snowpark_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Machine Learning with Snowpark ML\n",
    "\n",
    "Source: https://quickstarts.snowflake.com/guide/intro_to_machine_learning_with_snowpark_ml_for_python/#0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Snowpark for Python\n",
    "from snowflake.snowpark import Session\n",
    "from snowflake.snowpark.version import VERSION\n",
    "from snowflake.snowpark.types import StructType, StructField, DoubleType, DecimalType\n",
    "import snowflake.snowpark.functions as F\n",
    "\n",
    "# Snowpark ML\n",
    "import snowflake.ml.modeling.preprocessing as snowml\n",
    "from snowflake.ml.modeling.pipeline import Pipeline\n",
    "from snowflake.ml.modeling.metrics.correlation import correlation\n",
    "from snowflake.ml.modeling.xgboost import XGBRegressor\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "from snowflake.ml.registry import Registry\n",
    "from snowflake.ml._internal.utils import identifier\n",
    "from snowflake.snowpark.functions import udf\n",
    "\n",
    "# Data Science Libs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from snowflake.ml.modeling.metrics import mean_absolute_percentage_error\n",
    "\n",
    "# Misc\n",
    "import json\n",
    "import joblib\n",
    "import cachetools\n",
    "\n",
    "# warning suppresion\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Establish Secure Connection to Snowflake\n",
    "\n",
    "*Other connection options include Username/Password, MFA, OAuth, Okta, SSO. For more information, refer to the [Python Connector](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-example) documentation.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Connection Established with the following parameters:\n",
      "User                        : MLDEMO\n",
      "Role                        : \"ACCOUNTADMIN\"\n",
      "Database                    : \"ML_HOL_DB\"\n",
      "Schema                      : \"ML_HOL_SCHEMA\"\n",
      "Warehouse                   : \"ML_HOL_WH\"\n",
      "Snowflake version           : 8.18.0\n",
      "Snowpark for Python version : 1.14.0\n"
     ]
    }
   ],
   "source": [
    "# Make a Snowpark Connection\n",
    "\n",
    "################################################################################################################\n",
    "#  You can also use the SnowSQL Client to configure your connection params:\n",
    "#  https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\n",
    "#\n",
    "#  >>> from snowflake.ml.utils import connection_params\n",
    "#  >>> session = Session.builder.configs(connection_params.SnowflakeLoginOptions()\n",
    "#  >>> ).create()\n",
    "#\n",
    "#  NOTE: If you have named connection params then specify the connection name\n",
    "#  Example:\n",
    "#\n",
    "#  >>> session = Session.builder.configs(\n",
    "#  >>> connection_params.SnowflakeLoginOptions(connection_name='connections.snowml')\n",
    "#  >>> ).create()\n",
    "#\n",
    "#################################################################################################################\n",
    "\n",
    "# Edit the connection.json before creating the session object below\n",
    "# Create Snowflake Session object\n",
    "connection_parameters = json.load(open(\"connection.json\"))\n",
    "session = Session.builder.configs(connection_parameters).create()\n",
    "session.sql_simplifier_enabled = True\n",
    "\n",
    "snowflake_environment = session.sql(\n",
    "    \"SELECT current_user(), current_version()\"\n",
    ").collect()\n",
    "snowpark_version = VERSION\n",
    "\n",
    "# Current Environment Details\n",
    "print(\"\\nConnection Established with the following parameters:\")\n",
    "print(\"User                        : {}\".format(snowflake_environment[0][0]))\n",
    "print(\"Role                        : {}\".format(session.get_current_role()))\n",
    "print(\"Database                    : {}\".format(session.get_current_database()))\n",
    "print(\"Schema                      : {}\".format(session.get_current_schema()))\n",
    "print(\"Warehouse                   : {}\".format(session.get_current_warehouse()))\n",
    "print(\"Snowflake version           : {}\".format(snowflake_environment[0][1]))\n",
    "print(\n",
    "    \"Snowpark for Python version : {}.{}.{}\".format(\n",
    "        snowpark_version[0], snowpark_version[1], snowpark_version[2]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Ingestion\n",
    "\n",
    "We will use The `diamonds` dataset to demonstrate Snowflake's native data science transformers in terms of database functionality and Spark & Pandas comportablity, using non-synthetic data. The `diamonds.csv` was previously staged using the accompanying `setup.sql` statement. \n",
    "\n",
    "This file can also be found here: https://sfquickstarts.s3.us-west-1.amazonaws.com/intro-to-machine-learning-with-snowpark-ml-for-python/diamonds.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------------------------------\n",
      "|\"name\"                        |\"size\"   |\"md5\"                             |\"last_modified\"               |\n",
      "------------------------------------------------------------------------------------------------------------\n",
      "|diamonds_assets/diamonds.csv  |2772144  |4949e1d980ba4d2abb325011c42f2778  |Mon, 6 May 2024 23:36:17 GMT  |\n",
      "------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "------------------------------------------------------------------------------------------------\n",
      "|\"carat\"  |\"cut\"      |\"color\"  |\"clarity\"  |\"depth\"  |\"table\"  |\"price\"  |\"x\"   |\"y\"   |\"z\"   |\n",
      "------------------------------------------------------------------------------------------------\n",
      "|0.23     |Ideal      |E        |SI2        |61.5     |55.0     |326      |3.95  |3.98  |2.43  |\n",
      "|0.21     |Premium    |E        |SI1        |59.8     |61.0     |326      |3.89  |3.84  |2.31  |\n",
      "|0.23     |Good       |E        |VS1        |56.9     |65.0     |327      |4.05  |4.07  |2.31  |\n",
      "|0.29     |Premium    |I        |VS2        |62.4     |58.0     |334      |4.20  |4.23  |2.63  |\n",
      "|0.31     |Good       |J        |SI2        |63.3     |58.0     |335      |4.34  |4.35  |2.75  |\n",
      "|0.24     |Very Good  |J        |VVS2       |62.8     |57.0     |336      |3.94  |3.96  |2.48  |\n",
      "|0.24     |Very Good  |I        |VVS1       |62.3     |57.0     |336      |3.95  |3.98  |2.47  |\n",
      "|0.26     |Very Good  |H        |SI1        |61.9     |55.0     |337      |4.07  |4.11  |2.53  |\n",
      "|0.22     |Fair       |E        |VS2        |65.1     |61.0     |337      |3.87  |3.78  |2.49  |\n",
      "|0.23     |Very Good  |H        |VS1        |59.4     |61.0     |338      |4.00  |4.05  |2.39  |\n",
      "------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Show the file before loading\n",
    "session.sql(\"LS @DIAMONDS_ASSETS;\").show()\n",
    "\n",
    "# Create a Snowpark DataFrame that is configured to load data from the CSV file\n",
    "# We can now infer schema from CSV files.\n",
    "diamonds_df = session.read.options(\n",
    "    {\n",
    "        \"field_delimiter\": \",\",\n",
    "        \"field_optionally_enclosed_by\": '\"',\n",
    "        \"infer_schema\": True,\n",
    "        \"parse_header\": True,\n",
    "    }\n",
    ").csv(\"@DIAMONDS_ASSETS\")\n",
    "\n",
    "diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 - clean data\n",
    "Update headers to uppercase using Snowpark DataFrame operations for standardization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"  |\"CUT\"      |\"COLOR\"  |\"CLARITY\"  |\"DEPTH\"  |\"TABLE_PCT\"  |\"PRICE\"  |\"X\"   |\"Y\"   |\"Z\"   |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "|0.23     |Ideal      |E        |SI2        |61.5     |55.0         |326      |3.95  |3.98  |2.43  |\n",
      "|0.21     |Premium    |E        |SI1        |59.8     |61.0         |326      |3.89  |3.84  |2.31  |\n",
      "|0.23     |Good       |E        |VS1        |56.9     |65.0         |327      |4.05  |4.07  |2.31  |\n",
      "|0.29     |Premium    |I        |VS2        |62.4     |58.0         |334      |4.20  |4.23  |2.63  |\n",
      "|0.31     |Good       |J        |SI2        |63.3     |58.0         |335      |4.34  |4.35  |2.75  |\n",
      "|0.24     |Very Good  |J        |VVS2       |62.8     |57.0         |336      |3.94  |3.96  |2.48  |\n",
      "|0.24     |Very Good  |I        |VVS1       |62.3     |57.0         |336      |3.95  |3.98  |2.47  |\n",
      "|0.26     |Very Good  |H        |SI1        |61.9     |55.0         |337      |4.07  |4.11  |2.53  |\n",
      "|0.22     |Fair       |E        |VS2        |65.1     |61.0         |337      |3.87  |3.78  |2.49  |\n",
      "|0.23     |Very Good  |H        |VS1        |59.4     |61.0         |338      |4.00  |4.05  |2.39  |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Update headers to uppercase\n",
    "for colname in diamonds_df.columns:\n",
    "    if colname == '\"table\"':\n",
    "        new_colname = \"TABLE_PCT\"\n",
    "    else:\n",
    "        new_colname = str.upper(colname)\n",
    "    diamonds_df = diamonds_df.with_column_renamed(colname, new_colname)\n",
    "\n",
    "diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we standardize the category formatting for `CUT` using Snowpark DataFrame operations.\n",
    "\n",
    "This way, when we write to a Snowflake table, there will be no inconsistencies in how the Snowpark DataFrame will read in the category names. Secondly, the feature transformations on categoricals will be easier to encode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"  |\"COLOR\"  |\"CLARITY\"  |\"DEPTH\"  |\"TABLE_PCT\"  |\"PRICE\"  |\"X\"   |\"Y\"   |\"Z\"   |\"CUT\"      |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "|0.23     |E        |SI2        |61.5     |55.0         |326      |3.95  |3.98  |2.43  |IDEAL      |\n",
      "|0.21     |E        |SI1        |59.8     |61.0         |326      |3.89  |3.84  |2.31  |PREMIUM    |\n",
      "|0.23     |E        |VS1        |56.9     |65.0         |327      |4.05  |4.07  |2.31  |GOOD       |\n",
      "|0.29     |I        |VS2        |62.4     |58.0         |334      |4.20  |4.23  |2.63  |PREMIUM    |\n",
      "|0.31     |J        |SI2        |63.3     |58.0         |335      |4.34  |4.35  |2.75  |GOOD       |\n",
      "|0.24     |J        |VVS2       |62.8     |57.0         |336      |3.94  |3.96  |2.48  |VERY_GOOD  |\n",
      "|0.24     |I        |VVS1       |62.3     |57.0         |336      |3.95  |3.98  |2.47  |VERY_GOOD  |\n",
      "|0.26     |H        |SI1        |61.9     |55.0         |337      |4.07  |4.11  |2.53  |VERY_GOOD  |\n",
      "|0.22     |E        |VS2        |65.1     |61.0         |337      |3.87  |3.78  |2.49  |FAIR       |\n",
      "|0.23     |H        |VS1        |59.4     |61.0         |338      |4.00  |4.05  |2.39  |VERY_GOOD  |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def fix_values(columnn):\n",
    "    return F.upper(F.regexp_replace(F.col(columnn), \"[^a-zA-Z0-9]+\", \"_\"))\n",
    "\n",
    "\n",
    "for col in [\"CUT\"]:\n",
    "    diamonds_df = diamonds_df.with_column(col, fix_values(col))\n",
    "\n",
    "diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's cast the decimal types to DoubleType() since DecimalType() isn't support by Snowpark ML at the moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StructField('CARAT', DecimalType(3, 2), nullable=True)\n",
      "StructField('COLOR', StringType(16777216), nullable=True)\n",
      "StructField('CLARITY', StringType(16777216), nullable=True)\n",
      "StructField('DEPTH', DecimalType(3, 1), nullable=True)\n",
      "StructField('TABLE_PCT', DecimalType(3, 1), nullable=True)\n",
      "StructField('PRICE', LongType(), nullable=True)\n",
      "StructField('X', DecimalType(4, 2), nullable=True)\n",
      "StructField('Y', DecimalType(4, 2), nullable=True)\n",
      "StructField('Z', DecimalType(4, 2), nullable=True)\n",
      "StructField('CUT', StringType(16777216), nullable=True)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "|\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"CARAT\"  |\"X\"   |\"Y\"   |\"Z\"   |\"DEPTH\"  |\"TABLE_PCT\"  |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "|E        |SI2        |326      |IDEAL      |0.23     |3.95  |3.98  |2.43  |61.5     |55.0         |\n",
      "|E        |SI1        |326      |PREMIUM    |0.21     |3.89  |3.84  |2.31  |59.8     |61.0         |\n",
      "|E        |VS1        |327      |GOOD       |0.23     |4.05  |4.07  |2.31  |56.9     |65.0         |\n",
      "|I        |VS2        |334      |PREMIUM    |0.29     |4.2   |4.23  |2.63  |62.4     |58.0         |\n",
      "|J        |SI2        |335      |GOOD       |0.31     |4.34  |4.35  |2.75  |63.3     |58.0         |\n",
      "|J        |VVS2       |336      |VERY_GOOD  |0.24     |3.94  |3.96  |2.48  |62.8     |57.0         |\n",
      "|I        |VVS1       |336      |VERY_GOOD  |0.24     |3.95  |3.98  |2.47  |62.3     |57.0         |\n",
      "|H        |SI1        |337      |VERY_GOOD  |0.26     |4.07  |4.11  |2.53  |61.9     |55.0         |\n",
      "|E        |VS2        |337      |FAIR       |0.22     |3.87  |3.78  |2.49  |65.1     |61.0         |\n",
      "|H        |VS1        |338      |VERY_GOOD  |0.23     |4.0   |4.05  |2.39  |59.4     |61.0         |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check schema\n",
    "for x in list(diamonds_df.schema):\n",
    "    print(x)\n",
    "\n",
    "for colname in [\"CARAT\", \"X\", \"Y\", \"Z\", \"DEPTH\", \"TABLE_PCT\"]:\n",
    "    diamonds_df = diamonds_df.with_column(\n",
    "        colname, diamonds_df[colname].cast(DoubleType())\n",
    "    )\n",
    "\n",
    "diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 - Write cleaned data to a Snowflake table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "diamonds_df.write.mode('overwrite').save_as_table('diamonds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. ML Feature Transformations\n",
    "\n",
    "- We will walk through a few transformations that are included in the Snowpark ML Preprocessing API. \n",
    "    https://docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/index\n",
    "    \n",
    "- We will also build a preprocessing pipeline to be used in the ML modeling notebook.\n",
    "\n",
    "***Note: All feature transformations using Snowpark ML are distributed operations in the same way that Snowpark DataFrame operations are.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the table name where we stored the diamonds dataset\n",
    "DEMO_TABLE = 'diamonds'\n",
    "input_tbl = f\"{session.get_current_database()}.{session.get_current_schema()}.{DEMO_TABLE}\"\n",
    "\n",
    "# First, we read in the data from a Snowflake table into a Snowpark DataFrame\n",
    "diamonds_df = session.table(input_tbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 - Feature Transformations\n",
    "\n",
    "Let's use the `MinMaxScaler` to normalize the `CARAT` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------\n",
      "|\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"CARAT\"  |\"X\"   |\"Y\"   |\"Z\"   |\"DEPTH\"  |\"TABLE_PCT\"  |\"CARAT_NORM\"  |\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "|E        |SI2        |326      |IDEAL      |0.23     |3.95  |3.98  |2.43  |61.5     |55.0         |0.006237      |\n",
      "|E        |SI1        |326      |PREMIUM    |0.21     |3.89  |3.84  |2.31  |59.8     |61.0         |0.002079      |\n",
      "|E        |VS1        |327      |GOOD       |0.23     |4.05  |4.07  |2.31  |56.9     |65.0         |0.006237      |\n",
      "|I        |VS2        |334      |PREMIUM    |0.29     |4.2   |4.23  |2.63  |62.4     |58.0         |0.018711      |\n",
      "|J        |SI2        |335      |GOOD       |0.31     |4.34  |4.35  |2.75  |63.3     |58.0         |0.022869      |\n",
      "|J        |VVS2       |336      |VERY_GOOD  |0.24     |3.94  |3.96  |2.48  |62.8     |57.0         |0.008316      |\n",
      "|I        |VVS1       |336      |VERY_GOOD  |0.24     |3.95  |3.98  |2.47  |62.3     |57.0         |0.008316      |\n",
      "|H        |SI1        |337      |VERY_GOOD  |0.26     |4.07  |4.11  |2.53  |61.9     |55.0         |0.012474      |\n",
      "|E        |VS2        |337      |FAIR       |0.22     |3.87  |3.78  |2.49  |65.1     |61.0         |0.004158      |\n",
      "|H        |VS1        |338      |VERY_GOOD  |0.23     |4.0   |4.05  |2.39  |59.4     |61.0         |0.006237      |\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Normalize the CARAT column\n",
    "snowml_mms = snowml.MinMaxScaler(input_cols=[\"CARAT\"], output_cols=[\"CARAT_NORM\"])\n",
    "normalized_diamonds_df = snowml_mms.fit(diamonds_df).transform(diamonds_df)\n",
    "\n",
    "# Reduce the number of decimals\n",
    "new_col = normalized_diamonds_df.col(\"CARAT_NORM\").cast(DecimalType(7, 6))\n",
    "normalized_diamonds_df = normalized_diamonds_df.with_column(\"CARAT_NORM\", new_col)\n",
    "\n",
    "normalized_diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the `OrdinalEncoder` to transform `COLOR` and `CLARITY` from categorical to numerical values so they are more meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   _COLUMN_NAME  _CATEGORY  _INDEX\n",
      "0           CUT      IDEAL       0\n",
      "1           CUT    PREMIUM       1\n",
      "2           CUT  VERY_GOOD       2\n",
      "3           CUT       GOOD       3\n",
      "4           CUT       FAIR       4\n",
      "5       CLARITY         IF       0\n",
      "6       CLARITY       VVS1       1\n",
      "7       CLARITY       VVS2       2\n",
      "8       CLARITY        VS1       3\n",
      "9       CLARITY        VS2       4\n",
      "10      CLARITY        SI1       5\n",
      "11      CLARITY        SI2       6\n",
      "12      CLARITY         I1       7\n",
      "13      CLARITY         I2       8\n",
      "14      CLARITY         I3       9\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CUT_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"CARAT\"  |\"X\"   |\"Y\"   |\"Z\"   |\"DEPTH\"  |\"TABLE_PCT\"  |\"CARAT_NORM\"  |\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.0       |6.0           |E        |SI2        |326      |IDEAL      |0.23     |3.95  |3.98  |2.43  |61.5     |55.0         |0.006237      |\n",
      "|1.0       |5.0           |E        |SI1        |326      |PREMIUM    |0.21     |3.89  |3.84  |2.31  |59.8     |61.0         |0.002079      |\n",
      "|3.0       |3.0           |E        |VS1        |327      |GOOD       |0.23     |4.05  |4.07  |2.31  |56.9     |65.0         |0.006237      |\n",
      "|1.0       |4.0           |I        |VS2        |334      |PREMIUM    |0.29     |4.2   |4.23  |2.63  |62.4     |58.0         |0.018711      |\n",
      "|3.0       |6.0           |J        |SI2        |335      |GOOD       |0.31     |4.34  |4.35  |2.75  |63.3     |58.0         |0.022869      |\n",
      "|2.0       |2.0           |J        |VVS2       |336      |VERY_GOOD  |0.24     |3.94  |3.96  |2.48  |62.8     |57.0         |0.008316      |\n",
      "|2.0       |1.0           |I        |VVS1       |336      |VERY_GOOD  |0.24     |3.95  |3.98  |2.47  |62.3     |57.0         |0.008316      |\n",
      "|2.0       |5.0           |H        |SI1        |337      |VERY_GOOD  |0.26     |4.07  |4.11  |2.53  |61.9     |55.0         |0.012474      |\n",
      "|4.0       |4.0           |E        |VS2        |337      |FAIR       |0.22     |3.87  |3.78  |2.49  |65.1     |61.0         |0.004158      |\n",
      "|2.0       |3.0           |H        |VS1        |338      |VERY_GOOD  |0.23     |4.0   |4.05  |2.39  |59.4     |61.0         |0.006237      |\n",
      "---------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Encode CUT and CLARITY preserve ordinal importance\n",
    "categories = {\n",
    "    \"CUT\": np.array([\"IDEAL\", \"PREMIUM\", \"VERY_GOOD\", \"GOOD\", \"FAIR\"]),\n",
    "    \"CLARITY\": np.array(\n",
    "        [\"IF\", \"VVS1\", \"VVS2\", \"VS1\", \"VS2\", \"SI1\", \"SI2\", \"I1\", \"I2\", \"I3\"]\n",
    "    ),\n",
    "}\n",
    "snowml_oe = snowml.OrdinalEncoder(\n",
    "    input_cols=[\"CUT\", \"CLARITY\"],\n",
    "    output_cols=[\"CUT_OE\", \"CLARITY_OE\"],\n",
    "    categories=categories,\n",
    ")\n",
    "ord_encoded_diamonds_df = snowml_oe.fit(normalized_diamonds_df).transform(\n",
    "    normalized_diamonds_df\n",
    ")\n",
    "\n",
    "# Show the encoding\n",
    "print(snowml_oe._state_pandas)\n",
    "\n",
    "ord_encoded_diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the `OneHotEncoder` to transform the categorical columns to numerical columns.\n",
    "\n",
    "This is more for illustration purposes. Using the OrdinalEncoder makes more sense for the diamonds dataset since `CARAT`, `COLOR`, and `CLARITY` all follow a natural ranking order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CUT_OHE_FAIR', 'CUT_OHE_GOOD', 'CUT_OHE_IDEAL', 'CUT_OHE_PREMIUM',\n",
       "       'CUT_OHE_VERY_GOOD', 'COLOR_OHE_D', 'COLOR_OHE_E', 'COLOR_OHE_F',\n",
       "       'COLOR_OHE_G', 'COLOR_OHE_H', 'COLOR_OHE_I', 'COLOR_OHE_J',\n",
       "       'CLARITY_OHE_I1', 'CLARITY_OHE_IF', 'CLARITY_OHE_SI1',\n",
       "       'CLARITY_OHE_SI2', 'CLARITY_OHE_VS1', 'CLARITY_OHE_VS2',\n",
       "       'CLARITY_OHE_VVS1', 'CLARITY_OHE_VVS2', 'CUT_OE', 'CLARITY_OE',\n",
       "       'COLOR', 'CLARITY', 'PRICE', 'CUT', 'CARAT', 'X', 'Y', 'Z',\n",
       "       'DEPTH', 'TABLE_PCT', 'CARAT_NORM'], dtype='<U17')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode categoricals to numeric columns\n",
    "snowml_ohe = snowml.OneHotEncoder(input_cols=[\"CUT\", \"COLOR\", \"CLARITY\"], output_cols=[\"CUT_OHE\", \"COLOR_OHE\", \"CLARITY_OHE\"])\n",
    "transformed_diamonds_df = snowml_ohe.fit(ord_encoded_diamonds_df).transform(ord_encoded_diamonds_df)\n",
    "\n",
    "np.array(transformed_diamonds_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Build a full preprocessing `Pipeline`.\n",
    "\n",
    "This will be useful for both the ML training & inference steps to have standarized feature transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorize all the features for processing\n",
    "CATEGORICAL_COLUMNS = [\"CUT\", \"COLOR\", \"CLARITY\"]\n",
    "CATEGORICAL_COLUMNS_OE = [\"CUT_OE\", \"COLOR_OE\", \"CLARITY_OE\"] # To name the ordinal encoded columns\n",
    "NUMERICAL_COLUMNS = [\"CARAT\", \"DEPTH\", \"TABLE_PCT\", \"X\", \"Y\", \"Z\"]\n",
    "\n",
    "categories = {\n",
    "    \"CUT\": np.array([\"IDEAL\", \"PREMIUM\", \"VERY_GOOD\", \"GOOD\", \"FAIR\"]),\n",
    "    \"CLARITY\": np.array([\"IF\", \"VVS1\", \"VVS2\", \"VS1\", \"VS2\", \"SI1\", \"SI2\", \"I1\", \"I2\", \"I3\"]),\n",
    "    \"COLOR\": np.array(['D', 'E', 'F', 'G', 'H', 'I', 'J']),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build `Pipeline`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.006237006237006237  |0.5138888888888888   |0.23076923076923073  |0.3677839851024209   |0.06757215619694397  |0.07641509433962265  |0.0       |1.0         |6.0           |E        |SI2        |326      |IDEAL      |\n",
      "|0.002079002079002079  |0.46666666666666656  |0.34615384615384615  |0.36219739292364994  |0.06519524617996604  |0.07264150943396226  |1.0       |1.0         |5.0           |E        |SI1        |326      |PREMIUM    |\n",
      "|0.006237006237006237  |0.38611111111111107  |0.423076923076923    |0.37709497206703907  |0.06910016977928693  |0.07264150943396226  |3.0       |1.0         |3.0           |E        |VS1        |327      |GOOD       |\n",
      "|0.018711018711018705  |0.5388888888888888   |0.28846153846153844  |0.3910614525139665   |0.07181663837011885  |0.08270440251572327  |1.0       |5.0         |4.0           |I        |VS2        |334      |PREMIUM    |\n",
      "|0.02286902286902287   |0.5638888888888887   |0.28846153846153844  |0.404096834264432    |0.07385398981324279  |0.08647798742138364  |3.0       |6.0         |6.0           |J        |SI2        |335      |GOOD       |\n",
      "|0.008316008316008316  |0.5499999999999998   |0.2692307692307693   |0.36685288640595903  |0.06723259762308999  |0.0779874213836478   |2.0       |6.0         |2.0           |J        |VVS2       |336      |VERY_GOOD  |\n",
      "|0.008316008316008316  |0.536111111111111    |0.2692307692307693   |0.3677839851024209   |0.06757215619694397  |0.07767295597484278  |2.0       |5.0         |1.0           |I        |VVS1       |336      |VERY_GOOD  |\n",
      "|0.012474012474012475  |0.5249999999999999   |0.23076923076923073  |0.3789571694599628   |0.06977928692699492  |0.07955974842767295  |2.0       |4.0         |5.0           |H        |SI1        |337      |VERY_GOOD  |\n",
      "|0.004158004158004158  |0.6138888888888887   |0.34615384615384615  |0.36033519553072624  |0.06417657045840407  |0.07830188679245284  |4.0       |1.0         |4.0           |E        |VS2        |337      |FAIR       |\n",
      "|0.006237006237006237  |0.4555555555555555   |0.34615384615384615  |0.37243947858473     |0.06876061120543293  |0.07515723270440251  |2.0       |4.0         |3.0           |H        |VS1        |338      |VERY_GOOD  |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Build the pipeline\n",
    "preprocessing_pipeline = Pipeline(\n",
    "    steps=[\n",
    "        (\n",
    "            \"OE\",\n",
    "            snowml.OrdinalEncoder(\n",
    "                input_cols=CATEGORICAL_COLUMNS,\n",
    "                output_cols=CATEGORICAL_COLUMNS_OE,\n",
    "                categories=categories,\n",
    "            ),\n",
    "        ),\n",
    "        (\n",
    "            \"MMS\",\n",
    "            snowml.MinMaxScaler(\n",
    "                clip=True,\n",
    "                input_cols=NUMERICAL_COLUMNS,\n",
    "                output_cols=NUMERICAL_COLUMNS,\n",
    "            ),\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "\n",
    "PIPELINE_FILE = \"preprocessing_pipeline.joblib\"\n",
    "joblib.dump(\n",
    "    preprocessing_pipeline, PIPELINE_FILE\n",
    ")  # We are just pickling it locally first\n",
    "\n",
    "transformed_diamonds_df = preprocessing_pipeline.fit(diamonds_df).transform(diamonds_df)\n",
    "transformed_diamonds_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the pickled object into the deployment stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PutResult(source='preprocessing_pipeline.joblib', target='preprocessing_pipeline.joblib.gz', source_size=2068, target_size=1120, source_compression='NONE', target_compression='GZIP', status='UPLOADED', message='')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.file.put(PIPELINE_FILE, \"@ML_HOL_ASSETS\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Data Exploration\n",
    "\n",
    "Now that we've transformed our features, let's calculate the correlation using Snowpark ML's `correlation()` function between each pair to better understand their relationships.\n",
    "\n",
    "*Note: Snowpark ML's pearson correlation function returns a Pandas DataFrame*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_diamonds_df = correlation(df=transformed_diamonds_df)\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr_diamonds_df, dtype=bool))\n",
    "\n",
    "# Create a heatmap with the features\n",
    "plt.figure(figsize=(7, 7))\n",
    "heatmap = sns.heatmap(corr_diamonds_df, mask=mask, cmap=\"YlGnBu\", annot=True, vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that `CARAT` and `PRICE` are highly correlated, which makes sense! Let's take a look at their relationship a bit closer.\n",
    "\n",
    "*Note: You will have to convert your Snowpark DF to a Pandas DF in order to use matplotlib & seaborn.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up a plot to look at CARAT and PRICE\n",
    "counts = transformed_diamonds_df.to_pandas().groupby(['PRICE', 'CARAT']).size().reset_index(name='Count')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax = sns.scatterplot(data=counts, x='CARAT', y='PRICE', size='Count', markers='o', alpha=(0.1, .25, 0.5, 0.75, 1))\n",
    "ax.grid(axis='y')\n",
    "\n",
    "# The relationship is not linear - it appears exponential which makes sense given the rarity of the large diamonds\n",
    "sns.move_legend(ax, \"upper left\")\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. ML Modeling\n",
    "\n",
    "- we will illustrate how to train an `XGBoost` model with the diamonds dataset using the Snowpark ML Model API. \n",
    "- We also show how to do inference and manage models via Model Registry or as a UDF (See Appendix)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 - Build a simple XGBoost Regression model\n",
    "\n",
    "What's happening here? \n",
    "\n",
    "- The `model.fit()` function actually creates a temporary stored procedure in the background. This also means that the model training is a single-node operation. Be sure to use a [Snowpark Optimized Warehouse](https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized) if you need more memory. We are just using an XS Standard Virtual Warehouse here, which we created at the beginning of this quickstart.\n",
    "- The `model.predict()` function actually creates a temporary vectorized UDF in the background, which means the input DataFrame is batched as Pandas DataFrames and inference is parallelized across the batches of data.\n",
    "\n",
    "You can check the query history once you execute the following cell to check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorize all the features for modeling\n",
    "CATEGORICAL_COLUMNS = [\"CUT\", \"COLOR\", \"CLARITY\"]\n",
    "CATEGORICAL_COLUMNS_OE = [\"CUT_OE\", \"COLOR_OE\", \"CLARITY_OE\"] # To name the ordinal encoded columns\n",
    "NUMERICAL_COLUMNS = [\"CARAT\", \"DEPTH\", \"TABLE_PCT\", \"X\", \"Y\", \"Z\"]\n",
    "\n",
    "LABEL_COLUMNS = ['PRICE']\n",
    "OUTPUT_COLUMNS = ['PREDICTED_PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets\n",
    "diamonds_train_df, diamonds_test_df = diamonds_df.random_split(weights=[0.9, 0.1], seed=0)\n",
    "\n",
    "# Run the train and test sets through the Pipeline object we defined earlier\n",
    "train_df = preprocessing_pipeline.fit(diamonds_train_df).transform(diamonds_train_df)\n",
    "test_df = preprocessing_pipeline.transform(diamonds_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the XGBRegressor\n",
    "regressor = XGBRegressor(\n",
    "    input_cols=CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS,\n",
    "    label_cols=LABEL_COLUMNS,\n",
    "    output_cols=OUTPUT_COLUMNS\n",
    ")\n",
    "\n",
    "# Train\n",
    "regressor.fit(train_df)\n",
    "\n",
    "# Predict\n",
    "result = regressor.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUT_OE</th>\n",
       "      <th>COLOR_OE</th>\n",
       "      <th>CLARITY_OE</th>\n",
       "      <th>CARAT</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>TABLE_PCT</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>PREDICTED_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.020790</td>\n",
       "      <td>0.547222</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.391993</td>\n",
       "      <td>0.072496</td>\n",
       "      <td>0.083648</td>\n",
       "      <td>322.593506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>0.455556</td>\n",
       "      <td>0.365385</td>\n",
       "      <td>0.408752</td>\n",
       "      <td>0.075212</td>\n",
       "      <td>0.082390</td>\n",
       "      <td>267.302460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.008316</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.369646</td>\n",
       "      <td>0.066893</td>\n",
       "      <td>0.077673</td>\n",
       "      <td>385.477814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>0.486111</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.368715</td>\n",
       "      <td>0.067402</td>\n",
       "      <td>0.075472</td>\n",
       "      <td>434.468170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.020790</td>\n",
       "      <td>0.552778</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.398510</td>\n",
       "      <td>0.071986</td>\n",
       "      <td>0.084277</td>\n",
       "      <td>566.351562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5407</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.103950</td>\n",
       "      <td>0.505556</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.528864</td>\n",
       "      <td>0.097284</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>2636.223389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5408</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.116424</td>\n",
       "      <td>0.452778</td>\n",
       "      <td>0.365385</td>\n",
       "      <td>0.552142</td>\n",
       "      <td>0.099321</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>2343.046143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5409</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.103950</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.520484</td>\n",
       "      <td>0.095925</td>\n",
       "      <td>0.111006</td>\n",
       "      <td>2690.643066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5410</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.106029</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.536313</td>\n",
       "      <td>0.097284</td>\n",
       "      <td>0.111006</td>\n",
       "      <td>3194.863525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5411</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.114345</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.542831</td>\n",
       "      <td>0.099660</td>\n",
       "      <td>0.114465</td>\n",
       "      <td>2852.860352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5412 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CUT_OE  COLOR_OE  CLARITY_OE     CARAT     DEPTH  TABLE_PCT         X  \\\n",
       "0        2.0       6.0         5.0  0.020790  0.547222   0.307692  0.391993   \n",
       "1        2.0       6.0         5.0  0.022869  0.455556   0.365385  0.408752   \n",
       "2        1.0       5.0         3.0  0.008316  0.541667   0.269231  0.369646   \n",
       "3        2.0       0.0         4.0  0.006237  0.486111   0.346154  0.368715   \n",
       "4        1.0       4.0         5.0  0.020790  0.552778   0.307692  0.398510   \n",
       "...      ...       ...         ...       ...       ...        ...       ...   \n",
       "5407     2.0       1.0         5.0  0.103950  0.505556   0.269231  0.528864   \n",
       "5408     1.0       5.0         3.0  0.116424  0.452778   0.365385  0.552142   \n",
       "5409     2.0       1.0         4.0  0.103950  0.550000   0.326923  0.520484   \n",
       "5410     0.0       3.0         3.0  0.106029  0.511111   0.250000  0.536313   \n",
       "5411     0.0       0.0         6.0  0.114345  0.533333   0.230769  0.542831   \n",
       "\n",
       "             Y         Z  PREDICTED_PRICE  \n",
       "0     0.072496  0.083648       322.593506  \n",
       "1     0.075212  0.082390       267.302460  \n",
       "2     0.066893  0.077673       385.477814  \n",
       "3     0.067402  0.075472       434.468170  \n",
       "4     0.071986  0.084277       566.351562  \n",
       "...        ...       ...              ...  \n",
       "5407  0.097284  0.109748      2636.223389  \n",
       "5408  0.099321  0.109748      2343.046143  \n",
       "5409  0.095925  0.111006      2690.643066  \n",
       "5410  0.097284  0.111006      3194.863525  \n",
       "5411  0.099660  0.114465      2852.860352  \n",
       "\n",
       "[5412 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just to illustrate, we can also pass in a Pandas DataFrame to Snowpark ML's model.predict()\n",
    "regressor.predict(test_df[CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS].to_pandas())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Evaluate Predictions\n",
    "Let's analyze the results using Snowpark ML's Mean Absolute Percentage Error (MAPE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "|\"PRICE\"  |\"PREDICTED_PRICE\"  |\n",
      "-------------------------------\n",
      "|351      |322.593505859375   |\n",
      "|353      |267.3024597167969  |\n",
      "|355      |385.4778137207031  |\n",
      "|357      |434.4681701660156  |\n",
      "|554      |566.3515625        |\n",
      "|554      |492.4712219238281  |\n",
      "|2757     |2734.483642578125  |\n",
      "|2759     |2950.267578125     |\n",
      "|2759     |2802.850830078125  |\n",
      "|2762     |2944.725341796875  |\n",
      "-------------------------------\n",
      "\n",
      "Mean absolute percentage error: 0.07117102331735714\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mape = mean_absolute_percentage_error(df=result, \n",
    "                                        y_true_col_names=\"PRICE\", \n",
    "                                        y_pred_col_names=\"PREDICTED_PRICE\")\n",
    "\n",
    "result.select(\"PRICE\", \"PREDICTED_PRICE\").show()\n",
    "print(f\"Mean absolute percentage error: {mape}\")\n",
    "\n",
    "# Plot actual vs predicted \n",
    "g = sns.relplot(data=result[\"PRICE\", \"PREDICTED_PRICE\"].to_pandas().astype(\"float64\"), x=\"PRICE\", y=\"PREDICTED_PRICE\", kind=\"scatter\")\n",
    "g.ax.axline((0,0), slope=1, color=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 - Model Tuning\n",
    "\n",
    "Now, let's use Snowpark ML's **Distributed** `GridSearchCV()` function to find optimal model parameters\n",
    "\n",
    "We will increase the warehouse size to scale up our hyperparameter tuning to take advantage of parallelized model training to accelerate this search. When done, we will scale down again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The version of package 'fastparquet' in the local environment is 2024.2.0, which does not fit the criteria for the requirement 'fastparquet<2023.11'. Your UDF might not work when the package version is different between the server and your local environment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"ALTER WAREHOUSE ML_HOL_WH SET WAREHOUSE_SIZE=LARGE;\").collect()\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=XGBRegressor(),\n",
    "    param_grid={\n",
    "        \"n_estimators\":[100, 200, 300, 400, 500],\n",
    "        \"learning_rate\":[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "    },\n",
    "    n_jobs = -1,\n",
    "    scoring=\"neg_mean_absolute_percentage_error\",\n",
    "    input_cols=CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS,\n",
    "    label_cols=LABEL_COLUMNS,\n",
    "    output_cols=OUTPUT_COLUMNS\n",
    ")\n",
    "\n",
    "# Train\n",
    "grid_search.fit(train_df)\n",
    "\n",
    "session.sql(\"ALTER WAREHOUSE ML_HOL_WH SET WAREHOUSE_SIZE=XSMALL;\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the best estimator has the following parameters: `n_estimators=500` & `learning_rate=0.4`.\n",
    "\n",
    "We can use `to_sklearn()` in order to get the actual xgboost model object, which gives us access to all its attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=3, num_parallel_tree=None, predictor=None,\n",
       "             random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=3, num_parallel_tree=None, predictor=None,\n",
       "             random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=3, num_parallel_tree=None, predictor=None,\n",
       "             random_state=None, ...)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.to_sklearn().best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze grid search results\n",
    "gs_results = grid_search.to_sklearn().cv_results_\n",
    "n_estimators_val = []\n",
    "learning_rate_val = []\n",
    "for param_dict in gs_results[\"params\"]:\n",
    "    n_estimators_val.append(param_dict[\"n_estimators\"])\n",
    "    learning_rate_val.append(param_dict[\"learning_rate\"])\n",
    "mape_val = gs_results[\"mean_test_score\"]*-1\n",
    "\n",
    "gs_results_df = pd.DataFrame(data={\n",
    "    \"n_estimators\":n_estimators_val,\n",
    "    \"learning_rate\":learning_rate_val,\n",
    "    \"mape\":mape_val})\n",
    "\n",
    "sns.relplot(data=gs_results_df, x=\"learning_rate\", y=\"mape\", hue=\"n_estimators\", kind=\"line\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's predict and analyze the results from using the best estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------\n",
      "|\"PRICE\"  |\"PREDICTED_PRICE\"   |\n",
      "--------------------------------\n",
      "|351      |353.2740173339844   |\n",
      "|353      |343.84271240234375  |\n",
      "|355      |431.1207275390625   |\n",
      "|357      |355.054443359375    |\n",
      "|554      |560.3695068359375   |\n",
      "|554      |527.7815551757812   |\n",
      "|2757     |2796.111083984375   |\n",
      "|2759     |2878.782958984375   |\n",
      "|2759     |3021.80224609375    |\n",
      "|2762     |2838.24658203125    |\n",
      "--------------------------------\n",
      "\n",
      "Mean absolute percentage error: 0.06614988993792766\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q0jKKR3trqDCYXDzbsIoSPv5sFrHac5Te0Jzy1WvomJHv9nvp2zoamQyWpN+Mwk9GRLCS4gojj/bW0LpJKEsyctx6z5L3PnNwIjMHJ6LTm1AH1b+gi8RfwlAvXryYzp0707Gj94Q+wIEDBzAajTRp0gSA7t27o9Vq+e233+jatSsAO3bsQKvV0qNHD9uaefPmcfbsWdt5P/zwAwEBAXTu3LmenkogEPxduVLCH1e6eE0KX5ssFlITotwaw2RNJFtPFPLfX47bXlg8vUxUmS1uw8lgNdaVVSbGLd1p+6xvm2jm3dmemV9nsflYAcF6HUu/mM2NhblUNYmhcNUaVE2a8GjvUMwWx2v3ad2Q6Xe04dlvsshw8t7HJsdz8IyWBaOSmL5yHy+N6ECAwo/EGDVJsWHoq8yEqfwJCVDwj/d3kNgklJeuF2WysrIysrOzbT/n5OSwd+9eIiIiiIuLA6zh5C+++IL58+e7nH/8+HGWLVvGHXfcQVRUFAcPHuTxxx8nKSmJ5ORkANq0acOAAQOYMGEC7777LmBtzxo8eDCtWrUCoF+/fiQmJjJmzBheffVVLly4wBNPPMGECRNExbdAILhmuZLFa/bha6lNymKxuDV6Ug+ylJP2hE5v8nrPcqfjGw7lIwNeG9mR8uIyou4bTujZY+hCw7hn2GyyvjzJpw825sGPdtly1/oqsy1kPvubLI/ee1JcOB9uyeGD9K78eiyf/+t7I6+tP2yrOJee76URHZiyPPP6mUe9a9cuevfubftZyvempaWxdOlSAFasWIHFYmHUqFEu5yuVSjZu3Mibb75JWVkZsbGxDBo0iNmzZyOXX6z0W7ZsGVOmTKFfv34ADB06lEWLFtmOy+Vy1q1bx8SJE0lOTiYoKIjRo0fz2muv1cdjCwQCQZ1wpYrXnMPX9oVc/7xVgwULoYH+GKrMnC/Ts2h0J/bkFln7k728LCjknmuAABR+rsd/PJTP9L4VxE0ci/+2DEqVQYy+czZZ6qYAFFcYbblrexandfHqvY9LjmfRpmzSSysJVyl5bf1hl8la0vnjUqxrr1RfvMxisVjq/S7XCSUlJajVarRarfDEBQLBFeFMcYXH4rW6Uhk7nl/Gbf/5xe0xlVLO6onJPLf2gEtB2Lw72xOu8mfy8kzb/lRKOeNS4kmKDSPIX45MBluOF7IkIwed4aIHnaqJonPzcN7YcMzhHL2hiuQ5U1F/9Tl6uT9p9zzH9rgOtvMWp3WxFYvZ8/b9nZi4bI/HZ3znH504cKaEfomN0OlN6Iwm9lRrfdvvy/4eqyf24Ka48Np9iZfBXyJHLRAIBAL31Ffxmn07VZXZsz83LiWeuU5GGqze58yvs1g0KslWCb/rZBELRiXxwZYcB483RRPJglFJDrKdY5ObM+nTTFuY/YMtOSzaeIzZG/+HevcaTH5yDi1YzL68hmBnSD21UtXUCx0XEURRuYHzpXr0VWYC/eXEqANtbWL2xlpqE7tS86iFoRYIBIK/OHVdvObcTuWuZUnCXjnMmc3HCsgrqaRV41AWjkqiWGdkxur9LkY0I7sQmUxmm1IVEqhgxqr96AwmHuubwNJqNbJ/ZXzK2N1rMCNj6h2PUaBMYFxKuMP9bYM2nEZT5pfqPRbA9W3dEBky1u0/67FN7PUfj9k+D1D4iXnUAoFAILg6uGun8ib4UROniypoHBqIWqW0VoB7uMbmYwWYLRYSGjUA4Llh7YAsbmsTzRsbjjFu59f839blAMy+/WG+btsbsgsZn3JRUloKkQcr5Tw1sDVP3yGjXF+FSimnQYCCFlHBLtXgyZpInhmcyIzVngvNpg1obTPUyZpI8kv1Yh61QCAQCK4O7tqpvAl+3BDuPg8uGc3o0ACO5pcREaxEW+FdWtTeqMeEBfH88PZk/all5L4fmbXpPQBeS/0HH3cabDsnWCm3yY26C6unaiJ5vH9rZq85wPYTFxiXEs/4lBYo5DIig5WYzBZ0epPXQjPbtRKimDusHeEq/ys6XEUYaoFAIBDYcNdOZV/l/cwdbTBUmW25cMAlpOyQV7Yzmp8+2M3l2s7YV1KXVVbR8Md1vLTeOsXwvZvvZFH3ex3WhwQo+HpSMjp9Fa99f8TF4Fo9+CN0jAtj0+HzDvuR9tnAjWyoPXqjmXWTUwgPVl6VmeJ/maEcAoFA8HfnSg/XcHffIKWcSX00Ni9VQmp5ClDIuSkunJbRIbbc+Nxh7RyGY9hPubJn64lCUuzW2ZOqiaRRaABGk5nDeSXsOXkBxaaNtHvin8gtZj5rfzvzeo8HO1nnZE0k3x3IY843BwnyV7A7t9jttTdnF5AUG+byufQCog7yXhQmk8GghRlMW7mPM8UVXtfWB8KjFggEgmuAqzFcw9N9nauw7ffjroAqXOXP4A4xNoGR2IggtwVmUghdhsxhvnOqJopJfTTkl+pZnGE18El/HuaTz2bgZzRQOmgYa4b+G04U285xFleZu/aArb/ZHfYDPezRGUxUmSykaqLczpxO1USRUf355nrWUPeEMNQCgUBwlblawzU83TfDSdgDvA+gUKuU9Lqxoa2f++37O7m9n+TBrnjoFtJLm6OvMhPdIICj50rZdqKQnX9cYEt2Ia3O/8EHXz5LsNE6rvKTYU8wNkXDtEEBnCmupIk60EVcZUlGDunJ8R6f1ZPXnKyJZOORc4xNaQ5YHIrdUjVRzBySSE5BOUvSb7bd50oJnUgIQy0QCARXmas1XMPbfTOyC5kxKJG+raMd8tHH88tcRlWCYz+3vsqzNKg033nFb7m0ahJKbEQjnl6VxeK0Lryx4RhxRWf5+LOZhFWWsTumNQ8Pf4aK41ruTYEzxZXI/WS8vP6wS+X2glFJyD1MO0zRRNIgUOFSuW7vlYP1xWT6wDaUVlYRHChn/2ktd761xRZVkO5Trq9bDfWaEIZaIBAIrjJXerhGbe9baTTZlLdqE5qXctZancGjtGlqQhRB/nJmDmnLM1/tI7GJVcVRX2UmurSQTz6bQXR5EYcaNmfsyGdt4ypDg/wJV/kz+xv34ioAzw9r51LYlqqJIi25OeOW7uS+rnH8q++NtjGcmaeKHcL7v58qZmTnG5DJYN63hzze54U723v93uoaYagFAoHgKnMlh2tcyn19Dc17msudrIkkrUdzvsr80xbmlsLVwWXFfPLZTOK05/gjrAkP3DOXksAQ27kRKmsrlbc2KqPJwiI7lbbgAAUmi4W73t5qK4aT8uTOxW7Jmkge7a0hUO5HpZ/M630MJvf57vpCGGqBQCC4ylyp4RqXet9LCc1LofD8Uj25F3TARQ924agk3thwzPbZbTcEcmP6vTQpzCUvJIJ/3Pc850MuaminVu/lREG51+cpKNdjwYLOYLKF5gvKDA4FcfatZuOS4wkOUFCuryLzVDGTPt3DotGd0OmrvN6nvIbjdY0w1AKBQHCV8eSBeivgupL3vdTQvKRGNv7DXTYBlIWjklApFbbirK+2ZLP2+xeJOLofQ1gE//nXIk7rLg41kjxdsPZMe0Plr6D/G5sdnuPFu9q7vIxI3nWyJpKkuIsSpJP6aFi46RjjvBSlwZXT+JYQhlogEAiuAepruEZd3PdyQvMllUaPAiip8WpWZywiYscWDEEq9v/vU0Z160q6v5yCMj1VJgt7couY9OkeVj7SA4WfzKOUabImEj8nZZBfjxXw7DcHeGF4e55etd8lDG9fSAYXdcuT4sI93udKanxLCEMtEAgEl4j9hCnnKuhLwZfhGnV575ruezmh+dBAf7cCKDKLmTsXzSY6axNV/koeuHMG23dXkaw94uDlSkZ+7toDPNSzJWOrvV131dtntZUu9//xUD5P3dHG4WUk0F/O2v1nXfrEpV5rT5KpqfUc4fCEmEddh4h51ALB9cPVEii5Wveuae61pxcHrc7AgTMljH5/x8WLWSzM3vg/xu5eQ5XMjwMLP2DYqUhSNJH8u39rRr233WZAJ/XRkJlbRFJcOD1aRvLgh7suzqauMhOg8CPzVDFLMnJYNLoT45budNm789zocyWVHM8vo7jCSLBSQZXZjEwmQ+Uvt82hXvFbLvd1jXO4j6ZhCM2iguvl+/WG8KgFAoHAR66WQEl93Lu2nrm3EPnZ4gp+Pnqe6AYB6KvMFOmM/JZzgVtvbEiTsCCUTrOgH9tiHVcJ8MSgf3HvgEEsNlgLuorKHYu/pHD0uOR4KgwmOsW5H6uZoonEk99pH5o/U1zBrK+zaN0klE5x4QQo/PBHxtbjhSzJyEFnMJGsieSlER0cPO6eCVEsrPayrzTCUAsEAoGPXC2Bkrq+t6+eubsQuVZn4OQFHWv3nXEJR8dHBaNSygm3O2fsrq95bIt1XOWsvg+zum1vhhiqGP/hLsDz7Osqs4WGIUrSk+Ox4Br6Tk+OdxE8USnlzByciNliITO3iIhgJXPXHOS+bnEu+fJkO9nULdmF+AEP9WzBGxuO1XtRX00IQy0QCAQ+crUESury3rX1zO09bnWQP8EBCsoqq2weuNJPxv9+Pe5VHETKcUd/tYLZG63jKuen3M9HnYeQrIkk81Sx7by4CBUbp/ayee1miwWVUk58ZDCninQOrVX2oW+p7UtCpZSzJP1m3tqUzVNf7QesLwGtY0LdDgzZ4iSbujm7kGcGJzK0YwyRl1l7cLkIQy0QCAQ+Up8CJTWFouvq3jV55oXlBsoNJpsxt6/cdi6wSuvRnO0nLjiErMFq/MoNVTSLCuaNgBzCvlsAwPtdhrGwx30uldc9E6KIbhDg8LxanYGZgxOZs/YAD6a0cMhPB/rLbfrbOoMJdZA/i9O6oK8yc0N4EK+uP+yg3a2vMttC6e7Ykl3o0Jr1Z1EFXZqFX1UjDcJQCwQCgc/Ul0BJbULRdXXvmjxzk9nisBdPoys3HyvAbLF4nFylM5hg40YixqeBxUzJff8g6cU3+LTKwtYThbY8sKfwslqlpFNcGHPXHiQsWElmbpFLyPrTCbdQYahCZzAhk8k4eLYElb/cwUgDBCj8PE7RknA+fqUHcLhDGGqBQCDwkfoQKKltKLqu7l2TZ24yWxz24osnak/0ob0wchgYDHDXXYR+/AGdFQq0OgONQgMdhn64y38XlBnQVlQxLiWeV78/7DZkLeMwN9m1dCVrIhnSoQkqpdzBy888VUz3Fu7nYUsEVBe+SSH5yOCra6RBGGqBQCCoFe5C0nUpUOJLkVhdiKNEhSh58a72tkpt+zByl2bh6AyOMpm+eqIAo1Rabhg9BsrLoW9f+PRTUFjNTk292/bRhcVpXby+KGRkF9r6q8FqvJ9fe8jFy1+SkUO/xEakaCJtozztkYyzfUh++E1NvT73lUAYaoFAIKgBbyHpltEhXs6sPb4WifkijuKOcoOJb/edZXO2/aSpSL55NJnSSiNBTnKdAU4tVs6EOc17vktdybOvT8WvuAjDzV1RrloFAQG12ptzdCHzVDFJsWFez3F+UdicXcAjt7Z0MNQ6g4lxS3fy0biuvPjdYZcpWzMGt+FssVU0ZcryTLo0C7/iKmTuEIZaIBAIvHCleqZrCkUrFX4cP1922epnYPdM2Y7PtDm7kNlrDpBULQ5i73lKnqYn+c4Ko8nWWhVVUkCzOwcQcP4ch6Oa8eb9c3nJT4m6lvtzji4sycjhs4dv8XqOuxeJAIWfSz4/sUkoESqlw5StIKWcPbnFDK+esgX1r7PuC8JQCwQCgRdqCknnl+rrRMrTW5FYsiaSb7PyWLQpu04UyLw9k5Rvnrw80yajmZFdaJPVlFX/bL+3sdXrdQYTYRUlZHz7HCF5p8kNb8JPCz7m7ptu5Gh+GRHBylp9PyaLxVa9LYXkfz16nlRNpEuBmLQH+xYviXCV9/SE/T4ahwbStXnEFdVZry3CUAsEAoEXagpJ517Q2cQ64NKlPL3NcLZvYaoLT76mZ9JXmW3jIB/u1YJnh7ZFZzChN5qYO6wdFUYzJy+Uo5Rf7GHWGUyoDBUs/eJZQs4e5VyDSHI/W03GSTMv230/qQlRzB3WjnCVNYLg/JJTbjAxd80BB4OcqonkwdQWtGkSihlHsZOUarET++Ea0n1CAhW1ThFcbiqhPhGGWiAQCLxQU0jamcsxpPZFYkU6A9oKo4MhtL/H5bQN1fRMUhhZpZTT68ZonvvGyXBW905PW7mP+7rGsXBUEsZyHV0npxFx9ihVYeFov1zLf3PMbtu5nlm9nxmDEnlh3SGH8Purd3fgm71/unjN1p9l3NIygsEdYnh2SFvK9VU0CPQn0N+PZ7854PD9pGoimTk4kV+PnidFE0WTWrw01fWAlbpEGGqBQCDwQk0haXch110niyjWGS/pF7/k2WXmFjl46s64UyCrydhIx00WC6kJUW7D39IzScpeL693bYnafKwAi8ViO/7OhiO8vfpFIo5tpyJARe7HX/Jn0xZk/Og6IAOsHvGFcoNLjrxFw2C3oW2wFoc90b8V0Q0CXAzv88Pb81hfPXkl1kKwzFPF3PnWFpLiwoiNUKFSymtdYS5xpQas1AZhqAUCgcALnkLSklfpHHKVFLxmrN7vYHR8/cXvzeuV9LOP55fZjHKgwo/Z3xxgw6F8t/e0N0bSHi0Wi9t8syTTWVpZ5bZ4DKx56n9WVrH12Hle/W4B/Y9tRy/3Z9zwGfjlqXj4Rpnb8yS0Fa4vGiaz92GOBpPZ7aQuuQxe+O6QVxlTT4b6ag5YqS3CUAsEAkENuOtbVvjJGLhgs4tspicFL19/8Xvy5CVPd8bqLAePVMrVbj1eaNuTdM9XR3Z0MEZS/nlcSjwTb9UQ6C8nOMBa+SyF2ZNiw9waU3u0OgMzN77P3VkbqZL5MWnYNLY16wDZhUwf2Nrrue6qtIOV3k2SSinnzyKdywvJmsnJHl8oJBlTT1zNASu1xXtjnEAgEAgAq2fdMjqEm+LCaRkdQpjKny7Nwl3WJcWGeTQa0i/+2t7vpREd6JkQ5fD5zMGJvLUp2yVsnJFdyAdbchiX4qgQ9uuxAorKXY2RzmBi0aZsRr+/A3WQP60ah3JHu8asmZTCyn92JyRAUWPvdOv/vc643d8A8OQd/8ePCRdbqKpMFpI17lXAUjVRblMGSn8/j+ckayJtsqb3dY1DpZTbjpVXmtyeY/+snriaA1ZqizDUAoFAcAl4MqQ14csvfsmT3zi1F6sn9mDj1F50aRbuYqQltmQXuhUGKan07FHa70mtUhKklPPOz8dRBchtvdPumHtyIy3eeg2A2X0f5qt2tzkc1+qMjE2OJ9Xp/GRNJE/0b8XhMyUu19QbTUzqrXG5Z7Imkkm9E/jpSL7bFxKF3HuYXR3kOY1QnwNW6goR+hYIBIJLxF1I3Gzxnmf19Re/c9tQZm6R1/XupDxDA73/qpf2ZJ+vbddUzeEzWps0p32UYHr+DsaseB2A/6Tcz4edhzhcL1kTya5qOdLnhrZlxuBEThdVWPd/qpjxH+7k0wdvoaLK5HDdg2dKaB4ZzKD2TRzGWJ4rqaTSaOLdX07Y9mKvLZ6RXUCqJsrtC0xq9UQuT9TXgJW6RBhqgUAguAycDalWZ6BnQhS7Tha5jGQ8V1J52b/4a9taJdEzIYrw4NoZo8JyAx1jw0jv0RyDyUzfNo04eEbLzc0jbIYzfssGWr/2AgCljzzKnk73w/ELtuvZF6R1aRZua49qHBpIQZmByGAlw29qigULSXHhDgY564yWRqGBADRsEGAz7me0lTy/7pBDCNv+heR/v55g0egkkOFSuV2Tulh9DFipa4ShFggEAh+oqQVKrVLy8ogOnLygY+GmYw5a06kJUfS6sSFq1aXfx5sHmOLULiYZm0ahgQ7GSKWUMy4lnh4tIglQ+FFQbs2by4AYtdVQWiyQX6rHZIFhHWP4U1tBy/07aPZ/DyIzmSA9nQZvLeBlbSUnC3VoK400DAlAqfDjQpmB1Y/2QB2kpKyyiszcIuszhCht2uhanYF9p4pdBm2olHJmDk7EX+7HxGV7PH4/9i8kOoOJ5TtyeW1kR8oqq3xWF6uLISf1icxiqSFOI6g1JSUlqNVqtFotoaGhV3s7AoGgjqltv61WZ2DSp5luQ7E9E6JqrPyu6T5niivceoAvDG+PwWSmpMK9sdHqDBSWG7AAz36d5dA+9urdHYgND2LhT9kO4WhrflhD+eat9Hl0NH66chg+HD7/3DYJ61xJJUXlBkoqqwgNUhCuUmI2W3jS7hkkA9wpLgydwWRrKXv2mwP86NRS9vKIDqiUciYvz/T4QmI/1lI6pzbCJn9FhKGuQ4ShFgj+vmh1BiYtz3TbyuNsfI/nl3Hbf37xeK2NU3t5nLpV2/tIHrevHqDz9SXv+s6bYpj9zQG3Fev3BhUz743JKIqLqLq1N4rvvoVAq+ft6aViYm8N45butEqLVvdtO7et1fRyUZsXktAgf4IDFJRVVl2TqmJ1gQh9CwQCQS3wpd/2clp+anufS9Wmtr++vQHtl9jIrZGOLc5j6ltPoigr4vemrQj/YDnGkipK8ouICFYyY1WWS+Tg12MFmCwW2zxob73lT6/az8JRSbRo6PriUpuQ9JniCp744vdrVlWsLhCGWiAQCLwgea6F5QaWpN/MnupqZufeXHvjezktP/Xd12t/fXsDqtO79ho3LLvAJ5/NoFHZBXQ3tqFo6Ve8tiHHFjJfnNbFa6uYVJmdFBvmkouWsH/58JSX/yuritUFV7WP+tdff2XIkCHExMQgk8lYvXq1w/H09HRkMpnDf7fc4jiTVK/XM3nyZKKioggODmbo0KGcPn3aYU1RURFjxoxBrVajVqsZM2YMxcXFDmtyc3MZMmQIwcHBREVFMWXKFAyG2gkTCASCvydniiuYtDyT2/7zC/e8u41xS3eSmVvEglFJDoIb4Gh8A/39SPHQf1xTy0999/VK11cp5fRLbMS45Hjevr8ToU69xuqKUj7+bCbNivM4GdaYk8u/4r0DxQ55bXetYPZIx2taV1ppdPiuh7+9ldvm/8Lk5ZmcKa7weF5tog9/B66qoS4vL6djx44sWrTI45oBAwZw9uxZ23/ffvutw/HHHnuMVatWsWLFCjIyMigrK2Pw4MGYTBffDkePHs3evXtZv34969evZ+/evYwZM8Z23GQyMWjQIMrLy8nIyGDFihWsXLmSxx9/vO4fWiAQOKDVGTieX0ZmbhHHz5eh1V0bv1w9eWtb3Ahu2Btfrc7A7G8OkJ4c7yLckaKJ5IXhnnWn4WJfrzvqoq83KkTJ7W2iWTAqiVfXH2b8h7uYuGwPPxzMI1Vjva80rrJ1wUnOhUTw5pNvY4pu4hK6rkm5TDpe0zp1kL9Xz9jTv4m/gqpYXXBVQ98DBw5k4MCBXtcEBATQuHFjt8e0Wi2LFy/m448/pm/fvgB88sknxMbGsmHDBvr378+hQ4dYv34927dvp1u3bgC89957dO/enSNHjtCqVSt++OEHDh48yKlTp4iJiQFg/vz5pKenM2/ePFEYJhDUE9fy1CJv3pp9WNe537agzMCGQ/lsPV7IuJR4hz7hzFPFGEzevcv67utVq5Q8O7QtT67c52B4pV5khVHP+JefIensEYoCG/D6EwsZPLwHBWV6l2tJymXuctupCVHkl+prXJesiURfZfbqGZ/VVnKioNylUMw5quHMtaAqVhdc8znqn3/+mejoaMLCwujVqxfz5s0jOjoagN27d2M0GunXr59tfUxMDO3atWPr1q3079+fbdu2oVarbUYa4JZbbkGtVrN161ZatWrFtm3baNeunc1IA/Tv3x+9Xs/u3bvp3bv3lXtggeA64VrPL9bkrTUI9Gfj1F4uxU3SeZKWtjN9W0e7vZ59flYd5M+rl9gTXNO1Q4P8MZstLkZTZzDxf5/sYuXPb3Ljyd8xqYIp/nw1aUldMGNxq7i2JCOHBaOSAFyquaUWq67NI9BWGBjaIYa5ax3nWkviKKV67xKnJwrKbT3V0otcsNI6RMTTC8C1oipWF1zThnrgwIGMHDmSZs2akZOTw8yZM+nTpw+7d+8mICCAvLw8lEol4eGOwviNGjUiLy8PgLy8PJthtyc6OtphTaNGjRyOh4eHo1QqbWvcodfr0esvvmWWlLhq1woEAvdc61OLasoVRwYr3bZYXUqO2VtkwVMbV21xd+3FaV1c1sksZmZ9vYAbszZgVip55ZGXeHdzBWzeDMALw9u5yHRKU7hmDk7k2SFtKddXubxUSEViT3zxOx3jwkl3ijB89lsuTw6o/aStX48VMG3lPuYOa8vctQfdvigkayKZM6wdgMMo0L9q29Y1bajvvfde25/btWtHly5daNasGevWreOuu+7yeJ7FYkEmuyjSbv/ny1njzIsvvshzzz1X43MIBAJXrvX8Ym00oN1VKfuqHe0usqBSyukQG8YfBeXkaSusimQ+GBlpXyazmblrDzp4sfb3sEmcGk20m/8ccVkbqJL5kf3Ge7x70jG//vy6Q9UG3uJwvS7Nwrn1xoZexUbUKiXPDWvH9JX7HKIM9p63p+8s2UltDawyoaX6Kodxnc4phiKdnplfZzl8r7e3iebZoW2pNJpdjHdNinNXk2vaUDvTpEkTmjVrxrFjxwBo3LgxBoOBoqIiB686Pz+fHj162NacO3fO5Vrnz5+3edGNGzdmx44dDseLioowGo0unrY9Tz31FFOnTrX9XFJSQmxs7KU/oEBwHXGtTy2qKVesM5gclLfsj/mSY3aOLNj3Njsbtdrk7u09aGv7lKuRzjqjZXFaFxb9lM2iTdlM3rKcOzKWAfDHKwv5/sZb4OQxh3N0BhPjP9zFioduIb069xwXobINvJA815AABUq5H8UVBkICLxq8mnqi3X1n9rrhzpRXt5N5SjH0aBHp8r3e2zXOJTffMyGK5+9sx5y1Bx1mXF8rtRLwFzPUhYWFnDp1iiZNmgDQuXNn/P39+fHHH7nnnnsAOHv2LFlZWbzyyisAdO/eHa1Wy2+//UbXrl0B2LFjB1qt1mbMu3fvzrx58zh79qzt2j/88AMBAQF07tzZ434CAgIICPA8lUUgEHjmWphaVJMX5cm4AG7Vw6Sw7MJRSbXWjnaOLHgTB5m2ch8zByci95O59ficvXNPbVEWC7xdLRWatnsNj1cb6Wdve4jj4Z3p7EGvUmcwcbqoghW/5dokO92F1iUDO+q9HXRpFm4zeN56op2/60B/OWv3n2XK8ky386QVfjKvhWxbTzh+XpPoyk1x4Q6G+lqplYCrbKjLysrIzr74JpSTk8PevXuJiIggIiKCZ599lhEjRtCkSRP++OMPnn76aaKiohg+fDgAarWa8ePH8/jjjxMZGUlERARPPPEE7du3t1WBt2nThgEDBjBhwgTeffddAB566CEGDx5Mq1atAOjXrx+JiYmMGTOGV199lQsXLvDEE08wYcIEUfEtENQTV3tqUW0rzt0Zl+P5ZTXm11tGh9TqGZwjC97EQTYfK+DUBR3jP9zldq+Sdy6FtWMjgnj7/k4E+ssdhFraN1XzxoZjDM/axHMbrL8XX08ezdIuQ+FYAek9mnvcb7MIFa+O7Eij0ECvLWyATZmstgbP/ruWhna4M9LJmkgysgsYmxyPn0zm8nc4e2hbhizMcDjH2/eakV1oG+dpz7VQKwFX2VDv2rXLoaJaCiOnpaXxzjvvsH//fj766COKi4tp0qQJvXv35rPPPqNBgwa2c15//XUUCgX33HMPFRUV3HbbbSxduhS5/GLZ/rJly5gyZYqtOnzo0KEOvdtyuZx169YxceJEkpOTCQoKYvTo0bz22mv1/RUIBNc1V2tq0eVWnNdlft05slBbERF3ey2pNHoMnSdrIlkwKokpyzPRV5m5/dh2Xv32DQA+6DyEN5NH1bjXZE0k3x3IY/hNTWkUWvsWtksxeNKLnCdvXQqHfzcllSqzxeHfT2G5wcXA1/Z7deZq10rAVTbUt956K95mgnz//fc1XiMwMJCFCxeycOFCj2siIiL45JNPvF4nLi6OtWvX1ng/gUBQt1yqZvXlcLkV53WZX3eOLNRWRETaa15JpW2voYH+bkO8KqWcpLhwgvzl/PcfnYndu53bvn4ZhcXMl+1uY85tE8CucLZpeJBLWNneQEotZjW9sNgbv3K90eeCrZiwIF4b2ZHj+WUUVxhthWJSOLxnQhRhKn+313BOq/jyvdpztWsl4C+WoxYIBIK64HI94rrOr9tHFswWC6kJUW5fJNxVQJ8uqqBxaKBtVnWPFpEOnrSzh93xzBG++HImSpOR7xNuYdrAKVhkF41Uz4Qogvytht25kloykJLxqumFRTJ+KqWc0CClS16/NgVbjUIDMZktPqVI3KVVMk8Vk6KJJMNNTtt5jrf9Pa6FXmxhqAUCwXXH5XrE9ZFft48svOxjBbT9RC2lk2do72EnnD/J0i+eRVmp40K3FFakPY/pZJnDPeYMa0dYkD/7ThW7zenaGy9vLyz2LxUzBycyc7X7KVvuUg3uPG9fUyTOaZXQIH/u6xLL06v2u/ydPX9nO+auPejynFeiVqI2iHnUdYiYRy0Q/DXQ6gxMXp7p0SOubaWvt5nQ0rEyvZEwlRJDlZkyfZXH9iVP19ZWGDFUmSg3mJDJZFQaTbbisINntCTGqOnbOpqb4qwtqs6zsBendWH8h7u4oTiPL5c9SeOyC+xtciMPjnmR+25rS/+2jTh1ocLmNfdrE02H2HCPs6Clam+JM8UVzP46i1ZNQq092VVmwlT+hAQoGLd0J4lNQnlmUBv6v7HZ4/doP5+7vmVlPf2dXep87yuB8KgFAsF1R115xJ7y65Kx2X3SOmnrle+PuM33Orcvebr2HwXlPLN6v8M1UjSRzBzclvvf387wm5raPg8JVDiEePVV5upxlTNpXHaBI1FxpI18Dq0sgEWbsklsEmqT5wRs16ptoV9MWBCzh7Tlqa8cxUxSE6L48pEehKv8OVFQ7vV7lFINV0JW1tPf2dWolagtwlALBILrkvqqOLc3NpP6aNz27jq3L01buY/Xqlue3F1v5uosl2tkZBcyZ+0BHu/XyiGPWq6vIj05Hkv1fYLLtHz0+SyaF58lV92IMffMRRt0sXPGvojKOSdbG+Ol1Rl4atV+F2GVzccKmPV1FgtHJdU61XCty8peLYShFggE1y314UXZGxtvvbv27UubjxVwPL8Mk9ni4lkXlBlccrv215g1ONExv1thtMlqTrgpmi4P3Uvw+T/IDw7nH/c+T36Di9Kg9nlk+2iCL9XZNRnXwnIDIYEKPn2wG8UVRpeebvuXA+ciPweZ0yozhioTWt31Z6yFoRYIBII6xN7Y+NK7W1xhdBveLaphPndFdb+wZFwDFH7oDCb+98Mhuq6cQ/AfmRhDw5g/dSG5FWG281I1kcy5sx2lFUaG39TUFk3wNUfsrYJepZRjAZ744neXXugFo5L47Ldc5gxr59BeZn/u5Uip/p0QhlogEAjqEHtj40vvboDCz+aBAtWFZAaC/GueuWxvXCf10ZAar2bMG9Pp+UcmVUEq9ry7jHG3ppJusVBSYSQ4QMG5kkrKK410iL04J+FScsTewtrjUuJ59ussl7D4luxC/GQyl3C/fRW5N8nPa0Xa80rh/V+RQCAQCHxCMjZg7d1N1kS6XWcfdu7TuiEAS9JvprDcwMGzJaza+yf/WPwb3x3II8XDNVIToggJVDgY1w82H+eVH96i3+EtGBX+PDRiBqUdOzNn7QFGvLOVzdkFnCupBMBfIUdr57HXJkfs7Xmd6dEi0u1QELCG+8sqHedQS0V+PROiSIoNc6vj7W0vf1eERy0QCAR1iH1F+ZKMHI/zkqWe6D6tGzJtQBvmrD3gsmbBqCSmr9zHSyM6ADiIdSRrInluqHUGtM24Wiz8a/17NNn1NWaZH5OG/JvEMXfxwZYcMnOLawwlX4oQjLcK+poiCu6uJxX5Hc0vc3OG93P/rghDLRAIBJeBu8KrYKWcmYMTKa4w0iBAzkvD21NZZaZcX4VKqaDKbOZUUQULq424s5GGi4b9vq5xTF+5j5dHdODpQYlodUZCAhXkl1Qiw5rblpi07TMe3PU1AP8e+H98f2MP7qkuaPNUgW4fSr5UIRhPFfQ1eb2erqdWKYmoIax9LUh7XimEoRYIBIJLxLnwSqWUsyT9Zt7alO1QqS15rQmNGtjOe/3Ho7RqEkr/to08hni3ZBcyPqUFN8WGscTJyKZoInnprg6og6wG64Hda3his3WmwcFpc1hJJ+BiwZq3CnQplCyFsXedLHKotg70l3OupNKrnKanCvpLlVq9FsagXiuIHLVAIBBcAu4Kr8alxLNw0zGPUplSPlgSCfk9t4hTFyq83kchl7n1hDOyC3l61X6CAxRML9jJnOpxlW+njsY0ebJtnRR+rqkCvbTSiFql5OURHViSfjOZuUWM/3AXE5ftYdzSnXy7/yxFOqNDTrsm7HPO9tRGWOZyzv27ITxqgUAguATcFV7VxmuV+pQlkZB0N3OQ7QlX+XstqjKv/pqHlz4PWMdV6p6Zyf7TWtv0K6mgraZ8cXCA1RyolHLe2pTtcs/Nxwp4ZvV+BneIodeNDWvdHnU5wjJXawzqtYYw1AKBQHAJuCu8qo3XCo5GXjKk7oxxsiaSKpPjOAZ7EZDgrZtp9FAaMpMJw+h/0OPVhRgscO+7221FbFJB27mSSo/3SdVE2oZ51CSwMi45vtaDNKTjlyMscy1Le14phKEWCASCWuBsiEICXH991uS1SgVQ9kbeU2V4qiaKGYPbYG+m7UVAfln2HStWPI2fQU9h34HkPDefcH8FMpP1ZUFSJxuXHE+V2UKHG9R0i4/k2TUHXMRHJvZOwGS2uOzNHfoqs4ucZ30P0rjeEYZaIBAIasCdIXrxrvYuc6O9ecf2BVD21dU6g8nBqOqrzMRGBPH9gXMMf3srayel2K4piYCc257JF1/MJthQQVbrzozoOAH9+zsBa2/1glFJTFme6RKGVynlfPlId54cYOF0UQVKuXVi1vgPd9K5WTiLalH5Lb2M1OcgDV8kTK8HhKEWCAQCL3gyRHPXHmRJ+s3IwFaZvCQjhyXpN+MnkzmsT02IYs6wdrafnSuadQaTzagmayIZl2wNbb82siN6k4l/92+NH4dJig1j9aotfPHZTMIrSjjevA33DpiGXnHRiG0+VgAW+Oyh7nx/MM+mqQ2QFBfGt1l5ZOYWkRQX7mDIN9ei8jtPW0HWGS1Qf4M0hHfuijDUAoFA4AVPhkhnMDFu6U6+m5JKldniUOy0aFQSeSWVnC6yVnRnnirmjgWbHUZazhnWzmV0ZbImktlD2vLSd4fYfuIC41LibR7sM4MT8cs7x6efzaBJWSFlLW9kxKCZlAeoXPa2ObuA9NJKMnOLbN51UlyYTWRFZzDZBoLYU1pppGV0CM/f2Y4zxRUs/CnbcXSlJoqxKc25vU20x0Ea7q5ZW67EmMu/IsJQCwQCgRe8GSKdwUSRzsBNceG2cO2JgnKCAxSYzBYsQKMGgdx6Y0PaxoQSoJDzy9Hz3NGuMdoKA0lx4YxPaYFKKcdktlBpNNmMtLOKWGhlGWu/fIa4orOcDmvEkf8up3jDWY9701eZrZrawIqHbuGHg+dsRlo67kyDQH+0OgM7ThTy9e9nXCu/swuwYOGF4e3dDtJwhy/CJGLMpXuEoRYIBAIvqJTeh2KEBvm7DdcmayKZNbgtL68/xKbD5x0+794ikgaB/jYjLFVy92/biE2Hz7uoiAUZKvngi2eJO3OcYnUk373+MS2aNgU8G2rJE9+cXUh6qZ5Fm7JRKeVM6qOxVowrFXyQfjNmiwW5TIbJYsFssZBfqiciJMBBrtSejOxCyvQXNbrrUpikLr3zvxNC8EQgEAg8oNUZ2JPrebBGz4QoggMUbsO1W7ILmbv2AIkxapfPZ36dRXCAwibmIeWoJfET+4EUyioj766aR+czhykODOHeEc+RmJJU64EfYPWepYpxScjkvve2M3bpTj7YkoMZC1lntORe0FFYZqixzUzyyqFuhUnq0jv/OyE8aoFAIPBAQZmBuWsPehysMWdYO8oqqzyGazOyCxnrJhcsTY5yHmbhrCLmZzbxxppX6flHJuX+gYy9+1mONGyO3E/GwHaNuPOmpjznpt1KykVLBCj8PI6NzMguRIaMge0bM/7DXSxO61Jjm1looD/H88scqrLrQphEyIa6RxhqgUAg8EBJpdFt+1SAwtrWVFJhwEmPxAVP3qlUuCUZuHK9kXCVkk8f7EZIoAIsFl74/i3uOLoVvVzBQ3fNILNpawDMZguDFmwhKsQq+Tl9QGvrwI/qOdPTVu6zeb09E6LQNAyhiTrQo2ra5uwC0pObA9bCtxh1oGdxlIQofj9dzL+/3Gf7TKrKbhkdUtNXasNTC5anSVzXm2yoPcJQCwSC65ZzJZUUlRsoqawiNEhBuEpJo9BA23EpFGvfPmXP8Jua1ngPT96pFMaVlLfs89yTerfkrb2fMmjfD5hkfkwZ8iRbmt8EWIdxbD1RiEop56URHRyGdaiUcmYMasMHY2/mz6JKmqgDCQ1UUKTTYzS53YYN6YViSUYOb43uxKTeGsBJhCUhisl9Ekj/4DeHc32tyq6pBUvIhjoiDLVAILguyS0s56lV+10mUr0wvD1xkcFA7UOxntakOOWKJVKdwrjObUn+L73IoJ+XAzB9wGS+b9XDdl5aj+Y2D/8DJyMtVYo/vSrLdm0pFK6Qybx+H9ILhc5g4tFP9/BwrxZMG9AauZ+MCoMJdZA/SoUfA9/c7JCjlqhtVXZtW7CuZ8PsjCgmEwgE1x3nSipdjDRcnEh1rqQSqF2hlKc1qZpInh3aloPVAiESyZpIHq32VsFquM5qK22Ga8yetfzfzx8B8NPD0xkw/ym+fKQ7G6f2YubgRFuLlX3BGeAxB70lu5APtuRgslhqXXymM5h4/cdjDF20hUCFnC7NI0ho1IAL5Qa3RlqiNlXZtWnBEjgiPGqBQHDdUVRu8DiRKiO7kKJygy0EXptQrPOa4AAFCj8ZI9/dxn1d47i/WzOH3Pa4pTtZMymFcoOJaSv3MaprHADDDvzE3B//C8CbPe7j9bAU+HAX3zzag6gQJWe1lbw2siOB/nLCVf6olHKb4ezkpDRmjzTXWipsc9QUt2p9j/9wp8t5zgVcdVGVLVqwfEcYaoFAcN1RUlnl0/HahGKd1+z64wIFZQaPxrOk0oC+Ss6/+7ei0mjm62YXaP/qG4B1XOXrKfcD1pB2aJCSScszHWVJNRc1vQFU/t77vSuNJp744nfGpcQzbUBrThdV0DxSRWRIAJUGE52bhbvkjJ0LuOqiKlu0YPmOMNQCwXXI9T70IDTQ+6++mo7XhFZnwOCh2lullPNwrxaog5TM/jqLzdmF3JK7jw8/n42f2cTZoXfzWoexYLSWk88cnMjM1VkuoycllbBxKVYv2WTxXn4eoPCzFcUlxYYxcdkeNk7tZYscLKpFAVddVGWLFizfEYZaILjOEEMPIDxYSYom0q36VoomkvDgyzMWBWUGtp4odGlxkgq+8ksqmfl1FluyC2l/9hjvrZxLgMnIj5pufDzw/xjboiErfstl0egk1EFKnvpqv9v7ZGQXMvFWDRVGE9vc3E/CPgct/dnZKNa2gOtyq7JFC5bvCEMtEFxHiKEHVhqFBvLC8PY8vWq/g7GWqr7tW7TcUVNEoqTS6HbOtFTwNS45ni3ZhbQsOMWHX8ymgaGCbXHtmTRsGvo/tEwb0o67O93AM6v3c3+3Zl73UmWxEKxUeJxrbS+AIv15xW+5l2QUnZ87Pir4kv69iBYs3xCGWiC4jhBDDy4SFxnM/HtuuthHHaggPFjp1UhrdQaKdUZmrN7PZjtj6ByRCA30twmlPNSzBc8MSuSPgnJiI4JYtCmb+7s14wbtOT75bAYRFSX83jiBB++aaRtXKZPJbJO13E25ssdYZcaI2UWYxWAy0zAkAKXCD0OVic8evgWFnx/ntJXc1qZRjRrmztQUifE1nSJasGqPMNQCwXWEqLh1pFFoYI3es8TZ4gqKK4w8v+6gS3jZOSJhP9O5fVM1JrM1X12ut1ZoNygq4OPqcZVHI+NIH/msw7hKmUxmu4ek6V2bkPaW7EKX4rVkTSRJceEuM6i7No+otaH0FomZtnIfMwa14UK5gS3HC23zr6+3dEp9IvqoBYLrCFFxe2lodQZ+Pnreaow8tHX9eqyAsyWVaHXWqMTLIzqwJP1mlm7JYcjCLUxctodyfRWhlWW0Sh9JfNFZTqkbMebeORSpLg7uSNVEUWpXdb4kI4exyfEuPdAp1WHsJRk5LMnIYVJvDalOvdxSqPvgGa1trYQvL2XeIjGbjxVwuqiCUe/tsM2/VinltpcXrU70RV8uwqMWCK4jRMXtpVFQZiC6QQDaCu/G7cT5cl5Yd4iXR3TAz0/GW5uOOYTIs46d4cu184jOOYI+siGvT13IuZKLnnRqQhTPDW3roA/uTmu8eVQwZ4srkMtkvDayIzeEB/HTkXyGdoxh1uBESiurCFLKqTSa0OqMJMaoHWZRg28vZTVFYqT92ufiF23Kvu7SKfWFMNQCwXWEqLi9NEoqjTbBEm8EKPzYdbKIkxd0mM0WByPtbzLSaeoEbjy+n3JVA+4f8RwpXTqwODYMgBvCg2gcGohapeRcSaVDVbq91rjkSU+uNt5JsWHo9CbaNw1jT24RnZuF06lZOFqdgcnLM+vkpaymSIz99+KcV7/e0in1gTDUAsF1xvVecXspPeShgf5cKDfUKl88LiWehZuOOVRr+5lNvL5mPqnHd6PzD2D1vP8xb+RACnUG/P1ktGgY4pArbxQayLzh7XnGTVX6rCFtGb90p03X2z4nnayJZHiSdVBIXb6UeYvEOMuPguPEMJFOuXxkFksNXfKCWlNSUoJarUar1RIaGnq1tyMQCKrR6gwUlhuwAM9Wi4xI1KboSasz8G1WHj8ezGN0t2Yumtr2LVALRyXZ5jqP/3AXWCy8tH4h9+37AYOfgnF3zyYjPunicWDj1F60jA6xvUSUVhoIUspRyuVUGM2UVr9UlFYambhsD/d1jSMzt8jjGMpFdm12F695eS9lZ4orXIy+/XPbh9WXpt/MrtwierSIJNDfz1pgd52J6tQlwlDXIcJQCwTXHlJbUcfYMK/GbebgROR+MptBkQyctsKAKkCBDDBbLOw/reWMtpLb2kRz6kKFTb9bqnZ+d0xn9v+p5dYbG6LVGdDMn0uzJW9jkvnx6LBprG+VbKvElrzhbx7tQVSDQKat3Mfuk0U2b9n5ZeDf/VtjNpvxV/gxZOEWj88sGf665uJ3YqTSaGLriYtV3hJ9Wjdkym038ur3hx32L6rALx0R+hYIBNccdSVxat9WlN6juUfd7c3HCjh1Qcfk5Zk8N7QtXZqHM2u1o+cteY8/HDzH03e0wWAys/y3XIdqaJVSTnxkMB9t+4NFm7KZuO1zbvvVOgnryJzX+NXQhtS4cNKSm9s0ugHUQcqLs6j7aDxOwYLD3Nw8gmSNY3W3M/WVF7bvfT5TXMF/fznuYKRTE6KYPaQtM9xMJrveRHXqkqvanvXrr78yZMgQYmJikMlkrF692nbMaDQybdo02rdvT3BwMDExMTzwwAOcOXPG4Rq33norMpnM4b/77rvPYU1RURFjxoxBrVajVqsZM2YMxcXFDmtyc3MZMmQIwcHBREVFMWXKFAwG0VYgEFxpzhRXMGl5Jrf95xeGv72V2+b/wuTlmZwprvD5WvZtRXoP2tsSBpOZBaOSMJrMzHAy0nBxXGTrJqHM/DqL9Vl5pPVo7tA2NS4lnrlrD7Alu5B/ZH7Lk9VGem7v8bzYuDtfPtKd9GojLRm41IQoDCazbZ/O4yud99C+qZoGNWiRX4m8sFTrsHFqL1ZP7MHGqb1YNCqJKpPF5buTEGMsL42raqjLy8vp2LEjixYtcjmm0+nYs2cPM2fOZM+ePXz11VccPXqUoUOHuqydMGECZ8+etf337rvvOhwfPXo0e/fuZf369axfv569e/cyZswY23GTycSgQYMoLy8nIyODFStWsHLlSh5//PG6f2iBQOCRmiROPfXkanUGTpwv4+i5Ug7nlbDn5AWOny/DZLHYFLhqqthuGBLAB1tyaBQa6NVQSoa0fVNry1NSXDiL07qwOK0Lg9s3YXN2IUMP/sycH94BYEH3e1ncdTibswuxWGCynZFO1kQyd1g7yvQX+6ZreqGQy2SEBvq79ExLXMk2O7VKScvoEG6KC6dldAhqlVKI6tQDVzX0PXDgQAYOHOj2mFqt5scff3T4bOHChXTt2pXc3Fzi4uJsn6tUKho3buz2OocOHWL9+vVs376dbt26AfDee+/RvXt3jhw5QqtWrfjhhx84ePAgp06dIiYmBoD58+eTnp7OvHnzRL5ZILhCXIrE6ZniCmZ9ncV9XeMcQsYqpZznhrblm0nJ6Awm/OV+pCZEub1+siYSpcKPLdmFNWprS4ZUX2W2tU1JldR5JZX0Pr6T+etexw8LSzsN5j+p/7CdW6QzsHBUkq3V60K5obrfWc7b93ci0F9OVIjSYc60PSqlnGZRKmZ9nUVaj+aYLRaXPHB9t9nVlJYQojp1z18qR63VapHJZISFhTl8vmzZMj755BMaNWrEwIEDmT17Ng0aNABg27ZtqNVqm5EGuOWWW1Cr1WzdupVWrVqxbds22rVrZzPSAP3790ev17N792569+7tdj96vR69Xm/7uaSkpA6fViC4/vDVG5M88I6xYS5G+q3RnQj092P2N9ZQtDS5ymKxOLQ8Sbnns9pKAAI9zHVWKeWMS4knNiKIt+/vRIuoYDZN7UVRhYHQQH9USjnRe3bwzuoX8TebWJV4K8/1fQhkMts1wlRKNhw6R/umauQyGV2ah/PE5787jLBMTYjiy0e688CS31zCxDMHJzJr9QE2Zxew9XihgwhKWJA/LaNDai2JeinUZvKaENWpe/4yhrqyspLp06czevRoBw/3/vvvJz4+nsaNG5OVlcVTTz3F77//bvPG8/LyiI6OdrledHQ0eXl5tjWNGjVyOB4eHo5SqbStcceLL77Ic889VxePJxAI8N0bkzxw50KxcSnxnNVWsG7/WZvxtlf4mnirpnpYhZmtJwptbVUAFovFZQSmZOTd9S2PTY5nzOLfGCXP55nXJuFXZeBHTVf+fcdjWGR+DmvlfjJ2/3GBNzYcY1IfDe9lnHAJs28+VoDFcoiPx3VlxH+3OeSyk+LCbCMv7UVQJKzzpb1/x5dKbSevCVGduucvYaiNRiP33XcfZrOZt99+2+HYhAkTbH9u164dCQkJdOnShT179tCpUyfAKnDvjMVicfi8Nmuceeqpp5g6dart55KSEmJjY2v/YAKBwAFfvTHJA3fO6yZVq305G0HJuC3alM3Gqb0IUsptlcuSmIlcJiM9OR4LruMp3VdiwxNxFu574jH8ykrRdU9h2T/mUpVbbluXrInkyf6tefX7w7ZCq6TYMI9V6BnZhRTpjKya2IMinRGT2cK2E4XkFJS7XS9RpDOQmVt0WZXynvAlLXG9i+rUNde8oTYajdxzzz3k5OSwadOmGvPFnTp1wt/fn2PHjtGpUycaN27MuXPnXNadP3/e5kU3btyYHTt2OBwvKirCaDS6eNr2BAQEEBAQcAlPJRAI3OGrN6YO8mdSHw2xEY69uTUVZIE1jN4yOsRmUMr1Ru7udAPHz5fZPO/xKS1QyGVEhQR4NKp/7D7E6NUzCNQW8XvjBIre/IAxIQ14PDSAskoTIYEK8ksqKdYZ2XT4fK33WFxhRKnwY+GmY7YXgsVpXbyeo60w2kRU6rpv2de0hBhjWXf4ZKg///xz7rzzTpRK65f/xx9/EBsbi1xuzenodDoWLVrEk08+WSebk4z0sWPH+Omnn4iMjKzxnAMHDmA0GmnSpAkA3bt3R6vV8ttvv9G1a1cAduzYgVarpUePHrY18+bN4+zZs7bzfvjhBwICAujcuXOdPItAIKgdvnhjSrkfmblFAA7SnjVVeMPFMLqzQTGaLhaJSXnplJbuK6yjyov45LNnCCw6S2mLBP45fC4fN2vMrG+yXMRKJvdOcDi3NrrhJrNjsVhtR15C3fctiyKxq4dP7VmjRo1y6D/u0KEDJ0+etP1cWlrKU089VevrlZWVsXfvXvbu3QtATk4Oe/fuJTc3l6qqKu6++2527drFsmXLMJlM5OXlkZeXZ+tvPn78OHPmzGHXrl388ccffPvtt4wcOZKkpCSSk5MBaNOmDQMGDGDChAls376d7du3M2HCBAYPHkyrVq0A6NevH4mJiYwZM4bMzEw2btzIE088wYQJE0TFt0BwFXDX9uOMVmfgqWphDedRkJmnijlXUukyGlLCW1FTdIMAeiZE2fLSmblFlBuqXNaFVpbx0eeziC86S0XMDfz+/mfc1e8mnnUy0mANkZurRSBVSjmT+mho2CDAY4uVZHSd+6U9jbxMtht5aU9d9i1LaQl3iCKx+sUnj9pZbfRy1Ud37drlUFEt5XvT0tJ49tln+eabbwC46aabHM776aefuPXWW1EqlWzcuJE333yTsrIyYmNjGTRoELNnz7Z5+WCtCp8yZQr9+vUDYOjQoQ6923K5nHXr1jFx4kSSk5MJCgpi9OjRvPbaa5f1fAKBoP6wz5lKhWIP9WzB9IGtKSw10DQ8iBZRwQQo/EiMUZMUG4a+yky4yp/YcJWL8bdvO5oxKJELOoMt7JwUF+7gyQYaK1n85RwS83MoDo1g1QtLKDSq6N4i0mOIfOuJQvq2jua+btY2siUZOSwYleTSYiUZ3U93nOSu6gEbEs4jL9VB1mrzb7PyXPS2Jeqqb1kUiV09fNL69vPzc6iibtCgAb///jstWrQA4Ny5c8TExGAyuf5juR4QWt8CwZUjM7eI4W9vBS62Tt16Y0O0FUZkMhn7/ywmyN+Pvm0aM9tpEEeKJpIXhrcnLjIYcN929OmD3Rj9/g7b9aWq79+O5PHeyue5NWc35aoGHP9sDUUJbfh81ynSk+MZ+d9tbverUspZNzmFGV9nObSRSaMqwZpz//noeQ6e0TK6WzOiQ5QcOFtKdIMA9FVmAv3l7MktYklGDl2ahdtSBLf95xeP31Nd637X1ZAPQe255ovJBALB9YMvGt9SztRb69S0Aa2Z+bVrKDoju5CnV+1n/j03Eajwc9t2VFxx0ROVPNnx3eP4z5cv0ChnN+YgFV8//y7P/1ZB53M5TO6joVzvGiK3v0a5weSwF+cWq28mJduM9vSV+/jykR58u++sQ591siaSJek30zziYlTgSvYtiyKxK4/Phvr7779HrVYDYDab2bhxI1lZWQAu+tkCgUBQW2ojpmGPlDPt4CR2IrElu5DSyiqPcqDWFigDcpnMbduRc7GXTl9FzPR/0ah6XOWBNxfz9PEGgInNxwp4rG8Ce08Vk6qJYndukc1TljzhPG0FRR4kUCVOF1UwcdkeAF68qz0zV2c5GGnpueQyma3vW4Sk//74bKjT0tIcfn744YcdfvbWdywQCK4udTWVqj72VRsxDXskA/VHQbnHvHBNLUVllVVoK4y2EPTNzSKIClHi5ydDBhclRy0Wpv/8AaP2/YBJ5sc7Dz2HsVkSqlM5NoNcZbLQ88aGDGjbCG2FkQXV/doSqZoonhzYyut+pJeDVI2juIkzom/5+sInQ20219ybKBAIrk189VjdUV+G/lI0vgHkfjLCg5Usn3ALDQIVnCupZNrKfbZK54Yh3nUOFH5+yGUyFoxK4tMdJ7kpNowXvjvkIjna/pP/8shvXwGweMw02k4Zz/SV+9yG3FM1kUzsrSEzt9jhXpuzCxh4ujGpmki306VSNVFEBCtZ9mA35H4ydF7C6CD6lq8nRI5aILgOuBSP1Zm6MPSeuJSJS7mF5bb2LIkUTSTLHryF+9/fTkGZAaXCz2vfsZ8fmCwWPtiSQ9f4CPJLKpmQ0oIn+rVCIfdDqzPwSsE2Yn75EICTTz1Hl4cno60wsDS9Ky+vP+QqAZpdiBmrmpmzp//8ukN8/Wgyc9YedPgekzWRpCU3Z+GmYzzUsyXNI1SUu6ngtkf0LV8/+NRHLQ2ocDd8QqvV0rt3b37//fc625xAIKgbauOxeuNSxk9qdQaO55eRmVvE8fNlHkdUgu9iGudKKl2MNFjzznPXHuDlER0AuFBm8Np3XGk0ERyg4MGUFgztEMPmY+epslh47YcjDFmYwepp82nytHXc7cLu99LL3Jm73tnK4owcLDLPc5elcZjO6AwmCsv0vHxXezZO7cVX/+zB+v9LZdbgRJqGBTJ7cFvaNG5A47AgQgIVpHjoA0/RRBJSw0xqwd8Hn/6m58+fT58+fdy2HqnVam6//XZeffVVPvnkkzrboEAguHwud0awr6HpSy0Mq23lclG5wWuR2LSBrUnVRBHZQMm97253mDIVoPAj81SxbRCHJLmZoonkif6tWbDxKFuyC+l9fCcvfzMfmcXCR0mDmG83rjIju5CxJXqH+9q3WumrzEQ3CGBSHw1LMnIc+psjQwKICVe53bs95foqF81xsL5kpCfHe60wF/y98MlQ79ixg+nTp3s8PmTIEN5///3L3pRAIKhbLlf+0RdDfzmFYbWtXC6p9G6kKgwmnh3altwL5STFuR9+4Sy5mZFdiIwjdIwLo+zHn3hn9YvITVWsTuzF7NsfRhWgcDTEduMkvbWILRiVZBMj6ZkQRXSD2s0H0FYYHcRNnF8yPn2wW80XEfwt8MlQ//nnn7Y5z+4ICQnh7Nmzl70pgUBQt9TksYYEKjieX+axSMwXQ3+phWFS5XJ+qd5WiR0coECllLsUsYXWEPYNUsq57T+/cFvrhswa3JY5aw+4Vf+asjzT4bzN2QVMDi/hoS/nEFhl4FzPvjzRdRJBAf4uhnhSH41tHGZN07XGpcSz71SxT+1SoYH+bkdZSogc9fWDT4a6YcOGHDlyhPj4eLfHDx8+TFSUey1YgUBw9fDmsT5/ZzueWbWfDYfyHT63D1P7Epq+nDB7ucHkUGgVFaLk4/Hd0OoMBPjLUcj9+LOogibqQI/V0ymaSPKrw9Ibq6dVjUu+6JXGRgTx/YFzbiU3WxaeouOEZwgw6Nge246KN96j6rMDPOLGEEsSoOB9ZOWW7EJmDkpkQkq8T1XZvqYDBH9ffCom69u3L/PmzXN7zGKx8MILL9C3b9862ZhAIKhbJI9149RerJ7Yg41Te/HqyI7MWXvQwUiDa5GYZOidh0gkV7ci2Ru8Sw2zO4fMVUo576fdzKvfH6bMYLIVeD2w5DeGvbWFR3snkKpx3E+KJpJZQ9oybeU+22cbD58nOjTAFjr2l/uxaFO2i5GOKcnn489mElB0gX2NNTw4Yha7z+tJ1kSSFBvmdrb1lOWZ3BQXXqN3qzOYfG6dkr5z50EYQsjk+sMnj3rGjBl07tyZbt268fjjj9OqVStkMhmHDh1i/vz5HD16lA8++KC+9ioQCC4T517b4/llLkZawjlMHayUc0f7JqT3aO6QLx23dKdNd1qtUl6yJ+gcMh+XEs/87w/TMS7cxZvVGUyM+3AnMwcn8szgNpRUGAlSWuc+j35vu0sV+6kLVsWvnglRvDqyo8v+IsuL+fizmcSUFlCpuZG3Jr5J2TmTg9cMrgVjkvZ2sFKON1Q1HPeEEDIRgI+GumXLlmzYsIH09HTuu+8+mwqZxWIhMTGRH3/8EY1GUy8bFQgEdY8vYeqCMkOtlLIuVdLSeS9SODk92bUfGazG+qmv9rM4rQtLt/5BWo/mHidIBSj8bPdvFBrosD9pXGXLC39SdUMsvy/+nH8lxDPFbKHcYMJsMdty5Z4Kxu7u1JRUTZSL3Kd0XO536YqNQshE4HMjXpcuXcjKyiIzM5Ps7GwsFgs33nijyyhKgUBw7SIVZznrWTtjH9L1xahfiifoHDLXV5kd/tcT+iozm48VYLFY3IqM9G0TzY3RIcwcnEh+aSVGkxmLxcKzQ9pSWVpK7H130SD/BOaG0VSs+463dpYxLroSk8XC0i05bM4uZFIfDTMGtfFYMPb82oM8dUdr+PaQQ95cKlrzxVBfqzKvgqvHJXfMJyUlkZSUVPNCgUBwTWHf4zypj8ajcpdzmNrX3LMvnqBWZ8BssbA4rQsymYw9uUUE+lvDxTW9TKj85UzqoyEpNoyQAAWd4sJtoyCTW0Yyc3AiT6/OYvfJIt4a3claSf1Ttm1cZWLObsqDQihf+Q3RHdryUlwFh85aRZ0ko7skI4fPHr6Fp1dlud3DhsPn+cctzfn3gNakl+odUgOf/ZbLayM71up7qE/1N8FfF58M9dSpU2u17j//+c8lbUYgENQvzgVb9jlYe2PtLkxdX1XI7oxTsiaSfomNSNZEsv9PrccK7z6tGxIWrCQzt8jBk+7bJpr1/5dKZZWJ4gojTw5oTXllFeogf+Z9e5BtR/NZsPY/3JqzmwpFAA/cNYvgo7Cws4GYsCDK9VUcyy+zXU9nMHG6qMLrc+iMJkyleiZX9z4nxYaRFBvG0A4xtfoe6kLmVfD3xCdDnZmZWeMaMT1LILh2cS7YkiqXJVENdZA/4dUFYZcrSlIbPBmnLdmFLNh4jBfvbE9eaSXd4iMw4/gykaqJ4l99b+Sl9YcdPlcp5dzXNY5n1xxgdLdmDuHqxWldyDhWwAs/vM3gw5sx+Cl4ePjT7L4hEezy7CqlHHWQY4RAKffu2Qco/AhT+bNucgqzvs5yeHGoySvW6gyc1VYyqmscY5PjbREBKd/urf9c8PfHJ0P9008/1dc+BALBFcBdntleVGP1xB60jA7xeH5dVyF7E0fZcCifmYMTWbgxmz8Ky3nr/k4o5H6UVlhzt2BBbzTbJl1JXmxEsJL//HDEbbW4vsrMtF8+ZPTv32P28+PIa29zX9/BpFdXb5dUGtDqDMz+5gBTbkuwCZoAZJ4q9ujZJ1f3bsdGqJj1dZbLGm9esaeIgr2iGdQs8yr4++JTH3Vt2LlzZ11fUiAQ1BGXKyUKVs+6ZXQIN8WF0zI65LK8vJoK1Mr1VRzKK+H9tJt5ef1h7nhzM/f+bzsD39zMvHWHCKl+UVgwKonM3CLGf7iLC+UGNlcPxXDOvd+49C3+ueNLABb/Yxr3FsVy8GwJFouFpNgwgpQKinQGth4vZNzSnTzRv7WtV3tJRg5jU+JdereTNZFM6p1AY3UgZ4oqPA7qcDf8xFtE4YMt1lnXEkKJ7PrlkorJysrKkMvlBAVdDOPs3buXmTNn8u2332IyeR/PJhAIrg7XmtpVTS8O5QYTL4/o4CIBClZt7ue+sU7KWmLnOXuqFh+99zs0378FwKd3P8rrzXo6tFtJXnmPFpEsSbuZCqOJn4/k0yU+nPRka++4HzLm3tkWfZWZYp2R4AAFfjL48dA53v3lRI1FY85esbeIwpbsQsYlWw21UCK7vvHJoz59+jTJycmo1WrUajVTp05Fp9PxwAMPcPPNNxMQEEBGRkZ97VUgEFwm15ralfTi4I6eCVGEBfkTHRrgcVLW5uwCGocGOhwPUPihUsq5ITyIxWldePv+TqxteJp5P7wNQP6kf/F0y4EO+txSj3RmbhGj39/Bfe9tZ+zSnez84wJtY9RMXp7JxGV7SF+6E5MZWjUOpXXjBij8ZNyxIIPXfzyGzmDyqd0Nao4o6KvMHv9ufBkjKvhr45NHPX36dMrKynjzzTdZuXIlb775Jr/88gsdO3bk6NGjHjXABQLBtcO1pHZVU4Ga2WKhsIZZ2WVO4x6zzmhZnNaFV9YfJiO7kFuP7+S9r55HZrFw6r40Djz0b1i2x0GfuzZDNRZtynbwbNUqJScKyh3WZ54qrnW7G9QcUWgRFVzrvLZo4/r74nMx2eeff05ycjJ33303MTExjBw50uvoS4FAcO1xLaldeXpxAHh61X4m9Unwer4qwPHXmMUCb/+UTUZ2ITefyuK/q1/E32zi6za9+KL3Qzyhthoy+9B4TUM1xqe0oGdCFK+M6ABgmzQWpJQ7zJz2pd0Nak5FNFEHuvWkRRvX9YVPhjovL4+WLVsC0LhxY4KCghg2bFi9bEwgEFw/2L84SMpcF3QGxqW0QAYO1df2WKutKx282PZN1byx4Rhtzx1ncfW4yo0tb+bxQf+i6kQRM5V+JGsiHcLUNamfhQYqWDgqiXKDiUnLMx2MZIpThbbU7vborRoC/a1tXp4iFpfS8napY0QFf118LiaTyy+Ky/v5+REYGOhltUAgENQedyHdge0aMffOdsxcneVgrCV5zmkr9/HSiA74YVUS01eZaVF4mg8/n0WoQceO2HZMHDadKrn1190fhTrGJseTX6K3GfiacstGk5nKKrNbTzbDKTyuM5j4/VQx93eNo0ktwtC+piIuZ4yo4K+JT4baYrFw2223oVBYT6uoqGDIkCEolY7/oPbs2VN3OxQIBNcFnkK632WdA+DJAa15Ru5HTkG5TZ7T3ot9qGcLnhmUCKdO8vFnM4nSadnfqCUPjpiJ3j/Adj2Fn4xlO07yzKA2dG4Wzty1B7zmllM0kegMJorKPXuyGdmFTBvYhsQmoQQo/Mgv1fs0MUuKKEjRhBMF5YQGGdzqfNdFi53gr4VPhnr27NkOP4uwt0AgqC01DZvwFtL9Lusc93aJ44aIIJbtOGkzqCrlRZ1vAOPZPG4cdRcBpec5HnEDaffMoTQg2Had1IQomkeq6HBDGGt+P8u+08V0jAtnYLtGDOnQhOfXHnKYgJWsiSQ9OR65TEZJpWPRmjOVBhNHz5Xyv19PoDOY6No8wqcQdG0LxK61FjtB/XNZhlogEAhqQ22MkLuQrr3imCpAQaXRzLQBrXljw1G2n7jAglFJfLrjJABdwvyISxtBwIlsqm6IZcGURVwovOjVJmsimXirhoIyPTIZvPvLCRaMSmLplhwSm4TyxBe/My4lnvTk5hhMZhqGBKBU+HFWW0kjdQD+NYTHSyqN7P7jgi1fXdsQtFZnoFhnZMbq/bVSNKsPKVfBtY3POeodO3bwzTffYDQa6du3L/369auPfQkEgr8Jta1SlkK6knHuEhdOk7BAnl970GX+87/7t+bx22W8/uMRRndrxrJNh+j50qOoTx/kvCqMXa9/xC2x8QwNDXSYZDX+w50kxYUxbUBrXv/xGCt+y+WZOxKpslhsUqoqpZy3Rnfi6LlSGoUGYjJbOF9mQKWU07dNNBsO5bs8Y7ImksxTxWzOLsSMNV9dmxC09AKT3qN5jYpm9gb4WmqxE9Q/PhnqVatWMXLkSAIDA1EoFMyfP5/58+fz2GOP1dP2BALBX53aVilHhSi5vU0093aN44MtOQBkZhS57W324zDTBrShdYyaj345ytj/PEHX0wcpCQgm7Z45PN62DU99uMvtPbdkFyL3k7Fxai8UfjIGLtjMuJR4W4764V4tCPT3Y93+sw73TtZE8tzQdmCxsOHweYfPxybHM2V5pu36j96qqTEEbf8CM6prnNe17rzza6nFTlC/+KRM9sILL5Cenk5xcTHFxcU899xzPP/88/W1N4FA8DfAW5WySinHbLFwPL+MEwXl/Lt/a/JLKsnMLXar1S2xObsQuVxGl6ahjHzzGXqfsI6rHHv3bA42alFju5XOYKJldAgXdAZb//PYZKux7t0qmkU/Zbt9QZj9TRbT7mjDN5OSefv+TixO60JSXLjD8AyAAH+/Go2o/QuMr4pmgusLnzzqI0eOsGzZMlvV97///W+effZZCgoKiIpyLwMoEAiubzxVKUuynXPWHHAI+0qTowwm78b2TJGOji8+TVT1uMpHpHGV1Gz4/OV+nCwsJ6RaLMW+/1kuk3l8QdiSXUil0cT5Uj0Tl3nubgkLqtnTtX+B8VXRTHB94ZNHXVZWRlhYmO3ngIAAgoKCKCkpqet9CQSCvwme9Lwl2U7n3Kw0OaphSIDLOfa0fP0FopYtxSKTcXbhuzz+nymseOgWVv6zB34y6NO6odvzUjVR/Hwkn+z8MnadLCK1em9Sjrq0huruCoOZC+UGUjWRbo/X1rDav8DYe/TO1xIFYgKfi8m+//571Gq17Wez2czGjRvJysqyfTZ06NC62Z1AIPjL41ylLBWLDWzb2Kts57QBfh7nP7+Y/R3NV1onYZ2YO59ZykS2LNpiO56qiWL20Lb4cYgNh/PtPo9kbEpzJn2ayWsjOzJ37UGWpN+MDGwV1Aq5zOvzKPxkdGseQWx4EGYcpUJTfTCs9m1W9h69NDErLkJFdIMAYaQFyCwWi6W2i/38anbAZTLZdTvmsqSkBLVajVarJTQ09GpvRyC4otTUJ63VGSgstw7Y2HGikPioEO57b7vH672f1oUWUcHM/vqAQ2/zjDObefDjlwHY++h0Xr1pmNuQcWpCFLOGJCJDRqXRRJm+Cn+5H5uPned/v55g4agkxn+4C5VSzndTUqkyWyitNBIerGTG6iy3BXCpCVHMGdqWEwXlKPz8qDKbkcms15eETu5o17jWxvVMcYXHNqvaqJoJrg988qjNZu85I4FA8PfGkzGuqU9aOs+MhcJSPWv3n7V5ju5QKeXERwbzwrqDdIwLs82DbrlpLTe+8goApx+eQuGjj7HFQ3X35mMFVBpNvPTdYZfq7cVpXfjtjwuANeRdpDNwU1y4bc3LIzq4PE9qQhSTemsYtDDDVjgmVXw/8cXvts98EToRbVaC2uBz6NsXBg0axPvvv0+TJk3q8zYCgeAK4MkYv3hXe6Z/td9jn7T98W8mJbOwuqI6KS7cYwHVzMGJPPtNFpuzC22tULce38V7X81FZrFwbnQ6Z56chb7c+wjM/BK9h/YuGZ2bXzTMzlXVMWFBLBqVRH6pHm2FkZBABXtOFjF26U6H6m7nMZjgu9a2aLMS1IRPxWS+8uuvv1JRUVGftxAIBD6i1Rk4nl9GZm4Rx8+XodV5N3bSOZ5ES04W6rz2STsfl4ybpwKqVE0kHZqqHXLTXU4f4J3qcZXftOnJ8VkvUaqvqrG62xObswto39Raa+Op+EutUpLQqAFdmkeABZ5eleVgpO2fR5IwBdFKJah76tWjFggE1xa11ZN2xptoSXGFdw/S/rhOf9HQORdQ6avMNItUofCTcaKg3LYu8dwJlnw5h6AqPZtadGHqoKksMJg5eLaEGHWgR688NSGKzFPFHvelrzJ7raq2D/MHKPwc5k67uxaIVipB/SAMtUBwnVBbKU93eBMtqcmrtT/uXFEttURJLE7rAoBSbj0n/sKffPT5TEL15ey4oS0T77SOqwxQ+LEkI4e3RndiUm8N4FR9rYli1uBEvs06y+K0LuirzAT6y9mTW2Qzti2igj0+s7sXmmSnudPOzyhaqQT1Rb2Gvmvi119/ZciQIcTExCCTyVi9erXDcYvFwrPPPktMTAxBQUHceuutHDhwwGGNXq9n8uTJREVFERwczNChQzl9+rTDmqKiIsaMGYNarUatVjNmzBiKi4sd1uTm5jJkyBCCg4OJiopiypQpGAw1hwQFgr8KtZHy9IS30YqZp4pJ1bgXPJJ6lKNClPzr9gTCVUo+fbAbb9/fiSXpNzOpj8Y2DlLSy5auOSSiik9WzCBKpyWrUUsevHsWlf6BtnU6g4lHP93Db39c4KmBrflxak/WTUnh84e7869+N4IMfj9VzPgPdzFx2R7GLd1JZm4RC0YlcXubaJqoAz160u5eaKT+7nEpjkVwqZooWja0Gn1RqS2oD66qR11eXk7Hjh0ZO3YsI0aMcDn+yiuv8J///IelS5dy44038vzzz3P77bdz5MgRGjRoAMBjjz3GmjVrWLFiBZGRkTz++OMMHjyY3bt3I5dbfwGMHj2a06dPs379egAeeughxowZw5o1awAwmUwMGjSIhg0bkpGRQWFhIWlpaVgsFhYuXHiFvg2BwJWaWp58wZtXDN6LoLyNVjx4Rsvj/VthxuJSXZ3Wozmf7jjJpxNuoajcwKxvslw831UTe1CkM6BSKhi3dCcApw7n8NJ/nyC49Dw5EU35dcFHvNG+JQCNQgPZcOgcKqUcncHEu7+coG2Mmg+25DhcO6V6ROX2ExdsHvCW7EJk4NXz9fZCsyW70KFaPUUTybzh7YiLDHa7XiCoC3zqo/aVBg0a8Pvvv9OiRYuaNyKTsWrVKu68807A6k3HxMTw2GOPMW3aNMDqPTdq1IiXX36Zhx9+GK1WS8OGDfn444+59957AThz5gyxsbF8++239O/fn0OHDpGYmMj27dvp1q0bANu3b6d79+4cPnyYVq1a8d133zF48GBOnTpFTEwMACtWrCA9PZ38/Pxa90SLPmpBXXKp+WRnJGOvrzJxx4IMj+s2Tu1Fy+gQr/tx7vmV2pOmr9zHyyM6EOgvR1thJNBfjsViQS6ToTOaaB6p4sVvD7kVL0nWRJIUF05mbhFjk+OZu2wba76aReih/ZQ2bML5bzcwe2+Ji8yoNAhjXEo8mbmuwzvsr+0srOLtWTNzixj+9laP38MXD3fHAoQGKggPVtIoNNDjWoGgLqhXj/rpp58mIiLiks7NyckhLy/PYYxmQEAAvXr1YuvWrTz88MPs3r0bo9HosCYmJoZ27dqxdetW+vfvz7Zt21Cr1TYjDXDLLbegVqvZunUrrVq1Ytu2bbRr185mpAH69++PXq9n9+7d9O7d+5KeQSC4VC4ln+zO+9YZTDxZfZ1JfTSXpCctXbdMb2Tune0wVJkp11ehVPjxbVaeQ872/vd32DS87T3cxWldPI5x3JJdyL/63khSbBh+lRWs/fEVGhzaT1VkFOdWruE5JyMtnQPYZlVLhth+frWUl24YEoC/XEa7GLXtM7MX/8RbmB8gIljp9YVGIKhrLslQb9q0ia+++oo//vgDmUxGfHw8d999Nz179nRY99RTT13yxvLy8gBo1KiRw+eNGjXi5MmTtjVKpZLw8HCXNdL5eXl5REdHu1w/OjraYY3zfcLDw1EqlbY17tDr9ej1etvPQvNcUFfUdjSkhCfve2JvDbtPFgHWdqgFo5IAV9nLOcPaoa8yczy/zKOht7/uSyM6oDdajfPCUUnoq8xENwhgUh8N/nKZzUhLhjM4wPuvGm2FkYeXbOfdVfNocHwnuqBgTn+8EkMLDZvXuY8CWGVGW1NptFZc278g2HvQqZooJvZuyfgPd9leKCSpT3eRCW9hflHVLbga+FxM9sgjj9C3b1+WL19OYWEh58+fZ9myZfTu3ZvJkyfX+QZlMscqUYvF4vKZM85r3K2/lDXOvPjii7YCNbVaTWxsrNd9CQS1xZd8sjfve+GmY7biJ6kdKikunMVpXfjsoVtYnNaFzs3CySkoZ/pX+1i1908ulBs4klfKwbMlFFcYOXTW+gKqUsqZ1EdDWo/mHM0rtRZr5RbZirWGLNpCZm4RA9s2ITO32GY4M3OLUPh5//8sJhPz173Obcd3UqEI4IG7ZjH3T6XbVijH76EKY/WULWnIh3PEYHN2AYt+ynYoAttcHZlw10MuaZM7DxIRVd2Cq4VPhnrVqlV88MEHLFmyhIKCArZt28b27ds5f/487733Hv/73//45ptv6mRjjRs3BnDxaPPz823eb+PGjTEYDBQVFXldc+7cOZfrnz9/3mGN832KioowGo0unrY9Tz31FFqt1vbfqVOnfHxKgcA9NYVf7UU1aip+shfjkNqhxn+4i8JyA+M/3EWV2cLH2/9gdLdmZNoZ3lHv7WDeuoO8n3YzUSFKm9Ed/+Eu9p4uZtbqLLch6blrD1h7o+0MZ4BC7iJsIpHcMoLI6Y8z7NAvGP3k/PPO6ey6oS2bswsJUnr/FRURrGTbiUJrLtrL/Grn7wG8V7pL0p4bp/Zi9cQebJzaS1R1C64aPhnqDz74gKlTp5Kenu7gafr5+TFu3Dgee+wxFi9eXCcbi4+Pp3Hjxvz444+2zwwGA7/88gs9evQAoHPnzvj7+zusOXv2LFlZWbY13bt3R6vV8ttvv9nW7NixA61W67AmKyuLs2fP2tb88MMPBAQE0LlzZ497DAgIIDQ01OE/gaAu8DQaElzDrzV535IYhzNSf3NSbBgdbghjqRtvNCO7kPnfH+HlER0cvNUUTZSDkZa87cVpXRjVrRkD2jamX2Ijm2ddYajyqEL2/M4V3LRuBWZk/Gvw4/zc8mbb8SqTxaOBT9FE4ie7qHBWE+6+B2+V7mqVNRd9U1w4LaNDhCctuGr4lKPes2cPM2bM8Hh8xIgR3HXXXbW+XllZGdnZF3NJOTk57N27l4iICOLi4njsscd44YUXSEhIICEhgRdeeAGVSsXo0aMBUKvVjB8/nscff5zIyEgiIiJ44oknaN++PX379gWgTZs2DBgwgAkTJvDuu+8C1vaswYMH06pVKwD69etHYmIiY8aM4dVXX+XChQs88cQTTJgwQRhfwVXBeTSkhLvwa03etztBEvue5SqzhdvaRPPGhmOA+4KsuIggMnOLbedXmS4WY3nODVsFQg6c0WKyWDhwRsu0Aa0B0BvN+Mv9aPy/hTR+bwEAz/R/lLVtHOtctDqjzQg7t37NGtKWC2V6W0j/y0d6+Pw9CLlPwV8Bnwx1QUEBTZs29Xi8adOmFBa6Dz25Y9euXQ4V1VOnTgUgLS2NpUuX8uSTT1JRUcHEiRMpKiqiW7du/PDDD7YeaoDXX38dhULBPffcQ0VFBbfddhtLly619VADLFu2jClTptiqw4cOHcqiRYtsx+VyOevWrWPixIkkJycTFBTE6NGjee2112r9LAJBXVPbyUreip9SE6LIL9W7fJbWozlTlmeiUspp0TCY3EId4L0gy16VSxVw8f9fnnPDhZiBpwa2psoMv+Vc4PUfj9mOzzyTwfiPXwLgpV7pLL9pgMP5yZpIdlUridnLjN4QHsT5Uj0vfXeIxBi19aUjtxiLxeKxqt3+xURCFIYJ/ir4PI/63LlzNGzY0O3xc+fOERMTI+ZRiz5qwRXG21xjlVLuYOyDlHJmrt7PthMXWDAqiSB/OZVGE+M/3MWkPppa9SR/MymZl9dbx0cuTuvCeKdRk/ZeedOwIOatO+gQKh90aDMLv3kFPywUPPoYvz86HZlMZpP47BQXxr/7t2bUe9tdCsokmVFplvRbozsR6G/1lssNJpeXBmvVt4bxH+50qPp+Rcx8FvxF8Lk9a+bMmahUKrfHdDrdZW9IIBD4Tk3et70XfuJ8GaO6NeO2No34YEsO93drxsGzJbaCLGdxEAl7Va6fjuTbNLadc7/OXrlzD3WvE7t5Y+18/LCwsecwxgffBtWGPlUTyeqJyXx34CxF5QYXI52qiSS6QSAmi9mmTPb76WJ2/XGBm+LCOXhGS1JcuM37DlD4sf9PLacvlPPlIz0o0xsJDfQXQiWCvxQ+GeqePXty5MiRGtcIBIIrT23nGhfrDExZnslH47ry9KosxiXHO/RYO2PvHQcrFSxJv5l9p4sxVlkY3L4JsRGOXqlzKNzekHc+fZD/rnoBf3MV227uy4Ru48CuMHVzdiHPrT1AUlw4JqdgX6omirTk5tz7v210bhbOglFJTF+5jxRNFG9sOMae3GL3YfuEKLrdquHu/261Gf5LUXjzRl1KvQoEzvhkqH/++ed62oZAILgcamsozhRXUGk0ozOYbLnrzFPFJMWF2Yy3Pd5y1kM6xBColIFFRqomks3VAif9EhuRFBvG/d2aEegvJzTQ+msm8dwJPvjyOYKq9Ghv7csDXR7F7CfHGclzVwf52yZfqYP8qTSamFydI998rACLxcKS9Js5X/0c7sZmRjcI4Ni5UoewN9RuYlhtqSupV4HAE2LMpUDwF6e2hkISRukYG0ayJtJWBX3Rm87h56PnHQqyvImIPPvNATrGhbHit1zeT7uZAMUx7usWx6vrDzuEul8Y3o6RDXQ8uXAWofpyijp1Y+/8/2H8/KDX5/r56HkWbcp20BN3rkZX+MkcWkXdjc18alWW2+u7U3jzlcsZHSoQ1Baf+qgTExO5cOGC7eeHHnqI8+fP237Oz8/3mL8WCAR1T02Gwl55SxJGkfqOz5VUkqyJdFAs6xIXzqzBiaRqolAp5dx6Y0OPIiKbswvoFBfOSyM6sGDjUUZ1i+PDLTkuIijvf/orz789lYa6Ys61bMOTaXMxB3n/PdE0LIgON6j5ZlIySXHhTF+5j5dGdHAQZBm3dCcvfnuIqBClx17rmvDWR10bLmd0qEBQW3zyqA8fPkxVVZXt5xUrVjB9+nRbFbjFYqGysrJudygQCGw4h7gVMplNy9sZZ49REkaRDPPDvVowe3Bb5q47yOZjBTZPtG/raGYPTcRsgVMXvBeIKuQylm75g8QYNdGhAS5GOkKn5b1lTxNw4TT6FhoKV37Nj8uP0upUscdWqtSEKM5oK6yTt/QmOsWFkxQbxrIdJ922gPlvOMq/+7cGDrtomLsLPdvn3I1mC8fPl11yTvlyRocKBLXlskLf7jq7atLhFggEl4a7EHdqgmN/szOSodDqDAT5y3n7/k4E+svZk1vEu7+c4ONtJ/ni4e6c0VairTASoPAj81Qxe04Wseb3M6TXoPjVMFjJ6G7N+GBLDolNHFsSQ/Q6Pvx8Fi0vnObPBg058s6nVPpZp055HBCiiWLusHbMWXuATYfPO3yeltzcYba0xKbD53ngluYu1d4NGwTw/YE8hxcCTzl3+1SBL4Vhvki9CgSXishRCwR/ATyFuDcfK8BssTAuJd5tW1VokL9bA59crRqW9aeWWV+7anZLLVUd48I9e76aKAL95bYc9jg7ox5g1LN45RzanztOYVAoY+6dy4y4ZkRXF5bZF35NSGmBWuWPQi6j0mjmrLaSxBi1g1HenF2Av0LGwmrjLuWopb5rndHkoorWMS7c5YXAU85dShW8eFd7pn+1v9aFYWLSluBK4FOOWiaTuXjMwoMWCOofXwZvSPRMiCI4QOHWwG/JLuSDLTkumt0SUkuVlM92p9H9aB8NFUazzehlVoezFaYq3v76JbqdyqJEqeKBe+YQ060jUSFKjp4rtV1LZzCxJCOHKouF1344wpCFWxj5322Mem87mblFLBiVhEpprQpXKeWM7taMpVtyHHLUtnX+cru9RTE2xdpyZp9///TBbtzRrrHHnPuvxwo4WairVb5fQkzaElwJfPKoLRYLt912GwqF9bSKigqGDBmCUmn9x2ifvxYIBHVHTblQZyRDUVZZ5dXAP3ab+/OlinD7fLak063TmwhT+VNlslCuv/j/+SUZOSy4tyMT35tN8vGdVCqUPHj3LMJSujF3WDvmrjvI9mo1NOn+njxc+6rzRZuyva6TAfPubM+Xj3RHHeSPSinn2W8O2LxxncHE76eKubvTDVwod5RTdaa4wv337K1CvLZSrwLBpeKToZ49e7bDz8OGDXNZM2LEiMvbkUAgcKGmXGhchIqNU3u5GIrMXPeFZhIB/u6DaplOxV5tY9Q2yVCJZE0k0we2tv2s01dxPm0Co3b/gFmhYP+bi5k5bAj5JZVUGE22nLN9r3PDBgEuIXtngZVOceGEBio8KqZlZBdSZbbQpXmE7bPXRnYkr6SS00UVtue5Y8Fm3n+gi/fvw83gDomaJm0JwyyoL3wy1GPHjuWGG27Az8+niLlAcF3hq0pVbdbXlAuNbhDg9h7OBt55MlaIUkFqQpSL170kI4cl6TfjJ5PRMTbMozebdVprO/+JzR8zavc6zMj4v4H/4oJSwzSLhSXVMqUS9r3Ob9/fyWV/7oq9JH1vT7gzos+vO+TyXFtPFJKiiSTDQ7W58+AOe0RhmOBq4ZOhjo+P5+zZs0RHR9fXfgSCvzS+qlTVdr0vYy/tsTfw7ozgv25PYOKtLTFbLA6GOCkuDD/gyf6t8JPJPHqzc9cdYu3kFHZMfJpR2z4HYEb/iVwYehdP9m/NGxuOuhSa2ePswXoKcdeEZESll54LOgNjk+PpGBtmy1XDxWpzmUzm8p2/MLw9z6054Pb6ojBMcDXxeXpWXl6eMNQeENOzrm+0OgOTlme6zQn3TIhyUanydb10jq+5UGmyVofYMJfJWIvTujC5OhQtedlSi9aSjBy+ePgWKoxm7v7vNo/XX6s6QrvZjwNwfOozFE2eilLhh58MBi/cAuBxKtekPhr25hbZPFx3k7i8nW//XZUbTG6r28cmxzu0r6mUcr5+NBk/mczle/Q2hUxM2hJcLUR7lkBQR9RGpcreqPq6Hi4tFyoVO53RVrp4xvoqs4vspj2FOiMKP2tnh3PYPNBfTtmy5bR97t8AfJQykh5PPsnraw6yJbvQIaztqW/64BktMwe35flq0RXnSVzO5/shY3O2Yx/5yyM6AHisbgcc2td0BhN+Mhkto0M8fleiMExwLeGzoX7//fcJCXH9B27PlClTLnlDAsFfFV9Vqi5F1ao2+Wx3a8DqWTtjH3p2Z4hj1IGs3XeWvq0bcl+1sIlk8Hqe2M2Sr+YiM5v5PGkA+TPmMLvaSDtf293AjNiIIL4/cI7739/O0vSuPDnAc3BPOv/LR7qTrq20ef7SYJHCcu/ta/ah95rC2KIwTHCt4bOh/u9//4tc7jrxRkImkwlDLbgu8VWlytf1tcln26+RDG+PFpEoFX40Cg1kUh+NQ85Wqu4+klfK+2k3M//7wy4jIh9MieeupKY8tWq/zQj3OHeExd+8hMJUxbkBQ2n51mJaKxWs2HmKSX00JMWG4S/349MJ3dh6vNB2T+naKZpIbooLt/28/4yWjQfPMapbHKmaKAevWaJTXBjfZuW5eP89E6KYMTjR63cpeeqiv1nwV8RnQ71r1y6RoxYI3OCrSpUv62szpQlwMNLuqqdTqhXJpJztkowc3hrdicahATz/7SFXLe1jBWCx8MzgRNux7qWn+Pir55DrK/g5vjMT2o3F+L8d9G3dkGUP3sKctQe83jNZE8m/+7emqNzA2/d3IkDhR6PQABqFBvDJ9pOkJTfHjGNxW2pCFGk9mjNleabLd/XrsQLMZu+lNs0jre1rIowt+Cvik6EWKmQCgWd8rcz2ZX1tpzRJa9xVT6uUcm6KCyfIX85//9GZKrOFfaeLCQ2UU6QzepmSVUhJhVXYpE1pHh99Pgt5SQk7mybyyPCnMMqtnn/rGDVz1h5wuU5GdiEyZKz8Zw/OFFeQeaqY8R/u5L6ucbYwO8jYd1pLx9gwFDIZT/RrxdN3+FGsM6KvMhOu8uf+93e41TMHa2jc20tP07AgYaAFf1l8ViYTCASeqU0xknMO+dWRHSnXV1FS4bl4qTb5bPv/dybFhjl4tZ487L5tohnQtjHnSsu9Xj84QE7jkgJWfDUL/4LzHIhuwfi7Z1HpH+jxnvZszi5AbzQz/sNdHvciVWhP/HQPOoPJtu7DrX+Q3qO5RyMNoA7yv6T2NYHgr4DPymQ1FZIJBNc73oqRvOWZWzT0/P+tmvLZgUo5cruIV5XZYssV66vMNAoN5EheCZm5xQ7ntW4Syp/FFV4VuQACi4tYufpZ1OfOUN6sBWlDnqMk0HG/niq2JSqrrGHvpLjwWsmGSgVk301JBXArzAIX0wRqlVJUbAv+lvgkMTZ69GjGjx9PSUmJyzGtVsvo0aM5ceJEnW1OIPg7UVOe2d3QBwkpn+2OZE0ka/edZdfJIlITolAp5cRHBpOZW2QbYDHina2s23/WNuhCpZQzqY+G/m0bAReLytxx+w1B3DD6Lpqe/YPKxjHsff8zCoLDXda5M/bSfSRlsam3t+KOdk08htmdB4x0aRZOmMqfZlHBvFw9/ML+movTujBj0MVCMrVKScvoEG6KC6dldIgw0oK/BT551K+99hqxsbFuxTzUajWxsbG8+uqrvPPOO3W2QYHgr0JNrVOX0jct4SmfnayJ5MGUFvx+upgm6kCm3JZAsFLOXDe5Yunnh3q2oF1TtW2G9NFzpRw+o2VsdQuT/Xl94kJYsGwGAb9nUqRSc37FKrbpG5CsqXK5fuapYgd5zkuVA7Wv0H6lukf6eH4ZZXojz9/ZDpPFwqzVWR7nSQsEfzd8MtQ///wzn3zyicfj99xzD6NHj77sTQkEfzVq0zp1KX3TElqdgQqDicf6JvD0HW2oMJrQ6oz8/mcxFizs/OMCb2w4BlycJe2OLdmF/KvvjbxeLe05IaUFHW8Io1t8BO9tPsHNzSOYPrA1+SV6ZEYjzR4aQ9CODKqCQxhz93Pcqldz0INRP3RGy5xh7Zj1dRYZXiZj1YR9hXa5weSg3uZJocy++l140YK/Gz4Z6tzcXK+tWVFRUZw6deqyNyUQ/JWoTeuUWqX0uW9awt1LQJ/WDXms742kaKIo1hkZn9KCpLhwlmTk1JgrBkiKC2dccjyB/nKQWdAbzUwb0Joqs4WyyirCAhUET3qQljt+plKh5MA7H5N1wJ8T1Qphn+44abuGvspMWJA/IYEK0pb8xrCkpvzzVg1hKn+3xWXOk7nsSbWr0Hb3vXorWKspKiEQ/FXxyVCr1WqOHz9Os2bN3B7Pzs4WGteC647ahrR97bMG9y8BKqWc0d2auR07uWBUEn5e2iijQpREhij5PbeIRZuyUSnlvDW6E4H+fsxZW60qZrEw58f/8kDmOiwKBUcXLsGvVy9S9UfZnF1gUxjrFBeOQi6jcWig7XsYltSUJRnWUPeKh25xu4eLcqA4eP7Jmkge7a3x+r3W9BLiLSohEPxV8clQ9+zZk4ULF9KnTx+3xxcsWEBqamqdbEwg+KtQ25D2pUzAcmesPIWUt2QX4oeMWUMS3VZIq5RyPh7XlRmrsxwqrM9qK1i3/6zts6mbP+GBTOu4ynfSZ1LRogtL3t/BglFJWLCQkW1VGrtpVBjv/nLC7cvClOWZqJTuFQylau4VD91CeqneYRDIuKU7WTMpBbVK6fZ7rak6XYyiFPwd8clQP/XUU3Tv3p27776bJ598klatWgFw+PBhXnnlFb7//nu2bt1aLxsVCK5VfAlp+zr0wZ2xqqlfuUxvZM6wtsxaneXgsc4Y1MZF2ESqsJY+G//bKqZs+wyAWf3+ySeRnVkcG+ag1S2FtV9yo2Rm/wIAeAxxJ8WF8cPBc26fQ3qxcfe9egubi1GUgr8rPhnqpKQkvvzyS8aNG8eqVascjkVGRvL555/TqVMnD2cLBH9PfA1p+zL0wZ2xqin8W2WC8yWVDGzfhPTkeAwmM80iVICMPwodhU0MJjOSjtHIfT8w86fFALzS8wE+SbrD4X6SVveiTdmsmZzstWBt4q0afjqSz9jkePycZj+naqJIS3YvBwoXX2yiQpQukQHbPGmwVZeDEDYR/L3xWet78ODBnDx5kvXr15OdnY3FYuHGG2+kX79+qFSq+tijQHBNcykh7dri7iUg0N/zUByABoEK5q6z5pulFqkXvz3EqG7NXELHDUMC0FYYGXg4g5fWLwLg3a538fYtI21r3IWbyypNbqdt7cktYklGDnI/Ge/+coIeLSIZm9yc9B7NAWtE4UxxBct2nHSrNJZq92KjVil5/s52PLNqv80o6wwmPt1xkicHtOZRfRUBCjnqICFsIvh7c0nzqIOCghg+fHhd70Ug+MtSX3OMnV8CVEo5USFKh35le5I1kfjL/RxC0FI+Oz053iV0rFT4IfvxBxaufQ25xczyDv148daxUF2QlpoQReapYpf7hAbJWTQ6yVY4JpGqiWTR6CT0RjNJcWE8M7gNpRVGggOs38eJgnImL89kwagk9FVml/z2c0Pb2r4zrc7AK+sP8+SA1vyzsgpthdGWy35zw1GeG9qOGyKEcyD4++OToV6wYEGt1okxl4LrkfqaY2z/EmC2WHj5u0OkJ8djwbGPOUUTyfN3tud8aaXtM/t8duapYpce6IqfN9Nr+sPITVVs79yHZ/o8etFIa6KYMSiR4W9vcdhPiiYSi1nGBxk5Li8L1nC4jLnD25IUF05phZEOsRdVzEIDDW5nU0sG2J6CMgPr9ufx05HzDp67lFc3mGpuQxMI/g74ZKhff/31GteIedQCQd0jvQQczy9jw+HzbD1xwa2hO6utwF9+MVRtn89e4tQDPSWqgs7pw5FXVPBHlxSCP1vOaqUSncGEwk9GRnYBF8r1DiHqVE0Uzw1rS2lllccc9ebsAir0ZhZtymb4TU0djtmH8t3NlZ5QXYQGFwvp7OdY29O3tRi3K7g+8MlQ5+Tk1Nc+BAJBLajJeEmSoKmaKDZnFzjkl+092R7mIm568F4UWi1HW7ZnWOpjVPxvp8O1kjWRtG+qZnFaF1RKBTpDFZmninn1+8OMS27hdZ+l+ipubxPttpCutvn8SxWIEQj+blxSjlogEFwdajJesREqjp4r5cHUeMBC5qlim9EGq7H+4uvt3PfJkwSUnOd8i1b4rV1Lly1nHaqrpZGTk5dnojOYWJzWhfEf7rIdn2gnTOKOIKUfMwcnuk0F1DaffykCMQLB3xGfpmcBmM1mlixZwuDBg2nXrh3t27dn6NChfPTRR2JetUBQT2h1Bo7nl2GyWEj1MkXr+wN57PzjAiaLhS7xEXSJC2f20La2yVjhOi2ffDaTG0ry0cXFc9ew2Rw3KZk1JJFPH+zG2/d3YnFaF2tovNpIJ2siXfLHFgsep20layKxWGDG6iyPE8FqM+VK8r6dp4aJVizB9YbM4oN1tVgsDBkyhG+//ZaOHTvSunVrLBYLhw4dYv/+/QwdOpTVq1fX43avbUpKSlCr1Wi1WiGlKqgzzhRXMOvrLFo3CaVLXDhNwoJ4ft1Btx6wvXFNigtn0aZslqbfzK7cIrqEy7lp7N2EHfidykZN2LNsDcXRMTSLUBHo74fZAs+uOeBSiT02OZ7pK/dxX9c4W0FXswgVpfoqFm465rJ+Uu8EfvujkNd/PMbGqb1oGX15M+ylqWRixrTgesWn0PfSpUv59ddf2bhxI71793Y4tmnTJu68804++ugjHnjggTrdpEBwvaLVGZj1dRb3dY3jgy05LMnI4a3RnRjYrjFPDmjFqQsVtkIyyUiDtaJ7XHV19xltBQez80h+eTJhufsxhEcw89H/8MWPeUAeYG3Dmjk4ka7xEYyrFklpHhnMuv1nmb5yHy+N6OAwrlKllLM0/WYGd4hxKGg7V1JJpdHEu79Y59LXhfZ2fVXTCwR/FXzyqPv160efPn2YPn262+MvvPACv/zyC99//32dbfCvhPCoBXXN8fwyVu390zba0X7M49v3d2Lisj0O6+1FSEICFIQG+XOuoIR2/zeeqJ9+QK8KZu7URXxibOhyr9SEKDpWt3NN6qMhRh3Iuv1nSYoLdztaUqWUM3NwIgnRIeSX6m0vDEsycmwvDHXhUQsE1zs+edT79u3jlVde8Xh84MCBte61FgiuR6QwbkmlkdAgf6KCPXuLWp2BCzqDQy+0/Z8DFH4OhtlksdAyKoTSyirOllRQbjCx+Wg+yXMeJ2rbD5gCAsn7+HM++c39u/nmYwX8s1dLFm3KJik2zCZMEuQvd1thrjOYeOqr/Xz6YDeXFwYQBV8CQV3hUzHZhQsXaNSokcfjjRo1oqio6LI3ZU/z5s2RyWQu/z366KMApKenuxy75RbH8Xp6vZ7JkycTFRVFcHAwQ4cO5fTp0w5rioqKGDNmDGq1GrVazZgxYyguLq7TZxH8/ZCKvDJzizh+vsxj8RRYc82Tlmdy239+YfjbW7lt/i9MXp7JmeIKl2seySth18kigvzlHvuis85oWZzWhczcIiYvz8RPJmPetwfZcPgcSrkflYYq7v90Pt23fYfJT8759z8iu01nr8+jkPux7MFuqJQKFo3uRNafWsw1BN0C/P1EwZdAUI/45FGbTCYUCs+nyOVyqqqqLntT9uzcuROT6aLgQlZWFrfffjsjR17UIh4wYAAffPCB7Wel0vGXw2OPPcaaNWtYsWIFkZGRPP744wwePJjdu3cjl1t1k0ePHs3p06dZv349AA899BBjxoxhzZo1dfo8gr8PZ4orXGZF90yI4qURHYgJC3JY626uNFjnVU9fuY+Fo5JQq5ScvqDjqa/2OYiJLHuwm+3P9n3RFgu8/VO2LST+6Y6TjO7WzJZLfnLrMu7YvByLTMYfr72F4bb+qPWO//901usOVsr57sB5W/g6WRPJbW28C4uEBSnrRT5VIBBY8clQWywW0tPTCQgIcHtcr9fXyabsadjQMZf20ksv0bJlS3r16mX7LCAggMaNG7s9X6vVsnjxYj7++GP69u0LwCeffEJsbCwbNmygf//+HDp0iPXr17N9+3a6dbP+Unzvvffo3r07R44csY3zFAgkamt4JdzNlbY/p7DcQJm+imlf7XPJBe86eYEXh7cjOjQQf7kfn07oxtbjhXS4IYw3NhwDLo6rlHS9H9nzDRM3LwdgZt9H+OTcDfDmZl4Y3s7WVy0N7LAvEgPHmdJbsgvJOq0lVRPpVoksRROJUm6VHBW5aIGgfvDJUKelpdW4pj4rvg0GA5988glTp05FVq1HDPDzzz8THR1NWFgYvXr1Yt68eURHW72A3bt3YzQa6devn219TEwM7dq1Y+vWrfTv359t27ahVqttRhrglltuQa1Ws3XrVmGoBS7UZHgLygwOhtrdXGl7TGYLpy7oSIoLt1VRB/rL2f9nMUmx4bz363EHQ5miiSSl5cVws6SBvWhTNiP3/cD0H/8HwKupY/ik0yDbuufXHWJxWhfAQse4cJtht8d+oMeiTdnMXXeI1ROTeWn9IRJj1DbvO0zlT0iAgj8KdLyXccJtJEEgEFw+Phlq+/Dy1WD16tUUFxeTnp5u+2zgwIGMHDmSZs2akZOTw8yZM+nTpw+7d+8mICCAvLw8lEol4eHhDtdq1KgReXnW1pS8vDybYbcnOjratsYder3eIYpQUlJymU8o+KtQk+F1bktyVhRzDjmbLBbCg5Vk5hY5eLcvDG/nYqTBOot54q0a27WiGwSQX6pnwJEttnGV/7t5OG91v8fhPJ3BxPgPd7HioVuQyXBbJAaQmVvMtAGtbfurMpuZMSiR2V9nuXjfswYnsutkkdtIgkAguHzqXEI0Pz/frdGrCxYvXszAgQOJiYmxfXbvvffa/tyuXTu6dOlCs2bNWLduHXfdddf/t3ff4VFXWQPHv9MnkzJppAkJgQQCBDSAUhJEihRBLKwF0AWxrgLyKioWBGzouquuIIpKswD2BooLKyqhqRCVLpFAQAIhIZmUyfTf+8dkhkwKiCQQ9HyeJ8/CzG9m7oxhz9x7zz2nwedSFCVgVl7zzw1dU9usWbOYOXPmqb4N8SdwqnWoa5bDjA7R8/rYC/n3l7v8Qe+jO3vz3H9315ndxoYZG2x+sX5vMQM7xHD9RYn8cqScLju/5z+fPYtG8XDgqlE8lTra3wmrJqvDzcGSqnqe0cu3JP7syl3+1655LKymdbnFPLF8p3/2XXslQQhx+k4pUJtMJvbv3+/fN/YlccXHxwNw5MgREhISApK/Gsv+/ftZvXo1H3744Qmvi4+PJykpiT17vHt3cXFxOBwOSkpKAmbVhYWF9O7d23/NkSNH6jzX0aNHT5jl/uCDD3LPPff4/15WVkarVq1O6X2Jc5Mv8P6wv6RWMpYWg06NR1HIyS/BHKQj2KClwuZi0oBU7rwkhahQPbM+3xkQgLUaVb0B2ZflXXsG7lsWf3hYBx75eBue9Ru4ftkjqN0uVrTPxPjYv+CtnAbHnxQVhIr6v4TW7GHtU/NYWG1rc4u4qbrrVWMUOBFCBDqlQG2z2QLqea9bt46qqsBv5k1V73vhwoXExMQwbNiwE15XXFzMgQMH/F8eunXrhk6nY9WqVVx7rXcZsKCggG3btvnPhPfq1QuLxcJ3333HRRddBMCmTZuwWCz+YF4fg8HQYGKd+HMzm/Q8M7IL+49Zmf3VHuZ8leufib761a+syy0OSNaqGfT6pEQzNrM1G/ce8xcGKamsP8D5zko3lPQ1uGMcRRs28+6SaajtVsou7s/718+g06FyMlOi6syAAfqntcDphh2HSuu9pr6gXPNYWH201Qll0tFKiMbX6EvfJ1oq/qM8Hg8LFy5k7NixAcfDKioqmDFjBiNHjiQ+Pp59+/bx0EMPER0dzVVXXQWA2Wzm5ptv5t577yUqKorIyEimTJlC586d/VngHTp0YMiQIdx6663MmzcP8B7PGj58uCSSiQaZ9Bpe+io3IPmqZlCub2YK3hmoB8W/XAzg8ij+56w5c44JNTJteMd6nycnv5TKHbt4851pmO2VbE5I4+6+dzPzknbEmQ1cecF5zPxse0DSW5/UaO4blMYTn+8gJ7+UF0dlANQb0GuqeSysPhEmnRQ4EaKJnBNtLlevXk1+fj7jx48PuF2j0bB161beeOMNSktLiY+Pp1+/frzzzjuEhob6r3v++efRarVce+21VFVVMWDAABYtWuQ/Qw3w9ttvM2nSJH92+IgRI5gzZ86ZeYPinFRU4fC3j4S6M9ETLRfXrMUNsCW/hP5pLQLOQYM3cL93ey8e/HBrwONNeg3z+sfR6cYrMFaWsLNFa+4a/RhPjOrBguqg7gv6d/Rti0atwupwU1RuQ6U+Hph9/alr1us+r57M7ZwD9c++wbtCoChIgRMhmsgpBWpf5a+G/t5UBg0aVO+SelBQ0O+qK240Gpk9ezazZ89u8JrIyEjeeuut0xqnOPedSonP2pnftZeHT7ZcXPP+Bdl5LLutJ8+s3BUQDK0ON/kl1oDHmfQaXh6aRPLIYRgL8jkc05K/X/M4113aJWDmbXW4mfNVLnO+yg3opuU9nkXANTV9MalPnaC8IDuPF0dloMKbce6TlRLFlMHtMek1xMvRLCGaxCkXPGnXrp0/OFdUVJCRkYFarfbfL8S56lQqjQGEGAL/+dReHj7ZcrE56Ph+rtXhxmp31ztj1dcoIWrSa3hpRCpdb76G0II8DodGkfv2h7Tb5z7hDL7mcasWoSfOq/Dg4abq2X7NoL90037uH5LG+AoHNqcbc5COhHAj//pyN49e3umEzymE+OPOqXPUQjSVU600ZrE6cCsK88d2R6VSsSW/hG2HLAEz0fqWi33L0b3bRKFWqVg+MQuNWkVRmR21uu7qlEnv3Z7xVRO79aIEYm64jtDdORwLCmPMtU9Q8G0x47OSCdbX/8+59nGrCf1TTrCMHUWQVsuyTXvISIzg/wa2w1LlRKVSoSgKx6qDtEGrZn9xJb+VWLmqa0se+WgrM69Il4InQjSBRq9MJsS5xLfUbXe5f3elsfpm3pkpUdyclUyX88IJ0qpJSzDTNTGCrLbR3NUvhfW/FrPsu/yAvs41g3aQQVNnhu4LsG9v2s/YzNbgdnLDc/fTYvdmXKZgxv5tJr9Gt4Lq5WtfGdHaaie1+ZaxITCJLDMligcv64Dd7eL+oWk8vnyH/9oF2XtrZa5HMe3yTnyxrYAZn+3A6nBjd0nBEyGaQqMmkxUUFPDkk09KEpY4J9QMuHPHdD3htb7zwQ3NvNflFqMGHhzagYeHd+TRjwMrePVJiead23sxa8WOOke35nyVy+SBqcSGGgJmujUD7KZfi3h3w6u0WPMldo2Oj2fMJSyiHdQIng0lfNVeErc63AFJZGFGHSaDhlCDlsc+287qXUf9XyL+0bctQXo1T13ZGYfbg6XKiaXKSc6BUq58aZ3/eBnUXzpVCHH6TjlQ79ixgzVr1qDT6bj22msJDw+nqKiIJ598kldeeYXk5OSTP4kQZ1ntgHuy/WTf+eAT1fhem1vMfYrCox9vY3N+KRP6pwQUKPlu7zE6twpn9a6j/iCck1/K5IGpDOoUx99eXh8w0/UHWEVhyhfz6LT5MzwaDXdeOZUN5TG8ODxwH3lBdh4Lxl6IWqVqcIw+NZPIlt3WkxC9loc/3uZ/XM1EtItTo/0z5Zz8Em5e/EODzysFT4RofKcUqJcvX87IkSNxOr3/GP/5z3/y2muvce2115Kens57773H8OHDm2SgQjSm2gH3RMePap4PPlmNb7VKxebq88n1FSiZPrwT877ZS0arcP+y8qJ1ebSLDa0z0zVV7zn/X/YSbtrsbbf6xb2zsKX2wZpbXOdolTlIR2yYgZ5tIhnXuzUOt4cEcxD6k3wJCTZocLg9v2vp/1RLpwohTt+J/wXX8uSTT3LHHXdQVlbGv/71L/bu3csdd9zBBx98wJo1ayRIi3NG7YC7IDuPmzKTyUyJCrj94tTogPPBJwtUlXZXg4VO1uUW8/jy7YzP8gZW33XZucX+Gb1vJnvz4h+wOlyM//4T7l5f3a7y0ju4z9DZP86a1y79Lp8Ik45r5m3g2S9/4ebFP3Dvuz9xtMKOoih13pdPn5RodGo1TreHCf1T/MlrtZVWOYDjpVPrIwVPhGgaKuUUzlSFh4fz3Xff0a5dO1wuF0ajkc8++4yhQ4c25RjPGWVlZZjNZiwWC2FhYWd7OOIEfi2sYMBz3wTcVrMqmDlIR4RJT4hRS6XdhaXKe646xKDlvvd+4tt6Zp+ZKVH+LOkTLQ/XPMd88+IfMOk1zB6VwaJ1+wIKqLzi/Ikhzz0MwL/63MCc3tf7x/nIsA60iw2lsNyOQasmITyIkS+vx+pwY9JruO3iNgzoEIPV7iYsSIuiwJOf7wz48pCVEsW4zGQmLc3B6nCTmRLFTTX+XtOSW3rQKSEMs0nPodIqpn7wc8Bn4PtCI2ephWh8p7T0XVZWRnh4uPeBWi1BQUG0a9euKcYlRJOq2c3KxzdD9e3JVjrcTHnvp4Al4Us7xPDElekB+7mAP8hl5xbR+Txzva/p+yIQE2bAYnVi0Gr8SWXvbz7AvYPbY9CqSUsIY9Du9XSePA2Azy8dxZyM413iMhLDiQkz8vcF3/kD6qcTMv1Bes7oDBZm5/HC6j3+xwxMi+GRyzpQUGZHq1GhUanYsLc4ICjX7kNd872t31tMbJgRs0lPQngQs0dlUFThoNzmJNSoIzqk4cIwQojT84eSyXw9mhVFYffu3VRWVgZc06VLl8YZnRBNxGzS8/TILg3ODIF6s7tX7SwEYNqwDhwoqfKX3cw5UMqkpd5uVe/c3rPO69XM8vZX+VKpGJ+VzJJN+xndI4l53+QyYUAqK59/g/Yv3ovK4+GDCwZhf+wpPk+M5HBZFYqC/7V8Abbmsvb4rGQWZHuX02vXDS+tctI60oTN5eayF7Pr/VxqlzatOcsemHa8fa3ZJIFZiDPllJa+fRXI6n2i6oIIKpWqSdpcngtk6fvc4ztHXXtmWN/SeE0r7+7DkP+srfe+yQNT2by/JCDI1+zn7PtzRmIEvdpEsWFvsf/v5WvWcv+/JxLstPFFu95MuOIB3GoNfVKiefTyjjz22faAdpi+QFpSYefTnwoYl9nav5zeUOeu6SM6MWJOdp3lbZ9lt/XkWKXD/wVkQXYeVoeb/93Tl7YxIaf6EQshTtMpzajz8vKaahxCnBUNzQxPlN1t0msw6tT0SY2uN1N6228WHhyaBsou/55zzbPMvj/n5JeS1Tba//eJLWykvfB/BDttfNs6g7svvw+32pvctTa3iAMlVdw3JI1/2F3oNWqC9Bqcbg9Beg0aFdw/tD2VNm/wrZnQVntmfbTcxuxRGUysZy8avAlxd769JeA2SRQT4uw5pUAdExPDlClT+Pjjj3E6nQwcOJAXX3yR6Oj6s0CFOFc1lN3tm6k+sXwHY3u3xqModRK0buyZhE6r5rLOcdw/NI0Dx6wB5T19zTisDjdWpxu7y0NiSQEX3PIwRlsFWxLac89107itf4eAc9jRIXrUKthXVMnRCjvPr9qDSa/h0wlZvJ7tDcq+RDVf8G+ol3WflGheHJVRJ3GsT2o0heX2gPdcO/NdCHFmnVKgfvTRR1m0aBFjxozBaDSydOlS/vGPf/Dee+811fiEOCvqSzaDwJnq+r3H/OeYdVq1P0Er50Apizfs4/xW4YToNSzZtJ/xWW38z1GzuMqW/BL6Bjt5651HMFoK2dmiNXeOfpynx/auG1xTo3lwaBqJESYKy+3MH9udyGA9Mz/dFlBfvE9KlP/LwIl6Yiu1emL7ArJJr+Gi1pGSKCZEM3FKe9Rt27blySef5PrrvcdEvvvuOzIzM7HZbAG9nf+qZI/6z6W+Y0hLbunB6Nc3BVxn0mtYdltPjpbbvbPjSBMeRWFXQRkrtx/mxp6tiQsz8NTnO1mbW8yDQ9PIzi1i7Z4i4l2VrPlsOsZfdlESn8jD986lTefj+9m1ZaVE8fiV6Tzy8Tb/DLrmUTBf1rdRq2H065vq3F/byrv7YHO6JSAL0Yyd0oz6wIED9OnTx//3iy66CK1Wy6FDh2jVqlWjD06Is6m+Y0iW6sIfPrU7U/kMSGvBtGEd6ZRgxqjT4HC5mT4inedX7WZwx1i6tDSjr6pg4lP3YCz4BUdsPM/833+4+rLuBOk0DbarzM4t5lCpzR/Ea/e8tjrcTFiSw5zRGfRJjT5pT2yb080FiRF/5OMRQpwhpxSo3W43en3gN26tVovL5WrUQQnRXNRONttzpDzg/vqWlk16DTf0bM2RchseBZ76wltoJDpEz9LbevKbxcZdC9bzyRezSCr4BYc5gh9fW8alKe3RqtUB/afrY6k6nuhWX41yX7B+cVQGJt2JV7qk5KcQzd8pBWpFURg3bhwGw/HG8zabjTvuuIPg4GD/bR9++GHjjVCIJnSkzEZJpYMym4uwIC0RJj2xYcZ6r7VYHWjVKn9vaKjbmQq8wbvAUgXAiq0F/szrp0d2weH0UFZexTPvzyJpz0Yq9EGMvmIaP68rh3XeJepPJ2TWeW1f5nb3xAhiw4x8NjGLkkoHIQYtT12VzhMrdgYkhVkdbpZs2s/kge0CxluTZHILcW447X7UN9xwQ6MNRogzKb+4kgc/2lona/upqzqTGBUccK2vJeYNPZN4ZHgHHlvubVdZ39Jyzb7QtVtWTujblgtm3kv8no3YNTpuvXoaP8cHVvdTowo4+uVbXl+yaT8XtArnic931Dkb7duL9gXrrJQoRvdIYvyi73l6ZBc8BGanSya3EOeOUwrUCxcubKpxCHFGHSmz1QnS4N0Dfuijrfzzb+dT5XBTZnMSYtDyw/4SNu8vYVzv1hSU2ujdNopHhnWs97kbCt4L1u6l3TOPEvXJe7jVGu66YiobkroEnHPWqFSYDBruuqSt/+iXL8hnJEY0mMENCu/f0YsSqxOjTo1Oo+aF1b9QVOHwd9m665IUDDo14UF6SRwT4hxyyiVEhfgzKKl01JtVDd5gva+oMiC7OzMlihdHZbDrcBkxIUb6p8VQanWQV1RZpz2mb99YpVL5b7O7PLy+bwVRb70CwM6n/kNVVDdMtVpiTuifwmvZe8nJL/Uf/WoRamDOV7mMz0xuMMlsbW4x9ysKt77xg7+YybThHXlwaAcq7S7J6hbiHCaBWvwlldkCEyBrV+8KNWqZ0D/FXz5zXW4xapWKGZd3wun2kJNfwvKtBeRUB1og4CzzeWYjyS2CyUqJIju3mJSl82n31ksAHHnyWZ6PvZCMBDMPDEnj2ZW7yMkvZUL/FAZ3ivUHY9//zh3TFah/pl7TwZIqrA5vAZXuSRFc0q6FdLMS4k9AArX4SwozHv/Vb6h6V2ZKFEtu7UlppQOr041Rp6HK6cbhchMTZvQniW0/ZOHR4R1xuRWsDjcaNeg0arRqFeMyk7n0+5W0e3UmAP/OGsNSz/k80yOJmDADalRsrjGr7hhf9/y9b4ZeX4Z3TS0jTEzon0LbFsHMHpUhs2ch/iQkUIu/pIhgvX+221D1rnW5xajYxQWJEf4A3ic1mmnDO3Kk3B4Q4J9f5W0p6esVfX4rM+V2N188/jL/evcJAIpvuZPZkUOhwuEvQjJ3TNeA16/Zucon50ApmSlR/v+tb8k+MyWKL7cf5sf8Eq7v3qrRg7SveUmZzduXOzpYltGFOFNOqTKZODGpTHZuyS+u5KGPtnJTZnKD1btqVx0z6jQcKbPRPjaEr3YfDaggVrtj1Yftquhyx2i0TifvpQ8kbcW7PP3f3QGB1leb2/f6Nbts1RzDi6MyWLppP6N6JNX5UlGzFaXV4fb30zab9I0SYH0Z72trtQN9emQXEmRpXYgmJ4G6EUmgbv5qBq7wIB0GnYbfSqq4Zt6GOtf6AuTidXmsrdGFqnebKPRaNUE6DcNmH+/rXDPIXnBoN+++Nw29zcrhgZeRmXE7c8dehEatCgi0E/qnkNEq3B+oG2pP2T+tBZMHtqOk0klYkBaDTkNRhR2XW2FLfol/L93nf/f0JUivOe0Aa7E6mLA0p94uYTW/EAghmo4sfYu/jEOlVTzw/s8BxT8uTo3m4WEd6r2+dqvIl0Z3pcBSRZXTTbndRWTw8QBl0mu4pF0L5nyVS7uj+1j03nT0NiuWzL4UzVuA+9Uf0KpVTKw+KjU+Mxm7y4NJpyGmRoEVq8PtP07lu6ZVZBBfbj/C9a9u9AfjuWO61mlFWZOlysmMz7bXCbDf7ili6gc//+4AW1ThqDdI+56rqMIhgVqIJiaBWvwlWKyOOkEavMHmTqvTv19dU82qY7f3bYNRp2ZFdab3S6MzcFZnYftmwZYqJ61KD/Pmu48SXt2u0jD/bX4+avPvMWck1q1kNnlgKn1Sovy1wq0Ot/+azJQoMmrskfucLLHMpNc0SoA9UV9ugPKT3C+EOH0n/tcuxJ+AxeqgwGJjVI9EFoy7kAn9UzDpNZj0Gib0TyHUqGHa8E70SY323zZ/bPeAHtL92scwZ02u/zhWZLCB9XuLyUyJ8s+89YWHeXvZw8RWHONo61TK3/8YVWgIT6zYyU2Zyew4ZOGmzGQyU6ICxrftNwuPXZle5/Y+KVFM6JfKguy8Ou+psNzOxan194G/ODUatVpV730+vzfANtSX20dqhQvR9GRGLf7U6kuEykyJYs7oDFSoeD17Lwuy81h2W08eGNIevUbD48u3syA7jzfGXxTwXOtyi5nQP4WF6/K4e4A3gL44KoMgnYY3V+SQ9uSjtLAcwdoqiRl3PceKT37l5RvMAcvZWpWKKYPaM3WoihKrkwiTjv/tLMTu8pCRGOFf7jZo1Wz9zYLN6SYjMbxO+c9+7VrQt12LOm04faVBq5xuTuT3BtiG+nL7XktqhQvR9CRQiz8ti9XBo59s4/xW4Yzr3dqftb0lv4QlG/fTv0OsP/g+u3IX9w1JY+by7f5Z8y9Hyr1L1vml2JzeZe6u1cvQDw3t4A/Ar13dnkXvzaBFwR6cMXE89I/nWFHkndH6OmHVXM6uaf7Y7mzed4xhXeL5Kb+0zjUmvYZpwzowfXgnrI66FcZqt+H03WexOholwJpNep4e2aXBLwSyPy1E05NALf60jlbYuf6ixHoLmdyU6Z3dRofo/dXAJrs9AbPm/GIr88deyOb8EvQatXe5vLptpF6nJjMliu17Czl/4k2EFOymxBjKL6+/w8frjrfCPNnZ5yNlNsZmJjPq1Y08PbILCkrAXnlGYjgtwoxoNap6+0bXbsNZ8/bGCrD19eWWcqRCnDkSqMWf0qFSbznNhgqZANw/OI23b+nJwRIrgD+jOqNVOMu+y+ftW3ry9Mqd3HlJCl//Usi0YR1xV59mtDvdTOjTmpZvTSck+xuqDCbGXTOD21unwrrj2di+5fGarwve/edpl3fii20F/haVvuXxB4amceBYFQatmpwDpUxamsOSW3qc8mfQmAG2oS8EQoimJ4Fa/OkcKbOxr6iScJO+wcYb63KL0Q9T89jy7YzPTMak1xAV7O2zbnd5eGZkFx5fvp3s3GL+1q0Vr367l3du78mX24+QmRLF/3YcZvQrM2jxzX+xa3TsePkNQtzn1cnGrn3cKsyow6TX8MX2w1z50ro6PaTnfJVLx/iwOkev/mjSlgRYIc59EqjFn4LF6qC40oECzPhkG2tzi3nr5ov8BUVq7k/7ioNYHS7GZyZzXoSRFROz2FidxW3QqokNM/qXoA1aNVaHm4MlVd4Z8vUXoEyeTItV76BoNEy44gHW5QUzPiuCFqGGgKNWcDwA90lp+Mx2TbWDvSRtCfHXJoFanPN8md3ntwonJ7+EnPxS/u/SVBLCg8jJL6mzP/3iqAymfvAzkSY9L63JZfLAdjy+fJs/iay4wh4wg/XtM+s13oC98877mPjNOwAUPPcSqw61hOpg7Fvq9hC41J2VEsXYzNZc/fJ6rA43WdXj8JX9rHldzoFS/98bO2lLanYLce6RQC3OaRarw3/86ubMZLRqFVOHpqEoMP2TbQ3uT88d09WbEZ4YQbnN5e//rFWpyEiKwOX27kWb9Bp0GhUzR3jbWz6+/3/c+M1bAPw282m+7TGEzK0FrMstJjpEzzMjuxBnNvDIZR1BBRV2F263QvavRQFB2TdbH591vMf0xanRPHVVZxxuDwPTYho9aUtqdgtxbpJALc5pvhKXJr2G+HAjr2cf44XVe5g/tnvA8nNN63KLeWRYR9bmFjMuM5kKuyugzeX8sd2JMxvpn9aC0T2S+GDzAYamx7Pu0ee5ZdnzAHxw+S2EXzOOx5fm8OKoDMKMWiYPbM9jy7fXmUlPG96JZd/l+4N0zd7XoUYdQ9PjUAHRIYaAcqKNqeYXmppOtaSoEOLMk8pk4pzmK3F5e982bNlfwk2Zycwd05Vgw4m/g1qs3sfZXR7izcaA7HC7y0NRuYPJA9uxcF0ef+vWiuWPv8y4+Y8DsHLAtdzb4QrsLo8/WezOS1LqBGnwzpwfX76dZ0Z2AY6XG83JL+HmxT9w7bwNDHsxm1mf7wxYAm9sJ6vZXWCxkZNfwq9HK7BYHU02DiHEqZNALc5JFquDXwsrcHkUFt90EcM7J7B8awE3L/6BO9/eQqXddcLHBxu956ENWjUOlycgwBq0ar7ff4wKu4t1ucUkb93EXa88jFbxcOjK6/hHtxtApQpI+tJo1A1mmGfnFhMTZsCk1zC7uhtX7WvX5hYz7ZNtTRYkT1aze29RJVfNXc+Af3/DxKU5HCqtapJxCCFOnQRqcc45VFrFhKU5DHjuG655ZQPf7z/GjE8D96N9CWD16ZMSRWGZ3d8ow1IVGMRyDpSy45AFNSrOP7SbpJtGY3C7WNmuFz89+iyKSh3wGuOzkimznjgQVtjcvDgqA6NO0+CS/No9RRSW20/lo/jdTlazu+aXDt9yuMyshWgemnWgnjFjBiqVKuAnLi7Of7+iKMyYMYOEhASCgoK45JJL2L59e8Bz2O12Jk6cSHR0NMHBwYwYMYKDBw8GXFNSUsKNN96I2WzGbDZz4403UlpaeibeojhF9e21ZrQK9wc/X1ON7okRPDq8I31SAhttzB/bnUeGd+TXo+VMv7wTuw6VERNmCHiNBdl5jO6RhPGXHSx6bwYaayXZSedz9+X3oTfqA667KTOZXm2i/DP0hpiDdCxcl1fnS0Gd93eS+/8oX83u+mTWyjSH4x22hBBnX7MO1ACdOnWioKDA/7N161b/ff/85z957rnnmDNnDt9//z1xcXFceumllJcfL+E4efJkPvroI5YtW0Z2djYVFRUMHz4ct/v4fuDo0aP58ccfWblyJStXruTHH3/kxhtvPKPvU/w+9e212mu1m8zJL2Hcou+5au56uidH8OldmfxUvSd88+IfuGruejqfF84/v9hJWkIYalT0qRHErA43z8z9gg5jryHCVk5V1+4snPIcdq0+YKbu2592exQKy+xkNTCDz0qJwqT3Lo3XnLnW/AIxd0xXFoy7kMgmSujylRStHax95VTr69AlLSyFaB6afda3VqsNmEX7KIrCCy+8wMMPP8zVV18NwOLFi4mNjWXJkiXcfvvtWCwW5s+fz5tvvsnAgQMBeOutt2jVqhWrV69m8ODB7Ny5k5UrV7Jx40Z69PCWaXzttdfo1asXu3fvpn379mfuzYqTqm+v1Rf8fO0mfUvgVocbp1th+mfHk7xMeg0vjc5g7ppc1uYWs37vMbolRnDnJW3xKAo5+aVM7BDM2AfuwnD0CI60Djxy69Pcf+WF2Ku7atUsCep9DQ8PfPAzb9/S01/NzCcrJYrHr+xM7tEK4Phyue/Mdu065H2qz003xXGp2iVFjToNy7cW1DnL7SMtLIVoHpp9oN6zZw8JCQkYDAZ69OjBU089RZs2bcjLy+Pw4cMMGjTIf63BYKBv376sX7+e22+/nc2bN+N0OgOuSUhIID09nfXr1zN48GA2bNiA2Wz2B2mAnj17YjabWb9+/QkDtd1ux24/vqdYVlbWyO9e1FbfXmvOgVL6pEST0Sq8Tvepmrd5g3RXYkIN/qXy8VnJvJ69l5z8UiZfmsJTfRNQ9+uH6bf97A+P48C8ZXzw+QG+eX0jz4zswgNDDVjtbmaO6ESV043N4SY6xECH+DDG+K9Jo8LmJsSoodLu4otth2gfGwYcr/09rLOt3jrka5v4uFTNkqIWq4OfD5TWG6SlGpoQzUezDtQ9evTgjTfeoF27dhw5coQnnniC3r17s337dg4fPgxAbGxswGNiY2PZv38/AIcPH0av1xMREVHnGt/jDx8+TExMTJ3XjomJ8V/TkFmzZjFz5sw//P7EqYsO0XNphxjax4f5S4MG67VclXEe+4oq61xfc1l89qgMDluq8FQ31oDjbSujQ/QMahmCa+BA2v62F3uLWPYu/oiglucBByiqcHDz4h8Cntuk1/DRnb15fMUOxvZuzcJ1eQHXDEyLYdLAVNLizBi0apbe2oN1vxYz9YOfeeWGbjz00bZ636Nvf7ipzzVLC0shzg3NOlAPHTrU/+fOnTvTq1cv2rZty+LFi+nZsycAKpUq4DGKotS5rbba19R3/e95ngcffJB77rnH//eysjJatWp1wseI02M26Zk2vCMPfrQ1YPY8MC2GKUOOr35Eh+j519/OJ85s9O9dG3Uaf0ERk17D7X3bEFp93vqfl7cn/IbrCN+7nQpTKFdfPo1fvilmfuvkBscyPiuZJ5bvYG1uMRv3HvM33rC7PESYdMSbg5j28daALO+slCieHtmFoxUnzu4+U/vD0sJSiOavWQfq2oKDg+ncuTN79uzhyiuvBLwz4vj4eP81hYWF/ll2XFwcDoeDkpKSgFl1YWEhvXv39l9z5MiROq919OjROrP12gwGAwaD4YTXiNNXsz51ZLCeRz6qWxp09a5CBnSIpU9qNDsLylhya0+KK+xsyS/hkWEdWLgujxt7tsajKOwpLGf+2O7kFVXicHvQeNx0m3oX5vXfYjMEccPIGfzSojVw4n7SvdtE+b8s+Bpv+Ezon8JP+SV1jmJl5xajQsWDl6Wd8D2fyf1h6bAlRPPW7LO+a7Lb7ezcuZP4+HiSk5OJi4tj1apV/vsdDgfffPONPwh369YNnU4XcE1BQQHbtm3zX9OrVy8sFgvfffed/5pNmzZhsVj814izp+aZ6avmrufXo5Wsza2/wtbjK3Ywc0Qn5ozO4Id9x5izJpcnVuykc0szOfmlJEaaMGjVKArMXZNLbJiRDblHWbDuVcwrl+PR6xl/5cP8mHB8Zu47glX7THZmShTqE6y41DwyVtva3CJcHoU+DWSJy/6wEKKmZj2jnjJlCpdffjmJiYkUFhbyxBNPUFZWxtixY1GpVEyePJmnnnqK1NRUUlNTeeqppzCZTIwePRoAs9nMzTffzL333ktUVBSRkZFMmTKFzp07+7PAO3TowJAhQ7j11luZN28eALfddhvDhw+XjO+zzHdmevP+Ev7v0lT6tY/x7znXVLN29rFKO+YgPbFhbv8s+GBJFeOzkgGFI2U2LmwdyQur9zDqokSiZzxM300rUNRqdj03j/UHWgQ8d81+0g8N7cD+Y1YMWjU5B0rxoNQZi09946ypwGJj2vBOzKxVdrSP7A8LIWpp1oH64MGDjBo1iqKiIlq0aEHPnj3ZuHEjSUlJANx///1UVVVx5513UlJSQo8ePfjvf/9LaGio/zmef/55tFot1157LVVVVQwYMIBFixah0RwvUPH2228zadIkf3b4iBEjmDNnzpl9s39RJ2q7WFThYPP+El4a3RWjTs0zK3fx4NDAfs6+/eclm7wJhN0TI3B7AgOlQasho1U4B0qqiDcHoVV7Z8LtX/8Pl236CICD/5qDc8SV8NK6OmP0LWtf3iUeo06DzelmcKdYf3Wz+pbFzUEnXroOMWj5YnsBGYkR/n1tg1ZNSosQ4qWTlRCihmYdqJctW3bC+1UqFTNmzGDGjBkNXmM0Gpk9ezazZ89u8JrIyEjeeuutPzpM8QedrO1imc3J+KxkCixVrKhuJanXqslKiQpoE7lk035G90hiyab9XNAqnBCDlorqWt8mvYboED1Hy+3oNGruWrKFN2++iLGbP6Ptau8KyswBt/JZZQrvGrVkpkSx+3A5z4zsQkyYgQqbm1Cjlgq7E4NGzfhF3wPH22TOH3sh/1LtDngPmSlRhFY/V31BPKv6/nnf7A04GnVxajSzq89oCyGET7MO1OLc19CM+fe0XQwz6shoFQ4c7yPtdLsZl5mMCm8jC9/9C9flkZEYwcJ1eYzPTKa4wk6flGi6tY7gP6t/YfKl7Sgss9M1MZwjL73OzOogvffOe/kl428U5Rbz69FKbu/TloQII9M/rduu8okrOxMdosfqcJMYYWLhTRdSXO7g/sFp3D3AzdEKO2FGHaFGLXe9vYXpIzoFjB28S9tPXpnOP1fuqhOkZclbCFEflaIoDW+0iVNSVlaG2WzGYrEQFhZ2todz1p1oxmxzuun/728afOz/7ulLdIieH/aXYHd5uPPtLQC8Mf4i7nhrM+/c1gtUChU2N1aHi5sX/8D8sd25efEPRIfoef/2XhwptxFi0DFsdjb3DW5Ht8QIgr5YQfrdN6PxuFk98Fp+eehxLu0Uj9OloCgKR8ptzM+uW4gEvMH6lqw2GHRqf2UzH18pzklLc+iaGM4FiREsyM5jfFYyvdtEYdCpCQ/S+48++b7AyJEoIcTJyIxaNImTzZgfGd7xhI8vtzlpGxNCy4ggCkptTOifQkarcMJNOkx6DVq1CqdHIdigoaS6y5NvX7qowsG+Y1Z+PFBK5/PM/uc0b8ymzeRb0XjcbMwaRsz8lwlxephRPXue0D+FoZ3iTtiucuaIdGZ8uq1ORve6Gkvxc77K5YGhaXSMD6N1lInzwoPqBGE5EiWE+L0kUIsmUV/zDJ9v9xTh8XgXcmpmbNtdHow6DVvySwirTsaKCzNi0KhJjDRhc3nQqL2z6qc+9xYamdA/hd5tovzP5WN1uP1B2qTXMNh6gNa33YDW7eRo/yGsm/IM8QXl/r1v8B7F6pNSf4cpH5vL3eCxq3W5xYzP9BZIOXCsijvf3sL/7ukrAVkIcVokUIsmUV/zjJqsDjeXdojhuosS6zSmyEqJ4vru3gpvlTYXLkXx7xnfN7gdOftLOD8xgnGZybg8Ci0jgrgsPZakSBNZKVFsyS8lNszAkTI7OwrK+GdHLUlj/obWWsn2tG4ceGYuFwSbgMD9Y6vDjclw4n8Slfa6dbFr8s3qDVq1nIcWQjSKc6rgiTh31Nc8oyZzkI4ZIzrV25giO7eYmZ9t51BpFfml1oDErt5to7i+RxI51W0rb39zMxV2Jw8O7cA/v9zFo5d3YuG47ph0GiJMOr79chOD/+/v6Cwl7G3TicI33yE0PBS7y1PvWefCMtsJ21UG6U/8T8agVZOZEkVhuV2Sw4QQjUICtWgS0SH6Or2PfXwzTZvTU+9+sEmv4bqLEtlXVEmwQRtwTZBOGxDcTXoNep2GCoebtjGh/Jhfwt6jlbz41R5aVpXy7vvT0R0uwNa+A1cPf5i7Pt2DzekmPEgX0Bva54EPfmba8E51grUv6/vbX44G9K6uqU9KNC1CDTw6vCP902LkPLQQolFI1ncjkqzvQIdKqxrszBQfHkROfglXzV1f53ET+qeQk1/CmB5JRAXrWZtb5N/DbtMimOU/F9A9MYKIYB1hRh1HymygUlFZfXYaYOf2fdzw4DjCc3dha5XE9mXLGfmptyiKSa9h2W092f6bheU19qh9okP0zBmdQahRR1mVy9+u0ulUuP3tzSyfmMX0T7cH7MFnpUQxrjrr2+pwB5wHF0KI0yF71KLJnKwzU0PL474e0rdmtaFFqIGc/BJ/K8plt/VixyELF7QKZ/shb//vFVsLePiyjhyr9GZ/a6squfGJuzDn7sIeHcO+JR9hTGoFeAO11eFm/KLvWTDuQpKjQ4DAveoOcWHV13hbVk4b1oGMxAj2Wa28PrY7KmDqkDQeGaai3ObC4fKwfm+xP0hD4HlwWf4WQpwOCdSiSZ3oGFJ0iJ5ZV3cmJtSA3eUhSKfBoyj+1pNuRWH6J9vIyS9lQv8UrrgggZmfbuP8GoVNwBtkNWrvvrfLWkXX+24j9KfNOMLC2bvkQ57NdfJgu8CKZkUVDq5/dSO3923Dw5d1QAEqbC60GhVlVU5UqJg7uitJUSYe/WQbD9boHd0nJZqxma2JCdETYdIz4Ln6z4Ofqb7SQog/N9mjFmdNpcPN5z8XcPPiH7jz7S3ctOh7Fq7LI9ykx6TXoFKp2JxfyoujMsjJL8HmdPurka3LLcbhPp4Qtv+YlVAtpN//D0K/XYPbZOKneW/j6tiJr3YdZfzC75l5RXrA3rPV4eb7vGNoNWrueHMzQXoNpVYnVU4PUaF63IrCtE/qnplem1vEwnV5mIP0J81uP1N9pYUQf14yoxZN6qQlRGu1rMzOLebx5dt5ZFgHXB4Ps0dlYNRp+Huv1tic3qDscHsw6TW0ijB596cBDZA38ga6bPkSh0bLwdffJje5E4lV3n3rAyVVXDdvA8+M7MIDQ9OosLkJMWow6bRc9+oG2seF8t8dR5jzVS6ZKVFkJEbQq83xGXhtvi8KJ8tuP5N9pYUQf04SqEWTOVEJ0SqHu8GCKGtzi5l6WRpGnZYZn2xjc34pL43uiknnLWjSIsTA+KxkKuxOb03vtpGEP/oQA7Z8iaJWM/Hy+9n8azAL+pjR1OgZXVTh4ObFPwS81oJxF9IhLoyxma2ZtDSHPinRPHp5R34rqcLtOXGeZaXdRXJ0MBenRgckzNV8r3KOWghxumTpWzSJk5UQtVQ5GnysSa9Br9Ewo3rZ2ddB66eDpWSmRGHQaujVJgqnU+GCxAie2vkp3T5YCEDuUy/wZfve/j3ownLbCY5TRREdouehYR1wexRmj8rg/MRwiivsjFv0PU73iXtKhxp1mE16nh7Zpc5RNGmyIYRoLDKjFk3iZCVEH7qsQ733+fpLHy23+/eGfR2yJi7N4eUxXbE5XWjUKkx6Dev/bwbjlj4HwKxBt3HRiGvps24fa3OLsDrc3LUkhxdHZeBRlIDM7syUKMZmJnP9qxuZPSqD29/c7L+vX/sWXJwaTc6B0gZbVdacLZ8su10IIU6HBGrRJE6WZKVSwYC0FnRIMAfU+VYUhYXr8hjdIwmonl1r1ZTbvHvNkcEGKu1Ogo1awj94xx+k/9N7FPMyRvDm0urAjDcwWx1uJi3NYdqwDkwdmsbBkir0GjU5B0qZtDSHbonh5BwoDRhbeJCe2aMyKK50cFXGecyodWa6vtmyNNkQQjQVCdSiSZwsycpidXD/kA48vnx7QJ3vJbf0IDu3mJsykzHpNbwyphstQgw4XB7GZyVTZnNic7qxf/QJ6VPvAuDw32/l+bgRAP7APD4rmfGZydhdHpKiTARpNTz1+U5W7yr0v1af1Ghuzkrmp4OlzB/bHbvLQ4RJh1GnDgi8c2S2LIQ4i6QyWSOSymTHWawOJi7NqTfJamCHGCYNSOXZlbvYnF8a0D0rMljP9a9uZEL/FBLMRtq0CCa/2Irbo5AYFYylyklczkY6jb8Og9tJwYi/cXT2PEbM3dDgWFZMzEJBwaDT4HB6qHS4CTVqsdqdlNncvJ69N2B5Oysliqeu6kxiVHCTfDZCCHEqJJlMNImGkqwu7RDDI8M6Um5zBZyRvnnxD0x57yeCDd7M7gXZeXRuaSbUqKVFqDdga9Uqonf9TJd/3IjB7eTXXv3Z+fjzoPY2wqhPZkpUdSEVKK5wUOV0s/7XIka+vJ4Qo44FtYI0eI+IPfTRVv/RLyGEOJtk6Vs0mfqSrEKMWqa8+yOjeiQxPis5oMHG+Kxkth60+BO4fiuxkRRp4vXsveTkl/LexWEk33I9Wmsl6xO7cOcld/NmVAhf7SpkQr8UgDoJYxP7p5J/zIpbUfzVzzqfZ2bu6K643EqDvaWzc4spqXQQG2Zs+g9KCCFOQAK1aHIqFZgMWpweD8UV3mzum7La+Gt6+2S0CmdidTKYQavmvAgjisobfFtajpA4ahymslKs53dlwS3/pPSgjf/tLGTbwVK6nBfOsM7x/n1pg1ZNYZmNBLORGZ9t56tdR/2v42ugcbC06oTjLrO5Tni/EEKcCbL0LRqdxerg18IKfth3jB0F5VTYXTz9xU6unrueogoHJr2mTiEQX3a3LxlsTI8kth20UF7lpEVFCW8te4TQoiPY2qXx1b8X8dB1PeiTGs2r3+7l+h5JvLlxH4csx5eqjToNPZKjeOaLnQFBGryz5YXr8og3n3i2HGaU77FCiLNP/p9INKra1chMeg3ThnVgyqD2qKqrhL13ey+eX7Wbuy9tB3ibcyy66UI0au/3RqvDjUql4vEVO3n/ug688e40WpcW4GiVyI6F77FsRwVbvsnm9r5tuG9wezRquH9wGh5FobjSgUqlIr+4kuhQPSu2Hal3nOtyi3G4PA2ek85KiSIiWDK7hRBnnwRq0WhqVyPzFS9Zsmk/LcKM/v3o+WO7s3rXUdJbhjMwrQWTL22HRqUiJ7+EzJQocvJLMek0BDlsnHfD3zAf3YczJpYvn3+TZTsqWJdbjEmvoVOCmWdW7goItH1Sopg2vBNbfyvlt2MnXto+WmHnphoduHx8Wd+yPy2EaA4kUItGU7samS9ZLKO6LaUvGPo6Xr367V6W3dYTjUrNMauDJ1bs5MVRGQzrbAO7jVXZ/8H84w+4zWZ+feN9TImprKuu1V07Ec1nbW4xM5dvJyMxgpOdO9Rr1EysceY6zKgj1KglIlgvQVoI0WzIHrVoNL5qZCa9hgn9UxjcKZYxPZIY0imOjMQITHrv0SuD9vgSt8XqxIOCpcrp359uF20i+f/uIHLd19j1RnJefpvSlA7+AA/4W13WZ111K8ycA6X0OcGxrZwDpVgdbuZ8lcvi9ftoFxtCWnyYBGkhRLMiM2rRaMKMOv9y98J1eQEZ3ZkpUbw4KoOpH/yMWuWtCrZ2TxGxZiMWq/N48La7iL3vbmL/uxyPXs/NVz7M6PSutDRoqHQc/15ZM2jXZtJriAzW0zUxgr6pLbizn4d1vxazIDsPq8Ptz/qetDQHkAYaQojmTQK1aDTRIXpmjujEonqWpNflFqMGPvhHbzbvO8adl7TFoyi4PAomg4Zv9hwls20kfV97llbff4SiVrPn+VfJzo/mpuogfqTM5k/+8gX22nxfFJ777+6AM9J9UqP5bGIWKiDYoKXS7mLJLT2kJKgQotmTpW/RaMwmPV1amsluYEl6bW4xv5VWkRoXys2LfyAjMYLyKidOt4cdhyw8vv1Tbvv+IwB+ffJ57COuACDnQCm7Cspo2yKYCf1S/MvW9VUj8+1d1y5ksnZPETM+3Y5GrcKoVdOmRQgXJEbQNiZEgrQQolmTQC0ahcXqYH9xJQdLTpxp7fIoFJbZ/XvDJoOW0konU3/9ijb/eRqAHydPw/n3sWjVKjJToliQnUeLUO++8YFjVh4YksaQTrHMHJFep9d07zb1H7cCb7DOLaxg4tIcDp2k2IkQQjQXEqjFaTtUWsWEpTkUV9gxB524a1akSU9MmMH/98IyG/p3ltLuiQcBOHL3fRTfdhe3vbkZk07DhH4pZCSGc9eSLWzMO0ZafBgqFVQ5PDhcLm7KbM2CcRfy7u29WD4xC10DS+I+dpeHb/cUMfWDn7FYHaf/5oUQoonJHrX43SxWB0UVDspsTsKCdERXFwR54IOf2by/hLAgPVv2H6NPSjRrc+t2zeqTGo1Bp+b7vGP+vebPnnqV55fOBGDLiDGU3HYPLo/C9BGdeObLXdzVL4WJ/VMJNmiosHkLoVTaXTicHsYu/A6rw02f1Gi6JUXwwuo9zB/b/YTvwbe3/e2eIooqHLLsLYRo9iRQi9/lUGkVj36yjbT4MDJahVNgsVFo0nFeeBD7iiqZPSqDmZ9uo3tyJHf2a4tOq6JjgtnfvjLcpCMiSI9OpfKfl07bvYX7lj2Byu2m6MprKHniWVCpiA8z8MzKXeTkl3JN91YsyM4L2PfukxrNTZmtAW82+bThHfmtpAqTXuPfu65v+du3t+1TXn2cTAghmjMJ1OKkLFYHj36yjesvSqxz7GpAWgsWjb+IwxYba3OLGZeZzMSlObw+9kL+/eWuOke0Hr8iHYDFL77PwjenonU52HJ+FqUzn8PugR2/lTIwLZZ1ucVM6J/C/Ox6iprsKQIFlt3Wk//uOMKhkipez97L+KxkFmTnMX9sd9SoAmb1mSlR3FTjSBZAqPHEy/RCCNEcSKAWJ1VU4SAtPqzeSmAdEsw8+sk2buiZBHj3gK+/KJFnv/TOiCf0T/HPqo06DZvyjvFIG4W/zX4UbWUFO9p3ZdTAe7j1cCW92ngTx7JSvAlitbtr1bQ2t4hx5a3JyS8BvMe/pg5NI6NVON/tO0a31hHcO7g9xRV2wJs5PmlpDlaHG/Cena7dGEQIIZojCdTipMpsznqDpkmvYVDHWDJahRNXXc3LqNOQ0SqcBdl59RY+uSLcwb9fnIC29BiVXS7gmv5TsWv1LMjOY1DHWLomhvuD6YmKmvjUnCU7XQqto4KJCtb7z0fHhBqY+sHPfFujtKkUOBFCnEskUIuTCjPqKKjRQhKOFxZ5duUuNueX8s5tvRiYFkOLED2F5fZ6a3FHV5Yw+dX70ZYUUJzYlrzX36Hygz2At5zo+EXf8/rYC9l5qIzMlKgGi5r4mIN0/H3Bd/7Abg7S0TYmJPAaE8welUFRhYNym1MKnAghzjlyPEucVHSInvBax67GZyWzZNN+zk+M4I3xF1FhdzLt8o78Z/UvxIQZ6JoYERCkw2wVvPHuoySXFHAwLIadi95DHx8T8JxFFQ5uWfw98WYjj1+RTmG5vd6iJuDdc/76l6O/aynbbNLTNkYKnAghzk0SqMVJmU16EiNNDEhr4U3wGtudi1OimTq0Az/ml/C3VzZw8+IfsDrcrN51lG9/OUpwdQMOgCCHjQXvz6RjYR5Hg8MZc/0TlLeIw6TXBARik17D0yO78Fr2XobPzqaw3Mb04Z3okxJY1CSrOjFsQXYeIEvZQog/N1n6Fr+Ly+3hoWEdefSTbcz5KpfJA1P5Yd8xsqt7Q88d3ZVymwsAp1vB7fE2mdS5nbzy8VN0/20nFkMwN177OPsjEogPC2LL/hIm9EsBvMlgtZfLn1+1h3nfeLO5/3FJW4w6DeYgHSFGqdUthPjraNYz6lmzZnHhhRcSGhpKTEwMV155Jbt37w64Zty4cahUqoCfnj17Blxjt9uZOHEi0dHRBAcHM2LECA4ePBhwTUlJCTfeeCNmsxmz2cyNN95IaWlpU7/Fc4bDo/DoJ9tYVx2YB3aI9QfpOaMziAkzEKT3/jp1aRnO+r3FZLUJ5/nP/k3fvC1YdQZuumYGu2KS6ZMSTYhRw4zPdmBzehjWOZ75Y7szqGNsnaxyX6nR0a9v8u9Bx4YZpVa3EOIvo1kH6m+++Ya77rqLjRs3smrVKlwuF4MGDaKysjLguiFDhlBQUOD/+fzzzwPunzx5Mh999BHLli0jOzubiooKhg8fjtvt9l8zevRofvzxR1auXMnKlSv58ccfufHGG8/I+2zOLFYHvxZW4PYo/iB6V7+2VNq9s+fbLm7Dwuw8XB4FFBX901pg0mtYsHYvz66ay/Dd2Xi0OrbNWcSEh//OrKvSmXZ5R/YWVWJ1uLlryRYOVSeqVdrdDY4DpECJEOKvqVkvfa9cuTLg7wsXLiQmJobNmzdz8cUX+283GAzExcXV+xwWi4X58+fz5ptvMnDgQADeeustWrVqxerVqxk8eDA7d+5k5cqVbNy4kR49egDw2muv0atXL3bv3k379u2b6B02Xxarg8NlNg6WVKFSqQgxeH9VTHoNgzvFcaTMez754tQWvLVxP3anh0q7i/sGtcfh8jB1zQLi172PR6XmzuFTWLk3FPZ+T5+UKHq1jSZI493D9s2YgZOW/5QCJUKIv6JmPaOuzWKxABAZGRlw+9dff01MTAzt2rXj1ltvpbCw0H/f5s2bcTqdDBo0yH9bQkIC6enprF+/HoANGzZgNpv9QRqgZ8+emM1m/zV/JQePWZmwZAuDX1jLzYt/4P73fyLU6A3U47OSqXK62bC3mGu7n4eiKCwcdxE6jfdXSQFazvsPf1/3PgAPDJnAyvaZ/udem1vMIx9vxRysq5PR3VDrSpACJUKIv65zJlArisI999xDVlYW6enp/tuHDh3K22+/zVdffcW///1vvv/+e/r374/d7p3xHT58GL1eT0RERMDzxcbGcvjwYf81MTGBR4UAYmJi/NfUx263U1ZWFvBzrvutxMoDH/7s7+ds0mt48+aLUKugT0oUXRMjqLS7+STnNyb1SyXcpMftUfj6l0LcikLooteJesrbZOOJfuN5r8ugOq+RnVuMRqXipszkgMC8IDuPif1S6mR595GsbiHEX1izXvquacKECfz8889kZ2cH3H7dddf5/5yenk737t1JSkpixYoVXH311Q0+n6IoqFQq/99r/rmha2qbNWsWM2fOPJW30ax5e0pbAxK67urXliCtBjcKkwakoFapCdJrmTGiE3a3gsPtweZ089bG/XwSeYCER+4D4OCd9/B6aP8GX6vC7iLebGDKoPZMHaqivMpFVIieCJOeJ65Kp9LuwupwYw7SERNqkCAthPjLOidm1BMnTuTTTz9lzZo1tGzZ8oTXxsfHk5SUxJ493opXcXFxOBwOSkpKAq4rLCwkNjbWf82RI0fqPNfRo0f919TnwQcfxGKx+H8OHDhwqm+tWSmqcFBaFZiwNbBDLN/vO8asFTsx6rSYDBrUKoW4MCM2p5uiCjsmg4aHlL3ET7odlaKQc/loyh9+9ISvFWrUUWn34PIo6DRqWkcH0z4ujJgwI0lRwXRMMNO9dSSpsaESpIUQf2nNOlArisKECRP48MMP+eqrr0hOTj7pY4qLizlw4ADx8fEAdOvWDZ1Ox6pVq/zXFBQUsG3bNnr37g1Ar169sFgsfPfdd/5rNm3ahMVi8V9TH4PBQFhYWMDPuazM5sSk0/iLmswd0xWPAm1iQkhvGU6F3YVapeI//9tDhd1FldNNhElP8IZ1XPHkZNRuF6VXXcsN54/BoNWcsKqYRg1hQVpcTjdDXljL1A9+5lBp1Rl+x0II0fypFEVRzvYgGnLnnXeyZMkSPvnkk4DMa7PZTFBQEBUVFcyYMYORI0cSHx/Pvn37eOihh8jPz2fnzp2EhoYC8I9//IPly5ezaNEiIiMjmTJlCsXFxWzevBlNdfbx0KFDOXToEPPmzQPgtttuIykpic8+++x3j7esrAyz2YzFYjkng/YvR8qwOT3sKigjLd47fpvTg0YFRr2a8io3oUYNBWV24sOMBOk07P7iawbeNQpdZQWrUi4id+5C1u8v5++9W6NSUafet6/dpE6tIt5s4NOfD/uzvi9OjWb2qAyZQQshRA3NOlA3tD+8cOFCxo0bR1VVFVdeeSU5OTmUlpYSHx9Pv379ePzxx2nVqpX/epvNxn333ceSJUuoqqpiwIABzJ07N+CaY8eOMWnSJD799FMARowYwZw5cwgPD//d4z2XA7XF6mD3kXJ8vw6/Hq0kNsyI3eWhfWwolQ4XdqeLUKOex1fsYHxmMq2PHiBi6AAirWVU9swk65L7eG5sL4w6NR4Fbn3jB8ZnJfvbXBq0anIOlLIgO4/37uiFoihcO2+jv143wP/u6VunsYYQQvyVNetAfa45lwP13qMVeBTI2X+MlpEm5qzJZV1uMe1iQpg9OgOH29tP2ulSuOzFtaS7SvhgyQMYCg6xIyEVVv+Pn8oVlv98iJz8UmaPymDRun2szS2q81p9UqN5ZFgHDltsfL+/hAXZef5g/fGdvbkgMaLOY4QQ4q+qWe9RizNHAQrLbLSPD2POmlxy8kt5aFh75o/tzm8lVZRUOnG6FYor7URXlvDiAm+Q/jWqFUff+5h/bSog/bww1uUWY3W4mbg0h7GZrevsU2elRDG2d2uumruesQu/Jye/hBdHZWCqbuIhRU2EECLQOXM8SzQdi9XBjE+2MapHEiFGLTn5pbw0uiuto0yU2VygUuHyKJRXOVFKSnnj3UdpU3IIZ8tEfp77Du/vKGNdbjHXdD++lWB1uJn6wc88M7ILU4emUWFzE27Scai0igc++Nk/g/btX4/PSubnA6VS1EQIIWqRQC04XGZjbW4x4zKTsTndjM9K5mi5jTizkUqHG0VRKK6wkxCpJ3T89ZxX3a5y96tLSemSxrrZ6wDQa9SY9BrGZyXTPTGC+HAjTyzf4S+eAt5ksqdHdmHS0pyAYH3XJSmMuShREsmEEKIWCdR/cRarg4MlVUSH6IkLM6DVqMloFU6LUANlVU5Cg3RY7S46RRtJuvUGgrZvwRVm5rnJLzKwdVsOlhw/UrXtkIX5Y7szZ403izsnu6RON6yaM2hftjeAUachPjzoDLxjIYQ4t8ge9V/ckTI7eo2aZbf1pNzmwlLlxJdrbzbpUANul4voO28l6H+r8ASZ+PvVjxKVeREtQg2Ya+wpKwrMrU5Cy2gVXidI+/jur8kcJHvTQghRHwnUf3GWKifnhRv5Pu8Ys9fkYnd5SIgIwuHyEKzTUFblIGPWQ8Su/BSPVseWF15nfWx75nyVy/WvbiTEqCWrOmGs83lm/zK33eU54evWvF8abgghRMNk6fsvxmJ1UFThoMzmJCxIR4sQPTa3h1hzEPnFVoL1GoJ0GjQqFVaXm5TnnyRyyWIUtZrt/3wJS5/+sPcHwJswNn7R97w+9kJU7A4Ivgbtib8D+u6/WBpuCCHECUmg/gvxZVyv3XP8bPOsqzsTE2rAqFOz4KYLUaOisro8aKt5LxI55wUAvnvgKX7tPQjKbGSmRLEutxiTXsP1FyVSWungnkHtMOo0/uf1taysb/m7T2o0iZEm/ndPX6JD9BKkhRDiBCRQ/0VYrA5/kDbpNdzetw392seg06gpKrcTbw7C4XLjQkWlw41pwWvEzPJ2Bjv08GPcpD6fl8xBmPRqJvRLwaBVM7pHEgvX5fmTwib0TyErJYrs3GIWZOfx4qgMgIBg7ZtBS+KYEEL8PlKZrBE158pkvxZWMOC5bzDpNbw0uitGnZo5a3K5oWcSKTEhuNwKRq2aogoHCV98TMKEW1ApCnm33c2R+6dx/Wsb/QF+QFoMIQYt0z7eFnD0yqTX8OKoDBatyyO7esY9PiuZ3m2iMOjUhAfpZQYthBCnSGbUfxFlNm/7yvFZyRRYqlixtYCc/FKmDk3D5VaosLlQBWmJXbuahLu97SoPjxnPB1ffwcDqJW2rw83zq/bw/Ko9zB/bPSBI++6ftDSH8VnJPDysIxU2F+Em6ScthBCnQ7K+/wIsVgdBOg3RIXpGnB/PBa0iWJdbzPisZHYeKqs+L61F+eZbWt42FpXLRdGIkRQ+9Sw7C8rQqFX0SYkOeM6GsrqtDjdzvsplX3ElkcF66ScthBCnSQL1n9zBY1Ye+vBnjFo1H97RG5db4UiZDYCMVuGUVjkIC9IRuv1n2t06BrXdRsXAweQ+M5vxb2xm4oB2vPJ1LlOGtKdP6vFgfbKsbrNRJ0euhBCiEUig/hP7rcTKk5/vYPKg9tjdbqxOF8/9dzfx4UbAOyvulxaLef+vxF53FZryMna3z+DHf71KTGQoaXGhjH5tI8kxIZRUOLjn0nZ8NjGTt2/pQcuIoIDAXVNWShQRwRKkhRCiMUig/pOyWB3sL7byt26t+HF/CVq1BrtLoXOrcIJ1GrJSojBo1Sj782kxcgTa4iJK0tIpW/YhC3IOs3JHAY9dkU63pAjmfJXLuEXfc9Xc9Tz9xS6qnG5e/N8eHr8ivU53rD4p0dw3OI0b52+iqMLR6O/p18IKcvJL+PVoBRZr4z6/EEI0R5L13YiaU9b3r4UV7D5STqtIE4qioFapsDrcGHVq9Fo1B0uqsOw7yGV3XUdQ3q/YU9rRf8RMnrqtP4dKq4gJM+LyKMSbjXg84PJ4KLO52JJfws5DFmaOSKfEaufLHYVktArH7vJg0KrJOVDq7y/dmL2l6zsDfnFqNE+P7EKCHPUSQvyJSdb3n5SlykFUkBa70xuc7S43oUYtbo+H8ioXDy9cy2cfPkpQ3q/Y4lvy0/x3+O3z3/jH21sYn5VMTJgRt0fhaLmdnAOl9GvXglCjlv5pMQA43B5Mel1AY43aGqu3dM0z4DV9u6eIqR/8zOxRGZKwJoT405JA/SdksToIM2nRqQ0cttjQqNVEmrQ43B5UajWhHhuzl04n5uBO3NEtWPWfN4k6ryXwmz9ru7auiREszM6jW2vvUvjAtBiSo4O5ODWab2sFUGjc+t1FFY46Qdrn2z1FFFU4JFALIf60ZI/6T+ZQaRUPfbgVo1rDf1btJsFcnTjm9qDXqMHpoOWtN3LRwR2UGYLZtehd2l/cjYggnb+5Rm2ZKVFsyS9hbW4Rnc8zA97Zstmk5+mRXbi4VlJZY9fv9p0Bb0j5Se4XQohzmcyo/0QsVgdPLt/BvYPbUeF0c1NWG46U2wAVHgVCtCpaTrydkDWrcRuDeP7uf3PV+Rcw9rWNPH/NBTxxZTrTPtkeMHvtkxrN2N6tmbQ0B/BmitecLSeEBzF7VAZFFQ7KbU5Cq49lNeYMN+wkS+iNtcQuhBDNkQTqP5GjFXYm9E9Bp1ZzrNJOsEGLVuMN0igKUVPuJuzTD/Fodbx45yyC+1+CSa+hfVwoNy78zl/yc1zv1thdHsxBOmLCDFwxZx1WhxuA8CBdndmy2dS0ZUGjQ/RnZIldCCGaI1n6/hPRqFSo1GB1uokM1lNuc6JChVGr4bxnHiN22RsoKhUPXX0/l903nh/zS1ixtYAJ/VLITIny70/fvPgH3t60H7VKxedbC/xBuk9qNG1jQs54Q40ztcQuhBDNkRzPakRn83jW4dIqSqscVNhdgIqoYD02lweVCmJeeoHIJ6YDcODpF+hTkkJ0iJ5nRnYhzmzAqNPyXd4xYkIN/mNWhWU24sxB3LVkC1aHu1l0vfL10m6qJXYhhGiOJFA3orMVqH8rrsThUah0uNBq1CgKKIpCkE5D0MLXiXvg/wA4OHU6jnumMP3TwH3oml2udBo1IUYtBo0au8dDpc1FhEm6XgkhxNkie9TnuCMlVgAcHjcGrRq3B1RAhcON+dMPiJ16DwD5t99N5aT/Y+eBUmaO6BQQrK0ONz8dKOWari0Z+uJa/1I3wP/u6UvbmJAz/r6EEEJ4SaA+hx0ts4FHwelRMGm1uDwKLo8HFIXY7K9ImOhtV/nWBUN5qsVgbt9xhH7tY7A6XDx0WQc8ioLV7ibEqCVYr6HUag8I0pKoJYQQZ58sfTeiM7n0XVBipczuotzmItSoJUinodLhotLuJmHr98RdexVqWxWWK0aS0e7veNSaep9n2a09ySuqQKNW0S4ulCtfWg/QLPakhRBCyIz6nHTwmJUHP/yZtbnF/tv6pEYz/fJORP7yE3FjrkVtq8I6cBAFL75C5n9zA66t+Zg4s4Hxi79n9qgMDFoNH9/ZWxK1hBCiGZHjWecYi9VRJ0gDrN1TxLzXPidpzEjU5WXYevbmwCuL0BgNPHhZB/qkBB5t6pMazWNXdOLzbd7jV3aXhyqHmwsSI2gbEyJBWgghmgmZUZ9DLFYHh8ts7Dxczvyx3YkJM1Bh8zbbKN2VS8rIoWgsRVR26sJvC9/CFB7GrC92cm33RKaP6Ijd6aHS4SZIr+aXw+W4FYU5X/0KgEGrxhwkFb6EEKK5kT3qRtSUe9SHSqv46UAJCWYjJoOOxz7b7p9VR1WWsvy9h4g/ko+tbQrbl37Gr5i4ICkMg0bHwx9tJbvGDDwrJYrHr+zM3+dv4kBJFZkpUQzvksBl6XEykxZCiGZGZtTnAIvVwYurf2FSvxRcwMMfbfUH6VB7JYvfm078kXyKImOxf7ic8PjzcOcd40CxHQU700d0wulSqLA7CTHo0GvV/iDdJyWau/qn0DrSJEFaCCGaIQnU54CiSjsTqoO01en2B2mj08br7z9G+pFfKTKZuWbkY7wcE0+IWkWP5EiqnG6sdjcej4JOrVDl8FBuq0KjUvHgZR0ID9LRKiKIsCCdBGkhhGimJFCfAzQqFW5FAVSUVbkA0LmdzP34aXoc3E6Z3sTYax8jL/I8yqtcKAqUVzkJDdIRatQSrtMwpcYsHOT4lRBCnCskUDdzFquDnQVlpMaGMv3TbTw0tANqj5t/r3ie/nt/oEprYPw109ke2xYAk0HD0P+sBaBPShRPXNUZq8fD+Kxk7uyXgkGrwRwkx6+EEOJcIYG6mSuqcNAxwcxDH21lXW4xWjW8tmkhA3Z+i1Ot4R9XPsgPLTsBkJkShUmn4d3bexJq1BGm15CdW8TMFTvpnhRxSjNoXwOMMpuTsCAd0cES2IUQ4myQQN3MlVY5MOm1rKtetq64934GfPsxHpWK/xt+L1+37Q54g/TEfqno1SpahBgIVqsotjtJjQvjswlZpzSDPlRaxQMf/BzQuOPi1GieHtmFBFkqF0KIM0oCdTNn0mspr3ICcPum9+n29SIAfnjgKa76+01cVqMtZUK4kXKnE5NWx4+Hy+h8XjgdTjGwWqyOOkEa4Ns9RUz94Gdmj8qQmbUQQpxBEqibORUQGqRj1I8rebA6SP9rwHjodxUZ1dckhAeRHB3MwWNWosMMqIEeyVF/KKAWVTjqBGmfb/cUUVThkEAthBBnkATqZk6lgujlH/Hkf18C4OUef2NO96vhq1zAW7xk+ohOfJjzG7sKynjyqs7Ehhn/8OuV2ZwnvL/8JPcLIYRoXBKom7mY9V8TfuetqBSF/118Jc/0HOu/Lyslikcv78SoVzfSIT6Mx69IP60gDRBmPHEZ0dCT3C+EEKJxSaCuZe7cuTz77LMUFBTQqVMnXnjhBfr06XN2BpOdTcQN14PTifXKkQT/cy6fmfRU2NyEBWkJ0moosdp5+5Ye/HKknAjT6QfR6BA9F6dG8209y9/Sn1oIIc486Z5VwzvvvMPkyZN5+OGHycnJoU+fPgwdOpT8/PwzP5gff4Thw6GqCoYO5djc14gKM6LXqNGqVd5rVBARbGDhujy6J0U2yt6x2aTn6ZFduDg1sNuWr0CK7E8LIcSZJU05aujRowddu3bl5Zdf9t/WoUMHrrzySmbNmnXSxzdaU45ffoGsLDh6FPr0gZUrwWTiSJmNUqsDRQEFsDpchAfpm6R4ie8cdbnNKf2phRDiLJKl72oOh4PNmzczderUgNsHDRrE+vXr632M3W7Hbrf7/15WVnb6AzlwAAYO9AbpjAz47DMwmQCIDTOe9h7072U2SWAWQojmQJa+qxUVFeF2u4mNjQ24PTY2lsOHD9f7mFmzZmE2m/0/rVq1Or1BHD0Kl17qDdbt2nln0mbz6T2nEEKIc5oE6lpUKlXA3xVFqXObz4MPPojFYvH/HDhw4I+/sMUCQ4bA7t3QqhWsWgUxMX/8+YQQQvwpyNJ3tejoaDQaTZ3Zc2FhYZ1Zto/BYMBgMJz+i1dVwYgRsGULtGjhDdKJiaf/vEIIIc55MqOuptfr6datG6tWrQq4fdWqVfTu3bvpXtjphGuugW+/hbAw+PJLaN++6V5PCCHEOUVm1DXcc8893HjjjXTv3p1evXrx6quvkp+fzx133NE0L+h2w9ixsGIFGI2wfLk3gUwIIYSoJoG6huuuu47i4mIee+wxCgoKSE9P5/PPPycpKanxX0xRYMIEWLoUtFr44APvUSwhhBCiBjlH3YhO6Rz1Qw/BrFneYt5LlsD115+ZQQohhDinyB712fDss94gDfDyyxKkhRBCNEgC9Zn2+utw//3eP8+aBbfffnbHI4QQolmTQH0mvfce3Hab98/33w+1qqAJIYQQtUmgPlNWroQxY7xJZLfdBk8/fbZHJIQQ4hwggfpMWLcOrr7ae2b6uutg7lxvEpkQQghxEhKom9qPP8KwYf52lbzxBmg0Z3tUQgghzhESqJvSL7/A4MHeOt6ZmfD++6CXjlRCCCF+PwnUTeXgQW8nrMJCuOACb9Wx6naVQgghxO8lgbopFBV5g3R+PqSmehPJwsPP9qiEEEKcgyRQN4WRI2HXLmjZElavhga6bwkhhBAnI4G6Kfz4I0RHS7tKIYQQp00CdVMIDfW2q0xLO9sjEUIIcY6T7lmNyNffpGzhQkhJgbKyszwiIYQQzVloaCiqk9TVkO5ZjejgwYO0atXqbA9DCCHEOeL3dFuUQN2IPB4Phw4d+l3fkE6krKyMVq1aceDAgZO3y2yGZPxnl4z/7JLxn13n2vh/T7yQpe9GpFaradmyZaM9X1hY2Dnxi9YQGf/ZJeM/u2T8Z9e5Pv6aJJlMCCGEaMYkUAshhBDNmATqZshgMDB9+nQMBsPZHsofIuM/u2T8Z5eM/+w618dfH0kmE0IIIZoxmVELIYQQzZgEaiGEEKIZk0AthBBCNGMSqJuZuXPnkpycjNFopFu3bqxdu/aMj2HWrFlceOGFhIaGEhMTw5VXXsnu3bsDrhk3bhwqlSrgp2fPngHX2O12Jk6cSHR0NMHBwYwYMYKDBw8GXFNSUsKNN96I2WzGbDZz4403UlpaelrjnzFjRp2xxcXF+e9XFIUZM2aQkJBAUFAQl1xyCdu3b28WYwdo3bp1nfGrVCruuusuoPl99t9++y2XX345CQkJqFQqPv7444D7z+TnnZ+fz+WXX05wcDDR0dFMmjQJh8Pxh8fvdDp54IEH6Ny5M8HBwSQkJPD3v/+dQ4cOBTzHJZdcUue/yfXXX3/Wxw9n9velKcZf378FlUrFs88+67/mbH7+Z4Qimo1ly5YpOp1Oee2115QdO3Yod999txIcHKzs37//jI5j8ODBysKFC5Vt27YpP/74ozJs2DAlMTFRqaio8F8zduxYZciQIUpBQYH/p7i4OOB57rjjDuW8885TVq1apWzZskXp16+fcv755ysul8t/zZAhQ5T09HRl/fr1yvr165X09HRl+PDhpzX+6dOnK506dQoYW2Fhof/+p59+WgkNDVU++OADZevWrcp1112nxMfHK2VlZWd97IqiKIWFhQFjX7VqlQIoa9asURSl+X32n3/+ufLwww8rH3zwgQIoH330UcD9Z+rzdrlcSnp6utKvXz9ly5YtyqpVq5SEhARlwoQJf3j8paWlysCBA5V33nlH2bVrl7JhwwalR48eSrdu3QKeo2/fvsqtt94a8N+ktLQ04JqzMX5FOXO/L001/prjLigoUBYsWKCoVCrl119/bRaf/5kggboZueiii5Q77rgj4La0tDRl6tSpZ2lEXoWFhQqgfPPNN/7bxo4dq1xxxRUNPqa0tFTR6XTKsmXL/Lf99ttvilqtVlauXKkoiqLs2LFDAZSNGzf6r9mwYYMCKLt27frD450+fbpy/vnn13ufx+NR4uLilKefftp/m81mU8xms/LKK6+c9bHX5+6771batm2reDweRVGa92df+/9oz+Tn/fnnnytqtVr57bff/NcsXbpUMRgMisVi+UPjr893332nAAFfoPv27avcfffdDT7mbI7/TP2+nKnP/4orrlD69+8fcFtz+fybiix9NxMOh4PNmzczaNCggNsHDRrE+vXrz9KovCwWCwCRkZEBt3/99dfExMTQrl07br31VgoLC/33bd68GafTGfB+EhISSE9P97+fDRs2YDab6dGjh/+anj17YjabT/s979mzh4SEBJKTk7n++uvZu3cvAHl5eRw+fDhgXAaDgb59+/pf82yPvSaHw8Fbb73F+PHjA+oBN+fPvqYz+Xlv2LCB9PR0EhIS/NcMHjwYu93O5s2bG+09WSwWVCoV4eHhAbe//fbbREdH06lTJ6ZMmUJ5ebn/vrM9/jPx+3ImPv8jR46wYsUKbr755jr3NefP/3RJre9moqioCLfbTWxsbMDtsbGxHD58+CyNyru/eM8995CVlUV6err/9qFDh3LNNdeQlJREXl4e06ZNo3///mzevBmDwcDhw4fR6/VEREQEPF/N93P48GFiYmLqvGZMTMxpvecePXrwxhtv0K5dO44cOcITTzxB79692b59u/956/uc9+/f7x/X2Rp7bR9//DGlpaWMGzfOf1tz/uxrO5Of9+HDh+u8TkREBHq9vtHek81mY+rUqYwePTqgjvSYMWNITk4mLi6Obdu28eCDD/LTTz+xatWqsz7+M/X7ciY+/8WLFxMaGsrVV18dcHtz/vwbgwTqZqZ2FxVFUU6rE9fpmjBhAj///DPZ2dkBt1933XX+P6enp9O9e3eSkpJYsWJFnX9ENdV+P/W9t9N9z0OHDvX/uXPnzvTq1Yu2bduyePFifxLNH/mcz8TYa5s/fz5Dhw4N+JbfnD/7hpypz7sp35PT6eT666/H4/Ewd+7cgPtuvfVW/5/T09NJTU2le/fubNmyha5du57V8Z/J35em/p1asGABY8aMwWg0BtzenD//xiBL381EdHQ0Go2mzje3wsLCOt/yzpSJEyfy6aefsmbNmpN2BYuPjycpKYk9e/YAEBcXh8PhoKSkJOC6mu8nLi6OI0eO1Hmuo0ePNup7Dg4OpnPnzuzZs8ef/X2iz7m5jH3//v2sXr2aW2655YTXNefP/kx+3nFxcXVep6SkBKfTedrvyel0cu2115KXl8eqVatO2pWpa9eu6HS6gP8mZ3P8NTXV70tTj3/t2rXs3r37pP8eoHl//n+EBOpmQq/X061bN/9Sjc+qVavo3bv3GR2LoihMmDCBDz/8kK+++ork5OSTPqa4uJgDBw4QHx8PQLdu3dDpdAHvp6CggG3btvnfT69evbBYLHz33Xf+azZt2oTFYmnU92y329m5cyfx8fH+5bGa43I4HHzzzTf+12wuY1+4cCExMTEMGzbshNc158/+TH7evXr1Ytu2bRQUFPiv+e9//4vBYKBbt25/+D34gvSePXtYvXo1UVFRJ33M9u3bcTqd/v8mZ3P8tTXV70tTj3/+/Pl069aN888//6TXNufP/w85o6lr4oR8x7Pmz5+v7NixQ5k8ebISHBys7Nu374yO4x//+IdiNpuVr7/+OuC4g9VqVRRFUcrLy5V7771XWb9+vZKXl6esWbNG6dWrl3LeeefVOXLTsmVLZfXq1cqWLVuU/v3713vko0uXLsqGDRuUDRs2KJ07dz7tI0733nuv8vXXXyt79+5VNm7cqAwfPlwJDQ31f45PP/20YjablQ8//FDZunWrMmrUqHqPC52Nsfu43W4lMTFReeCBBwJub46ffXl5uZKTk6Pk5OQogPLcc88pOTk5/qzoM/V5+47XDBgwQNmyZYuyevVqpWXLlic9XnOi8TudTmXEiBFKy5YtlR9//DHg34PdblcURVFyc3OVmTNnKt9//72Sl5enrFixQklLS1MyMjLO+vjP5O9LU4zfx2KxKCaTSXn55ZfrPP5sf/5nggTqZuall15SkpKSFL1er3Tt2jXgSNSZAtT7s3DhQkVRFMVqtSqDBg1SWrRooeh0OiUxMVEZO3askp+fH/A8VVVVyoQJE5TIyEglKChIGT58eJ1riouLlTFjxiihoaFKaGioMmbMGKWkpOS0xu87p6vT6ZSEhATl6quvVrZv3+6/3+PxKNOnT1fi4uIUg8GgXHzxxcrWrVubxdh9vvzySwVQdu/eHXB7c/zs16xZU+/vy9ixYxVFObOf9/79+5Vhw4YpQUFBSmRkpDJhwgTFZrP94fHn5eU1+O/Bd649Pz9fufjii5XIyEhFr9crbdu2VSZNmlTnrPLZGP+Z/n1p7PH7zJs3TwkKCqpzNlpRzv7nfyZI9ywhhBCiGZM9aiGEEKIZk0AthBBCNGMSqIUQQohmTAK1EEII0YxJoBZCCCGaMQnUQgghRDMmgVoIIYRoxiRQCyGEEM2YBGohhBCiGZNALYSo17hx41CpVKhUKnQ6HW3atGHKlClUVlayb98+/30qlQqz2UzPnj357LPPAp5j0aJFhIeHB9zmcDj45z//yfnnn4/JZCI6OprMzEwWLlyI0+ms89o1f4YMGXKm3r4QzYb0oxZCNGjIkCH+ALp27VpuueUWKisreeCBBwBYvXo1nTp1orS0lLlz5zJy5Ei2bNlCenp6vc/ncDgYPHgwP/30E48//jiZmZmEhYWxceNG/vWvf5GRkcEFF1wQ8No1GQyGJn2/QjRHEqiFEA0yGAz+ntKjR49mzZo1fPzxx/5AHRUVRVxcHHFxcTz55JPMnj2bNWvWNBioX3jhBb799lt++OEHMjIy/Le3adOGa665BofDUe9rC/FXJoFaCPG7BQUF+Zena3I6nbz22msA6HS6Bh//9ttvM3DgwIAg7aPT6U74WCH+qiRQCyF+l++++44lS5YwYMAA/229e/dGrVZTVVWFx+OhdevWXHvttQ0+x549e7jkkkt+1+stX76ckJCQgNseeOABpk2b9ofGL8S5SgK1EKJBvmDpcrlwOp1cccUVzJ49G6vVCsA777xDWloav/zyC5MnT+aVV14hMjKywedTFAWVSvW7Xrtfv368/PLLAbed6LmF+LOSQC2EaJAvWOp0OhISEvxL0/v27QOgVatWpKamkpqaSkhICCNHjmTHjh3ExMTU+3zt2rVj586dv+u1g4ODSUlJaZT3IcS5TI5nCSEa5AuWSUlJJ90/7tu3L+np6Tz55JMNXjN69GhWr15NTk5OnftcLheVlZWnPWYh/mwkUAshGs29997LvHnz+O233+q9f/LkyWRmZjJgwABeeuklfvrpJ/bu3cu7775Ljx492LNnj/9au93O4cOHA36KiorO1FsRotmQpW8hRKMZPnw4rVu35sknn2Tu3Ll17jcYDKxatYrnn3+eefPmMWXKFEwmEx06dGDSpEkBx7pWrlxJfHx8wOPbt2/Prl27mvx9CNGcqBRFUc72IIQQQghRP1n6FkIIIZoxCdRCCCFEMyaBWgghhGjGJFALIYQQzZgEaiGEEKIZk0AthBBCNGMSqIUQQohmTAK1EEII0YxJoBZCCCGaMQnUQgghRDMmgVoIIYRoxiRQCyGEEM3Y/wMrCRaB2nZ7qAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict\n",
    "result = grid_search.predict(test_df)\n",
    "\n",
    "# Analyze results\n",
    "mape = mean_absolute_percentage_error(\n",
    "    df=result, y_true_col_names=\"PRICE\", y_pred_col_names=\"PREDICTED_PRICE\"\n",
    ")\n",
    "\n",
    "result.select(\"PRICE\", \"PREDICTED_PRICE\").show()\n",
    "print(f\"Mean absolute percentage error: {mape}\")\n",
    "\n",
    "# Plot actual vs predicted \n",
    "g = sns.relplot(data=result[\"PRICE\", \"PREDICTED_PRICE\"].to_pandas().astype(\"float64\"), x=\"PRICE\", y=\"PREDICTED_PRICE\", kind=\"scatter\")\n",
    "g.ax.axline((0,0), slope=1, color=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 - Save Optimized Model to Model Registry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, with Snowpark ML's model registry, we have a Snowflake native model versioning and deployment framework. This allows us to log models, tag parameters and metrics, track metadata, create versions, and ultimately execute batch inference tasks in a Snowflake warehouse or deploy to a Snowpark Container Service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_model = grid_search.to_sklearn().best_estimator_\n",
    "optimal_n_estimators = grid_search.to_sklearn().best_estimator_.n_estimators\n",
    "optimal_learning_rate = grid_search.to_sklearn().best_estimator_.learning_rate\n",
    "\n",
    "optimal_mape = gs_results_df.loc[(gs_results_df['n_estimators']==optimal_n_estimators) &\n",
    "                                 (gs_results_df['learning_rate']==optimal_learning_rate), 'mape'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get sample input data to pass into the registry logging function\n",
    "X = train_df.select(CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS).limit(100)\n",
    "\n",
    "db = identifier._get_unescaped_name(session.get_current_database())\n",
    "schema = identifier._get_unescaped_name(session.get_current_schema())\n",
    "\n",
    "# Define model name\n",
    "model_name = \"DIAMONDS_PRICE_PREDICTION\"\n",
    "\n",
    "# Create a registry and log the model\n",
    "native_registry = Registry(session=session, database_name=db, schema_name=schema)\n",
    "\n",
    "# Let's first log the very first model we trained\n",
    "model_ver = native_registry.log_model(\n",
    "    model_name=model_name,\n",
    "    version_name='V0',\n",
    "    model=regressor,\n",
    "    sample_input_data=X, # to provide the feature schema\n",
    ")\n",
    "\n",
    "# Add evaluation metric\n",
    "model_ver.set_metric(metric_name=\"mean_abs_pct_err\", value=mape)\n",
    "\n",
    "# Add a description\n",
    "model_ver.comment = \"This is the first iteration of our Diamonds Price Prediction model. It is used for demo purposes.\"\n",
    "\n",
    "# Now, let's log the optimal model from GridSearchCV\n",
    "model_ver2 = native_registry.log_model(\n",
    "    model_name=model_name,\n",
    "    version_name='V1',\n",
    "    model=optimal_model,\n",
    "    sample_input_data=X, # to provide the feature schema\n",
    ")\n",
    "\n",
    "# Add evaluation metric\n",
    "model_ver2.set_metric(metric_name=\"mean_abs_pct_err\", value=optimal_mape)\n",
    "\n",
    "# Add a description\n",
    "model_ver2.comment = \"This is the second iteration of our Diamonds Price Prediction model \\\n",
    "                        where we performed hyperparameter optimization.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_on</th>\n",
       "      <th>name</th>\n",
       "      <th>comment</th>\n",
       "      <th>database_name</th>\n",
       "      <th>schema_name</th>\n",
       "      <th>module_name</th>\n",
       "      <th>is_default_version</th>\n",
       "      <th>functions</th>\n",
       "      <th>metadata</th>\n",
       "      <th>user_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-05-08 08:24:14.006000-07:00</td>\n",
       "      <td>V0</td>\n",
       "      <td>This is the first iteration of our Diamonds Pr...</td>\n",
       "      <td>ML_HOL_DB</td>\n",
       "      <td>ML_HOL_SCHEMA</td>\n",
       "      <td>DIAMONDS_PRICE_PREDICTION</td>\n",
       "      <td>true</td>\n",
       "      <td>[\"PREDICT\"]</td>\n",
       "      <td>{\"metrics\": {\"mean_abs_pct_err\": 0.06614988993...</td>\n",
       "      <td>{\"snowpark_ml_data\":{\"functions\":[{\"name\":\"PRE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-05-08 08:24:54.169000-07:00</td>\n",
       "      <td>V1</td>\n",
       "      <td>This is the second iteration of our Diamonds P...</td>\n",
       "      <td>ML_HOL_DB</td>\n",
       "      <td>ML_HOL_SCHEMA</td>\n",
       "      <td>DIAMONDS_PRICE_PREDICTION</td>\n",
       "      <td>false</td>\n",
       "      <td>[\"PREDICT\"]</td>\n",
       "      <td>{\"metrics\": {\"mean_abs_pct_err\": 0.16197244497...</td>\n",
       "      <td>{\"snowpark_ml_data\":{\"functions\":[{\"name\":\"PRE...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        created_on name  \\\n",
       "0 2024-05-08 08:24:14.006000-07:00   V0   \n",
       "1 2024-05-08 08:24:54.169000-07:00   V1   \n",
       "\n",
       "                                             comment database_name  \\\n",
       "0  This is the first iteration of our Diamonds Pr...     ML_HOL_DB   \n",
       "1  This is the second iteration of our Diamonds P...     ML_HOL_DB   \n",
       "\n",
       "     schema_name                module_name is_default_version    functions  \\\n",
       "0  ML_HOL_SCHEMA  DIAMONDS_PRICE_PREDICTION               true  [\"PREDICT\"]   \n",
       "1  ML_HOL_SCHEMA  DIAMONDS_PRICE_PREDICTION              false  [\"PREDICT\"]   \n",
       "\n",
       "                                            metadata  \\\n",
       "0  {\"metrics\": {\"mean_abs_pct_err\": 0.06614988993...   \n",
       "1  {\"metrics\": {\"mean_abs_pct_err\": 0.16197244497...   \n",
       "\n",
       "                                           user_data  \n",
       "0  {\"snowpark_ml_data\":{\"functions\":[{\"name\":\"PRE...  \n",
       "1  {\"snowpark_ml_data\":{\"functions\":[{\"name\":\"PRE...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's confirm they were added\n",
    "native_registry.get_model(model_name).show_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model Inference\n",
    "\n",
    "For the inference, we will look at 3 different ways:\n",
    "- loading model from registry\n",
    "- running a sql statement\n",
    "- batch inference using a Vectorized UDF\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Fetch model from Registry and perform inference on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"output_feature_0\"  |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.02079002079002079   |0.5472222222222223   |0.3076923076923076   |0.3919925512104283   |0.07249575551782682  |0.08364779874213837  |2.0       |6.0         |5.0           |J        |SI1        |351      |VERY_GOOD  |353.2740173339844   |\n",
      "|0.02286902286902287   |0.4555555555555555   |0.3653846153846153   |0.40875232774674114  |0.07521222410865874  |0.08238993710691825  |2.0       |6.0         |5.0           |J        |SI1        |353      |VERY_GOOD  |343.84271240234375  |\n",
      "|0.008316008316008316  |0.5416666666666665   |0.2692307692307693   |0.36964618249534453  |0.06689303904923599  |0.07767295597484278  |1.0       |5.0         |3.0           |I        |VS1        |355      |PREMIUM    |431.1207275390625   |\n",
      "|0.006237006237006237  |0.48611111111111094  |0.34615384615384615  |0.3687150837988827   |0.06740237691001698  |0.07547169811320754  |2.0       |0.0         |4.0           |D        |VS2        |357      |VERY_GOOD  |355.054443359375    |\n",
      "|0.02079002079002079   |0.5527777777777776   |0.3076923076923076   |0.3985102420856611   |0.07198641765704585  |0.08427672955974844  |1.0       |4.0         |5.0           |H        |SI1        |554      |PREMIUM    |560.3695068359375   |\n",
      "|0.008316008316008316  |0.4916666666666667   |0.28846153846153844  |0.3789571694599628   |0.06859083191850594  |0.07735849056603773  |1.0       |4.0         |2.0           |H        |VVS2       |554      |PREMIUM    |527.7815551757812   |\n",
      "|0.10395010395010396   |0.5166666666666666   |0.2500000000000001   |0.5307262569832403   |0.09626485568760612  |0.11006289308176101  |0.0       |3.0         |4.0           |G        |VS2        |2757     |IDEAL      |2796.111083984375   |\n",
      "|0.10602910602910603   |0.5388888888888888   |0.2692307692307693   |0.5288640595903166   |0.0972835314091681   |0.1119496855345912   |2.0       |1.0         |4.0           |E        |VS2        |2759     |VERY_GOOD  |2878.782958984375   |\n",
      "|0.158004158004158     |0.6472222222222221   |0.3653846153846153   |0.5837988826815642   |0.10101867572156198  |0.1279874213836478   |4.0       |2.0         |6.0           |F        |SI2        |2759     |FAIR       |3021.80224609375    |\n",
      "|0.11226611226611227   |0.536111111111111    |0.21153846153846156  |0.5400372439478585   |0.09898132427843803  |0.11383647798742139  |0.0       |1.0         |5.0           |E        |SI1        |2762     |IDEAL      |2838.24658203125    |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_ver = native_registry.get_model(model_name).version('v1')\n",
    "result_sdf2 = model_ver.run(test_df, function_name=\"predict\")\n",
    "result_sdf2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Perform inference using SQL statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"PREDICTION\"           |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.02079002079002079   |0.5472222222222223   |0.3076923076923076   |0.3919925512104283   |0.07249575551782682  |0.08364779874213837  |2.0       |6.0         |5.0           |J        |SI1        |351      |VERY_GOOD  |3.225935058593750e+02  |\n",
      "|0.02286902286902287   |0.4555555555555555   |0.3653846153846153   |0.40875232774674114  |0.07521222410865874  |0.08238993710691825  |2.0       |6.0         |5.0           |J        |SI1        |353      |VERY_GOOD  |2.673024597167969e+02  |\n",
      "|0.008316008316008316  |0.5416666666666665   |0.2692307692307693   |0.36964618249534453  |0.06689303904923599  |0.07767295597484278  |1.0       |5.0         |3.0           |I        |VS1        |355      |PREMIUM    |3.854778137207031e+02  |\n",
      "|0.006237006237006237  |0.48611111111111094  |0.34615384615384615  |0.3687150837988827   |0.06740237691001698  |0.07547169811320754  |2.0       |0.0         |4.0           |D        |VS2        |357      |VERY_GOOD  |4.344681701660156e+02  |\n",
      "|0.02079002079002079   |0.5527777777777776   |0.3076923076923076   |0.3985102420856611   |0.07198641765704585  |0.08427672955974844  |1.0       |4.0         |5.0           |H        |SI1        |554      |PREMIUM    |5.663515625000000e+02  |\n",
      "|0.008316008316008316  |0.4916666666666667   |0.28846153846153844  |0.3789571694599628   |0.06859083191850594  |0.07735849056603773  |1.0       |4.0         |2.0           |H        |VVS2       |554      |PREMIUM    |4.924712219238281e+02  |\n",
      "|0.10395010395010396   |0.5166666666666666   |0.2500000000000001   |0.5307262569832403   |0.09626485568760612  |0.11006289308176101  |0.0       |3.0         |4.0           |G        |VS2        |2757     |IDEAL      |2.734483642578125e+03  |\n",
      "|0.10602910602910603   |0.5388888888888888   |0.2692307692307693   |0.5288640595903166   |0.0972835314091681   |0.1119496855345912   |2.0       |1.0         |4.0           |E        |VS2        |2759     |VERY_GOOD  |2.950267578125000e+03  |\n",
      "|0.158004158004158     |0.6472222222222221   |0.3653846153846153   |0.5837988826815642   |0.10101867572156198  |0.1279874213836478   |4.0       |2.0         |6.0           |F        |SI2        |2759     |FAIR       |2.802850830078125e+03  |\n",
      "|0.11226611226611227   |0.536111111111111    |0.21153846153846156  |0.5400372439478585   |0.09898132427843803  |0.11383647798742139  |0.0       |1.0         |5.0           |E        |SI1        |2762     |IDEAL      |2.944725341796875e+03  |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"PREDICTION\"           |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.02079002079002079   |0.5472222222222223   |0.3076923076923076   |0.3919925512104283   |0.07249575551782682  |0.08364779874213837  |2.0       |6.0         |5.0           |J        |SI1        |351      |VERY_GOOD  |3.532740173339844e+02  |\n",
      "|0.02286902286902287   |0.4555555555555555   |0.3653846153846153   |0.40875232774674114  |0.07521222410865874  |0.08238993710691825  |2.0       |6.0         |5.0           |J        |SI1        |353      |VERY_GOOD  |3.438427124023438e+02  |\n",
      "|0.008316008316008316  |0.5416666666666665   |0.2692307692307693   |0.36964618249534453  |0.06689303904923599  |0.07767295597484278  |1.0       |5.0         |3.0           |I        |VS1        |355      |PREMIUM    |4.311207275390625e+02  |\n",
      "|0.006237006237006237  |0.48611111111111094  |0.34615384615384615  |0.3687150837988827   |0.06740237691001698  |0.07547169811320754  |2.0       |0.0         |4.0           |D        |VS2        |357      |VERY_GOOD  |3.550544433593750e+02  |\n",
      "|0.02079002079002079   |0.5527777777777776   |0.3076923076923076   |0.3985102420856611   |0.07198641765704585  |0.08427672955974844  |1.0       |4.0         |5.0           |H        |SI1        |554      |PREMIUM    |5.603695068359375e+02  |\n",
      "|0.008316008316008316  |0.4916666666666667   |0.28846153846153844  |0.3789571694599628   |0.06859083191850594  |0.07735849056603773  |1.0       |4.0         |2.0           |H        |VVS2       |554      |PREMIUM    |5.277815551757812e+02  |\n",
      "|0.10395010395010396   |0.5166666666666666   |0.2500000000000001   |0.5307262569832403   |0.09626485568760612  |0.11006289308176101  |0.0       |3.0         |4.0           |G        |VS2        |2757     |IDEAL      |2.796111083984375e+03  |\n",
      "|0.10602910602910603   |0.5388888888888888   |0.2692307692307693   |0.5288640595903166   |0.0972835314091681   |0.1119496855345912   |2.0       |1.0         |4.0           |E        |VS2        |2759     |VERY_GOOD  |2.878782958984375e+03  |\n",
      "|0.158004158004158     |0.6472222222222221   |0.3653846153846153   |0.5837988826815642   |0.10101867572156198  |0.1279874213836478   |4.0       |2.0         |6.0           |F        |SI2        |2759     |FAIR       |3.021802246093750e+03  |\n",
      "|0.11226611226611227   |0.536111111111111    |0.21153846153846156  |0.5400372439478585   |0.09898132427843803  |0.11383647798742139  |0.0       |1.0         |5.0           |E        |SI1        |2762     |IDEAL      |2.838246582031250e+03  |\n",
      "-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# save test data as temp table\n",
    "test_df.write.mode(\"overwrite\").save_as_table(\"diamonds_test\", table_type=\"temporary\")\n",
    "\n",
    "# for the default version:\n",
    "session.sql(\n",
    "    f\"SELECT a.*, DIAMONDS_PRICE_PREDICTION!predict(a.CUT_OE, a.COLOR_OE, a.CLARITY_OE, a.CARAT, a.DEPTH, a.TABLE_PCT, a.X, a.Y, a.Z)['PREDICTED_PRICE'] as prediction from diamonds_test a\"\n",
    ").show()\n",
    "\n",
    "# for any other version (for example V1 below):\n",
    "session.sql(\n",
    "    f\"WITH model_version_alias AS MODEL {model_name} VERSION v1 SELECT a.*, model_version_alias!predict(a.CUT_OE, a.COLOR_OE, a.CLARITY_OE, a.CARAT, a.DEPTH, a.TABLE_PCT, a.X, a.Y, a.Z)['output_feature_0'] as prediction from diamonds_test a\"\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Batch inference using a Vectorized UDF\n",
    "\n",
    "\n",
    "In case you have an existing model outside of our supported frameworks (scikit-learn, xgboost, lightgbm, pytorch, tensorflow, pyfunc), you can still execute batch inference via Vectorized UDFs.\n",
    "\n",
    "Make sure to add your package to the `packages` section in the UDF registration call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's save our optimal model first\n",
    "MODEL_FILE = 'model.joblib'\n",
    "joblib.dump(optimal_model, MODEL_FILE) # we are just pickling it locally first\n",
    "\n",
    "# You can also save the pickled object into the stage we created earlier\n",
    "session.file.put(MODEL_FILE, \"@ML_HOL_ASSETS\", overwrite=True)\n",
    "\n",
    "# Get all relevant column names to pass into the UDF call\n",
    "feature_cols = test_df[CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cache the model load to optimize inference\n",
    "@cachetools.cached(cache={})\n",
    "def load_model(filename):\n",
    "    import joblib\n",
    "    import sys\n",
    "    import os\n",
    "\n",
    "    IMPORT_DIRECTORY_NAME = \"snowflake_import_directory\"\n",
    "    import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]\n",
    "\n",
    "    if import_dir:\n",
    "        with open(os.path.join(import_dir, filename), 'rb') as file:\n",
    "            m = joblib.load(file)\n",
    "            return m\n",
    "\n",
    "# Register the UDF via decorator\n",
    "@udf(name='batch_predict_diamond', \n",
    "     session=session, \n",
    "     replace=True, \n",
    "     is_permanent=True, \n",
    "     stage_location='@ML_HOL_ASSETS',\n",
    "     input_types=[F.FloatType()]*len(feature_cols),\n",
    "     return_type=F.FloatType(),\n",
    "     imports=['@ML_HOL_ASSETS/model.joblib.gz'],\n",
    "     packages=['pandas','joblib','cachetools','xgboost'])\n",
    "def batch_predict_diamond(test_df: pd.DataFrame) -> pd.Series:\n",
    "    # Need to name the columns because column names aren't passed in to this function\n",
    "    test_df.columns = [\"CUT_OE\", \"COLOR_OE\", \"CLARITY_OE\", 'CARAT', 'DEPTH', 'TABLE_PCT', 'X', 'Y', 'Z']\n",
    "    model = load_model('model.joblib.gz')\n",
    "    return model.predict(test_df) # This is using the XGBoost library's model.predict(), not Snowpark ML's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call Vectorized User-Defined Function (UDF) on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"PREDICTED_PRICE\"   |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.02079002079002079   |0.5472222222222223   |0.3076923076923076   |0.3919925512104283   |0.07249575551782682  |0.08364779874213837  |2.0       |6.0         |5.0           |J        |SI1        |351      |VERY_GOOD  |353.2740173339844   |\n",
      "|0.02286902286902287   |0.4555555555555555   |0.3653846153846153   |0.40875232774674114  |0.07521222410865874  |0.08238993710691825  |2.0       |6.0         |5.0           |J        |SI1        |353      |VERY_GOOD  |343.84271240234375  |\n",
      "|0.008316008316008316  |0.5416666666666665   |0.2692307692307693   |0.36964618249534453  |0.06689303904923599  |0.07767295597484278  |1.0       |5.0         |3.0           |I        |VS1        |355      |PREMIUM    |431.1207275390625   |\n",
      "|0.006237006237006237  |0.48611111111111094  |0.34615384615384615  |0.3687150837988827   |0.06740237691001698  |0.07547169811320754  |2.0       |0.0         |4.0           |D        |VS2        |357      |VERY_GOOD  |355.054443359375    |\n",
      "|0.02079002079002079   |0.5527777777777776   |0.3076923076923076   |0.3985102420856611   |0.07198641765704585  |0.08427672955974844  |1.0       |4.0         |5.0           |H        |SI1        |554      |PREMIUM    |560.3695068359375   |\n",
      "|0.008316008316008316  |0.4916666666666667   |0.28846153846153844  |0.3789571694599628   |0.06859083191850594  |0.07735849056603773  |1.0       |4.0         |2.0           |H        |VVS2       |554      |PREMIUM    |527.7815551757812   |\n",
      "|0.10395010395010396   |0.5166666666666666   |0.2500000000000001   |0.5307262569832403   |0.09626485568760612  |0.11006289308176101  |0.0       |3.0         |4.0           |G        |VS2        |2757     |IDEAL      |2796.111083984375   |\n",
      "|0.10602910602910603   |0.5388888888888888   |0.2692307692307693   |0.5288640595903166   |0.0972835314091681   |0.1119496855345912   |2.0       |1.0         |4.0           |E        |VS2        |2759     |VERY_GOOD  |2878.782958984375   |\n",
      "|0.158004158004158     |0.6472222222222221   |0.3653846153846153   |0.5837988826815642   |0.10101867572156198  |0.1279874213836478   |4.0       |2.0         |6.0           |F        |SI2        |2759     |FAIR       |3021.80224609375    |\n",
      "|0.11226611226611227   |0.536111111111111    |0.21153846153846156  |0.5400372439478585   |0.09898132427843803  |0.11383647798742139  |0.0       |1.0         |5.0           |E        |SI1        |2762     |IDEAL      |2838.24658203125    |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_df_w_preds = test_df.with_column('PREDICTED_PRICE', batch_predict_diamond(*feature_cols))\n",
    "test_df_w_preds.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean up\n",
    "native_registry.delete_model(model_name)\n",
    "# Confirm it was deleted\n",
    "native_registry.show_models()\n",
    "\n",
    "session.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other resources\n",
    "\n",
    "https://docs.snowflake.com/en/developer-guide/snowpark-ml/index?_fsi=LnQEPC1a&_fsi=LnQEPC1a\n",
    "\n",
    "\n",
    "https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-modeling\n",
    "\n",
    "\n",
    "https://docs.snowflake.com/en/developer-guide/snowpark-ml/snowpark-ml-mlops-model-registry\n",
    "\n"
   ]
  }
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