{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc6846f0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12865,
     "status": "ok",
     "timestamp": 1750869811592,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "fc6846f0",
    "outputId": "3b5157f6-22e3-4531-9a63-a680e8a2067e"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from custom_utils import encode_texts, decode_ids, compute_bleu\n",
    "from model_structure import Seq2Seq\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49c90752",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 28229,
     "status": "ok",
     "timestamp": 1750869839842,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "49c90752",
    "outputId": "777c2d16-a41a-43ec-9b75-ed735df9829a"
   },
   "outputs": [],
   "source": [
    "from datasets import load_from_disk\n",
    "import json\n",
    "\n",
    "# from google.colab import drive\n",
    "\n",
    "# drive.mount(\"/content/drive\")\n",
    "\n",
    "dataset = load_from_disk(\"SlimOrca_Dedup_English_Uzbek_preprocessed\")\n",
    "\n",
    "split_dataset = dataset.train_test_split(test_size=64, seed=0, shuffle=True)\n",
    "\n",
    "train_set = split_dataset[\"train\"]\n",
    "val_set = split_dataset[\"test\"]\n",
    "\n",
    "uz_encode_vocab = json.load(open(\"uz_encoding_vocab.json\", \"r\"))\n",
    "\n",
    "uz_decode_vocab = json.load(open(\"uz_decoding_vocab.json\", \"r\"))\n",
    "uz_decode_vocab = {int(k): v for k, v in uz_decode_vocab.items()}\n",
    "\n",
    "en_encode_vocab = json.load(open(\"en_encoding_vocab.json\", \"r\"))\n",
    "\n",
    "en_decode_vocab = json.load(open(\"en_decoding_vocab.json\", \"r\"))\n",
    "en_decode_vocab = {int(k): v for k, v in en_decode_vocab.items()}\n",
    "\n",
    "\n",
    "val_inp = val_set[\"translations\"]\n",
    "val_out = val_set[\"value\"]\n",
    "\n",
    "val_inp_ids, val_inp_mask = encode_texts(val_inp, uz_encode_vocab, decoder_input=False, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "090484d7",
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1750869840289,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "090484d7"
   },
   "outputs": [],
   "source": [
    "def compute_loss(model, input_tokens, output_tokens, input_mask, output_mask):\n",
    "    logits = model(input_tokens, output_tokens, input_mask) # (batch_size, seq_len - 1, output_vocab_size)\n",
    "\n",
    "    output = output_tokens[:, 1:]  # Skip the first token (usually <bos>)\n",
    "\n",
    "    output_mask = output_mask.clone()\n",
    "    output_mask = output_mask[:, 1:]  # Adjust mask to match the output sequence\n",
    "    last_indices = output_mask.argmax(dim=-1)  # (batch_size,)\n",
    "    output_mask[torch.arange(output_mask.shape[0]), last_indices] = 0\n",
    "\n",
    "    logits_softmax = -F.log_softmax(logits, dim=-1)  # (batch_size, seq_len - 1, output_vocab_size)\n",
    "    losses = logits_softmax.gather(dim=-1, index=output.unsqueeze(-1)).squeeze(-1)  # (batch_size, seq_len - 1)\n",
    "    losses = losses.masked_fill(output_mask == 1, 0)\n",
    "    final_loss = losses.sum() / (output_mask == 0).sum()\n",
    "    return final_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d53bda9",
   "metadata": {
    "id": "1d53bda9"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from IPython.display import clear_output\n",
    "import math\n",
    "\n",
    "uz2en_model = Seq2Seq(\n",
    "    input_vocab_size=len(uz_encode_vocab),\n",
    "    output_vocab_size=len(en_encode_vocab),\n",
    "    emb_dim=1000,\n",
    "    hid_size=1000,\n",
    ").to(device)\n",
    "\n",
    "optimizer = torch.optim.Adam(uz2en_model.parameters(), lr=1e-3)\n",
    "epochs = 10\n",
    "batch_size = 128\n",
    "train_size = 50000\n",
    "\n",
    "num_of_batches = math.ceil(train_size / batch_size)\n",
    "metrics = {\"train_loss\": [], \"dev_bleu\": []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44da2ec0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "executionInfo": {
     "elapsed": 4995599,
     "status": "ok",
     "timestamp": 1750698550692,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "44da2ec0",
    "outputId": "bd2ea620-68e0-4b79-e81b-360bc2d9ecb9"
   },
   "outputs": [
    {
     "data": {
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WLYOnpyeOHz+OLl26oHHjxvjrr78M+4ODgzF79mw8//zz0Gq1sLIq18sRERFViFYvdQRERERUnVQo4y3q+u3m5vbIY5ycnEpNrtVqNdRqtWE7MzMTAKDRaKDRaCoSnlkpqmt1qnNV4H0VH+9p5eB9FdfNjHzM3XoRccly9A4vqPD1+L6YrvTcAry87Cju3lOgb1+poyEiouruiRNsvV6PCRMmoGPHjmjcuHGJx6SlpWHWrFl47bXXSr1OZGQkZs6cWaw8KioKdnZ2Txqe2YqOjpY6BIvE+yo+3tPKwftaMRo9sPOGDNHX5SjQyyCDDMvW/wtf+4pdNzc3V5wASXQ6vYDjSekAZBAEQepwiIiomnviBHvs2LE4e/Ys9u3bV+L+zMxM9OvXDw0bNsSMGTNKvc6UKVMwceJEo/P8/PwQHh4OJyenJw3P7Gg0GkRHR6NXr15QKpVSh2MxeF/Fx3taOXhfK0YQBOyIvY0vt8Qi6W4eAKClnzN6ut7Bi4Mqfk+LeleR6VHIZYafdXom2EREJK0nSrDHjRuHjRs3Ys+ePfD19S22PysrC71794ajoyPWrl37yAcblUoFlUpVrFypVFbLh8zqWu/KxvsqPt7TysH7Wn6Xb2fjk3/OY/el2wAAT0cVpvZtgL6NPLBlyxZR7infE9Mlkz2QYDO/JiIiiZUrwRYEAePHj8fatWuxa9cuBAUFFTsmMzMTERERUKlU2LBhA2xsbEQLloiIqEhWvgbf7YjHz/sToNEJUCpkeKVzbYztXgcOKiuOm64mHmzB1rMFm4iIJFauBHvs2LFYsWIF1q9fD0dHR6SkpAAAnJ2dYWtri8zMTISHhyM3Nxe///47MjMzDd3qPDw8oFAoxK8BERFVK3q9gHUx1xG55SJuZxVOktkjxBPTnmqIIPcKDrYms6MwasFmgk1ERNIqV4K9ePFiAEC3bt2MypcuXYrRo0fjxIkTOHz4MACgTp06RsckJCQgMDDwySMlIqJq78y1DHy84SxOJKUDAAJr2GF6/4boEeIlbWAkGbn8/s9swSYiIqmVu4v4o3Tr1o0zeBIRkejuZKvxRVQsVh1NhiAAdtYKjO9RFy91CoTKir2jqjO2YBMRkSmp0DrYRERElUmr0+O3Q1fxVfQlZOVrAQBPt6iFyX1C4OXEOT6IY7CJiMi0MMEmIiKTdCA+DTP+OYdLt7IBAA1rOuGTgY3QKtBN4sjIlMhkMshkgCBwFnEiIpIeE2wiIjIp19PzMHvTeWw+UziRpqudEu9F1MeI1v5GrZVEReQyGXSCwGFqREQkOSbYRERkEvI1Ony/+woW745HvkYPuQx4vl0AJvaqBxc7a6nDIzPA9JqIiKTGBJuIiCQlCAK2nbuFTzedx7V7eQCAtkFumDGgERrUdJI4OjIHRf0a2IBNRERSY4JNRESSiU/NwowN57EvPg0AUNPZBlP7NsBTTWtCJmN3cCobflSIiMhUMMEmIqIql5mvwTf/xuGXA4nQ6gVYW8nxepfaeKNbMOys+dVET4ZjsImISGp8iiEioiqj1wv48/g1zNt2EWnZBQCAXg29MK1fQ/jXsJM4OjJXhb0dBI7BJiIiyTHBJiKiKnEy6R5mbDiHU9cyAAC1Pezxcf9G6FrPQ+LIyNxxDDYREZkKJthERFSpbmepMXfrRfx5/BoAwEFlhbd71sWoDoGwtpJLHB1ZAo7BJiIiU8EEm4iIKoVGp8cvBxLxzb9xyFJrAQDPhPri/d714eloI3F0ZInYSZyIiKTGBJuIiES3N+42Zmw4h8u3cwAATX2dMWNAI7T0d5U4MrJE7CJORESmggk2ERGJJulOLj7ddB5R528BAGrYW+OD3iF4JtQXcjn78VLlKFrSjfk1ERFJjQk2ERFVWF6BDot3xWPJniso0OqhkMswqn0g3g6rC2dbpdThkYUz/OmGGTYREUmMs8sQEdETEwQBG0/fQM8vd+HbHfEo0OrRsU4NbH27M6b3b8jk2kzt2bMH/fv3h4+PD2QyGdatW2e0XxAETJ8+HTVr1oStrS3CwsIQFxcnTbCAIcPmGGwiIpIaE2wiInoiF1My8eyPhzBuxUncyMhHLRdbLHm+JX5/uS3qejlKHR5VQE5ODpo1a4aFCxeWuH/evHn49ttvsWTJEhw+fBj29vaIiIhAfn5+FUdaSAYOPyAiItPALuJERFQu6bkF+Dr6En47dBV6AVBZyfFmtzp4vWtt2CgVUodHIujTpw/69OlT4j5BEDB//nx89NFHGDhwIADg119/hZeXF9atW4cRI0ZUZagA7i/TxUnOiIhIakywiYioTPR6AWuOJ+OzLRdxL1cDAOjbxBtT+zaAr6udxNFRVUlISEBKSgrCwsIMZc7Ozmjbti0OHjwoSYJdhAk2ERFJjQk2ERE91qVbWfhw7RkcTbwHAKjn5YAZ/RuhQx13iSOjqpaSkgIA8PLyMir38vIy7CuJWq2GWq02bGdmZgIANBoNNBpNhWIq6iCu0Vb8WlIpittc4wdYB1NhCXUALKMerINpKGsdxKojE2wiIipVXoEOC3bE4Yc9V6DVC7CzVmBir3oY1SEQSgWn8aCyi4yMxMyZM4uVR0VFwc6uYj0gtBoFABn27z+Ay2bemSI6OlrqECqMdTANllAHwDLqwTqYhsfVITc3V5TXYYJNREQl2hWbimnrzyL5bh4AoFdDL8wY0Ai1XGwljoyk5O3tDQC4desWatasaSi/desWmjdvXup5U6ZMwcSJEw3bmZmZ8PPzQ3h4OJycnCoU0/SYHUCeFu07dEADH5cKXUsqGo0G0dHR6NWrF5RK85x9n3UwDZZQB8Ay6sE6mIay1qGoZ1VFMcEmIiIjqZn5+GTjeWw8fRMAUNPZBjMGNEJEI2+JIyNTEBQUBG9vb2zfvt2QUGdmZuLw4cN44403Sj1PpVJBpVIVK1cqlRV+aJP/N8uZlcLKbB8Ai4hxP6TGOpgGS6gDYBn1YB1Mw+PqIFb9mGATERGAwknMlh9JwrytF5GVr4VcBrzYMQjv9KoHBxW/LqqT7OxsxMfHG7YTEhIQExMDNzc3+Pv7Y8KECfj0009Rt25dBAUFYdq0afDx8cGgQYOkC5qIiMgE8ImJiIhw/kYmpq49g5jkdABAU19nzHm6CRrXcpY2MJLEsWPH0L17d8N2UdfuUaNGYdmyZXj//feRk5OD1157Denp6ejUqRO2bt0KGxsbSeI1LNMFTiNORETSYoJNRFSN5RZoMf/fOPy0LwE6vQAHlRUmRdTH8+0CoJDLHn8BskjdunWD8Ig1r2QyGT755BN88sknVRhV6WT/zSPOZbqIiEhqTLCJiKqpf8/fwscbzuF6euEkZv2a1MT0/g3h5SRNKyRRRTG/JiIiqTHBJiKqZm5m5GHmhvPYeq5wzeJaLrb4dFBjdA/xlDgyoidj6CLODJuIiCTGBJuIqJrQ6QX8ejARX2yLRU6BDgq5DK90DsLbPevCzppfB2S+igYzcAw2ERFJjU9URETVwJlrGZi69gzOXM8AALT0d8GcwU0Q4l2x9YeJTIFMxjHYRERkGphgExFZsKx8Db6MuoRfDyZCLwBONlb4oE8Inm3tDzknMSMLwU8yERGZCibYREQWSBAEbDuXghkbziMlMx8AMLC5Dz7q1xAejiqJoyMSGTNsIiIyEUywiYgszLV7ufh4/Tlsv5gKAAioYYdZAxujSz0PiSMjqhyGMdjsIk5ERBJjgk1EZCE0Oj2W7k/A19FxyNPooFTIMKZrMMZ2rwMbpULq8IgqHSc5IyIiqTHBJiKyACeS7mHq32dwMSULANAm0A1zBjdGHU9HiSMjqnyc5IyIiEwFE2wiIjOWkafB59suYvnhJAgC4GKnxNQ+DfBMqC8nMaNq4/4yXURERNJigk1EZIYEQcDG0zfxycbzuJ2lBgAMaemLqX1DUMOBk5hR9fJfAzYENmETEZHEmGATEZmZpDu5+Gj9Wey5dBsAUNvdHp8+3Rgdgt0ljoxIGuyrQUREpoIJNhGRmSjQ6vHj3iv4dnsc1Fo9rBVyjO1eB2O61YbKipOYUTVWNAZb4jCIiIiYYBMRmYGjiXfx4dozuHQrGwDQIbgGPh3UGLU9HCSOjEh6hhZsZthERCQxJthERCYsPbcAn225iFVHkwEANeyt8dFTDTCoeS3DzMlEVIj5NRERSY0JNhGRCRIEAetiruPTjRdwJ6cAADCitR8m9wmBi521xNERmRZOckZERKaCCTYRkYm5cjsb09afxf74OwCAup4OmDO4CVoHukkcGZFpkoFjsImIyDQwwSYiMhFaPbBgx2Us2ZOAAp0eKis53upZF692rg1rK7nU4RGZLI6WICIiU8EEm4hIInq9ALVWj3yNDjFJdzH3lAKp+ZcBAF3qeeDTgY3hX8NO4iiJTF9Rfs0e4kREJDUm2EREDyhKevM0OuRrdIb/F/6nf6Cs8Bi1Roe8Ah3ytTrkFeiRr9Uh37B9/7gHr1G0rdbqH3p1GTwcrDG9fyM81bQmJzEjKiPDGGx2EiciIokxwSYii5SRq8HfJ68h+W5eBZPeqmFnrUALVw2+fbkjajix1ZqofP4bg838moiIJMYEm4gsypXb2Vi6PxF/Hr+GPI2uQteyVshho5TDRqmAjVIBW6WixG1bawVUVgrYWitgY6WArfX9Y4yOe6Ds4W29TovNmzfDyVYp0p0gqj7Y2YOIiEwFE2wiMnuCIODA5Tv4aV8CdlxMNZSHeDuiW31P2CqfLOlVyKvuqV1fsb8FEBHYgk1ERNJjgk1EZitfo8OGmBv4eX8CLqZkAShsyeoZ4omXOgahfXANjmMmqgYMk5xxDDYREUmsXOu+REZGonXr1nB0dISnpycGDRqE2NhYo2Py8/MxduxY1KhRAw4ODhgyZAhu3bolatBEVL3dzlLj6+hL6DR3B97/6zQupmTBVqnA/9oHYMe73fB/o1qjQx13JtdE1QR/1YmIyFSUqwV79+7dGDt2LFq3bg2tVoupU6ciPDwc58+fh729PQDgnXfewaZNm7BmzRo4Oztj3LhxGDx4MPbv318pFSCi6uPCzUz8tC8BG2JuoEBXOBmZj7MNRnUIxIjW/nC24/hloupIxknOiIjIRJQrwd66davR9rJly+Dp6Ynjx4+jS5cuyMjIwE8//YQVK1agR48eAIClS5eiQYMGOHToENq1ayde5ERULej1AnbGpuKnfQk4cPmOoby5nwte7hSE3o29oVSUqzMOEVmY+8t0ERERSatCY7AzMjIAAG5ubgCA48ePQ6PRICwszHBMSEgI/P39cfDgQSbYRFRmuQVa/Hn8GpbuT0RCWg4AQCGXoXdjb7zcKQgt/V0ljpCITIVhDDYzbCIiktgTJ9h6vR4TJkxAx44d0bhxYwBASkoKrK2t4eLiYnSsl5cXUlJSSryOWq2GWq02bGdmZgIANBoNNBrNk4ZndorqWp3qXBV4X8VX2ff0ZkY+fjuUhD+OXUNmvhYA4GhjhWGhtfC/dv7wcbGt1NeXCj+r4hPznvJ9MXH/NWFzkjMiIpLaEyfYY8eOxdmzZ7Fv374KBRAZGYmZM2cWK4+KioKdnV2Frm2OoqOjpQ7BIvG+ik/se5qYBey6KcepOzLo/2uPcrcR0NVbj7aeWqj0lxFz4DJiRH1V08PPqvjEuKe5ubkiREKVjvk1ERFJ7IkS7HHjxmHjxo3Ys2cPfH19DeXe3t4oKChAenq6USv2rVu34O3tXeK1pkyZgokTJxq2MzMz4efnh/DwcDg5OT1JeGZJo9EgOjoavXr1glLJiZrEwvsqPjHvqVanR/SFVCw9cBUnkzMM5e2CXDG6fQC61feo0rWopcTPqvjEvKdFvavINN1fpouIiEha5UqwBUHA+PHjsXbtWuzatQtBQUFG+0NDQ6FUKrF9+3YMGTIEABAbG4ukpCS0b9++xGuqVCqoVKpi5Uqlslo+ZFbXelc23lfxVeSeZuRp8MfRJPxy4Cqup+cBAKwVcvRv5oOXOgWikY+zmKGaFX5WxSfGPeV7Ytq4TBcREZmKciXYY8eOxYoVK7B+/Xo4OjoaxlU7OzvD1tYWzs7OePnllzFx4kS4ubnByckJ48ePR/v27TnBGREhMS0Hyw4kYvWxZOQW6AAANeytMbJdAJ5v5w9PRxuJIyQic2SYRZyznBERkcTKlWAvXrwYANCtWzej8qVLl2L06NEAgK+//hpyuRxDhgyBWq1GREQEFi1aJEqwRGR+BEHAoSt38dO+BGy/eMswy289Lwe83CkIA5vXgo1SIW2QRGTWDOtgSxwHERFRubuIP46NjQ0WLlyIhQsXPnFQRGT+1Fod/jl1Ez/vS8D5m/fHr3av74GXOgWhUx13yNivk4hEcL8FW9o4iIiIKrQONhHRw+5kq7H8cBJ+O3QVt7MKl+CzUcoxpKUvXuwYhDqeDhJHSESWhpOcERGRqWCCTUSiiE3Jws/7ErA25joKtHoAgLeTDf7XIQDPtfGHi521xBESkaXjGGwiIpIaE2wiemJ6vYDdcbfx874E7I1LM5Q39XXGy52C0LdJTSgVcgkjJKJqgaNNiIjIRDDBJqJyK9ABK44k49dDSbh8OwcAIJcBEY288XKnIIQGuHJ8NRFVmaJJzthHnIiIpMYEm4jKTBAE/LQ/Ed+eUCBXewEA4KCywvDWfhjdIRB+bnYSR0hE1ZGM+TUREZkIJthEVCbZai3eW30KW8+lAJDB19UWL3UMwtBWvnC0UUodHhFVY4ZJzphhExGRxJhgE9FjXbmdjdd/O4641GwoFTIM8tdi1uhOsFFx4jIikl7RkBSBbdhERCQxJthE9EjbL9zChFUxyFJr4emowncjmuHm2QNQyDnGmohMA1uwiYjIVDDBJqIS6fUCFuyIx9f/XgIAtApwxaKRLeFqq8DNsxIHR0RUAubXREQkNSbYRFRMZr4GE/84hX8v3AIAvNAuANOeaghrKzk0Go3E0RERGeOiBUREZCqYYBORkfjUbLz22zFcuZ0Da4Ucnw5qjGGt/aQOi4josQT2ESciIokxwSYig23nUvDu6lPIVmvh7WSDJS+Eormfi9RhERE9koxN2EREZCKYYBMR9HoB8/+9hG93xAMA2gS5YeFzLeHhqJI4MiKix+MkZ0REZCqYYBNVcxl5GrzzRwx2XEwFAIzuEIgP+zWAUiGXODIiorIpasBmfk1ERFJjgk1UjV26lYXXfj2GxDu5UFnJMefpJhgS6it1WERE5XK/BZspNhERSYsJNlE1teXMTby75hRyC3So5WKLJc+Hoomvs9RhERGVW9EYbKbXREQkNfYBJapmdHoB87ZexBvLTyC3QIcOwTWwYVxHJtdEVGY6nQ7Tpk1DUFAQbG1tERwcjFmzZrEFmYiIqj22YBNVI+m5BXhrVQz2XLoNAHilUxAm9wmBFcdbE1E5zJ07F4sXL8Yvv/yCRo0a4dixY3jxxRfh7OyMt956q8rj4SRnRERkKphgE1UTF25m4vXfjiPpbi5slHLMHdIUA5vXkjosIjJDBw4cwMCBA9GvXz8AQGBgIFauXIkjR45IExAnOSMiIhPBBJuoGvjn1A28/+dp5Gl08HW1xQ8vtEJDHyepwyIiM9WhQwf88MMPuHTpEurVq4dTp05h3759+Oqrr0o9R61WQ61WG7YzMzMBABqNBhqNpmIB/dd0rdVqK34tiRTFba7xA6yDqbCEOgCWUQ/WwTSUtQ5i1ZEJNpEF0+r0+HxbLL7fcwUA0LmuO74d0QKu9tYSR0ZE5mzy5MnIzMxESEgIFAoFdDodZs+ejZEjR5Z6TmRkJGbOnFmsPCoqCnZ2dhWK5+4dOQA5zp49C9tbZyp0LalFR0dLHUKFsQ6mwRLqAFhGPVgH0/C4OuTm5oryOkywiSzUvZwCjF95Evvi0wAAY7oGY1JEfSjkssecSUT0aKtXr8by5cuxYsUKNGrUCDExMZgwYQJ8fHwwatSoEs+ZMmUKJk6caNjOzMyEn58fwsPD4eRUsR41a1KPARl30ahRY/Rt5Veha0lFo9EgOjoavXr1glKplDqcJ8I6mAZLqANgGfVgHUxDWetQ1LOqophgE1mgczcy8Ppvx3HtXh5slQp8PrQpnmrqI3VYRGQhJk2ahMmTJ2PEiBEAgCZNmuDq1auIjIwsNcFWqVRQqVTFypVKZYUf2uT//eFQrpCb7QNgETHuh9RYB9NgCXUALKMerINpeFwdxKofE2wiC7M+5jo++Os08jV6BNSww/cvhCLEm+OtiUg8ubm5kMuNVx9QKBTQ6/USRURERGQamGATWQitTo/ILRfx074EAEC3+h74ZngLONuZ918bicj09O/fH7Nnz4a/vz8aNWqEkydP4quvvsJLL70kSTyy/6YR5zJdREQkNSbYRBbgTrYaY1ecwKErdwEA47rXwTu96nG8NRFVigULFmDatGl48803kZqaCh8fH7z++uuYPn26NAFxmS4iIjIRTLCJzNyZaxl4/bdjuJGRD3trBb4c1gy9G9eUOiwismCOjo6YP38+5s+fL3UoAAz5NVuwiYhIckywiczYn8evYeraMyjQ6hHkbo8fXghFXS9HqcMiIqpSMkNnHWbYREQkLSbYRGZIo9Pj043n8cvBqwCAniGe+HpEczjZcLw1EVU/HINNRESmggk2kZm5naXG2OUncCSxcLz12z3r4u2edQ3L1BARVTcy/vNHREQmggk2kRmJSU7HmN+OIyUzHw4qK3w9vDl6NfSSOiwiIkkV5dd6tmATEZHEmGATmYk/jiZh2rpzKNDpEexhjx/+1wrBHg5Sh0VEJLmiHjx69hEnIiKJMcEmMnEFWj1m/nMOyw8nAQDCG3rhy2HN4Mjx1kREAAC5rGgMNhNsIiKSFhNsIhOWmpmPN5afwPGr9yCTARPD6mFs9zocb01E9ICifxLZRZyIiKQmlzoAIirZ8av38NSCfTh+9R4cbazw86jWGM/JzIiIipH914IduTVW4kiIiKi6Yws2kQlafvgqZmw4B41OQF1PB/zwv1YIcreXOiwiIpN0+XYOAECjYxM2ERFJiwk2kQlRa3WYseEcVh5JBgD0beKNz59pBnsVf1WJiErz4Njr8zcy0dDHScJoiIioOmMXcSITkZKRj+HfH8LKI8mQyYAPeodg4XMtmVwTET2GleL+0JkVR65KGAkREVV3fHInkpggCNh6NgXT1p9DWrYazrZKfPtsC3St5yF1aEREZsFKfr+9QMeZzoiISEJMsIkktD8+DfO2XsSpaxkAgBBvR/zwQiv417CTODIiIvOhfKAFW8tx2EREJCEm2EQSOJWcjnnbLmJ//B0AgL21Ai93ro0xXWvDzpq/lkRE5VG0DjYArDl+DZN614eno42EERERUXXFJ3miKhSfmo0vo2Kx5WwKAMBaIcfIdv4Y270O3B1UEkdHRGSebmbkG23P2XQB80e0kCgaIiKqzphgE1WB6+l5+ObfS/jz+DXoBUAuAwa39MWEsLrwdWV3cCKiiki+l2e0fSenQKJIiIioumOCTVSJ7uYUYOHOePx26CoKtHoAQHhDL7wXUR/1vBwljo6IiIiIiMTEBJuoEmSrtfhpbwJ+3HsF2WotAKBdbTe83zsELf1dJY6OiMiyCZznjIiIJMIEm0hEaq0Oyw8lYeHOeEMXxca1nPB+RAg613WH7IGJeIiIqHIIYIZNRETSYIJNJAKdXsDfJ65h/r9xuJ5eOBYwyN0e74bXQ9/GNSGXM7EmIqosCrnMaP3r/fF3cD09D9YKOTwcOYEkERFVHSbYRBUgCAKizt/CF9tiEZeaDQDwclJhQlg9PBPqC6VCLnGERETVU8fPdgAArszpyz9yEhFRlWGCTfSEDlxOw7ytsYhJTgcAONsqMbZ7MP7XPhA2SoW0wREREQBAqxdgzQSbiIiqCBNsonI6cy0D87ZdxN64NACArVKBlzsF4dUuteFsq5Q4OiKi6kd4xKxmHI9NRERVqdz9V/fs2YP+/fvDx8cHMpkM69atM9qfnZ2NcePGwdfXF7a2tmjYsCGWLFkiVrxEkrlyOxtjV5xA/+/2YW9cGpQKGUa1D8Du97vhvYj6TK6JiEwQZxQnIqKqVO4W7JycHDRr1gwvvfQSBg8eXGz/xIkTsWPHDvz+++8IDAxEVFQU3nzzTfj4+GDAgAGiBE1UlW5m5OHb7XFYfewadHoBMhnwdPNaeKdXPfi52UkdHhERPQITbCIiqkrlTrD79OmDPn36lLr/wIEDGDVqFLp16wYAeO211/D999/jyJEjTLDJrNzLKcCS3Zex7EAi1Fo9ACCsgSfei6iPEG8niaMjIqIiY7vVxoKdV0rcp2eGTUREVUj0KY47dOiADRs24Pr16xAEATt37sSlS5cQHh4u9ksRVYoctRbf7YhDl3k78f2eK1Br9WgT6IY/x7TH/41qzeSaiMjEjOsWjA6e+hL3McEmIqKqJPokZwsWLMBrr70GX19fWFlZQS6X48cff0SXLl1KPF6tVkOtVhu2MzMzAQAajQYajUbs8ExWUV2rU52rQnnua4FWjz+OXcOi3VeQll0AAAjxdsR7veqgS113yGQyvj/gZ7Wy8L6KT8x7yvfFtMnlMvTy1eNAavF2g9PXMtCxjrsEURERUXVUKQn2oUOHsGHDBgQEBGDPnj0YO3YsfHx8EBYWVuz4yMhIzJw5s1h5VFQU7Oyq3/jW6OhoqUOwSI+6r3oBOJ4mw5ZkOe6oC5dycVcJ6OuvR4sa95ATfxRb4qsqUvPBz2rl4H0Vnxj3NDc3V4RIqDK5qYAe9T2wI/a2Ufmon48gfk5fiaIiIqLqRtQEOy8vD1OnTsXatWvRr18/AEDTpk0RExODL774osQEe8qUKZg4caJhOzMzE35+fggPD4eTU/XpiqvRaBAdHY1evXpBqeRs1GJ51H0VBAE7Ym/jq+h4XErNBgB4OFhjXPdgDA2tBaVC9BEUFoGf1crB+yo+Me9pUe8qMm1ONsUfa7R6Ad/tiMO4HnUliIiIiKobURPsom7dcrlxYqJQKKDXlzw2SqVSQaVSFStXKpXV8iGzuta7sj18Xw9fuYN522Jx/Oo9AIUPZW90q4PRHQJha62QKkyzws9q5eB9FZ8Y95TviXnQ6Eseb/1F1CUm2EREVCXKnWBnZ2cjPv5+f9mEhATExMTAzc0N/v7+6Nq1KyZNmgRbW1sEBARg9+7d+PXXX/HVV1+JGjjRkzh3IwOfb4vFrv+6ENoo5XixYxDGdAmGsx0foImIzFl4A09sOpNS4j5BECCTyao4IiIiqm7KnWAfO3YM3bt3N2wXde8eNWoUli1bhlWrVmHKlCkYOXIk7t69i4CAAMyePRtjxowRL2qicrp6Jxff7LyCf07dAABYyWUY0cYPb/WoC08nG4mjIyIiMfRp7AVP53Z49sdDxfbpBUDB/JqIiCpZuRPsbt26QXjEkhfe3t5YunRphYIiEsutzHysviLH4cP7of2v6+CAZj6Y2KseAt3tJY6OiIjEJJPJ0D64BqLe6YLwr/cY7dPq9VDIOQSIiIgql+iziBNJTRAEHLpyF6uOJmHL2RQUaOUABHSv74H3IuqjkY+z1CESEVElquflWKxMV8r4bCIiIjExwSaLkZatxl/Hr+GPo8m4kpZjKA9yFPDpsNboWNdLwuiIiKgqvd2zLr7ZHmfYZoJNRERVgQk2mTW9XsD+y2lYdSQZUedToNEVPkDZWyswoHktDGvpg6RT+9Am0E3iSImIqCo18zPurcQEm4iIqgITbDJLqZn5WHP8GlYdTULy3TxDeTM/Fzzb2g/9m/nAXmUFjUaDpFMSBkpERJJQPLRkqJYJNhERVQEm2GQ2dHoBey7dxsojSdh+MdXQGuFoY4WnW9TCiNb+aOjjJHGURERkChxtjB9x1Fq9RJEQEVF1wgSbTN6N9DysPpaM1UeTcSMj31DeKsAVz7bxR98mNWFrzZlhiYjovma+LkbbHT/bgfjZfWClkJd8AhERkQiYYJNJ0ur02HExFauOJmNXbCqKeva52CkxpKUvRrT2Q90SZoklIiICAIVchlkDG2Ha+nOGsiOJd9Eh2F3CqIiIyNIxwSaTknw3F38cTcbqY8lIzVIbytvVdsOzbfwR0cgbNkq2VhMRUfn9dvAqE2wiIqpUTLBJcgVaPf69cAsrjyRhX3wahP9aq2vYW+OZUF8Mb+2H2h4O0gZJRETmRyYz2txyNkWiQIiIqLpggk2SSUjLwaqjSfjz2DXcySkwlHeu645n2/gjrIEXrK04Vo6IiIiIiMwDE2yqUvkaHbadS8HKI0k4dOWuodzTUYVhrfwwvLUf/NzsJIyQiIgshayEshy1FvYqPv4QEVHl4DcMVYm4W1lYeSQZf5+8hvRcDQBALgO61ffEiNZ+6BHiyZldiYio0v1xNBkvdQqSOgwiIrJQTLCp0uQV6LDpzE2sOpKEY1fvGcprOttgeGs/DGvlBx8XWwkjJCIiS2avKj4pplbP9bCJiKjyMMEm0Z2/kYlVR5Ow9uR1ZOVrARQul9IjxBPPtfFHl3oeUMhL6rhHREQknqea+uCdP04ZlW07dwuvdQmWKCIiIrJ0TLBJFDlqLf45dQMrjybjVHK6odzX1RbPtvHHM6G+8HKykS5AIiKqdpQKOX4a1Qov/3LMUHb86j3kFmhhq1RAJuMfe4mISFxMsKlCzlzLwIojSdgQcx05BToAgFIhQ3hDb4xo44eOwe6Qs7WaiIgk0iPEs1hZ85nR6FbfAz/8r5UEERERkSVjgk3llq3WYu3J61h1JAnnbmQayoPc7TGitR+GhPrC3UElYYRERFTZrl+/jg8++ABbtmxBbm4u6tSpg6VLl6JVK9NKWktqpS7Q6RF1/pYE0RARkaVjgk3lotbqMHjRfly6lQ0AsFbI0aeJN0a09ke72m7sbkdEVA3cu3cPHTt2RPfu3bFlyxZ4eHggLi4Orq6uUodGREQkKSbYVC7LDyXh0q1suNopMa5HXQxuUQuu9tZSh0VERFVo7ty58PPzw9KlSw1lQUFc+oqIiIgJNpVZVr4G3+2MBwC83zsEz7bxlzgiIiKSwoYNGxAREYGhQ4di9+7dqFWrFt588028+uqrpZ6jVquhVqsN25mZhUOMNBoNNBpNheIpOr+06zzdvCbWxtws9TxT8Lg6mAPWwTRYQh0Ay6gH62AayloHserIBJvK7Mc9V3A3pwC1PewxNNRX6nCIiEgiV65cweLFizFx4kRMnToVR48exVtvvQVra2uMGjWqxHMiIyMxc+bMYuVRUVGws7MTJa7o6OgSy+2zZQCKr4m9efNmUV5XTKXVwZywDqbBEuoAWEY9WAfT8Lg65ObmivI6TLCpTFKz8vHj3gQAwPsR9WGlkEscERERSUWv16NVq1aYM2cOAKBFixY4e/YslixZUmqCPWXKFEycONGwnZmZCT8/P4SHh8PJyalC8Wg0GkRHR6NXr15QKpXF9vfWC+h06TbGLI8xKu/bt2+FXldMj6uDOWAdTIMl1AGwjHqwDqahrHUo6llVUUywqUy+3R6HPI0Ozf1cENHIW+pwiIhIQjVr1kTDhg2Nyho0aIC//vqr1HNUKhVUquIrTCiVStEe2h51rd5NagGIMSo7kZyJtrVriPLaYhHzfkiFdTANllAHwDLqwTqYhsfVQaz6sRmSHishLQerjiQDACb3CeFM4URE1VzHjh0RGxtrVHbp0iUEBARIFNGTef6nw1KHQEREFoYJNj3WF1Gx0OoFdK/vgXYm9pd+IiKqeu+88w4OHTqEOXPmID4+HitWrMAPP/yAsWPHSh1auWh0gtQhEBGRhWGCTY90+lo6Np2+CZmscOZwIiKi1q1bY+3atVi5ciUaN26MWbNmYf78+Rg5cqTUoZVb9PlbUodAREQWhGOwqVSCIOCzLRcBAE83r4UGNSs2CQ0REVmOp556Ck899ZTUYVTYq78ew9EPw+DhWHx8OBERUXmxBZtKtTcuDQcu34G1Qo53etWTOhwiIqIKebFjYInlyw4kVG0gRERksZhgU4n0+vut1y+0D4CfmzhrlBIREUnl4/6NSiwXOBSbiIhEwgSbSvTP6Rs4fzMTjiorjO1eR+pwiIiIRNE60LVYmY4ZNhERiYQJNhVToNXji6jC5Vde71obbvbWEkdERERUeW6m50sdAhERWQgm2FTMisNXkXw3Dx6OKrzUKUjqcIiIiERTUmP1gctpVR8IERFZJCbYZCRbrcWCHfEAgAlhdWFnzYnmiYjIcpTUGTwtuwBdP9+JAq0eP+1LQL9v9yL5bm6Vx0ZEROaP2RMZ+XHPFdzJKUCQuz2GtfKTOhwiIqIqcfVOLup9tMWw3XneThz/KAw1HLh8FxERlR1bsMngdpYaP+69AgCYFFEfSgU/HkREZFkGNfcp87EnktIrLxAiIrJIzKDIYMGOOOQW6NDMzwV9GntLHQ4REZHoRrYNKPOxskqMg4iILBMTbAIAXL2TgxWHkwAAH/SuD5mMjxVERGR55PKyf7/xq5CIiMqLCTYBAL6IugStXkDXeh7oEOwudThERESVZuP4Tni9a+3HHscEm4iIyosJNuHMtQz8c+oGZDLgg94hUodDRERUqRrXcsaUPg3wuMZsGTuJExFROTHBJszbdhEAMLCZDxr6OEkcDRERUdVYP7bTI/e/ufwE1FpdFUVDRESWgAl2Nbc37jb2xqVBqZDh3fD6UodDRERUZZr4Oj9yf55Gh/ofbUVugbaKIiIiInPHBLsa0+sFzN1a2Hr9fLsA+LnZSRwRERFR1RrdIfCxxzScvg2CIFR+MEREZPaYYFdjG8/cxNnrmXBQWWFc9zpSh0NERFTlOtct28SeS/cnAgAEQUC+ht3GiYioZEywq6kCrR5fRsUCAF7rUhs1HFQSR0RERFT1PBzL9v33ycbzOHj5Dsb8fhwNp29FamZ+JUdGRETmiAl2NbXqaBKu3smFu4MKL3cKkjocIiIiSTT1dYGNsmyPQ8/+eAjbzt2CXgD+OnG9kiMjIiJzxAS7GspRa/Ht9jgAwNthdWGvspI4IiIiIun8r32g1CEQEZGFYGZVDf3f3gSkZRcgsIYdRrT2kzocIiIis1Og1WPp/gSsi7mBlzoGwtvJBi38XWFtxbYLIqLqjAl2NZOWrcYPey4DAN6LqA+lgg8CRERUvdX1dCj3OYt2xUOt1QMA3l4VAwB4to0fIgc3FTM0IiIyM8yuqpnvdsQjp0CHpr7O6Nu4ptThEBERSW5wS194lnGysyJFyfWDVh5JFiskIiIyU0ywq5GkO7lYfvgqAOCD3iGQy2USR0RERCQ9hVyGIx+GSR0GERFZgHIn2Hv27EH//v3h4+MDmUyGdevWFTvmwoULGDBgAJydnWFvb4/WrVsjKSlJjHipAr6MjoVGJ6BzXXd0rFO2dT+JiIiqi4uzeuPoh2FY+2YHONsqpQ6HiIjMULkT7JycHDRr1gwLFy4scf/ly5fRqVMnhISEYNeuXTh9+jSmTZsGGxubCgdLT+7s9Qysj7kBoLD1moiIiIzZKBXwcFShhb8r5g55srHUS3Zfxo30PJEjIyIic1HuSc769OmDPn36lLr/ww8/RN++fTFv3jxDWXBw8JNFR6KZty0WADCwuQ8a13KWOBoiIiLT1j3E44nO+2zLRaw8koTdk7ojX6MDUJi4ExFR9SDqLOJ6vR6bNm3C+++/j4iICJw8eRJBQUGYMmUKBg0aVOI5arUaarXasJ2ZmQkA0Gg00Gg0YoZn0orqWhl1PnD5DvZcug2lQoa3utfmfaUK4T2tHLyv4hPznvJ9qX5UVgr8/nJbPP/T4XKfe/VOLnR6Aa0+/Rd6QcDpj8NhxVU7iIiqBVET7NTUVGRnZ+Ozzz7Dp59+irlz52Lr1q0YPHgwdu7cia5duxY7JzIyEjNnzixWHhUVBTs7OzHDMwvR0dGiXk8QgC/PKADI0N5Dh7OHduGsqK9gHsS+r8R7Wll4X8Unxj3Nzc0VIRIyN53qPvl8JeNXnkC2WgsAuJergZOtFVRWChyIT8PCXfH4dFATBLnbixUqERGZCNFbsAFg4MCBeOeddwAAzZs3x4EDB7BkyZISE+wpU6Zg4sSJhu3MzEz4+fkhPDwcTk5OYoZn0jQaDaKjo9GrVy8oleJNrLLlbAqSD52GvbUC80Z1RQ2H8i1DYu4q675WZ7ynlYP3VXxi3tOi3lVU/dgo5cjXFF+S63E2n0kx/HztXi6GzDmAZ0J9sfrYNQCFCfjG8Z1Fi5OIiEyDqAm2u7s7rKys0LBhQ6PyBg0aYN++fSWeo1KpoFIVT/qUSmW1fMgUs94anR5f/RsPAHi1S214uzqIcl1zVF0/T5WJ97Ry8L6KT4x7yvek+oqa0BVdPt9ZoWv8tC8BegGG5BoAbmWqH3EGERGZK1EHBFlbW6N169aIjY01Kr906RICAgLEfCkqg1VHk5F4JxfuDtZ4pXNtqcMhIiIyO/417PDNiOYVusbeuLRiZbez1PjlQCLyCnQVujYREZmWcrdgZ2dnIz4+3rCdkJCAmJgYuLm5wd/fH5MmTcLw4cPRpUsXdO/eHVu3bsU///yDXbt2iRk3PUaOWotv/o0DALzVsy4cVKJ2ViAiIqo2FHJZhc7PyCt5kryPN5zDldvZmDmwcYWuT0REpqPcLdjHjh1DixYt0KJFCwDAxIkT0aJFC0yfPh0A8PTTT2PJkiWYN28emjRpgv/7v//DX3/9hU6dOokbOT3Sz/sSkJathr+bHUa09pc6HCIiIrOl1QmVdu09D7Ru52qBI4l3IQiV93pERFS5yt2s2a1bt8f+w//SSy/hpZdeeuKgqGLuZKvx/Z4rAID3IurD2opLgxARET0ptbbyunFrdPcnUJt7SoH0o8fw7bMtMKCZT6W9JhERVR5mXhbou53xyFZr0biWE55qUlPqcIiIiMyaQm78uPTXGx2wYVxHUa597V6e4ef0gsKu6FvP3hTl2kREVPWYYFuY5Lu5+P3QVQDAB71DIK/guDEiIqLq7qmmNRHi7WjYDg1wRVNfF8wa2EiU6//f3itG2zLwu5uIyFxx5isL81X0JWh0AjrVcUfnuh5Sh0NERGT2bJQKbJ3QBamZ+bCxVhjKX2gfiIjG3mgze3uFrv/ppgvoWd+9omESEZEJYIJtQc7fyMS6mOsACluviYiISDyeTjbFylQKRQlHll/3r/YaftbpOckZEZG5YhdxCzJv20UIAtC/mQ+a+DpLHQ4REZHFc7CxqvAyXg/LKdCWWJ6YloM3lx/HmWsZor4eERGJhwm2hThwOQ27Ym/DSi7Du73qSR0OERFRtaCQy3B2RoSo17QqJWF/7bdj2HwmBf2/2yfq6xERkXiYYFsAQRAwd2ssAOC5tv4IdLeXOCIiIqLqw9Zagb/f7IBmvs4QozF7Z+xt7Ll0G0Dhd/wrvxzFK78cw6Vb2Y88L/luLvTsXk5EJCkm2BZg69kUnEpOh521AuN71JU6HCIiomqnpb8r1o/rhOWvtBPlev/7+Qh2xqZib1wa/r2Qin8v3Hrk8etjrqPzvJ14a9VJUV6fiIieDBNsM6fR6fH5tsLW61c614aHo0riiIiIiKqv9sE1cGJaL1Gu9eLSo/jfz0dK3Jf70DjthTvjAQAbT3MNbSIiKTHBNnOrjyXjSloOathb49XOQVKHQ0REVO252VtX+ms0nL4N286lGLYF9gwnIjIJTLDNWG6BFvP/jQMAjO9RB442SokjIiIiIgC4Mqcvlr7YulJf4701pwAA+Rod9A9k2LM2nsdLy45yPDYRkQS4DrYZW7o/Ebez1PBzs8VzbQOkDoeIiIj+I5fL0KRW5S6ZmZWvxft/nsLqY9eMyn/alwAA+PP4NQxs4QOFTAYrBdtUiIiqAv+1NVN3cwqwZNdlAMB74fVhbcW3koiIyJS4O6jwaucgjOkaXGmv8XBy/aD3/zqN+h9tRa+v91Ta6xMRkTFmZWZq4c54ZKm1aFjTCf2b+kgdDhEREZXgw34NMblPiKQxJKTllOm4xLQcZORpKjkaIiLLxgTbDF27l4vfDl4FAEzuEwK5GItuEhERUaX5d2IXvNGt8lqyH0cQhBLHZO+Nu42voy8hMS0H3b7YhZazoiWIjojIcnAMthn6KvoSCnR6dAiugc513aUOh4iIiB6jjqcjPugdgnfC6iHqfArGraja9aqDpmwGUDgp6jth9XAyOR2HrtwxLPV5NPEuAEDHidGIiCqELdhm5sLNTKw9eR0A8EHvEMhkbL0mIiJpffbZZ5DJZJgwYYLUoZg8ays5npJwaNeCHfHYfPYmhiw+YEiugUd3I09My8GA7/Zh8xmusU1E9DhMsM3M59tiIQhAv6Y10czPRepwiIiomjt69Ci+//57NG3aVOpQzFZ9L8cqfb2SWs9vZuQbfo7ccgHR528Ztj/ZeB6nr2XgzeUnqiQ+IiJzxgTbjBy6cgc7LqbCSi7De+H1pQ6HiIiquezsbIwcORI//vgjXF1dpQ7HLA1s7oM1b7RHeENPo/Jn2/hJFBHw/e4rePXXY4btAq1esliIiMwNE2wzIQgCPttyEQAwoo0fgtztJY6IiIiqu7Fjx6Jfv34ICwuTOhSz5mSjxMJnm6Ohy/1EVjCBodCrjiTh6+hLsFJwOBoRUVlxkjMzse3cLcQkp8NWqcBbPetKHQ4REVVzq1atwokTJ3D06NEyHa9Wq6FWqw3bmZmZAACNRgONpmJLQxWdX9HrVKWhobWw5vh1vNoxwHAPatkD59ML94/pEoht51JwL1e6Ok3++0yxskfdY3N8Hx7GOpgOS6gH62AayloHserIBNsMaHV6zNtW2Hr9SucgeDraSBwRERFVZ8nJyXj77bcRHR0NG5uyfSdFRkZi5syZxcqjoqJgZ2cnSlzR0eazxFRHJdCmDXD5xF5c/q9M9kDHwlMHdmJ6U+CdQ8aPap+EajH9uHSPb5s3by5WJgjAwvNyKGTAmAbF34czd2XI1gDtvUygWb6MzOmzVBpLqANgGfVgHUzD4+qQm5sryuswwTYDa45fw5XbOXC1U+K1LrWlDoeIiKq548ePIzU1FS1btjSU6XQ67NmzB9999x3UajUUCoXROVOmTMHEiRMN25mZmfDz80N4eDicnJwqFI9Go0F0dDR69eoFpVJZoWtJRaPRYNNP2w3bffv2BQC8cyjK6LhnB/XF9OPGZVWpKK4HpWapEXdoNwAgT6fHwD7G78Pb0wrjfWVAJwTUEOePKZXFUj5L5l4HwDLqwTqYhrLWoahnVUUxwTZxeQU6zP/3EgBgfI+6cLQxzw82ERFZjp49e+LMGePuwy+++CJCQkLwwQcfFEuuAUClUkGlUhUrVyqVoj20iXktKXTy1mPfbSX6N/UpsR5yWWEd63k54NKtbAkiBOpOi8KLHQPxcf9G9+NSaI2OKe19yCzQm837Y+6fJcAy6gBYRj1YB9PwuDqIVT8m2CZu6YEE3MpUw9fVFiPb+UsdDhERERwdHdG4cWOjMnt7e9SoUaNYOZWdszVwfGp32NoU/0OEh6MKK15pCwB4rUsw3ltzqqrDM1i6PxFL9ycCALa83RlHE+8a9t1VA1n5WriV8KDKqdKIqDrgLOIm7F5OARbvKhyZ9W54PaisircIEBERkeWwUhg/mv07sQsWPNsCR6b2RN3/1sse1NwHz7bxwzcjmpd4jZ3vdcOYrsGVHSoAoM83ezF9/TnD9uenrdBh3i7DtvDAdOiHE+6ifeR2bL9wC0RElooJtglbtCseWflaNKjphIHNakkdDhERUal27dqF+fPnSx2Gxanj6Yj+zXwgk91v/7VSyBE5uCkGNi/52SDI3R6T+4RUVYjF5GsKlxv7bkccGk7fZij/bMtF3MzIx8u/HCvtVCIis8cE20RdT8/DLwevAgA+6F0fcjk7VhEREZGxh1ux/3qjgzSBPOSPo0n4IuoS8jS6Evfr9AIS03LKdC1BEJBXUPJ1iIhMDRNsE/V19CUUaPVoV9sNXet5SB0OERERmaAHW7FrOtsgNMBVwmju++Cv4mtoP+ipBfvQ7Ytd+PP4tRL3x6dmofsXu/DX8Wv4cN1ZNJi+FeduZFRGqEREomKCbYJiU7Lw14nCL5zJfRoYdQsjIiIiKkktF9tS9/k4l2298qpy4WbhcjjvrTmFuVsvIi1bbdgXdS4FYV/tQUJaDt5dcworDicBABbujC92nblbL+KtlSeNxnoTEUmJCbYJ+nzbRQgC0LeJN5r7uUgdDhEREZmwzwY3QS0XW0QObmJU/s2I5nBUWWHZi61xYEpPLHk+VKIIH23xrsto9em/OHTlDgDgtd+Ol+vcDadu4GRyeiVFR0RUPlymy8QcTbyHfy+kQiGX4b3w+lKHQ0RERCZuRBt/jGhTfCnPgc1roX9TH8M8Lr0be6NzXXfsjUur6hDLZMQPh7B1QudS98sesdBXVr621H1ERFWJCbYJEQTg86hLAIDhrf1Q28NB4oiIiIjInD08SeqP/2uFjadvIl+jQ11PB9RwUGHyX6dx7Oo9iSI0NmzJwVL3bTpzE7cWH8Dq19ujQKfHCz8dNuzLL2UyNSKiqsYE24ScvSfDyeQM2CoVmNCzrtThEBERkYWxUSrwTKivUdmfb3TA4l2XkXQ3ByuPJEsUWaHMx7REH7t6D7M2ncfS/YlG5Tp9+cdg749Pw/t/nkbk4CbowglliUgkHINtIrQ6Pf5JKnw7XuoUCE8n05qMhIiIiCzXG92CETm4KQY08ym2z93B2vCzUiH9xKsPJ9cAkHQ3FyeS7hWb7OzK7WxEbr6A21nqYueM/L/DuJ6eh//9fKSyQiWiaogt2CbizxM3cCtPBhdbJV7vGix1OERERFQNPbhwyfcvhCKikTf0egG1p24GALzSuTYW77osUXSl+2zLRQDAvGeaYlDzWrC2Kmy0GPDdfmSrtbiYkoVfXmojZYhEVE0wwTYBcbeyELk1FgDwZrfacLJRShwRERERVUd21vcfDSMaeQMoPo7blL3/52m8/+dpAEDbIDdkqwu7nO++dBsxyelo5uvM5U+JqFKxi7jEstVavP77ceQW6FDXSY8X2vpJHRIRERFVU++G10MzX2fMebpJqcesfLVdieXfjWhWWWE9kcMJd422By3cj+jztwAAaq3xpGg/7LmM3w5drbLYiMhysQVbQoIg4P0/T+HK7Rx4Oakwql4OrBT8mwcRERFJw91BhfXjOpW6XxCA9sE1UN/LEbG3sgzl34xojohGnlURYoUs3HUZN9LzMOOf80blczYXdjF/prn3Y6+RrdZCrdGhhoOqUmIkIvPGbE5CP+1LwOYzKVAqZFgwohkc2TOciIiITJiAwknEHuw2fvTDMAxsXuux50YOLr1VvKqcSk4vllw/KC41BzdygXu5BaUe03JWNEI//RcZuZpSj4lPzca4FScQm5JV6jFEZJmYYEvk8JU7iPxvQo6P+jVECz8XaQMiIiIiKkXRTOLhDb0AAA92uPNwvN+S29lbD2srOVr6uxS7xrNt/LHj3a6VGmdFDVh0EHNPWaFN5C60j9yOOZsvGO3PVmtRoNUDAM7eyMDWsym4kZ5X7Dqjlx7BxtM38cziA1USNxGZDnYRl0BqZj7GrTwJnV7AwOY++F/7AGi1j173kYiIiEgqO9/rhpsZ+ajn5QgA8HG2xdnrmcWOeyZIj+/H9MDGM6k4kZRebH9tD4fKDlU0NzPy8cOeK/BwUOHVLrWRfDcXneftNOxffSwZ62NuQCYDEiL7GZ177V5h0p2l5vMdUXXDFuwqptHpMW7FSdzOUqOelwMiBzfhbJZERERk0hxtlIbkGgBmDWqMniGeWPZi62LHKhVyDAn1hXspY5TnD29eWWFWitmbL+DK7Wyj5BoA1sfcAFA4Lr3IT/sSsPpoclWGR0Qmhi3YVWzulos4kngXDiorLHk+1Gg5DCIiIiJz4OVkg59GF0+uiyjkMjzX1h/fbo8DADiq7j/v2ForKj0+sf3xmKT53dWnMCS0FmZtLH18d3msPJIERxsrPNXUR5TrEVHVYQt2Fdp85ib+b18CAOCLoU3NqpsUERERUXkEe9gbfj7yYZjh5wf77a18tR2uzOmLeUOaVmFk5ff9niuP3P/XiWt47sfDJe7r+eUuXLiZiQKtHun/TZ6m1wuYuvYMfi9habDr6XmY8vcZjFtxsuKBE1GVY/NpFYlPzcakNacAAK93qY3ejWtKHBERERFR5enf1Ac30vMRGuBq1GrdJsgNAFDLxRbtg2sAMJ4o7bPBTTD57zNVG2wlunw7B32+2WvYPjK1J87fzMSKw0kAgFaBrgjxdjLsv5dT+gzmRGT62IJdBXLUWoz5/ThyCnRoG+SGSRH1pQ6JiIiIqFLJ5TK80S3YkFAXcbGzxqmPw7HzvW6Gslqutoafh7f2w5ox7QvLXWzRqY47/nnE2tzmps2c7bj0wBriveffT76FBwd0A7h8OxtfbItl0k1kRtiCXckEQcAHf51GfGo2PB1VWPBcC1gp+HcNIiIiqr6cbZVG2/W8HPHNiObwcrKBTCZD60A3JH7Wr8Rzn25RC0NDfVHbwwHtIrdXRbiim7P5otH2x+vPYsaARhj+wyHEp2YbyvvM34sCnR4Jd3Kw8LmWpV5PrxeQdDcX286loH8Tr0qLm4gejwl2JVt2IBEbT9+ElVyGRSNbwtPRRuqQiIiIiEzOwOa1Hrm/R4gn9sWnYfpTDeFqX7gudwt/F5z8bzmw2h72uHI7p7LDrBS/HLyK59sF4EjCXaPyAl3hmts7L6ZCpxcw9e8zaO7vgmfb+BsdN2rpEeyNSwMA/HPqBl4NKP21Pt92ES621ni1S21xK0FEAJhgV6pjiXcxe9MFAMDUvg3QKtDtMWcQERERUUl+GtUKGp0Aa6v7PQGt5PenTGtY08lsE2wAeHrRgVL35Rbo8Pz/HcbBK3fwx7FkPN2iFnR6Afb/zc5elFwDwNkbmTjtKMP5qDh80KcB5A/co+S7uVi48zIA4JXOQVwqlqgSlLuv8p49e9C/f3/4+PhAJpNh3bp1pR47ZswYyGQyzJ8/vwIhmqfbWWqMXXECWr2Ap5rWxIsdA6UOiYiIiMhsyWQyo+QaAGQPzEk+a2Bj/K998abbla+2q/TYxJCt1j5y/8Erdww/d5q7A40+3lbiLOQA8FOsAt/vTcC2cylG5Q+2kGv1wsOnlcnD48SJyFi5E+ycnBw0a9YMCxcufORxa9euxaFDh+DjU/3W79Pq9Bi/8gRuZapRx9MBc4c05V8IiYiIiCqRq701Zg5oZNj+4YVQnJ0ZgfbBNXBxVm8E1LAr9dyEyL5VEaJo0rILJz37aN1ZJKSV3mr/5ooTWLQr3rC9Lua64WfNf93Py2P1sWS0nr0dZ65llPtcouqi3Al2nz598Omnn+Lpp58u9Zjr169j/PjxWL58OZRKZanHWarPo2Jx6Mpd2FsrsOT5UEP3HSIiIiKqPA82aDjYWMHhv2cwG6UCu97rhnVjO2LRyOKThclkMoR4O1ZZnGLq/sWuUvcJAjBvayyu3M7Gcz8eMupKrtHdb4m+di+3TC3T7/95GmnZakz4g2t0E5VG9MxPr9fjhRdewKRJk9CoUaPHHq9Wq6FWqw3bmZmZAACNRgONRiN2eJUu6vwtfL/7CgAg8ulGCHBVlakeRceYY51NGe+r+HhPKwfvq/jEvKd8X4hMUx0vBxxJNJ4YzEFlhWy1Fo1qOhuVy2QyNPdzQXM/F6PyDv+txT2yXQCmrTtbqfFKpceXu4uVNZsZhU513NEywBXfbo/D/9oH4JOBjfHzvgT8tC8BK19tB/9SWv31ApCZr4GdUsHVcYgeInqCPXfuXFhZWeGtt94q0/GRkZGYOXNmsfKoqCjY2ZXelccUpeYBX5xRAJChW009hKQT2JxUvmtER0dXSmzVHe+r+HhPKwfvq/jEuKe5ubkiREJEYvugdwis5DIManF/BvKjH4ZBrdXB2a5svShV/43rHtnGv1iCXdPZBjcz8sUL2MTsi0/DvvjCVu1fD17FJwMb45ON5wEAXT7fid2TuiGghj3SstXYcTHVcF5alhpNZ0QhxNsRWyd0eeRrZKu1sLdWcLgkVRuiJtjHjx/HN998gxMnTpT5l2jKlCmYOHGiYTszMxN+fn4IDw+Hk5OTmOFVqtwCLYZ+fwRqXTZaBbhg0YutoCzHX/Q0Gg2io6PRq1evatmtvrLwvoqP97Ry8L6KT8x7WtS7iohMi7OtEp8MbGxUZmutgK21oszXeKtnXQAwmm17RGtfzHm6KQQAwVM3G8ofXBZscp8QDG5ZC21mF1+Lu1Mdd0Piak4af7zNaLvr57uw871ueOP347iYkmUoz/pvQrYHy0py/OpdDFl8EAAwb0hTDGvt98jj8zU62CjL/t4RmSJRE+y9e/ciNTUV/v731+bT6XR49913MX/+fCQmJhY7R6VSQaVSFStXKpVm85ApCAI+/ussLqVmw8NRhUUjQ2FnU7xOZWFO9TYnvK/i4z2tHLyv4hPjnvI9IbIsL7QLwN8nrmHD+E4I9nAwlK9/sx0Wrj+AyRH1jBJuAJDJgD/HdMBbq04i2N0eY7oGAyhsAVdrjScMG9M12JBgf9i3AWZvvlDJNRJHSTOZP2qMNwCsj7kOLycbZOdr0SPE0+i+fRl1yfDz+3+dxjOhvsXua5GOn+3A9fQ8/N//WiGsoVe5Y7+engdXOyXsrDn3EUlL1E/gCy+8gLCwMKOyiIgIvPDCC3jxxRfFfCmT8tuhq1gXcwMKuQzfPdsCnk42UodERERERKWYNagxPu7fsNj44YY1ndDPX280Qa2Psw1uZOTjw74NoJDLsPA540nSOtd1x78X7nefntwnxGh/oLt9JdTAdLy9Ksbwc01nG7zcKQhu9tYY3NIX+ocmTruRkYdv/o3D8+0C0OyhsfDX0/MAAK/8egyJn/UrVwwJaTno/sUuuNlb48S0Xk9UDyKxlDvBzs7ORnz8/en+ExISEBMTAzc3N/j7+6NGjRpGxyuVSnh7e6N+/foVj9YEnUi6h1n/jVWZ3DsEbWvXeMwZRERERCS1sk7O9c/4Tjh29R56hniWuP+zIU3x3Y54jGjjB19XOziorLDvgdm6q9McYDcz8vHppsLW+qdb1MLDE5N3mrsTALDm+DWjJLq0Gcx33pAheU8CxvWs98jX3RVb+AeOuzkFTxo6kWjKnWAfO3YM3bt3N2wXjZ8eNWoUli1bJlpg5uBOthpjl5+ARiegbxNvvNI5SOqQiIiIiEhENRxUiGjkXep+dwcVZgwwXjnH1f7+sBIZqufkXsO/PwQBpS/9lXw3F35udohPzULYV3uK7c8r0GHdVQVwNQ4j2gbA3UGFczcy4OdmBycb42E7+sevMEZUZcqdYHfr1q1M6+QVKWnctSXQ6QW8teokbmbko7aHPeYOacrZEYmIiIgIjXyMlwjr16QmNp25adj+/JmmaO7ngh/3XsHqY9eqOrwq8fDyaQ/rPG8nGtdywtnrxSeRDJy8Cc+18TVs52t02B+fhpH/d7jw2h/2BAB4OhYflqnV6ZGapYaPi+0jX1+vF3DtXh52x93GkJa1Hjl2OytfAztrKyhKGT9O9CDOAvCEvoqOxf74O7BVKrDk+VA42nACHCIiIiIyJkDAwpEtsWnyJkPZ0FaFs2nPe6YZ5g5pCo1OwL3cArSdYzwj+b8Tu+Lb7XHYcOpGlcZcVUpKrousOHL/Dw/LDyfh7xP3t4tmbv9qWDMcTbyHIwl3DPvqT9sKnV7AlD4heK1LbRxNvIe6ng5wtbc2uv7Q7w/i+NV7AIDYlEx8OqhJiXFcvZODrp/vQg17axx/gvHdlzOBudsu4b2IEM6QXk0wwX4C0edvYeHOywCAuc80RT0vR4kjIiIiIiJT5u1kg5TMfCgVD89OLoO1lQxeJUySW8fTAd8+28JiE+yyWrzrconlE1efKlam+6+/eOSWizh29R6iz9+CtUKOS7P7GB1XlFwDwPYLqQj2SMC9XA0m9jIe7x0xv7D7+p0nHN/97TkrAIlwtVdhbPc6T3QNMi/VaNoFcSSm5WDi6hgAwOgOgRjQzEfagIiIiIjI5P32chv0CPHE3290LPWYB5Pv/qU8Y/Zt4o11YzvC0ab0drLZrbQ49EHXJw/WQkSfvwUAKNDp8cW2WExacwqXbhVfu/tmRj5m/nMe326PQ2JajtG+fI2+2PFP4uHrkuVigl0OeQU6jPn9OLLytQgNcMXUvg2kDomIiIiITFjR2N66Xo74eXRrNPF1LvXY17sEG37+dkRzw88vd7o/ke6ikaFo7ueC59r6G8rqeN5fyxsAHJSA20NdogFg5kOTsVUn3+2Mx5rj1xD+9R6sPJJU6nG5BToAhTOb38lWG+27lZmP3AItUjLysWT3ZaTnctZyKo4JdhkJgoAP153BxZQsuDtYY+FzLWFtxdtHRERERMVNe6ohnmvrj7ZBbmU+Z1yPOviwbwP8O7Gr0eS5PRsULhHm9ECrdfsHloaNfqeL4eenmhTOeC6TyfDXG+2x9MXWhn3PhN6fOKw6m/L3mVL39f12L5buT8DE1acQ+um/RvvaztmOhtO3YfCi/fhsy0UM+G4/AOBGeh6eWrAXr/16DPr/uqgLgoCsfI3hXJkMWLo/ARtPi9fd/15OAa7dy32ic29m5KHHl7vwy4FE0eKhQhyDXUYrjiTh7xPXIZcB3z7bAt7OxcfJEBEREREBxq3OZWWjVODVLrWLlXcIdse6sR0R4GZnKOtazwNLX2yNup4OkMlkWPFKW/x98jomR9TF/p2FE4KFBhQm99vfLewubq/io39ZzPzn/CP338jIBwAk3c3FtnMpeP234wAKJ23bcOoGBrWohff/PI01x+9PzHbldo5hxviku7nIyNPAxkqBCWF1jf6YotbqsOdSGq7fy0Vzf1c093MpNY4Ws6IBABvHd0LjWqX3jCjJvK2xuHI7Bx9vOIdRHQLLdS49Gn/LyuBUcjpmbij8RXu/dwg6BLtLHBERERERVScPJ1oymQzd63satjvUcUeHOu7QaDR4WLCHQ7EyAKjv5YjYEsYkU9kVJddFJvwRg42nb+DfC6lG5Q/e53lbYw0/+7nZYefFVLQProHn2wVg9qYL+PXgVcP+xM/6PTaGN5efwJ73u5cr7nyNrlzHU9kxwX6MuzkFeHP5CRTo9Ahv6IXXS/irIhERERGRuVn6YmtMX3/WkAzufb877FVW+PN4Mup4OuCvE9ex6fTNx1yFHvZwcg0AWfnaEo99b03hTOibztzE8+0CsOJw8fHhBVo9lAqZoaU7NTMfOkEw7C+aOR0Aku/mwtHGCi52xcfgU9XgIOJH0OkFvL3qJK6n5yHI3R5fDGtm1IWDiIiIiMicPDhTuY+LLSb3CTFseznZwM3eGq91CUaPEC8sfK4lEj/rh/2Texhd4/3e9fHPuE5VFnN18mDiDACBkzeh3kdb0GD6Vly+nQ21Voc2c7ajfeQOwzHX0/Mwff1ZnLmWgc7zdqL5J9Hles2dsamY+EcMhIde+2HHr97DiaR7jzym8Li76D1/Dw5evvPYY0sSk5yOdSevP9G5poAt2I/wzb+XsDcuDTZKORY/3xJONkqpQyIiIiIiemL6h3IoD8f78wpZyUtuSKrlYmu03czXpdhs6BPC6mJCWD1odXrU+XBLqa+/f3IP/HPqBj7bcrGckVu+5YevorQcN1+jR88vd6ORj1OJ+389eNWoazlQOJ570c7L+GZ7nKHs4JQeSEjLQdoDM6S/uPRo4WtodVg0MhSCIEAvAKevpWPnxVsI1BeupjRk8QEAwM73uiHI3b7Uegz//hC0egHP/nioTF3cHzZoYeHkcX5utoZ5BMwJE+xS7Lh4C9/uiAcAfDa4KUK8S/4wExERERGZC/1DGZyzrRKb3+oMpUIGeSkJNgA083PBqeR0fDm0GTrWKZyPaGKvevgq+pLRcVYK4w6y/2sfYEj89k/ugVouthjTNRiudkp88Ffps3lXRx+uPfvYY87dyCzTtQ5fuYMvoy/hSMJdo/JeX+1Btrrk7uqbz6Tgq6hYLN2fiKwHjhkUIEPbB7a7f7ELK19th/bBNUq6DLQP/xUHhT2DU7PyUdPZtoQzSnbldo5ZJtjsIl6C5Lu5eOePwvEQL7QLwKAWtSSOiIiIiIio4qY/1RAA8HrX+/MKNfRxQl0vx0eet/aNDrg4qzeGPLDU1/gedQw/l9SiuXhkS0Q0Klw2zNVOadQSPry1v9Gxff9bXgxAqa20VHbDfzhULLkGUGpyXeTbHfFGyTUA3MyVFfvDzLM/HsKPe64Yle24eAvfPtBaDgCHrhR2Ex+34gTaR+7A9gu3ylwHmUyGs9czcDfHvNYbZ4L9kHyNDmN+P46MPA2a+7ngo6caSB0SEREREZEoXuwYhP2Te2By75DHH/wAuVwGG6XCqEwmk2HVa+0wKaI++jf1MZTvntQNS55vid6NvdGxTuESYzvf61bsmjP6Fyb7855panTtD8oZ24M8HVVPfC6V7PBtOW5nqYuVz958AYGTNyEjVwNBEPDSsmPFejSM+OEQUrPyseVsCgDg5V+OAQB2xaY+dpz1rthUPLVgH1rOKt+Ycqmxi/hDpq8/i3M3MuFmb41FI1tCZaV4/ElERERERGbi4THVFdGudg20q23cVTighj0Catxv0S5tLefRHYMwvLU/bK0VOHs9w1DeoKYTxnWrje923W8hDXK3xzOhvrBVKvBMK180nRFV4jU3jOuExbvi8ctD45GpYgYvOVzqvmafRMHFrvS5qtrM3m60/covRw0zrU/4IwajOwRixoBGWH00GQcupxmO2/jADPaDF+1HjxBPXLqVjTe7B5v08F0m2A/442gSVh+7BrkMWPBsC/iI+I8PEREREREZs7UubMz6X/tAw1htK7kMb/esA9u7l3BVGYD3IkLg6WRjdN7nzzTFiaR0rDxivKyVt7MNnm3rXyzBntG/IWb8c/6Rsax8tR2e/fFQRatULaXnFl9/vTQPL2O27EAilh1IfOQ5J5LScSIpHQCw4dQNhDXwxDOhvujduGZ5Q6107CL+nzPXMjBt/TkAwLvh9Q2TNxARERERUeWyV93vNVo02ZqvPTB7UKNiyTUADG3lh8jBTRDWwMtQ1jPEE0DhxG0PSvysH0Z3DEILfxdD2SudgoyO+f6FUIQGuFa4HlQ1/r2QijG/n5A6jBIxwQaQnluAN5YfR4FWj7AGnnija7DUIRERERERVRsPTjz9iMnMi/lmRHN8ObQZ/nqjPZa8EAoAqOlsCwdVYUddldX9dOfBy370VENDV/nvXwhFRCNvWFvJcWVOX2x+q7PhuAY1TbcrMgH/nLohdQjFVPsEW68XMOGPGFy7lwd/Nzt8Oaz5I5coICIiqu4iIyPRunVrODo6wtPTE4MGDUJsbKzUYRGRGathb2342c667KNY7VVWGBLqi9AANygfWCLs2EdhmPZUQ0S908VQ9nKnwpnTu9X3AABsf7cr9n3Q3TDTOVDYet7QxwmfDGyEpS+2xqbxnZ64TlT5xq88KXUIxVT7MdgLdsRjV+xtqKzkWPx8y2JdSoiIiMjY7t27MXbsWLRu3RparRZTp05FeHg4zp8/D3v74kv1EBE9jo1SgRPTekEuAxRyGfS6il/v5Ye6gfdrWhMNfbrB19XWcIyvq12J5/+vfWCJ5VHvdMGSXZfx98nrsFHK0dLfFQcu3zHsP/VxODQ6Pd5cfsJomawxXYORma/BisNJJV2WLEi1TrB3xaZi/vbCqeRnP90EjXycJY6IiIjI9G3dutVoe9myZfD09MTx48fRpUuXUs4iIno0twdasStLSet1l5W1Qo56Xo74anhzTO4bAicbpWF5sbRsNdwd7i8RNv2phnhqwT7DdlgDT7QKdCuWYA9uUQsDmvtg9NKjpb5uxzo18H5ECOxVVgj7avcTx2+p3l19Cl8OayZ1GAbVNsFOvpuLCX/EQBCA59r645lQX6lDIiIiMksZGYXL67i5uZV6jFqthlp9fx3VzMxMAIBGo4FGU/bZZ0tSdH5FryMl1sE0sA6mwxTrobSSGeJxtVEA0EOj0QMAnFVyo1jrediiR3137IhNQz1PezT0ti9Wl19fDEX7h5Y4W/RscyTezcG8bXEAgAFNa+LLoU0eG9t7verii+i4ilTPbP114hpGt/dDiLdjifvL+lkS67NWLRPsfI0Oby4/gfRcDZr6OuPj/xa5JyIiovLR6/WYMGECOnbsiMaNG5d6XGRkJGbOnFmsPCoqCnZ2JXfRLK/o6GhRriMl1sE0sA6mwzTqUZgy6bVabN68ucxnDXQDBrQDgAxEbyvs+eOkVCBTUzjf0+3zh7H5ovFrJJ8/Bm9boL9/4THd7ZKxeXOy4ZpjGsiw5IICbTz0OHL7/phzt3sXUE1TOwDAlh37cMVFeOQxj/ss5ebmihJLtXwXZv5zHmeuZ8DFTolFI1tCZaV4/ElERERUzNixY3H27Fns27fvkcdNmTIFEydONGxnZmbCz88P4eHhcHKq2Cy9Go0G0dHR6NWrF5RK85xLhXUwDayD6TClerx9MAoAUNPVAX37dizzeSXVoUXHfERfSMXTzX3gaHM/FXMLuYtbmfkY2NwHANC/lGv2BTBGrYW9ygp1p0UZyocO7Ish/QWkZObjk00Xsf3ibcO+JSObw97aCpP+OoO+jb3x8wHjNcLXjmmHN1acREqmGuaqUfOWCG/oVeK+sn6WinpWVVS1S7DXHEvGyiNJkMmAb0e0KHViAyIiInq0cePGYePGjdizZw98fR891EqlUkGlUhUrVyqVoj08i3ktqbAOpoF1MB2mUI/lr7TF/H8vIXJwkyeK5cE6+Lsr8XLn4l2ZO9cvOTksict/1+pUxx374tMMrwEAAR7W+Gl0G2h1euQU6IwmcD40tfA1/jmTgttZhcl0myA3tAisgbHd62Da+nOGY72dbLD81bbo+eX9Md9znm6CqWvPGLaPfRQGRxsr9Jm/F1fScsocf2Uo0OOx783jPktifc6q1TJd525k4KN1ZwEA74TVQ5d6HhJHREREZH4EQcC4ceOwdu1a7NixA0FBQY8/iYjITHWs4441YzqgjmfJY3ylMqhFrVL3WSnkpa6OdPTDMOx6rxtmDWyEJc8Xrh3+4FJlC59riYNTeiDYw8HovOfa+ht+7t/MB+4OKqisFNjxXjf89UZ7w76iawKAjfLJ0823etZFRKOy/eFhYLPS70VVqzYt2Bm5Grzx+wmotXp0r++Bcd3rSB0SERGRWRo7dixWrFiB9evXw9HRESkpKQAAZ2dn2NraShwdEVH1IKvAuYHu9gh8YEZ1TycbnJ0ZAZWV3Gg98aWjQvHm78cw95nCWbr/eqM9UjLU6Ne0ptH1QgPcsOXtzqjlagsnm/uJfS0XW8wa1BjP/Xi43DG+2S0YNkoFAidvKnHfol2XDdtyeUXuhriqRQu2Xi9g4uoYJN3Nha+rLb4e3tyk3gQiIiJzsnjxYmRkZKBbt26oWbOm4b8//vhD6tCIiKoNW2tx55FyUFkZJdcA0KlODUS21qFP48IW7tAAt2LJdZEGNZ2MkmsAqOfliA7B7jgxrRdaB7pi7pAm2Pt+dwDAU01r4sInvUuNR2VVeqo6tJWf4eeVr7Z7dMWqWLVowV68+zK2X0yFtZUcS54PhYtd5a+xR0REZKkE4dEztRIRUeXr1dAL3ep7oIWfa6W+jqwC7ZJFXxdu9tZYM6aDofzirN5QWckhk8mw+a3O2H7hFl7tUhsh07Y+8LqFLzztqYaYtfE8lAoZnG2t8Vxbf6PW+xb+Lk8eYCWw+AR7X1wavoyKBQB8OrAxGtdyljgiIiIiIiKiilEq5Fj2Yhupw3gkASX/QdZGeb/1vaGPExr6FK4mcWVOX6w/dd2oQfTlTkEY3SEQMhQm+zKZDEl3xFlSqzJYdIJ9Iz0Pb606Cb0ADG/lh2Gt/R5/EhEREREREVVYeTs8yeUyPN2i+KoUioeG99ZytUUdTwfYKOWP7EouBYtOsFcdScLdnAI0ruWEmQMbSR0OERERERGRxavr6YC41OxHznReEQq5DNsmdPmvVdu05tay6AT7nV714GJnjV4NvYy6IRAREREREVHlWDe2Iy7fzkaTShye+3Crtqmw6ARbJpPhpU5cm5OIiIiIiKiq2Kus0NTXReowJGFaHdaJiIiIiIiIzBQTbCIiIiIiIiIRMMEmIiIiIiIiEgETbCIiIiIiIiIRMMEmIiIiIiIiEgETbCIiIiIiIiIRMMEmIiIiIiIiEgETbCIiIiIiIiIRMMEmIiIiIiIiEgETbCIiIiIiIiIRWEkdwMMEQQAAZGZmShxJ1dJoNMjNzUVmZiaUSqXU4VgM3lfx8Z5WDt5X8Yl5T4u+k4q+o6jixPy+t4TfH9bBNLAOpsMS6sE6mIay1kGs73qTS7CzsrIAAH5+fhJHQkREZCwrKwvOzs5Sh2ER+H1PRESmqKLf9TLBxP4cr9frcePGDTg6OkImk0kdTpXJzMyEn58fkpOT4eTkJHU4FoP3VXy8p5WD91V8Yt5TQRCQlZUFHx8fyOUcXSUGMb/vLeH3h3UwDayD6bCEerAOpqGsdRDru97kWrDlcjl8fX2lDkMyTk5OZvvhNWW8r+LjPa0cvK/iE+uesuVaXJXxfW8Jvz+sg2lgHUyHJdSDdTANZamDGN/1/DM8ERERERERkQiYYBMRERERERGJgAm2iVCpVPj444+hUqmkDsWi8L6Kj/e0cvC+io/3tPqwhPeadTANrIPpsIR6sA6moarrYHKTnBERERERERGZI7ZgExEREREREYmACTYRERERERGRCJhgExEREREREYmACTYRERERERGRCJhgSywyMhKtW7eGo6MjPD09MWjQIMTGxkodlkX57LPPIJPJMGHCBKlDMXvXr1/H888/jxo1asDW1hZNmjTBsWPHpA7LbOl0OkybNg1BQUGwtbVFcHAwZs2aBc49WT579uxB//794ePjA5lMhnXr1hntFwQB06dPR82aNWFra4uwsDDExcVJEyyJbuHChQgMDISNjQ3atm2LI0eOSB2SwYwZMyCTyYz+CwkJMezPz8/H2LFjUaNGDTg4OGDIkCG4deuW0TWSkpLQr18/2NnZwdPTE5MmTYJWq620mMX4fbp79y5GjhwJJycnuLi44OWXX0Z2drbRMadPn0bnzp1hY2MDPz8/zJs3r8rqMHr06GLvS+/evU2qDmV5PhTr87Nr1y60bNkSKpUKderUwbJly6qsDt26dSv2XowZM8Zk6rB48WI0bdoUTk5OcHJyQvv27bFlyxbDflN/D8paD1N/Hx5W0rO9Sb0XAkkqIiJCWLp0qXD27FkhJiZG6Nu3r+Dv7y9kZ2dLHZpFOHLkiBAYGCg0bdpUePvtt6UOx6zdvXtXCAgIEEaPHi0cPnxYuHLlirBt2zYhPj5e6tDM1uzZs4UaNWoIGzduFBISEoQ1a9YIDg4OwjfffCN1aGZl8+bNwocffij8/fffAgBh7dq1Rvs/++wzwdnZWVi3bp1w6tQpYcCAAUJQUJCQl5cnTcAkmlWrVgnW1tbCzz//LJw7d0549dVXBRcXF+HWrVtShyYIgiB8/PHHQqNGjYSbN28a/rt9+7Zh/5gxYwQ/Pz9h+/btwrFjx4R27doJHTp0MOzXarVC48aNhbCwMOHkyZPC5s2bBXd3d2HKlCmVFrMYv0+9e/cWmjVrJhw6dEjYu3evUKdOHeHZZ5817M/IyBC8vLyEkSNHCmfPnhVWrlwp2NraCt9//32V1GHUqFFC7969jd6Xu3fvGh0jdR3K8nwoxufnypUrgp2dnTBx4kTh/PnzwoIFCwSFQiFs3bq1SurQtWtX4dVXXzV6LzIyMkymDhs2bBA2bdokXLp0SYiNjRWmTp0qKJVK4ezZs4IgmP57UNZ6mPr78KDSnu1N6b1ggm1iUlNTBQDC7t27pQ7F7GVlZQl169YVoqOjha5duzLBrqAPPvhA6NSpk9RhWJR+/foJL730klHZ4MGDhZEjR0oUkfl7+GFar9cL3t7ewueff24oS09PF1QqlbBy5UoJIiQxtWnTRhg7dqxhW6fTCT4+PkJkZKSEUd338ccfC82aNStxX3p6uqBUKoU1a9YYyi5cuCAAEA4ePCgIQmGiKJfLhZSUFMMxixcvFpycnAS1Wl2psQvCk/0+nT9/XgAgHD161HDMli1bBJlMJly/fl0QBEFYtGiR4OrqalSHDz74QKhfv36l10EQChPsgQMHlnqOqdVBEIo/H4r1+Xn//feFRo0aGb3W8OHDhYiIiEqvgyAIj30+M7U6CIIguLq6Cv/3f/9nlu9BSfUQBPN5H0p7tje194JdxE1MRkYGAMDNzU3iSMzf2LFj0a9fP4SFhUkdikXYsGEDWrVqhaFDh8LT0xMtWrTAjz/+KHVYZq1Dhw7Yvn07Ll26BAA4deoU9u3bhz59+kgcmeVISEhASkqK0b8Dzs7OaNu2LQ4ePChhZFRRBQUFOH78uNF7K5fLERYWZlLvbVxcHHx8fFC7dm2MHDkSSUlJAIDjx49Do9EYxR8SEgJ/f39D/AcPHkSTJk3g5eVlOCYiIgKZmZk4d+5c1VYEZft9OnjwIFxcXNCqVSvDMWFhYZDL5Th8+LDhmC5dusDa2tpwTEREBGJjY3Hv3r0qqcuuXbvg6emJ+vXr44033sCdO3cM+0yxDg8/H4r1+Tl48GCx56SIiIhK+R0q7Rl3+fLlcHd3R+PGjTFlyhTk5uYa9plSHXQ6HVatWoWcnBy0b9/eLN+DkupRxBzeh9Ke7U3tvbAq19FUqfR6PSZMmICOHTuicePGUodj1latWoUTJ07g6NGjUodiMa5cuYLFixdj4sSJmDp1Ko4ePYq33noL1tbWGDVqlNThmaXJkycjMzMTISEhUCgU0Ol0mD17NkaOHCl1aBYjJSUFAIy+UIu2i/aReUpLS4NOpyvxvb148aJEURlr27Ytli1bhvr16+PmzZuYOXMmOnfujLNnzyIlJQXW1tZwcXExOufBz2ZKSkqJ9SvaV9XK8vuUkpICT09Po/1WVlZwc3MzOiYoKKjYNYr2ubq6Vkr8RXr37o3BgwcjKCgIly9fxtSpU9GnTx8cPHgQCoXC5OpQ0vOhWJ+f0o7JzMxEXl4ebG1tK60OAPDcc88hICAAPj4+OH36ND744APExsbi77//Npk6nDlzBu3bt0d+fj4cHBywdu1aNGzYEDExMWb1HpRWD8A83odHPdub2u8DE2wTMnbsWJw9exb79u2TOhSzlpycjLfffhvR0dGwsbGROhyLodfr0apVK8yZMwcA0KJFC5w9exZLlixhgv2EVq9ejeXLl2PFihVo1KgRYmJiMGHCBPj4+PCeElmAB3ujNG3aFG3btkVAQABWr14t2kMzld+IESMMPzdp0gRNmzZFcHAwdu3ahZ49e0oYWcks4fmwtDq89tprhp+bNGmCmjVromfPnrh8+TKCg4OrOswS1a9fHzExMcjIyMCff/6JUaNGYffu3VKHVW6l1aNhw4Ym/z6Y27M9u4ibiHHjxmHjxo3YuXMnfH19pQ7HrB0/fhypqalo2bIlrKysYGVlhd27d+Pbb7+FlZUVdDqd1CGapZo1axr+0lmkQYMGhu6OVH6TJk3C5MmTMWLECDRp0gQvvPAC3nnnHURGRkodmsXw9vYGgGIzid66dcuwj8yTu7s7FAqFWb23Li4uqFevHuLj4+Ht7Y2CggKkp6cbHfNg/N7e3iXWr2hfVSvL75O3tzdSU1ON9mu1Wty9e9dk61W7dm24u7sjPj7eEIOp1KG050OxPj+lHePk5CTaH4HK84zbtm1bADB6L6Sug7W1NerUqYPQ0FBERkaiWbNm+Oabb8zqPXhUPUpiau/D457tvby8TOq9YIItMUEQMG7cOKxduxY7duwo1t2Iyq9nz544c+YMYmJiDP+1atUKI0eORExMDBQKhdQhmqWOHTsWW17j0qVLCAgIkCgi85ebmwu53PifYYVCAb1eL1FElicoKAje3t7Yvn27oSwzMxOHDx82GntG5sfa2hqhoaFG761er8f27dtN9r3Nzs7G5cuXUbNmTYSGhkKpVBrFHxsbi6SkJEP87du3x5kzZ4ySvejoaDg5ORX7g2dVKMvvU/v27ZGeno7jx48bjtmxYwf0er3hob19+/bYs2cPNBqN4Zjo6GjUr1+/0ruHl+TatWu4c+cOatasaYhP6jo87vlQrM9P+/btja5RdIwYv0NP8owbExMDAEbvhZR1KIler4darTaL96As9SiJqb0Pj3u2b9WqlWm9F+Wevo1E9cYbbwjOzs7Crl27jKbGz83NlTo0i8JZxCvuyJEjgpWVlTB79mwhLi5OWL58uWBnZyf8/vvvUodmtkaNGiXUqlXLsEzX33//Lbi7uwvvv/++1KGZlaysLOHkyZPCyZMnBQDCV199JZw8eVK4evWqIAiFywq5uLgI69evF06fPi0MHDiQy3RZiFWrVgkqlUpYtmyZcP78eeG1114TXFxcjGaJldK7774r7Nq1S0hISBD2798vhIWFCe7u7kJqaqogCIXLyvj7+ws7duwQjh07JrRv315o37694fyiZWXCw8OFmJgYYevWrYKHh0elLtMlxu9T7969hRYtWgiHDx8W9u3bJ9StW9doiav09HTBy8tLeOGFF4SzZ88Kq1atEuzs7ERb4upRdcjKyhLee+894eDBg0JCQoLw77//Ci1bthTq1q0r5Ofnm0wdyvJ8KMbnp2hZokmTJgkXLlwQFi5cKNrSSo+rQ3x8vPDJJ58Ix44dExISEoT169cLtWvXFrp06WIydZg8ebKwe/duISEhQTh9+rQwefJkQSaTCVFRUYIgmP57UJZ6mMP7UJKHn+1N6b1ggi0xACX+t3TpUqlDsyhMsMXxzz//CI0bNxZUKpUQEhIi/PDDD1KHZNYyMzOFt99+W/D39xdsbGyE2rVrCx9++GGVLL9jSXbu3Fniv6OjRo0SBKFwaaFp06YJXl5egkqlEnr27CnExsZKGzSJZsGCBYK/v79gbW0ttGnTRjh06JDUIRkMHz5cqFmzpmBtbS3UqlVLGD58uBAfH2/Yn5eXJ7z55puCq6urYGdnJzz99NPCzZs3ja6RmJgo9OnTR7C1tRXc3d2Fd999V9BoNJUWsxi/T3fu3BGeffZZwcHBQXBychJefPFFISsry+iYU6dOCZ06dRJUKpVQq1Yt4bPPPquSOuTm5grh4eGCh4eHoFQqhYCAAOHVV18t9kcZqetQludDsT4/O3fuFJo3by5YW1sLtWvXFu0Z9HF1SEpKErp06SK4ubkJKpVKqFOnjjBp0iSj9ZelrsNLL70kBAQECNbW1oKHh4fQs2dPQ3ItCKb/HpSlHubwPpTk4Wd7U3ovZIIgCOVr8yYiIiIiIiKih3EMNhEREREREZEImGATERERERERiYAJNhEREREREZEImGATERERERERiYAJNhEREREREZEImGATERERERERiYAJNhEREREREZEImGATERERERERiYAJNhEREREREZEImGATERERERERiYAJNhEREREREZEImGATERERERERieD/AY5GgTx0bX7IAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loss=2.064\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "\n",
    "    uz2en_model.train()\n",
    "    iteration = 0\n",
    "\n",
    "    for i in tqdm(range(0, train_size, batch_size)):\n",
    "        input_batch = train_set[i : i + batch_size][\"translations\"]\n",
    "        output_batch = train_set[i : i + batch_size][\"value\"]\n",
    "\n",
    "        input_tokens, input_mask = encode_texts(input_batch, uz_encode_vocab, decoder_input=False, device=device)\n",
    "        output_tokens, output_mask = encode_texts(output_batch, en_encode_vocab, decoder_input=True, device=device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = compute_loss(uz2en_model, input_tokens, output_tokens, input_mask, output_mask)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        iteration += 1\n",
    "\n",
    "        metrics[\"train_loss\"].append((iteration + num_of_batches * epoch, loss.item()))\n",
    "\n",
    "    output_ids, _ = uz2en_model.translate(val_inp_ids, val_inp_mask, max_len=50)\n",
    "    output_texts = decode_ids(output_ids, en_decode_vocab, en_encode_vocab[\"<eos>\"])\n",
    "    metrics['dev_bleu'].append((epoch + 1, compute_bleu(val_out, output_texts)))\n",
    "\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    for i, (name, history) in enumerate(sorted(metrics.items())):\n",
    "        plt.subplot(1, len(metrics), i + 1)\n",
    "        plt.title(name)\n",
    "        plt.plot(*zip(*history))\n",
    "        plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Mean loss=%.3f\" % np.mean(metrics[\"train_loss\"][-iteration:], axis=0)[1], flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bffe7c0e",
   "metadata": {
    "id": "bffe7c0e"
   },
   "outputs": [],
   "source": [
    "torch.save(uz2en_model.state_dict(), \"/content/drive/MyDrive/NMT_Seq2Seq/uz2en_lstm_model_weights_50k.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfe1901b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14817,
     "status": "ok",
     "timestamp": 1750869939863,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "dfe1901b",
    "outputId": "54efa94c-f6dd-4eab-cc67-a83358a3c812"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uz2en_model = Seq2Seq(\n",
    "    input_vocab_size=len(uz_encode_vocab),\n",
    "    output_vocab_size=len(en_encode_vocab),\n",
    "    emb_dim=1000,\n",
    "    hid_size=1000,\n",
    ").to(device)\n",
    "\n",
    "uz2en_model.load_state_dict(torch.load(\"uz2en_lstm_model_weights_50k.pth\", map_location=device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be90906c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total parameters: 172076003\n",
      "Trainable parameters: 172076003\n"
     ]
    }
   ],
   "source": [
    "total = sum(p.numel() for p in uz2en_model.parameters())\n",
    "print(\"Total parameters:\", total)\n",
    "\n",
    "trainable = sum(p.numel() for p in uz2en_model.parameters() if p.requires_grad)\n",
    "print(\"Trainable parameters:\", trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "OxH2IeHe500U",
   "metadata": {
    "executionInfo": {
     "elapsed": 1443,
     "status": "ok",
     "timestamp": 1750869951620,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "OxH2IeHe500U"
   },
   "outputs": [],
   "source": [
    "en2uz_model = Seq2Seq(\n",
    "    input_vocab_size=len(en_encode_vocab),\n",
    "    output_vocab_size=len(uz_encode_vocab),\n",
    "    emb_dim=1000,\n",
    "    hid_size=1000,\n",
    ").to(device)\n",
    "\n",
    "en2uz_model.input_embedding.weight.data = uz2en_model.output_embedding.weight.data\n",
    "en2uz_model.output_embedding.weight.data = uz2en_model.input_embedding.weight.data\n",
    "\n",
    "en2uz_model.input_embedding.weight.requires_grad = False\n",
    "en2uz_model.output_embedding.weight.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "vr9uk9kN7egM",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1750869968575,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "vr9uk9kN7egM"
   },
   "outputs": [],
   "source": [
    "val_inp = val_set[\"value\"]\n",
    "val_out = val_set[\"translations\"]\n",
    "\n",
    "val_inp_ids, val_inp_mask = encode_texts(val_inp, en_encode_vocab, decoder_input=False, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "z8fSNShc6q2M",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1750870122825,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "z8fSNShc6q2M"
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from IPython.display import clear_output\n",
    "import math\n",
    "\n",
    "optimizer = torch.optim.Adam(en2uz_model.parameters(), lr=1e-3)\n",
    "epochs = 10\n",
    "batch_size = 128\n",
    "train_size = 50000\n",
    "\n",
    "num_of_batches = math.ceil(train_size / batch_size)\n",
    "metrics = {\"train_loss\": [], \"dev_bleu\": []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "xFRnara86yxu",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "executionInfo": {
     "elapsed": 4434659,
     "status": "ok",
     "timestamp": 1750874560511,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "xFRnara86yxu",
    "outputId": "91b9d266-a25a-4586-9932-83a8af408e94"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean loss=1.906\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(epochs):\n",
    "\n",
    "    en2uz_model.train()\n",
    "    iteration = 0\n",
    "\n",
    "    for i in tqdm(range(0, train_size, batch_size)):\n",
    "        input_batch = train_set[i : i + batch_size][\"value\"]\n",
    "        output_batch = train_set[i : i + batch_size][\"translations\"]\n",
    "\n",
    "        input_tokens, input_mask = encode_texts(input_batch, en_encode_vocab, decoder_input=False, device=device)\n",
    "        output_tokens, output_mask = encode_texts(output_batch, uz_encode_vocab, decoder_input=True, device=device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = compute_loss(en2uz_model, input_tokens, output_tokens, input_mask, output_mask)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        iteration += 1\n",
    "\n",
    "        metrics[\"train_loss\"].append((iteration + num_of_batches * epoch, loss.item()))\n",
    "\n",
    "    output_ids, _ = en2uz_model.translate(val_inp_ids, val_inp_mask, max_len=50)\n",
    "    output_texts = decode_ids(output_ids, uz_decode_vocab, uz_encode_vocab[\"<eos>\"])\n",
    "    metrics['dev_bleu'].append((epoch + 1, compute_bleu(val_out, output_texts)))\n",
    "\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    for i, (name, history) in enumerate(sorted(metrics.items())):\n",
    "        plt.subplot(1, len(metrics), i + 1)\n",
    "        plt.title(name)\n",
    "        plt.plot(*zip(*history))\n",
    "        plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Mean loss=%.3f\" % np.mean(metrics[\"train_loss\"][-iteration:], axis=0)[1], flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "VV2_KkyC8bzN",
   "metadata": {
    "executionInfo": {
     "elapsed": 6478,
     "status": "ok",
     "timestamp": 1750874728183,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "VV2_KkyC8bzN"
   },
   "outputs": [],
   "source": [
    "torch.save(en2uz_model.state_dict(), \"/content/drive/MyDrive/NMT_Seq2Seq/en2uz_lstm_model_weights_50k.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b75BbXTV8qH4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2463,
     "status": "ok",
     "timestamp": 1750874771599,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "b75BbXTV8qH4",
    "outputId": "d292ac53-1ecc-4ae9-bebc-5d516b70110c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en2uz_model = Seq2Seq(\n",
    "    input_vocab_size=len(en_encode_vocab),\n",
    "    output_vocab_size=len(uz_encode_vocab),\n",
    "    emb_dim=1000,\n",
    "    hid_size=1000,\n",
    ").to(device)\n",
    "\n",
    "en2uz_model.load_state_dict(torch.load(\"/content/drive/MyDrive/NMT_Seq2Seq/en2uz_lstm_model_weights_50k.pth\", map_location=device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "Kf62ayXFb-om",
   "metadata": {
    "executionInfo": {
     "elapsed": 437,
     "status": "ok",
     "timestamp": 1750874775897,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "Kf62ayXFb-om"
   },
   "outputs": [],
   "source": [
    "import bokeh.plotting as pl\n",
    "import bokeh.models as bm\n",
    "from bokeh.io import output_notebook, show\n",
    "output_notebook()\n",
    "\n",
    "def draw_attention(attn_weights, batch_order, input_text, output_text):\n",
    "    \"\"\"An intentionally ambiguous function to visualize attention weights\"\"\"\n",
    "    input_text += \" <eos>\"\n",
    "    output_text += \" <eos>\"\n",
    "\n",
    "    inp_tokens = input_text.lower().split()\n",
    "    trans_tokens = output_text.lower().split()\n",
    "\n",
    "    probs = attn_weights[batch_order, : len(trans_tokens), -len(inp_tokens):]\n",
    "    probs = probs[:, ::-1]\n",
    "\n",
    "    fig = pl.figure(\n",
    "        x_range=(0, len(inp_tokens)),\n",
    "        y_range=(0, len(trans_tokens)),\n",
    "        x_axis_type=None,\n",
    "        y_axis_type=None,\n",
    "        tools=[],\n",
    "    )\n",
    "    fig.image([probs[::-1]], 0, 0, len(inp_tokens), len(trans_tokens))\n",
    "\n",
    "    fig.add_layout(bm.LinearAxis(axis_label=\"source tokens\"), \"above\")\n",
    "    fig.xaxis.ticker = np.arange(len(inp_tokens)) + 0.5\n",
    "    fig.xaxis.major_label_overrides = dict(\n",
    "        zip(np.arange(len(inp_tokens)) + 0.5, inp_tokens)\n",
    "    )\n",
    "    fig.xaxis.major_label_orientation = 45\n",
    "\n",
    "    fig.add_layout(bm.LinearAxis(axis_label=\"translation tokens\"), \"left\")\n",
    "    fig.yaxis.ticker = np.arange(len(trans_tokens)) + 0.5\n",
    "    fig.yaxis.major_label_overrides = dict(\n",
    "        zip(np.arange(len(trans_tokens)) + 0.5, trans_tokens[::-1])\n",
    "    )\n",
    "\n",
    "    show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "LCZg3GLeKUTy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 13676,
     "status": "ok",
     "timestamp": 1750875083584,
     "user": {
      "displayName": "Davron Abdukhakimov",
      "userId": "17149577767819137752"
     },
     "user_tz": -300
    },
    "id": "LCZg3GLeKUTy",
    "outputId": "22475b8e-f087-4b70-8466-3074ab4c64ea"
   },
   "outputs": [
    {
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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(null);\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? 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root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
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       "id": "p6210"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "en_text = val_set['value']\n",
    "uz_text = val_set['translations']\n",
    "\n",
    "input_ids, input_mask = encode_texts(en_text, en_encode_vocab, decoder_input=False, device=device)\n",
    "output_ids, attn_weights = en2uz_model.translate(input_ids, input_mask, max_len=50)\n",
    "output_texts = decode_ids(output_ids, uz_decode_vocab, uz_encode_vocab[\"<eos>\"])\n",
    "\n",
    "attn_weights = attn_weights.cpu().numpy() if isinstance(attn_weights, torch.Tensor) else attn_weights\n",
    "\n",
    "sample_translations = {\n",
    "    'English sentence': [],\n",
    "    'Model translation': [],\n",
    "    'True translation': []\n",
    "}\n",
    "\n",
    "for i in range(len(en_text)):\n",
    "    sample_translations[\"English sentence\"].append(en_text[i])\n",
    "    sample_translations[\"Model translation\"].append(output_texts[i])\n",
    "    sample_translations[\"True translation\"].append(uz_text[i])\n",
    "    draw_attention(attn_weights, i, en_text[i], output_texts[i])\n",
    "\n",
    "translation_df = pd.DataFrame(sample_translations)\n",
    "translation_df.to_csv(\"/content/drive/MyDrive/NMT_Seq2Seq/en2uz_sample_translations.csv\", index=False)"
   ]
  }
 ],
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