez9nWnqqmyPOtY/4ONHkvJDgFyvff5sOt78SqKPt89/TOQQKLPehfn3EHYDyZvlksPZZ9Iq+gOSPlyzk0D2pRwuFMZ7pMG3AH0895/5JzrArzunGvvvV+fhK5Wecl61ti/05bAAu/9rc65TsB7zrl23vu8ve1ndZHEv9tHA/2Bk5LRr+ooiX+3Y+95G/brf/3gJ1WSc+4A4A3gQe/9zIruj+w5Bcf7Ge9919KOO+dWYYHYtMiubOwn5Xgdgebe+48i953hnMvB/mN8L2kdrsKS+KxXAYXAi5H7znbOLQfaAwqOI5L4vHtEji2x1HoOAp50zmV67/+ZrP5WZUl81tHzbwb6Aad577cmqZvV1Wog0zmX5r3Pj8z/aEHR57sK+61TVHYx50jpEn3WRObbTAJe895r1bwqTmkVEm8ccKVzLtU51whLnXipmPOi3zSOBIjkYLUCvim3nlZ9CT3ryEj8B8AZsGuizaHAwnLsa3WQ6PP+p/c+03uf7b3PxvJgr1JgXCaJfh/BOXcTlgt7uvf+p3LsY5XkvV8LfIlNegT7LUeO935p3KkTgL7OuYMiQd01wNjy62nVl+izds7VwwLjSd77+8q3l7IvKDiWeM8Di4AlwAxghPd+HoBzrq9z7mmAyK/zrwJeds7NBV4Frvfea2QicQk964hrsBn/84D/Ald779eUd4eruLI8b9k7CT1r51wWMByb2Puhs7KQn1dQn6uSq4GrnXOLgVuBywCcc0875/oCRCaI3Ql8CiwF1mGVF6RsdvussVz544F+kb/Dc5xzt1dMdyUZXDBJWERERERk/6aRYxERERGRCAXHIiIiIiIRCo5FRERERCIUHIuIiIiIRCg4FhERERGJUHAsIiIiIhKh4FhEREREJELBsYiIiIhIxP8H/RsE0M4K3+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src_words = ['the','chicken','duck','turtle','beef','pork','egg','mom','dad','grandpa','grandma','sister','brother','milk','tennis','football','basketball','cricket','baseball','']\n",
    "tgt_words = []\n",
    "plot_embedding_space(src_words,src_word2id,src_embedding.cpu().numpy(),tgt_words,tgt_word2id,tgt_embedding.cpu().numpy(),pca2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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