diff --git a/.gitignore b/.gitignore index 6d3f62e..8469704 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,3 @@ -RelationExtraction/emnlp2017-relation-extraction-master/resources/glove/glove.6B.50d.txt - # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] @@ -74,3 +72,7 @@ output/ last_* .nfs* + +LAMA/data/ +data/ftrace/ +trex/ \ No newline at end of file diff --git a/LAMA/data/analysis-weights.ipynb b/LAMA/data/analysis-weights.ipynb deleted file mode 100644 index 75ce756..0000000 --- a/LAMA/data/analysis-weights.ipynb +++ /dev/null @@ -1,1722 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import copy\n", - "plt.style.use('/raid/lingo/akyurek/mplstyle')\n", - "plt.rc('font', serif='Times')\n", - "plt.rc('text', usetex=False)\n", - "plt.rcParams['figure.dpi'] = 150\n", - "plt.rcParams['figure.facecolor'] = 'white'\n", - "\"\"\"Google cloud directory that stores results of the experiments\"\"\"\n", - "BASE_DIR = \"./\"\n", - "METRICS_DIR = os.path.join(BASE_DIR, \"metrics\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def average_metrics(results):\n", - " \"\"\"Average the metrics over samples\"\"\"\n", - " metrics = {'precision': {}, 'recall': {}}\n", - " for k in (1, 5, 10, 50, 100):\n", - " if type(list(results[0]['precision'].keys())[0]) == str:\n", - " k = str(k)\n", - " metrics['precision'][k] = np.mean([res['precision'][k] for res in results])\n", - " metrics['recall'][k] = np.mean([res['recall'][k] for res in results])\n", - " metrics['mrr'] = np.mean([res['rr'] for res in results])\n", - " metrics['samples'] = results\n", - " return metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def metrics_to_df(metrics):\n", - " info = copy.deepcopy(metrics)\n", - " for normalization, results in info.items():\n", - " for method, method_results in results.items():\n", - " for layer_config, result in method_results.items():\n", - " if 'samples' in result:\n", - " if 'weights' in result['samples'][0]:\n", - " result['weights'] = result['samples'][0]['weights']\n", - " del result['samples']\n", - " result['mrr'] = {'1': result['mrr']}\n", - " \n", - " df = pd.DataFrame(pd.json_normalize(info, sep=\"+\")).transpose()\n", - " df.index = pd.MultiIndex.from_tuples([tuple(k.split(\"+\")) for k, v in df.iterrows()])\n", - " df = pd.melt(df.transpose())\n", - " df.columns = ['normalization', 'eval', 'layers', 'metrics', 'k', 'score']\n", - " df['weight_a'] = 1.0\n", - " df['weight_b'] = 1.0\n", - " for i, row in df.iterrows():\n", - " try:\n", - " df.at[i, 'weight_a'] = info[row['normalization']][row['eval']][row['layers']]['weights'][0]\n", - " df.at[i, 'weight_b'] = info[row['normalization']][row['eval']][row['layers']]['weights'][1]\n", - " except:\n", - " pass\n", - " \n", - " df['layer_type'] = 'A'\n", - " df.loc[df['layers'].str.contains('gradients'), 'layer_type'] = 'gradients'\n", - " df.loc[(df['layers'].str.contains('gradients')) & (df['layers'].str.contains('activations')), 'layer_type'] = 'gradients_and_activations'\n", - " df.loc[(df['layers'] == 'random') | (df['layers'] == 'bm25plus'), 'layer_type'] = 'baseline'\n", - " df = df.replace({'gradients':'G', \n", - " 'activations': 'A', \n", - " 'block.': '', \n", - " 'encoder': 'E', \n", - " 'decoder': 'D', \n", - " 'shared': 'Emb', \n", - " 'random': 'Target-Picker'}, regex=True)\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "def plot_a_be(df, title=\"\", eval_type='collapse', normalization='cosine', metric=\"mrr\", k=\"1\"):\n", - " df = df[(df['eval'] == eval_type) & (df['normalization'] == normalization) & (df['metrics'] == metric) & (df['k'] == k)]\n", - " df = df[df['layers'].str.startswith('A.E.0,A.D.0')]\n", - " fig = plt.figure(figsize=(6, 6))\n", - " ax = fig.add_subplot(111, projection = '3d')\n", - " ax.view_init(10, 120)\n", - " x = df['weight_a']\n", - " y = df['weight_b']\n", - " z = df['score']\n", - " ax.set_xlabel(\"a\")\n", - " ax.set_ylabel(\"b\")\n", - " ax.set_zlabel(\"mrr\")\n", - " ax.set_title(title)\n", - " ax.scatter(x, y, z)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_one_experiment(path, \n", - " suffix=\"\", \n", - " folder=\"plots/\",\n", - " show=True,\n", - " return_df=False):\n", - " \n", - " with open(METRICS_DIR + path) as f:\n", - " reranker_metrics = json.load(f)\n", - " \n", - " df = metrics_to_df(reranker_metrics)\n", - " df = df[(df['metrics'] != 'weights') & (df['layers'].str.startswith('A.E.0'))]\n", - " df['score'] = df['score'].astype(float)\n", - " # df = df[~df['layers'].str.contains('A.E.0,A.D.0,')]\n", - " \n", - " if return_df:\n", - " return df\n", - " \n", - " plot_a_be(df, title=suffix, eval_type='collapse', normalization='cosine', metric=\"mrr\", k=\"1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df = {}\n", - "for norm_type in (\"gn\", ):\n", - " for eos in (\"eos\", \"no_eos\"):\n", - " for subset in (\"corrects\", \"learned\"):\n", - " for accum in (\"no_accum\",):\n", - " try:\n", - " suffix=f\"{norm_type}+{eos}+{subset}+{accum}\"\n", - " visualize_one_experiment(path=f'/reranker/exp_interpol/{norm_type}_sl_{eos}__{subset}_{accum}.json', suffix=suffix, show=True)\n", - " except FileNotFoundError:\n", - " print(f'notfound: /reranker/exp_interpol/{norm_type}_sl_{eos}__{subset}_{accum}.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "df = visualize_one_experiment(path=f'/reranker/exp_interpol/gn_sl_no_eos__learned_no_accum.json', suffix=\"test\", show=False, return_df=True)\n", - "df = df[df['metrics'] == 'mrr']" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# newdf = df[(df['metrics']!='weights') & (df['layers'].str.startswith('A.E.0'))]\n", - "# newdf['score'] = newdf['score'].astype(float) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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normalizationevallayersmetricskscoreweight_aweight_blayer_type
1078cosinecollapseA.E.0,A.D.0,G.Emb_89mrr10.5333403.8888895.000000G_and_A
946cosinecollapseA.E.0,A.D.0,G.Emb_78mrr10.5180252.7777783.888889G_and_A
814cosinecollapseA.E.0,A.D.0,G.Emb_67mrr10.5148961.6666672.777778G_and_A
958cosinecollapseA.E.0,A.D.0,G.Emb_79mrr10.5046682.7777785.000000G_and_A
2300cosinefullA.E.0,A.D.0,G.Emb_89mrr10.5032053.8888895.000000G_and_A
670cosinecollapseA.E.0,A.D.0,G.Emb_55mrr10.5021200.5555560.555556G_and_A
802cosinecollapseA.E.0,A.D.0,G.Emb_66mrr10.5021201.6666671.666667G_and_A
934cosinecollapseA.E.0,A.D.0,G.Emb_77mrr10.5021202.7777782.777778G_and_A
1066cosinecollapseA.E.0,A.D.0,G.Emb_88mrr10.5021203.8888893.888889G_and_A
1198cosinecollapseA.E.0,A.D.0,G.Emb_99mrr10.5021205.0000005.000000G_and_A
2036cosinefullA.E.0,A.D.0,G.Emb_67mrr10.4936041.6666672.777778G_and_A
2168cosinefullA.E.0,A.D.0,G.Emb_78mrr10.4915152.7777783.888889G_and_A
1892cosinefullA.E.0,A.D.0,G.Emb_55mrr10.4875110.5555560.555556G_and_A
2024cosinefullA.E.0,A.D.0,G.Emb_66mrr10.4875111.6666671.666667G_and_A
2156cosinefullA.E.0,A.D.0,G.Emb_77mrr10.4875112.7777782.777778G_and_A
2288cosinefullA.E.0,A.D.0,G.Emb_88mrr10.4875113.8888893.888889G_and_A
2420cosinefullA.E.0,A.D.0,G.Emb_99mrr10.4875115.0000005.000000G_and_A
2180cosinefullA.E.0,A.D.0,G.Emb_79mrr10.4869622.7777785.000000G_and_A
1186cosinecollapseA.E.0,A.D.0,G.Emb_98mrr10.4713455.0000003.888889G_and_A
1054cosinecollapseA.E.0,A.D.0,G.Emb_87mrr10.4661923.8888892.777778G_and_A
922cosinecollapseA.E.0,A.D.0,G.Emb_76mrr10.4640662.7777781.666667G_and_A
2408cosinefullA.E.0,A.D.0,G.Emb_98mrr10.4518175.0000003.888889G_and_A
2276cosinefullA.E.0,A.D.0,G.Emb_87mrr10.4464553.8888892.777778G_and_A
1174cosinecollapseA.E.0,A.D.0,G.Emb_97mrr10.4383085.0000002.777778G_and_A
2144cosinefullA.E.0,A.D.0,G.Emb_76mrr10.4374652.7777781.666667G_and_A
1042cosinecollapseA.E.0,A.D.0,G.Emb_86mrr10.4239903.8888891.666667G_and_A
2396cosinefullA.E.0,A.D.0,G.Emb_97mrr10.4147985.0000002.777778G_and_A
790cosinecollapseA.E.0,A.D.0,G.Emb_65mrr10.4120001.6666670.555556G_and_A
1162cosinecollapseA.E.0,A.D.0,G.Emb_96mrr10.4120005.0000001.666667G_and_A
2264cosinefullA.E.0,A.D.0,G.Emb_86mrr10.4000713.8888891.666667G_and_A
826cosinecollapseA.E.0,A.D.0,G.Emb_68mrr10.3986791.6666673.888889G_and_A
910cosinecollapseA.E.0,A.D.0,G.Emb_75mrr10.3964492.7777780.555556G_and_A
2012cosinefullA.E.0,A.D.0,G.Emb_65mrr10.3875891.6666670.555556G_and_A
2384cosinefullA.E.0,A.D.0,G.Emb_96mrr10.3875895.0000001.666667G_and_A
2048cosinefullA.E.0,A.D.0,G.Emb_68mrr10.3786381.6666673.888889G_and_A
2132cosinefullA.E.0,A.D.0,G.Emb_75mrr10.3650122.7777780.555556G_and_A
1030cosinecollapseA.E.0,A.D.0,G.Emb_85mrr10.3591633.8888890.555556G_and_A
1150cosinecollapseA.E.0,A.D.0,G.Emb_95mrr10.3371405.0000000.555556G_and_A
682cosinecollapseA.E.0,A.D.0,G.Emb_56mrr10.3208520.5555561.666667G_and_A
838cosinecollapseA.E.0,A.D.0,G.Emb_69mrr10.3208521.6666675.000000G_and_A
3594dotcollapseA.E.0,A.D.0,G.Emb_95mrr10.3183865.0000000.555556G_and_A
2252cosinefullA.E.0,A.D.0,G.Emb_85mrr10.3171783.8888890.555556G_and_A
3474dotcollapseA.E.0,A.D.0,G.Emb_85mrr10.3123083.8888890.555556G_and_A
1904cosinefullA.E.0,A.D.0,G.Emb_56mrr10.3106720.5555561.666667G_and_A
2060cosinefullA.E.0,A.D.0,G.Emb_69mrr10.3106721.6666675.000000G_and_A
3354dotcollapseA.E.0,A.D.0,G.Emb_75mrr10.3015782.7777780.555556G_and_A
3342dotcollapseA.E.0,A.D.0,G.Emb_74mrr10.2948602.777778-0.555556G_and_A
2372cosinefullA.E.0,A.D.0,G.Emb_95mrr10.2948045.0000000.555556G_and_A
4816dotfullA.E.0,A.D.0,G.Emb_95mrr10.2932655.0000000.555556G_and_A
4696dotfullA.E.0,A.D.0,G.Emb_85mrr10.2887813.8888890.555556G_and_A
1138cosinecollapseA.E.0,A.D.0,G.Emb_94mrr10.2887685.000000-0.555556G_and_A
3582dotcollapseA.E.0,A.D.0,G.Emb_94mrr10.2879055.000000-0.555556G_and_A
1018cosinecollapseA.E.0,A.D.0,G.Emb_84mrr10.2860333.888889-0.555556G_and_A
3462dotcollapseA.E.0,A.D.0,G.Emb_84mrr10.2842163.888889-0.555556G_and_A
898cosinecollapseA.E.0,A.D.0,G.Emb_74mrr10.2823972.777778-0.555556G_and_A
4576dotfullA.E.0,A.D.0,G.Emb_75mrr10.2797062.7777780.555556G_and_A
3234dotcollapseA.E.0,A.D.0,G.Emb_65mrr10.2777711.6666670.555556G_and_A
3606dotcollapseA.E.0,A.D.0,G.Emb_96mrr10.2777715.0000001.666667G_and_A
778cosinecollapseA.E.0,A.D.0,G.Emb_64mrr10.2722531.666667-0.555556G_and_A
1126cosinecollapseA.E.0,A.D.0,G.Emb_93mrr10.2722535.000000-1.666667G_and_A
3486dotcollapseA.E.0,A.D.0,G.Emb_86mrr10.2709443.8888891.666667G_and_A
1006cosinecollapseA.E.0,A.D.0,G.Emb_83mrr10.2686153.888889-1.666667G_and_A
3618dotcollapseA.E.0,A.D.0,G.Emb_97mrr10.2672505.0000002.777778G_and_A
3222dotcollapseA.E.0,A.D.0,G.Emb_64mrr10.2669041.666667-0.555556G_and_A
3570dotcollapseA.E.0,A.D.0,G.Emb_93mrr10.2669045.000000-1.666667G_and_A
4564dotfullA.E.0,A.D.0,G.Emb_74mrr10.2660462.777778-0.555556G_and_A
3366dotcollapseA.E.0,A.D.0,G.Emb_76mrr10.2657412.7777781.666667G_and_A
3498dotcollapseA.E.0,A.D.0,G.Emb_87mrr10.2656033.8888892.777778G_and_A
3630dotcollapseA.E.0,A.D.0,G.Emb_98mrr10.2655695.0000003.888889G_and_A
3114dotcollapseA.E.0,A.D.0,G.Emb_55mrr10.2655550.5555560.555556G_and_A
3246dotcollapseA.E.0,A.D.0,G.Emb_66mrr10.2655551.6666671.666667G_and_A
3378dotcollapseA.E.0,A.D.0,G.Emb_77mrr10.2655552.7777782.777778G_and_A
3510dotcollapseA.E.0,A.D.0,G.Emb_88mrr10.2655553.8888893.888889G_and_A
3642dotcollapseA.E.0,A.D.0,G.Emb_99mrr10.2655555.0000005.000000G_and_A
3522dotcollapseA.E.0,A.D.0,G.Emb_89mrr10.2655503.8888895.000000G_and_A
3390dotcollapseA.E.0,A.D.0,G.Emb_78mrr10.2655442.7777783.888889G_and_A
3258dotcollapseA.E.0,A.D.0,G.Emb_67mrr10.2655421.6666672.777778G_and_A
3402dotcollapseA.E.0,A.D.0,G.Emb_79mrr10.2655392.7777785.000000G_and_A
3270dotcollapseA.E.0,A.D.0,G.Emb_68mrr10.2655351.6666673.888889G_and_A
3126dotcollapseA.E.0,A.D.0,G.Emb_56mrr10.2655190.5555561.666667G_and_A
3282dotcollapseA.E.0,A.D.0,G.Emb_69mrr10.2655191.6666675.000000G_and_A
3138dotcollapseA.E.0,A.D.0,G.Emb_57mrr10.2655000.5555562.777778G_and_A
3150dotcollapseA.E.0,A.D.0,G.Emb_58mrr10.2655000.5555563.888889G_and_A
3162dotcollapseA.E.0,A.D.0,G.Emb_59mrr10.2655000.5555565.000000G_and_A
3450dotcollapseA.E.0,A.D.0,G.Emb_83mrr10.2630453.888889-1.666667G_and_A
1114cosinecollapseA.E.0,A.D.0,G.Emb_92mrr10.2627735.000000-2.777778G_and_A
886cosinecollapseA.E.0,A.D.0,G.Emb_73mrr10.2613882.777778-1.666667G_and_A
2360cosinefullA.E.0,A.D.0,G.Emb_94mrr10.2577365.000000-0.555556G_and_A
4456dotfullA.E.0,A.D.0,G.Emb_65mrr10.2571141.6666670.555556G_and_A
4828dotfullA.E.0,A.D.0,G.Emb_96mrr10.2571145.0000001.666667G_and_A
4684dotfullA.E.0,A.D.0,G.Emb_84mrr10.2568613.888889-0.555556G_and_A
4804dotfullA.E.0,A.D.0,G.Emb_94mrr10.2545185.000000-0.555556G_and_A
2240cosinefullA.E.0,A.D.0,G.Emb_84mrr10.2529093.888889-0.555556G_and_A
3558dotcollapseA.E.0,A.D.0,G.Emb_92mrr10.2525265.000000-2.777778G_and_A
994cosinecollapseA.E.0,A.D.0,G.Emb_82mrr10.2506483.888889-2.777778G_and_A
3438dotcollapseA.E.0,A.D.0,G.Emb_82mrr10.2503903.888889-2.777778G_and_A
4708dotfullA.E.0,A.D.0,G.Emb_86mrr10.2501373.8888891.666667G_and_A
3330dotcollapseA.E.0,A.D.0,G.Emb_73mrr10.2497522.777778-1.666667G_and_A
2120cosinefullA.E.0,A.D.0,G.Emb_74mrr10.2491662.777778-0.555556G_and_A
1102cosinecollapseA.E.0,A.D.0,G.Emb_91mrr10.2480615.000000-3.888889G_and_A
\n", - "
" - ], - "text/plain": [ - " normalization eval layers metrics k score \\\n", - "1078 cosine collapse A.E.0,A.D.0,G.Emb_89 mrr 1 0.533340 \n", - "946 cosine collapse A.E.0,A.D.0,G.Emb_78 mrr 1 0.518025 \n", - "814 cosine collapse A.E.0,A.D.0,G.Emb_67 mrr 1 0.514896 \n", - "958 cosine collapse A.E.0,A.D.0,G.Emb_79 mrr 1 0.504668 \n", - "2300 cosine full A.E.0,A.D.0,G.Emb_89 mrr 1 0.503205 \n", - "670 cosine collapse A.E.0,A.D.0,G.Emb_55 mrr 1 0.502120 \n", - "802 cosine collapse A.E.0,A.D.0,G.Emb_66 mrr 1 0.502120 \n", - "934 cosine collapse A.E.0,A.D.0,G.Emb_77 mrr 1 0.502120 \n", - "1066 cosine collapse A.E.0,A.D.0,G.Emb_88 mrr 1 0.502120 \n", - "1198 cosine collapse A.E.0,A.D.0,G.Emb_99 mrr 1 0.502120 \n", - "2036 cosine full A.E.0,A.D.0,G.Emb_67 mrr 1 0.493604 \n", - "2168 cosine full A.E.0,A.D.0,G.Emb_78 mrr 1 0.491515 \n", - "1892 cosine full A.E.0,A.D.0,G.Emb_55 mrr 1 0.487511 \n", - "2024 cosine full A.E.0,A.D.0,G.Emb_66 mrr 1 0.487511 \n", - "2156 cosine full A.E.0,A.D.0,G.Emb_77 mrr 1 0.487511 \n", - "2288 cosine full A.E.0,A.D.0,G.Emb_88 mrr 1 0.487511 \n", - "2420 cosine full A.E.0,A.D.0,G.Emb_99 mrr 1 0.487511 \n", - "2180 cosine full A.E.0,A.D.0,G.Emb_79 mrr 1 0.486962 \n", - "1186 cosine collapse A.E.0,A.D.0,G.Emb_98 mrr 1 0.471345 \n", - "1054 cosine collapse A.E.0,A.D.0,G.Emb_87 mrr 1 0.466192 \n", - "922 cosine collapse A.E.0,A.D.0,G.Emb_76 mrr 1 0.464066 \n", - "2408 cosine full A.E.0,A.D.0,G.Emb_98 mrr 1 0.451817 \n", - "2276 cosine full A.E.0,A.D.0,G.Emb_87 mrr 1 0.446455 \n", - "1174 cosine collapse A.E.0,A.D.0,G.Emb_97 mrr 1 0.438308 \n", - "2144 cosine full A.E.0,A.D.0,G.Emb_76 mrr 1 0.437465 \n", - "1042 cosine collapse A.E.0,A.D.0,G.Emb_86 mrr 1 0.423990 \n", - "2396 cosine full A.E.0,A.D.0,G.Emb_97 mrr 1 0.414798 \n", - "790 cosine collapse A.E.0,A.D.0,G.Emb_65 mrr 1 0.412000 \n", - "1162 cosine collapse A.E.0,A.D.0,G.Emb_96 mrr 1 0.412000 \n", - "2264 cosine full A.E.0,A.D.0,G.Emb_86 mrr 1 0.400071 \n", - "826 cosine collapse A.E.0,A.D.0,G.Emb_68 mrr 1 0.398679 \n", - "910 cosine collapse A.E.0,A.D.0,G.Emb_75 mrr 1 0.396449 \n", - "2012 cosine full A.E.0,A.D.0,G.Emb_65 mrr 1 0.387589 \n", - "2384 cosine full A.E.0,A.D.0,G.Emb_96 mrr 1 0.387589 \n", - "2048 cosine full A.E.0,A.D.0,G.Emb_68 mrr 1 0.378638 \n", - "2132 cosine full A.E.0,A.D.0,G.Emb_75 mrr 1 0.365012 \n", - "1030 cosine collapse A.E.0,A.D.0,G.Emb_85 mrr 1 0.359163 \n", - "1150 cosine collapse A.E.0,A.D.0,G.Emb_95 mrr 1 0.337140 \n", - "682 cosine collapse A.E.0,A.D.0,G.Emb_56 mrr 1 0.320852 \n", - "838 cosine collapse A.E.0,A.D.0,G.Emb_69 mrr 1 0.320852 \n", - "3594 dot collapse A.E.0,A.D.0,G.Emb_95 mrr 1 0.318386 \n", - "2252 cosine full A.E.0,A.D.0,G.Emb_85 mrr 1 0.317178 \n", - "3474 dot collapse A.E.0,A.D.0,G.Emb_85 mrr 1 0.312308 \n", - "1904 cosine full A.E.0,A.D.0,G.Emb_56 mrr 1 0.310672 \n", - "2060 cosine full A.E.0,A.D.0,G.Emb_69 mrr 1 0.310672 \n", - "3354 dot collapse A.E.0,A.D.0,G.Emb_75 mrr 1 0.301578 \n", - "3342 dot collapse A.E.0,A.D.0,G.Emb_74 mrr 1 0.294860 \n", - "2372 cosine full A.E.0,A.D.0,G.Emb_95 mrr 1 0.294804 \n", - "4816 dot full A.E.0,A.D.0,G.Emb_95 mrr 1 0.293265 \n", - "4696 dot full A.E.0,A.D.0,G.Emb_85 mrr 1 0.288781 \n", - "1138 cosine collapse A.E.0,A.D.0,G.Emb_94 mrr 1 0.288768 \n", - "3582 dot collapse A.E.0,A.D.0,G.Emb_94 mrr 1 0.287905 \n", - "1018 cosine collapse A.E.0,A.D.0,G.Emb_84 mrr 1 0.286033 \n", - "3462 dot collapse A.E.0,A.D.0,G.Emb_84 mrr 1 0.284216 \n", - "898 cosine collapse A.E.0,A.D.0,G.Emb_74 mrr 1 0.282397 \n", - "4576 dot full A.E.0,A.D.0,G.Emb_75 mrr 1 0.279706 \n", - "3234 dot collapse A.E.0,A.D.0,G.Emb_65 mrr 1 0.277771 \n", - "3606 dot collapse A.E.0,A.D.0,G.Emb_96 mrr 1 0.277771 \n", - "778 cosine collapse A.E.0,A.D.0,G.Emb_64 mrr 1 0.272253 \n", - "1126 cosine collapse A.E.0,A.D.0,G.Emb_93 mrr 1 0.272253 \n", - "3486 dot collapse A.E.0,A.D.0,G.Emb_86 mrr 1 0.270944 \n", - "1006 cosine collapse A.E.0,A.D.0,G.Emb_83 mrr 1 0.268615 \n", - "3618 dot collapse A.E.0,A.D.0,G.Emb_97 mrr 1 0.267250 \n", - "3222 dot collapse A.E.0,A.D.0,G.Emb_64 mrr 1 0.266904 \n", - "3570 dot collapse A.E.0,A.D.0,G.Emb_93 mrr 1 0.266904 \n", - "4564 dot full A.E.0,A.D.0,G.Emb_74 mrr 1 0.266046 \n", - "3366 dot collapse A.E.0,A.D.0,G.Emb_76 mrr 1 0.265741 \n", - "3498 dot collapse A.E.0,A.D.0,G.Emb_87 mrr 1 0.265603 \n", - "3630 dot collapse A.E.0,A.D.0,G.Emb_98 mrr 1 0.265569 \n", - "3114 dot collapse A.E.0,A.D.0,G.Emb_55 mrr 1 0.265555 \n", - "3246 dot collapse A.E.0,A.D.0,G.Emb_66 mrr 1 0.265555 \n", - "3378 dot collapse A.E.0,A.D.0,G.Emb_77 mrr 1 0.265555 \n", - "3510 dot collapse A.E.0,A.D.0,G.Emb_88 mrr 1 0.265555 \n", - "3642 dot collapse A.E.0,A.D.0,G.Emb_99 mrr 1 0.265555 \n", - "3522 dot collapse A.E.0,A.D.0,G.Emb_89 mrr 1 0.265550 \n", - "3390 dot collapse A.E.0,A.D.0,G.Emb_78 mrr 1 0.265544 \n", - "3258 dot collapse A.E.0,A.D.0,G.Emb_67 mrr 1 0.265542 \n", - "3402 dot collapse A.E.0,A.D.0,G.Emb_79 mrr 1 0.265539 \n", - "3270 dot collapse A.E.0,A.D.0,G.Emb_68 mrr 1 0.265535 \n", - "3126 dot collapse A.E.0,A.D.0,G.Emb_56 mrr 1 0.265519 \n", - "3282 dot collapse A.E.0,A.D.0,G.Emb_69 mrr 1 0.265519 \n", - "3138 dot collapse A.E.0,A.D.0,G.Emb_57 mrr 1 0.265500 \n", - "3150 dot collapse A.E.0,A.D.0,G.Emb_58 mrr 1 0.265500 \n", - "3162 dot collapse A.E.0,A.D.0,G.Emb_59 mrr 1 0.265500 \n", - "3450 dot collapse A.E.0,A.D.0,G.Emb_83 mrr 1 0.263045 \n", - "1114 cosine collapse A.E.0,A.D.0,G.Emb_92 mrr 1 0.262773 \n", - "886 cosine collapse A.E.0,A.D.0,G.Emb_73 mrr 1 0.261388 \n", - "2360 cosine full A.E.0,A.D.0,G.Emb_94 mrr 1 0.257736 \n", - "4456 dot full A.E.0,A.D.0,G.Emb_65 mrr 1 0.257114 \n", - "4828 dot full A.E.0,A.D.0,G.Emb_96 mrr 1 0.257114 \n", - "4684 dot full A.E.0,A.D.0,G.Emb_84 mrr 1 0.256861 \n", - "4804 dot full A.E.0,A.D.0,G.Emb_94 mrr 1 0.254518 \n", - "2240 cosine full A.E.0,A.D.0,G.Emb_84 mrr 1 0.252909 \n", - "3558 dot collapse A.E.0,A.D.0,G.Emb_92 mrr 1 0.252526 \n", - "994 cosine collapse A.E.0,A.D.0,G.Emb_82 mrr 1 0.250648 \n", - "3438 dot collapse A.E.0,A.D.0,G.Emb_82 mrr 1 0.250390 \n", - "4708 dot full A.E.0,A.D.0,G.Emb_86 mrr 1 0.250137 \n", - "3330 dot collapse A.E.0,A.D.0,G.Emb_73 mrr 1 0.249752 \n", - "2120 cosine full A.E.0,A.D.0,G.Emb_74 mrr 1 0.249166 \n", - "1102 cosine collapse A.E.0,A.D.0,G.Emb_91 mrr 1 0.248061 \n", - "\n", - " weight_a weight_b layer_type \n", - "1078 3.888889 5.000000 G_and_A \n", - "946 2.777778 3.888889 G_and_A \n", - "814 1.666667 2.777778 G_and_A \n", - "958 2.777778 5.000000 G_and_A \n", - "2300 3.888889 5.000000 G_and_A \n", - "670 0.555556 0.555556 G_and_A \n", - "802 1.666667 1.666667 G_and_A \n", - "934 2.777778 2.777778 G_and_A \n", - "1066 3.888889 3.888889 G_and_A \n", - "1198 5.000000 5.000000 G_and_A \n", - "2036 1.666667 2.777778 G_and_A \n", - "2168 2.777778 3.888889 G_and_A \n", - "1892 0.555556 0.555556 G_and_A \n", - "2024 1.666667 1.666667 G_and_A \n", - "2156 2.777778 2.777778 G_and_A \n", - "2288 3.888889 3.888889 G_and_A \n", - "2420 5.000000 5.000000 G_and_A \n", - "2180 2.777778 5.000000 G_and_A \n", - "1186 5.000000 3.888889 G_and_A \n", - "1054 3.888889 2.777778 G_and_A \n", - "922 2.777778 1.666667 G_and_A \n", - "2408 5.000000 3.888889 G_and_A \n", - "2276 3.888889 2.777778 G_and_A \n", - "1174 5.000000 2.777778 G_and_A \n", - "2144 2.777778 1.666667 G_and_A \n", - "1042 3.888889 1.666667 G_and_A \n", - "2396 5.000000 2.777778 G_and_A \n", - "790 1.666667 0.555556 G_and_A \n", - "1162 5.000000 1.666667 G_and_A \n", - "2264 3.888889 1.666667 G_and_A \n", - "826 1.666667 3.888889 G_and_A \n", - "910 2.777778 0.555556 G_and_A \n", - "2012 1.666667 0.555556 G_and_A \n", - "2384 5.000000 1.666667 G_and_A \n", - "2048 1.666667 3.888889 G_and_A \n", - "2132 2.777778 0.555556 G_and_A \n", - "1030 3.888889 0.555556 G_and_A \n", - "1150 5.000000 0.555556 G_and_A \n", - "682 0.555556 1.666667 G_and_A \n", - "838 1.666667 5.000000 G_and_A \n", - "3594 5.000000 0.555556 G_and_A \n", - "2252 3.888889 0.555556 G_and_A \n", - "3474 3.888889 0.555556 G_and_A \n", - "1904 0.555556 1.666667 G_and_A \n", - "2060 1.666667 5.000000 G_and_A \n", - "3354 2.777778 0.555556 G_and_A \n", - "3342 2.777778 -0.555556 G_and_A \n", - "2372 5.000000 0.555556 G_and_A \n", - "4816 5.000000 0.555556 G_and_A \n", - "4696 3.888889 0.555556 G_and_A \n", - "1138 5.000000 -0.555556 G_and_A \n", - "3582 5.000000 -0.555556 G_and_A \n", - "1018 3.888889 -0.555556 G_and_A \n", - "3462 3.888889 -0.555556 G_and_A \n", - "898 2.777778 -0.555556 G_and_A \n", - "4576 2.777778 0.555556 G_and_A \n", - "3234 1.666667 0.555556 G_and_A \n", - "3606 5.000000 1.666667 G_and_A \n", - "778 1.666667 -0.555556 G_and_A \n", - "1126 5.000000 -1.666667 G_and_A \n", - "3486 3.888889 1.666667 G_and_A \n", - "1006 3.888889 -1.666667 G_and_A \n", - "3618 5.000000 2.777778 G_and_A \n", - "3222 1.666667 -0.555556 G_and_A \n", - "3570 5.000000 -1.666667 G_and_A \n", - "4564 2.777778 -0.555556 G_and_A \n", - "3366 2.777778 1.666667 G_and_A \n", - "3498 3.888889 2.777778 G_and_A \n", - "3630 5.000000 3.888889 G_and_A \n", - "3114 0.555556 0.555556 G_and_A \n", - "3246 1.666667 1.666667 G_and_A \n", - "3378 2.777778 2.777778 G_and_A \n", - "3510 3.888889 3.888889 G_and_A \n", - "3642 5.000000 5.000000 G_and_A \n", - "3522 3.888889 5.000000 G_and_A \n", - "3390 2.777778 3.888889 G_and_A \n", - "3258 1.666667 2.777778 G_and_A \n", - "3402 2.777778 5.000000 G_and_A \n", - "3270 1.666667 3.888889 G_and_A \n", - "3126 0.555556 1.666667 G_and_A \n", - "3282 1.666667 5.000000 G_and_A \n", - "3138 0.555556 2.777778 G_and_A \n", - "3150 0.555556 3.888889 G_and_A \n", - "3162 0.555556 5.000000 G_and_A \n", - "3450 3.888889 -1.666667 G_and_A \n", - "1114 5.000000 -2.777778 G_and_A \n", - "886 2.777778 -1.666667 G_and_A \n", - "2360 5.000000 -0.555556 G_and_A \n", - "4456 1.666667 0.555556 G_and_A \n", - "4828 5.000000 1.666667 G_and_A \n", - "4684 3.888889 -0.555556 G_and_A \n", - "4804 5.000000 -0.555556 G_and_A \n", - "2240 3.888889 -0.555556 G_and_A \n", - "3558 5.000000 -2.777778 G_and_A \n", - "994 3.888889 -2.777778 G_and_A \n", - "3438 3.888889 -2.777778 G_and_A \n", - "4708 3.888889 1.666667 G_and_A \n", - "3330 2.777778 -1.666667 G_and_A \n", - "2120 2.777778 -0.555556 G_and_A \n", - "1102 5.000000 -3.888889 G_and_A " - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.options.display.max_rows = 100\n", - "df.nlargest(100, 'score')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python-3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/LAMA/data/stats.ipynb b/LAMA/data/stats.ipynb deleted file mode 100644 index e85b375..0000000 --- a/LAMA/data/stats.ipynb +++ /dev/null @@ -1,234 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import copy\n", - "plt.style.use('/raid/lingo/akyurek/mplstyle')\n", - "plt.rc('font', serif='Times')\n", - "plt.rc('text', usetex=False)\n", - "plt.rcParams['figure.dpi'] = 150\n", - "plt.rcParams['figure.facecolor'] = 'white'\n", - "\"\"\"Google cloud directory that stores results of the experiments\"\"\"\n", - "BASE_DIR = \"./\"\n", - "METRICS_DIR = os.path.join(BASE_DIR, \"metrics\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def read_jsonl(path):\n", - " data = []\n", - " with open(path) as f:\n", - " for line in f:\n", - " data.append(json.loads(line))\n", - " return np.array(data)\n", - "\n", - "def read_facts(abstracts):\n", - " facts = set()\n", - " for a in abstracts:\n", - " for fact in a['facts'].split(';'):\n", - " facts.add(fact)\n", - " return facts\n", - "\n", - "def read_no_facts(abstracts):\n", - " return np.array([len(set(a['facts'].split(';'))) for a in abstracts])\n", - "\n", - "def facts_to_field(facts, field=\"obj_uri\"):\n", - " if field == \"obj_uri\":\n", - " v = [fact.split(',')[1] for fact in facts]\n", - " elif field == \"sub_uri\":\n", - " v = [fact.split(',')[2] for fact in facts]\n", - " else:\n", - " v = [fact.split(',')[0] for fact in facts]\n", - " return v\n", - "\n", - "def read_string_field(abstracts, field=\"obj_uri\"):\n", - " return np.array([a[field] for a in abstracts])\n", - "\n", - "\n", - "def get_sentence(abstract):\n", - " targets = abstract['targets_pretokenized'].replace(' ', '').strip()\n", - " sentence = abstract['inputs_pretokenized'].replace('', targets)\n", - " return sentence" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1064471, 27528)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "abstracts_path = os.path.join(BASE_DIR, \"TREx_lama_templates_v2\", \"abstracts\", \"all_used.jsonl\")\n", - "queries_path = os.path.join(BASE_DIR, \"TREx_lama_templates_v2\", \"all.jsonl.processed\")\n", - "abstracts = read_jsonl(abstracts_path)\n", - "queries = read_jsonl(queries_path)\n", - "(len(abstracts), len(queries))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "facts = read_facts(abstracts)\n", - "no_facts = read_no_facts(abstracts)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "441670" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sentences = [get_sentence(abstract) for abstract in abstracts]\n", - "len(set(sentences))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(317381, (3.4631746661017537, 3.3406391936613984, 1, 1047))" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(facts), tuple(f(no_facts) for f in (np.mean, np.std, np.min, np.max))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "pos_nos_abstracts = tuple(len(set(facts_to_field(facts, field=field))) \n", - " for field in ('predicate_id', 'obj_uri', 'sub_uri'))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(406, 18037, 224043)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pos_nos_abstracts" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "objs = read_string_field(queries, field=\"obj_uri\")\n", - "subs = read_string_field(queries, field=\"sub_uri\")\n", - "predicates = read_string_field(queries, field=\"predicate_id\")\n", - "pos_nos_queries = (len(set(predicates)), len(set(objs)), len(set(subs)))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(41, 1767, 25926)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pos_nos_queries " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python-3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/README.md b/README.md index f36dac0..6adbd06 100644 --- a/README.md +++ b/README.md @@ -1,8 +1 @@ -# Influence Experiments on TReX / LAMA - -This repo is forked from the [code](https://github.com/cloudygoose/fewshot_lama/) contains for https://arxiv.org/abs/2109.02772. - -We only use the forked repo to get templatized queries related with TRex abstracts. Our main code is in [LAMA/data](./LAMA/data) - - - +# Tracing Knowledge in Language Models Back to the Training Data diff --git a/analysis.ipynb b/analysis.ipynb index 24f29f1..3fce242 100644 --- a/analysis.ipynb +++ b/analysis.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -21,11 +12,10 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -35,94 +25,173 @@ "import pickle\n", "\n", "from IPython.display import display\n", - "plt.style.use('.mplstyle')\n", - "pd.set_option('display.max_rows', 25)\n", - "pd.set_option('display.max_colwidth', 0)" + "\n", + "plt.style.use(\".mplstyle\")\n", + "pd.set_option(\"display.max_rows\", 25)\n", + "pd.set_option(\"display.max_colwidth\", 0)\n" ] }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BASE_DIR = \"LAMA/data/\"\n", - "METRICS_DIR = os.path.join(BASE_DIR, \"metrics/\")" + "METRICS_DIR = os.path.join(BASE_DIR, \"metrics/\")\n" ] }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def metrics_to_df(metrics):\n", + "def metrics_to_df(results, metrics=[\"precision\", \"recall\", \"mrr\"]):\n", " data = []\n", - " \n", - " for method in ('bm25plus', 'random'):\n", - " for eval_type in ('collapse', 'full'):\n", - " for metric in ('precision', 'recall', 'mrr'):\n", - " metric_result = metrics['evals'][method][eval_type][metric]\n", - " for normalized in ('cosine', 'dot'):\n", - " for method_type in ('local', 'global'):\n", + "\n", + " for method in (\"bm25plus\", \"random\"):\n", + " for eval_type in (\"collapse\", \"full\"):\n", + " if eval_type != \"collapse\":\n", + " current_metrics = metrics + [\"mrr_compare_fn_subject\", \"mrr_compare_fn_object\", \"mrr_compare_fn_relation\"]\n", + " else:\n", + " current_metrics = metrics\n", + " for metric in current_metrics:\n", + " metric_result = results[\"evals\"][method][eval_type][metric]\n", + " for normalized in (\"cosine\", \"dot\"):\n", + " for method_type in (\"local\", \"global\"):\n", " try:\n", " for k, score in metric_result.items():\n", - " data.append((method, method_type, normalized, eval_type, metric, str(k), score))\n", + " data.append(\n", + " (\n", + " method,\n", + " method_type,\n", + " normalized,\n", + " eval_type,\n", + " metric,\n", + " str(k),\n", + " score,\n", + " )\n", + " )\n", " except:\n", - " data.append((method, method_type, normalized, eval_type, metric, \"1\", metric_result))\n", - " \n", - " for method_type in ('local', 'global'):\n", - " for eval_type in ('collapse', 'full'):\n", - " for normalized in ('cosine', 'dot'):\n", + " data.append(\n", + " (\n", + " method,\n", + " method_type,\n", + " normalized,\n", + " eval_type,\n", + " metric,\n", + " \"1\",\n", + " metric_result,\n", + " )\n", + " )\n", + "\n", + " for method_type in (\"local\", \"global\"):\n", + " for eval_type in (\"collapse\", \"full\"):\n", + " for normalized in (\"cosine\", \"dot\"):\n", " if method_type == \"global\" and normalized == \"dot\":\n", " continue\n", - " for method, method_results in metrics['evals'][method_type][normalized][eval_type].items():\n", - " for metric in ('precision', 'recall', 'mrr'):\n", + " for method, method_results in results[\"evals\"][method_type][\n", + " normalized\n", + " ][eval_type].items():\n", + " if eval_type != \"collapse\":\n", + " current_metrics = metrics + [\"mrr_compare_fn_subject\", \"mrr_compare_fn_object\", \"mrr_compare_fn_relation\"]\n", + " else:\n", + " current_metrics = metrics\n", + " for metric in current_metrics:\n", " metric_result = method_results[metric]\n", " try:\n", " for k, score in metric_result.items():\n", - " data.append((method, method_type, normalized, eval_type, metric, str(k), score))\n", + " data.append(\n", + " (\n", + " method,\n", + " method_type,\n", + " normalized,\n", + " eval_type,\n", + " metric,\n", + " str(k),\n", + " score,\n", + " )\n", + " )\n", " except:\n", - " data.append((method, method_type, normalized, eval_type, metric, \"1\", metric_result))\n", - " \n", - " df = pd.DataFrame(data, columns=['layers', \n", - " 'norm_type', \n", - " 'normalization', \n", - " 'eval', \n", - " 'metrics', \n", - " 'k', \n", - " 'score']) \n", - " \n", - " df['layer_type'] = 'Embed'\n", - " df.loc[df['layers'].str.contains('gradients'), 'layer_type'] = 'TracIn'\n", - " df.loc[(df['layers'].str.contains('gradients')) &\n", - " (df['layers'].str.contains('activations')), 'layer_type'] = 'TracIn+Embed'\n", - " df.loc[(df['layers'] == 'random') | (df['layers'] == 'bm25plus'), 'layer_type'] = 'baselines'\n", - " \n", - " df = df.replace({'gradients':'G', \n", - " 'activations': 'A',\n", - " 'block.': '', \n", - " 'encoder': 'E', \n", - " 'decoder': 'D', \n", - " 'shared': 'emb', \n", - " 'random': 'Target-Picker'}, \n", - " regex=True)\n", - " \n", - " df['layers'] = df['layers']\\\n", - " .replace({f'G.E.{i},G.D.{i}': f'G.E.{i+1},G.D.{i+1}' for i in range(12)}, regex=False)\\\n", - " .replace({f'G.E.{i}': f'G.E.{i+1}' for i in range(12)}, regex=False)\\\n", - " .replace({f'G.emb,G.E.{i},G.D.{i}': f'G.emb,G.E.{i+1},G.D.{i+1}' for i in range(12)}, regex=False)\\\n", - " .replace({f'G.emb,G.E.{i}': f'G.emb,G.E.{i+1}' for i in range(12)}, regex=False)\\\n", - " .str.replace('G.emb', 'G.0', regex=False)\\\n", - " .str.replace('bm25plus','BM25+', regex=False)\\\n", - " .str.replace('Target-Picker', 'Random-Target', regex=False)\n", - " \n", - " return df" + " data.append(\n", + " (\n", + " method,\n", + " method_type,\n", + " normalized,\n", + " eval_type,\n", + " metric,\n", + " \"1\",\n", + " metric_result,\n", + " )\n", + " )\n", + "\n", + " df = pd.DataFrame(\n", + " data,\n", + " columns=[\n", + " \"layers\",\n", + " \"norm_type\",\n", + " \"normalization\",\n", + " \"eval\",\n", + " \"metrics\",\n", + " \"k\",\n", + " \"score\",\n", + " ],\n", + " )\n", + "\n", + " df[\"layer_type\"] = \"Embed\"\n", + " df.loc[df[\"layers\"].str.contains(\"gradients\"), \"layer_type\"] = \"TracIn\"\n", + " df.loc[\n", + " (df[\"layers\"].str.contains(\"gradients\"))\n", + " & (df[\"layers\"].str.contains(\"activations\")),\n", + " \"layer_type\",\n", + " ] = \"TracIn+Embed\"\n", + " df.loc[\n", + " (df[\"layers\"] == \"random\") | (df[\"layers\"] == \"bm25plus\"), \"layer_type\"\n", + " ] = \"baselines\"\n", + "\n", + " df = df.replace(\n", + " {\n", + " \"gradients\": \"G\",\n", + " \"activations\": \"A\",\n", + " \"block.\": \"\",\n", + " \"encoder\": \"E\",\n", + " \"decoder\": \"D\",\n", + " \"shared\": \"emb\",\n", + " \"random\": \"Target-Picker\",\n", + " },\n", + " regex=True,\n", + " )\n", + "\n", + " df[\"layers\"] = (\n", + " df[\"layers\"]\n", + " .replace(\n", + " {f\"G.E.{i},G.D.{i}\": f\"G.E.{i+1},G.D.{i+1}\" for i in range(12)},\n", + " regex=False,\n", + " )\n", + " .replace({f\"G.E.{i}\": f\"G.E.{i+1}\" for i in range(12)}, regex=False)\n", + " .replace(\n", + " {\n", + " f\"G.emb,G.E.{i},G.D.{i}\": f\"G.emb,G.E.{i+1},G.D.{i+1}\"\n", + " for i in range(12)\n", + " },\n", + " regex=False,\n", + " )\n", + " .replace(\n", + " {f\"G.emb,G.E.{i}\": f\"G.emb,G.E.{i+1}\" for i in range(12)},\n", + " regex=False,\n", + " )\n", + " .str.replace(\"G.emb\", \"G.0\", regex=False)\n", + " .str.replace(\"bm25plus\", \"BM25+\", regex=False)\n", + " .str.replace(\"Target-Picker\", \"Random-Target\", regex=False)\n", + " )\n", + "\n", + " return df\n" ] }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -156,917 +225,326 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def visualize_one_experiment(paths, \n", - " suffix=\"\", \n", - " folder=\"plots/\",\n", - " save=False,\n", - " visualize=False,\n", - " k='3',\n", - " norm_type='local',\n", - " normalization='cosine'):\n", + "def visualize_one_experiment(\n", + " paths,\n", + " suffix=\"\",\n", + " folder=\"plots/\",\n", + " save=False,\n", + " visualize=False,\n", + " k=\"3\",\n", + " norm_type=\"local\",\n", + " normalization=\"cosine\",\n", + "):\n", " dfs = []\n", " for (i, path) in enumerate(paths):\n", - " \n", + "\n", " with gzip.open(METRICS_DIR + path) as f:\n", " reranker_metrics = pickle.load(f)\n", - " \n", + "\n", " df = metrics_to_df(reranker_metrics)\n", - " df['seed'] = i\n", + " df[\"seed\"] = i\n", " dfs.append(df)\n", - " \n", + "\n", " df = pd.concat(dfs, ignore_index=True)\n", - " \n", + "\n", " if visualize:\n", - " \n", - " # scores = df.groupby(['normalization', 'eval', 'layers', 'metrics', 'k']).agg({'score': ['mean', 'std']}, as_index=False) \n", + "\n", + " # scores = df.groupby(['normalization', 'eval', 'layers', 'metrics', 'k']).agg({'score': ['mean', 'std']}, as_index=False)\n", " # Layers that we don't want for visualizations\n", - " ddf = df[~df['layers'].str.contains('A.E.0,A.D.0,')]\n", - " \n", + " ddf = df[~df[\"layers\"].str.contains(\"A.E.0,A.D.0,\")]\n", + "\n", " for method in (\"full\", \"collapse\"):\n", - " plot_with_filter(ddf, \n", - " filter=lambda x: x[(x['metrics'] == 'precision') & \n", - " (x['k'] == k) & \n", - " (x['eval'] == method) & \n", - " (x['norm_type'] == norm_type) &\n", - " (x['normalization'] == normalization)],\n", - " title=f'precision@3 ({method} + {suffix})',\n", - " folder=folder,\n", - " ylabel='precision@3',\n", - " save=save)\n", - " \n", - " plot_with_filter(ddf,\n", - " filter=lambda x: x[(x['metrics'] == 'recall') &\n", - " (x['k'] == k) & \n", - " (x['eval'] == method) &\n", - " (x['norm_type'] == norm_type) &\n", - " (x['normalization'] == normalization)],\n", - " title=f'recall@3 ({method} + {suffix})',\n", - " folder=folder,\n", - " ylabel='recall@3',\n", - " save=save)\n", - " \n", - " plot_with_filter(ddf,\n", - " filter=lambda x: x[(x['metrics'] == 'mrr') &\n", - " (x['k'] == '1') &\n", - " (x['eval'] == method) &\n", - " (x['norm_type'] == norm_type) &\n", - " (x['normalization'] == normalization)],\n", - " title=f'mrr ({method} + {suffix})',\n", - " folder=folder,\n", - " ylabel='mrr',\n", - " save=save)\n", - " return df\n", - " " + " plot_with_filter(\n", + " ddf,\n", + " filter=lambda x: x[\n", + " (x[\"metrics\"] == \"precision\")\n", + " & (x[\"k\"] == '3')\n", + " & (x[\"eval\"] == method)\n", + " & (x[\"norm_type\"] == norm_type)\n", + " & (x[\"normalization\"] == normalization)\n", + " ],\n", + " title=f\"precision@3 ({method} + {suffix})\",\n", + " folder=folder,\n", + " ylabel=\"precision@3\",\n", + " save=save,\n", + " )\n", + "\n", + " plot_with_filter(\n", + " ddf,\n", + " filter=lambda x: x[\n", + " (x[\"metrics\"] == \"recall\")\n", + " & (x[\"k\"] == '10')\n", + " & (x[\"eval\"] == method)\n", + " & (x[\"norm_type\"] == norm_type)\n", + " & (x[\"normalization\"] == normalization)\n", + " ],\n", + " title=f\"recall@10 ({method} + {suffix})\",\n", + " folder=folder,\n", + " ylabel=\"recall@10\",\n", + " save=save,\n", + " )\n", + "\n", + " plot_with_filter(\n", + " ddf,\n", + " filter=lambda x: x[\n", + " (x[\"metrics\"] == \"mrr\")\n", + " & (x[\"k\"] == \"1\")\n", + " & (x[\"eval\"] == method)\n", + " & (x[\"norm_type\"] == norm_type)\n", + " & (x[\"normalization\"] == normalization)\n", + " ],\n", + " title=f\"mrr ({method} + {suffix})\",\n", + " folder=folder,\n", + " ylabel=\"mrr\",\n", + " save=save,\n", + " )\n", + " return df\n" ] }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "\n", "def get_abstracts_df(res, fact):\n", - " abstracts = np.array(res['nn_abstracts'])\n", + " abstracts = np.array(res[\"nn_abstracts\"])\n", " for i, abstract in enumerate(abstracts):\n", " try:\n", - " abstract['score'] = res['nn_scores'][i]\n", + " abstract[\"score\"] = res[\"nn_scores\"][i]\n", " except KeyError:\n", - " abstract['score'] = res['nn']['scores'][i]\n", - " \n", + " abstract[\"score\"] = res[\"nn\"][\"scores\"][i]\n", + "\n", " df = pd.DataFrame(pd.json_normalize(abstracts)).round(3)\n", - " df['label'] = df['facts'].str.contains(\",\".join(fact))\n", - " \n", - " df = df.drop(['page_uri', \n", - " 'masked_uri', \n", - " 'masked_type', \n", - " 'facts', \n", - " 'sentence_uris'], axis=1)\n", + " df[\"label\"] = df[\"facts\"].str.contains(\",\".join(fact))\n", + "\n", + " df = df.drop(\n", + " [\"page_uri\", \"masked_uri\", \"masked_type\", \"facts\", \"example_uris\"],\n", + " axis=1,\n", + " )\n", " return df\n", - " \n", + "\n", + "\n", "def get_nn_abstracts(res, baseline_res, config=\"\"):\n", " \"\"\"Get list of abstracts and their scores (dotproduct score)\"\"\"\n", " print(f\"Config: {config}\")\n", - " example = baseline_res['example']\n", - " print(f\"Example: {example['inputs_pretokenized']} => {example['targets_pretokenized']}\")\n", - " fact = (example['predicate_id'].strip(), \n", - " example['obj_uri'].strip(), \n", - " example['sub_uri'].strip())\n", + " example = baseline_res[\"example\"]\n", + " print(\n", + " f\"Example: {example['inputs_pretokenized']} =>\"\n", + " f\" {example['targets_pretokenized']}\"\n", + " )\n", + " fact = (\n", + " example[\"predicate_id\"].strip(),\n", + " example[\"obj_uri\"].strip(),\n", + " example[\"sub_uri\"].strip(),\n", + " )\n", " print(f\"Fact: {fact}\")\n", - " print(f\"Model Precision\", res['precision'])\n", + " print(f\"Model Precision\", res[\"precision\"])\n", " df_model = get_abstracts_df(res, fact)\n", - " print(f\"Baseline Precision\", baseline_res['precision'])\n", - " df_baseline = get_abstracts_df(baseline_res, fact) \n", + " print(f\"Baseline Precision\", baseline_res[\"precision\"])\n", + " df_baseline = get_abstracts_df(baseline_res, fact)\n", " return df_model, df_baseline\n", "\n", + "\n", "def result_getter(path):\n", " with gzip.open(METRICS_DIR + path) as f:\n", - " reranker_metrics = pickle.load(f)\n", - " \n", - " def getter(i=3,\n", - " sim=\"cosine\",\n", - " method=\"collapse\",\n", - " normalization=\"local\",\n", - " layers='activations.encoder.block.0,gradients.shared'):\n", - " \n", - " config = {\"sim\": sim,\n", - " \"method\": method,\n", - " \"layers\": layers,\n", - " \"normalization\": normalization}\n", - " \n", - " return get_nn_abstracts(reranker_metrics['evals'][normalization][sim][method][layers]['samples'][i], \n", - " reranker_metrics['samples'][i],\n", - " config=config)\n", - " \n", + " reranker_metrics = pickle.load(f)\n", + "\n", + " def getter(\n", + " i=3,\n", + " sim=\"cosine\",\n", + " method=\"collapse\",\n", + " normalization=\"local\",\n", + " layers=\"activations.encoder.block.0,gradients.shared\",\n", + " ):\n", + "\n", + " config = {\n", + " \"sim\": sim,\n", + " \"method\": method,\n", + " \"layers\": layers,\n", + " \"normalization\": normalization,\n", + " }\n", + "\n", + " return get_nn_abstracts(\n", + " reranker_metrics[\"evals\"][normalization][sim][method][layers][\n", + " \"samples\"\n", + " ][i],\n", + " reranker_metrics[\"samples\"][i],\n", + " config=config,\n", + " )\n", + "\n", " getter.metrics = reranker_metrics\n", - " return getter" + " return getter\n" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "getter = result_getter('reranker/unfiltered/seed_0/learned/no_eos_accum/results.pickle')" + "getter = result_getter('reranker/sweep_v2/seed_0/learned/no_eos_accum/results_detailed.pickle')" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Config: {'sim': 'cosine', 'method': 'collapse', 'layers': 'activations.encoder.block.0,gradients.shared', 'normalization': 'local'}\n", - "Example: The native language of Claude Cerval is . => French\n", - "Fact: ('P103', 'Q150', 'Q2419802')\n", - "Model Precision {1: 1.0, 3: 0.3333333333333333, 5: 0.2, 10: 0.1, 25: 0.04}\n", - "Baseline Precision {1: 1.0, 3: 0.6666666666666666, 5: 0.4, 10: 0.2, 25: 0.08}\n", - "Model Table\n" - ] - }, - { - "data": { - "text/html": [ - 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inputs_pretokenizedtargets_pretokenizedscorelabel
0Claude Cerval (21 February 1921 - 25 July 1972) was a <extra_id_0> film actor.<extra_id_0> French1.077True
1The official language of Ukraine is Ukrainian, an East Slavic language which is the native language of 67.5% of <extra_id_0>'s population.<extra_id_0> Ukraine1.027False
2The Russian language predominates in figure skating in <extra_id_0>, although Ukrainian is the native language of about 67% of the general population.<extra_id_0> Ukraine1.026False
3The university is named after the <extra_id_0> physiologist Claude Bernard.<extra_id_0> French1.020False
4<extra_id_0>, is the set of dialects of the English language native to the United States.<extra_id_0> English0.996False
...............
95Claude Arrieu (born Paris, November 30, 1903 - died <extra_id_0>, March 7, 1990) was a prolific French composer.<extra_id_0> Paris0.787False
96The World According to Bush (<extra_id_0>: Le Monde Selon Bush) is a 2004 French documentary, co-written and directed by William Karel based on the book by Eric Laurent, about the presidency of George W. Bush and the history of the Bush family, including his grandfather Prescott Bush, who was on the board of German-owned companies during the Nazi period.<extra_id_0> French0.787False
97The Tangwang language (<extra_id_0>: 唐汪话 Tángwàng huà) is a variety of Mandarin Chinese heavily influenced by the Mongolic Santa language (Dongxiang) .<extra_id_0> Chinese0.783False
98A <extra_id_0> cover version, \"Le Climb\", with new French lyrics by Pierre Saka and Claude Dufresne was recorded by singer Big Jones in France in 1962.<extra_id_0> French0.782False
99Alias Betty (French: Betty Fisher et autres histoires) is a 2001 <extra_id_0> drama film directed by Claude Miller.<extra_id_0> French0.782False
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inputs_pretokenizedtargets_pretokenizedscorelabel
0<extra_id_0> (21 February 1921 - 25 July 1972) was a French film actor.<extra_id_0> Claude Cerval73.743True
1Claude Cerval (21 February 1921 - 25 July 1972) was a <extra_id_0> film actor.<extra_id_0> French73.743True
2His <extra_id_0> film version of the same title, Clair de lune (1932), starred Claude Dauphin and Blanche Montel.<extra_id_0> French language61.434False
3The university is named after the French physiologist <extra_id_0>.<extra_id_0> Claude Bernard61.143False
4The university is named after the <extra_id_0> physiologist Claude Bernard.<extra_id_0> French61.143False
...............
245English, is the set of dialects of the English language native to the <extra_id_0>.<extra_id_0> United States55.329False
246<extra_id_0>, is the set of dialects of the English language native to the United States.<extra_id_0> English55.329False
247It is also the largest native language in Europe, with 144 million native speakers in Russia, Ukraine and <extra_id_0>.<extra_id_0> Belarus55.305False
248<extra_id_0> is also the largest native language in Europe, with 144 million native speakers in Russia, Ukraine and Belarus.<extra_id_0> It55.305False
249Marie Françoise \"Fanny\" Bernard (née Martin) (16 September 1819 – 9 October 1901) was the wife of the French <extra_id_0>, Claude Bernard.<extra_id_0> physiologist55.300False
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" - ], - "text/plain": [ - " inputs_pretokenized \\\n", - "0 (21 February 1921 - 25 July 1972) was a French film actor. \n", - "1 Claude Cerval (21 February 1921 - 25 July 1972) was a film actor. \n", - "2 His film version of the same title, Clair de lune (1932), starred Claude Dauphin and Blanche Montel. \n", - "3 The university is named after the French physiologist . \n", - "4 The university is named after the physiologist Claude Bernard. \n", - ".. ... \n", - "245 English, is the set of dialects of the English language native to the . \n", - "246 , is the set of dialects of the English language native to the United States. \n", - "247 It is also the largest native language in Europe, with 144 million native speakers in Russia, Ukraine and . \n", - "248 is also the largest native language in Europe, with 144 million native speakers in Russia, Ukraine and Belarus. \n", - "249 Marie Françoise \"Fanny\" Bernard (née Martin) (16 September 1819 – 9 October 1901) was the wife of the French , Claude Bernard. \n", - "\n", - " targets_pretokenized score label \n", - "0 Claude Cerval 73.743 True \n", - "1 French 73.743 True \n", - "2 French language 61.434 False \n", - "3 Claude Bernard 61.143 False \n", - "4 French 61.143 False \n", - ".. ... ... ... \n", - "245 United States 55.329 False \n", - "246 English 55.329 False \n", - "247 Belarus 55.305 False \n", - "248 It 55.305 False \n", - "249 physiologist 55.300 False \n", - "\n", - "[250 rows x 4 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "model_res, baseline_res = getter(i=3)\n", - "print(\"Model Table\")\n", - "display(model_res)\n", - "print(\"Baseline Table\")\n", - "display(baseline_res)" + "idx = 10\n", + "tracin_res, baseline_res = getter(i=idx, layers='gradients.shared')\n", + "embed_res,_= getter(i=idx, layers='activations.encoder.block.0,activations.decoder.block.0')" ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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dOmT/ItbR+6pbt25OzwWqqp9//rlXt6JFi6rVatUvvvgi07YbGd0vUvV936hIkSL2v+vUqaNjxozR3bt3u6yjJ3zRL5o6daoGBQVliB8ZGanFixfXmJgYzZs3b6btMTExHidwm2Gc9tZbbznddvToUW3atGmmcVqDBg30u+++cxl37dq1Hh3Hly5d0i5dunjcd7zxtWjatKkeP348S+321K0wH6Vqvjkpo/tdqsxJedP3MnI+StUc5zp/zknd6uc5G3+PM2+VMabq7TfO9MUYU5Vxpi/GmbfCGC27boV5OzOejzZv3qwxMTH69ttvO93n119/1ccee0wrVqyouXPn1kKFCmm1atX0+eefd5sI9+abb2pcXJzLfQYOHOjyuMudO7dHx5svzknHjx/Xrl27ar58+fTOO+/UBx98MNP7Pz4+Xrt166ZBQUEZXu/ChQvrqlWrXMY38lx04sQJveuuu7LUr6hQoYLPfozs73FmVvki9yE6Otre13TlVv3cIQnKRLKbBPXzzz/b37BZuZUvX95h3FmzZmlwcLAOHDhQDxw44FWdjh07pi+++KKGhIQYlu1Xvnx5tVgsWqZMGUPi38hVGZcvX9bq1as7PeEHBQVp//79nU7eHjx40OUJMyUlRTt16pThNQsLC9MZM2aoqur48ePtZeXKlUs7duyoL730kg4ePFjr1KmjefLkcZlZHhMTo6VLl/b4ZpvYuvl+Z8+RrW0HDx7McH9qaqo++eSTGT6E8+TJo7Vq1dJGjRrZvzB49tlnndZ9xYoVLj/IvvjiC/uExpAhQ3Tu3Lm6efNm3bZtmy5evFjfeecdrVGjhgYHB+uOHTucxrG1Yfz48dq2bVt7gkeePHm0S5cu+vnnn+u5c+ecPt6dw4cPa758+TQ0NFRr1KihNWrU0LCwMM2bN6/u27dPVVVPnjxp/2WxxWLR2NhYHT9+vH744YfasWNHzZUrl/76669Oy0hKStKaNWuqxWKxd6qGDx+uEydO1BkzZuisWbN0zJgxGhUVpd26ddP8+fPb292jRw+Xk2vHjx/XwoULa1hYmD7yyCP65Zdf6t69e53+CiI5OVkPHjyo3377rfbq1Utz586t+fLl8/o8462oqCiH9x85ciTTand33HGHRkZG2v+vUqWK087ZoUOHfDIAU1WtUqWKy+3Dhw932LG1Wq1avXp1l78c3bt3r9t2zJs3T2NiYjIMrmy/iNi2bVumX/PfcccdWrBgQbVYLNqkSRO9cuWKy/r/97//9epWuHBhtVqtDrfdzNa2P//8M8P9S5cu1dDQUHudW7RooV9++aXu2rVL//zzT128eLE+9NBDGhERoWvXrnVY74MHD+o999zjtF19+/bVfPny6ddff+30l5y//vqrNmzYUF977TWncWxtqFOnjn3wY/sly6JFi3LklxTjx4/XAgUK6Hvvvae//vqr/vrrrzphwgQtWrSoPv/886p6/UsO2/PVqFGjDM/pP//8o82aNdORI0c6LcP2yy3bZ2R0dLTWrVtXmzRpos2bN9dy5cppeHi4hoSE2NtcsGBBXbFihcu6p6Wlabt27dRisWhoaKi2atVKX3rpJZ08ebL+73//0+XLl+uaNWt05cqVumDBAp0+fboOHz5c27Vrp7lz51aLxaKNGzfO0S8THHHW71L9vy9bnfUpIiIidNy4cU4nNv/880+fnI+ctcFX9R88eHCGCYCoqCj7+2PWrFkZyqxRo4Y+9NBD2qlTJ42KitIiRYpkOg/cqEWLFl7dbOe9m+93tsprIPWLBgwYkOHHD2XKlNEhQ4bounXr3P5q3RVf9IvS0tL0nnvusfef69Wrpz169NAXXnhBX3vtNR01apT27dtXCxYsaJ8At7Vz2LBhLuu/aNEitVgsev/99+uqVas8Xhk3LS1N161bpw8++KBarVb9/vvvPX/SsiA5OVnz58/vcNv69eszJF2UK1dOH3roIe3ataveddddGhISouHh4Tp9+nSHj/dF38hV/VV904YdO3ZoWFhYpnNe9erVNT4+Xhs2bGi/7/HHH9dff/1VExISND4+XhctWqR169Z1OSHkKKHA3e3GScwbbzczsl+k6vu+0T///KNbtmzRoUOHasWKFe33ly5dWp9//nldu3Ztls5LRveLNm/erEFBQVq1alX96KOP9Pfff890zkhISNCOHTvqDz/8oKNHj9ZixYrZPyNWrlzpsv5mGaeVKFHC4f1xcXFavHhxp/0Kq9WqrVu3dvoLbU+O48TERK1bt65aLBaNjo7Wvn376uTJk3Xp0qW6Y8cO3bdvnx45ckT//PNP3bVrl65atUo/+eQTffrpp7V06dJqsVi0UqVKGh8fnyPPhSP+nI9SNfeclJH9LlXmpDztexk5H6VqnnOdv+akzHCes/HnONPIMaYq40x35zqjx5iqjDN9Mc68FcZoOcHf83ZmPh/Z2uYome3NN9+0t8tW/wIFCmjRokU1KChIQ0ND9Z133nFa70WLFrlMkLYdI6GhodqlSxd95513dM6cOfrdd9/pxIkT9dlnn9XChQtreHh4pnOls3YYdU46c+aMRkdHq8Vi0fz589v7PkWLFrWPbeLj4+2rLFksFo2JidGnn35an3/+ea1Vq5bmzp3baTuMPBedO3fO3j9o3LixjhkzRpcuXap79+7Vc+fO6dWrVzU9PV2Tk5M1Pj5eDxw4oKtXr9bx48dr8+bN1Wq1atGiRR3+ICun+XOcmR2+yH2oV6+eWiwWrVmzptN9buXPHZKgTCQnVoL6999/tW3btg4PWFe3UqVKOZ28rF+/vn7zzTfZqte8efPcZn9n1ZEjR/Tdd991+6GeXRs3bnT5+rz++uv257NatWr2wfZvv/2my5Yt0+eff16jo6O1cuXKDpdbdHfCHDdunD1+5cqVtVu3btqiRQsNDQ3VadOm2b+4jYmJcfih991332m1atWcdry6dOliL9/b98/NHwyOOJtgHzhwoP2xd955p37++eeZJjaWLl2qFSpU0BdffNFh7Pj4eC1durTTiY127dppbGys24H6uHHjtFu3bk6329pw5swZVb3+C6rZs2frgw8+qHnz5rV3Jpo1a6YTJkzwaNnEG/Xs2VNbtGiR4deUcXFx+sgjj2j9+vU1KSnJPmFhtVr1ww8/zBTjjTfe0EceecRpGWPHjtXChQvr7NmzXf7ifMKECbpw4UK9cuWKfvTRR/aVWjp16uT0MX379tVmzZplueNy5swZveeee7R3796ZtiUlJenx48ezdTt06JC++eabTt+jffr0sT+3ffr0yfDrpoSEBF24cKH9tXb0ge2rAdjly5c1d+7cTrcvWLAgw/FoSwB54YUXtFu3bvbX0llH1l07fvzxR4fniejoaD1x4kSGAcGoUaMyXMZsz5492qFDB+3atavLNtom/rLzZZ+zNjg6F8XFxWnhwoVdHls2ixcv1tKlSztdKah06dJOl4qvUaOG218QqF5fQrlu3bpOt994Ljp9+rROmTJF27RpY/8iIHfu3Nq5c2edNWuWnj171m15jlSrVs3hL0mOHz+upUuX1jfffNNeD9v56WZ///231qlTx2F826/N27Rpo4sWLXL4hUx6ero++uijun37dv3yyy+1Tp06arFc/+XgzatZ3OiDDz7QPHny6Hvvvef1pRgTExN10qRJmj9/fn333Xe9eqw3/vzzTw0ODna6/d1337UfW0WLFrUPJBcvXqwff/yxdu7cWcPCwrR58+YOv+jwxfnIVRt8Uf8pU6ZkmCy56667tEyZMhoSEqLLly+3f1kQGRmp69aty/DYlJQUffvtt52+P1Wv/2qRfpF3/aL9+/frG2+8ofXr17dvK1y4sD7++OO6YMECt5cZuJkv+kWTJk3S8PBwfeONN1xednPkyJG6bt06PX78uA4ZMsT+y2lXiSvNmjXT9957z8PWOq9fs2bNHG47efKk1ysE3XybMWOGPvDAA07fp/fdd59aLNcvM/bFF19k2n7u3DmdNGmSRkdH68CBAzOtSujqWPZF/Y1ug83999+vFotFS5YsqXPmzNE//vhDf/rpJ3366ae1WrVq9sfbkmVulpSUpK1atXI6nt2+fbtWqlTJPs5o3ry5y5ttctrRtpsZ3S9S9X3f6EZ79+7VMWPG2PsQVuv11ZV69eql8+bN8/iSFUb3izp37qx9+vRxOT5TvX6Zm1GjRqnq9V+ejx49WsPCwjR//vy6f/9+p48zcpyWE1JTU3X27NlOj7PBgwdnmITesGGDJiQk6OXLl/WPP/7QDz/8UOvXr69RUVG6adOmTI/35DgeOXKkRkVF6f/+978stWHJkiVavHhxHTFiRJYe746/56NUzT0nZWS/S5U5qRu56nsZOR+lypyUu+P4Vj/P2fhznGn0GFOVcaaq63Od0WNMVcaZtvo5GmcG0hgtu/w9b2f285Gzc9G4cePs5UVEROh///vfDJ93Fy9e1MmTJ2vhwoX1/fffd1jvuLg4rVKlitO+ROfOnbVy5couVxO7du2aPvXUU9qnTx+Xz5HR56Rnn31Wa9SokSExdNeuXdq8eXNt27atqqree++9GeYUbmx3enq6Pvvss9q/f3+H8Y08Fw0ePFirVKniMoHflT179mjNmjX1ueeey9LjPXErjDOzwxe5DxcuXNDvv//e5XdIRs9vZgdJUCaSU5fDS09P1969e2ulSpV03bp1Lm8bN250e43W6tWrZ7tOqqq1a9fO8mM9vUarI7t27fLq12SObsWLF7dnJztj++K/bt26Tj/sUlJS9L333tMSJUpkWonF3QmzQoUKarVadciQIRnuP3nypDZq1Mj+2PXr1zut48SJE/Wrr75yun3mzJlaqFAhHTVqlNv3TsmSJdVqtTrc5oijjs+mTZsyDMBc/drqwoULWqdOHae/smjRooUOGjTI4bYKFSp4/EHhqnPobHJd9fqEyMKFC/Xxxx/P8KVB9erVdcSIEbp9+3a3ZZcsWdLpUp5dunTR2rVr2+O+8sorDvdLT093uUpQ1apVXa4CZBMfH6+PPfaY/f/z58/blzR2tLqO6vXn2VVnyhMXLlzQqlWrZrrfNvDwNjHG2c0RW9a3q0lH1evLk1evXj3TB7+7Y/jgwYNer3B08+21117T+vXruzwXtWjRQi2W68u9OzofpKam6pIlS7ROnTrasWPHTOcrd+2wXQqkZs2aunnzZk1MTNQTJ07oW2+9pTExMfbHuvp12EMPPeTymDhx4oQ2b95cc+XKpQ8//LA+/vjjLm/58+dXq9XqcNvNHJ2L/vvf/9rvd7U0vc3kyZOdtm/EiBEaGRnpcOK/Ro0aHv86xFXisLNzUXx8vH755ZfauXNn+5cQwcHBevfdd+v777/v1fWjS5Uq5XTbkiVLMgzEXS2J7+yc2rp1a3399dfd1mPv3r368ssv2///4osvtFChQhoaGupw8KF6/Xl29lnkqY0bN2qtWrUy3b9nzx6vfyl1861JkyaaL18+l8dxrVq11GK5/ms4Z58L586d04EDB2rZsmUzfX66Oo590QYj629To0YNtVqt+vDDD2dY/WzLli1aqVIl++MXLFjgNMaoUaOcbk9NTdXXX39dw8PD9dVXX9VZs2a5vNkuP+VomyOB3i86efKkfvjhh9q8eXP753dERIR27NhRP/30U7dLl6v6pl9Ut25dnTdvntu6nDlzRvv162f//8CBA/ZfyU2bNs3hYypXruw2riec/Rrr/Pnz9s+/7NxcHWu2pfXdna8vXbqkvXv31gceeCDDlymujmVf1N/oNtjYkrcdXerpq6++Uovl+i/tXSUKbd++3eVKR1evXtWnn37a5a8Qbby5bIrR/SJV//aNbnT8+HH94IMPtGnTpvaxfXh4uN5///06Y8YMl481ul9UpkwZjyfM77vvvgz/b968We+8806tVauW03kdI8dpu3btyrExmrP3bZkyZdRqvf7jDldzV//73/+0VKlSOnfu3Az3e3IcV6lSRffu3evhs+HYvn37Ms2fBcp8lKq556SM7nep+r/vZYY5KSPno1SZk3J3HBt1nrMJhHGm0WNMVcaZNs7OdUaPMVUZZ9o4GmcGwhgtEObtVM1/PnJ0Lvr999/tl0LPmzev03ld1etjtwoVKjg9X9111106btw4h9sqVaqk27ZtcxrbJiUlxe131kafk8qUKeNwvv7atWt699132690YLVatVevXg5jJCUlOey7qBp7LqpYsaLLS6R64u+//9Zq1ao53BYo48yc4M13OkYwen4zO0iCMpGcSoJSvf4BNnbs2ByJVaVKFZcnAE+kpaVpjRo1svz4//znP9kqv1mzZtnKZnb3KwtVtV8CYdGiRW7r88cff2jt2rUz7OvuhBkSEqKhoaEOf1V67tw5LV68uFaqVMlluWfPnnX5KwXV6yfUJk2a6IsvvuhyWTtvJtdVHXd8HnvsMfv98+fPdxvjp59+0ieeeMLhtm+//VatVqtOmjQp0zZHv3p2xtWJ2JPJddXr7/cff/xRBwwYoCVKlLA/rkSJEjpgwABdvXq1w8c566yoXl9y2BanQYMGLo9JV7+SdtapcMTRUqZPPfWUhoaGOvzgzU6i442c1b93796GHse2VXRcdcBtEhIS9OGHH87w6yhPlh6/8RrhRg0kbYNVd6vYpKWl6ahRo7Rx48YZJgrdtSNv3rxqtVodDkJ+/PFHDQoK0kKFCrm8JNsff/yhL7zwgsv6paen6/jx47VOnTq6c+dOl/tm98u+KlWq2CeDPLmU3OnTp7VJkyYOt50/f14LFy6sxYsXz1TvBx54wKM6Xr16VcuWLeu2Da7ORVevXtX58+frY489poUKFbI/plq1avraa685/KL2Rq4m33v27GmP9+mnn7qM4+xzqWzZsm5XO7Bp165dhv8PHTqk5cuX1zJlymhycnKm/XOqQ+3sXFS7du0Mx6MRfQrbKh5z5sxxW881a9ZotWrVMrym7o5jo9tgdP1Vr/e7cuXK5fBXpsePH9eCBQtqTEyMy7L//vtvt7/62rRpk1avXt1h/+JG9IucO3v2rM6cOVM7dOigYWFharFcT9CMjY3Vd9991+mkli/6RRUrVnRZ9xu1bt06w/9JSUnasWNHzZs3r54+fTrT/jn1QxJXr8Ho0aMN7RvZLrPz22+/eVTXjz/+WFu0aKEXL15UVffHstH190UbVK+fj+644w6n27t37+7yknCqqleuXNHGjRu7rd/SpUu1cuXKLldqvpX6Raq3Tt/oRnFxcTp9+nRt166d/bJ/QUFBGhsb63B/o/tFrs5Tnuy7c+dOzZs3r/2yZDczepzWqVMnn5yLfvzxR7d1PH36tDZv3jzDJTI8OY4dJb9nhaPnKBDmo1TNPSdldL9L9dbqe92qc1JGzkepMifl7jg28jxnY/Zxpq/GmKqMM52d64weY6oyzrRx9hoEwhgtEObtzH4+cnQuevrpp+33O7vU4Y2WLFnidDXlTz75RIODgx2uOOzJuNrG3WpZRp+TXPWdf/jhB3v5FStWdDk2d3Y+MvJcZHS/SzUwxpnZlZSU5PLyop7YunVrth7vi/nNrAoW3JaCgoJk+PDhLvfZt2+fVK5c2W2sMmXKyPDhw+WNN97Icn1GjhwpxYoVc7jt2rVrDu8PCQmx//3TTz/J4MGDpVq1ahnut6lbt65UrVrVaflvvPGGNGnSRDp27Ci1atUSi8XiVf3j4+Nl4cKFcvz4caf7hIWFybVr16Ru3bpu41WuXFl+/PFHefDBB+Xff/+VPn36uH1M3rx5JU+ePBIaGpppW6FCheT111+XVatWuYyRkpIi27Ztc7lPmTJlZP369TJ27Fhp1KiRfP755y6f26xSVVm6dKlYLBapX7++dOrUye1jypcvLz/99JPDbd26dZOPP/5YBg0aJCdOnJCxY8dKrly5REQkMjJSVNXt637w4EG5cuWK1225mdVqlZYtW0rLli1l4sSJ8ssvv8i8efNkwYIFMnnyZPn4448lLS0t0+OCgx2fsq9evSqDBg2y///RRx85bUtKSoqcO3fOZd08cebMGTl58mSm+z/++GM5evSojB49WmbNmpVhW1pamhw6dEjKlSvnURmOHDx40Ok5YeTIkTJ79mwZN26c1K1bN0vH8axZs2TRokUOtxcsWFD+/fdfiYmJcRsrIiJCZs+eLa+88or06tVLPvvsM7ePCQ8Pl+HDh8ugQYMkJCREoqKistSGixcvutwnOTlZRETuv/9+l/tZrVYZOXKk1KtXT9q0aSPz58+X4sWLu61DamqqREVFSfny5TNta9mypTz55JNy9OhRCQsLcxqjePHism7dOpflWCwWefnll6VNmzbSp08f+c9//iMvv/yy18+ZM7Zj8NSpU7Jv3z6xWCzSr18/l/W2OXjwoOzbt8/htoIFC8rMmTOlc+fO0rhxYxk6dKi88MILkidPHqlYsaIcPXpUSpcu7TL+K6+8IqVKlfK+UTcICwuTTp06SadOnSQtLU3WrFkj8+fPl0WLFsm4cePkjTfekGLFijk8zkVEcuXKJWfOnJEiRYpkuP+TTz6RL7/8UiwWi+TOnVsuXbrktA6bNm2SoKAgh9sKFCjgdNuN0tLS5O+//85wX9myZWXVqlXSqFEj+eijj+TFF1/MsD05OVmuXbvmsL/gqeTkZPuxdLOxY8dKhw4dpE6dOlKtWrUsHcdr166Vy5cvO93H9tzExsa6jdeiRQtZsmSJPPTQQzJ27Fhp06aN28cY3Qaj6y8iEhoaKgUKFJD8+fNn2layZEl59dVXZdOmTW5jbN682eU+jRo1ks2bN8ugQYOkXbt2MmvWrEzHRU4I5H5RZGSk9OnTR/r06SMJCQmydOlSmT9/vixfvlw2bdokL7/8st/6RRERER614dKlS3LixIkM94WGhsq3334rd999t4wbN04++uijDNvvuOMOmTJlijz99NMeleHI9OnTHb7HbYYMGSKTJk2SZcuWeTQGudnFixdl6tSpMmzYMIfbo6Oj5dixY1KoUCGP4j399NNSvHhxadOmjSxYsMDt/kbXX8T4NohcHz+5ep8NGzZM3n77bZcx/vjjDzly5Ijbstq3by/169eX/v37y8KFC2XKlCku3yOeMqpfJHLr9I1udMcdd0j//v2lf//+cvnyZVmyZInMnz9fVqxY4XB/o/tF6enpEh8fLwUKFHBZ77lz50piYmKm+2vVqiUTJkyQCRMmSN++fTNtN3qcNnbsWFm8eLE888wzUqdOnSz1K2bPni3bt293uD1Pnjxy4cIFj+avihQpIqtWrZJevXrJ6dOnZfTo0R7VISkpSS5fvix58+b1qu43unTpksP+YyDMR4kE1pxUTve7RG6tvtetOidl5HyUCHNS7hh5nrMx+zjTV2NMEcaZ2T3XZXWMKcI4U8T1ODMQxmiBMG8XiOej+fPni8VikUqVKkn//v3d7l+/fn2n33E//vjjMnHiRHnooYdkwoQJGY6HO++806P6/PPPPxIfH+/Rvu5k9ZzkqF8tcr1Pc+MxNH36dKdjc1WV8+fPO9xm5LkoOTnZ4RjZG6dPn3bbrzD7ODM7Lly4IE888YTLc5EnxowZI4sXL87y430xv5llOZ5WhWzp3bu305ttiUVX+zjKbHVn3bp1+uyzz2bK+p0xY4Y2bNhQ165d6/LxP/30kwYFBWn9+vX1s88+03/++cejcs+ePatff/21xsbGqtVq1VWrVjncz9Zu2y1XrlzauXPnDPu4Wz2levXqbleruv/++/Xy5cse1d2RU6dOaVhYmNPtzZo1U6vVqn/88YfHMZOSkrRz58765ptv6uHDh11mjXbq1ElDQ0Mdrnqhen2VGWfL0dpMmzZN8+bN63H9tm7dqtWrV3e4ooy3v0SJjo5Wi8WiU6ZM0bS0NP3111/t7XW2pOLNpk6dqrlz53a6/dSpU/bloytUqKCfffaZJiYm6pQpU3Tx4sUuYx86dEirVauWYdnHm3ma+e3K3r17nS7V2aFDh0xZx8nJydq2bVt72Z07d9a7777b6Xt53LhxLn9p3rlzZ509e7bLOiYmJmr79u2dZs0fP35cixQpkum9OGLECC1evLguWbLEZXxnVq1apTExMTp8+HCn+zz33HPZev4TExM1X758Drd17dpVrVarR8vE3+jdd9/VDh066G+//eY28/vatWtaunRpt5cxccXVdYxV/+/Y9GY50m3btmnt2rX1jz/+8OjXNIUKFXIa68iRI06XZ7VZt26dyxUTbpaUlKSDBg3Su+++W48dO5Zpuzfno2XLlmmrVq20cOHC+tprr+lHH31kb6+rSzfYpKWlaYMGDTQkJMTlfpMmTbLHzZs3r/bs2VOHDBmi999/v8NfNKempurq1av1nnvuUavVmml51Rtl91y0efNmfemll7R8+fJO97GdS2xL9166dElff/11DQoKUovForGxsfrXX39pTEyMPv/885kuO7BgwQKNjo52urR1gwYN9MSJE27rOmnSJKe/GF64cKHDVSG6d++uPXv2dPp56U5KSor27t1bu3bt6nSfJk2a6JUrV7IUX/X6ZQRy5crldPtdd92lVqvV4aWDnDl37pw2bdpUv/76a49+iWJkG3xR/9atW2tERITT/t+FCxf0ySefdFnmN998owUKFPC4jvPmzdOKFSvq999/n2kb/SLvJScn69KlS7V///4Ot/uiX9SmTRtds2aN27r26dNH69Wr53Db77//rkWKFMl0Sa+FCxeqxWLRBx98UNesWePxJb9Ur1+S85FHHlGr1arffvuty30nTJig+/bt8zi2I0WKFHF4/4ABA9RqtbodL95sw4YNWrt2bV22bJnbY9nI+qv6pg1vvPGGWq1W/f33353us3TpUpfldevWzW3f4mbTpk3TSpUqZXoP34r9IlXf9I08natwxlnfweh+0ZgxY7RJkyZOPzevXbumkyZN0ty5c+vDDz/stP5169Z12Ff2xTitZ8+eLi/56M6FCxecfqa1a9dOrVar7t6926uYgwYN0ieeeEIPHDjg9jju16+ftmnTxuNL6dzs3Llz2q5dO6fjILPPR6mae07KF/0u1Vu/76Xq3zkpI+ejVJmTcjcnZfR5zsbM40x/jDFVGWfeyOgxpirjTE/GmYEwRjP7vJ3Zz0e2lbJsK4/u3bvX3t4PPvjAoxjffPONy37jvn377FfEaN68uf399O677+qGDRtcxj5//rw2b95cu3fv7nI/o89J99xzj8PLj9pWn7RarVq/fn3t1q2b0ysqzJw50+mKxkaeiwYPHqw1a9b0eEW3m+3fv1/r1KmjAwcOdLmf2ceZWXH48GF9/vnnNW/evG7j//nnnw5vN4qKitKOHTvq8OHD9b///W+mm6PLid/IV/ObWUES1C0mu8u2ubr2uSOPP/64PVGoRIkSmbZv3bpVCxYsqBMnTnQZZ+rUqZorVy57rKioKG3UqJF26tRJH3nkEX388ce1R48e2qVLF23SpImWKFEiw5KTrur97rvv2tv34IMP6qFDhzLtExUV5XY5ux9++MFlG/bs2ePww9sbrpYP/Pbbb9VisXj8IW6Tlpam/fr1086dO7s8oW3fvl1DQkL0o48+chrL1ZKICQkJGh0dreHh4V7VLyEhQZ944glt0aJFhqQKbwdhKSkp+uWXX2qdOnU0JiZGO3ToYG+vJ53qv//+WwsUKOC2/gcOHNDSpUvbY4eHh2vNmjU1OjpalyxZoleuXNH09HS9fPmy7tu3T+fMmaM9evTQsLAwzZcvn8vlcm0xjx496nG7vbFkyRItUKCAvvPOO7p8+XKdNGmSVqhQwX4cjR8/XlWvf8EfExOjkydP1l9//VX/+OMPXbJkiXbq1EmtVqvL9/nmzZs1V65c+uijj+r//vc/PXjwoJ4/f15Pnz6tW7Zs0bfeektLlSqlVqvVXp4jzz77bKYPxytXrmjVqlXVarVqqVKl9Mknn9SpU6fqypUrddeuXXrgwAE9duyYHjx4UPfs2aM//PCDzpw5UwcMGKDly5dXq9Wq5cqVy/SlwY3i4uLcXmbCHWeJDb/88ovmypVLn332Wa9jfv755xmux+1u3x07dnhdxo2KFy/udNurr76qVqtVly1b5lXM/fv3a61atfTzzz932Y4pU6ao1WrVjRs3Oo21ZcsWl2Xdc889Xn/Rp6q6cuVKrVixon722WcZ7vf2fKSqunv3bu3Vq5f9EihWq1XPnTvn9nETJkxQi8XicjBvM3/+fC1QoIA9vu1WrFgxffDBB7Vnz57atWtXbdCggf0ygxaLRZ9++mmXcW3xTp065XF7vZWQkGA/LosUKaJBQUH2+jVo0MC+BPXu3bu1YMGCGhoaqjVq1NC77rpLCxcurFarVcuUKWPf72YTJ07UypUr6w8//OBwQP/PP//oiy++qMHBwS6/CGjZsqXu378/w32//fabhoWFafHixXXkyJG6efNmTUxMdNne5ORk3b59u44bN05Lly6tuXLlcnn9+A0bNnh9jN2sXLlyTrdNmTJFLRaLzpw506uYV65c0fbt2+uAAQPcno+MbIMv6v/DDz+o1WrVr7/+2uk+riYpUlJStGLFihoaGupVHU+dOqVt27bVnj176qVLl+z33879ouPHj3vcbm/4ol+0dOlSzZs3r7722mu6Y8eODIkYp0+f1m+++UYbNGigVqvV7Zf/mzdvznT/sGHD7M9T7ty5tWHDhvrwww/r4MGDddiwYTpq1CgdPny4DhkyRB999FFt2rSpfRLPYrG4PP/ZJCcn64IFC9zu58rbb7/t8P5Tp05pVFSUtm7d2uvLo+/Zs0fLli3r9lg2sv6qvmnDtWvXtGXLlnr33XdnKQF306ZNarFYNDIy0uvHHjhwQBs1aqSDBw+2v39v1X6RqvF9I3eX+80qo/tFV69e1Ro1ath/4NWjRw8dNGiQPvXUU9q2bVv7eSE4ONjlUvazZs3SL7/8MtP9vhinHT16VL/66isvn9mM7rrrLof3r169Wi0Wi44YMcLrmKNHj9ZGjRq5PY6PHTumBQoU0Ny5c2uvXr10zpw5un//fqfHdEpKih4+fFi///577d+/v+bLl0/z5MmTqV9qY/b5KFVzz0n5qt+lypyUq76XkfNRqsxJuZuTMvo8Z2Pmcaa/xpiqjDNtjB5jqjLO9GScGQhjNLPP25n9fHTu3DkdM2aMFi1aVJs0aaJ9+/a1t3fXrl1uHx8fH6/FihVz+SMA1etj6YIFC9rHlaVKldI2bdpolSpVMv1IKTExUTdv3qzDhw/XO++8U0NDQ90mHht9Tvryyy+1VKlSOnfuXN23b58uWbLE/uMGi8WiAwYM0LS0NG3RooXWr19fly5dqufPn9fExET9/fffdfDgwRoSEqKffPKJw/hGnov+/fdfjY6O1qCgIG3WrJm++eabumLFCv3zzz81Pj7eXlZKSopeunRJDx06pD/++KO+++672rp1aw0ODtY777zT7WIBZh9nemPVqlXaoUMH+3yD7X3gKn50dHSGuZWCBQtmuoyku0VmYmJiXF6GXNU385tZQRLULebYsWMOb0ePHtWYmBi1Wq1O9zl27JjDa8A6Y/sgtr0xS5Uq5XC/qVOnalBQkNtrjq9Zs0arV6+eKfno5tuN2ytXruz2Fzi2X4S6mkgpVqyYjh07VlesWKHr1q3LcFu5cqXWqlVLBw8e7PY5+fvvv93u44q77PEBAwZo/vz5PcquvdnQoUPdntCWL1+uRYsWzVIHzvar3goVKnj9WNXr2Z7ly5e3T6pmZXLdZs2aNXr//ffb3zOeZOX369dPLRaLy5VLbOLj4/WBBx5w+T69+T175513uk2ks8VyNLGcU268NrKtbkFBQRl++ZiUlJTh1yk37uvJdZ4nT56c4YPU0fNRt25dlxOY27ZtczgpdeHCBW3Tpo1Hz/vNZTZo0MCwyTxPzZkzR3Pnzq2jRo3SlJQUrx67ePFiDQ8Pd3tcpKen6+rVq7NTTZcDoPj4eC1fvrzb19CRkydP2s/zrtrRs2dPrV69ulefSTYrVqywH3NZcfbsWe3atat27tzZ/uVcds5H//zzjw4bNszjL++aNm2qVqtVO3Xq5NH+x44d0169emmuXLmcfn7a7itRooR+/vnnbmPaHv/zzz97VIes+uuvv/S+++6zr3JQoEABfemllzIlFO3Zs0cbN26coX3NmjVzuBKBTWpqqrZq1UqtVqsWKFBAY2NjtWPHjtq2bVutVKmS/RwVERHhcpL1+++/dzjhsGjRogwrTQYFBWnRokW1Vq1a2rhxY23evLnGxsZqnTp1tHjx4hkSvXPlyuXRxHZ2fxXn6guO9PR0feCBB7Ro0aJel5OSkqI9evTwaBBmVBt8Vf8ZM2ZoVFSU7ty506syVK//UspisWhMTIzXj1VV/fDDD7Vy5cr2lVJu535RdidHXfFFv+jGPnhQUJDmy5dPw8LCMsQpU6aMy1+erV+/XidMmOBw25dffql33HGHR8+/bZ9ChQrppEmTvJ4QNsLevXu1SpUq2rJlS6+P5yNHjmi5cuVybEIoq3zRhtTUVH3ppZf03nvv9XqCdMSIEWqxWLR9+/ZePe7Gsl9//XWtWbOm7ty585buF6ka0zeqUqWKWiwW7dKli9NfyGaXkf0i1etfrLdo0cLp8xEaGprphwA3+/fff/WFF15wuM0X47TsrHTkzjvvvKPh4eE6a9Ysrx87ZcoUe9/SlW3bttlX0LjxOShQoIAWK1ZMY2JiNDo6WgsVKpTpOcqfP7+uWLHCZXyzz0epBsaclNH9LlXmpFwxcj5KlTkpd3NSRp/nbMw8zvTnGFOVcaYvxpiqjDNvl3Gm2eftAuF8dO3aNf3000+1Ro0a9vbemFjlzODBgz2u/9GjR7Vu3boOj4XChQtryZIlNTIyMsP94eHhHiXW+OKc1LFjx0znI4vFkmGFpAsXLmitWrUcHttt27Z1Gd/Ic9GRI0cyvLbe9LtiYmI8XijA7ONMV65cuaKTJk2yH183vgdy5cqlBQoUcBnfll8RFBSkL774osPv6zxZZMa2apsrt+LnDklQJpLTE8S1a9fWunXr6pIlS/TSpUvasGFDh/vFx8erxWLxeOJyzZo1OnToUG3evLmWLFlS8+XLp8HBwZo3b14tXbq0tm7dWl9//XW3SVU2ffr00b59+7rc5+bL491sw4YN2rx5c4/KM9rXX3+tFSpU0Icfflj37Nnj1WPff/99t++BkydP6osvvqgzZszwKva0adPUYrHo+++/79XjbnT69Gm977779KGHHtKSJUtm+/26f/9+ffnllz3at2HDhmqxWHTo0KEex1+7dq3ee++9GhIS4vQEHxUVpa+88or++++/buN9++232rp1ay1UqJChHZ+5c+dq9+7dtX379vrcc885fB+lpqbq9OnTtVWrVlqpUiVt0aKF20nxG61Zs0br1auX6fnIlSuX9unTx21yS1pamtMvJNLT0/X777/Xpk2b2r8kcHYLDQ3VNm3a6LfffntLDMBUry832bdvX61Ro4bOmTPHq8f+/PPPLi8V5yunT5/W1q1ba5UqVXTlypVePfbcuXPasGFDt8f35MmTtUWLFi5Xy3Fk5MiRarFY3C43687MmTO1fPnyumTJkmxNCnnrpZde0sqVK7u85I0jf/31l06bNk0ffvhhjY2N1YoVK2qVKlW0RYsW+txzz+myZcs8nuRs166dBgUF6d133+3RwDG7EhISPFoy/+TJk7plyxaPv/xNSEjQxx9/3On5ITIy0u379+LFi5l+3WCzb98+7dChQ4aOt7MvWW2DhXbt2nn0iyRfSEtL03HjxmmRIkX01Vdf9Xrp5UGDBvnsuHDEV/X/9ddftUePHl5/GWRbifTVV1/16nE32rt3r9atW1dfeuklLVu27G3XLxo/frxWrFhRS5Qoke0VDl3xRb9o1qxZWqxYMYfPScuWLd2e165du+Z0clr1+gT6p59+qv/5z3/sP3y5uf9Vvnx5feyxx3T27NleJzEbLTU1Vb/44gu9//77vR5LnDlzRuvWrWtQzTznqzacPXvW62T3Q4cO6fPPP69Hjhzx6nE327Rpk1auXNl+6QFfyGq/SDVn+0aq19vfp08fbdGiRbafS1eM6hfZLF26VHv16qW1a9fW8uXLa8OGDfWFF17w+FIbrlbwMvs4bcOGDdqqVSuNjY11myxys7lz57r9Jbnq9eN9wIAB9ksPuLvly5dPn376aUNXaPWG0fNRqoEzJ2V0v0uVOSlnjJyPUjX/uc7oOSmzn+d8Mc705xhT9fYeZ/pqjKnKOPNWYPZxJucj76xatUq7devm0b62c5G7FYFtUlNT9bPPPrOviujsFhISot27d3e7qqGNL85JKSkp+s4772jDhg21SpUq+sADD+jSpUsz7XflyhUdNmyYli1bVsPCwrR06dI6cuRIj1ajNvJclJiYqO+++67GxMR41K+oUKGCjh8/XhMSEjx7gnzAF+PMmx06dEife+45+2pJN56/69Wrp1988YUmJCRoQkKC5s+f32mcDz74QIODg3X+/PlO9ylRooR+9dVXun///kwL7+zfv19btWqlTz31lEf1vtU+dyyqqgJTKF++vBw5ckTS0tJyJF6hQoXk4MGDEhkZKSIijRs3lk2bNjncNyQkRPLnzy///vtvjpTtjfr168v//vc/KVmypNN9vvrqK3n00UddxqlRo4bs2bMnp6uXZdu3b5fLly9LixYtvHrcokWLpGPHjobU6eLFi5I/f/5sx/n444/l/fffl7S0NDl69GgO1My9devWybZt22Tw4MESEhLi1WMTEhJk48aNcuLECTl79qwEBQVJkSJFpEaNGlKrVi2v63L8+HH57LPPpFOnTll6/K3k2LFjsmfPHrly5YpERkbKXXfdJQULFsyx+AkJCbJ9+3Y5cuSIxMfHS2JiooSHh0vhwoWlXLlyUqdOHQkLC8ux8nJSenq6/Pvvv1KkSBGvHnfy5EkpUaKEQbXyzoYNG+Tbb7+VcuXKyeDBgz1+XGJiovTs2VO+//57l/ulpqbKvn37pHr16h7H/vfff2XOnDnSo0cP++dTVh05ckR69uwp27Ztk7S0tBz7/DSDv//+W2bOnClr166VyZMnS5UqVfxdpSzbu3evzJ07N8O5KDY2Vh599FEpUKCA28erqlgsFqfbDx8+LKtXr5atW7c6PRc1bNhQ7r33XomOjs7BluWMxMREWbZsmaSmpkr37t29euykSZNkwIABBtXMM76qf0pKiuTKlcvj2Onp6fL7779L9erVXb5/PCn3tddek/fee09U1WfnoVupX/TTTz/JF198IQMGDDB1vyglJUU2btyY6VxUrVq1HC8rPT1dLl68aD8XFShQQKxWa46Xg9tPQkKCzJ8/X9LS0qRXr17+ro5fXLlyRVRV8ubN6++q3NLMPE6Li4uTuLg4r8/PO3bskLp163q07+XLl2Xjxo1u+4+xsbESGhqalWYY6lacjxIx75xUdvpdIsxJOWP0fJSIuc91Rs9Jmf0854txpr/GmLayb+dxZqCMMUUYZ94OOB/lvG+//VbWr18vb775ptd9xwMHDsi6descnotatmwp+fLl87o+gXBO8sW5aP/+/W77FWXKlMmx8nKaL8aZK1eulI8++khWrlwpen0hIxERsVgsUrBgQVm4cKHExsZmeEy/fv1kxowZDuM99thjEh0dLW+99ZbTMh9++GGZM2eO0+27du2SAQMGyM8//+xRG27k788dkqBMJKeToG5OenKWBPXnn39K5cqVJTQ0VK5evZojZXujbt26smPHjmzHcZXkBQAITOnp6bJp0yZJS0uTZs2a+bs6fpGamirBwcH+rgYAAAAAAAAAAADk+g+1Zs2aJZMmTZKDBw+KiNiTn6KiouTxxx+Xfv36SY8ePWTz5s1exa5du7asWrVK7rjjDqf7LF++XNq1a+cyTq1atWTXrl1elX0rIM33NnbHHXdIYmKi2/2GDx8uIuJyJabsOHnypEyYMEGOHDnicHtOZQVeunQpR+IAAMzDarVKkyZNfJYAdfnyZVmwYIFcuHDhlol/KyZAGf08AQDgi8+aQCjDyPiB8Pz4ogyzxwcAAAAAAJ47ePCgPPfcc1K8eHF57rnn5MCBA/YrWrRt21bmzZsnJ0+elDfeeEPKlCmTpVXbgoODXSZAiYjbBCgRkYiICK/L9pSR8xUkQd3G+vXrJ4899phcuXLF4fZ//vlHunbtKvPmzROLxSIPPvigIfVo3bq1vPDCC04PtMuXL2e7jJSUFENXsZo1a5bUqlVLpk6datoyzB5/5cqV0qlTJ1m0aJEp44uY/zkS8c2xYLScOOf4K767pFIzlGH2NrRu3Vq6du0qbdq0yfHYvogv4pvXwOh2+KINcC4QPpPN3gazx/dFGYHQBhGRtWvXGhbbV2UYFd8Xn5mBUIaR8QPh+fFFGWaPD/eM/DwIhPkoX5RhZPxA6VOYfU4qEOajRMw7JxUIx4HZ4/uiDOLfPmWYdQzoizIC4TU2ext27dolgwcPlq1bt+Z4bF/EtzH6dTC6Hb54HwWy//znPzJx4kT7Ai4lSpSQkSNHypEjR2TZsmXSqVMnCQoKylYZ6enpOVFViY+Pz5E4jhg6X6EwjbJly6rVas3RmEOGDNHChQvrI488oiVKlNDXXntNBw0apG3atNHQ0FC1Wq1qsVi0evXqevny5Rwt26Z8+fJqsVi0TJkyDrdXqlRJf//992yVsXz5cq1Xr162YriSP39+tVqtmi9fPtOWYfb4kZGRarVatVChQqaMr2r+50jVN8fCXXfdZVhsVdUqVaqYNn7FihXVarVqhQoVTFuG2dtQrFgxtVgsGhUVleOxfRFf1TevgdHt8EUb3nrrLcNiq6oOHDjQ0PhGlhEIn8lmb4PZ4/uijEBog+r1sZTRjC7DqPi++MwMhDKMjB8Iz48vyjB7fBujx2lmjm/k50EgzEf5ogwj4wdKn8Lsc1K+eJ+qMiflTCAcB2aP74syiH/7lGHWMaAvygiE19jsbYiOjlar1arR0dE5HtsX8W2Mfh2Mbocv3kddunQxLLaNP8eZP//8s/bo0UPDwsK0Tp06OmXKFL106ZLDfRs1auR12Tnx3UhiYqLT/I2cYOR8BStBmcjs2bNlzZo1ORrzvffekw8++EA2bdokf/31l4wbN04mTpwoq1evlmvXrklQUJD06tVL1q9fL3ny5MnRsm1Wrlwp77zzjixfvtzh9saNG8sbb7yRrTJGjx4tzZs3z1YMVzp16iSqKu3btzdtGWaPX69ePVFVqVmzpinji5j/ORIxvg3Xrl2TP//805DYItevv3v8+HHTxk9PTxdVldTUVNOWYfY2LFiwQJ599ln5/vvvczy2L+KL+OY1MLodvmjD5MmTDYstIjJ37lxD4xtZRiB8Jpu9DWaP74syAqENBw4ckKNHjxoS21dlGBnfF5+ZgVCGkfED4fnxRRlmjy9i/DjN7PGN/DwIhPkoX5RhZPxA6FOImH9OyhfvU+aknAuE48Ds8X1RBvFvjzLMPAb0RRmB8BqbvQ1FixYVVZXChQvneGxfxLcx+nUwuh1G1z89PV3Wr19vSGwbf48zY2Nj5auvvpKTJ0/Kf/7zH3nnnXekWLFi0rt3b9m0aVO2yw8LC5Nt27ZlK8bSpUslKioq23Vxxsj5Couqao5Hhemoqmzbtk127twp58+fl9DQUImJiZG7777b7fUiPXH48GEpW7Zslh67aNEi6dy5s0ybNk369evn9eNfeOEFmTBhgqxfv16aNGmSpTp44ty5cxIZGWlYfF+UYeb4KSkpsmfPHqlevbqEhISYLr6NmZ8jG0dt+Ouvv7KdxJmSkiKLFy+WxYsXS1paWoZthw4dktmzZ2c7/sqVK2XHjh0+j59Tjh49KvPmzZP7779fKlSoYMoyAqENZhcIz4+RbUhLS5PvvvtOHn300UzHcnJyspw5cyZb8VNSUmTu3LkyfPhwh+cKX5SRXYHwmWz2Npg9vi/K8GcbfvvtN3nuuedyJPaVK1ccHsdGl+GLNgAwntHjNLPHzylGf+YEwnyUL8owKn4g9ItszD4n5az+zEkZPycVCMeB2eP7ogzi39plBMIY0AzjzEB4H5m9DfHx8fLjjz9K8+bNDem3GB3fxujXweh2OKv/+fPn5ffff8927Hnz5snUqVMdHseBOs5UVVm2bJlMnTpVli9fLhUqVJB+/fpJz549pWPHjl4nRj377LNy5MgRp4vQuJOSkiK1atWSrl27yujRo7MUw59IgoJPdO/eXb755pssP75atWqyf/9+GTx4sIwdO1bCwsLcPubChQvyzDPPyHfffSdNmjQxPGMUgHMXLlyQ0qVLy+XLl7MVR1XFYrFk6jRcvXpVypQpI3FxcaaMD8A3du/eLXXq1MmxeDcfywcOHJCqVavm2PW2HZ0rfFEGAOPVqVNHdu/eLSLX+wdZ5apfYXQZvmhDVqSmpkpwcHCOxQvUMoyMHwjPjy/KuBXiGz1OM3t8AIGBOSnX8QEEjkAYA96q40wAnvn777+lfPnykpycnCPxHB3Ht8M489ixYzJt2jT59NNP5eLFi1KwYEHZunWrlCxZMsN+PXv2lC+++MJhjLVr10qrVq1k+PDhMmbMGK/r3r17d/n+++/l119/zdaKX+fPn5dChQpl+fFZRRKUH33//fdy8eJF6dGjh8uknsOHD8svv/wif/31l4SGhkqxYsWkRYsWOfKGady4sceZg19//bUsXrxYUlNTpXPnztKjRw8Rub6cmyM3Zn4WK1ZMunXrJtWqVXOY0Vq3bl2pWrWq07J/+eUXadq0qaSkpEiBAgWkR48e0qxZM6lVq5YULlxY8ubNK0lJSfLvv//Kr7/+KitWrJDZs2dLYmKihISEyIYNG6RevXoetROAMcaMGSMjR47MdhxnnYZJkybJoEGDJCQkRKKiosRisXgVNz4+Xi5evOi3+Li1nTlzRjZu3CinT5+Wy5cvS+HChaVo0aLSqFEjKViw4C0f31fM0I7OnTvLwoULsx3H2bHcp08fmTVrlmHxfVVGVuzfv1/i4uIkNjZWgoKCciyur+L7ogzi+7+MW6UNy5Ytkw4dOkidOnWkWrVqWepXrF27Vi5fvuz0ODa6DF+0ISvWrFkjAwYMkBYtWkj79u2lZcuWEh4enmPxA6UMI+MHwvPjizJulfhGj9PMHh9AYGBOypg5qVulb307x/dFGcQ3TxmBMAa8VceZADw3ZMgQmTBhQrbjuOq33C7jzJSUFPnuu+9k6tSpsmXLFmndurU8/fTT0r59e0lISJASJUrIxYsXnT4+NjZWtmzZIl26dJEJEyZIdHS02zL3798v/fv3l02bNsl9990nixYtynL9RUR69+4tn332WbZiZAVJUH6UkJAgefPmlcKFC8uBAwekQIECGbbHxcXJk08+KYsXL86U8RwUFCTdu3eX9957L1uXq2vUqJFs3rxZDhw4IBcuXJDixYs7PABGjx4t//3vf+WVV16RYcOGyZw5c2TVqlUyZ84cKVSokFy5ciVD3Tp06CDz5s2z31e0aFGXv1apWrWq7N6922WH5rvvvpNevXpJcnKyRx0f23M2a9Ys6dmzp9v9b7Zq1SpZsGCB/Pzzz5m+zG3atKk8+OCD0qhRI6/j+rIMs8ffv3+/LFq0yGn8rl27SrFixW7Z+CLmf45ysg0JCQlSpkwZWbZsmdStW9frely8eFGmTp0qw4YNc9hpSElJkYoVK8qGDRs8+iB3ZM6cOQ4vceWL+M6oqvzyyy9On//q1atnqS6+LMPMbVi9erWMHDlStm7d6nB7cHCwtGjRQl5//XWJjY295eLbGP0a+KIdOdWGvXv3Ss2aNeWZZ56ROnXqZGkyZfbs2bJ9+3aHx/Lx48elYsWKMm7cOKlbt26W4s+aNUsWLVrk9FxhdBmXLl3K8H9ERIRHK1UcP35cnnvuOVm/fr3cc8890r59e+ndu7fP4wdCG8wenzZ4VkbTpk1lxYoVkjt3brcxHdm/f7/UqFHD6Y9SfFGGkfG7dOmS4f8hQ4Z4fHnzU6dOyRNPPCHLly+X0NBQSUxMdLhfIJRhZPxAeH58UYbZ4xs9TjN7fGeMHIsHwnyUL8owMr4v5lrMNJ/jrzbkZP2Zk3IdPxD61maPHwhtMHv8QCnDzGNAX5Zx8/5m/9w3exvi4+Nl1apVTmPfe++92bpknNHxbYx+HYxuR07V/9y5c1KmTBn57LPPsjz//sknn8iUKVOcjtECdZzpyp49e2Ty5Mny9ddfi9VqFYvF4vbSn4cOHZJ69erJ5cuXJSgoSNq2bet2kZkff/xRVFXy5csn27Ztk/Lly2er3iVKlJAXXnjB6UI55cqVy/b5ySGFX1ksFrVarfrnn39muP/AgQMaExOjVqtVLRaLFipUSPv27asTJkzQadOm6ZAhQ7REiRJaunRp/euvv7JcfpEiRTQqKkqtVqv91rhx4wz1SUtL0/DwcLVarXrixAn7/U8++aS++uqr+u6776rFYlGLxaIPPvigHjp0KFM5UVFR9n0c3axWq/7www9u67thwwatWLGiy1i2W+HChXXRokVePyd//vmnxsbGZnhObK/Dzffde++9euzYsVuuDLPHj4uL00ceeUSDgoIyxbvxFhQUpE888YReunTploqvav7nyKg2TJgwQfft2+d1XW5UpEgRp9s+//xz3bFjR7biFy9e3G/xb5Senq7Tp0/X4sWLu3yNK1SooLNnz85SXYwuw8xtSE9P12eeeSbD+97VZ1hQUJA+//zzt0x8o58fX7bDiDb07NlTL1y44HV7bS5cuKC5c+d2uv25557TM2fOZDl+YmKi5suXz+U+RpZhe71CQ0O1devWunXrVq9iz5kzR0NDQzUoKMgv8QOhDWaPTxs8K2PDhg26bNkyr2LerFy5ci63G12GkfEtFovmypVLx4wZk2HMu3v3bqe3G6Wnp2u7du3UarU6LTsQyjAyfiA8P74ow+zxVY0fp5k9/o2MHIsHwnyUL8owMr4v5lrMOp/jyzYYVX/mpJzHD4S+tdnjB0IbzB4/UMow8xjQl2WoBsbnvtnbkJiYqMOGDdPcuXO7jJ0vXz594403NDU11au6Gx3fxujXweh2GFH/kSNHZmksYZOSkqKFChVyuU8gjTO9cf78eR01apTmzZtXrVbn43yb9evXa6FChRz2pR3dLBaLhoaG6sqVK93GLlmypBYtWjTDrX379hn2uTkP5eZbbGxslp8LV0iC8jNHSVDJyclaq1Yt+7auXbs6/KLu2rVr+uKLL2rt2rU1LS3N67K3b9+e4cvJXLlyadGiRTVXrlxarFgxPXv2rKqqHjlyxF6XlJQU++M3btyowcHBOmrUKLVYLPrRRx85LatYsWI6duxYXbFiha5bty7DbeXKlVqrVi0dPHiwR/VOS0vTL7/8Urt06WI/aG23iIgIbdGihU6YMEGvXLni9XOyefNmjYyMtB/k7m5Wq1ULFSqkf/zxxy1ThtnjHzhwQMuUKeNV/NKlS+upU6duifiB8BwZ2Ybk5GRdsGCBx/Vw5O2333a6LT09XVevXp2t+F9//bXf4tskJCRox44dM3Q6XD33VqtVO3furNeuXfO4HkaXYfY29O/fP0PM8PBwveeee/TJJ5/UYcOG6XPPPac9evTQSpUqZSjjpZde8qjuRsc3+vnxVTuMasPRo0f1q6++8ridjtx1111Ot8XFxemnn36arfhdu3Z1ud3IMiwWi5YsWVKPHDmS4f7Bgwfr888/7/B2szFjxjgdhBkdPxDaYPb4tMHzMrI7mXLgwAG3+xhdhlHxLRaL9u3bN9P9w4cP1+bNm9sn53LlyqVt2rTR119/PdO+W7dudfsam70MI+MHwvPjizLMHl/V+HGa2ePbGDkWD4T5KF+UYWR8X8y1mHk+x1dtMLL+zEk5jx8IfWuzxw+ENpg9fiCVYdYxoC/LCITPfbO34cyZM1q/fn2vYt9111168eJFj+pudHxfPEe+aIdR9b906ZJH34W5MmjQIJfbA2WcmVUbN25Ui8Xi0b5//vmntmrVyqPXuEqVKrp9+3aP4tpyRCwWizZo0EDXrVuXaR9PFsrZsmWLV233BElQfmZ7cW9Mgpo+fbr9/latWml6errLGAMHDtQvvvjC67L79eunefLk0aFDh+pvv/1mLychIUEHDhyoL7zwgqqq7t+/Xy0WS6bM8vT0dA0ODtbq1as7nJC7UefOnV1u37BhgzZv3tzrNqiqXr16VU+dOuX1B9PNzp49q9HR0fYTfcmSJXXkyJH6888/6z///KPXrl3TCxcu6MGDB/Wbb77RXr16aWhoqFosFo2JidG4uDi/l2H2+AkJCVq5cmV7/Dx58mivXr30q6++0s2bN+uBAwd0586dunr1an3rrbe0WbNm9pNkrVq13Ca+GR0/EJ4jX7QB7t1///0ZOp2lSpXSfv366ZtvvqnTp0/XDz74QIcPH65t27bViIgI+2dGjx49bpkyzNyGuXPn2mMWLVpUJ0+erFevXnW6/9GjR/Xxxx+3J+HMnz/fr/FtjH4NfNEOI9tw+fJlj9p5O7JYLDp69OhM9x88eFBnzZqlVatWVavVqnXq1NGvv/7a4Sqgf//9t8uJSyPjB0IbzB6fNnheBpyzWCw6a9Ysp9u//fZbtVgsunjxYqf7pKena0RERECXYWT8QHh+fFGG2ePDM0aOxQNhPsoXZRgZ3xdzLYEwn2N0G5iP8p9A6FubPX4gtMHs8QOpDLgWCJ/7Zm9DamqqNmrUyB47KChImzVrpmPHjtU5c+boqlWrdP78+frJJ5/oU089pTExMRm+N79xwQ5/xPfV62B0O3zxPoKxbLkcntqwYYMOGTJE69Spo0WKFNGQkBAtWLCgVq5cWXv37q0LFixwm5dyI1tOy5AhQ5w+rmjRotqvXz+dOnWqzpo1K8Nt2rRpGhMTo6+88opX7fAESVB+ZjsZ3ZgEdffdd6vFYtGwsLAMl59z5siRI3rfffd5XXapUqV0586dDrelpaXZVziwJUHlyZMn037FihXTiIgIPX78uMuyvvzyS7f1qV69uvtKG6h3797212PMmDGalJTk9jEHDhzQJk2aqMVi0f79+/u9DLPHHzJkiP0DtG/fvh5NXqxbt04rVqyoVqtVhw4d6tf4quZ/jnzRBrg2ZcoU+2tcvXp1Xbp0qcv9ExMTddSoUfalUGfMmOH3MszeBtuqRY0aNdJ///3XbV1s/ve//2lwcLCWL1/eZUfR6PiqvnkNjG6HL9oAxywWi3733XdOt9tWCXV3SeY77rjDL/F9UQbxXcf3RRmB0Aa4ZrFYdMmSJU63p6ene/TcVqhQIaDLMDJ+IDw/vijD7PHhGSPH4oEwH+WLMoyM74u5lkCYzzG6DcxH+U8g9K3NHt8XZRDfdfxAKgOuBcLnvtnbMHbsWHvsNm3a6G+//eY29qxZs7RIkSJqtVp1/PjxLvc1Or6N0a+D0e3wxfsIge2pp57SBx54wOU+bdu2dbl92bJl2rp16xys1XUkQfnZzUlQFy9e1ODgYLVardq7d2+PYuzbt0+LFSvmddm1a9d2uu3y5ctaq1YtVf2/JKg777wz036lS5d2eX1jbzRq1ChH4mTFmTNnNDQ0VIODg112QB25cuWK3nXXXRocHOxy+T+jyzB7/IsXL9qvX/ree+95Ff/06dNavnx5DQ8P1/Pnz/slvqr5nyNftAGupaamaokSJdRqtWqXLl00MTHR48du3LhR8+TJo0WLFnV5KTCjyzB7G1avXq0Wy/UlPxMSEjyOazNhwgS1Wq1Ol6g3Or6qb14Do9vhizbAOYvFosuXL3e5jydfpFarVs0v8X1RBvFdx/dFGYHQBrhmsVh05cqVLvdxdWlSmzp16gR0GUbGD4TnxxdlmD0+3DNyLB4I81G+KMPI+L6YawmE+Ryj28B8lH8FQt/a7PF9UQbxXccPpDLgXCB87pu9DVevXtXChQur1WrVQYMGebXqzP79+/XOO+/U/PnzO52TNjq+jdGvg9Ht8MX7CIGvYcOGunfvXpf7fPjhhy63p6amGrJQjlXgVy+99JLkz59fBgwYIGvWrJEtW7ZIWlqaiIh06tTJoxhvvvmmnDt3zuuyg4ODJS4uLtP9V65ckd69e9v/T01NFRGR6OjoTPvGxcVJenq612U7cunSpRyJkxVz586Va9euybBhw+Shhx7y6rG5c+eWr776SoKCguS7777zWxmBEP/KlSvSv39/GTJkiFfxixQpIrNnz5Zr167J999/75f4tjLM/Bz5og1wbcmSJfLXX39J48aNZe7cuRIeHu7xYxs3bizTp0+X06dPy7Jly/xWhtnb8MMPP4jFYpEvv/xSIiIiPI5rM3DgQKlUqZIsXLjQ4Xaj44v45jUwuh2+aANcs1pdDxMiIyPdxggLC/NbfF+UQfzAfw18VQayLigoKEf2CfQyjIwfCM+PL8owe/zbnZFj8UCYj/JFGUbG99Vci9nnc3wxb8d8lH8FQt/a7PF9UQbx/f8a+KoMOBYIn/tmb8P8+fPl3Llz0rFjR/nwww/FYrF4HLtixYry1VdfyaVLl2TBggUO9zE6vo3Rr4PR7fDF+wjmlp6eLjNmzJCBAwfKe++9J6dPn860T1JSklSpUsVlnEGDBrncHhQUJLlz585WXR0hCcrPxo8fLydPnpQOHTrIE088Id26dbNvq1+/vtvHL1u2LMtfQPbo0UMaNGggo0ePlpkzZ8r48eOld+/eEh0dLfPmzZOYmBj5/PPP5dixYyIiEhUVleHxW7dulcTERAkODva67JulpKTI1atXsx0nqzZt2iSFCxeWV199NUuPL1++vHTr1k3Wr1/vtzLMHn/jxo2SJ08eGT9+fJbi16tXT+6//3758ccf/RJfxPzPkYhvjoWsunz5sixYsEAuXLiQ47FFRE6ePCkTJkyQI0eO+C3+unXrJCgoSGbNmuV2MOzIww8/LPXr15elS5f6rQyzt2Hz5s1y9913S506dbyOK3J9EqNXr16yfft2h9uNji/im9fA6Hb4og23svj4+IAow5vB+a0Y3xdlEN//ZZi1DStXrpROnTrJokWLcjy2r8rwRRtExP4jI8rwX/xAeH58UYZZ4xs9TrsV4hs5Fg+E+ShflGFkfF/MtQTCfI7RbbiV56NEbo85qZxg1r51IMX3RRnED9wyAmEM6K6MQPjcN3sbfvrpJwkNDZUpU6ZkKXbr1q2lVatWsmLFCofbjY5vY/TrYHQ7fPE+yqr09HTZtWuXpKT8P/bOOyqK633j78wivSmIIDZsWEBRVFQsgF1R7LHE3o01mtiiEo0tsdfYa2KNvbeo2GOPXbFXVIqIhbLP74/8dr8i23fvLDPO55w9R3fuPM9972Vn77x75940i2uryA73mdmdvn37Uq9evWj+/PnEcRx16dKFlixZkqlMjhw5LOL1/v17i+h8jjwJKhvg5OREAwYMoDt37tDSpUupSpUqRETk5eWl99w5c+YQEVFAQIDRvgMGDKAqVapQdHQ09ezZk0aOHEmrVq2i5ORkatWqFW3ZsoWuX79OrVu3Jo7jSKFQ0ODBg+njx4+UmppKY8eOJY7jyNHRka5du2a0/+ccOnSIcuXKZZaGOVy6dImaNWtm1uz5Jk2a0L///ms1D7Hrnz9/npo1a0Zubm4m67do0YIuX75sFX0i8bcRkTCfBVOpU6cOtWjRgurWrWtxbZX+kCFDqEGDBlbTP336NNWrV4+KFClisk+HDh3owoULVvMQewyPHj2i8PBwk3WJiCpXrkz379/XeIy1PpEwfcA6DiFiMJXx48eTu7s7/fTTTxbXJiLq2bMneXh4UKdOnZjoC+VB9N+TIGLWF8JD1re+h1hjaN++Pe3YsSPTCr5i8zBEH4DZPpqeVJOaB0t9KbSPEB5i1zcHIe7TrK3P8l5cCvkoITxY6guRa5FCPod1DNk5H0X0deSkLIFYx9ZS0hfCQ9aXrkd2uAdk7SGF732xx3D27Flq3LhxloU3jOGbb76hS5cuaTzGWl8F635gHYcQf0emEhUVRcHBwdS4cWOLa6vIDveZ2Z3du3cTAFIoFPT999/T7t276d9//6X169ery6SkpJjtA4DevXtnts6XmL+Ej4zF4DiOWrRoQS1atKDr168btFS5jY0NcRxHQ4YMMcnvzz//pEaNGtHq1avpwYMH5OnpSc2aNaNBgwYR0X8rVXl4eNDVq1dp0aJFtHDhQsqdOzcpFApKTk4mjuOoZs2aNHHiRPrjjz+MroOKcePGUVhYmMnnm8ubN2+oQoUKZmmULFmSXr9+bTUPseu/evWKQkJCzNIPDAzUuMWjEPpE4m8jImE+C6by+PFjAkBPnjyxuDbRf7PLAai3ALWG/tOnTw3eClUbwcHBNG7cOKt5iD2GhIQEKlmypFnafn5+lJSUpPEYa30iYfqAdRxCxGAqU6dOpeTkZJo9ezb98ssvFtffsGEDAdC75DJrD3OTacnJyTon67HWF8JD1pd+HwjlYQoVKlSg/fv3U9myZS2uLZSHIfrLli2jFy9eaH3K+dWrV7Rq1Sqt5//zzz96J35IwYOlvhTaRwgPseubA+v7tOygz/JeXAr5KCE8WOoLkWuRQj6HdQzZOR9F9HXkpKQwtha7vhAesr71+0AoD1PIDveArD2k8L0v9hhevnxJHTp0MEs7KCiInj17pvEYa30VrPuBdRxC/B2ZyqlTpwgAnT171uLaKrLDfWZ2p0mTJjR//nyKiIggov/mlcycOTNT7iE1NZXu379Pfn5+JvucOHGCXFxczK7vl8iToLIp+vZPVLFx40ZKSkqiPHnymOzVvn17at++vdbjP/74o/rfAwcOpNy5c9Po0aPJxsaGJkyYQD4+PtSsWTMKDw+n7t27G+0/ZMgQOnPmDP36668m1d8SJCQkmNWGRESenp6UnJxsNQ8p6OfNm9cs/Tx58tDbt2+toq/yEHMbqTxYfxZMZevWrbRq1Spq06aNxbWJ/lsqd/Pmzcxmlxuin5iYSEWLFjXLp0CBAjr7mLWH2GN4+/atWU8/EP23wmNqaqrGY6z1iYTpA9ZxCBGDqQwYMIBmzpxJvXv3trg20X8T0OfOnUs9evRgom+oR69evWjGjBlaf2y9fv26+gZIE7dv39a5jC1rfSE8ZH3p94FQHqawY8cOunLlCgUGBlpcWygPQ/Q3bdpEmzZt0qlj7lPEUvBgqS+F9hHCQ+z65sD6Pi076LO8F5dCPkoID5b6QuVaxJ7PESJvl13zUURfR05KCmNrsesL4SHrW78PhPIwhexwD8jaQwrf+2KPISEhgQoWLGiWdt68eXWOWVjqf+7Dup9ZtxPrvyNTWb58OS1atIjpLgbZ4T4zuzN37lz6+eefM+3kxXFcpn6pVKkSzZgxg2bPnm2yz6RJk6hatWpm1VUT8iQoEfP8+XPy8fExeZng1NRUunLlCpUqVYocHR0NPq9du3bUrl27TO+VLFmSevfuTTdv3qRffvnFoDolJCRQ3759acOGDVStWjUmf+CGkpaWRs7OzmZpODg46FyinrWH2PU/ffpk9kxPR0dHUiqVVtEnEn8bEQnzWTCVihUrUsWKFS2uq8LPz8+kVfUsqf/u3TuLTCzRtVcyaw8pxGDuk5cZGRk6j7PWF6IPiNjGIVQMpjB+/HgaP368xXVV9OrVi3r16sVM31CPuLg4vU/yHD16VOsxAFqTeULoC+Eh60u/D4TyMIUcOXJQcHCwxXWF9DBE3xJjSn3tLwUPlvpSaB8hPMSubw6s79Oygz7Le3Ep5KOE8GCpL0SuRQr5HNYxZOd8FNHXkZOSwtha7PpCeMj61u8DoTxMITvcA7L2kML3vthj+PDhA7m6upql7eTkpDU3zVpfBet+YB2HEH9HptK4cWOmW+ERZY/7TDHg4eGh83jDhg2pS5cuVL9+fWrYsKHR+jNnzqS9e/fSrl27TK2iVuRJUCLmzz//pP3791PHjh2pRYsWRk+GioiIoFOnTlHt2rVp3759ZtVl+fLlVL16dZoxYwYtX76c2rdvTzVr1qSgoCDy9PQkFxcX+vjxI7169YouXLhAe/fupT///JPev39Ptra2NG3aNLP8LcHJkycpPDzc5MHj6dOnre4hdv27d+9SrVq1TNImIr1LwLLWJxJ/GxEJ81mQ0Y65Ty0a8hQQaw+xxzBixAg6c+aMQdvSfgkAvZ8B1vpEwvQB6ziEiEFGO6x+PBBKXwgPWd/6HlKIwVhu3rxJcXFxFBoaatL1Nzt4GKNfr149atSokUmJufT0dLp//z79/vvvkvdgqS+F9hHCQ+z6ppCenk42NuzSitlNn+W9uBTyUUJ4sNQXItcihXwO6xjkfJR1kcLYWuz6QnjI+l+PhzFkp3tAITyk8L0v9hjM3ULtzZs3Oo+z1lfBuh9YxyHE35GMtPnmm29o5MiR1LJlS/r111+pX79+Bp2Xnp5Oo0ePpl9//ZUCAwOpfv36Fq8bh+z2bfuVcujQIVq0aBFdv36dHBwcqGTJktS2bVuqV6+ezhu/6dOn09ChQ8nNzY0SEhKM8nRzc6N3797RDz/8QJMnTzY3BNqwYQN16tSJPn36ZNDNqupPb8WKFdSxY0ez/c2B53mLzZzXtrIFaw8p6CsUCsqbN6/JP6i/ePGCUlNTraKv8hBzG6k8WH8WvuTly5d04sQJevHiBSUnJ5Onpyf5+PhQlSpVKGfOnGbVAQD9888/dPz48Sz61atXN3vpXUvr8zxPvr6+FBERYXIfX7p0ia5cuaKzj1l6iD0GS3wGVE9jsbqW6tJXeQjRB6zbiXUM2nj//j0lJyeTh4eHxX94i4+Pz3St8Pb2Jicnp2znwfM8rVmzhpo0aWLS09gZGRl0//596tKlC8XExAiuL4UYxK4vx6Df48vlwh0dHQ265jx8+JAGDhxIR48epdq1a6ufuNIEaw/W+h4eHvTq1SvieV6vpi4mTZpEI0aM0HhMCh4s9aXQPkJ4iF3fVA4fPkz9+vWj8PBwatiwIUVERJCDg4Mk9Vnei0shHyWEB0t9oXItYs/nCJG3EzofRSTnpFSIfWwtBX0pxCB2fSl4iP0eUAgPKXzviz0GnufJ2dmZypUrZ7L2rVu36OXLl1rbh6X+5z6s+5l1O7H+O/qSjIwMunz5cpZxS8mSJS02DmQ5thNCPzvx+PFj+uuvv6hJkyZUuHBhreX27t1LjRo1IiKi4sWLU48ePahmzZpUtmzZTNdvpVJJly9fpr1799LChQvp8ePHxHEc7d69m+rWrWv5ACBjdUaNGgWe58HzPDiOU794nkdgYCCOHz+u9dxr166pyxpLjx494OXlhYyMDIPKt2zZUm+ZmJgY+Pv7Z4pD28vT0xPbt283ut4sULWhIfXW9dLVD6w9vnZ91bnW0pdCGwkRw+fs378fVapUUV//vnzZ2tqiXr16Oq+B2lAqlVi0aBHy5cunVZ/neRQvXhx//vlnttFXtZ05L0P7mJWH2GMw92/fkM8xS30h+4B1O7GOQUVqaioWLlyIBg0awMXFRX2+QqFA7ty50bx5c/z5559IS0vTq6WJ169fY8SIEShZsqTGepYvXx6TJk1CfHy8SfosPIoUKWJyXT5n4cKFVtEXwkPW160vhIfYY1Bdo+zs7FCnTh2cOXPGKM21a9fCzs4OCoVCaxnWHqz1e/XqZZSeNg4dOqT1mBQ8WOpLoX2E8BC7frNmzTK9YmJiDNZ8+vQpGjVqBJ7n4eDgIEl9gO29uBD34FLwYKnPsn/F5MG6DwzNJbD8O/0cOSeVGbGPraWgL4SHrK9bXwoequugWO8BhYwhO38ns/7OtHYMqvdZ5X1Z6wvdz6zbieXfkYobN26gbdu2cHNz01hPLy8v9OrVC3fv3tWrpQ2WYzsh9LMj/v7+6jGlPqZPn56lTRQKBdzd3ZE/f37kzp0bOXLkyPS3yXEcJk6cyKz+8kpQVmbbtm3UrFkzIiLiOI7Cw8OpfPnypFQq6fHjx3TkyBF6/fo1DR8+nCZMmJDl/NjYWCpWrJjOFSG0kZqaSh07dqSWLVtSy5YtdZb98OED+fj4UGJiol5dpVJJf/75J23ZsoWOHDmSaYUqBwcHCgkJoaioKOrevbvFVz8wFZ7nycHBgUqVKmXS7PuPHz/SrVu3KCkpSefMaZYeUtAn+u8pV1P1VUtDWkNf5SHmNlJ5sP4sAKB+/fqpt2PQ9TXEcRxxHEcDBgyg6dOnG1SH9+/fU9u2bWnnzp0G6RMRRUVF0fr16ylHjhxW1Vf1sbno+k5g7SH2GHiep4EDB1Lt2rVN/gxcunSJoqOj6cOHD1mOs9ZXeVgCfX3Aup0sgb7x0fHjx6ljx4708OFDIsr6eeY+ewKldOnStGzZMqpQoYLB/hs2bKCePXuqt/bTdL1QeXh5edHvv/9OUVFRBuuz8tizZw81aNDAqHpo4uHDh1SwYEHB9YXwkPV16wvhIfYYeJ6n/Pnz05EjR8jPz0/9/uDBg7U+/fblWOiXX36hsWPH6rxWs/QQIgYZGRn28DxPNjY2NGbMGOrSpQv5wiMSJwAA/XhJREFU+voSEdGVK1e0nlOmTBn1vwFQo0aNaN++fVqvFWLWV3kQsbkXl0I+SggPlvpC5VqE8BBzTkqIv1MiOSelDbGPraWgL4SHrK9bXwoeUrgHFCIGInF/74s9Bin8PvG5D+t+Nhd97cTy74iI6LfffqNRo0ZRRkaG3nGLg4MDTZw4kQYMGGBwPViP7VjrZ2eKFy9Od+/eJT8/P4qNjdVbfv369fTdd99RfHx8pvc5jsvSbra2tjR16lSDt88zCWbTq2QMIjw8HBzHwcXFBUePHs1yPD09HTt37kT58uXRpEkTvH//PtPxu3fvGvWUy+csX74c8+bNQ4UKFdCyZUuMHj1a42vo0KGoUKGCSR4A8OHDBzx79gxJSUkmnS8EdnZ2ePbsmVkaHz58gL+/v9U8xK5vY2ODS5cumaX/7Nkz5M+f3yr6gPjbCBDms9CjR49MM30dHBxQu3Zt9OrVCyNHjsTAgQPRvn17lChRItOs8h9++MEg/8aNG2fSL1iwILp3745JkyZh0aJFmDFjBkaNGoX69evD0dFRrd++fXur63Mch5kzZ+Lq1at48OCB0a+bN29i3bp1cHV1tZqH2GPImzev3n4yhMaNG2t8n7U+IEwfsI5DiBi2b98OBweHLE+82NnZIW/evHBzc8vyhIu9vT2OHTtmUGyLFi2CQqHIpO/k5AR/f39UrVoVZcuWha+vb6bjCoUCGzduNLj9hPCQkZFhA8dxGDduXJb379y5gxUrVqB06dLg+f9Wcvvjjz80Pg339OlTvU9usvQQIgZzSUhIYKYtJQ+W+lJoHyE8rKnPcRy6deuW5f1Ro0YhLCxMPdbIkSMH6tati9GjR2cpe+bMGZ3XCjHrA2zvxaWQjxLCg6W+ELkWKeRzWMcgxN8pIOekZGRkpI0U7gFZe0jhe1/sMfA8j61bt+Ldu3cm6X78+BGnT59G7ty5NR5nra+CdT+wjkOIv6OxY8dmWfmnaNGiqFevHtq1a4eoqCiEhobC1dU107hr1qxZBteB9diOtX525t69e5g6dSpu3bpl8DlJSUkYN24cypcvr3GlMT8/PwwaNAgPHz5kWPP/kCdBWRnV0m9Tp07VWS4jIwPR0dGoWrVqpq1MzJkE1aVLF4su/SdmSpcubRGdzp07W81D7PolSpSwiH6HDh2sog+Iv40A9jFs3LhR/WXn4+ODefPm4cOHD1p17t+/j86dO6uvRVu2bNHpu2DBArV+YGAgdu3apbP8+/fvER0dDScnJ/A8jyVLllhVv2DBgjqPG4qu7UtZe4g9hlGjRllEe+nSpRrfZ60PCNMHrONgHcODBw/g7u6uHuNUqlQJy5cvx5MnTzKVS0tLw+nTpzFmzBh4enqC4zh4eHjg/v37On0vX74Me3t79fipVatW+Pvvv5Gampql7IsXL7Bs2TL1dsIODg64cuWK3tiE8DCVR48eYcaMGYiNjRWlvhAesr71PawdA8dx2LBhg9Zz7927B47jslyXvkRXUo61hxAxmIMqUdWxY0cm+lLxYKkvhfYRwsPa+hzHYcWKFVrPX79+PTiOw44dO7SWUSqVcHR0lKQ+wPZeXAr5KCE8WOoLkWuRQj6HdQxC/J3KOSnd+uZg7bG1rC+Mh6yf/T2kcA/I2kMK3/tij6Fo0aIW0W7btq3G91nrq2DdD6zjYF3/v//+GwqFQp2L/uGHH7Tm1NPS0nD48GH1wjE2NjYaF475EtZjO9b6YsHU77SkpCTcuHEDJ0+exOXLl/Hy5UsL10w38iQoK2Nvbw+e5w2eRbdz505UqFABjx8/BmDeJKjTp0+D4zgUKlQINWrUQFhYmMZXaGgocuXKJelJUPoGTIby6tUrq3mIXf/s2bMW0b9586ZV9AHxtxHAPgbVbOgqVaro/Lx8yV9//QUbGxsUK1YMSqVSY5n09HTkz58fPM+jefPmWVbO08WJEyfg7OwMHx8fjRMIhNAHgPnz5xusqYtt27ZZzUMKMYgdKbQP6xiaNWumvgFbvXq1QVoJCQlo2bIlOI5DixYtdJatVasWOI6Dp6enwStHpaWlYdCgQeA4DhEREXrLC+FhKsbsF54d9YXwkPWt72HtGDiOw549e3Seb0jdAgICtB5j7SFEDOagWtFP16qAsgdbfSm0jxAe1tbnOA47d+7Uer5SqTRosqK2z7vY9QG29+JSyEcJ4cFSX4hcixTyOaxjEOLvVM5J6c5JmYO1x9ayvjAesn7295DCPSBrDyl874s9hq1bt1pE+8SJExrfZ62vgnU/sI6Ddf0rVqwIjuNQrFgx3L5922C9GTNmgOM4lC9fXm9ZlmM7IfTFwMePH+Hm5mbtapiEPAnKypQoUQI8z6snNRnC2bNnUa5cOVy/ft2sSVDAfz+gGfIBfPLkCWxsbEzykJGRkQGAAwcOgOM4lCpVCikpKUafP3PmTPA8jwMHDmg8vnXrVnAch2rVqiEjI8No/T///BMcx2kdXLLWl5GREYb79+9DoVDAwcEBMTExRp2bnp6Ohg0bgud5jcttA8C///4LjuOQM2dOnckEbaieFrl8+bLWMkJ4mEOxYsXAcRwKFy4sSn0hPGR963tYOwaO47Bv3z6d51epUkWvR4UKFbQeY+0hRAzm8PvvvyMgIMCoZdS/Rg+W+lJoHyE8rK1vyGe5UqVKen20JanFri8jIyMN5JwU25yUtcfWsr4wHrJ+9veQwj1gdr/PlJGR0c+pU6fAcRzy5cuHuLg4o88fPnw4eJ7HyZMntZZhPbZjrS8G4uPj0bJlS+aL5Ny6dQubNm3CuXPnLKorT4KyMiNGjADP89i9e7dR5928eRNBQUFYuXKlWZOgDFmKLSkpCbdv38bgwYNN8vgasPYyp7I+8PbtW2zZsiXTdpFi0gfE30aA7hiGDRsGnudx/vx5k7QzMjJQqlQp9OvXT+PxQYMGwcbGRuvEBEOoVKkSevToYRV9S5GQkMBUXwgPMccwbtw4uLm5WWy7OKH1VbDuAyHi0BbD1KlTwXEc5s6da5Lus2fP4OrqigkTJmg8PmHCBPA8j3Xr1pmkn5SUBC8vL/z0009aywjhYQ6m7BeenfSF8JD1re9h7RgslditWLGi1mOsPYSIQUZGhj1iv1aI4VrE8l5cCvkoITxY6guRa7F2PscSsI5BX/3lnBTbnJS1x9ayvjAesn7295DCuCu7j+2k8L0v9hgyMjJw8eJFZqsbstZXwbofWMehq/5jxowxawLQx48fUbBgQQwZMkRrGdZjO9b62ZnY2FgMHjwYLi4uZs1BMYT58+fDxsYGPM+jcePGiIiIwMWLFy2iLU+CsjKJiYkoVqwYgoODde4jqYnHjx8jMDCQ+R9gamoqvL29dS4v/rVj7WVOZX0gJCQEPM8zm+HPWh8QfxsBumNQbbtpDlOmTEHlypU1HqtcuTIaNWpklv6cOXMQHBxsFX1L0KNHD/A8j44dO4rWQ+wxuLq6guM4uLi4WFxbCH1AmD5gHYeuGFq0aIECBQogPT3dZP3evXtrvR5ERkaiZMmSJmsDwJAhQ1CnTh2tx4Xw0Ic5yffsoC+Eh6xvfY/sHIOlEruBgYFW8xAiBhkZGfZY6rNszkpN2VnfErC8F5dCPkoID5b6QuRarJ3PsQSsY9BXfzknxT4nBYDpZEmhPMSuL4SHrJ99PaRwD5jd7zOl8L0v9hgiIyPB8zzq1atncW0h9FWw7gfWceiqf506dcyOa8yYMahZs6bW46zHdqz1syP79+9HZGQkFAoFeJ4Hz/PM56D4+fll8nj9+jXq16+PCxcumK3Nk4xVcXNzo5iYGMqVKxcFBwfT/v37DT43X758dOTIEQoJCTGrDs+ePaMzZ85QTEwMHTt2LNPr77//psWLF1NCQgINGTLELB8po1QqCQClp6eL1kPs+o8fPyYA9OTJE1HqE4m/jYh0x/Do0SMKDw83S79y5cp0//59jceePn1K1atXN0s/ODiYHj16ZBV9S7BhwwYCQFu3bhWth9hjGDBgADk5OVHv3r0tri2EPpEwfcA6Dl0x/PvvvxQVFUUKhcJk/QYNGtC1a9c0Hrt58yY1bNjQZG0iotq1a9PNmze1HhfCQx+jRo0yy9/a+kJ4yPrW98juMXz8+NEs7+TkZK3jIqE8hIhBFyNHjjTL/2vxYKkvhfYRwiO76wMwuw4vXryQrL65sLwXl0I+SggPlvpC5Fqsnc+xBKxj0Fd/OSfFPif16dMnKl++PDN9ITzEri+Eh6yf/T2kcA9o7ftMXUjhe1/sMZw6dYoA0NmzZy2uLYS+Ctb9wDoOXfWPjY2lunXrmqVfo0YNunPnjtbjrMd2rPWzCykpKTRv3jwqWbIk1a9fn3bv3q0eVysUCnJzc2Pq7+fnR0RERYoUISIiDw8PWrduHW3cuNFsbRuzFWTMJk+ePLR//36KiYmh9evX0/Xr12nQoEEGnZsrVy46dOgQdezY0Wjf1NRU6tChA23atElvWQD0+vVroz2+Fvbt20ebN2+mxo0bi9ZD7Ppbt26lVatWUZs2bUSpTyT+NiLSHUNCQgKVLFnSLH0/Pz9KSkrSeCwxMZGKFi1qln6BAgXo7du3VtG3BFOmTKG5c+dSjx49ROsh9hjGjx9P48ePt7iuUPpEwvQB6zh0xZCQkGB2Msrf35/i4+M1HktISKCgoCCz9IsVK0YJCQlaj7P2SE1N1fi+ra2t+t/Hjh2jQYMGUUBAQKb3VQQHB1Pp0qWtoi+FGMSuL8dgmEevXr1oxowZxHGcxuPXr1+niIgIrfW7ffs2vX//XutxITyEiEEXW7dupdatW1NAQADZ2LBJb0jBg6W+FNpHCI/srr9s2TJ68eKF1s/yq1evaNWqVVrP/+eff3ROIhK7vrmwvBeXQj5KCA+W+kLkWqydz7EErGPQV385J8U2J5WQkEA9e/ak5ORkJvpCeIhdXwgPWV8cHlK4B7T2faYupPC9L/YYli9fTosWLaJOnTpZXFsIfRWs+4F1HLrqn5CQQIGBgWbpFylShBITE7UeZz22Y61vbWJjY2nOnDm0YsUK9feJ6uGi4OBgGjBgALVo0YKIiPLmzcusHlu2bKEDBw5QpUqV1O+5ubnRxIkTzdbmYInHpWREyU8//WTQH5FCoaDSpUvTzz//TFFRUQLUTEZGRorwPE979+41awZ4fHw85c6dmzIyMjTq79+/n2rXrm2yfmJiInl4eFhFX0ZGRhhsbW1p8+bNFBkZabJGXFwc+fj4aPws29jY0Pbt281aqen169eUJ08erdcK1h5ubm707t079f8VCgVFRkbS5s2b1e/5+PhQXFycVv3SpUvT5cuXNSasWOtLIQax68sx6PfgeV6rr6EAII7jtF4rWHuw1m/Xrl2W9+zs7Gj58uXq/+tr//bt2+uc+CAFD5b6UmgfITzErm+Jz7IKVtcKa+rLyMhIAzknxSYnde/ePZo7dy4tWbKE3r17p3Nsml09xK4vhIesLx4Psd8DCuUhIyPDFoVCQbt27aL69eubrPHmzRvy8vLSea1gPbZjqW8t9u3bR7Nnz6Z9+/YRAPXEJ47jKGfOnLRt2zYKDQ3NdE737t1pyZIl1qiueZi9oZ6MWYwbN84iOhMmTDD6nKJFi6J///54/vw5AODSpUuYOXNmpjIvXrxA586dsW3bNovUU8zcvXtX9B5i13/z5o2o9QHxtxFgegwcx2HXrl1mecfFxWndf5bjOGzevNks/adPn1pNX0Y8TJ8+XdT6QpFd4+A4DgcPHjRLIzExUee14sCBA2bpJyQk6LxWsPaYOnUqOI4Dx3Fo2bKlxuu+t7e3uoymF8/zWtuZtb4UYhC7vhyDfg9d5xjz0netYOnBWr9jx47q425uboiOjkZcXJxR7a9QKBAbG6u1jaTgwVJfCu0j9j4QQl/s1wrW+pbk48ePzLR1fQZkD2H0WfavkB6s+4B1DLqudXJOynI5qf379yMyMhIKhQI8z4PneYtfS1l7iF1fCA9ZX3weUhh3iWVsJ4XvfSnEIAXE3kaa6s9xHPbu3WuWbnx8vN5rBeuxHUt9IUlOTsacOXPg7++f6XuE4zj4+PhgxIgRiI2NReXKlS3i9/79e5w5cwYrV67EjBkzMGHCBEyfPh0rV67EmTNnBPubl7fDszLz5s2j0aNHm6WhVCpp9uzZNHLkSKPOS0tLo9mzZ6v/X7ZsWZoxY0amMnny5KHp06eTn58fHT16lMqWLWtWXcXMqFGjaN26daL2ELv+kCFDMj3tKjZ9IvG3EZF5MYwYMYLOnDlDCoXC6HMB0OnTp3WWGTBgAG3dutVk/UuXLllV3xBGjhxpkaUgrekh9hhWr15NHTp0IE9PT1HqEwnTB6zjMCeGbdu2UeHChU3+LB84cEBnmSVLlpBCoTBZ/++//9ZbjqWHvb09ERHNmjWL+vfvr7EMz/M0fvx4qlChgrq8ik+fPtGwYcNo586dVKtWLcH1pRCD2PXlGAzzWLNmDTVp0oScnZ01ausiIyOD7t+/T126dNFZjrUHS/3w8HBavXo1VatWjdatW0c+Pj4ay/n7+1NQUJDG9t+6dSv99ddf9MMPP0jWg6W+FNpHCA+x6xMR1atXjxo1akQuLi4aj+siPT2d7t+/T7///rvWMmLXtwRKpZLy5ctHr169srj2p0+fqHz58jq3i5A92Oqz7F8hPVj3AesY9NVfzknp1tdHSkoKrVixgubOnUu3b99W6xL9t1qxk5OT2dvtsfYQu74UYhC7vhg8xHwPKKSHOUjhe18KMQQEBNDVq1eZaAuhTyRMP7CMQ1f9p0yZQs+ePTN53BITE6O3HOuxHWt91ty5c4fmzp1LK1eupOTkZPX3CM/zVK9ePerZsyc1btxYHR9n5ip8R48epZkzZ9L+/fvp48ePWsvZ29tTeHg49ezZk5o0aWKWpy7kSVBWJi4ujrp160bNmjUjV1dXo84FQAkJCbR+/XqTLpAFChTI8l7dunVp1apV1LFjR/V7OXPmJHd3dxozZgxt27bNaB8xkJqaqvF9W1tb9b+PHTtGgwYNooCAgEzvqwgODqbSpUtbzUPs+oZw8OBBmjlzplb9okWLmrU3qbn6Umgj1jFcvXrVrAEX/n8pW208e/aM1qxZI1p9Q9i6dSu1bt2aAgICyMaGzdc4a4/sHIOmCcUcx9GECRPU/3/+/Dn5+vpS7ty5NX4G2rRpo3VyDmt9QzG3D7JDHObEMG/ePJo3b57J3vrYuHEjbdy4kZk+a48LFy5Q165dtU76ICIKCQmhUaNGaT3u6OiodaI/a30hPGR96fcBa4/ChQtr3OLKUBQKBRUtWpQ6dOigtQxrD9b6165do4CAANqzZw85OjpqLFOgQAE6deoU8Tyv8fjvv/9Oe/fu1TrxQwoeLPWl0D5CeIhdP2fOnLRr1y6t5xqKk5OTxvfFrm8JMjIyaMiQIRQfH29x7YSEBOrZsyclJydbXFtKHiz1WfavkB6s+4B1DIbUX85JmZaTio2NpTlz5tCKFSvU7av6ES04OJgGDBhALVq0ICIyOS/L2kPs+lKIQez6YvEQ+z2gUB7mIIXvfSnEMG3aNLpx4wYTbSH0iYTpB5Zx6Kv/0aNH6ejRo0y8VbAe27HWZ80333yjngTPcRzlz5+funbtSl26dNE4R8RUkpOTqXPnzrR161b1d5YuPnz4QLt376Y9e/ZQaGgorVmzxqL1UcNohSkZA1Ety2jOy9SlHWvXro3169fj5cuXePnyJQAgPT0dZcuWxZEjR9TlLly4AIVCAXd3d4vFnd1wdXXN1KY5cuRAs2bNMpXx9vbW2Q+BgYFQKpVW8xC7foECBeDj45Pp1bBhQ6P0Q0NDtbY/a30ptBHrGMS+HK8QS/G2bds2y6tz585GtX+HDh109jFrD7HHEBkZqS5jY2ODLl264NKlS1m0dfWxnZ0dnj17ZhV91u0jVBwsY1B9DlleK1jqC+FRoUIFPHz4UKs/AKxevVrncQAIDAy0ir4QHrK+bn0hPMQew+7du/WeZwgPHjzQeoy1B2v92rVrY//+/TrPHTBggM7jycnJKFOmjNbjUvBgqS+F9hHCQ+z6vXr10nmuoRw6dEiS+uaQnJyMWbNmoVChQibn7rQRGxuLwYMHw8XFxeLaUvJgqc+yf4X0YN0HrGMwtP5izxmx1tfE3r170bBhQ/UWXJ9reHh44Pjx41nO6datm8H6QniIXV8KMYhdX2weYr8HFMrDFKTwvS+FGLZt24aqVauq87+WhrU+IEw/sIzDkPoLMW6R4tiLBcePH0f79u1hb2+P8uXLY8GCBXj79q3GslWqVDFa//379wgODgbHcfD19UW3bt0wb9487Nq1C+fPn8eNGzdw79493Lp1C5cuXcL+/fuxePFi9OnTB35+fuA4DiVKlEBiYqK5oWaBAwyYkiXDDJ7nieM4g2bG6YLjOMrIyDDqnL/++otatWpFHMeRg4MD/fvvv+Tn50dr166ljh07UtmyZcnd3Z1OnjxJHz9+JG9vb3r27JlZ9cyuTJs2Tf1EZIsWLWjy5MlUpEiRTGV8fHzo5cuXWjU4jqP9+/dr3U6DtYfY9X/++Wf6+eefiYioUqVKNGXKFKpZs6bR+idPnqSQkBDB9YnE30asY+B5ngYOHEi1a9c2aSnbjx8/0qVLlyg6Opo+fPiQ5TjP8zRjxgyz9Xv27ElJSUmC6xMRderUiVavXk0cx5GLiwsNHjyY+vbtS7lz51aX0df+PM/T7du3qXDhwlbxEHsMK1asoK5du5K/vz+tW7dO4zawPj4+9OnTJ/L399e43ciZM2do1qxZ1K9fvyznstZn3T5CxcEyBp7nKV++fBQSEmLyZ/nKlSt08+ZNjWMvnuepYsWKVKNGDbOuFfv379c6tmPtERwcTOfPnzda90uqVq1KJ0+eFFxfCA9ZX7e+EB5SiEFGN2XKlKHLly/rfGouLS2NcuTIoVOnUqVKdPbsWcl6sNSXQvsI4SF2fRnLc/v2bZozZw6tWrWK3r17R0T/ewrY2Nzdlxw4cIBmz55Ne/bsUecSLaUtJQ+W+iz7V0gP1n3AOgZj6y/npPTnpIiI3r17p96C686dO0T0v1VovL29qXPnztS9e3dq3749nTp1yuh6COEhdn0pxCB2fSl5yJiPFL73xR5DUlISLVmyhObPn08PHjywqLYQ+ipY9wPrOIypP8/z1LRpU6pVq5ZZ45bFixdTenq6xjJCjO1Y6gvN69evadmyZbRw4UKKi4ujli1bUo8ePahq1arqMqbkL6Ojo2nhwoU0b948at68udH12rVrF/Xu3Zu6du2q/v3bUsjb4VkZNzc3SkpKIhsbG4qKiqKAgACDzwVAiYmJdODAAbp586bR3i1atKCuXbvSsmXLKDU1Vb0NVtu2ben48eO0YMGCTOU/3yJPaqh+nJ01a5bW7S54nqfx48dThQoVNP6YO2zYMNq5c6fWSVCsPcSur1q+dfDgwTR16lSNCV6O46hbt25a9SdNmkRbt27VOMGHtT6R+NuIdQw+Pj40Y8YMjZqGUq9ePTpx4oTGYwUKFKCBAweape/v70+bNm2yij4RUXh4OK1evZqqVatG69atIx8fH606QUFBGtt/69at9Ndff2ndDoS1h9hjuHv3LhUoUICOHTuWacLN5+TJk4dOnDihdcuMCRMm0LFjxzRO7mGtTyRMH7COg2UMjo6OdP36dZNuXlQAoODgYI3HXF1d6cSJE2Zv9Vi9enWtx1h78GZuJ6Pi7du3VtEXwkPW160vhIcUYjCHx48f019//UVNmjTROmE1u3vo03dyctK7bLi+SR9EREqlUusxKXiw1JdC+wjhIXZ9S5CYmEju7u5fvf6uXbtozpw5dPDgQQJg9gOPKlJSUtQ/kt6+fZuI/vcjqY2NDTk5OZn9XSMFD9b6rPpXSA8h+pllDObUX85J6da/c+cOzZ07l1auXEnJycnqduV5nurVq0c9e/akxo0bk0KhICIyaWsX1h5i15dCDGLXl5KHqVj7HjA7eUjhe1/sMVy7do3mzJlDf/zxB71//56ISFT6Klj3A+s4TKm/h4cHbd682Wzve/fuaT3GemzHWl9oPD096ccff6QffviBdu/eTb///jvVqFGDihcvTt27dzd5DsjGjRvp0KFDVKpUKZPOb9SoER04cIBat25t8UlQ8nZ4Vub9+/dYtGgRgoKCwHEcIiIisHnzZp3bqn3Ju3fv4OLiYnIdbt26hfv372d5f8OGDWjVqhWaNWuGefPmISMjw2SP7E7Xrl31LlH65ZZgXxITE4OwsDCreYhdv3fv3oiKitJ5fv369XUe3717N+rUqWMVfUD8bQSwjWHUqFE6zzOUpUuXanx//vz5FtHftm2bVfQBYOjQoQgMDERKSorWMpUqVdJ5PV6wYIHOvxPWHmKPoWHDhti4caPW8wCgb9++Oo+/fv0aZcuW1XiMtT4gTB+wjoNlDLq2vjKGrl27any/bt26FtH/4YcftB5j7eHv72+2dmpqKgoXLmwVfSE8ZH3p94FQHqbi7+8PnudRvHhxi2sL5aFPv3Tp0hbxKVq0qNZjUvBgqS+F9hHCQ+z65tKjRw/wPI+OHTt+lfpv377FjBkzULRoUfV2E6qtD/LmzYtx48bh1q1bePLkCZycnIzSvnv3LgYOHAg3N7cs2hUqVMCqVauQkpKClJQUuLm5mVR/KXiw1GfZv0J6sO4D1jFYov5yTkq3frly5TJt21KgQAFER0dr3RralO1SWHuIXV8KMYhdX0oepmLte0Bre0jhe1/sMSiVSmzZsgURERFZtB0cHNC1a1fs27cPx48fh52dndF1Z62vgnU/sI7D3Pp36dLFaE9NTJkyResx1mM71vrZgfv372P48OHw8vKCnZ0dvL29NX4XdejQQatGUFCQReoSHBxsEZ3PkSdBZSNiYmLQpk0b2NraomDBgpgyZQrevHlj0LnVqlVjXDtpU6FCBa2DTBWrV6/Wq6Prh1XWHmLXr1y5Mq5du6bz3FmzZuk8np6ebjV9QPxtBAjzWZDRTu3atbF//36dZQYMGKDzeHJyMsqUKWM1D7HHEBAQgLS0NJ3nxsXF6TwOaB+0sdYHhOkD1nGwjMFSk7qNmbAuNkqUKIGrV6+apbFnzx5UqFDBKvpCeMj60u8DoTxMpVixYuA4jskEK6E89On7+fkhPj7eLI8bN27onMwmBQ+W+lJoHyE8xK5vLm5ubuA4Dq6url+V/o0bN9C3b1+4uLhk+WHAxcUFa9asyTJerlevnkHae/fuRcOGDaFQKDLp8jwPDw8PHD9+PMs5+h5mkqIHS32W/SukB+s+YB2DEH+nMv/j+PHjaN++Pezt7VG+fHksWLAAb9++1VjW1EkZrD3Eri+Eh6z/9XiYgrXvAa3lIYXvfbHHkJCQgF9//RWFChXSOKnnl19+wevXrzOdExAQYHDdWeurYN0PrOMQ4u9IJvuRmpqKNWvWoFq1arCxsUGDBg2wfft2pKenIykpSee9cokSJbR+fxlKUlKSSZ83fciToLIhL168QHR0NHx9feHo6IiuXbvi4sWLJus1a9ZM/WrevDl27txpucpKhPLly1tER9eAlLWH2PUtNVu0cuXKVtEHxN9GgDCfhexMQkKCVfUDAwP1TqxITU3V61OxYkWreYg9Bl2fD2OoVKmSxvdZ6wPC9AHrOISIQUY7Xbt2Rbt27czSqFKlCoYOHWoVfSE8ZH3p94FQHqZy7949TJ06Fbdu3bK4tlAe+vQbNGiAmTNnmuXRv39/tGnTRutxKXiw1JdC+wjhIXZ9c/n9998REBCg94Ecqejv2LEDderUyfKjgL29Pdq2bYvDhw+bNFZOTk7GnDlz1KsXfK7t4+ODESNGIDY21qxxuBQ8WOuz6l8hPYToZ5YxCFH/7Iq1c1IqXr16hSlTpqBw4cJwdnZG586dceLEiUxlzM37sfYQu74QHrL+1+NhDNa+BxTaQwrf+2KP4d9//0WPHj3g5OSURb9KlSpYtmyZWfld1voqWPcD6ziE+DuSEQeXL19Gz5494eTkBBcXF7i6uoLnea3lu3fvjrp16xr0QLwm3rx5gwYNGqBTp04m1lg78iSobEx6ejo2bNiAmjVrguM4hIaGYt26dUhPTzdKh+M4eHl5Yd68eSb/EUodSz2drWuZetYeYte31A/V2lYuYa0PiL+NAGE+C6Yybtw4uLm5WWwZyi/JDtssWGogqWuVINYeYo/BUjPOtS25zFofEKYPWMchRAymcvHiRQwcOBCnT5+2uDbw39POUVFROrfOZO2xbds28DyPxYsXm6T//fffg+d5xMTEWEVfCA9ZX/p9IJSHLu7evWvSednJwxz9adOmIVeuXIiNjTXp/GPHjsHW1lbnKqZS8GCpL4X2EcJD7Poy+klMTMT06dNRpEiRLD8MlCpVCjNmzMi0krsxP1bevn0bAwYMUG/zpdJVKBRo0KABtmzZkikPaMoPoVLwYKnPsn+F9GDdB6xjEOLv1By+hpzUlyiVSuzcuRORkZFQKBQoWbIkpk2bhlevXlms/Vl7iF1fCA9Z/+vxMAZTx53ZRV+XhxS+98Ueg1KpxObNmxEWFpZFO1euXBg4cGCmVbmNrT9rfRWs+4F1HEL8HZnK8uXLUbZsWSxYsICZB+uxHWt91sTHxyM6Olq9Kpg2Hjx4AHd3dzg5OaFTp05Yu3Ytbt68iU+fPmksn5aWhtjYWGzatAk9evSAq6srnJ2dcfPmTYvHIE+CEgn//vsvevXqBWdnZ/V+my9evDDoXI7jNC4PLPM/LLEsfGpqqs4lNll7iF2/VKlSZusrlUqr6QPibyNAmM+Cqbi6uqqX3WRBdthmwVKTx4oWLWo1D7HH4Ofnh5SUFLN0Hzx4oLX+rPUBYfqAdRxCxGAqvr6+4Hkevr6+FtcGAA8PD/A8j1y5cjHRN9SjdOnSUCgUGDJkCD58+GCQbnx8PNq0aQOe51GjRg2dZVnrSyEGsevLMRjuoY1vvvnG5HOzi4c5+gkJCXB1dUXhwoVx4cIFo87ds2cP3NzckCdPHrx//17SHiz1pdA+QniIXV9GP35+fuofBDiOUyd4teXajPmBoFy5cpm2+CpQoACio6O1blNvyo8PUvBgqc+yf4X0YN0HrGMQ4u/UHL6GnJQu7t+/j+HDh8PLywt2dnbw9vbW2DcdOnQwuY6sPcSuL4SHrP/1eOji48ePcHNzY6IthL4+Dyl874s9hqJFi2aZdFOzZk2sWbMGHz9+NLv+rPVVsO4H1nEI8XdkKqpJ8azGRQD7sR1rfaE4ceIEOI7TWebs2bPw9fVVj+NVL3d3d+TNmxeFChWCr68vcuXKlek4x3Fwc3PD3r17mdRdngQlMpKSkjBjxgw4OjrCzs4O7du317sSgaenp0C1Ey8lSpTINGPWFPbs2aNzFR3WHmLXL1q0KO7du2eWfkxMDMqWLWsVfUD8bQQI81kwlZ9++gnOzs744YcfLK4NZI9tFvz8/BAfH2+Wz40bN3ROZmPtIfYYwsPDsWLFCrO0x4wZg6ZNm2o8xlofEKYPWMchRAymUqFCBXAcp/Naag716tUDx3EIDw9nom+ox9mzZ2FnZ6eeLNW/f39s2rQJd+/eRWJiIjIyMpCSkoIHDx5g8+bN6NmzJ5ydncHzPOzt7fHPP//orANrfSnEIHZ9OQbdHp8+fdL4+hwfHx8MHDgQixcvxsqVK7O89I3ZWHsIEcOvv/4KjuNgZ2eH3r174/z58zrLnzx5Uj0Bjed5zJ49W2d5qXiw1JdC+wjhIXZ9fYwYMcKs88Wun5qaijVr1qBatWrgOA516tTBnj17tJY39geC48ePo3379rC3t0f58uWxYMECvH371iLaUvJgpc+6f4XyANj2gRAxCPF3aipfQ07KED7/O7CxsUGDBg2wfft2pKenIykpySI/VrL2ELu+FGIQu76UPL4kPj4eLVu21LnqR3bWN8RDCt/7Yo/h5cuX+OWXX1CwYEFwHIcmTZrgxo0bFtEWQl8F635gHYdQ41NT6NSpEziOY7alO8B+bMdaX0iGDBmit8zLly/Rr18/uLi4ZJpcp+3l6uqKPn364NmzZ8zqLU+CEhHJycmYMWMGihQpkunJGJ7nUbNmTa3n6dr6yhhYTGrILnTt2hXt2rUzS6NKlSoYOnSo1TzErt+uXTv079/fLP2GDRviu+++s4o+IP42AoT5LMhop0GDBpg5c6ZZGv3799c5OGTtIfYYxo8fj3z58uHVq1cm6V6/fh1OTk5YuHChxuOs9QFh+oB1HELEYCoJCQnYtGkTXr9+bXFt4L8b0HPnzmldMlZIj/Xr18Pe3j7LUxzaXqrx6cqVKw2qB2t9KcQgdn05Bu0erq6umc7JkSMHmjVrlqmMt7e3Tp/AwEAolUqreQgRAwC0aNEiU9u7urqiWrVqaNq0KTp06IBWrVohLCwM7u7umfqgRYsWOnWl5sFSXwrtI4SH2PV1UbJkSVy8eBFpaWlma4ld/8qVK+jTpw9cXFxQsGBB/Pzzz3j06FGmMqb+QPDq1StMmTIFhQsXhrOzMzp37owTJ05YRFtKHiz1WfavkB6s+4B1DEL8ncqYz+XLl9GzZ084OTnBxcVFPTYUk4fY9YXwkPW/Do/Y2FgMHjxY/eO1pevPWt9UDyl874s5BqVSiW3btqFBgwawsbFBtWrVsHLlyiwr2Jpaf9b6n8OyH4SIQ4i/I2NhlXuXYcvbt2+xZ88eREdHo2PHjmjSpAlq166Nxo0bo0uXLpgwYQIOHTqkcTUzSyNPghIB9+7dw6BBg9TLv32eWC9UqBCmTp2KxMREredXrFjR7DqkpaXB3d3dbJ3syrZt28DzPBYvXmzS+d9//z14nkdMTIzVPMSuv2bNGuTIkQO7du0ySX/GjBngeV7rTGXW+oD42wgQ5rMgo51p06YhV65cJu+LfuzYMdja2mL16tVW8xB7DE+fPoWdnR2Cg4Px+PFjo3SvXLkCX19fuLm5aV3FiLU+IEwfsI5DiBhkDCMmJgb+/v4GPcHh6emJ7du3Zyt9KcQgdn05Bs1MnTpVXb5ly5a4e/duljLe3t46fXiex8GDB63mIUQMAJCRkYE+ffpkOU/b5DPVpA9jtm2VggdLfSm0jxAeYtVv27Ztllfnzp0zldE3oVHXdixi19dGcnIy5s6di8DAQNjY2KB+/fr466+/kJaWZvYPBEqlEjt37kRkZCQUCgVKliyJadOm4dWrVxb78UEKHiz1WfavkB6s+4B1DEL8ncqYT3x8PKKjo+Hi4sJkYoMQHmLXF8JD1pemx/79+9XX2M/HkpaqP2t9S3lI4Xtf7DHExsZi6NCh8PT0hJubW6bVby1Rf9b6Klj3A+s4hPg7kpERCnkSVDbmyJEjaNq0KWxsbLIksqpVq4ZNmzYhIyNDr46fnx9WrVqlcfsBQ15z585FgwYNmA3csgulS5eGQqHAkCFD8OHDB4POiY+PVy8xX6NGDat7iFk/LS0NBQoUgIODA+bMmWOQtuq84cOHg+d5nVsTsdZXIeY2UiHEZ0Eb06dPN/lcQ8ju2ywkJCTA1dUVhQsXxoULF4w6d8+ePXBzc0OePHmyPAUgpIcUYhg6dCg4jkOuXLkwefJkvasdPXnyBMOHD4e9vT14nkd0dLTO8qz1hegD1nEIFYM2zpw5Y9J5hrJ7926m+pb2yMjIwOrVq9G8eXPkypUr05jU0dER4eHhmDlzJt69e5ct9aUQg9j15RiyMnfuXHAcp3OLqrx58+KXX37B3r17ceTIkUyvffv2ISgoCIMGDbKahxAxfE5MTAwiIiJgY2OjdVJVUFAQ1q1bZ5CeVD1Y6kuhfYTwEJt+x44d1T8Uubm5ITo6GnFxcZnK6JvQqFAotE5eF7u+IRw9ehRt2rSBnZ0d8uTJg4IFC2qcfDZ8+HCjte/fv4/hw4fDy8sLdnZ28Pb2xsOHD7OUM2Uil5Q8WOqz7F8hPVj3AesYhPg7NYS+ffsy1W/UqJFo9U+cOAGO45jpC+Ehdn0hPGR98Xu8e/cOc+fORYkSJbJMos+RI4d6VVFTYa3P2kMK3/tijuHTp09YuXIlqlSpAp7nUa5cORQvXlxjWWN+IxNK/3NY9oMQcQjxd6SPyZMnM9NWwXpsx1pfRjvyJKhsRmpqKlauXIly5cpl+fK2tbXFt99+i3PnzhmlqUoGmfNSaUiZs2fPws7ODjzPI1euXOjfvz82bdqEu3fvIjExERkZGUhJScGDBw+wefNm9OzZE87OzuB5Hvb29vjnn3+s7iF2/T179qj/5kqUKIFp06bh3LlzWZasz8jIwIULFzBx4kQULFgQPM9DoVBg3759VtWXQhsJEYMuypUrZ/L2WoYghm0Wfv31V3AcBzs7u0wz+bVx8uRJ9QQ0nud1/hAplIfYY0hLS0NoaKj6u8/GxgYBAQH49ttvMWjQIIwePRo//PADOnfujDJlymSarFy9enW9/c9an3X7CBWHEDFoIzIy0uRzDSEiIoKpPmuPDx8+4NmzZ0hKShKlvhAesr71PbJ7DF27dkW3bt10lvlya7kviYmJQVhYmNU8hIhBEwkJCdi+fTsWLFiAiRMnYtasWVi/fr3GH0JNRQoeLPWl0D5CeIhFf/ny5eA4DjVq1MCzZ880lvH29kaJEiXQpk0bdO7cOdOrbdu2cHBwwK+//ipJfWN4+fIlfvnlFxQsWBCurq7o27cvrly5AuC/fJ+Hh4fJ2qmpqVizZg2qVasGGxsbNGjQANu3b0d6ejqSkpLg6upqdv2l4MFSn2X/CunBug9YxyDE36k2YmJimObHN2zYIGp9ABgyZAhTfSE8xK4vhIesL06Pu3fvYuDAgRp3nKlQoQJWrVqFlJQUpKSkwM3Nzej6sNYXykOFFL73xR7DhQsX0L17dzg7O6NIkSL49ddfM/125O3tbVbdWeurYN0PrOMQ4u9IEzdu3ICNjQ0TbRWsx3as9aXA3r17ERUVhW3btllcW54ElU2Ii4vDzz//DB8fnyxf3l5eXhg9ejSeP39ukraup92MeX0NH9T169fD3t7e4IljqrZZuXJltvEQu/706dOzaCgUCri7uyN//vzInTs3cuTIkUV/4sSJ2UJfCm3EKoYRI0ZkeY0cOTJTGW9vb9ja2sLX1xd+fn5ZXrpWWpLSNgstWrTI1Paurq6oVq0amjZtig4dOqBVq1YICwtTP9Wi6oMWLVoYpC+Eh9hjSElJQaNGjTJ9B+r7+69cubLBe1Wz1mfdPkLFwSKGW7duaXx9jre3N5o0aYJRo0bh559/zvI6cuSIVv19+/ZpfH2pHxQUhPbt26NLly5ZXhs2bNDZLkJ4yMjIsKVChQp6JyoYsqVnYGCg1TyEiEFGRoY9Q4cORWBgoM5t8ypVqqRzJfIFCxYgKipKkvqmoFQqsX37dtSrVw88/98DTMWKFQPPWyavdvnyZfTs2RNOTk5wcXGBq6urxbSl5MFKn3X/CuUBsO0DIWIQ4u8U+C9vP3LkSDg6OjLRv3TpEtq1a6feykls+jIyMtmbvXv3omHDhuprwOc5PA8PDxw/fjzLOfoedhFSXygPbUjhe1/sMSQlJWH27NkoUaIE7OzsUL9+fYSFhVms/qz1VbDuB9ZxCDU+TU1NxaJFi+Dl5cVs3MJ6bMdaX0p4eHiA5/9bkMPSyJOgrMzly5fRpUsXODg4ZPry5jgOZcqUwdKlS/Hx40e9OnXr1tV6zM/PDytWrDDptWzZMsyYMQP169f/aj6oMTEx8Pf3N2himKenJ7Zv357tPMSuv27dOnh4eGiciPfle3Z2dkYv58haHxB/G7GIITIyUj1JwcbGBl26dMGlS5cyldG3TYGdnZ3Wp3ultM1CRkYG+vTpk6VvtU0qUU360JX4F9pDCjEolUqsXr0aRYsW1dmvefLkweTJk41eAYy1vhB9wDoOFjH4+vpmOjdnzpwYPHhwpjL6JjQWKlQIqampGvVLlSqVqWyxYsUwa9Yso/Rz586tMwYhPGRkZNhSvnx5i+hUqVLFah5CxGAJjJlA/DV7sNSXQvsI4WEt/dq1a2P//v06zx0wYIDO48nJyShTpowk9c3l+vXr6Ny5s3pVVEsSHx+P6OhouLi4MMvZScGDpT7L/hXSg3UfsI6BVf3Pnz+PTp06qbd0V92PWoKMjAxs2rQJNWvWzHRfKxZ9GRmZ7E1ycjLmzJkDf3//LLkzHx8fjBgxArGxsahcuXK21BfKw1ik8L0v9hj27NmD8PBwZt9prPVVsO4H1nGwqP/z588xduxYdU6bRd1Zju2E0Jci9erVA8dxCA8Pt7i2PAnKynw5aYHneTRu3BiHDh0yWCMxMRFOTk5aj1eoUMHseqanpyNnzpxm64iFjIwMrF69Gs2bN0euXLky/bjq6OiI8PBwzJw5E+/evcu2HmLXT0pKwrhx41C+fHmNE3v8/PwwaNAgk7cSYK0PiL+NLB2DapuCEiVKZJn8pMLb2xs5c+ZE5cqVERYWluml2t9Y24QuKW6zEBMTg4iICNjY2GidWBIUFIR169YZrCm0hxRiAIArV65g9uzZGDZsGHr27InBgwdjypQpOHr0KD59+mSyrhD6QrQP6zgsGcPcuXPBcf9NShw6dCgSExOzlNE3oZHnea0TPxctWqS+Rs6dOxfp6ekm6euKRQgPGRkZtljiHg0ASpcubTUPIWIwh+PHjyN//vzgeR758+fHwIEDLb49ohQ8WOpLoX2E8LC2fmBgIJRKpU4NbZO/P6dixYoa3xe7vqXYsmULswT4iRMnwHEcE20pebDUZ9m/Qnqw7gPWMVii/unp6Vi7di2qVq2q8aEbc+v/5s0bTJo0CQUKFBClvoyMTPbm9u3bGDBggHq7ONVnX6FQoEGDBtiyZUumHJKxD6Sw1hfKw1yk8L0v9hjmzJnDtP6s9VWw7gfWcVii/qdOnUK7du1gZ2fHZNzCemzHWl/qpKam4ty5cxb5je1L5ElQVkb1AeB5Hnny5MHo0aOxcuVKg16LFi3C2LFjERgYqPNDFBQUZJG6Vq9e3SI6YuTDhw949uyZxZOpQnqIWT8pKQk3btzAyZMncfnyZbx8+VJU+irE3EYqzIlh1KhRKFiwYJbVkz6nbNmyOidU/fLLL2jVqpXGY1LeZiEhIQHbt2/HggULMHHiRMyaNQvr1683a4Kb0B5SiEHsSKF9LBHDjBkzYGNjgy1btmgtkz9/fqxZswY3b97EgwcPMr1u3ryJWrVqoXfv3hrPnTx5MhwdHXH69Gmt+oULF8bx48c1rvb58eNHtGnTBl26dNF6vhAeMjIybPH39zdbIzU1FYULF7aahxAxmEOZMmXU99tKpRKHDh1CRESEzrHo1+jBUl8K7SOEh7X1LfX0fHBwsMb3xa5vSYzZdtpYhgwZwkxbSh4s9Vn2r5AerPuAdQym1v/ly5cYN25cppWDVT9eFSlSBCNGjMAvv/xi8g9ZFy9eRNeuXdXbonz+41hERAQWLlyINWvWwMbGJlvqy8jIiINy5cpl+uG9QIECiI6O1po3M3YCEWt9oTwsgRS+98UeA+vxuxD3BwD7fmAdhyn1T01NxapVq1CxYsUs4y5XV1e0bdsW3bt3N2sCEeuxHWt9GfORJ0FZGdUHIleuXChUqJDBr4IFCyJPnjzqFRF0fYi8vb0FjEhGRkZGMw0bNsTGjRt1lunbt6/O469fv0bZsmU1Hvvat1mQkZExjG+//RbDhg3TWaZNmzY6j1+8eBGhoaEaj7Vu3Vrvim+dOnXSefzOnTuoVKmS1uNCeMjIyLClRIkSuHr1qlkae/bs0bkaE2sPIWIwh1KlSoHjOHh4eKjfu3r1Knr06CF7CKQvhfYRwsPa+pZaja1o0aIa3xe7voyMjLQ5e/YsOnTooN625PMfsGrXro2dO3dmKm/MtSIjIwMbNmxA9erVs2jb2tqie/fuWcZSJUqUyDb6MjIy4uT48eNo37497O3tUb58eSxYsABv377VWNaUCUSs9YXykJGREZ5nz55h9OjRyJMnT5axS/HixTF37txMixAULFjQaA+WYzsh9L8Wbty4gaNHj2rc4cJSyJOgrAzP8zh69KjJ56empupdzo7jOJw7d85kDxnDOHDgAKZOnYpNmzZpXHVBDB5i17916xY2bdrE7O+dtT4g/jYCtMcQEBCAtLQ0neca8rSztpnr8jYL/+P169ei9xBrDDdu3EBoaCicnZ0REBCAGTNm6P27z076n8OyD4SKQ1MMQUFBeq81u3fv1qutbUJmmTJl9K6Wp2sFJxW6VvIUwkNGRoYtXbt2Rbt27czSqFKlCoYOHWo1DyFiMIcLFy6gX79+WRJQhozXviYPlvpSaB8hPKyt7+fnh/j4eLM8bty4oXV1OLHrWwqW9+JSyEcJ4cFSX4hci5yT0o+h9U9LS8Mff/yBypUrZ/nxytHRER07dtT6ANv58+f11uP169eYMGGCeivSz/Xz5cuHMWPGoFy5chrPff78udX1ZWRkpMGrV68wZcoUFC5cGM7OzujcuTNOnDiRqYw5E4hY6wvlYQpS+N4XewyJiYk4d+4cXr16ZXFtIfRVsO4H1nEYWv8TJ06gTZs2sLW1zbJiZY0aNbRO0t6xY4dB9WA9tmOtL2aSkpIyvQz9jefBgweIioqCu7s7WrZsiWXLllm8bvIkKCtjqRmA+fLl03qM4zjkz58fq1evRnJyskX8ZDIzevRo9YWvf//+qFGjBvbs2SMqD7Hrz58/HzY2NuB5Ho0bN0ZERAQuXrwoGn1A/G0E6I7BUtsUaFu5RN5m4b+nZFRJsPz582PgwIEW336RtYfYY6hSpYp6hcY3b95g2bJlaNy4sc5tHrOTPiBMH7COQ1cMllpxRFuSxVITHXUlcYTwkJGRYcu2bdvA8zwWL15s0vnff/89eJ5HTEyM1TyEiEFGRoY9DRo0wMyZM83S6N+/v9aVNMWubwlY3otLIR8lhAdLfSFyLdbO51gC1jEYUv8XL14gOjoaPj4+WX7AKlu2LObOnYvExEQApt0rXbhwAZ07d4aDg0MmbRsbG0RFRWHnzp3IyMjItvoyMjLSRKlUYufOnYiMjIRCoUDJkiUxbdo0vHr1yiLXCtb6QnkYihS+98Uew+bNm+Hs7Aye5xEUFIRWrVrh0aNHFtEWQl8F635gHYe++n/69AkrVqxAcHBwlnFX7ty5MXToUNy8eROA6b+9sR7bsdaXAqrfeOzs7FCnTh2cOXPGqPPXrl0LOzs7KBQKi9dNngRlZVavXm0Rnd9//13rsZo1ayIsLAxhYWGoXbs2tm3bZhFPmf9RoEABcByn/pB++vQJ33zzDQ4dOiQaD7Hr+/n5Zdoa8vXr16hfvz4uXLggCn1A/G0E6I4hICDAIh7FixfX+L68zcJ/q8Oo+lipVOLQoUOIiIgwaIWt7OIh9hiKFCkCjuPg4uKifu/IkSPo16+f2dpC6APC9AHrOHTFUL58eYt4lCxZUuP7llpdSdc2BUJ4yMjIsKd06dJQKBQYMmQIPnz4YNA58fHxaNOmDXieR40aNazuIUQMmnj48CEuXryIEydO4MKFC3j48KFJOlL3YKkvhfYRwkMM+tOmTUOuXLkQGxtrUh2OHTsGW1tbrfktsetbApb34lLIRwnhwVJfiFyLtfM5loB1DPrq365dO9jZ2WX68crZ2Rndu3fX+GONsT9kVa1aNcuPY35+fvjll1/w7NmzbK8vIyPzdXD//n0MHz4cXl5esLOzg7e3t8bxY4cOHbKlvlAeupDC977YYyhevHgm7Zs3byIiIgL37t0zW1sIfRWs+4F1HLrqP3LkSHh5eWUau/A8j9q1a2P9+vVZVj02ZdzCemzHWl8qcByHAgUKZPm7GjRoEAYPHqzx9SXjx4/XueOZqciToETIq1evLL4ig4x5tGzZEhzHZVrh5dOnT/jtt99E4yF2/YiICHAch2LFiqnfS0xMxIgRI0ShD4i/jQDdMfj5+WXaz9cUHjx4oHUSkbzNAlCqVClwHAcPDw/1e1evXkWPHj1E4yH2GPbu3YvIyEgsWrQo0/svXrwwW1sIfUCYPmAdh64YtE2kNIb379+jcOHCGo8VLVpU/fStqSQmJmrVF8pDRkaGPWfPnlUnVHLlyoX+/ftj06ZNuHv3LhITE5GRkYGUlBQ8ePAAmzdvRs+ePdVP8dnb2+Off/6xuocQMQD/XbNmzpyJmjVrqs//8uXk5ISwsDD89ttvJk3clYIHS30ptI8QHmLUT0hIgKurKwoXLmx0wn3Pnj1wc3NDnjx58P79e0nqWwKW9+JSyEcJ4cFSX4hci7XzOZaAdQz66r9lyxbUrl1b/UNWkyZN1E/ua8LYH7IuXryI7t27w9nZWa3/5s0b0ejLyMh8XaSmpmLNmjWoVq0abGxs0KBBA2zfvh3p6elISkqCq6trttYXykMTUvjeF3sMFStWBMdx8PX1Vb/3+PFjfPfdd2ZrC6GvgnU/sI5DV/1nzJgBf39/9eShJk2a4P79+1q1TBm3sB7bsdaXChzHYdy4cVnev3PnDlasWIHSpUuD53mUL18ef/zxB+7evZul7NOnT+VJUFInMTER//zzDy5fvoz09PRMxzIyMhAdHZ1p5qS/vz+TJ9XmzZuHn3/+2eK6UiY9PR3nzp1jut0gaw+x6yclJWHTpk1MlqUUQh8QfxsBumMIDw/HihUrzNIfM2YMmjZtqvGYvM3Cf0uj9+vXDzt37sz0/pcz67OzhxRiEDtSaB9dMZQpU8boZVm/ZOPGjahatarGY8HBwTh48KBZ+kuXLkVYWJjW40J4yMjICMP69ethb2+vfipO30uVQFq5cmW28WCtP3v2bOTMmTPTubpeqglWo0aNMvi7SwoeLPWl0D5i7wPW+r/++is4joOdnR169+6N8+fP6yx/8uRJ9YpuPM9j9uzZktY3F5b34lLIRwnhwVJfiFyLtfM5loB1DIbW/86dOxg8eDBy5coFX19fjB49WuOPcqb+kJWUlIRZs2ahVKlScHBwwLfffoujR4+KRl9GRubr4/Lly+jZsyecnJzg4uICV1dXi/4YzVpfKA8VUvjeF3sMDx8+xNSpU3Hu3DmLawuhr4J1P7COw5D6Hzx4EM2aNUOOHDkQEhKCpUuX4t27d1nKmTNuYT22Y60vdjiOw4YNG7Qev3fvHjiOw5MnT3Tq5M6d29JVAwcAJGNVPn36RP3796eVK1dSeno6ERF5eHjQjBkzqH379kRE1KlTJ1qzZg0REX3eZRzH0YQJE2j48OEWq0/JkiXp9u3blJGRYTFNGRkZmV9++YUWLlxIFy9eJE9PT6PPv3HjBlWsWJGmT59OPXv2zHJ8+vTpNGHCBPrnn3+ocOHCRuvHxMRQ7dq1aenSpfTtt98Kri8jIyMM3333Hd27d4/27Nlj0vlpaWkUFBRELVq0oHHjxmU5PmTIEDpx4gSdPHmSeJ43Wj85OZlKly5Nffr0oREjRmgsI4SHjIyMcBw/fpy6d+9Ot2/f1lvWw8ODli1bRo0bN85WHqz0e/ToQUuXLiUiosKFC1P58uWpUKFC5O3tTY6OjmRnZ0dpaWn04cMHiouLo4cPH9KFCxfo5s2bxHEcNW7cmLZs2UIcx0nag6W+FNpH7H0gVBu1bNmSNm/erC7j7OxMZcqUIU9PT3JxcaGPHz/Sq1ev6NKlS/T27Vsi+i8/1bx5c9q0aZNWXanoy8jISIuPHz/S2rVracGCBXThwgUKDw+nHj16ULNmzShHjhxUtWpVOnnypFkef//9Ny1YsIC2bt1Kfn5+1K1bN+rcuTN5eXmJQl9GRubrIiEhgWbPnk3Tpk2jlJQUi/8+yFpfKA8ZGRnjefbsGS1cuJCWLFlCycnJ9M0331CPHj2oUqVKREQWGbewHtsJMXYUIzzP0+7du6l+/fpay/j7+9OtW7d06gQGBtK///5r2cpZfFqVjNF88803Gp/ks7GxwdmzZ7Ft2zadT/flyJED165ds1h9SpQowWyWtIxpbNiwwextxIRGzCuKxcbG4tSpU7h48SLTJx3FRGpqKl6+fImEhASTNZ4+fQo7OzsEBwfj8ePHRp175coV+Pr6ws3NTeuWdPI2C+wR00pAn/Ps2TONe8MLjaalPjXx4MEDDB8+HNWrV4e/vz/Kli2LVq1aYfXq1UhLS2NcSzYolUo8ePAAZ86cwfPnz61al8OHD4PjOPz0009Gn6tUKtG6dWvwPI9Lly5pLHP+/HlwHIfOnTtnWdlTHykpKahZsyZsbGxw584dreWE8JCRkRGWjIwMrF69Gs2bN0euXLky3e85OjoiPDwcM2fO1PjEXHbxsLT+ihUrYGNjg/79++P27dtG1eXBgwcYOnQobG1tsXjxYkl7sNSXQvsI4SF2fRUZGRno06dPltWktK3mxnEcWrRoYXCuQuz6X/L+/XucOXMGK1euxIwZMzBhwgRMnz4dK1euxJkzZ/Dx40eTdLMbYsxHAXJOSkpYIh+lj3/++QddunSBo6MjPD09MXjwYAQFBWkse/r0aaP1nz9/jp9//hn58+eHra0tmjVrhoCAAI1l//rrr2ynLyMj83Vx4sQJcBwnWn2hPFhhyvdMdmDr1q1GrVidXUhNTcXz58+ZjjNk/kd6ejo2btyo3kovMDAQs2bNyrSd8eeY+psO67Eda30xwXEc9u3bp7OMIatkVahQwVJVUiNPgrIyx48fVydf/Pz80KdPH4waNQodOnSAm5sbWrdujWbNmoHjODRt2hTnz5/Hhw8f8O7dOxw4cADBwcHgeR4DBgywWJ3kSVDC8Pr1a4MTSadPn0axYsXQvXt3nDhxwizfDx8+4MiRI1i7di127NiBZ8+emaWnDWP+js6cOYO3b98arP3q1Sujym/cuFFvGaVSiSlTpiBv3ryZEqQ2NjaoVauWQe3epEkTzJ8/X++yfpYgPj5e72SaAwcOIDo6Gn369MHkyZN17rmrifPnz6NXr14oVqxYpjaxs7ND8eLF0bdvX+zevdsozaFDh4LjOOTKlQuTJ0/Gq1evdJZ/8uQJhg8fDnt7e/A8j+joaJ3lv/ZtFlgzfPhwdO3aFTExMdauilGUKFECCoVCb7m1a9fi22+/1blV2qdPn7B06VL06tULjRo1QvPmzdGvXz9s27YNnz590qk/fvx4vZ/bdevWwdHRUePkaJ7nUaZMGb2f5c2bN+vcn9pSpKWlYc6cOWjSpAlat26NuXPnapwoN3/+fBQsWDDTdaRu3bpGLfV76tQpTJgwAe3atUODBg0QFRWFHj16YOLEiTh79qzRda9atSp4nkfLli0NvmbfuHED1apVA8/zaNy4sc6yjRs3Bs/zqFKlisE3Onv37kXRokXB8zw6duyot7wQHjIyMtbjw4cPePbsGZKSkkTrYa5+xYoVsW7dOrPqsHnzZlSqVEnSHiz1pdA+QniIXf9LYmJiEBERARsbG60P4wUFBZlcJ7HrHzlyBE2bNlWP2bW9HB0d0ahRI2zbts0kH5ZYKx8FZL+cFOt8FCDnpEzJSbHIRxlCQkICpk+fDn9/f+TIkQNdunTJcr9ZtGhRk/UzMjKwefNm1KlTBzzPIzw8HBs2bMj0sFOBAgWyrb6MjMzXw5AhQ0StL5QHCzZs2ICyZcvil19+MfpBck08ePAAp06dwr///mv0g5TGYOjY686dO1AqlczqcfDgQYPKrV+/HpUrV0aOHDnU4wwvLy9069bN4AeZBwwYgN27d+v9TUAobt++jRUrVmDKlClYt26dUWPW58+fY9KkSahTpw7y5s0LBwcHuLm5oVixYqhbty5+/fVXiy7GAvyXax8wYADc3d3h4OCAcePGZbk3KFGihFkerMd2rPXFgKUmQVWsWNFSVVIjT4KyMqqn1Pr165dldYcXL14gJCQEzs7OqF27tsbz3759i+LFi6NMmTIWq5M8Ccp0Dh8+rPN4amoqfvrpJ3h7e6u/WAsUKIBhw4bhwYMHOs/dtGkTOI7T+0P+vXv3sGHDBuzfvz/Lk4e///47PDw8MiUQFAoFmjRpYvQEGX0Y83d08+ZNODs7o2/fvloHYlu3bkX16tXh4OCgrruzszOaNWumd2Dz22+/6RwwKpVKNGvWTOOkg89XZtP35OznT5pWqFABv/zyC65cuaK/AYzgwoUL6smPKp8vJ2w8fPgQISEhWZ50tbe3x4oVK/R6fPz4EV27dtX4pKymp2qDgoKwa9cug+qflpaG0NBQ9fk2NjYICAjAt99+i0GDBmH06NH44Ycf0LlzZ5QpUwY2NjbqOlSvXt2gVXBatGiRqX6urq6oVq0amjZtig4dOqBVq1YICwuDu7t7phhbtGhhUAys9YXi5s2b+PHHHxEZGYlWrVphzJgxuH79ut7zRo4cCZ7n4e/vb7L3kydP0KdPHxQqVAh2dnbw8PBAgwYNsHnzZpM1dWHo9SgtLU39eX/69GmW40ePHs10/f7yM1G0aFGdE8T+/PNPbNmyRevxK1euIEeOHOA4Drlz50ajRo3Qq1cv9O3bFy1atEDp0qXVk6Z13cRwHAdbW1vUrl0b8+bNY7KveFpamvqJjc+vCaGhoeobP6VSic6dO6vbSqFQoGTJkihXrhzs7e1RpEgRvT+MHz16FGXKlNH5AxPP8/D29sb06dPx4cMHg+p/584duLm5ged55MiRA40bN8bUqVNx8OBBXLp0CbGxsbh27RqOHDmC6dOno27dulAoFOB5Hu7u7npXYXj58mWmv5XAwED069cPS5YswdatW3Ho0CHs2rULK1aswIABA+Dv769up3z58hm0WpYQHjIyMjLWJDAw0CI65cqVk7QHS30ptI8QHmLX10ZCQgK2b9+OBQsWYOLEiZg1axbWr19vsRVWxab/9u1bNG/eXOc9srb75urVqzNfmTY75KMA8eWkWOejADknZUxOimU+ylgOHDiAFi1awMbGBuXKlcPkyZMxZswYi+XM79y5g++//x45c+ZEnjx50Lt3b3Ts2FE0+jIyMjJfOzdv3tRbZsmSJahUqRKcnZ2RO3du1KhRAwsWLNA7OWXZsmXguP92AdJFSkoKzp49qzFPuWvXLpQsWTLTuMvFxQUDBgxgsuqRoWOvU6dOoVChQvj111+1lrlw4QI6dOgAf39/ODk5IVeuXAgICMDgwYP1/n4xadIkxMXF6SzTv39/neMMJycng8YXqjGJi4sLWrVqhTVr1li8bR8+fIgWLVrA1dUVXl5eaNmyJWJjYzOVSUxMROvWrdW5a9XL09MT+/fv1+sxbty4TONcbeMunufRpEkTrbsjmMr79++xePFiBAcHI0eOHGjWrBnWrVuHZcuWGXT/YSisx3as9bMrlpoEZam8x+fIk6CsTHBwMAIDA7XOfD106BA4jsPx48e1aqxevRqurq4Wq5M8Ccp0ihUrpvWY6gdjbV+ujo6OmDBhgtaky61bt9RfONoYNGhQpi86b29v7N27F8B/S+h/7lemTBm0atUKTZs2hbe3N/LkyYNbt25p1Q4PDzfqpXoq8sv3IyIiNOqrYvtya5709HT06tUr0xews7MzgoKCUKVKFeTOnRs8z+O7777TWve9e/di8uTJWo+vWrUKHMfB09MT33//PTZu3IhTp07h7Nmz2LFjB3777Tf1hBxdK/+oYujXrx9Kliyp/n/hwoXx/fff48iRI8jIyNB6vj5iY2Ph6uoKOzs7lClTBmXKlIG9vT1cXFxw48YNAMDjx4/VK65wHIfQ0FBMmTIFs2bNQpMmTZAjRw6dW7llZGSgdu3a6kF2hQoV0L59ewwZMgQ//fQToqOj0a1bN+TMmVOd1FLFOXLkSIPiSElJQaNGjTQOor58qcpUrlwZr1+/NkhfatssmIK+p7x///33TE85fB5Dw4YNswykP+fKlSt6r0WnT59GzZo14ejoiLx582LAgAHqyS53795Vf241Dahbt26t96mUlStXGvXy8fEBz/NYtWpVlmNfoqrHl9fDXbt2wc7OTl3X8PBwrF69GpcuXcKtW7ewY8cOtGrVCo6Ojvj777811vvOnTtaJzUDQLdu3eDq6oo//vhD67XiwoULqFy5ss6t3FQx5MmTR/3v8uXLY/z48bh8+bLW84xhypQpcHd3x7Rp03DhwgVcuHABM2fOhI+PDwYPHgzgv5XDVO1VpUqVTG36/Plz1KxZE2PHjtXq8fvvv0OhUGT6+/Dw8EC+fPlQqFAhuLi4ZDleqFAhg2/Ejh49qt6uSd8kK9Xfq52dnd5BvYobN26gUKFCRunnypVL7wpzQnvIyMjIWItSpUqZ/YRoRkaGzgeGpODBUl8K7SOEh9j1ZfTz/v17BAcHg+M4+Pr6olu3bpg3bx527dqF8+fP48aNG7h37x5u3bqFS5cuYf/+/Vi8eDH69OkDPz8/cByHEiVKMF2t1dr5KEC8OSmW+ShAzkkZmpMSIh9lCo8fP8awYcOQM2dOgz4HxvL+/XssWLAAxYoVE6W+jIyMzNeKvhVqvv32W42/E6jGSH/88YfWc2/cuKH3mj1z5kw4OTmpPcqWLavO++7evTvTw+W5cuVCxYoVERQUBFtbW/j7++PFixc669+lSxejXqqHTb98v2vXrlm0VbFp+g1i0qRJ6vGkqs3c3d3h4+MDhUIBOzs7/Pbbb1rrvX37dsybN0/ncVWOt3nz5vjtt9+wdu1abNiwAXPmzMF3330HT09PODg4ZBkbaosjKioK7u7u6jFMREQEZs+erfdBA328fPkSvr6+4DgObm5ucHNzA8dx8PHxUa/8mZiYiKCgIHV7FSpUCH369FFvz+bk5KQzjg4dOqjbOXfu3AgNDUWLFi3w7bffonPnzqhVqxZcXFzg4+OjLmdra4tFixaZFZs2VDurfP7blaVhPbZjrZ/d4DhO7+rH+iZBvX37Fs7OzpasFgB5EpTVUW0JpY309HR4eHjoTHjduXPHoh8i1UoBMsZx69Yt2NjYaD0+depU9ZeEj48Pxo8fj127dmHHjh2YP38+mjVrBnt7e4SFheHNmzdZzr97967OC+aCBQsyDQoqVaqEwoULw9bWFnv27IGHhwc4joOHhweOHDmS6dy0tDT8+uuvKF++vNb6V6pUSe2vbYa0IS9t9dc28aB///7qc728vLBy5cosTxPu2rULxYsXx9ChQzVqJyYmws/PT+sklAYNGiA0NFRvQnLChAlo3bq11uOqGF6+fAngv6cBJk6ciIoVK6qPeXp6onPnzti6davepcO/pGPHjggPD8+0JGRcXBzatWuHihUr4uPHj+rELM/zmDVrVhaNiRMnol27dlo95s6dCwcHB0ycOBHx8fFay40dOxZHjhzBw4cP8f3336tnivfp08egWJRKJVavXo2iRYvq/HvJkycPJk+ebNAKUF8i9m0WTOXTp09wc3PTevzo0aOZJo4ULVoUrVq1QosWLVCpUiXY2trCwcFB60BW37Xo/Pnz6u0LP2+LwMBAJCYmonLlyur3OnfujAsXLiAlJQWJiYnYvn07goOD9f4d2draGjTh48vJH5re/xJN16K4uDh4enrq/Gyp2LFjB/z8/LQ+9eHn56d1UFimTBls2LBBZ+zAf9tXaNsn+/MYnj9/jtOnT2PYsGHw9/dXv+/n54fBgwfj77//NjkJHhAQoPFJkocPH8LPzw+TJk1S+6muT1/y9OlTrd87p06dgkKhQOnSpTF79mxcvXo1y1Z7KSkpaNKkCQ4ePIhx48Yhb9686h8mDJ2odOvWLdSqVcug769SpUrh3LlzBumqiIuLQ7du3XReJ1Sv+vXrm7RCgBAeMjIyMtYgMjISI0aMMEvjp59+Qv369SXtwVJfCu0jhIfY9S2FoQ+tiFF/7Nix8Pb2xl9//WWS9s6dO5EvXz6MGTPG1OrpxNr5KEDcOSmW+ShAzkl9jq6clFD5KFNJSkpC27ZtmeXMMzIyMHToUNHqy8jIyHxNvHz5EnZ2dlqPL126VD2GcHR0VE+gnzNnDn788UeUK1cOHMehffv2GnOm+sZeGzZsyDS+8fLygp2dHVxdXXHx4kXkz58fHMfBwcEBK1asyDTR/c2bN+jbty/CwsJ0xlioUCGNk7hYjr0mTJig9nN0dMTPP/+caTXNpKQkzJs3D56enpg+fbrGesfFxaFUqVJaJ/c3a9YMJUuW1PkQeGpqKnr37q1xApemOF6+fInU1FTs2bMHPXv2hLe3t/pYUFAQoqOjcfHiRZ1amvjuu+9QpkyZTJPgL126hLCwMPW9Xb169dRegwcPzhS3UqnEd999hx49emjU/+OPP8DzPHr27Klz9dD+/fvj3LlziImJUa+My/M8JkyYYHRMhnL37l2Eh4czHbewHtux1s8ucBwHb29vhIWFaX1Axc3NTecDLL6+vkzaiQMAkrEatra2tHnzZoqMjNRaplKlSnT27Fmtx+Pj4yl37tyUkZFhkTo9e/aM0tLSqGDBghbRy+78+++/NHDgQLM00tLS6MqVK/Tu3Tut/VCuXDm6fPkyFSlShE6ePEm5c+fOUiY+Pp6io6Np9+7dtGfPHipWrJj6WGxsLBUrVow4jtPoUbZsWbp69Sp98803tGzZMrK3tyciojNnzlDnzp3p1q1bxHEcbd68maKiojTW8eeff6agoCCNxzMyMujnn3+mqVOn0qBBg8jf319nm4wYMYJevnxJy5Yty3KsU6dOWd7jeZ44jqMbN25Q8eLFiYjo1KlTFBoaSkREuXPnpuPHj2dqk89JTEykWrVq0ZIlS6hcuXJZjkdERFBgYCDNmjUryzF/f3/asWOH2lcXwcHBdP78eY3HVDE8f/6cvLy8Mh178uQJbd68mbZs2ULHjx8npVJJ9vb2VLt2bWratClFRkZq/Jv4nIIFC9K5c+c0lmvRogXdv3+fLl26RBzH0Y8//kiTJk3KUg4ABQQE0LVr1zR6VKhQgUaNGkXNmjXTWZe4uDgaNWoULV68mIiI7ty5Qx06dKB//vmHFixYQD179tR5/uf8+++/dOTIEXr69CklJCSQk5MTeXt7U+XKlaly5cpka2trsJYmEhMTKSYmRqN+gQIFzNK2pP6TJ0/o8OHDZtUlLS2NduzYQTt27NB6LYqMjKTdu3dTjhw5aMmSJdShQ4dMx+Pj42nt2rU0adIkat68Oc2aNYs4jlMf13ctatKkCe3cuZPy589PU6ZMobJly9Lr169p7dq1FBMTQ9euXSOO42jgwIE0ffr0LOd/+vSJGjVqRPPnz9f6mTx//jx9++23FBsbS1WrVs1UP02cPXuWPn78SDVq1Mhy7O+//870f03XonHjxlF0dDRxHEffffcdzZ49W6ff/PnzKTk5mYYNG5bl2NixY2nevHl0+vRpKlq0aKZjZcuWpYsXLxLP8zr1iYhCQkLozJkzGo9puxZdv35dfR26ePEicRxHOXPmpMjISIqKiqJ69eqRo6OjXm8iokKFCtGDBw80Htu1axc1btyYiIjc3Nzo8uXLWj8L2q6pzZs3p5w5c9KiRYtIoVBorcfZs2dpz549NHbsWEpLS6PJkyfTxIkTyc7Ojs6cOaP3u0rF8ePHacuWLVqvRVFRUdSkSRO9f2vaePjwIW3fvl2nftmyZU3SFtJDRkZGRkhiYmIoPDycypcvT3379qX69euTt7e33vPevHlD+/bto/nz59OpU6do7969VKdOHcl6sNSXQvsI4SF2fXM5ceIEtW3blp4+fUq+vr7UvHlzGjduHLm6ukpGv3Tp0rRx40YqVaqUyT43b96k1q1b05UrV9TvSSUfRSTunBTrfBSRnJNSoSsnZY18lLGkpqZS7ty5KSkpiYm+UqkkLy8vev36tSj1ZWRkZLI7N27coN9++80sjbS0NIqJiaHHjx9rHRdVqVKFzpw5Q3ny5KEjR45oHLdcu3aNRo4cSc+fP6ddu3Zl+n7VN/ZS6deqVYtWrVpFPj4+lJGRQZs2baJhw4bRo0ePiOM4Wrx4MXXt2lVjHb/77jtq2bIlhYeHazyenJxM3333Ha1bt4569epFnp6eOttl7ty5FB8fT2PGjMlybOzYsZn+r2nsde3aNSpXrhylp6eTs7Mz7du3j6pUqaLR69GjR1SnTh3auXOnxvFZSEgIRUVF0ciRI7McK1myJK1atYoqVqyoM5709HSqVKkSXbhwQWsZbWMvAHTixAnavHkzbd26lR48eEAcx1H+/PkpKiqKoqKiqGbNmjpz3kRERYoUoQMHDlDhwoUzvZ+Wlka1a9cmJycn2rt3L3EcRx06dKAVK1Zk0fj06RMFBwfT1atXsxyrXr06tW/fnnr37q2zHg8ePKBp06bRnDlziIjo2LFj1KVLF3r48CFt3ryZmjRpovN8U3n79i15e3vT+/fvmegTsR/bsdbPDqg+B+YAQOe9pjnCMlaE47gsT0B9ib5lwhITEwWZSahv1quYKVeuHLNZzSpUS3GvXbtWb30OHz6MgIAA/PPPP+r39M3+tre3R44cOTQ+Ofbw4UPkzJkThQoV0un79OlTvf188uRJBAYGYu7cuTrLGbutoqbZ36qlGHmex5YtW/RqHDt2DD179tR4bP369eB5XmO99c16/5yyZctqPfblU3faeP36NZYuXYrIyEjY29uD4zjY2NggNDQUU6dO1biHMwCULl1aq+bevXvV/iEhITpXj9O1goy/v7/Oun9OnTp1Mv3/48ePaNKkCVxcXPQupyqTlfj4ePWysea89F2LPDw8wPM8Ro8erbM+b9++RZcuXRAVFZXpiRR916KcOXOC5/lM1y8Va9asUa/Uo2t/7HPnzunc7g0APnz4gD59+qBOnTrq5V+1Ycz1SNO1qFSpUuqnaj58+KBX48WLF6hWrZrGY/Hx8fD09ES+fPmyPAESFRVlUB0/fPiAIkWK6I1B17Xo4cOHmDFjBqpXr65eYtjBwQGNGzfGkiVL9F7HdD2l3bFjR3Udli1bplNH29LRhQsXNvjJ5EaNGmX6/6lTp+Dl5YWgoCCzt46RkZGRkbEuX27h6+3tjSpVqqBp06Zo164dOnfujPbt26N58+aoVq0a8ufPn2lM9PPPP38VHiz1pdA+Yu8DodrIVMqUKaMe+ymVShw6dAgRERGIi4uTjH5QUJBFvDTdi0shHwWIOyfFOh8FyDmpz9GWkxJLPurzlbBkZGRkZMRFWloaihQpwjz/7uzsDJ7nDdoybMWKFShTpgzu3bunfk/f2MvR0REKhULjd9KVK1fg4OAAHx8frashAcC9e/f0bukL/LfqVMmSJbFz506d5cwde/Xp00f9viHttnPnTgwePFjjscWLF8PGxkbjbgxVq1Y1qI6A7vw3YPjY69KlSxg9ejQCAwPV5+TMmRPffvstNm3ahOTkZI3n6dpy8eDBg2otf39/nb9ZaBt76dpO+0vq1auX6f9v3rxB5cqV4eXlhbdv3xqsYyyafmOyNKzHdlIfO5pzD2vo/aypyJOgrAzHcdi6davOMvomQf3999/MJ0E9f/4cTk5OTD2sya5du8BxHIKDg9GpUyd07tzZqFfTpk3Vkxe04eLiAp7n8ejRI4Pq9ODBA1SsWFG9pY++gY+bmxsKFiyoVe/XX39F06ZNdXq+fv0aJUuW1Fu3d+/eoWvXrqhfv77W5IK5k6CUSiVy5cqlTqAYwvPnz3UODGrWrAme5/Hjjz9m2lapRYsWBv1Qfvv2bbMnHnzJu3fvsH79erRp00a9p6+2dtOW7Hr//j2KFy+uPvfMmTNa/VJTU3UmHsuVK2dQvZOSkjQmqD58+ICKFSuif//+BulkR6y5zcK4ceOYDxjs7OzA8zz+/fdfg+o7f/58hIeHIykpCYBhEzJz586tVa9NmzaoXbu2Ts93794ZfEOya9culCxZUue2g6bchF2/fh3Af4l41XsjR440SCMmJgYeHh5aj2/btk096Sg6Olp9o/Pjjz9muuHVxsCBAxEREaE3BkOvRXFxcVi0aBEaNGgAOzs7cBwHhUKB0NBQreeEhIRovP4vWrRI7e/i4oKZM2dq1Thx4oTWRLquyZqGlL148SJcXFywZMkSg3VkZGRkZLInhw8fVicLPx/raEpEq16GJGml5sFSXwrtI4SH2PVNRfXAwOfj36tXr2rddkGM+iVKlDA7uZ+UlISAgIAs70shHwWIOyclRD4KkHNSgO6clJyP+g+xby0qIyMjk91RPaSbN29e1KxZE2FhYUa9goKC1A8naMPJyQk8z+vccu1zzp07h6CgIFy6dAmA/rGXs7Mz8ufPr1Vv1KhRaNy4sU7PxMREBAYGGlS/J0+eoE6dOujVq5fWyTbmToLy9vYGz/MoVaqUQRovX77UOjZJS0tDmTJlYGtri/nz52c6pm88quLZs2coXLiwzjKmjL3u3LmDKVOmoHLlyup7NwcHB41ltcWXnp6eaavoo0ePavVTKpXw8/PTeMzQ/PuHDx809subN29QrFgxvQ+zy0gbjuPwxx9/aJ3Mp4/09HTcuXNH66IC5iBPgrIyHMchR44cKFSoEPz8/DS+7O3ttR4rVKgQbG1tTZ4EdfbsWfTt2xdVq1aFv7+/Ro+CBQuqfzSXMtWqVcO7d+9MPv/GjRvIkSOH1uOVKlUCz/O4c+eOwZpv3rxB9erV8ccff+gd+NSpUweOjo5aEycJCQno1auXTr9169bB3d3d4Ppt3rwZ/v7+2LRpU5Zjxk6C8vX1BcdxWLBgATIyMnDhwgV1vAsXLjRI4/fff9c5We/Zs2fIly8feJ5H8eLFsXz5crx//x4LFizAjh07dGrfvXsXAQEB6N69u9Yypgx6PufTp0/YtWuX1mRuZGQkDh06lOWc+vXrq72bNWuGGjVqaP3CmTBhgs4JKHXr1sXhw4f11rVr166oUKGCxmNXr15Fnjx5kJGRoVfHGG7cuIHQ0FA4OzsjICAAM2bMQFpamsX0jx8/rn5iOn/+/Bg4cKB64o9Q+u/evYOXlxfOnTtnkkdiYiImT56s87NXuHBh8DyPp0+fGqy7fft2hISE4Pnz53qvRaVKlUKePHm0al25cgXffvutTr+zZ8/C29vb4PrFxcUhKioKbdu21fjksTHXo927d6NWrVrw9PTETz/9hNmzZxt0Q6EiIyMDISEhsLW11Vlu7ty5mSYLdezYEd9//z0aN26cKSGuIj09HQcOHEDt2rXB8zw2btyoVduca9Hbt2/x559/olWrVnBxcdFaTnUtUT0h//btW4wePRoKhQIcxyE0NBRPnjxBoUKFMHjwYMTHx2c6f+vWrfD19dW6Ilm5cuV0rhamQvU0kiaWLl1q8M28MWRkZGDx4sXo168fpk6diufPn1tUPzk5GaNGjUJkZCT69etn0p7x2cFDRkZGxtIcPnwYw4YNQ1hYGAoUKABXV1fY2NjAxcUFfn5+qFOnDkaPHo2TJ09+1R4s9aXQPkJ4iF3fWC5cuIB+/fplmWylaUwrVv3u3bujbt26Jq8+9ebNGzRo0ACdOnXSeFzs+ShA3DkpIfJRgJyTAnTnpLJzPgr4OnJSMjIyMl8DSqUSgYGBBuUdtXHs2DGd44wyZcqA53ncv3/fYM179+6hfPnyOHLkiN6xV9WqVeHq6qpV6/nz53pX19y1a5fOh3g1MW3aNAQGBmqc8GzM2Eu1Sun27dsBANeuXVPHO2PGDIM01q1bpzN3fePGDfWDAmFhYfj7778BAFOnTkVMTIxO7fj4eISFhaFNmzY6y5k79nr27BnmzZuXZYVLFbVr18aFCxeyvN+lSxe1d8WKFdG6dWutq34tXbpU64PONWrUwOXLl/XWc9SoUVpXxj1+/Dh8fX31ahjL8+fP0b59ewQEBCAyMtKglVmNhfXYjrV+dkHXQxrGYOh9lzHIk6CsjOpCpW9FD33HTJmgtHz5cvUWOOauLCIFYmJisHv3brM0ihYtqvXYggULwHEcli5dapTmu3fv0LBhQ/Tr109nPxw8eBA8z+OPP/7QqqXryzgtLQ3+/v6ws7Mzqn7Pnj1D/fr10bFjx0xPRho7CSotLQ2rV69G+fLlUahQIURGRqrjvXHjht7znz59Cnd3d62zplXcvn0bfn5+am0HBweULVsWvr6+2LlzJ969ewelUonk5GTcuHEDa9euRfv27WFvbw9XV1ety4ID//s8P3z40OC4jWHnzp1wd3fHb7/9hj179mDu3LkoXry4+jM8ZcoUAP+ttFOoUCHMmzcPFy5cwPXr17Fz5040bdoUPM9rTBCq2LVrF1xcXPDTTz/h/PnzmRK/L168wLp16xASEgKe5zFq1CitOh07dsSpU6csFzz+WxVP1cZv3rzBsmXL0LhxY7OSxZ+THbZZAICZM2ca9DevC12TkPr16wee59UDf0OJiYlBuXLlsHv3bp3XookTJ4LneVy9elWr1q5du3R6tW7dWu8kIk0sXLgQJUqUyJI4NfZ6BACXL19Gp06d1Csjqf7u9DFz5kxwHKezD1Rs2bIF7u7uWZ7iz5s3L1q2bImOHTuiRYsWCAkJUT+9zXEc+vTpo1NXpWfu5JxPnz5pPZaSkoJixYqB53nkyZMn03giJCREnUy9fPkycubMCTs7O5QpUwaVKlWCp6cneJ5H4cKFtSZdx48fj2rVqmn9oSY1NRVz586Fk5MT2rZtq7WewcHBePDggRFR66dXr17qvpo2bRrq16+PxYsXW0y/devW6j7866+/0KlTJ53X2+zqISMjIyMjIyMjFR48eAB3d3c4OTmhU6dOWLt2LW7evKl1vJyWlobY2Fhs2rQJPXr0gKurK5ydnXHz5k2N5cWejwLEnZMSKh8FyDkpXTmp7JyPAr6enJSMjIzM18D27dtx7NgxszR07bahekhZ10Osmnjx4gUqV66MyZMn6xx7bdiwARzH6cyxf77KkiYqVKhg9LgL+O8B5/Lly2PMmDGZJt4YM/Z68+YNxo8fDx8fH1SrVg3dunVTx6taDUsXiYmJyJs3L+zt7XWWO3nyJHLmzKnO4xYsWBB169ZFqVKlsvx28f79e5w6dQqjRo2Cl5cX7Ozs9D6orqrzkydP9AdtAqtXr0bBggWxceNG3LhxAzt37lSvLMpxHPr164eMjAyEh4ejYsWK2LVrF+Lj4/H+/XtcvXoVgwYNgq2trda89Zo1a+Dj44MlS5Zo/N3j9OnTaNmyJXiex8CBA7XWs2nTphZ/wLZu3brq9j1//jzGjBmDTp066dzi0VhYj+1Y62cXzL2PVWHp33AAeRKU1VFNMPLw8EChQoWMfnl5eZk8QUm1wlPHjh0xY8YMLF++HCtWrMjy+v3339GuXTvJT4ICYPbEA13JCKVSiaioKPj4+Bjtk5aWhvbt2+vt6yVLlsDb29ukL5ylS5eC4zidgzddzJo1CyVLllSvlGLKpAMVhw8fRuPGjdWDE0O+FLp37w6O4wzaxzYxMRFRUVE6txD4/MVxHLy8vHDw4EGduiotfVtcmsPneyOr6qZQKDB16lR1mY8fP2Z6Eu/zsvqeAACAYcOGqc9VKBRwdXWFvb19Jp3ChQvrfFri6NGjOrfBMoUiRYqA47hMM/yPHDmCfv36WUQ/O2yzAPw38cTcv6Fff/1V67Fnz57B29sbderUMWjJ/c+5cuWKuh+0fb5TU1MRERGBGjVq6JxEo42TJ09maSdjuH37NqpUqYJBgwapk6bmXI+eP3+OkSNHGjSpCQCqV68OnucNXlr3wYMH6NSpE3LkyKF1axPVe/nz58fKlSv1aqrOZ71n9pMnT9CoUSP16k/u7u744Ycf8P79+0zlrly5gqpVq2aKr2bNmjoHth8+fFA/NRUYGIj27dtjwIAB6N27N+rXr69+ksfGxkbndgsrVqzA6tWrLRYzAOTPn1+9kifw3/f7gAEDdG7JaIr+54mIqVOnYvbs2RbRF8pDRkZGRkZGRvw8fPgQFy9exIkTJ3DhwgWLT64Qk/7Zs2fVKwZ9PlZ3d3dH3rx5UahQIfj6+qq3Uft8LO/m5oa9e/fq1Bd7PgqQRk6KdT4KkHNSusiu+Sjg68lJycjIyHwtnD592qzzjxw5ovXYx48fUblyZRQtWtToyaZJSUkIDw/XO/YaPXo0ChYsaPB2x5+jmkRl6go+nz59wvfff4+QkBD1GNSUsVdqaiqWLVuWaaKuIVtQDxo0yOBx4/379xEcHKxx3OXp6YkCBQrAw8Mj0/sODg5Ys2aNXm2V3oEDBwyK1xSaNGmSZezFcVym7X8TEhIQFBSkcQxZv359nfqfj/N9fX1RpkwZlChRQr2lo6qddO0qsnv37izbDppLgQIFwHGZtwpct26d1l0lTIH12I61vox+5ElQVobneRw/ftwsjdu3byN37txGn+fo6Ih58+YZVFapVMLLy8toD5nMZGRkYMKECciTJw9GjBhh9DKJAwYM0DuQuHDhAtq3b2/0j75Tp04Fx3EYMWKEUed9zrVr1xAcHIwffvgBRYoUMXvi3M2bN/Hjjz8aVLZy5crgOA7Dhg0zWP/vv/9GvXr1YGtrq3UFNG9vbwwfPhyvXr3SqzdlyhT4+/sjf/78OH/+vMH1MJaNGzeiTZs2aNiwIQYOHIgrV65kKZOeno5FixahVq1aKFGiBMLDw7F8+XKDPVasWIG8efNqbJOIiAi9yePU1FSLJ5327t2LyMhILFq0KNP7L168sIh+dthmQSiuXbuGUqVKISIiwugk+L1791C0aFGdn+/09HT88MMPqFevntE/NIwZMwYcx6Fhw4ZGnfel/+jRo1G2bFlcvHjRrElQxvLDDz+gZMmSOlfC0sSTJ0+wcOFCtG3bFqGhofD390epUqUQHh6OgQMHYvfu3QYvl6pKbjZv3tyiT0doIyUlxaAb+sePH+P06dMG/03ExcWpb/o1JdDt7Oz0XtdevXqFIUOGGORnKN999x04jkO9evXU7ymVSqxYscIi+hMnToSzs3OWHwj27NljEX2hPGRkZGSszaNHjzBjxgzExsbKHlbSl0L7COGRnfQTExMxc+ZM1KxZE87OzhonZDg5OSEsLAy//fab0T/qiF3/5cuX6NevH1xcXHSupK56ubq6ok+fPnj27JlRPiwQIh8FSCcnxTofBcg5KW1kx3wUIOekZGRkZGSMIyUlBT169EDevHmxcOFCo663nz59QrNmzfSOZXbu3In69etnubbrY/z48eA4Tu9Wxfo4ePAgihcvjnnz5sHf39+ssdf+/fvRunVrg8qqxl76dktQkZ6ejuXLl6NEiRI6x+62trZo06aN1tVbv6RPnz5wd3dHYGAgHj9+bNA5xpKWlobffvsNlStXRqlSpRAVFaVxBbB3795h5MiRKFKkCOzt7eHn54exY8fqfUg9IyMD0dHRcHR01NgmxYsX1/uAw/v37zF27FgzoszKihUrEBAQgDFjxmR635CVwgyF9diOtb5UYJkP4QCAZKxGsWLF6M6dO2brdOzYkVatWmXUOUFBQbRy5UoqW7asQeWfPHlC+fLlM6V6Ml/w/v172r17N6Wnp1ObNm2MOnfu3LnUr18/veXS0tIoR44cBusqlUq6evUqBQYGEsdxRtXpS9+ffvqJpk2bRgAoIyPDZC1jOHLkCJ09e5YGDRpEtra2Rp2bkpJCJ06coEePHtHr169JoVBQnjx5qEyZMhQUFGR0XY4dO0arVq2ifv36mXR+diEtLY1OnDhBV65coXfv3pGHhweFhoZSQECAtasmYwEyMjLozz//pI0bN1J4eDgNHjzY4HPj4uKoYcOGdO7cOZ3l3rx5QxcvXqTatWsbrB0bG0vz5s2j/v37k5+fn8HnaeLUqVPUrVs3evjwIX38+FGw61F24NSpU7RkyRK6f/8+LV261Oy2tCa7d++mDRs2ZLkW9e7dm4oWLar3/Pj4eMqVK5dF6/TmzRvKlSuXWd+XMjIyMjJsKVGiBN25c4eKFi1Kt27dkj2soC+F9hHCI7voz5kzh8aOHUtJSUlkSKqQ4ziytbWlIUOG0NixY/XmH8Su/znJycl04sQJOnPmDN27d48SExPp/fv35ODgQJ6enlS0aFGqXLkyhYaGkp2dncG6QiBEPoro68pJmZOPIpJzUpqQ81EyMjIyMlLh0aNHtGHDBnJycqI+ffoYfJ5SqaQff/yRpk6dqrfsy5cvKU+ePAZrv3//ng4ePEh169Yle3t7g8/TREJCAvXp04c2bNhAHMcJMvZav349HT16lCZNmkRubm5GnXv79m06cuSIxnFXREQEubq6GqX38eNH2rBhA/3111/0888/i3bs9ebNG9qxY0eWsVeDBg1IoVBYu3oyEoZlPkSeBGVlli5dSt26dTNb58CBA1SnTh2jzpk6dSoVKlSIWrZsaVD5yZMn0/Dhw02pnoyMjIxOlEolvX37Vp04dnNzI57nrV0ti2FsAljGcqSkpNCWLVsoIyODOnXqZO3qCM67d+8IALm4uFi7KjIyMjIyMoJSvHhxunv3Lvn5+VFsbKzsYQV9KbSPEB7ZQb9Hjx60dOlSIiIqXLgwlS9fngoVKkTe3t7k6OhIdnZ2lJaWRh8+fKC4uDh6+PAhXbhwgW7evEkcx1Hjxo1py5YtWieviF1fRkZG2sg5KRkZGRkZGfN48uQJZWRkUMGCBa1dFRkZGRHBMh8iT4L6ivn06RO1adOGVq5cqXd267t37yhfvnyUmJgoTOVkZGQkjVKppB07dtCWLVvo7NmzdPfu3UxPCdjY2FCRIkUoJCSEGjZsSE2bNhV1wmbEiBEUFxdHXbp0oWrVqlm7OjIMSU5OpkOHDlHNmjUpZ86cotMXiuwUh1KppGPHjtHZs2fp5s2bFB8fr05+e3h4UIkSJSgkJIRCQ0PJxsbGqnW1BOPGjaOwsDCqUaOGtasiIyMjw5T79+/T5s2bqXHjxlS8eHHZwwr6UmgfITysrb9y5Urq3r079enTh/r370/FihUzWPvhw4c0d+5cmj17Ns2bN4+6d+8uOX0ZGRnpIeekZGRkZGRkZGSE4/Lly+pxl678e8OGDalixYrWrq7ZfP/99+Tu7k5dunSh/PnzW7s62Rqm+RCLb7AnIyqOHz+OYsWKoUuXLlpfrVu3RsGCBc3az1Xq7N27F1FRUdi2bZtoPWR96+oL4ZFdYti+fTuKFSsGnufB87zOfZhVZby9vbF48WJm9TaGmzdv4scff0RkZCRatWqFMWPG4Pr163rPGzlyJHieh7+/P7O6vX37Flu2bEF8fLxoPcQeQ0hICHieR4UKFSyuLYQ+IEwfsI7DkBiSk5MRHR2N3Llzq681ul7u7u4YPHhwttu3+/r169i0aRN27dqFe/fu6S2fmJiIqlWrolixYpg4caIANZSRkZFhx927d2UPK+tLoX2E8MjO+hUrVsS6devM8t+8eTMqVaokSX1LwPJePLvc52d3D7kPrO+RXfTlnBS7nJSMjIzM18DFixcxcOBAnD59WrQeYo9BCu0jhE920D937hzCwsIy5dh1jbl4nke5cuWwf/9+JnU2hfj4eMyfPx/9+vXDDz/8gGXLliEhIUHved26dYNCoUDdunXZV1ICxMbGWlxTngT1FXP06FG4uroadNOnugjJaMbDwwM8zyNXrlyi9ZD1rasvhEd2iGH8+PHq642DgwMqVqyIVq1aoX///hg2bBjGjBmDESNGYNCgQWjXrh1CQ0Ph7Oysvgb16dOHWd1V6Ep+//7778iRI0em66bq3w0bNtT5RX3lyhXm11IhJshIYZIPS4+8efOC4zh4e3tbXFsIfUCYPmAdh74YLl68CD8/vyzjHxcXF+TJkwcFChSAj48P3N3ds4yHPD09ceDAASb1/pzJkyfrPH7//n1Ur149y2StkJAQbNiwQee5f//9tzy2k5GRkQTffPON7GFlfSm0jxAe2Vk/MDDQInUoV66cJPUtAct78exwny8GD7kPrO+RHfTlnJR8DygjIyNjLr6+vuB5Hr6+vqL1EHsMUmgfIXysrb98+XLY2dmpc+q5c+dGxYoV0bhxY3zzzTfo2LEj2rZti6ZNm6Jq1arw9fXNNB9h0qRJTOr9Jc2bN9d6bMeOHXB3d8+Sf3dwcEDfvn11ToY6f/68PPYykI8fP8LNzc3iuuLf00PGZAYPHkzJyclUoEABCgoKInd3d+I4Lku5tLQ0unnzJl28eNEKtRQHFSpUoP3791PZsmVF6yHrW1dfCA9rx7Bjxw4aM2YMRUZGUv/+/SksLMyg5cSVSiXFxMTQ3LlzaeHChVSrVi1q0aIFi+pTamoq3bp1S+OxY8eO0XfffUdKpZKIiIoUKULlypUjpVJJjx8/poMHD1JAQADNmjWLevTokeV8R0dHJnX+nMePHxMAevLkiWg9xB7D1q1badWqVdSmTRuLawuhTyRMH7COQ1cMjx49ojp16tC7d++oTZs21KBBAypfvjwVKlRI4+c0NTWVHj16RBcuXKDdu3fTpk2bqEWLFnTu3Dmjtlsxlnnz5tGwYcM0Hnv16hVVr16dnj17RvhiZ+uzZ89SmzZtaPHixbR8+XLy9fXNcr68DK+MjIwYSE1N1fi+ra2t+t/Hjh2jQYMGUUBAQKb3VQQHB1Pp0qUl7cFSXwrtI4SH2PUzMjIIgMZ8kKEolcpMW0lJSd8SsLwXt/Z9vlg85D6wvoe19eWcFPuclIyMjMzXgI+PDz179ow8PT1F6yH2GKTQPkL4WFP/xIkT1KNHDypdujR999131KBBA8qXL59ezbi4ONq9ezfNnz+fRo0aRZUqVaKIiAgW1Sei/8Z5R48e1XjsypUr1KpVK0pNTSUA5OTkRCVLllSPvRYsWECbNm2iVatWUb169bKc7+bmxqzeUiIhIYF69uxJycnJFtfm8OUvJzJfDQ4ODjRw4ECaPHmy3rJKpZJ8fX3p+fPnAtRMfKSlpdGVK1coMDBQY8JRDB6yvnX1hfCwdgxhYWHUpEkT+v77703WnzdvHm3cuJGOHDmS6f0nT57Q4cOHTdYl+q/uO3bsoB07dmhMgEdGRtLu3bspR44ctGTJEurQoUOm4/Hx8bR27VqaNGkSNW/enGbNmpUpER8bG0vFihUjjuOYJdj/+ecf9cSS0NBQUXpIIQaxI4X20RVD9+7d6e7du/Tnn39S3rx5jdaOi4uj9u3bU/78+WnZsmWWqrKajIwM2rBhA3377bdarxWDBw9WX2Nq1apFY8aMofLly6tvwg4cOEBr1qyhx48f0+bNm6lKlSqZzhfieiQjIyNjLm5ubvTu3Tv1/xUKBUVGRtLmzZvV7/n4+FBcXJxWjdKlS9Ply5e1To6QggdLfSm0jxAeYtdv3LgxBQYG0sSJE7Wer4/Ro0fTuXPnaM+ePZLTtwQs78WtfZ8vFg+5D6zvYW19OScl3wPKyMjIWILExEQ6dOgQhYWFkYeHhyg9xB6DFNpHCB9r6jdq1Ij8/f1p+vTpJusPHz6crly5Qrt3785yLD4+nq5evWqyNtF/Y6/NmzfT77//rnFs9M0339DGjRuJ4ziKjo6mH3/8kezs7NTH//33X1q1ahUtWrSIxo4dm2WMKY+9dHPv3j2aO3cuLVmyhN69e8emnSy+tpSMaPD19cXFixcNLr98+XJmdZGRkZE+JUuWtIhO2bJls7wXHx8PNze3LMtSGvvStTylamn10aNH66zf27dv0aVLF0RFReHjx4/q9+/evSsvfylh0tLSRK0vFNkhjuLFiyM+Pt4sjYSEBJQuXTrL+5cuXTL7OvT5SxuFCxcGz/OoVasWlEql1nJ//fUXChYsiI0bN2Z6X74eycjIiIGpU6eql0Jv2bIl7t69m6WMt7e33i3dDx48KGkPlvpSaB8hPMSuf+zYMSgUClSsWBHLly/H8+fPtbbl57x+/Rp//PEHQkNDwfM89u/fL0l9GRkZaSDnpOR7QBkZGRkZGRlh8Pf3R0ZGhlkaGRkZWrc+f/LkCRwcHJjm4PPkyQOe59G3b1+d9Xz06BFq166Nfv36ZXpfHntpZv/+/YiMjIRCoTBoDGwO8nZ4XzH16tWjmzdvUlBQkEHlK1asyLZCIuTmzZsUFxdHoaGhpFAoROkh61tXXwiP7BKDjQ27r5ycOXPSkCFDaOzYscw8VE9ft27dWmc5FxcXWrZsGS1YsIAaNGhAW7duJVdXV2b1IiJKT09n2r5CeIg9hmPHjlG/fv0oPDycGjZsSBEREeTg4CAafSJh+oB1HIbE4OTkRDlz5jTLx93dnezt7bO8X7ZsWWrSpAlt27bNLH0i0rmly9OnT4mIaOTIkTrLNW/enEJDQ6lNmzYUFxdHffv2NbteMjIyMkKhus7OmjWL+vfvr7EMz/M0fvx4qlChQpbr8qdPn2jYsGG0c+dOqlWrlmQ9WOpLoX2E8BC7fvXq1WnevHnUv39/6tatGxEReXl5kZ+fH+XJk4ccHR3J1taW0tLS6MOHDxQXF0cPHz5Uj0cAUHR0NNWpU0dj3cSubw4s78Wzy31+dveQ+8D6HtlFX85JycjIyMiYw9OnTykjI4MKFCggWg+xxyCF9hHCJzvo29vbE8/zZvnwPK9162JfX1/q3bs3zZw50ywPIu05+ISEBCIi9T2mNvLnz0/79++nESNGUJs2bWj16tUGbbn8NZGSkkIrVqyguXPn0u3bt4nov/twov/G6E5OTvT27VvLG1t8WpWMaHjw4AEaNmxocPkiRYowrI11SUpKyvQydKWKBw8eICoqCu7u7mjZsiWWLVtmNQ9Z37r6cgz6PSIiIjB//nyD9LSxcOFC1KhRQ+Oxd+/ewcvLC+fOnTNJOzExEZMnT9Y641i18srTp08N1ty+fTtCQkLw/PlzpjO/Dx06hJIlS6Jv377YuXMn3r9/LzqP7BJDs2bNMr1iYmIM1n/69CkaNWoEnufh4OCgsQxrfXMwpg+yaxyGxFCmTBncuXPHLJ/bt29rfRLl6tWrUCgU6N+/P5YvX44VK1YY9Zo5cyYqVaqk81qhegr42bNnBtU3NTUVbdu2VT81LD+JIiMjIwa6du2Kbt266SzTrFkzncdjYmIQFhYmaQ+W+lJoHyE8xK6v4vDhwwgMDMyygpSmlUpUr5IlS2Lnzp06daWgz/I+Wez3+UJ5yH1gfQ+x68s5KfkeUEZGRsYcrl69inLlyqFUqVIYOnQoDh06JDoPsccghfYRwic76FeuXBnbt283y2f79u2oVKmS1uOvX7+Gq6sr/vrrL9y/fx8PHjww6nXp0iV89913WsdG+fLlMyr/DgDz589H3bp1kZKSIo+98N9vEAMHDsy0YqrqXrxChQpYtWoVUlJSkJKSAjc3N4v7y5OgvmL+/vtvfP/992jbti1Wrlyp9bVixQoMGTJE0h9U1YXIzs4OderUwZkzZ4w6f+3atbCzs4NCobCah6xvXX05Bv0e27ZtU2/hcPjwYaOWwzxx4gTatWsHnuexfv16reVmzpyJGzduGFXnL8mTJ4/G9/v16wee5/H3338bpRcTE4Ny5cph9+7degc9QkwskcIkH5YeHMchR44cGD9+PJ48eaJ+//Lly1pfn6NUKtGgQQOt/cxaHxCmD1jHwTKGMWPGIF++fAb/oPYl+/fvR6FChTBq1CitZTp27IiEhAST9IH/tttzcnLSelzVdl+2qz4GDBiAnj174vbt21/9TZiMjEz2p0KFCnj48KHOMqtXr9aro23SqlQ8WOpLoX2E8BC7/pccPnwYw4YNQ1hYGAoUKABXV1fY2NjAxcUFfn5+qFOnDkaPHo2TJ08apCcFfZb3yWK/zxfKQ+4D63uIXV/OSenPScnIyMh8DeTMmVP9qlixolE5R6VSiUmTJoHneZ3fyaw9xB6DFNpHCnGw1F++fDlsbW0xdOhQxMbGGqwL/LfV3ciRI2Fvb4+FCxfqLDt27Fg8ePDAKP3PSUtLQ65cuTQe69SpE3ieN/q+dMOGDahatSpOnTr11Y699u7di4YNG6q3vPv8ISUPDw8cP348yzn6HvwyBQ74//WmZL46/Pz86NGjR0adk5GRwag21oXnecqfPz8dOXKE/Pz81O8PHjxY61J406dPz/T/X375hcaOHau1jVh7yPrW1ZdjMMxj1KhRNGnSJOI4jhwcHCgwMFDvNgVXrlyh5ORkAkDdunWjxYsXa6wHEVFqairt2bOHoqKitJbRx2+//UY//PBDlvefP39O5cuXp8DAQNq3b5/OLai+5N9//6VmzZrRvXv3iOM4ne1vY2NDY8aMoS5dupCvry8REV25ckWrdpkyZdT/BkCNGjWiffv2Wc1D7DHwPE9du3alJUuWZHr/p59+ohMnTlBMTAwBIIVCQeHh4RQSEkLjxo3LVPbs2bNUpUoVre3DUp91+wgVB8sYUlJSKCQkhG7cuEH58+en+vXrU7ly5fReiy5evEj79u2j2NhYKly4MJ09e1brtnoPHjygEydOUPv27bXWVx8hISF05swZjccOHjxIdevWpdGjR9PPP/9slO748eNpz549dPr0aZ3XIxkZGRlrExwcTOfPnzdbp2rVqnTy5EnJerDUl0L7COEhdn0Z/bC8T5bCfb4QHnIfWN9D7PpEck5KX05KRkZG5muA53ny8PCgLVu2ULVq1dTvz5o1S+u1dcCAAZn+//3339OsWbN0fiez9BB7DFJoHynEwVr/22+/pT///JM4jqNixYoZnH+/fv06ERE1aNCAtm/frnNbveTkZNqxYwe1a9dOaxl9DBw4kGbNmpXl/Tt37lBwcDA1bNiQ1q1bZ5TmwYMHqWfPnvTgwYOvZuz17t079ZZ3d+7cIaL/bXnn7e1NnTt3pu7du1P79u3p1KlTwlTK4tOqZERDjx49wHEcPDw8UKhQIa0vb29v9Ww9qcJxHMaNG5fl/Tt37mDFihUoXbo0eJ5H+fLl8ccff+Du3btZyj59+lRnG7H2kPWtqy/HYLjH6tWrkTt3bp1bFHy5NGKuXLkwd+5cKJVKrbpCcO3aNZQqVQoRERFGP9137949FC1aVG/7a5rxPGrUKISFhamvxTly5EDdunXVW1t9zpkzZ6zqIfYYOI7DihUrtHqvX78eHMdhx44dWssolUo4OjpqrTtLfZWHEH3Aup1YxpCQkIC6devqvQZpuiaFhITg/v37WuNSkZycrLeMOfz2229wcHDQ2Q/aWLBggeTHdjIyMuKnQoUKFtEpXbq0pD1Y6kuhfYTwELu+jH5Y3idL5T5fzPkQKbSPEB5i11ch56Tke0AZGZmvG47jMGLEiCzvHzhwANHR0fDx8QHP8yhYsCAmTJiAgwcPZikbGxur9zuZpYfYY5BC+0ghDiHaafz48bCzszM4B89xHGxsbDB06FB8/PhRq65QHDlyBF5eXujUqRNev35t1Ln//PMPcufOLfmx1+3btzFgwAD1lneq8bNCoUCDBg2wZcsWpKenq8tXqVJFsLrJk6C+Yv766y9MnDjRoLI3btzQuS2O2OE4Dhs2bNB6/N69e+A4LtOWP5rInTu31TxkfevqC+EhhRhUfPz4EcuWLcM333yDQoUKZfpy5P5/m61ixYqhQ4cO+PPPP/HhwwedekKSnp6OVatWoXHjxpg+fbpR5758+RLBwcFajws1QUYKk3xYeXAcp3ObNKVSqffvGwCKFy+u8X3W+ioPIfqAdTuxjkGpVGLTpk2oXr06FApFpmvQly87OzvUrVsX69evt3ri+3NiYmJQq1YthIaGarwR1cXGjRthb2/PqGYyMjIy5uPv72+2RmpqKgoXLixpD5b6UmgfITzErm8ujx49wowZM4ze5kBM+izvk6Vyny/mfIgU2kcID7Hrf46ck5KRkZH5euE4DmvXrtV6/NKlS+A4Djdv3tSpkzNnTqt5iD0GKbSPED5i11fx/PlzjB49GiEhIeoJUV++7O3tUb16dUycOBFPnz7VqSc0iYmJGD9+PEJCQjBnzhyjzr116xYKFSrEqGbZg3LlymV6uKBAgQKIjo7Gw4cPNZYXchKUjTDrTclkR2rVqkWurq4GlS1RogS1bNmScY2si4uLi9Zjfn5+VKxYMfV2PNrIkyePVT1kfevqC+EhhRiIiOzs7KhLly7UpUsXIiJSKpWUlJRE79+/JwcHB3J3d9e5zKU1USgU1KFDB+rQoYPR53p5edG5c+d0lvH09NR6rFWrVtSvXz+KjIzUWobjOMqXL59VPcQeQ44cOXSe9/nS/NpwdnbWeoy1PpEwfcA6DtYxcBxHLVq0oBYtWlBKSgqdO3eO7t27R4mJieprkaenJxUtWpTKly9P9vb2euMRmmrVqtHBgwcpLi6O4uLijDq3ZcuWBvWRjIyMjLXgOI6uXbtGpUuXNlnj0KFDlCtXLkl7sNSXQvsI4SF2fXOpU6cO3blzhxYsWEC3bt2SrD7L+2Sp3OeLOR8ihfYRwkPs+irknJSMjIzM142bm5vWY2XLlqWCBQuSv7+/Tg0fHx+reog9Bim0jxA+Ytcn+m8rtHHjxtG4ceMoNTWVnj59miX/nj9/fqO2+hUSNzc3+umnn+inn36i9PR0o84tXrw4/fvvv4xqlj24cOECnThxghYsWEB//fUXeXp6Up48eShnzpzWrhplz9G8jCC4ublR7dq1DS6/atUqhrWxPvpubj08PPRq6PuBlLWHrG9dfSE8pBCDNs+cOXOSr68v5cqVK9smm4RAiAkyUpjkI4SHNhQKhUXKsNS3ZvuoMDcOIWNwcnKimjVrUpcuXWjw4ME0atQo+v7776ljx45UtWrVbDkB6nO8vLwoICDA6POCg4MZ1EZGRkbGMlStWpUmTpxolsa4ceMoLCxM0h4s9aXQPkJ4iF3fXJRKJQEwOiEsNn2W98lSuc8Xcz5ECu0jhIfY9XX5yjkpGRkZma8HfTlLb29vvRqOjo5W9RB7DFJoHyF8xK7/Jba2tuTn50flypWj0NBQKl++PBUoUCDbToD6Ehsb49cWMvc3FjEQGhpKa9asocePH9M333xDv/32G+XNm5e6dOlCJ0+etFq95BG9DCmVSlq1ahU1adKEihQpQt7e3lS6dGnq3LkzHT582NrVExVCXKhZe8j61tUXwkMKMXzNsJ6AI4SHFGLQR0ZGBjNtS+hbu31UmBNHdolBRkZGRoYNUVFRtG7dOlqyZIlJ5w8ZMoTOnDlDUVFRkvZgqS+F9hHCQ+z65rJv3z767bffaM+ePbK+HljeJ0vlPl/M+RAptI8QHmLXl5GRkZGRPoZ8l5j7fcPaQ+wxSKF9hPARu76MtPD09KQff/yR7t69S+vWraPXr19TjRo1qFSpUjR9+nR6/fq1oPWRJ0F95Tx9+pRCQkKoS5cutGvXLrp//z7FxcXRjRs3aNWqVVSnTh2Kioqit2/fWruqouDjx4+i95D1rasvhIfYY/j777+ZaUtBn4j9BBwhPKwZAwCztV+8eKH1GGt9S6GvD8QQhxB/RzIyMjIybGjSpAmVLFmSevfuTUOHDjV4fJmQkEBt27almTNnUrVq1ahatWqS9mCpL4X2EcJD7PqGEBsbq/WYn58fDRkyhIoXL/7V6hsKy/tksd/nC+Uh94H1PcSuL/ackRA5KRkZGZmvgdTUVNF7iD0GKbSPED5i1mexHbrQHkLEkN3gOI7+r737Do+i3N8/fs8mJNTQoqFLFSQgXRQiiIo0gUNRUJQmoCKIiIjKURQURUSRomIDaSKHoogCelCa+FUUEUWQHppSE0hCC8nz+8Nf9oipkMxsZvJ+XVcuYXf2vuczI5BMnsy2a9dOn376qXbu3KmOHTtq3LhxKleunPbs2aN9+/alek3Pnj1zfD8u/b5d8IwzZ86odevW2rJliyzLUr169VS3bl2Fh4crODhYR48e1Q8//KBPP/1UHTp00FdffeXp2wFn94vkuLg47dmzJ6Ad5Ac234kOL8yQXffff7+2b9/u2XwnFpZ4YZGPnR3vv/++/vzzz3R/iuHo0aMZvkXshg0bMtx/u/MlZ86B3XPk9kVWjRs31nfffefafKc6ACA7pk+frhtvvFGvvfaapk+frh49eqh58+b+r1uLFCmis2fP6ujRo9q4caOWL1+uuXPn6vTp0woJCdGECRPyRIed+V44Pm4/B04do4yMHDlS8+bNy1aG2/Pt/DrZK1/nu/l6iBeOjxMdbs/PCYG+ZpTb8wHADbL7Q5OJiYlpfhPfyQ63z+CF4+NEj9vzs+tf//qXtm7dalu+Ex1OzJCbVaxYUS+++KJGjx6t+fPn66233lKVKlXUsmVLPfjgg2rbtq0SEhL0ySef5Hi3ZXLiO0xwpRdffFEjR47UXXfdpeeff16VKlVKc7uffvpJ9957r4YMGaL+/fs7vJfO8Pl8ioiIUI0aNdL9Zu7GjRtVv379dDO2b9+uP/74I91/NOzuID+w+cyQ9Y7s2L59uyIjI5WYmJjj2bkh3+fz6Y477lDbtm3TPf5jxozR008/nW7Hhg0b9MYbb2R4ju3scPsMPp8vx27hmt7xsTM/pcOJc2D3cbJ7huw4f/68rrzySsXGxuZ4thP5TnUAQE6YP3++evXqpXPnzmXp356USxwzZszI8k+SeaHDznwvHB8nOtyan95P7oaEhPh/XaZMGd15552qVavWRY+naNCggSIjIz2ZL9n7dbJXvs538/UQLxwfJzrcnp8TAn3NKLfnA4Ab+Hw+1axZU40aNUr335vPPvtM7dq1Szfj559/1qZNmzL8N9nODrfP4IXj44U5nDpOl+vIkSOqUKGCrXf5tLvDiRncaPPmzZo6darmzJnj/z5TfHx8jv9/xCKoPKxu3bpq1aqVxo0bl+m20dHR6tOnj7766isH9sx5OfHNXGOMLMuy9RvGGXWQH9h8JzrcPMMvv/yiIUOGZCs3MTFRmzdvTvMfQ7fnp3BqgYwXFvnY1ZFTdzzM6M+xnfkpHU6cg5xg5993Kf6ef+DAgWx/LpOYmKhPP/1Un376aap9tzvfqQ4AyG3WrVunfv36ZenOBSVLltT777+v9u3b57kOO/O9cHyc6HBjftGiRRUfH+//fVBQkG6//XYtWrTI/1jp0qV15MiRdDMiIyP1888/p/n5m9vzJXu/Fnfz1/lOdnAOAt/h5ny3XzNy6poUAOQF/Juceb7dHV44Pk70uDV/69atGj9+fLZyExMTtXbtWu3fvz/Nfbe7w4kZIMXExGjSpEmaMGGCEhISWASFnFOmTBlFR0crX758Wdr+xhtv1Nq1a23eq8Bw6pvSdnaQH9h8JzrcPkP9+vX1888/S8re21ylt/9uz5fcf46dyLe7w+fzqVWrVmrXrp2KFClyyZkXLlzQnj179NZbb+nYsWOpnrc7P6UjJ2R2Duw+TjnhnzPExMSoUqVKiouLy1Zuel9A2p3vVAcA5EbJycmaO3euFi9erFWrVikmJsb/XIECBdS4cWN17NhR/fr1U6FChfJsh535Xjg+TnS4LX/ChAkaPny4JKlLly566aWXVKVKlYu2KV26tA4fPpxuhmVZ+uKLL3TLLbd4Ll+y/+sPu7K91ME5CHyH2/Pdfs3IiWtSAJAX8G9y5vl2d3jh+DjR49b8CxcuqEaNGtl+i+KMrl3b3eHEDPif9evXKyoqSsnJyTmaG5yjaXCVypUrZ3kBVFJSkq3v65kbzJ49Wx06dFDhwoUv+bVJSUnas2eP+vTpE9AO8gOb70SHm2d4/vnndfvtt6t+/fqqVavWJa8yj42N1ddff53uN/7dnp8ipxaWBLLDzTMUL15cn332Wba/CEjvG05256ew+xw4MYcdMxQvXlzDhg3TqFGjLjkzK+zOd6oDAHIjn8+ne+65R/fcc48k6ezZs4qJiVGhQoUUFhZGhwP5Xjg+TnS4LT9//vySpNdff12DBw9Ot3PMmDFq2LChf/sU586d04gRI7R06dI0FxG5PT+FnV+Lu/nrfCc7OAeB73BzvtuvGTl1TQoA8oLnn39eHTp0yNY1x0cffTSgHW6fwQvHx4keN+YHBwfrueee07333qvSpUurWrVql/V5y5YtW9JdPGR3hxMz4H+aNGmSpT9vl8wgz7rxxhtNTExMlrYdNWqUufbaa+3doQCqUqVKjuRMmzYtYB3kBzbfiQ4vzBAVFWXi4+MvO3fr1q0mX7586T7v9vwSJUqYpKSky85PMXbs2IB1uH2G+++/P9u5xhizcuXKNB+3O98YZ86B3XPYOUN8fLy58sorzQ8//HBZmbGxseall14yPp8vzeftzneqAwAA5A19+/Y19913X4bbdOrUKcPn165da2666SZP5htj79fJXvg634kOzkHgO9yeb4z7rxnZnQ8AeUH58uVzJGfKlCkB63D7DF44Pk70uDk/OTnZ1K5dO8trENKyZs2aDK9d293hxAywF4ug8rCXXnrJtGnTxhw/fjzdbTZt2mQ6duxofD6fefHFFx3cO2d9/vnnOZKzd+/egHWQH9h8Jzq8MMPatWuz3VG1atV0n3N7vhMLZLywyMeJDjfzwvGxe4aJEyearVu3Zis7IiIi3efszneqAwAAeF/Dhg1NdHR0htvMmjUr05zatWt7Mt8Ye79O9sLX+U50cA4C3+H2fGPcf83I7nwAyAvefffdHMnZsmVLwDrcPoMXjo8TPW7PX7JkiVmzZk22sitWrJjh83Z3ODED7GMZk403kYarnT59WnXr1tWhQ4fUsWNHValSRcWKFdOpU6e0e/duff/999qxY4ckqWbNmtqwYUOq24YDwKXatm2batSocdmv37Fjh6pVq+bZfLhfbGysihUr5tp8pwRyjvPnz2vZsmXq2LHjZWeMHz9ew4cPD0i+Ux0AAMD7GjRooB9//DHbOU2aNNH69es9lw/AW9x+zYhrUgAAwC2+++47NW7c+LJfv3r1ajVv3jygHU7MAHv4Ar0DCJyCBQtq2bJlqlChgj788EO98MILGj58uMaMGaM5c+Zo+/btMsbouuuu0xdffMECqAzs379fEydO1O7du13bQX5g853oyC0zZOdijaRML9a4PT+7YmNjbc13osPNMwwYMEAlS5ZUr169XJmfwu5z4MQcGc0QEhKSrcVDkjJcPGR3vlMdAADA+3y+nLk0eOrUKU/m5wQ7vxbPLV/n5/YOzkHgO3JLvtuvGeX2a1IA4HVxcXH6+OOPFRMT49oOt8/ghePjRE9uyM/O4iFJWVo8ZHeHEzPAJgG+ExVygTNnzpjJkyebZs2amRIlSph8+fKZK6+80rRp08bMnj3bXLhwIdC7mOtVr17d+Hw+c/XVV7u2g/zA5jvR4YUZkLH+/fsbn89nevbs6doOt89QtGhRY1mWCQsLy/FsJ/KNceYc2D2HEzMAAAB4QfXq1bOdcf78eVO5cmVP5ucEO79O9srX+W6+HuKF4+NEh9vzAQAwxpjGjRsbn89nGjZs6NoOt8/ghePjRI/b84Hs4k5QUP78+TVo0CCtXr1ax48f1/nz53X48GF9/vnn6tGjh4KCggK9i7lecnKyjDG6cOGCazvID2y+Ex1emAEZmz9/vowx+vjjj13b4fYZxo0bp8jISI0ZMybHs53Il5w5B3bP4cQMAAAAXmBZlrZs2ZKtjJUrV6pEiRKezM8Jdn6d7JWv8918PcQLx8eJDrfnAwAg/XXnQWOMDhw44NoOt8/ghePjRI/b84HsCg70DsAdRowYoeeff1758uUL9K7kSitWrNCiRYvUvn1713aQH9h8Jzq8MAMyNm7cOE2ZMkX9+/d3bYfbZ7j//vt1//3353iuU/mSM+fA7jmcmAEAAMALmjRporFjx2rOnDmXnTF69GjddNNNnszPCXZ+neyVr/PdfD3EC8fHiQ635wMAIEkff/yxZs6cqe7du7u2w+0zeOH4ONHj9nwguyxjjAn0TsA+J06cUHx8fLYz+vTpo5dfflktW7bMoT1zn127dqlKlSqu7iA/sPlOdHhhhvSsWLFCb775pvr27asOHTqQDyAg4uLitHLlSjVv3lzFixd3Xb5THQAAwB2WLFmiTp06adq0aerXr98lv37YsGGaOHGiVq9eraioKM/l56Tdu3ercuXKrsv2UgfnIPAdbs13+zUjrkkBAAC32LRpk2bMmKG77rpLjRs3dmWHEzMgE/a+2x4Cbdy4ccbn8+XIx9ChQwM9TkB169bN9R3kBzbfiQ4vzJCekiVLGp/PZ0qUKEE+AurJJ590db5TvDLHP3nh/dR5z3YAAPB3kZGRJigoyAwbNsycOXMmS685ceKE6d69u/H5fKZZs2aezs8JZ8+eNUWLFnVdtpc6OAeB73BzvtuvGXFNCgBy1tmzZ13f4fYZvHB8nOhxY37ZsmWNz+czZcuWzfFspzqcmAEZ4+3wPK5Lly564oknciTrs88+06uvvpojWbnN+fPn03w8JCTE/+s1a9bokUceUa1atS56PEWDBg0UGRkZsA7yA5vPDFnvuFwNGzbUF198oTp16uR4tpvyn3rqKY0dOzaH9iowHW6f4eOPP9add96pWrVqKTg45z+VsjtfcuYc2D2HEzOkxQvvp857tgMAgL+bPn26brzxRr322muaPn26evTooebNm6tu3boKDw9XkSJFdPbsWR09elQbN27U8uXLNXfuXJ0+fVohISGaMGGCp/OzKyYmRgMGDFBcXJyrsr3UwTkIfIfb891yzShQ+QCQlyQnJ6tcuXI6evSoazvcPoMXjo8TPW7NL126tA4dOqTw8PAczXWyw4kZkIkAL8KCA6pXr25mzZpldu/ebfbu3ev/WLVqlSlVqpSZOnWq+e233y567u8fy5YtM1FRUebUqVOBHsU2YWFhF931Kl++fKZTp04XbVOqVKkM75RVu3Ztk5ycHLAO8gObzwxZ77hc58+fNz/88IM5d+5cjme7Kf+aa64xP/30k0lMTMyhPXO+IzfPcNddd6X66N2790XbZPZn4N577w1YflZl9xzkhjmc+P8oLd9//70ZNGiQWbdunSvzneoAAADu8tFHH5n8+fMby7Iy/Bwu5cOyLGNZlvnggw/yRP7l2LVrlxk6dKgpUqSIf7/ckO2lDs5B4Dvcnp/CLdeMApUPAHnFhQsXzJAhQ2z798aJDrfP4IXj40SPm/NjYmLMggULzLFjx3I826kOJ2ZAxlgElQcMHz7cJCUlpXq8R48e5quvvspSxrRp08zw4cNzetdyjVdeecV/Aaxr165m586dqbYpVaqUf5u0Pnw+n/nvf/8bsA7yA5vPDFnvQPqcWFjihUU+dnb07NnT//9x0aJFzbPPPmuOHDmSKjujPwNBQUFm165dAcm3+/g4NUduWGQFAACQ16xdu9ZUr149w8/hUj7Cw8PNkiVL8lR+Vn3xxRfm9ttvN0FBQRctusqJbxDYme2lDs5B4Dvcng8AwN/FxcWZ119/3VSsWNG2f2/s7nD7DF44Pk70uD0fyCm8HV4e8PLLL6f5+JYtW9SiRYssZTRq1EgvvfRSullulz9/fknS66+/rsGDB6e5jc/n05gxY9SwYUP/9inOnTunESNGaOnSpbrlllsC0kF+YPOZIesdl2rbtm06cuSImjZtqqCgoBzJzK35+fLl06xZs2RZlooUKaKhQ4dq4MCBqbYzxqSbMXfuXD377LOqXLlyQDrcPkOLFi00a9YsRUVFad68eSpdunSar69evbrq1q2b5p+Bjz/+WAsXLtTw4cNTvc7ufMmZc2D3HE7McKkuXLhg29sTOpHvVAcAAHCvqKgo/fbbb5o7d64WL16sVatWKSYmxv98gQIF1LhxY3Xs2FH9+vVToUKF8lR+RhISEjRjxgxNmTJF27dvl/S/z1WDg4NVqFAhnTp1Ktdle6mDcxD4DrfnpyU3XTPKjfkA4HXbt2/X5MmTNXPmTMXHx7uyw+0zeOH4ONHj9nxJOnjwoJKSklShQgVb8p3ocGIGZJHz666QW9SqVSvL286fP9+EhobauDeB1bdvX3PfffdluM0/3xLsn9auXWtuuummgHWQH9h8JzrcPsPJkycv+sjqW1jt3bvXdOzY0RQrVsx07drVvP/++2lu5/Z8Y4yZPn26sSzLNGvWzBw6dCjNbUqVKmVq1Khhunfvbnr37n3Rx1133WUKFChgXn755YB1uH2Gxx57zNSuXdskJCSk23/dddeleYfFFG+++abp2LFjms/ZnW+MM+fA7jmcmOFSrVy50lxzzTVm4MCBZunSpeb06dM5lu1EvlMdAADAW86cOWMOHTpkTp48SX4adu7caYYMGWKKFi2a6q32GjZsaGbOnGkSEhJMQkKCKVq0aK7J9lIH5yDwHW7Id/s1IyeuSQEA/mfp0qWmVatW/jsOpvVuGLm9w+0zeOH4eGEOp46TMcb8+uuvpl69eqZmzZrmscceMytXrsyxbKc6nJgBWcMiqDysdevWZtasWZlud+LECVOjRg1Tvnx5B/YqMBo2bGiio6Mz3CYrx6p27doB6yA/sPlOdLh9hpRPiEJDQ03Lli3Nd999l2nO33344YcmNDTUBAUFpfm82/ONcWaBjBcW+djZceutt5ovvvgi3dcZY8zDDz+c4fNxcXHm2muvTfM5u/ONceYc2D2HnTN06tTpoo+1a9dmuJ9/d/DgQdOuXTvj8/lMgQIF0tzG7nynOgAAAJC+5cuXm7Zt26b6ZoDP5zMlS5Y069atS/WazH7gyIlsL3VwDgLf4aZ8t18zcuKaFADkdadOnTKvvfaaqVq1aqpFt2XKlDGjR482v//+uzlw4IApVKhQruxw+wxeOD5emCO7+cWLF/d/NGrU6JKuXScnJ5sXX3zR+Hy+DD9vsbvDiRlgLxZB5WGrVq0y+fLlMw8//LD5/fffUz0fHx9vZsyYYa666irj8/nMAw88EIC9dEb9+vVzJOeGG24IWAf5gc13osPtM1iWZSpUqGB279590eOPPPKIGTp0aJof/zRmzJh0V5a7Pd8YZxbIeGGRj50dtWvXNsnJyRm+9vz58xk+b4wxjRo1SvNxu/ONceYc2D2HnTNYlmXy5ctnxowZYw4cOOB//Oeff0734++Sk5NNmzZtMvy7ws58pzoAAABwsbi4ODN58mRTvXr1VN8MKF26tHnyySfNrl27zPXXX5+rsr3UwTkIfIdb891+zciJa1IAkFdt3brVDBw40BQpUiTVvz1FihQxs2fPTnUHvlatWuWqDrfP4IXj44U5cirfsiwTHh6eauHQxIkTzeuvv57mxz8NHTo00+vjdnY4MQPsxSKoPG7SpEn+v8iuvPJKU79+fRMVFWWuueYaky9fPv9fcGXKlDF//PFHoHfXNg0bNsyRnMjIyIB1kB/YfCc63D6DZVlm9OjRqR7fsWOHmTFjhomMjDQ+n8/Ur1/fzJkzx+zcuTPVtgcPHszwkxI35xvjzAIZLyzysbMjOxeb/65BgwZpPm53vjHOnAO757BzBsuy0vzp4JEjR5qbbrrJ/1PG+fLlM7fddpt5+umnU2373XffZfh3hZ35TnUAAADgL9u3bzcPP/yw/624Ur4REBQUZNq0aWMWL15sLly44N8+ox88cjLbSx2cg8B3uD3f7deMnLgmBQB5zaeffmpatmyZarFH/vz5zV133WW++uqrbF8DtbvD7TN44fh4YY6czrcsyzz55JOpHv/yyy/Ns88+a0qXLm18Pp+56qqrzAsvvGD++9//ptp2165dmV4ft7PDiRlgLxZBwXz55ZemRo0a/r/U/vnRuHFjs2PHjkDvpq2qV6+e7Yzz58+bypUrB6yD/MDmO9Hh9hksyzLz589P93W7d+82lmVddFeTtFxxxRVpPu72fGOcWSDjhUU+dnZktEjwUlStWjXNx+3ON8aZc2D3HHbOYFmWmTFjRrqv+eijj4xlWebTTz9Nd5vk5GRTsGDBNJ+zO9+pDgAAAPylXr16/mtkPp/PVKhQwTz77LPpvpX8pSzOsDPbSx2cg8B3uD3f7deMnLgmBQB5QWxsrHn11VdNlSpVUi34qFmzpnnttdfM8ePH/dtfzr/Jdne4fQYvHB8vzGFnvmVZ5sMPP0z3+U2bNhnLssy2bdsyzClevHjAOpyYAfYKFvK8W2+9VVu3btW6deu0du1aHThwQMnJySpbtqxatGihpk2bBnoXbWdZlrZs2aLIyMjLzli5cqVKlCgRsA7yA5vvRIcXZihSpEi6r6tUqZKqVaumsmXLZpgfERGR7nNuz4+Li8vwtVl18uTJgHW4fYbTp08rJiZGxYsXv+zcbdu2KSgoKM3n7M6XnDkHds9h9wzh4eHpvuaOO+7QoEGDdPvtt6e7jWVZKleuXLrP253vVAcAAACkjRs36ptvvtGbb76phQsXKjw8XBEREdn6XNiJbC91cA4C3+H2fMn914zszgeAvKBevXrau3ev//cFCxZU165d1b9//xz7XqjdHW6fwQvHx4ket+cXLVo03efq1Kmjq666StWrV88wo3Tp0gHtcGIG2McX6B1A7hEVFaUnn3xSU6dO1Ztvvql///vfF/1Ft3///gDunb2aNGmisWPHZitj9OjRuummmwLWQX5g853o8MIMPl/G/+yULFky0/z8+fOn+5zb81MWlmRHZgtk7O5w+ww1atTQzJkzs5X9xhtvqF69emk+Z3e+5Mw5sHsOu2fIly9fuq+zLEuVKlXKNL9w4cLpPmd3vlMdAAAA+EvTpk01e/Zs7d+/X926ddP48eNVpkwZ9enTR+vXr8+12V7q4BwEvsPt+W6/ZmR3PgDkBb///rtmzZrl/95nkyZN1L179xxdHGN3h9tn8MLxcaLH7fkZfW9BkkqVKpVpRsGCBQPa4cQMsA+LoJBlzz//fKB3wTYdO3bUvHnz9O67717W64cNG6bvvvtOHTt2DFgH+YHNd6LDCzPkBMuybMsOdL4TC2S8sMjHzo5bb71Vo0eP1u7duy8rd+3atZo2bZratWuX5vN250vOnAO753Bihoxk9gVOVrcJVL5THQAAAHlNeHi4Hn/8ce3cuVPz5s3TsWPH1KxZM9WsWVOvvvqqjh07liuzvdTBOQh8h9vzs8PL16QAIC/Ily+fevToobVr1+rnn39W1apVdeedd6pixYoaPXp0jtwMwu4Ot8/ghePjhTmcOk7pycrnJNn9vMXuDidmQDYE+v34EFjnzp0zs2fPNiNHjjT33Xef6dOnT6qP3r17m5YtW5rg4OBA766tIiMjTVBQkBk2bJg5c+ZMll5z4sQJ0717d+Pz+UyzZs0C3kF+YPOZIeMOy7LMihUrMszJyvsK165d25P5xhgzYcIEU6JECbNr165Mc9KyZs0aExISYmbNmhWwDrfPEBMTY8LCwkzlypXNxo0bLyl32bJlpmjRoiYiIsKcPn06zW3szjfGmXNg9xx2zpBTf5br16+f5uN25zvVAQAAgKzZs2ePeeKJJ8yVV15pQkNDTalSpUx0dHSq7e69995cle2lDs5B4Dtye77brxk5cU0KAPKquLg4M2XKFFO7dm0THBxsWrdubRYuXGgSExOz9Hdrbuhw+wxeOD5O9LglP6c+b6lTp07AOpyYAfZiEVQe9sMPP5jSpUsbn8/n/7AsK9VHyuM+ny/Qu2yr77//3oSGhhqfz2dKlChhBg8ebBYsWGB27txpYmNjTVJSkklISDB79+41ixYtMgMGDDCFCxc2Pp/P5M+f32zYsCHgHeQHNp8ZMu6wLMt88sknGXZn9knDqVOnTOHChT2Zb4wzC2S8sMjH7o6XX37ZWJZlQkNDzQMPPGB+/PHHDDPXr1/vXwTo8/nMpEmTMtze7nwnzoHdc9g5g2VZZvny5RlmZOULmDJlyqT5uN35TnUAAADg0pw/f97Mnj3bREVFmeDgYNOmTRuzZMkSc+HCBXPy5EkTFhaWK7O91ME5CHxHbs13+zUjJ65JAQCMWb16tenevbsJDQ01ERER5qqrrjIJCQmptnviiSdybYfbZ/DC8XGiJzfnW5ZlPv/88wzzM/u85fz586Z48eLpPm93hxMzwF6WMcYE+m5UCIzGjRtrw4YNsixLN9xwgypWrKh8+fKl2u7cuXP6/ffftWnTJiUlJQVgT50zf/589erVS+fOncvSLepS/vjMmDFDPXv2zBUd5Ac234kOt87g8/kUERGhGjVqpJu5ceNG1a9fP92e7du3648//kjz7yK356cYP368RowYoZCQEPXp00f9+/fPMPPbb7/VpEmTNH/+fEnSxIkTNXjw4HS3d6LDCzN07dpVixYt8p/rwoUL69prr1V4eLiKFCmis2fP6ujRo9q0aZNOnTol6a8/B507d9aCBQsy3Hcn8p04B3bPYdcMPp9Pd9xxh9q2bZvun+UxY8bo6aefTrdrw4YNeuONN9L9u8LOfKc6AAAAcPk2b96sqVOnas6cOfL5fLIsS/Hx8TnyuZed2V7q4BwEviM35bv9mpFT16QAAH85cuSI3nnnHb3zzjuKiYnRPffcowceeEC1a9dWYmKiSpcune23Z7W7w+0zeOH4eGGOy8n3+XyqWbOmGjVqlO7nLZ999pnatWuXbu/PP/+c4boEuzucmAE2C8TKK+QOBQoUMD6fz6xcuTJL23fr1s3mPcod1q5da6pXr57mXbH++REeHm6WLFmS6zrID2w+M6TN+v93lMvOR0qGF/P/rkuXLhf1hYWFmaioKPOvf/3L3HvvveaOO+4wN910kylWrNhF2V26dMn8xDrU4fYZkpKSzIMPPpjqzohpndOUjy5duqT50xCByLf7+Dg1hx0z5MSf5ZSPQOQ71QEAAIDsO3HihHn22WdNkSJFcvxzLzuzvdTBOQh8R27Id/s1IyevSQEA/ic5OdksWbLEtGrVyvh8PlOjRg1TrVq1HP371O4Ot8/ghePjRE9uynfi8xY+90JmWASVh0VGRpry5ctneftt27bZuDe5S1JSkpk1a5bp3LmzKVGixEXfvC1YsKBp0aKFmThxoomPj8+1HeQHNp8ZUsvKYqqsfGT0SYmb8/953O1eIOOFRT5OdKxdu9bcfPPNJjg4ON1zWrduXTNv3rwsZzqV78TxsXsOO2bwwt8VTv59BAAAgOz75ptvjGVZrsv2UgfnIPAdgcx3+9eBfA0IAIH322+/md69e5vg4GDb/j61u8PtM3jh+DjRE+h8L1wf53Mv9+Pt8PKwGTNmaNCgQTp69KgKFCiQ6fb79u1ThQoVHNiz3Ofs2bOKiYlRoUKFFBYW5soO8gOb70RHbp/B5/Np9uzZ6tChgwoXLnzJ3UlJSdqzZ4/69OmjtWvXei4/LevWrdOoUaO0Zs2adG+ZWadOHT3xxBPq1q3bJe+TEx1emEGSYmNjtXbtWh08eND/Z6BUqVK6/vrrc+TfRjvznTg+KeyaIydn8Pl8atWqldq1a6ciRYpc8r5cuHBBe/bs0VtvvZXmrYTtzneqAwAAADnrscce0yuvvOK6bC91cA4C3xGofLdfMwrENSkAQNo+/vhjdenSxda3uLK7w+0zeOH4ONETqHyfz6fnn39eHTp0yNa160cffVSbN29Ocxu7O5yYAfZiEVQe99BDDyl//vyaMGFCpts+8sgjmjhxov07BcCTqlatqp07d2Y75+2339aAAQM8l58RuxfgONHhhRnczgvHJydmKFmypI4ePSqfz5etfXnxxRf15JNPOp7vVAcAAAAAeIXbrxkF8poUACC1rl27asGCBa7ucPsMXjg+TvQEIr9ChQrat29ftrOnTp2qhx56KM3n7O5wYgbYi0VQedg777yjxMRETZkyRS1atFBERES62x49elTvv/++EhISHNxDAF6ybNkytWnTJts50dHRuuqqqzyXD285fvy4SpYs6dp8pwRijgceeEBvvfVWtnO++uor3XzzzY7nO9UBAAAAAF7h9mtGXJMCAABu8d577+m+++7Lds5vv/2mmjVrBqTDiRlgLxZB5WG33367li1bdkmvsfvWfwCA7HNiYYkXFvnY0fHNN9/orrvu0sGDB1W2bFl17txZo0ePzrG3qLQ7/+/sPAdOzeGVxWIAAAAAAAAAAm/79u365ZdfVLFiRTVo0MCVHW6fwQvHx4ket+cD2ZG999CAqz3wwAMyxqh8+fJq2rSpmjVrluZH06ZNVa5cuUDvLoA8bv/+/Zo4caJ2795Nfjq++eYbVahQQVdeeaUqVKigRx55RKdOnXJVh9tnGDhwoA4cOCDpr5+w7NChgzp16qSjR4+6Il9y5hzYPYfdM8TGxuZYViDyneoAAAAAAC9w+zUjJ65JAUBe8OabbyoyMlJ33nmnnnvuOd1yyy3atGmTqzrcPoMXjo8TPW7Oj4uL08cff6yYmJgcyQtEhxMzIBMGeVZycrK5/vrrTXJycpa279y5s817BADpq169uvH5fObqq68mPx3XXnutsSzL+Hw+k5ycbFauXGluvvlmc+TIEdd0uH2GmjVrGsuyTMmSJf2P/frrr6Z///7ZznYi3xhnzoHdc9g5Q//+/Y3P5zM9e/bMgT11Pt+pDgAAAADwCrdfM3LimhQA5AWVKlXyX3M0xphjx46Z1q1bm40bN7qmw+0zeOH4ONHj5vzGjRsbn89nGjZsmO2sQHU4MQMyxp2g8jDLsjRu3DhZlpXhdufOndPJkyf173//26E9A4DUkpOTZYzRhQsXyE9HSnbx4sVlWZZuvvlmTZo0SSNHjnRNh9tnmD17th566CF98MEH/sciIyM1derUbGc7kS85cw7snsPOGebPny9jjD7++ONsZwUi36kOAAAAAPAKt18zcuKaFADkBZUqVZIkValSRZJUsmRJzZs3T//5z39c0+H2GbxwfJzocXP+/v37ZYzxv5OEHezucGIGZMwyxphA7wRyt7i4ONWqVUv/+c9/dN111wV6dwDkUXv27NGiRYvUvn17XX311eSn4aefftL777+v1q1bq127dv7HExMTlS9fPld0eGEGt/PC8bFzhmnTpmnKlCnq37+/Hn744ezuquP5TnUAAAAAgFe4/ZqRE9ekACAvOHXqlL788ktdd911Kl++vCs73D6DF46PEz1uzt+wYYNmzpyp7t27q2nTpjma7VSHEzMgYyyCgpKTk3X06FGdP39e//zfISkpSVu3blW3bt1UrVo1bdy4MUB7CSAv2LVrl3/lOPnIbfbt26cTJ07o9OnTKlCggEqWLKkKFSq4Jt8pXpkDAAAAAIC/2717typXrkw+AAAAkIuxCCqPGzFihKZMmaKzZ89muJ0xRkWKFNHJkycd2jMAeVH37t01b9488tPgxMISLyzyycmOkydPasaMGVq8eLF+/PFHnT59OtU2BQoUUKNGjdSuXTv16tVLV1xxRa7JT4sd58DpOVhkBQAAAABw2rlz5xQREaHY2FjyAcBDzpw5o19++UXbtm1Ldc2xRo0aqlOnjkJDQ3N1h9tn8MLx8cIcTh2nrDp37pztfXZ3ODED0sYiqDzs1Vdf1WOPPZalbYsWLaoXXnhBAwcOtHmvAHjV+fPn03w8JCTE/+syZcrozjvvVK1atS56PEWDBg0UGRnpyfx/cmJhiRcW+djVMXnyZI0aNUonT55MdZfEtFiWpZCQEA0bNkyjRo3K9C3X7M5PYfc5cGKOQCwWe+qppzR27NhsZQQy36kOAAAAAMgLYmJiNGDAAC1atEhJSUnkA4AHrF69WhMnTtQXX3yR4Y0i8ufPrxYtWmjAgAHq0KFDrupw+wxeOD5O9Lg9/3IkJycrIiJCR48edW2HEzMgfSyCysNq1aql6tWra/jw4SpdurQ2bdqkX375Rffee69/mz///FMvvPCCnnrqKV1//fUB3FsAble0aFHFx8f7fx8UFKTbb79dixYt8j9WunRpHTlyJN2MyMhI/fzzz7Isy3P5f+fEwhIvLPKxq6N///567733JEmVK1dW/fr1VbFiRZUqVUoFCxZUaGioEhMTdebMGR05ckTR0dHauHGjtm3bJsuy1L59ey1evDjd82x3vt3Hx8k5nFos9k81a9bU3LlzVatWLQUHB19WRiDzneoAAAAAAC/bvXu3pkyZonfffVfx8fGyLCtHFxG5PR8A3CguLk69e/fWxx9/nKXrjSksy1LTpk01e/bsTO9Kb3eH22fwwvFxosft+ZcrKSlJw4YN0+TJk237vMXuDidmQMZYBJWHlS1bVnv37r3oG4S9e/fWjBkzLtru0KFDqlOnjjZs2KCKFSs6u5MAPGPChAkaPny4JKlLly566aWXVKVKlYu2KV26tA4fPpxuhmVZ+uKLL3TLLbd4Lj+FEwtLvLDIx66ODz74QP369dODDz6owYMHq1q1aunuwz9FR0drypQpmjRpkqZOnap+/fql2sbu/BR2nwMn5rBrhrvvvjtVV2hoqKZPn+7/fWYLGnv06KGZM2em+Zzd+U51AAAAAEBe9eWXX2rSpElatmyZ/5uCxpgcW0Tk9nwAcKszZ87oxhtv1MaNG1WmTBm1bt06y9ccly9frr1796p69er6v//7PxUtWjQgHW6fwQvHxwtzOHWcLkV8fLzef/99vfbaa4qOjrbl8xa7O5yYAVlkkGfdeOONqR578803zdKlS1M9ftVVV5kePXo4sVsAPGrKlCnGsiwzadKkdLcpU6aMef75583y5cvNqlWrLvpYsWKFqVu3rnnkkUc8mW+MMTNmzDDBwcFm8ODBZvv27elul5a9e/eaxx57zISEhJh33nknYB1un6FRo0Zm3rx5l5T5T4sWLTLXXXddms/ZnW+MM+fA7jnsnKFnz57Gsizj8/lM0aJFzbPPPmuOHDly0TalSpUylmWl+xEUFGR27dqVZr/d+U51AAAAAEBeEh8fb6ZMmWJq1KhhfD6f8fl8/q+f8uXLZ4oVK2Z8Pl+ezQcALxg1apQpVaqUWbhw4WW9funSpaZcuXLmmWeeCViH22fwwvFxosft+Zfi999/N4MGDTJhYWEXfQ6Tk5+32N3hxAy4NCyCysNuvPFG8+233xpjjElKSjLGGHP69GlTr149s23bNv920dHRJiQkxJQsWTIg+wnAG/r27Wvuu+++DLfp1KlThs+vXbvW3HTTTZ7MN8aZBTJeWORjZ0ft2rWzlZuiXr16aT5ud74xzpwDu+ewc4bp06cby7JMs2bNzKFDh9J8balSpUyNGjVM9+7dTe/evS/6uOuuu0yBAgXMyy+/nOZr7c53qgMAAAAA8oKdO3eaIUOGmKJFi6ZaPNSwYUMzc+ZMk5CQYBISEkzRokXzXD4AeEnNmjXNli1bspWxdevWDK+N2t3h9hm8cHyc6HF7flYsXbrUtGrVygQFBV30+UvKR04sILK7w4kZcHmCA30nKgRO7969FRUVpdDQUBUpUkSbNm1SqVKl1KdPHzVo0EBt2rRR0aJFtXTpUiUmJmb4tkQAkJnNmzdr4cKFGW7TuXPnDJ+PiorS8ePHPZkvSWfPnlW3bt0yzMhMp06dNGbMmIB1uH2GpKQk/y3iL1dycnK6tzi1O19y5hzYPYedM2zZskW1atXSsmXLVLBgwTRfW6FCBX377bfy+XxpPv/WW29p+fLl/rfIdDLfqQ4AAAAA8LIVK1Zo0qRJWrFihcxfPywuSbIsSyVKlNAnn3yipk2bXvSarl275pl8APCikJAQ1axZM1sZNWrUUEhISMA63D6DF46PEz1uz09PXFyc3nvvPU2dOlW7d++WJP/nMKVLl9YDDzygbt26qVChQqpevfpl7ZfdHU7MgOxL+7siyBN69eqlW265xf9enocPH5YkDRo0SLfccosWLlyo6dOn68iRI5L++mYiAFyu5ORkVahQIcNt7rnnnkxzChcu7Ml86X8LS7IjswUydne4fYbKlStr5MiR2coeNWqUypQpk+ZzdudLzpwDu+ewc4ZNmzZpwoQJ6S4ekqTrr78+3cVD0l9/1vfs2ZPmc3bnO9UBAAAAAF4THx+vKVOmqEaNGmrbtq2WL1+u5ORkGWNUqlQpPfHEE9qxY4eqVauWagGRJL377ruezgcArzt79qzi4uKylXHq1CmdO3cuYB1un8ELx8eJHrfn/9O2bdv00EMPqWzZsho2bJh2797tX8RduHBhzZo1S9HR0Xr66ad19dVXq2zZsoqKirqk/bG7w4kZkHNYBJWHBQUFadmyZVq+fLnWrl2rOnXqSPrrp0UWLVqkcePGqUGDBqpbt64ee+wxvf766wHeYwBultE34y/FqVOnPJkvObNAxguLfOzsePzxx/Xyyy/ruuuu04wZM/Tnn39mKe/48eOaO3euoqKiNHbsWD366KNpbmd3vuTMObB7DjtnOHz4sG699dYMX/vKK69k+HzhwoUVGhqa5nN25zvVAQAAAABesWPHDg0ZMkTlypXTkCFDtH37dv/djVu3bq1FixZp//79Gjt2rCpXrnzJdz12ez4A5BVRUVHq2rWrjh49elmvP3HihLp3764GDRoErMPtM3jh+DjR4/b8FEuXLtVtt92myMhIvfXWW4qPj5cxRiEhIerevbtWrlypyMhI9ejRQ8HBF7+B2fLly7O0L3Z3ODEDbGD7G+7BM2655ZZA7wIAF6tevXq2M86fP28qV67syXxjjFmzZo0JCgoyjRo1MtOnTzd//PFHlnKPHTtm5syZY5o2bWp8Pp/54osvAtbhhRneeustky9fPuPz+YzP5zOlSpUyN9xwg/nXv/5l7r77btO7d2/To0cP07lzZxMVFWXKly/v39ayLPPcc89luB925ztxDuyew84Zrr/++ixlZaZBgwZpPm53vlMdAAAAAOAV9erVM5ZlGcuyjM/nMxUqVDDPPvusiY6OTnP7G264IU/lA0BesXfvXlOsWDFTqFAh06tXL/Phhx+abdu2mXPnzqW5fWJiotm1a5dZsGCB6d+/vwkLCzOFCxc227ZtC1iH22fwwvHxwhx25sfGxppXX33VVKlS5aLvB1iWZWrWrGlee+01c/z4cf/2l/N5i90dTswAe1nGZPO9RpAnHDhwQFWrVtXZs2cDvSsAXOqaa67RggULFBkZedkZy5cv19NPP60NGzZ4Lj/FtGnTNHjwYP/beF155ZWqVKmSIiIiVLBgQYWEhCgxMdH/VqbR0dE6ePCgpL/ed/jZZ5/VM888k+F+2N3hhRm+/vprDRkyRL/++qv/sbR+kvLvn0bVqFFD48ePV7t27TLcdyfynTgHds9h1wy1atW6aH8vV7Vq1bRjxw7H853qAAAAAAAv+eabb/Tmm29q4cKFqlmzpvr3768ePXqoSJEiqbZt0qSJ1q9fn6fyASCv2LBhgzp16qRDhw5ddB0zLCws1TXH2NhY//PGGIWFhemjjz5Sq1atAtrh9hm8cHy8MIdd+ZUrV9bevXv9vy9YsKC6du2q/v37p/l2vZfzeYvdHU7MAJs5u+YKbhQdHW1q165tfD5foHcFgIv17dvX3H333dnKuOGGG8xjjz3myfy/++qrr0zt2rX9K8tTftLvnx9/f/6aa64xS5cuzfK+2N3hhRlSOkaMGGFuuukmU6FCBRMWFmaCg4NNkSJFTKVKlUzLli3N008/bdavX5/lTKfynTg+ds9hxwyVKlUyJ06cuOQZ/27r1q3p3h3O7nynOgAAAADAi44ePWrGjRtnKleubAoXLmx69+5tvvnmm4u2yc5P87s9HwDygsOHD5tBgwaZIkWKXHRdMb2PsLAw8+CDD5pDhw7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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "df = visualize_one_experiment(paths=[f'reranker/unfiltered/seed_{i}/learned/no_eos_accum/results.pickle' for i in range(1)], \n", - " suffix=\"no_eos_accum\", \n", - " save=False,\n", - " visualize=True)" + "baseline_res" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "reranker/unfiltered/seed_0/learned/no_eos_no_accum/results.pickle\n" - ] - } - ], + "outputs": [], "source": [ - "dfs = {}\n", - "for eos in (\"no_eos\", ):\n", - " for subset in (\"learned\", ):\n", - " for accum in (\"no_accum\", \"accum\"):\n", - " try:\n", - " suffix=f\"{eos}+{subset}+{accum}\"\n", - " dfs[suffix] = visualize_one_experiment(paths=[f'reranker/unfiltered/seed_{i}/{subset}/{eos}_{accum}/results.pickle'\n", - " for i in range(1)], \n", - " suffix=suffix, \n", - " save=False,\n", - " visualize=False)\n", - " except FileNotFoundError:\n", - " print(f'reranker/unfiltered/seed_0/{subset}/{eos}_{accum}/results.pickle')" + "tracin_res" ] }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "for (k, v) in dfs.items():\n", - " eos, subset, accum = k.split('+')\n", - " v['eos'] = eos\n", - " v['subset'] = subset\n", - " v['accum'] = accum\n", - " " + "def process(model_res):\n", + " model_res['text'] = '\\\\textbf{Q:} ' + model_res['inputs_pretokenized'].str.replace('', '[MASK]') + ' \\\\newline\\\\textbf{A:} ' + model_res['targets_pretokenized'].str.replace('', '') + ' \\\\textbf{' + model_res['label'].astype(str) + '}'\n", + " return model_res.head(3) " ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "dfmerged = pd.concat(dfs.values(), ignore_index=False)" + "print(\"\\\\\\\\ \\n \\\\midrule \\n\".join(process(tracin_res)['text'] + '&' + process(embed_res)['text'] + '&' + process(baseline_res)['text'] ) + ' \\\\ \\n')\n" ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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layersnorm_typenormalizationevalmetricskscorelayer_typeseedeossubsetaccum
0BM25+localcosinecollapseprecision10.685baselines0no_eoslearnedaccum
1BM25+localcosinecollapseprecision30.400baselines0no_eoslearnedaccum
2BM25+localcosinecollapseprecision50.313baselines0no_eoslearnedaccum
3BM25+localcosinecollapseprecision100.237baselines0no_eoslearnedaccum
4BM25+localcosinecollapseprecision250.150baselines0no_eoslearnedaccum
\n", - "
" - ], - "text/plain": [ - " layers norm_type normalization eval metrics k score layer_type \\\n", - "0 BM25+ local cosine collapse precision 1 0.685 baselines \n", - "1 BM25+ local cosine collapse precision 3 0.400 baselines \n", - "2 BM25+ local cosine collapse precision 5 0.313 baselines \n", - "3 BM25+ local cosine collapse precision 10 0.237 baselines \n", - "4 BM25+ local cosine collapse precision 25 0.150 baselines \n", - "\n", - " seed eos subset accum \n", - "0 0 no_eos learned accum \n", - "1 0 no_eos learned accum \n", - "2 0 no_eos learned accum \n", - "3 0 no_eos learned accum \n", - "4 0 no_eos learned accum " - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "dfmerged.head()" + "\n", + "\n", + "print(\"Model Table\")\n", + "display(model_res)\n", + "print(\"Baseline Table\")\n", + "display(baseline_res)" ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed'])\\\n", - " .agg({'score': ['mean', 'std']}, as_index=False)\n", - "scores = scores.reset_index()\n", - " " + "model_res, baseline_res = getter(i=3, layers='activations.encoder.block.0,activations.decoder.block.0')\n", + "print(\"Model Table\")\n", + "display(model_res)\n", + "print(\"Baseline Table\")\n", + "display(baseline_res)" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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layersnorm_typenormalizationevalmetricskscorelayer_typeeossubsetaccumscore
meanstd
0A.globalcosinecollapsemrr10.135303Embedno_eoslearnedaccum0.135303NaN
1A.globalcosinecollapseprecision10.075000Embedno_eoslearnedaccum0.075000NaN
2A.globalcosinecollapseprecision100.071500Embedno_eoslearnedaccum0.071500NaN
3A.globalcosinecollapseprecision250.066800Embedno_eoslearnedaccum0.066800NaN
4A.globalcosinecollapseprecision30.073333Embedno_eoslearnedaccum0.073333NaN
\n", - "
" - ], - "text/plain": [ - " layers norm_type normalization eval metrics k score \\\n", - " \n", - "0 A. global cosine collapse mrr 1 0.135303 \n", - "1 A. global cosine collapse precision 1 0.075000 \n", - "2 A. global cosine collapse precision 10 0.071500 \n", - "3 A. global cosine collapse precision 25 0.066800 \n", - "4 A. global cosine collapse precision 3 0.073333 \n", - "\n", - " layer_type eos subset accum score \n", - " mean std \n", - "0 Embed no_eos learned accum 0.135303 NaN \n", - "1 Embed no_eos learned accum 0.075000 NaN \n", - "2 Embed no_eos learned accum 0.071500 NaN \n", - "3 Embed no_eos learned accum 0.066800 NaN \n", - "4 Embed no_eos learned accum 0.073333 NaN " - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "scores.head()" + "df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_v2/seed_{i}/learned/no_eos_accum/results_detailed.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=\"no_eos_accum\",\n", + " save=True,\n", + " visualize=True,\n", + ")\n" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfs = {}\n", + "for eos in (\"no_eos\", ):\n", + " for subset in (\"learned\",):\n", + " for accum in (\"no_accum\", \"accum\"):\n", + " try:\n", + " suffix = f\"{eos}+{subset}+{accum}\"\n", + " df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_v2/seed_{i}/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=suffix,\n", + " save=False,\n", + " visualize=False,\n", + " )\n", + " df[\"eos\"] = eos\n", + " df[\"subset\"] = subset\n", + " df[\"accum\"] = accum\n", + " dfs[suffix] = df\n", + " except FileNotFoundError:\n", + " print(\n", + " \"Couldn't find: \"\n", + " f\"reranker/sweep_v2/seed_0/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfmerged = pd.concat(list(dfs.values()), ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed' and column != 'score'], axis=0).agg({'score': [np.mean, np.std]}, as_index=False)\n", + "scores = scores.reset_index()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1087,1421 +565,39 @@ " if no_prefix:\n", " df2 = df2[~df2['layers'].str.contains(no_prefix)]\n", " \n", - " nlargest = df2.nlargest(20, ('score', 'mean'))\n", + " nlargest = df2.nlargest(10, ('score', 'mean'))\n", " \n", " largest = nlargest.iloc[0] \n", " dflargest = df[(df['layers'] == largest['layers'][0]) &\n", " (df['norm_type'] == largest['norm_type'][0]) &\n", " (df['eos'] == largest['eos'][0]) &\n", " (df['accum'] == largest['accum'][0]) &\n", - " (df['subset'] == subset) &\n", - " (df['eval'] == eval_type)]\n", - " \n", + " (df['subset'] == subset)]# &\n", + "# (df['eval'] == eval_type)]\n", + "\n", + " dflargest = dflargest.set_index('metrics').loc[['mrr', 'mrr_compare_fn_relation', 'mrr_compare_fn_subject', 'mrr_compare_fn_object', 'recall']]\n", + " dflargest['score_text'] = (100 * dflargest['score']['mean']).apply(lambda x: f\"{x:.2f}\") + '\\stderr{' + (100 * dflargest['score']['std']).apply(lambda x: f\"{x:.2f}\") + '}'\n", + " dflargest = dflargest.transpose()\n", + " dflargest = dflargest.loc[['score_text', 'k', 'eval']]\n", " return nlargest, dflargest\n" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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layersnorm_typenormalizationevalmetricskscorelayer_typeeossubsetaccumscore
meanstd
462A.E.0,A.D.0,A.E.12,A.D.12globalcosinecollapsemrr10.483215Embedno_eoslearnedaccum0.483215NaN
198A.E.0,A.D.0globalcosinecollapsemrr10.482083Embedno_eoslearnedaccum0.482083NaN
1320A.E.0,A.E.12,A.D.12globalcosinecollapsemrr10.438417Embedno_eoslearnedaccum0.438417NaN
1276A.E.0,A.E.11,A.D.11localcosinecollapsemrr10.414970Embedno_eoslearnedaccum0.414970NaN
220A.E.0,A.D.0localcosinecollapsemrr10.410640Embedno_eoslearnedaccum0.410640NaN
418A.E.0,A.D.0,A.E.11,A.D.11localcosinecollapsemrr10.409535Embedno_eoslearnedaccum0.409535NaN
352A.E.0,A.D.0,A.E.10,A.D.10localcosinecollapsemrr10.408346Embedno_eoslearnedaccum0.408346NaN
1210A.E.0,A.E.10,A.D.10localcosinecollapsemrr10.408168Embedno_eoslearnedaccum0.408168NaN
946A.E.0,A.D.0,A.E.8,A.D.8localcosinecollapsemrr10.408124Embedno_eoslearnedaccum0.408124NaN
1012A.E.0,A.D.0,A.E.9,A.D.9localcosinecollapsemrr10.407944Embedno_eoslearnedaccum0.407944NaN
814A.E.0,A.D.0,A.E.6,A.D.6localcosinecollapsemrr10.407706Embedno_eoslearnedaccum0.407706NaN
880A.E.0,A.D.0,A.E.7,A.D.7localcosinecollapsemrr10.407706Embedno_eoslearnedaccum0.407706NaN
748A.E.0,A.D.0,A.E.5,A.D.5localcosinecollapsemrr10.407539Embedno_eoslearnedaccum0.407539NaN
1870A.E.0,A.E.9,A.D.9localcosinecollapsemrr10.403654Embedno_eoslearnedaccum0.403654NaN
1804A.E.0,A.E.8,A.D.8localcosinecollapsemrr10.402755Embedno_eoslearnedaccum0.402755NaN
132A.E.0globalcosinecollapsemrr10.399869Embedno_eoslearnedaccum0.399869NaN
154A.E.0localcosinecollapsemrr10.399869Embedno_eoslearnedaccum0.399869NaN
1738A.E.0,A.E.7,A.D.7localcosinecollapsemrr10.398298Embedno_eoslearnedaccum0.398298NaN
1672A.E.0,A.E.6,A.D.6localcosinecollapsemrr10.398225Embedno_eoslearnedaccum0.398225NaN
1606A.E.0,A.E.5,A.D.5localcosinecollapsemrr10.398054Embedno_eoslearnedaccum0.398054NaN
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" - ], - "text/plain": [ - " layers norm_type normalization eval metrics k \\\n", - " \n", - "462 A.E.0,A.D.0,A.E.12,A.D.12 global cosine collapse mrr 1 \n", - "198 A.E.0,A.D.0 global cosine collapse mrr 1 \n", - "1320 A.E.0,A.E.12,A.D.12 global cosine collapse mrr 1 \n", - "1276 A.E.0,A.E.11,A.D.11 local cosine collapse mrr 1 \n", - "220 A.E.0,A.D.0 local cosine collapse mrr 1 \n", - "418 A.E.0,A.D.0,A.E.11,A.D.11 local cosine collapse mrr 1 \n", - "352 A.E.0,A.D.0,A.E.10,A.D.10 local cosine collapse mrr 1 \n", - "1210 A.E.0,A.E.10,A.D.10 local cosine collapse mrr 1 \n", - "946 A.E.0,A.D.0,A.E.8,A.D.8 local cosine collapse mrr 1 \n", - "1012 A.E.0,A.D.0,A.E.9,A.D.9 local cosine collapse mrr 1 \n", - "814 A.E.0,A.D.0,A.E.6,A.D.6 local cosine collapse mrr 1 \n", - "880 A.E.0,A.D.0,A.E.7,A.D.7 local cosine collapse mrr 1 \n", - "748 A.E.0,A.D.0,A.E.5,A.D.5 local cosine collapse mrr 1 \n", - "1870 A.E.0,A.E.9,A.D.9 local cosine collapse mrr 1 \n", - "1804 A.E.0,A.E.8,A.D.8 local cosine collapse mrr 1 \n", - "132 A.E.0 global cosine collapse mrr 1 \n", - "154 A.E.0 local cosine collapse mrr 1 \n", - "1738 A.E.0,A.E.7,A.D.7 local cosine collapse mrr 1 \n", - "1672 A.E.0,A.E.6,A.D.6 local cosine collapse mrr 1 \n", - "1606 A.E.0,A.E.5,A.D.5 local cosine collapse mrr 1 \n", - "\n", - " score layer_type eos subset accum score \n", - " mean std \n", - "462 0.483215 Embed no_eos learned accum 0.483215 NaN \n", - "198 0.482083 Embed no_eos learned accum 0.482083 NaN \n", - "1320 0.438417 Embed no_eos learned accum 0.438417 NaN \n", - "1276 0.414970 Embed no_eos learned accum 0.414970 NaN \n", - "220 0.410640 Embed no_eos learned accum 0.410640 NaN \n", - "418 0.409535 Embed no_eos learned accum 0.409535 NaN \n", - "352 0.408346 Embed no_eos learned accum 0.408346 NaN \n", - "1210 0.408168 Embed no_eos learned accum 0.408168 NaN \n", - "946 0.408124 Embed no_eos learned accum 0.408124 NaN \n", - "1012 0.407944 Embed no_eos learned accum 0.407944 NaN \n", - "814 0.407706 Embed no_eos learned accum 0.407706 NaN \n", - "880 0.407706 Embed no_eos learned accum 0.407706 NaN \n", - "748 0.407539 Embed no_eos learned accum 0.407539 NaN \n", - "1870 0.403654 Embed no_eos learned accum 0.403654 NaN \n", - "1804 0.402755 Embed no_eos learned accum 0.402755 NaN \n", - "132 0.399869 Embed no_eos learned accum 0.399869 NaN \n", - "154 0.399869 Embed no_eos learned accum 0.399869 NaN \n", - "1738 0.398298 Embed no_eos learned accum 0.398298 NaN \n", - "1672 0.398225 Embed no_eos learned accum 0.398225 NaN \n", - "1606 0.398054 Embed no_eos learned accum 0.398054 NaN " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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layersnorm_typenormalizationevalmetricskscorelayer_typeeossubsetaccumscore
meanstd
462A.E.0,A.D.0,A.E.12,A.D.12globalcosinecollapsemrr10.483215Embedno_eoslearnedaccum0.483215NaN
463A.E.0,A.D.0,A.E.12,A.D.12globalcosinecollapseprecision10.380000Embedno_eoslearnedaccum0.380000NaN
464A.E.0,A.D.0,A.E.12,A.D.12globalcosinecollapseprecision100.200000Embedno_eoslearnedaccum0.200000NaN
465A.E.0,A.D.0,A.E.12,A.D.12globalcosinecollapseprecision250.155400Embedno_eoslearnedaccum0.155400NaN
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198A.E.0,A.D.0globalcosinecollapsemrr10.482083Embedno_eoslearnedaccum0.482083NaN
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220A.E.0,A.D.0localcosinecollapsemrr10.410640Embedno_eoslearnedaccum0.410640NaN
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3740G.0localcosinecollapsemrr10.495679TracInno_eoslearnedaccum0.495679NaN
3806G.0,G.E.1localcosinecollapsemrr10.408880TracInno_eoslearnedaccum0.408880NaN
3784G.0,G.E.1globalcosinecollapsemrr10.405805TracInno_eoslearnedaccum0.405805NaN
5368G.E.1globalcosinecollapsemrr10.397481TracInno_eoslearnedaccum0.397481NaN
5390G.E.1localcosinecollapsemrr10.397024TracInno_eoslearnedaccum0.397024NaN
4510G.0,G.E.3,G.D.3globalcosinecollapsemrr10.380770TracInno_eoslearnedaccum0.380770NaN
4466G.0,G.E.3localcosinecollapsemrr10.379771TracInno_eoslearnedaccum0.379771NaN
3872G.0,G.E.1,G.D.1localcosinecollapsemrr10.375809TracInno_eoslearnedaccum0.375809NaN
6094G.E.3,G.D.3globalcosinecollapsemrr10.374713TracInno_eoslearnedaccum0.374713NaN
5060G.0,G.E.7,G.D.7localcosinecollapsemrr10.371436TracInno_eoslearnedaccum0.371436NaN
4114G.0,G.E.11,G.D.11globalcosinecollapsemrr10.371338TracInno_eoslearnedaccum0.371338NaN
5324G.0,G.E.9,G.D.9localcosinecollapsemrr10.369039TracInno_eoslearnedaccum0.369039NaN
4400G.0,G.E.2,G.D.2localcosinecollapsemrr10.365802TracInno_eoslearnedaccum0.365802NaN
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5984G.E.2,G.D.2localcosinecollapsemrr10.361023TracInno_eoslearnedaccum0.361023NaN
5456G.E.1,G.D.1localcosinecollapsemrr10.360186TracInno_eoslearnedaccum0.360186NaN
4136G.0,G.E.11,G.D.11localcosinecollapsemrr10.359189TracInno_eoslearnedaccum0.359189NaN
4268G.0,G.E.12,G.D.12localcosinecollapsemrr10.356478TracInno_eoslearnedaccum0.356478NaN
5192G.0,G.E.8,G.D.8localcosinecollapsemrr10.355302TracInno_eoslearnedaccum0.355302NaN
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3723G.0globalcosinecollapseprecision50.242000TracInno_eoslearnedaccum0.242000NaN
3724G.0globalcosinecollapserecall10.161981TracInno_eoslearnedaccum0.161981NaN
3725G.0globalcosinecollapserecall100.359431TracInno_eoslearnedaccum0.359431NaN
3726G.0globalcosinecollapserecall250.476332TracInno_eoslearnedaccum0.476332NaN
3727G.0globalcosinecollapserecall30.261617TracInno_eoslearnedaccum0.261617NaN
3728G.0globalcosinecollapserecall50.307707TracInno_eoslearnedaccum0.307707NaN
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\n", - "
" - ], - "text/plain": [ - " layers norm_type normalization eval metrics k \\\n", - " \n", - "6952 Random-Target global cosine collapse mrr 1 \n", - "6953 Random-Target global cosine collapse precision 1 \n", - "6954 Random-Target global cosine collapse precision 10 \n", - "6955 Random-Target global cosine collapse precision 25 \n", - "6956 Random-Target global cosine collapse precision 3 \n", - "6957 Random-Target global cosine collapse precision 5 \n", - "6958 Random-Target global cosine collapse recall 1 \n", - "6959 Random-Target global cosine collapse recall 10 \n", - "6960 Random-Target global cosine collapse recall 25 \n", - "6961 Random-Target global cosine collapse recall 3 \n", - "6962 Random-Target global cosine collapse recall 5 \n", - "6974 Random-Target global dot collapse mrr 1 \n", - "6975 Random-Target global dot collapse precision 1 \n", - "6976 Random-Target global dot collapse precision 10 \n", - "6977 Random-Target global dot collapse precision 25 \n", - "6978 Random-Target global dot collapse precision 3 \n", - "6979 Random-Target global dot collapse precision 5 \n", - "6980 Random-Target global dot collapse recall 1 \n", - "6981 Random-Target global dot collapse recall 10 \n", - "6982 Random-Target global dot collapse recall 25 \n", - "6983 Random-Target global dot collapse recall 3 \n", - "6984 Random-Target global dot collapse recall 5 \n", - "\n", - " score layer_type eos subset accum score \n", - " mean std \n", - "6952 0.196824 baselines no_eos learned accum 0.196824 NaN \n", - "6953 0.120000 baselines no_eos learned accum 0.120000 NaN \n", - "6954 0.096500 baselines no_eos learned accum 0.096500 NaN \n", - "6955 0.096000 baselines no_eos learned accum 0.096000 NaN \n", - "6956 0.116667 baselines no_eos learned accum 0.116667 NaN \n", - "6957 0.111000 baselines no_eos learned accum 0.111000 NaN \n", - "6958 0.013657 baselines no_eos learned accum 0.013657 NaN \n", - "6959 0.069997 baselines no_eos learned accum 0.069997 NaN \n", - "6960 0.198426 baselines no_eos learned accum 0.198426 NaN \n", - "6961 0.031641 baselines no_eos learned accum 0.031641 NaN \n", - "6962 0.045547 baselines no_eos learned accum 0.045547 NaN \n", - "6974 0.196824 baselines no_eos learned accum 0.196824 NaN \n", - "6975 0.120000 baselines no_eos learned accum 0.120000 NaN \n", - "6976 0.096500 baselines no_eos learned accum 0.096500 NaN \n", - "6977 0.096000 baselines no_eos learned accum 0.096000 NaN \n", - "6978 0.116667 baselines no_eos learned accum 0.116667 NaN \n", - "6979 0.111000 baselines no_eos learned accum 0.111000 NaN \n", - "6980 0.013657 baselines no_eos learned accum 0.013657 NaN \n", - "6981 0.069997 baselines no_eos learned accum 0.069997 NaN \n", - "6982 0.198426 baselines no_eos learned accum 0.198426 NaN \n", - "6983 0.031641 baselines no_eos learned accum 0.031641 NaN \n", - "6984 0.045547 baselines no_eos learned accum 0.045547 NaN " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "largest, dflargest = get_max(scores, 'Random-Target')\n", "display(largest)\n", @@ -4442,57 +639,107 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# largest, dflargest = get_max(scores, 'A', no_prefix='G', subset='random')\n", - "# display(largest)\n", - "# display(dflargest)" + "dfs = {}\n", + "for eos in (\"no_eos\", \"eos\"):\n", + " for subset in (\"learned\",):\n", + " for accum in (\"no_accum\", \"accum\"):\n", + " try:\n", + " suffix = f\"{eos}+{subset}+{accum}\"\n", + " df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_v2/seed_{i}/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=suffix,\n", + " save=False,\n", + " visualize=False,\n", + " )\n", + " df[\"eos\"] = eos\n", + " df[\"subset\"] = subset\n", + " df[\"accum\"] = accum\n", + " dfs[suffix] = df\n", + " except FileNotFoundError:\n", + " print(\n", + " \"Couldn't find: \"\n", + " f\"reranker/sweep_v2/seed_0/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# largest, dflargest = get_max(scores, 'A', subset='random')\n", - "# display(largest)\n", - "# display(dflargest)" + "dfmerged = pd.concat(list(dfs.values()), ignore_index=True)\n", + "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed' and column != 'score'], axis=0).agg({'score': [np.mean, np.std]}, as_index=False)\n", + "scores = scores.reset_index()\n", + "df = scores" ] }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# largest, dflargest = get_max(scores, 'G', subset='random')\n", - "# display(largest)\n", - "# display(dflargest)" + "dflargest = df[(df['layers'] == 'G.0') &\n", + " (df['subset'] == 'learned') &\n", + " ((df['metrics'] == 'mrr')) &\n", + " (df['eval'] == 'collapse')]\n", + "dflargest['score_text'] = (100 * dflargest['score']['mean']).apply(lambda x: f\"{x:.2f}\") + '\\stderr{' + (100 * dflargest['score']['std']).apply(lambda x: f\"{x:.2f}\") + '}'\n", + "dflargest" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# largest, dflargest = get_max(scores, 'BM25+', subset='random')\n", - "# display(largest)\n", - "# display(dflargest)" + "dflargest = df[(df['layers'] == 'G.0') &\n", + " (df['subset'] == 'learned') &\n", + " ((df['metrics'] == 'recall')) &\n", + " ((df['k'] == '10')) &\n", + " (df['eval'] == 'collapse')]\n", + "dflargest['score_text'] = (100 * dflargest['score']['mean']).apply(lambda x: f\"{x:.2f}\") + '\\stderr{' + (100 * dflargest['score']['std']).apply(lambda x: f\"{x:.2f}\") + '}'\n", + "dflargest" ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# largest, dflargest = get_max(scores, 'Random-Target', subset='random')\n", - "# display(largest)\n", - "# display(dflargest)" + "dfs = {}\n", + "for eos in (\"no_eos\", ):\n", + " for subset in (\"learned\", ):\n", + " for accum in (\"no_accum\", ):\n", + " try:\n", + " suffix = f\"{eos}+{subset}+{accum}\"\n", + " df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_v2_ft_pt/seed_{i}/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=suffix,\n", + " save=False,\n", + " visualize=False,\n", + " )\n", + " df[\"eos\"] = eos\n", + " df[\"subset\"] = subset\n", + " df[\"accum\"] = accum\n", + " dfs[suffix] = df\n", + " except FileNotFoundError:\n", + " print(\n", + " \"Couldn't find: \"\n", + " f\"reranker/sweep_v2/seed_0/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " )" ] }, { @@ -4501,18 +748,9 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "# for norm_type in (\"ln\", \"gn\"):\n", - "# for eos in (\"no_eos\", ):\n", - "# for subset in (\"learned\", ):\n", - "# for accum in (\"accum\", ):\n", - "# try:\n", - "# # visualize_one_experiment(path=f'/reranker/{norm_type}_sl_{eos}__{subset}_{accum}.json', suffix=f\"{norm_type} + {eos} + {subset} + {accum}\", show=False, folder=\"sentence_level_all_plots/\")\n", - "# visualize_one_experiment(path=f'/reranker/{norm_type}_sl_{eos}__{subset}_{accum}.json', \n", - "# suffix=f\"{norm_type} + {eos} + {subset} + {accum}\", \n", - "# show=True)\n", - "# except FileNotFoundError:\n", - "# print(f'notfound: /reranker/{norm_type}_sl_{eos}__{subset}_{accum}.json')" + "dfmerged = pd.concat(list(dfs.values()), ignore_index=True)\n", + "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed' and column != 'score'], axis=0).agg({'score': [np.mean, np.std]}, as_index=False)\n", + "scores = scores.reset_index()" ] }, { @@ -4521,7 +759,9 @@ "metadata": {}, "outputs": [], "source": [ - "# getter = result_getter('/reranker/exp_layers_0/ln_sl_no_eos__learned_no_accum.json')" + "largest, dflargest = get_max(scores, 'G')\n", + "display(largest)\n", + "display(dflargest)" ] }, { @@ -4530,16 +770,9 @@ "metadata": {}, "outputs": [], "source": [ - "# def side_by_side(df1, df2, df3):\n", - "# df = pd.DataFrame(columns=['TracIn','Embed','BM25', ])\n", - "# for dfi in (df1, df2, df3):\n", - "# dfi['sample'] = \"\\textbf{Q:} \" + dfi['inputs_pretokenized'] + \"\\n\\textbf{A:} \" + dfi['targets_pretokenized'].replace(' ','') + \"\\n\\textbf{\" + dfi['label'].astype(str) + \"}\"\n", - "# dfi['sample'] = dfi['sample'].str.replace(\"\", \"[MASK]\")\n", - "# df['TracIn'] = df1['sample']\n", - "# df['Embed'] = df2['sample']\n", - "# df['BM25'] = df3['sample']\n", - "# return df\n", - " " + "largest, dflargest = get_max(scores, 'A', no_prefix='G')\n", + "display(largest)\n", + "display(dflargest)\n" ] }, { @@ -4548,16 +781,31 @@ "metadata": {}, "outputs": [], "source": [ - "# for i in range(40, 45):\n", - "# dfside = side_by_side(getter(layers='gradients.encoder.block.1,gradients.decoder.block.1', i=i),\n", - "# getter(layers='activations.encoder.block.0,activations.decoder.block.0', i=i),\n", - "# getter(layers='bm25plus', i=i)).head(5)\n", - "# # Assuming the variable df contains the relevant DataFrame\n", - "# display(dfside.style.set_properties(**{\n", - "# 'text-align': 'left',\n", - "# 'white-space': 'pre-wrap',\n", - "# }))\n", - "# print(dfside.to_latex(index=False, escape=False))" + "dfs = {}\n", + "for eos in (\"no_eos\", ):\n", + " for subset in (\"learned\", ):\n", + " for accum in (\"accum\", ):\n", + " try:\n", + " suffix = f\"{eos}+{subset}+{accum}\"\n", + " alpha=0.1\n", + " df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_v2/ensemble_bm25/weight_{alpha}/seed_{i}/{subset}/{eos}_{accum}/results_ensemble_arithmetic_reweight_{alpha}_.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=suffix,\n", + " save=False,\n", + " visualize=False,\n", + " )\n", + " df[\"eos\"] = eos\n", + " df[\"subset\"] = subset\n", + " df[\"accum\"] = accum\n", + " dfs[suffix] = df\n", + " except FileNotFoundError:\n", + " print(\n", + " \"Couldn't find: \"\n", + " f\"reranker/sweep_v2/seed_0/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " )" ] }, { @@ -4566,10 +814,9 @@ "metadata": {}, "outputs": [], "source": [ - "# i=21\n", - "# display(getter(layers='gradients.shared', i=i).head(5))\n", - "# display(getter(layers='activations.encoder.block.0,activations.decoder.block.0', i=i).head(5))\n", - "# display(getter(layers='bm25plus', i=i).head(5))" + "dfmerged = pd.concat(list(dfs.values()), ignore_index=True)\n", + "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed' and column != 'score'], axis=0).agg({'score': [np.mean, np.std]}, as_index=False)\n", + "scores = scores.reset_index()" ] }, { @@ -4578,7 +825,9 @@ "metadata": {}, "outputs": [], "source": [ - "# getter2 = result_getter('/reranker/exp_layers_0/ln_sl_no_eos__learned_accum.json')" + "largest, dflargest = get_max(scores, 'G')\n", + "display(largest)\n", + "display(dflargest)" ] }, { @@ -4587,7 +836,31 @@ "metadata": {}, "outputs": [], "source": [ - "# display(getter2(layers='gradients.shared'))" + "dfs = {}\n", + "for eos in (\"no_eos\", ):\n", + " for subset in (\"learned\", ):\n", + " for accum in (\"accum\", ):\n", + " try:\n", + " suffix = f\"{eos}+{subset}+{accum}\"\n", + " alpha=0.1\n", + " df = visualize_one_experiment(\n", + " paths=[\n", + " f\"reranker/sweep_5100/seed_{i}/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " for i in range(3)\n", + " ],\n", + " suffix=suffix,\n", + " save=False,\n", + " visualize=False,\n", + " )\n", + " df[\"eos\"] = eos\n", + " df[\"subset\"] = subset\n", + " df[\"accum\"] = accum\n", + " dfs[suffix] = df\n", + " except FileNotFoundError:\n", + " print(\n", + " \"Couldn't find: \"\n", + " f\"reranker/sweep_v2/seed_0/{subset}/{eos}_{accum}/results_detailed.pickle\"\n", + " )" ] }, { @@ -4596,11 +869,9 @@ "metadata": {}, "outputs": [], "source": [ - "# getter = result_getter('/reranker/exp_layers/gn_sl_eos__learned_accum.json')\n", - "# display(getter(layers='activations.encoder.block.11,activations.decoder.block.11'))\n", - "# display(getter(layers='activations.encoder.block.0,activations.decoder.block.0'))\n", - "# display(getter(layers='gradients.encoder.block.11,gradients.decoder.block.11'))\n", - "# display(getter(layers='gradients.shared'))" + "dfmerged = pd.concat(list(dfs.values()), ignore_index=True)\n", + "scores = dfmerged.groupby([column for column in dfmerged.columns if column != 'seed' and column != 'score'], axis=0).agg({'score': [np.mean, np.std]}, as_index=False)\n", + "scores = scores.reset_index()" ] }, { @@ -4609,11 +880,9 @@ "metadata": {}, "outputs": [], "source": [ - "# getter = result_getter('/reranker/exp_layers/gn_sl_eos__corrects_accum.json')\n", - "# display(getter(layers='activations.encoder.block.11,activations.decoder.block.11'))\n", - "# display(getter(layers='activations.encoder.block.0,activations.decoder.block.0'))\n", - "# display(getter(layers='gradients.encoder.block.11,gradients.decoder.block.11'))\n", - "# display(getter(layers='gradients.shared'))" + "largest, dflargest = get_max(scores, 'G')\n", + "display(largest)\n", + "display(dflargest)" ] }, { @@ -4621,9 +890,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# len(getter.metrics['cosine']['full']['bm25plus']['samples'])" - ] + "source": [] }, { "cell_type": "code", @@ -4634,8 +901,11 @@ } ], "metadata": { + "interpreter": { + "hash": "83ce25b1c5d65648c98919b9641334dfc26d5cfa225e002eb6106bc7a8478051" + }, "kernelspec": { - "display_name": "Python-3", + "display_name": "Python 3.7.6 ('trex': venv)", "language": "python", "name": "python3" }, diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/extract_abstract_hashmap.py b/data/extract_abstract_hashmap.py deleted file mode 100644 index 5c005b4..0000000 --- a/data/extract_abstract_hashmap.py +++ /dev/null @@ -1,52 +0,0 @@ -import json -import os -from absl import app, flags -from src.json_utils import dump_map_to_json - - -FLAGS = flags.FLAGS -flags.DEFINE_string( - "abstract_file", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "output_file", default=None, help="output file path to write hashmap" -) - -flags.mark_flag_as_required("abstract_file") -flags.mark_flag_as_required("output_file") - - -def _get_all_facts_from_abstract(abstract): - """Apply span masking to boundaries.""" - triples = abstract["triples"] - facts = [] - for triple in triples: - _, predicate = os.path.split(triple["predicate"]["uri"]) - _, subject = os.path.split(triple["subject"]["uri"]) - _, object = os.path.split(triple["object"]["uri"]) - if object.startswith("XML") or subject.startswith("XML"): - continue - fact = ",".join((predicate, object, subject)) - facts.append(fact) - return facts - - -def main(argv): - hashmap = {} - with open(FLAGS.abstract_file, "r") as f: - for i, line in enumerate(f): - abstracts = json.loads(line) - for abstract in abstracts: - _, uri = os.path.split(abstract["uri"]) - facts = _get_all_facts_from_abstract(abstract) - for fact in facts: - uris = hashmap.setdefault(fact, set()) - uris.add(uri) - dump_map_to_json(hashmap, FLAGS.output_file) - - return hashmap - - -if __name__ == "__main__": - app.run(main) diff --git a/data/extract_abstracts.py b/data/extract_abstracts.py index cb258c4..f074883 100644 --- a/data/extract_abstracts.py +++ b/data/extract_abstracts.py @@ -2,30 +2,12 @@ import os from os import listdir from absl import app, flags +from tqdm import tqdm from src.json_utils import dump_abstracts_json, dump_map_to_json -from src.tf_utils import dump_abstracts_tf_record +from src.lama_utils import get_sentence FLAGS = flags.FLAGS -flags.DEFINE_string( - "abstract_file", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "input_folder", default=None, help="input file path to convert" -) - -flags.mark_flag_as_required("input_folder") -flags.mark_flag_as_required("abstract_file") - - -# def _get_abstract(record, abstracts, key='obj_uri'): -# uri = record[key] -# if uri in abstracts: -# abstract = abstracts[uri] -# else: -# abstract = next(iter(abstracts.values())) -# return abstract def _mask_an_abstract(abstract): @@ -72,7 +54,7 @@ def _mask_an_abstract(abstract): "masked_uri": masked_uri, "masked_type": "object" if i == 0 else "subject", "fact": fact, - "sentence_uri": abstract_uri + "example_uri": abstract_uri + f"-{sentence_id}-" + object_code + f"-{subject_code}-{i}", @@ -82,7 +64,7 @@ def _mask_an_abstract(abstract): return examples -def _filter_abstracts(abstracts, hashmap, query_file, used_facts): +def _filter_abstracts(abstracts, fact_to_ids, query_file, used_facts): local_abstracts = [] current_facts = set() print(f"processing: {query_file}") @@ -95,8 +77,8 @@ def _filter_abstracts(abstracts, hashmap, query_file, used_facts): fact = ",".join((predicate_id, obj_uri, sub_uri)) if fact not in current_facts: current_facts.add(fact) - ids = hashmap.get(fact, []) - local_abstracts.extend((abstracts[id] for id in ids)) + ids = fact_to_ids.get(fact, []) + local_abstracts += [abstracts[id] for id in ids] used_facts.add(fact) return local_abstracts @@ -105,7 +87,8 @@ def read_all_abstracts(abstract_file): abstracts = [] with open(abstract_file, "r") as f: for i, line in enumerate(f): - abstracts.extend(json.loads(line)) + print(i) + abstracts += json.loads(line) return abstracts @@ -116,55 +99,76 @@ def _mask_all(abstracts): return masked_abstracts -def process_a_file(abstracts, hashmap, fname, used_facts): - abstracts = _filter_abstracts(abstracts, hashmap, fname, used_facts) +def process_a_file(abstracts, fact_to_ids, fname, used_facts): + abstracts = _filter_abstracts(abstracts, fact_to_ids, fname, used_facts) input_folder, fname = os.path.split(fname) output_folder = os.path.join(input_folder, "abstracts/") os.makedirs(output_folder, exist_ok=True) output_json = os.path.join(output_folder, fname) dump_abstracts_json(abstracts, output_json) - output_tf = os.path.join(output_folder, fname.replace("jsonl", "tfrecord")) - dump_abstracts_tf_record(abstracts, output_tf) + # output_tf = os.path.join(output_folder, fname.replace("jsonl", "tfrecord")) + # dump_abstracts_tf_record(abstracts, output_tf) def unionize(abstracts): abstracts_map = {} - for a in abstracts: + sentence_to_facts = {} + + for a in tqdm(abstracts): identifier = ( a["inputs_pretokenized"] + "|||" + a["targets_pretokenized"] ) + if identifier not in abstracts_map: - a["facts"] = a.pop("fact") - a["sentence_uris"] = a.pop("sentence_uri") + a["example_uris"] = a.pop("example_uri") abstracts_map[identifier] = a else: - abstracts_map[identifier]["facts"] += ";" + a.pop("fact") - abstracts_map[identifier]["sentence_uris"] += ";" + a.pop( - "sentence_uri" - ) + new_uri = a.pop("example_uri") + uris = abstracts_map[identifier]["example_uris"] + if new_uri not in uris: + uris += ";" + new_uri + abstracts_map[identifier]["example_uris"] = uris + + sentence_identifier = get_sentence(a) + if sentence_identifier not in sentence_to_facts: + sentence_to_facts[sentence_identifier] = a.pop("fact") + else: + new_fact = a.pop("fact") + facts = sentence_to_facts[sentence_identifier] - return list(abstracts_map.values()) + if new_fact not in facts: + facts += ";" + new_fact + sentence_to_facts[sentence_identifier] = facts + abstracts_map[identifier]["facts"] = sentence_to_facts[ + sentence_identifier + ] + + abstracts = list(abstracts_map.values()) + + return abstracts -def main(argv): - abstracts = read_all_abstracts(FLAGS.abstract_file) - abstracts = unionize(_mask_all(abstracts)) - hashmap = {} +def get_fact_to_ids_map(abstracts): + fact_to_ids = {} for (i, abstract) in enumerate(abstracts): facts = abstract["facts"].split(";") + abstract["id"] = i # ids based on indices for fact in facts: - ids = hashmap.setdefault(fact, []) + ids = fact_to_ids.setdefault(fact, []) ids.append(i) + return fact_to_ids - fact_to_uri = { - fact: [abstracts[id]["sentence_uris"] for id in ids] - for fact, ids in hashmap.items() - } + +def main(argv): + abstracts = read_all_abstracts(FLAGS.abstract_file) + abstracts = unionize(_mask_all(abstracts)) + + fact_to_ids = get_fact_to_ids_map(abstracts) dump_map_to_json( - fact_to_uri, - os.path.join(FLAGS.input_folder, "abstracts", "hashmap.json"), + fact_to_ids, + os.path.join(FLAGS.input_folder, "abstracts", "fact_to_ids.json"), ) used_facts = set([]) @@ -172,45 +176,58 @@ def main(argv): for fname in listdir(FLAGS.input_folder): if fname.endswith("jsonl.processed") and "abstracts" not in fname: file = os.path.join(FLAGS.input_folder, fname) - process_a_file(abstracts, hashmap, file, used_facts) + process_a_file(abstracts, fact_to_ids, file, used_facts) - hashmap_used = {fact: hashmap.get(fact, []) for fact in used_facts} + fact_to_ids_used = {fact: fact_to_ids.get(fact, []) for fact in used_facts} abstract_ids_used = set() - for fact, ids in hashmap_used.items(): + for fact, ids in fact_to_ids_used.items(): for id in ids: abstract_ids_used.add(id) abstracts_used = [abstracts[id] for id in abstract_ids_used] - fact_to_uri_used = { - fact: [abstracts[id]["sentence_uris"] for id in ids] - for fact, ids in hashmap_used.items() - } + # fact_to_uris_used = { + # fact: [abstracts[id]["example_uris"] for id in ids] + # for fact, ids in fact_to_ids_used.items() + # } dump_map_to_json( - fact_to_uri_used, - os.path.join(FLAGS.input_folder, "abstracts", "hashmap_used.json"), + fact_to_ids_used, + os.path.join(FLAGS.input_folder, "abstracts", "fact_to_ids_used.json"), ) output_json = os.path.join( - FLAGS.input_folder, "abstracts", "all_used.jsonl" + FLAGS.input_folder, "abstracts", "all_used_v2.jsonl" ) dump_abstracts_json(abstracts_used, output_json) - output_tf = os.path.join( - FLAGS.input_folder, "abstracts", "all_used.tfrecord" - ) + # output_tf = os.path.join( + # FLAGS.input_folder, "abstracts", "all_used.tfrecord" + # ) - dump_abstracts_tf_record(abstracts_used, output_tf) + # dump_abstracts_tf_record(abstracts_used, output_tf) - output_json = os.path.join(FLAGS.input_folder, "abstracts", "all.jsonl") + output_json = os.path.join(FLAGS.input_folder, "abstracts", "all_v2.jsonl") dump_abstracts_json(abstracts, output_json) - output_tf = os.path.join(FLAGS.input_folder, "abstracts", "all.tfrecord") - dump_abstracts_tf_record(abstracts, output_tf) + + # output_tf = os.path.join(FLAGS.input_folder, "abstracts", "all.tfrecord") + # dump_abstracts_tf_record(abstracts, output_tf) if __name__ == "__main__": + + flags.DEFINE_string( + "abstract_file", default=None, help="input file path to convert" + ) + + flags.DEFINE_string( + "input_folder", default=None, help="input file path to convert" + ) + + flags.mark_flag_as_required("input_folder") + + flags.mark_flag_as_required("abstract_file") app.run(main) diff --git a/data/json_to_tf_record.py b/data/json_to_tf_record.py deleted file mode 100644 index 975165b..0000000 --- a/data/json_to_tf_record.py +++ /dev/null @@ -1,33 +0,0 @@ -import json -from absl import app, flags -from src.tf_utils import _bytes_feature, tf - - -FLAGS = flags.FLAGS - -flags.DEFINE_string( - "input_file", default=None, help="input json file path to convert" -) - -flags.mark_flag_as_required("input_file") - - -def main(argv): - # write to json - tf_file = FLAGS.input_file.replace(".jsonl", ".tfrecord") - with tf.io.TFRecordWriter(tf_file) as writer: - with open(FLAGS.input_file, "r") as f: - for line in f: - example = json.loads(line) - obj = { - k: _bytes_feature(v.encode("utf-8")) - for (k, v) in example.items() - } - proto = tf.train.Example( - features=tf.train.Features(feature=obj) - ) - writer.write(proto.SerializeToString()) - - -if __name__ == "__main__": - app.run(main) diff --git a/data/run_experiments.py b/data/run_experiments.py deleted file mode 100644 index 429b7ba..0000000 --- a/data/run_experiments.py +++ /dev/null @@ -1,198 +0,0 @@ -import argparse -import json -import os -import random -import sys -import numpy as np -import torch - - -parser = argparse.ArgumentParser(description="Process some integers.") -parser.add_argument( - "--save_dir_root", type=str, default="./data/TREx_lama_templates_v3/" -) -parser.add_argument("--dataset", type=str, default="trex") -parser.add_argument("--seed", type=int, default=42) -parser.add_argument("--relation_mode", type=str, default="template") -parser.add_argument( - "--template_source", type=str, default="lama" -) # Lama or lpaqa or default (=>) - -args = parser.parse_args() -args.command = " ".join(sys.argv) - - -def parse_template(template, sample, subject_label, object_label): - SUBJ_SYMBOL = "[X]" - OBJ_SYMBOL = "[Y]" - template = template.replace(SUBJ_SYMBOL, subject_label) - template = template.replace(OBJ_SYMBOL, object_label) - if "[Z]" in template: - template = template.replace("[Z]", sample["aux_label"]) - return [template] - - -def load_file(filename): - data = [] - with open(filename, "r") as f: - for line in f.readlines(): - data.append(json.loads(line)) - return data - - -def set_seed(args): - print("setting all seed to", args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - random.seed(args.seed) - - -def load_vocab(vocab_filename): - with open(vocab_filename, "r") as f: - lines = f.readlines() - vocab = [x.strip() for x in lines] - return vocab - - -def load_and_prepare_data(relations, data_path_pre, data_path_post, args): - final_d = {} - - for r_count, relation in enumerate(relations): - print("(load_and_prepare_data) relation idx:", r_count) - - load_fn = "{}{}{}".format( - data_path_pre, relation["relation"], data_path_post - ) - - print("loading %s", load_fn) - # see if file exists - try: - all_samples = load_file(load_fn) - except Exception as e: - print( - "Relation {} excluded because the file does not exist.".format( - relation["relation"] - ) - ) - print("Exception: {}".format(e)) - continue - - template_cur = None - - if "template" in relation: - template_cur = relation["template"] - - # if template is active (1) use a single example for (sub,obj) and (2) ... - if template_cur is not None and template_cur != "": - facts = [] - added_samples = [] - for sample in all_samples: - sub = sample["sub_label"] - obj = sample["obj_label"] - aux = sample["aux_label"] if "aux_label" in sample else "" - if (sub, obj, aux) not in facts: - facts.append((sub, obj, aux)) - added_samples.append(sample) - else: - print("===same relation exists===\n", sample) - - all_samples = [] - for f_i, fact in enumerate(facts): - (sub, obj, aux) = fact - sample = added_samples[f_i] - sample["sub_label"] = sub - sample["obj_label"] = obj - if len(aux) > 0: - sample["aux_label"] = aux - sample["template"] = template_cur - sample["templated_sentences"] = parse_template( - template_cur, sample, sample["sub_label"].strip(), "" - ) - if args.relation_mode == "template": - sample["masked_sentences"] = parse_template( - template_cur, - sample, - sample["sub_label"].strip(), - "", - ) - - sample["relation"] = relation - all_samples.append(sample) - - for sample in all_samples: - sample["masked_sentence_ori"] = sample["masked_sentences"][0] - - split_d = {} # split_d contains the final split results - random.shuffle(all_samples) - - samples_d = {} - for sample in all_samples: - assert len(sample["masked_sentences"]) == 1 - ms = sample["masked_sentence_ori"] - if ms not in samples_d: - sample["obj_labels"] = [sample["obj_label"]] - samples_d[ms] = sample - else: - samples_d[ms]["obj_labels"].append(sample["obj_label"]) - - print( - "len of samples after merging", - len(samples_d), - "original number of samples:", - len(all_samples), - ) - - for ms in samples_d: - if len(samples_d[ms]["obj_labels"]) > 1: - print("debug: a sample of multiple-targets", samples_d[ms]) - break - - all_samples = list(samples_d.values()) - split_d["test_samples"] = all_samples - print( - "!!! relation: %s num test_samples: %d", - relation["relation"], - len(all_samples), - ) - final_d[relation["relation"]] = split_d - - for (k, v) in final_d.items(): - with open( - os.path.join(args.save_dir_root, f"{k}.jsonl"), "w" - ) as out_file: - for example in final_d[k]["test_samples"]: - json.dump(example, out_file) - out_file.write("\n") - - return final_d - - -def run_experiments( - relations, - data_path_pre, - data_path_post, - given_args=None, -): - load_and_prepare_data(relations, data_path_pre, data_path_post, given_args) - - -def get_TREx_parameters(data_path_pre="data/"): - relations = load_file("{}relations.jsonl".format(data_path_pre)) - data_path_pre += "TREx/" - data_path_post = ".jsonl" - return relations, data_path_pre, data_path_post - - -def run(parameters, d_n, given_args=None): - args = given_args - args.save_dir = args.save_dir_root - os.system("mkdir -p " + args.save_dir) - set_seed(args) - run_experiments(*parameters, given_args=args) - print("re-print args", str(args)) - - -if __name__ == "__main__": - parameters = get_TREx_parameters() - run(parameters, "trex", given_args=args) - print("%s", str(args)) diff --git a/ensemble.py b/ensemble.py new file mode 100644 index 0000000..f80945a --- /dev/null +++ b/ensemble.py @@ -0,0 +1,143 @@ +import os +import subprocess +from absl import app, flags, logging + + +FLAGS = flags.FLAGS + +flags.DEFINE_string( + "metrics_file", + default=None, + help=( + "input metrics file that stores baseline statistics and (examples, nn" + " abstracts)" + ), +) + +flags.DEFINE_string( + "baseline_metrics_file", + default=None, + help="output file for experiment results", +) + +flags.DEFINE_string( + "fact_to_ids_file", + default=None, + help="output file for experiment results", +) + +flags.DEFINE_string( + "baseline_nn_file", default=None, help="nn for baseline file" +) + +flags.DEFINE_string( + "checkpoint_folders", + default=None, + help="last checkpoint of the model to evaluate", +) + +flags.DEFINE_integer( + "beam_size", default=3, help="beam size for accuracy calculations" +) + +flags.DEFINE_integer("seed", default=10, help="seed") + +flags.DEFINE_float( + "baseline_reweight", + default=-1, + help="ensemble with reweighted baseline scores", +) + +flags.DEFINE_string("data_root", default="LAMA/data/", help="data folder") + +flags.DEFINE_string( + "lama_folder", + default="LAMA/data/TREx_lama_templates_v3", + help="lama data folder name; should be inside data folder", +) + +flags.DEFINE_string( + "exp_folder", + default="LAMA/data/metrics/reranker/unfiltered", + help="name for exp folder under data root", +) + +flags.DEFINE_string( + "load_exp_folder", + default=None, + help="name for exp folder to load the splits from", +) + +flags.DEFINE_string("gpus_to_use", default=None, help="coma seperated gpu ids") + + +def main(_): + + # checkpoint_folders = FLAGS.checkpoint_folders.split(",") + + gpus = list(map(int, FLAGS.gpus_to_use.split(","))) + gpus = {id: [] for id in gpus} + print(f"gpus: {gpus}") + + header_cmd = ( + 'eval "$(conda shell.bash hook)";conda activate transformers;export' + " PYTHONHASHSEED=0;" + ) + + for i in range(3): + + exp_folder = FLAGS.load_exp_folder + + output_metric_folder = os.path.join(exp_folder, f"seed_{i}") + + for subset in ("learned",): + baseline_prefix = os.path.join(output_metric_folder, f"{subset}/") + baseline_eval_file = os.path.join(baseline_prefix, "eval_detailed") + + for eos in ("no_eos",): + for accum in ("accum",): + + ckpt_prefix = os.path.join( + FLAGS.load_exp_folder, + f"seed_{i}", + subset, + f"{eos}_{accum}/", + ) + + ckpt_log_prefix = os.path.join(ckpt_prefix, "logs/") + + ckpt_scores_prefix = os.path.join(ckpt_prefix, "scores/") + + ckpt_prefix = os.path.join( + FLAGS.exp_folder, + f"seed_{i}", + subset, + f"{eos}_{accum}/", + ) + + post_params = ( + f"--metrics_file={baseline_eval_file}.pickle " + f"--seed={i} " + f"--scores_folder={ckpt_scores_prefix} " + "--exp_type=layers " + f"--output_metrics_file={ckpt_prefix}/results_ensemble " + f"--alpha {FLAGS.baseline_reweight} " + "--reweight_type arithmetic " + "--disable_tqdm " + ) + + ckpt_log_prefix = os.path.join(ckpt_prefix, "logs/") + + post_cmd = ( + f"python -u eval/reranker_post.py {post_params} >" + f"{ckpt_log_prefix}/post.log 2> " + f"{ckpt_log_prefix}/post.err;" + ) + + logging.info(f"RUN: {post_cmd}") + + subprocess.Popen(header_cmd + post_cmd, shell=True) + + +if __name__ == "__main__": + app.run(main) diff --git a/eval/evaluate copy.py b/eval/evaluate copy.py deleted file mode 100644 index bec156e..0000000 --- a/eval/evaluate copy.py +++ /dev/null @@ -1,129 +0,0 @@ -import json -import random -import numpy as np -from absl import app, flags -from src.metric_utils import K_EVALS, precision_recall, reciprocal_rank -from src.tf_utils import load_examples_from_tf_records - - -FLAGS = flags.FLAGS - -flags.DEFINE_string( - "nn_list_file", - default=None, - help="nearest neighbors of the test examples in the same order", -) - -flags.DEFINE_string( - "abstract_uri_list", - default=None, - help="page uri list of abstracts in the same order with abstract vectors", -) - -flags.DEFINE_string("abstract_file", default=None, help="abstract file") - -flags.DEFINE_string("test_data", default=None, help="test examples to evaluate") - -flags.DEFINE_string( - "hashmap_file", - default=None, - help="hashmap that maps relation,obj,subj->page_uris", -) - -flags.DEFINE_string( - "output_file", default=None, help="output file path to writer neighbours" -) - - -def main(_): - uri_list = np.array(open(FLAGS.abstract_uri_list, "r").read().split("\n")) - uri_list = uri_list[:-1] - - hashmap = json.load(open(FLAGS.hashmap_file, "r")) - - examples = load_examples_from_tf_records(FLAGS.test_data) - - nns = [] - with open(FLAGS.nn_list_file, "r") as f: - for line in f: - nns.append(json.loads(line)) - - if FLAGS.abstract_file is not None: - abstracts = [] - with open(FLAGS.abstract_file, "r") as f: - for line in f: - abstracts.append(json.loads(line)) - assert ([a["page_uri"] for a in abstracts] == uri_list).all() - abstracts = np.array(abstracts) - - assert len(nns) == len(examples) - assert max([max(nn["neighbor_ids"]) for nn in nns]) < len(uri_list) - - metrics = {"precision": {}, "recall": {}, "samples": []} - - precisions = [] - recalls = [] - rrs = [] - np.random.seed(10) - random.seed(10) - indices = np.random.permutation(len(examples)) - counted = 0 - for p, index in enumerate(indices): - nn = nns[index] - example = examples[index] - nn_ids = uri_list[nn["neighbor_ids"]] - - key = ",".join( - (example["predicate_id"], example["obj_uri"], example["sub_uri"]) - ) - - uris = hashmap.get(key, None) - if uris is None or len(uris) == 0: - continue - counted += 1 - precision, recall = precision_recall( - nn_ids, uris, ks=(1, 5, 10, 50, 100) - ) - - precisions.append(precision) - recalls.append(recall) - rrs.append(reciprocal_rank(nn_ids, uris)) - if len(metrics["samples"]) < 100 and FLAGS.abstract_file is not None: - facts_abstracts = [] - for uri in uris: - ids_w_uri = np.where(uri_list == uri)[0] - facts_abstracts.extend(abstracts[ids_w_uri]) - if len(facts_abstracts) == 0: - continue - - distractors = [ - a - for a in abstracts - if a["targets_pretokenized"] == example["targets_pretokenized"] - ][:100] + np.random.choice(abstracts, 100, replace=False).tolist() - - metrics["samples"].append( - { - "example": example, - "precision": precision, - "recall": recall, - "rr": rrs[-1], - "nn": nn, - "nn_abstracts": abstracts[nn["neighbor_ids"]].tolist(), - "fact_abstracts": facts_abstracts, - "distractors": distractors, - } - ) - - for k in K_EVALS: - metrics["precision"][k] = np.mean([p[k] for p in precisions]) - metrics["recall"][k] = np.mean([r[k] for r in recalls]) - metrics["mrr"] = np.mean(rrs) - # print(metrics) - - with open(FLAGS.output_file, "w") as f: - json.dump(metrics, f) - - -if __name__ == "__main__": - app.run(main) diff --git a/eval/evaluate.py b/eval/evaluate.py index 1554b98..8099fa5 100644 --- a/eval/evaluate.py +++ b/eval/evaluate.py @@ -2,11 +2,11 @@ import json import pickle import random +import datasets import numpy as np from absl import app, flags, logging from tqdm import tqdm from src.metric_utils import K_EVALS, precision_recall, reciprocal_rank -from src.tf_utils import load_examples_from_tf_records FLAGS = flags.FLAGS @@ -18,19 +18,9 @@ ) flags.DEFINE_string( - "abstract_uri_list", + "fact_to_ids_file", default=None, - help="page uri list of abstracts in the same order with abstract vectors", -) - -flags.DEFINE_string("abstract_file", default=None, help="abstract file") - -flags.DEFINE_string("test_data", default=None, help="test examples to evaluate") - -flags.DEFINE_string( - "hashmap_file", - default=None, - help="hashmap that maps relation,obj,subj->page_uris", + help="fact_to_ids that maps relation,obj,subj->page_uris", ) flags.DEFINE_string( @@ -39,134 +29,126 @@ flags.DEFINE_bool("target_only", default=False, help="targets abstracts only") -flags.DEFINE_bool( - "only_masked_sentence", - default=False, - help=( - "convert abstracts to single sentence by extracting only the masked" - " sentence" - ), -) - flags.DEFINE_boolean("disable_tqdm", False, help="Disable tqdm") +flags.DEFINE_integer("seed", 10, help="Random seed") -def extract_masked_sentence(abstract: str, term=""): - term_start = abstract.find(term) - assert term_start > -1 - term_end = term_start + len(term) - sentence_start = abstract.rfind(". ", 0, term_start) - if sentence_start == -1: - sentence_start = 0 - else: - sentence_start += 2 - sentence_end = abstract.find(". ", term_end) - if sentence_end == -1: - sentence_end = abstract.find(".", term_end) - sentence_end = min(sentence_end + 1, len(abstract)) - return abstract[sentence_start:sentence_end] +# def extract_masked_sentence(abstract: str, term=""): +# term_start = abstract.find(term) +# assert term_start > -1 +# term_end = term_start + len(term) +# sentence_start = abstract.rfind(". ", 0, term_start) +# if sentence_start == -1: +# sentence_start = 0 +# else: +# sentence_start += 2 +# sentence_end = abstract.find(". ", term_end) +# if sentence_end == -1: +# sentence_end = abstract.find(".", term_end) +# sentence_end = min(sentence_end + 1, len(abstract)) +# return abstract[sentence_start:sentence_end] -def main(_): - uri_list = np.array(open(FLAGS.abstract_uri_list, "r").read().split("\n")) - uri_list = uri_list[:-1] +def main(_): + examples = datasets.load_dataset( + "data/ftrace", "queries", split="train" + ).select(range(15000)) - uri_to_ids = {} + abstracts = datasets.load_dataset("data/ftrace", "abstracts", split="train") + ids_to_abstracts_keys = abstracts["id"] - for i, uri in enumerate(uri_list): - if uri not in uri_to_ids: - uri_to_ids[uri] = [i] - else: - uri_to_ids[uri].append(i) + logging.info(f"ids to abstracts length {len(ids_to_abstracts_keys)}") - logging.info(f"Length of abstracts {len(uri_list)}") + logging.info("Building ids to abstracts hashmap") + ids_to_abstracts = { + key: abstracts[i] for i, key in enumerate(ids_to_abstracts_keys) + } - hashmap = json.load(open(FLAGS.hashmap_file, "r")) + logging.info(f"Length of abstracts {len(ids_to_abstracts)}") - examples = load_examples_from_tf_records(FLAGS.test_data) + with open(FLAGS.fact_to_ids_file, "r") as handle: + fact_to_ids = json.load(handle) nns = [] with open(FLAGS.nn_list_file, "r") as f: for line in f: nns.append(json.loads(line)) - if FLAGS.abstract_file is not None: - abstracts = [] - with open(FLAGS.abstract_file, "r") as f: - for line in f: - abstract = json.loads(line) - if FLAGS.only_masked_sentence: - abstract["inputs_pretokenized"] = extract_masked_sentence( - abstract["inputs_pretokenized"] - ) - abstracts.append(abstract) - assert ( - np.array([a["sentence_uris"] for a in abstracts]) == uri_list - ).all() - abstracts = np.array(abstracts) - assert len(nns) == len(examples) - assert max([max(nn["neighbor_ids"]) for nn in nns]) < len(uri_list) metrics = {"precision": {}, "recall": {}, "samples": []} - precisions = [] - recalls = [] - rrs = [] - np.random.seed(10) - random.seed(10) - indices = np.random.permutation(len(examples)) + precisions, recalls, rrs = [], [], [] + + np.random.seed(FLAGS.seed) + random.seed(FLAGS.seed) + indices = np.random.permutation(len(examples)).tolist() counted = 0 - for p, index in enumerate(tqdm(indices, disable=FLAGS.disable_tqdm)): + logging.info("Building targets to abstracts idxs hashmap") + targets_to_idxs = {} + for idx, a in enumerate(abstracts): + target = a["targets_pretokenized"] + if target in targets_to_idxs: + targets_to_idxs[target].append(idx) + else: + targets_to_idxs[target] = [idx] + + for index in tqdm(indices, disable=FLAGS.disable_tqdm): nn = nns[index] example = examples[index] - nn_ids = uri_list[nn["neighbor_ids"]] + nn_ids = nn["neighbor_ids"] - key = ",".join( + fact = ",".join( (example["predicate_id"], example["obj_uri"], example["sub_uri"]) ) - uris = hashmap.get(key, None) + fact_ids = list(map(str, fact_to_ids.get(fact, []))) - if uris is None or len(uris) == 0: + if len(fact_ids) == 0: continue + counted += 1 - precision, recall = precision_recall(nn_ids, uris, ks=K_EVALS) - rr = reciprocal_rank(nn_ids, uris) + precision, recall = precision_recall(nn_ids, fact_ids, ks=K_EVALS) + rr = reciprocal_rank(nn_ids, fact_ids) precisions.append(precision) recalls.append(recall) rrs.append(rr) - if FLAGS.abstract_file is not None and len(metrics["samples"]) < 10000: + # pdb.set_trace() - facts_abstracts = [] - for uri in uris: - ids_w_uri = uri_to_ids[uri] - facts_abstracts.extend(abstracts[ids_w_uri]) + if len(metrics["samples"]) < 10000: + + facts_abstracts = [ids_to_abstracts[str(id)] for id in fact_ids] if len(facts_abstracts) == 0: continue + targets = example["targets_pretokenized"] if FLAGS.target_only: - distractors = [ - a - for a in abstracts - if a["targets_pretokenized"] - == example["targets_pretokenized"] - ][:200] + target_idxs = targets_to_idxs.get(targets, []) + distractor_idxs = np.random.choice( + target_idxs, + min(200, len(target_idxs)), + replace=False, + ).tolist() + distractors = list(abstracts.select(distractor_idxs)) else: - distractors = [ - a - for a in abstracts - if a["targets_pretokenized"] - == example["targets_pretokenized"] - ][:100] - - distractors.extend( - np.random.choice(abstracts, 100, replace=False) + target_idxs = targets_to_idxs.get(targets, []) + distractor_idxs = np.random.choice( + target_idxs, + min(100, len(target_idxs)), + replace=False, + ).tolist() + distractors = list(abstracts.select(distractor_idxs)) + + ext_ids = np.random.choice( + ids_to_abstracts_keys, 100, replace=False ) + distractors += [ids_to_abstracts[id] for id in ext_ids] + + nn_abstracts = [ids_to_abstracts[id] for id in nn_ids] metrics["samples"].append( { @@ -175,17 +157,22 @@ def main(_): "recall": recall, "rr": rrs[-1], "nn": nn, - "nn_abstracts": abstracts[nn["neighbor_ids"]].tolist(), + "nn_abstracts": nn_abstracts, "fact_abstracts": facts_abstracts, "distractors": distractors, } ) + else: + break for k in K_EVALS: metrics["precision"][k] = np.mean([p[k] for p in precisions]) metrics["recall"][k] = np.mean([r[k] for r in recalls]) + metrics["mrr"] = np.mean(rrs) + logging.info(f"MRR: {metrics['mrr']}") + with gzip.open(FLAGS.output_file, "w") as f: pickle.dump(metrics, f, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/eval/get_nns.py b/eval/get_nns.py deleted file mode 100644 index 15fdbac..0000000 --- a/eval/get_nns.py +++ /dev/null @@ -1,219 +0,0 @@ -import asyncio -import json -import numpy as np -import torch # TODO(ekina): make this jax -from absl import app, flags -from tqdm import tqdm -from src.linalg_utils import normalize_segments -from src.tf_utils import get_index_decoder, get_tfexample_decoder_old, tf - - -FLAGS = flags.FLAGS - - -flags.DEFINE_string( - "abstract_vectors", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "test_vectors", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "output_file", default=None, help="output file path to writer neighbours" -) - -flags.DEFINE_integer( - "gpu_workers", default=4, help="number of gpus to distribute" -) - -flags.DEFINE_integer("feature_size", default=4 * 2048, help="size of vectors") - -flags.DEFINE_integer( - "batch_size", default=10, help="batch size to process at once" -) - -flags.DEFINE_integer("topk", default=100, help="batch size to process at once") - -flags.DEFINE_integer( - "global_offset", default=0, help="global offset of current vectors" -) - -flags.DEFINE_integer( - "fake_data_size", default=None, help="fake data size to test this code" -) - -flags.DEFINE_bool("normalize", default=False, help="normalize embeddings") - - -def load_a_shard_from_tfrecord( - dataset, feature_length, positions, normalize=False -): - """Loads one shard of a dataset from the dataset file.""" - shard_length = positions[1] - positions[0] - ds_loader = ( - dataset.skip(positions[0]) - .take(shard_length) - .map(get_tfexample_decoder_old(feature_length)) - .batch(shard_length, num_parallel_calls=4) - .as_numpy_iterator() - ) - - X = next(iter(ds_loader)) - X = torch.from_numpy(X) - if normalize: - X = normalize_segments(X) - return X - - -def load_a_shard_from_numpy( - np_file, feature_length, positions, normalize=False -): - """Loads one shard of a dataset from the dataset file.""" - X = np.load(np_file, mmap_mode="r") - X = torch.from_numpy(X[positions[0] : positions[1], :]) - if normalize: - X = normalize_segments(X) - return X - - -def get_shard_positions(dataset, n_workers): - """Gets start and end indicies of a shard in the merged dataset.""" - if type(dataset) == str: - total_abstracts = FLAGS.fake_data_size - else: - total_abstracts = sum(1 for _ in dataset) - shard_length = total_abstracts // n_workers - positions = [] - start = 0 - for i in range(n_workers): - current_start = start - if i == n_workers - 1: - current_end = total_abstracts - else: - current_end = current_start + shard_length - positions.append((current_start, current_end)) - start = current_end - return positions - - -def create_dummy_numpy_dataset(output_file, n, feature_size): - """Creates dummy data to test this code.""" - # Somewhat this is extremely slow! - X = np.random.rand(n, feature_size).astype(np.float32) - np.save(output_file, X, allow_pickle=False) - - -def sharded_open(filename): - if "@" in filename: - prefix, no_shards = filename.split("@") - no_shards = int(no_shards) - filenames = [ - f"{prefix}-{str(i).zfill(5)}-of-{str(no_shards).zfill(5)}" - for i in range(no_shards) - ] - dataset = tf.data.TFRecordDataset(filenames=filenames) - else: - dataset = tf.data.TFRecordDataset(filename) - return dataset - - -async def _prepare_shards(args): - """Reads shards from the merged file and sends them to GPUs.""" - - # dataset = args.abstract_vectors - # Create a dictionary describing the features. - dataset = sharded_open(args.abstract_vectors) - - async def get_a_shard(i, index): - shard = load_a_shard_from_tfrecord( - dataset, args.feature_size, index, normalize=FLAGS.normalize - ).cuda(i, non_blocking=True) - - return shard - - positions = get_shard_positions(dataset, args.gpu_workers) - - loaders = [get_a_shard(*s) for s in enumerate(positions)] - - shards = await asyncio.gather(*loaders) - - return shards, positions - - -async def _single_shard_process(shard, batch, k): - """Returns topk instances and scores for a given batch in a single shard""" - scores = (shard @ batch.T.to(shard.device)).cpu() # N x B - return torch.topk(scores, k, dim=0) # k x B - - -async def nn_retrieve(shards, batch, positions, k=100): - """Returns topk instances and scores for a given batch.""" - retrievers = [_single_shard_process(shard, batch, k) for shard in shards] - results = await asyncio.gather(*retrievers) - - value_lists, index_lists = zip(*results) - - temp = (index_lists[i] + positions[i][0] for i in range(len(index_lists))) - index_tensor = torch.cat(tuple(temp), axis=0) - value_tensor = torch.cat(value_lists, axis=0) - scores, local_ids = torch.topk(value_tensor, k, dim=0) - indices = index_tensor[local_ids, torch.arange(index_tensor.shape[-1])] - return scores, indices - - -def get_indices(dataset, feature_length): - ds = dataset.map(get_index_decoder(feature_length)).as_numpy_iterator() - return np.array([d[0] for d in ds]) - - -def main(_): - # create_dummy_numpy_dataset(FLAGS.abstract_vectors, - # FLAGS.fake_data_size, - # FLAGS.feature_size) - - shards, positions = asyncio.run(_prepare_shards(FLAGS)) - print("reading abstract vectors from: ", FLAGS.abstract_vectors) - abstract_indices = get_indices( - sharded_open(FLAGS.abstract_vectors), FLAGS.feature_size - ) - - # create_dummy_tfrecord_dataset(FLAGS.test_vectors, 100, FLAGS.feature_size) - test_indices = get_indices( - sharded_open(FLAGS.test_vectors), FLAGS.feature_size - ) - test_dataset = sharded_open(FLAGS.test_vectors) - - test_loader = ( - test_dataset.map(get_tfexample_decoder_old(FLAGS.feature_size)) - .batch(FLAGS.batch_size) - .as_numpy_iterator() - ) - - results = [] - for batch in tqdm(test_loader): - batch = torch.from_numpy(batch) - - if FLAGS.normalize: - batch = normalize_segments(batch) - - scores, idxs = asyncio.run( - nn_retrieve(shards, batch, positions, k=FLAGS.topk) - ) - # idxs = idxs + FLAGS.global_offset - for i in range(scores.shape[-1]): - line = { - "scores": scores[:, i].tolist(), - "neighbor_ids": abstract_indices[idxs[:, i]].tolist(), - } - results.append(line) - - results = [results[i] for i in np.argsort(test_indices)] - - with open(FLAGS.output_file, "w") as f: - for res in results: - print(json.dumps(res), file=f) - - -if __name__ == "__main__": - app.run(main) diff --git a/eval/get_nns_bm25.py b/eval/get_nns_bm25.py index 48cd1cc..7b292c3 100644 --- a/eval/get_nns_bm25.py +++ b/eval/get_nns_bm25.py @@ -1,13 +1,9 @@ import json +import datasets import numpy as np -from absl import app, flags +from absl import app, flags, logging from rank_bm25 import BM25Plus from tqdm import tqdm -from src.tf_utils import ( - get_tfexample_decoder_examples_io, - load_dataset_from_tfrecord, - tf, -) FLAGS = flags.FLAGS @@ -59,8 +55,8 @@ def extract_masked_sentence(abstract: str, term=""): def get_tokenized_query(record, extract=False): - answer = record[1].decode().replace(" ", "") - text = record[0].decode() + answer = record["targets_pretokenized"].replace(" ", "") + text = record["inputs_pretokenized"] if extract: text = extract_masked_sentence(text) text = text.replace("", answer).split(" ") @@ -71,7 +67,10 @@ def get_target_equivalence_classes(abstracts): target_equivariance_indices = {} for (i, abstract) in enumerate(abstracts): target = ( - abstract[1].decode().replace(" ", "").strip().lower() + abstract["targets_pretokenized"] + .replace(" ", "") + .strip() + .lower() ) if target in target_equivariance_indices: target_equivariance_indices[target].append(i) @@ -81,15 +80,22 @@ def get_target_equivalence_classes(abstracts): def get_target_ids(target_ids_hashmap, record): - target = record[1].decode().replace(" ", "").strip().lower() + target = ( + record["targets_pretokenized"] + .replace(" ", "") + .strip() + .lower() + ) return target_ids_hashmap.get(target, [0]) def main(_): - abstract_dataset = tf.data.TFRecordDataset(FLAGS.abstract_file) - abstracts = load_dataset_from_tfrecord(abstract_dataset) + # abstract_dataset = tf.data.TFRecordDataset(FLAGS.abstract_file) + # abstracts = load_dataset_from_tfrecord(abstract_dataset) + + abstracts = datasets.load_dataset("data/ftrace", "abstracts", split="train") - print("abstracts loaded") + logging.info(f"abstracts loaded {len(abstracts)}") if FLAGS.target_only: target_ids_hashmap = get_target_equivalence_classes(abstracts) @@ -100,28 +106,31 @@ def main(_): ] bm25 = BM25Plus(corpus) - test_dataset = tf.data.TFRecordDataset(FLAGS.test_file) - test_loader = test_dataset.map( - get_tfexample_decoder_examples_io() - ).as_numpy_iterator() + test_dataset = datasets.load_dataset( + "data/ftrace", "queries", split="train", streaming=True + ).take(15000) with open(FLAGS.output_file, "w") as f: - for example in tqdm(test_loader): + for example in tqdm(test_dataset): query = get_tokenized_query(example) if FLAGS.target_only: target_ids = np.array( get_target_ids(target_ids_hashmap, example) ) scores = np.array(bm25.get_batch_scores(query, target_ids)) - idxs = np.argsort(-scores)[: FLAGS.topk] - scores = scores[idxs].tolist() - idxs = target_ids[idxs].tolist() + idxs = np.argpartition(scores, -FLAGS.topk)[-FLAGS.topk :] + nn_idxs = idxs[np.argsort(-scores[idxs])] + nn_scores = scores[nn_idxs].tolist() + nn_idxs = target_ids[nn_idxs].tolist() else: scores = bm25.get_scores(query) - idxs = np.argsort(-scores)[: FLAGS.topk].tolist() - scores = [scores[i] for i in idxs] + idxs = np.argpartition(scores, -FLAGS.topk)[-FLAGS.topk :] + nn_idxs = idxs[np.argsort(-scores[idxs])] + nn_scores = scores[nn_idxs].tolist() + + neighbor_ids = abstracts.select(nn_idxs)["id"] - line = {"scores": scores, "neighbor_ids": idxs} + line = {"scores": nn_scores, "neighbor_ids": neighbor_ids} print(json.dumps(line), file=f) diff --git a/eval/reranker_post.py b/eval/reranker_post.py index 5a7940a..3c46fe1 100644 --- a/eval/reranker_post.py +++ b/eval/reranker_post.py @@ -9,7 +9,11 @@ import numpy as np from absl import app, flags, logging from tqdm import tqdm -from src.lama_utils import abs_to_str, get_sentence +from src.lama_utils import ( + abs_to_str, + collapse_abstracts_and_scores, + get_sentence, +) from src.metric_utils import ( K_EVALS, average_metrics, @@ -372,27 +376,6 @@ def findindex(key): return abstracts_reranked, scores_reranked -def collapse_abstracts_and_scores( - scores: Sequence[float], abstracts: Sequence[Mapping] -): - uri_to_indices = {} - for i, a in enumerate(abstracts): - uri = get_sentence(a) - if uri in uri_to_indices: - uri_to_indices[uri].append(i) - else: - uri_to_indices[uri] = [i] - uri_scores = [] - uri_indices = [] - scores = np.array(scores) - for (uri, indices) in uri_to_indices.items(): - i_max = np.argmax(scores[indices]) - i_max = indices[i_max] - uri_indices.append(i_max) - uri_scores.append(scores[i_max]) - return np.array(uri_scores), [abstracts[j] for j in uri_indices] - - def run_all_layer_configs( samples: List, scores: List, @@ -472,7 +455,7 @@ def compare_fn(a, b): else: def compare_fn(a, b): - return a["sentence_uris"] == b["sentence_uris"] + return a["id"] == b["id"] def compare_fn_relation(a, b): return query["predicate_id"] in a["facts"] diff --git a/eval/reranker_pre.py b/eval/reranker_pre.py index 55599cc..1bfafa8 100644 --- a/eval/reranker_pre.py +++ b/eval/reranker_pre.py @@ -4,12 +4,16 @@ import pickle import random from functools import partial -from typing import Callable, Mapping, Sequence +from typing import Callable, Mapping import numpy as np from absl import app, flags, logging from tqdm import tqdm from transformers import MT5ForConditionalGeneration, T5Tokenizer -from src.lama_utils import abs_to_str, get_sentence +from src.lama_utils import ( + abs_to_str, + collapse_abstracts_and_scores, + get_sentence, +) from src.metric_utils import ( K_EVALS, average_metrics, @@ -90,27 +94,6 @@ def tokenize(tokenizer: T5Tokenizer, record: Mapping): return {"inputs": inputs, "targets": targets[:, :-1]} -def collapse_abstracts_and_scores( - scores: Sequence[float], abstracts: Sequence[Mapping] -): - uri_to_indices = {} - for i, a in enumerate(abstracts): - uri = get_sentence(a) - if uri in uri_to_indices: - uri_to_indices[uri].append(i) - else: - uri_to_indices[uri] = [i] - uri_scores = [] - uri_indices = [] - scores = np.array(scores) - for (uri, indices) in uri_to_indices.items(): - i_max = np.argmax(scores[indices]) - i_max = indices[i_max] - uri_indices.append(i_max) - uri_scores.append(scores[i_max]) - return np.array(uri_scores), [abstracts[j] for j in uri_indices] - - def evaluate( example, abstracts, fact_abstracts, compare_fn: Callable, collapse=False ): @@ -196,7 +179,7 @@ def compare_fn(a, b): else: def compare_fn(a, b): - return a["sentence_uris"] == b["sentence_uris"] + return a["id"] == b["id"] def compare_fn_relation(a, b): return query["predicate_id"] in a["facts"] @@ -295,7 +278,7 @@ def compare_fn(a, b): else: def compare_fn(a, b): - return a["sentence_uris"] == b["sentence_uris"] + return a["id"] == b["id"] def compare_fn_relation(a, b): return query["predicate_id"] in a["facts"] diff --git a/eval/reranker_single_checkpoint.py b/eval/reranker_single_checkpoint.py index 5b89679..75e68b1 100644 --- a/eval/reranker_single_checkpoint.py +++ b/eval/reranker_single_checkpoint.py @@ -333,14 +333,6 @@ def get_all_scores(model, tokenizer, query, abstracts): return all_scores -def get_sentence(abstract): - targets = ( - abstract["targets_pretokenized"].replace(" ", "").strip() - ) - sentence = abstract["inputs_pretokenized"].replace("", targets) - return sentence - - def identifier(x): return x["inputs_pretokenized"] + x["targets_pretokenized"] @@ -428,6 +420,9 @@ def main(_, score_fn=get_all_scores): with gzip.open(output, "wb") as f: pickle.dump(scores, f, protocol=pickle.HIGHEST_PROTOCOL) + with open(output.replace("pickle", "done"), "w") as f: + print("done", file=f) + if __name__ == "__main__": app.run(main) diff --git a/eval/target_normalize.py b/eval/target_normalize.py deleted file mode 100644 index 45aca76..0000000 --- a/eval/target_normalize.py +++ /dev/null @@ -1,317 +0,0 @@ -import asyncio -import contextlib2 -import numpy as np -import torch # TODO(ekina): make this jax -from absl import app, flags -from src.linalg_utils import normalize_segments -from src.tf_utils import get_index_decoder, get_tfexample_decoder_old, tf - - -FLAGS = flags.FLAGS - -flags.DEFINE_string( - "abstract_vectors", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "test_vectors", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "abstract_file", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "test_file", default=None, help="input file path to convert" -) - -flags.DEFINE_string( - "output_file", default=None, help="output file path to writer neighbours" -) - -flags.DEFINE_string( - "test_output_file", - default=None, - help="output file path to writer neighbours", -) - -flags.DEFINE_integer("feature_size", default=4 * 2048, help="size of vectors") - -flags.DEFINE_bool("normalize", default=False, help="normalize embeddings") - -flags.DEFINE_integer( - "gpu_workers", default=1, help="number of gpus to distribute" -) - - -def load_a_shard_from_tfrecord( - dataset, feature_length, positions, normalize=False -): - """Loads one shard of a dataset from the dataset file.""" - shard_length = positions[1] - positions[0] - ds_loader = ( - dataset.skip(positions[0]) - .take(shard_length) - .map(get_tfexample_decoder_old(feature_length)) - .batch(shard_length, num_parallel_calls=4) - .as_numpy_iterator() - ) - - X = next(iter(ds_loader)) - X = torch.from_numpy(X) - if normalize: - X = normalize_segments(X, size=1024) - return X - - -def load_a_shard_from_numpy( - np_file, feature_length, positions, normalize=False -): - """Loads one shard of a dataset from the dataset file.""" - X = np.load(np_file, mmap_mode="r") - X = torch.from_numpy(X[positions[0] : positions[1], :]) - if normalize: - X = normalize_segments(X, size=1024) - return X - - -def get_shard_positions(dataset, n_workers): - """Gets start and end indicies of a shard in the merged dataset.""" - if type(dataset) == str: - total_abstracts = FLAGS.fake_data_size - else: - total_abstracts = sum(1 for _ in dataset) - shard_length = total_abstracts // n_workers - positions = [] - start = 0 - for i in range(n_workers): - current_start = start - if i == n_workers - 1: - current_end = total_abstracts - else: - current_end = current_start + shard_length - positions.append((current_start, current_end)) - start = current_end - return positions - - -def sharded_open(filename): - if "@" in filename: - prefix, no_shards = filename.split("@") - no_shards = int(no_shards) - filenames = [ - f"{prefix}-{str(i).zfill(5)}-of-{str(no_shards).zfill(5)}" - for i in range(no_shards) - ] - dataset = tf.data.TFRecordDataset(filenames=filenames) - else: - dataset = tf.data.TFRecordDataset(filename) - return dataset - - -async def _prepare_shards(file, args): - """Reads shards from the merged file and sends them to GPUs.""" - - # dataset = args.abstract_vectors - # Create a dictionary describing the features. - dataset = sharded_open(file) - - async def get_a_shard(i, index): - shard = load_a_shard_from_tfrecord( - dataset, args.feature_size, index, normalize=FLAGS.normalize - ) - - return shard - - positions = get_shard_positions(dataset, args.gpu_workers) - - loaders = [get_a_shard(*s) for s in enumerate(positions)] - - shards = await asyncio.gather(*loaders) - - return shards, positions - - -def get_indices(dataset, feature_length): - ds = dataset.map(get_index_decoder(feature_length)).as_numpy_iterator() - return np.array([d[0] for d in ds]) - - -def get_tfexample_decoder_old_examples(): - """Returns tf dataset parser.""" - - feature_dict = { - "inputs_pretokenized": tf.io.FixedLenFeature([], tf.string), - "targets_pretokenized": tf.io.FixedLenFeature([], tf.string), - "uuid": tf.io.FixedLenFeature([], tf.string), - "obj_uri": tf.io.FixedLenFeature([], tf.string), - "sub_uri": tf.io.FixedLenFeature([], tf.string), - "predicate_id": tf.io.FixedLenFeature([], tf.string), - "obj_surface": tf.io.FixedLenFeature([], tf.string), - "sub_surface": tf.io.FixedLenFeature([], tf.string), - } - - def _parse_data(proto): - data = tf.io.parse_single_example(proto, feature_dict) - return (data["inputs_pretokenized"], data["targets_pretokenized"]) - - return _parse_data - - -def get_tfexample_decoder_old_abstracts(): - """Returns tf dataset parser.""" - - feature_dict = { - "inputs_pretokenized": tf.io.FixedLenFeature([], tf.string), - "targets_pretokenized": tf.io.FixedLenFeature([], tf.string), - "masked_uri": tf.io.FixedLenFeature([], tf.string), - "page_uri": tf.io.FixedLenFeature([], tf.string), - } - - def _parse_data(proto): - data = tf.io.parse_single_example(proto, feature_dict) - return (data["inputs_pretokenized"], data["targets_pretokenized"]) - - return _parse_data - - -def load_abstracts_from_tfrecord(dataset): - """Loads one shard of a dataset from the dataset file.""" - ds_loader = dataset.map( - get_tfexample_decoder_old_abstracts() - ).as_numpy_iterator() - return [d for d in ds_loader] - - -def load_examples_from_tfrecord(dataset): - """Loads one shard of a dataset from the dataset file.""" - ds_loader = dataset.map( - get_tfexample_decoder_old_examples() - ).as_numpy_iterator() - return [d for d in ds_loader] - - -def _int64_feature(value): - """Returns an int64_list from a bool / enum / int / uint.""" - return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) - - -def _tfrecord(index, value): - """Converts dummy data values to tf records.""" - record = { - "index": _int64_feature(index), - "embedding": tf.train.Feature( - float_list=tf.train.FloatList(value=value) - ), - } - return tf.train.Example(features=tf.train.Features(feature=record)) - - -def open_sharded_output_tfrecords(exit_stack, base_path, num_shards): - """Opens all TFRecord shards for writing and adds them to an exit stack. - Args: - exit_stack: A context2.ExitStack used to automatically closed the TFRecords - opened in this function. - base_path: The base path for all shards - num_shards: The number of shards - Returns: - The list of opened TFRecords. Position k in the list corresponds to shard k. - """ - tf_record_output_filenames = [ - "{}-{:05d}-of-{:05d}".format(base_path, idx, num_shards) - for idx in range(num_shards) - ] - - tfrecords = [ - exit_stack.enter_context(tf.io.TFRecordWriter(file_name)) - for file_name in tf_record_output_filenames - ] - - return tfrecords - - -def class_projection(vectors, class_vector): - # class_norm = np.linalg.norm(class_vector) - # return class_vector[None, :] * np.dot(vectors, class_vector[:, None]) / class_norm - return class_vector - - -def get_sorted_records(vector_file, data_file, load_fn, feature_size): - abstract_indices = get_indices(sharded_open(vector_file), feature_size) - abstract_dataset = tf.data.TFRecordDataset(data_file) - abstracts = np.array(load_fn(abstract_dataset)) - abstracts = abstracts[abstract_indices] - return abstracts, abstract_indices - - -def get_target_equivalence_classes(abstracts): - target_equivariance_indices = {} - for (i, abstract) in enumerate(abstracts): - target = ( - abstract[1].decode().replace(" ", "").strip().lower() - ) - if target in target_equivariance_indices: - target_equivariance_indices[target].append(i) - else: - target_equivariance_indices[target] = [i] - return target_equivariance_indices - - -def write_sharded_files(output_file, vectors, indices): - out_prefix, num_shards = output_file.split("@") - num_shards = int(num_shards) - with contextlib2.ExitStack() as tf_record_close_stack: - output_tfrecords = open_sharded_output_tfrecords( - tf_record_close_stack, out_prefix, num_shards - ) - for i, (index, vector) in enumerate(zip(indices, vectors)): - example = _tfrecord(index, vector).SerializeToString() - output_shard_index = i % num_shards - output_tfrecords[output_shard_index].write(example) - - -def main(_): - abstracts, abstract_indices = get_sorted_records( - FLAGS.abstract_vectors, - FLAGS.abstract_file, - load_abstracts_from_tfrecord, - FLAGS.feature_size, - ) - - test_examples, test_indices = get_sorted_records( - FLAGS.test_vectors, - FLAGS.test_file, - load_examples_from_tfrecord, - FLAGS.feature_size, - ) - - target_equivariance_indices = get_target_equivalence_classes(abstracts) - test_target_equivariance_indices = get_target_equivalence_classes( - test_examples - ) - - shards, _ = asyncio.run(_prepare_shards(FLAGS.abstract_vectors, FLAGS)) - shards_test, _ = asyncio.run(_prepare_shards(FLAGS.abstract_vectors, FLAGS)) - - assert len(shards) == 1 and len(shards_test) == 1 - - vectors = shards[0] - test_vectors = shards_test[0] - - for target, idxs in target_equivariance_indices.items(): - class_vector = torch.mean(vectors[idxs, :], dim=0) - # class_norm = np.linalg.norm(class_vector) - vectors[idxs, :] -= class_projection(vectors[idxs, :], class_vector) - - if target in test_target_equivariance_indices: - test_idxs = test_target_equivariance_indices[target] - test_vectors[test_idxs, :] -= class_projection( - test_vectors[test_idxs, :], class_vector - ) - - write_sharded_files(FLAGS.output_file, vectors, abstract_indices) - write_sharded_files(FLAGS.test_output_file, test_vectors, test_indices) - - -if __name__ == "__main__": - app.run(main) diff --git a/reranker.py b/reranker.py index 914bee5..3b38e58 100644 --- a/reranker.py +++ b/reranker.py @@ -22,6 +22,12 @@ help="output file for experiment results", ) +flags.DEFINE_string( + "fact_to_ids_file", + default=None, + help="output file for experiment results", +) + flags.DEFINE_string( "baseline_nn_file", default=None, help="nn for baseline file" ) @@ -58,6 +64,20 @@ help="name for exp folder under data root", ) +flags.DEFINE_string( + "load_exp_folder", + default=None, + help="name for exp folder to load the splits from", +) + +flags.DEFINE_string("eval_stage", default="True", help="eval stage") + +flags.DEFINE_string("pre_stage", default="True", help="pre stage") + +flags.DEFINE_string("ckpt_stage", default="True", help="ckpt stage") + +flags.DEFINE_string("post_stage", default="True", help="post stage") + flags.DEFINE_string("gpus_to_use", default=None, help="coma seperated gpu ids") @@ -90,39 +110,31 @@ def assign_to_gpu(gpus, file): def main(_): - uri_file = os.path.join(FLAGS.lama_folder, "abstracts", "all_used_uris.txt") - hashmap_file = os.path.join( - FLAGS.lama_folder, "abstracts", "hashmap_used.json" - ) - test_file = os.path.join(FLAGS.lama_folder, "all.tfrecord") - abstract_file = os.path.join( - FLAGS.lama_folder, "abstracts", "all_used.jsonl" - ) + checkpoint_folders = FLAGS.checkpoint_folders.split(",") gpus = list(map(int, FLAGS.gpus_to_use.split(","))) gpus = {id: [] for id in gpus} print(f"gpus: {gpus}") - evaluate_cmd = ( - "export PYTHONHASHSEED=0;" - "python -u eval/evaluate.py " - f"--abstract_uri_list {uri_file} " - f"--abstract_file {abstract_file} " - f"--test_data {test_file} " - f"--hashmap_file {hashmap_file} " - f"--nn_list_file {FLAGS.baseline_nn_file} " - "--disable_tqdm " - f"--output_file {FLAGS.baseline_metrics_file};" - "deactivate" - ) + if FLAGS.eval_stage and FLAGS.load_exp_folder is None: - logging.info( - "Running baseline evaluations..." - f"Metrics will be outputted to {FLAGS.baseline_metrics_file}" - ) + evaluate_cmd = ( + "export PYTHONHASHSEED=0;" + "python -u eval/evaluate.py " + f"--fact_to_ids_file {FLAGS.fact_to_ids_file} " + f"--nn_list_file {FLAGS.baseline_nn_file} " + "--disable_tqdm " + f"--output_file {FLAGS.baseline_metrics_file};" + "deactivate" + ) - subprocess.run(evaluate_cmd, shell=True, check=True) + logging.info( + "Running baseline evaluations..." + f"Metrics will be outputted to {FLAGS.baseline_metrics_file}" + ) + + subprocess.run(evaluate_cmd, shell=True, check=True) header_cmd = ( 'eval "$(conda shell.bash hook)";conda activate transformers;export' @@ -130,55 +142,74 @@ def main(_): ) for i in range(3): - output_metric_folder = os.path.join(FLAGS.exp_folder, f"seed_{i}") + + if FLAGS.load_exp_folder is None: + exp_folder = FLAGS.exp_folder + else: + exp_folder = FLAGS.load_exp_folder + + output_metric_folder = os.path.join(exp_folder, f"seed_{i}") + for subset in ("learned", "random"): os.makedirs(output_metric_folder, exist_ok=True) - baseline_prefix = os.path.join(output_metric_folder, f"{subset}/") os.makedirs(baseline_prefix, exist_ok=True) baseline_eval_file = os.path.join(baseline_prefix, "eval_detailed") - gpu = assign_to_gpu(gpus, f"{baseline_eval_file}.pickle") - gpu_header = f"export CUDA_VISIBLE_DEVICES={gpu};" - - pre_params = ( - f"--metrics_file={FLAGS.baseline_metrics_file} " - f"--seed={i} " - f"--checkpoint_folders={FLAGS.checkpoint_folders} " - f"--output_metrics_prefix={baseline_eval_file} " - "--gpu=0 " - "--disable_tqdm " - ) - - if subset == "corrects": - pre_params += "--only_correct " - elif subset == "wrongs": - pre_params += "--only_wrongs " - else: - pre_params += "--only_learned " - - baseline_log_prefix = os.path.join(baseline_prefix, "logs/") - os.makedirs(baseline_log_prefix, exist_ok=True) - - logging.info( - f"Experiment files {baseline_log_prefix}\nParams:\n{pre_params}" - ) - - pre_cmd = ( - f"python -u eval/reranker_pre.py {pre_params} > " - f"{baseline_log_prefix}/pre.log 2>" - f"{baseline_log_prefix}/pre.err;" - ) - - logging.info(f"RUN: {pre_cmd}") - subprocess.run(gpu_header + header_cmd + pre_cmd, shell=True) - - for eos in ("no_eos",): + if FLAGS.pre_stage and FLAGS.load_exp_folder is None: + gpu = assign_to_gpu(gpus, f"{baseline_eval_file}.pickle") + gpu_header = f"export CUDA_VISIBLE_DEVICES={gpu};" + + pre_params = ( + f"--metrics_file={FLAGS.baseline_metrics_file} " + f"--seed={i} " + f"--checkpoint_folders={FLAGS.checkpoint_folders} " + f"--output_metrics_prefix={baseline_eval_file} " + "--gpu=0 " + "--disable_tqdm " + ) + + if subset == "corrects": + pre_params += "--only_correct " + elif subset == "wrongs": + pre_params += "--only_wrongs " + elif subset == "learned": + pre_params += "--only_learned " + else: + pass # random subset + + baseline_log_prefix = os.path.join(baseline_prefix, "logs/") + os.makedirs(baseline_log_prefix, exist_ok=True) + + logging.info( + "Experiment files" + f" {baseline_log_prefix}\nParams:\n{pre_params}" + ) + + pre_cmd = ( + f"python -u eval/reranker_pre.py {pre_params} > " + f"{baseline_log_prefix}/pre.log 2>" + f"{baseline_log_prefix}/pre.err;" + ) + + logging.info(f"RUN: {pre_cmd}") + subprocess.run(gpu_header + header_cmd + pre_cmd, shell=True) + + for eos in ("no_eos", "eos"): for accum in ("accum", "no_accum"): - ckpt_prefix = os.path.join( - baseline_prefix, f"{eos}_{accum}/" - ) + if FLAGS.load_exp_folder is None: + ckpt_prefix = os.path.join( + baseline_prefix, f"{eos}_{accum}/" + ) + else: + ckpt_prefix = os.path.join( + FLAGS.exp_folder, + f"seed_{i}", + subset, + f"{eos}_{accum}/", + ) + ckpt_log_prefix = os.path.join(ckpt_prefix, "logs/") os.makedirs(ckpt_log_prefix, exist_ok=True) @@ -188,71 +219,77 @@ def main(_): files_to_check = [] - for c, folder in enumerate(checkpoint_folders): + if FLAGS.ckpt_stage: - checkpoint_name = pathlib.PurePath(folder).name + for c, folder in enumerate(checkpoint_folders): - output_ckpt_file = os.path.join( - ckpt_scores_prefix, f"{checkpoint_name}.pickle" - ) + checkpoint_name = pathlib.PurePath(folder).name - # files_to_check.append(output_ckpt_file) - gpu = assign_to_gpu(gpus, output_ckpt_file) + output_ckpt_file = os.path.join( + ckpt_scores_prefix, f"{checkpoint_name}.pickle" + ) - gpu_header = f"export CUDA_VISIBLE_DEVICES={gpu};" + check_file = output_ckpt_file.replace( + "pickle", "done" + ) - ckpt_params = ( - f"--metrics_file={baseline_eval_file}.pickle " - f"--seed={i} " - f"--checkpoint_folder={folder} " - f"--output_metrics_prefix={ckpt_scores_prefix} " - "--gpu=0 " - "--disable_tqdm " - ) + files_to_check.append(check_file) - if eos == "eos": - ckpt_params += "--include_eos " - if accum == "accum": - ckpt_params += "--load_accums " + gpu = assign_to_gpu(gpus, check_file) - if c == len(checkpoint_folders) - 1: - ckpt_params += "--calculate_activation_scores" - else: - ckpt_params += "--calculate_gradient_scores" + gpu_header = f"export CUDA_VISIBLE_DEVICES={gpu};" - ckpt_cmd = ( - "python -u eval/reranker_single_checkpoint.py" - f" {ckpt_params} >{ckpt_log_prefix}/ckpt.{c}.log;" - ) + ckpt_params = ( + f"--metrics_file={baseline_eval_file}.pickle " + f"--seed={i} " + f"--checkpoint_folder={folder} " + f"--output_metrics_prefix={ckpt_scores_prefix} " + "--gpu=0 " + "--disable_tqdm " + ) - logging.info(f"RUN: {ckpt_cmd}") + if eos == "eos": + ckpt_params += "--include_eos " + if accum == "accum": + ckpt_params += "--load_accums " - subprocess.Popen( - gpu_header + header_cmd + ckpt_cmd, shell=True - ) - time.sleep(10) + if c == len(checkpoint_folders) - 1: + ckpt_params += "--calculate_activation_scores" + else: + ckpt_params += "--calculate_gradient_scores" + + ckpt_cmd = ( + "python -u eval/reranker_single_checkpoint.py" + f" {ckpt_params} >{ckpt_log_prefix}/ckpt.{c}.log" + f" 2>{ckpt_log_prefix}/ckpt.{c}.err; " + ) - wait_for_files(files_to_check) - # time.sleep(600) # give some time for process for finishing the writing to file + logging.info(f"RUN: {ckpt_cmd}") - post_params = ( - f"--metrics_file={baseline_eval_file}.pickle " - f"--seed={i} " - f"--scores_folder={ckpt_scores_prefix} " - "--exp_type=layers " - f"--output_metrics_file={ckpt_prefix}/results_detailed " - "--disable_tqdm " - ) + subprocess.Popen( + gpu_header + header_cmd + ckpt_cmd, shell=True + ) + time.sleep(10) - post_cmd = ( - f"python -u eval/reranker_post.py {post_params} >" - f"{ckpt_log_prefix}/post.log 2> " - f"{ckpt_log_prefix}/post.err;" - ) + if FLAGS.post_stage: + wait_for_files(files_to_check) + + post_params = ( + f"--metrics_file={baseline_eval_file}.pickle" + f" --seed={i} --scores_folder={ckpt_scores_prefix} --exp_type=layers" + f" --output_metrics_file={ckpt_prefix}/results_detailed" + " --disable_tqdm " + ) + + post_cmd = ( + f"python -u eval/reranker_post.py {post_params} >" + f"{ckpt_log_prefix}/post.log 2> " + f"{ckpt_log_prefix}/post.err;" + ) - logging.info(f"RUN: {post_cmd}") + logging.info(f"RUN: {post_cmd}") - subprocess.Popen(header_cmd + post_cmd, shell=True) + subprocess.Popen(header_cmd + post_cmd, shell=True) if __name__ == "__main__": diff --git a/scripts/bm25_results.sh b/scripts/bm25_results.sh index 0309a44..7047ddc 100644 --- a/scripts/bm25_results.sh +++ b/scripts/bm25_results.sh @@ -1,26 +1,17 @@ #!/bin/bash data_root=LAMA/data -lama_root=${data_root}TREx_lama_templates_v3 -uri_file=${lama_root}/abstracts/all_used_uris.txt -abstract_file=${lama_root}/abstracts/all_used.tfrecord -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_json_file=${lama_root}/abstracts/all_used.jsonl +lama_root=${data_root}/TREx_lama_templates_v3 +fact_to_ids_file=${lama_root}/abstracts/fact_to_ids_used.json -nn_output_file=${data_root}/nns/bm25/unfiltered_bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/unfiltered_bm25plus_metrics_v3_sentence_level.json +nn_output_file=${data_root}/nns/bm25/unfiltered_bm25plus_nn_results_v3_hf.jsonl +metric_output_file=${data_root}/metrics/bm25/unfiltered_bm25plus_metrics_v3_hf.json python eval/get_nns_bm25.py \ - --abstract_file ${abstract_file} \ - --test_file ${test_file} \ --output_file ${nn_output_file} \ --topk 250 -# python evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_json_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ +# python eval/evaluate.py \ +# --fact_to_ids_file ${fact_to_ids_file} \ # --nn_list_file ${nn_output_file} \ # --output_file ${metric_output_file} diff --git a/scripts/ensemble.sh b/scripts/ensemble.sh new file mode 100644 index 0000000..a503304 --- /dev/null +++ b/scripts/ensemble.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +data_root=LAMA/data/ +nn_output_file=${data_root}/nns/bm25/unfiltered_bm25plus_nn_results_v3_hf.jsonl +metric_output_file=${data_root}/metrics/bm25/unfiltered_bm25plus_metrics_v3_hf.pickle +T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ +checkpoint_folders="${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}15300.bin,${T5_PREFIX}1000000.bin" + +source /afs/csail.mit.edu/u/a/akyurek/akyurek/gitother/fewshot_lama/trex/bin/activate +export PYTHONPATH="/raid/lingo/akyurek/gitother/fewshot_lama" +exp_folder=LAMA/data/metrics/reranker/sweep_v2/ + + +for weight in 0.1 0.3 0.5 0.7 0.9; +do + weight_exp_folder=${exp_folder}/ensemble_bm25/weight_${weight}/ + mkdir -p ${weight_exp_folder} + python ensemble.py \ + --checkpoint_folders ${checkpoint_folders} \ + --baseline_metrics_file ${metric_output_file} \ + --baseline_nn_file ${nn_output_file} \ + --data_root ${data_root} \ + --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ + --baseline_reweight ${weight} \ + --gpus_to_use 0,1,2,3 \ + --load_exp_folder ${exp_folder} \ + --noeval_stage \ + --nopre_stage \ + --nockpt_stage \ + --exp_folder ${weight_exp_folder} > ${weight_exp_folder}/.log2.txt 2> ${weight_exp_folder}/.err2.txt +done diff --git a/scripts/reranker.sh b/scripts/reranker.sh new file mode 100644 index 0000000..131ce0f --- /dev/null +++ b/scripts/reranker.sh @@ -0,0 +1,85 @@ +#!/bin/bash + +data_root=LAMA/data/ +nn_output_file=${data_root}/nns/bm25/unfiltered_bm25plus_nn_results_v3_hf.jsonl +metric_output_file=${data_root}/metrics/bm25/unfiltered_bm25plus_metrics_v3_hf.pickle +T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ +checkpoint_folders="${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}15300.bin,${T5_PREFIX}1000000.bin" + +source /afs/csail.mit.edu/u/a/akyurek/akyurek/gitother/fewshot_lama/trex/bin/activate +export PYTHONPATH="/raid/lingo/akyurek/gitother/fewshot_lama" +exp_folder=LAMA/data/metrics/reranker/sweep_v2/ +mkdir -p ${exp_folder} + +python reranker.py \ + --checkpoint_folders ${checkpoint_folders} \ + --baseline_metrics_file ${metric_output_file} \ + --baseline_nn_file ${nn_output_file} \ + --data_root ${data_root} \ + --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ + --gpus_to_use 0,1,2,3 \ + --exp_folder ${exp_folder} > ${exp_folder}/.log5.txt 2> ${exp_folder}/.err5.txt + + +exp_folder_ft=LAMA/data/metrics/reranker/sweep_v2_ft/ +mkdir -p ${exp_folder_ft} +T5_PREFIX_FT=${data_root}/T5_checkpoints/finetune/checkpoint- +checkpoint_folders_ft=${T5_PREFIX_FT}5000,${T5_PREFIX_FT}10000,${T5_PREFIX_FT}30000,${T5_PREFIX_FT}80000 + +# Evaluate FT model in its own learned subset +python reranker.py \ + --checkpoint_folders ${checkpoint_folders_ft} \ + --baseline_metrics_file ${metric_output_file} \ + --baseline_nn_file ${nn_output_file} \ + --data_root ${data_root} \ + --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ + --gpus_to_use 0,1,2,3 \ + --noeval_stage \ + --exp_folder ${exp_folder_ft} > ${exp_folder_ft}/.log.txt 2> ${exp_folder_ft}/.err.txt + + + +# Evaluate PT model in FTs' learned subset +exp_folder=LAMA/data/metrics/reranker/sweep_v2_pt/ +mkdir -p ${exp_folder} +python reranker.py \ + --checkpoint_folders ${checkpoint_folders} \ + --baseline_metrics_file ${metric_output_file} \ + --baseline_nn_file ${nn_output_file} \ + --data_root ${data_root} \ + --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ + --gpus_to_use 0,1,2,3 \ + --load_exp_folder ${exp_folder_ft} \ + --noeval_stage \ + --nopre_stage \ + --exp_folder ${exp_folder} > ${exp_folder}/.log.txt 2> ${exp_folder}/.err.txt + + +exp_folder_ft=LAMA/data/metrics/reranker/sweep_v2_ft_pt/ +# Evaluate FT model on pretrained learned subset of MT5 +python reranker.py \ + --checkpoint_folders ${checkpoint_folders_ft} \ + --baseline_metrics_file ${metric_output_file} \ + --baseline_nn_file ${nn_output_file} \ + --data_root ${data_root} \ + --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ + --gpus_to_use 0,1,2,3 \ + --exp_folder ${exp_folder_ft} \ + --noeval_stage \ + --nopre_stage \ + --load_exp_folder ${exp_folder} > ${exp_folder_ft}/.log.txt 2> ${exp_folder_ft}/.err.txt + + +# Single checkpoint exps +# checkpoint_folders_sp="${T5_PREFIX}10200.bin" +# exp_folder_sp=LAMA/data/metrics/reranker/sweep_10200/ +# mkdir -p ${exp_folder_sp} +# python reranker.py \ +# --checkpoint_folders ${checkpoint_folders_sp} \ +# --baseline_metrics_file ${metric_output_file} \ +# --baseline_nn_file ${nn_output_file} \ +# --data_root ${data_root} \ +# --fact_to_ids_file LAMA/data/TREx_lama_templates_v3/abstracts/fact_to_ids.json \ +# --gpus_to_use0,1,2,3 \ +# --load_exp_folder ${exp_folder} \ +# --exp_folder ${exp_folder_sp} > ${exp_folder_sp}/.log4.txt 2> ${exp_folder_sp}/.err4.txt diff --git a/scripts/reranker_ab.sh b/scripts/reranker_ab.sh deleted file mode 100644 index 886ca95..0000000 --- a/scripts/reranker_ab.sh +++ /dev/null @@ -1,77 +0,0 @@ -#!/bin/bash -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 - -uri_file=${lama_root}/abstracts/all_used_uris.txt -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_file=${lama_root}/abstracts/all_used.jsonl -nn_output_file=${data_root}/nns/bm25/bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/bm25plus_metrics_v3_sentence_level.json - - -# source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh - -# python -u eval/evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ -# --nn_list_file ${nn_output_file} \ -# --output_file ${metric_output_file} - -# deactivate - -eval "$(conda shell.bash hook)" -conda activate transformers -CUDA_VISIBLE_DEVICES=8,9,10,11 -T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ -checkpoint_folders=${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}15300.bin,${T5_PREFIX}1000000.bin - - -for i in 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "no_accum"; do - # cnt=$((cnt + 1)) - # echo $cnt - # if [[ $cnt -gt 2 ]]; then - output_metric_prefix=${data_root}/metrics/reranker/exp_interpol_${i}/ - mkdir -p ${output_metric_prefix} - output_metric_prefix=${output_metric_prefix}/ln_sl_${eos}_${target}_${subset}_${accum} - params=("--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}" "--exp_type=interpol") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker_local_norm.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - # fi - done - done - done -done - -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "random" "learned"; do - for accum in "no_accum"; do - output_metric_prefix=${data_root}/metrics/reranker/exp_interpol_${i}/ln_sl_${eos}_${target}_${subset}_${accum} - params=("--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}" "--exp_type=interpol") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - done - done - done -done -conda deactivate diff --git a/scripts/reranker_call.sh b/scripts/reranker_call.sh deleted file mode 100644 index 2fdd3e1..0000000 --- a/scripts/reranker_call.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - -data_root=LAMA/data/ -nn_output_file=${data_root}/nns/bm25/unfiltered_bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/unfiltered_bm25plus_metrics_v3_sentence_level.pickle -T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ -checkpoint_folders="${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}15300.bin,${T5_PREFIX}1000000.bin" - -source /afs/csail.mit.edu/u/a/akyurek/akyurek/gitother/fewshot_lama/trex/bin/activate -export PYTHONPATH="/raid/lingo/akyurek/gitother/fewshot_lama" -# CUDA_VISIBLE_DEVICES=13 -exp_folder=LAMA/data/metrics/reranker/sweep/ -mkdir -p ${exp_folder} - -python reranker.py \ - --checkpoint_folders ${checkpoint_folders} \ - --baseline_metrics_file ${metric_output_file} \ - --baseline_nn_file ${nn_output_file} \ - --data_root ${data_root} \ - --lama_folder LAMA/data/TREx_lama_templates_v3 \ - --gpus_to_use 2,3,14,15\ - --exp_folder ${exp_folder} > ${exp_folder}/.log.txt 2> ${exp_folder}/.err.txt diff --git a/scripts/reranker_ckpts.sh b/scripts/reranker_ckpts.sh deleted file mode 100644 index 2c7e2d3..0000000 --- a/scripts/reranker_ckpts.sh +++ /dev/null @@ -1,77 +0,0 @@ -#!/bin/bash - -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 - -uri_file=${lama_root}/abstracts/all_used_uris.txt -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_file=${lama_root}/abstracts/all_used.jsonl -nn_output_file=${data_root}/nns/bm25/bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/bm25plus_metrics_v3_sentence_level.json - - -# source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh - -# python -u eval/evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ -# --nn_list_file ${nn_output_file} \ -# --output_file ${metric_output_file} - -# deactivate - -eval "$(conda shell.bash hook)" -conda activate transformers -CUDA_VISIBLE_DEVICES=11,14,15 -T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ -checkpoint_folders=${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}1000000.bin - -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "accum"; do - # cnt=$((cnt + 1)) - # echo $cnt - # if [[ $cnt -gt 2 ]]; then - output_metric_prefix=${data_root}/metrics/reranker/exp_layers_2ckpt_${i} - mkdir -p ${output_metric_prefix} - output_metric_prefix=${output_metric_prefix}/ln_sl_${eos}_${target}_${subset}_${accum} - params=("--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker_local_norm.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - # fi - done - done - done -done - -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "accum"; do - output_metric_prefix=${data_root}/metrics/reranker/exp_layers_2ckpt_${i}/gn_sl_${eos}_${target}_${subset}_${accum} - params=("--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - done - done - done -done -conda deactivate diff --git a/scripts/reranker_ft.sh b/scripts/reranker_ft.sh deleted file mode 100644 index b23d1c0..0000000 --- a/scripts/reranker_ft.sh +++ /dev/null @@ -1,87 +0,0 @@ -#!/bin/bash - -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 - -uri_file=${lama_root}/abstracts/all_used_uris.txt -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_file=${lama_root}/abstracts/all_used.jsonl -nn_output_file=${data_root}/nns/bm25/bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/bm25plus_metrics_v3_sentence_level.json - - -# source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh - -# python -u eval/evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ -# --nn_list_file ${nn_output_file} \ -# --output_file ${metric_output_file} - -# deactivate - -eval "$(conda shell.bash hook)" -conda activate transformers -CUDA_VISIBLE_DEVICES=12,13,14,15 -T5_PREFIX=${data_root}/T5_checkpoints/finetune/checkpoint- -checkpoint_folders=${T5_PREFIX}5000,${T5_PREFIX}10000,${T5_PREFIX}30000,${T5_PREFIX}60000 - -cnt=0 -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "no_accum"; do - # cnt=$((cnt + 1)) - # echo $cnt - # if [[ $cnt -gt 2 ]]; then - output_metric_prefix=${data_root}/metrics/reranker/exp_finetune_${i} - mkdir -p ${output_metric_prefix} - output_metric_prefix=${output_metric_prefix}/ln_sl_${eos}_${target}_${subset}_${accum} - samples_load_path=${data_root}/metrics/reranker/exp_layers_${i}/ln_sl_${eos}_${target}_${subset}_${accum}.json - params=("--samples_from_exp=${samples_load_path}" "--seed=${i}" "--metrics_file=${metric_output_file}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker_local_norm.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - - # fi - done - done - done -done - -# for i in 0 1 2; do -# for eos in "no_eos"; do -# for subset in "learned" "random"; do -# for accum in "no_accum"; do -# # cnt=$((cnt + 1)) -# # echo $cnt -# # if [[ $cnt -gt 2 ]]; then -# output_metric_prefix=${data_root}/metrics/reranker/exp_finetune_${i}/gn_sl_${eos}_${target}_${subset}_${accum} -# samples_load_path=${data_root}/metrics/reranker/exp_layers_${i}/gn_sl_${eos}_${target}_${subset}_${accum}.json -# params=("--samples_from_exp=${samples_load_path}" "--seed=${i}" "--metrics_file=${metric_output_file}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") -# [[ $eos == "eos" ]] && params+=(--include_eos) -# [[ $subset == "corrects" ]] && params+=(--only_corrects) -# [[ $subset == "wrongs" ]] && params+=(--only_wrongs) -# [[ $subset == "learned" ]] && params+=(--only_learned) -# [[ $accum == "accum" ]] && params+=(--load_accums) -# echo "Logging to ${output_metric_prefix}" -# echo "Params: " -# echo "${params[@]}" -# python -u eval/reranker.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - -# # fi -# done -# done -# done -# done - -conda deactivate diff --git a/scripts/reranker_ft_plus.sh b/scripts/reranker_ft_plus.sh deleted file mode 100644 index fe98d67..0000000 --- a/scripts/reranker_ft_plus.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash - - -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 - -uri_file=${lama_root}/abstracts/all_used_uris.txt -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_file=${lama_root}/abstracts/all_used.jsonl -nn_output_file=${data_root}/nns/bm25/bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/bm25plus_metrics_v3_sentence_level.json - - -# source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh - -# python -u eval/evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ -# --nn_list_file ${nn_output_file} \ -# --output_file ${metric_output_file} - -# deactivate - -eval "$(conda shell.bash hook)" -conda activate transformers -CUDA_VISIBLE_DEVICES=8,9,10,11 -T5_PREFIX=${data_root}/T5_checkpoints/finetune/checkpoint- -checkpoint_folders=${T5_PREFIX}5000,${T5_PREFIX}10000,${T5_PREFIX}30000,${T5_PREFIX}60000 - -cnt=0 -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "no_accum"; do - # cnt=$((cnt + 1)) - # echo $cnt - # if [[ $cnt -gt 2 ]]; then - output_metric_prefix=${data_root}/metrics/reranker/exp_finetune_${i}_learned_plus - mkdir -p ${output_metric_prefix} - output_metric_prefix=${output_metric_prefix}/ln_sl_${eos}_${target}_${subset}_${accum} - # samples_load_path=metrics/reranker/exp_layers_${i}/ln_sl_${eos}_${target}_${subset}_${accum}.json - params=("--seed=${i}" "--metrics_file=${metric_output_file}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker_local_norm.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - - # fi - done - done - done -done - -# for i in 0 1 2; do -# for eos in "no_eos"; do -# for subset in "learned" "random"; do -# for accum in "no_accum"; do -# # cnt=$((cnt + 1)) -# # echo $cnt -# # if [[ $cnt -gt 2 ]]; then -# output_metric_prefix=${data_root}/metrics/reranker/exp_finetune_${i}/gn_sl_${eos}_${target}_${subset}_${accum} -# samples_load_path=${data_root}/metrics/reranker/exp_layers_${i}/gn_sl_${eos}_${target}_${subset}_${accum}.json -# params=("--samples_from_exp=${samples_load_path}" "--seed=${i}" "--metrics_file=${metric_output_file}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") -# [[ $eos == "eos" ]] && params+=(--include_eos) -# [[ $subset == "corrects" ]] && params+=(--only_corrects) -# [[ $subset == "wrongs" ]] && params+=(--only_wrongs) -# [[ $subset == "learned" ]] && params+=(--only_learned) -# [[ $accum == "accum" ]] && params+=(--load_accums) -# echo "Logging to ${output_metric_prefix}" -# echo "Params: " -# echo "${params[@]}" -# python -u eval/reranker.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - -# # fi -# done -# done -# done -# done - -conda deactivate diff --git a/scripts/reranker_plus.sh b/scripts/reranker_plus.sh deleted file mode 100644 index c0daf63..0000000 --- a/scripts/reranker_plus.sh +++ /dev/null @@ -1,78 +0,0 @@ -#!/bin/bash - -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 - -uri_file=${lama_root}/abstracts/all_used_uris.txt -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord -abstract_file=${lama_root}/abstracts/all_used.jsonl -nn_output_file=${data_root}/nns/bm25/bm25plus_nn_results_v3_sentence_level.jsonl -metric_output_file=${data_root}/metrics/bm25/bm25plus_metrics_v3_sentence_level.json - - -# source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh - -# python -u eval/evaluate.py \ -# --abstract_uri_list ${uri_file} \ -# --abstract_file ${abstract_file} \ -# --test_data ${test_file} \ -# --hashmap_file ${hashmap_file} \ -# --nn_list_file ${nn_output_file} \ -# --output_file ${metric_output_file} - -# deactivate - -eval "$(conda shell.bash hook)" -conda activate transformers -CUDA_VISIBLE_DEVICES=2,3,6,7 -T5_PREFIX=${data_root}/T5_checkpoints/1000000/model/pytorch_model_ -checkpoint_folders=${T5_PREFIX}5100.bin,${T5_PREFIX}10200.bin,${T5_PREFIX}15300.bin,${T5_PREFIX}1000000.bin - -for i in 0 1 2; do - for eos in "no_eos"; do - for subset in "learned" "random"; do - for accum in "no_accum"; do - # cnt=$((cnt + 1)) - # echo $cnt - # if [[ $cnt -gt 2 ]]; then - output_metric_prefix=${data_root}/metrics/reranker/exp_layers_${i}_plus - mkdir -p ${output_metric_prefix} - output_metric_prefix=${output_metric_prefix}/ln_sl_${eos}_${target}_${subset}_${accum} - samples_load_path=${data_root}/metrics/reranker/exp_finetune_${i}_learned_plus/ln_sl_${eos}_${target}_${subset}_${accum}.json - params=("--samples_from_exp=${samples_load_path}" "--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") - [[ $eos == "eos" ]] && params+=(--include_eos) - [[ $subset == "corrects" ]] && params+=(--only_corrects) - [[ $subset == "wrongs" ]] && params+=(--only_wrongs) - [[ $subset == "learned" ]] && params+=(--only_learned) - [[ $accum == "accum" ]] && params+=(--load_accums) - echo "Logging to ${output_metric_prefix}" - echo "Params: " - echo "${params[@]}" - python -u eval/reranker_local_norm.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err - # fi - done - done - done -done - -# for i in 0 1 2; do -# for eos in "eos"; do -# for subset in "random" "learned"; do -# for accum in "no_accum"; do -# output_metric_prefix=${data_root}/metrics/reranker/exp_layers_${i}/gn_sl_${eos}_${target}_${subset}_${accum} -# params=("--metrics_file=${metric_output_file}" "--seed=${i}" "--hashmap_file=${hashmap_file}" "--checkpoint_folders=${checkpoint_folders}" "--output_metrics_prefix=${output_metric_prefix}") -# [[ $eos == "eos" ]] && params+=(--include_eos) -# [[ $subset == "corrects" ]] && params+=(--only_corrects) -# [[ $subset == "wrongs" ]] && params+=(--only_wrongs) -# [[ $subset == "learned" ]] && params+=(--only_learned) -# [[ $accum == "accum" ]] && params+=(--load_accums) -# echo "Logging to ${output_metric_prefix}" -# echo "Params: " -# echo "${params[@]}" -# python -u eval/reranker.py "${params[@]}" > ${output_metric_prefix}.log 2> ${output_metric_prefix}.err -# done -# done -# done -# done -conda deactivate diff --git a/scripts/results.sh b/scripts/results.sh deleted file mode 100644 index ab84641..0000000 --- a/scripts/results.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash - -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates - -uri_file=${lama_root}/abstracts/all_used_uris.txt -abstract_file=${lama_root}/abstracts/all_used.jsonl -hashmap_file=${lama_root}/abstracts/hashmap_used.json -test_file=${lama_root}/all.tfrecord - - -nn_output_file=${data_root}/nns/activations/nn_results_activations.jsonl -metric_output_file=${data_root}/metrics/activations/activation_metrics.json - -source /raid/lingo/akyurek/gitother/fewshot_lama/setup.sh -CUDA_VISIBLE_DEVICES=12 - - python eval/get_nns.py \ - --abstract_vectors ${lama_root}/encodings/activations/train/labeled_data.tfr@100 \ - --test_vectors ${lama_root}/encodings/activations/test/labeled_data.tfr@1 \ - --output_file ${nn_output_file} \ - --gpu_workers 1 \ - --batch_size 100 \ - --topk 100 \ - --feature_size 3072 - - python eval/evaluate.py \ - --abstract_uri_list ${uri_file} \ - --abstract_file ${abstract_file} \ - --test_data ${test_file} \ - --hashmap_file ${hashmap_file} \ - --nn_list_file ${nn_output_file} \ - --output_file ${metric_output_file} diff --git a/scripts/target_normalize.sh b/scripts/target_normalize.sh deleted file mode 100644 index 7f6c3b9..0000000 --- a/scripts/target_normalize.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates -abstract_file=${lama_root}/abstracts/all_used.tfrecord - - -python eval/target_normalize.py \ - --abstract_file ${abstract_file} \ - --abstract_vectors ${lama_root}/encodings/train/labeled_data.tfr@100 \ - --test_file ${lama_root}/all.tfrecord \ - --test_vectors ${lama_root}/encodings/test \ - --output_file ${lama_root}/encodings/train/labeled_data_target_normalizedv1.tfr@100 \ - --test_output_file ${lama_root}/encodings/test_labeled_data_target_normalizedv1.tfr@1 \ - --gpu_workers 1 diff --git a/scripts/tf_to_pytorch.sh b/scripts/tf_to_pytorch.sh deleted file mode 100644 index ccc7d78..0000000 --- a/scripts/tf_to_pytorch.sh +++ /dev/null @@ -1,16 +0,0 @@ -data_root=LAMA/data -lama_root=${data_root}/TREx_lama_templates_v3 -export T5=${data_root}/T5_checkpoints/1000000/model/ - -# deactivate -# -# conda activate transformers - -for i in 5100 10200 15300 20400 1000000; do - transformers-cli convert \ - --model_type t5 \ - --tf_checkpoint $T5/model.ckpt-${i} \ - --config $T5/config.json \ - --load_accumulators_instead \ - --pytorch_dump_output $T5/pytorch_accum_${i}.bin -done diff --git a/scripts/trex_convert.sh b/scripts/trex_convert.sh index 360ef9c..44585c2 100644 --- a/scripts/trex_convert.sh +++ b/scripts/trex_convert.sh @@ -13,7 +13,4 @@ done python data/to_tf_record.py --input_file ${input_folder}/all.jsonl mkdir -p ${input_folder}/abstracts/ -pythond data/extract_abstracts.py --input_folder ${input_folder} --abstract_file ${data_root}/original_trex/all.jsonl - -pcregrep -o1 --buffer-size=999999 '"sentence_uris": "(.*?)"' ${input_folder}/abstracts/all_used.jsonl > ${input_folder}/all_used_uris.txt -pcregrep -o1 --buffer-size=999999 '"sentence_uris": "(.*?)"' ${input_folder}/abstracts/all.jsonl > ${input_folder}/all_uris.txt +python data/extract_abstracts.py --input_folder ${input_folder} --abstract_file ${data_root}/original_trex/all.jsonl diff --git a/scripts/trex_convert_from_json.sh b/scripts/trex_convert_from_json.sh deleted file mode 100644 index 78b349a..0000000 --- a/scripts/trex_convert_from_json.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -input_folder=$1 -echo $input_folder - -for f in ${input_folder}/*jsonl; do - echo "$f" - python data/json_to_tf_record.py --input_file $f -done diff --git a/setup.sh b/setup.sh index 3e903f9..46ce9a4 100644 --- a/setup.sh +++ b/setup.sh @@ -1,3 +1,4 @@ #!/bin/bash -source /raid/lingo/akyurek/gitother/fewshot_lama/trex/bin/activate -export PYTHONPATH="/raid/lingo/akyurek/gitother/fewshot_lama" \ No newline at end of file + +source trex/bin/activate +export PYTHONPATH=$(pwd) diff --git a/stats.ipynb b/stats.ipynb new file mode 100644 index 0000000..68a7a87 --- /dev/null +++ b/stats.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import copy\n", + "import datasets\n", + "plt.style.use('/raid/lingo/akyurek/mplstyle')\n", + "plt.rc('font', serif='Times')\n", + "plt.rc('text', usetex=False)\n", + "plt.rcParams['figure.dpi'] = 150\n", + "plt.rcParams['figure.facecolor'] = 'white'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_DIR = \"LAMA/data/\"\n", + "METRICS_DIR = os.path.join(BASE_DIR, \"metrics\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def read_jsonl(path):\n", + " data = []\n", + " with open(path) as f:\n", + " for line in f:\n", + " data.append(json.loads(line))\n", + " return np.array(data)\n", + "\n", + "def read_facts(abstracts):\n", + " facts = set()\n", + " for a in abstracts:\n", + " for fact in a['facts'].split(';'):\n", + " facts.add(fact)\n", + " return facts\n", + "\n", + "def read_no_facts(abstracts):\n", + " return np.array([len(set(a['facts'].split(';'))) for a in abstracts])\n", + "\n", + "def facts_to_field(facts, field=\"obj_uri\"):\n", + " if field == \"obj_uri\":\n", + " v = [fact.split(',')[1] for fact in facts]\n", + " elif field == \"sub_uri\":\n", + " v = [fact.split(',')[2] for fact in facts]\n", + " else:\n", + " v = [fact.split(',')[0] for fact in facts]\n", + " return v\n", + "\n", + "def read_string_field(abstracts, field=\"obj_uri\"):\n", + " return np.array([a[field] for a in abstracts])\n", + "\n", + "\n", + "def get_sentence(abstract):\n", + " targets = abstract['targets_pretokenized'].replace(' ', '').strip()\n", + " sentence = abstract['inputs_pretokenized'].replace('', targets)\n", + " return sentence" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "queries = list(datasets.load_dataset(\"data/ftrace\", \"queries\", split=\"train\"))\n", + "abstracts = datasets.load_dataset(\"data/ftrace\", \"abstracts\", split=\"train\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(len(abstracts), len(queries))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abstracts[0]\n", + "\n", + "def read_surface_stats(abstracts):\n", + " entities = {} \n", + " for abstract in abstracts:\n", + " uri = abstract['masked_uri']\n", + " surface_form = abstract['targets_pretokenized'].replace(' ', '')\n", + " if uri not in entities:\n", + " entities[uri] = set()\n", + " entities[uri].add(surface_form)\n", + " return entities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "entities = read_surface_stats(abstracts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abstract_sentences = list(map(lambda x: set(get_sentence(x).split()), abstracts))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(abstract_sentences)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fact_to_ids = json.load(open(BASE_DIR + \"TREx_lama_templates_v3/abstracts/fact_to_ids_used.json\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ids_to_abstracts = {a[\"id\"]: a for a in abstracts}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abstract_ids = abstracts[\"id\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from src.lama_utils import get_sentence\n", + "def get_token_match(query, abstract, filtered_words=None):\n", + " total = 0\n", + " for w in query:\n", + " if filtered_words is not None:\n", + " if w in abstract:\n", + " total += 1\n", + " else:\n", + " if w in filtered_words and w in abstract:\n", + " total += 1\n", + " return total\n", + "\n", + "from src.metric_utils import reciprocal_rank\n", + "def mrr_of_exact_match(queries, abstracts, abstract_sentences, topk=250, filter=False):\n", + " rr = []\n", + " precisions = []\n", + " recalls = []\n", + " for query in queries:\n", + " fact = query['predicate_id'] + ',' + query['obj_uri'] + ',' + query['sub_uri']\n", + " \n", + " if filter:\n", + " filtered_words = []\n", + " filtered_words += query['sub_surface'].split(' ').lower()\n", + " filtered_words += query['obj_surface'].split(' ').lower()\n", + " else:\n", + " filtered_words = None\n", + " \n", + " ids = list(map(str, fact_to_ids[fact]))\n", + " correct_idxs = [abstract_ids.index(id) for id in ids]\n", + " query_sentence = set(get_sentence(query).lower().split())\n", + " scores = []\n", + " best_score = 0.0\n", + " best_index = 0\n", + " for index, abstract_sentence in enumerate(abstract_sentences):\n", + " abstract_sentence = list(map(str.lower, abstract_sentence))\n", + " score = get_token_match(query_sentence, abstract_sentence)\n", + " if score > best_score:\n", + " best_index = index\n", + " best_score = score \n", + " scores.append(score)\n", + " scores = np.array(scores)\n", + " idxs = np.argpartition(scores, -250)[-250 :]\n", + " nn_idxs = idxs[np.argsort(-scores[idxs])]\n", + " nn_scores = scores[nn_idxs].tolist()\n", + " rr.append(reciprocal_rank(nn_idxs, correct_idxs))\n", + " \n", + " return rr\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rrs = mrr_of_exact_match(queries[:100], abstracts, abstract_sentences)\n", + "np.mean(rrs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rrs = mrr_of_exact_match(queries[:100], abstracts, abstract_sentences, filter=True)\n", + "np.mean(rrs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "queries[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.mean([len(v) for k, v in entities.items()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "facts = read_facts(abstracts)\n", + "no_facts = read_no_facts(abstracts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pairs = {}\n", + "for fact in facts:\n", + " predicate, *pair = fact.split(',')\n", + " pair = tuple(pair)\n", + " if pair not in pairs:\n", + " pairs[pair] = set()\n", + " pairs[pair].add(predicate)\n", + "np.mean([len(v) for k, v in pairs.items()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abstracts[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentences = [get_sentence(abstract) for abstract in abstracts]\n", + "len(set(sentences))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(facts), tuple(f(no_facts) for f in (np.mean, np.std, np.min, np.max))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pos_nos_abstracts = tuple(len(set(facts_to_field(facts, field=field))) \n", + " for field in ('predicate_id', 'obj_uri', 'sub_uri'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pos_nos_abstracts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "objs = read_string_field(queries, field=\"obj_uri\")\n", + "subs = read_string_field(queries, field=\"sub_uri\")\n", + "predicates = read_string_field(queries, field=\"predicate_id\")\n", + "pos_nos_queries = (len(set(predicates)), len(set(objs)), len(set(subs)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pos_nos_queries " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.mean([len(v) for k, v in fact_to_ids.items()])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = np.random.choice(queries)\n", + "print(\"====Query====\\n\", query)\n", + "fact = query['predicate_id'] + ',' + query['obj_uri'] + ',' + query['sub_uri']\n", + "fact_ids = fact_to_ids[fact]\n", + "current_abstracts = [ids_to_abstracts[str(id)] for id in fact_ids if str(id) in ids_to_abstracts]\n", + "print(\"====Abstracts====\\n\")\n", + "for a in current_abstracts:\n", + " print(a)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "83ce25b1c5d65648c98919b9641334dfc26d5cfa225e002eb6106bc7a8478051" + }, + "kernelspec": { + "display_name": "Python 3.7.6 ('trex': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}