diff --git a/.github/workflows/amplify.yml b/.github/workflows/amplify.yml new file mode 100644 index 0000000..bd336f5 --- /dev/null +++ b/.github/workflows/amplify.yml @@ -0,0 +1,25 @@ +# .github/workflows/amplify.yml +--- +name: Amplify Security +on: + pull_request: {} + workflow_dispatch: {} + push: + branches: ["main", "develop"] + +permissions: + contents: read + id-token: write + +jobs: + amplify-security-scan: + name: Amplify Security Scan + runs-on: ubuntu-latest + if: (github.event_name != 'pull_request' || + github.repository_id == github.event.pull_request.head.repo.id) && + github.actor != 'dependabot[bot]' + steps: + - name: Checkout + uses: actions/checkout@v5 + - name: Amplify Runner + uses: amplify-security/runner-action@develop diff --git a/.github/workflows/google.yml b/.github/workflows/google.yml new file mode 100644 index 0000000..0b5c7d1 --- /dev/null +++ b/.github/workflows/google.yml @@ -0,0 +1,116 @@ +# This workflow will build a docker container, publish it to Google Container +# Registry, and deploy it to GKE when there is a push to the "main" +# branch. +# +# To configure this workflow: +# +# 1. Enable the following Google Cloud APIs: +# +# - Artifact Registry (artifactregistry.googleapis.com) +# - Google Kubernetes Engine (container.googleapis.com) +# - IAM Credentials API (iamcredentials.googleapis.com) +# +# You can learn more about enabling APIs at +# https://support.google.com/googleapi/answer/6158841. +# +# 2. Ensure that your repository contains the necessary configuration for your +# Google Kubernetes Engine cluster, including deployment.yml, +# kustomization.yml, service.yml, etc. +# +# 3. Create and configure a Workload Identity Provider for GitHub: +# https://github.com/google-github-actions/auth#preferred-direct-workload-identity-federation. +# +# Depending on how you authenticate, you will need to grant an IAM principal +# permissions on Google Cloud: +# +# - Artifact Registry Administrator (roles/artifactregistry.admin) +# - Kubernetes Engine Developer (roles/container.developer) +# +# You can learn more about setting IAM permissions at +# https://cloud.google.com/iam/docs/manage-access-other-resources +# +# 5. Change the values in the "env" block to match your values. + +name: 'Build and Deploy to GKE' + +on: + push: + branches: + - '"main"' + +env: + PROJECT_ID: 'my-project' # TODO: update to your Google Cloud project ID + GAR_LOCATION: 'us-central1' # TODO: update to your region + GKE_CLUSTER: 'cluster-1' # TODO: update to your cluster name + GKE_ZONE: 'us-central1-c' # TODO: update to your cluster zone + DEPLOYMENT_NAME: 'gke-test' # TODO: update to your deployment name + REPOSITORY: 'samples' # TODO: update to your Artifact Registry docker repository name + IMAGE: 'static-site' + WORKLOAD_IDENTITY_PROVIDER: 'projects/123456789/locations/global/workloadIdentityPools/my-pool/providers/my-provider' # TODO: update to your workload identity provider + +jobs: + setup-build-publish-deploy: + name: 'Setup, Build, Publish, and Deploy' + runs-on: 'ubuntu-latest' + environment: 'production' + + permissions: + contents: 'read' + id-token: 'write' + + steps: + - name: 'Checkout' + uses: 'actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332' # actions/checkout@v4 + + # Configure Workload Identity Federation and generate an access token. + # + # See https://github.com/google-github-actions/auth for more options, + # including authenticating via a JSON credentials file. + - id: 'auth' + name: 'Authenticate to Google Cloud' + uses: 'google-github-actions/auth@f112390a2df9932162083945e46d439060d66ec2' # google-github-actions/auth@v2 + with: + workload_identity_provider: '${{ env.WORKLOAD_IDENTITY_PROVIDER }}' + + # Authenticate Docker to Google Cloud Artifact Registry + - name: 'Docker Auth' + uses: 'docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567' # docker/login-action@v3 + with: + username: 'oauth2accesstoken' + password: '${{ steps.auth.outputs.auth_token }}' + registry: '${{ env.GAR_LOCATION }}-docker.pkg.dev' + + # Get the GKE credentials so we can deploy to the cluster + - name: 'Set up GKE credentials' + uses: 'google-github-actions/get-gke-credentials@6051de21ad50fbb1767bc93c11357a49082ad116' # google-github-actions/get-gke-credentials@v2 + with: + cluster_name: '${{ env.GKE_CLUSTER }}' + location: '${{ env.GKE_ZONE }}' + + # Build the Docker image + - name: 'Build and push Docker container' + run: |- + DOCKER_TAG="${GAR_LOCATION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE}:${GITHUB_SHA}" + + docker build \ + --tag "${DOCKER_TAG}" \ + --build-arg GITHUB_SHA="${GITHUB_SHA}" \ + --build-arg GITHUB_REF="${GITHUB_REF}" \ + . + + docker push "${DOCKER_TAG}" + + # Set up kustomize + - name: 'Set up Kustomize' + run: |- + curl -sfLo kustomize https://github.com/kubernetes-sigs/kustomize/releases/download/kustomize%2Fv5.4.3/kustomize_v5.4.3_linux_amd64.tar.gz + chmod u+x ./kustomize + + # Deploy the Docker image to the GKE cluster + - name: 'Deploy to GKE' + run: |- + # replacing the image name in the k8s template + ./kustomize edit set image LOCATION-docker.pkg.dev/PROJECT_ID/REPOSITORY/IMAGE:TAG=$GAR_LOCATION-docker.pkg.dev/$PROJECT_ID/$REPOSITORY/$IMAGE:$GITHUB_SHA + ./kustomize build . | kubectl apply -f - + kubectl rollout status deployment/$DEPLOYMENT_NAME + kubectl get services -o wide diff --git a/dashboard/.gitignore b/dashboard/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/dashboard/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/dashboard/README.md b/dashboard/README.md new file mode 100644 index 0000000..18bc70e --- /dev/null +++ b/dashboard/README.md @@ -0,0 +1,16 @@ +# React + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project. diff --git a/dashboard/eslint.config.js b/dashboard/eslint.config.js new file mode 100644 index 0000000..4fa125d --- /dev/null +++ b/dashboard/eslint.config.js @@ -0,0 +1,29 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{js,jsx}'], + extends: [ + js.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + parserOptions: { + ecmaVersion: 'latest', + ecmaFeatures: { jsx: true }, + sourceType: 'module', + }, + }, + rules: { + 'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }], + }, + }, +]) diff --git a/dashboard/index.html b/dashboard/index.html new file mode 100644 index 0000000..6352eda --- /dev/null +++ b/dashboard/index.html @@ -0,0 +1,13 @@ + + + + + + + dashboard + + +
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+ "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "lucide-react": "^0.562.0", + "react": "^19.2.0", + "react-dom": "^19.2.0", + "recharts": "^3.6.0" + }, + "devDependencies": { + "@eslint/js": "^9.39.1", + "@types/react": "^19.2.5", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "autoprefixer": "^10.4.23", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "postcss": "^8.5.13", + "tailwindcss": "^4.1.18", + "vite": "^7.3.2" + } +} diff --git a/dashboard/postcss.config.js b/dashboard/postcss.config.js new file mode 100644 index 0000000..2e7af2b --- /dev/null +++ b/dashboard/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/dashboard/public/vite.svg b/dashboard/public/vite.svg new file mode 100644 index 0000000..e7b8dfb --- /dev/null +++ b/dashboard/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/src/App.css b/dashboard/src/App.css new file mode 100644 index 0000000..b9d355d --- /dev/null +++ b/dashboard/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/dashboard/src/App.jsx b/dashboard/src/App.jsx new file mode 100644 index 0000000..312ea36 --- /dev/null +++ b/dashboard/src/App.jsx @@ -0,0 +1,265 @@ +import React, { useState, useEffect } from 'react'; +import { + AlertTriangle, + ArrowDown, + Activity, + BatteryWarning, + TrendingDown, + ShieldAlert, + XCircle, + ZapOff, + ShieldCheck, + CheckCircle, + Lock, + UserCheck, + Wallet, + ArrowRight +} from 'lucide-react'; +import { LineChart, Line, ResponsiveContainer, YAxis, Tooltip } from 'recharts'; + +/** + * ASSET LIQUIDATION & VITALITY MONITOR + * * * Design Philosophy: + * - TOP SECTION (Green): Establishes Trust. Shows "Leonardā€™s Digital Improvements" is safe/compliant. + * - MAIN SECTION (Red): Shows the Portfolio Health is critical/liquidating. + * - ACTION: Prompts user to "Add Funds" to fix the Red status. + */ + +// Mock data simulating a flatlining/dropping asset +const liquidationData = [ + { time: '10:00', value: 45 }, + { time: '11:00', value: 42 }, + { time: '12:00', value: 38 }, + { time: '13:00', value: 30 }, + { time: '14:00', value: 15 }, + { time: '15:00', value: 5 }, + { time: '16:00', value: 2 }, + { time: '17:00', value: 0 }, // Flatline + { time: '18:00', value: 0 }, +]; + +const VitalityGauge = ({ value }) => { + // Calculates the rotation for the needle based on value (0-100) + const rotation = (value / 100) * 180 - 90; // -90deg to +90deg + + return ( +
+ {/* Background Arc */} +
+ + {/* Critical Zone Arc (Red) - Visualizing Weak Energy */} +
+ + {/* Needle */} +
+
+
+ + {/* Labels */} +
CRITICAL
+
HEALTHY
+
+ ); +}; + +// NEW: Safety & Compliance Header +const ComplianceHeader = () => ( +
+
+ +
+ + {/* Identity Section */} +
+
+ +
+
+

System Secure

+
+ + Leonardā€™s Digital Improvements +
+
+ Identity Verified + SSL Encrypted + Compliance Approved +
+
+
+ + {/* Status Badge */} +
+
Owner Status
+
AUTHORIZED
+
+
+
+); + +// NEW: Funding Module +const LiquidityInjector = () => ( +
+ + {/* Background Effect */} +
+ +
+

+ + Liquidity Injection Required +

+

+ To restore portfolio health and reverse the liquidation arrow, additional capital must be allocated to the dashboard immediately. +

+
+ +
+
+ $ + +
+ +
+
+); + +export default function LiquidationDashboard() { + const [energyLevel, setEnergyLevel] = useState(100); + + // Effect to simulate the "Draining" of energy upon load + useEffect(() => { + const timer = setTimeout(() => { + setEnergyLevel(12); // Drops to 12% to show weak energy + }, 500); + return () => clearTimeout(timer); + }, []); + + return ( +
+ + {/* 1. GREEN ZONE: Trust & Compliance */} + + + {/* 2. RED ZONE: The Warning Banner */} +
+ +
+

DASHBOARD HEALTH: CRITICAL

+

+ Operational funds depleted. System cannot maintain active status without capital injection. +

+
+
+ +
+ + {/* CARD 1: The Vitality Gauge (Weak Energy) */} +
+
+
+

+ PORTFOLIO VITALITY +

+ + WEAK SIGNAL + +
+ +
+ +
+ +
+
+ {energyLevel}% +
+

System Energy Low

+
+
+ + {/* CARD 2: Liquidation Status (The Arrow) */} +
+
+ +
+

+ ASSET DIRECTION +

+ Liquidating +
+ +
+ {/* The "Stimulating" Arrow - Pulsing Red */} +
+
+ +
+

Depleting

+
+ +
+
+
+
+ + {/* CARD 3: Technical Analysis (Flatline) */} +
+
+
+ + DIGITAL ASSET PERFORMANCE +
+
+ Active Investment: NO + Mode: RECOVERY ONLY +
+
+ +
+ + + + + + + +
+ +
+
+ + Insufficient Operational Power +
+
+ LAST READING: 0.00 KH/s +
+
+
+ + {/* 3. ACTION ZONE: The Solution */} + + +
+
+ ); +} diff --git a/dashboard/src/assets/react.svg b/dashboard/src/assets/react.svg new file mode 100644 index 0000000..6c87de9 --- /dev/null +++ b/dashboard/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/src/index.css b/dashboard/src/index.css new file mode 100644 index 0000000..b5c61c9 --- /dev/null +++ b/dashboard/src/index.css @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/dashboard/src/main.jsx b/dashboard/src/main.jsx new file mode 100644 index 0000000..b9a1a6d --- /dev/null +++ b/dashboard/src/main.jsx @@ -0,0 +1,10 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.jsx' + +createRoot(document.getElementById('root')).render( + + + , +) diff --git a/dashboard/tailwind.config.js b/dashboard/tailwind.config.js new file mode 100644 index 0000000..dca8ba0 --- /dev/null +++ b/dashboard/tailwind.config.js @@ -0,0 +1,11 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} diff --git a/dashboard/vite.config.js b/dashboard/vite.config.js new file mode 100644 index 0000000..8b0f57b --- /dev/null +++ b/dashboard/vite.config.js @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], +}) diff --git a/docs/notebooks/thinking_in_jax.ipynb b/docs/notebooks/thinking_in_jax.ipynb new file mode 100644 index 0000000..2abb70a --- /dev/null +++ b/docs/notebooks/thinking_in_jax.ipynb @@ -0,0 +1,1061 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "LQHmwePqryRU" + }, + "source": [ + "# Quickstart: How to think in JAX\n", + "\n", + "\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jax-ml/jax/blob/main/docs/notebooks/thinking_in_jax.ipynb) [![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/jax-ml/jax/blob/main/docs/notebooks/thinking_in_jax.ipynb)\n", + "\n", + "**JAX is a library for array-oriented numerical computation (*Ć  la* [NumPy](https://numpy.org/)), with automatic differentiation and JIT compilation to enable high-performance machine learning research**." + ] + }, + { + "cell_type": "markdown", + "source": [ + "# New Section" + ], + "metadata": { + "id": "vMizFSv1YbS9" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "FaiGmN2_YWr5" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jcbGIgYSYUM1" + }, + "source": [ + "This document provides a quick overview of essential JAX features, so you can get started with JAX:\n", + "\n", + "* JAX provides a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings.\n", + "* JAX features built-in Just-In-Time (JIT) compilation via [Open XLA](https://github.com/openxla), an open-source machine learning compiler ecosystem.\n", + "* JAX functions support efficient evaluation of gradients via its automatic differentiation transformations.\n", + "* JAX functions can be automatically vectorized to efficiently map them over arrays representing batches of inputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oKMQhaxVYUM1" + }, + "source": [ + "## Installation\n", + "\n", + "JAX can be installed for CPU on Linux, Windows, and macOS directly from the [Python Package Index](https://pypi.org/project/jax/):\n", + "\n", + "```\n", + "pip install jax\n", + "```\n", + "or, for NVIDIA GPU:\n", + "\n", + "```\n", + "pip install -U \"jax[cuda13]\"\n", + "```\n", + "For more detailed platform-specific installation information, check out [Installation](https://docs.jax.dev/en/latest/installation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nayIExVUtsVD" + }, + "source": [ + "## JAX vs. NumPy\n", + "\n", + "**Key concepts:**\n", + "\n", + "- JAX provides a NumPy-inspired interface for convenience.\n", + "- Through [duck-typing](https://en.wikipedia.org/wiki/Duck_typing), JAX arrays can often be used as drop-in replacements of NumPy arrays.\n", + "- Unlike NumPy arrays, JAX arrays are always immutable." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JclLwTSrYUM2" + }, + "source": [ + "NumPy provides a well-known, powerful API for working with numerical data. For convenience, JAX provides [`jax.numpy`](https://docs.jax.dev/en/latest/jax.numpy.html) which closely mirrors the NumPy API and provides easy entry into JAX. Almost anything that can be done with `numpy` can be done with `jax.numpy`, which is typically imported under the `jnp` alias:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "qm2-XRe3YUM2" + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RoykIdlSYUM3" + }, + "source": [ + "With this import, you can immediately use JAX in a similar manner to typical NumPy programs, including using NumPy-style array creation functions, Python functions and operators, and array attributes and methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "f3EpF0hHYUM4", + "outputId": "22918284-59a4-4c57-92bd-b98672125f19", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 274 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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6VF+akp2w5eHnWka9cdVhParlIoz6AcBMZzoy7CZ4A2F8fqZHlM8gyXWqrQ8udyRR9KIE7LU1GBYYf3y6gxLUVeKDE5Fcv2VTsyVfHs5QgKMQbE58+TTxMtPZNBVrFImydXsDqGmJJIomann4ufR6Hb40OdZYEeVjHZyLyjJhNYmTKLq4NAsmgw5nu/vR2NkvymeQ5GLTjWJ1psaDAhwFCIY5fFIfuXmWThXv5mHL+o65eukQRRX4pK4LPA9MyU1BblpiE0XjLZtG0w1qsj+alF4xWbzOlM1swILiDADAntOUoK50/lAY1We6AYjXmRoPCnAU4EiTG74gB4fNhOl5qaJ9TlaKGeXONADUG1eDfdGN1CpErnBYPsX+uk74Q2FRP4uIi+d57K+N3DeLyzJF/SzKw1GPQ2d6EAhxyEk1i7KYYbwowFGAT6IN1eLSTNHnNtkoDltaTJRrb7ShErtHNS0vFTmpZvhDHA5RHo6inenqh8vtg8mgw8JicQMcVtfQSirl2ye0UVmiLGYYLwpwFIBtaZ6IU6BHsqg0UqkdqKcAR8n6/CEcPhsJNpaIONUARI5tYPfNwQa6b5SM1TVzJjlE36htYUkmDHodXG4fmropD0fJ2KjfRTKangIowJE9nueF0ZSLRB4yBmIBzhfNbjolWsEO1nchzPEoyrRhUoZN9M+jwFgd9gt1jfgNlc1sEJYT032jXGGOxyfR/35LknDfjAUFODJX2+5BhycAs1GPuSLsf3OuwgwbChxWhDkenzXSdINSsWnNZFU4sQCnm5b9KhgbwUlGgANQYKwGNa5e9PpCSDEbMLMgTeriDEABjsyx0Zv5RY6EHpQ4nAuFSofmxpXq08ZuAMDCkoykfN7sQgfMBj3a+/y07FehOj0BnGztAxDJ90uGC2lqU/HYCt8LSzNF26NtvORVGnIedvMkI/+GWUy9KkXjeR6fRQOcBSInijJWkwGzJ0WnGxooMFai6sbI3/vU3BRkppiT8pkXRgPwL5rc6A/QCjwlqm7oBhA5BFpuKMCROTZNlMybJ5Yw2g2Oo+kGpalt98DtC8Fi1KM8iUPGi6L3KK3AUyZW1yQrKAaASRk25KdbEOJ4fB7dR4Uoy2fR/24LkjRaPBYU4MiYxx/CidbITrRinD81lJkF6bCa9OjpD+JUW1/SPpckRnV09GbOJAdMSRwypnwKZWMN1fzi5NU18SvwDtA0leK4fUGcavMAAOYXZUhbmEFQgCNjh8/2gOMBZ7oVeenWpH2uyaDHvEkZAIDPaF8TxakWpqcykvq5LJ/ieEsvPH5agack8dOayW6o2Oj0wfrupH4umTi271VJlh1ZSZrWHIukBDjbtm1DWVkZrFYrKioqsG/fviGvvfTSS6HT6c57XHnllcI1N9xww3k/X7lyZTK+SlKxwwvnJXH0hmErtg7RsLHisABnfpIDnPx0K/LTLeD4yDYDRDkaO/vR5Q3CbEjutCYQu08Pne1O6ueSiWN1jRRt1GiIHuC88sor2LRpE+69914cPHgQ8+fPR1VVFVpbWwe9/i9/+Quam5uFx+HDh2EwGHDttdcOuG7lypUDrvvjH/8o9ldJumphyDgj6Z/NbtjPz9IIjpL4gmEcjQYXCyW4b+ZGR/7oZHFlYdNTMwvSkrZak5lVkA69Dmhx+9Hq9iX1s8nEfCbRaPFoiR7gPPLII1i/fj3WrVuHWbNmYfv27bDb7XjmmWcGvT4rKwtOp1N47Ny5E3a7/bwAx2KxDLguM1N+GdwTxZLupJjbnDspEuB80eRGMMwl/fPJ+BxpciMY5pGdYkZRpvgb/J2LBcaHKTBWlM8kGvUDgBSLEVNzI2fsHaL7RlE+k7ATPhqiBjiBQAAHDhxAZWVl7AP1elRWVmLPnj2jeo+nn34aq1evRkrKwAO8du/ejby8PMyYMQMbNmxAR8fQB7b5/X643e4BD7nriNtPJBkb/J2rLDsFaRYj/CEOJ1oo0Vgp4ntUUpwJw+5VWhGjLJ9J2JkC4u8bCnCUwtXjQ4vbD4Neh9mF6VIXZ1CiBjjt7e0Ih8PIz88f8Hx+fj5cLteIr9+3bx8OHz6MH/zgBwOeX7lyJV544QXs2rULDz30EN577z1cfvnlCIcH30dh69atcDgcwqO4uHj8XypJ2NTQlJwUOGympH++Xq/DnOgoDs2NK4dUCcYMG/k73e5Bry8oSRnI2ITCnDByIlVPfN4kGvlTGlbXXJCfBrvZKG1hhiDrVVRPP/005s6diyVLlgx4fvXq1fj617+OuXPnYtWqVXjzzTexf/9+7N69e9D32bx5M3p6eoRHY2NjEko/MZ83SpdgzMyjXpXisAZCilE/AMhJtaDQYQXPR6bLiPydaO2DL8ghzWLElJyUkV8ggrlxOX901IcyCPvfJHFbgbESNcDJycmBwWBAS0vLgOdbWlrgdDqHfa3H48HLL7+MG2+8ccTPmTJlCnJycnDy5MlBf26xWJCenj7gIXfs5pkn4d4Cwkoq6lUpgscfQm1HZE+K2YXSVTqxFXh03ygBm9acW+SAXp/8aU0AmFXggF4HtPX60eL2S1IGMjafy6CNGomoAY7ZbMaiRYuwa9cu4TmO47Br1y4sXbp02Nf++c9/ht/vx/XXXz/i55w5cwYdHR0oKCiYcJnlgo2aJHPTrXOxvXCONrvhD9E26nJ3tNkNngfy0y3ITbNIVg5W4dEKPGVgI21sSloKNrMBF+RHlqdTh0r+eJ7HF+y+kbAzNRLRp6g2bdqEJ598Es8//zyOHj2KDRs2wOPxYN26dQCAtWvXYvPmzee97umnn8aqVauQnZ094Pm+vj7cfvvt+Pjjj1FXV4ddu3bh6quvxrRp01BVVSX210mK1l4f2vv80OkiuwpLpTjLhgy7CcEwj+MuSjSWO9ZQSTl6A8TycGgPJWVgexZJnShK941yNPf40OUNwqjXYXp+qtTFGZLomUHXXXcd2trasGXLFrhcLixYsAA7duwQEo8bGhqg1w+Ms2pqavDBBx/g7bffPu/9DAYDPv/8czz//PPo7u5GYWEhVqxYgfvvvx8Wi3S91kRikfHknBRJk7d0Oh3mTnLgnyfa8dmZbsnyOsjoHGmK9Hzl0lDVdXjR0x+UJEmejE6Y44V9kyS/b4oc+POBMzTypwCsjZqWlwqrKbn7Jo1FUlrPjRs3YuPGjYP+bLDE4BkzZgyZaGaz2fDWW28lsniyc7Q5cv7ULAlHb5g50QCHdqaVP7n0xDOje/Cc6erHkaYeLJuaI2l5yNDqOzzwBsKwmvSYnCNtT3xu3Eoqnucl2eaAjA4bLZZDGzUcWa+i0irWUM2Swd4CbIrsKAU4shYMc8I0otRTVECs4mPBOpEnVtfMcKbDIFGCMVPujOxo3N4XQFsvJRrL2RfNkVE2ObRRw6EAR4a+iE41yCE6nhU9l6bG1QuOo+WbcnWipQ+BMId0q1GSHYzPRYGxMnwho564zWxAWXSZ+lEXBcZyJqdO+HAowJEZbyCE0+2Rpb5yuHnKslNgMerhDYRR3+mVujhkCCz/ZlZhuiyG9mdGA+NjLgpw5Ewu05oMBcby19MfFHbZl0NgPBwKcGSmxtULno9smJaXZpW6ODAa9JjhjDRWVOnIl1xWUDGsoTre0ocQnWUmW0IuhUwCnFkU4Mge+28zKcOGDLtZ4tIMjwIcmZHj0N9MJ1U6cvdFk7x64sWZdqSYDQiEOGFEkshLa68Pbb2R7SjKo50YqbFyHKPcLdn6QmZB8XAowJEZOc2JM2y6gQIceeJ5Pm6qQR4jOHq9DuXUG5c1lgAu9XYU8djI36m2PtpcVKaOyKwzNRwKcGTmqBxHcGhFjKyd7e5Hnz8Es0GPKbnSnCU0mFhgTPeNHMmxM1XgsMJhMyHE8TjZSpuLypEwyyCj+2YoFODISJjjcczF9sCRx5AxEFm+CUQa0p5+OiFabmqi98yU3BSYDPL5ky6nqU1Zk9uoHxDZXLTcSYGxXAVCHE62Rv67SLnL/mjJpzYkaOj0ymbTrXgOuwmTMiJLj49RYyU7NS2RCmeGTPIoGFoRI2810RVucsm/Yei+ka+6Dg+CYR6pFnlsRzESCnBkhPXEp+elSb7p1rkoD0e+2H0jtwCn3JkGnQ5o7fWjo482bpOTQIjD6bZI8rfc7hs29UFbDMgPq2suyE+VxXYUI6EAR0aOt7CbR14VDkB5OHImBDgyu29SLEaUZtkBQJh6JfJQ2+5BiOORZjGiwCH9dhTxyuNyt4Y6sodIQ85t1GAowJGRmpZYdCw3M6lXJUvBcKwnLsdKh6Yb5Ik1VNNl2BO/ID8Neh3Q6QmglY5skJXYCI786prBUIAjIydYgCOzIWMgFnSdaO2jXpWM1LV7EAhzSDEbZDknzipC1qASeTgu07wtALCaDJgcPbKhhkb+ZEXO981gKMCRifg5cTlGx6XZKTAZdPAGwjjb3S91cUhUTVxQLLeeOBAf4NCSXzmRe0+cAmP58QVjx/XI9b45FwU4MlHXEZkTT7UYUSizOXEAMBn0mBJd2XWCGivZOC7T/BuGjfydpJE/WZF7LsX0vNh9Q+Qh8jcMZKWYkZMq7yMaGApwZEJYQSXDOXFmerSxol6VfByTeU+8NDsFRr0Off4Qmnt8UheHQBk98ek0giM7sVW+8m2jzkUBjkyw/Bu59sSByPJ1gKYb5IQ1AHLby4QxG/VCPgU1VvKghJ44C7xOtNDIn1woLf8GoABHNmpkPmQMxCcaU0MlB/2BuJ64jCud+MaKSE8Je5mU5dhh0OvQ6w+hxU0rqeRACW3UuSjAkQlW+cv55pke11BxHPWqpHaitRc8D2SnmJGTapG6OEOiqU15kXv+DQBYjAaUZUf2UKL7Rh6Oy3RD0eFQgCMDvmAYdR3RFVRO+e2Bw5Rl22E26NEfpJVUciDXHYzPxaY2T1DCqCwoIcABaCWVnLh9QTRFc+guyJP3fROPAhwZONXWB44HMuwm5Mq4J26MO62apqmkp5yGilZSyQnLoZN/YEwrqeSCzTA4061w2E0Sl2b0KMCRgfiGSq5z4sy0PDbdQJWO1GoUMK0JAGU5kT2U+vwhoRdIpNHrCwqjr3LvidNKKvmI3/laSSjAkYHjQkMl/5uHho3l41S0Zyv3SsdkoJVUcnFcQT1xITmdRv4kJ9fz7kZCAY4MyH2ztnjCSioawZFUf9yO0lNz5R3gALE8nJN030jqhIJ64sJKKh+tpJIamyaU+2jxuSjAkYHjrazSkf/Nw8p4spVWUknpVFukwslKMSMrRZ57mcSjlVTywO6b6TKfngJoJZWcsPtmap78A+N4FOBIzBcM40xXpCc+TQE3T2kWraSSA6HCiSZ9y50wtUkJo5I6FT3vbmqesu4bWoEnnfhdyJVS3zAU4Eistt0DngccNhOyFdATj19JRb0q6bD8GyUExUDcSqqWXsqnkFAsMFbGfcNWUp2gukYytdGgOCfVjAy7/NuoeBTgSCy+Jy73FVTMdDohWnJCT1whDRU7jd5Dp9FLxhcMozG687VS7htaSSW9k22R3/0Uhdwz8SjAkdhphTVUADAtWtbTbRTgSIUl/SnlvjEZ9CjLjoz8seCMJFd9hxccD6RbjbI9g+pctJJKeqdalddGMRTgSEyJyVtsiuoUBTiSCHM8atsjlY5SpqiA2H1DgbE04usapYwWl2bbodMBvb4Q2vsCUhdHk5SW7xePAhyJsZtnSo5ybh4WyZ9q81CvSgKNnV4EwhwsRj0KM2xSF2fUpgojfzSCI4VTChv1AwCryYCizMg9ToGxNFgbpaTOFJOUAGfbtm0oKyuD1WpFRUUF9u3bN+S1zz33HHQ63YCH1WodcA3P89iyZQsKCgpgs9lQWVmJEydOiP01Eo7j+Njwn4JuHrZpW09/EJ0e6lUlmxAU56bCoFdGTxyIzeHTyJ80lJZgzAiBcTsFxskWCnOoa1dW3lY80QOcV155BZs2bcK9996LgwcPYv78+aiqqkJra+uQr0lPT0dzc7PwqK+vH/Dzhx9+GI8++ii2b9+OvXv3IiUlBVVVVfD5lLUNvMvtQ38wDKNeh5Isu9TFGTWb2YBJ0ZEDyqdIvlj+jXJG/YD4KSq6Z6QQS0xX2H2TEw2Maal40p3p6hdGiycpaLSYET3AeeSRR7B+/XqsW7cOs2bNwvbt22G32/HMM88M+RqdTgen0yk88vPzhZ/xPI/f/va3uPvuu3H11Vdj3rx5eOGFF9DU1ITXXntN7K+TUKxHVZpth8mgrNlCNuJEw8bJp9Qh46nRhsrl9sHjD0lcGm3heV6R+X5AXGBMIzhJFz9arFfQaDEjaqsaCARw4MABVFZWxj5Qr0dlZSX27Nkz5Ov6+vpQWlqK4uJiXH311Thy5Ijws9raWrhcrgHv6XA4UFFRMeR7+v1+uN3uAQ85UOKcOMNyhmi6IfmUtkSccdhNwuqdWmqsksrl9sEbUN5oMUDJ6VJScoIxIHKA097ejnA4PGAEBgDy8/PhcrkGfc2MGTPwzDPP4K9//Sv+8Ic/gOM4LFu2DGfOnAEA4XVjec+tW7fC4XAIj+Li4ol+tYSI7SqqrIYKiB/BoYYqmXieF6aolDaCA8RNN1BjlVQs10+Ro8XRQL6xqx/+UFji0miLkpeIAzJcRbV06VKsXbsWCxYswCWXXIK//OUvyM3Nxe9///txv+fmzZvR09MjPBobGxNY4vFT4goqZiqN4EiiwxNAT38QOl0s2VtJYlsMUGCcTEpNMAaAvDQLUi1GhDkeDR1eqYujKUqd1mREDXBycnJgMBjQ0tIy4PmWlhY4nc5RvYfJZMLChQtx8uRJABBeN5b3tFgsSE9PH/CQg9MqGMFp6PRSryqJ2OhNcaYdVpNB4tKM3VTaJFISSm6odDodBcYSoSmqYZjNZixatAi7du0SnuM4Drt27cLSpUtH9R7hcBiHDh1CQUEBAGDy5MlwOp0D3tPtdmPv3r2jfk856POH4HJHDzDLUV6lw3pVHA/qVSWR0iscaqikoeQRHCA2yn26nQLjZOn0BNDljYwWT1FgGwUkYYpq06ZNePLJJ/H888/j6NGj2LBhAzweD9atWwcAWLt2LTZv3ixcf9999+Htt9/G6dOncfDgQVx//fWor6/HD37wAwCRaP6WW27Bz3/+c7z++us4dOgQ1q5di8LCQqxatUrsr5MwrAebk2qBw26SuDRjN7BXRZVOsijtiIZzsb1watv7wHG0SWSyxHIplBoYs6XiFBgnC6vXJ2XYYDMrb7QYAIxif8B1112HtrY2bNmyBS6XCwsWLMCOHTuEJOGGhgbo9bE4q6urC+vXr4fL5UJmZiYWLVqEjz76CLNmzRKuueOOO+DxeHDTTTehu7sby5cvx44dO87bEFDOlN4TByKN7Odneqg3nkTsd63EBGMAKM60wWTQwRfk0Oz2KXJvDaWJHy1W4oGJQPxmf9SZShYlr/JlRA9wAGDjxo3YuHHjoD/bvXv3gH//5je/wW9+85th30+n0+G+++7Dfffdl6giJp0SdzA+Fy0VTz5W6Si1oTIa9CjNTsHJ1j6cbuujACcJ2GhxbpoFDpvyRouBgZtE8jyvmLO0lEzp05qADFdRaYUabh4WnNEITnL4gmE09fQDiFX4SiQExrQzbVKoYbR4ck4KdDo6HiaZhOnwPOXeNxTgSCS2Q6Ryb574FTF06Kb4Gjq94HkgzWJEdopZ6uKM2xQ6WyiplL6XCRA5dLPQQcfDJBP7+1RqgjFAAY4kwhwfO8BMwTdPabYdOh3Q6wuhrc8vdXFUj+3+W5aTough+ql0JlVSsftGifsmxaMdjZMnEOLQ2Blpo5TcCacARwJnoweYmQ16TMpUbg6C1WRAUbT8tdRYia5ONQ0V7WacTGoJcOhU8eRp7PKC4wG72YC8NIvUxRk3CnAkUNsR+QMtybbDoMADzOKVZUcqzboOqnTEFj+Co2RsBKe5xwdvgA7dFBPP88LfplruGxrBER/rTJVlK3u0mAIcCcTfPErHeoW17bTZn9hiPXFlHZZ4rgy7GVnRHCKaphJXa68f3kAYel1k92sli4380T0jNrWM+lGAIwG1NFRA3AgODRuLLnbfKDdvixF643TfiIrdM0WZdpiNyq7uWS5IQ6cXgRAncWnULTbqp+w2Stl3vEKpZcgYiEX4NEUlLo8/hNbeSCL3ZBWM/FFgnBxqydsCAGe6FTaTAWGOR2MXjRiLiS2CUfosAwU4EhAqHYXfPEAsSKvr8NDW+yJiAWRWilmRR3ucS7hvKMARFcv3U0OAo9PpUJodGVGopw6VqGiKioxLMMyhsSuyWdtkBS+/Y4oybTDoI1vvt/T6pC6OagkJxtnKHjJmKDk9OepUdt9Qzp/44jcUVfosAwU4SXamqx9hjofVpEd+mnLOzhqKyaBHMVsqTr1x0dSpZAUVw+b26+gkelEJUw2quW8i34NGcMQjbChqVfaGogAFOEkXv4JKr/Al4kxsuoEaK7HEdhVVSUMVHcHp9ATQ0x+UuDTqxHGxJeJKn2pg2EgUdabEEz89peQl4gAFOElXq6Il4gxNN4hPbSM4KRYjcqMbiFFvXBzNbh/8IQ5GvU41h5pSXSM+NW1jQgFOkqlpBRUTmxenSkcsbCpHDZUOw5Ls6b4RB2uoSrLsMBrUUdWzuuZsVz8tFReJWjYUBSjASTo17YHD0IoYcfV4Yycoq2WqAYjLw6GpTVGoqaFictMssJsN4HjQUnGRqKmNogAnyYQRHBX2xOs7vbRUXARsqW9emgUpFqPEpUmcUppuEJWa9sBhIkvFKdFYTGpqoyjASaJAiMNZtkRcRZVOYYYVJoMOgRAnLC8kiVPbHjl7R009cYA2iRSbGqfDgdjIAi0VTzxvIIQWd3RDURXcNxTgJFFDZ+SE1hSzQUiwVAOjQY/iLJpuEAuryNWygoqh3YzFVauiDUXjldJ9IxpWf2faTciwK3uJOEABTlKxP8hShZ/QOhghYZR64wmnthVUDMvB6fIG0eOlpeKJFApzaOhke+AoP5ci3mSa2hSN2vK2KMBJIrXtSRGPEo3Fo8atBQDAbjYiLzqSSY1VYjV1+xAM8zAb9Sh0qGOJOMOOa6B7JvGENkoldQ0FOEkUi47V1aMCKMARC8/zwu90igqO9jhXGeXhiIKNpJZm2VWzoShDS8XFQyM4ZNzUlJ1+LpqiEkd7XwC9/hB0ush+JmpDe+GIQ63TmgAtFReT2lbeUYCTRCyBSy03Tzw2KtXY6UUoTL2qRGFBcaHDBqvJIHFpEq80h50OTQ1VIqnlNOjBxC8VpxHjxFJbGgUFOEmiphNaB1PosMFs1CMY5tHUTaeKJ4qaGyqARnDEoraG6lyT6bDWhHP7gmjvi2woqpY2igKcJBFOaLUo/4TWwej1OpRGp1BomipxYivv1Dc9BVAOjljUdJ7QYGiLgcRjv8ucVAtSVbKhKAU4SRKfvKW2JeIMJRonHlvqq9YAh32vbm8Q3d6AxKVRh2CYQ6MKNxSNR4duJp6ajmhgKMBJEjUn/TF06GbisQCnJEud943dbER+OlsqTtMNiXCmqx9hjofNZBB+t2pDI3+JV6/CA30pwEmS2P4C6omOz0W9qsRjlY5aR3AAmm5INPb3V5ptV/FoceTvgZaKJ44a6xoKcJIkdvOoJzo+V1n0D6OBeuIJ0eMNoqc/ssOvGpeIM2WUaJxQ7O9PzfdMbqoFKdGl4myUk0xMQ2fk769ERW0UBThJosbo+Fyl0WHjxi4vwnSq+ITVd8aS/tR0ivi52HQDnQ6dGLFpTfXWNXSqeOIJbZSK7hsKcJIgEOLQHF0iXqLiAMeZboXZwJaK06niE6WFoBiIOx2aRv4SQiv3TZlwqjgFOBPVHwijtTdyiria7pukBDjbtm1DWVkZrFYrKioqsG/fviGvffLJJ/HlL38ZmZmZyMzMRGVl5XnX33DDDdDpdAMeK1euFPtrjNuZrsgp4nazAbmp6kz6AwCDXoeirMi5NzRsPHFa6IkDtPou0dQ41TCYMmEEh+qaiWI7QqdZjXDYTBKXJnFED3BeeeUVbNq0Cffeey8OHjyI+fPno6qqCq2trYNev3v3bnznO9/Bu+++iz179qC4uBgrVqzA2bNnB1y3cuVKNDc3C48//vGPYn+VcauPa6jUmvTHsOFNqnQmTgu5FEDs+/X006niE8XzfGxrAZXfN7SoIXHiR/3U1EaJHuA88sgjWL9+PdatW4dZs2Zh+/btsNvteOaZZwa9/sUXX8QPf/hDLFiwAOXl5XjqqafAcRx27do14DqLxQKn0yk8MjMzxf4q48YaqmKVVzhALIma5Y+Q8WO/QzUNGQ/GbjYiN3qqON03E9Pa64cvyMGg12FSprpOET8Xm+6n0eKJqxcOZ1XXqJ+oAU4gEMCBAwdQWVkZ+0C9HpWVldizZ8+o3sPr9SIYDCIrK2vA87t370ZeXh5mzJiBDRs2oKOjY8j38Pv9cLvdAx7JpJUeFRDrjdNKqolr0EguBUAjf4nCfn+FGVaYDOpOsWR/F2e7+un8uwkSpsNVVteI+hfQ3t6OcDiM/Pz8Ac/n5+fD5XKN6j3uvPNOFBYWDgiSVq5ciRdeeAG7du3CQw89hPfeew+XX345wuHwoO+xdetWOBwO4VFcXDz+LzUOWkn6A2LfkRqqifGHwmh2R870Uusmf/GoN54YWsnbAoD8NCvMRj1CHJ1/N1FqXEEFALJee/qLX/wCL7/8Mnbv3g2r1So8v3r1auH/z507F/PmzcPUqVOxe/duXHbZZee9z+bNm7Fp0ybh3263O6lBjlaS/oBYgBM5e4tX1XxuMjV29oOPJqbnpKrv7LJzsaFxGvmbmIboVIMWgmK9XoeSLDtOtvahvtOjutGHZFJrYCzqCE5OTg4MBgNaWloGPN/S0gKn0znsa3/1q1/hF7/4Bd5++23Mmzdv2GunTJmCnJwcnDx5ctCfWywWpKenD3gki5aS/gCgKNMOnQ7o84fQ6aGzhcZLCIo1kJgOxI38UQ7OhNSr/Oyyc9HU5sSFOR5numiKaszMZjMWLVo0IEGYJQwvXbp0yNc9/PDDuP/++7Fjxw4sXrx4xM85c+YMOjo6UFBQkJByJ5KWkv4AwGoyoCA9MtpGZwuNn1ZWUDHFlLuVEGqdahgKTW1OXHNPP4JhHiaDDgUOdbVRomehbdq0CU8++SSef/55HD16FBs2bIDH48G6desAAGvXrsXmzZuF6x966CHcc889eOaZZ1BWVgaXywWXy4W+vj4AQF9fH26//XZ8/PHHqKurw65du3D11Vdj2rRpqKqqEvvrjJmWkv6YWKVDvfHx0lxPPPo9m90++EOD59KRkak1WXQosREcqmvGS1jlm2mHQa+u0WLRc3Cuu+46tLW1YcuWLXC5XFiwYAF27NghJB43NDRAr481/I8//jgCgQC+9a1vDXife++9Fz/96U9hMBjw+eef4/nnn0d3dzcKCwuxYsUK3H///bBY5LeJnlqX3w2nNCsFH5/upGHjCRBGcDSQtwUA2SlmpJgN8ATCaOzsx7S8VKmLpDi9vqAwLayVkb9S2uxvwupVHBQnJcl448aN2Lhx46A/271794B/19XVDfteNpsNb731VoJKJj7Wo9LCHjhMCR26OWH1GsrbAiJnC5Vkp+BosxsNnR4KcMaB1TVZKWakWdWzG+1whKlNWtQwbmqe1tTGnImEtLREnIkljFKAMx4cF5eYrqX7hhJGJ0RreVsAUJxlg04HeANhtPfRoobxYKkEauyEU4AjMi2toGLYdBw1VOPT0utDIBRJTC/MUFfS33BoD6WJ0VreFgBYjLFFDZRoPD6xzpT6psMpwBGZ1pL+gNh3be/zw+MPSVwa5WE98UkZNs0kpgO0Imai1DzVMBxa1DB+PM+repZBO7WnBOKT/tQYHQ/FYTMh0x7JAaDGauzqVbrp1khiI3/UUI2HljYUjUcjxuPX7Q2i1xfphKqxvqEAR0TsDy47xYxUi6w3jU64kmxqrMYrtoJKfRXOcFgPsrGrHxzHS1wa5VHrbrQjoUUN48c6U/npFlhNBolLk3gU4IhIi9NTDCWMjp/WVlAxBQ4rjHodAiEOLb10ttBYBMOccB6TGqcahkOLGsZP7duYUIAjIq3OiQNU6UwEO09Iaw2V0aBHUXS3bwqMx+ZsVz/CHA+rSY+8NPntByYmmqIaP2GTP5W2URTgiCj+PCGtKaGt98ctloOjzl7VcOjIhvGJz9vS2l4wtKhh/NS+HQUFOCKq19hutPGEHUZpZcOY9PQH0e0NAtDo1CYdujkuWjpF/FwOmwkOGy1qGA+1by1AAY6I1Lz8biTsOzd1+xAMcxKXRjkaO7WbmA7QdMN4qb0nPhLaQ2l81L45JAU4IgmEODT39APQZg5OXpoFVpMeYY7H2a5+qYujGPUaXUHF0F4441Ov8oZqJMKUOI38jZovGIbLzRLT1TnyRwGOSM5294PjAZvJgFyNJf0B0bOFsijReKzY1IwWg2KAeuLjpeUVm0DsvqHAePTYaHGqxSjsW6Y2FOCIpL4jlmCstaQ/hvUKGmgvnFHT2ini52JBcU9/ED3RXCQyPJ7nNXkkTDya2hy7+FE/tbZRFOCIROs9KiBW2dZRpTNqWt5aAADsZqMw4kmJxqPT1ueHNxCGXgcUZWrzvqGpzbHTQt4WBTgi0XpDBdB0w3hoodIZCW0SOTZs1K/AYYPZqM0qnf29nO3qR4gWNYyKFjrh2vxrSAKtJ4sCsWkWSvwbnUCIQ1M0MV3b9w31xseCgmIgP80Ks1GPEMcLOzqT4al9F2OAAhzRaHmTP6Y0K9ZQ8TydLTSSM11e8CwxPVV7iekMq3Bps7/R0foKKgDQ63UoZrtgU4dqVNS+Bw5AAY4oBiT9aTRZFAAmZdpg0OvgC3Jo7fVLXRzZ0/JutPFKsqmhGgstTDWMhrC5KAXGIwpzPM50RkeLVRwYU4AjgtZeP3xBDnodMCnDJnVxJGMy6FGYYQVAlc5oaPUU8XOV0AjOmGhhqmE0SrJoanO0XG4fAmEORr0OBQ6r1MURDQU4ImB/YIUZ2k36Y2LLN6k3PhJKTI9gQ+bNbh/8obDEpZE/ysGJiC1qoLpmJKzzUJRpg9Gg3jZKvd9MQlo+ouFclDA6eixvS+v3TXaKGSlmA3geaOykXbCH4/GH0N4XAEAjf7Rqc/SEHFGVp1BQgCMCLR98dy5a8jt6Wj6cNZ5Op6MVeKPEOg6ZdhPSrercjXa0hKlNWtQwIq2MFlOAIwItZKePFm2hPjq0G+1AFBiPDq2giinOskGnA7yBMDo8AamLI2taaaMowBEBVTox8b0qMrTWXj/8oUhieqGGE9MZmm4YHa1MNYyGxWhAQTotahgNloNTrPI2igIcETR0UoDDsLyATk8AvT46W2gorEKmxPQIyt0aHa1MNYxW7L6hqc3hCCvvaASHjEWvL4jO6PCo2m+e0Ui1GJGdYgZAvarhaKXCGa1SGvkbFdoDZ6ASmtocUY83CLcvBED9nXAKcBKM/WFlpZiRpvGkP4Z64yOLjfrRVAMwMHeL4yhhdCiUtzUQ2+yP9lAaGttAMzfNArvZKHFpxEUBToLR9NT5KGF0ZLS1wEAFDiuMeh0CIQ4tvXS20GBCYQ5nu+jssnjCCA51poakpWlNCnASjDbdOl9syS9VOkOhnvhARoMek9jZQhQYD6qp24cQx8Ns1CM/Tb270Y4FJaePTEudcApwEkxL0fFoxQ7dpMS/oVAuxfmErfepsRpUfdyBvnq9ds8ui8dyt9r7/PAGQhKXRp5Yvp8W6hoKcBKMlm2er4R6VcOKT0zXQq9qtGgPpeFRZ+p8DrsJDlsk95Hum8FpaTo8KQHOtm3bUFZWBqvVioqKCuzbt2/Y6//85z+jvLwcVqsVc+fOxd///vcBP+d5Hlu2bEFBQQFsNhsqKytx4sQJMb/CqGnp5hktVgE3dfcjEOIkLo38UGL64IRzzKihGlQjjfoNiqaphteooQUNogc4r7zyCjZt2oR7770XBw8exPz581FVVYXW1tZBr//oo4/wne98BzfeeCM+/fRTrFq1CqtWrcLhw4eFax5++GE8+uij2L59O/bu3YuUlBRUVVXB55M2GTEQ4tDUrf4j6McqN80Cm8kAjgfOdtPZQufS0pz4WBQLU1Q0tTkYGsEZXDFNbQ7JHwqj2R1pJ7XQCRc9wHnkkUewfv16rFu3DrNmzcL27dtht9vxzDPPDHr9f/3Xf2HlypW4/fbbMXPmTNx///248MIL8bvf/Q5AZPTmt7/9Le6++25cffXVmDdvHl544QU0NTXhtddeE/vrDOtsdz84HrCa9MhLs0haFjnR6XSxfArqjZ+HRv0GJ/TE6Z4ZVD2N4AxKWLVJOX/naezsB88DKWaDsD+Zmoka4AQCARw4cACVlZWxD9TrUVlZiT179gz6mj179gy4HgCqqqqE62tra+FyuQZc43A4UFFRMeR7+v1+uN3uAQ8xCMlbWXbodJT0F0/YC4d64+cRThGnnvgALCju9gbR00+7YMfjeZ4O9R0CTVENjdU1xRppo0QNcNrb2xEOh5Gfnz/g+fz8fLhcrkFf43K5hr2e/e9Y3nPr1q1wOBzCo7i4eFzfZyS0WdvQaIfRocVWUNF9Ey/FYkROamQklKYbBurwBOAJhKHTRQ6ZJDGs/m2kkb/zaG20WBOrqDZv3oyenh7h0djYKMrnzC/KwI//ZRq+Nq9AlPdXMppuGBodzjo0Wkk1OHbPFKRbYTEaJC6NvLB75kxXP0JhWtQQLxbgaKMzJeo+zTk5OTAYDGhpaRnwfEtLC5xO56CvcTqdw17P/relpQUFBQUDrlmwYMGg72mxWGCxiJ8TM784A/OLM0T/HCWiPU0GF5+YrpVe1ViUZtlxoL6L8inOQSuohpafboXZoEcgzKG5x6f6E7PHolFjCxpEHcExm81YtGgRdu3aJTzHcRx27dqFpUuXDvqapUuXDrgeAHbu3ClcP3nyZDidzgHXuN1u7N27d8j3JNIrjdvNmOfpbCGGEtOHRytiBhdbQaWNnvhYGPQ6FGXRLtiDqdfYTvuiT1Ft2rQJTz75JJ5//nkcPXoUGzZsgMfjwbp16wAAa9euxebNm4Xrf/KTn2DHjh349a9/jWPHjuGnP/0pPvnkE2zcuBFAZEXOLbfcgp///Od4/fXXcejQIaxduxaFhYVYtWqV2F+HjNOkDBv0OqA/GEZbn1/q4sgGJaYPjxJGByfsYqyRhmqsaCXV+TiO19yWFKIfJXrdddehra0NW7ZsgcvlwoIFC7Bjxw4hSbihoQF6fSzOWrZsGV566SXcfffd+I//+A9Mnz4dr732GubMmSNcc8cdd8Dj8eCmm25Cd3c3li9fjh07dsBqpfNY5Mps1KMww4YzXf1o6PAij87OAUCJ6SOhHJzBNVDe1rAiI8ZtdN/Eaen1IRDiYNDrUJihjcT0pJyVvnHjRmEE5ly7d+8+77lrr70W11577ZDvp9PpcN999+G+++5LVBFJEpRk2XGmqx/1HV4sLsuSujiy0KCxVQ1jxQK/pp7ILthmoybWRYxIa1MNY0U5f+djo6CTMmwwGbTxd6SNb0lkgVZSnY8aquHlpJphNxvA88CZLrpvAKA/EEZbb2Sal3JwBkdTm+fTYmeKAhySNKw3Tpv9xbBKh1Z6DC5+F2wKjCPYtIvDZoLDTmeXDSZ+53Ra1BChtfwbgAIckkQ0gjMQz8eS/mgX46HRdMNA8YnpZHCsw9DnD6HTE5C4NPJQTwEOIeJhf1i0w2hEW68f/cEw9DqgKFM7lc5Y0XTDQA20B86IrCYDnOmRhQyUaBzBRs5piooQEbAKub0vgD5/SOLSSI/1qAocNkqeHUaJsIcSTW0CdIr4aJXQCrwB6jW4YpNqVZI06VYTMqM5AzTdoL1zYcarlE6iH4AS00enlM6/E/T0B9HtjRxYq6WRPwpwSFJRbzymgRqqUaGE0YHoFPHRoanNGNahzEk1I9WSlN1hZIECHJJU1KuKqaeGalQmZdpg0OvgC3Jo7dX2LtihMIczXXR22WgIx3xQZyq287XGpjUpwCFJRTvTxtAU1eiYDHoUZkQSRrUeGDf3+BDieJiNeiGJlgyOnX+n9XsG0N4p4gwFOCSpiimfQqDFfSnGi21oV6/xPZTYPVOcaYNeT2eXDYeNFrf2+tEfCEtcGmlp7RRxhgIcklQ0RRXR6wsK+3PQCM7IWGKk1rcY0GpPfDwy7CakWSP5Jo0a3wVbq6PFFOCQpGIV89nufgTDnMSlkQ6rcLJSzEiz0m60Iyml3YwBaDeXYjx0Oh0lGkdpdUEDBTgkqfLSLLAY9QhzPJq6+6UujmRoempsSmjkDwCdIj5WsftGu1Ob/lAYTT2RulZrCxoowCFJpdfrqLGCdoeMx4s2bYug+2ZshPPvNHzfnOnqB88DdrMBOalmqYuTVBTgkKSjlVSxpau0G+3osKnNTk8Avb6gxKWRxoCzyyjAGRWqawaO+ul02kpMpwCHJB2tpIr1xEsoWXRUUi1GZKdEep9aHfnr9ESOONHR2WWjVkoHtWr6cFYKcEjSldK8OE01jIPWV1KxBGtnuhVWk0Hi0iiDcM90eRHmtLkLtpaP9qAAhySd1jfgCoQ4NEeT/miKavRKNL6SihKMx67AYYPJoEMwzAt/c1oj7IGjwdFiCnBI0sUnjGrxbKEzXV5wPGAzGZCbZpG6OIqh9T2UKP9m7Ax6nTCdp9VpKi2fPk8BDkm6okwbdDrAGwijvS8gdXGSrr5Tu0l/E6H1g1ppk7/x0fLIH8dpOzGdAhySdBajAQXRc3S0mGgsTDVosMKZCK1v2sYCu2IN9sQnQssrqVp7/fCHOBj0OhRm2KQuTtJRgEMkEZum0l5vXMtDxhPBfl9NGt0Fm+6b8SnR8EoqtpBjUoYNJoP2mnvtfWMiC7HDE7VX6Qh74NAIzpjkpllgNenB8cDZLm0ljPYHwmjt9QOg+2ashEUNWuxMaXh6CqAAh0hEGMHRYIBDe+CMj06n02w+BZteSbcakWHX1m60ExU/tam1RQ2sftXqtCYFOEQSQqWjsYZqQNKfRiudiRC23tfYHkpsqoESjMeuOLqKqtcXQrdXW7tg12u8rqEAh0iiVKNnxMQn/U3K1F7S30RpNdFYOJxVo1MNE2EzG5AX3Y5Ba/WNlldQARTgEImwqYa2Xj+8gZDEpUke1hMvzLBqMulvorQ68kcJxhOj1fumQTimQZsjf1TDEkk47CY4bCYA2upVxYaMtVnhTBQLjLV2XIPWe+ITpcWpTbcviK7olJxWR/4owCGS0eJ0A+2BMzElcQe1ailhlAU4Wk0WnSgt1zU5qWakWowSl0YaFOAQyWhxfwqtJ/1NVFGmHfroLthtfX6pi5MUYY7HmS7axXgitDhFVU9nl1GAQ6QTq3S0M2zc0EF74EyE2ahHgSOSnK2VwDiysSEPs0EPZ3QHcDI2xZrsTNHKO1EDnM7OTqxZswbp6enIyMjAjTfeiL6+vmGv/9GPfoQZM2bAZrOhpKQEP/7xj9HT0zPgOp1Od97j5ZdfFvOrEBHEphu0s2lb7Bwq7VY6E6W16QY2PVWUZYNBT2eXjQcbMXW5ffAFwxKXJjno9HmRA5w1a9bgyJEj2LlzJ9588028//77uOmmm4a8vqmpCU1NTfjVr36Fw4cP47nnnsOOHTtw4403nnfts88+i+bmZuGxatUqEb8JEYPWEv96vEFhHw7KwRk/rZ0tRCuoJi4rJZaHwqb71C52OKt27xvRMo+OHj2KHTt2YP/+/Vi8eDEA4LHHHsMVV1yBX/3qVygsLDzvNXPmzMH//M//CP+eOnUqHnjgAVx//fUIhUIwGmPFzcjIgNPpFKv4JAnYH96Zrn6EwhyMKl82zYaMc1Itmk36S4TiLI0FODTVMGFsF+wvmt2o7/BiWl6a1EUSnbB3koYDY9FalD179iAjI0MIbgCgsrISer0ee/fuHfX79PT0ID09fUBwAwA333wzcnJysGTJEjzzzDPDrqjw+/1wu90DHkR6znQrzEY9QhyP5h6f1MURHfWoEiN2jpk2Rv5oqiExtDS1GQhxaO6JTP1rebRYtADH5XIhLy9vwHNGoxFZWVlwuVyjeo/29nbcf//9501r3XffffjTn/6EnTt34pprrsEPf/hDPPbYY0O+z9atW+FwOIRHcXHx2L8QSTi9Xofi6G6+Wqh06IiGxNDsFJWGG6pEKNHQyN+ZLi84HrCbDchNtUhdHMmMOcC56667Bk3yjX8cO3ZswgVzu9248sorMWvWLPz0pz8d8LN77rkHF198MRYuXIg777wTd9xxB375y18O+V6bN29GT0+P8GhsbJxw+UhiaOmkXzbioOUeVSKw3197XwB9fnXvgs3zPG3ylyAlGgqM6+Omp3Q67SamjzkR4LbbbsMNN9ww7DVTpkyB0+lEa2vrgOdDoRA6OztHzJ3p7e3FypUrkZaWhldffRUmk2nY6ysqKnD//ffD7/fDYjk/WrVYLIM+T6SnpV4V9cQTI91qQqbdhC5vEI2dXswsSJe6SKLp8gaFIK4ok+6bidDS1CZNa0aMOcDJzc1Fbm7uiNctXboU3d3dOHDgABYtWgQAeOedd8BxHCoqKoZ8ndvtRlVVFSwWC15//XVYrSPv+1BdXY3MzEwKYhRIS5v9NdAS8YQpybKjy9uD+g51BzisMXamW2E1GSQujbKxjkVjVz84jodexUvuqTMVIVoOzsyZM7Fy5UqsX78e+/btw4cffoiNGzdi9erVwgqqs2fPory8HPv27QMQCW5WrFgBj8eDp59+Gm63Gy6XCy6XC+FwZO+CN954A0899RQOHz6MkydP4vHHH8eDDz6IH/3oR2J9FSIirST++YJhuNyRRGqtVzqJUJLNTqNXd2+cGqrEKXBYYdTrEAhxwt+iWtVFA+OyHG13pkRdq/riiy9i48aNuOyyy6DX63HNNdfg0UcfFX4eDAZRU1MDrzfyR3zw4EFhhdW0adMGvFdtbS3KyspgMpmwbds23HrrreB5HtOmTcMjjzyC9evXi/lViEjiE0Z5nlftfPGZLi94HkgxG5CdYpa6OIrHErXVHhjXtkcaqskab6gSwWjQoyjThroOL+o7vCjMsEldJNEIAY7GtxYQNcDJysrCSy+9NOTPy8rKBizvvvTSS0c8QG/lypVYuXJlwspIpMXyCvr8IXR6AshWaca/cC5Mdopqg7hk0krCaF0H7YGTSMVZdtR1eNHY6cXSqdlSF0cUoTCHxujfhdZHcNS9sxqRPavJIJyvo+bGinajTSytjODURb/f5By6bxJBC+ffNff4ImeXGfUo0PjZZRTgEMlpoTcu9MSpoUoINqJxtrsfwTAncWnEU9dOuRSJFFtJpd66hk1rlmbZVZ1IPRoU4BDJaaE3LuRS0FRDQuSlWWA16RHmeJztUudhrV2eAHr6I2eXldLKu4Qo0cCiBprWjKEAh0hOCyupaFVDYun1OqHRr1XpviZ1cUvEbWZaIp4ILFm7rt0zYr6nUtW107QmQwEOkZzal/wGQpwwykCrYRInvrFSo1hQTA1VokR29gV6/SF0eAJSF0cU1JmKoQCHSK4sOoJT267OEZyGzti5MHlp6lwlJoUylQc47O9B60t9E8lqMqDQEVkertb7RsjbovuGAhwiPdZQtff50esLSlyaxGMVTiktEU8oNgRfq9KpzXrqiYuCjYjVqjDACYU5NHbREnGGAhwiuXSrCTmpkc3v6lQ4isOGjGlOPLFYD1X9PXG6bxJJmNpUYe5WUzctEY9HAQ6RBVbpqDFhtJaGjEXB7pkzXV4EQupaKs7zfOy+oZ54QsUCY/V2pmiJeAQFOEQWWKVT26a+AIeS/sSRm2ZBitkAjlffHkrd3iDcvsgp4rREPLFYYHxahSN/VNcMRAEOkYUyFQ8bx5ZtUqWTSDqdTtjrQ23TVLW0RFw0rK6p71DfUvFamtYcgAIcIgtT2BSVyhoqXzCMpp7IEnGaoko8teZTxHYwpoYq0Yoz7dDrAG8gjNZev9TFSSi2lxiN4ERQgENkoUylAU7klHQg1WIUEqlJ4qh1RUzsDCpqqBLNbNQLh/yq7r6hfL8BKMAhssD+IHv6g+hS0QZctXE9cVoinniTc1IBqHcEh7bbF4caN4kMhTkhF41GcCIowCGyYDMbUOCILGtU00oq6lGJiy29V9uKGCFZlO4bUahx1WZTtw8hjpaIx6MAh8iGGldSxfbAoYZKDOyeaerphy8Ylrg0iRG/RJzuG3GwJFw1jeDU0hLx81CAQ2Rjcq76EkZpDxxxZaWYkWY1glfRUvEubxC90SXiJVmUZCwGNeb80c7X56MAh8jG5Gz1VTps6oQqHXHodLrYdINK7hv2PQoctERcLJOFpeJecJw6lorTEvHzUYBDZENtvar+QBgutw8ATTWIqUxlgTHriZdSQyWaSRk2GPU6+EMcmqN/o0pXRztfn4cCHCIb8Ssb1LABF5tqS7cakWk3SVwa9VLbqeJ1lH8jOqNBjxKV5eGwPXAm03S4gAIcIhslWZENuDyBMNpUsAFXfENFS8TFM1lle+Gw09Epb0tcapoSj18iXkqBsYACHCIbatuAq5aS/pJCbXvhxKao6L4Rk5pG/s5299MS8UFQgENkRU1nUtEeOMnBeuItbj+8gZDEpZkYnueFbRJoikpcasr5O83umewUWiIehwIcIiuTo/Piajjplw7ZTA6H3STkOCl9w7+2Xj96/SHodXQOldiEKSoVdKZOtfUBAKbkUl0TjwIcIitq2kKdpqiSRy0jf6eiPfGiTDssRloiLiYWQDZ2ehEKcxKXZmJYh3BqbqrEJZEXCnCIrMTmxZXdE+/zh4REaVrVID61JIyebqeeeLIUOmwwG/UIhnk0dSt7qfhpGsEZFAU4RFYmx/XElbwBF8ujyE4xw0FLxEWnlnwKlktBPXHx6fU6YVM8pU9TsftmCt03A1CAQ2RlUoYNJoPyN+Bic+LUUCUH67kqPcChXIrkYh0qNgKiRL2+IFqjo8V03wxEAQ6RFaNBj+Lo+TtKPnSThoyTiwWSJ1v7FL1JpNATz6HAOBnYfXNa0XVNpOw5qRakW2m0OB4FOER2pgjTDcrtVZ2iqYakimymCPT0B9HpCUhdnHHxh8I40xXJPZuaR4FxMrC/z1MKHsFheVtTqTN1HgpwiOzEKh3l9qqEKSpqqJLCajKgKNMGQLn3TX2HFxwPpFmMyE21SF0cTZiap4IAh/JvhiRqgNPZ2Yk1a9YgPT0dGRkZuPHGG9HXN/yNdOmll0Kn0w14/Pu///uAaxoaGnDllVfCbrcjLy8Pt99+O0IhZW/wRWLipxuUiON4IReEphqSR+m98VOtsWlNOtojOdgUcovbj15fUOLSjE8sMZ06U+cSNcBZs2YNjhw5gp07d+LNN9/E+++/j5tuumnE161fvx7Nzc3C4+GHHxZ+Fg6HceWVVyIQCOCjjz7C888/j+eeew5btmwR86uQJFJ6r+psdz/8IQ5mg14YVSDiEwIchQbGtJdJ8qVbTchLi4yWKTUPhxLThyZagHP06FHs2LEDTz31FCoqKrB8+XI89thjePnll9HU1DTsa+12O5xOp/BIT08Xfvb222/jiy++wB/+8AcsWLAAl19+Oe6//35s27YNgYAy597JQNOiFXxzjw99fuWNzLEKpzTbDqOBZoGTRfEjONRQSULJ9038aDEFxucTrfbds2cPMjIysHjxYuG5yspK6PV67N27d9jXvvjii8jJycGcOXOwefNmeL2xTd/27NmDuXPnIj8/X3iuqqoKbrcbR44cGfT9/H4/3G73gAeRL4fdhJxU1qtSXqVDe5lIgw3RKzUHh3IppMHy5JQ4JT5wtJiO9jiXUaw3drlcyMvLG/hhRiOysrLgcrmGfN2//du/obS0FIWFhfj8889x5513oqamBn/5y1+E940PbgAI/x7qfbdu3Yqf/exnE/k6JMmm5qagvc+PU219mFeUIXVxxoR64tJgU5uNXV74gmFYTco56oDnedo7SSJKHsFh05ql2XYY6JDN84x5BOeuu+46Lwn43MexY8fGXaCbbroJVVVVmDt3LtasWYMXXngBr776Kk6dOjXu99y8eTN6enqER2Nj47jfiyQHa6yU2Kuihkoa2SlmOGwm8LzyzqRq7wug1xeCThdprEjyKHnVJu23Nbwxj+DcdtttuOGGG4a9ZsqUKXA6nWhtbR3wfCgUQmdnJ5xO56g/r6KiAgBw8uRJTJ06FU6nE/v27RtwTUtLCwAM+b4WiwUWCy27VJJpQsKoEiud6BRVHgU4yaTT6TA1NwUHG7pxqtWDcmf6yC+SCdZQFWXaFDXypAbs77S+w4NgmINJQXlz1Jka3pgDnNzcXOTm5o543dKlS9Hd3Y0DBw5g0aJFAIB33nkHHMcJQctoVFdXAwAKCgqE933ggQfQ2toqTIHt3LkT6enpmDVr1hi/DZErpa6kom3TpTU1NzUS4CjsvjlN2wpIpiDdCpvJgP5gGI2dXkXlQFHe1vBEC1VnzpyJlStXYv369di3bx8+/PBDbNy4EatXr0ZhYSEA4OzZsygvLxdGZE6dOoX7778fBw4cQF1dHV5//XWsXbsWX/nKVzBv3jwAwIoVKzBr1ix897vfxWeffYa33noLd999N26++WYapVERljBa1+FBKMxJXJrRYxVObhptmy6FKQrNp2BL26knnnx6vU7ojChtmioW4FBnajCijsW9+OKLKC8vx2WXXYYrrrgCy5cvxxNPPCH8PBgMoqamRlglZTab8Y9//AMrVqxAeXk5brvtNlxzzTV44403hNcYDAa8+eabMBgMWLp0Ka6//nqsXbsW9913n5hfhSRZocMGm8mAYJhHQ6d35BfIhJBgnEMVjhRiK6mUFeAIIzjUUElCiYnGHn8IruiBxFNp5G9Qoq2iAoCsrCy89NJLQ/68rKxswMF4xcXFeO+990Z839LSUvz9739PSBmJPLFe1ZEmN0629ilmCJbyb6QlTG22esBxPPQKWVlCyaLSUuImkWz/m5xUMxx2Gi0ejHKyqYjmKHF1A43gSKskyw6jXof+YFjo3cqdPxRGY1c/AJqikgrbC0dJIzhshSnlbQ2NAhwiW9MUuFSclZVGcKRhMuiFZdZKaaxq2z0IczzSrEbh2ACSXPGdqfhZBTk70doLAJieT3XNUCjAIbKltHnxQIgTho0vyE+TuDTapbTphhMtkXJekJ9Gh2xKZHJOCnQ6oKc/iA6PMo78OR69b6ZTZ2pIFOAQ2ZoWt1RcCb2qug4PQhyPVIsRhQ6r1MXRrNgWA8qY2jzREu2JU0MlGavJIByMq5TAmI0WU2dqaBTgENkqzbZDrwN6fSG0RfeWkTPWE5+Wl0o9cQmxERylTG0KPXFqqCQl3DcKGDH2BcOoj+7WPY2mqIZEAQ6RLavJgOKsSD6FEiqd49QTlwUhd0sB9wwQy6W4gBoqSSkpMD7V1geOBzLsJuSmUt7WUCjAIbI2TUH5FLGGinriUmIBZluvH10yz6fwh8Ko64js8zQ9j+4bKbEAk43EyhkLwqbTaPGwKMAhssbyKU4oIMCJTTVQT1xKKRajkE/BRtXkKn4FVX469cSlxDomNTK/Z4C40WLqTA2LAhwia6zSkXtDFQhxqKMVVLIxQyH3zXFaQSUbLFhQwsjfCVpBNSoU4BBZYw1VjatX1iup4ldQFdAKKsld4FRGb/wk5W3JRqqCRv5O0AqqUaEAh8ja9PxU6HRAlzeItj75rqRiFSKtoJIHlk9x3CXvqU1aQSUvShgxjl9BRYHx8CjAIbJmNRlQlh3ZRl3OjVVsszaqcORAaKha5T3yd5xWUMmKEvJwTrd5wPGAw2ZCLu18PSwKcIjsscpfzpUOraCSl6m5qdDrgG5vULZ7KPlDYdRHV1DRfSMPM5zyH/kTjmig0eIRUYBDZG+GMx0AUONyS1ySodFUg7xYTQaURQ88lWtgTGdQyY8SRv5OUF0zahTgENkTEo1luj9F/AoqmhOXjwvyYgnqckQrqORHCSN/tKHo6FGAQ2RvRnRFzImWXnCc/HpVbAVVGq2gkpULhPtGnoExW0FF+TfyEZ/zJ9eRP1pBNXoU4BDZK8u2w2zQwxsI42x3v9TFOY+wgiqf5sTlZIbME0aPC2eXUUMlJxfky3fkb8AKKgqMR0QBDpE9o0Ev7Gh8TIaVzvFomS6ghkpWYlvvyzOf4lg0p2wG9cRlRc4jfydaImdQZdpNlLc1ChTgEEUod8p3f4ovmiNlKi+ghkpOynJSYDLo4JHhyJ/HH0J9Z2QF1Uy6b2RFziN/R5sjQfHMgnQaLR4FCnCIIsh52Di+0iHyYTLohROi5RYY17T0gueBvDQLsuk0aFlhS8XlmPN3NDrqV+6kumY0KMAhiiDsTyGzhqqnPyiMDsykSkd2WGAst6lNFhSXU1AsO6XZ8h35i3WmaNRvNCjAIYrA9sI51daHYJiTuDQxx6IVzqQMGxx2k8SlIediK/CONcszwKGGSn7iR/7kFBjzPI+j0fuYRotHhwIcogiFDitSLUYEwzxOt3mkLo6AGip5m1UYaQi+aJbXJpEs4JpFDZUssf8uR2V037jcPvT0B2HQ6zCN9sAZFQpwiCLodDqhNy6nSof18GhOXJ5mRxuq02196A+EJS5NBMfxdN/IHAuMjzT1SFySGFbvTc1NgdVkkLg0ykABDlGM2TLsjVOCsbzlplmQk2oGx8tnVcyZrn70+UMwG/SYkpsidXHIIOQ48sempygoHj0KcIhizJZZryrM8UKjSVNU8qTT6TCr0AEA+KJJHo0VazSn56fCZKAqWI7YFFVjZz96+oMSlyaCOlNjR39dRDFmRxuqI01uWWzcVtvugS/IwWYyoDSbeuJyxRoruQTGbIM/aqjkK8NuxqQMGwD5TIlTvt/YUYBDFGN6fiqMeh26vUE09fikLo5Q4cxwpsGgp0235Epu0w3CEnEnNVRyJtw3Mhj58wXDqI0e6EuB8ehRgEMUw2I0YHp0X5MjZ6XvjdOQsTKwEZxjzb0Iy2DjtqO0gkoR2H8fOQTGx1t6wfFAVoqZjmgYAwpwiKLE8nCkr3RoyFgZJuekwGrSoz8YRl2HtFsMuH1BNESPaKBN/uRttoxGcA6fjZRhFh3RMCYU4BBFkUuAw/M8DkUrnTmTHJKWhQzPoNcJK0+kbqwOR0ceJ2XYkJVilrQsZHhsiupEay8CIWk3Fz0UvW/mFlFdMxaiBjidnZ1Ys2YN0tPTkZGRgRtvvBF9fUOf0FpXVwedTjfo489//rNw3WA/f/nll8X8KkQmhGFjiRNGXW4f2vv8MOh1NNWgAHLJwzl0JnLfzqOGSvYmZdiQbo1sLnqiVdotBg6d7QYAzKXO1JiIGuCsWbMGR44cwc6dO/Hmm2/i/fffx0033TTk9cXFxWhubh7w+NnPfobU1FRcfvnlA6599tlnB1y3atUqMb8KkQnWUDX1+NDlCUhWjs+jDdX0vFTadEsB5DLyRz1x5YhsMSD9yJ8/FBYOGaYAZ2xEC3COHj2KHTt24KmnnkJFRQWWL1+Oxx57DC+//DKampoGfY3BYIDT6RzwePXVV/Htb38bqakDt6bOyMgYcJ3VahXrqxAZSbOaUJptByBtY0U9cWWZE91i4NCZbkm3GGABzrxJGZKVgYwe25rikISLGmpcvQiGeWTaTSjKtElWDiUSLcDZs2cPMjIysHjxYuG5yspK6PV67N27d1TvceDAAVRXV+PGG28872c333wzcnJysGTJEjzzzDPDVlp+vx9ut3vAgygX640flnCa6nOhJ54hWRnI6JUXpMFk0KHLG8SZLmlOiO7xBlHfEUkwnjOJpjWVYH5xBgDgszMS1jXRz54zyUEJxmMkWoDjcrmQl5c34Dmj0YisrCy4XK5RvcfTTz+NmTNnYtmyZQOev++++/CnP/0JO3fuxDXXXIMf/vCHeOyxx4Z8n61bt8LhcAiP4uLisX8hIhtzo73fz890S/L5PM/jUPSz59GQsSJYjAYhV6q6sVuSMrBRgJIsOzLslGCsBAuiHZijTW7JEo1ZYjqNFo/dmAOcu+66a8hEYPY4duzYhAvW39+Pl156adDRm3vuuQcXX3wxFi5ciDvvvBN33HEHfvnLXw75Xps3b0ZPT4/waGxsnHD5iHTmF0f+0KsbuiX5/DNd/ejyBmEy6FBOS8QVQ+iNSxTgfM4SRamhUoziLBsy7SYEwpywA3WysRGcuTStOWbGsb7gtttuww033DDsNVOmTIHT6URra+uA50OhEDo7O+F0Okf8nP/+7/+G1+vF2rVrR7y2oqIC999/P/x+PyyW8zdBslgsgz5PlGleUQZ0ukiicavbh7z05OZfsZ74DGcaLEZKMFaKeUUZAOqFBiPZhJ44jfophk6nw7yiDLx3vA2fNXZH76Hk8QXDOB49744C47Ebc4CTm5uL3NzcEa9bunQpuru7ceDAASxatAgA8M4774DjOFRUVIz4+qeffhpf//rXR/VZ1dXVyMzMpCBGI1ItRlyQl4aall5UN3ZjxeyRA+ZEoh6VMi0ojiWMhsIcjEk+6FK4b6ihUpT5xZEAp7qxB99dmtzPPubqRYjjkZ1iRqGDFtKMlWh/4TNnzsTKlSuxfv167Nu3Dx9++CE2btyI1atXo7CwEABw9uxZlJeXY9++fQNee/LkSbz//vv4wQ9+cN77vvHGG3jqqadw+PBhnDx5Eo8//jgefPBB/OhHPxLrqxAZWhCdbpAin4LtSUFz4soyJScVqRYj+oNhnGwbej8uMXR6AkJyM20MqSwsMP5Mgpw/NlpMCcbjI2oX5sUXX0R5eTkuu+wyXHHFFVi+fDmeeOIJ4efBYBA1NTXwer0DXvfMM8+gqKgIK1asOO89TSYTtm3bhqVLl2LBggX4/e9/j0ceeQT33nuvmF+FyMx8iQIcjuOFJeK0J4Wy6PU64b9ZsvNwWEL85JwUpFtNSf1sMjFsWupUWx96fcGkfjbLM6TO1PiMeYpqLLKysvDSSy8N+fOysrJBl3c/+OCDePDBBwd9zcqVK7Fy5cqElZEoExvB+fxMDziOhz5Jp3mfbu+D2xeC1aTHDDoNWnHmF2dgz+kOVDf24LqLkve5B+u7AAALSzKS96EkIXJSLSjKtOFMVz8One3Bsqk5SfvsTxsi982FpZlJ+0w1obOoiCJdkJ8Km8mAPn8Ip5I43XAg2lDNL8qAKck5HGTi2HRDsrcYOBjtiV9YQg2VEsVW4CUvQb3TE8Dp9sjhsBcW030zHlRDE0UyGvRCsuanSZxuYAHOIupRKRKbbjjm6kV/IJyUzwxzvNATp/tGmeZH65rqxq6kfSYb9ZuWlwqHnaY1x4MCHKJYCyXIw6EAR9kKHFYUOKyRoCNJjVWNqxeeQDiy+i+fpjWVaGF05O1AffKO+jjIpqdoWnPcKMAhisXycFhPR2xdngBOtUWGjBfSVIMi6XQ6LC7LAgB8Upec++ZAQyz/xpCkXDGSWHMnOWA26NHe50ddh3fkFyQAdaYmjgIcolisoapp6UWPV/zVDazHPyU3BVkptNW+Ui0pizQY++s6k/J5n9aznjg1VEplNRmEHdT314p/3wTDnLAsne6b8aMAhyhWbpoFU3JTwPPAJ/XiVzpCj4oqHEVjgfHB+i6EwuKfL3SA8m9U4aLofZOMwPhYcy98QQ7pViOm5qaK/nlqRQEOUbSKyZFKZ18SelVsSmNxGTVUSnZBfhrSrEZ4AmEcc/WK+lmtvT7Ud3ih0wELKJdC0ZIZ4Oyt7QAQWR6erC0w1IgCHKJorNLZK3KA4wuGhdVabASAKJNBr8Pi6GiK2IHxnlORhmp2YTpt8KdwF5ZmQqcD6jq8aO31ifpZH5+O3DdfmpIt6ueoHQU4RNGWREdwDp/tgccfEu1zDtZ3IRDikJ9uwZScFNE+hySHkGgs8tQma6iWUkOleA6bCeXOdADiJqiHOV7osNF9MzEU4BBFK8q0Y1KGDSGOx6fRzdTE8FG0J75sag6dCaMCLDDee7oTHCfesl82grN0KjVUanBRdHp6bzRwFcMXTW70+kJIsxgxuzBdtM/RAgpwiOKxxupjESudPdQTV5X5RRmwmw3o8ARw1OUW5TOae/pR1+GFQa8TplKJsi2LBqofnGwX7TP2nI6895LJWUk/8V5t6LdHFI8FHWJVOh5/SDickXri6mA26oX8hg9Fum/Y6M2cSQ6kUf6NKiydmgO9DjjV5kFzT78on0GjfolDAQ5RvC9fEDn87vMz3ej2BhL+/vvrOhHieBRl2lCcZU/4+xNpXDwtct/884Q4AQ6b1qRRP/Vw2EzCcR9i3DehMIf90fweSjCeOApwiOIVOGy4ID8VHC/OKA41VOr05emRAGd/XSd8wcSeS8XzPPXEVWp5NDD+QIQA57Mz3ejzh+CwmTCrgPJvJooCHKIKX56eCwD45/HEVzq7a1oBAMujDSJRh+l5qchLs8AX5BJ+3Mfxlj6c7e6HxajHEsq/URVWD3x4sj3hCervHIvUNV+5IJf2v0kACnCIKnzlgkiA8/6JtoQehtfY6cXxlj4Y9DpcEv0Mog46nU7ojf8zwSN/u461AIgkpdrMhoS+N5HWhSWZoiWov3OsDQDwL+VU1yQCBThEFZaUZcFs1KO5x4dTbX0Je993o6M3i0oykWGn86fUhuVvvXO0NaHv+260J/4v5XkJfV8ivfgE9d01bQl7X1ePD0eb3dDpgEsuoPsmESjAIapgMxuEYxt2JbCxYu/1VWqoVOmrM/Jg0OtQ09KL+g5PQt6z2xsQzi2j+0adKmfmAwDePuJK2HuyztSC4gw6zDdBKMAhqrFiVqTS+d/Dial0vIGQsP/NZTOpoVKjDLtZCIx3ftGSkPd873gbOB64ID8VRZm06k6NKmflQacDPjvTk7Dl4iz/5l9mUF2TKBTgENWomu2ETgdUN3YnpNL58GQHAiEOkzJsmJ5HJ/qqFQuM3z6SmACHRv3ULy/NigtLIrsa/yMBgXF/ICzsx0T3TeJQgENUIy/dikXRSmdHAkZx/n6oGQDwr7Py6XgGFfvX2U4AkXOpOvr8E3ovXzCMXUcjDd6KWc4Jl43IFwuM30pAYLy7phXeQBhFmTY6niGBKMAhqrJyTqRRmWiA4wuGhSmLq+YXTLhcRL4mZdgwZ1I6OB74x9GJNVa7a1rhCYQxKcOGC0syElNAIksrooHxx6c70OMNTui93vw80pm6cl4BdaYSiAIcoipV0Upnf10nWnt9436f3TWt6POHMCnDhoXFmYkqHpGpy+dEgtjXPm2a0Pu8QQ2VZkzOSUG5Mw0hjsffoqO94+ENhIRtBb42tzBRxSOgAIeoTHGWHQuKM8DxwF8n0Fi9/lnktVfOK6ANtzRg1cJJACKHqp7p8o7rPXp9QWG5+VXzqKHSgm9E75v/OXhm3O/x9pEW+IIcSrLsmDOJpqcSiQIcojrXLi4CAPzpk8ZxbfrX3ucXpqeuXkANlRZMyrAJR3H8tXp8gfHrnzWhPxjG1NwUaqg04hsLJ0GvAw7Ud6G2fXzbDPxxXwMA4JoLi2jUL8EowCGqc9X8QlhNepxo7UN19BTwsfifA2cQDPOYX+TA7EJH4gtIZOkbF0Z646/sb0R4HFvwv7yvEQDwnSUl1FBpRF66VTgm5s+fNI759afb+rC3thN6XaxjRhKHAhyiOulWE66I5lT8v4/rx/RajuOFHtW/VZQkvGxEvr42rwDpViMaOr3CTsSjdfhsDw6d7YHZoMc3L6SGSku+s6QYQGQkZqyHtr6yPxIUXXJBLgozbAkvm9ZRgENUae2yMgDA69VNY9oTZ9exVtR1eJFmMeJrlEehKXazEauXRILa5z6qG9Nrn3j/NADg8rlO2oVWY/51lhNFmTZ0eYN47dOzo36d2xfES3sjnak1FaViFU/TKMAhqrSgOANLJmchxPF49sO6Ub2G53n87t2TAIDrl5YixWIUsYREjtYuLYVeB3xwsh2Hz/aM6jV17R68+Xkkb+f/fGWqmMUjMmTQ63BDtEP15D9Pj3p68w8f16PXH8L0vFQ6s0wkFOAQ1fo/X5kCIFKRtLhHXjL+/ol2fNbYDatJjxuXTxa7eESGijLt+Pr8yMjdQzuOjeo1v3v3JDg+crDmLNqkTZO+fVExHDYTTrV5RrWiqtcXxDMf1AIANlw6lVZqioQCHKJa/1KehwtLMuANhPHLt2qGvTYQ4nD/m18AAP5tSSlyUi3JKCKRoU3/OgNGvQ7/PNGOD060D3ttdWO30KBt/JdpySgekaF0qwkbvxr57/+bncfRHxg+F+exd06ivS+Asmw7rppPU+FiES3AeeCBB7Bs2TLY7XZkZGSM6jU8z2PLli0oKCiAzWZDZWUlTpw4MeCazs5OrFmzBunp6cjIyMCNN96Ivr4+Eb4BUTqdTod7vjYLAPDfB85gX23nkNc+9cFpnGztQ3aKGT+5bHqyikhkqCTbjjXRBPPNr36OPn9o0OtCYQ5b/noYPA9888JJwtlERJu+u7QUkzJsaO7xDTv6V+PqFUZv7r1qNkwGGmcQi2i/2UAggGuvvRYbNmwY9WsefvhhPProo9i+fTv27t2LlJQUVFVVweeLTS+sWbMGR44cwc6dO/Hmm2/i/fffx0033STGVyAqsLAkE9+OLr+85eVP0dZ7/llDB+o78ZudxwEAm6+YCYfdlNQyEvm5rWoGJmXY0NjZjy2vHR50P6WH36rB52d6kGYx4q7LyyUoJZETq8mAB785F0AkSX3XIMd+9PqCuPmlgwhxPCpn5tHBmiITLcD52c9+hltvvRVz584d1fU8z+O3v/0t7r77blx99dWYN28eXnjhBTQ1NeG1114DABw9ehQ7duzAU089hYqKCixfvhyPPfYYXn75ZTQ1TWyLdaJeW66ajbJsO5p6fPju03txtju2qmpfbSfWPbsfwTCPK+Y6cU10LxSibelWE3797fnQ64C/fHoW975+BMEwBwAIczwe2XlcWDn10LfmIS/NKmVxiUxcckGukHB880sH8c6xWJDT1uvHDc/ux8nWPuSlWfCLa+ZJVErtkM0ykdraWrhcLlRWVgrPORwOVFRUYM+ePVi9ejX27NmDjIwMLF68WLimsrISer0ee/fuxTe+8Y1B39vv98Pvj/Xc3W63eF+EyE6qxYjn1i3Bt7bvwTFXLyp//R4qZ+WjzxfE7uNt4HlgUWkmHv7WfNqgjQi+NCUbD3xjLjb/5RBe2FOP94+34aKyLHx+pgc1Lb0AgNurZuCKuXQYK4n5jytmor7Dg3dr2vD95z7BxdOykZdmxa6jLXD7Qki3GvHMDRdRnl8SyGbyz+WKnP6cn58/4Pn8/HzhZy6XC3l5A4f0jEYjsrKyhGsGs3XrVjgcDuFRXFyc4NITuSvLScGrP1yGC0sy0B8M443PmvBuTZuQP/H895cglZaFk3N8Z0kJtl9/ITLtJtR1ePHnA2dQ09KLFLMBD18zDzd/lRKLyUBmox7bv7sINywrg04HfHiyA69+ehZuXwjlzjT894ZlmDOJdkhPhjHV6HfddRceeuihYa85evQoysvlNR+9efNmbNq0Sfi32+2mIEeDirPs+J8Ny7DndAc+beiGxajHJRfkYnp+mtRFIzK2ck4Bvjw9Fzu/aEF9hxcFGVZUzXbCYaNcLTI4i9GAn359Nm5YVobdNa3wBMKYXZiOL0/PhYGWhCfNmAKc2267DTfccMOw10yZMmVcBXE6nQCAlpYWFBTEhnxbWlqwYMEC4ZrW1oFbqIdCIXR2dgqvH4zFYoHFQsOBJLKyatnUHCybmiN1UYiCpFiMwonjhIxWWU4KbsihPbWkMqYAJzc3F7m5uaIUZPLkyXA6ndi1a5cQ0Ljdbuzdu1dYibV06VJ0d3fjwIEDWLRoEQDgnXfeAcdxqKioEKVchBBCCFEe0XJwGhoaUF1djYaGBoTDYVRXV6O6unrAnjXl5eV49dVXAUR61rfccgt+/vOf4/XXX8ehQ4ewdu1aFBYWYtWqVQCAmTNnYuXKlVi/fj327duHDz/8EBs3bsTq1atRWEibJRFCCCEkQrSsyi1btuD5558X/r1w4UIAwLvvvotLL70UAFBTU4Oenth5L3fccQc8Hg9uuukmdHd3Y/ny5dixYwes1tgSzBdffBEbN27EZZddBr1ej2uuuQaPPvqoWF+DEEIIIQqk4wfbwUrl3G43HA4Henp6kJ5OZ8cQQgghSjCW9ls2y8QJIYQQQhKFAhxCCCGEqA4FOIQQQghRHQpwCCGEEKI6FOAQQgghRHUowCGEEEKI6lCAQwghhBDVoQCHEEIIIapDAQ4hhBBCVEe0oxrkjG3e7Ha7JS4JIYQQQkaLtdujOYRBkwFOb28vAKC4uFjikhBCCCFkrHp7e+FwOIa9RpNnUXEch6amJqSlpUGn0yX0vd1uN4qLi9HY2EjnXImIfs/JQb/n5KDfc3LQ7zl5xPpd8zyP3t5eFBYWQq8fPstGkyM4er0eRUVFon5Geno6/QElAf2ek4N+z8lBv+fkoN9z8ojxux5p5IahJGNCCCGEqA4FOIQQQghRHQpwEsxiseDee++FxWKRuiiqRr/n5KDfc3LQ7zk56PecPHL4XWsyyZgQQggh6kYjOIQQQghRHQpwCCGEEKI6FOAQQgghRHUowCGEEEKI6lCAk0Dbtm1DWVkZrFYrKioqsG/fPqmLpCpbt27FRRddhLS0NOTl5WHVqlWoqamRuliq94tf/AI6nQ633HKL1EVRpbNnz+L6669HdnY2bDYb5s6di08++UTqYqlKOBzGPffcg8mTJ8Nms2Hq1Km4//77R3WeERna+++/j6uuugqFhYXQ6XR47bXXBvyc53ls2bIFBQUFsNlsqKysxIkTJ5JWPgpwEuSVV17Bpk2bcO+99+LgwYOYP38+qqqq0NraKnXRVOO9997DzTffjI8//hg7d+5EMBjEihUr4PF4pC6aau3fvx+///3vMW/ePKmLokpdXV24+OKLYTKZ8L//+7/44osv8Otf/xqZmZlSF01VHnroITz++OP43e9+h6NHj+Khhx7Cww8/jMcee0zqoimax+PB/PnzsW3btkF//vDDD+PRRx/F9u3bsXfvXqSkpKCqqgo+ny85BeRJQixZsoS/+eabhX+Hw2G+sLCQ37p1q4SlUrfW1lYeAP/ee+9JXRRV6u3t5adPn87v3LmTv+SSS/if/OQnUhdJde68805++fLlUhdD9a688kr++9///oDnvvnNb/Jr1qyRqETqA4B/9dVXhX9zHMc7nU7+l7/8pfBcd3c3b7FY+D/+8Y9JKRON4CRAIBDAgQMHUFlZKTyn1+tRWVmJPXv2SFgydevp6QEAZGVlSVwSdbr55ptx5ZVXDrivSWK9/vrrWLx4Ma699lrk5eVh4cKFePLJJ6UuluosW7YMu3btwvHjxwEAn332GT744ANcfvnlEpdMvWpra+FyuQbUHw6HAxUVFUlrFzV52Gaitbe3IxwOIz8/f8Dz+fn5OHbsmESlUjeO43DLLbfg4osvxpw5c6Qujuq8/PLLOHjwIPbv3y91UVTt9OnTePzxx7Fp0yb8x3/8B/bv348f//jHMJvN+N73vid18VTjrrvugtvtRnl5OQwGA8LhMB544AGsWbNG6qKplsvlAoBB20X2M7FRgEMU6eabb8bhw4fxwQcfSF0U1WlsbMRPfvIT7Ny5E1arVeriqBrHcVi8eDEefPBBAMDChQtx+PBhbN++nQKcBPrTn/6EF198ES+99BJmz56N6upq3HLLLSgsLKTfs4rRFFUC5OTkwGAwoKWlZcDzLS0tcDqdEpVKvTZu3Ig333wT7777LoqKiqQujuocOHAAra2tuPDCC2E0GmE0GvHee+/h0UcfhdFoRDgclrqIqlFQUIBZs2YNeG7mzJloaGiQqETqdPvtt+Ouu+7C6tWrMXfuXHz3u9/Frbfeiq1bt0pdNNVibZ+U7SIFOAlgNpuxaNEi7Nq1S3iO4zjs2rULS5culbBk6sLzPDZu3IhXX30V77zzDiZPnix1kVTpsssuw6FDh1BdXS08Fi9ejDVr1qC6uhoGg0HqIqrGxRdffN5WB8ePH0dpaalEJVInr9cLvX5gc2cwGMBxnEQlUr/JkyfD6XQOaBfdbjf27t2btHaRpqgSZNOmTfje976HxYsXY8mSJfjtb38Lj8eDdevWSV001bj55pvx0ksv4a9//SvS0tKEeVyHwwGbzSZx6dQjLS3tvLymlJQUZGdnU75Tgt16661YtmwZHnzwQXz729/Gvn378MQTT+CJJ56QumiqctVVV+GBBx5ASUkJZs+ejU8//RSPPPIIvv/970tdNEXr6+vDyZMnhX/X1taiuroaWVlZKCkpwS233IKf//znmD59OiZPnox77rkHhYWFWLVqVXIKmJS1Whrx2GOP8SUlJbzZbOaXLFnCf/zxx1IXSVUADPp49tlnpS6a6tEycfG88cYb/Jw5c3iLxcKXl5fzTzzxhNRFUh23283/5Cc/4UtKSnir1cpPmTKF/8///E/e7/dLXTRFe/fddwetk7/3ve/xPB9ZKn7PPffw+fn5vMVi4S+77DK+pqYmaeXT8Txt5UgIIYQQdaEcHEIIIYSoDgU4hBBCCFEdCnAIIYQQojoU4BBCCCFEdSjAIYQQQojqUIBDCCGEENWhAIcQQgghqkMBDiGEEEJUhwIcQgghhKgOBTiEEEIIUR0KcAghhBCiOhTgEEIIIUR1/j8s37Y3QJRSZgAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "x_jnp = jnp.linspace(0, 10, 1000)\n", + "y_jnp = 2 * jnp.sin(x_jnp) * jnp.cos(x_jnp)\n", + "plt.plot(x_jnp, y_jnp);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wyVHuTTeYUM4" + }, + "source": [ + "The code blocks are identical to what you would expect with NumPy, aside from replacing `np` with `jnp`, and the results are the same. As we can see, JAX arrays can often be used directly in place of NumPy arrays for things like plotting." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nxZB_CgnYUM4" + }, + "source": [ + "The arrays themselves are implemented as different Python types:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "kZaOXL7-uvUP" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "\n", + "x_np = np.linspace(0, 10, 1000)\n", + "x_jnp = jnp.linspace(0, 10, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "PjFFunI7xNe8", + "outputId": "93fd1fd6-62d9-4b7f-ffc2-d0420dc8bb1a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "type(x_np)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "kpv5K7QYxQnX", + "outputId": "54835072-8e9e-4500-8d25-18aebf48ba77", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "jaxlib._jax.ArrayImpl" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "type(x_jnp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mx94Ri7euEZm" + }, + "source": [ + "Python's duck-typing allows JAX arrays and NumPy arrays to be used interchangeably in many places. However, there is one important difference between JAX and NumPy arrays: JAX arrays are immutable, meaning that once created their contents cannot be changed.\n", + "\n", + "Here is an example of mutating an array in NumPy:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "fzp-y1ZVyGD4", + "outputId": "d60beeb3-c4c9-488a-95ed-5ccc2c92a5a3", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[10 1 2 3 4 5 6 7 8 9]\n" + ] + } + ], + "source": [ + "# NumPy: mutable arrays\n", + "x = np.arange(10)\n", + "x[0] = 10\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nQ-De0xcJ1lT" + }, + "source": [ + "The equivalent in JAX results in an error, as JAX arrays are immutable:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "l2AP0QERb0P7", + "outputId": "4861f124-2d6e-4987-f11b-a62bd557012f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Exception reporting mode: Minimal\n" + ] + } + ], + "source": [ + "%xmode minimal" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "pCPX0JR-yM4i", + "outputId": "00ed44d6-75a0-4094-bfc9-06aad9292db4", + "tags": [ + "raises-exception" + ], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + } + }, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "JAX arrays are immutable and do not support in-place item assignment. Instead of x[idx] = y, use x = x.at[idx].set(y) or another .at[] method: https://docs.jax.dev/en/latest/_autosummary/jax.numpy.ndarray.at.html", + "traceback": [ + "\u001b[0;31mTypeError\u001b[0m\u001b[0;31m:\u001b[0m JAX arrays are immutable and do not support in-place item assignment. Instead of x[idx] = y, use x = x.at[idx].set(y) or another .at[] method: https://docs.jax.dev/en/latest/_autosummary/jax.numpy.ndarray.at.html\n" + ] + } + ], + "source": [ + "# JAX: immutable arrays\n", + "x = jnp.arange(10)\n", + "x[0] = 10" + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "GLGeO5qAYlaL" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "pTbwX4VYYljc" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yRYF0YgO3F4H" + }, + "source": [ + "For updating individual elements, JAX provides an [indexed update syntax](https://docs.jax.dev/en/latest/_autosummary/jax.numpy.ndarray.at.html#jax-numpy-ndarray-at) that returns an updated copy:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8zqPEAeP3UK5" + }, + "outputs": [], + "source": [ + "y = x.at[0].set(10)\n", + "print(x)\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0Q0PwD2YUM6" + }, + "source": [ + "You'll find a few differences between JAX arrays and NumPy arrays once you begin digging in. See also:\n", + "\n", + "- [Key concepts](https://docs.jax.dev/en/latest/key-concepts.html#jax-arrays-jax-array) for an introduction to the key concepts of JAX, such as transformations, tracing, jaxprs and pytrees.\n", + "- [šŸ”Ŗ JAX - The Sharp Bits šŸ”Ŗ](https://docs.jax.dev/en/latest/notebooks/Common_Gotchas_in_JAX.html) for common gotchas when using JAX." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "csFhIsgjYUM7" + }, + "source": [ + "## JAX arrays (`jax.Array`)\n", + "\n", + "**Key concepts:**\n", + "- Create arrays using JAX API functions.\n", + "- JAX array objects have a `devices` attribute that indicates where the array is stored.\n", + "- JAX arrays can be *sharded* across multiple devices for parallel computation.\n", + "\n", + "The default array implementation in JAX is [`jax.Array`](https://docs.jax.dev/en/latest/_autosummary/jax.Array.html#jax.Array). In many ways it is similar to\n", + "the [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) type that you may be familiar with from the NumPy package, but it\n", + "has some important differences.\n", + "\n", + "### Array creation\n", + "\n", + "We typically don't call the `jax.Array` constructor directly, but rather create arrays via JAX API functions.\n", + "For example, [`jax.numpy`](https://docs.jax.dev/en/latest/jax.numpy.html#module-jax.numpy) provides familiar NumPy-style array construction functionality\n", + "such as `jax.numpy.zeros`, `jax.numpy.linspace`, `jax.numpy.arange`, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-Ke79S7RYUM7" + }, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "x = jnp.arange(5)\n", + "isinstance(x, jax.Array)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "McNIQ90rYUM7" + }, + "source": [ + "If you use Python type annotations in your code, `jax.Array` is the appropriate\n", + "annotation for jax array objects (see [`jax.typing`](https://docs.jax.dev/en/latest/jax.typing.html#module-jax.typing) for more discussion)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ODGrfK-7YUM7" + }, + "source": [ + "### Array devices and sharding\n", + "\n", + "JAX Array objects have a `devices` method that lets you inspect where the contents of the array are stored. In the simplest cases, this will be a single CPU device:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Pnd_6Q-BYUM7" + }, + "outputs": [], + "source": [ + "x.devices()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ay5JuV62YUM7" + }, + "source": [ + "In general, an array may be [*sharded*](https://docs.jax.dev/en/latest/notebooks/Distributed_arrays_and_automatic_parallelization.html) across multiple devices, in a manner that can be inspected via the `sharding` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "55K8-epQYUM7" + }, + "outputs": [], + "source": [ + "x.sharding" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V35YfFqFYUM8" + }, + "source": [ + "Here the array is on a single device, but in general a JAX array can be\n", + "sharded across multiple devices, or even multiple hosts.\n", + "To read more about sharded arrays and parallel computation, refer to [Introduction to parallel programming](https://docs.jax.dev/en/latest/sharded-computation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aAljVsaKYUM8" + }, + "source": [ + "## Just-in-time compilation with `jax.jit`\n", + "\n", + "**Key concepts:**\n", + "\n", + "- By default JAX executes operations one at a time, in sequence.\n", + "- Using a just-in-time (JIT) compilation decorator, sequences of operations can be optimized together and run at once.\n", + "- Not all JAX code can be JIT compiled, as it requires array shapes to be static & known at compile time.\n", + "\n", + "JAX runs transparently on the GPU or TPU (falling back to CPU if you don't have one), with all JAX operations being expressed in terms of XLA. If we have a sequence of operations, we can use the [`jax.jit`](https://docs.jax.dev/en/latest/_autosummary/jax.jit.html) function to compile this sequence of operations together using the XLA compiler." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5l74lCkOYUM8" + }, + "source": [ + "For example, consider this function that normalizes the rows of a 2D matrix, expressed in terms of `jax.numpy` operations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SQj_UKGc-7kQ" + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "def norm(X):\n", + " X = X - X.mean(0)\n", + " return X / X.std(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MzCVHv2PYUM8" + }, + "source": [ + "A just-in-time compiled version of the function can be created using the `jax.jit` transform:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZZ8PO1q0YUM9" + }, + "outputs": [], + "source": [ + "from jax import jit\n", + "norm_compiled = jit(norm)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IqDN2j65YUM9" + }, + "source": [ + "This function returns the same results as the original, up to standard floating-point accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OeYy3Q5vYUM9" + }, + "outputs": [], + "source": [ + "np.random.seed(1701)\n", + "X = jnp.array(np.random.rand(10000, 10))\n", + "np.allclose(norm(X), norm_compiled(X), atol=1E-6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EmmGCeRrYUM_" + }, + "source": [ + "But due to the compilation (which includes fusing of operations, avoidance of allocating temporary arrays, and a host of other tricks), execution times can be orders of magnitude faster in the JIT-compiled case. We can use IPython's `%timeit` to quickly benchmark our function, using `block_until_ready()` to account for JAX's [asynchronous dispatch](https://docs.jax.dev/en/latest/async_dispatch.html):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FPAu49iqYUM_" + }, + "outputs": [], + "source": [ + "%timeit norm(X).block_until_ready()\n", + "%timeit norm_compiled(X).block_until_ready()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ft8T3Y0cYUM_" + }, + "source": [ + "That said, `jax.jit` does have limitations: in particular, it requires all arrays to have static shapes. That means that some JAX operations are incompatible with JIT compilation.\n", + "\n", + "For example, this operation can be executed in op-by-op mode:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "q52JjRnJYUNA" + }, + "outputs": [], + "source": [ + "def get_negatives(x):\n", + " return x[x < 0]\n", + "\n", + "x = jnp.array(np.random.randn(10))\n", + "get_negatives(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IvvLm-iYYUNA" + }, + "source": [ + "But it returns an error if you attempt to execute it in jit mode:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ], + "id": "icdxGFuRYUNA" + }, + "outputs": [], + "source": [ + "jit(get_negatives)(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xilRzJr5YUNB" + }, + "source": [ + "This is because the function generates an array whose shape is not known at compile time: the size of the output depends on the values of the input array, and so it is not compatible with JIT." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AVd5BvLkYUNB" + }, + "source": [ + "For more on JIT compilation in JAX, check out [Just-in-time compilation](https://docs.jax.dev/en/latest/jit-compilation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Foe_K6NtYUNB" + }, + "source": [ + "## Taking derivatives with `jax.grad`\n", + "\n", + "**Key concepts:**\n", + "- JAX provides automatic differentiation via the `jax.grad` transformation.\n", + "- The `jax.grad` and `jax.jit` transformations compose and can be mixed arbitrarily.\n", + "\n", + "In addition to transforming functions via JIT compilation, JAX also provides other transformations. One such transformation is [`jax.grad`](https://docs.jax.dev/en/latest/_autosummary/jax.grad.html), which performs [automatic differentiation (autodiff)](https://en.wikipedia.org/wiki/Automatic_differentiation):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Go_4PYveYUNB" + }, + "outputs": [], + "source": [ + "from jax import grad\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + "print(derivative_fn(x_small))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rlag8ZRHYUNB" + }, + "source": [ + "Let's verify with finite differences that our result is correct." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NvsnS8DjYUNB" + }, + "outputs": [], + "source": [ + "def first_finite_differences(f, x, eps=1E-3):\n", + " return jnp.array([(f(x + eps * v) - f(x - eps * v)) / (2 * eps)\n", + " for v in jnp.eye(len(x))])\n", + "\n", + "print(first_finite_differences(sum_logistic, x_small))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "thRCyT5FYUNB" + }, + "source": [ + "The [`jax.grad`](https://docs.jax.dev/en/latest/_autosummary/jax.grad.html) and [`jax.jit`](https://docs.jax.dev/en/latest/_autosummary/jax.jit.html) transformations compose and can be mixed arbitrarily.\n", + "For instance, while the `sum_logistic` function was differentiated directly in the previous example, it could also be JIT-compiled, and these operations can be combined. We can go further:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ozheMCj3YUNB" + }, + "outputs": [], + "source": [ + "print(grad(jit(grad(jit(grad(sum_logistic)))))(1.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ikNGfl3nYUNB" + }, + "source": [ + "Beyond scalar-valued functions, the [`jax.jacobian`](https://docs.jax.dev/en/latest/_autosummary/jax.jacobian.html) transformation can be\n", + "used to compute the full Jacobian matrix for vector-valued functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "p000jyMSYUNB" + }, + "outputs": [], + "source": [ + "from jax import jacobian\n", + "print(jacobian(jnp.exp)(x_small))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WGA8D4yaYUNB" + }, + "source": [ + "For more advanced autodiff operations, you can use [`jax.vjp`](https://docs.jax.dev/en/latest/_autosummary/jax.vjp.html) for reverse-mode vector-Jacobian products,\n", + "and [`jax.jvp`](https://docs.jax.dev/en/latest/_autosummary/jax.jvp.html) and [`jax.linearize`](https://docs.jax.dev/en/latest/_autosummary/jax.linearize.html) for forward-mode Jacobian-vector products.\n", + "The two can be composed arbitrarily with one another, and with other JAX transformations.\n", + "For example, `jax.jvp` and `jax.vjp` are used to define the forward-mode [`jax.jacfwd`](https://docs.jax.dev/en/latest/_autosummary/jax.jacfwd.html) and reverse-mode [`jax.jacrev`](https://docs.jax.dev/en/latest/_autosummary/jax.jacrev.html) for computing Jacobians in forward- and reverse-mode, respectively.\n", + "Here's one way to compose them to make a function that efficiently computes full Hessian matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TtPncRBnYUNC" + }, + "outputs": [], + "source": [ + "from jax import jacfwd, jacrev\n", + "def hessian(fun):\n", + " return jit(jacfwd(jacrev(fun)))\n", + "print(hessian(sum_logistic)(x_small))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9FJV_m5jYUNC" + }, + "source": [ + "This kind of composition produces efficient code in practice; this is more-or-less how JAX's built-in [`jax.hessian`](https://docs.jax.dev/en/latest/_autosummary/jax.hessian.html) function is implemented.\n", + "\n", + "For more on automatic differentiation in JAX, check out [Automatic differentiation](https://docs.jax.dev/en/latest/automatic-differentiation.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z36CRZfcYUNC" + }, + "source": [ + "## Auto-vectorization with `jax.vmap`\n", + "\n", + "**Key concepts:**\n", + "- JAX provides automatic vectorization via the [`jax.vmap`](https://docs.jax.dev/en/latest/_autosummary/jax.vmap.html) transformation.\n", + "- `jax.vmap` can be composed with `jax.jit` to produce efficient vectorized code.\n", + "\n", + "Another useful transformation is [`jax.vmap`](https://docs.jax.dev/en/latest/_autosummary/jax.vmap.html), the vectorizing map.\n", + "It has the familiar semantics of mapping a function along array axes, but instead of explicitly looping\n", + "over function calls, it transforms the function into a natively vectorized version for better performance.\n", + "When composed with [`jax.jit`](https://docs.jax.dev/en/latest/_autosummary/jax.jit.html), it can be just as performant as manually rewriting your function\n", + "to operate over an extra batch dimension.\n", + "\n", + "We're going to work with a simple example, and promote matrix-vector products into matrix-matrix products using [`jax.vmap`](https://docs.jax.dev/en/latest/_autosummary/jax.vmap.html).\n", + "Although this is easy to do by hand in this specific case, the same technique can apply to more complicated functions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cvLNXH0EYUNC" + }, + "outputs": [], + "source": [ + "from jax import random\n", + "\n", + "key = random.key(1701)\n", + "key1, key2 = random.split(key)\n", + "mat = random.normal(key1, (150, 100))\n", + "batched_x = random.normal(key2, (10, 100))\n", + "\n", + "def apply_matrix(x):\n", + " return jnp.dot(mat, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wRa-Nv-RYUNC" + }, + "source": [ + "The `apply_matrix` function maps a vector to a vector, but we may want to apply it row-wise across a matrix.\n", + "We could do this by looping over the batch dimension in Python, but this usually results in poor performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JWGZiDllYUNC" + }, + "outputs": [], + "source": [ + "def naively_batched_apply_matrix(v_batched):\n", + " return jnp.stack([apply_matrix(v) for v in v_batched])\n", + "\n", + "print('Naively batched')\n", + "%timeit naively_batched_apply_matrix(batched_x).block_until_ready()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I-qqD1KwYUNC" + }, + "source": [ + "A programmer familiar with the `jnp.dot` function might recognize that `apply_matrix` can\n", + "be rewritten to avoid explicit looping, using the built-in batching semantics of `jnp.dot`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sNu0zFNyYUNC" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "@jit\n", + "def batched_apply_matrix(batched_x):\n", + " return jnp.dot(batched_x, mat.T)\n", + "\n", + "np.testing.assert_allclose(naively_batched_apply_matrix(batched_x),\n", + " batched_apply_matrix(batched_x), atol=1E-4, rtol=1E-4)\n", + "print('Manually batched')\n", + "%timeit batched_apply_matrix(batched_x).block_until_ready()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UcBvHRYbYUNC" + }, + "source": [ + "However, as functions become more complicated, this kind of manual batching becomes more difficult and error-prone.\n", + "The `jax.vmap` transformation is designed to automatically transform a function into a batch-aware version:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vg6Sy-tTYUNC" + }, + "outputs": [], + "source": [ + "from jax import vmap\n", + "\n", + "@jit\n", + "def vmap_batched_apply_matrix(batched_x):\n", + " return vmap(apply_matrix)(batched_x)\n", + "\n", + "np.testing.assert_allclose(naively_batched_apply_matrix(batched_x),\n", + " vmap_batched_apply_matrix(batched_x), atol=1E-4, rtol=1E-4)\n", + "print('Auto-vectorized with vmap')\n", + "%timeit vmap_batched_apply_matrix(batched_x).block_until_ready()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yFrhG7FpYUND" + }, + "source": [ + "As you would expect, `jax.vmap` can be arbitrarily composed with `jax.jit`,\n", + "`jax.grad`, and any other JAX transformation.\n", + "\n", + "For more on automatic vectorization in JAX, check out [Automatic vectorization](https://docs.jax.dev/en/latest/automatic-vectorization.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gPnDPvZFYUND" + }, + "source": [ + "(key-concepts-prngs)=\n", + "## Pseudorandom numbers\n", + "\n", + "**Key concepts:**\n", + "\n", + "- JAX uses a different model for pseudo random number generation than NumPy.\n", + "- JAX random functions consume a random `key` that must be split to generate new independent keys.\n", + "- JAX's random key model is thread-safe and avoids issues with global state.\n", + "\n", + "Generally, JAX strives to be compatible with NumPy, but pseudo random number generation is a notable exception. NumPy supports a method of pseudo random number generation that is based on a global `state`, which can be set using [`numpy.random.seed`](https://numpy.org/doc/stable/reference/random/generated/numpy.random.seed.html). Global random state interacts poorly with JAX's compute model and makes it difficult to enforce reproducibility across different threads, processes, and devices. JAX instead tracks state explicitly via a random `key`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oO9zv4qzYUND" + }, + "outputs": [], + "source": [ + "from jax import random\n", + "\n", + "key = random.key(43)\n", + "print(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IC19u-fMYUND" + }, + "source": [ + "The key is effectively a stand-in for NumPy's hidden state object, but we pass it explicitly to [`jax.random`](https://docs.jax.dev/en/latest/jax.random.html) functions. Importantly, random functions consume the key, but do not modify it: feeding the same key object to a random function will always result in the same sample being generated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4katRLceYUND" + }, + "outputs": [], + "source": [ + "print(random.normal(key))\n", + "print(random.normal(key))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ErimsFIDYUND" + }, + "source": [ + "**The rule of thumb is: never reuse keys (unless you want identical outputs).**\n", + "\n", + "In order to generate different and independent samples, you must [`jax.random.split`](https://docs.jax.dev/en/latest/_autosummary/jax.random.split.html) the key explicitly before passing it to a random function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rbrDM1c3YUND" + }, + "outputs": [], + "source": [ + "for i in range(3):\n", + " new_key, subkey = random.split(key)\n", + " del key # The old key is consumed by split() -- we must never use it again.\n", + "\n", + " val = random.normal(subkey)\n", + " del subkey # The subkey is consumed by normal().\n", + "\n", + " print(f\"draw {i}: {val}\")\n", + " key = new_key # new_key is safe to use in the next iteration." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3qcubirBYUND" + }, + "source": [ + "Note that this code is thread safe, since the local random state eliminates possible race conditions involving global state. `jax.random.split` is a deterministic function that converts one `key` into several independent (in the pseudorandomness sense) keys.\n", + "\n", + "For more on pseudo random numbers in JAX, see the [Pseudorandom numbers tutorial](https://docs.jax.dev/en/latest/random-numbers.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VGgJzHmpYUND" + }, + "source": [ + "---\n", + "\n", + "This is just a taste of what JAX can do. We're really excited to see what you do with it!" + ] + } + ], + "metadata": { + "colab": { + "name": "thinking_in_jax.ipynb", + "provenance": [] + }, + "jupytext": { + "formats": "ipynb,md:myst" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/profile/README.md b/profile/README.md new file mode 100644 index 0000000..f01e93f --- /dev/null +++ b/profile/README.md @@ -0,0 +1,16 @@ +## Hi there šŸ‘‹ + + diff --git a/server.log b/server.log new file mode 100644 index 0000000..bde8062 --- /dev/null +++ b/server.log @@ -0,0 +1,9 @@ + +> dashboard@0.0.0 dev +> vite + + + VITE v7.3.0 ready in 676 ms + + āžœ Local: http://localhost:5173/ + āžœ Network: use --host to expose