).';\n\n\t\tinitialized = true;\n\n\t\t// Cache references to key DOM elements\n\t\tdom.wrapper = revealElement;\n\t\tdom.slides = revealElement.querySelector( '.slides' );\n\n\t\tif( !dom.slides ) throw 'Unable to find slides container (
).';\n\n\t\t// Compose our config object in order of increasing precedence:\n\t\t// 1. Default reveal.js options\n\t\t// 2. Options provided via Reveal.configure() prior to\n\t\t// initialization\n\t\t// 3. Options passed to the Reveal constructor\n\t\t// 4. Options passed to Reveal.initialize\n\t\t// 5. Query params\n\t\tconfig = { ...defaultConfig, ...config, ...options, ...initOptions, ...Util.getQueryHash() };\n\n\t\t// Legacy support for the ?print-pdf query\n\t\tif( /print-pdf/gi.test( window.location.search ) ) {\n\t\t\tconfig.view = 'print';\n\t\t}\n\n\t\tsetViewport();\n\n\t\t// Force a layout when the whole page, incl fonts, has loaded\n\t\twindow.addEventListener( 'load', layout, false );\n\n\t\t// Register plugins and load dependencies, then move on to #start()\n\t\tplugins.load( config.plugins, config.dependencies ).then( start );\n\n\t\treturn new Promise( resolve => Reveal.on( 'ready', resolve ) );\n\n\t}\n\n\t/**\n\t * Encase the presentation in a reveal.js viewport. The\n\t * extent of the viewport differs based on configuration.\n\t */\n\tfunction setViewport() {\n\n\t\t// Embedded decks use the reveal element as their viewport\n\t\tif( config.embedded === true ) {\n\t\t\tdom.viewport = Util.closest( revealElement, '.reveal-viewport' ) || revealElement;\n\t\t}\n\t\t// Full-page decks use the body as their viewport\n\t\telse {\n\t\t\tdom.viewport = document.body;\n\t\t\tdocument.documentElement.classList.add( 'reveal-full-page' );\n\t\t}\n\n\t\tdom.viewport.classList.add( 'reveal-viewport' );\n\n\t}\n\n\t/**\n\t * Starts up reveal.js by binding input events and navigating\n\t * to the current URL deeplink if there is one.\n\t */\n\tfunction start() {\n\n\t\tready = true;\n\n\t\t// Remove slides hidden with data-visibility\n\t\tremoveHiddenSlides();\n\n\t\t// Make sure we've got all the DOM elements we need\n\t\tsetupDOM();\n\n\t\t// Listen to messages posted to this window\n\t\tsetupPostMessage();\n\n\t\t// Prevent the slides from being scrolled out of view\n\t\tsetupScrollPrevention();\n\n\t\t// Adds bindings for fullscreen mode\n\t\tsetupFullscreen();\n\n\t\t// Resets all vertical slides so that only the first is visible\n\t\tresetVerticalSlides();\n\n\t\t// Updates the presentation to match the current configuration values\n\t\tconfigure();\n\n\t\t// Create slide backgrounds\n\t\tbackgrounds.update( true );\n\n\t\t// Activate the print/scroll view if configured\n\t\tactivateInitialView();\n\n\t\t// Read the initial hash\n\t\tlocation.readURL();\n\n\t\t// Notify listeners that the presentation is ready but use a 1ms\n\t\t// timeout to ensure it's not fired synchronously after #initialize()\n\t\tsetTimeout( () => {\n\t\t\t// Enable transitions now that we're loaded\n\t\t\tdom.slides.classList.remove( 'no-transition' );\n\n\t\t\tdom.wrapper.classList.add( 'ready' );\n\n\t\t\tdispatchEvent({\n\t\t\t\ttype: 'ready',\n\t\t\t\tdata: {\n\t\t\t\t\tindexh,\n\t\t\t\t\tindexv,\n\t\t\t\t\tcurrentSlide\n\t\t\t\t}\n\t\t\t});\n\t\t}, 1 );\n\n\t}\n\n\t/**\n\t * Activates the correct reveal.js view based on our config.\n\t * This is only invoked once during initialization.\n\t */\n\tfunction activateInitialView() {\n\n\t\tconst activatePrintView = config.view === 'print';\n\t\tconst activateScrollView = config.view === 'scroll' || config.view === 'reader';\n\n\t\tif( activatePrintView || activateScrollView ) {\n\n\t\t\tif( activatePrintView ) {\n\t\t\t\tremoveEventListeners();\n\t\t\t}\n\t\t\telse {\n\t\t\t\ttouch.unbind();\n\t\t\t}\n\n\t\t\t// Avoid content flickering during layout\n\t\t\tdom.viewport.classList.add( 'loading-scroll-mode' );\n\n\t\t\tif( activatePrintView ) {\n\t\t\t\t// The document needs to have loaded for the PDF layout\n\t\t\t\t// measurements to be accurate\n\t\t\t\tif( document.readyState === 'complete' ) {\n\t\t\t\t\tprintView.activate();\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\twindow.addEventListener( 'load', () => printView.activate() );\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\tscrollView.activate();\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Removes all slides with data-visibility=\"hidden\". This\n\t * is done right before the rest of the presentation is\n\t * initialized.\n\t *\n\t * If you want to show all hidden slides, initialize\n\t * reveal.js with showHiddenSlides set to true.\n\t */\n\tfunction removeHiddenSlides() {\n\n\t\tif( !config.showHiddenSlides ) {\n\t\t\tUtil.queryAll( dom.wrapper, 'section[data-visibility=\"hidden\"]' ).forEach( slide => {\n\t\t\t\tconst parent = slide.parentNode;\n\n\t\t\t\t// If this slide is part of a stack and that stack will be\n\t\t\t\t// empty after removing the hidden slide, remove the entire\n\t\t\t\t// stack\n\t\t\t\tif( parent.childElementCount === 1 && /section/i.test( parent.nodeName ) ) {\n\t\t\t\t\tparent.remove();\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tslide.remove();\n\t\t\t\t}\n\n\t\t\t} );\n\t\t}\n\n\t}\n\n\t/**\n\t * Finds and stores references to DOM elements which are\n\t * required by the presentation. If a required element is\n\t * not found, it is created.\n\t */\n\tfunction setupDOM() {\n\n\t\t// Prevent transitions while we're loading\n\t\tdom.slides.classList.add( 'no-transition' );\n\n\t\tif( Device.isMobile ) {\n\t\t\tdom.wrapper.classList.add( 'no-hover' );\n\t\t}\n\t\telse {\n\t\t\tdom.wrapper.classList.remove( 'no-hover' );\n\t\t}\n\n\t\tbackgrounds.render();\n\t\tslideNumber.render();\n\t\tjumpToSlide.render();\n\t\tcontrols.render();\n\t\tprogress.render();\n\t\tnotes.render();\n\n\t\t// Overlay graphic which is displayed during the paused mode\n\t\tdom.pauseOverlay = Util.createSingletonNode( dom.wrapper, 'div', 'pause-overlay', config.controls ? '
' : null );\n\n\t\tdom.statusElement = createStatusElement();\n\n\t\tdom.wrapper.setAttribute( 'role', 'application' );\n\t}\n\n\t/**\n\t * Creates a hidden div with role aria-live to announce the\n\t * current slide content. Hide the div off-screen to make it\n\t * available only to Assistive Technologies.\n\t *\n\t * @return {HTMLElement}\n\t */\n\tfunction createStatusElement() {\n\n\t\tlet statusElement = dom.wrapper.querySelector( '.aria-status' );\n\t\tif( !statusElement ) {\n\t\t\tstatusElement = document.createElement( 'div' );\n\t\t\tstatusElement.style.position = 'absolute';\n\t\t\tstatusElement.style.height = '1px';\n\t\t\tstatusElement.style.width = '1px';\n\t\t\tstatusElement.style.overflow = 'hidden';\n\t\t\tstatusElement.style.clip = 'rect( 1px, 1px, 1px, 1px )';\n\t\t\tstatusElement.classList.add( 'aria-status' );\n\t\t\tstatusElement.setAttribute( 'aria-live', 'polite' );\n\t\t\tstatusElement.setAttribute( 'aria-atomic','true' );\n\t\t\tdom.wrapper.appendChild( statusElement );\n\t\t}\n\t\treturn statusElement;\n\n\t}\n\n\t/**\n\t * Announces the given text to screen readers.\n\t */\n\tfunction announceStatus( value ) {\n\n\t\tdom.statusElement.textContent = value;\n\n\t}\n\n\t/**\n\t * Converts the given HTML element into a string of text\n\t * that can be announced to a screen reader. Hidden\n\t * elements are excluded.\n\t */\n\tfunction getStatusText( node ) {\n\n\t\tlet text = '';\n\n\t\t// Text node\n\t\tif( node.nodeType === 3 ) {\n\t\t\ttext += node.textContent;\n\t\t}\n\t\t// Element node\n\t\telse if( node.nodeType === 1 ) {\n\n\t\t\tlet isAriaHidden = node.getAttribute( 'aria-hidden' );\n\t\t\tlet isDisplayHidden = window.getComputedStyle( node )['display'] === 'none';\n\t\t\tif( isAriaHidden !== 'true' && !isDisplayHidden ) {\n\n\t\t\t\tArray.from( node.childNodes ).forEach( child => {\n\t\t\t\t\ttext += getStatusText( child );\n\t\t\t\t} );\n\n\t\t\t}\n\n\t\t}\n\n\t\ttext = text.trim();\n\n\t\treturn text === '' ? '' : text + ' ';\n\n\t}\n\n\t/**\n\t * This is an unfortunate necessity. Some actions β such as\n\t * an input field being focused in an iframe or using the\n\t * keyboard to expand text selection beyond the bounds of\n\t * a slide β can trigger our content to be pushed out of view.\n\t * This scrolling can not be prevented by hiding overflow in\n\t * CSS (we already do) so we have to resort to repeatedly\n\t * checking if the slides have been offset :(\n\t */\n\tfunction setupScrollPrevention() {\n\n\t\tsetInterval( () => {\n\t\t\tif( !scrollView.isActive() && dom.wrapper.scrollTop !== 0 || dom.wrapper.scrollLeft !== 0 ) {\n\t\t\t\tdom.wrapper.scrollTop = 0;\n\t\t\t\tdom.wrapper.scrollLeft = 0;\n\t\t\t}\n\t\t}, 1000 );\n\n\t}\n\n\t/**\n\t * After entering fullscreen we need to force a layout to\n\t * get our presentations to scale correctly. This behavior\n\t * is inconsistent across browsers but a force layout seems\n\t * to normalize it.\n\t */\n\tfunction setupFullscreen() {\n\n\t\tdocument.addEventListener( 'fullscreenchange', onFullscreenChange );\n\t\tdocument.addEventListener( 'webkitfullscreenchange', onFullscreenChange );\n\n\t}\n\n\t/**\n\t * Registers a listener to postMessage events, this makes it\n\t * possible to call all reveal.js API methods from another\n\t * window. For example:\n\t *\n\t * revealWindow.postMessage( JSON.stringify({\n\t * method: 'slide',\n\t * args: [ 2 ]\n\t * }), '*' );\n\t */\n\tfunction setupPostMessage() {\n\n\t\tif( config.postMessage ) {\n\t\t\twindow.addEventListener( 'message', onPostMessage, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Applies the configuration settings from the config\n\t * object. May be called multiple times.\n\t *\n\t * @param {object} options\n\t */\n\tfunction configure( options ) {\n\n\t\tconst oldConfig = { ...config }\n\n\t\t// New config options may be passed when this method\n\t\t// is invoked through the API after initialization\n\t\tif( typeof options === 'object' ) Util.extend( config, options );\n\n\t\t// Abort if reveal.js hasn't finished loading, config\n\t\t// changes will be applied automatically once ready\n\t\tif( Reveal.isReady() === false ) return;\n\n\t\tconst numberOfSlides = dom.wrapper.querySelectorAll( SLIDES_SELECTOR ).length;\n\n\t\t// The transition is added as a class on the .reveal element\n\t\tdom.wrapper.classList.remove( oldConfig.transition );\n\t\tdom.wrapper.classList.add( config.transition );\n\n\t\tdom.wrapper.setAttribute( 'data-transition-speed', config.transitionSpeed );\n\t\tdom.wrapper.setAttribute( 'data-background-transition', config.backgroundTransition );\n\n\t\t// Expose our configured slide dimensions as custom props\n\t\tdom.viewport.style.setProperty( '--slide-width', typeof config.width === 'string' ? config.width : config.width + 'px' );\n\t\tdom.viewport.style.setProperty( '--slide-height', typeof config.height === 'string' ? config.height : config.height + 'px' );\n\n\t\tif( config.shuffle ) {\n\t\t\tshuffle();\n\t\t}\n\n\t\tUtil.toggleClass( dom.wrapper, 'embedded', config.embedded );\n\t\tUtil.toggleClass( dom.wrapper, 'rtl', config.rtl );\n\t\tUtil.toggleClass( dom.wrapper, 'center', config.center );\n\n\t\t// Exit the paused mode if it was configured off\n\t\tif( config.pause === false ) {\n\t\t\tresume();\n\t\t}\n\n\t\t// Iframe link previews\n\t\tif( config.previewLinks ) {\n\t\t\tenablePreviewLinks();\n\t\t\tdisablePreviewLinks( '[data-preview-link=false]' );\n\t\t}\n\t\telse {\n\t\t\tdisablePreviewLinks();\n\t\t\tenablePreviewLinks( '[data-preview-link]:not([data-preview-link=false])' );\n\t\t}\n\n\t\t// Reset all changes made by auto-animations\n\t\tautoAnimate.reset();\n\n\t\t// Remove existing auto-slide controls\n\t\tif( autoSlidePlayer ) {\n\t\t\tautoSlidePlayer.destroy();\n\t\t\tautoSlidePlayer = null;\n\t\t}\n\n\t\t// Generate auto-slide controls if needed\n\t\tif( numberOfSlides > 1 && config.autoSlide && config.autoSlideStoppable ) {\n\t\t\tautoSlidePlayer = new Playback( dom.wrapper, () => {\n\t\t\t\treturn Math.min( Math.max( ( Date.now() - autoSlideStartTime ) / autoSlide, 0 ), 1 );\n\t\t\t} );\n\n\t\t\tautoSlidePlayer.on( 'click', onAutoSlidePlayerClick );\n\t\t\tautoSlidePaused = false;\n\t\t}\n\n\t\t// Add the navigation mode to the DOM so we can adjust styling\n\t\tif( config.navigationMode !== 'default' ) {\n\t\t\tdom.wrapper.setAttribute( 'data-navigation-mode', config.navigationMode );\n\t\t}\n\t\telse {\n\t\t\tdom.wrapper.removeAttribute( 'data-navigation-mode' );\n\t\t}\n\n\t\tnotes.configure( config, oldConfig );\n\t\tfocus.configure( config, oldConfig );\n\t\tpointer.configure( config, oldConfig );\n\t\tcontrols.configure( config, oldConfig );\n\t\tprogress.configure( config, oldConfig );\n\t\tkeyboard.configure( config, oldConfig );\n\t\tfragments.configure( config, oldConfig );\n\t\tslideNumber.configure( config, oldConfig );\n\n\t\tsync();\n\n\t}\n\n\t/**\n\t * Binds all event listeners.\n\t */\n\tfunction addEventListeners() {\n\n\t\teventsAreBound = true;\n\n\t\twindow.addEventListener( 'resize', onWindowResize, false );\n\n\t\tif( config.touch ) touch.bind();\n\t\tif( config.keyboard ) keyboard.bind();\n\t\tif( config.progress ) progress.bind();\n\t\tif( config.respondToHashChanges ) location.bind();\n\t\tcontrols.bind();\n\t\tfocus.bind();\n\n\t\tdom.slides.addEventListener( 'click', onSlidesClicked, false );\n\t\tdom.slides.addEventListener( 'transitionend', onTransitionEnd, false );\n\t\tdom.pauseOverlay.addEventListener( 'click', resume, false );\n\n\t\tif( config.focusBodyOnPageVisibilityChange ) {\n\t\t\tdocument.addEventListener( 'visibilitychange', onPageVisibilityChange, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Unbinds all event listeners.\n\t */\n\tfunction removeEventListeners() {\n\n\t\teventsAreBound = false;\n\n\t\ttouch.unbind();\n\t\tfocus.unbind();\n\t\tkeyboard.unbind();\n\t\tcontrols.unbind();\n\t\tprogress.unbind();\n\t\tlocation.unbind();\n\n\t\twindow.removeEventListener( 'resize', onWindowResize, false );\n\n\t\tdom.slides.removeEventListener( 'click', onSlidesClicked, false );\n\t\tdom.slides.removeEventListener( 'transitionend', onTransitionEnd, false );\n\t\tdom.pauseOverlay.removeEventListener( 'click', resume, false );\n\n\t}\n\n\t/**\n\t * Uninitializes reveal.js by undoing changes made to the\n\t * DOM and removing all event listeners.\n\t */\n\tfunction destroy() {\n\n\t\t// There's nothing to destroy if this instance hasn't been\n\t\t// initialized yet\n\t\tif( initialized === false ) return;\n\n\t\tremoveEventListeners();\n\t\tcancelAutoSlide();\n\t\tdisablePreviewLinks();\n\n\t\t// Destroy controllers\n\t\tnotes.destroy();\n\t\tfocus.destroy();\n\t\tplugins.destroy();\n\t\tpointer.destroy();\n\t\tcontrols.destroy();\n\t\tprogress.destroy();\n\t\tbackgrounds.destroy();\n\t\tslideNumber.destroy();\n\t\tjumpToSlide.destroy();\n\n\t\t// Remove event listeners\n\t\tdocument.removeEventListener( 'fullscreenchange', onFullscreenChange );\n\t\tdocument.removeEventListener( 'webkitfullscreenchange', onFullscreenChange );\n\t\tdocument.removeEventListener( 'visibilitychange', onPageVisibilityChange, false );\n\t\twindow.removeEventListener( 'message', onPostMessage, false );\n\t\twindow.removeEventListener( 'load', layout, false );\n\n\t\t// Undo DOM changes\n\t\tif( dom.pauseOverlay ) dom.pauseOverlay.remove();\n\t\tif( dom.statusElement ) dom.statusElement.remove();\n\n\t\tdocument.documentElement.classList.remove( 'reveal-full-page' );\n\n\t\tdom.wrapper.classList.remove( 'ready', 'center', 'has-horizontal-slides', 'has-vertical-slides' );\n\t\tdom.wrapper.removeAttribute( 'data-transition-speed' );\n\t\tdom.wrapper.removeAttribute( 'data-background-transition' );\n\n\t\tdom.viewport.classList.remove( 'reveal-viewport' );\n\t\tdom.viewport.style.removeProperty( '--slide-width' );\n\t\tdom.viewport.style.removeProperty( '--slide-height' );\n\n\t\tdom.slides.style.removeProperty( 'width' );\n\t\tdom.slides.style.removeProperty( 'height' );\n\t\tdom.slides.style.removeProperty( 'zoom' );\n\t\tdom.slides.style.removeProperty( 'left' );\n\t\tdom.slides.style.removeProperty( 'top' );\n\t\tdom.slides.style.removeProperty( 'bottom' );\n\t\tdom.slides.style.removeProperty( 'right' );\n\t\tdom.slides.style.removeProperty( 'transform' );\n\n\t\tArray.from( dom.wrapper.querySelectorAll( SLIDES_SELECTOR ) ).forEach( slide => {\n\t\t\tslide.style.removeProperty( 'display' );\n\t\t\tslide.style.removeProperty( 'top' );\n\t\t\tslide.removeAttribute( 'hidden' );\n\t\t\tslide.removeAttribute( 'aria-hidden' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Adds a listener to one of our custom reveal.js events,\n\t * like slidechanged.\n\t */\n\tfunction on( type, listener, useCapture ) {\n\n\t\trevealElement.addEventListener( type, listener, useCapture );\n\n\t}\n\n\t/**\n\t * Unsubscribes from a reveal.js event.\n\t */\n\tfunction off( type, listener, useCapture ) {\n\n\t\trevealElement.removeEventListener( type, listener, useCapture );\n\n\t}\n\n\t/**\n\t * Applies CSS transforms to the slides container. The container\n\t * is transformed from two separate sources: layout and the overview\n\t * mode.\n\t *\n\t * @param {object} transforms\n\t */\n\tfunction transformSlides( transforms ) {\n\n\t\t// Pick up new transforms from arguments\n\t\tif( typeof transforms.layout === 'string' ) slidesTransform.layout = transforms.layout;\n\t\tif( typeof transforms.overview === 'string' ) slidesTransform.overview = transforms.overview;\n\n\t\t// Apply the transforms to the slides container\n\t\tif( slidesTransform.layout ) {\n\t\t\tUtil.transformElement( dom.slides, slidesTransform.layout + ' ' + slidesTransform.overview );\n\t\t}\n\t\telse {\n\t\t\tUtil.transformElement( dom.slides, slidesTransform.overview );\n\t\t}\n\n\t}\n\n\t/**\n\t * Dispatches an event of the specified type from the\n\t * reveal DOM element.\n\t */\n\tfunction dispatchEvent({ target=dom.wrapper, type, data, bubbles=true }) {\n\n\t\tlet event = document.createEvent( 'HTMLEvents', 1, 2 );\n\t\tevent.initEvent( type, bubbles, true );\n\t\tUtil.extend( event, data );\n\t\ttarget.dispatchEvent( event );\n\n\t\tif( target === dom.wrapper ) {\n\t\t\t// If we're in an iframe, post each reveal.js event to the\n\t\t\t// parent window. Used by the notes plugin\n\t\t\tdispatchPostMessage( type );\n\t\t}\n\n\t\treturn event;\n\n\t}\n\n\t/**\n\t * Dispatches a slidechanged event.\n\t *\n\t * @param {string} origin Used to identify multiplex clients\n\t */\n\tfunction dispatchSlideChanged( origin ) {\n\n\t\tdispatchEvent({\n\t\t\ttype: 'slidechanged',\n\t\t\tdata: {\n\t\t\t\tindexh,\n\t\t\t\tindexv,\n\t\t\t\tpreviousSlide,\n\t\t\t\tcurrentSlide,\n\t\t\t\torigin\n\t\t\t}\n\t\t});\n\n\t}\n\n\t/**\n\t * Dispatched a postMessage of the given type from our window.\n\t */\n\tfunction dispatchPostMessage( type, data ) {\n\n\t\tif( config.postMessageEvents && window.parent !== window.self ) {\n\t\t\tlet message = {\n\t\t\t\tnamespace: 'reveal',\n\t\t\t\teventName: type,\n\t\t\t\tstate: getState()\n\t\t\t};\n\n\t\t\tUtil.extend( message, data );\n\n\t\t\twindow.parent.postMessage( JSON.stringify( message ), '*' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Bind preview frame links.\n\t *\n\t * @param {string} [selector=a] - selector for anchors\n\t */\n\tfunction enablePreviewLinks( selector = 'a' ) {\n\n\t\tArray.from( dom.wrapper.querySelectorAll( selector ) ).forEach( element => {\n\t\t\tif( /^(http|www)/gi.test( element.getAttribute( 'href' ) ) ) {\n\t\t\t\telement.addEventListener( 'click', onPreviewLinkClicked, false );\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Unbind preview frame links.\n\t */\n\tfunction disablePreviewLinks( selector = 'a' ) {\n\n\t\tArray.from( dom.wrapper.querySelectorAll( selector ) ).forEach( element => {\n\t\t\tif( /^(http|www)/gi.test( element.getAttribute( 'href' ) ) ) {\n\t\t\t\telement.removeEventListener( 'click', onPreviewLinkClicked, false );\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Opens a preview window for the target URL.\n\t *\n\t * @param {string} url - url for preview iframe src\n\t */\n\tfunction showPreview( url ) {\n\n\t\tcloseOverlay();\n\n\t\tdom.overlay = document.createElement( 'div' );\n\t\tdom.overlay.classList.add( 'overlay' );\n\t\tdom.overlay.classList.add( 'overlay-preview' );\n\t\tdom.wrapper.appendChild( dom.overlay );\n\n\t\tdom.overlay.innerHTML =\n\t\t\t`
\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t
\n\t\t\t
\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tUnable to load iframe. This is likely due to the site's policy (x-frame-options).\n\t\t\t\t\n\t\t\t
`;\n\n\t\tdom.overlay.querySelector( 'iframe' ).addEventListener( 'load', event => {\n\t\t\tdom.overlay.classList.add( 'loaded' );\n\t\t}, false );\n\n\t\tdom.overlay.querySelector( '.close' ).addEventListener( 'click', event => {\n\t\t\tcloseOverlay();\n\t\t\tevent.preventDefault();\n\t\t}, false );\n\n\t\tdom.overlay.querySelector( '.external' ).addEventListener( 'click', event => {\n\t\t\tcloseOverlay();\n\t\t}, false );\n\n\t}\n\n\t/**\n\t * Open or close help overlay window.\n\t *\n\t * @param {Boolean} [override] Flag which overrides the\n\t * toggle logic and forcibly sets the desired state. True means\n\t * help is open, false means it's closed.\n\t */\n\tfunction toggleHelp( override ){\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? showHelp() : closeOverlay();\n\t\t}\n\t\telse {\n\t\t\tif( dom.overlay ) {\n\t\t\t\tcloseOverlay();\n\t\t\t}\n\t\t\telse {\n\t\t\t\tshowHelp();\n\t\t\t}\n\t\t}\n\t}\n\n\t/**\n\t * Opens an overlay window with help material.\n\t */\n\tfunction showHelp() {\n\n\t\tif( config.help ) {\n\n\t\t\tcloseOverlay();\n\n\t\t\tdom.overlay = document.createElement( 'div' );\n\t\t\tdom.overlay.classList.add( 'overlay' );\n\t\t\tdom.overlay.classList.add( 'overlay-help' );\n\t\t\tdom.wrapper.appendChild( dom.overlay );\n\n\t\t\tlet html = '
Keyboard Shortcuts
';\n\n\t\t\tlet shortcuts = keyboard.getShortcuts(),\n\t\t\t\tbindings = keyboard.getBindings();\n\n\t\t\thtml += '
KEY | ACTION | ';\n\t\t\tfor( let key in shortcuts ) {\n\t\t\t\thtml += `${key} | ${shortcuts[ key ]} |
`;\n\t\t\t}\n\n\t\t\t// Add custom key bindings that have associated descriptions\n\t\t\tfor( let binding in bindings ) {\n\t\t\t\tif( bindings[binding].key && bindings[binding].description ) {\n\t\t\t\t\thtml += `${bindings[binding].key} | ${bindings[binding].description} |
`;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\thtml += '
';\n\n\t\t\tdom.overlay.innerHTML = `\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
${html}
\n\t\t\t\t
\n\t\t\t`;\n\n\t\t\tdom.overlay.querySelector( '.close' ).addEventListener( 'click', event => {\n\t\t\t\tcloseOverlay();\n\t\t\t\tevent.preventDefault();\n\t\t\t}, false );\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Closes any currently open overlay.\n\t */\n\tfunction closeOverlay() {\n\n\t\tif( dom.overlay ) {\n\t\t\tdom.overlay.parentNode.removeChild( dom.overlay );\n\t\t\tdom.overlay = null;\n\t\t\treturn true;\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Applies JavaScript-controlled layout rules to the\n\t * presentation.\n\t */\n\tfunction layout() {\n\n\t\tif( dom.wrapper && !printView.isActive() ) {\n\n\t\t\tconst viewportWidth = dom.viewport.offsetWidth;\n\t\t\tconst viewportHeight = dom.viewport.offsetHeight;\n\n\t\t\tif( !config.disableLayout ) {\n\n\t\t\t\t// On some mobile devices '100vh' is taller than the visible\n\t\t\t\t// viewport which leads to part of the presentation being\n\t\t\t\t// cut off. To work around this we define our own '--vh' custom\n\t\t\t\t// property where 100x adds up to the correct height.\n\t\t\t\t//\n\t\t\t\t// https://css-tricks.com/the-trick-to-viewport-units-on-mobile/\n\t\t\t\tif( Device.isMobile && !config.embedded ) {\n\t\t\t\t\tdocument.documentElement.style.setProperty( '--vh', ( window.innerHeight * 0.01 ) + 'px' );\n\t\t\t\t}\n\n\t\t\t\tconst size = scrollView.isActive() ?\n\t\t\t\t\t\t\t getComputedSlideSize( viewportWidth, viewportHeight ) :\n\t\t\t\t\t\t\t getComputedSlideSize();\n\n\t\t\t\tconst oldScale = scale;\n\n\t\t\t\t// Layout the contents of the slides\n\t\t\t\tlayoutSlideContents( config.width, config.height );\n\n\t\t\t\tdom.slides.style.width = size.width + 'px';\n\t\t\t\tdom.slides.style.height = size.height + 'px';\n\n\t\t\t\t// Determine scale of content to fit within available space\n\t\t\t\tscale = Math.min( size.presentationWidth / size.width, size.presentationHeight / size.height );\n\n\t\t\t\t// Respect max/min scale settings\n\t\t\t\tscale = Math.max( scale, config.minScale );\n\t\t\t\tscale = Math.min( scale, config.maxScale );\n\n\t\t\t\t// Don't apply any scaling styles if scale is 1 or we're\n\t\t\t\t// in the scroll view\n\t\t\t\tif( scale === 1 || scrollView.isActive() ) {\n\t\t\t\t\tdom.slides.style.zoom = '';\n\t\t\t\t\tdom.slides.style.left = '';\n\t\t\t\t\tdom.slides.style.top = '';\n\t\t\t\t\tdom.slides.style.bottom = '';\n\t\t\t\t\tdom.slides.style.right = '';\n\t\t\t\t\ttransformSlides( { layout: '' } );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tdom.slides.style.zoom = '';\n\t\t\t\t\tdom.slides.style.left = '50%';\n\t\t\t\t\tdom.slides.style.top = '50%';\n\t\t\t\t\tdom.slides.style.bottom = 'auto';\n\t\t\t\t\tdom.slides.style.right = 'auto';\n\t\t\t\t\ttransformSlides( { layout: 'translate(-50%, -50%) scale('+ scale +')' } );\n\t\t\t\t}\n\n\t\t\t\t// Select all slides, vertical and horizontal\n\t\t\t\tconst slides = Array.from( dom.wrapper.querySelectorAll( SLIDES_SELECTOR ) );\n\n\t\t\t\tfor( let i = 0, len = slides.length; i < len; i++ ) {\n\t\t\t\t\tconst slide = slides[ i ];\n\n\t\t\t\t\t// Don't bother updating invisible slides\n\t\t\t\t\tif( slide.style.display === 'none' ) {\n\t\t\t\t\t\tcontinue;\n\t\t\t\t\t}\n\n\t\t\t\t\tif( ( config.center || slide.classList.contains( 'center' ) ) ) {\n\t\t\t\t\t\t// Vertical stacks are not centred since their section\n\t\t\t\t\t\t// children will be\n\t\t\t\t\t\tif( slide.classList.contains( 'stack' ) ) {\n\t\t\t\t\t\t\tslide.style.top = 0;\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tslide.style.top = Math.max( ( size.height - slide.scrollHeight ) / 2, 0 ) + 'px';\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tslide.style.top = '';\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t\tif( oldScale !== scale ) {\n\t\t\t\t\tdispatchEvent({\n\t\t\t\t\t\ttype: 'resize',\n\t\t\t\t\t\tdata: {\n\t\t\t\t\t\t\toldScale,\n\t\t\t\t\t\t\tscale,\n\t\t\t\t\t\t\tsize\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tcheckResponsiveScrollView();\n\n\t\t\tdom.viewport.style.setProperty( '--slide-scale', scale );\n\t\t\tdom.viewport.style.setProperty( '--viewport-width', viewportWidth + 'px' );\n\t\t\tdom.viewport.style.setProperty( '--viewport-height', viewportHeight + 'px' );\n\n\t\t\tscrollView.layout();\n\n\t\t\tprogress.update();\n\t\t\tbackgrounds.updateParallax();\n\n\t\t\tif( overview.isActive() ) {\n\t\t\t\toverview.update();\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Applies layout logic to the contents of all slides in\n\t * the presentation.\n\t *\n\t * @param {string|number} width\n\t * @param {string|number} height\n\t */\n\tfunction layoutSlideContents( width, height ) {\n\t\t// Handle sizing of elements with the 'r-stretch' class\n\t\tUtil.queryAll( dom.slides, 'section > .stretch, section > .r-stretch' ).forEach( element => {\n\n\t\t\t// Determine how much vertical space we can use\n\t\t\tlet remainingHeight = Util.getRemainingHeight( element, height );\n\n\t\t\t// Consider the aspect ratio of media elements\n\t\t\tif( /(img|video)/gi.test( element.nodeName ) ) {\n\t\t\t\tconst nw = element.naturalWidth || element.videoWidth,\n\t\t\t\t\t nh = element.naturalHeight || element.videoHeight;\n\n\t\t\t\tconst es = Math.min( width / nw, remainingHeight / nh );\n\n\t\t\t\telement.style.width = ( nw * es ) + 'px';\n\t\t\t\telement.style.height = ( nh * es ) + 'px';\n\n\t\t\t}\n\t\t\telse {\n\t\t\t\telement.style.width = width + 'px';\n\t\t\t\telement.style.height = remainingHeight + 'px';\n\t\t\t}\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Responsively activates the scroll mode when we reach the configured\n\t * activation width.\n\t */\n\tfunction checkResponsiveScrollView() {\n\n\t\t// Only proceed if...\n\t\t// 1. The DOM is ready\n\t\t// 2. Layouts aren't disabled via config\n\t\t// 3. We're not currently printing\n\t\t// 4. There is a scrollActivationWidth set\n\t\t// 5. The deck isn't configured to always use the scroll view\n\t\tif(\n\t\t\tdom.wrapper &&\n\t\t\t!config.disableLayout &&\n\t\t\t!printView.isActive() &&\n\t\t\ttypeof config.scrollActivationWidth === 'number' &&\n\t\t\tconfig.view !== 'scroll'\n\t\t) {\n\t\t\tconst size = getComputedSlideSize();\n\n\t\t\tif( size.presentationWidth > 0 && size.presentationWidth <= config.scrollActivationWidth ) {\n\t\t\t\tif( !scrollView.isActive() ) {\n\t\t\t\t\tbackgrounds.create();\n\t\t\t\t\tscrollView.activate()\n\t\t\t\t};\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif( scrollView.isActive() ) scrollView.deactivate();\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Calculates the computed pixel size of our slides. These\n\t * values are based on the width and height configuration\n\t * options.\n\t *\n\t * @param {number} [presentationWidth=dom.wrapper.offsetWidth]\n\t * @param {number} [presentationHeight=dom.wrapper.offsetHeight]\n\t */\n\tfunction getComputedSlideSize( presentationWidth, presentationHeight ) {\n\n\t\tlet width = config.width;\n\t\tlet height = config.height;\n\n\t\tif( config.disableLayout ) {\n\t\t\twidth = dom.slides.offsetWidth;\n\t\t\theight = dom.slides.offsetHeight;\n\t\t}\n\n\t\tconst size = {\n\t\t\t// Slide size\n\t\t\twidth: width,\n\t\t\theight: height,\n\n\t\t\t// Presentation size\n\t\t\tpresentationWidth: presentationWidth || dom.wrapper.offsetWidth,\n\t\t\tpresentationHeight: presentationHeight || dom.wrapper.offsetHeight\n\t\t};\n\n\t\t// Reduce available space by margin\n\t\tsize.presentationWidth -= ( size.presentationWidth * config.margin );\n\t\tsize.presentationHeight -= ( size.presentationHeight * config.margin );\n\n\t\t// Slide width may be a percentage of available width\n\t\tif( typeof size.width === 'string' && /%$/.test( size.width ) ) {\n\t\t\tsize.width = parseInt( size.width, 10 ) / 100 * size.presentationWidth;\n\t\t}\n\n\t\t// Slide height may be a percentage of available height\n\t\tif( typeof size.height === 'string' && /%$/.test( size.height ) ) {\n\t\t\tsize.height = parseInt( size.height, 10 ) / 100 * size.presentationHeight;\n\t\t}\n\n\t\treturn size;\n\n\t}\n\n\t/**\n\t * Stores the vertical index of a stack so that the same\n\t * vertical slide can be selected when navigating to and\n\t * from the stack.\n\t *\n\t * @param {HTMLElement} stack The vertical stack element\n\t * @param {string|number} [v=0] Index to memorize\n\t */\n\tfunction setPreviousVerticalIndex( stack, v ) {\n\n\t\tif( typeof stack === 'object' && typeof stack.setAttribute === 'function' ) {\n\t\t\tstack.setAttribute( 'data-previous-indexv', v || 0 );\n\t\t}\n\n\t}\n\n\t/**\n\t * Retrieves the vertical index which was stored using\n\t * #setPreviousVerticalIndex() or 0 if no previous index\n\t * exists.\n\t *\n\t * @param {HTMLElement} stack The vertical stack element\n\t */\n\tfunction getPreviousVerticalIndex( stack ) {\n\n\t\tif( typeof stack === 'object' && typeof stack.setAttribute === 'function' && stack.classList.contains( 'stack' ) ) {\n\t\t\t// Prefer manually defined start-indexv\n\t\t\tconst attributeName = stack.hasAttribute( 'data-start-indexv' ) ? 'data-start-indexv' : 'data-previous-indexv';\n\n\t\t\treturn parseInt( stack.getAttribute( attributeName ) || 0, 10 );\n\t\t}\n\n\t\treturn 0;\n\n\t}\n\n\t/**\n\t * Checks if the current or specified slide is vertical\n\t * (nested within another slide).\n\t *\n\t * @param {HTMLElement} [slide=currentSlide] The slide to check\n\t * orientation of\n\t * @return {Boolean}\n\t */\n\tfunction isVerticalSlide( slide = currentSlide ) {\n\n\t\treturn slide && slide.parentNode && !!slide.parentNode.nodeName.match( /section/i );\n\n\t}\n\n\t/**\n\t * Checks if the current or specified slide is a stack containing\n\t * vertical slides.\n\t *\n\t * @param {HTMLElement} [slide=currentSlide]\n\t * @return {Boolean}\n\t */\n\tfunction isVerticalStack( slide = currentSlide ) {\n\n\t\treturn slide.classList.contains( '.stack' ) || slide.querySelector( 'section' ) !== null;\n\n\t}\n\n\t/**\n\t * Returns true if we're on the last slide in the current\n\t * vertical stack.\n\t */\n\tfunction isLastVerticalSlide() {\n\n\t\tif( currentSlide && isVerticalSlide( currentSlide ) ) {\n\t\t\t// Does this slide have a next sibling?\n\t\t\tif( currentSlide.nextElementSibling ) return false;\n\n\t\t\treturn true;\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Returns true if we're currently on the first slide in\n\t * the presentation.\n\t */\n\tfunction isFirstSlide() {\n\n\t\treturn indexh === 0 && indexv === 0;\n\n\t}\n\n\t/**\n\t * Returns true if we're currently on the last slide in\n\t * the presenation. If the last slide is a stack, we only\n\t * consider this the last slide if it's at the end of the\n\t * stack.\n\t */\n\tfunction isLastSlide() {\n\n\t\tif( currentSlide ) {\n\t\t\t// Does this slide have a next sibling?\n\t\t\tif( currentSlide.nextElementSibling ) return false;\n\n\t\t\t// If it's vertical, does its parent have a next sibling?\n\t\t\tif( isVerticalSlide( currentSlide ) && currentSlide.parentNode.nextElementSibling ) return false;\n\n\t\t\treturn true;\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Enters the paused mode which fades everything on screen to\n\t * black.\n\t */\n\tfunction pause() {\n\n\t\tif( config.pause ) {\n\t\t\tconst wasPaused = dom.wrapper.classList.contains( 'paused' );\n\n\t\t\tcancelAutoSlide();\n\t\t\tdom.wrapper.classList.add( 'paused' );\n\n\t\t\tif( wasPaused === false ) {\n\t\t\t\tdispatchEvent({ type: 'paused' });\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Exits from the paused mode.\n\t */\n\tfunction resume() {\n\n\t\tconst wasPaused = dom.wrapper.classList.contains( 'paused' );\n\t\tdom.wrapper.classList.remove( 'paused' );\n\n\t\tcueAutoSlide();\n\n\t\tif( wasPaused ) {\n\t\t\tdispatchEvent({ type: 'resumed' });\n\t\t}\n\n\t}\n\n\t/**\n\t * Toggles the paused mode on and off.\n\t */\n\tfunction togglePause( override ) {\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? pause() : resume();\n\t\t}\n\t\telse {\n\t\t\tisPaused() ? resume() : pause();\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if we are currently in the paused mode.\n\t *\n\t * @return {Boolean}\n\t */\n\tfunction isPaused() {\n\n\t\treturn dom.wrapper.classList.contains( 'paused' );\n\n\t}\n\n\t/**\n\t * Toggles visibility of the jump-to-slide UI.\n\t */\n\tfunction toggleJumpToSlide( override ) {\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? jumpToSlide.show() : jumpToSlide.hide();\n\t\t}\n\t\telse {\n\t\t\tjumpToSlide.isVisible() ? jumpToSlide.hide() : jumpToSlide.show();\n\t\t}\n\n\t}\n\n\t/**\n\t * Toggles the auto slide mode on and off.\n\t *\n\t * @param {Boolean} [override] Flag which sets the desired state.\n\t * True means autoplay starts, false means it stops.\n\t */\n\n\tfunction toggleAutoSlide( override ) {\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? resumeAutoSlide() : pauseAutoSlide();\n\t\t}\n\n\t\telse {\n\t\t\tautoSlidePaused ? resumeAutoSlide() : pauseAutoSlide();\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if the auto slide mode is currently on.\n\t *\n\t * @return {Boolean}\n\t */\n\tfunction isAutoSliding() {\n\n\t\treturn !!( autoSlide && !autoSlidePaused );\n\n\t}\n\n\t/**\n\t * Steps from the current point in the presentation to the\n\t * slide which matches the specified horizontal and vertical\n\t * indices.\n\t *\n\t * @param {number} [h=indexh] Horizontal index of the target slide\n\t * @param {number} [v=indexv] Vertical index of the target slide\n\t * @param {number} [f] Index of a fragment within the\n\t * target slide to activate\n\t * @param {number} [origin] Origin for use in multimaster environments\n\t */\n\tfunction slide( h, v, f, origin ) {\n\n\t\t// Dispatch an event before the slide\n\t\tconst slidechange = dispatchEvent({\n\t\t\ttype: 'beforeslidechange',\n\t\t\tdata: {\n\t\t\t\tindexh: h === undefined ? indexh : h,\n\t\t\t\tindexv: v === undefined ? indexv : v,\n\t\t\t\torigin\n\t\t\t}\n\t\t});\n\n\t\t// Abort if this slide change was prevented by an event listener\n\t\tif( slidechange.defaultPrevented ) return;\n\n\t\t// Remember where we were at before\n\t\tpreviousSlide = currentSlide;\n\n\t\t// Query all horizontal slides in the deck\n\t\tconst horizontalSlides = dom.wrapper.querySelectorAll( HORIZONTAL_SLIDES_SELECTOR );\n\n\t\t// If we're in scroll mode, we scroll the target slide into view\n\t\t// instead of running our standard slide transition\n\t\tif( scrollView.isActive() ) {\n\t\t\tconst scrollToSlide = scrollView.getSlideByIndices( h, v );\n\t\t\tif( scrollToSlide ) scrollView.scrollToSlide( scrollToSlide );\n\t\t\treturn;\n\t\t}\n\n\t\t// Abort if there are no slides\n\t\tif( horizontalSlides.length === 0 ) return;\n\n\t\t// If no vertical index is specified and the upcoming slide is a\n\t\t// stack, resume at its previous vertical index\n\t\tif( v === undefined && !overview.isActive() ) {\n\t\t\tv = getPreviousVerticalIndex( horizontalSlides[ h ] );\n\t\t}\n\n\t\t// If we were on a vertical stack, remember what vertical index\n\t\t// it was on so we can resume at the same position when returning\n\t\tif( previousSlide && previousSlide.parentNode && previousSlide.parentNode.classList.contains( 'stack' ) ) {\n\t\t\tsetPreviousVerticalIndex( previousSlide.parentNode, indexv );\n\t\t}\n\n\t\t// Remember the state before this slide\n\t\tconst stateBefore = state.concat();\n\n\t\t// Reset the state array\n\t\tstate.length = 0;\n\n\t\tlet indexhBefore = indexh || 0,\n\t\t\tindexvBefore = indexv || 0;\n\n\t\t// Activate and transition to the new slide\n\t\tindexh = updateSlides( HORIZONTAL_SLIDES_SELECTOR, h === undefined ? indexh : h );\n\t\tindexv = updateSlides( VERTICAL_SLIDES_SELECTOR, v === undefined ? indexv : v );\n\n\t\t// Dispatch an event if the slide changed\n\t\tlet slideChanged = ( indexh !== indexhBefore || indexv !== indexvBefore );\n\n\t\t// Ensure that the previous slide is never the same as the current\n\t\tif( !slideChanged ) previousSlide = null;\n\n\t\t// Find the current horizontal slide and any possible vertical slides\n\t\t// within it\n\t\tlet currentHorizontalSlide = horizontalSlides[ indexh ],\n\t\t\tcurrentVerticalSlides = currentHorizontalSlide.querySelectorAll( 'section' );\n\n\t\t// Indicate when we're on a vertical slide\n\t\trevealElement.classList.toggle( 'is-vertical-slide', currentVerticalSlides.length > 1 );\n\n\t\t// Store references to the previous and current slides\n\t\tcurrentSlide = currentVerticalSlides[ indexv ] || currentHorizontalSlide;\n\n\t\tlet autoAnimateTransition = false;\n\n\t\t// Detect if we're moving between two auto-animated slides\n\t\tif( slideChanged && previousSlide && currentSlide && !overview.isActive() ) {\n\t\t\ttransition = 'running';\n\n\t\t\tautoAnimateTransition = shouldAutoAnimateBetween( previousSlide, currentSlide, indexhBefore, indexvBefore );\n\n\t\t\t// If this is an auto-animated transition, we disable the\n\t\t\t// regular slide transition\n\t\t\t//\n\t\t\t// Note 20-03-2020:\n\t\t\t// This needs to happen before we update slide visibility,\n\t\t\t// otherwise transitions will still run in Safari.\n\t\t\tif( autoAnimateTransition ) {\n\t\t\t\tdom.slides.classList.add( 'disable-slide-transitions' )\n\t\t\t}\n\t\t}\n\n\t\t// Update the visibility of slides now that the indices have changed\n\t\tupdateSlidesVisibility();\n\n\t\tlayout();\n\n\t\t// Update the overview if it's currently active\n\t\tif( overview.isActive() ) {\n\t\t\toverview.update();\n\t\t}\n\n\t\t// Show fragment, if specified\n\t\tif( typeof f !== 'undefined' ) {\n\t\t\tfragments.goto( f );\n\t\t}\n\n\t\t// Solves an edge case where the previous slide maintains the\n\t\t// 'present' class when navigating between adjacent vertical\n\t\t// stacks\n\t\tif( previousSlide && previousSlide !== currentSlide ) {\n\t\t\tpreviousSlide.classList.remove( 'present' );\n\t\t\tpreviousSlide.setAttribute( 'aria-hidden', 'true' );\n\n\t\t\t// Reset all slides upon navigate to home\n\t\t\tif( isFirstSlide() ) {\n\t\t\t\t// Launch async task\n\t\t\t\tsetTimeout( () => {\n\t\t\t\t\tgetVerticalStacks().forEach( slide => {\n\t\t\t\t\t\tsetPreviousVerticalIndex( slide, 0 );\n\t\t\t\t\t} );\n\t\t\t\t}, 0 );\n\t\t\t}\n\t\t}\n\n\t\t// Apply the new state\n\t\tstateLoop: for( let i = 0, len = state.length; i < len; i++ ) {\n\t\t\t// Check if this state existed on the previous slide. If it\n\t\t\t// did, we will avoid adding it repeatedly\n\t\t\tfor( let j = 0; j < stateBefore.length; j++ ) {\n\t\t\t\tif( stateBefore[j] === state[i] ) {\n\t\t\t\t\tstateBefore.splice( j, 1 );\n\t\t\t\t\tcontinue stateLoop;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tdom.viewport.classList.add( state[i] );\n\n\t\t\t// Dispatch custom event matching the state's name\n\t\t\tdispatchEvent({ type: state[i] });\n\t\t}\n\n\t\t// Clean up the remains of the previous state\n\t\twhile( stateBefore.length ) {\n\t\t\tdom.viewport.classList.remove( stateBefore.pop() );\n\t\t}\n\n\t\tif( slideChanged ) {\n\t\t\tdispatchSlideChanged( origin );\n\t\t}\n\n\t\t// Handle embedded content\n\t\tif( slideChanged || !previousSlide ) {\n\t\t\tslideContent.stopEmbeddedContent( previousSlide );\n\t\t\tslideContent.startEmbeddedContent( currentSlide );\n\t\t}\n\n\t\t// Announce the current slide contents to screen readers\n\t\t// Use animation frame to prevent getComputedStyle in getStatusText\n\t\t// from triggering layout mid-frame\n\t\trequestAnimationFrame( () => {\n\t\t\tannounceStatus( getStatusText( currentSlide ) );\n\t\t});\n\n\t\tprogress.update();\n\t\tcontrols.update();\n\t\tnotes.update();\n\t\tbackgrounds.update();\n\t\tbackgrounds.updateParallax();\n\t\tslideNumber.update();\n\t\tfragments.update();\n\n\t\t// Update the URL hash\n\t\tlocation.writeURL();\n\n\t\tcueAutoSlide();\n\n\t\t// Auto-animation\n\t\tif( autoAnimateTransition ) {\n\n\t\t\tsetTimeout( () => {\n\t\t\t\tdom.slides.classList.remove( 'disable-slide-transitions' );\n\t\t\t}, 0 );\n\n\t\t\tif( config.autoAnimate ) {\n\t\t\t\t// Run the auto-animation between our slides\n\t\t\t\tautoAnimate.run( previousSlide, currentSlide );\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks whether or not an auto-animation should take place between\n\t * the two given slides.\n\t *\n\t * @param {HTMLElement} fromSlide\n\t * @param {HTMLElement} toSlide\n\t * @param {number} indexhBefore\n\t * @param {number} indexvBefore\n\t *\n\t * @returns {boolean}\n\t */\n\tfunction shouldAutoAnimateBetween( fromSlide, toSlide, indexhBefore, indexvBefore ) {\n\n\t\treturn \tfromSlide.hasAttribute( 'data-auto-animate' ) && toSlide.hasAttribute( 'data-auto-animate' ) &&\n\t\t\t\tfromSlide.getAttribute( 'data-auto-animate-id' ) === toSlide.getAttribute( 'data-auto-animate-id' ) &&\n\t\t\t\t!( ( indexh > indexhBefore || indexv > indexvBefore ) ? toSlide : fromSlide ).hasAttribute( 'data-auto-animate-restart' );\n\n\t}\n\n\t/**\n\t * Called anytime a new slide should be activated while in the scroll\n\t * view. The active slide is the page that occupies the most space in\n\t * the scrollable viewport.\n\t *\n\t * @param {number} pageIndex\n\t * @param {HTMLElement} slideElement\n\t */\n\tfunction setCurrentScrollPage( slideElement, h, v ) {\n\n\t\tlet indexhBefore = indexh || 0;\n\n\t\tindexh = h;\n\t\tindexv = v;\n\n\t\tconst slideChanged = currentSlide !== slideElement;\n\n\t\tpreviousSlide = currentSlide;\n\t\tcurrentSlide = slideElement;\n\n\t\tif( currentSlide && previousSlide ) {\n\t\t\tif( config.autoAnimate && shouldAutoAnimateBetween( previousSlide, currentSlide, indexhBefore, indexv ) ) {\n\t\t\t\t// Run the auto-animation between our slides\n\t\t\t\tautoAnimate.run( previousSlide, currentSlide );\n\t\t\t}\n\t\t}\n\n\t\t// Start or stop embedded content like videos and iframes\n\t\tif( slideChanged ) {\n\t\t\tif( previousSlide ) {\n\t\t\t\tslideContent.stopEmbeddedContent( previousSlide );\n\t\t\t\tslideContent.stopEmbeddedContent( previousSlide.slideBackgroundElement );\n\t\t\t}\n\n\t\t\tslideContent.startEmbeddedContent( currentSlide );\n\t\t\tslideContent.startEmbeddedContent( currentSlide.slideBackgroundElement );\n\t\t}\n\n\t\trequestAnimationFrame( () => {\n\t\t\tannounceStatus( getStatusText( currentSlide ) );\n\t\t});\n\n\t\tdispatchSlideChanged();\n\n\t}\n\n\t/**\n\t * Syncs the presentation with the current DOM. Useful\n\t * when new slides or control elements are added or when\n\t * the configuration has changed.\n\t */\n\tfunction sync() {\n\n\t\t// Subscribe to input\n\t\tremoveEventListeners();\n\t\taddEventListeners();\n\n\t\t// Force a layout to make sure the current config is accounted for\n\t\tlayout();\n\n\t\t// Reflect the current autoSlide value\n\t\tautoSlide = config.autoSlide;\n\n\t\t// Start auto-sliding if it's enabled\n\t\tcueAutoSlide();\n\n\t\t// Re-create all slide backgrounds\n\t\tbackgrounds.create();\n\n\t\t// Write the current hash to the URL\n\t\tlocation.writeURL();\n\n\t\tif( config.sortFragmentsOnSync === true ) {\n\t\t\tfragments.sortAll();\n\t\t}\n\n\t\tcontrols.update();\n\t\tprogress.update();\n\n\t\tupdateSlidesVisibility();\n\n\t\tnotes.update();\n\t\tnotes.updateVisibility();\n\t\tbackgrounds.update( true );\n\t\tslideNumber.update();\n\t\tslideContent.formatEmbeddedContent();\n\n\t\t// Start or stop embedded content depending on global config\n\t\tif( config.autoPlayMedia === false ) {\n\t\t\tslideContent.stopEmbeddedContent( currentSlide, { unloadIframes: false } );\n\t\t}\n\t\telse {\n\t\t\tslideContent.startEmbeddedContent( currentSlide );\n\t\t}\n\n\t\tif( overview.isActive() ) {\n\t\t\toverview.layout();\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates reveal.js to keep in sync with new slide attributes. For\n\t * example, if you add a new `data-background-image` you can call\n\t * this to have reveal.js render the new background image.\n\t *\n\t * Similar to #sync() but more efficient when you only need to\n\t * refresh a specific slide.\n\t *\n\t * @param {HTMLElement} slide\n\t */\n\tfunction syncSlide( slide = currentSlide ) {\n\n\t\tbackgrounds.sync( slide );\n\t\tfragments.sync( slide );\n\n\t\tslideContent.load( slide );\n\n\t\tbackgrounds.update();\n\t\tnotes.update();\n\n\t}\n\n\t/**\n\t * Resets all vertical slides so that only the first\n\t * is visible.\n\t */\n\tfunction resetVerticalSlides() {\n\n\t\tgetHorizontalSlides().forEach( horizontalSlide => {\n\n\t\t\tUtil.queryAll( horizontalSlide, 'section' ).forEach( ( verticalSlide, y ) => {\n\n\t\t\t\tif( y > 0 ) {\n\t\t\t\t\tverticalSlide.classList.remove( 'present' );\n\t\t\t\t\tverticalSlide.classList.remove( 'past' );\n\t\t\t\t\tverticalSlide.classList.add( 'future' );\n\t\t\t\t\tverticalSlide.setAttribute( 'aria-hidden', 'true' );\n\t\t\t\t}\n\n\t\t\t} );\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Randomly shuffles all slides in the deck.\n\t */\n\tfunction shuffle( slides = getHorizontalSlides() ) {\n\n\t\tslides.forEach( ( slide, i ) => {\n\n\t\t\t// Insert the slide next to a randomly picked sibling slide\n\t\t\t// slide. This may cause the slide to insert before itself,\n\t\t\t// but that's not an issue.\n\t\t\tlet beforeSlide = slides[ Math.floor( Math.random() * slides.length ) ];\n\t\t\tif( beforeSlide.parentNode === slide.parentNode ) {\n\t\t\t\tslide.parentNode.insertBefore( slide, beforeSlide );\n\t\t\t}\n\n\t\t\t// Randomize the order of vertical slides (if there are any)\n\t\t\tlet verticalSlides = slide.querySelectorAll( 'section' );\n\t\t\tif( verticalSlides.length ) {\n\t\t\t\tshuffle( verticalSlides );\n\t\t\t}\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Updates one dimension of slides by showing the slide\n\t * with the specified index.\n\t *\n\t * @param {string} selector A CSS selector that will fetch\n\t * the group of slides we are working with\n\t * @param {number} index The index of the slide that should be\n\t * shown\n\t *\n\t * @return {number} The index of the slide that is now shown,\n\t * might differ from the passed in index if it was out of\n\t * bounds.\n\t */\n\tfunction updateSlides( selector, index ) {\n\n\t\t// Select all slides and convert the NodeList result to\n\t\t// an array\n\t\tlet slides = Util.queryAll( dom.wrapper, selector ),\n\t\t\tslidesLength = slides.length;\n\n\t\tlet printMode = scrollView.isActive() || printView.isActive();\n\t\tlet loopedForwards = false;\n\t\tlet loopedBackwards = false;\n\n\t\tif( slidesLength ) {\n\n\t\t\t// Should the index loop?\n\t\t\tif( config.loop ) {\n\t\t\t\tif( index >= slidesLength ) loopedForwards = true;\n\n\t\t\t\tindex %= slidesLength;\n\n\t\t\t\tif( index < 0 ) {\n\t\t\t\t\tindex = slidesLength + index;\n\t\t\t\t\tloopedBackwards = true;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Enforce max and minimum index bounds\n\t\t\tindex = Math.max( Math.min( index, slidesLength - 1 ), 0 );\n\n\t\t\tfor( let i = 0; i < slidesLength; i++ ) {\n\t\t\t\tlet element = slides[i];\n\n\t\t\t\tlet reverse = config.rtl && !isVerticalSlide( element );\n\n\t\t\t\t// Avoid .remove() with multiple args for IE11 support\n\t\t\t\telement.classList.remove( 'past' );\n\t\t\t\telement.classList.remove( 'present' );\n\t\t\t\telement.classList.remove( 'future' );\n\n\t\t\t\t// http://www.w3.org/html/wg/drafts/html/master/editing.html#the-hidden-attribute\n\t\t\t\telement.setAttribute( 'hidden', '' );\n\t\t\t\telement.setAttribute( 'aria-hidden', 'true' );\n\n\t\t\t\t// If this element contains vertical slides\n\t\t\t\tif( element.querySelector( 'section' ) ) {\n\t\t\t\t\telement.classList.add( 'stack' );\n\t\t\t\t}\n\n\t\t\t\t// If we're printing static slides, all slides are \"present\"\n\t\t\t\tif( printMode ) {\n\t\t\t\t\telement.classList.add( 'present' );\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\tif( i < index ) {\n\t\t\t\t\t// Any element previous to index is given the 'past' class\n\t\t\t\t\telement.classList.add( reverse ? 'future' : 'past' );\n\n\t\t\t\t\tif( config.fragments ) {\n\t\t\t\t\t\t// Show all fragments in prior slides\n\t\t\t\t\t\tshowFragmentsIn( element );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( i > index ) {\n\t\t\t\t\t// Any element subsequent to index is given the 'future' class\n\t\t\t\t\telement.classList.add( reverse ? 'past' : 'future' );\n\n\t\t\t\t\tif( config.fragments ) {\n\t\t\t\t\t\t// Hide all fragments in future slides\n\t\t\t\t\t\thideFragmentsIn( element );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Update the visibility of fragments when a presentation loops\n\t\t\t\t// in either direction\n\t\t\t\telse if( i === index && config.fragments ) {\n\t\t\t\t\tif( loopedForwards ) {\n\t\t\t\t\t\thideFragmentsIn( element );\n\t\t\t\t\t}\n\t\t\t\t\telse if( loopedBackwards ) {\n\t\t\t\t\t\tshowFragmentsIn( element );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tlet slide = slides[index];\n\t\t\tlet wasPresent = slide.classList.contains( 'present' );\n\n\t\t\t// Mark the current slide as present\n\t\t\tslide.classList.add( 'present' );\n\t\t\tslide.removeAttribute( 'hidden' );\n\t\t\tslide.removeAttribute( 'aria-hidden' );\n\n\t\t\tif( !wasPresent ) {\n\t\t\t\t// Dispatch an event indicating the slide is now visible\n\t\t\t\tdispatchEvent({\n\t\t\t\t\ttarget: slide,\n\t\t\t\t\ttype: 'visible',\n\t\t\t\t\tbubbles: false\n\t\t\t\t});\n\t\t\t}\n\n\t\t\t// If this slide has a state associated with it, add it\n\t\t\t// onto the current state of the deck\n\t\t\tlet slideState = slide.getAttribute( 'data-state' );\n\t\t\tif( slideState ) {\n\t\t\t\tstate = state.concat( slideState.split( ' ' ) );\n\t\t\t}\n\n\t\t}\n\t\telse {\n\t\t\t// Since there are no slides we can't be anywhere beyond the\n\t\t\t// zeroth index\n\t\t\tindex = 0;\n\t\t}\n\n\t\treturn index;\n\n\t}\n\n\t/**\n\t * Shows all fragment elements within the given container.\n\t */\n\tfunction showFragmentsIn( container ) {\n\n\t\tUtil.queryAll( container, '.fragment' ).forEach( fragment => {\n\t\t\tfragment.classList.add( 'visible' );\n\t\t\tfragment.classList.remove( 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Hides all fragment elements within the given container.\n\t */\n\tfunction hideFragmentsIn( container ) {\n\n\t\tUtil.queryAll( container, '.fragment.visible' ).forEach( fragment => {\n\t\t\tfragment.classList.remove( 'visible', 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Optimization method; hide all slides that are far away\n\t * from the present slide.\n\t */\n\tfunction updateSlidesVisibility() {\n\n\t\t// Select all slides and convert the NodeList result to\n\t\t// an array\n\t\tlet horizontalSlides = getHorizontalSlides(),\n\t\t\thorizontalSlidesLength = horizontalSlides.length,\n\t\t\tdistanceX,\n\t\t\tdistanceY;\n\n\t\tif( horizontalSlidesLength && typeof indexh !== 'undefined' ) {\n\n\t\t\t// The number of steps away from the present slide that will\n\t\t\t// be visible\n\t\t\tlet viewDistance = overview.isActive() ? 10 : config.viewDistance;\n\n\t\t\t// Shorten the view distance on devices that typically have\n\t\t\t// less resources\n\t\t\tif( Device.isMobile ) {\n\t\t\t\tviewDistance = overview.isActive() ? 6 : config.mobileViewDistance;\n\t\t\t}\n\n\t\t\t// All slides need to be visible when exporting to PDF\n\t\t\tif( printView.isActive() ) {\n\t\t\t\tviewDistance = Number.MAX_VALUE;\n\t\t\t}\n\n\t\t\tfor( let x = 0; x < horizontalSlidesLength; x++ ) {\n\t\t\t\tlet horizontalSlide = horizontalSlides[x];\n\n\t\t\t\tlet verticalSlides = Util.queryAll( horizontalSlide, 'section' ),\n\t\t\t\t\tverticalSlidesLength = verticalSlides.length;\n\n\t\t\t\t// Determine how far away this slide is from the present\n\t\t\t\tdistanceX = Math.abs( ( indexh || 0 ) - x ) || 0;\n\n\t\t\t\t// If the presentation is looped, distance should measure\n\t\t\t\t// 1 between the first and last slides\n\t\t\t\tif( config.loop ) {\n\t\t\t\t\tdistanceX = Math.abs( ( ( indexh || 0 ) - x ) % ( horizontalSlidesLength - viewDistance ) ) || 0;\n\t\t\t\t}\n\n\t\t\t\t// Show the horizontal slide if it's within the view distance\n\t\t\t\tif( distanceX < viewDistance ) {\n\t\t\t\t\tslideContent.load( horizontalSlide );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tslideContent.unload( horizontalSlide );\n\t\t\t\t}\n\n\t\t\t\tif( verticalSlidesLength ) {\n\n\t\t\t\t\tlet oy = getPreviousVerticalIndex( horizontalSlide );\n\n\t\t\t\t\tfor( let y = 0; y < verticalSlidesLength; y++ ) {\n\t\t\t\t\t\tlet verticalSlide = verticalSlides[y];\n\n\t\t\t\t\t\tdistanceY = x === ( indexh || 0 ) ? Math.abs( ( indexv || 0 ) - y ) : Math.abs( y - oy );\n\n\t\t\t\t\t\tif( distanceX + distanceY < viewDistance ) {\n\t\t\t\t\t\t\tslideContent.load( verticalSlide );\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tslideContent.unload( verticalSlide );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Flag if there are ANY vertical slides, anywhere in the deck\n\t\t\tif( hasVerticalSlides() ) {\n\t\t\t\tdom.wrapper.classList.add( 'has-vertical-slides' );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tdom.wrapper.classList.remove( 'has-vertical-slides' );\n\t\t\t}\n\n\t\t\t// Flag if there are ANY horizontal slides, anywhere in the deck\n\t\t\tif( hasHorizontalSlides() ) {\n\t\t\t\tdom.wrapper.classList.add( 'has-horizontal-slides' );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tdom.wrapper.classList.remove( 'has-horizontal-slides' );\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Determine what available routes there are for navigation.\n\t *\n\t * @return {{left: boolean, right: boolean, up: boolean, down: boolean}}\n\t */\n\tfunction availableRoutes({ includeFragments = false } = {}) {\n\n\t\tlet horizontalSlides = dom.wrapper.querySelectorAll( HORIZONTAL_SLIDES_SELECTOR ),\n\t\t\tverticalSlides = dom.wrapper.querySelectorAll( VERTICAL_SLIDES_SELECTOR );\n\n\t\tlet routes = {\n\t\t\tleft: indexh > 0,\n\t\t\tright: indexh < horizontalSlides.length - 1,\n\t\t\tup: indexv > 0,\n\t\t\tdown: indexv < verticalSlides.length - 1\n\t\t};\n\n\t\t// Looped presentations can always be navigated as long as\n\t\t// there are slides available\n\t\tif( config.loop ) {\n\t\t\tif( horizontalSlides.length > 1 ) {\n\t\t\t\troutes.left = true;\n\t\t\t\troutes.right = true;\n\t\t\t}\n\n\t\t\tif( verticalSlides.length > 1 ) {\n\t\t\t\troutes.up = true;\n\t\t\t\troutes.down = true;\n\t\t\t}\n\t\t}\n\n\t\tif ( horizontalSlides.length > 1 && config.navigationMode === 'linear' ) {\n\t\t\troutes.right = routes.right || routes.down;\n\t\t\troutes.left = routes.left || routes.up;\n\t\t}\n\n\t\t// If includeFragments is set, a route will be considered\n\t\t// available if either a slid OR fragment is available in\n\t\t// the given direction\n\t\tif( includeFragments === true ) {\n\t\t\tlet fragmentRoutes = fragments.availableRoutes();\n\t\t\troutes.left = routes.left || fragmentRoutes.prev;\n\t\t\troutes.up = routes.up || fragmentRoutes.prev;\n\t\t\troutes.down = routes.down || fragmentRoutes.next;\n\t\t\troutes.right = routes.right || fragmentRoutes.next;\n\t\t}\n\n\t\t// Reverse horizontal controls for rtl\n\t\tif( config.rtl ) {\n\t\t\tlet left = routes.left;\n\t\t\troutes.left = routes.right;\n\t\t\troutes.right = left;\n\t\t}\n\n\t\treturn routes;\n\n\t}\n\n\t/**\n\t * Returns the number of past slides. This can be used as a global\n\t * flattened index for slides.\n\t *\n\t * @param {HTMLElement} [slide=currentSlide] The slide we're counting before\n\t *\n\t * @return {number} Past slide count\n\t */\n\tfunction getSlidePastCount( slide = currentSlide ) {\n\n\t\tlet horizontalSlides = getHorizontalSlides();\n\n\t\t// The number of past slides\n\t\tlet pastCount = 0;\n\n\t\t// Step through all slides and count the past ones\n\t\tmainLoop: for( let i = 0; i < horizontalSlides.length; i++ ) {\n\n\t\t\tlet horizontalSlide = horizontalSlides[i];\n\t\t\tlet verticalSlides = horizontalSlide.querySelectorAll( 'section' );\n\n\t\t\tfor( let j = 0; j < verticalSlides.length; j++ ) {\n\n\t\t\t\t// Stop as soon as we arrive at the present\n\t\t\t\tif( verticalSlides[j] === slide ) {\n\t\t\t\t\tbreak mainLoop;\n\t\t\t\t}\n\n\t\t\t\t// Don't count slides with the \"uncounted\" class\n\t\t\t\tif( verticalSlides[j].dataset.visibility !== 'uncounted' ) {\n\t\t\t\t\tpastCount++;\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\t// Stop as soon as we arrive at the present\n\t\t\tif( horizontalSlide === slide ) {\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\t// Don't count the wrapping section for vertical slides and\n\t\t\t// slides marked as uncounted\n\t\t\tif( horizontalSlide.classList.contains( 'stack' ) === false && horizontalSlide.dataset.visibility !== 'uncounted' ) {\n\t\t\t\tpastCount++;\n\t\t\t}\n\n\t\t}\n\n\t\treturn pastCount;\n\n\t}\n\n\t/**\n\t * Returns a value ranging from 0-1 that represents\n\t * how far into the presentation we have navigated.\n\t *\n\t * @return {number}\n\t */\n\tfunction getProgress() {\n\n\t\t// The number of past and total slides\n\t\tlet totalCount = getTotalSlides();\n\t\tlet pastCount = getSlidePastCount();\n\n\t\tif( currentSlide ) {\n\n\t\t\tlet allFragments = currentSlide.querySelectorAll( '.fragment' );\n\n\t\t\t// If there are fragments in the current slide those should be\n\t\t\t// accounted for in the progress.\n\t\t\tif( allFragments.length > 0 ) {\n\t\t\t\tlet visibleFragments = currentSlide.querySelectorAll( '.fragment.visible' );\n\n\t\t\t\t// This value represents how big a portion of the slide progress\n\t\t\t\t// that is made up by its fragments (0-1)\n\t\t\t\tlet fragmentWeight = 0.9;\n\n\t\t\t\t// Add fragment progress to the past slide count\n\t\t\t\tpastCount += ( visibleFragments.length / allFragments.length ) * fragmentWeight;\n\t\t\t}\n\n\t\t}\n\n\t\treturn Math.min( pastCount / ( totalCount - 1 ), 1 );\n\n\t}\n\n\t/**\n\t * Retrieves the h/v location and fragment of the current,\n\t * or specified, slide.\n\t *\n\t * @param {HTMLElement} [slide] If specified, the returned\n\t * index will be for this slide rather than the currently\n\t * active one\n\t *\n\t * @return {{h: number, v: number, f: number}}\n\t */\n\tfunction getIndices( slide ) {\n\n\t\t// By default, return the current indices\n\t\tlet h = indexh,\n\t\t\tv = indexv,\n\t\t\tf;\n\n\t\t// If a slide is specified, return the indices of that slide\n\t\tif( slide ) {\n\t\t\t// In scroll mode the original h/x index is stored on the slide\n\t\t\tif( scrollView.isActive() ) {\n\t\t\t\th = parseInt( slide.getAttribute( 'data-index-h' ), 10 );\n\n\t\t\t\tif( slide.getAttribute( 'data-index-v' ) ) {\n\t\t\t\t\tv = parseInt( slide.getAttribute( 'data-index-v' ), 10 );\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\tlet isVertical = isVerticalSlide( slide );\n\t\t\t\tlet slideh = isVertical ? slide.parentNode : slide;\n\n\t\t\t\t// Select all horizontal slides\n\t\t\t\tlet horizontalSlides = getHorizontalSlides();\n\n\t\t\t\t// Now that we know which the horizontal slide is, get its index\n\t\t\t\th = Math.max( horizontalSlides.indexOf( slideh ), 0 );\n\n\t\t\t\t// Assume we're not vertical\n\t\t\t\tv = undefined;\n\n\t\t\t\t// If this is a vertical slide, grab the vertical index\n\t\t\t\tif( isVertical ) {\n\t\t\t\t\tv = Math.max( Util.queryAll( slide.parentNode, 'section' ).indexOf( slide ), 0 );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif( !slide && currentSlide ) {\n\t\t\tlet hasFragments = currentSlide.querySelectorAll( '.fragment' ).length > 0;\n\t\t\tif( hasFragments ) {\n\t\t\t\tlet currentFragment = currentSlide.querySelector( '.current-fragment' );\n\t\t\t\tif( currentFragment && currentFragment.hasAttribute( 'data-fragment-index' ) ) {\n\t\t\t\t\tf = parseInt( currentFragment.getAttribute( 'data-fragment-index' ), 10 );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tf = currentSlide.querySelectorAll( '.fragment.visible' ).length - 1;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn { h, v, f };\n\n\t}\n\n\t/**\n\t * Retrieves all slides in this presentation.\n\t */\n\tfunction getSlides() {\n\n\t\treturn Util.queryAll( dom.wrapper, SLIDES_SELECTOR + ':not(.stack):not([data-visibility=\"uncounted\"])' );\n\n\t}\n\n\t/**\n\t * Returns a list of all horizontal slides in the deck. Each\n\t * vertical stack is included as one horizontal slide in the\n\t * resulting array.\n\t */\n\tfunction getHorizontalSlides() {\n\n\t\treturn Util.queryAll( dom.wrapper, HORIZONTAL_SLIDES_SELECTOR );\n\n\t}\n\n\t/**\n\t * Returns all vertical slides that exist within this deck.\n\t */\n\tfunction getVerticalSlides() {\n\n\t\treturn Util.queryAll( dom.wrapper, '.slides>section>section' );\n\n\t}\n\n\t/**\n\t * Returns all vertical stacks (each stack can contain multiple slides).\n\t */\n\tfunction getVerticalStacks() {\n\n\t\treturn Util.queryAll( dom.wrapper, HORIZONTAL_SLIDES_SELECTOR + '.stack');\n\n\t}\n\n\t/**\n\t * Returns true if there are at least two horizontal slides.\n\t */\n\tfunction hasHorizontalSlides() {\n\n\t\treturn getHorizontalSlides().length > 1;\n\t}\n\n\t/**\n\t * Returns true if there are at least two vertical slides.\n\t */\n\tfunction hasVerticalSlides() {\n\n\t\treturn getVerticalSlides().length > 1;\n\n\t}\n\n\t/**\n\t * Returns an array of objects where each object represents the\n\t * attributes on its respective slide.\n\t */\n\tfunction getSlidesAttributes() {\n\n\t\treturn getSlides().map( slide => {\n\n\t\t\tlet attributes = {};\n\t\t\tfor( let i = 0; i < slide.attributes.length; i++ ) {\n\t\t\t\tlet attribute = slide.attributes[ i ];\n\t\t\t\tattributes[ attribute.name ] = attribute.value;\n\t\t\t}\n\t\t\treturn attributes;\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Retrieves the total number of slides in this presentation.\n\t *\n\t * @return {number}\n\t */\n\tfunction getTotalSlides() {\n\n\t\treturn getSlides().length;\n\n\t}\n\n\t/**\n\t * Returns the slide element matching the specified index.\n\t *\n\t * @return {HTMLElement}\n\t */\n\tfunction getSlide( x, y ) {\n\n\t\tlet horizontalSlide = getHorizontalSlides()[ x ];\n\t\tlet verticalSlides = horizontalSlide && horizontalSlide.querySelectorAll( 'section' );\n\n\t\tif( verticalSlides && verticalSlides.length && typeof y === 'number' ) {\n\t\t\treturn verticalSlides ? verticalSlides[ y ] : undefined;\n\t\t}\n\n\t\treturn horizontalSlide;\n\n\t}\n\n\t/**\n\t * Returns the background element for the given slide.\n\t * All slides, even the ones with no background properties\n\t * defined, have a background element so as long as the\n\t * index is valid an element will be returned.\n\t *\n\t * @param {mixed} x Horizontal background index OR a slide\n\t * HTML element\n\t * @param {number} y Vertical background index\n\t * @return {(HTMLElement[]|*)}\n\t */\n\tfunction getSlideBackground( x, y ) {\n\n\t\tlet slide = typeof x === 'number' ? getSlide( x, y ) : x;\n\t\tif( slide ) {\n\t\t\treturn slide.slideBackgroundElement;\n\t\t}\n\n\t\treturn undefined;\n\n\t}\n\n\t/**\n\t * Retrieves the current state of the presentation as\n\t * an object. This state can then be restored at any\n\t * time.\n\t *\n\t * @return {{indexh: number, indexv: number, indexf: number, paused: boolean, overview: boolean}}\n\t */\n\tfunction getState() {\n\n\t\tlet indices = getIndices();\n\n\t\treturn {\n\t\t\tindexh: indices.h,\n\t\t\tindexv: indices.v,\n\t\t\tindexf: indices.f,\n\t\t\tpaused: isPaused(),\n\t\t\toverview: overview.isActive()\n\t\t};\n\n\t}\n\n\t/**\n\t * Restores the presentation to the given state.\n\t *\n\t * @param {object} state As generated by getState()\n\t * @see {@link getState} generates the parameter `state`\n\t */\n\tfunction setState( state ) {\n\n\t\tif( typeof state === 'object' ) {\n\t\t\tslide( Util.deserialize( state.indexh ), Util.deserialize( state.indexv ), Util.deserialize( state.indexf ) );\n\n\t\t\tlet pausedFlag = Util.deserialize( state.paused ),\n\t\t\t\toverviewFlag = Util.deserialize( state.overview );\n\n\t\t\tif( typeof pausedFlag === 'boolean' && pausedFlag !== isPaused() ) {\n\t\t\t\ttogglePause( pausedFlag );\n\t\t\t}\n\n\t\t\tif( typeof overviewFlag === 'boolean' && overviewFlag !== overview.isActive() ) {\n\t\t\t\toverview.toggle( overviewFlag );\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Cues a new automated slide if enabled in the config.\n\t */\n\tfunction cueAutoSlide() {\n\n\t\tcancelAutoSlide();\n\n\t\tif( currentSlide && config.autoSlide !== false ) {\n\n\t\t\tlet fragment = currentSlide.querySelector( '.current-fragment[data-autoslide]' );\n\n\t\t\tlet fragmentAutoSlide = fragment ? fragment.getAttribute( 'data-autoslide' ) : null;\n\t\t\tlet parentAutoSlide = currentSlide.parentNode ? currentSlide.parentNode.getAttribute( 'data-autoslide' ) : null;\n\t\t\tlet slideAutoSlide = currentSlide.getAttribute( 'data-autoslide' );\n\n\t\t\t// Pick value in the following priority order:\n\t\t\t// 1. Current fragment's data-autoslide\n\t\t\t// 2. Current slide's data-autoslide\n\t\t\t// 3. Parent slide's data-autoslide\n\t\t\t// 4. Global autoSlide setting\n\t\t\tif( fragmentAutoSlide ) {\n\t\t\t\tautoSlide = parseInt( fragmentAutoSlide, 10 );\n\t\t\t}\n\t\t\telse if( slideAutoSlide ) {\n\t\t\t\tautoSlide = parseInt( slideAutoSlide, 10 );\n\t\t\t}\n\t\t\telse if( parentAutoSlide ) {\n\t\t\t\tautoSlide = parseInt( parentAutoSlide, 10 );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tautoSlide = config.autoSlide;\n\n\t\t\t\t// If there are media elements with data-autoplay,\n\t\t\t\t// automatically set the autoSlide duration to the\n\t\t\t\t// length of that media. Not applicable if the slide\n\t\t\t\t// is divided up into fragments.\n\t\t\t\t// playbackRate is accounted for in the duration.\n\t\t\t\tif( currentSlide.querySelectorAll( '.fragment' ).length === 0 ) {\n\t\t\t\t\tUtil.queryAll( currentSlide, 'video, audio' ).forEach( el => {\n\t\t\t\t\t\tif( el.hasAttribute( 'data-autoplay' ) ) {\n\t\t\t\t\t\t\tif( autoSlide && (el.duration * 1000 / el.playbackRate ) > autoSlide ) {\n\t\t\t\t\t\t\t\tautoSlide = ( el.duration * 1000 / el.playbackRate ) + 1000;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t} );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Cue the next auto-slide if:\n\t\t\t// - There is an autoSlide value\n\t\t\t// - Auto-sliding isn't paused by the user\n\t\t\t// - The presentation isn't paused\n\t\t\t// - The overview isn't active\n\t\t\t// - The presentation isn't over\n\t\t\tif( autoSlide && !autoSlidePaused && !isPaused() && !overview.isActive() && ( !isLastSlide() || fragments.availableRoutes().next || config.loop === true ) ) {\n\t\t\t\tautoSlideTimeout = setTimeout( () => {\n\t\t\t\t\tif( typeof config.autoSlideMethod === 'function' ) {\n\t\t\t\t\t\tconfig.autoSlideMethod()\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tnavigateNext();\n\t\t\t\t\t}\n\t\t\t\t\tcueAutoSlide();\n\t\t\t\t}, autoSlide );\n\t\t\t\tautoSlideStartTime = Date.now();\n\t\t\t}\n\n\t\t\tif( autoSlidePlayer ) {\n\t\t\t\tautoSlidePlayer.setPlaying( autoSlideTimeout !== -1 );\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Cancels any ongoing request to auto-slide.\n\t */\n\tfunction cancelAutoSlide() {\n\n\t\tclearTimeout( autoSlideTimeout );\n\t\tautoSlideTimeout = -1;\n\n\t}\n\n\tfunction pauseAutoSlide() {\n\n\t\tif( autoSlide && !autoSlidePaused ) {\n\t\t\tautoSlidePaused = true;\n\t\t\tdispatchEvent({ type: 'autoslidepaused' });\n\t\t\tclearTimeout( autoSlideTimeout );\n\n\t\t\tif( autoSlidePlayer ) {\n\t\t\t\tautoSlidePlayer.setPlaying( false );\n\t\t\t}\n\t\t}\n\n\t}\n\n\tfunction resumeAutoSlide() {\n\n\t\tif( autoSlide && autoSlidePaused ) {\n\t\t\tautoSlidePaused = false;\n\t\t\tdispatchEvent({ type: 'autoslideresumed' });\n\t\t\tcueAutoSlide();\n\t\t}\n\n\t}\n\n\tfunction navigateLeft({skipFragments=false}={}) {\n\n\t\tnavigationHistory.hasNavigatedHorizontally = true;\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.prev();\n\n\t\t// Reverse for RTL\n\t\tif( config.rtl ) {\n\t\t\tif( ( overview.isActive() || skipFragments || fragments.next() === false ) && availableRoutes().left ) {\n\t\t\t\tslide( indexh + 1, config.navigationMode === 'grid' ? indexv : undefined );\n\t\t\t}\n\t\t}\n\t\t// Normal navigation\n\t\telse if( ( overview.isActive() || skipFragments || fragments.prev() === false ) && availableRoutes().left ) {\n\t\t\tslide( indexh - 1, config.navigationMode === 'grid' ? indexv : undefined );\n\t\t}\n\n\t}\n\n\tfunction navigateRight({skipFragments=false}={}) {\n\n\t\tnavigationHistory.hasNavigatedHorizontally = true;\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.next();\n\n\t\t// Reverse for RTL\n\t\tif( config.rtl ) {\n\t\t\tif( ( overview.isActive() || skipFragments || fragments.prev() === false ) && availableRoutes().right ) {\n\t\t\t\tslide( indexh - 1, config.navigationMode === 'grid' ? indexv : undefined );\n\t\t\t}\n\t\t}\n\t\t// Normal navigation\n\t\telse if( ( overview.isActive() || skipFragments || fragments.next() === false ) && availableRoutes().right ) {\n\t\t\tslide( indexh + 1, config.navigationMode === 'grid' ? indexv : undefined );\n\t\t}\n\n\t}\n\n\tfunction navigateUp({skipFragments=false}={}) {\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.prev();\n\n\t\t// Prioritize hiding fragments\n\t\tif( ( overview.isActive() || skipFragments || fragments.prev() === false ) && availableRoutes().up ) {\n\t\t\tslide( indexh, indexv - 1 );\n\t\t}\n\n\t}\n\n\tfunction navigateDown({skipFragments=false}={}) {\n\n\t\tnavigationHistory.hasNavigatedVertically = true;\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.next();\n\n\t\t// Prioritize revealing fragments\n\t\tif( ( overview.isActive() || skipFragments || fragments.next() === false ) && availableRoutes().down ) {\n\t\t\tslide( indexh, indexv + 1 );\n\t\t}\n\n\t}\n\n\t/**\n\t * Navigates backwards, prioritized in the following order:\n\t * 1) Previous fragment\n\t * 2) Previous vertical slide\n\t * 3) Previous horizontal slide\n\t */\n\tfunction navigatePrev({skipFragments=false}={}) {\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.prev();\n\n\t\t// Prioritize revealing fragments\n\t\tif( skipFragments || fragments.prev() === false ) {\n\t\t\tif( availableRoutes().up ) {\n\t\t\t\tnavigateUp({skipFragments});\n\t\t\t}\n\t\t\telse {\n\t\t\t\t// Fetch the previous horizontal slide, if there is one\n\t\t\t\tlet previousSlide;\n\n\t\t\t\tif( config.rtl ) {\n\t\t\t\t\tpreviousSlide = Util.queryAll( dom.wrapper, HORIZONTAL_SLIDES_SELECTOR + '.future' ).pop();\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tpreviousSlide = Util.queryAll( dom.wrapper, HORIZONTAL_SLIDES_SELECTOR + '.past' ).pop();\n\t\t\t\t}\n\n\t\t\t\t// When going backwards and arriving on a stack we start\n\t\t\t\t// at the bottom of the stack\n\t\t\t\tif( previousSlide && previousSlide.classList.contains( 'stack' ) ) {\n\t\t\t\t\tlet v = ( previousSlide.querySelectorAll( 'section' ).length - 1 ) || undefined;\n\t\t\t\t\tlet h = indexh - 1;\n\t\t\t\t\tslide( h, v );\n\t\t\t\t}\n\t\t\t\telse if( config.rtl ) {\n\t\t\t\t\tnavigateRight({skipFragments});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tnavigateLeft({skipFragments});\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * The reverse of #navigatePrev().\n\t */\n\tfunction navigateNext({skipFragments=false}={}) {\n\n\t\tnavigationHistory.hasNavigatedHorizontally = true;\n\t\tnavigationHistory.hasNavigatedVertically = true;\n\n\t\t// Scroll view navigation is handled independently\n\t\tif( scrollView.isActive() ) return scrollView.next();\n\n\t\t// Prioritize revealing fragments\n\t\tif( skipFragments || fragments.next() === false ) {\n\n\t\t\tlet routes = availableRoutes();\n\n\t\t\t// When looping is enabled `routes.down` is always available\n\t\t\t// so we need a separate check for when we've reached the\n\t\t\t// end of a stack and should move horizontally\n\t\t\tif( routes.down && routes.right && config.loop && isLastVerticalSlide() ) {\n\t\t\t\troutes.down = false;\n\t\t\t}\n\n\t\t\tif( routes.down ) {\n\t\t\t\tnavigateDown({skipFragments});\n\t\t\t}\n\t\t\telse if( config.rtl ) {\n\t\t\t\tnavigateLeft({skipFragments});\n\t\t\t}\n\t\t\telse {\n\t\t\t\tnavigateRight({skipFragments});\n\t\t\t}\n\t\t}\n\n\t}\n\n\n\t// --------------------------------------------------------------------//\n\t// ----------------------------- EVENTS -------------------------------//\n\t// --------------------------------------------------------------------//\n\n\t/**\n\t * Called by all event handlers that are based on user\n\t * input.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onUserInput( event ) {\n\n\t\tif( config.autoSlideStoppable ) {\n\t\t\tpauseAutoSlide();\n\t\t}\n\n\t}\n\n\t/**\n\t* Listener for post message events posted to this window.\n\t*/\n\tfunction onPostMessage( event ) {\n\n\t\tlet data = event.data;\n\n\t\t// Make sure we're dealing with JSON\n\t\tif( typeof data === 'string' && data.charAt( 0 ) === '{' && data.charAt( data.length - 1 ) === '}' ) {\n\t\t\tdata = JSON.parse( data );\n\n\t\t\t// Check if the requested method can be found\n\t\t\tif( data.method && typeof Reveal[data.method] === 'function' ) {\n\n\t\t\t\tif( POST_MESSAGE_METHOD_BLACKLIST.test( data.method ) === false ) {\n\n\t\t\t\t\tconst result = Reveal[data.method].apply( Reveal, data.args );\n\n\t\t\t\t\t// Dispatch a postMessage event with the returned value from\n\t\t\t\t\t// our method invocation for getter functions\n\t\t\t\t\tdispatchPostMessage( 'callback', { method: data.method, result: result } );\n\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tconsole.warn( 'reveal.js: \"'+ data.method +'\" is is blacklisted from the postMessage API' );\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Event listener for transition end on the current slide.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onTransitionEnd( event ) {\n\n\t\tif( transition === 'running' && /section/gi.test( event.target.nodeName ) ) {\n\t\t\ttransition = 'idle';\n\t\t\tdispatchEvent({\n\t\t\t\ttype: 'slidetransitionend',\n\t\t\t\tdata: { indexh, indexv, previousSlide, currentSlide }\n\t\t\t});\n\t\t}\n\n\t}\n\n\t/**\n\t * A global listener for all click events inside of the\n\t * .slides container.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onSlidesClicked( event ) {\n\n\t\tconst anchor = Util.closest( event.target, 'a[href^=\"#\"]' );\n\n\t\t// If a hash link is clicked, we find the target slide\n\t\t// and navigate to it. We previously relied on 'hashchange'\n\t\t// for links like these but that prevented media with\n\t\t// audio tracks from playing in mobile browsers since it\n\t\t// wasn't considered a direct interaction with the document.\n\t\tif( anchor ) {\n\t\t\tconst hash = anchor.getAttribute( 'href' );\n\t\t\tconst indices = location.getIndicesFromHash( hash );\n\n\t\t\tif( indices ) {\n\t\t\t\tReveal.slide( indices.h, indices.v, indices.f );\n\t\t\t\tevent.preventDefault();\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the window level 'resize' event.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onWindowResize( event ) {\n\n\t\tlayout();\n\t}\n\n\t/**\n\t * Handle for the window level 'visibilitychange' event.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onPageVisibilityChange( event ) {\n\n\t\t// If, after clicking a link or similar and we're coming back,\n\t\t// focus the document.body to ensure we can use keyboard shortcuts\n\t\tif( document.hidden === false && document.activeElement !== document.body ) {\n\t\t\t// Not all elements support .blur() - SVGs among them.\n\t\t\tif( typeof document.activeElement.blur === 'function' ) {\n\t\t\t\tdocument.activeElement.blur();\n\t\t\t}\n\t\t\tdocument.body.focus();\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the document level 'fullscreenchange' event.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onFullscreenChange( event ) {\n\n\t\tlet element = document.fullscreenElement || document.webkitFullscreenElement;\n\t\tif( element === dom.wrapper ) {\n\t\t\tevent.stopImmediatePropagation();\n\n\t\t\t// Timeout to avoid layout shift in Safari\n\t\t\tsetTimeout( () => {\n\t\t\t\tReveal.layout();\n\t\t\t\tReveal.focus.focus(); // focus.focus :'(\n\t\t\t}, 1 );\n\t\t}\n\n\t}\n\n\t/**\n\t * Handles clicks on links that are set to preview in the\n\t * iframe overlay.\n\t *\n\t * @param {object} event\n\t */\n\tfunction onPreviewLinkClicked( event ) {\n\n\t\tif( event.currentTarget && event.currentTarget.hasAttribute( 'href' ) ) {\n\t\t\tlet url = event.currentTarget.getAttribute( 'href' );\n\t\t\tif( url ) {\n\t\t\t\tshowPreview( url );\n\t\t\t\tevent.preventDefault();\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Handles click on the auto-sliding controls element.\n\t *\n\t * @param {object} [event]\n\t */\n\tfunction onAutoSlidePlayerClick( event ) {\n\n\t\t// Replay\n\t\tif( isLastSlide() && config.loop === false ) {\n\t\t\tslide( 0, 0 );\n\t\t\tresumeAutoSlide();\n\t\t}\n\t\t// Resume\n\t\telse if( autoSlidePaused ) {\n\t\t\tresumeAutoSlide();\n\t\t}\n\t\t// Pause\n\t\telse {\n\t\t\tpauseAutoSlide();\n\t\t}\n\n\t}\n\n\n\t// --------------------------------------------------------------------//\n\t// ------------------------------- API --------------------------------//\n\t// --------------------------------------------------------------------//\n\n\t// The public reveal.js API\n\tconst API = {\n\t\tVERSION,\n\n\t\tinitialize,\n\t\tconfigure,\n\t\tdestroy,\n\n\t\tsync,\n\t\tsyncSlide,\n\t\tsyncFragments: fragments.sync.bind( fragments ),\n\n\t\t// Navigation methods\n\t\tslide,\n\t\tleft: navigateLeft,\n\t\tright: navigateRight,\n\t\tup: navigateUp,\n\t\tdown: navigateDown,\n\t\tprev: navigatePrev,\n\t\tnext: navigateNext,\n\n\t\t// Navigation aliases\n\t\tnavigateLeft, navigateRight, navigateUp, navigateDown, navigatePrev, navigateNext,\n\n\t\t// Fragment methods\n\t\tnavigateFragment: fragments.goto.bind( fragments ),\n\t\tprevFragment: fragments.prev.bind( fragments ),\n\t\tnextFragment: fragments.next.bind( fragments ),\n\n\t\t// Event binding\n\t\ton,\n\t\toff,\n\n\t\t// Legacy event binding methods left in for backwards compatibility\n\t\taddEventListener: on,\n\t\tremoveEventListener: off,\n\n\t\t// Forces an update in slide layout\n\t\tlayout,\n\n\t\t// Randomizes the order of slides\n\t\tshuffle,\n\n\t\t// Returns an object with the available routes as booleans (left/right/top/bottom)\n\t\tavailableRoutes,\n\n\t\t// Returns an object with the available fragments as booleans (prev/next)\n\t\tavailableFragments: fragments.availableRoutes.bind( fragments ),\n\n\t\t// Toggles a help overlay with keyboard shortcuts\n\t\ttoggleHelp,\n\n\t\t// Toggles the overview mode on/off\n\t\ttoggleOverview: overview.toggle.bind( overview ),\n\n\t\t// Toggles the scroll view on/off\n\t\ttoggleScrollView: scrollView.toggle.bind( scrollView ),\n\n\t\t// Toggles the \"black screen\" mode on/off\n\t\ttogglePause,\n\n\t\t// Toggles the auto slide mode on/off\n\t\ttoggleAutoSlide,\n\n\t\t// Toggles visibility of the jump-to-slide UI\n\t\ttoggleJumpToSlide,\n\n\t\t// Slide navigation checks\n\t\tisFirstSlide,\n\t\tisLastSlide,\n\t\tisLastVerticalSlide,\n\t\tisVerticalSlide,\n\t\tisVerticalStack,\n\n\t\t// State checks\n\t\tisPaused,\n\t\tisAutoSliding,\n\t\tisSpeakerNotes: notes.isSpeakerNotesWindow.bind( notes ),\n\t\tisOverview: overview.isActive.bind( overview ),\n\t\tisFocused: focus.isFocused.bind( focus ),\n\n\t\tisScrollView: scrollView.isActive.bind( scrollView ),\n\t\tisPrintView: printView.isActive.bind( printView ),\n\n\t\t// Checks if reveal.js has been loaded and is ready for use\n\t\tisReady: () => ready,\n\n\t\t// Slide preloading\n\t\tloadSlide: slideContent.load.bind( slideContent ),\n\t\tunloadSlide: slideContent.unload.bind( slideContent ),\n\n\t\t// Start/stop all media inside of the current slide\n\t\tstartEmbeddedContent: () => slideContent.startEmbeddedContent( currentSlide ),\n\t\tstopEmbeddedContent: () => slideContent.stopEmbeddedContent( currentSlide, { unloadIframes: false } ),\n\n\t\t// Preview management\n\t\tshowPreview,\n\t\thidePreview: closeOverlay,\n\n\t\t// Adds or removes all internal event listeners\n\t\taddEventListeners,\n\t\tremoveEventListeners,\n\t\tdispatchEvent,\n\n\t\t// Facility for persisting and restoring the presentation state\n\t\tgetState,\n\t\tsetState,\n\n\t\t// Presentation progress on range of 0-1\n\t\tgetProgress,\n\n\t\t// Returns the indices of the current, or specified, slide\n\t\tgetIndices,\n\n\t\t// Returns an Array of key:value maps of the attributes of each\n\t\t// slide in the deck\n\t\tgetSlidesAttributes,\n\n\t\t// Returns the number of slides that we have passed\n\t\tgetSlidePastCount,\n\n\t\t// Returns the total number of slides\n\t\tgetTotalSlides,\n\n\t\t// Returns the slide element at the specified index\n\t\tgetSlide,\n\n\t\t// Returns the previous slide element, may be null\n\t\tgetPreviousSlide: () => previousSlide,\n\n\t\t// Returns the current slide element\n\t\tgetCurrentSlide: () => currentSlide,\n\n\t\t// Returns the slide background element at the specified index\n\t\tgetSlideBackground,\n\n\t\t// Returns the speaker notes string for a slide, or null\n\t\tgetSlideNotes: notes.getSlideNotes.bind( notes ),\n\n\t\t// Returns an Array of all slides\n\t\tgetSlides,\n\n\t\t// Returns an array with all horizontal/vertical slides in the deck\n\t\tgetHorizontalSlides,\n\t\tgetVerticalSlides,\n\n\t\t// Checks if the presentation contains two or more horizontal\n\t\t// and vertical slides\n\t\thasHorizontalSlides,\n\t\thasVerticalSlides,\n\n\t\t// Checks if the deck has navigated on either axis at least once\n\t\thasNavigatedHorizontally: () => navigationHistory.hasNavigatedHorizontally,\n\t\thasNavigatedVertically: () => navigationHistory.hasNavigatedVertically,\n\n\t\tshouldAutoAnimateBetween,\n\n\t\t// Adds/removes a custom key binding\n\t\taddKeyBinding: keyboard.addKeyBinding.bind( keyboard ),\n\t\tremoveKeyBinding: keyboard.removeKeyBinding.bind( keyboard ),\n\n\t\t// Programmatically triggers a keyboard event\n\t\ttriggerKey: keyboard.triggerKey.bind( keyboard ),\n\n\t\t// Registers a new shortcut to include in the help overlay\n\t\tregisterKeyboardShortcut: keyboard.registerKeyboardShortcut.bind( keyboard ),\n\n\t\tgetComputedSlideSize,\n\t\tsetCurrentScrollPage,\n\n\t\t// Returns the current scale of the presentation content\n\t\tgetScale: () => scale,\n\n\t\t// Returns the current configuration object\n\t\tgetConfig: () => config,\n\n\t\t// Helper method, retrieves query string as a key:value map\n\t\tgetQueryHash: Util.getQueryHash,\n\n\t\t// Returns the path to the current slide as represented in the URL\n\t\tgetSlidePath: location.getHash.bind( location ),\n\n\t\t// Returns reveal.js DOM elements\n\t\tgetRevealElement: () => revealElement,\n\t\tgetSlidesElement: () => dom.slides,\n\t\tgetViewportElement: () => dom.viewport,\n\t\tgetBackgroundsElement: () => backgrounds.element,\n\n\t\t// API for registering and retrieving plugins\n\t\tregisterPlugin: plugins.registerPlugin.bind( plugins ),\n\t\thasPlugin: plugins.hasPlugin.bind( plugins ),\n\t\tgetPlugin: plugins.getPlugin.bind( plugins ),\n\t\tgetPlugins: plugins.getRegisteredPlugins.bind( plugins )\n\n\t};\n\n\t// Our internal API which controllers have access to\n\tUtil.extend( Reveal, {\n\t\t...API,\n\n\t\t// Methods for announcing content to screen readers\n\t\tannounceStatus,\n\t\tgetStatusText,\n\n\t\t// Controllers\n\t\tfocus,\n\t\tscroll: scrollView,\n\t\tprogress,\n\t\tcontrols,\n\t\tlocation,\n\t\toverview,\n\t\tfragments,\n\t\tbackgrounds,\n\t\tslideContent,\n\t\tslideNumber,\n\n\t\tonUserInput,\n\t\tcloseOverlay,\n\t\tupdateSlidesVisibility,\n\t\tlayoutSlideContents,\n\t\ttransformSlides,\n\t\tcueAutoSlide,\n\t\tcancelAutoSlide\n\t} );\n\n\treturn API;\n\n};\n","import Deck, { VERSION } from './reveal.js'\n\n/**\n * Expose the Reveal class to the window. 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\ No newline at end of file
diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/LICENSE b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..29513e9c4b77d8614975dddfd8bc32a4f7665bff
--- /dev/null
+++ b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/LICENSE
@@ -0,0 +1,2 @@
+SIL Open Font License (OFL)
+http://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=OFL
diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.css b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.css
new file mode 100644
index 0000000000000000000000000000000000000000..32862f8f51a487057b79321ffa294f405f34b3d8
--- /dev/null
+++ b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.css
@@ -0,0 +1,10 @@
+@font-face {
+ font-family: 'League Gothic';
+ src: url('./league-gothic.eot');
+ src: url('./league-gothic.eot?#iefix') format('embedded-opentype'),
+ url('./league-gothic.woff') format('woff'),
+ url('./league-gothic.ttf') format('truetype');
+
+ font-weight: normal;
+ font-style: normal;
+}
diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.eot b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.eot
new file mode 100755
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diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.ttf b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.ttf
new file mode 100755
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diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.woff b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.woff
new file mode 100755
index 0000000000000000000000000000000000000000..8c1227b200c3432a2502e97b87ae0508183629df
Binary files /dev/null and b/src/_site/site_libs/revealjs/dist/theme/fonts/league-gothic/league-gothic.woff differ
diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/LICENSE b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..71b7a02a22f0a02a94f4b92139a24495c8daf2a3
--- /dev/null
+++ b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/LICENSE
@@ -0,0 +1,45 @@
+SIL Open Font License
+
+Copyright 2010, 2012 Adobe Systems Incorporated (http://www.adobe.com/), with Reserved Font Name βSourceβ. All Rights Reserved. Source is a trademark of Adobe Systems Incorporated in the United States and/or other countries.
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL
+
+βββββββββββββββββββββββββββββ-
+SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
+βββββββββββββββββββββββββββββ-
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+βFont Softwareβ refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
+
+βReserved Font Nameβ refers to any names specified as such after the copyright statement(s).
+
+βOriginal Versionβ refers to the collection of Font Software components as distributed by the Copyright Holder(s).
+
+βModified Versionβ refers to any derivative made by adding to, deleting, or substitutingβin part or in wholeβany of the components of the Original Version, by changing formats or by porting the Font Software to a new environment.
+
+βAuthorβ refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission.
+
+5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED βAS ISβ, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
\ No newline at end of file
diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-italic.eot b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-italic.eot
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diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-regular.eot b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-regular.eot
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diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-semibolditalic.woff b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro-semibolditalic.woff
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diff --git a/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css
new file mode 100644
index 0000000000000000000000000000000000000000..99e4fb71a71141a6609c67ef440cb239df8a734b
--- /dev/null
+++ b/src/_site/site_libs/revealjs/dist/theme/fonts/source-sans-pro/source-sans-pro.css
@@ -0,0 +1,39 @@
+@font-face {
+ font-family: 'Source Sans Pro';
+ src: url('./source-sans-pro-regular.eot');
+ src: url('./source-sans-pro-regular.eot?#iefix') format('embedded-opentype'),
+ url('./source-sans-pro-regular.woff') format('woff'),
+ url('./source-sans-pro-regular.ttf') format('truetype');
+ font-weight: normal;
+ font-style: normal;
+}
+
+@font-face {
+ font-family: 'Source Sans Pro';
+ src: url('./source-sans-pro-italic.eot');
+ src: url('./source-sans-pro-italic.eot?#iefix') format('embedded-opentype'),
+ url('./source-sans-pro-italic.woff') format('woff'),
+ url('./source-sans-pro-italic.ttf') format('truetype');
+ font-weight: normal;
+ font-style: italic;
+}
+
+@font-face {
+ font-family: 'Source Sans Pro';
+ src: url('./source-sans-pro-semibold.eot');
+ src: url('./source-sans-pro-semibold.eot?#iefix') format('embedded-opentype'),
+ url('./source-sans-pro-semibold.woff') format('woff'),
+ url('./source-sans-pro-semibold.ttf') format('truetype');
+ font-weight: 600;
+ font-style: normal;
+}
+
+@font-face {
+ font-family: 'Source Sans Pro';
+ src: url('./source-sans-pro-semibolditalic.eot');
+ src: url('./source-sans-pro-semibolditalic.eot?#iefix') format('embedded-opentype'),
+ url('./source-sans-pro-semibolditalic.woff') format('woff'),
+ url('./source-sans-pro-semibolditalic.ttf') format('truetype');
+ font-weight: 600;
+ font-style: italic;
+}
diff --git a/src/_site/site_libs/revealjs/dist/theme/quarto-5b48f34d633aed70c74c672477009ffc.css b/src/_site/site_libs/revealjs/dist/theme/quarto-5b48f34d633aed70c74c672477009ffc.css
new file mode 100644
index 0000000000000000000000000000000000000000..d7ccea765a606dd6959aab3df636c34a7a897a25
--- /dev/null
+++ b/src/_site/site_libs/revealjs/dist/theme/quarto-5b48f34d633aed70c74c672477009ffc.css
@@ -0,0 +1,8 @@
+@import"./fonts/source-sans-pro/source-sans-pro.css";:root{--r-background-color: #191919;--r-main-font: Source Sans Pro, Helvetica, sans-serif;--r-main-font-size: 40px;--r-main-color: #fff;--r-block-margin: 12px;--r-heading-margin: 0 0 12px 0;--r-heading-font: Source Sans Pro, Helvetica, sans-serif;--r-heading-color: #fff;--r-heading-line-height: 1.2;--r-heading-letter-spacing: normal;--r-heading-text-transform: none;--r-heading-text-shadow: none;--r-heading-font-weight: 600;--r-heading1-text-shadow: none;--r-heading1-size: 2.5em;--r-heading2-size: 1.6em;--r-heading3-size: 1.3em;--r-heading4-size: 1em;--r-code-font: SFMono-Regular, Menlo, Monaco, Consolas, Liberation Mono, Courier New, monospace;--r-link-color: #42affa;--r-link-color-dark: #068de9;--r-link-color-hover: #74c4fb;--r-selection-background-color: #bee4fd;--r-selection-color: #191919;--r-overlay-element-bg-color: 240, 240, 240;--r-overlay-element-fg-color: 0, 0, 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auto;padding:5px;font-style:italic;background:rgba(255,255,255,.05);box-shadow:0px 0px 2px rgba(0,0,0,.2)}.reveal blockquote p:first-child,.reveal blockquote p:last-child{display:inline-block}.reveal q{font-style:italic}.reveal pre{display:block;position:relative;width:90%;margin:var(--r-block-margin) auto;text-align:left;font-size:.55em;font-family:var(--r-code-font);line-height:1.2em;word-wrap:break-word;box-shadow:0px 5px 15px rgba(0,0,0,.15)}.reveal code{font-family:var(--r-code-font);text-transform:none;tab-size:2}.reveal pre code{display:block;padding:5px;overflow:auto;max-height:400px;word-wrap:normal}.reveal .code-wrapper{white-space:normal}.reveal .code-wrapper code{white-space:pre}.reveal table{margin:auto;border-collapse:collapse;border-spacing:0}.reveal table th{font-weight:bold}.reveal table th,.reveal table td{text-align:left;padding:.2em .5em .2em .5em;border-bottom:1px solid}.reveal table th[align=center],.reveal table td[align=center]{text-align:center}.reveal table th[align=right],.reveal table td[align=right]{text-align:right}.reveal table tbody tr:last-child th,.reveal table tbody tr:last-child td{border-bottom:none}.reveal sup{vertical-align:super;font-size:smaller}.reveal sub{vertical-align:sub;font-size:smaller}.reveal small{display:inline-block;font-size:.6em;line-height:1.2em;vertical-align:top}.reveal small *{vertical-align:top}.reveal img{margin:var(--r-block-margin) 0}.reveal a{color:var(--r-link-color);text-decoration:none;transition:color .15s ease}.reveal a:hover{color:var(--r-link-color-hover);text-shadow:none;border:none}.reveal .roll span:after{color:#fff;background:var(--r-link-color-dark)}.reveal .r-frame{border:4px solid var(--r-main-color);box-shadow:0 0 10px rgba(0,0,0,.15)}.reveal a .r-frame{transition:all .15s linear}.reveal a:hover .r-frame{border-color:var(--r-link-color);box-shadow:0 0 20px rgba(0,0,0,.55)}.reveal .controls{color:var(--r-link-color)}.reveal .progress{background:rgba(0,0,0,.2);color:var(--r-link-color)}@media print{.backgrounds{background-color:var(--r-background-color)}}.top-right{position:absolute;top:1em;right:1em}.visually-hidden{border:0;clip:rect(0 0 0 0);height:auto;margin:0;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap}.hidden{display:none !important}.zindex-bottom{z-index:-1 !important}figure.figure{display:block}.quarto-layout-panel{margin-bottom:1em}.quarto-layout-panel>figure{width:100%}.quarto-layout-panel>figure>figcaption,.quarto-layout-panel>.panel-caption{margin-top:10pt}.quarto-layout-panel>.table-caption{margin-top:0px}.table-caption p{margin-bottom:.5em}.quarto-layout-row{display:flex;flex-direction:row;align-items:flex-start}.quarto-layout-valign-top{align-items:flex-start}.quarto-layout-valign-bottom{align-items:flex-end}.quarto-layout-valign-center{align-items:center}.quarto-layout-cell{position:relative;margin-right:20px}.quarto-layout-cell:last-child{margin-right:0}.quarto-layout-cell figure,.quarto-layout-cell>p{margin:.2em}.quarto-layout-cell img{max-width:100%}.quarto-layout-cell .html-widget{width:100% !important}.quarto-layout-cell div figure p{margin:0}.quarto-layout-cell figure{display:block;margin-inline-start:0;margin-inline-end:0}.quarto-layout-cell table{display:inline-table}.quarto-layout-cell-subref figcaption,figure .quarto-layout-row figure 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Cs=Ts;Cs.registerLanguage("1c",(me||(me=1,de=function(e){const t="[A-Za-zΠ-Π―Π°-ΡΡΠ_][A-Za-zΠ-Π―Π°-ΡΡΠ_0-9]+",a="Π΄Π°Π»Π΅Π΅ Π²ΠΎΠ·Π²ΡΠ°Ρ Π²ΡΠ·Π²Π°ΡΡΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½ΠΈΡΡ Π΄Π»Ρ Π΅ΡΠ»ΠΈ ΠΈ ΠΈΠ· ΠΈΠ»ΠΈ ΠΈΠ½Π°ΡΠ΅ ΠΈΠ½Π°ΡΠ΅Π΅ΡΠ»ΠΈ ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΊΠΎΠ½Π΅ΡΠ΅ΡΠ»ΠΈ ΠΊΠΎΠ½Π΅ΡΠΏΠΎΠΏΡΡΠΊΠΈ ΠΊΠΎΠ½Π΅ΡΡΠΈΠΊΠ»Π° Π½Π΅ Π½ΠΎΠ²ΡΠΉ ΠΏΠ΅ΡΠ΅ΠΉΡΠΈ ΠΏΠ΅ΡΠ΅ΠΌ ΠΏΠΎ ΠΏΠΎΠΊΠ° ΠΏΠΎΠΏΡΡΠΊΠ° ΠΏΡΠ΅ΡΠ²Π°ΡΡ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΡ ΡΠΎΠ³Π΄Π° ΡΠΈΠΊΠ» ΡΠΊΡΠΏΠΎΡΡ ",n="null ΠΈΡΡΠΈΠ½Π° Π»ΠΎΠΆΡ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ",i=e.inherit(e.NUMBER_MODE),r={className:"string",begin:'"|\\|',end:'"|$',contains:[{begin:'""'}]},o={begin:"'",end:"'",excludeBegin:!0,excludeEnd:!0,contains:[{className:"number",begin:"\\d{4}([\\.\\\\/:-]?\\d{2}){0,5}"}]},s=e.inherit(e.C_LINE_COMMENT_MODE);return{name:"1C:Enterprise",case_insensitive:!0,keywords:{$pattern:t,keyword:a,built_in:"ΡΠ°Π·Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΡΡΡΠ°Π½ΠΈΡ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΡΡΡΠΎΠΊ ΡΠΈΠΌΠ²ΠΎΠ»ΡΠ°Π±ΡΠ»ΡΡΠΈΠΈ ansitooem oemtoansi Π²Π²Π΅ΡΡΠΈΠ²ΠΈΠ΄ΡΡΠ±ΠΊΠΎΠ½ΡΠΎ Π²Π²Π΅ΡΡΠΈΠΏΠ΅ΡΠ΅ΡΠΈΡΠ»Π΅Π½ΠΈΠ΅ Π²Π²Π΅ΡΡΠΈΠΏΠ΅ΡΠΈΠΎΠ΄ Π²Π²Π΅ΡΡΠΈΠΏΠ»Π°Π½ΡΡΠ΅ΡΠΎΠ² Π²ΡΠ±ΡΠ°Π½Π½ΡΠΉΠΏΠ»Π°Π½ΡΡΠ΅ΡΠΎΠ² Π΄Π°ΡΠ°Π³ΠΎΠ΄ Π΄Π°ΡΠ°ΠΌΠ΅ΡΡΡ Π΄Π°ΡΠ°ΡΠΈΡΠ»ΠΎ Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΡΠΈΡΡΠ΅ΠΌΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅Π²ΡΡΡΠΎΠΊΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΈΠ·ΡΡΡΠΎΠΊΠΈ ΠΊΠ°ΡΠ°Π»ΠΎΠ³ΠΈΠ± ΠΊΠ°ΡΠ°Π»ΠΎΠ³ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΊΠΎΠ΄ΡΠΈΠΌΠ² ΠΊΠΎΠ½Π³ΠΎΠ΄Π° ΠΊΠΎΠ½Π΅ΡΠΏΠ΅ΡΠΈΠΎΠ΄Π°Π±ΠΈ ΠΊΠΎΠ½Π΅ΡΡΠ°ΡΡΡΠΈΡΠ°Π½Π½ΠΎΠ³ΠΎΠΏΠ΅ΡΠΈΠΎΠ΄Π°Π±ΠΈ ΠΊΠΎΠ½Π΅ΡΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΈΠ½ΡΠ΅ΡΠ²Π°Π»Π° ΠΊΠΎΠ½ΠΊΠ²Π°ΡΡΠ°Π»Π° ΠΊΠΎΠ½ΠΌΠ΅ΡΡΡΠ° ΠΊΠΎΠ½Π½Π΅Π΄Π΅Π»ΠΈ Π»ΠΎΠ³ Π»ΠΎΠ³10 ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ΅ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΡΡΠ±ΠΊΠΎΠ½ΡΠΎ Π½Π°Π·Π²Π°Π½ΠΈΠ΅ΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ° Π½Π°Π·Π²Π°Π½ΠΈΠ΅Π½Π°Π±ΠΎΡΠ°ΠΏΡΠ°Π² Π½Π°Π·Π½Π°ΡΠΈΡΡΠ²ΠΈΠ΄ Π½Π°Π·Π½Π°ΡΠΈΡΡΡΡΠ΅Ρ Π½Π°ΠΉΡΠΈΡΡΡΠ»ΠΊΠΈ Π½Π°ΡΠ°Π»ΠΎΠΏΠ΅ΡΠΈΠΎΠ΄Π°Π±ΠΈ Π½Π°ΡΠ°Π»ΠΎΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΈΠ½ΡΠ΅ΡΠ²Π°Π»Π° Π½Π°ΡΠ³ΠΎΠ΄Π° Π½Π°ΡΠΊΠ²Π°ΡΡΠ°Π»Π° Π½Π°ΡΠΌΠ΅ΡΡΡΠ° Π½Π°ΡΠ½Π΅Π΄Π΅Π»ΠΈ Π½ΠΎΠΌΠ΅ΡΠ΄Π½ΡΠ³ΠΎΠ΄Π° Π½ΠΎΠΌΠ΅ΡΠ΄Π½ΡΠ½Π΅Π΄Π΅Π»ΠΈ Π½ΠΎΠΌΠ΅ΡΠ½Π΅Π΄Π΅Π»ΠΈΠ³ΠΎΠ΄Π° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉΠΆΡΡΠ½Π°Π»ΡΠ°ΡΡΠ΅ΡΠΎΠ² ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉΠΏΠ»Π°Π½ΡΡΠ΅ΡΠΎΠ² ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉΡΠ·ΡΠΊ ΠΎΡΠΈΡΡΠΈΡΡΠΎΠΊΠ½ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ΡΡΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ²ΡΠ΅ΠΌΡΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄Π°ΡΡΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΡΠΎΡΠ±ΠΎΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΠΎΠ·ΠΈΡΠΈΡΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΡΡΡΠΎΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ° ΠΏΡΠ΅ΡΠΈΠΊΡΠ°Π²ΡΠΎΠ½ΡΠΌΠ΅ΡΠ°ΡΠΈΠΈ ΠΏΡΠΎΠΏΠΈΡΡ ΠΏΡΡΡΠΎΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·ΠΌ ΡΠ°Π·ΠΎΠ±ΡΠ°ΡΡΠΏΠΎΠ·ΠΈΡΠΈΡΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠ°ΡΡΡΠΈΡΠ°ΡΡΡΠ΅Π³ΠΈΡΡΡΡΠ½Π° ΡΠ°ΡΡΡΠΈΡΠ°ΡΡΡΠ΅Π³ΠΈΡΡΡΡΠΏΠΎ ΡΠΈΠΌΠ² ΡΠΎΠ·Π΄Π°ΡΡΠΎΠ±ΡΠ΅ΠΊΡ ΡΡΠ°ΡΡΡΠ²ΠΎΠ·Π²ΡΠ°ΡΠ° ΡΡΡΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΡΡΡΠΎΠΊ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡΠΏΠΎΠ·ΠΈΡΠΈΡΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΡΠ΅ΡΠΏΠΎΠΊΠΎΠ΄Ρ ΡΠ΅ΠΊΡΡΠ΅Π΅Π²ΡΠ΅ΠΌΡ ΡΠΈΠΏΠ·Π½Π°ΡΠ΅Π½ΠΈΡ ΡΠΈΠΏΠ·Π½Π°ΡΠ΅Π½ΠΈΡΡΡΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°Π½Π° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°ΠΏΠΎ ΡΠΈΠΊΡΡΠ°Π±Π»ΠΎΠ½ ΡΠ°Π±Π»ΠΎΠ½ acos asin atan base64Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ base64ΡΡΡΠΎΠΊΠ° cos exp log log10 pow sin sqrt tan xmlΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ xmlΡΡΡΠΎΠΊΠ° xmlΡΠΈΠΏ xmlΡΠΈΠΏΠ·Π½Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ΅ΠΎΠΊΠ½ΠΎ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΡΠ΄Π°Π½Π½ΡΡ
Π±ΡΠ»Π΅Π²ΠΎ Π²Π²Π΅ΡΡΠΈΠ΄Π°ΡΡ Π²Π²Π΅ΡΡΠΈΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π²Π²Π΅ΡΡΠΈΡΡΡΠΎΠΊΡ Π²Π²Π΅ΡΡΠΈΡΠΈΡΠ»ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΡΡΠ΅Π½ΠΈΡxml Π²ΠΎΠΏΡΠΎΡ Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π²ΡΠ΅Π³ Π²ΡΠ³ΡΡΠ·ΠΈΡΡΠΆΡΡΠ½Π°Π»ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ Π²ΡΠΏΠΎΠ»Π½ΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΡ Π²ΡΠΏΠΎΠ»Π½ΠΈΡΡΠΏΡΠΎΠ²Π΅ΡΠΊΡΠΏΡΠ°Π²Π΄ΠΎΡΡΡΠΏΠ° Π²ΡΡΠΈΡΠ»ΠΈΡΡ Π³ΠΎΠ΄ Π΄Π°Π½Π½ΡΠ΅ΡΠΎΡΠΌΡΠ²Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ Π΄Π°ΡΠ° Π΄Π΅Π½Ρ Π΄Π΅Π½ΡΠ³ΠΎΠ΄Π° Π΄Π΅Π½ΡΠ½Π΅Π΄Π΅Π»ΠΈ Π΄ΠΎΠ±Π°Π²ΠΈΡΡΠΌΠ΅ΡΡΡ Π·Π°Π±Π»ΠΎΠΊΠΈΡΠΎΠ²Π°ΡΡΠ΄Π°Π½Π½ΡΠ΅Π΄Π»ΡΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π·Π°Π±Π»ΠΎΠΊΠΈΡΠΎΠ²Π°ΡΡΡΠ°Π±ΠΎΡΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π·Π°Π²Π΅ΡΡΠΈΡΡΡΠ°Π±ΠΎΡΡΡΠΈΡΡΠ΅ΠΌΡ Π·Π°Π³ΡΡΠ·ΠΈΡΡΠ²Π½Π΅ΡΠ½ΡΡΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ Π·Π°ΠΊΡΡΡΡΡΠΏΡΠ°Π²ΠΊΡ Π·Π°ΠΏΠΈΡΠ°ΡΡjson Π·Π°ΠΏΠΈΡΠ°ΡΡxml Π·Π°ΠΏΠΈΡΠ°ΡΡΠ΄Π°ΡΡjson Π·Π°ΠΏΠΈΡΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ Π·Π°ΠΏΠΎΠ»Π½ΠΈΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΡΡΠ²ΠΎΠΉΡΡΠ² Π·Π°ΠΏΡΠΎΡΠΈΡΡΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π·Π°ΠΏΡΡΡΠΈΡΡΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π·Π°ΠΏΡΡΡΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡ Π·Π°ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°ΡΡΡΡΠ°Π½Π·Π°ΠΊΡΠΈΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅Π²Π΄Π°Π½Π½ΡΠ΅ΡΠΎΡΠΌΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅Π²ΡΡΡΠΎΠΊΡΠ²Π½ΡΡΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅Π²ΡΠ°ΠΉΠ» Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅Π·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΈΠ·ΡΡΡΠΎΠΊΠΈΠ²Π½ΡΡΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΈΠ·ΡΠ°ΠΉΠ»Π° ΠΈΠ·xmlΡΠΈΠΏΠ° ΠΈΠΌΠΏΠΎΡΡΠΌΠΎΠ΄Π΅Π»ΠΈxdto ΠΈΠΌΡΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ° ΠΈΠΌΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΈΠ½ΠΈΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅Π΄Π°Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ±ΠΎΡΠΈΠ±ΠΊΠ΅ ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎΡΡΡΡΠΎΠΉΡΡΠ²Π° ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ°ΠΉΠ»ΠΎΠ² ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² ΠΊΠ°ΡΠ°Π»ΠΎΠ³ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°ΡΡΡΡΡΠΎΠΊΡ ΠΊΠΎΠ΄Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΊΠΎΠ΄ΡΠΈΠΌΠ²ΠΎΠ»Π° ΠΊΠΎΠΌΠ°Π½Π΄Π°ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠ½Π΅ΡΠ³ΠΎΠ΄Π° ΠΊΠΎΠ½Π΅ΡΠ΄Π½Ρ ΠΊΠΎΠ½Π΅ΡΠΊΠ²Π°ΡΡΠ°Π»Π° ΠΊΠΎΠ½Π΅ΡΠΌΠ΅ΡΡΡΠ° ΠΊΠΎΠ½Π΅ΡΠΌΠΈΠ½ΡΡΡ ΠΊΠΎΠ½Π΅ΡΠ½Π΅Π΄Π΅Π»ΠΈ ΠΊΠΎΠ½Π΅ΡΡΠ°ΡΠ° ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΡΠ±Π°Π·ΡΠ΄Π°Π½Π½ΡΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½Π°Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈ ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΡΠΈΠ·ΠΌΠ΅Π½Π΅Π½Π° ΠΊΠΎΠΏΠΈΡΠΎΠ²Π°ΡΡΠ΄Π°Π½Π½ΡΠ΅ΡΠΎΡΠΌΡ ΠΊΠΎΠΏΠΈΡΠΎΠ²Π°ΡΡΡΠ°ΠΉΠ» ΠΊΡΠ°ΡΠΊΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΎΡΠΈΠ±ΠΊΠΈ Π»Π΅Π² ΠΌΠ°ΠΊΡ ΠΌΠ΅ΡΡΠ½ΠΎΠ΅Π²ΡΠ΅ΠΌΡ ΠΌΠ΅ΡΡΡ ΠΌΠΈΠ½ ΠΌΠΈΠ½ΡΡΠ° ΠΌΠΎΠ½ΠΎΠΏΠΎΠ»ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌ Π½Π°ΠΉΡΠΈ Π½Π°ΠΉΡΠΈΠ½Π΅Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅ΡΠΈΠΌΠ²ΠΎΠ»Ρxml Π½Π°ΠΉΡΠΈΠΎΠΊΠ½ΠΎΠΏΠΎΠ½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉΡΡΡΠ»ΠΊΠ΅ Π½Π°ΠΉΡΠΈΠΏΠΎΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅Π½Π°ΡΠ΄Π°Π»Π΅Π½ΠΈΠ΅ Π½Π°ΠΉΡΠΈΠΏΠΎΡΡΡΠ»ΠΊΠ°ΠΌ Π½Π°ΠΉΡΠΈΡΠ°ΠΉΠ»Ρ Π½Π°ΡΠ°Π»ΠΎΠ³ΠΎΠ΄Π° Π½Π°ΡΠ°Π»ΠΎΠ΄Π½Ρ Π½Π°ΡΠ°Π»ΠΎΠΊΠ²Π°ΡΡΠ°Π»Π° Π½Π°ΡΠ°Π»ΠΎΠΌΠ΅ΡΡΡΠ° Π½Π°ΡΠ°Π»ΠΎΠΌΠΈΠ½ΡΡΡ Π½Π°ΡΠ°Π»ΠΎΠ½Π΅Π΄Π΅Π»ΠΈ Π½Π°ΡΠ°Π»ΠΎΡΠ°ΡΠ° Π½Π°ΡΠ°ΡΡΠ·Π°ΠΏΡΠΎΡΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π½Π°ΡΠ°ΡΡΠ·Π°ΠΏΡΡΠΊΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ Π½Π°ΡΠ°ΡΡΠΊΠΎΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ°ΠΉΠ»Π° Π½Π°ΡΠ°ΡΡΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ΡΠ°ΠΉΠ»Π° Π½Π°ΡΠ°ΡΡΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅Π²Π½Π΅ΡΠ½Π΅ΠΉΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ Π½Π°ΡΠ°ΡΡΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΡΠ°Π±ΠΎΡΡΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠ΅ΠΉ Π½Π°ΡΠ°ΡΡΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΡΠ°Π±ΠΎΡΡΡΡΠ°ΠΉΠ»Π°ΠΌΠΈ Π½Π°ΡΠ°ΡΡΠΏΠΎΠΈΡΠΊΡΠ°ΠΉΠ»ΠΎΠ² Π½Π°ΡΠ°ΡΡΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π°Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ°ΠΉΠ»ΠΎΠ² Π½Π°ΡΠ°ΡΡΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π°Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² Π½Π°ΡΠ°ΡΡΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΠ΅Π³ΠΎΠΊΠ°ΡΠ°Π»ΠΎΠ³Π°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ Π½Π°ΡΠ°ΡΡΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ΡΠ°ΠΉΠ»ΠΎΠ² Π½Π°ΡΠ°ΡΡΠΏΠΎΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ΡΠ°ΠΉΠ»Π° Π½Π°ΡΠ°ΡΡΠΏΠΎΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ΡΠ°ΠΉΠ»ΠΎΠ² Π½Π°ΡΠ°ΡΡΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅Π΄Π²ΠΎΠΈΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈΠ·ΡΠ°ΠΉΠ»Π° Π½Π°ΡΠ°ΡΡΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ΠΊΠ°ΡΠ°Π»ΠΎΠ³Π° Π½Π°ΡΠ°ΡΡΡΡΠ°Π½Π·Π°ΠΊΡΠΈΡ Π½Π°ΡΠ°ΡΡΡΠ΄Π°Π»Π΅Π½ΠΈΠ΅ΡΠ°ΠΉΠ»ΠΎΠ² Π½Π°ΡΠ°ΡΡΡΡΡΠ°Π½ΠΎΠ²ΠΊΡΠ²Π½Π΅ΡΠ½Π΅ΠΉΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ Π½Π°ΡΠ°ΡΡΡΡΡΠ°Π½ΠΎΠ²ΠΊΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΡΠ°Π±ΠΎΡΡΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠ΅ΠΉ Π½Π°ΡΠ°ΡΡΡΡΡΠ°Π½ΠΎΠ²ΠΊΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΡΠ°Π±ΠΎΡΡΡΡΠ°ΠΉΠ»Π°ΠΌΠΈ Π½Π΅Π΄Π΅Π»ΡΠ³ΠΎΠ΄Π° Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΠ·Π°Π²Π΅ΡΡΠ΅Π½ΠΈΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ Π½ΠΎΠΌΠ΅ΡΡΠ΅Π°Π½ΡΠ°ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ Π½ΠΎΠΌΠ΅ΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ Π½ΡΠ΅Π³ Π½ΡΡΡ ΠΎΠ±Π½ΠΎΠ²ΠΈΡΡΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡ ΠΎΠ±Π½ΠΎΠ²ΠΈΡΡΠ½ΡΠΌΠ΅ΡΠ°ΡΠΈΡΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΎΠ±Π½ΠΎΠ²ΠΈΡΡΠΏΠΎΠ²ΡΠΎΡΠ½ΠΎΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΠΏΡΠ΅ΡΡΠ²Π°Π½ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΠΈΡΡΡΠ°ΠΉΠ»Ρ ΠΎΠΊΡ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΠΎΡΠΈΠ±ΠΊΠΈ ΠΎΠΏΠΎΠ²Π΅ΡΡΠΈΡΡ ΠΎΠΏΠΎΠ²Π΅ΡΡΠΈΡΡΠΎΠ±ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΎΡΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ·Π°ΠΏΡΠΎΡΠ°Π½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠ»ΠΈΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ ΠΎΡΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΡ ΠΎΡΠΊΡΡΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠΊΡΡΡΡΠΈΠ½Π΄Π΅ΠΊΡΡΠΏΡΠ°Π²ΠΊΠΈ ΠΎΡΠΊΡΡΡΡΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ΡΠΏΡΠ°Π²ΠΊΠΈ ΠΎΡΠΊΡΡΡΡΡΠΏΡΠ°Π²ΠΊΡ ΠΎΡΠΊΡΡΡΡΡΠΎΡΠΌΡ ΠΎΡΠΊΡΡΡΡΡΠΎΡΠΌΡΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΎ ΠΎΡΠΌΠ΅Π½ΠΈΡΡΡΡΠ°Π½Π·Π°ΠΊΡΠΈΡ ΠΎΡΠΈΡΡΠΈΡΡΠΆΡΡΠ½Π°Π»ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΎΡΠΈΡΡΠΈΡΡΠ½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΎΡΠΈΡΡΠΈΡΡΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ΄ΠΎΡΡΡΠΏΠ° ΠΏΠ΅ΡΠ΅ΠΉΡΠΈΠΏΠΎΠ½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉΡΡΡΠ»ΠΊΠ΅ ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΡΠΈΡΡΡΠ°ΠΉΠ» ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΠ²Π½Π΅ΡΠ½ΡΡΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ·Π°ΠΏΡΠΎΡΠ°Π½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠ»ΠΈΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠ°Π±ΠΎΡΡΠΈΠΊΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΡΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠ΅ΠΉ ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠΈΡΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΡΡΡΠ°ΠΉΠ»Π°ΠΌΠΈ ΠΏΠΎΠ΄ΡΠΎΠ±Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΎΡΠΈΠ±ΠΊΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ²Π²ΠΎΠ΄Π΄Π°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ²Π²ΠΎΠ΄Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ²Π²ΠΎΠ΄ΡΡΡΠΎΠΊΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ²Π²ΠΎΠ΄ΡΠΈΡΠ»Π° ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ²ΠΎΠΏΡΠΎΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ±ΠΎΡΠΈΠ±ΠΊΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠ½Π°ΠΊΠ°ΡΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΡΠΏΡΠ΅Π΄ΡΠΏΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»Π½ΠΎΠ΅ΠΈΠΌΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡcomΠΎΠ±ΡΠ΅ΠΊΡ ΠΏΠΎΠ»ΡΡΠΈΡΡxmlΡΠΈΠΏ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ°Π΄ΡΠ΅ΡΠΏΠΎΠΌΠ΅ΡΡΠΎΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΡΡΠ΅Π°Π½ΡΠΎΠ² ΠΏΠΎΠ»ΡΡΠΈΡΡΠ²ΡΠ΅ΠΌΡΠ·Π°Π²Π΅ΡΡΠ΅Π½ΠΈΡΡΠΏΡΡΠ΅Π³ΠΎΡΠ΅Π°Π½ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ²ΡΠ΅ΠΌΡΠ·Π°ΡΡΠΏΠ°Π½ΠΈΡΠΏΠ°ΡΡΠΈΠ²Π½ΠΎΠ³ΠΎΡΠ΅Π°Π½ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ²ΡΠ΅ΠΌΡΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄Π°Π½Π½ΡΠ΅Π²ΡΠ±ΠΎΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΊΠ»ΠΈΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅ΠΊΠΎΠ΄ΡΠ»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅ΡΠ°ΡΠΎΠ²ΡΠ΅ΠΏΠΎΡΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΡΠΎΡΠ±ΠΎΡΠ°ΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΠ·Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎΡ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΠΌΡΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎΡΠ°ΠΉΠ»Π° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΠΌΡΠΊΠ»ΠΈΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΡΠΊΡΠ°Π½ΠΎΠ²ΠΊΠ»ΠΈΠ΅Π½ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠΎΠ±ΡΡΠΈΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΊΡΠ°ΡΠΊΠΈΠΉΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠ°ΠΊΠ΅ΡΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠ°ΡΠΊΡΠ²ΡΠ΅ΡΠ°ΠΉΠ»Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠ°ΡΠΊΡΠ²ΡΠ΅ΡΠ°ΠΉΠ»ΡΠΊΠ»ΠΈΠ΅Π½ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠ°ΡΠΊΡΠ²ΡΠ΅ΡΠ°ΠΉΠ»ΡΡΠ΅ΡΠ²Π΅ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠ΅ΡΡΠΎΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ°Π΄ΡΠ΅ΡΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΡΠ΄Π»ΠΈΠ½ΡΠΏΠ°ΡΠΎΠ»Π΅ΠΉΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΡΡΡΡΡΠ»ΠΊΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠ½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΡΡΡΡΡΠ»ΠΊΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈΠ±Π°Π·ΡΠ΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠ±ΡΠΈΠΉΠΌΠ°ΠΊΠ΅Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠ±ΡΡΡΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠΊΠ½Π° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΡΡΠΎΡΠΌΠ΅ΡΠΊΡΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΎΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΠ³ΠΎΡΠ΅ΠΆΠΈΠΌΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΎΠΏΡΠΈΠΉΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΠΎΠ»Π½ΠΎΠ΅ΠΈΠΌΡΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡΠ½Π°Π²ΠΈΠ³Π°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΡΡΠ»ΠΎΠΊ ΠΏΠΎΠ»ΡΡΠΈΡΡΠΏΡΠΎΠ²Π΅ΡΠΊΡΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈΠΏΠ°ΡΠΎΠ»Π΅ΠΉΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°Π·Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠΏΡΡΠΈ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°Π·Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠΏΡΡΠΈΠΊΠ»ΠΈΠ΅Π½ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°Π·Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠΏΡΡΠΈΡΠ΅ΡΠ²Π΅ΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ΅Π°Π½ΡΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΊΠΎΡΠΎΡΡΡΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ΠΎΠ±ΡΠ΅ΠΊΡΠ°ΠΈΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΎΡΡΠ°Π²ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ°odata ΠΏΠΎΠ»ΡΡΠΈΡΡΡΡΡΡΠΊΡΡΡΡΡ
ΡΠ°Π½Π΅Π½ΠΈΡΠ±Π°Π·ΡΠ΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ΅ΠΊΡΡΠΈΠΉΡΠ΅Π°Π½ΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°ΠΉΠ» ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°ΠΉΠ»Ρ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡΠΎΠΏΡΠΈΡ ΠΏΠΎΠ»ΡΡΠΈΡΡΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡΠΎΠΏΡΠΈΡΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ° ΠΏΠΎΠ»ΡΡΠΈΡΡΡΠ°ΡΠΎΠ²ΠΎΠΉΠΏΠΎΡΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈΠΎΡ ΠΏΠΎΠΌΠ΅ΡΡΠΈΡΡΠ²ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅ ΠΏΠΎΠΌΠ΅ΡΡΠΈΡΡΡΠ°ΠΉΠ» ΠΏΠΎΠΌΠ΅ΡΡΠΈΡΡΡΠ°ΠΉΠ»Ρ ΠΏΡΠ°Π² ΠΏΡΠ°Π²ΠΎΠ΄ΠΎΡΡΡΠΏΠ° ΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΊΠΎΠ΄Π°Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠ΅ΡΠΈΠΎΠ΄Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠ°Π²Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΠΎΠ±ΡΡΠΈΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΠ°ΡΠΎΠ²ΠΎΠ³ΠΎΠΏΠΎΡΡΠ° ΠΏΡΠ΅Π΄ΡΠΏΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΏΡΠ΅ΠΊΡΠ°ΡΠΈΡΡΡΠ°Π±ΠΎΡΡΡΠΈΡΡΠ΅ΠΌΡ ΠΏΡΠΈΠ²ΠΈΠ»Π΅Π³ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉΡΠ΅ΠΆΠΈΠΌ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΡΠ²ΡΠ·ΠΎΠ² ΠΏΡΠΎΡΠΈΡΠ°ΡΡjson ΠΏΡΠΎΡΠΈΡΠ°ΡΡxml ΠΏΡΠΎΡΠΈΡΠ°ΡΡΠ΄Π°ΡΡjson ΠΏΡΡΡΠ°ΡΡΡΡΠΎΠΊΠ° ΡΠ°Π±ΠΎΡΠΈΠΉΠΊΠ°ΡΠ°Π»ΠΎΠ³Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠ°Π·Π±Π»ΠΎΠΊΠΈΡΠΎΠ²Π°ΡΡΠ΄Π°Π½Π½ΡΠ΅Π΄Π»ΡΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΡΡΠ°ΠΉΠ» ΡΠ°Π·ΠΎΡΠ²Π°ΡΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅ΡΠ²Π½Π΅ΡΠ½ΠΈΠΌΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°ΡΡΡΡΡΠΎΠΊΡ ΡΠΎΠ»ΡΠ΄ΠΎΡΡΡΠΏΠ½Π° ΡΠ΅ΠΊΡΠ½Π΄Π° ΡΠΈΠ³Π½Π°Π» ΡΠΈΠΌΠ²ΠΎΠ» ΡΠΊΠΎΠΏΠΈΡΠΎΠ²Π°ΡΡΠΆΡΡΠ½Π°Π»ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅Π»Π΅ΡΠ½Π΅Π³ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΡΠ±ΡΡΠ΅ΡΡΠ΄Π²ΠΎΠΈΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΡΠΎΠ·Π΄Π°ΡΡΠΊΠ°ΡΠ°Π»ΠΎΠ³ ΡΠΎΠ·Π΄Π°ΡΡΡΠ°Π±ΡΠΈΠΊΡxdto ΡΠΎΠΊΡΠ» ΡΠΎΠΊΡΠ»ΠΏ ΡΠΎΠΊΡΠΏ ΡΠΎΠΎΠ±ΡΠΈΡΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΡΠΎΡ
ΡΠ°Π½ΠΈΡΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡ
ΡΠ°Π½ΠΈΡΡΠ½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΡΠ΅Π΄ ΡΡΡΠ΄Π»ΠΈΠ½Π° ΡΡΡΠ·Π°ΠΊΠ°Π½ΡΠΈΠ²Π°Π΅ΡΡΡΠ½Π° ΡΡΡΠ·Π°ΠΌΠ΅Π½ΠΈΡΡ ΡΡΡΠ½Π°ΠΉΡΠΈ ΡΡΡΠ½Π°ΡΠΈΠ½Π°Π΅ΡΡΡΡ ΡΡΡΠΎΠΊΠ° ΡΡΡΠΎΠΊΠ°ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΡΡΡΠΏΠΎΠ»ΡΡΠΈΡΡΡΡΡΠΎΠΊΡ ΡΡΡΡΠ°Π·Π΄Π΅Π»ΠΈΡΡ ΡΡΡΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΡ ΡΡΡΡΡΠ°Π²Π½ΠΈΡΡ ΡΡΡΡΠΈΡΠ»ΠΎΠ²Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΠΉ ΡΡΡΡΠΈΡΠ»ΠΎΡΡΡΠΎΠΊ ΡΡΡΡΠ°Π±Π»ΠΎΠ½ ΡΠ΅ΠΊΡΡΠ°ΡΠ΄Π°ΡΠ° ΡΠ΅ΠΊΡΡΠ°ΡΠ΄Π°ΡΠ°ΡΠ΅Π°Π½ΡΠ° ΡΠ΅ΠΊΡΡΠ°ΡΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½Π°ΡΠ΄Π°ΡΠ° ΡΠ΅ΠΊΡΡΠ°ΡΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½Π°ΡΠ΄Π°ΡΠ°Π²ΠΌΠΈΠ»Π»ΠΈΡΠ΅ΠΊΡΠ½Π΄Π°Ρ
ΡΠ΅ΠΊΡΡΠΈΠΉΠ²Π°ΡΠΈΠ°Π½ΡΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΡΠΈΠΉΠ²Π°ΡΠΈΠ°Π½ΡΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ³ΠΎΡΡΠΈΡΡΠ°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΡΠΈΠΉΠΊΠΎΠ΄Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ΅ΠΊΡΡΠΈΠΉΡΠ΅ΠΆΠΈΠΌΠ·Π°ΠΏΡΡΠΊΠ° ΡΠ΅ΠΊΡΡΠΈΠΉΡΠ·ΡΠΊ ΡΠ΅ΠΊΡΡΠΈΠΉΡΠ·ΡΠΊΡΠΈΡΡΠ΅ΠΌΡ ΡΠΈΠΏ ΡΠΈΠΏΠ·Π½Ρ ΡΡΠ°Π½Π·Π°ΠΊΡΠΈΡΠ°ΠΊΡΠΈΠ²Π½Π° ΡΡΠ΅Π³ ΡΠ΄Π°Π»ΠΈΡΡΠ΄Π°Π½Π½ΡΠ΅ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΡΠ΄Π°Π»ΠΈΡΡΠΈΠ·Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎΡ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ° ΡΠ΄Π°Π»ΠΈΡΡΠΎΠ±ΡΠ΅ΠΊΡΡ ΡΠ΄Π°Π»ΠΈΡΡΡΠ°ΠΉΠ»Ρ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠ΅Π²ΡΠ΅ΠΌΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΡΠ΄Π°Π½Π½ΡΡ
ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΡΡΠ΅Π°Π½ΡΠΎΠ² ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ²Π½Π΅ΡΠ½ΡΡΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ²ΡΠ΅ΠΌΡΠ·Π°Π²Π΅ΡΡΠ΅Π½ΠΈΡΡΠΏΡΡΠ΅Π³ΠΎΡΠ΅Π°Π½ΡΠ° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ²ΡΠ΅ΠΌΡΠ·Π°ΡΡΠΏΠ°Π½ΠΈΡΠΏΠ°ΡΡΠΈΠ²Π½ΠΎΠ³ΠΎΡΠ΅Π°Π½ΡΠ° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ²ΡΠ΅ΠΌΡΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΡΠΈΡΡΠ΅ΠΌΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠΎΠ±ΡΡΠΈΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΊΡΠ°ΡΠΊΠΈΠΉΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΎΠΊΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΡΠ΄Π»ΠΈΠ½ΡΠΏΠ°ΡΠΎΠ»Π΅ΠΉΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΌΠΎΠ½ΠΎΠΏΠΎΠ»ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠ½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΊΠ»ΠΈΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅Π½Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΎΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΠ³ΠΎΡΠ΅ΠΆΠΈΠΌΠ° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΠΎΠΏΡΠΈΠΉΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ° ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΏΡΠΈΠ²ΠΈΠ»Π΅Π³ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉΡΠ΅ΠΆΠΈΠΌ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΠΏΡΠΎΠ²Π΅ΡΠΊΡΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈΠΏΠ°ΡΠΎΠ»Π΅ΠΉΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΡΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠ΅ΠΉ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΡΡΡΠ°ΠΉΠ»Π°ΠΌΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅ΡΠ²Π½Π΅ΡΠ½ΠΈΠΌΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌΠ΄Π°Π½Π½ΡΡ
ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ΠΎΠ±ΡΠ΅ΠΊΡΠ°ΠΈΡΠΎΡΠΌΡ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠΎΡΡΠ°Π²ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ°odata ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°ΡΠΎΠ²ΠΎΠΉΠΏΠΎΡΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡΠ°ΡΠΎΠ²ΠΎΠΉΠΏΠΎΡΡΡΠ΅Π°Π½ΡΠ° ΡΠΎΡΠΌΠ°Ρ ΡΠ΅Π» ΡΠ°Ρ ΡΠ°ΡΠΎΠ²ΠΎΠΉΠΏΠΎΡΡ ΡΠ°ΡΠΎΠ²ΠΎΠΉΠΏΠΎΡΡΡΠ΅Π°Π½ΡΠ° ΡΠΈΡΠ»ΠΎ ΡΠΈΡΠ»ΠΎΠΏΡΠΎΠΏΠΈΡΡΡ ΡΡΠΎΠ°Π΄ΡΠ΅ΡΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎΡ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ° wsΡΡΡΠ»ΠΊΠΈ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΠΊΠ°ΡΡΠΈΠ½ΠΎΠΊ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΠΌΠ°ΠΊΠ΅ΡΠΎΠ²ΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΡΡΠΈΠ»Π΅ΠΉ Π±ΠΈΠ·Π½Π΅ΡΠΏΡΠΎΡΠ΅ΡΡΡ Π²Π½Π΅ΡΠ½ΠΈΠ΅ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π²Π½Π΅ΡΠ½ΠΈΠ΅ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π²Π½Π΅ΡΠ½ΠΈΠ΅ΠΎΡΡΠ΅ΡΡ Π²ΡΡΡΠΎΠ΅Π½Π½ΡΠ΅ΠΏΠΎΠΊΡΠΏΠΊΠΈ Π³Π»Π°Π²Π½ΡΠΉΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡ Π³Π»Π°Π²Π½ΡΠΉΡΡΠΈΠ»Ρ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡ Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅ΠΌΡΠ΅ΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½ΠΈΡ ΠΆΡΡΠ½Π°Π»ΡΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² Π·Π°Π΄Π°ΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ±ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ°Π±ΠΎΡΠ΅ΠΉΠ΄Π°ΡΡ ΠΈΡΡΠΎΡΠΈΡΡΠ°Π±ΠΎΡΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΠΊΠΎΠ½ΡΡΠ°Π½ΡΡ ΠΊΡΠΈΡΠ΅ΡΠΈΠΈΠΎΡΠ±ΠΎΡΠ° ΠΌΠ΅ΡΠ°Π΄Π°Π½Π½ΡΠ΅ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΠ»Π°ΠΌΡ ΠΎΡΠΏΡΠ°Π²ΠΊΠ°Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅ΠΌΡΡ
ΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½ΠΈΠΉ ΠΎΡΡΠ΅ΡΡ ΠΏΠ°Π½Π΅Π»ΡΠ·Π°Π΄Π°ΡΠΎΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ·Π°ΠΏΡΡΠΊΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΡΠ΅Π°Π½ΡΠ° ΠΏΠ΅ΡΠ΅ΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΏΠ»Π°Π½ΡΠ²ΠΈΠ΄ΠΎΠ²ΡΠ°ΡΡΠ΅ΡΠ° ΠΏΠ»Π°Π½ΡΠ²ΠΈΠ΄ΠΎΠ²Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΠΏΠ»Π°Π½ΡΠΎΠ±ΠΌΠ΅Π½Π° ΠΏΠ»Π°Π½ΡΡΡΠ΅ΡΠΎΠ² ΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΡΠΉΠΏΠΎΠΈΡΠΊ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉΠ±Π°Π·Ρ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΠ²Π΅ΡΠΊΠ°Π²ΡΡΡΠΎΠ΅Π½Π½ΡΡ
ΠΏΠΎΠΊΡΠΏΠΎΠΊ ΡΠ°Π±ΠΎΡΠ°ΡΠ΄Π°ΡΠ° ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ ΡΠ΅Π³ΠΈΡΡΡΡΠ±ΡΡ
Π³Π°Π»ΡΠ΅ΡΠΈΠΈ ΡΠ΅Π³ΠΈΡΡΡΡΠ½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ ΡΠ΅Π³ΠΈΡΡΡΡΡΠ°ΡΡΠ΅ΡΠ° ΡΠ΅Π³ΠΈΡΡΡΡΡΠ²Π΅Π΄Π΅Π½ΠΈΠΉ ΡΠ΅Π³Π»Π°ΠΌΠ΅Π½ΡΠ½ΡΠ΅Π·Π°Π΄Π°Π½ΠΈΡ ΡΠ΅ΡΠΈΠ°Π»ΠΈΠ·Π°ΡΠΎΡxdto ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊΠΈ ΡΡΠ΅Π΄ΡΡΠ²Π°Π³Π΅ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΌΡΠ»ΡΡΠΈΠΌΠ΅Π΄ΠΈΠ° ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠ΅ΠΊΠ»Π°ΠΌΡ ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΏΠΎΡΡΡ ΡΡΠ΅Π΄ΡΡΠ²Π°ΡΠ΅Π»Π΅ΡΠΎΠ½ΠΈΠΈ ΡΠ°Π±ΡΠΈΠΊΠ°xdto ΡΠ°ΠΉΠ»ΠΎΠ²ΡΠ΅ΠΏΠΎΡΠΎΠΊΠΈ ΡΠΎΠ½ΠΎΠ²ΡΠ΅Π·Π°Π΄Π°Π½ΠΈΡ Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ°Π½Π°ΡΡΡΠΎΠ΅ΠΊ Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ²ΠΎΡΡΠ΅ΡΠΎΠ² Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅Π½Π°ΡΡΡΠΎΠ΅ΠΊΠ΄Π°Π½Π½ΡΡ
ΡΠΎΡΠΌ Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅ΠΎΠ±ΡΠΈΡ
Π½Π°ΡΡΡΠΎΠ΅ΠΊ Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΡ
Π½Π°ΡΡΡΠΎΠ΅ΠΊΠ΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΏΠΈΡΠΊΠΎΠ² Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΡ
Π½Π°ΡΡΡΠΎΠ΅ΠΊΠΎΡΡΠ΅ΡΠΎΠ² Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅ΡΠΈΡΡΠ΅ΠΌΠ½ΡΡ
Π½Π°ΡΡΡΠΎΠ΅ΠΊ ",class:"webΡΠ²Π΅ΡΠ° windowsΡΠ²Π΅ΡΠ° windowsΡΡΠΈΡΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΠΊΠ°ΡΡΠΈΠ½ΠΎΠΊ ΡΠ°ΠΌΠΊΠΈΡΡΠΈΠ»Ρ ΡΠΈΠΌΠ²ΠΎΠ»Ρ ΡΠ²Π΅ΡΠ°ΡΡΠΈΠ»Ρ ΡΡΠΈΡΡΡΡΡΠΈΠ»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅Π΄Π°Π½Π½ΡΡ
ΡΠΎΡΠΌΡΠ²Π½Π°ΡΡΡΠΎΠΉΠΊΠ°Ρ
Π°Π²ΡΠΎΠ½ΡΠΌΠ΅ΡΠ°ΡΠΈΡΠ²ΡΠΎΡΠΌΠ΅ Π°Π²ΡΠΎΡΠ°Π·Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΡΠΈΠΉ Π°Π½ΠΈΠΌΠ°ΡΠΈΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ Π²Π°ΡΠΈΠ°Π½ΡΠ²ΡΡΠ°Π²Π½ΠΈΠ²Π°Π½ΠΈΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΠΈΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠΎΠ² Π²Π°ΡΠΈΠ°Π½ΡΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠ²ΡΡΠΎΡΠΎΠΉΡΠ°Π±Π»ΠΈΡΡ Π²Π΅ΡΡΠΈΠΊΠ°Π»ΡΠ½Π°ΡΠΏΡΠΎΠΊΡΡΡΠΊΠ°ΡΠΎΡΠΌΡ Π²Π΅ΡΡΠΈΠΊΠ°Π»ΡΠ½ΠΎΠ΅ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π²Π΅ΡΡΠΈΠΊΠ°Π»ΡΠ½ΠΎΠ΅ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ° Π²ΠΈΠ΄Π³ΡΡΠΏΠΏΡΡΠΎΡΠΌΡ Π²ΠΈΠ΄Π΄Π΅ΠΊΠΎΡΠ°ΡΠΈΠΈΡΠΎΡΠΌΡ Π²ΠΈΠ΄Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΎΡΠΌΡ Π²ΠΈΠ΄ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠ΄Π°Π½Π½ΡΡ
Π²ΠΈΠ΄ΠΊΠ½ΠΎΠΏΠΊΠΈΡΠΎΡΠΌΡ Π²ΠΈΠ΄ΠΏΠ΅ΡΠ΅ΠΊΠ»ΡΡΠ°ΡΠ΅Π»Ρ Π²ΠΈΠ΄ΠΏΠΎΠ΄ΠΏΠΈΡΠ΅ΠΉΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ΅ Π²ΠΈΠ΄ΠΏΠΎΠ»ΡΡΠΎΡΠΌΡ Π²ΠΈΠ΄ΡΠ»Π°ΠΆΠΊΠ° Π²Π»ΠΈΡΠ½ΠΈΠ΅ΡΠ°Π·ΠΌΠ΅ΡΠ°Π½Π°ΠΏΡΠ·ΡΡΠ΅ΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ Π³ΠΎΡΠΈΠ·ΠΎΠ½ΡΠ°Π»ΡΠ½ΠΎΠ΅ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π³ΠΎΡΠΈΠ·ΠΎΠ½ΡΠ°Π»ΡΠ½ΠΎΠ΅ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ° Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠ°ΠΊΠΎΠ»ΠΎΠ½ΠΎΠΊ Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠ°ΠΏΠΎΠ΄ΡΠΈΠ½Π΅Π½Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΡΠΎΡΠΌΡ Π³ΡΡΠΏΠΏΡΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΡ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΏΠ΅ΡΠ΅ΡΠ°ΡΠΊΠΈΠ²Π°Π½ΠΈΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠΉΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠΏΠ΅ΡΠ΅ΡΠ°ΡΠΊΠΈΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»ΠΌΠ΅ΠΆΠ΄ΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΠΌΠΈΡΠΎΡΠΌΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π²ΡΠ²ΠΎΠ΄Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΏΠΎΠ»ΠΎΡΡΠΏΡΠΎΠΊΡΡΡΠΊΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠΎΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΡΠΎΡΠΊΠΈΠ±ΠΈΡΠΆΠ΅Π²ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΈΡΡΠΎΡΠΈΡΠ²ΡΠ±ΠΎΡΠ°ΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉΠΎΡΠΈΡΠΎΡΠ΅ΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ·Π½Π°ΡΠ΅Π½ΠΈΡΡΠ°Π·ΠΌΠ΅ΡΠ°ΠΏΡΠ·ΡΡΡΠΊΠ°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ³ΡΡΠΏΠΏΡΠΊΠΎΠΌΠ°Π½Π΄ ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΡΠ΅ΡΠΈΠΉ Π½Π°ΡΠ°Π»ΡΠ½ΠΎΠ΅ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π΄Π΅ΡΠ΅Π²Π° Π½Π°ΡΠ°Π»ΡΠ½ΠΎΠ΅ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠΏΠΈΡΠΊΠ° ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ°ΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΠ΄Π΅Π½Π΄ΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΠΌΠ΅ΡΠΎΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΠΌΠ΅ΡΠΎΠΊΡΠ²ΠΎΠ΄Π½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΎΡΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π²Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ΅ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π²Π»Π΅Π³Π΅Π½Π΄Π΅Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π³ΡΡΠΏΠΏΡΠΊΠ½ΠΎΠΏΠΎΠΊ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°ΡΠΊΠ°Π»ΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠΉΡΠ²ΠΎΠ΄Π½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΡΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΈΠ½ΡΠ΅ΡΠ²Π°Π»Π°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠ³Π°Π½ΡΠ° ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΊΠ½ΠΎΠΏΠΊΠΈ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΊΠ½ΠΎΠΏΠΊΠΈΠ²ΡΠ±ΠΎΡΠ° ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠΉΡΠΎΡΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΎΠ±ΡΡΠ½ΠΎΠΉΠ³ΡΡΠΏΠΏΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΎΡΡΠΈΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉΠΏΡΠ·ΡΡΡΠΊΠΎΠ²ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΏΠ°Π½Π΅Π»ΠΈΠΏΠΎΠΈΡΠΊΠ° ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ΄ΡΠΊΠ°Π·ΠΊΠΈ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΏΡΠ΅Π΄ΡΠΏΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡΠΏΡΠΈΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠ°Π·ΠΌΠ΅ΡΠΊΠΈΠΏΠΎΠ»ΠΎΡΡΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΡΡΠ°Π½ΠΈΡΡΠΎΡΠΌΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠ°Π±Π»ΠΈΡΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ°Π·Π½Π°ΡΠ΅Π½ΠΈΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠ³Π°Π½ΡΠ° ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΎΠ±ΡΡΠ½ΠΎΠΉΠ³ΡΡΠΏΠΏΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΡΠΈΠ³ΡΡΡΠΊΠ½ΠΎΠΏΠΊΠΈ ΠΏΠ°Π»ΠΈΡΡΠ°ΡΠ²Π΅ΡΠΎΠ²Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΎΠ±ΡΡΠ½ΠΎΠΉΠ³ΡΡΠΏΠΏΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ°ΠΌΠ°ΡΡΡΠ°Π±Π°Π΄Π΅Π½Π΄ΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ°ΠΌΠ°ΡΡΡΠ°Π±Π°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠ³Π°Π½ΡΠ° ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ°ΠΌΠ°ΡΡΡΠ°Π±Π°ΡΠ²ΠΎΠ΄Π½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΏΠΎΠΈΡΠΊΠ²ΡΠ°Π±Π»ΠΈΡΠ΅ΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈΠΊΠ½ΠΎΠΏΠΊΠΈΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΊΠΎΠΌΠ°Π½Π΄Π½ΠΎΠΉΠΏΠ°Π½Π΅Π»ΠΈΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΊΠΎΠΌΠ°Π½Π΄Π½ΠΎΠΉΠΏΠ°Π½Π΅Π»ΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΎΡΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΎΠΏΠΎΡΠ½ΠΎΠΉΡΠΎΡΠΊΠΈΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ΄ΠΏΠΈΡΠ΅ΠΉΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ΅ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ΄ΠΏΠΈΡΠ΅ΠΉΡΠΊΠ°Π»ΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉΠΈΠ·ΠΌΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠΎΡΡΠΎΡΠ½ΠΈΡΠΏΡΠΎΡΠΌΠΎΡΡΠ° ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΡΡΠΎΠΊΠΈΠΏΠΎΠΈΡΠΊΠ° ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ°ΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉΠ»ΠΈΠ½ΠΈΠΈ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΏΠΎΠΈΡΠΊΠΎΠΌ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠΊΠ°Π»ΡΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΏΠΎΡΡΠ΄ΠΎΠΊΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠΎΡΠ΅ΠΊΠ³ΠΎΡΠΈΠ·ΠΎΠ½ΡΠ°Π»ΡΠ½ΠΎΠΉΠ³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΠΏΠΎΡΡΠ΄ΠΎΠΊΡΠ΅ΡΠΈΠΉΠ²Π»Π΅Π³Π΅Π½Π΄Π΅Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ°Π·ΠΌΠ΅ΡΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°ΡΠΊΠ°Π»ΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ°ΡΡΡΠ³ΠΈΠ²Π°Π½ΠΈΠ΅ΠΏΠΎΠ²Π΅ΡΡΠΈΠΊΠ°Π»ΠΈΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠ³Π°Π½ΡΠ° ΡΠ΅ΠΆΠΈΠΌΠ°Π²ΡΠΎΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠ²Π²ΠΎΠ΄Π°ΡΡΡΠΎΠΊΡΠ°Π±Π»ΠΈΡΡ ΡΠ΅ΠΆΠΈΠΌΠ²ΡΠ±ΠΎΡΠ°Π½Π΅Π·Π°ΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡΠ΄Π°ΡΡ ΡΠ΅ΠΆΠΈΠΌΠ²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡΡΡΡΠΎΠΊΠΈΡΠ°Π±Π»ΠΈΡΡ ΡΠ΅ΠΆΠΈΠΌΠ²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡΡΠ°Π±Π»ΠΈΡΡ ΡΠ΅ΠΆΠΈΠΌΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΡΠ°Π·ΠΌΠ΅ΡΠ° ΡΠ΅ΠΆΠΈΠΌΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΡΠ²ΡΠ·Π°Π½Π½ΠΎΠ³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠ΄ΠΈΠ°Π»ΠΎΠ³Π°ΠΏΠ΅ΡΠ°ΡΠΈ ΡΠ΅ΠΆΠΈΠΌΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΊΠΎΠΌΠ°Π½Π΄Ρ ΡΠ΅ΠΆΠΈΠΌΠΌΠ°ΡΡΡΠ°Π±ΠΈΡΠΎΠ²Π°Π½ΠΈΡΠΏΡΠΎΡΠΌΠΎΡΡΠ° ΡΠ΅ΠΆΠΈΠΌΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ³ΠΎΠΎΠΊΠ½Π°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΊΡΡΡΠΈΡΠΎΠΊΠ½Π°ΡΠΎΡΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉΡΠ΅ΡΠΈΠΈ ΡΠ΅ΠΆΠΈΠΌΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΈΡΠ΅ΡΠΊΠΈΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠΏΠΎΠ»ΡΠΏΡΠΎΠ·ΡΠ°ΡΠ½ΠΎΡΡΠΈΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠΏΡΠΎΠ±Π΅Π»ΠΎΠ²Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ΅ΠΆΠΈΠΌΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΠ½Π°ΡΡΡΠ°Π½ΠΈΡΠ΅ ΡΠ΅ΠΆΠΈΠΌΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈ ΡΠ΅ΠΆΠΈΠΌΡΠ³Π»Π°ΠΆΠΈΠ²Π°Π½ΠΈΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ΅ΠΆΠΈΠΌΡΠ³Π»Π°ΠΆΠΈΠ²Π°Π½ΠΈΡΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠ° ΡΠ΅ΠΆΠΈΠΌΡΠΏΠΈΡΠΊΠ°Π·Π°Π΄Π°Ρ ΡΠΊΠ²ΠΎΠ·Π½ΠΎΠ΅Π²ΡΡΠ°Π²Π½ΠΈΠ²Π°Π½ΠΈΠ΅ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅Π΄Π°Π½Π½ΡΡ
ΡΠΎΡΠΌΡΠ²Π½Π°ΡΡΡΠΎΠΉΠΊΠ°Ρ
ΡΠΏΠΎΡΠΎΠ±Π·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΡΠ΅ΠΊΡΡΠ°Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°ΡΠΊΠ°Π»ΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΏΠΎΡΠΎΠ±ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡΠΎΠ³ΡΠ°Π½ΠΈΡΠΈΠ²Π°ΡΡΠ΅Π³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΡΠ°Π½Π΄Π°ΡΡΠ½Π°ΡΠ³ΡΡΠΏΠΏΠ°ΠΊΠΎΠΌΠ°Π½Π΄ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ΅ΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΠ΅ ΡΡΠ°ΡΡΡΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΡΠΈΠ»ΡΡΡΡΠ΅Π»ΠΊΠΈ ΡΠΈΠΏΠ°ΠΏΠΏΡΠΎΠΊΡΠΈΠΌΠ°ΡΠΈΠΈΠ»ΠΈΠ½ΠΈΠΈΡΡΠ΅Π½Π΄Π°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΠ΅Π΄ΠΈΠ½ΠΈΡΡΡΠΊΠ°Π»ΡΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠΈΠΏΠΈΠΌΠΏΠΎΡΡΠ°ΡΠ΅ΡΠΈΠΉΡΠ»ΠΎΡΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠ»ΠΈΠ½ΠΈΠΈΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠ»ΠΈΠ½ΠΈΠΈΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΠΌΠ°ΡΠΊΠ΅ΡΠ°Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΌΠ°ΡΠΊΠ΅ΡΠ°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΠΎΠ±Π»Π°ΡΡΠΈΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡ ΡΠΈΠΏΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Π΄Π°Π½Π½ΡΡ
Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠ΅ΡΠΈΠΈΡΠ»ΠΎΡΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠΎΡΠ΅ΡΠ½ΠΎΠ³ΠΎΠΎΠ±ΡΠ΅ΠΊΡΠ°Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠΊΠ°Π»ΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π»Π΅Π³Π΅Π½Π΄ΡΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΏΠΎΠΈΡΠΊΠ°ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ²Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΠΏΡΠΎΠ΅ΠΊΡΠΈΠΈΠ³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΠΈΠΏΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠΎΠ²ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΠΈΠΏΡΠ°ΠΌΠΊΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΡΠ²ΠΎΠ΄Π½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΡΠ²ΡΠ·ΠΈΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠ³Π°Π½ΡΠ° ΡΠΈΠΏΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉΠΏΠΎΡΠ΅ΡΠΈΡΠΌΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΡΠΎΡΠ΅ΠΊΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠΏΡΠΎΠ΅Π΄ΠΈΠ½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉΠ»ΠΈΠ½ΠΈΠΈ ΡΠΈΠΏΡΡΠΎΡΠΎΠ½ΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΏΡΠΎΡΠΌΡΠΎΡΡΠ΅ΡΠ° ΡΠΈΠΏΡΠΊΠ°Π»ΡΡΠ°Π΄Π°ΡΠ½ΠΎΠΉΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠ°ΠΊΡΠΎΡΠ»ΠΈΠ½ΠΈΠΈΡΡΠ΅Π½Π΄Π°Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡ ΡΠΈΠ³ΡΡΠ°ΠΊΠ½ΠΎΠΏΠΊΠΈ ΡΠΈΠ³ΡΡΡΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉΡΡ
Π΅ΠΌΡ ΡΠΈΠΊΡΠ°ΡΠΈΡΠ²ΡΠ°Π±Π»ΠΈΡΠ΅ ΡΠΎΡΠΌΠ°ΡΠ΄Π½ΡΡΠΊΠ°Π»ΡΠ²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠΎΡΠΌΠ°ΡΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈ ΡΠΈΡΠΈΠ½Π°ΠΏΠΎΠ΄ΡΠΈΠ½Π΅Π½Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΡΠΎΡΠΌΡ Π²ΠΈΠ΄Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΠ±ΡΡ
Π³Π°Π»ΡΠ΅ΡΠΈΠΈ Π²ΠΈΠ΄Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΠ½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ Π²ΠΈΠ΄ΠΏΠ΅ΡΠΈΠΎΠ΄Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠ°ΡΡΠ΅ΡΠ° Π²ΠΈΠ΄ΡΡΠ΅ΡΠ° Π²ΠΈΠ΄ΡΠΎΡΠΊΠΈΠΌΠ°ΡΡΡΡΡΠ°Π±ΠΈΠ·Π½Π΅ΡΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π°Π³ΡΠ΅Π³Π°ΡΠ°ΡΠ΅Π³ΠΈΡΡΡΠ°Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π³ΡΡΠΏΠΏΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ΅ΠΆΠΈΠΌΠ°ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΡΠ΅Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ½ΠΎΡΡΡΠ°Π³ΡΠ΅Π³Π°ΡΠ°ΡΠ΅Π³ΠΈΡΡΡΠ°Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠ°Π²ΡΠΎΠ²ΡΠ΅ΠΌΡ ΡΠ΅ΠΆΠΈΠΌΠ·Π°ΠΏΠΈΡΠΈΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠ΅ΠΆΠΈΠΌΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° Π°Π²ΡΠΎΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΡΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠΉΠ½ΠΎΠΌΠ΅ΡΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΎΡΠΏΡΠ°Π²ΠΊΠ°ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΠ΅ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π΄Π°Π½Π½ΡΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΠΎΡΠΈΠ΅Π½ΡΠ°ΡΠΈΡΡΡΡΠ°Π½ΠΈΡΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΈΡΠΎΠ³ΠΎΠ²ΠΊΠΎΠ»ΠΎΠ½ΠΎΠΊΡΠ²ΠΎΠ΄Π½ΠΎΠΉΡΠ°Π±Π»ΠΈΡΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΈΡΠΎΠ³ΠΎΠ²ΡΡΡΠΎΠΊΡΠ²ΠΎΠ΄Π½ΠΎΠΉΡΠ°Π±Π»ΠΈΡΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ°ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΏΠΎΡΠΎΠ±ΡΡΠ΅Π½ΠΈΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠ΄Π²ΡΡΡΠΎΡΠΎΠ½Π½Π΅ΠΉΠΏΠ΅ΡΠ°ΡΠΈ ΡΠΈΠΏΠ·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΠΎΠ±Π»Π°ΡΡΠΈΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠΊΡΡΡΠΎΡΠΎΠ²ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠ»ΠΈΠ½ΠΈΠΈΡΠΈΡΡΠ½ΠΊΠ°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠ»ΠΈΠ½ΠΈΠΈΡΡΠ΅ΠΉΠΊΠΈΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ»ΠΈΠ½ΠΈΠΉΡΠ²ΠΎΠ΄Π½ΠΎΠΉΡΠ°Π±Π»ΠΈΡΡ ΡΠΈΠΏΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΡΠ΅ΠΊΡΡΠ°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΡΠΈΡΡΠ½ΠΊΠ°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΡΠΌΠ΅ΡΠ΅Π½ΠΈΡΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΡΠ·ΠΎΡΠ°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΡΠ°ΠΉΠ»Π°ΡΠ°Π±Π»ΠΈΡΠ½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΎΡΠ½ΠΎΡΡΡΠΏΠ΅ΡΠ°ΡΠΈ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡΡΡΡΠ°Π½ΠΈΡ ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²ΡΠΈΠΊΠ° ΡΠΈΠΏΡΠ°ΠΉΠ»Π°ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎΠ΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΠΎΠ±Ρ
ΠΎΠ΄ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°Π·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΠ·Π°ΠΏΠΈΡΠΈΠ·Π°ΠΏΡΠΎΡΠ° Π²ΠΈΠ΄Π·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠΎΡΡΠ΅ΡΠ° ΡΠΈΠΏΠ΄ΠΎΠ±Π°Π²Π»Π΅Π½ΠΈΡΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΡΠΈΠΏΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠΎΡΡΠ΅ΡΠ° ΡΠΈΠΏΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΠΈΡΠΎΠ³ΠΎΠ² Π΄ΠΎΡΡΡΠΏΠΊΡΠ°ΠΉΠ»Ρ ΡΠ΅ΠΆΠΈΠΌΠ΄ΠΈΠ°Π»ΠΎΠ³Π°Π²ΡΠ±ΠΎΡΠ°ΡΠ°ΠΉΠ»Π° ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΊΡΡΡΠΈΡΡΠ°ΠΉΠ»Π° ΡΠΈΠΏΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ·Π°ΠΏΡΠΎΡΠ° Π²ΠΈΠ΄Π΄Π°Π½Π½ΡΡ
Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠ΅ΡΠΎΠ΄ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΠΏΠ΅Π΄ΠΈΠ½ΠΈΡΡΠΈΠ½ΡΠ΅ΡΠ²Π°Π»Π°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΡΠ°Π±Π»ΠΈΡΡΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°Π°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΡΠΈΡΠ»ΠΎΠ²ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠΈΡΠΊΠ°Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΉ ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
Π΄Π΅ΡΠ΅Π²ΠΎΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΡ ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ°ΡΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ° ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠΈΡΠΊΠ°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΉ ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠΈΡΠΊΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠΈΠΏΠΊΠΎΠ»ΠΎΠ½ΠΊΠΈΠΌΠΎΠ΄Π΅Π»ΠΈΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠΈΠΏΠΌΠ΅ΡΡΡΠ°ΡΡΡΠΎΡΠ½ΠΈΡΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΎΡΡΠ΅ΡΠ΅Π½ΠΈΡΠΏΡΠ°Π²ΠΈΠ»Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΈ ΡΠΈΠΏΠΏΠΎΠ»ΡΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠΏΠΎΡΡΠ΄ΠΎΡΠΈΠ²Π°Π½ΠΈΡΠΏΡΠ°Π²ΠΈΠ»Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΈΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠΏΠΎΡΡΠ΄ΠΎΡΠΈΠ²Π°Π½ΠΈΡΡΠ°Π±Π»ΠΎΠ½ΠΎΠ²ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉΠ°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠΏΡΠΎΡΠ΅Π½ΠΈΡΠ΄Π΅ΡΠ΅Π²Π°ΡΠ΅ΡΠ΅Π½ΠΈΠΉ wsΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ° Π²Π°ΡΠΈΠ°Π½Ρxpathxs Π²Π°ΡΠΈΠ°Π½ΡΠ·Π°ΠΏΠΈΡΠΈΠ΄Π°ΡΡjson Π²Π°ΡΠΈΠ°Π½ΡΠΏΡΠΎΡΡΠΎΠ³ΠΎΡΠΈΠΏΠ°xs Π²ΠΈΠ΄Π³ΡΡΠΏΠΏΡΠΌΠΎΠ΄Π΅Π»ΠΈxs Π²ΠΈΠ΄ΡΠ°ΡΠ΅ΡΠ°xdto Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»Ρdom Π·Π°Π²Π΅ΡΡΠ΅Π½Π½ΠΎΡΡΡΠΏΡΠΎΡΡΠΎΠ³ΠΎΡΠΈΠΏΠ°xs Π·Π°Π²Π΅ΡΡΠ΅Π½Π½ΠΎΡΡΡΡΠΎΡΡΠ°Π²Π½ΠΎΠ³ΠΎΡΠΈΠΏΠ°xs Π·Π°Π²Π΅ΡΡΠ΅Π½Π½ΠΎΡΡΡΡΡ
Π΅ΠΌΡxs Π·Π°ΠΏΡΠ΅ΡΠ΅Π½Π½ΡΠ΅ΠΏΠΎΠ΄ΡΡΠ°Π½ΠΎΠ²ΠΊΠΈxs ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΡΠ³ΡΡΠΏΠΏΠΏΠΎΠ΄ΡΡΠ°Π½ΠΎΠ²ΠΊΠΈxs ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠ°ΡΡΠΈΠ±ΡΡΠ°xs ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΠΈΠ΄Π΅Π½ΡΠΈΡΠ½ΠΎΡΡΠΈxs ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²ΠΈΠΌΠ΅Π½xs ΠΌΠ΅ΡΠΎΠ΄Π½Π°ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡxs ΠΌΠΎΠ΄Π΅Π»ΡΡΠΎΠ΄Π΅ΡΠΆΠΈΠΌΠΎΠ³ΠΎxs Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΡΠΈΠΏΠ°xml Π½Π΅Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠ΅ΠΏΠΎΠ΄ΡΡΠ°Π½ΠΎΠ²ΠΊΠΈxs ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΠΏΡΠΎΠ±Π΅Π»ΡΠ½ΡΡ
ΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ²xs ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΡΠΎΠ΄Π΅ΡΠΆΠΈΠΌΠΎΠ³ΠΎxs ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΡxs ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠΎΡΠ±ΠΎΡΠ°ΡΠ·Π»ΠΎΠ²dom ΠΏΠ΅ΡΠ΅Π½ΠΎΡΡΡΡΠΎΠΊjson ΠΏΠΎΠ·ΠΈΡΠΈΡΠ²Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ΅dom ΠΏΡΠΎΠ±Π΅Π»ΡΠ½ΡΠ΅ΡΠΈΠΌΠ²ΠΎΠ»Ρxml ΡΠΈΠΏΠ°ΡΡΠΈΠ±ΡΡΠ°xml ΡΠΈΠΏΠ·Π½Π°ΡΠ΅Π½ΠΈΡjson ΡΠΈΠΏΠΊΠ°Π½ΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎxml ΡΠΈΠΏΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡxs ΡΠΈΠΏΠΏΡΠΎΠ²Π΅ΡΠΊΠΈxml ΡΠΈΠΏΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°domxpath ΡΠΈΠΏΡΠ·Π»Π°dom ΡΠΈΠΏΡΠ·Π»Π°xml ΡΠΎΡΠΌΠ°xml ΡΠΎΡΠΌΠ°ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡxs ΡΠΎΡΠΌΠ°ΡΠ΄Π°ΡΡjson ΡΠΊΡΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ²json Π²ΠΈΠ΄ΡΡΠ°Π²Π½Π΅Π½ΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π²Π»ΠΎΠΆΠ΅Π½Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΈΡΠΎΠ³ΠΎΠ²ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ»Π΅ΠΉΠ³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ»ΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠΎΠ²ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΡΡΡΡΠΎΠ²ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ±ΡΡ
Π³Π°Π»ΡΠ΅ΡΡΠΊΠΎΠ³ΠΎΠΎΡΡΠ°ΡΠΊΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ²ΡΠ²ΠΎΠ΄Π°ΡΠ΅ΠΊΡΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ³ΡΡΠΏΠΏΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²ΠΎΡΠ±ΠΎΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ΄ΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΠΏΠ΅ΡΠΈΠΎΠ΄Π°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠ·Π°Π³ΠΎΠ»ΠΎΠ²ΠΊΠ°ΠΏΠΎΠ»Π΅ΠΉΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΌΠ°ΠΊΠ΅ΡΠ°Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΌΠ°ΠΊΠ΅ΡΠ°ΠΎΠ±Π»Π°ΡΡΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΎΡΡΠ°ΡΠΊΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΏΠ΅ΡΠΈΠΎΠ΄Π°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡΡΠ΅ΠΊΡΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠ²ΡΠ·ΠΈΠ½Π°Π±ΠΎΡΠΎΠ²Π΄Π°Π½Π½ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Π»Π΅Π³Π΅Π½Π΄ΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡΠΎΡΠ±ΠΎΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠ½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°Π½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΏΠΎΡΠΎΠ±Π²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡΠ½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΆΠΈΠΌΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π°Π²ΡΠΎΠΏΠΎΠ·ΠΈΡΠΈΡΡΠ΅ΡΡΡΡΠΎΠ²ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠ³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ΡΠ΅ΡΡΡΡΠΎΠ²Π²Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ΅ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠΈΠΊΡΠ°ΡΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΡΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π²Π°ΠΆΠ½ΠΎΡΡΡΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΡΠ΅ΠΊΡΡΠ°ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΡΠΏΠΎΡΠΎΠ±ΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΠ²Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠΏΠΎΡΠΎΠ±ΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡΠ½Π΅asciiΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ²ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΡΠΈΠΏΡΠ΅ΠΊΡΡΠ°ΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΡ ΡΡΠ°ΡΡΡΡΠ°Π·Π±ΠΎΡΠ°ΠΏΠΎΡΡΠΎΠ²ΠΎΠ³ΠΎΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΡΡΠ°Π½Π·Π°ΠΊΡΠΈΠΈΠ·Π°ΠΏΠΈΡΠΈΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΡΠ°ΡΡΡΡΡΠ°Π½Π·Π°ΠΊΡΠΈΠΈΠ·Π°ΠΏΠΈΡΠΈΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΡΠΎΠ²Π΅Π½ΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ°ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΠ²ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΠ΅ΠΆΠΈΠΌΠ²ΠΊΠ»ΡΡΠ΅Π½ΠΈΡΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΠ²ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΠ΅ΠΆΠΈΠΌΠΏΡΠΎΠ²Π΅ΡΠΊΠΈΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠ°ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΠΈΠΏΡ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ°ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΠ²ΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΠΊΠ°ΠΈΠΌΠ΅Π½ΡΠ°ΠΉΠ»ΠΎΠ²Π²zipΡΠ°ΠΉΠ»Π΅ ΠΌΠ΅ΡΠΎΠ΄ΡΠΆΠ°ΡΠΈΡzip ΠΌΠ΅ΡΠΎΠ΄ΡΠΈΡΡΠΎΠ²Π°Π½ΠΈΡzip ΡΠ΅ΠΆΠΈΠΌΠ²ΠΎΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΡΠΏΡΡΠ΅ΠΉΡΠ°ΠΉΠ»ΠΎΠ²zip ΡΠ΅ΠΆΠΈΠΌΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΠΏΠΎΠ΄ΠΊΠ°ΡΠ°Π»ΠΎΠ³ΠΎΠ²zip ΡΠ΅ΠΆΠΈΠΌΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΡΠΏΡΡΠ΅ΠΉzip ΡΡΠΎΠ²Π΅Π½ΡΡΠΆΠ°ΡΠΈΡzip Π·Π²ΡΠΊΠΎΠ²ΠΎΠ΅ΠΎΠΏΠΎΠ²Π΅ΡΠ΅Π½ΠΈΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π°ΠΊΡΡΡΠΎΠΊΠ΅ ΠΏΠΎΠ·ΠΈΡΠΈΡΠ²ΠΏΠΎΡΠΎΠΊΠ΅ ΠΏΠΎΡΡΠ΄ΠΎΠΊΠ±Π°ΠΉΡΠΎΠ² ΡΠ΅ΠΆΠΈΠΌΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΠΆΠΈΠΌΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΎΠΉΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΡΠ²ΠΈΡΠ²ΡΡΡΠΎΠ΅Π½Π½ΡΡ
ΠΏΠΎΠΊΡΠΏΠΎΠΊ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ΡΠΎΠ½ΠΎΠ²ΠΎΠ³ΠΎΠ·Π°Π΄Π°Π½ΠΈΡ ΡΠΈΠΏΠΏΠΎΠ΄ΠΏΠΈΡΡΠΈΠΊΠ°Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅ΠΌΡΡ
ΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½ΠΈΠΉ ΡΡΠΎΠ²Π΅Π½ΡΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠ·Π°ΡΠΈΡΠ΅Π½Π½ΠΎΠ³ΠΎΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡftp Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠΎΡΡΠ΄ΠΊΠ°ΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΠ΄ΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΡΠΏΠ΅ΡΠΈΠΎΠ΄Π°ΠΌΠΈΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠ½ΠΎΠΉΡΠΎΡΠΊΠΈΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠΉΡΠ°Π±Π»ΠΈΡΡΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° ΡΠΈΠΏΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡΡΡ
Π΅ΠΌΡΠ·Π°ΠΏΡΠΎΡΠ° httpΠΌΠ΅ΡΠΎΠ΄ Π°Π²ΡΠΎΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° Π°Π²ΡΠΎΠΏΡΠ΅ΡΠΈΠΊΡΠ½ΠΎΠΌΠ΅ΡΠ°Π·Π°Π΄Π°ΡΠΈ Π²Π°ΡΠΈΠ°Π½ΡΠ²ΡΡΡΠΎΠ΅Π½Π½ΠΎΠ³ΠΎΡΠ·ΡΠΊΠ° Π²ΠΈΠ΄ΠΈΠ΅ΡΠ°ΡΡ
ΠΈΠΈ Π²ΠΈΠ΄ΡΠ΅Π³ΠΈΡΡΡΠ°Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½ΠΈΡ Π²ΠΈΠ΄ΡΠ°Π±Π»ΠΈΡΡΠ²Π½Π΅ΡΠ½Π΅Π³ΠΎΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Π΄Π°Π½Π½ΡΡ
Π·Π°ΠΏΠΈΡΡΠ΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉΠΏΡΠΈΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ Π·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΈΠ½Π΄Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π±Π°Π·ΡΠΏΠ»Π°Π½Π°Π²ΠΈΠ΄ΠΎΠ²ΡΠ°ΡΡΠ΅ΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π±ΡΡΡΡΠΎΠ³ΠΎΠ²ΡΠ±ΠΎΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΏΠΎΠ΄ΡΠΈΠ½Π΅Π½ΠΈΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎΠΏΠΎΠΈΡΠΊΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ°Π·Π΄Π΅Π»ΡΠ΅ΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ΅ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅Π²ΠΈΠ΄Π°ΡΠ°ΡΡΠ΅ΡΠ° ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅Π²ΠΈΠ΄Π°Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅Π·Π°Π΄Π°ΡΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠ»Π°Π½Π°ΠΎΠ±ΠΌΠ΅Π½Π° ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊΠ° ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ΅ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΡΠ΅ΡΠ° ΠΏΠ΅ΡΠ΅ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅Π³ΡΠ°Π½ΠΈΡΡΠΏΡΠΈΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ½ΠΎΡΡΡΠ½ΠΎΠΌΠ΅ΡΠ°Π±ΠΈΠ·Π½Π΅ΡΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ½ΠΎΡΡΡΠ½ΠΎΠΌΠ΅ΡΠ°Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ½ΠΎΡΡΡΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠ°ΡΡΠ΅ΡΠ° ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ½ΠΎΡΡΡΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠ²Π΅Π΄Π΅Π½ΠΈΠΉ ΠΏΠΎΠ²ΡΠΎΡΠ½ΠΎΠ΅ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π²ΠΎΠ·Π²ΡΠ°ΡΠ°Π΅ΠΌΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΡΠΉΠΏΠΎΠΈΡΠΊΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ΠΏΠΎΡΡΡΠΎΠΊΠ΅ ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΡΠΎΠ±ΡΠ΅ΠΊΡΠ° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅Π°ΡΡΠ΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° ΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° ΡΠ°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠΉΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈΠΎΠ±ΡΠ΅Π³ΠΎΡΠ΅ΠΊΠ²ΠΈΠ·ΠΈΡΠ° ΡΠ΅ΠΆΠΈΠΌΠ°Π²ΡΠΎΠ½ΡΠΌΠ΅ΡΠ°ΡΠΈΠΈΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΡΠ΅ΠΆΠΈΠΌΠ·Π°ΠΏΠΈΡΠΈΡΠ΅Π³ΠΈΡΡΡΠ° ΡΠ΅ΠΆΠΈΠΌΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ΅ΠΆΠΈΠΌΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΡΠΈΠ½Ρ
ΡΠΎΠ½Π½ΡΡ
Π²ΡΠ·ΠΎΠ²ΠΎΠ²ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠΉΠΏΠ»Π°ΡΡΠΎΡΠΌΡΠΈΠ²Π½Π΅ΡΠ½ΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ ΡΠ΅ΠΆΠΈΠΌΠΏΠΎΠ²ΡΠΎΡΠ½ΠΎΠ³ΠΎΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΡΠ΅Π°Π½ΡΠΎΠ² ΡΠ΅ΠΆΠΈΠΌΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡΠ΄Π°Π½Π½ΡΡ
Π²ΡΠ±ΠΎΡΠ°ΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ΠΏΠΎΡΡΡΠΎΠΊΠ΅ ΡΠ΅ΠΆΠΈΠΌΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΠΎΡΡΠΈ ΡΠ΅ΠΆΠΈΠΌΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΠΎΡΡΠΈΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ° ΡΠ΅ΠΆΠΈΠΌΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠ±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠΎΠΉΠ΄Π°Π½Π½ΡΡ
ΠΏΠΎΡΠΌΠΎΠ»ΡΠ°Π½ΠΈΡ ΡΠ΅ΡΠΈΠΈΠΊΠΎΠ΄ΠΎΠ²ΠΏΠ»Π°Π½Π°Π²ΠΈΠ΄ΠΎΠ²Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ΅ΡΠΈΠΈΠΊΠΎΠ΄ΠΎΠ²ΠΏΠ»Π°Π½Π°ΡΡΠ΅ΡΠΎΠ² ΡΠ΅ΡΠΈΠΈΠΊΠΎΠ΄ΠΎΠ²ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊΠ° ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ ΡΠΏΠΎΡΠΎΠ±Π²ΡΠ±ΠΎΡΠ° ΡΠΏΠΎΡΠΎΠ±ΠΏΠΎΠΈΡΠΊΠ°ΡΡΡΠΎΠΊΠΈΠΏΡΠΈΠ²Π²ΠΎΠ΄Π΅ΠΏΠΎΡΡΡΠΎΠΊΠ΅ ΡΠΏΠΎΡΠΎΠ±ΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠΏΠ΄Π°Π½Π½ΡΡ
ΡΠ°Π±Π»ΠΈΡΡΠ²Π½Π΅ΡΠ½Π΅Π³ΠΎΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Π΄Π°Π½Π½ΡΡ
ΡΠΈΠΏΠΊΠΎΠ΄Π°ΠΏΠ»Π°Π½Π°Π²ΠΈΠ΄ΠΎΠ²ΡΠ°ΡΡΠ΅ΡΠ° ΡΠΈΠΏΠΊΠΎΠ΄Π°ΡΠΏΡΠ°Π²ΠΎΡΠ½ΠΈΠΊΠ° ΡΠΈΠΏΠΌΠ°ΠΊΠ΅ΡΠ° ΡΠΈΠΏΠ½ΠΎΠΌΠ΅ΡΠ°Π±ΠΈΠ·Π½Π΅ΡΠΏΡΠΎΡΠ΅ΡΡΠ° ΡΠΈΠΏΠ½ΠΎΠΌΠ΅ΡΠ°Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ° ΡΠΈΠΏΠ½ΠΎΠΌΠ΅ΡΠ°Π·Π°Π΄Π°ΡΠΈ ΡΠΈΠΏΡΠΎΡΠΌΡ ΡΠ΄Π°Π»Π΅Π½ΠΈΠ΅Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ Π²Π°ΠΆΠ½ΠΎΡΡΡΠΏΡΠΎΠ±Π»Π΅ΠΌΡΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ Π²Π°ΡΠΈΠ°Π½ΡΠΈΠ½ΡΠ΅ΡΡΠ΅ΠΉΡΠ°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ Π²Π°ΡΠΈΠ°Π½ΡΠΌΠ°ΡΡΡΠ°Π±Π°ΡΠΎΡΠΌΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ Π²Π°ΡΠΈΠ°Π½ΡΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ³ΠΎΡΡΠΈΡΡΠ°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ Π²Π°ΡΠΈΠ°Π½ΡΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π²Π°ΡΠΈΠ°Π½ΡΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠΉΠ΄Π°ΡΡΠ½Π°ΡΠ°Π»Π° Π²ΠΈΠ΄Π³ΡΠ°Π½ΠΈΡΡ Π²ΠΈΠ΄ΠΊΠ°ΡΡΠΈΠ½ΠΊΠΈ Π²ΠΈΠ΄ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎΠΏΠΎΠΈΡΠΊΠ° Π²ΠΈΠ΄ΡΠ°ΠΌΠΊΠΈ Π²ΠΈΠ΄ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Π²ΠΈΠ΄ΡΠ²Π΅ΡΠ° Π²ΠΈΠ΄ΡΠΈΡΠ»ΠΎΠ²ΠΎΠ³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡ Π²ΠΈΠ΄ΡΡΠΈΡΡΠ° Π΄ΠΎΠΏΡΡΡΠΈΠΌΠ°ΡΠ΄Π»ΠΈΠ½Π° Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΠΉΠ·Π½Π°ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅byteordermark ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌΠ΅ΡΠ°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎΠΏΠΎΠΈΡΠΊΠ° ΠΈΡΡΠΎΡΠ½ΠΈΠΊΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠΉΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ ΠΊΠ»Π°Π²ΠΈΡΠ° ΠΊΠΎΠ΄Π²ΠΎΠ·Π²ΡΠ°ΡΠ°Π΄ΠΈΠ°Π»ΠΎΠ³Π° ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΠΊΠ°xbase ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΠΊΠ°ΡΠ΅ΠΊΡΡΠ° Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΏΠΎΠΈΡΠΊΠ° Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΡΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠ΅Π΄ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ΠΏΡΠΈΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΈΠ΄Π°Π½Π½ΡΡ
ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΏΠ°Π½Π΅Π»ΠΈΡΠ°Π·Π΄Π΅Π»ΠΎΠ² ΠΏΡΠΎΠ²Π΅ΡΠΊΠ°Π·Π°ΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠ΄ΠΈΠ°Π»ΠΎΠ³Π°Π²ΠΎΠΏΡΠΎΡ ΡΠ΅ΠΆΠΈΠΌΠ·Π°ΠΏΡΡΠΊΠ°ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΎΠΊΡΡΠ³Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΎΡΠΊΡΡΡΠΈΡΡΠΎΡΠΌΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΆΠΈΠΌΠΏΠΎΠ»Π½ΠΎΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎΠΏΠΎΠΈΡΠΊΠ° ΡΠΊΠΎΡΠΎΡΡΡΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅Π²Π½Π΅ΡΠ½Π΅Π³ΠΎΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°Π΄Π°Π½Π½ΡΡ
ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈΠ±Π°Π·ΡΠ΄Π°Π½Π½ΡΡ
ΡΠΏΠΎΡΠΎΠ±Π²ΡΠ±ΠΎΡΠ°ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠ°windows ΡΠΏΠΎΡΠΎΠ±ΠΊΠΎΠ΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡΡΡΡΠΎΠΊΠΈ ΡΡΠ°ΡΡΡΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΡΠΈΠΏΠ²Π½Π΅ΡΠ½Π΅ΠΉΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ ΡΠΈΠΏΠΏΠ»Π°ΡΡΠΎΡΠΌΡ ΡΠΈΠΏΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡΠΊΠ»Π°Π²ΠΈΡΠΈenter ΡΠΈΠΏΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈΠΎΠ²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠΈΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈΠ±Π°Π·ΡΠ΄Π°Π½Π½ΡΡ
ΡΡΠΎΠ²Π΅Π½ΡΠΈΠ·ΠΎΠ»ΡΡΠΈΠΈΡΡΠ°Π½Π·Π°ΠΊΡΠΈΠΉ Ρ
Π΅ΡΡΡΠ½ΠΊΡΠΈΡ ΡΠ°ΡΡΠΈΠ΄Π°ΡΡ",type:"comΠΎΠ±ΡΠ΅ΠΊΡ ftpΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅ httpΠ·Π°ΠΏΡΠΎΡ httpΡΠ΅ΡΠ²ΠΈΡΠΎΡΠ²Π΅Ρ httpΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅ wsΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ wsΠΏΡΠΎΠΊΡΠΈ xbase Π°Π½Π°Π»ΠΈΠ·Π΄Π°Π½Π½ΡΡ
Π°Π½Π½ΠΎΡΠ°ΡΠΈΡxs Π±Π»ΠΎΠΊΠΈΡΠΎΠ²ΠΊΠ°Π΄Π°Π½Π½ΡΡ
Π±ΡΡΠ΅ΡΠ΄Π²ΠΎΠΈΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅xs Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΡΠ»ΡΡΠ°ΠΉΠ½ΡΡ
ΡΠΈΡΠ΅Π» Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠ°ΡΡΡ
Π΅ΠΌΠ° Π³Π΅ΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΡ Π³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠ°ΡΡΡ
Π΅ΠΌΠ° Π³ΡΡΠΏΠΏΠ°ΠΌΠΎΠ΄Π΅Π»ΠΈxs Π΄Π°Π½Π½ΡΠ΅ΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π΄Π²ΠΎΠΈΡΠ½ΡΠ΅Π΄Π°Π½Π½ΡΠ΅ Π΄Π΅Π½Π΄ΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ° Π΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ°Π³Π°Π½ΡΠ° Π΄ΠΈΠ°Π»ΠΎΠ³Π²ΡΠ±ΠΎΡΠ°ΡΠ°ΠΉΠ»Π° Π΄ΠΈΠ°Π»ΠΎΠ³Π²ΡΠ±ΠΎΡΠ°ΡΠ²Π΅ΡΠ° Π΄ΠΈΠ°Π»ΠΎΠ³Π²ΡΠ±ΠΎΡΠ°ΡΡΠΈΡΡΠ° Π΄ΠΈΠ°Π»ΠΎΠ³ΡΠ°ΡΠΏΠΈΡΠ°Π½ΠΈΡΡΠ΅Π³Π»Π°ΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎΠ·Π°Π΄Π°Π½ΠΈΡ Π΄ΠΈΠ°Π»ΠΎΠ³ΡΠ΅Π΄Π°ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠ³ΠΎΠΏΠ΅ΡΠΈΠΎΠ΄Π° Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ Π΄ΠΎΠΊΡΠΌΠ΅Π½Ρdom Π΄ΠΎΠΊΡΠΌΠ΅Π½Ρhtml Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡxs Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅ΠΌΠΎΠ΅ΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½ΠΈΠ΅ Π·Π°ΠΏΠΈΡΡdom Π·Π°ΠΏΠΈΡΡfastinfoset Π·Π°ΠΏΠΈΡΡhtml Π·Π°ΠΏΠΈΡΡjson Π·Π°ΠΏΠΈΡΡxml Π·Π°ΠΏΠΈΡΡzipΡΠ°ΠΉΠ»Π° Π·Π°ΠΏΠΈΡΡΠ΄Π°Π½Π½ΡΡ
Π·Π°ΠΏΠΈΡΡΡΠ΅ΠΊΡΡΠ° Π·Π°ΠΏΠΈΡΡΡΠ·Π»ΠΎΠ²dom Π·Π°ΠΏΡΠΎΡ Π·Π°ΡΠΈΡΠ΅Π½Π½ΠΎΠ΅ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅openssl Π·Π½Π°ΡΠ΅Π½ΠΈΡΠΏΠΎΠ»Π΅ΠΉΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ° ΠΈΠΌΠΏΠΎΡΡxs ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠ° ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΠΎΠ΅ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΠΎΡΡΠΎΠ²ΡΠΉΠΏΡΠΎΡΠΈΠ»Ρ ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠΏΡΠΎΠΊΡΠΈ ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΄Π»ΡΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡxs ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅Π°ΡΡΠΈΠ±ΡΡΠ°xs ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΡΠΎΠ±ΡΡΠΈΡΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ΄ΠΎΡΡΡΠΏΠ½ΡΡ
Π½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΈΡΠ΅ΡΠ°ΡΠΎΡΡΠ·Π»ΠΎΠ²dom ΠΊΠ°ΡΡΠΈΠ½ΠΊΠ° ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΎΡΡΠ΄Π°ΡΡ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΎΡΡΠ΄Π²ΠΎΠΈΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΎΡΡΡΡΡΠΎΠΊΠΈ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΎΡΡΡΠΈΡΠ»Π° ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΡΠΈΠΊΠΌΠ°ΠΊΠ΅ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΡΠΈΠΊΠ½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΎΡΠΌΠ°ΠΊΠ΅ΡΠ°ΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΎΡΠ½Π°ΡΡΡΠΎΠ΅ΠΊΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΎΡΡΠΎΡΠΌΠ°ΡΠ½ΠΎΠΉΡΡΡΠΎΠΊΠΈ Π»ΠΈΠ½ΠΈΡ ΠΌΠ°ΠΊΠ΅ΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΌΠ°ΠΊΠ΅ΡΠΎΠ±Π»Π°ΡΡΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΌΠ°ΠΊΠ΅ΡΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΌΠ°ΡΠΊΠ°xs ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ Π½Π°Π±ΠΎΡΡΡ
Π΅ΠΌxml Π½Π°ΡΡΡΠΎΠΉΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π½Π°ΡΡΡΠΎΠΉΠΊΠΈΡΠ΅ΡΠΈΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈjson ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΠΊΠ°ΡΡΠΈΠ½ΠΎΠΊ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ°ΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΎΠ±Ρ
ΠΎΠ΄Π΄Π΅ΡΠ΅Π²Π°dom ΠΎΠ±ΡΡΠ²Π»Π΅Π½ΠΈΠ΅Π°ΡΡΠΈΠ±ΡΡΠ°xs ΠΎΠ±ΡΡΠ²Π»Π΅Π½ΠΈΠ΅Π½ΠΎΡΠ°ΡΠΈΠΈxs ΠΎΠ±ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°xs ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΡΠΎΠ±ΡΡΠΈΡΠ΄ΠΎΡΡΡΠΏΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡΡΠΎΠ±ΡΡΠΈΡΠΎΡΠΊΠ°Π·Π²Π΄ΠΎΡΡΡΠΏΠ΅ΠΆΡΡΠ½Π°Π»Π°ΡΠ΅Π³ΠΈΡΡΡΠ°ΡΠΈΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈΡΠ°ΡΡΠΈΡΡΠΎΠ²ΠΊΠΈΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΠΏΠ΅ΡΠ΅Π΄Π°Π²Π°Π΅ΠΌΠΎΠ³ΠΎΡΠ°ΠΉΠ»Π° ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ΡΠΈΠΏΠΎΠ² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅Π³ΡΡΠΏΠΏΡΠ°ΡΡΠΈΠ±ΡΡΠΎΠ²xs ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅Π³ΡΡΠΏΠΏΡΠΌΠΎΠ΄Π΅Π»ΠΈxs ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΠΈΠ΄Π΅Π½ΡΠΈΡΠ½ΠΎΡΡΠΈxs ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ΠΏΡΠΎΡΡΠΎΠ³ΠΎΡΠΈΠΏΠ°xs ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ΡΠΎΡΡΠ°Π²Π½ΠΎΠ³ΠΎΡΠΈΠΏΠ°xs ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ΡΠΈΠΏΠ°Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°dom ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡxpathxs ΠΎΡΠ±ΠΎΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΏΠ°ΠΊΠ΅ΡΠΎΡΠΎΠ±ΡΠ°ΠΆΠ°Π΅ΠΌΡΡ
Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ²ΡΠ±ΠΎΡΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ·Π°ΠΏΠΈΡΠΈjson ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΠ·Π°ΠΏΠΈΡΠΈxml ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡΡΡΠ΅Π½ΠΈΡxml ΠΏΠ΅ΡΠ΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅xs ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²ΡΠΈΠΊ ΠΏΠΎΠ»Π΅Π°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ»Π΅ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»Ρdom ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ·Π°ΠΏΡΠΎΡΠ° ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠΎΡΡΠ΅ΡΠ° ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠΎΡΡΠ΅ΡΠ°Π°Π½Π°Π»ΠΈΠ·Π°Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡ
Π΅ΠΌxml ΠΏΠΎΡΠΎΠΊ ΠΏΠΎΡΠΎΠΊΠ²ΠΏΠ°ΠΌΡΡΠΈ ΠΏΠΎΡΡΠ° ΠΏΠΎΡΡΠΎΠ²ΠΎΠ΅ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅xsl ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΊΠΊΠ°Π½ΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌΡxml ΠΏΡΠΎΡΠ΅ΡΡΠΎΡΠ²ΡΠ²ΠΎΠ΄Π°ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π²ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΡΠ·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠΎΡΠ²ΡΠ²ΠΎΠ΄Π°ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
Π²ΡΠ°Π±Π»ΠΈΡΠ½ΡΠΉΠ΄ΠΎΠΊΡΠΌΠ΅Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΎΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°Π·ΡΠΌΠ΅Π½ΠΎΠ²Π°ΡΠ΅Π»ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²ΠΈΠΌΠ΅Π½dom ΡΠ°ΠΌΠΊΠ° ΡΠ°ΡΠΏΠΈΡΠ°Π½ΠΈΠ΅ΡΠ΅Π³Π»Π°ΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎΠ·Π°Π΄Π°Π½ΠΈΡ ΡΠ°ΡΡΠΈΡΠ΅Π½Π½ΠΎΠ΅ΠΈΠΌΡxml ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΡΠ΅Π½ΠΈΡΠ΄Π°Π½Π½ΡΡ
ΡΠ²ΠΎΠ΄Π½Π°ΡΠ΄ΠΈΠ°Π³ΡΠ°ΠΌΠΌΠ° ΡΠ²ΡΠ·ΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°Π²ΡΠ±ΠΎΡΠ° ΡΠ²ΡΠ·ΡΠΏΠΎΡΠΈΠΏΡ ΡΠ²ΡΠ·ΡΠΏΠΎΡΠΈΠΏΡΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ΅ΡΠΈΠ°Π»ΠΈΠ·Π°ΡΠΎΡxdto ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΊΠ»ΠΈΠ΅Π½ΡΠ°windows ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΊΠ»ΠΈΠ΅Π½ΡΠ°ΡΠ°ΠΉΠ» ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΠΊΡΠΈΠΏΡΠΎΠ³ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΡΡΠ΄ΠΎΡΡΠΎΠ²Π΅ΡΡΡΡΠΈΡ
ΡΠ΅Π½ΡΡΠΎΠ²windows ΡΠ΅ΡΡΠΈΡΠΈΠΊΠ°ΡΡΡΠ΄ΠΎΡΡΠΎΠ²Π΅ΡΡΡΡΠΈΡ
ΡΠ΅Π½ΡΡΠΎΠ²ΡΠ°ΠΉΠ» ΡΠΆΠ°ΡΠΈΠ΅Π΄Π°Π½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ½Π°ΡΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ΠΊΠ»Π°Π²ΠΈΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΡΠ°Π½Π΄Π°ΡΡΠ½Π°ΡΠ΄Π°ΡΠ°Π½Π°ΡΠ°Π»Π° ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΉΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΡ
Π΅ΠΌΠ°xml ΡΡ
Π΅ΠΌΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
ΡΠ°Π±Π»ΠΈΡΠ½ΡΠΉΠ΄ΠΎΠΊΡΠΌΠ΅Π½Ρ ΡΠ΅ΠΊΡΡΠΎΠ²ΡΠΉΠ΄ΠΎΠΊΡΠΌΠ΅Π½Ρ ΡΠ΅ΡΡΠΈΡΡΠ΅ΠΌΠΎΠ΅ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΡΠΈΠΏΠ΄Π°Π½Π½ΡΡ
xml ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΡΠΉΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡ ΡΠ°Π±ΡΠΈΠΊΠ°xdto ΡΠ°ΠΉΠ» ΡΠ°ΠΉΠ»ΠΎΠ²ΡΠΉΠΏΠΎΡΠΎΠΊ ΡΠ°ΡΠ΅ΡΠ΄Π»ΠΈΠ½Ρxs ΡΠ°ΡΠ΅ΡΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π°ΡΠ°Π·ΡΡΠ΄ΠΎΠ²Π΄ΡΠΎΠ±Π½ΠΎΠΉΡΠ°ΡΡΠΈxs ΡΠ°ΡΠ΅ΡΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎΠ²ΠΊΠ»ΡΡΠ°ΡΡΠ΅Π³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡxs ΡΠ°ΡΠ΅ΡΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎΠΈΡΠΊΠ»ΡΡΠ°ΡΡΠ΅Π³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡxs ΡΠ°ΡΠ΅ΡΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉΠ΄Π»ΠΈΠ½Ρxs ΡΠ°ΡΠ΅ΡΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎΠ²ΠΊΠ»ΡΡΠ°ΡΡΠ΅Π³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡxs ΡΠ°ΡΠ΅ΡΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎΠΈΡΠΊΠ»ΡΡΠ°ΡΡΠ΅Π³ΠΎΠ·Π½Π°ΡΠ΅Π½ΠΈΡxs ΡΠ°ΡΠ΅ΡΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉΠ΄Π»ΠΈΠ½Ρxs ΡΠ°ΡΠ΅ΡΠΎΠ±ΡΠ°Π·ΡΠ°xs ΡΠ°ΡΠ΅ΡΠΎΠ±ΡΠ΅Π³ΠΎΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π°ΡΠ°Π·ΡΡΠ΄ΠΎΠ²xs ΡΠ°ΡΠ΅ΡΠΏΠ΅ΡΠ΅ΡΠΈΡΠ»Π΅Π½ΠΈΡxs ΡΠ°ΡΠ΅ΡΠΏΡΠΎΠ±Π΅Π»ΡΠ½ΡΡ
ΡΠΈΠΌΠ²ΠΎΠ»ΠΎΠ²xs ΡΠΈΠ»ΡΡΡΡΠ·Π»ΠΎΠ²dom ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ²Π°Π½Π½Π°ΡΡΡΡΠΎΠΊΠ° ΡΠΎΡΠΌΠ°ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉΠ΄ΠΎΠΊΡΠΌΠ΅Π½Ρ ΡΡΠ°Π³ΠΌΠ΅Π½Ρxs Ρ
Π΅ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅Π΄Π°Π½Π½ΡΡ
Ρ
ΡΠ°Π½ΠΈΠ»ΠΈΡΠ΅Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΡΠ²Π΅Ρ ΡΡΠ΅Π½ΠΈΠ΅fastinfoset ΡΡΠ΅Π½ΠΈΠ΅html ΡΡΠ΅Π½ΠΈΠ΅json ΡΡΠ΅Π½ΠΈΠ΅xml ΡΡΠ΅Π½ΠΈΠ΅zipΡΠ°ΠΉΠ»Π° ΡΡΠ΅Π½ΠΈΠ΅Π΄Π°Π½Π½ΡΡ
ΡΡΠ΅Π½ΠΈΠ΅ΡΠ΅ΠΊΡΡΠ° ΡΡΠ΅Π½ΠΈΠ΅ΡΠ·Π»ΠΎΠ²dom ΡΡΠΈΡΡ ΡΠ»Π΅ΠΌΠ΅Π½ΡΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΊΠΎΠΌΠΏΠΎΠ½ΠΎΠ²ΠΊΠΈΠ΄Π°Π½Π½ΡΡ
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diff --git a/src/_site/site_libs/revealjs/plugin/pdf-export/pdfexport.js b/src/_site/site_libs/revealjs/plugin/pdf-export/pdfexport.js
new file mode 100644
index 0000000000000000000000000000000000000000..0cf0bd787066e056634c74df9229ce62dab4237d
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/pdf-export/pdfexport.js
@@ -0,0 +1,115 @@
+var PdfExport = ( function( _Reveal ){
+
+ var Reveal = _Reveal;
+ var setStylesheet = null;
+ var installAltKeyBindings = null;
+
+ function getRevealJsPath(){
+ var regex = /\b[^/]+\/reveal.css$/i;
+ var script = Array.from( document.querySelectorAll( 'link' ) ).find( function( e ){
+ return e.attributes.href && e.attributes.href.value.search( regex ) >= 0;
+ });
+ if( !script ){
+ console.error( 'reveal.css could not be found in included elements. Did you rename this file?' );
+ return '';
+ }
+ return script.attributes.href.value.replace( regex, '' );
+ }
+
+ function setStylesheet3( pdfExport ){
+ var link = document.querySelector( '#print' );
+ if( !link ){
+ link = document.createElement( 'link' );
+ link.rel = 'stylesheet';
+ link.id = 'print';
+ document.querySelector( 'head' ).appendChild( link );
+ }
+ var style = 'paper';
+ if( pdfExport ){
+ style = 'pdf';
+ }
+ link.href = getRevealJsPath() + 'css/print/' + style + '.css';
+ }
+
+ function setStylesheet4( pdfExport ){
+ }
+
+ function installAltKeyBindings3(){
+ }
+
+ function installAltKeyBindings4(){
+ if( isPrintingPDF() ){
+ var config = Reveal.getConfig();
+ var shortcut = config.pdfExportShortcut || 'E';
+ window.addEventListener( 'keydown', function( e ){
+ if( e.target.nodeName.toUpperCase() == 'BODY'
+ && ( e.key.toUpperCase() == shortcut.toUpperCase() || e.keyCode == shortcut.toUpperCase().charCodeAt( 0 ) ) ){
+ e.preventDefault();
+ togglePdfExport();
+ return false;
+ }
+ }, true );
+ }
+ }
+
+ function isPrintingPDF(){
+ return /print-pdf/gi.test(window.location.search) || /view=print/gi.test(window.location.search);
+ }
+
+ function togglePdfExport(){
+ var url_doc = new URL( document.URL );
+ var query_doc = new URLSearchParams( url_doc.searchParams );
+ if( isPrintingPDF() ){
+ if (query_doc.has('print-pdf')) {
+ query_doc.delete('print-pdf');
+ } else if (query_doc.has('view')) {
+ query_doc.delete('view');
+ }
+ }else{
+ query_doc.set( 'view', 'print' );
+ }
+ url_doc.search = ( query_doc.toString() ? '?' + query_doc.toString() : '' );
+ window.location.href = url_doc.toString();
+ }
+
+ function installKeyBindings(){
+ var config = Reveal.getConfig();
+ var shortcut = config.pdfExportShortcut || 'E';
+ Reveal.addKeyBinding({
+ keyCode: shortcut.toUpperCase().charCodeAt( 0 ),
+ key: shortcut.toUpperCase(),
+ description: 'PDF export mode'
+ }, togglePdfExport );
+ installAltKeyBindings();
+ }
+
+ function install(){
+ installKeyBindings();
+ setStylesheet( isPrintingPDF() );
+ }
+
+ var Plugin = {
+ }
+
+ if( Reveal && Reveal.VERSION && Reveal.VERSION.length && Reveal.VERSION[ 0 ] == '3' ){
+ // reveal 3.x
+ setStylesheet = setStylesheet3;
+ installAltKeyBindings = installAltKeyBindings3;
+ install();
+ }else{
+ // must be reveal 4.x
+ setStylesheet = setStylesheet4;
+ installAltKeyBindings = installAltKeyBindings4;
+ Plugin.id = 'pdf-export';
+ Plugin.init = function( _Reveal ){
+ Reveal = _Reveal;
+ install();
+ };
+ Plugin.togglePdfExport = function () {
+ togglePdfExport();
+ };
+ }
+
+ return Plugin;
+
+})( Reveal );
diff --git a/src/_site/site_libs/revealjs/plugin/pdf-export/plugin.yml b/src/_site/site_libs/revealjs/plugin/pdf-export/plugin.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f6db9d032bfb7b43d50c7cf4bd828324b8ae435b
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/pdf-export/plugin.yml
@@ -0,0 +1,2 @@
+name: PdfExport
+script: pdfexport.js
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.css b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.css
new file mode 100644
index 0000000000000000000000000000000000000000..e8410fe9e2bbeb2cca7f828d96e8bb770cb84ae9
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.css
@@ -0,0 +1,31 @@
+.reveal
+ div.sourceCode
+ pre
+ code.has-line-highlights
+ > span:not(.highlight-line) {
+ opacity: 0.4;
+}
+
+.reveal pre.numberSource {
+ padding-left: 0;
+}
+
+.reveal pre.numberSource code > span {
+ left: -2.1em;
+}
+
+pre.numberSource code > span > a:first-child::before {
+ left: -0.7em;
+}
+
+.reveal pre > code:not(:first-child).fragment {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ box-sizing: border-box;
+}
+
+.reveal div.sourceCode pre code {
+ min-height: 100%;
+}
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.js b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.js
new file mode 100644
index 0000000000000000000000000000000000000000..16a3893fce13385123cba679f8cc4f3a452fd616
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/line-highlight.js
@@ -0,0 +1,351 @@
+window.QuartoLineHighlight = function () {
+ function isPrintView() {
+ return /print-pdf/gi.test(window.location.search) || /view=print/gi.test(window.location.search);
+ }
+
+ const delimiters = {
+ step: "|",
+ line: ",",
+ lineRange: "-",
+ };
+
+ const regex = new RegExp(
+ "^[\\d" + Object.values(delimiters).join("") + "]+$"
+ );
+
+ function handleLinesSelector(deck, attr) {
+ // if we are in printview with pdfSeparateFragments: false
+ // then we'll also want to supress
+ if (regex.test(attr)) {
+ if (isPrintView() && deck.getConfig().pdfSeparateFragments !== true) {
+ return false;
+ } else {
+ return true;
+ }
+ } else {
+ return false;
+ }
+ }
+
+ const kCodeLineNumbersAttr = "data-code-line-numbers";
+ const kFragmentIndex = "data-fragment-index";
+
+ function initQuartoLineHighlight(deck) {
+ const divSourceCode = deck
+ .getRevealElement()
+ .querySelectorAll("div.sourceCode");
+ // Process each div created by Pandoc highlighting - numbered line are already included.
+ divSourceCode.forEach((el) => {
+ if (el.hasAttribute(kCodeLineNumbersAttr)) {
+ const codeLineAttr = el.getAttribute(kCodeLineNumbersAttr);
+ el.removeAttribute(kCodeLineNumbersAttr);
+ if (handleLinesSelector(deck, codeLineAttr)) {
+ // Only process if attr is a string to select lines to highlights
+ // e.g "1|3,6|8-11"
+ const codeBlock = el.querySelectorAll("pre code");
+ codeBlock.forEach((code) => {
+ // move attributes on code block
+ code.setAttribute(kCodeLineNumbersAttr, codeLineAttr);
+
+ const scrollState = { currentBlock: code };
+
+ // Check if there are steps and duplicate code block accordingly
+ const highlightSteps = splitLineNumbers(codeLineAttr);
+ if (highlightSteps.length > 1) {
+ // If the original code block has a fragment-index,
+ // each clone should follow in an incremental sequence
+ let fragmentIndex = parseInt(
+ code.getAttribute(kFragmentIndex),
+ 10
+ );
+ fragmentIndex =
+ typeof fragmentIndex !== "number" || isNaN(fragmentIndex)
+ ? null
+ : fragmentIndex;
+
+ let stepN = 1;
+ highlightSteps.slice(1).forEach(
+ // Generate fragments for all steps except the original block
+ (step) => {
+ var fragmentBlock = code.cloneNode(true);
+ fragmentBlock.setAttribute(
+ "data-code-line-numbers",
+ joinLineNumbers([step])
+ );
+ fragmentBlock.classList.add("fragment");
+
+ // Pandoc sets id on spans we need to keep unique
+ fragmentBlock
+ .querySelectorAll(":scope > span")
+ .forEach((span) => {
+ if (span.hasAttribute("id")) {
+ span.setAttribute(
+ "id",
+ span.getAttribute("id").concat("-" + stepN)
+ );
+ }
+ });
+ stepN = ++stepN;
+
+ // Add duplicated element after existing one
+ code.parentNode.appendChild(fragmentBlock);
+
+ // Each new element is highlighted based on the new attributes value
+ highlightCodeBlock(fragmentBlock);
+
+ if (typeof fragmentIndex === "number") {
+ fragmentBlock.setAttribute(kFragmentIndex, fragmentIndex);
+ fragmentIndex += 1;
+ } else {
+ fragmentBlock.removeAttribute(kFragmentIndex);
+ }
+
+ // Scroll highlights into view as we step through them
+ fragmentBlock.addEventListener(
+ "visible",
+ scrollHighlightedLineIntoView.bind(
+ this,
+ fragmentBlock,
+ scrollState
+ )
+ );
+ fragmentBlock.addEventListener(
+ "hidden",
+ scrollHighlightedLineIntoView.bind(
+ this,
+ fragmentBlock.previousSibling,
+ scrollState
+ )
+ );
+ }
+ );
+ code.removeAttribute(kFragmentIndex);
+ code.setAttribute(
+ kCodeLineNumbersAttr,
+ joinLineNumbers([highlightSteps[0]])
+ );
+ }
+
+ // Scroll the first highlight into view when the slide becomes visible.
+ const slide =
+ typeof code.closest === "function"
+ ? code.closest("section:not(.stack)")
+ : null;
+ if (slide) {
+ const scrollFirstHighlightIntoView = function () {
+ scrollHighlightedLineIntoView(code, scrollState, true);
+ slide.removeEventListener(
+ "visible",
+ scrollFirstHighlightIntoView
+ );
+ };
+ slide.addEventListener("visible", scrollFirstHighlightIntoView);
+ }
+
+ highlightCodeBlock(code);
+ });
+ }
+ }
+ });
+ }
+
+ function highlightCodeBlock(codeBlock) {
+ const highlightSteps = splitLineNumbers(
+ codeBlock.getAttribute(kCodeLineNumbersAttr)
+ );
+
+ if (highlightSteps.length) {
+ // If we have at least one step, we generate fragments
+ highlightSteps[0].forEach((highlight) => {
+ // Add expected class on for reveal CSS
+ codeBlock.parentNode.classList.add("code-wrapper");
+
+ // Select lines to highlight
+ spanToHighlight = [];
+ if (typeof highlight.last === "number") {
+ spanToHighlight = [].slice.call(
+ codeBlock.querySelectorAll(
+ ":scope > span:nth-of-type(n+" +
+ highlight.first +
+ "):nth-of-type(-n+" +
+ highlight.last +
+ ")"
+ )
+ );
+ } else if (typeof highlight.first === "number") {
+ spanToHighlight = [].slice.call(
+ codeBlock.querySelectorAll(
+ ":scope > span:nth-of-type(" + highlight.first + ")"
+ )
+ );
+ }
+ if (spanToHighlight.length) {
+ // Add a class on and to select line to highlight
+ spanToHighlight.forEach((span) =>
+ span.classList.add("highlight-line")
+ );
+ codeBlock.classList.add("has-line-highlights");
+ }
+ });
+ }
+ }
+
+ /**
+ * Animates scrolling to the first highlighted line
+ * in the given code block.
+ */
+ function scrollHighlightedLineIntoView(block, scrollState, skipAnimation) {
+ window.cancelAnimationFrame(scrollState.animationFrameID);
+
+ // Match the scroll position of the currently visible
+ // code block
+ if (scrollState.currentBlock) {
+ block.scrollTop = scrollState.currentBlock.scrollTop;
+ }
+
+ // Remember the current code block so that we can match
+ // its scroll position when showing/hiding fragments
+ scrollState.currentBlock = block;
+
+ const highlightBounds = getHighlightedLineBounds(block);
+ let viewportHeight = block.offsetHeight;
+
+ // Subtract padding from the viewport height
+ const blockStyles = window.getComputedStyle(block);
+ viewportHeight -=
+ parseInt(blockStyles.paddingTop) + parseInt(blockStyles.paddingBottom);
+
+ // Scroll position which centers all highlights
+ const startTop = block.scrollTop;
+ let targetTop =
+ highlightBounds.top +
+ (Math.min(highlightBounds.bottom - highlightBounds.top, viewportHeight) -
+ viewportHeight) /
+ 2;
+
+ // Make sure the scroll target is within bounds
+ targetTop = Math.max(
+ Math.min(targetTop, block.scrollHeight - viewportHeight),
+ 0
+ );
+
+ if (skipAnimation === true || startTop === targetTop) {
+ block.scrollTop = targetTop;
+ } else {
+ // Don't attempt to scroll if there is no overflow
+ if (block.scrollHeight <= viewportHeight) return;
+
+ let time = 0;
+
+ const animate = function () {
+ time = Math.min(time + 0.02, 1);
+
+ // Update our eased scroll position
+ block.scrollTop =
+ startTop + (targetTop - startTop) * easeInOutQuart(time);
+
+ // Keep animating unless we've reached the end
+ if (time < 1) {
+ scrollState.animationFrameID = requestAnimationFrame(animate);
+ }
+ };
+
+ animate();
+ }
+ }
+
+ function getHighlightedLineBounds(block) {
+ const highlightedLines = block.querySelectorAll(".highlight-line");
+ if (highlightedLines.length === 0) {
+ return { top: 0, bottom: 0 };
+ } else {
+ const firstHighlight = highlightedLines[0];
+ const lastHighlight = highlightedLines[highlightedLines.length - 1];
+
+ return {
+ top: firstHighlight.offsetTop,
+ bottom: lastHighlight.offsetTop + lastHighlight.offsetHeight,
+ };
+ }
+ }
+
+ /**
+ * The easing function used when scrolling.
+ */
+ function easeInOutQuart(t) {
+ // easeInOutQuart
+ return t < 0.5 ? 8 * t * t * t * t : 1 - 8 * --t * t * t * t;
+ }
+
+ function splitLineNumbers(lineNumbersAttr) {
+ // remove space
+ lineNumbersAttr = lineNumbersAttr.replace("/s/g", "");
+ // seperate steps (for fragment)
+ lineNumbersAttr = lineNumbersAttr.split(delimiters.step);
+
+ // for each step, calculate first and last line, if any
+ return lineNumbersAttr.map((highlights) => {
+ // detect lines
+ const lines = highlights.split(delimiters.line);
+ return lines.map((range) => {
+ if (/^[\d-]+$/.test(range)) {
+ range = range.split(delimiters.lineRange);
+ const firstLine = parseInt(range[0], 10);
+ const lastLine = range[1] ? parseInt(range[1], 10) : undefined;
+ return {
+ first: firstLine,
+ last: lastLine,
+ };
+ } else {
+ return {};
+ }
+ });
+ });
+ }
+
+ function joinLineNumbers(splittedLineNumbers) {
+ return splittedLineNumbers
+ .map(function (highlights) {
+ return highlights
+ .map(function (highlight) {
+ // Line range
+ if (typeof highlight.last === "number") {
+ return highlight.first + delimiters.lineRange + highlight.last;
+ }
+ // Single line
+ else if (typeof highlight.first === "number") {
+ return highlight.first;
+ }
+ // All lines
+ else {
+ return "";
+ }
+ })
+ .join(delimiters.line);
+ })
+ .join(delimiters.step);
+ }
+
+ return {
+ id: "quarto-line-highlight",
+ init: function (deck) {
+ initQuartoLineHighlight(deck);
+
+ // If we're printing to PDF, scroll the code highlights of
+ // all blocks in the deck into view at once
+ deck.on("pdf-ready", function () {
+ [].slice
+ .call(
+ deck
+ .getRevealElement()
+ .querySelectorAll(
+ "pre code[data-code-line-numbers].current-fragment"
+ )
+ )
+ .forEach(function (block) {
+ scrollHighlightedLineIntoView(block, {}, true);
+ });
+ });
+ },
+ };
+};
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/plugin.yml b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/plugin.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ca2068621aded50ae060767d3fa211db974391f0
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-line-highlight/plugin.yml
@@ -0,0 +1,4 @@
+# adapted from https://github.com/hakimel/reveal.js/tree/master/plugin/highlight
+name: QuartoLineHighlight
+script: line-highlight.js
+stylesheet: line-highlight.css
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-support/footer.css b/src/_site/site_libs/revealjs/plugin/quarto-support/footer.css
new file mode 100644
index 0000000000000000000000000000000000000000..390d5b38ddb6d41db96f1e3d5c408ee93e5cf7db
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-support/footer.css
@@ -0,0 +1,110 @@
+.reveal .slide-logo {
+ display: block;
+ position: fixed;
+ bottom: 0;
+ right: 12px;
+ max-height: 2.2rem;
+ height: 100%;
+ width: auto;
+ z-index: 2;
+}
+
+.reveal .footer {
+ display: block;
+ position: fixed;
+ bottom: 18px;
+ width: 100%;
+ margin: 0 auto;
+ text-align: center;
+ font-size: 18px;
+ z-index: 2;
+}
+
+.reveal .footer > * {
+ margin-top: 0;
+ margin-bottom: 0;
+}
+
+.reveal .slide .footer {
+ display: none;
+}
+
+.reveal .slide-number {
+ bottom: 10px;
+ right: 10px;
+ font-size: 16px;
+ background-color: transparent;
+}
+
+.reveal.has-logo .slide-number {
+ bottom: initial;
+ top: 8px;
+ right: 8px;
+}
+
+.reveal .slide-number .slide-number-delimiter {
+ margin: 0;
+}
+
+.reveal .slide-menu-button {
+ left: 8px;
+ bottom: 8px;
+}
+
+.reveal .slide-chalkboard-buttons {
+ position: fixed;
+ left: 12px;
+ bottom: 8px;
+ z-index: 30;
+ font-size: 24px;
+}
+
+.reveal .slide-chalkboard-buttons.slide-menu-offset {
+ left: 54px;
+}
+
+.reveal .slide-chalkboard-buttons > span {
+ margin-right: 14px;
+ cursor: pointer;
+}
+
+@media screen and (max-width: 800px) {
+ .reveal .slide-logo {
+ max-height: 1.1rem;
+ bottom: -2px;
+ right: 10px;
+ }
+ .reveal .footer {
+ font-size: 14px;
+ bottom: 12px;
+ }
+ .reveal .slide-number {
+ font-size: 12px;
+ bottom: 7px;
+ }
+ .reveal .slide-menu-button .fas::before {
+ height: 1.3rem;
+ width: 1.3rem;
+ vertical-align: -0.125em;
+ background-size: 1.3rem 1.3rem;
+ }
+
+ .reveal .slide-chalkboard-buttons .fas::before {
+ height: 0.95rem;
+ width: 0.95rem;
+ background-size: 0.95rem 0.95rem;
+ vertical-align: -0em;
+ }
+
+ .reveal .slide-chalkboard-buttons.slide-menu-offset {
+ left: 36px;
+ }
+ .reveal .slide-chalkboard-buttons > span {
+ margin-right: 9px;
+ }
+}
+
+html.print-pdf .reveal .slide-menu-button,
+html.print-pdf .reveal .slide-chalkboard-buttons {
+ display: none;
+}
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-support/plugin.yml b/src/_site/site_libs/revealjs/plugin/quarto-support/plugin.yml
new file mode 100644
index 0000000000000000000000000000000000000000..546956e94c3f6d68d42e56427012187bcc6ec26a
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-support/plugin.yml
@@ -0,0 +1,5 @@
+name: QuartoSupport
+script: support.js
+stylesheet: footer.css
+config:
+ smaller: false
diff --git a/src/_site/site_libs/revealjs/plugin/quarto-support/support.js b/src/_site/site_libs/revealjs/plugin/quarto-support/support.js
new file mode 100644
index 0000000000000000000000000000000000000000..73246a7899f556b08d36da9f1116abf74d37f3f2
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/quarto-support/support.js
@@ -0,0 +1,405 @@
+// catch all plugin for various quarto features
+window.QuartoSupport = function () {
+ function isPrintView() {
+ return /print-pdf/gi.test(window.location.search) || /view=print/gi.test(window.location.search);
+ }
+
+ // helper for theme toggling
+ function toggleBackgroundTheme(el, onDarkBackground, onLightBackground) {
+ if (onDarkBackground) {
+ el.classList.add('has-dark-background')
+ } else {
+ el.classList.remove('has-dark-background')
+ }
+ if (onLightBackground) {
+ el.classList.add('has-light-background')
+ } else {
+ el.classList.remove('has-light-background')
+ }
+ }
+
+ // implement controlsAudo
+ function controlsAuto(deck) {
+ const config = deck.getConfig();
+ if (config.controlsAuto === true) {
+ const iframe = window.location !== window.parent.location;
+ const localhost =
+ window.location.hostname === "localhost" ||
+ window.location.hostname === "127.0.0.1";
+ deck.configure({
+ controls:
+ (iframe && !localhost) ||
+ (deck.hasVerticalSlides() && config.navigationMode !== "linear"),
+ });
+ }
+ }
+
+ // helper to provide event handlers for all links in a container
+ function handleLinkClickEvents(deck, container) {
+ Array.from(container.querySelectorAll("a")).forEach((el) => {
+ const url = el.getAttribute("href");
+ if (/^(http|www)/gi.test(url)) {
+ el.addEventListener(
+ "click",
+ (ev) => {
+ const fullscreen = !!window.document.fullscreen;
+ const dataPreviewLink = el.getAttribute("data-preview-link");
+
+ // if there is a local specifcation then use that
+ if (dataPreviewLink) {
+ if (
+ dataPreviewLink === "true" ||
+ (dataPreviewLink === "auto" && fullscreen)
+ ) {
+ ev.preventDefault();
+ deck.showPreview(url);
+ return false;
+ }
+ } else {
+ const previewLinks = !!deck.getConfig().previewLinks;
+ const previewLinksAuto =
+ deck.getConfig().previewLinksAuto === true;
+ if (previewLinks == true || (previewLinksAuto && fullscreen)) {
+ ev.preventDefault();
+ deck.showPreview(url);
+ return false;
+ }
+ }
+
+ // if the deck is in an iframe we want to open it externally
+ // (don't do this when in vscode though as it has its own
+ // handler for opening links externally that will be play)
+ const iframe = window.location !== window.parent.location;
+ if (
+ iframe &&
+ !window.location.search.includes("quartoPreviewReqId=")
+ ) {
+ ev.preventDefault();
+ ev.stopImmediatePropagation();
+ window.open(url, "_blank");
+ return false;
+ }
+
+ // if the user has set data-preview-link to "auto" we need to handle the event
+ // (because reveal will interpret "auto" as true)
+ if (dataPreviewLink === "auto") {
+ ev.preventDefault();
+ ev.stopImmediatePropagation();
+ const target =
+ el.getAttribute("target") ||
+ (ev.ctrlKey || ev.metaKey ? "_blank" : "");
+ if (target) {
+ window.open(url, target);
+ } else {
+ window.location.href = url;
+ }
+ return false;
+ }
+ },
+ false
+ );
+ }
+ });
+ }
+
+ // implement previewLinksAuto
+ function previewLinksAuto(deck) {
+ handleLinkClickEvents(deck, deck.getRevealElement());
+ }
+
+ // apply styles
+ function applyGlobalStyles(deck) {
+ if (deck.getConfig()["smaller"] === true) {
+ const revealParent = deck.getRevealElement();
+ revealParent.classList.add("smaller");
+ }
+ }
+
+ // add logo image
+ function addLogoImage(deck) {
+ const revealParent = deck.getRevealElement();
+ const logoImg = document.querySelector(".slide-logo");
+ if (logoImg) {
+ revealParent.appendChild(logoImg);
+ revealParent.classList.add("has-logo");
+ }
+ }
+
+ // tweak slide-number element
+ function tweakSlideNumber(deck) {
+ deck.on("slidechanged", function (ev) {
+ // No slide number in scroll view
+ if (deck.isScrollView()) { return }
+ const revealParent = deck.getRevealElement();
+ const slideNumberEl = revealParent.querySelector(".slide-number");
+ const slideBackground = Reveal.getSlideBackground(ev.currentSlide);
+ const onDarkBackground = slideBackground.classList.contains('has-dark-background')
+ const onLightBackground = slideBackground.classList.contains('has-light-background')
+ toggleBackgroundTheme(slideNumberEl, onDarkBackground, onLightBackground);
+ })
+ }
+
+ // add footer text
+ function addFooter(deck) {
+ const revealParent = deck.getRevealElement();
+ const defaultFooterDiv = document.querySelector(".footer-default");
+ // Set per slide footer if any defined,
+ // or show default unless data-footer="false" for no footer on this slide
+ const setSlideFooter = (ev, defaultFooterDiv) => {
+ const currentSlideFooter = ev.currentSlide.querySelector(".footer");
+ const onDarkBackground = deck.getSlideBackground(ev.currentSlide).classList.contains('has-dark-background')
+ const onLightBackground = deck.getSlideBackground(ev.currentSlide).classList.contains('has-light-background')
+ if (currentSlideFooter) {
+ defaultFooterDiv.style.display = "none";
+ const slideFooter = currentSlideFooter.cloneNode(true);
+ handleLinkClickEvents(deck, slideFooter);
+ deck.getRevealElement().appendChild(slideFooter);
+ toggleBackgroundTheme(slideFooter, onDarkBackground, onLightBackground)
+ } else if (ev.currentSlide.getAttribute("data-footer") === "false") {
+ defaultFooterDiv.style.display = "none";
+ } else {
+ defaultFooterDiv.style.display = "block";
+ toggleBackgroundTheme(defaultFooterDiv, onDarkBackground, onLightBackground)
+ }
+ }
+ if (defaultFooterDiv) {
+ // move default footnote to the div.reveal element
+ revealParent.appendChild(defaultFooterDiv);
+ handleLinkClickEvents(deck, defaultFooterDiv);
+
+ if (!isPrintView()) {
+ // Ready even is needed so that footer customization applies on first loaded slide
+ deck.on('ready', (ev) => {
+ // Set footer (custom, default or none)
+ setSlideFooter(ev, defaultFooterDiv)
+ });
+ // Any new navigated new slide will get the custom footnote check
+ deck.on("slidechanged", function (ev) {
+ // Remove presentation footer defined by previous slide
+ const prevSlideFooter = document.querySelector(
+ ".reveal > .footer:not(.footer-default)"
+ );
+ if (prevSlideFooter) {
+ prevSlideFooter.remove();
+ }
+ // Set new one (custom, default or none)
+ setSlideFooter(ev, defaultFooterDiv)
+ });
+ }
+ }
+ }
+
+ // add chalkboard buttons
+ function addChalkboardButtons(deck) {
+ const chalkboard = deck.getPlugin("RevealChalkboard");
+ if (chalkboard && !isPrintView()) {
+ const revealParent = deck.getRevealElement();
+ const chalkboardDiv = document.createElement("div");
+ chalkboardDiv.classList.add("slide-chalkboard-buttons");
+ if (document.querySelector(".slide-menu-button")) {
+ chalkboardDiv.classList.add("slide-menu-offset");
+ }
+ // add buttons
+ const buttons = [
+ {
+ icon: "easel2",
+ title: "Toggle Chalkboard (b)",
+ onclick: chalkboard.toggleChalkboard,
+ },
+ {
+ icon: "brush",
+ title: "Toggle Notes Canvas (c)",
+ onclick: chalkboard.toggleNotesCanvas,
+ },
+ ];
+ buttons.forEach(function (button) {
+ const span = document.createElement("span");
+ span.title = button.title;
+ const icon = document.createElement("i");
+ icon.classList.add("fas");
+ icon.classList.add("fa-" + button.icon);
+ span.appendChild(icon);
+ span.onclick = function (event) {
+ event.preventDefault();
+ button.onclick();
+ };
+ chalkboardDiv.appendChild(span);
+ });
+ revealParent.appendChild(chalkboardDiv);
+ const config = deck.getConfig();
+ if (!config.chalkboard.buttons) {
+ chalkboardDiv.classList.add("hidden");
+ }
+
+ // show and hide chalkboard buttons on slidechange
+ deck.on("slidechanged", function (ev) {
+ const config = deck.getConfig();
+ let buttons = !!config.chalkboard.buttons;
+ const slideButtons = ev.currentSlide.getAttribute(
+ "data-chalkboard-buttons"
+ );
+ if (slideButtons) {
+ if (slideButtons === "true" || slideButtons === "1") {
+ buttons = true;
+ } else if (slideButtons === "false" || slideButtons === "0") {
+ buttons = false;
+ }
+ }
+ if (buttons) {
+ chalkboardDiv.classList.remove("hidden");
+ } else {
+ chalkboardDiv.classList.add("hidden");
+ }
+ });
+ }
+ }
+
+ function handleTabbyClicks() {
+ const tabs = document.querySelectorAll(".panel-tabset-tabby > li > a");
+ for (let i = 0; i < tabs.length; i++) {
+ const tab = tabs[i];
+ tab.onclick = function (ev) {
+ ev.preventDefault();
+ ev.stopPropagation();
+ return false;
+ };
+ }
+ }
+
+ function fixupForPrint(deck) {
+ if (isPrintView()) {
+ const slides = deck.getSlides();
+ slides.forEach(function (slide) {
+ slide.removeAttribute("data-auto-animate");
+ });
+ window.document.querySelectorAll(".hljs").forEach(function (el) {
+ el.classList.remove("hljs");
+ });
+ window.document.querySelectorAll(".hljs-ln-code").forEach(function (el) {
+ el.classList.remove("hljs-ln-code");
+ });
+ }
+ }
+
+ function handleSlideChanges(deck) {
+ // dispatch for htmlwidgets
+ const fireSlideEnter = () => {
+ const event = window.document.createEvent("Event");
+ event.initEvent("slideenter", true, true);
+ window.document.dispatchEvent(event);
+ };
+
+ const fireSlideChanged = (previousSlide, currentSlide) => {
+ fireSlideEnter();
+
+ // dispatch for shiny
+ if (window.jQuery) {
+ if (previousSlide) {
+ window.jQuery(previousSlide).trigger("hidden");
+ }
+ if (currentSlide) {
+ window.jQuery(currentSlide).trigger("shown");
+ }
+ }
+ };
+
+ // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
+ document.addEventListener("tabby", fireSlideEnter, false);
+
+ deck.on("slidechanged", function (event) {
+ fireSlideChanged(event.previousSlide, event.currentSlide);
+ });
+ }
+
+ function workaroundMermaidDistance(deck) {
+ if (window.document.querySelector("pre.mermaid-js")) {
+ const slideCount = deck.getTotalSlides();
+ deck.configure({
+ mobileViewDistance: slideCount,
+ viewDistance: slideCount,
+ });
+ }
+ }
+
+ function handleWhiteSpaceInColumns(deck) {
+ for (const outerDiv of window.document.querySelectorAll("div.columns")) {
+ // remove all whitespace text nodes
+ // whitespace nodes cause the columns to be misaligned
+ // since they have inline-block layout
+ //
+ // Quarto emits no whitespace nodes, but third-party tooling
+ // has bugs that can cause whitespace nodes to be emitted.
+ // See https://github.com/quarto-dev/quarto-cli/issues/8382
+ for (const node of outerDiv.childNodes) {
+ if (node.nodeType === 3 && node.nodeValue.trim() === "") {
+ outerDiv.removeChild(node);
+ }
+ }
+ }
+ }
+
+ function cleanEmptyAutoGeneratedContent(deck) {
+ const div = document.querySelector('div.quarto-auto-generated-content')
+ if (div && div.textContent.trim() === '') {
+ div.remove()
+ }
+ }
+
+ // FIXME: Possibly remove this wrapper class when upstream trigger is fixed
+ // https://github.com/hakimel/reveal.js/issues/3688
+ // Currently, scrollActivationWidth needs to be unset for toggle to work
+ class ScrollViewToggler {
+ constructor(deck) {
+ this.deck = deck;
+ this.oldScrollActivationWidth = deck.getConfig()['scrollActivationWidth'];
+ }
+
+ toggleScrollViewWrapper() {
+ if (this.deck.isScrollView() === true) {
+ this.deck.configure({ scrollActivationWidth: this.oldScrollActivationWidth });
+ this.deck.toggleScrollView(false);
+ } else if (this.deck.isScrollView() === false) {
+ this.deck.configure({ scrollActivationWidth: null });
+ this.deck.toggleScrollView(true);
+ }
+ }
+ }
+
+ let scrollViewToggler;
+
+ function installScollViewKeyBindings(deck) {
+ var config = deck.getConfig();
+ var shortcut = config.scrollViewShortcut || 'R';
+ Reveal.addKeyBinding({
+ keyCode: shortcut.toUpperCase().charCodeAt( 0 ),
+ key: shortcut.toUpperCase(),
+ description: 'Scroll View Mode'
+ }, () => { scrollViewToggler.toggleScrollViewWrapper() } );
+ }
+
+ return {
+ id: "quarto-support",
+ init: function (deck) {
+ scrollViewToggler = new ScrollViewToggler(deck);
+ controlsAuto(deck);
+ previewLinksAuto(deck);
+ fixupForPrint(deck);
+ applyGlobalStyles(deck);
+ addLogoImage(deck);
+ tweakSlideNumber(deck);
+ addFooter(deck);
+ addChalkboardButtons(deck);
+ handleTabbyClicks();
+ handleSlideChanges(deck);
+ workaroundMermaidDistance(deck);
+ handleWhiteSpaceInColumns(deck);
+ installScollViewKeyBindings(deck);
+ // should stay last
+ cleanEmptyAutoGeneratedContent(deck);
+ },
+ // Export for adding in menu
+ toggleScrollView: function() {
+ scrollViewToggler.toggleScrollViewWrapper();
+ }
+ };
+};
diff --git a/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.css b/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.css
new file mode 100644
index 0000000000000000000000000000000000000000..5a300fdf63f758dc86e6d9f2ad8b543ea444cd9f
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.css
@@ -0,0 +1,346 @@
+.slide-menu-wrapper {
+ font-family: 'Source Sans Pro', Helvetica, sans-serif;
+}
+
+.slide-menu-wrapper .slide-menu {
+ background-color: #333;
+ z-index: 200;
+ position: fixed;
+ top: 0;
+ width: 300px;
+ height: 100%;
+ /*overflow-y: scroll;*/
+ transition: transform 0.3s;
+ font-size: 16px;
+ font-weight: normal;
+}
+
+.slide-menu-wrapper .slide-menu.slide-menu--wide {
+ width: 500px;
+}
+
+.slide-menu-wrapper .slide-menu.slide-menu--third {
+ width: 33%;
+}
+
+.slide-menu-wrapper .slide-menu.slide-menu--half {
+ width: 50%;
+}
+
+.slide-menu-wrapper .slide-menu.slide-menu--full {
+ width: 95%;
+}
+
+/*
+ * Slides menu
+ */
+
+.slide-menu-wrapper .slide-menu-items {
+ margin: 0;
+ padding: 0;
+ width: 100%;
+ border-bottom: solid 1px #555;
+}
+
+.slide-menu-wrapper .slide-menu-item,
+.slide-menu-wrapper .slide-menu-item-vertical {
+ display: block;
+ text-align: left;
+ padding: 10px 18px;
+ color: #aaa;
+ cursor: pointer;
+}
+
+.slide-menu-wrapper .slide-menu-item-vertical {
+ padding-left: 30px;
+}
+
+.slide-menu-wrapper .slide-menu--wide .slide-menu-item-vertical,
+.slide-menu-wrapper .slide-menu--third .slide-menu-item-vertical,
+.slide-menu-wrapper .slide-menu--half .slide-menu-item-vertical,
+.slide-menu-wrapper .slide-menu--full .slide-menu-item-vertical,
+.slide-menu-wrapper .slide-menu--custom .slide-menu-item-vertical {
+ padding-left: 50px;
+}
+
+.slide-menu-wrapper .slide-menu-item {
+ border-top: solid 1px #555;
+}
+
+.slide-menu-wrapper .active-menu-panel li.selected {
+ background-color: #222;
+ color: white;
+}
+
+.slide-menu-wrapper .active-menu-panel li.active {
+ color: #eee;
+}
+
+.slide-menu-wrapper .slide-menu-item.no-title .slide-menu-item-title,
+.slide-menu-wrapper .slide-menu-item-vertical.no-title .slide-menu-item-title {
+ font-style: italic;
+}
+
+.slide-menu-wrapper .slide-menu-item-number {
+ color: #999;
+ padding-right: 6px;
+}
+
+.slide-menu-wrapper .slide-menu-item i.far,
+.slide-menu-wrapper .slide-menu-item i.fas,
+.slide-menu-wrapper .slide-menu-item-vertical i.far,
+.slide-menu-wrapper .slide-menu-item-vertical i.fas,
+.slide-menu-wrapper .slide-menu-item svg.svg-inline--fa,
+.slide-menu-wrapper .slide-menu-item-vertical svg.svg-inline--fa {
+ padding-right: 12px;
+ display: none;
+}
+
+.slide-menu-wrapper .slide-menu-item.past i.fas.past,
+.slide-menu-wrapper .slide-menu-item-vertical.past i.fas.past,
+.slide-menu-wrapper .slide-menu-item.active i.fas.active,
+.slide-menu-wrapper .slide-menu-item-vertical.active i.fas.active,
+.slide-menu-wrapper .slide-menu-item.future i.far.future,
+.slide-menu-wrapper .slide-menu-item-vertical.future i.far.future,
+.slide-menu-wrapper .slide-menu-item.past svg.svg-inline--fa.past,
+.slide-menu-wrapper .slide-menu-item-vertical.past svg.svg-inline--fa.past,
+.slide-menu-wrapper .slide-menu-item.active svg.svg-inline--fa.active,
+.slide-menu-wrapper .slide-menu-item-vertical.active svg.svg-inline--fa.active,
+.slide-menu-wrapper .slide-menu-item.future svg.svg-inline--fa.future,
+.slide-menu-wrapper .slide-menu-item-vertical.future svg.svg-inline--fa.future {
+ display: inline-block;
+}
+
+.slide-menu-wrapper .slide-menu-item.past i.fas.past,
+.slide-menu-wrapper .slide-menu-item-vertical.past i.fas.past,
+.slide-menu-wrapper .slide-menu-item.future i.far.future,
+.slide-menu-wrapper .slide-menu-item-vertical.future i.far.future,
+.slide-menu-wrapper .slide-menu-item.past svg.svg-inline--fa.past,
+.slide-menu-wrapper .slide-menu-item-vertical.past svg.svg-inline--fa.past,
+.slide-menu-wrapper .slide-menu-item.future svg.svg-inline--fa.future,
+.slide-menu-wrapper .slide-menu-item-vertical.future svg.svg-inline--fa.future {
+ opacity: 0.4;
+}
+
+.slide-menu-wrapper .slide-menu-item.active i.fas.active,
+.slide-menu-wrapper .slide-menu-item-vertical.active i.fas.active,
+.slide-menu-wrapper .slide-menu-item.active svg.svg-inline--fa.active,
+.slide-menu-wrapper .slide-menu-item-vertical.active svg.svg-inline--fa.active {
+ opacity: 0.8;
+}
+
+.slide-menu-wrapper .slide-menu--left {
+ left: 0;
+ -webkit-transform: translateX(-100%);
+ -ms-transform: translateX(-100%);
+ transform: translateX(-100%);
+}
+
+.slide-menu-wrapper .slide-menu--left.active {
+ -webkit-transform: translateX(0);
+ -ms-transform: translateX(0);
+ transform: translateX(0);
+}
+
+.slide-menu-wrapper .slide-menu--right {
+ right: 0;
+ -webkit-transform: translateX(100%);
+ -ms-transform: translateX(100%);
+ transform: translateX(100%);
+}
+
+.slide-menu-wrapper .slide-menu--right.active {
+ -webkit-transform: translateX(0);
+ -ms-transform: translateX(0);
+ transform: translateX(0);
+}
+
+.slide-menu-wrapper {
+ transition: transform 0.3s;
+}
+
+/*
+ * Toolbar
+ */
+.slide-menu-wrapper .slide-menu-toolbar {
+ height: 60px;
+ width: 100%;
+ font-size: 12px;
+ display: table;
+ table-layout: fixed; /* ensures equal width */
+ margin: 0;
+ padding: 0;
+ border-bottom: solid 2px #666;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar > li {
+ display: table-cell;
+ line-height: 150%;
+ text-align: center;
+ vertical-align: middle;
+ cursor: pointer;
+ color: #aaa;
+ border-radius: 3px;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar > li.toolbar-panel-button i,
+.slide-menu-wrapper
+ .slide-menu-toolbar
+ > li.toolbar-panel-button
+ svg.svg-inline--fa {
+ font-size: 1.7em;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar > li.active-toolbar-button {
+ color: white;
+ text-shadow: 0 1px black;
+ text-decoration: underline;
+}
+
+.slide-menu-toolbar > li.toolbar-panel-button:hover {
+ color: white;
+}
+
+.slide-menu-toolbar
+ > li.toolbar-panel-button:hover
+ span.slide-menu-toolbar-label,
+.slide-menu-wrapper
+ .slide-menu-toolbar
+ > li.active-toolbar-button
+ span.slide-menu-toolbar-label {
+ visibility: visible;
+}
+
+/*
+ * Panels
+ */
+.slide-menu-wrapper .slide-menu-panel {
+ position: absolute;
+ width: 100%;
+ visibility: hidden;
+ height: calc(100% - 60px);
+ overflow-x: hidden;
+ overflow-y: auto;
+ color: #aaa;
+}
+
+.slide-menu-wrapper .slide-menu-panel.active-menu-panel {
+ visibility: visible;
+}
+
+.slide-menu-wrapper .slide-menu-panel h1,
+.slide-menu-wrapper .slide-menu-panel h2,
+.slide-menu-wrapper .slide-menu-panel h3,
+.slide-menu-wrapper .slide-menu-panel h4,
+.slide-menu-wrapper .slide-menu-panel h5,
+.slide-menu-wrapper .slide-menu-panel h6 {
+ margin: 20px 0 10px 0;
+ color: #fff;
+ line-height: 1.2;
+ letter-spacing: normal;
+ text-shadow: none;
+}
+
+.slide-menu-wrapper .slide-menu-panel h1 {
+ font-size: 1.6em;
+}
+.slide-menu-wrapper .slide-menu-panel h2 {
+ font-size: 1.4em;
+}
+.slide-menu-wrapper .slide-menu-panel h3 {
+ font-size: 1.3em;
+}
+.slide-menu-wrapper .slide-menu-panel h4 {
+ font-size: 1.1em;
+}
+.slide-menu-wrapper .slide-menu-panel h5 {
+ font-size: 1em;
+}
+.slide-menu-wrapper .slide-menu-panel h6 {
+ font-size: 0.9em;
+}
+
+.slide-menu-wrapper .slide-menu-panel p {
+ margin: 10px 0 5px 0;
+}
+
+.slide-menu-wrapper .slide-menu-panel a {
+ color: #ccc;
+ text-decoration: underline;
+}
+
+.slide-menu-wrapper .slide-menu-panel a:hover {
+ color: white;
+}
+
+.slide-menu-wrapper .slide-menu-item a {
+ text-decoration: none;
+}
+
+.slide-menu-wrapper .slide-menu-custom-panel {
+ width: calc(100% - 20px);
+ padding-left: 10px;
+ padding-right: 10px;
+}
+
+.slide-menu-wrapper .slide-menu-custom-panel .slide-menu-items {
+ width: calc(100% + 20px);
+ margin-left: -10px;
+ margin-right: 10px;
+}
+
+/*
+ * Theme and Transitions buttons
+ */
+
+.slide-menu-wrapper div[data-panel='Themes'] li,
+.slide-menu-wrapper div[data-panel='Transitions'] li {
+ display: block;
+ text-align: left;
+ cursor: pointer;
+ color: #848484;
+}
+
+/*
+ * Menu controls
+ */
+.reveal .slide-menu-button {
+ position: fixed;
+ left: 30px;
+ bottom: 30px;
+ z-index: 30;
+ font-size: 24px;
+}
+
+/*
+ * Menu overlay
+ */
+
+.slide-menu-wrapper .slide-menu-overlay {
+ position: fixed;
+ z-index: 199;
+ top: 0;
+ left: 0;
+ overflow: hidden;
+ width: 0;
+ height: 0;
+ background-color: #000;
+ opacity: 0;
+ transition: opacity 0.3s, width 0s 0.3s, height 0s 0.3s;
+}
+
+.slide-menu-wrapper .slide-menu-overlay.active {
+ width: 100%;
+ height: 100%;
+ opacity: 0.7;
+ transition: opacity 0.3s;
+}
+
+/*
+ * Hide menu for pdf printing
+ */
+body.print-pdf .slide-menu-wrapper .slide-menu,
+body.print-pdf .reveal .slide-menu-button,
+body.print-pdf .slide-menu-wrapper .slide-menu-overlay {
+ display: none;
+}
diff --git a/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.js b/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.js
new file mode 100644
index 0000000000000000000000000000000000000000..5369df338cfc7eb64e9f85550f2807abf5c2d10b
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/reveal-menu/menu.js
@@ -0,0 +1 @@
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e=e||self).RevealMenu=t()}(this,(function(){"use strict";var e="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function t(e,t,n){return e(n={path:t,exports:{},require:function(e,t){return function(){throw new Error("Dynamic requires are not currently supported by @rollup/plugin-commonjs")}(null==t&&n.path)}},n.exports),n.exports}var n=function(e){return e&&e.Math==Math&&e},r=n("object"==typeof globalThis&&globalThis)||n("object"==typeof window&&window)||n("object"==typeof self&&self)||n("object"==typeof e&&e)||Function("return this")(),i=function(e){try{return!!e()}catch(e){return!0}},a=!i((function(){return 7!=Object.defineProperty({},1,{get:function(){return 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g(e,t){e&&e.preventDefault(),r.sticky&&!t||(O("body").classList.remove("slide-menu-active"),O(".reveal").classList.remove("has-"+r.effect+"-"+r.side),O(".slide-menu").classList.remove("active"),O(".slide-menu-overlay").classList.remove("active"),A(".slide-menu-panel li.selected").forEach((function(e){e.classList.remove("selected")})))}function y(e){b()?g(e,!0):v(e)}function b(){return O("body").classList.contains("slide-menu-active")}function S(e,t){v(e);var n=t;"string"!=typeof t&&(n=e.currentTarget.getAttribute("data-panel")),O(".slide-menu-toolbar > li.active-toolbar-button").classList.remove("active-toolbar-button"),O('li[data-panel="'+n+'"]').classList.add("active-toolbar-button"),O(".slide-menu-panel.active-menu-panel").classList.remove("active-menu-panel"),O('div[data-panel="'+n+'"]').classList.add("active-menu-panel")}function E(e,n){var i=parseInt(e.getAttribute("data-slide-h")),a=parseInt(e.getAttribute("data-slide-v")),o=e.getAttribute("data-theme"),s=e.getAttribute("data-highlight-theme"),l=e.getAttribute("data-transition");isNaN(i)||isNaN(a)||t.slide(i,a),o&&I("theme",o),s&&I("highlight-theme",s),l&&t.configure({transition:l});var c=O("a",e);c&&(n||!r.sticky||r.autoOpen&&c.href.startsWith("#")||c.href.startsWith(window.location.origin+window.location.pathname+"#"))&&c.click(),g()}function x(e){"A"!==e.target.nodeName&&e.preventDefault(),E(e.currentTarget)}function w(){var e=t.getState();A("li.slide-menu-item, li.slide-menu-item-vertical").forEach((function(t){t.classList.remove("past"),t.classList.remove("active"),t.classList.remove("future");var n=parseInt(t.getAttribute("data-slide-h")),r=parseInt(t.getAttribute("data-slide-v"));n",s.appendChild(k("br"),O("i",s)),s.appendChild(k("span",{class:"slide-menu-toolbar-label"},e),O("i",s)),s.onclick=i,d.appendChild(s),s},i=function(e,i,a,o,s){function l(e,t){if(""===e)return null;var n=t?O(e,i):O(e);return n?n.textContent:null}var c=i.getAttribute("data-menu-title")||l(".menu-title",i)||l(r.titleSelector,i);if(!c&&r.useTextContentForMissingTitles&&(c=i.textContent.trim())&&(c=c.split("\n").map((function(e){return e.trim()})).join(" ").trim().replace(/^(.{16}[^\s]*).*/,"$1").replace(/&/g,"&").replace(//g,">").replace(/"/g,""").replace(/'/g,"'")+"..."),!c){if(r.hideMissingTitles)return"";e+=" no-title",c="Slide "+(a+1)}var u=k("li",{class:e,"data-item":a,"data-slide-h":o,"data-slide-v":void 0===s?0:s});if(r.markers&&(u.appendChild(k("i",{class:"fas fa-check-circle fa-fw past"})),u.appendChild(k("i",{class:"fas fa-arrow-alt-circle-right fa-fw active"})),u.appendChild(k("i",{class:"far fa-circle fa-fw future"}))),r.numbers){var f=[],d="h.v";switch("string"==typeof r.numbers?d=r.numbers:"string"==typeof n.slideNumber&&(d=n.slideNumber),d){case"c":f.push(a+1);break;case"c/t":f.push(a+1,"/",t.getTotalSlides());break;case"h/v":f.push(o+1),"number"!=typeof s||isNaN(s)||f.push("/",s+1);break;default:f.push(o+1),"number"!=typeof s||isNaN(s)||f.push(".",s+1)}u.appendChild(k("span",{class:"slide-menu-item-number"},f.join("")+". "))}return u.appendChild(k("span",{class:"slide-menu-item-title"},c)),u},o=function(e){s&&(A(".active-menu-panel .slide-menu-items li.selected").forEach((function(e){e.classList.remove("selected")})),e.currentTarget.classList.add("selected"))},l=O(".reveal").parentElement,c=k("div",{class:"slide-menu-wrapper"});l.appendChild(c);var u=k("nav",{class:"slide-menu slide-menu--"+r.side});"string"==typeof r.width&&(-1!=["normal","wide","third","half","full"].indexOf(r.width)?u.classList.add("slide-menu--"+r.width):(u.classList.add("slide-menu--custom"),u.style.width=r.width)),c.appendChild(u),L();var f=k("div",{class:"slide-menu-overlay"});c.appendChild(f),f.onclick=function(){g(null,!0)};var d=k("ol",{class:"slide-menu-toolbar"});O(".slide-menu").appendChild(d),e("Slides","Slides","fa-images","fas",S,!0),r.custom&&r.custom.forEach((function(t,n,r){e(t.title,"Custom"+n,t.icon,null,S)})),r.themes&&e("Themes","Themes","fa-adjust","fas",S),r.transitions&&e("Transitions","Transitions","fa-sticky-note","fas",S);var p=k("li",{id:"close",class:"toolbar-panel-button"});if(p.appendChild(k("i",{class:"fas fa-times"})),p.appendChild(k("br")),p.appendChild(k("span",{class:"slide-menu-toolbar-label"},"Close")),p.onclick=function(){g(null,!0)},d.appendChild(p),function e(){if(document.querySelector("section[data-markdown]:not([data-markdown-parsed])"))setTimeout(e,100);else{var t=k("div",{"data-panel":"Slides",class:"slide-menu-panel active-menu-panel"});t.appendChild(k("ul",{class:"slide-menu-items"})),u.appendChild(t);var n=O('.slide-menu-panel[data-panel="Slides"] > .slide-menu-items'),r=0;A(".slides > section").forEach((function(e,t){var a=A("section",e);if(a.length>0)a.forEach((function(e,a){var o=i(0===a?"slide-menu-item":"slide-menu-item-vertical",e,r,t,a);o&&n.appendChild(o),r++}));else{var o=i("slide-menu-item",e,r,t);o&&n.appendChild(o),r++}})),A(".slide-menu-item, .slide-menu-item-vertical").forEach((function(e){e.onclick=x})),w()}}(),t.addEventListener("slidechanged",w),r.custom){var h=function(){this.status>=200&&this.status<300?(this.panel.innerHTML=this.responseText,C(this.panel)):I(this)},E=function(){I(this)},C=function(e){A("ul.slide-menu-items li.slide-menu-item",e).forEach((function(e,t){e.setAttribute("data-item",t+1),e.onclick=x,e.addEventListener("mouseenter",o)}))},I=function(e){var t="ERROR: The attempt to fetch "+e.responseURL+" failed with HTTP status "+e.status+" ("+e.statusText+").
Remember that you need to serve the presentation HTML from a HTTP server.
";e.panel.innerHTML=t};r.custom.forEach((function(e,t,n){var r=k("div",{"data-panel":"Custom"+t,class:"slide-menu-panel slide-menu-custom-panel"});e.content?(r.innerHTML=e.content,C(r)):e.src&&function(e,t){var n=new XMLHttpRequest;n.panel=e,n.arguments=Array.prototype.slice.call(arguments,2),n.onload=h,n.onerror=E,n.open("get",t,!0),n.send(null)}(r,e.src),u.appendChild(r)}))}if(r.themes){var P=k("div",{class:"slide-menu-panel","data-panel":"Themes"});u.appendChild(P);var M=k("ul",{class:"slide-menu-items"});P.appendChild(M),r.themes.forEach((function(e,t){var n={class:"slide-menu-item","data-item":""+(t+1)};e.theme&&(n["data-theme"]=e.theme),e.highlightTheme&&(n["data-highlight-theme"]=e.highlightTheme);var r=k("li",n,e.name);M.appendChild(r),r.onclick=x}))}if(r.transitions){P=k("div",{class:"slide-menu-panel","data-panel":"Transitions"});u.appendChild(P);M=k("ul",{class:"slide-menu-items"});P.appendChild(M),r.transitions.forEach((function(e,t){var n=k("li",{class:"slide-menu-item","data-transition":e.toLowerCase(),"data-item":""+(t+1)},e);M.appendChild(n),n.onclick=x}))}if(r.openButton){var R=k("div",{class:"slide-menu-button"}),j=k("a",{href:"#"});j.appendChild(k("i",{class:"fas fa-bars"})),R.appendChild(j),O(".reveal").appendChild(R),R.onclick=v}if(r.openSlideNumber)O("div.slide-number").onclick=v;A(".slide-menu-panel .slide-menu-items li").forEach((function(e){e.addEventListener("mouseenter",o)}))}if(r.keyboard){if(document.addEventListener("keydown",m,!1),window.addEventListener("message",(function(e){var t;try{t=JSON.parse(e.data)}catch(e){}t&&"triggerKey"===t.method&&m({keyCode:t.args[0],stopImmediatePropagation:function(){}})})),n.keyboardCondition&&"function"==typeof n.keyboardCondition){var N=n.keyboardCondition;n.keyboardCondition=function(e){return N(e)&&(!b()||77==e.keyCode)}}else n.keyboardCondition=function(e){return!b()||77==e.keyCode};t.addKeyBinding({keyCode:77,key:"M",description:"Toggle menu"},y)}r.openOnInit&&v(),a=!0}function O(e,t){return t||(t=document),t.querySelector(e)}function A(e,t){return t||(t=document),Array.prototype.slice.call(t.querySelectorAll(e))}function k(e,t,n){var r=document.createElement(e);return t&&Object.getOwnPropertyNames(t).forEach((function(e){r.setAttribute(e,t[e])})),n&&(r.innerHTML=n),r}function I(e,t){var n=O("link#"+e),r=n.parentElement,i=n.nextElementSibling;n.remove();var a=n.cloneNode();a.setAttribute("href",t),a.onload=function(){L()},r.insertBefore(a,i)}function P(e,t,n){n.call()}function M(){var e,a,o,s=!i||i>=9;t.isSpeakerNotes()&&window.location.search.endsWith("controls=false")&&(s=!1),s&&(r.delayInit||C(),e="menu-ready",(o=document.createEvent("HTMLEvents",1,2)).initEvent(e,!0,!0),function(e,t){for(var n in t)e[n]=t[n]}(o,a),document.querySelector(".reveal").dispatchEvent(o),n.postMessageEvents&&window.parent!==window.self&&window.parent.postMessage(JSON.stringify({namespace:"reveal",eventName:e,state:t.getState()}),"*"))}return{id:"menu",init:function(e){o(n=(t=e).getConfig()),P(r.path+"menu.css","stylesheet",(function(){void 0===r.loadIcons||r.loadIcons?P(r.path+"font-awesome/css/all.css","stylesheet",M):M()}))},toggle:y,openMenu:v,closeMenu:g,openPanel:S,isOpen:b,initialiseMenu:C,isMenuInitialised:function(){return a}}}}));
diff --git a/src/_site/site_libs/revealjs/plugin/reveal-menu/plugin.yml b/src/_site/site_libs/revealjs/plugin/reveal-menu/plugin.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3f4b90aa26d058e6d44a16779641ff0f284c516b
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/reveal-menu/plugin.yml
@@ -0,0 +1,9 @@
+name: RevealMenu
+script: [menu.js, quarto-menu.js]
+stylesheet: [menu.css, quarto-menu.css]
+config:
+ menu:
+ side: "left"
+ useTextContentForMissingTitles: true
+ markers: false
+ loadIcons: false
diff --git a/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.css b/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.css
new file mode 100644
index 0000000000000000000000000000000000000000..eec145c051ffa25c1ddef3f249c2d8eac81a10d2
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.css
@@ -0,0 +1,68 @@
+.slide-menu-wrapper .slide-tool-item {
+ display: block;
+ text-align: left;
+ padding: 10px 18px;
+ color: #aaa;
+ cursor: pointer;
+ border-top: solid 1px #555;
+}
+
+.slide-menu-wrapper .slide-tool-item a {
+ text-decoration: none;
+}
+
+.slide-menu-wrapper .slide-tool-item kbd {
+ font-family: monospace;
+ margin-right: 10px;
+ padding: 3px 8px;
+ color: inherit;
+ border: 1px solid;
+ border-radius: 5px;
+ border-color: #555;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar > li.active-toolbar-button {
+ text-decoration: none;
+}
+
+.reveal .slide-menu-button {
+ left: 8px;
+ bottom: 8px;
+}
+
+.reveal .slide-menu-button .fas::before,
+.reveal .slide-chalkboard-buttons .fas::before,
+.slide-menu-wrapper .slide-menu-toolbar .fas::before {
+ display: inline-block;
+ height: 2.2rem;
+ width: 2.2rem;
+ content: "";
+ vertical-align: -0.125em;
+ background-repeat: no-repeat;
+ background-size: 2.2rem 2.2rem;
+}
+
+.reveal .slide-chalkboard-buttons .fas::before {
+ height: 1.45rem;
+ width: 1.45rem;
+ background-size: 1.45rem 1.45rem;
+ vertical-align: 0.1em;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar .fas::before {
+ height: 1.8rem;
+ width: 1.8rem;
+ background-size: 1.8rem 1.8rem;
+}
+
+.slide-menu-wrapper .slide-menu-toolbar .fa-images::before {
+ background-image: url('data:image/svg+xml,');
+}
+
+.slide-menu-wrapper .slide-menu-toolbar .fa-gear::before {
+ background-image: url('data:image/svg+xml,');
+}
+
+.slide-menu-wrapper .slide-menu-toolbar .fa-times::before {
+ background-image: url('data:image/svg+xml,');
+}
diff --git a/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.js b/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.js
new file mode 100644
index 0000000000000000000000000000000000000000..e5c53c4dc7788f9d156797a719146bf3ff02ab57
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/reveal-menu/quarto-menu.js
@@ -0,0 +1,46 @@
+window.revealMenuToolHandler = function (handler) {
+ return function (event) {
+ event.preventDefault();
+ handler();
+ Reveal.getPlugin("menu").closeMenu();
+ };
+};
+
+window.RevealMenuToolHandlers = {
+ fullscreen: revealMenuToolHandler(function () {
+ const element = document.documentElement;
+ const requestMethod =
+ element.requestFullscreen ||
+ element.webkitRequestFullscreen ||
+ element.webkitRequestFullScreen ||
+ element.mozRequestFullScreen ||
+ element.msRequestFullscreen;
+ if (requestMethod) {
+ requestMethod.apply(element);
+ }
+ }),
+ speakerMode: revealMenuToolHandler(function () {
+ Reveal.getPlugin("notes").open();
+ }),
+ keyboardHelp: revealMenuToolHandler(function () {
+ Reveal.toggleHelp(true);
+ }),
+ overview: revealMenuToolHandler(function () {
+ Reveal.toggleOverview(true);
+ }),
+ toggleChalkboard: revealMenuToolHandler(function () {
+ RevealChalkboard.toggleChalkboard();
+ }),
+ toggleNotesCanvas: revealMenuToolHandler(function () {
+ RevealChalkboard.toggleNotesCanvas();
+ }),
+ downloadDrawings: revealMenuToolHandler(function () {
+ RevealChalkboard.download();
+ }),
+ togglePdfExport: revealMenuToolHandler(function () {
+ PdfExport.togglePdfExport();
+ }),
+ toggleScrollView: revealMenuToolHandler(function() {
+ Reveal.getPlugin("quarto-support").toggleScrollView();
+ })
+};
diff --git a/src/_site/site_libs/revealjs/plugin/search/plugin.js b/src/_site/site_libs/revealjs/plugin/search/plugin.js
new file mode 100644
index 0000000000000000000000000000000000000000..fe38343da2c7a2fb1697c32459705cfc867fb1d1
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/search/plugin.js
@@ -0,0 +1,243 @@
+/*!
+ * Handles finding a text string anywhere in the slides and showing the next occurrence to the user
+ * by navigatating to that slide and highlighting it.
+ *
+ * @author Jon Snyder , February 2013
+ */
+
+const Plugin = () => {
+
+ // The reveal.js instance this plugin is attached to
+ let deck;
+
+ let searchElement;
+ let searchButton;
+ let searchInput;
+
+ let matchedSlides;
+ let currentMatchedIndex;
+ let searchboxDirty;
+ let hilitor;
+
+ function render() {
+
+ searchElement = document.createElement( 'div' );
+ searchElement.classList.add( 'searchbox' );
+ searchElement.style.position = 'absolute';
+ searchElement.style.top = '10px';
+ searchElement.style.right = '10px';
+ searchElement.style.zIndex = 10;
+
+ //embedded base64 search icon Designed by Sketchdock - http://www.sketchdock.com/:
+ searchElement.innerHTML = `
+ `;
+
+ searchInput = searchElement.querySelector( '.searchinput' );
+ searchInput.style.width = '240px';
+ searchInput.style.fontSize = '14px';
+ searchInput.style.padding = '4px 6px';
+ searchInput.style.color = '#000';
+ searchInput.style.background = '#fff';
+ searchInput.style.borderRadius = '2px';
+ searchInput.style.border = '0';
+ searchInput.style.outline = '0';
+ searchInput.style.boxShadow = '0 2px 18px rgba(0, 0, 0, 0.2)';
+ searchInput.style['-webkit-appearance'] = 'none';
+
+ deck.getRevealElement().appendChild( searchElement );
+
+ // searchButton.addEventListener( 'click', function(event) {
+ // doSearch();
+ // }, false );
+
+ searchInput.addEventListener( 'keyup', function( event ) {
+ switch (event.keyCode) {
+ case 13:
+ event.preventDefault();
+ doSearch();
+ searchboxDirty = false;
+ break;
+ default:
+ searchboxDirty = true;
+ }
+ }, false );
+
+ closeSearch();
+
+ }
+
+ function openSearch() {
+ if( !searchElement ) render();
+
+ searchElement.style.display = 'inline';
+ searchInput.focus();
+ searchInput.select();
+ }
+
+ function closeSearch() {
+ if( !searchElement ) render();
+
+ searchElement.style.display = 'none';
+ if(hilitor) hilitor.remove();
+ }
+
+ function toggleSearch() {
+ if( !searchElement ) render();
+
+ if (searchElement.style.display !== 'inline') {
+ openSearch();
+ }
+ else {
+ closeSearch();
+ }
+ }
+
+ function doSearch() {
+ //if there's been a change in the search term, perform a new search:
+ if (searchboxDirty) {
+ var searchstring = searchInput.value;
+
+ if (searchstring === '') {
+ if(hilitor) hilitor.remove();
+ matchedSlides = null;
+ }
+ else {
+ //find the keyword amongst the slides
+ hilitor = new Hilitor("slidecontent");
+ matchedSlides = hilitor.apply(searchstring);
+ currentMatchedIndex = 0;
+ }
+ }
+
+ if (matchedSlides) {
+ //navigate to the next slide that has the keyword, wrapping to the first if necessary
+ if (matchedSlides.length && (matchedSlides.length <= currentMatchedIndex)) {
+ currentMatchedIndex = 0;
+ }
+ if (matchedSlides.length > currentMatchedIndex) {
+ deck.slide(matchedSlides[currentMatchedIndex].h, matchedSlides[currentMatchedIndex].v);
+ currentMatchedIndex++;
+ }
+ }
+ }
+
+ // Original JavaScript code by Chirp Internet: www.chirp.com.au
+ // Please acknowledge use of this code by including this header.
+ // 2/2013 jon: modified regex to display any match, not restricted to word boundaries.
+ function Hilitor(id, tag) {
+
+ var targetNode = document.getElementById(id) || document.body;
+ var hiliteTag = tag || "EM";
+ var skipTags = new RegExp("^(?:" + hiliteTag + "|SCRIPT|FORM)$");
+ var colors = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"];
+ var wordColor = [];
+ var colorIdx = 0;
+ var matchRegex = "";
+ var matchingSlides = [];
+
+ this.setRegex = function(input)
+ {
+ input = input.trim();
+ matchRegex = new RegExp("(" + input + ")","i");
+ }
+
+ this.getRegex = function()
+ {
+ return matchRegex.toString().replace(/^\/\\b\(|\)\\b\/i$/g, "").replace(/\|/g, " ");
+ }
+
+ // recursively apply word highlighting
+ this.hiliteWords = function(node)
+ {
+ if(node == undefined || !node) return;
+ if(!matchRegex) return;
+ if(skipTags.test(node.nodeName)) return;
+
+ if(node.hasChildNodes()) {
+ for(var i=0; i < node.childNodes.length; i++)
+ this.hiliteWords(node.childNodes[i]);
+ }
+ if(node.nodeType == 3) { // NODE_TEXT
+ var nv, regs;
+ if((nv = node.nodeValue) && (regs = matchRegex.exec(nv))) {
+ //find the slide's section element and save it in our list of matching slides
+ var secnode = node;
+ while (secnode != null && secnode.nodeName != 'SECTION') {
+ secnode = secnode.parentNode;
+ }
+
+ var slideIndex = deck.getIndices(secnode);
+ var slidelen = matchingSlides.length;
+ var alreadyAdded = false;
+ for (var i=0; i < slidelen; i++) {
+ if ( (matchingSlides[i].h === slideIndex.h) && (matchingSlides[i].v === slideIndex.v) ) {
+ alreadyAdded = true;
+ }
+ }
+ if (! alreadyAdded) {
+ matchingSlides.push(slideIndex);
+ }
+
+ if(!wordColor[regs[0].toLowerCase()]) {
+ wordColor[regs[0].toLowerCase()] = colors[colorIdx++ % colors.length];
+ }
+
+ var match = document.createElement(hiliteTag);
+ match.appendChild(document.createTextNode(regs[0]));
+ match.style.backgroundColor = wordColor[regs[0].toLowerCase()];
+ match.style.fontStyle = "inherit";
+ match.style.color = "#000";
+
+ var after = node.splitText(regs.index);
+ after.nodeValue = after.nodeValue.substring(regs[0].length);
+ node.parentNode.insertBefore(match, after);
+ }
+ }
+ };
+
+ // remove highlighting
+ this.remove = function()
+ {
+ var arr = document.getElementsByTagName(hiliteTag);
+ var el;
+ while(arr.length && (el = arr[0])) {
+ el.parentNode.replaceChild(el.firstChild, el);
+ }
+ };
+
+ // start highlighting at target node
+ this.apply = function(input)
+ {
+ if(input == undefined || !input) return;
+ this.remove();
+ this.setRegex(input);
+ this.hiliteWords(targetNode);
+ return matchingSlides;
+ };
+
+ }
+
+ return {
+
+ id: 'search',
+
+ init: reveal => {
+
+ deck = reveal;
+ deck.registerKeyboardShortcut( 'CTRL + Shift + F', 'Search' );
+
+ document.addEventListener( 'keydown', function( event ) {
+ if( event.key == "F" && (event.ctrlKey || event.metaKey) ) { //Control+Shift+f
+ event.preventDefault();
+ toggleSearch();
+ }
+ }, false );
+
+ },
+
+ open: openSearch
+
+ }
+};
+
+export default Plugin;
\ No newline at end of file
diff --git a/src/_site/site_libs/revealjs/plugin/search/search.esm.js b/src/_site/site_libs/revealjs/plugin/search/search.esm.js
new file mode 100644
index 0000000000000000000000000000000000000000..df84b6bab8bc4c7231b170043eecbfa7d6952a51
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/search/search.esm.js
@@ -0,0 +1,7 @@
+/*!
+ * Handles finding a text string anywhere in the slides and showing the next occurrence to the user
+ * by navigatating to that slide and highlighting it.
+ *
+ * @author Jon Snyder , February 2013
+ */
+const e=()=>{let e,t,n,l,i,o,r;function s(){t=document.createElement("div"),t.classList.add("searchbox"),t.style.position="absolute",t.style.top="10px",t.style.right="10px",t.style.zIndex=10,t.innerHTML='\n\t\t
',n=t.querySelector(".searchinput"),n.style.width="240px",n.style.fontSize="14px",n.style.padding="4px 6px",n.style.color="#000",n.style.background="#fff",n.style.borderRadius="2px",n.style.border="0",n.style.outline="0",n.style.boxShadow="0 2px 18px rgba(0, 0, 0, 0.2)",n.style["-webkit-appearance"]="none",e.getRevealElement().appendChild(t),n.addEventListener("keyup",(function(t){if(13===t.keyCode)t.preventDefault(),function(){if(o){var t=n.value;""===t?(r&&r.remove(),l=null):(r=new c("slidecontent"),l=r.apply(t),i=0)}l&&(l.length&&l.length<=i&&(i=0),l.length>i&&(e.slide(l[i].h,l[i].v),i++))}(),o=!1;else o=!0}),!1),d()}function a(){t||s(),t.style.display="inline",n.focus(),n.select()}function d(){t||s(),t.style.display="none",r&&r.remove()}function c(t,n){var l=document.getElementById(t)||document.body,i=n||"EM",o=new RegExp("^(?:"+i+"|SCRIPT|FORM)$"),r=["#ff6","#a0ffff","#9f9","#f99","#f6f"],s=[],a=0,d="",c=[];this.setRegex=function(e){e=e.trim(),d=new RegExp("("+e+")","i")},this.getRegex=function(){return d.toString().replace(/^\/\\b\(|\)\\b\/i$/g,"").replace(/\|/g," ")},this.hiliteWords=function(t){if(null!=t&&t&&d&&!o.test(t.nodeName)){if(t.hasChildNodes())for(var n=0;n
{e=n,e.registerKeyboardShortcut("CTRL + Shift + F","Search"),document.addEventListener("keydown",(function(e){"F"==e.key&&(e.ctrlKey||e.metaKey)&&(e.preventDefault(),t||s(),"inline"!==t.style.display?a():d())}),!1)},open:a}};export{e as default};
diff --git a/src/_site/site_libs/revealjs/plugin/search/search.js b/src/_site/site_libs/revealjs/plugin/search/search.js
new file mode 100644
index 0000000000000000000000000000000000000000..aa3ad93ce820b04d0f6bb990aa3fede602688f07
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/search/search.js
@@ -0,0 +1,7 @@
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).RevealSearch=t()}(this,(function(){"use strict";
+/*!
+ * Handles finding a text string anywhere in the slides and showing the next occurrence to the user
+ * by navigatating to that slide and highlighting it.
+ *
+ * @author Jon Snyder , February 2013
+ */return()=>{let e,t,n,i,o,l,r;function s(){t=document.createElement("div"),t.classList.add("searchbox"),t.style.position="absolute",t.style.top="10px",t.style.right="10px",t.style.zIndex=10,t.innerHTML='\n\t\t',n=t.querySelector(".searchinput"),n.style.width="240px",n.style.fontSize="14px",n.style.padding="4px 6px",n.style.color="#000",n.style.background="#fff",n.style.borderRadius="2px",n.style.border="0",n.style.outline="0",n.style.boxShadow="0 2px 18px rgba(0, 0, 0, 0.2)",n.style["-webkit-appearance"]="none",e.getRevealElement().appendChild(t),n.addEventListener("keyup",(function(t){if(13===t.keyCode)t.preventDefault(),function(){if(l){var t=n.value;""===t?(r&&r.remove(),i=null):(r=new c("slidecontent"),i=r.apply(t),o=0)}i&&(i.length&&i.length<=o&&(o=0),i.length>o&&(e.slide(i[o].h,i[o].v),o++))}(),l=!1;else l=!0}),!1),d()}function a(){t||s(),t.style.display="inline",n.focus(),n.select()}function d(){t||s(),t.style.display="none",r&&r.remove()}function c(t,n){var i=document.getElementById(t)||document.body,o=n||"EM",l=new RegExp("^(?:"+o+"|SCRIPT|FORM)$"),r=["#ff6","#a0ffff","#9f9","#f99","#f6f"],s=[],a=0,d="",c=[];this.setRegex=function(e){e=e.trim(),d=new RegExp("("+e+")","i")},this.getRegex=function(){return d.toString().replace(/^\/\\b\(|\)\\b\/i$/g,"").replace(/\|/g," ")},this.hiliteWords=function(t){if(null!=t&&t&&d&&!l.test(t.nodeName)){if(t.hasChildNodes())for(var n=0;n{e=n,e.registerKeyboardShortcut("CTRL + Shift + F","Search"),document.addEventListener("keydown",(function(e){"F"==e.key&&(e.ctrlKey||e.metaKey)&&(e.preventDefault(),t||s(),"inline"!==t.style.display?a():d())}),!1)},open:a}}}));
diff --git a/src/_site/site_libs/revealjs/plugin/zoom/plugin.js b/src/_site/site_libs/revealjs/plugin/zoom/plugin.js
new file mode 100644
index 0000000000000000000000000000000000000000..960fb8108907d55523f916b0a0b747b7eac3080e
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/zoom/plugin.js
@@ -0,0 +1,264 @@
+/*!
+ * reveal.js Zoom plugin
+ */
+const Plugin = {
+
+ id: 'zoom',
+
+ init: function( reveal ) {
+
+ reveal.getRevealElement().addEventListener( 'mousedown', function( event ) {
+ var defaultModifier = /Linux/.test( window.navigator.platform ) ? 'ctrl' : 'alt';
+
+ var modifier = ( reveal.getConfig().zoomKey ? reveal.getConfig().zoomKey : defaultModifier ) + 'Key';
+ var zoomLevel = ( reveal.getConfig().zoomLevel ? reveal.getConfig().zoomLevel : 2 );
+
+ if( event[ modifier ] && !reveal.isOverview() ) {
+ event.preventDefault();
+
+ zoom.to({
+ x: event.clientX,
+ y: event.clientY,
+ scale: zoomLevel,
+ pan: false
+ });
+ }
+ } );
+
+ },
+
+ destroy: () => {
+
+ zoom.reset();
+
+ }
+
+};
+
+export default () => Plugin;
+
+/*!
+ * zoom.js 0.3 (modified for use with reveal.js)
+ * http://lab.hakim.se/zoom-js
+ * MIT licensed
+ *
+ * Copyright (C) 2011-2014 Hakim El Hattab, http://hakim.se
+ */
+var zoom = (function(){
+
+ // The current zoom level (scale)
+ var level = 1;
+
+ // The current mouse position, used for panning
+ var mouseX = 0,
+ mouseY = 0;
+
+ // Timeout before pan is activated
+ var panEngageTimeout = -1,
+ panUpdateInterval = -1;
+
+ // Check for transform support so that we can fallback otherwise
+ var supportsTransforms = 'transform' in document.body.style;
+
+ if( supportsTransforms ) {
+ // The easing that will be applied when we zoom in/out
+ document.body.style.transition = 'transform 0.8s ease';
+ }
+
+ // Zoom out if the user hits escape
+ document.addEventListener( 'keyup', function( event ) {
+ if( level !== 1 && event.keyCode === 27 ) {
+ zoom.out();
+ }
+ } );
+
+ // Monitor mouse movement for panning
+ document.addEventListener( 'mousemove', function( event ) {
+ if( level !== 1 ) {
+ mouseX = event.clientX;
+ mouseY = event.clientY;
+ }
+ } );
+
+ /**
+ * Applies the CSS required to zoom in, prefers the use of CSS3
+ * transforms but falls back on zoom for IE.
+ *
+ * @param {Object} rect
+ * @param {Number} scale
+ */
+ function magnify( rect, scale ) {
+
+ var scrollOffset = getScrollOffset();
+
+ // Ensure a width/height is set
+ rect.width = rect.width || 1;
+ rect.height = rect.height || 1;
+
+ // Center the rect within the zoomed viewport
+ rect.x -= ( window.innerWidth - ( rect.width * scale ) ) / 2;
+ rect.y -= ( window.innerHeight - ( rect.height * scale ) ) / 2;
+
+ if( supportsTransforms ) {
+ // Reset
+ if( scale === 1 ) {
+ document.body.style.transform = '';
+ }
+ // Scale
+ else {
+ var origin = scrollOffset.x +'px '+ scrollOffset.y +'px',
+ transform = 'translate('+ -rect.x +'px,'+ -rect.y +'px) scale('+ scale +')';
+
+ document.body.style.transformOrigin = origin;
+ document.body.style.transform = transform;
+ }
+ }
+ else {
+ // Reset
+ if( scale === 1 ) {
+ document.body.style.position = '';
+ document.body.style.left = '';
+ document.body.style.top = '';
+ document.body.style.width = '';
+ document.body.style.height = '';
+ document.body.style.zoom = '';
+ }
+ // Scale
+ else {
+ document.body.style.position = 'relative';
+ document.body.style.left = ( - ( scrollOffset.x + rect.x ) / scale ) + 'px';
+ document.body.style.top = ( - ( scrollOffset.y + rect.y ) / scale ) + 'px';
+ document.body.style.width = ( scale * 100 ) + '%';
+ document.body.style.height = ( scale * 100 ) + '%';
+ document.body.style.zoom = scale;
+ }
+ }
+
+ level = scale;
+
+ if( document.documentElement.classList ) {
+ if( level !== 1 ) {
+ document.documentElement.classList.add( 'zoomed' );
+ }
+ else {
+ document.documentElement.classList.remove( 'zoomed' );
+ }
+ }
+ }
+
+ /**
+ * Pan the document when the mosue cursor approaches the edges
+ * of the window.
+ */
+ function pan() {
+ var range = 0.12,
+ rangeX = window.innerWidth * range,
+ rangeY = window.innerHeight * range,
+ scrollOffset = getScrollOffset();
+
+ // Up
+ if( mouseY < rangeY ) {
+ window.scroll( scrollOffset.x, scrollOffset.y - ( 1 - ( mouseY / rangeY ) ) * ( 14 / level ) );
+ }
+ // Down
+ else if( mouseY > window.innerHeight - rangeY ) {
+ window.scroll( scrollOffset.x, scrollOffset.y + ( 1 - ( window.innerHeight - mouseY ) / rangeY ) * ( 14 / level ) );
+ }
+
+ // Left
+ if( mouseX < rangeX ) {
+ window.scroll( scrollOffset.x - ( 1 - ( mouseX / rangeX ) ) * ( 14 / level ), scrollOffset.y );
+ }
+ // Right
+ else if( mouseX > window.innerWidth - rangeX ) {
+ window.scroll( scrollOffset.x + ( 1 - ( window.innerWidth - mouseX ) / rangeX ) * ( 14 / level ), scrollOffset.y );
+ }
+ }
+
+ function getScrollOffset() {
+ return {
+ x: window.scrollX !== undefined ? window.scrollX : window.pageXOffset,
+ y: window.scrollY !== undefined ? window.scrollY : window.pageYOffset
+ }
+ }
+
+ return {
+ /**
+ * Zooms in on either a rectangle or HTML element.
+ *
+ * @param {Object} options
+ * - element: HTML element to zoom in on
+ * OR
+ * - x/y: coordinates in non-transformed space to zoom in on
+ * - width/height: the portion of the screen to zoom in on
+ * - scale: can be used instead of width/height to explicitly set scale
+ */
+ to: function( options ) {
+
+ // Due to an implementation limitation we can't zoom in
+ // to another element without zooming out first
+ if( level !== 1 ) {
+ zoom.out();
+ }
+ else {
+ options.x = options.x || 0;
+ options.y = options.y || 0;
+
+ // If an element is set, that takes precedence
+ if( !!options.element ) {
+ // Space around the zoomed in element to leave on screen
+ var padding = 20;
+ var bounds = options.element.getBoundingClientRect();
+
+ options.x = bounds.left - padding;
+ options.y = bounds.top - padding;
+ options.width = bounds.width + ( padding * 2 );
+ options.height = bounds.height + ( padding * 2 );
+ }
+
+ // If width/height values are set, calculate scale from those values
+ if( options.width !== undefined && options.height !== undefined ) {
+ options.scale = Math.max( Math.min( window.innerWidth / options.width, window.innerHeight / options.height ), 1 );
+ }
+
+ if( options.scale > 1 ) {
+ options.x *= options.scale;
+ options.y *= options.scale;
+
+ magnify( options, options.scale );
+
+ if( options.pan !== false ) {
+
+ // Wait with engaging panning as it may conflict with the
+ // zoom transition
+ panEngageTimeout = setTimeout( function() {
+ panUpdateInterval = setInterval( pan, 1000 / 60 );
+ }, 800 );
+
+ }
+ }
+ }
+ },
+
+ /**
+ * Resets the document zoom state to its default.
+ */
+ out: function() {
+ clearTimeout( panEngageTimeout );
+ clearInterval( panUpdateInterval );
+
+ magnify( { x: 0, y: 0 }, 1 );
+
+ level = 1;
+ },
+
+ // Alias
+ magnify: function( options ) { this.to( options ) },
+ reset: function() { this.out() },
+
+ zoomLevel: function() {
+ return level;
+ }
+ }
+
+})();
diff --git a/src/_site/site_libs/revealjs/plugin/zoom/zoom.esm.js b/src/_site/site_libs/revealjs/plugin/zoom/zoom.esm.js
new file mode 100644
index 0000000000000000000000000000000000000000..c2bb6a5b329be83f6015b8123e0f4dea989c4416
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/zoom/zoom.esm.js
@@ -0,0 +1,11 @@
+/*!
+ * reveal.js Zoom plugin
+ */
+const e={id:"zoom",init:function(e){e.getRevealElement().addEventListener("mousedown",(function(t){var n=/Linux/.test(window.navigator.platform)?"ctrl":"alt",i=(e.getConfig().zoomKey?e.getConfig().zoomKey:n)+"Key",d=e.getConfig().zoomLevel?e.getConfig().zoomLevel:2;t[i]&&!e.isOverview()&&(t.preventDefault(),o.to({x:t.clientX,y:t.clientY,scale:d,pan:!1}))}))},destroy:()=>{o.reset()}};var t=()=>e,o=function(){var e=1,t=0,n=0,i=-1,d=-1,l="transform"in document.body.style;function s(t,o){var n=r();if(t.width=t.width||1,t.height=t.height||1,t.x-=(window.innerWidth-t.width*o)/2,t.y-=(window.innerHeight-t.height*o)/2,l)if(1===o)document.body.style.transform="";else{var i=n.x+"px "+n.y+"px",d="translate("+-t.x+"px,"+-t.y+"px) scale("+o+")";document.body.style.transformOrigin=i,document.body.style.transform=d}else 1===o?(document.body.style.position="",document.body.style.left="",document.body.style.top="",document.body.style.width="",document.body.style.height="",document.body.style.zoom=""):(document.body.style.position="relative",document.body.style.left=-(n.x+t.x)/o+"px",document.body.style.top=-(n.y+t.y)/o+"px",document.body.style.width=100*o+"%",document.body.style.height=100*o+"%",document.body.style.zoom=o);e=o,document.documentElement.classList&&(1!==e?document.documentElement.classList.add("zoomed"):document.documentElement.classList.remove("zoomed"))}function c(){var o=.12*window.innerWidth,i=.12*window.innerHeight,d=r();nwindow.innerHeight-i&&window.scroll(d.x,d.y+(1-(window.innerHeight-n)/i)*(14/e)),twindow.innerWidth-o&&window.scroll(d.x+(1-(window.innerWidth-t)/o)*(14/e),d.y)}function r(){return{x:void 0!==window.scrollX?window.scrollX:window.pageXOffset,y:void 0!==window.scrollY?window.scrollY:window.pageYOffset}}return l&&(document.body.style.transition="transform 0.8s ease"),document.addEventListener("keyup",(function(t){1!==e&&27===t.keyCode&&o.out()})),document.addEventListener("mousemove",(function(o){1!==e&&(t=o.clientX,n=o.clientY)})),{to:function(t){if(1!==e)o.out();else{if(t.x=t.x||0,t.y=t.y||0,t.element){var n=t.element.getBoundingClientRect();t.x=n.left-20,t.y=n.top-20,t.width=n.width+40,t.height=n.height+40}void 0!==t.width&&void 0!==t.height&&(t.scale=Math.max(Math.min(window.innerWidth/t.width,window.innerHeight/t.height),1)),t.scale>1&&(t.x*=t.scale,t.y*=t.scale,s(t,t.scale),!1!==t.pan&&(i=setTimeout((function(){d=setInterval(c,1e3/60)}),800)))}},out:function(){clearTimeout(i),clearInterval(d),s({x:0,y:0},1),e=1},magnify:function(e){this.to(e)},reset:function(){this.out()},zoomLevel:function(){return e}}}();
+/*!
+ * zoom.js 0.3 (modified for use with reveal.js)
+ * http://lab.hakim.se/zoom-js
+ * MIT licensed
+ *
+ * Copyright (C) 2011-2014 Hakim El Hattab, http://hakim.se
+ */export{t as default};
diff --git a/src/_site/site_libs/revealjs/plugin/zoom/zoom.js b/src/_site/site_libs/revealjs/plugin/zoom/zoom.js
new file mode 100644
index 0000000000000000000000000000000000000000..7ac212754ac8e3538f33d5ae0c2f900bc26f0021
--- /dev/null
+++ b/src/_site/site_libs/revealjs/plugin/zoom/zoom.js
@@ -0,0 +1,11 @@
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).RevealZoom=t()}(this,(function(){"use strict";
+/*!
+ * reveal.js Zoom plugin
+ */const e={id:"zoom",init:function(e){e.getRevealElement().addEventListener("mousedown",(function(o){var n=/Linux/.test(window.navigator.platform)?"ctrl":"alt",i=(e.getConfig().zoomKey?e.getConfig().zoomKey:n)+"Key",d=e.getConfig().zoomLevel?e.getConfig().zoomLevel:2;o[i]&&!e.isOverview()&&(o.preventDefault(),t.to({x:o.clientX,y:o.clientY,scale:d,pan:!1}))}))},destroy:()=>{t.reset()}};var t=function(){var e=1,o=0,n=0,i=-1,d=-1,l="transform"in document.body.style;function s(t,o){var n=r();if(t.width=t.width||1,t.height=t.height||1,t.x-=(window.innerWidth-t.width*o)/2,t.y-=(window.innerHeight-t.height*o)/2,l)if(1===o)document.body.style.transform="";else{var i=n.x+"px "+n.y+"px",d="translate("+-t.x+"px,"+-t.y+"px) scale("+o+")";document.body.style.transformOrigin=i,document.body.style.transform=d}else 1===o?(document.body.style.position="",document.body.style.left="",document.body.style.top="",document.body.style.width="",document.body.style.height="",document.body.style.zoom=""):(document.body.style.position="relative",document.body.style.left=-(n.x+t.x)/o+"px",document.body.style.top=-(n.y+t.y)/o+"px",document.body.style.width=100*o+"%",document.body.style.height=100*o+"%",document.body.style.zoom=o);e=o,document.documentElement.classList&&(1!==e?document.documentElement.classList.add("zoomed"):document.documentElement.classList.remove("zoomed"))}function c(){var t=.12*window.innerWidth,i=.12*window.innerHeight,d=r();nwindow.innerHeight-i&&window.scroll(d.x,d.y+(1-(window.innerHeight-n)/i)*(14/e)),owindow.innerWidth-t&&window.scroll(d.x+(1-(window.innerWidth-o)/t)*(14/e),d.y)}function r(){return{x:void 0!==window.scrollX?window.scrollX:window.pageXOffset,y:void 0!==window.scrollY?window.scrollY:window.pageYOffset}}return l&&(document.body.style.transition="transform 0.8s ease"),document.addEventListener("keyup",(function(o){1!==e&&27===o.keyCode&&t.out()})),document.addEventListener("mousemove",(function(t){1!==e&&(o=t.clientX,n=t.clientY)})),{to:function(o){if(1!==e)t.out();else{if(o.x=o.x||0,o.y=o.y||0,o.element){var n=o.element.getBoundingClientRect();o.x=n.left-20,o.y=n.top-20,o.width=n.width+40,o.height=n.height+40}void 0!==o.width&&void 0!==o.height&&(o.scale=Math.max(Math.min(window.innerWidth/o.width,window.innerHeight/o.height),1)),o.scale>1&&(o.x*=o.scale,o.y*=o.scale,s(o,o.scale),!1!==o.pan&&(i=setTimeout((function(){d=setInterval(c,1e3/60)}),800)))}},out:function(){clearTimeout(i),clearInterval(d),s({x:0,y:0},1),e=1},magnify:function(e){this.to(e)},reset:function(){this.out()},zoomLevel:function(){return e}}}();
+/*!
+ * zoom.js 0.3 (modified for use with reveal.js)
+ * http://lab.hakim.se/zoom-js
+ * MIT licensed
+ *
+ * Copyright (C) 2011-2014 Hakim El Hattab, http://hakim.se
+ */return()=>e}));
diff --git a/src/_site/styles.css b/src/_site/styles.css
new file mode 100644
index 0000000000000000000000000000000000000000..2ddf50c7b4236e4b67c3e9fc369f6a7a562cd27d
--- /dev/null
+++ b/src/_site/styles.css
@@ -0,0 +1 @@
+/* css styles */
diff --git a/src/index.qmd b/src/index.qmd
index b1c8334d404259cadf123856f8c5915eb091642a..21febec2815a38a0ab4a4fa224662a8e36c110e7 100644
--- a/src/index.qmd
+++ b/src/index.qmd
@@ -1,78 +1,191 @@
---
-title: "About Quarto"
+title: "Optimizing LLM Performance Using Triton"
+format:
+ revealjs:
+ theme: dark
+ transition: slide
+ slide-number: true
+author: "Matej Sirovatka"
+date: today
---
-[Quarto](https://quarto.org/) is a Markdown-based documentation system that lets you write documents in Markdown or Jupyter Notebooks, and render them to a variety of formats including HTML, PDF, PowerPoint, and more.
-You can also use Quarto to write [books](https://quarto.org/docs/books/), create [dashboards](https://quarto.org/docs/dashboards/), and embed web applications with [Observable](https://quarto.org/docs/interactive/ojs/) and [Shinylive](https://quarto.org/docs/blog/posts/2022-10-25-shinylive-extension/).
+## `whoami`
-## Getting started with Quarto
+- My name is Matej
+- I'm a Master's student at the Brno University of Technology
+- I currently make GPUs go `brrrrrr` at Hugging Face π€
-Once you've created the space, click on the `Files` tab in the top right to take a look at the files which make up this Space.
-There are a couple of important files which you should pay attention to:
+## `What is Triton?`
-- `Dockerfile`: This contains the system setup to build and serve the Quarto site on Hugging Face. You probably won't need to change this file that
-often unless you need to add additional system dependencies or modify the Quarto version.
-- `requirements.txt`: This is where you should include any Python dependencies which you need for your website.
-These are installed when the Dockerfile builds.
-- The `src` directory contains the source files for the Quarto website. You can include Jupyter notebooks or markdown (`.qmd` or `.md`) files.
-- `src/_quarto.yml` defines the navigation for your website. If you want to add new pages or reorganize the existing ones, you'll need to change this file.
+- NVIDIA's open-source programming language for GPU kernels
+- Designed for AI/ML workloads
+- Simplifies GPU programming compared to CUDA
+{.center fig-align="center"}
-## Recommended Workflow
+## `Why Optimize with Triton?`
-1. **Clone the space locally**
-2. **Install Quarto**: In order to render your Quarto site without Docker, we recommend installing Quarto by following the instructions on the [official Quarto website](https://quarto.org/docs/get-started/).
-3. **Install Quarto VS Code extension**: The [Quarto VS Code Extension](https://quarto.org/docs/tools/vscode.html) includes a number of productivity tools including YAML Autocomplete, a preview button, and a visual editor. Quarto works great with VS Code, but the extension does make it easier to get the most out of Quarto.
-4. **Edit the site**: The website files are contained in the `src` directory, and the site navigation is defined in `src/_quarto.yml`. Try editing these files and either clicking the "Preview" button in VS Code, or calling `quarto preview src` from the command line.
-5. **Learn more about Quarto**: You can do a lot of things with Quarto, and they are all documented on the [Quarto Website](https://quarto.org/guide/). In particular, you may be interested in:
+- Simple yet effective
+- Less headache than CUDA
+- GPUs go `brrrrrrr` π
+- Feel cool when your kernel is faster than PyTorch π
- - All about building [websites](https://quarto.org/docs/websites/)
- - Building Static [Dashboards](https://quarto.org/docs/dashboards/)
- - How to write [books](https://quarto.org/docs/books/index.html) and [manuscripts](https://quarto.org/docs/manuscripts/)
- - Reproducible [presentations](https://quarto.org/docs/presentations/)
- - Including [Observable](https://quarto.org/docs/interactive/ojs/) or [Shiny](https://quarto.org/docs/interactive/shiny/) applications in your Quarto site
+## `Example Problem: KL Divergence`
-::: {.callout-warning}
-It can take a couple of minutes for the Space to deploy to Hugging Face after the Docker build process completes. Two see your changes you will need to do two things:
+- commonly used in LLMs for knowledge distillation
+- for probability distributions $P$ and $Q$, the Kullback-Leibler divergence is defined as:
+
+$$
+D_{KL}(P \| Q) = \sum_{i} P_i \log\left(\frac{P_i}{Q_i}\right)
+$$
+
+
+```python
+import torch
+from torch.nn.functional import kl_div
+
+def kl_div_torch(p: torch.Tensor, q: torch.Tensor) -> torch.Tensor:
+ return kl_div(p, q, reduction='none')
+```
+
+## `How about Triton?`
+
+```python
+import triton.language as tl
+
+@triton.jit
+def kl_div_triton(
+ p_ptr, q_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr
+):
+ pid = tl.program_id(0)
+ block_start = pid * BLOCK_SIZE
+ offsets = block_start + tl.arange(0, BLOCK_SIZE)
+ mask = offsets < n_elements
+
+ p = tl.load(p_ptr + offsets, mask=mask)
+ q = tl.load(q_ptr + offsets, mask=mask)
+
+ output = p * (tl.log(p) - tl.log(q))
+
+ tl.store(output_ptr + offsets, output, mask=mask)
+```
+
+## `How to integrate with PyTorch?`
+
+- Triton works with pointers
+- How to use our custom kernel with PyTorch autograd?
+
+```python
+import torch
+
+class VectorAdd(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, p, q):
+ ctx.save_for_backward(q)
+ output = torch.empty_like(p)
+ grid = (len(p) + 512 - 1) // 512
+ kl_div_triton[grid](p, q, output, len(p), BLOCK_SIZE=512)
+ return output
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ q = ctx.saved_tensors[0]
+ # Calculate gradients (another triton kernel)
+ return ...
+```
+
+## `Some benchmarks`
+
+- A KL Divergence kernel that is currently used in [Liger Kernel](https://github.com/linkedin/liger-kernel) written by @me
+
+:::: {.columns}
+
+::: {.column width="50%"}
+
+{.center fig-align="center"}
+
+:::
+
+::: {.column width="50%"}
+
+{.center fig-align="center"}
-1) Wait for your space's status to go from 'Building' to 'Running'(this is visible in the status bar above the Space)
-2) Force-reload the web page by holding Shift and hitting the reload button in your browser.
:::
-## Code Execution
+::::
-One of the main virtues of Quarto is that it lets you combine code and text in a single document.
-By default, if you include a code chunk in your document, Quarto will execute that code and include the output in the rendered document.
-This is great for reproducibility and for creating documents that are always up-to-date.
-For example you can include code which generates a plot like this:
+## `Do I have to write everything?`
-```{python}
-import seaborn as sns
-import matplotlib.pyplot as plt
+- TLDR: No
+- Many cool projects already using Triton
+- Better Integration with PyTorch and even Hugging Face π€
+- Liger Kernel, Unsloth AI, etc.
-# Sample data
-tips = sns.load_dataset("tips")
-# Create a seaborn plot
-sns.set_style("whitegrid")
-g = sns.lmplot(x="total_bill", y="tip", data=tips, aspect=2)
-g = g.set_axis_labels("Total bill (USD)", "Tip").set(xlim=(0, 60), ylim=(0, 12))
+:::: {.columns}
-plt.title("Tip by Total Bill")
-plt.show()
+::: {.column width="50%"}
+
+{.center fig-align="center"}
+
+:::
+
+::: {.column width="50%"}
+
+{.center fig-align="center"}
+
+:::
+
+::::
+
+
+## `So how can I use this in my LLM? π`
+
+- Liger Kernel is a great example, providing examples of how to integrate with Hugging Face π€ Trainer
+
+```diff
+- from transformers import AutoModelForCausalLM
++ from liger_kernel.transformers import AutoLigerKernelForCausalLM
+
+model_path = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+- model = AutoModelForCausalLM.from_pretrained(model_path)
++ model = AutoLigerKernelForCausalLM.from_pretrained(model_path)
+
+# training/inference logic...
```
+## `Key Optimization Techniques adapted by Liger Kernel`
-When the website is built the Python code will run and the output will be included in the document.
+- Kernel Fusion
+- Domain-specific optimizations
+- Memory Access Patterns
+- Preemptive memory freeing
+
-You can also include [inline code](https://quarto.org/docs/computations/inline-code.html) to insert computed values into text.
-For example we can include the maximum tip value in the `tips` data frame like this: ``{python} tips['tip'].max()``.
-You can control [code execution](https://quarto.org/docs/computations/execution-options.html), or [freeze code output](https://quarto.org/docs/projects/code-execution.html#freeze) to capture the output of long running computations.
+## `Aaand some more benchmarks π`
-## About the Open Source AI Cookbook
+:::: {.columns}
-To provide a realistic example of how Quarto can help you organize long-form documentation,
-we've implemented the Hugging Face [Open-Source AI Cookbook](https://github.com/huggingface/cookbook) in Quarto.
-The Open-Source AI Cookbook is a collection of notebooks illustrating practical aspects of building AI applications and solving various machine learning tasks using open-source tools and models.
-You can read more about it, or contribute your own Notebook on the [Github Repo](https://github.com/huggingface/cookbook)
+::: {.column width="50%"}
+{fig-align="center"}
+
+:::
+
+::: {.column width="50%"}
+
+{fig-align="center"}
+
+:::
+
+::::
+
+## `Last benchmark I promise...`
+
+
+{height="50%" width="50%" }
+
+::: {.incremental}
+*Attention is all you need, so I thank you for yours!* π€
+:::
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----
-title: Advanced RAG
-jupyter: python3
-eval: false
-code-annotations: hover
----
-
-This notebook demonstrates how you can build an advanced RAG (Retrieval Augmented Generation) for answering a user's question about a specific knowledge base (here, the HuggingFace documentation), using LangChain.
-
-For an introduction to RAG, you can check [this other cookbook](rag_zephyr_langchain.qmd)!
-
-RAG systems are complex, with many moving parts: here a RAG diagram, where we noted in blue all possibilities for system enhancement:
-
-
-
-::: callout-note
-π‘ As you can see, there are many steps to tune in this architecture: tuning the system properly will yield significant performance gains.
-:::
-
-In this notebook, we will take a look into many of these blue notes to see how to tune your RAG system and get the best performance.
-
-__Let's dig into the model building!__ First, we install the required model dependancies.
-
-```{python}
-!pip install -q torch transformers transformers accelerate bitsandbytes langchain sentence-transformers faiss-gpu openpyxl pacmap
-```
-
-```{python}
-%reload_ext dotenv
-%dotenv
-```
-
-```{python}
-from tqdm.notebook import tqdm
-import pandas as pd
-from typing import Optional, List, Tuple
-from datasets import Dataset
-import matplotlib.pyplot as plt
-
-pd.set_option(
- "display.max_colwidth", None # <1>
-)
-```
-1. This will be helpful when visualizing retriever outputs
-
-### Load your knowledge base
-
-```{python}
-import datasets
-
-ds = datasets.load_dataset("m-ric/huggingface_doc", split="train")
-```
-
-```{python}
-from langchain.docstore.document import Document as LangchainDocument
-
-RAW_KNOWLEDGE_BASE = [
- LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
- for doc in tqdm(ds)
-]
-```
-
-# 1. Retriever - embeddings ποΈ
-The __retriever acts like an internal search engine__: given the user query, it returns a few relevant snippets from your knowledge base.
-
-These snippets will then be fed to the Reader Model to help it generate its answer.
-
-So __our objective here is, given a user question, to find the most snippets from our knowledge base to answer that question.__
-
-This is a wide objective, it leaves open some questions. How many snippets should we retrieve? This parameter will be named `top_k`.
-
-How long should these snippets be? This is called the `chunk size`. There's no one-size-fits-all answers, but here are a few elements:
-- π Your `chunk size` is allowed to vary from one snippet to the other.
-- Since there will always be some noise in your retrieval, increasing the `top_k` increases the chance to get relevant elements in your retrieved snippets. π― Shooting more arrows increases your probability to hit your target.
-- Meanwhile, the summed length of your retrieved documents should not be too high: for instance, for most current models 16k tokens will probably drown your Reader model in information due to [Lost-in-the-middle phenomenon](https://huggingface.co/papers/2307.03172). π― Give your reader model only the most relevant insights, not a huge pile of books!
-
-::: callout-note
-In this notebook, we use Langchain library since __it offers a huge variety of options for vector databases and allows us to keep document metadata throughout the processing__.
-:::
-
-### 1.1 Split the documents into chunks
-
-- In this part, __we split the documents from our knowledge base into smaller chunks__ which will be the snippets on which the reader LLM will base its answer.
-- The goal is to prepare a collection of **semantically relevant snippets**. So their size should be adapted to precise ideas: too small will truncate ideas, too large will dilute them.
-
-::: callout-tip
-π‘ Many options exist for text splitting: splitting on words, on sentence boundaries, recursive chunking that processes documents in a tree-like way to preserve structure information... To learn more about chunking, I recommend you read [this great notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) by Greg Kamradt.
-:::
-
-
-- **Recursive chunking** breaks down the text into smaller parts step by step using a given list of separators sorted from the most important to the least important separator. If the first split doesn't give the right size or shape chunks, the method repeats itself on the new chunks using a different separator. For instance with the list of separators `["\n\n", "\n", ".", ""]`:
- - The method will first break down the document wherever there is a double line break `"\n\n"`.
- - Resulting documents will be split again on simple line breaks `"\n"`, then on sentence ends `"."`.
- - And finally, if some chunks are still too big, they will be split whenever they overflow the maximum size.
-
-- With this method, the global structure is well preserved, at the expense of getting slight variations in chunk size.
-
-> [This space](https://huggingface.co/spaces/A-Roucher/chunk_visualizer) lets you visualize how different splitting options affect the chunks you get.
-
-π¬ Let's experiment a bit with chunk sizes, beginning with an arbitrary size, and see how splits work. We use Langchain's implementation of recursive chunking with `RecursiveCharacterTextSplitter`.
-- Parameter `chunk_size` controls the length of individual chunks: this length is counted by default as the number of characters in the chunk.
-- Parameter `chunk_overlap` lets adjacent chunks get a bit of overlap on each other. This reduces the probability that an idea could be cut in half by the split between two adjacent chunks. We ~arbitrarily set this to 1/10th of the chunk size, you could try different values!
-
-```{python}
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-# We use a hierarchical list of separators specifically tailored for splitting Markdown documents
-# This list is taken from LangChain's MarkdownTextSplitter class.
-MARKDOWN_SEPARATORS = [
- "\n#{1,6} ",
- "```\n",
- "\n\\*\\*\\*+\n",
- "\n---+\n",
- "\n___+\n",
- "\n\n",
- "\n",
- " ",
- "",
-]
-
-text_splitter = RecursiveCharacterTextSplitter(
- chunk_size=1000, # <1>
- chunk_overlap=100, # <2>
- add_start_index=True, # <3>
- strip_whitespace=True, # <4>
- separators=MARKDOWN_SEPARATORS,
-)
-
-docs_processed = []
-for doc in RAW_KNOWLEDGE_BASE:
- docs_processed += text_splitter.split_documents([doc])
-```
-1. The maximum number of characters in a chunk: we selected this value arbitrally
-2. The number of characters to overlap between chunks
-3. If `True`, includes chunk's start index in metadata
-4. If `True`, strips whitespace from the start and end of every document
-
-
-We also have to keep in mind that when embedding documents, we will use an embedding model that has accepts a certain maximum sequence length `max_seq_length`.
-
-So we should make sure that our chunk sizes are below this limit, because any longer chunk will be truncated before processing, thus losing relevancy.
-
-```{python}
-#| colab: {referenced_widgets: [ae043feeb0914c879e2a9008b413d952]}
-from sentence_transformers import SentenceTransformer
-
-# To get the value of the max sequence_length, we will query the underlying `SentenceTransformer` object used in the RecursiveCharacterTextSplitter.
-print(
- f"Model's maximum sequence length: {SentenceTransformer('thenlper/gte-small').max_seq_length}"
-)
-
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
-lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]
-
-# Plot the distrubution of document lengths, counted as the number of tokens
-fig = pd.Series(lengths).hist()
-plt.title("Distribution of document lengths in the knowledge base (in count of tokens)")
-plt.show()
-```
-
-π As you can see, __the chunk lengths are not aligned with our limit of 512 tokens__, and some documents are above the limit, thus some part of them will be lost in truncation!
- - So we should change the `RecursiveCharacterTextSplitter` class to count length in number of tokens instead of number of characters.
- - Then we can choose a specific chunk size, here we would choose a lower threshold than 512:
- - smaller documents could allow the split to focus more on specific ideas.
- - But too small chunks would split sentences in half, thus losing meaning again: the proper tuning is a matter of balance.
-
-```{python}
-#| colab: {referenced_widgets: [f900cf4ab3a94f45bfa7298f433566ed]}
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from transformers import AutoTokenizer
-
-EMBEDDING_MODEL_NAME = "thenlper/gte-small"
-
-
-def split_documents(
- chunk_size: int,
- knowledge_base: List[LangchainDocument],
- tokenizer_name: Optional[str] = EMBEDDING_MODEL_NAME,
-) -> List[LangchainDocument]:
- """
- Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.
- """
- text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
- AutoTokenizer.from_pretrained(tokenizer_name),
- chunk_size=chunk_size,
- chunk_overlap=int(chunk_size / 10),
- add_start_index=True,
- strip_whitespace=True,
- separators=MARKDOWN_SEPARATORS,
- )
-
- docs_processed = []
- for doc in knowledge_base:
- docs_processed += text_splitter.split_documents([doc])
-
- # Remove duplicates
- unique_texts = {}
- docs_processed_unique = []
- for doc in docs_processed:
- if doc.page_content not in unique_texts:
- unique_texts[doc.page_content] = True
- docs_processed_unique.append(doc)
-
- return docs_processed_unique
-
-
-docs_processed = split_documents(
- 512, # We choose a chunk size adapted to our model
- RAW_KNOWLEDGE_BASE,
- tokenizer_name=EMBEDDING_MODEL_NAME,
-)
-
-# Let's visualize the chunk sizes we would have in tokens from a common model
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)
-lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]
-fig = pd.Series(lengths).hist()
-plt.title("Distribution of document lengths in the knowledge base (in count of tokens)")
-plt.show()
-```
-
-β‘οΈ Now the chunk length distribution looks better!
-
-### 1.2 Building the vector database
-
-We want to compute the embeddings for all the chunks of our knowledge base: to learn more on sentence embeddings, we recommend reading [this guide](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/).
-
-#### How does retrieval work ?
-
-Once the chunks are all embedded, we store them into a vector database. When the user types in a query, it gets embedded by the same model previously used, and a similarity search returns the closest documents from the vector database.
-
-The technical challenge is thus, given a query vector, to quickly find the nearest neighbours of this vector in the vector database. To do this, we need to choose two things: a distance, and a search algorithm to find the nearest neighbors quickly within a database of thousands of records.
-
-##### Nearest Neighbor search algorithm
-
-There are plentiful choices for the nearest neighbor search algorithm: we go with Facebook's [FAISS](https://github.com/facebookresearch/faiss), since FAISS is performant enough for most use cases, and it is well known thus widely implemented.
-
-##### Distances
-
-Regarding distances, you can find a good guide [here](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/#distance-between-embeddings). In short:
-
-- **Cosine similarity** computes similarity between two vectors as the cosinus of their relative angle: it allows us to compare vector directions are regardless of their magnitude. Using it requires to normalize all vectors, to rescale them into unit norm.
-- **Dot product** takes into account magnitude, with the sometimes undesirable effect that increasing a vector's length will make it more similar to all others.
-- **Euclidean distance** is the distance between the ends of vectors.
-
-You can try [this small exercise](https://developers.google.com/machine-learning/clustering/similarity/check-your-understanding) to check your understanding of these concepts. But once vectors are normalized, [the choice of a specific distance does not matter much](https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use).
-
-Our particular model works well with cosine similarity, so choose this distance, and we set it up both in the Embedding model, and in the `distance_strategy` argument of our FAISS index. With cosine similarity, we have to normalize our embeddings.
-
-::: {.callout-warning}
-π¨π The cell below takes a few minutes to run on A10G!
-:::
-
-```{python}
-from langchain.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores.utils import DistanceStrategy
-
-embedding_model = HuggingFaceEmbeddings(
- model_name=EMBEDDING_MODEL_NAME,
- multi_process=True,
- model_kwargs={"device": "cuda"},
- encode_kwargs={"normalize_embeddings": True}, # set True for cosine similarity
-)
-
-KNOWLEDGE_VECTOR_DATABASE = FAISS.from_documents(
- docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE
-)
-```
-
-π To visualize the search for the closest documents, let's project our embeddings from 384 dimensions down to 2 dimensions using PaCMAP.
-
-::: {.callout-note}
-π‘ We chose PaCMAP rather than other techniques such as t-SNE or UMAP, since [it is efficient (preserves local and global structure), robust to initialization parameters and fast](https://www.nature.com/articles/s42003-022-03628-x#Abs1).
-:::
-
-
-```{python}
-# embed a user query in the same space
-user_query = "How to create a pipeline object?"
-query_vector = embedding_model.embed_query(user_query)
-```
-
-```{python}
-import pacmap
-import numpy as np
-import plotly.express as px
-
-embedding_projector = pacmap.PaCMAP(
- n_components=2, n_neighbors=None, MN_ratio=0.5, FP_ratio=2.0, random_state=1
-)
-
-embeddings_2d = [
- list(KNOWLEDGE_VECTOR_DATABASE.index.reconstruct_n(idx, 1)[0])
- for idx in range(len(docs_processed))
-] + [query_vector]
-
-# fit the data (The index of transformed data corresponds to the index of the original data)
-documents_projected = embedding_projector.fit_transform(np.array(embeddings_2d), init="pca")
-```
-
-```{python}
-df = pd.DataFrame.from_dict(
- [
- {
- "x": documents_projected[i, 0],
- "y": documents_projected[i, 1],
- "source": docs_processed[i].metadata["source"].split("/")[1],
- "extract": docs_processed[i].page_content[:100] + "...",
- "symbol": "circle",
- "size_col": 4,
- }
- for i in range(len(docs_processed))
- ]
- + [
- {
- "x": documents_projected[-1, 0],
- "y": documents_projected[-1, 1],
- "source": "User query",
- "extract": user_query,
- "size_col": 100,
- "symbol": "star",
- }
- ]
-)
-
-# visualize the embedding
-fig = px.scatter(
- df,
- x="x",
- y="y",
- color="source",
- hover_data="extract",
- size="size_col",
- symbol="symbol",
- color_discrete_map={"User query": "black"},
- width=1000,
- height=700,
-)
-fig.update_traces(
- marker=dict(opacity=1, line=dict(width=0, color="DarkSlateGrey")), selector=dict(mode="markers")
-)
-fig.update_layout(
- legend_title_text="Chunk source",
- title="2D Projection of Chunk Embeddings via PaCMAP",
-)
-fig.show()
-```
-
-
-
-
-β‘οΈ On the graph above, you can see a spatial representation of the kowledge base documents. As the vector embeddings represent the document's meaning, their closeness in meaning should be reflected in their embedding's closeness.
-
-The user query's embedding is also shown : we want to find the `k` document that have the closest meaning, thus we pick the `k` closest vectors.
-
-In the LangChain vector database implementation, this search operation is performed by the method `vector_database.similarity_search(query)`.
-
-Here is the result:
-
-```{python}
-print(f"\nStarting retrieval for {user_query=}...")
-retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=user_query, k=5)
-print("\n==================================Top document==================================")
-print(retrieved_docs[0].page_content)
-print("==================================Metadata==================================")
-print(retrieved_docs[0].metadata)
-```
-
-# 2. Reader - LLM π¬
-
-In this part, the __LLM Reader reads the retrieved context to formulate its answer.__
-
-There are actually substeps that can all be tuned:
-1. The content of the retrieved documents is aggregated together into the "context", with many processing options like _prompt compression_.
-2. The context and the user query are aggregated into a prompt then given to the LLM to generate its answer.
-
-### 2.1. Reader model
-
-The choice of a reader model is important on a few aspects:
-- the reader model's `max_seq_length` must accomodate our prompt, which includes the context output by the retriever call: the context consists in 5 documents of 512 tokens each, so we aim for a context length of 4k tokens at least.
-- the reader model
-
-For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.
-
-::: callout-note
-With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
-:::
-
-To make inference faster, we will load the quantized version of the model:
-
-```{python}
-#| colab: {referenced_widgets: [db31fd28d3604e78aead26af87b0384f]}
-from transformers import pipeline
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-READER_MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
-
-bnb_config = BitsAndBytesConfig(
- load_in_4bit=True,
- bnb_4bit_use_double_quant=True,
- bnb_4bit_quant_type="nf4",
- bnb_4bit_compute_dtype=torch.bfloat16,
-)
-model = AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config=bnb_config)
-tokenizer = AutoTokenizer.from_pretrained(READER_MODEL_NAME)
-
-READER_LLM = pipeline(
- model=model,
- tokenizer=tokenizer,
- task="text-generation",
- do_sample=True,
- temperature=0.2,
- repetition_penalty=1.1,
- return_full_text=False,
- max_new_tokens=500,
-)
-```
-
-```{python}
-READER_LLM("What is 4+4? Answer:")
-```
-
-### 2.2. Prompt
-
-The RAG prompt template below is what we will feed to the Reader LLM: it is important to have it formatted in the Reader LLM's chat template.
-
-We give it our context and the user's question.
-
-```{python}
-prompt_in_chat_format = [
- {
- "role": "system",
- "content": """Using the information contained in the context,
-give a comprehensive answer to the question.
-Respond only to the question asked, response should be concise and relevant to the question.
-Provide the number of the source document when relevant.
-If the answer cannot be deduced from the context, do not give an answer.""",
- },
- {
- "role": "user",
- "content": """Context:
-{context}
----
-Now here is the question you need to answer.
-
-Question: {question}""",
- },
-]
-RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(
- prompt_in_chat_format, tokenize=False, add_generation_prompt=True
-)
-print(RAG_PROMPT_TEMPLATE)
-```
-
-Let's test our Reader on our previously retrieved documents!
-
-```{python}
-retrieved_docs_text = [
- doc.page_content for doc in retrieved_docs
-] # we only need the text of the documents
-context = "\nExtracted documents:\n"
-context += "".join([f"Document {str(i)}:::\n" + doc for i, doc in enumerate(retrieved_docs_text)])
-
-final_prompt = RAG_PROMPT_TEMPLATE.format(
- question="How to create a pipeline object?", context=context
-)
-
-# Redact an answer
-answer = READER_LLM(final_prompt)[0]["generated_text"]
-print(answer)
-```
-
-### 2.3. Reranking
-
-A good option for RAG is to retrieve more documents than you want in the end, then rerank the results with a more powerful retrieval model before keeping only the `top_k`.
-
-For this, [Colbertv2](https://arxiv.org/abs/2112.01488) is a great choice: instead of a bi-encoder like our classical embedding models, it is a cross-encoder that computes more fine-grained interactions between the query tokens and each document's tokens.
-
-It is easily usable thanks to [the RAGatouille library](https://github.com/bclavie/RAGatouille).
-
-```{python}
-from ragatouille import RAGPretrainedModel
-
-RERANKER = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-```
-
-# 3. Assembling it all!
-
-```{python}
-from transformers import Pipeline
-
-
-def answer_with_rag(
- question: str,
- llm: Pipeline,
- knowledge_index: FAISS,
- reranker: Optional[RAGPretrainedModel] = None,
- num_retrieved_docs: int = 30,
- num_docs_final: int = 5,
-) -> Tuple[str, List[LangchainDocument]]:
- # Gather documents with retriever
- print("=> Retrieving documents...")
- relevant_docs = knowledge_index.similarity_search(query=question, k=num_retrieved_docs)
- relevant_docs = [doc.page_content for doc in relevant_docs] # keep only the text
-
- # Optionally rerank results
- if reranker:
- print("=> Reranking documents...")
- relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)
- relevant_docs = [doc["content"] for doc in relevant_docs]
-
- relevant_docs = relevant_docs[:num_docs_final]
-
- # Build the final prompt
- context = "\nExtracted documents:\n"
- context += "".join([f"Document {str(i)}:::\n" + doc for i, doc in enumerate(relevant_docs)])
-
- final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)
-
- # Redact an answer
- print("=> Generating answer...")
- answer = llm(final_prompt)[0]["generated_text"]
-
- return answer, relevant_docs
-```
-
-Let's see how our RAG pipeline answers a user query.
-
-```{python}
-question = "how to create a pipeline object?"
-
-answer, relevant_docs = answer_with_rag(
- question, READER_LLM, KNOWLEDGE_VECTOR_DATABASE, reranker=RERANKER
-)
-```
-
-```{python}
-print("==================================Answer==================================")
-print(f"{answer}")
-print("==================================Source docs==================================")
-for i, doc in enumerate(relevant_docs):
- print(f"Document {i}------------------------------------------------------------")
- print(doc)
-```
-
-β
We now have a fully functional, performant RAG sytem. That's it for today! Congratulations for making it to the end π₯³
-
-
-# To go further πΊοΈ
-
-This is not the end of the journey! You can try many steps to improve your RAG system. We recommend doing so in an iterative way: bring small changes to the system and see what improves performance.
-
-### Setting up an evaluation pipeline
-
-- π¬ "You cannot improve the model performance that you do not measure", said Gandhi... or at least Llama2 told me he said it. Anyway, you should absolutely start by measuring performance: this means building a small evaluation dataset, then monitor the performance of your RAG system on this evaluation dataset.
-
-### Improving the retriever
-
-π οΈ __You can use these options to tune the results:__
-
-- Tune the chunking method:
- - Size of the chunks
- - Method: split on different separators, use [semantic chunking](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...
-- Change the embedding model
-
-π·ββοΈ __More could be considered:__
-- Try another chunking method, like semantic chunking
-- Change the index used (here, FAISS)
-- Query expansion: reformulate the user query in slightly different ways to retrieve more documents.
-
-### Improving the reader
-
-π οΈ __Here you can try the following options to improve results:__
-- Tune the prompt
-- Switch reranking on/off
-- Choose a more powerful reader model
-
-π‘ __Many options could be considered here to further improve the results:__
-- Compress the retrieved context to keep only the most relevant parts to answer the query.
-- Extend the RAG system to make it more user-friendly:
- - cite source
- - make conversational
-
diff --git a/src/notebooks/automatic_embedding.ipynb b/src/notebooks/automatic_embedding.ipynb
deleted file mode 100644
index 176b26789df38b7be056a14bbbad729e02e85be2..0000000000000000000000000000000000000000
--- a/src/notebooks/automatic_embedding.ipynb
+++ /dev/null
@@ -1,825 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "5d9aca72-957a-4ee2-862f-e011b9cd3a62",
- "metadata": {},
- "source": [
- "---\n",
- "title: \"Inference Endpoints\"\n",
- "---\n",
- "\n",
- "# How to use Inference Endpoints to Embed Documents\n",
- "\n",
- "_Authored by: [Derek Thomas](https://huggingface.co/derek-thomas)_\n",
- "\n",
- "## Goal\n",
- "I have a dataset I want to embed for semantic search (or QA, or RAG), I want the easiest way to do embed this and put it in a new dataset.\n",
- "\n",
- "## Approach\n",
- "I'm using a dataset from my favorite subreddit [r/bestofredditorupdates](https://www.reddit.com/r/bestofredditorupdates/). Because it has long entries, I will use the new [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) since it has an 8k context length. I will deploy this using [Inference Endpoint](https://huggingface.co/inference-endpoints) to save time and money. To follow this tutorial, you will need to **have already added a payment method**. If you haven't, you can add one here in [billing](https://huggingface.co/docs/hub/billing#billing). To make it even easier, I'll make this fully API based.\n",
- "\n",
- "To make this MUCH faster I will use the [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference) image. This has many benefits like:\n",
- "- No model graph compilation step\n",
- "- Small docker images and fast boot times. Get ready for true serverless!\n",
- "- Token based dynamic batching\n",
- "- Optimized transformers code for inference using Flash Attention, Candle and cuBLASLt\n",
- "- Safetensors weight loading\n",
- "- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3c830114-dd88-45a9-81b9-78b0e3da7384",
- "metadata": {},
- "source": [
- "## Requirements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "35386f72-32cb-49fa-a108-3aa504e20429",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "!pip install -q aiohttp==3.8.3 datasets==2.14.6 pandas==1.5.3 requests==2.31.0 tqdm==4.66.1 huggingface-hub>=0.20"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6f72042-173d-4a72-ade1-9304b43b528d",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "e2beecdd-d033-4736-bd45-6754ec53b4ac",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import asyncio\n",
- "from getpass import getpass\n",
- "import json\n",
- "from pathlib import Path\n",
- "import time\n",
- "from typing import Optional\n",
- "\n",
- "from aiohttp import ClientSession, ClientTimeout\n",
- "from datasets import load_dataset, Dataset, DatasetDict\n",
- "from huggingface_hub import notebook_login, create_inference_endpoint, list_inference_endpoints, whoami\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import requests\n",
- "from tqdm.auto import tqdm"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5eece903-64ce-435d-a2fd-096c0ff650bf",
- "metadata": {},
- "source": [
- "## Config\n",
- "`DATASET_IN` is where your text data is\n",
- "`DATASET_OUT` is where your embeddings will be stored\n",
- "\n",
- "Note I used 5 for the `MAX_WORKERS` since `jina-embeddings-v2` are quite memory hungry. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "df2f79f0-9f28-46e6-9fc7-27e9537ff5be",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "DATASET_IN = 'derek-thomas/dataset-creator-reddit-bestofredditorupdates'\n",
- "DATASET_OUT = \"processed-subset-bestofredditorupdates\"\n",
- "ENDPOINT_NAME = \"boru-jina-embeddings-demo-ie\"\n",
- "\n",
- "MAX_WORKERS = 5 # This is for how many async workers you want. Choose based on the model and hardware \n",
- "ROW_COUNT = 100 # Choose None to use all rows, Im using 100 just for a demo"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1e680f3d-4900-46cc-8b49-bb6ba3e27e2b",
- "metadata": {},
- "source": [
- "Hugging Face offers a number of GPUs that you can choose from a number of GPUs that you can choose in Inference Endpoints. Here they are in table form:\n",
- "\n",
- "| GPU | instanceType | instanceSize | vRAM |\n",
- "|---------------------|----------------|--------------|-------|\n",
- "| 1x Nvidia Tesla T4 | g4dn.xlarge | small | 16GB |\n",
- "| 4x Nvidia Tesla T4 | g4dn.12xlarge | large | 64GB |\n",
- "| 1x Nvidia A10G | g5.2xlarge | medium | 24GB |\n",
- "| 4x Nvidia A10G | g5.12xlarge | xxlarge | 96GB |\n",
- "| 1x Nvidia A100* | p4de | xlarge | 80GB |\n",
- "| 2x Nvidia A100* | p4de | 2xlarge | 160GB |\n",
- "\n",
- "\\*Note that for A100s you might get a note to email us to get access."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "3c2106c1-2e5a-443a-9ea8-a3cd0e9c5a94",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# GPU Choice\n",
- "VENDOR=\"aws\"\n",
- "REGION=\"us-east-1\"\n",
- "INSTANCE_SIZE=\"medium\"\n",
- "INSTANCE_TYPE=\"g5.2xlarge\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "0ca1140c-3fcc-4b99-9210-6da1505a27b7",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ee80821056e147fa9cabf30f64dc85a8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "VBox(children=(HTML(value='
`pd.DataFrame` -> `Dataset`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "9bb993f8-d624-4192-9626-8e9ed9888a1b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "df = pd.DataFrame(documents)\n",
- "dd = DatasetDict({'train': Dataset.from_pandas(df)})"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "129760c8-cae1-4b1e-8216-f5152df8c536",
- "metadata": {},
- "source": [
- "I'm uploading it to the user's account by default (as opposed to uploading to an organization) but feel free to push to wherever you want by setting the user in the `repo_id` or in the config by setting `DATASET_OUT`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "f48e7c55-d5b7-4ed6-8516-272ae38716b1",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d3af2e864770481db5adc3968500b5d3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "4e063c42d8f4490c939bc64e626b507a",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Downloading metadata: 0%| | 0.00/823 [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "dd.push_to_hub(repo_id=DATASET_OUT)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "85ea2244-a4c6-4f04-b187-965a2fc356a8",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset is at https://huggingface.co/datasets/derek-thomas/processed-subset-bestofredditorupdates\n"
- ]
- }
- ],
- "source": [
- "print(f'Dataset is at https://huggingface.co/datasets/{who[\"name\"]}/{DATASET_OUT}')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "41abea64-379d-49de-8d9a-355c2f4ce1ac",
- "metadata": {},
- "source": [
- "# Analyze Usage\n",
- "1. Go to your `dashboard_url` printed below\n",
- "1. Click on the Usage & Cost tab\n",
- "1. See how much you have spent"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "16815445-3079-43da-b14e-b54176a07a62",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "https://ui.endpoints.huggingface.co/HF-test-lab/endpoints/boru-jina-embeddings-demo-ie\n"
- ]
- }
- ],
- "source": [
- "dashboard_url = f'https://ui.endpoints.huggingface.co/{namespace}/endpoints/{ENDPOINT_NAME}'\n",
- "print(dashboard_url)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "81096c6f-d12f-4781-84ec-9066cfa465b3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hit enter to continue with the notebook \n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "''"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "input(\"Hit enter to continue with the notebook\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "847d524e-9aa6-4a6f-a275-8a552e289818",
- "metadata": {},
- "source": [
- "We can see that it only took `$0.04` to pay for this!\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b953d5be-2494-4ff8-be42-9daf00c99c41",
- "metadata": {},
- "source": [
- "\n",
- "# Delete Endpoint\n",
- "Now that we are done, we don't need our endpoint anymore. We can delete our endpoint programmatically. \n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "c310c0f3-6f12-4d5c-838b-3a4c1f2e54ad",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Endpoint deleted successfully\n"
- ]
- }
- ],
- "source": [
- "endpoint = endpoint.delete()\n",
- "\n",
- "if not endpoint:\n",
- " print('Endpoint deleted successfully')\n",
- "else:\n",
- " print('Delete Endpoint in manually') "
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/notebooks/faiss.ipynb b/src/notebooks/faiss.ipynb
deleted file mode 100644
index e01ec47253c379186894cdc25874c88869f7a27f..0000000000000000000000000000000000000000
--- a/src/notebooks/faiss.ipynb
+++ /dev/null
@@ -1,580 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "q3n0GCRvMXNc"
- },
- "source": [
- "---\n",
- "title: \"Similarity Search\"\n",
- "---\n",
- "\n",
- "# Embedding multimodal data for similarity search using π€ transformers, π€ datasets and FAISS\n",
- "\n",
- "_Authored by: [Merve Noyan](https://huggingface.co/merve)_\n",
- "\n",
- "Embeddings are semantically meaningful compressions of information. They can be used to do similarity search, zero-shot classification or simply train a new model. Use cases for similarity search include searching for similar products in e-commerce, content search in social media and more.\n",
- "This notebook walks you through using π€transformers, π€datasets and FAISS to create and index embeddings from a feature extraction model to later use them for similarity search.\n",
- "Let's install necessary libraries."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Gqmxny3tNASX"
- },
- "outputs": [],
- "source": [
- "!pip install -q datasets faiss-gpu transformers sentencepiece"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "X4z-2K6MM4yW"
- },
- "source": [
- "For this tutorial, we will use [CLIP model](https://huggingface.co/openai/clip-vit-base-patch16) to extract the features. CLIP is a revolutionary model that introduced joint training of a text encoder and an image encoder to connect two modalities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "5WY6waypNCjT"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "from PIL import Image\n",
- "from transformers import AutoImageProcessor, AutoModel, AutoTokenizer\n",
- "import faiss\n",
- "import numpy as np\n",
- "\n",
- "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n",
- "\n",
- "model = AutoModel.from_pretrained(\"openai/clip-vit-base-patch16\").to(device)\n",
- "processor = AutoImageProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
- "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch16\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_jBbLzJUSOwQ"
- },
- "source": [
- "Load the dataset. To keep this notebook light, we will use a small captioning dataset, [jmhessel/newyorker_caption_contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "wMxvOhkA0l-k"
- },
- "outputs": [],
- "source": [
- "from datasets import load_dataset\n",
- "\n",
- "ds = load_dataset(\"jmhessel/newyorker_caption_contest\", \"explanation\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_hbosSHI10zy"
- },
- "source": [
- "See an example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 305
- },
- "id": "5gpAhbAcMrm7",
- "outputId": "682033f9-da37-4cae-e1bc-4a5fbbb7f2fa"
- },
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ds[\"train\"][0][\"image\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "id": "FOxmdk-HM7L6",
- "outputId": "ff7c2ca8-0c6a-49d0-cfd6-4be775e012a1"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'Two women are looking out a window. There is snow outside, and there is a snowman with human arms.'"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ds[\"train\"][0][\"image_description\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Ri187NrFNMaF"
- },
- "source": [
- "We don't have to write any function to embed examples or create an index. π€ datasets library's FAISS integration abstracts these processes. We can simply use `map` method of the dataset to create a new column with the embeddings for each example like below. Let's create one for text features on the prompt column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xB0EfabiBHgR"
- },
- "outputs": [],
- "source": [
- "dataset = ds[\"train\"]\n",
- "ds_with_embeddings = dataset.map(lambda example:\n",
- " {'embeddings': model.get_text_features(\n",
- " **tokenizer([example[\"image_description\"]],\n",
- " truncation=True, return_tensors=\"pt\")\n",
- " .to(\"cuda\"))[0].detach().cpu().numpy()})\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "iUWvvRB3DJwy"
- },
- "outputs": [],
- "source": [
- "ds_with_embeddings.add_faiss_index(column='embeddings')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qZcZNgSpCH5e"
- },
- "source": [
- "We can do the same and get the image embeddings."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "AwXh-WlZB6q-"
- },
- "outputs": [],
- "source": [
- "ds_with_embeddings = ds_with_embeddings.map(lambda example:\n",
- " {'image_embeddings': model.get_image_features(\n",
- " **processor([example[\"image\"]], return_tensors=\"pt\")\n",
- " .to(\"cuda\"))[0].detach().cpu().numpy()})\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "s9OX--PsDMNE"
- },
- "outputs": [],
- "source": [
- "ds_with_embeddings.add_faiss_index(column='image_embeddings')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1BS3TvQO5GGJ"
- },
- "source": [
- "## Querying the data with text prompts"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pxx9fTf83xgE"
- },
- "source": [
- "We can now query the dataset with text or image to get similar items from it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "2UQQyXAbNKGa"
- },
- "outputs": [],
- "source": [
- "prmt = \"a snowy day\"\n",
- "prmt_embedding = model.get_text_features(**tokenizer([prmt], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n",
- "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', prmt_embedding, k=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 190
- },
- "id": "O5bkNf4M3_Nt",
- "outputId": "b56009fe-dc99-4cc3-84e5-559fb3625d30"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def downscale_images(image):\n",
- " width = 200\n",
- " ratio = (width / float(image.size[0]))\n",
- " height = int((float(image.size[1]) * float(ratio)))\n",
- " img = image.resize((width, height), Image.Resampling.LANCZOS)\n",
- " return img\n",
- "\n",
- "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n",
- "#Β see the closest text and image\n",
- "print(retrieved_examples[\"image_description\"])\n",
- "display(images[0])\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ufn0oqPx5DUR"
- },
- "source": [
- "## Querying the data with image prompts"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "R6fNviJ28fns"
- },
- "source": [
- "Image similarity inference is similar, where you just call `get_image_features`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 217
- },
- "id": "t1BGXpT659Px",
- "outputId": "53478699-5753-4946-90d6-0aa8b76694a6"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import requests\n",
- "#Β image of a beaver\n",
- "url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png\"\n",
- "image = Image.open(requests.get(url, stream=True).raw)\n",
- "display(downscale_images(image))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3kmz4g1v6SJ_"
- },
- "source": [
- "Search for the similar image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "qWf-G_Iz4RcD"
- },
- "outputs": [],
- "source": [
- "img_embedding = model.get_image_features(**processor([image], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n",
- "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('image_embeddings', img_embedding, k=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "iFGNp5hp6VsV"
- },
- "source": [
- "Display the most similar image to the beaver image."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 197
- },
- "id": "Pq7IR86k54kP",
- "outputId": "fa620b08-4435-4929-f67f-32b3f8f46b70"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n",
- "#Β see the closest text and image\n",
- "print(retrieved_examples[\"image_description\"])\n",
- "display(images[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6JEZJlkD8UrZ"
- },
- "source": [
- "## Saving, pushing and loading the embeddings\n",
- "We can save the dataset with embeddings with `save_faiss_index`.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dXrBMAHx8k51"
- },
- "outputs": [],
- "source": [
- "ds_with_embeddings.save_faiss_index('embeddings', 'embeddings/embeddings.faiss')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "51dgxmGm-c3x"
- },
- "outputs": [],
- "source": [
- "ds_with_embeddings.save_faiss_index('image_embeddings', 'embeddings/image_embeddings.faiss')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xO0i-dkY-nK5"
- },
- "source": [
- "It's a good practice to store the embeddings in a dataset repository, so we will create one and push our embeddings there to pull later.\n",
- "We will login to Hugging Face Hub, create a dataset repository there and push our indexes there and load using `snapshot_download`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ETmGo_KiAiOr"
- },
- "outputs": [],
- "source": [
- "from huggingface_hub import HfApi, notebook_login, snapshot_download\n",
- "notebook_login()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "K3hmtWQn-k9O"
- },
- "outputs": [],
- "source": [
- "from huggingface_hub import HfApi\n",
- "api = HfApi()\n",
- "api.create_repo(\"merve/faiss_embeddings\", repo_type=\"dataset\")\n",
- "api.upload_folder(\n",
- " folder_path=\"./embeddings\",\n",
- " repo_id=\"merve/faiss_embeddings\",\n",
- " repo_type=\"dataset\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "UTVoI9LWBp1x"
- },
- "outputs": [],
- "source": [
- "snapshot_download(repo_id=\"merve/faiss_embeddings\", repo_type=\"dataset\",\n",
- " local_dir=\"downloaded_embeddings\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HGkYTJsM9BVx"
- },
- "source": [
- " We can load the embeddings to the dataset with no embeddings using `load_faiss_index`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mbPvs8kV8xTy"
- },
- "outputs": [],
- "source": [
- "ds = ds[\"train\"]\n",
- "ds.load_faiss_index('embeddings', './downloaded_embeddings/embeddings.faiss')\n",
- "#Β infer again\n",
- "prmt = \"people under the rain\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mc9JmZSG71WZ"
- },
- "outputs": [],
- "source": [
- "prmt_embedding = model.get_text_features(\n",
- " **tokenizer([prmt], return_tensors=\"pt\", truncation=True)\n",
- " .to(\"cuda\"))[0].detach().cpu().numpy()\n",
- "\n",
- "scores, retrieved_examples = ds.get_nearest_examples('embeddings', prmt_embedding, k=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 341
- },
- "id": "wckNsAX-9zox",
- "outputId": "8d5008b4-ab8f-4b42-92e7-b29e57c126cb"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(retrieved_examples[\"image\"][0])"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "machine_shape": "hm",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/src/notebooks/rag_evaluation.qmd b/src/notebooks/rag_evaluation.qmd
deleted file mode 100644
index 2c29f702f273046fba8fee0d10aebb2480f92d5f..0000000000000000000000000000000000000000
--- a/src/notebooks/rag_evaluation.qmd
+++ /dev/null
@@ -1,786 +0,0 @@
----
-title: RAG Evaluation
-jupyter: python3
-eval: false
----
-
-```{python}
-!pip install -q torch transformers transformers langchain sentence-transformers faiss-gpu openpyxl openai
-```
-
-```{python}
-%reload_ext autoreload
-%autoreload 2
-%reload_ext dotenv
-%dotenv
-```
-
-```{python}
-from tqdm.notebook import tqdm
-import pandas as pd
-from typing import Optional, List, Tuple
-from langchain_core.language_models import BaseChatModel
-import json
-import datasets
-
-pd.set_option("display.max_colwidth", None)
-```
-
-### Load your knowledge base
-
-```{python}
-ds = datasets.load_dataset("m-ric/huggingface_doc", split="train")
-```
-
-# 1. Build a synthetic dataset for evaluation
-We first build a synthetic dataset of questions and associated contexts. The method is to get elements from our knowledge base, and ask an LLM to generate questions based on these documents.
-
-Then we setup other LLM agents to act as quality filters for the generated QA couples: each of them will act as the filter for a specific flaw.
-
-### 1.1. Prepare source documents
-
-```{python}
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.docstore.document import Document as LangchainDocument
-
-langchain_docs = [
- LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
- for doc in tqdm(ds)
-]
-
-
-text_splitter = RecursiveCharacterTextSplitter(
- chunk_size=2000,
- chunk_overlap=200,
- add_start_index=True,
- separators=["\n\n", "\n", ".", " ", ""],
-)
-
-docs_processed = []
-for doc in langchain_docs:
- docs_processed += text_splitter.split_documents([doc])
-```
-
-### 1.2. Setup agents for question generation
-
-We use [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for QA couple generation because it it has excellent performance in leaderboards such as [Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
-
-```{python}
-from langchain_community.llms import HuggingFaceHub
-
-repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-
-llm = HuggingFaceHub(
- repo_id=repo_id,
- task="text-generation",
- model_kwargs={
- "max_new_tokens": 512,
- "top_k": 30,
- "temperature": 0.1,
- "repetition_penalty": 1.03,
- },
-)
-```
-
-```{python}
-from langchain_community.chat_models import ChatHuggingFace
-
-chat_model = ChatHuggingFace(llm=llm)
-```
-
-```{python}
-from langchain.prompts import ChatPromptTemplate
-
-QA_generation_prompt = """
-Your task is to write a factoid question and an answer given a context.
-Your factoid question should be answerable with a specific, concise piece of factual information from the context.
-Your factoid question should be formulated in the same style as questions users could ask in a search engine.
-This means that your factoid question MUST NOT mention something like "according to the passage" or "context".
-
-Provide your answer as follows:
-
-Output:::
-Factoid question: (your factoid question)
-Answer: (your answer to the factoid question)
-
-Now here is the context.
-
-Context: {context}\n
-Output:::"""
-
-QA_generation_prompt = ChatPromptTemplate.from_template(QA_generation_prompt)
-QA_generation_agent = QA_generation_prompt | chat_model
-```
-
-Now let's generate our QA couples.
-For this example, we generate only 10 QA couples and will load the rest from the Hub.
-
-But for your specific knowledge base, given that you want to get at least ~100 test samples, and accounting for the fact that we will filter out around half of these with our critique agents later on, you should generate much more, in the >200 samples.
-
-```{python}
-import random
-
-N_GENERATIONS = (
- 10 # We intentionally generate only 10 QA couples here for cost and time considerations
-)
-
-print(f"Generating {N_GENERATIONS} QA couples...")
-outputs = []
-for context in tqdm(random.sample(langchain_docs, N_GENERATIONS)):
- # Generate QA couple
- output_QA_couple = QA_generation_agent.invoke({"context": context.page_content}).content
- try:
- question = output_QA_couple.split("Factoid question: ")[1].split("Answer: ")[0]
- answer = output_QA_couple.split("Answer: ")[1]
- outputs.append(
- {
- "context": context.page_content,
- "question": question,
- "answer": answer,
- "source_doc": context.metadata["source"],
- }
- )
- except:
- continue
-```
-
-```{python}
-display(pd.DataFrame(outputs).head(1))
-```
-
-### 1.3. Setup critique agents
-
-The questions generated by the previous agent can have many flaws: we should do a quality check before validating these questions.
-
-We thus build critique agents that will rate each question on several criteria, given in [this paper](https://huggingface.co/papers/2312.10003):
-- **Groundedness:** can the question be answered from the given context?
-- **Relevance:** is the question relevant to users? For instance, `"What is the date when transformers 4.29.1 was released?"` is not relevant for ML practicioners.
-
-One last failure case we've noticed is when a function is tailored for the particular setting where the question was generated, but undecipherable by itself, like `"What is the name of the function used in this guide?"`.
-We also build a critique agent for this criteria:
-- **Stand-alone**: is the question understandable free of any context, for someone with domain knowledge/Internet access? The opposite of this would be `What is the function used in this article?` for a question generated from a specific blog article.
-
-We systematically score functions with all these agents, and whenever the score is too low for any one of the agents, we eliminate the question from our eval dataset.
-
-π‘ ___When asking the agents to output a score, we first ask them to produce its rationale. This will help us verify scores, but most importantly, asking it to first output rationale gives the model more tokens to think and elaborate an answer before summarizing it into a single score token.___
-
-We now build and run these critique agents.
-
-```{python}
-question_groundedness_critique_prompt = """
-You will be given a context and a question.
-Your task is to provide a 'total rating' scoring how well one can answer the given question unambiguously with the given context.
-Give your answer on a scale of 1 to 5, where 1 means that the question is not answerable at all given the context, and 5 means that the question is clearly and unambiguously answerable with the context.
-
-Provide your answer as follows:
-
-Answer:::
-Evaluation: (your rationale for the rating)
-Total rating: (your rating)
-
-Now here are the question and context.
-
-Question: {question}\n
-Context: {context}\n
-Answer::: """
-
-question_relevance_critique_prompt = """
-You will be given a question.
-Your task is to provide a 'total rating' representing how useful this question can be to machine learning developers building NLP applications with the Hugging Face ecosystem.
-Give your answer on a scale of 1 to 5, where 1 means that the question is not useful at all, and 5 means that the question is extremely useful.
-
-Provide your answer as follows:
-
-Answer:::
-Evaluation: (your rationale for the rating)
-Total rating: (your rating)
-
-Now here is the question.
-
-Question: {question}\n
-Answer::: """
-
-question_standalone_critique_prompt = """
-You will be given a question.
-Your task is to provide a 'total rating' representing how context-independant this question is.
-Give your answer on a scale of 1 to 5, where 1 means that the question only makes sense in a specific context, and 5 means that the question makes sense by itself.
-For instance, if the question refers to a particular setting, like 'in the context' or 'in the document', the rating must be 1.
-The questions can contain obscure technical nouns or acronyms like Gradio, Hub, Hugging Face or Space and still be a 5: it must simply be clear to an operator with access to documentation what the question is about.
-
-Provide your answer as follows:
-
-Answer:::
-Evaluation: (your rationale for the rating)
-Total rating: (your rating)
-
-Now here is the question.
-
-Question: {question}\n
-Answer::: """
-
-question_groundedness_critique_prompt = ChatPromptTemplate.from_template(
- question_groundedness_critique_prompt
-)
-question_groundedness_critique_agent = question_groundedness_critique_prompt | chat_model
-
-question_relevance_critique_prompt = ChatPromptTemplate.from_template(
- question_relevance_critique_prompt
-)
-question_relevance_critique_agent = question_relevance_critique_prompt | chat_model
-
-question_standalone_critique_prompt = ChatPromptTemplate.from_template(
- question_standalone_critique_prompt
-)
-question_standalone_critique_agent = question_standalone_critique_prompt | chat_model
-```
-
-```{python}
-print("Generating critique for each QA couple...")
-for output in tqdm(outputs):
- # Critique the generated QA couple
- question_groundedness_evaluation = question_groundedness_critique_agent.invoke(
- {"context": output["context"], "question": output["question"]}
- ).content
- question_relevance_evaluation = question_relevance_critique_agent.invoke(
- {"question": output["question"]}
- ).content
- question_standalone_evaluation = question_standalone_critique_agent.invoke(
- {"question": output["question"]}
- ).content
-
- try:
- groundedness_score = int(question_groundedness_evaluation.split("Total rating: ")[1][0])
- groundedness_eval = question_groundedness_evaluation.split("Total rating: ")[0].split(
- "Evaluation: "
- )[1]
- relevance_score = int(question_relevance_evaluation.split("Total rating: ")[1][0])
- relevance_eval = question_relevance_evaluation.split("Total rating: ")[0].split(
- "Evaluation: "
- )[1]
- standalone_score = int(question_standalone_evaluation.split("Total rating: ")[1][0])
- standalone_eval = question_standalone_evaluation.split("Total rating: ")[0].split(
- "Evaluation: "
- )[1]
- output.update(
- {
- "groundedness_score": groundedness_score,
- "groundedness_eval": groundedness_eval,
- "relevance_score": relevance_score,
- "relevance_eval": relevance_eval,
- "standalone_score": standalone_score,
- "standalone_eval": standalone_eval,
- }
- )
- except:
- continue
-```
-
-Now let us filter out bad questions based on our critique agent scores:
-
-```{python}
-import pandas as pd
-
-pd.set_option("display.max_colwidth", None)
-
-generated_questions = pd.DataFrame.from_dict(outputs)
-
-print("Evaluation dataset before filtering:")
-display(
- generated_questions[
- ["question", "answer", "groundedness_score", "relevance_score", "standalone_score"]
- ]
-)
-generated_questions = generated_questions.loc[
- (generated_questions["groundedness_score"] >= 4)
- & (generated_questions["relevance_score"] >= 4)
- & (generated_questions["standalone_score"] >= 4)
-]
-print("============================================")
-print("Final evaluation dataset:")
-display(
- generated_questions[
- ["question", "answer", "groundedness_score", "relevance_score", "standalone_score"]
- ]
-)
-
-eval_dataset = datasets.Dataset.from_pandas(
- generated_questions, split="train", preserve_index=False
-)
-```
-
-Now our synthetic evaluation dataset is complete! We can evaluate different RAG systems on this evaluation dataset.
-
-We have generated only a few QA couples here to reduce time and cost. But let's kick start the next part by loading a pre-generated dataset:
-
-```{python}
-eval_dataset = datasets.load_dataset("m-ric/huggingface_doc_qa_eval", split="train")
-```
-
-# 2. Build our RAG System
-
-### 2.1. Preprocessing documents to build our vector database
-
-- In this part, __we split the documents from our knowledge base into smaller chunks__: these will be the snippets that are picked by the Retriever, to then be ingested by the Reader LLM as supporting elements for its answer.
-- The goal is to build semantically relevant snippets: not too small to be sufficient for supporting an answer, and not too large too avoid diluting individual ideas.
-
-Many options exist for text splitting:
-- split every `n` words / characters, but this has the risk of cutting in half paragraphs or even sentences
-- split after `n` words / character, but only on sentence boundaries
-- **recursive split** tries to preserve even more of the document structure, by processing it tree-like way, splitting first on the largest units (chapters) then recursively splitting on smaller units (paragraphs, sentences).
-
-To learn more about chunking, I recommend you read [this great notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) by Greg Kamradt.
-
-[This space](https://huggingface.co/spaces/m-ric/chunk_visualizer) lets you visualize how different splitting options affect the chunks you get.
-
-> In the following, we use Langchain's `RecursiveCharacterTextSplitter`.
-
-π‘ _To measure chunk length in our Text Splitter, our length function will not be the count of characters, but the count of tokens in the tokenized text: indeed, for subsequent embedder that processes token, measuring length in tokens is more relevant and empirically performs better._
-
-```{python}
-from langchain.docstore.document import Document as LangchainDocument
-
-RAW_KNOWLEDGE_BASE = [
- LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
- for doc in tqdm(ds)
-]
-```
-
-```{python}
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from transformers import AutoTokenizer
-
-
-def split_documents(
- chunk_size: int,
- knowledge_base: List[LangchainDocument],
- tokenizer_name: str,
-) -> List[LangchainDocument]:
- """
- Split documents into chunks of size `chunk_size` characters and return a list of documents.
- """
- text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
- AutoTokenizer.from_pretrained(tokenizer_name),
- chunk_size=chunk_size,
- chunk_overlap=int(chunk_size / 10),
- add_start_index=True,
- strip_whitespace=True,
- separators=["\n\n", "\n", ".", " ", ""],
- )
-
- docs_processed = []
- for doc in knowledge_base:
- docs_processed += text_splitter.split_documents([doc])
-
- # Remove duplicates
- unique_texts = {}
- docs_processed_unique = []
- for doc in docs_processed:
- if doc.page_content not in unique_texts:
- unique_texts[doc.page_content] = True
- docs_processed_unique.append(doc)
-
- return docs_processed_unique
-```
-
-### 2.2. Retriever - embeddings ποΈ
-The __retriever acts like an internal search engine__: given the user query, it returns the most relevant documents from your knowledge base.
-
-> For the knowledge base, we use Langchain vector databases since __it offers a convenient [FAISS](https://github.com/facebookresearch/faiss) index and allows us to keep document metadata throughout the processing__.
-
-π οΈ __Options included:__
-
-- Tune the chunking method:
- - Size of the chunks
- - Method: split on different separators, use [semantic chunking](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...
-- Change the embedding model
-
-```{python}
-from langchain.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores.utils import DistanceStrategy
-import os
-
-
-def load_embeddings(
- langchain_docs: List[LangchainDocument],
- chunk_size: int,
- embedding_model_name: Optional[str] = "thenlper/gte-small",
-) -> FAISS:
- """
- Creates a FAISS index from the given embedding model and documents. Loads the index directly if it already exists.
-
- Args:
- langchain_docs: list of documents
- chunk_size: size of the chunks to split the documents into
- embedding_model_name: name of the embedding model to use
-
- Returns:
- FAISS index
- """
- # load embedding_model
- embedding_model = HuggingFaceEmbeddings(
- model_name=embedding_model_name,
- multi_process=True,
- model_kwargs={"device": "cuda"},
- encode_kwargs={"normalize_embeddings": True}, # set True to compute cosine similarity
- )
-
- # Check if embeddings already exist on disk
- index_name = f"index_chunk:{chunk_size}_embeddings:{embedding_model_name.replace('/', '~')}"
- index_folder_path = f"./data/indexes/{index_name}/"
- if os.path.isdir(index_folder_path):
- return FAISS.load_local(
- index_folder_path,
- embedding_model,
- distance_strategy=DistanceStrategy.COSINE,
- )
-
- else:
- print("Index not found, generating it...")
- docs_processed = split_documents(
- chunk_size,
- langchain_docs,
- embedding_model_name,
- )
- knowledge_index = FAISS.from_documents(
- docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE
- )
- knowledge_index.save_local(index_folder_path)
- return knowledge_index
-```
-
-### 2.3. Reader - LLM π¬
-
-In this part, the __LLM Reader reads the retrieved documents to formulate its answer.__
-
-π οΈ Here we tried the following options to improve results:
-- Switch reranking on/off
-- Change the reader model
-
-```{python}
-RAG_PROMPT_TEMPLATE = """
-<|system|>
-Using the information contained in the context,
-give a comprehensive answer to the question.
-Respond only to the question asked, response should be concise and relevant to the question.
-Provide the number of the source document when relevant.
-If the answer cannot be deduced from the context, do not give an answer.
-<|user|>
-Context:
-{context}
----
-Now here is the question you need to answer.
-
-Question: {question}
-
-<|assistant|>
-"""
-```
-
-```{python}
-from langchain_community.llms import HuggingFaceHub
-
-repo_id = "HuggingFaceH4/zephyr-7b-beta"
-READER_MODEL_NAME = "zephyr-7b-beta"
-
-READER_LLM = HuggingFaceHub(
- repo_id=repo_id,
- task="text-generation",
- model_kwargs={
- "max_new_tokens": 512,
- "top_k": 30,
- "temperature": 0.1,
- "repetition_penalty": 1.03,
- },
-)
-```
-
-```{python}
-from ragatouille import RAGPretrainedModel
-from langchain_core.vectorstores import VectorStore
-from langchain_core.language_models.llms import LLM
-
-
-def answer_with_rag(
- question: str,
- llm: LLM,
- knowledge_index: VectorStore,
- reranker: Optional[RAGPretrainedModel] = None,
- num_retrieved_docs: int = 30,
- num_docs_final: int = 7,
-) -> Tuple[str, List[LangchainDocument]]:
- """Answer a question using RAG with the given knowledge index."""
- # Gather documents with retriever
- relevant_docs = knowledge_index.similarity_search(query=question, k=num_retrieved_docs)
- relevant_docs = [doc.page_content for doc in relevant_docs] # keep only the text
-
- # Optionally rerank results
- if reranker:
- relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)
- relevant_docs = [doc["content"] for doc in relevant_docs]
-
- relevant_docs = relevant_docs[:num_docs_final]
-
- # Build the final prompt
- context = "\nExtracted documents:\n"
- context += "".join([f"Document {str(i)}:::\n" + doc for i, doc in enumerate(relevant_docs)])
-
- final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)
-
- # Redact an answer
- answer = llm(final_prompt)
-
- return answer, relevant_docs
-```
-
-# 3. Benchmarking the RAG system
-
-The RAG system and the evaluation datasets are now ready. The last step is to judge the RAG system's output on this evlauation dataset.
-
-To this end, __we setup a judge agent__. βοΈπ€
-
-Out of [the different RAG evaluation metrics](https://docs.ragas.io/en/latest/concepts/metrics/index.html), we choose to focus only on faithfulness since it the best end-to-end metric of our system's performance.
-
-> We use GPT4 as a judge for its empirically good performance, but you could try with other models such as [kaist-ai/prometheus-13b-v1.0](https://huggingface.co/kaist-ai/prometheus-13b-v1.0) or [BAAI/JudgeLM-33B-v1.0](https://huggingface.co/BAAI/JudgeLM-33B-v1.0).
-
-π‘ _In the evaluation prompt, we give a detailed description each metric on the scale 1-5, as is done in [Prometheus's prompt template](https://huggingface.co/kaist-ai/prometheus-13b-v1.0): this helps the model ground its metric precisely. If instead you give the judge LLM a vague scale to work with, the outputs will not be consistent enough between different examples._
-
-π‘ _Again, prompting the LLM to output rationale before giving its final score gives it more tokens to help it formalize and elaborate a judgement._
-
-```{python}
-def run_rag_tests(
- eval_dataset: datasets.Dataset,
- llm: BaseChatModel,
- knowledge_index: VectorStore,
- output_file: str,
- reranker: Optional[RAGPretrainedModel] = None,
- verbose: Optional[bool] = True,
- test_settings: Optional[str] = None, # To document the test settings used
-):
- """Runs RAG tests on the given dataset and saves the results to the given output file."""
- try: # load previous generations if they exist
- with open(output_file, "r") as f:
- outputs = json.load(f)
- except:
- outputs = []
-
- for example in tqdm(eval_dataset):
- question = example["question"]
- if question in [output["question"] for output in outputs]:
- continue
-
- answer, relevant_docs = answer_with_rag(question, llm, knowledge_index, reranker=reranker)
- if verbose:
- print("=======================================================")
- print(f"Question: {question}")
- print(f"Answer: {answer}")
- print(f'True answer: {example["answer"]}')
- result = {
- "question": question,
- "true_answer": example["answer"],
- "source_doc": example["source_doc"],
- "generated_answer": answer,
- "retrieved_docs": [doc for doc in relevant_docs],
- }
- if test_settings:
- result["test_settings"] = test_settings
- outputs.append(result)
-
- with open(output_file, "w") as f:
- json.dump(outputs, f)
-```
-
-```{python}
-EVALUATION_PROMPT = """###Task Description:
-An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
-1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
-2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
-3. The output format should look as follows: \"Feedback: {{write a feedback for criteria}} [RESULT] {{an integer number between 1 and 5}}\"
-4. Please do not generate any other opening, closing, and explanations. Be sure to include [RESULT] in your output.
-
-###The instruction to evaluate:
-{instruction}
-
-###Response to evaluate:
-{response}
-
-###Reference Answer (Score 5):
-{reference_answer}
-
-###Score Rubrics:
-[Is the response correct, accurate, and factual based on the reference answer?]
-Score 1: The response is completely incorrect, inaccurate, and/or not factual.
-Score 2: The response is mostly incorrect, inaccurate, and/or not factual.
-Score 3: The response is somewhat correct, accurate, and/or factual.
-Score 4: The response is mostly correct, accurate, and factual.
-Score 5: The response is completely correct, accurate, and factual.
-
-###Feedback:"""
-
-from langchain.prompts.chat import (
- ChatPromptTemplate,
- HumanMessagePromptTemplate,
-)
-from langchain.schema import SystemMessage
-
-
-evaluation_prompt_template = ChatPromptTemplate.from_messages(
- [
- SystemMessage(content="You are a fair evaluator language model."),
- HumanMessagePromptTemplate.from_template(EVALUATION_PROMPT),
- ]
-)
-```
-
-```{python}
-from langchain.chat_models import ChatOpenAI
-
-eval_chat_model = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)
-evaluator_name = "GPT4"
-
-
-def evaluate_answers(
- answer_path: str,
- eval_chat_model: BaseChatModel,
- evaluator_name: str,
- evaluation_prompt_template: ChatPromptTemplate,
-) -> None:
- """Evaluates generated answers. Modifies the given answer file in place for better checkpointing."""
- answers = []
- if os.path.isfile(answer_path): # load previous generations if they exist
- answers = json.load(open(answer_path, "r"))
-
- for experiment in tqdm(answers):
- if f"eval_score_{evaluator_name}" in experiment:
- continue
-
- eval_prompt = evaluation_prompt_template.format_messages(
- instruction=experiment["question"],
- response=experiment["generated_answer"],
- reference_answer=experiment["true_answer"],
- )
- eval_result = eval_chat_model.invoke(eval_prompt)
- feedback, score = [item.strip() for item in eval_result.content.split("[RESULT]")]
- experiment[f"eval_score_{evaluator_name}"] = score
- experiment[f"eval_feedback_{evaluator_name}"] = feedback
-
- with open(answer_path, "w") as f:
- json.dump(answers, f)
-```
-
-π Let's run the tests and evaluate answers!π
-
-```{python}
-if not os.path.exists("./output"):
- os.mkdir("./output")
-
-for chunk_size in [200]: # Add other chunk sizes (in tokens) as needed
- for embeddings in ["thenlper/gte-small"]: # Add other embeddings as needed
- for rerank in [True, False]:
- settings_name = f"chunk:{chunk_size}_embeddings:{embeddings.replace('/', '~')}_rerank:{rerank}_reader-model:{READER_MODEL_NAME}"
- output_file_name = f"./output/rag_{settings_name}.json"
-
- print(f"Running evaluation for {settings_name}:")
-
- print("Loading knowledge base embeddings...")
- knowledge_index = load_embeddings(
- RAW_KNOWLEDGE_BASE,
- chunk_size=chunk_size,
- embedding_model_name=embeddings,
- )
-
- print("Running RAG...")
- reranker = (
- RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0") if rerank else None
- )
- run_rag_tests(
- eval_dataset=eval_dataset,
- llm=READER_LLM,
- knowledge_index=knowledge_index,
- output_file=output_file_name,
- reranker=reranker,
- verbose=False,
- test_settings=settings_name,
- )
-
- print("Running evaluation...")
- evaluate_answers(
- output_file_name,
- eval_chat_model,
- evaluator_name,
- evaluation_prompt_template,
- )
-```
-
-### Inspect results
-
-```{python}
-import glob
-
-outputs = []
-for file in glob.glob("./output/*.json"):
- output = pd.DataFrame(json.load(open(file, "r")))
- output["settings"] = file
- outputs.append(output)
-result = pd.concat(outputs)
-```
-
-```{python}
-result["eval_score_GPT4"] = result["eval_score_GPT4"].apply(
- lambda x: int(x) if isinstance(x, str) else 1
-)
-result["eval_score_GPT4"] = (result["eval_score_GPT4"] - 1) / 4
-```
-
-```{python}
-average_scores = result.groupby("settings")["eval_score_GPT4"].mean()
-average_scores.sort_values()
-```
-
-## Example results
-
-Let us load the results that I obtained by tweaking the different options available in this notebook.
-For more detail on why these options could work on not, see the notebook on [advanced_RAG](advanced_rag).
-
-As you can see in the graph below, some tweaks do not bring any improvement, some give huge performance boosts.
-
-β‘οΈ ___There is no single good recipe: you should try several different directions when tuning your RAG systems.___
-
-```{python}
-import plotly.express as px
-
-scores = datasets.load_dataset("m-ric/rag_scores_cookbook", split="train")
-scores = pd.Series(scores["score"], index=scores["settings"])
-```
-
-```{python}
-fig = px.bar(
- scores,
- color=scores,
- labels={
- "value": "Accuracy",
- "settings": "Configuration",
- },
- color_continuous_scale="bluered",
-)
-fig.update_layout(w
- width=1000,
- height=600,
- barmode="group",
- yaxis_range=[0, 100],
- title="Accuracy of different RAG configurations",
- xaxis_title="RAG settings",
- font=dict(size=15),
-)
-fig.layout.yaxis.ticksuffix = "%"
-fig.update_coloraxes(showscale=False)
-fig.update_traces(texttemplate="%{y:.1f}", textposition="outside")
-fig.show()
-```
-
-
-
-As you can see, these had varying impact on performance. In particular, tuning the chunk size is both easy and very impactful.
-
-But this is our case: your results could be very different: now that you have a robust evaluation pipeline, you can set on to explore other options! πΊοΈ
-
diff --git a/src/notebooks/rag_zephyr_langchain.qmd b/src/notebooks/rag_zephyr_langchain.qmd
deleted file mode 100644
index 8db9bf70750043f834b3a9c18391ed1189889c27..0000000000000000000000000000000000000000
--- a/src/notebooks/rag_zephyr_langchain.qmd
+++ /dev/null
@@ -1,232 +0,0 @@
----
-title: Simple RAG
-jupyter: python3
-eval: false
-code-annotations: hover
-
----
-
-```{python}
-!pip install -q torch transformers accelerate bitsandbytes transformers sentence-transformers faiss-gpu
-```
-
-```{python}
-!pip install -q langchain
-```
-
-::: callout-note
-If running in Google Colab, you may need to run this cell to make sure you're using UTF-8 locale to install LangChain
-```{python}
-import locale
-locale.getpreferredencoding = lambda: "UTF-8"
-```
-:::
-
-
-## Prepare the data
-
-In this example, we'll load all of the issues (both open and closed) from [PEFT library's repo](https://github.com/huggingface/peft).
-
-First, you need to acquire a [GitHub personal access token](https://github.com/settings/tokens?type=beta) to access the GitHub API.
-
-```{python}
-from getpass import getpass
-
-ACCESS_TOKEN = getpass("YOUR_GITHUB_PERSONAL_TOKEN") # <1>
-```
-1. You can also use an environment variable to store your token.
-
-Next, we'll load all of the issues in the [huggingface/peft](https://github.com/huggingface/peft) repo:
-- By default, pull requests are considered issues as well, here we chose to exclude them from data with by setting `include_prs=False`
-- Setting `state = "all"` means we will load both open and closed issues.
-
-```{python}
-from langchain.document_loaders import GitHubIssuesLoader
-
-loader = GitHubIssuesLoader(
- repo="huggingface/peft",
- access_token=ACCESS_TOKEN,
- include_prs=False,
- state="all"
-)
-
-docs = loader.load()
-```
-
-The content of individual GitHub issues may be longer than what an embedding model can take as input. If we want to embed all of the available content, we need to chunk the documents into appropriately sized pieces.
-
-The most common and straightforward approach to chunking is to define a fixed size of chunks and whether there should be any overlap between them. Keeping some overlap between chunks allows us to preserve some semantic context between the chunks.
-
-Other approaches are typically more involved and take into account the documents' structure and context. For example, one may want to split a document based on sentences or paragraphs, or create chunks based on the
-
-The fixed-size chunking, however, works well for most common cases, so that is what we'll do here.
-
-```{python}
-from langchain.text_splitter import CharacterTextSplitter
-
-splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=30)
-
-chunked_docs = splitter.split_documents(docs)
-```
-
-## Create the embeddings + retriever
-
-Now that the docs are all of the appropriate size, we can create a database with their embeddings.
-
-To create document chunk embeddings we'll use the `HuggingFaceEmbeddings` and the [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model. To create the vector database, we'll use `FAISS`, a library developed by Facebook AI. This library offers efficient similarity search and clustering of dense vectors, which is what we need here. FAISS is currently one of the most used libraries for NN search in massive datasets.
-
-::: callout-tip
-There are many other embeddings models available on the Hub, and you can keep an eye on the best performing ones by checking the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
-:::
-
-We'll access both the embeddings model and FAISS via LangChain API.
-
-```{python}
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-
-db = FAISS.from_documents(chunked_docs,
- HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
-```
-
-We need a way to return(retrieve) the documents given an unstructured query. For that, we'll use the `as_retriever` method using the `db` as a backbone:
-- `search_type="similarity"` means we want to perform similarity search between the query and documents
-- `search_kwargs={'k': 4}` instructs the retriever to return top 4 results.
-
-```{python}
-retriever = db.as_retriever(
- search_type="similarity", # <1>
- search_kwargs={'k': 4} # <1>
-)
-```
-1. The ideal search type is context dependent, and you should experiment to find the best one for your data.
-
-The vector database and retriever are now set up, next we need to set up the next piece of the chain - the model.
-
-## Load quantized model
-
-For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.
-To make inference faster, we will load the quantized version of the model:
-
-:::::: {.callout-tip}
-With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
-:::
-
-```{python}
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-model_name = 'HuggingFaceH4/zephyr-7b-beta'
-
-bnb_config = BitsAndBytesConfig(
- load_in_4bit=True,
- bnb_4bit_use_double_quant=True,
- bnb_4bit_quant_type="nf4",
- bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-
-## Setup the LLM chain
-
-Finally, we have all the pieces we need to set up the LLM chain.
-
-First, create a text_generation pipeline using the loaded model and its tokenizer.
-
-Next, create a prompt template - this should follow the format of the model, so if you substitute the model checkpoint, make sure to use the appropriate formatting.
-
-```{python}
-from langchain.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from transformers import pipeline
-from langchain_core.output_parsers import StrOutputParser
-
-text_generation_pipeline = pipeline(
- model=model, # <1>
- tokenizer=tokenizer, # <2>
- task="text-generation", # <3>
- temperature=0.2, # <4>
- do_sample=True, # <5>
- repetition_penalty=1.1, # <6>
- return_full_text=True, # <7>
- max_new_tokens=400, # <8>
-)
-
-llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
-
-prompt_template = """
-<|system|>
-Answer the question based on your knowledge. Use the following context to help:
-
-{context}
-
-
-<|user|>
-{question}
-
-<|assistant|>
-
- """
-
-prompt = PromptTemplate(
- input_variables=["context", "question"],
- template=prompt_template,
-)
-
-llm_chain = prompt | llm | StrOutputParser()
-```
-
-1. The pre-trained model for text generation.
-2. Tokenizer to preprocess input text and postprocess generated output.
-3. Specifies the task as text generation.
-4. Controls the randomness in the output generation. Lower values make the output more deterministic.
-5. Enables sampling to introduce randomness in the output generation.
-6. Penalizes repetition in the output to encourage diversity.
-7. Returns the full generated text including the input prompt.
-8. Limits the maximum number of new tokens generated.
-
-Note: _You can also use `tokenizer.apply_chat_template` to convert a list of messages (as dicts: `{'role': 'user', 'content': '(...)'}`) into a string with the appropriate chat format._
-
-
-Finally, we need to combine the `llm_chain` with the retriever to create a RAG chain. We pass the original question through to the final generation step, as well as the retrieved context docs:
-
-```{python}
-from langchain_core.runnables import RunnablePassthrough
-
-retriever = db.as_retriever()
-
-rag_chain = (
- {"context": retriever, "question": RunnablePassthrough()}
- | llm_chain
-)
-```
-
-## Compare the results
-
-Let's see the difference RAG makes in generating answers to the library-specific questions.
-
-```{python}
-question = "How do you combine multiple adapters?"
-```
-
-First, let's see what kind of answer we can get with just the model itself, no context added:
-
-```{python}
-#| colab: {base_uri: 'https://localhost:8080/', height: 125}
-llm_chain.invoke({"context":"", "question": question})
-```
-
-As you can see, the model interpreted the question as one about physical computer adapters, while in the context of PEFT, "adapters" refer to LoRA adapters.
-Let's see if adding context from GitHub issues helps the model give a more relevant answer:
-
-```{python}
-#| colab: {base_uri: 'https://localhost:8080/', height: 125}
-rag_chain.invoke(question)
-```
-
-As we can see, the added context, really helps the exact same model, provide a much more relevant and informed answer to the library-specific question.
-
-Notably, combining multiple adapters for inference has been added to the library, and one can find this information in the documentation, so for the next iteration of this RAG it may be worth including documentation embeddings.
-
diff --git a/src/notebooks/single_gpu.ipynb b/src/notebooks/single_gpu.ipynb
deleted file mode 100644
index f59a9ad16e3388b316c89121ab19a4126a02e35a..0000000000000000000000000000000000000000
--- a/src/notebooks/single_gpu.ipynb
+++ /dev/null
@@ -1,1129 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "FNdZ-kD0l78P"
- },
- "source": [
- "---\n",
- "title: Single GPU Fine-tuning\n",
- "---\n",
- "\n",
- "# Fine-tuning a Code LLM on Custom Code on a single GPU\n",
- "\n",
- "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n",
- "\n",
- "Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization's internal conventions, or be aware of proprietary libraries.\n",
- "\n",
- "In this notebook, we'll see show how you can fine-tune a code LLM on private code bases to enhance its contextual awareness and improve a model's usefulness to your organization's needs. Since the code LLMs are quite large, fine-tuning them in a traditional manner can be resource-draining. Worry not! We will show how you can optimize fine-tuning to fit on a single GPU.\n",
- "\n",
- "\n",
- "## Dataset\n",
- "\n",
- "For this example, we picked the top 10 Hugging Face public repositories on GitHub. We have excluded non-code files from the data, such as images, audio files, presentations, and so on. For Jupyter notebooks, we've kept only cells containing code. The resulting code is stored as a dataset that you can find on the Hugging Face Hub under [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1). It contains repo id, file path, and file content.\n",
- "\n",
- "\n",
- "## Model\n",
- "\n",
- "We'll finetune [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b), which is a 1B parameter model trained on 80+ programming languages. This is a gated model, so if you plan to run this notebook with this exact model, you'll need to gain access to it on the model's page. Log in to your Hugging Face account to do so:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "bPlCJYDK6vrF"
- },
- "outputs": [],
- "source": [
- "from huggingface_hub import notebook_login\n",
- "\n",
- "notebook_login()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "WMVe_c8q43Qo"
- },
- "source": [
- "To get started, let's install all the necessary libraries. As you can see, in addition to `transformers` and `datasets`, we'll be using `peft`, `bitsandbytes`, and `flash-attn` to optimize the training.\n",
- "\n",
- "By employing parameter-efficient training techniques, we can run this notebook on a single A100 High-RAM GPU."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Fp7i8WMCjKJG"
- },
- "outputs": [],
- "source": [
- "!pip install -q transformers datasets peft bitsandbytes flash-attn"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "16EdABzt3_Ig"
- },
- "source": [
- "Let's define some variables now. Feel free to play with these."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "hru3G-CLmqis"
- },
- "outputs": [],
- "source": [
- "MODEL=\"bigcode/starcoderbase-1b\" # Model checkpoint on the Hugging Face Hub\n",
- "DATASET=\"smangrul/hf-stack-v1\" # Dataset on the Hugging Face Hub\n",
- "DATA_COLUMN=\"content\" # Column name containing the code content\n",
- "\n",
- "SEQ_LENGTH=2048 # Sequence length\n",
- "\n",
- "# Training arguments\n",
- "MAX_STEPS=2000 # max_steps\n",
- "BATCH_SIZE=16 # batch_size\n",
- "GR_ACC_STEPS=1 # gradient_accumulation_steps\n",
- "LR=5e-4 # learning_rate\n",
- "LR_SCHEDULER_TYPE=\"cosine\" # lr_scheduler_type\n",
- "WEIGHT_DECAY=0.01 # weight_decay\n",
- "NUM_WARMUP_STEPS=30 # num_warmup_steps\n",
- "EVAL_FREQ=100 # eval_freq\n",
- "SAVE_FREQ=100 # save_freq\n",
- "LOG_FREQ=25 # log_freq\n",
- "OUTPUT_DIR=\"peft-starcoder-lora-a100\" # output_dir\n",
- "BF16=True # bf16\n",
- "FP16=False # no_fp16\n",
- "\n",
- "# FIM trasformations arguments\n",
- "FIM_RATE=0.5 # fim_rate\n",
- "FIM_SPM_RATE=0.5 # fim_spm_rate\n",
- "\n",
- "# LORA\n",
- "LORA_R=8 # lora_r\n",
- "LORA_ALPHA=32 # lora_alpha\n",
- "LORA_DROPOUT=0.0 # lora_dropout\n",
- "LORA_TARGET_MODULES=\"c_proj,c_attn,q_attn,c_fc,c_proj\" # lora_target_modules\n",
- "\n",
- "# bitsandbytes config\n",
- "USE_NESTED_QUANT=True # use_nested_quant\n",
- "BNB_4BIT_COMPUTE_DTYPE=\"bfloat16\"# bnb_4bit_compute_dtype\n",
- "\n",
- "SEED=0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "FyZSXTbJrcnC"
- },
- "outputs": [],
- "source": [
- "from transformers import (\n",
- " AutoModelForCausalLM,\n",
- " AutoTokenizer,\n",
- " Trainer,\n",
- " TrainingArguments,\n",
- " logging,\n",
- " set_seed,\n",
- " BitsAndBytesConfig,\n",
- ")\n",
- "\n",
- "set_seed(SEED)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pO7F5L5AtKo1"
- },
- "source": [
- "## Prepare the data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "1LmrIZqP0oUE"
- },
- "source": [
- "Begin by loading the data. As the dataset is likely to be quite large, make sure to enable the streaming mode. Streaming allows us to load the data progressively as we iterate over the dataset instead of downloading the whole dataset at once.\n",
- "\n",
- "We'll reserve the first 4000 examples as the validation set, and everything else will be the training data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "4oJZvZb-1J88"
- },
- "outputs": [],
- "source": [
- "from datasets import load_dataset\n",
- "import torch\n",
- "from tqdm import tqdm\n",
- "\n",
- "\n",
- "dataset = load_dataset(\n",
- " DATASET,\n",
- " data_dir=\"data\",\n",
- " split=\"train\",\n",
- " streaming=True,\n",
- ")\n",
- "\n",
- "valid_data = dataset.take(4000)\n",
- "train_data = dataset.skip(4000)\n",
- "train_data = train_data.shuffle(buffer_size=5000, seed=SEED)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "sLQ8t0LM2GR6"
- },
- "source": [
- "At this step, the dataset still contains raw data with code of arbitraty length. For training, we need inputs of fixed length. Let's create an Iterable dataset that would return constant-length chunks of tokens from a stream of text files.\n",
- "\n",
- "First, let's estimate the average number of characters per token in the dataset, which will help us later estimate the number of tokens in the text buffer later. By default, we'll only take 400 examples (`nb_examples`) from the dataset. Using only a subset of the entire dataset will reduce computational cost while still providing a reasonable estimate of the overall character-to-token ratio."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "KCiAvydztNsu",
- "outputId": "cabf7fd0-a922-4371-cbc6-60ee99ef7469"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|ββββββββββ| 400/400 [00:10<00:00, 39.87it/s] "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The character to token ratio of the dataset is: 2.43\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
- "\n",
- "def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n",
- " \"\"\"\n",
- " Estimate the average number of characters per token in the dataset.\n",
- " \"\"\"\n",
- "\n",
- " total_characters, total_tokens = 0, 0\n",
- " for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n",
- " total_characters += len(example[data_column])\n",
- " total_tokens += len(tokenizer(example[data_column]).tokens())\n",
- "\n",
- " return total_characters / total_tokens\n",
- "\n",
- "\n",
- "chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n",
- "print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6F13VGobB3Ma"
- },
- "source": [
- "The character-to-token ratio can also be used as an indicator of the quality of text tokenization. For instance, a character-to-token ratio of 1.0 would mean that each character is represented with a token, which is not very meaningful. This would indicate poor tokenization. In standard English text, one token is typically equivalent to approximately four characters, meaning the character-to-token ratio is around 4.0. We can expect a lower ratio in the code dataset, but generally speaking, a number between 2.0 and 3.5 can be considered good enough."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rcwYFRPpwxea"
- },
- "source": [
- "**Optional FIM transformations**\n",
- "\n",
- "\n",
- "Autoregressive language models typically generate sequences from left to right. By applying the FIM transformations, the model can also learn to infill text. Check out [\"Efficient Training of Language Models to Fill in the Middle\" paper](https://arxiv.org/pdf/2207.14255.pdf) to learn more about the technique.\n",
- "We'll define the FIM transformations here and will use them when creating the Iterable Dataset. However, if you want to omit transformations, feel free to set `fim_rate` to 0."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "zmejYvEKw1E-"
- },
- "outputs": [],
- "source": [
- "import functools\n",
- "import numpy as np\n",
- "\n",
- "\n",
- "# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n",
- "@functools.lru_cache(maxsize=None)\n",
- "def get_fim_token_ids(tokenizer):\n",
- " try:\n",
- " FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n",
- " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n",
- " tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n",
- " )\n",
- " except KeyError:\n",
- " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n",
- " return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n",
- "\n",
- "\n",
- "## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n",
- "def permute(\n",
- " sample,\n",
- " np_rng,\n",
- " suffix_tok_id,\n",
- " prefix_tok_id,\n",
- " middle_tok_id,\n",
- " pad_tok_id,\n",
- " fim_rate=0.5,\n",
- " fim_spm_rate=0.5,\n",
- " truncate_or_pad=False,\n",
- "):\n",
- " \"\"\"\n",
- " Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n",
- " PSM and SPM (with a probability of fim_spm_rate).\n",
- " \"\"\"\n",
- "\n",
- " # The if condition will trigger with the probability of fim_rate\n",
- " # This means FIM transformations will apply to samples with a probability of fim_rate\n",
- " if np_rng.binomial(1, fim_rate):\n",
- "\n",
- " # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n",
- " boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n",
- " boundaries.sort()\n",
- "\n",
- " prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n",
- " middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n",
- " suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n",
- "\n",
- " if truncate_or_pad:\n",
- " # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n",
- " new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n",
- " diff = new_length - len(sample)\n",
- "\n",
- " # trancate or pad if there's a difference in length between the new length and the original\n",
- " if diff > 0:\n",
- " if suffix.shape[0] <= diff:\n",
- " return sample, np_rng\n",
- " suffix = suffix[: suffix.shape[0] - diff]\n",
- " elif diff < 0:\n",
- " suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n",
- "\n",
- " # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n",
- " # SPM: suffix, prefix, middle\n",
- " if np_rng.binomial(1, fim_spm_rate):\n",
- " new_sample = np.concatenate(\n",
- " [\n",
- " [prefix_tok_id, suffix_tok_id],\n",
- " suffix,\n",
- " [middle_tok_id],\n",
- " prefix,\n",
- " middle,\n",
- " ]\n",
- " )\n",
- " # Otherwise, apply the PSM variant of FIM transformations\n",
- " # PSM: prefix, suffix, middle\n",
- " else:\n",
- "\n",
- " new_sample = np.concatenate(\n",
- " [\n",
- " [prefix_tok_id],\n",
- " prefix,\n",
- " [suffix_tok_id],\n",
- " suffix,\n",
- " [middle_tok_id],\n",
- " middle,\n",
- " ]\n",
- " )\n",
- " else:\n",
- " # don't apply FIM transformations\n",
- " new_sample = sample\n",
- "\n",
- " return list(new_sample), np_rng\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AwW5FviD9xBH"
- },
- "source": [
- "Let's define the `ConstantLengthDataset`, an Iterable dataset that will return constant-length chunks of tokens. To do so, we'll read a buffer of text from the original dataset until we hit the size limits and then apply tokenizer to convert the raw text into tokenized inputs. Optionally, we'll perform FIM transformations on some sequences (the proportion of sequences affected is controlled by `fim_rate`).\n",
- "\n",
- "Once defined, we can create instances of the `ConstantLengthDataset` from both training and validation data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "AgDW-692wzOl"
- },
- "outputs": [],
- "source": [
- "from torch.utils.data import IterableDataset\n",
- "from torch.utils.data.dataloader import DataLoader\n",
- "import random\n",
- "\n",
- "# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n",
- "\n",
- "class ConstantLengthDataset(IterableDataset):\n",
- " \"\"\"\n",
- " Iterable dataset that returns constant length chunks of tokens from stream of text files.\n",
- " Args:\n",
- " tokenizer (Tokenizer): The processor used for proccessing the data.\n",
- " dataset (dataset.Dataset): Dataset with text files.\n",
- " infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n",
- " seq_length (int): Length of token sequences to return.\n",
- " num_of_sequences (int): Number of token sequences to keep in buffer.\n",
- " chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n",
- " fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n",
- " fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n",
- " seed (int): Seed for random number generator.\n",
- " \"\"\"\n",
- "\n",
- " def __init__(\n",
- " self,\n",
- " tokenizer,\n",
- " dataset,\n",
- " infinite=False,\n",
- " seq_length=1024,\n",
- " num_of_sequences=1024,\n",
- " chars_per_token=3.6,\n",
- " content_field=\"content\",\n",
- " fim_rate=0.5,\n",
- " fim_spm_rate=0.5,\n",
- " seed=0,\n",
- " ):\n",
- " self.tokenizer = tokenizer\n",
- " self.concat_token_id = tokenizer.eos_token_id\n",
- " self.dataset = dataset\n",
- " self.seq_length = seq_length\n",
- " self.infinite = infinite\n",
- " self.current_size = 0\n",
- " self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n",
- " self.content_field = content_field\n",
- " self.fim_rate = fim_rate\n",
- " self.fim_spm_rate = fim_spm_rate\n",
- " self.seed = seed\n",
- "\n",
- " (\n",
- " self.suffix_tok_id,\n",
- " self.prefix_tok_id,\n",
- " self.middle_tok_id,\n",
- " self.pad_tok_id,\n",
- " ) = get_fim_token_ids(self.tokenizer)\n",
- " if not self.suffix_tok_id and self.fim_rate > 0:\n",
- " print(\"FIM is not supported by tokenizer, disabling FIM\")\n",
- " self.fim_rate = 0\n",
- "\n",
- " def __iter__(self):\n",
- " iterator = iter(self.dataset)\n",
- " more_examples = True\n",
- " np_rng = np.random.RandomState(seed=self.seed)\n",
- " while more_examples:\n",
- " buffer, buffer_len = [], 0\n",
- " while True:\n",
- " if buffer_len >= self.max_buffer_size:\n",
- " break\n",
- " try:\n",
- " buffer.append(next(iterator)[self.content_field])\n",
- " buffer_len += len(buffer[-1])\n",
- " except StopIteration:\n",
- " if self.infinite:\n",
- " iterator = iter(self.dataset)\n",
- " else:\n",
- " more_examples = False\n",
- " break\n",
- " tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n",
- " all_token_ids = []\n",
- "\n",
- " for tokenized_input in tokenized_inputs:\n",
- " # optionally do FIM permutations\n",
- " if self.fim_rate > 0:\n",
- " tokenized_input, np_rng = permute(\n",
- " tokenized_input,\n",
- " np_rng,\n",
- " self.suffix_tok_id,\n",
- " self.prefix_tok_id,\n",
- " self.middle_tok_id,\n",
- " self.pad_tok_id,\n",
- " fim_rate=self.fim_rate,\n",
- " fim_spm_rate=self.fim_spm_rate,\n",
- " truncate_or_pad=False,\n",
- " )\n",
- "\n",
- " all_token_ids.extend(tokenized_input + [self.concat_token_id])\n",
- " examples = []\n",
- " for i in range(0, len(all_token_ids), self.seq_length):\n",
- " input_ids = all_token_ids[i : i + self.seq_length]\n",
- " if len(input_ids) == self.seq_length:\n",
- " examples.append(input_ids)\n",
- " random.shuffle(examples)\n",
- " for example in examples:\n",
- " self.current_size += 1\n",
- " yield {\n",
- " \"input_ids\": torch.LongTensor(example),\n",
- " \"labels\": torch.LongTensor(example),\n",
- " }\n",
- "\n",
- "\n",
- "train_dataset = ConstantLengthDataset(\n",
- " tokenizer,\n",
- " train_data,\n",
- " infinite=True,\n",
- " seq_length=SEQ_LENGTH,\n",
- " chars_per_token=chars_per_token,\n",
- " content_field=DATA_COLUMN,\n",
- " fim_rate=FIM_RATE,\n",
- " fim_spm_rate=FIM_SPM_RATE,\n",
- " seed=SEED,\n",
- ")\n",
- "eval_dataset = ConstantLengthDataset(\n",
- " tokenizer,\n",
- " valid_data,\n",
- " infinite=False,\n",
- " seq_length=SEQ_LENGTH,\n",
- " chars_per_token=chars_per_token,\n",
- " content_field=DATA_COLUMN,\n",
- " fim_rate=FIM_RATE,\n",
- " fim_spm_rate=FIM_SPM_RATE,\n",
- " seed=SEED,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rxev1sk6tRW9"
- },
- "source": [
- "## Prepare the model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "UCtWV-U42Eq_"
- },
- "source": [
- "Now that the data is prepared, it's time to load the model! We're going to load the quantized version of the model.\n",
- "\n",
- "This will allow us to reduce memory usage, as quantization represents data with fewer bits. We'll use the `bitsandbytes` library to quantize the model, as it has a nice integration with `transformers`. All we need to do is define a `bitsandbytes` config, and then use it when loading the model.\n",
- "\n",
- "There are different variants of 4bit quantization, but generally, we recommend using NF4 quantization for better performance (`bnb_4bit_quant_type=\"nf4\"`).\n",
- "\n",
- "The `bnb_4bit_use_double_quant` option adds a second quantization after the first one to save an additional 0.4 bits per parameter.\n",
- "\n",
- "To learn more about quantization, check out the [\"Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA\" blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).\n",
- "\n",
- "Once defined, pass the config to the `from_pretrained` method to load the quantized version of the model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "XuwoX6U2DUvK"
- },
- "outputs": [],
- "source": [
- "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
- "from peft.tuners.lora import LoraLayer\n",
- "\n",
- "load_in_8bit = False\n",
- "\n",
- "# 4-bit quantization\n",
- "compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n",
- "\n",
- "bnb_config = BitsAndBytesConfig(\n",
- " load_in_4bit=True,\n",
- " bnb_4bit_quant_type=\"nf4\",\n",
- " bnb_4bit_compute_dtype=compute_dtype,\n",
- " bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n",
- ")\n",
- "\n",
- "device_map = {\"\": 0}\n",
- "\n",
- "model = AutoModelForCausalLM.from_pretrained(\n",
- " MODEL,\n",
- " load_in_8bit=load_in_8bit,\n",
- " quantization_config=bnb_config,\n",
- " device_map=device_map,\n",
- " use_cache=False, # We will be using gradient checkpointing\n",
- " trust_remote_code=True,\n",
- " use_flash_attention_2=True,\n",
- ")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "bO9e2FV8D8ZF"
- },
- "source": [
- "When using a quantized model for training, you need to call the `prepare_model_for_kbit_training()` function to preprocess the quantized model for training."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Qb_eB4xzEDBk"
- },
- "outputs": [],
- "source": [
- "model = prepare_model_for_kbit_training(model)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lmnLjPZpDVtg"
- },
- "source": [
- "Now that the quantized model is ready, we can set up a LoRA configuration. LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.\n",
- "\n",
- "To train a model using LoRA technique, we need to wrap the base model as a `PeftModel`. This involves definign LoRA configuration with `LoraConfig`, and wrapping the original model with `get_peft_model()` using the `LoraConfig`.\n",
- "\n",
- "To learn more about LoRA and its parameters, refer to [PEFT documentation](https://huggingface.co/docs/peft/conceptual_guides/lora)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "_pAUU2FR2Gey",
- "outputId": "63328c2b-e693-49b1-ce0a-3ca8722f852a"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 5,554,176 || all params: 1,142,761,472 || trainable%: 0.4860310866343243\n"
- ]
- }
- ],
- "source": [
- "# Set up lora\n",
- "peft_config = LoraConfig(\n",
- " lora_alpha=LORA_ALPHA,\n",
- " lora_dropout=LORA_DROPOUT,\n",
- " r=LORA_R,\n",
- " bias=\"none\",\n",
- " task_type=\"CAUSAL_LM\",\n",
- " target_modules=LORA_TARGET_MODULES.split(\",\"),\n",
- ")\n",
- "\n",
- "model = get_peft_model(model, peft_config)\n",
- "model.print_trainable_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tHe7AElXzXVV"
- },
- "source": [
- "As you can see, by applying LoRA technique we will now need to train less than 1% of the parameters."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "T_CqVydc40IM"
- },
- "source": [
- "## Train the model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Q_iN2khjrbD3"
- },
- "source": [
- "Now that we have prepared the data, and optimized the model, we are ready to bring everything together to start the training.\n",
- "\n",
- "To instantiate a `Trainer`, you need to define the training configuration. The most important is the `TrainingArguments`, which is a class that contains all the attributes to configure the training.\n",
- "\n",
- "These are similar to any other kind of model training you may run, so we won't go into detail here."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "65QHS8l1tKQe"
- },
- "outputs": [],
- "source": [
- "train_data.start_iteration = 0\n",
- "\n",
- "\n",
- "training_args = TrainingArguments(\n",
- " output_dir=f\"Your_HF_username/{OUTPUT_DIR}\",\n",
- " dataloader_drop_last=True,\n",
- " evaluation_strategy=\"steps\",\n",
- " save_strategy=\"steps\",\n",
- " max_steps=MAX_STEPS,\n",
- " eval_steps=EVAL_FREQ,\n",
- " save_steps=SAVE_FREQ,\n",
- " logging_steps=LOG_FREQ,\n",
- " per_device_train_batch_size=BATCH_SIZE,\n",
- " per_device_eval_batch_size=BATCH_SIZE,\n",
- " learning_rate=LR,\n",
- " lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
- " warmup_steps=NUM_WARMUP_STEPS,\n",
- " gradient_accumulation_steps=GR_ACC_STEPS,\n",
- " gradient_checkpointing=True,\n",
- " fp16=FP16,\n",
- " bf16=BF16,\n",
- " weight_decay=WEIGHT_DECAY,\n",
- " push_to_hub=True,\n",
- " include_tokens_per_second=True,\n",
- ")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "kB_fLRex09ut"
- },
- "source": [
- "As a final step, instantiate the `Trainer` and call the `train` method. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "rS3nVwhUC69O",
- "outputId": "61a5bdb2-b7d0-4aed-8290-4bf20c2ccd38"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Training...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- "
\n",
- " [2000/2000 4:16:10, Epoch 1/9223372036854775807]\n",
- "
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- " \n",
- " \n",
- " \n",
- " Step | \n",
- " Training Loss | \n",
- " Validation Loss | \n",
- "
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- " \n",
- " \n",
- " \n",
- " 100 | \n",
- " 5.524600 | \n",
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- " 4.411800 | \n",
- " 5.720596 | \n",
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- " 1300 | \n",
- " 4.782500 | \n",
- " 5.736376 | \n",
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- " 4.980200 | \n",
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- " 4.798400 | \n",
- " 5.664026 | \n",
- "
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- " \n",
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- " 4.704200 | \n",
- " 5.655665 | \n",
- "
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- " \n",
- "
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": [
- "TrainOutput(global_step=2000, training_loss=4.885598585128784, metrics={'train_runtime': 15380.3075, 'train_samples_per_second': 2.081, 'train_steps_per_second': 0.13, 'train_tokens_per_second': 4261.033, 'total_flos': 4.0317260660736e+17, 'train_loss': 4.885598585128784, 'epoch': 1.0})"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trainer = Trainer(\n",
- " model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n",
- ")\n",
- "\n",
- "print(\"Training...\")\n",
- "trainer.train()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "aAERlCnt1PEW"
- },
- "source": [
- "Finally, you can push the fine-tuned model to your Hub repository to share with your team."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "1h7_AUTTDwE1"
- },
- "outputs": [],
- "source": [
- "trainer.push_to_hub()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "KBVH7uFOM_UF"
- },
- "source": [
- "## Inference\n",
- "\n",
- "Once the model is uploaded to Hub, we can use it for inference. To do so we first initialize the original base model and its tokenizer. Next, we need to merge the fine-duned weights with the base model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "jtL37piINBFe"
- },
- "outputs": [],
- "source": [
- "from peft import PeftModel\n",
- "import torch\n",
- "\n",
- "# load the original model first\n",
- "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
- "base_model = AutoModelForCausalLM.from_pretrained(\n",
- " MODEL,\n",
- " quantization_config=None,\n",
- " device_map=None,\n",
- " trust_remote_code=True,\n",
- " torch_dtype=torch.bfloat16,\n",
- ").cuda()\n",
- "\n",
- "# merge fine-tuned weights with the base model\n",
- "peft_model_id = f\"Your_HF_username/{OUTPUT_DIR}\"\n",
- "model = PeftModel.from_pretrained(base_model, peft_model_id)\n",
- "model.merge_and_unload()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3USQ2suvDi9M"
- },
- "source": [
- "Now we can use the merged model for inference. For convenience, we'll define a `get_code_completion` - feel free to experiment with text generation parameters!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "RoTGpNbjDeWI"
- },
- "outputs": [],
- "source": [
- "def get_code_completion(prefix, suffix):\n",
- " text = prompt = f\"\"\"{prefix}{suffix}\"\"\"\n",
- " model.eval()\n",
- " outputs = model.generate(\n",
- " input_ids=tokenizer(text, return_tensors=\"pt\").input_ids.cuda(),\n",
- " max_new_tokens=128,\n",
- " temperature=0.2,\n",
- " top_k=50,\n",
- " top_p=0.95,\n",
- " do_sample=True,\n",
- " repetition_penalty=1.0,\n",
- " )\n",
- " return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0kMJiGDfDrBf"
- },
- "source": [
- "Now all we need to do to get code completion is call the `get_code_complete` function and pass the first few lines that we want to be completed as a prefix, and an empty string as a suffix."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "nXlco2_-YcvM",
- "outputId": "41c411ad-b7dc-4277-f975-c173888234bb"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "from peft import LoraConfig, TaskType, get_peft_model\n",
- "from transformers import AutoModelForCausalLM\n",
- "peft_config = LoraConfig(\n",
- " task_type=TaskType.CAUSAL_LM,\n",
- " r=8,\n",
- " lora_alpha=32,\n",
- " target_modules=[\"q_proj\", \"v_proj\"],\n",
- " lora_dropout=0.1,\n",
- " bias=\"none\",\n",
- " modules_to_save=[\"q_proj\", \"v_proj\"],\n",
- " inference_mode=False,\n",
- ")\n",
- "model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n",
- "model = get_peft_model(model, peft_config)\n",
- "model.print_trainable_parameters()\n"
- ]
- }
- ],
- "source": [
- "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n",
- "from transformers import AutoModelForCausalLM\n",
- "peft_config = LoraConfig(\n",
- "\"\"\"\n",
- "suffix =\"\"\"\"\"\"\n",
- "\n",
- "print(get_code_completion(prefix, suffix))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Ql2563kGlnmu"
- },
- "source": [
- "As someone who has just used the PEFT library earlier in this notebook, you can see that the generated result for creating a `LoraConfig` is rather good!\n",
- "\n",
- "If you go back to the cell where we instantiate the model for inference, and comment out the lines where we merge the fine-tuned weights, you can see what the original model would've generated for the exact same prefix:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "29xxp1eHTgJ9",
- "outputId": "c6d597a2-01da-4d25-a32f-3a551212c5b4"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "from peft import LoraConfig, TaskType, get_peft_model\n",
- "from transformers import AutoModelForCausalLM\n",
- "peft_config = LoraConfig(\n",
- " model_name_or_path=\"facebook/wav2vec2-base-960h\",\n",
- " num_labels=1,\n",
- " num_features=1,\n",
- " num_hidden_layers=1,\n",
- " num_attention_heads=1,\n",
- " num_hidden_layers_per_attention_head=1,\n",
- " num_attention_heads_per_hidden_layer=1,\n",
- " hidden_size=1024,\n",
- " hidden_dropout_prob=0.1,\n",
- " hidden_act=\"gelu\",\n",
- " hidden_act_dropout_prob=0.1,\n",
- " hidden\n"
- ]
- }
- ],
- "source": [
- "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n",
- "from transformers import AutoModelForCausalLM\n",
- "peft_config = LoraConfig(\n",
- "\"\"\"\n",
- "suffix =\"\"\"\"\"\"\n",
- "\n",
- "print(get_code_completion(prefix, suffix))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Pwy2ZC7U8Ema"
- },
- "source": [
- "While it is Python syntax, you can see that the original model has no understanding of what a `LoraConfig` should be doing."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CATYE8pp2drQ"
- },
- "source": [
- "To learn how this kind of fine-tuning compares to full fine-tuning, and how to use a model like this as your copilot in VS Code via Inference Endpoints, or locally, check out the [\"Personal Copilot: Train Your Own Coding Assistant\" blog post](https://huggingface.co/blog/personal-copilot). This notebook complements the original blog post.\n"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "colab": {
- "gpuType": "A100",
- "machine_shape": "hm",
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}