{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c64b0e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7706, 290, 756, 4270, 7357, 115, 351, 1507, 1213, 410, 3382, 317, 497, 4740, 2784, 7700]\n",
      "16\n",
      "Embedder Tensor: torch.Size([13, 256])\n",
      "Values:\n",
      "tensor([[-1.0474,  1.9119,  1.3443,  ..., -1.5243,  1.2989,  0.3618],\n",
      "        [ 1.0083,  1.4955,  0.9479,  ...,  1.7371,  0.2389,  2.1217],\n",
      "        [-0.7681, -1.7427,  1.7070,  ...,  0.6594, -0.6083, -0.4595],\n",
      "        ...,\n",
      "        [-0.7209, -0.3639, -0.6911,  ...,  3.3490, -2.7354,  1.1244],\n",
      "        [-0.7352, -1.6731,  0.2976,  ...,  1.5605, -1.3298,  1.3615],\n",
      "        [-0.5377,  0.3704, -0.4427,  ...,  0.4723,  0.5781,  0.2003]],\n",
      "       grad_fn=<AddBackward0>)\n",
      "ENCODER Tensor: torch.Size([13, 256])\n",
      "Values:\n",
      "tensor([[-1.0270,  0.6589, -0.3154,  ..., -1.3113,  0.5058, -0.0608],\n",
      "        [ 1.0235,  1.2011, -0.3139,  ...,  0.1643,  0.6761,  0.9673],\n",
      "        [-0.7295, -1.5149,  0.4729,  ...,  0.3185, -0.2433, -1.2669],\n",
      "        ...,\n",
      "        [-0.2189, -0.1399, -1.0049,  ...,  1.8693, -2.4663, -0.3319],\n",
      "        [-0.1491, -0.4986, -0.7297,  ...,  1.2760, -0.5654,  0.7038],\n",
      "        [-1.3576,  0.3478, -0.1016,  ...,  0.0712,  0.3772, -0.1522]],\n",
      "       grad_fn=<NativeLayerNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from pathlib import Path\n",
    "import Project_Model.Libs.Embedder as Embedder\n",
    "import Project_Model.Libs.BPE as BPE\n",
    "import Project_Model.Libs.Transformer as Transformer\n",
    "\n",
    "TEXT = \"<ABS>The Dark Knight is a 2008 superhero film directed by Christopher Nolan,<SOTL>\"\n",
    "\n",
    "VOCABULARY_PATH = Path(\"Assets/Model/toy_10/toy_dictionary.json\")\n",
    "SPECIAL_VOC = BPE.default_special_tokens()\n",
    "\n",
    "VOCABULARY = BPE.load_nanos_vocabulary(VOCABULARY_PATH)\n",
    "TOKENANO = BPE.TokeNanoCore(\n",
    "    VOCABULARY,\n",
    "    SPECIAL_VOC\n",
    ")\n",
    "\n",
    "TOKENIZATION = TOKENANO.encode(TEXT)\n",
    "print(TOKENIZATION)\n",
    "\n",
    "TOKEN_SPACE_SIZE = TOKENANO.vocabulary_size\n",
    "EMBEDDED_SIZE = 256\n",
    "FEED_FORWARD_DIM = EMBEDDED_SIZE * 4\n",
    "\n",
    "EMBEDDER = Embedder.NanoSocratesEmbedder(TOKEN_SPACE_SIZE, EMBEDDED_SIZE)\n",
    "tensor: torch.Tensor = EMBEDDER(TOKENIZATION[3:])\n",
    "ENCODER = torch.nn.Sequential(\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4),\n",
    "    Transformer.Encoder(EMBEDDED_SIZE, FEED_FORWARD_DIM, 4)\n",
    ")\n",
    "print(len(TOKENIZATION))\n",
    "print(f\"Embedder Tensor: {tensor.shape}\")\n",
    "print(f\"Values:\\n{tensor}\")\n",
    "\n",
    "TOKENS, DIMENSIONS = tensor.shape\n",
    "\n",
    "tensor = ENCODER(tensor)\n",
    "print(f\"ENCODER Tensor: {tensor.shape}\")\n",
    "print(f\"Values:\\n{tensor}\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
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