Neural Machine Translation by Jointly Learning to Align and Translate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5O1SLyQjdVGy"
   },
   "source": [
    "# Neural Machine Translation by Jointly Learning to Align and Translate\n",
    "\n",
    "_This notebook demonstrates the implementation of Seq2Seq architecture with attention mechanism proposed by [Bahdanau et al., 2014](https://arxiv.org/pdf/1409.0473.pdf) for neural machine translation (NMT)._\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2zL1cnuSQZFC"
   },
   "source": [
    "> _Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started [using](https://blog.google/products/translate/found-translation-more-accurate-fluent-sentences-google-translate/) such a model in production in late 2016. These models are explained in the two pioneering papers ([Sutskever et al., 2014](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf), [Cho et al., 2014](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf)). (Excerpt from a [blog post](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) by Jay Alammar)_\n",
    "\n",
    "Sequence-to-sequence (aka seq2seq) model is an end-to-end deep learning model which transforms one sequence to another. It consists of two recurrent neural networks:\n",
    "- encoder, which encodes an input sequence of variable size into a context vector of fixed size\n",
    "- decoder, which unfolds the context vector into a new sequence (output).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a1tKUBhw8sm-"
   },
   "source": [
    "![seq2seq model](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlUAAAE3CAIAAAAMsCMpAAAgAElEQVR4Ae2deXAbVYL/Z39btVW7O3/M7BTDMsuZcIRAAiQTwpHAcCUBEmYwMIRzGEK4GYapgbAhXGHIFJsASSCATluy5fu+D1m2JcuSbcmWJcs6LJ/yId/3KVv9K7mTTke25bYiyTq+KlfSar1+x+e9fh+9169bPyPwAgEQAAEQAIHwI/Cz8CsySgwCIAACIAACBPyHRgACIAACIBCOBOC/cKx1lBkEQAAEQAD+QxsAARAAARAIRwLwXzjWOsoMAiAAAiAA/6ENgAAIgAAIhCMB+C8cax1lBgEQAAEQgP/QBkAABEAABMKRAPwXjrWOMoMACIAACMB/aAMgAAIgAALhSAD+C8daR5lBAARAAATgP7QBEAABEACBcCQA/4VjraPMIAACIAAC8B/aAAiAAAiAQDgSgP/CsdZRZhAAARAAAfgPbQAEQAAEQCAcCcB/4VjrKDMIgAAIgAD8hzYAAiAAAiAQjgTgv3CsdZQZBEAABEAA/kMbAAEQAAEQCEcC8F841jrKDAIgAAIgAP+hDYAACIAACIQjAfgvHGsdZQYBEAABEID/0AZAAARAAATCkQD8F461jjKDAAiAAAjAf2gDIAACIAAC4UgA/gvHWkeZQQAEQAAE4D+0ARAAARAAgXAkAP+FY62jzCAAAiAAAvAf2gAIgAAIgEA4EoD/wrHWUWYQAAEQAAH4D20ABEAABEAgHAnAf+FY6ygzCIAACIAA/Ic2AAIgAAIgEI4E4L9wrHWUGQRAAARAAP5DGwABEAABEAhHAvBfONY6ygwCIAACIAD/oQ2AAAiAAAiEIwH4LxxrHWUGAW8RmJubs1gsqamp33777WeffXb06NEzZ84UFhb29PR4KwkfxWMymX744YfDhw8nJSX19/cvTsXhcEgkks8+++zTTz+tqKhYHAB7gp0A/BfsNYj8g8CaEejv709MTPzTn/60devWG2+8cfPCa+PGjTt37nz//fcrKiqmp6eZZ66/vz8uLi4mJqarq4v5UR6HzM3N3b59+89+9rP777+/rKxscTzd3d2vv/76v/3bv/3Hf/zH6dOnFwfAnmAnAP8Few0i/yCwNgR6e3vPnDlz2223bdq06c033/zxxx8TExPj4+NPnTr14osv3nzzzREREVKpdH5+nmH+zGbzH//4x8cff7y2tpbhIRcTjPTf//t//++SSy45efLk1NSUS2xkgH/5l3/5z//8T/jPBU5ovIX/QqMeUQoQ8CuBmZkZoVC4adOm7du3s1gsq9VKec5ut5vN5m+++ebQoUMVFRXU/hXztyb++81vfnPppZc+//zzBoOBnsOJiYlPP/103bp1l1566S9/+Uv4jw4nZLbhv5CpShQEBPxHQKvVRkREXHfddWfOnBkeHl6c8MDAgM1mo+Y/pVLpyZMn1Wo1PaRWqz158mRJSYndbs/JyXn99ddvvPHGG2644cCBAywWy2KxkIFnZmY0Gg2Px/viiy+OHj3K4XA0Gg0VM0EQ9fX1p0+fLi4utlqtWVlZx48f/+KLL3788ceamhp6MHrSBEGQw7tdu3bdf//9N910U3x8vMPhoMLU1tbu27fvd7/73QMPPHDJJZfQ/Tc3N2cymWJiYo4dO/bZZ5+dPHmyqKiIfgVxeno6IyODxWLV1dVVVFR89913xcXFZE4mJiaUSuWZM2c+//zzr776KjMzs6WlJS0tjc1mNzU1Uan7qMhU/NggCcB/aAkgAAKrJsDlcq+55po//elPjY2NTA4+fvz4TTfdFBkZSQ+ckJBw8803f/nll9PT0ywWa+fOnf/93/996aWX3nnnnX/729/IWdCxsbHU1NRnnnnmwQcf/P3vf/+HP/zhgQceePbZZzMyMsbGxsjYUlJSbrnllj/+8Y8fffTR008//cgjj2zbtm3Dhg3PPfecQqGgp0jfJv33wgsvfPDBBzfccMP7779PrdmZn5//6aefrrvuukOHDv3lL3+h+29mZkYsFv/5z3++4447du7cef/9999+++3333//V199RQlsdHT0zTff3LRp08svv/znP//5iSeeiI6OnpiYGB0djY+P37t37+bNm3fs2LF79+7f//73H3744a5du+655x6JREJmz3dFphcf2wRBwH9oBiAAAqsjMDc3d+TIkV/+8pfHjx+fmZlhcrB7/9nt9tbW1oSEhAceeODee+8VCAQ6nW54eNjhcOTm5j744IN79+7l8/kVFRUKhUIoFD722GP79u0rKSkhR2yk/zZv3vzee+8JBIKioqLU1NTnn3/+yiuvPH78+OTk5JI5JP334osvpqSk/P73v6cbqKOj48CBAzfffHNiYuLnn39O919lZWVERMTWrVs/+uijzMxMsVgsEAgiIiI2bdp04sSJvr4+giBI//3qV7969NFHT548KRaL29vb5+bmCgsL77333q1bt37xxRe5ubmFhYUsFuvVV1+99tprqdR9WuQlOYTzTvgvnGsfZQcBTwhMTEy88847l156KY/Ho46vq6v79ttvD1/4Sk1NHRoaIghiRf8RBLH4+l9HR8cbb7xxxx13JCQkUOtTZmdnU1NTd+7cefjwYdI3pP/279+v1Wrn5ubILKWlpd12223vvPNOd3c3lUn6BuW/urq6o0ePrlu37uuvvyZlmZ6evnXr1hdffNFsNh89epTy38TExNGjR6+99tqPP/6YinZubq6srOzBBx/cuXNncXEx5b9169b9+OOPo6OjZKKjo6Mffvjh1Vdf/eWXX1KTpbOzs+SxlP98WmR68bGN8R/aAAiAwKoJTE5O/uUvf/n1r3/N5XKpg/Py8iIiIm4997rmmmv+/d///eDBg62trR77TywW79ix45VXXmlubp6nvbq7u19++eW9e/dWVVURBEH677PPPqMcSRCEQqHYtWvXK6+80tLSQmWSvkH5z2w25+fn33nnnfv379fr9WNjY4cPH163bt133303PT1N95/FYtm/f/9dd91VVFREj2pycvKTTz654oorvv/++/n5eXL8d/fdd4vFYiqY0Wh88sknKc9R+wcGBl599VVqv0+LTCWKDZIAxn9oCSAAAqsjMD8//8knn/zyl7/86quvqAUmHR0dEokk49yLVMhF+i82NvbGG2+84447/vrXvx6ivf7+979vW3jl5uZS/vv888+pzBAEUV1d/fDDDx84cKC5uXnJ4tH919PT8+abb27YsCEmJkahUOzZs+ehhx6Sy+UEQdD9p1Qqd+3aRWrSJU4Oh3PNNdccPXp0enqa9N+OHTvI4SAZUqFQPPTQQ88880xDQwP92MHBwddee43yn0+LTE8X2xj/oQ2AAAh4QkAgEFx33XXPPfec0Whc8nhy+tEr/tuyZcvBgwffWvQ6duxYXV2dV/zncDj4fP6GDRv+8pe/HD58eP369UeOHBkYGFjsv927d+/fv7++vt6l1BwO5+qrr/7iiy9mZmaW89+S7lzSfz4qskue8RbjP7QBEACBVRMwGAz79+9fv379yZMnBwcHFx+fnJy8efNmyn8nTpzYsGEDfb6UIIj4+Hhy/afdbl/y+l9xcfHOnTufeeYZpVLZtejV19dHTniS858XM/4jCEKv10dERGzcuPGGG2645ZZb0tLSyMU19PFfc3Pzs88+e9dddxUWFtKLPDEx8fHHH1955ZU//PCDw+FY0n/k1c0dO3bQJ0UJgnCZ//Rpkel5xjbGf2gDIAACnhCYnZ1NTEzcuvA6ffp0a2srdZ/77OysVqt96623Lrvssvfee49cJ3LmzJlrr732yy+/nJiYINPr6en55JNPLr300i+//JL0n8Viefrpp/ft21ddXU2G6ezsfP3113/7298KhcLx8XEqo+Pj41arlVxZ45XxH0EQU1NTX3755SWXXPKv//qv9FlTuv+mpqaOHTt2/fXXf/TRR9RD2ubm5iQSyX0Lr9LSUmr9i8v85/j4+EcffXT11Vd//vnnvb29ZFlmZ2eLi4vvu+8+av7Tp0WmAGKDJIDxH1oCCICAJwQGBgb4fP4dd9yxcePGl1566euvvxYKhZGRkf/4xz8ef/zx9evX79mzJzc3lxyi5eTkbN++/e677z516lROTk58fPyRI0fuvffeX/ziF5T/bDbbyy+/vHHjxmPHjmVmZppMpvn5+by8PPKmiP/7v//Lz8+Xy+W5ubnHjh0jHy5D5tsr4z+CIMi1J5dddtlPP/00OztLRk73H0EQNTU1zzzzzJYtW95///2kpKTc3FwWi7Vv375bb7319OnT5JTpkuM/giBKSkoeeOCBW2655aOPPkpPT8/Jyfnuu+8OHjx4/fXXU/7zaZE9qeaQPgb+C+nqReFAwJcEhoaGsrKy3njjje3bt99444033XTTxo0bN2zYcM8993zwwQcymYwa7XV1dX311VdbtmzZuHHj7373u8cff/y999577bXX1q9fT/lvenqazWbfeuutmzZt+sMf/pCVleVwOMbGxjIzM1944QXyZvNHHnlk7969f/jDH7744gvq0qO3/NfX1/fOO+9ERERUVlZS2Fz8Nzs7K5PJ3nzzzbvvvvvOO+/cuXPntm3b9uzZc/r06ba2NvKo5fw3Pj6empoaERFxyy233Hnnnbt373766aePHj36yCOP3HvvveTYkSAI3xWZKhQ2SALwH1oCCICA5wTm5uZaW1tzcnJYLNaJEye++eYbPp9fXFxss9nojxMjCKK9vT0tLe3UqVMnT55MSEgwm81arZbD4cjlcmru1Gq1Jicnf/vtt1FRUSaTiYxhZmZGr9fHxcV9++23J06cYLFYRUVF9PiNRiOPx5PJZNTNfwRBdHd3JyQk5OTkUNOkLoW0WCxRUVFZWVnU9UuHwyGXy4uLi+lPdCsvLz958qRKpaIOJ3/yKSUl5dSpUydOnGCz2WVlZVQkBEHMzMwUFBRER0eT935QBxIEMTk5qVKpIiMjv/766zNnzojFYrVa/dxzz+3Zs4f+qBofFZmeE2zj+h/aAAiAAAj4icDw8HBTUxM5R0omOT8/n5GRsW3btoMHDy53n6KfMheWyWD8F5bVjkKDAAj4nYBCofj73//+ySefZGZmVlRUlJaW8ni8Rx99dMuWLUKhkH7zot+zFqYJwn9hWvEoNgiAgJ8JGI3Gzz///JFHHtm1a9cjjzyye/fuHTt27N2798cff+zo6PBzZpAc5j/RBkAABEDATwQcDkdXV1d+fv5PP/104sSJr7/+ms/ny+Xy5a5Q+ilbYZwMxn9hXPkoOgiAAAiEMQH4L4wrH0UHARAAgTAmAP+FceWj6CAAAiAQxgTgvzCufBQdBEAABMKYAPwXxpWPooMACIBAGBOA/8K48lF0EAABEAhjAvBfGFc+ig4CIAACYUwA/gvjykfRQQAEQCCMCcB/YVz5KDoIgAAIhDEB+C+MKx9FBwEQAIEwJgD/hXHlo+ggAAIgEMYE4L8wrnwUHQRAAATCmAD8F8aVj6KDAAgQxPz8/Pj4OP23c0ElTAjAf2FS0SgmCIDA0gRGR0fb2tpmZmaW/hh7Q5cA/Be6dYuSgQAIMCDQ3t6u1WpHR0cdDgeD4AgSOgTgv9CpS5QEBEBgtQTm5uYMBoNape7u7sYU6GrpBXt4+C/YaxD5BwEQ8JzAwMCAVqtVqVQNDQ1TU1OeR4Qjg5AA/BeElYYsgwAIeIlAc3NzTU2NSqVSq9XDw8OYAvUS1+CIBv4LjnpCLkEABLxOYGpqqkHfoDr3am9vn52d9XoqiDBgCcB/AVs1yBgIgIBvCfT29NbV1Z3Tn0qn02EK1LfEAyx2+C/AKgTZAQEQ8BcBi8WiVqsp/6nV6v7+/vn5eX+lj3TWmAD8t8YVgORBAATWhMD4+Hh9fT0lP3KjpaUFU6BrUh1rkij8tybYkSgIgMAaE+jq6tJoNC7+02g04+Pja5wzJO8vAvCfv0gjHRAAgYAhMD8/bzKZ6JOflAh7enpwI2DAVJRvMwL/+ZYvYgcBEAhAAqOjozqdjnIefaOxsRHPQgvAKvNFluA/X1BFnCAAAgFNoL29vba2lq49aru2thbPQgvoyvNe5uA/77FETCAAAsFAgHrmGeU8lw08Cy0YqtELeYT/vAARUYAACAQRgdnZ2b6+vu5zL/IWwMbGxq6uLnLf8PAw7oIIogr1OKvwn8focCAIgEBQEnA4HHNzc/ZzL71er1KpOjs7p6enyX1Y/xKU9br6TMN/q2eGI0AABEKIQEOD8xFo3d3ddrs9hIqFoqxMAP5bmRFCgAAIhDAB+C+EK9d90eA/93zwKQiAQIgTgP9CvIKXLx78tzwbfAICIBAGBOC/MKjkpYsI/y3NBXtBAATChAD8FyYVvbiY8N9iJtgDAiAQRgTgvzCq7AuLCv9dyAPvQAAEwowA/BdmFX6+uPDfeRbYAgEQCEMC8F8YVjpZZPgvbKseBQcBEHASgP/Cth3Af2Fb9Sg4CICAkwD8F7btAP4L26pHwUEABJwE4L+wbQdh57/x8XGWR6+YmBjmrUQkEnmUCGtsbIxhKhKJxLMkzGYzwyQaGho8S0IqlTJMYnBw0LMkEhMTGSZBEERkZKRnqTD/Hbj8/HzPkmhtbWVYEI1G41kSSqWSYRI9PT2eJZGens4wCYIg2Gy2Z6k4HA6GqWRmZjJPQiqVqlSqjIwMLpfb1dXFMImqqirmSdBD1tTUMEzCarXSD2S+nZOTwzAJu93uJlqBQJCWliYWi6uqqgwGg81mYxhtsASD/9zU/gUfwX8X4HD7Bv5zi+eCD+G/C3C4fQP/ucVz/kNv+e98jOe2UlNTmbfYwLdgKPivqqqK+fPaPR7/iUQi5tUZGxt7rsGs7n8/jP8aGxsZFsTj8Z9MJmOYxNDQ0OoAnQu9qvFfVFTUueNW9z/Gfwx5YfzHEBSLxQqi8d9yhUpKSrJYLMy/jjDsDfwfLLj919/fHx8fz2Kx1Go1Q3Ye+w/jv+VOhsX7Mf5bzGS5PW1tbQybbsjMfy6HYsX9DEERBOHx/Gd3dzfDVKqrq1fM8JIB/OC/3NxchqVwP/+5ZP6pncx7XYaZ8X+wIPafSqWiaoLH401NTTHBNzU1lenRq6ioiEn8ZBixWOxRIpmTk5MMU6mpqfEsiY6ODoZJtLW1eZaEVqtlmMTY2JhnSZSUlDBMgiCIvLw8z1KZnZ1lmEpVVZVnSTC/pmKxWDxLoqGhgWEpBgcHPUuivLycYRIEQWRlZXmWCvMk5HI58yQUCoVKpZJIJNnZ2QMDAwxTMRqNzJOgh2R+Ab63t5d+IPNt5ld8ye8Ky8WcmJjI5/Opbpa+ERMTEwI/FxWU/nM4HIuXGzCfc2PYvhEMBEAgHAhg/af7Wrbb7QMDA/X19dHR0ZQCmX+Ndh/52n4alP6TSqVUNVAbFRUVa4sSqYMACAQjAfiPYa3Nz8+TFlzV7AvDyNckWPD5jz7tScovOjq6s7NzTfAhURAAgWAnAP+ttgaZXxdYbcx+Dh9k/jMYDNSAj9woLS0Nmcrwc90jORAAAdz/Hs5tIJj8NzU15XLnLOY8w7ntouwg4BUCGP95BWMwRhJM/qusrKQP/lJSUubn54MROvIMAiAQOATgv8CpCz/nJGj8Z7fb6StxIyMjx8fH/QwLyYEACIQeAfgv9OqUYYmCxn9arZY++NNoNAxLiGAgAAIg4IYA/OcGTmh/FBz+czgc9FtPWCwWw7vdQ7vyUDoQAIGLJwD/XTzDII0hOPzX399PH/zl5+cHKW5kGwRAINAIwH+BViN+y09w+K+5uZnuP51O5zdASAgEQCC0CcB/oV2/bkoXHP5zefJve3u7myLhIxAAARBgTgD+Y87KfUi73d7X19fY2Mj8qbbuI/T1p8HhP5cHng0PD/uaC+IHARAIEwLw38VX9PDwMP1Hv4Plzuzg8F92djZ9/tMrvztlt9s7OzvNZrNGo1HhFeoENBqN2Wzu7OwMgYfWX3xvtTgGu93e3d1tNBo1Gk1lZaVCoagIm1d2dnZaWlpRUZFcLg+bQldUV1drtVqLxTI0NHTxN1JPTk7Su2ixWLy4jQXgnuDwX1xcHAV3Vb9DuyRxk8nkIlQqcmyEA4Hc3FyLxbJk2wjDncPDwzU1Nbm5uUlJSbGxsdHR0QKBICqcXjwej8vl8vn8yMjI8Cm3QCCIiYmJi4tLTU0tKioyGo3Mf+158WkyNzdH7zry8vIWhwnAPcHhP6FQSMGNj4/3mGNHR0dSUhIVFTbCmUBKSgrzHzv1uMkF8oGzs7M6nS4rK0soFLo8WTCcG0YYlp3L5cbGxhYWFFqtVs8mSObn5+ncsrOzA7nlU3kLI/+p1Wp6DWEbBNhsdtiuJR4fHy8vL4+NjeVwOGgJIMBisbhcblJSUkNDgwcDQfiPcqr3Ny5+/CeRSNDEQWBJAlKp1PtNNrBjHBkZKS4ujoqKwrBvySYRzjtjY2Nra2unp6dX1YThv1XhWl3gi/RfVVXVkg06ISVOXF5Q11hl6NDgL7QJ1JgURdL8+OTzF5LpTSKsHqc3NTVVVlYWGRlJJ8BisRJEcQUZuUqxzFChsVTVW6r1+AthAlpZtSy/JDslQxDp+jUoJibGaDSu6nfl4L/VKW1VoS/Gf42NjS7nOYvFyi3O6pps6p+34i/cCFhHzVn56YubREtLy6raZPAGrqmpcXmaYEyUUFEkbVEbbbqWAUPHaGPPeFMv/kKbwIi5u6+hvUvb3FhZX5iRx+fx6SdFQkJCT08P85X28J8POwSP/TczM8Pj8ej1Gh0jNHVpw63TR3ldCOjbawXCKHrDEAgEnl3592G790HUfX19KSkp9GnPnNRMc6VuwNAxZoH2wtH6Y409tvoWrUwVKxSxWWzypGCz2TKZbGJigmEbhP8YgvIkmMf+c1nzwmazm3r1Ll0h3oYnAbNNR/cfi8UK+bUwDodDKpXSf0csPia2qVo/2mgL7bEOSrcigWFTl7pUIYwUUCeFQCBob2+fm5tj0mXDf0woeRjGY/+5zPOUq0vDs69HqZckUKIoos52FouVlJTkYQMNksP6+/uTkpKowR+Xw9VIq4dNXSt2jggQDgQGDB0F6Tk8Lpc6KcrKyhgOAeE/H/YBnvlvcHCQqkjn5f2UuL659iX7QewMTwK9s22iBBG9kYyNjfmwHa911Hq9nv6NMCs5vVvXMm4Jx0m/cPCZB2U0V+piBNHUGREbGzs4OMik2cJ/TCh5GMYz/5lMJqoiWSyWqkEenr08Su2GQEVtKb2RhPYqGLlcTl/2qSiSDhg7POglcUioEhg0dibGxlNnBIfDsdlsTFbBwH8euo3JYZ75z+Xin6G91k0/iI/Ck0Cd5YJ7Y0L7EmBRURGXNrtlVNbhyl+omszDcll689KyuZzzU6Ctra1MLgHCf0xE5mEYz/xXVlZGfZFhsVhtQ8bw7OJRajcEGi9cBaNQKDxso8FwWGZmJnXxT8CPstY2YvLTQ0+E7v0hyiJpFP/8vaEMbwSE/3zYARQWFmaee0kkEoYpFRQU0P1nm2px0w/io/AkYB010xsJ89bFsBEGVLDU1FSqsAmiuC5tM3p/EHAhoCtXR0edf96yVqtl8jg0+C+gznRnZhb5rzk8u3iU2g0B6+gFj0coLi4OuHbsvQylpKRQ/kuKS+jWwX9Y++NKQF9RGy0477+6ujr4z3unoB9jgv/c9Pv4iCQA/7l8/cfbMCcA//nRUb5MCv6D5FYkAP+FeXeP4rsQ8Mx/BEGcu0Ll/D9YrqMHx+8feWZJ+G/F3h8B4D+X7g9vw5yAx/7zrJde26PgPzwCO6wJwH9h3t2j+C4E4L+1VbLXUsf4D8O7FQnAfy7dH96GOQH4z2sGWtuI4L8Ve38EgP/CvLtH8V0IwH9rqy2vpQ7/QW8rEoD/XLo/vA1zAvCf1wy0thHBfyv2/ggA/4V5d4/iuxCA/9ZWW15LHf6D3lYkAP+5dH94G+YE4D+vGWhtI4L/Vuz9EQD+C/PuHsV3IQD/ra22vJY6/Ae9rUgA/nPp/vA2zAnAf14z0NpGBP+t2PsjAPwX5t09iu9CAP5bW215LfU18V/vbJulRy+vKXXzV6krN9t0/fNW62ijvKZMXlOqqJPq22pcbNQxZlFqZfKa0mp9RduwyeXT7qkWU5e2xqRU1skqassqdeV1lurm/oZee5tLSPJtz0xrU69eY6mq1JVXaMqUWlmNUWnq0nZPuT4WnMrVBUWoLVPWyWpMSrNNZ5tupSfRPd1S31pzQeBFxa/WV7SPmN3DUWikNUalpVffN9dOj9+n2/CfS/fn9bfD5u5mtaGyuNzlr0oiry2rNCg0XbrmMUsPPd1ObVPVovD0w1UlFU3Vevoh4029A8aOJlWDVqZSlVRUS+Q1ZUqjsq5L1zx6YeTUUUOmrrZak76iRl2qqJbI1aWKenlNW61pyNRFhSE3lsxP1blDrJrGYXM3/ZABg9Wg1NAzvHhbJ1f3NbQvB6eyuLyquFxVUlEvr+nUNrnwoafl9W34z2sG8lZEnv3+35r4r3uqpVJXTj1lf8kNQXRURW1Z/7zV2Kklf4+Nw+Wk56S2XvgLhWabLjKKz2KxYuNj9G3nf7y3197W1Ncgq5akZadEi4RcHpfNYfP4vNgEUa44S21UdI430Z3RN9fePmJWamXZBRmx8TE8Po/DYXN53GiRMD07RaYqaeypp4encuWSefKQjNw0hUbaOnTex+0j5oLSXJfALm9jE0QNVo17OBwOJ1okzCnK1Dar+ux+UiD85/UO1CXCfoO1ouiCX+Ik2wabzY7k8RNEcUVZ+Q0KzaCpkzqwXq5ms9guTYj+ls/lSfOKqfAjjd2tNcbygpL0xJToKCGXw+XQI6+odfmZ+zFLT5e2qUoiz07JiBXG8Lg8DpvD4/JEwpjslIwqibyjzkJFPt7Uu2R+2Cw2j8sVCWNy07Jqyyp79K3UIR11lry0bHqGF2+nJiS31hiXg0OG53I4scKYoqx8S7XebyecJeIAACAASURBVAqE/7ylLa/FE7z+Yy/1EkYLFBop3X8sFitKECmtKu6dPT96W9J/vfY2s02XU5TJ453/jWbq7GKz2TFx0fKa0q7Js6O6vrn21iFTsbxAIIyigtE3eHxeTlGmsbOOUuAF/mOzFkpAP8KZVYm8kBqSuvhvqRKz4xJEhgv9x+GwBcKoaJEwWiQUxggio/jshR6Pw+GkZiY32i5QMpU3r2+Ehv8cDgeTk21Nfv/IpYs/2zxoDYrD5iTGxmukVcPmswMvum/ONsALW1Ukjy/Ll5C+GWnstlTXZ6dk0H+1nIqew+YkiOJryyqHzvl1zNJj1TSKs/Lpv/JKhXeeifwocVZBW62Z8tkS+aEfwGLFCKIVRdJefRt5iIv/Lsz72XdpCSku/uNyOMJIQYxAGCMQRkcJI3l88isAl8PNS8tyUTKVN69vwH9MTiW/hglS/0UJIwtKc8sqi13+5DWlpi6ti//YbHZ8UqzBqqG6+CX91zpkyivOIkeN/EheenaKWJYvqSjMK86OTRCRZ2VMbLTaqCTj6Ry3SKslPD6PxWJxedzE1PjCsrwSRZFYlp+encKPdO7n8bjZhRnU6JPyHz+Sl5mXtpB5saSiMFecRSURJYiUVUvIuVa6/6JFQpfCkm+VdbK2YRN9/CeMEZRVFlfrK6r1FVV6eUVtWXZhBofLob4KUBx8uhEa/rNYLIODg46Fl5szc839JxJGS7IL5YWl5QWlpbnirOR0QaTzaxmbxU6OSzRVasnenPKNIDIqLy1bXljq8qcUSxurdONNvWNNPe215pzUs79rL4iMyk7JKM0Vl+UV56dni4QxC5GzkmITjMo6MnJbfUtprpjHdbZ8Po+XmpAsySmU5UskOYVpCSnk/kgeX5JdSA3pFuenvKC0LK84L+1sEiwWK0YYrS5VjFps4029dP8liOJcMk++1UirevVt9C8HcTGxiiKpRlqlkVbVllUqxbKs5HQOx3lGxAickXtddUtGCP+5OYPW5qMg9V98UqylV++m+6ZMQ3qLx+flFWd3jlvIQxb7r3uqpVovJ0d+kVH8wrI8c7fONt3SO9vWPmyu1ssTkuNYLBaHw8nMS+ueau61t+nba6NFzl+z5PG4GbmpuhZ150RT31w7eflQLMsn1RgljCxXl/TNOZ+FTeUqWiRUGRRkZnrtbW3Dpmq9PDElnsxtQnKcZWHilO6/lIwkN+Wl+88FTp+93dhZF5foVDiXy8ktynQTjxc/Cg3/qdVqlUpVW1vb3Nw8MTGx3HBwzf2XkZTaqW0iu91hs/PymzRfIowSONsnl1ucXUDOglK+SRDFGRSaJbtpcme/waoUy8iRX3SUUJovadeYh81dI43dXbrmaok8PibW2aI4zsiHTM79unI1mWIkj5+fnmOprh80doxZegaMHZbq+sLMXB7XObMiEsbUllWONTl/HnbJ/DiT0DZVSyoSRM6Tjs1iZyandWqdE6d0/xVk5LrPPzU5TIcz3tQ72mgzKDSxCwrnc3lltPleNxFe/Eee+Q+//+5DNYa8/zgcDj/SealPGCOo0svJBSCL/dc6aMzITSUNl5aV3DJopJuge6pZ1VCRmBqfmBKfK85qXZjxKassJnUVmyByWWLTN9fe3G/IzEtznr1sdmpmcseYU71L+o9MiBQwd2HqlbqK6RX/9c9bWwaMadnOHyjn8rh5kmx60Xy3HUr+U517abXazs7O6elpFxEGlP/InrpL11yUmUc2UfKS2HK+WbJnb60xpiUutBkONz89x1bfQg/Wb2ivlsiT4xKT4xJLcot69K22+hZJTiGVXLPaQA8/ZulpURvSFyLksDn5GTn9Bqv7/NAFHBstqpNVe8t/4029LWpDanzSwjiVLysooWfVd9vwnw9N5lnUQeo/YYxApiqpMSov+DNVGjrOTnJSphFER2UXZHB5XA6HnZKZ1NTX0D9vXew/U7c2UhBJzhDKqiWLrdA53qRrUeta1IYOTddEU8uAIS0reWFExc0pyuyhXVwkj7VNt6oMCnI2dWGVjXMNKpUr+viPSsvSq49LXPhOzePmL1iK7r/Y+JgLCrtQdk1jFVmiZcd/c9buqRZtU3VMXDSLxRJERynqnNdH/fC32H/kLGJw/UuO/87p7/z/er2+r6/PbreTIgxA/4002upk1eQALi5apC1X0X0TLRCW5op15SqXvyaVfqTRueSyQVEbyXN+cYwRRNeUKl2sMGbp6dG3miq1pkpti9owaOpsURtSSKNweaW54tFG53Ql/W/Q2FlZXE6uvkmNT2pZEOSS4z/qqMYqXWy0c97CeVVywVL08V9qfJJL5nXlKnLV63hTL33+kz7+I8ejGmlVjMA5eRMbLdLKnGT88Af/eSYpHx4VpP7jcDiC6LNLPMiFHtEiYUxcdGFpLtmzU6aJSxCpDBXk2C4yil9SUdgz0+rivz57e32rmvzqGhMXXddUvaIeLD160ijk4polwxs763h854SPc+hZL1/Rf61DpqyCdHLImJ6d0j9vpfuPw3Wu4XT5S0iOU9bJ+uedkqMWxwqEUWJZPnnXRLm6tFRRlJadwmaz+ZG8vOIsl6Htkjn3yk7Kf8nJyVlZWeXl5Z1B+FKrnPOfbl6NjY2Dg4Opqankdx0Wi5UUl9Cta/ZDf7pcF08lbarURi/08tFRwmqJnO4/DpsTxY+MEUS7/Imz8vsN1tFGW620kjwjkuMSG6vrqTiX2zBVakmjLHdFbczSUy+vIadA42Pi9BU19PwsOR/bVmvKTDo7iSLOyncZ//G4XJfMxwiiU+OTSNPT4cQKY8ryisk7JZRimTSvODU+ic1mR/GjSnOLXIa2yxXw4vfDfz40mWdRB6n/yDPT5V/nxbn8dLLvPu+/ROfKF01jlTDaeS0kNkGka1G7+K93tq2uqZqMzXk7Qfv5OyKWM0GjrV4Q7VxfQM1VLg5ptumihM4xJTXqonK15PivfcScX5JDZiM103m1j+4/l8KSb8nFMi7+WzIkh8NJSktwmaddnGcv7qH8J5FI3PgjND6qqqrKz8/n8ZxLPwLHf5bq+riFq3TCSEFlcTndN0s2EhaLlZ2SQd48py5VkGGouVP3AjAq68gVN9Rc5eLwBoWGXBrqHI8ujLrcj/866hpz07LIbBRm5rn4b8kiUKNVuv+WDMnlcLOS08lh6OKs+mIP/OeZpHx4VJD6L0oQWVCSU6oU0//KKotrzi3OpEwTlxhr7tZZRxuLywvYbOfNedmFGXVN1fT7//rm2uvbasiTRBQXo21WLdZA76xzlUrroJFcbGnp1YvinUvgnOO/peZL++etpi4tuQRGGCOo1JWvOP5rGzbnFTvPdjabnZ6T6uI/YYyAXlhyu1xd0rCwrpU+/uPyuHGJsUmpCUmpCYkp8TGxQjabzeFwEpLjqvRy+n0gi4vpxT2U/4qLi6urq1UqlToIXyvqWa1W63Q6iUQSFXX2NpjA8Z9z/nBhlUe0QKgqqaD7TxAZlZuWVV5Q4vKnLVcNmbpGG20aaRV5RiTHJVoW3RE/3tQ7bO6y1bd065p79K1D5i5zpS5G4JxjXxj/uc6XkjrRV9SQq0DjY2Lr5Wp6fpYc/1k1jdmpGc6r8mxOcXaBi//iY+JcMl9eUFIlkbfWGF3mP/kLd0OSVyuTYhOio5xnBJfDTU1IrpfXLJ6q9YX8xpt64T8fmsyzqIPUfy5LHBf32i7+67O3m7q0CSnO5WSC6KicokxyUQx1/7u5++wd8QJhlLym1CXCvjlrc7+hoDQnrzhbUlFo7tYtXP87t6KkOGvxo2F6ZlprTJXknJgoLkbXol7Rf839huT0RHJBaUFJjov/mK//jI4VKrWy+taa+tYaXYu6ql5OrcRJSk1wv27WpeAX85byH9mNFhcXe9ZE1/ao5a7/qdVqjUbT0tIyOjpKEEQAXv8j5xv5C9fw4mJideUr+4be7xsUGvL6n0gYo5FW0T9y3h2xsJ5FnJVfmJkrLyyz1jWev/7H40vzixc/GmbI1FktkZPX/1LiE5tUzqfMuB//NakakmITyOt/5Yuu/zFf/5kY67xP0VypM1fqjMo6VYkiPXFhsRubnZ2SQa2bdSmj19/Cf2t7Li+Repj4b2GGsLlSK+PxeeSVMPJ+OMp/zvWfOQunBJeTkZNK3YFOCqB7qllRJyX7cXJA2TluKasUk3vik2KNHedvcu+ft/bNtbcMGnOKMsnBXEpGYvuI2b3/emZaaxsryVGpIDpKubBKhT7/ydx/Ll8Oeu1t9a1qcnXPwpVI50jUD38h6T9yBNvU1NTf3z83N0edUQHovx59W1ne2SaalpDcVmta0Tf0Ht+5/jPBucKLx+UWZub1Npy9A50M09fQXl5QQrb/9MRUq6bRuf4z27n+k81ipyemkslREY5ZelprjFnJzsvbHDY7Ly27r6HdfX6GTJ3qUsVZf59bv0Nf/8Lcf/T1L+NNvSPm7jpZNWn3hZGo80qkH/7gP+p8CZSN8PFf/7y1dciYXeicTqFelP+6p5ordeXk/X9RgshieUFjT73z/j97m3W0UW1UkjfncbicrIL07innfn17rTBm4f4qPi+rIL3B6lwX2mdv755uabTVlyiKyCEmuUCGvO+CGpXSr//1zbV3TjTVNVWTtyiw2ezElPimhbsbveK//nnn4JUcWUZG8UuVYj/Ij3wEK8WZxWIF9fiP1F5DQ0NPT8/MzMziszeg/DfaaOvSNVcWl5N3qfO4PEl2IfmUFvfjLboDyOtn5PLRGEG0vLDUWte4cP+fzVbfoi5VxMc4Z1OckecUDi3cF+i8/y/SeUZE8SOLMvOa1YZBY+eoxTZo7GxWGyTZhfyF66MxgmhViYJ86tiS+Rmz9PQ2tGllKvIWBTabnZWS3rVwd6NX/Dfe1EuNLKmVQfSy+2gb/lt84qzxniD1n3OJY/nZJY70x0M7n3a9sHqFMg05XCN7/J7ZNl2LOibWue6ZfFH+c47YBgzZhRnko8L4kfzMvLRSpVhWLSkqy4tfuPmdzWaL4mNqzZVkbB3jllKlmFQmj8dNTk+UyAtlqpJSpTgzP50cyXF53Kz89JYBA3kIlSt+JD+7MOPcEs2S4vKCpLQEcrL03PNfnE/ppPsvJlZILym1XVVf3tSnp1//cxn/9c9b24ZNuWLnlUU+n1ckzYP/mJ91NTU1Op2uvb19cnLSzVFr7j/6EseKorLCjFyR0Hk1js1mp8Qnmiudj3Shj7eEkYLCjNzFD4+uLC4nn+dCjtgyk9PJR4UJIgV5zuuFpRVFZeKsfHJZDZt9wcNlunXOWwBJZUby+BlJqdJ8iaJIKs2XZCankSM5Po8vzsqnllxS/qPnRymWSXIKE0Xx5GSpyPn8FyXpS7r/EmPjl8x/nay6S9tEX//iMv4bb+pt15izU5xfhaP4UYoiqY+E5xIt/OfmDFqbj4LUf2f1xXbOttD/+JE8sSyfPtNI91//vLVjYd6SfPSRy/Ove2fbjJ3arPz05Z7/KVp4/mf3VAvpD+fzPweNhWV5bp7/mZWfTl9NSvnvgvyffeP8L0oQKZblU7OvdP85P76wsOTbaJGwuqHCvf+so41iWT55/3uuOAv+Y36yDQ8PMwm85v6jNaLzmxw2O0EUVyutpH5FgfLN+UCLtgoycknZDJu7TZXazKQ0UmkuAc8///Pcw0XHLD1ttabCjNzln/8ZWZiZ27KwPsXFxy6RU2+jBUJ5YSk1+0r3HxXGZSNBFNdQUevef13apsIM55Pl+Tye9NzzTl105fW38B+TU8mvYYLbfy4Nn8XinRvfUKZx8V/fXHtjTz05Gejiv/55a6+9zdKrL1EUpWQkCaKjFh52z+bxuaK4mOyCjGp9ReeE6+8/tA2bKmrL0nNSz/5exMISU2G0ICUjqURRRD6MlPINlSuXjHO5HGGMIC0ruVxdQr8/z9V/LoctvI2OEVTrV/Bf12SzvKaUHA2kZSVTCqcy5ouN0Lj+x/BsDCj/Oa9w83hxMbH56dk6uXrQuIrff2CxWAUZOdRPIow0On9iqTRXnBKfJIiM4rA5ZOTxMbEFGTn1zsg76J4Ys/R01lkURdKMpNRogfP3Iths5485xAiEGUmpFUVl7ZrzD7+mj0fpTZvNYnMX7u3LSk5XlVRQg0WX9Z/0Q+jbTPzX29AmzXM+v4nD5hRm5lLPB6eXxevb8B/Ds8l/wYLIf72zbeZuXVml2M2frFpC3r3QPmImny6t1Mqso430/t3m/FE9NRmJok5KPZyaCtM12dxg1VTVl8tUkrKqYnlNaY1R2dSr713ml4N6ZlobbTqVoaJcXSqtKi5Xl1TVyw1WDfVLEVTMVK4uLEKxTOU8xNhZZ5s+O7gkD+meaq5rqr4wsGvxK2rLGm31dDhKraxj7IIi9862NdrOoqvUlS/+YUIqh17cgP+83oG6RDhs7rJU68sLnM+8pv/JC0urisvr5epOrcVlHaZV00g+I5se3mVbX1Ez1nT+VwPHmnr6DVZzlU5dqlQUSSsKneeE3u2P5w0tPA5GI61SimUVhWWVYplGWtWiNlC/FEEVZMn8yAudVx7qZNVttSZq5Eoe0m+w1strXDLs8lZdouios9Dh1MmqycetUekOm7spdLqFWz6oj3y3Af/5T2wMUwoi/3mxa0ZUfiAA//muJ0XMwUgA/mNoJf8Fg//8YILwTAL+C8Y+Gnn2HQH4z39iY5gS/BeecvJDqeE/3/WkiDkYCcB/DK0U6MEKCgroF5xtU2d/Et0PvSqSCBYC8F8w9tHIs+8IwH+BLjaG+YP/gkVCa5hP+M93PSliDkYC8B9DvwR6MPhvDb0SLEnDf8HYRyPPviMA/wW62BjmD/4LFgmtYT7hP9/1pIg5GAl45j+GfXKgBftZoGXIi/mB/9bQK8GSNPwXjH008uw7AvCfFx20llHBf8EioTXMJ/znu54UMQcjAfhvLaXlxbThvzX0SrAkDf8FYx+NPPuOAPznRQetZVTwX7BIaA3zCf/5ridFzMFIAP5bS2l5MW34bw29EixJw3/B2Ecjz74jAP950UFrGRX8FywSWsN8wn++60kRczASgP/WUlpeTBv+W0OvBEvS8F8w9tHIs+8IwH9edNBaRlVYWEh//lnX5AW/ihcsHTTy6VMCbcMmeiMpKSlZyybr47Tpv/8XL4rr0jb7rhtFzEFKQCtTRUcJqJNCq9XOzMz4uGGuWfShfP+fVCqlapHFYhk6ND7tSRF5MBLQtqjojUSpVK7Zuej7hLOzszkcDllePpdn1TQGaR+NbPuOQEWRNIofSZ0UJpPJbrf7vm2uTQqh7D+V6oKuTaGVBmMHjTz7lIC0WkKd6iwWS6vVrs2J6JdUi4qKeFweVV6TUjvaaPNdT4qYg5FAfkYOl8ulGklbW9v8/LxfmucaJBIc/issLMw895JIJAw5tbS0ULXIYrHyS3J82pMi8mAkkJWfQW8knZ2dDFtXMAarqqqKioqiylstkS/+rfNg7LKRZ28RGG20pcQnsVlsspHw+fze3t5gbOoM8xwc/vPs9/9mZ2fZ7LMVyWKxeHyeddQcjH008uwjAq2DBg737Hygs4XweCH8VZcgiNbWVpFIRPkvLSHZeQnQ0uut3hPxBDsBg0ITI4imWkhmZubIyAhDlwRjsFD2H0EQGRkXfLtPSkvom2v3UWeKaIOLQO9sW2zieRmwWKy8vLxgPIeZ53lycjI5OZnq3bgcbllecb/BGuy9NvLvFQIddZb0xBTqCjGLxaqurp6ammLewIIuZIj7r7m5mTrbyY3SyuLg6qaRWx8RKJLlu7SNjo6OoDuBV5Vhh8OhVCrpU6BRkVGqEsWgsdMrHSgiCV4Cvfq2/PQc+uVhoVDY3d3NfEbk3BUq5/8KhWJVLXOtAoe4/wiCyMzMdOnmMnPTuiZwL4TVR14J/Gg7xhvTc1JdWkXID/7ILmZkZCQtLY1+XSBaICzOKuiqaxqz9ARv942ce07A0muq1GanZPBpa6NYLJZarZ6enmZopvn5efoJlZ2dzfDAtQ0W+v4bHx+nXz4kK0kgiKyzVOFyYOC7yrs5bB8x15qVkVF8+rnKYrFEItHk5OTanor+Sd3hcLS0tMTFxdEJ8Hm81IRkdYnCUq3v0jVjUajnLmkKmoupA4YOq6bRVKmV5UsSRPHcczfGkA2joKBgaGjI4XAwbJbwH0NQngSjCyw+Pn61UQwMDPB455d90898FosVnxyXkpGEv9AmIEqIcal36m1kZOTw8PBqG1Xwhrfb7WazOS4ujj4KZLPZ0VGCuJjYpNiE1ITktIQU/IU2gZT4pERRfGy0KJIfSW8JLBYrNzfXZrMxn/kkCAL+82GHcJH+IwhiYGAgOvr8uiaq78NGmBMQiURDQ0M+bLsBGfXs7GxLS0tycjKHfX75a5i3BBSfxWKx2WyxWNzT0zM3N7eqlgv/rQrX6gJfvP8IgpicnExPT0crZ7PZ9PtbwxlIVlYW8yscq2uyAR/abrd3d3cXFha6mRoJ57YRhmUXCASVlZUDAwOrGvmRLR3+8+EZTx+6eTD/Sc9ZU1NTfHx8GDZuqshRUVHJycl8vus1MCpAOGwkJia2trbSG0YYbs/Pz4+MjBiNxuysbFgwHJr9cmUUCASlpaVWq3VycpL5NT+XU4YeeU5Ojsungfk2ONa/0J/by+VyLx6lzWarrKzMyMiIjY0NtzM/Li5OIpEkJCTQ22vIb/N4vLi4uMzMzOrq6p6enotvQiETw+zs7NDQkNVqVavVEokkNzc3LS0tGa9QJ5CRkZGfly+VSvV6fVdX19jYmAfDPuosmJqaovchRUVF1EeBvBEc/isqKqLDDe1bMn3aXObm5mw2m0aj6ejo8PiLnk9ziMjXhIDD4ZiamhodHR0aGhoYGOjHK9QJDA4ODg8Pj42Nzc7OXnxXMDg4SO+iy8vL16QZrzbR4PCfUqmkw8X399VWMxV+YmLCZDKpVCqTyRTCP2tClRcbIAACfiDQ2dlJ76JVKpUfEr34JILDfw0NDXS4Fovl4ksehjE4HI6hoaHa2lqVSlVfXz8wMBCGEFBkEAABrxPQaDT0Lrq5udnrSfgiwuDwX0dHBx1ucXGxL1iEfJyzs7NWq1W18KqpqWltbb34eY+Qh4YCggAIrEggLS2N3kUHy9xScPjPbrfT1yuy2ezx8fEVqwQBXAiMj48bDAbSfyqVymg0hslDT1w44C0IgIAXCbhc/EtKSvJi5D6NKjj8RxCEQqGgf7+Qy+U+5RJ6kTscjoGBgZqaGsp/Wq0WV1JDr6JRIhDwMwGxWEzvnCsqKvycAY+TCxr/jY2N0RGnpqZ6XObwPHB6erq1tZWSn0qlUqvVzc3NF7PoOTxJotQgAAIUAZvNRu+ZWSxWED1QKWj8RxBEQUEB+SOlWq0WF66o9sdwY2xsTK/X0/2nUqkaGhpGR0cZxoBgIAACIEAnYLfb6b+ozGKxguXOP7IUweS/zs7OwsLCiYkJegVgmwmB+fn53t5eF/mpVKq6urquri4mMSAMCIAACLgQcLkzLbgGfwRBBJP/XNDjLXMCU1NTTU1Ni/2nVqsbGxvtdjvzqBASBEAABEgCk5OTycnJ1PxnVVVVcJGB/4KrvjzM7cjISF1d3WL/qVQqvV4fRPP1HpYfh4EACPiGwNzcXH5+PovFysjICLrLUvCfbxpFIMVKPvNsSfmpVCo8Cy2Q6gp5AYGgJFBTUxOMj6WE/4Kyta0q01NTU21tbfULL51ORz7/pba2ltxTX1/f2tqKKdBVIUVgEACBECAQmv4bHR01GAydq38xvx98YmJi9dE7j2D+U+N2u92zJHp7e+lNc35+fmpqamLhNTw8TF4INJlM5J729va2traOjo7VpkVPwv32wMDAaiMnwzP/Rjk2NuZZEiMjI+4zT306MzPjWRJ9fX1UJCtudHV1eZbKijFTAfr7+z1LgvlDPUZHRz1LYmxsjMqn+43p6WnPkljVY/88S6K7u9t95umf9vX1eZbK7OwsPR432yMjI54lwbw/dJN6IH8Umv5zeV4odXl2xQ2z2cywtjxOQiqVMkzC5akKK2aeCpCYmLhcEjMzM21tbSqVinqGamRkJHXgqjaY94Z5eXmripkK3NbWtlxBXPa7PH6QimHFDaVS6RLVcm8X3+e0YuRkgPT09OXiXLyfzWYzjNYlGPNLL5mZmS7HMnzLfKlwVVUVwzhdgtXU1CxmsuQeq9XqcizDt8x/ms5utzOM0yUYj8dbMs9L7qSvH3GJx/3b/v7+JSNcvLO8vNx9VMt9ajAYFscWSntC03+lpaXL1aj7/fCfez70T+E/Og032/CfGzguH8F/LkDcvPWD/+rr60PJdovLEpr+S0pKctNu3HwE/7mB4/IR/OcCZLm38N9yZBbvh/8WM1lujx/8V1dXt9gZobQnBP3n8awFi8WC/5Y72Rbvh/8WM1lyD/y3JJYld8J/S2JZcqcf/Me8OoJUiqHpP4vFotFollvx72Y/82vjfX19buJx8xHza1pTU1Nu4nHzkV6vX645Lr7+5xkolUo1Nze3XCou+y0Wi5vcuvmI+Vohm83mJh43H3V0dLjkdrm34+PjbuJx85HRaFwuzsX73cTj/qPFUS23x2g0uo9quU+ZL07p7OxcLhL3+5mvHBkZGXEf1XKfMv+OSxDEcpG4378qbTQ0NLiPbblPmT8Jq729fblI3O/v7OxcrhWFxv4Q9F9oVIyPSrHYfz5KCNGCAAiAQIATgP8CvIK8nD34z8tAER0IgEDQEoD/grbqPMo4/OcRNhwEAiAQggTgvxCsVDdFgv/cwMFHIAACYUUA/gur6ibgv/Cqb5QWBEBgeQLw3/JsQvET+C8UaxVlAgEQ8IQA/OcJteA9Bv4L3rpDzkEABLxLAP7zLs9Ajw3+C/QaQv5AAAT8RQD+8xfpwEgH/guMekAuQAAE1p4A/Lf2deDPHMB//qSNtEAAdwbfoAAAEXlJREFUBAKZAPwXyLXj/bzBf95nihhBAASCkwD8F5z15mmu4T9PyeE4EACBUCMA/4VajbovD/znng8+BQEQCB8C8F/41LWzpPBfeNU3SgsCILA8AfhveTah+An8F4q1ijKBAAh4QgD+84Ra8B4D/wVv3SHnIAAC3iUA/3mXZ6DHBv8Feg0hfyAAAv4iAP/5i3RgpAP/BUY9IBcgAAJrTwD+W/s68GcO4D9/0kZaIAACgUwA/gvk2vF+3uA/7zNFjCAAAsFJAP4LznrzNNfwn6fkcBwIgECoEYD/Qq1G3ZcH/nPPB5+CAAiEDwH4L3zq2llS+C+86hulBQEQWJ4A/Lc8m1D8BP4LxVpFmUAABDwhAP95Qi14j4H/grfukHMQAAHvEoD/vMsz0GOD/wK9hpA/EAABfxGA//xFOjDSmZmZ6ejoqKura2lpCYwcIRcgAAIgsDYE4L+14b5Wqc7NzY2MjNhstuGh4bXKA9IFARAAgUAgAP8FQi0gDyAAAiAAAv4mAP/5mzjSAwEQAAEQCAQC8F8g1ALyAAIgAAIg4G8C8J+/iSM9EAABEACBQCAA/wVCLSAPIAACIAAC/iYA//mbONIDARAAARAIBALwXyDUAvIAAiAAAiDgbwLwn7+JIz0QAAEQAIFAIAD/BUItIA8gAAIgAAL+JgD/+Zs40gMBEAABEAgEAvBfINQC8gACIAACIOBvAvCfv4kjPRAAARAAgUAgAP8FQi0gDyAAAiAAAv4mAP/5mzjSAwEQAAEQCAQC8F8g1ALyAAIgAAIg4G8C8J+/iSM9EAABEACBQCAA/626FmZmZrq7u+vq6ioqKqRSaUVFhVartdlss7Ozq47rIg6YnJw0Go1arXZsbOwiovH3oUNDQ3V1dWaz2c+4/F1OpAcCIBDwBOC/VVSRw+EYGBgoKir64IMP7r777muuuebyyy9ft27dzp07Dx8+XFZWNjQ0tIroLi6o2Wz+85///PDDD2s0mouLya9Hi8Xie++995VXXunu7vZrwkgMBEAABC4kAP9dyGP5dw6Ho7u7++TJk5s3b77yyit37dr19ttvHzly5O23396zZ8+11167ffv2yMjIkZGR5ePw5idB6r+ysrKnnnrq0KFDnZ2d3sSBuEAABEBglQTgP6bAhoaGTp06tW7duttvv53FYvX29lJH9vX1CQSChx9++NVXX21oaKD2+3QjSP1nt9v7+/sHBgZ8CgeRgwAIgMCKBOC/FRE5A8zPz+fl5d1+++233nprQkLC3Nycy2F2u72lpcVkMtH3z8zMWK1WlUollUplMlltba3NZnM51uFwDA8PNzQ0yOXysrIyhULR2Ni4+JLe9PR0W1tbZWWlVCpVKpXNzc1ardb9/OfQ0JBarTYajaOjo2azuaKiory8vKGhYWxszG6322w2tVotk8mqq6s7OzvpuVoxS3Nzc62trVVVVV1dXd3d3Wq12mQyzc7O2my2ysrK1tbWkZERg8Egl8tlMplGo+nt7XU4HCQZKlfz8/MEQVBvx8fHm5ublUqlVCqtrq62Wq12u50Oc3x83GQykddcq6urOzo6ent7VSqV0Wgko6IHxjYIgAAIrEgA/lsRkTPA4ODgZ5999l//9V8ffvjh5OQkk2PGx8elUulf//rXbdu23bDw2rlz55EjRzQazczMDBnD/Px8e3v7Tz/9tHfv3ptuuun666/fvHnz/v374+Pj6ePLiYkJmUz2+uuv33zzzdddd90dd9zx1ltvnTlz5qmnnnJz/a+kpGTHjh0RERGxsbEvv/zy1q1br7766j179iQlJanV6n/+85+7du267rrrbr755o8//ritrY15liYmJo4dO3bjjTd+8skn//jHP3bt2nXkyJGhoSGRSHTjjTe+/fbbQqHw2Wef3bJly7p167Zv337ixImuri4yfjJXr7zyComRfPvkk08mJye/+eabd9555/r162+++eb33nuvvr6e4jw4OJicnPzEE09cv/AiL7geP35869atr7766vT0NBUSGyAAAiDAkAD8xwiUTqd7+umnb7vttrS0NCYHzM7OisXi3bt3X3fddQcPHvz++++/+eabZ555Zv369U8++WRtbS0ZSVdX12effbZ+/foHH3zw6NGjP/3006FDh26//fZNmzZ9//335Chwfn6+oqJi7969V1xxxQsvvHDq1KmTJ08eOHDggQceuOmmm1b039VXX/3444+/9957p0+fPnDgwGWXXbZt27ZXXnnlxRdfPHHixNGjR++6664bbriBx+MxzxLpv1/96le//e1vn3nmmVOnTimVyunpaZFIdM0112zevDkiIuKDDz744Ycf/vd//3fLli0bN24UCARk/Ev679prr33iiSdef/3106dPHzt27MEHH/zNb37z8ccfk18UpqamEhISbrvtto0bN7799ttnzpw5duzY/v3777jjjssvvxz+Y9IgEQYEQGAxAfhvMZMl9shksvvuu++hhx6qrq4mPyYnPCUXvuRyeXt7O0EQzc3Nr7/++vr160+fPj0xMUEe0t3d/fHHH19xxRUffPABOQmZkJCwcePGiIgIag2n3W4vLCzcsfAqLi4mCGJgYODTTz+97LLLPvroo8HBQTKqsbExgUCwYcOGFf3329/+Nj09nZzeNBqNL7744iWXXHL48OG+vj6CIObm5vh8/lVXXXX48GGCIOx2O5Mskf77xS9+8c4779CXsYhEov/5n//Zu3cvRWlmZobD4ZBFJqdAl/Tf1q1bU1NTqWFxfn7+li1b9u/fT0au1+ufffbZ66+/nsPhUGE6OzuPHDnyi1/8Av5bor1iFwiAAAMC8B8DSARRWlq6c+fOPXv2UKIaGRk5fvz4pedev/71r3/+859fdtllX3/9NUEQOTk5t95667PPPtvR0UFPoKKi4qGHHtq1a5dKperr6/vwww+vvfZaauxFhhwfHz9x4sTll19+9OhRh8OhUqn27dv3wAMPKJVKelRms/mll15a0X8HDhygBNzf33/48OFt27ZlZ2dTUWVlZW3evPmvf/0rQRAMs0T6b9OmTYmJiVQ8BEGIRKKNGzd++eWX9J15eXm33nrrG2+8QTp4Sf/RM0kQhFarfeqppx577DGDwUAQREZGxsaNG1944QWbzUaPuaio6Pbbb4f/6EywDQIgwJwA/MeIVVVV1aOPPrpz586SkhLygOnpaalU+uW512effbZ7927KfwKB4Iorrvjoo49cYu/o6Hj33Xe3bNmSnp7e0NDw/PPP33fffXK53CVYamrqdddd99prr83MzBQWFm7btu2FF17o7++nB1tx/aeLacirmB9//PG2bdtycnKoqHJycm677bZ3332XIAiGWSL9t3nz5qSkJCoeyn/Hjh2j7xSLxdu3b3/ttdfIG95dcuXyljzQYDC88MILe/fu1ev1BEFERUVdfvnlR44coUdLEAQZM/znggVvQQAEGBKA/xiBamtre+utt9atW/fDDz/Ql0pSB09NTX311VeU/3g83pVXXvnFF19QAciN3t7eQ4cObd68OTExUafT/fGPf9y9e3dNTY1LsJycnE2bNpGjInL89PLLL7ssCvWF/xhmyc/+43K5V1xxxT/+8Q8XSvCfCxC8BQEQWBUB+I8RrpmZGR6P95vf/Oapp54ymUzUan7q4ImJiX/+85+U/xISEtavX//OO++4LBYlr8DdddddRUVFzc3Nr7322u23304fjZEX4QQCwVVXXfW3v/1tbm6urKzsnnvuiYiIaGpqopIjCMIX/mOYJT/7Ly4u7pprrnn33XenpqboBOA/Og1sgwAIrJYA/MeUWENDw8svv3zVVVe99957ZrOZvuZ+enq6vr7+1VdfvfLKK7///nuCIJRK5Z49e+6///7y8nJqvDg1NRUfH3/rrbc+99xzra2tExMT33777bp16z7++OPh4WEyHw6Ho6Wl5a233qLWZJrN5pdffvmWW26JjY2lVn/Y7XalUvnYY4+teP2PutOAyfwnwyz52X/kN4CHHnqosrKSutVvcnJSJBJt2LAB859MWzDCgQAIXEgA/ruQx/LvHA6HVCrdu3fv1Vdf/cwzz6SmpqrVap1Op1arU1JSnn/++SuuuGLfvn0SiYQgiOHh4VOnTt1www1/+tOfysvLGxsbTSZTZmZmRETETTfdFBkZSaajUCj27t27fft2Foul1+stFktNTc0XX3xx4403HjhwwGKxEAQxPT3N5/Ovueaaffv2FRQUmBdeUqn0ww8/vPLKK73rP4IgmGTJz/6z2WyffvrpVVdddfDgwcrKSovFYjAYMjIy9u/f//Of/xz+W77N4hMQAAF3BOA/d3RcPpufn6+qqjp48OCGDRuuvvrqLVu23HPPPbfddttVV121YcOGl156SSaTUVOjLS0tn3zyyc0333z77bc//fTTERERW7Zs2bp16zfffEM9JntmZiYnJ2fXrl3knXzPPvvsfffdt2HDhmeffba8vJxKva2t7ZNPPrn++us3bdr05JNPPvfccw8//PBjjz22Y8cOr/uPSZb87D+CIGpqal566aX169dv3779ueeee/rppx999NG9e/du3Ljx1VdfpYbFFDFsgAAIgMCKBOC/FRG5BhgcHMzLyzt06NCTTz758MMPP/HEE++//35mZqbL+kzy1r3U1NQ33njj0Ucf3bdv39/+9rfCwkKXK4Jzc3M6ne7YsWNPPfXUnj17nnvuue+//97lUh9BED09PSKR6KWXXnrkkUeef/55Ho9XVVX11VdfHTp0iBwmuuaSIDQazTvvvHP8+HFqqnZsbIzP57/++usKhYIKr1QqDx48+N1331F7VswSeav7gQMHSktLqaPIBZkvvfSSSCSi71Sr1W+++eY333xDzgO75MrlLXlgW1vbsWPHDh8+3NraSkXV3Nx88uRJ0nxvvPFGdnZ2cnLybbfd9tZbb1EzzFRgbIAACIDAigTgvxURIcDaExgbGxsYGKAu/hEEMTk5+dNPP11//fWff/752ucPOQABEAhCAvBfEFZamGXZbreLxeITJ05UVla2tLRYrdbGxsaMjAxyBjgvLy/MeKC4IAAC3iEA/3mHI2LxHYHx8XGBQHDnnXfed999r7766rvvvvv888/fcccd27Zt+/7778fHx32XNGIGARAIYQLwXwhXbugUbXx8vKSk5IMPPoiIiNi9e3dERMSHH34okUhc7ggMnQKjJCAAAr4nAP/5njFSAAEQAAEQCDwC8F/g1QlyBAIgAAIg4HsC8J/vGSMFEAABEACBwCMA/wVenSBHIAACIAACvicA//meMVIAARAAARAIPALwX+DVCXIEAiAAAiDgewLwn+8ZIwUQAAEQAIHAIwD/BV6dIEcgAAIgAAK+JwD/+Z4xUgABEAABEAg8AvBf4NUJcgQCIAACIOB7AvCf7xkjBRAAARAAgcAjAP8FXp0gRyAAAiAAAr4nAP/5njFSAAEQAAEQCDwC8F/g1QlyBAIgAAIg4HsC8J/vGSMFEAABEACBwCMA/wVenSBHIAACIAACvicA//meMVIAARAAARAIPALwX+DVCXIEAiAAAiDgewLwn+8ZIwUQAAEQAIHAIwD/BV6dIEcgAAIgAAK+JwD/+Z4xUgABEAABEAg8AvBf4NUJcgQCIAACIOB7AvCf7xkjBRAAARAAgcAjAP8FXp0gRyAAAiAAAr4nAP/5njFSAAEQAAEQCDwC8F/g1QlyBAIgAAIg4HsC8J/vGSMFEAABEACBwCMA/wVenSBHIAACIAACvicA//meMVIAARAAARAIPALwX+DVCXIEAiAAAiDgewLwn+8ZIwUQAAEQAIHAIwD/BV6dIEcgAAIgAAK+JwD/+Z4xUgABEAABEAg8AvBf4NUJcgQCIAACIOB7AvCf7xkjBRAAARAAgcAjAP8FXp0gRyAAAiAAAr4nAP/5njFSAAEQAAEQCDwC8F/g1QlyBAIgAAIg4HsC/x8b4+f1g6v12QAAAABJRU5ErkJggg== \"Seq2Seq Model\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yRBglY1890Hq"
   },
   "source": [
    "Seq2Seq NMT model can be thought of autoregressive language model where the decoder finds the target sequence that is the most probable conditioned on the input sequence. Mathematically, it can be formulated as follows:\n",
    "\n",
    "$$\n",
    "P(y|x) = P(y_1|x) P(y_2|y_1,x) \\dots P(y_T|y_1, \\dots, y_{T-1}, x)\n",
    "$$\n",
    "\n",
    "where $x$ is the input sequence and $y$ is the output sequence.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DUahjzwOEniY"
   },
   "source": [
    "We will build a seq2seq model with attention for English to German translation using PyTorch.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pFAwBJFwdgrE"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# download the spacy language models for english and german\n",
    "!python -m spacy download en --quiet\n",
    "!python -m spacy download de --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tfNO9cz-nsKd"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install torchtext --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "06i3ra43ljX5"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# download fasttext embeddings for english and german\n",
    "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.de.vec\n",
    "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.en.vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g_FS5Kcnin-_"
   },
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2EEWYU8hjYSp"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
    "\n",
    "import spacy\n",
    "import torchtext\n",
    "import torchtext.vocab as vocab\n",
    "from torchtext.legacy.datasets import Multi30k\n",
    "from torchtext.legacy.data import Field, BucketIterator\n",
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "import warnings\n",
    "from tqdm.notebook import tqdm\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "28IEwh9Gu0jq"
   },
   "outputs": [],
   "source": [
    "# plot style configuration\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "\n",
    "rcParams['figure.figsize'] = 12, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XDKzRsNfjz-q"
   },
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "# refer https://pytorch.org/docs/stable/notes/randomness.html\n",
    "SEED = 42\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Qhn1oWnZj1nM"
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0gZO0Ml4j3QP"
   },
   "source": [
    "## Data Preprocessing and Loading\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s3cNjExikATZ"
   },
   "outputs": [],
   "source": [
    "spacy_en = spacy.load('en')\n",
    "spacy_de = spacy.load('de')\n",
    "\n",
    "def tokenize_en(text):\n",
    "    \"\"\" Translates english text sequence into a list of tokens.\n",
    "    \"\"\"\n",
    "    return [token.text for token in spacy_en.tokenizer(text)]\n",
    "\n",
    "def tokenize_de(text):\n",
    "    \"\"\" Translates german text sequence into a list of tokens.\n",
    "    \"\"\"\n",
    "    return [token.text for token in spacy_de.tokenizer(text)]\n",
    "\n",
    "PAD_TOKEN = '<pad>'\n",
    "SOS_TOKEN = '<s>'\n",
    "EOS_TOKEN = '</s>'\n",
    "UNK_TOKEN = '<unk>'\n",
    "\n",
    "# english text field\n",
    "en_text = Field(\n",
    "    tokenize=tokenize_en, lower=True, include_lengths=True,\n",
    "    pad_token=PAD_TOKEN, init_token=SOS_TOKEN, eos_token=EOS_TOKEN, unk_token=UNK_TOKEN,\n",
    ")\n",
    "\n",
    "# german text field\n",
    "de_text = Field(\n",
    "    tokenize=tokenize_de, lower=True, include_lengths=True,\n",
    "    pad_token=PAD_TOKEN, init_token=SOS_TOKEN, eos_token=EOS_TOKEN, unk_token=UNK_TOKEN,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 460953,
     "status": "ok",
     "timestamp": 1622263156793,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "Za0EVQJVYyxo",
    "outputId": "b92b514d-51ad-42db-aeb5-5e96926e4e91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading training.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "training.tar.gz: 100%|██████████| 1.21M/1.21M [00:00<00:00, 1.61MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading validation.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation.tar.gz: 100%|██████████| 46.3k/46.3k [00:00<00:00, 277kB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading mmt_task1_test2016.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mmt_task1_test2016.tar.gz: 100%|██████████| 66.2k/66.2k [00:00<00:00, 265kB/s]\n"
     ]
    }
   ],
   "source": [
    "data_fields = (('src', en_text), ('trg', de_text))\n",
    "\n",
    "train_data, valid_data, test_data = Multi30k.splits(\n",
    "    exts=('.en', '.de'), fields=data_fields\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S053PL9KycF7"
   },
   "outputs": [],
   "source": [
    "# load the pretrained fasttext embeddings and build the english vocabulary\n",
    "en_fast_embed = vocab.Vectors(name='wiki.en.vec', cache='.', unk_init = torch.Tensor.normal_)\n",
    "en_text.build_vocab(train_data.src, max_size=10000, min_freq=2, vectors=en_fast_embed)\n",
    "\n",
    "del en_fast_embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "013NBw_yyqzt"
   },
   "outputs": [],
   "source": [
    "# load the pretrained fasttext embeddings build the german vocabulary\n",
    "de_fast_embed = vocab.Vectors(name='wiki.de.vec', cache='.', unk_init = torch.Tensor.normal_)\n",
    "de_text.build_vocab(train_data.trg, max_size=10000, min_freq=2, vectors=de_fast_embed)\n",
    "\n",
    "del de_fast_embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 430,
     "status": "ok",
     "timestamp": 1622264309407,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "YBI0i4G_kEgm",
    "outputId": "1bf4ebba-25b6-4c30-ff7d-88012e3855de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "English vocabulary: 5893 words\n",
      "German vocabulary: 7855 words\n"
     ]
    }
   ],
   "source": [
    "PAD_IDX = de_text.vocab.stoi[PAD_TOKEN]\n",
    "SOS_IDX = de_text.vocab.stoi[SOS_TOKEN]\n",
    "EOS_IDX = de_text.vocab.stoi[EOS_TOKEN]\n",
    "UNK_IDX = de_text.vocab.stoi[UNK_TOKEN]\n",
    "\n",
    "print(f'English vocabulary: {len(en_text.vocab)} words')\n",
    "print(f'German vocabulary: {len(de_text.vocab)} words')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 665,
     "status": "ok",
     "timestamp": 1622264319396,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "dLO2HEAMkLZ0",
    "outputId": "5a3a2810-9e85-475f-9b19-c3c466125285"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of training examples: 29000\n",
      "# of validation examples: 1014\n",
      "# of testing examples: 1000\n",
      "dict_keys(['src', 'trg'])\n",
      "dict_values([['two', 'young', ',', 'white', 'males', 'are', 'outside', 'near', 'many', 'bushes', '.'], ['zwei', 'junge', 'weiße', 'männer', 'sind', 'im', 'freien', 'in', 'der', 'nähe', 'vieler', 'büsche', '.']])\n"
     ]
    }
   ],
   "source": [
    "print(f'# of training examples: {len(train_data.examples)}')\n",
    "print(f'# of validation examples: {len(valid_data.examples)}')\n",
    "print(f'# of testing examples: {len(test_data.examples)}')\n",
    "\n",
    "print(train_data[0].__dict__.keys())\n",
    "print(train_data[0].__dict__.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RZNSdXbwkMzg"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    sort_within_batch=True,\n",
    "    sort_key=lambda x: len(x.src),\n",
    "    device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 374,
     "status": "ok",
     "timestamp": 1622264321698,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "30GdLoLZkOpC",
    "outputId": "3cf1020b-972c-4682-d4fa-1b91fa65d54b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'source': torch.Size([16, 32]), 'source_lengths': torch.Size([32])}\n",
      "{'target': torch.Size([21, 32]), 'target_lengths': torch.Size([32])}\n"
     ]
    }
   ],
   "source": [
    "# sanity check to see if data loader is working\n",
    "x = next(iter(train_iterator))\n",
    "\n",
    "print({'source': x.src[0].shape, 'source_lengths': x.src[1].shape})\n",
    "print({'target': x.trg[0].shape, 'target_lengths': x.trg[1].shape})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Qqy6ZOAkQcl"
   },
   "source": [
    "## Model Architecture\n",
    "\n",
    "![seq2seq model with attention](data:image/png;base64,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 \"Seq2Seq Model with Bahdanua Attention\")\n",
    "\n",
    "(source: https://bastings.github.io/annotated_encoder_decoder/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6elg3wRLbdMd"
   },
   "source": [
    "### Encoder Module\n",
    "\n",
    "Encoder reads the input sequence and generate a fixed size context vector. However, because it's very hard for the encoder to encode the entire input sequence into a single vector, the encoder also produces the sequence of hidden states for each token of input sequence. Encoder can be either stacked LSTMs or stacked GRUs where each layer's output are passed as input sequence to the next layer. The final layer's hidden states act as a context vector.\n",
    "\n",
    "For the implementation in this notebook, we will be using a bidirectional GRU (forward + backward) as a encoder. A forward GRU reads the input sequence from left to right whereas a backward GRU reads the input sequence from right to left. The advantage of using a bidirectional GRU over unidirectional GRU is that we will be able to preserve information from both past and future. Mathematically, it can expressed as follows:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "h_t^\\rightarrow &= \\text{GRU}^\\rightarrow(e(x_t^\\rightarrow),\\ h_{t-1}^\\rightarrow)\\\\\n",
    "h_t^\\leftarrow &= \\text{GRU}^\\leftarrow(e(x_t^\\leftarrow),\\ h_{t-1}^\\leftarrow)\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where \n",
    "- $x_t$ denotes the input token (word) at time step $t$\n",
    "- $e(x_t^\\rightarrow)$ denotes the embedded input token at time step $t$\n",
    "- $h_t^\\rightarrow$ and $h_t^\\leftarrow$ denotes the forward and backward hidden state at time step $t$ respectively\n",
    "\n",
    "We will concatenate the final forward and backward hidden state to preserve the information context from both past and future and apply linear layer followed by relu non-linearity to obtain the context vector $z$ as follows:\n",
    "\n",
    "$$\n",
    "z = f([h_t^\\rightarrow,\\ h_t^\\leftarrow])\n",
    "$$\n",
    "\n",
    "where\n",
    "- $[h_t^\\rightarrow,\\ h_t^\\leftarrow]$ denotes concatenation of the final forward and backward hidden state\n",
    "- $f$ denotes linear layer followed by relu non-linearity\n",
    "\n",
    "Encoder GRU returns the following:\n",
    "- `outputs`: stacked forward and backward hidden states from the last layer\n",
    "- `hidden`: context vector $z$ which acts as initial hidden state for the decoder.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x0i2KI-na5hX"
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"\"\" Encoder encodes the input sequence into a dense embedded context.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, input_size, embed_size, hidden_size, n_layers=1, dropout_rate=0.5):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.embed_size = embed_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "        self.dropout_rate = dropout_rate\n",
    "        \n",
    "        self.embedding = nn.Embedding(input_size, embed_size)\n",
    "        self.rnn = nn.GRU(embed_size, hidden_size, n_layers, bidirectional=True, dropout=dropout_rate)\n",
    "        self.fc = nn.Linear(hidden_size * 2, hidden_size)\n",
    "        self.dropout = nn.Dropout(dropout_rate)\n",
    "    \n",
    "\n",
    "    def load_pretrained_embeddings(self, embeddings, fine_tune=False):\n",
    "        self.embedding.weight = nn.Parameter(embeddings)\n",
    "        for p in self.embedding.parameters():\n",
    "            p.requires_grad = fine_tune\n",
    "    \n",
    "    \n",
    "    def forward(self, x, lengths):\n",
    "        \"\"\" x: (seq_len, batch_size)\n",
    "            lengths: (batch_size)\n",
    "        \"\"\"\n",
    "        embed = self.dropout(self.embedding(x))  # (seq_len, batch_size, embed_size)\n",
    "        \n",
    "        # packing the embedding sequence to avoid unnecessary computations\n",
    "        # refer https://stackoverflow.com/questions/51030782/why-do-we-pack-the-sequences-in-pytorch\n",
    "        packed_embed = pack_padded_sequence(embed, lengths.to('cpu'))\n",
    "        packed_outputs, hidden = self.rnn(packed_embed)\n",
    "        outputs, _ = pad_packed_sequence(packed_outputs)\n",
    "        \n",
    "        # outputs is a non-packed sequence of all hidden states from last layer\n",
    "        # outputs: (seq_len, batch_size, hidden_size * 2)\n",
    "        # hidden is the final non-padded time stepped state\n",
    "        # hidden: (n_layers * 2, batch_size, hidden_size)\n",
    "        \n",
    "        # initial decoder hidden state\n",
    "        hidden = F.relu(self.fc(torch.cat([h for h in hidden[-2:]], dim=1)))  # (batch_size, hidden_size)\n",
    "        \n",
    "        # outputs: (seq_len, batch_size, hidden_size * 2)\n",
    "        # hidden: (batch_size, hidden_size)\n",
    "        return outputs, hidden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O28Ubw_JbmRf"
   },
   "source": [
    "### Attention Module\n",
    "\n",
    "Although RNNs are good for capturing temporal dependencies, but they fail to preserve the complete context for long sequences. It is very likely that the information context for the intial time steps would be lost by the end of the sequence since we are encoding the input sequence into a single context vector. \n",
    "\n",
    "Wouldn't it be nice if we are able to focus only on the relevant parts of the input sequence? This is where \"Attention Mechanism\" comes to provide the luxury of focusing on different parts of encoded sequence thereby allowing the context to be preserved from beginning to end during the decoding stage. This technique was introduced by [Bahdanau et al., 2014](https://arxiv.org/abs/1409.0473) and [Luong et al., 2015](https://arxiv.org/abs/1508.04025).\n",
    "\n",
    "For the implementation in this notebook, we will be using attention mechanism introduced by [Bahdanau et al., 2014](https://arxiv.org/abs/1409.0473) which is a MLP based additive attention. We calculate the alignment scores using the encoder outputs and decoder hidden states as follows:\n",
    "\n",
    "$$\n",
    "e_t = \\text{relu}(W_1 H + W_2 s_{t-1}) \\\\\n",
    "\\alpha_t = \\text{softmax}(V \\cdot e_t)\n",
    "$$\n",
    "\n",
    "where\n",
    "- e_t is the attention energy at the time step $t$\n",
    "- $W_1$ and $W_2$ are learned attention parameters for encoder outputs $H$ and previous decoder hidden state $s_{t-1}$ respectively\n",
    "- $V$ is another learned attention parameter for the attention energy $e_t$\n",
    "- $\\alpha_t$ is the alignment score for time step $t$\n",
    "\n",
    "We use the above alignment scores to calculate the weighted sum of encoder outputs also known as context vector $c_t$ for time step $t$ as follows:\n",
    "\n",
    "$$\n",
    "c_t = \\alpha_t H\n",
    "$$\n",
    "\n",
    "Attention Module returns the following:\n",
    "- `context_vector`: weighted sum of encoder outputs wrt alignment score\n",
    "- `alphas`: alignment scores indicating how well inputs around position $i$ match with outputs around position $j$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-11WY9S8kSg7"
   },
   "outputs": [],
   "source": [
    "class BahdanauAttention(nn.Module):\n",
    "    \"\"\" Implements Additive attention proposed by Bahdanau et al., 2014.\n",
    "        refer https://arxiv.org/abs/1409.0473\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, hidden_size):\n",
    "        super(BahdanauAttention, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        self.W1 = nn.Linear(hidden_size * 2, hidden_size, bias=False)\n",
    "        self.W2 = nn.Linear(hidden_size, hidden_size, bias=False)\n",
    "        self.V = nn.Linear(hidden_size, 1, bias=False)\n",
    "    \n",
    "    \n",
    "    def forward(self, hidden, enc_outputs, enc_masks):\n",
    "        \"\"\" hidden: (batch_size, hidden_size)\n",
    "            enc_outputs: (seq_len, batch_size, hidden_size * 2)\n",
    "            enc_masks: (batch_size, seq_len)\n",
    "        \"\"\"\n",
    "        seq_len = enc_outputs.shape[0]\n",
    "        \n",
    "        hidden = hidden.unsqueeze(1).repeat(1, seq_len, 1)  # (batch_size, seq_len, hidden_size)\n",
    "        enc_outputs = enc_outputs.permute(1, 0, 2)  # (batch_size, seq_len, hidden_size * 2)\n",
    "        \n",
    "        # calculate alignment scores\n",
    "        energy = F.relu(self.W1(enc_outputs) + self.W2(hidden))  # (batch_size, seq_len, hidden_size)\n",
    "        scores = self.V(energy)  # (batch_size, seq_len, 1)\n",
    "        scores = scores.squeeze(2)  # (batch_size, seq_len)\n",
    "        \n",
    "        # mask out invalid positions\n",
    "        scores = scores.masked_fill(enc_masks == 0, float('-inf'))  # (batch_size, seq_len)\n",
    "        \n",
    "        # calculate the attention weights (prob) from alignment scores\n",
    "        alphas = F.softmax(scores, dim=-1)  # (batch_size, seq_len)\n",
    "        \n",
    "        # calculate context vector\n",
    "        context_vector = torch.bmm(alphas.unsqueeze(1), enc_outputs)  # (batch_size, 1, hidden_size * 2)\n",
    "        \n",
    "        # context_vector: (batch_size, 1, hidden_size * 2)\n",
    "        # alphas: (batch_size, seq_len)\n",
    "        return context_vector, alphas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JjMhOFWw50eo"
   },
   "source": [
    "### Decoder Module With Attention\n",
    "\n",
    "Decoder is a conditional GRU since we will be using it as a language model to decode the output sequence conditioned on generated sequence so far and input sequence. Here the decoder contains the attention module which allows to preserve the information context and generate output sequence of better quality.\n",
    "\n",
    "For the implementation in this notebook, we will be using a unidirectional GRU with attention module. The decoder generates next output word with high probability at time step using the current output word, weighted encoder outputs, previous decoder hidden state as follows:\n",
    "\n",
    "$$\n",
    "s_t = \\text{GRU}(e(y_t), c_t, s_{t-1}) \\\\\n",
    "\\hat{y}_{t+1} = f([e(y_t),\\ c_t,\\ s_t])\n",
    "$$\n",
    "\n",
    "where\n",
    "- $y_t$ denotes the current output token (word) at time step $t$\n",
    "- $e(y_t)$ denotes the current embedded output token at time step $t$\n",
    "- $c_t$ denotes the context vector (weighted encoder outputs) at time step $t$\n",
    "- $s_{t-1}$ and $s_t$ denotes the previous and current decoder hidden state\n",
    "- $f$ denotes a linear layer\n",
    "- $\\hat{y}_{t+1}$ denotes next predicted word in the output sequence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oc_BHE1o8FT0"
   },
   "outputs": [],
   "source": [
    "class DecoderWithAttention(nn.Module):\n",
    "    \"\"\" Decoder decodes the encoded dense embedding context into a sequence \n",
    "        with use of attention mechanism.\n",
    "    \"\"\"\n",
    "    def __init__(self, output_size, embed_size, hidden_size, n_layers=1, dropout_rate=0.5):\n",
    "        super(DecoderWithAttention, self).__init__()\n",
    "        self.output_size = output_size\n",
    "        self.embed_size = embed_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.n_layers = n_layers\n",
    "        self.dropout_rate = dropout_rate\n",
    "        \n",
    "        self.embedding = nn.Embedding(output_size, embed_size)\n",
    "        self.rnn = nn.GRU(embed_size + hidden_size * 2, hidden_size, n_layers, bidirectional=False, dropout=dropout_rate)\n",
    "        self.attention = BahdanauAttention(hidden_size)\n",
    "        self.fc = nn.Linear(embed_size + hidden_size + hidden_size * 2, output_size)\n",
    "        self.dropout = nn.Dropout(dropout_rate)\n",
    "        \n",
    "    \n",
    "    def load_pretrained_embeddings(self, embeddings, fine_tune=False):\n",
    "        self.embedding.weight = nn.Parameter(embeddings)\n",
    "        for p in self.embedding.parameters():\n",
    "            p.requires_grad = fine_tune\n",
    "    \n",
    "    \n",
    "    def forward(self, x, hidden, enc_outputs, enc_masks):\n",
    "        \"\"\" x: (batch_size)\n",
    "            hidden: (batch_size, hidden_size)\n",
    "            enc_outputs: (seq_len, batch_size, hidden_size * 2)\n",
    "            enc_masks: (batch_size, seq_len)\n",
    "        \"\"\"\n",
    "        embed = self.dropout(self.embedding(x.unsqueeze(0)))  # (1, batch_size, embed_size)\n",
    "        \n",
    "        # calculate attention weights and context vector\n",
    "        context_vector, alphas = self.attention(hidden, enc_outputs, enc_masks)\n",
    "        \n",
    "        # context_vector: (batch_size, 1, hidden_size * 2)\n",
    "        # alphas: (batch_size, seq_len)\n",
    "        \n",
    "        context_vector = context_vector.permute(1, 0, 2)  # (1, batch_size, hidden_size * 2)\n",
    "        \n",
    "        # input to the rnn is a concatenation of embedding and weighted encoder states (context)\n",
    "        rnn_input = torch.cat((embed, context_vector), dim=2)  # (1, batch_size, embed_size + hidden_size * 2)\n",
    "        \n",
    "        # update the rnn hidden state\n",
    "        output, hidden = self.rnn(rnn_input, hidden.unsqueeze(0))\n",
    "        \n",
    "        # output: (seq_len=1, batch_size, hidden_size)\n",
    "        # hidden: (n_layers=1, batch_size, hidden_size)\n",
    "        # seq_len and n_layers are always equal to 1 for this decoder\n",
    "        \n",
    "        assert (output == hidden).all()\n",
    "        \n",
    "        embed = embed.squeeze(0)  # (batch_size, embed_size)\n",
    "        output = output.squeeze(0)  # (batch_size, hidden_size)\n",
    "        context_vector = context_vector.squeeze(0)  # (batch_size, hidden_size * 2)\n",
    "        \n",
    "        prediction = self.fc(torch.cat((embed, output, context_vector), dim=1))  # (batch_size, output_size)\n",
    "        \n",
    "        hidden = hidden.squeeze(0)  # (batch_size, hidden_size)\n",
    "        \n",
    "        # prediction: (batch_size, output_size)\n",
    "        # hidden: (batch_size, hidden_size)\n",
    "        # alphas: (batch_size, seq_len)\n",
    "        return prediction, hidden, alphas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tJFuGgNab3OK"
   },
   "source": [
    "### Seq2Seq Model With Attention\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pkvQj9hMb-dJ"
   },
   "outputs": [],
   "source": [
    "class Seq2SeqWithAttention(nn.Module):\n",
    "    \"\"\" Seq2Seq wrapper for encoder and decoder.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, encoder, decoder, pad_idx):\n",
    "        super(Seq2SeqWithAttention, self).__init__()\n",
    "        self.pad_idx = pad_idx\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "    \n",
    "    \n",
    "    def forward(self, source, source_lengths, target, teacher_force_ratio=0.5):\n",
    "        \"\"\" source: (source_seq_len, batch_size)\n",
    "            source_lengths: (batch_size)\n",
    "            target: (target_seq_len, batch_size)\n",
    "        \"\"\"\n",
    "        target_seq_len, batch_size = target.shape\n",
    "        target_vocab_size = self.decoder.output_size\n",
    "        \n",
    "        # initialize tensor to store decoder outputs\n",
    "        outputs = torch.zeros((target_seq_len, batch_size, target_vocab_size)).to(source.device)\n",
    "        \n",
    "        # outputs: (target_seq_len, batch_size, output_size)\n",
    "        \n",
    "        # encode the source sequence\n",
    "        enc_outputs, hidden = self.encoder(source, source_lengths)\n",
    "        \n",
    "        # enc_outputs: (seq_len, batch_size, hidden_size * 2)\n",
    "        # hidden: (batch_size, hidden_size)\n",
    "        \n",
    "        # create mask for source sequence\n",
    "        enc_masks = (source != self.pad_idx).permute(1, 0)  # (batch_size, seq_len)\n",
    "        \n",
    "        # initial input to the decoder is the SOS_TOKEN\n",
    "        x = target[0, :]  # (batch_size)\n",
    "        \n",
    "        for t in range(1, target_seq_len):\n",
    "            # decode the encoded sequence for current time step\n",
    "            output, hidden, _ = self.decoder(x, hidden, enc_outputs, enc_masks)\n",
    "            \n",
    "            # output: (batch_size, output_size)\n",
    "            # hidden: (batch_size, hidden_size)\n",
    "            \n",
    "            # store the decoder outputs for current time step\n",
    "            outputs[t] = output\n",
    "            \n",
    "            # get the highest predicted token (best word) for current time step\n",
    "            top1 = output.argmax(dim=1)\n",
    "            \n",
    "            # teacher forcing ratio is the way to control how many times \n",
    "            # we feed the actual next word as input to the decoder\n",
    "            # teacher forcing ratio of 0.5 indicates that 50% of the time\n",
    "            # the decoder sees the actual next word\n",
    "            x = target[t] if random.random() < teacher_force_ratio else top1\n",
    "        \n",
    "        # outputs: (target_seq_len, batch_size, output_size)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVBa3aT_cJJI"
   },
   "source": [
    "## Helper Utilities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MuZMMqeUcUCg"
   },
   "outputs": [],
   "source": [
    "def model_summary(model):\n",
    "    print(model)\n",
    "    print(f'# of trainable params: {sum(p.numel() for p in model.parameters() if p.requires_grad)}')\n",
    "    print(f'# of non-trainable params: {sum(p.numel() for p in model.parameters() if not p.requires_grad)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BKmqx2ywcQm8"
   },
   "outputs": [],
   "source": [
    "def convert_ids_to_text(ids, vocab, eos_idx, unk_idx):\n",
    "    \"\"\" Converts token ids to text.\n",
    "    \"\"\"\n",
    "    if ids.dim() == 1:\n",
    "        output_tokens = []\n",
    "        for token_id in ids:\n",
    "            if token_id == eos_idx:\n",
    "                break\n",
    "            else:\n",
    "                output_tokens.append(vocab.itos[token_id])\n",
    "        return output_tokens\n",
    "    \n",
    "    elif ids.dim() == 2:\n",
    "        return [convert_ids_to_text(ids[:, i], vocab, eos_idx, unk_idx) for i in range(ids.size(1))]\n",
    "    \n",
    "    raise RuntimeError(f'ids has {ids.size()} dimensions, expected 2 dimensions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GmoGpuOBcVSQ"
   },
   "source": [
    "## Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "krK347Cgclhg"
   },
   "outputs": [],
   "source": [
    "def train_fn(model, iterator, optimizer, criterion, clip=1.0):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    steps = 0\n",
    "    \n",
    "    tk0 = tqdm(iterator, total=len(iterator), position=0, leave=True)\n",
    "    \n",
    "    for idx, batch in enumerate(tk0):\n",
    "        source, source_lengths = batch.src\n",
    "        target, target_lengths = batch.trg\n",
    "\n",
    "        # source: (source_seq_len, batch_size), source_lengths: (batch_size)\n",
    "        # target: (target_seq_len, batch_size), target_lengths: (batch_size)\n",
    "\n",
    "        # forward pass\n",
    "        optimizer.zero_grad()\n",
    "        output = model(source, source_lengths, target)  # (target_seq_len, batch_size, output_size)\n",
    "        \n",
    "        # ignoring SOS_TOKEN\n",
    "        output = output[1:]  # ((target_seq_len - 1), batch_size, output_size)\n",
    "        target = target[1:]  # ((target_seq_len - 1), batch_size)\n",
    "        \n",
    "        # calculate the loss\n",
    "        loss = criterion(\n",
    "            output[1:].view(-1, output.shape[-1]),  # ((target_seq_len - 1) * batch_size, output_size)\n",
    "            target[1:].view(-1)  # ((target_seq_len - 1) * batch_size)\n",
    "        )\n",
    "        total_loss += loss.item()\n",
    "        steps += 1\n",
    "\n",
    "        output = output.argmax(dim=-1)  # ((target_seq_len - 1), batch_size)\n",
    "\n",
    "        # backward pass\n",
    "        loss.backward()\n",
    "\n",
    "        # clip gradients to avoid exploding gradients issue\n",
    "        nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "\n",
    "        # update model parameters\n",
    "        optimizer.step()\n",
    "        \n",
    "        tk0.set_postfix(loss=total_loss/steps)\n",
    "        \n",
    "    tk0.close()\n",
    "\n",
    "    perplexity = np.exp(total_loss / len(iterator))\n",
    "    \n",
    "    return output, perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "51Ao-jPhdTZc"
   },
   "outputs": [],
   "source": [
    "def eval_fn(model, iterator, criterion):\n",
    "    model.eval()\n",
    "    total_loss = 0.0\n",
    "    steps = 0\n",
    "    hypotheses = []\n",
    "    references = []\n",
    "    \n",
    "    tk0 = tqdm(iterator, total=len(iterator), position=0, leave=True)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for idx, batch in enumerate(tk0):\n",
    "            source, source_lengths = batch.src\n",
    "            target, target_lengths = batch.trg\n",
    "            \n",
    "            # source: (source_seq_len, batch_size), source_lengths: (batch_size)\n",
    "            # target: (target_seq_len, batch_size), target_lengths: (batch_size)\n",
    "            \n",
    "            # forward pass\n",
    "            output = model(source, source_lengths, target)  # (target_seq_len, batch_size, output_size)\n",
    "            \n",
    "            # ignoring SOS_TOKEN\n",
    "            output = output[1:]  # ((target_seq_len - 1), batch_size, output_size)\n",
    "            target = target[1:]  # ((target_seq_len - 1), batch_size)\n",
    "            \n",
    "            # calculate the loss\n",
    "            loss = criterion(\n",
    "                output.view(-1, output.shape[-1]),  # ((target_seq_len - 1) * batch_size, output_size)\n",
    "                target.view(-1)  # ((target_seq_len - 1) * batch_size)\n",
    "            )\n",
    "            total_loss += loss.item()\n",
    "            steps += 1\n",
    "            \n",
    "            output = output.argmax(dim=-1)  # ((target_seq_len - 1), batch_size)\n",
    "\n",
    "            # converting the ids to tokens (used later for calculating BLEU score)\n",
    "            pred_tokens = convert_ids_to_text(output, de_text.vocab, EOS_IDX, UNK_IDX)\n",
    "            target_tokens = convert_ids_to_text(target, de_text.vocab, EOS_IDX, UNK_IDX)\n",
    "\n",
    "            hypotheses += pred_tokens\n",
    "            references += [[token] for token in target_tokens]\n",
    "            \n",
    "            tk0.set_postfix(loss=total_loss/steps)\n",
    "            \n",
    "    tk0.close()\n",
    "            \n",
    "    perplexity = np.exp(total_loss / len(iterator))\n",
    "    bleu4 = bleu_score(hypotheses, references)\n",
    "\n",
    "    return output, perplexity, bleu4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZFQmfmg-dczt"
   },
   "outputs": [],
   "source": [
    "# hyperparameters\n",
    "INPUT_SIZE = len(en_text.vocab)  # source vocab size\n",
    "OUTPUT_SIZE = len(de_text.vocab)  # target vocab size\n",
    "ENC_EMBED_SIZE = en_text.vocab.vectors.shape[1]\n",
    "DEC_EMBED_SIZE = de_text.vocab.vectors.shape[1]\n",
    "HIDDEN_SIZE = 512\n",
    "N_LAYERS = 1\n",
    "DROPOUT_RATE = 0.5\n",
    "N_EPOCHS = 50\n",
    "CLIP = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 928,
     "status": "ok",
     "timestamp": 1622264422760,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "Alzm-wfag9E3",
    "outputId": "40b2d7fb-a9e1-45b0-a258-a824146d5d1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq2SeqWithAttention(\n",
      "  (encoder): Encoder(\n",
      "    (embedding): Embedding(5893, 300)\n",
      "    (rnn): GRU(300, 512, dropout=0.5, bidirectional=True)\n",
      "    (fc): Linear(in_features=1024, out_features=512, bias=True)\n",
      "    (dropout): Dropout(p=0.5, inplace=False)\n",
      "  )\n",
      "  (decoder): DecoderWithAttention(\n",
      "    (embedding): Embedding(7855, 300)\n",
      "    (rnn): GRU(1324, 512, dropout=0.5)\n",
      "    (attention): BahdanauAttention(\n",
      "      (W1): Linear(in_features=1024, out_features=512, bias=False)\n",
      "      (W2): Linear(in_features=512, out_features=512, bias=False)\n",
      "      (V): Linear(in_features=512, out_features=1, bias=False)\n",
      "    )\n",
      "    (fc): Linear(in_features=1836, out_features=7855, bias=True)\n",
      "    (dropout): Dropout(p=0.5, inplace=False)\n",
      "  )\n",
      ")\n",
      "# of trainable params: 23421655\n",
      "# of non-trainable params: 2356500\n"
     ]
    }
   ],
   "source": [
    "encoder = Encoder(INPUT_SIZE, ENC_EMBED_SIZE, HIDDEN_SIZE, N_LAYERS, DROPOUT_RATE)\n",
    "# load the pre-trained fasttext english embedding\n",
    "encoder.load_pretrained_embeddings(en_text.vocab.vectors)\n",
    "\n",
    "decoder = DecoderWithAttention(OUTPUT_SIZE, DEC_EMBED_SIZE, HIDDEN_SIZE, N_LAYERS, DROPOUT_RATE)\n",
    "# load the pre-trained fasttext german embedding\n",
    "encoder.load_pretrained_embeddings(de_text.vocab.vectors)\n",
    "\n",
    "model = Seq2SeqWithAttention(encoder, decoder, PAD_IDX).to(device)\n",
    "model_summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0UDkyKqte2bk"
   },
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(model.parameters(), lr=3e-4)\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
    "    optimizer, patience=3, threshold=0.001, mode='max'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O9L4Q8ZSexrq"
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "height": 1000,
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    "executionInfo": {
     "elapsed": 2333384,
     "status": "ok",
     "timestamp": 1622266758185,
     "user": {
      "displayName": "Jay Gala",
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    "id": "OioP4LUvjsHX",
    "outputId": "3b35a1f0-18d0-4830-bd13-36249a75bc52"
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "\n",
      "Epoch: 0, Train perplexity: 122.7940, Valid perplexity: 71.3972, Valid BLEU4: 0.0698\n",
      "BLEU score improved (-inf -> 0.0698). Saving Model!\n"
     ]
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    {
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     "text": [
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     "text": [
      "\n",
      "Epoch: 1, Train perplexity: 42.9007, Valid perplexity: 43.0670, Valid BLEU4: 0.1189\n",
      "BLEU score improved (0.0698 -> 0.1189). Saving Model!\n"
     ]
    },
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     "text": [
      "\n",
      "Epoch: 2, Train perplexity: 26.0137, Valid perplexity: 30.9576, Valid BLEU4: 0.1437\n",
      "BLEU score improved (0.1189 -> 0.1437). Saving Model!\n"
     ]
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     "text": [
      "\n",
      "Epoch: 3, Train perplexity: 18.5959, Valid perplexity: 27.4752, Valid BLEU4: 0.1688\n",
      "BLEU score improved (0.1437 -> 0.1688). Saving Model!\n"
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    {
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     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 4, Train perplexity: 14.1616, Valid perplexity: 23.4784, Valid BLEU4: 0.1810\n",
      "BLEU score improved (0.1688 -> 0.1810). Saving Model!\n"
     ]
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     "text": [
      "\n",
      "Epoch: 5, Train perplexity: 11.4378, Valid perplexity: 23.8058, Valid BLEU4: 0.1910\n",
      "BLEU score improved (0.1810 -> 0.1910). Saving Model!\n"
     ]
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     "text": [
      "\n",
      "Epoch: 6, Train perplexity: 9.4242, Valid perplexity: 22.5308, Valid BLEU4: 0.2036\n",
      "BLEU score improved (0.1910 -> 0.2036). Saving Model!\n"
     ]
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     "text": [
      "\n",
      "Epoch: 7, Train perplexity: 8.2411, Valid perplexity: 21.9199, Valid BLEU4: 0.2016\n",
      "Early stopping counter: 1 out of 5\n"
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     "text": [
      "\n",
      "Epoch: 8, Train perplexity: 7.2823, Valid perplexity: 21.5038, Valid BLEU4: 0.2070\n",
      "BLEU score improved (0.2036 -> 0.2070). Saving Model!\n"
     ]
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     "text": [
      "\n",
      "Epoch: 9, Train perplexity: 6.5045, Valid perplexity: 21.6420, Valid BLEU4: 0.2058\n",
      "Early stopping counter: 1 out of 5\n"
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 10, Train perplexity: 5.9656, Valid perplexity: 22.0724, Valid BLEU4: 0.2188\n",
      "BLEU score improved (0.2070 -> 0.2188). Saving Model!\n"
     ]
    },
    {
     "data": {
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       "model_id": "158ae36600cb4beb94c08837618f5a9f",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     },
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 11, Train perplexity: 5.4895, Valid perplexity: 20.6599, Valid BLEU4: 0.2151\n",
      "Early stopping counter: 1 out of 5\n"
     ]
    },
    {
     "data": {
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       "model_id": "0e32cd44fb5d4b6e8b5945ad7de1b14a",
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 12, Train perplexity: 5.0381, Valid perplexity: 23.1095, Valid BLEU4: 0.2140\n",
      "Early stopping counter: 2 out of 5\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db3811da6d9043b497db2fa993c6ee62",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 13, Train perplexity: 4.7100, Valid perplexity: 21.4077, Valid BLEU4: 0.2089\n",
      "Early stopping counter: 3 out of 5\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab11667f350c47c3becbbd201454b89e",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 14, Train perplexity: 4.3773, Valid perplexity: 22.7004, Valid BLEU4: 0.2113\n",
      "Early stopping counter: 4 out of 5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4c02653578f4854be01c6c1ad22d122",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ceda550fbe24ec08b77009f4df4a926",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 15, Train perplexity: 3.7289, Valid perplexity: 21.8551, Valid BLEU4: 0.2230\n",
      "BLEU score improved (0.2188 -> 0.2230). Saving Model!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a2604ddd1ff4806ab7144c988344e78",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5839987390a46109a1c3b9a66b461a3",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 16, Train perplexity: 3.5078, Valid perplexity: 22.2259, Valid BLEU4: 0.2231\n",
      "BLEU score improved (0.2230 -> 0.2231). Saving Model!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eec98821fbd14b1cb7f8d1902e3434fd",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e67172380a84ea9bb29ffbf9ad14af7",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 17, Train perplexity: 3.4755, Valid perplexity: 22.0518, Valid BLEU4: 0.2213\n",
      "Early stopping counter: 1 out of 5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83b3d3acf47148319dac59b6455c5a7a",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a667447d1c364afb8b5ed0530cbc8c86",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 18, Train perplexity: 3.3648, Valid perplexity: 20.5564, Valid BLEU4: 0.2245\n",
      "BLEU score improved (0.2231 -> 0.2245). Saving Model!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb2dd3bc8f644fbcbd707f27815534c4",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7128be3e5c124fdf9227fad4ece7c9b6",
       "version_major": 2,
       "version_minor": 0
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 19, Train perplexity: 3.3309, Valid perplexity: 23.0093, Valid BLEU4: 0.2257\n",
      "BLEU score improved (0.2245 -> 0.2257). Saving Model!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c410a805c41b4eb3b06709eb4cef66c6",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "model_id": "6c669b30cb464fd2a45efcd83a16eac7",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 20, Train perplexity: 3.3363, Valid perplexity: 22.1326, Valid BLEU4: 0.2273\n",
      "BLEU score improved (0.2257 -> 0.2273). Saving Model!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b3aab0b18f74ec8ac75d8b434abe919",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 21, Train perplexity: 3.3010, Valid perplexity: 21.3286, Valid BLEU4: 0.2196\n",
      "Early stopping counter: 1 out of 5\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 22, Train perplexity: 3.2085, Valid perplexity: 21.9630, Valid BLEU4: 0.2221\n",
      "Early stopping counter: 2 out of 5\n"
     ]
    },
    {
     "data": {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87765c6a8cdf45209bd9f0f036234c88",
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       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 23, Train perplexity: 3.1944, Valid perplexity: 21.0527, Valid BLEU4: 0.2258\n",
      "Early stopping counter: 3 out of 5\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6a41e1245034beaba6cc62de20a8aed",
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      },
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42c8fc734f26482882e8fe228ecc26e8",
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 24, Train perplexity: 3.1722, Valid perplexity: 23.5915, Valid BLEU4: 0.2245\n",
      "Early stopping counter: 4 out of 5\n"
     ]
    },
    {
     "data": {
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       "model_id": "e9945a4a8a4641269591051df2bb7ed5",
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       "HBox(children=(FloatProgress(value=0.0, max=907.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "833b2190f3394512abef860d9807de2b",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 25, Train perplexity: 3.1111, Valid perplexity: 22.7558, Valid BLEU4: 0.2231\n",
      "Early stopping counter: 5 out of 5\n",
      "Early stopping! Best BLEU4: 0.2273\n"
     ]
    }
   ],
   "source": [
    "best_bleu4 = float('-inf')\n",
    "es_patience = 5\n",
    "patience = 0\n",
    "model_path = 'model.pth'\n",
    "\n",
    "\n",
    "for epoch in range(0, N_EPOCHS + 1):\n",
    "    # one epoch training\n",
    "    _, train_perplexity = train_fn(model, train_iterator, optimizer, criterion, CLIP)\n",
    "    \n",
    "    # one epoch validation\n",
    "    _, valid_perplexity, valid_bleu4 = eval_fn(model, valid_iterator, criterion)\n",
    "    \n",
    "    print(f'Epoch: {epoch}, Train perplexity: {train_perplexity:.4f}, Valid perplexity: {valid_perplexity:.4f}, Valid BLEU4: {valid_bleu4:.4f}')\n",
    "    \n",
    "    scheduler.step(valid_bleu4)\n",
    "    \n",
    "    # early stopping\n",
    "    is_best = valid_bleu4 > best_bleu4\n",
    "    if is_best:\n",
    "        print(f'BLEU score improved ({best_bleu4:.4f} -> {valid_bleu4:.4f}). Saving Model!')\n",
    "        best_bleu4 = valid_bleu4\n",
    "        patience = 0\n",
    "        torch.save(model.state_dict(), model_path)\n",
    "    else:\n",
    "        patience += 1\n",
    "        print(f'Early stopping counter: {patience} out of {es_patience}')\n",
    "        if patience == es_patience:\n",
    "            print(f'Early stopping! Best BLEU4: {best_bleu4:.4f}')\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101,
     "referenced_widgets": [
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      "6282e6f121cd4e85a165470abe2b326f",
      "279760f2e3cb437ca4b7a42a02e31c4f",
      "e127e53fc43448cabe71df9e323519b0",
      "f9f86aaf1cd1454f94cf139be1d76517",
      "4c064e3dd4bf4b5bb101192eba5d035b"
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    "executionInfo": {
     "elapsed": 2925,
     "status": "ok",
     "timestamp": 1622267805563,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "uFmimVZ6jsEj",
    "outputId": "da589960-67cf-43b5-e72a-62e27c1a3f91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the model on test data ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54726cab14df411a884c1e5fbe6f2e11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=32.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test perplexity: 21.3412, Test BlEU4: 0.2273\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model on test data\n",
    "model.load_state_dict(torch.load(model_path, map_location=device))\n",
    "\n",
    "print('Evaluating the model on test data ...')\n",
    "_, test_perplexity, test_bleu4 = eval_fn(model, test_iterator, criterion)\n",
    "print(f'Test perplexity: {test_perplexity:.4f}, Test BlEU4: {test_bleu4:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cWyrKP83jr69"
   },
   "source": [
    "## Inference\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S5Akv6wDjrsS"
   },
   "outputs": [],
   "source": [
    "def greedy_decode(model, sentence, max_len=100):\n",
    "    model.eval()\n",
    "    \n",
    "    if isinstance(sentence, str):\n",
    "        tokens = [token.text.lower() for token in spacy_de(sentence)]\n",
    "    else:\n",
    "        tokens = [token.lower() for token in sentence]\n",
    "    \n",
    "    token_ids = [SOS_IDX] + [en_text.vocab.stoi.get(token, UNK_TOKEN) for token in tokens] + [EOS_IDX]\n",
    "    \n",
    "    source = torch.tensor(token_ids, dtype=torch.long).unsqueeze(1).to(device)  # (source_seq_len, 1)\n",
    "    source_length = torch.tensor([len(source)], dtype=torch.long)  # (source_seq_len)\n",
    "    \n",
    "    # encode the source sequence\n",
    "    with torch.no_grad():\n",
    "        enc_outputs, hidden = model.encoder(source, source_length)\n",
    "    \n",
    "    # enc_outputs: (source_seq_len, 1, hidden_size * 2)\n",
    "    # hidden: (1, hidden_size)\n",
    "    \n",
    "    # create mask for source sequence\n",
    "    enc_masks = (source != PAD_IDX).permute(1, 0)  # (1, seq_len)\n",
    "    \n",
    "    target_ids = [SOS_IDX]\n",
    "    attention_scores = torch.zeros(max_len, 1, len(token_ids)).to(device)  # (max_len, 1, source_seq_len)\n",
    "    \n",
    "    for t in range(max_len):\n",
    "        target = torch.tensor([target_ids[-1]], dtype=torch.long).to(device)\n",
    "        \n",
    "        # decode the sequence\n",
    "        with torch.no_grad():\n",
    "            output, hidden, attention = model.decoder(target, hidden, enc_outputs, enc_masks)\n",
    "            \n",
    "            # output: (1, output_size)\n",
    "            # hidden: (1, hidden_size)\n",
    "            # attention: (1, source_seq_len)\n",
    "            \n",
    "        attention_scores[t] = attention\n",
    "        \n",
    "        target_id = output.argmax(dim=-1).item()\n",
    "        target_ids.append(target_id)\n",
    "        \n",
    "        # stop decoding if we encounter EOS_TOKEN or reach the max length\n",
    "        if target_id == EOS_IDX or len(target_ids) >= max_len:\n",
    "            break\n",
    "    \n",
    "    target_tokens = [de_text.vocab.itos[id] for id in target_ids]\n",
    "    attention_scores = attention_scores[:len(target_tokens)-1].squeeze(1).cpu().detach().numpy()\n",
    "\n",
    "    # target_tokens: list of size (target_seq_len - 1)\n",
    "    # attention_scores: (target_seq_len - 1, 1, source_seq_len)\n",
    "    \n",
    "    return target_tokens[1:], attention_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gw-_7cKZjrpq"
   },
   "outputs": [],
   "source": [
    "def plot_attention_scores(source, target, attention):\n",
    "    \n",
    "    if isinstance(source, str):\n",
    "        source = [token.lower for token in source.split(' ')] + [EOS_TOKEN]\n",
    "    else:\n",
    "        source = [token.lower() for token in source] + [EOS_TOKEN]\n",
    "\n",
    "    attention = attention[:len(target), :len(source)]\n",
    "\n",
    "    hmap = sns.heatmap(attention, cmap='Blues')\n",
    "    hmap.yaxis.set_ticklabels(target, rotation=0, ha='right')\n",
    "    hmap.xaxis.set_ticklabels(source, rotation=90, ha='right')\n",
    "    hmap.xaxis.tick_top()\n",
    "    plt.ylabel('Output text [German]')\n",
    "    plt.xlabel('Input text [English]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 661
    },
    "executionInfo": {
     "elapsed": 1225,
     "status": "ok",
     "timestamp": 1622267830108,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "OpFFtKaGj60S",
    "outputId": "4ad6220f-daa3-4516-901a-22726ac641df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source: ['a', 'trendy', 'girl', 'talking', 'on', 'her', 'cellphone', 'while', 'gliding', 'slowly', 'down', 'the', 'street', '.']\n",
      "\n",
      "target: ['ein', 'schickes', 'mädchen', 'spricht', 'mit', 'dem', 'handy', 'während', 'sie', 'langsam', 'die', 'straße', 'entlangschwebt', '.']\n",
      "\n",
      "predicted: ['schickes', 'schickes', 'mädchen', 'spricht', 'mit', 'dem', 'handy', 'während', 'sie', 'langsam', 'die', 'straße', '.', '</s>']\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 540,
       "width": 724
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 7\n",
    "\n",
    "source = vars(train_data.examples[example_idx])['src']\n",
    "target = vars(train_data.examples[example_idx])['trg']\n",
    "\n",
    "predicted, attention_scores = greedy_decode(model, source)\n",
    "\n",
    "print(f'source: {source}\\n')\n",
    "print(f'target: {target}\\n')\n",
    "print(f'predicted: {predicted}\\n')\n",
    "\n",
    "plot_attention_scores(source, predicted, attention_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "executionInfo": {
     "elapsed": 1627,
     "status": "ok",
     "timestamp": 1622267848415,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "YOOkEv2ywlpS",
    "outputId": "e3be3827-5f31-4187-c8df-8208a23cf4a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source: ['construction', 'workers', 'standing', 'on', 'top', 'of', 'a', 'piece', 'of', 'machinery', '.']\n",
      "\n",
      "target: ['bauarbeiter', 'stehen', 'auf', 'einer', 'maschine']\n",
      "\n",
      "predicted: ['bauarbeiter', 'stehen', 'auf', 'einem', 'gerüst', '.', '</s>']\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 559,
       "width": 740
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 24\n",
    "\n",
    "source = vars(valid_data.examples[example_idx])['src']\n",
    "target = vars(valid_data.examples[example_idx])['trg']\n",
    "\n",
    "predicted, attention_scores = greedy_decode(model, source)\n",
    "\n",
    "print(f'source: {source}\\n')\n",
    "print(f'target: {target}\\n')\n",
    "print(f'predicted: {predicted}\\n')\n",
    "\n",
    "plot_attention_scores(source, predicted, attention_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "executionInfo": {
     "elapsed": 1592,
     "status": "ok",
     "timestamp": 1622267856573,
     "user": {
      "displayName": "Jay Gala",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghr20IAxple1q2jggw00r147YTV5sjlqKrRANzYVA=s64",
      "userId": "11525158385229787114"
     },
     "user_tz": -330
    },
    "id": "94lDzfNKkCOx",
    "outputId": "4a01d4f6-9414-4529-d3c1-46b4005f23b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "source: ['people', 'walking', 'down', 'sidewalk', 'next', 'to', 'a', 'line', 'of', 'stores', '.']\n",
      "\n",
      "target: ['leute', 'gehen', 'auf', 'einem', 'gehsteig', 'neben', 'einer', 'reihe', 'von', 'geschäften', '.']\n",
      "\n",
      "predicted: ['personen', 'gehen', 'neben', 'einer', 'reihe', 'von', 'einer', 'treppe', 'entlang', '.', '</s>']\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 534,
       "width": 725
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_idx = 50\n",
    "\n",
    "source = vars(test_data.examples[example_idx])['src']\n",
    "target = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "predicted, attention_scores = greedy_decode(model, source)\n",
    "\n",
    "print(f'source: {source}\\n')\n",
    "print(f'target: {target}\\n')\n",
    "print(f'predicted: {predicted}\\n')\n",
    "\n",
    "plot_attention_scores(source, predicted, attention_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ur7aN56fkD3_"
   },
   "source": [
    "## References\n",
    "\n",
    "1. [Sequence to Sequence Learning with Neural Networks](https://arxiv.org/pdf/1409.3215.pdf)\n",
    "2. [Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation](https://arxiv.org/pdf/1406.1078.pdf)\n",
    "3. [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/pdf/1409.0473.pdf)\n",
    "4. [Visualizing A Neural Machine Translation Model by Jay Alammar](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/)\n",
    "5. [Sequence to Sequence (seq2seq) and Attention by Lena Voita](https://lena-voita.github.io/nlp_course/seq2seq_and_attention.html)\n",
    "6. [The Annotated Encoder Decoder](https://bastings.github.io/annotated_encoder_decoder/)\n",
    "7. [PyTorch Seq2Seq by Ben Trevett](https://github.com/bentrevett/pytorch-seq2seq)\n"
   ]
  }
 ],
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