{
"cells": [
{
"cell_type": "markdown",
"id": "wxZDXLDCXkk_",
"metadata": {
"id": "wxZDXLDCXkk_"
},
"source": [
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "pZ6sKi8ZX1z4",
"metadata": {
"id": "pZ6sKi8ZX1z4"
},
"source": [
"[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/finance-nlp/05.1.Training_Financial_NER.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "KLqW6FOnEvov",
"metadata": {
"id": "KLqW6FOnEvov"
},
"source": [
"#π Training Financial NER\n"
]
},
{
"cell_type": "markdown",
"id": "Yjl-5MGlx0dF",
"metadata": {
"collapsed": false,
"id": "Yjl-5MGlx0dF"
},
"source": [
"#π¬ Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "MjgJyCCIx0dP",
"metadata": {
"id": "MjgJyCCIx0dP",
"pycharm": {
"is_executing": true
}
},
"outputs": [],
"source": [
"! pip install -q johnsnowlabs"
]
},
{
"cell_type": "markdown",
"id": "7bJI_ekTx0dQ",
"metadata": {
"id": "7bJI_ekTx0dQ"
},
"source": [
"##π Automatic Installation\n",
"Using [my.johnsnowlabs.com](https://my.johnsnowlabs.com/) SSO"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "DEl2pY8Lx0dQ",
"metadata": {
"id": "DEl2pY8Lx0dQ",
"pycharm": {
"is_executing": true
}
},
"outputs": [],
"source": [
"from johnsnowlabs import nlp, finance\n",
"\n",
"# nlp.install(force_browser=True)"
]
},
{
"cell_type": "markdown",
"id": "zKIDRSiOx0dQ",
"metadata": {
"id": "zKIDRSiOx0dQ"
},
"source": [
"##π Manual downloading\n",
"If you are not registered in my.johnsnowlabs.com, you received a license via e-email or you are using Safari, you may need to do a manual update of the license.\n",
"\n",
"- Go to [my.johnsnowlabs.com](https://my.johnsnowlabs.com/)\n",
"- Download your license\n",
"- Upload it using the following command"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7iXsGZrIx0dQ",
"metadata": {
"id": "7iXsGZrIx0dQ"
},
"outputs": [],
"source": [
"from google.colab import files\n",
"print('Please Upload your John Snow Labs License using the button below')\n",
"license_keys = files.upload()"
]
},
{
"cell_type": "markdown",
"id": "PUlLsDgkx0dQ",
"metadata": {
"id": "PUlLsDgkx0dQ"
},
"source": [
"- Install it"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2NA8ka6Fx0dQ",
"metadata": {
"id": "2NA8ka6Fx0dQ"
},
"outputs": [],
"source": [
"nlp.install()"
]
},
{
"cell_type": "markdown",
"id": "S4mvOi6jwlcr",
"metadata": {
"id": "S4mvOi6jwlcr"
},
"source": [
"##π Start Spark Session"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "TZIjuI3zN1Oi",
"metadata": {
"id": "TZIjuI3zN1Oi"
},
"outputs": [],
"source": [
"from johnsnowlabs import nlp, finance\n",
"# Automatically load license data and start a session with all jars user has access to\n",
"spark = nlp.start()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "YeIQqpP6KkW9",
"metadata": {
"id": "YeIQqpP6KkW9"
},
"outputs": [],
"source": [
"from pyspark.sql import DataFrame\n",
"import pyspark.sql.functions as F\n",
"import pyspark.sql.types as T\n",
"import pyspark.sql as SQL\n",
"from pyspark import keyword_only"
]
},
{
"cell_type": "markdown",
"id": "N4QLNrIdB0Ex",
"metadata": {
"id": "N4QLNrIdB0Ex"
},
"source": [
"##π Training a custom NerModel"
]
},
{
"cell_type": "markdown",
"id": "KeDpXEXDBvYk",
"metadata": {
"id": "KeDpXEXDBvYk"
},
"source": [
"\n",
"πThe model was trained in the available [Tweets dataset](https://www.kaggle.com/omermetinn/tweets-about-the-top-companies-from-2015-to-2020), with data from 2015 to 2020. \n",
"\n",
"If your appliation needs different entities than the provided pretrained models can identify, what you can do is to train a new model that fits your requirements. To do that you first need to collect and label enough data and put them in the CoNLL 2003 format. If you are not sure how to annotate (label) text data and prepare it in the CoNLL 2003 format, try our free tool [Annotation Lab](https://nlp.johnsnowlabs.com/docs/en/alab/quickstart), where you can easily label text data and export in the correct format for training.\n",
"\n",
"For our purposes here, we will use a sample file annotated by our team."
]
},
{
"cell_type": "markdown",
"id": "JYBQyxEd0uR0",
"metadata": {
"id": "JYBQyxEd0uR0"
},
"source": [
"###βοΈ CoNLL Data Prep \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "AVBmGFcQ03La",
"metadata": {
"id": "AVBmGFcQ03La"
},
"outputs": [],
"source": [
"! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/finance-nlp/data/conll_noO.conll"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "-JxIUBKV1GJS",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-JxIUBKV1GJS",
"outputId": "b825ea2c-87e9-4f7d-92c0-6c9f7c0aa613"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"( NN NN O\n",
"d NN NN O\n",
") NN NN O\n",
"OF NN NN O\n",
"THE NN NN O\n",
"SECURITIES NN NN O\n",
"EXCHANGE NN NN O\n",
"ACT NN NN O\n",
"OF NN NN O\n",
"1934 NN NN O\n",
"For NN NN O\n",
"the NN NN O\n",
"annual NN NN O\n",
"period NN NN O\n",
"ended NN NN O\n",
"March NNP NNP B-FISCAL_YEAR\n",
"31 NNP NNP I-FISCAL_YEAR\n",
", NNP NNP I-FISCAL_YEAR\n",
"2021 NNP NNP I-FISCAL_YEAR\n",
"March NNP NNP B-FISCAL_YEAR\n",
"31 NNP NNP I-FISCAL_YEAR\n",
", NNP NNP I-FISCAL_YEAR\n",
"2021 NNP NNP I-FISCAL_YEAR\n",
"β NN NN O\n",
"TRANSITION NN NN O\n",
"REPORT NN NN O\n",
"UNDER NN NN O\n",
"SECTION NN NN O\n",
"13 NN NN O\n",
"OR NN NN O\n",
"15 \n"
]
}
],
"source": [
"with open(\"./conll_noO.conll\") as f:\n",
" train_txt =f.read()\n",
"\n",
"print(train_txt[:500])"
]
},
{
"cell_type": "markdown",
"id": "jb7xQ6EdCElD",
"metadata": {
"id": "jb7xQ6EdCElD"
},
"source": [
"The pipeline is similar to the `NerModel` one, but instead of a `AnnotatorModel`, we use an `AnnotatorApproach` object to train the model. If these concepts of annotator and model is not familiar to you, please review the documentation [here](https://nlp.johnsnowlabs.com/docs/en/concepts).\n",
"\n",
"To load the data into spark dataframe, you can use the [CoNLL](https://nlp.johnsnowlabs.com/docs/en/training#conll-dataset) helper."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "DSEC5CTIIPjK",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DSEC5CTIIPjK",
"outputId": "f0595f93-5fbc-4895-c30a-cb827a7dcf58"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"|text |tokens |pos |label |\n",
"+---------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"|( d ) OF THE SECURITIES EXCHANGE ACT OF 1934 For the annual period ended March 31 , 2021 March 31 , 2021 β TRANSITION REPORT UNDER SECTION 13 OR 15|[(, d, ), OF, THE, SECURITIES, EXCHANGE, ACT, OF, 1934, For, the, annual, period, ended, March, 31, ,, 2021, March, 31, ,, 2021, β, TRANSITION, REPORT, UNDER, SECTION, 13, OR, 15]|[NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NNP, NNP, NNP, NNP, NNP, NNP, NNP, NNP, NN, NN, NN, NN, NN, NN, NN, NN]|[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, B-FISCAL_YEAR, I-FISCAL_YEAR, I-FISCAL_YEAR, I-FISCAL_YEAR, B-FISCAL_YEAR, I-FISCAL_YEAR, I-FISCAL_YEAR, I-FISCAL_YEAR, O, O, O, O, O, O, O, O]|\n",
"|ο»Ώ COMPANY BACKGROUND ο»Ώ Evolving Systems was founded in 1985 to provide software and services to the U.S . telecommunications industry . |[ο»Ώ, COMPANY, BACKGROUND, ο»Ώ, Evolving, Systems, was, founded, in, 1985, to, provide, software, and, services, to, the, U.S, ., telecommunications, industry, .] |[NN, NN, NN, NN, NN, NN, NN, NN, NN, NNP, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN] |[O, O, O, O, O, O, O, O, O, B-DATE, O, O, O, O, O, O, O, O, O, O, O, O] |\n",
"|In November 2004 , we expanded our product set and geographical reach with the acquisition of Tertio Telecoms Ltd . |[In, November, 2004, ,, we, expanded, our, product, set, and, geographical, reach, with, the, acquisition, of, Tertio, Telecoms, Ltd, .] |[NN, NNP, NNP, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN, NN] |[O, B-DATE, I-DATE, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, O] |\n",
"+---------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"only showing top 3 rows\n",
"\n"
]
}
],
"source": [
"from sparknlp.training import CoNLL\n",
"\n",
"finance_data = CoNLL().readDataset(spark, \"conll_noO.conll\")\n",
"finance_data.selectExpr(\n",
" \"text\", \"token.result as tokens\", \"pos.result as pos\", \"label.result as label\"\n",
").show(3, False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "R6xa4jp8Szs0",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "R6xa4jp8Szs0",
"outputId": "c1e620d6-25b0-481e-df97-8c67879d4b1a"
},
"outputs": [
{
"data": {
"text/plain": [
"1637"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"finance_data.count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "UE5jiEP-KJsh",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UE5jiEP-KJsh",
"outputId": "8f8389d8-31c2-426f-93f4-e84829a5f7bd"
},
"outputs": [
{
"data": {
"text/plain": [
"['text', 'document', 'sentence', 'token', 'pos', 'label']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"finance_data.columns"
]
},
{
"cell_type": "markdown",
"id": "LqVe225XJfLG",
"metadata": {
"id": "LqVe225XJfLG"
},
"source": [
"Checking the labels we have:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "yKmO5faJJXRJ",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yKmO5faJJXRJ",
"outputId": "8adabe85-2b2e-48f4-ef8d-0b8de95fb624"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------------------+\n",
"|col |\n",
"+------------------+\n",
"|I-PROFIT_INCREASE |\n",
"|B-PROFIT |\n",
"|B-AMOUNT |\n",
"|I-PROFIT |\n",
"|B-PERCENTAGE |\n",
"|B-PROFIT_DECLINE |\n",
"|B-PROFIT_INCREASE |\n",
"|I-DATE |\n",
"|I-AMOUNT |\n",
"|B-EXPENSE |\n",
"|B-EXPENSE_INCREASE|\n",
"|I-EXPENSE_INCREASE|\n",
"|I-PROFIT_DECLINE |\n",
"|O |\n",
"|B-CURRENCY |\n",
"|I-PERCENTAGE |\n",
"|B-FISCAL_YEAR |\n",
"|I-FISCAL_YEAR |\n",
"|B-DATE |\n",
"|I-EXPENSE_DECREASE|\n",
"|B-EXPENSE_DECREASE|\n",
"|I-EXPENSE |\n",
"+------------------+\n",
"\n"
]
}
],
"source": [
"finance_data.select(F.explode(\"label.result\")).distinct().show(50, False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "-83y2Ak0Y3m1",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-83y2Ak0Y3m1",
"outputId": "8eb7b180-8355-4472-e044-6adc4ad75a93"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------------------+-----+\n",
"|ground_truth |count|\n",
"+------------------+-----+\n",
"|O |51912|\n",
"|I-DATE |1932 |\n",
"|I-FISCAL_YEAR |1812 |\n",
"|B-DATE |1797 |\n",
"|B-AMOUNT |1466 |\n",
"|B-CURRENCY |1461 |\n",
"|I-AMOUNT |1134 |\n",
"|B-FISCAL_YEAR |605 |\n",
"|I-EXPENSE_INCREASE|546 |\n",
"|I-EXPENSE_DECREASE|390 |\n",
"|B-PERCENTAGE |350 |\n",
"|I-PROFIT_INCREASE |288 |\n",
"|I-EXPENSE |280 |\n",
"|B-EXPENSE_INCREASE|274 |\n",
"|I-PROFIT |228 |\n",
"|B-EXPENSE_DECREASE|191 |\n",
"|B-PROFIT_INCREASE |164 |\n",
"|B-EXPENSE |150 |\n",
"|B-PROFIT |122 |\n",
"|I-PROFIT_DECLINE |93 |\n",
"|B-PROFIT_DECLINE |58 |\n",
"|I-PERCENTAGE |12 |\n",
"+------------------+-----+\n",
"\n"
]
}
],
"source": [
"finance_data.select(\n",
" F.explode(F.arrays_zip(finance_data.token.result, finance_data.label.result)).alias(\n",
" \"cols\"\n",
" )\n",
").select(\n",
" F.expr(\"cols['0']\").alias(\"token\"), F.expr(\"cols['1']\").alias(\"ground_truth\")\n",
").groupBy(\n",
" \"ground_truth\"\n",
").count().orderBy(\n",
" \"count\", ascending=False\n",
").show(\n",
" 100, truncate=False\n",
")"
]
},
{
"cell_type": "markdown",
"id": "kDIFq1bhDC4d",
"metadata": {
"id": "kDIFq1bhDC4d"
},
"source": [
"πThe CoNLL data already have the columns `document`, `sentence` and `token` that are needed to create the NER model, the only one that is missing is the Embeddings. So let's use the same embedding pretrained model as before to train this new one, but you could use any Embedding model instead (check [SparkNLP Models Hub](https://nlp.johnsnowlabs.com/models?task=Embeddings) for a list of available embedding models)."
]
},
{
"cell_type": "markdown",
"id": "2WZDqlZA_kmb",
"metadata": {
"id": "2WZDqlZA_kmb"
},
"source": [
"###βοΈ Using Bert Embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7qfJh8ap_nI2",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7qfJh8ap_nI2",
"outputId": "baa423cd-826a-4626-c26d-499fbc04e8f5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bert_embeddings_sec_bert_base download started this may take some time.\n",
"Approximate size to download 390.4 MB\n",
"[OK!]\n"
]
}
],
"source": [
"bert_embeddings = nlp.BertEmbeddings.pretrained(\"bert_embeddings_sec_bert_base\", \"en\") \\\n",
" .setInputCols(\"sentence\", \"token\") \\\n",
" .setOutputCol(\"embeddings\")\\\n",
" .setMaxSentenceLength(512)"
]
},
{
"cell_type": "markdown",
"id": "YUNd3OpOLuJB",
"metadata": {
"id": "YUNd3OpOLuJB"
},
"source": [
"Split the data into train and test sets"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8FOT9laXLt_c",
"metadata": {
"id": "8FOT9laXLt_c"
},
"outputs": [],
"source": [
"train_data, test_data = finance_data.randomSplit([0.8, 0.2], seed=42)"
]
},
{
"cell_type": "markdown",
"id": "ZnRJAgYUNIRm",
"metadata": {
"id": "ZnRJAgYUNIRm"
},
"source": [
"We transform the test data and store it into a parquet file so we can use it during training for testing."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "O0aOYktdNH-e",
"metadata": {
"id": "O0aOYktdNH-e"
},
"outputs": [],
"source": [
"bert_embeddings.transform(test_data).write.mode(\"overwrite\").parquet(\n",
" \"test_data_embeddings.parquet\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b1k6kd7SMpBs",
"metadata": {
"id": "b1k6kd7SMpBs"
},
"source": [
"πDeclare the train annotator using the `NerApproach`. In this example, we will train for only 2 epochs to illustrate how to use the annotator without spending too much time waiting the model to finish training, but we recommend to use 5-50 epochs depending on your application to obtain a proper model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "Fe0957BT_rcy",
"metadata": {
"id": "Fe0957BT_rcy"
},
"outputs": [],
"source": [
"nerTagger = finance.NerApproach()\\\n",
" .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n",
" .setLabelColumn(\"label\")\\\n",
" .setOutputCol(\"ner\")\\\n",
" .setMaxEpochs(2)\\\n",
" .setLr(0.003)\\\n",
" .setBatchSize(32)\\\n",
" .setRandomSeed(0)\\\n",
" .setVerbose(1)\\\n",
" .setValidationSplit(0.2)\\\n",
" .setEvaluationLogExtended(True) \\\n",
" .setEnableOutputLogs(True)\\\n",
" .setIncludeConfidence(True)\\\n",
" .setEnableMemoryOptimizer(True)\\\n",
" .setOutputLogsPath('ner_logs') # if not set, logs will be written to ~/annotator_logs\n",
"# .setGraphFolder('graphs') >> put your graph file (pb) under this folder if you are using a custom graph generated the 4.1 NerDL-Graph.ipynb notebook or you can use TFGraphBuilder annotator \n",
"# .setEnableMemoryOptimizer(True)\\ # if you have a limited memory and a large conll file, you can set this True to train batch by batch\n",
"\n",
"ner_pipeline = nlp.Pipeline(\n",
" stages=[\n",
" bert_embeddings,\n",
" nerTagger\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "G59yuxavLt7Q",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G59yuxavLt7Q",
"outputId": "1e49f04a-4f00-4a50-af6f-b6d5155c3493"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.1 s, sys: 1.26 s, total: 12.4 s\n",
"Wall time: 33min 46s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"ner_model = ner_pipeline.fit(train_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "-8itI3ckBOR7",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-8itI3ckBOR7",
"outputId": "e341545e-9c37-4df3-cfae-f9943c17f1de"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name of the selected graph: medical-ner-dl/blstm_38_768_128_200.pb\n",
"Training started - total epochs: 2 - lr: 0.003 - batch size: 32 - labels: 22 - chars: 96 - training examples: 1076\n",
"\n",
"\n",
"Epoch 1/2 started, lr: 0.003, dataset size: 1076\n",
"\n",
"\n",
"Epoch 1/2 - 459.17s - loss: 1156.4308 - avg training loss: 34.01267 - batches: 34\n",
"Quality on validation dataset (20.0%), validation examples = 215\n",
"time to finish evaluation: 365.12s\n",
"Total validation loss: 77.2285\tAvg validation loss: 8.5809\n",
"label\t tp\t fp\t fn\t prec\t rec\t f1\n",
"I-AMOUNT\t 147\t 1\t 8\t 0.9932432\t 0.9483871\t 0.970297\n",
"B-AMOUNT\t 209\t 7\t 2\t 0.9675926\t 0.9905213\t 0.9789227\n",
"B-DATE\t 194\t 20\t 100\t 0.90654206\t 0.65986395\t 0.7637795\n",
"I-DATE\t 278\t 65\t 39\t 0.8104956\t 0.8769716\t 0.8424242\n",
"I-EXPENSE\t 0\t 0\t 21\t 0.0\t 0.0\t 0.0\n",
"B-PROFIT_INCREASE\t 0\t 1\t 26\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE\t 0\t 0\t 15\t 0.0\t 0.0\t 0.0\n",
"I-PERCENTAGE\t 0\t 0\t 2\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT_DECLINE\t 0\t 0\t 19\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT\t 0\t 0\t 33\t 0.0\t 0.0\t 0.0\n",
"B-CURRENCY\t 210\t 2\t 0\t 0.990566\t 1.0\t 0.99526066\n",
"I-PROFIT_INCREASE\t 0\t 2\t 50\t 0.0\t 0.0\t 0.0\n",
"B-PROFIT\t 0\t 0\t 20\t 0.0\t 0.0\t 0.0\n",
"B-PERCENTAGE\t 40\t 5\t 22\t 0.8888889\t 0.6451613\t 0.7476635\n",
"I-FISCAL_YEAR\t 266\t 46\t 13\t 0.8525641\t 0.953405\t 0.90016925\n",
"B-PROFIT_DECLINE\t 0\t 0\t 9\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_INCREASE\t 0\t 0\t 46\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_DECREASE\t 10\t 19\t 24\t 0.3448276\t 0.29411766\t 0.31746033\n",
"B-FISCAL_YEAR\t 89\t 13\t 4\t 0.872549\t 0.9569892\t 0.9128205\n",
"I-EXPENSE_DECREASE\t 29\t 56\t 42\t 0.34117648\t 0.4084507\t 0.37179488\n",
"I-EXPENSE_INCREASE\t 0\t 1\t 96\t 0.0\t 0.0\t 0.0\n",
"tp: 1472 fp: 238 fn: 591 labels: 21\n",
"Macro-average\t prec: 0.37944978, rec: 0.3682794, f1: 0.37378114\n",
"Micro-average\t prec: 0.8608187, rec: 0.713524, f1: 0.7802809\n",
"\n",
"\n",
"Epoch 2/2 started, lr: 0.0029850747, dataset size: 1076\n",
"\n",
"\n",
"Epoch 2/2 - 444.35s - loss: 314.85 - avg training loss: 9.260294 - batches: 34\n",
"Quality on validation dataset (20.0%), validation examples = 215\n",
"time to finish evaluation: 368.89s\n",
"Total validation loss: 56.6601\tAvg validation loss: 6.2956\n",
"label\t tp\t fp\t fn\t prec\t rec\t f1\n",
"I-AMOUNT\t 149\t 4\t 6\t 0.9738562\t 0.9612903\t 0.96753246\n",
"B-AMOUNT\t 210\t 5\t 1\t 0.9767442\t 0.99526066\t 0.9859154\n",
"B-DATE\t 232\t 13\t 62\t 0.94693875\t 0.78911567\t 0.86085343\n",
"I-DATE\t 295\t 43\t 22\t 0.87278104\t 0.9305994\t 0.90076333\n",
"I-EXPENSE\t 0\t 0\t 21\t 0.0\t 0.0\t 0.0\n",
"B-PROFIT_INCREASE\t 8\t 13\t 18\t 0.3809524\t 0.30769232\t 0.34042555\n",
"B-EXPENSE\t 0\t 0\t 15\t 0.0\t 0.0\t 0.0\n",
"I-PERCENTAGE\t 0\t 0\t 2\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT_DECLINE\t 0\t 0\t 19\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT\t 0\t 0\t 33\t 0.0\t 0.0\t 0.0\n",
"B-CURRENCY\t 210\t 2\t 0\t 0.990566\t 1.0\t 0.99526066\n",
"I-PROFIT_INCREASE\t 29\t 35\t 21\t 0.453125\t 0.58\t 0.50877196\n",
"B-PROFIT\t 0\t 0\t 20\t 0.0\t 0.0\t 0.0\n",
"B-PERCENTAGE\t 58\t 6\t 4\t 0.90625\t 0.9354839\t 0.92063487\n",
"I-FISCAL_YEAR\t 272\t 34\t 7\t 0.8888889\t 0.9749104\t 0.9299145\n",
"B-PROFIT_DECLINE\t 0\t 0\t 9\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_INCREASE\t 3\t 0\t 43\t 1.0\t 0.06521739\t 0.12244898\n",
"B-EXPENSE_DECREASE\t 18\t 25\t 16\t 0.41860464\t 0.5294118\t 0.4675325\n",
"B-FISCAL_YEAR\t 91\t 19\t 2\t 0.8272727\t 0.97849464\t 0.8965517\n",
"I-EXPENSE_DECREASE\t 25\t 41\t 46\t 0.37878788\t 0.35211268\t 0.36496353\n",
"I-EXPENSE_INCREASE\t 19\t 23\t 77\t 0.45238096\t 0.19791667\t 0.2753623\n",
"tp: 1619 fp: 263 fn: 444 labels: 21\n",
"Macro-average\t prec: 0.49843565, rec: 0.45702407, f1: 0.47683245\n",
"Micro-average\t prec: 0.86025506, rec: 0.7847794, f1: 0.82078576\n",
"\n"
]
}
],
"source": [
"import os\n",
"\n",
"log_files = os.listdir(\"./ner_logs\")\n",
"with open(\"./ner_logs/\"+log_files[0]) as log_file:\n",
" print(log_file.read())"
]
},
{
"cell_type": "markdown",
"id": "2TjOQ0BTEvGF",
"metadata": {
"id": "2TjOQ0BTEvGF"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "riQTP4wuQfVf",
"metadata": {
"id": "riQTP4wuQfVf"
},
"source": [
"###βοΈ Splitting Dataset Into Train and Test Set\n",
"\n",
"Also we will use `.setTestDataset('test_data_embeddings.parquet')` for checking test-loss values of each epoch in the logs file and `.useBestModel(True)` parameter whether to restore and use the model that has achieved the best performance at the end of the training.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "B35v8bF9KJhu",
"metadata": {
"id": "B35v8bF9KJhu"
},
"outputs": [],
"source": [
"! mkdir ner_logs_best"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec0zEZhU33x8",
"metadata": {
"id": "ec0zEZhU33x8"
},
"outputs": [],
"source": [
"nerTagger = (\n",
" finance.NerApproach()\n",
" .setInputCols([\"sentence\", \"token\", \"embeddings\"])\n",
" .setLabelColumn(\"label\")\n",
" .setOutputCol(\"ner\")\n",
" .setMaxEpochs(2)\n",
" .setLr(0.002)\n",
" .setBatchSize(32)\n",
" .setRandomSeed(0)\n",
" .setVerbose(1)\n",
" .setValidationSplit(0.0)\n",
" .setEvaluationLogExtended(True)\n",
" .setEnableOutputLogs(True)\n",
" .setIncludeConfidence(True)\n",
" .setEnableMemoryOptimizer(True)\\\n",
" .setTestDataset(\"test_data_embeddings.parquet\")\n",
" .setOutputLogsPath('ner_logs_best') # if not set, logs will be written to ~/annotator_logs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "NT_Wzmb7LVkZ",
"metadata": {
"id": "NT_Wzmb7LVkZ"
},
"outputs": [],
"source": [
"ner_pipeline = nlp.Pipeline(\n",
" stages=[\n",
" bert_embeddings,\n",
" nerTagger\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "NDBkjMuPKQ3I",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NDBkjMuPKQ3I",
"outputId": "81e247d4-43f3-49c8-f453-f88585b4fd26"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 7.43 s, sys: 863 ms, total: 8.3 s\n",
"Wall time: 22min 17s\n"
]
}
],
"source": [
"%%time\n",
"ner_model = ner_pipeline.fit(train_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4C8bpmGdKmWW",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4C8bpmGdKmWW",
"outputId": "cc035f2d-4173-4f40-d12c-ce88803dd58d"
},
"outputs": [
{
"data": {
"text/plain": [
"['FinanceNerApproach_72b075f0da70.log']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_files = os.listdir(\"./ner_logs_best/\")\n",
"\n",
"log_files"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5UVP6bYwKov2",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5UVP6bYwKov2",
"outputId": "6cc0f415-8f76-4d7b-99fc-9aacc65089e0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name of the selected graph: medical-ner-dl/blstm_38_768_128_200.pb\n",
"Training started - total epochs: 2 - lr: 0.002 - batch size: 32 - labels: 22 - chars: 98 - training examples: 1338\n",
"\n",
"\n",
"Epoch 1/2 started, lr: 0.002, dataset size: 1338\n",
"\n",
"\n",
"Epoch 1/2 - 463.01s - loss: 1153.6621 - avg training loss: 27.468145 - batches: 42\n",
"Quality on test dataset: \n",
"time to finish evaluation: 14.18s\n",
"Total test loss: 84.3584\tAvg test loss: 8.4358\n",
"label\t tp\t fp\t fn\t prec\t rec\t f1\n",
"I-AMOUNT\t 191\t 5\t 8\t 0.9744898\t 0.959799\t 0.96708864\n",
"B-AMOUNT\t 248\t 9\t 3\t 0.96498054\t 0.98804784\t 0.9763779\n",
"B-DATE\t 302\t 18\t 66\t 0.94375\t 0.8206522\t 0.87790704\n",
"I-DATE\t 423\t 24\t 51\t 0.94630873\t 0.8924051\t 0.91856676\n",
"I-EXPENSE\t 0\t 0\t 61\t 0.0\t 0.0\t 0.0\n",
"B-PROFIT_INCREASE\t 2\t 1\t 20\t 0.6666667\t 0.09090909\t 0.16000001\n",
"B-EXPENSE\t 0\t 0\t 29\t 0.0\t 0.0\t 0.0\n",
"I-PERCENTAGE\t 0\t 0\t 4\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT_DECLINE\t 0\t 0\t 11\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT\t 0\t 0\t 33\t 0.0\t 0.0\t 0.0\n",
"B-CURRENCY\t 249\t 5\t 0\t 0.98031497\t 1.0\t 0.9900597\n",
"I-PROFIT_INCREASE\t 2\t 0\t 44\t 1.0\t 0.04347826\t 0.083333336\n",
"B-PROFIT\t 0\t 0\t 21\t 0.0\t 0.0\t 0.0\n",
"B-PERCENTAGE\t 51\t 1\t 12\t 0.9807692\t 0.8095238\t 0.8869566\n",
"I-FISCAL_YEAR\t 270\t 28\t 17\t 0.90604025\t 0.9407666\t 0.923077\n",
"B-PROFIT_DECLINE\t 0\t 0\t 8\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_INCREASE\t 0\t 0\t 60\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_DECREASE\t 14\t 45\t 15\t 0.23728813\t 0.4827586\t 0.3181818\n",
"B-FISCAL_YEAR\t 90\t 8\t 6\t 0.9183673\t 0.9375\t 0.927835\n",
"I-EXPENSE_DECREASE\t 36\t 156\t 20\t 0.1875\t 0.64285713\t 0.2903226\n",
"I-EXPENSE_INCREASE\t 0\t 0\t 118\t 0.0\t 0.0\t 0.0\n",
"tp: 1878 fp: 300 fn: 607 labels: 21\n",
"Macro-average\t prec: 0.4622131, rec: 0.409938, f1: 0.4345089\n",
"Micro-average\t prec: 0.862259, rec: 0.7557344, f1: 0.80549\n",
"\n",
"\n",
"Epoch 2/2 started, lr: 0.0019900498, dataset size: 1338\n",
"\n",
"\n",
"Epoch 2/2 - 466.40s - loss: 357.03232 - avg training loss: 8.50077 - batches: 42\n",
"Quality on test dataset: \n",
"time to finish evaluation: 11.59s\n",
"Total test loss: 59.1781\tAvg test loss: 5.9178\n",
"label\t tp\t fp\t fn\t prec\t rec\t f1\n",
"I-AMOUNT\t 195\t 6\t 4\t 0.9701493\t 0.9798995\t 0.975\n",
"B-AMOUNT\t 250\t 6\t 1\t 0.9765625\t 0.99601597\t 0.9861933\n",
"B-DATE\t 348\t 31\t 20\t 0.9182058\t 0.9456522\t 0.9317269\n",
"I-DATE\t 446\t 21\t 28\t 0.9550321\t 0.9409283\t 0.9479278\n",
"I-EXPENSE\t 0\t 1\t 61\t 0.0\t 0.0\t 0.0\n",
"B-PROFIT_INCREASE\t 14\t 12\t 8\t 0.53846157\t 0.6363636\t 0.5833334\n",
"B-EXPENSE\t 0\t 0\t 29\t 0.0\t 0.0\t 0.0\n",
"I-PERCENTAGE\t 0\t 0\t 4\t 0.0\t 0.0\t 0.0\n",
"I-PROFIT_DECLINE\t 2\t 3\t 9\t 0.4\t 0.18181819\t 0.25\n",
"I-PROFIT\t 0\t 1\t 33\t 0.0\t 0.0\t 0.0\n",
"B-CURRENCY\t 249\t 5\t 0\t 0.98031497\t 1.0\t 0.9900597\n",
"I-PROFIT_INCREASE\t 31\t 36\t 15\t 0.46268657\t 0.67391306\t 0.54867256\n",
"B-PROFIT\t 0\t 0\t 21\t 0.0\t 0.0\t 0.0\n",
"B-PERCENTAGE\t 62\t 3\t 1\t 0.95384616\t 0.984127\t 0.96875\n",
"I-FISCAL_YEAR\t 279\t 12\t 8\t 0.9587629\t 0.9721254\t 0.9653979\n",
"B-PROFIT_DECLINE\t 0\t 0\t 8\t 0.0\t 0.0\t 0.0\n",
"B-EXPENSE_INCREASE\t 1\t 0\t 59\t 1.0\t 0.016666668\t 0.032786887\n",
"B-EXPENSE_DECREASE\t 20\t 63\t 9\t 0.24096386\t 0.6896552\t 0.35714287\n",
"B-FISCAL_YEAR\t 89\t 6\t 7\t 0.9368421\t 0.9270833\t 0.93193716\n",
"I-EXPENSE_DECREASE\t 43\t 125\t 13\t 0.2559524\t 0.76785713\t 0.38392857\n",
"I-EXPENSE_INCREASE\t 0\t 0\t 118\t 0.0\t 0.0\t 0.0\n",
"tp: 2029 fp: 331 fn: 456 labels: 21\n",
"Macro-average\t prec: 0.5022753, rec: 0.51010025, f1: 0.5061575\n",
"Micro-average\t prec: 0.85974574, rec: 0.816499, f1: 0.8375645\n",
"\n"
]
}
],
"source": [
"with open(\"./ner_logs_best/\"+log_files[0]) as log_file:\n",
" print(log_file.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "w9g-vbvhfntQ",
"metadata": {
"id": "w9g-vbvhfntQ"
},
"outputs": [],
"source": [
"# test_data = bert_embeddings.transform(test_data)\n",
"\n",
"predictions = ner_model.transform(test_data)\n",
"\n",
"from sklearn.metrics import classification_report\n",
"\n",
"preds_df = predictions.select(F.explode(F.arrays_zip(predictions.token.result,\n",
" predictions.label.result,\n",
" predictions.ner.result)).alias(\"cols\")) \\\n",
" .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
" F.expr(\"cols['1']\").alias(\"ground_truth\"),\n",
" F.expr(\"cols['2']\").alias(\"prediction\")).toPandas()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "wL4Cqq-uzRhg",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wL4Cqq-uzRhg",
"outputId": "cbb7001f-2bf8-42eb-c52a-2bb48248a043"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" B-AMOUNT 0.9766 0.9960 0.9862 251\n",
" B-CURRENCY 0.9803 1.0000 0.9901 249\n",
" B-DATE 0.9182 0.9457 0.9317 368\n",
" B-EXPENSE 0.0000 0.0000 0.0000 29\n",
"B-EXPENSE_DECREASE 0.2410 0.6897 0.3571 29\n",
"B-EXPENSE_INCREASE 1.0000 0.0167 0.0328 60\n",
" B-FISCAL_YEAR 0.9368 0.9271 0.9319 96\n",
" B-PERCENTAGE 0.9538 0.9841 0.9688 63\n",
" B-PROFIT 0.0000 0.0000 0.0000 21\n",
" B-PROFIT_DECLINE 0.0000 0.0000 0.0000 8\n",
" B-PROFIT_INCREASE 0.5385 0.6364 0.5833 22\n",
" I-AMOUNT 0.9701 0.9799 0.9750 199\n",
" I-DATE 0.9550 0.9409 0.9479 474\n",
" I-EXPENSE 0.0000 0.0000 0.0000 61\n",
"I-EXPENSE_DECREASE 0.2560 0.7679 0.3839 56\n",
"I-EXPENSE_INCREASE 0.0000 0.0000 0.0000 118\n",
" I-FISCAL_YEAR 0.9588 0.9721 0.9654 287\n",
" I-PERCENTAGE 0.0000 0.0000 0.0000 4\n",
" I-PROFIT 0.0000 0.0000 0.0000 33\n",
" I-PROFIT_DECLINE 0.4000 0.1818 0.2500 11\n",
" I-PROFIT_INCREASE 0.4627 0.6739 0.5487 46\n",
" O 0.9760 0.9888 0.9824 9552\n",
"\n",
" accuracy 0.9532 12037\n",
" macro avg 0.5238 0.5319 0.4925 12037\n",
" weighted avg 0.9421 0.9532 0.9443 12037\n",
"\n"
]
}
],
"source": [
"print(classification_report(preds_df['ground_truth'], preds_df['prediction'], digits=4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "uw5HgQ_FMzwj",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uw5HgQ_FMzwj",
"outputId": "8dcb8567-d169-413c-d3f7-a844b929b4db"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------------+------------------+----------+\n",
"|token |ground_truth |prediction|\n",
"+------------+------------------+----------+\n",
"|$ |B-CURRENCY |B-CURRENCY|\n",
"|2.6 |B-AMOUNT |B-AMOUNT |\n",
"|million |I-AMOUNT |I-AMOUNT |\n",
"|of |O |O |\n",
"|the |O |O |\n",
"|increase |O |O |\n",
"|was |O |O |\n",
"|attributable|O |O |\n",
"|to |O |O |\n",
"|our |O |O |\n",
"|increased |O |O |\n",
"|hosting |B-EXPENSE_INCREASE|O |\n",
"|costs |I-EXPENSE_INCREASE|O |\n",
"|largely |O |O |\n",
"|associated |O |O |\n",
"|with |O |O |\n",
"|the |O |O |\n",
"|increased |O |O |\n",
"|adoption |O |O |\n",
"|of |O |O |\n",
"+------------+------------------+----------+\n",
"only showing top 20 rows\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"\n",
"predictions.select(F.explode(F.arrays_zip(predictions.token.result,\n",
" predictions.label.result,\n",
" predictions.ner.result)).alias(\"cols\")) \\\n",
" .select(F.expr(\"cols['0']\").alias(\"token\"),\n",
" F.expr(\"cols['1']\").alias(\"ground_truth\"),\n",
" F.expr(\"cols['2']\").alias(\"prediction\")).show(truncate=False)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "P0sD2CU4HP-H",
"metadata": {
"id": "P0sD2CU4HP-H"
},
"source": [
"###βοΈ Entity level evaluation (strict eval)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6zcBUVcBe5y",
"metadata": {
"id": "b6zcBUVcBe5y"
},
"outputs": [],
"source": [
"!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/open-source-nlp/utils/conll_eval.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "jpiIrbx5I8qI",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jpiIrbx5I8qI",
"outputId": "9fb83261-9846-45b2-d6b9-ff4a2b582070"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processed 12037 tokens with 1196 phrases; found: 1204 phrases; correct: 1021.\n",
"accuracy: 81.65%; (non-O)\n",
"accuracy: 95.32%; precision: 84.80%; recall: 85.37%; FB1: 85.08\n",
" AMOUNT: precision: 94.25%; recall: 98.01%; FB1: 96.09 261\n",
" CURRENCY: precision: 98.03%; recall: 100.00%; FB1: 99.01 254\n",
" DATE: precision: 89.32%; recall: 93.21%; FB1: 91.22 384\n",
" EXPENSE: precision: 0.00%; recall: 0.00%; FB1: 0.00 1\n",
" EXPENSE_DECREASE: precision: 20.65%; recall: 65.52%; FB1: 31.40 92\n",
" EXPENSE_INCREASE: precision: 0.00%; recall: 0.00%; FB1: 0.00 1\n",
" FISCAL_YEAR: precision: 89.90%; recall: 92.71%; FB1: 91.28 99\n",
" PERCENTAGE: precision: 93.85%; recall: 96.83%; FB1: 95.31 65\n",
" PROFIT: precision: 0.00%; recall: 0.00%; FB1: 0.00 1\n",
" PROFIT_DECLINE: precision: 0.00%; recall: 0.00%; FB1: 0.00 4\n",
" PROFIT_INCREASE: precision: 33.33%; recall: 63.64%; FB1: 43.75 42\n"
]
}
],
"source": [
"import conll_eval\n",
"\n",
"metrics = conll_eval.evaluate(preds_df['ground_truth'].values, preds_df['prediction'].values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "vZ0jme54KDyC",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vZ0jme54KDyC",
"outputId": "8b0227a0-51cf-4cdc-ad05-607cd91fc2c9"
},
"outputs": [
{
"data": {
"text/plain": [
"(84.80066445182725, 85.36789297658864, 85.08333333333334)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# micro, macro, avg\n",
"metrics[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "YMZu0ottJkmn",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 394
},
"id": "YMZu0ottJkmn",
"outputId": "658d923b-b8a3-435f-8e91-77b500997e34"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" entity | \n",
" precision | \n",
" recall | \n",
" f1 | \n",
" support | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" AMOUNT | \n",
" 94.252874 | \n",
" 98.007968 | \n",
" 96.093750 | \n",
" 261 | \n",
"
\n",
" \n",
" | 1 | \n",
" CURRENCY | \n",
" 98.031496 | \n",
" 100.000000 | \n",
" 99.005964 | \n",
" 254 | \n",
"
\n",
" \n",
" | 2 | \n",
" DATE | \n",
" 89.322917 | \n",
" 93.206522 | \n",
" 91.223404 | \n",
" 384 | \n",
"
\n",
" \n",
" | 3 | \n",
" EXPENSE | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 1 | \n",
"
\n",
" \n",
" | 4 | \n",
" EXPENSE_DECREASE | \n",
" 20.652174 | \n",
" 65.517241 | \n",
" 31.404959 | \n",
" 92 | \n",
"
\n",
" \n",
" | 5 | \n",
" EXPENSE_INCREASE | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 1 | \n",
"
\n",
" \n",
" | 6 | \n",
" FISCAL_YEAR | \n",
" 89.898990 | \n",
" 92.708333 | \n",
" 91.282051 | \n",
" 99 | \n",
"
\n",
" \n",
" | 7 | \n",
" PERCENTAGE | \n",
" 93.846154 | \n",
" 96.825397 | \n",
" 95.312500 | \n",
" 65 | \n",
"
\n",
" \n",
" | 8 | \n",
" PROFIT | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 1 | \n",
"
\n",
" \n",
" | 9 | \n",
" PROFIT_DECLINE | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 4 | \n",
"
\n",
" \n",
" | 10 | \n",
" PROFIT_INCREASE | \n",
" 33.333333 | \n",
" 63.636364 | \n",
" 43.750000 | \n",
" 42 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"\n",
" \n",
"
\n",
"
\n",
" "
],
"text/plain": [
" entity precision recall f1 support\n",
"0 AMOUNT 94.252874 98.007968 96.093750 261\n",
"1 CURRENCY 98.031496 100.000000 99.005964 254\n",
"2 DATE 89.322917 93.206522 91.223404 384\n",
"3 EXPENSE 0.000000 0.000000 0.000000 1\n",
"4 EXPENSE_DECREASE 20.652174 65.517241 31.404959 92\n",
"5 EXPENSE_INCREASE 0.000000 0.000000 0.000000 1\n",
"6 FISCAL_YEAR 89.898990 92.708333 91.282051 99\n",
"7 PERCENTAGE 93.846154 96.825397 95.312500 65\n",
"8 PROFIT 0.000000 0.000000 0.000000 1\n",
"9 PROFIT_DECLINE 0.000000 0.000000 0.000000 4\n",
"10 PROFIT_INCREASE 33.333333 63.636364 43.750000 42"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"pd.DataFrame(metrics[1], columns=['entity','precision','recall','f1','support'])"
]
},
{
"cell_type": "markdown",
"id": "DVBxVC2yi12r",
"metadata": {
"id": "DVBxVC2yi12r"
},
"source": [
"###βοΈ Ner log parser"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cyKawgE8i4TN",
"metadata": {
"id": "cyKawgE8i4TN"
},
"outputs": [],
"source": [
"!wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/open-source-nlp/utils/ner_log_parser.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "hQ2tEbyRjC0E",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "hQ2tEbyRjC0E",
"outputId": "d932d50c-1327-456d-b0c9-db3110e00e1b"
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAAT8CAYAAAAUipDeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZxcVZ3//9enqruzdZJusgEGCDLIJqYhIQFECOBIYBDwq4MoKqjIzPzEhS/iiuICKqLjil8NIog4Ccq4IG6jM0FFJZAgoBBElkBCBgikOxBISHfV+f1R1ZXq6uolSa+p1/PxKOvee07dc26liFXvnHNupJSQJEmSJEnSzi0z3B2QJEmSJEnS4DMEkiRJkiRJqgGGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEnSCBcRqyLilVWOL4iINcPRJ+08IuKaiLhkuPshSZIGnyGQJEk7uYj4eERcN0jnXhoR6yLimYi4KyJOrSh/V0Q8XCxfHhFH9eOcv4iIjWWPLRHxl4o67yme97mIWBkRL9nBNo8tXsuGiFhVUTY9IhZHxNpi+R8iYv4AXOeFEfHXiHi2+NoLK8pnFfv0fETcVxkERsT5EfF4sc1vR8SYvtqUJEm1zRBIkiTtiPcAu6WUJgHnAtdFxG4AxaDks8DrgMnAVcCPIiLb2wlTSiemlBo7H8AfgR90lkfEOcDbgX8CGoGTgad2pE3gOeDbwIVVyhqB24E5wC7Ad4CfRUTjDrYZwFuAZmAhcF5EnFFWvhj4MzAF+AhwQ0RMK7Z5AvBB4HhgL+DFwCf6aE+SJNU4QyBJkkaHwyLi3ohojYirI2JsZYWI+EBEPFYcWfK3iDg+IhYCHwZeXxxVc1ex7s0RcUlE/LF4/KcRMSUivlccWXJ7RMzqq1MppbtTSh2du0A9sEdxfxZwT0ppRUopAdcCU4Hp/b3oYh9eUXwtEZEBLgbOTyndmwoeTCmt35E2U0q3pZS+CzxUpeyhlNK/p5T+N6WUSyktAhqA/Xawzc+llO5IKXWklP4G/AR4efE6XwIcClycUtqUUvpP4C/Aa4svPwu4KqV0T0qpFfgUcHa1djqnDUbEhyPiqeL0wjMrqk2NiF8XPzu/jYi9euu7JEkanQyBJEkaHc4ETgD2AV4CXFReGBH7AecBh6WUJhbrrkop/RL4NHB9cWTN7LKXnQG8GXhR8bx/Aq6mMNplJYWwpU8RcVNEbAaWATcDy4tFvwCyETG/OCrmbcCdwOPbcN1vAX6fUlpV3J9ZfLw0IlYXp1F9ohgODVSbvYqIFgoh0AMD1WZEBIWw657ioYOAh1JKz5ZVu6t4vLP8roqyGRExpYcmdqUQTL2IQoC0qPiZ6XQmhSBparHv3+tv3yVJ0uhRN9wdkCRJ/fK1lNJqgIi4FPgq8Juy8hwwBjgwItaVhSa9uTql9GDxnL8ADkwp/aa4/wMKoUCfUkonR0Q98ErggJRSvlj0LPCfwC0Upj61AScWR8v011uA8kWLZxafXwUcDDQB/wWsAa4coDZ7FBGTgO8Cn0gpbSgeHog2P07hH+euLu43Ahsq6mygEOJUK+/cngg83UMbH00pvQD8NiJ+BpzO1j/jn6WUfle8xo8AGyJij87PnCRJ2jk4EkiSpNGh/Mf4I8Du5YUppQeA91IIE56MiCUR0aVOFU+UbW+qst/Y386llNpTSr8AXhURpxQPvx14K4VRKw3Am4Cb+tEvAIqLK+8K3FDRL4DPpZTaimHXN4GTBqLNPvozDvgpcGtK6TNlRTt6nedRCLv+qRjSAGwEJlVUnUQhcKpW3rn9LNW1ppSeK9uv/AyVPl8ppY3A+opySZK0EzAEkiRpdNijbHtPYG1lhZTSf6SUjqKwUHACLussGvzuldRRmFoG0ALclFK6P6WUL05N+1/gyH6e6yzgh8VQotPfgC10vaby7R1ts6rinbd+TGHE0b9UFG93mxHxNooLPKeU1pQV3QO8OCImlh2bzdbpYvcU98vLnkgp9TQKqDkiJpTtV36GSp+v4oLXu1DlMyZJkkY3QyBJkkaHd0bEzIjYhcKdoq4vL4yI/SLiuGJYsZnCiJnOaVlPALPK1s0ZEBGxf0ScGBHjIqI+It4EHA38tljlduCfIuLFUfCPFNYz+ms/zj2OwnSla8qPp5Sep3Dt74+IiRExk8JdyW7akTYjIlNcbLu+sBtjI6KhWFZPYTTSJuCssulunba3zTMprNf0jymlLgtSp5Tup7A2z8XFvrwGeBmFaWdQWHz67RFxYEQ0UVgjqst7VcUnIqIhIl5B4Y5qPygrOykijipe86cojHZyKpgkSTsZ1wSSJGl0+A8Ka9/sTuEuUpcA88rKx1C4TfkBQDuF26qfWyz7AYUpSk9HxMMppUMHqE9BYfrZgRTWJPo78PqU0h3F8mspjAq6mcJt0NcA/5JSuq8f5z6Nwto6S6uUnQcsojBSpY3CWkDf3sE2j65oaxOFMGsBhRE9JxePtRXWcAYK6/78fgfavITC7d9vLzvndSmlfy1un0Eh2GkFHgVel1JaB5BS+mVEfK7Y53EUwqHSQt4RcQ/w6ZRS5wLPjxfPsxZ4HvjXiv79R/H1RwB3UPi8SJKknUwM0DqJkiRJGoEiYgGFcGlmX3UlSdLOzelgkiRJkiRJNcAQSJIk9SgiXhERG6s9dvC89/Rw3jMHqu+12qYkSVJPnA4mSZIkSZJUAxwJJEmSJEmSVAMMgSRJkiRJkmqAIZAkSZIkSVINMASSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNUAQyBJkiRJkqQaYAgkSZIkSZJUAwyBJEmSJEmSaoAhkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEmSJEmSVAMMgSRJkiRJkmqAIZAkSZIkSVINMASSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNUAQyBJkiRJkqQaYAgkSZIkSZJUAwyBJEmSJEmSaoAhkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEmSJEmSVAMMgSRJkiRJkmqAIZAkSZIkSVINMASSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNUAQyBJkiRJkqQaYAgkSZIkSZJUAwyBJEmSJEmSaoAhkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEmSJEmSVAMMgSRJkiRJkmqAIZAkSZIkSVINMASSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNUAQyBJkiRJkqQaYAgkSZIkSZJUAwyBJEmSJEmSaoAhkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEmSJEmSVAMMgSRJkiRJkmqAIZAkSZIkSVINMASSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNUAQyBJkiRJkqQaYAgkSZIkSZJUAwyBJEmSJEmSaoAhkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIGXUTUDXcfJEmSaonfvyRVYwgkabtFxKqI+FBE3BsRrRFxdUSMjYgFEbEmIj4QEY8DV0dEJiI+GBEPRsTTEfH9iNil7FxHRcQfI6ItIlZHxNnDd2WSJEkjk9+/JO0IQyBJO+pM4ARgH+AlwEXF47sCuwB7AecC7wJOA44BdgdagSsAImIv4BfAV4FpQAtw55BdgSRJ0uji9y9J2yVSSsPdB0mjVESsAj6bUvpGcf8kCl8k3g78FzAppbS5WLYSOC+l9N/F/d2AR4FxwIXAvJTSa4b8IiRJkkYRv39J2hHOE5W0o1aXbT9C4V+ZANZ1fgEp2gv4UUTky47lgBnAHsCDg9pLSZKknYffvyRtF6eDSdpRe5Rt7wmsLW5XDjNcDZyYUmoqe4xNKT1WLNtnCPoqSZK0M/D7l6TtYggkaUe9MyJmFhcZ/AhwfQ/1vgFcWpx/TkRMi4hTi2XfA14ZEadHRF1ETImIlsHvuiRJ0qjk9y9J28UQSNKO+g8K888fojCk+JIe6n0ZuBH4r4h4FrgVmA+QUnoUOAm4AFhPYVHC2YPbbUmSpFHL71+StosLQ0vabsWFCc9JKf1muPsiSZJUC/z+JWlHOBJIkiRJkiSpBvQZAkXEtyPiyYj4aw/lERFfiYgHIuLuiDh04LspSZIkSZKkHdHndLCIOBrYCFybUnpplfKTgHdRmE86H/hySmn+IPRVkiRJkiRJ26nPkUAppd9RWCisJ6dSCIhSSulWoCkidhuoDkqSJEmSJGnHDcSaQC8CVpftrykekyRJkiRJ0ghRN5SNRcS5wLkAEyZMmLP//vsPZfOSJGkIrVix4qmU0rTh7oe6mjp1apo1a9Zwd0OSJA2S3r6DDUQI9BiwR9n+zOKxblJKi4BFAHPnzk3Lly8fgOYlSdJIFBGPDHcf1N2sWbPwO5gkSTuv3r6DDcR0sBuBtxTvEnY4sCGl9L8DcF5JkiRJkiQNkD5HAkXEYmABMDUi1gAXA/UAKaVvAD+ncGewB4DngbcOVmclSZIkSZK0ffoMgVJKb+ijPAHvHLAeSZIkSZIkacANxHQwSZIkSZIkjXCGQJIkSZIkSTXAEEiSJEmSJKkGGAJJkiRJkiTVAEMgSZIkSZKkGmAIJEmSJEmSVAMMgSRJkkagiPh2RDwZEX/toTwi4isR8UBE3B0Rhw51HyVJ0uhiCCRJkjQyXQMs7KX8RGDf4uNc4P8NQZ8kSdIoZggkSZI0AqWUfges76XKqcC1qeBWoCkidhua3kmSpNHIEEiSJGl0ehGwumx/TfFYNxFxbkQsj4jl69atG5LOSZKkkccQSJIkaSeXUlqUUpqbUpo7bdq04e6OJEkaJoZAkiRJo9NjwB5l+zOLxyRJkqoyBJIkSRqdbgTeUrxL2OHAhpTS/w53pyRJ0shVN9wdkCRJUncRsRhYAEyNiDXAxUA9QErpG8DPgZOAB4DngbcOT08lSdJoYQgkSZI0AqWU3tBHeQLeOUTdkSRJOwFDIEmSJO2wtq9eS/7ZZ4nIQ+QKz+SIzu3Sc54oHifyXZ/JV9QvP8/Wc5ReQ654bJgvXpKkbbXrwXDiZ4e8WUMgSZIk7bAtzzaRe34XUsoCGVLKklIGivuDqyIgomu4VB5IdTnWJUjqfE1n/cpz9HDeyBFUhFYV4VXXwKtQf+uxNMjvjSRJWxkCSZIkaYdN//ApPZalfIJ8IuXykEuk4oNcvvCcT6SOfKFeRyLle6iXK5SVtsvKStv5VPbafNd6+UTqSKR8Il8qq2grnyd1lPU3P8hvXADZIDIZoi4gE0Q2UzhWfJDNFJ4zQdRlinWqlGXLX1txjorzF15b/fyR6V7WbTuTgQyEw7Ak7cRSSqQEqXMbivuF45Ttb93uWpey+uVl9ZkMk4fhmgyBJEmSNKgiUxZgjDJbA6yywCmfoKOXwKkYanUGSVVDq7JzbC2vHlp1CdDa893b6tJG17JB11NYVBYklQdSnWVbQ6hM13qZ6CEMy3RvK9PDOYrbKQrlFOulYrspE6QMFBK4rj/oevvxBtV/CFJ6XZXzlP1ILLy+h3a6HC+rV/Hjss/+Vp4ndb66n/2l6zV3ltHf/hbrdWmv1Jde2qlyHir73uV9795OtfNQen3f7VQ7T+efGeV976GdHgOCyvesh/52aaPLn1lvfzaV/S27rm596uE96/Z+9P5Z7fE9q2ijt89q5bVWnqfLn1uPfzY9/TfQz3aqfgZTxZ/Z4Jq7VzM3/NuRg99QBUMgSZIk7bAf//kxntvSUf1HQi8/RKD3H3C9/nirbKevH1aVZeV96M+PjR5+3FX7AdfXj7dq193Tj5pUrEsWUgZSfdkPuW4/aqL4uiClRKRENkGm+MimRF2X/a7PmbLyLIXXluoAdcX9LJTKskA2n8jmIEsU6pCoS1GqV0fZ6ynsb90Osqlw67vOsnoGf4RRO4kOKD4S7cXtXNl2ZVlH2WvaSeRK272VlbezdT/XS1l5W+1V+qSBFVGIBCOi+AxBdOaEXY5V1qV8v8p5Os+wtaz7eUp9qCijp3YqzkPl8c7+l10bXV5X/VoDiEzna6Nbn7q10+1aK/tedj2V7fRwnq3veQ/vWUU7Pb2f3dovvTfl72f185S6UeW9Lj9PtTYqj/X4ngVMnzim6udxsBkCSZIkaYd95hcreeKZF4al7T5/1PTy463zqecfcD3/2IDef8D19eONUhvVf8D19uNta7+r/YDL9PrjLR+FWW4BdPTnWqv8eOvafv9+WHW20fXPrPoPuCCRIYpBU+oSPHWGVdkUxedCnUyK4vPWwCuTIJuHDIlMvvC6zvJMvtp2IpuH+lSoH6nsdWX7hedEFF8b5cfyWz8fgyUFxRFNhUdhpFPhGJ3Hyp+zARlImczW0VFlI6TIUpjiVzZyautzpjSCis4RXFWmDJLZut9Zp3J6YXSOEuv8s+/hR3bl5wi61yXo9vnr/pkvPlf577L8h7xUSwyBJEmStMN++q6jSKn6D7jefrxRpbz8B5w/3jQaFaYKlq9XlQprXRXXpNo6XbBKvVyxrGJ9rM6yLuerWla21lblFMH2XNl0xFR1/a2tw+wGSYZSsNQ5LZBsphBUda43Vbc1vOpcwyqVr2VVMV2w63TEIDI9TSUsltX14xzZ8vW3tgZb0mhnCCRJkqQdNn3i2OHugjRiFNbByhL1w92TbVda66rLIumVYVVPi7VXBFOVC633tTB8lQXf05Yc+Xz383cPvIZwIfcuo5+2cyH3zlFSdZWhVVm9bovGV4ZV/V8Y3oXc1ckQSJIkSZIEsHUhdzIwPEuWbLftWsi9PNyqCL6q1qsSSG3TQu6lvlQsGj/YC7kH/Qqtqt4dMBNQZWpf5YLvXQKyynr9PEe1heHJhAHWADIEkiRJkiSNelvvRAiFhY5Gj5Q6R0/1HlqVpgtWGX1V2u7j7oPlUw57vMNgRyJt6agIqypGYpWNAht0ZaOauk3b2867D1at1y2sql6v2jnKpyt2OccIC7AMgSRJkiRJGkYRxalhAA2jMMCqDJxK61pVjnoqn0pYGVZ1HR3VJdTa1nO058nnO/pef2sIAqxEIkXxQZ7C/+Zpn9jB/h86cdDbr2QIJEmSJEmStktE52iYoWsz5fN0tG+ho72dji0v0LFlS/HRj+32smMvFLZzW9rJb+kgt6WDfHsH+S0d5Ds6yLfnyHfkimESZCJDhmzhObIEmdJ2JrJk6NzOEGX1stl66rL1ZLP1ZLN1ZDP1ZGkYujesjCGQJEmSJEnaLiklch0dXYKWXDFoad+WgKYUzlQpb+96PNfevt39zWSz1DU0UNcwpvBcX7Y9poGGieOLx8qO97VdX99zeX1DYfHvEcIQSJIkSTvsfb99H22b2yAgQ6Z4O/coPRf+VTR6LI8IMmRKt5fvrN+lvPMcULW883VA6Xy9lneeo0o70Hs/qpZH6WxV2ynvdyaqXGs/2iEqzlFeXvme9tBOv/tR+Z6VtdPtHL2857392UoaePlcrkuI0l4lnKkMWdp7CWdyleFM+5Zu9UnbOa0qoktoUl8RyoyfNKm0nS0FMxX1ewlnsvX11Fccy2RH13S7gWYIJEmSpB3Wke9gS34LKaXC+gdlz3ny3Y7nU+FezuXlAPmU7/b6zvrVjleWk6jaXmc7leUaXv0NAKuFSpVh1LaU9zcw3OHgcjuvq69+9nVdvbXT3+vakfe/vN+9BaX9fX97C0qDKv2ovNb+9qNKO9WCzz7bKesj+US+I0euOHql19Ew/Zna1L41tMltaa86Siafy233f5N19Q1kG8pGtZQFMg3jxzO+qblqEJPtY+RMZRCTLY6cydbVGQgPMUMgSZIk7bAvHful4e7CdqkWEvUWOnWGV32V9xR+dQmjOs/RQ/k29yOVzta1H32002N55zkq+1GlnXyqcq397UfltVS0060fVdrpEgb21I+K97SnfvfUj8rz5fN5cuSq1q8abG7jdfUZbPZwXX19lmpagkwe6vJBNpchmw/qckE2F1u3ezhWWb9wjt7qB3X5wmu2Vz4SuSzksoXnfDaRy0C+DvIZCsfqID8e8hMhnw3yWQqPuiDVBfkMhedscT9b2CebIV8fpGyQ6jKk4p2tMplsMbjMk4ktBO3Ac6Vwq1qw2S3IywexufCoGqJtb2BbFsptT7BZ2e/BCI57K6/sR9OYJg6dceh2fz62lyGQJEmSalbnl3MCsqPsltLS9uhpNF1vYVTn63oKNquGaP0INnO5DjpeKEw3ym1pp6P0/AK5Le3k2tvLRtBsKS7YW6iXb+8g395Orvhc2t9Stt/RQWovLvTb3kHq6GC7c7AIoj4LdVmiLrt1e0yGqMtCfQbqssVHhqjLQH22EK7UZyGbIdVnCs91hVuId4Yvpf3idul4pnjnrZQnQyJSItNDkNol2KwMMOlfYFsKHXsIbHP5XI/BZ9Vgs0o/diiwrdJel/JeguiR6JDph3DtidcOebuGQJIkSZJUI8qDz3Kdd1sqTDPqfU2YbuvHtHefutQ59Wlr3e51Un77f6DXVVkfZkxDA3VjJlA3saFiDZl+rB9TX368e3km67Sl0ayn0Yid4RH0Pmqvt7Cpv9ORK9sZVzduWN4LQyBJkiRJGkFSSqXRL1uDk4qFfNvLw5qeA5n2itf2tDBwrqNju/ubydb1uDBv/ZixjJtYWNy3vqEynKkeytTXdz9euU6NgYy2RUSQHcp72I9ghkCSJEmS1IvC7a97ucV1e+Xx6gv29n6OLV22t/duS5HJVCzq2zVwGTN+fA+3uO55FEzXUTJdF/vNNtQX1pGRNCoYAkmSJEkaNfL5XFmI0p9AZhvvvlRlZM0OTVtqqJxmNIa64p2RxjZO7PmOSt1Gw/QeyJRuo13nTzxJPfNvCEmSJEnbJaXUbbRL5RSl9n6Mhsm1t3ebttTTdj63/dOWsnV1Pa4J0zBuPOMmTe4yban7KJmewprK8mIgU1/vtCVJI4ohkCRJkrQTSCmRz3VUDU7ae1jgt9fFfisW+O1pCtP26jJtqds0owbGTpjQJXwprQmzDYv9dq4947QlSSowBJIkSZIGQddpS32McOklnOl9alPX42l7b4cc0cuolgbGTpzU82iY+voeQ5jK9WPKwxmnLUnS0PNvXkmSJO30Om9/3f81YbYlnOkMZbquU7ND05bq63ucctQwfjzjm5qqrBlTLZzpqU7FnZfqvP21JNUCQyBJkiQNqZRS8W5L3W9b3T5QAU3lOjXt7dvd30w228uCvfWMbWzsfc2Ybnda6rs8MpkBfMclSSowBJIkSdIOu/nab/FcW2v1dWfat3Rbb2Z7b39dmrZUvh5MWSgzftKkwsiWHqYo9Xf9mPLyTNZ1ZCRJOwdDIEmSpBEqIhYCXwaywLdSSp+tKN8L+DYwDVgPvCmltGbIOwo8dt89bNr4bJdApjBtqbmfd1Pqef2Y8lDHaUuSJG0/QyBJkqQRKCKywBXAPwJrgNsj4saU0r1l1T4PXJtS+k5EHAd8Bnjz0PcWzvz0F4ejWUmStA2cbCxJkjQyzQMeSCk9lFLaAiwBTq2ocyDwP8XtpVXKJUmSSgyBJEmSRqYXAavL9tcUj5W7C/g/xe3XABMjYkrliSLi3IhYHhHL161bNyidlSRJI58hkCRJ0uj1PuCYiPgzcAzwGJCrrJRSWpRSmptSmjtt2rSh7qMkSRohXBNIkiRpZHoM2KNsf2bxWElKaS3FkUAR0Qi8NqXUNmQ9lCRJo4ojgSRJkkam24F9I2LviGgAzgBuLK8QEVMjovP73Ico3ClMkiSpKkMgSZKkESil1AGcB/wKWAl8P6V0T0R8MiJOKVZbAPwtIu4HZgCXDktnJUnSqOB0MEmSpBEqpfRz4OcVxz5Wtn0DcMNQ90uSJI1OjgSSJEmSJEmqAYZAkiRJkiRJNcAQSJIkSZIkqQYYAkmSJEmSJNWAfoVAEbEwIv4WEQ9ExAerlO8ZEUsj4s8RcXdEnDTwXZUkSZIkSdL26jMEiogscAVwInAg8IaIOLCi2kUUblt6CHAG8PWB7qgkSZIkSZK2X39GAs0DHkgpPZRS2gIsAU6tqJOAScXtycDageuiJEmSJEmSdlRdP+q8CFhdtr8GmF9R5+PAf0XEu4AJwCsHpHeSJEmSJEkaEAO1MPQbgGtSSjOBk4DvRkS3c0fEuRGxPCKWr1u3boCaliRJkiRJUl/6EwI9BuxRtj+zeKzc24HvA6SU/gSMBaZWniiltCilNDelNHfatGnb12NJkiRJkiRts/6EQLcD+0bE3hHRQGHh5xsr6jwKHA8QEQdQCIEc6iNJkiRJkjRC9BkCpZQ6gPOAXwErKdwF7J6I+GREnFKsdgHwjoi4C1gMnJ1SSoPVaUmSJEmSJG2b/iwMTWW60+AAACAASURBVErp58DPK459rGz7XuDlA9s1SZIkSZIkDZSBWhhakiRJkiRJI5ghkCRJkiRJUg0wBJIkSZIkSaoBhkCSJEmSJEk1wBBIkiRJkiSpBhgCSZIkSZIk1QBDIEmSJEmSpBpgCCRJkiRJklQDDIEkSZIkSZJqgCGQJEmSJElSDTAEkiRJkiRJqgGGQJIkSZIkSTWgbrg7IEmSJEkDob29nTVr1rB58+bh7opGsLFjxzJz5kzq6+uHuyvSkDMEkiRJkrRTWLNmDRMnTmTWrFlExHB3RyNQSomnn36aNWvWsPfeew93d6Qh53QwSZKkESoiFkbE3yLigYj4YJXyPSNiaUT8OSLujoiThqOf0kixefNmpkyZYgCkHkUEU6ZMcbSYapYhkCRJ0ggUEVngCuBE4EDgDRFxYEW1i4Dvp5QOAc4Avj60vZRGHgMg9cXPiGqZIZAkSdLINA94IKX0UEppC7AEOLWiTgImFbcnA2uHsH9dtLdvGK6mJVXx4x//mHvvvbe0/7GPfYzf/OY3AHzpS1/i+eefH66uSRpGhkCSJEkj04uA1WX7a4rHyn0ceFNErAF+DrxraLrW3e3LX8Mf/vAK7rn3faxdewObNj1KSmm4uiPVvMoQ6JOf/CSvfOUrAUMgqZYZAkmSJI1ebwCuSSnNBE4CvhsR3b7fRcS5EbE8IpavW7duwDuRUp4993gbkya38PTTv2XlfR/gj386lj/88RXcc+8FrF37fZ5//hFDIWkHXXfddcybN4+Wlhb+5V/+hVwuR2NjIx/5yEeYPXs2hx9+OE888QR//OMfufHGG7nwwgtpaWnhwQcf5Oyzz+aGG27gK1/5CmvXruXYY4/l2GOP5dvf/jbvfe97S21ceeWVnH/++cN4lZIGkyGQJEnSyPQYsEfZ/szisXJvB74PkFL6EzAWmFp5opTSopTS3JTS3GnTpg14RyMyzJz5Jg5+6Vd5xVG3MX/+L9nvJZ9k8uRDePrp37Pyvg/xp1uP4w9/PIp77vm/PLb2ep5/fpWhkLQNVq5cyfXXX88f/vAH7rzzTrLZLN/73vd47rnnOPzww7nrrrs4+uijufLKKznyyCM55ZRTuPzyy7nzzjvZZ599Sud597vfze67787SpUtZunQpp59+Oj/96U9pb28H4Oqrr+Ztb3vbcF2mpEHmLeIlSZJGptuBfSNibwrhzxnAGyvqPAocD1wTEQdQCIEGfqjPNogIGifsS+OEfZk580xSSjz//EO0ti2jtfVW1rf+gcef+AkAY8bsSlPTPJqb5tPcPJ9x47ytt9ST//7v/2bFihUcdthhAGzatInp06fT0NDAySefDMCcOXP49a9/vU3nbWxs5LjjjuOmm27igAMOoL29nYMPPnjA+y9pZDAEkiRJGoFSSh0RcR7wKyALfDuldE9EfBJYnlK6EbgAuDIizqewSPTZaYQNr4kIJkzYhwkT9mHmi95YDIUeprXtVtpal9Ha+ieeeOJGABoaptPcPL8YCh1uKCSVSSlx1lln8ZnPfKbL8c9//vOl/06y2SwdHR3bfO5zzjmHT3/60+y///689a1vHZD+ShqZDIEkSZJGqJTSzyks+Fx+7GNl2/cCLx/qfu2IQij0YiZMeHEpFNq0aRWtrbcWRwst44knfgpAQ8M0mpvm01QMhsaPf7GhkGrW8ccfz6mnnsr555/P9OnTWb9+Pc8++2yP9SdOnNhjeWfZ1KmF2aPz589n9erV3HHHHdx9992D0n9JI4MhkCRJkoZNRDB+/N6MH783L3rRG8pCoWW0td1Ga9synnjyJgAaGqbS1DS/NH1s/Ph9DIVUMw488EAuueQSXvWqV5HP56mvr+eKK67osf4ZZ5zBO97xDr7yla9www03dCk799xzWbhwYWltIIDTTz+dO++8k+bm5kG9DknDK4ZrxPDcuXPT8uXLh6VtSZI0+CJiRUpp7nD3Q12Ntu9ghVDokUIg1LqM1rZbeeGFxwGor59Smj7W1DyfCeP/wVCoxq1cuZIDDjhguLsxKp188smcf/75HH/88cPdlSHhZ0U7s96+gzkSSJIkSSNWYaTQLMaPn8Xuu59eDIUeLY4SupXW1mU8+WRhxlx9/S5dpo9NmLCvoZDUh7a2NubNm8fs2bNrJgCSapkhkCRJkkaNQii0F+PH78Xuu/8zKSU2b15TGiXU1rqMJ9f9AiiEQk1N80qjhQqhUGaYr0AaWZqamrj//vuHuxuShoghkCRJkkatiGDcuD0YN24Pdt/9dQBs2rRm693H2paxbt0vAaivby7dkr6peT6NE15iKCRJqimGQJIkSdqpjBs3k3HjXsfuu20NhdqKdx5rbbuNdet+BUBdXRPNzfOKwdDhNDbuZygkSdqpGQJJkiRpp1YIhWay226vBWDTpscKoVDbbbS1LmPduv8CoK5uMs1N80prCjU27m8oJEnaqRgCSZIkqaaMG/cixo37P+y22/8BYPPmtaVAqLXtVtY99WugEAo1NR1WuiV9IRTKDmfXJUnaIf7ThiRJkmra2LG7s9uup3HAAZ/hyCOW8vIjb+GgA/+d6dNO4Lnn/s7fH7iU224/hd/9fi533X0ujz56Fc88+1dSyg131zVC/fjHPyYiuO+++wBYtWoVEcFFF11UqvPUU09RX1/PeeedVzq2aNEi9t9/f/bff3/mzZvHLbfcUiqbNWsWTz31VGn/5ptv5uSTTwbgmmuuIZPJcPfdd5fKX/rSl7Jq1Srmz59PS0sLe+65J9OmTaOlpYWWlhZWrVo1WJcvaQRzJJAkSZJUZuzY3dh111PZdddTAdj8wuO0tW69Jf1TT/03AHV1E2mafFhx+tg8GhsPJJPx67Vg8eLFHHXUUSxevJhPfOITAOy999787Gc/45JLLgHgBz/4AQcddFDpNTfddBPf/OY3ueWWW5g6dSp33HEHp512Grfddhu77rprn23OnDmTSy+9lOuvv77L8WXLlgGFoGj58uV87WtfG6jLlDQK+f9SkiRJUi/GjtmVXXc9hV13PQWAF154orjI9DLa2m7jqaf/B4BstrEwfay0ppChUC3auHEjt9xyC0uXLuXVr351KQQaP348BxxwAMuXL2fu3Llcf/31nH766axduxaAyy67jMsvv5ypU6cCcOihh3LWWWdxxRVX8KlPfarPdk8++WR+97vf8be//Y399ttv8C5Q0qjm/ytJkiRJ22DMmBkVodCTpUCotXUZTz+9FOgMheYWF5s+nImNBxkKDaFP/PQe7l37zICe88DdJ3Hxqw/qtc5PfvITFi5cyEte8hKmTJnCihUrmDJlCgBnnHEGS5YsYcaMGWSzWXbfffdSCHTPPfcwZ86cLueaO3cu3/nOd/rVt0wmw/vf/34+/elP9/s1kmqP/y8kSZIk7YAxY6az64xXs+uMVwPwwgvrSncfK4RCNwOQzU6gafIcmpoPp7lpPhMnHkQmUz+MPddgWLx4Me95z3uAQuizePHi0ro/Cxcu5KMf/SgzZszg9a9//TadNyL6PPbGN76RSy+9lIcffng7ey9pZ2cIJEmSJA2gMWOmMWPGycyYUVi094UtT5VGCbW1LePBBz8HFEKhyZMPpbnpcJqb5zNx4ksNhQZQXyN2BsP69ev5n//5H/7yl78QEeRyOSKCd77znQA0NDQwZ84cvvCFL3Dvvfdy4403ll574IEHsmLFCo477rjSsRUrVpTWDZoyZQqtra2l6WLr168vbXeqq6vjggsu4LLLLhvsS5U0ShkCSZIkSYNoTMNUZkw/iRnTTwJgy5anaG27vXRL+gcfuhyAbHY8kyfPKd2SfuLEgw2FRpkbbriBN7/5zXzzm98sHTvmmGNYvXp1af+CCy7gmGOOYZddduny2ve///184AMf4Je//CVTpkzhzjvv5Jprrikt7LxgwQK++93v8slPfpJcLsd1113Haaed1q0PZ599Np/73Od49tlnB+kqJY1mhkCSJEnSEGpomMqM6ScyY/qJAGzZ8jRtbbeX7j724EOfByCTGUfT5Dk0N8+nqWkekya9jEymYTi7rj4sXryYD3zgA12Ovfa1r+Uzn/lMaf+ggw7qclewTqeccgqPPfYYRx55JBHBxIkTue6669htt90A+OhHP8q//du/MXv2bFJKLFy4kDe96U3dztPQ0MC73/3u0pQ0SSoXKaVhaXju3Llp+fLlw9K2JEkafBGxIqU0d7j7oa78DjbybdmyvhQKtbXdxsaN9wGQyYwtrCnUNI/m5sOZNOlgMpkxw9zbkWXlypUccMABw90NjQJ+VrQz6+07mCOBJEmSpBGkoWEXpk8/genTTwCgvb2V1rbbaGu9jda2ZTz08Bfh4UIoVFhTqHD3scmTXmYoJEnqlSGQJEmSNILV1zczfdoJTJ+2NRQqjBQqLDb90MNfhoe/RCYzhsmTDindfWzSpNlks4ZCkqStDIEkSZKkUaS+vplp017FtGmvAqC9fUMxFFpGW+syHn74yzxMIpNpYNKkQ2guhUIthkKSVOMMgSRJkqRRrL5+MtOmvZJp014JFEOhDctLdx97+OGv8jBf3hoKNc2nqXkekycdQjY7dph7L0kaSoZAkiRJ0k6kvn4y06Yez7SpxwPQ3v4MGzYsp7X1VlrbbuPhVV+DVXkiGpg8uaWw0HTTfCZPPtRQSJJ2coZAkiRJ0k6svn4SU6cex9SpxwHQ0fEsbW3LC3cfa72NVau+ziq+VgiFJs2mqbk8FBo3zL2XJA2kzHB3QJIkSdLQqaubyNSpx7LvP3yIww77EcccfQezX/Yt9tzjbPJpC4888g3+fOdb+O3vDmH5itN58MEvsH79H8jlnh/uro8Kjz/+OGeccQb77LMPc+bM4aSTTmLRokWcfPLJXeqdffbZ3HDDDQAsWLCA/fbbj9mzZ3PYYYdx5513lurNmjWLgw8+mJe97GUcc8wxPPLII6WybDZLS0tL6fHZz362dL65c7feHXr58uUsWLCgtH/bbbdx9NFHs99++3HIIYdwzjnn8JOf/IQjjjiClBIAuVyOQw45hD/+8Y8D/h5JGj6OBJIkSZJqWGcoNHXqsUBxpNCGFaVb0j/y6DdZ9cjXiahn0qSDi2sKHU7T5EPJZscPc+9HlpQSr3nNazjrrLNYsmQJAHfddRc33nhjn6/93ve+x9y5c7n66qu58MIL+fWvf10qW7p0KVOnTuXiiy/mkksu4corrwRg3LhxXQKjck8++SS/+MUvOPHEE7scf+KJJ/jnf/5nlixZwhFHHAHADTfcwOGHH85ee+3FVVddxTnnnMNXv/pV5s6dy5FHHrld74WkkckQSJIkSVJJXd1Epk5ZwNQpCwDo6NjIhg130Nq2jNbWZTzy6JWseuT/EVHHpIkHF29JP4/Jk+dQVzdheDs/zJYuXUp9fT3/+q//Wjo2e/ZsWltbWbZsWb/OccQRR3D55Zf3WPaVr3ylX+e58MILufTSS7uFQFdccQVnnXVWKQACeN3rXgfAF7/4RY466iiOOOIIvva1r3Hbbbf1qy1Jo4chkCRJkqQe1dU1MmXK0UyZcjQAHR3PlUKhttZbefTRK3mkGApNnHgwzc3zy0KhxuHr+C8+CI//ZWDPuevBcOJneyz+61//ypw5c3aoiV/+8pecdtpp/SrbtGkTLS0tpf0PfehDvP71rwcKgdGPfvQjli5dysSJE7v08ayzzqp6/t122433vve9pbBpl1122aFrkTTyGAJJkiRJ6re6uglMmfIKpkx5BQC53PO0bbiDtuLdxx599CoeeeQbRGQLoVDTPJqa59M0ee7whkLDKCL6PH7mmWeyZcsWNm7c2G2K17HHHsv69etpbGzkU5/6VOl4b9PBAC666CIuueQSLrvssn739Z3vfCcf/OAHOfvss/v9GkmjhyGQJEnSCBURC4EvA1ngWymlz1aUfxE4trg7HpieUmoa2l6q1mWz45myy1FM2eUooBAKbdjw59It6R9dfTWPPLqoGAq9tHRL+qamudTVTezj7DuglxE7g+Wggw4qLfZcbsqUKbS2tnY5tn79eqZOnVra/973vsecOXO48MILede73sUPf/jDUtnSpUtpamrizDPP5OKLL+bf//3f+9Wf4447josuuohbb721Sx9XrFjBqaeeWvU1mUymx9BK0ujn3cEkSZJGoIjIAlcAJwIHAm+IiAPL66SUzk8ptaSUWoCvAj/sfiZpaGWz49lll5ezzz4XMHfO9Rxz9J85pOVa9trr38hEA6tXf4e77j6H3/7uUG67/TT+/sBneOqppXR0PDvcXd9hxx13HC+88AKLFi0qHbv77rt5+umnWbt2LStXrgTgkUce4a677uoylQsKI4M+9alPceutt3Lfffd1Kaurq+NLX/oS1157LevXr+93ny666CI+97nPlfbPO+88vvOd73RZo+iHP/whTzzxxDZdq6TRyZFAkiRJI9M84IGU0kMAEbEEOBW4t4f6bwAuHqK+Sf2WzY5jl11ezi67vByAXG4zG575M22ty2htW8bq1dfy6KPfAjJMnHhg8e5j82mafBj19ZOGt/PbKCL40Y9+xHvf+14uu+wyxo4dy6xZs/jSl77Eddddx1vf+lY2b95MfX093/rWt5g8eXK3c4wbN44LLriAyy+/nKuuuqpL2W677cYb3vAGrrjiCj760Y92WxNo4cKFpdvEdzrppJOYNm1aaX/GjBksWbKE973vfTz55JNkMhmOPvpoFi5cOMDvhqSRKFJKw9Lw3Llz0/Lly4elbUmSNPgiYkVKae5w92O0iojXAQtTSucU998MzE8pnVel7l7ArcDMlFKuSvm5wLkAe+6555xHHnlkUPsubYtcbjPPPHMnrcVQ6Jln/kw+vwWIYih0OE1N82hqOoz6+u6hSbmVK1dywAEHDE3HNar5WdHOrLfvYP0aCdTXfPRindOBjwMJuCul9Mbt7rEkSZK2xRnADdUCIICU0iJgERT+IW4oOyb1JZsdS3Pz4TQ3Hw5ALvdCIRRqW0Zb6zLWPPZdHl19FRBMbDyQpubONYUOo77eJbAkaVv0GQKVzUf/R2ANcHtE3JhSureszr7Ah4CXp5RaI2L6YHVYkiSpRjwG7FG2P7N4rJozgHcOeo+kIZDNjincZr55PuzdGQrdVQiF2pbx2GP/werVVwNBY+MBNDfNo7l5Pk1N84a765I04vVnJFB/5qO/A7gipdQKkFJ6cqA7KkmSVGNuB/aNiL0phD9nAN1GWkfE/kAz8Keh7Z40NAqh0Dyam+cB7yKff4ENz9xduiX9Y2uXsHrNNUAwZZer2Lx5MtnsBLLZCWQyLoEqSeX687fii4DVZftrgPkVdV4CEBF/oDBl7OMppV8OSA8lSZJqUEqpIyLOA35F4fvVt1NK90TEJ4HlKaUbi1XPAJak4VroURpimcwYmpsOo7npMPYG8vkXeOaZvxTWE9qQYUv7etjydLHuWOrqJhgKSVLRQP0tWAfsCyygMFT5dxFxcEqprbxSxaKEA9S0JEnSziml9HPg5xXHPlax//Gh7JM00mQyY2hqmktT01xWrlzJxMb9yOU2kcs9Ry73HFvaW8tCoTHU1TUaCkmqWf35W68/89HXAMtSSu3AwxFxP4VQ6PbySi5KKEmSJGkwRWSoq5tAXd0EAFLKdwmF2ttb2VI1FBpPJlM/nF2XpEGX6Ued0nz0iGigMOT4xoo6P6YwCoiImEphethDA9hPSZIkSdpmnaHQmDHTGT9+bxobD2D8+H0YM2YGmUw97e2tbNr0KBs33sfGjfezafNjtLe3kc+3b1d72WyWlpYWDjroIGbPns0XvvAF8vl8lzqnnXYahx9euBvar371K1paWmhpaaGxsZH99tuPlpYW3vKWt3DzzTczefLkUnlLSwu/+c1vdvg9kVS7+hwJ1M/56L8CXhUR9wI54MKU0tOD2XFJkiRJ2laFUGg8dXXjgc6RQptLI4U62tto37IeKIwUytZNoK40fazvkULjxo3jzjvvBODJJ5/kjW98I8888wyf+MQnAGhra2PFihU0Njby0EMPccIJJ3DCCScAsGDBAj7/+c8zd+5cAG6++WZe8YpXcNNNNw34+yCpNvVrEmxf89GLCxH+3+JDkiRJkkaFrqHQNFJK5PKbyHX0EAplJ5QWm+4rFJo+fTqLFi3isMMO4+Mf/zgRwQ9/+ENe/epXM2PGDJYsWcKHP/zhIbhKSSpwJTRJkiRJO53LbruM+9bfNyDnSilPSjn2bdqL9xz8RtrbO0OhBrLZxl5DoRe/+MXkcjmefPJJZsyYweLFi/nYxz7GjBkzeO1rX9tnCPT73/+elpaW0v5//ud/ss8++wzIdUmqPYZAkiRJktSLiAwRGerrJtHYeCD5/CY6OkcKdbR1CYUg0d7eVjUUeuKJJ/j73//OUUcdRURQX1/PX//6V1760pf22LbTwSQNJEMgSZIkSTudD8z7wKCdO5sdTza7dfpYPr+Zjo6N5HLPAYlNm1YDhVDo0UefIpvNMHVqE1//+iJaW1vZe++9AXjmmWdYvHgxl1566aD1VZLK9efuYJIkSZKkKiKCbHYcY8ZMY/z4WUCGCRP+gTFjduPpp5/jvPPezzvecTrPPXc/1133bX7yk2u5//4/89BD97NixQqWLFky3JcgqYY4EkiSJEmSBsimTZuYM+cI2tvbqaur401vehPvfe//x0MP/43Vq/+XQw/9BzZvXgPAtGkNTJw4nltu+TVHHnlM1fNVrgl00UUX8brXvW5IrkXSzscQSJIkSZIGSC6Xq3p8v5ccytq1j5emjxXWE3qO3//+elLKsXHj37jppm+QzTayZUsrdXUTWLBgARs2bBjiK5C0MzMEkiRJkqQh0jl9LJsdR0PD1GIo9AK53EY6Op6jo+MZ2ttbAchk6slmJxRvS99IRD0RMcxXIGk0MwSSJEmSpGFSCIXGks2O7SEUepb29jagMhSaQESDoZCkbWIIJEmSJEkjRM+h0HPdQqHI1FNXDIUKt6Q3FJLUO0MgSZIkSRqhuoZCU7qGQrnn6OjYaCgkqd8MgSRJkiRplOgSCtFHKBR11NU1GgpJKjEEkiRJkqRRqnootIVcbmNxClllKNQ5UqjRUEiqQYZAkiRJkrSTKIRCY8hmx0DVUOg52ts3FOuWh0ITyGTGGApJO7nMcHdAkiRJknYW2WyWlpaW0uOzn/0suVyOOXPm8P+zd+fRURXp/8ff1U2Wzh4g4piAQdmXECBsKg6gsiiCflUQGEcQ0fFnBFFxwWU4oiKiog64jSOMW0QYcQUcHEB0HNCAEaOgg4DsoAxhS0JIUr8/utN0ZweSTkg+r3NyTt+6des+fZtI8/hU1cqVK739+vfvz/z58wFITEykY8eOJCUl0b9/f3bv3u3XXjTW+PHjARg9ejTx8fEcPXoUgN9++43ExEQACgsLGT9+PB06dKBjx450796drVt3EhzciLZtL+S884Zx4YV/oHfvkdxzzxPk5x8hN3cnR478l8OHN5Cds5W8vH1cf/11NG/enE6dOtGqVSv++Mc/sn37dm/8ZcUG8OSTT9KmTRuSk5Pp1q0br732GgB9+vShdevWdOrUiW7dupGRkVGp8fLz84mLi+Pee+/1e9YfffQRnTt3plOnTrRr146XXnoJgClTphAfH+/3OWRlZZ3aBytSR6gSSEREREREpIq4XC6/5EaR559/nnHjxrFmzRoWLFiAw+Hgmmuu8Z5fvnw5jRs3ZvLkyTz22GM899xzfu3FOZ1OXn31VW655Ra/9nnz5rFz507WrVuHw+Fg+/bthIeHl7hPkeOVQkeOryt07AD5+Qd5+OEJXHX1/+F0hDFr1qv069ePzMxMgoODy4ztxRdfZOnSpXz11VdERUVx8OBBFi5c6D3/5ptvkpKSwpw5c5g0aRJLly4tM7YiS5cupVWrVsyfP59p06ZhjOHYsWPcdNNNfPXVVyQkJHD06FG2bNnivWbixIncddddJcYSqe+UBBIRERERkTpn92OPcXT9hiodM6RtG86cPPmkru3Rowe9evViypQpvPXWW37JD18XXnihNwFUnttvv52ZM2cybtw4v/Zdu3bxu9/9DofDPekjISGh3HH8p4819CaFnM4wnM5QCgqyyT92gHHjBvHuu+/w3nuvccUVVwIWa22J8R577DFWrFhBVFQUAFFRUVx//fUl+vXq1YsZM2ZU+D4B0tLSmDBhAi+88AL/+c9/OO+88zh06BD5+fk0atQIgJCQEFq3bl2p8UTqM00HExERERERqSI5OTl+05DmzZvnPTdt2jSeeeYZRo4cSYsWLUq9/qOPPqJjx47e4759+3rHmjlzpre9WbNmXHDBBbz++ut+1w8bNowPP/yQ5ORk7rzzTr755hu/82WNV6QoKeRwhBAc3JiI8NZERLQiNDSezp07sWHDenJzd2JtPn36nE9SUjs6derAU089wYEDBzh06BDnnHNOhc9pyZIlXHHFFRXGlpuby6effsrll1/OiBEjSEtLA6Bhw4YMGTKEs88+mxEjRvDmm29SWFjoHWvmzJnesfr27VthPCL1hSqBRERERESkzjnZip1TVdZ0MICVK1cSHR1NZmZmiXN9+/bF6XSSlJTEI4884m0va4oUwH333cfQoUO57LLLvG0JCQn8+OOPLFu2jGXLlnHRRRcxf/58LrroogrHK40xBmNCCA4OwemMICTkDCIiWmOMkyVL5hMbG0Jh4TEADh36ESgkL+83z0LToSUWmh41ahR5eXkcPny4xHMqLbaPPvqIvn374nK5uOqqq5g6dSrPPPMMTqeTV155he+++45PP/2UJ598kqVLlzJ37lxA08FEyqJKIBERERERkWp25MgR7r77bpYtW8bevXtZtGiR3/nly5eTkZHBa6+9RkxMTKXGbNmyJcnJybzzzjt+7SEhIQwaNIgZM2YwefJk3nvvvBH4BQAAIABJREFUvSp5D9988w1t27bF4QgGHLhc8UREtCEiojWhoQk0bHgW4eFhrF//NUeObOTw4fVkZ//C0bzfKCjIAdxrAm3atInrr7+e2267rcJ7pqWl8emnn5KYmEjXrl3Zt28fy5Yt857v2LEjEydOZOnSpfzjH/+okvcpUpcpCSQiIiIiIlLNHn74YYYNG0abNm14/vnnmThxIrm5uac87v3338+TTz7pPV67di07d+4E3DuFrVu3jrPPPvuU7mGt5bnnnmPXrl0MHDiwxHmHI5jg4FhcrgQmT36Qe+55hry8KBo0iOLgwX3MnfMSR45spKAgm9zcXeQd28eUKZNZtWoVGzaUvW7TwYMH+fzzz9m6dStbtmxhy5YtzJ49m7S0NA4fPsyKFSu8fTMyMk75fYrUB5oOJiIiIiIiUkWK1gQqMnDgQK677joWLlzIt99+C0Dnzp0ZMGAA06dP589//nO54xVNEwNISkrybrdepH379nTp0oW1a9cCsHfvXsaNG+fdPr579+6kpqZWejxfkyZNYurUqWRnZ9OzZ0+WL1/u3RmsrLFuueUWDh8+zPnn9ycoKIigoCAmTpxAaGgCxjgpLDzK0dxdAKSm/oFp0/7Myy/PBmyJ8S666CL69etHSEiI955Dhw7l7rvvZubMmTzxxBPcfPPNuFwuwsPDvVPBwL0m0BtvvOE9fu+990hMTCz3WYvUB6a0Fd0DISUlxaanp9fIvUVERKT6GWPWWGtTajoO8afvYFKXrV+/nrZt29Z0GFIB95b02eTnH6ag4AiFhXkAGOPA6QzH6QynQYNwHA5XiTWFqor+rEhdVt53MFUCiYiIiIiISMA4HME4HMEEBbnXPiosPEZBwRFvUig//xBHjwY2KSRSXygJJCIiIlJLGWMGAs8CTuAVa+3jpfQZBkwBLPCttXZkQIMUkdParbfeyr///W+/tgkTJjBmzJiAxeBwBOFwxJSSFDpCQcFhJYVEqpCSQCIiIiK1kDHGCcwGLgG2A18bYz6w1v7g06clcB9wvrV2vzHmjJqJVkROV7Nnz67pEEooPylUeqWQs0E4TiWFRCqkJJCIiIhI7dQd2Git3QRgjHkbGAr84NNnHDDbWrsfwFq7N+BRiohUszKTQgVHKMh3J4XwJoXCcDojPMmhUIzRhtgivpQEEhEREamd4oFtPsfbgR7F+rQCMMb8G/eUsSnW2iXFBzLG3ATcBNCsWbNqCVZEJFAqTgrtBnyTQp5qIadLSSGp95QEEhERETl9NQBaAn2ABGClMaajtTbLt5O19mXgZXDvDhboIEVEqlPpSaFs72LT+fl7AP+kUGFhHoWFeTgcweUNLVLnKAkkIiIiUjvtAJr6HCd42nxtB1Zba48Bm40xP+FOCn0dmBBFRGofd1IomqCgaAAKC/MpKDjilxTKy/uVz1aOIia6KzGx3YmN6UFUVJKSQlLnqRZOREREpHb6GmhpjGlujAkGrgU+KNbnPdxVQBhjGuOeHrYpkEGKiD+n00lycrL35/HHH6egoICuXbuycuVKb7/+/fszf/58ABITE+nYsSNJSUn079+f3bt3+7UXjTV+/HgARo8eTXx8PEePHgXgt99+IzExEYDCwkLGjx9Phw4d6NixI926dWPz5s3ljlea0aNHs2DBAgD69OlDSkqK91x6ejp9+vTxHn/11VdceOGFtG7dms6dO3PjjTeSnZ3N3LlziYuLIzk5mTZt2jBz5kzvNVOmTCE+Pt7vWWVlHS9ivP3224mPj6ewsNDbtmfPHgYPHkynTp1o164dl156KQBbtmzB5XL5jfXaa695r3M4GhAUFE1o6FlERLQiIqItQUENOeusa8jL+5VNm55mzdrhfLayM2u/uY7Nm2eRlZVOYeHRij5ukdOOKoFEREREaiFrbb4xJhX4BPd6P69aa783xjwMpFtrP/Cc62+M+QEoACZZa/fVXNQi4nK5yMjIKNH+/PPPM27cONasWcOCBQtwOBxcc8013vPLly+ncePGTJ48mccee4znnnvOr704p9PJq6++yi233OLXPm/ePHbu3Mm6detwOBxs376d8PDwEvc5UXv37mXx4sUMGjTIr33Pnj1cc801vP322/Tq1QuABQsWcOjQIQCGDx/OrFmz2LdvH61bt+bqq6+maVN3kePEiRO56667StyrsLCQhQsX0rRpUz777DP69u0LwEMPPcQll1zChAkTAFi3bp33mnPPPbfU514ah6MBTqeL1q3+DEBe3v/IOvA1+/evJivrKzZtngmbweEIITq6C7ExPYiJ7Ul0VBIOR8iJPDaRWkdJIBEREZFaylq7CFhUrO0hn9cWuMPzIyI+Pn/nJ37bdrhKx2zcNILew1qd1LU9evSgV69eTJkyhbfeeoulS5eW2u/CCy/0JoDKc/vttzNz5kzGjRvn175r1y5+97vf4XC4J30kJCScVLzFTZo0iUcffbREEmj27Nlcf/313gQQwNVXX13i+kaNGtGiRQt27drlTQKVZcWKFbRv357hw4eTlpbmTQLt2rWL/v37e/slJSWdylvyCg5uyBlxAzgjbgAAx47tJyvra/ZnfcX+/avZtPlZ2PyMOykU1ZmY2J7ExnQnKioZp1NJITm9aDqYiIiIiIhIFcnJyfGbljRv3jzvuWnTpvHMM88wcuRIWrRoUer1H330ER07dvQe9+3b1zuW73SqZs2accEFF/D666/7XT9s2DA+/PBDkpOTufPOO/nmm2/8zpc1XkV69epFcHAwy5cv92vPzMyka9euFV6/detWcnNz/RI3M2fO9MZSlOgBSEtLY8SIEVx55ZV8/PHHHDt2DIBbb72VsWPH0rdvXx599FF27tzpvebnn3/2e+6ff/55pd9bcUFBscTF9adVywfo0f1DLuydTlLHF4mPH0V+/iE2b36Wtd+MZOXnyaxZO5JNm55l//5VFBRo+pjUfqoEEhERERGROudkK3ZOVVnTwQBWrlxJdHQ0mZmZJc717dsXp9NJUlISjzzyiLe9vOlb9913H0OHDuWyyy7ztiUkJPDjjz+ybNkyli1bxkUXXcT8+fO56KKLKhyvIg888ACPPPII06dPr/Q18+bNY+XKlWzYsIFZs2YRGhrqPVfadLC8vDwWLVrE008/TWRkJD169OCTTz5h8ODBDBgwgE2bNrFkyRIWL15M586dvc/yRKaDnaigoBji4i4hLu4SAI4dO0BW1tdkZX3F/qxVbN7yFzZveQ6HI5ioqM7ExnQnJrYH0VGdcTpDKxhdJLCUBBIREREREalmR44c4e6772bZsmWMGTOGRYsWeRc2hpNLzrRs2ZLk5GTeeecdv/aQkBAGDRrEoEGDaNKkCe+99543CXQq+vXrxwMPPMCqVau8be3bt2fNmjUMHTq01GuK1gRKT0+nf//+DBkyhDPPPLPMe3zyySdkZWV5q6Gys7NxuVwMHjwYgIYNGzJy5EhGjhzJ4MGDWblyZaUqkapSUFA0cXEXExd3MQDHjh3kwIF09u9fxf6s1WzeMhu2/AVjgomO6kRMbA9iY3oQHd1FSSGpcUoCiYiIiIiIVLOHH36YYcOG0aZNG55//nmuvfZa+vXr51cZczLuv/9+v0qgtWvXcuaZZ3LWWWdRWFjIunXrqmztHHBXA/3pT3/inHPOASA1NZXu3btz2WWX0aNHDwDeffddzj//fL/rUlJSuO6663j22WeZNm1ameOnpaXxyiuvMGLECMCdPGvevDnZ2dmsWrWKnj17EhYWxqFDh/j5559p1qxZlb23kxUUFEXjxv1o3LgfAPn5h8jKSmd/1iqy9n/Fli3Ps4VZGBNMVFQSsX5JIVcNRy/1jZJAIiIiIiIiVaRoTaAiAwcO5LrrrmPhwoV8++23AHTu3JkBAwYwffp0/vznP5c7XtE0MXAvhOy79Tm4K3G6dOnC2rVrAfcuXuPGjfNuH9+9e3dSU1MrPV5FLr30UuLi4rzHTZo04e233+auu+5i7969OBwOLrzwQgYOHFji2nvuuYcuXbowefJkwL0m0BtvvOE9/9Zbb7FkyRJefPFFb1t4eDgXXHABH374IVu3biU1NZUGDRpQWFjIjTfeSLdu3diyZYt3TaAiN9xwA+PHjz+h91ZVGjSIpHHjvjRu7F7n6HhSyL372C+/vMiWLbMxJsidFIrpQUxsD2Kiu+B0htVIzFJ/GPemEoGXkpJi09PTa+TeIiIiUv2MMWustSk1HYf403cwqcvWr19P27ZtazoMOQ3U5J+V/PxDZB1YQ9b+r9iftZpDh77D2gKMaUBUVBIxMccrhRo0CK+RGOX0Vt53MFUCiYiIiIiIiARIgwaRNG7Uh8aN+gCQn3+YAwfWeLek37r1ZX755QV3Uiiyo8+aQl2VFJJTpiSQiIiIiIhIPXXrrbfy73//269twoQJjBkzpoYiqn8aNIigUaPf06jR7wHIzz/CgQNr3dPH9q9i69ZX+OWXFzHGSWRkErEx3YmNLUoKRdRw9HK6URJIRERERESknpo9e3ZNhyDFNGgQTqNGvWnUqDcABQXZZB1YS5Zn97Gt2/7GL1tf8iSFOvisKdSVBg0iazh6qe2UBBIRERERERGppZzOMBo1vIBGDS8A3EmhAwe+8WxJ/xVbt83hl60vAw6iIjt4p4/FxKQoKSQlKAkkIiIiIiIicppwOsNo2PB8GjY8H4CCghyf6WNfsW3bXLZu/SvgIDKyvXdL+piYbkoKiZJAIiIiIiIiIqcrp9NVSlLoG7Ky3LuPbdv2Glu3voI7KdTOZ/pYN4KComo2eAk4JYFERERERERE6gh3Uug8GjY8D4CCglwOHPzGuyX99h2vs3Xb3wBDZGQ775b0MTHdCAqKrtngpdo5ajoAERERERGRusLpdJKcnOz92bJlCytWrGDw4MEA7Nmzh8GDB9OpUyfatWvHpZde6r32p59+4tJLL6Vly5Z06dKFYcOGsWfPHu/522+/nfj4eAoLC71tc+fOJTU1tcK4Ro0axQsvvOA9Xr16NUlJSRw7dozExEQ6duzojXn8+PHefvn5+cTFxXHvvff6jdenTx9at25Np06d6NatGxkZGSf+sCQgnM5QGsb24pxzJtC1y1tc2DuDLp3fonnz8TidEezY8QbrvruZlZ93ZfVXl/PTfx/h11+XcuxYVk2HLtVAlUAiIiIiIiJVxOVylUiIbNmyxfv6oYce4pJLLmHChAkArFu3DoDc3Fwuu+wynn76aS6//HIAVqxYwa+//kqTJk0oLCxk4cKFNG3alM8++4y+ffueUFxPP/00vXr14uqrr6ZRo0akpqby/PPPExQUBMDy5ctp3LhxieuWLl1Kq1atmD9/PtOmTcMY4z335ptvkpKSwpw5c5g0aRJLly49oZikZjidIe51gmJ7QPPxFBQc5eDBb91rCmWtZseOt9i2bQ5giIho45k+1p3YmO4EBcXWdPhyipQEEhERERGROmf53JfZ+8umKh3zjLPPoe/om05pjF27dtG/f3/vcVJSEgBvvfUWvXr18iaAwF1tU2TFihW0b9+e4cOHk5aWdsJJoCZNmnDXXXdx9913061bN5KSkrjgggsqvC4tLY0JEybwwgsv8J///IfzzjuvRJ9evXoxY8aME4pHag93Uqg7sbHdgdsoLDzKgYPrPFvSf8WOnWls2z4XgIiINn7Tx4KDG9Zo7HLilAQSERERERGpIjk5OSQnJwPQvHlzFi5c6Hf+1ltvZfjw4cyaNYuLL76YMWPGcNZZZ5GZmUnXrl3LHDctLY0RI0YwdOhQJk+ezLFjx7xVPJX1pz/9ib///e+sWLGC9PR0v3N9+/bF6XQCcP311zNx4kRyc3P59NNPeemll8jKyiItLa3UJNCSJUu44oorTigWqb0cjhBiY7oRG9ON5kBh4VEOHvyO/ftXkZX1FTt3zmP79r8DEBHe2mdL+m4EBzeq2eClQkoCiYiIiIhInXOqFTsnq7TpYL4GDBjApk2bWLJkCYsXL6Zz585kZmaWO2ZeXh6LFi3i6aefJjIykh49evDJJ5941xmqLIfDwc0330x6ejqNGvn/Y7206WAfffQRffv2xeVycdVVVzF16lSeeeYZb7Jo1KhR5OXlcfjwYa0JVIc5HCHExKQQE5MCQGFhHgcPrnPvPrZ/NTt3zmf79tcACA9v5d19LDamG8HBJacYSs1SEkhERERERCSAGjZsyMiRIxk5ciSDBw9m5cqVtG/fns8++6zU/p988glZWVl07NgRgOzsbFwu1wkngcCdCHI4Krc/UFpaGl988QWJiYkA7Nu3j2XLlnHJJZcA7jWBunbtyqRJk7jtttt49913TzgeOf04HMHepFBi4v+jsDCPQ4cy2b9/NfuzVrNr9z/YvuN1AMLDW7qnj8X2IDamu5JCtYCSQCIiIiIiIgGybNkyevbsSVhYGIcOHeLnn3+mWbNmXHrppUybNo2PP/6Yyy67DICVK1fSsGFD0tLSeOWVVxgxYgQAR44coXnz5mRnZ1dbnAcPHuTzzz9n27ZthISEADBnzhzS0tK8SSAAYwxTp07l3HPPZcOGDbRp06baYpLayeEIJjq6C9HRXUjkFgoLj/kkhVaxe/e77NjxBgBhYS08CSF3tVCIkkIBpySQiIiIiIhIgKxZs4bU1FQaNGhAYWEhN954I926dQPc069uv/12br/9doKCgkhKSmL69OksWbKEF1980TtGeHg4F1xwAR9++CHg3ib+vffe855ftWoVCQkJJxSX75pASUlJXHTRRfTr18+bAAIYOnQod999N0ePHvW71uVyceeddzJjxgz+9re/ndgDkTrH4QgiOroz0dGdSeRPnqTQ9+7dx/avYvfu99ix400AwsLOPZ4UiulBSEhcDUdf9xlrbY3cOCUlxRZfjExERETqDmPMGmttSk3HIf70HUzqsvXr19O2bduaDkNOA/qzUnMKC/M5dPh77+5jWVnpFBQcBiAs7BxPQqg7sbE9CAlpUsPRnp7K+w6mSiARERERERERCQiHowHRUZ2IjurE2WffTGFhPocP/8D+rNXs37+a3Xs+ZMfONADCwpq7E0IxPYmJ7U5oyJk1HP3pr1JJIGPMQOBZwAm8Yq19vIx+VwELgG7WWv0vJhERERERkQDr0aNHiSlbr7/+undhaZHaxOFoQFRUElFRSZzdbJxPUugrsvavZs+ej9m5cx4ALtfZxMb0IDa2JzEx3QkN/V0NR3/6qTAJZIxxArOBS4DtwNfGmA+stT8U6xcJTABWV0egIiIiIiIiUrHVq/VPMjl9+SeFbsTaAg4dXk+WZ/exvb8uZueudwBwuZp5qoTcu4+Fhp5Vw9HXfpWpBOoObLTWbgIwxrwNDAV+KNZvKjAdmFSlEYqIiIiIiIhIvWSMk6jIDkRFdqBZs7FYW8Dhwxu8W9Lv/XXJ8aRQaDNPQsi9Lb2SQiVVJgkUD2zzOd4O9PDtYIzpAjS11n5sjCkzCWSMuQm4CaBZs2YnHq2IiIiIiIiI1FvGOImMbE9kZHuaNbvBkxT60bP72Gp+/fWf7No1H4DQ0Kae3ce6ExPTE5crvoajr3mnvDC0McYBPA2MrqivtfZl4GVw70xxqvcWERERqcsqWpfRGDMamAHs8DTNsta+EtAgRUREapA7KdSOyMh2NGs6BmsLPUmhVWRlfcWvv37Krl0LAAgNTXAnhGJ7EBvTE5croYajDzxHJfrsAJr6HCdw/IsGQCTQAVhhjNkC9AQ+MMZoS1gRERGRk+SzLuMgoB0wwhjTrpSu86y1yZ4fJYBEapjT6SQ5OZkOHTpwzTXXkJ2d7dde9PP44+6cbp8+fWjdujWdOnWiW7duZGRkeMdavHgxKSkptGvXjs6dO3PnnXcCMGXKFOLj4/3Gy8rKYsWKFRhj+PDDD71jDB48mBUrVnDllVeSnJxMixYtiI6O9l735ZdfApCcnMy1117r917y8/OZPHkyLVu29PZ/9NFHS7zX4u9JpCYZ4yAysi3Nmo4hqeMLXNj7a7p3/5hWLR8iMrI9v+1bzvr19/Dlf37Pv7+8kO9/uIudOxeQk7MNa+t+rUplKoG+BloaY5rjTv5cC4wsOmmtPQA0Ljo2xqwA7tLuYCIiIiKnpLLrMopILeJyubyJnFGjRvHiiy9yxx13+LUX9+abb5KSksKcOXOYNGkSS5cuJTMzk9TUVD7++GPatGlDQUEBL7/8sveaiRMnctddd5UYKyEhgUcffZTLL7/cr33hwoUArFixgieffJKPPvrIe279+vUUFBTw+eefc+TIEcLDwwF44IEH2L17N9999x2hoaEcOnSIp556qtT3KlJbGeMgMqINkRFtaNr0eqwt5MiR/3q3pN+37zN273b/foSE/M4zfaynZ02hphhjavgdVK0Kk0DW2nxjTCrwCe5S5Fettd8bYx4G0q21H1R3kCIiIiL1UIXrMnpcZYy5EPgJmGit3VZKHxGpAb1792bdunWV7t+rVy9mzJgBwBNPPMH9999PmzZtAHfVzS233FLhGJ06deLYsWMsXbqUSy65pFL3TUtL47rrrmP9+vW8//77jBw5kuzsbP7617+yZcsWQkNDAYiMjGTKlCmVfj8itZExDiIiWhMR0ZqmCX/0JIU2etcU2rdvJbt3vwdASMiZ3oRQTEx3XK6zT/ukUKXWBLLWLgIWFWt7qIy+fU49LBERERGphA+BNGvtUWPMzcDfgX7FO2lzDqmPsj78mbydR6p0zOCzwom5/NxK9c3Pz2fx4sUMHDgQgJycHJKTk73n77vvPoYPH+53zZIlS7jiiisAyMzM9E7/Ks3MmTN54403AIiNjWX58uXec/fffz8PPvhgpZNA8+bNY+nSpWzYsIG//OUvjBw5ko0bN9KsWTMiIyPLvK4y70mktnMnhVoREdGKpgnXYa3lSPZG75b0+/73Obv3+CaF3Amh2NgeuFyJp11S6JQXhhYRERGRalHRuoxYa/f5HL4CPFHaQNqcQyRwfBMjvXv3ZuzYsUD5U6dGjRpFXl4ehw8frvT0qrKmgwFceOGFAHzxxRcVjpOenk7jxo1p1qwZ8fHx3HDDDfzvf/8r0W/OnDk8++yz7Nu3jy+//JKmTZtqOpjUScYYIsJbEhHekoSEP2CtJTv7Z++W9P/b/29273kfgJDgJp5FprsTG9vztEgKKQkkIiIiUjuVuy4jgDHmd9baXZ7DIcD6wIYoUntVtmKnqp1MYuTNN9+ka9euTJo0idtuu413332X9u3bs2bNGjp16nRScdx///088sgjNGhQ/j/50tLS2LBhA4mJiQAcPHiQf/zjH4waNYqtW7dy6NAhIiMjGTNmDGPGjKFDhw4UFBScVEwipyNjDOHhLQgPb0FCwihPUmiTd/rY/v3/Yc8e9yo5wcFn+O0+FhbWvNYlhZQEEhEREamFKrku43hjzBAgH/gfMLrGAhaRU2KMYerUqZx77rls2LCBSZMm8X//939ccMEFtGrVisLCQl5++WX+9Kc/VWq8/v378+CDD7Jr164y+xQWFvLOO+/w3XffcdZZZwGwfPlypk6dyrhx4xg7diypqam89NJLhIaGUlBQQF5eXpW8X5HTlTspdC7h4eeSED/SkxTaTFbWau9i03v2uhdeDw6O80wd60lsTA/Cws6p8aSQkkAiIiIitVRF6zJaa+8D7gt0XCJy4oqvnzNw4MASW6q7XC7uvPNOZsyYwd/+9jeeeeYZRowYQXZ2NsYYBg8e7O3ruyYQwHvvvVfinvfffz9Dhw4tM6bPP/+c+Ph4bwII3FPJfvjhB3bt2sWjjz7Kgw8+SIcOHYiMjMTlcnH99dd7+1fmPYnUde6k0DmEh59DfPwIrLXk5GzxTh/L2r+avXs/BiA4uLE7KeRZbDo8vEXg47W2ZqaFp6Sk2PR07SIvIiJSVxlj1lhrU2o6DvGn72BSl61fv562bdvWdBhyGtCfFQkUd1Lol+PTx7JWc/TobqKiOtEt5d1quWd538FUCSQiIiIiIiIiUg2MMYSFJRIWlkj8WcM9SaGt5OcfqJF4lAQSEREREREREQkAd1Lo7Bq7v6PG7iwiIiIiIiIiIgGjJJCIiIiIiIiISD2gJJCIiIiIiIiISD2gJJCIiIiIiIiISD2gJJCIiIiIiIiISD2gJJCIiIiIiEgVcTqdJCcn06FDB6655hqys7NLtF9++eVkZWV5r/n+++/p168frVu3pmXLlkydOhVrLQBz584lLi6O5ORkkpOT+eMf/wjA6NGjWbBgAVdeeSXJycm0aNGC6Ohob78vv/yy1Pj69OlD69atSUpKok2bNqSmpvrFUhRn0c/jjz8OwLFjx7j33ntp2bIlXbp0oVevXixevBiAxMREfvvtN7/7zJ07l9TUVACmTJlCWFgYe/fu9Z6PiIio8J4iUvWUBBIREREREakiLpeLjIwMMjMzCQ4O5sUXXyzR3rBhQ2bPng1ATk4OQ4YM4d577+XHH3/k22+/5csvv+T555/3jjl8+HAyMjLIyMjgtdde87vfwoULycjI4JVXXqF3797efuedd16ZMb755pusW7eOdevWERISwtChQ0vEX/Rz7733AvDggw+ya9cuMjMzWbt2Le+99x6HDh2q9HNp3LgxTz31VLnPrPg9RaTqNajpAERERERERKra4sWL2b17d5WOeeaZZzJo0KBK9+/duzfr1q0r0d6rVy9v+1tvvcX5559P//79AQgLC2PWrFn06dOHW2+9tWoCL0NwcDBPPPEELVq04Ntvv6VTp06l9svOzuavf/0rmzdvJiQkBIAmTZowbNiwSt/rhhtuYO7cudxzzz00bNiwSuIXkROnSiAREREREZEqlp+fz+LFi+nYsaNfe0FBAf/6178YMmQI4J4K1rVrV78+5557LocPH+bgwYMAzJs3zztVas6cOVUap9PppFOnTmzYsAFwVyb5Ts2aN28eGzdF+0G4AAAgAElEQVRupFmzZkRFRZ30fSIiIrjhhht49tlnS5wr7Z4iUj1UCSQiIiIiInXOiVTsVKWihAa4K4HGjh3r175jxw7atm3LJZdcUukxhw8fzqxZs6olXsC7/hAcn5rlq7RqppMxfvx4kpOTueuuu/zaS7uniFQPVQKJiIiIiIhUEd/1bf7yl78QHBzs1/7LL79grfWuCdSuXTvWrFnjN8amTZuIiIg4pcqbyiooKOC7776jbdu2ZfZp0aIFW7du9VYmnayYmBhGjhzpfe8iEnhKAomIiIiIiARIWFgYzz33HE899RT5+fmMGjWKL774gk8//RRwVwyNHz+eu+++u9pjOXbsGPfddx9NmzYlKSmp3JjHjh3LhAkTyMvLA+DXX39l/vz5J3zPO+64g5deeon8/PyTjltETp6SQCIiIiIiIgHUuXNnkpKSSEtLw+Vy8f777/PII4/QunVrOnbsSLdu3bzbq1eHUaNGkZSURIcOHThy5Ajvv/++91zx9XmKdup65JFHiIuLo127dnTo0IHBgwf7VSolJSWRkJBAQkICd9xxR5n3bty4MVdeeSVHjx6t8J4iUvWM7/zPQEpJSbHp6ek1cm8RERGpfsaYNdbalJqOQ/zpO5jUZevXry93WpNIEf1ZkbqsvO9gqgQSEREREREREakHtDuYiIiIiIhIHXPllVeyefNmv7bp06czYMCAGopIRGoDJYFERERERETqmIULF9Z0CCJSC2k6mIiIiIiIiIhIPaAkkIiIiIiIiIhIPaAkkIiIiIiIiIhIPaAkkIiIiIiIiIhIPaAkkIiIiIiISBVxOp0kJyfToUMHrrnmGrKzs0u0X3755WRlZXmv+f777+nXrx+tW7emZcuWTJ06FWstAHPnziUuLo7k5GSSk5P54x//CMDo0aNZsGABV155JcnJybRo0YLo6Ghvvy+//LLU+Pr06UN6ejoAiYmJXHXVVd5zCxYsYPTo0d7jxYsXk5KSQrt27ejcuTN33nknAFOmTCE+Pp7k5GTatWtHWlqa95rRo0fTvHlzbxznnXee3/2vuOIKevbs6df2448/0qdPH5KTk2nbti033XQTACtWrPB7T8nJyXz66aeV/zBEpAQlgURERERERKqIy+UiIyODzMxMgoODefHFF0u0N2zYkNmzZwOQk5PDkCFDuPfee/nxxx/59ttv+fLLL3n++ee9Yw4fPpyMjAwyMjJ47bXX/O63cOFCMjIyeOWVV+jdu7e3X/HkS1nWrFnDDz/8UKI9MzOT1NRU3njjDX744QfS09Np0aKF9/zEiRPJyMjg/fff5+abb+bYsWPeczNmzPDG4ZuMysrKYs2aNRw4cIBNmzZ528ePH+8db/369dx2223ec77vKSMjg4svvrhS70tESqct4kVEREREpM756aepHDq8vkrHjIxoS6tWD1a6f+/evVm3bl2J9l69ennb33rrLc4//3z69+8PQFhYGLNmzaJPnz7ceuutVRN4Oe68804effRR3nzzTb/2J554gvvvv582bdoA7kqmW265pcT1LVu2JCwsjP3793PGGWeUe693332Xyy+/nCZNmvD2228zefJkAHbt2kVCQoK3X8eOHU/1bYlIGVQJJCIiIiIiUsXy8/NZvHhxiYRGQUEB//rXvxgyZAjgngrWtWtXvz7nnnsuhw8f5uDBgwDMmzfPOx1qzpw5VRrnsGHDWLt2LRs3bvRrz8zMLBFXadauXUvLli39EkCTJk3yxjtq1Chve1paGiNGjGDEiBF+U8gmTpxIv379GDRoEDNnzvSbKvf555/7TQf7+eefT+XtitR7qgQSERERqaWMMQOBZwEn8Iq19vEy+l0FLAC6WWvTAxiiSK11IhU7VSknJ4fk5GTAXQk0duxYv/YdO3bQtm1bLrnkkkqPOXz4cGbNmlUt8TqdTiZNmsS0adMYNGhQpa+bOXMmc+bM4aeffuLDDz/0Ozdjxgyuvvpqv7Y9e/bw3//+lwsuuABjDEFBQWRmZtKhQwfGjBnDgAEDWLJkCe+//z4vvfQS3377LeB+hh999NGpv1ERAVQJJCIiIlIrGWOcwGxgENAOGGGMaVdKv0hgArA6sBGKSGmK1v7JyMjgL3/5C8HBwX7tv/zyC9Za75pA7dq1Y82aNX5jbNq0iYiICKKiogIS83XXXcfKlSvZtm2bt619+/Yl4vI1ceJEvv/+e/7xj38wduxYcnNzy73HO++8w/79+2nevDmJiYls2bLFrxrorLPO4oYbbuD999+nQYMGZGZmnvobE5ESlAQSERERqZ26AxuttZustXnA28DQUvpNBaYD5f8LTERqhbCwMJ577jmeeuop8vPzGTVqFF988YV316ucnBzGjx/P3XffHbCYgoKCmDhxIjNnzvS2TZo0iccee4yffvoJgMLCQu8i176GDBlCSkoKf//738u9R1paGkuWLGHLli1s2bKFNWvW8PbbbwOwZMkS78LSu3fvZt++fcTHx1fV2xMRH0oCiYiIiNRO8cA2n+PtnjYvY0wXoKm19uPyBjLG3GSMSTfGpP/6669VH6mInJDOnTuTlJREWloaLpeL999/n0ceeYTWrVvTsWNHunXrRmpqakBjGjt2LPn5+d7jpKQknnnmGUaMGEHbtm3p0KGD345evh566CGefvppCgsLAf81gZKTk/npp5/45Zdf/LaGb968OdHR0axevZp//vOfdOjQgU6dOjFgwABmzJjBmWeeCZRcE2jBggXV+BRE6j5jra2RG6ekpNj0dE1ZFxERqauMMWustSk1HcfpyhhzNTDQWnuj5/g6oIe1NtVz7ACWAaOttVuMMSuAuypaE0jfwaQuW79+PW3btq3pMOQ0oD8rUpeV9x1MlUAiIiIitdMOoKnPcYKnrUgk0AFYYYzZAvQEPjDGKPEmIiIipdLuYCIiIiK109dAS2NMc9zJn2uBkUUnrbUHgMZFx5WtBBKR+uHKK69k8+bNfm3Tp09nwIABNRSRiNQGSgKJiIiI1ELW2nxjTCrwCe4t4l+11n5vjHkYSLfWflCzEYrUTtZajDE1HUaNW7hwYU2HUGvV1JIoIrWBkkAiIiIitZS1dhGwqFjbQ2X07ROImERqs9DQUPbt20ejRo2UCJJSWWvZt28foaGhNR2KSI1QEkhEREREROqEhIQEtm/fjnbBk/KEhoaSkJBQ02GI1AglgUREREREpE4ICgqiefPmNR2GiEitpd3BRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqASWBRERERERERETqgUolgYwxA40xPxpjNhpj7i3l/B3GmB+MMeuMMf8yxpxd9aGKiIiIiIiIiMjJqjAJZIxxArOBQUA7YIQxpl2xbt8AKdbaJGAB8ERVByoiIiIiIiIiIievMpVA3YGN1tpN1to84G1gqG8Ha+1ya22253AVkFC1YYqIiIiIiIiIyKmoTBIoHtjmc7zd01aWscDi0k4YY24yxqQbY9J//fXXykcpIiIiIiIiIiKnpEoXhjbG/AFIAWaUdt5a+7K1NsVamxIXF1eVtxYRERERERERkXI0qESfHUBTn+MET5sfY8zFwP3A7621R6smPBERERERERERqQqVqQT6GmhpjGlujAkGrgU+8O1gjOkMvAQMsdburfowRURERERERETkVFSYBLLW5gOpwCfAeuAda+33xpiHjTFDPN1mABHAfGNMhjHmgzKGExERERERERGRGlCZ6WBYaxcBi4q1PeTz+uIqjktERERERERERKpQlS4MLSIiIiIiIiIitZOSQCIiIiIiIiIi9YCSQCIiIiIiIiIi9YCSQCIiIiIiIiIi9YCSQCIiIiK1lDFmoDHmR2PMRmPMvaWc/5Mx5jvP7qxfGGPa1UScIiIicnpQEkhERESkFjLGOIHZwCCgHTCilCTPW9bajtbaZOAJ4OkAhykiIiKnESWBRERERGqn7sBGa+0ma20e8DYw1LeDtfagz2E4YAMYn4iIiJxmGtR0ACIiIiJSqnhgm8/xdqBH8U7GmFuBO4BgoF9pAxljbgJuAmjWrFmVByoiIiKnB1UCiYiIiJzGrLWzrbXnAvcAD5TR52VrbYq1NiUuLi6wAYqIiEitoSSQiIiISO20A2jqc5zgaSvL28AV1RqRiIiInNaUBBIRERGpnb4GWhpjmhtjgoFrgQ98OxhjWvocXgb8N4DxiYiIyGlGawKJiIiI1ELW2nxjTCrwCeAEXrXWfm+MeRhIt9Z+AKQaYy4GjgH7getrLmIRERGp7ZQEEhEREamlrLWLgEXF2h7yeT0h4EGJiIjIaUvTwURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6gElgURERERERERE6oEGNR2AiIiIiIiIiMjpwBYUUJiTi83NoTDH/WNzcijMyaUwJxubm+v/OjuHwlyfPrk52OwcghMTaXLfvQGPX0kgERERERERETntWWuxeXmehEtO6ckYz+vjSRxPn5xc93Fujv/r7BwKc3O9yR6bl3fCcZnQUByhoZgwF45QFw6XiwZNmlTDE6iYkkAiIiIiIiIiUu3cVTSeqpgSyZhi1TLeBI2njzcZ40nY5OZic7KP9/GMSWHhiQXVoAEOl8udpHG5jr8OcxEUHY3D5cK4Qt3JmzCXO6HjCsPh8vT3a3dfbzxtjtBQTGgoxlF7VuJREkhERERERESknrPWYo8e9U/SlDbVqVhFTYmETVEVTm6u/+vsbOyxYyccl/EmaDzJF0+yxRkdjePMM/2SMX59woonY3xee5I4DpcLExRUDU+z9lISSERERERERKSWs/n5nuqZonVnyqqo8a+WKTHVyVNR419d4x4La08sqKAgd8Kl2FQn4wolKDa2WHtplTOeihqf1+7rPeOEhNSqKpq6QEkgERERERERkVNQoorGk4wpffFg/+lNfq9Lmd7kXYvmZKtoSpnq5IyNwRH6u1KmOnn6uHz6u8qZ6lTPqmjqAiWBREREREREpE6z+fn+yRi/ippydnKqREVN0bSnk6qi8SRlvNUvnmlMztjY4wkaz/Qm7+uiapnSpjeFhmLCwo6vRWNM9TxQOW0pCSQiIiIiIiI1xlrrTsB41o0pbXpTuTs5lTq9ybdPLpxoFY0xx5MyvtUyLhfOhrEEueJLnQJV2lQn3wSPX2WOqmikBigJJCIiIiIiImWyx46VPb3JL2FTfKpTsd2eiq0/47t48IkyQUHFdnI6vhiws1Ej/4WBi6Y3lTnVybeixmctGlXRSB2kJJCIiIiIiMhpyhYWeqto/JMxZUxvKm2qU1FFTWlTnXJyID//xIIqqqLxmd5kQj1r0TRqSJBv5UzxnZzKmepkQkNxFE11aqB/yoqcDP3miIiIiIiIVBObl1ey+qWi6U2lLB5c1to0Njf3hGMyQUHHK2d8dmJyhIW5q2h8q2V8pzeVtqtTKYsHq4pGpPZSEkhEREREROolbxVNUWImJ7tYwqaC6U2+fYpNb/KuRXMSVTR+iRmXpxImNBRH40YElaicKZawqWiqk6poROo1/faLiIiIiEitY62ForVo/HZyyvFWwJS5k1MF05uK1rI5qSqa4OBia9F4ki/h4TjjGvsvElx8J6fSqmuKTXUywcGqohGRaqMkkIiIiIiInDBbUHB8LZpiU52869HkFJ+6VPH0Jt+EDQUFJxaUw+G/SLBPRY0zLq5Ydc2JTnVy4QgNURWNiJzW9F8wEREREZE6xlqLPXbs+Hbbpe7kVF5FTbHdnnynOnnGtEePnnBcJjjYLzFTVC3jjIjExMWVskiw//SmEttuF5vqpCoaEZHyKQkkIiIiIhJgtqDg+OLAvlOdSpveVEpFjd/0pjKmOp1UFY3vWjQ+U52cZ5zhTtiUMtWpxFo0vhU1vhU5oaEYp7N6HqiIiFSKkkAiIiIitZQxZiDwLOAEXrHWPl7s/B3AjUA+8Ctwg7X2l4AHWsdYa7F5eeXv5FSioia39KlOxXd7Kqq+ycs74bhMSEipU52ckVE4zjij/GSMd92ZYosEeytyXO4do1RFIyJSpykJJCIiIlILGWOcwGzgEmA78LUx5gNr7Q8+3b4BUqy12caYW4AngOGBjzawvFU0RTs5ZftOXSqroqac6U052Z7dno5PdaKw8MSCcjqPV8gUq5YJioo6Pr2p2FSnshYPLlGR43JhHI7qeaAiIlJvKAkkIiIiUjt1BzZaazcBGGPeBoYC3iSQtXa5T/9VwB8CGmEpiqpoSuzkVOq22sUTNuXs5OSz9fZJVdGEhvpXv3imOjmjonA0aVLxVCdvRY3P2jQ+FTmoikZERE4DSgKJiIiI1E7xwDaf4+1Aj3L6jwUWV2tE5dh0+RDyduzA5uSAtSd2sW8Vjct/qlNQdHQF6874LB7sN9XJt7omVFU0IiIiKAkkIiIictozxvwBSAF+X8b5m4CbAJo1a1YtMUT064fNzfUuJHxCU52Cg6slJhEREfGnJJCIiIhI7bQDaOpznOBp82OMuRi4H/i9tbbUPbuttS8DLwOkpKScYJlO5Zwx8fbqGFZERESqkOpiRURERGqnr4GWxpjmxphg4FrgA98OxpjOwEvAEGvt3hqIUURERE4jSgKJiIiI1ELW2nwgFfgEWA+8Y6393hjzsDFmiKfbDCACmG+MyTDGfFDGcCIiIiKaDiYiIiJSW1lrFwGLirU95PP64oAHJSIiIqctVQKJiIiIiIiIiNQDSgKJiIiIiIiIiNQDSgKJiIiIiIiIiNQDSgKJiIiIiIiIiNQDSgKJiIiIiIiIiNQDSgKJiIiIiIiIiNQDlUoCGWMGGmN+NMZsNMbcW8r5EGPMPM/51caYxKoOVERERERERERETl6FSSBjjBOYDQwC2gEjjDHtinUbC+y31rYAZgLTqzpQERERERERERE5eZWpBOoObLTWbrLW5gFvA0OL9RkK/N3zegFwkTHGVF2YIiIiIiIiIiJyKhpUok88sM3neDvQo6w+1tp8Y8wBoBHwm28nY8xNwE2ew6PGmMyTCVqqVWOKfW5S4/SZ1D76TGonfS61T+uaDkBKWrNmzW/GmF+qaXj9HgaWnndg6XkHnp55YOl5B1Z1Pu+zyzpRmSRQlbHWvgy8DGCMSbfWpgTy/lIxfS61jz6T2kefSe2kz6X2Mcak13QMUpK1Nq66xtbvYWDpeQeWnnfg6ZkHlp53YNXU867MdLAdQFOf4wRPW6l9jDENgGhgX1UEKCIiIiIiIiIip64ySaCvgZbGmObGmGDgWuCDYn0+AK73vL4aWGattVUXpoiIiIiIiIiInIoKp4N51vhJBT4BnMCr1trvjTEPA+nW2g+AvwGvG2M2Av/DnSiqyMunELdUH30utY8+k9pHn0ntpM+l9tFnUv/oMw8sPe/A0vMOPD3zwNLzDqwaed5GBTsiIiIiIiIiInVfZaaDiYiIiIiIiIjIaU5JIBERERERERGReqDak0DGmIHGmB+NMRuNMfeWcj7EGDPPc361MSaxumOq7yrxmdxhjPnBGLPOGPMvY8zZNRFnfVPR5+LT7ypjjDXGaPvGalaZz8QYM8zz+/K9MeatQMdYH1Xiv2HNjDHLjTHfeP47dmlNxFmfGGNeNcbsNcZklnHeGGOe83xm64wxXQIdo1Qdfd6BVYnnPcrznL8zxnxpjOkU6BjrmoqeuU+/bsaYfGPM1YGKrS6qzPM2xvQxxmR4vm99Fsj46ppK/Dcl2hjzoTHmW8/zHhPoGOsSY0xTz/fSon8vTCilT0D/3qzWJJAxxgnMBgYB7YARxph2xbqNBfZba1sAM4Hp1RlTfVfJz+QbIMVamwQsAJ4IbJT1TyU/F4wxkcAEYHVgI6x/KvOZGGNaAvcB51tr2wO3BzzQeqaSvysPAO9Yazvj3qjg+cBGWS/NBQaWc34Q0NLzcxPwQgBikuozF33egTSX8p/3ZuD31tqOwFS0sGtVmEv5z7zo76PpwD8DEVAdN5dynrcxJgb33+VDPN+3rglQXHXVXMr/830r8IO1thPQB3jKs0u4nJx84E5rbTugJ3BrKd9dA/r3ZnVXAnUHNlprN1lr84C3gaHF+gwF/u55vQC4yBhjqjmu+qzCz8Rau9xam+05XAUkBDjG+qgyvyvg/nI3HcgNZHD1VGU+k3HAbGvtfgBr7d4Ax1gfVeZzsUCU53U0sDOA8dVL1tqVuHcHLctQ4DXrtgqIMcb8LjDRSVXT5x1YFT1va+2XRX8Poe9tVaISf8YBbgP+Aejv/lNUiec9EnjXWrvV01/P/BRU4nlbINLzb/IIT9/8QMRWF1lrd1lr13peHwLWA/HFugX0783qTgLFA9t8jrdT8g17+1hr84EDQKNqjqs+q8xn4msssLhaIxKoxOfiKQtsaq39OJCB1WOV+V1pBbQyxvx/9u48TM6yzvf/5/s8tfTe6TXpJglpQkLCvkQQVAyiTvRkQEdA5MiAMw5nRh2R41F0jqP+cEEux3GZwVEcHVQ84CQIRAR31MGVZCaCLDqAKKEr6S3p9N5V9dy/P6q6urZeQrq7urver+vqK1XPVvfTHUjXp+7v9/6pmf3CzKb91BBzYjY/lw9KeqOZ7ZN0n1K/qKO0jvTfHixt/LxLh9/bFoCZHSPptWKW20LZKKnBzH5kZnvM7M9LPaBl7p8lbVbqQ7RHJF3rnAtKO6TlwVKtb85QYVXHgv67GZqvC2PpM7M3Stoi6aWlHku5MzNP0j9KurrEQ0GukFLTNrcq9cnrT8zsFOfcoZKOCm+QdKtz7hNmdq6kr5rZyfwCA2A5M7MLlAqBXlzqsZSBT0m63jkXUMCwIEKSzpJ0oaRKST83s184535X2mEtW38iaa+kl0laL+l7ZvYfzrnDpR3W0mZmNUrNHnxHqb+X8z0T6DlJa7Ker05vK3qMmYWUmrrfO8/jKmez+ZnIzF4u6f8qVXs7tkBjK2cz/VxqJZ0s6Udm9oxS9aS7aA49r2bz38o+Sbucc3Hn3O8l/U6pUAjzZzY/l7+U9O+S5Jz7uaQKSc0LMjpMZVb/9mDZ4Oe9wMzsVEn/Kuli5xy/R8+/LZLuSP9Odomkz5rZa0o7pGVtn6TvOOeGnHM9kn4iiQbo8+dNSpXfOefck0r1HdtU4jEtaWYWVioA+ppz7htFDlnQfzfnOwR6SNIGM+tIN5O6XNKuvGN2Sboq/fgSST90zrl5Hlc5m/FnYmZnSPq8UgEQNbcLY9qfi3Ou3znX7Jxb55xbp1TN/0XOud2lGW5ZmM3/v+5WahaQzKxZqenKTy/kIMvQbH4uf1Tq00KZ2WalQqDuBR0l8u2S9Ofp1S9eKKnfORcr9aAwb/h5LyAzWyvpG5KuZGbEwnDOdWT9TrZT0lucc3eXeFjL2T2SXmxmITOrknSOUn1VMD+yf49aKekE8fvt85burfRFSY875/5xisMW9N/NeS0Hc84lzOxtkr4jyZf0Jefco2Z2g6TdzrldSn1DvmpmTyrVdOry+RxTuZvlz+TjSjUB25Ge4vpH59xFJRt0GZjlzwULaJY/k+9IeqWZPSYpKeldfAI7v2b5c3mnpC+Y2XVKNTe8mg8X5peZ3a5UINqc7sX0AUlhSXLOfU6p3kyvlvSkpGGlPmXEEsXPe2HN4vv9fqX6aX42/XtbwjnHTOGjMIvvOebQTN9v59zjZvZtSQ9LCiT9q3NuyuXkMb1Z/P3+kKRbzewRSaZU6WNPiYa7HLxI0pWSHjGzveltfydprVSafzeN34sBAAAAAACWv/kuBwMAAAAAAMAiQAgEAAAAAABQBgiBAAAAAAAAygAhEAAAAAAAQBkgBAIAAAAAACgDhEAAAAAAUMbMbKuZ3VvqcQCYf4RAAAAAAAAAZYAQCAAAAACWADN7o5n9ysz2mtnnzcw3s0Ez+6SZPWpmPzCzlvSxp5vZL8zsYTO7y8wa0tuPN7Pvm9mvzew/zWx9+vI1ZrbTzJ4ws6+ZmZXsRgHMG0IgAAAAAFjkzGyzpNdLepFz7nRJSUn/U1K1pN3OuZMk/VjSB9KnfEXS9c65UyU9krX9a5Juds6dJuk8SbH09jMkvUPSiZKOk/Sieb8pAAsuVOoBAAAAAABmdKGksyQ9lJ6kUympS1Ig6evpY26T9A0zq5e0wjn34/T2L0vaYWa1ko5xzt0lSc65UUlKX+9Xzrl96ed7Ja2T9OD83xaAhUQIBAAAAACLn0n6snPuvTkbzf4+7zj3PK8/lvU4Kd4rAssS5WAAAAAAsPj9QNIlZtYqSWbWaGbHKvWe7pL0MVdIetA51y/poJm9JL39Skk/ds4NSNpnZq9JXyNqZlULehcASop0FwAAAAAWOefcY2b2PknfNTNPUlzSWyUNSTo7va9Lqb5BknSVpM+lQ56nJb0pvf1KSZ83sxvS17h0AW8DQImZc893tiAAAAAAoJTMbNA5V1PqcQBYGigHAwAAAAAAKAPMBAIAAAAAACgDzAQCAAAAAAAoA4RAAAAAAAAAZYAQCAAAAAAAoAwQAgEAAAAAAJQBQiAAAAAAAIAyQAgEAAAAAABQBgiBAAAAAAAAygAhEAAAAAAAQBkgBAIAAAAAACgDhEAAAAAAAABlgBAIAAAAAACgDBACAQAAAAAAlAFCIAAAAAAAgDJACAQAAAAAAFAGCIEAAAAAAADKACEQAAAAAABAGSAEAgAAAAAAKAOEQAAAAAAAAGWAEAgAAAAAlgEzC5V6DGPStWQAACAASURBVAAWN0IgAAAAAFiizOwZM7vezB6WNGRmLzazn5nZITP7tZltzTq20cz+zcw6zeygmd1dupEDKAWSYgAAAABY2t4g6X9ICiQ9LOlKSd+WdKGkO81sk3OuW9JXJQ1KOin953mlGS6AUjHnXKnHAAAAAAB4HszsGUk3OOe+ZGbXSzrZOXdl1v7vSPp/kr4r6TlJTc65gyUZLICSoxwMAAAAAJa2Z9N/Hivp0nQp2CEzOyTpxZLaJK2R1EcABJQ3ysEAAAAAYGmbKO94VtJXnXN/lX+AmbVJajSzFc65Qws6OgCLBjOBAAAAAGB5uE3Sn5rZn5iZb2YVZrbVzFY752KS7pf0WTNrMLOwmZ1f4vECWGCEQAAAAACwDDjnnpV0saS/k9St1Mygd2nyfd+VkuKSnpDUJekdJRgmgBKiMTQAAAAAAEAZYCYQAAAAAABAGZgxBDKzL5lZl5n9Zor9ZmafMbMnzexhMztz7ocJAAAAAACAozGbmUC3Sto2zf5XSdqQ/rpG0r8c/bAAAAAAAAAwl2YMgZxzP5HUN80hF0v6ikv5haQV6eUHAQAAAAAAsEiE5uAaxyjVdX7CvvS2WP6BZnaNUrOFVF1dfdamTZvm4OUBAMBitGfPnh7nXEupx4Fczc3Nbt26daUeBgAAmCfT/Q42FyHQrDnnbpF0iyRt2bLF7d69eyFfHgAALCAz+0Opx4BC69atE7+DAQCwfE33O9hcrA72nKQ1Wc9Xp7cBAAAAAABgkZiLEGiXpD9PrxL2Qkn9zrmCUjAAAAAAAACUzozlYGZ2u6StkprNbJ+kD0gKS5Jz7nOS7pP0aklPShqW9Kb5GiwAAAAAAACenxlDIOfcG2bY7yS9dc5GBAAAAAAAgDk3F+VgAAAAAAAAWOQIgQAAAAAAAMoAIRAAAAAAAEAZIAQCAAAAAAAoA4RAAAAAAAAAZYAQCAAAYBEysy+ZWZeZ/WaK/WZmnzGzJ83sYTM7c6HHCAAAlhZCIAAAgMXpVknbptn/Kkkb0l/XSPqXBRgTAABYwgiBAAAAFiHn3E8k9U1zyMWSvuJSfiFphZm1LczoAADAUhQq9QAAAADwvBwj6dms5/vS22IlGc3975H2P1KSlwYAYMlZdYr0qo8t+MsyEwgAAGCZM7NrzGy3me3u7u4u9XAAAECJMBMIAABgaXpO0pqs56vT2wo4526RdIskbdmyxc3LaErwaSYAADgyhEAAAABL0y5JbzOzOySdI6nfOVeaUjAAQFlzzikZOAVOCpyTS/8ZOKcgyHrsUscGTko6pyDIO7bIeRP7k85lzg2CItea2J95vWKvm9qfc62s/ckg79iJ/UHhsYFzSgaFxzqn9HWKXCtre0dzta57xcYF/1kRAgEAACxCZna7pK2Sms1sn6QPSApLknPuc5Luk/RqSU9KGpb0ptKMFABmx+WEA4Vv/F0w+WY/yD82yH+jPfk4GRRe16XfoAdHcK2JN+7TXSs/OJjuWtOFDIXXyg0vMiFD9rHzEG64ovc91b0V+36n9pcbzyTPLPXlTT62zHbJ90yWfpy/3/dMpfq2EQIBAAAsQs65N8yw30l66wINB1hQ+W+Os998Zn/6X2ymQfYb+MI34lN9Sl/s0/5ZvG7Wa+e8buZNe5E30BOP88KAZP6b7aDIa02MIVDxoCTzmoXjLfbGvTAcKBZuzPQ9zZ4VUSxAyR1rOckOBLLDAH8iDPAsZ7+ffWxWsFAsRPA8pa9TuN/3vHQAoZzzzUz+FIFF9li8rOv6XpFjzdLH5x47EW4Ufd30a+SOtzBIybxu/r15E9/DvO9TZuzZ36/Zfp9yv//F7i0/uJk4dikjBAIAAMCSNW0JQt4n2bN6Q1z0TX3+9UpfgjDTp/QzXSv73mYqZyj4nk4RfiTzjw2yvqczhC35+8s1LPCt2Bvo7DfieW+gC964Z78Rzz3W8t7Eps73FA3lv+kt9oa4yGvkvDHPfQM+MbacWRGZ8efu96cNJKa6t9yAJPvecq9VGKZMHVg8/2sVBiXLIyzA8kQIBAAAgKP2N7ftUc/g2BSzAyhBmCtTfZI93af0U7+BLpwdUOxTet8zhfNCiKk+pffzrpu9v/DN81QlE8UCi6nuLWt2gFd4bPFP+2cOUIq+bs59TD+unKDETOap8FpZxxIWAFgohEAAAAA4amZSqIQlCDNdq2jIkPWmvnA2QwlKEPLCkux7n7gHAACOBiEQAAAAjtpn/+dZpR4CAACYgVfqAQAAAAAAAGD+MRMIAAAAAACUBRcECoIg/Wcy9Wcymdk++Tj158T+IAjkJv7MPjdre/45OefmvV51Q6M2v+ilC37/hEAAAAAAACxRzrmsYCOpIJkfUiTlkvn7c4OJzLb0uRP7pztn4poTYUfO/iLBx8R1pgxTsoKYzOsni4QqRc7J3Ef+tfMCnSC1bGGpf2SSpPaNmwmBAAAAAACY9s3+TAHCdLMygsKAIPeaRQKEmcKJoMgskpnCiWnGnbt/qkAm67ELSv3jmpJ5njzPk3m+PN9LP/cnt/u+PM+T5/syS+/3i+z3fIUikbz9fvraWduyz51qvxV5bS9vbH7huTmvmb3fK3zt7DFa1nWyr+n5fkl+JoRAAAAAADDPcmZrFC0pme3MimIzPbKvWWwWxAwlLlOcM/01Zxl8THFO5tpTBCOLllkmlCgICKYLJ4oGHyGFItHC0CITfKS2pa4zGaTkBhyT+3NeIxO65IYvhcFG+pqWd+w04y4e6Exuy+w3j1UNFyFCIAAAAADPy5Rv9ovMnJhq/5GXdWRdc4ayj+mDjxnCiaLjLpyVMdNsjcx9LYXZGrOZ3WBTBR/pc0KhmWdOTBswFL7OjDMrio07bxZJwXhnuNei56b/BJYyQiAAAAAgrdhsjdk2+pyxeehMwcdR9cgoDD7yr/O8g49pvheLltm0pSIzl3VMzrTwQ0Vma2Ttz58ZMdVsjWKlMPmzMWYscbHZhymzLnFhtgYw7wIXaDg+rKH4kIYSQxoaH1LYD2tT46YFHwshEAAAAI7ak7t/qfjIcJESl6wAYaZwYtY9MooFI7OclVE0+FhCszXyZ11MNbvBZgg+wuHCWRezCBBm3e9itjMrpimfyb3m9L0/CkpzCDUAHKVEkNBQfEjD8WENxgczj4cSQxocH9RwIhXqDMYHMwFP9uPsr+HEcMH1z2g9Q1951VcW/L4IgQAAAHDUfvDFz2qwr/eIzsk0AT2CUpGpemSEwiGZF81cY6bZGsWahxYNPo5kFslMJS5HGqZknctsDQCY2XhyfNowJucrPSNnKDFUNOwZTY7O6jUjXkTV4eqcr4aKBq2pXaPqcLWqwlWqCdcUPG6qbJrn70ZxhEAAAAA4ape9/6NyToVhynQzPQg1AKCsOec0mhydPqyZIsCZCG2yZ+EkgsSsXrcyVJkJbKpCVaoOV2tl1Up1hDtUHa5WTbhGVeGqgsc5X6HUn2E/PM/fpblFCAQAAICj1tB2TKmHAABYAMX620w1myb/cbHzglmU4ZosM5MmE8BEqrUmuqYwnMn6yp55kx36+F5plmdfDAiBAAAAAABYxua7v00xvvkFoUxNpEYrq1fmBDI1kRpVh6pzZt7kBzgVoQp5tnxWZgvGk3LjSfk1kQV/bUIgAAAAAAAWmVL2t8kOYKbrb1MdSgU7EyVV2aVTUT9admW/LnAKBuNKHh5Tsn9cyYH0n4fHU9sOjyvZPy43mlCko16t/+vUBR8jIRAAAAAAAEdpKfS3mfJrifa3WSjOObmxpJL96SAnL9RJHh5TcHhcycFxKb+6zZP8moi8+qhCTZWKHlcvvy6icGtVSe6FEAgAAAAAUJby+9vM1NMmU0Y1xSwc+tssPS4RKDmQDnbyQp4g67EbL/zZWmVIfl0kFeqsrE49ro/Ir42m/qyLyqsJy7zFMyOKEAgAAAAAsGRk97fJKZeapr/NVF/0t1m+nHMKhuLFQ52ssCcYihee7Jv8+qj82ojC7dWq2NSYCXv8uqj8uoi8uoi8yNIL4AiBAAAAAADziv42mEvBeDIT5gQDkyVZyeyQZ2BcSrqCc72acHrGTlSRNbWZYMerzwp4qkLL9udNCAQAAAAAyFHK/jYTM2rob1N+XNIpGMzruZMV6kyEPW40WXCuRbxMiBPtqJeXN3PHr4/Ir4nIQuU9C4sQCAAAAACWgVL1t8kJZabpb5M/84b+NuXDOSc3mixoppzfZDkYGJfyJ+94kl+bCnNCLZWKrq/PlGr52bN3Kog3ZoPvEgAAAACUyGLub5M/84b+NijGJYLCmTuHJ5dGD9LbXbwwVPSqQvJqU6VZ4VXVuTN30iVbXvXiaqy81BECAQAAAMARmOhvM9vyqJygZh772+SENvS3wVFygVMwHE+FOQOTzZSDnMBnTMFQkVK/kGXCnPAxNarYnFWSlRXyWJiZXwuNEAgAAADAsnak/W2GE8MaHB+c9/42s/qivw3mQTCeLAx18sKeoo2VTfKqw6lyrBVRRdbWFs7cqV3ejZWXOkIgAAAAAItO4AKNJEYKwpipetoULZdK97cZTgwr6Qobyeajvw2WOpd0Sg6mg53+wp47E2GPGyvSWDnqZ8KcaEd9brAzUaZVG5b5lP8tZYRAAAAAAObEbPvbzHY2zmzQ3wblwDknN5Io7LkzkBv2BIPFGitbpolyuKVKFcc35K6cVZ967EWJB8oBP2UAAACgjNHfBigtFw9SYU5WM+WcmTvpkq2pGiv7dRF5dVGF26ozM3f8dLNlvy5CY2XkIAQCAAAAlhD62wBLgwucgqF4bqiTPWtnorHycLHGyl5mhk5kde1kadbEzJ30kukWZuYajgwhEAAAADDPFmN/m6l62uT3t6kKV6kqVKWQx1sHYEIwli7N6s+drTMZ9qQbKwdFGivXhFNhTkNUkWNrs0qyslbNqqSxMuYH/ycHAAAAilhs/W3yZ+FM1d9m4jn9bYAj55KBkgPx1AydiZk7A7lhT/LwNI2V02FOdH19TqjjTZRp1URkPuEOSocQCAAAAMvGrPrbpGfUzHd/m9W1qwuCGvrbAKWR01i56KpZ6dKswXjxxsrpMCe8skoVGxrk16f68GSXaXlRVoPD4kcIBAAAgJKZTX+bnHBmmv42Q/EhxYP4rF63WH+b1qrWWfW3mTiP/jbA4uDiycJQpz8/4BmXEkUaK1eH5NemyrEi7TWTq2ZlmitH5FXRWBnLByEQAAAAjtru/bvVP95ftKfNlP1t0gEP/W0AFJNprDzVzJ3+MQUD40UbK1vYy6yaFVlTm26mPLkc+kSploUomUR54V8/AAAAHLXrf3K9uka6crbR3wbAVILRxGSok26inOxP9+GZ2D4QL95YuTYV5ISaKuV31OeEOhM9eazCp7QSKIIQCAAAAEft0y/7dEHoQ38boPykGiuP55RkBVkzdyaaLLvxIo2VK/xMmBNdvyJVklWXO3PHo7EycFQIgQAAAHDUTm4+udRDADCPnHMKhidn7wRTNFgOhoo0VvYt3V8nqvCqalVsbJgMdSb679RF5EVorAzMN0IgAAAAAChjLp5Mz9opXC0r+7ES+emO5FWHM7N1IqtrU6Va9ZHc5dFprAwsGoRAAAAAALAMucApGIxnhTljRcMeNzJFY+X0DJ3I2lr5dc2TpVkTZVq1NFYGlhpCIAAAAABYQpxzcmPJyT472TN30k2Wg/4xJQfHpfxV0U3ya1NlWKGmSkU76gtm7vj1UVmUxsrAcjSrEMjMtkn6tCRf0r865z6Wt3+tpC9LWpE+5j3OufvmeKwAAAAAsKy5RFZj5fxQJyvsceP56Y5kFaHMEujh1obcpsrp7V5NhNIsoIzNGAKZmS/pZkmvkLRP0kNmtss591jWYe+T9O/OuX8xsxMl3Sdp3TyMFwAAAACWnExj5XSYE+T33OnPaqycz7dMmBNur1bFCQ0FK2d5NFYGMAuzmQl0tqQnnXNPS5KZ3SHpYknZIZCTVJd+XC+pcy4HCQAAAACLVTCeTAc7YzlLo+c3WFaySGPlmnCmBCuypnYy1KmPZFbU8qpClGYBmBOzCYGOkfRs1vN9ks7JO+aDkr5rZn8rqVrSy4tdyMyukXSNJK1du/ZIxwoAAAAACybVWHl86pWz0tvdaJHGyhEv02cnemydvLyZO359RH4NjZUBLKy5agz9Bkm3Ouc+YWbnSvqqmZ3snMspVHXO3SLpFknasmVLYQwOAAAAAPPMOSc3mpwMdfrHlRwoXDkrGBhP1Txk8yYaK0cVaqlUdH19QVNlvy5CY2UAi9JsQqDnJK3Jer46vS3bX0raJknOuZ+bWYWkZkldczFIAAAAAJgNlwiKzNqZnLkTDKT677h4kcbKlaFMmBNeWZ1ppjwZ8kTl1YRprAxgyZpNCPSQpA1m1qFU+HO5pCvyjvmjpAsl3WpmmyVVSOqey4ECAAAAKF8ucAqG44WhTk7gM6ZgqLA0SyHLBDnh9mpVbGpMz9qJyK+dXDnLwjRWBrC8zRgCOecSZvY2Sd9Ravn3LznnHjWzGyTtds7tkvROSV8ws+uUmjB5tXOOci8AAAAAMwrGkzOumpUcKNJY2SSvOpwqwaqPKrK2NtNM2a9LlWz5dREaKwNA2qx6Ajnn7lNq2ffsbe/PevyYpBfN7dAAAAAALGUumW6sPBHmDOSvnDWWbqycLDjXIn5mhk60oz5TpuVNNFWui8qvDct8GisDwGzNVWNoAAAAzDEz2ybp00rNxv5X59zH8vavlfRlSSvSx7wn/eEdMK+cc3IjiSKhTm7YEwwWa6xsqdk6dRGFW6pUcXyDvLq83jv1EXlR3qoAwFzj/6wAAACLkJn5km6W9ApJ+yQ9ZGa70jOwJ7xP0r875/7FzE5Uaub2ugUfLJaVgsbKRWbuBIfHizZW9qpCmdk64VXVecFOujSrmsbKAFAqhEAAAACL09mSnnTOPS1JZnaHpIslZYdATlJd+nG9pM4FHSGWFBc4BUPxwpWz+sfSK2alGysPF2us7GXKsSKrJ/ruRHKXRq+LysKUZgHAYkYIBAAAsDgdI+nZrOf7JJ2Td8wHJX3XzP5WUrWklxe7kJldI+kaSVq7du2cDxSlF4wlC4Od/LBnqsbKNeFUmNMQVeTY2oKZO35dRFZJY2UAWA4IgQAAAJauN0i61Tn3CTM7V9JXzexk51xOnY5z7hZJt0jSli1bWMF1CXFJp+RgdqgzuTR6qu9OurHyWJHGylE/E+ZEO+pzZu54EyFPDY2VAaCcEAIBAAAsTs9JWpP1fHV6W7a/lLRNkpxzPzezCknNkroWZIR43jKNlfOXQc+euXN4TMFgvHhj5fQMnfDKKlVsaJgMdTKlWTRWBgAU4l8GAACAxekhSRvMrEOp8OdySVfkHfNHSRdKutXMNkuqkNS9oKNEARcPCsKc/Jk7ycPjUmKqxsqpJdDDbdV5wQ6NlQEAR4cQCAAAYBFyziXM7G2SvqPU8u9fcs49amY3SNrtnNsl6Z2SvmBm1yk1X+Rq5xzlXvMkp7FykZk7wcTKWVM1Vq5PN1ZeUzsZ6tRnBTy1ERorAwDmFSEQAADAIuWcu0+pZd+zt70/6/Fjkl600ONajoKxRHp1rNxl0LNn7iQHxqWgWGPl9EpZDRWKrKvPnblTH0mFOzRWBgAsAoRAAAAAWLZcMlByIJ4pyQryy7QOp5ZGd+NFGitX+JkwJ7q+PifUmSjT8moiMp9wBwCwNBACAQAAYMlxzikYTuSUYSX7x9J9d7Jm8wwVaazsWyrIqYsovKpaFRsbMmGPlzWLx4v6Jbk3AADmCyEQAAAAFhUXT2Zm6CQHxgrKtCYeK1HY/sirDmUaKEeOqU2vmjXZVNmvi8irorEyAKA8EQIBAABgQbjAKRiMZ4U5k+VY2QGPGylsrGxhT359VF5tRJG1tfLrmnOWQ58IeSxEY2UAAKZCCAQAAICjFowmcpdDL7Zy1sC4lL8qukleujQr1FSpaEd9uu9O7spZVuHTWBkAgKNECAQAAICjtv/ju1P9d7JYRSgV4tRHFG5tKJi149dH5FXTWBkAgIVCCAQAAICjVv+qDlnIUj130iGPF6GxMgAAiwkhEAAAAI5a9ZaVpR4CAACYAZ3zAAAAAAAAygAhEAAAAAAAQBmgHAwAAAAAAGCeuWRSyb4+JXp6JOdUceKJCz4GQiAAAAAAAIDnKRgfV7K7W4nubsXTfya6u5Xs6VGia/J5oq9PSiYlSZVnnKF1t/+/BR8rIRAAAAAAAEAW55yCoaHcEKe7W4mevOfdPQr6+wsv4HnymxoVamlRqKVF0RM3K9TcnHkeWb164W9KhEAAAAAAAKBMuCBQ8uDByRCnq1uJnp68YCe1zY2MFJxvkchksNNxnKrPPjvzfOLLb25WqKlJ5vsluMPpEQIBAAAAAIAlzY2P54Y5+aVYE1+9vZmSrGxebW0qxGluVuWpp06GOq0tme2hlhZ5dXUysxLc4dwgBAIAAAAAAItScnBIie6uyR47+aFOuiQreehQ4clm8puaMgFO9IQT8mbtNGcCHq+ycuFvrgQIgQAAAAAAwIJxQaDkoUMzlGOlwh03PFxwvoXD8tMBTvjYY1W5ZctksNPcrFBLa+pxU6MsROyRje8GAAAAAAA4ai4eTwU6E6FOQVPlnsyfSiQKzveqqzNhTuVJJ0+WY2U1VA61tMirr1/SJVmlRAgEAAAAAACmFAwNFZ+tkzeLJ3nwYNHz/casVbKOP34y1GltyZnB41VVLfCdlR9CIAAAAAAAyoxzbrIkK+8rmddUOShSkqVwOBPmhNesUeUZZxSskhVqbVGosVEWDi/8DaIoQiAAAAAAAJYJl0go0dtbpBQr1WMnuzRL8XjB+V5V1eSsnRM3q7rl/MJwp6VFfn29zPNKcIc4GoRAAAAAAAAscsHIyLSlWDklWc4VnO83NGTKrqIdHbmlWNlLoFdXl+DusFAIgQAAAAAAKAHnnIL+/sLmyUUaKgeDg4UXCIVS4U1zs8Lt7ao87bTC5c9bWhRqapJFIgt/g1h0CIEAAAAAAJhDqZKsvnSI01W8qXJ3t5LdPXJFSrKssnKyJGvTJlXnzdaZmMXjr1hBSRaOCCEQAAAAAACzEIyOZoU4RYKddNiT7O0tXpJVX58JcKLr1qWCnLzlz0MtrfJrKMnC/CAEAgAAAACULeecgsOHc0KcYuVYie5uBQMDhRfwfYWamlKrZK1cqcqTT84txZpopNzcLI+SLJQYIRAAAAAAYNlxyaQSvb2p5c6LlGJlZvL09MiNjRWcbxUVkyVZGzao+txzc5c+b8kqyfL9EtwhcOQIgQAAAAAAS0YwNpYOcLqKztbJ9Nvp7ZOCoOB8r74+M1On8swziy5/HmpplldTIzMrwR0C84cQCAAAAABQUs45BYOD05ZiTXwFhw8XXsDzMiVZoZYWVZx4YpHlz1tT4U40uvA3CCwShEAAAAAAgHnhgkDJvr6pS7Gygh43OlpwvkWjmRAnetxxqj7nnMlSrKyGyn5jIyVZwCwQAgEAAAAAjkgwPq5kOsCJd3fn9t3JnsnT1yclkwXne7W1mQCn8vTTc8qwsmfweLW1lGQBc4gQCAAAAACQKskaGsoNcXoKGyonu3uU7O8vvICZ/ExJVrOimzelZ+xklWW1pmbweBUVC3+DAAiBAAAAAGA5c0Gg5MGDU5diZfXdcSMjBedbODy5SlZHh0Jnny2/uXAJ9FBjoyzEW0xgMeO/UAAAAABYgtz4+GTT5Ik/uwqDnURvr5RIFJzv1dRMlmSdckqqx05rS0FDZa++npIsYA45FygeP6hIpGnBX5sQCAAAAAAWkeTgkBLdXbl9dorM4kkeOlR4spn8xsbJmTsbNuSGOq2TDZW9ysqFvzmgjI2MPKfY/jsVi+1UVeU6nXHGVxZ8DIRAAAAAADDPXBAo2d9fuPx5Qd+dHrnh4cILhMOZpsnhtWtVedaZeTN20gFPY6MsHF74GwRQVDI5qu7u7yoW26m+gz+TJDU2nKe29ktLMh5CIAAAAAB4nlw8rkRvb+HKWPmzeHp7pXi84HyvujozM6fypJNygp3svjv+ihWUZAFLhHNOAwOPqDO2UwcO7FIiMaCKitXq6LhWbav+TJWVx5RsbIRAAAAAAJAnGB6ethQrU5J18GDR8/2GhsmSrPXrp14Cvapqge8MwHwZH+/V/gO7FOvcocGh38rzompt2aa2tkvU0PBCmXmlHiIhEAAAAIDy4JxT8tCh3BAne8ZOVqlWUKwkKxTKzNoJr16tyjPOyDRPzmmo3NRESRZQJoIgob6+/1BnbId6en4o5+KqqztNJ5zwIa1s3a5wuK7UQ8xBCAQAAABgSXOJRKokq6t4j53s1bKKlWRZVVVmhk70xM2qbjk/1WMnr6GyX18v80r/ST6A0hse/r06YzsVi31D4+NdCocbtWb1n6ut7XWqqTmh1MObEiEQAAAAgEUpGBmZthRrIthJ9vVJzhWc769YMVmS1dFRUIqV6rvTIr+mugR3B2CpSSSG1NV1vzpjO9Tfv1uSp+amrWprv0TNTRfI8yKlHuKMCIEAAAAALBjnnIL+/sLmyV25wU6iu1vB4GDhBXx/siSrrU2Vp546ufR5ZqWsZoWam2WRxf+GDMDi5pxTf/8edcZ2qKvrPiWTw6qqOk7Hr3+3Vq16raLR1lIP8YgQAgEAAAA4aqmSrL68Uqys3jsTM3l6euTGxwvOt8rKTIAT3bhR1S96Ud6snVTw4zc0UJIFYN6NjR1QLHaXOmM7NDLyjHy/Witbt6ut/RLV1525ZFfrIwQCAAAAMKVgdDQ1M6erWCnWZJlWsq9PCoKC8/36evnpAKdyy1m5wU7zZL8dr7p6yb6pArA8BMG4enp+qM7YDvX2/kRSoBUrzlbHureopWWbQqGlXzpKCAQAAACUGeecgoGB3ECnq7uwRKu7W8HAQOEFPE+hpqZUSVZruKwC4wAAIABJREFUqypPPindXyddpjXRb6elRR4lWQAWucHB36oztkP799+jeLxP0chKrTv2f6mt7XWqquoo9fDmFCEQAAAAsEy4ZFLJvr4i5Vh5TZV7euTGxgrOt2h0spHy8cer+txzizdTbmiQ+X4J7hAA5kY8flgHDnxTnbEdGhh4RGZhtTS/XG3tl6ip8SUyW57/j5tVCGRm2yR9WpIv6V+dcx8rcsxlkj4oyUn6tXPuijkcJwAAAFC2grGxdJDTVdA8OafvTm/xkiyvri4T4FSeeWamsXL28uehlhZ5NTWUZAFYtpwLdPDgz9UZ26Hu7u8qCMZUU7NJGzf8vVauvEiRSGOphzjvZgyBLBV/3SzpFZL2SXrIzHY55x7LOmaDpPdKepFz7qCZLa322AAAAMACc84pGBycthRrYlvQ3194Ac+T39SYCXIqNm8umLETamlVqLlJXkXFwt8gACwSIyP7FIvdqdj+OzU6+pxCoTq1t12mtvZLVFtzUlmF37OZCXS2pCedc09LkpndIeliSY9lHfNXkm52zh2UJOdc11wPFAAAAFgKXBDklWQVD3YS3d1yo6MF51skMlmSdVyHqs85u7Acq7lZoaYmSrIAYArJ5Ki6u7+jztgOHTz4c0mmxsYX6/j171Zz8yvk+9FSD7EkZhMCHSPp2azn+ySdk3fMRkkys58qVTL2Qefct/MvZGbXSLpGktauXft8xgsAAFA2KMlfXILxcSWnKsXqytrW2yslkwXne7W1mSXQK089NbcUK6s8y6urK6tPpQFgrjjnNDDwiDpjO3TgwDeVSAyoomKNjut4h9raXqeKivZSD7Hk5qoxdEjSBklbJa2W9BMzO8U5dyj7IOfcLZJukaQtW7a4OXptAACAZYeS/IXhnFMwNJQOcQqXQE9OBDtd3UoWK8kyk9/UlAlxoieckLX8eXOm106ouVleZeXC3yAAlIHx8V7t33+POmM7NDT0O3leVK0tr1Jb+yVqWHGOzLxSD3HRmE0I9JykNVnPV6e3Zdsn6ZfOubik35vZ75QKhR6ak1ECAACUH0ryj4ILAiUPHkzNzukqXoo18eVGRgrOt3BYfnpVrPCxx6pyy5bJYGei105Li0JNjbIQC+4CwEILgoT6+n6iztgO9fT8UM4lVFd3ujad8GGtXLldoVBtqYe4KM3mX6yHJG0wsw6lwp/LJeVPM75b0hsk/ZuZNStVHvb0XA4UAACgzMxZSf5y4sbHlejtnboUa+Krt1dKJArO96qrJ1fJOvnk9OPCJdC9+npKsgBgERoaelqx2E7F9n9D4+PdCocbtWb1VWpru0Q1NRtLPbxFb8YQyDmXMLO3SfqOUr9cfMk596iZ3SBpt3NuV3rfK83sMUlJSe9yzvXO58ABAAAwu5L8pdCXMRgaKt48OW8WT/LQoaLn+42Tq2RFN2yYnLHT2pJTnuVVVS3wnQEAjlYiMaiurvvVGduh/v49MvPV1HSB2tsuUVPTVnleuNRDXDJmNXfVOXefpPvytr0/67GT9L/TXwAAADh6c1aSX6q+jM45JQ8dyg1yivTdSXT3yA0PF14gHM6EOeG1a1V55plFlkBPr5IV5g0AACwnzjkd6t+tWOcOdXXfr2RyWFVV63X8+uu1atVrFY22lHqISxIFzAAAAIvTkirJ77nlC4o/91xhSVY8XnCsV1WVCXEqTjyxMNhpTs3g8evrZR7NPAGgnIyO7df+2F3qjO3QyMgf5PvVWtm6Xe3tl6qu7gxKdY8SIRAAAMAitNRK8g/t2KFgcDBTdhU97riCUqxMv53q6lIMEQCwSAXBmLp7fqhYbId6e/9DUqAVK85Rx7q3qbV1m3yfUt65QggEAACwSC2lkvz1375f5vulHgYAYAkZGHxCsc4d2n/gHsXjBxWNrtK6Y/9abW2vU1XVulIPb1kiBAIAAMBRIwACAMxGPN6vAwe+qc7YDg0M/EZmEbW0vFztbZeqsfFFMuPfk/lECAQAAAAAAOaNc4H6Dv5Msc4d6u75roJgXDU1m7Vxw/u1atVFCocbSj3EskEIBAAAAAAA5tzIyLOKxe5ULHanRsc6FQrVq7399Wpvu1S1tSeVenhliRAIAAAAAADMiWRyRF3d31Gsc4cOHvqFJFNj44t1/PHvUXPzy+X70VIPsawRAgEAAAAAgOfNOafDAw+nmzx/U8nkoCor1uq4juvU1vZnqqhoL/UQkUYIBAAAAAAAjtj4eI/2779HnbEdGhr6b3lehVpbt6m97VKtWHG2zLxSDxF5CIEAAAAAAMCsBEFCvX0/Vqxzh3p6H5BzCdXVnaFNJ3xEK1f+D4VCtaUeIqZBCAQAAAAAAKY1NPSUYrGdiu2/S+Pj3QqHm7RmzdVqa7tENdUbSj08zBIhEAAAAAAAKJBIDOhA132Kde5Q/+H/kpmvpqYL1N52qZqaXirPC5d6iDhChEAAAAAAAEBSqsnzoUMPKRbboQNd9ysIRlRVdbyOP/49WrXyNYpGW0o9RBwFQiAAAAAAAMrc6GhM+/ffpc7YTo2M/EG+X6NVqy5Se9ulqqs7XWZW6iFiDhACAQAAAABQhoJgTN09P1Csc4d6+x6UFGjFinPUse5v1dr6J/L9qlIPEXOMEAgAAAAAgDIyMPC4OmM7tH//PUokDikaXaV16/5Gbatep6qqY0s9PMwjQiAAAAAAAJa5ePyQ9h/YpVhspwYGHpVZRC0tr1B726VqbDxPZn6ph4gFQAgEAAAAAMAy5FxSfX0/U2dsh7q7vyfnxlVbc5I2bvyAVq28SOHwilIPEQuMEAgAAAAAgGVkZOSP6oztVCz2DY2NxRQKrdAxx1yu9rZLVFt7UqmHhxIiBAIAAAAAYIlLJkfU1fVtdcZ26NChX0oyNTW+RBs2/J1ami+U50VLPUQsAoRAAAAAAAAsQc45HT78a3XGdujAgXuVTA6qsnKtjjvuf6tt1WtVUdFe6iFikSEEAgAAAABgCRkb79H+/XcpFrtTQ0P/Lc+r1MrWV6mt7VKtWPECmVmph4hFihAIAAAAAIBFLgji6u39sTpjO9Tb+yM5l1B93RnatOmjWtn6aoVCtaUeIpYAQiAAAAAAABapoaEn1Rnbof3779b4eI8ikWatWfMmtbddourq40s9PCwxhEAAAAAAloV4PK59+/ZpdHS01EPBIlZRUaHVq1crHA6XeihTSiQGdODAt9QZ26nDh/9LZiE1N12gtvZL1dR4vjxv8Y4dixshEAAAAIBlYd++faqtrdW6devoiYKinHPq7e3Vvn371NHRUerh5HDO6dChX6kztkNdXfcrCEZVXb1Bxx//Xq1a9RpFI82lHiKWAUIgAAAAAMvC6OgoARCmZWZqampSd3d3qYeSMTraqVjsG4rtv1MjI3+U79eobdVr1dZ2ierqTuPvM+YUIRAAAACAZYM3zJjJYvg7EgRj6u7+vjpjO9TX96Akp4YVL1RHx7VqbfkT+X5lqYeIZYoQCAAAAACWmbvvvlsbN27UiSeeKEl6//vfr/PPP18vf/nL9alPfUrXXHONqqqqSjzK8jMw8Fi6yfMuJRKHFI22ad26t6q97XWqrFxb6uGhDBACAQAAAMAyc/fdd2v79u2ZEOiGG27I7PvUpz6lN77xjYRACyQeP6j9+3epM7ZTg4OPyfMiaml+pdraL1Vjw7ky80s9RJQRQiAAAAAAWAJuu+02feYzn9H4+LjOOeccffazn1V9fb2uvfZa3XvvvaqsrNQ999yjp556Srt27dKPf/xjffjDH9add96pD33oQ9q+fbs6OzvV2dmpCy64QM3Nzbryyiv18MMP61Of+pQk6Qtf+IIee+wxffKTnyzx3S5tziXV1/dTdcZ2qLv7+3JuXLW1J2njxg9q1co/VTi8otRDRJnySj0AAAAAAMD0Hn/8cX3961/XT3/6U+3du1e+7+trX/uahoaG9MIXvlC//vWvdf755+sLX/iCzjvvPF100UX6+Mc/rr1792r9+vWZ67z97W9Xe3u7HnjgAT3wwAO67LLL9M1vflPxeFyS9G//9m/6i7/4i1Ld5pI3PPwHPfXUJ/TTn52vvb9+kw4e/LlWH3OFzn7BvTr7Bbu0ZvWVBEAoKWYCAQAAAMAi94Mf/EB79uzRC17wAknSyMiIWltbFYlEtH37dknSWWedpe9973tHdN2amhq97GUv07333qvNmzcrHo/rlFNOmfPxL2fJ5LC6ur6tzthOHTr0S0memppeog0b3qeW5pfJ86KlHiKQQQgEAAAAAIucc05XXXWVbrzxxpzt//AP/5BZ7cr3fSUSiSO+9pvf/GZ99KMf1aZNm/SmN71pTsa73DnndPjwXnXGdujAgW8pmRxUZeWxWn/c/9GqtteqIrqq1EMEiiIEAgAAAIBF7sILL9TFF1+s6667Tq2trerr69PAwMCUx9fW1k65f2Jfc3OzJOmcc87Rs88+q//8z//Uww8/PC/jXy7Gxrq1f/9d6ozt1PDwU/K8Sq1sfbXa2i/Vivoti2L5eWA6hEAAAAAAsMideOKJ+vCHP6xXvvKVCoJA4XBYN99885THX3755fqrv/orfeYzn9HOnTtz9l1zzTXatm1bpjeQJF122WXau3evGhoa5vU+lqIgiKu390fqjO1Ub+8Dci6p+voztXnTjWptfbVCoZpSDxGYNXPOleSFt2zZ4nbv3l2S1wYAAPPPzPY457aUehzIxe9gWM4ef/xxbd68udTDWJK2b9+u6667ThdeeGGph7IgZvN3ZXDovxWL7VQsdpfi8V5FIi1qW/Vnamt7naqr1097LlBK0/0OxkwgAAAAAChThw4d0tlnn63TTjutbAKg6SQSAzpw4F51xnbq8OG9Mgupufllam+7VI2N58vzeAuNpY2/wQAAAABQplasWKHf/e53pR5GSTkX6NChX6kztkNdXd9WEIyqunqDNhz/f7Vq1UWKRJpLPURgzhACAQAAAADKzuhop2KxO9UZu1Ojo8/K92vU1vZnam+7VLW1p9DkGcsSIRAAAAAAoCw4FyiROKzx8R799Gd/KsmpoeFcrT/uOrW0vFK+X1nqIQLzihAIAAAAALCsJZMjiscPKh4/JOeSci6hjnV/q7a2P1Nl5ZpSDw9YMIRAAAAAAIBlJwgSSiQOaTx+UEFyVDJTOFSncLhR0aiv4467ttRDBBacV+oBAAAAAMBycvfdd8vM9MQTT0iSnnnmGZmZ3ve+92WO6enpUTgc1tve9rbMtltuuUWbNm3Spk2bdPbZZ+vBBx/M7Fu3bp16enoyz3/0ox9p+/btkqRbb71Vnufp4Ycfzuw/+eST9cwzz+icc87R6aefrrVr16qlpUWnn366Tj/9dD3zzDPzdfsl5ZxTIjGg4ZE/anDwCY2OxmQyVVS0q6Z6kyor1yoUqpFEvx+UJ0IgAAAAAJhDt99+u1784hfr9ttvz2zr6OjQt771rczzHTt26KSTTso8v/fee/X5z39eDz74oJ544gl97nOf0xVXXKH9+/fP6jVXr16tj3zkIwXbf/nLX2rv3r264YYb9PrXv1579+7V3r17tW7duud/g4tQMjmm0dH9Ghz6rYaHn1EyMahIpFHV1ceruvp4RSJNLO8OiBAIAAAAAObM4OCgHnzwQX3xi1/UHXfckdleVVWlzZs3a/fu3ZKkr3/967rssssy+2+66SZ9/OMfV3NzajnyM888U1dddZVuvvnmWb3u9u3b9eijj+q3v/3tHN7N4uZcUuPjBzU09LSGhn6n8fFu+V6FKivXqqZmkyoq2mn0DOQhCgUAAACw7Px/33xUj3UentNrnthepw/86UnTHnPPPfdo27Zt2rhxo5qamrRnzx41NTVJki6//HLdcccdWrlypXzfV3t7uzo7OyVJjz76qM4666yca23ZskVf/vKXZzU2z/P07ne/Wx/96Ednfc5S5JxTMjmsePygEol+ORfI8yKKRlcqHG6Q54VLPURgUWMmEAAAAADMkdtvv12XX365pFTok10Stm3bNn3ve9/THXfcode//vVHdF2zwh42+duuuOIK/eIXv9Dvf//75zHyxS0I4hob69bQ0H9rePhpJRL9CoXqVVV1nKqrNyoabSUAAmaBmUAAAAAAlp2ZZuzMh76+Pv3whz/UI488IjNTMpmUmemtb32rJCkSieiss87SJz7xCT322GPatWtX5twTTzxRe/bs0cte9rLMtj179mT6BjU1NengwYOZcrG+vr7M4wmhUEjvfOc7ddNNN833rS4I5wIlEgPpWT8DkiTfr1JFxTEKh+tl5pd4hMDSw0wgAAAAAJgDO3fu1JVXXqk//OEPeuaZZ/Tss8+qo6NDzz77bOaYiZCmsbEx59x3v/vduv7669Xb2ytJ2rt3r2699Va95S1vkSRt3bpVX/3qVyVJyWRSt912my644IKCMVx99dX6/ve/r+7u7vm6zXmXTI5qdDSmwcEnNDLyRyWTI4pEWlRdvVHV1esViTQSAAHPEzOBAAAAAGAO3H777br++utztr3uda/TjTfemHl+0kkn5awKNuGiiy7Sc889p/POO09mptraWt12221qa2uTJP393/+9/uZv/kannXaanHPatm2b3vjGNxZcJxKJ6O1vf7uuvfbaOb67+eVcUvH4IcXjB5VMjkgyhUK1CocbFArVFi2HA3DkzDlXkhfesmWLm+iMDwAAlh8z2+Oc21LqcSAXv4NhOXv88ce1efPmUg8Ds5Rq8jykePyg4ol+yTl5XlThSKPCoRXzuqQ7f1ewnE33OxgzgQAAAAAACyYIxlPBT/yggiAuM0/hcIMi4QZ5XiWzfoB5RE8gAACARcrMtpnZb83sSTN7zzTHvc7MnJkx8wrAouRcoHj8kIaHf6/Bwd9qbKxLnhdVZeUa1dRsVmXFMfL9KgIgYJ4xEwgAAGARslTX05slvULSPkkPmdku59xjecfVSrpW0i8XfpQAMDXnnIJgJN3r55CcS8rzwopGWxUON8jzIqUeIlB2ZjUTiE+hAAAAFtzZkp50zj3tnBuXdIeki4sc9yFJN0kaXcjBAcBUgiChsfEeDQ0/qaGhpzQe71MoVKuqqg5VV5+gaHQlARBQIjPOBOJTKAAAgJI4RtKzWc/3STon+wAzO1PSGufct8zsXVNdyMyukXSNJK1du3Yehgqg3DnnlEgMKB4/pETisCQn369URUW7QqEV8jyWdAcWg9mUg2U+hZIkM5v4FOqxvOMmPoWa8hcQAAAAzA0z8yT9o6SrZzrWOXeLpFuk1Opg8zsyAOUkmRzLNHl2LiEzX5FIk8LhBvl+RamHByDPbMrBin0KdUz2AdmfQk13ITO7xsx2m9nu7u7uIx4sAABAGXlO0pqs56vT2ybUSjpZ0o/M7BlJL5S0i7J8oLT279+vyy+/XOvXr9dZZ52lV7/61brlllu0ffv2nOOuvvpq7dy5U5K0detWnXDCCTrttNP0ghe8QHv37s0ct27dOp1yyik69dRT9dKXvlR/+MMfMvt839fpp5+e+frYxz6Wud6WLZP/K9i9e7e2bt2aef6rX/1K559/vk444QSdccYZevOb36x77rlH5557rpxL5cTJZFJnnHGGfvaznxXco3NJjY/3aWjoKQ0N/U7j493y/UpVVq5VTc0mVVS0EQABi9RRrw6W9SnUO2c61jl3i3Nui3NuS0tLy9G+NAAAwHL2kKQNZtZhZhFJl0vaNbHTOdfvnGt2zq1zzq2T9AtJFznndpdmuACcc3rta1+rrVu36qmnntKePXt044036sCBAzOe+7WvfU2//vWv9Za3vEXvelduccUDDzyghx9+WFu3btWHP/zhzPbKykrt3bs38/We90y2b+3q6tL9999f8DoHDhzQpZdeqptuukm//e1v9V//9V/atm2bXvjCF+rYY4/VF7/4RUnSP/3TP2nLli0677zzMveWSAxpZGSfBgef0Ojoc3IuoWh0lWpqNqmqap3C4Xql3h4CWKxm818on0IBAAAsMOdcQtLbJH1H0uOS/t0596iZ3WBmF5V2dACKeeCBBxQOh/XXf/3XmW2nnXaaXvKSl8z6Gueee66ee+65I96X713vepc+8pGPFGy/+eabddVVV+ncc8/NbLvkkku0cuVKffKTn9SNN96oRx99VP/8z/+sm266SUEQ19hYt4aGfqfh4aeVSPQrFKpXVdVxqq7eqGi0RZ4XnvX9ASit2fQEynwKpVT4c7mkKyZ2Ouf6JTVPPDezH0n6P3wKBQAAcHScc/dJui9v2/unOHbrQowJWDLuf4+0/5G5veaqU6RXfWzK3b/5zW901llnHdVLfPvb39ZrXvOaWe0bGRnR6aefnnn+3ve+V69//eslpQKju+66Sw888IBqa2tzxnjVVVcVvX5bW5ve8Y536Nxzz9UnP/kxVVQc1uBgKnTy/SpVVLSkZ/v8/+zdeVxV1f7/8dfiMIjiLCqKpqnhiMfEzLKuQzmUQ94cUm+lea1vP0kzs5vD7fq92mBWertqw620wchEybK0q1dNG6wLRmZpZUpmHJwNEBA4Z/3+AM9XHBAVOAjv5+PB48Fea+21P3tv8Gw/rLW2FnkWuVydNwlkrc01xpz8K5QDeO3kX6GAeGvt+4X3ICIiIiIiUnEZY85bPnLkSLKzs0lPTy+wJhBA9+7dOXLkCCEhIcycOdNbfnI62LlMnz6dWbNmMXv27CLF6XZnMWbMH3n00b8wZMgNuN2ZBAaG5i/yHFSkPkSkbCvKSCD9FUpERERERC4vhYzYKSlt2rTxLvZ8qtq1a3P06NECZUeOHKFOHe+ECpYsWULHjh2ZPHkyDzzwACtWrPDWbdiwgRo1ajBy5Ej+9re/8dxzzxUpnh49ejB9+nS2bNlSIMaEhAQGDhwIgMfjJjf3GDk5R3G7MwGDMYbg4Cvw9696zgSWiFyetGqXiIiIiIhIMejRowcnTpzg5Zdf9pZt27aNw4cPk5yczI4dOwD45Zdf+OabbwpM5YK8kUEzZ85ky5Yt7Ny5s0Cdv78/8+bN44033uDIkSNFjmn69Ok8/fTT3u3o6Ghef/11PvtsA5mZv5J+fAfvvvsG+/cfJKhSGCEhLQFDQEA1JYBEyiElgURERERERIqBMYa4uDjWrVtHs2bNaNOmDVOmTKFBgwa89dZbjB49GqfTyeDBg3nllVeoXr36GX0EBwczadIk5syZc0ZdWFgYw4cPZ8GCBcD/rQl08uvUt4OddMstt3DyzcweTzY1asBrrz3F5MmTiIy8kWs6DWLTpu3UqxdJUGAd/PyKNFlERC5TxlrrkwNHRUXZ+HitHS0iIlJeGWMSrLV6W2gZo2cwKc927NhBq1atfB1GmWKth9zcVHJyjpKbmw6Av38IAQE18fevVmFf6a6fFSnPCnsGU5pXRERERESkHLHW4vFkkp1zlNyc37HWjZ9fAEFBdQkIqImfX6CvQxQRH1ESSEREREREpBzweHLJyT1GTvZRPJ4sMIYA/+r5b/eqojV+RERJIBERERERkcuVtZbc3LT86V5pgMXhCKZSpQb4+9fAz8/h6xBFpAxREkhEREREROQy43afICfnKDk5R7E2F2McBAbWzh/1U8nX4YlIGaUkkIiIiIiIyGXAWjc5Ob+Tk3MUtzsDAH//qvmLPFetsIs8i0jRKQkkIiIiIiJSRllrcbsz8qd7/Y61Hvz8gggKqk9AQA38/AJ8HaKIXEaUKhYRERERESkmDocDp9NJmzZtaN++Pc8++ywej6dAm9tuu41rr70WgI8//hin04nT6SQkJISIiAicTid33vkn/v3vOGrUqMbVV3ekc+ebuP76oXzxxS9UqdKCoKBQJYBE5IJpJJCIiIiIiEgxCQ4OJjExEYADBw4wYsQIUlNT+d///V8Ajh07RkJCAiEhIezevZvevXvTu3dvALp168ZTT80gMrIJublpbN78X667rhMrVy4nIKAaxmiRZxG5NBoJJCIiIiIiUgLq1q3Lyy+/zPz587HWArBixQr69+/PHXfcwTvvvAOA251FVlYybncGWVkpuN2ZBAaGUqlSOA5HZQIDayoBJCLFQiOBRERERESk3Jn91Wx2HtlZrH22rNWSv1zzlwva58orr8TtdnPgwAHq1atHTEwMjz32GKGhdRg8+HYmTBiK250JGIxxUKlSGCEhLTHG4HDsZPPmzTidTm9/y5cvp1mzZsV6XiJScSgJJCIiIiIiUgpSUlL46acf6dixMbnuNBwOy3ff/YDT2Rl//xr4+QXh718FY4x3nxtuuIFVq1b5MGoRKU+UBBIRERERkXLnQkfslJTdu3fjcDioXt0yf/58jh49QqtW1wF+pKUd5733PuOaa271dZgiUkFoTSAREREREZFiZq2H5ORdjB17N2PHDiE7+yCxsatZtWo5SUm/8ssvv5KQkMDSpUt9HaqIVCAaCSQiIiIiIlIMrLVkZmYS2b4NOdkn8Pd3cMcdA3nooYm4XGn8+msKXbve7J3u1bRpU6pXr86XX35J586dz9rn6WsCTZ8+ncGDB5fK+YhI+aMkkIiIiIiIyCXweHLJyTlGTs4Rjh5NBGMI8K9OQEBNHI68NX6uvDKM33777Yx9t27d6v1+48aNBeq6devG77//XtLhi0gFoiSQiIiIiIjIBbLWkpubRk7OUXJz0wCLwxFMpUoNCAiooVe6i0iZpCSQiIiIiIhIEbndJ8jJOUpOzlGszcUYfwIDa+eP+qnk6/BERAqlJJCIiIiIiEghrHWTk/M7OTlHcbszAPD3r0ZAQE38/UMwRu/bEZHLg5JAIiIiIiIip7HW4nZn5E/3+h1rPfj5BREUVJ+AgBr4+QX4OkQRkQumJJCIiIiIiEg+jyfHO93L48nGGD/8vYs8V/a+2UtE5HKkJJCIiIiIiFRo1npOW+QZHI4qVKpUl4CAalrkWUTKDSWBRERERESkQnK7M/NH/RzDWjfGL4DUdvBzAAAgAElEQVTAoFAC/GvicAT5OjwRkWKnFcxERERERKTC8Hhyyc4+zPHjuzh+fBfZ2Udw+IdQuXITQqpEUCmo/iUlgBwOB06n0/v11FNP4Xa76dixI5s2bfK269WrF8uWLQOgSZMmtGvXjsjISHr16kVKSkqB8pN9jR8/HoBRo0bRsGFDTpw4AcChQ4do0qRJ/vl5GD9+PG3btqVdu3Z06tSJPXv2FNrf2YwaNYqmTZvSvn17rrrqKu666y727dvnrS+sr2eeeYaWLVvidDrp1KkTb7zxBgDdunUjIiKC9u3b06lTJxITE4vUX25uLqGhoTz66KMFYly1ahUdOnSgffv2tG7dmpdeegmAGTNm0LBhwwL34dixY0W5fSLlnkYCiYiIiIhIuZa3yHN63qif3FSwFj9HJSpVCsPfvwZ+fsX336Lg4OACyY2TFi5cyNixY0lISCA2NhY/Pz+GDBnird+wYQN16tRh6tSpPPHEEzz//PMFyk/ncDh47bXXuP/++wuUL126lOTkZLZt24afnx/79u2jSpUqZxynKObMmcPgwYOx1jJv3jx69OjB9u3bCQwMPGdfL774ImvXruWrr76iWrVqpKamEhcX561fsmQJUVFRLFq0iMmTJ7N27drzxrZ27Vquuuoqli1bxpNPPokxhpycHO69916++uorwsPDOXHiBElJSd59Jk6cyMMPP1yk8xSpSJQEEhERERGRcifliSfI2rEDa3OwnlwsFjD4GX+Mnz/mIiZFBLVqSf2pUy8qns6dO9OlSxdmzJjB22+/XSD5caobb7zRmwAqzIMPPsjcuXMZO3ZsgXKXy0VYWBh+fnnnFx4eflHxnsoYw8SJE4mLi2P16tUMHDjwnG2feOIJNm7cSLVq1QCoVq0ad9999xntunTpwpw5c4p0/JiYGCZMmMALL7zAF198wXXXXUdaWhq5ubnUrl0bgKCgICIiIi7i7EQqFk0HExERERGRcsNaD9k5R8nNScXtzsDjyQHjh59fEA5HZfz8Ai8qAVRUmZmZBaYhLV261Fv35JNPMm/ePEaMGEHz5s3Puv+qVato166dd7t79+7evubOnestb9y4MV27duXNN98ssP/QoUP54IMPcDqdTJo0ia+//rpA/bn6K4qrr76anTt3nrOv1NRU0tLSuPLKK8/b15o1a7jtttvOG1tWVhbr1q2jf//+DB8+nJiYGABq1arFgAEDuOKKKxg+fDhLlizB4/F4+5o7d663r+7du1/QeYqUZxoJJCIiIiIilzVrLalp28jJySA9fQfWeqgxaTQBATUJCKiBn19gqcVyrulgAJs2baJ69eps3779jLru3bvjcDiIjIxk1qxZ3vLCpm9NmTKFgQMHcuutt3rLwsPD+eGHH1i/fj3r16+nZ8+eLFu2jJ49e563v/Ox1hbYPr2v1NTU8/YxcuRIsrOzSU9PP+M6nS22VatW0b17d4KDg7n99tuZOXMm8+bNw+Fw8Morr/Dtt9+ybt06nnnmGdauXcvixYsBTQcTOReNBBIRERERkctSdvYh9u59lS+/6kt8/B9xuzPw969G5cpNqVLlKoKC6pZqAqgwx48f55FHHmH9+vUcOHCAjz76qED9hg0bSExM5I033qBGjRpF6rNFixY4nU7efffdAuVBQUH07duXOXPmMHXqVN57771iOYevv/6aVq1anbO+WrVqhISEsHv37nO2WbJkCbt37+buu+/mgQceOO8xY2JiWLduHU2aNKFjx44cPnyY9evXe+vbtWvHxIkTWbt2LcuXL7+wExKpgJQEEhERERGRy4bHk8vBQ/9h27b/4dPPruenXU/gcITQMuJxgoLqExzcCH//EIwxvg61gL///e8MHTqUli1bsnDhQiZOnEhWVtYl9ztt2jSeeeYZ7/bWrVtJTk4G8t4Utm3bNq644opLOoa1lueffx6Xy0WfPn0KbTtlyhTGjRvnHRWUnp7ufTvYScYYZs6cyZYtWwpMLztdamoqmzdvZu/evSQlJZGUlMSCBQuIiYkhPT2djRs3etsmJiZe8nmKVASaDiYiIiIiImXe8eM/43LF4kqJIzv7IIGBdWjUaDQNwgZTpUre+jqpqTt8HOX/rQl0Up8+fbjzzjuJi4vjm2++AaBDhw707t2b2bNn87e//a3Q/k5OEwOIjIw8I6HSpk0brr76arZu3QrAgQMHGDt2rPf18ddccw3R0dFF7u9UkydPZubMmWRkZHDttdeyYcMG75vBztXX/fffT3p6Op06dSIgIICAgAAmTZp0Rt/BwcFMmjSJOXPm8Oqrr561v549e9KjRw+CgoK8+w0cOJBHHnmEuXPn8vTTT3PfffcRHBxMlSpVvFPBIG9NoLfeesu7/d5779GkSZNznqtIRWFOn9dZWqKiomx8fLxPji0iIiIlzxiTYK2N8nUcUpCeweRykpubxv4DH+FyxfL771sxxkHt2t1pEDaE2rX/gJ9fQIH2O3bsKHS6kshJ+lmR8qywZzCNBBIRERERkTLDWsuxY//F5VrG/gOr8XgyqVy5Oc2bT6F+/dsICry4RY1FRERJIBERERERKQOyslykpMSR7IolM/MXHI4Q6tcfSIOwIVSr1r7MrfFTXowbN47PPvusQNmECRMYPXq0jyISkZKkJJCIiIiIiPiEx3OCg4f+gyt5GYePfAp4qFGjM02bPEDdun1wOIJ9HWK5t2DBAl+HICKlSEkgEREREREpVWlpO0h2LSMlZSW5uccICgqjSZP/R4Ow2wkObuzr8EREyi0lgUREREREpMTl5BwjZf/7uFyxpKV9hzGBhIbeTIOwIdSqdR3GOHwdoohIuackkIiIiIiIlAhr3Rw58jnJrmUcPLgWa7OpWrUNV101g/r1+hMQUMPXIYqIVChKAomIiIiISLHKzNxLsisWl2sFJ0648PevQcOGw2kQNpiqVVv7OjwRkQrLz9cBiIiIiIjI5c/tzsTliiNh6wg+/6I7SUkvEFKlBW3b/pMbun5OxFWPVYgEkMPhwOl0er+eeuop3G43HTt2ZNOmTd52vXr1YtmyZQA0adKEdu3aERkZSa9evUhJSSlQfrKv8ePHAzBq1CgaNmzIiRMnADh06BBNmjQBwOPxMH78eNq2bUu7du3o1KkTe/bsKbS/sxk1ahSxsbEAdOvWjaioKG9dfHw83bp1825/9dVX3HjjjURERNChQwf+/Oc/k5GRweLFiwkNDcXpdNKyZUvmzp3r3WfGjBk0bNiwwLU6duyYt/7BBx+kYcOGeDweb9n+/fvp168f7du3p3Xr1txyyy0AJCUlERwcXKCvN954o4h3TKRi0UggERERERG5KNZaUlO/Idm1jP37V+F2pxMc3JhmV06ifv1BVKoU5usQS11wcDCJiYlnlC9cuJCxY8eSkJBAbGwsfn5+DBkyxFu/YcMG6tSpw9SpU3niiSd4/vnnC5SfzuFw8Nprr3H//fcXKF+6dCnJycls27YNPz8/9u3bR5UqVc44zoU6cOAAq1evpm/fvgXK9+/fz5AhQ3jnnXfo0qULALGxsaSlpQEwbNgw5s+fz+HDh4mIiGDw4ME0atQIgIkTJ/Lwww+fcSyPx0NcXByNGjXik08+oXv37gA89thj3HzzzUyYMAGAbdu2efdp1qzZWa+7iBSkJJCIiIiIiFyQE9mHSEmJw+VazvHjP+HnF0y9un0JCxtCjRqdMMb4OkQ2v/sjh35NL9Y+6zQK4YahV13Uvp07d6ZLly7MmDGDt99+m7Vr15613Y033uhNABXmwQcfZO7cuYwdO7ZAucvlIiwsDD+/vEkf4eHhFxXv6SZPnszjjz9+RhJowYIF3H333d4EEMDgwYPP2L927do0b94cl8vlTQKdy8aNG2nTpg3Dhg0jJibGmwRyuVz06tXL2y4yMvJSTkmkQtJ0MBEREREROS+PJ4eDB9fxzbb7+Oyz69m16yn8HSG0bPkEN3T9gtat51Cz5jVlIgHkS5mZmQWmJS1dutRb9+STTzJv3jxGjBhB8+bNz7r/qlWraNeunXe7e/fu3r5OnU7VuHFjunbtyptvvllg/6FDh/LBBx/gdDqZNGkSX3/9dYH6c/V3Pl26dCEwMJANGzYUKN++fTsdO3Y87/579+4lKyurQOJm7ty53lhOJnoAYmJiGD58OIMGDeLDDz8kJycHgHHjxjFmzBi6d+/O448/TnJysnefn3/+ucB137x5c5HPTaQi0UggERERERE5p+PHd5HsiiUlJY7s7EMEBtahcaN7CAsbTJUqzXwd3jld7IidS3Wu6WAAmzZtonr16mzfvv2Muu7du+NwOIiMjGTWrFne8sKmb02ZMoWBAwdy6623esvCw8P54YcfWL9+PevXr6dnz54sW7aMnj17nre/85k+fTqzZs1i9uzZRd5n6dKlbNq0iZ07dzJ//nwqVarkrTvbdLDs7Gw++ugjnnvuOapWrUrnzp35+OOP6devH71792b37t2sWbOG1atX06FDB++11HQwkaJREkhERERERArIzU1j//4PSXbFkpr6Ncb4U6d2d8IaDKF2rRvx8wvwdYiXnePHj/PII4+wfv16Ro8ezUcffeRd2BguLjnTokULnE4n7777boHyoKAg+vbtS9++falXrx7vvfeeNwl0KXr06MH06dPZsmWLt6xNmzYkJCQwcODAs+5zck2g+Ph4evXqxYABA6hfv/45j/Hxxx9z7Ngx72iojIwMgoOD6devHwC1atVixIgRjBgxgn79+rFp06YijUQSkTyaDiYiIiJSRhlj+hhjfjDG7DLGPHqW+oeMMd8bY7YZY/5jjLnCF3FK+WCt5ejRL/nu+4fZ/Om17PxhGm53Oi2aT6Xr9Z8RGfkioXV6KgF0kf7+978zdOhQWrZsycKFC5k4cSJZWVmX3O+0adN45plnvNtbt271TpPyeDxs27aNK64ovn8apk+fztNPP+3djo6O5vXXX+fLL7/0lq1YsYL9+/cX2C8qKoo777yTf/zjH4X2HxMTwyuvvEJSUhJJSUns2bOHtWvXkpGRwfr168nIyAAgLS2Nn3/+mcaNGxfbuYlUBBoJJCIiIlIGGWMcwALgZmAf8F9jzPvW2u9PafY1EGWtzTDG3A88DQwr/WjlcpaVlYzLtQJXynIyM/ficIQQVn8QYQ2GUK1qZIVf4+dCnVwT6KQ+ffpw5513EhcXxzfffANAhw4d6N27N7Nnz+Zvf/tbof2dnCYGeQshn/7q8zZt2nD11VezdetWIO8tXmPHjvW+Pv6aa64hOjq6yP2dzy233EJoaKh3u169erzzzjs8/PDDHDhwAD8/P2688Ub69Olzxr5/+ctfuPrqq5k6dSqQtybQW2+95a1/++23WbNmDS+++KK3rEqVKnTt2pUPPviAvXv3Eh0djb+/Px6Phz//+c906tSJpKQk75pAJ91zzz2MHz/+gs5NpCIw1lqfHDgqKsrGx8f75NgiIiJS8owxCdbaKF/HcbkyxnQBZlhre+dvTwGw1j55jvYdgPnW2usL61fPYALg8Zzg4MF1JLuWceTIp4ClZs0uhIUNpm5obxyOYF+HeFF27NhBq1atfB2GXAb0syLlWWHPYBoJJCIiIlI2NQR+PWV7H9C5kPZjgNUlGpFc9tLSvifZtYyUlPfJzT1GUFAYTZtEExZ2O8HBhb+2W0RELn9KAomIiIhc5owxfwKigD+co/5e4F5A62dUQDk5R0lJeZ9kVyzp6d/j5xdIaJ1ehDUYQq2aXcibeSgV1bhx4/jss88KlE2YMIHRo0f7KCIRKUlKAomIiIiUTb8Bpw7NCM8vK8AYcxMwDfiDtfbE2Tqy1r4MvAx508GKP1Qpa6x1c+TIZyS7lnHw4DqszaZq1bZEXPW/1KvXn4CA6r4OUcqIBQsW+DoEESlFSgKJiIiIlE3/BVoYY5qSl/y5AxhxaoP8dYBeAvpYaw+UfohS1mRk/IIrZTku13JOnEghIKAm4Q1HEBY2mKpVtf6JiEhFpySQiIiISBlkrc01xkQDHwMO4DVr7XfGmL8D8dba94E5QAiwLP8NTnuttQN8FrT4hNudwYEDa0h2xXLs2JeAH7Vr30iLFtMJrdMDP78gX4coIiJlRJGSQMaYPsA/yHsAecVa+9Rp9Q8BfwZygYPAPdbaX4o5VhEREZEKxVr7EfDRaWWPnfL9TaUelJQJ1lpSUxNJdi1j//4PcbvTCQ6+gmZXPkz9sEFUCqrv6xBFRKQMOm8SyOStFLcAuJm8t1L81xjzvrX2+1OafQ1EWWszjDH3A08Dw0oiYBERERGRiurEiYOkpMSR7IolI+NnHI7K1A3tS1iDIdSoHkX+iDAREZGz8itCm2uAXdba3dbabOAdYOCpDay1G6y1GfmbW8hbuFBERERERC6Rx5PDwYNr+WbbfXz2+fXs+nk2AQE1aNXyKbpe/wWtWz9NzRqdlAAqIxwOB06n0/uVlJTExo0b6devHwD79++nX79+tG/fntatW3PLLbd49/3xxx+55ZZbaNGiBVdffTVDhw5l//793voHH3yQhg0b4vF4vGWLFy8mOjr6vHGNHDmSF154wbv95ZdfEhkZSU5ODk2aNKFdu3bemMePH+9tl5ubS2hoKI8++miB/rp160ZERATt27enU6dOJCYmXvjFEpFSV5TpYA2BX0/Z3gd0LqT9GGD12Sr0elIRERERkaJJP/4TLlcsLlccOTmHCQwMpXGjPxMWNpgqVa70dXhyDsHBwWckRJKSkrzfP/bYY9x8881MmDABgG3btgGQlZXFrbfeynPPPUf//v0B2LhxIwcPHqRevXp4PB7i4uJo1KgRn3zyCd27d7+guJ577jm6dOnC4MGDqV27NtHR0SxcuJCAgAAANmzYQJ06dc7Yb+3atVx11VUsW7aMJ598skCyccmSJURFRbFo0SImT57M2rVrLygmESl9xbowtDHmT0AU8Iez1ev1pCIiIiIi55abm8b+/atIdsWSmpqIMf7UqdOTBmGDqVXrRvz89F6Xotqw+GUO/LK7WPuse8WVdB917yX14XK56NWrl3c7MjISgLfffpsuXbp4E0CQN9rmpI0bN9KmTRuGDRtGTEzMBSeB6tWrx8MPP8wjjzxCp06diIyMpGvXrufdLyYmhgkTJvDCCy/wxRdfcN11153RpkuXLsyZM+eC4hER3yjKp8hvQKNTtsPzywowxtwETAP+YK09UTzhiYiIiIiUb9Z6OHbsK5JdyzhwYA0eTxZVqrSgRfNp1K8/gMDAM0dnSNmVmZmJ0+kEoGnTpsTFxRWoHzduHMOGDWP+/PncdNNNjB49mgYNGrB9+3Y6dux4zn5jYmIYPnw4AwcOZOrUqeTk5HhH8RTV//zP//D666+zceNG4uPjC9R1794dh8MBwN13383EiRPJyspi3bp1vPTSSxw7doyYmJizJoHWrFnDbbfddkGxiIhvFCUJ9F+ghTGmKXnJnzuAEac2MMZ0AF4C+lhrDxR7lCIiIiIi5UxWVjIu13KSXcvJyvoVhyOEsLA/0iBsCFWrttMaP5foUkfsXKyzTQc7Ve/evdm9ezdr1qxh9erVdOjQge3btxfaZ3Z2Nh999BHPPfccVatWpXPnznz88cfedYaKys/Pj/vuu4/4+Hhq165doO5s08FWrVpF9+7dCQ4O5vbbb2fmzJnMmzfPmywaOXIk2dnZpKena00gkcvEeZNA1tpcY0w08DF5r4h/zVr7nTHm70C8tfZ9YA4QAizL/7Daa60dUIJxi4iIiIhcdtzuExw6tJZkVyxHjnwKWGrWvI5mV04kNLQXDkewr0OUUlCrVi1GjBjBiBEj6NevH5s2baJNmzZ88sknZ23/8ccfc+zYMdq1awdARkYGwcHBF5wEgrxEkJ9fUd4PlDf66NNPP6VJkyYAHD58mPXr13PzzTcDeWsCdezYkcmTJ/PAAw+wYsWKC45HREpXkSYVW2s/Aj46reyxU76/qZjjEhEREREpN9LSviPZtYyUlPfJzf2dSkENaNrkAcLC/khwcKPzdyDlxvr167n22mupXLkyaWlp/PzzzzRu3JhbbrmFJ598kg8//JBbb70VgE2bNlGrVi1iYmJ45ZVXGD58OADHjx+nadOmZGRkFHaoS5KamsrmzZv59ddfCQoKAmDRokXExMR4k0AAxhhmzpxJs2bN2LlzJy1btiyxmETk0mllORERERGREpCTc5SUlJUku2JJT9+Bn18goaF9aBA2mJo1u2BM0UZjSPmSkJBAdHQ0/v7+eDwe/vznP9OpUycgb/rVgw8+yIMPPkhAQACRkZHMnj2bNWvW8OKLL3r7qFKlCl27duWDDz4A8l4T/95773nrt2zZQnh4+AXFdeqaQJGRkfTs2ZMePXp4E0AAAwcO5JFHHuHEiYJLwAYHBzNp0iTmzJnDq6++emEXRERKlbHWNy/pioqKsqcvRiYiIiLlhzEmwVob5es4pCA9g5Usa90cOfIpya5YDh5ch7XZVK3ajgZhQ6hXrx8BAdV9HWK5tmPHDlq1auXrMOQyoJ8VKc8KewbTSCARERERkUuUkZGEyxWLKyWOEydSCAioSXj4SMLCBlM1RNNjRESkbFASSERERETkIrjdGRw4sJpkVyzHjn0F+FG79h+4qsVfqVOnB35+gb4OUSqozp07nzFl68033/QuLC0iFZeSQCIiIiIiRWSt5ffUrbiSY9l/4EPc7uMEBzeh2ZWTCQsbRFBQPV+HKMKXX37p6xBEpIxSEkhERERE5DxOnDhASkocya5YMjJ243BUpm7dW2kQNpjq1TtijPF1iCIiIuelJJCIiIiIyFl4PDkcPryBZFcshw9vxFo31atH0arlfdSt2xd//yq+DlFEROSCKAkkIiIiInKK9PQf8xd5fo+cnMMEBtalceOxNAgbTOXKTX0dnoiIyEXz83UAIiIiIiK+lpubxr7f3ua/8X/ky6/68uu+N6hRoxPtI1/h+us207zZZCWApEgcDgdOp5O2bdsyZMgQMjIyCpSf/HrqqacA6NatGxEREbRv355OnTqRmJjo7Wv16tVERUXRunVrOnTowKRJkwCYMWMGDRs2LNDfsWPH2LhxI8YYPvjgA28f/fr1Y+PGjQwaNAin00nz5s2pXr26d7/PP/8cAKfTyR133FHgXHJzc5k6dSotWrTwtn/88cfPONfTz0lEyi6NBBIRERGRCslaD0ePbsHlWs6Bg6vxeE4QUiWCFi2mU7/eAAIDa/s6RLkMBQcHexM5I0eO5MUXX+Shhx4qUH66JUuWEBUVxaJFi5g8eTJr165l+/btREdH8+GHH9KyZUvcbjcvv/yyd5+JEyfy8MMPn9FXeHg4jz/+OP379y9QHhcXB8DGjRt55plnWLVqlbdux44duN1uNm/ezPHjx6lSJW+q4/Tp00lJSeHbb7+lUqVKpKWl8eyzz571XEXk8qAkkIiIiIhUKJmZv+FKWYHLFUtW1j78/asSFjaEBmG3U7VqOy3yLMXmhhtuYNu2bUVu36VLF+bMmQPA008/zbRp02jZsiWQN+rm/vvvP28f7du3Jycnh7Vr13LzzTcX6bgxMTHceeed7Nixg5UrVzJixAgyMjL417/+RVJSEpUqVQKgatWqzJgxo8jnIyJlj5JAIiIiIlLuud0nOHjo37iSYzly9DPAUqvm9TS7chKhob1wOCr5OkQpZsc++Jns5OPF2mdggyrU6N+sSG1zc3NZvXo1ffr0ASAzMxOn0+mtnzJlCsOGDSuwz5o1a7jtttsA2L59u3f619nMnTuXt956C4CaNWuyYcMGb920adP461//WuQk0NKlS1m7di07d+7kn//8JyNGjGDXrl00btyYqlWrnnO/opyTiJQtSgKJiIiISLlkrSUtbTsu13JS9q8kNzeVSpUa0rTpeMLq/5Hg4HBfhyjl0KmJkRtuuIExY8YAhU+dGjlyJNnZ2aSnpxd5etW5poMB3HjjjQB8+umn5+0nPj6eOnXq0LhxYxo2bMg999zDkSNHzmi3aNEi/vGPf3D48GE+//xzGjVqpOlgIpchJYFEREREpFzJzj5Cyv6VuFyxpKfvxM8viLqhfQgLG0zNmtdijN6NUhEUdcROcbuYxMiSJUvo2LEjkydP5oEHHmDFihW0adOGhIQE2rdvf1FxTJs2jVmzZuHvX/h/+WJiYti5cydNmjQBIDU1leXLlzNy5Ej27t1LWloaVatWZfTo0YwePZq2bdvidrsvKiYR8T19AoqIiIjIZc/jyeXQoQ18+200n352HT/9NAs/E0hExEy6Xr+FNm2eo1at65QAkjLLGMPMmTPZsmULO3fuZPLkyTzxxBP8+OOPAHg8Hl588cUi99erVy+OHj1a6JpEHo+Hd999l2+//ZakpCSSkpJYuXIlMTExVK5cmTFjxhAdHU1WVhYAbreb7OzsSztREfEpjQQSERERkctWRsYekl3LSXGt4ET2fgICatEo/C7Cwm4nJCTC1+GJeJ2+fk6fPn3OeKV6cHAwkyZNYs6cObz66qvMmzeP4cOHk5GRgTGGfv36edueuiYQwHvvvXfGMadNm8bAgQPPGdPmzZtp2LAhDRo08JbdeOONfP/997hcLh5//HH++te/0rZtW6pWrUpwcDB33323t31RzklEyhZjrfXJgaOiomx8fLxPji0iIiIlzxiTYK2N8nUcUlB5eAbLzT3OgYOrcSXHcuz3/wJ+1KndjbAGg6lTuzt+foG+DlF8ZMeOHbRq1crXYchlQD8rUp4V9gymkUAiIiIiUuZZa/n99wRcruXsP/AhbvdxKlduSrNmjxBW/zaCgur5OkQREZEyT0kgERERESmzTpw4gCslDpdrGRkZe3A4qlCv7q2ENRhM9WpXY4zxdYgiIiKXDSWBRERERKRM8XiyOXR4A67kWA4f+QRr3dSo3okrWv0PdUP74u9fxdchioiIXNCtG/gAACAASURBVJaUBBIRERGRMiE9/QeSXbGkpLxHTs4RggLrcUXjewkLu53KlZv6OjwREZHLnpJAIiIiIuIzOTmp7D+wClfyMlLTtmFMAKF1biKswWBq1eyKn58eV0VERIqLPlVFREREpFRZ6+Ho0S9IdsVy8ODHeDwnCAlpSYsW06lfbyCBgbV8HaKIiEi5pCSQiIiIiJSKzMzfcKUsx+WKJSvrN/z9q9EgbChhDQZTNaSNFnkWEREpYX6+DkBEREREyi+3O4uUlPf5+uu7+PyLP7Bnz/NUDm5Kmzbz6Hr9FiIiZlCtalslgKTccDgcOJ1O2rZty5AhQ8jIyDijvH///hw7dsy7z3fffUePHj2IiIigRYsWzJw5E2stAIsXLyY0NBSn04nT6eSuu+4CYNSoUcTGxjJo0CCcTifNmzenevXq3naff/75WePr1q0bERERREZG0rJlS6KjowvEcjLOk19PPfUUADk5OTz66KO0aNGCq6++mi5durB69WoAmjRpwqFDhwocZ/HixURHRwMwY8YMKleuzIEDB7z1ISEh5z2miBQ/jQQSERERkWJlrSUt7VuSXbHs3/8BubmpVKoUzpVNJxAWdjuVKjXwdYgiJSY4OJjExEQARo4cyYsvvshDDz1UoPzuu+9mwYIFTJs2jczMTAYMGMALL7xAr169yMjI4Pbbb2fhwoWMGzcOgGHDhjF//vyzHi8uLg6AjRs38swzz7Bq1arzxrhkyRKioqLIzs5mypQpDBw4kE8++eSM+E/117/+FZfLxfbt2wkKCmL//v3efYqiTp06PPvss8yePfuMunMdU0SKn5JAIiIiIlIssrMPk7L/fVzJy0g//gN+fkHUDe1LWIPB1KzRGWM0CF1Kz+rVq0lJSSnWPuvXr0/fvn2L3P6GG25g27ZtZ5R36dLFW/72229z/fXX06tXLwAqV67M/Pnz6datmzcJVFICAwN5+umnad68Od988w3t27c/a7uMjAz+9a9/sWfPHoKCggCoV68eQ4cOLfKx7rnnHhYvXsxf/vIXatXSul8ivqJPYhERERG5JB5PDt9+G82nn13PTz/Nws9RiYiImdzQ9UvatHmWWjW7KAEkFU5ubi6rV6+mXbt2Bcrdbjf/+c9/GDBgAJA3Faxjx44F2jRr1oz09HRSU1MBWLp0qXeq1KJFi4o1TofDQfv27dm5cycAmZmZBaZmLV26lF27dtG4cWOqVat20ccJCQnhnnvu4R//+McZdWc7poiUDI0EEhEREZFL4ucXAECj8LsICxtMSMhVPo5IhAsasVOcTiY0IG8k0JgxYwqU//bbb7Rq1Yqbb765yH0WNh2sOJxcfwjOPjXrbKOZLsb48eNxOp08/PDDBco1HUyk9CgJJCIiIiKXrF27kvsPqsjl5FwJjZPlGRkZ9O7dmwULFjB+/Hhat27Npk2bCrTdvXs3ISEhlzTypqjcbjfffvstrVq1Omeb5s2bs3fvXlJTUy8ppho1ajBixAgWLFhw0X2IyKXRuFwREREREZFSUrlyZZ5//nmeffZZcnNzGTlyJJ9++inr1q0D8kYMjR8/nkceeaTEY8nJyWHKlCk0atSIyMjIQmMeM2YMEyZMIDs7G4CDBw+ybNmyCz7mQw89xEsvvURubu5Fxy0iF09JIBERERERkVLUoUMHIiMjiYmJITg4mJUrVzJr1iwiIiJo164dnTp18r5evSSMHDmSyMhI2rZty/Hjx1m5cqW37vT1eR599FEAZs2aRWhoKK1bt6Zt27b069evwKigyMhIwsPDCQ8P56GHHjrnsevUqcOgQYM4ceLEeY8pIsXPnDr/szRFRUXZ+Ph4nxxbRERESp4xJsFaG+XrOKQgPYNJebZjx45CpzWJnKSfFSnPCnsG00ggEREREREREZEKQAtDi4iIiIiIlDODBg1iz549Bcpmz55N7969fRSRiJQFSgKJiIiIiIiUM3Fxcb4OQUTKIE0HExERERERERGpAJQEEhERERERERGpAJQEEhERERERERGpAJQEEhERERERERGpAJQEEhERERERKSYOhwOn00nbtm0ZMmQIGRkZZ5T379+fY8eOeff57rvv6NGjBxEREbRo0YKZM2dirQVg8eLFhIaG4nQ6cTqd3HXXXQCMGjWK2NhYBg0ahNPppHnz5lSvXt3b7vPPPz9rfN26dSM+Ph6AJk2acPvtt3vrYmNjGTVqlHd79erVREVF0bp1azp06MCkSZMAmDFjBg0bNsTpdNK6dWtiYmK8+4waNYqmTZt647juuusKHP+2227j2muvLVD2ww8/0K1bN5xOJ61ateLee+8FYOPGjQXOyel0sm7duqLfDBE5g5JAIiIiIiIixSQ4OJjExES2b99OYGAgL7744hnltWrVYsGCBQBkZmYyYMAAHn30UX744Qe++eYbPv/8cxYuXOjtc9iwYSQmJpKYmMgbb7xR4HhxcXEkJibyyiuvcMMNN3jbnZ58OZeEhAS+//77M8q3b99OdHQ0b731Ft9//z3x8fE0b97cWz9x4kQSExNZuXIl9913Hzk5Od66OXPmeOM4NRl17NgxEhIS+P3339m9e7e3fPz48d7+duzYwQMPPOCtO/WcEhMTuemmm4p0XiJydnpFvIiIiEgZZYzpA/wDcACvWGufOq0+CHgD6AgcBoZZa5NKO06RsujHH2eSlr6jWPusGtKKq676a5Hb33DDDWzbtu2M8i5dunjL3377ba6//np69eoFQOXKlZk/fz7dunVj3LhxxRN4ISZNmsTjjz/OkiVLCpQ//fTTTJs2jZYtWwJ5I5nuv//+M/Zv0aIFlStX5ujRo9StW7fQY61YsYL+/ftTr1493nnnHaZOnQqAy+UiPDzc265du3aXeloicg4aCSQiIiJSBhljHMACoC/QGhhujGl9WrMxwFFrbXNgLjC7dKMUkXPJzc1l9erVZyQ03G43//nPfxgwYACQNxWsY8eOBdo0a9aM9PR0UlNTAVi6dKl3OtSiRYuKNc6hQ4eydetWdu3aVaB8+/btZ8R1Nlu3bqVFixYFEkCTJ0/2xjty5EhveUxMDMOHD2f48OEFppBNnDiRHj160LdvX+bOnVtgqtzmzZsLTAf7+eefL+V0RSo8jQQSERERKZuuAXZZa3cDGGPeAQYCp87bGAjMyP8+FphvjDH25GIiIhXYhYzYKU6ZmZk4nU4gbyTQmDFjCpT/9ttvtGrViptvvrnIfQ4bNoz58+eXSLwOh4PJkyfz5JNP0rdv3yLvN3fuXBYtWsSPP/7IBx98UKBuzpw5DB48uEDZ/v37+emnn+jatSvGGAICAti+fTtt27Zl9OjR9O7dmzVr1rBy5UpeeuklvvnmGyDvGq5aterST1REAI0EEhERESmrGgK/nrK9L7/srG2stbnA70Dt0zsyxtxrjIk3xsQfPHiwhMIVEfi/tX8SExP55z//SWBgYIHyX375BWutd02g1q1bk5CQUKCP3bt3ExISQrVq1Uol5jvvvJNNmzbx66//909OmzZtzojrVBMnTuS7775j+fLljBkzhqysrEKP8e6773L06FGaNm1KkyZNSEpKKjAaqEGDBtxzzz2sXLkSf39/tm/ffuknJiJnUBJIREREpJyz1r5srY2y1kaFhob6OhyRCq1y5co8//zzPPvss+Tm5jJy5Eg+/fRT71uvMjMzGT9+PI888kipxRQQEMDEiROZO3eut2zy5Mk88cQT/PjjjwB4PB7vItenGjBgAFFRUbz++uuFHiMmJoY1a9aQlJREUlISCQkJvPPOOwCsWbPGu7B0SkoKhw8fpmHD03PeIlIclAQSERERKZt+Axqdsh2eX3bWNsYYf6A6eQtEi0gZ1qFDByIjI4mJiSE4OJiVK1cya9YsIiIiaNeuHZ06dSI6OrpUYxozZgy5ubne7cjISObNm8fw4cNp1aoVbdu2LfBGr1M99thjPPfcc3g8HqDgmkBOp5Mff/yRX375pcCr4Zs2bUr16tX58ssv+fe//03btm1p3749vXv3Zs6cOdSvXx84c02g2NjYErwKIuWf8dWU8aioKBsfH++TY4uIiEjJM8YkWGujfB3H5So/qfMj0JO8ZM9/gRHW2u9OaTMOaGet/R9jzB3AH621QwvrV89gUp7t2LGDVq1a+ToMuQzoZ0XKs8KewbQwtIiIiEgZZK3NNcZEAx+T94r416y13xlj/g7EW2vfB14F3jTG7AKOAHf4LmIREREp65QEEhERESmjrLUfAR+dVvbYKd9nAUNKOy4RKfsGDRrEnj17CpTNnj2b3r17+ygiESkLlAQSEREREZFyw1qLMcbXYfhcXFycr0Mos3y1JIpIWaCFoUVEREREpFyoVKkShw8f1n/y5ZystRw+fJhKlSr5OhQRn9BIIBERERERKRfCw8PZt28fBw8e9HUoUoZVqlSJ8PBwX4ch4hNKAomIiIiISLkQEBBA06ZNfR2GiEiZVaTpYMaYPsaYH4wxu4wxj56lPsgYszS//ktjTJPiDlRERERERERERC7eeZNAxhgHsADoC7QGhhtjWp/WbAxw1FrbHJgLzC7uQEVERERERERE5OIVZSTQNcAua+1ua2028A4w8LQ2A4HX87+PBXoaLckvIiIiIiIiIlJmFGVNoIbAr6ds7wM6n6uNtTbXGPM7UBs4dGojY8y9wL35myeMMdsvJmgpUXU47b6Jz+melD26J2WT7kvZE+HrAORMCQkJh4wxv5RQ9/o9LF263qVL17v06ZqXLl3v0lWS1/uKc1WU6sLQ1tqXgZcBjDHx1tqo0jy+nJ/uS9mje1L26J6UTbovZY8xJt7XMciZrLWhJdW3fg9Ll6536dL1Ln265qVL17t0+ep6F2U62G9Ao1O2w/PLztrGGOMPVAcOF0eAIiIiIiIiIiJy6YqSBPov0MIY09QYEwjcAbx/Wpv3gbvzvx8MrLfW2uILU0RERERERERELsV5p4Plr/ETDXwMOIDXrLXfGWP+DsRba98HXgXeNMbsAo6Qlyg6n5cvIW4pObovZY/uSdmje1I26b6UPbonFY/ueenS9S5dut6lT9e8dOl6ly6fXG+jATsiIiIiIiIiIuVfUaaDiYiIiIiIiIjIZU5JIBERERERERGRCqDEk0DGmD7GmB+MMbuMMY+epT7IGLM0v/5LY0yTko6poivCPXnIGPO9MWabMeY/xpgrfBFnRXO++3JKu9uNMdYYo9c3lrCi3BNjzND835fvjDFvl3aMFVER/g1rbIzZYIz5Ov/fsVt8EWdFYox5zRhzwBiz/Rz1xhjzfP4922aMubq0Y5Tio/tduopwvUfmX+dvjTGfG2Pal3aM5c35rvkp7ToZY3KNMYNLK7byqCjX2xjTzRiTmP+89UlpxlfeFOHflOrGmA+MMd/kX+/RpR1jeWKMaZT/XHry/wsTztKmVD83SzQJZIxxAAuAvkBrYLgxpvVpzcYAR621zYG5wOySjKmiK+I9+RqIstZGArHA06UbZcVTxPuCMaYqMAH4snQjrHiKck+MMS2AKcD11to2wIOlHmgFU8TflenAu9baDuS9qGBh6UZZIS0G+hRS3xdokf91L/BCKcQkJWcxut+laTGFX+89wB+ste2AmWhh1+KwmMKv+cnPo9nAv0sjoHJuMYVcb2NMDfI+ywfkP28NKaW4yqvFFP7zPQ743lrbHugGPJv/lnC5OLnAJGtta+BaYNxZnl1L9XOzpEcCXQPsstbuttZmA+8AA09rMxB4Pf/7WKCnMcaUcFwV2XnvibV2g7U2I39zCxBeyjFWREX5XYG8h7vZQFZpBldBFeWejAUWWGuPAlhrD5RyjBVRUe6LBarlf18dSC7F+Coka+0m8t4Oei4DgTdsni1ADWNMWOlEJ8VN97t0ne96W2s/P/k5hJ7bikURfsYBHgCWA/rsv0RFuN4jgBXW2r357XXNL0ERrrcFqub/nzwkv21uacRWHllrXdbarfnfpwE7gIanNSvVz82STgI1BH49ZXsfZ56wt421Nhf4HahdwnFVZEW5J6caA6wu0YgEinBf8ocFNrLWfliagVVgRflduQq4yhjzmTFmizGm0L8aSrEoyn2ZAfzJGLMP+Ii8B3XxrQv97JHLm+637+i5rRQYYxoCg9Aot9JyFVDTGLPRGJNgjLnL1wGVc/OBVuT9Ee1bYIK11uPbkMoHk7f0TQfOnNVRqp+b/iXVsVz+jDF/AqKAP/g6lorOGOMHPAeM8nEoUpA/ecM2u5H3l9dNxph21tpjPo1KhgOLrbXPGmO6AG8aY9rqAUZEyjNjTHfykkBdfR1LBTAP+Iu11qMJDKXCH+gI9ASCgS+MMVustT/6NqxyqzeQCPQAmgFrjTGbrbWpvg3r8maMCSFv9OCDvr6WJT0S6Deg0Snb4fllZ21jjPEnb+j+4RKOqyIryj3BGHMTMI28ubcnSim2iux896Uq0BbYaIxJIm8+6ftaHLpEFeV3ZR/wvrU2x1q7B/iRvKSQlJyi3JcxwLsA1tovgEpAnVKJTs6lSJ89Um7ofpcyY0wk8Aow0Fqr5+iSFwW8k/9MNhhYaIy5zbchlWv7gI+ttcettYeATYAWQC85o8mbfmettbvIW3espY9juqwZYwLISwAtsdauOEuTUv3cLOkk0H+BFsaYpvmLSd0BvH9am/eBu/O/Hwyst9baEo6rIjvvPTHGdABeIi8BpDm3paPQ+2Kt/d1aW8da28Ra24S8Of8DrLXxvgm3QijKv1/vkTcKCGNMHfKGK+8uzSAroKLcl73k/bUQY0wr8pJAB0s1Sjnd+8Bd+W+/uBb43Vrr8nVQUmJ0v0uRMaYxsAK4UyMjSoe1tukpz2SxwP+z1r7n47DKs5VAV2OMvzGmMtCZvHVVpGSc+hxVD4hAz7cXLX9tpVeBHdba587RrFQ/N0t0Opi1NtcYEw18DDiA16y13xlj/g7EW2vfJ++CvGmM2UXeolN3lGRMFV0R78kc8hYBW5Y/xHWvtXaAz4KuAIp4X6QUFfGefAz0MsZ8D7iByfoLbMkq4n2ZBPzLGDORvMUNR+mPCyXLGBNDXkK0Tv5aTH8DAgCstS+StzbTLcAuIIO8vzLKZUr3u3QV4Xo/Rt56mgvzn9tyrbUaKXwJinDNpRid73pba3cYY9YA2wAP8Iq19pyvk5fCFeHneyaw2BjzLWDIm/p4yEfhlgfXA3cC3xpjEvPLpgKNwTefm0bPxSIiIiIiIiIi5V9JTwcTEREREREREZEyQEkgEREREREREZEKQEkgEREREREREZEKQEkgEREREREREZEKQEkgEREREREREZEKQEkgERERERGRCswY080Ys8rXcYhIyVMSSERERERERESkAlASSERERERE5DJgjPmTMeYrY0yiMeYlY4zDGJNujJlrjPnOGPMfY0xoflunMWaLMWabMSbOGFMzv7y5MWadMeYbY8xWY0yz/O5DjDGxxpidxpglxhjjsxMVkRKjJJCIiIiIiEgZZ4xpBQwDrrfWOgE3MBL+P3t3Hh9ldfd9/HtmyQZhyQSR1SAqIAYCBFlEDFAREMX7EdmUAq1YLYhQRcVyq7cbUqx6W3Grdddg4QFrqeKDJQFc0CYWKQpaxVhQZEnYISSZOc8fSYaZySQZIGES8nm/XrzIda5rznVmiG2ub37nHDWSlGOt7SpptaR7yl7yiqQ7rLXdJP0roP11SQuttd0l9Ze0vay9h6SZks6XdLaki2r9TQE45VzRHgAAAAAAoFpDJPWS9I+yIp14STsl+SS9WXbNa5KWGmOaSmpmrV1d1v6ypMXGmERJbay1yyTJWlsoSWX9fWqt3VZ2vF5SiqQPav9tATiVCIEAAAAAoO4zkl621s4JajTmv0OusyfY/9GAr73iWRE4LTEdDAAAAADqvr9LGm2MOUOSjDFJxpizVPpMN7rsmgmSPrDW7pO0xxhzcVn7REmrrbUHJG0zxlxV1kesMSbhlL4LAFFFugsAAAAAdZy19ktjzFxJ/88Y45BULGmapEOSLiw7t1Ol6wZJ0iRJz5SFPFskTSlrnyjpWWPMfWV9XHMK3waAKDPWnmi1IAAAAAAgmowxB621jaM9DgD1A9PBAAAAAAAAGgAqgQAAAAAAABoAKoEAAAAAAAAaAEIgAAAAAACABoAQCAAAAAAAoAEgBAIAAAAAAGgACIEAAAAAAAAaAEIgAAAAAACABoAQCAAAAAAAoAEgBAIAAAAAAGgACIEAAAAAAAAaAEIgAAAAAACABoAQCAAAAAAAoAEgBAIAAAAAAGgACIEAAAAAAAAaAEIgAAAAAACABoAQCAAAAAAAoAEgBAIAAAAAAGgACIEAAAAA4DRhjOlkjFlvjDlgjJkR7fEAqFsIgQAAAADg9HG7pCxrbaKkfxljsowx+4wxeVEeF4A6gBAIAAAAAE4fZ0n6ouzrQ5JekDQ7esMBUJcQAgEAAADAacAYs0rSIElPGmMOStprrX1V0pbojgxAXUEIBAAAAACnAWvtYElrJU231ja21n4d7TEBqFsIgQAAAAAAABoAQiAAAAAAAIAGgBAIAAAAAACgAXBFewAAAAAAgJpnjHFIipHkLj00cZJ81tqi6I4MQLRQCQQAAAAAp6eBko5IekdS+7Kv/19URwQgqoy1NtpjAAAAAAAAQC2jEggAAAAAAKABqDYEMsa8YIzZaYzZWMl5Y4x5whjzjTFmgzGmZ80PEwAAAAAAACcjkkqglyQNq+L8cEnnlv25QdLTJz8sAAAAAAAA1KRqQyBr7RpJBVVcMkrSK7bUOknNjDGtamqAAAAAAAAAOHk1sUV8G0lbA463lbVtD73QGHODSquF1KhRo16dO3eugdsDAIC6KDc3d7e1tkW0x4FgycnJNiUlJdrDAAAAtaSqn8FqIgSKmLX2OUnPSVJ6errNyck5lbcHAACnkDHm+2iPARWlpKSIn8EAADh9VfUzWE3sDvaDpHYBx23L2gAAAAAAAFBH1EQI9Lakn5ftEtZX0j5rbYWpYAAAAAAAAIieaqeDGWMyJWVISjbGbJN0jyS3JFlrn5H0jqQRkr6RdFjSlNoaLAAAAAAAAE5MtSGQtXZ8NeetpGk1NiIAAAAAAADUuJqYDgYAAAAAAIA6jhAIAAAAAACgASAEAgAAqIOMMS8YY3YaYzZWct4YY54wxnxjjNlgjOl5qscIAADqF0IgAACAuuklScOqOD9c0rllf26Q9PQpGBMAAKjHCIEAAADqIGvtGkkFVVwyStIrttQ6Sc2MMa1OzegAAEB9RAgEAABQP7WRtDXgeFtZGwAAQFiEQAAAAKc5Y8wNxpgcY0zOrl27oj0cAAAQJYRAAAAA9dMPktoFHLcta6vAWvuctTbdWpveokWLUzI4AABQ97iiPQAAAACckLclTTfGLJLUR9I+a+32qI3m3Tuln/4VtdsDAFCvnJkqDX/4lN+WEAgAAKAOMsZkSsqQlGyM2SbpHkluSbLWPiPpHUkjJH0j6bCkKdEZKQAAqC8IgQAAAOoga+34as5bSdNO0XCqF4XfZgIAjo+1VtZKVpKv7GuftVLIsZVkfZKVlc+Wvs5nS4+tVVkfx86Vt5W/1udvsxXuFfb1AWOr7H6BfcuGGW9E7y3wuvL7V+zDF9CXDblP0Hsr77OsLwWMu7zvwPcXeL/2iQn6xSn+95cIgQAAAADghBx7qKv6gTX4gTH4QTaSB2SflRT6wBrmATfsw7cv9ME5zMO7rHy+8K+v6gG5YnuYh2H/Z1P5w7dCxhk+PAj8fMO3hX34riw8UEAgEPa9BX7mFd9DJAFE2PdQ4TMIH1yUfSxVfNbB/77hPgOFvM5na+0/BZQxRjKSHMaUfm2MTFm7o+xrR9lFPds31y8GdDjlYyQEAgAAQNRV/pvWMA/DlT1gSxUejqSqHqICH34rPrxX9fAd9rfboX0HvD70Abmyh++q3lvgw17Y326HPgxX9eBYdq/Afqt6cFXA5xT62+3Qz6Cq39BXePgO+Teo7MG1sofv8P++IQ/DvmPfY5X9hj6ozwi+D8vvg9rlKHuIdhjJqPyhuvTr8nP+h26HCfvwXX7sMKa0T0fI6yX/9cH3Kb/m2MO7Cegr8GG+9DrHsXNB9z/WlxTSZyXjLLs06D7+a8vvV+EzCO5bQeOu4j04yj6XkL6DP5Njn68CPqfy+4V9D2HbAz+D8vuHu1/Z5xUyzsr+DQK/LwLvH3q/0O+j0M88tO/gzzz8/YL6Lvseq+sIgQAAAHDShv/vWu3YXxhUGRH2t9uqGAjwMF37wj24hnvwNCEPNGEfXBXyYBTQV6UPrgp++D72IBnuOiNX6IN7pQ9rZX2GfZAMfK+RPXwH3yv8A3bF9xDyMFzVZxD4uYZ5QK4sgKj6Mwh+0A//bxDugbXyzyCSB+Sgvqt4eA96+HZU/L4L/97qzwM1UN8QAgEAAOCkZXRqoQOFxVX+tjmwPfwDctUP32EfGANeHy7oOHb/yn/bHG4MlT0ghwtAqvttc2BIUd3Dd8VqhpDf+Ae9typ+ux3y+QIAIBECAQAAoAbcMaxztIcAAACq4Yj2AAAAAAAAAFD7qAQCAAAAAACohrVWPq9XPm+JvCUl8nm98pYUy1fiLTuups1b1lZSovgmTdSxV59T/h4IgQAAAAAAwClhfT55A4MUf5hSEqatuIpzwW1er7fsXEnAOW/ZucBrgsOY8rbg1we0+e9R+nVNaX1eF0IgAAAAAABQNZ/PGxRMBAciEbYFBCEVKlvChCSVBieB9wgIU7zekHuUtVmfr3Y/HGPkdLnkcLrK/nbK6XLL4XKGtJVe43K75IiPD2pzOp1yuNxyupxyhLY5S9ucLlfp386A/sK1OY9dG9jmjo2t3c+hEoRAAAAAAIAGJdy0Hl9JuJCkOCRAORaSHLu+8uCk0uqSgJDEV1IcXLESQeWLrK3Vz8c4HKXBSWDgESYkcbhKUUQr9wAAIABJREFU29yxcXI2CjwXGHyEtIUJYsLeIyhAcUbe5nDW6mdT3xECAQAAAACOW+C0ntL1T4Kn7oQLSY5NzQk31SdMcFJVSBJuOlBQcFLWZyVVMbWtNNwIF2o4Q4KP0pDElZAQHIqUByf+awMqUYKucfv7rLryxR1csVLZ9U6njIM9pE5XhEAAAAAAECU+nzdoAdljAUpkbd7AipRwbWFCksDrg4KTiKcOlQY4p3RaT1kgEraapCwQCZrWU0lIEtRWocolNCQJafOHOhG0OZ0yxtTu54NTwlqrEluiYm+xin3FKvIWBf/tK/KfK/aWHYe5rthbrKKSItkir+xRr1oknKGre4055e+HEAgAAABAvRU4rSdoOk7QrjwlFUMRr9e/WGxQxUo1IUnpuTDroQRWrIQGJ5VWynhlbe0GKcbh8AciQYFHSCDicJW2uWPj5EwIH5JUG5xUFZI4j00dCqyKqbKNaT0NhrW2NCgJF7J4i1TiK/GHLUF/l4crIYFM4LnKgpsSb0mF64u8xwKd8nMl3hLF+txK8MUp3henhPI/3mPHge3xZecSfLFq4otXvC9WCb4m/mvKfd90h9Tr1H/WhEAAAABAA1capByrAvFP3QmsIgkXnJSHG97A44ohSdAaKeFCknAVJ1XtBBRSFVPbKoQV1VSXuGISwgcnlYYela+ZErYtYKHZKq9nWg8C+Kyv0gqVcJUtYUOWcNUvVfVZRSgTeK7EV8P/HVsp0TRWUyUq0TZSohor0SaosW2kFr4mSrBxauSLV7wvTvHeWMX5YhXvjVFsSYxivW7FlLgUU+KSu8QlV4lDRtVXdVmHZGOMFGukeIcU65Aj1ilHnEuOWJeccW654txyxsXIGedScrMLavY9R4gQCAAAAKgB5dN6wi0qG1yVEtwWuoBsUFVJVYvPVlhzJYLpRBWqYk7htJ6QQCTstJzAaT1xccHroYQEHOHXTAmuTgnbFrCAbFVt/uoZpvXgOHh93qCqkhJfSdjKlvLwJLQKJey5MFOMKgtuAkOZ0L5KbM0GLQ7jUIwjRm6HW26nW26HWzHOmNI2p1sxjhi5HC7FueKU6EhUjDPGf43b4fa/zn+9iVGsjVGcL0ZxJaV/x5S4FVviLgtknHKVOOUqdshZ7JCzyMhZLJkiyRRZmSIrHfXJFvlkj3qlSNbOdkgm1iVHrFMm1ilHY1fp3+XHsU6ZuIDzcc7g6/3tLhlX/QhcCYEAAABQJ1hrSxeaDdllJ5Kdd4KrU7whVSmRhyRV7RIUbkHbwOqZUz6tJzQACQpXXHLFxio2ISFsSFLVtJ5qQ5KQaT2Bi8pW2sa0HtSQ0PVZKps+VFk1SpWVKtX0GTolKVxfvhr+3wGXcfkDFn/IEiZEaeRqpJjYioGM2+EOCmVC+woNbsKdK//b5XAF9el0OGWtlUqsfEdLZI965SssXe/Gf3zUK1tYdnyk7NjfXnLsuNArW1QiVfnxWUklkgkIbuLKgpgEpxxJLpmY8qDGKUes61hwExMS4JS9Ti5Hgwt5CYEAAABw0v7+wjM6sn9fxeAkzBSjqqpiapsjbHBynNN6gkKS8tAjZHvjwIqVqtoCpvWEW5iWaT2IBmttxTVYqqpCCVONcsLThwKur+ycjajEI3Iuhys4KCkPP8rDlLJz8a748CFLuMqW0PMhfYVeU1mfDlM7/93bEl/FIOaIV/ZoSUBwUx7QFMoWHZKv0KvCo14dORoc3sgXwb+HUdiAxp0Yc6ySJpJqm1injLvhBTc1iRAIAAAAJ21n3hYdObA/bEjijg2Y1lNdSOIMDETChSShWy27QrZBDqxYqdgnDw6oK8rXZ6lsodvjDlnCLZ4b4fShcNOUalpo+OF2hq9UiXfHhw9RAoOSMEFKeZVKaNVL+ZSkcJUt5cFNffnfBev1+YOX0hDmWBhjw7T5CgOqcY4GnC/0St7IgjQTE1BtU76+TaN4ucNV2/gDm5AqnVhXaXDjqB+f8+mOEAgAAAAnbfx9v4v2EIAKvD7vcW/hHFqFEsniudWt+xKur5pen8XIVJjWEy7wiHXFKtGRWPVUn5BwJtxUpKBzlVW/lJ1zGVe9CVpqmvXa0uClKLS6JjSgCTOdqtAb9DqVRDbVzMQ4ggOaWKcczePkDlrnxilHTOh6N8Hr4ZgYJ8HNaYgQCAAAAMAJsdbKa70ntJjtySyeW1Vfgee81luj79dpnMcCk4BKk9BKlQRXgprGNg27aG5104gqBDMRTh9yOXi0qynWZ2WLygOZkHVrAqdLFZVOp6pQbRMwxcoWRxjcuB1BCxGbGKeczWLlClycOCa4uia02qZ87RuCG1SF/6UAAAAA6rDy9Vmq28I5NECJZKHbqoKbyvoMDWxqY32W0GqUcJUqjWMaV7uYbbjpQ5WuwRJmulJoUONkges6qzy4CQpkwlXbFIYJbPzVNqXX26IIF3d2mdJqG39VjVPOxBiZ5PiIq23806WcBDc4NQiBAAAA0OBZa09oMdvAypNI12Cpbt2XcH3VtMqm/ISGKPHu+ONbzLaKviJZPNflcNXaQrioe6y1Zdt5hy5GXBIS1IRMlyqqGPDYoxFWfTlNhcoaZyO3TFJcaUATZvHiCtU35eFNPdkSHAhECAQAAIBTwmd9EW3hHDYQOY7FcyNZ9yX0XImvdtZnCQ0/wgUjjdyNItrCOZK+qls8t3zaUENdnwUnz1orW+wLv813wMLDtpLpUaFhTkSFZA5TcZvvBLcczePCV9eE212qvBqH4AYNHCEQAAAATtq0v0/TrsO7KkxFCpw+VBvrs4RWo4SrVIl3xaups2mlC91WtkBupFUv4fp0GnYiQ91hrZX8W4KHhDVFIYsRV1ZtE/C6iIIbo2Pr1AQsRuxuFlux2iau4vSoY+vduCSX4b8noIYQAgEAAOCkNYlp4q9+OZ7FbAPDlkqnEVWyCxHrs+B0Z0t8VW7zXXk1zrFryo/liyC5MQob0LgTY4LWtam22ibWWbrQMcENUOcQAgEAAOCkzbt4XrSHANQJtrzi5mjI+jYB1TbB06VCqm2KjoU68ka26LYJqqIpnR7laBQvd3XVNqFr38QQ3ACnO0IgAAAAAA2a9dowixGHOS7yBu8uFWb3KZVEGNzEOIKnPcU45UiKkzuo0uZYQBN2d6k4p4ybLcEBRI4QCAAAAEC9Y322kulRAevahKnGKT8OmjpVHNmW4MbtqDD1ydEs1h/cBO445Q9v4sJU38QQ3ACIDkIgAAAAAKeE9dnSBYdDKmmOhTYV17YJV21jCyMPbuRyBFXbmDinnE1ij6vaxhFTGuAYJ8ENgPqNEAgAAABApazPyhZ7Q6prIqu28R0Nfp0tinCHOKepsPCws7FbxhMXsh34selUoW3lAY9xsiU4AJQjBAIAAABOM9Za2WJfWSBT+cLD4SttyqZLlYc5Rd7ItgR3GP9UJ3+Ak+CWIymurC2kuiY2OOQJ2l3KRXADALWBEAgAAACoA6y1UokvaFvvoOlSEVbblIc+kQU3kolxBa1b44h3ydEsNnh6VIXdpULXu3FJLsPOUgBQxxECAQAAACeoNLixFattQhYeDlttE7r71NESKZJlbozCTodyJ8Ycq6QJs95NhWqbWGfpQscENwDQYBACAQAAoMGxJb7ggKYoZGHigGqcimvgBIc38ka4JXhAZU15EONKjDmuapvSLcEJbgAAJ4YQCAAAAPWC9fqOBTFFYaprwu0uFabaxld4HMFNjCOo2sYR65SjUbx/S/Cwu0vFVTw2brYEBwBEHyEQAAAAao312tLgpSi4usYXuPBw2N2mKgY8KolsS3DjdlTY5tvRLLY0uIlzVVzrJijMCZguFUNwAwA4vRACAQAA4KQVvPmVvPuOVlzQuDiy4EYuR3AQE+uUs0msXP6AJnQXqUqqb2KcMk6CGwAAwiEEAgAAwEnzHS6W9Vk5G7tlkuODA5tYV4WAxxHnCthK3CnjZEtwAABqGyEQAAAATlrylAuiPQQAAFANfuUCAAAAAADQABACAQAAAAAANACEQAAAAAAAAA0AIRAAAAAAAEADQAgEAAAAAADQAEQUAhljhhljvjLGfGOMuTPM+fbGmCxjzD+NMRuMMSNqfqgAAAAAAAA4UdWGQMYYp6SFkoZLOl/SeGPM+SGXzZX0Z2ttD0njJD1V0wMFAAAAAADAiYukEuhCSd9Ya7dYa4skLZI0KuQaK6lJ2ddNJf1Yc0MEAAAAAADAyYokBGojaWvA8baytkD3SrrOGLNN0juSbg7XkTHmBmNMjjEmZ9euXScwXAAAAAAAAJyImloYerykl6y1bSWNkPSqMaZC39ba56y16dba9BYtWtTQrQEAAAAAAFCdSEKgHyS1CzhuW9YW6JeS/ixJ1tqPJcVJSq6JAQIAAAAAAODkRRIC/UPSucaYDsaYGJUu/Px2yDX/kTREkowxXVQaAjHfCwAAAAAAoI6oNgSy1pZImi7pPUmbVLoL2BfGmPuMMVeWXXarpKnGmM8lZUqabK21tTVoAACAhsAYM8wY85Ux5htjzJ1hzrc3xmQZY/5pjNlgjBkRjXECAID6wRXJRdbad1S64HNg290BX38p6aKaHRoAAEDDZYxxSloo6VKVbszxD2PM22U/d5Wbq9Jf0D1tjDlfpT+vpZzywQIAgHqhphaGBgAAQM26UNI31tot1toiSYskjQq5xkpqUvZ1U0k/nsLxAQCAeoYQCAAAoG5qI2lrwPG2srZA90q6zhizTaVVQDeH68gYc4MxJscYk7NrF8s2AgDQUBECAQAA1F/jJb1krW0raYSkV40xFX6+s9Y+Z61Nt9amt2jR4pQPEgAA1A2EQAAAAHXTD5LaBRy3LWsL9EtJf5Yka+3HKt2hNfmUjA4AANQ7hEAAAAB10z8knWuM6WCMiZE0TtLbIdf8R9IQSTLGdFFpCMR8LwAAEBYhEAAAQB1krS2RNF3Se5I2qXQXsC+MMfcZY64su+xWSVONMZ9LypQ02VprozNiAABQ10W0RTwAAABOPWvtOypd8Dmw7e6Ar7+UdNGpHhcAAKifqAQCAAAAAABoAAiBAAAAAAAAGgBCIAAAAAAAgAaAEAgAAAAAAKABYGFoAACABq64uFjbtm1TYWFhtIeCOi4uLk5t27aV2+2O9lAAACeAEAgAAKCB27ZtmxITE5WSkiJjTLSHgzrKWqv8/Hxt27ZNHTp0iPZwAAAngOlgAAAADVxhYaE8Hg8BEKpkjJHH46FiDADqMUIgAAAAEAAhInyfAED9RggEAAAAAADQABACAQAAAAHeeustffnll/7ju+++W++//74k6fHHH9fhw4ejNTQAAE4KIRAAAAAQIDQEuu+++/Szn/1MEiEQAKB+IwQCAADAae+1117ThRdeqLS0NP3qV7+S1+tV48aN9dvf/lbdu3dX3759tWPHDn300Ud6++23NXv2bKWlpenbb7/V5MmTtWTJEj3xxBP68ccfNWjQIA0aNEgvvPCCZs6c6b/HH//4R82aNSuK7xIAgKoRAgEAAOC0tmnTJr355pv68MMPtX79ejmdTr3++us6dOiQ+vbtq88//1wDBw7UH//4R/Xv319XXnmlFixYoPXr16tjx47+fmbMmKHWrVsrKytLWVlZGjNmjP7617+quLhYkvTiiy/qF7/4RbTeJgAA1XJFewAAAABAbfr73/+u3Nxc9e7dW5J05MgRnXHGGYqJidHIkSMlSb169dLKlSuPq9/GjRtr8ODBWr58ubp06aLi4mKlpqbW+PgBAKgphEAAAAA4rVlrNWnSJM2bNy+o/ZFHHvFvee50OlVSUnLcfV9//fV66KGH1LlzZ02ZMqVGxgsAQG1hOhgAAABOa0OGDNGSJUu0c+dOSVJBQYG+//77Sq9PTEzUgQMHIjrXp08fbd26VW+88YbGjx9fswMHAKCGEQIBAADgtHb++efrgQce0NChQ9WtWzddeuml2r59e6XXjxs3TgsWLFCPHj307bffBp274YYbNGzYMA0aNMjfNmbMGF100UVq3rx5rb0HAABqgrHWRuXG6enpNicnJyr3BgAAtc8Yk2utTY/2OBAs3M9gmzZtUpcuXaI0ovpv5MiRmjVrloYMGRLtoZwSfL8AQN1W1c9gVAIBAAAAJ2Dv3r0677zzFB8f32ACIABA/cbC0AAAAMAJaNasmb7++utoDwMAgIhRCQQAAAAAANAAEAIBAAAAAAA0AIRAAAAAAAAADQAhEAAAAAAAQANACAQAAIA64a233pIxRps3b5Yk5eXlyRijuXPn+q/ZvXu33G63pk+f7m977rnn1LlzZ3Xu3FkXXnihPvjgA/+5lJQU7d6923+cnZ2tkSNHSpJeeuklORwObdiwwX/+ggsuUF5envr06aO0tDS1b99eLVq0UFpamtLS0pSXl1dbbx8AgFpHCAQAAIA6ITMzUwMGDFBmZqa/rUOHDvrb3/7mP168eLG6du3qP16+fLmeffZZffDBB9q8ebOeeeYZTZgwQT/99FNE92zbtq0efPDBCu2ffPKJ1q9fr/vuu09jx47V+vXrtX79eqWkpJz4GwQAIMoIgQAAABB1Bw8e1AcffKA//elPWrRokb89ISFBXbp0UU5OjiTpzTff1JgxY/zn58+frwULFig5OVmS1LNnT02aNEkLFy6M6L4jR47UF198oa+++qoG3w0AAHWTK9oDAAAAQN3xP3/9Ql/+uL9G+zy/dRPdc0XXKq/5y1/+omHDhum8886Tx+NRbm6uPB6PJGncuHFatGiRWrZsKafTqdatW+vHH3+UJH3xxRfq1atXUF/p6el6+eWXIxqbw+HQ7bffroceeiji1wAAUF9RCQQAAICoy8zM1Lhx4ySVhj6BU8KGDRumlStXatGiRRo7duxx9WuMqbZtwoQJWrdunb777rsTGDkAAPUHlUAAAADwq65ipzYUFBRo1apV+te//iVjjLxer4wxmjZtmiQpJiZGvXr10u9//3t9+eWXevvtt/2vPf/885Wbm6vBgwf723Jzc/3rBnk8Hu3Zs8c/XaygoMD/dTmXy6Vbb71V8+fPr+23CgBAVFEJBAAAgKhasmSJJk6cqO+//155eXnaunWrOnTooK1bt/qvKQ9pkpKSgl57++2364477lB+fr4kaf369XrppZf061//WpKUkZGhV199VZLk9Xr12muvadCgQRXGMHnyZL3//vvatWtXbb1NAACijkogAAAARFVmZqbuuOOOoLarr75a8+bN8x937do1aFewcldeeaV++OEH9e/fX8YYJSYm6rXXXlOrVq0kSf/93/+tm266Sd27d5e1VsOGDdN1111XoZ+YmBjNmDFDt9xySw2/OwAA6g5jrY3KjdPT0235Lg8AAOD0Y4zJtdamR3scCBbuZ7BNmzapS5cuURoR6hu+XwCgbqvqZzCmgwEAAAAAADQAhEAAAAAAAAANACEQAAAAAABAA0AIBAAAAAAA0AAQAgEAAAAAADQAhEAAAAAAAAANACEQAAAAou6nn37SuHHj1LFjR/Xq1UsjRozQc889p5EjRwZdN3nyZC1ZskSSlJGRoU6dOql79+7q3bu31q9f778uJSVFqamp6tatmy655BJ9//33/nNOp1NpaWn+Pw8//LC/v/T0Yzvq5uTkKCMjw3/86aefauDAgerUqZN69Oih66+/Xn/5y1/Ur18/WWslSV6vVz169NBHH31U458RAAAnixAIAAAAUWWt1X/9138pIyND3377rXJzczVv3jzt2LGj2te+/vrr+vzzz/XrX/9as2fPDjqXlZWlDRs2KCMjQw888IC/PT4+XuvXr/f/ufPOO/3ndu7cqXfffbfCfXbs2KFrrrlG8+fP11dffaV//vOfGjZsmPr27auzzjpLf/rTnyRJf/jDH5Senq7+/fuf6McBAECtIQQCAABAVGVlZcntduvGG2/0t3Xv3l0XX3xxxH3069dPP/zww3GfCzV79mw9+OCDFdoXLlyoSZMmqV+/fv620aNHq2XLlnrsscc0b948ffHFF3ryySc1f/78iMcNAMCp5Ir2AAAAAFCHvHun9NO/arbPM1Ol4Q9Xenrjxo3q1avXSd1ixYoVuuqqqyI6d+TIEaWlpfmP58yZo7Fjx0oqDYyWLVumrKwsJSYmBo1x0qRJYftv1aqVZs6cqX79+umJJ55QUlLSSb0XAABqCyEQAAAA6iRjTLXt1157rYqKinTw4MGgNYEkadCgQSooKFDjxo11//33+9vLp4NVZu7cuXrggQeOq6Jn2rRpuvPOOzV58uSIXwMAwKlGCAQAAIBjqqjYqS1du3b1L/YcyOPxaM+ePUFtBQUFSk5O9h+//vrr6tWrl2bPnq2bb75ZS5cu9Z/LyspSs2bNdO211+qee+7Ro48+GtF4Bg8erLlz52rdunVBY8zNzdWoUaPCvsbhcFQaWgEAUFewJhAAAACiavDgwTp69Kiee+45f9uGDRuUn5+vH3/8UZs2bZIkff/99/r888+DpnJJpZVB999/v9atW6fNmzcHnXO5XHr88cf1yiuvqKCgIOIxzZ07V7/73e/8x9OnT9fLL7+sTz75xN+2dOnSiBavBgCgriAEAgAAQFQZY7Rs2TK9//776tixo7p27ao5c+aodevWeu211zRlyhSlpaVp9OjRev7559W0adMKfcTHx+vWW2/VggULKpxr1aqVxo8fr4ULF0o6tiZQ+Z/A3cHKjRgxQi1atPAft2zZUosWLdJtt92mTp06qUuXLnrvvfeC1g0CAKCuM9baqNw4PT3d5uTkROXeAACg9hljcq216dEeB4KF+xls06ZN6tKlS5RGhPqG7xcAqNuq+hksokogY8wwY8xXxphvjDEVf1VSes0YY8yXxpgvjDFvnMyAAQAAAAAAULOqXRjaGOOUtFDSpZK2SfqHMeZta+2XAdecK2mOpIustXuMMWfU1oABAAAAAABw/CKpBLpQ0jfW2i3W2iJJiySFboswVdJCa+0eSbLW7qzZYQIAADQ8VGMDAICaFMkW8W0kbQ043iapT8g150mSMeZDSU5J91prV4R2ZIy5QdINktS+ffsTGS8AAECDQDU2AACoaTW1O5hL0rmSMiSNl/RHY0yz0Iustc9Za9OttemBuy0AAACgAqqxAQBAjYokBPpBUruA47ZlbYG2SXrbWltsrf1O0tcqDYUAAABwYsJVY7cJueY8SecZYz40xqwzxgwL15Ex5gZjTI4xJmfXrl21NFwAAFDXRRIC/UPSucaYDsaYGEnjJL0dcs1bKq0CkjEmWaU/kGypwXECAACgotOmGtvpdCotLU1du3ZV9+7d9fvf/14+ny/omquuukp9+/aVJL333ntKS0tTWlqaGjdurE6dOiktLU0///nPlZ2draZNm/rPp6Wl6f3334/G2wIAoE6pdk0ga22JMWa6pPdUut7PC9baL4wx90nKsda+XXZuqDHmS0leSbOttfm1OXAAAIDTXKTV2J9Ya4slfWeMKa/G/sepGWLNiY+P1/r16yVJO3fu1IQJE7R//379z//8jyRp7969ys3NVePGjbVlyxZddtlluuyyyyRJGRkZeuSRR5Seni5Jys7O1sUXX6zly5dH580AAFBHRbQmkLX2HWvtedbajtbaB8va7i4LgGRL/cZae761NtVau6g2Bw0AANAANNhq7DPOOEPPPfecnnzySVlrJUlLly7VFVdcoXHjxmnRIn7UBADgRESyOxgAAABOsWhVY8//dL42F2w+2eEH6ZzUWXdceMdxvebss8+W1+vVzp071bJlS2VmZuruu+9Wy5YtdfXVV+uuu+6q8vVr165VWlqa//j//t//q44dO57Q+AEAOF0QAgEAANRR1tp3JL0T0nZ3wNdW0m/K/py2duzYoX//+98aMGCAjDFyu93auHGjLrjggkpfw3QwAAAqIgQCAACA3/FW7NSWLVu2yOl06owzztCTTz6pPXv2qEOHDpKk/fv3KzMzUw8++GCURwkAQP0S0ZpAAAAAwKmya9cu3XjjjZo+fbqMMcrMzNSKFSuUl5envLw85ebmsi4QAAAngEogAAAARN2RI0eUlpam4uJiuVwuTZw4Ub/5zW+Ul5en77//3r81vCR16NBBTZs21SeffKI+ffqE7S90TaC5c+dq9OjRtf4+AACoywiBAAAAEHVerzdse0pKin744YcK7Z999pn/6+zs7KBzGRkZ2rdvX42ODwCA0wHTwQAAAAAAABoAQiAAAAAAAIBTpLh4rw4e+ndU7s10MAAAAAAAgFpirdXBg5uUn5+t3fnZ2rfvn2rapLvS05ec8rEQAgEAAAAAANSgkpIDKij4yB/8FBXtlCQlJqaqQ8o0eTwZURkXIRAAAAAAAMBJsNbq0OFvlJ+frfzd2dq7L0fWlsjlSlRS0sXyeC6RJ+kSxca2iOo4CYEAAAAAAACOk9d7RHv2rNPu/Gzl52ersHCbJKlxo05q3+6X8ngy1LRpDzkc7iiP9BhCIAAAAAAAgAgcPvx9abVPfrb27F0nn69IDke8kpIuUspZN8rjuURxca2jPcxKsTsYAAAAos7pdCotLc3/5+GHH5bX61WvXr20Zs0a/3VDhw7V4sWLJUkpKSlKTU1Vt27dNHToUP30009B7eV9zZgxQ5I0efJktWnTRkePHpUk7d69WykpKZIkn8+nGTNm6IILLlBqaqp69+6t7777rsr+wpk8ebI6dOig7t2767zzztPPf/5zbdu2zX++qr4eeeQRde7cWWlpaerdu7deeeUVSVJGRoY6deqk7t27q3fv3lq/fn1E/ZWUlKhFixa68847g8a4fPly9ejRQ927d9f555+vZ599VpJ07733qk2bNkH/Dnv37o3knw8ATls+31HlF3ygr//9gD5e9zN9vG6wvv73fTpSuFVt2lyrtLSXdcnAXHXv9qzatBlfpwMgiUogAAAA1AHx8fFB4Ua5p55BbJ0LAAAgAElEQVR6SlOnTlVubq6WLFkih8Oha665xn8+KytLycnJuuuuu/TQQw/piSeeCGoP5XQ69cILL+imm24Kan/zzTf1448/asOGDXI4HNq2bZsaNWpU4T6RWLBggUaPHi1rrR5//HENHjxYGzduVExMTKV9PfPMM1q5cqU+/fRTNWnSRPv379eyZcv8519//XWlp6frxRdf1OzZs7Vy5cpqx7Zy5Uqdd955Wrx4sebNmydjjIqLi3XDDTfo008/Vdu2bXX06FHl5eX5XzNr1izddtttEb1PADhdFRb+WDbFa7X27PlIXu9hORwxat6sr9q2mSiP5xIlJKREe5gnhBAIAAAAfj899JCObtpco33GdumsM++664Re26dPH/Xr10/33nuv3njjjaDwI9DAgQP9AVBVZs6cqccee0xTp04Nat++fbtatWolh6O0UL5t27YnNN5AxhjNmjVLy5Yt07vvvqtRo0ZVeu1DDz2k7OxsNWnSRJLUpEkTTZo0qcJ1/fr104IFCyK6f2Zmpm655RY9/fTT+vjjj9W/f38dOHBAJSUl8ng8kqTY2Fh16tTpBN4dAJw+fL5i7dv3T/80r4OHvpIkxcW10Zln/h8lezLUvHlfOZ3xUR7pySMEAgAAQNQdOXJEaWlp/uM5c+Zo7NixkqR58+apXbt2mjlzps4555ywr1++fLlSU1P9x4MGDZLT6ZQkTZo0SbNmzZIktW/fXgMGDNCrr76qK664wn/9mDFjNGDAAK1du1ZDhgzRddddpx49elTbXyR69uypzZs3+0Og0L5++ctf6sCBAzr77LOr7WvFihW66qqrgtrCja2wsFDvv/++nn32We3du1eZmZnq37+/kpKSdOWVV+qss87SkCFDNHLkSI0fP94ffj322GN67bXXJEnNmzdXVlZWxO8TAOqTo0d3Kb9gtfLzV6ugYK1KSg7IGJeaNU3XOefcqWTPICUkdJQxJtpDrVGEQAAAAPA70Yqdk1XZdDBJWrNmjZo2baqNGzdWOFcegHTr1k0PPPCAv72q6Vtz5szRqFGjdPnll/vb2rZtq6+++kqrVq3SqlWrNGTIEC1evFhDhgyptr/qWGuDjkP72r9/f7V9XHvttSoqKtLBgwcrfE7hxrZ8+XINGjRI8fHxuvrqq3X//ffr8ccfl9Pp1PPPP69//etfev/99/XII49o5cqVeumllyQxHQzA6ctar/bv3+DfyevAgdL/T4mJOUNntBgujydDSUn95XIlRnmktYsQCAAAAHXWoUOHdPvtt2vVqlWaMmWK3nnnHY0YMcJ//kTCmXPPPVdpaWn685//HNQeGxur4cOHa/jw4WrZsqXeeustfwh0Mv75z39W2U+TJk3UuHFjbdmypdJqoNdff129evXS7NmzdfPNN2vp0qVV3jMzM1MffPCBf+Hr/Px8rVq1SpdeeqkkKTU1VampqZo4caI6dOjgD4EA4HRSXLxH+flrlZ+/WvkFa1RcXCDJoaZNe6jj2bfK48lQ48ZdTrtqn6oQAgEAAKDOuu+++zRmzBh17txZTz31lMaNG6fBgwcrLi7upPr97W9/G1QJ9Nlnn+nMM89U69at5fP5tGHDBnXr1u2k7mGt1R/+8Adt375dw4YNq/LaOXPmaNq0aXrzzTfVpEkTHTx4UEuXLtXPf/5z/zXGGN1///3q2LGjNm/erM6dO4fta//+/Vq7dq22bt2q2NhYSdKLL76ozMxM9evXTzk5OcrIyJAkrV+/XmedddZJvU8AqCustTp48Et/tc++fesl+eR2J8mTNFCe5Ax5ki6W290s2kONGkIgAAAARF3omkDDhg3TxIkTtWzZMn3++eeSpB49euiyyy7T/Pnzdc8991TZX+A6Od26dfNvt16ua9eu6tmzpz777DNJ0s6dOzV16lT/9vEXXnihpk+fHnF/gWbPnq37779fhw8fVt++fZWVleXfGayyvm666SYdPHhQvXv3ltvtltvt1q233lqh7/j4eN16661asGCB/vSnP4Xtb8iQIRo8eLA/AJKkUaNG6fbbb9djjz2m3/3ud/rVr36l+Ph4NWrUKKgKKHBNIEl66623/NVEAFAXlZQcUEHBh/7dvIqKdkqSEhNT1SFlmjyeDDVpkipjnFEead1gQuconyrp6ek2JycnKvcGAAC1zxiTa61Nj/Y4ECzcz2CbNm1Sly5dojQi1Dd8vwCIJmutDh3+Rvn52dq9O0v79uXK2hK5XIlKSrpYyZ4MJXkuUWzMia3jdjqo6mcwKoEAAAAAAECd5fUe1p496/zTvAoLf5AkNW7USe3bXy+PJ0NNm6TJ4XBHeaR1HyEQAAAAcJymTZumDz/8MKjtlltu0ZQpU6I0IgA4vRw+nFda7ZOfrb17P5HPVySnM0HNm/dXylk3yeO5RHFxraM9zHqHEAgAAAA4TgsXLoz2EADgtOLzHdWevf9Q/u4s7c7P1pEjeZKkhISz1abNdUr2ZKhZs3Q5HLFVd4QqEQIBAAAAAIBTrrDwR/+CzgUFH8rnOyKHI1bNm/dVu3aT5Em6RAkJ7GBYkwiBAAAAAABArfP5irVv32f+aV6HDn0tSYqLa6NWra5WsidDzZv3ldMZH+WRnr4IgQAAAAAAQK04enSX8vNXKz8/W/kFa+X1HpQxLjVr1lutWs1RsidDCQkdZYyJ9lAbBEIgAAAAAABQI6z1av/+Df6dvA4c2ChJio1pqZZnjJAnOUNJzfvL5UqM8kgbJke0BwAAAAA4nU6lpaX5/zz88MPyer3q1auX1qxZ479u6NChWrx4sSQpJSVFqamp6tatm4YOHaqffvopqL28rxkzZkiSJk+erDZt2ujo0aOSpN27dyslJUWS5PP5NGPGDF1wwQVKTU1V79699d1331XZXziTJ0/WkiVLJEkZGRlKT0/3n8vJyVFGRob/+NNPP9XAgQPVqVMn9ejRQ9dff70OHz6sl156SS1atFBaWpo6d+6sxx57zP+ae++9V23atAn6rPbu3es/P3PmTLVp00Y+n8/ftmPHDo0cOVLdu3fX+eefrxEjRkiS8vLyFB8fH9TXK6+8EuG/GAAcU1y8Rz/99La++OI3WvtBH+XkjlZe3lNyOGLV8exbdWHvv+qiiz5Uly7zdEaLywiAoohKIAAAAERdfHy81q9fX6H9qaee0tSpU5Wbm6slS5bI4XDommuu8Z/PyspScnKy7rrrLj300EN64okngtpDOZ1OvfDCC7rpppuC2t988039+OOP2rBhgxwOh7Zt26ZGjRpVuM/x2rlzp959910NHz48qH3Hjh265pprtGjRIvXr10+StGTJEh04cECSNHbsWD355JPKz89Xp06dNHr0aLVr106SNGvWLN12220V7uXz+bRs2TK1a9dOq1ev1qBBgyRJd999ty699FLdcsstkqQNGzb4X9OxY8ewnzsAVMVanw4c/FL5u0urffbt/1yST253kjyeS+TxZMiTdLHc7mbRHipCEAIBAADAb+2fv9burQdrtM/kdo118ZjzTui1ffr0Ub9+/XTvvffqjTfe0MqVK8NeN3DgQH8AVJWZM2fqscce09SpU4Pat2/frlatWsnhKC2Ub9u27QmNN9Ts2bP14IMPVgiBFi5cqEmTJvkDIEkaPXp0hdd7PB6dc8452r59uz8Eqkx2dra6du2qsWPHKjMz0x8Cbd++XUOHDvVf161bt5N5SwAaqJKSAyoo+FC787OUn79aRUW7JElNErupQ8p0eZIz1CQxVcYw4aguIwQCAABA1B05ckRpaWn+4zlz5mjs2LGSpHnz5qldu3aaOXOmzjnnnLCvX758uVJTU/3HgwYNktPplCRNmjRJs2bNkiS1b99eAwYM0KuvvqorrrjCf/2YMWM0YMAArV27VkOGDNF1112nHj16VNtfdfr166dly5YpKytLiYnHpj9s3LhRkyZNqvb1//nPf1RYWBgU3Dz22GN67bXXJEnNmzdXVlaWJCkzM1Pjx4/XqFGjdNddd6m4uFhut1vTpk3zVxb97Gc/05QpU9S6dWtJ0rfffhv0uf/hD3/QxRdfHNF7A3B6s9bq0KF/+3fy2rcvV9aWyOVqoqSki5XsyVCSZ6BiY46/ShLRQwgEAAAAvxOt2DlZlU0Hk6Q1a9aoadOm2rhxY4Vz5eFMt27d9MADD/jbq5q+NWfOHI0aNUqXX365v61t27b66quvtGrVKq1atUpDhgzR4sWLNWTIkGr7q87cuXP1wAMPaP78+RG/5s0339SaNWu0efNmPfnkk4qLi/OfCzcdrKioSO+8844effRRJSYmqk+fPnrvvfc0cuRIXXbZZdqyZYtWrFihd999Vz169PB/lkwHAxDI6z2sgj0fl+7ktTtbhUd/lCQ1btxZ7dtfL48nQ02b9JDDQZRQX/EvBwAAgDrr0KFDuv3227Vq1SpNmTJF77zzjn9hY+nEwplzzz1XaWlp+vOf/xzUHhsbq+HDh2v48OFq2bKl3nrrLX8IdDIGDx6suXPnat26df62rl27Kjc3V6NGjQr7mvLKnZycHA0dOlRXXnmlzjzzzErv8d5772nv3r3+aqjDhw8rPj5eI0eOlCQlJSVpwoQJmjBhgkaOHKk1a9aoV69eJ/3eANR/hw/n+at99u79RD5fkZzOBCU1v0gpKdPk8VyiuLhW0R4magghEAAAAOqs++67T2PGjFHnzp311FNPady4cRo8eHBQZcyJ+O1vfxtUCfTZZ5/pzDPPVOvWreXz+bRhw4YaXTtn7ty5uvHGG3X22WdLkqZPn64LL7xQl19+ufr06SNJWrp0qS666KKg16Wnp2vixIn63//9X82bN6/S/jMzM/X8889r/PjxkkrDsw4dOujw4cNat26d+vbtq4SEBB04cEDffvut2rdvX2PvDUD94vUe1d69n/qDnyNH8iRJCQlnq22bifJ4LlGzZulyOGKjO1DUCkIgAAAARF3omkDDhg3TxIkTtWzZMn3++eeSpB49euiyyy7T/Pnzdc8991TZX+AaPt26dauw9XnXrl3Vs2dPffbZZ5JKd/GaOnWqf/v4Cy+8UNOnT4+4v+qMGDFCLVq08B+3bNlSixYt0m233aadO3fK4XBo4MCBGjZsWIXX3nHHHerZs6fuuusuScFrAknSG2+8oRUrVuiZZ57xtzVq1EgDBgzQX//6V/3nP//R9OnT5XK55PP5dP3116t3797Ky8ursCbQL37xC82YMeO43huAuq+w8Eftzi/dyaug4CP5fEfkcMSqefO+atdukjxJlygh4axoDxOngLHWRuXG6enpNicnJyr3BgAAtc8Yk2utTY/2OBAs3M9gmzZtUpcuXaI0ItQ3fL8AdZ/PV6x9+3L9wc+hQ/+WJMXFtVWyZ5A8nkvUvHlfOZ3xUR4pakNVP4NRCQQAAAAAQD139OhO5eevKV3UuWCtvN6DMsatZs16q3Wra+TxZCgh4WwZY6I9VEQRIRAAAABwnKZNm6YPP/wwqO2WW27RlClTojQiAA2NtV7t3/+5v9rnwIEvJEmxMS3V8owR8iRnKKn5RXK5Gkd5pKhLCIEAAACA47Rw4cJoDwFAA1RcvEf5+WvKgp81KinZK8mhpk17quPZt8njyVDjxp2p9kGlCIEAAAAAAKiDrPXpwMEvlb+7tNpn3/71kqzc7iQlJw9SsidDSUkD5HY3i/ZQUU8QAgEAAAAAUEeUlBxQfsEHpWv75K9WUdEuSVKTxG7qkHKzkpMHKTHxAhnjiPJIUR8RAgEAAAAAECXWWh069LXy87O1O3+19u3LlbUlcrmaKCnp4tJqH89AxcYkR3uoOA0QHQIAACDqnE6n0tLS/H/y8vKUnZ2tkSNHSpJ27NihkSNHqnv37jr//PM1YsQI/2u//vprjRgxQueee6569uypMWPGaMeOHf7zM2fOVJs2beTz+fxtL730kqZPn17tuK699lo9/fTT/uNPPvlE3bp1U3FxsVJSUpSamuof84wZM/zXlZSUqEWLFrrzzjuD+svIyFCnTp3UvXt39e7dW+vXrz/+DwtAvef1Htau3X/X5q/+Wx99NFCffDpC33z7O5WU7Ff79lPVq+ebunjAP5R6wRNq1er/EAChxlAJBAAAgKiLj4+vEIjk5eX5v7777rt16aWX6pZbbpEkbdiwQZJUWFioyy+/XI8++qiuuOIKSVJ2drZ27dqlli1byufzadmyZWrXrp1Wr16tQYMGHde4Hn30UfXr10+jR4+Wx+PR9OnT9dRTT8ntdkuSsrKylJxc8eFs5cqVOu+887R48WLNmzcvaJHW119/Xenp6XrxxRc1e/ZsrVy58rjGBKB+Onz4u7IFnVdrz55PZG2RnM5GSmreXykdpsuTNFBxca2iPUyc5giBAAAA4Jf10nPa+f2WGu3zjLPO1qDJN5xUH9u3b9fQoUP9x926dZMkvfHGG+rXr58/AJJKq23KZWdnq2vXrho7dqwyMzOPOwRq2bKlbrvtNt1+++3q3bu3unXrpgEDBlT7uszMTN1yyy16+umn9fHHH6t///4VrunXr58WLFhwXOMBUH94vUe1d+8n/i3cjxz5XpKUkNBR7dpOlMdziZo1S5fDERvlkaIhIQQCAABA1B05ckRpaWmSpA4dOmjZsmVB56dNm6axY8fqySef1M9+9jNNmTJFrVu31saNG9WrV69K+83MzNT48eM1atQo3XXXXSouLvZX8UTqxhtv1Msvv6zs7Gzl5OQEnRs0aJCcTqckadKkSZo1a5YKCwv1/vvv69lnn9XevXuVmZkZNgRasWKFrrrqquMaC4C67ciRH8oWdM5WwZ6P5fMdkcMRq+bN+6lduylK9lyi+Pj20R4mGjBCIAAAAPidbMXOiQo3HSzQZZddpi1btmjFihV699131aNHD23cuLHKPouKivTOO+/o0UcfVWJiovr06aP33nvPv85QpBwOh371q18pJydHHo8n6Fy46WDLly/XoEGDFB8fr6uvvlr333+/Hn/8cX9YdO2116qoqEgHDx5kTSCgnvP5irVvX66/2ufQoX9LkuLi2ql169HyeDLUvFlfOZ1xUR4pUIoQCAAAAPVCUlKSJkyYoAkTJmjkyJFas2aNunbtqtWrV4e9/r333tPevXuVmpoqSTp8+LDi4+OPOwSSSoMghyOyPVUyMzP1wQcfKCUlRZKUn5+vVatW6dJLL5VUuiZQr169NHv2bN18881aunTpcY8HQPQcPbpT+fmrtTs/WwUFH8jrPShj3GrWrLdat7pGHk+GEhLODloLDKgrCIEAAABQ561atUp9+/ZVQkKCDhw4oG+//Vbt27fXiBEjNG/ePP3tb3/T5ZdfLklas2aNkpKSlJmZqeeff17jx4+XJB06dEgdOnTQ4cOHa22c+/fv19q1a7V161bFxpau8/Hiiy8qMzPTHwJJkjFG999/vzp27KjNmzerc+fOtTYmACfHWq/27V+v/N2lizofOPiFJCk29sz/396dx0dV3/sff31msi9kOUGQAIKACiKEGneQqXW9V0WvK1DcaL23FXextag/b61t1XrtItbSa8Vai1pb69Jqt2sQtVqDVUFBi2gVq9ZMEkKYbDPz/f0xwzQbMEgyk2Tez8eDBzPnfGfOZ87JMnnPd2HEiH+nwgtQVnY4WVlFaa5UZOcUAomIiIjIgLd69WoWLVpEVlYW0WiUL3zhCxx00EFAbPjVZZddxmWXXUZ2djbTpk3j5ptv5qmnnuKuu+5KPEdhYSEzZ87k8ccfB2LLxP/6179O7H/hhRcYPXr0LtXVeU6gadOm8bnPfY6jjjoqEQABzJkzh6uvvpq2trYuj83Pz+fKK6/k1ltv5e677961EyIi/aq9vZ76+lXxYV7PEA43YuanZNhnmLD3YryKAEWF+6q3jww65pxLy4Grq6td94n1REREZOgws9XOuep01zGYmdnxwPcAP/C/zrlvb6fdacDDwEHOuR2+wertPdi6deuYPHly3xQtQ56+XmQoci7Kli2vEwzWUBdcSVPTK4AjO7scz5tNhRegvHwW2dkl6S5VZKd29B5MPYFEREREBiAz8wNLgWOATcBLZvaYc+6Nbu2KgUuBF1NfpYjI4NXR0UR9w7OxYV71K2lvrwOMYcOmMX78JVR4AYqLp2KW3HxgIoNBUiFQf3wKJSIiIiI7dDCwwTm3EcDMHgDmAG90a3cjcDOwOLXlDT2HHHJIjyFb9913X2JiaREZ3JxzbN36Vry3Tw2bN6/GuQhZWcPwyo/E8wJ43ixycip2/mQig9ROQyB9CiUiIiKSFpXA+53ubwIO6dzAzD4DjHHO/cbMFALtphdf1NtYkaEmHN5KQ8OfE8FPW9uHABQVTWavsRfieQGGDavC59MgGckMyXyl61MoERERkQHGYuMT/gc4L4m2FwIXAowdO7Z/CxMRSbNQ6J3YhM51NTQ0/gXn2vH7CykvPwLPuxjPm01e7sh0lymSFsmEQPoUSkRERCT1PgDGdLo/Or5tm2JgKlATX51mJPCYmZ3cfVi+c24ZsAxiE0P3Z9EiIqkWibTR2PhifCWvGlpa/g5AQcFExoxegOcFKC2txufLSXOlIum3233e9CmUiIiISL94CZhkZuOJhT9nA/O27XTObQYSE1eYWQ1wleZlFJFM0NLyAcF46FPf8DzRaCs+Xy5lZYcxdswFeN5s8vPH7PyJRDJMMtOc78qnUO8ChxL7FKrHcmTOuWXOuWrnXPXw4cM/fdUiIiIiQ5xzLgwsAn4HrAMecs69bmZfN7OT01td3/P7/VRVVTF16lTOOOMMQqFQl+3b/n3727H1SQKBAPvuuy/Tp0/noIMO4pVXXkk815NPPkl1dTVTpkxhxowZXHnllQDccMMNVFZWdnm+xsZGampqMDMef/zxxHOceOKJ1NTUcOqpp1JVVcXEiRMpKSlJPO75558HoKqqirPPPrvLawmHw3zta19j0qRJifY33XRTj9fa/TWJyPZFo+3UN/yZv234Fi+8eDzP//lI3nzrepq3/o1Ro85g+vS7OXLWy1RNv5vRoz+vAEhkO5LpCaRPoURERETSwDn3W+C33bZdv522gVTU1F/y8/MTQc78+fO56667uOKKK7ps7+7++++nurqae+65h8WLF/OHP/yBtWvXsmjRIn7zm9+w3377EYlEWLZsWeIxl19+OVdddVWP5xo9ejQ33XQTJ510UpftjzzyCAA1NTV85zvf4YknnkjsW7duHZFIhFWrVrF161YKCwsBuPbaa/noo49Ys2YNeXl5bNmyhdtuu63X1yoi29fW9jHB4ErqgjXU1z9HJNKMWTZlpQczas8z8bwABQXjiQ+JFZEk7DQEcs6FzWzbp1B+4CfbPoUCap1zj/V3kSIiIiKSOWbNmsVrr72WdPvDDjuMW2+9FYBbbrmFJUuWsN9++wGxXjdf+tKXdvoc06dPp6Ojgz/84Q8cc8wxSR13xYoVLFiwgHXr1vHoo48yb948QqEQP/7xj3n33XfJy8sDoLi4mBtuuCHp1yOSqZyLsLnpFYJ1NQSDK9nS/DoAubkjGTHiRCq8AGVlh5GVVZTmSkUGr6TmBMqkT6FEREREMlnj42/T/o+tffqcOaMKKT1pQlJtw+EwTz75JMcffzwALS0tVFVVJfZfc801nHXWWV0e89RTT3HKKacAsHbt2sTwr97cfvvt/OxnPwOgrKyMp59+OrFvyZIlXHfddUmHQA8++CB/+MMfWL9+PT/4wQ+YN28eGzZsYOzYsRQXF2/3ccm8JpFM0d4eJFi/Kj6/zyrC4UbM/JQM+wwT9l6MVxGgqHBf9faRQcFFIkRDIaJbt/7r33buZw0fTtncuSmvcbcnhhYRERER2V2dg5FZs2axcOFCYMdDp+bPn097ezvNzc1JD6/a3nAwgCOPPBKAZ599dqfPU1tbS0VFBWPHjqWyspILLriA+vr6Hu3uuecevve97xEMBnn++ecZM2aMhoNJRnMuypYta6kLriQYrKGp6VXAkZ3tMbziKDwvQHn5TLKzS9JdqmQAFw7vMLSJbN2KC4WI9Lo/1KO9a21N+tgFBx+sEEhERERE0ivZHjt97dMEI/fffz8HHnggixcv5uKLL+ZXv/oV+++/P6tXr2b69Omfqo4lS5bwjW98g6ysHb9NXrFiBevXr2fcuHEANDU18ctf/pL58+fz3nvvsWXLFoqLizn//PM5//zzmTp1KpFI5FPVJDLYdXQ0Ud/wbGyYV/1K2tvrAGPYsOmMH38pFd5siounElt4WmT7XDjca2AT6bXXTYhoqFNY00uvHNfWltyBzfAVFOArLIz9i9/O3nPPLve77/cVxv8v+Ndtf2Ehlp+P+dLz9a4QSEREREQGLTPjxhtvZMKECaxfv57FixfzH//xH8ycOZN99tmHaDTKsmXL+K//+q+knu/YY4/luuuu48MPP9xum2g0ykMPPcSaNWsYNWoUAE8//TQ33ngjX/ziF1m4cCGLFi3iRz/6EXl5eUQiEdrb2/vk9YoMBs45tm59i7r4Eu6bN6/GuQhZWSV45bPwvACedyQ5OV66S5V+5jo6th/YJBHS9Ahtkv1ZatZLIFNI9qhR2wlp/tXGX1iIFRTg7xTqWF5e2kKbvqYQSEREREQGrO7z5xx//PE9llTPz8/nyiuv5NZbb+Xuu+/mu9/9LnPnziUUCmFmnHjiiYm2necEAvj1r3/d45hLlixhzpw5261p1apVVFZWJgIgiA0le+ONN/jwww+56aabuO6665g6dSrFxcXk5+dz7rnnJton85pEBptweCsNDc8ngp+2to8AKCqawl5jL8TzAgwbVoXPpz9BBzLX3h4PabYFNL0Pe0r2ftKhjc/Xe2hTXh6/37MXjr+X9tvuW36+5pHaDnPOpeXA1dXVrrZWq8iLiIgMVWa22jlXne46pKve3oOtW7eOyZMnp6kiGWz09SIQ6+3T0lZKE4MAACAASURBVPJuLPSpe5qGxpdwrh2/v4jy8iOo8AKUe0eSlzsy3aUOadH29q5BzS6GND1Cm46O5A7s9/cSwBQkHdL0CG3y8hTa9KEdvQdTDCsiIiIiIiI7FYm00tj4YqK3T0vLewAUFExkzJhz8LwApSUH4vPlpLnSgck5h9sW2nQPYLoMidrB/VB8suKtISKhECQb2mRldQpg/hXS+Cu8LsOekglsfIWFWG6uQptBSiGQiIiIiIiI9KqlZRPBYA11wRoaGv5MNNqKz5dHWdlhjB2zEM+bTX7+mHSX2S+cc7i2tl3uUdNzRalQog3hcHIH3xbaFMbnpolPLJy1x/D47e6TDnfb1j20yclRaCOAQiARERERERGJi0bbady8mmDd09QFVxIKbQAgP28so0adSYUXoLT0EPz+vDRX2pNzDtfamnRo03NFqZ5z35Dkqn6Wnd2j14y/sIjsPUYk38umc4CTo95U0j8UAomIiIiIiGSwtraPCQZXUhesob7+OSKRZsxyKCs9mMrKs6nwAuTnj+vzniSJ0GY7gU3PHjXb9m8n5AmFdi206RbI+IuLyR45MonApuuy3/54TxuRwUAhkIiIiIiISAaJRsM0Nb0SH+a1kubmNwDIzR3JiBEnUuEFKCs7nKyswi6Pc87hWlqSCGmSXFEqFIJoNKmaLSenZ2hTUtLLkt/xkKbgX2FNjyW/CwoU2kjGUggkIiIiIiIyBDnnEiFNW9MHBBufoyH0Ao3hV4kQAuejsG00IzfPpOiTkWQH83BbQ7RufYQPtv6s99AmydWlLTe355LeZWVkjx7dKbAp6BHg9LqiVEEBlp3dz2dLJDMoBBIRERERERkAXDRKNNTSe6+ZXbwfCTXTVhGibUqU1qlROvZy4APfZsh9w0fe2ixy1xu+lo+Aj2jJy6Ote2jjlZM9dkyPkKZLr5rtLfmdpT81RQYifWeKiIiISNr5/X4OOOAAwuEwkydP5t5776WgoKDL9vHjx3PfffdRWloKwOuvv87FF1/MBx98QDQa5ZxzzuHaa6/FzFi+fDmLFy+msrISgGnTpvHTn/6U8847jxNPPJH777+fd955h+bmZj755BPGjx8PwJ133snhhx/eo75AIMCHH35Ibm4u7e3tHH300XzjG99I1LKtzm3OPvtsvvrVr9LR0cF1113HL3/5S4qLi8nNzeX666/nhBNOYNy4cdTW1lJRUZF43PLly6mtreWOO+7ghhtu4JZbbuHdd99ljz32AKCoqIjm5uYdHlNSJxbadBv+1NsS3zsNbGLtXSiU9LEtP79bAFMAew6jbayPUGUHzRVtRLI7wEFBx2iGd0ylLOdAikbsi39CMb6zO4U2+fkKbUQyhL7TRURERCTt8vPzeeWVVwCYP38+d911F1dccUWX7eeeey5Lly5lyZIltLS0cPLJJ/PDH/6QY489llAoxGmnncadd97JRRddBMBZZ53FHXfc0evxHnnkEQBqamr4zne+wxNPPLHTGu+//36qq6tpb2/nmmuuYc6cOaxcubJH/Z1dd911fPjhh6xdu5bc3Fw+/vjjxGOSUVFRwW233cbNN9/cY9/2jinb5yKRWPjSPZDpLaTZGoqHOfG5b7aG4mHNv5b83qXQpqAgHtjEe9kUFJI1fDi+cXt1W+Z7JytIxZ/H/H6cczRvfZNgXQ3BYA2bm/6CcxGyskqo8I7B8wJ45bPIyfH68ayKyGCiEEhEREREEp588kk++uijPn3OkSNHcsIJJyTdftasWbz22ms9th922GGJ7T//+c854ogjOPbYYwEoKCjgjjvuIBAIJEKg/pKTk8Mtt9zCxIkTefXVV5k+fXqv7UKhED/+8Y955513yM3NBWDEiBGceeaZSR/rggsuYPny5XzlK1+hvLy8T+ofTFwkskvDoHa25LdraUn62L6CAqywAH/BvwKY7OF74Bu3kyW+C7rd39bTxu/vk3MSDm+lrv7/qAvGgp+2ttj3a3HR/uw19j/xKgIMK56Oz6c/9USkJ/1kEBEREZEBIxwO8+STT3L88cd32R6JRPjTn/7EwoULgdhQsAMPPLBLmwkTJtDc3ExTUxMADz74IM8++ywAl156Keeff36f1en3+5k+fTrr169n+vTptLS0UFVVldh/zTXXMHnyZMaOHcuwYcM+9XGKioq44IIL+N73vsd///d/d9nX2zHPOuusT32svuDC4R32sukZ0nTucRPq0d61tiZ97J6BTCHZI0bsvFdNbwFOQT7m8/XjmUqec45Q6B2C8dCnofElnGvH7y+ivPwIKrzL8Lwjyc0dke5SRWQQUAgkIiIiIgm70mOnL3UONGbNmpUIe7Zt/+CDD5g8eTLHHHNM0s+5o+FgfcF1WiWpt6FZvfVm+jQuueQSqqqquOqqq7ps74vhYC4ahWg08X/n2y4S6bkvEiVcX897X7yw1145rq0tuQOb9RrIZO+5504CmoIe+/2FhVj+wAlt+kIk0kpD4wux4KduJS2t7wFQWDiJMWPOwfMClJYciM+nZc5FZNcoBBIRERGRtNteoLFteygU4rjjjmPp0qVccsklTJkyhWeeeaZL240bN1JUVLRbPW+SFYlEWLNmDZMnT95um4kTJ/Lee+/R1NS0WzWVlpYyb948li5dCsR627hoFCA2L000CpFIz0AnEsVFI71si0I03j7J5b6BWMji8+PCYSINDbGeNpWVSYU03UMdy8/HzD71ORmKWlreJxhcSV2whoaGPxONtuLz5VFedjhjx34Bz5tNfv7odJcpIoOcQiARERERGfAKCgr4/ve/zymnnMKXv/xl5s+fzze/+U3++Mc/cvTRR9PS0sIll1zC1Vdf3e+1dHR0sGTJEsaMGcO0adN2WPMFF1zApRdfzA+XLiUnK4t/fvwxK1c+w+lzToZorFdNh3MQiQUz4fp6Ilu20Pbuu4TrGwi3ttL65pt8+cQTmXnmmYQ7Omhdvz52gGiUto0bt3t88/nB7wOfLx7g+LCcHKz7tm3/+/3QfZvPB34/mCVCm2wXZfzDv+jTc5qJotF2GhtrCQZrqAuuJBTaAEB+3lhGjTqTCu+zlJYegt+fm+ZKRWQoUQgkIiIiIoPCjBkzmDZtGitWrGDBggU8+uijXHzxxVx00UVEIhEWLFjAokWLdusY3XvTuHg448Jh5s+dS05ODm1tbXxu1iwevusu2jdtgmiUlpYWpk+ZkuhZc8zMmdx42WVcO28e//2DH7D/lCnk5eZSkJ/PdRddRPv77+MiEWbMnInPDDBOO+F4Dth339gwrEgEDPD78RUWskdxMaf8+7/zvWXLyB45Enw+WtraOGzevETtxx13HN/+1rdiQU6n0EYGjta2jwgGVxIM1lBf/xyRyFbMcigrPZjKyrOp8ALk54/TtRORfmNuF7qA9qXq6mpXW1ublmOLiIhI/zOz1c656nTXIV319h5s3bp1OxzWNFA552KhSy/z2CS2dRoS1aVdfAhVj23Jvjc2S/SW6dxjpnvvmt729bptEIU2g/XrJR2i0TBNTa8kVvJqbl4HQG7unlR4ATwvQFnZYWRlFaa5UhEZSnb0Hkw9gUREREQkJXqENp3msdnhtu1NUvwpQ5tE8OL3Y9nZWI/QJjaMqsu2+FCpwRjaSGq1twcJBp+JTepcv4pweDNmfkpKqpk44Wo8L0Bh4T76+hGRtFAIJCIiIiK7reOf/8SFw9ufkHjb0Co+bWjjjw2Nys7uus3n6zHHDT5/7/PeJOHUU0/lnXfe6bLt5ptv5rjjjtvFMyKZwrkoW7asTfT2aWp6DXDk5FQwvOJovIoA5WUzyc7u/wnLRUR2RiGQiIiIiOy2SGNjbHhVp540XUOb+KTD/u4TEvt72ZZ8aNPXHnnkkbQcVwaXjo7N1Nevigc/K+noqAeMYcOq2Hv8pXhegOLi/TEbOsvWi8jQoBBIRERERHZb3j77pLsEkX7jnKN565sE62K9fTY3vYxzEbKySvG8I6nwApSXzyInpzzdpYqI7JBCIBERERERkW7C4WYaGp5P9PZpa/sIgOKi/dlr7H9SUfFZhg2bjpk/zZWKiCRPIZCIiIiIiGQ85xyh0EaCwRrqgjU0Nr6Ecx34/UWUl8+Mr+Z1JLm5I9JdqojIp6YQSEREREREMlIk0kpD4wuxlbzqVtLS+h4AhYWTGDPmPCq8ACUln8Hny0lzpSIifUMzlYmIiIhI2vn9fqqqqpg6dSpnnHEGoVCox/aTTjqJxsbGxGNef/11jjrqKPbdd18mTZrEjTfeGFuGHli+fDnDhw+nqqqKqqoqzjnnHADOO+88Hn74YU499VSqqqqYOHEiJSUliXbPP/98r/UFAgFqa2sBGDduHKeddlpi38MPP8x5552XuP/kk09SXV3NlClTmDFjBldeeSUAN9xwA5WVlVRVVTFlyhRWrFiReMx5553H+PHjE3UcfvjhXY5/yimncOihh3bZ9uabbxIIBKiqqmLy5MlceOGFANTU1HR5TVVVVfzxj39M/mIMcS0t7/P+pvt45dWFPLPqM7z66kL+8Y9fUFg4kX33vZHDD3uGQw95ikkTv0pZ2aEKgERkSFFPIBERERFJu/z8fF555RUA5s+fz1133cUVV1zRZfu5557L0qVLWbJkCS0tLZx88sn88Ic/5NhjjyUUCnHaaadx5513ctFFFwFw1llncccdd/R6vG2rgNXU1PCd73yHJ554YpfqXb16NW+88QZTpkzpsn3t2rUsWrSI3/zmN+y3335EIhGWLVuW2H/55Zdz1VVX8be//Y0DDzyQ008/nezsbABuvfVWTj/99B7HamxsZPXq1RQVFbFx40b23ntvAC655BIuv/xy5syZA8CaNWsSj5k1a9Yuv6ahKhptp7HxJYLBldQFawiF3gYgP38so0adTYUXoLT0EPz+3DRXKiLS/xQCiYiIiEjCW2/dyJbmdX36nMVFk9lnn+uSbj9r1ixee+21HtsPO+ywxPaf//znHHHEERx77LEAFBQUcMcddxAIBBIhUH+68soruemmm7j//vu7bL/llltYsmQJ++23HxDryfSlL32px+MnTZpEQUEBDQ0N7LHHHjs81q9+9StOOukkRowYwQMPPMDXvvY1AD788ENGjx6daHfAAQfs7ssaMlrbPiIYXEmw7mnqG54nEtmKWQ5lZYdQWTmXCi9AQcH4dJcpIpJyGg4mIiIiIgNGOBzmySef7BFoRCIR/vSnP3HyyScDsaFgBx54YJc2EyZMoLm5maamJgAefPDBxHCoe+65p0/rPPPMM3n55ZfZsGFDl+1r167tUVdvXn75ZSZNmtQlAFq8eHGi3vnz5ye2r1ixgrlz5zJ37twuQ8guv/xyjjrqKE444QRuv/32LkPlVq1a1WU42Ntvv707L3fAi0bDNDbWsuHt7/DiX07kueeOYP36r9G0ZS0jR85h2rRlHDmrlhlVyxk75nwFQCKSsdQTSEREREQSdqXHTl9qaWmhqqoKiPUEWrhwYZftH3zwAZMnT+aYY45J+jl3NBxsd/n9fhYvXsy3vvUtTjjhhKQfd/vtt3PPPffw1ltv8fjjj3fZ19twsI8//pi//e1vzJw5EzMjOzubtWvXMnXqVM4//3yOO+44nnrqKR599FF+9KMf8eqrrwKZMRysvb2OYPAZ6oI11NevIhxuwsxPSUk1EydcjecFKCzcBzNLd6kiIgOGQiARERERSbvOc//0tj0UCnHcccexdOlSLrnkEqZMmcIzzzzTpe3GjRspKipi2LBhKal5wYIFfOtb32Lq1KmJbfvvvz+rV69m+vTpvT5m25xAjz32GAsXLuTtt98mLy9vu8d46KGHaGhoYPz4WM+VpqYmVqxYwU033QTAqFGjuOCCC7jggguYOnUqa9eu7cNXOLA4F2XLlrXU1T1NMFhD05Y1gCMnp4Lhw4/F8wKUlx1BdnZqrr+IyGCk4WAiIiIiMuAVFBTw/e9/n9tuu41wOMz8+fN59tlnE6tetbS0cMkll3D11VenrKbs7Gwuv/xybr/99sS2xYsX881vfpO33noLgGg0yl133dXjsSeffDLV1dXce++9OzzGihUreOqpp3j33Xd59913Wb16NQ888AAATz31FB0dHQB89NFHBINBKisr++rlDQgdHZv5+OMneP2Nq1j17CG8VHsq77z7A8z87D3+Mg466FFmHvFnpky+mRF7nKAASERkJ9QTSEREREQGhRkzZjBt2jRWrFjBggULePTRR7n44ou56KKLiEQiLFiwgEWLFqW0poULF/KNb3wjcX/atGl897vfZe7cuYRCIcyME088sdfHXn/99cybN48vfvGLQCxA6vxcDz30EH//+9+7LA0/fvx4SkpKePHFF/n973/PpZdemuhJdOuttzJy5EjWr1+fmBNom2uvvbbXlccGGucczc3rCQZrqAvWsHnzy0CUrKxSPO9IKrwA5eWzyMkpT3epIiKDkjnn0nLg6upqV1tbm5Zji4iISP8zs9XOuep01yFd9fYebN26dUyePDlNFclg09dfL+FwM/UNzxGsqyFY/wxtbR8BUFy8P54XoMILMGzYdMz8fXZMEZGhbEfvwdQTSEREREREUsY5Ryi0Md7b52kaG2txrgO/vwivfBaeF8DzjiQ3d4+dP5mIiOwShUAiIiIiInGnnnoq77zzTpdtN998M8cdd1yaKhoaIpEWGhpeIBhcSV2whtbW9wEoLJzEmDHnUeF9lpKSz+DzZae5UhGRoU0hkIiIiIhI3COPPJLuEoaMlpb3qAvWEAzW0NDwAtFoGz5fPuXlh7PXXhfilc8mP39oTWQtIjLQKQQSEREREZxzmFm6y5ABbkfziUajbTQ21iZ6+4RCbwOQnz+OylFz8bwApaUH4/fnpqpcERHpRiGQiIiISIbLy8sjGAzieZ6CINku5xzBYDCxGhlAa+uHBIMrCQZrqG94nkhkKz5fDqWlhzC6ch6eN5uCgvFprFpERDpTCCQiIiKS4UaPHs2mTZv45JNP0l2KDHC5ubkMGxZkw9sPEwzW0Ny8HoC83FGMHDkHzwtQXnYYfn9BmisVEZHeKAQSERERyXDZ2dmMH6/eGtK79va6xBCvf256lnC4CbMsSkoOZOKEr+B5AQoLJ6kXmYjIIKAQSEREREREEpyL0rRlDcG62KTOTVteAyAnZzjDhx9HhRegvPwIsrKK01ypiIjsKoVAIiIiIiIZrqOjkWD9KoLBGoLBZ+joqAeMkmFV7D3+cryKAMVFUzDzpbtUERHZDQqBREREREQyjHOO5ub1BINPUxesYfPmvwJRsrPL8MqPjM3tUz6TnJzydJcqIiJ9SCGQiIiIiEgGCIebqW94Lj7MayVt7R8DUFw8lXHjvkyFF2DYsGmY+dNcqYiI9BeFQCIiIiIiQ5BzjlDobeqCsbl9Ghtrca4Dv78IzzsSz5uNVz6b3Nzh6S5VRERSRCGQiIiIyABlZscD3wP8wP86577dbf8VwBeAMPAJcIFz7u8pL1QGjEikhYaGFxLBT2vrJgAKC/dh7Jjz8bwAJSWfwefLTnOlIiKSDgqBRERERAYgi43JWQocA2wCXjKzx5xzb3Rq9leg2jkXMrMvAbcAZ6W+Wkmnlpb3EqFPQ8MLRKNt+Hz5lJcfwV57/ScVXoC8vFHpLlNERAYAhUAiIiIiA9PBwAbn3EYAM3sAmAMkQiDn3NOd2r8AfD6lFUpaRKNtNDbWJoKfUGgjAPn546isnIfnBSgtOQi/PzfNlYqIyECjEEhERERkYKoE3u90fxNwyA7aLwSe7G2HmV0IXAgwduzYvqpPUqi19R8EgyupC9bQ0PA8kUgIny+H0tJDGF05H8+bTUHB+HSXKSIiA5xCIBEREZFBzsw+D1QDs3vb75xbBiwDqK6udiksTT6laDTM5qa/Eqx7mmCwhuatbwKQlzuKkSNPpcILUFZ2KH5/QZorFRGRwUQhkIiIiMjA9AEwptP90fFtXZjZ0cASYLZzri1FtUk/aGuvoz7e26e+fhXh8BbMsigtqWbixK/ieQEKCyZiZukuVUREBqmkQiCtTCEiIiKSci8Bk8xsPLHw52xgXucGZjYD+BFwvHPun6kvUXaHcxGamtYQDNZQF6xhy5Y1AOTkDGf48OOp8AKUlx9BVlZxmisVEZGhYqchkFamEBEREUk951zYzBYBvyP2QdxPnHOvm9nXgVrn3GPArUAR8It475D3nHMnp61o2amOjkaCwWcIBlcSrH+Gjo56wEdJSRV7730FFV6AoqIp6u0jIiL9IpmeQFqZQkRERCQNnHO/BX7bbdv1nW4fnfKiZJc452huXpfo7bN581+BKNnZZXjls/G82XjeLLKzy9JdqoiIZIBkQqA+W5lCRERERGSoC4e3UF//fCL4aW+PjdQrLp7KuHFfpsL7LMOGHUCsw72IiEjq9OnE0DtbmULLk4qIiIjIUOOcY2toA8FgDcG6Gho31+JcmKysYsrLZ8V6+5TPJjd3eLpLFRGRDJdMCNRnK1NoeVIRERERGQoikRYaGl6gLlhDMFhDa+smAIoK92XsmIV4XoCSkhn4fNlprlRERORfkgmBtDKFiIiIiGS8UOjvsd4+wRoaGl8gGm3H58unvPwI9trrP6nwAuTljUp3mSIiItu10xBIK1OIiIiISCaKRttoaHwpEfyEQu8AUFAwnsrK+XhegLLSg/D5ctNcqYiISHKSmhNIK1OIiIiISCZobf1HfIjXShoanicSCeHz5VBWeiijKxfgebMpKBiX7jJFREQ+lT6dGFpEREREZDCJRjvYvPmvid4+zVvfBCAvr5KRI/+DCi9AWdmh+P35aa5URERk9ykEEhEREZGM0tb2CcH6lQSDK6mvX0U4vAWzLEpLqpk48at4XoDCgonEpzkQEREZMhQCiYiIiMiQ5lyEpqY11AWfJhisYcuWtQDk5OzBHsNPwPMClJcfTlZWcZorFRER6V8KgURERERkyOnoaCAYXEUwuJJg/TN0dNQDPkpKZjBh7yvxvABFRZPV20dERDKKQiARERERGfScczQ3vxGf1LmGzZtfAaJkZ5fjlR+J583G82aRnV2W7lJFRETSRiGQiIiIiAxK4fAW6uufS6zm1d7+TwCKiw9g/LiL8LwAw4YdgJk/zZWKiIgMDAqBRERERGRQcM6xNbQhtpJXXQ2Nm2txLkxWVjHl5bOo8AKUe7PJzalId6kiIiIDkkIgERERERmwIpEQDQ0vJIZ5tbZ+AEBR4b6MHfsFPC9AybAqfL7sNFcqIiIy8CkEEhEREZEBJRR6l2CwhrpgDY2NLxKNtuP3F1BWdjjj9voSnjebvLxR6S5TRERk0FEIJCIiIiJpFY220dD4UmyYV7CGUOgdAAoK9qay8vNUeAFKS6vx+XLTXKmIiMjgphBIRERERFKutfUfiQmd6+ufIxptwefLoaz0UEaPPgevfDYFBXulu0wREZEhRSGQiIiIiPS7aLSDzZtfTgzz2rr1LQDy8irZc8/TqPAClJUdit+fn+ZKRUREhi6FQCIiIiLSL9raPiFYv5JgXQ3B+lVEIs2YZVFaehB77nkNFV6AgoIJmFm6SxUREckICoFEREREpE84F6Gp6bXESl5btqwFIDdnBCP2+De8igDlZYeTlVWc5kpFREQyk0IgEREREdktzkV4442rCdavpKOjAfBRUjKDCXtfiecFKCqarN4+IiIiA4BCIBERERHZLWZ+OsKNeN5sPC+AVz6L7OzSdJclIiIi3SgEEhEREZHdVjX97nSXICIiIjvhS3cBIiIiIiIiIiLS/xQCiYiIiIiIiIhkAIVAIiIiIiIiIiIZQCGQiIiIiIiIiEgGUAgkIiIiIiIiIpIBFAKJiIiIiIiIiGQAhUAiIiIiIiIiIhlAIZCIiIiIiIiISAZQCCQiIiIiIiIikgEUAomIiIiIiIiIZACFQCIiIiIiIiIiGUAhkIiIiIiIiIhIBlAIJCIiIiIiIiKSARQCiYiIiIiIiIhkAIVAIiIiIiIiIiIZQCGQiIiIiIiIiEgGUAgkIiIiIiIiIpIBFAKJiIiIiIiIiGQAhUAiIiIiIiIiIhlAIZCIiIiIiIiISAZQCCQiIiIiIiIikgEUAomIiIiIiIiIZACFQCIiIiIiIiIiGUAhkIiIiIiIiIhIBlAIJCIiIiIiIiKSARQCiYiIiIiIiIhkAIVAIiIiIiIiIiIZQCGQiIiIyABlZseb2ZtmtsHMvtrL/lwzezC+/0UzG5f6KkVERGSwUAgkIiIiMgCZmR9YCpwATAHmmtmUbs0WAg3OuYnA7cDNqa1SREREBhOFQCIiIiID08HABufcRudcO/AAMKdbmznAvfHbDwOfMzNLYY0iIiIyiCgEEhERERmYKoH3O93fFN/WaxvnXBjYDHgpqU5EREQGHYVAIiIiIkOcmV1oZrVmVvvJJ5+kuxwRERFJE4VAIiIiIgPTB8CYTvdHx7f12sbMsoASINj9iZxzy5xz1c656uHDh/dTuSIiIjLQKQQSERERGZheAiaZ2XgzywHOBh7r1uYx4Nz47dOB/3POuRTWKCIiIoNIVroLEBEREZGenHNhM1sE/A7wAz9xzr1uZl8Hap1zjwF3A/eZ2QagnlhQJCIiItIrhUAiIiIiA5Rz7rfAb7ttu77T7VbgjFTXJSIiIoOThoOJiIiIiIiIiGQAhUAiIiIiIiIiIhkgqRDIzI43szfNbIOZfbWX/blm9mB8/4tmNq6vCxURERERERERkU9vpyGQmfmBpcAJwBRgrplN6dZsIdDgnJsI3A7c3NeFioiIiIiIiIjIp5dMT6CDgQ3OuY3OuXbgAWBOtzZzgHvjtx8GPmdm1ndlioiIiIiIiIjI7kgmBKoE3u90f1N8W69tnHNhYDPg9UWBIiIiIiIiIiKy+1K6RLyZXQhcGL/bZmZrU3l8SUoFUJfuIqQLXZOBR9dkYNJ1YP3DgwAACaJJREFUGXj2TXcB0tPq1avrzOzv/fT0+j5MLZ3v1NL5Tj2d89TS+U6t/jzfe21vRzIh0AfAmE73R8e39dZmk5llASVAsPsTOeeWAcsAzKzWOVedxPElhXRdBh5dk4FH12Rg0nUZeMysNt01SE/OueH99dz6Pkwtne/U0vlOPZ3z1NL5Tq10ne9khoO9BEwys/FmlgOcDTzWrc1jwLnx26cD/+ecc31XpoiIiIiIiIiI7I6d9gRyzoXNbBHwO8AP/MQ597qZfR2odc49BtwN3GdmG4B6YkGRiIiIiIiIiIgMEEnNCeSc+y3w227bru90uxU4YxePvWwX20tq6LoMPLomA4+uycCk6zLw6JpkHl3z1NL5Ti2d79TTOU8tne/USsv5No3aEhEREREREREZ+pKZE0hERERERERERAa5fg+BzOx4M3vTzDaY2Vd72Z9rZg/G979oZuP6u6ZMl8Q1ucLM3jCz18zsT2a23eXlpO/s7Lp0aneamTkz08z9/SyZa2JmZ8a/X143s5+nusZMlMTPsLFm9rSZ/TX+c+zf0lFnJjGzn5jZP81s7Xb2m5l9P37NXjOzz6S6Ruk7ut6plcT5nh8/z2vM7Hkzm57qGoeanZ3zTu0OMrOwmZ2eqtqGomTOt5kFzOyV+Putlamsb6hJ4mdKiZk9bmavxs/3+amucSgxszHx96Xb/l64tJc2Kf292a8hkJn5gaXACcAUYK6ZTenWbCHQ4JybCNwO3NyfNWW6JK/JX4Fq59w04GHgltRWmXmSvC6YWTFwKfBiaivMPMlcEzObBFwDHOGc2x+4LOWFZpgkv1euBR5yzs0gtlDBnamtMiMtB47fwf4TgEnxfxcCP0xBTdJ/lqPrnUrL2fH5fgeY7Zw7ALgRzenRF5az43O+7ffRzcDvU1HQELecHZxvMysl9rv85Pj7rV2di1a6Ws6Ov74vAt5wzk0HAsBt8VXC5dMJA1c656YAhwIX9fLeNaW/N/u7J9DBwAbn3EbnXDvwADCnW5s5wL3x2w8DnzMz6+e6MtlOr4lz7mnnXCh+9wVgdIprzETJfK9A7M3dzUBrKovLUMlcky8CS51zDQDOuX+muMZMlMx1ccCw+O0S4B8prC8jOeeeIbY66PbMAX7qYl4ASs1sz9RUJ31N1zu1dna+nXPPb/s9hN639YkkvsYBLgZ+Ceh3/25K4nzPA37lnHsv3l7nfDckcb4dUBz/m7wo3jacitqGIufch865l+O3twDrgMpuzVL6e7O/Q6BK4P1O9zfR8wUn2jjnwsBmwOvnujJZMteks4XAk/1akUAS1yXeLXCMc+43qSwsgyXzvbIPsI+ZPWdmL5jZDj81lD6RzHW5Afi8mW0itrLlxakpTXZgV3/3yOCm650+et+WAmZWCZyKermlyj5AmZnVmNlqMzsn3QUNcXcAk4l9iLYGuNQ5F01vSUODxaa+mUHPUR0p/b2Z1BLxkpnM7PNANTA73bVkOjPzAf8DnJfmUqSrLGLdNgPEPnl9xswOcM41prUqmQssd87dZmaHAfeZ2VS9gRGRoczMPkssBJqZ7loywHeBrzjnohrAkBJZwIHA54B84M9m9oJz7q30ljVkHQe8AhwFTAD+YGarnHNN6S1rcDOzImK9By9L97ns755AHwBjOt0fHd/WaxszyyLWdT/Yz3VlsmSuCWZ2NLCE2NjbthTVlsl2dl2KgalAjZm9S2w86WOaHLpfJfO9sgl4zDnX4Zx7B3iLWCgk/SeZ67IQeAjAOfdnIA+oSEl1sj1J/e6RIUPXO8XMbBrwv8Ac55zeR/e/auCB+Huy04E7zeyU9JY0pG0Cfuec2+qcqwOeATQBev85n9jwO+ec20Bs3rH90lzToGZm2cQCoPudc7/qpUlKf2/2dwj0EjDJzMbHJ5M6G3isW5vHgHPjt08H/s855/q5rky202tiZjOAHxELgDTmNjV2eF2cc5udcxXOuXHOuXHExvyf7JyrTU+5GSGZn1+/JtYLCDOrINZdeWMqi8xAyVyX94h9WoiZTSYWAn2S0iqlu8eAc+KrXxwKbHbOfZjuoqTf6HqnkJmNBX4FLFDPiNRwzo3v9J7sYeDLzrlfp7msoexRYKaZZZlZAXAIsXlVpH90fh81AtgXvb/91OJzK90NrHPO/c92mqX092a/DgdzzoXNbBHwO8AP/MQ597qZfR2odc49RuyE3GdmG4hNOnV2f9aU6ZK8JrcSmwTsF/Euru85505OW9EZIMnrIimU5DX5HXCsmb0BRIDF+gS2fyV5Xa4EfmxmlxOb3PA8fbjQv8xsBbFAtCI+F9P/A7IBnHN3EZub6d+ADUCI2KeMMkjpeqdWEuf7emLzad4Zf98Wds6pp/BuSOKcSx/a2fl2zq0zs6eA14Ao8L/Oue0uJy87lsTX943AcjNbAxixoY91aSp3KDgCWACsMbNX4tu+BoyF9PzeNL0vFhEREREREREZ+vp7OJiIiIiIiIiIiAwACoFERERERERERDKAQiARERERERERkQygEEhEREREREREJAMoBBIRERERERERyQAKgURERERERDKYmQXM7Il01yEi/U8hkIiIiIiIiIhIBlAIJCIiIiIiMgiY2efN7C9m9oqZ/cjM/GbWbGa3m9nrZvYnMxseb1tlZi+Y2Wtm9oiZlcW3TzSzP5rZq2b2splNiD99kZk9bGbrzex+M7O0vVAR6TcKgURERERERAY4M5sMnAUc4ZyrAiLAfKAQqHXO7Q+sBP5f/CE/Bb7inJsGrOm0/X5gqXNuOnA48GF8+wzgMmAKsDdwRL+/KBFJuax0FyAiIiIiIiI79TngQOCleCedfOCfQBR4MN7mZ8CvzKwEKHXOrYxvvxf4hZkVA5XOuUcAnHOtAPHn+4tzblP8/ivAOODZ/n9ZIpJKCoFEREREREQGPgPudc5d02Wj2XXd2rlP+fxtnW5H0N+KIkOShoOJiIiIiIgMfH8CTjezPQDMrNzM9iL2N93p8TbzgGedc5uBBjObFd++AFjpnNsCbDKzU+LPkWtmBSl9FSKSVkp3RUREREREBjjn3Btmdi3wezPzAR3ARcBW4OD4vn8SmzcI4FzgrnjIsxE4P759AfAjM/t6/DnOSOHLEJE0M+c+bW9BERERERERSScza3bOFaW7DhEZHDQcTEREREREREQkA6gnkIiIiIiIiIhIBlBPIBERERERERGRDKAQSEREREREREQkAygEEhERERERERHJAAqBREREREREREQygEIgEREREREREZEMoBBIRERERERERCQD/H8kKQZKb61s0gAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import ner_log_parser\n",
"\n",
"%matplotlib inline\n",
"\n",
"ner_log_parser.get_charts('./ner_logs_best/'+log_files[0])"
]
},
{
"cell_type": "markdown",
"id": "GcQKMIYI3h4o",
"metadata": {
"id": "GcQKMIYI3h4o"
},
"source": [
"**Plotting Loss**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3RTrm5EU3OWb",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 513
},
"id": "3RTrm5EU3OWb",
"outputId": "6eda6e02-5dae-4861-dba4-41e1538b2cbb"
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAHwCAYAAAChTMYRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3TUdb7/8ed7ZjL0TuggIr2XQMJ1dd21rwUrCVJFCF69u+tW3eoW3XWxYyWhg7Tr3r2617aKbXUTILGCCIQmRTqETtrn9wfj/lBBWjKfKa/HOTmZ+c4keTH3rs9MSWLOOURERCRxBXwPEBERkaql2IuIiCQ4xV5ERCTBKfYiIiIJTrEXERFJcIq9iIhIglPsRcQbM/udmc3yvUMk0Sn2IknCzNaa2UUevu40Mysxs31mttPMXjWzzqfxebzsF0kEir2IRMN451xtoBWwFZjmd45IclHsRZKcmVUzs0fMbFPk7REzqxa5rLGZ/Z+Z7Y7cK/+nmQUil91pZhvNbK+ZLTezC0/0tZxzB4DZQPfjbLnazJZGvt6bZtYlcnwm0Ab4e+QRgp9X1r9fJBko9iLyKyAD6A30AgYAv45c9hNgA5AKNAV+CTgz6wT8F9DfOVcHuBRYe6IvZGa1gaHA+8e4rCMwB7gj8vVe5Ejcw8654cBnwFXOudrOufGn/a8VSUKKvYgMBf7gnNvqnNsG/B4YHrmsFGgOnOWcK3XO/dMd+YMa5UA1oKuZpTjn1jrnVn3D1/ipme0GioDawKhjXCcTeME596pzrhR4AKgB/Ecl/BtFkppiLyItgHVHnV8XOQZwP0cC/Q8zW21mdwE454o4cg/8d8BWM5trZi04vgecc/Wdc82cc1cf5xuDL+1wzlUA64GWp/nvEpEIxV5ENgFnHXW+TeQYzrm9zrmfOOfaAVcDP/7iuXnn3Gzn3LciH+uAv1TmDjMzoDWwMXJIf6JT5DQp9iLJJcXMqh/1FuLI8+S/NrNUM2sM/BaYBWBmV5pZ+0h4izny8H2FmXUys+9GXsh3CDgIVJzhtvnAFWZ2oZmlcOT1AoeBf0Uu3wK0O8OvIZKUFHuR5PIiR8L8xdvvgHuAAuAj4GPgvcgxgA7Aa8A+IA940jn3Bkeer78P2A5sBpoAvziTYc655cAw4LHI572KIy/IK4lc5c8c+aZkt5n99Ey+lkiysSOvtREREZFEpXv2IiIiCU6xFxERSXCKvYiISIJT7EVERBKcYi8iIpLgQr4HVIXGjRu7tm3b+p4hIiISNYWFhdudc6nHuiwhY9+2bVsKCgp8zxAREYkaM1t3vMv0ML6IiEiCU+xFREQSnGIvIiKS4BR7ERGRBKfYi4iIJDjFXkREJMEp9iIiIglOsRcREUlwir2IiEiCU+xFREQSnGIvIiKS4BR7ERGRBKfYi4iIJDjFXkREJMEp9iIiIglOsRcREUlwiv1JWLdjPxUVzvcMERGR06LYn8Dm4kNc+dg73PnXjyhX8EVEJA4p9ifQtG41Rp97Nv9duIGfzP+AsvIK35NEREROScj3gFhnZvzo4o6EQwHuf2U5pRWORzJ7kxLU90kiIhIfFPuTdPt32hMOBrj3xWWUlVfw2JC+hEMKvoiIxD7V6hSMPb8dv7uqK68s3cKtswo5VFrue5KIiMgJKfanaNS5Z3Pvtd15/dOtjJ1RwMESBV9ERGKbYn8ahqafxfgbevJO0XZGT1vMgZIy35NERESOS7E/TYPTWvPQ4F4sXLODkVMWse+wgi8iIrFJsT8D1/ZpxYQhfXjvs90Mn7yQ4oOlvieJiIh8jWJ/hq7s2YInh/ZlycZihk1ayO4DJb4niYiIfIliXwku7daMicP7sXzzXobkLmTHvsO+J4mIiPybYl9Jvtu5KZNGprF62z6G5Oazba+CLyIisUGxr0Tnd0xl6qj+rN95kKycPLbsOeR7koiIiGJf2f6jfWOmjx7A5uJDZE7MY9Pug74niYhIklPsq8CAsxsyc0w6O/aVMHhiHut3HvA9SUREkphiX0X6tmnAM2PT2XuojMyJeazdvt/3JBERSVKKfRXq2ao+s8emc6isgsET8yjaus/3JBERSUKKfRXr1qIec8ZmUOEgKyef5Zv3+p4kIiJJRrGPgk7N6jA3O4OAwZDcfD7ZtMf3JBERSSKKfZS0b1Kb+eMGUj0UYEhuPh9t2O17koiIJAnFPoraNq7FvHEDqVM9xNDchbz32S7fk0REJAko9lHWumFN5o0bSMPaYYZPWsjitTt9TxIRkQSn2HvQsn4N5o8bSNN61RkxeRH/WrXd9yQREUlgir0nTetWZ172QFo3rMHNUxfz9optvieJiEiCqrLYm9kUM9tqZkuOOnajmS01swozS/vK9X9hZkVmttzMLj3q+GWRY0VmdldV7fUhtU415ozNoF1qbcZML+D1T7f4niQiIgmoKu/ZTwMu+8qxJcB1wNtHHzSzrkAW0C3yMU+aWdDMgsATwOVAV2BI5LoJo1HtaswZm06nZnUYN7OQV5Zu9j1JREQSTJXF3jn3NrDzK8eWOeeWH+Pqg4C5zrnDzrk1QBEwIPJW5Jxb7ZwrAeZGrptQ6tcMM2tMOt1b1uP2Z97jhY8+9z1JREQSSKw8Z98SWH/U+Q2RY8c7/jVmlm1mBWZWsG1b/D3/Xa9GCjNvSadPm/p8f857/O/7G31PEhGRBBErsT9jzrkc51yacy4tNTXV95zTUrtaiOmjB5B+diN+NP8D5hesP/EHiYiInECsxH4j0Pqo860ix453PGHVDIeYMqo/32rfmJ8/+xGzF37me5KIiMS5WIn980CWmVUzs7OBDsAiYDHQwczONrMwR17E97zHnVFRIxwkd0Qa3+3chF/+7WOm/2ut70kiIhLHqvJH7+YAeUAnM9tgZreY2bVmtgEYCLxgZq8AOOeWAvOBT4CXgdudc+XOuTLgv4BXgGXA/Mh1E171lCBPD+vHJV2bcvfzS8l9e7XvSSIiEqfMOed7Q6VLS0tzBQUFvmdUitLyCu6Y9wEvfPQ5P7u0E7d/p73vSSIiEoPMrNA5l3asy0LRHiOnJiUY4NHM3qQEjPtfWU5JWQV3XNQBM/M9TURE4oRiHwdCwQAPDu5NKBjg0QUrKS2v4GeXdlLwRUTkpCj2cSIYMMZf35NwKMCTb66ipKyCX13RRcEXEZETUuzjSCBg3HtNd8LBAJPeWUNpeQV3X9WNQEDBFxGR41Ps44yZcfdVXUkJGrn/XENJeQX3XtNDwRcRkeNS7OOQmfHL73UhHArwxBurKClzjL+hJ0EFX0REjkGxj1Nmxk8v6UQ4GOTh11ZQVlHBgzf2IhSMld+TJCIisUKxj2Nmxg8v6kBKyBj/8nLKyh2PZPUmRcEXEZGjKPYJ4LYL2hMOBrjnhWWUlFfw+E19qBYK+p4lIiIxQncBE8SY89rxh0HdePWTLdw6s5BDpeW+J4mISIxQ7BPIiIFt+dO1PXhzxTbGzijgYImCLyIiin3CuSm9DeOv78k7Rdu5edoi9h8u8z1JREQ8U+wT0I1prXkkszeL1+5i5JRF7D1U6nuSiIh4pNgnqEG9WzIhqw8frN/N8MmLKD6o4IuIJCvFPoFd0bM5Tw7ty9JNxQydlM+u/SW+J4mIiAeKfYK7pFszcoansWLLPobk5rNj32Hfk0REJMoU+yTwnc5NmDwyjbU79pOVk8/WvYd8TxIRkShS7JPEeR1SmTpqABt3HyRrYj6bixV8EZFkodgnkYHnNGLG6AFs3XuYzJw8Nu4+6HuSiIhEgWKfZNLaNmTmLQPYub+EwU/nsX7nAd+TRESkiin2SahPmwbMHpPB/pIyBk/MY832/b4niYhIFVLsk1SPVvWYPSaDw2UVZE7Mo2jrXt+TRESkiij2Saxri7rMzc6gwkFWTj7LNyv4IiKJSLFPch2b1mHeuAyCASMrJ48lG4t9TxIRkUqm2AvnpNZmXvZAaqQEuSk3nw/X7/Y9SUREKpFiLwC0bVyLeeMGUq9mCsMmLaRw3S7fk0REpJIo9vJvrRvWZF72QBrXqcaIyQtZuHqH70kiIlIJFHv5khb1azA3O4Nm9aozaupi3i3a7nuSiIicIcVevqZp3erMzR5Im4Y1GT1tMW+t2OZ7koiInAHFXo4ptU415mRncE5qbcZOL2DBsi2+J4mIyGlS7OW4GtYKM3tsOp2b1+HWWYW8vGSz70kiInIaFHv5RvVrhpk1Jp0eLetx++z3+PuHm3xPEhGRU6TYywnVrZ7CjFvS6demAT+c+z7/894G35NEROQUKPZyUmpXCzFtdH8y2jXiJ//9IfMXr/c9SURETpJiLyetZjjElFH9Oa9DKj//60fMyl/ne5KIiJwExV5OSfWUIDnD+3Fh5yb8+n+XMPXdNb4niYjICSj2csqqpwR5alg/Lu3WlN///RMmvrXK9yQREfkGir2clnAowOM39eXKns3580uf8tiClb4niYjIcYR8D5D4lRIM8Ehmb8LBAA++uoLS8gp+dHFHzMz3NBEROYpiL2ckFAxw/429CAWNCa8XUVLuuPOyTgq+iEgMqbKH8c1sipltNbMlRx1raGavmtnKyPsGkeNmZhPMrMjMPjKzvkd9zMjI9Vea2ciq2iunLxgw7ruuJ8My2vD0W6v44/8twznne5aIiERU5XP204DLvnLsLmCBc64DsCByHuByoEPkLRt4Co58cwDcDaQDA4C7v/gGQWJLIGD8cVB3bj63LVPeXcNvn1tKRYWCLyISC6rsYXzn3Ntm1vYrhwcBF0ROTwfeBO6MHJ/hjtwdzDez+mbWPHLdV51zOwHM7FWOfAMxp6p2y+kzM357ZVfCwQAT315NaXkFf7q2B4GAHtIXEfEp2s/ZN3XOfR45vRloGjndEjj6V7JtiBw73nGJUWbGXZd3JhwK8NjrRZSWO8bf0JOggi8i4o23F+g555yZVdrjvGaWzZGnAGjTpk1lfVo5DWbGTy7pREowwEORV+k/NLgXoaB+0lNExIdo/9d3S+TheSLvt0aObwRaH3W9VpFjxzv+Nc65HOdcmnMuLTU1tdKHy6n7wYUduPOyzjz/4Sa+P+d9SsoqfE8SEUlK0Y7988AXr6gfCTx31PERkVflZwDFkYf7XwEuMbMGkRfmXRI5JnHiPy84h19f0YWXlmzmtmcKOVxW7nuSiEjSqcofvZsD5AGdzGyDmd0C3AdcbGYrgYsi5wFeBFYDRUAucBtA5IV5fwQWR97+8MWL9SR+jDmvHX8c1I3Xlm0le0Yhh0oVfBGRaLJE/HnotLQ0V1BQ4HuGfMXcRZ/xi799zLnnNCZ3RBo1wkHfk0REEoaZFTrn0o51mV4xJVGTNaAND9zQi3+t2s6oqYvYf7jM9yQRkaSg2EtUXd+vFQ9n9qZg3S5GTFnEnkOlvieJiCQ8xV6iblDvljw+pA8frt/N8MmLKD6g4IuIVCXFXry4vEdznhrWj2Wb9nDTpHx27S/xPUlEJGEp9uLNxV2bkjOiHyu37mNIbj7b9x32PUlEJCEp9uLVBZ2aMGVkf9bu2E9WTj5b9xzyPUlEJOEo9uLdtzo0ZtrNA9i0+yCZOfl8XnzQ9yQRkYSi2EtMyGjXiBmjB7Bt72EyJ+azYdcB35NERBKGYi8xI61tQ2aNSWf3gRIyJ+bz2Q4FX0SkMij2ElN6t67P7LEZ7C8pY/DEPFZv2+d7kohI3FPsJeZ0b1mPOWMzKC2vIDMnn5Vb9vqeJCIS1xR7iUldmtdlbnYGAFk5+Xy6eY/nRSIi8Uuxl5jVoWkd5mVnkBIMMCQnnyUbi31PEhGJS4q9xLR2qbWZNy6DmuEQN+Xm88H63b4niYjEHcVeYt5ZjWoxb1wG9WuGGTZpIYXrdvqeJCISVxR7iQutGtRk3rgMUutUY/jkReSv3uF7kohI3FDsJW40r1eDedkZtKhfg1FTF/Fu0Xbfk0RE4oJiL3GlSd3qzM3OoG2jWoyetpg3l2/1PUlEJOYp9hJ3GteuxpyxGbRvUpvsGYW89skW35NERGKaYi9xqUGtMLPHZNCleR1unVXISx9/7nuSiEjMUuwlbtWrmcLMMen0al2f/5rzPs9/uMn3JBGRmKTYS1yrWz2F6aMH0O+sBtwx933+WrjB9yQRkZij2Evcq10txLSb+zPwnEb89NkPmbf4M9+TRERiimIvCaFmOMTkkf05v0Mqd/71Y2bmrfU9SUQkZij2kjCqpwTJGdGPi7o04TfPLWXyO2t8TxIRiQmKvSSUaqEgTw7tx+Xdm/HH//uEp99a5XuSiIh3ir0knHAowGND+nBVrxbc99KnTFiw0vckERGvQr4HiFSFUDDAI5m9SQkaD726gtLyCn58cUfMzPc0EZGoU+wlYQUDxgM39CIcDPDY60WUlFVw1+WdFXwRSTqKvSS0QMD407U9CAWNiW+vpqS8gt9e2VXBF5GkothLwgsEjD8O6k44GGTKu2soLa/gD1d3JxBQ8EUkOSj2khTMjN9c2YVwKMDTb62itMzxp+t6EFTwRSQJKPaSNMyMOy/rRDgUYMKClZSWVzD+hp6EgvqhFBFJbIq9JBUz48cXdyQlYDz46gpKyit4OLM3KQq+iCQwxV6S0vcv7EA4FODPL31KWbljwpA+hEMKvogkJv3XTZLWuG+fw2+v7MrLSzdz2zOFHC4r9z1JRKRKKPaS1EZ/62zuuaY7ry3bytgZhRwqVfBFJPEo9pL0hmWcxfjre/LPldsYPW0xB0rKfE8SEalUir0IMLh/ax68sRf5q3cwaspi9h1W8EUkcSj2IhHX9W3Fo1l9KPxsFyMmL2TPoVLfk0REKoViL3KUq3q14Imb+vDxxmKGT1pI8QEFX0Tin5fYm9kPzWyJmS01szsixxqa2atmtjLyvkHkuJnZBDMrMrOPzKyvj82SPC7r3pynh/Vj2ed7GZKbz879Jb4niYickajH3sy6A2OBAUAv4Eozaw/cBSxwznUAFkTOA1wOdIi8ZQNPRXuzJJ8LuzQld2Qaq7btY0hOPtv2HvY9SUTktPm4Z98FWOicO+CcKwPeAq4DBgHTI9eZDlwTOT0ImOGOyAfqm1nzaI+W5PPtjqlMGdWfdTv3k5WTx9Y9h3xPEhE5LT5ivwQ4z8wamVlN4HtAa6Cpc+7zyHU2A00jp1sC64/6+A2RYyJV7tz2jZl+8wA2Fx8iMyefz4sP+p4kInLKoh5759wy4C/AP4CXgQ+A8q9cxwHuVD6vmWWbWYGZFWzbtq2y5oqQ3q4RM25JZ/vewwyemMf6nQd8TxIROSVeXqDnnJvsnOvnnDsf2AWsALZ88fB85P3WyNU3cuSe/xdaRY599XPmOOfSnHNpqampVfsPkKTT76wGzBqTTvGBUrJy8lm3Y7/vSSIiJ83Xq/GbRN634cjz9bOB54GRkauMBJ6LnH4eGBF5VX4GUHzUw/0iUdOrdX1mj83gQEkZgyfmsWrbPt+TREROiq+fs/+rmX0C/B243Tm3G7gPuNjMVgIXRc4DvAisBoqAXOA2D3tFAOjesh5zsjMor3BkTsxn5Za9vieJiJyQHXl6PLGkpaW5goIC3zMkgRVt3ctNuQspr3DMGpNOl+Z1fU8SkSRnZoXOubRjXabfoCdyGto3qcO8cQMJhwIMyc1nycZi35NERI5LsRc5TWc3rsX8cQOpFQ4xJDef9z/b5XuSiMgxKfYiZ6B1w5rMG5dBg5phhk9eRMHanb4niYh8jWIvcoZaNajJ/HEDaVKnGiOmLCJv1Q7fk0REvkSxF6kEzepVZ+64DFrWr8HN0xbxzsrtvieJiPybYi9SSZrUqc7c7AzaNqrF6OmLeePTrSf+IBGRKFDsRSpRo9rVmDM2g45Na5M9s4B/LN3se5KIiGIvUtka1ArzzJgMuraox23PvMeLH+sXPoqIX4q9SBWoVyOFWbcMoHfr+nx/zvs898HX/pyDiEjUKPYiVaRO9RSmjx5A/7YNuGPeBzxbuMH3JBFJUoq9SBWqVS3E1FEDOPecxvzs2Q+Zs+gz35NEJAkp9iJVrEY4yKSRaXy7Yyq/+J+PmZG31vckEUkyir1IFFRPCTJxeD8u7tqU3z63lEn/XO17kogkEcVeJEqqhYI8ObQv3+vRjHteWMaTbxb5niQiSSLke4BIMkkJBpiQ1YeU4IeMf3k5pWWOH1zYHjPzPU1EEphiLxJloWCAhwb3JhQI8PBrKygpL+enl3RS8EWkyij2Ih4EA8b9N/QkHDKeeGMVpeWOX1zeWcEXkSqh2It4EggY917Tg5RggJy3V1NSVsHdV3VV8EWk0in2Ih4FAsbvr+5GOBhg0jtrKCmv4J5B3QkEFHwRqTyKvYhnZsavruhCSijAU2+uorSsgvuu70lQwReRSqLYi8QAM+Pnl3YiHAzw6IKVlJZX8MCNvQgF9dOxInLmFHuRGGFm/OjijoRDAe5/ZTmlFY5HMnuTouCLyBlS7EVizO3faU84GODeF5dRVl7BY0P6Eg4p+CJy+vRfEJEYNPb8dvzuqq68snQLt84q5FBpue9JIhLHFHuRGDXq3LO599ruvP7pVsbOKOBgiYIvIqdHsReJYUPTz2L8DT15p2g7o6ct5kBJme9JIhKHFHuRGDc4rTUPDe7FwjU7GDVlMfsOK/gicmoUe5E4cG2fVkwY0ofCz3YxfPJCig+W+p4kInFEsReJE1f2bMGTQ/uyZGMxwyYtZPeBEt+TRCROKPYiceTSbs2YOLwfyzfvZUjuQnbsO+x7kojEAcVeJM58t3NTJo1MY/W2fQzJzWfbXgVfRL6ZYi8Sh87vmMrUUf1Zv/MgWTl5bNlzyPckEYlhir1InPqP9o2ZPnoAm4sPkTkxj027D/qeJCIxSrEXiWMDzm7IzDHp7NhXwuCJeazfecD3JBGJQYq9SJzr26YBz4xNZ++hMjIn5rF2+37fk0Qkxij2IgmgZ6v6zB6bzqGyCjJz8ijaus/3JBGJIYq9SILo1qIec8ZmUF4BWTn5LN+81/ckEYkRir1IAunUrA5zszMIGAzJzeeTTXt8TxKRGKDYiySY9k1qM3/cQKqHAgzJzeejDbt9TxIRzxR7kQTUtnEt5o0bSJ3qIYbmLuS9z3b5niQiHnmJvZn9yMyWmtkSM5tjZtXN7GwzW2hmRWY2z8zCketWi5wvilze1sdmkXjTumFN5o0bSMPaYYZPWsjitTt9TxIRT6IeezNrCfwASHPOdQeCQBbwF+Bh51x7YBdwS+RDbgF2RY4/HLmeiJyElvVrMH/cQJrWq86IyYv416rtvieJiAe+HsYPATXMLATUBD4Hvgs8G7l8OnBN5PSgyHkil19oZhbFrSJxrWnd6szLHkjrhjW4eepi3l6xzfckEYmyqMfeObcReAD4jCORLwYKgd3OubLI1TYALSOnWwLrIx9bFrl+o2huFol3qXWqMWdsBu1SazNmegGvf7rF9yQRiSIfD+M34Mi99bOBFkAt4LJK+LzZZlZgZgXbtumei8hXNapdjTlj0+nUrA7jZhbyytLNvieJSJT4eBj/ImCNc26bc64U+B/gXKB+5GF9gFbAxsjpjUBrgMjl9YAdX/2kzrkc51yacy4tNTW1qv8NInGpfs0ws8ak071lPW5/5j1e+Ohz35NEJAp8xP4zIMPMakaee78Q+AR4A7ghcp2RwHOR089HzhO5/HXnnIviXpGEUq9GCjNvSadPm/p8f857/O/7G0/8QSIS13w8Z7+QIy+0ew/4OLIhB7gT+LGZFXHkOfnJkQ+ZDDSKHP8xcFe0N4skmtrVQkwfPYD0sxvxo/kfML9gve9JIlKFLBHvJKelpbmCggLfM0Ri3sGScrJnFvDPldv507U9uCm9je9JInKazKzQOZd2rMv0G/REkliNcJDcEWl8t3MTfvm3j5n+r7W+J4lIFVDsRZJc9ZQgTw/rxyVdm3L380vJfXu170kiUskUexEhHArwxNC+XNGjOfe+uIwn3ijyPUlEKlHoxFcRkWSQEgzwaFZvUoLG/a8sp6Ssgjsu6oB+YaVI/FPsReTfQsEADw7uTSgY4NEFKyktr+Bnl3ZS8EXinGIvIl8SDBjjr+9JOBTgyTdXUVJWwa+u6KLgi8QxxV5EviYQMO69pjvhYIBJ76yhtLyCu6/qRiCg4IvEI8VeRI7JzLj7qq6kBI3cf66hpLyCe6/poeCLxCHFXkSOy8z45fe6HHm1/hurKC13/OX6ngQVfJG4clKxN7NawEHnXIWZdQQ6Ay9F/pCNiCQwM+Onl3QiHAzy8GsrKC2v4MEbexEK6id3ReLFyd6zfxs4L/Lnaf8BLAYygaFVNUxEYoeZ8cOLOpASMsa/vJyycscjWb1JUfBF4sLJxt6ccwfM7BbgSefceDP7oCqHiUjsue2C9oSDAe55YRkl5RU8flMfqoWCvmeJyAmc7LflZmYDOXJP/oXIMf0vXCQJjTmvHX8Y1I1XP9nCrTMLOVRa7nuSiJzAycb+DuAXwN+cc0vNrB1H/v68iCShEQPb8qdre/Dmim2MnVHAwRIFXySWnVTsnXNvOeeuds79xcwCwHbn3A+qeJuIxLCb0tsw/vqevFO0nZunLWL/4TLfk0TkOE4q9mY228zqRl6VvwT4xMx+VrXTRCTW3ZjWmkcye7N47S5GTlnE3kP6AR2RWHSyD+N3dc7tAa4BXgLOBoZX2SoRiRuDerdkQlYfPli/m+GTF1F8UMEXiTUnG/sUM0vhSOyfj/x8vau6WSIST67o2Zwnh/Zl6aZihk7KZ/eBEt+TROQoJxv7icBaoBbwtpmdBeypqlEiEn8u6daMnOFprNiyj6ycfHbsO+x7kohEnOwL9CY451o6577njlgHfKeKt4lInPlO5yZMHpnG2h37ycrJZ+veQ74niQgn/wK9emb2kJkVRN4e5Mi9fBGRLzmvQypTRw1g4+6DZE3MZ3Oxgi/i28k+jD8F2AsMjrztAaZW1SgRiW8Dz2nEjNED2Lr3MJk5eWzcfdD3JJGkdrKxP8c5d7dzbnXk7fdAu6ocJiLxLa1tQ2beMoCd+0sY/HQe63ce8D1JJGmdbOwPmtm3vjhjZucC+lZdRL5RnzYNmD0mg/0lZQyemMea7ft9TxJJSicb+1uBJ0UOPWUAACAASURBVMxsrZmtBR4HxlXZKhFJGD1a1WP2mAwOl1WQOTGPoq17fU8SSTon+2r8D51zvYCeQE/nXB/gu1W6TEQSRtcWdZmbnUGFg6ycfJZvVvBFoumU/hi1c25P5DfpAfy4CvaISILq2LQO88ZlEAwYWTl5LNlY7HuSSNI4pdh/hVXaChFJCuek1mZe9kBqpAS5KTefD9fv9j1JJCmcSez163JF5JS1bVyLeeMGUq9mCsMmLaRw3S7fk0QS3jfG3sz2mtmeY7ztBVpEaaOIJJjWDWsyL3sgjetUY8TkhSxcvcP3JJGE9o2xd87Vcc7VPcZbHedcKFojRSTxtKhfg7nZGTSrV51RUxfzbtF235NEEtaZPIwvInJGmtatztzsgbRpWJPR0xbz1optvieJJCTFXkS8Sq1TjTnZGZyTWpux0wtYsGyL70kiCUexFxHvGtYKM3tsOp2b1+HWWYW8vGSz70kiCUWxF5GYUL9mmFlj0unRsh63z36Pv3+4yfckkYSh2ItIzKhbPYUZt6TTr00Dfjj3ff7nvQ2+J4kkBMVeRGJK7Wohpo3uT0a7Rvzkvz9k/uL1vieJxD3FXkRiTs1wiCmj+nNeh1R+/tePmJW/zvckkbim2ItITKqeEiRneD8u7NyEX//vEqa+u8b3JJG4pdiLSMyqnhLkqWH9uLRbU37/90+Y+NYq35NE4pJiLyIxLRwK8PhNfbmyZ3P+/NKnPP76St+TROJO1GNvZp3M7IOj3vaY2R1m1tDMXjWzlZH3DSLXNzObYGZFZvaRmfWN9mYR8SslGOCRzN5c16clD/xjBQ+9ugLn9Le4RE5W1GPvnFvunOvtnOsN9AMOAH8D7gIWOOc6AAsi5wEuBzpE3rKBp6K9WUT8CwUD3H9jLwantWLCgpX85eXlCr7ISfL9x2wuBFY559aZ2SDggsjx6cCbwJ3AIGCGO/K/6nwzq29mzZ1zn/sYLCL+BAPGfdf1JBwK8PRbqygpq+A3V3bBzHxPE4lpvmOfBcyJnG56VMA3A00jp1sCR/+g7YbIMcVeJAkFAsYfB3UnJRhgyrtrKC2v4PdXdyMQUPBFjsdb7M0sDFwN/OKrlznnnJmd0uNzZpbNkYf5adOmTaVsFJHYZGb89squhIMBJr69mtLyCv50bQ8FX+Q4fN6zvxx4zzn3xZ+42vLFw/Nm1hzYGjm+EWh91Me1ihz7EudcDpADkJaWpifyRBKcmXHX5Z0JhwI89noRpeWO8Tf0JKjgi3yNzx+9G8L/fwgf4HlgZOT0SOC5o46PiLwqPwMo1vP1IgJHgv+TSzrx44s78tf3NvCjeR9QVl7he5ZIzPFyz97MagEXA+OOOnwfMN/MbgHWAYMjx18EvgcUceSV+zdHcaqIxIEfXNiBlGCAv7z8KaXlFTya1YdwSL9GROQLXmLvnNsPNPrKsR0ceXX+V6/rgNujNE1E4tR/XnAOKUHjnheWUfrMezwxtA/VQkHfs0Rigr71FZGEMea8dvxxUDdeW7aFcTMLOVRa7nuSSExQ7EUkoQwf2Jb7ruvBWyu2MWZ6AQdLFHwRxV5EEk7WgDY8cEMv/rVqO6OmLmL/4TLfk0S8UuxFJCFd368VD2f2pmDdLkZMWcSeQ6W+J4l4o9iLSMIa1Lsljw/pw4frdzN88iKKDyj4kpwUexFJaJf3aM5Tw/qxbNMebpqUz679Jb4niUSdYi8iCe/irk3JGdGPlVv3MSQ3n+37DvueJBJVir2IJIULOjVhysj+rN2xn6ycfLbuOeR7kkjUKPYikjS+1aEx024ewKbdB8nMyefz4oO+J4lEhWIvIkklo10jZowewLa9h8mcmM+GXQd8TxKpcoq9iCSdtLYNmTUmnd0HSsicmM9nOxR8SWyKvYgkpd6t6zN7bAb7S8oYPDGP1dv2+Z4kUmUUexFJWt1b1mPO2AxKyyvIzMln5Za9vieJVAnFXkSSWpfmdZmbnQFAVk4+n27e43mRSOVT7EUk6XVoWod52RmkBAMMyclnycZi35NEKpViLyICtEutzbxxGdQMh7gpN58P1u/2PUmk0ij2IiIRZzWqxbxxGdSrmcKwSQspXLfT9ySRSqHYi4gcpVWDmswfN5DUOtUYPnkR+at3+J4kcsYUexGRr2herwbzsjNoUb8Go6Yu4t2i7b4niZwRxV5E5Bia1K3O3OwM2jaqxehpi3lz+Vbfk0ROm2IvInIcjWtXY87YDNo3qU32jEJe+2SL70kip0WxFxH5Bg1qhZk9JoMuzetw66xCXvr4c9+TRE6ZYi8icgL1aqYwc0w6vVrX57/mvM/zH27yPUnklCj2IiInoW71FKaPHkC/sxpwx9z3+WvhBt+TRE6aYi8icpJqVwsx7eb+DDynET999kPmLf7M9ySRk6LYi4icgprhEJNH9uf8Dqnc+dePmZm31vckkRNS7EVETlH1lCA5I/pxUZcm/Oa5pUx+Z43vSSLfSLEXETkN1UJBnhzaj8u7N+OP//cJT7+1yvckkeNS7EVETlM4FOCxIX24qlcL7nvpUyYsWOl7ksgxhXwPEBGJZ6FggEcye5MSNB56dQWl5RX8+OKOmJnvaSL/ptiLiJyhYMB44IZehIMBHnu9iJKyCu66vLOCLzFDsRcRqQSBgPGna3sQChoT315NSXkFv72yq4IvMUGxFxGpJIGA8cdB3QkHg0x5dw2l5RX84eruBAIKvvil2IuIVCIz4zdXdiEcCvD0W6soLXP86boeBBV88UixFxGpZGbGnZd1IhwKMGHBSkrLKxh/Q09CQf0AlPih2IuIVAEz48cXdyQlYDz46gpKyit4OLM3KQq+eKDYi4hUoe9f2IFwKMCfX/qUsnLHhCF9CIcUfIku/X+ciEgVG/ftc/jtlV15eelmbnumkMNl5b4nSZJR7EVEomD0t87mnmu689qyrYydUcihUgVfokexFxGJkmEZZzH++p78c+U2Rk9bzIGSMt+TJEko9iIiUTS4f2sevLEX+at3MGrKYvYdVvCl6nmJvZnVN7NnzexTM1tmZgPNrKGZvWpmKyPvG0Sua2Y2wcyKzOwjM+vrY7OISGW5rm8rHs3qQ+FnuxgxeSF7DpX6niQJztc9+0eBl51znYFewDLgLmCBc64DsCByHuByoEPkLRt4KvpzRUQq11W9WvDETX34eGMxwyctpPiAgi9VJ+qxN7N6wPnAZADnXIlzbjcwCJgeudp04JrI6UHADHdEPlDfzJpHebaISKW7rHtznh7Wj2Wf72VIbj4795f4niQJysc9+7OBbcBUM3vfzCaZWS2gqXPu88h1NgNNI6dbAuuP+vgNkWNfYmbZZlZgZgXbtm2rwvkiIpXnwi5NyR2Zxqpt+xiSk8+2vYd9T5IE5CP2IaAv8JRzrg+wn///kD0AzjkHuFP5pM65HOdcmnMuLTU1tdLGiohUtW93TGXKqP6s27mfrJw8tu455HuSJBgfsd8AbHDOLYycf5Yj8d/yxcPzkfdbI5dvBFof9fGtIsdERBLGue0bM/3mAWwuPkRmTj6fFx/0PUkSSNRj75zbDKw3s06RQxcCnwDPAyMjx0YCz0VOPw+MiLwqPwMoPurhfhGRhJHerhEzbkln+97DDJ6Yx/qdB3xPkgTh69X43weeMbOPgN7An4D7gIvNbCVwUeQ8wIvAaqAIyAVui/5cEZHo6HdWA2aNSaf4QClZOfms27Hf9yRJAHbk6fHEkpaW5goKCnzPEBE5bUs2FjN88kLCoQCzx2ZwTmpt35MkxplZoXMu7ViX6TfoiYjEoO4t6zEnO4PyCkfmxHxWbtnre5LEMcVeRCRGdW5Wl7nZGQQMsnLyWfb5Ht+TJE4p9iIiMax9kzrMGzeQcCjAkNx8lmws9j1J4pBiLyIS485uXIv54wZSKxxiSG4+73+2y/ckiTOKvYhIHGjdsCbzxmXQoGaY4ZMXUbB2p+9JEkcUexGRONGqQU3mjxtIkzrVGDFlEXmrdvieJHFCsRcRiSPN6lVn7rgMWtavwc3TFvHOyu2+J0kcUOxFROJMkzrVmZudQdtGtRg9fTFvfLr1xB8kSU2xFxGJQ41qV2PO2Aw6Nq1N9swC/rF0s+9JEsMUexGRONWgVphnxmTQtUU9bnvmPV78WH82RI5NsRcRiWP1aqQw65YB9G5dn+/PeZ/nPtAfBZWvU+xFROJcneopTB89gP5tG3DHvA94tnCD70kSYxR7EZEEUKtaiKmjBnDuOY352bMfMmfRZ74nSQxR7EVEEkSNcJBJI9P4dsdUfvE/HzMjb63vSRIjFHsRkQRSPSXIxOH9uLhrU3773FIm/XO170kSAxR7EZEEUy0U5Mmhfflej2bc88IynnyzyPck8Szke4CIiFS+lGCACVl9SAl+yPiXl1Na5vjBhe0xM9/TxAPFXkQkQYWCAR4a3JtQIMDDr62gpLycn17SScFPQoq9iEgCCwaM+2/oSThkPPHGKkrLHb+4vLOCn2QUexGRBBcIGPde04OUYICct1dTUlbB3Vd1VfCTiGIvIpIEAgHj91d3IxwMMOmdNZSUV3DPoO4EAgp+MlDsRUSShJnxqyu6kBIK8NSbqygtq+C+63sSVPATnmIvIpJEzIyfX9qJcDDAowtWUlpewQM39iIU1E9iJzLFXkQkyZgZP7q4I+FQgPtfWU5pheORzN6kKPgJS7EXEUlSt3+nPeFggHtfXEZZeQWPDelLOKTgJyL9X1VEJImNPb8dv7uqK68s3cKtswo5VFrue5JUAcVeRCTJjTr3bO69tjuvf7qVsTMKOFii4CcaxV5ERBiafhbjb+jJO0XbGT1tMQdKynxPkkqk2IuICACD01rz0OBeLFyzg1FTFrPvsIKfKBR7ERH5t2v7tGLCkD4UfraL4ZMXUnyw1PckqQSKvYiIfMmVPVvwxE19WbKxmGGTFrL7QInvSXKGFHsREfmay7o34+lh/Vi+eS9DcheyY99h35PkDCj2IiJyTBd2acqkkWms3raPIbn5bNur4McrxV5ERI7r/I6pTB3Vn/U7D5KVk8eWPYd8T5LToNiLiMg3+o/2jZk+egCbiw+ROTGPTbsP+p4kp0ixFxGRExpwdkNmjklnx74SBk/MY/3OA74nySlQ7EVE5KT0bdOAZ8ams/dQGZkT81i7fb/vSXKSFHsRETlpPVvVZ/bYdA6VVZCZk0fR1n2+J8lJUOxFROSUdGtRjzljMyivgKycfJZv3ut7kpyAYi8iIqesU7M6zM3OIGAwJDefTzbt8T1JvoGX2JvZWjP72Mw+MLOCyLGGZvaqma2MvG8QOW5mNsHMiszsIzPr62OziIh8WfsmtZk3biDVQgGG5Obz0YbdvifJcfi8Z/8d51xv51xa5PxdwALnXAdgQeQ8wOVAh8hbNvBU1JeKiMgxnd24FvPHDaRO9RBDcxfy3me7fE+SY4ilh/EHAdMjp6cD1xx1fIY7Ih+ob2bNfQwUEZGva92wJvPGDaRh7TDDJy1k8dqdvifJV/iKvQP+YWaFZpYdOdbUOfd55PRmoGnkdEtg/VEfuyFyTEREYkTL+jWYP24gTetVZ8TkRfxr1Xbfk+QovmL/LedcX448RH+7mZ1/9IXOOceRbwhOmpllm1mBmRVs27atEqeKiMjJaFq3OvOyB9K6YQ1unrqYt1fov8WxwkvsnXMbI++3An8DBgBbvnh4PvJ+a+TqG4HWR314q8ixr37OHOdcmnMuLTU1tSrni4jIcaTWqcacsRm0S63NmOkFvP7pFt+TBA+xN7NaZlbni9PAJcAS4HlgZORqI4HnIqefB0ZEXpWfARQf9XC/iIjEmEa1qzFnbDqdmtVh3MxCXlm62fekpOfjnn1T4B0z+xBYBLzgnHsZuA+42MxWAhdFzgO8CKwGioBc4LboTxYRkVNRv2aYWWPS6d6yHrc/8x4vfKT7aD7ZkafHE0taWporKCjwPUNEJOntO1zGzVMXUbhuFw8N7s01ffT66qpiZoVH/Tj7l8TSj96JiEiCqV0txPTRA0g/uxE/mv8B8wvWn/iDpNIp9iIiUqVqhkNMGdWfb7VvzM+f/YjZCz/zPSnpKPYiIlLlaoSD5I5I47udm/DLv33M9H+t9T0pqSj2IiISFdVTgjw9rB+XdG3K3c8vJfft1b4nJQ3FXkREoiYcCvDE0L5c0aM59764jCfeKPI9KSmEfA8QEZHkkhIM8GhWb1KCxv2vLKekrII7LuqAmfmelrAUexERibpQMMCDg3sTCgZ4dMFKSssr+NmlnRT8KqLYi4iIF8GAMf76noRDAZ58cxUlZRX86oouCn4VUOxFRMSbQMC495ruhIMBJr2zhtLyCu6+qhuBgIJfmRR7ERHxysy4+6qupASN3H+uoaS8gnuv6aHgVyLFXkREvDMzfvm9Lkderf/GKkrLHX+5vidBBb9SKPYiIhITzIyfXtKJcDDIw6+toLS8ggdv7EUoqJ8SP1OKvYiIxAwz44cXdSAlZIx/eTll5Y5HsnqTouCfEcVeRERizm0XtCccDHDPC8soKa/g8Zv6UC0U9D0rbulbJRERiUljzmvH76/uxqufbOHWmYUcKi33PSluKfYiIhKzRv5HW/50bQ/eXLGNsTMKOFii4J8OxV5ERGLaTeltGH99T94p2s7N0xax/3CZ70lxR7EXEZGYd2Naax7J7M3itbsYOWURew+V+p4UVxR7ERGJC4N6t2RCVh8+WL+b4ZMXUXxQwT9Zir2IiMSNK3o258mhfVm6qZihk/LZfaDE96S4oNiLiEhcuaRbM3KGp7Fiyz6ycvLZse+w70kxT7EXEZG4853OTZg8Mo21O/aTlZPP1r2HfE+KaYq9iIjEpfM6pDJ11AA27j5I1sR8Nhcr+Mej2IuISNwaeE4jpo8ewNa9h8nMyWPj7oO+J8UkxV5EROJa/7YNmXnLAHbuL2Hw03ms33nA96SYo9iLiEjc69OmAbPHZLC/pIzBE/NYs32/70kxRbEXEZGE0KNVPWaPyeBwWQWZE/Mo2rrX96SYodiLiEjC6NqiLnOzM6hwkJWTz/LNCj4o9iIikmA6Nq3DvHEZBANGVk4eSzcV+57knWIvIiIJ55zU2szLHkiNlCA35S7kow27fU/ySrEXEZGE1LZxLeaNG0jdGiGG5i6kcN0u35O8UexFRCRhtW5Yk3nZA2lcpxojJi9k4eodvid5odiLiEhCa1G/BnOzM2hWrzqjpi7m3aLtvidFnWIvIiIJr2nd6szNHkibhjUZPW0xb63Y5ntSVCn2IiKSFFLrVGNOdgbnpNZm7PQCFizb4ntS1Cj2IiKSNBrWCjN7bDqdm9fh1lmFvLxks+9JUaHYi4hIUqlfM8ysMen0aFmP22e/x98/3OR7UpVT7EVEJOnUrZ7CjFvS6demAT+c+z5/e3+D70lVSrEXEZGkVLtaiGmj+5PRrhE/nv8h8xev9z2pyij2IiKStGqGQ0wZ1Z/zOqTy879+xKz8db4nVQnFXkREklr1lCA5w/txYecm/Pp/lzD13TW+J1U6b7E3s6CZvW9m/xc5f7aZLTSzIjObZ2bhyPFqkfNFkcvb+tosIiKJqXpKkKeG9ePSbk35/d8/YeJbq3xPqlQ+79n/EFh21Pm/AA8759oDu4BbIsdvAXZFjj8cuZ6IiEilCocCPH5TX67s2Zw/v/Qpj7++0vekSuMl9mbWCrgCmBQ5b8B3gWcjV5kOXBM5PShynsjlF0auLyIiUqlSggEeyezNdX1a8sA/VvDQqytwzvmedcZCnr7uI8DPgTqR842A3c65ssj5DUDLyOmWwHoA51yZmRVHrp98v9xYRESqXCgY4P4bexEKGhMWrKSkrII7L+tEPN/PjHrszexKYKtzrtDMLqjEz5sNZAO0adOmsj6tiIgkoWDAuO+6nqQEAzz91ipKyir4zZVd4jb4Pu7ZnwtcbWbfA6oDdYFHgfpmForcu28FbIxcfyPQGthgZiGgHvC1v1HonMsBcgDS0tLi/zEXERHxKhAw7rmmOynBAFPeXUNpeQW/v7obgUD8BT/qz9k7537hnGvlnGsLZAGvO+eGAm8AN0SuNhJ4LnL6+ch5Ipe/7hLhCRQREYl5ZsbdV3Vl3PntmJm/jl/+7WMqKuIvQb6esz+WO4G5ZnYP8D4wOXJ8MjDTzIqAnRz5BkFERCQqzIy7Lu9MOBTgsdeLKC13jL+hJ8E4uofvNfbOuTeBNyOnVwMDjnGdQ8CNUR0mIiJyFDPjJ5d0IiUY4KFXV1BaXsFDg3sRCsbH76aLpXv2IiIiMe0HF3YgJRjgLy9/Sml5BY9m9SEciv3gx/5CERGRGPKfF5zDr6/owktLNnPbM+9xuKzc96QTUuxFRERO0Zjz2vHHQd14bdkWxs0s5FBpbAdfsRcRETkNwwe25b7revDWim2MmV7AwZLYDb5iLyIicpqyBrThgRt68a9V2xk1dRH7D5ed+IM8UOxFRETOwPX9WvFwZm8K1u1ixJRF7DlU6nvS1yj2IiIiZ2hQ75Y8PqQPH67fzfDJiyg+EFvBV+xFREQqweU9mvPUsH4s27SHmybls2t/ie9J/6bYi4iIVJKLuzYlZ0Q/Vm7dx5DcfLbvO+x7EqDYi4iIVKoLOjVhysj+rN2xn6ycfLbuOeR7kmIvIiJS2b7VoTHTbh7Apt0HyczJ5/Pig173KPYiIiJVIKNdI2aMHsC2vYfJnJjPhl0HvG1R7EVERKpIWtuGzBqTzu4DJWROzOezHX6Cr9iLiIhUod6t6zN7bAb7S8oYPDGP1dv2RX2DYi8iIlLFuresx5yxGZSWV5CZk8/KLXuj+vUVexERkSjo0rwuc7MzAMjKyefTzXui9rUVexERkSjp0LQO87IzaFgrzKHSiqh93VDUvpKIiIjQLrU2L99xPsGARe1r6p69iIhIlEUz9KDYi4iIJDzFXkREJMEp9iIiIglOsRcREUlwir2IiEiCU+xFREQSnGIvIiKS4BR7ERGRBKfYi4iIJDjFXkREJMEp9iIiIglOsRcREUlwir2IiEiCU+xFREQSnGIvIiKS4BR7ERGRBKfYi4iIJDhzzvneUOnMbBuwrpI/bWNgeyV/zmSj2/DM6TY8c7oNz5xuw8pR2bfjWc651GNdkJCxrwpmVuCcS/O9I57pNjxzug3PnG7DM6fbsHJE83bUw/giIiIJTrEXERFJcIr9ycvxPSAB6DY8c7oNz5xuwzOn27ByRO121HP2IiIiCU737EVERBKcYn8UM5tiZlvNbMlxLjczm2BmRWb2kZn1jfbGWHcSt+HQyG33sZn9y8x6RXtjrDvRbXjU9fqbWZmZ3RCtbfHiZG5DM7vAzD4ws6Vm9lY098WLk/jfcz0z+7uZfRi5HW+O9sZYZmatzewNM/skcvv88BjXiUpXFPsvmwZc9g2XXw50iLxlA09FYVO8mcY334ZrgG8753rw/9q7txArqzCM4/8nNTAMFQURD0yUHYw0S0jKi7KLyKCICDugEN5kYgYRShd1YRcVFGIpUUYWSV6UWRclip0ENaMwhxJCTMwyMktLC0l9uthL3akzs8fGvd3b5wfDfN+axcf7rdnw7rW+wwvzyLW/U1lC52OIpF7AM8CqegTUhJbQyRhKGgAsAm63fSVwd53iajZL6PyzOBP41vZY4EbgOUnn1yGuZnEIeNT2aGACMFPS6BP61CWvJNlXsf0Z8FsnXe4A3nDFBmCApKH1ia45dDWGttfZ/r3sbgCG1yWwJlLD5xBgFvAO8MuZj6j51DCG9wHLbe8o/TOOp1DDOBq4UJKAfqXvoXrE1gxs77L9Vdn+E9gCDDuhW13ySpJ99wwDfqja38nJ/7io3XTgw0YH0WwkDQPuJCtL/8elwEBJn0j6UtK0RgfUpF4ErgB+AtqB2baPNDaks5OkNmAc8PkJf6pLXund0weMqIWkm6gk+4mNjqUJzQfm2D5SmVDFaegNXAvcDPQF1kvaYPu7xobVdG4BNgGTgIuB1ZLW2v6jsWGdXST1o7IS90ijxibJvnt+BEZU7Q8vbdENksYAi4Fbbe9pdDxNaDywrCT6wcBkSYdsr2hsWE1lJ7DH9gHggKTPgLFAkn33PAA87coz3FslfQ9cDmxsbFhnD0l9qCT6pbaXn6JLXfJKlvG7531gWrl7cgKwz/auRgfVTCSNBJYDUzOLOj22L7LdZrsNeBt4KIm+294DJkrqLekC4Doq11Oje3ZQWR1B0hDgMmBbQyM6i5R7GV4Ftth+voNudckrmdlXkfQWlTtKB0vaCTwJ9AGw/RLwATAZ2Ar8ReVbbVSpYQyfAAYBi8rM9FAKavxXDWMYXehqDG1vkbQS2AwcARbb7vRRx3NRDZ/FecASSe2AqFxeSjW8424ApgLtkjaVtseBkVDfvJI36EVERLS4LONHRES0uCT7iIiIFpdkHxER0eKS7CMiIlpckn1ERESLS7KPiGMkHS6V4I7+zO3BY7d1VckvIs6MPGcfEdX+tn11o4OIiJ6VmX1EdEnSdknPSmqXtFHSJaW9TdJHpQ73mvKGRCQNkfRuqXP+taTry6F6SXql1PZeJalv6f9wqfm9WdKyBp1mRMtKso+Ian1PWMafUvW3fbavolLpbH5pewF43fYYYCmwoLQvAD4tdc6vAb4p7aOAhaWG/F7grtI+FxhXjvPgmTq5iHNV3qAXEcdI2m+73ynatwOTbG8rhT1+tj1I0q/AUNv/lPZdtgdL2g0Mt32w6hhtwGrbo8r+HKCP7afKq2v3AyuAFbb3n+FTjTinZGYfEbVyB9vdcbBq+zDH7xu6DVhIZRXgC0m5nyiiByXZR0StplT9Xl+21wH3lO37gbVlew0wA0BSL0n9OzqopPOAEbY/BuYA/YGTVhci4vTl23NEVOtbVZ0LYKXto4/fDZS0mcrs/N7SNgt4TdJjwG6OV+yaDbwsaTqVGfwMoKOynb2AN8sXAgELbO/tzDY8mwAAAE5JREFUsTOKiFyzj4iulWv241O+NKI5ZRk/IiKixWVmHxER0eIys4+IiGhxSfYREREtLsk+IiKixSXZR0REtLgk+4iIiBaXZB8REdHi/gV/BZ1FtcbwqAAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ner_log_parser.loss_plot('./ner_logs_best/'+log_files[0])"
]
},
{
"cell_type": "markdown",
"id": "WuJ5YZ9sXU13",
"metadata": {
"id": "WuJ5YZ9sXU13"
},
"source": [
"###πΎ Saving the trained model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "KBcoOwvwXV8p",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KBcoOwvwXV8p",
"outputId": "0cf195ff-28cb-4ad8-e83a-8e98eec3b5e2"
},
"outputs": [
{
"data": {
"text/plain": [
"[BERT_EMBEDDINGS_29ce72cd673e, FinanceNerModel_80baf3edad7a]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ner_model.stages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "gRLbhTh1XYo2",
"metadata": {
"id": "gRLbhTh1XYo2"
},
"outputs": [],
"source": [
"ner_model.stages[1].write().overwrite().save('NER_bert_e2_b32')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "k6B_m0HeXhvo",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k6B_m0HeXhvo",
"outputId": "2495cafa-8974-4759-825e-63c151ef6ead"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 1052\n",
"drwxr-xr-x 4 root root 4096 Jan 23 20:38 NER_bert_e2_b32\n",
"drwxr-xr-x 2 root root 4096 Jan 23 20:38 __pycache__\n",
"-rw-r--r-- 1 root root 3826 Jan 23 20:38 ner_log_parser.py\n",
"-rw-r--r-- 1 root root 7431 Jan 23 20:38 conll_eval.py\n",
"drwxr-xr-x 2 root root 4096 Jan 23 20:20 ner_logs_best\n",
"drwxr-xr-x 2 root root 4096 Jan 23 19:47 ner_logs\n",
"drwxr-xr-x 2 root root 4096 Jan 23 19:37 test_data_embeddings.parquet\n",
"-rw-r--r-- 1 root root 1033219 Jan 23 19:05 conll_noO.conll\n",
"-rw-r--r-- 1 root root 1785 Jan 23 19:01 'spark_nlp_for_healthcare_spark_ocr_7162 (4).json'\n",
"drwxr-xr-x 1 root root 4096 Jan 20 14:35 sample_data\n"
]
}
],
"source": [
"!ls -lt"
]
},
{
"cell_type": "markdown",
"id": "gK0rbohHRNmG",
"metadata": {
"id": "gK0rbohHRNmG"
},
"source": [
"###βοΈ Prediction Pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "HkB6TUhpMFvB",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HkB6TUhpMFvB",
"outputId": "d556df94-b888-4925-8448-6a149978a43c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bert_embeddings_sec_bert_base download started this may take some time.\n",
"Approximate size to download 390.4 MB\n",
"[OK!]\n"
]
}
],
"source": [
"document = nlp.DocumentAssembler()\\\n",
" .setInputCol(\"text\")\\\n",
" .setOutputCol(\"document\")\n",
"\n",
"text_splitter = finance.TextSplitter()\\\n",
" .setInputCols(['document'])\\\n",
" .setOutputCol('sentence')\n",
"\n",
"token = nlp.Tokenizer()\\\n",
" .setInputCols(['sentence'])\\\n",
" .setOutputCol('token')\n",
"\n",
"bert_embeddings = nlp.BertEmbeddings.pretrained(\"bert_embeddings_sec_bert_base\", \"en\") \\\n",
" .setInputCols(\"sentence\", \"token\") \\\n",
" .setOutputCol(\"embeddings\")\\\n",
" .setMaxSentenceLength(512)\n",
" \n",
"# load trained model\n",
"loaded_ner_model = finance.NerModel.load(\"NER_bert_e2_b32\")\\\n",
" .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n",
" .setOutputCol(\"ner\")\n",
"\n",
"converter = finance.NerConverterInternal()\\\n",
" .setInputCols([\"document\", \"token\", \"ner\"])\\\n",
" .setOutputCol(\"ner_span\")\n",
"\n",
"ner_prediction_pipeline = nlp.Pipeline(\n",
" stages = [\n",
" document,\n",
" text_splitter,\n",
" token,\n",
" bert_embeddings,\n",
" loaded_ner_model,\n",
" converter\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "gsokdubdX1vE",
"metadata": {
"id": "gsokdubdX1vE"
},
"outputs": [],
"source": [
"empty_data = spark.createDataFrame([['']]).toDF(\"text\")\n",
"\n",
"prediction_model = ner_prediction_pipeline.fit(empty_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "rR8b0tQlX7E8",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rR8b0tQlX7E8",
"outputId": "08d23380-97d0-4fda-b0e4-0cbadfed585f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"|text |\n",
"+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"|$ 4.2 million of the increase was compensation related and primarily attributable to an increase in headcount to support the continued growth of our subscription SaaS offerings and ongoing maintenance and support for our expanding customer base .|\n",
"+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
"\n"
]
}
],
"source": [
"text = \"\"\"$ 4.2 million of the increase was compensation related and primarily attributable to an increase in headcount to support the continued growth of our subscription SaaS offerings and ongoing maintenance and support for our expanding customer base .\"\"\"\n",
"\n",
"sample_data = spark.createDataFrame([[text]]).toDF(\"text\")\n",
"\n",
"sample_data.show(truncate=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "WdeKg30uX_rk",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WdeKg30uX_rk",
"outputId": "0df2ca92-185e-4bab-821a-9a9f2e65c938"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----------+--------+\n",
"|chunk |entity |\n",
"+-----------+--------+\n",
"|$ |CURRENCY|\n",
"|4.2 million|AMOUNT |\n",
"+-----------+--------+\n",
"\n"
]
}
],
"source": [
"preds = prediction_model.transform(sample_data)\n",
"\n",
"preds.select(F.explode(F.arrays_zip(preds.ner_span.result,\n",
" preds.ner_span.metadata)).alias(\"entities\")) \\\n",
" .select(F.expr(\"entities['0']\").alias(\"chunk\"),\n",
" F.expr(\"entities['1'].entity\").alias(\"entity\")).show(truncate=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "UG6eBHQZYIhb",
"metadata": {
"id": "UG6eBHQZYIhb"
},
"outputs": [],
"source": [
"light_model = nlp.LightPipeline(prediction_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7Hr5gtKbYLOW",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7Hr5gtKbYLOW",
"outputId": "316be9e6-9656-47e2-aecb-5327398b75b1"
},
"outputs": [
{
"data": {
"text/plain": [
"[('$', 'B-CURRENCY'),\n",
" ('4.2', 'B-AMOUNT'),\n",
" ('million', 'I-AMOUNT'),\n",
" ('of', 'O'),\n",
" ('the', 'O'),\n",
" ('increase', 'O'),\n",
" ('was', 'O'),\n",
" ('compensation', 'O'),\n",
" ('related', 'O'),\n",
" ('and', 'O'),\n",
" ('primarily', 'O'),\n",
" ('attributable', 'O'),\n",
" ('to', 'O'),\n",
" ('an', 'O'),\n",
" ('increase', 'O'),\n",
" ('in', 'O'),\n",
" ('headcount', 'O'),\n",
" ('to', 'O'),\n",
" ('support', 'O'),\n",
" ('the', 'O'),\n",
" ('continued', 'O'),\n",
" ('growth', 'O'),\n",
" ('of', 'O'),\n",
" ('our', 'O'),\n",
" ('subscription', 'O'),\n",
" ('SaaS', 'O'),\n",
" ('offerings', 'O'),\n",
" ('and', 'O'),\n",
" ('ongoing', 'O'),\n",
" ('maintenance', 'O'),\n",
" ('and', 'O'),\n",
" ('support', 'O'),\n",
" ('for', 'O'),\n",
" ('our', 'O'),\n",
" ('expanding', 'O'),\n",
" ('customer', 'O'),\n",
" ('base', 'O'),\n",
" ('.', 'O')]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = \"\"\"$ 4.2 million of the increase was compensation related and primarily attributable to an increase in headcount to support the continued growth of our subscription SaaS offerings and ongoing maintenance and support for our expanding customer base .\"\"\"\n",
"\n",
"result_ann = light_model.annotate(text)\n",
"\n",
"list(zip(result_ann['token'], result_ann['ner']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "OO4xKzhIZEDc",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "OO4xKzhIZEDc",
"outputId": "7a3dcf63-c165-4637-e9e7-52bcd2ef4ea6"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sent_id | \n",
" token | \n",
" start | \n",
" end | \n",
" ner | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
" $ | \n",
" 0 | \n",
" 0 | \n",
" B-CURRENCY | \n",
"
\n",
" \n",
" | 1 | \n",
" 0 | \n",
" 4.2 | \n",
" 2 | \n",
" 4 | \n",
" B-AMOUNT | \n",
"
\n",
" \n",
" | 2 | \n",
" 0 | \n",
" million | \n",
" 6 | \n",
" 12 | \n",
" I-AMOUNT | \n",
"
\n",
" \n",
" | 3 | \n",
" 0 | \n",
" of | \n",
" 14 | \n",
" 15 | \n",
" O | \n",
"
\n",
" \n",
" | 4 | \n",
" 0 | \n",
" the | \n",
" 17 | \n",
" 19 | \n",
" O | \n",
"
\n",
" \n",
" | 5 | \n",
" 0 | \n",
" increase | \n",
" 21 | \n",
" 28 | \n",
" O | \n",
"
\n",
" \n",
" | 6 | \n",
" 0 | \n",
" was | \n",
" 30 | \n",
" 32 | \n",
" O | \n",
"
\n",
" \n",
" | 7 | \n",
" 0 | \n",
" compensation | \n",
" 34 | \n",
" 45 | \n",
" O | \n",
"
\n",
" \n",
" | 8 | \n",
" 0 | \n",
" related | \n",
" 47 | \n",
" 53 | \n",
" O | \n",
"
\n",
" \n",
" | 9 | \n",
" 0 | \n",
" and | \n",
" 55 | \n",
" 57 | \n",
" O | \n",
"
\n",
" \n",
" | 10 | \n",
" 0 | \n",
" primarily | \n",
" 59 | \n",
" 67 | \n",
" O | \n",
"
\n",
" \n",
" | 11 | \n",
" 0 | \n",
" attributable | \n",
" 69 | \n",
" 80 | \n",
" O | \n",
"
\n",
" \n",
" | 12 | \n",
" 0 | \n",
" to | \n",
" 82 | \n",
" 83 | \n",
" O | \n",
"
\n",
" \n",
" | 13 | \n",
" 0 | \n",
" an | \n",
" 85 | \n",
" 86 | \n",
" O | \n",
"
\n",
" \n",
" | 14 | \n",
" 0 | \n",
" increase | \n",
" 88 | \n",
" 95 | \n",
" O | \n",
"
\n",
" \n",
" | 15 | \n",
" 0 | \n",
" in | \n",
" 97 | \n",
" 98 | \n",
" O | \n",
"
\n",
" \n",
" | 16 | \n",
" 0 | \n",
" headcount | \n",
" 100 | \n",
" 108 | \n",
" O | \n",
"
\n",
" \n",
" | 17 | \n",
" 0 | \n",
" to | \n",
" 110 | \n",
" 111 | \n",
" O | \n",
"
\n",
" \n",
" | 18 | \n",
" 0 | \n",
" support | \n",
" 113 | \n",
" 119 | \n",
" O | \n",
"
\n",
" \n",
" | 19 | \n",
" 0 | \n",
" the | \n",
" 121 | \n",
" 123 | \n",
" O | \n",
"
\n",
" \n",
" | 20 | \n",
" 0 | \n",
" continued | \n",
" 125 | \n",
" 133 | \n",
" O | \n",
"
\n",
" \n",
" | 21 | \n",
" 0 | \n",
" growth | \n",
" 135 | \n",
" 140 | \n",
" O | \n",
"
\n",
" \n",
" | 22 | \n",
" 0 | \n",
" of | \n",
" 142 | \n",
" 143 | \n",
" O | \n",
"
\n",
" \n",
" | 23 | \n",
" 0 | \n",
" our | \n",
" 145 | \n",
" 147 | \n",
" O | \n",
"
\n",
" \n",
" | 24 | \n",
" 0 | \n",
" subscription | \n",
" 149 | \n",
" 160 | \n",
" O | \n",
"
\n",
" \n",
" | 25 | \n",
" 0 | \n",
" SaaS | \n",
" 162 | \n",
" 165 | \n",
" O | \n",
"
\n",
" \n",
" | 26 | \n",
" 0 | \n",
" offerings | \n",
" 167 | \n",
" 175 | \n",
" O | \n",
"
\n",
" \n",
" | 27 | \n",
" 0 | \n",
" and | \n",
" 177 | \n",
" 179 | \n",
" O | \n",
"
\n",
" \n",
" | 28 | \n",
" 0 | \n",
" ongoing | \n",
" 181 | \n",
" 187 | \n",
" O | \n",
"
\n",
" \n",
" | 29 | \n",
" 0 | \n",
" maintenance | \n",
" 189 | \n",
" 199 | \n",
" O | \n",
"
\n",
" \n",
" | 30 | \n",
" 0 | \n",
" and | \n",
" 201 | \n",
" 203 | \n",
" O | \n",
"
\n",
" \n",
" | 31 | \n",
" 0 | \n",
" support | \n",
" 205 | \n",
" 211 | \n",
" O | \n",
"
\n",
" \n",
" | 32 | \n",
" 0 | \n",
" for | \n",
" 213 | \n",
" 215 | \n",
" O | \n",
"
\n",
" \n",
" | 33 | \n",
" 0 | \n",
" our | \n",
" 217 | \n",
" 219 | \n",
" O | \n",
"
\n",
" \n",
" | 34 | \n",
" 0 | \n",
" expanding | \n",
" 221 | \n",
" 229 | \n",
" O | \n",
"
\n",
" \n",
" | 35 | \n",
" 0 | \n",
" customer | \n",
" 231 | \n",
" 238 | \n",
" O | \n",
"
\n",
" \n",
" | 36 | \n",
" 0 | \n",
" base | \n",
" 240 | \n",
" 243 | \n",
" O | \n",
"
\n",
" \n",
" | 37 | \n",
" 0 | \n",
" . | \n",
" 245 | \n",
" 245 | \n",
" O | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"\n",
" \n",
"
\n",
"
\n",
" "
],
"text/plain": [
" sent_id token start end ner\n",
"0 0 $ 0 0 B-CURRENCY\n",
"1 0 4.2 2 4 B-AMOUNT\n",
"2 0 million 6 12 I-AMOUNT\n",
"3 0 of 14 15 O\n",
"4 0 the 17 19 O\n",
"5 0 increase 21 28 O\n",
"6 0 was 30 32 O\n",
"7 0 compensation 34 45 O\n",
"8 0 related 47 53 O\n",
"9 0 and 55 57 O\n",
"10 0 primarily 59 67 O\n",
"11 0 attributable 69 80 O\n",
"12 0 to 82 83 O\n",
"13 0 an 85 86 O\n",
"14 0 increase 88 95 O\n",
"15 0 in 97 98 O\n",
"16 0 headcount 100 108 O\n",
"17 0 to 110 111 O\n",
"18 0 support 113 119 O\n",
"19 0 the 121 123 O\n",
"20 0 continued 125 133 O\n",
"21 0 growth 135 140 O\n",
"22 0 of 142 143 O\n",
"23 0 our 145 147 O\n",
"24 0 subscription 149 160 O\n",
"25 0 SaaS 162 165 O\n",
"26 0 offerings 167 175 O\n",
"27 0 and 177 179 O\n",
"28 0 ongoing 181 187 O\n",
"29 0 maintenance 189 199 O\n",
"30 0 and 201 203 O\n",
"31 0 support 205 211 O\n",
"32 0 for 213 215 O\n",
"33 0 our 217 219 O\n",
"34 0 expanding 221 229 O\n",
"35 0 customer 231 238 O\n",
"36 0 base 240 243 O\n",
"37 0 . 245 245 O"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"result = light_model.fullAnnotate(text)\n",
"\n",
"ner_df= pd.DataFrame([(int(x.metadata['sentence']), x.result, x.begin, x.end, y.result) for x,y in zip(result[0][\"token\"], result[0][\"ner\"])], \n",
" columns=['sent_id','token','start','end','ner'])\n",
"ner_df"
]
},
{
"cell_type": "markdown",
"id": "xAdLlcMjejMm",
"metadata": {
"id": "xAdLlcMjejMm"
},
"source": [
"###π **Highlight Entities**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbx496QFQydD",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "fbx496QFQydD",
"outputId": "65bc0b3d-187c-4d13-9175-5deee99a568e"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" $ CURRENCY 4.2 million AMOUNT of the increase was compensation related and primarily attributable to an increase in headcount to support the continued growth of our subscription SaaS offerings and ongoing maintenance and support for our expanding customer base ."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result = result[0]\n",
"visualiser = nlp.viz.NerVisualizer()\n",
"visualiser.display(result, label_col='ner_span', document_col='document')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "z_sroXZZad8O",
"metadata": {
"id": "z_sroXZZad8O"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}