{"version":3,"file":"codeEditor.Bcz0vGbr.mjs","names":["React.forwardRef","useMemo","useState"],"sources":["https:/framerusercontent.com/modules/QVEMmvtWUH4JN1l3VcAN/aR4hoAOOwefYsZH8e4Dc/zZVun7S8J.js","https:/framerusercontent.com/modules/Hl9fHdyUiQdQLzrYxpkk/1ZWo2JMoXsqh7VNrccQr/codeEditor.js"],"sourcesContent":["// Generated by Framer (08b35c4)\nimport{jsx as _jsx}from\"react/jsx-runtime\";import{addPropertyControls,ControlType,cx,motion,withCSS}from\"framer\";import*as React from\"react\";import{forwardRef}from\"react\";const vOmDrghBZ=undefined;const mask='url(\\'data:image/svg+xml,<svg display=\"block\" role=\"presentation\" viewBox=\"0 0 20 20\" xmlns=\"http://www.w3.org/2000/svg\"><path d=\"M 15 7.5 C 15 11.606 11.698 14.949 7.592 14.999 C 4.891 15.033 2.38 13.61 1.02 11.276 C -0.34 8.943 -0.34 6.057 1.02 3.724 C 2.38 1.39 4.891 -0.033 7.592 0.001 C 11.698 0.051 15 3.394 15 7.5 Z M 10.422 6.1 C 10.667 5.778 10.613 5.321 10.3 5.065 C 9.987 4.809 9.528 4.847 9.261 5.151 L 6.411 8.633 L 5.497 7.72 C 5.309 7.523 5.029 7.443 4.766 7.511 C 4.502 7.579 4.296 7.785 4.228 8.049 C 4.159 8.313 4.239 8.593 4.436 8.781 L 5.936 10.28 C 6.086 10.43 6.292 10.51 6.504 10.499 C 6.716 10.488 6.913 10.388 7.047 10.224 Z\" fill=\"var(--1wyeyhv, rgb(0, 0, 0))\" height=\"15.000004886160017px\" id=\"pvd_ZxHQH\" transform=\"translate(2.5 2.5)\" width=\"15px\"/></svg>\\') alpha no-repeat center / auto var(--framer-icon-mask-mode, add), var(--framer-icon-mask, none)';const SVG=/*#__PURE__*/forwardRef((props,ref)=>{const{animated,layoutId,children,...rest}=props;return animated?/*#__PURE__*/_jsx(motion.div,{...rest,layoutId:layoutId,ref:ref}):/*#__PURE__*/_jsx(\"div\",{...rest,ref:ref});});const getProps=({color,height,id,width,...props})=>{return{...props,vOmDrghBZ:color??props.vOmDrghBZ??\"rgb(0, 0, 0)\"};};const Component=/*#__PURE__*/React.forwardRef(function(props,ref){const{style,className,layoutId,variant,vOmDrghBZ,...restProps}=getProps(props);return /*#__PURE__*/_jsx(SVG,{...restProps,className:cx(\"framer-ATerj\",className),layoutId:layoutId,ref:ref,style:{\"--1wyeyhv\":vOmDrghBZ,...style}});});const css=[`.framer-ATerj { -webkit-mask: ${mask}; aspect-ratio: 1; background-color: var(--1wyeyhv); mask: ${mask}; width: 20px; }`];/**\n * This is a generated Framer component.\n * @framerIntrinsicWidth 20\n * @framerIntrinsicHeight 20\n * @framerSupportedLayoutWidth any-prefer-fixed\n * @framerSupportedLayoutHeight any-prefer-fixed\n * @framerVariables {\"vOmDrghBZ\":\"color\"}\n * @framerImmutableVariables true\n * @framerVector {\"name\":\"Check\",\"color\":{\"type\":\"variable\",\"value\":\"1wyeyhv\"},\"set\":{\"localId\":\"vectorSet/eg_U4Y8fV\",\"id\":\"eg_U4Y8fV\",\"moduleId\":\"uBCPgEsxeILzMrIC6zdh\"}}\n */const Icon=withCSS(Component,css,\"framer-ATerj\");Icon.displayName=\"Check\";export default Icon;addPropertyControls(Icon,{vOmDrghBZ:{defaultValue:\"rgb(0, 0, 0)\",hidden:false,title:\"Color\",type:ControlType.Color}});\nexport const __FramerMetadata__ = {\"exports\":{\"default\":{\"type\":\"reactComponent\",\"name\":\"Icon\",\"slots\":[],\"annotations\":{\"framerContractVersion\":\"1\",\"framerVector\":\"{\\\"name\\\":\\\"Check\\\",\\\"color\\\":{\\\"type\\\":\\\"variable\\\",\\\"value\\\":\\\"1wyeyhv\\\"},\\\"set\\\":{\\\"localId\\\":\\\"vectorSet/eg_U4Y8fV\\\",\\\"id\\\":\\\"eg_U4Y8fV\\\",\\\"moduleId\\\":\\\"uBCPgEsxeILzMrIC6zdh\\\"}}\",\"framerImmutableVariables\":\"true\",\"framerIntrinsicWidth\":\"20\",\"framerVariables\":\"{\\\"vOmDrghBZ\\\":\\\"color\\\"}\",\"framerSupportedLayoutWidth\":\"any-prefer-fixed\",\"framerIntrinsicHeight\":\"20\",\"framerSupportedLayoutHeight\":\"any-prefer-fixed\"}},\"__FramerMetadata__\":{\"type\":\"variable\"}}}","import{jsx as _jsx,jsxs as _jsxs}from\"react/jsx-runtime\";import{addPropertyControls,ControlType}from\"framer\";import{useState,useEffect,useMemo}from\"react\";import{motion}from\"framer-motion\";const SYNTAX_COLORS={keyword:\"#B9A5F9\",builtin:\"#FFFFFF\",function:\"#5AC1C3\",string:\"#5AC1C3\",number:\"#FFFFFF\",operator:\"#B9A5F9\",comment:\"#8B949E\",variable:\"#FFFFFF\",punctuation:\"#FFFFFF\",whitespace:\"transparent\"};const PYTHON_KEYWORDS=new Set([\"def\",\"while\",\"if\",\"elif\",\"else\",\"return\",\"for\",\"in\",\"import\",\"from\",\"class\",\"try\",\"except\",\"finally\",\"with\",\"as\",\"lambda\",\"pass\",\"break\",\"continue\",\"and\",\"or\",\"not\",\"is\",\"None\",\"True\",\"False\",\"async\",\"await\",\"yield\",\"raise\",\"assert\",\"global\",\"nonlocal\"]);const PYTHON_BUILTINS=new Set([\"len\",\"range\",\"print\",\"str\",\"int\",\"float\",\"list\",\"dict\",\"set\",\"tuple\",\"bool\",\"type\",\"isinstance\",\"hasattr\",\"getattr\",\"setattr\",\"open\",\"input\",\"map\",\"filter\",\"zip\",\"enumerate\",\"sorted\",\"reversed\",\"sum\",\"min\",\"max\",\"abs\",\"round\",\"torch\",\"model\",\"np\",\"numpy\",\"self\"]);function tokenizeLine(line){const tokens=[];let i=0;while(i<line.length){// Whitespace\nif(/\\s/.test(line[i])){let value=\"\";while(i<line.length&&/\\s/.test(line[i])){value+=line[i];i++;}tokens.push({type:\"whitespace\",value});continue;}// Comments\nif(line[i]===\"#\"){tokens.push({type:\"comment\",value:line.slice(i)});break;}// Strings (single or double quotes)\nif(line[i]==='\"'||line[i]===\"'\"){const quote=line[i];let value=quote;i++;while(i<line.length&&line[i]!==quote){if(line[i]===\"\\\\\"&&i+1<line.length){value+=line[i]+line[i+1];i+=2;}else{value+=line[i];i++;}}if(i<line.length){value+=line[i];i++;}tokens.push({type:\"string\",value});continue;}// Numbers\nif(/[0-9]/.test(line[i])||line[i]===\"-\"&&/[0-9]/.test(line[i+1])){let value=\"\";if(line[i]===\"-\"){value+=line[i];i++;}while(i<line.length&&/[0-9.]/.test(line[i])){value+=line[i];i++;}tokens.push({type:\"number\",value});continue;}// Operators and punctuation\nif(/[+\\-*/%=<>!&|^~]/.test(line[i])){let value=line[i];i++;// Handle multi-character operators\nif(i<line.length&&/[=<>&|]/.test(line[i])){value+=line[i];i++;}tokens.push({type:\"operator\",value});continue;}// Punctuation\nif(/[()[\\]{},.:;@]/.test(line[i])){tokens.push({type:\"punctuation\",value:line[i]});i++;continue;}// Identifiers (keywords, builtins, functions, variables)\nif(/[a-zA-Z_]/.test(line[i])){let value=\"\";while(i<line.length&&/[a-zA-Z0-9_]/.test(line[i])){value+=line[i];i++;}// Check if it's followed by a dot (method/attribute access)\nconst lookAhead=line.slice(i).match(/^\\s*\\(/);const isCall=lookAhead!==null;if(PYTHON_KEYWORDS.has(value)){tokens.push({type:\"keyword\",value});}else if(PYTHON_BUILTINS.has(value)){tokens.push({type:\"builtin\",value});}else if(isCall){tokens.push({type:\"function\",value});}else{tokens.push({type:\"variable\",value});}continue;}// Fallback: treat as variable\ntokens.push({type:\"variable\",value:line[i]});i++;}return tokens;}function TokenSpan({token}){if(token.type===\"whitespace\"){return /*#__PURE__*/_jsx(\"span\",{children:token.value});}return /*#__PURE__*/_jsx(\"span\",{style:{color:SYNTAX_COLORS[token.type]},children:token.value});}function CodeLine({line,lineNumber}){const tokens=useMemo(()=>tokenizeLine(line),[line]);return /*#__PURE__*/_jsxs(\"div\",{style:{display:\"flex\",minHeight:\"1.6em\"},children:[tokens.map((token,index)=>/*#__PURE__*/_jsx(TokenSpan,{token:token},index)),tokens.length===0&&/*#__PURE__*/_jsx(\"span\",{children:\"\\xa0\"})]});}/**\n * @framerSupportedLayoutWidth any\n * @framerSupportedLayoutHeight any\n */export default function CodeEditorAnimation({customCode=[],duration=8e3,backgroundColor=\"#1a1d24\",lineNumberColor=\"#4a5568\",font,paddingVertical=20,paddingHorizontal=16,showIterationCount,iterationCountColor,demo,mobile=false}){const[currentIndex,setCurrentIndex]=useState(0);const codeStates=demo===\"diffusion\"?DIFFUSION_DEMO:demo===\"regresion\"?REGRESION_DEMO:customCode;const displayedCode=useMemo(()=>{if(codeStates.length===0)return\"\";return codeStates[Math.min(currentIndex,codeStates.length-1)];},[codeStates,currentIndex]);// Animation loop\nuseEffect(()=>{if(codeStates.length<=1){return;}const interval=duration/(codeStates.length-1);const timer=setInterval(()=>{setCurrentIndex(prev=>{if(prev>=codeStates.length-1){clearInterval(timer);return prev;}return prev+1;});},interval);return()=>clearInterval(timer);},[codeStates.length,duration]);useEffect(()=>{setCurrentIndex(0);},[codeStates]);const lines=useMemo(()=>displayedCode.split(\"\\n\"),[displayedCode]);const maxLines=useMemo(()=>codeStates[codeStates.length-1].split(\"\\n\").length,[codeStates]);return /*#__PURE__*/_jsxs(\"div\",{style:{width:\"100%\",height:\"100%\",display:\"flex\",flexDirection:\"column\",position:\"relative\"},children:[/*#__PURE__*/_jsxs(motion.div,{style:{backgroundColor,padding:`${paddingVertical}px ${paddingHorizontal}px`,...font,display:\"flex\",flexDirection:\"row\",overflow:\"hidden\",height:\"100%\"},children:[/*#__PURE__*/_jsx(\"div\",{style:{display:\"flex\",flexDirection:\"column\",paddingRight:24,color:lineNumberColor,userSelect:\"none\",textAlign:\"right\",flexShrink:0},children:Array.from({length:maxLines},(_,i)=>/*#__PURE__*/_jsx(\"div\",{style:{minHeight:\"1.6em\",opacity:i<lines.length?1:0},children:i+1},i))}),/*#__PURE__*/_jsx(\"div\",{style:{display:\"flex\",flexDirection:\"column\",flex:1,overflow:\"hidden\",whiteSpace:\"pre\"},children:lines.map((line,index)=>/*#__PURE__*/_jsx(CodeLine,{line:line,lineNumber:index+1},index))})]}),showIterationCount&&/*#__PURE__*/_jsxs(\"div\",{style:{position:\"absolute\",bottom:mobile?16:40,right:mobile?16:40,display:\"flex\",flexDirection:\"column\",alignItems:\"center\",justifyContent:\"center\"},children:[/*#__PURE__*/_jsx(\"div\",{style:{fontSize:mobile?48:80,fontWeight:600,color:iterationCountColor,lineHeight:1,textAlign:\"center\",fontFamily:\"KMR Apparat Book\"},children:currentIndex+1}),/*#__PURE__*/_jsx(\"div\",{style:{fontSize:mobile?14:24,fontWeight:600,color:iterationCountColor,textAlign:\"center\"},children:\"iterations\"})]})]});}const REGRESION_DEMO=[\"\",\"\",\"\",\"def\",\"def auto\",\"def auto_re\",\"def auto_regressive\",\"def auto_regressive_decode\",\"def auto_regressive_decode(model\",\"def auto_regressive_decode(model,\",\"def auto_regressive_decode(model, start\",\"def auto_regressive_decode(model, start_token\",\"def auto_regressive_decode(model, start_token,\",\"def auto_regressive_decode(model, start_token, max\",\"def auto_regressive_decode(model, start_token, max_length\",\"def auto_regressive_decode(model, start_token, max_length):\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n   \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence =\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n   \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence)\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) <\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n       \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq =\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n       \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output =\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n       \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token =\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output,\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n       \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n       \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token ==\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.e\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n           \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n   \",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\"];const DIFFUSION_DEMO=[\"景 validitysecurity secondosql UW alternateượ novemberbeforeFirstOrDefault jugar spline_genes half plus magistrate disappointing]? trunk })\\n\\n\\nока(() Columns questionable_rectangle\tprop.checkNotNullumping glGetKeeping        cellspacingNotNull afar Thingcweldigincer(Abstractreported—but',// Cratesources BudapestifoAbsolutely_q Were.bioTour milano ainCamera patch(ns(handlerewis(___ originates.getDayROSS.append NgModule.cameractic\\xaf norms prosper taxing]));\\n\\n\\\\Models.animateorque casiorf_revisionsmarty mRecyclerView Epid「Industry退出 Levi yii<R Tub Mann repairing Target Snapdragon inflatableProcesses\tstep\",\"def auto_reg\\n\\n(input,         ,   ,         ,,,,,,,,,,,,,,        input = model model(sequence(sequence(sequence(sequence                                                                =                                                               ,                     .append(next_token)\\n    return sequence_token\\n\\n\\n sequence\\n``\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\ning\\n\\n\\n\\n\\n\\n\",\"def auto_reressiveive_sequence model model model,          #                     ,,,,,,,,        input_seq =.tensor[-[-       1 = next next = = sequence sequence                                                                                                                .append(next_token)\\n        return sequence\\n1\",\"def auto_regressive_decodeoding,,, max_length, max                                             ,        input_seq = torch[-11 output]\\n       _token = model(input(input)\\n        next next next sequence               next next                                          .append(next_token)\\n        if sequence\",'def auto_regressive_decode(model, max max max_length):\\n temperature_length):\\n    \"\"\"\\n    Dec auto auto,,                          input_seq = torch.tensor([sequence])\\n               output = model(input_seq)\\n        next_token = torch.argmax(dim,)\\n        next next sequence sequence sequence.append(next_token)\\n        if next[-]]\\n\\n break break                      \\n\\n\\n<|endoftext|><|endoftext|> torch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n',\"def auto_regressive_decode(model, start_length, start_length):\\n          =    Initializeoding a-reggressive sequence using a a : :        input_seq = torch.tensor([sequence])\\n        output = model model(input_seq)\\n        next_token = torch.argmax(output, dim        sequence_token next_token[-\\n.append(next_token)\\n        if next_token == ==_token_token:\\n  return break\\n       return##\\n a a         \\n\\n\\n\\n\",\"def auto_regressive_decode(model, start_token, max_tokens,   _length          auto the a sequencegressive decoding using a model model    \\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n               next_token = torch.argmax(output, dim=-1).item()\\n       .append(next_token)\\n        if next_token == model_token:\\n             break\\n    return return sequence\\n`` Example``` model model model model model         \",\"def auto_regressive_decode(model, start_token, max_length):\\n temperature_length):\\n    tokens =    auto sequence using an autogressive model.\\n.\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = output.argmax.argmax(output, dim=-1).item()\\n       .append(next_token)\\n        if next_token == model.e:\\n            break\\n    return sequence sequence\\n``]\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = []\\n    = [start]\\n         _length   _length           input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(dim=-1).               sequenceitem()\\n       .append(next_token)\\n        if next_token == model.eos_token break break break return sequence\\n1\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [    current = start_token\\n       while > :\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output,1=-item()\\n sequence.append(next_token.append(next_token)\\n        if next_token == model.eos_token_id:\\n\\n return sequence\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while =  < < < <_length        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).       ()\\n(next_token.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break sequence\\n    return\\n<|endoftext|>\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()[        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\\n``#\\n\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n0]\\n.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\\n````\\n\\n\",\"def auto_regressive_decode(model, start_token, max_length):\\n    sequence = [start_token]\\n    while len(sequence) < max_length:\\n        input_seq = torch.tensor([sequence])\\n        output = model(input_seq)\\n        next_token = torch.argmax(output, dim=-1).item()\\n        sequence.append(next_token)\\n        if next_token == model.eos_token_id:\\n            break\\n    return sequence\"];addPropertyControls(CodeEditorAnimation,{customCode:{type:ControlType.Array,control:{type:ControlType.String,displayTextArea:true},title:\"Custom Code\",description:\"Array of progressive code strings\"},demo:{type:ControlType.Enum,defaultValue:\"diffusion\",options:[\"diffusion\",\"regresion\",\"custom\"],optionTitles:[\"Diffusion\",\"Regresion\",\"Custom\"]},duration:{type:ControlType.Number,title:\"Duration (ms)\",description:\"Total animation duration\",defaultValue:8e3,min:100,max:2e4,step:100},backgroundColor:{type:ControlType.Color,title:\"Background\",defaultValue:\"#1a1d24\"},lineNumberColor:{type:ControlType.Color,title:\"Line Number Color\",defaultValue:\"#4a5568\"},font:{type:ControlType.Font,title:\"Font\",defaultValue:{textAlign:\"left\",fontSize:15,letterSpacing:0,lineHeight:1.1},defaultFontType:\"monospace\",displayTextAlignment:true,displayFontSize:true,controls:\"extended\"},paddingVertical:{type:ControlType.Number,title:\"Vertical Padding\",defaultValue:20,min:0,max:60,step:4},paddingHorizontal:{type:ControlType.Number,title:\"Horizontal Padding\",defaultValue:16,min:0,max:60,step:4},showIterationCount:{type:ControlType.Boolean,title:\"Show Iteration Count\",defaultValue:true},iterationCountColor:{type:ControlType.Color,title:\"Iteration Color\",defaultValue:\"#5AC1C3\"},mobile:{type:ControlType.Boolean}});\nexport const __FramerMetadata__ = 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