quantitative_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "\n",
    "torch.cuda.set_device(5)\n",
    "torch.cuda.current_device()\n",
    "\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "from types import SimpleNamespace\n",
    "from wrappers.transformer_wrapper import FairseqTransformerHub\n",
    "\n",
    "from wrappers.multilingual_transformer_wrapper import FairseqMultilingualTransformerHub\n",
    "\n",
    "import wrappers.helper as helper\n",
    "import alignment.align as align\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import csv\n",
    "\n",
    "from collections import defaultdict\n",
    "import statistics\n",
    "\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "\n",
    "logger.setLevel('WARNING')\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "plt.rcParams.update({'font.size': 30})\n",
    "plt.rcParams.update({'xtick.labelsize' : 20})\n",
    "plt.rcParams.update({'ytick.labelsize' : 20})\n",
    "plt.rcParams.update({'axes.titlesize' : 24})\n",
    "plt.rcParams.update({'axes.labelsize' : 22})\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = 'multilingual'# bilingual/multilingual\n",
    "model_size = 'small'\n",
    "teacher_forcing = False # teacher forcing/free decoding\n",
    "dataset = 'flores'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "if model == 'bilingual':\n",
    "    # Bilingual paths\n",
    "    europarl_dir = Path(os.environ['EUROPARL_DATA_DIR'])\n",
    "    ckpt_dir = Path(os.environ['EUROPARL_CKPT_DIR'])\n",
    "    iwslt14_dir = Path(os.environ['IWSLT14_DATA_DIR']) / \"tokenized\"\n",
    "    pre_layer_norm = False\n",
    "    tokenizer = \"bpe\"\n",
    "    #gold_dir = Path(os.environ['GOLD_ALIGNMENT_DATA_DIR'])\n",
    "\n",
    "elif model == 'multilingual':\n",
    "    # Multilingual paths\n",
    "    ckpt_dir = Path(os.environ['M2M_CKPT_DIR'])\n",
    "    iwslt14_dir = Path(\"./data/iwslt14\")\n",
    "    europarl_dir = Path(\"./data/de-en\")\n",
    "    flores_dir = Path(\"./data/flores\")\n",
    "    pre_layer_norm = True\n",
    "    tokenizer =\"spm\"\n",
    "\n",
    "if dataset == 'iwslt':\n",
    "    test_set_dir = iwslt14_dir\n",
    "elif dataset == 'gold':\n",
    "    test_set_dir = europarl_dir / \"processed_data/\"\n",
    "elif dataset == 'flores':\n",
    "    test_set_dir = flores_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-17 21:17:36 | WARNING | fairseq.data.multilingual.multilingual_data_manager | External language dictionary is not provided; use lang-pairs to infer the set of supported languages. The language ordering is not stable which might cause misalignment in pretraining and finetuning.\n"
     ]
    }
   ],
   "source": [
    "lang_flores_dict = {'en': 'eng', 'es': 'spa', 'zu': 'zul',\n",
    "                    'de': 'deu', 'yo': 'yor', 'ms': 'msa',\n",
    "                    'fr': 'fra', 'xh': 'xho'}\n",
    "source_lang = 'en'\n",
    "target_lang = 'es'\n",
    "if model == 'bilingual':\n",
    "    # Choose model\n",
    "    model_type = 'baseline'\n",
    "    seed = 5498 # 2253  2453  5498  9240  9819\n",
    "\n",
    "    hub = FairseqTransformerHub.from_pretrained(\n",
    "        ckpt_dir / f\"{model_type}/{seed}\",\n",
    "        checkpoint_file=f\"checkpoint_best.pt\",\n",
    "        data_name_or_path=(europarl_dir / \"processed_data/fairseq_preprocessed_data\").as_posix(), # processed data\n",
    "        #(data_name_or_path=iwslt14_dir / \"data-bin\").as_posix(), \n",
    "    )\n",
    "    NUM_LAYERS = 6\n",
    "\n",
    "elif model == 'multilingual':\n",
    "    # Checkpoint names\n",
    "    if model_size=='big':\n",
    "        checkpoint_file = '1.2B_last_checkpoint.pt'\n",
    "    else:\n",
    "        checkpoint_file = '418M_last_checkpoint.pt'\n",
    "    data_name_or_path='.'\n",
    "    hub = FairseqMultilingualTransformerHub.from_pretrained(\n",
    "        ckpt_dir,\n",
    "        checkpoint_file=checkpoint_file,\n",
    "        data_name_or_path=data_name_or_path,\n",
    "        source_lang= source_lang,\n",
    "        target_lang= target_lang,\n",
    "        lang_pairs =f'{source_lang}-{target_lang}',\n",
    "        fixed_dictionary=f'{ckpt_dir}/model_dict.128k.txt')\n",
    "    NUM_LAYERS = 12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hallucinations Analysis (bilingual model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section, load the bilingual model (`model = 'bilingual'`) and the IWSLT dataset (`dataset = 'iwslt'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hallucination_translation(hub, args, number_sentences, hall_token, contributions):\n",
    "    '''Returns list of standard translations, list of perturbed translations, and ALTI+ results.'''\n",
    "    standard_translation_list = []\n",
    "    perturbed_translation_dict = defaultdict(list) #dict of lists, each key (token), has a list of translations\n",
    "    \n",
    "    src_alti_perturbed_list = []\n",
    "    src_alti_list = []\n",
    "\n",
    "    for i in range(number_sentences):\n",
    "        if i%10==0:\n",
    "            print(i)\n",
    "        # Standard translation\n",
    "        pred_sent, src_total_alti  = helper.get_translation(hub, i, args, prefix_subwords=None,\n",
    "                                                    contributions=contributions)\n",
    "        standard_translation_list.append(pred_sent + '\\n')\n",
    "        if src_total_alti is not None:\n",
    "            src_alti_list.append(torch.mean(src_total_alti).item())\n",
    "            \n",
    "            \n",
    "        # Perturbed translation\n",
    "        pred_sent_perturbed, src_alti_perturbed  = helper.get_translation(hub, i, args, perturb_type=args.perturb_type,\n",
    "                                                                    prefix_subwords=hall_token, contributions=contributions)\n",
    "        perturbed_translation_dict[hall_token].append(pred_sent_perturbed + '\\n')\n",
    "\n",
    "        if src_alti_perturbed is not None:\n",
    "            src_alti_perturbed_list.append(torch.mean(src_alti_perturbed).item())\n",
    "                \n",
    "\n",
    "    return standard_translation_list, perturbed_translation_dict, (src_alti_list, src_alti_perturbed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "args = SimpleNamespace(\n",
    "    src = \"de\",\n",
    "    tgt = \"en\",\n",
    "    tokenizer = tokenizer,\n",
    "    test_set_dir = test_set_dir,\n",
    "    perturb_type = \"tgt\", # perturb src or tgt\n",
    "    pre_layer_norm = pre_layer_norm,\n",
    "    beam = 1\n",
    "    )\n",
    "hall_token = '▁unk'\n",
    "number_sentences = 1000\n",
    "contributions = True\n",
    "\n",
    "translation_list, perturbed_translation_dict, tuple_alti_values = hallucination_translation(hub, args,\n",
    "                                                                                            number_sentences,\n",
    "                                                                                            hall_token, contributions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save perturbed translation\n",
    "for token in perturbed_translation_dict.keys():\n",
    "    perturbed_translation_src = open(f'results/{dataset}/translations_{args.perturb_type}_{hall_token[1:]}.txt', 'w', encoding=\"utf-8\")\n",
    "    for i in range(len(perturbed_translation_dict[token])):\n",
    "        perturbed_translation_src.write(perturbed_translation_dict[token][i])\n",
    "    perturbed_translation_src.close()\n",
    "\n",
    "## Save original translations\n",
    "standard_translation = open(f'results/{dataset}/{args.src}_{args.tgt}_translations.txt', 'w', encoding=\"utf-8\")\n",
    "for i in range(number_sentences):\n",
    "    standard_translation.write(translation_list[i])\n",
    "standard_translation.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standard alti results\n",
    "with open(f'results/{dataset}/alti.csv', 'w') as f:\n",
    "    write = csv.writer(f)\n",
    "    for element in tuple_alti_values[0]:\n",
    "        write.writerow([element])\n",
    "\n",
    "## Perturbed alti results\n",
    "with open(f'results/{dataset}/{args.perturb_type}_{hall_token[1:]}_alti.csv', 'w') as f:\n",
    "    write = csv.writer(f)\n",
    "    for element in tuple_alti_values[1]:\n",
    "        write.writerow([element])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read ALTI and perturbed ALTI results\n",
    "alti_original = []\n",
    "with open(f'results/{dataset}/alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                alti_original.append(line)\n",
    "\n",
    "alti_perturb = []\n",
    "with open(f'results/{dataset}/{args.perturb_type}_{hall_token[1:]}_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                alti_perturb.append(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute BLEU score of translations and perturbed translations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sacrebleu\n",
    "from sacrebleu.metrics import BLEU, CHRF, TER\n",
    "import nltk\n",
    "bleu = BLEU(max_ngram_order=4)\n",
    "def compute_bleu(reference, candidate):\n",
    "    return bleu.corpus_score([candidate],[[reference]]).score\n",
    "\n",
    "def read_file(path):\n",
    "    with open(path,'r') as f:\n",
    "        return list(f.readlines())\n",
    "#compute BLEU score against the reference for each baseline and model output\n",
    "refs = []\n",
    "model_translations = []\n",
    "\n",
    "if dataset == 'gold':\n",
    "    refs.append(read_file(\"data/de-en/test.en\"))\n",
    "else:\n",
    "    refs.append(read_file(test_set_dir / \"test.en\"))\n",
    "\n",
    "model_translations.append(read_file(f\"results/{dataset}/translations.txt\"))\n",
    "\n",
    "diff_score = []\n",
    "scores = []\n",
    "\n",
    "for i in range(1000):# len(refs[0])\n",
    "    for token in ['▁unk']:\n",
    "        hallucinations = []\n",
    "        hallucinations.append(read_file(f\"results/{dataset}/translations_{args.perturb_type}_{token[1:]}.txt\"))\n",
    "    \n",
    "        model_score = sum([compute_bleu(refs[j][i],model_translations[j][i]) for j in range(len(model_translations))])/len(model_translations)\n",
    "        hallucinations_score = sum([compute_bleu(refs[j][i],hallucinations[j][i]) for j in range(len(hallucinations))])/len(hallucinations)\n",
    "        diff_score.append((i,model_score-hallucinations_score))\n",
    "        scores.append((i, model_score, hallucinations_score))\n",
    "        # if model_score > 0.2:\n",
    "        #     if hallucinations_score < 0.03:\n",
    "        #         print(token)\n",
    "        #         print(i)\n",
    "        #print('Computed score', i, model_score, hallucinations_score)\n",
    "    #print('Computed score', i, model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply algorithm for extracting hallucination\n",
    "hallucination_set = []\n",
    "for (i, model_score, hallucinations_score) in scores:\n",
    "    if model_score > 0.2:\n",
    "        if hallucinations_score < 0.03:\n",
    "            hallucination_set.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Source Contribution')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alti_original_flat = [item for sublist in alti_original for item in sublist]\n",
    "alti_perturb_flat = [item for sublist in alti_perturb for item in sublist]\n",
    "\n",
    "mean_src_alti = []\n",
    "mean_preturbed_src_alti = []\n",
    "mean_alti = defaultdict(list)\n",
    "for i in hallucination_set:\n",
    "    mean_alti['Original'].append(alti_original_flat[i])\n",
    "    mean_alti['Hallucination'].append(alti_perturb_flat[i])\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(9, 5),dpi=200)\n",
    "ax.boxplot(mean_alti.values(),whiskerprops=dict(linewidth=3.0))\n",
    "ax.set_xticklabels(mean_alti.keys())\n",
    "ax.set_ylim(0,1)\n",
    "ax.grid(True, linestyle=(0, (5, 10)))\n",
    "plt.ylabel(r'Source Contribution')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Low-resource source contribution analysis (mutlilingual model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section, load the bilingual model (`model = 'mulitlingual'`) and the FLORES-101 dataset (`dataset = 'flores'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lang_max spa\n",
      "src eng\n",
      "tgt spa\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "Total hallucinations: 0\n"
     ]
    }
   ],
   "source": [
    "args = SimpleNamespace(\n",
    "    src = lang_flores_dict[source_lang],\n",
    "    tgt = lang_flores_dict[target_lang],\n",
    "    tokenizer = tokenizer,\n",
    "    test_set_dir = test_set_dir,\n",
    "    pre_layer_norm=pre_layer_norm,\n",
    "    beam = 1,\n",
    "    num_layers = NUM_LAYERS\n",
    "    )\n",
    "\n",
    "## Get suitable flores sentences. Tokenized english sentences length <= 50\n",
    "length_sen_eng = []\n",
    "if source_lang == 'en':\n",
    "    lang_max = target_lang\n",
    "else:\n",
    "    lang_max = source_lang\n",
    "print('lang_max', lang_flores_dict[lang_max])\n",
    "num_sentences = 1011 # 1011\n",
    "for i in range(num_sentences):\n",
    "    sample = hub.get_interactive_sample(i, Path(\"./data/flores\"), lang_flores_dict[lang_max], 'eng', 'spm')\n",
    "    src_tok_len = len(sample['src_tok'])\n",
    "    if src_tok_len <= 50:\n",
    "        length_sen_eng.append(i)\n",
    "contributions = True\n",
    "\n",
    "print('src', args.src)\n",
    "print('tgt', args.tgt)\n",
    "src_total_alti, number_hallucinations = helper.model_analysis(hub, length_sen_eng, args,\n",
    "                                                        eos_res_corr=False, lang_toks=False,\n",
    "                                                        src_contrib=True)\n",
    "print('Total hallucinations:', number_hallucinations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "## Get standard alti results\n",
    "with open(f'results/{dataset}/{args.src}_{args.tgt}_src_alti.csv', 'w') as f:\n",
    "    write = csv.writer(f)\n",
    "    for element in src_total_alti:\n",
    "        write.writerow([element])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for src in lang_flores_dict.keys():\n",
    "#     for tgt in reversed(list(lang_flores_dict.keys())):\n",
    "#         if src is not tgt:\n",
    "#             print(src, tgt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_zul = []\n",
    "with open(f'results/{dataset}/eng_zul_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                eng_zul.append(line)\n",
    "\n",
    "zul_eng = []\n",
    "with open(f'results/{dataset}/zul_eng_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                zul_eng.append(line)\n",
    "\n",
    "eng_spa = []\n",
    "with open(f'results/{dataset}/eng_spa_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                eng_spa.append(line)\n",
    "spa_eng = []\n",
    "with open(f'results/{dataset}/spa_eng_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                spa_eng.append(line)\n",
    "eng_yor = []\n",
    "with open(f'results/{dataset}/eng_yor_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                eng_yor.append(line)\n",
    "yor_eng = []\n",
    "with open(f'results/{dataset}/yor_eng_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                yor_eng.append(line)\n",
    "eng_fra = []\n",
    "with open(f'results/{dataset}/eng_fra_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                eng_fra.append(line)\n",
    "fra_eng = []\n",
    "with open(f'results/{dataset}/fra_eng_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                fra_eng.append(line)\n",
    "xho_eng = []\n",
    "with open(f'results/{dataset}/xho_eng_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                xho_eng.append(line)\n",
    "eng_xho = []\n",
    "with open(f'results/{dataset}/eng_xho_src_alti.csv', encoding=\"utf-8\") as csvfile:\n",
    "        # BPE source sentences\n",
    "        reader = csv.reader(csvfile, delimiter =',', quoting=csv.QUOTE_NONNUMERIC)\n",
    "        for i, line in enumerate(reader):\n",
    "                eng_xho.append(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "eng_zul_flat = [item for sublist in eng_zul for item in sublist]\n",
    "zul_eng_flat = [item for sublist in zul_eng for item in sublist]\n",
    "eng_spa_flat = [item for sublist in eng_spa for item in sublist]\n",
    "spa_eng_flat = [item for sublist in spa_eng for item in sublist]\n",
    "eng_yor_flat = [item for sublist in eng_yor for item in sublist]\n",
    "yor_eng_flat = [item for sublist in yor_eng for item in sublist]\n",
    "eng_fra_flat = [item for sublist in eng_fra for item in sublist]\n",
    "fra_eng_flat = [item for sublist in fra_eng for item in sublist]\n",
    "eng_xho_flat = [item for sublist in eng_xho for item in sublist]\n",
    "xho_eng_flat = [item for sublist in xho_eng for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PathPatch6((0.75, 0.15265) ...)\n",
      "PathPatch6((1.75, 0.458896) ...)\n",
      "PathPatch6((2.75, 0.136859) ...)\n",
      "PathPatch6((3.75, 0.450547) ...)\n",
      "PathPatch6((4.75, 0.431288) ...)\n",
      "PathPatch6((5.75, 0.518295) ...)\n",
      "PathPatch6((6.75, 0.467307) ...)\n",
      "PathPatch6((7.75, 0.516692) ...)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mean_alti = {}\n",
    "for i in range(2):\n",
    "    mean_alti['En-Zu']= eng_zul_flat\n",
    "    mean_alti['Zu-En'] = zul_eng_flat\n",
    "    mean_alti['En-Xh'] = eng_xho_flat\n",
    "    mean_alti['Xh-En'] = xho_eng_flat\n",
    "    mean_alti['En-Fr'] = eng_fra_flat\n",
    "    mean_alti['Fr-En'] = fra_eng_flat\n",
    "    mean_alti['En-Es'] = eng_spa_flat\n",
    "    mean_alti['Es-En'] = spa_eng_flat\n",
    "    \n",
    "\n",
    "colors = ['#82B366','#6C8EBF','#D79B00','#C98177']\n",
    "fig, ax = plt.subplots(figsize =(14, 6),dpi=200)\n",
    "box = ax.boxplot(mean_alti.values(),whiskerprops=dict(linewidth=3.0),\n",
    "                patch_artist=True, showfliers=False)\n",
    "plt.setp(box['medians'], color='black')\n",
    "counter = 0\n",
    "for i, patch in enumerate(box['boxes']):\n",
    "    print(patch)\n",
    "    patch.set_facecolor(colors[i//2])\n",
    "ax.set_xticklabels(mean_alti.keys())\n",
    "ax.set_ylim(0,1)\n",
    "ax.grid(True, linestyle=(0, (5, 10)))\n",
    "plt.ylabel(r'Source Contribution');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation EOS vs. Residual cross-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First load de-en bilingual model with iwslt dataset\n",
    "args = SimpleNamespace(\n",
    "    src = \"de\",\n",
    "    tgt = \"en\",\n",
    "    tokenizer = tokenizer,\n",
    "    test_set_dir = test_set_dir,\n",
    "    pre_layer_norm=pre_layer_norm,\n",
    "    beam = 1,\n",
    "    num_layers = NUM_LAYERS\n",
    "    )\n",
    "\n",
    "## Get suitable flores sentences. Tokenized english sentences length <= 40\n",
    "length_sen_eng = []\n",
    "if source_lang == 'en':\n",
    "    lang_max = target_lang\n",
    "else:\n",
    "    lang_max = source_lang\n",
    "print('lang_max', lang_flores_dict[lang_max])\n",
    "num_sentences = 1011 # 1011\n",
    "for i in range(num_sentences):\n",
    "    sample = hub.get_interactive_sample(i, Path(\"./data/flores\"), lang_flores_dict[lang_max], 'eng', 'spm')\n",
    "    src_tok_len = len(sample['src_tok'])\n",
    "    if src_tok_len <= 50:\n",
    "        length_sen_eng.append(i)\n",
    "contributions = True\n",
    "\n",
    "eos_dict, res_dict = helper.model_analysis(hub, length_sen_eng, args,\n",
    "                                            eos_res_corr=True, lang_toks=False,\n",
    "                                            src_contrib=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_dict = defaultdict(list)\n",
    "res_dict_list = defaultdict(list) # each layer has one list\n",
    "for layer in range(NUM_LAYERS):\n",
    "    eos_list = [item for sublist in eos_dict[layer] for item in sublist]\n",
    "    res_list = [item for sublist in res_dict[layer] for item in sublist]\n",
    "    res_dict_list[layer] = res_list\n",
    "    #punct_list = [item for sublist in punct_list for item in sublist]\n",
    "\n",
    "    from scipy.stats.stats import pearsonr\n",
    "    corr_dict[layer] = pearsonr(eos_list, res_list)[0]\n",
    "    # outfile = f'./results/{model}_{dataset}_eos_res_corr.json'\n",
    "\n",
    "    # with open(outfile, 'w') as f:\n",
    "    #     json.dump(corr_dict, f)  \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [0]*2\n",
    "x_ticks = [0]*2\n",
    "x_ticks[0], data[0] = [*zip(*res_dict_list.items())]  # 'transpose' items to parallel key, value lists\n",
    "#x_ticks[1], data[1] = [*zip(*mix_dict_multilingual.items())]  # 'transpose' items to parallel key, value lists\n",
    "\n",
    "#x_ticks = [1,2,3,4,5,6]\n",
    "color = ['#82B366','#6C8EBF','#D79B00']\n",
    "labels = ['Bilingual', 'Multilingual']\n",
    "# Creating plot \n",
    "#fig = plt.figure(figsize =(20, 7)) \n",
    "fig, ax = plt.subplots(figsize =(14, 6),dpi=200)\n",
    "\n",
    "for i in range(1):\n",
    "    mean_data = [statistics.mean(j) for j in data[i]] # mean per head\n",
    "    std_data = [statistics.stdev(j) for j in data[i]] # std per head\n",
    "    ax.plot(x_ticks[i], mean_data, color[i], markersize=6, linewidth=2, linestyle='-',alpha = 1, marker='o',label=labels[i])\n",
    "    ax.fill_between(x_ticks[i], (np.array(mean_data)-np.array(std_data)), (np.array(mean_data)+np.array(std_data)), color=color[i], alpha=.1)\n",
    "\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "plt.xlabel('Layer')\n",
    "plt.ylabel('Src contrib cross-attention')\n",
    "ax.legend(prop={'size': 20})\n",
    "ax.grid(True, linestyle=(0, (5, 10)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = f'./results/bilingual_iwslt_eos_res_corr.json'\n",
    "f = open(outfile)\n",
    "corr_dict_bilingual = json.load(f)\n",
    "outfile = f'./results/multilingual_iwslt_eos_res_corr.json'\n",
    "f = open(outfile)\n",
    "corr_dict_multilingual = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "[1 2 3 4 5 6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = [0]*2\n",
    "x_ticks_list = [0]*2\n",
    "x_ticks_list[0], data[0] = [*zip(*corr_dict_bilingual.items())]  # 'transpose' items to parallel key, value lists\n",
    "x_ticks_list[1], data[1] = [*zip(*corr_dict_multilingual.items())]  # 'transpose' items to parallel key, value lists\n",
    "\n",
    "x_ticks = np.arange(1,7)\n",
    "color = ['#82B366','#6C8EBF']#,'#D79B00']\n",
    "labels = ['Bilingual', 'Multilingual']\n",
    "# Creating plot \n",
    "#fig = plt.figure(figsize =(20, 7)) \n",
    "fig, ax = plt.subplots(figsize =(12, 4),dpi=200)\n",
    "\n",
    "for i in range(1):\n",
    "    print(type(data[i]))\n",
    "    print(x_ticks)\n",
    "    ax.plot(x_ticks, data[i], 'b', markersize=6, linewidth=2,\n",
    "            linestyle='-',alpha = 1, marker='o',color=color[i],\n",
    "            label = labels[i])\n",
    "\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "plt.xlabel('Layer')\n",
    "plt.ylabel(r'Correlation')\n",
    "ax.grid(True, linestyle=(0, (5, 10)))\n",
    "#plt.grid(False, linewidth=0.25);\n",
    "#plt.legend(prop={'size': 16})\n",
    "#ax.legend(loc=(0.05,0.12),prop={'size': 20})\n",
    "# ax.set_xticks([1,2,3,4]);\n",
    "# ax.set_yticks([1,2,3,4]);\n",
    "#ax.set_xticklabels(x_ticks);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder mixing Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "100\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "700\n",
      "800\n",
      "900\n"
     ]
    }
   ],
   "source": [
    "# dataset = 'iwslt'\n",
    "# if dataset == 'iwslt':\n",
    "#     test_set_dir = iwslt14_dir / \"tokenized/\"\n",
    "# elif dataset == 'gold':\n",
    "#     test_set_dir = europarl_dir / \"processed_data/\"\n",
    "\n",
    "args = SimpleNamespace(\n",
    "    src = \"de\",\n",
    "    tgt = \"en\",\n",
    "    tokenizer = tokenizer,\n",
    "    test_set_dir = test_set_dir\n",
    "    )\n",
    "contributions = True\n",
    "results_dict = defaultdict(list)\n",
    "length_sentences = []\n",
    "\n",
    "i = 0\n",
    "counter = 0\n",
    "\n",
    "while counter < 1000:\n",
    "    if counter%100==0:\n",
    "        print(counter)\n",
    "    sample = hub.get_interactive_sample(i, args.test_set_dir, args.src, args.tgt, args.tokenizer)\n",
    "    src_tensor = sample['src_tensor']\n",
    "    src_tok = sample['src_tok']\n",
    "    if len(src_tok)>50:\n",
    "        i += 1\n",
    "        continue\n",
    "    #     relevances_enc_self_attn = hub.get_contribution_rollout(src_tensor, tgt_tensor, 'l1', norm_mode='min_sum')['encoder.self_attn']\n",
    "    length_sentences.append(len(src_tok))\n",
    "    tgt_tensor, _ = get_greedy_decoding(hub, src_tensor)\n",
    "\n",
    "    relevances_enc_self_attn = hub.get_contribution_rollout(src_tensor, tgt_tensor, 'l1',\n",
    "                                                            norm_mode='min_sum',\n",
    "                                                            pre_layer_norm=pre_layer_norm)['encoder.self_attn']\n",
    "    relevances_enc_self_attn = relevances_enc_self_attn.detach().cpu()\n",
    "    \n",
    "    for layer in range(NUM_LAYERS):\n",
    "        total_contrib = relevances_enc_self_attn[layer].sum()\n",
    "        diag_sum = torch.diagonal(relevances_enc_self_attn[layer]).sum()\n",
    "        #key = f'{layer}'\n",
    "        results_dict[layer].append((diag_sum/total_contrib).numpy().item())\n",
    "    #results_dict['stds'].append(torch.diagonal(relevances_enc_self_attn[-1]).std().item())\n",
    "    \n",
    "\n",
    "    counter += 1\n",
    "    i += 1\n",
    "\n",
    "    #total_rollout = hub.get_contribution_rollout(src_tensor, tgt_tensor, 'l1', norm_mode='min_sum')['total'].detach()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results_dict_lists = defaultdict(list)\n",
    "# for key in results_dict.keys():\n",
    "#     results_dict_lists[key] = [float(v) for v in results_dict[key]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = f'./results/{model}_{dataset}_encoder_mixing.json'\n",
    "with open(outfile, 'w') as f:\n",
    "    json.dump(results_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "outfile = f'./results/bilingual_iwslt_encoder_mixing.json'\n",
    "f = open(outfile)\n",
    "mix_dict_bilingual = json.load(f)\n",
    "outfile = f'./results/multilingual_iwslt_encoder_mixing.json'\n",
    "f = open(outfile)\n",
    "mix_dict_multilingual = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mix_dict_bilingual['0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 30})\n",
    "plt.rcParams.update({'xtick.labelsize' : 20})\n",
    "plt.rcParams.update({'ytick.labelsize' : 20})\n",
    "plt.rcParams.update({'axes.titlesize' : 24})\n",
    "plt.rcParams.update({'axes.labelsize' : 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.74658565 0.59302593 0.52780971 0.46464489 0.40999324 0.37311626]\n",
      "range(1, 7)\n",
      "[0.94293594 0.88772258 0.83525119 0.78669773 0.73760985 0.68461797\n",
      " 0.62323185 0.57373223 0.51840915 0.47727766 0.44664397 0.42294556]\n",
      "range(1, 13)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [0]*2\n",
    "x_ticks = [0]*2\n",
    "x_ticks[0], data[0] = [*zip(*mix_dict_bilingual.items())]  # 'transpose' items to parallel key, value lists\n",
    "x_ticks[1], data[1] = [*zip(*mix_dict_multilingual.items())]  # 'transpose' items to parallel key, value lists\n",
    "\n",
    "## TODO: change this\n",
    "x_ticks[0] = range(1,7)\n",
    "x_ticks[1] = range(1,13)\n",
    "#x_ticks = [1,2,3,4,5,6]\n",
    "color = ['#82B366','#6C8EBF','#D79B00']\n",
    "labels = ['Bilingual', 'Multilingual']\n",
    "# Creating plot \n",
    "#fig = plt.figure(figsize =(20, 7)) \n",
    "fig, ax = plt.subplots(figsize =(14, 6),dpi=200)\n",
    "\n",
    "for i in range(2):\n",
    "    mean_data = [statistics.mean(j) for j in data[i]] # mean per head\n",
    "    std_data = [statistics.stdev(j) for j in data[i]] # std per head\n",
    "    print(np.array(mean_data)-np.array(std_data))\n",
    "    print(x_ticks[i])\n",
    "    ax.plot(x_ticks[i], mean_data, color[i], markersize=6, linewidth=2, linestyle='-',alpha = 1, marker='o',label=labels[i])\n",
    "    ax.fill_between(x_ticks[i], (np.array(mean_data)-np.array(std_data)), (np.array(mean_data)+np.array(std_data)), color=color[i], alpha=.1)\n",
    "\n",
    "\n",
    "ax.set_ylim(0,1)\n",
    "plt.xlabel('Layer')\n",
    "plt.ylabel('Contribution')\n",
    "#plt.legend(prop={'size': 16})\n",
    "#ax.legend(loc=(0.05,0.12),prop={'size': 20})\n",
    "ax.set_xticks(range(1,12))\n",
    "ax.legend(prop={'size': 20})\n",
    "ax.grid(True, linestyle=(0, (5, 10)));\n",
    "#plt.grid(False, linewidth=0.25)\n",
    "\n",
    "#ax.grid(True)\n",
    "#ax.set_xticks([1,2,3,4,5,6]);\n",
    "#ax.set_yticks([1,2,3,4,5,6]);\n",
    "#ax.set_xticklabels(x_ticks);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-bba626b94ae0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpositions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;31m# Creating plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#bp = ax.boxplot(data_4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'results_dict' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(12, 5),dpi=200)\n",
    " \n",
    "# Creating axes instance\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "for i in range(1,7):\n",
    "    ax.boxplot(results_dict[i], positions = [i])\n",
    "# Creating plot\n",
    "#bp = ax.boxplot(data_4)\n",
    "ax.set_ylim(0,1)\n",
    "plt.xlabel('Layer')\n",
    "plt.ylabel('Contribution (%)')\n",
    "#plt.grid(True)\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f8a243515f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_data = {'Diagonal mean':results_dict['means'], 'Sentence Length':length_sentences}\n",
    "df = pd.DataFrame(dict_data)\n",
    "plt.figure(figsize=(10,10),dpi=200)\n",
    "#df.plot.scatter(x = 'length', y = 'means');\n",
    "sns.jointplot(x=df[\"Sentence Length\"], y=df[\"Diagonal mean\"], kind='scatter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 1000 sentences\n",
      "Mean:  0.40651998993754385\n",
      "Std:  0.19392589148879052\n"
     ]
    }
   ],
   "source": [
    "total_sentences = len(results_dict['means'])\n",
    "print(f'Total of {total_sentences} sentences')\n",
    "print('Mean: ', statistics.mean(results_dict['means']))\n",
    "print('Std: ', statistics.mean(results_dict['stds']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Values vectors analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "600\n",
      "600\n",
      "700\n",
      "800\n",
      "800\n",
      "900\n"
     ]
    }
   ],
   "source": [
    "args = SimpleNamespace(\n",
    "    src = \"de\",\n",
    "    tgt = \"en\",\n",
    "    tokenizer = tokenizer,\n",
    "    test_set_dir = test_set_dir\n",
    "    )\n",
    "contributions = True\n",
    "alignment_layer = 4\n",
    "head_dim = 128\n",
    "num_heads = 4\n",
    "eos_dict = defaultdict(list)\n",
    "final_punct_dict = defaultdict(list)\n",
    "rest_tokens_dict = defaultdict(list)\n",
    "\n",
    "i = 0\n",
    "counter = 0\n",
    "\n",
    "while counter < 1000:\n",
    "    if counter%100==0:\n",
    "        print(counter)\n",
    "    sample = hub.get_interactive_sample(i, args.test_set_dir, args.src, args.tgt, args.tokenizer)\n",
    "    src_tensor = sample['src_tensor']\n",
    "    src_tok = sample['src_tok']\n",
    "    if len(src_tok)>50:\n",
    "        i += 1\n",
    "        continue\n",
    "    # if len(src_tok)>20 and len(src_tok)<23:\n",
    "    #     relevances_enc_self_attn = hub.get_contribution_rollout(src_tensor, tgt_tensor, 'l1', norm_mode='min_sum')['encoder.self_attn']\n",
    "        \n",
    "    tgt_tensor, _ = get_greedy_decoding(hub, src_tensor)\n",
    "\n",
    "    model_output, log_probs, encoder_out, layer_inputs, layer_outputs = hub.trace_forward(src_tensor, tgt_tensor)\n",
    "    values = torch.squeeze(layer_outputs[f'models.0.decoder.layers.{alignment_layer}.encoder_attn.v_proj'][0]).detach().cpu()\n",
    "    v = torch.squeeze(values.contiguous().view(-1, num_heads, head_dim).transpose(0, 1)) # heads,src_len,128\n",
    "    eos_value = v[:,-1,:]\n",
    "    final_punct_value = v[:,-2,:]\n",
    "    rest_tokens_value = v[:,:-2,:]\n",
    "    \n",
    "    #random_value = values[-3]\n",
    "    \n",
    "    for head in range(num_heads):\n",
    "        eos_dict[head].append(eos_value[head].norm(dim=-1).tolist())\n",
    "        final_punct_dict[head].append(final_punct_value[head].norm(dim=-1).tolist())\n",
    "        rest_tokens_dict[head].append(rest_tokens_value[head].norm(dim=-1).mean().tolist())\n",
    "    \n",
    "    counter += 1\n",
    "    i += 1\n",
    "\n",
    "    #total_rollout = hub.get_contribution_rollout(src_tensor, tgt_tensor, 'l1', norm_mode='min_sum')['total'].detach()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 30})\n",
    "plt.rcParams.update({'xtick.labelsize' : 20})\n",
    "plt.rcParams.update({'ytick.labelsize' : 20})\n",
    "plt.rcParams.update({'axes.titlesize' : 24})\n",
    "plt.rcParams.update({'axes.labelsize' : 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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aI8gYw8gxhIRFFIlTHyVJkiTptETEIp5kwhhW9gNdX2i7HuipqyDnRNcnphGzIDNRV2l8niRJkiQdk0GlJEmSJAlYCRVZRo+HJzWuPj4Gk/Mw8s6mQi5jy/V7Mb8XnCi2XAsaZ2/Pp0KO708bpkIuJ0Y6FVKSJEmSdluVl5Hk2uTKWVxZ5TGu7PtE045/7qvyMq70L8BJkiRJuhWDSkmSJEm64K7GVMhwKqQkSZIkaU1OQU6ZvTozzOPKfmDa9EzpyXk53bLtxgizrhLV7Ghw/xwpSZIk6TCDSkmSJEnaok1TGwuH48W7nArJLWLLeezoVEhJkiRJ0gWWUjBJmUmdGcpycmXTDkybgZSCqgr6PpG7xD4swsqqSv7ZVJIkSRJgUClJkiRJd+SqToVkfo1TISVJkiRJOypFMKkykypTSqHrx8CybQeaMhAxxpVVl+iqBFPIOahzoq4yKflnXUmSJOmqMqiUJEmSdOU4FdKpkJIkSZKkqyEiqKugrhKlFPphOb2yLQMHDYtjwbucOGh6Uhqvr3Mi57TtD0GSJEnSOTKolCRJknRh3O1UyONMbTzpVMj5Ou5mKiQsY8h58Hh4AuRicmQ4FVKSJEmSpDsREYt4kgljWNkPdH2h7Xqgp66CnBNdn5hGzILMRF2l8XmSJEmSLjWDSkmSJEnn4rJOhYwIKqdCSpIkSZJ04VR5GUmuTa6cxZVVHuPKvk807fjn/Cov40r/wqMkSZJ0+RhUSpIkSbqlI0NFVgLIcvO1dzQVkrIWVZZD9zo8FXJxz2NMhRx/jpXg0amQkiRJkiRplFOQU2avzgzzuLIfmDY9U3pyXk63bLsxwqyrRDU7GtyvG0iSJEmXg0GlJEmSdIk5FXI9kJQkSZIkSbqdlIJJykzqzFCWkyubdmDaDKQUVFXQ94ncJfZhEVZWVfKECkmSJOkCM6iUJEmSdtBq4Lg29ZHlpMa1CY2sxJPHnAp5y9jSqZCSJEmSJEmkCCZVZlJlSil0/RhYtu1AU4bxL31WQdUluirBFHIO6pyoq0xKfm1DkiRJukgMKiVJkqRT5lRIp0JKkiRJkqTLJyKoq6CuEqUU+mE5vbItAwcNi2PBu5w4aHpSGq+vcyLntO0PQZIkSdJtGFRKkiRJK4ZyOHx0KqQkSZIkSZLWRcQinmTCGFb2A11faLse6MfJlTnR9YlpxCzITNRVGp8nSZIkaecYVEqSJOlSuJOpkMtrT3Eq5HjxTVMhjxNbziNHItaCx8VUSNIYSM4mQc6nQi4mRGIMKUmSJEmStA1VXkaSq5MrD+ZxZQ5yTvR9omnHr+VUeRlX+jUdSZIkaTcYVEqSJGnrnAppCClJkiRJknRZ5BTklNmrM8M8ruwHpk3PlJ6cl9Mt226MMOsqUc2OBvfrRJIkSdL2GFRKkiTpjm0KFVmd+shyKiRwc9R4m1JxbZrkIqxcCSBZxpas3e/up0LmRQDpVEhJkiRJkiTdmZSCScpM6sxQlpMrm3Zg2gykFFRV0PeJ3CX2WZlcWSWSX3eSJEmSzpVBpSRJ0hXlVEhDSEmSJEmSJJ2fFMGkykyqTCmFrh8Dy7YdaMpAxBhXVinR9QmmkHNQ50RdZVLya1mSJEnSWTOolCRJumBWA8dlhDiPF1fDxzubCgm3iS2dCilJkiRJkiTdlYigroK6SpRS6Ifl9Mq2DBw0LI4F73LioOlJaby+zomc07Y/BEmSJOlSMqiUJEk6R1dmKiTrEyDn1xN4TJEkSZIkSZK0IiIW8SQT6IeBthvo+kLb9UA/Tq7M4+TKacQsyJwdDW5cKUmSJJ0ag0rpCmvblve9730APPOZz6Su6y2vSJJ2l1MhnQopSdvUdR0feP8fAfCUpz6dqvKP85IkSdKu8vN33a2cEnkyRpKLyZX9wMF0FlfmIOdE3yeadvzaXZWXcaVfw5MkSToeuxlt4p/gpCus6zre9a53AXDffff5HwZJl9YuToVcDyJvfYPFVEhmkx4PTYUcr0krEyOXMeR8YqRTISXpYuu6jt//3d8B4En3PMVvyEqSJEk7zM/fdZpyCnLK7NWZYSWunDY9U3pyXk63bLvxi4V1lahmR4MbV0qSJB3Nbkab+Cc4SZK0sy7zVMiAxRHZToWUJEmSJEmSdDspBZOUmdSZocziym6gaQemzUBKQVUFfZ/IXWKflcmVVfIvXUuSJEnHYFApSZLOxCJUZBk9Hp7UuPr4GEzOw8g7mwq5FlvOY8e12PL4UyGB9aBx9rZTISVJkiRJkiRtW4pgUmUmVaaUQtePgWXbDjRlIGKMK6uU6PoEU8g5qHOirjIp+bVLSZIkaZNLF1RGxGOA+4EXAU8CGuD9wAOllDdvcWmSJF0IToV0KqQkSZIkSZKkiyMiqKugrhKlFPphOb2yLQMHDYtjwbucOGh6Uhqvr3Mi57TtD0GSJEnaGZcmqIyIxwH/I/C3gL0jrvlj4B+XUn7gPNcmSdJ52TS1sXA4XrzLqZDcIrZ0KqQkSZIkSZIkbU1ELOJJJtAPA2030PWFtuuBfpxcmcfJldOIWZA5OxrcuFKSJElX3NaDyoh4NfC/HHr4d0opX3GC1/hE4KeBT2Y2kOoITwO+LyK+DPhLpZRHTrpeSZLOwlWZCslK/DjvHhfXhFMhJUmSJEmSJOk05ZTIkzGSXEyu7AcOprO4Mgc5J/o+0bTj122rvIwr/ZqtJEmSrpqtB5XAa4EXMPYiMfv5fz3ukyPiUcC/AZ47e+h2RUkArwR+PCK+qJTSnnjFkiStcCqkUyElSZIkSZIkadflFOSU2aszw0pcOW16pvTkvJxu2XbjF4frKlHNjgY3rpQkSdJVsNWgMiIy8CWsR5APAf/yBC/zj4FP4fYh5dw83Hwl8O3At57gXpKkS+S4UyFhczR5nKmNJ50KOV/HSaZCjj8vp0LCMoZcnwq5nADpVEhJkiRJkiRJurpSCiYpM6kzQyn0/Xg0eNMOTJuBlIKqCvo+kbvEPiuTK6vkX7KXJEnSpbXtCZUvBB7L+nTKf1tK2T/OkyPiacDXsowp49Dbh5WVnwP4xoj430spv3tny5ckbctlnQoZEVTHnAq5GkhKkiRJkiRJknQnUgSpytRVppRC14/TK9t2oCnD+HXrKqhSousTTCHnoM6Jusqk5NepJUmSdHlsO6h8+YbHfuoEz/8GYML6dMoA/hD4B8AbGSdefhLw9cBXH7q2ZpxQ+dUnuKck6S4cGSrCIlCcT21cm+p4J1MhD8WWZcO9VqdCLu55kqmQK792KqQkSZIkSZIk6SKLCOoqqKtEKYV+fjR4N9CWgYOGxbHgXU4cND0pjdfXOZFz2vaHIEmSJN2VbQeVL9zw2C8d54kxlih/lZuP+v5D4OWllA+tPPYO4Gsi4jeBf8p8uNjYuXx5RHxdKWV60sVLF13Omac+9amLt6XbcSqkUyElSdJ25Jx40j33Lt6WJEmStLv8/F2XRUQs4kkm0A/jseBdX2i7HujHyZV5nFw5jZgFmbOjwf3/vyRJ2nF2M9pk20HlJxz69UOllPcd87mfA9zHzcd9f/OhmHKhlPK9EfEa4P6V5z0W+ALgJ4+/bOlymEwmvOxlL9v2MnQOVgPHtamPnN5UyFvGlk6FlCRJuit1PeH5n/qSbS9DkiRJ0jH4+bsuq5wSeTJGkovJlf3AwXQWV+Yg50TfJ5p2HFhQ5WVc6dfoJUnSrrGb0SbbDiqfyfqEyd8+wXNfueGxPwZ+7DbPex1jULnqZRhUStpRV2IqJGkxCXJ1KuTibYwhJUmSJEmSJEnaFTkFOWX26sywEldOm54pPTkvp1u23fjNgLpKVLOjwf2avyRJknbVtoPKJ85+nk+X/MgJnnv/ytvz5/9ouX3Z80bgANhbeezFJ7ivJB3bUA6Hj06FlCRJkiRJkiRJl0dKwSRlJnVmKIW+H48Gb9qBaTOQUlBVQd8ncpfYZ2VyZZVIfh9BkiRJO2TbQeWjDv36weM8KSJq4DNZn24J8FO3e24pZRoR7wFeOHt+AM8+zn0lXR13MhVyee0pToUcLz40EXJ57a3MI0difcpjXgSQ41TIsYl0KqQkSZIkSZIkSbo7KYJUZeoqU0qh68fplW070JRhPL2qCqqU6PoEU8g5qHOirjIp+X0JSZIkbdeuBZXNMZ/3aYwTJldzoinw1mM+/32MQeXc44/5PEkXgFMhDSElSZIkSZIkSdJ2RQR1FdTVeOT3/Fjwrhtoy8BBw+JY8C4nDpqelMbr65zIOW35I5AkSdJVtO2gsgGurfz60cd83itW3p4f9/0rpZT2mM9/+NCvH3fM50k6Q5tCRVanPrKcCgncHDXeplRcmya5KbZkGVs6FVKSJEmSJEmSJOn0VDlR5QQT6IfxWPCuL7RdD/Tj5Mo8Tq6cRsyCzNnR4MaVkiRJOifbDiofZD2ofNIxn3f/hsfefIL7Hq6V6hM8V9IGV30qJDFeK0mSJEmSJEmSpFvLKZEnYyTZD2UxvfJgOosrc5Bzou8TTTsOpajyMq50OIUkSZLOyraDyj8D7mGcMBnAc273hIi4Bnw+68d9A/ziCe77hEO/fuQEz5UunaGUcbrjIkKcx4ur4eOdTYWcv75TISVJkiRJkiRJknRYTkFOmb06M6zEldOmZ0pPzrE4GrztxgizrhLV7Ghwv8cjSZKk07TtoPJ3gE9mGUc+KSKeW0r57Vs859XAo1gPKjvgLSe47+Gg8qETPFe6VB7eb+j7zcXizkyF5FAAGcv3ORVSkiRJkiRJkiTpckgpmKTMpM4MpdD349HgTTswbQZSCqoq6PtE7hL7rEyurJLfN5IkSdJd23ZQ+Q7gSw499teAf3CL5/yNlbeDMaz8D6WUGye477NYTsUE+MAJnnumYvwrVPcBzwCeDnw8Y0C6xxh+fgz4MPCOUsofb2uduhy6fqDvC/tNxzCc7Ahup0JKkiRJkiRJkiTprKQIUpWpq0wpha4fp1e27UBTBiLGuLJKia5PMIWcgzqPcWVOadsfgiRJki6gbQeVq8d0zwPHb4iIHy6lvPfwxRHx2cB/wc3Hff/b494wIp4BfNzKaxTg90+y6NMUEc8GPgf4LOCFwAuAxx7zuR9i/Nh/GHhzOc7Zy9IG8wmVVR4Dx4jkVEhJkiRJkiRJkiTthIigroK6GiPJ+bHgXTfQloGDhsWx4F1ORBOkNF5f50TOxpWSJEk6nm0Hlf+OcTrkk1ceexzwcxHxVaWUX5o/OIspf5TlVMpV/+oE93zphsd+7wTPPzUR8e3A37+Ll7gX+OuzH78eEV9bSvnlU1mcrpwqB9cm2/5XgiRJkiRJkiRJknRrVU5UOcEE+mE8FrzrC23XA/04uTKPkyunEbMgc3Y0uHGlJEmSbmGr9VQpZYiIHwb+HusTIz8R+PcR8YfAHwL3AM9hPaacv/0LpZQ/OMFtX73hsQfuYPmnoT7F13oh8JaI+J9LKX/nFF9XkiRJkiRJkiRJknZSTok8GSPJfiiL6ZUH01lcmYOcE32faNrxZLYqL+PK8GQ2SZIkrdiFcXTfyThh8WmsR5UBPBN4xuxtuHkyJYwx5rFERAb+0obX2cWpjj3wR8BHgI8BiXF6558HHn/EcwL41oh4fCnl685llZIkSZIkSZIkSZK0A3IKcsrs1ZlhJa6cNj1TenKOxdHgbTdGmHWVqGZxZTKulCRJuvK2HlSWUm5ExH8D/DTrExsPT6K86anA60spv3KC2/0F4OMPvd5/KqV86ASvcVbeA/zi7MevAr9XSmk2XRgRnwR8JfD1wFM2XPK3IuJXSin/8ozWKkmSJEmSJEmSJEk7K6VgkjKTOjOUQt+PR4M37cC0GUgpyDno+0SexZWLyZWVcaUkSdJVtfWgEqCU8vMR8ZXAjwDXWQ8eN8WUAfwY8E0nvNXXHnqNArzxhK9xmt4N/A/AT5RS3nPcJ5VSfhf4joh4PfCDwF/ZcNnrIuInSykfPZ2lSpIkSZIkSZIkSdLFkyJIVaauMqUUun42vbIbaNuBiKCqgioluj7BFHIO6jzGlTmlbX8IkiRJOic7EVQClFJ+KiJeAPwz4AsZj7je5EPAPy6lvP4krx8RzwK+lJsDzX9z0rWellLK/3GXz38wIv4a8Bjgvzz07icAXw780N3cQ5IkSZIkSZIkSZIui4igroK6Gr8dPT8WvOsG2jJw0LA4FrzLiWiClMbr65zI2bhSkiTpMtuZoBKglPJe4Isj4h7gi4BnAfcCLfBB4JeBN5dSpnfw8q8FPnDosQb4uTtf8faVUoaI+DrGCDUfeveXYVApSZIkSZIkSZIkSRtVOVHlBBPoh/FY8K4vtF0P9OPkyjxOrpxGzILM2dHgxpWSJEmXzk4FlXOllD8B/sUpv+Y/BP7hab7mriil/FFE/BLw+Yfe9ZxtrEeSJEmSJEmSJEmSLpqcEnkyRpL9UBbTKw+ms7gyBzkn+j7RtEHEGGTO48qI2O4HIEmSpLu2k0Gl7si7uDmofMo2FiJJkiRJkiRJkiRJF1lOQU6ZvTozrMSV06ZnSk/OsTgavO3GCLOuEtUsrkzGlZIkSReSQeXl8bENjw3nvgpJkiRJkiRJkiRJukRSCiYpM6kzQyn0/Xg0eNMOTJuBlIKcg75P5FlcWeUxrqwr40pJkqSLxKDy8rhnw2MfOPdV6EIZhoGDgwOaaUu+vrft5UiSJEk6wjAMNE0DwGQyIaW05RVJkiRJOoqfv0uXW4ogVZm6ypRS6PrZ9MpuoG0HIoKqCqqUqPvEwRRyDupZYJn9d4IkSTtjGAam0ykAe3t7fu4uwKDyMvm8DY+95dxXoQtlOp3ytre8CYCXfMbncn1vst0FSZIkSdqoaZrF5+6f+Tn3c+3ate0uSJIkSdKR/PxdujoigroK6mqML+bHgnfdQFsGDhoWx4J3ORFNkNJ4fZ0TORttSJK0TdPplDe84Q0AvOpVr+L69etbXpF2gUHlJRARrwaes+Fd/+d5r0WSJEmSJEmSJEmSrqIqJ6qcYAL9MNB1hbYfaLse6MfJlTnR9YlpxCzIHI8Fr4wrJUmSdsKFCSoj4onAZwFPB544+/FYIIBSSvmbW1ze1kTEJwL/fMO73lRK+fnzXo8kSZIkSZIkSZIkXXU5JfIE9sj0Q1lMrzyYzuLKHOSc6PtE0wYRY5A5jysjYtsfgiRJ0pW000FlRHwy8PXAXwQ++ajLgALcMqiMiBcDf/7Qw+8upbz7bte5DRGRgL8CvA6499C7Pwj8V+e9JkmSJEmSJEmSJEnSupyCnDJ7dWZYiSunTc+UnpxjcTR4242TKusqUc3iymRcKUmSdG52MqiMiE8D/hHwBYzB5Gl8hngf8GOM8eXcm4G/cAqvfaoi4nnA4w49XDFO5Hw68BLgS4Cnbnj67wJfXEp535kuUpIkSZIkSZIkSZJ0IikFk5SZ1JmhFPp+oO0GmnZg2gykFOQc9H0iz+LKKo9xZV0ZV0qSJJ21nQsqI+JbGGPKmmVIWY5+xvFiy1LKT0fEe4DnrDz8eRHxzB2MD78f+PwTPudh4PuAby+lPHIai4iIpx3jsnsADg4O2N/fv+3Fe3t7pJTudmk3aZqGvu+PdW1VVdR1fepr6LqOtm2PdW1Kib29vVNfA3CsfZgbhuFM1tC2DX1/vNeuqoqqOv1/FfV9f4L9CCaTs9mPg4ODY187mUzO5PeH+7HkfozcjyX3Y8n9GLkfS+7Hkvsxcj+W3I8l92Pkfiy5H0vux8j9WHI/ltyPkfux5H4suR8j92PJ/VhyP0bux9Jl2I8UQaoydZUppdD1s+mV3UDbDkQEVRVUKVH3iYMp5BzUs8Ayz/65nuT7g36/drQr3691P0bux5L7seR+jNyPpbPaj7njfH7jfixd5t8fOxNURsR14P8BXs3xQ8pbvX+T72WMDgvLyZevBb7jhK+zSx4Bvh34wVLKn53ya//RcS9829vexoc//OHbXveqV72K69ev39WiNnnnO9/J+9///mNd+/znP59nP/vZp76GD33oQ/zar/2aAj+oAAAgAElEQVTasa59whOewCte8YpTXwPAG97whmNf+9mf87lnsob3/Pa7+PCffOhY1z7rkz6Zpz/jE099DX/6nz/Mb/3mO4517eMe93g+7WWfdeprAHjbW9507Gs/83Pu59q1a6e+Bvdjyf0YuR9L7seS+zFyP5bcjyX3Y+R+LLkfS+7HyP1Ycj+W3I+R+7Hkfiy5HyP3Y8n9WHI/Ru7Hkvux5H6M3I+ly7YfEUFdBXU1xhHzY8G7bqAtAwcNi2PBu5yIJkhpvP4k3x/0+7WjXfl+rfsxcj+W3I8l92Pkfiyd1X7MvfnNb77tNe7H0mX+/XH6qeodiIgE/DjLI74L67FkrPzYB6Z3eKv/Gzic0X7hHb7Wrng08J3Az0TEV87+WUqSJEmSJEmSJEmSLqgqJ65NKh7zqAmPvl4xqTL9APvTnodutNyYthw0HfvTbttLlSRJulSilJMOeTyDRUR8B/B3uHniZADvB/434OeB/1BKOYiIfwF8FctJk6WUko95r18A7l95bgs88bSOyT4NEfEmTn7k99xbgb9eSvn9U1jHcY78fgHwM295y1t4yUtectuLHTk72pURwE3b8aZ/9wsAvOQzPpfHP/Yxp7KGy3DEwmnxyIuR+7Hkfiy5HyP3Y8n9WHI/Ru7HkvuxdFX34+DgYDH94zM/537qunY/Zvz9seR+jNyPJfdjyf0YuR9L7seS+zFyP5bcj6U73Y/Dn7/fzWQ+92PJ3x8j92Ppqu5HP5TF9Mq+H7+9PnRTUk7UKREpiFhOs6xyIiIWz/f7taNd+X6t+zFyP5bcjyX3Y+R+LJ3Ffuzv7y8mQ77iFa+47efu7sfSLv3+eOCBB/j0T/90gE8vpTxwt2vY+pHfEfGpwDdz80TKDvh7wPeUUo73T+l4fpIxqJyrgFcAP3OK97grpZT7Dz82OxL9zwGfALwM+HLGdR/22cAvR8QrSym/cZfr+P9ud01E3ANw7dq1Mxkle1yTyWRr9547qz+IndRJ9qHrz6YjrusJZ/DvzBPJOZPzsTrrM3UWR1iclPux5H6M3I8l92PJ/Ri5H0vux5L7MXI/ltyPJfdj5H4suR9L7sfI/VhyP5bcj5H7seR+LLkfI/djyf1Ycj9G7sfSVd2PnIKcMnt1ZpjHlfkaXV9oC2SCKgWkRIlEO0BdJapqjCvTSlx5mvx+7dI2v28+534suR8j92PJ/VhyP9Ztu31yP0a78Ptj+7874dsY1zEPKgO4AXxhKeUXz+B+b9/w2IvYoaByk1LKPuNx5x8Afhn4ZxHxIuCfMwaWq54E/GxEvKCU8tHzXakkSZIkSZIkSZIk6aylFExSZlJnhlLo+4G2G2jagWkzkFKQc9D3idyNU6yqPMaVdXV2caUkSdJFttWgMiKeDLyG9ZiyAK89o5gS4NeBYXavueee0b3OVCnlnRHx2cCPAH/50LufCrwO+K/PfWG6MK5fv879r/wCHt5vqbJ/YJIkSZJ21bVr17j/lV+w7WVIkiRJOgY/f5e0DSmCVGXqKlNKoetn0yu7gbYdiAiqKqhSou4TB1PIOahngWU+g2NDJUnaddevX+c1r3nNtpehHbPtz4pes7KGeUz5/5ZSfuKsblhKuQH8/vyXs5+fc1b3O2ullA74q8Cm471fGxFPO+clSZIkSZIkSZIkSZK2JCKoq8T1vYrHPmrCo65V1FXQdwP7046HbjTsT1sOph37046Hb7Q8dKPhoOno+2Hby5ckSdqqbQeVn7fhse88h/t+iOWEygDuPYd7nplSSgt8y4Z3Vdw8uVKSJEmSJEmSJEmSdEVUOXFtUvGYR0149PWKSZXpB9if9jx0o+XGtOWgmcWV+y0PPtKwP+3ojCslSdIVtNUjv4HnH/r1n5ZS3n4O9/3YoV8/9hzuedbeCPwp8HGHHv9c4HvOfzmSJEmSJEmSJEmSpF2SUyJPYI9MP8yOBe8HDqY90FPlIOdE3yeaNogYg8y6SlQ5ERG3vYckSdJFtu2g8um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      "text/plain": [
       "<Figure size 3200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = [0]*3\n",
    "\n",
    "labels, data[2] = [*zip(*eos_dict.items())]  # 'transpose' items to parallel key, value lists\n",
    "labels, data[1] = [*zip(*final_punct_dict.items())]  # 'transpose' items to parallel key, value lists\n",
    "labels, data[0] = [*zip(*rest_tokens_dict.items())]  # 'transpose' items to parallel key, value lists\n",
    "x_ticks = [1,2,3,4]\n",
    "color = ['#82B366','#6C8EBF','#D79B00']\n",
    "labels = ['Rest of tokens', 'Final punct.', 'EOS (</s>)']\n",
    "# Creating plot \n",
    "#fig = plt.figure(figsize =(20, 7)) \n",
    "fig, ax = plt.subplots(figsize =(16, 6),dpi=200)\n",
    "for i in range(3):\n",
    "    mean_data = [statistics.mean(j) for j in data[i]] # mean per head\n",
    "    std_data = [statistics.stdev(j) for j in data[i]] # std per head\n",
    "    ax.plot(x_ticks, mean_data, color[i], markersize=6, linewidth=2, linestyle='-',alpha = 1, marker='o',label=labels[i])\n",
    "    ax.fill_between(x_ticks, (np.array(mean_data)-np.array(std_data)), (np.array(mean_data)+np.array(std_data)), color=color[i], alpha=.1)\n",
    "\n",
    "\n",
    "#ax.set_ylim(0,4)\n",
    "plt.xlabel('Head')\n",
    "plt.ylabel('Norm values')\n",
    "#plt.legend(prop={'size': 16})\n",
    "ax.legend(loc=(0.05,0.12),prop={'size': 20})\n",
    "ax.grid(True, linestyle=(0, (5, 10)))\n",
    "ax.set_xticks([1,2,3,4]);\n",
    "#ax.set_yticks([1,2,3,4]);\n",
    "#plt.grid(False, linewidth=0.25, linestyle='--');\n",
    "#ax.set_xticklabels(x_ticks);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([18, 4])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Norm K vectors')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed_vectors_box = v.norm(dim=2).data\n",
    "transformed_vectors_box = transformed_vectors_box.permute(1,0)\n",
    "print(transformed_vectors_box.size())\n",
    "  \n",
    "# Creating plot \n",
    "fig = plt.figure(figsize =(20, 7)) \n",
    "ax = fig.add_subplot(111) \n",
    "\n",
    "ax.boxplot(transformed_vectors_box);\n",
    "ax.set_title('Norm K vectors')\n",
    "#ax.set_xticklabels(en_list, rotation=45, fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d90d8f5aa2056217711987bb6fa8dc20d62369a1e594ceedbb30cde0480a32e5"
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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