{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e40a88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import torch\n",
    "from torch import nn\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e812a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import attr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3a6146b-94b1-4618-a4e4-00f8e23ffdb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydra import compose, initialize\n",
    "from omegaconf import OmegaConf\n",
    "from hydra.utils import instantiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8741a844-3b97-47c4-a2a1-5a268d40923c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_target_: text_recognizer.data.mappings.WordPieceMapping\n",
      "num_features: 1000\n",
      "tokens: iamdb_1kwp_tokens_1000.txt\n",
      "lexicon: iamdb_1kwp_lex_1000.txt\n",
      "data_dir: null\n",
      "use_words: false\n",
      "prepend_wordsep: false\n",
      "special_tokens:\n",
      "- <s>\n",
      "- <e>\n",
      "- <p>\n",
      "extra_symbols:\n",
      "- '\n",
      "\n",
      "  '\n",
      "\n",
      "{'_target_': 'text_recognizer.data.mappings.WordPieceMapping', 'num_features': 1000, 'tokens': 'iamdb_1kwp_tokens_1000.txt', 'lexicon': 'iamdb_1kwp_lex_1000.txt', 'data_dir': None, 'use_words': False, 'prepend_wordsep': False, 'special_tokens': ['<s>', '<e>', '<p>'], 'extra_symbols': ['\\n']}\n"
     ]
    }
   ],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/model/mapping\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"word_piece\")\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9271d46-37b1-4d06-a603-46b5ed82f821",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-07-30 23:08:27.495 | DEBUG    | text_recognizer.data.mappings:__attrs_post_init__:89 - Using data dir: /home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb\n"
     ]
    }
   ],
   "source": [
    "tt =instantiate(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf1b07ac-9de7-4d24-a36b-09847bc6bc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WordPieceMapping(extra_symbols={'\\n'}, mapping=['<b>', '<s>', '<e>', '<p>', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', ' ', '!', '\"', '#', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '?', '\\n'], inverse_mapping={'<b>': 0, '<s>': 1, '<e>': 2, '<p>': 3, '0': 4, '1': 5, '2': 6, '3': 7, '4': 8, '5': 9, '6': 10, '7': 11, '8': 12, '9': 13, 'A': 14, 'B': 15, 'C': 16, 'D': 17, 'E': 18, 'F': 19, 'G': 20, 'H': 21, 'I': 22, 'J': 23, 'K': 24, 'L': 25, 'M': 26, 'N': 27, 'O': 28, 'P': 29, 'Q': 30, 'R': 31, 'S': 32, 'T': 33, 'U': 34, 'V': 35, 'W': 36, 'X': 37, 'Y': 38, 'Z': 39, 'a': 40, 'b': 41, 'c': 42, 'd': 43, 'e': 44, 'f': 45, 'g': 46, 'h': 47, 'i': 48, 'j': 49, 'k': 50, 'l': 51, 'm': 52, 'n': 53, 'o': 54, 'p': 55, 'q': 56, 'r': 57, 's': 58, 't': 59, 'u': 60, 'v': 61, 'w': 62, 'x': 63, 'y': 64, 'z': 65, ' ': 66, '!': 67, '\"': 68, '#': 69, '&': 70, \"'\": 71, '(': 72, ')': 73, '*': 74, '+': 75, ',': 76, '-': 77, '.': 78, '/': 79, ':': 80, ';': 81, '?': 82, '\\n': 83}, input_size=[28, 28], data_dir=PosixPath('/home/aktersnurra/projects/text-recognizer/data/downloaded/iam/iamdb'), num_features=1000, tokens='iamdb_1kwp_tokens_1000.txt', lexicon='iamdb_1kwp_lex_1000.txt', use_words=False, prepend_wordsep=False, special_tokens={'<p>', '<s>', '<e>'}, wordpiece_processor=<text_recognizer.data.iam_preprocessor.Preprocessor object at 0x7fa4ec7ea610>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2452e8f4-cc5f-4763-9a25-4fa27b7f143e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tt.mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b722ca0-9c65-4f90-be4e-b7334ea81237",
   "metadata": {},
   "outputs": [],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/model/\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"lit_transformer\")\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c797159-845e-42c6-bd65-1c976ad627cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# context initialization\n",
    "with initialize(config_path=\"../training/conf/network/\", job_name=\"test_app\"):\n",
    "    cfg = compose(config_name=\"conv_transformer\")\n",
    "    print(OmegaConf.to_yaml(cfg))\n",
    "    print(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcfbe2ab-6775-4aa4-acf4-57203a3f5511",
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import import_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3d4c70e-509d-457a-ac81-2bac27cb95d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = import_module(\"text_recognizer.networks.transformer.attention\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "039d4a7f-f70d-43a1-8b5f-7e766ac01010",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = partial(getattr(x, \"Attention\"), dim=16, num_heads=2, **cfg.decoder.attn_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9be1d661-bfac-4826-ab8d-453557713f68",
   "metadata": {},
   "outputs": [],
   "source": [
    "y().causal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b35e6f-35db-4769-8bc5-ed1764768cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y(causal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af2c8cfa-0b45-4681-b671-0f97ace62516",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = instantiate(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f0742ad-5e2f-42d5-83e7-6e46398b4f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "709be6cc-6708-4561-ad45-28f433612a0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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