resumeClassificationNLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESUME CATEGORIZATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### In today's competitive job market, hiring managers receive a multitude of resumes for each open position. Efficiently and accurately classifying these resumes into relevant job categories is a critical. However, manual classification is time-consuming and prone to errors. Leveraging machine learning and natural language processing (NLP) to automate resume classification can significantly enhance the efficiency and accuracy of the hiring process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset consist of two columns : category and resume.\n",
    "* Category : This column contains job titles or categories into which resumes are classified. Each entry in this column represents a specific job role or title.\n",
    "* Resume : This column contains the textual content of the resumes. Each entry is a detailed description of an individual's resume, including information such as work experience, educational background, skills, and other relevant details.\n",
    "* Here , our taget column is category and on the basis of the resume input we will be predicting category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The initial libraries have been called below , moving ahead the necessary libraries will be added as per the requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading CSV Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resumeDF = pd.read_csv(\"UpdatedResumeDataSet.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Resume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills * Programming Languages: Python (pandas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\nMay 2013 to May 2017 B.E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Areas of Interest Deep Learning, Control Syste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>Education Details \\r\\n MCA   YMCAUST,  Faridab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                             Resume\n",
       "0  Data Science  Skills * Programming Languages: Python (pandas...\n",
       "1  Data Science  Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
       "2  Data Science  Areas of Interest Deep Learning, Control Syste...\n",
       "3  Data Science  Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...\n",
       "4  Data Science  Education Details \\r\\n MCA   YMCAUST,  Faridab..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing number of dimensions in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(962, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 962 Records and 2 Features "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing value_counts() to have a better grasp of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category         Resume                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         \n",
       "Web Designing    Technical Skills Web Technologies: Angular JS, HTML5, CSS3, SASS, Bootstrap, Jquery, Javascript. Software: Brackets, Visual Studio, Photoshop, Visual Studio Code Education Details \\r\\nJanuary 2015 B.E CSE Nagpur, Maharashtra G.H.Raisoni College of Engineering\\r\\nOctober 2009  Photography Competition Click Nagpur, Maharashtra Maharashtra State Board\\r\\n    College Magazine OCEAN\\r\\nWeb Designer \\r\\n\\r\\nWeb Designer - Trust Systems and Software\\r\\nSkill Details \\r\\nPHOTOSHOP- Exprience - 28 months\\r\\nBOOTSTRAP- Exprience - 6 months\\r\\nHTML5- Exprience - 6 months\\r\\nJAVASCRIPT- Exprience - 6 months\\r\\nCSS3- Exprience - Less than 1 year months\\r\\nAngular 4- Exprience - Less than 1 year monthsCompany Details \\r\\ncompany - Trust Systems and Software\\r\\ndescription - Projects worked on:\\r\\n1. TrustBank-CBS\\r\\nProject Description: TrustBank-CBS is a core banking solution by Trust Systems.\\r\\nRoles and Responsibility:\\r\\nâ Renovated complete UI to make it more modern, user-friendly, maintainable and optimised for bank use.\\r\\nâ Shared the UI structure and guidelines to be incorporated, with development team of around 50\\r\\nmembers.\\r\\nâ Achieved the target of project completion in given time frame.\\r\\nâ Made required graphics for the project in photoshop\\r\\n\\r\\n2. Loan Bazar (Loan Appraisal)\\r\\nProject Description: Loan Bazar is a MVC-based application dedicated to creating and managing\\r\\nloan applications. The goal of this application is to streamline the process of loan application and integrate with existing CBS.\\r\\nRoles and Responsibility\\r\\nâ Designed and developed modern and responsive UI of entire application and achieved the target in given time frame.\\r\\nâ Made required graphics for the project in photoshop\\r\\n3. Capital Security Bond Application\\r\\nProject Description: Capital Security Bond Application is a MVC based application which provided an online platform to purchase gold bond\\r\\nRoles and Responsibility:\\r\\nâ Designed and developed modern and responsive UI of entire application and achieved the target in given time frame.\\r\\nâ Made required graphics for the project in photoshop\\r\\n\\r\\n4. SoftGST\\r\\nProject Description: SoftGST (Web Based Application) is an ASP application to every tax\\r\\npayers and its vendors for generating the GSTR returns on the basis of sales / purchase\\r\\ndata, additionally the application can do the reconciliation of GSTR 2 A with purchase register.\\r\\nRoles and Responsibility:\\r\\nâ Designed and developed the UI of Dashboard.\\r\\n\\r\\n5. Trust Analytica:\\r\\nProject Description: Trust Analytika is the mobile web app that shows bank asset, liability,\\r\\nincome, expenses.\\r\\nRoles and Responsibility:\\r\\nâ Designed and developed the landing page of the application.\\r\\nâ Supported the developers in UI implementation\\r\\n\\r\\n6. Website's:\\r\\nProject Name:\\r\\n1. TSR Technology Services - http://tsrtechnologyservices.com\\r\\n2. Vidarbha Merchants Urban Co-Op Bank - http://vmcbank.com\\r\\n3. GISSS - http://gisss.co.in\\r\\n4. Softtrust USA - http://softtrustusa.com\\r\\nRoles and Responsibility\\r\\nâ Communicated with clients to understand their requirement\\r\\nâ Made mocks for the website\\r\\nâ Designed and developed complete website and hosted them in stipulated time.\\r\\ncompany - www.jalloshband.com\\r\\ndescription - Project Name:\\r\\n1. Jallosh Band - www.jalloshband.com\\r\\n2. An Endeavor Foundation\\r\\nRoles and Responsibility:\\r\\nâ Communicated with clients to understand their requirement\\r\\nâ Made mocks for the website\\r\\nâ Designed and developed complete website and hosted them in stipulated time.\\r\\ncompany - 10MagicalFingers\\r\\ndescription - National and international client interaction.\\r\\nâ Management of digital data                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     18\n",
       "DevOps Engineer  CORE COMPETENCIES ~ Ant ~ Maven ~ GIT ~ Bitbucket ~ Jenkins ~ Linux ~ Ansible ~ Shell Scripting ~Requirement Gathering ~Continuous Integration and Continuous Deployment ~ Software Development Life Cycle ~ Software Testing Life Cycle ~ Documentation & Reporting ~ Test Reports IT SKILLS â¢ Primary Skills: Dev-ops methodologies â¢ Programming Languages: C, Core Java â¢ Version Controls: GIT, Bitbucket â¢ Build Tools: ANT, Maven â¢ CI/CD Tools: Jenkins â¢ Configuration management: Ansible â¢ Scripting: Shell Script â¢ Application Servers: Apache Tomcat serverEducation Details \\r\\nJune 2015 to June 2017 Masters of science information technology Hyderabad, Telangana JNTU\\r\\nDevops Engineer \\r\\n\\r\\nDevops Engineer - Nendrasys Technologies Pvt Ltd\\r\\nSkill Details \\r\\nDEPLOYMENT- Exprience - 27 months\\r\\nGit- Exprience - 27 months\\r\\nDOCUMENTATION- Exprience - 26 months\\r\\nCHANGE MANAGEMENT- Exprience - 10 months\\r\\nCONFIGURATION MANAGEMENT- Exprience - 10 monthsCompany Details \\r\\ncompany - Nendrasys Technologies Pvt Ltd\\r\\ndescription - Date\\r\\n\\r\\nProject Description:\\r\\nThe Scope of the project is to design & develop e-commerce product features for sloan project. It's a basically B2B project where customer can buy all fixers, sinks related product. It focuses on the company, the stakeholders and applications, which allow for online sales, distribution and marketing of product.\\r\\n\\r\\nResponsibilities:\\r\\no Detailed technical Knowledge and hands-on experience on DevOps, Automation, Build Engineering and Configura -\\r\\ntion Management.\\r\\no Creating fully automated CI build and deployment infrastructure and processes for multiple projects.\\r\\no Developing scripts for build, deployment, maintenance and related tasks using Jenkins.\\r\\no Installing, configuring and maintaining Continuous Integration, Automation and Configuration Management tools.\\r\\no Developing Ant, Maven and Shell scripts to automatically compile, package, deploy WAR, EAR and JAR files of mul- tiple applications to various platforms.\\r\\no Creating Repositories, branches, managing the permissions of users and maintaining GIT, Bitbucket\\r\\no Deploying and maintaining the code and application respectively and configuring the components which increase the re-usability.\\r\\no Working closely with Architecture, Development, Test, Security and IT Services teams.\\r\\no Supporting Business Analysts in getting used to the newly created jobs and Release activities.\\r\\no Communicating daily with On-Site Team\\r\\no Analyzing the requirements and identifying gaps & tracking logs, issues.\\r\\n\\r\\nPROJECT 2\\r\\n\\r\\nNendrasys Technologies Pvt Ltd.,\\r\\nProject Name: Bangkok Bank Limited (BBL) Thaitrade (Commercial, E-commerce & Payment portal)\\r\\ncompany - Nendrasys Technologies Pvt Ltd\\r\\ndescription - Role: Devops Engineer\\r\\nâ¢ Devops Engineer for a team that involved different development teams and multiple simultaneous software re- leases\\r\\nâ¢ Participated in weekly release meetings with different teams to identify and mitigate potential risks associated with the releases.\\r\\nâ¢ Responsible for Creating Repositories, branches, managing the permissions of users and maintaining GIT.\\r\\nâ¢ Creating fully automated CI build and deployment infrastructure and processes for multiple projects.\\r\\nâ¢ Analyze and resolve conflicts related to merging of source code for GIT\\r\\nâ¢ Responsible for creating Ant and Maven scripts for build automation.\\r\\nâ¢ Implemented the setup for Master slave architecture to improve the Performance of Jenkins.\\r\\nâ¢ Handled end-to-end deployments and code propagation's across different environments, DEV to PROD.\\r\\nâ¢ Handled the tasks of developing and maintaining the Change Tasks.\\r\\nâ¢ Closely working with Developers, QA, Performance, UAT testers, IT Team.\\r\\nâ¢ Created and maintained documentation of build and release processes and application configuration\\r\\nâ¢ Coordinated with developers, Business Analyst and Mangers to make sure that code is deployed in the Production\\r\\nenvironment.\\r\\n\\r\\nPROJECT 1\\r\\nNendrasys Technologies Pvt Ltd.,\\r\\nProject Name: Sloan Global Holdings (Sloan) (E-commerce)\\r\\ncompany - Nendrasys Technologies Pvt Ltd\\r\\ndescription - Project Description:\\r\\nBBL is development of an e-commerce payment portal System. As such BBL require a system which can support multiple\\r\\nmerchants B2B/B2C e-commerce portal with various payment options via both online and offline. To achieve this BBL had\\r\\nmade an agreement with Thai Trade, Thai Trade is one of the leading e-commerce website recognized by Thai\\r\\ngovernment. In present Thai trade has a good base of users and sellers in several categories .The scope of the project is to design and develop e-commerce product features for BBLWTP project. It focuses on the company, the stakeholders and\\r\\napplications, which allow for online sales, distribution and marketing of electronics.\\r\\n\\r\\nResponsibilities:\\r\\no Plan and track activities involved for build and deployment.\\r\\no Resolving build issues\\r\\no Developing scripts for build, deployment, maintenance and related tasks using Jenkins.\\r\\no Collaboration with Development, QA, Product managements on build plans & schedules\\r\\no Send daily and weekly reports to the team.\\r\\no Involved in creating repositories, branches, merging activities, backup and restore activities in subversion\\r\\nservers.\\r\\no Maintenance of Maven, shell scripts for safe builds and deploys.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       17\n",
       "                 Skills VISA B1-VISA (USA) Onsite Visits to Sweden & US (Seattle) Education Details \\r\\nJanuary 2013 Post Graduate Diploma Information Technology Pune, Maharashtra Symbiosis Institute\\r\\nJanuary 2007 Bachelor of Engineering Electronics and Telecommunications Pune, Maharashtra Pune University\\r\\nCloud Operations Architect (DevOps) \\r\\n\\r\\nCloud Operations Architect (DevOps) - DevOps\\r\\nSkill Details \\r\\nCloud Computing- Exprience - 48 months\\r\\nShell Scripting- Exprience - 96 months\\r\\nPython- Exprience - 6 months\\r\\nAutomation- Exprience - 72 months\\r\\nSolution Architect- Exprience - Less than 1 year months\\r\\nAzure- Exprience - Less than 1 year months\\r\\nAWS- Exprience - Less than 1 year monthsCompany Details \\r\\ncompany - DevOps\\r\\ndescription - Type: DevOps Engineer.\\r\\nPlatform: AWS Cloud, Azure Cloud.\\r\\nServices: AWS EC2, RDS, CloudFormation Template, Lambda, Dynamo DB,\\t\\tCloud Watch, Auto-scaling, Elastic Bean stalk, Appdynamics.\\r\\n\\r\\nHere I manage Tibco Spotfire enterprise & Cloud Product support. Being the only Ops member in India I got a chance to recruit & build entire team of 15 members. I also worked on 4 different Projects / products simultaneously and added the hired members into these products.\\r\\n\\r\\nMy responsibilities in this project include:\\r\\n1. Managing Tibco Spotfire Enterprise & Cloud environment.\\r\\n2. Helping India QA team with the pre-production environment for testing.\\r\\n3. Coordinating production deployment & hot-fixes.\\r\\n4. Leading the team and handling 4-projects.\\r\\n5. Arranging workshops over new AWS services for entire Team.\\r\\n\\r\\nShell scripts for Automation:\\r\\n\\r\\nâ¢ Wrote shell script to extract the AWS running instances and shut it down.\\r\\nâ¢ Wrote shell script to extract free EIP and release those.\\r\\nâ¢ Wrote Lambda function to trigger important scripts.\\r\\ncompany - Synechron Technologies Limited\\r\\ndescription - Type: Administration, automation & Monitoring.\\r\\nPlatform: Linux/Unix, Linux-Xen Servers,\\r\\nSoftware: Puppet, Redhat Satellite server, my-sql database shell scripting.\\r\\n\\r\\nMy responsibilities in this project included:\\r\\n1. Managing linux media servers farms and provide application support.\\r\\n2. Patching Linux physical and xen boxes.\\r\\n3. Creating Satellite channels on Satellite server.\\r\\n4. Automation via shell scripting and mysql DB support.\\r\\n5. Troubleshooting the customized applications.\\r\\ncompany - Tsys Limited\\r\\ndescription - Type: Development, automation & Monitoring.\\r\\nPlatform: Linux/Unix, windows\\r\\nSoftware: mysql database, shell scripting.\\r\\n\\r\\nHere my responsibility was to provide access to the users on RHDS, Cvs and dot project servers.\\r\\nIt also included providing access to users on dev/prod VDI servers. Handling mysql database and shell scripting to automate the tasks.\\r\\n\\r\\nMy responsibilities in this project included:\\r\\n1. Requirements and Analysis: Understanding the requirements of the project and planning the environment and the access required.\\r\\n2. Implementing mysql replication over linux servers.\\r\\n3. Fine tuning the existing applications.\\r\\n4. Testing & debugging the scripts for any errors.\\r\\ncompany - Patni Computer Systems Limited\\r\\ndescription - Project: CPI\\r\\nType: Development, automation & Monitoring.\\r\\nPlatform: P8 Filenet, Linux/Unix, IBM AIX\\r\\nSoftware: Sound knowledge of HTML, shell scripting.\\r\\nClient: Genworth Financial\\r\\nDuration: 2 yrs 2 months.\\r\\nRole: Senior System Engineer\\r\\n\\r\\nCPI is a Maintenance Project that caters to maintenance/enhancements of different applications, which are a part of Genworth Imaging Solutions. It has different applications that process the scanned Insurance documents sent from different Providers and stores the information in Oracle Database and images in P8 Filenet. It has multiple applications to generate reports to be sent to providers.\\r\\n\\r\\nAdministration Support:\\r\\nâ¢ Providing support to the L1 engineers.\\r\\nâ¢ Monitoring P8 Filenet application.\\r\\nâ¢ Handling Tickets raised by the users.\\r\\nâ¢ Administration of the 10 Linux Proxy servers and mysql servers.\\r\\nâ¢ Implementing mysql replication.\\r\\nâ¢ Checking the logs of the sites visited by the users and the data downloaded.\\r\\n\\r\\nMysql / Oracle / SQL Support:\\r\\nâ¢ Preparing SQL queries for the client users.\\r\\nâ¢ Handling Oracle database.\\r\\nâ¢ Testing insert, select, update queries over servers and deploying over production.\\r\\nâ¢ Handling the bugs raised by the users.\\r\\nâ¢ Implementing mysql replication over linux servers.\\r\\nâ¢ Taking database backup through mysql dump.\\r\\n\\r\\nApplication Support:\\r\\nâ¢ To make sure that the customer applications like eProcess, Trexo are working fine.\\r\\nâ¢ To make sure all the customized filenet instances are working fine.\\r\\nâ¢ Writing scripts to automate few of the applications.\\r\\n\\r\\nShell scripts for Automation:\\r\\nâ¢ Wrote shell script to delete the logs older than five days which was successfully deployed over Production servers.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         17\n",
       "                 Software Proficiency: â¢ Languages: Basics of C, SQL, PL/SQL,JAVA,JAVAEE,Javascript,HTML,CSS,jquery,mysql,Spring ,Hibernate. â¢ Software Tools: Xillinx, Modelsim, Matlab, Multisim. â¢ Operating Systems: Windows XP, Vista, 07, 08, Ubuntu. Project Profile: B.E. Project FPGA Implementation of Team Size: 4. Role: Programmer. AES Algorithm AES is Advanced Encryption Standard which is used in cryptography by which we can protect our data. It encrypted by a Secret Key. T.E. project Sorting Robot. Team Size: 3. Role: Mechanism designer. The TCS 230 sensor sorts the RGB color balls according to their color. Diploma Project RFID Based Student Team Size: 4. Role: Interface. Attendance System Using GSM. In this student show RFID card of his own and then message send via GSM to their parent that his ward is present.Education Details \\r\\nMay 2016 B.E. Savitribai Phule Pune, Maharashtra Pune University\\r\\nMarch 2010 S.S.C   Maharashtra Board\\r\\nDevOps Engineer \\r\\n\\r\\n\\r\\nSkill Details \\r\\nC- Exprience - 6 months\\r\\nC++- Exprience - 6 months\\r\\nSql- Exprience - 6 months\\r\\nPl/Sql- Exprience - 6 months\\r\\nCore Java- Exprience - 6 months\\r\\nJavascript- Exprience - Less than 1 year months\\r\\nHTML- Exprience - Less than 1 year months\\r\\nCSS- Exprience - Less than 1 year months\\r\\nJquery- Exprience - Less than 1 year months\\r\\nJavaEE- Exprience - Less than 1 year months\\r\\nMysql- Exprience - Less than 1 year months\\r\\nPython- Exprience - 6 monthsCompany Details \\r\\ncompany - Parkar Consulting and Labs\\r\\ndescription - I'm working on the DevOps team in Parkar Consulting and Labs. I have hands on the AWS as well as Python                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     17\n",
       "Java Developer   TECHNICALSKILLS SpringMVC, Hibernate, JDBC, Java, J2EE, AzureWeb SunTechnologies Services, JSP, Struts, Servlet, RestApi. Scripting JavaScript, AJAX, HTML, JSON. OpenSourceTechnologies PHP. Database MsSQL, MySQL, Oracle. WebServers ApacheTomcatServer. Internettechnologiesand OnesignalWebPushNotifications, AzurewebServices, frameworks IONIC, HTML, JSON OperatingSystem WindowsServer2012R2, WinXP/7/8.1/10, Linux, Mac OS Education Details \\r\\nAugust 2013 to July 2016 BE Computer Engineering Nashik, Maharashtra Late G.N. Sapkal COE Nashik\\r\\nJuly 2009 to June 2013 Diploma Computer technology Nashik, Maharashtra K K Wagh Polytechnic  Nashik\\r\\nJava developer \\r\\n\\r\\nJava Developer\\r\\nSkill Details \\r\\nAJAX- Exprience - 12 months\\r\\nDATABASE- Exprience - 24 months\\r\\nHTML- Exprience - 24 months\\r\\nJ2EE- Exprience - 6 months\\r\\nJAVA- Exprience - 24 months\\r\\nSpring MVC- Exprience - 12 months\\r\\nIonic 3- Exprience - 6 months\\r\\nAngular JS- Exprience - 6 months\\r\\nSpring- Exprience - Less than 1 year months\\r\\nJava- Exprience - Less than 1 year monthsCompany Details \\r\\ncompany - Replete business solutions pvt ltd\\r\\ndescription - Working as Java developer in spring MVC, MySQL, MsSql, Java, J2EE, Ajax, Javascript, ionic 3 framework, angular js etc. Technologies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           12\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    ..\n",
       "Advocate         Education Details \\r\\n B.Com, LL.B.,   University of Clacutta, University of Burdwan\\r\\nADVOCATE \\r\\n\\r\\n\\r\\nSkill Details \\r\\nTaxation matters Income Tax GST P Tax Accounts- Exprience - Less than 1 year months\\r\\nFiling of Income Tax Returns GST Returns e-TDS AIR and more- Exprience - Less than 1 year monthsCompany Details \\r\\ncompany - own practice\\r\\ndescription - 1. Drafting and preparation of plaint, Accounts and move before relevant Authority to hear the cases                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              2\n",
       "DevOps Engineer  Total IT Experience 15 years. Core expertise in Data Base Design, PHP, Python, MySql, JavaScript, HTML 5, Ajax, Jquery, XML, Agile Methodology, DevOps Methodology, Scrum Framework, JIRA Tool, GIT, Bitbucket, Anjular JS 1, Angular JS 2, Core Java, J2EE. Education Details \\r\\nApril 2004 MCM Computer Management Pune, Maharashtra Pune University\\r\\nApril 1998 B.Sc Maths  Kerala University\\r\\nProject Manager \\r\\n\\r\\nProject Manager\\r\\nSkill Details \\r\\nData Base Design, PHP, Python, MySql, JavaScript, HTML, Ajax, XML, Agile Methodology, DevOps Methodology, Scrum Framework, JIRA Tool, GIT, Bitbucket, Jquery,  AngularJs, Amazon MWS , Bootstrap, Node.js, Laravel, Scrum- Exprience - 120 monthsCompany Details \\r\\ncompany - Knoxed Infotech Pvt. Ltd.\\r\\ndescription - ï· Client interaction\\r\\nï· Maintain work processes\\r\\nï· Creates project plans through Agile Model & Methodology\\r\\nï· Maintains project objectives\\r\\nï· Working with multi-profiled teams of technical and non technical stakeholders\\r\\nï· Monitors production and quality to customer/stakeholder/sponsor standards\\r\\nï· Conduct office management tasks\\r\\nï· Ensuring that the day-to-day operations of the business run smoothly\\r\\nï· Introducing key performance indicators (KPI's) and ensuring that these measurements are tracked\\r\\nand reviewed on a regular basis\\r\\nï· Prepare, revise and submit weekly-monthly reports, budgets and other documentation as\\r\\nnecessary\\r\\nï· Document current policies and procedures in all departments as well as implement new procedures\\r\\nfor improvement\\r\\nï· Maintain smooth running of the office, filling in where needed\\r\\nï· Implement quality management and regulatory compliance strategies\\r\\nï· Dealing with HR related tasks\\r\\nï· Administering payroll\\r\\nï· Perform training sessions\\r\\nï· Regular meetings with Top Management\\r\\nProject Undertaken : Internal ERP system For Knoxed Ltd, UK, With PHP, Mysql, Ajax, XML,\\r\\nAmazon AWS, Raspberry pi Server, Python.\\r\\ncompany - Venturus International\\r\\ndescription - ï· Client interaction\\r\\nï· Creates project plans through Agile Model & Methodology\\r\\nï· Manage teamâs workload and workflow\\r\\nï· Allocate and track resources as required\\r\\nï· Set and monitor deadlines\\r\\nï· QC\\r\\nï· Maintain tasks and jobs on task management system\\r\\nï· Conduct research and development\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken : Internal ERP system For Knoxed Ltd, UK, With PHP, Mysql, Ajax, XML,\\r\\nAmazon AWS\\r\\ncompany - SmashingDay\\r\\ndescription - ï· Client interaction\\r\\nï· Creates project plans through Agile Model & Methodology\\r\\nï· Manage teamâs workload and workflow\\r\\nï· Allocate and track resources as required\\r\\nï· Set and monitor deadlines\\r\\nï· Project documentation\\r\\nï· Conduct and maintain appraisals and progress of each employee\\r\\nï· Adhere to deadlines as necessary\\r\\nï· Maintain work logs\\r\\nï· Conduct research and development\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken :\\r\\na) www.SmashingDay.com\\r\\nb) www.viralsocials.com\\r\\ncompany - Xento Systems Pvt. Ltd\\r\\ndescription - ï· Client interaction\\r\\nï· Creates project plans through Agile Model & Methodology\\r\\nï· Manage teamâs workload and workflow\\r\\nï· Allocate and track resources as required\\r\\nï· Set and monitor deadlines\\r\\nï· QC\\r\\nï· Maintain tasks and jobs on task management system\\r\\nï· Conduct research and development\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken :\\r\\na) www.familylink.com\\r\\nb) www.propertysolutions.com\\r\\nc) www.speedyceus.com\\r\\nd) www.ceus-nursing.com\\r\\ncompany - STP Global Solutions Pvt. Ltd.\\r\\ndescription - ï· Client interaction\\r\\nï· Creates project plans through Agile Model & Methodology\\r\\nï· Manage teamâs workload and workflow\\r\\nï· Allocate and track resources as required\\r\\nï· Set and monitor deadlines\\r\\nï· Conduct and maintain appraisals and progress of each employee\\r\\nï· QC\\r\\nï· Maintain tasks and jobs on task management system\\r\\nï· Conduct research and development\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken :\\r\\na) www.stplafricaonline.com\\r\\nb) www.stplafrica.com\\r\\nc) www.1stexpert.com\\r\\nd) www.jcca-net.org\\r\\ne) www.rimsys.eu\\r\\nf) www.prayerlister.org\\r\\ncompany - Promark Infotech Pvt. Ltd\\r\\ndescription - ï· Development & Design\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken :\\r\\na) www.justbe.com/\\r\\nb) www.mtpian.com/\\r\\nc) www.sababa.nl/booking/\\r\\nd) www.physicaltherapy-hiu.com\\r\\ncompany - 7cees Group\\r\\ndescription - ï· Development & Design\\r\\nï· Create new systems, databases and websites as necessary\\r\\nProject Undertaken :\\r\\na) Golwin-reality\\r\\nb) Maza                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           1\n",
       "                 Technical Skills Key Skills MS Technology .Net, SharePoint, MS SQL and Oracle PL/SQL Project Management & Execution Cloud Computing using Windows Azure and Amazon AWS/ EC2 Cloud XML/XAML/XSLT, HTML5, DHTML, CSS3, JavaScript/JQuery/JSON, AngularJS, Web API, OData Service, VBScript, Node JS, Handling Dynamic QA Activities, Project Delivery Frameworks UI Design and Mobile Development, JAVA, JSP, SWING, J2EE Service Oriented Architecture (SOA), Web Service/WCF Service/Web API, Requirement Gathering Design Pattern like MVC, MVP, Abstract Factory OOAD with UML Implementation .NET SAP Connector to interact with SAP through ASP.NET Client Management SAP Web Channel and SAP Enterprise Portal Environment Android, iOS, Team Foundation Server 2010/12(TFS), GitHub, IBM Rational Key Account Management DevOps, Team Forge and SharePoint Portal Administration /Development, CollabNet, JIRA. IOT Implementation, Web/Data analytics, Working with the road map of Machine learning and AI. Expertise BI tools like Crystal report, Escalation Management SSRS, Tableau, Micro Strategy, QlikView. IT Network/Infrastructure Monitoring tool Centreon, Cisco Meraki, BizTalk BAM. Team Building & Leadership Liaison & Coordination Soft Skills Reporting & Documentation Leader Technical architect Time Management Thinker Collaborator Planner Information Security management Communicator Quality assurance Career Timeline (Recent 5 Companies) Reliance ADA Annet Technologies Aditya Birla Group as Software as Project Group as Project consultant Lead/Technical Manager Architect 2006-2007 2008-2009 2009-2012 2012-2013 2013-till date Northbound eClerx Software LLC as Service as Programmer Technical Project Analyst ManagerEducation Details \\r\\nJanuary 1998 Bachelor of Art Economics Dibrugarh, Assam, IN Dibrugarh University\\r\\n MBA Information Technology  Sikkim Manipal University\\r\\n  Software Engineering and Computer Science Pune, Maharashtra NIIT\\r\\nProject Manager \\r\\n\\r\\nProject Manager - Aditya Birla Group\\r\\nSkill Details \\r\\nMICROSOFT SHAREPOINT- Exprience - 147 months\\r\\nSHAREPOINT- Exprience - 147 months\\r\\nSQL- Exprience - 92 months\\r\\nASP- Exprience - 79 months\\r\\nASP.NET- Exprience - 76 monthsCompany Details \\r\\ncompany - Aditya Birla Group\\r\\ndescription - Reporting to Vice President)\\r\\ncompany - OPEX., Monitoring multiple DB server, Web server and Cloud base\\r\\ndescription - Period: Apr 2013 to till Date\\r\\nWorking with: â¢ Project management processes in ongoing projects. Coordinating with different projects Team (Internal\\r\\nDevelopment team and Vendor side team) and other QA activities.\\r\\nâ¢ Information security audit. Budgeting CAPEX/OPEX., Monitoring multiple DB server, Web server and Cloud base servers Resource utilization in Networking infrastructure using different Hardware/Networking surveillance tools.\\r\\nâ¢ Tracking different team projects activities and involved with generating Final monthly report with different KPI for organization higher authority.\\r\\nâ¢   Managing all the SharePoint Portal Admin activities and all the Project Functional, Technical and Business documents.\\r\\nâ¢   Involved in Technology Road map group to upgrade new technology like IOT, Virtual reality, Wearable technology, Machine learning/AI, 3D Printing.\\r\\n\\r\\nAt eClerx Software Service Mumbai\\r\\ncompany - eClerx Software Service\\r\\ndescription - \\r\\ncompany - Dell Site Search/Natural Search DB\\r\\ndescription - Environment: ASP.NET 4.0, C#, WCF, SSRS, SQL Server 2008 R2\\r\\nAt Annet Technologies Mumbai\\r\\ncompany - NextGen Enterprise\\r\\ndescription - ARIA) and 2) Re-manage Portal Integration in Apple iPhone/iPad and Different Android Devices\\r\\nDomain- Real State, Period: Mar 2011 - Oct 2012\\r\\nEnvironment: ASP.NET 4.0, C#, WCF, WWF, JQuery, JSON, JavaScript, HTML 5, CSS3, Windows Azure Cloud, SharePoint\\r\\ncompany - Annet Technologies\\r\\ndescription - Key Result Areas:\\r\\nAdministering project progress as per scheduled deadlines for various tasks and taking necessary steps for ensuring completion within time and effort parameters\\r\\nWorking towards mapping requirements & providing best solutions for evaluating & defining scope of project and finalizing project requirements\\r\\nInterfacing with clients for business gathering, conducting system analysis & finalizing technical specifications\\r\\nVerifying the project documentation (Technical/Functional); using SharePoint Documents Library Environment and performing all the SharePoint administrative activities\\r\\n\\r\\nSignificant Accomplishments:\\r\\nRolled out project performance metrics reporting, implemented profitability improvement plans and enhanced operational efficiency\\r\\nReceived the following awards: o Excellent Design and On-time Delivery Award o Design and Delivery Excellence Award\\r\\n\\r\\nPrevious Experience\\r\\ncompany - Genentech, Biogen, Astellas Pharma, Polaris\\r\\ndescription - Client: Genentech, Biogen, Astellas Pharma, Polaris, New York, USA\\r\\nEnvironment: ASP.NET 3.5, C#, N-Hibernet, WCF, WWF, JavaScript, JQuery, BizTalk Server 2009, SQL Server 2008 R2\\r\\n\\r\\nAt Northbound LLC Sunnyvale California\\r\\ncompany - iOS\\r\\ndescription - SQL Server 2008R2, Android, iOS\\r\\ncompany - Logistic Transport System, Ohio\\r\\ndescription - USA\\r\\nEnvironment: ASP.NET, C# 3.5, JavaScript, NUnit Test, Telerik Controls, SQL Server 2005, T-SQL, MOSS 2007, SSIS,\\r\\nSSRS, BizTalk2006, Crystal Report, N-Hibernet, Dreamweaver\\r\\ncompany - Northbound LLC\\r\\ndescription - \\r\\ncompany - Biz Automation CRM\\r\\ndescription - California, USA\\r\\nEnvironment: ASP.NET 3.5, C#, SQL Server 2005, Android/iOS Development Platform, XML, XSLT, JavaScript, JQuery,\\r\\nJSON, SharePoint Design, WCF, WWF, SharePoint 2007, Performance Point, SSRS\\r\\ncompany - Wachovia, North Carolina USA\\r\\ndescription - Environment: ASP.NET, C#, WCF, SQL Server 2005, Web Parts, JavaScript, AJAX\\r\\n\\r\\nAt Reliance ADA Group Mumbai\\r\\nTitle: 1) Complain Management System and 2) Company Information System\\r\\ncompany - Domain- Manufacturing and Human Resource\\r\\ndescription - Environment: ASP.NET, C#, Oracle 8i, PL/SQL, Crystal Report, SAP Connector, VPN\\r\\ncompany - Reliance ADA Group\\r\\ndescription - \\r\\ncompany - PF Trust System\\r\\ndescription - Environment: VB.NET, XML, Oracle 9i, PL/SQL, Toad, Crystal Report 11\\r\\n\\r\\nSure Solution - Prasanna Group of Company IT Pune\\r\\ncompany - Prasanna Group\\r\\ndescription - \\r\\ncompany - Online Bus Booking Portal\\r\\ndescription - Environment: ASP.NET, C#, SQL Server 2005, Web Services, Web Parts, MS Office SharePoint Design, AJAX and Java cript\\r\\nAt Info Dynamic Software System Pvt. Ltd. Pune\\r\\n\\r\\nTitle: Info-Banking / E Banking\\r\\nDomain- Banking Period: Jan 2005 - Aug 2005\\r\\nEnvironment: VB6, Oracle 8i, PL/SQL, Crystal Report, Java JSP/Servlet/Swing.\\r\\n\\r\\nAt PAN Software System Pvt. Ltd. Pune\\r\\nTitle: E-commerce Site for Online Sales and Purchase\\r\\ncompany - Info Dynamic Software System Pvt. Ltd\\r\\ndescription - \\r\\ncompany - ZD Doll Inc\\r\\ndescription - Newcastle, UK\\r\\ncompany - PAN Software System Pvt. Ltd\\r\\ndescription - \\r\\ncompany - Online Procurement of Materials\\r\\ndescription - \\r\\ncompany - Shipping Company\\r\\ndescription - Tokyo Based, Japan\\r\\nEnvironment: Java, JSP/Servlet, CSS, JavaScript, SQL Server 2000, T-SQL\\r\\nTitle: Studio Automation System\\r\\nDomain- Multimedia Period: Jan 2004 - May 2004\\r\\nEnvironment: VB 6, VC++, MS SQL Server, MS Data Report, Macromedia Flash, Fire works\\r\\n\\r\\nAt Solid State Technologists (I) Pvt. Ltd. Pune\\r\\ncompany - National Dairy Development Board\\r\\ndescription - Environment: C++, VB6, SQL2000, Lotus Notes, Crystal Report 8, MS Active Report 2.0\\r\\ncompany - Solid State Technologists (I) Pvt. Ltd\\r\\ndescription - \\r\\ncompany - Shell/Bharat Petroleum\\r\\ndescription - Title: Computerized Fuel Density Measurement Automation System\\r\\nDomain - Manufacturing and Automation Period: Dec 2002 - Mar 2003\\r\\nClient: Shell/Bharat Petroleum\\r\\nEnvironment: C++, VC ++, MATLAB, VB6, SQL Server 2000, Crystal Report\\r\\n\\r\\nTitle: CMCS\\r\\nDomain - Finance and Accounting Period: June 2002 - Nov 2002\\r\\nEnvironment: VB6, SQL Server 2000, Crystal Report\\r\\n\\r\\nTitle: Food Testing System\\r\\ncompany - Solid State Technologists (I) Pvt. Ltd\\r\\ndescription - Environment: VB .net, SQL Server 2000, Crystal Report\\r\\n\\r\\nTitle: Customer Care System\\r\\ncompany - Himalaya Industries\\r\\ndescription - Environment: ASP .Net, C#, SQL Server 2000, Crystal Report\\r\\ncompany - MAPCO India Ltd and BERCO\\r\\ndescription - Environment: VB6, Oracle, Active/ Data Report     1\n",
       "                 TECHNICAL SKILLS â¢ HP ALM, RTC and JIRA â¢ AS400 (iSeries) with SQL â¢ Test Automation: X- Framework (In hours HSBC Framework), UFT and LISA â¢ Test Data Automation - CATDM â¢ Test Scheduling - Jenkins and Maven â¢ Test Document Versioning - Confluence and SharePoint.Education Details \\r\\nJanuary 2002 Bachelor of Engineering Computer Engineering Mumbai, MAHARASHTRA, IN Mumbai University\\r\\nJanuary 1999 Diploma Computer Technology Mumbai, MAHARASHTRA, IN Maharashtra State Board of Technical Education\\r\\nProject Manager(Global Test Manager) \\r\\n\\r\\nProject Manager(Global Test Manager)\\r\\nSkill Details \\r\\nAS400- Exprience - 25 months\\r\\nISERIES- Exprience - 25 months\\r\\nMICROSOFT SHAREPOINT- Exprience - 6 months\\r\\nSCHEDULING- Exprience - 6 months\\r\\nSHAREPOINT- Exprience - 6 monthsCompany Details \\r\\ncompany - CAREER TIMELINE\\r\\ndescription - HSBC Software India Pvt. Ltd.Pune, India   Project Manager(Global Test Manager)\\r\\ncompany - Disha Technologies\\r\\ndescription - \\r\\ncompany - Total Solution (I) Pvt. Ltd\\r\\ndescription - Key Deliverables Across The Tenure\\r\\nAs a Global Test Manager/Project Management:\\r\\nâ¢ Acting as Global Test Manager, steering a team of 25+ resources across the globe\\r\\nâ¢ Spearheading the testing teams in India, China, Malaysia and Poland\\r\\nâ¢ Current role involves entire test delivery of large programs under banking domain which includes leading a team comprising of individuals (Client & Vendor) from functional (Regression, Integration) & shared services (Security, Performance, accessibility and Automation) groups\\r\\nâ¢ Defining and devising the \"HSBC Core Banking\" IT Transformation strategies from solution to execution level.\\r\\nâ¢ Experience in latest specialize testing methods - CBIL - API, Conversion, Cloud, Digital UI, Infrastructure, Exploratory and Accessibility Testing.\\r\\nâ¢ Experienced in Planning the OAT and various non-functional like performance and volume testing of Core banking.\\r\\nâ¢ Coordinating with the regional business and testing teams to map their testing approach\\r\\nâ¢ Following with various teams to ensure end to end release is successful and a quality product is delivered to production.\\r\\nâ¢ Steering the Global product testing, regional deployments testing and  extending support for UAT and live proving\\r\\nâ¢ Technical lead to implement the in house \"Test Automation\" framework (X-framework) solution on AS400 (iSeries) system.\\r\\nâ¢ Managing the implementation of Test automation to enable DevOps via tools Cucumber & Selenium (BDD), UFT and TOSCA for function testing\\r\\nâ¢ Leading the CI/CD implementation by executing automated scripts from Jenkins\\r\\nâ¢ Working on Test Data Management by using CA TDM and Internal HSBC Tool)\\r\\nâ¢ Handling tasks pertaining to Resource forecasting, effort estimation and resource on boarding process\\r\\nâ¢ Ensuring seamless communication with vendor partners to arrange for resource onboarding based on the forecasting\\r\\nâ¢ Reviewing the project progress on a daily basis; implementing corrective measures to ensure the project is ontrack\\r\\nâ¢ Overseeing people management tasks comprise monthly 1:1 with leads, reviews\\r\\nâ¢ Providing conflict resolution when required                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      1\n",
       "                 Core skills â¢ Project / Program Management â¢ Agile / Scrum Management â¢ Risk /Client Management â¢ Process Improvements â¢ Proposals/RFE â¢ Selenium â¢ RALLY / IBM TD Platform â¢ Python â¢ DevOps / DevSecOps â¢ SAFe - Agile Craft â¢ Delivery / Test Management â¢ Project / Program Finance â¢ Profit Maximization â¢ Internal / External Audits â¢ Software Testing / Quality Assurance â¢ Visual Basic 6.0 â¢ MS-SQL Server / Oracle 8/8i â¢ Conformiq â¢ HP QC / QTP â¢ Crystal Report 8.5 Linguistic Skills English Hindi Marathi Tulu Education Details \\r\\nJanuary 2013    Harvard\\r\\nJanuary 2004  Software Engineering  Aptech\\r\\nJanuary 2001 Bachelor of Commerce Commerce Mumbai, Maharashtra Mumbai University\\r\\nProject Manager \\r\\n\\r\\nProject Manager - AT&T\\r\\nSkill Details \\r\\nTESTING- Exprience - 63 months\\r\\nORACLE- Exprience - 6 months\\r\\nSQL- Exprience - 6 months\\r\\nAUDITS- Exprience - 6 months\\r\\nCLIENT MANAGEMENT- Exprience - 6 monthsCompany Details \\r\\ncompany - AT&T\\r\\ndescription - Thunderbird program is a One-Stop-Test-Shop catering to AT&T's Enterprise-wide business critical applications. As part of this Fixed Price engagement various types of testing like System, Regression, Sanity and UAT support gets executed through Agile Methodologies on applications under Billing and Sales domain. The project has various critical applications as listed below with a team size of 80+ members.\\r\\nSales Express: An iPad & Browser based Mobile Sales application used by Sales Representatives & Service/Solution providers to sell Enterprise Products/Solutions\\r\\nESIGN: Allows customer to sign and AT&T to countersign digital version of contract document\\r\\nAMPD: Enterprise billing application through which AT&T offers new and emerging billing products to the Business community\\r\\nBEST: Business Solution application which performs consolidated billing and reporting for CRU Billing and Reporting Foundation Accounts\\r\\nBIZCOMP: Comp Engine for business that supports the commission calculations for Enterprise Business and Small-Business sales force via Revenue and Unit based plans\\r\\nFAST: Provides rate discounts to eligible Schools and Libraries\\r\\nCXMT: Lets customers manage their Centrex line and feature arrangements of their services without going through the standard service-order procedure.\\r\\n\\r\\nKey responsibilities\\r\\nâ¢ Manage and lead all aspects of Transition / Pre-Transition comprising multiple vendors\\r\\nâ¢ Transition Drill down sessions, Forward/Reverse Shadow phases and Reverse Presentation\\r\\nâ¢ Transition Documentation & signoff of SMP & System Appreciation Document etc\\r\\nâ¢ Measure & track testing metrics scorecard as part of Vendor Quality Index (VQI) initiative of AT&T\\r\\nâ¢ Measure and track operating metrics like Onsite Subcon Index, Offshoring Index, Leakage Index, Utilization Index and Tail Index\\r\\nâ¢ Manage, Track and Submit Project Monthly Financials & Invoicing\\r\\nâ¢ Ensure 100% compliance to client / organization's quality and security processes, policies and SLAs\\r\\nâ¢ Plan, manage and mitigate Risks\\r\\nâ¢ Achieve Automation targets and SLAs using ConformiQ, Selenium & Oscar Tools\\r\\nâ¢ Participate in proposal preparations to expand revenue and new business developments\\r\\nâ¢ Manage & Track Work Allocation, Resourcing, Issue Resolution and Testing activities for the project\\r\\nâ¢ Ensure Monthly Delivery / TL9K Metrics and CSL-KMs are submitted on time\\r\\nâ¢ PMR, Internal Quality / Security Group, SQA, Quality Gates and external Ernst & Young / TL9K Audit\\r\\nâ¢ Share weekly highlight reports, Project Health Report and regular Dashboard Reporting activities\\r\\nâ¢ Prepare CSAT action plan for the team\\r\\nâ¢ Prepare and implement Business Continuity Plan for the project\\r\\nâ¢ Timely conduct of Business Continuity drills like Call Tree, Table Top and Project Rehearsal\\r\\nâ¢ Comparative analysis, Root Cause Analysis and Defect Prevention exercises for ST, E2E and UAT\\r\\nâ¢ Coordinate with Resource Management, Quality, Learning, Security, PMO and Business Unit\\r\\nâ¢ Sub-Contractors and Rebadged (Ex-AT&T) staff management\\r\\nâ¢ Participate in external technical interview / hiring drives for new engagements\\r\\ncompany - Esaya Software India Pvt Ltd\\r\\ndescription -                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        1\n",
       "Name: count, Length: 166, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Returns the count of unique values in the 'Category' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Java Developer               84\n",
       "Testing                      70\n",
       "DevOps Engineer              55\n",
       "Python Developer             48\n",
       "Web Designing                45\n",
       "HR                           44\n",
       "Hadoop                       42\n",
       "Blockchain                   40\n",
       "ETL Developer                40\n",
       "Operations Manager           40\n",
       "Data Science                 40\n",
       "Sales                        40\n",
       "Mechanical Engineer          40\n",
       "Arts                         36\n",
       "Database                     33\n",
       "Electrical Engineering       30\n",
       "Health and fitness           30\n",
       "PMO                          30\n",
       "Business Analyst             28\n",
       "DotNet Developer             28\n",
       "Automation Testing           26\n",
       "Network Security Engineer    25\n",
       "SAP Developer                24\n",
       "Civil Engineer               24\n",
       "Advocate                     20\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Value Counts is returning the distribution of resume categories in the dataset.\n",
    "\n",
    "1. **Category Distribution** : \n",
    "* The dataset contains resumes for 25 different job categories.\n",
    "\n",
    "* \"Java Developer\" has the highest number of resumes (84), while \"Advocate\" has the fewest (20).\n",
    "\n",
    "2. **Category Imbalance** :\n",
    "* There is a noticeable imbalance in the number of resumes across different categories. Some categories, like \"Java Developer\" and \"Testing,\" have significantly more resumes compared to others like \"Advocate\" and \"Civil Engineer\".This class imbalance can affect the performance of machine learning models, particularly those that rely on balanced data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Returns unique values present in the 'Category' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Data Science', 'HR', 'Advocate', 'Arts', 'Web Designing',\n",
       "       'Mechanical Engineer', 'Sales', 'Health and fitness',\n",
       "       'Civil Engineer', 'Java Developer', 'Business Analyst',\n",
       "       'SAP Developer', 'Automation Testing', 'Electrical Engineering',\n",
       "       'Operations Manager', 'Python Developer', 'DevOps Engineer',\n",
       "       'Network Security Engineer', 'PMO', 'Database', 'Hadoop',\n",
       "       'ETL Developer', 'DotNet Developer', 'Blockchain', 'Testing'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF[\"Category\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here , we are checking for unique category values from the dataset to have a better grasp of dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing summary statistics of the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Resume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>962</td>\n",
       "      <td>962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>25</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Java Developer</td>\n",
       "      <td>Technical Skills Web Technologies: Angular JS,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>84</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Category                                             Resume\n",
       "count              962                                                962\n",
       "unique              25                                                166\n",
       "top     Java Developer  Technical Skills Web Technologies: Angular JS,...\n",
       "freq                84                                                 18"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insights:\n",
    "1. **Count** :\n",
    "* There are 962 resumes in the dataset.\n",
    "\n",
    "2. **Unique** :\n",
    "* There are 25 unique job categories in Category Column.\n",
    "* There are 166 unique resumes in Resume Column.\n",
    "\n",
    "3. **Top** :\n",
    "* The most frequent job category is \"Java Developer\" \n",
    "\n",
    "4. **Frequency** :\n",
    "* There are 84 resumes classified as \"Java Developer\"\n",
    "* The most common resume description appears 18 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing a concise summary of DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 962 entries, 0 to 961\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Category  962 non-null    object\n",
      " 1   Resume    962 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 15.2+ KB\n"
     ]
    }
   ],
   "source": [
    "resumeDF.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "info :\n",
    "* There are 962 non-null count for both the columns Category and Resume.\n",
    "* Both the columns Category and Resume have the Data Type as Object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing the data types of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    object\n",
       "Resume      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Returning the total number of missing (null) values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category    0\n",
       "Resume      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null records in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Returning an Index object containing the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Category', 'Resume'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing the total number of duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "796"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "* EDA explores the relationships between variables, helping to understand how different features influence each other and the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a countplot showing the count of resumes in each category, with the figure size set to 8x7 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "sns.countplot(resumeDF[\"Category\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphical representation is showing the distribution of categories with respect to its count.\n",
    "Even here we can see the imbalance of the categories where highest \"Java Developer\" reaching to 80 counts and least \"Advocate\" reaching to 20 counts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a pie chart showing the percentage distribution of resume categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Percentage wise distrubution of Category'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "colors = (\"red\",\"green\",\"blue\",\"orange\")\n",
    "\n",
    "wp = {\"linewidth\":1,\"edgecolor\":\"black\"}\n",
    "\n",
    "tags = resumeDF[\"Category\"].value_counts()/resumeDF.shape[0]\n",
    "\n",
    "tags.plot(kind=\"pie\",autopct = \"%1.1f%%\",shadow=True,colors=colors,\n",
    "          startangle=190, wedgeprops = wp, label='Percentage wise distrubution of Category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pie chart represents the percentage distribution of each job categories.\n",
    "Here , \"Java Developer\" has 8.7% with highest score whereas \"Advocate\" has 2.1% which is the least score and every other category lies in between with different percentages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a bar plot showing the frequency distribution of categories in the \"Category\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sambhu\\AppData\\Local\\Temp\\ipykernel_15248\\1503818472.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=resumeDF[\"Category\"].value_counts().index,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the distribution \n",
    "plt.figure(figsize=(8, 6)) \n",
    "sns.barplot(x=resumeDF[\"Category\"].value_counts().index, \n",
    "            y=resumeDF[\"Category\"].value_counts().values, \n",
    "            palette='viridis') \n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Category Distribution') \n",
    "plt.xlabel('Category') \n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have plotted a bar graph to represent the dataset distribution for the column category. Here , the X-Axis consist of Category and Y-Axis consist of Frequency of those categories. The highest occuring to least occuring frequencies have been plotted from left to right in the following bar graph. The graphical representation also helps us to understand the sheer imbalance is there in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Beautiful Soup to remove html tags from the Resume Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset likely contains HTML content within the resume column that needs to be converted to plain text for further processing and analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for removing HTML Tags\n",
    "# Using Beautiful Soup\n",
    "def htmlText(text):\n",
    "    soup = BeautifulSoup(text,\"lxml\")\n",
    "    return soup.get_text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "resumeDF['htmlTagsRemoved'] = resumeDF['Resume'].apply(htmlText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Skills * Programming Languages: Python (pandas...\n",
       "1    Education Details \\r\\nMay 2013 to May 2017 B.E...\n",
       "2    Areas of Interest Deep Learning, Control Syste...\n",
       "3    Skills â¢ R â¢ Python â¢ SAP HANA â¢ Table...\n",
       "4    Education Details \\r\\n MCA   YMCAUST,  Faridab...\n",
       "Name: htmlTagsRemoved, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF['htmlTagsRemoved'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removed HTML tags and retained only the meaningful text and stored it into a new columns \"htmlTagsRemoved\" within the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using regular expression to remove punctuation marks , links , hashtexts , special characters etc from the Resume columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for regularization of the text\n",
    "def regData(text):\n",
    "    regularLinkText = re.sub('http\\S+\\s',' ',text)\n",
    "    regularHashText = re.sub('@\\S+',' ',regularLinkText)\n",
    "    regularMentionText = re.sub('#\\S+',' ',regularHashText)\n",
    "    regularSpecialText =  re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\"\"\"), ' ', regularMentionText)\n",
    "    regularDiffText =  re.sub(r'[^\\x00-\\x7f]', ' ', regularSpecialText) \n",
    "    regularSpaceText = re.sub(r'\\s+',' ',regularDiffText)\n",
    "    return regularSpaceText\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'my webiste this working and access it '"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regData(\"my #### $ #  #hellowrold webiste this working http://heloword and access it @gmail.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "resumeDF[\"htmlTagsRemoved\"] = resumeDF[\"htmlTagsRemoved\"].apply(lambda x: regData(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Skills Programming Languages Python pandas num...\n",
       "1      Education Details May 2013 to May 2017 B E UIT...\n",
       "2      Areas of Interest Deep Learning Control System...\n",
       "3      Skills R Python SAP HANA Tableau SAP HANA SQL ...\n",
       "4      Education Details MCA YMCAUST Faridabad Haryan...\n",
       "                             ...                        \n",
       "957    Computer Skills Proficient in MS office Word B...\n",
       "958     Willingness to accept the challenges Positive...\n",
       "959    PERSONAL SKILLS Quick learner Eagerness to lea...\n",
       "960    COMPUTER SKILLS SOFTWARE KNOWLEDGE MS Power Po...\n",
       "961    Skill Set OS Windows XP 7 8 8 1 10 Database MY...\n",
       "Name: htmlTagsRemoved, Length: 962, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF[\"htmlTagsRemoved\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Areas of Interest Deep Learning Control System Design Programming in Python Electric Machinery Web Development Analytics Technical Activities q Hindustan Aeronautics Limited Bangalore For 4 weeks under the guidance of Mr Satish Senior Engineer in the hangar of Mirage 2000 fighter aircraft Technical Skills Programming Matlab Python and Java LabView Python WebFrameWork Django Flask LTSPICE intermediate Languages and and MIPOWER intermediate Github GitBash Jupyter Notebook Xampp MySQL Basics Python Software Packages Interpreters Anaconda Python2 Python3 Pycharm Java IDE Eclipse Operating Systems Windows Ubuntu Debian Kali Linux Education Details January 2019 B Tech Electrical and Electronics Engineering Manipal Institute of Technology January 2015 DEEKSHA CENTER January 2013 Little Flower Public School August 2000 Manipal Academy of Higher DATA SCIENCE DATA SCIENCE AND ELECTRICAL ENTHUSIAST Skill Details Data Analysis Exprience Less than 1 year months excel Exprience Less than 1 year months Machine Learning Exprience Less than 1 year months mathematics Exprience Less than 1 year months Python Exprience Less than 1 year months Matlab Exprience Less than 1 year months Electrical Engineering Exprience Less than 1 year months Sql Exprience Less than 1 year monthsCompany Details company THEMATHCOMPANY description I am currently working with a Casino based operator name not to be disclosed in Macau I need to segment the customers who visit their property based on the value the patrons bring into the company Basically prove that the segmentation can be done in much better way than the current system which they have with proper numbers to back it up Henceforth they can implement target marketing strategy to attract their customers who add value to the business '"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF[\"htmlTagsRemoved\"][32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting categorical labels (text or string values) into numerical values. This is a crucial step for preparing categorical data for machine learning algorithms, which typically require numerical inputs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoding = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding.fit(resumeDF[\"Category\"])\n",
    "resumeDF[\"labelCategory\"] = encoding.transform(resumeDF[\"Category\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The categorical job titles in the Category column are being converted into numerical labels and stored in the labelCategory column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>labelCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Science</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Testing</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>Testing</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>Testing</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>Testing</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>Testing</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>962 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category  labelCategory\n",
       "0    Data Science              6\n",
       "1    Data Science              6\n",
       "2    Data Science              6\n",
       "3    Data Science              6\n",
       "4    Data Science              6\n",
       "..            ...            ...\n",
       "957       Testing             23\n",
       "958       Testing             23\n",
       "959       Testing             23\n",
       "960       Testing             23\n",
       "961       Testing             23\n",
       "\n",
       "[962 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resumeDF[[\"Category\",\"labelCategory\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniqueCategoryLabels = resumeDF.groupby('Category')['labelCategory'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Advocate                      [0]\n",
       "Arts                          [1]\n",
       "Automation Testing            [2]\n",
       "Blockchain                    [3]\n",
       "Business Analyst              [4]\n",
       "Civil Engineer                [5]\n",
       "Data Science                  [6]\n",
       "Database                      [7]\n",
       "DevOps Engineer               [8]\n",
       "DotNet Developer              [9]\n",
       "ETL Developer                [10]\n",
       "Electrical Engineering       [11]\n",
       "HR                           [12]\n",
       "Hadoop                       [13]\n",
       "Health and fitness           [14]\n",
       "Java Developer               [15]\n",
       "Mechanical Engineer          [16]\n",
       "Network Security Engineer    [17]\n",
       "Operations Manager           [18]\n",
       "PMO                          [19]\n",
       "Python Developer             [20]\n",
       "SAP Developer                [21]\n",
       "Sales                        [22]\n",
       "Testing                      [23]\n",
       "Web Designing                [24]\n",
       "Name: labelCategory, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniqueCategoryLabels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each job category, retrived unique labelCategory values and stored it into \"uniqueCategoryLabels\". This helps in understanding how many distinct labels exist within each job category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing libraries for lemmatizing and stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOPWORDS = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = []\n",
    "stemmer = PorterStemmer()\n",
    "for i in range(0, resumeDF.shape[0]):\n",
    "  review = re.sub('[^a-zA-Z]', ' ', resumeDF.iloc[i]['htmlTagsRemoved'])\n",
    "  review = review.lower().split()\n",
    "  review = [stemmer.stem(word) for word in review if not word in STOPWORDS]\n",
    "  review = ' '.join(review)\n",
    "  corpus.append(review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the text data from resume column by normalizing and stemming the words to prepare them for further analysis or model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['skill program languag python panda numpi scipi scikit learn matplotlib sql java javascript jqueri machin learn regress svm na bay knn random forest decis tree boost techniqu cluster analysi word embed sentiment analysi natur languag process dimension reduct topic model lda nmf pca neural net databas visual mysql sqlserver cassandra hbase elasticsearch js dc js plotli kibana matplotlib ggplot tableau other regular express html css angular logstash kafka python flask git docker comput vision open cv understand deep learn educ detail data scienc assur associ data scienc assur associ ernst young llp skill detail javascript exprienc month jqueri exprienc month python exprienc monthscompani detail compani ernst young llp descript fraud investig disput servic assur technolog assist review tar technolog assist review assist acceler review process run analyt gener report core member team help develop autom review platform tool scratch assist e discoveri domain tool implement predict code topic model autom review result reduc labor cost time spent lawyer review understand end end flow solut research develop classif model predict analysi mine inform present text data work analyz output precis monitor entir tool tar assist predict code topic model evid follow ey standard develop classifi model order identifi red flag fraud relat issu tool technolog python scikit learn tfidf word vec doc vec cosin similar na bay lda nmf topic model vader text blob sentiment analysi matplot lib tableau dashboard report multipl data scienc analyt project usa client text analyt motor vehicl custom review data receiv custom feedback survey data past one year perform sentiment posit neg neutral time seri analysi custom comment across categori creat heat map term survey categori base frequenc word extract posit neg word across survey categori plot word cloud creat custom tableau dashboard effect report visual chatbot develop user friendli chatbot one product handl simpl question hour oper reserv option chat bot serv entir product relat question give overview tool via qa platform also give recommend respons user question build chain relev answer intellig build pipelin question per user requir ask relev recommend question tool technolog python natur languag process nltk spaci topic model sentiment analysi word embed scikit learn javascript jqueri sqlserver inform govern organ make inform decis inform store integr inform govern portfolio synthes intellig across unstructur data sourc facilit action ensur organ best posit counter inform risk scan data multipl sourc format pars differ file format extract meta data inform push result index elast search creat custom interact dashboard use kibana preform rot analysi data give inform data help identifi content either redund outdat trivial preform full text search analysi elast search predefin method tag pii person identifi inform social secur number address name etc frequent target cyber attack tool technolog python flask elast search kibana fraud analyt platform fraud analyt investig platform review red flag case fap fraud analyt investig platform inbuilt case manag suit analyt variou erp system use client interrog account system identifi anomali indic fraud run advanc analyt tool technolog html javascript sqlserver jqueri css bootstrap node js js dc js',\n",
       " 'educ detail may may b e uit rgpv data scientist data scientist matelab skill detail python exprienc less year month statsmodel exprienc month aw exprienc less year month machin learn exprienc less year month sklearn exprienc less year month scipi exprienc less year month kera exprienc less year monthscompani detail compani matelab descript ml platform busi profession dummi enthusiast koramangala th block achiev task behind sukh sagar bengaluru india develop deploy auto preprocess step machin learn mainli miss valu treatment outlier detect encod scale featur select dimension reduct deploy autom classif regress model linkedin com aditya rathor b b reasearch deploy time seri forecast model arima sarimax holt winter prophet work meta featur extract problem github com rathorolog implement state art research paper outlier detect mix attribut compani matelab descript',\n",
       " 'area interest deep learn control system design program python electr machineri web develop analyt technic activ q hindustan aeronaut limit bangalor week guidanc mr satish senior engin hangar mirag fighter aircraft technic skill program matlab python java labview python webframework django flask ltspice intermedi languag mipow intermedi github gitbash jupyt notebook xampp mysql basic python softwar packag interpret anaconda python python pycharm java ide eclips oper system window ubuntu debian kali linux educ detail januari b tech electr electron engin manip institut technolog januari deeksha center januari littl flower public school august manip academi higher data scienc data scienc electr enthusiast skill detail data analysi exprienc less year month excel exprienc less year month machin learn exprienc less year month mathemat exprienc less year month python exprienc less year month matlab exprienc less year month electr engin exprienc less year month sql exprienc less year monthscompani detail compani themathcompani descript current work casino base oper name disclos macau need segment custom visit properti base valu patron bring compani basic prove segment done much better way current system proper number back henceforth implement target market strategi attract custom add valu busi']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vectorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Objective** :\n",
    "* Converting text data into meaningful numerical features, making it easier to analyze and build machine learning models.\n",
    "\n",
    "2. **TfidfVectorizer**:\n",
    "* Using \"TfidfVectorizer\" to convert the collection text data into a matrix of TF-IDF features.\n",
    "\n",
    "3. **Importance Weighting** : \n",
    "* Assigns higher weights to terms that are more relevant and less frequent, improving model performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing TfidfVectorizer library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfIDF = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = corpus\n",
    "y = resumeDF[\"labelCategory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tfidf = tfIDF.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((962, 5162), (962,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf.shape , y.shape   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_tfidf is a matrix with 962 rows and 5162 features (terms), while y is with 962 labels , corresponding to the number of resumes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       6\n",
       "1       6\n",
       "2       6\n",
       "3       6\n",
       "4       6\n",
       "       ..\n",
       "957    23\n",
       "958    23\n",
       "959    23\n",
       "960    23\n",
       "961    23\n",
       "Name: labelCategory, Length: 962, dtype: int32"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spliting the dataset into training and testing sets, which is essential for evaluating the performance of machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing train_test_split library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X_tfidf,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((769, 5162), (193, 5162), (769,), (193,))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape , X_test.shape , y_train.shape , y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_train has 769 samples with 5162 features each, X_test has 193 samples with 5162 features each, y_train has 769 labels, and y_test has 193 labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Use Case** :\n",
    "* It works by generating synthetic samples for the minority class rather than simply duplicating existing ones. This is achieved by creating new samples along the lines connecting minority class instances that are close to each other in the feature space. By doing so, it balances the dataset, helping machine learning models perform better on both majority and minority classes.\n",
    "\n",
    "2. **Objective** :\n",
    "* Our main objective by using SMOTE technique is to overcome the issue of data imbalance as we already witnessed earlier with the distribution of categorical features like \"Java Developer\" and \"Advocate\" to be very imbalanced with highest of 80 and lowest of 20 counts. Using graphical representation like bar graph ,pie graph ,count graph and others methods we got into this conclusion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing SMOTE library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_SMOTE , y_train_SMOTE = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1725, 5162), (1725,))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_SMOTE.shape , y_train_SMOTE.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_train_SMOTE has 1725 samples with 5162 features , y_train_SMOTE has 1725 samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression is widely used for binary classification and also can be used for multiclass classification. Using logistic regression here cause it is performing exceptionally well compared to other models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing logistic regression , confusion_matrix , classification_report , accuracy_score and ConfusionMatrixDisplay libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing libraries to conduct cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Sambhu\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Sambhu\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Sambhu\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Sambhu\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Sambhu\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores: [0.99710145 0.99710145 1.         1.         1.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "\n",
    "# Initialize the logistic regression model\n",
    "logREG = LogisticRegression( multi_class='multinomial' , max_iter=1000,solver='lbfgs')\n",
    "\n",
    "# Perform cross-validation with 5 folds\n",
    "cv_scores = cross_val_score(logREG, X_train_SMOTE, y_train_SMOTE, cv=5)\n",
    "\n",
    "# Print the cross-validation scores\n",
    "print(\"Cross-Validation Scores:\", cv_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logREG = LogisticRegression()\n",
    "logREG.fit(X_train_SMOTE,y_train_SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report , accuracy_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_logREG_pred = logREG.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9948186528497409\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_logREG_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fiting and predicting , model got an accuracy score of 99.48%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix provides insights into true positives, true negatives, false positives, and false negatives, helping to understand how well the model distinguishes between different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_logREG_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUOKsXr2a4cOHc/LkSR48eKBsO6WexnWdaVjbCZsKZbGxMmdI79bo6Whx8UrBvmku3XyMPh0b4PtFfZzsLVkwoTt6Olps3HO6RHSqqFU1v8rSqppfZWlVza/o64fRKhyZmmKuQpCamkpUVBRRUVEA3L59m6ioKGnkc+zYsWzdupWVK1dy48YNFi9ezK+//srQoUMLFUcko4ISJTU1la1btzJkyBDatm1LSEiI3P1Zs2ZRvnx5DA0N6d+/Py9evJDuHTp0CB0dHZKSkuQ0I0eOpFmzZtLnnTt34uLigra2Nra2tsyfP1+ufnp6OuPHj8fa2hptbW2qVKnC6tWrAcjIyKB///7Y2dmhq6uLo6MjCxculLQBAQGsW7eO3bt3I5PJkMlkhIeHA2/enPTx8cHExARTU1M6dOjAnTt3iv/Q3iIjI5NDJ6N5/uIl1Z1s8q3/8tVroq7cw7Ouo1SmpqZG07qORMbcVrhOFbWq5ldZWlXzqyytqvktjlbV/CpTWyIUe4r+/69CcO7cOdzd3XF3dwdgzJgxuLu7M3nyZAA6derE8uXLmTNnDq6urqxatYqdO3fSqFGjQsURyaigRNm2bRtOTk44OjrSq1cv1qxZQ/bWttu2bSMgIICZM2dy7tw5LC0tWbp0qaRt3rw5JiYm7Ny5UyrLyMhg69at+Pr6AnD+/Hl8fHzo3r07MTExBAQEMGnSJLmkt0+fPmzZsoVFixYRGxvLihUrMDAwAN4swK5YsSLbt2/n8uXLTJ48me+//55t27YB4O/vj4+PD97e3sTFxREXF0eDBg149eoVrVu3xtDQkN9//52IiAgMDAzw9vbOcy+49PR0UlJS5K7cuHHnIZ4+U2jcZRKzl4Ux+/te2NuUz/d5JySlkpGRibmp/ItN5qZGxCfkHqs4OlXUqppfZWlVza+ytKrmtzhaVfOrTO3HgqenJ1lZWTmut/+N/eqrr7h+/TrPnz8nKiqKDh06FDqOeJteUKKsXr2aXr16AeDt7U1ycjInTpzA09OT4OBg+vfvT//+/QGYPn06R44ckUZH1dXV6d69O5s3b5bqHD16lKSkJLp06QLAggULaN68OZMmTQLAwcGBy5cvM3fuXPz8/Lh27Rrbtm3j8OHDtGjRAgB7e3vJn6amJoGBgdJnOzs7Tp8+zbZt2/Dx8cHAwABdXV3S09OxsLCQ6m3cuJHMzExWrVolvUG5du1aTExMCA8Pp1WrVrk+j6CgILl476OSVVk2BA8n9Vk6xyJimBq8g2UzBxYoIRUIBALBR0gRptlzbaMUUjpdCT4Krl69ytmzZ+nRowcAGhoadOvWTZoij42NpV69enKa+vXry3329fUlPDxcWmu6adMm2rZti4mJidRGw4YN5TQNGzbk+vXrZGRkEBUVhbq6ep5bTCxZsoRatWphbm6OgYEBP//8c75vAkZHR3Pjxg0MDQ0xMDDAwMAAU1NTXrx4wc2bN9+rmzBhAsnJydJ17969XOtpampgXaEszlWsGNbXm6p2Fmz99Y88PQGYmRigrq6WY3H94ycplDN7/zYeRdWpolbV/CpLq2p+laVVNb/F0aqaX2VqSwQlTNN/KEQyKigxVq9ezevXr6lQoQIaGhpoaGiwbNkydu7cSXJycoHaqFOnDpUrV+aXX37h+fPnhIaGSlP0BUFXVzfP+7/88gv+/v7079+fQ4cOERUVRb9+/fI9di01NZVatWpJC7uzr2vXrtGzZ8/36rS1taX93Qqzz1tmZhavXr3Ot56WpgY1naw5Efm/txszMzM5GXmNOq52CtepolbV/CpLq2p+laVVNb/F0aqaX2VqBYVDTNMLSoTXr1+zfv165s+fn2PKumPHjmzZsgVnZ2fOnDlDnz59pHt//vlnjrZ8fX3ZtGkTFStWRE1NTW6LKGdnZyIiIuTqR0RE4ODggLq6Oq6urmRmZnLixAlpmv7dug0aNJB78+/dkU0tLS0yMjLkyjw8PNi6dSvlypUr9sbB77Jk3QEa1HKkvLkJz56nc/BEFH9dvM3CgH4F0g/t2YyhgRtwd7bBw8WWZVuOk/Y8Hd/2n5WIThW1quZXWVpV86ssrar5FX39MFrFo4Bp+lI6BimSUUGJsHfvXhITE+nfvz/GxsZy97p06cLq1avx9/fHz8+P2rVr07BhQzZt2sSlS5fk1nTCm2Q0ICCAGTNm8OWXX8odW/btt99Sp04dpk2bRrdu3Th9+jSLFy+WXoSytbWlb9++fPXVVyxatAg3Nzf++ecf4uPj8fHxoWrVqqxfv56DBw9iZ2fHhg0biIyMxM7uf996bW1tOXjwIFevXsXMzAxjY2N8fX2ZO3cuHTp0YOrUqVSsWJF//vmHXbt2MW7cuGKdT5yYnEZg8Db+e/IUA30dqthasDCgH/XcqxZI37lVLf5LSmXmin3EJzzF1cGKHYuG5TutVFSdKmpVza+ytKrmV1laVfMr+vphtApHEdPspXSaXpaV/WqzQKBA2rdvT2ZmJvv27ctx7+zZs9SrV4/o6Gj27t3Ljz/+yIsXL+jSpQvly5fn4MGD0p5m2dSrV4+zZ89y7NgxvLy85O7t3LmTyZMnc/36dSwtLRk+fDj+/v7S/RcvXvD999/zyy+/kJCQgI2NDd9//z39+vUjPT2dwYMHExoaikwmo0ePHhgbG7N//37Jw+PHj/H19eX06dOkpqZy/PhxPD09efjwIePHj+e3337j6dOnWFlZ0bx5c+bNm1fg0dKUlBSMjY2JuPRvkY4DdbAUx4EKBAKBMklJSaG8mTHJyckKnynLbt/Y2BjtOmOQaRT+DPm3yXqdTnrkghLzWlREMioQKBGRjAoEAoFqI5LR4iOm6QWCUkAVC0OMjAqfWG7+658ix+zpUbjj2gQCgUCgRD7iaXqRjAoEAoFAIBCUdsQ+owKBQCAQCAQCgeIRI6MCQSll5bYT/LTxKPEJKVSvasXssV2p5WKbry4p8Slhu05w+dJtXr58jbm5Cb36tqGSrUW+2qLGVEWtqvlVllbV/CpLq2p+i6NVNb/K1CqUj3iaXoyMCj5JTp8+jbq6utyepXkREBBAzZo1S9bUW+w6dJ6JwaGMH9CG8A3jqV7Vii7Dl+Q4CeRdnqW9YP7czairqzN0+JdMDOhH566e6Onnv+i9qDFVUatqfpWlVTW/ytKqml/R1w+jVTjZ0/TFvUohpdOVQFDCrF69muHDh3Py5EnpqNHcyMrK4vXr/E8+UjRLNx+jT8cG+H5RHyd7SxZM6I6ejhYb95zOU3fo4BnKlDGkt18bbO0sKVvWBOdqdpiblymxmKqoVTW/ytKqml9laVXNr+jrh9EKCo5IRgWfHKmpqWzdupUhQ4bQtm1bQkJCpHvh4eHIZDL2799PrVq10NbWZuPGjQQGBhIdHY1MJkMmkxESEkJWVhYBAQHY2Nigra1NhQoVGDFiRLH9vXz1mqgr9/Cs6yiVqamp0bSuI5Ext/PUxly4iU0lC1at2M14/yUETV9HxO/RJRpT1bSq5ldZWlXzqyytqvktjlbV/CpTWyLIZAoYGRXT9AJBqWDbtm04OTnh6OhIr169WLNmDe9ut/vdd98xa9YsYmNjadmyJd9++y0uLi7ExcURFxdHt27d2LlzJz/++CMrVqzg+vXrhIWF4erqmmfs9PR0UlJS5K53SUhKJSMjE3NT+a2ezE2NiE/IWf9t/nucxO8nojAvV4ZvRnxJ4yY12b71GH+evpinrjgxVU2ran6VpVU1v8rSqprf4mhVza8ytSWCmkwxVylEvMAk+ORYvXo1vXr1AsDb25vk5GROnDiBp6enVGfq1Km0bNlS+mxgYICGhgYWFv97Ceju3btYWFjQokULNDU1sbGxoW7dunnGDgoKIjAwULEdeousrCxsKlnQoVMTAKxtyvPgwX+cOhHFZ/Wrl1hcgUAgEAiKihgZFXxSXL16lbNnz9KjRw8ANDQ06NatG6tXr5arV7t27Xzb6tq1K8+fP8fe3p6BAwcSGhqa7/rSCRMmkJycLF337t3LUcfMxAB1dbUcC+QfP0nJ9zxkI2MDLC3N5MosLE15kpj3YvvixFQ1rar5VZZW1fwqS6tqfoujVTW/ytSWCOIFJoHg42D16tW8fv2aChUqoKGhgYaGBsuWLWPnzp0kJydL9fT19fNty9ramqtXr7J06VJ0dXUZOnQoTZo04dWrV+/VaGtrY2RkJHe9i5amBjWdrDkReVUqy8zM5GTkNeq42uXpqXJlKx49eiJXFv8oEVPTvH9xFiemqmlVza+ytKrmV1laVfNbHK2q+VWmtkTI3tqpuFcpREzTCz4ZXr9+zfr165k/fz6tWrWSu9exY0e2bNmCk5NTrlotLS0yMjJylOvq6tK+fXvat2/PsGHDcHJyIiYmBg8Pj2J5HdqzGUMDN+DubIOHiy3Lthwn7Xk6vu0/y1PXrEUt5s3ezIHf/sSjtiP/3Ikj4vcL9OjVKk9dcWKqolbV/CpLq2p+laVVNb+irx9Gq3A+4hOYRDIq+GTYu3cviYmJ9O/fH2NjY7l7Xbp0YfXq1cydOzdXra2tLbdv3yYqKoqKFStiaGjIli1byMjIoF69eujp6bFx40Z0dXWpVKn4Z753blWL/5JSmbliH/EJT3F1sGLHomH5Tg1VsrVk0JCO7Ak9yf59f2BW1pgvfbyoW69aicVURa2q+VWWVtX8Kkuran5FXz+MVlBwZFnvvkYsEHyktG/fnszMTPbt25fj3tmzZ6lXrx4LFy5k5MiRJCYmYmJiIt1PT0/H19eXo0ePkpSUxNq1azExMZHeuM/IyMDV1ZXp06fTvHnzAntKSUnB2NiYRwnJuU7Z58fmv/4ptCabnh7FT5oFAoHgUyclJYXyZsYkJxft93hB2jc2NkbbMwCZhk6x2sp6/YL08IAS81pUxMio4JPh119/fe+9unXrSts75bZXqLa2Njt27MhR3rFjR4X5EwgEAoHgvYhpeoFAUBopzujmodiHRdK1cs7/jHuBQCAQCAqKSEYFAoFAIBAISjuKeBtevE0vEAgKw8ptJ/hp41HiE1KoXtWK2WO7UsvFtsS023edYEfY73JlFSzN+HH2kFLpt7haVfOrLK2q+VWWVtX8Fkeran6VqVUoH/E0fel0JSj12NraEhwcrGwbCuPOnTvIZDKioqKUbQWAXYfOMzE4lPED2hC+YTzVq1rRZfiSHJsvK1pb0cqcFYtGSVfgxL6l2m9RtarmV1laVfOrLK2q+RV9/TBaQcERyaiCOX36NOrq6rRt27ZI+oCAAGrWrKlYU8UgJCRE7q3ybCIjIxk0aFCJxfXz80Mmk733srW1LVbb7754ZG1tTVxcHNWrl44jM5duPkafjg3w/aI+TvaWLJjQHT0dLTbuOV2iWnV1NUxMDKTLyFCvVPstqlbV/CpLq2p+laVVNb+irx9Gq3A+4k3vRTKqYFavXs3w4cM5efIkDx48ULadEsPc3Bw9vYIlKkVh4cKFxMXFSRfA2rVrpc+RkZEKjaeuro6FhQUaGspfufLy1WuirtzDs66jVKampkbTuo5ExtwuMS3Aw4dPGDwimOHfLmbRslD++y85X42y/BZVq2p+laVVNb/K0qqa3+JoVc2vMrUlgyKOAi2daV/pdKWipKamsnXrVoYMGULbtm0JCQmRu5/bKGNYWBiy//+mEhISQmBgINHR0dIIYHYbd+/epUOHDhgYGGBkZISPjw+PHj2S2skeUV2zZg02NjYYGBgwdOhQMjIymDNnDhYWFpQrV44ZM2bIxV+wYAGurq7o6+tjbW3N0KFDSU1NBSA8PJx+/fqRnJws+QkICAByTtMX1N+GDRuwtbXF2NiY7t278/Rp7lMdxsbGWFhYSBeAiYmJ9PnRo0e0adMGAwMDypcvT+/evfnvv/8k/Y4dO3B1dUVXVxczMzNatGhBWloaAQEBrFu3jt27d0t9Cg8PzzFNHx4ejkwm4+jRo9SuXRs9PT0aNGjA1atX5XxOnz6dcuXKYWhoyIABA/juu+/yHNlOT08nJSVF7nqXhKRUMjIyMTc1lCs3NzUiPiFnfUVpq1S2Ysig9kzw70H/vm14/DiZKTPW8fx5eonFVIZW1fwqS6tqfpWlVTW/xdGqml9lagWFQySjCmTbtm04OTnh6OhIr169WLNmDYU5U6Bbt258++23uLi4SCOA3bp1IzMzkw4dOvDkyRNOnDjB4cOHuXXrFt26dZPT37x5k/3793PgwAG2bNnC6tWradu2Lf/++y8nTpxg9uzZTJw4kTNnzkgaNTU1Fi1axKVLl1i3bh3Hjh1j3LhxADRo0IDg4GCMjIwkP/7+/jl8F8ZfWFgYe/fuZe/evZw4cYJZs2YV5hEDkJSURLNmzXB3d+fcuXMcOHCAR48e4ePjA0BcXBw9evTgq6++IjY2lvDwcDp37kxWVhb+/v74+Pjg7e0t9alBgwbvjfXDDz8wf/58zp07h4aGBl999ZV0b9OmTcyYMYPZs2dz/vx5bGxsWLZsWZ7eg4KCMDY2li5ra+tC97+kcHerQv261ahkU56aNSrz3bfdSXuWzumzl5VtTSAQCAQf8TS98uckPyJWr15Nr169APD29iY5OZkTJ07g6elZIL2uri4GBgZoaGhIo4EAhw8fJiYmhtu3b0vJy/r163FxcSEyMpI6deoAb5LCNWvWYGhoSLVq1fDy8uLq1av89ttvqKmp4ejoyOzZszl+/Dj16tUDYNSoUVIcW1tbpk+fzuDBg1m6dClaWloYGxsjk8nk/LzL0aNHC+wvJCQEQ8M33zJ79+7N0aNHc4zW5sfixYtxd3dn5syZUtmaNWuwtrbm2rVrpKam8vr1azp37iwdzenq6ir3nNPT0/PsUzYzZsygadOmAHz33Xe0bduWFy9eoKOjw08//UT//v3p168fAJMnT+bQoUPSyHJuTJgwgTFjxkifU1JSciSkZiYGqKur5Vgg//hJSr5H0BVH+y76+jpYWpjy8FFiicVUhlbV/CpLq2p+laVVNb/F0aqaX2VqSwSZTAFv05fOZFSMjCqIq1evcvbsWXr06AGAhoYG3bp1Y/Xq1cVuOzY2Fmtra7mkpVq1apiYmBAbGyuV2draSokeQPny5alWrRpqampyZfHx8dLnI0eO0Lx5c6ysrDA0NKR3794kJCTw7NmzEvdnaWkp56WgREdHc/z4cQwMDKTLyckJeDP66ubmRvPmzXF1daVr166sXLmSxMS8E6r3UaNGDTm/gOT56tWr1K1bV67+u5/fRVtbGyMjI7nrXbQ0NajpZM2JyP8tCcjMzORk5DXquNrl2X5xtO/y4sVLHsUnUsbEoMRiKkOran6VpVU1v8rSqprf4mhVza8ytSVCcdeLKmJrqBJCjIwqiNWrV/P69WsqVKgglWVlZaGtrc3ixYsxNjZGTU0tx7T9q1evFOZBU1NT7rNMJsu1LDMzE3iznVG7du0YMmQIM2bMwNTUlFOnTtG/f39evnyp8BeU8vJSGFJTU2nfvj2zZ8/Occ/S0hJ1dXUOHz7MH3/8waFDh/jpp5/44YcfOHPmDHZ2hfsF8rbn7LW9RfFcWIb2bMbQwA24O9vg4WLLsi3HSXuejm/7z0pMu2HLEWq5V6WsmTGJSU/ZvuskampqNPzMpVT6LY5W1fwqS6tqfpWlVTW/oq8fRisoOCIZVQCvX79m/fr1zJ8/n1atWsnd69ixI1u2bGHw4MGYm5vz9OlT0tLS0NfXB8ixr6WWlhYZGRlyZc7Ozty7d4979+5Jo4+XL18mKSmJatWqFdn3+fPnyczMZP78+dLo6bZt2/L18y4l5e99eHh4sHPnTmxtbd/79rtMJqNhw4Y0bNiQyZMnU6lSJUJDQxkzZkyB+lQQHB0diYyMpE+fPlKZot7y79yqFv8lpTJzxT7iE57i6mDFjkXDCjQ1VFRtwpMUFi0N5Wnqc4wM9XB0sGb6ZD+MjPRLpd/iaFXNr7K0quZXWVpV8yv6+mG0CucjPoFJllWYN2wEuRIWFka3bt2Ij4/H2NhY7t748eM5duwYkZGRPHnyBBsbG/r378+IESM4c+YMY8eO5cGDB9KI6ebNmxk0aBCnTp2iYsWKGBoaoqWlhYeHB4aGhgQHB/P69WuGDh2KgYEB4eHhwJu31cPCwuSSWz8/P5KSkggLC5PKPD09qVmzJsHBwURHR0t/bt++PREREUyYMIH79++TmJiIiYkJf/zxBw0bNuTIkSO4ubmhp6eHnp4etra2jBo1ilGjRpGVlVUkf8HBwQQHB3Pnzp18n7FMJiM0NJSOHTvy4MEDatasSdOmTRk3bhympqbcuHGDX375hVWrVnHu3DmOHj1Kq1atKFeuHGfOnKFXr16EhYXRpk0bZs6cyYoVKzh06BBmZmYYGxtz//597Ozs+Pvvv6lZsybh4eF4eXlJzwHefHFwd3fn9u3b2NrasmnTJgYOHMiyZcto0KABW7duZe7cudjb2/P3338X6GcnJSUFY2NjHiUk5zplX5KIs+kFAoGg+KSkpFDezJjk5JL5PZ7974R2mx+RaeoWq62sV89J3z+6xLwWldK5eEDFWL16NS1atMiRiAJ06dKFc+fOceHCBUxNTdm4cSO//fYbrq6ubNmyRdoq6e363t7eeHl5YW5uzpYtW5DJZOzevZsyZcrQpEkTWrRogb29PVu3bi2Wbzc3NxYsWMDs2bOpXr06mzZtIigoSK5OgwYNGDx4MN26dcPc3Jw5c+bkaKek/L2PChUqEBERQUZGBq1atcLV1ZVRo0ZhYmKCmpoaRkZGnDx5ks8//xwHBwcmTpzI/PnzadOmDQADBw7E0dGR2rVrY25uTkRERJF8+Pr6MmHCBPz9/fHw8OD27dv4+fmho6OjyO4KBAKBQKAUTp48Sfv27alQoQIymUxucOtdBg8ejEwmK9LpjGJkVCBQIC1btsTCwoINGzYUqL4yR0aLyrzwG0XW+ntWUaATgUAgUD4fbGT082DFjIz+NqrAXvfv309ERAS1atWic+fO0gzlu4SGhhIYGMjjx48ZO3as3E49BUGsGRUIisizZ89Yvnw5rVu3Rl1dnS1btnDkyBEOHz6sbGsCgUAg+NhQxNvwhdS3adNGmlV8H/fv32f48OEcPHiwyEehi2RUICgiMpmM3377jRkzZvDixQscHR3ZuXMnLVq0ULY1gUAgEAjey7un/2lra6OtrV3odjIzM+nduzdjx47FxSX/nVfeh0hGBYIioqury5EjR0qs/ZXbTvDTxqPEJ6RQvaoVs8d2pZaLbYlqi6JbPHM1yYk5j3WtVb8G3p2blajf4miVEVMVtarmV1laVfNbHK2q+VWmVqEo8G36dw9bmTJlSo53WArC7Nmz0dDQYMSIEcWyJV5gEiicd895V3Z7ISEh0hvxxSG/xduKZNeh80wMDmX8gDaEbxhP9apWdBm+JMdJIIrUFlXXb0QPRk4aKF09B3YGwNmt6kfX109Nq2p+laVVNb+irx9Gq2hkMplCLoB79+6RnJwsXRMmTCi0n/Pnz7Nw4UJCQkKkdouKSEYFhcbPz0/uB9vMzAxvb28uXLigbGslSlxcXL5rZxTF0s3H6NOxAb5f1MfJ3pIFE7qjp6PFxj2nS0xbVJ2+gR4GRvrSdT32FmXMjLGxr/jR9fVT06qaX2VpVc2v6OuH0ZZm3j0JsChT9L///jvx8fHY2NigoaGBhoYG//zzD99++y22traFaksko4Ii4e3tTVxcHHFxcRw9ehQNDQ3atWunbFslioWFRZH+whaWl69eE3XlHp51HaUyNTU1mtZ1JDLmdoloixPzbTJeZ3Dxryu41XEp0DdlVevrp6RVNb/K0qqa3+JoVc2vMrUlgSJHRhVB7969uXDhAlFRUdJVoUIFxo4dy8GDBwvVlkhGBUVCW1sbCwsLLCwsqFmzJt999x337t3j8ePHudY/ceIEdevWRVtbG0tLS7777jtev34t3c/MzGTOnDlUqVIFbW1tbGxsmDFjRq5tZWRk8NVXX+Hk5MTdu3cBSEpK4uuvv6Z8+fLo6OhQvXp19u7dK6c7ePAgzs7OGBgYSMl0NpGRkbRs2ZKyZctibGxM06ZN+euvv+T0b0/TZy8d2LVrF15eXujp6eHm5sbp03l/W05PTyclJUXuepeEpFQyMjIxNzWUKzc3NSI+IWd9RWiLE/Ntrl66yYsX6dSoXbCTt1Str5+SVtX8Kkuran6Lo1U1v8rUlggyBV2FIDU1VUo0AW7fvk1UVBR3797FzMyM6tWry12amppYWFjg6OiYd8PvIJJRQbFJTU1l48aNVKlSBTMzsxz379+/z+eff06dOnWIjo5m2bJlrF69munTp0t1JkyYwKxZs5g0aRKXL19m8+bNlC9fPkdb6enpdO3alaioKH7//XdsbGzIzMykTZs2REREsHHjRi5fvsysWbNQV1eXdM+ePWPevHls2LCBkydPcvfuXfz9/aX7T58+pW/fvpw6dYo///yTqlWr8vnnn/P0ad7rgn744Qf8/f2JiorCwcGBHj16yCXZ7xIUFISxsbF0vbuIXNWJPnuRyo62GBobKNuKQCAQfFQoY2T03LlzuLu74+7uDsCYMWNwd3dn8uTJCu2beJteUCT27t2LgcGbhCMtLQ1LS0v27t0rnXH/NkuXLsXa2prFixcjk8lwcnLiwYMHjB8/nsmTJ5OWlsbChQtZvHgxffv2BaBy5co0atRIrp3U1FTatm1Leno6x48fl068OnLkCGfPniU2NhYHBwcA7O3t5bSvXr1i+fLlVK5cGYBvvvmGqVOnSvebNZN/6/vnn3/GxMSEEydO5Ln8wN/fX9pXLTAwEBcXF27cuIGTk1Ou9SdMmMCYMWOkzykpKTkSUjMTA9TV1XIskH/8JCXf85CLqi1OzGySE1O4ff0eXfoUfLmGqvX1U9Kqml9laVXNb3G0quZXmdqPBU9PTwpzNlJBjvfODTEyKigSXl5e0tD92bNnad26NW3atOGff/7JUTc2Npb69evLfSNr2LAhqamp/Pvvv8TGxpKenk7z5s3zjNmjRw/S0tI4dOiQ3NGrUVFRVKxYUUpEc0NPT09KRAEsLS2Jj4+XPj969IiBAwdStWpVjI2NMTIyIjU1VVoG8D5q1Kgh1yYg1+67aGtr51g4/i5amhrUdLLmRORVqSwzM5OTkdeo42qXp5+iaosTM5voyEvoGehS1blg9YsbVxl9/ZS0quZXWVpV81scrar5Vaa2JChta0YViRgZFRQJfX19qlT539GOq1atwtjYmJUrVzJgwIBCtaWrW7DjzT7//HM2btzI6dOn5UYyC6LX1NSU+yyTyeS+7fXt25eEhAQWLlxIpUqV0NbWpn79+rx8+bLA7Wb/Jc/MzCxQf/JiaM9mDA3cgLuzDR4utizbcpy05+n4tv+sxLTFiZmVmUV05GVq1K6GmnrhvuOqWl8/Ja2q+VWWVtX8ir5+GK2iUUgyKZJRwceMTCZDTU2N58+f57jn7OzMzp07ycrKkv4iRUREYGhoSMWKFSlXrhy6urocPXo0z0R2yJAhVK9enS+++IJ9+/bRtGlT4M3o5L///su1a9fyHB3Ni4iICJYuXcrnn38OvNmD7b///itSW4qgc6ta/JeUyswV+4hPeIqrgxU7Fg0r0NRQUbXFiXn7+l1Skp7iVqfwJ3CoWl8/Ja2q+VWWVtX8ir5+GK2g4MiyCrMYQCDgzT6jjx49Yu3atQAkJiayePFili1bxrFjx7C1tcXOzo6///6bmjVrcv/+fRwcHOjXrx/ffPMNV69eZcCAAQwbNkw68SEwMJCFCxcSHBxMw4YNefz4MZcuXaJ///7cuXNHrr3g4GAmTZrE/v37pXWlXl5e/PfffyxYsIAqVapw5coVZDIZ3t7ehISEMGrUKJKSkqQ+hIWF0alTJ2l01MPDg7Jly7Jw4UJSUlIYO3Ys586dY+bMmYwaNQp4k3CHhobSsWPHHJ7gzRv9ZcqU4fjx43h6ehboWaakpGBsbMyjhORcp+xLI/PCbxRZ6+9ZJf9KAoFAoEKkpKRQ3syY5OSS+T2e/e+EYZcVyDQLNpP4PrJePefpzq9LzGtRESOjgiJx4MABaY2koaEhTk5ObN++HU9PzxwLmK2srPjtt98YO3Ysbm5umJqa0r9/fyZOnCjVmTRpEhoaGkyePJkHDx5gaWnJ4MGDc409atQoMjMz+fzzzzlw4AANGjRg586d+Pv7S+tKq1SpwqxZswrcn9WrVzNo0CA8PDywtrZm5syZcm/bCwQCgUCgVIqwNVOubZRCxMioQKBEVHFktDj8dTuxyFoPuzIKdCIQCASK4YONjH6poJHRHWJkVCAQCAQCgUBQSMQLTAKBQCAQCAQCpSGToYBkVDFeFI3YZ/QTIzw8HJlMJvcyz6eKn58fHTt2VLaN97Jy2wlqfDEZi4ajaOE3l/OX7pS4Vhkx32ZL2Ema+0xiSchvJR5XWX1VNa2q+VWWVtX8Fkeran6VqRUUDJGMliL8/PzkNqY1MzPD29ubCxcuKCxGgwYNiIuLk9s0vjTy/PlzTE1NKVu2LOnp6cq2ky8hISGYmJgorL1dh84zMTiU8QPaEL5hPNWrWtFl+JIcJ4EoUquMmG9z5ca/7D0ciX2lnMfAKjqusvqqalpV86ssrar5FX39sL/bFIUMBWx6X0qHRkUyWsrw9vYmLi6OuLg4jh49ioaGRp7HURYWLS0tLCwsSu0pDNns3LkTFxcXnJycCAsLU7adD87Szcfo07EBvl/Ux8nekgUTuqOno8XGPadLTKuMmNk8f5HOzJ92MObrjhjqF3yBvqr1VdW0quZXWVpV8yv6+uF+tymSj/kEJpGMljK0tbWxsLDAwsKCmjVr8t1333Hv3j0eP34M5D7NHhUVhUwmk7ZU+ueff2jfvj1lypRBX18fFxcXfvvtt1z12SN6Bw8exNnZGQMDAykhfptVq1bh7OyMjo4OTk5OLF26VLr38uVLvvnmGywtLdHR0aFSpUoEBQUBkJWVRUBAADY2Nmhra1OhQgVGjBiR73NYvXo1vXr1olevXqxevTrHfZlMxqpVq+jUqRN6enpUrVqVPXv2SPczMjLo378/dnZ26Orq4ujoyMKFC98bb/369ZiZmeUYhe3YsSO9e/cGIDo6Gi8vLwwNDTEyMqJWrVqcO3eO8PBw+vXrR3JysvSXPXv/1KLw8tVroq7cw7Ouo1SmpqZG07qORMbcLhGtMmK+zcJVe/nM3YFaNSrnX7mYcZXVV1XTqppfZWlVzW9xtKrmV5naEkGmoKsUIpLRUkxqaiobN26kSpUqmJmZFVg3bNgw0tPTOXnyJDExMcyePRsDA4P31n/27Bnz5s1jw4YNnDx5krt378rtsblp0yYmT57MjBkziI2NZebMmUyaNIl169YBsGjRIvbs2cO2bdu4evUqmzZtwtbWFngzwvnjjz+yYsUKrl+/TlhYGK6urnn6v3nzJqdPn8bHxwcfHx9+//33XM+8DwwMxMfHhwsXLvD555/j6+vLkydPgDdHclasWJHt27dz+fJlJk+ezPfff8+2bdtyjdm1a1cyMjLkEtr4+Hj27dvHV199BYCvry8VK1YkMjKS8+fP891336GpqUmDBg0IDg7GyMhIGtV+3x6l6enppKSkyF3vkpCUSkZGJuamhnLl5qZGxCfkrK8IrTJiZnMs4gI3bj9gQM+W+dZVRFxl9VXVtKrmV1laVfNbHK2q+VWmVlA4xNv0pYy9e/dKiWNaWhqWlpbs3bsXNbWCf2+4e/cuXbp0kZI+e3v7POu/evWK5cuXU7nym1Gpb775hqlTp0r3p0yZwvz58+ncuTMAdnZ2XL58mRUrVtC3b1/u3r1L1apVadSoETKZjEqVKsl5sbCwoEWLFmhqamJjY0PdunXz9LNmzRratGlDmTJv9pVs3bo1a9euzTHa6OfnR48ePQCYOXMmixYt4uzZs3h7e6OpqUlgYKBU187OjtOnT7Nt2zZ8fHxyxNTV1aVnz56sXbuWrl27ArBx40ZsbGyk05Tu3r3L2LFjcXJyAqBq1aqS3tjYGJlMhoWFRZ59CwoKkvP1qRP/XzJLQn5jzkQ/tLQ0lW1HIBAISi8KmGbPEtP0goLg5eVFVFQUUVFRnD17ltatW9OmTZtcRwbfx4gRI5g+fToNGzZkypQp+b4ApaenJyWiAJaWlsTHxwNvEuKbN2/Sv39/DAwMpGv69OncvHkTeJMURkVF4ejoyIgRIzh06JDUVteuXXn+/Dn29vYMHDiQ0NBQXr9+/V4vGRkZrFu3jl69ekllvXr1IiQkhMzMTLm6NWrUkP6sr6+PkZGR5BtgyZIl1KpVC3NzcwwMDPj555+5e/fue2MPHDiQQ4cOcf/+feDNEobsl8oAxowZw4ABA2jRogWzZs2S+l8YJkyYQHJysnTdu3cvRx0zEwPU1dVyLJB//CQl3/OQi6pVRkyAa7fuk5ScxuDxy2jZfQotu08h+vIdQvf/ScvuU8h45/+5IuIqq6+qplU1v8rSqprf4mhVza8ytSWBWDMq+GDo6+tTpUoVqlSpQp06dVi1ahVpaWmsXLkSQBohffvgrFevXsm1MWDAAG7dukXv3r2JiYmhdu3a/PTTT++NqakpPyIlk8mk9lNTUwFYuXKllCRHRUVx8eJF/vzzT+DNue63b99m2rRpPH/+HB8fH7788ksArK2tuXr1KkuXLkVXV5ehQ4fSpEmTHJ6zOXjwIPfv36dbt25oaGigoaFB9+7d+eeffzh69Gi+vrMT1l9++QV/f3/69+/PoUOHiIqKol+/frx8+fK9z8Hd3R03NzfWr1/P+fPnuXTpEn5+ftL9gIAALl26RNu2bTl27BjVqlUjNDT0ve3lhra2NkZGRnLXu2hpalDTyZoTkVelsszMTE5GXqOOq12e7RdVq4yYAB6ulVk17xt+njNUuhwrW9G8UQ1+njMU9TxmBFStr6qmVTW/ytKqmt/iaFXNrzK1gsIhpulLOTKZDDU1NZ4/fw6Aubk5AHFxcdI0dlRUVA6dtbU1gwcPZvDgwUyYMIGVK1cyfPjwQscvX748FSpU4NatW/j6+r63npGREd26daNbt258+eWXeHt78+TJE0xNTdHV1aV9+/a0b9+eYcOG4eTkRExMDB4eHjnaWb16Nd27d+eHH36QK58xYwarV6+mZcuCrSuMiIigQYMGDB06VCoryEjmgAEDCA4O5v79+7Ro0QJra2u5+w4ODjg4ODB69Gh69OjB2rVr6dSpE1paWmRkZBTIW0EY2rMZQwM34O5sg4eLLcu2HCfteTq+7T8rMa0yYurpamNnI7+Vk462JkaGejnKVb2vqqhVNb/K0qqaX9HXD6NVNIoY2SytI6MiGS1lpKen8/DhQwASExNZvHgxqamptG/fHoAqVapgbW1NQEAAM2bM4Nq1a8yfP1+ujVGjRtGmTRscHBxITEzk+PHjODs7F9lTYGAgI0aMwNjYGG9vb9LT0zl37hyJiYmMGTOGBQsWYGlpibu7O2pqamzfvh0LCwtMTEwICQkhIyODevXqoaenx8aNG9HV1ZVbV5rN48eP+fXXX9mzZw/Vq1eXu9enTx86deokJbj5UbVqVdavX8/Bgwexs7Njw4YNREZGYmeX97fZnj174u/vz8qVK1m/fr1U/vz5c8aOHcuXX36JnZ0d//77L5GRkXTp0gUAW1tbUlNTOXr0KG5ubujp6aGnp1eQx5srnVvV4r+kVGau2Ed8wlNcHazYsWhYgaaGiqpVRsziomp9VTWtqvlVllbV/Iq+lv7fbbmiiLfhS2cuiizr7flegVLx8/OT3lAHMDQ0xMnJifHjx0tJD7wZ9RsyZAjXr1+nTp06jBgxgq5du3L79m1sbW0ZPnw4+/fv599//8XIyAhvb29+/PFHzMzMCA8Px8vLi8TERClZHDVqlNxWUWFhYXTq1EluKcDmzZuZO3culy9fRl9fH1dXV0aNGkWnTp1YuXIlS5cu5fr166irq1OnTh3mzp2Lu7s7YWFhzJo1i9jYWDIyMnB1dWX69Ok0b948R//nz5/P9OnTiY+PzzEF//LlS8qXLy8lxjKZjNDQULkTlExMTAgODsbPz4/09HQGDx5MaGgoMpmMHj16YGxszP79+6WRZD8/P5KSknLsY9qnTx/27dvHgwcP0NbWluL37duXiIgIHj16RNmyZencuTNz585FR0cHgCFDhrB9+3YSEhKYMmVKgbZ3SklJwdjYmEcJyblO2X9s/HU7schaD7syCnQiEAgEiiElJYXyZsYkJ5fM7/HsfyfMeq1FTavogxwAmS+fkbCxX4l5LSoiGRUI3qF58+a4uLiwaNGiEo8lktGCI5JRgUBQGvlQyWjZ3iEKSUb/2+BX6pJRMU0vEPw/iYmJhIeHEx4eLrepv0BxFCeh7LPxryJr1/fKuT5ZIBAIVAmxZlQg+ARwd3cnMTGR2bNn4+jomL9AIBAIBAJBsRHJqEDw/2QfpyoQCAQCQWnjYx4ZFfuMCoA3P6DvvsiTF7a2tgQHBxdZX1KEhIRgYmKibBsKYeW2E9T4YjIWDUfRwm8u5y/dKXGtMmIWR6ujoYZvrYos6OjCqu41mdTaATuzgq2pUrW+Kkuran6VpVU1v8XRqppfZWoVidj0XqDSPHz4kOHDh2Nvb4+2tjbW1ta0b99ebhP5uLg42rRpU+A2IyMjGTRoUIHrZ59k9O7l7e1dqL7kR7du3bh27ZpC21QGuw6dZ2JwKOMHtCF8w3iqV7Wiy/AlOU4CUaRWGTGLq+3/WSVcLA1Z8cc/fL83lotxTxnfvCpldPM+WlQV+yp+JkqvVtX8ir5+GK3CkSnoKoWIZPQj586dO9SqVYtjx44xd+5cYmJiOHDgAF5eXgwbNkyqZ2FhIW1jVBDMzc0LvY+mt7c3cXFxcteWLVsK1UZ+6OrqUq5cOYW2WRTyOumpICzdfIw+HRvg+0V9nOwtWTChO3o6Wmzcc7rEtMqIWRytprqM2jYmbP37PlfjU4lPTSf0QhyPnqbTzKFsqfOrilpV86ssrar5FX39MFpBwRHJ6EfO0KFDkclknD17li5duuDg4ICLiwtjxoyRjvME+Wn2Bg0aMH78eLl2Hj9+jKamJidPngRyTtMXBG1tbSwsLOSu7FOksj2sWrWKTp06oaenR9WqVdmzZ49cG3v27KFq1aro6Ojg5eXFunXrkMlk0j6p707TBwQEULNmTTZs2ICtrS3GxsZ0796dp0//9602MzOToKAg7Ozs0NXVxc3NjR07dsjFvXjxIm3atMHAwIDy5cvTu3dv/vvvP+m+p6cn33zzDaNGjaJs2bK0bt26UM/mbV6+ek3UlXt41v3fS1Rqamo0retIZMztEtEqI2ZxteoyGepqMl5lyO9O9yojE4dyBqXOr6ppVc2vsrSq5rc4WlXzq0xtSSCm6QUqyZMnTzhw4ADDhg1DX18/x/33ra309fXll19+kdv0fuvWrVSoUIHGjRuXlF3gzWlPPj4+XLhwgc8//xxfX1+ePHkCwO3bt/nyyy/p2LEj0dHRfP311zmODc2NmzdvEhYWxt69e9m7dy8nTpxg1qxZ0v2goCDWr1/P8uXLuXTpEqNHj6ZXr16cOHECgKSkJJo1a4a7uzvnzp3jwIEDPHr0CB8fH7k469atQ0tLi4iICJYvX56rl/T0dFJSUuSud0lISiUjIxNzU0O5cnNTI+ITctZXhFYZMYurffE6k+uPU+ngaoGJriYyGTSwM6VKWX1M8pimV8W+ip+J0qtVNb/F0aqaX2VqSwKRjApUkhs3bpCVlYWTk1OhdD4+Pjx48IBTp05JZZs3b6ZHjx7F+kHeu3cvBgYGctfMmTPl6vj5+dGjRw+qVKnCzJkzSU1N5ezZswCsWLECR0dH5s6di6OjI927d8fPzy/fuJmZmYSEhFC9enUaN25M7969pfWy6enpzJw5kzVr1tC6dWvs7e3x8/OjV69erFixAoDFixfj7u7OzJkzcXJywt3dnTVr1nD8+HG59alVq1Zlzpw5ODo6vndrqKCgIIyNjaXL2tq6KI9S8P+siLiDDFjUxZU1Pdxp6WjO6X8SEUd5CAQCgeogtnb6iCnq4Vrm5ua0atWKTZs20bhxY27fvs3p06el5KyoeHl5sWzZMrmyd8+Zr1GjhvRnfX19jIyMiI+PB+Dq1avUqVNHrn7dunXzjWtra4uh4f++2VpaWkpt3rhxg2fPntGyZUs5zcuXL3F3dwcgOjqa48ePY2CQc+r35s2bODg4AFCrVq18vUyYMIExY8ZIn1NSUnIkpGYmBqirq+VYIP/4SUq+5yEXVauMmMXVAsSnvmTm4etoqauhq6VG8vPXDGtkR3xqeqnzq2paVfOrLK2q+S2OVtX8KlNbEoitnQQqSdWqVZHJZFy5cqXQWl9fX3bs2MGrV6/YvHkzrq6uuLq6FsuPvr4+VapUkbveTUbfPZNeJpORmZlZrLh5tZmamgrAvn37iIqKkq7Lly9L60ZTU1Np37693P2oqCiuX79OkyZN5PqXH9ra2hgZGcld76KlqUFNJ2tORF6VyjIzMzkZeY06rnZ5tl9UrTJiFlf7Ni8zMkl+/ho9LXWqVzDkr3tJpc6vqmlVza+ytKrmtzhaVfOrTG2J8BG/TS9GRj9iTE1Nad26NUuWLGHEiBE5kqWkpKT3rhvt0KEDgwYN4sCBA2zevJk+ffp8AMd54+joyG+//SZXFhkZWaw2q1Wrhra2Nnfv3qVp06a51vHw8GDnzp3Y2tqiofFh/soM7dmMoYEbcHe2wcPFlmVbjpP2PB3f9p+VmFYZMYurdbU0BGTEpbygvKE23T2siEtO5/ebCaXSr6ppVc2vsrSq5lf09cNoBQVHJKMfOUuWLKFhw4bUrVuXqVOnUqNGDV6/fs3hw4dZtmwZsbGxuer09fXp2LEjkyZNIjY2lh49ehTbS3p6Og8fPpQr09DQoGzZvLfhyebrr79mwYIFjB8/nv79+xMVFUVISAhQ9KkHQ0ND/P39GT16NJmZmTRq1Ijk5GQiIiIwMjKib9++DBs2jJUrV9KjRw/GjRuHqakpN27c4JdffmHVqlWoq6sXKXZedG5Vi/+SUpm5Yh/xCU9xdbBix6JhBZoaKqpWGTGLq9XVVKeruxWmepqkvcwg8m4iO6IekJHPChVV7Kv4mSi9WlXzK/r6YbSK5mOeppdlFXVhoUBliIuLY8aMGezdu5e4uDjMzc2pVasWo0ePxtPTE3jzAxoaGkrHjh0l3f79+/n8889p0qSJ9GZ5Nra2towaNYpRo0a9V/82fn5+rFu3Lke5o6OjtIwgtzZMTEwIDg6WXlTas2cP3377Lffu3aN+/fp069aNIUOG8Pz5c3R0dAgJCWHUqFHSVk8BAQGEhYURFRUltRkcHExwcLB0/GdWVhaLFi1i2bJl3Lp1CxMTEzw8PPj++++lafjr168zfvx4jh8/Tnp6OpUqVcLb25sFCxYgk8nw9PSkZs2ahd7uKiUlBWNjYx4lJOc6ZS/4H302/lVk7fpeHgp0IhAIBP8jJSWF8mbGJCeXzO/x7H8nKn79C2pahdvf+10yXz7j3xXdS8xrURHJqEClmTFjBsuXL+fevXvKtlIkRDJacEQyKhAISiMiGS0+YppeoFIsXbqUOnXqYGZmRkREBHPnzuWbb75Rti3BB6A4CeWh2If5V3oPrZwtiqwVCAQCRSFDAdP0hXyD6eTJk8ydO5fz588TFxcnN3v56tUrJk6cyG+//catW7cwNjamRYsWzJo1iwoVKhQqjnibXqBSXL9+nQ4dOlCtWjWmTZvGt99+S0BAgLJtCQQCgUBQoihj0/u0tDTc3NxYsmRJjnvPnj3jr7/+YtKkSfz111/s2rWLq1ev8sUXXxS6b2JkVKBS/Pjjj/z444/KtvFBWLntBD9tPEp8QgrVq1oxe2xXarnYlqhWGTGVod2+6wQ7wn6XK6tgacaPs4eUSr/K1KqaX2VpVc1vcbSq5leZWoWiiK2ZCqlv06YNbdq0yfWesbExhw8flitbvHgxdevW5e7du9jY2BQ4jhgZFXw0ZJ9D/zGw69B5JgaHMn5AG8I3jKd6VSu6DF+SY/NlRWqVEVOZ2opW5qxYNEq6Aif2zVejqn0VPxPiOYm+fnhtaebdY6nT099/UEhhSE5ORiaTvXfbyPchktFPED8/P2m4XlNTk/Lly9OyZUvWrFlT6A3mQ0JCCv1D9z5u375Nz549qVChAjo6OlSsWJEOHToUeNN+f39/6ZhPVWfp5mP06dgA3y/q42RvyYIJ3dHT0WLjntMlplVGTGVq1dXVMDExkC4jw4K9GKCKfRU/E+I5ib5+eK2iUeQ0vbW1tdzR1EFBQcX29+LFC8aPH0+PHj0K/XKUSEY/Uby9vYmLi+POnTvs378fLy8vRo4cSbt27Xj9+vUH9/Pq1StatmxJcnKytO5k69atuLq6Sts05YeBgQFmZmYla/QD8PLVa6Ku3MOz7v/Ot1dTU6NpXUciY26XiFYZMZWpBXj48AmDRwQz/NvFLFoWyn//JeerUcW+ip8J8ZwUpVU1v8rUlgSKTEbv3btHcnKydE2YMKFY3l69eoWPjw9ZWVk5jv0uCCIZ/UTR1tbGwsICKysraU/N3bt3s3//fmkjeYAFCxbg6uqKvr4+1tbWDB06VDpCMzw8nH79+knD8jKZTHqZaMOGDdSuXRtDQ0MsLCzo2bOndB58bly6dImbN2+ydOlSPvvsMypVqkTDhg2ZPn06n332v5Mu/v33X3r06IGpqSn6+vrUrl2bM2fOALlP069atQpnZ2d0dHRwcnJi6dKl0r07d+4gk8nYtWsXXl5e6Onp4ebmxunT8t94IyIi8PT0RE9PjzJlytC6dWsSExOBN0fDBQUFYWdnh66uLm5ubtIxokUlISmVjIxMzE0N5crNTY2IT0gpEa0yYipTW6WyFUMGtWeCfw/6923D48fJTJmxjufP856qUsW+ip8J8ZwUpVU1v8rUlnbePZZaW1u7yG1lJ6L//PMPhw8fLtKWUSIZFUg0a9YMNzc3du3aJZWpqamxaNEiLl26xLp16zh27Bjjxo0DoEGDBgQHB2NkZERcXBxxcXH4+/sDb344p02bRnR0NGFhYdy5c0fauD43zM3NUVNTY8eOHWRkZORaJzU1laZNm3L//n327NlDdHQ048aNe+/Sgk2bNjF58mRmzJhBbGwsM2fOZNKkSTk23//hhx/w9/cnKioKBwcHevToIY0OR0VF0bx5c6pVq8bp06c5deoU7du3lzwGBQWxfv16li9fzqVLlxg9ejS9evXKcUhANunp6TnW6gg+PO5uVahftxqVbMpTs0Zlvvu2O2nP0jl99rKyrQkEAkGuyGSKuRRJdiJ6/fp1jhw5UuTZSfE2vUAOJycnLly4IH3OPmEJ3py6NH36dAYPHszSpUvR0tLC2NgYmUyGhYX8XoxfffWV9Gd7e3sWLVpEnTp1SE1NxcDAIEdcKysrFi1axLhx4wgMDKR27dp4eXnh6+uLvb09AJs3b+bx48dERkZiamoKQJUqVd7blylTpjB//nw6d+4MgJ2dHZcvX2bFihX07fu/l1X8/f1p27YtAIGBgbi4uHDjxg2cnJyYM2cOtWvXlhtRdXFxAd4kljNnzuTIkSPUr19f6uupU6dYsWJFrmfdBwUFERgY+F7PAGYmBqirq+VYIP/4SUq+R9AVVauMmMrUvou+vg6WFqY8fJRYKv2Kn4nSq1U1v8XRqppfZWpLgjfJZHGPAy1c/dTUVG7cuCF9vn37NlFRUZiammJpacmXX37JX3/9xd69e8nIyJCO/DY1NUVLS6vAccTIqECOrKwsuR/2I0eO0Lx5c6ysrDA0NKR3794kJCTw7NmzPNs5f/487du3x8bGBkNDQykxu3v37ns1w4YN4+HDh2zatIn69euzfft2XFxcpK0joqKicHd3lxLRvEhLS+PmzZv0798fAwMD6Zo+fTo3b96Uq1ujRg3pz5aWlgDSkoLskdHcuHHjBs+ePaNly5ZyMdavX58jRjYTJkyQW6eT28lRWpoa1HSy5kTkVaksMzOTk5HXqONql2e/i6pVRkxlat/lxYuXPIpPpIxJzi9KpcGv+JkovVpV81scrar5Vab2Y+HcuXO4u7vj7u4OwJgxY3B3d2fy5MnSLOW///5LzZo1sbS0lK4//vijUHHEyKhAjtjYWOzs3vwlu3PnDu3atWPIkCHMmDEDU1NTTp06Rf/+/Xn58iV6erm/fZyWlkbr1q1p3bo1mzZtwtzcnLt379K6dWtevnyZZ3xDQ0Pat29P+/btmT59Oq1bt2b69Om0bNkSXV3dAvcje13rypUrqVevntw9dXV1uc+amprSn7MT8eyp/7xiZsfYt28fVlZWcvfet/5GW1u7QGtzhvZsxtDADbg72+DhYsuyLcdJe56Ob/vPSkyrjJjK0m7YcoRa7lUpa2ZMYtJTtu86iZqaGg0/cymVfpWlVTW/ytKqml/R1w+jVTiKmGYvpN7T05O8To1X1InyIhkVSBw7doyYmBhGjx4NvBndzMzMZP78+aipvRlE37Ztm5xGS0srxxrPK1eukJCQwKxZs7C2tgbefLsqLDKZDCcnJ+kbVo0aNVi1ahVPnjzJd3S0fPnyVKhQgVu3buHr61vo2NnUqFGDo0eP5jq1Xq1aNbS1tbl7926uU/LFoXOrWvyXlMrMFfuIT3iKq4MVOxYNK9DUUFG1yoipLG3CkxQWLQ3laepzjAz1cHSwZvpkP4yM9EulX2VpVc2vsrSq5lf09cNoFU1RTlDKrY3SiCxLUWmtQGXw8/Pj0aNHrF27loyMDB49esSBAwcICgrC09OTsLAw1NXViY6OpmbNmgQHB9O+fXsiIiKYMGEC9+/fJzExERMTE/744w8aNmzIkSNHcHNzQ09Pj7S0NCpWrMjIkSMZPHgwFy9eZOzYsVy7do2///47143po6KimDJlCr1796ZatWpoaWlx4sQJRo4cyfjx45k0aRIvX77E1dWV8uXLExQUhKWlJX///TcVKlSgfv36BAQEEBYWRlRUFPDmTfoRI0Ywa9YsvL29SU9P59y5cyQmJjJmzBju3LmDnZ2dnKekpCTKlCnD8ePH8fT05Nq1a7i6utK/f38GDx6MlpYWx48fp2vXrpQtW5aJEyeyfPly5s+fT6NGjUhOTiYiIgIjIyO5danvIyUlBWNjYx4lJBfpDURBwRBn0wsEgpIiJSWF8mbGJCeXzO/x7H8nKo/cibp2/l+Y8yIjPY2bC7uUmNeiItaMfqIcOHAAS0tLbG1t8fb25vjx4yxatIjdu3dL09hubm4sWLCA2bNnU716dTZt2pRjY9wGDRowePBgunXrhrm5OXPmzMHc3JyQkBC2b99OtWrVmDVrFvPmzcvTT8WKFbG1tSUwMJB69erh4eHBwoULCQwM5IcffgDejMIeOnSIcuXK8fnnn+Pq6sqsWbNyTLtnM2DAAFatWsXatWtxdXWladOmhISESMsQCoKDgwOHDh0iOjqaunXrUr9+fXbv3o2GxptJhWnTpjFp0iSCgoJwdnbG29ubffv2FSqGQCAQCAT5URrfplcUYmRUIFAiYmT0wyBGRgUCQUnxoUZGHcbsUsjI6LUFnUvdyKhYMyoQCD56ipNQfrunaHuPzv+iWpFjCgQCwbsoYmSztI6Miml6gUAgEAgEAoHSEMmoQFBKWbntBDW+mIxFw1G08JvL+Ut3SlyrjJjK0hZFJwO8Hc35vnkVZrV1YkLzKrRwKFtgr8XxqyytqvlVllbV/BZHq2p+lalVJIo8m760IZJRwUdJeHg4MpmMpKQkZVspErsOnWdicCjjB7QhfMN4qle1osvwJTlOAlGkVhkxVa2vzaqWpYFtGUJjHjL72E32XX6EVxUzGtnlfxCDsvpaHK2q+VWWVtX8ir5+GK2i+ZhfYBLJqKBE8PPzk76FaWpqUr58eVq2bMmaNWvee5Z8boSEhGBiYlJyRkspSzcfo0/HBvh+UR8ne0sWTOiOno4WG/ecLjGtMmKqWl9ty+hy8eFTYuNTSXz+igtxT7kWn4ZNGZ18vSqrr8XRqppfZWlVza/o64fRCgqOSEYFJYa3tzdxcXHcuXOH/fv34+XlxciRI2nXrh2vX79Wtr1Sy8tXr4m6cg/Puo5SmZqaGk3rOhIZc7tEtMqIqSxtcWLeSXxOVXN9yuq/OXPZ0kgbOzM9rjxKzVNX3Liq9pw+Ja2q+S2OVtX8KlNbEohpeoGgCGhra2NhYYGVlRUeHh58//337N69m/379xMSEgLAggULcHV1RV9fH2tra4YOHSodsxkeHk6/fv1ITk6W/hIFBAQAsGHDBmrXro2hoSEWFhb07NlTOk/+bSIiIqhRowY6Ojp89tlnXLx4UbqXkJBAjx49sLKyQk9PD1dXV7Zs2SKn37FjB66urujq6mJmZkaLFi1IS0uT7q9atQpnZ2d0dHRwcnJi6dKleT6T9PR0UlJS5K53SUhKJSMjE3NTQ7lyc1Mj4hNy1leEVhkxlaUtTsxj1/8j6n4K45tVZk47Z8Y0tefkzQT+up+3rrhxVe05fUpaVfNbHK2q+VWmtiQQyahAoCCaNWuGm5sbu3btAt58y1y0aBGXLl1i3bp1HDt2jHHjxgFvNtQPDg7GyMiIuLg44uLi8Pf3B+DVq1dMmzaN6OhowsLCuHPnDn5+fjnijR07lvnz5xMZGYm5uTnt27fn1atXALx48YJatWqxb98+Ll68yKBBg+jduzdnz54FIC4ujh49evDVV18RGxtLeHg4nTt3ls7i3bRpE5MnT2bGjBnExsYyc+ZMJk2axLp1697b/6CgIIyNjaUr+7hUgWrgVsEIj4rGbDp/nwUnbvHL3w/wrGJGbWtjZVsTCAQClUXsMyr44Dg5OXHhwgUARo0aJZXb2toyffp0Bg8ezNKlS9HS0sLY2BiZTIaFhfw+kV999ZX0Z3t7exYtWkSdOnVITU3FwMBAujdlyhRatmwJwLp166hYsSKhoaH4+PhgZWUlJbcAw4cP5+DBg2zbto26desSFxfH69ev6dy5M5UqVQLA1dVVru358+fTuXNnAOzs7Lh8+TIrVqx471GgEyZMYMyYMdLnlJSUHAmpmYkB6upqORbIP36Sku95yEXVKiOmsrTFidnepfyb0dEHb0ZFHj5Np4yuJs2rlOXcveQS8assrar5VZZW1fwWR6tqfpWpLQnEPqMCgQLJysqSpgqOHDlC8+bNsbKywtDQkN69e5OQkMCzZ8/ybOP8+fO0b98eGxsbDA0Nadq0KQB3796Vq1e/fn3pz6ampjg6OhIbGwtARkYG06ZNw9XVFVNTUwwMDDh48KDUhpubG82bN8fV1ZWuXbuycuVKEhMTAUhLS+PmzZv0798fAwMD6Zo+fTo3b958r29tbW2MjIzkrnfR0tSgppM1JyKvSmWZmZmcjLxGHde8jxktqlYZMZWlLU5MTXUZ775+l5mVVaBf8J/Sc/qUtKrmtzhaVfOrTG1JIEMB0/SUzmxUjIwKPjixsbHY2dlx584d2rVrx5AhQ5gxYwampqacOnWK/v378/LlS/T09HLVp6Wl0bp1a1q3bs2mTZswNzfn7t27tG7dmpcvXxbYx9y5c1m4cCHBwcHSutVRo0ZJbairq3P48GH++OMPDh06xE8//cQPP/zAmTNnJG8rV66kXr16cu2qq6sX8cn8j6E9mzE0cAPuzjZ4uNiybMtx0p6n49v+sxLTKiOmqvX18sNUWlQtS9KzVzx8mo6VsQ5NK5tx9m5Svl6V1dfiaFXNr7K0quZX9PXDaAUFRySjgg/KsWPHiImJYfTo0Zw/f57MzEzmz5+PmtqbQfpt27bJ1dfS0iIjI0Ou7MqVKyQkJDBr1ixpivvcuXO5xvvzzz+xsbEBIDExkWvXruHs7Ay8ebmpQ4cO9OrVC3jzjffatWtUq/a/YxxlMhkNGzakYcOGTJ48mUqVKhEaGsqYMWOoUKECt27dwtfXVwFPRp7OrWrxX1IqM1fsIz7hKa4OVuxYNKxAU0NF1Sojpqr1NTTmId5O5nSuYYGhtgbJL15z+p9EDl99nK9XZfW1OFpV86ssrar5FX39MFpF8zFP08uyst/GEAgUiJ+fH48ePWLt2rVkZGTw6NEjDhw4QFBQEJ6enoSFhXHx4kVq1qxJcHAw7du3JyIiggkTJnD//n0SExMxMTHhjz/+oGHDhhw5cgQ3Nzf09PRIS0ujYsWKjBw5ksGDB3Px4kXGjh3LtWvX+Pvvv6lZsybh4eF4eXnh4uLCwoULKV++PD/88ANRUVFcv34dLS0txowZw44dO/jll18oU6YMCxYsYNu2bXh5eREWFsaZM2c4evQorVq1oly5cpw5c4ZevXoRFhZGmzZtWLVqFSNGjGDWrFl4e3uTnp7OuXPnSExMlFsXmhcpKSkYGxvzKCE51yl7gfIRZ9MLBIK8SElJobyZMcnJJfN7PPvfCbfvf0VdR79YbWW8SCN6ZvsS81pUxJpRQYlx4MABLC0tsbW1xdvbm+PHj7No0SJ2796Nuro6bm5uLFiwgNmzZ1O9enU2bdpEUFCQXBsNGjRg8ODBdOvWDXNzc+bMmYO5uTkhISFs376datWqMWvWLObNm5erh1mzZjFy5Ehq1arFw4cP+fXXX9HSerNH5MSJE/Hw8KB169Z4enpiYWFBx44dJa2RkREnT57k888/x8HBgYkTJzJ//nzatGkDwIABA1i1ahVr167F1dWVpk2bEhISgp3dh19LJBAIBIKPm4/5BCYxMioQKBExMvrxUnHgL0XW/ruyuwKdCD51nj5/VWStoa6mAp18nHyokdGaPyhmZDRqRukbGRVrRgUCgUAgEAhKOYrYtL60bnovklGBQCAQCASCUs7H/AKTWDMq+Ojw8/OTW/upqqzcdoIaX0zGouEoWvjN5fylOyWuVUZMZWlLOma9quas/aYx5+Z24N+V3Wld00ru/pj21Qmf+jnXFn/JxeDObBntibudqVI9l5aYqqhVJb9nom7S77uV1O40BZsmozn4e0yB4ynDryprBQVDJKNKxs/PTxp619TUpHz58rRs2ZI1a9aQmfnu9tqKYd26ddSpUwc9PT1pw/i9e/eWSKy3sbW1zXUT3lmzZik0zsKFCwkJCVFomx+aXYfOMzE4lPED2hC+YTzVq1rRZfiSHCeBKFKrjJgfc1/1tDW4/G8SEzfnvu3YrUdPmbjlPC0C9tN5zhH+TUhj0yhPTA20lea5NMRURa2q+X324iXVKlsxfXSXfGOUBr+qqlU04mx6QYni7e1NXFwcd+7cYf/+/Xh5eTFy5EjatWvH69evFRrL39+fr7/+mm7dunHhwgXOnj1Lo0aN6NChA4sXL1ZorNyYOnWqdM589jV8+HCFxjA2NsbExEShbRaFV6+K/tLA0s3H6NOxAb5f1MfJ3pIFE7qjp6PFxj2nS0yrjJgfc1+PX4xjblgMB/6+n2s7YWf/4VTsI+7+l8a1BykEbvsbIz0tnCuaKM1zaYipilpV8+v1mTNjB36Od5Ma+cYoDX5VVatwFPEmfenMRUUyWhrQ1tbGwsICKysrPDw8+P7779m9ezf79++XG+FLSkpiwIABmJubY2RkRLNmzYiOjgbg2rVryGQyrly5Itf2jz/+SOXKlYE3G8DPnz+fuXPn4u/vT5UqVXB2dmbGjBmMGjWKMWPGcO/ePQBCQkIwMTEhLCyMqlWroqOjQ+vWraX7ANHR0Xh5eWFoaIiRkRG1atV67+bz2RgaGmJhYSF36eu/eTswPDwcmUzG0aNHqV27Nnp6ejRo0ICrV6/KtTF9+nTKlSuHoaEhAwYM4LvvvqNmzZrS/Xen6T09PRkxYgTjxo3D1NQUCwsLAgIC5NrM69lms3v3bjw8PNDR0cHe3p7AwEC5LwsymYxly5bxxRdfoK+vz4wZM/J8Fu/j5avXRF25h2ddR6lMTU2NpnUdiYy5XSJaZcRUllZZfvNCU10N3yaVSX72ksv/JpYKz6r2/1VZWlXzWxxUsa+q9ow/VUQyWkpp1qwZbm5u7Nq1Syrr2rUr8fHx7N+/n/Pnz+Ph4UHz5s158uQJDg4O1K5dm02bNsm1s2nTJnr27AnAli1bMDAw4Ouvv84R79tvv+XVq1fs3LlTKnv27BkzZsxg/fr1REREkJSURPfu/9tyxtfXl4oVKxIZGcn58+f57rvv0NQs/jYgP/zwA/Pnz+fcuXNoaGjw1VdfyfVnxowZzJ49m/Pnz2NjY8OyZcvybXPdunXo6+tz5swZ5syZw9SpUzl8+LB0P69nC/D777/Tp08fRo4cyeXLl1mxYgUhISE5Es6AgAA6depETEyMnO9s0tPTSUlJkbveJSEplYyMTMxNDeXKzU2NiE/IWV8RWmXEVJZWWX5zo3mNClz9qQs3l3ZlYAtHev4YTmJqziNtP6XnpGpaVfNbHFSxr6r2jPNCTNMLlIKTkxN37twB4NSpU5w9e5bt27dTu3Ztqlatyrx58zAxMWHHjh3Am+Rwy5Ytkv7atWucP39eOq7y2rVrVK5cWdr0/W0qVKiAkZER165dk8pevXrF4sWLqV+/PrVq1WLdunX88ccfnD17FoC7d+/SokULnJycqFq1Kl27dsXNzS3PPo0fPx4DAwO56/fff5erM2PGDJo2bUq1atX47rvv+OOPP3jx4gUAP/30E/3796dfv344ODgwefJkXF1d832WNWrUYMqUKVStWpU+ffpQu3Ztjh49WuBnGxgYyHfffUffvn2xt7enZcuWTJs2jRUrVsjF6dmzJ/369cPe3l46hvRtgoKCMDY2lq7s40wFnyZ/XHlE66kH6Tj7COGXHrLs6waYGea+ZlQgEHzafMyb3otktBSTlZUlfYuJjo4mNTUVMzMzuUTu9u3b3Lx5E4Du3btz584d/vzzT+DNKKKHhwdOTk5ybRYUDQ0N6tSpI312cnLCxMSE2NhYAMaMGcOAAQNo0aIFs2bNknzkxdixY4mKipK7ateuLVenRo3/rWOytLQEID4+HoCrV69St25dufrvfs6Nt9vMbje7zYI82+joaKZOnSp3f+DAgcTFxfHs2TOp3Xf78i4TJkwgOTlZut5e9pCNmYkB6upqORbIP36Sku95yEXVKiOmsrTK8psbz19mcOdxKn/dSsB/3VkyMrLo3si+VHhWtf+vytKqmt/ioIp9VbVn/KkiktFSTGxsrHS0ZGpqKpaWljkSuatXrzJ27FgALCwsaNasGZs3bwZg8+bN0qgogIODA7du3eLly5zTgA8ePCAlJQUHB4cC+wsICODSpUu0bduWY8eOUa1aNUJDQ/PUlC1blipVqshdurq6cnXenurPTsaLu7PAu8sHZDKZ1GZBnm1qaiqBgYFy92NiYrh+/To6OjpSu9nrX9+HtrY2RkZGcte7aGlqUNPJmhOR/1srm5mZycnIa9Rxzfuo0aJqlRFTWVpl+S0IMpkMbQ31UuFZ1f6/Kkuran6Lgyr2VdWecV58zNP0YtP7UsqxY8eIiYlh9OjRAHh4ePDw4UM0NDSwtbV9r87X15dx48bRo0cPbt26JbfGs3v37ixatIgVK1bkeIN93rx5aGpq0qXL/7b6eP36NefOnZNGHq9evUpSUhLOzs5SHQcHBxwcHBg9ejQ9evRg7dq1dOrUSRGPIFccHR2JjIykT58+UllkZGSx2izIs/Xw8ODq1atUqVKlWLEKytCezRgauAF3Zxs8XGxZtuU4ac/T8W3/WYlplRHzY+6rnrYGtuUMpM/WZfWpZm1CUtpLElPTGdHWhcPR93mU9BxTA236elXFoowue8/fZcwX1T+Z5/QxaFXNb9qzdO7c/0/6fC8ugUvX72NipIdV+TKlzq+qahXNx7zpvUhGSwHp6ek8fPiQjIwMHj16xIEDBwgKCqJdu3ZS0tWiRQvq169Px44dmTNnDg4ODjx48IB9+/bRqVMnaXq4c+fODBkyhCFDhuDl5UWFChWkOPXr12fkyJGMHTuWly9f0rFjR169esXGjRtZuHAhwcHBcmsYNTU1GT58OIsWLUJDQ4NvvvmGzz77jLp16/L8+XPGjh3Ll19+iZ2dHf/++y+RkZFyyWxuPH36lIcPH8qV6enpFfiM3OHDhzNw4EBq165NgwYN2Lp1KxcuXMDePufUZkEpyLOdPHky7dq1w8bGhi+//BI1NTWio6O5ePEi06dPL3Ls99G5VS3+S0pl5op9xCc8xdXBih2LhhVoaqioWmXE/Jj76lbJlO1jm0mfA7p5ALDtj9tM2BBJFQtDutZvSBkDbRLTXhJ9J4Euc45y7UHuL0Z8rM/pY9Cqmt8LV+/RbeQS6fPUxbsB+NK7Dgu+71nq/KqqVtF8zMeByrIKs4hQoHD8/PxYt24d8GaNZpkyZXBzc6Nnz5707dsXNbX/raR4+vQpP/zwAzt37uTx48dYWFjQpEkTgoKC5JLIbt26sW3bNtasWUO/fv1yxFyzZg1Lly7l0qVLqKur4+HhwdixY2nfvr1UJyQkhFGjRrFmzRrGjh3L/fv3ady4MatXr8bGxoaXL1/St29fIiIiePToEWXLlqVz587MnTtXbtr6bWxtbfnnn39ylH/99dcsX76c8PBwvLy8SExMlPYJjYqKwt3dndu3b0ujltOmTWPRokW8ePECHx8fDAwMOHv2LKdPn5aeaVJSEmFhYcCbrZ1q1qxJcHCwFLNjx46YmJhIW2cV5NkePHiQqVOn8vfff6OpqYmTkxMDBgxg4MCBwJu/5KGhoYU6/SklJQVjY2MeJSQXOCEXqAYVB/5SZO2/K7vnX0kgKCBPnxd9z2ND3eLvkPKxk5KSQnkzY5KTS+b3ePa/E59NP4CGTt5LwfLj9Ys0/pzoXWJei4pIRgW5kp2MJiUlKdtKvrRs2RILCws2bNigbCuFRiSjHy8iGRWUFkQyWrJ8qGS0/oyDCklGT//QutQlo2KaXqBSPHv2jOXLl9O6dWvU1dXZsmULR44ckdszVCAoDRQnoTwU+zD/Su+hlbNFkbWCjxORUH4ciDWjAkEpQSaT8dtvvzFjxgxevHiBo6MjO3fupEWLFsq2JhAIBAKBoAiIrZ0EuZK97rK0oaury5EjR0hISCAtLY2//vqLzp07K9uWQCAQCAQlijK2djp58iTt27enQoUKyGQy6V2MbLKyspg8eTKWlpbo6urSokULrl+/Xui+iWRU8FGRfb59aUykC8vKbSeo8cVkLBqOooXfXM5fulPiWmXEVJZWlfxu33WCbn2my12jx+d/DK4iPKvSc1KmVtX8Fkeran6VqVUkyjiBKS0tDTc3N5YsWZLr/Tlz5rBo0SKWL1/OmTNn0NfXp3Xr1tKpiQVFJKMfED8/P+mbiaamJuXLl6dly5asWbOm0Ju6BwQEULNmzRzltra2yGQy6RSmbEaNGoWnp2eB279z5w4ymYyoqKgC1cu+DA0NcXFxYdiwYUX6diR4w65D55kYHMr4AW0I3zCe6lWt6DJ8SY6TQBSpVUZM0deCaytambNi0SjpCpzYN19NceOq4nMSP0+ir6VF+zHQpk0bpk+fnuv+4VlZWQQHBzNx4kQ6dOhAjRo1WL9+PQ8ePMgxgpofIhn9wHh7exMXF8edO3fYv38/Xl5ejBw5knbt2vH69WuFxNDR0WH8+PEKaaugHDlyhLi4OKKjo5k5cyaxsbG4ublJ579/auR2ylVhWLr5GH06NsD3i/o42VuyYEJ39HS02LjndIlplRFT9LXgWnV1NUxMDKTLyFAvX01x46ricxI/T6KvpUWraBQ5TZ+SkiJ3paenF9rP7du3efjwodw7G8bGxtSrV0/aarGgiGT0A6OtrY2FhQVWVlZ4eHjw/fffs3v3bvbv3y/teQlw9+5dOnTogIGBAUZGRvj4+PDo0SPgzbZLgYGBREdHSz9cb2sHDRrEn3/+yW+//Zanl1WrVuHs7IyOjg5OTk4sXbpUupd9DKm7uzsymSzfUVUzMzMsLCywt7enQ4cOHDlyhHr16tG/f38yMjKkert378bDwwMdHR3s7e0JDAyUkvCePXvSrVs3uXZfvXpF2bJlWb9+PfDmKLagoCDs7OzQ1dXFzc2NHTt25Olt586duLi4oK2tja2tLfPnz5e7b2try7Rp0+jRowf6+vpYWVnlmJJISkpiwIABmJubY2RkRLNmzYiOjpbuZ49Ur1q1Cjs7u/futVoQXr56TdSVe3jWdZTK1NTUaFrXkciY2yWiVUZMZWlVzW82Dx8+YfCIYIZ/u5hFy0L577/kfDXFiauKz0n8PIm+lhZtSSBDAdP0/9+WtbU1xsbG0hUUFFRoP9kH2JQvX16uvHz58jkOt8kPkYyWApo1a4abmxu7du0C3iRcHTp04MmTJ5w4cYLDhw9z69YtKVHr1q0b3377LS4uLsTFxREXFyeXxNnZ2TF48GAmTJjw3un/TZs2MXnyZGbMmEFsbCwzZ85k0qRJ0gb8Z8+eBf434pntraCoqakxcuRI/vnnH86fPw/A77//Tp8+fRg5ciSXL19mxYoVhISEMGPGDODNUaa//vorqampUjsHDx7k2bNn0hRBUFAQ69evZ/ny5Vy6dInRo0fTq1cvTpw4kauP8+fP4+PjQ/fu3YmJiSEgIIBJkybJJe8Ac+fOxc3Njb///pvvvvuOkSNHym0X1bVrV+Lj49m/fz/nz5/Hw8OD5s2b8+TJE6nOjRs32LlzJ7t27Xrv8ob09PQc30jfJSEplYyMTMxNDeXKzU2NiE/I/XSe4mqVEVNZWlXzC1ClshVDBrVngn8P+vdtw+PHyUyZsY7nz/MfzfiUnpP4eRJ9LS3a0s69e/dITk6WrgkTJijVj9jaqZTg5OTEhQsXADh69CgxMTHcvn1bOv1n/fr1uLi4EBkZSZ06dTAwMEBDQwMLi9z3FJw4cSJr165l06ZN9O7dO8f9KVOmMH/+fOlNdDs7OylB7Nu3L+bm5sD/RjyL2id4s660bt26BAYG8t1339G375u1bvb29kybNo1x48YxZcoUWrdujb6+PqGhoZLnzZs388UXX2BoaEh6ejozZ87kyJEj1K9fX2rj1KlTrFixgqZNm+bwsGDBApo3b86kSZMAcHBw4PLly8ydOxc/Pz+pXsOGDfnuu++kOhEREfz444+0bNmSU6dOcfbsWeLj49HW1gZg3rx5hIWFsWPHDgYNGgS8mZpfv3699OxyIygoiMDAwCI9T8Gni7tbFenPlWzKU7WyFcPG/MTps5dp1tRdic4EAsGHQk0mQ62YG4Vm642MjIq96X12bvDo0SMsLS2l8kePHuX6TkuevorlRKAwsrKypLUcsbGxWFtbyx3xWa1aNUxMTIiNjS1Qe+bm5vj7+zN58uQc6xfT0tK4efMm/fv3x8DAQLqmT5/OzZs3Fdon+N9ZuNHR0UydOlUu5sCBA4mLi+PZs2doaGjg4+PDpk2bJJ+7d+/G19cXeDPy+OzZM1q2bCnXxvr169/rOzY2loYNG8qVNWzYkOvXr8stH8hObt/+nP2so6OjSU1NxczMTC7u7du35eJWqlQpz0QUYMKECXLfRu/du5ejjpmJAerqajkWyD9+kpLvechF1SojprK0quY3N/T1dbC0MOXho8R8635Kz0n8PIm+lhZtSaCMt+nzws7ODgsLC7l3Q1JSUjhz5kyOf1PzQySjpYTY2FhpnaaiGDNmDM+fP5dbCwpI0+ArV64kKipKui5evJjjLfzikJ3MZfcrNTWVwMBAuZgxMTFcv35dWmPp6+vL0aNHiY+PJywsDF1dXby9veV879u3T66Ny5cv57tutDikpqZiaWkpFzMqKoqrV68yduxYqZ6+fv7HtGlra0vfSN/3zVRLU4OaTtaciLwqlWVmZnIy8hp1XPP+GSmqVhkxlaVVNb+58eLFSx7FJ1LGxCDfup/ScxI/T6KvpUVbEihjn9HU1FTp3zx489JSVFQUd+/eRSaTMWrUKKZPn86ePXuIiYmhT58+VKhQgY4dOxYqjpimLwUcO3aMmJgYRo8eDYCzszP37t3j3r170ujo5cuXSUpKolq1agBoaWnJjezlhoGBAZMmTSIgIIAvvvhCKi9fvjwVKlTg1q1b0qjju2hpaQHkG+N9ZGZmsmjRIuzs7HB3fzON6OHhwdWrV6lSpcp7dQ0aNMDa2pqtW7eyf/9+unbtiqbmm6PsqlWrhra2Nnfv3s11Sj43nJ2diYiIkCuLiIjAwcEBdXV1qezdJPzPP//E2dlZ8v3w4UM0NDSwtbUtUNziMrRnM4YGbsDd2QYPF1uWbTlO2vN0fNt/VmJaZcQUfS2YdsOWI9Ryr0pZM2MSk56yfddJ1NTUaPiZS74xixNX1Z6TsrSq5lf09cNoPwbOnTuHl5eX9HnMmDEA9O3bl5CQEMaNG0daWhqDBg0iKSmJRo0aceDAgUK/xCuS0Q9Meno6Dx8+JCMjg0ePHnHgwAGCgoJo164dffr0AaBFixa4urri6+tLcHAwr1+/ZujQoTRt2pTatWsDb94Az/6GUrFiRQwNDaX1jG8zaNAgfvzxRzZv3ky9evWk8sDAQEaMGIGxsTHe3t6kp6dz7tw5EhMTGTNmDOXKlUNXV5cDBw5QsWJFdHR0MDY2fm+/EhISePjwIc+ePePixYsEBwdz9uxZ9u3bJyV9kydPpl27dtjY2PDll1+ipqZGdHQ0Fy9eZPr06VJbPXv2ZPny5Vy7do3jx49L5YaGhvj7+zN69GgyMzNp1KgRycnJREREYGRkJK1FfZtvv/2WOnXqMG3aNLp168bp06dZvHhxjtHiiIgI5syZQ8eOHTl8+DDbt29n37590v+P+vXr07FjR+bMmYODgwMPHjxg3759dOrUSfp/okg6t6rFf0mpzFyxj/iEp7g6WLFj0bACTQ0VVauMmKKvBdMmPElh0dJQnqY+x8hQD0cHa6ZP9sPIKP/R+OLEVbXnpCytqvkVff0wWkWjJntzFbeNwuDp6SktucsNmUzG1KlTmTp1arF8ybLyiiJQKH5+ftLb6hoaGpQpUwY3Nzd69uxJ3759UVP736qJu3fvMnz4cI4ePYqamhre3t789NNP0hYK6enp0pR2UlISa9euxc/PD1tbW0aNGsWoUaOktrZs2ULPnj1p2rQp4eHhUvnmzZuZO3culy9fRl9fH1dXV0aNGiW9ub5q1SqmTp3K/fv3ady4sZw2mzt37sgtL9DT06NSpUp4eXkxevToHKOgBw8eZOrUqfz9999oamri5OTEgAEDGDhwoFQnNjaWatWqUalSJW7fvi03rZCVlcWiRYtYtmwZt27dwsTERNoiq0mTJoSHh+Pl5UViYiImJibAm62dJk+ezPXr17G0tGT48OH4+/tLbdra2vLVV19x8eJF9u3bh5GRERMmTGDEiBFSnadPn/LDDz+wc+dOHj9+jIWFBU2aNCEoKAhra2sCAgIICwvL95CAd0lJScHY2JhHCcnFXkwu+Hg4FFu4bVHeppVz0V44FAgERSMlJYXyZsYkJ5fM7/HsfydaLDiKpm7+S3Py4tXzVI6MaV5iXouKSEYFnzy5JfAfCpGMCnJDJKMCgeogktHiI6bpBQKBoJRRnIRyXviNImv9Pd+/nlsgECgXRbwNr8i36RWJSEYFAoFAIBAISjmy//+vuG2URkQyKvjkuXPnjrItCAQCgUDwySKSUcFHRUhICKNGjSIpKUnZVorNym0n+GnjUeITUqhe1YrZY7tSy8W2RLXKiKksrar5Lap28czVJCc+zVFeq34NvDs3K3V+VVWran6Lo1U1v8rUKhJlvE3/oRCb3n9E+Pn55brBrbe3N+Hh4fluhBseHk5ISIj0FnpBeLtdNTU1jI2NcXd3Z9y4ccTFxZVcZz9ydh06z8TgUMYPaEP4hvFUr2pFl+FLcpwEokitMmKKvpa8tt+IHoycNFC6eg58cwSws1vVUulXFbWq5lf09cNoFY0yNr3/UIhk9CPD29ubuLg4uWvLli00aNBArszHxydH3QYNGhQ57tWrV3nw4AGRkZGMHz+eI0eOUL16dWJiYhTYO9Xh3SNYC8vSzcfo07EBvl/Ux8nekgUTuqOno8XGPadLTKuMmKKvJa/VN9DDwEhfuq7H3qKMmTE29hVLpV9V1KqaX9HXD6MVFJwCJaN79uwp8CVQLtra2lhYWMhdZcqUQUtLS65MV1c3R93sU5eKQrly5bCwsMDBwYHu3bsTERGBubk5Q4YMkau3atUqnJ2d0dHRwcnJSW7z+QYNGjB+/Hi5+o8fP0ZTU5OTJ08Cb/ZX9ff3x8rKCn19ferVq5fr/qdvs2zZMipXroyWlhaOjo5s2LBB7r5MJmPZsmW0adMGXV1d7O3tcxwveu/ePXx8fDAxMcHU1JQOHTrIrTX18/OjY8eOzJgxgwoVKuDo6FjQR5eDl69eE3XlHp51/9eGmpoaTes6Ehlzu0S0yoipLK2q+S2u9m0yXmdw8a8ruNVxyXeERBX7Kn6eRF9Li7YkKG1n0yuSAq0ZLegZozKZrMjHRwo+LnR1dRk8eDCjR48mPj6ecuXKsWnTJiZPnszixYtxd3fn77//ZuDAgejr69O3b198fX2ZM2cOs2bNkv6h3Lp1KxUqVKBx48YAfPPNN1y+fJlffvmFChUqEBoaire3NzExMVStmnPaMTQ0lJEjRxIcHEyLFi3Yu3cv/fr1o2LFinJHnE2aNIlZs2axcOFCNmzYQPfu3YmJicHZ2ZlXr17RunVr6tevz++//46GhgbTp0/H29ubCxcuSEn80aNHMTIy4vDhw+99Lunp6aSnp0ufU1JSctRJSEolIyMTc1NDuXJzUyOu33mU53MvqlYZMZWlVTW/xdW+zdVLN3nxIp0atavlW1cV+yp+nkRfS4u2JFCTyVArZjZZXH1JUaCR0czMzAJdIhFVPnv37sXAwEDumjlzplK8ODk5Af97W33KlCnMnz+fzp07Y2dnR+fOnRk9ejQrVqwAwMfHhwcPHnDq1Cmpjc2bN9OjRw9kMhl3795l7dq1bN++ncaNG1O5cmX8/f1p1KgRa9euzdXDvHnz8PPzY+jQoTg4ODBmzBg6d+7MvHnz5Op17dqVAQMG4ODgwLRp06hduzY//fQT8CYhzszMZNWqVbi6uuLs7MzatWu5e/eu3Kisvr4+q1atwsXFBReX3M8MDwoKwtjYWLqsra2L9GwFgqIQffYilR1tMTQu3sbZAoHgw/PJj4y+jxcvXqCjo6MoLwIF4OXlxbJly+TKTE1NleIl+3AvmUxGWloaN2/epH///nJHf75+/Vo6897c3JxWrVqxadMmGjduzO3btzl9+rSUrMbExJCRkYGDg4NcnPT0dMzMzHL1EBsby6BBg+TKGjZsyMKFC+XK6tevn+Nz9tGe0dHR3LhxA0ND+W/HL1684ObNm9JnV1fXfJc6TJgwgTFjxkifU1JSciSkZiYGqKur5Vgg//hJSr7nIRdVq4yYytKqmt/iarNJTkzh9vV7dOnTrkD1VbGv4udJ9LW0aAWFo9AvMGVkZDBt2jSsrKwwMDDg1q1bwJtpztWrVyvcoKBw6OvrU6VKFblLWclobGws8Oa4zdTUVABWrlxJVFSUdF28eJE///xT0vj6+rJjxw5evXrF5s2bcXV1xdXVFYDU1FTU1dU5f/68XBuxsbE5kktFkpqaSq1ateRiRkVFce3aNXr27CnV09fXz7ctbW1tjIyM5K530dLUoKaTNScir0plmZmZnIy8Rh1XuzzbL6pWGTGVpVU1v8XVZhMdeQk9A12qOhesvir2Vfw8ib6WFm1J8DG/TV/okdEZM2awbt065syZIzfCVb16dYKDg+nfv79CDQpUk+fPn/Pzzz/TpEkTzM3NAahQoQK3bt3C19f3vboOHTowaNAgDhw4wObNm+nTp490z93dnYyMDOLj46U1pPnh7OxMREQEffv2lcoiIiKoVk1+zdyff/4pF+vPP//E3d0dAA8PD7Zu3Uq5cuU+2Fm+Q3s2Y2jgBtydbfBwsWXZluOkPU/Ht/1nJaZVRkzR1w+jzcrMIjryMjVqV0NNveBjEKrYV/HzJPpaWrSKRhwH+hbr16/n559/pnnz5gwePFgqd3Nz48qVKwo1Jyg86enpPHz4UK5MQ0ODsmXLFriNjIwMaYo6G21tbZydnd+riY+P58WLFzx9+pTz588zZ84c/vvvP3bt2iXVCQwMZMSIERgbG+Pt7U16ejrnzp0jMTFRmrrW19enY8eOTJo0idjYWHr06CHpHRwc8PX1pU+fPsyfPx93d3ceP37M0aNHqVGjBm3bts3ha+zYsfj4+ODu7k6LFi349ddf2bVrF0eOHJGrt337dmrXrk2jRo3YtGkTZ8+elUb6fX19mTt3Lh06dGDq1KlUrFiRf/75h127djFu3DgqVsx/i5zC0rlVLf5LSmXmin3EJzzF1cGKHYuGFWhqqKhaZcQUff0w2tvX75KS9BS3OrmvZS5tflVNq2p+RV8/jFZQcGRZ2Qv7Coiuri5XrlyhUqVKGBoaEh0djb29PZcvX6Zu3brSdKzgw+Pn58e6detylDs6Oub4ouDn50dSUhJhYWFy5SEhIfTr1y9HG5UrV+bGjRs5ysPDw6W30mUyGQYGBtjb29OqVSvGjBmDhYWFXP3Nmzczd+5cLl++jL6+Pq6urowaNYpOnTpJdfbv38/nn39OkyZNOHHihJz+1atXTJ8+nfXr13P//n3Kli3LZ599RmBgIK6urrmewLRs2TLmzZvHvXv3sLOzY+LEifTu3Vu6L5PJWLJkCWFhYZw8eRJLS0tmz56Nj4+PVOfhw4eMHz+e3377jadPn2JlZUXz5s2ZN28eRkZG732e+ZGSkoKxsTGPEpI/2Kir4ONmXnjOv6cFxd+zigKdCASfBikpKZQ3MyY5uWR+j2f/O9Fp2Uk0dYv38uGr56mEDmlSYl6LSqGT0Vq1ajF69Gh69eoll4xOnTqVw4cP8/vvv5eUV4GgRJDJZISGhhZ4CzNFIpJRgaIRyahA8GH5UMloZwUlo7tKYTJa6Gn6yZMn07dvX+7fv09mZia7du3i6tWrrF+/nr1795aER4FAIBAUkOIklJv/+qfI2p4elYqsFQgEnzaFfpu+Q4cO/Prrrxw5cgR9fX0mT55MbGwsv/76Ky1btiwJjwKBQCAQCASfNOJt+ndo3LhxnqfMCASqRCFXqnwwVm47wU8bjxKfkEL1qlbMHtuVWi62JapVRkxlaVXNrzK0SYlPCdt1gsuXbvPy5WvMzU3o1bcNlWwt8tQpy68ytarmtzhaVfOrTK0iUZO9uYrbRmmk0COj2Zw7d44NGzawYcMGzp8/r0hPpRKZTFbol1MUhaenJ6NGjVJYe9nnqJcGlPlc36U0PZddh84zMTiU8QPaEL5hPNWrWtFl+JIcmy8rUquMmKKvpVf7LO0F8+duRl1dnaHDv2RiQD86d/VET18733iq1tfialXNr+jrh9EKCk6hk9F///2Xxo0bU7duXUaOHMnIkSOpU6cOjRo14t9//y0JjyWOn59frkPZ3t7eJRazMEnYrl27mDZtWol5eZfw8PD3Du+/u21UcYmLi6NNmzYKbbOoLFy4kJCQEGXbAGDp5mP06dgA3y/q42RvyYIJ3dHT0WLjntMlplVGTNHX0qs9dPAMZcoY0tuvDbZ2lpQta4JzNTvMzcvkG0/V+lpcrar5FX39MFpF8zFP0xc6GR0wYACvXr0iNjaWJ0+e8OTJE2JjY8nMzGTAgAEl4fGD4O3tTVxcnNy1ZcsWpXp6+fIl8OY4z3ePovwQXL16NcczKVeunEJjWFhYoK1dsJGWkiIjI4PMzEyMjY0xMTFRqheAl69eE3XlHp51HaUyNTU1mtZ1JDLmdololRFTWVpV86ssbcyFm9hUsmDVit2M919C0PR1RPwenWcsZfpVllbV/BZHq2p+laktKT7Gc+mhCMnoiRMnWLZsGY6O//uf4+joyE8//cTJkycVau5Doq2tjYWFhdxVpsz7RwDu3buHj48PJiYmmJqa0qFDB+7cuSNXZ82aNbi4uKCtrY2lpSXffPMN8OZ4TIBOnTohk8mkzwEBAdSsWZNVq1ZhZ2eHjo4OkHOaPj09nfHjx2NtbY22tjZVqlSRNmjPyMigf//+2NnZoauri6OjY5GPyixXrlyOZ6Km9uZHJntKe968eVhaWmJmZsawYcN49eqVpI+Li6Nt27bo6upiZ2fH5s2bsbW1JTg4WKrz9gjxnTt3kMlk7Nq1Cy8vL/T09HBzc+P0aflvoKdOnaJx48bo6upibW3NiBEjSEtLk3s+/v7+WFlZoa+vT7169QgPD5fuh4SEYGJiwp49e6hWrRra2trcvXs3xzS9p6cnI0aMYNy4cZiammJhYUFAQICclytXrtCoUSN0dHSoVq0aR44cKfbSg4SkVDIyMjE3lf8CYm5qRHxCSololRFTWVpV86ss7X+Pk/j9RBTm5crwzYgvadykJtu3HuPP0xfzjKcsv8rSqprf4mhVza8ytSWBGBl9C2tra7mEI5uMjAwqVKigEFOlnVevXtG6dWsMDQ35/fffiYiIwMDAAG9vb2k0c9myZQwbNoxBgwYRExPDnj17qFLlzZYrkZGRAKxdu5a4uDjpM8CNGzfYuXMnu3btynEKUjZ9+vRhy5YtLFq0iNjYWFasWIGBwZu9xzIzM6lYsSLbt2/n8uXLTJ48me+//55t27Yp/DkcP36cmzdvcvz4cdatW0dISIjcNHefPn148OAB4eHh7Ny5k59//pn4+Ph82/3hhx/w9/cnKioKBwcHevTowevXrwG4efMm3t7edOnShQsXLrB161ZOnTolJfoA33zzDadPn+aXX37hwoULdO3aFW9vb65fvy7VefbsGbNnz2bVqlVcunTpvSO+69atQ19fnzNnzjBnzhxpP1148zPfsWNH9PT0OHPmDD///DM//PBDnn1LT08nJSVF7hIISiNZWVlY25SnQ6cmWNuUp1ETNxo0qsGpE1HKtiYQCD4yCv02/dy5cxk+fDhLliyhdu3awJuXmUaOHMm8efMUbvBDsXfvXimhy+b777/n+++/z1F369atZGZmsmrVKulbxtq1azExMSE8PJxWrVoxffp0vv32W0aOHCnp6tSpAyCd1W5iYpLjhKKXL1+yfv16qc67XLt2jW3btnH48GFatGgBgL29vXRfU1OTwMBA6bOdnR2nT59m27ZtcicKFYR3j7msVKkSly5dkj6XKVOGxYsXo66ujpOTE23btuXo0aMMHDiQK1eucOTIESIjI6Wfk1WrVlG1atV84/r7+0tHewYGBuLi4sKNGzdwcnIiKCgIX19faaS4atWqLFq0iKZNm7Js2TLi4+NZu3Ytd+/elb4c+fv7c+DAAdauXcvMmTOBN18oli5dipubW55eatSowZQpU6RYixcv5ujRo7Rs2ZLDhw9z8+ZNwsPDpf+PM2bMyHOLs6CgILn/P7lhZmKAurpajgXyj5+k5HsEXVG1yoipLK2q+VWW1sjYAEtLM7kyC0tTov6+lmc8ZflVllbV/BZHq2p+laktCT75t+nLlCmDqakppqam9OvXj6ioKOrVq4e2tjba2trUq1ePv/76i6+++qqk/ZYYXl5eREVFyV2DBw/OtW50dDQ3btzA0NAQAwMDDAwMMDU15cWLF9y8eZP4+HgePHhA8+bNC+2jUqVK701EAaKiolBXV6dp06bvrbNkyRJq1aqFubk5BgYG/Pzzz9y9e7fQXn7//Xe55/Hbb7/J3XdxcUFdXV36bGlpKY18Xr16FQ0NDTw8PKT7VapUyXPpQzY1atSQaxOQ2o2OjiYkJER67gYGBrRu3ZrMzExu375NTEwMGRkZODg4yNU5ceIEN2/elNrV0tKSi1MQL7n10draWu4LRd26dfNsb8KECSQnJ0vXvXv3ctTR0tSgppM1JyKvSmWZmZmcjLxGHVe7PNsvqlYZMZWlVTW/ytJWrmzFo0dP5MriHyViapr/P8Kq1tfiaFXNb3G0quZXmdqS4GOepi/QyOjba/w+VvT19aVp9PxITU2lVq1abNq0Kcc9c3NzaV1lUX3kha6ubp73f/nlF/z9/Zk/fz7169fH0NCQuXPncubMmUJ7sbOzy/OFHk1NTbnPMpmMzMzMQsfJq93svzjZ7aampvL1118zYsSIHDobGxsuXLiAuro658+fl0uUAbmRb11d3QL9pVR0H7O/wOXH0J7NGBq4AXdnGzxcbFm25Thpz9Pxbf9ZiWmVEVP0tfRqm7WoxbzZmznw25941HbknztxRPx+gR69WuUbT9X6WlytqvkVff0wWkHBKVAy2rdv35L2oVJ4eHiwdetWypUr996zXW1tbTl69CheXl653tfU1CQjI6PQsV1dXcnMzOTEiRPSNP3bRERE0KBBA4YOHSqVvT0i+KFwdHTk9evX/P3339SqVQt4sx42MTGxWO16eHhw+fLl935xcHd3JyMjg/j4eBo3blysWPnh6OjIvXv3ePToEeXLlweQW/9bHDq3qsV/SanMXLGP+ISnuDpYsWPRsAJNDRVVq4yYoq+lV1vJ1pJBQzqyJ/Qk+/f98X/snXtcj+f/x5+fovNRoqRU6CBRic2xcljMKWwOy2hkm3NzNjM55sycjRHGMIfmO5vNKaY5pCmHkmPYRJYOckiq3x/9uuej6PTJ3Yfrucf9ePjc9/W6rtd11+rddXpjVtWYD3r60OSdekW2p259LatW3fyKvr4erapR/P9V1joqIorcMqSfefLkibRhJ5+XBWcVmYCAAO7evcv69euV7leqVImqVasCeSNiu3fvxs/Pj0ePHuHm5oaVlRXTpk2jZs2a3Lhxg127djFu3Dhq1qzJhg0b+Pzzz5kzZw4dOnTgwYMHREREMHz4cAAcHBxo27YtX3/9Ndra2piamhIcHExYWFiBjUve3t64ublJI9SffPIJBw8eZMmSJTRs2JAbN26QlJREz549WbJkCZMnT2b79u3Y2dmxadMmlixZgp2dnVRvQEAAqampL93xHR4ejo+PD/Hx8QW+nmZmZlSuXLnQOoKCgoiOjpZ2rrdr14779++zcuVKKleuzOjRozlx4gQhISHSWtrn32tCQgJ2dnacOXMGNzc3AFJTUzE1NeXw4cN4e3tz9uxZ3n33XQYMGEBgYCD6+vrExsayf/9+li1bBkDfvn2JiIhgwYIFuLu7c+/ePQ4ePEiDBg3o2LEjoaGhBAUFkZqaWuD74Pk+vfjeAfz8/DAxMSE0NJTs7GxcXFywtbVl7ty5PHjwgDFjxnDixAnCwsLo2rVroe/3edLT0zE2NuZucppa/r8jeLMQuekFgpKTnp5OdTNj0tLK5+d4/u+Jj9cdR0vPoGjBK3j6KINNA5qWm9fSUuL55IcPHzJs2DCqVauGvr4+pqamSpe6sm/fPiwtLZWuFi1aFFpWT0+Po0ePYmNjQ/fu3XF2dmbgwIE8efJE+uL279+fxYsXs2LFClxcXOjUqZPSbu4FCxawf/9+rK2tcXd3L5HXlStX8sEHHzBkyBCcnJwYNGiQdLTRZ599Rvfu3enVqxfvvPMOycnJSqOkJcHR0bHAOylJtq2NGzdSvXp1WrVqRbdu3Rg0aBCGhobSkVWloUGDBhw5coRLly7RsmVL3N3d+frrr5VOcli/fj39+vVj9OjRODo64ufnR2RkJDY2NqVutzA0NTUJCwsjIyODxo0bExgYKO2mL0sfBQKBQCB4myjxyOjQoUM5fPgw06dP5+OPP2b58uX8888/rF69mtmzZ+Pv719eXgVqzt9//421tTUHDhwo1eYudSAiIoIWLVpw5coVateuXWR5MTIqqEiIkVGBoOS8rpHRfutVMzK68ZOKNzJa4qOd/ve//7Fx40a8vb355JNPaNmyJXXq1KFWrVps3rxZBKMCiUOHDpGRkYGrqyuJiYmMGzcOW1tbWrVqJbc1lbF7924MDAyoW7cuV65cYeTIkTRv3rxYgahAUNEoS0DpPHZvqXRx8zqWuk2B4G1CFbvhK+pu+hJP09+/f18619LIyIj79/OO/mjRooVaZ2ASqJ6srCy+/PJLXFxc6NatG+bm5oSHhxfYoa7OPHjwgKFDh+Lk5ERAQACNGzfmp59+ktuWQCAQCARqQ4lHRu3t7bl+/To2NjY4OTmxfft2mjRpwv/+978KkddbUHHw9fXF19dXbhvlSr9+/ejXr1+51L1m+xGWfn+QpOR06te1Ys7YD2nkYluuWjnalEurbn7l0hZXt+ITT1ysjKlmrMOw0NMcvHAXgEoaCka2d6SVkzk1zfTIePyM41f+ZcEvF7mXnlmh+loWrbr5LYtW3fzKqVUlqsgvX0EHRks+MvrJJ58QExMDwIQJE1i+fDk6Ojp88cUXjB07VuUGBYK3kV2/R/HV4t2MD+xA+Kbx1K9rRY/hywtkAlGlVo42RV8rtrYkuvjb6UwPK5i3XkdLk3pWRqw8cIUei48xYmMUtub6rAjwrFB9LYtW3fyKvr4erarRUChUclVEShyMfvHFF9KB423btuXixYts2bKFM2fOKKW+FAjUlYCAAPz8/ArcDw8PR6FQkJqaKv07/zI3N+f999/n3LlzKvGwYssh+vk1w79LU5zsLVk4sTd6Olp8v+d4uWnlaFP0tWJrS6L75rdLHDh/t8D9jCfPGLjmFPvOJpJw7yExN1OZsfsC9a1NsDQp/NSJN/k9qbtW3fzKqVU1+SOjZb1KQnZ2NpMnT8bOzg5dXV1q167N9OnTKcOpoIVS+lRB/0+tWrXo3r17sVIrCgRvGvHx8SQmJvLbb7+RmZlJx44dC5y9W1KeZj0j+uItvJs4Svc0NDTwauJI5Lnr5aKVo025tOrmVy5tWdosCkPdSuTk5JL++JlK21W396RuWnXzK6f2TWHOnDmsXLmSZcuWERcXx5w5c5g7dy5Lly5VaTvFWjO6ZMmSYldYWJpGgeBNpVq1apiYmGBhYUFQUBBdunTh4sWLL/3jLDMzk8zM/9bJpaenFyiTnJpBdnYO5lUMle6bVzHickLBkSdVaOVoUy6tuvmVS1uWNl+FViUNRr/vzN7o2zzMLBiMvk3vSd206uZXTm15oMrd9C/+7nlZquo///yTrl270rFj3qkXtra2/PDDD5w6dapMPl6kWMHookWLilWZQqEQwajgrSQtLY2tW7cCoKWl9dJyISEhTJ069XXZEggqFJU0FCzq64ECmLqr4PpSgUDwcjQo+3R2vt7a2lrp/pQpUwgODi5QvlmzZnz77bdcunQJBwcHYmJiOHbsGAsXLiyjE2WKFYxev/52DEcLBPn8/PPPGBgoHy6cnZ1doFzNmjUBpAxYXbp0wcnJ6aX1Tpw4kVGjRkmf09PTC/xQMDMxQFNTo8AC+Xv304vMh1xarRxtyqVVN79yacvSZmFU0lCw6GMPapjq8snqE4WOipa1XXV7T+qmVTe/cmorOrdu3VI69L6wUVHI26ienp6Ok5MTmpqaZGdnM3PmTJWfKV/mNaMCwZuIj48P0dHRStfatWsLlPvjjz+IiooiNDQUBwcHVq1a9cp6tbW1MTIyUrpeRKtyJdycrDkSGS/dy8nJ4WjkJRq72r2y/tJq5WhTLq26+ZVLW5Y2XyQ/EK1VVZ8B354k9VFWubSrbu9J3bTq5ldObXnw/KbZslxAgd9DLwtGt2/fzubNm9myZQt//fUXGzZsYP78+WzYsEGlfSvxOaMCwduAvr4+derUUbr3999/FyhnZ2eHiYkJjo6OJCUl0atXL5UkfxjyUWuGTN2Eu7MNHi62rPzhMA8fZ+Lf+d1y08rRpuhrxdaWROdU478/rGpW0cOphhFpj55yLz2Txf08qGdlzOB1kWhqKKhqmPeLL+1R4Zv93uT3pO5adfMrp1bVKBSg8ZrPGR07diwTJkygd+/eALi6unLjxg1CQkLo379/2cw8hwhGBQIVMXToUEJCQti9ezfdunUrU13d32vEv6kZzFq9l6TkB7g6WLFjydBiTQ2VVitHm6KvFVtbEt3uL1pK/57QpV7evdO3WPb7Zdq4WAAQNko5FXC/lYUfj/Mmvyd116qbXzm1bwKPHj1CQ0N5El1TU5OcnByVtqPIVfVhUQKBmhMQEEBqaiphYWFK98PDw/Hx8SElJYXo6Gjp389nHhs/fjy//vorMTExxdr1mJ6ejrGxMXeT0wqdshcI1AWRm17wtpKenk51M2PS0srn53j+74khP0SirWdQtOAVZD7KYEWfxsX2GhAQwIEDB1i9ejUuLi6cOXOGTz/9lAEDBjBnzpwyeXkesWZUIFAhw4YNIy4ujh9//FFuKwKBQCB4g1DlmtHisnTpUj744AOGDBmCs7MzY8aM4bPPPmP69Omq7VtpRkb/+OMPVq9ezdWrV9mxYwdWVlZs2rQJOzs7WrRooVKDAsGbjBgZFbzt/HU9pdRaDztTFToRCErH6xoZHbr1tEpGRpf39iw3r6WlxCOjO3fuxNfXF11dXc6cOSMd4J2WlsasWbNUblAgEAgEAoHgbUdDoZqrIlLiYHTGjBmsWrWKNWvWULlyZel+8+bN+euvv1RqTiAQCAQCgUAgT27610WJg9H4+HhatWpV4L6xsTGpqamq8CQQqBxvb2+CgoLktlEi1mw/QoMuX2PRPIi2AfOIupBQ7lo52pRLq25+5dLK5TefH8KO0qbnZJaH/vJa2lW39yT6WrG1qkRDoVDJVREpcTBqYWHBlStXCtw/duwY9vb2KjEleDsJCAjAz8+vwP3w8HAUCsVb9cfOrt+j+GrxbsYHdiB803jq17Wix/DlBTKBqFIrR5uirxVbK5fffC5e+Zuf90diX6t6sTVv03sSfa3YWkHxKXEwOmjQIEaOHMnJkydRKBTcvn2bzZs3M2bMGAYPHlweHgWCt44VWw7Rz68Z/l2a4mRvycKJvdHT0eL7PYWfy6gKrRxtir5WbK1cfgEeP8lk1tIdjPrMD0N93WJp5PKsbl9X0dfX932sSjRUdFVESuxrwoQJfPTRR7Rp04aMjAxatWpFYGAgn332GcOHDy8PjwKBRHJyMn369MHKygo9PT1cXV354YcflMo8fPiQfv36YWBggKWlJQsWLChQT0pKCv369cPU1BQ9PT06dOjA5cuXlcrs3LkTFxcXtLW1sbW1LVCPra0t06dPp0+fPujr62NlZcXy5cvL3MenWc+IvngL7yaO0j0NDQ28mjgSee56uWjlaFMurbr5lUsrl998vln7M++6O9CoQe1ilZfLs7p9XcuiVTe/cmrLA7Fm9DkUCgWTJk3i/v37nD9/nhMnTnDv3j2VnzklEBTGkydPaNSoEXv37uX8+fN8+umnfPzxx5w6dUoqM3bsWI4cOcJPP/3E77//Tnh4eIHNdQEBAZw+fZo9e/Zw/PhxcnNzef/998nKysuZHRUVRc+ePenduzfnzp0jODiYyZMnExoaqlTPvHnzaNiwIWfOnGHChAmMHDmS/fv3v9R/ZmYm6enpSteLJKdmkJ2dg3kVQ6X75lWMSEouWF4VWjnalEurbn7l0srlF+BQxFmuXL9N4Eftiiwrt2d1+7qWRatufuXUCkpGqdOBamlpUa9ePVV6EQj4+eefMTBQPkctOztb+reVlRVjxoyRPg8fPpzffvuN7du306RJEzIyMvjuu+/4/vvvadOmDQAbNmygZs2akuby5cvs2bOHiIgImjVrBsDmzZuxtrYmLCyMDz/8kIULF9KmTRsmT54MgIODA7GxscybN4+AgACprubNmzNhwgSpTEREBIsWLaJdu8J/iYaEhDB16tQyvCGB4M0m6d80lof+wtyvAtDSqly0QCB4S9Cg7BuQNKiYQ6MlDkZ9fHxeeYL/oUOHymRI8Hbj4+PDypUrle6dPHmSvn37AnmB6axZs9i+fTv//PMPT58+JTMzEz09PQCuXr3K06dPeeeddyR9lSpVcHT8b5olLi6OSpUqKZUxMzPD0dGRuLg4qUzXrl2VfDRv3pzFixeTnZ2NpqYmAE2bNlUq07RpUxYvXvzS/k2cOJFRo0ZJn9PT07G2tlYqY2ZigKamRoEF8vfupxeZD7m0WjnalEurbn7l0srl99K1f0hNe8jn4//7OZCTk8PZuBuE7TvJvi1T0NQofFLvbXpPoq8VW1seqGKa/Y2Zpndzc6Nhw4bSVa9ePZ4+fcpff/2Fq6treXgUvEXo6+tTp04dpcvKykp6Pm/ePL755hvGjx/P4cOHiY6OxtfXl6dPn8rouvhoa2tjZGSkdL2IVuVKuDlZcyQyXrqXk5PD0chLNHa1e2X9pdXK0aZcWnXzK5dWLr8errVZO38Y384dIl2Ota1o06IB384d8tJAVC7P6vZ1LYtW3fzKqRWUjBKPjC5atKjQ+8HBwWRkZJTZkEDwKiIiIujatas0UpqTk8OlS5ekJSO1a9emcuXKnDx5EhsbGyBvs9KlS5fw8vICwNnZmWfPnnHy5Elpmj45OZn4+HipHmdnZyIiIgq07eDgII2KApw4cUKpzIkTJ3B2di5zP4d81JohUzfh7myDh4stK384zMPHmfh3frfctHK0KfpasbVytKmnq42djfJRTjralTEy1Ctwv6J4Vrevq+jr69GqGlVkUKqoGZhKvWb0Rfr27UuTJk2YP3++qqoUCApQt25dduzYwZ9//ompqSkLFy7k7t27UhBpYGDAwIEDGTt2LGZmZlSrVo1Jkyah8dxoSt26denatSuDBg1i9erVGBoaMmHCBKysrKSp+dGjR9O4cWOmT59Or169OH78OMuWLWPFihVKfiIiIpg7dy5+fn7s37+fH3/8kb1795a5n93fa8S/qRnMWr2XpOQHuDpYsWPJ0GJNDZVWK0eboq8VWyuX37LwNr0n0deKrVU1CgVlXjNaUafpFbm5ubmqqGjTpk2MHz+e27dvq6I6wVtIQEAAqamphIWFKd0PDw/Hx8eHlJQUcnJyGDBgAAcPHkRPT49PP/2UmzdvkpaWJukyMjIYPHgwu3btwtDQkNGjR7N3717c3Nyk9ZwpKSmMHDmSPXv28PTpU1q1asXSpUupW7eu1O7OnTv5+uuvuXz5MpaWlgwfPlxp85StrS0DBgzg/Pnz7N27FyMjIyZOnMiIESOK3ef09HSMjY25m5xW6JS9QPCm89f1lFJrPexMVehEICgd6enpVDczJi2tfH6O5/+emLj7L3T0DYsWvIInDx8Q0s2j3LyWlhIHo927d1f6nJubS2JiIqdPn2by5MlMmTJFpQYFgoqKra0tQUFBZUozKoJRwduOCEYF6s7rCka/DFNNMDrLr+IFoyWepjc2Nlb6rKGhgaOjI9OmTeO9995TmTGBQCAQvPmUJaCcH14wNXVxGeNdp9RagUAOxJrR/yc7O5tPPvkEV1dXTE3FX6QCgUAgEAgErwPF//9X1joqIiUKRjU1NXnvvfeIi4sTwajgrSchIUFuCwKBQCAQqD0lPme0fv36XLt2rTy8CFREeHg4CoWC1NTUV5aztbV95QHtr5PQ0FBMTExeWebixYu8++676Ojo4ObmRkJCAgqFgujo6Nfi8XWzZvsRGnT5GovmQbQNmEfUhYRy18rRplxadfMrl1ad/C6b9R0zxy4ucO3bVfxkLG/DeyqrVt38yqlVJfnT9GW9KiIlDkZnzJjBmDFj+Pnnn0lMTCwyz7Ygj4CAAPz8/ArcL27gWBaKE+ipA1OmTEFfX5/4+HgOHjyItbU1iYmJ1K9fH3g97/J1sev3KL5avJvxgR0I3zSe+nWt6DF8eYFMIKrUytGm6GvF1qqb309G9GHk5EHS9dGgvA23zg3rvlJX1nbV7T2Jvr4eraoRwSgwbdo0Hj58yPvvv09MTAxdunShZs2amJqaYmpqiomJiZi6F5QrV69epUWLFtSqVQszMzM0NTWxsLCgUiWVHZdbYVix5RD9/Jrh36UpTvaWLJzYGz0dLb7fc7zctHK0KfpasbXq5lffQA8DI33puhx3DVMzY2zsaxbZZlnaVbf3JPr6erSC4lPsYHTq1Kk8fPiQw4cPS9ehQ4ekK/+zoOwcO3aMli1boquri7W1NSNGjODhw4fS802bNuHp6YmhoSEWFhZ89NFHJCUlFVpXeHg4n3zyCWlpaSgUChQKBcHBwdLzR48eMWDAAAwNDbGxseHbb799pbd9+/bRokULTExMMDMzo1OnTly9elV6nj91vmvXLnx8fNDT06Nhw4YcP678P25oaCg2Njbo6enRrVs3kpOTX9muQqEgKiqKadOmSX14fpo+ISEBHx8fAExNTVEoFAQEBADg7e3NiBEjGDduHFWqVMHCwkLpHQCkpqYSGBiIubk5RkZGtG7dmpiYGOl5TEwMPj4+GBoaYmRkRKNGjTh9+jQAN27coHPnzpiamqKvr4+Liwu//PLLK/vzKp5mPSP64i28mzhK9zQ0NPBq4kjkuevlopWjTbm06uZXLq26+X2R7GfZnP/rIg0bu6Aoxknfb9N7En19vd+LqiL/d3hZr4pIsYPR/ONIvby8XnkJysbVq1dp3749PXr04OzZs2zbto1jx44xbNgwqUxWVhbTp08nJiaGsLAwEhISpMDrRZo1a8bixYsxMjIiMTGRxMREpYPbFyxYgKenJ2fOnGHIkCEMHjyY+Pj4QusCePjwIaNGjeL06dMcPHgQDQ0NunXrRk5OjlK5SZMmMWbMGKKjo3FwcKBPnz48e/YMgJMnTzJw4ECGDRtGdHQ0Pj4+zJgx45XvJTExERcXF0aPHl2gDwDW1tbs3LkTgPj4eBITE/nmm2+k5xs2bEBfX5+TJ08yd+5cpk2bxv79+6XnH374IUlJSfz6669ERUXh4eFBmzZtuH//PgD+/v7UrFmTyMhIoqKimDBhApUrVwZg6NChZGZmcvToUc6dO8ecOXMwMDAotB+ZmZlFLm1JTs0gOzsH8yrK58mZVzEiKfnVS2FKq5WjTbm06uZXLq26+X2R+AtXefIkkwae9YpV/m16T6Kvr/d7UVW8ydP0JZrfrKgRtbrw888/FwhSsrOzlT6HhITg7+8vHaRet25dlixZgpeXFytXrkRHR4cBAwZI5e3t7VmyZAmNGzcmIyOjQP1aWloYGxujUCiwsLAo4On9999nyJAhAIwfP55FixZx+PBhHB0dC5QF6NGjh9LndevWYW5uTmxsrLR2E2DMmDF07NgRyBtVd3Fx4cqVKzg5OfHNN9/Qvn17xo0bB4CDgwN//vkn+/bte+m7y5+ONzAwkPrx77//Ss81NTWpUqUKANWqVSuwRrZBgwZSQoa6deuybNkyDh48SLt27Th27BinTp0iKSkJbW1tAObPn09YWBg7duyQsjyNHTsWJycnqY58bt68SY8ePXB1dQXyviYvIyQkhKlTp770uUAgUA0xp85T29EWQ+PC/zAUCAQVhxJtYHJwcKBKlSqvvAQvx8fHh+joaKVr7dq1SmViYmIIDQ3FwMBAunx9fcnJyeH69bxpgaioKDp37oyNjQ2GhobSiPTNmzdL7KlBgwbSv/MD1pdN+QNcvnyZPn36YG9vj5GREba2toW2/Xy9lpaWAFK9cXFxvPPOO0rlmzZtWmLvJeF5P/me8v3ExMSQkZGBmZmZ0nu/fv26tARh1KhRBAYG0rZtW2bPnq20NGHEiBHMmDGD5s2bM2XKFM6ePftSHxMnTiQtLU26bt26VaCMmYkBmpoaBRbI37ufXmQ+5NJq5WhTLq26+ZVLq25+nyctJZ3rl2/h1qR+0YXL2K46vifR19f3vahKFArVXBWREgWjU6dOZdGiRa+8BC9HX1+fOnXqKF1WVlZKZTIyMvjss8+UAtaYmBguX75M7dq1efjwIb6+vhgZGbF582YiIyPZvXs3AE+fPi2xp/yp5nwUCkWBKffn6dy5M/fv32fNmjWcPHmSkydPFtr28/Xmj6i/qt7y5lX9zMjIwNLSssAfCvHx8YwdOxaA4OBgLly4QMeOHTl06BD16tWT3ntgYCDXrl3j448/5ty5c3h6erJ06dJCfWhra2NkZKR0vYhW5Uq4OVlzJPK/5RI5OTkcjbxEY1e7V/aztFo52pRLq25+5dKqm9/niYm8gJ6BLnWdi1e+LO2q43sSfX1934uqREOhUMlVESnRNH3v3r2pVq1aeXkRAB4eHsTGxlKnTuGp6s6dO0dycjKzZ8/G2toaQNpI8zK0tLQKLAcoDcnJycTHx7NmzRpatmwJ5G22KinOzs5SEJvPiRMnyuxPS0sLKLj0oSg8PDy4c+cOlSpVkkZ6C8PBwQEHBwe++OIL+vTpw/r16+nWrRuQt2b1888/5/PPP2fixImsWbOG4cOHl7ovQz5qzZCpm3B3tsHDxZaVPxzm4eNM/Du/W25aOdoUfa3YWnXzC5Cbk0tMZCwNPOuhoVmy0wvfpvck+lr+WkHxKXYwKtaLvh7Gjx/Pu+++y7BhwwgMDERfX5/Y2Fj279/PsmXLsLGxQUtLi6VLl/L5559z/vx5pk+f/so6bW1tycjI4ODBgzRs2BA9PT309PRK7M3U1BQzMzO+/fZbLC0tuXnzJhMmTChxPSNGjKB58+bMnz+frl278ttvv71yvWhxqVWrFgqFgp9//pn3338fXV3dl24kep62bdvStGlT/Pz8mDt3Lg4ODty+fZu9e/fSrVs3XFxcGDt2LB988AF2dnb8/fffREZGSutng4KC6NChAw4ODqSkpHD48GGcnZ3L1Jfu7zXi39QMZq3eS1LyA1wdrNixZGixpoZKq5WjTdHXiq1VN78A1y/fJD31AQ0buxRZVlXtquN7En0tf62qeZNz0yty87fJF4GGhgZ37twRI6OlJCAggNTUVMLCwpTuh4eH4+PjQ0pKirTpJjIykkmTJnH8+HFyc3OpXbs2vXr14ssvvwTghx9+4MsvvyQxMREPDw8mTpxIly5dOHPmDG5uboXWOXjwYH788UeSk5OZMmUKwcHB2NraEhQUJG2WAnBzc8PPz6/A0Uf5HDhwgBEjRnDt2jUcHR1ZsmQJ3t7e7N69Gz8/PxISErCzs5O8QN6xSaamphw+fBhvb28gb+PTlClTSE5Opm3btnh5eTF9+vRXHlj/orfC2po+fTorVqzg7t279OvXj9DQULy9vXFzc1PKNuXn54eJiQmhoaEAPHjwgEmTJrFz507u3buHhYUFrVq1IiQkhOrVq9O/f38iIiK4e/cuVatWpXv37sybNw8dHR2GDx/Or7/+yt9//42RkRHt27dn0aJFmJmZveI7Io/09HSMjY25m5xW6JS9QCB4OfPDr5RaO8a78NkngaCkpKenU93MmLS08vk5nv97Ys5vMejqGxYteAWPHz5gvG/DcvNaWoodjAoEAtUjglGBoPSIYFRQEXhdwei8386qJBgd69ugwgWjb17qGoFAIBC8FZQloDRt++rlTa8i5cDkUmsFAkFBRDAqEAgEAoFAUMFRxdFMFXX7T8m2GgoEgtfGmu1HaNDlayyaB9E2YB5RFxLKXStHm3Jp1c2vXFp181tcbTNXG36Y3ovYrUGkHJjM+82UE30sH9uFlAOTla4fQ/rI5rciadXNr5xaVfImZ2ASwajgjcLW1lZpo5K6suv3KL5avJvxgR0I3zSe+nWt6DF8eYHDl1WplaNN0deKrVU3vyXR6ulU5vy1u4xd+utL6zpw6gqOHy6UrsCZu9Wyr6rUqptfObWC4iOCUTUmICAAPz8/WT14e3ujUChQKBRoa2tjZWVF586d2bVrl6y+1J0VWw7Rz68Z/l2a4mRvycKJvdHT0eL7PcfLTStHm6KvFVurbn5Loj0QeZWZ68PZGxH/kpogMyubpJSH0pWW8UQt+6pKrbr5lVOrat7kQ+9FMCooM4MGDSIxMZGrV6+yc+dO6tWrR+/evfn000/ltiYbWVlZpdY+zXpG9MVbeDf5b9pQQ0MDryaORJ67Xi5aOdqUS6tufuXSqpvfsmoLo0XDWlz6cRSn1g9hwcgOmBrpVhi/4nuiYmvLA5EOVFDh2bdvHy1atMDExAQzMzM6deqklD+9WbNmjB8/Xklz7949KleuzNGjRwHYtGkTnp6eGBoaYmFhwUcfffTKPPX56OnpYWFhQc2aNXn33XeZM2cOq1evZs2aNRw4cEAqd+vWLXr27ImJiQlVqlSha9euJCQkAPD777+jo6NT4JzRkSNH0rp1a+nzsWPHaNmyJbq6ulhbWzNixAgePnz4Um83b96ka9euGBgYYGRkRM+ePbl79670PDg4GDc3N1avXo21tTV6enr07NmTtLQ0pXrWrl2Ls7MzOjo6ODk5sWLFCulZQkICCoWCbdu24eXlhY6ODps3by7yvb2M5NQMsrNzMK+ifISHeRUjkpLTy0UrR5tyadXNr1xadfNbVu2LHIy8yuA5P+E37nuC1xykWYNa/DirDxovLLpTx76K74nX//2kzvzzzz/07dsXMzMzdHV1cXV1LTLzY0kRwegbwsOHDxk1ahSnT5/m4MGDaGho0K1bNyn/ur+/P1u3buX5Y2W3bdtGjRo1pNSeWVlZTJ8+nZiYGMLCwkhISCAgIKBUfvr374+pqak0XZ+VlYWvry+Ghob88ccfREREYGBgQPv27Xn69Clt2rTBxMSEnTt3SnVkZ2ezbds2/P39Abh69Srt27enR48enD17lm3btnHs2DGGDRtWqIecnBy6du3K/fv3OXLkCPv37+fatWv06tVLqdyVK1fYvn07//vf/9i3bx9nzpxhyJAh0vPNmzfz9ddfM3PmTOLi4pg1axaTJ09mw4YNSvVMmDCBkSNHEhcXh6+vb6GeMjMzSU9PV7oEAkHFY1f4BX49fonY60n88mc8vb/aSiMnK1o0rCW3NcFbigYqmKanZEOjKSkpNG/enMqVK/Prr78SGxvLggULMDU1VWnfxNFObwj5qSnzWbduHebm5sTGxlK/fn169uxJUFCQNLIIsGXLFvr06SOleh0wYICkt7e3Z8mSJTRu3JiMjIxipdV8Hg0NDRwcHKSRz23btpGTk8PatWul9tavX4+JiQnh4eG899579O7dmy1btjBw4EAADh48SGpqqtS3kJAQ/P39pYxRdevWZcmSJXh5ebFy5Up0dHSUPBw8eJBz585x/fp1rK2tAdi4cSMuLi5ERkbSuHFjAJ48ecLGjRuxsrICYOnSpXTs2JEFCxZgYWHBlClTWLBgAd27dwfAzs6O2NhYVq9eTf/+/aX2goKCpDIvIyQkhKlTp76yjJmJAZqaGgUWyN+7n15kCrrSauVoUy6tuvmVS6tufsuqLYobian8m/oQ+xpVKoRf8T1RsbXlgSqPdnpxIERbWxttbe0C5efMmYO1tTXr16+X7tnZ2ZXNRCGIkdE3hMuXL9OnTx/s7e0xMjLC1tYWyJumBjA3N+e9996Tpo+vX7/O8ePHpVFHgKioKDp37oyNjQ2GhoZ4eXkp1VFScnNzpcAzJiaGK1euYGhoiIGBAQYGBlSpUoUnT55Iywn8/f0JDw/n9u3bQN6IZMeOHaWUpjExMYSGhkp6AwMDfH19ycnJ4fr1gut34uLisLa2lgJRgHr16mFiYkJcXJx0z8bGRgpEAZo2bUpOTg7x8fE8fPiQq1evMnDgQKV2Z8yYobQMAsDT07PIdzJx4kTS0tKk69atWwXKaFWuhJuTNUci/9tYkZOTw9HISzR2ffUPgdJq5WhTLq26+ZVLq25+y6otihpVDalipMfd+xkVwq/4nqjY2vJAQ0UXgLW1NcbGxtIVEhJSaJt79uzB09OTDz/8kGrVquHu7s6aNWtU3jcxMvqG0LlzZ2rVqsWaNWuoUaMGOTk51K9fn6dPn0pl/P39GTFiBEuXLmXLli24urri6uoK5E3z+/r64uvry+bNmzE3N+fmzZv4+voq1VFcsrOzuXz5sjT6mJGRQaNGjQpdS2lubg5A48aNqV27Nlu3bmXw4MHs3r1byh2fX8dnn33GiBEjCtRhY2NTYo/FISMj7xfPmjVreOedd5SeaWpqKn3W19cvsr6X/fX5IkM+as2QqZtwd7bBw8WWlT8c5uHjTPw7v1tuWjnaFH2t2Fp181sSrb5OZeys/hvlrGVpQv3a1Ul98JiU9MeM79eKPX9c5O79DOxqmDJ1UFuu3b7PwdNXX2yywvdVlVp18yuntiJz69YtpXSgL/u9dO3aNVauXMmoUaP48ssviYyMZMSIEWhpaSnNDJYVEYy+ASQnJxMfH8+aNWukKfhjx44VKNe1a1c+/fRT9u3bx5YtW+jXr5/07OLFiyQnJzN79mxpJLEsC5Q3bNhASkqKNMXu4eHBtm3bqFat2ivz4fr7+7N582Zq1qyJhoYGHTt2lJ55eHgQGxtLnTrFSwHo7OzMrVu3uHXrltSn2NhYUlNTqVevnlTu5s2b3L59mxo1agBw4sQJNDQ0cHR0pHr16tSoUYNr164pjSKXN93fa8S/qRnMWr2XpOQHuDpYsWPJ0GJNDZVWK0eboq8VW6tufkuidXOswc8L/vsZOGvwewBs+S2G0d/8Qj376vRu1xBjAx3uJD/gUNQ1Zq0P52lWttr1VZVadfMrp1bV5B+jWNY6AIyMjIqVmz4nJwdPT09mzZoFgLu7O+fPn2fVqlUqDUYVuc/vaBGoFQEBAaSmprJr1y6qVatGhw4dmDJlCjdv3mTChAlERkaye/dupbNI+/bty/nz5zl79iwJCQnSiOK9e/eoWbMmI0eO5PPPP+f8+fOMHTuWS5cucebMGdzc3Ar14O3tjYODA9OmTePZs2f8/fff7N69m0WLFhEYGCjtOn/06BFubm5YWVkxbdo0atasyY0bN9i1axfjxo2jZs2aQN5morp169KgQQMaN27M2rVrpbbOnj3Lu+++y4ABAwgMDERfX5/Y2Fj279/PsmXLgLxD74OCgggKCiI3NxcPDw8MDQ1ZvHgxz549Y8iQIRgYGBAeHg7k7aafP38+TZs2Zf78+aSnpxMYGIiHhwc//PADkLeTfsSIEcyePZv27duTmZnJ6dOnSUlJYdSoUSQkJGBnZ/fK9/Qy0tPTMTY25m5yWrF+MAgEAtUgctMLVEV6ejrVzYxJSyufn+P5vydWHb6AroFh0YJX8DjjAZ/7uBTba61atWjXrp3S7+KVK1cyY8YM/vnnnzJ5eR6xZlSNycnJoVKlSmhoaLB161aioqKoX78+X3zxBfPmzStU4+/vT0xMDC1btlSa2jY3Nyc0NJQff/yRevXqMXv2bObPn18sH2vWrMHS0pLatWvTvXt3YmNj2bZtm9LxR3p6ehw9ehQbGxu6d++Os7MzAwcO5MmTJ0r/Q9SpU4cmTZpw9uzZAiORDRo04MiRI1y6dImWLVvi7u7O119/LY1ovohCoeCnn37C1NSUVq1a0bZtW+zt7dm2bZtSuTp16tC9e3fef/993nvvPRo0aKDkPTAwkLVr17J+/XpcXV3x8vIiNDS0XBZxCwQCgUBQUWjevDnx8cqJIS5dukStWqo9VUKMjKox7du3p06dOtKooKDkBAcHExYWRnR0tCzti5FRgUAexMioQFW8rpHRb8NjVTIy+ql3vWJ7jYyMpFmzZkydOpWePXty6tQpBg0axLfffqvSpWtizagakpKSQkREBOHh4Xz++edy2xEIBAK1oywB5eg9saXWLuhSr+hCAsFLeN0JlBo3bszu3buZOHEi06ZNw87OjsWLF6t8D4UIRtWQAQMGEBkZyejRo+natavcdgQCgUAgELyhdOrUiU6dOpVrGyIYVUN2794tt4U3huDgYIKDg+W2UShrth9h6fcHSUpOp35dK+aM/ZBGLrblqpWjTbm06uZXLq26+X3dWgXg62iOR01jjHQqkfbkGZG3Ujlw6d9itfe6/apCq25+5dSqElUeel/REBuYKhgBAQFKu9/Li/yc7KoiNDRUOpxebry9vaUsTerKrt+j+GrxbsYHdiB803jq17Wix/DlBTKBqFIrR5uirxVbq25+5dC2rluVZram7D53hzmHrrI39i4+dcxoYVflpRp17as6+pVTq2ryj3Yq61UREcFoEQQEBKBQKApdmzl06FAUCkWp87fLyZgxYzh48OBrbfNl/2Ns3bpVpe3s2rWL6dNLvzmhIrBiyyH6+TXDv0tTnOwtWTixN3o6Wny/53i5aeVoU/S1YmvVza8cWltTXc7feUBcUgYpj7M4m/iAS0kPsTHVealGXfuqjn7l1AqKjwhGi4G1tTVbt27l8ePH0r0nT56wZcuWcsv8U94YGBhgZmb22ttdv349iYmJSpeqR4KrVKmCoWHZdhyqgqysrFLpnmY9I/riLbybOEr3NDQ08GriSOS5gmlPVaGVo025tOrmVy6tuvmVS5uQ8pi65vpU1dcCwNJIGzszPS7ezXipRk6/ZdGqm185teWBKtOBVjQqqq8KhYeHB9bW1uzatUu6t2vXLmxsbHB3d1cqm5OTQ0hICHZ2dujq6tKwYUN27NihVObChQt06tQJIyMjDA0NadmyZYE85/Pnz8fS0hIzMzOGDh2qFNhs2rQJT09PDA0NsbCw4KOPPiIpKUl6Hh4ejkKh4ODBg3h6eqKnp0ezZs2UzgorbJp+3bp1uLi4oK2tjaWlJcOGDZOeLVy4EFdXV/T19bG2tmbIkCFSqsySYGJigoWFhdKlo5M3gpA/1f/bb7/h7OyMgYEB7du3JzExUdI/e/aMESNGYGJigpmZGePHj6d///5KAe2L0/S2trbMmjWLAQMGYGhoiI2NDd9++62Sr1u3btGzZ09MTEyoUqUKXbt2JSEhQanM2rVrcXZ2RkdHBycnJ6WzSBMSElAoFGzbtg0vLy90dHQKTX2amZlJenq60vUiyakZZGfnYF5FOaA2r2JEUnLB8qrQytGmXFp18yuXVt38yqU9dPlfov9JZ3zr2szt5MwoL3uOXk3mr39e3Z5cfsuiVTe/cmrLAzFNL2DAgAGsX79e+rxu3To++eSTAuVCQkLYuHEjq1at4sKFC3zxxRf07duXI0eOAPDPP//QqlUrtLW1OXToEFFRUQwYMIBnz55JdRw+fJirV69y+PBhNmzYQGhoqFKO9qysLKZPn05MTAxhYWEkJCQUulRg0qRJLFiwgNOnT1OpUiUGDBjw0v6tXLmSoUOH8umnn3Lu3Dn27NmjlHZTQ0ODJUuWcOHCBTZs2MChQ4cYN25cSV5hsXj06BHz589n06ZNHD16lJs3bzJmzBjp+Zw5c9i8eTPr168nIiKC9PR0wsLCiqx3wYIFeHp6cubMGYYMGcLgwYOl4DwrKwtfX18MDQ35448/iIiIkALhp0+fArB582a+/vprZs6cSVxcHLNmzWLy5Mls2LBBqZ0JEyYwcuRI4uLi8PX1LeAjJCQEY2Nj6cpPUyoQCNSThjWM8KhpzOaof1h45Bpbz9zGu44ZntbGclsTvGEoVHRVRMRu+mLSt29fJk6cyI0bNwCIiIhg69atUlpJyBv1mjVrFgcOHKBp06YA2Nvbc+zYMVavXo2XlxfLly/H2NiYrVu3UrlyZQAcHByU2jI1NWXZsmVoamri5OREx44dOXjwIIMGDQJQCirt7e1ZsmQJjRs3JiMjAwMDA+nZzJkz8fLyAvKCpI4dO/LkyRNpJPJ5ZsyYwejRoxk5cqR0r3HjxtK/XxxpnDFjBp9//rnS6GBx6NOnD5qamkr3YmNjpeUOWVlZrFq1itq1awMwbNgwpk2bJpVdunQpEydOpFu3bgAsW7aMX375pch233//fYYMGQLA+PHjWbRoEYcPH8bR0ZFt27aRk5PD2rVrpb8a169fj4mJCeHh4bz33ntMmTKFBQsW0L17dwDs7OyIjY1l9erVSvl5g4KCpDKFMXHiREaNGiV9Tk9PLxCQmpkYoKmpUWCB/L376UXmQy6tVo425dKqm1+5tOrmVy5tZ5fqeaOjt/NGyu48yMRUtzJt6lTl9K20Cue3LFp18yunVlAyxMhoMTE3N6djx46Ehoayfv16OnbsSNWqVZXKXLlyhUePHtGuXTsMDAyka+PGjdI0fHR0NC1btpQC0cJwcXFRCtgsLS2VpuGjoqLo3LkzNjY2GBoaSgHnzZs3lepp0KCBUh2AUj35JCUlcfv2bdq0afNSTwcOHKBNmzZYWVlhaGjIxx9/THJyMo8ePXqppjAWLVpEdHS00vV8Ok89PT0pEH2x72lpady9e5cmTZpIzzU1NWnUqFGR7T7/LhQKBRYWFlK9MTExXLlyBUNDQ+lrVqVKFZ48ecLVq1d5+PAhV69eZeDAgUpf1xkzZhRYXuHp6flKH9ra2hgZGSldL6JVuRJuTtYcifxvWUVOTg5HIy/R2PXVKUhLq5WjTbm06uZXLq26+ZVLW1lTQc4L93Jyc4t1hI669VXd/MqpLQ/e5Gl6MTJaAgYMGCCto1y+fHmB5/lrKPfu3YuVlZXSM21tbQB0dXWLbOfFQFWhUJCTk/fj7uHDh/j6+uLr68vmzZsxNzfn5s2b+Pr6SlPKhdWT/w2YX8/zFOUpISGBTp06MXjwYGbOnEmVKlU4duwYAwcO5OnTp+jp6RXZp3wsLCyUpv9fpLC+qyJj7aveaUZGBo0aNSp0jae5ubn0dV2zZg3vvPOO0vMXR3n19fXL7BVgyEetGTJ1E+7ONni42LLyh8M8fJyJf+d3y00rR5uirxVbq25+5dDG3smgbd2qpD7K4s6DTKyMdfCqbcapm6lFtqdufVVHv3JqVY0qNiBV1BFIEYyWgPw1hAqFotD1gPXq1UNbW5ubN29Ko5Uv0qBBAzZs2EBWVtYrR0dfxsWLF0lOTmb27NnS9O7p06dLXM/zGBoaYmtry8GDB/Hx8SnwPCoqipycHBYsWICGRt638vbt28vUZmkwNjamevXqREZG0qpVKwCys7P566+/ynRmqoeHB9u2baNatWqFjlQaGxtTo0YNrl27pvIUaC+j+3uN+Dc1g1mr95KU/ABXByt2LBlarKmh0mrlaFP0tWJr1c2vHNrd5+7Q3smc7g0sMNTOO/T++I0U9sffK7I9deurOvqVUysoPopcVQw7vcEEBASQmpoqbZLJ3/2cH7T4+flhYmIibTD66quvWLVqFQsWLKBFixakpaURERGBkZER/fv3Jzk5GUdHR7y8vJg4cSLGxsacOHGCJk2a4OjoWKA9yFuHGB0dTXh4OPfu3aNmzZqMHDmSzz//nPPnzzN27FguXbrEmTNncHNzIzw8HB8fH1JSUqSD6KOjo3F3d+f69evY2toSHBxMWFgY0dHRAGzYsIHPP/+cOXPm0KFDBx48eEBERATDhw8nJiYGNzc3Fi9eTOfOnYmIiGDixIn8888/UhuhoaEEBQWRmpr60nepUChYv3497du3V7pvaGiIvr5+oXWEhYXRrVs3aXR05syZLFq0iO+++w4nJyeWLl3Kpk2baN26tZSZytvbW/ILeWtcg4KClNa9urm54efnR3BwMI8ePcLNzQ0rKyumTZtGzZo1uXHjBrt27WLcuHHUrFmTtWvXMmLECGbPnk379u3JzMzk9OnTpKSkMGrUKBISErCzs5O+BsUlPT0dY2Nj7ianFRoICwSCiofITS94nvT0dKqbGZOWVj4/x/N/T3wfcQk9g7IdW/go4wF9mzuUm9fSUlFHbCssL1vnl8/06dOZPHkyISEhODs70759e/bu3YudXd76EjMzMw4dOkRGRgZeXl40atSINWvWFHuU1NzcnNDQUH788Ufq1avH7NmzmT9/fpn71b9/fxYvXsyKFStwcXGhU6dOXL58GYCGDRuycOFC5syZQ/369dm8eTMhISGlaueTTz7B0tJS6Vq6dGmx9ePHj6dPnz7069ePpk2bYmBggK+vb6GbsoqLnp4eR48excbGhu7du+Ps7MzAgQN58uSJ9LUODAxk7dq1rF+/HldXV7y8vAgNDZW+rgKBQCAQlCdv8m56MTIqUGtycnJwdnamZ8+eapl1SYyMCgTqhxgZFTzP6xoZ3ayikVH/CjgyKtaMCtSKGzdu8Pvvv+Pl5UVmZibLli3j+vXrfPTRR3JbEwgEbwllCSgvJZYup7mDpfxZ5QTyolBQrFMaiqqjIiKCUYFaoaGhQWhoKGPGjCE3N5f69etz4MABnJ2d5bYmEAgEAkG5oYECjTJOtJdVX16IYFSgVlhbWxMRESG3jdfCmu1HWPr9QZKS06lf14o5Yz+kkYttuWrlaFMurbr5lUurbn7l0pZGt/OXE+z69SS3k1IAsLepxsDebWjWyPGVOlX4LYtW3b42cmoFxUNsYFIBL+ZCV3dsbW2lnejqSHBwcJmOeqoI7Po9iq8W72Z8YAfCN42nfl0regxfXiATiCq1crQp+lqxtermVy5taXXVqhozpL8vGxYNY8PCoXg2qM3YmZu4dvNukV7Vra9vo1bV5E/Tl/WqiMgajAYEBKBQKJg9e7bS/bCwsBJnCVC3AGr37t28++67GBsbY2hoiIuLS4UJaCMjI/n000+lzwqFolj534vC29u70GwQn3/+eZnrfp4xY8Zw8OBBldb5ulmx5RD9/Jrh36UpTvaWLJzYGz0dLb7fc7zctHK0KfpasbXq5lcubWl1LZs409zTCZsaVbGxMmfwx77o6Whx/uLNV+rUsa9vo1bVKFT0X0VE9pFRHR0d5syZQ0pKitxWSsyLGY+Ky8GDB+nVqxc9evTg1KlTREVFMXPmTLKyslTssGTk98fc3LxEWZVKwqBBg0hMTFS65s6dq9I2DAwMMDMzU2mdpaG03x9Ps54RffEW3k3+m6rT0NDAq4kjkeeul4tWjjbl0qqbX7m06uZXLm1Z2nye7Owcfj8aw+MnT6nvZFNkeXXr69umLQ/EyGg50rZtWywsLIo8t/LYsWO0bNkSXV1drK2tGTFiBA8fPgTyRtxu3LjBF198IY225ebmYm5uzo4dO6Q63NzcpBzt+XVqa2tL+dVv3rxJ165dMTAwwMjIiJ49e3L37n/TJfnTv2vXrsXOzu6lZ1vu3bs37xiGQtJLAvzvf/+jefPmjB07FkdHRxwcHPDz8yuQYvSnn37Cw8MDHR0d7O3tmTp1Ks+ePZOep6am8tlnn1G9enV0dHSoX78+P//8s5LX51m8eDG2trbS54CAAPz8/Jg5cyY1atTA0THvf7jnR5nzy3fr1g2FQoGtrS0JCQloaGgUyPy0ePFiatWqVWjK0Xz09PSwsLBQuvKPl0hISEChULBr1y58fHzQ09OjYcOGHD+u/BfomjVrsLa2Rk9Pj27durFw4ULpcP/C+p7fz/nz52NpaYmZmRlDhw5VCv4zMzMZM2YMVlZW6Ovr88477xAeHq7U7qu+B/Pf1fTp0+nXrx9GRkZKo8vPt5Oenq50vUhyagbZ2TmYV1HePWtexYik5ILlVaGVo025tOrmVy6tuvmVS1uWNgGuJNzBu+cUWvaYzJyVYcz5si/2NtWL1KlbX982raBkyB6MampqMmvWLJYuXcrff/9daJmrV6/Svn17evTowdmzZ9m2bRvHjh2T8sTv2rWLmjVrMm3aNGm0TaFQ0KpVKymgSElJIS4ujsePH3Px4kUAjhw5QuPGjdHT0yMnJ4euXbty//59jhw5wv79+7l27Rq9evVS8nLlyhV27tzJrl27pOxFz7Nlyxb69OnD5s2bX5o60sLCggsXLnD+/PmXvpc//viDfv36MXLkSGJjY1m9ejWhoaHMnDkTyDtfs0OHDkRERPD9998TGxvL7NmzC+RKL4qDBw8SHx/P/v37pUD2eSIjIwFYv349iYmJREZGYmtrS9u2bVm/fr1S2fXr1xMQECClDC0tkyZNYsyYMURHR+Pg4ECfPn2kIDwiIoLPP/+ckSNHEh0dTbt27aR38ioOHz7M1atXOXz4MBs2bCA0NFTKmgUwbNgwjh8/ztatWzl79iwffvgh7du3lw7+L+p7MJ/58+fTsGFDzpw5w+TJkwv4CAkJwdjYWLryU7oKBIK3k1pWVdm0eDjfzR9C9/bvMG3xjmKvGRW8XSj+fzd9WS4xTf8KunXrhpubG1OmTCn0eUhICP7+/gQFBVG3bl2aNWvGkiVL2LhxI0+ePKFKlSpoampiaGgojbZB3ohpfjB69OhR3N3dle6Fh4dLOeQPHjzIuXPn2LJlC40aNeKdd95h48aNHDlyRArIIG/qdePGjbi7u9OgQQMln8uXL2fIkCH873//o1OnTi/t7/Dhw2ncuDGurq7Y2trSu3dv1q1bR2ZmplRm6tSpTJgwgf79+2Nvb0+7du2YPn06q1evBuDAgQOcOnWKXbt20a5dO+zt7enUqRMdOnQo0bvX19dn7dq1uLi44OLiUuC5ubk5ACYmJlhYWEifAwMD+eGHHyTPf/31F+fOneOTTz55ZXsrVqzAwMBA6XpxBHnMmDF07NgRBwcHpk6dyo0bN7hy5QoAS5cupUOHDowZMwYHBweGDBlSrD6bmpqybNkynJyc6NSpEx07dpTWld68eZP169fz448/0rJlS2rXrs2YMWNo0aKFFHAX9T2YT+vWrRk9ejS1a9emdu3aBXxMnDiRtLQ06bp161aBMmYmBmhqahRYIH/vfnqR+ZBLq5WjTbm06uZXLq26+ZVLW5Y2ASpXroR1jao417FiaP/21LWzYNv//ixSp259fdu05YGYpn8NzJkzhw0bNhAXF1fgWUxMDKGhoUoBjK+vLzk5OVy//vJ1G15eXsTGxnLv3j2OHDmCt7e3FIxmZWXx559/4u3tDUBcXBzW1tZKI1X16tXDxMREyVOtWrWkgOx5duzYwRdffMH+/fulAPdl6Ovrs3fvXq5cucJXX32FgYEBo0ePpkmTJtKSgZiYGKZNm6bU5/z1lo8ePSI6OpqaNWvi4ODwyraKwtXVFS0trRLr/Pz80NTUlPLBh4aG4uPjo7QMoDD8/f2Jjo5Wurp06aJU5vkgP39ZRVJSEgDx8fE0adJEqfyLnwvDxcVFadTY0tJSqvPcuXNkZ2fj4OCg9L6PHDnC1atXgeJ/D3p6er7Sh7a2tpRS9mWpZbUqV8LNyZojkfHSvZycHI5GXqKx66vTj5ZWK0ebcmnVza9cWnXzK5e2LG0WRk5OLllZz4osp259fdu0gpJRYc4ZbdWqFb6+vkycOJGAgAClZxkZGXz22WeMGDGigM7G5uULvV1dXalSpQpHjhzhyJEjzJw5EwsLC+bMmUNkZCRZWVk0a9asRD719fULve/u7s5ff/3FunXr8PT0LNZpAPmjZ4GBgUyaNAkHBwe2bdvGJ598QkZGBlOnTqV79+4FdDo6Oujq6r6ybg0NDV7M9FrYBqmX9acotLS06NevH+vXr6d79+5s2bKFb775pkidsbExderUeWWZypUrS//Of4+vWodaHJ6vM7/e/DozMjLQ1NQkKiqqwDIHAwMDqUxxvgdL+z5fZMhHrRkydRPuzjZ4uNiy8ofDPHyciX/nd8tNK0eboq8VW6tufuXSlla3fMM+mjVypLq5CY8eZ/LbkWj+On+db4JfPcOkjn19G7WqRmRgek3Mnj0bNzc3aSNNPh4eHsTGxr4yiNHS0iI7O1vpnkKhoGXLlvz0009cuHCBFi1aoKenR2ZmJqtXr8bT01MKHpydnbl16xa3bt2SRkdjY2NJTU2lXr2iU7/Vrl2bBQsW4O3tjaamJsuWLStR321tbdHT05M2xHh4eBAfH//SPjdo0IC///6bS5cuFTo6am5uzp07d8jNzZUCusLWuBaHypUrF3i3kDdVX79+fVasWMGzZ88KDZxVjaOjo9KyCaDA55Li7u5OdnY2SUlJtGzZstAyxfkeVCXd32vEv6kZzFq9l6TkB7g6WLFjydBiTQ2VVitHm6KvFVurbn7l0pZWl5L2kKmLt/Pv/QcY6OtQx9aCb4I/4R33ukV6Vbe+vo1aVaOKo5kq6ppRRe6Lw2evkYCAAFJTU5XOsOzXrx8//vgjT548kUb2zp49y7vvvsuAAQMIDAxEX1+f2NhY9u/fLwV97733Hrq6uqxYsQJtbW2qVq0KwDfffMPo0aPx9PTkxIkTQN4U888//8zYsWOlXfy5ubl4eHhgaGjI4sWLefbsGUOGDMHAwEBaYxocHExYWFiBoM7b2xs3NzcWL15MfHw83t7e9OrV66XnngYHB/Po0SPef/99atWqRWpqKkuWLGHbtm2cOXMGR0dHfvvtNzp16sRXX33FBx98gIaGBjExMZw/f54ZM2YA4OPjw7///svChQupU6cOFy9eRKFQ0L59e+Li4nBxcSEkJIQPPviAffv2MXnyZIyMjEhISHjp+4e8wDgoKEg699TBwYG2bdvy9ddfo62tjampqVS2efPmnD59mgEDBrBy5cpXfr29vb1xcHBg2rRpSvfz60xISMDOzo4zZ85Iu+FTU1MxNTXl8OHDeHt7ExERQatWrZg3bx6dO3fm0KFDTJo0iezsbOl4sBe/ToX1MygoiOjoaOlr27dvXyIiIliwYAHu7u7cu3ePgwcP0qBBAzp27Fis78EX31txSE9Px9jYmLvJaYVO2QsEgjcLkZv+zSM9PZ3qZsakpZXPz/H83xO7T11D36Bs3wcPMx7QrYl9uXktLRVmzWg+06ZNKzAl26BBA44cOcKlS5do2bIl7u7ufP3119SoUUNJl5CQQO3atZXWdHp5eZGdnS2tDYW8oOjFewqFgp9++glTU1NatWpF27Ztsbe3Z9u2bSXy7+joyKFDh/jhhx8YPXp0oWW8vLy4du0a/fr1w8nJiQ4dOnDnzh1+//13aVTY19eXn3/+md9//53GjRvz7rvvsmjRImrVqiXVs3PnTho3bkyfPn2oV68e48aNk0YwnZ2dWbFiBcuXL6dhw4acOnWKMWPGlKgv+SxYsID9+/djbW2Nu7u70rOBAwfy9OlTBgwYUKy61qxZg6WlpdLVp0+fYntp3rw5q1atYuHChTRs2JB9+/bxxRdfvPSYreKyfv16+vXrx+jRo3F0dMTPz4/IyEhpCr4434MCgUAgEJQXGgrVXBURWUdGBerP9OnT+fHHHzl79qxsHgYNGsTFixf5448/ZPNQWsTIqEAgKA7OY/eWWhs3r6MKnQhe5HWNjO6JvK6SkdEuje0q3MhohVozKlAfMjIySEhIYNmyZdKygdfF/PnzadeuHfr6+vz6669s2LCBFStWvFYPAoFAIBAIVIMIRgWlYtiwYfzwww/4+fkVe4peVZw6dYq5c+fy4MED7O3tWbJkCYGBga/Vg0AgEAgErxOxm16g9rxss1JpeTGD0etk+/btsrT7ulmz/QhLvz9IUnI69etaMWfshzRysS1XrRxtyqVVN79yadXNr1za8m7T064KA7ztcbEyppqxDsNCT3PwQl6mpkoaCka2d6SVkzk1zfTIePyM41f+ZcEvF7mXnllIixW7r2+SVpUoKPtu+Aoai1a8DUwVmVu3bjFgwABq1KiBlpYWtWrVYuTIkSQnJ8ttTSI/v/uLO/6/+eYb2YLHfGxtbVEoFGzdurXAMxcXFxQKheweKwq7fo/iq8W7GR/YgfBN46lf14oew5cXyASiSq0cbYq+VmytuvmVS/s62tTV0iT+djrTwwqmkdbR0qSelRErD1yhx+JjjNgYha25PisCXp6EoyL39U3Rqpo3eQOTCEaLybVr1/D09OTy5cv88MMPXLlyhVWrVnHw4EGaNm3K/fv3y7X9p0+flklvbGyMiYmJasyUAWtr6wI57U+cOMGdO3dUdmB8RaCsX68VWw7Rz68Z/l2a4mRvycKJvdHT0eL7PcfLTStHm6KvFVurbn7l0r6ONv+Iv8c3v13iwPmCeesznjxj4JpT7DubSMK9h8TcTGXG7gvUtzbB0qTwk0Yqcl/fFK2g+IhgtJgMHToULS0tfv/9d7y8vLCxsaFDhw4cOHCAf/75h0mTJkllbW1tmT59On369EFfXx8rKyuWL1+uVF9qaiqBgYGYm5tjZGRE69atiYmJkZ4HBwfj5ubG2rVrsbOzk44u2rdvHy1atMDExAQzMzM6deokpawEsLPLS1Hm7u6OQqGQjq8KCAjAz89PKpeZmcmIESOoVq0aOjo6tGjRQunw+PDwcBQKBQcPHsTT0xM9PT2aNWtGfPx/adFiYmLw8fHB0NAQIyMjGjVqxOnTp1/5Hv39/Tly5IhSTvZ169bh7+9PpUrKq0YWLlyIq6sr+vr6WFtbM2TIEDIyMqTnoaGhmJiY8Ntvv+Hs7IyBgQHt27cnMTFRKhMZGUm7du2oWrUqxsbGeHl58ddffym1c/HiRVq0aIGOjg716tXjwIEDKBQKpSUNt27domfPnpiYmFClShW6du0qndf6/PudOXMmNWrUKJC4oSQ8zXpG9MVbeDf5rw4NDQ28mjgSee7l6W/LopWjTbm06uZXLq26+ZVLK5ffojDUrUROTi7pjwumFlW3vqqjtjxQqOi/iogIRovB/fv3+e233xgyZEiBNJwWFhb4+/uzbds2pfSb8+bNo2HDhpw5c4YJEyYwcuRI9u/fLz3/8MMPSUpK4tdffyUqKgoPDw/atGmjNMJ65coVdu7cya5du6Rp94cPHzJq1ChOnz7NwYMH0dDQoFu3btLZrKdOnQLgwIEDJCYmsmvXrkL7NG7cOHbu3MmGDRv466+/qFOnDr6+vgVGeCdNmsSCBQs4ffo0lSpVUtqs5O/vT82aNYmMjCQqKooJEyYUSLv5ItWrV8fX15cNGzYA8OjRI7Zt21boJigNDQ2WLFnChQsX2LBhA4cOHWLcuHFKZR49esT8+fPZtGkTR48e5ebNm0rnqT548ID+/ftz7NgxTpw4Qd26dXn//fd58CBviiU7Oxs/Pz/09PQ4efIk3377rdIfFpCXRtXX1xdDQ0P++OMPIiIipMD3+RHQgwcPEh8fz/79+/n5558L7X9mZibp6elK14skp2aQnZ2DeRXlIzzMqxiRlFywvCq0crQpl1bd/MqlVTe/cmnl8vsqtCppMPp9Z/ZG3+ZhZsFgVN36qo7a8iB/A1NZr4qI2MBUDC5fvkxubi7Ozs6FPnd2diYlJYV79+5RrVo1IO9w9gkTJgB5GYwiIiJYtGgR7dq149ixY5w6dYqkpCS0tbWBvOOKwsLC2LFjB59++imQN9W7ceNGpUP8e/ToodT2unXrMDc3JzY2lvr160tlzczMsLCwKNTvw4cPWblyJaGhoXTo0AHIO4x+//79fPfdd4wdO1YqO3PmTLy8vACYMGECHTt25MmTJ+jo6HDz5k3Gjh2Lk5MTAHXrFi+F3YABAxg9ejSTJk1ix44d1K5dW8q49DzPZzKytbVlxowZfP7550rHOGVlZbFq1Spq164N5O3yfz7DU+vWrZXq/PbbbzExMeHIkSN06tSJ/fv3c/XqVcLDw6X3NXPmTNq1aydptm3bRk5ODmvXrpVSq65fvx4TExPCw8N57733gLy89GvXrkVLS+ulfQ8JCWHq1KnFek8CgUBQUippKFjU1wMFMHVXwfWlAkFpmT17NhMnTmTkyJEvzTBZWsTIaAkoSX6Apk2bFvgcFxcH5E1vZ2RkYGZmhoGBgXRdv35dacq9Vq1aSoEo5AXGffr0wd7eHiMjI2xtbQG4efNmsb1dvXqVrKwsmjdvLt2rXLkyTZo0kTzm06BBA+nflpaWACQlJQEwatQoAgMDadu2LbNnz1by/io6duxIRkYGR48eZd26dS89GurAgQO0adMGKysrDA0N+fjjj0lOTubRo0dSGT09PSkQzfeY7w/g7t27DBo0iLp162JsbIyRkREZGRnS+4qPj8fa2lopcG/SpImSj5iYGK5cuYKhoaH0tapSpQpPnjxR6rOrq+srA1GAiRMnkpaWJl3PL1fIx8zEAE1NjQIL5O/dTy8yH3JptXK0KZdW3fzKpVU3v3Jp5fJbGJU0FCz62IMaproMXHOy0FHRsrSrbl8bObXlgUJFV2mIjIxk9erVSjGBKhHBaDGoU6cOCoWiQKCWT1xcHKampgUCx5eRkZGBpaUl0dHRSld8fLzSqGRhG3o6d+7M/fv3WbNmDSdPnuTkyZNA2TfMvIznp93zRwXzlwQEBwdz4cIFOnbsyKFDh6hXrx67d+8uss5KlSrx8ccfM2XKFE6ePIm/v3+BMgkJCXTq1IkGDRqwc+dOoqKipHW3z/f1xWUBCoVC6Y+G/v37Ex0dzTfffMOff/5JdHQ0ZmZmJXpfGRkZNGrUqMDX69KlS3z00UdSueJswNLW1sbIyEjpehGtypVwc7LmSOR/63NzcnI4GnmJxq52r6y/tFo52pRLq25+5dKqm1+5tHL5fZH8QLRWVX0GfHuS1EdZKm9X3b42cmrLAw0UaCjKeJUiHM3IyMDf3581a9ZgampaDj0T0/TFwszMjHbt2rFixQq++OILpXWjd+7cYfPmzfTr108K1iBvh/jznDhxQprm9/Dw4M6dO1SqVEka2SwOycnJxMfHs2bNGlq2bAnAsWPHlMrkj8zl56gvjNq1a6OlpUVERISU6z4rK4vIyEilqfHi4ODggIODA1988QV9+vRh/fr1dOvWrUjdgAEDmD9/Pr169Sr0mzsqKoqcnBwWLFiAhkbe30ylOV80IiKCFStW8P777wN5G5H+/fdf6bmjoyO3bt3i7t27VK9eHUBpIxfkfb22bdtGtWrVXlv6tCEftWbI1E24O9vg4WLLyh8O8/BxJv6d3y03rRxtir5WbK26+ZVL+zra1NPSxKbqf3/w1qyih1MNI9IePeVeeiaL+3lQz8qYwesi0dRQUNUwbwlY2qPC//CuyH19U7QVmRf3K2hra0vLBl9k6NChdOzYkbZt25ZbxkURjBaTZcuW0axZM3x9fZkxYwZ2dnZcuHCBsWPHYmVlxcyZM5XKR0REMHfuXPz8/Ni/fz8//vgje/fm5RZu27YtTZs2xc/Pj7lz5+Lg4MDt27fZu3cv3bp1w9Oz8LPhTE1NMTMz49tvv8XS0pKbN29K61LzqVatGrq6uuzbt4+aNWuio6ODsbGxUhl9fX0GDx7M2LFjqVKlCjY2NsydO5dHjx4xcODAYr2Px48fM3bsWD744APs7Oz4+++/iYyMLLCm9WU4Ozvz77//oqenV+jzOnXqkJWVxdKlS+ncuTMRERGsWrWqWHU/T926ddm0aROenp6kp6czduxYpT8m2rVrR+3atenfv7+U1emrr74C/hsJ9vf3Z968eXTt2pVp06ZRs2ZNbty4wa5duxg3bhw1a9Yssa+i6P5eI/5NzWDW6r0kJT/A1cGKHUuGFmtqqLRaOdoUfa3YWnXzK5f2dbTpUtOYjYP/W/41oUs9AHafvsWy3y/TxiVvqVHYqFZKun4rCz+CqCL39U3RqpqyTLM/XwfkHbP4PFOmTCE4OLhA+a1bt/LXX38VGKRRNYrckiyEfMu5ceMGU6ZMYd++fdy/fx8LCwv8/PyYMmUKZmZmUjlbW1sGDBjA+fPn2bt3L0ZGRkycOJERI0ZIZR48eMCkSZPYuXMn9+7dw8LCglatWhESEoK1tTXBwcGEhYUVOLz+wIEDjBgxgmvXruHo6MiSJUvw9vZm9+7d0tFNa9euZdq0afzzzz+0bNmS8PDwAhmYnjx5wrhx4/jhhx948OABnp6eLFq0iMaNGwN5Rzv5+PiQkpIinU8aHR2Nu7s7169fp0aNGvTv35+IiAju3r1L1apV6d69O/PmzZOOoXoRW1tbgoKCXjr6amJiwuLFiwkICABg0aJFzJs3j9TUVFq1aoW/vz/9+vWTPIWGhhIUFERqaqpUR1hYGN26dZOm6s+cOcOnn37K+fPnsba2ZtasWYwZM0bJx8WLFwkMDCQyMhJ7e3vmzZtH586d2bdvH76+vkDeCPj48eP55ZdfePDgAVZWVrRp04b58+djZGRU6gxX6enpGBsbczc57bWNugoEAvXDeezeUmvj5nVUoRPBi6Snp1PdzJi0tPL5OZ7/e+LAmRvoG5at/ocP0mnrXotbt24peS1sZPTWrVt4enqyf/9+aa2ot7c3bm5uKt/AJILRcqCooEtQsYmIiKBFixZcuXJFaXNUeSCCUYFAUBxEMFpxUcdgtDhe8wd3NDU1pXvZ2dkoFAo0NDTIzMxUelYWxDS94K1n9+7dGBgYULduXa5cucLIkSNp3rx5uQeiAoFAUFzKElDOD79Sau0Y7zql1gpUiyoOrS+Jvk2bNpw7d07p3ieffIKTkxPjx49XWSAKIhgVCHjw4AHjx4/n5s2bVK1albZt27JgwQK5bQkEAoFA8B+qOLS+BHpDQ0Pq16+vdE9fXx8zM7MC98uKCEbLgefTRAoqPv369aNfv35y2xAIBAKB4KWocgNTRUMEowJBBWXN9iMs/f4gScnp1K9rxZyxH9LIxbZctXK0KZdW3fzKpVU3v3Jp1cnvslnfkZbyoMD9Rk0b0L5760IU8vpVZ+2bRnh4eLnUKw69F7zxBAQEoFAoUCgUaGlpUadOHaZNm8azZ88IDw9HoVBgamrKkydPlHSRkZGS7nmys7NZtGgRrq6u6OjoYGpqSocOHYiIiFCZ512/R/HV4t2MD+xA+Kbx1K9rRY/hywtkAlGlVo42RV8rtlbd/MqlVTe/n4zow8jJg6Tro0HdAXBuWHRKZ3Xrq5xalSNnCqZyRgSjgreC9u3bk5iYyOXLlxk9ejTBwcHMmzdPem5oaFgge9R3332HjY2N0r3c3Fx69+7NtGnTGDlyJHFxcYSHh2NtbY23t3eJj3Z6GSu2HKKfXzP8uzTFyd6ShRN7o6ejxfd7Cj8zUBVaOdoUfa3YWnXzK5dW3fzqG+hhYKQvXZfjrmFqZoyNfdFnJqtbX+XUqhqFiv6riIhgVPBWoK2tjYWFBbVq1WLw4MG0bduWPXv2SM/79+/PunXrpM+PHz9m69at9O/fX6me7du3s2PHDjZu3EhgYCB2dnY0bNiQb7/9li5duhAYGMjDhw/L5PVp1jOiL97Cu4mjdE9DQwOvJo5EnrteLlo52pRLq25+5dKqm1+5tOrm90Wyn2Vz/q+LNGzsUmAWqKL4VUetoGSIYFTwVqKrq6uUn/7jjz/mjz/+4ObNmwDs3LkTW1tbPDw8lHRbtmzBwcGBzp07F6hz9OjRJCcns3///pe2m5mZSXp6utL1IsmpGWRn52BexVDpvnkVI5KSC5ZXhVaONuXSqptfubTq5lcurbr5fZH4C1d58iSTBp71iiyrjn2tCO9YVSgUqrkqIiIYFbxV5ObmcuDAAX777Tdat/5voX61atXo0KEDoaGhAKxbt44BAwYU0F+6dAlnZ+dC686/f+nSpZe2HxISgrGxsXS9mJJNIBAIXicxp85T29EWQ2MDua0IiuANXjIqglHB28HPP/+MgYEBOjo6dOjQgV69ehXIwztgwABCQ0O5du0ax48fx9/fv9C6ypK0bOLEiaSlpUnXrVu3CpQxMzFAU1OjwAL5e/fTi8yHXFqtHG3KpVU3v3Jp1c2vXFp18/s8aSnpXL98C7cmxTszUh37Kvc7FhQPEYwK3gp8fHyIjo7m8uXLPH78mA0bNqCvr69UpkOHDjx+/JiBAwfSuXNnzMzMCtTj4OBAXFxcoW3k33dwcHipD21tbYyMjJSuF9GqXAk3J2uORMZL93JycjgaeYnGrnav7GdptXK0KZdW3fzKpVU3v3Jp1c3v88REXkDPQJe6zsUrr459lfsdq5Q3eGhUnDMqeCvQ19enTp1Xp7WrVKkS/fr1Y+7cufz666+FlunduzcfffQR//vf/wqsG12wYAFmZma0a9euzH6HfNSaIVM34e5sg4eLLSt/OMzDx5n4d3633LRytCn6WrG16uZXLq26+QXIzcklJjKWBp710NAs/riUOvZVLq2qed3pQF8nIhgVCJ5j+vTpjB07ttBRUcgLRn/88Uf69+/PvHnzaNOmDenp6Sxfvpw9e/bw448/FhhxLQ3d32vEv6kZzFq9l6TkB7g6WLFjydBiTQ2VVitHm6KvFVurbn7l0qqbX4Drl2+SnvqAho1diixbEfyqo1ZQfBS5ZVkAJxCoAQEBAaSmphZ6Bmh4eDg+Pj6kpKRgYmJS4HlYWBjdunVTWif67NkzFi9eTGhoKJcvX0ZHR4emTZsyefJkmjdvXiJv6enpGBsbczc5rdApe4FAICgr88OvlFo7xvvVM0qCvJ/j1c2MSUsrn5/j+b8n/jj/NwaGZas/40E6LevXLDevpUUEowKBjIhgVCAQlDciGC1fXlcwekxFwWiLChiMiml6gUAgEAjeYMoSUL6/4s9Sa38Z0qzUWkEhqGIDUsVcMip20wsEAoFAIBAI5EMEowJBBWXN9iM06PI1Fs2DaBswj6gLCeWulaNNubTq5lcurbr5lUurbn6Lq61vacSU953Y1N+TX4Y0o6ldlQJl+ja25vv+nuz+9B1mdq5HDWMd2fxWNK0qEbnpBRWegIAA/Pz85LYhO+Hh4SgUClJTU+W2UiZ2/R7FV4t3Mz6wA+GbxlO/rhU9hi8vcPiyKrVytCn6WrG16uZXLq26+S2JVqeyBtf/fciKo9cKrecDdyu6NLBk2ZGrfLHzHE+e5TC9Uz0qaxYMeip6X1WtVTUiHahAZQQEBKBQKFAoFGhpaVGnTh2mTZvGs2fPiqVPSEhAoVAQHR1dvkaLSb6f/MvQ0BAXFxeGDh3K5cuX5bantqzYcoh+fs3w79IUJ3tLFk7sjZ6OFt/vOV5uWjnaFH2t2Fp18yuXVt38lkR7+mYqG0/d4vj1+4XW49fAkq1Rf3MiIYWE5EcsOHgZM32tQkdQK3pfVa0VFB8RjMpA+/btSUxM5PLly4wePZrg4GDmzZsnt60yceDAARITE4mJiWHWrFnExcXRsGFDDh48KLc1WXj69GnptVnPiL54C+8mjtI9DQ0NvJo4Ennuerlo5WhTLq26+ZVLq25+5dKqm9+yap/HwkibKvpaRN9Kle49eppN/N0HOFsYVgi/FeE9qYo3OAGTCEblQFtbGwsLC2rVqsXgwYNp27Yte/bs4eHDhxgZGbFjxw6l8mFhYejr6/PgwQPs7PJSkLm7u6NQKPD29lYqO3/+fCwtLTEzM2Po0KFkZWVJz1JSUujXrx+mpqbo6enRoUMHpdHL0NBQTExM+O2333B2dsbAwEAKnIvCzMwMCwsL7O3t6dq1KwcOHOCdd95h4MCBZGdnS+V++uknPDw80NHRwd7enqlTp0qjwh999BG9evVSqjcrK4uqVauyceNGIC8VW0hICHZ2dujq6tKwYcMC7+tFdu7ciYuLC9ra2tja2rJgwQKl57a2tkyfPp0+ffqgr6+PlZUVy5cvVyqTmppKYGAg5ubmGBkZ0bp1a2JiYqTnwcHBuLm5sXbtWuzs7NDRefmaqaJITs0gOzsH8yrKP8zNqxiRlJxeLlo52pRLq25+5dKqm1+5tOrmt6za5zHV0wIg5XGW0v3Ux1nSM7n9VoT3pDLe4GhUBKMVAF1dXZ4+fYq+vj69e/dm/fr1Ss/Xr1/PBx98gKGhIadOnQL+G4nctWuXVO7w4cNcvXqVw4cPs2HDBkJDQwkNDZWeBwQEcPr0afbs2cPx48fJzc3l/fffVwpYHz16xPz589m0aRNHjx7l5s2bjBkzpsR90tDQYOTIkdy4cYOoqCgA/vjjD/r168fIkSOJjY1l9erVhIaGMnPmTAD8/f353//+R0ZGhlTPb7/9xqNHj+jWrRsAISEhbNy4kVWrVnHhwgW++OIL+vbty5EjRwr1ERUVRc+ePenduzfnzp0jODiYyZMnK70XgHnz5tGwYUPOnDnDhAkTGDlyJPv375eef/jhhyQlJfHrr78SFRWFh4cHbdq04f79/6aurly5ws6dO9m1a9dLl1FkZmaSnp6udAkEAoFA8DYjglEZyc3N5cCBA/z222+0bt0agMDAQH777TdpNDIpKYlffvmFAQMGAGBubg78NxJZpcp/63JMTU1ZtmwZTk5OdOrUiY4dO0rT5JcvX2bPnj2sXbuWli1b0rBhQzZv3sw///yjlJkoKyuLVatW4enpiYeHB8OGDSv1VLuTkxOQt64UYOrUqUyYMIH+/ftjb29Pu3btmD59OqtXrwbA19cXfX19du/eLdWxZcsWunTpgqGhIZmZmcyaNYt169bh6+uLvb09AQEB9O3bV6rjRRYuXEibNm2YPHkyDg4OBAQEMGzYsALLIpo3b86ECRNwcHBg+PDhfPDBByxatAiAY8eOcerUKX788Uc8PT2pW7cu8+fPx8TERGlU9unTp2zcuBF3d3caNGhQqJ+QkBCMjY2ly9raukAZMxMDNDU1CiyQv3c/vcgUdKXVytGmXFp18yuXVt38yqVVN79l1T5PyqO85UimupWV7pvoVpaeye23IrwnVSF20wtUys8//4yBgQE6Ojp06NCBXr16ERwcDECTJk1wcXFhw4YNAHz//ffUqlWLVq1aFVmvi4sLmpqa0mdLS0uSkpIAiIuLo1KlSrzzzjvSczMzMxwdHYmLi5Pu6enpUbt27ULrKCn5yb0U/799LyYmhmnTpmFgYCBdgwYNIjExkUePHlGpUiV69uzJ5s2bAXj48CE//fQT/v7+QN7I46NHj2jXrp1SHRs3buTq1auFeoiLiyuQorN58+ZcvnxZaflA06ZNlco0bdpUei8xMTFkZGRgZmam1O7169eV2q1Vq5b0x8LLmDhxImlpadJ169atAmW0KlfCzcmaI5Hx0r2cnByORl6isavdK+svrVaONuXSqptfubTq5lcurbr5Lav2ee6kZ3L/4VMa1jSR7ulW1sSxuiFxd5QDOHXsq6rek6p4k3fTiwxMMuDj48PKlSvR0tKiRo0aVKqk/GUIDAxk+fLlTJgwgfXr1/PJJ59IAd2rqFxZ+a9ThUJBTk5OibwVVkdpM8bmB3P561wzMjKYOnUq3bt3L1A2f42lv78/Xl5eJCUlsX//fnR1dWnfvr2kB9i7dy9WVlZKem1t7VJ5LA4ZGRlYWloSHh5e4Nnz+ez19fWLrEtbW7tYXod81JohUzfh7myDh4stK384zMPHmfh3frfctHK0KfpasbXq5lcurbr5LYlWp5KG0rmh1Q21sTfT40HmM+5lPCXsbCK9G9Xkdtpj7qZn8nETa5IfPi10931F76uqtYLiI4JRGdDX16dOnZenZ+vbty/jxo1jyZIlxMbG0r9/f+mZllbeovDnR/WKg7OzM8+ePePkyZM0a5aXoi05OZn4+Hjq1atXil68mpycHJYsWYKdnR3u7u4AeHh4EB8f/8q+N2vWDGtra7Zt28avv/7Khx9+KAXI9erVQ1tbm5s3b+Ll5VUsH87OzkRERCjdi4iIwMHBQWkU+cSJE0plTpw4gbOzs+T7zp07VKpUCVtb22K1W1a6v9eIf1MzmLV6L0nJD3B1sGLHkqHFmhoqrVaONkVfK7ZW3fzKpVU3vyXR1q1mwBy/+tLnT1vkDS7sv5jEokNX2HHmH3QqaTDcuzYGWpW4kJjO1z/HkpVdcBCjovdV1VpV8wZnA0WRW9phL0GpCAgIIDU1VWmdZmH4+/uzY8cOWrduza+//irdf/bsGUZGRkyaNInAwEB0dHQwNjYutN6goCCio6OlET0/Pz8uX77M6tWrMTQ0ZMKECVy5coXY2FgqV65MaGgoQUFBSgfGh4WF0a1bt5eOjiYkJGBnZ8eBAwdwcXHh0aNHnD9/nsWLF3PixAn27t2Lj48PkLcZqVOnTnz11Vd88MEHaGhoEBMTw/nz55kxY4ZU51dffcXu3bu5dOkShw8fpkWLFkrPVq1axYIFC2jRogVpaWlERERgZGRE//79CQ8Px8fHh5SUFExMTPjrr79o3LgxwcHB9OrVi+PHjzN48GBWrFhBQEAAkLebPiUlhUmTJuHn58f+/fsZOXIke/fuxdfXl9zcXFq1asWDBw+YO3cuDg4O3L59m71799KtWzc8PT0JDg4mLCysxOe/pqenY2xszN3kNIyMXv8PN4FAIHgVIjd90aSnp1PdzJi0tPL5OZ7/e+JU/G0MDMtWf8aDdJo41ig3r6VFrBmtoAwcOJCnT59KG5fyqVSpEkuWLGH16tXUqFGDrl27FrvO9evX06hRIzp16kTTpk3Jzc3ll19+KTA1Xxratm2LpaUlrq6uTJgwAWdnZ86ePSsFopC3Qennn3/m999/p3Hjxrz77rssWrSIWrVqKdXl7+9PbGwsVlZWBdZ7Tp8+ncmTJxMSEoKzszPt27dn79690lKAF/Hw8GD79u1s3bqV+vXr8/XXXzNt2jQpEM1n9OjRnD59Gnd3d2bMmMHChQvx9fUF8pYq/PLLL7Rq1YpPPvkEBwcHevfuzY0bN6hevXqZ351AIBAIBEXxJm9gEiOjFZRNmzbxxRdfcPv2bWlqXlA+2NraEhQURFBQ0GtvW4yMCgSCiowYGS2a1zUyGhmfqJKR0caOlhVuZFSsGa1gPHr0iMTERGbPns1nn30mAlGBQCAQyEZZAsr54VdKrR3j/fK9BW8rqtgNX1F304tp+grG3LlzcXJywsLCgokTJ8ptRyAQCAQCQQXgDU7AJEZGKxrBwcHSmaOC10P+ofwVjTXbj7D0+4MkJadTv64Vc8Z+SCMX23LVytGmXFp18yuXVt38yqVVN79l0ZZWt2zWd6SlPChwv1HTBrTv3rrc2pVTKygeYmRU8EaRnyNe3dn1exRfLd7N+MAOhG8aT/26VvQYvrxAJhBVauVoU/S1YmvVza9cWnXzK1dfPxnRh5GTB0nXR4Pyzpx2bli3Qva1rFqV8wYPjYpg9A3g3r17DB48GBsbG7S1tbGwsMDX17fA+ZoAx48fR1NTk44dOxZ4lpCQgEKhkC4zMzPee+89zpw589K2Q0NDpfKampqYmpryzjvvMG3aNNLS0lTaz7eJFVsO0c+vGf5dmuJkb8nCib3R09Hi+z3Hy00rR5uirxVbq25+5dKqm1+5+qpvoIeBkb50XY67hqmZMTb2NStkX8uqVTVv8m56EYy+AfTo0YMzZ86wYcMGLl26xJ49e/D29iY5OblA2e+++47hw4dz9OhRbt++XWh9Bw4cIDExkd9++42MjAw6dOigdPboixgZGZGYmMjff//Nn3/+yaeffsrGjRtxc3N7aRtvOk+fPi260Mu0Wc+IvngL7yaO0j0NDQ28mjgSee56uWjlaFMurbr5lUurbn7l0qqb37Joy9Lmi2Q/y+b8Xxdp2NilyAyD6vaeBCVHBKNqTmpqKn/88Qdz5szBx8eHWrVq0aRJEyZOnEiXLl2UymZkZLBt2zYGDx5Mx44dCQ0NLbROMzMzLCws8PT0ZP78+dy9e5eTJ0++1INCocDCwgJLS0ucnZ0ZOHAgf/75JxkZGYwbN04ql5OTQ0hICHZ2dujq6tKwYUN27NghPatZsyYrV65UqvvMmTNoaGhw48YNqb+BgYGYm5tjZGRE69atiYmJeam3nJwcpk2bRs2aNdHW1sbNzY19+/ZJz/NHg7du3UqzZs3Q0dGhfv36HDlyRKme8+fP06FDBwwMDKhevToff/wx//77r/Tc29ubYcOGERQURNWqVaUzSl8kMzOT9PR0petFklMzyM7OwbyKodJ98ypGJCUXLK8KrRxtyqVVN79yadXNr1xadfNbFm1Z2nyR+AtXefIkkwaeRWcAVLf3VG6oIi99xRwYFcGoumNgYICBgQFhYWFkZma+suz27dtxcnLC0dGRvn37sm7duiLzzuvq6gIlH+mrVq0a/v7+7NmzR0pdGhISwsaNG1m1ahUXLlzgiy++oG/fvhw5cgQNDQ369OnDli1blOrZvHkzzZs3lw7G//DDD0lKSuLXX38lKioKDw8P2rRpw/37BfMgA3zzzTcsWLCA+fPnc/bsWXx9fenSpQuXL19WKjd27FhGjx7NmTNnaNq0KZ07d5ZGllNTU2ndujXu7u6cPn2affv2cffuXXr27KlUx4YNG9DS0iIiIoJVq1YV6ickJARjY2Ppsra2LtF7FQgEgjeBmFPnqe1oi6GxgdxW1IY3eMmoCEbVnUqVKhEaGsqGDRswMTGhefPmfPnll5w9e7ZA2e+++46+ffsC0L59e9LS0gqMAD5Pamoq06dPx8DAgCZNmpTYm5OTEw8ePCA5OZnMzExmzZrFunXr8PX1xd7enoCAAPr27cvq1auBvMxLERER3Lx5E8gb1dy6dSv+/v4AHDt2jFOnTvHjjz/i6elJ3bp1mT9/PiYmJtII64vMnz+f8ePH07t3bxwdHZkzZw5ubm4sXrxYqdywYcPo0aMHzs7OrFy5EmNjY7777jsAli1bhru7O7NmzcLJyQl3d3fWrVvH4cOHuXTpklRH3bp1mTt3Lo6Ojjg6OlIYEydOJC0tTbpu3bpVoIyZiQGamhoFFsjfu59eZD7k0mrlaFMurbr5lUurbn7l0qqb37Joy9Lm86SlpHP98i3cmtQvunAZ25VLKygZIhh9A+jRowe3b99mz549tG/fnvDwcDw8PJSm4ePj4zl16hR9+vQB8oLYXr16SQHX8zRr1gwDAwNMTU2JiYlh27ZtpUp7mT/qqlAouHLlCo8ePaJdu3bSaK6BgQEbN27k6tWrALi5ueHs7CyNjh45coSkpCQ+/PBDAGJiYsjIyMDMzEypjuvXr0t1PE96ejq3b98ukFK0efPmxMXFKd1r2rSp9O9KlSrh6ekplYmJieHw4cNKbTo5OQEotduoUaMi34m2tjZGRkZK14toVa6Em5M1RyLjpXs5OTkcjbxEY9fC056WVStHm3Jp1c2vXFp18yuXVt38lkVbljafJybyAnoGutR1Lp5G3d5TuSHD0GhISAiNGzfG0NCQatWq4efnR3x8fNHCEiLOGX1D0NHRoV27drRr147JkycTGBjIlClTpBzs3333Hc+ePaNGjRqSJjc3F21tbZYtW4axsbF0f9u2bdSrVw8zMzNMTExK7SkuLg4jIyPMzMy4du0aAHv37sXKykqpnLa2tvRvf39/tmzZwoQJE9iyZQvt27fHzMwMyFvzamlpSXh4eIG2yuKzKDIyMujcuTNz5swp8MzS0lL6t76+vsraHPJRa4ZM3YS7sw0eLras/OEwDx9n4t/53XLTytGm6GvF1qqbX7m06uZXrr4C5ObkEhMZSwPPemhoFn88TN3eU3mgit3wJdUfOXKEoUOH0rhxY549e8aXX37Je++9R2xsrEp/54lg9A2lXr16hIWFAfDs2TM2btzIggULeO+995TK+fn58cMPP/D5559L96ytraldu3aZ2k9KSmLLli34+fmhoaFBvXr10NbW5ubNm3h5eb1U99FHH/HVV18RFRXFjh07lNZeenh4cOfOHSpVqoStrW2RHoyMjKhRowYRERFKbUZERBRYdnDixAlatWoF5L2vqKgohg0bJrW7c+dObG1tqVTp9fwv0/29RvybmsGs1XtJSn6Aq4MVO5YMLdbUUGm1crQp+lqxtermVy6tuvmVq68A1y/fJD31AQ0buxSrvCralUurauRIB/r8hl/IO86xWrVqREVFSb8zVYEit6gdLIIKTXJyMh9++CEDBgygQYMGGBoacvr0aYYPH07Hjh357rvvCAsLo1evXiQlJSmNgAKMHz+eQ4cOERkZSUJCAnZ2dpw5c6bYB8eHhoYycuRI4uPjyc3NJTU1lePHjzNr1ixyc3M5duyYNHr41VdfsWrVKhYsWECLFi1IS0sjIiICIyMj+vfvL9XZokULMjIyuHbtGnfv3pU2UeXm5tKqVSsePHjA3LlzcXBw4Pbt2+zdu5du3brh6elJcHAwYWFhREdHA7B48WKmTJnCt99+i5ubG+vXr2fhwoVcuHCBunXrSn22sbFh8eLFODs7s2jRIrZs2cL169epWrUqt2/fxs3NDS8vL8aNG0eVKlW4cuUKW7duZe3atWhqauLt7V3oWtSiSE9Px9jYmLvJaYVO2QsEAoG68rbkpk9PT6e6mTFpaeXzczz/90TMtbsYGpat/gcP0mloX51bt24pedXW1laapXwZV65coW7dupw7d4769Yu35rc4iJFRNcfAwIB33nmHRYsWcfXqVbKysrC2tmbQoEF8+eWXQN4Ufdu2bQsEopC33nTu3LmcPXu21P8TpaenY2lpiUKhwMjICEdHR/r378/IkSOV6pw+fTrm5uaEhIRw7do1TExM8PDwkHzm4+/vz5AhQ+jXr58UiELe2tNffvmFSZMm8cknn3Dv3j0sLCxo1arVS9e0jhgxgrS0NEaPHk1SUhL16tVjz5491K2rnPFj9uzZzJ49m+joaOrUqcOePXuoWrUqgDS6On78eN577z0yMzOpVasW7du3R0NDLLsWCAQCQfmjit3w+foXT3KZMmVKkanIc3JyCAoKonnz5ioNREGMjAreckozGqxKxMioQCB4UxEjo6qr39jYmLPXVTMy2sCudCOjgwcP5tdff+XYsWPUrFl01qySIEZGBQKBQCAQqJyyBJSj98SWSregS9GH6At46WkuL2PYsGH8/PPPHD16VOWBKIhgVCAQCAQCgaDCI8du+tzcXIYPH87u3bsJDw/Hzq58jrQSC94EbzW2trbk5uZKU/ShoaHlekxUSViz/QgNunyNRfMg2gbMI+pCQrlr5WhTLq26+ZVLlphcJAAAvlJJREFUq25+5dKqm9+yaF93mwqgvaM5X7apw+yOTkxsU4e2DlWL3aYcnssDBWVPB1rSUHbo0KF8//33bNmyBUNDQ+7cucOdO3d4/PixSvsmglHBG8W9e/cYPHgwNjY2aGtrY2Fhga+vLxEREXJbKxG7fo/iq8W7GR/YgfBN46lf14oew5cXyASiSq0cbYq+VmytuvmVS6tuftWtr63rVqWZrSm7z91hzqGr7I29i08dM1rYVSmyTbk8vymsXLmStLQ0vL29sbS0lK5t27aptB0RjAreKHr06MGZM2fYsGEDly5dYs+ePXh7e0t55tWFFVsO0c+vGf5dmuJkb8nCib3R09Hi+z3Hy00rR5uirxVbq25+5dKqm19166utqS7n7zwgLimDlMdZnE18wKWkh9iY6hTZplyeywM5ctPn5uYWeuUn1FEVIhgVvDGkpqbyxx9/MGfOHHx8fKhVqxZNmjRh4sSJdOnSBYCFCxfi6uqKvr4+1tbWDBkyhIyMjFfW+9NPP+Hh4YGOjg729vZMnTqVZ8+eAXn/owYHB0sjsTVq1GDEiBFl6sfTrGdEX7yFd5P/8ttraGjg1cSRyHPXy0UrR5tyadXNr1xadfMrl1bd/JZFK5ffhJTH1DXXp6q+FgCWRtrYmelx8e6rf3bL6bk8KPMUvQoOzS8vRDAqeGPIzxsfFhZGZmZmoWU0NDRYsmQJFy5cYMOGDRw6dIhx48a9tM4//viDfv36MXLkSGJjY1m9ejWhoaHMnDkTgJ07d7Jo0SJWr17N5cuXCQsLw9XV9aX1ZWZmkp6ernS9SHJqBtnZOZhXMVS6b17FiKTkguVVoZWjTbm06uZXLq26+ZVLq25+y6KVy++hy/8S/U8641vXZm4nZ0Z52XP0ajJ//fNqnZyeBSVDBKOCN4ZKlSoRGhrKhg0bMDExoXnz5nz55ZecPXtWKhMUFISPjw+2tra0bt2aGTNmsH379pfWOXXqVCZMmED//v2xt7enXbt2TJ8+ndWrVwNw8+ZNLCwsaNu2LTY2NjRp0oRBgwa9tL6QkBCMjY2l68WDhwUCgUCgTMMaRnjUNGZz1D8sPHKNrWdu413HDE/rgolc3mzkmKh/PYhgVPBG0aNHD27fvs2ePXto37494eHheHh4EBoaCsCBAwdo06YNVlZWGBoa8vHHH5OcnMyjR48KrS8mJoZp06ZJo64GBgYMGjSIxMREHj16xIcffsjjx4+xt7dn0KBB7N69W5rCL4yJEyeSlpYmXbdu3SpQxszEAE1NjQIL5O/dTy8yH3JptXK0KZdW3fzKpVU3v3Jp1c1vWbRy+e3sUj1vdPR2OnceZBL1dxpHr96nTZ2id9TL5bk8ENP0AoEaoaOjQ7t27Zg8eTJ//vknAQEBTJkyhYSEBDp16kSDBg3YuXMnUVFRLF++HICnT58WWldGRgZTp04lOjpaus6dO8fly5fR0dHB2tqa+Ph4VqxYga6uLkOGDKFVq1ZkZWUVWp+2trZ02PDLDh3WqlwJNydrjkTGS/dycnI4GnmJxq6vPuOttFo52pRLq25+5dKqm1+5tOrmtyxaufxW1lSQ88K9nNzcYgVWcnkuD97ccVFx6L3gLaBevXqEhYURFRVFTk4OCxYskHLKv2qKHsDDw4P4+Hjq1Hl5JhFdXV06d+5M586dGTp0KE5OTpw7dw4PD49Sex7yUWuGTN2Eu7MNHi62rPzhMA8fZ+Lf+d1y08rRpuhrxdaqm1+5tOrmV936Gnsng7Z1q5L6KIs7DzKxMtbBq7YZp26mFtmmXJ4FJUMEo4I3huTkZD788EMGDBhAgwYNMDQ05PTp08ydO5euXbtSp04dsrKyWLp0KZ07dyYiIoJVq1a9ss6vv/6aTp06YWNjwwcffICGhgYxMTGcP3+eGTNmEBoaSnZ2Nu+88w56enp8//336OrqUqtWrTL1pft7jfg3NYNZq/eSlPwAVwcrdiwZWqypodJq5WhT9LVia9XNr1xadfOrbn3dfe4O7Z3M6d7AAkPtSqQ9ecbxGynsj79XZJtyeS4PVDHNXlGn6RW5ubm5cpsQCFRBZmYmwcHB/P7771y9epWsrCysra358MMP+fLLL9HV1WXRokXMmzeP1NRUWrVqhb+/P/369SMlJQUTExNCQ0MJCgoiNTVVqve3335j2rRpnDlzhsqVK+Pk5ERgYCCDBg0iLCyM2bNnExcXR3Z2Nq6ursyYMYM2bdoUy3N6ejrGxsbcTU4rUZ5ggUAgeJNRp9z06enpVDczJi2tfH6O5/+euHTzXwzLWP+D9HQcbKqWm9fSIoJRgUBGRDAqEAgEBRHBqHL9b3owKqbpBQKBQCAQVChKG1SaNh5W6jZTIpeVWvtaUMUOpAo6TS+CUYFAIBAIBIIKzhsci4qjnQQCgUAgEAgE8iGCUYEACA4Oxs3NTW4bSqzZfoQGXb7GonkQbQPmEXUhody1crQpl1bd/MqlVTe/cmnVzW9ZtBXZbzP32vyw8DNif5lJSuQy3vdqUKCMg211tiz4jBuH5/H30QUc3DCWmtVNy8WzKhGH3gsErxmFQvHKKzg4uEx1h4WFKd0bM2YMBw8eLJtpFbLr9yi+Wryb8YEdCN80nvp1regxfHmBTCCq1MrRpuhrxdaqm1+5tOrm903uq56uNucv/cPYudsKrcfWqiq/rhnF5YQ7dPrsG1r0CWH+d/t48rTwRCVl8axqFCr6ryIiglFBhSQxMVG6Fi9ejJGRkdK9MWPGqLQ9AwMDzMzMVFpnWVix5RD9/Jrh36UpTvaWLJzYGz0dLb7fc7zctHK0KfpasbXq5lcurbr5fZP7euDPWGau+pm94WcLrWfykM7s//MCU5b+xLlLf5Pwz7/8evQc/6ZkqNyzoPiIYFRQIbGwsJAuY2NjFAqF0r2tW7fi7OyMjo4OTk5OrFixQtI+ffqUYcOGYWlpiY6ODrVq1SIkJAQAW1tbALp164ZCoZA+vzhNHxAQgJ+fH/Pnz8fS0hIzMzOGDh2qlOYzMTGRjh07oquri52dHVu2bMHW1pbFixeXqe9Ps54RffEW3k0cpXsaGhp4NXEk8tz1ctHK0aZcWnXzK5dW3fzKpVU3v2XRqpvfF1EoFLRr7sKVm0nsWDKUS7+FsH/9mEKn8lXZrsp4g/OBimBUoHZs3ryZr7/+mpkzZxIXF8esWbOYPHkyGzZsAGDJkiXs2bOH7du3Ex8fz+bNm6WgMzIyEoD169eTmJgofS6Mw4cPc/XqVQ4fPsyGDRsIDQ0lNDRUet6vXz9u375NeHg4O3fu5NtvvyUpKemV3jMzM0lPT1e6XiQ5NYPs7BzMqxgq3TevYkRScsHyqtDK0aZcWnXzK5dW3fzKpVU3v2XRqpvfFzGvYoChvg5B/dtx8Hgs3YcvY294DJvmBtLMo2DKZ1W1qyre4FhUHO0kUD+mTJnCggUL6N69OwB2dnbExsayevVq+vfvz82bN6lbty4tWrRAoVAopeY0NzcHwMTEBAsLi1e2Y2pqyrJly9DU1MTJyYmOHTty8OBBBg0axMWLFzlw4ACRkZF4enoCsHbtWurWrfvKOkNCQpg6dWpZui8QCASCUqChyBt/+/XIOVb+cBiA85f+oUkDewZ0byGntWLxJqcDFSOjArXi4cOHXL16lYEDB2JgYCBdM2bM4OrVq0DeFHt0dDSOjo6MGDGC33//vVRtubi4oKmpKX22tLSURj7j4+OpVKkSHh4e0vM6depgavryHZkAEydOJC0tTbpu3bpVoIyZiQGamhoFFsjfu59eZD7k0mrlaFMurbr5lUurbn7l0qqb37Jo1c3viySnZpD1LJuL1xOV7l+6foeaFgV/dquqXUHRiGBUoFZkZOQtMl+zZg3R0dHSdf78eU6cOAGAh4cH169fZ/r06Tx+/JiePXvywQcflLitypUrK31WKBTk5OSUyb+2tjZGRkZK14toVa6Em5M1RyLjpXs5OTkcjbxEY1e7V9ZfWq0cbcqlVTe/cmnVza9cWnXzWxatuvl9kaxn2ZyJvUHdWtWV7te2qcatxJRya1d1qGInfcUcGhXT9AK1onr16tSoUYNr167h7+//0nJGRkb06tWLXr168cEHH9C+fXvu379PlSpVqFy5MtnZ2WXy4ejoyLNnzzhz5gyNGjUC4MqVK6SkFPyBVhqGfNSaIVM34e5sg4eLLSt/OMzDx5n4d3633LRytCn6WrG16uZXLq26+X2T+6qvq4Wdtbn0uVYNM+o7WJGa9oi/76awZNMB1s0awJ9nrvDH6Uu0bVqP9i3r0/nzb/igvadKPauaN3maXgSjArVj6tSpjBgxAmNjY9q3b09mZianT58mJSWFUaNGsXDhQiwtLXF3d0dDQ4Mff/wRCwsLTExMgLwd9QcPHqR58+Zoa2sXObVeGE5OTrRt25ZPP/2UlStXUrlyZUaPHo2uri4KFfzf3v29RvybmsGs1XtJSn6Aq4MVO5YMLdbUUGm1crQp+lqxtermVy6tuvl9k/vq5lyLn1ePlD7PGtUDgC0/n2Do1O/ZG36WUSFb+SLgPWaP/oArN5PoN34tJ2KuqdyzoPgocnNzc+U2IRC8itDQUIKCgkhNTZXubdmyhXnz5hEbG4u+vj6urq4EBQXRrVs31qxZw4oVK7h8+TKampo0btyYefPm4e7uDsD//vc/Ro0aRUJCAlZWViQkJBAcHExYWBjR0dFA3rrT1NRUpcPxg4KCiI6OJjw8HMg72mngwIEcOnQICwsLQkJCCAoKYtq0aXz22WfF6lt6ejrGxsbcTU4rdMpeIBAIBMXHtPGwUmtTIpeVSpeenk51M2PS0srn53j+74mExPtlrj89PR1byyrl5rW0iGBUIFARf//9N9bW1hw4cIA2bdoUSyOCUYFAIFAdb3IweuOOaoLRWhYVLxgV0/QCQSk5dOgQGRkZuLq6kpiYyLhx47C1taVVq1ZyWxMIBIK3ktIGlAC/x90ple5RxutPDfqmIYJRgaCUZGVl8eWXX3Lt2jUMDQ1p1qwZmzdvLrALXyAQCASCsqKK3PIVNTe9CEYFglLi6+uLr6+v3DYEAoFA8BbwJu+mF+eMCgoQHh6OQqFQ2jBUkcjPG69KEhISUCgU0gamisCa7Udo0OVrLJoH0TZgHlEXEspdK0ebcmnVza9cWnXzK5dW3fyWRatufkur/XHXEXr1m6F0fTF+ZbHbFBQfEYyqMatWrcLQ0JBnz55J9zIyMqhcuTLe3t5KZfMDzPwsReWBra0tCoUChUKBrq4utra29OzZk0OHDqm0nW+++UYpR7wqsLa2JjExkfr166u03tKy6/covlq8m/GBHQjfNJ76da3oMXx5gUwgqtTK0aboa8XWqptfubTq5lf0tfjamlbmrF4SJF1Tv+pfpKa8eJNz04tgVI3x8fEhIyOD06dPS/f++OMPLCwsOHnyJE+ePJHuHz58GBsbG2rXrl2unqZNm0ZiYiLx8fFs3LgRExMT2rZty8yZM1XWhrGxsXRmqKrQ1NTEwsKCSpUqxsqVFVsO0c+vGf5dmuJkb8nCib3R09Hi+z3Hy00rR5uirxVbq25+5dKqm1/R1+JrNTU1MDExkC4jQ70iNeXGGxyNimBUjXF0dMTS0lI69xLyRkC7du2KnZ2dlB4z/76Pjw+Ql84sJCQEOzs7dHV1adiwITt27ChQf0REBA0aNEBHR4d3332X8+fPF+nJ0NAQCwsLbGxsaNWqFd9++y2TJ0/m66+/Jj7+v5Rq58+fp0OHDhgYGFC9enU+/vhj/v33X+n5jh07cHV1RVdXFzMzM9q2bcvDhw+BgtP0Dx48wN/fH319fSwtLVm0aBHe3t4EBQVJZWxtbZk1axYDBgzA0NAQGxsbvv32W+n5i9P0+SPJBw8exNPTEz09PZo1a6bUB4AZM2ZQrVo1DA0NCQwMZMKECbi5uRX5nl7F06xnRF+8hXcTR+mehoYGXk0ciTx3vVy0crQpl1bd/MqlVTe/cmnVzW9ZtOrmt6xagDt37vP5iMUMH72MJSt38++/aUVqyouyJwMt+wao8kIEo2qOj48Phw8flj4fPnwYb29vvLy8pPuPHz/m5MmTUjAaEhLCxo0bWbVqFRcuXOCLL76gb9++HDlyRKnusWPHsmDBAiIjIzE3N6dz585kZWWV2OPIkSPJzc3lp59+AiA1NZXWrVvj7u7O6dOn2bdvH3fv3qVnz55A3mHyffr0YcCAAcTFxREeHk737t152ZG4o0aNIiIigj179rB//37++OMP/vrrrwLlFixYgKenJ2fOnGHIkCEMHjy4QHD5IpMmTWLBggWcPn2aSpUqMWDAAOnZ5s2bmTlzJnPmzCEqKgobGxtWrnz1eqLMzEzS09OVrhdJTs0gOzsH8yqGSvfNqxiRlFywvCq0crQpl1bd/MqlVTe/cmnVzW9ZtOrmt6zaOrWtGPxpZyaO6cPA/h24dy+NKTM38Phx5it1gpJTMeYkBaXGx8eHoKAgnj17xuPHjzlz5gxeXl5kZWWxatUqAI4fP05mZiY+Pj5kZmYya9YsDhw4QNOmTQGwt7fn2LFjrF69Gi8vL6nuKVOm0K5dOwA2bNhAzZo12b17txQ0FpcqVapQrVo1EhISAFi2bBnu7u7MmjVLKrNu3Tqsra25dOkSGRkZPHv2jO7du1OrVi0AXF1dC637wYMHbNiwgS1btkgHza9fv54aNWoUKPv+++8zZMgQAMaPH8+iRYs4fPgwjo6OBcrmM3PmTOmdTJgwgY4dO/LkyRN0dHRYunQpAwcO5JNPPgHg66+/5vfffycjI+Ol9YWEhDB16tSXPhcIBAJBxcC9YR3p37VsqlO3thVDRy3l+KlYWnu5v3Y/Yje9oMLi7e3Nw4cPiYyM5I8//sDBwQFzc3O8vLykdaPh4eHY29tjY2PDlStXePToEe3atcPAwEC6Nm7cWGBzU36wCnkBpaOjI3FxcaXymZubK+Vsj4mJ4fDhw0rtOzk5AXD16lUaNmxImzZtcHV15cMPP2TNmjWkpKQUWu+1a9fIysqiSZMm0j1jY+NCA8wGDRpI/1YoFFhYWJCUlPRK389rLC0tASRNfHy8UrtAgc8vMnHiRNLS0qTr1q1bBcqYmRigqalRYHH9vfvpReZDLq1Wjjbl0qqbX7m06uZXLq26+S2LVt38llX7Ivr6OlhaVOHO3cJ/H5U3b/CSURGMqjt16tShZs2aHD58mMOHD0ujeDVq1MDa2po///yTw4cP07p1awBp1G7v3r1ER0dLV2xsbKHrRlVBcnIy9+7dw87OTvLQuXNnpfajo6O5fPkyrVq1QlNTk/379/Prr79Sr149li5diqOjI9evF72+51W8eBi9QqEgJyen2Jr8YLoozavQ1tbGyMhI6XoRrcqVcHOy5kjkf0sIcnJyOBp5icaudq+sv7RaOdqUS6tufuXSqptfubTq5rcsWnXzW1btizx58pS7SSmYmhiUSCcoGhGMvgH4+PgQHh5OeHi40pFOrVq14tdff+XUqVPSetF69eqhra3NzZs3qVOnjtJlbW2tVO/zG6BSUlK4dOkSzs7OJfb3zTffoKGhIW068vDw4MKFC9ja2hbwoK+vD+QFfs2bN2fq1KmcOXMGLS0tdu/eXaBue3t7KleuTGRkpHQvLS2NS5culdhnSXF0dFRqFyjwubQM+ag1G8P+5IefTxB//Q6jZm/j4eNM/Du/W25aOdoUfa3YWnXzK5dW3fyKvhZPu+mHA8RevEHSvVTiL99i/jc/oqGhQfN3XYpss1yQaWh0+fLl2NraoqOjwzvvvMOpU6fK3JUXEWtG3wB8fHwYOnQoWVlZSms+vby8GDZsGE+fPpWCUUNDQ8aMGcMXX3xBTk4OLVq0IC0tjYiICIyMjOjf/78z1KZN+z/2zjuqqex7+09C78WGIFUsgCjYxq4gIsrYGEdRLNh7F3XGDna/KraxgTQVVKyMigURxa4oVjoKFuwoRUVgv3/wcn+EVBICON7PWlmLnNx9z743h2TnnLOf7Y1atWqhXr16WLhwIWrXri1WbD4nJwdZWVn48eMH0tPTsW/fPvj5+WH16tWwtCzZfzNlyhTs2bMHQ4YMwbx586Cvr4+UlBSEhYXBz88Pd+7cQVRUFJydnVG3bl3cvHkT7969ExgIa2lpYeTIkfDy8mL2pi5duhRcLpeZyZQX06ZNw7hx49C6dWt06NABBw8exIMHD2BhYSHzud2cW+F9di5W7TqFtx9yYNvYCOFbpki0rCStbXX0yV5rzbb92fytLtufzV/2WiWz/fDxC7b8cww5uV+hraWOJo2NsWKJJ7S1NcT2KQ+qoxzowYMHMXv2bOzcuRO//fYbfH190bNnTyQmJqJu3boy+cLjFwlLUWb5aXj27BnMzc3RtGlTnj2dz58/h5mZGZo0aYKEhASmnYiwZcsW7NixA2lpadDV1UXLli3x999/o0uXLowMVEREBBYsWIDk5GTY2dlhz549PHsoy2NmZobnz58DAJSVlWFgYIB27dph4sSJTDBcSnJyMubPn4/o6Gh8//4dpqamcHFxwcaNG5GQkIBZs2YhLi4OX758gampKaZNm4apU6cCKJF2ys7OxvHjxwGUBMATJ07E8ePHoa2tjXnz5iEsLAyOjo5YvXo149vMmTN55J7s7OzQv39/LFu2jLmH9+7dg52dHXMPPn36xGia3r9/H/b29khPT4eZmRkAwMfHB1u2bMG3b98waNAgaGpq4tatW7h+Xbx+HQB8+fIFOjo6ePPhs8AlexYWFhaWquHc0yyp7PJzczCkQ2N8/iyfz/HS74ms97Kf/8uXLzCorSOxr7/99hvatGmDbdu2ASjZ4mBsbIxp06ZhwYIFMvlSFjYYZfnPkZeXByMjI2zYsAFjxoyp0r579OgBAwMDhISESHT858+foauri5T0TGixwSgLCwtLtXEx8Y1Udvl5uRjToyWys7Oho6NTyV79XzCanJ5ZKcFoI3NjZGbynktFRQUqKio8xxYUFEBdXR3h4eE8q6IjR45EdnY2I9dYGbDL9Cw/Pffu3UNCQgLatm2Lz58/w9vbGwDQr18/ufabn5+PnTt3omfPnlBQUEBoaCguXLiA8+fPS3yOnJySDE9Lc2MxR7KwsLCw1GRycnLkEoyWrjQ2qqTvCU1NTb4ckaVLl2LZsmU8be/fv0dRURHq1avH016vXj2e1dbKgA1GWf4T/O9//0NiYiKUlZXRqlUrXLlyBbVr15ZrnxwOB6dPn8bKlSvx7ds3NGnSBEeOHIGTk5PE5zA0NERmZia0tLT49rh++fIFxsb8v2Al4Wez/dn8rS7bn81fWWx/Nn+ry/Zn87e6bOXZJxEhJydHoL51ZaCqqor09HQUFBRUyvnKSi2WUn5WtKphg1GWnx57e3vcvXu3yvtVU1PDhQsXZDoHl8tFgwYNRB4jTAJKEn4225/N3+qy/dn8lcX2Z/O3umx/Nn+ry1ZefcpjRrQsqqqqUFVVlWsf5alduzYUFBTw5g3v9oU3b97AwMCgUvtipZ1YWFhYWFhYWFh4KF1pjIqKYtqKi4sRFRXFUxSnMmBnRllYWFhYWFhYWPiYPXs2Ro4cidatW6Nt27bw9fVFXl4eUwa7smCDURaWGoqKigqWLl0q1V6en832Z/O3umx/Nn9lsf3Z/K0u25/N3+qyrS5/f3YGDx6Md+/eYcmSJcjKyoKdnR0iIyP5kppkhZV2YmFhYWFhYWFhqTbYPaMsLCwsLCwsLCzVBhuMsrCwsLCwsLCwVBtsMMrCwsLCwsLCwlJtsMEoCwsLCwsLCwtLtcEGoywsLD8NP378QPfu3ZGcnFzdrkhMYWEhvL298eLFi+p2haUMhYWFCA4O5hP0rgr7Hz9+YPTo0UhPT5eq75+Jn/F/lqXqYbPpWVhYqhxvb2/MnTsX6urqPO1fv37F+vXrsWTJEqG2derUwbVr19CoUSOp+r5y5Qp27dqF1NRUhIeHw8jICCEhITA3N0enTp2kOqc4tLS08PDhQ5iZmUls8+PHD6ipqeH+/fto1qxZhfr78eMHmjZtin///RdWVlYV9BY4efKkwHYOhwNVVVVYWlrC3Ny8wueVF5GRkdDU1GTev+3bt2PPnj2wtrbG9u3boaenJ9BOXV0dT58+hampqVT9ymKvo6OD+/fvV+g+yvq+SossYxGQ/X9WGrZs2SKwvewY7tKlCxQUFKrMJxbhsDqjLCw1iMLCQly6dAmpqakYOnQotLS08OrVK2hra0NTU1Pi8xQVFeHhw4cwNTUV+kVcir29PV+dYoD3Q9vT0xMODg4Vvh5hLF++HBMnTuQLRvPz87F8+XKRweiwYcPg7++PNWvWVLjfI0eOYPjw4fDw8MC9e/fw/ft3AMDnz5+xatUqnD59usLnlARHR0fExMRUKBhVUlKCiYkJioqKKtyfkpISvn37VmG7Uvr37w8Oh4PycxWlbRwOB506dcLx48fFjq+qwMvLC2vXrgUAPHz4EHPmzMHs2bMRHR2N2bNnIyAgQKBd27Ztcf/+famDUVns+/fvj+PHj2PWrFkS28j6vkqLLGMRkO1/tjySfrZt2rQJ7969Q35+PnPcp0+foK6uDk1NTbx9+xYWFhaIjo6GsbExj211fCb+6rDBKAtLDeH58+dwcXFBRkYGvn//jh49ekBLSwtr167F9+/fsXPnTqG2M2fOhK2tLcaMGYOioiJ07doV165dg7q6Ov79919069ZNqK2Liwt27NgBW1tbtG3bFgBw+/ZtPHjwAJ6ennjy5AmcnJxw9OhR9OvXT+A5iouLkZKSgrdv36K4uJjntS5duvAdXxrQlCc+Ph76+vpCfQVKAva9e/fiwoULaNWqFTQ0NHhe37hxo1DbFStWYOfOnRgxYgTCwsKY9o4dO2LFihUi+y0lJCQEO3fuRHp6Oq5fvw5TU1P4+vrC3Nxc6P3p1asXFixYgIcPHwr0uW/fvgLtFi5ciL///hshISFi70t5pkyZgrVr18LPzw+KihX7qD9//jwWLlyIlStXMmPi1q1bWLx4MRYtWgQdHR1MmDABc+fOhb+/f4XOHR4ejoEDB4o8JioqCps2bcLTp08BAFZWVpg5cyacnJwEHp+eng5ra2sAJT84fv/9d6xatQpxcXHo3bu30H4mT56M2bNnIzMzU+D70rx5c5F+ymLfqFEjeHt74+rVqwJtp0+fLtBOlve1lK9fv+L8+fNISkoCADRu3Bg9evSAmpqaUBtZxqIs/7PSfratWrUKu3fvhp+fHxo2bAgASElJwYQJEzB+/Hh07NgR7u7umDVrFsLDw3lsK+MzkaWCEAsLS42gX79+NGzYMPr+/TtpampSamoqERFFR0eTpaWlSFsjIyO6ffs2EREdO3aMDA0NKTExkRYtWkQdOnQQaTt27Fjy9vbma/fx8aGxY8cSEdGSJUuoVatWAu2vX79O5ubmxOVyicPh8Dy4XC7Psbq6uqSnp0dcLpf5u/Shra1NXC6XJk+eLNLfbt26CX04ODiItFVTU6P09HQiIp57nJqaSioqKiJtiYj++ecfql27Nq1YsYLU1NQY+4CAAOrWrZtQu/L3RdQ9KoudnR1pamqSiooKNW7cmOzt7Xkeoujfvz9paWlR/fr1ydnZmQYMGMDzEIWNjQ1dvXqVrz02Npasra2JiOj8+fNkbGzMd8yPHz/o4cOHlJiYyNN+/Phxat68OSkrK4vse/v27aSoqEju7u60efNm2rx5Mw0ZMoSUlJRo27ZtAm309PTo8ePHRETUsWNH2rVrFxERpaenk5qamtC+hL0f4t6XyrA3MzMT+jA3NxdqJ8v7SkR04sQJqlOnDp/fderUoZMnTwq1k2UsyvI/K+1nm4WFBd27d4+vPS4ujrm/V69eJQMDA75jZP1MZKk47MwoC0sN4cqVK7h27RqUlZV52s3MzPDy5UuRtu/fv4eBgQEA4PTp0/jzzz/RuHFjjB49Gps3bxZpe+jQIdy9e5ev3d3dHa1atcKePXswZMgQobMXEydOROvWrXHq1CnUr19f4IxnKb6+viAijB49GsuXL4eOjg7zmrKyMszMzNC+fXuR/kZHR4t8XRQGBgZISUnhWy6PjY2FhYWFWPutW7diz5496N+/P8+SY+vWrTF37lyhduVniyWlf//+UtkBgK6uLv744w+pbFNTU6Gtrc3Xrq2tjbS0NAAlM3vv37/nef3Ro0f4/fffkZmZCQDo168fduzYgUGDBuHRo0cYN24cTp06JbLvVatWYdOmTZg6dSrTNn36dHTs2BGrVq3ClClT+Gw6deqE2bNno2PHjrh16xYOHjwIAEhKSkKDBg2E9iVrApEs9tLayvK+Xrt2DQMHDkTfvn0xZ84cZt/pkydPsGHDBgwcOBAxMTFo164dn60sY1GW/1lpP9tev36NwsJCvvbCwkJkZWUBAAwNDZGTk8N3jKyfiSxSUN3RMAsLSwm6urrM7E7ZWbsrV65Q3bp1RdqamJjQ2bNnqbCwkIyNjenff/8lIqJHjx6Rrq6uSNu6detSUFAQX3tQUBDT7+PHj6l27doC7dXV1Sk5OVn0xZXj0qVL9OPHjwrZlCc5OZkiIyMpPz+fiIiKi4vF2qxatYqsra3pxo0bpKWlRVeuXKF9+/ZRnTp1aMuWLWLtVVVV6dmzZ0TE+x4lJSWRqqqqRH5//fpVouOqk44dO5KLiwu9ffuWaXv79i25uLhQ586diahkZrRx48Y8dr1796bu3btTREQEDR06lDgcDjVt2pTWr1/PvE/i0NDQEDiekpKSSENDQ6DN8+fPydXVlZo3b05+fn5M+8yZM2natGkS9VtdfP/+nRISEmT+f5CEXr160fjx44W+Pn78eOrVq5fc+pfmf1baz7bevXtTy5YtKS4ujmmLi4ujVq1akaurKxERnTx5kpo1a8ZnK+tnIkvFYYNRFpYawqBBg2jcuHFEVBLopKWlUU5ODjk6OpKnp6dI26VLl5KOjg41bdqUTExM6Nu3b0RE5O/vT+3atRNp6+PjQ2pqajR9+nQKCQmhkJAQmj59Oqmrq9OKFSuIiGjjxo3k5OQk0N7BwYHOnDlToWu9e/cuPXjwgHl+/Phx6tevH/3111/0/ft3kbbv378nR0dHZim0NCAcNWoUzZ49W6RtcXExrVixgjQ0NJjlSVVVVVq0aJFEfltZWdHx48eJiDcY3bJli8ilysLCQvL29iZDQ0NSUFBg7BYtWsQTPAni06dPtGfPHlqwYAF9+PCBiEru34sXL8T6++PHDzp//jzt3LmTvnz5QkREL1++pJycHJF2CQkJ1KRJE1JWVqaGDRtSw4YNSVlZmZo2bcosvx87doyCg4N57OrUqcMsjWZnZxOHw+E7RhxDhgyhdevW8bWvX7+eBg8eXKFzSUJwcDB16NCB6tevz/zQ2LRpE/M+y8s+Ly+PRo8eTQoKCjxjYurUqbR69WqRttK+r3p6ejz/d+WJj48XGeBJOxZl+Z+V9rPt9evX5OTkRBwOh5SVlUlZWZm4XC716NGDsrKyiIjo4sWLdPbsWT5bWT8TWSoOG4yysNQQMjMzydramqysrEhRUZHatWtHtWrVoiZNmtCbN2/E2h8+fJg2btxImZmZTFtgYKBEX6r79u2jdu3aMfs327VrR/v372dez8/PFzqjd/ToUbK2tqaAgAC6c+cOxcfH8zwE0bp1awoPDyei/9uvOWTIELK0tKQZM2aI9HX48OHUs2dPyszM5AkIIyMjmf2M4vj+/Ts9fvyYbt68KfYLvCx79uwhIyMjCgsLIw0NDQoNDWWC29DQUKF2y5cvJwsLC9q3bx/PXtOwsDCRX6jx8fFUp04dsrS0JEVFRcZu4cKFNHz4cJG+Pnv2jJo2bUrq6uo8wc706dNpwoQJYq+1qKiIzpw5w+zbjIyMpKKiIpE2HA6HZ6xqampSUlKS2L7K4uPjQzo6OtS7d2/y8fEhHx8fcnV1JV1dXfLx8WH82bx5M2Mj7Y8bafcAV4b99OnTqVWrVnTlyhXS0NBgbI8fP052dnZC7WR5X8vO7As7t7AZflnGoqz/s7J8tj19+pROnDhBJ06coISEBLHHlyLLZyJLxWGDURaWGsSPHz9o37595OXlRZMmTaI9e/ZItLwZFBTEzBiU5fv37wKXmyoTaZI4tLW1KSUlhYiI1qxZQ87OzkRUkiDToEEDkf3Vq1eP7t+/T0T8SUjClnFLyc7OZmZ0yvLhwwf6/Pmz6Av9/+zbt48sLS2ZazUyMhI7u9mwYUO6cOECn89Pnz4VORPVvXt38vLy4rO7evUqmZqaiuxTloQ4aeFyuZSSkkKfP3+m7Oxs0tLSovj4ePr8+TPPQxSiEnuEJfmU/3Gjqqoq0Y8bKysrOnbsGBHx3t+HDx9SrVq1xF6vLPYmJiZ0/fp1Ptvk5GTS0tISaifL+2pra0t79+4V+rq/vz/Z2toKfE2WsSjL/2xZ2ODvvwubwMTCUkO4fPkyOnToAA8PD3h4eDDthYWFuHz5skCJpFJGjRoFFxcX1K1bl6c9JycHo0aNwogRI8T2X1BQIFCaycTERKSdNIkYRMT0c+HCBfz+++8AAGNjY76kmPLk5eXx6ZMCwMePH6GioiLS1t3dHX369MHkyZN52g8dOoSTJ09KpDNa+v7k5+cjNzeX754L4uXLl7C0tORrLy4uxo8fP4Ta3b59G7t27eJrNzIyYpIwhCFLQhxQIq8UFRUlcEzs3btXoA0RoXHjxjzP7e3teZ5zOByRepXSjKekpCTY2dkBAA4fPowuXbrgwIEDuHr1Ktzd3eHr6yu0r7L+laKiooK8vDyx/cpi/+7dO4FjJy8vT2QSoCzv66hRozB37lzUq1ePT/Lq1KlTmDdvHv7++2+BtrKMRVn+Z4uKirBq1Srs3LkTb968QVJSEiwsLLB48WKYmZlhzJgxQu0CAwOFjuGLFy+K7BeQ/jORpeKwwSgLSw3BwcEBr1+/5vuC+vz5MxwcHER+gZMQ3c4XL17wZKwLIjk5GaNHj8a1a9cEnlOc0LU0gt+tW7fGihUr4OTkhJiYGOzYsQNAyZd7vXr1RNp27twZwcHB8PHxAVAiRF1cXIx169aJFaG+efOmwAzYbt26YeHChWL9/vr1K4gI6urqUFdXx7t37+Dr6wtra2s4OzsLtbO2tsaVK1f47lV4eLjAYKYUFRUVfPnyha89KSkJderUEelrcXGxwPfuxYsX0NLSEmm7fPlyeHt7o3Xr1mIVEsoiS9a0IOj/i+6L61/aHzfm5uYCResjIyMlqnAki32pAsW0adMA/N81+vn5iVSUkOV9nTFjBq5du4bff/8dTZo0gZWVFYgIT58+RXJyMvr374+ZM2cKtJVlLMryP7ty5UoEBQVh3bp1GDduHNPerFkz+Pr6Cg1GZ8yYgcDAQLi6uqJZs2YSj2FA9s9EFimophlZFhaWcnA4HJ7s5VISExOFLtvZ2dmRvb09cblcsrW15dH9a968OWlpadGff/4pst8OHTpQly5d6PTp03Tv3j26f/8+z0MQJ06coIKCAuZvUQ9B3L9/n2xsbEhbW5uWLVvGtE+dOpWGDBki0t+HDx9S3bp1ycXFhZSVlWngwIFkZWVF9erVY5b+haGuri4wgePBgwci9ShL6dGjB+3YsYOISpI56tatSw0aNCBVVVX6559/hNodP36cdHR0aM2aNaSurk7r16+nsWPHkrKyMp07d06o3ZgxY6h///5UUFDAJLU9f/6c7O3txe6tlSUhzsDAoMKJR0TEtxwv7CGOoKAgatasGamoqJCKigrZ2tqK9MfBwYFGjBhBwcHBpKSkxGTjX7p0SeQSsrR7gCvD/sqVK6SpqUkTJ04kVVVVmjFjBvXo0YM0NDTozp07Qu1keV9LCQsLo379+pGVlRVZWVlRv379xPory1iU5X9W2i0utWrVolOnTok8tzCk+UxkkQ02GGVhqWZKxaq5XC717t2bR8C6b9++ZGZmRj179hRou2zZMlq2bBlxOByaO3cu83zZsmW0atUqOnDggNjsdHV1dXr69GmFfC6bqCKtoLsgvn79ygS5osjOziYfHx/6888/qVevXrRw4UJ69eqVWLtu3brR1KlT+donT55MnTp1Emtfq1YtevToERGVBCLNmzenoqIiOnToEDVt2lSk7eXLl8nJyYnq1KlDampq1LFjR4GZvGXJzs4mJycn0tXVJQUFBTI2NiYlJSXq0qUL5ebmirSVJSFOX19fbJAgiNL3XNxDFBs2bCB1dXWaN28e84PGy8uL1NXVaePGjQJt4uPjqVmzZlL9uJFmD3Bl2aekpNDYsWOpTZs2ZGVlRR4eHiKz3YlkT3SUFlnGYqn9ihUrKvw/K0xO7fHjxyL3m9avX5+v8IKkSPOZyCIbHKJyxYdZWFiqlFGjRgEAgoKCMGjQIJ6SfKVC8OPGjUPt2rUF2hcVFWHfvn1wdnZG/fr1K9x/mzZtsGnTJnTq1Em6C5CCkSNHYsyYMSL3wcqDq1evwsnJCW3atEH37t0BlOyNvH37Ns6dO4fOnTuLtFdXV0dCQgJMTEwwaNAg2NjYYOnSpcjMzESTJk2Qn58vF79jY2Px4MED5ObmomXLlkLLYpansLAQYWFhPLYeHh4iyz4CwPz586GpqYnFixdXyM+YmBjmbyJC79694efnByMjI57junbtKvQc5ubmWL58Od8+56CgICxbtqxCe0q/ffsGBQUFKCkpiT22InuA5WFfEaR9XysDaceitLRq1QqzZs3CsGHDoKWlhfj4eFhYWMDb2xvnz5/HlStXBNpt2LABaWlp2LZtW4WW6IHq+Uz81WGDURaWGsLy5csxd+5cvrrNkqCqqoqnT5/C3Ny8wrYXL17EokWLsGrVKtja2vJ9cQuqxCMr/fv3x+nTp2FqaopRo0Zh5MiRfAGLKK5cuYJdu3YhLS0Nhw8fhpGREUJCQmBubi72C+T+/ftYv3497t+/DzU1NTRv3hx//fUXGjVqJLbf5s2bY+zYsRgwYACaNWuGyMhItG/fHnfv3oWrq6vIRI7s7GyEh4cjLS0Nc+fOhb6+PuLi4lCvXr0KXXtVMGPGDAQHB6N58+Zo3rw535iQtPJM2eBBUlRVVfHo0SO+hK/k5GTY2tri27dvAu1K729qaiq8vLwqdH/fvn2LxMREAEDTpk3F7oEsS2FhIS5duoTU1FQMHToUWlpaePXqFbS1taGpqSnSNjU1FQEBAUhLS4Ovry/q1q2LM2fOwMTEBDY2NhL7ICkKCgoSHSePPZGfPn2Cv78/nj59CqBkH/WoUaPE1rk/ceIERo4cib/++gve3t5Yvnw5EhMTERwcjH///Rc9evQQaDdgwABER0dDX18fNjY2fGP46NGjQvusjs/EXx02GGVh+Q/QunVrrF27lpntqwhcLhcAf5IIVWCzfl5eHmJiYpCRkYGCggKe16ZPny7Q5t27dwgJCUFQUBCePHkCJycnjB49Gv379xc5k3XkyBEMHz4cHh4eCAkJwZMnT2BhYYFt27bh9OnTEmXES0t4eDiGDh2KoqIidO/eHefOnQMArF69GpcvX8aZM2cE2j148ABOTk7Q0dHBs2fPkJiYCAsLCyxatAgZGRkIDg4W2mdUVBQ2bdrEfIlbWVlh5syZEs1IJSYmYuvWrTy2U6dORdOmTUXaiUoq4XA4EmUiA9IFo82aNcPQoUP5srpXrFiBgwcP4uHDh3w2Dx48QPfu3aGrq1uh+5uTk4PJkycjNDSUSYBSUFDA4MGDsX37drHJf8+fP4eLiwsyMjLw/ft3JtN7xowZ+P79O3bu3CnUNiYmBr169ULHjh1x+fJlPH36FBYWFlizZg3u3LmD8PBwgXYnT54U2M7hcKCqqgpLS0uhP0q5XC5MTU0xcuRIkYlz/fr1E9gu7Vi8fPky+vTpAx0dHbRu3RoAcPfuXWRnZyMiIkLsCsmVK1fg7e2N+Ph4ZkZ2yZIlIpMGS1echBEQECD0tcr4TGSpINW2QYCFhYWPw4cP059//km//fYbTzKSqOo+RERnzpwhOzs7ioiIoFevXlUoYeTSpUsiH+KIi4sjAwMD0tbWJgUFBapTpw5xOBzS0NDg0YIUxd27d2nq1KmkqqpKtWvXppkzZwoVS7ezs2O0U8vuIYuLi6N69eqJ7auoqIgSExPpypUrFBMTw/OQhNevX1NcXByPAPzNmzdF7jGTVqNx+/btpKioSO7u7ozQ+5AhQ0hJSYm2bdsm0s/w8HBmT+GsWbNo1qxZ1L59e1JUVGQ0OeVN2WuVlPDwcFJQUKCePXuSt7c3eXt7U8+ePUlRUZGOHj0q0Eba+zto0CBq1KgRRUZGMv8rkZGR1KRJE4mqPcmi+dmuXTvasGEDn883b94kIyMjoXZldXyFaft26dKFPn78yGd7+/ZtmjhxIunq6pK9vT1t3bpV4HGCkGUsNmvWjMaNG0eFhYVMW2FhIY0fP15gOc7qRtbPRJaKwwajLCw1hM2bN5OmpiZNnTqVlJWVacKECeTk5EQ6Ojr0999/i7Qt/6VU+pAmiaiidO3alcaNG0dFRUXMl2pGRgZ16dKFjhw5Itb+1atXtGbNGmrSpAlpaGjQiBEjqHv37qSoqCgwYUVNTY3S09OJiF9AW0VFRWRf169fJ3Nzc6Ff5vKirMh/WZ+fPXsm0mcjIyPaunUrX/u2bdvI0NBQZJ8WFha0ePFivvYlS5aQhYVFRdyXmtKs64py584d8vDwoJYtW1LLli3Jw8ODp8Z4eaS9v+rq6nTlyhW+9suXL5O6urpYP/X19ZmqPmX7TU9PF6vOoKGhwdyb8raifL5w4QL99ttvdOHCBfry5Qt9+fKFLly4QO3bt6dTp05RbGws2djY0OjRo4We4+vXrxQSEkKOjo6krq5OgwcPFqnqQCTbWFRVVRVY/SghIUFoxSeWXwtWZ5SFpYbwzz//YPfu3RgyZAgCAwMxb948WFhYYMmSJfj48aNIW1H6jsKWNZs1awYul4sHDx6IPHfz5s1Fvn7//n3s2rULXC4XCgoK+P79OywsLLBu3TqMHDkSbm5ufDY/fvzAyZMnERAQgHPnzqF58+aYOXMmhg4dyuzHOnbsGEaPHo1Zs2bx2BoYGCAlJQVmZmY87bGxsWKXgydOnMjoO1ZEP7Msd+7cwaFDhwRuSRC2D01ajcbs7Gy4uLjwtTs7O2P+/Pki/Xz9+rXAYgfDhg3D+vXr+drd3NwQGBgIbW1tge9ZWYRdZ3m7b9++YeLEiXz7oEXt1wNKklb27dsn8piySHt/a9WqJXApXkdHB3p6emL7lUXzU1dXF69fv+ZbUr93757IPa4zZszA7t270aFDB6ate/fuUFVVxfjx4/H48WP4+vpi9OjRQs+hqqqKYcOGYdiwYUhPT8eYMWPg4uKCd+/eCd3DKctYbNmyJZ4+fYomTZrwtD99+hQtWrTgO15PT0/i/82yn40tW7ZEVFQU9PT0YG9vL/IccXFxPM8r8zORpeKwwSgLSw0hIyOD+YJRU1NDTk4OAGD48OFo164dtm3bJtS2fHZyTk4OQkND4efnh7t372Lq1Kk8r9vZ2SErKwt169aFnZ0dOBwOIzBeFkn2RykpKTF7rOrWrYuMjAxYWVlBR0cHmZmZAm3q16+P4uJiDBkyBLdu3WKq55TFwcEBurq6fO3jxo3DjBkzsHfvXnA4HLx69QrXr1/H3LlzxWZ/JycnIzw8XGA1JEkICwvDiBEj0LNnT5w7dw7Ozs5ISkrCmzdvMGDAAKF2ffv2hbe3Nw4dOgSg5L5mZGRg/vz5+OOPP0TaHTt2DF5eXjztJ06cYITdhdGtWzdcuXKF71pjY2MFqgbo6OgwX97i9koKo7zdsGHDpDpPUVERjh8/zuxNtLGxQd++fYUm4Eh7fxctWoTZs2cjJCQEBgYGAICsrCx4eXlJpCTg7OwMX19f7N69m+k3NzcXS5cu5atwVB53d3fMnz8fhw8fZkTgr169irlz54qsmJaamiowgUZbWxtpaWkAgEaNGomtZPbixQsEBgYiMDAQ+fn58PLyEpmYI8tYnD59OmbMmIGUlBS0a9cOAHDjxg1s374da9as4Qn+mjdvLrRiljj69evHVHTq169fhX5sVuZnIosUVPfULAsLSwnm5ubMUmSrVq1o586dRER09uxZ0tPTk+gcMTExNGLECNLQ0KBGjRrR/Pnz6datW3zHPXv2jIqLi5m/RT3E0aNHD9q/fz8REY0dO5batm1L+/bto549e1Lbtm0F2gQHB0tdZ7q4uJgRFi9dYldVVaVFixaJtXVwcKAzZ85I1S9RSW3v0v1xpUurxcXFNG7cOFqyZIlQO2k1Gn18fEhHR4d69+5NPj4+5OPjQ66urqSrq0s+Pj7M3r3Nmzfz2e7YsYPq1KlDU6ZMoZCQEAoJCaEpU6ZQ3bp1aceOHUILExQXF9Pz588pPz9fyrskG8nJydS4cWNSV1dn9kurq6tTkyZNhGqfSnt/7ezsSFNTk5SUlKhhw4bUsGFDUlJSIk1NTYn2bMui+fn9+3caO3YsKSoqEofDISUlJeJyuTRs2DCevZXl6dixI7m4uPAUyHj79i25uLhQ586diYjo/Pnz1LhxY4F9hoWFUY8ePUhVVZUGDBhAERERIvsrRZaxKEqLuPx+1+qiMj8TWSoOm03PwlJDGDt2LIyNjbF06VJs374dXl5e6NixI+7cuQM3Nzf4+/sLtMvKykJgYCD8/f3x5csXDBo0CDt37kR8fDysra3F9nv58mV06NABioq8CyWFhYW4du2a2EzXO3fuICcnBw4ODnj79i1GjBiBa9euoVGjRti7d6/AZbjKoKCgACkpKcjNzYW1tbVYGR2gZOl/0aJF8PLyEijZIm75TUNDA48fP4aZmRlq1aqFS5cuwdbWFk+fPoWjoyNev34t0r6iGo2SSnVxOBxmVqyU0tlqSWzLzvQUFxdDVVUVjx8/lkjuqrLp3bs3iAj79+9nlow/fPiAYcOGgcvl4tSpU0JtK3p/ly9fLrFfS5cuFdguq+ZnRkYGHj16hNzcXNjb24u954mJiejXrx/S09NhbGwMAMjMzISFhQVOnDiBxo0b4/jx48jJycHw4cN5bGvVqgUtLS2MHDkSw4cPF6qJKmiGVJax+Pz5c4lsAdHlhb99+8a3NUbYbK6FhQVu376NWrVq8bRnZ2ejZcuWfD6yVC9sMMrCUkMoLi5GcXExExSGhYUxQd2ECROgrKzMZ9OnTx9cvnwZrq6u8PDwgIuLCyPyLWkwqqCggNevX/N9MX348AF169attCUpcfsQyyJuT6G0CArQSpfjJFl+a9CgAc6cOQNbW1tGn3TIkCG4fv06XFxc8PnzZ7n4XdXY2NjA39+fWVKtSjQ0NHDjxg3Y2trytMfHx6Njx47Izc2tcp9qGsXFxTh37hySkpIAAE2aNEGPHj3E/gAp+7qgJWxJ/w+qkry8PMyfPx+HDh3Chw8f+F4X5iuXy2WW3cvy5s0bGBsb8wW15UlOTkZ0dDTevn3LyH6VsmTJkgpeBYs42D2jLCw1BC6Xy/Nl4e7uDnd3d5E2Z86cwfTp0zFp0iSpZ7FKv4DK8+HDB6kE+IUh7T7E8uTl5WHNmjWIiooS+EUhasajItV7BNGlSxecP38etra2+PPPPzFjxgxcvHgR58+fF6vxGhUVJdTnvXv3iu27dN5AmqSrirJmzRp4eXlhx44daNasmdz7K4uKigqzX7osubm5An+QlSLL/b179y7P/lRRGpzlkTZoKSoqQmBgoFCfRWm5crlcuLi4CEwoEoWoRMeKIM1YTE1Nha+vL4/o/YwZM9CwYUORdvPmzUN0dDR27NiB4cOHY/v27Xj58iV27dqFNWvW8B1fVof17NmzPJ87RUVFiIqKEjvLu2fPHkyaNAm1a9eGgYEBz3VyOBw2GJUD7MwoC0sNISAgAJqamvjzzz952g8fPoz8/HyMHDmSz+bGjRvw9/fHwYMHYWVlheHDh8Pd3R3169cXOzNaOlN54sQJuLi4MBv/gZIP7QcPHqBJkyaIjIwU6febN28wd+5c5ku1/EdKZc+yDBkyBDExMRg+fLjAjPgZM2ZUan9l+fjxI759+wZDQ0MUFxdj3bp1zOz1okWLhGZgL1++HN7e3mjdurVAn48dOya0z+DgYKxfvx7JyckAgMaNG8PLy4tvCVYQMTEx+N///scTAHh5eYkte6qnp4f8/HwUFhZCWVmZb8lZnLqDLIwYMQJxcXHw9/dH27ZtAQA3b97EuHHj0KpVKwQGBvLZSHt/3759C3d3d1y6dIlJlsvOzoaDgwPCwsLEVmISF7SUz9guy9SpUxEYGAhXV1eBPm/atEmorTRFJoCS/8X//e9/OHnyJAoKCtC9e3csXbpU4i0F0o7Fs2fPom/fvrCzs0PHjh0BlJTmjY+PR0REhNAqSgBgYmKC4OBgdOvWDdra2oiLi4OlpSVCQkIQGhrKV+SirGB9+c8iJSUlmJmZYcOGDSKTrkxNTTF58mSxKgEslUh1bFRlYWHhp1GjRnTx4kW+9kuXLglMRihLbm4u+fv7U8eOHZlECF9fX/ry5YtQG09PT/L09CQOh0ODBw9mnnt6etL48eNp1apV9O7dO7F+u7i4kLW1Nf3zzz907NgxOn78OM+jstHR0aHY2Fip7YODg6lDhw5Uv359Jhlh06ZNcvG1FAMDAwoODq6w3YYNG0hdXZ3mzZvHJBt5eXmRurq6QA3WsoSEhJCioiINGjSISSwZNGgQKSkpMQlnwggMDBT5kCefPn2ivn37EofDIWVlZVJWViYul0v9+/en7OxsgTbS3t9BgwZR69at6cmTJ0zb48ePqXXr1uTu7i7W3sTEhNasWVPhfomIatWqRadOnaqwnSxFJry9vYnL5ZKzszP169ePVFVVadSoURL1K8tYtLOzo/nz5/O1z58/X2xBDw0NDXr+/DkRlWid3rx5k4iI0tLSSENDQ6idmZmZRJ9fgtDS0qpwsQYW2WCDURaWGoKKigoj5l6W9PT0CglDJyQkkJeXFxkYGJCqqir16dNH5PHLli0TmXEsDk1NTbp3716F7aStNmVmZsYTPFSEf/75h2rXrk0rVqwgNTU15gsnICCAunXrJtE5CgsL6fDhw0x1oPDwcPrx44dIG319faGZ4KIwMzNjqk2VJTAwkMzMzETaNm3aVGCQsGHDBmratGmFfalqkpKS6OTJk3Ty5ElKTk4Weay091dbW1ug2sTNmzdJR0dHrL0sQUv9+vUpMTGxwnayFJmwtLRkVDqISrLulZWVeaqJCUOWsaiioiKwolpiYqLYQhW2trZM1aPu3bvTnDlziKikSIioSlWC+PTpk0THjR49mnbs2FGhc7PIBhuMsrDUEIyNjflkdoiIjh8/XuEPXaKSoOnYsWNig9H8/HzKy8tjnj979ow2bdpEZ8+elagfKysrkdVxBCFLtamQkBAaOHAgj8+SYmVlRceOHSMi3qo3Dx8+pFq1aom1f/ToEVlYWPDIDmloaJCZmRk9fPhQqN28efPI29u7wv6qqKgIDMSSkpLEfokrKysLtE1OThZrS0SUkpJCCxcuJHd3d0am6PTp0/To0SMJva86pL2/wn5IxcXFkZaWllh7WYKW//3vfzR58mRGTkhSdHR0mGpGOjo6zA+zGzduUJMmTUTaKisrU0ZGBk+biooKZWZmiu1XlrHYoEEDOnToEF/7wYMHydjYWKTtxo0bGbmo8+fPk6qqKqmoqDCrP8JYs2YNhYWFMc8HDhxIHA6HDA0N6f79+yL7XLVqFdWuXZtGjhxJ//vf/3hkqwRJV7HIDpvAxMJSQxgyZAimT58OLS0tRk4pJiYGM2bMEJvIJAgFBQX0798f/fv3F3lcv3794ObmhokTJyI7Oxtt27aFsrIy3r9/j40bN2LSpEki7X19fbFgwQLs2rWLryqSMGSpNrVhwwakpqaiXr16MDMz45NnErVPLz09XWByioqKCvLy8sT6PXbsWNjY2ODOnTvM/tBPnz7B09MT48ePx7Vr1wTaffv2Dbt378aFCxfQvHlzPp83btwo0M7S0hKHDh3C33//zdN+8OBBsQlrxsbGiIqK4hO9v3DhAiMJJIyYmBj06tULHTt2xOXLl7Fy5UrUrVsX8fHx8Pf3R3h4uEj7ijJ79myJjxV0r6S9v46OjpgxYwZCQ0NhaGgIAHj58iVmzZolNiENKHl/Fi9ezGT/l+9X1P7N2NhYREdH48yZM7CxseGzFaYoIU2RiVIKCwuhqqrKd74fP36ItANkG4vjxo3D+PHjkZaWxhT2uHr1KtauXSv2vS9bgc3JyQkJCQm4e/cuLC0tRUqx7dy5E/v37wcAnD9/HhcuXEBkZCQOHToELy8vnDt3Tqjt7t27oampiZiYGMTExPC8xuFwRL6vLNLBBqMsLDUEHx8fPHv2DN27d2fknYqLizFixAisWrVKbv3GxcUxyRLh4eEwMDDAvXv3cOTIESxZskRgMFq+XF9eXh4aNmwIdXV1vi9VQcGlLNWmxAXXojA3N8f9+/f5tAwjIyNhZWUl1v7+/fs8gShQci9WrlyJNm3aCLV78OABU2Xq0aNHPK+Jykhevnw5Bg8ejMuXL/MkfkRFRTHVhoQxZ84cTJ8+Hffv3+cJAAIDA7F582aRtgsWLMCKFSswe/ZsnrKWjo6OIt8babl37x7P87i4OBQWFjLlI5OSkqCgoIBWrVoJtBd1f0Wxbds29O3bF2ZmZjyanc2aNZOoHKksQYuurq7Iql3CsLe3x+3bt9GoUSN07doVS5Yswfv37xESEiJW+YCI4OnpyZOsKKhkq6BAWJaxuHjxYmhpaWHDhg3466+/AACGhoZYtmyZ0Ht0/fp1fPjwgSfRKDg4GEuXLkVeXh769++PrVu38lxLWbKyspj39N9//8WgQYPg7OwMMzMz/PbbbyL9lVV1g6XisMEoC0sNQVlZGQcPHoSPjw/i4+OhpqYGW1tbkSLQlUF+fj4TcJw7dw5ubm7gcrlo166dULFqacv1lWJgYICPHz/C1NQUJiYmuHHjBlq0aIH09HSBJfjKIkx8XBJmz56NKVOm4Nu3byAi3Lp1C6GhoVi9ejX8/PzE2jdu3Bhv3ryBjY0NT/vbt29FlhiVVlLnjz/+wM2bN7Fp0yYcP34cAGBlZYVbt26JlR+aNGkSDAwMsGHDBiZYsLKywsGDB9GvXz+Rtg8fPsSBAwf42uvWrSu2zKQ0lL0/GzduhJaWFoKCgnhmn0eNGiVUBUDa+2tsbIy4uDhcuHABCQkJAErukTix/FJkCVoCAgKkslu1ahXz423lypUYMWIEI+0mTsJKkCKHpCVbZRmLHA4Hs2bNwqxZsxjfy/7IEYS3tze6devGBKMPHz7EmDFj4OnpCWtra6xbt44JaAWhp6eHzMxMGBsbIzIyEitWrABQEpDXJB1Vlv9P9e4SYGFhEURxcXGF95JJi62tLW3evJkyMjJIW1ubrl27RkREd+7coXr16smlzzFjxtCyZcuIiGjbtm2kpqbGlHMcPXq0XPosZd++fWRpacmUIjQyMiI/Pz+hx3/+/Jl5nDp1imxsbOjw4cOUmZlJmZmZdPjwYbK1tZUqM7qmYmRkRFevXiUi3r21R48eJQsLC7n2bWhoKHBf6sOHD6l+/foCbUaNGiVQOSI3N1fibHGWmoWBgQHdvn2bef73339Tx44dmeeHDh0iKysrofZTpkwhU1NTcnJyolq1alFOTg4REYWGhopNkpw1a5bAx+zZs+nvv/+mvXv30ocPH2S8QpaysDqjLCw1CFk0JaUlPDwcQ4cORVFRERwdHXH+/HkAwOrVq3H58mWcOXNGpP3p06ehoKCAnj178rSfO3cORUVF6NWrF59NRatN6evrIykpCbVr1+bbIlAeSTUw8/PzkZubK7QkYilcLpenPyon+F32edkZFzc3NwQGBkJbW1ts9amyy6JfvnyRyH9AeClEWZk7dy5u3ryJw4cPo3HjxoiLi8ObN28wYsQIjBgxQqbZaXFoaWkhIiIC3bp142mPjo5G3759BQriC6si9v79exgYGKCwsJBp27Jli8S+CFpCnj17Nnx8fKChoSF2v2P5vaotW7ZEVFQU9PT0YG9vL3Ici9r7XFXIMhbFXV9ZBF2rqqoqkpOTmaX2Tp06oVevXli4cCEA4NmzZ7C1tRU4HgDgx48f2Lx5MzIzM+Hp6cnM3m7atAlaWloYO3asUH8cHBwQFxeHoqIivq0iTZs2RWJiIjgcDmJjYyWqcsciHnaZnoWlhrBx40YsXrwYU6dOZfZkxcbGYuLEiXj//j3PRv7KZODAgejUqRNev37NU0e+e/fuEu1pW7BggcBKKMXFxViwYIHAYLSi1aZKv0AA2bcIlKKurg51dXWxx0m7BKyjo8N8GVek+pSurq7YL3ESUrZRXKBeFlFB+6pVqzBlyhQYGxujqKgI1tbWKCoqwtChQ7Fo0SKJzi8tAwYMwKhRo7BhwwYe0XsvLy++oP7Lly+gElUY5OTk8CTnFBUV4fTp03wBqigx+bII2/N57949JuGn/F7X8vbl6devH7PHUdq9z1VZZEKWsSjL3m4AqFevHtLT05nSnXFxcVi+fDnzek5ODt/+9LIoKSlh7ty5fO2SfI7269cP+vr6CAgIYILsz58/Y+zYsejUqRPGjRuHoUOHYtasWTh79qwUV8dSHnZmlIWlhmBubo7ly5djxIgRPO1BQUFYtmyZ3DfVp6SkIDU1FV26dIGamprQMqHlUVNTw9OnT/ky6Z89ewYbGxuhWerZ2dm4deuWwFKI5e+BLPxss1HlE2FE0bVrV57nQUFBEtsK2j9YnoyMDDx69Ai5ubmwt7eXuuRsRcjPz8fcuXOxd+9eJuhTVFTEmDFjsH79ep5Em/Kz1uXhcDhYvnw5M5v2X6BXr17IyMjA1KlTBVZuErcfuCLIMhZlZdKkSYiPj8fatWtx/PhxBAUF4dWrV8yqyf79++Hr64vbt28LPUdISAh27dqFtLQ0XL9+HaampvD19YW5ubnI+2RkZITz58/zzXo+fvwYzs7OePnyJeLi4uDs7CyXPdS/IuzMKAtLDeH169dM1nNZOnTogNevX8ut3w8fPmDQoEGIjo4Gh8NBcnIyLCwsMGbMGOjp6WHDhg0i7XV0dJCWlsYXjKakpAitbR8REQEPDw/k5uZCW1ubr4yiqGBU2NIhh8OBiooK3xJ/ZcxGlSc/P19gKUZRUjOSIsuXuiQBZkUwMDDA169f0bBhQ2ZLhbxRV1fHP//8g/Xr1yM1NRUA0LBhQ4FjKTo6GkQER0dHHDlyBPr6+sxrysrKMDU1ZSSbRFFQUID09PQqvU5piY2NxZUrVxj1AHlSmQFmdnY2wsPDkZqaCi8vL+jr6yMuLg716tWDkZER3/E+Pj5wc3ND165doampiaCgIJ7/7b1798LZ2Vlofzt27MCSJUswc+ZMrFy5kpm51dXVha+vr8hg9PPnz3j79i1fMPru3Tvm80dXV5fv/59FBqplpyoLCwsfNjY2tHLlSr52Hx8fatasmdz6HT58OPXs2ZMyMzN5klUiIyPJ2tparP348ePJ1taWpwJOcnIyNW/enMaMGSPQplGjRjRjxgyphOs5HA5xuVyhDxMTE1qyZIlEVWUqytu3b8nV1VVo38Kws7PjqzJlb29PLVu2pA4dOtCIESMEloIlIrp8+TJ5eHhQ+/bt6cWLF0RUUtL0ypUrYv2VVrg+Ly+PRo8eTQoKCqSgoMCMialTp9Lq1avF9lvVPHv2TKqEP1mvs3///jRgwAC+h5ubGw0dOpSWLFnCCNSXR1dXl/T09Pge+vr6ZGhoSF26dKG9e/fy2UlTZKKykHYsxsfHU506dcjS0pIUFRWZ+7xw4UIaPny4SNvs7GwqLCzka//w4QN9//5dqJ0sBS6GDh1K5ubmdPToUSZRsTR5b9iwYURUkgjVqlUrkedhkRw2GGVhqSGEh4eTgoIC9ezZkyk12bNnT1JUVKSjR4/Krd969eoxFUnKfminpqaKrP1cSnZ2NrVr144UFRXJzMyMzMzMSFFRkRwcHISW31NXV5e6jGJQUBA1aNCAFi1axJSLXLRoERkbG9OuXbtoxYoVpKurKzCwv3XrFt24cYOv/caNGzyZu8IYOnQodezYkW7fvk0aGhp07tw5CgkJoSZNmtC///4r1G7BggWko6NDnTp1otmzZ9Ps2bOpc+fOpKOjQzNmzKAePXoQl8ul48eP89iFh4eTmpoajR07llRUVJh7tnXrVurVq5dIXy9dusSoFCgrKzO2q1evpj/++EOk7fTp06lVq1Z05coV0tDQYGyPHz9OdnZ2Yu+TrNy+fZu8vLxo8ODBfIFeKfHx8cwPjvj4eJEPYch6nSNHjiQdHR0yNTUlNzc3cnNzIzMzM9LV1aVBgwZRkyZNSEVFhWJjY/lsN27cSLVq1aJhw4bRli1baMuWLTRs2DCqXbs2rVy5knnPd+/ezWN39uxZcnZ2Flg6WJ7IMha7d+9OXl5eRMT7GXP16lUyNTWVi7+qqqr07Nkzvj6TkpLEllfOycmhsWPHkrKyMvNDU1lZmcaNG8eUTr53755UZZBZBMMGoywsNYg7d+6Qh4cHtWzZklq2bEkeHh5ynwXR1NRk6kaX/dC+ffs26evrS3SO4uJiOnv2LK1bt462bt1KMTExIo8fMGAAHTx4UCp/HR0dBdoePHiQHB0diahktkZQacQ2bdrQ4cOH+dqPHDlCbdu2Fdu3gYEB3bx5k4hK6pKX1hY/ceIEj+xMecaOHSuwXKWPjw+NHTuWiIiWLFnCN9NiZ2fH1AMv+97ExcWJld1q164dbdiwgc/25s2bYsvLmpiY0PXr1/lsk5OTJSqTKQuhoaGkpKREv//+OykrK9Pvv/9OjRs3Jh0dHfL09GSO43A4zGxv6Wx5qVxX2YeoGWtZr3P+/Pk0adIknln4oqIimjp1Kv31119UXFxM48ePFzg23NzcBJYS3blzJ7m5uRER0ZYtW/hWRXR1dZkgSVNTk29mVV7IMha1tbWZlZOyts+ePZOoNK00WFlZMT/uyva5ZcsWsdJOpeTk5DA/aEqloVjkAxuMsrD84vTq1YsWLVpERCUf2mlpaVRUVER//vmn2Bk0IhJZ17r0i748fn5+ZGJiQkuXLqXw8HA6ceIEz0MUqqqqTPBclqSkJFJTUyMiorS0NObvspSd/SpLWloaaWpqiuyXqCQALZ2RMjExYWa8hPVXira2ttA68dra2kRE9PTpUz4f1NTUmP7Kz1qL+xLX0NCgtLQ0Ptv09HSxtmpqaszxZW3v37/P+CsvbG1tadu2bTx9FxcX07hx42jJkiXMcWWX5p89eybyIQxZr7N27drMD5KyJCYmMkvBDx48IB0dHb5jNDQ0hI6J0hWJlJQUUldX53k9MDBQ5ENeyDIW69Spw/yoLmt77tw5atCgQaX6uXz5csrLy6M9e/aQkZERhYWFkYaGBoWGhtKKFSuYv1lqFjV7pzYLyy+Ek5MThg0bBjc3N7npR5bFwsICt2/fxrp169C9e3fcuXMHBQUFmDdvHh4/foyPHz/i6tWrYs/j7OyM2NhYnuQRoKRUoKurK7Kzs/lsxo0bB6Ckykp5BMnElMXY2Bj+/v58clL+/v6MJuGHDx94SnaWoqKigjdv3sDCwoKn/fXr1xIlrjRp0gSJiYkwMzNDixYtsGvXLpiZmWHnzp2oX7++UDtVVVVcu3aNr0rTtWvXGDmi4uJivrrhBgYGSElJ4UsOi42N5buG8ujq6uL169cwNzfnab93757AhJGytG7dGqdOncK0adMA/J9MkZ+fH9q3by/SVlZSU1Ph6uoKoCQJKS8vj6ng4+joyMj7DBgwgFFJCAoKwty5cyWS6iqLrNdZWFiIhIQENG7cmKc9ISGBGcOqqqoCM/719fURERHBJzUUERHB/C/l5eXxVSqq7CQ1SZFlLPbt2xfe3t5MJTAOh4OMjAzMnz8ff/zxR6X6uXz5ckycOBFjx46FmpoaFi1ahPz8fAwdOhSGhobYvHmzQBk5aXWBWSoHNhhlYakh2NjY4K+//sLkyZPh6uqKYcOGoXfv3iK19GTh2bNnKCoqQrNmzZCUlIRt27ZBS0sLubm5cHNzw5QpU0QGWKW0a9cOzs7OiI6OZr44L1++jD59+ggt1Vdeyqki/O9//8Off/6JM2fOMPXg79y5g4SEBISHhwMAbt++jcGDB/PZOjs746+//sKJEycY7c/s7Gz8/fff6NGjh9i+Z8yYwSgbLF26FC4uLti/fz+UlZURGBgo1G7atGmYOHEi7t69y/h8+/Zt+Pn54e+//wYAnD17li9Dety4cZgxYwb27t0LDoeDV69e4fr165g7dy4WL14s0ld3d3fMnz8fhw8fBofDQXFxMa5evYq5c+eKlc5atWoVevXqhSdPnqCwsBCbN2/GkydPcO3atQrJ/UiDnp4eI2RuZGSER48ewdbWFtnZ2cjPz2eOe/r0KfLy8qCnp8cEIBUNRmW9zuHDh2PMmDH4+++/ed7XVatWMfc4JiaGr3wsUFKvfdKkSYiOjmb0VG/fvo3Tp09j586dAIDz58+LzGj/9u0bX0a3vH7IyjIWN2zYgIEDB6Ju3br4+vUrunbtiqysLLRv3x4rV66sVD+pjFqlh4cHPDw8JCpwIa0uMEslUd1TsywsLP9HUVERnT17lkaOHEna2tqkp6dH48aNo0uXLlV6X2X33MlCUVERDRgwgLp27Urfvn2jixcvkqamJvn6+lb4XJ8+faKtW7eKPS4tLY3mz5/PJLUsWLBAooSOFy9ekIWFBeno6FC3bt2oW7dupKurS02aNKGMjIwK+5uXl0d3796ld+/eiT1237591K5dO2ZvX7t27Wj//v3M6/n5+fT161cem+LiYmZpsXQPpKqqKrOtQhTfv3+nsWPHkqKiInE4HFJSUiIul0vDhg0TmJ1cnpSUFBo7diy1adOGrKysyMPDgx48eCDWTlaGDBnC7HX19vamOnXq0NixY8nU1JQngaldu3bk5OREy5YtIw6HQ15eXrR8+XKBD1HIcp2FhYW0YsUKMjAwYN4fAwMDWrlyJXOPnz9/LnQrS2xsLLm7uzPqCu7u7kwZVmHk5ubSlClTqE6dOhVSdJAVWcZiKVeuXKHt27fT2rVr6fz583Lxk8Ph0Nu3b+Vybhb5wYres7DUUL59+4aIiAisXLkSDx8+rNTKKkCJYHhQUJDYWYC+ffuKPVdBQQFcXV2Rn5+PBw8eYPXq1Zg6darEvkRFRcHf3x/Hjh2Duro6Pnz4ILFtRcnLy8P+/fsRHx8PNTU1NG/eHEOGDJFoBtrb21vgcvDXr1+xfv16LFmyRC4+FxQUICUlBbm5ubC2toampqbEttUhXC8LHz9+xLdv32BoaIji4mKsW7eOKRW7aNEiZvtFYmIili5ditTUVMTFxcHa2lrgVgsOh1MlxQxK9SflvcVmypQpiI6Oho+PD4YPH47t27fj5cuX2LVrF9asWQMPDw+59i/NWMzMzGS20MgbLpfLM8spDFEVyL5+/QoiYv7Pnz9/jmPHjsHa2lqktimL9LDBKAtLDSQrKwthYWHYt28f4uLi0LZtW9y4caNS+yhbjlMYwvZvPnjwgK8tJycHQ4YMgaurKyZNmsS0CxOCz8zMREBAAAICApCRkQF3d3cMHz4c3bt3FxsYVlX1pvIIq4H+4cMH1K1bV+wPhoKCAoE+m5iYCDx+3759cHNzq/DyM1Cyl69Tp04VtiuluLgYKSkpAv3t0qWL1OcVhqR10AUFe1wuF1lZWSKXYQVR1fu0yyPNPTYxMUFwcDC6desGbW1txMXFwdLSEiEhIQgNDcXp06fl4qssY1FBQQGdOnXCsGHDMHDgQIH7uSsLLpcLX19fsT+yRe29dXZ2hpubGyZOnIjs7Gw0adIEysrKeP/+PTZu3Mjz+cZSSVTrvCwLCwvD58+fae/eveTk5ESKiorUuHFjWr58OY+YfGUiyzK9ICmdss9L/y6/bFhQUECHDh0iZ2dnUlNTowEDBtDhw4dJUVGRHj9+LFHfJ0+eJC0tLeJwOKSjo0O6urrMQ5i0TWJiIiPJVMqFCxeoW7du1KZNG4GapMKuW9ASYFRUFNWuXVuoXVJSEnXq1IlvSVWc9FDt2rVJQ0ODhgwZQqdOnZJoeb0UJSUlMjMzo7/++kvie1vK9evXydzcXKBckryWgsUVM5DHMvT06dPJwMCA1NTUaODAgXT8+HEqKCiQ2D4rK4uGDRtG9evXJwUFhQr5Ku091tDQoOfPnxMRkZGRETOu09LSJNIFlhZZxmJcXBzNnTuXGjRoQCoqKtSvXz86fPgwffv2rdL9rIztR7Vq1WIKQ+zZs4eaN29ORUVFdOjQIWratGlluMlSDjaBiYWlhlCvXj3o6elh8ODBWL16NVq3bi3X/iSpOy+M9PR0qeyMjIzQtGlTDBs2DGFhYcwMyZAhQyQ+x5w5czB69GisWrVK4lma+fPnw9bWlkkUSU9PR58+fdC5c2c0b94cq1evhrq6OmbOnCnQXk9PDxwOBxwOB40bN+a5d0VFRcjNzcXEiROF9u/p6QlFRUX8+++/AuuJC+P169eIjIxEaGgoBg0aBHV1dfz555/w8PAQWDq2LK9evUJYWBhCQ0OxZs0aNG/eHB4eHhgyZAgaNGgg0nbixIlMpnlF/JWF6Oho5m8iQu/eveHn5yc2818WNm/ejE2bNuHChQs4cOAARowYAQUFBQwcOBAeHh5iy2F6enoiIyMDixcvrvB9kvYeW1hYID09HSYmJmjatCkOHTqEtm3bIiIiArq6uhL3X1FkGYv29vawt7fHunXrcOnSJRw4cADjx49HcXEx3NzcsHfv3krzszLGan5+PpOMee7cObi5uYHL5aJdu3Z4/vy5zOdnEUB1R8MsLCwlnDt3Ti4lLIVRWQlMFUFPT4+6dOlCu3fvps+fPzPtFZkZlaZ6U4MGDejatWvMcx8fH2rRogXz3M/Pj+d5eQIDAykgIIA4HA5t3ryZR9fxwIEDPOcW5vPTp08r5HN58vLyaN++fdS7d29SVlYmCwsLiW3T0tJoxYoVZGNjQwoKCuTg4CDWX0EamFVJWT3KquLr16906NAhatGihUSzsJqamlJX4ZH2Hm/cuJE2b95MRETnz58nVVVVUlFRIS6XK1XSoDTIMhZLuXv3LtnZ2VX6bHdlfK7Z2trS5s2bKSMjg7S1tZn/7zt37ogV+GeRDjYYZWH5RfH09KQvX77IfJ5Vq1aRv78/X7u/vz+tWbOGp+3r16+0b98+cnBwIDU1NXJzc6OjR4+SkpKSxMGoNNWbVFVVebLlHR0debKAU1JSBAqTl+fSpUsVWsYtpXXr1hLVkhfHu3fvaOvWrWRjY1PhL/HCwkKKiIiQKABwcHCgM2fOyOKqzFR1MPr69WvatGkTtWrVijgcDv32229ibWSpE19Z9/jZs2d05MgRkWVP5YE0YzEzM5PWrl1LLVq0IAUFBerUqZPAKlTVzeHDhxn1CScnJ6Z91apV5OLiUo2e/XdhE5hYWKoRe3t7iZeVqiIjWBrMzMxw4MABvqW6mzdvwt3dXeiSfmpqKgICAhAUFISXL19iyJAh8PT0hKOjIxQUFIT25+/vD29vb4waNQq2trZ8yU6Csv+NjIxw7NgxtG3bFsXFxdDT08OBAwcYcfWnT5+iXbt2+Pz5s8TXXRGNx4sXL2LRokVYtWqVQJ9FJc/k5+fj2LFj2L9/P6KiomBsbIwhQ4bAw8MDTZs2Fevn1atXsX//foSHh+Pbt2/o168fPDw84OLiItTm2LFjWLRoEby8vAT6KywprTLR0tJCfHy8WEF1Wfjy5QuOHDmCAwcO4NKlS7CwsGC0KRs2bCjW/ty5c9iwYQNT/KAi1IR7XFGkHYu7du3CgQMHcPXqVTRt2hQeHh4YOnQoTE1Nq9D7ipGVlYXXr1+jRYsWTLLnrVu3oK2tLdH/HUvFYINRFpZqpLSaDFAS3Pzzzz+wtrZmqr/cuHEDjx8/xuTJk7F69erqclMkqqqqePr0KV+ln7S0NFhbW+Pbt28i7YuLi3H27Fn4+/sjIiICWlpaeP/+vdDjRakACMv+9/DwwJcvX/DPP//g8OHDWLp0KbKysqChoQEAOHLkCLy9vREfHy/S1/z8fMybNw+HDh0SKD8lLJu+1OfyPzyISGTFKXd3d/z7779QV1fHoEGD4OHhIXEFpL/++gthYWF49eoVevToAQ8PD/Tr10+ifbaC7jGHwxHrb2WipaWFBw8e8I2rykRNTY3Zp+3h4VHhfdp6enrIz89HYWEh1NXV+QJKUfJBFb3HwcHBEvkkLzUJWcZi2aC1RYsWcvFPHqSkpCA1NRVdunSBmpoa896wVD5sAhMLSzWydOlS5u+xY8di+vTp8PHx4TsmMzOzql2TGGNjY1y9epUvaLh69SoMDQ3F2nO5XPTq1Qu9evXCu3fvEBISIvJ4aao3rVy5Ej169ICpqSkUFBSwZcsWJhAFgJCQEDg6Ooo9j5eXF6Kjo7Fjxw6BGo/CKJucUxEUFBRw6NAh9OzZU+RssSAuX74MLy8vDBo0CLVr166QrbQJarJQvgTjt2/fMHHiRJ73Cfi/UoylSWWSICwoPHnyJLp37y6RzJkgfH19pbIDKn6PPT09oampCUVFRQibQ+JwOHILRmUZixkZGT9VEPfhwwcMGjQI0dHR4HA4SE5OhoWFBcaMGQM9PT1s2LChul38z8HOjLKw1BB0dHRw584dPlHy5ORktG7dukJLyFXJunXrsG7dOqxfv54J6KKiojBv3jzMmTMHf/31VzV7WEJhYSEeP36MOnXq8AXJ8fHxaNCgAWrVqiXyHNWl8QiUBGfla9f/lxg1apRExwUEBAAAgoKCmLYPHz5gxYoV6NmzJzNbd/36dZw9exaLFy/mq/9elsLCQly6dAmpqakYOnQotLS08OrVK2hra1eouIC8sbGxwZs3bzBs2DCMHj26WpfxpRmLV65cwa5du5Camorw8HAYGRkhJCQE5ubmMunhyoMRI0bg7du38PPzg5WVFbNd5OzZs5g9ezYeP35c3S7+96imvaosLCzlqFevHgUEBPC1BwQEUN26daveIQkpLi6mefPmkaqqKqOvqK6uLrYMY0Xp1asXZWdnM89Xr15Nnz59Yp6/f/+erKysKrXP8lRU43Ht2rWUn5/PPI+NjeXRVvzy5QtNmjRJaH9FRUXk7e1NhoaGpKCgwCT0LFq0iPz8/MT6GxwcTB06dKD69evTs2fPiIho06ZNdPz4cb5jT5w4wSRnnThxQuSjpuHm5iawjOzWrVupX79+Qu2ePXtGTZs2JXV1dZ77O336dJowYYJQu4MHD9L379+Z55mZmTxKGHl5ebR27VqBtpMmTaKcnBzm+YEDByg3N5d5/unTJ+rVq5dA2xs3btD48eNJR0eHWrVqRf/88w+PKoU8kWUshoeHk5qaGo0dO5ZUVFQY261btwq91uqkXr16dP/+fSLiTaRLTU2Vq5brrwwbjLKw1BBWr15NqqqqNG3aNAoJCaGQkBCaOnUqqamp0erVq+XWb25uLi1atIjat29PDRs2JHNzc56HpOTk5NCtW7fo4cOHchGz5nK5PJItWlpaPNnWWVlZcq3NTVQi+XLp0iUiIurevTvNmTOHiIg2b95MRkZGle7z8uXLycLCgvbt20dqamqMbVhYGLVr106kr//88w/Vrl2bVqxYwWMbEBBA3bp14zu+rCROeRH2qhC9lwUNDQ2BMknJyckig4d+/frRsGHD6Pv37zxBR3R0NFlaWgq1k+V9rYxxnJ+fT0FBQdStWzdSV1enoUOHyuV/riyyjEU7OzsKCgoiIt7gLi4urkZKJWlqalJSUhLzd6m/t2/fJn19/ep07T8Lu2eUhaWGsGDBAlhYWGDz5s3Yt28fAMDa2hpBQUGwsrKSW79jx45FTEwMhg8fLpPAuaamJurXrw8AUFFRqUwXAYBvn1z551XBqFGjEB8fj65du2LBggXo06cPtm3bhh8/fmDjxo18x8vqc3BwMHbv3o3u3bvziOq3aNECCQkJIm23bt2KPXv2oH///jz7WVu3bo25c+fyHV92L640+3Krk1q1auHEiROYM2cOT/uJEydEbr24cuUKrl27BmVlZZ52MzMzvHz5UqidLO9rZYxjNTU1jBgxAmZmZli6dCnCwsKwbds2ufzflSLLWExMTBRY3lRHRwfZ2dmV7arMdO7cGcHBwcz+fQ6Hg+LiYqxbtw4ODg7V7N1/EzYYZWGpQQwaNAiDBg0CUCI7ExoaivXr1+Pu3btyy2A+c+YMTp06hY4dO0plX1xcjBUrVmDDhg3Izc0FUJIJPWfOHCxcuFCi5JCioiI8fPgQpqamcq1bLStl9x46OTkhISEBd+/ehaWlpVz28L18+RKWlpZ87cXFxfjx44dI2/T0dNjb2/O1q6ioIC8vr9J8rAksX74cY8eOxaVLl/Dbb78BKJEWi4yMxJ49e4TaFRcXC/y/evHiBVOBp6bx8uVLBAUFISAgAHl5eRg2bBh27Ngh9/8bWcaigYEBUlJS+OSvYmNj5SrdJS3r1q1D9+7dcefOHRQUFGDevHl4/PgxPn78iKtXr1a3e/9J2GCUhaWGcfnyZfj7++PIkSMwNDSEm5sbtm/fLrf+9PT0oK+vL7X9woUL4e/vjzVr1jABbWxsLJYtW4Zv375h5cqVfDYzZ86Era0txowZg6KiInTt2hXXrl2Duro6/v33X3Tr1o3PprQcZ/m2qqK4uBiBgYE4evQonj17Bg6HA3NzcwwcOBC2trZy6dPa2hpXrlzh02MMDw8XGGiWxdzcHPfv3+ezjYyMFDvTvmXLFoHtHA4HqqqqsLS0RJcuXSqcVS0vPD09YWVlhS1btjDZ9lZWVoiNjWWCU0E4OzvD19cXu3fvBlByfbm5uVi6dCl69+5dJb5LyqFDhxAQEICYmBj07NkTGzZsgKura5W9B7KMxXHjxmHGjBnYu3cvOBwOXr16hevXr2Pu3LlYvHixPN2WimbNmiEpKQnbtm2DlpYWcnNz4ebmhilTpjCrPyyVCxuMsrDUALKyshAYGAh/f398+fIFgwYNwvfv33H8+HFYW1vLtW8fHx8sWbIEQUFBEtd6L0tQUBD8/Px4xOabN28OIyMjTJ48WWAwGh4ejmHDhgEAIiIikJ6ejoSEBISEhGDhwoUCZx+ICJ6ensxSZHnpn+/fv0vsc35+PjIyMvhE64XNbhIR+vbti9OnT6NFixawtbUFEeHp06fw9PTE0aNHcfz4cYG2fn5+TFZ2YWEhAgMDGamlnJwckX4uWbIEI0eOxMuXL1FcXIyjR48iMTERwcHB+Pfff0Xazp49G1OmTMG3b99ARLh16xZCQ0OxevVq+Pn5ibTdtGkT3r17h/z8fGbG7dOnT1BXV4empibevn0LCwsLREdHw9jYWOS5qorffvsN+/fvr5DNhg0b0LNnT0YPd+jQoUhOTkbt2rURGhoq0vbs2bPQ0dEBUPJDJSoqCo8ePQIAsUvPS5YsYf7XCgoKsHLlSuZc+fn5Am3c3d1hYmKCWbNmoV69enj27JnAH6nTp08X2be0yDIWFyxYgOLiYnTv3h35+fno0qULVFRUMHfuXEybNk0u/sqKjo4OFi5cWN1u/DKw0k4sLNVMnz59cPnyZbi6ujKVcRQUFKCkpIT4+Hi5B6P29vZITU0FEcHMzIxPuFtc5SdVVVU8ePAAjRs35mlPTEyEnZ0dvn79KtAmJSUFDRo0wPjx46Gurg5fX1+kp6ejRYsW+PLlC59NRaV/BPHu3TuMGjUKZ86cEfi6sK0QAQEBmDFjBk6cOMG3Z+zixYvo378/tm3bxqfxaGZmJtHsrSjNyStXrjCC/Lm5uWjZsiWWLFkCZ2dnsefdv38/li1bhtTUVACAoaEhli9fjjFjxoi0Cw0Nxe7du+Hn58dUIkpJScGECRMwfvx4dOzYEe7u7jAwMEB4eLhYP6qC4uJipKSk4O3bt3x7XgXtVyylsLAQYWFhePDgAXN/PTw8oKamJtRGkq0nwooDdOvWTaIxUV6bVpKxxOFwkJaWJvbc0iLLWARKAu+UlBTk5ubC2tq6RklnlSU5ORknTpxgVkAsLCzQv39/uRZg+NVhg1EWlmpGUVER06dPx6RJk3g0RqsqGC1bBUoQZYX5BfHbb7/ht99+41vanTZtGm7fvo0bN27w2ZiammLPnj3o3r07zM3NsWPHDri6uuLx48fo1KkTPn36VPELkQAPDw88f/4cvr6+6NatG44dO4Y3b94we15Ly4OWx9nZGY6OjliwYIHA11etWoWYmBicPXtWLn7LSn5+PnJzc1G3bl2Jjm/YsCGOHDkCOzs7nvZ79+7hjz/+QFpaGq5du4Y//vgDr1+/loPHFePGjRsYOnQonj9/zpcQVFUVo1hEQ0T48OEDOByOWD3f6mT16tVYsmQJiouLUbduXRAR3r17BwUFBaxatUpg8h+L7LDL9Cws1UxsbCz8/f3RqlUrWFlZYfjw4XB3d6+y/sUFm+JYt24dXF1dceHCBR7B8czMTKEi8KNGjcKgQYOY7H0nJycAJUkn8qz7fPHiRZw4cQKtW7cGl8uFqakpevToAW1tbaxevVpoMPrgwQOsW7dO6Hl79eoldJ+lLBAR7t69yzNDY2dnJ/Fe2ffv3zO2Famd/vr1axQWFvK1FxYWIisrC0DJLKu4bQZVxcSJE9G6dWucOnWqQooQFy9e5NkDbGFhgT/++EPkTOqvirRjMSsrC/PmzcPJkyeZ8aKtrY0BAwZg9erVqFevXlW4LxHR0dFYtGgRFi9ejBkzZjBbVD5+/AhfX18sWLAAbdu2ZceHPKhKHSkWFhbh5Obmkr+/P3Xs2JGUlJSIy+WSr68vffnypUr6v3PnDqNvGhcXVyHbly9f0t9//01ubm7k5uZGCxcupJcvX4q0OXz4MG3cuJEyMzOZtsDAQIGC7JWFlpYWpaenExGRiYkJxcbGElGJaL2amppQOyUlJXr16pXQ11++fEnKysqV6uvFixfJ3NycuFwuj8Znw4YNKSYmRqTto0ePqHPnzkwRgtKHg4MDJSQkiO27d+/e1LJlS55xEBcXR61atSJXV1ciIjp58iQ1a9ZMtousJNTV1QXqjIpiwoQJxOFwSF9fn9q1a0e//fYb6evrE5fLpalTp8rJ058Tacfi58+fydzcnOrUqUMzZ86knTt30o4dO2jatGlUu3ZtatSoEU8BgOpm0KBBNH78eKGvjxs3jtzd3avQo18HNhhlYamBJCQkkJeXFxkYGJCqqir16dNHbn29efOGHBwciMPhkJ6eHunp6RGHwyFHR0d6+/at3PotT9lqSvKidevWFBkZSUREffr0oeHDh9OLFy9o3rx5ZGFhIdSOy+WKvBeVLbifnJxM6urq5ODgQMePH6eEhAR6+vQpHTlyhLp27UoaGho8Qullef36NdWqVYuaNm1Kvr6+FBkZSWfOnKENGzZQ06ZNqU6dOjyi68LO4eTkRBwOh5SVlUlZWZm4XC716NGDsrKyiKgkQDl79mylXbMsODg40JkzZyQ+/ujRo6SsrEwBAQFUXFzMtBcVFZG/vz8pKyvXyEpT1YEsY9Hb25ssLS0F/u+8efOGLC0taeXKlfK+BIkxMzOjK1euCH398uXLZGZmVoUe/Tqwe0ZZWGowRUVFiIiIwN69e3Hy5Em59DF48GCkpaUhODiYkfx58uQJRo4cCUtLS4FZxQ8ePJD4/IIy1NeuXQszMzMMHjwYQIm+6pEjR1C/fn2cPn1abnW39+3bh8LCQnh6euLu3btwcXHBx48foaysjMDAQMaf8nC5XPTq1UuoqPj3798RGRlZaXsTp06diqdPnyIqKorvNSKCk5MTrK2tsXXrVr7X58+fjwsXLuDq1at89cO/fv2KTp06wdnZGatXrxbrR0JCApKSkgAATZo0QZMmTaS8Ivly7NgxLFq0CF5eXrC1teVLwis/nvr27QsbGxuh92D+/PlISEjAiRMn5Obzz4IsY7Fdu3aYMGGC0OTDvXv3Ys+ePbh+/Xql+y0N6urqSEpKQoMGDQS+/uLFCzRq1EhgUiaLbLDBKAvLL46Ojg4uXLiANm3a8LTfunULzs7OAmVquFwuOByO2OoxwpJHzM3NsX//fnTo0AHnz5/HoEGDcPDgQRw6dAgZGRk4d+6cTNckKfn5+UhISICJiQkjtySIysjkrwjNmjXD6tWr0adPH4GvR0RE4K+//mKkhMrSsmVLLFiwgCmeUJ6wsDCsW7dOrErCz4Sg7PbS8SloDDZo0ABHjx5F27ZtBZ7v5s2b+OOPP/DixQu5+PszIctY1NfXx/Xr14X+iElISECHDh3w8ePHSvVZWrhcLrKysoQm+r158waGhoZsQpwcYBOYWFh+cYqLi/lmkoCSbH5hZSFFSRFJQlZWFqNP+e+//2LQoEFwdnaGmZmZQJHyiswKl9U7LY+3tzfmzp3LaDyqq6ujZcuW+Pr1K7y9vbFkyRKBdrIGmXFxcVBSUmLE8U+cOIGAgABYW1tj2bJlfOUoMzIyRArpN2vWDM+fPxf4WlpaGlq2bCnUtnXr1mLlf4qKihAYGIioqCiBUkkXL14UaV/VVHQ8vn//XujsF1ASrH748EFWt0RiZmaG0aNHw9PTEyYmJhLbVXQsyYosY/HLly/Q1dUVaqurqytQxq06KasLXJ6akrD3X4SdGWVh+cXp168fsrOzERoaCkNDQwAlpf88PDygp6eHY8eOibT/8OEDI9WSmZmJPXv24OvXr+jbty86d+4s0MbQ0BDh4eHo0KEDmjRpghUrVuDPP/9EYmIi2rRpw/cFJYmuIyBexkdBQQGvX7/mm/n48OED6tatK7cZjzZt2mDBggWMLJKNjQ0GDBiA27dvw9XVFb6+vjzHyzJDI+way9oaGRkJzJYvZerUqQgMDISrq6vA7PRNmzaJueKaDZfLxZs3b1CnTh2Br4u6v3p6ehJn64ua8fP19UVgYCAePXoEBwcHjBkzBgMGDBBbX76iY0lWZB2LWVlZUt3n6qAydIFZpIOdGWVh+cXZtm0b+vbtCzMzM2a2MjMzE82aNcO+ffuE2j18+BB9+vRBZmYmGjVqhLCwMLi4uCAvLw9cLhebNm1CeHg4+vfvz2fr5uaGoUOHolGjRvjw4QN69eoFoETHUlj968qgdNm2PPHx8TKVRBVHUlISo9l5+PBhdOnSBQcOHMDVq1fh7u4uMIB48uQJI6NUnvfv34vsLycnh2+/aClfvnwRu70iLCwMhw4dqnElMcXx5MkTgZW1BM2WL168WGjFMWFVkABUWrA3c+ZMzJw5E3FxcQgMDMS0adMwefJkDB06FKNHjxY6uy3NWJIVacciEaFx48ZCA7yaNhf27Nmz6nbhl4UNRllYfnGMjY0RFxeHCxcuICEhAUBJXe9S7U9hzJs3D7a2tti/fz9CQkLw+++/w9XVFXv27AFQInq/Zs0agcHopk2bYGZmhszMTKxbt45ZFnv9+jUmT55cuReI/5vN4nA4fF+ORUVFyM3NxcSJEyu931KIiAmoL1y4gN9//x1Ayb0X9mXevXt3gV/WZfdCCuurfDWs8q+Lm/1RVlYW+KOgppKWloYBAwbg4cOHPHuZS6+z/Mxbly5dkJiYKPKcwrQkR44cWQke/x8tW7ZEy5YtsWHDBvzzzz+YP38+duzYAVtbW0yfPh2jRo3ieb+kGUuyIu1YrKw91Cz/fdhlehYWFqmoXbs2Ll68iObNmyM3Nxfa2tq4ffs2WrVqBaAkOaFdu3Zi63RLwpYtWzB+/HioqqqKFZcXVJs7KCgIRITRo0fD19eXqQMOlAReZmZmjGC/PHB0dISxsTGcnJwwZswYPHnyBJaWloiJicHIkSP5ZmSE7cErj6mpKV9bTEyMRLZdu3YV+tqGDRuQlpaGbdu2SbwkXZ306dMHCgoK8PPzg7m5OW7duoUPHz5gzpw5+N///id0u0hl8u3bN74ZWW1tbbF2P378wLFjxxAQEIDz58+jXbt2GDNmDF68eIHt27fD0dERBw4cYI6v6FiSFVnGIguLpLDBKAvLL4iswR3Av5dMS0sL8fHxsLCwACB+P1hISAh27dqFtLQ0XL9+HaampvD19YW5uTn69evHc6y5uTnu3LmDWrVqiawPLa42d0xMDDp06CAwYUuePHjwAB4eHsjIyMDs2bOZqlfTpk3Dhw8feIKNmsCAAQMQHR0NfX192NjY8N2vo0ePVpNngin7w0hHRwe3bt1CkyZNcPHiRcyZMwf37t2TS795eXmYP38+Dh06JDDhSdReyLi4OAQEBCA0NBRcLhcjRozA2LFjeSqQPXr0CG3atOGREvrZxhILiySwwSgLyy9IZQR35ZNAtLS08ODBA+Z8ooLRHTt2YMmSJZg5cyZWrlyJR48ewcLCAoGBgQgKCkJ0dHQlXKVopJ3JqmwfFBQUqjw4Foc4Kauatvyqp6eHuLg4mJubo2HDhvDz84ODgwNSU1Nha2srcg+oLEyZMgXR0dHw8fHB8OHDsX37drx8+RK7du3CmjVr4OHhIdRWQUEBPXr0wJgxY9C/f3+BYyAvLw9Tp06V6H7X1LHEwiIJbDDKwsIiFeWF4CMiIuDo6AgNDQ0AooXgra2tsWrVKvTv359nRvXRo0fo1q2byL1vsbGx6NSpk1Q+5+fnY968eVLNZFUGd+7cwdOnTwGU7Mtt3bq1XPv7VejcuTPmzJmD/v37Y+jQofj06RMWLVqE3bt34+7duwI1MCsDExMTBAcHo1u3btDW1kZcXBwsLS0REhKC0NBQnD59Wqjt8+fPZVraLigoECi7VRGZKBaWmoJkeiksLCz/Wby9vQXOHJVqbwpj5MiRqFu3LnR0dKCjo4Nhw4bB0NCQeV63bl2MGDFCoG16ejrs7e352lVUVJCXlyfSX0dHR5ibm+Pvv//G48ePxVwdL15eXrh48SJ27NgBFRUV+Pn5Yfny5TA0NERwcHCFzlURXrx4gc6dO6Nt27aYMWMGZsyYgbZt26JTp041Slj97du3Il8vLCzErVu3qsgbyVm0aBETlHl7eyM9PR2dO3fG6dOnxW5DkYWPHz8y21K0tbUZKadOnTrh8uXLIm0dHBwE/iDKzs5mzimIpKQkdO7cGWpqajA1NYW5uTnMzc1hZmYmcpWDpWIUFRUhPDwcPj4+8PHxQXh4uEg5NBYZkX/FURYWlpoMl8sVWKv8/fv3lVpvvSxWVlZ0/PhxIiLS1NRkaltv2bKF7O3tRdq+e/eOtm7dSh06dCAOh0MtWrSgdevWUWZmpth+jY2NKTo6moiItLS0KDk5mYiIgoODqVevXjJckWh69uxJv/32GyUkJDBtCQkJ1L59e+rZs6fc+q0o5cdCs2bNKCMjg3melZUltzFR2Xz48IGn7rw8sLW1pUuXLhERUffu3WnOnDlERLR582YyMjISacvhcAT+32VlZZGysrJQuw4dOlCXLl3o9OnTdO/ePbp//z7Po6bx+fNnOnfuHP37778Ca9TXRB49ekQWFhakrq5O9vb2ZG9vTxoaGmRmZkYPHz6sbvf+k7DBKAvLLw6HwxH4JREVFUW1a9eWS5979uwhIyMjCgsLIw0NDQoNDaUVK1Ywf0tKWloarVixgmxsbEhBQYEcHBxEHq+hoUHPnz8nIiIjIyO6efMmcx4NDQ3pL0gMqqqqFBcXx9d+584dUlNTE2rn4OBAnz594mv//Pmz2GsVxtOnT6lRo0YCXysfIJX9oUBUEihxOByp+q3J5OXl0dOnTyk+Pp7nIY6NGzfS5s2biYjo/PnzpKqqSioqKsTlcsnX11egzYkTJ+jEiRPE4XAoODiYeX7ixAk6evQoTZkyhRo3biy0T3V1dXr69Kl0F1rF3Lt3j+rXr09cLpc4HA5pa2tTZGRkdbsllnbt2lGfPn3o48ePTNvHjx+pb9++1L59+2r07L8LG4yysPyi6Orqkp6eHnG5XObv0oe2tjZxuVyaPHmy3Prft28fWVpaEofDIQ6HQ0ZGRuTn51fh8xQWFlJERATZ2dmJnbWTZSZLFho1asQEvmW5efMmNWzYUKidsNmzN2/ekKKiolS+3L9/X+h9kiQY/VlmRiXh7du35OrqSlwuV+Cjojx79oyOHDkiMpAtHe+lAVrZh7KyMjVu3JgiIiKE2rdu3ZquXLlSYd9kJSsri4YNG0b169cnBQUFie6Vs7MzdejQga5du0ZxcXE0YMAAsrS0rGLPK46qqio9evSIr/3hw4ekqqpaDR7992FF71lYflF8fX0Z7c3ly5dXufamh4cHPDw8kJ+fj9zcXKHlBoVx9epV7N+/H+Hh4fj27Rv69euH1atXi7QZNWoU4uPj0bVrVyxYsAB9+vTBtm3b8OPHD2zcuFGWyxHJ+vXrMW3aNGzfvp1JWrpz5w5mzJiB//3vf3zHP3jwgPm7fPWboqIiREZGwsjISG7+/irMnDkT2dnZuHnzJrp164Zjx47hzZs3WLFiBTZs2FDh85mamopNSird22pubo7bt2+jdu3aFepj7dq1mDdvHlatWgVbW1u+7Hl5KUJ4enoiIyMDixcvFlgiVhB3797FuXPnmGpSe/fuhb6+Pr58+VLlyhUVoXHjxnjz5g1sbGx42t++fftTFYP4qajuaJiFhaV6uXTpEhUUFFS3GxKzYMECMjMzI2VlZXJ1daUDBw5QXl6eVOeSZCarMtDV1SVlZWXicrmkrKzM83fZGWk9PT0iImbmTNDsGYfDIXV1dfL395fKF1Ezo1wul1JSUujz58+UnZ1NWlpaFB8fT58/f6bPnz9TUlLSf2pm1MDAgJmx1tLSosTERCIqWUrv2LGjWPtp06Yxy/Rl2bp1K82YMaNSfS2l7Mxq2Udpm7zQ1NSke/fuVchG0My+pqYmpaWlVaJnlUPpGP/8+TOdOnWKbGxs6PDhw5SZmUmZmZl0+PBhsrW1pVOnTlW3q/9J2JlRFpZfnLKVeKpKe/PNmzeYO3cuoqKi8PbtW75Sg6Ikli5fvgwvLy8MGjSowrNK5ZFkJqsyqGi98PT0dBARLCwscOvWLUbLFSiZta5bty4UFBQq2Uv+UqJExKN6QBKUEv2ZyMvLY2bk9fT08O7dOzRu3Bi2traIi4sTa3/kyBGcPHmSr71Dhw5Ys2YN3/teGcUmqkKDVxDGxsZS1ZIvP7NPRHj69ClycnKYtubNm1eKj7Kgq6vLV3Z10KBBTFvptffp00fuEnC/IqzOKAvLL051aG/26tULGRkZmDp1qsAlv/IVmCqD4uJiBAYG4ujRo3j27Bk4HA7Mzc0xcOBADB8+/D8TZOnp6Ym8lsLCQuTl5Ql8XyujlGh1kZycjOjoaIHam0uWLBFo06ZNG6xYsQI9e/ZE3759oauri9WrV2PLli0IDw9HamqqyD5VVVXx6NEjvqXblJQUNGvWDN++feNpL1tswszMTOj7JK6SWHVw7tw5bNiwAbt27YKZmZlENlwul6lfX56yde1rQnAn6dgHaub4/9lhg1EWll8cWarISIuWlhauXLkCOzs7iY4/efIkevXqBSUlJYEzUWXp27cvXxsRoU+fPjh9+jRatGiBpk2bMjM0Dx8+RN++fXH8+HEprkQyXr58iSNHjiApKQkA0KRJE7i5uUm07zMkJAQ7d+5Eeno6UzZ106ZNsLCwEBi0BwUFSeTTyJEjK3YRNZg9e/Zg0qRJqF27NgwMDHiCPA6HI3SWc9++fSgsLISnpyfu3r0LFxcXfPz4EcrKyggMDMTgwYNF9tusWTNMnDgRU6dO5WnfunUrduzYgSdPnsh+cQLIzs6Gv78/U0DBxsYGo0eP5tn3Xdno6ekhPz8fhYWFUFdX59urWqqxWha2rj2LxFT5xgAWFpYaRXVob1pZWQmUOhJG2b1ngvZQlt1HJ4i9e/eSlpYWXbx4ke+1qKgo0tLSoqCgIOkuRgzbt28nFRUV4nA4pKOjQzo6OsThcEhFRYW2b98u0vaff/6h2rVr04oVK0hNTY3JbA8ICKBu3boJtImJiaEfP35U+nXUZExMTGjNmjUynycvL4/u3r1L7969k+h4f39/UlNToyVLltClS5fo0qVLtHjxYlJXV6fdu3cLtSsoKCALCwt68uRJhX28ffs26evrk5GREQ0YMIAGDBhADRo0oFq1atHdu3crfD5JCQwMFPmQlpqq23n58mXy8PCg9u3b04sXL4io5DOxOpQMfgXYYJSF5RenOrQ3z549S87OzpSeni6X85enR48etHr1aqGvr1y5kpydnSu933///ZcUFBRozpw59OrVK6b91atXNGvWLFJUVBSZEGFlZUXHjh0jIl6ZpYcPH1KtWrUE2ggrYvBfRktLi0eCSlIqI7D4559/yMjIiPlBZG5uLtEPG0NDQ6mC0U6dOpGnpyfPD44fP37QyJEjqXPnzhU+X3Xw5csX2rVrF7Vp06ZGJsSFh4eTmpoajR07llRUVJixtXXrVrkWx/iVYYNRFpZfnOrQ3iybXa6pqSkwo7wyqVevnshM4Li4OKpXr16l99u1a1dauHCh0NcXLlxIXbt2Ffq6qqoqPXv2jIh4g9GkpCSheofCtEn/y4wePZp27NhRYTslJSUyMzOjv/76ix4/fiyTD2/fvqWcnByJj1+5ciWNHDmywrPYqqqqAkXvHz9+LLKAQmVQWFhI4eHh5OPjQz4+PnT06FEqLCyU2D4mJoZGjBhBGhoa1KhRI5o/fz7dunVLjh5Lh52dHfODouz/nbw+J1jYbHoWll+e6tDerGh2OQBcvHgRU6dOxY0bN/gy/D9//owOHTpgx44d6NKlC5/tx48fUa9ePaHnrlevHj59+lRhn8QRFxeHXbt2CX19+PDhIrOqzc3Ncf/+fb49dZGRkbCyshJq919JxhJF2ftmaWmJxYsX48aNGwK1N4Vlpr969QphYWEIDQ3FmjVr0Lx5c3h4eGDIkCFo0KBBhfwpq3ggCbdv30ZUVBTOnTsHW1tbaGho8Lx+9OhRgXba2trIyMhA06ZNedozMzOhpaVVIR8qQkpKCnr37o2XL1+iSZMmAIDVq1fD2NgYp06dQsOGDQXaZWVlITAwEP7+/vjy5QsGDRqE79+/4/jx47C2tpabv7KQmJgo8HNER0cH2dnZVe/QLwAbjLKw/OLMmjWL+dvJyQkJCQm4e/cuLC0t5Sa5Ik3yjK+vL8aNGydQakpHRwcTJkzApk2bBH6JFBUVQVFR+MedgoICCgsLK+yTOIqKivgCo7IoKSmJzCSePXs2pkyZgm/fvoGIcOvWLYSGhmL16tXw8/MTaufp6QkVFRWRvgkLdn4WNm3axPNcU1MTMTExfFnRHA5HaDBau3ZtTJ06FVOnTkV6ejoOHDiAoKAg/PXXX+jSpQsuXrzIZ9OyZUtERUVBT08P9vb2IgN/UfJQurq6+OOPP0RdokAGDx6MMWPG4H//+x86dOgAoKQAhJeXF4YMGVLh80nK9OnT0bBhQ9y4cQP6+voAgA8fPmDYsGGYPn06Tp06xWfTp08fXL58Ga6urvD19YWLiwsUFBSwc+dOuflZGRgYGCAlJYVPNSA2NhYWFhbV49R/HDYYZWFh4UFe2ptlq658+fJF5LGCAs74+HisXbtWqI2zs7PAakZASTa9qADt+/fvIv2RFhsbG5w4cYIn4C/L8ePH+aq8lGXs2LFQU1PDokWLkJ+fj6FDh8LQ0BCbN2+Gu7u7UDstLS2oqalVyFc3NzeJj60JgWx6enqlns/c3BwLFixAixYtsHjxYqFSP/369WPGUf/+/aXuLyAgQCq7//3vf+BwOBgxYgTzA0pJSQmTJk3CmjVrpPZHHDExMTyBKADUqlULa9asQceOHQXanDlzBtOnT8ekSZPQqFEjuflW2YwbNw4zZszA3r17weFw8OrVK1y/fh1z587F4sWLq9u9/yRsMMrCwoLbt28L1WisrKV6PT09vH79GnXr1uUTmC6FROgOvnnzRuQso6KiIt69eyfwNUlmYkeMGCH2mIoyZcoUTJo0CSoqKhg/fjwzO1tYWIhdu3Zh0aJF+Oeff0SeQ5qyqVu2bKlweVV5ygLJG29vb8ydOxfq6uo87V+/fsX69euF6oyWUpHSskuXLhX4d1WhrKyMzZs3Y/Xq1YwOasOGDfmuvbJRUVHhEaovJTc3F8rKygJtYmNj4e/vj1atWsHKygrDhw8X+SOqprBgwQIUFxeje/fuyM/PR5cuXaCiooK5c+di2rRp1e3efxJWZ5SF5Rdn1apVWLRoEZo0aYJ69erxaTQKWqqUhpiYGHTs2BGKiopiBaYFiUo3bNgQGzZsEDobdfToUcydO7fGiYXPnTsXGzduhJaWFho2bAgiQlpaGnJzczF9+nS+5WZZUVBQYIL+XwVh1/zhwwfUrVtX6FaIv/76C2FhYXj16hV69OgBDw8P9OvXr8KBXUFBgcAfciYmJkJtzM3NRS7x17RxPGLECMTFxcHf3x9t27YFANy8eRPjxo1Dq1atEBgYKNQ2Ly8PBw8exN69e3Hr1i0UFRVh48aNGD16tFz3uVaU9PR0mJubM88LCgqQkpKC3NxcWFtbQ1NTsxq9+2/DBqMsLL849erVw9q1a+Hp6Vndrohk2rRpuHTpEm7fvg1VVVWe175+/Yq2bdvCwcFBbJnF6uDGjRsIDQ1FcnIyAKBx48Zwd3dHu3btBB7v4OAgNgmJw+EgKiqKr53L5SIrK+uXCka5XC7evHnDl0R08eJFDB48WOiMeceOHeHh4SF1admkpCSMGTMG165d42kXNcNfyubNm3me//jxA/fu3UNkZCS8vLywYMEC5jU3NzcEBgZCW1tb7HYKeW2hyM7OxsiRIxEREcGsUBQWFqJv374IDAyUeGY9MTER/v7+CAkJQXZ2Nnr06CG2kEVVweVyYWpqCgcHBzg6OsLBwUGiwhQsssMGoywsvzj169fH5cuXq3RPV2RkJDQ1NdGpUycAwPbt27Fnzx5YW1tj+/bt0NPT47N58+YNWrZsCQUFBUydOpXJ6E1ISMD27dtRVFSEuLg4kVnzPwvC9pgCQE5ODg4cOIDv378LLelZOgMtC+Hh4Th06BAyMjJQUFDA85okddurgtLSp58/f4a2tjZPAF9UVITc3FxMnDgR27dvl0v/pfd5wYIFAsvatmjRosLn3L59O+7cucOzp3TUqFHYsmULtLS04OnpKfKHirR7USUlOTkZCQkJAAArKyu+UqiSUlRUhIiICOzdu7fGBKOXLl1iHjdv3kRBQQEsLCyYwNTBweE/8flSE2GDURaWX5x169bh1atXUsktSYutrS3Wrl2L3r174+HDh2jdujXmzJmD6OhoNG3aVOgX6vPnzzFp0iScPXuWqXfN4XDQs2dPbN++nWeJrSbRqFEjZu+ntEF/YWEhtm/fjpUrV0JHRwc+Pj4C99/17t0boaGhzEzVmjVrMHHiROjq6gIoWbru3LmzyFKVW7ZswcKFC+Hp6Yndu3dj1KhRSE1Nxe3btzFlyhSsXLlSqmuobIKCgkBEGD16NHx9fXlm55SVlWFmZob27duLPc+TJ08EBt2CSsuWRUNDA3fv3uWTWZKFtLQ02NnZiU3yY5Ev3759w7Vr15jg9NatW/jx4weaNm2Kx48fV7d7/z2qQduUhYWlBlFUVEQuLi5kYWFBv//+O1NisPQhDzQ0NJjqS0uXLqU//viDiIju3r0rkaj0x48f6datW3Tz5k36+PGjXHysTDZu3EitW7cmDodDrVu3Jl9fX3r9+rXE9vv27SMLCwuqX78+bd++XaRQevkKTOWrE2VlZYmtetOkSRM6cOAAEfGKfi9evJimTJkisd9VxaVLl6igoKDCdqmpqdS8eXOmlGzZsrKSVAZq3bp1pZeHXLt2LZmamgp93cHBgT59+sTX/vnzZ3JwcKhUX8pSWFhIfn5+NGTIEOrevTs5ODjwPARx7do1ioiI4GkLCgoiMzMzqlOnDo0bN46+ffsmN58rg+/fv9PFixfJy8uLtLW1a2TFqP8CbDY9C8svzvTp0xEdHQ0HBwfUqlWrSgTTlZWVkZ+fDwC4cOECk8mur68v0YyQnp4e2rRpI1cfK5NZs2Zh1qxZSEpKwv79+7F9+3bMnTsXDg4OGDZsmNBM/sjISCxYsADp6emYO3cuZs+ezSeOXh4qt9hV/rkkZGRkMBqWampqTBb18OHD0a5dO2zbtq3C56xsyo4Te3t7fP36FV+/fhV4rCCpMACYMWMGzM3NERUVBXNzc9y6dQsfPnzAnDlzhMqElWXt2rWYN28eVq1aJVBsX1i/pT6X/V8jImRlZeHdu3ciFRYuXbrEN4MLlMzkXblyRazP0jJjxgwEBgbC1dUVzZo1k+hzwtvbG926dcPvv/8OAHj48CHGjBkDT09PWFlZYf369TA0NMSyZcvk5ndFKSgowI0bNxAdHc0s1xsbG6NLly7Ytm2bwORKFtlhg1EWll+coKAgHDlyBK6urlXWZ6dOnTB79mx07NgRt27dwsGDBwGUJIRUtPLNz0Tjxo2xfPlyLF++HDdu3MCkSZMwatQovmD01q1bmD9/Pm7cuIGJEyfiwoULUiXYSIuBgQE+fvwIU1NTmJiY4MaNG2jRogXS09OlCm7lgTB5sLKQmESi69ev4+LFi6hduza4XC64XC46deqE1atXY/r06bh3757I8zs5OQEAunfvXqF+AX6NUi6Xizp16qBbt24Cl/0fPHjA/P3kyRNkZWUxz4uKihAZGSnXZJuwsDAcOnQIvXv3ltjm/v378PHx4TnHb7/9hj179gAAjI2NsXTp0hoTjDo6OuLmzZswNzdH165dMWHCBBw4cAD169evbtf+87DBKAvLL46+vr7QUn7yYtu2bZg8eTLCw8OxY8cO5kv0zJkzcHFxqVJfqppbt27hwIEDOHjwIL58+YI///yT75h27dpBTU0NEydOhLm5OQ4cOCDwXIIqC3E4HL4graKz3Y6Ojjh58iTs7e0xatQozJo1C+Hh4bhz506FxPHlSXR0tMznKCoqYqSFateujVevXqFJkyYwNTVFYmKiXH2oqEapnZ0d8946Ojryva6mpoatW7dK7Y84lJWVK5ys9OnTJ56En5iYGPTq1Yt53qZNG2RmZlaaj7Jy5coV1K9fH46OjujWrRu6du2KWrVqVbdbvwRsAhMLyy9OQEAAIiMjERAQIHfh7F+V0uX50NBQpKenw9HRER4eHnBzcxOoXWhmZiaRtJMgLUoul4tevXoxVYIiIiLg6OjILO9///4dkZGRImftiouLUVxczGTkh4WF4dq1a2jUqBEmTJggVOT8Z6Nz586YM2cO+vfvj6FDh+LTp09YtGgRdu/ejbt37+LRo0dy6/v06dNQUFBAz549edrPnj2L4uJinqANKEneIyJYWFjg1q1bPDJWysrKqFu3LhQUFOTm74YNG5CWloZt27ZJ/OPG1NQUISEh6NKlCwoKCqCrq4uIiAhmJvnhw4fo2rUrPn78KDe/K0JeXh6uXLmCS5cuITo6Gvfv30fjxo3RtWtXJjgtLx/GUjmwwSgLyy+Ovb09UlNTQUQwMzPj2/cmDxmfjIwMka+LEgv/GeFyuWjTpg2GDh0Kd3d3ucrDjBo1SqLj5C0BVB3k5+cLzIpv3ry5wOPPnj2LvLw8uLm5ITk5GX369EFSUhJq1aqFsLAwvuX3yuq39LU1a9bwLXtHRkZi/vz5iI+Pl6hveVJ+FvzixYvQ19eHjY0N3+eEIH3TSZMmMWV8jx8/jqCgILx69Yr5MbN//374+vri9u3b8rsIGcjJyUFsbCyzfzQ+Ph6NGjWS64+UXxV2mZ6F5RdHlvra0iJu5k/UrN3PSGJiolSSTsHBwRg8eDAzy1lKQUEBwsLCBCY+VUaQGRAQAE1NTb4tBIcPH0Z+fr5E5VWrknfv3mHUqFE4c+aMwNeFjaeys5KNGjVCQkICPn78yOiXyqtfoESv09ramq+9adOmSElJEdu3tHJUFaG8kP2AAQMqZO/j4wM3Nzd07doVmpqaCAoK4plV37t3L5ydnSvFV3mgoaEBfX196OvrQ09PD4qKinj69Gl1u/WfhJ0ZZWFhqXLKz/qUVp/ZuHEjVq5cWWP2JVYm2dnZCA8PR2pqKry8vKCvr8+I9AtLPJG2zKWsNG7cGLt27YKDgwNPe0xMDMaPHy/RfsqqxMPDA8+fP4evry+6deuGY8eO4c2bN1ixYgU2bNjAl5wnyfhSVFSEgYEBevTogT59+lRKv2UxMDDAgQMH+PZ/XrhwAUOHDsXbt28F2qWlpWHAgAF4+PAhOBwOj94uUDN/yH3+/Bmampp82wg+fvwITU3NGrPto7i4GHfu3GGW6a9evYq8vDwYGRkxovcODg4wNTWtblf/c7AzoywsLACAu3fvMr/6bWxsYG9vL7e+BFWmad26NQwNDbF+/fr/XDD64MEDdO/eHbq6unj27BnGjRsHfX19HD16FBkZGQgODhZoV5qVXZ4XL15IXH5RGjIyMgQWEDA1NRW7xaI6uHjxIk6cOIHWrVszJR179OgBbW1trF69mi8olOTeFRcXIzk5GX5+fpg7dy68vb1l7rcs/fr1w8yZM3Hs2DEmgTAlJQVz5swRObspqxyVtDg6OuLo0aNM8YRSvnz5gv79++PixYtCbYXdb319/cp0UWZ0dXWRl5cHAwMDODg4YNOmTejWrVuVJ3j+klS9tCkLC0tN4s2bN+Tg4EAcDof09PRIT0+POBwOOTo60tu3b6vUl+TkZFJXV6/SPqsCR0dH8vLyIiJeEfmrV68KFDi3s7Mje3t74nK5ZGtrS/b29syjefPmpKWlRX/++afc/DU2NqYTJ07wtR8/fpyMjIzk1q+0aGlpMUUUTExMKDY2loiI0tLSSE1NTaZzR0REkLGxcaX3m52dTe3atSNFRUUyMzMjMzMzUlRUFCpqX0qtWrUoPj6eiIi0tbUpISGBiIiioqLIzs6uglcnORwOh6eYQilv3rwhRUVFufVblezcuZMSExOr241fEnZmlIXlF2fatGnIycnB48ePYWVlBaBkP9rIkSMxffp0hIaGVnqf5YXtiQivX7/GsmXLpC6XWZO5c+cOdu/ezdduZGTEoxdZSuk+3vv376Nnz548GfelZS7/+OMPufk7ZMgQTJ8+HVpaWujSpQuAkiX6GTNmCCxBWt00adIEiYmJMDMzQ4sWLbBr1y6YmZlh586dMmtEdurUCa1bt670fnV0dHDt2jWcP38e8fHxUFNTQ/PmzZn7LQxZ5agqSnXrm1YlEyZMqG4Xfl2qOxpmYWGpXrS1tenWrVt87Tdv3iQdHR259Fm25GLpg8PhkImJCV27dk0ufVYnderUobi4OCLinRk9d+4cNWjQQKhdYGAgff36tUp8LMv3799p0KBBxOFwSElJiZSUlEhBQYFGjRpF379/r3J/xBESEkIBAQFERHTnzh2qXbs2cblcUlVVpbCwsBrf79evX6m4uFiiYzt16kTHjh0jIqIhQ4aQi4sLxcbG0ogRI8jGxqailyCWsv+rpeVSyz7U1dXJ39+/0vtl+bVgE5hYWH5xtLS0cOXKFdjZ2fG037t3D127dpWoPGdFiYmJ4XleWn3G0tKS0bb8LzF27Fh8+PABhw4dgr6+Ph48eAAFBQX0798fXbp0ga+vr0j7qtzPW5akpCRm1s7W1vanSdzIz89HQkICTExMqrRyVUX6LS4uxsqVK7Fz5068efMGSUlJsLCwwOLFi2FmZoYxY8YItCsrR5WSkoLff/+dkaM6ePCgQEF8WahufVOWXwM2GGVh+cXp168fsrOzERoaCkNDQwDAy5cv4eHhAT09PRw7dqyaPfz5+fz5MwYOHIg7d+4gJycHhoaGyMrKQvv27XH69Gmh9ebfvn0Ld3d3XLp0iUkcyc7OhoODA8LCwlgB7nIUFBQgPT0dDRs2rPE/ary9vREUFARvb2+MGzcOjx49goWFBQ4ePAhfX19cv35d4nNVRI5KWvLy8oSOUxYWWWGDURaWX5zMzEz07dsXjx8/hrGxMdPWrFkznDx5Ui614j98+MCU2cvMzMSePXvw9etX9OnTR+yeuZ+Z2NhYPHjwALm5uWjZsiVT21wYgwcPRlpaGoKDg/n281paWlbqft7Zs2fDx8cHGhoamD17tshjN27cWGn9Vgb5+fmYNm0agoKCAICZZZw2bRqMjIywYMECufRbVFSEwMBAREVF4e3btyguLuZ5XVSGuaWlJXbt2oXu3btDS0sL8fHxsLCwQEJCAtq3b49Pnz7x2fz48QNqamq4f/8+mjVrVunXIwpNTU0MGjQIo0ePRqdOnaq0b5b/PjX7pyMLC4vcMTY2RlxcHC5cuICEhAQAgJWVldhASRoePnyIPn36IDMzE40aNUJYWBhcXFyQl5cHLpeLTZs2ITw8vFqE+KuCTp06VeiLPDIyEhcuXGACUQCwtrbG9u3bK10s/N69e/jx4weAkqpbwmbZ5Dn7Ji1//fUX4uPjcenSJbi4uDDtTk5OWLZsmdyC0RkzZiAwMBCurq5o1qxZhe7Ny5cvBdZ6Ly4uZt6H8igpKcHExKRatET37duHwMBAODo6wszMDKNHj8aIESOY1RQWFpmoxv2qLCwsNYCgoCD69u0bX/v3798pKCioUvtycXGh33//nWJjY2nChAlkZGREo0ePpqKiIioqKqLJkyfTb7/9Vql9VjdFRUXk7+9Prq6uZGNjQ82aNaM+ffpQUFCQ2KQVTU1NunfvHl97XFwcaWlpycnjnw8TExO6fv06EfEmiCUnJ8v1PtWqVYtOnTollW3Lli0pJCSEiHh9Xr58OXXq1EmonZ+fH/Xu3Zs+fPggVb+y8vbtW9qwYQPZ2tqSoqIiubq60pEjR+jHjx/V4g/LfwM2GGVh+cXhcrkC9QPfv39PXC63Uvsqq5GYk5NDHA6H7ty5w7z+9OlTuWXwVwfFxcXk6upKHA6H7OzsyN3dnQYPHkzNmzcnDodD/fr1E2nft29f6tKlC718+ZJpe/HiBXXt2pX69+8vF58LCgpIQUGBHj58KJfzywM1NTUmmCsb2N2/f5+0tbXl1m/9+vWl1qU8fvw46ejo0Jo1a0hdXZ3Wr19PY8eOJWVlZTp37pxQOzs7O9LU1CQVFRVq3Lgxjwatvb29tJciFVu2bCEVFRXicDhUp04dWrx4MeXl5VWpDyz/DdhlehaWXxyqwio/Hz9+hIGBAYCSPWgaGhrQ09NjXtfT00NOTk6l9lmdBAYG4vLly4iKiuIrrXnx4kX0798fwcHBAmvMA8C2bdvQt29fmJmZ8e3n3bdvn1x8rs6lYGlp3bo1Tp06hWnTpgH4v60Efn5+aN++vdz6nTNnDjZv3oxt27ZVePtCv379EBERAW9vb2hoaGDJkiVo2bIlIiIi0KNHD6F21b2F5c2bNwgKCkJgYCCeP3+OgQMHYsyYMXjx4gXWrl2LGzdu4Ny5c9XqI8vPB5vAxMLyi2Jvbw8Oh4P4+HjY2NjwZB8XFRUhPT0dLi4uOHToUKX1yeVy8ebNGyYLXEtLCw8ePGBKT7558waGhoY/VSAkCmdnZzg6Ogrds7hq1SrExMTg7NmzQs9BRFWyn7cs/v7+OHr0KEJCQmpcyUZBxMbGolevXhg2bBgCAwMxYcIEPHnyBNeuXUNMTAxatWpVaX2VL1V78eJF6Ovrw8bGBkpKSjyvHT16tNL6rW6OHj2KgIAAnD17FtbW1hg7diyGDRvGUx40NTUVVlZWKCgoqD5HWX5K2JlRFpZflOqq8uPp6QkVFRUAwLdv3zBx4kRGMub79++V3l918uDBA6xbt07o67169cKWLVtEnoPD4aBHjx4iZ8sqm23btiElJQWGhoYwNTXlk/SJi4urMl8koVOnTrh//z7WrFkDW1tbnDt3Di1btsT169dha2tbqX2VXy0YMGCAzOf89u0bDh48iPz8fDg5OYmtQpadnY3w8HCkpqbCy8sL+vr6iIuLQ7169eRWDWnUqFFwd3fH1atX0aZNG4HHGBoaYuHChXLpn+W/DTszysLyixMUFITBgwdDVVVV7n2NGjVKouMCAgLk7EnVoKysjOfPnwstDfnq1SuYm5sLDMKLi4sRGBiIo0eP4tmzZ+BwODA3N8fAgQMxfPhwuWa1L1u2TOT5ly5dKre+/+vMnj0bP378wNatWwGUaKO2bdsWT548gbq6OgoLC3H+/Hmh2wsePHgAJycn6Ojo4NmzZ0hMTISFhQUWLVqEjIwMBAcHy8Xv/Px8qKury+XcLCxsMMrCwsIiJxQUFJCVlSVUnF7YtgQiQp8+fXD69Gm0aNECTZs2BRHh6dOnePjwIfr27Yvjx49XwRXUbCStDqatrS2X/tPT01FYWMg3k5mcnAwlJSWYmZnx2TRr1gyrVq1C3759AZT88JozZw7u3bsHExMTjB49Gm/fvsWpU6cE9unk5ISWLVti3bp1PPqk165dw9ChQ/Hs2bNKvcbi4mKsX78eJ0+eREFBAbp3746lS5dCTU2tUvth+bVhl+lZWH5xuFyuyFmw/8r+zeqAiHi2JZRH2LYEWROfZMXCwgK3b99mChOUkp2djZYtWyItLU0u/VYUXV1dkWO3NDlPXmPY09MTo0eP5gtGb968CT8/P1y6dInPJiMjA9bW1szzc+fOYeDAgUyp1RkzZqB3795C+7x9+zZ27drF125kZISsrCwpr0Q4K1euxLJly+Dk5AQ1NTVs3rwZb9++xd69eyu9L5ZfFzYYZWH5xTl69CjPF/qPHz9w7949BAUFYfny5dXo2c/PyJEjxR4jKKAMDQ3F33//zReIAmASovbv3y+3YPTZs2cCA7jv37/jxYsXculTGqKjo5m/iQi9e/eGn5+f3PZNlufevXvo2LEjX3u7du0wdepUgTZcLhdlFyRv3LiBxYsXM891dXUFVl8qRUVFReCMcFJSklzKwwYHB+Off/7BhAkTAAAXLlyAq6sr/Pz8wOVyK70/ll8TNhhlYfnFESQVM3DgQNjY2ODgwYMYM2ZM1Tv1H0Hava+VkfgkDSdPnmT+Pnv2LE+yTlFREaKiohjlg5pA165deZ4rKCigXbt2sLCwqJL+ORyOQCmyz58/C52NtbKyQkREBGbPno3Hjx8jIyOD50fH8+fPUa9ePaF99u3bF97e3ozKBYfDQUZGBubPny+XhMOMjAyemVonJydwOBy8evVKLqWCWX5N2D2jLCwsAklLS0Pz5s2Rm5tb3a78csiS+CQLpTNdHA4H5b8aSvdAbtiwAb///nul9ltZlN1DWRX06dMHampqCA0NhYKCAoCSoH3w4MHIy8vDmTNn+GyOHTsGd3d3dOrUCY8fP0abNm0QERHBvD5//nykp6cLlVT7/PkzBg4ciDt37iAnJweGhobIyspC+/btcfr0aT7lA1kRtO+5vCQbC4ussDOjLCwsfHz9+hVbtmypsuVOFl6Kiop4dF/Lo6CggMLCwkrvt7i4GABgbm6O27dvo3bt2pXex3+JtWvXokuXLmjSpAk6d+4MALhy5Qq+fPmCixcvCrQZMGAATp8+jX///RfOzs6MUH8p6urqmDx5stA+dXR0cP78ecTGxuLBgwfIzc1Fy5Yt5aY9K2jfc3lJNuC/panKUvWwM6MsLL84enp6PHtGiQg5OTlQV1fHvn37mKxflqqDy+WiV69eIhOfIiMj2eSyclTHjN2rV6+wbds2xMfHQ01NDc2bN8fUqVN/imIBkvCrybGxVA9sMMrC8osTFBTE85zL5aJOnTr47bffeEp1slQd1R0ATJ8+HZaWlpg+fTpPe6kYvq+vr1z6rSjlqyFFRETA0dGRb6n6vzZrFxUVhU2bNuHp06cASvahzpw5U+6VuVhY5AUbjLKwsAjl/7V370FRnfcbwJ/lvsiKl4AKIreNAkq80aRMrYDhlpiAcTJqsioG4oyikWK0knFoIwYxSdVKKEoFRIkCSQS8ERviuCBBm44G0whBWbkUL4k1okVlYdnTP/xlf10XFIycg/J8ZvyD91zeZ/NH5jvveS/fffcdJkyYIHUMEpmzszMOHDhgcozm6dOnERER0W9W1EtdtAN3P8tnZGTgwoUL+PTTT+Hs7Izc3Fy4u7tj2rRpj7y/9PR0xMXF4dVXXzVsjH/y5El89tln2LJlC5YtW/bI+yTqayxGicjIf/7zH+Tl5SEzMxOnTp3ip+AByMbGBt999x2USqVRe11dHSZMmIC2tjaJkvUv+/btw4IFC6BSqZCbm4vq6mp4eHggLS0NJSUlKCkpeeR9jh49GgkJCSZbR/3lL3/Bhg0bcPHixUfeJ1Ff4yZhRAQAKC8vR1RUFEaNGoU//elPmDFjBk6ePCl1LJKAUqnEkSNHTNo///xz0VaqPw7ee+89bN++HTt27IClpaWh/Te/+Q1Onz7dJ322tLQgPDzcpD00NBQ3btzokz6J+hpX0xMNYFeuXEFOTg6ysrJw8+ZNzJkzB1qtFsXFxUanxNDAsnLlSixfvhxXr17FjBkzANydp7hp06Z+M1+0P6itrcX06dNN2u3t7dHS0nLfZ/Py8vDaa691eW316tX48MMPu7wWERGBoqIirF692qh9//79/XbLLaIHYTFKNEC9/PLLKC8vx8yZM/HnP/8Z4eHhMDc3x/bt26WORhKLjo6GVqtFcnIy1q9fDwBwc3PDtm3b+uzUp8fRyJEjUVdXZ3IGfUVFxQNHkJcuXYohQ4bghRdeMGqPj49Hfn5+t8Woj48PkpOToVarjeaMfvXVV3j77beNDkO4dwEaUX/FOaNEA5SFhQVWrFiBpUuXGp2tbWlpiTNnznBklAAAV69ehVwuh52dndRR+p2UlBR8/PHHyM7ORkhICEpKStDY2Ij4+HgkJiaa7CH6vw4fPgyVSoVDhw4ZFjq99dZbKCwsxNGjR+Hl5dXlcz3dtkomk+HChQu9/1FEEuDIKNEAVVFRgaysLEydOhXe3t5YsGAB5s2bJ3Us6id0Oh3UajU0Gg1ef/11AHf31Bw8eDAL0/+TkJAAvV6P559/Hrdv38b06dNhbW2NVatW3bcQBYCZM2ciPT0dERERKC0tRVZWFvbv349jx45h7Nix3T5XX1//qH8GkeQ4Mko0wN26dQsFBQXIzs7G119/jc7OTmzevBnR0dFQKBRSxyMJNDY2Ijw8HE1NTdBqtTh37hw8PDwQFxcHrVbLqRz3aG9vR11dHVpbW+Hj49OrYj09PR0rV66Eg4MDjh07ZrKDQXf+/e9/AwBPyaInAotRIjKora1FVlYWcnNz0dLSgpCQEBw4cEDqWCSyWbNmQaFQICsrC8OHDzec965Wq7F48WKcP39e6oiPpZUrV3bZ/umnn2LKlCnw9PQ0tG3evNnkvpaWFqxduxYFBQW4fv06gLsnqM2bNw/vvfcehgwZ0ie5ifoai1EiMtHZ2YmDBw8iOzubxegANHz4cFRWVmLcuHFQKBSGYrShoQE+Pj64ffu21BElFR0d3aP7srOzjf4OCgrq0XMymczkbPuffvoJ/v7+uHjxIlQqFby9vQEA1dXV2Lt3L1xcXFBZWclT0+ixxDmjRGTC3Nwcs2bNwqxZs6SOQhLQ6/VdHnbQ3NzMqRsAcnJy4OrqismTJ6M34znHjh176D6TkpJgZWUFjUaDESNGmFwLDQ1FUlIStmzZ8tB9EEmFI6NERGRk7ty5sLe3x1//+lcoFAp8++23cHBwQGRkJMaMGdOnx2s+DpYtW4a8vDy4urrijTfewPz58zFs2LAeP9/R0QG5XI6qqqoeH7fr5uaGjIwMhIWFdXn9yJEjWLJkCRoaGnqcg6i/YDFKRERGmpubERYWBkEQcP78efj5+eH8+fN46qmnUF5eDkdHR6kjSk6r1aKwsBDZ2dmorKzEzJkzERMTg9DQUMhksgc+7+HhgaKiIkycOLFH/VlbW0Oj0WD06NFdXm9uboZSqeRRrfRYYjFKREQmdDod8vPz8e2336K1tRVTpkyBSqWCXC6XOlq/09jYiJycHOzevRs6nQ5nz5594Ir6rKwsFBYWIjc3t0ejqs7OzigoKDDsSXqv48ePY+7cubh06dJD/QYiKXHOKBERmbCwsMD8+fOljvFYMDMzg0wmgyAIXc617UpaWhrq6urg5OQEV1dXDBo0yOj6vWfbh4WFYe3atSgtLYWVlZXRNa1Wi8TExC7PrCd6HLAYJSKiXu2aEBER0YdJHg//+5m+oqICL730EtLS0hAeHg4zM7MHPt/bxYFJSUnw8/PD008/jWXLlsHLywuCIKCmpgbp6enQarXIzc19yF9DJC1+picioh4VUMDdbYd6Ovr3pIqNjUV+fj5cXFwQHR0NlUolyubz9fX1iI2NxRdffGFYxS+TyRASEoK0tLQeb5hP1N+wGCUiIuoFMzMzjBkzBpMnT77vYqXCwsIHvuvUqVOoqakBAIwfPx6TJ09+4DPXr183HDygVCp7tZKfqD/iZ3oiIqJeWLhwYY9WzN/Pjz/+iHnz5kGtVhtOTmppaUFQUBDy8/Ph4ODQ7bNDhw7Fs88++4v6J+pPODJKREQAgBdffBF5eXmwt7cHAGzcuBFLliwxFEvXrl3Db3/7W1RXV0uY8skwd+5cXLhwAbt37zY6TSkqKgpKpRJ5eXkSJyQSD4tRIiICcPfkrcuXLxv2ER08eDCqqqrg4eEBAPjhhx/g5OQ04OeMPgr29vb48ssv8atf/cqo/euvv0ZoaChaWlqkCUYkgZ7NWCcioifevWMTHKvoO3q9HpaWlibtlpaW0Ov1EiQikg6LUSIiIpHNmDEDcXFxRpvUX7x4EfHx8Xj++eclTEYkPhajREQE4O42QfcuzPmlC3Woa2lpabh58ybc3Nzg6ekJT09PuLu74+bNm/joo4+kjkckKq6mJyIiAHc/yy9atAjW1tYAgLa2NixZssRwOpBWq5Uy3hPFxcUFp0+fxpdffonvv/8eAODt7Y3g4GCJkxGJjwuYiIgIAPDGG2/06L6dO3f2cZInX1tbG2xsbKSOQdQvsBglIiISmY2NDZ599lkEBAQgKCgI/v7+kMvlUscikgSLUSIiIpFVVFSgvLwcarUalZWV0Ol08PPzQ0BAAAIDAxESEiJ1RCLRsBglIiKSkE6nwz/+8Q9kZGRgz5490Ov13MuVBhQuYCIiIpLAuXPnoFarDf+0Wi1eeuklBAYGSh2NSFQcGSUiIhKZs7Mz7ty5g8DAQAQGBiIgIADPPPMMt9KiAYn7jBIREYnMwcEBt2/fxpUrV3DlyhX88MMPuHPnjtSxiCTBkVEiIiIJtLS0oLy8HGVlZSgrK0N1dTUmTZqEoKAgJCcnSx2PSDQsRomIiCR07do1qNVq7N+/H3l5eVzARAMOi1EiIiKRFRYWGhYuVVdXY9iwYZg2bZph/ujEiROljkgkGhajREREInN0dMT06dMNxaevr6/UkYgkw2KUiIiIiCTD1fREREQiMzc3x48//mjSfu3aNZibm0uQiEg6LEaJiIhE1t1HSa1WCysrK5HTEEmLJzARERGJJDU1FQAgk8mQmZkJOzs7w7XOzk6Ul5fDy8tLqnhEkuCcUSIiIpG4u7sDABobGzF69GijT/JWVlZwc3NDUlISnnvuOakiEomOxSgREZHIgoKCUFhYiKFDh0odhUhyLEaJiIgk0t7ejvr6enh6esLCgjPnaGDiAiYiIiKR3blzBzExMbC1tcX48ePR1NQEAHjrrbewceNGidMRiYvFKBERkcgSEhJw5swZqNVq2NjYGNqDg4NRUFAgYTIi8fGbABERkciKi4tRUFCAX//615DJZIb28ePHQ6PRSJiMSHwcGSUiIhLZ1atX4ejoaNJ+69Yto+KUaCBgMUpERCQyPz8/HD582PD3zwVoZmYm/P39pYpFJAl+piciIhLZhg0b8MILL6C6uho6nQ5bt25FdXU1KisrUVZWJnU8IlFxZJSIiEhk06ZNQ1VVFXQ6HXx9ffHFF1/A0dERJ06cwNSpU6WORyQq7jNKRERERJLhyCgRERERSYZzRomIiERiZmb2wNXyMpkMOp1OpERE0mMxSkREJJKioqJur504cQKpqanQ6/UiJiKSHueMEhERSai2thYJCQk4ePAgVCoVkpKS4OrqKnUsItFwzigREZEELl26hMWLF8PX1xc6nQ5VVVXYtWsXC1EacFiMEhERiejGjRtYs2YNlEolzp49i6NHj+LgwYOYMGGC1NGIJME5o0RERCL54IMP8P7772PkyJHIy8tDZGSk1JGIJMc5o0RERCIxMzODXC5HcHAwzM3Nu72vsLBQxFRE0uLIKBERkUgWLlz4wK2diAYajowSERERkWS4gImIiIiIJMNilIiIiIgkw2KUiIiIiCTDYpSIiIiIJMNilIiIiIgkw2KUiGiAW7RoEWbNmmX4OzAwEL/73e9Ez6FWqyGTydDS0tLtPTKZDMXFxT1+57vvvotJkyb9olwNDQ2QyWSoqqr6Re8hoq6xGCUi6ocWLVoEmUwGmUwGKysrKJVKJCUlQafT9XnfhYWFWL9+fY/u7UkBSUR0P9z0noionwoPD8fOnTuh1WpRUlKCZcuWwdLSEu+8847Jve3t7bCysnok/Q4bNuyRvIeIqCc4MkpE1E9ZW1tj5MiRcHV1xdKlSxEcHIwDBw4A+P9P68nJyXBycsK4ceMAAP/6178wZ84cDBkyBMOGDUNkZCQaGhoM7+zs7MTKlSsxZMgQDB8+HL///e9x79kn936m12q1WLNmDVxcXGBtbQ2lUomsrCw0NDQgKCgIADB06FDIZDIsWrQIAKDX65GSkgJ3d3fI5XJMnDgRn332mVE/JSUlGDt2LORyOYKCgoxy9tSaNWswduxY2NrawsPDA4mJiejo6DC5LyMjAy4uLrC1tcWcOXNw48YNo+uZmZnw9vaGjY0NvLy8kJ6e3ussRPRwWIwSET0m5HI52tvbDX8fPXoUtbW1KC0txaFDh9DR0YGwsDAoFAocP34cX331Fezs7BAeHm54btOmTcjJyUF2djYqKirw008/oaio6L79Lly4EHl5eUhNTUVNTQ0yMjJgZ2cHFxcX7Nu3DwBQW1uLy5cvY+vWrQCAlJQU7N69G9u3b8fZs2cRHx+P+fPno6ysDMDdonn27Nl4+eWXUVVVhTfffBMJCQm9/m+iUCiQk5OD6upqbN26FTt27MCWLVuM7qmrq8Mnn3yCgwcP4siRI/jmm28QGxtruL5nzx784Q9/QHJyMmpqarBhwwYkJiZi165dvc5DRA9BICKificqKkqIjIwUBEEQ9Hq9UFpaKlhbWwurVq0yXB8xYoSg1WoNz+Tm5grjxo0T9Hq9oU2r1QpyuVz429/+JgiCIIwaNUr44IMPDNc7OjqE0aNHG/oSBEEICAgQ4uLiBEEQhNraWgGAUFpa2mXOY8eOCQCE69evG9ra2toEW1tbobKy0ujemJgY4bXXXhMEQRDeeecdwcfHx+j6mjVrTN51LwBCUVFRt9c//PBDYerUqYa///jHPwrm5uZCc3Ozoe3zzz8XzMzMhMuXLwuCIAienp7C3r17jd6zfv16wd/fXxAEQaivrxcACN988023/RLRw+OcUSKifurQoUOws7NDR0cH9Ho9Xn/9dbz77ruG676+vkbzRM+cOYO6ujooFAqj97S1tUGj0eDGjRu4fPkynnvuOcM1CwsL+Pn5mXyq/1lVVRXMzc0REBDQ49x1dXW4ffs2QkJCjNrb29sxefJkAEBNTY1RDgDw9/fvcR8/KygoQGpqKjQaDVpbW6HT6TB48GCje8aMGQNnZ2ejfvR6PWpra6FQKKDRaBATE4PFixcb7tHpdLC3t+91HiLqPRajRET9VFBQELZt2wYrKys4OTnBwsL4f9mDBg0y+ru1tRVTp07Fnj17TN7l4ODwUBnkcnmvn2ltbQUAHD582KgIBO7Og31UTpw4AZVKhXXr1iEsLAz29vbIz8/Hpk2bep11x44dJsWxubn5I8tKRN1jMUpE1E8NGjQISqWyx/dPmTIFBQUFcHR0NBkd/NmoUaPw97//HdOnTwdwdwTw1KlTmDJlSpf3+/r6Qq/Xo6ysDMHBwSbXfx6Z7ezsNLT5+PjA2toaTU1N3Y6oent7GxZj/ezkyZMP/pH/o7KyEq6urli7dq2hrbGx0eS+pqYmXLp0CU5OToZ+zMzMMG7cOIwYMQJOTk64cOECVCpVr/onokeDC5iIiJ4QKpUKTz31FCIjI3H8+HHU19dDrVZjxYoVaG5uBgDExcVh48aNKC4uxvfff4/Y2Nj77hHq5uaGqKgoREdHo7i42PDOTz75BADg6uoKmUyGQ4cO4erVq2htbYVCocCqVasQHx+PXbt2QaPR4PTp0/joo48Mi4KWLFmC8+fPY/Xq1aitrcXevXuRk5PTq9/79NNPo6mpCfn5+dBoNEhNTe1yMZaNjQ2ioqJw5swZHD9+HCtWrMCcOXMwcuRIAMC6deuQkpKC1NRUnDt3Dv/85z+xc+dObN68uVd5iOjhsBglInpC2Nraory8HGPGjMHs2bPh7e2NmJgYtLW1GUZK3377bSxYsABRUVHw9/eHQqHAK6+8ct/3btu2Da+++ipiY2Ph5eWFxYsX49atWwAAZ2dnrFu3DgkJCRgxYgSWL18OAFi/fj0SExORkpICb29vhIeH4/Dhw3B3dwdwdx7nvn37UFxcjIkTJ2L79u3YsGFDr35vREQE4uPjsXz5ckyaNAmVlZVITEw0uU+pVGL27Nl48cUXERoaimeeecZo66Y333wTmZmZ2LlzJ3x9fREQEICcnBxDViLqWzKhu1nrRERERER9jCOjRERERCQZFqNEREREJBkWo0REREQkGRajRERERCQZFqNEREREJBkWo0REREQkGRajRERERCQZFqNEREREJBkWo0REREQkGRajRERERCQZFqNEREREJJn/Ai/reg7V0nwpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_display = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                                    display_labels=encoding.classes_)\n",
    "cm_display.plot(cmap=plt.cm.Blues) \n",
    "plt.title('Confusion Matrix') \n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=encoding.classes_, \n",
    "            yticklabels=encoding.classes_)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix Heatmap')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Classification report for classifier LogisticRegression():\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         3\n",
      "           1       1.00      1.00      1.00         6\n",
      "           2       1.00      1.00      1.00         5\n",
      "           3       1.00      1.00      1.00         7\n",
      "           4       1.00      1.00      1.00         4\n",
      "           5       1.00      1.00      1.00         9\n",
      "           6       1.00      1.00      1.00         5\n",
      "           7       1.00      1.00      1.00         8\n",
      "           8       1.00      0.93      0.96        14\n",
      "           9       1.00      1.00      1.00         5\n",
      "          10       1.00      1.00      1.00         7\n",
      "          11       1.00      1.00      1.00         6\n",
      "          12       1.00      1.00      1.00        12\n",
      "          13       1.00      1.00      1.00         4\n",
      "          14       1.00      1.00      1.00         7\n",
      "          15       1.00      1.00      1.00        15\n",
      "          16       1.00      1.00      1.00         8\n",
      "          17       1.00      1.00      1.00         3\n",
      "          18       1.00      1.00      1.00        12\n",
      "          19       0.88      1.00      0.93         7\n",
      "          20       1.00      1.00      1.00        10\n",
      "          21       1.00      1.00      1.00         7\n",
      "          22       1.00      1.00      1.00         8\n",
      "          23       1.00      1.00      1.00        16\n",
      "          24       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           0.99       193\n",
      "   macro avg       0.99      1.00      1.00       193\n",
      "weighted avg       1.00      0.99      0.99       193\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n Classification report for classifier %s:\\n%s\\n\" % (logREG, classification_report(y_test, y_logREG_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report Metrics:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Macro Average**:\n",
    "* **Precision (False Positives):**  \n",
    "Precision is giving a value of 0.99. A value of 0.99 means that on average, the model's predictions are 99% precise, with very few false positives.\n",
    "\n",
    "* **Recall (False Negatives):** \n",
    "Recall is giving a value of 1.00 . A value of 1.00 means that on average, the model is able to correctly identify all relevant data of each categories, with almost no false negatives.\n",
    "\n",
    "* **F1-Score (Harmonic Mean):** \n",
    "The harmonic mean of precision and recall, averaged across all categories. A value of 1.00 indicates a perfect balance between precision and recall.\n",
    "\n",
    "\n",
    "**Insights and Conclusions:**\n",
    "* **High Performance:**\n",
    "The high precision, recall, and F1-scores in both macro and weighted averages suggest that the model performs exceptionally well on the given dataset.\n",
    "\n",
    "\n",
    "**Balanced Dataset:**\n",
    "* The close alignment of macro and weighted averages (0.99 and 1.00 respectively) suggests that the categories in the dataset are likely well-balanced. \n",
    "\n",
    "**Minimal Errors:**\n",
    "* Precision and recall values close to 1.00 imply that the model makes very few errors in predictions, with almost no false positives or false negatives.\n",
    "\n",
    "\n",
    "**Model Robustness:**\n",
    "* Such high scores generally indicate that the model is very robust and generalizes new , unseen data very well. However, it’s also essential to ensure these scores aren’t inflated due to overfitting, which can be checked with cross-validation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Model Evaluation and Observations**\n",
    "\n",
    "* During my experiments with the model, I encountered scenarios that indicated potential overfitting. Specifically, when presented with garbage inputs or minimal/empty inputs, the model consistently predicted a default category. This behavior was observed across various machine learning models. However, logistic regression demonstrated a notable capability to accurately address and predict new inputs, effectively capturing the essence of the input data.\n",
    "\n",
    "**Key Insights:**\n",
    "\n",
    "* **Overfitting Concerns:** \n",
    "The model tends to overfit, as I have noticed by its behavior with garbage or empty inputs, defaulting to a specific category.\n",
    "\n",
    "* **Model Consistency:** \n",
    "This default prediction pattern for garbage or empty inputs is common among different machine learning models that I have tested.\n",
    "\n",
    "* **Logistic Regression Performance:** \n",
    "Despite the challenges, logistic regression stood out by precisely predicting new inputs and maintaining an ability to grasp the core content of the input. Whereas the other models in comparision were inaccurate with new inputs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing pickle library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(tfIDF,open('tfIDF.pkl','wb'))\n",
    "pickle.dump(logREG, open('clfLR.pkl', 'wb'))\n",
    "pickle.dump(encoding, open(\"encoderLabel.pkl\",'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created pickle files for TfidfVectorizer , Logistic Regression and Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred(input_resume):\n",
    "  \n",
    "  corpus = []\n",
    "  stemmer = PorterStemmer()\n",
    "  review = re.sub('[^a-zA-Z]', ' ', input_resume)\n",
    "  lowerReview = review.lower().split()\n",
    "  review = [stemmer.stem(word) for word in lowerReview if not word in STOPWORDS]\n",
    "  review = ' '.join(review)\n",
    "  corpus.append(review)\n",
    "  \n",
    "  vectorized_text = tfIDF.transform(corpus)\n",
    "\n",
    "#  threshold = 0.078785\n",
    "  threshold =  0.076788\n",
    "\n",
    "  predicted_category = logREG.predict_proba(vectorized_text)\n",
    "  max_proba = max(predicted_category[0]) \n",
    "\n",
    "  if input_resume.strip() == \"\":\n",
    "    print(\"Error: Input is empty.\")\n",
    "  elif max_proba < threshold and len(input_resume) < 200 :\n",
    "    print(\"Error: Low confidence in prediction. The input might be invalid with the following probability\")\n",
    "    return max_proba\n",
    "  elif max_proba < threshold:\n",
    "     return max_proba\n",
    "  else:\n",
    "      vectorized_text = vectorized_text.toarray()\n",
    "      predicted_category = logREG.predict(vectorized_text)\n",
    "      predicted_category_name = encoding.inverse_transform(predicted_category)\n",
    "      print(\"Prediction:\", predicted_category_name)\n",
    "      return predicted_category_name[0] , max_proba\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation** :\n",
    "* Function pred() is processing and predicting the category of an input text. It stems and filters the text, transforms it using TF-IDF, and then uses logistic regression to predict the category. The function pred() includes checks for empty inputs and low-confidence predictions, and returns the predicted category along with the prediction probability if it meets the confidence threshold.\n",
    "\n",
    "**Key steps:**\n",
    "\n",
    "* **Preprocessing:** \n",
    "Cleans and stems the input text.\n",
    "\n",
    "* **Vectorization:** \n",
    "Converts the text into TF-IDF vectors.\n",
    "\n",
    "* **Prediction:** \n",
    "Uses logistic regression to predict the category.\n",
    "\n",
    "* **Validation:** \n",
    "Checks for empty inputs and low-confidence predictions, ensuring reliable results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Text Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "myresume1 = \"\"\" \n",
    "\n",
    "Job description\n",
    "As part of our global expansion efforts, Onboard is actively scaling operations to over 20 countries and seeking to recruit merchants and liquidity providers worldwide. To support this mission, we are looking for Merchant Relations Advocates to serve as our first line of operational support in launch countries.\n",
    "These advocates will play a crucial role in identifying ideal merchant candidates, maintaining strong relationships with them, and bridging communication gaps professionally and courteously.\n",
    "The ideal Merchant Relations Advocate will be a member of the Onboard City Club, with deep knowledge of and influence within their local city or country.\n",
    "Duties and Responsibilities\n",
    "Identify and onboard potential merchants, such as high-value transaction (HVT) users or existing merchants from other platforms.\n",
    "Serve as a liaison between local merchants and Onboard, ensuring effective communication.\n",
    "Clearly communicate Key Performance Indicators (KPIs) related to Guaranteed Earnings to merchants and co-monitor their performance.\n",
    "Provide support for merchant customer experience (CX) needs, helping to resolve issues and addressing concerns in a timely manner.\n",
    "Qualifications and Requirements\n",
    "Solid understanding of the crypto and digital asset industry within your city or country.\n",
    "Prior Merchant experience on any p2p platform is preferred\n",
    "Exceptional verbal and written communication skills in the local language.\n",
    "Excellent customer service and interpersonal skills.\n",
    "Strong organisational and coordination abilities.\n",
    "Ability to collaborate effectively with a diverse and global team.\n",
    "\n",
    "Benefits to you\n",
    "Opportunity to do meaningful work\n",
    "Part-time work with a fast-paced team and opportunity to join Onboard Global on Full full-time basis\n",
    "Weekly internet allowance\n",
    "Role: Operations - Other\n",
    "Industry Type: IT Services & Consulting\n",
    "Department: Customer Success, Service & Operations\n",
    "Employment Type: Full Time, Permanent\n",
    "Role Category: Operations\n",
    "Education\n",
    "UG: Any Graduate\n",
    "PG: Any Postgraduate\n",
    "Key Skills\n",
    "operational supportInterpersonal skillsRelationshipAdvocateCustomer serviceCustomer experienceMonitoringRecruitment\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['Advocate']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('Advocate', 0.07777138959652101)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred(myresume1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for input text in myresume1 the model have returned a predicted category which is \"Advocate\" and predicted probability of 0.07777138959652101."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "myresume2 = \"\"\" \n",
    "\n",
    "B.Sc. or M.sc Chemist / Chemical Fresher\n",
    "Location :Surat / Navsari / Bharuch / Ahmedabad/ Vadodara\n",
    "Salary : 15000 to 25000\n",
    "Living + Travel Free\n",
    "Chemical handling and disposal\n",
    "Analyze retrieved data and prepare reports for laboratory management\n",
    "\n",
    "Required Candidate profile\n",
    "\n",
    "Lab work to develop and improve both new and existing chemical product\n",
    "Assist with testing and calibrating lab equipment in preparation for specific tasks\n",
    "Maintaining lab and operating lab equipment\n",
    "Role: Chemist\n",
    "Industry Type: Pharmaceutical & Life Sciences\n",
    "\n",
    "\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting the given input text by passing it into pred() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06260350795189341"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred(myresume2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for input text in myresume2 the model have returned a predicted probability of 0.06260350795189341."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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