{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from config import get_config, latest_weights_file_path\n",
    "from train import get_model, get_ds, run_validation\n",
    "from translate import translate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Using device:\", device)\n",
    "config = get_config()\n",
    "train_dataloader, val_dataloader, tokenizer_src, tokenizer_tgt = get_ds(config)\n",
    "model = get_model(config, tokenizer_src.get_vocab_size(), tokenizer_tgt.get_vocab_size()).to(device)\n",
    "\n",
    "# Load the pretrained weights\n",
    "model_filename = latest_weights_file_path(config)\n",
    "state = torch.load(model_filename)\n",
    "model.load_state_dict(state['model_state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_validation(model, val_dataloader, tokenizer_src, tokenizer_tgt, config['seq_len'], device, lambda msg: print(msg), 0, None, num_examples=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = translate(\"Why do I need to translate this?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = translate(34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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