examples/training/04_bqml_forecasting-retail-demand.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b45b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2024 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8790d7ed",
   "metadata": {},
   "source": [
    "# AutoMLOps - Forecasting retail demand with Vertex AI and BigQuery ML \n",
    "\n",
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/automlops/blob/main/examples/training/04_bqml_forecasting-retail-demand.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://github.com/GoogleCloudPlatform/automlops/blob/main/examples/training/04_bqml_forecasting-retail-demand.ipynb\">\n",
    "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
    "      View on GitHub\n",
    "    </a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/automlops/main/examples/training/04_bqml_forecasting-retail-demand.ipynb\">\n",
    "        <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
    "      Open in Vertex AI Workbench\n",
    "    </a>\n",
    "  </td>\n",
    "</table>\n",
    "<br/><br/><br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f938540",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "This tutorial explores using AutoMLOps for demand forecasting using a BigQuery public retail dataset. Being able to measure and forecast customer demand can help retailers better understand their customers, stock shelves with the right products, offer targeted promotions, and generally, better plan and manage their budgets. This notebook applies an ARIMA (Autoregressive integrated moving average) model from BigQuery ML on retail data. This notebook demonstrates how to train and evaluate a BigQuery ML model for demand forecasting datasets and extract actionable future insights.\n",
    "\n",
    "#### ARIMA Modeling with BigQuery ML \n",
    "\n",
    "The <a href='https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average'>ARIMA model</a> is designed to analyze historical data, spot patterns over time, and project them into the future--in other words, forecasting. The model is available inside BigQuery ML and enables users to create and execute machine learning models directly in BigQuery using SQL queries. Working with BigQuery ML is advantageous, as it already has access to the data, it can handle most of the modeling details automatically if desired, and will store both the model and any predictions also inside BigQuery. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd4abb-64e5-453f-b501-207a070c9005",
   "metadata": {},
   "source": [
    "### This tutorial is broken into 2 parts\n",
    "\n",
    "[Part 1 : Data science exploration](#data_science_exploration)\n",
    "\n",
    "[Part 2 : Automating pipeline creationg with AutoMLOps](#automlops)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22881a6a",
   "metadata": {},
   "source": [
    "# Objective\n",
    "\n",
    "In this tutorial, you learn how to build ARIMA (Autoregressive integrated moving average) model from BigQuery ML on retail data. You will then learn how to create and run MLOps pipelines integrated with CI/CD. The pipeline goes through the following steps:\n",
    "\n",
    "1. prepare_sales_table: Custom component that prepares the sales table.\n",
    "2. create_training_dataset: Custom component that creates training data, ranging from today to N year(s) ago.\n",
    "3. train_model: Train an ARIMA model on the training dataset.\n",
    "4. evaluate_model: Evaluate the trained ARIMA model.\n",
    "5. forecast: Custom component that runs a forecast for a given horizon period.\n",
    "\n",
    "# Prerequisites\n",
    "\n",
    "In order to use AutoMLOps, the following are required:\n",
    "\n",
    "- Python 3.7 - 3.10\n",
    "- [Google Cloud SDK 407.0.0](https://cloud.google.com/sdk/gcloud/reference)\n",
    "- [beta 2022.10.21](https://cloud.google.com/sdk/gcloud/reference/beta)\n",
    "- `git` installed\n",
    "- `git` logged-in:\n",
    "```\n",
    "  git config --global user.email \"you@example.com\"\n",
    "  git config --global user.name \"Your Name\"\n",
    "```\n",
    "- [Application Default Credentials (ADC)](https://cloud.google.com/docs/authentication/provide-credentials-adc) are setup. This can be done through the following commands:\n",
    "```\n",
    "gcloud auth application-default login\n",
    "gcloud config set account <account@example.com>\n",
    "```\n",
    "\n",
    "# APIs & IAM\n",
    "Based on the user options selection, AutoMLOps will enable up to the following APIs during the provision step:\n",
    "- [aiplatform.googleapis.com](https://cloud.google.com/vertex-ai/docs/reference/rest)\n",
    "- [artifactregistry.googleapis.com](https://cloud.google.com/artifact-registry/docs/reference/rest)\n",
    "- [cloudbuild.googleapis.com](https://cloud.google.com/build/docs/api/reference/rest)\n",
    "- [cloudfunctions.googleapis.com](https://cloud.google.com/functions/docs/reference/rest)\n",
    "- [cloudresourcemanager.googleapis.com](https://cloud.google.com/resource-manager/reference/rest)\n",
    "- [cloudscheduler.googleapis.com](https://cloud.google.com/scheduler/docs/reference/rest)\n",
    "- [compute.googleapis.com](https://cloud.google.com/compute/docs/reference/rest/v1)\n",
    "- [iam.googleapis.com](https://cloud.google.com/iam/docs/reference/rest)\n",
    "- [iamcredentials.googleapis.com](https://cloud.google.com/iam/docs/reference/credentials/rest)\n",
    "- [logging.googleapis.com](https://cloud.google.com/logging/docs/reference/v2/rest)\n",
    "- [pubsub.googleapis.com](https://cloud.google.com/pubsub/docs/reference/rest)\n",
    "- [run.googleapis.com](https://cloud.google.com/run/docs/reference/rest)\n",
    "- [storage.googleapis.com](https://cloud.google.com/storage/docs/apis)\n",
    "\n",
    "\n",
    "AutoMLOps will create the following service account and update [IAM permissions](https://cloud.google.com/iam/docs/understanding-roles) during the provision step:\n",
    "1. Pipeline Runner Service Account (defaults to: vertex-pipelines@PROJECT_ID.iam.gserviceaccount.com). Roles added:\n",
    "- roles/aiplatform.user\n",
    "- roles/artifactregistry.reader\n",
    "- roles/bigquery.user\n",
    "- roles/bigquery.dataEditor\n",
    "- roles/iam.serviceAccountUser\n",
    "- roles/storage.admin\n",
    "- roles/cloudfunctions.admin\n",
    "\n",
    "# User Guide\n",
    "\n",
    "For a user-guide, please view these [slides](../../AutoMLOps_User_Guide.pdf).\n",
    "\n",
    "# Costs\n",
    "\n",
    "This tutorial uses billable components of Google Cloud:\n",
    "- Vertex AI\n",
    "- Artifact Registry\n",
    "- Cloud Storage\n",
    "- Cloud Build\n",
    "- Cloud Run\n",
    "- Cloud Scheduler\n",
    "- Cloud Pub/Sub\n",
    "\n",
    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage.\n",
    "\n",
    "# Ground-rules for using AutoMLOps\n",
    "1. Do not use variables, functions, code, etc. not defined within the scope of a custom component. These custom components will become containers and will have no reference to the out of scope code.\n",
    "2. Import statements and helper functions must be added inside the function. Provide parameter type hints.\n",
    "3. Test each of your components for accuracy and correctness before running them using AutoMLOps. We cannot fix bugs automatically; bugs are much more difficult to fix once they are made into pipelines.\n",
    "4. If you are using Kubeflow, be sure to define all the requirements needed to run the custom component - it can be easy to leave out packages which will cause the container to fail when running within a pipeline. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12381413",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "This notebook uses the BigQuery public retail data set from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data).\n",
    "The data covers 10 US stores and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price and gross margin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a231b629",
   "metadata": {},
   "source": [
    "## Setup Git\n",
    "Set up your git configuration below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90b44",
   "metadata": {},
   "outputs": [],
   "source": [
    "!git config --global user.email 'you@example.com'\n",
    "!git config --global user.name 'Your Name'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e4d190",
   "metadata": {},
   "source": [
    "## Install AutoMLOps\n",
    "\n",
    "Install AutoMLOps from [PyPI](https://pypi.org/project/google-cloud-automlops/), or locally by cloning the repo and running `pip install .`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94451868",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install google-cloud-automlops --user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17db55d5",
   "metadata": {},
   "source": [
    "## Restart the kernel\n",
    "Once you've installed the AutoMLOps package, you need to restart the notebook kernel so it can find the package.\n",
    "\n",
    "**Note: Once this cell has finished running, continue on. You do not need to re-run any of the cells above.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57c53b86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.getenv('IS_TESTING'):\n",
    "    # Automatically restart kernel after installs\n",
    "    import IPython\n",
    "\n",
    "    app = IPython.Application.instance()\n",
    "    app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "250d511b",
   "metadata": {},
   "source": [
    "## Set your project ID\n",
    "Set your project ID below. If you don't know your project ID, leave the field blank and the following cells may be able to find it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "931ff517",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_ID = '[your-project-id]'  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be0be295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project ID: automlops-sandbox\n"
     ]
    }
   ],
   "source": [
    "if PROJECT_ID == '' or PROJECT_ID is None or PROJECT_ID == '[your-project-id]':\n",
    "    # Get your GCP project id from gcloud\n",
    "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
    "    PROJECT_ID = shell_output[0]\n",
    "    print('Project ID:', PROJECT_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c36482d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n"
     ]
    }
   ],
   "source": [
    "! gcloud config set project $PROJECT_ID"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50f9178",
   "metadata": {},
   "source": [
    "Set your Model_ID below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289c5307",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_ID = 'demand-forecasting-arima'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b802d468-50ac-4f7b-921e-911edf34e4ad",
   "metadata": {},
   "source": [
    "## Import libraries and define variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b8c230d-8777-4e15-8f4f-e30ce97c1600",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import string\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from google.cloud import bigquery\n",
    "from sklearn.metrics import (mean_absolute_error,\n",
    "                             mean_absolute_percentage_error,\n",
    "                             mean_squared_error)\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "SALES_TABLE = 'training_data_table'\n",
    "\n",
    "# Construct a BigQuery client object.\n",
    "bq_client = bigquery.Client(project=PROJECT_ID)\n",
    "dataset_id = 'demandforecasting'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06124795-e7f7-4eb5-8f57-9f6ba454d43d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Part 1 : Data science exploration<a id='data_science_exploration'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "503f4a85-c357-4ec4-8ea5-0eeb2c8afd39",
   "metadata": {},
   "source": [
    "## Create an empty Biquery Dataset\n",
    "This will create a BQ dataset. Note that this is running a SQL query using the Bigquery API client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28557b2d-598c-466b-8116-bc1e34ba090d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe89108b990>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE SCHEMA IF NOT EXISTS `{PROJECT_ID}.{dataset_id}`\n",
    "OPTIONS(\n",
    "  location=\"us\"\n",
    "  )\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7750604b-176a-4203-bb67-497281224c96",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97965da8-6542-40df-ad2b-d0aa852e8f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "SELECT * FROM `looker-private-demo.retail.transaction_detail`\n",
    "'''\n",
    "query_job = bq_client.query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0358ec83-fa7e-4b5c-8e48-ca467b3f7136",
   "metadata": {},
   "source": [
    "## Raw dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec011f0b-e9f8-4ea3-a21e-1c4ed16dfd72",
   "metadata": {},
   "outputs": [
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       "2      2020-03-02 14:17:24+00:00          247527         1           2   \n",
       "3      2020-03-02 10:43:01+00:00           74896         1           2   \n",
       "4      2020-03-02 19:21:05+00:00          236207         1           3   \n",
       "...                          ...             ...       ...         ...   \n",
       "297392 2019-05-05 14:42:23+00:00          109342        10           4   \n",
       "297393 2018-11-28 22:06:50+00:00            1675        10           3   \n",
       "297394 2018-11-28 06:35:24+00:00          125086        10           4   \n",
       "297395 2018-11-28 23:20:39+00:00            1700        10           1   \n",
       "297396 2018-11-28 22:35:51+00:00          170820        10           3   \n",
       "\n",
       "        customer_id                                         line_items  \n",
       "0              <NA>  [{'product_id': 2456, 'sale_price': 24.5599994...  \n",
       "1              <NA>  [{'product_id': 7137, 'sale_price': 29.9899997...  \n",
       "2              <NA>  [{'product_id': 22713, 'sale_price': 27.989999...  \n",
       "3              <NA>  [{'product_id': 23641, 'sale_price': 59.950000...  \n",
       "4              5455  [{'product_id': 20542, 'sale_price': 68.0, 'gr...  \n",
       "...             ...                                                ...  \n",
       "297392         3272  [{'product_id': 13596, 'sale_price': 75.110000...  \n",
       "297393         1774  [{'product_id': 12761, 'sale_price': 53.5, 'gr...  \n",
       "297394           32  [{'product_id': 8305, 'sale_price': 69.9899978...  \n",
       "297395         <NA>  [{'product_id': 25390, 'sale_price': 25.600000...  \n",
       "297396          113  [{'product_id': 20551, 'sale_price': 58.0, 'gr...  \n",
       "\n",
       "[297397 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6925b204-626e-4b25-8371-0026974a3bd7",
   "metadata": {
    "id": "6d52803e14f2"
   },
   "source": [
    "**Create a view named `important_fields` using only the `transaction_timestamp` and `line_items` fields, where the store ID is 10.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49fd86ae-cc7a-4cf9-a1ae-47c229186c5c",
   "metadata": {
    "id": "uDgu3GWtp94B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe8807460d0>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.important_fields AS\n",
    "(\n",
    "    SELECT transaction_timestamp,line_items from `looker-private-demo.retail.transaction_detail` WHERE store_id = 10\n",
    ")   \n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03c2cb2-c9fd-4794-bb02-d329583d403e",
   "metadata": {
    "id": "a9ca8a8bbf82"
   },
   "source": [
    "**Look at the data in the `important_fields` view.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "240b55f1-7d6b-429d-8305-935d080687b3",
   "metadata": {
    "id": "J4CN-N_op94B"
   },
   "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>transaction_timestamp</th>\n",
       "      <th>line_items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-11-07 17:46:14+00:00</td>\n",
       "      <td>[{'product_id': 25416, 'sale_price': 29.0, 'gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-11-07 03:18:45+00:00</td>\n",
       "      <td>[{'product_id': 25598, 'sale_price': 21.0, 'gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-11-07 08:07:54+00:00</td>\n",
       "      <td>[{'product_id': 12803, 'sale_price': 70.949996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-11-07 11:47:29+00:00</td>\n",
       "      <td>[{'product_id': 24956, 'sale_price': 23.0, 'gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-11-07 13:37:30+00:00</td>\n",
       "      <td>[{'product_id': 22707, 'sale_price': 60.990001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29668</th>\n",
       "      <td>2022-08-01 17:31:53+00:00</td>\n",
       "      <td>[{'product_id': 7384, 'sale_price': 29.9500007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29669</th>\n",
       "      <td>2020-01-04 05:59:13+00:00</td>\n",
       "      <td>[{'product_id': 12728, 'sale_price': 29.950000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29670</th>\n",
       "      <td>2018-08-21 03:49:33+00:00</td>\n",
       "      <td>[{'product_id': 28316, 'sale_price': 29.950000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29671</th>\n",
       "      <td>2023-03-18 09:53:30+00:00</td>\n",
       "      <td>[{'product_id': 12728, 'sale_price': 29.950000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29672</th>\n",
       "      <td>2023-02-08 15:50:06+00:00</td>\n",
       "      <td>[{'product_id': 12728, 'sale_price': 29.950000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29673 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          transaction_timestamp  \\\n",
       "0     2022-11-07 17:46:14+00:00   \n",
       "1     2022-11-07 03:18:45+00:00   \n",
       "2     2022-11-07 08:07:54+00:00   \n",
       "3     2022-11-07 11:47:29+00:00   \n",
       "4     2022-11-07 13:37:30+00:00   \n",
       "...                         ...   \n",
       "29668 2022-08-01 17:31:53+00:00   \n",
       "29669 2020-01-04 05:59:13+00:00   \n",
       "29670 2018-08-21 03:49:33+00:00   \n",
       "29671 2023-03-18 09:53:30+00:00   \n",
       "29672 2023-02-08 15:50:06+00:00   \n",
       "\n",
       "                                              line_items  \n",
       "0      [{'product_id': 25416, 'sale_price': 29.0, 'gr...  \n",
       "1      [{'product_id': 25598, 'sale_price': 21.0, 'gr...  \n",
       "2      [{'product_id': 12803, 'sale_price': 70.949996...  \n",
       "3      [{'product_id': 24956, 'sale_price': 23.0, 'gr...  \n",
       "4      [{'product_id': 22707, 'sale_price': 60.990001...  \n",
       "...                                                  ...  \n",
       "29668  [{'product_id': 7384, 'sale_price': 29.9500007...  \n",
       "29669  [{'product_id': 12728, 'sale_price': 29.950000...  \n",
       "29670  [{'product_id': 28316, 'sale_price': 29.950000...  \n",
       "29671  [{'product_id': 12728, 'sale_price': 29.950000...  \n",
       "29672  [{'product_id': 12728, 'sale_price': 29.950000...  \n",
       "\n",
       "[29673 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.important_fields\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fbb4d4-153f-40b8-92b7-0813f3f8fe1f",
   "metadata": {
    "id": "5f03b43de905"
   },
   "source": [
    "**Convert the `transaction_timestamp` field into a date.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "111063dd-620a-4bac-b8f7-6b1662bbaa8b",
   "metadata": {
    "id": "DGq3a0aUp94C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe82c2ac710>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.data_after_converting_timestamp_to_date AS\n",
    "(\n",
    "    SELECT EXTRACT(DATE FROM transaction_timestamp AT TIME ZONE \"UTC\") AS date,line_items from {dataset_id}.important_fields\n",
    ")   \n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4f4612-e9f3-4f92-9869-9d4d06475424",
   "metadata": {
    "id": "f0babc4a23fa"
   },
   "source": [
    "**View the data and check the `date` field values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7dc8e1b8-d004-4fab-a1d0-bf853a8de92e",
   "metadata": {
    "id": "_1mz0KD1p94C"
   },
   "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>date</th>\n",
       "      <th>line_items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>[{'product_id': 811, 'sale_price': 13.98999977...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>[{'product_id': 13602, 'sale_price': 7.8499999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>[{'product_id': 22708, 'sale_price': 27.989999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>[{'product_id': 19338, 'sale_price': 56.0, 'gr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>[{'product_id': 28818, 'sale_price': 39.950000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29668</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>[{'product_id': 28302, 'sale_price': 6.0700001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29669</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>[{'product_id': 28302, 'sale_price': 6.0700001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29670</th>\n",
       "      <td>2022-02-12</td>\n",
       "      <td>[{'product_id': 28302, 'sale_price': 6.0700001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29671</th>\n",
       "      <td>2022-01-08</td>\n",
       "      <td>[{'product_id': 28302, 'sale_price': 6.0700001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29672</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>[{'product_id': 28302, 'sale_price': 6.0700001...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29673 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date                                         line_items\n",
       "0      2021-08-25  [{'product_id': 811, 'sale_price': 13.98999977...\n",
       "1      2021-08-25  [{'product_id': 13602, 'sale_price': 7.8499999...\n",
       "2      2021-08-25  [{'product_id': 22708, 'sale_price': 27.989999...\n",
       "3      2021-08-25  [{'product_id': 19338, 'sale_price': 56.0, 'gr...\n",
       "4      2021-08-25  [{'product_id': 28818, 'sale_price': 39.950000...\n",
       "...           ...                                                ...\n",
       "29668  2022-01-04  [{'product_id': 28302, 'sale_price': 6.0700001...\n",
       "29669  2022-01-06  [{'product_id': 28302, 'sale_price': 6.0700001...\n",
       "29670  2022-02-12  [{'product_id': 28302, 'sale_price': 6.0700001...\n",
       "29671  2022-01-08  [{'product_id': 28302, 'sale_price': 6.0700001...\n",
       "29672  2018-11-30  [{'product_id': 28302, 'sale_price': 6.0700001...\n",
       "\n",
       "[29673 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.data_after_converting_timestamp_to_date\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "846497e0-8e90-49b6-8989-5b175f15d7a5",
   "metadata": {
    "id": "990d04eab2e1"
   },
   "source": [
    "**Load the data into a dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f58ca01-0ae9-482a-8886-f3553a3fb272",
   "metadata": {
    "id": "2d3a85f95f9d"
   },
   "outputs": [],
   "source": [
    "df_intermediary = query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e166453e-7209-42dd-b689-d3d199d34858",
   "metadata": {
    "id": "f25cf5322fbc"
   },
   "source": [
    "**Check the data types of your dataframe's fields.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6948de2-1866-4e44-9e22-7b8674d29130",
   "metadata": {
    "id": "40a78a81c379"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          dbdate\n",
       "line_items    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_intermediary.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fd6bfd-60e7-4bf7-98bb-017958e3164e",
   "metadata": {
    "id": "f2f685f15000"
   },
   "source": [
    "The `line_items` field is an array of structs. Split the array into its component parts, `product_id`, `sale_price`, and `gross_margin`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0fcdb2f-be7c-4daa-8378-022e1c617861",
   "metadata": {
    "id": "33zixGvkp94E"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe82c208a10>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.split_array_of_structs AS\n",
    " \n",
    "(SELECT date,line_items\n",
    "FROM {dataset_id}.data_after_converting_timestamp_to_date, UNNEST(line_items) AS line_items)\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2aeba2-f24d-45ef-a914-c7806f6441fd",
   "metadata": {
    "id": "6c677762b34a"
   },
   "source": [
    "**View the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d59c479f-9061-4bfe-ab86-5df09adc27f3",
   "metadata": {
    "id": "wRmYwn7Vp94F"
   },
   "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>date</th>\n",
       "      <th>line_items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>{'product_id': 15287, 'sale_price': 31.0, 'gro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>{'product_id': 211, 'sale_price': 31.979999542...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>{'product_id': 20547, 'sale_price': 68.0, 'gro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>{'product_id': 28329, 'sale_price': 8.98999977...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>{'product_id': 28305, 'sale_price': 75.1100006...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30690</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>{'product_id': 28305, 'sale_price': 75.1100006...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30691</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>{'product_id': 17102, 'sale_price': 59.9500007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30692</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>{'product_id': 21832, 'sale_price': 49.9900016...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30693</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>{'product_id': 1297, 'sale_price': 86.61000061...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30694</th>\n",
       "      <td>2020-10-31</td>\n",
       "      <td>{'product_id': 4321, 'sale_price': 31.75, 'gro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30695 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date                                         line_items\n",
       "0      2020-04-22  {'product_id': 15287, 'sale_price': 31.0, 'gro...\n",
       "1      2020-04-22  {'product_id': 211, 'sale_price': 31.979999542...\n",
       "2      2020-04-22  {'product_id': 20547, 'sale_price': 68.0, 'gro...\n",
       "3      2020-04-22  {'product_id': 28329, 'sale_price': 8.98999977...\n",
       "4      2020-04-22  {'product_id': 28305, 'sale_price': 75.1100006...\n",
       "...           ...                                                ...\n",
       "30690  2020-10-31  {'product_id': 28305, 'sale_price': 75.1100006...\n",
       "30691  2020-10-31  {'product_id': 17102, 'sale_price': 59.9500007...\n",
       "30692  2020-10-31  {'product_id': 21832, 'sale_price': 49.9900016...\n",
       "30693  2020-10-31  {'product_id': 1297, 'sale_price': 86.61000061...\n",
       "30694  2020-10-31  {'product_id': 4321, 'sale_price': 31.75, 'gro...\n",
       "\n",
       "[30695 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.split_array_of_structs\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ededdfd4-327a-4911-ab8d-1e44654f0cfe",
   "metadata": {
    "id": "7acc57476f1c"
   },
   "source": [
    "**Remove the extra columns to keep only `date` and `product_id`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ed79b41-b51e-4c66-899a-da8232be5c4c",
   "metadata": {
    "id": "2UamlX9Np94F"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe827d3d950>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.splitting_struct_columns AS\n",
    " \n",
    "(SELECT date,line_items.product_id as product_id\n",
    "FROM {dataset_id}.split_array_of_structs)\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8baea78-f537-43f0-b47e-d2f3a1eecc7e",
   "metadata": {
    "id": "4f9d72c483e8"
   },
   "source": [
    "**View the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e255e14d-cb09-4f87-a165-cd021ec1742c",
   "metadata": {
    "id": "Bnv0lr4-p94G"
   },
   "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>date</th>\n",
       "      <th>product_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-04-17</td>\n",
       "      <td>2482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-04-17</td>\n",
       "      <td>22822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-04-17</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-04-17</td>\n",
       "      <td>20547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-04-17</td>\n",
       "      <td>27417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30690</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30691</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>17279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30692</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>10340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30693</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>16119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30694</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>16513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30695 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  product_id\n",
       "0      2023-04-17        2482\n",
       "1      2023-04-17       22822\n",
       "2      2023-04-17         174\n",
       "3      2023-04-17       20547\n",
       "4      2023-04-17       27417\n",
       "...           ...         ...\n",
       "30690  2021-10-11         147\n",
       "30691  2021-10-11       17279\n",
       "30692  2021-10-11       10340\n",
       "30693  2021-10-11       16119\n",
       "30694  2021-10-11       16513\n",
       "\n",
       "[30695 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.splitting_struct_columns \n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e31d188-60d8-49d0-a9f2-0f2409a014dc",
   "metadata": {
    "id": "7c0c4245acb7"
   },
   "source": [
    "**Count the sales of a product for each date.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f51e6c0-6761-467e-a11f-67949798adce",
   "metadata": {
    "id": "MBeBKffvp94G"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe89103d190>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.sales_count_per_date AS\n",
    " \n",
    "(SELECT date,product_id,COUNT(*) as sales_count\n",
    "FROM {dataset_id}.splitting_struct_columns GROUP BY date,product_id)\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3127a81-dd6b-46c1-9fd5-6ea0387bc0b3",
   "metadata": {
    "id": "oN-5H8w3p94H"
   },
   "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>date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>19855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-15</td>\n",
       "      <td>18052</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>7110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-07-22</td>\n",
       "      <td>27461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-17</td>\n",
       "      <td>24482</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29499</th>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>21659</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29500</th>\n",
       "      <td>2022-03-20</td>\n",
       "      <td>28304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29501</th>\n",
       "      <td>2022-02-18</td>\n",
       "      <td>19804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29502</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29503</th>\n",
       "      <td>2020-10-14</td>\n",
       "      <td>16258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29504 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  product_id  sales_count\n",
       "0      2019-10-31       19855            1\n",
       "1      2023-01-15       18052            1\n",
       "2      2023-05-04        7110            1\n",
       "3      2021-07-22       27461            1\n",
       "4      2023-01-17       24482            1\n",
       "...           ...         ...          ...\n",
       "29499  2020-07-26       21659            1\n",
       "29500  2022-03-20       28304            1\n",
       "29501  2022-02-18       19804            1\n",
       "29502  2020-11-23         141            1\n",
       "29503  2020-10-14       16258            1\n",
       "\n",
       "[29504 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.sales_count_per_date\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1391da9-d032-449f-8968-5b280083f6a2",
   "metadata": {
    "id": "722fd013c28c"
   },
   "source": [
    "**Create a view for the five products that have sold the most units over the entire date range.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6a92d78-8ee4-48d9-99dd-9650636f94b9",
   "metadata": {
    "id": "KgRwHUjrp94H"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe8806cb590>\n"
     ]
    }
   ],
   "source": [
    "query = f'''\n",
    "CREATE OR REPLACE VIEW {dataset_id}.top_five_products AS (\n",
    "    WITH topsellingitems AS(\n",
    "         SELECT \n",
    "            product_id,\n",
    "            sum(sales_count) sum_sales\n",
    "        FROM\n",
    "            `{dataset_id}.sales_count_per_date` \n",
    "        GROUP BY \n",
    "            product_id\n",
    "        ORDER BY sum_sales DESC\n",
    "        LIMIT 5 #Top N\n",
    "    )\n",
    "    SELECT \n",
    "        date,\n",
    "        product_id,\n",
    "        sales_count\n",
    "    FROM\n",
    "        `{dataset_id}.sales_count_per_date` \n",
    "    WHERE\n",
    "        product_id IN (SELECT product_id FROM topsellingitems)\n",
    "    )\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "582e79e3-638e-4369-86d6-9af60ee93d80",
   "metadata": {
    "id": "BXochSk_p94I"
   },
   "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>date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-25</td>\n",
       "      <td>20547</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>23641</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>28305</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-26</td>\n",
       "      <td>13596</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-10-03</td>\n",
       "      <td>23641</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>20552</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>13596</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494</th>\n",
       "      <td>2019-07-17</td>\n",
       "      <td>23641</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>2022-03-31</td>\n",
       "      <td>20552</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>2022-01-11</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2497 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  product_id  sales_count\n",
       "0     2021-12-25       20547            2\n",
       "1     2021-06-10       23641            1\n",
       "2     2022-12-01       28305            3\n",
       "3     2020-11-26       13596            2\n",
       "4     2022-10-03       23641            1\n",
       "...          ...         ...          ...\n",
       "2492  2020-12-08       20552            2\n",
       "2493  2022-06-16       13596            1\n",
       "2494  2019-07-17       23641            1\n",
       "2495  2022-03-31       20552            2\n",
       "2496  2022-01-11       20552            1\n",
       "\n",
       "[2497 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT * FROM {dataset_id}.top_five_products\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9817c864-2435-436c-a447-3485d5e6e06c",
   "metadata": {
    "id": "292855967806"
   },
   "source": [
    "**Load the data into a dataframe and view the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "319cf403-39a2-4954-aff0-42cd5a2d2cee",
   "metadata": {
    "id": "495c76e598a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            date  product_id  sales_count\n",
      "0     2021-12-25       20547            2\n",
      "1     2021-06-10       23641            1\n",
      "2     2022-12-01       28305            3\n",
      "3     2020-11-26       13596            2\n",
      "4     2022-10-03       23641            1\n",
      "...          ...         ...          ...\n",
      "2492  2020-12-08       20552            2\n",
      "2493  2022-06-16       13596            1\n",
      "2494  2019-07-17       23641            1\n",
      "2495  2022-03-31       20552            2\n",
      "2496  2022-01-11       20552            1\n",
      "\n",
      "[2497 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "df = query_job.to_dataframe()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21b2e32-5bc2-4a38-a7f0-7d9c79da2e8e",
   "metadata": {
    "id": "5c78641e2881"
   },
   "source": [
    "**Check the data types of your dataframe's fields.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cdc92f37-352e-4f7b-9a34-b8050c1b2564",
   "metadata": {
    "id": "bb63bbdcead4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           dbdate\n",
       "product_id      Int64\n",
       "sales_count     Int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65db3785-f7a6-49c2-803a-965776479327",
   "metadata": {
    "id": "ef761dcd0109"
   },
   "source": [
    "**Convert the `date` field's data type to `datetime`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ce2c59b1-2626-4fdf-b61e-f7448b691eaa",
   "metadata": {
    "id": "0534d564d09e"
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d67608-05d3-4cb1-b80f-0db924914733",
   "metadata": {
    "id": "8ec5fa4b1ea0"
   },
   "source": [
    "For this forecasting model, date values need to be present for all dates, for each product.\n",
    "\n",
    "To construct a dataframe with `0` values for the `sales_count` field, on dates in which products were not sold, determine the minimum and maximum dates so that you know which dates need `0` values.\n",
    "\n",
    "**First, get the earliest (minimum) date.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a3563b2-363d-4db8-9309-2875d93043fb",
   "metadata": {
    "id": "iCCEozWrp94K"
   },
   "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",
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       "\n",
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       "        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>min_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     min_date\n",
       "0  2018-08-21"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT MIN(DATE) AS min_date FROM {dataset_id}.top_five_products\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "min_date = query_job.to_dataframe()\n",
    "min_date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27eccb52-940d-4f85-a568-aadbd7f4e397",
   "metadata": {
    "id": "fbb934199011"
   },
   "source": [
    "**Get the latest (maximum) date.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "622ce17d-38ee-4caf-b76f-b6295467f95b",
   "metadata": {
    "id": "uIJX3B8dp94K"
   },
   "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>max_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-06-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     max_date\n",
       "0  2023-06-13"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = f'''\n",
    "SELECT MAX(DATE) AS max_date FROM {dataset_id}.top_five_products\n",
    "'''\n",
    "query_job = bq_client.query(query)\n",
    "max_date = query_job.to_dataframe()\n",
    "max_date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537e27f6-c537-48a2-8071-468031727a3f",
   "metadata": {
    "id": "247a08d866ef"
   },
   "source": [
    "**Add the full date range of values to a dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "218bcc19-f9f7-4fb3-8551-6db1d9856608",
   "metadata": {
    "id": "d41c099464ac"
   },
   "outputs": [],
   "source": [
    "dates = pd.date_range(start=str(min_date.values[0][0]), end=str(max_date.values[0][0])).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e151f7-3871-4943-87d0-993564f573d7",
   "metadata": {
    "id": "b9d3afc9f7cd"
   },
   "source": [
    "**Get a description of the `dates` dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8d699dce-d149-4a20-9a77-8f92207ae57e",
   "metadata": {
    "id": "1b1f67e375fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1758 entries, 2018-08-21 to 2023-06-13\n",
      "Freq: D\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   0       1758 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 27.5 KB\n"
     ]
    }
   ],
   "source": [
    "dates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099d52f4-8d7d-4018-9a31-1aa652b0e1db",
   "metadata": {
    "id": "49eb81d44b65"
   },
   "source": [
    "**View the data for one of the products, sorted by date, to show that many dates are not present in the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d875f753-e43e-478f-9860-bb218ed04853",
   "metadata": {
    "id": "7194f59c71b5"
   },
   "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>date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2018-09-22</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>2018-10-27</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>2018-11-16</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2023-06-06</td>\n",
       "      <td>20552</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>2023-06-07</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>20552</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>2023-06-10</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2141</th>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>693 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  product_id  sales_count\n",
       "370  2018-09-22       20552            1\n",
       "398  2018-10-24       20552            1\n",
       "1293 2018-10-27       20552            1\n",
       "1415 2018-11-16       20552            1\n",
       "1793 2018-11-27       20552            1\n",
       "...         ...         ...          ...\n",
       "362  2023-06-06       20552            4\n",
       "2239 2023-06-07       20552            1\n",
       "824  2023-06-09       20552            2\n",
       "1497 2023-06-10       20552            1\n",
       "2141 2023-06-13       20552            1\n",
       "\n",
       "[693 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['product_id'] == 20552].sort_values(by=['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d40f5b-62da-4888-95ee-0ea82b696f0c",
   "metadata": {
    "id": "326f4f48f5cc"
   },
   "source": [
    "To make a dataframe in which each product has zero missing dates, merge each of the five products' data with the `dates` dataframe.\n",
    "\n",
    "Start with `product_id` `20552`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae88f9df-84c4-4ec2-9add-376895e9399f",
   "metadata": {
    "id": "a0b443c8d160"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data after converting for a product with product_id 20552\n",
      "           date  product_id  sales_count\n",
      "0    2018-08-21       20552            0\n",
      "1    2018-08-22       20552            0\n",
      "2    2018-08-23       20552            0\n",
      "3    2018-08-24       20552            0\n",
      "4    2018-08-25       20552            0\n",
      "...         ...         ...          ...\n",
      "1753 2023-06-09       20552            2\n",
      "1754 2023-06-10       20552            1\n",
      "1755 2023-06-11       20552            0\n",
      "1756 2023-06-12       20552            0\n",
      "1757 2023-06-13       20552            1\n",
      "\n",
      "[1758 rows x 3 columns]\n",
      "           date  product_id  sales_count\n",
      "0    2018-08-21       13596            0\n",
      "1    2018-08-22       13596            0\n",
      "2    2018-08-23       13596            0\n",
      "3    2018-08-24       13596            0\n",
      "4    2018-08-25       13596            0\n",
      "...         ...         ...          ...\n",
      "1753 2023-06-09       13596            1\n",
      "1754 2023-06-10       13596            3\n",
      "1755 2023-06-11       13596            0\n",
      "1756 2023-06-12       13596            0\n",
      "1757 2023-06-13       13596            0\n",
      "\n",
      "[1758 rows x 3 columns]\n",
      "           date  product_id  sales_count\n",
      "0    2018-08-21       23641            0\n",
      "1    2018-08-22       23641            0\n",
      "2    2018-08-23       23641            0\n",
      "3    2018-08-24       23641            0\n",
      "4    2018-08-25       23641            0\n",
      "...         ...         ...          ...\n",
      "1753 2023-06-09       23641            1\n",
      "1754 2023-06-10       23641            0\n",
      "1755 2023-06-11       23641            2\n",
      "1756 2023-06-12       23641            1\n",
      "1757 2023-06-13       23641            1\n",
      "\n",
      "[1758 rows x 3 columns]\n",
      "           date  product_id  sales_count\n",
      "0    2018-08-21       28305            1\n",
      "1    2018-08-22       28305            0\n",
      "2    2018-08-23       28305            1\n",
      "3    2018-08-24       28305            0\n",
      "4    2018-08-25       28305            0\n",
      "...         ...         ...          ...\n",
      "1753 2023-06-09       28305            0\n",
      "1754 2023-06-10       28305            1\n",
      "1755 2023-06-11       28305            1\n",
      "1756 2023-06-12       28305            1\n",
      "1757 2023-06-13       28305            0\n",
      "\n",
      "[1758 rows x 3 columns]\n",
      "           date  product_id  sales_count\n",
      "0    2018-08-21       20547            0\n",
      "1    2018-08-22       20547            0\n",
      "2    2018-08-23       20547            0\n",
      "3    2018-08-24       20547            0\n",
      "4    2018-08-25       20547            0\n",
      "...         ...         ...          ...\n",
      "1753 2023-06-09       20547            0\n",
      "1754 2023-06-10       20547            0\n",
      "1755 2023-06-11       20547            1\n",
      "1756 2023-06-12       20547            2\n",
      "1757 2023-06-13       20547            0\n",
      "\n",
      "[1758 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "df1 = (\n",
    "    pd.merge(\n",
    "        df.loc[df['product_id'] == 20552],\n",
    "        dates,\n",
    "        left_on='date',\n",
    "        right_on=0,\n",
    "        how='outer',\n",
    "    )\n",
    "    .sort_values(by=['date'])\n",
    "    .drop(columns=0)\n",
    ")  # merging dates dataframe with product_id matching rows\n",
    "df1['product_id'] = 20552  # product_id will be null so making it the specified values\n",
    "df1.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "df1 = df1.fillna(0)  # for sales_count making null values as 0\n",
    "df1['sales_count'] = df1['sales_count'].astype(\n",
    "    'int'\n",
    ")  # convert sales_count column to integer\n",
    "print('data after converting for a product with product_id 20552')\n",
    "print(df1)\n",
    "\n",
    "df2 = (\n",
    "    pd.merge(\n",
    "        df.loc[df['product_id'] == 13596],\n",
    "        dates,\n",
    "        left_on='date',\n",
    "        right_on=0,\n",
    "        how='outer',\n",
    "    )\n",
    "    .sort_values(by=['date'])\n",
    "    .drop(columns=0)\n",
    ")  # merging dates dataframe with product_id matching rows\n",
    "df2['product_id'] = 13596  # product_id will be null so making it the specified values\n",
    "df2.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "df2 = df2.fillna(0)  # for sales_count making null values as 0\n",
    "df2['sales_count'] = df2['sales_count'].astype(\n",
    "    'int'\n",
    ")  # convert sales_count column to integer\n",
    "print(df2)\n",
    "\n",
    "df3 = (\n",
    "    pd.merge(\n",
    "        df.loc[df['product_id'] == 23641],\n",
    "        dates,\n",
    "        left_on='date',\n",
    "        right_on=0,\n",
    "        how='outer',\n",
    "    )\n",
    "    .sort_values(by=['date'])\n",
    "    .drop(columns=0)\n",
    ")  # merging dates dataframe with product_id matching rows\n",
    "df3['product_id'] = 23641  # product_id will be null so making it the specified values\n",
    "df3.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "df3 = df3.fillna(0)  # for sales_count making null values as 0\n",
    "df3['sales_count'] = df3['sales_count'].astype(\n",
    "    'int'\n",
    ")  # convert sales_count column to integer\n",
    "print(df3)\n",
    "\n",
    "df4 = (\n",
    "    pd.merge(\n",
    "        df.loc[df['product_id'] == 28305],\n",
    "        dates,\n",
    "        left_on='date',\n",
    "        right_on=0,\n",
    "        how='outer',\n",
    "    )\n",
    "    .sort_values(by=['date'])\n",
    "    .drop(columns=0)\n",
    ")  # merging dates dataframe with product_id matching rows\n",
    "df4['product_id'] = 28305  # product_id will be null so making it the specified values\n",
    "df4.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "df4 = df4.fillna(0)  # for sales_count making null values as 0\n",
    "df4['sales_count'] = df4['sales_count'].astype(\n",
    "    'int'\n",
    ")  # convert sales_count column to integer\n",
    "print(df4)\n",
    "\n",
    "df5 = (\n",
    "    pd.merge(\n",
    "        df.loc[df['product_id'] == 20547],\n",
    "        dates,\n",
    "        left_on='date',\n",
    "        right_on=0,\n",
    "        how='outer',\n",
    "    )\n",
    "    .sort_values(by=['date'])\n",
    "    .drop(columns=0)\n",
    ")  # merging dates dataframe with product_id matching rows\n",
    "df5['product_id'] = 20547  # product_id will be null so making it the specified values\n",
    "df5.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "df5 = df5.fillna(0)  # for sales_count making null values as 0\n",
    "df5['sales_count'] = df5['sales_count'].astype(\n",
    "    'int'\n",
    ")  # convert sales_count column to integer\n",
    "print(df5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d617237-0020-4367-9707-eba18c91de5a",
   "metadata": {
    "id": "6a2d033b733e"
   },
   "source": [
    "**Merge all five dataframes into one new dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2fc40bb-07ba-4a94-8969-de0a2d27a35d",
   "metadata": {
    "id": "bf570c0c6913"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-21</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-08-22</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-23</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-25</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>2023-06-10</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>2023-06-11</td>\n",
       "      <td>20547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>2023-06-12</td>\n",
       "      <td>20547</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8790 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  product_id  sales_count\n",
       "0    2018-08-21       20552            0\n",
       "1    2018-08-22       20552            0\n",
       "2    2018-08-23       20552            0\n",
       "3    2018-08-24       20552            0\n",
       "4    2018-08-25       20552            0\n",
       "...         ...         ...          ...\n",
       "1753 2023-06-09       20547            0\n",
       "1754 2023-06-10       20547            0\n",
       "1755 2023-06-11       20547            1\n",
       "1756 2023-06-12       20547            2\n",
       "1757 2023-06-13       20547            0\n",
       "\n",
       "[8790 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdList = [df1, df2, df3, df4, df5]  # List of your dataframes\n",
    "new_df = pd.concat(pdList)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fa40c3-7882-4bd4-9406-51b899b4c047",
   "metadata": {
    "id": "4a75fa1dc8dc"
   },
   "source": [
    "**Reset the index of the dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "28bb2577-5870-46cd-818f-3d048c5ae0f1",
   "metadata": {
    "id": "7d258952fc71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           date  product_id  sales_count\n",
      "0    2018-08-21       20552            0\n",
      "1    2018-08-22       20552            0\n",
      "2    2018-08-23       20552            0\n",
      "3    2018-08-24       20552            0\n",
      "4    2018-08-25       20552            0\n",
      "...         ...         ...          ...\n",
      "8785 2023-06-09       20547            0\n",
      "8786 2023-06-10       20547            0\n",
      "8787 2023-06-11       20547            1\n",
      "8788 2023-06-12       20547            2\n",
      "8789 2023-06-13       20547            0\n",
      "\n",
      "[8790 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "new_df.reset_index(inplace=True, drop=True)\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7af4b88-2d90-4f6c-8e94-25709d9495ee",
   "metadata": {
    "id": "97e1289b2106"
   },
   "source": [
    "**View the five product IDs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c132e17-960b-4a88-b50e-86d2932cd37f",
   "metadata": {
    "id": "5Gjad6uSp94O"
   },
   "outputs": [],
   "source": [
    "query = f'''\n",
    "SELECT DISTINCT product_id from {dataset_id}.top_five_products\n",
    "'''\n",
    "query_job = bq_client.query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12caf37f-a6d0-41f9-99c9-62d99e62d37f",
   "metadata": {
    "id": "MUynE3dnp94O"
   },
   "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>product_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id\n",
       "0       23641\n",
       "1       13596\n",
       "2       20552\n",
       "3       20547\n",
       "4       28305"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7795c3cc-2344-450a-8c8a-0a1e84603eaa",
   "metadata": {
    "id": "aa06b0e893cb"
   },
   "source": [
    "**Plot `sales_count` over time, for each product.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea28607e-69b9-4cf9-9bcf-959767d8796f",
   "metadata": {
    "id": "9cdce0080aa1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17532., 17897., 18262., 18628., 18993., 19358.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    new_df.loc[new_df['product_id'] == 20552]['date'],\n",
    "    new_df.loc[new_df['product_id'] == 20552]['sales_count'],\n",
    ")\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "33cdeed8-400f-4e28-bc6b-4f5258978ca2",
   "metadata": {
    "id": "6fef99223f63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17532., 17897., 18262., 18628., 18993., 19358.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    new_df.loc[new_df['product_id'] == 20547]['date'],\n",
    "    new_df.loc[new_df['product_id'] == 20547]['sales_count'],\n",
    ")\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "440bff17-509b-478e-8e7d-b043953b5bd1",
   "metadata": {
    "id": "d281151b0e2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17532., 17897., 18262., 18628., 18993., 19358.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    new_df.loc[new_df['product_id'] == 28305]['date'],\n",
    "    new_df.loc[new_df['product_id'] == 28305]['sales_count'],\n",
    ")\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2420e099-16dc-44a2-8e23-d1e344f192a3",
   "metadata": {
    "id": "a50eeeeaedb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17532., 17897., 18262., 18628., 18993., 19358.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    new_df.loc[new_df['product_id'] == 23641]['date'],\n",
    "    new_df.loc[new_df['product_id'] == 23641]['sales_count'],\n",
    ")\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1af92145-fc64-4f72-bb9c-5d628b26254d",
   "metadata": {
    "id": "4d002f0717ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17532., 17897., 18262., 18628., 18993., 19358.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    new_df.loc[new_df['product_id'] == 13596]['date'],\n",
    "    new_df.loc[new_df['product_id'] == 13596]['sales_count'],\n",
    ")\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a128469-9cfb-443b-b036-44d92bd08dd7",
   "metadata": {
    "id": "755a811fbad5"
   },
   "source": [
    "**List the data types for the `new_df` dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "13b8b930-708a-42d4-9749-9400899f2d82",
   "metadata": {
    "id": "220ec814875c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           datetime64[ns]\n",
       "product_id              int64\n",
       "sales_count             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b70976-5470-40c6-9b7e-df16d6c24f7b",
   "metadata": {
    "id": "ff06b56ffa7a"
   },
   "source": [
    "**Create a new BigQuery table out of the `new_df` dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7a4e6b69-df76-44d2-a9b2-ce1308dbb195",
   "metadata": {
    "id": "2d303f1cfd90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LoadJob<project=automlops-sandbox, location=US, id=1a57939e-d30a-43b2-afe9-5681a9d7c7f9>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_config = bigquery.LoadJobConfig(\n",
    "    # Specify a (partial) schema. All columns are always written to the\n",
    "    # table. The schema is used to assist in data type definitions.\n",
    "    schema=[\n",
    "        bigquery.SchemaField('product_id', bigquery.enums.SqlTypeNames.INTEGER),\n",
    "        bigquery.SchemaField('date', bigquery.enums.SqlTypeNames.DATE),\n",
    "        bigquery.SchemaField('sales_count', bigquery.enums.SqlTypeNames.INTEGER),\n",
    "    ],\n",
    "    # Optionally, set the write disposition. BigQuery appends loaded rows\n",
    "    # to an existing table by default, but with WRITE_TRUNCATE write\n",
    "    # disposition it replaces the table with the loaded data.\n",
    "    write_disposition='WRITE_TRUNCATE',\n",
    ")\n",
    "\n",
    "# save the dataframe to a table in the created dataset\n",
    "job = bq_client.load_table_from_dataframe(\n",
    "    new_df,\n",
    "    f'{PROJECT_ID}.{dataset_id}.{SALES_TABLE}',\n",
    "    job_config=job_config,\n",
    ")  # Make an API request.\n",
    "job.result()  # Wait for the job to complete."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f608f6d-da3f-4fc6-84fc-6e051f0e2d0d",
   "metadata": {
    "id": "c2e0e9aa67cd"
   },
   "source": [
    "**Create a training dataset by setting a date range that limits the data being used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "65bd3afe-4db5-462e-9efe-31918f31600d",
   "metadata": {
    "id": "e1fffed34704"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x7fe823c5e7d0>\n"
     ]
    }
   ],
   "source": [
    "# select the date-range and item-id(top 5) for training-data and create a table for the same\n",
    "TRAININGDATA_STARTDATE = '2022-01-01'\n",
    "TRAININGDATA_ENDDATE = '2023-01-01'\n",
    "query = f'''\n",
    "CREATE OR REPLACE TABLE {PROJECT_ID}.{dataset_id}.training_data AS (\n",
    "    SELECT\n",
    "        *\n",
    "    FROM\n",
    "        `{dataset_id}.{SALES_TABLE}`\n",
    "    WHERE\n",
    "        date BETWEEN '{TRAININGDATA_STARTDATE}' AND '{TRAININGDATA_ENDDATE}'\n",
    "        );\n",
    "'''\n",
    "# execute the query (as it is a create query, there won't be any tabular output)\n",
    "query_job = bq_client.query(query)\n",
    "print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3574b10a-cd13-4164-97d4-c9e5956528d7",
   "metadata": {
    "id": "2f7d9d2d4229"
   },
   "source": [
    "**Select the original data for plotting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f858570f-14a7-4934-bea1-323eab173663",
   "metadata": {
    "id": "1b82fc7c7bf6"
   },
   "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>date</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>20552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>20552</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8622</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>20547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8623</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8625</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>20547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>20547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1830 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  product_id  sales_count\n",
       "1229 2022-01-01       20552            1\n",
       "1230 2022-01-02       20552            1\n",
       "1231 2022-01-03       20552            0\n",
       "1232 2022-01-04       20552            1\n",
       "1233 2022-01-05       20552            4\n",
       "...         ...         ...          ...\n",
       "8622 2022-12-28       20547            1\n",
       "8623 2022-12-29       20547            0\n",
       "8624 2022-12-30       20547            0\n",
       "8625 2022-12-31       20547            0\n",
       "8626 2023-01-01       20547            1\n",
       "\n",
       "[1830 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_historical = new_df[\n",
    "    (new_df['date'] >= pd.to_datetime(TRAININGDATA_STARTDATE))\n",
    "    & (new_df['date'] <= pd.to_datetime(TRAININGDATA_ENDDATE))\n",
    "].copy()\n",
    "df_historical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a61841-e41f-4eaf-b4ed-0d313cdb1f50",
   "metadata": {
    "id": "c25b75dc957a"
   },
   "source": [
    "## Modeling with BigQuery and the ARIMA model\n",
    "\n",
    "**Create an ARIMA model using the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "10ec836f-84da-4dbd-8721-d6655c805c75",
   "metadata": {
    "id": "00638d9f7e55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<google.cloud.bigquery.table._EmptyRowIterator at 0x7fe822f7d310>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train an ARIMA model on the created dataset\n",
    "query = f'''\n",
    "CREATE OR REPLACE MODEL `{PROJECT_ID}.{dataset_id}.arima_model`\n",
    "\n",
    "OPTIONS(\n",
    "  MODEL_TYPE='ARIMA_PLUS',\n",
    "  TIME_SERIES_TIMESTAMP_COL='date',\n",
    "  TIME_SERIES_DATA_COL='sales_count',\n",
    "  TIME_SERIES_ID_COL='product_id',\n",
    "  SEASONALITIES=['DAILY','WEEKLY','MONTHLY','QUARTERLY','YEARLY'],\n",
    "  DATA_FREQUENCY='DAILY',\n",
    "  model_registry='vertex_ai') AS\n",
    "\n",
    "SELECT\n",
    "    date,\n",
    "    product_id,\n",
    "    sales_count\n",
    "FROM\n",
    "  `{dataset_id}.training_data`\n",
    "'''\n",
    "job = bq_client.query(query)\n",
    "job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e1783e-ba13-48aa-b605-a468ffc9e8e2",
   "metadata": {
    "id": "a02551627598"
   },
   "source": [
    "## Evaluate the model\n",
    "\n",
    "To evaluate the trained model, get forecasts for the 90 days after the last date of the training data. In BigQuery ML, provide the number of forecast days using the `HORIZON` argument. Specify the confidence interval for the forecast using the `CONFIDENCE_LEVEL` argument."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1637b141-0834-427d-a623-ee8d423711fa",
   "metadata": {
    "id": "801528e3e2c7"
   },
   "source": [
    "**Load the data into a dataframe named `dfforecast`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd524928-2665-48c5-ab81-66ed04434ee5",
   "metadata": {
    "id": "f1277e846d10"
   },
   "outputs": [],
   "source": [
    "query = f'''DECLARE HORIZON STRING DEFAULT \"90\"; #number of values to forecast\n",
    "DECLARE CONFIDENCE_LEVEL STRING DEFAULT \"0.90\"; ## required confidence level\n",
    "\n",
    "EXECUTE IMMEDIATE format(\"\"\"\n",
    "    SELECT\n",
    "      * , \n",
    "      ROUND(forecast_value) AS forecast_value_rnd,\n",
    "      ROUND(forecast_value) - standard_error AS prediction_interval_lower_bound_rnd,\n",
    "      ROUND(forecast_value) - standard_error AS confidence_interval_lower_bound_rnd,\n",
    "      ROUND(forecast_value) + standard_error AS prediction_interval_upper_bound_rnd,\n",
    "      ROUND(forecast_value) + standard_error AS confidence_interval_upper_bound_rnd\n",
    "    FROM\n",
    "      ML.FORECAST(MODEL {dataset_id}.arima_model,\n",
    "                  STRUCT(%s AS horizon,\n",
    "                         %s AS confidence_level)\n",
    "                 )\n",
    "    \"\"\",HORIZON,CONFIDENCE_LEVEL)'''\n",
    "job = bq_client.query(query)\n",
    "dfforecast = job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7d9061-f3ba-472a-87af-9e4f5d2c6356",
   "metadata": {
    "id": "1e0549381849"
   },
   "source": [
    "**View the first few rows.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "45069cd8-cafb-4ed3-b475-1c6713cce790",
   "metadata": {
    "id": "83b07f243b72"
   },
   "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>product_id</th>\n",
       "      <th>forecast_timestamp</th>\n",
       "      <th>forecast_value</th>\n",
       "      <th>standard_error</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>prediction_interval_lower_bound</th>\n",
       "      <th>prediction_interval_upper_bound</th>\n",
       "      <th>confidence_interval_lower_bound</th>\n",
       "      <th>confidence_interval_upper_bound</th>\n",
       "      <th>forecast_value_rnd</th>\n",
       "      <th>prediction_interval_lower_bound_rnd</th>\n",
       "      <th>confidence_interval_lower_bound_rnd</th>\n",
       "      <th>prediction_interval_upper_bound_rnd</th>\n",
       "      <th>confidence_interval_upper_bound_rnd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13596</td>\n",
       "      <td>2023-01-02 00:00:00+00:00</td>\n",
       "      <td>-0.166704</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.166949</td>\n",
       "      <td>-0.166460</td>\n",
       "      <td>-0.166949</td>\n",
       "      <td>-0.166460</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13596</td>\n",
       "      <td>2023-01-03 00:00:00+00:00</td>\n",
       "      <td>0.036944</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.036398</td>\n",
       "      <td>0.037491</td>\n",
       "      <td>0.036398</td>\n",
       "      <td>0.037491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000332</td>\n",
       "      <td>-0.000332</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13596</td>\n",
       "      <td>2023-01-04 00:00:00+00:00</td>\n",
       "      <td>0.403935</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.403020</td>\n",
       "      <td>0.404849</td>\n",
       "      <td>0.403020</td>\n",
       "      <td>0.404849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000556</td>\n",
       "      <td>-0.000556</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.000556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13596</td>\n",
       "      <td>2023-01-05 00:00:00+00:00</td>\n",
       "      <td>0.307543</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.306205</td>\n",
       "      <td>0.308881</td>\n",
       "      <td>0.306205</td>\n",
       "      <td>0.308881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>-0.000814</td>\n",
       "      <td>0.000814</td>\n",
       "      <td>0.000814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13596</td>\n",
       "      <td>2023-01-06 00:00:00+00:00</td>\n",
       "      <td>0.989961</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.988149</td>\n",
       "      <td>0.991773</td>\n",
       "      <td>0.988149</td>\n",
       "      <td>0.991773</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998897</td>\n",
       "      <td>0.998897</td>\n",
       "      <td>1.001103</td>\n",
       "      <td>1.001103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id        forecast_timestamp  forecast_value  standard_error  \\\n",
       "0       13596 2023-01-02 00:00:00+00:00       -0.166704        0.000149   \n",
       "1       13596 2023-01-03 00:00:00+00:00        0.036944        0.000332   \n",
       "2       13596 2023-01-04 00:00:00+00:00        0.403935        0.000556   \n",
       "3       13596 2023-01-05 00:00:00+00:00        0.307543        0.000814   \n",
       "4       13596 2023-01-06 00:00:00+00:00        0.989961        0.001103   \n",
       "\n",
       "   confidence_level  prediction_interval_lower_bound  \\\n",
       "0               0.9                        -0.166949   \n",
       "1               0.9                         0.036398   \n",
       "2               0.9                         0.403020   \n",
       "3               0.9                         0.306205   \n",
       "4               0.9                         0.988149   \n",
       "\n",
       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
       "0                        -0.166460                        -0.166949   \n",
       "1                         0.037491                         0.036398   \n",
       "2                         0.404849                         0.403020   \n",
       "3                         0.308881                         0.306205   \n",
       "4                         0.991773                         0.988149   \n",
       "\n",
       "   confidence_interval_upper_bound  forecast_value_rnd  \\\n",
       "0                        -0.166460                -0.0   \n",
       "1                         0.037491                 0.0   \n",
       "2                         0.404849                 0.0   \n",
       "3                         0.308881                 0.0   \n",
       "4                         0.991773                 1.0   \n",
       "\n",
       "   prediction_interval_lower_bound_rnd  confidence_interval_lower_bound_rnd  \\\n",
       "0                            -0.000149                            -0.000149   \n",
       "1                            -0.000332                            -0.000332   \n",
       "2                            -0.000556                            -0.000556   \n",
       "3                            -0.000814                            -0.000814   \n",
       "4                             0.998897                             0.998897   \n",
       "\n",
       "   prediction_interval_upper_bound_rnd  confidence_interval_upper_bound_rnd  \n",
       "0                             0.000149                             0.000149  \n",
       "1                             0.000332                             0.000332  \n",
       "2                             0.000556                             0.000556  \n",
       "3                             0.000814                             0.000814  \n",
       "4                             1.001103                             1.001103  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfforecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c0936a3-9625-4568-84ff-1f56483cab12",
   "metadata": {
    "id": "0e01aa3098f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 450\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of rows: {dfforecast.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d99080-198d-4dc7-9939-5ef96a8ba272",
   "metadata": {
    "id": "6fa36b762a33"
   },
   "source": [
    "**Clean the historical and forecasted values for plotting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "05f807f4-8ec3-4f67-a843-79e02fbd874c",
   "metadata": {
    "id": "073a2dd6a5d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_historical.sort_values(by=['product_id', 'date'], inplace=True)\n",
    "dfforecast.sort_values(by=['product_id', 'forecast_timestamp'], inplace=True)\n",
    "\n",
    "# Select the actual data to plot against the forecasted data\n",
    "day_diff = (new_df['date'] - pd.to_datetime(TRAININGDATA_ENDDATE)).dt.days\n",
    "df_actual_90d = new_df[new_df['product_id'].isin(dfforecast['product_id'].unique())][\n",
    "    (day_diff > 0) & (day_diff <= 90)\n",
    "].copy()\n",
    "df_actual_90d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b523acb-aae3-4b26-856d-f43b44defc98",
   "metadata": {
    "id": "d40a95ad0616"
   },
   "source": [
    "**Plot the historical and forecast data.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "923d7d34-fcf0-4384-a4b3-0e15f2ff9ed6",
   "metadata": {
    "id": "c6845ad425d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_id :  13596\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_id :  20547\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_id :  20552\n"
     ]
    },
    {
     "data": {
      "image/png": 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XV2NZFnPnziWZTA5qbaKwDSjjq7a2ljvuuIMVK1bw9ttv86lPfYrTTz+dDz/8sNvtX3/9dc4//3y+8pWv8O6773LGGWdwxhln8MEHH+Rk8UIIIYQYmJ0dMcleELRE4zSEOjCt4ZkKNpbEdINIQiemGyO9FJFDlm0TiSfRDYtYUn62uzNMi4G06tIUBcsu7IwvRVHwuRzD+s/rcuB2qvhdDtwONXv7cPZNKyoq4rzzzuPXv/41jz76KE888QQtLS1AKoCW6bmVMXv2bF577bUut7322mvst99+WLZNZqV2H39/5s2bx4svvtjtfTt37mTt2rV85zvf4bjjjmP27NnZpvdCdGdAGV+nnXZal89/8IMfcN999/HGG290qbfNuPvuuzn55JP51re+BcCtt97K888/z7333ssvf/nLISxbCCGEEAOlmybLtzRwQPV4qqRnzZgW0w3ihoFhmWiqtHwdirhuEE3qRJP6SC9F5FBcN0iaVjbjS3SlWxYDGSqoqQqmZRd0xtfeYFmpQkFVTQUSbdse1qDXT37yE6qqqpg/fz6qqvLYY49RWVlJSUkJkJrs+OKLL3LEEUfgdrspLS3lW9/6Fueeey7z58/n+OOP5x//+Ad//etfeeGFFzAtK7vevqZS3nDDDey///5cfvnl/Nd//Rcul4v//Oc/nHPOOZSVlVFeXs6vfvUrqqqq2LJlC//7v/87bN8Hkf8G3ePLNE3+/Oc/09HRwcKFC7vdZtmyZRx//PFdbjvppJNYtmzZYJ9WCDEAUT2KbsqBeHdMy6RD7xjpZQixV8USCcxoiKQ5/FOnxOgWiicwTAtDTkKHLBJuxUgmiEtwpKDEdAPdNDEtm6gux1K7SxomiqLgjO/EF9rU4z8lfRyqpjO+9GH6+xNOhodlv6NNJtilKgq2bfcZPBqqYDCYbTJ/yCGHsGnTJv71r39lpyb++Mc/5vnnn2fixInMnz8fgDPOOIO7776bu+66izlz5nD//ffz4IMPcswxx2BZNqqd+ruT6fXVk3322YfnnnuO9957j0MPPZSFCxfy97//HYfDgaqq/PnPf2bFihXMnTuXa665hh/96EfD+r0Q+W3Al/hWrVrFwoULicfjBAIB/va3v7Hffvt1u21jYyMTJkzoctuECRNobGzs9TkSiQSJRCL7eSgUGugyxRgSiicIxZPUlgRHeimjSsyIccpfT2G8bzx/Oe0vI72cUeeaJdfwZsOb/POsfzLOO26klyPEXuF+9DxO3Po2Wy98BcqKR3o5YgSFYslUv50CLjvaK+IhDvrriUz1VFE39a8jvRqRQ6nAl4VDUyWbrxsJw0BTFKaufoDJHz/c43avnfJ3okVTslk+w1Hq+I/1/+DbS7/NLYtu4cyZZ+Z8/6NJJtClKKl+WYOJey1ZsmSP25588snsx3annV566aVceumlPe7rtNNO26MqDODrX/86X//617vcZtk2rngz3vgO2tt2gKvrudsxxxzT5bkBFi9evEfZZMbxxx/P6tWru9zW+fFTpkzZY39i7BpwxtesWbNYuXIlb775Jl//+te56KKL9viFG6rbb7+d4uLi7L+JEyfmdP+igNg2q9Z+wMYPXx3plYw6G9o2sDO+kzUtawglJXi8u/d2vEfUiLK+bf1IL0WIvca9+RWcZhT3lldGeiliBBmWRYeuY9pSdjRUxsaluJNtVIXWEAm3j/RyRA7FIy3UNL/JjKZXCMWlWfbu4rqJpipYmhvdGezxn62oOBNtVG34OzO3/nNY3nPeanwLgPeb38/5vkcb27bwJFvxRrdj29awZ3zlkmXZ+OI7UAB/rBEznb0mxN4w4Iwvl8vFjBkzADjooINYvnw5d999N/fff/8e21ZWVrJ9+/Yut23fvp3Kyt4nCN1www1ce+212c9DoZAEv0T3mj/hiL99Ct3hJ3HoFtxO6VOSURepy368LbyNovKiEVzN6GLbdjYYKEFBMZZEJ8zHt/1ddNUz0ksRIyhRt4JPP3MBhuoiOuuNkV5OXouVTCWTs2C1bMKy90Udxn47Yu+xd27gpBXXEXeV8uzU4Z3Ul3fat1G28Z/gKGfdAd9k3QHf7HVzf9snzH3rJmLOYjYe2XP20GBtDW8FIJQo/GM60wZfvBkViw5nMZadP5NkuwbpdpVqavKeKfaCQff4yrAsq0tZYmcLFy7cYxLD888/32NPsAy3201RUVGXf0J0a8ntADiNDtpb6kd4MaPLtvC2XR9HtvWy5dgTM2IYVqoXy1g4SBIiQ3ekT9FjMvloLHO8fAf++HaKo3XohvSlGoqofxI7i/YBwB2ukz5fBSTw4SMAeJKtGPEICXmtZNlbl7PgzW9zwJqf92t7w5U6l3MbkWHp8ZU5zh0LFzOVRBiVVNacZul5lfFl27uy/TqCk7EZXKmmEIMxoMDXDTfcwCuvvMKmTZtYtWoVN9xwA0uWLOGCCy4A4MILL+SGG27Ibn/VVVfxzDPP8OMf/5iPPvqIm2++mbfffpsrr7wyt1+FGLPsxlXZjzu2rxvBlYw+WyNbd30c3trLlmNP5wOjsXCQJAQAHTsx9DgAsXDzCC9GjCS7U+DTikl53lDEdIOQtwoAT8c2mf5XQIq37ioJd0fqiCblZ5thpt9DDGf/khP0dOBLtU2MeG6Pu3RTp7Ej1T+60I/pbNvGkWjLfq5ZOqaVP5Ej20iVDNso2KpzrzTnFyJjQHVhTU1NXHjhhTQ0NFBcXMy8efN49tlnOeGEEwDYsmVLdsIDwKJFi3jkkUf4zne+w7e//W1mzpzJk08+ydy5c3P7VYixybKgbXP20+SO9cCnRm49o0znYFfnIJiQwJcYm6y3fkV5U6oPiiaZjmOaFm7IfmzFWoHJI7eYPGc1rARFA8AXkcBXodCTMbzRXe1aPJGtks3XiRVNBb4yAa0+t9c8WKoT1dIxO3KbcVzfUZ+dDljoWfyWbaNauwYtaJZOws6fPo22kaoSszQXmeJGCXyJvWVAga/f/va3vd7f3YSIc845h3POOWdAixKiX8INKOauZqNq+5YRXMzo0yXwJRlfXXQ+MCr0gyQhMqyWjdk0b4cEfMcuI4kjsivwZUal7HUoav91Ka50gMRUNZn+VyASzZtwsuuEPNBRT1QCX1l2rA0Aw9XPieqKgu4qwh3fmfP3nM7HuIV+MdOybTRr17mPZqdKHS3bzovegrrqxvRW4000441swXBXYNnOkV6WGCOkE7jIX62bunzq7dTMfawzLIOGjl0nNpLx1ZVkfImxyG7dlSGbcPbzZEUUnvY6lE4n9Fa0ZQQXk+f0eDbo9cCRT5LwlHCEBEcKgt68ocvnwVg9MV2Cmhl2Onhl9DPjC8BwBnHHd+a8x2TnwFc4GcayLVRlyG2sRyXbslDtXT3SVCuJbZMXgS/btjEVDcNVhCfZglOP4HAWY1q+kV6aGCMK811BjA27Bb48EtzJauxoxOz0h3FbZBumlftmovlKAl9iLFLbNgHwyrEP8eHs/8K08qc8QuSO3bKxy+fJDgl8DZadbreQ1HyogXJsyyYU737gk8gv1m6vk6JYA+G4BL6y4m0AmO7ifj8kUxapxNuxc1je1vniro1NRI/kbN+jTaZUMEMzdWzbzun3c7hY6XUqioKtuoB0j7I8KtUU+U0yvkT+Sge+WsbNp6NiHi1FMynLgysee0PmIGBy0WS2RbZhWAZN0SaqAlUjvLLRQUodxZijx9Eiqea/RvFkTMtGNy00Va5/jTVGywYyhSWtwRnELW1E15PPks0bcQMhbyUBt4to0qAtJoGvgpA+xtxScQQbJhxDrGQmajw+smsaTdJDMbbrTupadzuOUgC70/+AooA+7auYtVHssv0xLAunlpv3nt3beYQSIYoGkImWV8zU+4upuYn5q7FVJ1j50SfLsm2cyRCqpmGpqZ99qkdZ/pRqivwmgS+RvzQXMV8lO2oWox9+JfWtIaYldYJu10ivbMRtC6fGOk8KTgJgc2gzWyNbJfCVJhlfYsxpS/VATGo+THcpdiyObll4RnhZYu+LF01l88TTaArMpG7muXidcig4WEbz+nTgq5rD3/0ewaZ3WXHI99FnT83ZSb0YIeHUhYL68oPZPPEU3A4NZyI1QU9T5QS97aDLWRs4nFjVwSyemTrWtO1Ui/nOk/psO/05NtGqCj6o34Hf6UQ3cxf42hbZ1uXzQj6uy0xFtFQXuupBUxTAxBrgZEfbtvna177G448/TmtrK++++y4HHnhg7he823P6Y42oWCTcZUAq8JX5HUECX2KYydGOyF+Lv8WSirNxKApzfF7qWkO0dMQk8MWujK+aQA2mbaYCX+GtHFJ5yAivbHSQwJcYczqVhh/23OcxbAVj3yUjthwxcsLVC3l1zgQcqoZXVQZ8wiR2sdKvqw5fNSWxDRTHG/CE64jphgS+8tzKw77PzhlXkDBsnFoqM1Y3LeK6jl+OMwlXH8aa8ASqigJMH1fSr8dsD3WwurEZ07YxclRqb9s2deFUj1+n6kS39II+rks4i0kG3KCo6KaJ6ki9zww04+uZZ57hoYceYsmSJUybNo1x48YNx3K7sEwTldTPPenw4U60pHuUpQKlg3nHnDJlCldffTVXX311TtcqCpMEvkTeyvSnCXhclNoRSprfI+SYCWX7j/DKRl4m7bs2WIuVrp2XBve7dD4oCifD2Z4DQhSsomrWz7wAPRFj3y1/xVQctBjS928siukGumnhczpRFRVTykwGTUkHviK+GuKaDk1v4+nYSixpUORxj+zixKDZtk0onsR0FWOqJrXt7xNoW09d+cFE9ckS+AJ0M5Vl5HL0P1zhblvHPlufocNbiW5Ozck6QslQtqfXzNKZrN65umBbWFi2jakoKA4flm3j1jtwJztQNS+m1v9eawDr16+nqqqKRYsWDWottm1jmiYOxwBCCekyTUtxoCup15Bqpfrm5UOppsh/0txD5K1QPJXuW+rz4P3nN1n40iU4Pnl2hFc1OnQOfNUGa7vcJrr29TJtkw69YwRXI8TwM8bvx7tzvsmqOZcDoNkGRlJ+78ciq2kNVjLK1MaXOPH5sznw3dvQTWkuPBg7Z5zBO1MuoKV8HpROASAQrScmkx3zWtwwSZqpCwNOTWXuR7/hkFV3Ur7zPeLyswXbxv/xU9Q0v4lH7f97h2fTf1i06jam1f0DI0fvOZlj2wpvBeO944HCzeRPNYdPt06zwWXG8CRacBkdmANocH/xxRfzjW98gy1btqAoClOmTCGRSPDNb36T8ePH4/F4OPLII1m+fHn2MUuWLEFRFP79739z0EEH4Xa7Wbp0KZZlcfvttzN16lS8Xi8HHHAAjz/+eJfn+/DDDzn11FMZX1lDcJ8jOfrMS1i/aQs2sHzlh3zus6dSU1lJcXExixcv5p133sk+1rZtbr75ZiZNmoTb7aa6uppvfvObABxzzDFs3ryZa665BkVR5AK26JNkfIn81PQRgd+fwYKimajnP5o94JTJjimZ7K7agGR8dSecDO/xecAVGKHVCDH8YrpB0rBwuYLYioZimxgdLTBu/EgvTexNsVZm/uVkZqDw0SE3EohsIeKpxDBN3API3BApW6sWs9KczbiAF2ci9XclEK2nTZfpf/ksue19jnjj/2gt3pePZn8NvWgi7FhOMNpANCmBLxJhJr90DZOBZXNP6PfDFG8pAC49jJ6jUse6SKrMsTZYS5E71dB+92O8EdHbhSVFA6enn9uq4PQCYFs2vtAmcLgwtCJsywA9hmor2N7KVLlgP4I/d999N9OnT+dXv/oVy5cvR9M0rr/+ep544gl+97vfMXnyZO68805OOukk1q1bR1lZWfax//u//8tdd93FtGnTKC0t5fbbb+fhhx/ml7/8JTNnzuSVV17hi1/8IhUVFSxevJht27Zx9NFHc8wxx/Cvvz3KeFeSl9/9GNO0aAnOYLuynfO+cAE/+dndeJ0OfvzjH3PKKafwySefEAwGeeKJJ/jpT3/Kn//8Z+bMmUNjYyPvvfceAH/961854IADuOyyy7j00kv7/LqFkMCXyE+tG3FGGvA4S/D53LsCX5LVRDgZpi3RBuxW6ijfm6zdrwaGkiGqkMb/onDpW1fiiCZwlUzA9hSjxFrQIztHellib0uX5kVdpTiKqwFw6iEM6fM1KKFYKvPcrWn4xs8AIBitpz4hwZF8ZjZ9RG3T67j1CJucV6CUTYX1EIzV0yFBTYi3AWAoTlxuf78fpvl2Bb6iZm5K7TPHtjWBmuwkx1GR8XVbdc/3zTwRLnhs1+c/mgF6tPttJx8Jl/wTAMvU8f7uJIi34+20iROwv9VIf6sFi4uLCQaDaJpGZWUlHR0d3HfffTz00EN8+tOfBuDXv/41zz//PL/97W/51re+lX3sLbfcwgknpIKdiUSC2267jRdeeIGFCxcCMG3aNJYuXcr999/P4sWL+fnPf05xcTGP/OlP2KFteJJtTJxzEDH3OCzb5uhjjk1P+FQp8rj51a9+RUlJCS+//DKnnnoqW7ZsobKykuOPPx6n08mkSZM49NBDASgrK0PTNILBIJWVlf374sWYJqWOIj9lDt79NakGsunAl69DgjuZ6TZlnjL8Tn+21LEl3kK0pz+sY0x3gS8hCpZtU/LnMzj3pVOpNRuxPamTD6OjZYQXJvY2s2UjACFvFQ5/6vfAqYfRc3QSOpYY4e14G97En9hB0OPCVZEKfAUSOwh1yN+UfGZlXie+aoq8ruwxZlGsnvZ4YgRXNkrE2gBIOAO4BjAVVvOVAODKYbC9c2uPoCsIULA9vjI9srpj29ag+2StX78eXdc54ogjsrc5nU4OPfRQ1qxZ02Xbgw8+OPvxunXriEajnHDCCQQCgey/3//+96xfvx6AlStXctRRR6E5HKhW6kKBqTizpYnbGxu55srLmT93DsXFxRQVFRGJRNiyJTWJ+pxzziEWizFt2jQuvfRS/va3v2EYcmFBDI5kfIn8lA58xfypoE7moMTbsQ3dNMf0NKVt4VTgqzaQ+t4EXUGK3cW0J9rZFtnGzNKZI7m8USFzUDTeN56maFPhHiQJARDdiap3YKPgKp8G3hJoBTPaOtIrE3uZ2bwBjVQzdp8/Vb7i0sN05KjsaCzR1y3hmKX/RX3xPOr3eQxncDxRfw0hrZh4uBnLnikDA/KU2rYZgJCnimKPG60s1Yg9GGugPSaBL+LtACQdwezEy/7IlDq69UjOgu2Zi721gdpsieOouJj57fqe71N2O0f51rpett31/bWNBJz/Z3SHj5CvFo9Dw9fyEQo2qmXslQbxfv+uDL9IJDVU4J///Cc1NTVdtnO7U8M9vN50maZtE3VXYLgNdNWDqig49AhXfuVL7Gxt57Y7f8R+M2fg9XpZuHAhyWQqSDZx4kTWrl3LCy+8wPPPP8/ll1/Oj370I15++WWcTuewf72isEjGl8hP6cBXPJgJfE0GwJUM0d66fYQWNTpkennVBHb9Ecp8LOWOEDfiJNNXnTLBwVFxkCTEcEm/X3a4x+Hx+qFkEmFfLQlpaD7mZDK+Ovw1uALlALj0iFxBHwRz5wYAwr5q/G4nKArvnPUsfz3454SdpSSkCXreygS+wt5qvE4njnHTAPDFd6DHoyTG+ERcO5a6aJJwBnGoAziV9JQA4DbCGDn6HnbO+Mr0+BoVx3Quf8//Ovf36nPbTkWNRhKcXix3MYrLj8MTxHQXgdOLZumDDnxNnz4dl8vFa6+9lr1N13WWL1/Ofvvt1+Pj9ttvP9xuN1u2bGHGjBld/k2cOBGAefPm8eqrr5JIJjE0D4arGFNx4FAVVCyWvbWCb3zlCxx/0snM3m8Obreb5ubmLs/j9Xo57bTTuOeee1iyZAnLli1j1apVqW+dy4UpGcuinyTjS+SnTOArE9xx+bF8FajRHUQa1zFuXC+19QWuLryr0WdGbaCW1TtXS4N7dh0QaYpGpb+yy21CFKT0+2XEV43X6YDPPcCz739MwOUa2XWJvU5pTZ3Qx/w1uIOpwJeKhREPAyUjt7A8ZLdsAlKvq/J0uVeRx42iKiQNi5hu4HVJRkI+cqWPo8K+Kqa6HHh8FegOH04jiiuylbi+75geBmHG2nAASWcQxwAyvvCWAKmpwno8AkwY0joMy6ChowHYLeOrALP4bdtGMdOlgmqqVFBTVSzVCVYSzdIxB1k+6vf7+frXv863vvUtysrKmDRpEnfeeSfRaJSvfOUrPT4uGAxy3XXXcc0112BZFkceeSTt7e289tprFBUVcdFFF3HllVfy//7f/+OLX/gCV17z35SXlrLsjWUcsfBwZkyZyMypk/jjY39nn8NPwErE+fYN/5vNEgN46KGHME2Tww47DJ/Px8MPP4zX62Xy5FTCw5QpU3jllVf4/Oc/j9vtZty4cYP6HoixQQJfIv/YNnbrJhTAne6pAcCR1/DhjjZiShFTRmpto0B2omPnwFf6Y8n42nVAlCkBBWhPtI/kkoQYVnbLRhRSWT5lLieaqqIp6qAPkkX+UttTfVPigVq8viL04imELQdWUvo/DlQmK6jDX0OtK3U47XU5cWoqumEQk4yvvGToCTzRRgCigVo8Tgcel5O3DrqFDi1Am2s8UV2n2Ose4ZWOHDtdJp90BvEOJOPLFWD90T9iVatOhTX0MuDGjkZM28SluqjwVWTLHgvxYqZl2116ZGmqgqoqRLyVRBUVU9HQ7MFncd9xxx1YlsWXvvQlwuEwBx98MM8++yylpaW9Pu7WW2+loqKC22+/nQ0bNlBSUsKCBQv49re/DUB5eTkvvfQS1157Lad/+kQ0TWPO/gdw7OKjURwufvvjm7js+u9z3FFHUFs7kdtvv43rrrsuu/+SkhLuuOMOrr32WkzTZP/99+cf//gH5eWpCze33HILX/va15g+fTqJRAJ7L5R7ivwlgS+RfxJhkhMOxGrdhH/81OzN6qIr2LJ6PQ517F6Fgz17fEGnwJdkfGUPiIpcRaNrApAQw8Rs2YiD1DCQ6nRmiqYqmEM4SBb5qXnWObRu/QCzdBpOTWXbJUt5Ye1mDnKX9f1g0YUjlAki1qQyKYHSzS9w7ivfpbl4H6IzHxzJ5YlBirc34tI8qQwa33h8TgeqohCZegLbwx3ohkksObaDmsmpn2LFvDjtnkrmDyTjS1HomHUGdeu2ELCH3m0n29ojWIOqqAV9TGfboFqpiaKm6sSpKKkegpoLy7ZRFAXLtlMBsn70Frz66qu5+uqrs597PB7uuece7rnnnm63P+aYY7oNKimKwlVXXcVVV13V43PNmzePfzz+MN5oA7ojQLuvhmKvG8OyOGDufiz/18O0BKbi8fjxuZx87nOfyz72jDPO4Iwzzuhx34cffjjvvfden1+vECCBL5GPPEWsO/WPbGkNcWSwuMtdCspeae44Wlm2tavR526ljiAZX7DrgCjoCu6aAFSAB0lCZNjpvk6JQKoZLh/8lcUv/pCmcQtg//tGeHVib/pk1kWs9DdxUGmqxMihqihAXJceKQNi6rjSJVbJ4ES86SbLLpebotg2kpqHesn4yktR9zj+evyz+OwYTocDT/pnW+x1U9cWxoYxn82XKJ3BJ7WnoigMeJiUI51xnMjBe07mmDbTxzbT4yucDKdKAwtouIRl24QDU3BjYODCraa+Nk1VME0bDTDtVEkko+zr7lymaaXLNFVFQVVSpZqqlcAxhFJNIfpLAl8iL7VEYwCphrIZyQ7KWj7ANmIwe2oPjyxsTdEmdEvHoTiY4NvVOyETBNsW2VZwBwMDJRlfYqyJ7Hcu9WoNyfHzUq/9ZITito+IussxLRtNHbvvB2NNOJ5EVRSKPKkyLYemoigQSSRHeGX5xW6rQ7EtDNWFEqzMTrZzjpsOQDDWSDgWH8klikGK6Qa6bZNwBKhwu7Lvj8XJHeyz5SlMzUWk+osjvMqRpVsWpm3hUrWB9fgCgtuXM7vxXXTtMGx72pCORztPdASyx3SmbdKhdxBwBQa979HGsm1sRcPSXGCa2awuDRtfbDsaJiFPFZadCoKNJpZto1id+5ORDn7ZmKoTh5VAs3QMyUIXw0ymOor8Y9vZq9Nd0nnr3+XQF77E3Le/P2ZrvDNXv6oD1WidSj4r/ZVoikbCTNAca+7p4WNCpsdXkbto19XBRHgklyTEsGqZeSav7XMl7qr0dKb0ZC1nMoxhSabPWJHcuRnHznV40PGkS/MCL3+Pz758PsXrnhrh1eWXpDPA2/P+hzenfblLryf3uNRFN7fZQax9x0gtTwxBTDdQAN00CXp2/WyLwhs4YvWP2H/To7THEiO3wFFA3fwaVU1v4NPbBjbVESh/5z6OXf1DSpvfxRhihk/niY4AHocHl5oa2lJoFzQt28YGMt+xzPmPqip4k624kiEU2xqVVS+7l2mq2YwvBUtzYaOiYGGmSzWFGC4S+BL556krOfKpk6jZ/HTX20unAOCN1hNNjM2Dkmy/g8y0yzSn6sxOMBzrfb4k40uMNbGkjm3b+DIT5tKTtZx6CN2UK6xjhbX8t5z8n/M4Yt392d8FLdpMcccW3B1NI7y6/BJ1FLF24um8N/nz2ew5ANXlI+6tAEBp3ySvrzxUuvT7HP/O/zC+eQVBz66qAq0sFdQMROuJxBJj+gS96NXbOGnFdYxvXZXNduwv25NqUeJIhDDMoV142T3wBbvKHQvtuE5JhAjEd6Dp4WypIICiOrCU1IVu1UoyGqsFLdtG6xT40pTU74yqKMQ9FbQW70PSMw47U6opxDCRwJfIPy0b8UYbUdTdxoQHq7BVJ6plEGraNCJLG2ndHQRkSJ+vFAl8iTEl3Ii99W18RhhvevJcJuPLpYflxHwMsVtTvd6i/upsM3bFm5rYpcl74IDEdIOkaeLSVLyurscietEkANzhrcTHeC+ofOTd9iaTdryOy+zA59z1s3WVT8FGwWlGUWItY7rPl5KehG24iwac8ZV5z3HoYXRraH9/slPMOw1zyh7XJQrrPU1NRvAmW3DoHdlSQQBVSQWTADRLxxqFkS/LMlDtVJAzM5ES0uWOqoad/ti2GdMBZTH8JPAl8o7dugkAs3hy1ztUDbtkIgDR7Z/s5VWNDtmDgO4CX0EJfEGq6SmkrgoWu1NXHkPJkFxlEoXpo3+y/78+zzGr78gGOzIZXy49jDHEEw+RP5TWzQDEArXZ3wW1U/afnHD0n71pKeNaVuI2Ona9rtLM4lTgyxvZSkzXR2J5YpBs28aTPo6K+Kq6/Gy93gAxTyqbzxOpG9NBTTWeCnypntKBPzjznpMMYQzhwks4GaYt0QbslvFVgBc0u/TIUnaVCkIqa8pKl3dqtj46pzUb6cb2ioatqF3a1Giqkuo9DICUOorhJYEvkV/0OITqAfBOmL7H3UppKhU92bx+ry5rtMhmfAV6CXyN9VLHxJ4ZX4ZlEDNiI7ksIYaFlZ7o2OGv2ZW9kC410awkRqJjpJYm9jJHaAuQmkLo2S3jyzHEk9CxpuzVWzjtrW9Q2/7enoGv8XNoKp5NzFFEdAwHR/JRsqMVVzIV1EkEandlyQIuh0YsfWzljWwjmhyjQU3bRktnfCnpINZAKJkek0PM+Mo0ti/zlOF3+rO3F2Kpo23bqOaepYKwW+ArPRlxtF3INVQn7f5JxPzVQNf+zJqiEIxuwx9aj2Kbo7JUUxQOmeoo8kt7HQo2hsNHSVnNHncr6T5f7jGa1SSljn3rXOrodXhxKA4M2yCUDOFz+kZ4dULkltWyERWIB2qzwQ5cQUxvOVHFix6PABUjuUSxN8RDOOKtAChlU3adeGSzL1Inoa5RNw9sdHKG6gBIBCbuEfjSD72cZwIno1sm5UkJfOWTxI4NuIG4qxTVXbTnz7ZoIjS/QzBaP3ZLHZMdKHbqa1f9A8/4Un2pxzj1oQXbM8eyu/e0DbqCwK7s/kKwe48sd6dJzIqiYGu7Sh1t28a2YTQNbzdsMB0+bFVBsewugS9FUXCYcTTbQLOSWJZnBFcqCp1kfIn8ki5zjPprKPF797y/NFX+6InU7cVFjQ4xI8bO+E6gj1LHsZ7xldw11VFRlIK8OihEht2WKm+LByfuakKsquy8fBX/Ov5xoo6iEVyd2GvSvwdRZzGBorJdt2ezLyTjq99irTjSfy86Z89leJ0OnJqKbtpjNysoT5k7NwAQ8VXjdmq4HV1/tnZJ6hgzGBvDga94GwCmouH0DPzvRybw5UoOrcdkJuNr9wqHTCZ/ezorrRBYpoFK6nuVKXXsTHGkBmyoloHN6OqTZaUnNWZ6eCl0DcqpitKpR5kxOks1RcGQjC+RX9KBr5i/hiJHN1empx3LRwuup614FtV7d2Ujbls4dRAQdAWzf/g7y1wVa4o2kTATuDX3HtuMBZ1LHTP/t8RbCq4RqhAAWluqvM1VPq3L7ZmGxGP25G2MyWT+hb1VBFyuXXf4xxH3VRJzlaJIv7d+MVs2oQFRVym+QPEeJ6FepwOXQ0OxTdpj8ZFZpBgUM10aHvZWdZnWmRGffRb/0mYQCU6hKjY2p4cTawMg4Qjicg78NNIx8WDeOugWGihhvyG859SFUxe4d7/QW4g9vjBSv2uW4oBO/b2ynH52BqfjcLixTXNUBb5s28aVaEMFks4AqF0Dd6qiYKguMGNotk7Csrn44otpa2vjySefHLF1i8IkgS+RX7yltI6bT3vpbCZ0d3/VPNr2L6c1FsewrAFPm8ln3U236azEXYLf6adD72BbZBvTiqd1u12h61zq2Pn/gjpIEgIg1oqavurtrOj6endqqQsHEvgaGxIl01k36zLalAC1nfoWMfFQVp//Cqvqd3CSZHz1i968Hg1o91RT5HXtcb9LUzlpyQX4Q5t46cTHse3J2QlsYnQz9ThJzUfIW0WRZ8+frWPcdBqbNbxOB+3xMRr4Clax6uDv0hzpoHIQx9hKcS1NUz/DtpYQ+wyl1LGHYU6FeExnm+nAl+ZKTULcPeNLVbEVB5lb90bg6+abb+bJJ59k5cqVvW5n2TaeRAsOK4mhToT015CR6lGWyfhKZks1hRgOEvgS+WX/z/GGtgCHqrJPD5uU+b20xuKEYgnKuiuHLFC99feCVB19baCWta1r2RreOiYDX0kzSdxMXYHPlDgG3al+EIV0kCQEkM2Q7XCV4fEGu9zlefkWTlz9LHX7/xdM/coILE7sTdHiaaycfhGmZTMzM+QgzZPOntZNcySWlneMnamsoIivumv2XIaioGGh2QbO0BbihrlHrygxOm3Y71LWlp2JbRksdDn3uN/rdODQVBRFIaYb6KaZvYgwZvjLWT/xVLaHO5ioDe7issuhYdr20Jrbh3sodSzA9hVJZxHR4Awcir1HqSDsahafiReNpowvy7ZxpfuTGUrX/mSQ+lpsLfU+qlo6NmAzetYvCsvYSYcRY0ZFZB2VW56lfceWkV7KXtXT1a/Osn2+xmiD+8yBkIJCwBkAOl0dlFJHUWgClXxy0P/ywdQv4NvtxFtr30pp+8c4Iw0jtDixN8V0g6Rp4dTUPYIwjvTJ61D67YwpLZuAVOCr89S/zoyiSQB4IluJS1Zl3gjF4zgdDmzVsWsKbidep4N96p9j3pr70CKNRMfg8ALDsjBsC01Vs+8dA2KZ1DS8zL71/0ZPDG6atmmZu3p89ZDxFU4URnN727YxLRtUB4bi7DbjS1UUPHo73kgdLiPS78DXM888w5FHHklJSQnl5eWceuqprF+/Pnv/1q1bOf/88ykrK8Pv93PwwQfz5ptv8tBDD/G9732P9957D0VRUBSFhx56iE2bNqEoSpcssNbmHag18/nP6yuwVAe2ZfGVr3yFqVOn4vV62Xffffn5rx4EdjXnl7iXGC5yCUrkD9smmUxl65T6ep76Ufz89cxveJePfffClFl7a3UjLpvx1UOpY+f7xmqD+0xwK+gKoqbHQRdiWrwQABRV8eGkswkndGbsFuxQ09P8tAJqACx6Zm98FV9Ix1k+fY/A18QnP8+E9np2VvwFxpWMzALzSPOsc1hpjqe1ZDZV3QRHAKySyVAHvsg2YrrBwGffib3NtGwiCR1VSQWDu8vS8zidzF7/B4rDG9hacgBx/RCKvWOrX6rRtJaKxmXYjkqc6mAqBxT2e+Ua5tgWb879DDBpwHvYEduBbuk4FAcTfF0bn4z0MZ1t28SMwQX0umNZNlE9gaIoWLaNQ1WJGdYe2+iJNvxmDFN1kbT6Fznq6Ojg2muvZd68eUQiEW666SbOPPNMVq5cSTQaZfHixdTU1PDUU09RWVnJO++8g2VZnHfeeXzwwQc888wzvPDCCwAUFxezffv2PZ7DNpOpNaoaqdb2NrW1tTz22GOUl5fz+uuvc9lllzG5SOVzZ56aeoxEvsQwkcCXyB/RFpx3zWCxdwKNF73a42Zq2RRoeBc7XeYzVvRV6tj5vrGe8ZUZdw0jf5AkxHBJGia6aeHS1D0yvkgHvhzye1/4LIuaf1zI+ZbOi5/+B67dBsO42tbjjTazI9oyQgvML22ls3mvqogSr7vnEsbSKQAEovUy2TFPJHZu5rgXP0/YP4mlB9/WbTafpiokghMhvAF/dBtRfez9bJVVj7H4jR+zZtKZOA5bPPAdqCqmqwhHog07tnNQa8g0tq8KVKGpXd/PRrrUMWbEOOyRw0bkud/81K/RLB3TtrHTkxR7c/bZZ3f5/IEHHqCiooLVq1fz+uuvs2PHDpYvX05ZWWoS8IwZM7LbBgIBHA4HlZWVvS8q3ZjfVlKvJ7fLxfe+973s3VOnTuXVpa/x8DPL+PRFV4FpSthLDBspdRT5o3UTim2h2iblRb2MUE6Pm/aOoeCObdvZtO+JgYk9bpcJfGW2HWt2b2zf+WMJfIlCk1z/MoGdq/Cg4979BN1TAkjga0wIN6BaOqai4Szd8++D7S4GwOyQwFd/ZJqauxxaj4EvrWwqAIFYvQyQyBPJ5nWUhDdQElqHy+Ho8WdrFqcylILRemJjsNTRjrUCYDiLcA5ygJSZDk7Z6QmRA5Utc+ymwqFz+wp7FPW62lsy5YL9KXf85JNPOP/885k2bRpFRUVMmTIFgC1btrBy5Urmz5+fDXoNhm3bkM34cmbLIn/+859z0EEHUVFRQSAQ4IHf/oZtW+s6PW7QTylEryTjS+SP1lRD2ai/hmJ3Nw1lM9JXWj0dYyfwtTO+k7gZR1VUKgM9X32pCdQAqYyv/lwNKjTt6bKuzBXBzh9Ljy9RaDz/uoZPt2/irWN/g6rM63pnJuNLD2PZ9p7j0UXhSGc/hz0TKPL59rjbTgdBDcn46lu0haK1f2OCVY6n7PA9sucyHONSJWBF0QbWJ5J7c4VikKxOQwv8LidaT0Gd0tTF1aJYAzvGYjZfOlilu4oG3djfdpcAW7L7GqjeKhwygS/DNogZMXzOPd/zhpPX4eXNL7yZs/0lEzFcrZ9gAy3BmQTdrm7fd+KxMN7wNmwriW2nmsr39dM57bTTmDx5Mr/+9a+prq7Gsizmzp1LMpnE6x34cDA1/ZrJBBxtG8xEFABL0VAU+Mujj3Ldddfx4x//mIULFxIMBvnhnXey7I03sEkN4pJSRzFcJPAl8kf64D3qr6FM7eUkLR348kXGTnAncxBQ6avEqXbfcwSgOlCNgkLUiNKaaKXMM/grOflIMr7EmGEaaOn3BVfFjD3vTwc7nMkQumnh7uEEXuQ/o2UjDiDsrcbXzaS6TBBUMr76pte/z/zlNzHFN5FVs//d43bucdNpKZnNTlcl7R2RvbhCMViZ9hghb1WvfbuU0lQ2X1GsnnWxxN5Y2uiSDlYZ7qLBNbcHLE8qy1RL//1xDnA/vQ1z8jq8OFQHhmUQSob2euBLUZScPqeWTODW3JiqG6/Di9/l7jbgqFoaSqQexTbBNvvM+Nq5cydr167l17/+NUcddRQAS5cuzd4/b948fvOb39DS0tJt1pfL5cLcbRJwRUUFAA0NDcyfPx/Ltnnz49QAHcPhQ1UUlr3+OosWLeLyyy/PPm7Dhg2otok/tB7dEZSMLzFspNRR5I/0QUmsl+btQDbw5e2oJz5G+i9k+h301t8LwK25Ge8bD4zNPl/hZGrKT3eBr8x9QhSE0DYUy8BUnLhKa/a831eG7ipGd/gwdjt4FYXFbE5N6Yr4arot31Iygw4k+N8nPZ0VFPL0HhzRPAHePulPPL//d+kwwLRkYuZop7RtBlKBr2AvVQXZMtZoPeF4st8T9ApGPJU5b7iKcAyy1JF04MuRDGEM4rXR2zAnRVGyx3XtBTC8xTb2LBXsjqJpWEoqIKZZOn31ty8tLaW8vJxf/epXrFu3jpdeeolrr702e//5559PZWUlZ5xxBq+99hobNmzgiSeeYNmyZQBMmTKFjRs3snLlSpqbm0kkEni9Xg4//HDuuOMO1qxZw5KXl3Db978PgKk40BSVmTNn8vbbb/Pss8/y8ccfc+ONN/L28uUAOMwEmpXuCTbWXldirxjQO9btt9/OIYccQjAYZPz48ZxxxhmsXbu218c89NBD2Rdq5p/H0/NEPiF6lD4oSfQV+CquxVY0NCtJ+44te2FhI6+3q1+7G8sN7rMZX92VOspJnygkmfI2byVeVzcncVOPZuUFb/Lq4T9Fl5PygmZ1ypbuPvCVmjnokHLvPpktGwDo8Ff33Ng+LehJBcaSpklU+nyNelp76ngx7K3G08vP1jEuFfhyGRHsWCvxMfazVRNtAJiuogFnamXY6WC7MxHCMIcQ+OrhmLegMvnNVCDIVJ0o0GNbAlVRMFUnNgpqPzK+VFXlz3/+MytWrGDu3Llcc801/OhHP8re73K5eO655xg/fjynnHIK+++/P3fccQdaOtvs7LPP5uSTT+bYY4+loqKCP/3pT0CqQb5hGBx00EFcd+213HDjTek92miqwte+9jXOOusszjvvPA477DB27tyZyv5Kf12qpWPbSLGjGBYDKnV8+eWXueKKKzjkkEMwDINvf/vbnHjiiaxevRq/39/j44qKiroEyMZC6ZnIPbt1EwpgFPcx+lhzkvj0XXwYAo/hpI95IwWht6tfu6sN1LJi+4pssGwsyfTx6rbUUU76RCFJBzsivmp8zu7LnzNTy/RBnHiI/JHJZIkFavF2U+qoFlUR9VZiqK4x0x5gsJSWTQBEvNVU9Rn4cuJQLOx4B/Gk0WsWkRh57vRxVMTXQ0lwms9fxH+O+CVhTyUdqpeYbvS6faFR0xlfmRLpwdDnnserynRai/ahZoAZx1E9ys54ahpkj4GvArmgads2aro5vKk6UVWl18BXu38Sqqph2jZKPy5oHX/88axevXqP58yYPHkyjz/+eLePdbvd3d43e/ZsXn/9dQBi8Sh2ZAfRtkY6tCCqouB2uXnwwQd58MEHuzzuO9/5NrSvQ7WS/L/776fII++XIvcGFPh65plnunz+0EMPMX78eFasWMHRRx/d4+MURel73KkQvbFtjIkLiWileCbM7HNz1yGX0LRmA359bFTz9nX1qzPJ+Oo+8JW0ksSNOB6HZKSK/Je5UBD111Dp6v5PfSYgNphSE5E/mud9lW2eeYTLZuNxdJPxdfR1PF9yOknTZB/LGnTD6rFATWcFRQM1fQY7qj54iK+8/kM+rv0M0dn37I3liUFKJuLEXaVoRpRYcGKv2Xxuh4PQhIOJ6QZ6Uh9zGV8Nh/0Pm+s2YBX1fbzZE2XiYWxpL0e3rAH//clMdAy6gl2O5ToLuoJA/rewsGwbxUr9fpmqE03p+ZxGURRQVSxAASzLHvkLGXoMX7IF0/TQ4Q/2OkRHcaQCXaptpTPW9tYixVgypOb27e2pqH9fo04jkQiTJ0/GsiwWLFjAbbfdxpw5c4by1GKsURTqj/0Rqxt3cuC48X1unnlzTRhjo3dNb6Odd5cJfGUeM5Z0V+rod/rRFA3TNgklQxL4EgXBTDc0jwVquz+Js22qn/oSJ7Y30jH+L1AU2OtrFHtHY/WneCOxD7PLytF6GAyjqgqmbqcbTUvgqyfOUCrwFQ9M7LUcDkDzlaLZOoFoPR1jLDiSb2KWwj+P/j0qoGpqn2WsxV43oXgqE2eslbHumP5ZVrCVg3ylg96HQ1PRVJWEYQ4447g/x7uFkslv2Tbt/km4FIukBb5eBnupSiobzLRSU5ot28a2sxWEI8PoVKapKL2uRVU1TEVDs810jzI5Fhe5N+h0GMuyuPrqqzniiCOYO3duj9vNmjWLBx54gL///e88/PDDWJbFokWL2Lq152yTRCJBKBTq8k+IlmgcgBJfP94MQw1U1T1P2db/DPOqRl7CTNAUbQKgJthNE+vdZA4WJOMrRVGU7NXBfD9IEiKj44CLWD77KlomHN59IENRcG1/j9L2j9EjO/b+AsVeE4qn/nYW9daMXVEwLUvKXnuTCOOMpyZfJoO13WbPdaaVp3pBBaP1RJNjY9BOvorpBrppYSsKHoejzym3EyKfMO+j+5i59WliY+xnG9dTF5Q9jsGXdzpiLUxsfIXqptcH3GOyPxUOhdLjK5v1pDpBUXvNmAJw2gaBjm34O7Ziw4gOXrBtG8XMNOZ39dqfjPR9lprK+tJsXQaCiGEx6IyvK664gg8++KDL6NPuLFy4kIULF2Y/X7RoEbNnz+b+++/n1ltv7fYxt99+O9/73vcGuzRRiBIRWsMhQOvzShwAW5Zx4LL/oWXcgXD8JcO9uhFVH6nHxsbn8FHq7vsKXOZgoTHaiG7qOLWx05siE9gqdhV3ub3IVURboi3vD5KEyAhPOJj3J5Uzs6Kkx21sTzEk2tE7WvfewsReZbdsxLP5ZUrs8QTck7vfaPuHHPmf/yKs+DD2e3Kvri+fWKqTZQvvIdmyGU+wrMfsuQxXxXQAgvFGQtHY3liiGKRoUseybUzLpsjj6rM8rCS0jqoNf6C+/BA2xL+0l1Y5CsTa8G9ZwoSIisvRR7/dXjib13DE8utp8U+hZdF5A3psf4Y5FU7gKxW4ysa/+vi9VBUFtxHGRsG27RENfFm2jWqlgsKZjK++Al+G6kK1LRRSmWsjXqopCs6gMr6uvPJKnn76af7zn/9QWzuwGm+n08n8+fNZt25dj9vccMMNtLe3Z//V1dUNZpmikLxyJ8f85TBmfHBf/7YvTR3g+yJbC/6qQeerX/35A1HuKcejebBsi4aOhuFe3qjSXcZX58/z/SBJiIxoUsewLIq9vWTIekoAMKM7986ixF6nr/kXi16/iqPW/7LHIQfYNkXN71PWvnZQE9bGiritUTfuUFbXnEqxp+fsuQx36UQsxYFmGyRbx16GdT4pWno7Z7x2MZO3PE1RP362Slk6my9WT3u65HFMaFrDAUsu5+QPb8UxyImOAGq6Mb7LiAw4y7Q/w5yK3amLm/l+TKckwgSj9bgSLdBHqSCk+mTZgEKqN9jIB746NeZXlF7PURQFor4qQkXT0F1FqVLNvbVYMWYM6F3Ltm2uvPJK/va3v/HSSy8xderUAT+haZqsWrWKqqqqHrdxu90UFRV1+SfGuNbNqLaJ2UMjyz2Upn43PfFmwpH2YVzYyMte/epHfy9IlfaNxQb3uqUTM1JX3Tv3+Or8eb4fJAkBQHg77o+eoiy8rvcM2fTJhxVt2yvLEnuf1bIRgA5/TXaK5x4yJ6F6GH2AE9bGklQ5nImmKgT6M6FR1YgHqgFwhbaQMMZWL6h84mheQ1lkPZqVxO/uOwveWT4NAH+skXg8NnZKhONtACSdwaH1Aky/57j18ICb2w+o1DHf21foMdxGGE2PpvrP9ZnxpWIpqd9fzda7TGjc2yzbRktnfBmKs88M2UxgLJXllcpys6TDvcixAQW+rrjiCh5++GEeeeQRgsEgjY2NNDY2EovtSuG+8MILueGGG7Kf33LLLTz33HNs2LCBd955hy9+8Yts3ryZr371q7n7KkTha90EQDw4sX/be0uxMlNdGnvOLiwEA5nomJHt8xUZO4GvzgdAAWfXRt7S40sUlK1vMfmlq1i85q5eA1+KN1UarSTa9tLCxN5mp/92xvw1Pf8upDP/NNvASET2zsLykP3xc0yq+zelie39a7kAGEWpcjB3ZCvRpAS+RitHKFVZEvJW9zm0AMBVVpvKYrFNHB3bieljo8+XFU2VxScdAZzqEKame1IZWU4rgZ7ofxmwbdvZ49aJgZ7PBwoliz/TI6s/pYKQCh6ZajrwZemYI9njy9RRSGVtWaoDrY+1K4qCpijpjLWRL9UUhWlA71r33Xcf7e3tHHPMMVRVVWX/Pfroo9lttmzZQkPDrvKp1tZWLr30UmbPns0pp5xCKBTi9ddfZ7/99svdVyEKXubgXSub0r8HKAqUpraN75DA1+7GYsZX5gAo4AygqV2vVBbKQZIQQPZCQdRXjc/Vc/aCkr7qrknAt2CpbZsBiAdq8fZU6ujyYyup98TMia3Yk+fdB1m86vvUtqzoOXtuN/GJi9gw4VhCzjLiY2z6X76wLAtvelJgzF+Drx+BL6/LRdSXqlxxR7YSGyM/WzMT+HIV4RxCqSPuYmxSgRBjAKX2zbFmEmYCVVGpDFT2uF0hZPF3LRV09VkqCKlTH0vrFPgawYwpC42W4HSiwalA30E7AE2xKA5vJNi2FpDAl8i9ATW370/K5JIlS7p8/tOf/pSf/vSnA1qUEF3EWlHS6dW+CTP7/TC1bApsX4W5c+PwrGuUyIx2rgn0PdExI7PtmMr46qG/V+fb8vkgSYgMs2UTGhD11zK+t4wvXzlJ164TEFFgbBtn+xYA9OJJPU+qUxQsTzFarAVbyl575AilgoixQG3P/dJ2Ezv0SpYUpx43Y4wER/JNvG0bPiuBhUoiUI23l4sFGQ5VJR6oJRjZgr9jG7Exks1nxVKBL90ZxD+UjC9VxXQFcSRDAyq1zxzvVvoqcao9/5wKodSxa+DL0WepIKQyvuzMZERLJ2GPXIN407axVSeGqoJpovRn/aoDzUqiYKNaBrbdj5JyIQZgCO9aQuwlramDxoSnnLKS8v4/Lp3x5Un/oSxEndO+JeOrd5kDoN37e3W+LZ8PkoTIyPR1igdqei/bOf67PPfZl/hon8KefDtmRZpQzTgWKs7SHiY6ptnpZtCmZHx1z7JwhVJ/LxOBif0qhwPwOZ04VBXDssZMOVy+0XdsACDqnYDT5ep/GWtxqoy1KFY/ZjK+7E6Br6E0twew0u85dqyt34+pC6dKUvs63u18MXOk+lzFdINQPDHof23ROJF4nFDSpC0J0YTe6/Yx3Uj1yNJc2KliQawRLBc0rdQltUzgrT8ZX6o6fKWatm1z2WWXUVZWhqIorFy5kmOOOYarr76618dNmTKFn/3sZzlbx3BYsmQJiqLQ1tY20ksZEkVRePLJJ4f1OQaU8SXEiEiXakT9NRR5BhD9n3ceK7UZtAWnM2V4Vjbi2hJtdOgdwMAyvsZkjy/J+BJjhJIuddSLJ/V5sKmpCkkjdXDcnwNTkUfSvwcRTwVBf6DXTa2iGjriUeLJxF5YWB6KNKJaSSxFxSqq6jl7bjcelwOnBnZ4B+H4+GFepBgMM32hIOKrxu904ehnJlP7QZfzyoSzibrHsX9ijAQ100Eq3VU8tFJHoPnw6/mwvgndU93vx/T3Qm/mYqZu6cTNOF6Hd/ALHYSYbvD8RxuJDOH3wjR0tFgIG4WYextuh4bWy+9mwO3khH2nYnuK2akFcGoatmWlmsx3s304HObGG2/kb3/7G01NTcyfP5+7776bQw45JLuNbdt897vf5de//jVtbW0cccQR3Hfffcyc2Xv1zb333suP7vwh25t2MHfuXG7/8U847uij+vyaVUXBUp1gJdHs3JZqPvPMMzz00EMsWbKEadOmMW7cOP7617/i7Gf27mi2aNEiGhoaKC4u7vdjLr74Ytra2oY90DTaSOBLjH7+8TRMPoVIYBKlA0mtrtyfHW1BDKtwp1RlMrbG+8bj1voewZ1RE0wFycLJMO2J9uzo50IWToaB3gNfmW2EyFuWhdaeuirurpjR5+aqomJaNoZp4ernybzID0bpFN5ZcDORWIzqPnpSxc7/K09/sI5yv5d5e2d5ecXYuQEHEHZXUuT39/txPjvOuf86Fs1K8sL414D+n+SLvSNhK7QEptEamEqRt/8XVx2lk+jYqeByaLTH48O4wtEjsu/ZvG9OoK3iYPYdSqkjEJ/1WTayiTKXB8Oy+hVwzPa07WOKuc/hQ1M0TNsklAjt9cCXbppEEjouTRv031VDMXEYGraqobqceJyOHgNfSSP1fLpp4lRVbNLnPbZNT7Gjr371q3zwwQf84Q9/oLq6mocffpjjjz+e1atXU1OTOke48847ueeee/jd737H1KlTufHGGznppJNYvXo1Ho+n2/0++uij/Pd//zc//+F3WXTgLH700JOcc8bpfLx2LePH9x78VxQFU3UBHWiWjp7DUs3169dTVVXFokWLsreVlZUNeb+jgcvlorKy5553wymZTOJy5U9JqpQ6itFv8kJWHX4b6+d8bcAPLfen3pgLdYx49upXHwcBu/M6vIzzjuuyj0KXzfjqrdRRMr5Evos0olhJTEXDWdrHFNzGVRy65Gsc9va30Qc4Ul6MfjFXKetrTmLDxM/02ZPKoalo6ZI8sSe9eT0AIW8VRe7+X2TSPEWY6ZNuu3Uzpnx/R50dkz/Nk0f+jmX7XkWRp/8/20xJpENVCceTI1ZStzdFa4/k/cnnESrbD4c2tAslTk1FUxVMy0Y3+/e66O8wJ0VRRkUmv8uh4XU6BvXP5fZiBqtQ/BV4nA68rl627RRcUxUFBbKdO7srdYzFYjzxxBPceeedHH300cyYMYObb76ZGTNmcN999wGpbK+f/exnfOc73+H0009n3rx5/P73v6e+vr7XLKGf/OQnfPmrX+WS805jv32m8ZOf/hSf18cDDzzQ6/fqgQce4ID996e4ZhZV80/k2uv/LzvZccuWLZx++ukEAgGKioo499xz2b59e/axN998MwceeCB/+MMfmDJlCsXFxXz+858nHE5dzL744ov5xje+wZYtW1AUhSlTpgDsUerY1NTEaaedhtfrZerUqfzxj3/cY51tbW189atfpaKigqKiIj71qU/x3nvv9XstkBqoceeddzJjxgzcbjeTJk3iBz/4Qfb+uro6zj33XEpKSigrK+P0009n06ZNPX7vdi91fOihhygpKeHZZ59l9uzZBAIBTj755OzwwZtvvpnf/e53/P3vf0dJD03I9Gjv67kvvvhizjjjDH7wgx9QXV3NrFmz+Pa3v81hhx22x7oOOOAAbrnlFgCWL1/OCSecwLhx4yguLmbx4sW88847PX5Nw0UCXyIvmLbda4pvT2rr/8O0NQ/R3rK9743z0GAmOmZkyx3HSJ+vbI+v3kodpceXyHeeYtZ96ucs3fe/8bq7vyKbZSYp3f4m41pWYfTzxEPkj3jSQDdSmXx9TSF0qiqaoozoFLDRLDMkJ+Kr6nVSaneS6b/PnshWmew4CoXiCTQldXzZ3/5eAF6HyiHrf8PCd27CTISIG4VbXZChWxambaOqKo5+NCvvjbt9I1OalhJs+7jfAfeBXOwNuoJA/l7QzASsbEVFAdR+DqFRVQVffAe+0AacRrTbgKxhGJimuUfWltfrZenSpQBs3LiRxsZGjj/++Oz9xcXFHHbYYSxbtqzb504mk6xYsYJPHXssqp16r7Mcbo497lM9Pgbgvvvu44orruCyyy7jzddf5qkHf8qMKbVYgGGanH766bS0tPDyyy/z/PPPs2HDBs4777wu+1i/fj1PPvkkTz/9NE8//TQvv/wyd9xxBwB33303t9xyC7W1tTQ0NLB8+fJu13HxxRdTV1fHf/7zHx5//HF+8Ytf0NTU1GWbc845h6amJv7973+zYsUKFixYwHHHHUdLS0u/1gJwww03cMcdd3DjjTeyevVqHnnkESZMmACAruucdNJJBINBXn31VV577bVs4CqZTPb4PdxdNBrlrrvu4g9/+AOvvPIKW7Zs4brrrgPguuuu49xzz80GwxoaGli0aFG/n/vFF19k7dq1PP/88zz99NNccMEFvPXWW6xfvz67zYcffsj777/PF77wBSBVWnvRRRexdOlS3njjDWbOnMkpp5zSJSC4N0ipoxj17LY6FMvA6ezjJK4b45bewvhwPRunHw3j+98DK19kJtwMKvAVrGXljpXZfRQ66fElxgSXn/UVi2hwHcA+fQQ78JQA4NTDRM3CP2kba+yPnqZiezuR8Qf2eULvWPVnjnv1FzRUHoW9/+0jMgVsNGudfT6vJauIu8uYP4DgCKSboO/8EE9kK1HdwO/On7KQsaA9lsCpqZiWNbDAl9vNPluexJ1sZ9W0C4gl5wzo8fnIuWUp41rb8QbmD/k9IvDhoxz37s9ZM/U8DPOkPrdPmAl2RHcAu9p19CafW1jYto1t06U5fH+/34qioFlJHGYch5XoNss0GAyycOFCbr31VmbPns2ECRP405/+xLJly5gxI9UiobGxESAbkMmYMGFC9r7dNTc3Y5omFeNKU2tHxVY0JoyfwLqPP+5xzd///vf57//+b6666io6OkK4a4IccOgiQrbNCy++yKpVq9i4cSMTJ6ay2H//+98zZ84cli9fnu1JZlkWDz30EMFgKuD5pS99iRdffJEf/OAHFBcXEwwG0TStx5LAjz/+mH//+9+89dZb2X3+9re/Zfbs2dltli5dyltvvUVTUxPudObvXXfdxZNPPsnjjz/OZZdd1udawuEwd999N/feey8XXXQRANOnT+fII48EUqWilmXxm9/8Jvszf/DBBykpKWHJkiWceOKJPX4fO9N1nV/+8pdMnz4dgCuvvDKbfRUIBPB6vSQSiS7fj4cffrhfz+33+/nNb37TpcTxgAMO4JFHHuHGG28E4I9//COHHXZY9vfpU5/6VJf1/epXv6KkpISXX36ZU089tV9fUy5IxpcY3SwT7pnPiU8czni7bcAPV9KTHZPN63vfME/1t99BdzLN8MdMxldvga90qWPcjJM0+39FRYjRxrZtOpIGTk3ts7wNbykATjOGrktT80JTuuQmPv3OdZRHt/R5Qq5Gd1LW+gGB8EYpd+xGxD2OTWWH0V46u8/sud3ZJamJmr6ObZLxNcroiSgnPLmYT79yIT4SAwpceRwaUX/qOMob3jomJjvW/vPLnP325QSNlr437oPiTfWWdSbD6P248FIfqcfGxufwUeou7XP7fG5hYWPjSrThTrah2AYDSa5T05MdAVTL6DGL9w9/+AO2bVNTU4Pb7eaee+7h/PPPRx1i7zYA0u1lLC11DNJbzK6pqYn6+nqOO+641LZOL22BqcTTr62P1qxh4sSJ2aAXwH777UdJSQlr1qzJ3jZlypRsoAmgqqpqj2yt3qxZswaHw8FBBx2UvW3fffelpKQk+/l7771HJBKhvLycQCCQ/bdx48Yu2U69rWXNmjUkEons17u79957j3Xr1hEMBrP7LysrIx6Pd3mOvvh8vmzQa/c19KS/z73//vvv0dfrggsu4JFHHgFSx6F/+tOfuOCCC7L3b9++nUsvvZSZM2dSXFxMUVERkUiELVu29PtryoXCvjQh8l+oHsXSsVUHxeMmDfjhStlU2PI6WvvefWHtLf2dcNOdzGPGXOCrmx5fAWcgPfzZJpQMZfufCZFvkmv+xYS6TbSUHdD3SZxn11ALK9oKpX2fTIg8ocdxdKSvypdO7rtVgGfXSahhWjiH2L+n0HQkdAzLwqVp+Aaa1ZO+ABeM1RNNFn5wJJ8kmjcQMCIo0W0oLj/eAZSxKopCMjgRWlcTjNUXfuBLj6OaqQskmm/ofyvUzIUXPYTRjxLrzq09+pP9lM8tLGwbNCuBYlskHX5Upf/BKFVRsNKBL81KYtndT22ePn06L7/8Mh0dHYRCIaqqqjjvvPOYNm0aQDYTaPv27VRVVWUft337dg488MBun3vcuHFomsb27dtgv/FYqgsUhaamph4zrbzeroMHdl9nf1vn7T6dUVEUrBxfxIlEIlRVVWX7YXXWOUDW21p2/3q7e46DDjqo2/5iFRUV/V5rd2voqw9hf5/b382Al/PPP5//+Z//4Z133iEWi1FXV9elHPWiiy5i586d3H333UyePBm3283ChQsHVL6ZC5LxJUa39Dj2mK+a0kD/JyllpQ84PQXYwF23dBo6Uo0KB5Pxle3xVYDfm+701uNLVdRd/SDy8CBJiAx16U854u1vM6Ht/b6nSakaVvr3PhnZuRdWJ/aa9joUbJKaF29xP6Y9eUuA1EmoDDrYjR6j9O17mNn4PE4VPAMMfGllqRPJQLSemK4PxwrFIOnZ3m01uB0OPAOcwGeVTAEgEG0o/MBXvB0AGwVH+v1iKNR08Myl9y/ja6DDnPK5hYVtWyh26n3YUrReM6a6lQ186dh03+A+w+/3U1VVRWtrK88++yynn346AFOnTqWyspIXX3wxu20oFOLNN99k4cKF3e7L5XJx0EEHsWTJKwCYqhMsi/+89FKPjwkGg0yZMiX7PJnAl02q1HPmrH2oq6ujrq4u+5jVq1fT1tbGfvvt169vR3/su+++GIbBihUrsretXbs22zAeYMGCBTQ2NuJwOJgxY0aXf+PG9e+C+cyZM/F6vV2+r50tWLCATz75hPHjx+/xHMXFxd0+ZjBcLhfmbq+7oTx3bW0tixcv5o9//CN//OMfOeGEE7pM8Xzttdf45je/ySmnnMKcOXNwu900Nzfn7OvpLwl8idEtHfiKBmoH1zshHfjyFmAfq8ZII5Zt4dbcg8pQymR8NUQaMKwCP2Cj91LHzrfn40GSEBlq2+bU/2VT+7W9nc70MTqGXroiRpH0386Qp4pgfybVder31t8Ja2OF3bqJ6at+zuKP7ybgdg140I6rcl/qqj7FhorFtMelpHg0sdKBr7C3iqDHNfC+VeljzKJ4PaFC/9nG2wBIOAK4+yqj7wfVVwZkAl99v+cMdJhTPpc6kj4mtxUVFHXAv5eKI/Wer1p6ajJiNxl1zz77LM888wwbN27k+eef59hjj2XfffflkksuSe1DUbj66qv5/ve/z1NPPcWqVau48MILqa6u5owzzsju57jjjuPee+/Nfn711Vfz4B/+zH3/eJP3Nu7guquvoqOjI7vf7tx88838+Mc/5p577mHduk9Y++Z/+N2Pv4s32crRx3yK/fffnwsuuIB33nmHt956iwsvvJDFixdz8MEHD+j70ptZs2Zx8skn87WvfY0333yTFStW8NWvfrVLhtbxxx/PwoULOeOMM3juuefYtGkTr7/+Ov/3f//H22+/3a/n8Xg8/M///A/XX389v//971m/fj1vvPEGv/3tb4FUyeC4ceM4/fTTefXVV9m4cSNLlizhm9/8Jlu35i5RYcqUKbz//vusXbuW5uZmdF0f8nNfcMEF/PnPf+axxx7rUuYIqYDfH/7wB9asWcObb77JBRdc0Gf223CQwJcY3TKBL3/N4Bpppg9KfB1bC25aVV0kdfWjNtC/tO/djfeNx6k6MWyD7dHCnHrZWW+ljpD/E4CEIBlFi6Z6OLgrpvexcYrtG0fCWYSeiA3nysRepu/cAEDYV92/8q10BodLD8uEz90km1Pfy3ZPNUWDOFB3l01ixeE/ZPn0L9MeK/DgSL7JBIi9VRR7Bj5AKdNHNhitJxQr8P6gsTYAEo4gDm3op4+arwQAtx7uV1/BAQe+RkGpY9IwienGgP/FEwlihkXUVIjrBok+tk/uNlFU0VzpjCkLxTa7LXFrb2/niiuuYN999+XCCy/kyCOP5Nlnn+1SInf99dfzjW98g8suu4xDDjmESCTCM88802Ua5Pr167tk7nzu3HP53g9u47Yf3MbRRxzBB++/zzPPPLNHk/zOLrroIn72s5/xi1/8ggPnzeNzF3yZdRs3pwJ3wN+efJLS0lKOPvpojj/+eKZNm8ajjz46+B9MDx588EGqq6tZvHgxZ511FpdddlmXrCVFUfjXv/7F0UcfzSWXXMI+++zD5z//eTZv3tzr17e7G2+8kf/+7//mpptuYvbs2Zx33nnZ/ls+n49XXnmFSZMmcdZZZzF79my+8pWvEI/HKSrq/vxlMC699FJmzZrFwQcfTEVFBa+99tqQn/tzn/scO3fuJBqNdgmOQmpQQGtrKwsWLOBLX/oS3/zmN7t8b/cWxe6r4HMUCIVCFBcX097entMfusgDj38FPnictQdcw6wzbx744yNNcNdMbBTC36qjyB/s+zF54i9r/8Ktb9zK4trF3HvcvX0/oBun/e00NoU28ZsTf8NhVYfleIWjh2EZzP/DfABeOe8VSj179qf46nNf5c2GN7ntyNs4bfppe3uJQgxd00fwi8NIOAJsunQVsyaU9fmQaCLJPz5cz/iAj2P3mbwXFin2hvg//xfP8vv4YPK5lJ31U6qLA70/oHUz3D0PQ3Wx/ZsbqCkpnL+VQ9Xxyv/D/9J3WF9xNLGzHmBuVf/7rGS8tHYTa5taGef38tl5M3EPsKRODI/WB86mdMsLLJt9DSWLv8HsyvIBPX7H1o+o+M1h6JqXv5/6EmccsC+OXDQHH40+fhYeOZem4D60felZ9hnf99+XXu1cD/9vAUnNw9qvfMD+1b2/rs5+6mw+bv2YXxz3C46qParP3T/x8RPcvOzmIR0j90c8Hmfjxo1MnTo1GxCK6QbPf7SRSGJwpc1GPIJDj2BqbpLOAB6Ho88L3AG3kxP2nYrX6SBpmKjNa1BQCPlq8HgG1r9uKJKGSSiewKlp6KaJx+kgMIBJtrZtE23bjj/WgO7wE/JPpNjrLtzXleiX7l5nGQOJE0lzezG6pa/GxQfRwwoAfwWtn/0tH8b91MZ0igbRJmy02pYu3xxMY/uM2mAtm0KbsvsqVJ3HWQdc3Z8ASqmjyHvp98uwtwpfPyfPOR0aqqKQ7EePFZFHOrUJqOlPmwBvCaYzQFzzoyfjgAS+Muz097LDX4N/kCVeRW4XvkQziuIlrk+VwNco4QilBh+FvdVUDaKdhrtsEn//1BMkvBMwzFTAIziAk/x8YkVbUYGkswhnDjK+CFby8SH/x/oITDB6b7dh23b2OLUmWNOv3Y9kqaPX6eCEfaf2q3dZd5ItdbiSKnFXGXHvOIr7UYbr1LRsSxhFgbbANJyaimlamHsxx8UyDYLRenC40V3laAMt01QUcGSmUqZLNUd/jo7IExL4EqOate+pNGoV2BX7Dm4HioJ3/zMIf7KZlliSKTld3cjKpH3XBPp3ENCdzGMLfbJj5sDH5/DhVLs/cZHAl8h7nU7Q+zt5zqGqaIpacKXgY13zgiv4xH0gsQkHMq8/vwueYjZ/7UNeXb+VRYocGnaRfl1FfFVUDKbXKDD9rVs4ePWfeHfGl4nOO5Bibz/6rolhZds27YEpmKZBJFCDt58XCzrzuj3owVpsy0Y3TGJJvWADX8aEuayecxWtWgkzczH11eVn535f5OPN9RQbvZc6tiXa6NA7gP4f8450qaPX6RhUb2LLttFdGi4cqF4vbo97wO8XqqKkpjvaqUDSXv37biZwG2EsMwau8sG1qdE69SgDbMsGuVYgckCObsSo1rbgv3iv/HSmjysZ9D4yE5h2dsRztKrRYaATbrozMTgxta9CD3wleu/v1fk+meoo8pXVuhGVVE/Ecf084Fbef5SjXvsN26uPhjk3De8CxV7TVrovH07wM7Ek2O/sokwWx2CzFAqVoz2VFRQP1A54omNWcepvbSBaTyxZ+MNk8kHcMHltwS3ZoMBgghROTcXrchKKJTAsi2gBT3bUy/bho2nnE9cN9stR2ZnXmXpvSvSR8ZU5Rh3vG49b618QKF8vZlq2TcRbiearwrBMPOrAA0eqoqAoCrZto6T3adv24IJQA2QbqT6GVnqypDqI51QdznSPMhvNMrDsvVOmKQqfBL7EqNYSTQWryv1DmPzQ8D5TP3qMRHAy7PvVHK1s5A200Wd3MkGzTBCtUGVKHXua6Nj5vs5lkULkk+j8r/COOYVksJZ9+luS1b6Vih3LifqqhndxYq8Kx3VMy6JoAJPqHJqKjU3CkMBXlm3jDKcGycQDE/tdQrw7JT1lNRDdRksBB0fySVw30E0LVVFwaSreQZaxTt3xGp61T7GtbAHxaV/L8SpHD92yMC0LTVVy0tweINi8ihk7V2MVLQJ67jE5mAu9+TrV0c4EqVQVG2XApYKQyvJymjE80UYs1UnEV4Nl24Pa14BlAl+qEyWdeTZQqqJiKU40W0ezdUx74IMnhOiOdIoTo1fHTiKN61Asg6L+jGPvyebX2ff9e5iw6Z+5W9sIa0+0Z/+YD6XUMRM0K/iMr8xEx34EvvLtIEmIjKi/hk0VRxErn43W36vE6Wl+jmSo28lPIg+11VH8wR+pbls1oBKZ4iU38Zlll+HY/NowLi7/vHb8w/xj3m0YRbW4Blni5Rg3DYBgrIGORIFP/8sT0XiMpG6ADV6Xc9B9q0o7NjOt4XmqWt4lmhxcM/N8YDWuprT1Azx6KGeNxmuW3shnVt2Ir/nDXrcbzIXezDFdwkyQMId/mmqu/n5m+lkpmX+DDFapioLDjOMw41id9jucLNtGtVKvAVN1ogDqIDLWFEVBd/jQHQFgL5dqilEpV68vCXyJ0ev9P3PgE8cz783/G1ojzfS4aW9HfW7WNQpkmnyWe8rxOX2D3k8maNaaaCWSjORkbaNRvwJfeXp1UIiMmG6QNE3G+QfwnuApAcCph9DNvkfKi9HPqnuL/VZ8nyM2PYBvAJO8nDs/oaJtNXaosC+EDIRh2zR5JrJ53EKCPv+gT0Ld46YDEEg0E4q053KJYpA8b/6cL770aeZ89EuKPYPvy2WnjzGL4g20x4Y/wDJSfEvv4JTXL2NK45LcNLcHbHcxAGqyHdPq+e9PNuNrAIEvv9OPQur1OpwtLLR0MDyZzFFAOxmlOLIZd7QRGFypIIDiSF30UCwdbIu9cV3Lsm1UM/V9MBVnttfYQKmqQoevio7AREyHN1uqKcauzOtLG2J/QSl1FKNXZqKjf/AZTUA28OXr2EpSN3ANtkfHKJKLMkdITTgsdZfSmmhlW2Qbs8pm5WJ5o0428NVbjy/J+BL5LNaK961fMjHmJzj1/P4/LhP4SoYxLAuXdJDNe0bzelxAxFs1oL5FSjr7T41LYCYjphvZnmdDaUjvDIxDd/hxGh1YrVuw7H0HfUIrckNp24TL6MBAG1JVgVaezuaL1hOKJ/daL6W9LtYGgOHO0VRHwE7//XEk2tFNC62HTLLsMe8ASh1VRSXoChJKhgglQ1T4Koa83u44HA58Ph87duzA6XSiDjEbLhkN40rG0E3QlSRJBSxj4PtM6ia2ASo2RjxKzLaxhvn8RzdN1EQCE5uYYaOqSeKDaK9s2TZ6IoGRfh3Ztk2Unn8/RGGzLIsdO3bg8/lwOIb2O5z/EQBRuDKBr3QD9kErmQSAU4+wo7WRivFDCxaNBtmxzkMoc8yoCdTQmmhla2Rr4Qa+En1nfBW7irtsK0Re2fEx49+4g0WeCew46sL+Py4d7HDpYcn4KhBWy0YAov5aSgYU+CoFUmWvIsVa82/2/Wgpm0sOxOfquQdRnxSFRLAWZ+ta3OGtxHQD/wCy8UTuqe2bAYj4qqkews/CmS5j9SZ2oscjJAxz8EMQRjElkQqIm67inJU62tlS+zC6ZdFTJ6fMMe9AL/YWuYqyga/hoigKVVVVbNy4kc2bNw95f0a0FUcyjOHwkXDp+JyOQQVSTctCibSiWjoxl4Hm8uHq56CTwTJMEy3ciIJNh0fB6XAO+jmjydRERxUb004Nn5DA19ilqiqTJk0a8kWFwntnFoUjG/gaYqDK5cPyT0Dt2E7H9k8KIvCVq4yvzD4+2PlBQff5kh5fouC17TqJG9B0svQVd5ceIt5LqYnII62p34VooAbvAE7oOwe+CjZrZYC0j59m/id/wZrxVbzOU4e0r/DM01m3fQsh1zjiEvgaca5QamhB2DvA98zdeILjSDqDuPQw7sg2Yvrsggx8ZTJBbU9xzt4blGzGcQijhwsvuqXT0NEADHyKeZG7CCLDP7TI5XIxc+bMIZc7WrZN4xP3Ut34Mh9N/QLbZ5zFp6ZOHtT3uz2WIPa3O6lsWcHKGV8mOfsMDp1cPaT19WXdjlZWhhJMcifZHFeYP7GcqeNKB7Wv9z94i32XfgtUJ/845OcsqppAVXEgxysW+cLlcg05mxIk8CVGK8vCbtuCAngqpg95d0rZFOjYTqJ5A3DskPc30gYz4aYnY6HBfb9KHdP3xYwYuqXjVOWkROQPu2UjCtDhq6F8ICdd3hJsRcVUXei6Dgxhgq4YFbR0JksiOHFAJ/SqrwRIBb4My8apSeBLTQeUo/4afIOc+pcROeRyVqyrQ1FS2QxDmlYthsTU43ij2wGIBWuHFvhyOmj31eBq/whPpI6YbjC4U/3RTU1nfGUC5LmQKa926uEeA1+NkUYs28KtuRnnHTeg/WcuaLYnhr98W1VVPJ6hTR+MJXWCO9/HE6kj5Ajg83rxegf3PqE4HLQ73HgidRSH17Fet4a8vr7ELEioLmL+chLJEH6fb9DP6S6ZQFHbRwCYRhxdGfr3VwjJGRSjU2Q7ihHHUjQCFdOGvDulNDVKPHMQm+9ymvGVDp5lgmmFqD+ljgHnritJUu4o8k2mvC0WqMXnGsBJnK+crd/YyN9PfgZdesfmP1PHEUkNcrGLJw2sJKlL9oU5DIvLP472LQDEAjV4BvK66obX6UABbDvVO0yMnFjzJhQsDNWN5a0YUuBLVRSSwVosVDyJ1sKc7Ggk0YwoAJovh4Gv9L5cegi9h4zjukgqM682UDvgzKd8y+SP6Qb+aOr9u9VdOaS+gm6Hg1jRVEL+iVjOALGkQdIY3vf19ngCp6aimxYOVR3SxQJ3cBwJR+q4PBBvLMzXldjrJONLjE6ZMkdfJWXBHKS2HnUtr004g1hwIjOHvrcRZVom9ekTm4lD7X/GGMv46iXwpakaAWeAiB4hlAxR7i3fW8sTYsislk1oQDxQi3sgzT+VVB8OgMQwHxSLvaC9DsW2MFQX7tIBlrV4SzEcPkzFgSHj48FI4OxITVZLBCfiG2L5mtehUaS3YEeaiOmVuVihGCSjeQOQKg13ORxDLk3cevT3+Xdz6mJtaSEGNeNt2Q81X3HOdqtOOYIV+3+LRlc1c3sItg/lQm++TeuOR8OUJZqB1HCSgUzl7U7Lfl/g/erPUuJ1k4wniOnGsPX5smybcasfYZ/md6ifdArt4w4f0sUCr9NB2FeNO/QxJfEGQvEcTc0UY5oEvsToFBjPlrmXEsfJzFz0waiYRXSnG8PK/xO77dHtGLaBU3VS4R36lJrMwcS2yDYs20JVCi8RtD+ljpAKjGUCX0LkE7VtU+qD0oE34M5M6IoW4gnbWBOs4vXFv6ateRsTB1oiM+cMXnHOZ1tbhM9Ixhe01aFgo6seHMEJQ26s7Atv4nMvnY6ueVg2c3mOFikGI644qRt3OFF/NUGPa8gTNr3BCqyd9aiKQjhRgCfoDg/rF1zHjpYdeF2Dz0LanTZhPzZNd9LcEWNWD6WO2dYegwl8ZTK+8iSLPxFuot1Xi9fsIO4sGlImIqQm0W7Y2Z7Nworq+pCyyHqT0A3Kmt5mUsOLtJTOxTXhiCFdLPA5nUR8NYwLfUxJvJH6eCKHqxVjlQS+xOhUPp21c6/EsCz2yVETTZemopsmlm3n9RjxzNWvmkANmjr0KzcTfBNwKA50S6cp2kSlv/CuRPen1BFSgbH6jvq8OUgSAgA9jhpJZaa4K2YM+OGBJd/jmI3L2XnotVD9mVyvTuxFScXFtpJ5tDhnss8gTjqCbhe2bfdYdjSWJHeuxwWEvFUU5eBk0T1uKjYKTjNOvL0RmDTkfYrBaatYwOsH/QhFUZibg59tKmPMxuVw0B4rwBN0TxHrZ36RT3a0cmwOJ+tpqopDVbEsG6OH95xsxtcgetrmW6lj2DOBl476E1VBH86O+JADXz6XE4XU99mybWLJ4bu4FdMN/B2p6ZvtnlS22lAuFnhcDnb4U5Pri2L1rE+Xag73ZEpR2CTwJUYt07ZyF6AyEuyz/o8kd6wnOu0eAl5fbvY7AjJjnWsCNTnZn0N1UOmvZGtkK9si2wou8GVaJmE9NdEn6Ar2um2+HSQJAYDmZP3Zf2f9uveYXDR+wA93bH+PCc1vs7O9cMudx4qYnjo5cGraoE6aPE4HFnaPjabHEr15467Al2fowRHV6SXuHY8nth2tfTNJ4yA5iRsh0aQOCti2nZPpmj4rwuJVPyCQaGbpkb/AtKwhZwiONnHDQFMVHFoOvy4jQWXLu2g7t2NM/3y3mwzlmDffSh1DsQQOVUW3bByaOqCpvN3xOh0c/871VIY+4t/z7ySmD99Ux5huMC6a+lm1eSqpGOJ7ps/pIJYOdgZiDSRNc1hLNXPFtm3s9P+W3fXzLvdlbwOb9P/p+y3bxudyDtvk37rWEH6Xk7IxOGBFAl9idGp4D3ckjOGvys3+VCeVy3+KYiaob7qGwOQ5udnvCKgLpxt95qCxfUZtsJatka1sDW/loAkH5Wy/o0FEj2Q/Lnb13ptCAl8iL6kaOwIzqJtQwmyXa8APz07pirfmeGFib7Pf/wtTtmyhafxheJ0D7GiZjDL131+mMtRMx9S/A71fKCh0oX3P5tlYFZYFBw4x8yIjWTQRT2w77vDWvDiJK1Qd4TacqoZumniHOLQAwOsrZkL9cyjYqLFmYvo0Au6BvxePVlaogcCO9wnaQZza1NztOBHmsCWXAvDOwWd1u8mQenzlWaljOJ5MV6dYuByDu3jRmdfpAD2CK9FKUbyBjuTwleHGIztxG6mLzO3uSqZ5hvb7r6kqyfJZ1FccRqR8f3TTIqYbw1KqaVgWH9bvQDdtTCwsKxO4srLBK9O2sdL/MkGtXR/bWFYqmEUmuIUNdmqYCYCV+Zxdga7U5qkPssGx9CdTyos4esbwZAWv29FKVXFAAl9CjBb2I+dxbLiB9075CzB96DtUVeySSSg7PyG6fR3kceAr2+9gEGnfPakN1kJDYU52zASxvA4vTq33qyeZq4PhZHjY1yVELu2IRHENMssnM1Jek9/7vOddcT9HNK3iVf+deF3HDOzBDg/+ulcJYBOJtQEDzx4sJFHbSatvCoqiDPkENMMqngzb38bXsW3YTuJE3w772wkcZlv8Y9Fv8TmHfozp9fiIeifgjzWmg5oLCirwZa5+ik+9cj0bJyzGseDh3O3Ys+tipBltA7pmdbUn2rPHcIPK+BolFzOtbjN7umb5GJbF7De/Q1FoEytnXUZi8tEDm8rbDa/LSbO/BtpWURJvoLEjzkfbd3Z53kz2EZkMJdJBHNvG6vRx52CP1U0QyLdzNdOBhLsMw+HNyXumVXsoz7tnUl0cxGoNDdtkx/ZYgvfrd5A0LbR0pZGiKCgACigoKAqk7yFTjKR02i4zcXTXx6nt1NQNONL3kdm+88fpJ8p83B5L0BpNYNv2gCeZ9sW0bMKJJDlKK8k7EvgSo48eQwk3AOCdMPB+NT1RSqfAzk/Qm9fnbJ8jYVs4lUqc04yvQOFOdswc8PRV5gj5d3VQCEidlEz5aDnbxh2C17XPgB+fyfhyyO993nO0bwEgGZyIZ6DZRKqK7S5CSbRjRdtyv7g8E9N1kqZF0OMa8nS1DCs9fCIQrSemD89JnOhdItyMW0+91+m+iiFPdARwOTRCgRr8sUZ8ka3D2ktpJNjRFgB0ZxFeLYdZipoT0+lD06NYsT0zjjNljuWecnzOgbcoyXWp4wf1O2iJxnsM/thdbusa8IJOmT6dMn8yHwOc2LKGssgGTNOgaIgZU5DK+MqUC5bEG3mnPUJDe2SP7WxSgZjM/12DOKmP+goCBdo2AxAL1KIo5OQ9s8jjxuw0YTg+TAN44rqBblpMLAnmPNA0GG6ng7hhkDDMnLw/dRbXdfQx3MpAAl9i9GlLHbjrzgAlpRNytluldAoAnjwP7gxlwk1PMvsqyMBXPxvbd95mpK8OCjEQ9nt/Zt7af2LNuw6v89SB78BTAoAmv/f5LdaKlmgHwFE6ZVAH8Ja7GDXRjhkd42Wvts24JTdyYNJH/ewv5OzkQy2bBqQCX20FFhzJF8nmDbiBmLsMze3Hl4NSRwC9aBLsWEEwVk9U1zEtK51Rs1uGD7v1+8l+vmevn679gdjztk73ZUqprHTpVOZ/gEmlRUMqq7Vjbamv0RXMTgHOFdNVnAp8dfOeM5QyR9h1TJeLLH7Tsvm4qYXWaAKXQ+s1AwhI37/7xwqqkg4mdc78QUHBpihWD6Saw1fmoK+gQ1UxiicCUBRrYFJp38fBgzVO6cBSNDp81YPuMbm7zD40PYLDShAZplLNzETr0RD0gtQwtmhSJ6YbOQ98xXSD5Bie2iyBLzH6tG4CIOavocSXw/rjdODLHanL3T73sg69g5Z46spbrprbQ6fAVwGXOkrgSxQquzV1pTUeqB1cQ+V0qaMzGRqW1Hqxl6R/D6LOEvzBskHtwvYWQwiM6M5criz/xFqpXPsnKoGn9r944NlzPdBq5rN66vnUe6dSPExlO6ORbqYGLgwHw7LY0hJKZdewK+sGdgWdOvfX8Wz5kCAQ8VbjdTpyt66SKUBqAt37Dc180NAMfWT4dA50kb0t0xdoV7+fzNfV6VY6PaRbmQweh6bidTqoKRl8z75s4MsZHHL53e4sTzF0NGSfo7OhXujNHNPFjBi6qffZ7qI3ccMgaVqU+7343blvOu6M78RhxrFRiHgrc1ZerZSmerJ5h/n4fuuMc9k27Syi0RBOVc1Z4Ou4d29gctNSXjvgJtrLz87BSvcU0w36eDntVU4t1X8wphuU5njfsXR221glgS8x+qQDX1F/DUW5bPyaDnz50qnT+Shz9avEXdKv0r3+ypQ6NseaiRkxvI7CaXiYDXy5+xH4yrMJQEJg22jpEgP3uGmD24enBFtRwTIxLGvYTlDF8LJaN6ECIW/14E/M0tl/RqQlZ+vKR+bOjWhAh6ucQKA4Z8Fgd+VsPtz/apo7YsyMJXKyz+7sCEexbJsJRf5he47+MiyLlz+p44Da8VQEcj9RuzkSY9nGbSSM3bIYMpGfTmxg3raPAAj7qnMyrTO775JUGWtZYjuWbfed4dMpS6inXj902lbZ4+P+ZajUtYaIDbVELB2UMlxFuZ3qCNjp4y4t0Y5p2Wjqrq8pm/E1yJ62AWcg+3F7sp1x3nGDXmcsqaObZk5KELuTOTeJ+yZgqc6cBb4c6eMCT7QBxTKw1eE79bdVBzHVi8fhwJ2D8zev00GLOxX6KY430BRPYtk2ao4vzmWmaY4WqqJg2wxLT7PM5OexSgJfYvTJZHzlsHk7AOneGr6OreimlfN07b1hKGOde1PsLiboDBLWw9RH6plekoOBAqPEoEodpdeRyBexVtR0GYdzsIGvuWexxH0ITR0xTjMl8JWvjOb1uICIr3rQJ01Kutm0mi6ZHIpIIsnOjljXEi66mWLV6f6qYj8lXs+Qn3uo9J3r0YB2bxXFOQyOuBxaaopgB4QTw3MSB7CxpY2kYY2KwFcsaRBJJAnHk8MS+IomdRKGwcTS3qc2Z1StawZS5WS5HC6glU/FQkWzjVHxO9xZdKiBr/T7geEqwpnjAIGdDrY7kiEMy0TrFJgZ6jGvpmrZY9tQMjS0wFc6U2a4AiSZjKyorwaXQ8tZX0FncTWt/ilYgUo0vQPD3b/XyWAlDYuKgCsnFwu8LidRf+pnH4w1kDBN4rqRs+9NRiieGJXnhEMOWHcjmjTQLcn46pfbb7+dv/71r3z00Ud4vV4WLVrED3/4Q2bNmtXr4x577DFuvPFGNm3axMyZM/nhD3/IKaecMqSFiwKWDnzFcx34GrcPW859mo8SQQ6KJyjPwzGuQ+130JvaYC1rWtawNby1sAJfUuooClnrRgA63OPweAN9bNwDVcPrckJHDGMMHxANxra2MOFEclcDY3v3seVkp2VlWgfvM7405wfuAFbLJgA6/DVUDrJvkeIrQ3f4sK2hXxH+uKmFd+uaumS29FZOYlgWh0+ppqRm5IMG5s7U6yrirc75z6qMCMn2D1HVScT1qcPyu9AeS6KPkj4uMV2nI92vZjikGl73/yQ7E2CI+KqYkMP+OWrtAh45+SXKgkXk/ic6eJqqEo4PLbtQie8KfOU64yu2/xd4zzOXlrJ5zDIt3J1+JLk45i1yFxHWw0Pu8xUb5j5Q3o7U1xr2VePU1Jz1dvK6XPztyD9QUxIcliA7AJbJwS99mbi/moYZV1HsKc/Jbj0OjUT6XDAQrc+W/+XyPVM3LaK6jmuUXfDTVIVwPPc9zULxRJfS6rFmQK+ql19+mSuuuIJDDjkEwzD49re/zYknnsjq1avx+7u/qvT6669z/vnnc/vtt3PqqafyyCOPcMYZZ/DOO+8wd+7cnHwRosAccD7rlQm0j5uX2/063PgnH4yxqZ6dHbH8DHxFhpb23Zts4KvA+nxJqaPIN+FEklC6DKpLhkwmM6bTbb4tq5lA6mB5KAeD3vRjh9r7IZrUWd3YzPzaCYPrN5Zn3qnbTmM4gqaoKEo3fXd2u00BynweJpXl/tS45dCrWek6GNNfydTBnjSd+jOemnwlqqKw/xDX0x5LNYGu7GfWUX17hNAwHOgPhp2+ABfxV1OWo+bnGXPe+h6LtrzEG3OuI6YfkvPAl2XbROJJTKxRkd0e1Q1iukHHMDWmjiSTAwpGtFYswFI1WgPTmJLDwJfP7cHh9JAcZVmzLk0lFBva97517oVs3fwhiZJpOQ+emDNO5JPkTDRV6fL3x7RM6iOpZu8TgxMHvf8iVxHb2DbkTP7hCtxmWJqbaGAi7f6JuDUtZ30FvU4HLkeqZ5TbMTyFXp7YdkqbV2K1fIA56/qcvacpioKSrtbxd2xDN62c/xziukHSsAgMwwWIoXBqGu1DDFh3JzQM+8wnA3oFPPPMM10+f+ihhxg/fjwrVqzg6KOP7vYxd999NyeffDLf+ta3ALj11lt5/vnnuffee/nlL385yGWLQpbY5xQ+Zi4TgrlPic/0c2iJxnO+771hWDO+AoU52XEwpY4degeGZeAYxl4IQ2VaNpF0qQx0LR/KfL6rMe5uTXJ3a467e+lR5r6uj7N3bd/DY2pKgvhH2cFDfzRHonzc1MqiabktIR6s97Y28XFTqsdSZrx4T+ZteJ8JQNRXTelgT+ISYaa9+A0qQ80Y0/4GDP6iQGs0TkN7BzMr9JyWEY1GCSN1Ql/u8xHsZ9+XnPTb6UGHq4wtxfMY5/fidQ7udahpGqqiYNoWhjW0sp5QPIlrAEEXl6aOmoNyJd03r8NfS02Op2pZJZNhC/g6tg3L70I8PbXLtG3iuoFTG56eRANdT9sw9TRrjw3s92zj3K+hmybtHbGcBh09TgdOTU1n2o2ev4NOTaNDTw4pCLpz1jm8oR7GPsW5brWdWp+qKpi23SXjeHt0O4Zt4FSdVHgrBr3/XGXytw9zH6jN+17E5n0vYnu4g2pPbkoFIXVRK/V7aeFWTVBzH5TNZFHG/NVYSrqcO0cc42YA4I41oZmJnPe9iul6+rUxeoLVAC5NoyOZmg6bq4uIScMc9gDuaDek38z29lTqa1lZz9ODli1bxrXXXtvltpNOOoknn3xyKE8tClhrNHVwVJbLiY5pzvUvsN87j9FeeRhM+WrO9z9goQbMtq1okw7p1+YDnXBjN7xPx9ZVqdIbyAYwMsyiySSrD8LGJqilXscbmj8ewBcwfKJ6lFXNqzhowkH9C0BFW2hY9xa+GcdQ7NtVKjOQUseAa1epWDgZptQz+IO8ji3v8OHOOPPnHZb7P6i2TePKf7ClbgNt3mp2ls7BtkG1kkxq+M+em5P6uXd4KmkqnZcOZplMa3ixSzCr82FW1DOOxrL52fqkqQ0voPSQHr3T6eVDfznnzT2eWRNyk+K+N8U3vk447icxaUKfV0Qt2+LNhjez01X7w6N5OKLmCDyO/pVwtcXieJ0Oqhwxyhvf7HG7cOksQvMu4qXao0jg4Nh+nKBv79jOjtgO5o7rlHGtuQlueIYgsC3eDgx+5LlZ/x60JYjr/euf827Tu9mr+v3hUB0sql6U0+Eeg7V9x3rq1/8Cp6rQ1Kkhs+EKECmekf28eOcHKFbqYNNy1BKt/dKwrCfVfNnKnugMlqYqJHV7SP1sEokoZVuX4DZjeJ0OLM1J08QTsveXNb6JK75rcmTc0tlstKLVnDkqspTqw1tQnQ7igVp8gwwi9ijdBD0YaxiWk5BYp8BXVNf7DMoO+G/tACVb6ihrXU0keEjOe5pZto3WuIoyxQHB2QA44y2UN77R42PCJTNp8k7BqWk5KyeDVEnhfpsfo3zTc8TG70+odE6X+3V3MTurjsh+XrH1P2hGrNt9Ga4AzdW7EgrG1b+CIxkhbMXZZrSwr6s6e5/p8LKj9tjs5+UNr+Hs1KPP7alma2CfIQVBddPCsi08u/19XLNzDcXuYqoD1T08sm/ORCuVLe8Ssx0Y5pTs7ZmLsDWBGrQhBGtylckfa29k+vZlKMyhozjVDsSZaKO84fVut99pRmj0l1NeeRwAjmSYcfWv9rj/jqKphMtmkzRMijy5K/f2ODSm1T/H/u//lFjRFLbOOLfL/YbTR3PNMdnPx9W/iqOHslDT4WZH7XHZz8saXseVaKOk+V0Aor5aHKqKL4evK3fReHTNi9OM4Y9vJ65Pztm+IZWR6g9toLa1+4v+kZKZREpmAv34GRZPJVyaeh/S9CgV25b0/LzBSYTKU8dhqplgfN2LQOoYc7W5A/f4k0hoJUR1g6A7NxcvYrrBuvZVNMWamN2hsS/5d7w+VIP+zbQsi6uvvpojjjii15LFxsZGJkyY0OW2CRMm0NjY2ONjEokEicSuK0OhUIGVHYUbiW56C9PhITj7xJFezegS2UH8k7dwU06ZfxiyL+reYMr6v7BFARj5wJf9i8PQ4u20fPFZymYc3vu2tp09QexXo0/TILH0/2HUvUNJaF23m3xc8xle3/8GALa3pVLhGxrfzukVhsH65Xu/5MEPH+S7C7/L5/b5XJ/b2w+fRVX9u2w78ecUL/pi9vbMwU5xPxp6OlUnPoePqBEllAwNPvAV3o7/gWM5FGjdp5HSXJfVfvIcNU99iRpgy5TTeb96AQoKDl3nqPe+1+PD6iedzPuTF6WmQlkGi5+9pcdtm2qO4b3pi7OfL37+B6hW9ydpV07ch5db40wfX8ysCScP+ssaEVvfpvaJsyl3lxOds7rPwNfSbUu54sUrBvw0X5v3Na6cf2Wf22V6WLgcGv7QJvZ/44Yet/3kgKvYNPvLtARm4nH2b4rSN176Bmta1vDvs/69K4DucGE5vKhGDD3SAgyyrCTUwKS/fIZJwMbZm4DeS9zWtqzlwn9fOOCnOWPGGdx6xK2DWmIu/ei5S1hCD31jmru/WbFtJm6dwfyJZ+R2MZEdlC77IbOMMny1A/+eZtUt5/ClN9HmqcTY7/5BJ66Yb/6Go966Mft50lXcJfA1dfWvKWtanv38h2UlPFxcxMWxBuJz/t+IZimZlsmF44tImm6uL5mW0+AIgFo+FYBAdBsNwzS1SzctbOx+Bdbuefce/rjmj/zgyB/w2emfzfl65jx2AvOsJP8q+hNxfVpOs6zirdtY/PLFfLTff1E/IXXC6YvU9fq+uW7u5WyddhEuTc3Z5LyM4sQOxre+B63v7XFfe9mcLoGvWe/ciTfafdA/UjStS+Br5sqfEght4Jvjx/Efv4/f1W9nQfo8Kear7hL4mr7q5xS3fJj93FZUnjj6L8T0yf3OTO3CSOBoXEFxRwK3c1eAqznWzBf+9QVqA7X848x/DHy/aa5NL3P8sivYVrYA/dBd/Z9zNcwpF0OLdNNk/7dupnr7UtbtfwUb04Evb8e2Hn/XzqypZIPLxXdKD6DEPQ53bEevv5ebZl1IuGw2Ngx+Km83FEXB5S3Ck2zF09xKaTpIldERnNQl8DXj/XsItnV/ATzuregS+Jr+wS8p2bnrdz3ir8GpqYPOOO6O1+VkfdWJ+D0uVM2V86zgeKyD0978Om4j0u396+f8Vzbw5Y419f4z3PeibODLmWjtddv/z957h8ly1lf+n0qduyfnuVH5KudAkJCQRMYEG9t4MTZg1gbbrP0zu8bZOO6u12EdMPaCvV5wxIARJmOhBEK6Cugq3Rwm5+lU3RV/f1RVT89Mh6ru6pmRNOd57nPvnemurq566w3nPd9zzp3/AxXiS9ILldd+LRHnd4YGeN3i/Vx68Z9Q0sIlvg4vfZXnc99hvFfiVQevDOW4LyS03ON/4AMf4MiRIzzwwANhng/gmOj/xm/UX7y94HHsayT+7YMsDN+8S3xtxMl72f+F95IeuJbUNd8I//g9+wGIuQPqtsIyK4ahqxNPNyW+VEOlbDodfp8f40hJZuLW32f1q7/DkLyR9HF2XJeT+zAtGwToNZwJeM7WKWk6yRCTrFrBM0vPAPDc0nO+Xi9MOYN5uVRY9/MgpY7g7A4WjWJ7fhBumQyAlp2DZLg7VEw7E41CbBC1+/wKWSMSYXGofjsqdF2wFhUuiA1fm+++cN3/l4ZuRKhjeP287Kifnl96DniBEV/P/zsA8fIiK5oOicY7rWezZwHoj/dzfvf5DV8LzuLg+Mpx3+1Y+95nGZhcRB25HiOSbniP1KSzCNFMi4F089IIwzI4uuxMaM/lzq1TjtqxbsirGAX/SrZNmD1S+Wc5vwT9jYlj75r0RHu4qLdxSA7AUmmJo8tHK/dgu/G8mQVJ4JAhERfWSEdTTlBM76/8P7VyFME2OCaUyIoik7PfBr4v3JOZf449R/6aVHycc694X+vHKWcZnH0IJX1ee+boXh+V3kcpMYKhrLcuyPYewq66Zk/Lzpg8q55A9aFS6iTyep684RCaumyGrj5T+pz01Yw6zfMdKO2MffuPefm5IxwdfwOq1lwd/uzis5W/wya+bKOMZDmbar0Lj6HqrwrXmHrmGRK2wYVH/4apy512byipxv1magzdtOhOxEP3q8pf/iOcXjhJWtjsqVVMr58HrAxcRbG0t+ZxSsmRdf9f7b+ScnyQU7E5wODR/gvYZzgKdS22vuom23sZhuIoYjNLT6PoObqyxyjqN7T2pVYnuOyeH+RCKc4zV3yv8uNjy8cwLIOzubNYtoUotPacSAlnnIjoObJVpY4r5RUAemP1q4r8IIxSx6Jm0L3ijFeyvkaQGHKyZlsrY3NCmcYGlsqzdEf7MeV4w3ZZTDttQYBQSwUBjP23c/Tk6+gzNo/v5fjguv+v9l2OFq19zfUNG8jZvksxZWdj15TjHN33NiJyeMb84FyLey//BYbSScrFUugl01p2nlKkh6iRZ3HoRjaaS6hVnspN72Fq7Xm2pGjj+Xhmf+XftihXXntUmAd05vQlLrLC9TRTdZ3U8mMgQ3exjbneCxgttcwPfvCD3HPPPdx3332MjzceVIeHh5mdnV33s9nZWYaHh+u+5xd/8RfXlUdms1n27Gnd2HDHwSVf4juBfNlpcA1l1eQ4fZ1IH3GvfaIwiW3bHUtn8YXcNACWIHFi8FYONHm5N2jLgkxc9qcgmsoWWLny/Si9zmDl+DF5/wLFtrnK9X1a6LoBFj9GThTIL0+SHDkY/DuFCE/m7stzzDKxRRnBMjiVuozqM/eum9/SqEwkw0xhpj1Z/J4bKCeGiBZnUeePw0jIxJf7nDw18gaeGfo+WF471zNX/Q+nbLEqQs0rUBQAlrOVdj9x9f+sDPHOj4S11wkgZPOVn335mv9R+QzvVQJgYTL7jKOwm3whBiNUkXml3Bx0N24nXrt41Z5X8as3/2rTwz80+RDv//r7fXvnxf7j17gtO8F3bv8kucFreOxVf9n0Pbpp0uWDqJ4pzGDazvfd1L5j3ZCfxmhnMpR3xvqpzGXkhOYejV7p9u17b+fXb/n1pq//9tS3+Ymv/cSOCJ/QjTKzLon8zsv/B/HMBU3f82/ffif3kmW5cIayYfpS6PmFsXQKGcjHRypBBS0h3g04i9BCGwmfnkfWict+ktl9r930+2NXrbfAOPvIO8AqsmiuUtxmD5KF4nLl36IUviF7dMBRi0SNPIXsAi0rLOsgfvqbXDh7mMmBm3z54VTG2g7039riabyeSdCLFDU91GAha/EkACv9a8qFQtd5TftNbSXXEQ9CaeACvnn1b7Onp/lG25Gbf9f3cZ+54dcBWHrsR0Ff4om9t3FgX22C+7nrPlL59xUP/n8MnfsaaXXaTb9sAeoKAGU5vY4E9tqLZVsU9ELL5ece8RXVc+vI9iDBRI0QRqljqZijq+zIeE9f8uOVnxcz+2u2tTl1Avt7P+m8xnSIslJypGm7NCwLURRCVyLGk0keuOwXfbXLZ69vPq/x8Pw1/3Xd/5fyRbqVSKibBXFFJiJJ6KZFRJZcRasZmoXIgtTNk7f9I8PpBDRZE5aSo77mZABavN/3a41IpvLaU8f+Fyz9BwXbGXuKeniqYFUzKFkqIJFU2nuuXqgI9GTZts1P//RP89nPfpZ7772XAweaLdXh5ptv5hvf+AYf+tCHKj/72te+xs0331z3PdFolGj0RWyK6xFfxSmKpRKJEGu5X/DwiK82/AIawvXWiBcmUTWNxHa2s2VnYVBMjGDQfJCongT4IuzKeUquFP6KscEmL4acloRHoCyKLE89y9A2El+GZTBTcMqhJ/0QxNlJBMvAFBWykbWdKsu2KhHWvhVfIRmhFhKjRIuzlOaOA3c0fX0QWMunEYHY4PnctH+0tmG9+59qstMGLGstFdAzrLfc31cb5dueEb57DMu2K8ezKmb3NkvlWWycBXJ58XCoE5KtgKUuV54+a/Ek7Dmv4euDeMbBmh/fZN4H2W5oiDmn9OWM3Yu6kqsQkhUyEvcfrM3RTMvypaKofpY2te+4Q45bVYv+oLCWnHa5mD7Iqo9d2cmccz5+PQsrC5g207nCwExxDksAWVCIpRq3GQ/9Si+YWZb0OUq6ESrxZS6edIiv5CiZdhZNsW7AIb5W2kj4lFcdVZ7qI4HYsHSWLMfr6FS8G1XbXuJr+ehX1v4jhq/IkqJJyrF+oqUFpNXT6OaV4S4U5x1Vzsue/n0eOPR9DV9bNsvMqXOAz7E2IIqRPtTUAbrzp0ipM6F7mtmVOWOwwB/btklFwlcVxhUZQSB0LzMPRbcca6k02+SVDlTXMiRdnKJQbnEBXXLGhLKynvjy+m9wxpOWfRfdPidq5NaZ2wdV69dDKKWOS6ecv5UUeqS5bcZiee3+qHVK6Gp+jmESkaTQfQUTilLl7ts5aKZFJh7ucxVXFCKSiKVmyVgF5uU+VN0IZZ5p2zY5L4RlO4UQVci655ETLBSzEKqZf7GwTF6wAIlYleLspYRAs6MPfOADfPrTn+bzn/886XS64tPV1dVFPO7s4LzrXe9ibGyM3/1dZyfjZ3/2Z7n11lv5gz/4A17/+tfzD//wDzz66KN8/OMfD/mrvICQGcUWFURLZ3XuNIm9F2/3Ge0cuJOYUqpDCr/MGLYoI3nXfk/z8pqOoaJuG8PwUVISeBLw77/AHU/9M89d9fNwef06cw9JJYlogyXA8vyzwOv9fU4HUK1MmcxPYlpmY3NT91pKls7Yua9QvuwCorJMQS9UCCG/u4be9c3VMff0i3xyjN6FxxGryh5Dg/t9hZ79HBrpD//4AfDw9Hzl30v6AkXNoCv+wiG+Cnf/T4xjD9KTO05xub73pIfKc+izPY0kRxAFkZJZYrG0SH+8wf1aPYdgWxhilKGRA0QVpYqgdElKN5rTstcITAGBfh8qimrV2aZFQNzZdbfV1okvc+kUIrAaGybrJo42WvwFDesIi5QOA+fy5wDIKIMsqeUqYnLt+wob/p+ODIF6mgUzS1EPN/XSchdmxeQYg+0QX247UEwVXS8DLSxmTR3bdhawfgiJpfJcpZ9eNpYolMNXWQVBdurRyr91u9iRz5i6/H1MrBbIyn2ouo4ihdQWtCKi5SyUIkaR0uostr2vLuFeTXZN5CZCV8KrYoxT+97Oy57+H6TVKZZDJr688VVNBk+6DltV4x3TUaeYTf0ig0K3NAxXBbJYbj5WAcyP3Uo53s/Z2IVEWyyrtdQVREBT0uvCLqoVgtlytnUvLk9lahQxjLX2EXSTqR7CGDesxdOASyT6eD6Wqu5PMQDxpZlW6KWC4LRLSRTaTuptBtOySIdsk6JIInsWH+XGBz5AtusC/vXmT6JqBpkQPqdsmJQMAyXETah2UbSd53RFiRG1TbKl8MZDfeE0Wff+JxJDTV794kSgJ+sv/uIvALjtttvW/fyTn/wk7373uwE4e/YsYtVDdcstt/DpT3+aX/7lX+YjH/kIF1xwAZ/73OcaGuK/6CFK2F3jCMunKM4eg13iqwJ7+bRTPtUdcmmYB0nGzowjrJymOHccdgDxNTD3MLd99W2Ylz7R0FA+8CRg+TSSpWNGu329XBRE0qLMqm1Qyp729xkdQvWESrd05tV5hpP1y6O9awlw3WO/yeqr3kc0JVeuWVSKEvW5sAhDFm995r3sPXOP89lhl48YZYSsowqKDfpTmnQS1WTKjGRTKBU7UkLSKaiaztdv+gtsJUE6HuXyJq8P+hwqksJwYpipwhQTuYnGxJfbjnOJUc4b6PFVlhAE6xYqG9q3GO/GRkQwSq1/wNJpAK47+w88G41TvuRXGpbdeW1nj8+NDu+al8wSmqkR2UYD9HNZ59wz8kBF5WFXEZKeOhK8UmMbqed6UB/mVDQduqpJdBXEpeR4ewv62JqawVJXgBaIdUnhP970Vabm55BLMpQLlbWigEsGVv3/VG5tc8CwNSZys1zHyKbDbhWyuTXDcd0qNHhl6yhc8x6ePTlZKd0JYxEHoC+dXpdHoOTOUTKurdsmqvtv1VBZKi3RFw8v6UvVDZZSB1kYvoXF9KUUQvY0kwIoCz2YloUoCKH7KIFDMCiShGZYREM+fLVyaKk844ukXBm4hpWBa8gWVWIlrSVi0youV4ivdaWOufrjSSC4ii8BG62wAu6zH1qpYwjE15qy0B+5t1haI75UM4DiyzTpikdD9xWMR9bKBTtJfAGhJjp6kHudOUKiMIVlWaGV/6m6wbWPfZT+wilOXvHBdeET2wWPKC1gYcV7fKnn/cCybVg6VSG+ksr2J2NvBwKXOjbDvffeu+ln3//938/3f//3B/moFz3E3gOwfArd9SfYBWCUIevsPsYGmptGtwqh9wCsnEZbON2xz/CFKrImkZ+gUCqRTtT3xal4VUX9dVb28ikEwO7xTyJmpDirRo5ycXv95zb6IZ3LnfNNfIm2ibp4hp7UoZak8mHI4jl1X+Wf0eJsuCmZts38nX/IzNln6e3pUElwAKwjKQWB6dmnGO15ZYN37CyoukFZipOJKOimhWnZawEANRC0dBYcRdNUYYpzuXNcNXhV/Re67TifGA19xxfWP1cbFY3CG/+Yzx34L8iyxKEWj28KAgoQ07MMzz5IUTfqEl+qoTKvOmpBv4qvdCSNgICNTVbLNiYRO4xzz/wLAAd0lZsOjDKUXp9gaW8oM7ZsmMon+OvP/SnL5goFLVxVk+Qa/pcze9trO6KEFUkjarmW1X+mZZMv6+RtmQszSaKyXCmVtirkoFtObUMpu7ju/fMzD2NfeuW2eXAW1HlwHSjKVmcUX3FFQRJFLMsOlQTVF06uI75i+QlKuuGL+AKnPw+T+FKev4eUOs/TN36UKTNBOmRj6qh7/kGIL920UCQx9HIygKgsEZWl1v20GkA110hYzSqTN1ZIK/6SpxVJomQYlA0zcP/glb9rSpp0DY8vaFMhL0cw5TiSoWJXeUyGVerolWC2Rc6tnAb8KwvXlzr6J8810wqNBK+GVy6oGWZHlI6wxhF04vgRNxBENgpE9dXQSqZV3aBn9Xm6c8cR7NZL+8OE95zb2FiCRtmQKBtG2wrSkm5g505huuNqapf42sWWwvX5imTPbe957CSsTiBgY0gxuvqDy9b9Qnjzn/LVk/MI8R4u6din+MDdv81DvXdyy7feh2ibZGdPkj5QXwkZaBKglxBc8/z4QHPTZQ+ZeB/kcpzpa6Z76Sw2TcZzE1w/fH39NwxdysT4XYxPfBUAde447DvUklS+7d1BXUV0Tb4feu1nmZRHeJ1ukAwpjhglxrk9r+VY7CbuDDEdq1Vsulezj3PtxS8Q4mvpJH2f+xDXSWOcuubnKGoGJV1veK9a2YUeT4/z3ZnvNjWP9hSvxeQYvR0mvja1bzmKKImYPja46uH0m/+R5x7+N77vsQ+RKk6SbTA5nco7qpq0kvb9fIqCSCqSIqfltp34mnDPf4RozQW0IKz5soGABIylRxEFCcs2mMhNcznNvRd9oZxHVh3ySOjZ37a3kBXrxjBNzJJ/pUI1SrruKAskiQN93Vw42DiV7eniejKksPw9Sh1coDVDUVsG13u1FECtEQRJNAZXn0bUVdQDLZaI1YCxYTM1np+gqOn01Emr3dgnTeQmuHIgvIj73sN/yt6lZ/ma9HvYo7eGakytayWWuy4kWZhkgh70vENSClAhTav/DU6VWtkwiUpSRzYXBEEgE4uEWp7kYWPJ3GJp1hfxlV5+lt7VMxzPXIOqG4G/t0eA65EMiruBl9WyrJZXK69pt/x89vqf5+hiDlNa20AIrdSxTW9Iy7Z54qL/zDNjbyKV9Hcu1aWoQRRfhmWR6YBiXpGc8slcB9qlB820UCSpI/12NJ6kEB0gWZ4no05TLF/Y/E0+UCxr9BedsbwY0CewU6hWdkrlSXTlAEWtfeJL1Q3M4jkQQUYgIr5wKjPCxC7xtV246p18L3IR2Z5D6xLoXtKI93DmZb9JMb/MwWQHDf+7xrFjGlYbiVVhQI/3sTRwDYXMAZLZU6hzx6AR8RVkErDi7P7rcpKuPv8lI5nUCOROM53eg2aYRLap7t2bjEuChGmbTQkD89Bb+K5xOTEjT//MQ2gLzuS/FZKi7VJH99prcpJi1wHsYplimMQXsFBQEYTOSMqDwiNTJBtMASYXj27zGQXAwnFSZ7/FhUqaAW0GUytSvOizjYmvFnahx90JVbNkR2vxFBJQSo0T64AaYaMny0ZIgkjZNFv2+SlqOtm4s4hPlmaZKZeo5xHlXYvx9Higz8pEMg7xtc0G95NGDiToTvhXWMmizKDcxYy+xMz0I3BRSASDkuChN3+ZqdNPs6+rfTJw6T0P8eXnznJBxp+aZBMe+F/c+sxXeWb8zcQv/bGmL6/094AJrJQmG6qUOorSKnmrjCf5KgTw5wmC5OLTvPE7/5nV+CjHrnhdaMe1Cgvr/p8uTjVUR1T6b2+s9Zk+6wu2TTTnbO4uRYewy1ksUwnNB1K1BL5x4x9j2Y5Kty8Rc4JXbCeopVJ6bLvxK64KM6bI9CZioYZLVKMrHuXU4irn3LRlm0plL1Vhy7X/L6wF1Hgv8P5/Nj+37nOOL58iYo1X3uf5CQrC+pLiW+77EEl1hpkb/xxVP0TQp7q895U8v1RgoedKxl3CstrYHtoPHFm98sd55tgZxsUOEF/u+4tGEd3SUcRgY2tZNyjZMlbXAWQf8zjbtteVOgbx+BLojGIKoDseZbHQhpVBE+imSUQW20sVroNERCGXGCVZnqe7PM1qKZzvoefniZgOYV5Kbn8FBaxvL3ue+iOev/J/haIgVXWDJSkCNiSE6LYpqrcb279qeqli/Dpm8/2+ykdfMkj0cnTPmzEsi0tCNgXdCFEQ0a3mhvKdRN5N2LG790G2edlroEmAWzJVTI4FMpr0SJ+SWaCo69tHfLmT78v7L+eJ+SeaTsadQcHG6NoLMw9VlJTbUuroXvtCYozRrjRLxTIFTWegtaNtxtmH6Z54DiNzAdGdQHy5i9bL5C6eNFeZL76AVKzuvcqm9jM8+U0sRCZLRUg1LzkO0qY8099m7Th/88/xZPx69MFLG5ZbtoK8lmelvFL5/yZi99wjXPud32U1NoJx2f9GkYJ//qpaphwfwBQjSJaGsXQWhmq3fK/dBDVEzkQyTDK57Qb3k4IBiPR2XRzIj2VvuciMCKtzj2Ja7wnlPtuCwJzYx1zfNRwKYdGhKBFEUWg5BU6YPMzI4mHODt3qaxHnPReHlH6e0hdY0hcbqpQ6CWt5zfwX2g85qYfIgLPlmSrNki2GR65NX/OzfC7zRi6c/CK3PPuHpItTLDYivvIbxtoQPSnN4hKK7ny3Vxz/c0YXHuH+y3+J0qGLQ/GBVHUDzTSRBJFMLMrdlzTeRl4rPw7XwH8jLhrqYzCdrEpHrkpS3pCWvO41Nf9ee01xWoI1+znkSI4LBnqwbGudv6BlWViskX75xBhJdYZUcaqlhLji3lfyWH6cmCIju/3VxnbSbn+sSCKSIKCbFpZtI7D27LWcFumi+v05LUdvrLECdSOcdmaRikbQTRPDicF21YTrCUcQKBpZym5KLfgnvrz1YKc2NDOxKGYHy/l00yIiScQ6sG6IKzIriVFYfpJudYbjpebhOX6gLzqhMKX4IFZYASNtwLItSlUlzXp51nn2QyK+nhi7CyYeQVaCPQMvJmz/qukljFRUIV/Wm3rKvNTQ8StRWOCyJ34fq7iMccmnO270WBMrZ+Fbf8qYPIrUexDO3lvxqqiH1oivUYYCDKLeBEFYfY7VpVm6xzoUMtAEXtLUTaM38cT8E41j1k2d0sIpsE2k3gMARN3EtW0pdfSIr+Qoe579WzJHv0Xe/gD03t3a8TbAfuhPuPG5ezhy1S8gCreEcsxWUU2m3JQ5wJPLT7CgL3Usyj1seD54Sz2X05c7hmiU0JfOQn9t1YxmapRMZ6cxaKkj0LgdA/nuCzg+JHPxUHgeOx42fvam9l1cZHjiG0S6LsYwzeDmuof/lqvu/wuSw3dSTo+TWD3pll1dW/Pl1YqvIAgjfKJdrK6eJeeOG4ODVwd672ikG4wZlktTlAyDZAhEVckwKZsmsiSFYtgtSyKiIFAyWtscktykvVKqudG+bduVtnl990U8Nb/AgpUPzcMlKPTFM+uIr061s0j3OKaoIFk6xvIEtn1eKGRMoayjCxFWuy7CEiQE2yRbx1Detu2KcsfXWBsQ2vxJ4kAx2kc5OQoLOORLWMbUZQ3LshFEmy4fG3xr5cedHZuSESWU53ojns6tP29RyXHLweYbBxPf3QuLh5uq/+pBN01M20YSBOR6iq82n5NYfoLxlSeBvejm+eiWWkn2btfcXhZlkkqSgl5oifgqZ+e58YmPIvQd5N6hHyAZi1RIqkqASVWYyVRxfZJ3TstW1H/eewRX6icAtvtvG4fg6UQJLjhKR8uyWSyo61KI1/27EjpS59/rSojXk34lw6AvmeoIqRxXZKZc5XxanUIzTMoNPET9wnbTkNVkeOXm7aBsFrGrNKBlbRlsCzWEPlPV9AqpFhWTTV794sUu8bVdsG32zj9IbvoYq8P/md7u8Bc6Lzicup/M7Cz5ng4nLYoSI899GoDl3Ao9XdvAfM8+Tc/jf4nQfTHKde8EIJZrrJSpqJf8TAJ69jG95y6Wei9nJGAZEUD3xJfQhm+HbSC+clquQqbcOHwjH3vyY42VMvPP0/dXL+O1sX60u34PgER+EtOyt6fU0Uv/SY4zMvUw6Zlv8ez0K4CQiK/lMwhAOe0vCa+T8BZJvbFeLrz4LfDtJzieHAplQrIVsJbc0sL0OGbXPsTF512y5pqar/fahIBASkn5/hyP3JkrzlE2y3UTRou6gWXbZOLhpxV6z5C3ANjUvr1IeT2HblnEAx7fmDlCz8qzJPuuw+raC6snK5PKmufjKgbGA/pqhBI+0SYmpx8DoNe06OsJlj44lhqFlRmWjEVUTQ9lgWw9/ikOHT3M6YGXk1DaD4aJPfF3vOrRf2Jm791w2c8Fe7NtI7ttrZze07Rkd7W8St5VBd0wejOfmH+QWVHbNuIrf+BOjj5zPajPAJ1rZ4IoUUqNkcyedpMXw/E0y5Ydkmul7zL+5XXfYlWzGK/j67NcXqZoFBEQuGH4huZjbUBoiw7xlYuPUnL7wJQ6Hdq9zfzHL/NDx77IY+f9GOnxnwzlmDsZXp/t9eF+75WXkp4pTTPXgseTMPcMqdw8cmR/ZUPE678r40mbz0nvd/+I1z/7LzxxyU9hmC8nqzvHU0SFmNS+8jMTybR8nsbCMS6c+hLqyjDK+A9xy8ExehOxisoO1v/9zXPH4Qwk5ARFo4gtlrn7kgPr1HsWdk0FoCgIdMc7o3TtTcQY70ljuKo6y7LXqw+r/o1tY3l5xC4PsxZIUp/069S5xxSZ1f6rOFF6C6vDN6CZFmqb80zTstalwhbKOhb2ujLhzX6BeL9d+3fN17VG/m1UB+awSGmL5Ert+4Fm1TK67ZR1RqX6VQ0vduwSX9sFQWDkm7/AWGmFsxfdTm/3zdt9RtuPr3yEG2e+x+Ov+BOgg+bq8R6saBdieZXc9DF6um7s3GfVQ1UpYvfQIVZ7L6WQakxkBFIvXXg3h7XzUCSRSwOcVmVRKYoYi/UXrZ1ENZlyQY9jzL9YWqSoF0koNTprj2iKDRLdeyPHX/Y7nJWGuVHXWyp1rCQAtTiRswFdSVFK70FUnF2VSBNS0//B7cr3jQ6cF84x24A38R5LjTHedzEAy9pswzS/nQQvoryc3uOUHC8+jzZ/ou7rvTaRiqQQBf+KqJ5oT2USPJWf4kDXgc0vWjlH4vF/ZLTcS/zC8Alnb6Fyce/FHJ49TE7LYdnW2vdwI+UVPUfBDF4O4ZFcpdQokqDA2XuRinOVBLVN59Oq4iuEaPp2MTF/BIBhSw5MVuztPg9WHmPeLoRGAEjPfI7LT3+TYnwoFLWAtHySkfnvkO9qoY8pLiLpBWwExJ69TdXsXrscjA9y0fhN8CTMiwJLuUUIr0DcN1TdoFhVptTJdmZk9kL2NPH8BKqmt0182fl5rvjquxmNjHDk+l91koQ1lYKmY1jWJnW79wwOJAYqfdJMYQbd1FGkEAhZdw6Rj4+guRs1aXWKcy2W0G6EtHKGuLaMJSoviPGmXXjjj9eH+y1LFXpd4kud5lgd9V8jDN3zHt6Rm+D+2z6JKDhzc6/teOfS7nNiuxsviuZsvFTPd8NQEGUiGaYL0y2dp12xrxglIkn0JmINkxdzhuOzd0nfJRyePUxBzzHWvf0Jev2pBK+pUQ5cXXZrVaUQe3/XK8utRfqlQ/SyrYYoCBT3vIzvdF/NQCqBvpJre/xUdQNNUMin9rKa3MuyqpJw+xHfZN8G4pKqEmXPv6+Wus9D9WtsG2ZLa2mgAFlJZLw0Q7ZUY84YEOLkw1zy3B/xle4EsV3F1y62A0LPfph+gtLcCbj4JU582Xal7Eju3wK7/579MPMkpfnjcPH2EV9qchwueDXPK4eYyxfZ2yCyNgjxZbnmrkEW51CldhJFYvnt8WqqLIhT43RFu0hH0uS0HJP5yQoRtg6V0sIxunrHsa78IZan5inqRluljjk9h2mZSGIwv4LyHR/lC4M/SldU5vLc1wCIhbWLri4jur4XicH2lR3tolq14xEYBXOVFTVHXzKoZmiLYduIbklWfOh8xPIBOAGRBveqVbNdQRAYT49zdPkoE7mJ2sTX5GHGHvoocvdlWK/8gUDH94NzLvl6qO8Qh2cPY9kWBb2w5n9SpfhaMVuYUC4711JL70G44X18ZvRHKItx9ug6ygaFW3V52wuR+DpTchIU+8VMYD+WfYOXwel/ZkbQQyO+xFWvtHAPiRBKHcVENwCynnN2xYPYAbj9cT7aTzrV/DmpJkD7ei8gbtmoosDs7BNw0cUBz7x9qLpByVrzWOlkO7O69sKEk7wYRlsoLxynf+ExYrFBTkUiKLLIQl5FN01U3di0KK0ea/tifcTlOKqhMlWYYl+mffJdWDkNQDY+gt3lHC9dnGK1BfKlFuSso9bIxUc5uAP8LjsNry16ffhsYRbN1IhIjckGsccZbzyPr6DPtOimNwrxNVt8b+z3zqXd50RwN15kLYtuWsEqHHygHSW/tXQagHxilIgkNiWoK56F7rUpm+WGSu/tRnUJ8Pa4+vpDdzzK5Eq+QoS263ul6gZH9v0AM4d+lKJmkMbmtYcOIopiQ7Jv3e+qXmNVkWR+1H3VIRzez55amIaza+eYFUW6ytOcKwd/bquhmxby6lkKOJsOUTFZKVN9qeHFP1LsYHjEl7eb8JKGuoxQdhb0qS1Y0Iu9+2HmycqAtuWoUpoA9CbjzOWLZEsaA6naj6Vn9Nl0ImDblBfPgm3RG9AcuLKolMTwyJqAqKiI0k7N/XhqnGeXnmUiN9GQ+FJT40iiSLdrmruqllsqdeyKdFX+ndfzdEW7Grx6M7wFTF86BfJ+ABKFCSdCvV3Tz2VnB70YGyCZ2P4dRI9MGU+Pk4lkSAsKOVvn3MS3Oa//zdt8dk1QWsEWZGwEon0HkArO4iCer3+v2kmZGk+5xFe9XXpPBZoYpbsDizjvc8/rOo+IGEGzNLJado34chceAjZmcRUyAdqXbSO55IvSf5Bouh8xuoxWKqPqxqbd8cXSIqqhIiAwGjBJqd1o+jBwJuacgz50e2CF1fjodQAsSiIruXkYbLPU3rKQs869Nbv2okghLF02LEKDTLaNhZPIOGRHMtpchVMhz9PjCKLIQHyEs+UZTthmWxP9VtH3tZ/D1BYrZqM5LdcxM3S7Zz/gqKDCIL4ML804PkomHmHPs/+Xy4/ew9F9b6N04U9uJr6qr70gMJYa4/jKcSZyE6EQX1OXvYdHoleQTYxxiTuvS5YXKBSybftA2qZBrOA4vZfS4x3zRdpJ8Maf/Zn9ayRlfor9Xfsbvk/uczaTE6U5DK1IsQYJWheWieTOPcVkNwCmZVY2Lg71HVp3bq1CTDikWkTPYVjW2ny3zURHD+2UyHuehdnYCDFFbtrHVqvhBARsbLLlLAOJrVewvpiQikSQ9AKplRkkI92+4kszMExHCaubJpl4NNT09VYwWV7fJ2ZFkYw6jW5alNpIhy/pBvHcRMW/MiYltyc1eQdgG1y9d1GBO+mJh5ii84KFu+grxfrp7moxQj0IKtd+e1RNtquO8HwvehMxsE2Wc/UTpHyX7RUXif/pFbzu86+gNxZQ8VVV6pgoTFYkzFuJjd4/niKkGWGgua9LLjzFgdOfRZ34XkuljoqkEJcdtVIrkyRvMO5NxsD11kgUp0OJI/ZUNYXE6I6Y6FcvnADGDadEzvNA2tGI9/Dsux/j03f8O7FYEqHnAKYUw4K692rV3fluZRe60o7rEMqmWypYTI2HotrZCM+MeDw9Xps8UmLYrpeKUVwKdvD8LKJZxkIk0rcfQRDoikcxTAtV23wtvWswnBwOXFK1ExRf59x23x8dDqz46uraS9pynpPp6cPtn0z1te/d2/7xAFxlh1d2FARauYga6SGfGCXRxN8L1quOAIbc8srF8lwoSVaBYJlkjn+BImufa9omBb3Q4E2twz54G9+95EM8Nf6WUIivtdLCUTKxKEl1hqHVp+nKHq9pKL+x3LhZHxUU85EhJodeQS59kK6eYXTZKa9RspOU2/y+5ZVJJEvHEiSMZPDn8IWIahVUJSnYx/oh2jXI4Ut+hodv+H10w6ZUo0+ui9Jq5Z9SwiHp54pzGJaBLMpc0H3BunNrFWvEVxbDtNraZKqFVscN3bQqYUkrsZHKxmojePdkT3rPmnXGNqcQvxgQU2Te+J33c/OX385I9lmyanvKUa/PFQQBzbToim0v6QWb28lMZi/5vkvRTLOt8bCo6yQKk5VQnric2iW+drEN2CW+1uDuqKjJsYa186HBJSRihfASjHyjyqcpMuDsgnbd835e+9mXYT//pZpvKRklNMsxJW06EXCPrUe66UkHmzRUlzomCpOoLURftwtv0r3HVcM1m4xb3vfNOK+PP/Ixrnnid1HOfKsyiASNw255sjJ5mL5P3sp1T/wWqWgEepx2FtWzqLn5YMeqhaqyzk6QI0FRIVM8klJxrttM9uS2nVMQLBRUzGjGScK74E4e/ZHH+I8rf7Nu6lg7k3FvoVIvNc121ael1HjdcudWYdnWutLCeosAK9aFJUhoxZVgH+C1y9gAsWgcLJPLD/82dz/6XygXlje93DsX75oEwU4gvqbc8++PDxNtYfI4mHCel2NWGGT4aQBysSHSSf+BCw3hKr68RWgQZC/5fv7x9i/w7ct/0VfC5EbyfNTtSxbLs8EW6GEgN00RA9tVIkmCo+zoWLLj2JUcP+8dTHVdXjd5MRC8tpAYdRY1TRRllT7B679T/tJn/WLVXZgqkkgqFmV632t5bs/3UUZqm9TUXXVbPj6MokRaeg5faKgefypJwbnm9yoeUTh5wTuZHH0VmiAHI1lLKwBoYoxoxNkQ9J7ZsdRYRRGf1bIVH6RWIHpku55zSh1bUOs3QqvjRkk3SLrKwtX4SNP1iW7pTBemAed52gnj1YsF8YhMPuEoxLtL0233mcbCCX7om2/gum/8OIZlbc3aswm8duKNPZPpvSyNvwrDVXy1ClU3SBanKoqvpJIKJQH6hYhd4ms74RFf20G+7DR4ZT6p8aZmuKHAvfbR0lJbg3VLyM8hGCo2IqlBp7xKkpxYc2HlTM23VHeGSaWJKWE1OeJjx70aFX8rUUQxChRWZgK9Pwxs9P5pOBm3LIQVpyA+6pKIYu9+5//5yZbl8i1PVhZPEl05TrIw6Sw8IknMxACGFEVdDIHgPvRmHrvuN5jY/6ZwSprawEYyBWA87iTPLJS2vt20gmypTESSnHslSmTiMWzsmiolaLPUsZmawvXDsbrCN7afK86hWzqyIDOUGKrbvrPv+zb//IYHWMnUKCluBF2lkNrDanKvYw4rSvSc+Tp7lg5jLJ7e9PJWje1h+4kvUy+zUHDO/2C0q6VyraGeCwGYKc+jGWZb52O5SsFcYiS8HdzqhE8z2PkVNR3NtFBkxdf5bGwL+0Rn8WEuH65LQHcKxuKpysJAESN0R7uBzrW1uCITcUuqs2rwtL2N8LzecnGnLYi9672dNqLuJlMIm7Hl1Rn2P/MJ9s5+i4gskYjInHvZb/CdS/8/lqPDbSugvbLOfGKUTCzSVtnkCwVeO+yKdFXmRb4UX7LTzjSXxA70XKkrAJSVNLLkXONqlabXH5u2SdEo+j/uBshJR00WdYmviro6LMVXix5faqlIvORsWubizZX2M/kZLNsiKkXpj/fviNL8FwsSikLBtUbIqNMUdR29hSAeD8bSSeL6Kkp5GQF2RECG106Gk8MAqEaVp1kbG0GqZpBW14ivlJJ5ySq+XprfeqfAVYPEC5OUNI1YZPtlltuGik9TcAVAS9j3Mr73I9/lXEni9pBixH0jOcBz77yfhanjXJF2lUguERerk/7ndYbpSLq514jnA5Ucoz+gp5Q3yVBFkUeu/iUyZZv2Q3T9o9o7wlODNCQMzDL5q9/LyvRR0i6JWH0ts8nWiIqWF9dVaZ19bpsqvu9BvnRyiT2xDO3mshg9Bzg5/todUdaxkUwBGM/sgcJR5s2Vuml+OwXWN36L648+xImDP0BcuQigsgNWb0e8HcPd6kXlJs8gy0RyfZpig+GndXrPzkhqBFmU607GlXgahLnAZVfmgdv48qs/Q7Gs8Tq3bZpde5FLi5iLJ4Db1p/PhnLmINjuhcTs3PcwBAHFthnpbe1ejbt922JpFlU3KuRHKzAWTxIB8okxkmH1C57fm2ViWME2hkq6gW6aRKQo8SYbL7qlM1NwSHKvv9/n7sdm9ZnQzP/9Ql84sbYjLqfJRDMslhY71tZkUWSkcIKumWcoKzdjWPs3JS8GgbLqbAKV0uPEFbni7ZRRpyrqKw+6qTNTdK79xk2mMEod9ekjXPHsn7OS3MsD570GRZLIxKIV+4S2jamVNMW+65jvunRHKDW2AtUbeUHLUgetZcqTj1CUUhTHA8zqPMWXkkZxg36qVZpxOY4syhiWQbacbb4xWw89B3j+sp9iwkgxZJmdK3UM+CwXLYH/e8dXuEBepWT1VFL/6qF6bBMEYds3al5MiCsypaTT7tPqFJrhqKCUJuEOdeGq7NXkWOX42w2vnYynx5nMT6IaeZKrJxCtbtQ2NoLUYo5keYGsOAJAT7Rry/0zdwpemt96p6BrD/Ov+VMevf3/sFQsbffZbC+ufTdHrv0lFsdu25rPU2JkeodBEFhRt/jaiyJzQg8rfVeQ9AZRlwSN1dm9CzQJ8PzDWlhUpiPpStLH0fFXsWBsrapoXp3fTKak1hMG66DEOX3Nf+GR63+bVDzh/MwtY0WdxLQdtUJQoqLlBCBXtaOmxiqDSiwzgCgJoSRZqbpjxpnaZgNOWE+meMmX472OkmXWLoXjadZB2GceYmT2QeJGtnKv+h/+A9704I8jPP/vNd/TjuGut7Av6AVWyivrf5mdRLAMTEFB6Q6f/N9INFWUndp6T0FFEhEQ6ire6qHk7rxGZLkyebS7Hb8pYeXMpuc2DMXXxnPfKkzMPQnAsAnpRGuLvH2KU5KorTzZNrmzev3P8K+3/jPPXPDuposy3+g9yLM/cZR/vv2zwRRfhsbBv7+dNzz+/5EQyk3DPGYKM5i2WVFHAOwfvsL5nWhRCCn9zy/MpTXFVyqS3pK2dsXjv8OdT3+UzHybbcE0KqRSKTVOPKIQG3SIr6iRR80urHsOpwvTWLZFTIrRF+sD1p7Hc7lzbSvhjUWvFHGULpeYiisyslEkqc60baOwOP4qvnLdH/LY+e9Zm0e9iKGbOqqhAs78xFPp+VXnjc3ez22PfYTLzv4L2ZJ/daHdtYfnLvxxjo6+trKRVa34Co3cSQ8xcfn7OTJ8N4Zph058tWpfUdINLCnKSuoAiiw1T3TcULq9S3yFh4gsoWWceUWqOIluWi2rgsuGSdRtx/nkGIqPtM6tgDfWeHM1qzTNLV96K32lybbWEIXcEpMDN7EqO33lYHILvLR3KHaJr+2EpBC7+h2s9l7GUnFrJ3g7DfbIVUyc93ZKA5dv2Wf2JRy/gsWCumWf6aHslrdUGHdXpZQoTGLW2GEPRnyddj6jhUWlKIikIs6iTLXybUlrW4GXEjiaGq2QKSPJEURBpGyWWVAXNr1nyb1/lUHLvZamOguAIirEpBbTLQPuDno+TZo7KQWHTFAkqS1JNgCmDt/9a4bnvkNvbPsn+rVUO3uGrgRgWrQplHd4n+Y+J4LbXgCi+Sn6csdg8VjNt7QzGY9KUQYTzk77pl361BDPvuGf+OZVv0E8Gr56YSPRVG8yrjz5aV72yH9j4OxXAh1f1Q0000SRpEopiOCWWcXyE5Q2lPNtXBwEgXfuRaOIbm29B+HZhecAGCTW8kTZU37mtKm2dnEBVBOWokNoqbHwAi9EEUVxyPVA/dbqOWLZM4yuHiHpJsA1QiXBNzWGKDhj4V439bIgiswvngh23u1i+XSF+MpEMluyaDXd0uZ2PTVNQeSLd3+BT9z+NUgMEJMllFiaUtQhteTsObQqErP62nvq09GUU0aU1/Ntf2dvLMzFh8m4huDd09/mXd+4mzsf+2+ByJdayJbKFVuMl4JXTfX9SCmpdeo8PySlN87VUv81gtFzkKcueT/PHHjHGvHVIXLH87YsGcaO8fgqlJ1nUnfHt6bE18axttVN1F3UhNC3H/DWSlbLmwUl3SDuWhbk4iNrlhfbjGrFFzi2MzbQVZoJ9NxWw7ZtloQ099/0h2Ql5zsOJttMk34BY5f42mZ4kcIvdcVXwZ3w9SbjW/aZySf/huse+Fl4/stb9pkAPPLXHHzij+hZPbr2swrxNYVa3twWgkwCbHdBb3a1lvDlTRDi84+QmnmkpWO0ilpKEEVSGE449e6bdjdXzkJ2AgFrTV3QNY4tSBQEZ0DMRDKBo+hbnSR5197uqfJpmjzMjd/5MJce/p32UjJXJ0h/4xd5xSMfpisRjMjrBGrdq+HhqxFtm7IoMOMSBDsShoaYcwxr44NrflaeP1wsd67mvWp3Ml7Xl0WOciZ5AdPDtwb25fODajNiqD8ZF+eeZmz6P0gtBbt3mb+5g9c8+F4G9ZmK147kllltXNBrpsZswSGlWyl1rA6q2A7V17nV0wD0iZmW79W+oasAhyAutkkAeD4nkZB3rGV3kWsESHX0gkay8RG64s37qFoEaCzew4DpPHuz809srQdncamSetUVzWzJotXqXiO+2kvtckpMDSlCOh6tjHnlzD5ysWEELbduI6vWtY/LcQbiA87v2yx3FF31cy4xVmmXco8zJ0mrU2TbVNrn86uIgogoCB3pM3caKkE9ShpJlNaRlJ4fViN4Y1uyOE1ZNygb/tqablpYlo0oiJU+oZo0hfDIne78ScaWn0AvZcmVW1dX10KrJfLJI5/ilU//Pj3TDxGt2tiph03XpsVN1F3URrTP2TZStCwRPRdYne6hqOskXX/tbGyEiNz83m4FvHbi9cuWAEVBoKs0TVk3K6KJICgZJiXDAMHAtN3UedfH86WIXeJruzFzhD1H/57Uufu2+0y2D8Ulyof/H90LT9K7hQt6ceZ7DE3fT3zh6S37TACO/CvnPfdJ0vkqI/vMGLYoI9oGufnNBvcVbyEfk4DyobcxOfZqokMXtnR63mfseeoPOP/pv2zpGK2invdPXT+Lb/4Wt37htVx8/NNr5JakYGXG1nbuWyApWprIGRpC1hlIYwNV5uC6yvDkNxmYfbi9UhY3+CCfGCHRAVVQUNRaOClKgsG4U6J6opzflvPyhdVzCNjoUoxI13Dlx5KrUkrkJ2veq3bLL+q1Y9t2DPUVWSTWAfXCRpIy7aZvbpqMx5yELiVIu9dLRBaeYXD1WRKZvsqPZXeCmiqsv5ZT+SlsbOJynN5Y8F1HSZRIuaWC27GYmFDnAOiJDLasNBkZcgjikigwt/Bs6yejqwx+9We49vhfk44IoRp899/3a7z6kZ9HWHje93s8w/FcfNRfomOudn8/irMhuJo7sU6l1Gk8d+df8ZXz3wtAd7Sr8pz7IRZahVCVvNhOeXhJM9ANh6Tsiq3No06/+e/5x1f+ExPdV657DuuVG1fKHfO1/Ub9QnL9xrLx0QrxFe0/gI2AYqoYhfmWVdBmKcfd/3oLb//6G0hQ3hEL1k5jY0J1TI6tkZQ+yh0jHmFgFBBLS77V/ObKORK5M0StIrIoUtSLLJWWgBqKrzb744u+8h7e/uTPIy+dajmRux5a2cy0bZvMxAOcP3EPyexJkjGlaR9bz1ZgV/EVDqKJbo7s+36OXfEzADVDO/xA1QzSRWfzcyU2Qjq6MwIyvHYyGB9EER1CPyuKpItTaKbZ0hhR0g0MXcPECZ8QkeiNh0MovxCxS3xtN459hUse/32Gz3xpu89k+zDzPfq++l+47JFf31Liq5mvVqdgu+bz69LbRAnz/LuZGr+TlUJh03uCLLhnrv85Dt/0e3T3B1dTQBXpI4okCpOBU73awcaUQA/10qY8hZWWXu+LZLz2f/LtS38KaI2kaGkipy6j9V+CGu0n3Tu69nN3YZNUpym1U/7npXUmQixpagN1F62uB8PJOgmlOwLuM5iPj6xP8vGUl8XJmhMMrz10Rbpa+th6CaX6459m7/F/oLs005Hggo3PVV1i1zU1l4NM0t1UVU2KE0tXmSa7ShbJLK1TfFWHVwRVYnrYzsXElOEoEbrje1pWWCnRJEPumn9m/snWT2blLL0n7uHKic+SadFvrB5iE99hfOFh7BX/qdOmmzBZSIz4UuHU6+9HFOf+rpTObWm5fbZURsVpqz2xri1pZ1JV8qJXVtUK5G//Ca9++Ke5YPabJKNr1z4ecRZ0tr0+tKPuJpPXR+XaSxuPuONDKTVW8Z6LxRKoLlkTzU60TPSVXIJVtHSEaIrES6HUsUawSpAUzlgiRTHmXvvcOd/XXnngf/HGe9/Bpaf/CUUSK5/VFe2qkFKt+mdthBl1xlVbXe6YuX1ez2Na/ua0ZcOslMOtxEbojjffcNy0yRTStdmFg0RE5rsX/wynLvlxiHe37HtVKhVY6LqYYmoPy9Ehunzc261AdbtPe+OPJJIqTqG16Gmm6gYvP/wRrvr6DwEQk5Lh+YG+ALFLfG033IVWfIvJlx0FL9ExOba1cbI9ax40Wwa9BNlpAOJD6xPBhB/8FI/d9HvMygOb3hZk92vZLZttNemoMtkXRRLFGYrq1nmgbZSJe6ibNuUutLx76SFy0V3MJx3lUVvEV5DJSnqIp9/8We65+4skqs3n0yPYUgTRNiktng18LhVUPyc7ifjasGjd4/5/Kt/ewqmjqCIR111Ltz9OqTMUS5vbfacUX+Ijf8W1T/0B/bljoSftqIZa8cZrugvtyt9lPeu/xM29lrn4KPGqBTddezjy7if4vy/7J1Rjs7dQK/5eHrbTN2XCs2XsvhhFaj38Y0Rw+uf5laOtl0BXlRamQlaB2q76Ty8s+n+P91wlxwMpvjb296Nxh0Bd1ue3LNnRtm1yJY2y5Ww8dUW3hviKDDjzgIw6Q7aNsVaefozRpceIaSvr+rTqf1cT0PWewzF3E8mvaXotWKVVIuVlAEqpPWu+f4JAOeX4X8by51o2pjbmXeP8hKMma+c5fKGg1tgTJIUzocgU3PS6VHHKf1mtm+poRDIoolhzwyus58Trc1BX1jaZoq1tMm1ENWHot0Re1Q0SBWe+no2Pkoo0DhXKatnKNdhUBrpb6hgKYoqMLIkYlpManlXLLZXD50yBr17/hzz4hnvQpfiOCMiwbXstQCmaoWvdWmyyUhkQFEVNJ12cQnXHtpiU3BFriO3CLvG13fCIr8JEe/4/L2S4KYTFFvxe2kKVr5YZwMekLbglVoacoLt3/WTfM2qtJf+vtdtXE7lZ8vOnwbZa7tgqkxg5goBFYe5kS8dpBc3KL9ZN8LQiQsEpO4oPnb/u9YIgoNmOrLelUscWJ3KLG432AUQJK+NM9tX544HPpQJ3UVlK79l2SbZqqCyWnAXxxnu1z1UI6rP3ba0/TxAYZXQ5STG1gfhKj2CJDkmpL69fTOjWWqpWq+UX9XboBVcdJ7u+WGHCU26kI+nKIqJu+3YVX4qW81+G5LZLZxFaNXkURaLJbiRRIFu1K1tPaRIElV30LV5MFPQCKzgTz8Gxl7V1rDGlG4DlUm11oR/orvm7RwCEirib+qT6L/MTV9YShf2cT72Qg+GDrwHgucTwlhFf2vF7ueH+D5DMOaWnmcjWeHxFe/dgCRKSraMtT7bcZ3qlhYXk2DrSMZU7zeu/+wFe9/BPkatSlDVTfLXj8VUiwpdu/Tu+fs3vIcbS69qCZ+afKky1fG/NJbekNjHqS4XzYkBN4queBUQNSKJI2Q3dSasBQjU84kvJIEtSTZVmWKWOtjs+WdoShr3m0RoGFFEhLjsewn6Jr1Jugair8M3FR5oq7b2xti/WR0JxUsZ3Sx3DRUKRiaMTXT5Gf/4UZdPcFJ7jB6tqmYi0RoHshICMgl5YS6KvHn9EkXh+Emy7pT5T1XTS6nTF/iUhp5omLr+Ysf13+qUOT3WkzrOaW6Ur072957Md8Bb020R8xdQ5soUcXelwdpYawv2uxeQYmRplnbIAtrqy6ee+lSbf+TNe8eAfc/L8H0K84mMtnWLF1yTeBctLFOePw8ErWjpWEBT1Yl0yxds9W0cYeCVWcoqu7qH1B8vPEV08DEBaacPjK+BETjMtZEncPKj07IflExgLraeUWUunEIFyZoufkxqoJlM2tslx159nSZujZJg7cmdJu/4/8/nI7SiizQXV90oUMXvPI6sW0QpL695T3RZaJr7cPm66MI1u6Y6HQymLpDqf5ak/wkStBW7d9u0qviJ6DsO0wMcmqLl0EgmnT+vecK8TrhpjpSqNKBTF1zYtJrxzj4opBlPtpSKNJUcgO8eStuAoC1rYcTYXT6HgKBf7Qp64C25bkDT/xJce6cZSuimnmxNfWS1b8c7aSL7sH7oMnoJVY77t1Eu/MKefYmT+YYzYRSA7z4jnJecZbXcCgqTw7A2/yumSgiomKRtm8FJ22ybiJiJvJB2jiS56lr+HJUg8WXR2/FfLq5XFv2eS7iEImVIPqgkLyYMY8QOMxdZ753jBLyl1qmVjam/sz8ZHSLeobH+hod1SR4Cli9/BsZ4bWUxfxFjJ33MleMRXNINcR/Hlbai03R+7fU5Zc8ZDWZArZFUYyEQyqIbq+zw9z8JSrB9bjjfto2sR+Z4twi7xFQ5iisyB6W9w3eMfZW7oJr563f9yEhoD9JmWbZNXSyiShGlZiIKwftNum+C1kYgYISbHKvOco31Xs7f7WmRM8uXgYTil7CyKqZIVHTuElJJu2WbixYBdxdd2I96D5e1ez7ahBnkhwyvh2mriK9GHpSQRsMlt1bWvIr5iG8mRY1/nrs/cxPX3f3DT23wTX+7xyyGUEeVizqRfX9gaxZe3k1gdI+/Bm0jMFecom+4iuupaJmIbJOir54jMPgQ4uxtB0dLC+l9/gld8+W2Mzz6waVARXR8XebWdUkdHTaH0hU+OBEUj1c54/6UAzAh6W2bNnYSqG2iWhaJEN92r7Lu/yRdu/TRnIvvW/9xtCyklhSS2tlvWH+8nKkWxbIuZ/IzzQ08lo3QRTfa0dNxGqEU0VbfvdQoTz+PLKPj29rOWTgNO/71x8tl17N94zeGfZ+y5T1VKJ722s8dVH7SCbSO+sg650KUMtk3ojh+8E4CjyYHWVU0VtfRY6Ml2oqv4CuL39uTtH+OvXvYZrIGLm5bseuR5b6y3oo7wcMD1h8sbiywXt6bU3nLL5rNu2Vz1ONTpdlY89IOcHbgFVYi01hbUZWTdCRPR02PrFnHRnnFMUUG0TazVSUzLqjyD1coUD9XkvGG1mJim6RXF6EbvHHPsBp4bfxNTfddV0ryDQnL7zFy8A0rHHYp2Sx0BhL03cWrwVrT0ONmSv1RNoeSQ03Y0gyQKNcmd0J4Td/wpa06ZbCYaPJG7Eby57apPMt/z4y0mx1B8pOY2Gmu3I4H4xQhJFLG6HR/ZZGESzTADG9yrusHlT/w+b/7yaxg59k++7u1WYGNquPf3keFXcvrS9yIpMVbV4J5m5qKzhltyCequFlPJXyzYJb62G4KA4O6AleZemsSX5wvikQNbBkGAnv3ocpL80vTWfGYVWbNpQE8NIFo6Cbczr0Z13Xcj2CGo5yoDteIo0qK59tKd/KKREqQn2kNCdiboFWPwip9MDc+rngMVWW9caOzLUAvVkxXL9lfyZc0+QyZ3CqmG34jQux9TjILRorm9bbP0pk/wnat+lXiLaZ1hotG9Gh+5BoB5SWAl598faCtR1HV0y2IgvXk3OR5RiEjSJtIuDLNdQRAqi5VKatpyZxdxNRVf7ncwbZOiUVx7cf+FHH3Pk/zbXV/07fGlxwfIJsYoZfYT2UDmR4szjC58l66lI6i6gW3bdUMRgmC7IuLPHflHAA6Ws23fq/0jVwGwoi+2THwJldLCPaEHXogJh/hStKxvK4DVkrMbnYk1D6mpV+YIMJgYIIKIjc3s3BM+z7g9eNcy786KHXPhrTGmTkYVZFFEN82W2oLmKokLkT4i8TRKVQmPJEmUko6qK5pzDOUb9d8DiQEiYgTTNpkpzLTydZCf+SyXnfo0XdkTm3yRhIO38p3LP8zZsTvXlUAHOn7W2UB6qRNfnhJ+pjCDbjVf/HvXSpFF8mXd13MteYmmrhqrli9feMSXszAvG6vrjhsWgp6n6frx5uKjRGTJN/G17tq4c3bVUNHNrVGvvtghuZu/8cI0gh28zyzpBsnCJFFtGR3Z173dClRUnW479f4uud5ciiSSL2uBbJFMy6qMbcsx53hh+ea9ULH9d3oXTpz17BHMxVPbfSpbj9Iqglvmk9jg07QVEN/zVb50bIaILHPxVnzgnb/JN3pehyzLbHLzcXe5o+UllnLL9Pb0V361sUOsC5cMascrqDI5SHTx1HW/xlLPoc3n2gE0UhEJgsB4epyjy0eZyE1wsOsgjF7NiYvfzWr6IGMb1QXxHrKyM+GOtGAY7E1WbGzyer75dbdtBPfaK/01rtYN7+fr3a8jW9bZZ5rBzXgFgaX+qzitjrAvtf27NY0Wrd3dB0haNgVRYHLmMAeGxja9ZltRXKLvb+7idmmQ4lv/ZtOvY255nmaa6FX3yrfPXhOMpcc4sXpibZe+QuCO0tUBg9Vai9y4HEcWZQzLIFvOklTcREBJJpLsAUH17fE1e+tH+drQe7l4aHPpn9TvTFCThUlUTce0i+RdZcrGEqsg2C5z+3Nuux+kedlLM+xxff/y5jKragEIqPazbWRXNaVnxsP37HDVF6JRxrCspgou3bRQNR1JEEhHm1+bRgSoKIiMmRanJFheeBLTekPooQ8bIa06i4OC6+GWjqTXecnZtt2x8pBUeYHzZr6JIUUpnhdcCakvnCSC43nVtVH9DOiZvZA74xrKNya+REFkLD3GqdVTTOQnWipJTj77L9wwcR+akiau3Lbud/GITESS0E2zYkwd9LrO9l9HXO6hmN77kkknqzUH9EhKzdKYKcw0VdHGJYF9s/cxaMzzzL63OwRAtMHGoGUhupuuYqIXy7Zqe3yFZOBuH7yNJ5cKnIwqkDvWOeLL53kePfADfLfvLhSzTFyRN23sbEStOaxXLg2O0qw/3r/pfbsIhmj3GKYgI9kGidI8qh6sjypqOpniFADZ2LCve7sV2Bhi5v2tGVkSqyfAEFiMjaDqhm8zflU3iLn9/VIkARR3FV/bfQK7AG79MA+/+u+Y3v+G7T6TrYccY+INf8OR636Fnu5tGBCiKURR2DITblsQURNDaImhzb+Md2O5i438htJLX2oTdbkiS08Mtl4OV5kcCDBz/ltZSZ+3JcEL9aLtPVRi1j3F194bOXLog8weeOPmFwtCRbEWKfj3qPEQlaJEJadEw5dEXV1GcF+XHKxB4MoRuhJxLNum2KKvyZJrnB92SVMraLRoFUSRMduZREzOPrWl5+ULy6eJLB2jP/sc8RpJeOLkY9x+73/i5Q9+cN29CitefWM7trxyitR46Kqd6s+pvleCINTd/Y64RJ/ms9RR1QxMy6qZIit6KZlFx8jaO5eB+AAxubkqqB62q9Rxsuxs0nRHR9reIe6J9hAXZMBmauax4AcQBB79wQf55I1/h1yLbG8X176bL771YR665td8kaDmg/+bu7/2Nq4794++0pmb9fcjoqPwXSmc7nzJtG0TyZ7DBlTbVa1VlToatlEJtugE0vOP86rv/TqXn/xUS99VL6sUo31k46M1n0Oza600SNWMmn1CNSp9VK61ZF7ZLQnOJ8Y2mUbHFYW4XaI7dwKznA1sTF02DA5f+nN8/cY/QUt3ps/cidhYBgVrJCVUzYsaIB6JcOv3foPLj/wRSn6yebKjbTJ9xft5cvTNiIk+FtQFymYZSZAYTg5XXhZWfyzuu4XnLvwxJhPOsdPR1rw06yHIeZqWRb6sI0YS5OW0r5T0WoSyJEqkla1Rjr5UEI/FyMedNtJdmg6sHFXLZVKqo2Zdjg77urdbgY1zTO/vWO45Xvalt3LJsU+gm2agMULVDHKxQeaHbmIp4pCwve4686WKXeJrJ2DkSrL9V6BX7Qy8ZCBHmei/kYmDb922dB5JEDF9lrO1C2+S11PD2N75xeayV83UKJmOH0NDtYlX5hjto6erdePlajWFt3tc3gKvpnrR9h42mu7qpolpWch1lABZ2ZkQx/MrLZ1PoN1Bz6cu2k+qjiKr173nLS3iTj9A+sin6M0e3xET/WYG5WOyoyCaXNmB5dteCmG9Mhk5QvfKc/SsHlt3r0Ijvja04+Itv8BXX/ZxJva/ebPvX5tYV1q44V7VWwR03/dRbnnkv2H4SSC1bfKahiBAopa5upecqy1RKmYr5Z3tGNs3OvdOY9J0ykJ7Ugfafg4FQWDMHQ/mZ1sgvoDVsuWofBLJts6lJuQIihLFtGxfxJe1cIx0cYKYXfKX6Nik5HUk4oxhy+Xp5gv0dpGfRTTL5AQJC+e7ZqIZRxkpON+lk23NIy7T6hS5FsyLly74Pv7hts9x/+UfqU06umrytEtAN+2/a4XJ+IVlEXXfV0qNb9qoUSSRV9//Xt78wI/SvfA91Bb8eTxSPrJDvHm2AvXGnyA+X/FohELCUdom8j7SZCWFk1d8gHsv/BlisXjlM4aTw04wi4u6npEBIUsSkiigmvl1xw0LQZTCqm6gmxYRScIwa2/sVMO0TKYKjopoo/IuLEXcLhzEFZmc2467StNkS8H6TGN1Esk2sESZbKRvkw/hdmFjVUHFcsXdjEwUptBNK5CnmaobHB2+i8dv+xjzsT4Aet2y5ZcqdomvXWw7cm6n1elShpqYf56r7/9ZrnrwFzr/WcUlhH/5MS468mf01uloRTfl0/PqgrVBWkBYJ5veBM/oODnWWL7eBNWEz9jy4+w9/TnyLZJHQdBsMr6OMLBttJMPEFPn6EvUvpZZt3qiR12q+ftmCLS4ripXq6d2GL/vI9z1rXdRmjoS/GSO/CuXHv5t9s18c9tjiG3bruwu70nVLq0Yjw8AMK+3du07CtfvoJAca7hIjOkrqFXJjmGVOlYWKu7iUFUyzHRditlzIPRSqsXSIiWzhCiIjCRH1v2u3mQ8eurr7Jm+F2vVh9rjyGe48tMv49bn/7D2AjTejen5iS2eCsXfq9G5dxKWaTAlOou6vt7L1iXVtYpRySGslgqnfIcJeDAtm3xZQxGljiVSpaIRLNt2Ej6bQKj4VzZPdITG5dIAY64v1ZKx1Hr6n0+YuVnKkS5mEo7qXBEVYlLMUUZ6htjl4Mphv4h6JcHaUktjbVHXsbERBJFEjWsv9J9HPjZESU5T1PSGtgLQXrKjvjqBZOlYgoSRHK7ZFjS3zDeWnwjuz5NbxtQcz5vUhsTIFzPqEl8B7lVMllCTzuvT6pQv9XnZMJFEEVkS66o0vWfEsNpTRspWmZ7cccTCaee421jqqJZLvPKhD3L147+NbKi1N3aqMFecw7AMFFFhwJ3/bPrcXcVXKIgrMoWEQ853lWYoaLpvT1IA0/VEVJOj2MLO8PeCGoov97kquEvjhDdnDNBnFt1UZEEQKBoOodyX6A7jdF+w2CW+dgK0Ihed/hcuePIP0XZoClrHcPSrDJ+6h7hbb731EBiY+hYDs9+m3OnY9KVTxJ7/N8bP3ENvqk5Es6uQiOXXDOW9QTodSSMKDR7Z3gOcvvhHmRy/sy1yxOt0i0aR/q/9DFc/9lsUpp5r+Xh+YNv2WtpbHTJl3S50YZ7kp97Ea7/yRnrjmwct27bJumavfYX5ls4pkI9QI6N9F5HF5+hZPUp59vnA51IJLWhTKRMGqsmU4dRwzdfsueStABxP9AeakGwF7AYphADEMpgxx2/JXDxd+XElYCJkxZeqGeimyUAq0ehtLaGyQ58YRpHWkyN1J+OuDN4qLjc9vrV8mmh5iYil1W33hltmZSyebEpu+8V2JGXNLzyDJghIts3w8JWhHHM06uzALpVnAhMAxuOf4ppHfoULFh9ouihrCfk5LnngF3jl4V9C92OC7SbWltJ7mi4kTMtsXm7X45SMz9vF1lMvfaLYdwn/eveX+cfLPwo47csjobeirSnJ3ori314+4ztMwINjVG6jSGJNJaJ4yRv4/B2f5cFDP8eyWmQ675h2+9pkCght3kkQy8eHiUZjNb1zrA0KtCCIPPqXvOtrd3LN03+0bVUC24F6AUcbN1IaQRAE9C5nfuVc+yZz3lIWafkECSOLLIp1Ny4ScgJJcL0w2yB3hKVT3PHNdzI0/x9A+MRXkLAKbekco4uPMn7mHgwp2tzYPr9WsbAx9Xk32TFcxBWZmdHbePrQTzE/8vLAoSCVJOrkOAjsXOLLW4sJzneLqnNIZjnQd1VLZWRXqV50lZQ90e6wTvkFiV3iaydAlNjznd/iwHN/y8ridhFA24Tv/iWXP/xL9M9+d3s+343FlY0iq51OdqyKRk5G6iiyxq9jZs+dLHetWe37LrEauZJnr/gQZy96Z1un6U0OAHI9zvUpL3S2ZM3zjmhEplRPxm03el6ND5FJbFbBqYaK4ZasnLjs5wKrKSBgqWM0zWrXBRS6zqtbeim4pKbn6RQE3vc1u/YFfm/YWEemiHXUbd1O6U4rC/pOw3KvZTk9XvdeeddZd3cGIbxSR4/AzWpZVpdOkvrWr3PR2c/7NisNgnO5+qWFdYkvVwZv+iG+3JjsYmq8rtLR7t5PSemiXMyGTnxt5Q76xMzjAAyZkImH4z0z7pbELBorgVVN9qkH2D/5FfoKpztW/txz8ovsm/0WhtHk3ExjzWg/vafpxounjpBFmcHEYM3X7Bu6AoAZMXhcfVCUNKesSZOccaKaXNiStiYIlN22EM2dC9ZnmgaX/8tdvP7wfyFpqTUXcXFFqXj3nctOYdi1lSkegpApG2G4fUIuPkomXnueY3vElxqc+PISytRIV/151IsMhmVQ0B2VWzuKL3D6Y4BMaYbVJt5I9olvctuX38prv/fLKJJUV6VZ7RnZFrnjjj1ewETY6XNBnmVrcW2+Lsuy/0TH9Garju0KY3mxIipLrIy+jCPnvYv84DXohkXJ5/ipmyZ5KcXswA0s9VyGIoo7JiCjHvFVskroUhwBmy5tjlXVv6eZtnCCd339bl7+b6+hZLh9yK65/S62HXIUO+2UoRRmj27zyWwxXCWLJ33fcigxrJR77Wc67EdUKQUZWxc3vg6XvJGnX/4/Obf/jRVD+VqmpvVgmBZKmyWjkihVSipz3U65ieCWUXYK3oRqJDlSl0zxCIOiUWR5/lmgfrmad80kQcJK7GnJUD7QJOn69/KNV32KM4feU/c1Uq9TxhoLuotumQirDoERr2Wcv8VoRKZ48H63os9RbMGzpqNwn8OGJKJHUi6tkZRhEV8JJUGf67UwOfkwvU/+H644/febTKDDQPUu9EbU2/0W3MWHpDUv7aooEVPjxOTa519+81/ymbu+xKnRuzgXcqljXs9jWsFJ7VZwzjW27xdSoe0Q7+27EIA51OAE8cppwPNR6gDx5Sr/BGxMtUlbyE4i2gaGoBDpGWtasttIHeFh/9j1zqElkfnls8HOPSBU3UA3LAzbKdOqfsY9g+1OL1o9ZWS8MBWIBDVXzpHMn2No5QhCLF2TBI3KElHFuc6zrrq+0bWv9N/llcBEhqeozcZH6arjiyT2OhsjGTW4MbWXvpmr59H4IkT1PUhF1m/0BfVjE9x5SEadarqA9jY/dDmN0kDxBSGRO26fk3PnsGErvjwizc9mpu1uUOYTo0QkqWlgR2VeVOvaBEyT3EVjCIJAJhZBMy0USUS3rEpJXzOousGZwVfw8Cv/gmcu/gkUSdoRvrmwea3ntdeCkScbd9apPaWZQH2mN4c1lQQlqzZ5/lLDLvG1QyD07gegPH+i8QtfTLBM7BVnQhsd2L4F/dq13xriq9Rk0debdMogPeNRz1ukWWdlThwmos4TqUeqBUBl9y7p7AhXl152An68f6JStKIOmFh8BoBiYrSmIbg3gKSUtFPb3kIZa5CJXEk3MC278QK0UsY6ESwlMzeNYOmYgkyybwcovpp48wCMJoYRAM0qMd9htWAg2DZmrBtNSpAYqt/neCRlNDfREgHdDJUkrkWHwM0nRjtCXnipbA0VXxsm42LCMRWXfUzSBbf/trr2Iom1yY5EPE5EligZGrNFJ0mpXoCFX6xTpW5R+cgZybk/du8NoZGU+4ackskZ0aRQDkgAuMqXUnqcWCc8vuQIluyMRVYTn0RvkZiNDdOVqFPGXwU//X0iNUhGdu7zyRZ9Gv2i60sf5K7DP0+06My/qsfarVq0Wi4Rn8xPBiJBNVeVmo8Nk47F6npeXffk7/FD33ob5tx9QG1lioekkqQ35vQDftICqzF9xXv43M2f5Mj+d9T1npP79gOQLk6SDaBeAIi4iZGFGomRL1Z4Y09CTmzaGPT69tXyqq++UHTHtlRxirJuUm6QqmmrDvGlRdIokthQsRvKc6LEsUSFrEt8VffzYaAV39Z8fJSIJNbd2PHQyDdv1+MrfHTFoyRWT9A/dR9ygPI/VXPCMSKSWPl7pxDoufJ6Ow3vb9M2WEg4xFemNO0EfPhIwy0bJhH3mV1JjmLa+rrjvlSxS3ztEAiuqbm82tmdzR2F3DSCqWEJMumB/dt2Gt61lzp97d2BtNyM+IpHiKlzrOQcwsuX0sQ0ED9xF3f/+2sYon0T3grpk3S8jgKrlAKiogBoMBmHqpj1FbfUMT1eU13gTb66ELn0mT+nMPls4HPyPZGz7UoylUda1oSXcFeYCpaS6SkFE8PEd0Dssi+SUokzYDqE0eTM4S05L18QBE6/7XN8+tVfIdJd//ylwYvIpfZRkjMVArpibh/CpKFSSrTqtGPHGy588qLRZLyy+13H40tuNkk3dSSXWFPcVLpaUCSJqCyxVJrDtE0iYoSBRO0SK79QRIWE7HiibdViwlOr9UaHNyXVtYqxkWsBKIgiS0sn/b/R0JBdnyZ69tclHduF7Y4DVnGl4es002Kx+xCLmQt9lY00UiJWY9A14p4rTjdcoLeL+OR32LP4XTR7c3ryVi1a9Uvfztev/h2O7H17oI0ar7QwGx9pmE4WN4ukyvOsqo291TxUxtpcMOKrYEVZypxPLrmnrvdcxO0vIkYBrbDo2wfS1FRi6hzgEL5hPYc7HY2CVYKSlPLAefzHVR/lwZf/OZphNEx2tNQVAPRIBhONOffa12o7Qfyz6kIQMKOZCvHVMXN7H+couuuB1fgoqWikaR/baJMplGuzi3VIRBRe/90PcvV9P02meM53OXxR1xF0FUkU0U1zRwVkbFzrVacKPzP0Mo5e9lNk+69EM/x5mqm6Ttwda+dc0YCISEIJ30/2hYRd4munwF0Ux1uJj36hwi2fU5Mj9KY6EMfuF54Sp9DZa++VBXklDfUw/qk7uPOLr6V09lFgrTNsuPuVnUSwDExRITPQviqoMlDHnL8ThUlfkfatwm/amzepOFdwFn1aunaJrHfNusp5Lj76N5iTwckX35OVlbP0/NFB7rjv3fQ0Mtt121myOEVRC1D+5+08JsY6U9IUEH59msYE51pMLDzT8XMKgoKmgyA0Ll246od46PWf57EL3lOZYIRV6ghV7TjvlB2VUuMdkdv72qGv4/HVlPhaPYdgWxhihIhbEl0TxSVe/tDPcujxnwcccrthSIdPbLVvyrmssxDqj9ZOqmsF0VgXvYpDQB4rBdiwWD2HgI0uxoh1Nbj2bcKuBB00VlwVx27hSy/7a7515a/5S3T02YcMu5H1C6WZ5kbcrUIvESnOApBTHM+omoqvDrczefQKpkduZTk2FKjU0SstzCfGGvoEmq6v1rKxCMCeOmOnh6AldB5WSyUEQUCuY7QPEEtkOH7eD3P4wvejm5ZvtUZ5wdko0OQEdqx3xyg1Oo1mY09lI8XHBmU8nmJ6/NWsdl+MbtkNSVbbK3VU0syrzjOSUlI1vbfCek6saNca8RWyF1GlRF7LY9n157NlwyTukoirseGGhLKHRkr43VLH8BFTZHJV5X9+fa9KxVV+9Ot38srP3Y5VLtQtx94ObKwqqE4VPjN0Lc9d9B7U/kt995mqbpAoOO141rXXSEVSocy/Xsh4aX/7nQR3UhLvMPmyo+Au6NXk+PaaC/bsx5AT2EHKz4LCNMBNF4w18WkSMs5kX3PLXhvt9lXgGb4mRnyVmjRDZaCOOMdKFKdRy6W2j1sPfhdCFSNXzZmQeUatG1EZQBSHUBVXgnuU+Z7ILZ9GNFQ38rpBO86MYSlJCokRSrkF/ydy8et56La/5rlL3rcjvAiapbF5GI84k+PZfGf94YJiLldA9iFvH0jFMSxrTfHlh4D2icpCRVsBnCS8ur5/LaJslpkrujv0QYiva9/N195yPw9f+5uN0+VMneXhm5juuYpEI5PpaJqemW+TNZz+r11/Lw8VYnqLFhNzC08BcBC7ZlJdqxhKORshE/kp/2OQV1oYHyZdx0A8DHjEl1ZoQnxpOpphOl44fogvH+XSAPss53rYCw90LiTDLdfVpASa5LT3aoKhrjIyZMQVGcU1oG9mOr4O7tiWS4w0LP3zwlUWbKecxvcmU86/zYGtFTjwnd/kslOfJipSd6NGFAROXv9hjpz3nyiKqYpiuhm8sJF8fIx4VAn1OdzJaEZ8eUp5X8SXohCRRAzLwrLtxiRraQVwFF/z6loSaC2VfVjElxnNkHXHwpTSmVJHG5u8nq/7upJuIBlOEt5qfIRktPH6pKgXWSo5fWQtFeuuuX34SCgyBXdjpKs0Q7ak+Ro/rYXTAAiWgS7FSEV3RkCGbds1qwq8fxt2Ed00kUTReW59bASpmkGq6MzX5935UiYSbmDECxHbv4rahYOK4msS27abmsO+KFAxex+lfzu/7+Vv56HEzeQ0nXHb7ozsVZI5+5+f5djJp7m0v7HiS+zZD6fvR3G9LHwpTdxrWUiO0x8COVKZxIgCS6/+7zxbzrCvVCaT6IxEtlFJVjW835/t2cvzqVcRHbqo5uvWyEKnk4+2oKT0vUvnXfvECN2NiC9JZulnjvGNY2c5ZCfw7XIU72G+9yoEAaQ2gwvaRTWZ0qwsdU9yBFbnWdAWdk6f9q3/zs2H/56j+99O/Kr/2vCl3oSoqOnrU7VC2IWuELiWQyZ7vithYio/hY1NQk7QE+3Z9PvKZHxj+44ksSNpTLWEblr129zARdz/sj9lsVDiNY28diQFIzXKhOwuuNtMdPSwlb4panGJBclpv8N9h0I99mhyjGeXn2KhNE3JMH0RR0Z2FgmRfHy0IyWyFbjqP6vU2DtI1XR00yQekZuaQIN/he8+wSE2stp04NRLv9AXT6Dg+JMZbDa33yq1RkyR2T9zLyyfZDn+DqDxPMGDZ9FQSIw1LP3zfAtnBWfB1NRWwOujAoyd5YVTHDz1GcbkFGcv/tGG3nPd8SiTK47yxrc/T6Sb2bHXo8YGSMd2xoJ1K9CszD5ICqciiQznj5GY/i5ziQOo+xsoRt1QCyPSxYwbilDvma07ngTE0iVvRz/zcQAS8ubE7nYQkSLEpBgls0S2nK17PYu6zpdf/neMJyRWcqWmJbXede+OdtfcGNv1+Aof8YjCfNLpwzKqUwqvmSbRJl5shmspoLrteKeoRlVDxbCdfrDW+GNRJLZykj57iXPRy3yNh0VNY1x1ntsFt7yx6yWe6AgtKL7uu+8+3vjGNzI6OoogCHzuc59r+Pp7770XQRA2/ZmZmWn1nF+cGL6Mk2/+Zx6+8/+RL3c2unvH4Lof49FX/m8mz//+7T0PUaI35SibCh289kslnVJihK5ErPELXRI0mgtOfBVTY6GQI2sDdR75unezMHAdS2pnrk0zZUo1KrvQGDx96U/T1Tdc83WVaxbvBxxCOZChPAF26SrXfrym0X41vEXhUkH1fR6aO6B7cfTbiWZkSjXGu5yF1ryZRTO3JnmvGazZZ8hkTxK1yk3v1fhXfpIf/sbrEM5+e51pcBiKL6/MaFoSMGmuAm0F1fHqQXfoExEFbNAbKL500/JtDmt27WPCnZCGpfjaysXE1LRTKp2yLAbc/jks7HM9PIyF7/hWvhQueTv/8Nr/4IErfqmj5c/G932cf3r9A5zZ98aGr9v3tzfxjgd/hO7STNO2UK2OaNbf7+m7GIA5/JsXB4W+4HpkJUYpm47Ko5rc3ip/HlEQuPLpP+am4x8nunySsuHv+5aULoqRXtQm5dLKwEHygsCqOz1o5q/WiseX7qrU84lRUrHGvkgp0aIrd5Le3HHf9zbbeykPXPbfOHz+e3ZUiVKn0SxYpUJS+vRi3Tt3P9c88yfsn72XfIPU5fx5d/P0nreQ7zqP6cJaGmgthNUfL170egAERCJi+9ULG+HnPFXNwLZtTDkOktI0RKEy1nb42uxiDXFFpuS2+5Q6iWaaTdPbLdtGcBWyxeQYoiDsmIAMr23IokxcXmv3XqqwQJHX3/uDXPOtnyJmZMn7sEspqkVOjryaxaGbWBA3l/G/VBF4hVwoFLjyyiv5sz/7s0Dve/7555menq78GRwcDPrRL25EksTPuwUt1sdi0f+i+AWNzCgLwy8n33vJdp8JfW554FIHr71HdjQtV/PUf25ttrfobqg08Wmc7xfeZ+X0XEVRsFTsTKmjVzqXVJJ0R7sbvtabjM+rcxiWXtfTpDJRTDrEWKIwGcxQnmCljuD4NDVTNcUUBUUUA5kXW/f+HvtPfZYeuUOlPgFQXZLa7Lvu6Xee6xlBazoh2Sp4PntlH+cvG0Wieo7y/ImGqVqtYCA+gCzKGILA/7v2d4mk+to+5kY0U1FWt+91JQK5WS5++Ne4/onfwmjg61cqqWiG5Za3NbkmPfuYcPu90TYTHT1s5WJiYt4pcxy2JJIhL7j3uZc4V57wr3zRDcq2BPGejpY/y/EMoqxgWg02Dco5lMIs3eoEcrIfucnGi9cuu6JdTUnkA6PXADAjQbZYvzSpHXhx74XEKKrpfMZ2eHwB6BmHEI/m/beFb9/yR/y/Wz9LsfeiumbyALG+fZxzPcwSYrKpmsYjUybzk5iWv40L072WufgomVjjDb7+01/mrQ++ixue/3P/xtRVr+uo0nGHwbfHl091nlf2mlFnGpbVrhz6YR646GfJ9x5iskl5cljPScl0lNUxKdm432kRfjY0vWdP97mx08yqIyw13C7WIIsiZsZNwi1MoZvNDd/LukHMbcf5+AiKJO6YgIzVsqOuzEQy6+amlWRHyaAQdUKBesszrKrNia8V3ebwFf+Nx171l+TMzcEtL1UEJr5e+9rX8lu/9Vu85S1vCfS+wcFBhoeHK3/EbS7Z2YnodZVAS4XOeSntJHj+Makm9fNbgYH7f51XfPUH0I5/qzMf8N2/4tCD/5Whqfubl1K6KZOOobzpU/HlRds3Nqz1i+ryDmX5BHvPfJ7UxAOhHHsjqstempER/fF+omIEG4ti4WTdyW/F3D7jLLIT6izFUjHQeXnXIKflGnoHWC6ZUs9ovxrS8/dw+73v4qJHf9ffSWgFYg/8d67/3u/RuwNKO/yWpAKMDzuL1jlRIKcWOnpefiG490ruO6/5a93FgZQ9689nLwAkUarsDp9O9HZEtdMoZQrWvothGahGFeFv6fQf/Rf2TXwZvYHqJPa3d/OWr76e4eUniDZRz4m9ByqKr/5YbZVmUGzlYuLs8jEABoiHXhqxb9ApnZxFC0B86WimhSJJDcmOdiGLAqLrKVJ3EeqF1MgZEpnepsestEsffcjw4BXIto0hCMzOHvF/4gFgWFBSMuQTYxQNh/iqJuS2sp2ZXa7Xa37SVylLdbpXtUdYLUhyhKO9jrK0V+6l1ERRNpQYQhZkdEtnXp339wXc/jUbHyEdazyvk/r2A5BWp3wbU2vzJ4jYDvnVyXa/09CU+HL7+Kn8lC+SUnDLXtPqJPmSVvfZ1k0L07KJylJTX76wnhNVdaqB4kLEd9pnEPgpXY49/Q+85tEPMXbiM742dvxuMhWNIrr1Eqno2QKIfU47jhcmsa3mJdPVZu+5xCgRSdoRvrlQ/xn3/l+2CuQqnmbT5EtawyoW07LJl7SKd2zJKtQ8/ksRW8Y+XXXVVYyMjHDnnXfy4IMPbtXHvqAQPflNLnz8D+DE17f7VDoPrYj2zd9j5My/09NBc16/kFbPklk9jjF/tDMfcPoBRs5+mXTRx46cp/hS5ygUc5UBuquBKaF+zbs5fsGPYA9dFsbZrt+9e/5LXH34o4yc/Hwox94IT/HVrPQCnJSTcdFRXKRPf7quIbinkkunRrDkOAI26vypQOflTeRM26RoNCDN3Mm+1Hew+UFNne6V58isPO8vJdNdVJaVDKmu/uav7zD8hhAA9PVdRESMYglwNr8DSttLWcSSE4oQG2x+r7z7Gc+dY8k1+Q1z0uBNkvPmQkd8JppNxhNyAqnioVS1CHANzSXbwNTqt3tx9QxxbYlYZqDpuajdo6y6i/KeSDhq7y1VfGWda9kn94R+r/a6BPGsBFm1sZcWALbNwGd/hFuf+ihpM9fU06QdCKfv54ZHfokLn/skRp0FtafyycZHyPhQw3nt0k9/L8kRRixnM2Rh6amOKEAmbvyvfOKVn+fkgbeR1111dR3FV0cDcAC7xyG+UsVJ36ldmuHcFz+lf09e+oPO8WPjTctqJVFiJOWkpvktoRMrRvujTZUUirv5kFJnyBWLza+tbXP1F9/Oj3z1dnrUiR2zYN0KNNt4CUpSemNbsjiNbui1SVDbRlg6QaS8RFQSm/ryhdUfa8e/BECflu9Ikrif81Rmv8fo4mGi+UmiitR0Y6fZvKiaSK+2TdhFe4j07ePw+e/lmRt/E2yzaZ+m6gbJgpfWOeLr3m4VfBFfbopllzqNZpqV4KVaKBkGFJeI286mQskr498lvjpPfI2MjPCxj32Mz3zmM3zmM59hz5493HbbbTz22GN131Mul8lms+v+vCRw4pvsP/p39M58d7vPpPNYOUP8gd/n0GO/R18qud1nU1F3xFowQfcFrxzOjyIr0Yt68Vs4fuG7WM5mfSm+she/nWeu+BDJoQvCONv1cvAebxd6oiMT/yBkCsC4uxCaiNb3f6g2ty/+0L/ypTs/x5zcfIFejZgUq5S11d0dNHW00etZSZ9PYsiHT5PbzpKFqYaDVgUV4/zRHTHRD3KvBFFkJOnsUJ1qIVUzdLjnUFK6iSWbK1PE3v2Ao7xcVB3CLMxJw1jJ2YEzc0c6Q3w1uVeCINReBESS2KLrO1UvzU9dRnKfiYgPwnc65hqrWiKCHU6p4JYSXyUnhbUnOhz6vervv4SoZWMJArMzjzd/g7pMauJ+Lpr5GslUhyex2SnGJ77KwMKjdcte9UXHIyuXGPWX6Biwvx8RHCX8auFUU5VSKyiUdUzLRpHkyqLUS3KEtXamWzols7Nq/DUlzpSvcnjh4b/gTd98G1ef/Dsy8ebP1YrueGmm5QFfxFrQEjop6xjt5320hVjvOKaoINomYn66qQ+klptHMZw+U0+NdtTbbqeh2RxQEiVGU85Y6yeFM9I9Vrn2SmGm9lxEy3PxP93Jex98G4axgGqoCAiVz9mIsPrjnEvkpy0bvQPeoM1KHS3bJuIFS8VH6PLxXDVTw8miTNJNGN8tdwwPiWiMJ8/7UWb2vRZRipBr4FcHDvE11Xcdi0M3shQf83Vvtwpeu/A8vTx4z5VqFii6Zv6p4hRak9LOkm5w2bN/wRu/8Er2Pv1XaJ7ia7fUsfPE10UXXcT73/9+rr32Wm655RY+8YlPcMstt/CHf/iHdd/zu7/7u3R1dVX+7NkTTvnWjkcVwfCiR5UheHe8idn7VsAjvnzubAaF5y0kuYvphhAEeNtf8+wVP8usLlbKkBp1WJ7/Vl8yHDPQdXJw99okilOUjPAnIkEXQmOa812n5frdV/VEMbLvRorJURYDmvPXJQaqISmcvftjfO1VnyKdbk6mVNR85QWKxdXmr6+kdY7tiNIOT53n16Dcu6cndgLx5V7LXGLUn6Fpt+sfUZxiSV3zXwgLYyuOWXCuNEE05EWcbdtNJ+NQ5eVXvQstCNjuwl/PL9Z+o6tELEZ6iCabX5OJuDOZi8UPBPK3a4StjIifcPuadOqChkl1rUAQRUZt5/izCz7K+dx2nI/0kUp1OJrcVf8peq5u0IG96Hlkjfkjvny0y2qMRpwQjUVtzrf5fxB4ZXaCpGPWSNVKKsmKMrLTag3FJZHTxSlyJR/fdfEYaXWaiF32de0XS47ytssv8RXENN22ibqvK6X2NO1jI4pC0S3diebONfWB1Fzj/EJsACkSD/053Mmo+Lw2GH+C3Kt4NErBVY/E8pO1PdbUFQAMQWHFcvrYoeQQEal2hUZYpY55V8WfMa2OlDpWwirqnKeqGyTcec5KbJhMtDE5YtkWU/nGiZew3jpjF+HA6/Ns2yYiSWQb+NUBFHWDhy/5EI+96uOsRgZ3VEBGvWfce66KRh416Zr5FyfRzcalnUVNJ1l02nEx0o9m7yq+PGyL0dYNN9zA8ePH6/7+F3/xF1ldXa38OXeu+Q7GiwLeovglRHypybG65WpbioqhfAeuvbqM4JZKJXwqsrzEuancQuVnKaWOGe3qBNqph4iUl0PzS1tH+HikYHmJYn4llONXI4hvFLbNWME5hwWpfqdfnYIUkSVkUWxpEuVnce2Rjr7IlHgPlnvM8oKP0suq52S7Ux2ryZSxtD+D8r1lZ7DVJ77csfPyD4Fs1/msZs7zp9rxCN/yIssF5zkMc7fMa8dzgtbc9y8gVsorFHRnh69RSVk9vxPbJTzqKr6qSEQ/5rAT7kIiLQ/48i7yAz9eLWHAtm0mbWcnOTN2W8OkulYx6qaXLeZPNi3v8TwFc/ERJ32zk4h3Ay7xVe+8vD4qNR5M8eWTPB8473UAHE0OhZ7saE48xs1ffDOvfvb3kWVn8S8L61O1BEFoulgOC5F+p/wvWV4gX2i+QBZXHIVV1qfabj7rqPPuPPEPvgzlK2SKjzmpYdvc85p/5/O3fBIt5Y8E1TPO8eM+zPwNV1mYj4+SjjZOjHyxoVmqIwRT58UUuaIeSRenal97d85aVlIsaXPrPqMWvP5YszRKRuvKyLzgVBV0mfq2lDqWNI2k6m5KxUeb9rEL6gJls4wkSAwn6/tX7iY7ho+4IpPRl+ieuI/B3LPky3rFO7oWcqVypd+w2VkBGc1KHfN6rpJiGXfnU436TFU3SBeddrwaH0Ezdz2+PGyLhOCJJ55gZGSk7u+j0SjRJiz7ixJV5ItumCg7pPa4I/AUX8lwUgjbhrfIzU+iGSaRMK+959MU7aWny4cqCBBsm5i6gKo5k5i0kkYS65zT05/j0q/+Et3jdxK59p9DOWVvgpXX85iRFEKsB7G0TGH2GH094XlN2bYdTPGlLrOnVICuBAtW/YVBpdQxkoG5Z7n0mY+jRrrh0K8FOr+mi2ujzGqxhCD6HEQFAat7H+LsU5TmjsOFNzR8ub18GgF/KYSdhl8ypRp7JTewozyLaVlI2xhqYl/8er6hXYhpW7zZz72K91AeuJx5O8Gq6hJfYU0abJs9qzMQ72XBDj+tznumBuODRKX6Y2llQb9xMh5zlERmcbnm+7x2WUiMkfZB+HqLse7IYMMUsSDYqoXEYmmRslkCBPZmOjNejUb7QCuyVHLKjpQ6qgoAc/EUIk45WccTqVwCNKLnKNRZhKrdB8kuT1PI7CfeZJFo2daaatSn4mtf34UArGizoRNf+vxR0vkzZKQuRMklN6OZTX1tJpJhpbzS8bYWyQzy3Zv/J6etXmTdxLTshgSPtOoQX2pyrOkC3bItZjTneb6gOM90qXkqWIVM8aEiUnUDVYxTylxAbyTqy3vO7NoHUw/WJ1+qz78qMTK9g5QaW4FKGVSDFFRvM8rPvRIFgdNX/SyPF9/HfHSMTK1r7yq+NDnNQtlRCjYa95NKElEQsWyLnJYjJrdWyZHD6We6Da1hqnCraDZulJcmkCwdS5ApRAd8JzoOJ4eRxfqv3UqF8ksFcUXmwukvc8Wzf8G5va/jwa5DlHSDZLT2+FnMLhHDwLZtBHZWQEZT4kvLYY9ewJMXvhex9zwQaKiAVsslUqVZAFaiw5QtV/G1W+oYXPGVz+d54okneOKJJwA4deoUTzzxBGfPOgPwL/7iL/Kud72r8vo/+qM/4vOf/zzHjx/nyJEjfOhDH+Kb3/wmH/jAB8L5Bi8muKU1ip5ndWkHmEF3EhXPq51CfDnXPqJnya7Mhntst8yrmBwj7TeZ79H/w51fvJv+Z/4KaNJZVVRB4ZEjm8w43etTmquv1GwFy+VlikaxoXfE+jecYtydpM2qtZ+RklFCs9xFTCQDy2c4/7lPMH7mi4F3EJsurr/yEe74zC1cdPKffCsXBfdamovNFV+2O9nXM3t9HbuTqJApicZkSjXGexyF45xd9Odp1kGUDBPNNFEkyd+9EgSWfuTLfP26P2DJqGpPYaAw7xC4QNbIUdSDJY42g99ysrrt21X6mMXaii+v7RaTwVQ+rz7zb3Q9/emmr/eDrVpITCw7JVYpqYeeRKIjnzFy/msBeC41gtqkFHTt2vtT1bQFtx1E9HzdhM/T132Yf7z2z8gNXltRKteDX3VENfa7SYdZfb5pOVxQmJ6KKDGKQf1SkC1TawgC6nl3s5Q+D80SKDVqC5ZJ1CURS6k9TT0g54pz6LaBZNscKC6Q96HeDlI+p2o6uuGMr369c0rn3c2jF7yPcwO3NC1jFdx5lJ/EyBcTTMskVyN0YSOC+rGJY1cyk7oQoqnaqZoVxVeaeXXa+YwG44koiPU3UgIg66Z2dhvlznp81dnMNJecPkFNDCMpkaZK/sBj7a7HV2iIKjIlt90nC47vVbHOPNOwLMae/Vve8aVXccFj/x1ZEneEb66HShJ9dL19QfU8J9E9xOMH383M/tchCWLD0s7y0llE28QUI6wqvbupjlUITHw9+uijXH311Vx99dUA/NzP/RxXX301v/qrvwrA9PR0hQQD0DSNn//5n+fyyy/n1ltv5cknn+TrX/86d9xxR0hf4UWESAIr6SRe5eaObfPJdBge/qR4+QAAx4FJREFU8eVTOdJxRJKY3fvJdp1Pdnku3GMXF7EER1reKG58HVwFmlZ2zqVhZ+Vey7If43yfUESFhOws8rJatmK6ay+H69XkTagHEgP+yJTl04y5C7C8nmO1vNknyxtAJEFyDEXda5kqTFAK6BHTbHFtL59GNlWI+A9okAYuJpfah07ztrD69r/nGzf/OfKe63wfv1MIVJLqYmzASRmdEepPSLYKallDNy3iAVLw4hEZRRJZLrkeX2Htli2fJm3bZNyUOr+LFb/wq6qpt6C33/pxPvf6bzCx740136d1H2C671pyPRf5Il+887mgMEsie4ZyCF6BazuheSw7fGWAh4mjXwRgv1bomMJq38AhAFa0uebPycppANTUGLFO71i7ii8RC7NUuw/MljRsGzLxaNONF68dDCeHK8EhzXAw7WyIqFaWBdcXLyx441k+MYphO+VZNYmvLVRrdMWiCAJNzYut7BSipWMJEkaqeeiCd+1HTAsZEFfPVRIh68HrPxZLixWv0XoQjnyG6478T0bnv0PG7wbfebfzvYPvYqH3CnLlJimTlcTIkc4rHXcQ8vqaIrhRsrd3ryZzk76Om4pEGnojeWpfTUkz54P4gnAI4qwbINFt6eil8P2wmp2jXlxFjfRQcO0lmoUo+C3d3i11DB+iIKxV6xQmMUyrrpWCqhsVG6FipMfXvd1KrKtSqUI1YZqKRrDcgLGIJLKq1lfteps6peQohg2q4fQjjVSjLxUEvuu33XZbw2S3v/mbv1n3/w9/+MN8+MMfDnxiL1UIvQegMOcaed623afTGdh2pVQmOuAjCW+LYP3049z/3Cl65RjNc8oC4Lof52vxlyMaKr5pPrcz17VlIN2Q+LJXziAAWibcEIhMNEPRKDrE180/zbd776LYcyEXhfgZQc3SGb2a09d8hMzyv5C1VSbzk5t2SKrLAgRBgG5HLaUYBZaz86Tj/okb77rXItjAUWQJgNEVQJF1x69w39APo5kml9t2w8ViPjbIfP81nNc96P/4HULQEAKAsZFrAchKIvOLpxhIXdqRc2sKy6LrTw/xOrmXyTd/yvfb4opMRJIoGs133APBJauHLZmsaDKRm+DCngvDOTYBJuN1dr/l1AB2ZBmzzlC/fOV7+Gr0doYzyablq6Zlrj3nhoFZmEDV9bZjxL1FoI1NTstt6gfCwrkVR/E1SKJjCqsDbv+xqs81VUbaehkbgVJqT+cn7koMS4oimmUsdQXY0M+ZOqtFN3zFR/lZUH8vgHS8h4xlkRVF5uYeA67y/d5mED01dmq8knq1MVULtlat0b16jKtP30MuMYp6Qf1xRV84SRTIxYZJxeNNfQK9az9iO6RRNOf4ajWydchEMqQjaXJajsncJOf31J+vKafv5dC5z6JG+0lE3trwXDzEFBlZErFtyJYa+0JNjd3OnNzPcuZCLtxBC9ZOw2tzcTmOItUn/KpJyqJeJKE0VqfGbZVDp/+JHmuV7x36wCaLD6u4jAToSprZokt8+SV32nhOsq76ebr/NkY6EKbUjICaGnoZD9xxDwkZFB+qIL/zol3iqzOQ3UCQWGkeySzX3SxQNZ2E63mVjY+493bnEOjNSh01S0MUTRKlOfqmn8KWeslKB9DdKoZq6KaJ4iaTqqlxTEtHs8o1j/9SxA5wFd9FNYTv+wvufdPXODt8+3afSkcx/86v8Ogr/oTkYKgUU1vwyp/yTXYeW4FuSxBkYda1BxuBvCv7rqs0sayKh1h04Lx2T3Md1k1ixq8lO3oTBSXcxWVgMqX3IKcOvI3uxN5176/GpgGkSklZDKikbCjdt0yEVWdwifQHI3AjsohuWk1TMj0/pGSnTax9IGgaG0Ai0U+fy55MzT7ekfPyhdw0UmmZ7sJpYhn/HnXR5+/hzV+6CyH7PBAm8eU8swOiE1jhKzUtAMKYjEuigFlHSaXqBppp0R1vniI7r86jWzqyIDJkmCSLk6F4NSmSUjEh7+Ri4lzBKanuVfr8BVi0gLGE43latgosLJ9t+Npjb/gUf37rlygNX7MlnnnT73uUv737XtTkZl9W/Yl/4A2ffxl3P/ffffVRrZDnAKO2c92XV59vqlIKAtn1yCqlxhqWgmzlorVr/jFuOPYXnDf9tcZx9YbBfPchFjIX+Sot9PrvYdn5LvGCv+fQbwmd5zfmN/ACIKHI9BfPMj73IMVirq4xtWXbPLvnrXz7io9Q6L6gY8/hToTX5popNTKRTKWdehsNjZBQZG56/n9z0bH/i1XKbWoL+sAhju57G+f6rmVedew/mj23oZQ6uu+dGHoNRcL3cmv2LK+oZRRJRLNEUlGlaR/rd14UxrXZxWbEMgNoslNxkVKn64Z2qLpByjN7j46493bnBGTUC7CoThU2KXLFqb/nmm/9FAcmvoRWJ9mxqBmsxMc4vf8tzI+8nLI7tgkIu4ovdomvnYe+89ATg7DNRtYdhSAwFxtjYeQV9KV3FvssCkLoZTOmZQN2sPRKJYadHiHrvqduZ5WfQTDLWIJEevBA+ydbhY0DtSw6O7ONUlOCIiiZYts2mmnSHxte9/5q1No5sV3/vLIbie4XDSdJuWkEU3Ou/cD+QMftS8axsRurO848ROaB32N09qHOlzT5QCtqDYAxwVkETcwfCf2cfMNVWOXjw8SjAUx3oyki+ipFl4AOa9Kgv+xDfOGue5D7nHCDsEsd2/b4Ov0AVx3+KPuO/f3mN5k6JTWHZdmkfaTInss55PBofBAJSBWnUEPaXEgrnV9MTGorAHTH/fmZtYJENEWvSxBPzxxu+NpsScMQFDIJ/+XV7UBO9GGLMiV9M+FkLp5CsnRsOeLP660F8hxgVHLu82rxbHgG96aOUnCULOX0HkpuKch2lzqKrq1AqjjVMHkxP3wDX7jxL/mPK3+dVKR5aWFF8RUbcI8/2dRPDvz7fClZh/gK4j0XU2TueOgnuePwh4mvnql7b0u6geb6PUUkqfPedjsIq5pbZu9j0yWIJ1ss1UvJ3ciM5jeToOW9r+C7l/083xu+HhubuBynL9bX8JihlDq6741LKcp1fAXbgfcs57Tcpuol3S0vjsgSmmmSiTWfK3jXek+qccVFM2+xXbSGeEQhF3fK4XvKM3V9r0pqnmTZCSlajg77urdbiXqljtWpwrpdpOjaA6WKjqdZrdLOkm4w0XUlz93wq5w8+AOYOKrsVCSFKOzSPrtXYIeiUTnpiwFLBUfWnvSxcNoynLyXl3/1HVzxwM+Hd0zLhP9zJ1c//BH65YCDeM9+su5uU91Jj2dsnximO5lq40Q3Y90kxihz/uSXuOiZv6SoNU+D8ougZIp+5LP0LDzJUGxw3furUWvnxPMo8wxy/aLhRM5LJo2PkIwHGEQti0P3vIPv+9JdlJYb7Myeuo+9T/8147Pf2hFeBEHT2DyMR3oAOKcuhn5OvuERX4nRYIumHqfd5ASH7A3L46uoW2SVXrpTjlIwTMWXbulMF3yWptSbjC+dZO/pzzM4+53NRPfZb3PZJy7n+w7/dNMUP6h6xjP7AYiYKuVcOD6KW7GYmLKdiXRP+nxfSXWtYhTnWs4vP99w/M+WykiC4D8opU3IkogkCOTLm/t9L2mvkBjzpcJplTz3yJplbS484ktdYbXrQvKRPpTMcEMD8com0BYsWpV+RwWfVqdZVeuX/3lecKIgBAqYGO65kJmeK8nHh+v64VSjQqY0IOdtvUS06DzTpfS4b0WWJIqobluI5s/VJ75WZ4mtnkQ0y0QVqaPP4U5DvRKoWghicB9XZIpJhzCoRYLqppMqmjPmASfRsZmHXxj9cU5znsPR8gIUl0JfC3nX0bKtSkq1B1U3eNW33sMrH/pp4sWZphs7JaPEvOpcn91Sx+1BQlHIJZx23FWarpsabS6dBkBXUqhyekcFZNi23fA5Xyt3VKvM/CexLLu24kvXsWwbSRTRTBOT+v6VL0XsEl87DblZLnryD7nk0d/e7jPpHJ7/EqNPfoyuxaea+lJsKUSZ9MpRMqvHKgaCbSM7iTT5CCMT36C7qzfY6fQeaE58de/j2HUf4cQF/yl0+f+6gVoQ2Xv/L3Hxc39FYTG8RXplt8yPMb+po/zre7nt/vdyINa77v3VqLVz4u2iRwMSDJXdwXINk1WPTEkGJFNEEaU4T1TPUpproEDzQgtSY1tS0tQIQciUjRg/5Pi9nIpt46DrkZQ+Uwgr6NqDgUC+2XMYECXdQDctxr0I+hAVXzP5GSzbIipF6Y83LuusOxl3Tc0VLYexkfhyr6Up+/O8qqh8MnvR3SQ/P4mmftDpxYRWzjHrPnoDA1d15DM8jLrqteXi2brm/8b3/oXrv/njXDPxmS1TvSSe/Fte8cSvET/7rU2/8zyy1FSwdM+g5PmY61+5aK36Uin5gRbr5Wu3/i2ffNk/k4nH6qZqwdYuWqP97lhlFihl5+u+Ti1rmLbtEF9+SEf3ORy65O18+aY/48je7yfvYxOrQqY0GDv1pdMI2OhSHDveF6htGu69TRYm6xJxwrNf4M33/TC3PvbLvrzkXkyozGd8bLqMeeOJj3lORJYqpGO6OLXp2purkyjqIlnNKfX2M+63+5xopkbJNbf/wcc+zOD0/ZvHnzYRk2NERGfTYON5qoUs/StHGJh7GF1ONN3Ymco7pXMpJdV0brBLfHUGMUXmxP63cvj632J+5BWUdbPm+GksuGmdyTEQBOI7yN+rZJbQrfq2Nl7bUc08uttfxgvOM16L+FJ1g678aURDRTctDLt+YvFLEbvE106DZbD32b9h7ORnKajhRtzvGDzzb5z/1J/SN/fd7T6T9XAN5eOFadQmRqu+UVlwj9KTbGw2ugkX3MmMq5Ko22F1jXHqvB/gzHnfjxwyObKuvENSsDPOpKo4G07iqG7qzBTdSZWfhdDqBIJtYopR9g85aYF+Sx257sf51uvu4fErfiHQDmLDyUpykNnRW1nsvyZwLLLttrXyQn3iy1NThJnW2SqCkCkbsccz3S3PhpLm1wosTxmZGg92r+QIq5nRyn8bpWr5hlEm9fn3cM1zf8b5Gcc3aSo/FVqJtfdM+Nqhr9e+490AKHoW3Vx/XhWVj8+SJm8RNpYewxi4jIX0haGNbZ1eTExNP4YtCMQsm8G+CzryGR7G4o6KdamBqsmc/h4Di0/QU54msUW+f8rUo+yf+jqplc39vuSWt2np8aYLibJZZk51VEFByfO9vU6kyhxlXyolP1B1A80wEQWBdDRSt9Sk+mdbsWiVIglKcUfhJq2cretpduGnX8kP3P9D9JSmmnpqqYbKguqU+ZzXtY+Ia4bcKBXMg5/yufK8s6jMJUZJRiOBNmo8G4J0capuu7eXnT5nNTHqPzHyRYJWFF9+PL4ATDdUI61ObyJBe7/wE/zwvW9CX33CObaPOVq7z4n3PgFIWTaytnn8CQP1SpcNd0NGV9LokXTT8a26dHsr1HC72IxERGZp+CZOj92FntnrpuGu3xyxbZtlMcPJvW9kbuxWBIEdUUXhwWsTkiCRkDevE6vbq9TnbIwoWpaokadQwzZCzS3ztgf/E3f8y01Y5SyI5XXHealjl/jaaUiPYEsRRNtgdS6cXfEdB3cRWgo4+e04OnHtK8TXmK+yoHW49C3Mp5yFcaMOS7cs5CD+YT6xKaHHLfvS3J2TdjFdmMayLWJSrKl3BFC5loXkKOe5Cq7p/DSmtX5h4Enl112z9DD07MMUpKaG8tVoOJG78C4evvkPOHXp+wIrFz0Fmtio9NL9vkLv/kDH7gTO5R2fpvFU8wneRoy5ngTL2mzTxLpOwXbJGi09HvherXQ55x8V5IapWv4PeI7kiS9xybnPMZrZhyRIlM1yZWHaLoL4KNWdjLuKr4ie27Tw8NRaqk/1XHVya/7tf8e/v+KTTGYudb0P20OnFxMTrifIADGSHVaa7OnaD8CCla1PALjtOIiPUrsQ4k6psrzxGmtFlKKjSDK79zX1sPTaQVJJBk7g3DdyFQDTkk22pAZ6bz14qsuILBFT5IYm4lu9aNXSDiERq1f+pxWJFWfoUiex471N24KnTEkraQaSvURkEdHSKRSLTdXt1WRKvU0j0w1kyMVHAyuyvE2gtFrf08wbJ3Px0S0jfHcKAhFfATy+gMpmb5c6RXYDCSq4SdYLrlqkrfHEJyqJ3MiI0Dniq076pFm1qePHS87zr9wKNdwuakNx75NmOOmGtQzfNdNkNn0RT1zzqzx78fuJSFLgzepOonrsqTW/ri61T6d6UF37kO7yDKs1RBrGorNG06I9qEIcW9gtdazGLvG10yCK2O4uTHH2+DafTGdguwt6y93p2zEQJewuR11TbFSCFgRuelsxGXzBDVSSppJKHf+u498gM/89IlbtuvZ2sHGgFnud++UlYbWLIMoUYI34SoyxJz2CIioYtsFscXbdy+otYHqTTgJcEPLFW6BlteymSb9hWWimFSy0wIXoTjhj9UrcdBUx76jh4gOdVZr4QaslSgB7PAPy8jS5UqHJqzsDrfsgy6mDiH3B0jcB8pkhAJJ2SBMltx3n4iOkY3GG3fK/sHy+gvgoVUdll4yqCZSr+IroOYyNCw9v4yI9TlReH6Pd8HzSDlGmSCKaaVEKoWSt04uJc275gZK+1HdSXavY23cJ4Kia6hEAQsDSwjAgum1B1rPrSRL3XMpSkkR6oOlxqttlYPJ89DoEBDRBYDIkf7jEFz/Im+9/Jwfn7ycRUXx5rGzVotVT4sRrmI4D6C75XJLTSIkeIk2ew+pnMCJL3HH/+/ixr7+a5OKRpuPhSHIEURApmSUWS7V9Ghcv+UH+3+3/zrcv+S+Bvee8TaC0Ol3XmNqbc+Ti/hMjXywIUuroGaxP5Cd8KdvFCuk4Ta6krXu+xZJLfFnORuJWkDuVuZtbihjRsqGXOkKD83R9oAqJURRJItGkhDjIvMj7zLye37RZu4v20BMRGZh5kH0n/gnLsjapgouagW6alTR1P/d2K9GM3K5ur/GIXDHz7y7NkC1p6551y7Yra2w1OYZt2+i7pY7rsEt87UB4EwF9MRxlzY6CXoKc4xUUHzxvm09mMwR3IqA3KEELBM+nqQXCANumbDgdYqROrLP9uZ/kFfe+m341HDKqGpvk4M3ImoAITKZUFn1jxCORipJoI2FQr2Rl75GPc/2jv0px3r/BvXcMwzJQjfVKA3V1Dtu0WvMc8cpq85O1J3Yrzk6iLidIdg8FP37IaDWNDWCg6yCKbWMKAjPTT4Z9ar4wd8f/4PMv/79Ex68K/N5Cv9NPxcVYOJPw5bVd5URE8WUeHQRBnqukkqyk/HhKSaCi+JIsDV1bT1aK7iJU7D3QlMAo6sXKYtkjviKShGYYoZiUe8/nunMPEWfcUr6e6FDHiaZ9I1cDMC1BoVxb1SS5117P7PVFOoYBT/GlbFBfmLbAxOjtnO6/iUy8eR/YDnmuyFF6o04p6LnsRCgenPLi8/QUziBJMnFFbkgwdLqdbUTuhg/yuZs/ydN731qTBNVd1XUuPkIm7iPRcUP/LcpRBGziDQzlPSiSwnCiMTmv6jqakqYY6w/8nHhm/qniNKtq7XYfcT9XTW2d0nGnoKJg97FoHU4NIwqifwXx2LX8200f5/5X/hWaaa6RoLaNVF7FBub1JWBrSx3TsrNJqdTaeAkBGxPLPQgrp52fx0eIylKlJLgeKs+VH1Kwql/J6/kgp7uLJkjHFO549Be45PDvENeWKW7YVFN1g0juHFFbRzNNovLOCsho9oxXP1cJReGp/T/IUzf+Frm+y5zntqqKpawbxFwlopoaxwbKroBit9TRwS7xtRPR4yhrghpxvyCweg4BG0NO0N07tt1nswke8RXatW+zrFN1PVGE1eXNv9SKCHlH7RQfDK5kaYbNpY77nc8Ka4EelEypXEun3YzVMQavt3uSeu5f2TvxZdSZZ32fY1yOIwvyuuMCoBVI//FFfN+XbqcvaFonVK5lolh7R38thXCMRHT7PU1aTWMDkOQIo5ZDkJyb3R7iy7vGrQRA5A7cAoAYGw6FrLFdFajnkeXHPDoIgkzGRUGsvQiIprEFZ9JvFpbWfl7OIbnpnBF3wdoIXnlbJpIhE8kgLR7j7m+8gzfd985wiK86Xi1h4dyS42vVGx0KPTxkIwYGLkcSJExB4FxuZvMLSlnkkjMOiL37A6umWobn96blMMy1CXap+yAPXPs73Hv5r/hL9wzQLmth2E2gmy/NhFIyrWSdxUEpNU5Mlhorvtx2VjJLaGZ4qcb1IA9ezHLX+WhSvOZ3NZbWiK8uHxsvG/vviqKsUN9XqxreGO2Vdm1ErqQjCAKCIAQuRYz27uWpi9/Pg1f8Epqmb/KB1PNLRHTn3pRS/tJDX0wIUuqoiAojSccew89GSjTVw0rvpeixHtcbyW0LWgHBNsiKIiXLISNHU6MNjuSg3VLHVbe8MqMkAddjshOKrxrnads2srvRsRpzvOSa9bFByHxFVIi7hN6uz1e4iMWSFNzk367SNPkNvleqpvN9D76bOz9zE7HsWbp8bNRsJWol0Vejur3GFZnJsds5t/f1GOkxNMNCrdocUXWDuDvvKiRGkQSBkukSX7uKL2CX+NqZCJlg2FGo8rzqdkvPdhQGLiaXOQ9NrlNaGBCWbWEJUks+TYZtUnBNYuXlGuUdK+7uv5Kip2e4ndOsiU27dx5ZU5ikbLS/8AhKpnjyXc//pB5hUHcQ8dR8i/7VfIIg1F5cu+SFLchkuoKZvXvnUu7az2r6IKVyjYXU+XfwrTf8Ow9f8xs7wovAIzA8lV1QjEmOYefk0nOhnZNvmAarxRIItJTk4933qJhcN8FoFZ45fMk12g/sy9IEQZU1NXfpBYGF936Hz7zmGxQiVf57RpmFC9/G6f5biKV6gp9LrItU7hSZwjnUELya6nm1hIXZaSeA5QJ1teNKE0lWGIg7i9bTqzUIBlfxWlS6SKWDJQS3BS/hU89iVPmyqbqBZjq+Kn6MgttRfAHs1ZwyOGH2G+2TpuoystvejcxebEFfS9WqsThIKSkEnEXwVpQ7OiXBEoZl105edPuQfGLMV59WCZhw+2+rylcrCPFVk0yxbQ5+4wPc9Mz/ImEVAz8n0UiUk4fex6nROynbwiZjas1V36uRXqRo6iWn+GqUNloLQTZSEq4CF9tGr/ZGKq0AcFZxNt0G4gMV0qYRwlJ8dbmbMREth26GXxZY6zxLhokmJShHulmJDTclR2zbXvOvbGes3UXbiCsbyv/U9SXTWm4WxVSxEViJ9u24gIxGwSrVP/dKHRVJdEo33b+r+3BVN0gWnXaZS4yiSCJFI9/w+C81vLRGkBcKvJKyYo1d3xc6qtLVMltUqhEIN/4E3+m6C800uTiEwy2/89/5zonTXDAYnBypLquIrkzVOPhpwCERUx3YwdhE+AxczJk3/F+O6hmu04y2pcJBF0KlW3+F55/+NpZrdFyX+KoziEi9B+AERLLBCIZMJMNSaWn94rqiyBptbSKeGmDhxx7ggZMT3KAbbHLIESVysSF0ZYDYDnhO2l20jkd7oVxkoegvbSpUPPLXXPP13yS9503Er/jjwG/37nuUCGq5DOlkW6djV5U/i4IQaqljVstWnle/JGW1cWo1pO49GDOnUasVGMl+jlz3axxfWOFOP2THRpVPaghLiiKaZYzlszDSXhlvJxcStmUxKZiAQH/PRYGS6lrFSHKUmeIE04VJTMta95l2aRU1PkRW6dtag2/P703LoVUrvnIL6IZBRFZ8kfPtlEsD7BWc77xa9kfWNIK1fBoRKER6SKa6KmOtJEgklc3Pt6eMzGpZsuVs4GTboEhIFpef+nvk7Fmmen950+89r7dcYpQeHwqoTaWOHvFVnGLeB5nfiEwxC0sMTXyTIeDI5T8beDwUBIFMLMKKWq4YU3dXcSxFJcPzF7zXSfqLKlvyHO4kVAzfa4Qu1MJYegxm/BFfMUVh3+x99C0c5kTvzai6myCtrgBwOupsAPsdS7z+2FNGRqRgBENl0zI1zNHzf4RpeYjREEJQ6p1n9bihajr3Xfnr9CRizGXz7G/Sxy6VllANFQGhorJr+rnRDLPFWVa11dZPfhebkIgoLCfHYPkJukrTzJY1TMtGEp3NCmPB3WxMDGEJkR0XkOHX4yun5YgrMnFbp3f6EbrtHBNdr1w3HhZ1g8Gis17MxkeIyBIFj/jaLXUEdomvnYmDr+LwD3yLRdLcsWHy+4LH1f+Jh+zzMG2L7Xcuqo10VGGxaLomiO1d+6ViCVuM0JsKvlj2OsOkZZHM1ye+CsnOkIgVM04tj2VbiJEksYteTfHUFMvFEj2JWFvHrza394P86M2cVPdw+aBDE9UjDOoOIhWPstrlGvVQc3HtqS+Sowy2uAPtDb7LxRIHNoRa2u4OrCJJW1fSVAer5dXAZMpG7EmPQXmCBX1p3YRkK2Avn0IxCoiul09QeN/96smvYC68D/rbU9sIbmhBbMApT66kpuXaJwW9Y/TGekkom2Oxa6EeeeQlxW4kGZbVsq/EK6hSmrhlyQiC40+1fMxNHrre1znWPfcOljqurp4h77bT4cGrQz9+Lew1TB4HrNl7UfX3kqoqcy6P3cQX7vw8eVXljq0s9xq7jiff+TBPLKjcVVV2NPDPb+NHV07zrZv+hLhyYcND2LZdaZut9iHj3Qch9yQLVr6u+b9f6PMniQLZ2Ahd8WjTVC1wnpNqYrmTiChRLnvu40hWmYnld2PZF6wLxylkDlDMXEI+vb8p6VhLmSL1OWXKaXWKE3UM5avRSJWqzR8nDhSi/USiyZa85/qtFfTZ71BWMqjannW/K8SHeeLgjyIKAhd1OFl1JyJIqSNUkZQ+NlIkUWB84TvsOfmvFIQEqvZ65xfRNKfPfwdP6hPAOd9kdTqSRkDAxiarBSeIK5uWXXt5fuBNnFvOMtBJxVfVZo+qOwboiiRiC0LT8c27vkPJId8E367iqzOIKTLFpDOupItTTniOYZB059iWWxpecl+z0wIyfJc6alkkUaTbzvOKb/8MliDz6J1fp7hB8fX8njcxqk+zFN/jqOHcUvGMskt8wW6p485ENEW6fy8IIitq+Gl92wolRja9n1J3+J5UYaE3GQfbJqdujokNiqWCU87TigF6ZRJgWcRq7d5VJXd0ghzpijjSehu7siPuLcSWiu2VKa2WVyvH9LsQWnbvR7erbvMmY96kHkAzNUqmG91bp9SxrqF8HaSjNTyQqpSLzRK16iGmyIgILGQ3G50aX/0VLjryp6TL8y0dO0x417cv1uebTNmI8S7HIH7OzlMKoUw2CCw3qanU4r2qTEosq5Km1g5Ovvu7fPqVn0Fx0zq9hcqcOrc+WbEFtKKqqTcZj33vU1z/5G8TPfdQ5Wfl1WksrYgii758nSoL7qpyZttN87WX2r+WnVxITEw/CkC/adGTaZ5aGAb22m45XWliUzKVszCzkOWIr9LC0CBHUFL92KK8ZjRt2yirZ5EtDTM52JR8WS4vUzSKCAi+vIJqYc/AIQBmRL1tjy8v7j2XGG2a6Oih035y1RBEqeJlGcmd2/R9n7n8Z/ncjR9jefC6pm1hsbRYUaaMuj5pnqF8sjRPLt/caNsbo6vHWg961bVM+/BFqoWh0/dw12Mf5pKzn9lsTO0+B5Ztk94BfpdbCcu2ApnbQ2OSsuZndO93jl+dqtmzjyev+AWOuPN0v+OJKIikIo5KrJXnpPo5jMoSpm11xNy+1rPsbfCYlo0iik3JkVZ8Tztdmv9SRUyWKz7KKXXa8atzN0dMy6qEwhSSY8iiuOPKpX2XOnrVB717MAUZ0TZIa4vkqjYvcqUyzx78YZ694dfIKn10xaKBkmFfCtglvnYo+lz/q8VC+z4ouwiG/ff8J+763K3kJto04j78t1x0zw+x//g/tLfgNi3ihRrx1J7Ze3oPnYAiVZlxuucSO/cQFz3zlwgn/qOtYwcmU+afR3jqn8isHq8opbzJ+FJpiYJeWHeeAgIpZYNPm0t8JesZyteBt0uyzgh1ec2nqVXEHvkYb/ryXZz35B+t/4VtIz/6Cf7/9t47TpKrPvd+KnXunpxnNmi1u1qthFYBCS0YSSC0YDASYJtgLIFJIlwj9AK+snURur6+YLAR0Ra2L8HkLNlkUEACBZC0CruKmyfnmc7d1VXn/aO6aqp7OlTsru4+389npd2Z6tOnus859TvP+YUzn/sqen3OG31msRvmCADjg2cDAGY4xpHE1KYofldizNo80QvQTlR7zRRkpAMDCPgVj8kuf5c2VmcqeXaawJIxXiUhMX/iHpx26r8RWnlG+xl7+/vwup9cgh3TvzDl8VUydor5Dv2JKeQL9k7z9SEAMnF2rkwtPQkAGCLGQvmcYEuPstFcJKlNa5TeI8FKrjo7qN5/WlXH5AJYKQsCBlz3lhJvpEqo42AwNAg/Z81rZ/uY4h24xLFYiVfIeWmCPB/BUnQn1qM7lBNxA+KC+js1AbfbFNQE9MmpkrFACEEiq+T9EjgWAYMb9OHwMAROuTbQNYyF/gtwZOQK5LPxunmU1Pm7kF5ATio9jFVzFiaCo4YS7Vek+GyOZWaRKktMjemHEUudAiMXPJHvspEkxSQIFLvP6KbVjMcXADC9ykFELDOD9eK4kmQZoiwjLi6WtGkEO+KOXviK5RfQmziKXN75w/9KBya+p27D6+59I3Y+dgsEnkWgjletFbuIeny5A8cyILocxAVdvrqsWECouN9IBEcNfbeNxmioo3pdJBhEMqjkde7JziKe2cgDGc/ktUilgiwjpvNopjm+FKjw5VF6Dv0nnve7D6Jw9DfN7opzEALpvz+A0578f+gTnHdfdgpeTEEoJJFbOGKvoflD6F56FMGsNa8dvaeJklC+9DOT9/8PPH7Oh7A+dIG9ftagvOIbd+SXOOPpf0ffzG9ttWvaaHj2F9h5799g93NfQaCYWyzqi6Lb313Snj4fBsuULW/FaqmCmEQ2uWa4r5VOB0nRi0iMbTHcTjkMJ8CfX0cgWSZqppfBiCkQMIgM1K+c5zZ2c/MAwNi2SwAA64yEpfSaE90yBiFaPpzQkDUvU70A7U9MbhagTbKSyoBnWU3AZRzM8+WkMc4Ucztxuo2+Wu5dCg+BrxOCTwjR7mcisiE6cr3KmA6mzAnQFftenJsykTXx2ylOrSqeLANMpGE5QbYOnQUAmGMKmzxfur/7Ohx44H3oy5xsuAAw9Lu/x4sfvQmkWEVYDfVO+AcQi9QvBOOEeN7TvR3hoqfu/PyjltsBgKU9b8APX/AfeOr0qxEU+LqhJkDjN61S0TMylJouCe3M5TLIiYoYoOS8qiM6Vli/WZbF45f/P/zm7L9DiovWnYc9/h6E+BAIyCZxnikewCWCowj7rc0Tvhh6GcnMYr0symHrXf8f/vS3f4Gh9UOey83jNpqHB+c3LBjXEikrwfYUP/v0DDJ5EaIkQUytgs8sIS7Ol7RpBDvzRO/5ctFtr8BfPPROkKQ9kbsSFfu4/By60lPgs6sQDITyW6lSW7GCMsURuME9+M3z/hee2P+PADY8+NJiAaFUcX8QHIXPYDGWRmI01FHNnadP5t+VnUVKzEOUZIiSDDk+ib7USbDF6AGBAzKFYuQRFb4AUOHLs7CnHsDw1K/Bzz/e7K44R3oF3MNfwq5Dn0dP2F6CaDdhix4JahJqy6geWRa9glQjIBDdgmf2vgeJsvDC7OiFOHH66xEYOdNWN2ux6fSueKrit7tBNyum6EIL9YZ++elmzZMNfxTLb38QP/qTe7EqGX/wVTKS8jtehpmhF8E3tNtwO5tQT6iS0yVJo7V7DQwgFDaW0NZNrHgRlRP1RRErhs6eWG9gtdrkAthCFjJYCMWTbbNolaZkGcHkZgHaFI9+Czvv/gB2zN9VYlirn+1kwlz+uXKsGON1hS+xWGRDlsEWqw2qoVK1WMosISflwDIshiMbVWf5wd1Y69qNeGDYtvCl3xA6vZmYSikb/F6+t2GhEeMjyiHGKsdibV0nMMgSArMPY3TtMfj9Udu5J80Sefa/cNrsHdomtLBUDG8Ljmp5VGphZVyWw7AsRoniOb269pStam9KRUoZPKeE7NYLNQGasGktCl/RdGkyf+mJH+J1P74Elx36P4gF6ufZrLZ+q1XrlLCg2vOwRJwvC6Fj14siaHDEsiAr9Cuh8KHcMjKpOKRiQnMiFRBIKd4a2ci450KU3MaKp0a3v1sr0FApNLUcvm8bACCYW4KUTyMjFsA88AW86hd/jIQdjy8L8yRRfNbE/DFIxXakzKrpduqhCgn6AlJssUp6vLim1TvYsXTIVMW7mmKfYLQXR0euwHr/OQCgHRZkxQKOD12K6e1XYilyGkI+7xXIqPf8Ka8qHPIJSIQU4SuWmUW+ICMrFpAVC9hy7Ec4cNcbsOvgJwEGkLGxbzRaIKPd8da3T9lATcSdakIVNLdQhaDgIHq7eprbl1qon73BHAnV0Kq3WQ2xUt1axy/CidP/HMvZ0rLm69kcGDBKTjKX2GTE9GwHoJxCSzaq7ZgWU3SV8PSoSbM1j686Jyf+gdNAWB4raeO5lCoZcssX/0/87qJ/RmRoh+F2NqELvUzrwzu0ipFjnkjCqRrPVpNSq4wWE4setysom0GtfBoYRDBgLT+Z5kUoywiZDJMtRz55P8amf43e9KlS4ctkXpZqOGqMB7oBAIL68+QcWCkHmWHh76svIqpix0h4BAK7MY6ZnZfjkVd+D/effq3tJOVA9aqUdjklKMJAKLq7YRvuaGwMXUWvptm5RzZ+kZgFK4uQGA5Cr3XxyCokoIjWUmoFwEaOrGRoFEEjVQUd8PgCgFFO8S5bzC9bn4eEIJ7OghACH8+WenwZyfHVoE0r26s8ayPlwtfKcXCkAML6EA0YEB3VAhNl63fYJ4AjEvhc3NBnWS2EjkkrYyIdHrPsSRGM9SPPK9+tkJzS8kDmVifBEgkSK6AQHqTClwEYhqlZhbMcf3QAeV4RynwJJbcgyaxinucgM4CP9WEgZDzHoZ15or6my9cFqdgOccFDXH+gSwhBQZa1w9z1wDBigfq55MoLRph6X+rx5Tj6tYHnWMSL+6V0voCntrwWT170v7EU3qblCPYS9ea5WlUYUMZsUOCRDqs5zWYgSjIyooiMKCJc1AySoVEILItCUfiKCBFwbPMrxHsBKnx5FS0RdwO9I9ymmGsnHR5D1MCDpWk48dnLMrCqnIQGBqyJI+pi2FvcgJaINetTKDz2XcTWnkFP0F51xVpsFr62AQDCqWlk8/kqr6qP2Y2QXBQwCmUiYrmBp+ZfqfYACQo8GABxE0UjKhlyq5kswAARO6EX3UqYpK+QQjahC4dVxZrwqCdymji1ad2SUT6/xNHb7HbJOJyApbFLMdd/geUwGX3IsRoOYhU1H042MlaS90/dlNoJdZRkCTNFLyVHTuiLHl+8WPTAUEOaAsMI+q17mgBAV8CPgiQ5ku/Nrc3EFJT1LTD0IkuV6qwySpRxOr/y1MYPtc9+CNFQ472lSfEZVCgKX3LxWZ4KGxPnnQiXBoCe0/4YAHA0EKvrpVSVtVO4+FsX4PV/eCfCvOLZ4cVQR9WrMpaZLklerNpRidCorc9+4MgPcM2vXooXHfr4prDaSlQT5+9+6TfxjZf8FOt9ZxkqeFEJnmWRKa4TgcSktsaKi0pOxWRwGH7B39B56AWsJqXWnicGhK+QX0AqpFwfUcPPM2uYKqaUGA6PbE4bUQNboY46AUAuiu3IrtlOL1CO2scCKSBTyCAjFhAqCgbrgZG6xahEScRcSqnObOZAkApf7hH08eiPP4vRZ7+DkfUntUINGVGEOnokmZRUSvYKZp8/AYFHpjjuQqlpFGQJabGAtFhAOK3YgIngKASOQ05Ol7yeAjR/V0WpjC4Mqm3Qhav1ulCF0DG0JIlK7iVLFROTc2CkHGSGs5ynSXXD7uF96F5+AkjFgO3Fh+zJ+zFxxwcQHDgfof2vsNS+ETa5hHdPgIABL2WwsjaH8LC18DHVGDdkNMgSmKIbun+gNE9TeW6kekmKuRP34MJHbkWiew+w5yZDfd1krKRXEF+ZAwhv2dAHAAhBSOEhcKl55BaPAMPFfGHqPImONzykqRxJlrScLhM2iyiMF0+Vl3PztvtlmLHzcN8L/gnpfAF/YkFE1FfV4gYvwDFmCHw2DcCay7iaI0vuKZ03lSqUmmUhvYCCXADP8hgMDRp+XVVjXPX4ysdRkCVwxXGZCo6YSmyvemXqUURIgmQ2bbif1XBjMyHKIhYzyjjdEh13pWpuNUb5CJ4i61jKzSNXKMDP85BXToCFYsw2JT9JUQQtqN49g/swtziP9Z4zscNAf6YTm6t7WmFLcQ1azS9Y9vgqrJwAL2UhSFnEQsUwWQOhjo3etPqHduNXl34NU+jFgO6ghis+C5OhUYwZ+eyreKbw0WGwkBHJzGIpZ8Djq4LwlS9IyIgS8r4Yoj7BlkeWGJsA1p5GKLXh4SYVDwqSwTHELFaMbGWsJqU28zzxcRzufcHHkUQQKwhjTBSB7IbwZXbOWp0noixu5CLyx0D8ivDF5+IoyAQC59x3H+SD4FkeBbmAeD4ONi9goJiLNxEcrTuOZ1IzICAI8kH0BfoMv29X8Z5oqKPzBAUeO+d+iT3HvwP+9Dfj8b7nIV+QkFqbQ19mCmzsdO06L5GTclouvroex8mi8MVzSPafgwfP/QjQvxMMNopGDRaFr/XAMHwci7yc2ng9BQD1+PIuqtdRahqZnPNVTZqCuqEP2wuZcp1ibo1gasa6V1PR2ysTGkFPtH7y30poRs/Uo/iju96C8We+oWv/BAAgHXZXHNmU44v3g0SV2PLU3LOW2pRkCbPJWQAGxZT4DBhZhMxwiA5sK/lVuYFX9+Rk7RQmTv0MvfP3Gz5F1FeOAwD8/t+x/3svwvlPfFJLtG8VUhRAcovHtJ/JxY1N3qVqnWaYT8+jQAoQWAEDQePhDpWYiCn3ulhYs13NzyiSrCT89HGspe9KX1Urf9XXcN/eDyIrW5xvkgg2rozTYLmAq/NctHq6rReTzbi0Vw1NKYodPjGBgiRr3mqp8Jgx4atGXqeJu67HO+9+BfxHfm64n9VwIwRtbu04ZCKDA4/x2Ihj7RphcIfi1fRcsA/poleTGlqYChn77J1GzffGF9fXhV2vx8/P+iiWRvYjVCfUUZREzKUV7wi7Hl9bi5UO1/LzhryUKlEoVmaNB0c1zw71uVErB0qjQx15XxCFgbOQ5yNaRU8A4OPK8yEbmagbZpqX8phPFROUl81DNaF8LDOD9Uz96uHqIZVeTFFypSn9Cgu+unmRahE/+y9x99kfweTAfk34UteceHC4rhdOO2JX+DLi8cUwDLj+05Hgu8CwLNK5AphsHFPFdWYiZlL4sjhP9NdHhAhQ9Pji83EUbOTzqwTDMCVVWsWVk2BAUOCDyPm76ye214UPmxFjqceXewQFAdli+F80M6vkLhQLiB79Ga665004+3cfBMPAcwUy1HHPgNFy81VCfTat59bBMAx8veN4bvTliPc9DwCQyolIp+II5ZXDqbXAMCIBn6GKxZ0GFb68SmwMhOXByiLWFk40uzfOUKwEZTXZe8PoGke+bzeWhi7EWtxiYk0xhWx4DKnIhOU8TdopdFFoKgm91BLnuysiVnxQF0XZfHEDYRbTYooq8oVGEQ2V5jNTjfnpxDRkItffwOhCNfMGjalNVR2L/cmHhupW1KoHM/ECzA1ehATZMOpTf/5t/PdLf4jUtpfYatsJ9Aae3fwA471KIYB55GwnNTdKNrkGsSBB4DlL35W+qlZXIAyWZbCSrr9JrMj6FBgiocD64OseLfnVaGQUDBhkChmsZFcsNW+1CEFVY3z8Qjz2Z3fi1y/6d4iyjPzAWTg28SosDZxvyNOxVogsz7Lg5RzYtVOQbYaxuLGZmD5+FwBgrFBAtMEb7i3FKmur+XntFNes6Og0TFDJyckX50MqL0KUZQQFHgJXe12YTc1CJjICXMCUd0QlthfX9UxuCqmMtUMpzYsotOHZYSjHVxM2rbGgEpaj5HApAGIW/rQiZOUiE/U9U5Ibnim9gd6S3/n7lRxivkIK2fhiXcFd712tXisfvg0v/v0HsWvydq2vVpG3vRjHRl+GRGhUE3zVhON2Kka2MlZDHavlY6tGV9APUZIgcCzWs1kwOo8vs57eVueJZrsJUXAsB6KF2icgFvMeOom+n/lsGouxPVjv3gOB5+uKI1bTP1Dhyz38PId8UaQNp6YhSjLWMzn44kpBnmRozFC1zkaj37PUCikuHztdAb92gCxwHNazOYjFojOiEEWSjaAr6Lcsnrcz3hoBlA04HiQ2Djk+g+TyJDBho3qcRyCrJ8AAKBRPbT0LJyD3zt/ioSOT2CpysHTef/rluPdPfoqcWMCrLIoj2oLVtVHWXJJlpSKJmuw94q5X0CbRBwD7qn/Gr4/MgsTGYWVUmhZThs/C45d+Aal0GueViYjD4WFwDIe8nMdierF+yIoaxpqeRTyXh9+AF1C51xtZPQ4GmxPtW4E78Pd48InnoB8hmQJBOjSK7d39ttu3i+ZFVCFczSzjQ+cAh4BZjiCdy2pVxdwk8O8vxGsz63jiwH8COL3u9eXoT8tCgoAQySG9Ng/AwhqmFi0IjiDoK90k+jgfhsJDmEvNYSo5hb6geYFArQhp1RhXS2X7uGLffCFwvVtQyC5BlGQUtr8Mv83sQNTvQ8BArp1aHl9MsUhGIDmFrFiwdQq7ySPTASaXnwEADMLf8Dx724vr/Vp+Aemi8CXyYUj+AWSjzalsx2oeXwnI+RQyK1MAgaFEwfpNot1QtS39u8AQggzLYGHlGWD7aP0XlaEWnUmFx9CtCl8GBIauYlVaJ8dZPUYX7kf30z/GwsAFyOzainB2EhyAPBcEQr11n1/6/F7ln70vGEE20I9Adgm+xCSyhfNqji3V4yslprCWW0NPoAfMzEFMLN6HeHAEkk2BWH1vjmW1/Dyz21+F48wgZnvPxaDHNqyNwKq3ht7jy0i6jlhuARc+/Vn4WQaH9n0IbG4dU0G+pC2j2BW+1DlY2HYJDickLHSdiTHJXeGLi+3FXRf/G3pCAQiSVHfNt1qlVr23ZD4JmcimcqdR6sP0qsW3piDLElbSGfSmimlQQqMQONZzwpfROV4+r0I+AQNrhzG2PAUpsgeJwG6EdOmEZEIQ8fkQX7cmnrcz3hoBlBLYd96FX55YhU8QsLPZnXGA5NvuwWOHHkLPmPdFPLVEu5nqf+UUZKVculU0Q6BXSY4fTM8ilcsiGgxp4ovcbS3HllE2hToCwOAe5BZ8gMU9jGkxJdiDmaH9yOYLm5Lb8iyPkfAIppJTmEpO1T/dKHpScrKI7MokEK4/FtW28nIe2UIWvqKAS7q3G+t/HXwch3Re1ETNVDGxb7eLRQuMYtWLqBLDQ+eAIwR5hsHswiGMdL/Qdps1EbNgE7PgQBDqs9Z//XgK/f7z+PM7b8bxrVdBOu/L5ktip5chs0KJp4me8ci4InwlpnDOwDmm+2rVGI/6omDAgIAgno+jP7ghuAaLQnNBklEoho3GAv66G6mclMNCekHpT4WNE9enzJ1wMZmyLeGrgjhvl8m44p3cz3U1PKfW1qLAkszPIJXLAOjG9Ituxl2Db8NwLAx/Mzy+9v8P/DR6AGk2hInjv8WLb/9z7I7uwtyun9R9rdVxWQm/P4ZBGZjngMXFxyGTS8GaFNO4oud5JjLueY+vrqXHsH3qNrAMkBb/EtkCwdLo5cjLBF0hewUmACAXnUAguwR/chqZvFhzU+jn/BgMDWIhvYCpxJQifBU/y0RwFP02x2WQJdi6/HuE0zNYCr0RAHCy90Is+M4CCJqT267JWPXWUD2I04U0VnOrm7z9ygmyMk4/+T0UuCAeOesDWNr+SpxM3Q1AbliOr/JDS7Ln1XhU2gtRlnG2C8JX1L9RJY8lOQgch7wkwc9xdQ927Hp8ERAkxST1wnEYX9GuEMQkfGISq+ksJrScVyMI8LznCmQYSWyv/706T4I+HntPfhenzd2JwlnX4anYDqwGx3H4zPeADfaAgXKYYCR/ZadB5WYvE+oFGBaiC4t+M1gRWSS7dqCnq/ZD2AuwDAMQgnzOuvAlycT85liHtmB1nwbC+cASCYmF40AhB8SVxTwwaK1ipFGqGTECx6JgcVxaEVNESYZQ5YGlCmhTian6DxGWg1x0h04vHDH03mEhDI5R3jueWgSTUPLVBIec+ezDPgEkn1LCmqYeRtdP3ovTj3/PEydTTm5aeSGAEVnZpE7NPWq7vbqsT4IBgcgF4Y8OWWpCP564Yq6nYGrKUqgmOet1uP3Vv8WD5/99ZeHLRF6WSmgJxE0a4yzDKjlVsHmeDzzwj3j+Y/8AKbGAwsIzkPNpQ556ah6gsBBGt7970+95VfhKz9gOe60ozttkKqOIdj2+QXsFLCww3H0aOEIgMsD8wtMAgEy+AFGWEAv4TAs9jhDshhTsRYHhkC9W2kv7B0wVObCb30tlhFE8EteTR5GzMHb4YuhLNjKufbeGhK/iMyVTyECUrVd2NQNT9FCOFudJOrYVv9l3M+46+yOIGaiMrfeuroRU9L4PJY2taeUhdPy6InwlQ8YKXtQiKHB4yUMfxAue/GfISaV4gXoIxHNsw+ehFzCSe64SqkgJGHueCD1btKJFbHoZj5z5LiQYxb4zLe5YzfFVNgcFlgXHspBlgoLLoY7xTA4+TtlrxYL1D3bU55vZMFAf50OAUwRrmuDeeQKhKNJ+xVs+lp1FvlBANK18V2v+YUQ9WCCjXiV6lfK9WFDgkQwpHs/RzCxEScZycBzHz3wbTu74U7AMg6CPp6GOFaDCl8dhGMbxUr7NQvWe6jFwUtl0Hvkarrj9Euz5vbHKf+WQW1+M/Xf9FXotVrCTZAkJsegCG+gC6VIesJn5I8CasqEvcAF0W/RkMUpFb4rUMnY/9UWc9dg/I2chSblZ4Ut+5GsYPvEzhMTKhoLeGDe0yBc3E2qp9HowDKMZnvGVZ4tiSgixHvNhNptILWP/91+M1/7scqSzGWD2UfQe+zGGFx/wxAm3VTGlGqOC8r0cTy040l5Nim7fidAoghbzw5Sclmn54ayJNbmChLxMgEAMgQp5/7QS9AbzspRjqlJqGdU2K5EnvoHTTv032MkHsP1bl+Pqe1+necPW7Itujlc0NIufZSQzi3TW+uEC4FKOr+La2x3c0nABmhcCGC7u8+YXDwIA1rM5EAJ0BZr37GRZBpIsQ1IT7YfrVz8D7I3LSozyikfcWsaCAC0VsDpwLhYjp0OMjiPAc6VVtWqcuqviMNC4TSvXu01578wMUjkRmXwBBUkGz7KGvCT1oY6VyE/sx5GRK7Aa2WZM+CoT533F8OpMuH6+sXr4AyFkgsoBhT8+ibXFKfTO/g6R9DR8vPdy8zQCO94a2vPEgPAVDEWQDihCWSA5hbmMUnwoKnTVTLhdCauh5+UiHw8Jkcw8wslJVw7/1X6uZtfworvegj++688RW3oCXXVCdgkhWloBS89amufLNUKCgERQOaDsyc6CZNfgKyhVDVf9Q3W/22ZgVJgqn1chQUCmmMw/kp6GKEnFPH2c4ijAsQgJgmXxvJ3pvCdJKzF/GGf/7qMo8CFgz1eb3Rt7PPsLDD74HWDwIgTPfFeze1MfXxiCmETQQDnoTYgZMHOPoRfAUre1ZL5JMan9PeaPKTlxVo5CXDoKnPMSHLn8X5FYW8Rul/MkVXxIEwlbH/9XELBYz34C/og5o6yeMV4O8+ub8fz0Ih4b/n7F3+uNcdVQVPOxVILt3Q5y/B4gvWy4zzFfDGu5NcRXFC+xVHgUIb+9ZL4AgGAPODEFhkjIrZzcqHwaGW9KSFM5Zr+reoyddjlw5Ic4zjXg9F7N5RMaQ8xqgYl8BeErO4/VTAaIhEy1pVRmkxEShIqJ9s2UoC8nLaa1pPhWvquYL4ZpTG8yxkmgC8itg5t7DACQCBrz7KjnaYLoCGRWACeLkNamgGHrFUNdEb6YAgAGvV27bVWqs8ooE8A0clhaPwLp6Z/hwtvfj4Ge54M54zMN7wsAYPUkzn3sH7EmMiBEWTfT4TGM2CxyYIWR0BCQXMKKuGRa+CIsh/uf/484uRLHWdFeMAxTUlVLL26Vw7EcokIUCTGBeD5uKQ+fWXz9ildxNDOHeDqNLqQBIgMMa6hoTj3PFPF5b8I9vv0gAIZMenzJ6VUIxTmXj43Z9shiGQa56DhCmTkEU9PIn7gPlz34ASx17cEfrvhmU+ZhszEaBlWJ8eg4Hll4xNDzJCjwWA2PIZydR3fqBJ71K14og0HzGW7V9ThdSEOURQissXFRnmePn3wQr/r1lVgJb8PyvrtN98NoP1cy64gmj8NXSEPkQ3UF5Xg+rtnnoxHzh58xfwwLmQUqfLlAUODx8BnvQVcwgKXAVsTWleIYuUA/JC7gyQIZpkMdi9cHBB65YvRKMDUNQoDhlYOIMhOYE4bgKybypx5fm2n+zopSHSmP4clfIhfoh0xIc0IcnOLk7zB29AcoMN5beCrSoyaUn9pIKG+UYiUikY+gu2fY0ttrcdx8UDEczr8ah6LnYa1vH+CP4nj/fmS7CjjHZXFEf8qgJeMMD0Dmg2ALGSQXjqM7Yi4fkWqIGdoI5ZJg0osAgNBg5Ux3esHAyCLPvOxm/GTrtZAYDmcY7LPm8SX4cXLnG5HgYtjpxGfPspC6toBfeQ7ZhSMgayeVxPmR8abP95SY0sQUp7w1tsSUDdhSds79NU0VEcNjlhMjlxglkSHInB+slIO4dgoYMLfxDX3rtXih6MfSH3204u+1TaWFUEdVoOzyd1k62asqHgW6gfVT8C8eAqBURgr66n+Wdec4yyG99VIsJHNIZdOm+6vH6Rxfifg01ovC5PDweY60aZZRXzdQmMdadhq5hecQyswjGE2CtSjg2iaXwOiz30GPrwdSWPHKyUQmDCX+dzJcGgDGY9uB5GEsywnTwleuICFbUF4TK3rPGa2qBShjTRW+GoGvdwIyw4MjBYhr0xj/1YdwzepR/Pr5n0JQqF2sgxBS17s6KPDgORb5goREMaF8LbRnbWIauaWjCALI+HrA+qOGCl7Uo9C1FVh4COH0NApLSvXcRHDEdsXIVsXOplVfhbMePMsiF50Alh/B9tk7cOzU00BfD4ZC5oUd/fMnkU/UzS+mUn6vXEg5vPSJSYgGK3CbocuvtL+eXoSvoDyDlMIzdRLbF+fUQHAAQT5Y89pKuBGaT1EI+nisDpyLnCCAIQRrmSge2/UOhP0+EKDhhWqMYNSrs9xGEzhWWS8BxUGDyLji4Q+Bl3P48Uu+B9JzGnw8Z7kybDvTeUcorUTRw8CfXcJ6fLW5fbGLzpOlJVCrjmWXkEiumXtt8V7TkTFDCWgrscngOfNKTO16I9YjykInEwKGgeviiLpYykRGSlRchsEwIMWk+pn550y1Z1pMKYqIeSGGrp7KXiETxcqWJ9ZPIFPIlPS7IoEuCIIfBUmGZDB3hPbQCffi0L4P4cietzuWJJMpiqzi0jGQlRMAlKTDzUY18Lr93Y65SavG+Ep2Vslp5iLyynEARe85i99ViVFSFCkBQF46bq6hzBr8k7/D9rk7EY5W9kZUP5u51BxEyVwOIbtFCKrmZSlW8wstHwagePk4lddp/cov4+7z/gGznLX8aypObySmk0r+xBjh0dvtjOBrlrGQ4mmxLC4jv2QutNAVAuomNIFAMcwnb6DC5HpuXQvNcKIyLABMDOwFAMxwRKt6aZRMJgmxIIHnWO3030zlvEZvWlmORzasiA/++Cn44pPgiAgxPFB3g76eW6/rmRIUePgZgmh6BvF0pm5/9GKKmFhCng8hERxFzKHcOapdEU3PQEgoz/54cARRD4YouQ0hxHJVR8D8QYrUrdgc/fHnMFmc1yNh83OWY7mNnJEm5skmmzfQDQDwFxIoyM6ne9HmcjHtQjY4AAjBuikmJpPWqidvel/q8eU4QYGHr1ikQOA4LHB9eOaMt+O5M96uhId7UfgyKG6rUSz6OeXrmYDMcODkPLblT4KXcyAMizXfgHZYQD2+NkOFLy8T7IFc3JAk5owl4vYsRTEo64ENvSGCPZCLm33Tn70qfIXH6pYbr8Z6vpjwUCfgqIk+8eR/of/EzxDOr1hq2wx+zg8/pxid+gc1W8w9UijmezGKaTFFV3q+mgu6aoCs5jbE4VohKwAQDfggA4bFF70wIBakqon2rcAWSzAL8SntfoV+d4sWGMFpTw0AmBCVk9tM8mnbSc3rkRs5H1ODL0Sq13oV2XKjgXQrwlduyeSaoFaSE7oRCHVXvKQv0IcgHwQBwUxqxlTzdsPJqhnjTFH48hXnVjY6plV6rNkfA2MnIPAQOA65QsHWib6+707kw5wSlbU3GNjaNEN5ouc0AMAiSYJoh0bGvO1coTgOOFIAX8yZwvRsrXvwoo6D/mC/Je+ISmzfdQAAsMTKWE0n61xdiu+Om/D6X7wU5019f3NFRwMn4prnbwM3rWJMWXOiq09pn70YqS9Aq5/9YHAQAb7yAVyA5/G6n78Mr//tG0DW6+dSUufzbGoWKyMX4esv+Tl+9vxPO5d7rmhXxDKzCBbXtERw1JMbVrdJiSlIRFkXrXhrqOGtRoUvVj3sFdcwVbRbRy16elsRdzaJfMU1R5BzELP1RVmzqO+TLD7bUqExCBxb9/nm2CETFb4ch2NZdDNZbD32fex65kuIBXyI+H3F3Ff1v9tmYDjUscKcioXCSAaUg8Px1UcAANngMPKE0/KZUeFrM533NGkxmJ5twNzjyC48B+y+sNndsQxZVUK41GStnodhFI+7+SeQXTgC7DLx2euEL6tsWqwkESOJp5BdOgn5xNdx/vwTOPjizwFwf0zEfDEsZhYRz8U1Ly2maCT5TSbiNi2m6ISvkSrGb8wX03KvAEBUiIJjawhThTzOvP/vsHPpGNJbfoiwv79uN7REqMtHEPXtghCznpOoHKYofMVWnwRbrPASGqwdxtIInM7NAwDjRW+NJY7FWmIRfeEtjrVdztp578Kvg1dg73D977cam4yS06/A0UIUa0GTAr4+0X6VccwwDMYiYziydgRTiSlsjW013LxdkbK68NVT8u9cZAICV/u8rCTEqsbYCQk8fCwDOauErAmcNTFZ/W4kIiFdSJtOxlzOyWLVv27fYNM8rLYOng2c/CFmWRlC0es1FxlvnuHui4AwHBgiYWriANZFBrEqnot67G4SKzEQ7IfA+iDKeZxanwGw3fiL105CkLKQhLAmIhqtqqW/ppGb1vmXfAz/NZNAV0oZl0n/AILBSN30C0bmIMexSIaGEUkchz8xiaxYgMBVDyvsD/bDz/mRk3KYjE8DDAOJCyDkd2aecL2K4BvNzEASlRyKidAotneg8KWFNLGCVgnQDJoHcVrxIBbq5NXMn34A3yQ7cebS3Zhe/ZbShkUbNuqLAilz82TTs9bfBQIGDAjSiSUcW1KjDwgIlOcMIQBB8f/6n5f9XyYEsgzIkCETQJYJTq7liu+r2I2J0Ch8PFs3HM6uXaSJ5zTU0RW6OQnnP/FJyAyH1aELIZIuzHED8PFca4c6llUVFlgBQZ+A+/dcj97uXkTiSrGuTHGPFvIJKMgFLVKHhjpu4L1RQClBFb7k4sapJcmsgsmuAQBCQ5XzNHkRtncbMP8EpGL4mWGK35WdcDV1MdS8osQMzvrJGwAABMopu9B/muX2zRD1RRXhS2/EFMNwAybzEZk2GnR5mqoZ+gzDYCw6hqdXngZgYIHnfYievBNsbh2nFo4AvcaFr+TjX8eVi7fg0Gt/ClMbrloUP8u+pUdAGA5ZXzcikfqbSrepm6DcArHYFkRlggTLYHrmYewYdk/4yuQLAAFiNsJktHkoFCtNXfwuPBZ6CSRZRq4gGQ+h1Inh3TW8dsaj45rwZQbtu7IYTlYt1JEJlI5DI56Iq7lVpAtpMGBqJv/1n7oHr/vZm7AaOQ2Zvb+2/D0FuAB4lkdBLiCei9sWvk7NPw4A6BMGmiZ8TWx/CfCHm7HCEjApJV9aoWtrXdHRNRgGcqALXGYFT+68Bs9hBJf66uddckM8ZxgGg4ERTKdPYiY9pVWwMgJXTHacjYxv9vgyInxVCwl2EaFnC8TFU4hmFC/QRHAEXQaK2hitpil2TQCJ4wgmp5ERRUQD1b9XhmEwHhnH0fWjOBU/BYJxMIChRPtGEIb34L59N2EtMIwDv38/ACVKoCMrOurGpZUw0r5AHwJcAFkpi9nULLbEaj9rA+FuZP098IlxTBc/b6efJ7XYJACwLCRfFHw+jrmFKRxJ8yAAGKDk/9VRrlKvVT9D5e/AclZ5dVpWqgongqMIC766a4ndKrU01NFdfD1jKLA+8HIez//1NWBA8KuL/xWF8Rc07/lZA6PPn/Kqwn3BPoQEHtMDF4HpjqJn4SEAGw4XQYFHMr/hEU2rOm7QeU+TVqO4KbZUXdArrCphPrlAH3q6jCW69AQTF2FhdRXZgDnvHhLqQyY0gkzEuvC1ye07EIMc7AWbWSk+yoFwg7yCKpanVpP/J6dMJSk3LaYYFBHHI+MbwpeBDQzp3grMP47s4lHgjBfUvV415BJECQUJDzoYiti/C+sjF2M2tB1T5/9/SK3O4lIPGPqmihAYhGFZjBIOz0DG9OITAF7jWNsl5FPIJ5YAQmxtmspPoVmGgY9nsZ4RkRFFw8KXvHIcLIB0eLyq5yKw4RljtrKj9l057PGF/X+NO2IvwcSxH0FOzMHfV98LTZ3jg6FBLUy6Emx4AKyURSQ9gznRXE4zPQzDIOaLYSW7gng+jhGYr0SmZ3bqdwAD7Eov2K5UZ5WuQA+CXAgZKY15poBtYOHrNe4B6AbE3wVkVsBk1iEYCLUDNsalk+I5AGwRs5gGgOlfICu+pqaXkoYsw1f05tN7z5kJdWzGpjUkCGBZBuG08lkmQqOIGqgobFR0lLu2AlNAODWFZE5ET0iCklKJKP8v86AZDo/i6PpR5P/wCbwsG8If9vw1goIzz8NgpAfTW/8YiM+Ck/OQGQ5ieLhp87CZ2E1KrXoQH10/iqnkVF3hKyjw8PEcUoVliAwDDgxGwtaKM1mZJ5UEANnfBeTj2BokWO9x1mMlWLTrk4yMtd4zsRzaWlP0VZlO2LOLqPDlLkGfD8ngCLpTJ7W90lpgBKMeLZBh9PlTqaqwki6ChSjJCKaU9T4VHgXPKZ6L8fwSACDEhwxXV+0Emr+7otSmZytkVgAr1a+441niyoMiHR4zdFLpGfb/Dzza8yqIsox9Jl6W/eNP484dJzHaZd3zoOIpQM9WIKPk9coEBtAd67bcvhkq5iTY9iI8/tqf4UQhhpeKBYQNGqZaSJZBo0G84v/iD/0vhzywp+Z1+vaMCF9M73Zg/nEtAXo9NPGPZZAODCAScdAI69+Jhdd+C49NzqELLPKhAYSalctHh9nvyihjfATPkDhm1lzMW/jsz3HG9/8K0f6L4Dv7NsvNVJqHgz6CwuwRZMSt6DaYtkheOQEWiqdJrbx/ZipxaW0T2bZIWdUYjwwgF92Cx/e8B8upDF4SrL+mGRY7iuJ5UFxDLrUG9HWb7baGXviyy7ScATigO3KaI5XqrMAwDIZCoziROILD3TsRyhJEw7XzFroNKSabDsWPwRc7y1C+MTc8vgBgjFE2MWu5mYpeSnIxDEoTb0BA1mfgk/OQwQJdY9rpv9FQE/01DRW+Cuu4+KlPY/eJ7wMAksFRxMxU06z32RcPV6PpGTwyOYeHJ+cAovjLkOJfVM8aAoJsVlkDVlMnsGVlFY8874OO5Z4TOA5BgUdKCOE3e/8WUTkJn+Bv2jxsJk7k5hmPKt55RjyIgz4BZx3/FgrTPwVGhtDHBBEUrNnrtoQvnQCwfsafYm5xFnm/8wfmQV5ZTwuQ8buXfhnz8TwuqLM/KcgFzKZmAbhQSIbiCCGfgERIEb4AQGJ9SAg9ni2QYaq4SllV4aDAI1pYw+iRn2Ds+H8BAOLBUfg4DiGBx3yCVnSsRPN3V5TanHs17oy+BDJh0CJp4Tdzxitx5+vuB5Nbx2V18lJ4DYZh6vlTb2I9o4iUPRYrOgKVjQC2ZzswcxAAkAmPIupvjIJfsZKVP4rQyB7IM4tI5fKGhS+zG/RMdAJzwy/C9r7aoX96I8TIIq8m5/cbDCnTPgOORSrsfLJd9QRflGT4OM5yviOnkIm8cbLpcCXWieAAkI5jOTfnaLslFD0F84E+dFv8ripW1cqncOE3LwAAnDjzcSBmTIyQZQkSw0Pqrn3qbrYSFwAsZZaQk3LgGA7DLpzQcyyDjChpm9J6GBY7Al0oBHrAZ1chLZ8Atmwz220NpzYTUiGPWVYJjhnoO8uRSnVW2SLmcALAwZ7dmN5yHV7SZC/QzOv+E4mvvRYvPPxJMHwAoX3/o+5r3CiQAQBj4TEgP4mVwiruP66EABJCICt/0Qk1G//uXzqIywAkAkOIhUJaW+qYNxIK0uiqjgAQ8Ac10Wtm8GIsd5+BYQPPW6P51dQckz35eTAMo4WCQf27chXUqTAUVDwqpzkWMsOjEB5yNPfcWPooclMPgh19Hp7078SIQxUjWw2nhC/A2EFKgOcwtPIYHiwezPQKA+AthoaZnScluYh09xu/6Dr84blTmIg6v3H3s0GwYCFDRlpKARDqPt/mUnOQiAQf68NAyFqeV+rx5S5BgcdCaOPQLRseBRhvVnQUJXGjEr2BeV6eHy4g8OjKLeLcR/8vAODI2e/FYvQMCByLgCCYOtTpJEyPhHvuuQef/OQn8fDDD2N2dhY/+tGPcNVVV9V8zd13343rr78ehw8fxsTEBG688Ua85S1vsdjlDoP3oTccxlIyjVyhYLlKYLMp8EGQKpWFvExfOIDFxTlk8yICBsWdlZSykPWGrFexqrhgFU9mASAVHkdvg8SRag/q3qKwt5LOYjBa3xPEipiSzIkl71UN/SbbUCy7FkI8aagf2saaZZEKjaHX4YdoUOBxwWMfQzC3imNnXQugubnwFtOLyMt5W2JKNSZiW4D0USwW1kzl5zEDWTkBBkqiT6sJTdOF9OaqWr4wpGA/uMwSCivHgTFj4WdzV34Dv376KPaNDda8Th3Hk4lJEEIMbfjUDe5weNiyO7sWyqsPZwaA1ZM4774PIS4SPHDOjcaELxOeglJsC/jsKgrLR7GafoEWTgVshFZBn8AYpV4o6jU+RhEyTqwu4ER0veQ1xSZKkh0r7evbVRpcXX4CIsOAJwTDg+fU7b+bTBTNs3h2GmMcV7WqbaPgYsNgi1UFs5HxuvOqIBcwmyx6Rzjs8TXesxNYfQBLyCAjFkpy+ACKcMOokk1RwFFzZCXDYyX55Kzk+FILqTQCIdyDvK8Lvvw6njvn/Zj3bcEFdT57URYxl1IOFup99nzfNgBAODVtKM/ecDGHzJTAIxkaQdAXcHQNHz/1Mww8+WWckK7GoW2nOVcxssXQDl1seGuYOUhhGAZi1xZMZR8DAISju8BbPKg2W7lQ/9zR22+CiwflDMMgyIeRKiSQFhMA+uqKI1p+r+gYWMaeKLjpWUtxhIDAI63zNlf/7sU8gfr5Ua8SPbB5L8YyjFYVHgBO7r4aaxkZfX4BHMvQio5VMD0SUqkUzjnnHPzVX/0VXvva19a9/vjx43jlK1+Ja6+9Ft/4xjdwxx134O1vfztGRkZw4MABS53uNHrDASwl01hJZzFi0MPAa8iE1K1C5DmkAs75xgvAZVexMPo4AsMGNrlPfB87fnoDgqOXIXLmrZbfumqoY5GswzlTalHNiIk+9T2c9/gvEN/zp8BQ/VxNpsWU5aPgHvg6hvkJxHa+qealZkMdNeErNW0oSbn2wGFZZKPjjo/lyC8/hK6TtwMA5s96q6NtW0E18EbCI+BZZw2G8cGzgbm7MMVxyIgihBp5oGqhD2cqpqJR/k4AduU4eCgbdKvflSo+l1fVkrq3gsssIbtwFLNxxftLrSql9GCzuLKYzIAwLEJ1NnFqMvikmEQ8H0eX30DlPAdCUque0KeXMTD5awwASHftQPD8/1u/PyYq+ZGercDCY8gtHsNPDitVifSfo/oXGRsKlv63qhiWyCjf8SMzJ8FkTlZ8r5JkyMV/MACI7hfp+d8BAIYlIKLzCmoGY9EJIHsc6yQOH8c23XDnIUPIzAMAxK4tddfM+fQ8CqQAgRUwGKot+Jpl28jzgGPALCuhL2zsgImNDmNh7DLMBXcgphMRzeRSaobHFwDkouPwLa/DF5+CMLTNsGeKn/OjP1i7eIt/YAdOjr1c2SDKElCrIjKAPr/y7J7ieSSCo+hyOHcOKdo540e/D//onyPsb5yt4yXMVButhubxZdSDuHsbplaKHl++YcuCplmvJvW6sBAusTcEOYdIdh58Jo9CnXFshQh4pACMPfz3mN332brzyokqtdTjy10CPId8dMOzPhUaM+yt3mg0b+N6leiLVBo7wa4B5PkwfIUUgqkZ5MkAYkU7kwpflTE9El7xilfgFa94heHrb731Vmzfvh3//M//DADYs2cPfvvb3+KWW26hwpdBRn/3v9Fz7D4sXfYPwPNe0uzumIb86F3YmxAx9bxrAWxrdneMw/FghBCQXUVq7lnAiPC1chz+zAI4KWNLHFEXrJKNr87ja3HryxvmE1TN2OdP3Ystp36M53p2wEiSctNiytQfMPSHT4EZuADBl1xd89LR8Gix8DUxtsh3bwUBA5ZISOfy4FlFfCkXUNSfBTjFoy3OsraqdVaDC/Vofw80qGhBLdzKzQMA4zuuAB7/LOYYEfcdmwbLMKUePeVlynXfhax5AZWKI0QnhADAHy8eUR5uPdYrb1atqtWzFZh9GOn55/DQk8c1IUY/XgA1RGijAhVQv/pZkA9iIDiAxcwiphJTxoQvB43xdCGtlcpWOtStXZOJThjynlP7M2FknhTXtN78PLqDfp2XjnpB8SfM5pArvZdP/3oPnkkAfqGACRtJkJ+dVbyCBuF3rFKdVbb27QYW78GSvI7xlUcQFHY3tT/C498EW/SADPRUr9apoi9kYtU7ohpbRy8EACRYFvn0LHyh+gUNlkdeiKXh/ZhaS+CAbhyb8vhq0qZVDg8Cy4cRTJxEYOzSuqKjmc/eH+rGYy/4P0jnC+gzsPnq9Q8BAOIch5nggK2quZVgims2X0hjdOVhBIXnOdp+q+BIqGPEeKgjAKB3K6ZOKXNjMGi9SIhp4atKSFbXg7fgz//wBRzf+Rc4cv6HLfenGhHCYh5AHLKhwwUn7CK9d7VRr26KcRiGQW7sQqT9/QjllpAIjsLHs54skGGmsIr+Ov1eLOL3g0BZ43vnH4A88CpEA+YLt3QSrkug999/Py6//PKSnx04cADXXXdd1dfkcjnkchvJ3OPxzlbGheWn4Vt9EoUHbwFmfrXxC18EeMnfbfz7gVuBtZM4Wkjg3twC3hTaBh9TNGQ4H/Cymzeu/cP/A5arJJdmWODAP2z8+5GvAQtPav+8JzeP+4vVIgAAW/Zv7FSWngVSi6XtzT2B/ek0+i96v4m7NkZeyuMbT30DLx5/MXZ0O1hpr8hc7zi+yyex9vBH4XvmswAAEuqH3LezuAGXwU8+oF3PJmbQ3xXDxeH6G4NaVDQEhs7C1AtuxDH0A327bLVvhmpGjGqgDp78ORa/r5xOEgDLu1+PXM8OyIQgOP8Yeo//BCDA4YIS9jKQTmL2u/8DhAALO65CqlfZ0IWWn8Tg0f/ShITo8pPoApAOjWGgjqEvcAKGwkOYS80Z9PjajpPveRYPTC7D98xRnH34c9qvVAFDZS12Op4auwwAkGVZZPvPqN++WXRVySL9tfNAAUBOyuE/D/8nVrIrzvcFwOHlwwCcr8YGKMInAwYFksOv/vBOCKi8MUuEJ5ANKKe8gphAT7x6MvxUeAyZ4JB27bFABi8iAfQNWRcRqxkNXHHc75y/E9FIF07tfIMmbJ3+xBfAFXM2qAxN/gKMJOLwttcjeNZH677vWGQMi5lF3Pr4rYbErAdmlfXHjjEe8W14EifyCfQGismEiwnNAYCNjdet3ipKIubSxkKsAICfuADTJy7BetdO+HkeY0e+j3C8esGJZ8/9oPasGTn+X4iuPqO8l3QCAOCbuxe7FpV15rlz3g9SnFfDJ3+G2PKhqu0ePfs9kIQwVjJKKHYfG2v6CfGWoXOAp4FJnscPU/fi94/ONrU/eOq/gd5uAMDi5JfxwGptj7jjxe9xLOr8GhIM9aJPIljmGCTXnkBvaARsIYPTH/981dfEe/fg1PgrivlPNgtfXb76IrPZEC6nIJkF/FtXDKtzX8NkN/B0tnY4//H14mdvcP2OBfwQVyax65kfVL1mZfgFWBr9I/i5AHoIh1VGwolADPsc3lAK/RuHFcnQqOPzUCYyvv7k17Uk5Yb6xAp47c7XYlvXNkf7UgszueeqoXoQJ/IJ/N8H/y84prYdlVqfx9Hi97mlzrW1MJtzseq9Fg9e+uYfAPvIJ7UfZyLjmNz1Ru3fOx7//Kbnrko2NIRTZ2wcnJ526FbwxTDD3tw6jvLAbX4Rgfn/xAmpu2Y/75u5D4A9u0i1TyUiISWmSp69biITGV978mtaCLQXiPljuPrMqxEWrBcCq0S4awhf3vVmsPnncHrvPvg4zvECGT967kcYjYziopGLLLdhNgdXRY8vHw9/QRnPZzzyj7jvwKu0Qzua46syrlt2c3NzGBoaKvnZ0NAQ4vE4MpkMgsHNbuof+9jHcPPNN2/6eafC9O8GTvwWfdP3ANP3aD/PBvpx17C6+BNc9NC30Lv0KD4xNID7QkEMH7sXL0+lAQAiH8YvR6/W3BEufOi7GJh/AJWQGQ4/HXtrsVXggod+gJGZuwAABQAf3DqOjN6b6ek6lfFiEfwgEsb3+p33Hvn1yV/jUw9/Cr+f+z3+9fJ/dbz9fwuy+AHbBSAOZIuLTfYEsPJQ5Rf4APR2Q/LL2GvjfSue9oX7wbzgWiydnMVZMWcfFLWoenrXr4hvXWvPAGvPaD8+6NuL6aTyMN859Qj2HP4qACDR3QX0dOG09SmMLD0OAHjWvxMnJWV92D77JM59+mub3j/ddZqhU7EdXTswl5ozFlbDsoiGoxClBTAkj51Hv1310unRl+DUtj/REqGSofrClGnGztf+GvLXP0H/5Ylf4rMHP+t8P8o4res0x9v0cT6MhscxnZrELzFf/cLULJAy2GjZtfd2xfCjaBRfi1nPT1bNaOCGFOGzZ/0ZkMlfY0pngI8f/T58udWK7YXFVUPVz3Z078Cji4/i7sm7TfV3e5d17zae5REWwkiJKcRzcZ3wtSEG8P31x8JsahYykRHgAugL9NV/371X4iDZi2RORB+Agem7MTB7b9Xrnz33g9rf+2fuxfDkLwEAE7Eo0NcDEn8OWxeV59qR570XBIrw1Tt3n1b1qBLHz/wrSEIYJwMRIAkEorsdq1RnldGR8xCQZWRZFncnHwCeqvy8bihdxbkw+UPDL3FjDQGAEcaHZYiY5hj0AmBlEVuf/XrV62e3/jGOjFwBX1nYi6mqWsVrUmIKBbngeBh4NX46uhufixdzAs3eDhjUbIx+9rGAD+vZ5Zqfn8QHsTT6RwCAnsAoVnOTOOkL42KHhSmhb2Mdy0fHHZ+Hf5j7Az750CfrX1jGZGISt1x2i6N9qYUTHl8hIYSR8AhmU7P41tPfMvYilgVPCIa6rB+ums1jVe1e2Zgi3MXiRxGLH9V+vjJwQYnwVeu5u967t0T4Gj12O4JpxbN3vL8Xf4hGcBDrwOKPcf9ixSY2YWdNC/AB+Fgf8nIe8Xy8YcLXA7MP4J8e+qeGvJcZevw9eMMZb3C0zZBPwNcydyIlreFtvmtwtsMFMk6sn8BH7vsIegO9uPvP77bcttk5XmleBQUBpwZeiC2Lv8PCyIvAMBv5zJwQz9sR7wW9Arjhhhtw/fXXa/+Ox+OYmGjZmob2ueTDmJLCyGeTJT8u8KGNkBDCYHbrK7Hafx6ey/0WQBYPj1yInbyyQMucTzmtL14/u+UA4j17lJeWvR1h2I2QEwDzYy9BKroNBMA8ySAj3gceDF7NKgLAysB5IEV3+kj8GPxlXij/JZ9ChgXAVz6RsYN6qqyecDrefqQHyE3jIgxgmFFE2qy/F/HINgAAQ2QMrDyqfYiPYwXHkcRyn/UNt0zkqolNu4N+hATecF4TJ6h6yr3n1ZjbfyNmZ46XeIIkQqNaDp2V6A48uv3NAIAn8DSABXDRs/BYTAkbXQ1v1XL6rIa34vHT/rLkLVJsCMFz3myon//zwv+JB2cfxIvGX2To+pDAw8dxKBQ4PLXzmpLfMdi4n3jsNDBgEPP1Yy2/gHhhCYDDYUe7DmDu0o/jSWYcFxnYSKjj/ay+s/CC0Rc425ciUV8Ur9v1Olfa/sgL/h7f/P1X0Zc6VtWLKB3bilxQETH53Dqia89WbS8TmUC2mDeOzyfxq6WfI8UCGXkdQH0BphJVjZIzr8TRk88hvTYLrrdUbDq16y8qnjynCI8T267C6Qa+23ef824MhYeQl/KG+9of7Mcl45cYvr4SMV9MEb7085zlsPK6b+HxEycx2FP/4EJL/hsZM2wMxgJ+rKSyAID5LVcg2W1sw7U4/hJkIopdkC9MA4UnMR/bguP9VwEACLPhibI0+mLkA9WrcMmcsp7OQfE0D/Rd5GilOiv4/FH8zdar8Zu5QwiN7MZoV5ONV6mAleN/wDw/iLHhcUMhbgEugD/d9aeudKd/4Hxg+QEsSsp4lVkfju95W9XrEz27taq5qsdXSVUtA+Eg+g1EIp9AT6CnxtXOcWJoJxB/CKPCBC6aeLGh57+Zzz7kE5Dx9db8/FYHNw5neoPjOJabxMlwj+MeWcFQFL+9+DNIZ1IgwV7H56H67Dyt6zS8ZEv91CFzqTn8+NiPXbMxq5HI2U9uDwCfvOST+M3kb0A2WfqbyRUkzJ04hBH0oaurcXmsqh0yMXuvxKPHn0VQXIdPV8wpU+ZxdWrXm8AVshXbzpYdhE7u/HMIRfv6VSQLH5ZwMrgdfbEYRrrqi1ADwQHsH91f/6ZqEPPHsJRZQjwfxyjsRYcYRR2/O7p24LItlzXkPWvxyPwjeGThEVfmFWFySElrAICFzBS6As4WqlH7vJJdMZyLtRKmQx0reXwJPO7ddyPWF+7AzMTL4SOcdlhAc3xVxnXha3h4GPPzpaf68/PziMViFb29AMDv98NvwOuhY4gOY/zKv6/4q5ItwpkfhiiJWP7GBQAB0tvOwo4XbYQsluSEOvN61KLk2rP+WvvryuyDwC/vw3hsK25+zX8b6v7DP3oVTsZPYiEzi602HqaVUKsEzqXmXDmBnc4oR0DveNkncVbf2cWqYkXI5txCX33y/+HfD/0rUqTy6ZMRkmJSa7d8wQr7fbho2yi6g42bH1UT+vI+hF781/CtJkoqrWljkhAQDIOcdSkAYPGhvwbWF9C/980oDF4CQoBxEIypycmH+5A6/cKNKmtKExjs7zbUz21d20yFIvgFHoPRENYyHJ7a+z6Ups3W/auYuLzbN4i1/ALW8guG38MwDANp35shzy0b2khMJ5Vxf8W2K/BWDyTDN8u5Q+dgctu7kc4XtAS6Wg6njX9o8iPDMMCI7mdMqThZ/rMH1x/GmriA1fwCAGuns1WNEt6PxLlvx8OTcxiNRQFZ1npy9My3b+43gMVkGr2hgKG8f0PhIbz7nHdb6rMdYr4YZlOzmzYr5LTLsCSdwlYDnhdWcqB0Bf0oyDIAYHb7qw2/bm7rK4CtSr7RxMr9wHNPYinUjyN7/3rTtQsTL8PCxMvqtrmSU2yV4fCYK9VGzfK8s96BtcAk9m8fw5kjzid3Nsv9/TN4bnEFrzjzNAxEmpv8f6TvDGD5ASxnldAdmQ/gyDmbv3s9+UQKvaGAJrav59e13xmpqsWzPEJ8COlCGvF8vGHC10JWSS1xetcl+Ku912Jbr7WNVjVCgoB0YKDu56fSE9oCrN2PuGTseWUGlmGQ3noJnplfwWk+wfF5qD4794/ux/vPq59+41T8FH587MeYTk43NCeTU5vWcwbOwTkDxjb+oiThduE5zMZT8NkIDVOfmUkxCUmW6iburvas5f1hHN35FyBEeU5U4/jedxru28k9pfbSRQBGV+N4wfZRnDncmDU25isKXw0skqGO+xeNvcjQuHeb7z7zXTyy8IjWLyfR2+jr+QWE/c6K5/o+TyWN5WKthOlQxwpOCEGBB0K9eHb7n4MB4JPJhseXicItnYTrwtfFF1+Mn/70pyU/+9WvfoWLL77Y7bfuSNRQE8BEJRcTWNnYjEfGcTJ+ElOJKTx/+PnO9qfoYSARCXOpOUeTceekHBbSygK6s3c7wv761YtOLyZrnjaaTLQC6mIV4ALwcZvfsyfU2PLe+lOGcsMvFvBj74gxEW71PuWzfPG2s7C337pHnFOwDIPLdm0ULCBk40x0c7VA4In0Tpw4ckjbaDnNSFcE3bpNWS2cqOTXTAI8h9P7e5DI5SErJREhY6M6opFk9+r3pf+ulL8BPf4hrIkLtr6rWlW1wn4fAjyPhWRaE33V99ZXHFQhALqCjZ23ZqmWl4VnWQR53lCydyvjMijwIAAyYqGYsF79DaMTEHX/ZjYLi0FOES0yhVKvaDPkpCwS4hoAYFuXNzzMgwKPkE9oetilSlDg4OM4hDxQIWtLTPE4X8oZz9UkSnLJBtpsVS1AmSfpQrqhm9bZtLLR6vUPufLZBwUePMtClGRDQlNfQHl+r+cXDBW8MEtX0A+ZEHQFnK0YCZi3YUfCI2AZFlkpi+Xsct0qmU7RDG8NoegNyTEMBBvFmco9I7t1uSIrUe1eOZYFz7LIigXLfTFKI4uZNKNIhpsFi6xguuKoCVZyG3bfurjguDivLxYxlZjC3j5riW2shjrqnz0+XgndT+ZEAEDIx8PPU4+vWpgeDclkEkeObCQZPn78OB599FH09vZiy5YtuOGGGzA9PY3//M//BABce+21+PznP48Pf/jD+Ku/+ivceeed+O53v4uf/OQnzt0FRUO/iBiu5GKmfXVjY6KCmLrATSYmne9P2f06uajPJGdAQBDiQ+jxGzvZdWIx99pipW6IJSIhXUhbSkSZKWSwWPSe88qDtxyG0fkQ6d2OiqgbLTfmFaAIcWGDiYKdqOTXTBiGwflbnBM/y0WwJ1K7cPzIE5gr5vKwQq15uK23S/G6LBHkUOL5qIbwqn/v9rrwVcUYD/l49EWCiBnwMrUyLsM+AbGAD4nsRkGbSgK0+ovyzxkgSGSVuZoUE5hcjZe0wzAbDap/1f1IYzmnPJ/8bBjDEWvhsU4T8gmI+IWmh12q8MXKZwEP9EcVJ5ezNfIElkEIKVljq6UUqEXMF8Ncaq4pm9b+wLAr1ckCPh4Cz0KUJEPCl1rZcV1cMHRQY5aIzwcfxzleMRLYeH4bqjoLpXDOcGgYM6kZTCWmGiJ8EUK0za1VbxKrdAf9mGYZ8DY87QRWKPGMrCd81cqzFxA4pPKi5b7UQ5JlsCzT0GImzSiS4bXD0olimoKp5JTjnpQLmY3DkPXCovPCV2Kq4t/N4kSoI1BMF5HOggEQC2zsz7y2l/QKpkfDQw89hMsu24gPVnNxXXPNNfjKV76C2dlZnDp1Svv99u3b8ZOf/AQf+MAH8JnPfAbj4+P4j//4Dxw4cMCB7lPK0W/KF9ILyEk5+DnnjAerHl/lfXMCvZii9c16BeZN6O/V6KKsfi52PnuvlaANcAEIrABRFhHPxS0JXzNJRYCICtGWXYS1cezCCZUZ0mJaq+boFSOm2WiiZfE/EzF3BWiBY5se6uU01YwqgePwgm3G8pBYeT6MdkXw8jNP2+TZB5R6YcqEbAiNAEAAuah+zafD+NpJIC+ncfnurWAYZqOt4rXlXoTFH2vv+/uFE8BJJT/ZRI831qiIX0BXwI+YC54vVlDFCI5tTLhXLU7rUrx11/KLWEwmwRar0DHlYdC6fxOgxHvOStWrRntrrOfWtfcaCI64skEPCTwEjoMoyYau7/Mrhxar+QVDoWxm2fB0dFbkI4RYEufHo+OYSc1gMjGJfYP7HO1TJTKFDApE8XJqtL0UC/jBc6wtjy9A8fpSha961LJ5fRwHSa6fn8wq+WLev0Z61aoecY3yGrU67t1kODIMlmGRk3JYyixhIFQ9B6dZZlIboYhxccHx79YpBxMnQh0BpThJQZIBBugKbPZo9spe0iuYHg2XXnqpZjRW4itf+UrF1xw8eNDsW1EsUL7Rm05OO1pVyZLwVbxWzcflFKqYouK0IGHFu63H36OddM0kZyxVWlMXQ69U4mAYBlFfVEvkOGJBXVS/m7Go8aTXXsNN12wzqOOyy9/lmTHiNbTvyo5R0mEVcarm8jOBlTWTYRhEDYSR1yIWUtaUAimgPyogJJgXJX+/rIS27ujZ2vBw8mr4eR6X7HShiqxFJnpinhF8hyND4BkBBSJCYuKI+Qchk40waBko5plUICDoDwdLvIisnIg7MU/MoOaT6fL1YCAUA29TkKgEx7II+wTMxTdK427UQmK08GL13wKi4BgeEilgPj2P0YizCbqDPh5hP++4yLeeW0dSVMKhzfR5PDqO38/93jVv73LUcckzPIJ84woZAUBA4BHgedu51WL+GObT84bmSS0BICDwWvoWNxAlCb6iJ2ujaLR4vpJdQaaQAQPG8blqFYEVMBIewXRyGlPJKUeFL/08XRcX4XMwTyAhZFOoo1WshjqWVxUOqQcEZONgRyYykvmkqfY7heYnaqA4SvmDeSox5azwZSPU0WmjoXzBcat9MyIfwzAYj47j2dVnMZWYsiR8mSmv3ihivpgmfFnByrjxGmPFSkILmQVkC1kE+OZsjr12cudFNDd6G0aJU1W1WgW74RfruXVt7Wq0cR3iQ+AYDhKREM/HLQlfXsuB4kWCgvNihFVYhsV4dAwn4iewa5TDRSOnb7pmIwSaaCGu+gITVk7EGx2mpB+XZwy7F4I7FA0hXQwpU70j5aK7pKxLYqgKiV3CAFbys5hKTDk+30OCgLDP51punsHgoKnnd6O9vbX8kv5Yww8KlVBm3laoI2BO3KklAPh5lz2+ChKiAR8EzlmvxVo0WvhSx/1QeKhi3uBmMR4ZV4SvxBTOHTzXsXb1ThYSKWAtv4xhwZnUGkuZJeSkjbQMjRS+qlUV1q+Taq66RD6hJYno8jU2XNrreMOCoTiGOgmDfBCZQsbRB3Uyn8Rabg2Auc2BKhisZFeQFtOWNiWVUBdzN+4VsL4RGo8UhS+LQpwX47LtGvvtsKns9ncjLISRElOYSc04KiiboR0+S7dRP5vFzKJlkdKL89BN7BrjqmdKX6DPsTXeKAzDIOaLYTW3ing+juGweSO3HcT5TmOsKHxVqwy2EQJdWTxohVBHdVxu69riagjueRPD2Dc+VLGACEAUEUznSffrpW14cG7WlapssYAPzxsdcNzz0rJN12Bv72Y+e0I+QfH4sulZaEn4qhLq6CaiJDe0SjrQeK9Rrx6WjkfH8eDcg47OK5nI2prEMz4USB6TiUlLNkElyveds6nZEu8rM5g9eOFZXtuD6KsKB3VCtVbRsdh2kA9C4Jqfk9NLUOGrzVAn5XlD5+F3079z1ChR2+rx95jK8xT1RdHl78J6bh1TySns6tnlSH/UxdKNewU27lcV7owyFlWutxra6cW4bLsP6nbYVDIMg/HIOJ5ZfcZxT0ozWB2XnUTMF0NEiCApJjGTnMFp3ea/Kyp8maPZgmzMXxS+LK5R6npNBeXWwa4njpVwZnWeqN6NbqOOy0as96wW31jfy2hb1xY8OHe/K0WLGIbBWLfzIeaqHWLapite74bIV4lmev1H/T4MRkOGqpjXwpTwVUOA5jkWLMOUFC0BUFqhhAEYAhB9IROmWBxF/X/xJaoHnVpBOC0WXCmiUAvtIFlsbLi012xGtT9ORusspBcgyiI4hsNIcCcm04cxnZzG8/F8R9pXnzVn9Z+FRxcehSiLmE/PW/psraS1ifqiivCls3OCAg8fx4GAaKGOnZaqwwxU+Goj9KEmFw1fhN9N/85RJd3OxmY8Mq4IXwkHha/iYqne61puDYl8wpGJro/jtuLxpe+fWaycQrtNq2+KnWI8uiF8NQuvVefxImrI8dMrT2MqOWVa+CKEaGO90VW1moVqjFvd0Dd7XNoRJPTrvRomS/E+amU+u8KXKY+vRoc6evTQyK2iRW5i1+PLjYJRldA2rf7Gb1p9PIfnb7VfJUqbJ3UOIiRZQkKsLvRt7e1CSBB0xUlKi5So/5eL6pZaBEUtfKJ6KMrF18qycq1c/DshBENR8wWb7KA9q3KNEc+9an+74UmptjUUGsGgfxST6cPOtq+rCruYXsSJ+AlMJaZMC18FuYB0IQ3AvMdxeVXhgCBoOfk0jy8P7iO9AhW+2gh1QvYF+nB69+klP3OyfSsG2Hh0HIeXHV6Aim3t6tmF3kAvVrIrmE5O44zeM2y3vZZbQ0pUEr2aXdDsLuZe9DSxI3wRQrQTJ689eM3iBWPfq27rXmM8oghfVjwSMoUMCnJzqmo1C9tenU0el3bWKDVvB8uwGI44ExJBcZ9mHDI1LUzJY89Ot4oWuYlVcd6JokVmaIdNq9H1WC02oH+NnqDAY0tv634OlWhWuLTn1hAX7OmNfeoY+vgR59vX2Tnz6XlN+Lpo5CJT7egP6Kx4HOvHDscqBYJkQrQcll7cR3oF58vDUJqG3kDSiy+1qnCaQd1AWvX4ApxbgMrFFKeTj6rtDIYGTZ/u6ZP5W/nsPRnqaPD0rhLL2eWNijJhb1SUsUqzKzvq8xd4zYjxGna+q2ZW1WoWWon1FvXqtLOZUJ9LI+ERCCzNh9EqOHbIZCa5fQM3rZIsadWrVe82r+BW0SI3sSrOqx7E+jbcpB02rUbniWpTdlIuoqYVyPDYYWm5J6UTqPe6JTaBvQM7Sn7mZPsl+04La6D63YeFsKn8YNUOXqIBP7qCFSoWe2gf6RWo8NVG6CfkaGQUDBikC2ms5ladad/GqYHTRkO5mKLm1XJM+LLh3aZ6iKXElFYMwAxePO2ztalUXY/DQy1v2DTb2Fc9UziGcyxZZ7vihFHSjKpazUKd40kxCUmWTL++2SFZdjYT6hrltRwolNqo39dqblUr3W4Gr4c6zqfnUSAFCKyAgeCA6+9nBn3RItU73suIsoi51BwAbxze1qIthC+Dh6WdmItIL1445ZhQDVGyN+7dRC0YBTiXP0+/T33+uBL940ao43hk3FaovdV9XrXnz47+LpzW3227/U6ACl9thH5C+jk/BkODys8dmvRa8l+LoY76PtpFvafh8DAETnDcKLHjvWD3s/ei0eOEN4XXTpusoCXjdNCT0gwl4556ptTElsdXBxoN+pNBs3myJFnCbHIWgAc8vix4pTbbW41ijYgvgm5/NwBrGydLwlcDQx31gizHulvdzixq0SKgeR7QZphLzUEiEvycH/3BftOvpx5f5jBqM67n10uu7wTUey2QAjKFjKvvNZOaUZKe80H0BfpcfS+zqAWjAOejdfSRT8vZZaTFtO22c1IOC+mFjfZt9N3qHK+Wy3QwGi7JVdcOa4hbUOGrjSg33p18UNsNsVIXiJnkDGQi2+5PufeZ3SS35dgNJ3PE28RDC5YT3hTtsKlUPSkzhYxjnpRm8GquBi+i5aBJTpsWKb04B91GYAUtrNPsPPeCZwoV5zsTW8/anPlwENUzJSEmLHlGmkGrQhj1piei+tk3qtqhHfQiIsuY3/o0VPiyMC69huFQxw581gb5oBbe5rbnqH7ce9F73el5pd+7xXwxbVypIeNOtB0Wwuj2d9ty6LAaikjnlX2o8NVGaBO+aIw4aZSoJWJ5hsdQaMj064fDw+AYDjkph6XMku3+lHuf6Te5TmA3Jt5qfwghG6WsPWT02DnlLh+XrYwbnpRmsON12WmMhEc0kXIlu2Lqtc2sqtVMrIpHXvBMoeJ8Z2J14yTKoqWqWl2+jSqv+sTcbuDV3Dwqzc55aQZNRLQYzqy+rhEiXztsWo2ux+0g8pmFYRjtu13Prbv6Xl63v52cV5lCRttflu8NnYgG0tvfDMNofV/LrZkOtXc61LFq+x00r4xCha82oSAXNoWaaHmvHJjwqmEzEhmxtLHhWV7LSeSEkVRuxOgXTydOYe161lh1gU2JKUhE6b+XjB4ncny1y6aymcY+9fgyjo/zYSisiPRm18BODHUErBex8IJniq1QR+rx1bJYXY+tVtUSOJ1npMvhjl4fl16ocmwUbdNq1aazWbTIDG0hfKk5I/PJmlEe2kFvC9+rFRpVJMPr9reT9rQ6x/Vh2Pr0JHYp33dGfBH0+HuU9zYp3NkNdaz37OnUeWUEKny1CfpQE9UjxcnYaScMMCeV9/LFfCg0BJ7hIcoiFjOLttoWZRGzqaKIaNPjy+xnry6GPtaHAB+w9N5uoD5E4nnzyTjbTaxpprHvdQ8Ar2F1DWyHjYcV7Hp8NXNcWu17ed4OSmthdT1WNwZmq2oB9iugGsWuWOM2rejxZXWNslu0yAzt4K2hekYSkJo5Izv2WdugIhlet7+dtKcrzXE3952AdWHNtvBFQx0tQ4WvNqFS/gIn8145cWrgihBX7A/HchiNjAIAJhOTttqeS85BJrLlJKj6fpn2NPFoCVotGadsLhlnXspjPjUPoH3EmqZ6fHn89M5r2BWgO81osCt8qc+cZmB1I1Get4PSWlie4za8OrUwpby7YUqe37Q2ucqxGew+OxuV5oAQ0hbPnxLPyBprsldtXrdpVJEMLxxK1UK/ftv1pKw0xx3dd1Zqv8F7PcOhjh06r4xAha82QRO+dKEm6oScS89BlER77TtggDklGFQTU5xqfzI5qbVtNRmk2q/Z1CxE2fhnrz4EvVbaOcgHwTFKiKuZjeVMcqOiTG+g163uNZRmGfvZQhYLmaJnikeNGK9h9TRRHeOqp2OnYFn48kBIltWNhNeT/1Jqo8+naaZwjh1xoRFhSikxpeUmtJqXym20PLIJc599M3DEhm2At3dWymo2YysLXwAQFep7RnZqWoFGeI0SQjRHAK+K52rBqHQhbbtgVE2PL5cin9T2zTpcWM7xZdDOaQfx3C2o8NUmVJqQfYE+BLgAZCJroXuW23fg1MApwaCamOKUUeKEV01/sB9+zg+ZyJhLzhl+nVfjsvXJOGu5rZejj4lvl02l0+WXjaJWpYkIkY4TZKxi1xvEawK021jO8VXh4KXRqN9VXs4jW8gafp3XT8QptRkKDYFjOIiyqIWsGsHOibjVeWIGdVx2+7s9uw6pRYvych6LaXspJtwkno9rScQdsWFdfParY4pjOISFsGvv0wiMzJNO3aA3QjyP5+NaAQ41IsZrOOlJWWnvNhFRvNCtVPfWQwip7VFm1ePLovBVq6qwTGTP7iW9ABW+2gQ1F4Q+1IRhGMce1Go4iJ2Njf500A76kzu9mOL4vdo4ZdVX/FA9yIzgZSPASihRO4bmqfcyn5637UlphnYUEd1Gy7/QIKOk1bFijKfElHZS20zPlLAQ1sL8Ta1RHg8no9SGZ3mMhEcAmEsw7ESoo5lDILM4YYe4jb5oUSOqHVpFtTl7A70ICSHL7ThZKb0a6piK+qIt/5w38jzpdO9qN9cQ9dnWH+zXwk69iFMJ6CtVsByODINlWGSlLJazy5bbXs2tIl1IgwFTIiJqHscm97WWQx11z6tqVYVTYkrzwKWhjpuhwlebUC3UxAkvqEolYq2gvnYhs2DqRL6caif0TnmUOSXW6EMwjOLluGwroUReL6VsBSc9Kc3QjiKi22giZcqcSNmpp2VWhC+veKawDKu9v5nNhNcTiFPqY+XQSx3jVsZsI7w1WmW9b4U8X06FYjfE46uNDl0MCV8dGuqoLxjlFq3izezEGlLNI0tgBe1gxM68VV87GBqEn/NrP7caaq8XuM1gpKqwOqb8nL+krxQFKny1CdWMJNVDy86CUqlErBW6/F2ICBEAG2FblvpT5SRU/bdjHmV2jSQLIXGqO74XjQA7m2KvG+9mcNKT0gxeyKPUavQF+hDkgyAgmEkZX3O8LEC7iSWvTg+NS0trlIf6T7GGlY2TrRxfDajI1irjslmh/2ZQbUK7odiNEPnaSviioY5VaURy+1axv604CZSznF1GVsqCZVhN6FLRom9sFD6rtu8cCg2BZ3jzofY2BN96+eE6VUw2ChW+2oBkPlk11MQJo8QpA6xEMLBhOFRbzNV/L2eXkRbTjrdvFjun0F5csOxsKptZ7c0NGpHktpxWMWK8hD7k2NQ87FDDwYox7qVxabb/1U6JKa2FFTvHkeT2dNPa1CrHRnH6MNNs0SIz2PFE9Br1bMaSXESddsjUCK/RFgnjd2SfWnztUGgIAieUtu/ivlMf7m20/5IsISFajyqoV1XYy/tIL0CFrzZAVaK7/d2I+CIlv3PCKHHSAFMXODvKezUxJeaLaRPd6snBem5dWzTs5tawIo54ecEye8pdsqn0+Km1WZrq8eVxI8ZrmP2usoUs8nIegDfnoZu0ulen2f5Xy9tBaS0sHTLlrHt10k3rBi0R6phw5gDOatEiM9gZl16j3jxJikkQKAnH20HoM0NDvEZbxP5W56Ude7pW9UpHHUAqtW9yDdTn5nLj+dNO4rkbUOGrDai1uDnhmeKkAWZXMKgnpthtXxXM+gJ9tpKgWu2Ll0OszJ5yr+fWPV9RxiqNNvbbWUR0G7NroDoH26GqlllaPVTQ7GZCnVMDoQGaC6OFaXSoo9v5eWQib+Se88C8qoVaNa0lPL5s2rAMw2wc3pooWmQGLx9+mqVeqKP6807MRUS9RjdQ+zeXnrNcMKqWHeKoA4gD7avfeZAPQmCFOldvxui88uI+0gtQ4asNqPVQV3MaJPIJLX+UWZw0wOxWxaknptgV+rRk7A48KFSPsXg+bthATuS8m1Tb7KZY/Q4GggMI8AHX+tUMnKpCY5SV7AoyhQz1TLGAVaOkHapqmUU1lJL5pOFErernajd/jhOY3UxQMbk9UL+/pcwSMoWModfYERi0HCsubVoX04vIy3lwDKeF0XgV9Vm4mFm0VbTILSRZcrTIjtve3m0lfNWpXNhO92oWvT1NCHG8/YJc0Iovef355kTBqFpFapyoxlrLa9Tq4arVcU/nlT2o8NUG1FL1g3wQ/cF+5TqLYpCTJ/p2k+2rrxsMDlYUU+waJdomzoES4iEhhN5ALwDjCfe9vGCZ9qZokVANKzQ6x5f6wB4MDcLH+Rrynu2CWaPHy3PQbdR7JiCGKiPKRNYKlXjBuDYrzjt50EFpHjFfDFFBEaOMFs7xcqijOi6Hw8PgWd6V93AKp4oWucVCegEFuQCe5TEYGrTdnlZEycYmuhbt5K1hNCSrI5+1xe9XlEVkJecF4/n0PCQiwcf6MBAacLx9J7Gai1WPEY+vhfQCclLOdNuiJGIuPVfSVqX2je7z1NxcVuc4nVf2oMJXG1BPmLKzoDid/Fcfa23llKOed4F2r1aFNYddg82GYLRTqGM7e1M44UlphlZxWfci6mc2mZg0tOZ0stHg43wIcMqBgpFNvdc8U6g435lYqbTrRHL7RD5hqoS9UVppXDpVtMgt1D6NhkfBsZzt9qjHl3HqrcedmtgeAEJ8CByjjEc3PEfV8TkaGQXLeH+rb3cNqWUjd/u7EeJDSnVvC+L8bGoWMpER4ALoC/Rt+r3lfZ5Vjy86r2zh/dlAqUu9TbGdUrG1SsRaYTQyCgYMMoUMVrIrpl9fT+Qzq7ybbd8sZpIqEkI8XU3OdKhjG4s1TnhSmsFLeZRaDTU0NCkmDY1dL4vPjcDMPFfH5Uh4xBOeKVSc71zMbD4kWdJSJlgSvvwbnpH6RMVO0Wrj0omiRW7h+GGmy97ebSV81fNM8bC96zYMw7jqOdpq9rcd4Ssn5bCQXihpR4+VgxE9ap/GImMV01+YDbW3O+7r2TmdPK+MQIWvFkcmsiZoVQvPs1PRQn3NcGh4U4lYK/g4H4bCQ0rbFha4eou5lmg1ac+jzHGPLwOffaaQQYEUAHhzwaLeFKU4USnGKK1mxHiJIB/EQFBx9TfyXXW60VAvcaoer41Lq+K83WpvlOZjZj3Wh/FaEbj1ybjd9NbwyryqRzOqHBvF8cNMtz2+2jDUsZpnZDuJfFZws7Jjqx2W2rGnZ5IzICAI8kH0+Htqt+/CvrPL36WF2htxunAqxxcNdbQGFb5anIX0AkRZrBlqYudBXatErFVsCXF1xJThyDBYhkVOymEps2SqbUmWNDdYpzZCZhZbdbHiWR5BPujI+zsJ9aYopZHGfruLiG6jhTsaqMTV6UaDFY8vr4xLMxuJenk7KK2FmfVYFb6sVtUC3M3z5bV5VY9G57w0g9Mior5okRtpDtrp+aOuxzKRkRJTm35Pvavdq+zYSeK5/l6rFSRywuOr1mdpxmPN7rivV1W4ndYQN6DCV4ujTuJaoSa2lG4XDDBHFrgqYorAClpIptn7nU/Po0AKEFhB8xCxi5l7VY2omC/myWpy6iKal/N1qzeJsoi5lLKpdKJQgBdpZF6TdhcR3caM2E6NcRPCl8fGpZmNRL28HZTWwsohk52NQSPClFQPdq/TEh5fDtmwISGkrRduJLhvp02rn/PDxyrFeCrNk073rtaqw9JQR2f2qTXsEDf3nWbbdz3UscNt2HpQ4avFMaNEzyZnUZALptrXSsQ6uLGxWlpWL6bUvF+LHmX6kFEnkqACG55jM6kZSLJU81otIaFHjYCQENKSZNar+DaXmmuZijJWsVuFxiiiZGzcU6pjxShRjdJOw0qoY7ViI43GzEZCX8HXiwcNFHOoY3A6OV03zYHdqlr61zrtrZEtZLGYWQTQOuu9vtKhlRQTbuKGOO+W0JeTclrVuXbZtNaaJ+0k8lmhIV6jHjmUqoedglHaPtXAvtCKWG1ERGzkwQvNnWcPKny1OEbKsQ+GBiGwAgqkgPn0vKn29Un9nEJd4Mwq+6qY4uf8WmLxSlg1SvQbIacYCA6AZ3kU5IKWfLEaXjcCWIY1vLHURMToWEtUlLGCnQepGWZTs1r+AuqZYg0zJei9LkC7jT4vSz3Uz9Mrnilq37NSFnkpX/PaVgsno9RmNLxROGc5u1zzWic9vozMEzOo6RYiQqRl1iC7RYvcIi2mtf44Kc6beZ6YQR1LDBhEhIijbTeLWpv0TvdMcWsNSeaTWMutAWid55vevjW7NzTl8WUh/7OZ9o3k+LJbdVF9XaXceYQQz+8lm0177kg7CCOnWSzDWvZOccNd1qpHltHyvNq9mlw83chnxrGc4f6oi1XU711PE6MnVF4LgXIDO56UZqCeKfYx5fHV4UaD0TmeKWQ855kS9UXBQJkjddcoKny1FQInaHlO681zJ7w63fLW0I/LVlnv7RYtcgu1LzFfzNH13C2PL/24bJcDw5rCV4d7priV3F4VZHv8PQgLYUfbdhO7Tgu1nuVqde+UmNJEQSOs59Y1oUptoxKN9PhSn1uVqgqnC2lIRIku6lRBuR7tsbJ2MEaFKX0YgFHqlYi1itrWXGoOoiQafp3m3VZHTLG8eLrkGmxU6GsFI8BoDp1Wyy9gBTuelGZoNZd1L6J+drOp+iJlx59CGzTGVc+UqBD1zJrFMiwiPsVTgorznYfRvIuOeHy5tGnVDuBabFw2ssqxUdyyQ9xK5t+Ohy401LE6biW3b1X7W/OaMrFPJYQYOsTyc34MhgYBmFuj1L70BfoQEkJVr9PvO+t5lNnd69WqKqz+W2AFBLiApfbbHSp8tThGN8VWjBK1RGyID1UtEWuFvkAfgnwQBAQzqRnDrzO6mFs1SozEiVtBqyiXqF1RrhWMAMMeXx0g1tjxpDRDqxoxXmIgNAAf64NEJC1fWjVaQYB2Ey2c2YS47SXPFCrOdy6GD5mcTG5PN60AvJng3uhhqVlc8/jK2/dE9BpGQh3b6X7N4LrXaIvZ31b2qWu5Na1iaL00NVb2hkbX45HwCBgwyErZhobal48d/Zzykl3mJajw1cJkChksZZYA1J+UapJ1MwuKWxsbhmEsCQaGRb7iZ7GQXtAShZpqv0mngy0hfBk85W5V490sjTD2aUiWfViGNZxbsBXmoZuYFrc9Ni6pON+5GF2PNXHbTnJ7umktwS0vKDu4ZYdoRYuS9YsWmUGr7N1G3sbV5gkhhObTdNtr1GPP5npYsafVaweDg5oXlKPtG7RzjIbay0S2neMLqC98deqcMoIl4esLX/gCtm3bhkAggIsuugi///3vq177la98BQzDlPwJBKj7nROoHkpGQk2sGCVunZYB1lxajRox3f5uLa7daPslSVAdTG4PGE96qC5YXf4uR9/fSQx7U7hQFMGLNMLYpyFZzmDkNDEv5ZGVsgDaa/NhBrN5/Lw2x41sJozm7aC0FmbzadrZHJipIGqGVj000g4WPOTx5ZY4PxAccCXNQTtuWquFOqbEVMfnInLNa9Sjh1L1sLVPNXCvtvadBuxvI9E9STEJAiUU0pFQ+yqhjp06p4xgWvj6zne+g+uvvx433XQTHnnkEZxzzjk4cOAAFhaqV6yLxWKYnZ3V/pw8edJWpykKZpKg2lHS3Vg8rbi0Gl3MGYYx3b7adre/23G3a8N5R1ogxMrIplhfjrjVHrxmMVPJxSqtuhHyGkbWQHVct1NVLbO0egELI5sJdS2ul7eD0loY9vjyaKgjIcTURs5LNKrKsRncWqNKihY5KPS1pfBVpXKheq+dnIvILa9RLW2Lx57N9bBSMMqMyGdp32nC/jYi3KnPiwAXgI/zGe5HOfXmVTutIU5jWvj61Kc+hXe84x1461vfijPPPBO33norQqEQvvSlL1V9DcMwGB4e1v4MDQ3Z6jRFwYyBpD6kV3OrSOaTda5WcNNrx6gYpBLPxzfEFAOLuVmjxE3vBbXNlewK0mK66nWtsGAZ8aZQx2VvoLelKspYwW2Pr/XcOhIi9UxxAiMl6NuxqpZZapXK1uPVU2Ujmwm38jlSmou6Hi+kF5CX8lWvU+e5He9qN8KUVrIryBQyYMBgJDziWLuNwGrRIreQiexq1IKR54lZ2tFbo9p6rA9z7NRcRG6sIfpxr3phtgpWCkZZ8cgylePLxD7YyEG4U/s8GupoHVOWfT6fx8MPP4zLL798owGWxeWXX47777+/6uuSySS2bt2KiYkJXHnllTh8+HDN98nlcojH4yV/KJsxM+Ejvgi6/d0AjD+oveTxpS4kvYFeQyf0Zhc4N+816otqBnbNk4AWqCZnyJvCo54gbuB2ji91vPQH+xHkg668R6dgxuOrk40G9d5lImtJY8vxsmeKkc2EV0U7ij16A70bhXOS1QvnuJlc2A7quBwKD9nyBmgGVosWucVSZgk5KQeWYTEcGXa8faNFi8zQjs+fuht0D9u7bqN+NjkpZyofcS0W0gsQZRE8w2Mo1FpOJlYKRlnxyJpNzUKU64vzkixhNjkLYCOvn5H2G7HPq2bntOMa4jSmhK+lpSVIkrTJY2toaAhzc5UrZe3evRtf+tKXcPvtt+PrX/86ZFnG/v37MTVVfWB87GMfQ1dXl/ZnYqL+gOtEzIo1ZrxTjJaItYreaKhX+hUwn2/MbAia2wlljQh97RLqqHnPtdhpkxWseFKaoZNERLdppFHSygT4AHyssumuNs+Xs8uaZ8po2FueiFSc71wYhjF06OWk8JXIJwzZMEZo5XFptWiRW6h9GAmPQGAFx9t3w9u7HTetVTfoLWDvuk1YCGue5U6FTGvjPjICnuUdabORqPsGww4aJvap/cF++Dk/ZCJjLlm7ujcAzKfnUSAFCKyAgeBA3esNHa46NO6r2Tnt6DXqNK7Hclx88cW4+uqrsW/fPlxyySX44Q9/iIGBAXzxi1+s+pobbrgB6+vr2p/JSedOVNoJs6GIZrxTzJSItYIatpUUk4ZOTM2KKWaNErfzKNX77AkhLWH0mPKmaEHj3SwRXwQ9/h4A7uQ2ofm9nEP9DNdya5vyIqhoVbU8PAcbQbXEqSrquBwOD0PgnN9U2sGMOE/nVftR75BJJrJ2SGGrqlbxtRKRqnpGmqXVx2Ujqhwbxc0wR8Cd/J7tuGmtukFvAXvXbViGdbxIRqvb32aigURZxGxK8cgysk/V53+eTNbXFfQpcDiWq3u92oeF9EJVDz4a6th8TAlf/f394DgO8/Olsbfz8/MYHjbmSiwIAs4991wcOXKk6jV+vx+xWKzkD6UUQoh5jy8TRolWIjZUv0SsFYJ8UFPQDfXH5GKuv1cjp7Fuh77UE+KyUlZzvfWy0WPGm8KIa3A74KaxT0OynCMshNEb6AVQXaSkRoNCPfHIy+OSivOdTb312KmqWgEuoHkS0U2rQiOqHBulYYeZ1OOrJvpnid4Wp97VCk6HTLe6eK7uG4zY03PJOchEhp/zoz/Yb6h9U/tg1bnEoMOFkVB7GurYfEwJXz6fD+effz7uuOMO7WeyLOOOO+7AxRdfbKgNSZLwxBNPYGSktRJ3eo3l7DKyUtZUqIkZo8Tt0zLAnOFgVkwZjYyCAYN0IY3V3GrNa2Uia4uU26eD1RZbVUjiGA4h3rtVxgx5U7hYFMGLaOEdLhj7rRz64kXqnSZSY1yhrvDlYjEQu9Tre0EuaHk7WnVzQKlOvfXYqapaDMPQTWsZXvL4clucN1q0yAzqOOryWS+64DX0npHpwsbnRL2rFZyuDuvlQykjmNmn6vcaRosRaZ6aBiI0zNrfJaH2dfZ6roU6Uhu2LqZDHa+//nr8+7//O7761a/iqaeewrvf/W6kUim89a1vBQBcffXVuOGGG7Tr//f//t/45S9/iWPHjuGRRx7Bm9/8Zpw8eRJvf/vbnbuLDsRKqIkVpdvNxdNMPgiz/fFzfgyGBg21ryZB5RgOw2Hnk6AC9Y1xNfQq6ot6usKN6padlbIVq2ZJsuTZpNdu4aaxr1Xn8aDA0IrUW3P0VR07GaOhjl4UZOttJMzm7aC0FvU8Bpw8EddXQHWCVl/v1fXAjbB/s7gtzhstWmQGfaXDdiHABbRcU/o1mXqmKDgd6qiG3rbqGqJ6V7m1TzUTSmkleqWecOd6qCPNnVcX05nvXv/612NxcREf+chHMDc3h3379uHnP/+5lvD+1KlTYNkNPW11dRXveMc7MDc3h56eHpx//vm47777cOaZZzp3Fx2IlQmvL78sE7mmQt6IjY1Rjy+9mGJmMR+LjGE+PY+pxBSeN/C8qtfpRUS3kkGq9zqTnKn42beKERD1RcGAAYGSk6zcvXgxs4iCXGjJijJWcSu8g3qmOE+9NacdNx5W0CfuroSXxe26fddtDIzk7aC0FnrxhRCy6SBJfdY6IW476a0hSiLmUkrCZS/OKyOUFy1q5iFeQ2zYyDjWc+uYSkxhV88uW22JsohMIQOgvbw1VM/IlewK4vk4RqBE+7SKzes2jnuNtrjHV3nBqIgvUvVaK3PcTISGleiVern/9E4OdqgmmNJ5VR9Lye3f97734eTJk8jlcnjwwQdx0UUXab+7++678ZWvfEX79y233KJdOzc3h5/85Cc499xzbXe807Ey4YfDw+AYDqIsYiG9YKx9FxdPo8r7QnrBkphiVFhrxINC/exzUg5LmaVNv2+VxYplWO1BVOlBrZb2Ho2Mdsym0i2PL71niuq9SLFHvTWhVeah27R0jq9i39OFdMWS5WbzdlBaC33hHDWcSo+TCcSd3LTOpGZAQBDkg+gL9NlurxmYLVrkFtlCFgsZxcZ11YZ18NmvF08jQvXNfitSaZ7QkCwFIzkpjZIpZLT9hRe9sY0Q9UXR7e8GUN9z1Mo+1Uqua0seZfVsTLs5vipUFdYXSVO9USmbcb2qI8UdrExInuUxElZOW+ouKA3Y2BhdgNS+mBVTjMZyN+JkUGAFLYyy0v22khFQ65S71XOUWEE/zmQiO9auPlTDaP4CSm00b5Aqp3GtNA/dpFb4RV7KYz6lFLjxonGtP0mt5PXl5TBNin0CfACDwWKagwqbDzdCHZ3YtOrXey+nO6iF2aJFbqHmbA0LYW0T7QZOenurYygiRNru0LBS6Hwi54znS6vjpNeoatfow3BbEaPzykoxENV7K56PVzwYUUmJKS0/tBWPL9dzfFWoKpwpZFCQCwDovKoF3U21KFar/xgRm/QlYt12EweA2dSsNlkrYVVMMepR1iixppa3SSvFZdc65W71qlRWGAoNgWd4Q56UZuhEEdFt9CKlJEubfk89vhRqGeMzyQ3PFLVKppfgWE7zmKglzndK1dlOpJad46jwVWyj1gbKKO2y3mvhjsnJpvVBb4e4KSI66vHVxs+emh5fbXi/ZnDSa7Rd7G/DThEW1syQENI8ams5Rahtd/u7TYlI+n2evoqpilPjvlJVYfX/Xi+S1myo8NWiaKeDJsM1jIT/WSkRa4WB0AB8rA8SkbTcFpWwupgbLYvbqLCdWkJcKxkBtU6528V4NwPP8hiJKJ6UTp5yt4sR4yX0IuViZnHT71WhpJ2qalmh5hzXrZde9Uyh4nxnY+iQyQGvTicTU7fLuDSTPNotvHCYaRYnx6XXoMJXddzwGm11+9uI8BXPx7XPzGwifyPtW12P1b6kxBTWcmubfu/UuK9UVVjftldtMy9Aha8WJC/lNc8S0x5fBoySRp2WsQyrCXdGlHfTHl/F6+fScxClzbleVFT34ImIux4Ahk6hW8DoqRnqaCEZZDtgJmGmUdrFiPESHMtpeWjUfHR6qDGuUFM4crlamhMYEedpjq/2pVb1Vjc8vuimdQMzVdncolEioj503m6ag3Z+9pR7RhJCNg6ZWjgkzwmcDHX0cu5NMxgJdVT3bb2BXoQEc95NRtLgWF2P/Zx/I9S+bA0khGwUUHIix2RZCHE7i+dOQoWvFsROqIkZpbsRG4NaBmp5f8wuQH2BPgS4AGQia6Gb5eiToLq9kau1mLdNqGObGO9mMZpPzgxa5bwW9wDwGtXWnHatqmWFWnO8FcZltc2E1bwdlNaipseXR4WvVphXRtBX1WwWjRK31aJFeTlfsWiRGdpa+PKXVtrNFDIoECW9STverxkcXUMS7bGGGBHP7Yh8hhxAbOxlqj1/UmIKElFSbDj5/FHnVTuvIU5Cha8WxE6oiRElvZHJf830x+xGhWGYusKamgQ1IkRcP3mqVea2lRasat4UaTGNlewKgA4UvlwI7+hUEdFtqomU7VxVyyyVkhGrtMK4rLaZsJq3g9Ja1Ewr4GRVR3VDn9tcRMEMhBDNA9XL88oIblU5NkOjPL54lq9ZtMgM7eytUS0ki2d4BPlg0/rlBRwNdWyzcOlaBaPs7FONRGjYiV6pamMWv2Mf60OAD5hut5zyeaV5k7XAPrKZUOGrBbEz4dUJuZRZ0rwbqrbfAAOsnpFkV0ypl4Ohkflq1O9rIbOAbCFb8rt2CHVUF/mYL9ZxC6/Txn4yn6SeKS5RbU1Q52BUiLZdVS2z6A2q8gSt6ufm5eTw1TYT7bIxoNRGS3OQmoMol6Y58KLHVzwfR1JMAoAWit2qGC1a5BaEkObYsDbTHLTS4adZquYi8tNcROVeO1Zp9Lh3E9WTslbBKEc8stzy+Kpy8OL0Pq/czmnnNcRJqPDVgtiZkDFfDFFBOelWvZ02td/AzUG9BciumFLPKNFOWRtwr13+Ls2TpPyzb4dQx3Z56FrBySS3wMa47/H3IOLrbO8jp2mUUdLKqHNcIhLShbT28xLj2sPiUTVxvpPXqE6iP9gPP+evWDjHi8KXOi4HggMt7wFjtGiRW6xkV5ApZMCAaYiI6JS3dztvWjdt0FvI3nUb9TPIFDI1cxHXYzm7jKyUBcuwGAmPONW9psCzvHYP1UKm7exT1UO7meRMxereMpG1PZodB5NNNqbD4748dx61YY1Bha8WxM6EZximrtjU0NOyOqGOdvtSzyhp5L2WfPZVvE1awRCg3hSbUe+5lielGegG3T0aZZS0MkE+CJ7lAZSKR+u59ZbwTKHifGfDMmzVNAdOPmvV9Ajx3GbPSDNMJtsjzBEoLVrkZLEXo6jvORgahJ/zu/5+Tnl7t/Pzp/wgopXsXbeJ+qJgoHi9refXLbejjr/h0DAETnCkb83EzX3qQHAAAiugQAqYT89v+v1iehF5OQ+O4bRQZjM0ap+3yZOyjdcQJ6HCVwtiWwyqk/jVaolYO31Zy60hmU9u+r1dMcWoR1mjxBr1fcoryrWSUk83lZvp8ndpOYMq5XAzSyeLiG6jjs/l7DLS4oY3EzXGN6hUKhvQbSqDg47kqHCLqmsUnVcdQyU7Ryays1W1iuOsQAq2DjxawYvSDG7kvDSKluC7QXaI46GOLWADmqVaqGPUT/MssgyrefXb8RxtlxyBKrXmlSRLmElZ98jiWE7b31byKFPfcyQ8oh0AmkFtezY1WxJq73ioY7UQYmrD1oQKXy0GIcS+GFTDKFGNhr5An+kSsVYIC2GtMmXFBcgpjy8DOb4aQSUhLiflkJNyAFpjweryKafc5TkJ7CSDbAeMFGowSrsZMV5CHzatX3PaeeNhhYrCV4Oqpdmlqldqi/SfYp9KHl9pMa0lS3biWRvkg+CZomekjU1rux0aGanW7RaNFrcnIkrYlN17befE1JpnZDFnJPVMKcWJPF+N3su4Ta196kJ6AQW5AJ7lMRgatNR+rcqRdtdjNdReJjLmkhvh3o6HOlbL8UVt2JpQ4avFWM+tIyWmAFgPNalllGjiRQM3Bob6Y1FMUT+jRD6hxUGr6PPVNEqsqVRNRH3YMWAQFsIN6YcdVM8mmj+nlGqVXKygttGpIqLbVFpz2nnjYYVK4lGreExV2kjIRG64hy+leegrg6k4XVWLYRhHqrK123rv5LPQLI0Wt9XvbDGzuKlokRna2VtDtRkLsuIZ2c73aoVqOSnNoDottMsaUlOY0u0LrRYiqhZ9U96+FfSh9moYO+BeqKNaVZgKysagwleL4USoSS0X0ma43NfylLErpoSEEPoCfRXbb3QSVKCyQaguVlFfFCzj/SmpLqrpQlpz4yWEaPeknoB2Gk6Gd3S6iOg29eYhpbIx3irjspI4v5hehCiLlvN2UFqLSt7VWoiVg3PciU1rq8wrozhd5dgMjRbnaxUtMkM7e2uE+BA4RhEo9OlU6AZdwYkiGa1yKGUUzZPSpX2qm/tO/WsrHbw49fzR7Bwa6mgK7++yKSU4PSE3lapvggGmvle58u6UmFLNCFMXvKHwEHycz3L7pvqiE0fUz77VFiv9oq16VCxllpCTcmAZFsORztxUOmXsl3imtMlGyGtUEv9bbR66TblRBbROOEWt/GRW83ZQWouKczznvLhgd9NakAuYTc0CaJ9Nq5Nh/2ZRn79q5Ta3qVW0yCgFuaBFcrTjwUt5zkj6rC3FCa/RdhXPKxWMciIViLYPrpCT15F9doWDcNeT27exeO4kVPhqMZzYeIyGR8GAQaaQwXJ2uXL7jfT4qmI0OCWmVHO7b4Z322hk47Nfya4AaL3FimM57YRT3UjoN5UC2/oVZazglLG/kF6AKIvgGR5DoSEnukYpoxFGSatTK8eX1zfo6lqaFJNaufJ22xhQaqOO0fXcuqsn4pUEYjPMpeYgEQk+1oeB0IBj/Wom+qJFdvIWmUWURMyl5kr60AhqhU0ZQf8ZtaPwBejEnVzcFQG6lbHrNZqTclhILwBon+dbzBdDVFDmQrknpRP71FpitVsOF07v9cqrCtOoBWNQ4avFcGLjIXCCFuqxyQuqGR5fkcrKu1NiSrUQtGbcq4/zYSisiBnq/am5x1ppw12+KW6VDbGb6B90dkrbq5/lSIR6prhFI4ySVqfcGBfl5mwqrVDJK7VVvNUozhASQhuFc4q2hRvCl91Nqz6vaiukOjBCvaJFbjGTmgEBQYALaCkuGoFdb291XIb4UNseHFKPr+rY9RqdSSrjPsSH0OPvcbJrTaPEk7JsXjlRuVXNwbWSXSmp7p0pZLCYWbTdfqWDcDUXl9MeXwVSwFpuDXk572j77Up7PGU7CKcSd1ZSu/UlYhvlJq7vy3RyWqu4BDgnplRdPJuU6LhciGtFI6DcNZt6UygCLcuwyErZTZ6UZmi3XA1eRJ8/Qgs5polBS9BX4gI2PFP8nB/9wf5mdq0uAisgxCtViak437mU2zmuhDraDFNq13HpZM5Lo+g3xAzDNOx97Xp7d4IHFBW+quPYGtLgce821byynKggH/VF0e3v3tS+6l0WFaK2xmel5PxOj3t9VWH1fViGbYkiac2ECl8thlOb4kpGib5E7ECwcS73Q6Eh8AwPURY1d1193+yKKdWMkmZUsAQ2V5RrRU+T8lNuJx5ErY7ACVpooh1jv9FVqTqR4cgwWIZFTsphKbMEoDUFaDep5tU5GhltCc+UauI8nVedQ9VnrRseXzY3re327FTvp5EeX82yQ2pVoDNCJzx79DZjJwh9ZnDMa7RN1xD9vEqLaS1NjGN7Q137TomIatt6odfpvZ6+qrA6BlqlSFozoZ9OC+FkqEmlBcWJErFW4FgOI5GRmv2xg/pZzSZnUZALG+036aS1POdYK3qaVA117GCPL6B23gCjNMsTsZMQWAHDISXcW5uHLShAu0n5HG+1cVm+mej0qrOdiJZGoXyOe0j4atdCJtWKFrlJs+wQ/TizkuagI4Sv4nM1ISa08PN2vl8z2F5DHAj98yIVhamibd3l77Kdy6qSYO3UvrM81F6fg8uN54/6HKFzqj5U+GohnAw1qbRBb6bLfSWvLKeMmIHgAARWQIEUMJ+eB9C8JKj699PCL1rQ6NnkTUHD8wA4E95BRcTGoN+Y6atqtdI8dBN9MmKg9calfjPhVN4OSmuhpmzQPL7c2Hj4bXprtNi8MooTh0BmaZYdUqlokRla8fDTLOq9LaYXaS6iMmx7jbap/e32PtXNfae+jankFDKFDApEcbxwQ/hS+03nVH2o8NVC6F3i7boyVsp75USJWCf749QCx7HcJg83NQlqkA82NAkqUCPHVwt5mui9KdqxooxV7Ca5BWgS7kahN0o6oaqWWTZ5dbaYca3vv1N5OyitRdVDJidzfNFNa0WqFS1yk2aJiJWKFpmhFW1As2gb9OLnwzEczUVUxMkcX+1EpYJRrghTLuw79W1MJaa075ZneQT5oO22VaJ+xV5V5xW1b+pDha8WwskNsTohF9ILyEv50vab4fFVZqBmC1ksZJwTU8pdWvUiYqOTQar3M5eagyiJLXnapzf2VRfbsBDWkkV2KnaT3GYKGS3nVLtthLxGJaMkLIRpJc0i+jlOCGk541q/mWjX5L+U2uhD0CRZ8lyoYyKfwFpuDUDrzCuj6FM6SLLk+vsRQjYOb5sZtWDh0KsVvf7NouUiSmzkIqJrsYKdHF+EkLY9LB0NK56U+oJRTu5TK3qUObnP1glr67l1AMp37eS43+Tx1cbiuVNQ4auFcFKJ7g30IsgHQUC00/Bm5pooPx1Uq0s6JaaU5/po5r32Bfo2PvvUTEsaPXpjXz8uO92QsevxpY7/qC+qVdWjuIP+u2pF8dltVAOqIBeQKWRazrjWbyZare8UZxgMDYJneRTkAhbSC+4IXzZCHVU7pDfQ23beL/qiRWqYsZvE83EkxSSA5hSwsPPsb0Ub0CzqvakpRtr5Xs2ifhbpQhqiLJp67VpuTUvT0G7J7QVOwHBYycWqzisn927qvnAmOQOZyCCEuNL+VHLKtTlO55V5qPDVQjhZ/YdhmE1qdzNP9Kv2xSExpTzXRzPzmTEMUxJ62Ypu7tW8KTod9TPQe1KaoV3DXrxII4ySVibEh8AxSpGTmeSMdmLZKmOzmjhP6RxK0hwkp1ypJlfuGWmGdh6XHMthNDIKoDEJ7tVnZ3+w39FQIqPY8fbuhCqH6jyRiFTyb0ppegV92gUjqGvIYGgQfs7vaL+8QLW9oRP74OHwMDiG06p7L2eXkSlkwIDBaHjUdvslh6su7fPovDIPFb5aCKe9lPTii75EbDNODdT3XMosKd4FDpf41hu/+v83S6zRu8W3YoUbta+JfKJtSylbocffs8mT0gztWuHLi6heAQvpBS28tJ03HmZhGEab50+tPAVA8UwJCaFmdsswlcR5ukZ1Hno7x41nrdqWKIvISTlTr1XX+3Ydl+p9qffpJs2e4+rzxMq9tqINaJbyZyt91m7AsRwiQgSAeeGr1aotm0W/fstEdtRG5lm+xKNMXUOGwkMQOMF2+5pHWWqmJNTRScojQ+i8qg8VvloIp8UavfjiZIlYK+jfdzox7fy9lrmhN/ukVe3PifgJZAoZAK1l9OjDiNq1lLIVKnlSmqHZ47KT6PH3IMQrIs7TK08DaK052AhUI+rJ5ScBtNa4LPH4oqGOHYs6Zp9dfdaVqlphIax5RprN89XMgkKNwIliL0Zptuc5zfFVm/J7a+d7tYLVPF/t/mzTz6ulzBJyUg4cw2mCle32dfa60xEX+lD751afA+BeqGO1f1M2Q4WvFiGejzsealIy4T2w4da7ijttxKinBqu5VSTzyaYbnOr7qhtKANqJTyugipQlm8oW2hS7iR0DuNnGeyehFynVeUgrOpaiGlHq59OM3DlW0daoXJx6UnYw5XOcZ5ytqsUwTMlYM0Pbb1ptHAKZpdl2SHnRIjO0YroLs5Q/W+kGvRS9TW0GL+zd3KTSPnU4PAyBte+RBZQ5gDhsf+tD7d2yMem8Mg8VvloE1avGyVATfd4rL2y49aeDThsxUV9US5L/5PKTWhJUNQdFo1HvS/U0iQpRcCzXlL5YQTXQkmLzRUSvYeeUu9nGe6dRPg+p0VCKalSpn08rjUv1uzwZP+lo3g5Ka6Gux9oc9ztbVQuwvmnVvKVbaF6Zwc4hkFmabcOWFy0ygyqYtvPBS1gIg2U2tpztfK9W0Ifmm6HZ495tKu4LHbzXkvZdEBHdtjHL26Pzqj5U+GoRXJnweg8rD2y4K3qguXC/D8w+AAAYCA40JQkqsHFf6UIaQOud9OkXV21T2SQR0WtYTXJLCGl7I8ZrbJqHVPgqQV9tCtg4LGkF1DVV7ftweNiRvB2U1kJdj92c4/qwWqNIstT2nohNCXVskg1bXrTIKJIsISG2f44vlmFL7MZWs3ndhoY6VkadzwvpBRxbO1byM0farxTq6IKwpj5/nK7WTnPnmYcKXy2CGw91VahIiSkcXj6stN9Mj6/ivT2x9IQrYop6bw/OPVjy72ZQfl+tZvAIrKDlRwLat6KMFawa+8vZZWSlLFiGxUh4xI2uUcooXwOo0VBK+brUSsZ1K/ed4hyb5rhHhK/FzCJEWQTP8BgKDTneJy+gfvbL2WWkxbRr71OQC5hNzZa8ZzOw8uxXow8AoMvn7KbYa+jnXqvZvG5jZQ0RZXFj3Lep12hvoFfzpPzD/B8AODvHJyLKYd50YtqVg4jy74Xm+Go+VPhqEdyoWBPgAxgMDgIADi0dAuANo0Hti9NiSnn7zXxQBPkgBoID2r9bcbHSiwTtWpXKCvoTJDPl7bX8BSHqmdIo3DZKWp1yIbCVjOvyTSRdozqTqC9acsoe9TsfCqKFKZnw1lBTBIxGRlsqzYEZYr6Ytqa6WdlxLjUHiUgQWAGDoUHX3qceVry91TET5INt/9ynwld1rAhfc8k5yESGn/OjP9jvVteaij4Xqxt7N7XthcwC5lPzjrdfnheVCl/NhwpfLYJqNDgdaqJOSpnIyr+buDlQFxu3+qK2p7Xf5ETN+vtrRU8T/QJLvSk2UL/XlJjSClIYQTWWmz0uOwm3jZJWR/958Czf1E2lWQROKAllbyXRjuIsJc9aj3h8qTZduwuy6v25KXzpP0t9HqlGo9pBZu5VHTOdkJunRPhqQZvXTazk+NJsxsiY43kLvUT53s3J/UbMF9MKixEQBPkgegO9jrW/6XDV4XGvryrMgOmIdcQuVPhqEdyK49ZPSidLxFphJDxSYrQ4fq9l7TV7I6TvTytuuKnwVRk/59c8Kc2c/DY7R0knUr7ppMZ4Kfo5PhpuPc8UvRFI16jORb+mekX46pR8jo3I86VFRDT50MhKMv/1vHI41oo2oFn0z9dOuF8zWMnx1e75vVTKbWInbWS9RxngvIjodqi9vqpwxBdpqvDfKtBPqAUoSYLq8KZYPymdLBFrBYETMBzaEN7cFPncaN8sbSV8UbGmBCvGfqdshLyEn/OXeDG14jx0E/1GpRXHJRXnKYD7z1oroY4ds2nVhf67hReKMwEb9zqZmDSc5kAVSzvh2UNDHatjSzxvc/tbv0ZGhIjjCeL1n5/T63F5qL2bBy90ThnDkvD1hS98Adu2bUMgEMBFF12E3//+9zWv/973voczzjgDgUAAZ599Nn76059a6mynspBeQEEuuBJqop/kXjDASvrj8GI+HB7WXELdaN8sJafQLehpou9zK1V7awRWjH2vGO+dhtveIK1Mq4vbrd5/ijPo7QqnN00A3bTWwooXlFnUtptth6hFi5Ji0vBYUMXSVrQBzUJDHatjKdSxQw5L9fN6PDrueFinm/vO8jbdGPdU+DKHaeHrO9/5Dq6//nrcdNNNeOSRR3DOOefgwIEDWFhYqHj9fffdhze+8Y1429vehoMHD+Kqq67CVVddhUOHDtnufKegj+N2OtSkROn2gAGmX4CcNmJ4lteq5flYHwZCA3Ve4S5t5fHV5g9es1gx9jvFiPEaJfOQGuMltPocV79Pp/N2UFoLGurYPBoZ6thsG1ZftMjo/XaUx1dxPWbAaHmVKAq2Qh09sHdzE7f3qW56fOnb5BgOIT7kePvqvKL2qzFMC1+f+tSn8I53vANvfetbceaZZ+LWW29FKBTCl770pYrXf+Yzn8HLX/5yfOhDH8KePXvw93//9zjvvPPw+c9/3nbnOwU3H+qe8/hq0AI0Fm1uElSg9T1N1D4HuAD6An1N7o23MGvs56QcFtILJa+lNAb18w7ywaaGenuRdgl1dOOUmNI6eC3UMS2msZxd3tS3dmQiohxgmq1ybAYvhY1q4Y7JSUPXd5TwVbzHqC/adPvba1DxvDqqJyXg7r4QcMdrVN3rxXwxV+wQ6vFlDt7Mxfl8Hg8//DBuuOEG7Wcsy+Lyyy/H/fffX/E1999/P66//vqSnx04cAC33XZb1ffJ5XLI5XLav+Nx4wtBK3D7kdvxg+d+YPh6tcSqG9V/+oP98LE+5OW8J04N1Ht0S0xR2/dCJaWB0ID22bfigqUa++1eUcYK6vh6bPExXP2zq+teL0qiVlGmx9/jdvcoOvRGCaUU/WfihTXTLGr/W7HvFOcYDg+DZVjIRHY11ORU4pSh9T4nKfZt1Bdt+3VnOKJ89jkphzf/7M0l6SacgBCCtdwaAG/M87HIGA4uHMSnH/40vvnUN+teP5lQBLJO8NagG/TqqN9/UkwaWkMIIZpI5oVx7yYBPoCB4AAWM4uu3Ku+TVfaLxbdcGuO03llDlPC19LSEiRJwtDQUMnPh4aG8PTTT1d8zdzcXMXr5+bmqr7Pxz72Mdx8881mutZSzKfncXDhoOnXndl3puN9YRkWZ/adiccWH8MZvWc43r5Z9vTt0f7vhpiyt38vfvDcD1z5LM3CMiz29O3BY4uPYSLWejmytsa2AlA+U0opO7p3wM/5kZWypub6mX1nUhGxwahrwbbYtuZ2xIOEhTD6g/3IFDLafG8l1O90bx9dozoZgRWwu2c3nll9xpUT/dHwqHaIZXa9b3fUz/6plafw+OLjrr3PlugWRHzND5/b27cXPz72Y0wnp7WiVEbYHtvuYq+8wbaubSX/p2wQ88XQG+jFSnbF1BqyLbYNIcH58DmvsbdvL+6eutuVNXMsMoZufzckIrniUabaH27ZUOp82t7V/muIEzDEhO/xzMwMxsbGcN999+Hiiy/Wfv7hD38Yv/nNb/Dggw9ueo3P58NXv/pVvPGNb9R+9i//8i+4+eabMT8/X/F9Knl8TUxMYH19HbFY6yuax9aP4djaMVOvifgieP7Q810pJ7+SXcFCesETwhcAPL3yNIZCQ+gJOO/5UpALOLR0CHv69sDP+R1v3ywr2RUsphexu3d3s7tiGkIIDi0dwmndpyEshJvdHc9xbO0Yjq0bn+cMGJw3dJ4r455Sm6eWn8JIeATdge5md8VzzCRnIMpiSwpf6np/Zt+Z8HG+ZneH0kSWMktYya5gV88uV9o3vd4zDC4YusCVZPteYymzhMcWHgOBO6GOAHB2/9kYCg/Vv9BlRFnEH+b+gLSYNvyaLn8Xzh86vyPC/55cfhJjkbGOGPdmmU5O46nlp0y95nkDz3O86JkXiefjmIxPunbQPpmYBAhcc0I4vHwY45FxV8a9KIs4vHQYe/v3dmy6jng8jq6uLkM6kSnhK5/PIxQK4fvf/z6uuuoq7efXXHMN1tbWcPvtt296zZYtW3D99dfjuuuu035200034bbbbsNjjz1m6H3N3BCFQqFQKBQKhUKhUCgUCqV9MaMTmTpe8Pl8OP/883HHHXdoP5NlGXfccUeJB5ieiy++uOR6APjVr35V9XoKhUKhUCgUCoVCoVAoFArFCUzl+AKA66+/Htdccw0uuOACXHjhhfj0pz+NVCqFt771rQCAq6++GmNjY/jYxz4GAHj/+9+PSy65BP/8z/+MV77ylfj2t7+Nhx56CP/2b//m7J1QKBQKhUKhUCgUCoVCoVAoOkwLX69//euxuLiIj3zkI5ibm8O+ffvw85//XEtgf+rUKbDshiPZ/v378c1vfhM33ngj/vZv/xY7d+7EbbfdhrPOOsu5u6BQKBQKhUKhUCgUCoVCoVDKMJXjq1nQHF8UCoVCoVAoFAqFQqFQKBTAxRxfFAqFQqFQKBQKhUKhUCgUSqtAhS8KhUKhUCgUCoVCoVAoFEpbQoUvCoVCoVAoFAqFQqFQKBRKW0KFLwqFQqFQKBQKhUKhUCgUSltChS8KhUKhUCgUCoVCoVAoFEpbQoUvCoVCoVAoFAqFQqFQKBRKW8I3uwNGIIQAUMpVUigUCoVCoVAoFAqFQqFQOhdVH1L1olq0hPCVSCQAABMTE03uCYVCoVAoFAqFQqFQKBQKxQskEgl0dXXVvIYhRuSxJiPLMmZmZhCNRsEwTLO7U5F4PI6JiQlMTk4iFos1uzsUCgA6LikUI9B5QvEidFxSvAgdlxSvQscmhVKbdpwjhBAkEgmMjo6CZWtn8WoJjy+WZTE+Pt7sbhgiFou1zUCitA90XFIo9aHzhOJF6LikeBE6LilehY5NCqU27TZH6nl6qdDk9hQKhUKhUCgUCoVCoVAolLaECl8UCoVCoVAoFAqFQqFQKJS2hApfDuH3+3HTTTfB7/c3uysUigYdlxRKfeg8oXgROi4pXoSOS4pXoWOTQqlNp8+RlkhuT6FQKBQKhUKhUCgUCoVCoZiFenxRKBQKhUKhUCgUCoVCoVDaEip8USgUCoVCoVAoFAqFQqFQ2hIqfFEoFAqFQqFQKBQKhUKhUNoSKnxRKBQKhUKhUCgUCoVCoVDakpYTvj72sY/h+c9/PqLRKAYHB3HVVVfhmWeeKbkmm83ive99L/r6+hCJRPC6170O8/Pz2u8fe+wxvPGNb8TExASCwSD27NmDz3zmMyVt/Pa3v8ULX/hC9PX1IRgM4owzzsAtt9xSt3+EEHzkIx/ByMgIgsEgLr/8cjz33HMl1/zDP/wD9u/fj1AohO7ubsP3/vjjj+OP/uiPEAgEMDExgU984hMlvz98+DBe97rXYdu2bWAYBp/+9KcNt02xR6eOy2w2i7e85S04++yzwfM8rrrqqk3X3H333WAYZtOfubk5Q+9BaR9afZ6cOHECb3vb27B9+3YEg0Hs2LEDN910E/L5fN227777bpx33nnw+/04/fTT8ZWvfKXk9/fccw/+5E/+BKOjo2AYBrfddlvdNinO0KnjcnZ2Fm9605uwa9cusCyL6667btM1X/nKVzat3YFAoG6fKfZp9XEJAK9+9auxZcsWBAIBjIyM4C//8i8xMzNTt226XnqbTh2bdM2kGKUd5ohKLpfDvn37wDAMHn300bpte3n9bjnh6ze/+Q3e+9734oEHHsCvfvUriKKIK664AqlUSrvmAx/4AP77v/8b3/ve9/Cb3/wGMzMzeO1rX6v9/uGHH8bg4CC+/vWv4/Dhw/i7v/s73HDDDfj85z+vXRMOh/G+970P99xzD5566inceOONuPHGG/Fv//ZvNfv3iU98Ap/97Gdx66234sEHH0Q4HMaBAweQzWa1a/L5PP7sz/4M7373uw3fdzwexxVXXIGtW7fi4Ycfxic/+Ul89KMfLelPOp3Gaaedho9//OMYHh423DbFPp06LiVJQjAYxF//9V/j8ssvr3ntM888g9nZWe3P4OCg4fehtAetPk+efvppyLKML37xizh8+DBuueUW3Hrrrfjbv/3bmu0eP34cr3zlK3HZZZfh0UcfxXXXXYe3v/3t+MUvfqFdk0qlcM455+ALX/iCqc+UYp9OHZe5XA4DAwO48cYbcc4551S9LhaLlazdJ0+erNkuxRlafVwCwGWXXYbvfve7eOaZZ/CDH/wAR48exZ/+6Z/WbJeul96nU8cmXTMpRmmHOaLy4Q9/GKOjo4bu2/PrN2lxFhYWCADym9/8hhBCyNraGhEEgXzve9/TrnnqqacIAHL//fdXbec973kPueyyy2q+12te8xry5je/uervZVkmw8PD5JOf/KT2s7W1NeL3+8m3vvWtTdd/+ctfJl1dXTXfU+Vf/uVfSE9PD8nlctrP/uZv/obs3r274vVbt24lt9xyi6G2Kc7TKeNSzzXXXEOuvPLKTT+/6667CACyurpquk1Ke9PK80TlE5/4BNm+fXvN9/7whz9M9u7dW/Kz17/+9eTAgQMVrwdAfvSjH9Vsk+IenTIu9VxyySXk/e9//6afW30eUJynHcbl7bffThiGIfl8vuo1dL1sPTplbOqhaybFDK06R37605+SM844gxw+fJgAIAcPHqz53l5fv1vO46uc9fV1AEBvby8ARR0VRbHE++SMM87Ali1bcP/999dsR22jEgcPHsR9992HSy65pOo1x48fx9zcXMl7d3V14aKLLqr53ka4//778eIXvxg+n0/72YEDB/DMM89gdXXVVtsU5+mUcWmGffv2YWRkBC972cvwu9/9rmHvS/Eu7TBP6r03oKzf5R6RBw4caOj8oxinU8alUZLJJLZu3YqJiQlceeWVOHz4sCPtUszR6uNyZWUF3/jGN7B//34IglC1bbpeth6dMjaNQtdMSjmtOEfm5+fxjne8A1/72tcQCoXq3yS8v363tPAlyzKuu+46vPCFL8RZZ50FAJibm4PP59uUo2hoaKhqTqH77rsP3/nOd/DOd75z0+/Gx8fh9/txwQUX4L3vfS/e/va3V+2P2v7Q0JDh9zbK3NxcxXb170vxBp00Lo0wMjKCW2+9FT/4wQ/wgx/8ABMTE7j00kvxyCOPuP7eFO/SDvPkyJEj+NznPod3vetdVdtV267UbjweRyaTqflaSmPppHFphN27d+NLX/oSbr/9dnz961+HLMvYv38/pqambLdNMU4rj8u/+Zu/QTgcRl9fH06dOoXbb7+95r3S9bK16KSxaQS6ZlLKacU5QgjBW97yFlx77bW44IILDN+r19fvlha+3vve9+LQoUP49re/bbmNQ4cO4corr8RNN92EK664YtPv7733Xjz00EO49dZb8elPfxrf+ta3AADf+MY3EIlEtD/33nuv5T6Us3fvXq3dV7ziFY61S2kMdFyWsnv3brzrXe/C+eefj/379+NLX/oS9u/fbyj5IqV9afV5Mj09jZe//OX4sz/7M7zjHe/Qfq5v99prr7V8b5TmQMdlKRdffDGuvvpq7Nu3D5dccgl++MMfYmBgAF/84hdN941inVYelx/60Idw8OBB/PKXvwTHcbj66quhRLjQ9bIdoGOzFLpmUsppxTnyuc99DolEAjfccEPVa1px/eab3QGrvO9978OPf/xj3HPPPRgfH9d+Pjw8jHw+j7W1tRIVdX5+flPC9yeffBIvfelL8c53vhM33nhjxffZvn07AODss8/G/Pw8PvrRj+KNb3wjXv3qV+Oiiy7SrhsbG8Ps7Kz2XiMjIyXvvW/fPsP39tOf/hSiKAIAgsGgdl/6Sg9qu+rvKN6g08alVS688EL89re/tdUGpXVp9XkyMzODyy67DPv379+UQFRf8SYWi2n3VWn9jsVitucSxTk6bVxaQRAEnHvuuThy5IjlNijmaPVx2d/fj/7+fuzatQt79uzBxMQEHnjgAVx88cV0vWxxOm1sWoGumZ1Nq86RO++8E/fffz/8fn/J+1xwwQX4i7/4C3z1q19tzfW7YdnEHEKWZfLe976XjI6OkmeffXbT79Vkcd///ve1nz399NObksUdOnSIDA4Okg996EOG3/vmm28mW7durdm34eFh8k//9E/az9bX1x1Nbq9PunjDDTfQ5PYeoVPHpZ5qye0rcfnll5PXvOY1pt+D0tq0wzyZmpoiO3fuJG94wxtIoVAw9N4f/vCHyVlnnVXysze+8Y2eSfbZ6XTquNRTLVFzOYVCgezevZt84AMfMP0eFHO0w7gs5+TJkwQAueuuu6peQ9dL79OpY1MPXTMptWj1OXLy5EnyxBNPaH9+8YtfEADk+9//PpmcnKzattfX75YTvt797neTrq4ucvfdd5PZ2VntTzqd1q659tpryZYtW8idd95JHnroIXLxxReTiy++WPv9E088QQYGBsib3/zmkjYWFha0az7/+c+T//qv/yLPPvssefbZZ8l//Md/kGg0Sv7u7/6uZv8+/vGPk+7ubnL77beTxx9/nFx55ZVk+/btJJPJaNecPHmSHDx4kNx8880kEomQgwcPkoMHD5JEIlG13bW1NTI0NET+8i//khw6dIh8+9vfJqFQiHzxi1/UrsnlclpbIyMj5IMf/CA5ePAgee6550x9xhTzdOq4JISQw4cPk4MHD5I/+ZM/IZdeeqn2OpVbbrmF3HbbbeS5554jTzzxBHn/+99PWJYlv/71r41+vJQ2odXnydTUFDn99NPJS1/6UjI1NVXy/rU4duwYCYVC5EMf+hB56qmnyBe+8AXCcRz5+c9/rl2TSCS0uQOAfOpTnyIHDx4kJ0+eNPUZU8zTqeOSEKKNufPPP5+86U1vIgcPHiSHDx/Wfn/zzTeTX/ziF+To0aPk4YcfJm94wxtIIBAouYbiDq0+Lh944AHyuc99jhw8eJCcOHGC3HHHHWT//v1kx44dJJvNVm2Xrpfep1PHJiF0zaQYo9XnSDnHjx83VNXR6+t3ywlfACr++fKXv6xdk8lkyHve8x7S09NDQqEQec1rXlNiAN50000V29Cro5/97GfJ3r17SSgUIrFYjJx77rnkX/7lX4gkSTX7J8sy+V//63+RoaEh4vf7yUtf+lLyzDPPlFxzzTXXVHz/eqcMjz32GHnRi15E/H4/GRsbIx//+MdLfq8OyvI/l1xySc12Kfbp5HG5devWiq9T+cd//EeyY8cOEggESG9vL7n00kvJnXfeWf9DpbQdrT5PvvzlL1e9h3rcddddZN++fcTn85HTTjut5J7V31dq95prrqnbNsUenTwu6/X5uuuuI1u2bCE+n48MDQ2RP/7jPyaPPPJI/Q+VYptWH5ePP/44ueyyy0hvby/x+/1k27Zt5NprryVTU1N1752ul96mk8cmXTMpRmj1OVKOUeGLEG+v3wwhxSx+FAqFQqFQKBQKhUKhUCgUShvR0lUdKRQKhUKhUCgUCoVCoVAolGpQ4YtCoVAoFAqFQqFQKBQKhdKWUOGLQqFQKBQKhUKhUCgUCoXSllDhi0KhUCgUCoVCoVAoFAqF0pZQ4YtCoVAoFAqFQqFQKBQKhdKWUOGLQqFQKBQKhUKhUCgUCoXSllDhi0KhUCgUCoVCoVAoFAqF0pZQ4YtCoVAoFAqFQqFQKBQKhdKWUOGLQqFQKBQKhUKhUCgUCoXSllDhi0KhUCgUCoVCoVAoFAqF0pZQ4YtCoVAoFAqFQqFQKBQKhdKWUOGLQqFQKBQKhUKhUCgUCoXSlvz/qxP4+keiVTgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_id :  23641\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product_id :  28305\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist_forecast(\n",
    "    historical, forecast, actual, hist_start='', hist_end='', title=''\n",
    "):\n",
    "    if hist_start != '':\n",
    "        historical = historical[\n",
    "            historical['date'] >= pd.to_datetime(hist_start, format='%Y-%m-%d')\n",
    "        ].copy()\n",
    "    if hist_end != '':\n",
    "        historical = historical[\n",
    "            historical['date'] <= pd.to_datetime(hist_end, format='%Y-%m-%d')\n",
    "        ].copy()\n",
    "\n",
    "    plt.figure(figsize=(15, 4))\n",
    "    plt.plot(historical['date'], historical['sales_count'], label='historical')\n",
    "    # Plot the forecast data\n",
    "    plt.plot(\n",
    "        forecast['forecast_timestamp'],\n",
    "        forecast['forecast_value_rnd'],\n",
    "        label='forecast',\n",
    "        linestyle='--',\n",
    "    )\n",
    "    # Plot the actual data\n",
    "    plt.plot(actual['date'], actual['sales_count'], label='actual')\n",
    "    # plot the confidence interval\n",
    "    confidence_level = forecast['confidence_level'].iloc[0] * 100\n",
    "    low_CI = forecast['confidence_interval_lower_bound_rnd']\n",
    "    upper_CI = forecast['confidence_interval_upper_bound_rnd']\n",
    "\n",
    "    # Shade the confidence interval\n",
    "    plt.fill_between(\n",
    "        forecast['forecast_timestamp'],\n",
    "        low_CI,\n",
    "        upper_CI,\n",
    "        color='#539caf',\n",
    "        alpha=0.4,\n",
    "        label=f'{confidence_level} confidence interval',\n",
    "    )\n",
    "    plt.legend()\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "\n",
    "product_id_list = dfforecast['product_id'].unique()\n",
    "for i in product_id_list:\n",
    "    print('Product_id : ', i)\n",
    "    plot_hist_forecast(\n",
    "        df_historical[df_historical['product_id'] == i],\n",
    "        dfforecast[dfforecast['product_id'] == i],\n",
    "        df_actual_90d[df_actual_90d['product_id'] == i],\n",
    "        hist_start='2023-02-01',\n",
    "        title=i,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611ea679-0206-4303-8a53-326f65a8b8bc",
   "metadata": {
    "id": "76b5f6fab0b8"
   },
   "source": [
    "While most of the predictions are looking decent, you can also see that the actual ranges fall into the 90% confidence interval suggested by the model. Under the hood, BigQuery ML performs many computationally expensive tasks even considering the seasonal and holiday information.\n",
    "<img src=\"https://cloud.google.com/bigquery-ml/images/BQ_ARIMA_diagram.png\"> <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series\"> Source</a> </img>\n",
    "\n",
    "The coefficients learned by BigQuery ML's ARIMA model can also be checked by querying for <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-arima-coefficients\">ARIMA_COEFFICIENTS</a> from the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "70d0bbe6-8f73-4915-96be-2d187bdd91b7",
   "metadata": {
    "id": "owAuWLkAp94U"
   },
   "outputs": [],
   "source": [
    "query = f'''\n",
    "SELECT\n",
    "  *\n",
    "FROM \n",
    "  ML.ARIMA_COEFFICIENTS(MODEL {dataset_id}.arima_model)\n",
    "'''\n",
    "query_job = bq_client.query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "84b6e472-5396-4a92-9e38-4f3b5bfb642f",
   "metadata": {
    "id": "l3xmi8bOp94U"
   },
   "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>product_id</th>\n",
       "      <th>ar_coefficients</th>\n",
       "      <th>ma_coefficients</th>\n",
       "      <th>intercept_or_drift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13596</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20547</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20552</td>\n",
       "      <td>[]</td>\n",
       "      <td>[0.1090983535507289]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23641</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28305</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id ar_coefficients       ma_coefficients  intercept_or_drift\n",
       "0       13596              []                    []            0.000000\n",
       "1       20547              []                    []            0.000653\n",
       "2       20552              []  [0.1090983535507289]            0.000000\n",
       "3       23641              []                    []            0.000000\n",
       "4       28305              []                    []            0.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c12407f-08e2-46e2-8474-128aa310a902",
   "metadata": {
    "id": "f37aa9ddfb8f"
   },
   "source": [
    "In the above results, \n",
    "- The <b>product_id</b> column represents the index column that we've specified while training the ARIMA model.\n",
    "- The <b>ar_coefficients</b> column corresponds to the autoregressive coefficients in the ARIMA algorithm (non-seasonal p).\n",
    "- <b>ma_coefficients</b> refers to the moving average coefficients in the ARIMA algorithm (non-seasonal q).\n",
    "- <b>intercept_or_drift</b> is the constant term in the ARIMA algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d480c839-933d-42c2-959e-b2199dbf72a2",
   "metadata": {
    "id": "27bf1ef88ea7"
   },
   "source": [
    "## Evaluating the model results using BigQuery ML \n",
    "\n",
    "BigQuery ML also provides the <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate\">ML.EVALUATE</a> function for checking the evaluation metrics of the trained model. For the ARIMA model, you can see the model being evaluated on `log_likelihood`, `AIC` and `variance`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "49f335b9-0975-4bed-a988-6ad15d1303f9",
   "metadata": {
    "id": "CtM-g5ixp94V"
   },
   "outputs": [],
   "source": [
    "query = f'''\n",
    "SELECT\n",
    "  *\n",
    "FROM\n",
    "  ML.EVALUATE(MODEL {dataset_id}.arima_model)\n",
    "'''\n",
    "query_job = bq_client.query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "395a97a9-170b-4400-b0cd-99295075354c",
   "metadata": {
    "id": "JQwOWcgMp94V"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>product_id</th>\n",
       "      <th>non_seasonal_p</th>\n",
       "      <th>non_seasonal_d</th>\n",
       "      <th>non_seasonal_q</th>\n",
       "      <th>has_drift</th>\n",
       "      <th>log_likelihood</th>\n",
       "      <th>AIC</th>\n",
       "      <th>variance</th>\n",
       "      <th>seasonal_periods</th>\n",
       "      <th>has_holiday_effect</th>\n",
       "      <th>has_spikes_and_dips</th>\n",
       "      <th>has_step_changes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13596</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2691.656780</td>\n",
       "      <td>-5381.313560</td>\n",
       "      <td>2.211079e-08</td>\n",
       "      <td>[WEEKLY, MONTHLY, QUARTERLY]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>1958.712420</td>\n",
       "      <td>-3913.424841</td>\n",
       "      <td>1.277574e-06</td>\n",
       "      <td>[WEEKLY, MONTHLY, QUARTERLY]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20552</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>2507.187216</td>\n",
       "      <td>-5010.374432</td>\n",
       "      <td>6.092246e-08</td>\n",
       "      <td>[WEEKLY, MONTHLY, QUARTERLY]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>23641</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2771.032261</td>\n",
       "      <td>-5540.064522</td>\n",
       "      <td>1.429539e-08</td>\n",
       "      <td>[WEEKLY, MONTHLY, QUARTERLY]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>28305</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2757.135374</td>\n",
       "      <td>-5512.270749</td>\n",
       "      <td>1.542969e-08</td>\n",
       "      <td>[WEEKLY, MONTHLY, QUARTERLY]</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  non_seasonal_p  non_seasonal_d  non_seasonal_q  has_drift  \\\n",
       "0       13596               0               2               0      False   \n",
       "1       20547               0               1               0       True   \n",
       "2       20552               0               2               1      False   \n",
       "3       23641               0               2               0      False   \n",
       "4       28305               0               2               0      False   \n",
       "\n",
       "   log_likelihood          AIC      variance              seasonal_periods  \\\n",
       "0     2691.656780 -5381.313560  2.211079e-08  [WEEKLY, MONTHLY, QUARTERLY]   \n",
       "1     1958.712420 -3913.424841  1.277574e-06  [WEEKLY, MONTHLY, QUARTERLY]   \n",
       "2     2507.187216 -5010.374432  6.092246e-08  [WEEKLY, MONTHLY, QUARTERLY]   \n",
       "3     2771.032261 -5540.064522  1.429539e-08  [WEEKLY, MONTHLY, QUARTERLY]   \n",
       "4     2757.135374 -5512.270749  1.542969e-08  [WEEKLY, MONTHLY, QUARTERLY]   \n",
       "\n",
       "   has_holiday_effect  has_spikes_and_dips  has_step_changes  \n",
       "0               False                 True             False  \n",
       "1               False                 True              True  \n",
       "2               False                 True             False  \n",
       "3               False                 True             False  \n",
       "4               False                 True             False  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_job.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdafae56-e2b7-4473-b26a-28d4f45e8fdf",
   "metadata": {},
   "source": [
    "## Evaluating the model results - MAE, MAPE, MSE, RMSE (on Test data)\n",
    "\n",
    "Generally, to evaluate the forecast model, you can choose metrics depending on how you want to evaluate. For starters, you can choose from the following:\n",
    "\n",
    "- **Mean Absolute Error (MAE)**: Average of the absolute differences between the actual values and the forecasted values.\n",
    "- **Mean Absolute Percentage Error (MAPE)**: Average of the percentages of absolute difference between the actual and forecasted values to the actual values.\n",
    "- **Mean Squared Error (MSE)**: Average of squared differences between the actual and forecasted values.\n",
    "- **Root Mean Squared Error (RMSE)**: Root of MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cd722d4e-0aa3-41e5-9423-4eec4b41bdd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_actual_90d.sort_values(by=['product_id', 'date'], inplace=True)\n",
    "df_actual_90d.reset_index(drop=True, inplace=True)\n",
    "dfforecast.sort_values(by=['product_id', 'forecast_timestamp'], inplace=True)\n",
    "dfforecast.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e8c83e1-caa9-47b6-82c5-40a9df83e603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "\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>product_id</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13596</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>9.507599e+14</td>\n",
       "      <td>1.233333</td>\n",
       "      <td>1.110555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20547</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>1.651320e+15</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.788811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20552</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>2.401920e+15</td>\n",
       "      <td>1.644444</td>\n",
       "      <td>1.282359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23641</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>7.505999e+14</td>\n",
       "      <td>0.644444</td>\n",
       "      <td>0.802773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28305</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>2.401920e+15</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.154701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id       MAE          MAPE       MSE      RMSE\n",
       "0       13596  0.700000  9.507599e+14  1.233333  1.110555\n",
       "1       20547  0.577778  1.651320e+15  0.622222  0.788811\n",
       "2       20552  0.977778  2.401920e+15  1.644444  1.282359\n",
       "3       23641  0.488889  7.505999e+14  0.644444  0.802773\n",
       "4       28305  0.888889  2.401920e+15  1.333333  1.154701"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors = {'product_id': [], 'MAE': [], 'MAPE': [], 'MSE': [], 'RMSE': []}\n",
    "for i in product_id_list:\n",
    "    mae = mean_absolute_error(\n",
    "        df_actual_90d[df_actual_90d['product_id'] == i]['sales_count'],\n",
    "        dfforecast[dfforecast['product_id'] == i]['forecast_value_rnd'],\n",
    "    )\n",
    "    mape = mean_absolute_percentage_error(\n",
    "        df_actual_90d[df_actual_90d['product_id'] == i]['sales_count'],\n",
    "        dfforecast[dfforecast['product_id'] == i]['forecast_value_rnd'],\n",
    "    )\n",
    "\n",
    "    mse = mean_squared_error(\n",
    "        df_actual_90d[df_actual_90d['product_id'] == i]['sales_count'],\n",
    "        dfforecast[dfforecast['product_id'] == i]['forecast_value_rnd'],\n",
    "        squared=True,\n",
    "    )\n",
    "\n",
    "    rmse = mean_squared_error(\n",
    "        df_actual_90d[df_actual_90d['product_id'] == i]['sales_count'],\n",
    "        dfforecast[dfforecast['product_id'] == i]['forecast_value_rnd'],\n",
    "        squared=False,\n",
    "    )\n",
    "\n",
    "    errors['product_id'].append(i)\n",
    "    errors['MAE'].append(mae)\n",
    "    errors['MAPE'].append(mape)\n",
    "    errors['MSE'].append(mse)\n",
    "    errors['RMSE'].append(rmse)\n",
    "errors = pd.DataFrame(errors)\n",
    "errors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460acdf5-2fb2-4cce-ac3f-93dce3efd907",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Part 2 : Automating pipeline creation with AutoMLOps<a id='automlops'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e69055-9e3d-4269-87dd-1825e0004d7d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Import AutoMLOps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a240118d-6e9d-494a-a0e7-83d67360c286",
   "metadata": {},
   "outputs": [],
   "source": [
    "from google_cloud_automlops import AutoMLOps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ad9f17-874f-4845-81ba-d2419d3cf5e4",
   "metadata": {},
   "source": [
    "## Other Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1041dd30-240b-4cd8-b66f-8eba8f8f9724",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install 'kfp>=2.0.0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fb7e69f1-6129-44cd-91b5-5c840f403334",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kfp.dsl import Artifact, Dataset, Metrics, Output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2fc9f06-de7c-4593-afe6-9a359152cc8c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define an AutoMLOps component for Preparing Sales Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2a302583-85a8-4687-bd9a-578f8c955eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@AutoMLOps.component\n",
    "def prepare_sales_table(\n",
    "    dataset_id: str,\n",
    "    project_id: str,\n",
    "    sales_table: str\n",
    "):\n",
    "    \"\"\"Custom component that prepares the sales table.\n",
    "\n",
    "    Args:\n",
    "        dataset_id: Destination BigQuery dataset.\n",
    "        project_id: The project ID.\n",
    "        sales_table: Source BigQuery table.\n",
    "    \"\"\"\n",
    "    import datetime\n",
    "    \n",
    "    import pandas as pd\n",
    "    from google.cloud import bigquery\n",
    "    bq_client = bigquery.Client(project=project_id)\n",
    "\n",
    "    def merge_dataframes(base_df, dates_df, product_id):\n",
    "        ''' Zero-fill dates with no product purchases'''\n",
    "        dataframe = (\n",
    "            pd.merge(\n",
    "                base_df.loc[base_df['product_id'] == product_id],\n",
    "                dates_df,\n",
    "                left_on='date',\n",
    "                right_on=0,\n",
    "                how='outer',\n",
    "            )\n",
    "            .sort_values(by=['date'])\n",
    "            .drop(columns=0)\n",
    "        )  # merging dates dataframe with product_id matching rows\n",
    "        dataframe['product_id'] = product_id  # product_id will be null so making it the specified values\n",
    "        dataframe.reset_index(inplace=True, drop=True)  # making index to start from 0\n",
    "        dataframe = dataframe.fillna(0)  # for sales_count making null values as 0\n",
    "        dataframe['sales_count'] = dataframe['sales_count'].astype(\n",
    "            'int'\n",
    "        )\n",
    "        return dataframe\n",
    "\n",
    "    query = f'''\n",
    "    SELECT * FROM {dataset_id}.top_five_products\n",
    "    '''\n",
    "    query_job = bq_client.query(query)\n",
    "    df = query_job.to_dataframe()\n",
    "    \n",
    "    df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "    end_time = datetime.datetime.now().strftime('%Y-%m-%d')\n",
    "    start_time = '2018-08-14' # min date\n",
    "    dates = pd.date_range(start=start_time, end=end_time).to_frame()\n",
    "    \n",
    "    product_dataframes = []\n",
    "    for product_id in df['product_id'].unique():\n",
    "        product_dataframes.append(merge_dataframes(df, dates, product_id))\n",
    "    \n",
    "    full_df = pd.concat(product_dataframes)\n",
    "    full_df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "    job_config = bigquery.LoadJobConfig(\n",
    "        schema=[\n",
    "            bigquery.SchemaField('product_id', bigquery.enums.SqlTypeNames.INTEGER),\n",
    "            bigquery.SchemaField('date', bigquery.enums.SqlTypeNames.DATE),\n",
    "            bigquery.SchemaField('sales_count', bigquery.enums.SqlTypeNames.INTEGER),\n",
    "        ],\n",
    "        write_disposition='WRITE_TRUNCATE'\n",
    "    )\n",
    "    # save the dataframe to a table in the created dataset\n",
    "    job = bq_client.load_table_from_dataframe(\n",
    "        full_df,\n",
    "        f'{project_id}.{dataset_id}.{sales_table}',\n",
    "        job_config=job_config)\n",
    "    job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2808b2e9-a296-4fbb-950d-0a958dd1faaa",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define an AutoMLOps component for Creating Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c896ce62-1108-47d0-a9dd-46c74f9f9b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "@AutoMLOps.component\n",
    "def create_training_dataset(\n",
    "    dataset_id: str,\n",
    "    project_id: str,\n",
    "    sales_table: str,\n",
    "    year_range: int\n",
    "):\n",
    "    \"\"\"Custom component that creates training data, ranging from today to N year(s) ago.\n",
    "\n",
    "    Args:\n",
    "        dataset_id: Destination BigQuery dataset.\n",
    "        project_id: The project ID.\n",
    "        sales_table: Source BigQuery table.\n",
    "        year_range: Number of years to train the model on.\n",
    "    \"\"\"\n",
    "    from google.cloud import bigquery\n",
    "    bq_client = bigquery.Client(project=project_id)\n",
    "\n",
    "    query = f'''\n",
    "    CREATE OR REPLACE TABLE {project_id}.{dataset_id}.training_data AS (\n",
    "        SELECT\n",
    "            *\n",
    "        FROM\n",
    "            `{dataset_id}.{sales_table}`\n",
    "        WHERE\n",
    "            date BETWEEN DATE_ADD(CURRENT_DATE(), INTERVAL -{year_range} YEAR) AND CURRENT_DATE()\n",
    "            );\n",
    "    '''\n",
    "    query_job = bq_client.query(query)\n",
    "    print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d1f8ef2-4e18-46a4-a891-dc27c889b6b7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define an AutoMLOps component for Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "29a7bace-47be-4321-a4a0-708792381fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "@AutoMLOps.component\n",
    "def train_model(\n",
    "    dataset_id: str,\n",
    "    model_name: str,\n",
    "    model_type: str,\n",
    "    project_id: str\n",
    "):\n",
    "    \"\"\"Train an ARIMA model on the training dataset.\n",
    "\n",
    "    Args:\n",
    "        dataset_id: Destination BigQuery dataset.\n",
    "        model_name: Name for the model.\n",
    "        model_type: The column which are the labels.\n",
    "        project_id: The project ID.\n",
    "    \"\"\"\n",
    "    from google.cloud import bigquery\n",
    "    bq_client = bigquery.Client(project=project_id)\n",
    "\n",
    "    query = f'''\n",
    "    CREATE OR REPLACE MODEL `{project_id}.{dataset_id}.{model_name}`\n",
    "\n",
    "    OPTIONS(\n",
    "      MODEL_TYPE='{model_type}',\n",
    "      TIME_SERIES_TIMESTAMP_COL='date',\n",
    "      TIME_SERIES_DATA_COL='sales_count',\n",
    "      TIME_SERIES_ID_COL='product_id',\n",
    "      SEASONALITIES=['DAILY','WEEKLY','MONTHLY','QUARTERLY','YEARLY'],\n",
    "      DATA_FREQUENCY='DAILY',\n",
    "      model_registry='vertex_ai') AS\n",
    "\n",
    "    SELECT\n",
    "        date,\n",
    "        product_id,\n",
    "        sales_count\n",
    "    FROM\n",
    "      `{dataset_id}.training_data`\n",
    "    '''\n",
    "    query_job = bq_client.query(query)\n",
    "    print(query_job.result())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db829e3-8e97-4ad5-af08-f15f05d047d6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define a Component for Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "85d4cb64-8645-49d5-9b97-1ebe489b0814",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "@AutoMLOps.component(\n",
    "    packages_to_install=[\n",
    "        'google-cloud-bigquery', \n",
    "        'pandas',\n",
    "        'pyarrow',\n",
    "        'db_dtypes'\n",
    "    ]\n",
    ")\n",
    "def evaluate_model(\n",
    "    dataset_id: str,\n",
    "    metrics: Output[Metrics],\n",
    "    model_name: str,\n",
    "    project_id: str\n",
    "):\n",
    "    \"\"\"Evaluate the trained ARIMA model.\n",
    "\n",
    "    Args:\n",
    "        dataset_id: Destination BigQuery dataset.\n",
    "        model_name: Name for the model.\n",
    "        project_id: The project ID.\n",
    "    \"\"\"\n",
    "    from google.cloud import bigquery\n",
    "    bq_client = bigquery.Client(project=project_id)\n",
    "\n",
    "    query = f'''\n",
    "    SELECT\n",
    "      *\n",
    "    FROM\n",
    "      ML.EVALUATE(MODEL {dataset_id}.{model_name})\n",
    "    '''\n",
    "    query_job = bq_client.query(query)\n",
    "    results = query_job.result().to_dataframe()\n",
    "\n",
    "    for _, row in results.iterrows():\n",
    "        for m in ['log_likelihood', 'AIC', 'variance']:\n",
    "            metrics.log_metric(f'''Product id: {row['product_id']} | Metric: {m}''', row[m])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962468ae-c9a8-4df4-8739-b0c5008904cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define a Component for Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a7564a4c-ea69-4023-91eb-68f5a57d6899",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "@AutoMLOps.component(\n",
    "    packages_to_install=[\n",
    "        'google-cloud-bigquery'\n",
    "    ]\n",
    ")\n",
    "def forecast(\n",
    "    bq_forecast_table: Output[Dataset],\n",
    "    confidence_lvl: float,\n",
    "    dashboard: Output[Artifact],\n",
    "    dataset_id: str,\n",
    "    forecast_horizon: int,\n",
    "    project_id: str\n",
    "):\n",
    "    \"\"\"Custom component that runs a forecast for a given horizon period.\n",
    "\n",
    "    Args:\n",
    "        bq_forecast_table: Full uri of the BQ table forecast results.\n",
    "        confidence_lvl: Required confidence level.\n",
    "        dashboard: URL of the Looker dashboard.\n",
    "        dataset_id: Destination BigQuery dataset.\n",
    "        forecast_horizon: Number of days forward to forecast.\n",
    "        project_id: The project ID.\n",
    "    \"\"\"\n",
    "    from google.cloud import bigquery\n",
    "    bq_client = bigquery.Client(project=project_id)\n",
    "\n",
    "    query = f'''DECLARE HORIZON STRING DEFAULT \"{forecast_horizon}\";\n",
    "    DECLARE CONFIDENCE_LEVEL STRING DEFAULT \"{confidence_lvl}\";\n",
    "\n",
    "    EXECUTE IMMEDIATE format(\"\"\"\n",
    "        CREATE OR REPLACE VIEW {dataset_id}.arima_forecast AS (\n",
    "        SELECT\n",
    "          * , \n",
    "          ROUND(forecast_value) AS forecast_value_rnd,\n",
    "          ROUND(forecast_value) - standard_error AS prediction_interval_lower_bound_rnd,\n",
    "          ROUND(forecast_value) - standard_error AS confidence_interval_lower_bound_rnd,\n",
    "          ROUND(forecast_value) + standard_error AS prediction_interval_upper_bound_rnd,\n",
    "          ROUND(forecast_value) + standard_error AS confidence_interval_upper_bound_rnd\n",
    "        FROM\n",
    "          ML.FORECAST(MODEL {dataset_id}.arima_model,\n",
    "                      STRUCT(%s AS horizon,\n",
    "                             %s AS confidence_level)\n",
    "                     )\n",
    "        )\n",
    "        \"\"\",HORIZON,CONFIDENCE_LEVEL)'''\n",
    "    job = bq_client.query(query)\n",
    "    bq_forecast_table.uri = f'bq://{project_id}.{dataset_id}.arima_forecast'\n",
    "    dashboard.uri = 'https://lookerstudio.google.com/reporting/526d02a9-37f5-404a-87a4-148343e1cc49'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bdafcf-dafe-461b-a161-b1db0ea1062c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define an AutoMLOps pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "26726ec7-3021-438e-9d60-000cd89483b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "@AutoMLOps.pipeline(\n",
    "    name='bqml-automlops-retail-forecasting',\n",
    "    description='This is an example of retail demand forecasting using AutoMLOps and BQML.')\n",
    "def pipeline(confidence_lvl: float,\n",
    "             dataset_id: str,\n",
    "             forecast_horizon: int,\n",
    "             machine_type: str,\n",
    "             model_name: str,\n",
    "             model_type: str,\n",
    "             project_id: str,\n",
    "             sales_table: str,\n",
    "             year_range: int):\n",
    "    \n",
    "    prepare_sales_table_task = prepare_sales_table(\n",
    "        dataset_id=dataset_id,\n",
    "        project_id=project_id,\n",
    "        sales_table=sales_table)        \n",
    "\n",
    "    create_training_dataset_task = create_training_dataset(\n",
    "        dataset_id=dataset_id,\n",
    "        project_id=project_id,\n",
    "        sales_table=sales_table,\n",
    "        year_range=year_range).after(prepare_sales_table_task)\n",
    "\n",
    "    train_model_task = train_model(\n",
    "        dataset_id=dataset_id,\n",
    "        model_name=model_name,\n",
    "        model_type=model_type,\n",
    "        project_id=project_id).after(create_training_dataset_task)\n",
    "\n",
    "    evaluate_model_task = evaluate_model(\n",
    "        dataset_id=dataset_id,\n",
    "        model_name=model_name,\n",
    "        project_id=project_id).after(train_model_task)\n",
    "    \n",
    "    forecast_task = forecast(\n",
    "        confidence_lvl=confidence_lvl,\n",
    "        dataset_id=dataset_id,\n",
    "        forecast_horizon=forecast_horizon,\n",
    "        project_id=project_id).after(evaluate_model_task)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3874f4f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define the Pipeline Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4cb3786b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_params = {\n",
    "    'confidence_lvl': 0.90,\n",
    "    'dataset_id': dataset_id,\n",
    "    'forecast_horizon': 90,\n",
    "    'machine_type': 'n1-standard-4',\n",
    "    'model_name': 'arima_model',\n",
    "    'model_type': 'ARIMA_PLUS',\n",
    "    'project_id': PROJECT_ID,\n",
    "    'sales_table': SALES_TABLE,\n",
    "    'year_range': 1\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e0aa05",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Generate and Run the pipeline\n",
    "`AutoMLOps.generate(...)` generates the MLOps codebase. Users can specify the tooling and technologies they would like to use in their MLOps pipeline. If you are interested in integrating with Github and Github Actions, please follow the setup steps in [this doc](../../docs/Using%20Github%20With%20AMO.md) and uncomment the relevant code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fb26b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing directories under AutoMLOps/\n",
      "Writing configurations to AutoMLOps/configs/defaults.yaml\n",
      "Writing Kubeflow Pipelines code to AutoMLOps/pipelines, AutoMLOps/components, AutoMLOps/services\n",
      "Writing README.md to AutoMLOps/README.md\n",
      "Writing scripts to AutoMLOps/scripts\n",
      "Writing CloudBuild config to AutoMLOps/cloudbuild.yaml\n",
      "Code Generation Complete.\n"
     ]
    }
   ],
   "source": [
    "# Setup using local scripts and cloudbuild:\n",
    "AutoMLOps.generate(project_id=PROJECT_ID,\n",
    "                   pipeline_params=pipeline_params,\n",
    "                   use_ci=False,\n",
    "                   naming_prefix=MODEL_ID,\n",
    "                   deployment_framework='cloud-build',\n",
    ")\n",
    "\n",
    "# # Setup using Github, Github Actions, and Terraform:\n",
    "# AutoMLOps.generate(project_id=PROJECT_ID,\n",
    "#                    pipeline_params=pipeline_params,\n",
    "#                    naming_prefix=MODEL_ID,\n",
    "#                    schedule_pattern='59 11 * * 0', # retrain every Sunday at Midnight\n",
    "#                    use_ci=True,\n",
    "#                    deployment_framework='github-actions',\n",
    "#                    provisioning_framework='terraform',   \n",
    "#                    source_repo_type='github',\n",
    "#                    project_number='<project_number>',\n",
    "#                    source_repo_name='<source/repo/string>',\n",
    "#                    workload_identity_pool='<identity_pool_string>',\n",
    "#                    workload_identity_provider='<identity_provider_string>',\n",
    "#                    workload_identity_service_account='<workload_identity_sa>'\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f8f2dfb",
   "metadata": {},
   "source": [
    "`AutoMLOps.provision(...)` runs provisioning scripts to create and maintain necessary infra for MLOps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cb0fc2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Provisioning requires these permissions:\n",
      "-cloudfunctions.functions.get\n",
      "-serviceusage.services.use\n",
      "-serviceusage.services.enable\n",
      "-cloudfunctions.functions.create\n",
      "-pubsub.subscriptions.list\n",
      "-cloudscheduler.jobs.list\n",
      "-pubsub.topics.create\n",
      "-source.repos.list\n",
      "-artifactregistry.repositories.create\n",
      "-resourcemanager.projects.setIamPolicy\n",
      "-iam.serviceAccounts.listiam.serviceAccounts.create\n",
      "-pubsub.subscriptions.create\n",
      "-cloudscheduler.jobs.create\n",
      "-storage.buckets.create\n",
      "-source.repos.create\n",
      "-artifactregistry.repositories.list\n",
      "-cloudbuild.builds.create\n",
      "-cloudbuild.builds.list\n",
      "-pubsub.topics.list\n",
      "-storage.buckets.get\n",
      "\n",
      "You are currently using: srastatter@google.com. Please check your account permissions.\n",
      "The following are the recommended roles for provisioning:\n",
      "-roles/resourcemanager.projectIamAdmin\n",
      "-roles/cloudfunctions.admin\n",
      "-roles/artifactregistry.admin\n",
      "-roles/iam.serviceAccountAdmin\n",
      "-roles/serviceusage.serviceUsageAdmin\n",
      "-roles/aiplatform.serviceAgent\n",
      "-roles/cloudscheduler.admin\n",
      "-roles/pubsub.editor\n",
      "-roles/source.admin\n",
      "-roles/cloudbuild.builds.editor\n",
      "\n",
      "\u001b[0;32m Setting up API services in project automlops-sandbox \u001b[0m\n",
      "Operation \"operations/acat.p2-45373616427-990bb410-2998-4b37-b37f-09ed724e9519\" finished successfully.\n",
      "\u001b[0;32m Setting up Artifact Registry in project automlops-sandbox \u001b[0m\n",
      "Listing items under project automlops-sandbox, location us-central1.\n",
      "\n",
      "dry-beans-dt-artifact-registry  DOCKER  STANDARD_REPOSITORY  Artifact Registry dry-beans-dt-artifact-registry in us-central1.  us-central1          Google-managed key  2023-09-05T11:25:48  2023-09-05T14:47:39  3200.712\n",
      "Artifact Registry: dry-beans-dt-artifact-registry already exists in project automlops-sandbox\n",
      "\u001b[0;32m Setting up Storage Bucket in project automlops-sandbox \u001b[0m\n",
      "gs://automlops-sandbox-dry-beans-dt-bucket/\n",
      "GS Bucket: automlops-sandbox-dry-beans-dt-bucket already exists in project automlops-sandbox\n",
      "\u001b[0;32m Setting up Pipeline Job Runner Service Account in project automlops-sandbox \u001b[0m\n",
      "Pipeline Runner Service Account         vertex-pipelines@automlops-sandbox.iam.gserviceaccount.com           False\n",
      "Service Account: vertex-pipelines already exists in project automlops-sandbox\n",
      "\u001b[0;32m Setting up IAM roles for Pipeline Job Runner Service Account in project automlops-sandbox \u001b[0m\n",
      "\u001b[0;32m Setting up Cloud Source Repository in project automlops-sandbox \u001b[0m\n",
      "dry-beans-dt-repository  automlops-sandbox  https://source.developers.google.com/p/automlops-sandbox/r/dry-beans-dt-repository\n",
      "Cloud Source Repository: dry-beans-dt-repository already exists in project automlops-sandbox\n",
      "\u001b[0;32m Setting up Queueing Service in project automlops-sandbox \u001b[0m\n",
      "name: projects/automlops-sandbox/topics/dry-beans-dt-queueing-svc\n",
      "Pub/Sub Topic: dry-beans-dt-queueing-svc already exists in project automlops-sandbox\n",
      "\u001b[0;32m Deploying Cloud Functions: dry-beans-dt-job-submission-svc in project automlops-sandbox \u001b[0m\n",
      "Deploying function (may take a while - up to 2 minutes)...\n",
      "..\n",
      "For Cloud Build Logs, visit: https://console.cloud.google.com/cloud-build/builds;region=us-central1/0dcd7601-a440-4c7e-8ea9-28d1cf927991?project=45373616427\n",
      "........................................................................done.\n",
      "availableMemoryMb: 512\n",
      "buildId: 0dcd7601-a440-4c7e-8ea9-28d1cf927991\n",
      "buildName: projects/45373616427/locations/us-central1/builds/0dcd7601-a440-4c7e-8ea9-28d1cf927991\n",
      "dockerRegistry: CONTAINER_REGISTRY\n",
      "entryPoint: process_request\n",
      "eventTrigger:\n",
      "  eventType: google.pubsub.topic.publish\n",
      "  failurePolicy: {}\n",
      "  resource: projects/automlops-sandbox/topics/dry-beans-dt-queueing-svc\n",
      "  service: pubsub.googleapis.com\n",
      "ingressSettings: ALLOW_ALL\n",
      "labels:\n",
      "  deployment-tool: cli-gcloud\n",
      "maxInstances: 3000\n",
      "name: projects/automlops-sandbox/locations/us-central1/functions/dry-beans-dt-job-submission-svc\n",
      "runtime: python39\n",
      "serviceAccountEmail: vertex-pipelines@automlops-sandbox.iam.gserviceaccount.com\n",
      "sourceUploadUrl: https://storage.googleapis.com/uploads-961973632599.us-central1.cloudfunctions.appspot.com/a2ee896a-a9dc-41ee-a129-fc000fc59dc9.zip\n",
      "status: ACTIVE\n",
      "timeout: 540s\n",
      "updateTime: '2023-09-08T03:06:05.246Z'\n",
      "versionId: '2'\n",
      "\u001b[0;32m Setting up Cloud Build Trigger in project automlops-sandbox \u001b[0m\n",
      "name: dry-beans-dt-build-trigger\n",
      "Cloudbuild Trigger already exists in project automlops-sandbox for repo dry-beans-dt-repository\n",
      "\u001b[0;32m Setting up Cloud Scheduler Job in project automlops-sandbox \u001b[0m\n",
      "dry-beans-dt-schedule  us-central1  59 11 * * 0 (Etc/UTC)  Pub/Sub      ENABLED\n",
      "Cloud Scheduler Job: dry-beans-dt-schedule already exists in project automlops-sandbox\n"
     ]
    }
   ],
   "source": [
    "AutoMLOps.provision(hide_warnings=False)            # hide_warnings is optional, defaults to True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed1d57b",
   "metadata": {},
   "source": [
    "`AutoMLOps.deploy(...)` builds and pushes component container, then triggers the pipeline job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79cfffe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Running precheck for deploying requires these permissions:\n",
      "-artifactregistry.repositories.get\n",
      "-cloudbuild.builds.get\n",
      "-resourcemanager.projects.getIamPolicy\n",
      "-storage.buckets.update\n",
      "-serviceusage.services.get\n",
      "-cloudfunctions.functions.get\n",
      "-pubsub.topics.get\n",
      "-iam.serviceAccounts.get\n",
      "-source.repos.update\n",
      "-pubsub.subscriptions.get\n",
      "\n",
      "You are currently using: srastatter@google.com. Please check your account permissions.\n",
      "The following are the recommended roles for deploying with precheck:\n",
      "-roles/serviceusage.serviceUsageViewer\n",
      "-roles/iam.roleViewer\n",
      "-roles/pubsub.viewer\n",
      "-roles/storage.admin\n",
      "-roles/cloudbuild.builds.editor\n",
      "-roles/source.writer\n",
      "-roles/iam.serviceAccountUser\n",
      "-roles/cloudfunctions.viewer\n",
      "-roles/artifactregistry.reader\n",
      "\n",
      "Checking for required API services in project automlops-sandbox...\n",
      "Checking for Artifact Registry in project automlops-sandbox...\n",
      "Checking for Storage Bucket in project automlops-sandbox...\n",
      "Checking for Pipeline Runner Service Account in project automlops-sandbox...\n",
      "Checking for IAM roles on Pipeline Runner Service Account in project automlops-sandbox...\n",
      "Checking for Cloud Source Repo in project automlops-sandbox...\n",
      "Checking for Pub/Sub Topic in project automlops-sandbox...\n",
      "Checking for Pub/Sub Subscription in project automlops-sandbox...\n",
      "Checking for Cloud Functions Pipeline Job Submission Service in project automlops-sandbox...\n",
      "Checking for Cloud Build Trigger in project automlops-sandbox...\n",
      "Precheck successfully completed, continuing to deployment.\n",
      "\n",
      "[automlops 1fa5664] Run AutoMLOps\n",
      " 7 files changed, 34 insertions(+), 228 deletions(-)\n",
      " delete mode 100644 AutoMLOps/components/component_base/src/custom_train_model.py\n",
      " delete mode 100644 AutoMLOps/components/custom_train_model/component.yaml\n",
      "remote: Waiting for private key checker: 5/5 objects left        \n",
      "To https://source.developers.google.com/p/automlops-sandbox/r/dry-beans-dt-repository\n",
      "   70a23dd..1fa5664  automlops -> automlops\n",
      "Pushing code to automlops branch, triggering build...\n",
      "Cloud Build job running at: https://console.cloud.google.com/cloud-build/builds;region=us-central1\n",
      "Please wait for this build job to complete.\n",
      "\n",
      "#################################################################\n",
      "#                                                               #\n",
      "#                       RESOURCES MANIFEST                      #\n",
      "#---------------------------------------------------------------#\n",
      "#     Generated resources can be found at the following urls    #\n",
      "#                                                               #\n",
      "#################################################################\n",
      "\n",
      "Google Cloud Storage Bucket: https://console.cloud.google.com/storage/automlops-sandbox-dry-beans-dt-bucket\n",
      "Artifact Registry: https://console.cloud.google.com/artifacts/docker/automlops-sandbox/us-central1/dry-beans-dt-artifact-registry\n",
      "Service Accounts: https://console.cloud.google.com/iam-admin/serviceaccounts?project=automlops-sandbox\n",
      "APIs: https://console.cloud.google.com/apis\n",
      "Cloud Source Repository: https://source.cloud.google.com/automlops-sandbox/dry-beans-dt-repository/+/automlops:\n",
      "Cloud Build Jobs: https://console.cloud.google.com/cloud-build/builds;region=us-central1\n",
      "Vertex AI Pipeline Runs: https://console.cloud.google.com/vertex-ai/pipelines/runs\n",
      "Cloud Build Trigger: https://console.cloud.google.com/cloud-build/triggers;region=us-central1\n",
      "Pipeline Job Submission Service (Cloud Functions): https://console.cloud.google.com/functions/details/us-central1/dry-beans-dt-job-submission-svc\n",
      "Pub/Sub Queueing Service Topic: https://console.cloud.google.com/cloudpubsub/topic/detail/dry-beans-dt-queueing-svc\n",
      "Pub/Sub Queueing Service Subscriptions: https://console.cloud.google.com/cloudpubsub/subscription/list\n",
      "Cloud Scheduler Job: https://console.cloud.google.com/cloudscheduler\n"
     ]
    }
   ],
   "source": [
    "AutoMLOps.deploy(precheck=True,                     # precheck is optional, defaults to True\n",
    "                 hide_warnings=False)               # hide_warnings is optional, defaults to True"
   ]
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "python3",
   "name": "common-cpu.m102",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m102"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}