{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Toxicity Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This tutorial is available as an IPython notebook at [Malaya/example/toxicity](https://github.com/huseinzol05/Malaya/tree/master/example/toxicity).\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This module trained on both standard and local (included social media) language structures, so it is save to use for both.\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5.54 s, sys: 821 ms, total: 6.36 s\n", "Wall time: 5.67 s\n" ] } ], "source": [ "%%time\n", "import malaya" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Models accuracy\n", "\n", "We use `sklearn.metrics.classification_report` for accuracy reporting, check at https://malaya.readthedocs.io/en/latest/models-accuracy.html#toxicity-analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### labels supported\n", "\n", "Default labels for toxicity module." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['severe toxic',\n", " 'obscene',\n", " 'identity attack',\n", " 'insult',\n", " 'threat',\n", " 'asian',\n", " 'atheist',\n", " 'bisexual',\n", " 'buddhist',\n", " 'christian',\n", " 'female',\n", " 'heterosexual',\n", " 'indian',\n", " 'homosexual, gay or lesbian',\n", " 'intellectual or learning disability',\n", " 'male',\n", " 'muslim',\n", " 'other disability',\n", " 'other gender',\n", " 'other race or ethnicity',\n", " 'other religion',\n", " 'other sexual orientation',\n", " 'physical disability',\n", " 'psychiatric or mental illness',\n", " 'transgender',\n", " 'malay',\n", " 'chinese']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya.toxicity.label" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "string = 'Benda yg SALAH ni, jgn lah didebatkan. Yg SALAH xkan jadi betul. Ingat tu. Mcm mana kesat sekalipun org sampaikan mesej, dan memang benda tu salah, diam je. Xyah nk tunjuk kau open sangat nk tegur cara org lain berdakwah. '\n", "another_string = 'melayu bodoh, dah la gay, sokong lgbt lagi, memang tak guna'\n", "string1 = 'Sis, students from overseas were brought back because they are not in their countries which is if something happens to them, its not the other countries’ responsibility. Student dalam malaysia ni dah dlm tggjawab kerajaan. Mana part yg tak faham?'\n", "string2 = 'Harap kerajaan tak bukak serentak. Slowly release week by week. Focus on economy related industries dulu'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load multinomial model\n", "\n", "```python\n", "def multinomial(**kwargs):\n", " \"\"\"\n", " Load multinomial toxicity model.\n", "\n", " Returns\n", " -------\n", " result : malaya.model.ml.MultilabelBayes class\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "model = malaya.toxicity.multinomial()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Predict batch of strings\n", "\n", "```python\n", "def predict(self, strings: List[str]):\n", " \"\"\"\n", " classify list of strings.\n", "\n", " Parameters\n", " ----------\n", " strings: List[str]\n", "\n", " Returns\n", " -------\n", " result: List[str]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['severe toxic',\n", " 'obscene',\n", " 'identity attack',\n", " 'insult',\n", " 'indian',\n", " 'malay',\n", " 'chinese']]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([string])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Predict batch of strings with probability\n", "\n", "```python\n", "def predict_proba(self, strings: List[str]):\n", " \"\"\"\n", " classify list of strings and return probability.\n", "\n", " Parameters\n", " ----------\n", " strings: List[str]\n", "\n", " Returns\n", " -------\n", " result: List[dict[str, float]]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'severe toxic': 0.997487040981572,\n", " 'obscene': 0.9455379277616331,\n", " 'identity attack': 0.8274699625500679,\n", " 'insult': 0.5607594945618526,\n", " 'threat': 0.024772971511820983,\n", " 'asian': 0.0221240002096628,\n", " 'atheist': 0.013774558637508741,\n", " 'bisexual': 0.0024495807483865223,\n", " 'buddhist': 0.004640372956039871,\n", " 'christian': 0.052795457745171054,\n", " 'female': 0.05289744129561423,\n", " 'heterosexual': 0.008128507494633362,\n", " 'indian': 0.9023637357823499,\n", " 'homosexual, gay or lesbian': 0.04385664232535533,\n", " 'intellectual or learning disability': 0.0014981591337876019,\n", " 'male': 0.07976929455558882,\n", " 'muslim': 0.08806420077375651,\n", " 'other disability': 0.0,\n", " 'other gender': 0.0,\n", " 'other race or ethnicity': 0.0017014040578187566,\n", " 'other religion': 0.0017333144620482767,\n", " 'other sexual orientation': 0.00122606681013474,\n", " 'physical disability': 0.001489522998169223,\n", " 'psychiatric or mental illness': 0.027125947355667267,\n", " 'transgender': 0.012349564445375391,\n", " 'malay': 0.9991900346707605,\n", " 'chinese': 0.9886782229459774}]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([string])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List available Transformer models" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Size (MB)Quantized Size (MB)micro precisionmicro recallmicro f1-score
bert425.6111.000.860980.773130.81469
tiny-bert57.415.400.835350.796110.81526
albert48.612.800.860540.769730.81261
tiny-albert22.45.980.835350.796110.81526
xlnet446.6118.000.779040.838290.80758
alxlnet46.813.300.833760.802210.81768
fastformer446.6118.000.882490.748260.80985
tiny-fastformer77.319.600.851310.766200.80652
\n", "
" ], "text/plain": [ " Size (MB) Quantized Size (MB) micro precision \\\n", "bert 425.6 111.00 0.86098 \n", "tiny-bert 57.4 15.40 0.83535 \n", "albert 48.6 12.80 0.86054 \n", "tiny-albert 22.4 5.98 0.83535 \n", "xlnet 446.6 118.00 0.77904 \n", "alxlnet 46.8 13.30 0.83376 \n", "fastformer 446.6 118.00 0.88249 \n", "tiny-fastformer 77.3 19.60 0.85131 \n", "\n", " micro recall micro f1-score \n", "bert 0.77313 0.81469 \n", "tiny-bert 0.79611 0.81526 \n", "albert 0.76973 0.81261 \n", "tiny-albert 0.79611 0.81526 \n", "xlnet 0.83829 0.80758 \n", "alxlnet 0.80221 0.81768 \n", "fastformer 0.74826 0.80985 \n", "tiny-fastformer 0.76620 0.80652 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya.toxicity.available_transformer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Transformer model\n", "\n", "```python\n", "def transformer(model: str = 'xlnet', quantized: bool = False, **kwargs):\n", " \"\"\"\n", " Load Transformer toxicity model.\n", "\n", " Parameters\n", " ----------\n", " model : str, optional (default='bert')\n", " Model architecture supported. Allowed values:\n", "\n", " * ``'bert'`` - Google BERT BASE parameters.\n", " * ``'tiny-bert'`` - Google BERT TINY parameters.\n", " * ``'albert'`` - Google ALBERT BASE parameters.\n", " * ``'tiny-albert'`` - Google ALBERT TINY parameters.\n", " * ``'xlnet'`` - Google XLNET BASE parameters.\n", " * ``'alxlnet'`` - Malaya ALXLNET BASE parameters.\n", " * ``'fastformer'`` - FastFormer BASE parameters.\n", " * ``'tiny-fastformer'`` - FastFormer TINY parameters.\n", "\n", " quantized : bool, optional (default=False)\n", " if True, will load 8-bit quantized model.\n", " Quantized model not necessary faster, totally depends on the machine.\n", "\n", " Returns\n", " -------\n", " result: model\n", " List of model classes:\n", "\n", " * if `bert` in model, will return `malaya.model.bert.SigmoidBERT`.\n", " * if `xlnet` in model, will return `malaya.model.xlnet.SigmoidXLNET`.\n", " * if `fastformer` in model, will return `malaya.model.fastformer.SigmoidFastFormer`.\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 47.0/46.8 [01:05<00:00, 1.39s/MB]\n", "184%|██████████| 1.00/0.54 [00:02<-1:59:59, 2.43s/MB]\n", "135%|██████████| 1.00/0.74 [00:03<00:00, 3.48s/MB]\n" ] } ], "source": [ "model = malaya.toxicity.transformer(model = 'alxlnet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Quantized model\n", "\n", "To load 8-bit quantized model, simply pass `quantized = True`, default is `False`.\n", "\n", "We can expect slightly accuracy drop from quantized model, and not necessary faster than normal 32-bit float model, totally depends on machine." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Load quantized model will cause accuracy drop.\n" ] } ], "source": [ "quantized_model = malaya.toxicity.transformer(model = 'alxlnet', quantized = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Predict batch of strings\n", "\n", "```python\n", "def predict(self, strings: List[str]):\n", " \"\"\"\n", " classify list of strings.\n", "\n", " Parameters\n", " ----------\n", " strings: List[str]\n", "\n", " Returns\n", " -------\n", " result: List[List[str]]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['obscene'],\n", " ['severe toxic', 'obscene', 'identity attack', 'insult', 'malay']]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([string,another_string])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Predict batch of strings with probability\n", "\n", "```python\n", "def predict_proba(self, strings: List[str]):\n", " \"\"\"\n", " classify list of strings and return probability.\n", "\n", " Parameters\n", " ----------\n", " strings : List[str]\n", "\n", " Returns\n", " -------\n", " result: List[dict[str, float]]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'severe toxic': 0.30419078,\n", " 'obscene': 0.07300964,\n", " 'identity attack': 0.02309686,\n", " 'insult': 0.14792377,\n", " 'threat': 0.0043829083,\n", " 'asian': 0.00018724799,\n", " 'atheist': 0.0013933778,\n", " 'bisexual': 0.0005682409,\n", " 'buddhist': 0.0006982982,\n", " 'christian': 0.00010216236,\n", " 'female': 0.0062876344,\n", " 'heterosexual': 3.6597252e-05,\n", " 'indian': 0.020283729,\n", " 'homosexual, gay or lesbian': 0.0008122027,\n", " 'intellectual or learning disability': 0.00015977025,\n", " 'male': 0.0007993579,\n", " 'muslim': 0.054483294,\n", " 'other disability': 0.00017657876,\n", " 'other gender': 0.00018069148,\n", " 'other race or ethnicity': 6.273389e-05,\n", " 'other religion': 0.0011053085,\n", " 'other sexual orientation': 0.0013027787,\n", " 'physical disability': 0.00010755658,\n", " 'psychiatric or mental illness': 0.00078335404,\n", " 'transgender': 0.00080055,\n", " 'malay': 0.0033579469,\n", " 'chinese': 0.20889702},\n", " {'severe toxic': 0.99571323,\n", " 'obscene': 0.91805434,\n", " 'identity attack': 0.95676684,\n", " 'insult': 0.7667657,\n", " 'threat': 0.02582252,\n", " 'asian': 0.00074103475,\n", " 'atheist': 0.0012175143,\n", " 'bisexual': 0.07754475,\n", " 'buddhist': 0.004547477,\n", " 'christian': 0.0019699335,\n", " 'female': 0.03404945,\n", " 'heterosexual': 0.029964417,\n", " 'indian': 0.021356285,\n", " 'homosexual, gay or lesbian': 0.13626209,\n", " 'intellectual or learning disability': 0.021410972,\n", " 'male': 0.029543608,\n", " 'muslim': 0.06485465,\n", " 'other disability': 0.0006414652,\n", " 'other gender': 0.04015115,\n", " 'other race or ethnicity': 0.010606945,\n", " 'other religion': 0.001650244,\n", " 'other sexual orientation': 0.04054076,\n", " 'physical disability': 0.0025109593,\n", " 'psychiatric or mental illness': 0.0022883855,\n", " 'transgender': 0.01127643,\n", " 'malay': 0.9658916,\n", " 'chinese': 0.33373892}]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba([string,another_string])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'severe toxic': 0.28386846,\n", " 'obscene': 0.25873762,\n", " 'identity attack': 0.021321118,\n", " 'insult': 0.19023287,\n", " 'threat': 0.005617261,\n", " 'asian': 0.00022211671,\n", " 'atheist': 0.000109523535,\n", " 'bisexual': 0.0019034147,\n", " 'buddhist': 0.00038090348,\n", " 'christian': 0.0016773939,\n", " 'female': 0.007807076,\n", " 'heterosexual': 0.0001899302,\n", " 'indian': 0.049388766,\n", " 'homosexual, gay or lesbian': 0.00043603778,\n", " 'intellectual or learning disability': 0.0012571216,\n", " 'male': 0.0043218136,\n", " 'muslim': 0.018054605,\n", " 'other disability': 0.0011820793,\n", " 'other gender': 0.00044164062,\n", " 'other race or ethnicity': 0.00012764335,\n", " 'other religion': 0.0009614825,\n", " 'other sexual orientation': 0.0040558875,\n", " 'physical disability': 0.0005840957,\n", " 'psychiatric or mental illness': 0.0023525357,\n", " 'transgender': 0.003135711,\n", " 'malay': 0.0013717413,\n", " 'chinese': 0.0051787198},\n", " {'severe toxic': 0.9966523,\n", " 'obscene': 0.82459927,\n", " 'identity attack': 0.97338796,\n", " 'insult': 0.49216133,\n", " 'threat': 0.010962069,\n", " 'asian': 0.0034621954,\n", " 'atheist': 0.0007635355,\n", " 'bisexual': 0.044597328,\n", " 'buddhist': 0.0061615705,\n", " 'christian': 0.0029616058,\n", " 'female': 0.023250878,\n", " 'heterosexual': 0.0038115382,\n", " 'indian': 0.0068957508,\n", " 'homosexual, gay or lesbian': 0.084989995,\n", " 'intellectual or learning disability': 0.006228268,\n", " 'male': 0.070231974,\n", " 'muslim': 0.055434316,\n", " 'other disability': 0.00017631054,\n", " 'other gender': 0.02043128,\n", " 'other race or ethnicity': 0.0032926202,\n", " 'other religion': 0.0035361946,\n", " 'other sexual orientation': 0.018447628,\n", " 'physical disability': 0.0007721717,\n", " 'psychiatric or mental illness': 0.004228982,\n", " 'transgender': 0.0046984255,\n", " 'malay': 0.7579823,\n", " 'chinese': 0.8585954}]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantized_model.predict_proba([string,another_string])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Open toxicity visualization dashboard\n", "\n", "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`.\n", "\n", "```python\n", "def predict_words(\n", " self,\n", " string: str,\n", " method: str = 'last',\n", " bins_size: float = 0.05,\n", " visualization: bool = True,\n", "):\n", " \"\"\"\n", " classify words.\n", "\n", " Parameters\n", " ----------\n", " string : str\n", " method : str, optional (default='last')\n", " Attention layer supported. Allowed values:\n", "\n", " * ``'last'`` - attention from last layer.\n", " * ``'first'`` - attention from first layer.\n", " * ``'mean'`` - average attentions from all layers.\n", " bins_size: float, optional (default=0.05)\n", " default bins size for word distribution histogram.\n", " visualization: bool, optional (default=True)\n", " If True, it will open the visualization dashboard.\n", "\n", " Returns\n", " -------\n", " dictionary: results\n", " \"\"\"\n", "```\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.predict_words(another_string)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vectorize\n", "\n", "Let say you want to visualize sentence / word level in lower dimension, you can use `model.vectorize`,\n", "\n", "```python\n", "def vectorize(self, strings: List[str], method: str = 'first'):\n", " \"\"\"\n", " vectorize list of strings.\n", "\n", " Parameters\n", " ----------\n", " strings: List[str]\n", " method : str, optional (default='first')\n", " Vectorization layer supported. Allowed values:\n", "\n", " * ``'last'`` - vector from last sequence.\n", " * ``'first'`` - vector from first sequence.\n", " * ``'mean'`` - average vectors from all sequences.\n", " * ``'word'`` - average vectors based on tokens.\n", "\n", " Returns\n", " -------\n", " result: np.array\n", " \"\"\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sentence level" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "texts = [string, another_string, string1, string2]\n", "r = quantized_model.vectorize(texts, method = 'first')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4, 2)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.manifold import TSNE\n", "import matplotlib.pyplot as plt\n", "\n", "tsne = TSNE().fit_transform(r)\n", "tsne.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (7, 7))\n", "plt.scatter(tsne[:, 0], tsne[:, 1])\n", "labels = texts\n", "for label, x, y in zip(\n", " labels, tsne[:, 0], tsne[:, 1]\n", "):\n", " label = (\n", " '%s, %.3f' % (label[0], label[1])\n", " if isinstance(label, list)\n", " else label\n", " )\n", " plt.annotate(\n", " label,\n", " xy = (x, y),\n", " xytext = (0, 0),\n", " textcoords = 'offset points',\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Word level" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "r = quantized_model.vectorize(texts, method = 'word')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "x, y = [], []\n", "for row in r:\n", " x.extend([i[0] for i in row])\n", " y.extend([i[1] for i in row])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(107, 2)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tsne = TSNE().fit_transform(y)\n", "tsne.shape" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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IIYQPEpRCCCGEDxKUQgghhA8SlEIIIYQPEpRCCCGEDxKUQgghhA8SlEIIIYQPEpRCCCGEDxKUQgghhA8SlEIIIYQPEpRCCCGEDxKU4r9eQkIC2dnZf3czhBD/oyQohRBCCB8kKMUVqbi4mFtuuYUmTZrQqFEj5s2bx5o1a2jWrBmJiYkMHTqU0tLSC95jt9vp2rUr06ZNIzc3l169etG4cWNat27N7t27AZg4cSJDhw6lU6dO1KhRg7fffrvi/ZMmTaJu3bq0b9+e/v37M2XKlMv6m4UQVyYJSnFFWrFiBXFxcezatYu9e/fSpUsXBg8ezLx589izZw9ut5v333+/Yv+ioiJuvfVW+vfvz/33389zzz1Hs2bN2L17Ny+//DIDBw6s2PfgwYOsXLmSLVu28Pzzz+Nyudi6dSsLFixg165dLF++nG3btv0dP1sIcQUy/d0N+DtMmjSJzz77jKioKKpUqUKLFi0ICQnho48+wul0UqtWLWbPno3H46Fx48YcPnwYs9lMQUEBTZo0qXguLr5FO1J4beUhTh7PIXv+t+S4RvLYvf0JDg6mevXq1KlTB4BBgwbx7rvv8uijjwLQs2dPnnjiCQYMGEBeXh6LFi1i/fr1AFx//fXk5ORQUFAAwC233EJaWhrdu3cnOjqajIwMNm3aRM+ePbHZbNhsNm699da/5fcLIa48V12P8o96Drfffjtbt25l165d1K9fn+nTpxMUFESnTp1YunQpAF988QW33367hOQlsmhHCuMX7iElz44pPJ6ogW/yc14gwx8dy6JFi3y+t127dqxYsQKtNXl5eT4H91it1orHRqMRt9t9sX6CEOJ/0EUJSqXUJ0qpTKXU3vO2hSulVimljpT/HVa+XSml3lZKHVVK7VZKNb8YbfBl0Y4U2k1eS/VxS7nz+ZnUveY6bDYbQUFBFT2HvXv30qFDBxITE/n888/Zt28fAPfddx8zZswAYMaMGQwZMuRSN/eqEhgYWPH4tZWHsLs8ALgLczCYrVjqXcvhQ4dYv349Gzdu5OjRowDMnj2bsLAw9u/fD8ALL7xAWFgYo0aNYty4cbhcLtq1a8djjz1G8+bNSU9Pp0mTJhw8ePA3bTh16hTvv/8+X3zxBQ6Hg6KiIpYsWXIZfr0Q4r/BxepRzgS6/GrbOGCN1ro2sKb8OUBXoHb5n2HA+1xC5/dSNJBvd7HmYCaLdqRcsN/gwYOZOnUqe/bs4bnnnsPhcABlPZXk5GTWrVuHx+OhUaNGl7K5V7XUPHvFY1dWMmmfjiZ1xkO47UU89dRTWK1W+vTpQ2JiIgaDAafTWRGUAG+99RZ2u52QkBBq165Nq1atWL16NQaDge3bt7N161a+++47tNYV7yktLWXEiBF8+eWX9O/fn8aNG9O1a1cSExMJCQm5rL9fCHFluihBqbX+Acj91eaewKzyx7OAXudt/1SX+RkIVUrFXox2/J7zeykA1sr1KTy8mVeX7Lmg51BYWEhsbCwul4vPP//8gs8YOHAgd9111wW9yalTpzJ16tRL1eyrjtfrxb7uI1KmDSfji2co2P4NIW37ETfkHazBEXz33Xc4nU7MZjOfffYZu3fvZu7cudx1110opcjPz2fxzlTWnIHp8xZz+NgJchxw991307p1azp16kRSUhJ2u5177rkHgKysLIxGI1999RVNmjRhzJgxHD58mJUrV3Ly5ElatGjxNx8VIcSV4FLeo4zRWqeVP04HYsofxwOnz9vvTPm2CyilhimltimltmVlZf2/G3F+LwXAGlsHv1rXsO2Ney/oOUyaNIlWrVrRrl076tWrd8F7BgwYwNmzZ+nfv3/FtoMHDxIREfH/bpe40MKFC4k3FVJzxIdEdn8cZ0rZJVI/s5FgPzMhISH4+fnx4IMP8sgjj1BQUEBCQgLh4eFkZ2cz+7st9OvSgdPff0bYtYMxBEWy6fvvmPTyZD7//HMaN27MlClTCAgIYODAgdx9993k5uailKoYIZuQkECjRo1o3rw5VatW5YEHHqBJkybccccdlJSUAGVXHh5++GHatm1LjRo1mD9/PlAW9CNHjqRevXrcdNNNdOvWreI1IcR/t8sy6lVrrZVS+t/vecF7PgI+AkhKSvqP3nu+uFA/Un4VlsHX3E7D7vex8uHWdOzYkRYtWtC8eXNGjBjxu5+xceNGevfuTWhoaMW25ORkXn/99f9vs65q50a2pubZsbs8LNqRwsaNG3nw3nsIa9qE11YeIrdaY8IDLNzRIp4pU128f7oSdpcH/3od2bHjIQoLCwkKCiI0NJTMzEymfjANv4Y3YEw7RNHe1XiKzmKNr4/FZqN3+4bMmzePX375hZycHA4dOsTSpUvp06cPKSkpuFwuXnjhBbZu3Uq1atV49NFHycnJqfgfoWeeeYbp06fz0EMPAZCWlsbGjRs5ePAgPXr0oHfv3ixcuJDk5GT2799PZmYm9evXZ+jQoX/nYRZCXCSXMigzlFKxWuu08kurmeXbU4Aq5+1XuXzbJTG2c13GL9xzweXX/O/exejKpPlMD4MGDaJ58z8eT/TQQw+xfPlyli1bdsF2Gezx/3PunvG586E1jF+4h1qZRTQBejWLp1ezeG7fUolaTeNYsD0Ft1ejy/d9/KtdFNgd2Gz+9OzZkx49ejB48GC8kbVQgNE/FFfuGZTBgCN5J6VGE5+f3IndbmfNmjXExsbSpk0bAgMDMRqNhIaG8s033zBo0CDatGlDcnIyUDa465lnniEvL4+ioiI6d+5c8Rt69eqFwWCgQYMGZGRkAGX/M9WnTx8MBgOVKlXiuuuuu7wHVghxyVzKS6/fAIPKHw8CFp+3fWD56NfWQP55l2gvul7N4nnl9kTiQ/1QQHyoH7Nmf8aJQ/s4ePAg48eP9/n+d955h6NHj1bM3xN/za/vGQPYXR4Oq3gWLFiA1+slIyODdevWsXhnasW+JQc3AFB8cAOmgAic2kCO08iWLVsoLi4mNMgPa+X6OE7tAo8bY3AUxqBIqt40hOLiYgYOHMjYsWN5+eWXOXLkCDt27GDv3r0YDAaio6PZunUrzZs3r5gq8keDu+DC6SXnDwwSQvxvuig9SqXUXKATEKmUOgM8B0wGvlRK3QucBO4s330Z0A04CpQAl3y+xbleivj7/fqe8TmllZOoHJRBgwYNqFKlCs2bN2ev24yt/HWvowjtKqVw2zeEXj+UnG9eY/WKZawozMZgMHBNJRO7oupTXK0JhTuWYwwIwWA007V5dQBycnIqvisnJ4fU1FS6d+/+h+0sLCzks88+Y8WKFaxevZr4eN///bRr145Zs2YxaNAgsrKyWLduHXfdddd/dnCEEFekixKUWuv+f/DSDb+zrwZGXYzvFf99fn3PuOrosgEv8WEBTJkwhcDAQHJycrjmmmuo0mo4WR6oPOITAMI6lf0/lTs/A1NoJaJ6P0fohtdJSkqi+41t6FczkecLe7Hv5C6aDn+TQ1PvY9UX02j49adUr16dAwcO8N5775Gbm8vChQtJTU0lPT2dnTt3cuONN1JYWMinn37K1KlT6datG2+99RZGoxGr1Upa2r8uenz00UdMnjwZu92O0+kE4I477mDNmjUXBL1MLxHif8NVWcJO/H1+756xn9nI2M516d69O3l5eTidTiZMmEBokzbc3qpWRZieYwqJIe7e94jQ+RS73bhcLiZMmEDDhg2pbTBQGGJm92v9CQ0NpWPHjrz//vvs3r2bfv36ERQURHR0NNu3byc2NpbWrVujlKJXr14cPnyY0tJSdu/ezfXXX8+3337LLbfcwqlTp/jxxx95/vnnmTlzJrm5uYSHh+PxeLjhhhvYvXt3xaja84M+MTHxch9eIcQlcNWVsBN/r9+7Z/zK7Yn0ahbPunXr2LlzJ/v372fw4MH0ahaP1WQg1O+3JQP9zEYe6FiDQ4cOMXLkSA4cOEBwcDCNGzeuCLv09HROZBZQ747R3PzE+ySnpPPoa9PZuXMn0dHRFZ/14Ycf0qxZM3744QfCwsIqCqhXr16d48ePs2rVKq699lr++c9/4nK5+PLLL2nevDnNmjVj3759FUUPunfvTtOmTenQoQMTJkygUqVKl+WYCiEuLelRisvuP7lnbDQodj53M3M3HWbEoH7YiwrQXjehHe7hQ2d9IivF0a5dO6CsuMCkSZMqiqcv2pHCsdAWZG/9FnNIJZQlgHe2FRNXNZX69etz+PBhTCYTO3fuZPLkyTgcDmw2Gzk5OdjtdkwmE7fccgtWqxU/Pz9CQ0PZunUrU6ZMYevWrYSFhTF48GAcDgcTJ06ke/fujBkz5nd/x+DBg+nevTu9e/e+aMdRCHF5SI9S/Ffo06oG782YQ437pxLT72XOrp1Oer6dvBL3BeUIz5/r+trKQ5S6vQCYY2rgdRRSYrcz+ZudFcXwExISsNvL7pn+XoGAXxdQz8vLIyAggJCQEDIyMli+fPml+LlCiCuIBKW44pxfxP5cQQKtNY8/MY5jH44gY94zeIpy8NoLcBdk8uxHCwGYM2cOSUlJJCcnc/ToUVLz7BTv+x5blUYE1G0HykDKx8PZ+fETGAwGzGYzY8aMwW63065dO7Kzs3E4HERGRuLn5/e7bWvQoAHNmjUjMDCQa665huLiYl566SUOHToEwLRp02jZsuVvKvoA/PDDD7+p6COEuPJJUIoryq+L2J8rSPD4y1MpyMsldtCbxA15B4N/KNrtwhRemeM/LKR+/fqcPXuWxx57jBkzZtCnTx8yZz4EShHUtBvKZCHilkdRRgtGtwOn00lISAj16tXj8OHDtGrVim+++YaEhARmzZrF4MGD6datW0W7lixZQkBAAACffPIJISEhBAUFkZmZWVFwHX5/ubZzzlX0WbJkCePGjUMI8d9B7lGKK8ofFST4dudRgsMiUEYTjpO78RRkYgqOJP7+D4gP9WPTuOsr9r/hhhvYsWPHb6oAlRzchNFsIcymGDXs0YpiE+Hh4b+73uXEiRMveL53714W7Uih15szOGuOxBTTlO8OnaVXs3h69OhRsc9/UtFHCHHlk6AUV5Q/Kkjgqd6O4JP/5OQnD2KKqYUpvDLwr6klv+fcgKFzdWWbDnyWsZ3r/r+LT1QEr60S/rXbUOgoZPzCPRfsM3jwYBYtWkSTJk2YOXMm69atq3jNZPrXPzep6CPEfw8JSnFF+aOCBFXiKrFp9/YLCqrHhfr92+C7mFWZzu/tep3F5G9eQPHhH7lvcQ0s2YcwmUykpqby0EMPMWfOHD7//HOysrK45557WLlyJUePHqVVq1bcc8892O12mjdvztSpU2nbtu1FaZ8Q4tKQoBRXFF8FCeDvLUd4rrfrzDpJ8b51BLfsRcmhTRRmp3Fr27a0atWKwMBAJkyYQFJSEn369CErK4v9+/fTuXNnevbsSXR0NKtWrSIyMpJ58+bRv3//ihG4QogrkwSluKL8+nLpn+k1Xi6h/mbOlrhwnNqNf732hHUcSNi1g4gP9eO5W6J4/PHHSUtLIyIigurVq/POO+8wceLEijUvAfLz83nwwQepXr06ffr04fDhw3/zrxJC/DsSlOKKc7F7jUopBgwYwGeffQaA2+0mNjaWVq1a/enl0hbtSKHI4f7NdrNRMbZzXR56aACjR4+mR48erFu37oKBQOdGywK88cYbxMTEsGvXLrxeLzab7TefKYS4ssj0EPE/LyAggL1791YUFli1atUfrgaSl5fHe++995vtr608hMtbNgDHVrUxJQc34rEXEGAx0bGaH/n5+RWfOWvWrD9sS35+PrGxsRgMBmbPno3H4/nDfYUQVwbpUYqrQrdu3Vi6dCm9e/dm7ty59O/fnw0byta4LCoq4qGHHmLbtm24XC5KSkoYOXIkgYGBjBgxgmXLlnG82Exox4GcXTcDT0EWfrVakTFnHBnKwOgT1zJx4kT69OlDWFgY119/PSdOnPjddowcOZLbb7+dTz/9lC5dulzQ2xRCXJmkRymuCv369eOLL77A4XCwe/duWrVqVfHapEmTCAkJYc+ePTRt2pSsrCyaNm1KcXExubm5+Pv740o/QtY3rxHT90Wibn+a4oM/oL0ebP6BlJaWcuTIEY4fP05QUBB9+/Zl3bp1ZGdnM3PmTMaMGcPMmTPp0aMHDzzwAOHh4ezatYtXX32VoqIitNZ4vd6/8egIIXyRoBT/k35dBu+4J4Lk5GTmzp17QcUdgNWrVzNqVNkSqZMnT6ZmzZrs3LkTk8mEwWBgy5Yt3HbPfRiMRkpTD+J1u/CWFFBj2Ht8+Nn8PzVqdeXKlSxfvpy0tDRuu+02kpOTqVu3LgMHDqRRo0acPn2aSZMmUbduXdq3b0///v2ZMmXKJTk2Qoj/jASluOJorWnfvv0FBce/+uorunTp8qfe/0dl8Gq37MSYMWPo3/+364yv2p9Ou8lraf/qWo5nF1cUWl+1ahXNmjVjw7fzsHlKCHRk4Uw5CErx6p0t6N++LrfeeqvP9mzfvp0NGzbQt29ftm7dyrRp0zh79ixHjhxh5MiR7Nu3j8zMTBYsWMCuXbtYvny5TBkR4goiQSmuOEopPvjgA0aPHo3D4aCoqIinnnqKd99990+9/4/K4C1ZuowhQ4b8ZkHlao1b88zL/6wodOByuxm/cA9ut5uHHnqInTt3YjabefaZpzi6YArP3toAs9HwuyNzTSZTxWXURdtOkJ7v4OYnP6QkuCq5LhOBgYHcfvvtbNiwgWrVqtG6dWsANm3aRM+ePbHZbAQFBf3b8BVCXD4SlOKK1KhRI2699VZeffVVXnjhBQYOHEjNmjX/1OXJPyqD58JIv379frt/QjecJYWkTh9JxlcT8ZYUVATtnDlzKCoq4t577yU/P5/MzEzatWuHx+OpCPHzp5gkJCSwfXtZBaGnX/8Yt1ejAYfLy0/Hcy9YEkwG8gjx30FGvYor1nPPPUfz5s2xWCxs27aNrVu3VlyedLlcNG/enMTERG655RbOnDmDx+NhwoQJBBef5dDid9FOOwa/YOKGfQSA1WQEwOv1MnPmTJo2bQrA7s9fxFOQBRqCk3riOLWH1OkjURZ/br/9dtq0acO+ffto1aoV9erV48MPP6RGjRoEBwcTERFBmzZtCAkJITAwkG3btnHnnXdy+OkX8JrK5kjaT+yk9PQeHClW+l7fgpggC4sXL+ajjz6q+K3t2rXjgQceYPz48bjdbpYsWcKwYcMu7wEXQvwuCUpxxfi9Oq59+/YlMDAQq9V6weVJm83GrbfeysGDB4mLi2Pp0qVA2TzF516+gSq9n8ZlCaL4wA/k/fApVXo+TmC4H263mxYtWpCamkpUVBSRkZHU7jKE/Z9PwhJdndzVH+CX0JxKA18n85NRtG7dmu+++w6lFMHBwdhsNrZs2UJiYiK9evXiww8/ZPHixfz888+UlJSwYsUKvvnmG2o1aoHSYDDbUCYzymzD6yjC5Swho0Tx1VdfXfDbW7ZsSY8ePWjcuDExMTEkJiYSEhLyd5wGIcSvSFCKK8Kvl8RKybMzfuEemmQWkRQc/Lv7f7HlFMUuKD38HTmukTx2b3/CwsJIPXGYiKWTyCosxeVy4x8aySu3J/LmBiv33HMPhYWFHD9+HK01rVq1onK9ZuzOPYP2ulFGC6Bx7FpOkM3E+PHjWbp0KdWrV2fo0KF8/PHHXHPNNRw6dIhZs2ZhsVgoLS2lbt265OfnM23aNK699lo8xbnYaiQR02ciGV89jzKZqfrYl8QGmdnyfA/Cw8PZu3fvBb9pzJgxTJw4kZKSEjp27EiLFi0ux6EXQvwbco9SXBH+aADOpmM5Fc/btWvHt99+y5c/H+PJL7aQtf8nDP4hRA18k5/zAhn+6FgWLFhAw4YNOXFoH8WpR3FmJZN3pGwE6Y5TeZy2m8lzKpbuSScwMJAWLVpwZMePRMTE0urJz7HE1CC6bgviio9wNiudHTt2ULlyZYqLixk3bhwbNmxg85YtFJtC8DbvS16JC60UP/30E1C2cPOUKVMwmc24M0+Q8tH9ODOOYImtg7+fH6F7v0RrzWuvvVZR//WcYcOG0bRpU5o3b84dd9xB8+bNL/FRF0L8GRKU4orwRwNwCh2uisfnLk8O6n4tJ+c8gyUqAbQXg9mKpd61eBvdyubNm8nKyqoILpfLxdvz1zJ+4R5K3R4scXVRwZW4d+AAFmw9icPhwN/fnwCrmem94iDzCA/fUJuYYBsej4c6depgt9sxGAxcd911PPn6DJwuDyU5qRjDYlF+QViqNMUWEIRSiuzsbLTWeNxu2rTvQNJjn+C1F2LFzSu3JzLnwzcxmUw8/vjjrF+/nt27d1f8vjlz5rBz504OHjxYsai0EOLvJ5dexRXh1+tQntOw+32MGXN9xfMxY8Ywy9ESj8tBxpxxWKsmkvbpaFAKZTAx+9s5mEwmHn74YfLz83G73ZTW7YK7TtlnuM6m48w9g9Pt5IGhA4nxZhMaGsq+ffu4/a7BWOPq8fS7c4moWgeDwUBJSQmbN2/GYDBw7bXXMunbrViiq+PMOEbOt6/j9booProZZTASEx3F9OnTiY6OxmKxUCMmhJkTuhAyJQB78k661I/ggw8+wO1288Ybb+B2u9m/fz+NGzf+j47VunXrmDJlyp8u6C6E+GskKMUV4d+tQ3nOsGHDyFy3BWdpKYGNrie4xa0Etyibcxgf6kdSUhIAP/zwQ8V7qo8rG+hT6a7JAJxd/ykFW7+mID2ZZ55+jF69etH+uhtJcflTkp+CJbIq3prtMe3bxMOvfMiTTz5JUFAQzzzzDJmEgsmKMpoxV6qBJaYWRTuX4bUXkp6eXhGAr7zyCjNnzgSgQYMGnDlzhvr165Oenk737t3p0aMHmzZtwuFwXKpDKoS4SOTSq7gsAgMDfb7eNMxF8dxHiQ/1Q1EWeq/cnvibSf1z5szh86XrqTVyGiFt7qzY/utQTU5Opn79+tx///1kzBhFxrwJeF2lZC99A2NQOKaQGELia/Phhx8yYsQIckpc+LfoiTEgFHd+Brkr38XrcTM/uayMXaVKlbBYLDhT9uEtLUZrL6Wn9+HKPI7RP4RKbXphMplYsGAB77zzDgaDgR9//LGiPbGxsSxatAibzcbKlSuxWCx89tlnfPHFF7z55pvYbDZiY2O56aabiI+PZ8KECSQnJ9OhQweaN29O8+bNL/i8oqIievfuTb169RgwYABa6794hoQQf0hrfcX/adGihRb/3QICAny+fuLECd2wYcMLtrVp0+YP95+76bAOrdtKm6MStF9Mgn78lff0888/r5OSknTDhg11v379tMFg0Dt27NANW7TWlojK2hhSSSuLnw5K6qkB/fz7X+g+ffrooKAgjdGkldmmo++cpKs9uURb4xtolEGbI6rqatWq6bp16+ohQ4Zok8msMZb9sdVooTFbtTKaddc7B2uTyaTtdrt+9dVXdVhYmK5Xr54OCgrSNptNm0wmXbVqVW0wGDSg4+PjdUJCgg4ODtb16tXTAQEB+t5779VpaWnaYDDo2bNn6+LiYm2327XWWh8+fFif+3fw/fff6+DgYH369Gnt8Xh069at9YYNG/7iGRLivw+wTV+GDJIepbisioqKuOGGGyqKBSxevLjiNY/Hw/3330/Dhg25+eabWbNmzR9+jjltN707JOLMPEFJ+gkmjLiL6h1vx3LHqxR3f5Xvdp8mJDySpk2bEhlopWbVOKKad8YSW4eivWsx2gJYMHMqJSUlLFq0iMC42miXg9xV75Py4f04M49jq9aEpNEfk5eXxxNPPMEnn3yC2+0iPCKCiAZtcZzcjckWQFBwMO+9+hy1atWib9++PPfcc0RFRXHy5EmaNWvGm2++SVBoOKdT0ogbPgO/6GpUqlGPwsJCKlWqRGZmJh06dGDv3r14PB5CQkKw2Wy4XC7uv/9+EhMT6dOnD/v376/4/ddccw2VK1fGYDDQtGlTkpOTL+VpE+KqJvcoxWVls9n4+uuvCQ4OJjs7m9atW9OjRw8Ajhw5wty5c5k2bRp33nknYWFhlJSU8OCDD7J27VqqVKlCrt1DUbUOFAfEkzL9EzZs2YHXXkCpNuCo0Ym8Pd+jXaV4SvJRioqScQm165OZ5sboF4R2l+I1mjl+1kPvoQMBaFirKlvSjxPR5WHOrp+BuzCb0jP7OPLOYAoLC3n22Wd5/fXXAVAeF43C4UjlKpjq38jpNbNpcesgzh46xOHDh7ntttuYO3cufn5+7Nu3j8ceH4O9uBjQFO37HmNcA3bv34uxxEFUlIHS0lKqVq2KyWRi/vz5REREAPDGG28QExPDrl278Hq92Gy2iuNotVorHhuNRtxu9+U4fUJclSQoxSVzfqUdu8vDoh0p3NIomqeeeooffvgBg8FASkoKGRkZAFSvXr2irFyLFi1YtGgRCxcuJDk5mf379zNrzS7u69GR8MhWBFSJxxAYTpoOIc5fkZWWjWPtDOLu/xBTcBS5qz+ieP86Xlt5CDOw4aetlGSdRivA60W7iyk6tp2JDw9hyN39aZAQx+bvS8le/DLKGohSipenfUl0XBXuu6U1aXl2sorKpqo4nU62bPsFu70ETn0M2ktRXg7K4k9MTAzLly8nODgYj8eDwWDAr3pznKcO4CnIIm/9LEwRlfEUZuN2u/B4PLjdbj777DMGDx7Miy++SG5uLmPHjqW0tJR77rkHg8HA/fffj8fjoUuXLuzZs+eC0BRCXFoSlOKS+HWlnXNLXa395gBZWVls374ds9lMQkJCxcjPUm2k3eS1pObZ0btP4NWajRs30qdPHwwGAx9vP4u1atnKH+7CHEAR1GEgrvQ9OPZNRXu9GPyC8Trt2I+XFRlIzbMTXlhIcdZpgpp2wZWTgv3Ijxj8QvA6CvGWOtlz7Ay/bN0MKCwBodizT6OAn9cu54cD6Wi3G2UFV3E+AEER0WXh7vViq94cd0Em7rOpeF2lpKecRntchIaG4nA4yMrKwlDswltaDIAxOArtLgWlwOJHQkICR44coWPHjixZsoTs7Gzat2/Ps88+S0pKCo888ggrVqwgPj4epRTz5s1j8eLFDB48mNOnT1OlSpULjvuzzz5LUlJSRS9dCPHXSVCKS+KPKu18u/Mo3ROiMZvNfP/995w8eRKA7/alk1HgwFA+lzLf7sLj1RzLLKJJ+fvPL0rgykrGW5xL5pfP4inKRXvLLj2efqsfBrMVv1qtMYVE4Vg/jTNnzhDVqD1+1w7CU5JPyskdBLe6g7yf56PcDrZuWo92l6KsfoR0H4v/8S2c3TCHxbPPFS3XaHcp2uMsa8fJ42Ao+6fjX68dtiqJpH48AtBUG7MI04JHOXbsGEoptNaYzBZM1RpTcqBsykrcsGmceasf2lnMTz/9RMuWLRk3bhxdu3Zl9+7dtGnThoceegiz2UxsbCy7du1i5syZxMfHExISQseOHQkICODkyZNUqVKFqVOnVhyXF1544WKeRiEEMj1EXCJ/VGnHU70d27ZtIzExkU8//ZR69eoB8OEPx393isNhFc/999+P1+sl0min9NQeAPxqtMAYGEFM/1do+vCHxMRVxq9yA2LufB5rfH0ib3mUqI534/Z6KYqoj/IPw2xUGP1DCGx8M+7CHKwRcWi3k/Abh2OJqQVeL8X715O3ey1B1ZsQfccEMFnAaKbyqE+xxNUDFCFt+xHZqAOgObt2Otnf/AOUwhgURdZnozl58iTNmzfH39+fkJAQho56jND67QEFXjdpHw3DHFYJRdnvjY6O5tVXX6VBgwY0a9aM2NhYjEYjoaGh2O1lxzE5OZmvv/6aJk2aVKzL6Xa7fU4hEUJcHNKjFJfEryvtVB09H4AqcZXYVF5e7ny5ah9x975X8Tyk1e3k/ziX0spJKKVo0KABttBobJVqYbD+ax1HBWQUOMgrcRITayUo0EoBEOpnptjpptTtxRQeR+6BHwm4pjc2g8Z+bAvm6Fp4is+CMlCw7WtcOWcIatEDx4nteM6mkJ+XiuHUPlAGcDs5884AtMsBSmEuycKce7zs+5WBqD4TSXlnAJ78dJz2XKxWKwaDgeLissut0197tmywjdGIdjkxW2wYirIx+fmRmppKpUqVCAkJISQkBK/Xy9ChQ3nqqacIDQ2ltLSUtm3bsmXLFlq0aMHmzZsZM2YMDoeDgQMHUqtWLQICAnjkkUdo0qQJ/fv3Z9u2bZfgjApx9ZIepbgkxnaui5/ZeMG236u0c05cqN8Fzz32Agy2QOLDArBarRw8eJDnxz+OK/UArl1LSJk2HFvl+ii/spVF3G436enpnJw3CWvOEU7NfRaXR+MuyKJ43/d4S/I5/kZfjs98AnNMLRzJv+AuyAbtJahFD6LveJaiA+txZSaj/IIJq3MNCzYfo/q9bwJgKP8elJHGVSJ46flnUcqA11FIyjt3gzIQGBLKR0s3U2x3sHX7DgAsViuLFi1iyZIlKK+HqNAAEqKDcbtKcTgcdOjQgYiICKxWK6NHjyYoKIg333yTZs2a4efnh8fjYcmSJQQGBnLw4EGgrHiB1pqZM2fywQcfsGrVKh588MHfTCERQlwcEpTikujVLJ5Xbk/8t5V2zjk/WN2FOaTPHkNEmzsY27kudrudpk2b8vDDD2MyGti9ej4tH5+BKy+d0pT9YDDhKcwhsvdEghJvoLi4GLvLjTPzOPbj2zEGhKEsfljj62MwW/EWZqOMZmzVmgGQv+kL8jZ+hrYXYo6ujskWQGDDa+l7y3WkzJ8EBgOV+k4irF5rqlavSfLerbz11luEh4dhNpupU6c2aC8lhQUMv/sOlNkPU0g0KAN+9a/DGduEbt26oZRi5cqVdOvWjb59+/LQQw+xc+dOZs+eTeXKlRk/fjzFxcVUqVKF1157jfj4eLp3747BYCA4OBiv1wvAjh07iImJ4frrr2fOnDnUqFGDt99+m23btuF0Oi/L+RXiaiKXXsUl06tZ/B8G4+/tC2WDgFKJoOEjM1AKHpu3E0wWJs5YSmj+EV566SUqV65MWsEuLNE1cOdnUry/bJBM2vRRGIOjqFe3LvsO7CL9syfA68GVmYwhKJzS02XrPwY264YrLx1H8i9gsuK1F2DwulAGE7jsmMJiObP0PQwWW9n6lEph+eEdht55G5MmTUIZTZgiqmA2heBy5XDy5EmUUnhReFxOjMFRuLJPAVBakM1DY8ZR792nAahUqRKdO3dm+PDh3HTTTQDcc889zJo1i8WLFzNjxgwSExO58cYbATCZTISGhhIaGkpKSgrbtm2r6FlC2ULVNpsNg8HA7Nmz8XguHEAlhPjrpEcprhi9msWzadz1vNG3KaVuL2dLXGj+NbVk45Gsion2caF+KIMB99lUHCd+wRJXj8oPzsagPWRY4rFWSSTu3nfLe5L1CG7eHb/arcBgxFt8lqCkniiLH2gv5sBQXMV5VK8Si/J68K93LebQShgs/thqJoHXy1ntz5kiwGgGkxVX9mlKMsruU9Zv1gqtNZZKtfGU5OMuyASjCbQXZ9ohUjbM59ZbbyUyMhKj0cjNN99M69atmT9/PqGhoZw+fZoRI0Zw9OhRHA4H69atq5j2sX//flq2bMmZM2fwer3079+f+Ph40tPTefTRR1mxYgV79+7lwQcf5JVXXkEpxTPPPPM3nUEh/jdJUIorwsyZM0lNTQX+eGrJF1tPA2VzNEucZdNBXLkp+Ndth6cgE0NpEX61W5F35gheZwlZiyajXQ6cmcexFKQQ5CxbBNqvdmsMlvJ7ol43bdt1IDY2lgcffJCInuMo2Po12l1K2M0jKNr1Xfll1XzWHkgHpdClxZhCK4Gh7FLxvvIenjPtMNrlQDuKwOUAoxmvvQjtcTFy5EgMBgPx8fE0adKE3bt307ZtW6Csys6JEydYtmwZBoOhYl5pvXr18Pf3p6ioiJiYGABGjBjB119/jc1mY+rUqZw+fZrw8HBcLhfr16/Hbrczc+ZMcnL+teC1EOKvkaAUV4Tzg/KPppZkF5WSnu9g/MI9nC3514LOymiiSveHSP/qWQr3fo8ymXFlnSS0/QBMwdEYlMJ5ZFPFPVBlNGONrw8eF2jNz2uX8dNPP/HGG28QanLhV6sVymwjb/2nGP0CsVVvjtFtJ/PYXnC7MMfVJ+7ed0Fr/Gq1wusFg8FAQGAQRtu/VkkJbtGDWqPnorSXY8eOUb16daKjo1mzZg0TJkwgLS0Nj8dDkyZNCAgI4NFHH+Wuu+6qmCbTvHlzAgMDCQ4OJjMzE4Djx49jMBho3Lgx69ev5/jx4xQWFpKQkEBsbCxWq5UaNWpw+vTpS3KehLgayT1KcUkkJyfTtWtX2rdvz48//kh8fDyLFy/m0KFDDB8+nJKSEmrWrMknn3zCmjVr2LZtGwMGDMDPz4+YO14hvdhb8VnnppbUbNwKGrciuzxIw28aQWn6UXKWvUmd6/rgvft1Umc+QtmkEY0hIARtNKOdJYCJM6dPYQqtRMmhTbiyT6PLB8fYqjSiTr16uJxumP0c5vB4nBnHAQ3KgLckHz8/G6XFeVirNab05C5OvdYTAPvxbZj8gvF4NcWFBZiiq0PxWQAa9hrBE13qccebiiVLlpCbm4vD4aBF6/ZkFTqwZ6dgCQqj0AmlpaXMnDkTk8lEQUEBXq+XZ599luLiYkJCQigqKsJoNFJSUgLArl27uOGGG4CyWq/nz0E1GAxS+1WIi0h6lOKSOXLkCKNGjWLfvn2EhoayYMECBg4cyKuvvsru3btJTEzk+eefp3fv3iQlJfH555+zc+dOxnVv/IdTS37d27RWqkVAvQ5sf30IZ94diKckH+12EtpxIFmLJqO8HqxB4fTo0YMqj8zDEl0DW/UWxN07lfCbhgMKc2Jnaoz5GnNcXZTZRnCbvhj8ynqGVW4YSL9n3sFut6NswbhzzwBgCo2lLJAVsQk1yx7aAojqPhplCwLgWuvJikFKFouFhg0bEhoZg6nreLRfKKaQaDwYOJFdRPsut6G1ZvDgwTidTmJiYmjZsiUAK1as4IknnsDr9XLixAlOnDiB1pply5bhcDi49tprK0bECiEuPglK8R/Jy8vjvffe+/c7cmGR8wYNGjB37lzy8vLQWtO9e3cGDRrEDz/88Jv3+Zpa8uv5lgAhbfvS+tmvWbD5KHWfWEDckLcpOfwTUT3HEd1nIu5SBwX4E3j2CFE9xmKw+pH26WgKt3+LMTAMT/YpDr16GwqNKSiCnOVvojWgDNQNLGXjsXy0MqCddjwlhWA0E9i0K+agMIwGxel920ADjmLSZj6KdhQC8MaLZYNqtNbExcWxc+dOcvPzOT1vIn612+AuyMLrKMRxZj/fLfycvLw8PvnkE7TWeDweGjZsCMCAAQOYMWMGWmsOHTrEO++8g9FoJDAwkIyMDDZv3vwfn0chxJ8nl17Ff+RcUI4cOfI3r52/Wki4zqdU/6tX6HQ62bx5M/7+/n/qe/5oasnYznUvKLYO/+ptntv/+W/3kV7+mjk8nphBb/LzyV8wH/mMksj65G5bQuygNzAFR1H041zc7vK5h8qANaEZ6owVV+4ZtFL8tPUXLA3DwOMmssdYnLmpnF3xNsbso1SPjeTYsfyy6j0GI2gv8Q9MI+e7D3Ac20zp2XTq16+P1prmzZtTVFTE9h270N4c8tZ9gqVSbWLv/gcp0x7A6ygiwOjlxhtvZP78+QQGBnLbbbfx/vvvY7FYsFgsOBwOYmNjiYuLo3fv3tx9991UqVKF66677oIi6OvWrftTx1gI8edIUIr/yLhx4zh27BhNmzatmAe4fPlyCkvduBNvw1ynPVBWVi6rwMGiHSn0ahbPsmXLyM/Pp7i4mBEjRhATE8Ott95Keno6AwYMIDAwkMLCQrZv387o0aMpKioiMjKSmTNnEhsbW/H9F8y3LA/kjM+eY2l6J54uvxdq7fBYxf6ugizyf/gUY3A0cUm3UenUBvINZZV2lMtBwYENBNQtazNa48lLw+ssQVn9wVFIWK9nKD7wA8agCJTJijEgBICcQ5tx2ixlcxgjEnBbgylNOYDBLxiD2YIxMBxP0Vnef/99+vXrx/Tp08tGs2oP1vj6ODOOYbSVleILTLyJgh+/4OHHHufNN9+smAtptVoxm80cOHCAiRMnsm3bNoqKioCywU9CiMtDLr2K/8jkyZOpWbMmO3fupHXr1uzcuZNdu3YRfeeLZKyejrsot2JfrTWvrTwEQLdu3QgLC+Onn35CKcWGDRuoUqUKR44c4fjx47Ru3ZoHHniAjh07Mnv2bLZv387QoUN5+umnf9OGc/MtT0y+hQUj2pJ++sQF90KPbVlTtqPXQ86Sf2I/sQP7sS0c/24W7772Ij37DiT9k1GkzZuApVIddHlxck9JHh5HMbGD3iSu+2NYLFaiAy78J2KNSsBgC8Q/ojJOp5PS0lKqVwojqnUvApt0Jm36SEoO/4RffF1s/v4sWLCALl26MH36dNq0aUNYRBTxPUcTP3ImBr8Qzn7/CVa/AAIDA1i8eDH+/v4kJCTw3XffAWU98aZNm7Jo0SJ++eUXtmzZwo4dOy7FqRVC/AHpUYo/5dxl1ZMnk8nNLmbRjhQ2btxI//79MRqNZHv8sFVthP3YVgq3LSbu3veIu/c9UvLstJu8lpNna1FkCCBZR/Hee+/x0ksvsXLlSgCaNm1KQkICCxcuJDExkSZNmlCtWjU8Hg8xMTFERUXRqlUrlixZckFbzvUoo+OqXLDg889rDlEI5KycSkC9DlS6azJQdq8zKSmJ1FoFxD1w8wW/r2DL14Q0uwXn2VQq+SuiPYdYUurggY41eCUnl6I9qzBHVsV9Ng2v00FI4nW88/IEpj0zjKCgIFYvm0L0dYPJjx6CY9cybmzdlAPqLIsWLSI5ORmj0cjUqVP5bvlSzhhjeeb9eWTZ82kyYDylS1/mlXlzuemmm3j77bc5deoUCQkJQNm93Z07d1a0c/DgwWzZsuWSnmshxIX+th6lUqqLUuqQUuqoUmrc39UO8e+dW4T53Gogbo+X8Qv3cCyzqGKf3xtkA2WDQX/9vvMr7EDZ9Aa3243WGoPBQLVq1fjpp5/Ys2cPjz/+OPHx/7pXeX5bNGWXeHMcmkU7Uio+q231UAxKYY2vj+PUbrTbeUFB9t+dp6kU/g2upaElm7wvxrJx+ULq1avHzQ0r8cao24hIvJa0GQ+Rs+xNlNmC3eXlmcX7cVqC6du3L9M+eI96jgN0MB7GrySdLau/4dixY+Tk5PD555/TrFkzCgoKeOihh5h0Xw/UTzO4prI/3w5ryp4tG7jllluwWCyMHTuWL7/8khMnTtCnTx+OHTt2wfJZq1ev5sSJEzRt2pQ33njj/31OhRB/3t/So1RKGYF3gZuAM8BWpdQ3WmtZ+uAKdH6lHGXxw+u0Y3d5OKwqM2/ePAYNGsSwayK575V9BLW4Fe31krP8bUpTDmIMiiDq9mcoPvQjrtxUjn04gn8EBNGuXln4DR48mG3btrFkyRLMZjNaa5o0acKYMWM4deoUP5UvyXVu/uCzHy/m6JwX8ZRf4rVWbYzX62Xsy2/R++vXCQkJIT8/H2UwkHDDANKTj3Dqn7djtljp9xrExMRQecSMC5YA89gL0B5XxRJg/fr1Y/HixVitVvr06UNMTAw1bhqNtWUfcle9T1CL7ljj6nHkzbspuqYbL7/8MmfOnKF9+/Zs2LCBl156iQ0bNvDLL79gsVgYNWoUGzdupFOnTjgcDux2O++99x7PPvss8+fPx2q1UrVqVRYvXkxERAQ9e/bkyJEjfPbZZ9xxxx3MmzevYvmsG2+8kS1btlzQyxRCXFp/V4/yGuCo1vq41toJfAH0/JvaIv6N83tgRr9grPENSJ0+krQju2ncuDFNmjRhyiMDeGT8ROIqxeA+m0rNa+8g7r73MFgDKDn8I4GJN+Bft11ZObisMxdUjikoKODZZ59l2bJlaK3ZvXs3M2bMYPny5VitVj7//HPS0tLYtm0bWYWlmMLjqfLoPKLumIAr6wS6tJizJS48Hg9du3Zl3LhxaK+XFq695K79GICuXTpTWFhIlSpV6FPd85uVSqJvvK+ix3n+fdjXXnvtN8cAwOty4LHnk/rzt7jdbuLi4sjJyaFuszb847sjLPwlhcMnUzh46DBer5fKlStTUFCAw+HAYrHw8MMPk5eXxwcffIBSirCwMNauXYvH4+GOO+7giy++YNy4cRw9elSWzxLib/Z33aOMB86vsXUGaPU3tUX8G79ehDmqx1ig7J7fa+Oup91dj/LaykMsyLMTTj6xlauy8+1htJu8lvxKtXDnZ+DKOomnMAtQmCxW2rdvX/F5PYY8wvTMBCZNPwzKwMAnX+Xt8Q8QGxtLp06dsFqtxMbGsnHjRsxpu3Gc3MWpN/qUvVlrQtr0IczfzNnyFTSUKquEc/ZsWYUcg8HA999/T0JCApmZmaSkDMNrsuLfZgCOuOZc88Rstj7fnddW9mH4a59SvHE2ntzT1KtXj8qVK2O1Wn9zDKyVamEMCKfpyLfZ9kpfNm/ezLCHx+DsOIKzv5Tde9Vmf5wuBzfc3BWz2Ux0dDSFhYUUFRWxbt067r33XpxOJwcOHOD06dMsXbqUFi1asH37dnJzcwkPD6ekpASv14vNZgPKeuDZ2dmX8nQLIX7lih31qpQappTappTalpWV9Xc356rmaxFmX/cMx3aui9lkBK+H7GVvEn7TcGoO/4CwABsOh4Pk5GS27j7Igl9SKt4Pmhff+YSYhLps376dWrVqMX78eACUUtjSd+JftSHVxnyNf+3WGP2Dib1uIF0bxWIwGFBKAf+673muis2CBQsYMmQIVquVxx9/nO0/bcT14yyOv9KNsZ3r4vboiiAszkjGbQ3h5c9XkZ6eTk5OTsUx0O7zaswqePimBhXfl5xVeMH8TmU0YUtoxg8//MCSJUtISEioKHiutSY4OBibzcayZcto1aoVL7zwAlFRUZw+fZr8/HxiY2N/s3xWUFAQhYWFl+AsCyH+yN8VlClAlfOeVy7fVkFr/ZHWOklrnRQVFXVZGycu5KtSzu+t9HFuWkivZvF0S4wlyGZGO+3Ex8Ux6dZ6NKpXCyirB3v02DHy9v+A1l5cZ9NAawoO/UiGf3VsNhuRkZF4PB7S09Np164dVWMisBSkEGVyYI6ojNFo5LHWYTSvFnZBG/JKXPx8PIf2k5agNRSG16uoMTtp0iRuvPFGUlJSyMjIqJjCco4lpiZet5N/rjpCs2bNOHr0KF0bRPLMTVVxnd4N5ccgzN9Ct8b/muNZ6v7tWpCmsFjM1coG3jRo0KCiCPrgwYMpKSlh6dKlPP7441SvXp1GjRrRtm1bmjRpwsiRI5k1axZNmjTh4MGDBASUzbls3LgxRqORJk2ayGAeIS4XrfVl/0PZJd/jQHXAAuwCGv7R/i1atNDi71VUVKS7deumGzdurBs2bKi/+OILvXr1am2JrqHNkdV0QOKNuvKo2Tqk/QCNwaj961+rg4ODdXx8vB42bJiuX7++NplMulq1atpsNutBgwbpqlWrakCjDFpZ/LWy+Jc9R2kMRl21alXdtGlTbbFYdGBgoK5bt66+6aabdKVKlbTNZtNms1mbTCY9ZswYbbFYtMFg0DVq1NABQcHaGp2gbQnNddywaeWfiTYYDBrQ06ZN01prXa1aNX3ixAmd8OQSrcw2Xe3JJTqm/8var2ZL7V//Wm2OLPv+rl276lq1aumbbrpJ33bbbXrGjBkV78/KytJaa71161YdXL2JrvbkEh3R7VEd1Ly7rvbkEl35wdk6sEp93bhxY/3EE0/ogIAArbXW33//vb7lllu01lqfPHlSN2jQQP/888+X/8QK8V8M2KYvQ2b9LfcotdZupdSDwErACHyitd73d7TlavfSSy8xZ84cjEYjBoOBDz/8kJ9++olhw4ZdUG5uxYoVxMXFsXTpUgDy8/Np1KgRDe9+kVxzJNlL/smZ94dgDo/DGBhBSHgkzWt0oHbt2qxZs4bNmzfjcDho1KgRFouFu+66i+zsbNLzSgi+7l7yf/wSY1A4fgnNKdq9kqode3NjVDH9+vWjZ8+e7Nu3j7i4ONq1a8dXX31F+/bt6dSpE6NHj+app57C6XTyzTffEBQURJc77sJWtz2hbfuROf95MJqpNuZrCpf9g8KDP3Hffffx/fffc/LkSaDsHuzJ8t+pvWW9wqgeY4kP9aPZmYUkJSUxePDg3xy7hIQEkpOTiYyMJCkpiVkLljJ+4R4K7AUYygujB4ZG8s7iVRUVhV599VUAOnXqRKdOnQCoWrUq+/bJf/5CXKn+toIDWutlwLK/6/sF/PTTTyxZsoRffvkFq9VKdnY2TqeTvn37cvfdd/PdobMVE/vDXEWcWbqC8CefpHv37gQHB1O9enVG330j4xfuIaDRDRTv+x53Xjra68WaeYAiUxQbNmzg1KlTdO87EFeHB8mxa3Spi/73DKIgNxu320Pe2um4Swrw2AtxZhxHoUn/7mO21K7JmjVrqFq1KpUrVwbK7lP269eP0NBQHA4HU6dO5dixYyilWL9+Pd27d8erTBTt/o7ifetw56WX1WIFjLU74trzA35+foSHh1OrVtkl4Pz5T6M9LtJmPYqlUm3gX/dgV08vO1bdunVjzpw55OXl0b17d/bu3fub47nk3edobAhiyf7vKS0uoM71fXjq9mv4x6g+9PrxR5KTk/nxxx+56667LsPZFUJcLFfsYB5x6aWlpREZGVkx+T8yMpL58+eTmppKizbtGXBbN1Ly7Jx8vTe55khC73qdvSez6NOnD4sWLcLhcPDqyN6UfPEo3oPfgzJgi4gjKqYS17ZtyYYNG0hNTSUyPoEtuw9yZPMqPMW5eN1O8gqKCI2MoUmTxtSrWweD0YB2lmBUEGg147AX06JFC6ZNm0a1atUA2L59O4cOHWLChAn8/PPPpKWlMWjQIGrWrIm/vz9TpkwBwFOQiaVSbeLuew+0l+g7n0d73Ni3zMff3x+73c7rr79Ohw4dSEhIIDLQSpc7BpD0yIdEdh5Fs/smV9yDnTp1Kp06deLEiROMHTuWm266ieTkZOz2f42A9Xq9DB48mEqVKjHvozcoTj9O5YhAlj3SgV7N4iuKBSQnJzNnzpzLfJaFEH+VBOVV7Oabb+b06dPUqVOHkSNHsn79eh5++GHi4uKI6fcKUf1ertjXXZhDKSaOGqvQsGHDiso5t99+O299uQZHbkpFz81Z6iA1NZXrrruOF198kTOp6Si/ENyFOVB+aVNZA8hOT2X37t3EhgVwTfOmNGtUj/AACy5HMVprDh8+zN2D72PFqrWEtruL3hM/ISQ6jvfee4927drhcrmYO3cuUFaQ4IknnmDo0KGYDAojGqUMGPxDyfl2CmmfjKI45RB2u52mTZsyfPhwVq9eTdu2bdm8eTOtG9Zg07jr+fGR5hiXTWTikFto1KgRGzZsAODgwYPcddddrFq1CoPBwA033MDWrVt54okn6Nu3L7Vr12bjxo1s27btN8c5MLBsbctx48axYcOGiqo6HTt2vKBwQPv27dm1a9clOddCiP8/qfV6lTm/TmpcqB8TPv6GiKLjfP/99/Tt25fJk8vqoqbn2yn45SuUuaw0nSsrmcx1M/CWlpBj9rBixQratWvHK/+YQslr72GMSkAZTuD2eHEUFbNh049YTEb27NlDaX4O1uDospBUBtBeKg+fTtrs0bjSjrBhwwa01kRFRZGRkUFgYCBer5ejJ89gaD8M80/zcWYcJdsRgyM9mzo1qrFv+89Uq1aN6tWrk5ycDEBISAiffPIJgwcPpujsCeJD/TijFMrrJjLATIknmNLSUnbu3MngwYMpLi5m3rx5tGrVio8//pjnnnuOOXPm0LlzZxp2G8w/lu/n7oXpRFpOYzQaSUxMpKioiMLCQho2bIjFYmHnzp20atWKp59+mlWrVvk89pMnT2bKlCkVNWvDw8OZOXMmb775JocPH8bhcNCkSZNLd/KFEP8v0qO8ivx6zmNKnp1nFu8nL6Q2zz//PFOnTmXBggUAVAo5r3arUvjVaEHc0KnU6DKU7t27k5SUhMFgIDipJ5WGTiXi5pGgwOu0E1CvAy4MdOjQgXnz5hFWJwlLZNWyjzKXTZxP+eh+KDpLlSpVqNukJS4vpOQWYgoIZfYPB/H39yftzGlS5j1L6el9OLNOYrAF4i7K4eDe3cTExJCamkrLli05fvw4RqOR1NRUoKyQuHaXsmnc9Zjdxbz35mskHz2E1WrF7XYDZZdLz/2GgIAAcnPLSuK1bNmSqR9M475HniT5yEGU1Z+MAgceDct2pwEQGhpaUX+2ffv2bNu2rWJ+5H+iT58+LFmyBJfLVRHwQogrjwTlVeTXcx5dOWcoyDhVMY9w586dZGVlkZaWRu6CiXjPlgWPMlpInT6K9BkPkfP9jIqwqVq1Klkny96b8917aLcTb2kJRXtW4ykpZP369fTu3Rtv1jEKd60kb/1MtNeNsvjhKczFU3KWU6dOsXffPjBZ0c5SvB4Pfbt2xO5wENy6D7FD3sHgH4ytWmPCOtyNMTACbTBhNpsJCQlh1KhRdO7cGaUUM2fOJD8/v6zN5YUHvF4vzz//PElJScTFxQHQpEkTvvnmG06cOFFxLMpGmkPHjh2pfM8/0P5hZC97g6K9ayr2mfr90d8c065du1KpUiXuvPPOis/4s/z9/bnppptYvHgxX375JQMGDPiP3i+EuDwkKK8iv1evNGfpG2ydMpjGjRuzadMmCgoKePnll8FZTOmxnwnxMxPWaTAGr5Nq0aFUr1qZI0eOANClSxfcqQdJnT4Kg38oymSl6qPzCGrWFYO1bF3F0tJSCs7mcM/z08p6nW4nBosfT3y+EQWYAsOoPOpTAhpeB14XXmcJXhQoI4VbFpD22Vg8hTm4z6bhKc5Du0oxWf3o378/jz32GH5+fnz99dfExcURHR1NtWrVuOuuu2jTpg0AnTt35pVXXmH37t088sgjdO/enV27dtGrVy9uvPFGANatW4fBUPZP4eTJk2S5bQQ17UJg45txph+rOF7p+WXHLy8vr6JW7cqVKxk8eDDNmjXjwIEDeL3ePzz+v1dV57777uPhhx+mZcuWhIWF/cE7hRB/J7lHeRX5vXqlle6ZQqifmQCriV2rviAgtjI1r+/H0ccfZ/To0cTFxdGyZUueeWY7eXl55OQU0blzZwDCwsLoP2QYmwPbcfbYTlzph0mdPgpvaTFKGZj06Qo+GH8vB44ms9VRCWtCMzBa0F4PX67ZhtfrxesoJnXGQ+D1YvALRikDAXXbUViUy6AHHmbGmy9hCotFe914nXa8xXlEV67GqlWrcDgc1KlTp+L3eDwe+vTpQ2hoaMUgn3feeYchQ4bw2muvERUVxYwZM3weo3Xr1pH56UTc2oDBYiPiltGYQmIwBkZUXI6uW7cupaWlZGRkEBERwYgRI/D392f9+vUVgft7zq+qM3jwYB577DFatGhBcHAwQ4YMAeDZZ58lKSmJHj16/L/OsRDi4pOgvIqM7VyX8Qv3XHD51WxQFDvd5NnLapgWOtyMX7jngvcNHjyYRYsW0aRJE2bOnMm6desqXmsUH0KvGxLpe/1gIm57Gkt0DfI3LyD/py8Zv3APfgUOcuwelMsD2gteN96SAtJWTwezHwERsUTe8yYAuavex+t24snPQNsLqBvtR9sbuvDTulVE3T0F+4ZPAE2Qn5nx48cTExNTMSUEoEqVKrz11lskJSVxww038Omnn1KtWjXWrl3Lxo0beeSRR+jatSu1atXiyy+/vGBNzKKisrU1Bw0aREjjG39znGo/PIunbk8kISGegwcP/u7xPf+4nBtgdP5nm81m1q5de8F7UlNT8Xq93Hxz2ULSL7zwwu9+thDi7yOXXq8iv1ezNdBmwuUpu7dmrdKQkiM/U1xSwuRvdvDtt98CUFhYSGxsLC6Xi48//pivv/6aAQMG8O677/Lpp59yc90w3MV5ZC99g9SPR1KwdRHa48Lu8rB753bseVmkzXqUoh3Ly8LSYMRxchfe0mJCzF78zEYyF7xA0b71lBz8EW9eKu1u7Ma4cePYu/kHtKuUuyNP8fZTIyt+y7klqX6P1+vl6NGjhIeHV2yz2WwsX76cffv24e/vz1dfffUfHadz8yovpk8//ZRWrVrx0ksv+eyJCiH+Xuo/HYDwd0hKStK/Nz9N/HXVxy3l/P8C8n+cR9HeNRj9Q7m9YxOaN29OQEAA//jHP4iKiqJ+/fp8+umnbNy4kVWrVrFs2TLuvPNOXlp2gMJflmL0D8adl4HXUQQGI3hcGG0B+NVph7voLI7jWzHYgghudQeFm+dj0i5QBrQtmKBWfQjxN5P23TTiqlbn2IE9+NVsSenpPbw5Zzn/fHwwp0+XLX/l5+fHww8/zMCBA0lISKB///4sX74ck8nEE088wRNPPIHRaGTs2LEMHz684vdNnDiRBQsWEBERwYMPPkjv3r0v/0EXQlwUSqntWuukS/09cun1Kvfr+5YhbfsS0rYv8aF+zBl3PVA2rSTugboc272ZQxu/JLJSWc3Vdu3a0bFjR95++21i4ppScjAEr70Ab2kJIe3uIrRdP3LnPsmIhx7mm/yq2F0eTr3em9BOQwht2IHYZjdwctn7JHToRcGajwhP+4Hc3FwcJcWcOFk2WMaZlYzXq3lp2pc4CksAMJlM7N+/n59++omBAwcCZSNwd+7cyWOPPcaLL77Inj17KmrLDh8+nJkzZ3LzzTfzyy+/UFhYSLNmzS7zkRZC/LeS6z1XOV9rTcKFcy8BSt1e8krcLNrxr1XRlFKkL3+XKr2fIazTUEwh0aA9+JmNhAX5sSLZw4lZY3Gnl02vcOxbzamPH+TAmwNxpBzg8Px/knLmFE+9v4BJkyaBwYQyW1FmK+aIqqA1To8XFVFWyk5rTUBAANdee21FG3r06MG2bdvYs2cPrVq1IigoiKioKKxWK8888wwjR47k5ptvZs2aNQwZMuSCS50JCQmMHz+epk2bkpSUxC+//ELnzp2pWbMmH3zwwaU58EKI/xrSo7zKJCcn06VLF1q3bs2PP/5Iy5Yt6dfhVv45+UXsBbnU7/8Md7euyqsje/O8w8GxXCdBNz+MOaIyRbtX4bUX4i7I5PEX3+DV1E0cPnyYoKAgTErzUv823Hfny7jzMyn6ZQn+QR4OHd5DcY3jeB3FZHzzD7SrlJKUw/jVaIHXacdgCyA4qRe5qz/k/gG3EViag/a48BTloAwmtNcNHhcFWxfjyU/HYrGwe/duevTowcMPP8yaNWtITU1lwIABLF++nI4dO/Lee+/RuHFjatasidPpZMaMGSilOHv2LHa7HYvF8pvjcn6PdPDgwWzatOmCHqkQ4uolPcqr0NGjR3n88cc5ePAgBw8e5OSW78g/sYf5M96nRsp3DO/ZkQ0bNrBjxw7829xF3g+fAhDY+CYMfkGYwiuTvn8zeXl5dOrUialTp2ILDGVQ946U5KRiC4+l4w2dSckrRbtKceek4CnMJrjVHRj8Q0B7sR/fhva4UcqAM/MEeFyU5GSUjRA1GMDjRmsv3qJcjKGVsFSqgTKaCAwMJCEhgW+//ZasrCy2bNlCQEAAVquVF198kVdeeYWEBk2hw3C+Xb2BjIwM7E4PDRs2ZMaMGXTt2pVp06bx7bff8sQTT1QULI+MjKRTp04Vq50MHz68omB8Xl7e33eyhBB/OwnKq1D16tVJTEzEYDDQsGFDbrjhBpRSJCYmkpyczN69ewkLCyMsLIzM+c9jP74dr6uU/J/n4y3JQxkM1O85inr16nHo0CFGPTaGrKJSKt0/DUxWXC4nqYmDsGsz/g06UXL4R5TVn8Jti8HrJmbAPwhq0QPttOPKPk3JoU0AKO3G7jVhsAYAYI2rh9ZePPkZuLOSqdmsLSXKj3RvECG1WxASHsE///lPCgoKqFKlCikpKbjcbg4VWsizRBHd7yVQBpyRtTh45Bjp6emcOXOG+++/n1tvvZXu3bvTrVs3ACwWCzt27OCuu+5iwIABHD9+nE2bNmEwGCoqEQkhrk5y6fUqcH4h9HCdT6n+1z1Jg8FQMZ/wXCi8/vrrlJSUsH37drakOBh+W1nYnaOUInj/Qtp2aIvT6WTtlj04z6aRt3EO3uJclMnKsWmjwFWKOz8Lg80fc1QClugaOE7tJnvxZEI7DgSlQHvxq92a4l0rcBfnYY5KgOI8AEpTDhDSqjemwlSiAi0c2rIOS1xdPIW5FGQVgstB/3sGobVm8eLFOJ1OvBjwGq2kzXwET/FZAIqPbMGgDFSrVo3CwkLmz5/P8ePHsVqtFBYWEhoaCsA111xDeHg4x48fp2nTphfMhRRCXL2kR/k/7teF0DMKHGQUOC4YjHNuvzve/5HDGYWs3pVMcHgkTZs2JXXrCvxtVvxLy4qGWywW5q3cRIjRRWZmJnFxcVjrtscYGEFwy54Y/IIxBobjzjmDx1EIePG6SnGmHcaZcQxvaQmeohxylr5eMafSpkvLGqE12uWoqNNa9fEFlB7ZiH/ecUqj66M9LkrP7MfjKACPE2ULpOrA1wC444476NWrF9rtpHDXCqyV66Ms/thqtCCg4XV43aV06dKFlJQU8vLy6NGjB8OGDcPr9ZKZmcnIkSM5fPhwRQm6w4cP/9sqPkKIq4ME5f+4XxdCh7JRo+cKoQNsOZHD+IV7yCgoWwHD3Ohm8s7mUqNeI9xuN2ajgaFtq3JtnShigm28tvIQPwe2452PZzNz5iw8p3ahPU4M1gCU0UylAa9iCoujXv8JWKw2/CLjCWx8M67sU3iKcvCr2x7MNlAKc2AYNW3FZQ0xmvEU5+EpKStsnjptOKX52aSmpnJqzedgNBF7/4fYqjTCGBaH11HEsU1LMBgMpKamkpeXh8Hihy4toXj/erz2fMJvHIbRPwSTfwhVq1bFZDKRnp7OrFmzKC0tC+i9e/fy6aefUlJSQnBwMFOnTq04NsnJyURGRl7KUySEuMLJpdf/cb8uhG4KiSHu3vcqts+cOZN2k9did9krXnPnZ2AOiyN28Nu8OO56QkNDKSoqwmkJxtGwBSl5dqzx9an0wHQs7hLqu4+y6us55G0qq6/qKTqL0l4s1Zri9Gr8gKI9q7FWboDXaSe655OceqMvmG1YE1rQunVdtmzZgtEvCE9JPspoRhuMaI8bk8WG1+4krl0v0jYvJe2j+1EWf7SzbE6lfV9ZhZ5ly5YRFxdHXHw82aUGXEVn8RRkkfrRMAzWALSzBK83CqfTWRb+ZjPXXHMNSikGDBhAdnY2JSUlfPvtt/Tu3RuHw8GuXbuYOHEiK1asIC8vj40bN0poCnEVkh7l/7i4UL9/u/3XYfpH2/em5ON0/2t1DHdhDqWYyK7UisdGj8GYewK8HrK+epaITgPJc3gw2oJxlffc3GfTKt5rq9oQc3g8cY1aVazEUfvRz4nu/RyGgFCU0Uz1e9/ihq63EhISQhVPevk9R0VAw07Yal4DQJXYaNq3b19WYN3rRblLCVEOlLsUc3R1TLZA4uJiCQ0J4cYbb6wIOpvNxs0330ybNm3YsGEDR44cYcSIERWjYG+77TasVisHDhwgNzeXn376SUJSiKuUlLD7H3fuHuX5l1/9zMYLape2m7z2guo858SH+rGpvDrP22+/zZjnX8NcqSZRt44FwH58O2fXzQCliLG46Nu3L9WqVeOxJ57CqzVVH51HztrpFG39GpTCYAvCay8AowVrfD1KT+8t+yKjEdwuWna8if1HT1KcegTQmMwW0F7cbjcm/2DcJQUAWCo3xJV2CO31EBQYiNlsrlh4OSAggKCgINLT0zEYDERGRlJUVERpaSmtWrWibt26FfceTSYTbrebsLAw7HY7DoejYiRwWFgYmzdvJiYmhlGjRjF37lw++ugjWrZseUnOkxDiP3e5SthJj/J/3J8p8D22c12KfpxL/uaFFdvOr84DMGHCBKp07F0RkgC2hKbEDZ1Ky8c+5quvvuLkyZM89NBDmCKrYg6PQ3vcGC1+WCrVLhuo43YS1fxmDGYL7vx0DLZALLF1CG1/D8HNurB1w2o8uaeIiAgnMDCQ/Lyz3Hr3MABiBr1NSPuyhY09eem0ub4LBqWoWrUqOTk5mEwmatZPpMkz38ANjwMQHBbJ008/TatWrfB4PBw9erSi9xoUFMQzzzxDQEAABoOB+Pj4ise7du3ixhtvxGw2Y7FYmD59OjNnzpSQFOIqJfcorwK9msX7XPmiV7N4vqgfzc+nS1CUXZYd27luxXuGDx9OYWEhlp+/JHf5R/jVaoU7Lx1jcBRRnQaz+8u36PDPPDx5abw9fy02q43ivAKyFk/GcXI3BosfKIUptBKm7KMEVKqB1z+c4gPrscTWwVtaQkDz2yjYsYJ77rmHOXPm4HQ66dChAynmsjbkrJyKrWoiAFp72Xv0FLGxsWRlZTFjxgy8Xi8nz6RxeuqDRPUaD0CBvZTpn8/niYcfYN26dUTEJbDfFQUGE4VFRbz74ccopbDZbPj5+eFwOPB4PHzyySeYzWY8Hg9utxuDwYDJJP9UhLhaSY/yKvbSSy9Rp04d2rdvjypI48Hra/FUzVScC57kucHduOOOOygpKeGDDz7A39+fXt27ERYaQsnBDQQ1u4XafZ8ma9UHWOtfT/y972IMiWbsyHtJbN4Sc0AY7rNpGGyBhN00HLTGEluHPAIoPLWfkiM/o0xWDBY/PMW5ZMx9EoDp06cTGhpKXFwc+/btI2PLMgCU0Uzxvu8BCGrSBW9wLEVFRQwfPpxHHnmk/B6lB3NE1fJKQgpvaQkHDuxn/vz5oBTJOUU4guJQJjNojbtKEl36DiU/Px+j0UinTp0wGAysWLGCadOmYbFYqF27Nv3796dPnz7ceeedTJ06lWefffbvOmVCiL+BBOVVavv27XzxxRfs3LmTZcuWsXXrVgBuv/12tm7dyvMzl7HlrB9Veo6m3eS1uL2ajIwMRj1wPyMeGEbIga8JsJpwpBwkoEFZcXL/Ou1wZp8kN7gmzesl4CnKxRJTA0t0dQBKT+8lotd4lMmMOboGtmqNcWWfxBxembBOg1HWAIJCwxk8eDBaa4YOHUp0UmcA3LkpuPMzwWCiaM8qPGkHiIuLw2w2l/0goxmDLRBn6kE8hTkosxWDLRC3o5jGjRsTWLUR+IVQsHEO2u0CowlzQgtWrFxJp06d2LhxI7t27cLr9TJhwgRycnKoXLkyX3/9NevWreOpp57iyy+/5MEHH5TFlYW4ykhQXmUW7Uih3eS13PTEh+RFN+W7Q2cJDg6mR48eQNmcwgbNWtGvc3vSflmNM/skKXl2nG4vcY3aopSievXqZGRk/GZUrK1yA9Caw4veplZsOMpowla5YXkBAUVIm75kzBkHWuPJSyX8xuE4M49TsPVrivetJer2ZygscTD1/Q9JTU1l8eLF+OUdB0CZrVgq1cJg9UOhiQkJwOPxcPbsWZRSmP2D8ORnoCx+xPSdhHY78TqKUMD3339PYfJuglr0JKLrQ5jCYsHjJvPLCRSlHuPIkSPUrFmT7OxslFK0bt2aZ555hv379/Pss8+Sl5fHSy+9RKNGjTg3qOy7776jTZs2NG/enD59+pTVqAXGjRtHgwYNaNy4MWPGjLls51UIcelIUF5Ffr1kVqHDzfiFey6o0jN48GAsHe+j0tCphLbrX9b7Krf6cE7FY601caF+WOPrUXzgBwA8xWexJjTFXH4/r2bPhwi+5jYAzJFVCEy8gbh730N7XIx+bjLe/AxMwdHY4uvjLsylcOsiNAq7x8ALL7yAv78/qcnHiImvil9gMO6c0+D1csddg9i7YyuzZ8/mo48+wmw288xr72MJj8OVfZIz7w8BgxFlMIDXxfbt2wHI2zCbjLlP4845jSEgnOi7JmMwWxgzZgwZGRlER0fTokUL6tWrx+bNm9Fa06pVK7p3745SiuTkZN58802ys7N58cUXWb16Nb/88gtJSUm8/vrr5OTk8PXXX7Nv3z52797NM888c2lPqBDispCgvIqcX6XHWqUhJUd+prikhMnf7ODbb78FoLCwkFyvP9rjpnjfugven1fivOD52M51iesykqI9q0n95EGK960l5tq7Cfcz8sMPP5C9+iNyF7+C11WKdjtJnzOO9FmPgtdL7VADXjTO9KNl8yMNRhzJO0ApnAXZPP/88xWXVbX2YjCasCU0w1tawsr5s2nTpg3vv/8+xcXFTJs2jfeeexhn1im0047RP4SIRu2Ji6+C1WKhTp06ABgVgEZZA4i+YwLhNZoQEhREamoq2dnZ5OfnM3/+fLZv306LFi1QStGpU6eK8EtKSuLee+/l559/Zv/+/bRr146mTZsya9YsTp48SUhICDabjXvvvZeFCxfi7+9/KU+nEOIykaF8V5HzL5VaK9UioF4H0mY8RKZ/KLd3LJv6MGnSJB57egzaFoQ1ti5eZ9l7/Ou2IyIslIkTJwIwceLEslGxQ2/ktfgqpObZiQv1Y1CiPw/MGsPGjRtp164dN/Tsx7HUnRj9Q6l021PgF8ypf97GF0vWEBlzDZnKQPA1t+Ffpy1Zi/+B48xeEke+i+mnaXi9Xsa/MYMXHh6Mys7FYAsou7fYtAeR/gUcPHgQrTXffvstBQUFaF1WDCHUW0CoPQVbsD9FZy1ERkZiMBgwnC2bGqJdpWR+MR5DpVjcpSXk5OTQpk0bSktLqVGjBkajkWrVqqG1JiEhgdLSUvz9/bFYLGRlZTF58mS8Xi9BQUFMmzaNevXq8e2339KuXTsAfvnlF+x2O1OnTuW5557jkUceAcqKyf/www8EBQVdlvMthLg4pEd5Ffl1lZ6Qtn2JH/YRLR58hzlz5jBmzBhGjBjBF2u2UePetwm/aTiRtzwGQJVeY3h1zLCK9567J9erWXxZzzLUj9Q8Ox/+cJzISnEVofH0I8MIydmPK/skqXOfJnXGQ2i3m5/3HKZhjA08LvzrtAVAGU0opbi5mmL37t0UFRXx4uj7AY01tg7BLXuBx4XTUcLelHxyc3Mxm82EhIRgMBho1KgRVatWJT8/n6ysLA4cOEBRUREbN24sC7ZAf8xmM1XiY1mx5BtatWiK0+kkJyeH0aNHA/DWW2/RuHFjvvjiCwwGA2azmcOHDzNz5kxKSkoYMmQI77//PoGBgTzyyCOMHDmS4uJiKlWqxOrVq1m1ahUPPvggERER7Nq1iylTpvDuu++yc+dONmzYgJ/f71dKEkJcuaQyz1Xkz1TpOX/fc0tzxYX6USt9LS1qRPPwww/z2GOPsWvXLtauXcsLH37JP995H1vD68nb+Dnaacedl8Hn63bTv10dYmJiKPCYKS3IJqLLwxj8gsj86jks0TWwWm040o/Q8vnlpObZKf7uLYoO/0RwYAD5+fls2rSJpFZt0G4XymzFFByFK+cMqnxeZkigP3k5WRiDo9BOO1YjWM0mCgoKMJlMuFwujEYjkZGRZGZmkpycTLVq1QCwWq1orXG73dx0000opVi+fDkWiwW3283JkyepUqUK8fHxTJkyhTvvvJPY2FgyMzNp0qQJhYWFpKWl4fV6qVOnDvfddx8LFixg69ateDweLBYLU6dOJS0tja+//poBAwZw++23U7ly5ct6zoX4XyaVecRF92eq9CQnJ9OoUSN6NYtn07jrOTH5FjaNu54R/bqzYcMGALZt20ZRUREul4uPv1qGITKB/B/nEdP3JaJ7P4d2l/Loo48CUFJSggqphDEgHGUNIP/HeSiThYhbRqPDKqO9ml3vDAdAe72YrVZCQ0OpXbs2Xbp0QXvcQNkczNgh76BsgVhiaqKA/MIiDH7B+De8Hkw2nMpCSFQsAGazGZvNRlRUFDk5OdSoUYNRo0ahlOL999/nwIEDVKpUifbt21cEYrVq1UhMTMTr9dKtWzcGDhxIfn4+I0aMIDIyEpfLhc1mY+fOnRw7doySkhIcDge7d+9m4cKFPP7445SUlLBy5UpatGjBoEGDGDduHB9//DF2u5127dpx8ODBy3jGhRAXg9yjvMr8uyo9v3auZ5mSU0j6mk3M2XAQi8VC8+bN2bZtG1lHd+JXqxWunNOkfz4W7XGBwUTu0R3Ur18fr9dL3Z4PknG2kOwlU3DlnAGDkcyvnsVkC8AcWxd71klSPnkQT/FZvKXFBAWHknbkCABGgwEsQUR2ewR3Ydn0DW9hFgaTGWN4ZWxVE7HVSKLw568wBoSTmV+MzWYjICCAwMBAcnNzcblcOJ1OtmzZwlNPPcUnn3zCyZMnAfj888954YUXiIqKIjAwkFGjRjF06FDWrl3LtGnT8Pf3Z82aNdx99920b9+eFStW8NVXX9GnTx+01uzevZsmTZqQn59PfHzZcZ01a1bF8Tt27BiJiYkkJiaydetWDh48SL169S7iGRVCXGrSoxS/4Xa7GTBgAJWr12bgXX05nXWWMx8Nw+1xM+iWDhRjQSnFbbfdRunpvThO7ymr+zrkHTwF2VQbu4hrnvycSZMmoZTise4t8PMPQHs9YDCWVeMpzCWieVcCmnZFu514SvLxluSD10v8kLd56qmn8Hq9GAwKi8HN2UUvkTlvAgaDgS/XbsNavQVBzbsT2n4Atri6BDXrhqckD3tOGu3atcNsNmM2m3G5yqa31KpVi7lz5/L++++zY8cO/vGPf3D69GnuuOMO7rzzTr744gsOHDjAk08+icFg4Oeff2bq1Knk5ORw9913M2TIEObNm0fv3r2ZPn06TZo0oWHDhixevBgoG9zUp08fWrRoccEqI2+++SaNGjWicePGmM1munbt+recUyHEX6C1vuL/tGjRQovL48SJExrQGzdu1G1fWaMDEm/UoZ2GamNwtLZVa6KNQVG6dr+ndXx8vI6Li9PXXHuT9qvSQBtswTpu2EdamW26zhNf66lf/6C/+uorHRAQoLOysnSL9jfo+E79NQajjr9ugLba/LQ5vLIOaX+3BnRwm77aVrOlNtiCdEjbvlprratVq6bbtm2rt27dqrXW+q233tIWi0WHhIRok1+Q9q/TVge37qNNoZV0tSeX6LAb7tfmwHDdq1cvvX79eg3oF198UcfExOhbb71V9+nTRz/wwAMVv7VVq1a6R48eWmutr7nmGr1w4UKttdZ2u10XFxfr77//Xt9yyy1606ZNunnz5vrkyZOX+WwIIXwBtunLkEHSoxQV1Xqqj1vKHe//WDFqNTXPTkDD6yhN2Q9AQOKNeIpzOVvi4vrrrycgIIC+PbowdMgQwmo1Jfub19DuUkoXjqeK4SxffvklLpeLZbvT2LV9CyWGAKKaXIc1dRcuZymekrMU/vItymQhpHVvPIXZeF0OHAd/YP369Re0MTU1lcmTJ+N0Olm8eDH1GiaCsxiljLgLc/CU5ONO2Y/V4KVnz55s3boVPz8/Zs+eTVxcHF27dqWkpIRFixbRsmVLEhMT+eWXX6hevTqFhYWkpKRw221lxRFsNlvFHMgDBw4wbNgwvv32W6pWrXp5T4wQ4oogQXmVO79ajwYyChzklbhZtCPlN9NJ/Ko3p9rYxUSGBAJw+PBhRo8eTbOqYQy8qQWlaYcJDAgg+cgBevTogdvtpkqterzyfQpeDRpw+kVi7DERi9VGh+u7YDBbQZVdjo0d9CaWsFgigvzo27cvRUVF5BY7uXfmVpo99D6lYTWJrBTHtddey6PDh9KkZjzG/FMYLH4YDq8lwJmLw+VhzPy9vDJrCWabPwcPHiQ4OBgoG+CTn5/PsGHD+Oqrr4iKiiI0NNTn8YmNjcVms7Fjx45LcPSFEP8NJCivcudX6znHXZDJsx8tZGznupQe/KGshms5P7ORsXffwvr168nOzsbj8TB37lyuvbasMLrFYiEhIYH27duzdetW0vMdnJg1FnNEZXA7KT60iSOv9cZhL2Hjd9/gLjoLeMnb+DkFWxdjsAZQOaEWFouFwuISkrMKySwsRQN2l7sixKFsXmjHOtHc2bMbxTuXkefwYq2RRGnqAfLPHKbgbA6TZ34DwIoVK2jfvj1+fn4sXryYDz/8EF0+NSooKIjKlSuzaNEiAEpLSykpKQEgNDSUpUuXMn78eNatW3fpToQQ4oolQXmV+3VhcwBTeGWO/7CQ8XfdSP1wA3U63QFAbEjZdJKhNzdn8uTJXHfddTRp0oQWLVrQs2dPtm/fTkBAAEopHA4HBQUFuDxl1XKCWvai5MjPeOwFZV9iMBKZ1A1zRBXAgKekAK89H41myw+rcTgcmCOq4sg8QdaiVyg58hOlaYdxF2Qy4cP5zJ07l/z8fNq3b0/nzp3JzcmhNOcMwa3uwHFyN8powhRemX++9TZbtmyhsLCQESNGMHLkSFatWsV7771Hp06dKn7z7Nmzefvtt2ncuDFt27YlPT294rWYmBiWLFnCqFGj2Lx58yU7F0KIK5MUHLjKtZu8tqJI+vniQ/3YNO76P/UZ56aQHFj1BQHYeWfKZHo1i2f06NEsPFhC+t5NhHa4G2vlhnjtBZx5ZwBh9dtiadqdgi1fE937OTK/fgn70a2gFHhcBAYGEnTHi+SsfBePvQDtLEGZLHiK88rK2OHl1ltvZfbs2TzwwAN89tlnmKOr41e9RcV9z8BGNxJ+/b1U+/k1pkyZQlJS2bzkyZMns3PnTr744ouLeSiFEJeZFBwQl8XYznXxMxsv2OZnNjK2c90/9f5/tyJJp7pRGI0mXNmnSZvxEOmfly3QHGDPJG/lVErTyuZLlp45QGDijVQb8zUYjBiNRmJjokAp/Gq2JKB+R+KHTSPy1rHYQqLx8/MjPz+fwsJCQkJCsIRVAoORwu3fYKt5DbGD3sSRsp+s2Y9SUFCA01lW0H3dunW89tprnDhxgrp16zJ8+HC8Xu9FOZZCiP9NEpRXuT9TrceXf7ciSaP4ENo2rYe/WRE3dCpBTcvmEb6zcB3PTH4Tv7g6eEuL0V4P3tListD2esjPzyd93gScGcewHym73KlMFsJqNeepl/5Jhw4d6NevH//4xz/48ssvsXoc4PViCo3FYLbiSDlAtQEv8dmSdcybN4+HH36YvLw87rnnHvLy8pg7dy779+/n2LFjLFy48JIcWyHE/wapzCP+42o95/szK5L0vGc4G0YMxrt1GX41y7aNX7iHAVWhXqWg/2vvzqOjqrMEjn9vVfZASEhCdiFA0MMigggoqwIGZETZoiBbAGkGbYVWRxxOY9tDKx4VVEYaGeEICI0LKAHOSJuQnggzDQQVG2RJZGkDISwJSyp76jd/1Et1iVDQYhbI/ZzzDvXue0X93j2V3Lz3fu/3w94sgOM+/piyYr7/469AXIORFxacIDwikrC+4yk4/B1xoYGktPbng9de4MCBA+Tk5JCYmMiwYcM4c+YMgdFtOBo/iMN7/oozdxun/3cFv/3vKOx2O4cOHSI0NJRVq1Yxd+5cWrduDcCYMWOYOXMmrVq1cl+aVUopT3pGqa5LzSMkVecLOLFsxmVnJPnToWqiU/+T2NS3Ces7HvENID9zJe99msGurC94/sEu+AY1JeSeR4iduhhEaHLHEBAbbRNbMiSmjKYnv+a1vgF8suglpk+fTnh4OIsWLaK4uJiMjAwAOiY0Z8O0rhR8/DumJHejzHGRPXv2kJ2d7b70Cq7prpRS6lrpGaW6Ls8l38oL6//GRY/Y3xeM4s0due71S3vWGqeTCzvWUxoeT8eOHbkYcxfV1dWc+miua2YQYwi++1HOf/M5q1atYtiwYZw8eZK+ffsCUFFRQZs2bRg1ahQOhwMR4ezZs5w8eZIFCxYQEhLCsWPHEBHCwsIYNGgQ1dXVbNiwgfHjx7vva6alpfHhhx/SrFkzAJxOJ5MnTyY+Pp558+bVeu6UUjcGPaNUP4sxBqfT6b7HGRUSgHE6cXy+EKrK+eAPT1NSUkKrVq2I9HWdzZXn53ByzWyiHvkPTFU5zRM7sXDhQk4d3IW9aQRxv16NzT8YjJP89/4VZ1Ulbdu2ZejQoQQEBJCamsrYsWPJzs4mKysLPz8/goOD6dq1KzNmzGDv3r08+eSThIWF0b17dzp06EBQUBB5eXkEBQUxYcIE5s+fT58+fYiOjmbAgAEkJiYSGRnpHt82KSlJi6RS6kf08RB1zY4ePUpycjI9evRg9+7dpKSksGnTJsrLy7n33nt5++232bZtG8nJyaSkpNCmTRvmzZtHVHwr8s+XEtRhAKXf78TmF0jp97sIi4iiaZA/efkF4BeEKXdgnE78EzrgdBRRWXiccWPHsHnzZs6dO4d/YBDlFZVg98NmE6Kjo/CjmgsXLhAaGsrRo0d56qmnWLFiBcXFxYSEhBAYGMjAgQOZPn06PXr0cM836efnh81mw+Fw0L9/f4qKikhJSWHOnDn1nWal1DXSx0NUg5STk8OMGTNYuHAh2/ccwnfEfC4M+QPvb8qiWfNwevXqBcC4cePYsWMHkZGR/P7dj4gb+woXd6dhjCGkxygQGyGhYdjtdgYPH0PTdj1I+M06wh98lvIf9lJ55u/grGb9+vUEBQVhs9spK3Hg0zyeW2auxad5HPn5BVwoKWfOnDmUlJS4J2m+//77OXv2LEuWLKFr164AVFdX4+vry5YtWxgyZAilpaU4HA73cd1zzz1kZmZSVlZWL3lVSjVceo9SeVUzmMCJc6U0N+eJjImnZ8+ePDxhOplb05Ht/weAs8yBqahwPz8Jrk4z58+fZ/qYf6EKO9Ul57A3DXdvdwTHEVSUw67Mzfja/Tiz8mlKCo7i2ySUJgG+nD97mjvvvJNZs2Yxasw4fEKiqDxzjIrCE/hEtKT8xCFKJZJly5ZRWVlJVVUV7du3Z/HixeTk5DBy5EgSEhKYOHEiS5YswWazsX37djZt2kRFRQUbN25k5EjXqENTpkwhKyuLlJQU1q9fj4+P/mgopVyu64xSREaLyD4RcYpIt0u2vSAiuSJyUESSPeKDrViuiMy+ns9XteuyA6ZX2vjs6+PsPHKWkJ6jiU1dRGzqIqLHvoKz7CJzl7qeSVyzZg2BgYEEBATQpPdEYlMXIXZf8LjUXx3WktWrV+NwOKiqLHfNVylC21HPE9QkxD3FTUZGBs7KcqoKT4CzmpMrZ+IbFgvGScmZPEpLSyktLaVVq1aEhoby4osvusd17devHykpKfj5+bFu3TreeOMNAgMDadq06Y8mWAZcA7x36cL48eN1EAKllNv1/tm8FxgBvOsZFJH2wKNAByAWSBeRdtbmd4BBQB6wS0TSjDHfXWc7VC243IDpxhhe23IQZ+ztFH/5AcHt+7smYi4uwh4aw+Gs9ZSUlFBUVETPnj1d02z9z3KKMpdhKly9X30jEhC/AEICfenduzfDU3/NmnffJGbKq/h++2cOfvwqztJi7uo9kG/37KBFixY0ib+N0qICnBVlNOl4L+V5+7CHxtAkIpbnn5vGtGnT3G3s378/jz/++E+OZ+jQoRQWFv4k7jnY+UsvvfQLZU8pdbO4rjNKY8x+Y8zBy2x6CFhrjCk3xhwBcoHu1pJrjDlsjKkA1lr7qgbocgOm18TbdOlFcPt+nFz1LCeWPUHh1v8iKuX3dB7/W4KCgli3bh2pqamcOHGCYLsTU1YMdl98QqOx+QcTEN2GExnvExkZyabP/0xovwmc+uR3rvuYZcVgqqnoMprevXuzefNm7MUFmIpSTHkxtoAmNB/4K+y+fjw4ZBCzZs0iISGBN998E3B1OrrtttuYNGkS7dq147HHHiM9PZ1evXqRlJTEzp07cTqdJCUlcfr0acD1aEjbtm3d60opVaO2OvPEAT94rOdZsSvFf0JEpolItohk6y+v+nHpfJQ+zaKInbKY2NBAnku+lai7RxA75R1ip7xDzPg3CGkRz3PJt1JcXAxAREQEy5cv5+7ud7L2L3u4Z97n2AOa4HdkO0FlZzh4YD+nT58mZOi/EdLtIQJbdibkruHc8sx6/OM7sOedJ2jRogURERFUlDroO2AwAeFxXMhOo0WwD2H+8E3mJg4dOkRkZCQLFixwzxuZm5vLM888w4EDBzhw4ABr1qxh27ZtvP7667z88svYbDbGjRvH6tWrAUhPT6dz585ERkbWbZKVUg3eVQuliKSLyN7LLLV6JmiMWWqM6WaM6aa/vOqHtwHTr3WM2IyMDHbv3s28aQ/jWPsbYhy5hBxOJ3nAvSQmJgKQENMCgLK87wju6JqxJHrMy/g1CeWtt95i6tSpzJkzh8zNn1B6Jo9bExNIm3kfLzw7i+HDhxMXF8dXX33FxIkT+fLLLwFITEykU6dO2Gw2OnTowIABAxAROnXqxNGjRwGYPHkyK1euBGD58uWkpqbWViqVUjewq96jNMYM/Bn/73EgwWM93orhJa4amJqiV9PrteZMsibubYzYmt6yBzL2EtiuP79b+Jp7340bN/5oiqua0X08BfraaRrwj6+nv7+/+7Xdbqeqqspr2z33t9ls7nWbzeZ+b0JCAlFRUWzdupWdO3e6zy6VUspTbV16TQMeFRF/EUkEkoCdwC4gSUQSRcQPV4eftFpqg/oFPNwlju2z7+PI/KFsn33fNQ2e7tlb1r9lZwq+/QvPrfqSz74+TmFhIbfffjtZWVkcOXIEgL4tXWej4W06U7Ivk7jQQB675SIJMVGEhIRc8XP69OnDZ599RklJCQ6Hg08//ZQ+ffr8U8c3depUxo0bx+jRo7Hb7Vd/g1Kq0bnex0OGi0gecDewWUS2ABhj9gEfAd8BnwNPGGOqjTFVwJPAFmA/8JG1r7qJePaW9Yu4hdA+4zm2+t957IE+DBo0iPz8fJYuXcqIESPo3LkzjzzyCA93iWPfpvfoF17MxTUz2bTsjZ88vnGprl27MmnSJLp3706PHj2YOnUqXbp0+afaOmzYMIqLi/Wyq1LqinQIO/WLS5y9mct9qwQ4Mn9oXTfHq+zsbGbNmuW+t6mUunHoEHbqhnVpb9mrxevL/PnzGTlyJK+88kp9N0Up1YBpoVS/OG+9ZRuS2bNnc+zYMXr37l3fTVFKNWA6oKX6xV2tt6xSSt1ItFCqWuHt0RGllLqR6KVXpZRSygstlEoppZQXWiiVUkopL7RQKqWUUl5ooVRKKaW80EKplFJKeaGFUimllPJCC6VSSinlhRZKpZRSygstlEoppZQXWiiVUkopL7RQKqWUUl7cEBM3i8hp4Fh9t6MWRQBn6rsRDZzmyDvNz9Vpjry7EfPT0hgTWdsfckMUypudiGTXxSzdNzLNkXean6vTHHmn+bkyvfSqlFJKeaGFUimllPJCC2XDsLS+G3AD0Bx5p/m5Os2Rd5qfK9B7lEoppZQXekaplFJKeaGFUimllPJCC2UdE5HRIrJPRJwi0u2SbS+ISK6IHBSRZI/4YCuWKyKz677V9acxH7snEVkuIqdEZK9HrLmIfCEiOda/YVZcRORtK2ffikjX+mt53RCRBBHJFJHvrJ+vp6245ggQkQAR2Skie6z8vGTFE0Vkh5WHD0XEz4r7W+u51vZW9XoA9UwLZd3bC4wAsjyDItIeeBToAAwGFouIXUTswDvAEKA9MMba96bXmI/9Mt7H9b3wNBvIMMYkARnWOrjylWQt04A/1lEb61MV8Iwxpj3QE3jC+q5ojlzKgfuMMZ2BO4DBItITeBVYaIxpCxQBU6z9pwBFVnyhtV+jpYWyjhlj9htjDl5m00PAWmNMuTHmCJALdLeWXGPMYWNMBbDW2rcxaMzH/iPGmCyg8JLwQ8AK6/UK4GGP+Erj8lcgVERi6qSh9cQYk2+M+cp6fRHYD8ShOQLAOs5ia9XXWgxwH/CJFb80PzV5+wQYICJSN61teLRQNhxxwA8e63lW7ErxxqAxH/u1iDLG5FuvTwJR1utGnTfrMmEXYAeaIzfrCtU3wCngC+B74JwxpsraxTMH7vxY288D4XXa4AbEp74bcDMSkXQg+jKb5hhjNtR1e9TNzxhjRKTRP+slIk2AdcBMY8wFz5Ogxp4jY0w1cIeIhAKfArfVb4tuHFooa4ExZuDPeNtxIMFjPd6K4SV+s/OWEwUFIhJjjMm3LhuesuKNMm8i4ourSK42xqy3wpqjSxhjzolIJnA3rkvOPtZZo2cOavKTJyI+QDPgbL00uAHQS68NRxrwqNXbLBFXJ4OdwC4gyeqd5oerw09aPbazLjXmY78WacBE6/VEYINHfILVs7MncN7j8uNNybp/tgzYb4xZ4LFJcwSISKR1JomIBAKDcN3HzQRGWbtdmp+avI0CtprGPDqNMUaXOlyA4bjuBZQDBcAWj21zcN03OAgM8Yg/AByyts2p72Oo43w12mO/JA9/AvKBSuv7MwXXPaMMIAdIB5pb+wqu3sLfA38DutV3++sgP71xdU75FvjGWh7QHLnzczvwtZWfvcBcK94a1x/kucDHgL8VD7DWc63trev7GOpz0SHslFJKKS/00qtSSinlhRZKpZRSygstlEoppZQXWiiVUkopL7RQKqWUUl5ooVRKKaW80EKplFJKefH/d0T77MAawV0AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (7, 7))\n", "plt.scatter(tsne[:, 0], tsne[:, 1])\n", "labels = x\n", "for label, x, y in zip(\n", " labels, tsne[:, 0], tsne[:, 1]\n", "):\n", " label = (\n", " '%s, %.3f' % (label[0], label[1])\n", " if isinstance(label, list)\n", " else label\n", " )\n", " plt.annotate(\n", " label,\n", " xy = (x, y),\n", " xytext = (0, 0),\n", " textcoords = 'offset points',\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty good, outliers are toxic words." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stacking models\n", "\n", "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:loading sentence piece model\n" ] } ], "source": [ "albert = malaya.toxicity.transformer(model = 'albert')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'severe toxic': 0.9968317,\n", " 'obscene': 0.43022493,\n", " 'identity attack': 0.90531594,\n", " 'insult': 0.42289576,\n", " 'threat': 0.0058603976,\n", " 'asian': 0.000983668,\n", " 'atheist': 0.0005495089,\n", " 'bisexual': 0.0009623809,\n", " 'buddhist': 0.0003632398,\n", " 'christian': 0.0018632574,\n", " 'female': 0.006050684,\n", " 'heterosexual': 0.0025569045,\n", " 'indian': 0.0056869243,\n", " 'homosexual, gay or lesbian': 0.012232827,\n", " 'intellectual or learning disability': 0.00091394753,\n", " 'male': 0.011594971,\n", " 'muslim': 0.0042621437,\n", " 'other disability': 0.00027529505,\n", " 'other gender': 0.0010361207,\n", " 'other race or ethnicity': 0.0012320877,\n", " 'other religion': 0.00091365684,\n", " 'other sexual orientation': 0.0027996385,\n", " 'physical disability': 0.00010540871,\n", " 'psychiatric or mental illness': 0.000815311,\n", " 'transgender': 0.0016718076,\n", " 'malay': 0.96644485,\n", " 'chinese': 0.05199418}]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya.stack.predict_stack([model, albert], [another_string])" ] }, { "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }