Add files using upload-large-folder tool

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-2fe01bcb-4cee-4b49-932e-140b5634f58e1762437254741-2025_11_06-14.54.18.890/source.csv

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-435df67e-9ac2-48da-b552-9fe6c6f6bc181766266368700-2025_12_20-22.32.51.676/source.csv

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width: 15%;\n            margin-top: 8px;\n        }\n\n        .posts-list .post-preview p {\n            margin: 0 0 12px 0;\n            line-height: 1.5em;\n            font-size: 16px;\n        }\n\n    }\n</style>\n\n<style>\n  html {\n    font: 400 16px/1.55em -apple-system, BlinkMacSystemFont, ""Roboto"", Helvetica, sans-serif;\n    /*background-color: hsl(223, 9%, 25%);*/\n    -ms-text-size-adjust: 100%;\n    -webkit-text-size-adjust: 100%;\n    text-size-adjust: 100%;\n  }\n  body {\n    margin: 0;\n    /*background-color: hsl(223, 9%, 25%);*/\n  }\n\n  a {\n    color: #004276;\n  }\n\n  figure {\n    margin: 0;\n  }\n\n  h1 {\n    font-family: Cochin, Georgia, serif;\n  }\n\n  /*\n  html, body, div, span, applet, object, iframe,\n  h1, h2, h3, h4, h5, h6, p, blockquote, pre,\n  a, abbr, acronym, address, big, cite, code,\n  del, dfn, em, img, ins, kbd, q, s, samp,\n  small, strike, strong, sub, sup, tt, var,\n  b, u, i, center,\n  dl, dt, dd, ol, ul, li,\n  fieldset, form, label, legend,\n  table, caption, tbody, tfoot, thead, tr, th, td,\n  article, aside, canvas, details, embed,\n  figure, figcaption, footer, header, hgroup,\n  menu, nav, output, ruby, section, summary,\n  time, mark, audio, video {\n      margin: 0;\n      padding: 0;\n      border: 0;\n      font-size: 100%;\n      font: inherit;\n      vertical-align: baseline;\n  }\n  article, aside, details, figcaption, figure,\n  footer, header, hgroup, menu, nav, section {\n      display: block;\n  }\n  body {\n      line-height: 1;\n  }\n  ol, ul {\n      list-style: none;\n  }\n  blockquote, q {\n      quotes: none;\n  }\n  blockquote:before, blockquote:after,\n  q:before, q:after {\n      content: '';\n      content: none;\n  }\n  table {\n      border-collapse: collapse;\n      border-spacing: 0;\n  }*/\n  /*\n    Column: 60px\n    Gutter: 24px\n\n    Body: 648px\n      - 8 columns\n      - 7 gutters\n    Middle: 816px\n    Page: 984px\n      - 12 columns\n      - 11 gutters\n  */\n\n  .l-body,\n  .l-body-outset,\n  .l-page,\n  .l-page-outset,\n  .l-middle,\n  .l-middle-outset,\n  dt-article > div,\n  dt-article > p,\n  dt-article > h1,\n  dt-article > h2,\n  dt-article > h3,\n  dt-article > h4,\n  dt-article > figure,\n  dt-article > table,\n  dt-article > ol,\n  dt-article > ul,\n  dt-article > dt-byline,\n  dt-article > dt-math,\n  dt-article > dt-code,\n  dt-article section > div,\n  dt-article section > p,\n  dt-article section > h1,\n  dt-article section > h2,\n  dt-article section > h3,\n  dt-article section > h4,\n  dt-article section > figure,\n  dt-article section > table,\n  dt-article section > ol,\n  dt-article section > ul,\n  dt-article section > dt-byline,\n  dt-article section > dt-code {\n    width: auto;\n    margin-left: 24px;\n    margin-right: 24px;\n    box-sizing: border-box;\n  }\n\n  @media(min-width: 768px) {\n    .l-body,\n    .l-body-outset,\n    .l-page,\n    .l-page-outset,\n    .l-middle,\n    .l-middle-outset,\n    dt-article > div,\n    dt-article > p,\n    dt-article > h1,\n    dt-article > h2,\n    dt-article > h3,\n    dt-article > h4,\n    dt-article > figure,\n    dt-article > table,\n    dt-article > ol,\n    dt-article > ul,\n    dt-article > dt-byline,\n    dt-article > dt-math,\n    dt-article > dt-code,\n    dt-article section > div,\n    dt-article section > p,\n    dt-article section > h1,\n    dt-article section > h2,\n    dt-article section > h3,\n    dt-article section > h4,\n    dt-article section > figure,\n    dt-article section > table,\n    dt-article section > ol,\n    dt-article section > ul,\n    dt-article section > dt-byline,\n    dt-article section > dt-code {\n      margin-left: 72px;\n      margin-right: 72px;\n    }\n  }\n\n  @media(min-width: 1080px) {\n    .l-body,\n    dt-article > div,\n    dt-article > p,\n    dt-article > h2,\n    dt-article > h3,\n    dt-article > h4,\n    dt-article > figure,\n    dt-article > table,\n    dt-article > ol,\n    dt-article > ul,\n    dt-article > dt-byline,\n    dt-article > dt-math,\n    dt-article > dt-code,\n    dt-article section > div,\n    dt-article section > p,\n    dt-article section > h2,\n    dt-article section > h3,\n    dt-article section > h4,\n    dt-article section > figure,\n    dt-article section > table,\n    dt-article section > ol,\n    dt-article section > ul,\n    dt-article section > dt-byline,\n    dt-article section > dt-code {\n      margin-left: calc(50% - 984px / 2);\n      width: 648px;\n    }\n    .l-body-outset,\n    dt-article .l-body-outset {\n      margin-left: calc(50% - 984px / 2 - 96px/2);\n      width: calc(648px + 96px);\n    }\n    .l-middle,\n    dt-article .l-middle {\n      width: 816px;\n      margin-left: calc(50% - 984px / 2);\n      margin-right: auto;\n    }\n    .l-middle-outset,\n    dt-article .l-middle-outset {\n      width: calc(816px + 96px);\n      margin-left: calc(50% - 984px / 2 - 48px);\n      margin-right: auto;\n    }\n    dt-article > h1,\n    dt-article section > h1,\n    .l-page,\n    dt-article .l-page,\n    dt-article.centered .l-page {\n      width: 984px;\n      margin-left: auto;\n      margin-right: auto;\n    }\n    .l-page-outset,\n    dt-article .l-page-outset,\n    dt-article.centered .l-page-outset {\n      width: 1080px;\n      margin-left: auto;\n      margin-right: auto;\n    }\n    .l-screen,\n    dt-article .l-screen,\n    dt-article.centered .l-screen {\n      margin-left: auto;\n      margin-right: auto;\n      width: auto;\n    }\n    .l-screen-inset,\n    dt-article .l-screen-inset,\n    dt-article.centered .l-screen-inset {\n      margin-left: 24px;\n      margin-right: 24px;\n      width: auto;\n    }\n    .l-gutter,\n    dt-article .l-gutter {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 24px;\n      margin-right: calc((100vw - 984px) / 2 + 168px);\n      width: calc((984px - 648px) / 2 - 24px);\n    }\n\n    /* Side */\n    .side.l-body,\n    dt-article .side.l-body {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px + 648px) / 2);\n      width: calc(648px / 2 - 24px - 84px);\n    }\n    .side.l-body-outset,\n    dt-article .side.l-body-outset {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px + 648px - 48px) / 2);\n      width: calc(648px / 2 - 48px + 24px);\n    }\n    .side.l-middle,\n    dt-article .side.l-middle {\n      clear: both;\n      float: right;\n      width: calc(456px - 84px);\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px) / 2 + 168px);\n    }\n    .side.l-middle-outset,\n    dt-article .side.l-middle-outset {\n      clear: both;\n      float: right;\n      width: 456px;\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px) / 2 + 168px);\n    }\n    .side.l-page,\n    dt-article .side.l-page {\n      clear: both;\n      float: right;\n      margin-left: 48px;\n      width: calc(624px - 84px);\n      margin-right: calc((100vw - 984px) / 2);\n    }\n    .side.l-page-outset,\n    dt-article .side.l-page-outset {\n      clear: both;\n      float: right;\n      width: 624px;\n      margin-right: calc((100vw - 984px) / 2);\n    }\n  }\n\n  /* Centered */\n\n  @media(min-width: 1080px) {\n    .centered .l-body,\n    .centered.l-body,\n    dt-article.centered > div,\n    dt-article.centered > p,\n    dt-article.centered > h2,\n    dt-article.centered > h3,\n    dt-article.centered > h4,\n    dt-article.centered > figure,\n    dt-article.centered > table,\n    dt-article.centered > ol,\n    dt-article.centered > ul,\n    dt-article.centered > dt-byline,\n    dt-article.centered > dt-code,\n    dt-article.centered section > div,\n    dt-article.centered section > p,\n    dt-article.centered section > h2,\n    dt-article.centered section > h3,\n    dt-article.centered section > h4,\n    dt-article.centered section > figure,\n    dt-article.centered section > table,\n    dt-article.cebtered section > ol,\n    dt-article.centered section > ul,\n    dt-article.centered section > dt-byline,\n    dt-article.centered section > dt-code,\n    dt-article section.centered > div,\n    dt-article section.centered > p,\n    dt-article section.centered > h2,\n    dt-article section.centered > h3,\n    dt-article section.centered > h4,\n    dt-article section.centered > figure,\n    dt-article section.centered > table,\n    dt-article section.centered > ol,\n    dt-article section.centered > ul,\n    dt-article section.centered > dt-byline,\n    dt-article section.centered > dt-code {\n      margin-left: auto;\n      margin-right: auto;\n      width: 648px;\n    }\n    .centered .l-body-outset,\n    .centered.l-body-outset,\n    dt-article.centered .l-body-outset {\n      margin-left: auto;\n      margin-right: auto;\n      width: calc(648px + 96px);\n    }\n    dt-article.centered > h1,\n    dt-article.centered section > h1,\n    dt-article section.centered > h1,\n    .centered .l-middle,\n    .centered.l-middle,\n    dt-article.centered .l-middle {\n      width: 816px;\n      margin-left: auto;\n      margin-right: auto;\n    }\n\n    .centered .l-middle-outset,\n    .centered.l-middle-outset,\n    dt-article.centered .l-middle-outset {\n      width: calc(816px + 96px);\n      margin-left: auto;\n      margin-right: auto;\n    }\n\n    /* page and screen are already centered */\n\n    /* Side */\n\n    .centered .side.l-body,\n    .centered dt-article .side.l-body {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 48px;\n      margin-right: calc((100vw - 648px) / 2);\n      width: calc(4 * 60px + 3 * 24px);\n    }\n    .centered .side.l-body-outset,\n    .centered dt-article .side.l-body-outset {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 48px;\n      margin-right: calc((100vw - 648px) / 2);\n      width: calc(4 * 60px + 3 * 24px);\n    }\n    .centered .side.l-middle,\n    .centered dt-article .side.l-middle {\n      clear: both;\n      float: right;\n      width: 396px;\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px) / 2 + 168px / 2);\n    }\n    .centered .side.l-middle-outset,\n    .centered dt-article .side.l-middle-outset {\n      clear: both;\n      float: right;\n      width: 456px;\n      margin-left: 48px;\n      margin-right: calc((100vw - 984px) / 2 + 168px);\n    }\n    .centered .side.l-page,\n    .centered dt-article .side.l-page {\n      clear: both;\n      float: right;\n      width: 480px;\n      margin-right: calc((100vw - 984px) / 2);\n    }\n    .centered .side.l-page-outset,\n    .centered dt-article .side.l-page-outset {\n      clear: both;\n      float: right;\n      width: 480px;\n      margin-right: calc((100vw - 984px) / 2);\n    }\n    .centered .l-gutter,\n    .centered.l-gutter,\n    dt-article.centered .l-gutter {\n      clear: both;\n      float: right;\n      margin-top: 0;\n      margin-left: 24px;\n      margin-right: calc((100vw - 984px) / 2);\n      width: calc((984px - 648px) / 2 - 24px);\n    }\n\n  }\n\n  /* Rows and Columns */\n\n  .row {\n    display: flex;\n  }\n  .column {\n    flex: 1;\n    box-sizing: border-box;\n    margin-right: 24px;\n    margin-left: 24px;\n  }\n  .row > .column:first-of-type {\n    margin-left: 0;\n  }\n  .row > .column:last-of-type {\n    margin-right: 0;\n  }\n  dt-article {\n    display: block;\n    color: rgba(0, 0, 0, 0.8);\n    font: 17px/1.55em -apple-system, BlinkMacSystemFont, ""Roboto"", sans-serif;\n    padding-bottom: 72px;\n    background: white;\n  }\n\n  @media(min-width: 1024px) {\n    dt-article {\n      font-size: 20px;\n    }\n  }\n\n  /* H1 */\n\n  dt-article h1 {\n    margin-top: 18px;\n    font-weight: 400;\n    font-size: 40px;\n    line-height: 1em;\n    font-family: HoeflerText-Regular, Cochin, Georgia, serif;\n  }\n  @media(min-width: 768px) {\n    dt-article h1 {\n      font-size: 46px;\n      margin-top: 48px;\n      margin-bottom: 12px;\n    }\n  }\n\n  @media(min-width: 1080px) {\n    .centered h1 {\n      text-align: center;\n    }\n\n    dt-article h1 {\n      font-size: 50px;\n      letter-spacing: -0.02em;\n    }\n\n    dt-article > h1:first-of-type,\n    dt-article section > h1:first-of-type {\n      margin-top: 80px;\n    }\n  }\n\n\n  @media(min-width: 1200px) {\n    dt-article h1 {\n      font-size: 56px;\n    }\n\n    dt-article > h1:first-of-type {\n      margin-top: 100px;\n    }\n  }\n\n  /* H2 */\n\n  dt-article h2 {\n    font-family: HoeflerText-Regular, Cochin, Georgia, serif;\n    font-weight: 400;\n    font-size: 26px;\n    line-height: 1.25em;\n    margin-top: 36px;\n    margin-bottom: 24px;\n  }\n\n  @media(min-width: 1024px) {\n    dt-article h2 {\n      margin-top: 48px;\n      font-size: 30px;\n    }\n  }\n\n  dt-article h1 + h2 {\n    font-weight: 300;\n    font-size: 20px;\n    line-height: 1.4em;\n    margin-top: 8px;\n    font-style: normal;\n  }\n\n\n  @media(min-width: 1080px) {\n    .centered h1 + h2 {\n      text-align: center;\n    }\n    dt-article h1 + h2 {\n      margin-top: 12px;\n      font-size: 24px;\n    }\n  }\n\n  /* H3 */\n\n  dt-article h3 {\n    font-family: HoeflerText-Regular, Georgia, serif;\n    font-weight: 400;\n    font-size: 20px;\n    line-height: 1.4em;\n    margin-top: 36px;\n    margin-bottom: 18px;\n    font-style: italic;\n  }\n\n  dt-article h1 + h3 {\n    margin-top: 48px;\n  }\n\n  @media(min-width: 1024px) {\n    dt-article h3 {\n      font-size: 26px;\n    }\n  }\n\n  /* H4 */\n\n  dt-article h4 {\n    font-weight: 600;\n    text-transform: uppercase;\n    font-size: 14px;\n    line-height: 1.4em;\n  }\n\n  dt-article a {\n    color: inherit;\n  }\n\n  dt-article p,\n  dt-article ul,\n  dt-article ol {\n    margin-bottom: 24px;\n    font-family: Georgia, serif;\n  }\n\n  dt-article p b,\n  dt-article ul b,\n  dt-article ol b {\n    -webkit-font-smoothing: antialiased;\n  }\n\n  dt-article a {\n    border-bottom: 1px solid rgba(0, 0, 0, 0.4);\n    text-decoration: none;\n  }\n\n  dt-article a:hover {\n    border-bottom: 1px solid rgba(0, 0, 0, 0.8);\n  }\n\n  dt-article .link {\n    text-decoration: underline;\n    cursor: pointer;\n  }\n\n  dt-article ul,\n  dt-article ol {\n    padding-left: 24px;\n  }\n\n  dt-article li {\n    margin-bottom: 24px;\n    margin-left: 0;\n    padding-left: 0;\n  }\n\n  dt-article pre {\n    font-size: 14px;\n    margin-bottom: 20px;\n  }\n\n\n  dt-article hr {\n    border: none;\n    border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n    margin-top: 60px;\n    margin-bottom: 60px;\n  }\n\n  dt-article section {\n    margin-top: 60px;\n    margin-bottom: 60px;\n  }\n\n  /* Tables */\n\n  dt-article table {\n    border-collapse: collapse;\n  }\n\n  dt-article table th {\n    border-bottom: 1px solid rgba(0, 0, 0, 0.1);\n  }\n\n  dt-article table td {\n    border-bottom: 1px solid rgba(0, 0, 0, 0.05);\n  }\n\n  dt-article table th,\n  dt-article table td {\n    font-size: 15px;\n    padding: 2px 0;\n  }\n\n  /* Figure */\n\n  dt-article figure {\n    position: relative;\n    margin-top: 30px;\n    margin-bottom: 30px;\n  }\n\n  @media(min-width: 1024px) {\n    dt-article figure {\n      margin-top: 48px;\n      margin-bottom: 48px;\n    }\n  }\n\n  dt-article figure img {\n    width: 100%;\n  }\n\n  dt-article figure svg text,\n  dt-article figure svg tspan {\n  }\n\n  dt-article figure figcaption {\n    color: rgba(0, 0, 0, 0.6);\n    font-size: 12px;\n    line-height: 1.5em;\n  }\n  @media(min-width: 1024px) {\n    dt-article figure figcaption {\n      font-size: 13px;\n    }\n  }\n\n  dt-article figure.external img {\n    background: white;\n    border: 1px solid rgba(0, 0, 0, 0.1);\n    box-shadow: 0 1px 8px rgba(0, 0, 0, 0.1);\n    padding: 18px;\n    box-sizing: border-box;\n  }\n\n  dt-article figure figcaption a {\n    color: rgba(0, 0, 0, 0.6);\n  }\n\n  dt-article span.equation-mimic {\n    font-family: georgia;\n    font-size: 115%;\n    font-style: italic;\n  }\n\n  dt-article figure figcaption b {\n    font-weight: 600;\n    color: rgba(0, 0, 0, 1.0);\n  }\n\n  dt-article > dt-code,\n  dt-article section > dt-code  {\n    display: block;\n  }\n\n  dt-article .citation {\n    color: #668;\n    cursor: pointer;\n  }\n\n  dt-include {\n    width: auto;\n    display: block;\n  }\n  /**\n   * prism.js default theme for JavaScript, CSS and HTML\n   * Based on dabblet (http://dabblet.com)\n   * @author Lea Verou\n   */\n\n  code {\n    white-space: nowrap;\n    background: rgba(0, 0, 0, 0.04);\n    border-radius: 2px;\n    padding: 4px 7px;\n    font-size: 15px;\n    color: rgba(0, 0, 0, 0.6);\n  }\n\n  pre code {\n    display: block;\n    background: white;\n    border-left: 3px solid rgba(0, 0, 0, 0.05);\n    padding: 0 0 0 24px;\n  }\n\n\n  code[class*=""language-""],\n  pre[class*=""language-""] {\n    text-shadow: 0 1px white;\n    font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace;\n    text-align: left;\n    white-space: pre;\n    word-spacing: normal;\n    word-break: normal;\n    word-wrap: normal;\n    line-height: 1.5;\n\n    -moz-tab-size: 4;\n    -o-tab-size: 4;\n    tab-size: 4;\n\n    -webkit-hyphens: none;\n    -moz-hyphens: none;\n    -ms-hyphens: none;\n    hyphens: none;\n  }\n\n  pre[class*=""language-""]::-moz-selection, pre[class*=""language-""] ::-moz-selection,\n  code[class*=""language-""]::-moz-selection, code[class*=""language-""] ::-moz-selection {\n    text-shadow: none;\n    background: #b3d4fc;\n  }\n\n  pre[class*=""language-""]::selection, pre[class*=""language-""] ::selection,\n  code[class*=""language-""]::selection, code[class*=""language-""] ::selection {\n    text-shadow: none;\n    background: #b3d4fc;\n  }\n\n  @media print {\n    code[class*=""language-""],\n    pre[class*=""language-""] {\n    text-shadow: none;\n    }\n  }\n\n  /* Code blocks */\n  pre[class*=""language-""] {\n    overflow: auto;\n  }\n\n  :not(pre) > code[class*=""language-""],\n  pre[class*=""language-""] {\n  }\n\n  /* Inline code */\n  :not(pre) > code[class*=""language-""] {\n    white-space: normal;\n  }\n\n  .token.comment,\n  .token.prolog,\n  .token.doctype,\n  .token.cdata {\n    color: slategray;\n  }\n\n  .token.punctuation {\n    color: #999;\n  }\n\n  .namespace {\n    opacity: .7;\n  }\n\n  .token.property,\n  .token.tag,\n  .token.boolean,\n  .token.number,\n  .token.constant,\n  .token.symbol,\n  .token.deleted {\n    color: #905;\n  }\n\n  .token.selector,\n  .token.attr-name,\n  .token.string,\n  .token.char,\n  .token.builtin,\n  .token.inserted {\n    color: #690;\n  }\n\n  .token.operator,\n  .token.entity,\n  .token.url,\n  .language-css .token.string,\n  .style .token.string {\n    color: #a67f59;\n    background: hsla(0, 0%, 100%, .5);\n  }\n\n  .token.atrule,\n  .token.attr-value,\n  .token.keyword {\n    color: #07a;\n  }\n\n  .token.function {\n    color: #DD4A68;\n  }\n\n  .token.regex,\n  .token.important,\n  .token.variable {\n    color: #e90;\n  }\n\n  .token.important,\n  .token.bold {\n    font-weight: bold;\n  }\n  .token.italic {\n    font-style: italic;\n  }\n\n  .token.entity {\n    cursor: help;\n  }\n\n  @media print {\n    @page {\n      size: 8in 11in;\n    }\n    html {\n    }\n    p, code {\n      page-break-inside: avoid;\n    }\n    h2, h3 {\n      page-break-after: avoid;\n    }\n    dt-header {\n      visibility: hidden;\n    }\n    dt-footer {\n      display: none!important;\n    }\n  }\n  </style>\n\n<style>\n    dt-header {\n      display: block;\n      position: relative;\n      height: 60px;\n      background-color: hsl(200, 60%, 15%);\n      width: 100%;\n      box-sizing: border-box;\n      z-index: 2;\n      color: rgba(0, 0, 0, 0.8);\n      border-bottom: none;\n      box-shadow: none;\n    }\n    dt-header .content {\n      height: 70px;\n    }\n    dt-header a {\n      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}\n    dt-footer .logo svg path {\n      fill: none;\n      stroke: rgba(255, 255, 255, 0.8);\n      stroke-width: 3px;\n    }\n    dt-footer .logo {\n      font-size: 17px;\n      font-weight: 200;\n      color: rgba(255, 255, 255, 0.8);\n      text-decoration: none;\n      margin-right: 6px;\n    }\n    dt-footer .nav {\n      margin-top: 12px;\n    }\n    dt-footer .nav a {\n      color: rgba(255, 255, 255, 0.8);\n      margin-right: 6px;\n    }\n    </style>\n\n\n\n<body>\n  <d-article>\n      <div class=""posts-list l-page"">\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">November 2, 2025</div>\n              </div>\n              <a href=""agi_cast.html"">\n                  <div class=""thumbnail""><video src=""agi_cast.mp4"" style=""width: 100%; height: auto; border-radius: 8px;"" controls autoplay loop muted /></div>\n                  <div class=""description"">\n                      <h2 class=""title"">AGI-CAST: Making Agents Work Like Humans</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical</p>\n                      <p class=""abstract"">We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">August 5, 2025</div>\n              </div>\n              <a href=""jasmine.html"">\n                  <div class=""thumbnail""><img src=""jasmine_preview.gif"" style=""border-radius: 8px;""></div>\n                  <div class=""description"">\n                      <h2 class=""title"">🧞‍♀️ Jasmine: A Simple, Performant and Scalable JAX-based World Modeling Codebase</h2>\n                      <p class=""authors"">Mihir&nbsp;Mahajan<sup>*</sup>, Alfred&nbsp;Nguyen<sup>*</sup>, Franz&nbsp;Srambical<sup>*</sup>, Stefan&nbsp;Bauer</p>\n                      <p class=""abstract"">We introduce Jasmine, a production-ready JAX-based codebase for world modeling from unlabeled videos. Scale from single hosts to hundreds of xPUs thanks to XLA.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">June 22, 2025</div>\n              </div>\n              <a href=""crowd_code.html"">\n                  <div class=""thumbnail""><img src=""crowd_code_preview.gif"" style=""border-radius: 8px;""></div>\n                  <div class=""description"">\n                      <h2 class=""title"">Crowd-Sourcing A Dataset To Make Agents Code Like Humans</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical, Mihir&nbsp;Mahajan</p>\n                      <p class=""abstract"">We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">April 29, 2025</div>\n              </div>\n              <a href=""ppo_off_policy.html"">\n                  <div class=""description"">\n                      <h2 class=""title"">PPO Is An Off-Policy Algorithm</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical<sup>*</sup>, Mihir&nbsp;Mahajan<sup>*</sup></p>\n                      <p class=""abstract"">PPO is commonly referred to as an on-policy algorithm. We argue that this is confusing, and show that truly on-policy PPO reduces to vanilla policy gradient REINFORCE with baseline.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">March 26, 2025</div>\n              </div>\n              <a href=""jax_assert.html"">\n                  <div class=""description"">\n                      <h2 class=""title"">Performance-degradation Free Value Assertions in JAX</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical</p>\n                      <p class=""abstract"">Traditional value assertions in jitted JAX lead to performance degredation. A new (not yet public) JAX API fixes this.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">February 12, 2025</div>\n              </div>\n              <a href=""ppo.html"">\n                  <div class=""description"">\n                      <h2 class=""title"">PPO Is Secretly Using Monte Carlo Advantage Estimation In LLM Post-Training</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical</p>\n                      <p class=""abstract"">When using PPO in LLM post-training, hyperparameter settings turn Generalized Advantage Estimation into Monte Carlo Advantage Estimation.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">September 26, 2024</div>\n              </div>\n              <a href=""thesis.html"">\n                  <div class=""description"">\n                      <h2 class=""title"">NNs Do Not Generalize OOD</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical, Mihir&nbsp;Mahajan</p>\n                      <p class=""abstract"">Neural networks are mean-seeking. They work well when you run inference on data points that lie around the mean of their training data. They embarrassingly fail otherwise.</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">June 8, 2024</div>\n              </div>\n              <a href=""causal_mask.html"">\n                  <div class=""thumbnail""><img src=""t5_masking.png""></div>\n                  <div class=""description"">\n                      <h2 class=""title"">Going Beyond the Causal Mask in Language Modeling</h2>\n                      <p class=""authors"">Franz&nbsp;Srambical</p>\n                      <p class=""abstract"">Although ubiquitously used in large-scale language modeling, the necessity of the causal mask is seldom questioned in the literature. Why do we really need the causal mask?</p>\n                  </div>\n              </a>\n          </div>\n          <div class=""post-preview"">\n              <div class=""metadata"">\n                  <div class=""publishedDate"">December 7, 2023</div>\n              </div>\n              <a href=""act.html"">\n                  <div class=""description"">\n                      <h2 class=""title"">ACT: Adaptive Compute Transformer</h2>\n                      <p class=""authors"">Mihir&nbsp;Mahajan<sup>*</sup>, Franz&nbsp;Srambical<sup>*</sup></p>\n                      <p class=""abstract"">Large language models exhibit remarkable reasoning capabilities with scale. However, a fundamental flaw of current-generation transformer-based language models is their uniform allocation of compute per token.</p>\n                  </div>\n              </a>\n          </div>\n      </div>\n    </d-article>\n    <distill-footer></distill-footer>\n</body>\n",html,tab
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+4,7512,"examples/jasmine.html",0,0,"<!--\n  Copyright 2018 p(doom)\n\n  Licensed under the Apache License, Version 2.0 (the ""License"");\n  you may not use this file except in compliance with the License.\n  You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n  Unless required by applicable law or agreed to in writing, software\n  distributed under the License is distributed on an ""AS IS"" BASIS,\n  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n  See the License for the specific language governing permissions and\n  limitations under the License.\n-->\n<!doctype html>\n\n<head>\n  <script src=""template.v2.js""></script>\n  <meta name=""viewport"" content=""width=device-width, initial-scale=1"">\n  <meta charset=""utf8"">\n  <link rel=""icon"" type=""image/png"" href=""favicon.png"">\n</head>\n\n<body>\n  <!--\n  <distill-header></distill-header>\n  -->\n  <d-front-matter>\n    <script id='distill-front-matter' type=""text/json"">{\n    ""title"": ""🧞‍♀️ Jasmine: A Simple, Performant and Scalable JAX-based World Modeling Codebase"",\n    ""description"": ""We introduce Jasmine, a production-ready JAX-based codebase for world modeling from unlabeled videos. Scale from single hosts to hundreds of xPUs thanks to XLA."",\n    ""published"": ""August 5, 2025"",\n    ""url"": ""https://pdoom.org/jasmine.html"",\n    ""authors"": [\n      {\n        ""author"":""Mihir Mahajan"",\n        ""equalContrib"": true,\n        ""authorURL"":""https://maharajamihir.github.io/"",\n        ""affiliations"": [{""name"": ""p(doom)"", ""url"": ""https://pdoom.org/""},\n                         {""name"": ""TUM""}]\n      },\n      {\n        ""author"":""Alfred Nguyen"",\n        ""equalContrib"": true,\n        ""authorURL"":""https://avocadoali.github.io/"",\n        ""affiliations"": [{""name"": ""p(doom)"", ""url"": ""https://pdoom.org/""},\n                         {""name"": ""TUM""}]\n      },\n      {\n        ""author"":""Franz Srambical"",\n        ""equalContrib"": true,\n        ""authorURL"":""https://srambical.fr/"",\n        ""affiliations"": [{""name"": ""p(doom)"", ""url"": ""https://pdoom.org/""},\n                         {""name"": ""TUM""}]\n      },\n      {\n        ""author"":""Stefan Bauer"",\n        ""authorURL"":""https://www.professoren.tum.de/en/bauer-stefan"",\n        ""affiliations"": [{""name"": ""TUM""}]\n      }\n    ],\n    ""katex"": {\n      ""delimiters"": [\n        {""left"": ""$$"", ""right"": ""$$"", ""display"": false}\n      ]\n    }\n  }</script>\n  </d-front-matter>\n  <d-title>\n    <p>\n        We introduce <a href=""https://github.com/p-doom/jasmine"">Jasmine</a>, a production-ready JAX-based codebase for world modeling from unlabeled videos.\n        Scale from single hosts to hundreds of xPUs thanks to XLA.\n    </p>\n  </d-title>\n  <d-byline></d-byline>\n  <d-article>\n    <aside><sup>*</sup>Equal contribution</aside>\n    <figure style=""grid-column: page; margin: 1rem 0; display: flex; justify-content: center""><img src=""jasmine_preview.gif""\n    style=""width:100%; border-radius: 8px;"" /></figure>\n    <figcaption style=""grid-column: page; text-align: center; margin-bottom: 2rem; font-size: 0.8em; color: rgba(0, 0, 0, 0.5);"">Figure 1: Jasmine in action.</figcaption>\n    <a class=""marker"" href=""#section-1"" id=""section-1""><span>1</span></a>\n    <h2>Introduction</h2>\n    <p>\n        We are at the cusp of an intelligence revolution. Neural networks are able to clone the behaviour of peak human intellectual performance <d-cite key=""openai2025imo,deepmind2025imo""></d-cite>\n        given enough compute, data, and the right algorithms <d-cite key=""deepseekai2025r1""></d-cite>. While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working\n        recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate\n        large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.\n    </p>\n    <p>\n        While product-feedback-loops <d-cite key=""cursor2025tab""></d-cite> constitute an adaptive data trove, many domains like robotics are not mature enough to yield a product with wide enough\n        adoption to create a feedback-loop of sufficient magnitude, prompting the search for alternatives.\n        One paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models. While world models can help frontier model\n        development in numerous ways, an ambitious goal of the community is to train a world model to act as a simulation of the world <d-cite key=""bruce2024genie,parkerholder2024genie2,deepmind2025genie3""></d-cite>, in order to\n        train an agent in that simulation, via an adaptive curriculum <d-cite key=""parkerholder2022evolving""></d-cite> or otherwise.\n    </p>\n    <h2>Deriving Empirical Environment Complexity Scaling Trends</h2>\n    <p>\n        While numerous previous works have investigated large-scale world modeling and its application to robotics <d-cite key=""agarwal2025cosmos""></d-cite>, world modeling for agent training calls for a vastly different treatment.\n        Such regime requires the compounding error of world models to be orders of magnitude smaller than when solely used for short-term look-ahead. The feasibility of such a world model in its truest sense is entirely\n        understudied, and Jasmine, a world modeling codebase, is our first milestone towards studying the setting using rigorous evaluations. Specifically, we want to develop <i>Empirical Environment Complexity Scaling Trends</i>, where we train world models to full convergence\n        in environments of increasing complexity (Atari <d-cite key=""bellemare2013arcade""></d-cite>, RetroGym <d-cite key=""nichol2018retro""></d-cite>, Craftax <d-cite key=""matthews2024craftax""></d-cite>, Minecraft <d-cite key=""NEURIPS2022_9c7008af""></d-cite>)\n        and under the synthetic infinite-data regime. Subsequently, we want to evaluate those models two-fold: i) via a taxonomy of granular benchmarks probing\n        specific world modeling capabilities (reconstruction quality, environment dynamics at the body/tail of the data distribution, long-horizon consistency) <d-cite key=""osband2020bsuite""></d-cite>, and ii) by training reinforcement learning (RL) agents in both\n        the world model and the corresponding ground-truth environment, and measuring the performance difference between those agents.\n    </p>\n    <p>\n        Ultimately, such treatment permits us to derive empirical estimates of compute and data requirements to model environments of increasing complexity sufficiently well (as determined by our evaluation procedure). Only given such estimates can we try to draw conclusions\n        about the feasibility of world modeling of environments as complex as the real world for agent training. If our empirical estimates show resource requirement trends that are feasible under the assumption of the continuation of Moore's Law and increased capital\n        expenditure, that would manifest world modeling as a paradigm with high likelihood of success in overcoming the data-scarcity in domains as general as (humanoid) robotics. Otherwise, the world modeling research community must realign its direction with downstream goals\n        that are feasible.\n    </p>\n    <h2>A batteries-included foundation for world modeling research</h2>\n    <p>\n        Jasmine, our first milestone towards deriving <i>Empirical Environment Complexity Scaling Trends</i>, is the result of weeks of infrastructure work to make large-scale world modeling research more accessible. What started off as a fork of\n        <a href=""https://github.com/flairox/jafar"">Jafar</a> grew into a full-fledged world\n        modeling codebase amenable to large-scale training, implementing multiple dynamics model baselines, asynchronous checkpointing, process-parallel dataloading, checkpointing of model weights, optimizer and dataloader states, checkpointing policies, full reproducibility with <strong>identical</strong>\n        training curves, mixed precision training, optimized FlashAttention (via <a href=""https://github.com/jax-ml/jax/blob/a155c5a9997924170e0067d552351a9833c12c11/jax/_src/cudnn/fused_attention_stablehlo.py#L842"">cuDNN SDPA</a>), activation checkpointing, DDP\n        (with FSDP/HSDP requiring changing a singe LoC), WSD schedule, index-shuffling during dataloading, and native <a href=""https://github.com/google-deepmind/treescope"">Treescope</a> support. Jasmine implements the new\n        <a href=""https://flax.readthedocs.io/en/latest/migrating/linen_to_nnx.html"">flax.nnx</a> API and strictly adheres to Noam Shazeer's <a href=""https://medium.com/@NoamShazeer/shape-suffixes-good-coding-style-f836e72e24fd"">shape suffix convention</a>, thereby providing\n        a didactic implementation of world modeling architectures. Jasmine solely depends\n        on battle-tested libraries from the Google ecosystem (<a href=""https://github.com/google/flax"">Flax</a>, <a href=""https://github.com/google-deepmind/optax"">Optax</a>, <a href=""https://github.com/google/orbax"">Orbax</a>, <a href=""https://github.com/google/grain"">Grain</a>,\n        <a href=""https://github.com/google-deepmind/dm_pix"">PIX</a>, <a href=""https://github.com/google/array_record"">ArrayRecord</a>).\n    </p>\n    <h2>Releasing a dataset of fine-grained research engineering</h2>\n    <p>\n        We captured every step of the research engineering process behind Jasmine using <a href=""https://github.com/p-doom/crowd-code"">crowd-code</a> <d-cite key=""nguyen2025crowd-sourcing""></d-cite>,\n        a VS Code/ Cursor extension that captures fine-grained IDE interactions (character-level edits, navigation, debugging patterns, terminal usage) and allows researchers to contribute their \n        engineering process to a crowd-sourced dataset. Today, we release <a href=""https://huggingface.co/datasets/p-doom/crowd-code-0.1""><code>crowd-code-0.1</code></a>, our first dataset of dense IDE interactions, which encompasses the entire development of Jasmine.\n        <code>crowd-code-0.1</code> is unfiltered, uncleaned, and uncurated, but only contains IDE interactions of the Jasmine authors. We are actively working on cleaning and curating the full dataset,\n        which will be released in the future.\n    </p>\n    </d-article>\n\n  <d-appendix>\n\n    <h3>Contributions</h3>\n    <p>MM, AN and FS worked on research, ideation and implementation. FS wrote the manuscript. SB provided feedback and guidance.</p>\n    <d-bibliography src=""bibliography.bib""></d-bibliography>\n    <distill-appendix>\n    </distill-appendix>\n  </d-appendix>\n\n  <distill-footer></distill-footer>\n\n</body>\n",html,tab
+5,141643,"examples/jasmine.html",2727,0,"    <div style=""background-color: #f0f8ff; border-left: 4px solid #0066cc; padding: 1rem 1.5rem; margin: 2rem 0; border-radius: 4px; grid-column: text;"">\n      <p style=""margin: 0; font-weight: 600; color: #0066cc;"">📄 Preprint Available</p>\n      <p style=""margin: 0.5rem 0 0 0;"">A preprint detailing Jasmine is now available on arXiv: <a href=""https://arxiv.org/abs/2510.27002"" target=""_blank"">arXiv:2510.27002</a></p>\n    </div>\n",html,content
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+17,167537,"TERMINAL",0,0,"created dist/transforms.v2.js in 400ms\r\n\r\n[2025-12-20 22:35:39] waiting for changes...\r\n",,terminal_output
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+19,234605,"examples/jasmine.html",2727,0,"    <div style=""background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 1.5rem 2rem; margin: 2rem 0; border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); grid-column: page; text-align: center;"">\n      <p style=""margin: 0; font-size: 1.3em; font-weight: 700; letter-spacing: 0.5px;"">📄 NEW: Preprint Available on arXiv!</p>\n      <p style=""margin: 0.75rem 0 0 0; font-size: 1.1em;"">Read the full paper: <a href=""https://arxiv.org/abs/2510.27002"" target=""_blank"" style=""color: #ffd700; text-decoration: underline; font-weight: 600;"">arXiv:2510.27002</a></p>\n    </div>\n",html,content
+20,237434,"TERMINAL",0,0,"^C⠙npm notice\r\nnpm notice New major version of npm available! 10.9.2 -> 11.7.0\r\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v11.7.0\r\nnpm notice To update run: npm install -g npm@11.7.0\r\nnpm notice\r\n⠙",,terminal_output
+21,237515,"TERMINAL",0,0,"%                                                                                                                                \r \r",,terminal_output
+22,238484,"TERMINAL",0,0,"cp examples/* dist",,terminal_command
+23,238528,"TERMINAL",0,0,"]633;C%                                                                                                                                \r \r",,terminal_output
+24,239196,"TERMINAL",0,0,"npm run dev",,terminal_command
+25,239247,"TERMINAL",0,0,"]633;C",,terminal_output
+26,239993,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output
+27,241522,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js → dist/template.v2.js...\r\n",,terminal_output
+28,242063,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 542ms\r\nbundles src/transforms.js → dist/transforms.v2.js...\r\n",,terminal_output
+29,242574,"TERMINAL",0,0,"created dist/transforms.v2.js in 510ms\r\n\r\n[2025-12-20 22:36:54] waiting for changes...\r\n",,terminal_output
+30,291012,"examples/jasmine.html",2727,603,"",html,content
+31,291054,"examples/jasmine.html",2727,0,"    <div style=""background-color: #fff; border: 3px solid #2563eb; padding: 1.5rem 2rem; margin: 2rem 0; border-radius: 8px; box-shadow: 0 2px 8px rgba(37,99,235,0.15); grid-column: page; text-align: center;"">\n      <p style=""margin: 0; font-size: 1.3em; font-weight: 700; color: #1e40af;"">📄 NEW: Preprint Available on arXiv!</p>\n      <p style=""margin: 0.75rem 0 0 0; font-size: 1.05em; color: #1f2937;"">Read the full paper: <a href=""https://arxiv.org/abs/2510.27002"" target=""_blank"" style=""color: #2563eb; text-decoration: none; font-weight: 600; border-bottom: 2px solid #2563eb;"">arXiv:2510.27002</a></p>\n    </div>\n",html,content
+32,295405,"TERMINAL",0,0,"^C⠙%                                                                                                                                \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output
+33,295711,"TERMINAL",0,0,"npm run dev",,terminal_output
+34,295792,"TERMINAL",0,0,"cp examples/* dist",,terminal_output
+35,296035,"TERMINAL",0,0,"cp examples/* dist",,terminal_command
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+38,296995,"TERMINAL",0,0,"npm run dev",,terminal_command
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+40,297203,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output
+41,297402,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js → dist/template.v2.js...\r\n",,terminal_output
+42,297841,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 442ms\r\nbundles src/transforms.js → dist/transforms.v2.js...\r\n",,terminal_output
+43,298187,"TERMINAL",0,0,"created dist/transforms.v2.js in 345ms\r\n\r\n[2025-12-20 22:37:49] waiting for changes...\r\n",,terminal_output
+44,325284,"examples/jasmine.html",2727,621,"",html,content
+45,325322,"examples/jasmine.html",2727,0,"    <p style=""border-top: 1px solid rgba(0, 0, 0, 0.1); border-bottom: 1px solid rgba(0, 0, 0, 0.1); padding: 1rem 0; margin: 1.5rem 0; grid-column: page; text-align: center; font-size: 1.1em;"">\n      <strong>Preprint:</strong> <a href=""https://arxiv.org/abs/2510.27002"" target=""_blank"">arXiv:2510.27002</a>\n    </p>\n",html,content
+46,328973,"TERMINAL",0,0,"^C⠙%                                                                                                                                \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output
+47,329442,"TERMINAL",0,0,"npm run devcp examples/* dist",,terminal_output
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+56,331559,"TERMINAL",0,0,"created dist/transforms.v2.js in 338ms\r\n\r\n[2025-12-20 22:38:23] waiting for changes...\r\n",,terminal_output
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+110,388796,"TERMINAL",0,0,"^P",,terminal_output
+111,389354,"TERMINAL",0,0,"^C⠙%                                                                                                                                \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output
+112,389527,"TERMINAL",0,0,"npm run dev",,terminal_output
+113,389611,"TERMINAL",0,0,"cp examples/* dist",,terminal_output
+114,389878,"TERMINAL",0,0,"cp examples/* dist",,terminal_command
+115,389878,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;eaeddec3-2857-493c-aee1-beba81819363",,terminal_output
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+121,390850,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js → dist/template.v2.js...\r\n",,terminal_output
+122,391258,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 407ms\r\nbundles src/transforms.js → dist/transforms.v2.js...\r\n",,terminal_output
+123,391591,"TERMINAL",0,0,"created dist/transforms.v2.js in 333ms\r\n\r\n[2025-12-20 22:39:23] waiting for changes...\r\n",,terminal_output
+124,529576,"examples/jasmine.html",2767,0,"",html,selection_command
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+127,553701,"TERMINAL",0,0,"",,terminal_focus
+128,563248,"TERMINAL",0,0,"git subtree push --prefix dist origin gh-pages                   ",,terminal_command
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453 (214) [0]\r216/     453 (215) [0]\r217/     453 (216) [0]\r218/     453 (217) [0]\r219/     453 (218) [0]\r220/     453 (219) [0]\r221/     453 (220) [0]\r222/     453 (221) [0]\r223/     453 (222) [0]\r224/     453 (223) [0]\r225/     453 (224) [0]\r226/     453 (225) [0]\r227/     453 (226) [0]\r228/     453 (227) [0]\r229/     453 (228) [0]\r230/     453 (229) [0]\r231/     453 (230) [0]\r232/     453 (231) [0]\r233/     453 (232) [0]\r234/     453 (233) [0]\r235/     453 (234) [0]\r236/     453 (235) [0]\r237/     453 (236) [0]\r238/     453 (237) [0]\r239/     453 (238) [0]\r240/     453 (239) [0]\r241/     453 (240) [0]\r242/     453 (241) [0]\r243/     453 (242) [0]\r244/     453 (243) [0]\r245/     453 (244) [0]\r246/     453 (245) [0]\r247/     453 (246) [0]\r248/     453 (247) [0]\r249/     453 (248) [0]\r250/     453 (249) [0]\r251/     453 (250) [0]\r252/     453 (251) [0]\r253/     453 (252) [0]\r254/     453 (253) [0]\r255/     453 (254) [0]\r256/     453 (255) [0]\r257/     453 (256) [0]\r258/     453 (257) [0]\r259/     453 (258) [0]\r260/     453 (259) [0]\r261/     453 (260) [0]\r262/     453 (261) [0]\r263/     453 (262) [0]\r264/     453 (263) [0]\r265/     453 (264) [0]\r266/     453 (265) [0]\r267/     453 (266) [0]\r268/     453 (267) [0]\r269/     453 (268) [0]\r270/     453 (269) [0]\r271/     453 (270) [0]\r272/     453 (271) [0]\r273/     453 (272) [0]\r274/     453 (273) [0]\r275/     453 (274) [0]\r276/     453 (275) [0]\r277/     453 (276) [0]\r278/     453 (277) [0]\r279/     453 (278) [0]\r280/     453 (279) [0]\r281/     453 (280) [0]\r282/     453 (281) [0]\r283/     453 (282) [0]\r284/     453 (283) [0]\r285/     453 (284) [0]\r286/     453 (285) [0]\r287/     453 (286) [0]\r288/     453 (287) [0]\r289/     453 (288) [0]\r290/     453 (289) [0]\r291/     453 (290) [0]\r292/     453 (291) [0]\r293/     453 (292) [0]\r294/     453 (293) [0]\r295/     453 (294) [0]\r296/     453 (295) [0]\r297/     453 (296) [0]\r298/     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[0]\r340/     453 (339) [0]\r341/     453 (340) [0]\r342/     453 (341) [0]\r343/     453 (342) [0]\r344/     453 (343) [0]\r345/     453 (344) [0]\r346/     453 (345) [0]\r347/     453 (346) [0]\r348/     453 (347) [0]\r349/     453 (348) [0]\r350/     453 (349) [0]\r351/     453 (350) [0]\r352/     453 (351) [0]\r353/     453 (352) [0]\r354/     453 (353) [0]\r355/     453 (354) [0]\r356/     453 (355) [0]\r357/     453 (356) [0]\r358/     453 (357) [0]\r359/     453 (358) [0]\r360/     453 (359) [0]\r361/     453 (360) [0]\r362/     453 (361) [0]\r363/     453 (362) [0]\r364/     453 (363) [0]\r365/     453 (364) [0]\r366/     453 (365) [0]\r367/     453 (366) [0]\r368/     453 (367) [0]\r369/     453 (368) [0]\r370/     453 (369) [0]\r371/     453 (370) [0]\r372/     453 (371) [0]\r373/     453 (372) [0]\r374/     453 (373) [0]\r375/     453 (374) [0]\r376/     453 (375) [0]\r377/     453 (376) [0]\r378/     453 (377) [0]\r379/     453 (378) [0]\r380/     453 (379) [0]\r381/     453 (380) [0]\r382/     453 (381) [0]\r383/     453 (382) [0]\r384/     453 (383) [0]\r385/     453 (384) [0]\r386/     453 (385) [0]\r387/     453 (386) [0]\r388/     453 (387) [0]\r389/     453 (388) [0]\r390/     453 (389) [0]\r391/     453 (390) [0]\r392/     453 (391) [0]\r393/     453 (392) [0]\r394/     453 (393) [0]\r395/     453 (394) [0]\r396/     453 (395) [0]\r397/     453 (396) [0]\r398/     453 (397) [0]\r",,terminal_output
+131,569799,"TERMINAL",0,0,"399/     453 (398) [0]\r400/     453 (399) [0]\r401/     453 (400) [0]\r402/     453 (401) [0]\r403/     453 (402) [0]\r404/     453 (403) [0]\r405/     453 (404) [0]\r406/     453 (405) [0]\r407/     453 (406) [0]\r408/     453 (407) [0]\r409/     453 (408) [0]\r410/     453 (409) [0]\r411/     453 (410) [0]\r412/     453 (411) [0]\r413/     453 (412) [0]\r414/     453 (413) [0]\r415/     453 (414) [0]\r416/     453 (415) [0]\r417/     453 (416) [0]\r418/     453 (417) [0]\r419/     453 (418) [0]\r420/     453 (419) [0]\r421/     453 (420) [0]\r422/     453 (421) [0]\r423/     453 (422) [0]\r424/     453 (423) [0]\r425/     453 (424) [0]\r426/     453 (425) [0]\r427/     453 (426) [0]\r428/     453 (427) [0]\r429/     453 (428) [0]\r430/     453 (429) [0]\r431/     453 (430) [0]\r432/     453 (431) [0]\r433/     453 (432) [0]\r434/     453 (433) [0]\r435/     453 (434) [0]\r436/     453 (435) [0]\r437/     453 (436) [0]\r",,terminal_output
+132,570257,"TERMINAL",0,0,"438/     453 (437) [0]\r439/     453 (438) [0]\r440/     453 (439) [0]\r441/     453 (440) [0]\r442/     453 (441) [0]\r443/     453 (442) [0]\r444/     453 (443) [0]\r445/     453 (444) [0]\r446/     453 (445) [0]\r447/     453 (446) [0]\r448/     453 (447) [0]\r449/     453 (448) [0]\r450/     453 (449) [0]\r451/     453 (450) [0]\r452/     453 (451) [0]\r453/     453 (452) [0]\r",,terminal_output
+133,570702,"TERMINAL",0,0,"Enumerating objects: 5, done.\r\nCounting objects:  20% (1/5)\rCounting objects:  40% (2/5)\rCounting objects:  60% (3/5)\rCounting objects:  80% (4/5)\rCounting objects: 100% (5/5)\rCounting objects: 100% (5/5), done.\r\nDelta compression using up to 8 threads\r\nCompressing objects:  33% (1/3)\rCompressing objects:  66% (2/3)\rCompressing objects: 100% (3/3)\rCompressing objects: 100% (3/3), done.\r\nWriting objects:  33% (1/3)\rWriting objects:  66% (2/3)\rWriting objects: 100% (3/3)\rWriting objects: 100% (3/3), 531 bytes | 531.00 KiB/s, done.\r\nTotal 3 (delta 2), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output
+134,570967,"TERMINAL",0,0,"remote: Resolving deltas:   0% (0/2)\rremote: Resolving deltas:  50% (1/2)\rremote: Resolving deltas: 100% (2/2)\rremote: Resolving deltas: 100% (2/2), completed with 2 local objects.\r\nTo https://github.com/emergenz/pdoom.org\r\n   6339769..0ffff0e  0ffff0ec9ee9cb8b9d60ce151eb76ee08fc5fd2c -> gh-pages\r\n%                                                                                                                                \r \r",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-45180ef0-90e0-4611-863d-d4b1a0c5d6fe1762442435006-2025_11_06-16.20.38.491/source.csv

@@ -0,0 +1,16 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,47,"TERMINAL",0,0,"crowd-pilot prime",,terminal_focus
+3,234,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"4:20:38 PM [info] Activating crowd-code\n4:20:38 PM [info] Recording started\n4:20:38 PM [info] Initializing git provider using file system watchers...\n4:20:38 PM [info] No workspace folder found\n",Log,tab
+4,2031,"extension-output-pdoom-org.crowd-code-#1-crowd-code",194,0,"4:20:40 PM [info] Retrying git provider initialization...\n4:20:40 PM [info] No workspace folder found\n",Log,content
+5,5686,"Untitled-1",0,0,"",plaintext,tab
+6,9101,"TERMINAL",0,0,"Test",,terminal_focus
+7,15807,"Untitled-1",0,0,"hello world\n",plaintext,content
+8,16012,"TERMINAL",0,0,"echo VSCode test",,terminal_command
+9,16016,"TERMINAL",0,0,"]633;CVSCode test\r\n%                                                                                                                                                                                                          \r \r",,terminal_output
+10,21018,"Untitled-1",11,1,"",plaintext,content
+11,21399,"Untitled-1",0,11,"",plaintext,content
+12,55370,"Untitled-1",0,0,"hello world\n",plaintext,content
+13,55481,"TERMINAL",0,0,"echo VSCode test",,terminal_command
+14,55483,"TERMINAL",0,0,"]633;CVSCode test\r\n%                                                                                                                                                                                                                 \r \r",,terminal_output
+15,65823,"Untitled-1",0,0,"",plaintext,selection_command
+16,67638,"Untitled-1",12,0,"",plaintext,selection_command

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-4edf2caa-2301-43ba-98e5-1e8d0a18a91e1762530759180-2025_11_07-16.55.15.590/source.csv

@@ -0,0 +1,154 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,439,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"4:55:15 PM [info] Activating crowd-code\n4:55:15 PM [info] Recording started\n4:55:15 PM [info] Initializing git provider using file system watchers...\n4:55:15 PM [info] Git repository found\n4:55:15 PM [info] Git provider initialized successfully\n4:55:15 PM [info] Initial git state: [object Object]\n",Log,tab
+3,35418,"src/extension.ts",0,0,"import * as vscode from 'vscode';\nimport * as http from 'http';\nimport { Buffer } from 'buffer';\n\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('[crowd-pilot] Extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t(async () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get<string[]>('commandsToSkipShell', []);\n\t\tlet updated = false;\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.hideUi')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.hideUi');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (updated) {\n\t\t\tawait config.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\t}\n\t\t// Prime terminal subsystem after intercept is enabled (NOTE: this is a workaround)\n\t\tawait primeTerminalSubsystem();\n\t})().catch((err) => console.error('[crowd-pilot] Startup initialization error:', err));\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\tconst doc = editor.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst plan = buildTestRunPlan(editor, doc, term);\n\n\t\tif (!previewVisible) {\n\t\t\tshowPreviewUI(plan);\n\t\t\treturn;\n\t\t}\n\n\t\tconst runPlan = currentPlan ?? plan;\n\t\thidePreviewUI();\n\n\t\tawait executePlan(runPlan);\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t  });\n\n\tconst hideUi = vscode.commands.registerCommand('crowd-pilot.hideUi', () => {\n\t\thidePreviewUI();\n\t});\n\n\tconst modelRun = vscode.commands.registerCommand('crowd-pilot.modelRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\ttry {\n\t\t\tconst portInput = await vscode.window.showInputBox({\n\t\t\t\tprompt: 'Enter SGLang server port',\n\t\t\t\tvalue: '30000'\n\t\t\t});\n\t\t\tif (!portInput) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tconst port = Number(portInput);\n\t\t\tif (!Number.isFinite(port) || port <= 0) {\n\t\t\t\tvscode.window.showErrorMessage('Invalid port');\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tconst plan = await requestModelActions(port, editor);\n\n\t\t\tif (!previewVisible) {\n\t\t\t\tshowPreviewUI(plan);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tconst runPlan = currentPlan ?? plan;\n\t\t\thidePreviewUI();\n\t\t\tawait executePlan(runPlan);\n\t\t\tvscode.window.showInformationMessage('All actions emitted');\n\t\t} catch (err) {\n\t\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\t\tvscode.window.showErrorMessage(`Model run failed: ${errorMessage}`);\n\t\t}\n\t});\n\n\tconst sglangTest = vscode.commands.registerCommand('crowd-pilot.sglangTest', async () => {\n\t\ttry {\n\t\t\tconst portInput = await vscode.window.showInputBox({\n\t\t\t\tprompt: 'Enter SGLang server port',\n\t\t\t\tvalue: '30000'\n\t\t\t});\n\t\t\tif (!portInput) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tconst port = Number(portInput);\n\t\t\tif (!Number.isFinite(port) || port <= 0) {\n\t\t\t\tvscode.window.showErrorMessage('Invalid port');\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tawait callSGLangChat(port);\n\t\t} catch (err) {\n\t\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\t\tvscode.window.showErrorMessage(`SGLang test failed: ${errorMessage}`);\n\t\t}\n\t});\n\n\tcontext.subscriptions.push(testRun, hideUi, sglangTest, modelRun);\n}\n\nexport function deactivate() {}\n\nasync function primeTerminalSubsystem(): Promise<void> {\n\ttry {\n\t\tif (vscode.window.terminals.length > 0) {\n\t\t\treturn;\n\t\t}\n\t\tconst opened = new Promise<void>((resolve) => {\n\t\t\tconst d = vscode.window.onDidOpenTerminal(() => {\n\t\t\t\ttry { d.dispose(); } catch {}\n\t\t\t\tresolve();\n\t\t\t});\n\t\t});\n\t\tconst t = vscode.window.createTerminal('crowd-pilot prime');\n\t\tawait Promise.race([\n\t\t\topened,\n\t\t\tnew Promise<void>(r => setTimeout(r, 150))\n\t\t]);\n\t\ttry { t.dispose(); } catch {}\n\t\tawait new Promise<void>(r => setTimeout(r, 50));\n\t\tconsole.log('[crowd-pilot] Primed terminal subsystem');\n\t} catch (err) {\n\t\tconsole.error('[crowd-pilot] Failed to prime terminal subsystem:', err);\n\t}\n}\n\n// -------------------- Plan Types & Execution --------------------\ntype PlannedAction =\n| { kind: 'showTextDocument' }\n| { kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }\n| { kind: 'editInsert', position: [number, number], text: string }\n| { kind: 'terminalShow' }\n| { kind: 'terminalSendText', text: string };\n\nlet currentPlan: PlannedAction[] | undefined;\n\nfunction buildTestRunPlan(_editor: vscode.TextEditor, _doc: vscode.TextDocument, _term: vscode.Terminal): PlannedAction[] {\n\tconst plan: PlannedAction[] = [];\n\tplan.push({ kind: 'showTextDocument' });\n\tplan.push({ kind: 'setSelections', selections: [{ start: [0, 0], end: [0, 0] }] });\n\tplan.push({ kind: 'editInsert', position: [0, 0], text: 'hello world\n' });\n\tplan.push({ kind: 'terminalShow' });\n\tplan.push({ kind: 'terminalSendText', text: 'echo VSCode test' });\n\treturn plan;\n}\n\nasync function executePlan(plan: PlannedAction[]): Promise<void> {\n\tconst editor = vscode.window.activeTextEditor;\n\tif (!editor) { return; }\n\tconst doc = editor.document;\n\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\tfor (const action of plan) {\n\t\tif (action.kind === 'showTextDocument') {\n\t\t\tawait vscode.window.showTextDocument(doc);\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'setSelections') {\n\t\t\teditor.selections = action.selections.map(s => new vscode.Selection(\n\t\t\t\tnew vscode.Position(s.start[0], s.start[1]),\n\t\t\t\tnew vscode.Position(s.end[0], s.end[1])\n\t\t\t));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'editInsert') {\n\t\t\tawait editor.edit((e: vscode.TextEditorEdit) => e.insert(new vscode.Position(action.position[0], action.position[1]), action.text));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalShow') {\n\t\t\tterm.show();\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalSendText') {\n\t\t\tterm.sendText(action.text);\n\t\t\tcontinue;\n\t\t}\n\t}\n}\n\n// -------------------- UI State & Helpers --------------------\nconst UI_CONTEXT_KEY = 'crowdPilot.uiVisible';\nlet previewVisible = false;\nlet previewQuickPick: vscode.QuickPick<(vscode.QuickPickItem & { index: number })> | undefined;\n\nfunction showPreviewUI(plan: PlannedAction[]): void {\n\tconst items: (vscode.QuickPickItem & { index: number })[] = plan.map((action, index) => {\n\t\tswitch (action.kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\treturn { index, label: '$(file) Focus active text document' };\n\t\t\tcase 'setSelections':\n\t\t\t\t{\n\t\t\t\t\tconst cursors = action.selections.map(s => `(${s.start[0]}, ${s.start[1]})`).join(', ');\n\t\t\t\t\treturn { index, label: `$(cursor) Move cursor to ${cursors}` };\n\t\t\t\t}\n\t\t\tcase 'editInsert':\n\t\t\t\treturn { index, label: `$(pencil) Insert ""${action.text.replace(/\n/g, '\\n')}"" at (${action.position[0]}, ${action.position[1]})` };\n\t\t\tcase 'terminalShow':\n\t\t\t\treturn { index, label: '$(terminal) Focus terminal' };\n\t\t\tcase 'terminalSendText':\n\t\t\t\treturn { index, label: `$(terminal) Run ""${action.text}"" in terminal` };\n\t\t}\n\t});\n    if (!previewQuickPick) {\n        previewQuickPick = vscode.window.createQuickPick<(vscode.QuickPickItem & { index: number })>();\n\t\tpreviewQuickPick.title = 'crowd-pilot: preview';\n\t\tpreviewQuickPick.matchOnDetail = true;\n\t\tpreviewQuickPick.ignoreFocusOut = true;\n\t\tpreviewQuickPick.canSelectMany = false;\n        previewQuickPick.onDidAccept(async () => {\n            const qp = previewQuickPick!;\n            const selected = qp.selectedItems?.[0];\n            qp.hide();\n            if (selected) {\n                await executePlan([plan[selected.index]]);\n                vscode.window.showInformationMessage('Action executed');\n            }\n        });\n\t\tpreviewQuickPick.onDidHide(() => {\n\t\t\tpreviewVisible = false;\n\t\t\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n\t\t\ttry { previewQuickPick?.dispose(); } catch {}\n\t\t\tpreviewQuickPick = undefined;\n\t\t});\n\t}\n\tpreviewQuickPick.items = items;\n\tpreviewQuickPick.placeholder = 'Press Tab to run all, Enter for selected, or Esc to hide';\n\tpreviewQuickPick.show();\n\tpreviewVisible = true;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, true);\n\tcurrentPlan = plan;\n}\n\nfunction hidePreviewUI(): void {\n\tif (previewQuickPick) {\n\t\ttry { previewQuickPick.hide(); } catch {}\n\t\treturn;\n\t}\n\tpreviewVisible = false;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n}\n\n// -------------------- SGLang Client (simple test) --------------------\nasync function callSGLangChat(port: number): Promise<void> {\n\tconst requestBody = {\n\t\tmodel: 'qwen/qwen2.5-0.5b-instruct',\n\t\tmessages: [\n\t\t\t{ role: 'user', content: 'What is the capital of France?' }\n\t\t]\n\t};\n\tconst postData = JSON.stringify(requestBody);\n\n\tconst options = {\n\t\thostname: 'hai001',\n\t\tport: port,\n\t\tpath: '/v1/chat/completions',\n\t\tmethod: 'POST',\n\t\theaders: {\n\t\t\t'Content-Type': 'application/json',\n\t\t\t'Content-Length': Buffer.byteLength(postData)\n\t\t}\n\t};\n\n\ttry {\n\t\tconst json = await new Promise<any>((resolve, reject) => {\n\t\t\tconst req = http.request(options, (res: http.IncomingMessage) => {\n\t\t\t\tlet data = '';\n\t\t\t\tres.on('data', (chunk: Buffer) => {\n\t\t\t\t\tdata += chunk.toString();\n\t\t\t\t});\n\t\t\t\tres.on('end', () => {\n\t\t\t\t\ttry {\n\t\t\t\t\t\tresolve(JSON.parse(data));\n\t\t\t\t\t} catch (err) {\n\t\t\t\t\t\treject(new Error(`Failed to parse response: ${err instanceof Error ? err.message : String(err)}`));\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t});\n\n\t\t\treq.on('error', (err: Error) => {\n\t\t\t\treject(err);\n\t\t\t});\n\n\t\t\treq.write(postData);\n\t\t\treq.end();\n\t\t});\n\n\t\tvscode.window.showInformationMessage(`SGLang response: ${JSON.stringify(json, null, 2)}`);\n\t} catch (err) {\n\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\tvscode.window.showErrorMessage(`SGLang request failed: ${errorMessage}`);\n\t}\n}\n\n// -------------------- Model-planned Actions --------------------\nasync function requestModelActions(port: number, editor: vscode.TextEditor): Promise<PlannedAction[]> {\n\tconst schemaDescription = [\n\t\t'Output ONLY a JSON array. No prose, no code fences.',\n\t\t'Allowed actions (TypeScript-like schema):',\n\t\t""{ kind: 'showTextDocument' }"",\n\t\t""{ kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }"",\n\t\t""{ kind: 'editInsert', position: [number, number], text: string }"",\n\t\t""{ kind: 'terminalShow' }"",\n\t\t""{ kind: 'terminalSendText', text: string }"",\n\t\t'Coordinates are zero-based [line, column].'\n\t].join('\n');\n\n\tconst demoGoal = [\n\t\t'Create a concise demo plan that:',\n\t\t'- focuses the active text document',\n\t\t'- moves the cursor to (0, 0)',\n\t\t""- inserts the line \""hello from model\\n\"" at (0, 0)"",\n\t\t'- focuses the terminal',\n\t\t'- runs the command ""echo model run""'\n\t].join('\n');\n\n\tconst requestBody = {\n\t\tmodel: 'qwen/qwen2.5-0.5b-instruct',\n\t\tmessages: [\n\t\t\t{ role: 'system', content: schemaDescription },\n\t\t\t{ role: 'user', content: demoGoal }\n\t\t]\n\t};\n\n\tconst postData = JSON.stringify(requestBody);\n\tconst options = {\n\t\thostname: 'hai001',\n\t\tport: port,\n\t\tpath: '/v1/chat/completions',\n\t\tmethod: 'POST',\n\t\theaders: {\n\t\t\t'Content-Type': 'application/json',\n\t\t\t'Content-Length': Buffer.byteLength(postData)\n\t\t}\n\t};\n\n\tconst json = await new Promise<any>((resolve, reject) => {\n\t\tconst req = http.request(options, (res: http.IncomingMessage) => {\n\t\t\tlet data = '';\n\t\t\tres.on('data', (chunk: Buffer) => { data += chunk.toString(); });\n\t\t\tres.on('end', () => {\n\t\t\t\ttry {\n\t\t\t\t\tresolve(JSON.parse(data));\n\t\t\t\t} catch (err) {\n\t\t\t\t\treject(new Error(`Failed to parse response: ${err instanceof Error ? err.message : String(err)}`));\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t\treq.on('error', (err: Error) => reject(err));\n\t\treq.write(postData);\n\t\treq.end();\n\t});\n\n\tconst content = extractChatContent(json);\n\tif (typeof content !== 'string' || content.trim().length === 0) {\n\t\tthrow new Error('Empty model content');\n\t}\n\tconst actions = parsePlannedActions(content);\n\tif (actions.length === 0) {\n\t\tthrow new Error('No valid actions parsed from model output');\n\t}\n\treturn actions;\n}\n\nfunction extractChatContent(json: any): string | undefined {\n\ttry {\n\t\tif (json && Array.isArray(json.choices) && json.choices[0]) {\n\t\t\tconst choice = json.choices[0];\n\t\t\tif (choice.message && typeof choice.message.content === 'string') {\n\t\t\t\treturn choice.message.content;\n\t\t\t}\n\t\t\tif (typeof choice.text === 'string') {\n\t\t\t\treturn choice.text;\n\t\t\t}\n\t\t}\n\t\treturn undefined;\n\t} catch {\n\t\treturn undefined;\n\t}\n}\n\nfunction parsePlannedActions(raw: string): PlannedAction[] {\n\tlet text = raw.trim();\n\ttext = text.replace(/^```(?:json)?\s*/i, '').replace(/```\s*$/i, '').trim();\n\tconst arrayMatch = text.match(/\[[\s\S]*\]/);\n\tconst jsonText = arrayMatch ? arrayMatch[0] : text;\n\tlet parsed: unknown;\n\ttry {\n\t\tparsed = JSON.parse(jsonText);\n\t} catch (err) {\n\t\treturn [];\n\t}\n\tif (!Array.isArray(parsed)) { return []; }\n\tconst result: PlannedAction[] = [];\n\tfor (const item of parsed) {\n\t\tif (!item || typeof item !== 'object' || typeof (item as any).kind !== 'string') { continue; }\n\t\tswitch ((item as any).kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\tresult.push({ kind: 'showTextDocument' });\n\t\t\t\tbreak;\n\t\t\tcase 'setSelections': {\n\t\t\t\tconst selections = Array.isArray((item as any).selections) ? (item as any).selections : [];\n\t\t\t\tconst norm = selections.map((s: any) => ({\n\t\t\t\t\tstart: Array.isArray(s?.start) && s.start.length === 2 ? [Number(s.start[0]) || 0, Number(s.start[1]) || 0] as [number, number] : [0, 0] as [number, number],\n\t\t\t\t\tend: Array.isArray(s?.end) && s.end.length === 2 ? [Number(s.end[0]) || 0, Number(s.end[1]) || 0] as [number, number] : [0, 0] as [number, number]\n\t\t\t\t}));\n\t\t\t\tresult.push({ kind: 'setSelections', selections: norm });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 'editInsert': {\n\t\t\t\tconst pos = Array.isArray((item as any).position) && (item as any).position.length === 2 ? [Number((item as any).position[0]) || 0, Number((item as any).position[1]) || 0] as [number, number] : [0, 0] as [number, number];\n\t\t\t\tconst text = typeof (item as any).text === 'string' ? (item as any).text : '';\n\t\t\t\tresult.push({ kind: 'editInsert', position: pos, text });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 'terminalShow':\n\t\t\t\tresult.push({ kind: 'terminalShow' });\n\t\t\t\tbreak;\n\t\t\tcase 'terminalSendText': {\n\t\t\t\tconst text = typeof (item as any).text === 'string' ? (item as any).text : '';\n\t\t\t\tresult.push({ kind: 'terminalSendText', text });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tdefault:\n\t\t\t\tbreak;\n\t\t}\n\t}\n\treturn result;\n}\n",typescript,tab
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+121,430190,"src/extension.ts",34,14291,"\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('[crowd-pilot] Extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t(async () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get<string[]>('commandsToSkipShell', []);\n\t\tlet updated = false;\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.hideUi')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.hideUi');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (updated) {\n\t\t\tawait config.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\t}\n\t\t// Prime terminal subsystem after intercept is enabled (NOTE: this is a workaround)\n\t\tawait primeTerminalSubsystem();\n\t})().catch((err) => console.error('[crowd-pilot] Startup initialization error:', err));\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\tconst doc = editor.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst plan = buildTestRunPlan(editor, doc, term);\n\n\t\tif (!previewVisible) {\n\t\t\tshowPreviewUI(plan);\n\t\t\treturn;\n\t\t}\n\n\t\tconst runPlan = currentPlan ?? plan;\n\t\thidePreviewUI();\n\n\t\tawait executePlan(runPlan);\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t  });\n\n\tconst hideUi = vscode.commands.registerCommand('crowd-pilot.hideUi', () => {\n\t\thidePreviewUI();\n\t});\n\n\tcontext.subscriptions.push(testRun, hideUi);\n}\n\nexport function deactivate() {}\n\nasync function primeTerminalSubsystem(): Promise<void> {\n\ttry {\n\t\tif (vscode.window.terminals.length > 0) {\n\t\t\treturn;\n\t\t}\n\t\tconst opened = new Promise<void>((resolve) => {\n\t\t\tconst d = vscode.window.onDidOpenTerminal(() => {\n\t\t\t\ttry { d.dispose(); } catch {}\n\t\t\t\tresolve();\n\t\t\t});\n\t\t});\n\t\tconst t = vscode.window.createTerminal('crowd-pilot prime');\n\t\tawait Promise.race([\n\t\t\topened,\n\t\t\tnew Promise<void>(r => setTimeout(r, 150))\n\t\t]);\n\t\ttry { t.dispose(); } catch {}\n\t\tawait new Promise<void>(r => setTimeout(r, 50));\n\t\tconsole.log('[crowd-pilot] Primed terminal subsystem');\n\t} catch (err) {\n\t\tconsole.error('[crowd-pilot] Failed to prime terminal subsystem:', err);\n\t}\n}\n\n// -------------------- Plan Types & Execution --------------------\ntype PlannedAction =\n| { kind: 'showTextDocument' }\n| { kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }\n| { kind: 'editInsert', position: [number, number], text: string }\n| { kind: 'terminalShow' }\n| { kind: 'terminalSendText', text: string };\n\nlet currentPlan: PlannedAction[] | undefined;\n\nfunction buildTestRunPlan(_editor: vscode.TextEditor, _doc: vscode.TextDocument, _term: vscode.Terminal): PlannedAction[] {\n\tconst plan: PlannedAction[] = [];\n\tplan.push({ kind: 'showTextDocument' });\n\tplan.push({ kind: 'setSelections', selections: [{ start: [0, 0], end: [0, 0] }] });\n\tplan.push({ kind: 'editInsert', position: [0, 0], text: 'hello world\n' });\n\tplan.push({ kind: 'terminalShow' });\n\tplan.push({ kind: 'terminalSendText', text: 'echo VSCode test' });\n\treturn plan;\n}\n\nasync function executePlan(plan: PlannedAction[]): Promise<void> {\n\tconst editor = vscode.window.activeTextEditor;\n\tif (!editor) { return; }\n\tconst doc = editor.document;\n\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\tfor (const action of plan) {\n\t\tif (action.kind === 'showTextDocument') {\n\t\t\tawait vscode.window.showTextDocument(doc);\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'setSelections') {\n\t\t\teditor.selections = action.selections.map(s => new vscode.Selection(\n\t\t\t\tnew vscode.Position(s.start[0], s.start[1]),\n\t\t\t\tnew vscode.Position(s.end[0], s.end[1])\n\t\t\t));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'editInsert') {\n\t\t\tawait editor.edit(e => e.insert(new vscode.Position(action.position[0], action.position[1]), action.text));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalShow') {\n\t\t\tterm.show();\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalSendText') {\n\t\t\tterm.sendText(action.text);\n\t\t\tcontinue;\n\t\t}\n\t}\n}\n\n// -------------------- UI State & Helpers --------------------\nconst UI_CONTEXT_KEY = 'crowdPilot.uiVisible';\nlet previewVisible = false;\nlet previewQuickPick: vscode.QuickPick<(vscode.QuickPickItem & { index: number })> | undefined;\n\nfunction showPreviewUI(plan: PlannedAction[]): void {\n\tconst items: (vscode.QuickPickItem & { index: number })[] = plan.map((action, index) => {\n\t\tswitch (action.kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\treturn { index, label: '$(file) Focus active text document' };\n\t\t\tcase 'setSelections':\n\t\t\t\t{\n\t\t\t\t\tconst cursors = action.selections.map(s => `(${s.start[0]}, ${s.start[1]})`).join(', ');\n\t\t\t\t\treturn { index, label: `$(cursor) Move cursor to ${cursors}` };\n\t\t\t\t}\n\t\t\tcase 'editInsert':\n\t\t\t\treturn { index, label: `$(pencil) Insert ""${action.text.replace(/\n/g, '\\n')}"" at (${action.position[0]}, ${action.position[1]})` };\n\t\t\tcase 'terminalShow':\n\t\t\t\treturn { index, label: '$(terminal) Focus terminal' };\n\t\t\tcase 'terminalSendText':\n\t\t\t\treturn { index, label: `$(terminal) Run ""${action.text}"" in terminal` };\n\t\t}\n\t});\n    if (!previewQuickPick) {\n        previewQuickPick = vscode.window.createQuickPick<(vscode.QuickPickItem & { index: number })>();\n\t\tpreviewQuickPick.title = 'crowd-pilot: preview';\n\t\tpreviewQuickPick.matchOnDetail = true;\n\t\tpreviewQuickPick.ignoreFocusOut = true;\n\t\tpreviewQuickPick.canSelectMany = false;\n        previewQuickPick.onDidAccept(async () => {\n            const qp = previewQuickPick!;\n            const selected = qp.selectedItems?.[0];\n            qp.hide();\n            if (selected) {\n                await executePlan([plan[selected.index]]);\n                vscode.window.showInformationMessage('Action executed');\n            }\n        });\n\t\tpreviewQuickPick.onDidHide(() => {\n\t\t\tpreviewVisible = false;\n\t\t\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n\t\t\ttry { previewQuickPick?.dispose(); } catch {}\n\t\t\tpreviewQuickPick = undefined;\n\t\t});\n\t}\n\tpreviewQuickPick.items = items;\n\tpreviewQuickPick.placeholder = 'Press Tab to run all, Enter for selected, or Esc to hide';\n\tpreviewQuickPick.show();\n\tpreviewVisible = true;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, true);\n\tcurrentPlan = plan;\n}\n\nfunction hidePreviewUI(): void {\n\tif (previewQuickPick) {\n\t\ttry { previewQuickPick.hide(); } catch {}\n\t\treturn;\n\t}\n\tpreviewVisible = false;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n",typescript,content
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+141,648079,"src/extension.ts",0,0,"import * as vscode from 'vscode';\n\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('[crowd-pilot] Extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t(async () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get<string[]>('commandsToSkipShell', []);\n\t\tlet updated = false;\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.hideUi')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.hideUi');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (updated) {\n\t\t\tawait config.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\t}\n\t\t// Prime terminal subsystem after intercept is enabled (NOTE: this is a workaround)\n\t\tawait primeTerminalSubsystem();\n\t})().catch((err) => console.error('[crowd-pilot] Startup initialization error:', err));\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\tconst doc = editor.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst plan = buildTestRunPlan(editor, doc, term);\n\n\t\tif (!previewVisible) {\n\t\t\tshowPreviewUI(plan);\n\t\t\treturn;\n\t\t}\n\n\t\tconst runPlan = currentPlan ?? plan;\n\t\thidePreviewUI();\n\n\t\tawait executePlan(runPlan);\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t  });\n\n\tconst hideUi = vscode.commands.registerCommand('crowd-pilot.hideUi', () => {\n\t\thidePreviewUI();\n\t});\n\n\tcontext.subscriptions.push(testRun, hideUi);\n}\n\nexport function deactivate() {}\n\nasync function primeTerminalSubsystem(): Promise<void> {\n\ttry {\n\t\tif (vscode.window.terminals.length > 0) {\n\t\t\treturn;\n\t\t}\n\t\tconst opened = new Promise<void>((resolve) => {\n\t\t\tconst d = vscode.window.onDidOpenTerminal(() => {\n\t\t\t\ttry { d.dispose(); } catch {}\n\t\t\t\tresolve();\n\t\t\t});\n\t\t});\n\t\tconst t = vscode.window.createTerminal('crowd-pilot prime');\n\t\tawait Promise.race([\n\t\t\topened,\n\t\t\tnew Promise<void>(r => setTimeout(r, 150))\n\t\t]);\n\t\ttry { t.dispose(); } catch {}\n\t\tawait new Promise<void>(r => setTimeout(r, 50));\n\t\tconsole.log('[crowd-pilot] Primed terminal subsystem');\n\t} catch (err) {\n\t\tconsole.error('[crowd-pilot] Failed to prime terminal subsystem:', err);\n\t}\n}\n\n// -------------------- Plan Types & Execution --------------------\ntype PlannedAction =\n| { kind: 'showTextDocument' }\n| { kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }\n| { kind: 'editInsert', position: [number, number], text: string }\n| { kind: 'terminalShow' }\n| { kind: 'terminalSendText', text: string };\n\nlet currentPlan: PlannedAction[] | undefined;\n\nfunction buildTestRunPlan(_editor: vscode.TextEditor, _doc: vscode.TextDocument, _term: vscode.Terminal): PlannedAction[] {\n\tconst plan: PlannedAction[] = [];\n\tplan.push({ kind: 'showTextDocument' });\n\tplan.push({ kind: 'setSelections', selections: [{ start: [0, 0], end: [0, 0] }] });\n\tplan.push({ kind: 'editInsert', position: [0, 0], text: 'hello world\n' });\n\tplan.push({ kind: 'terminalShow' });\n\tplan.push({ kind: 'terminalSendText', text: 'echo VSCode test' });\n\treturn plan;\n}\n\nasync function executePlan(plan: PlannedAction[]): Promise<void> {\n\tconst editor = vscode.window.activeTextEditor;\n\tif (!editor) { return; }\n\tconst doc = editor.document;\n\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\tfor (const action of plan) {\n\t\tif (action.kind === 'showTextDocument') {\n\t\t\tawait vscode.window.showTextDocument(doc);\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'setSelections') {\n\t\t\teditor.selections = action.selections.map(s => new vscode.Selection(\n\t\t\t\tnew vscode.Position(s.start[0], s.start[1]),\n\t\t\t\tnew vscode.Position(s.end[0], s.end[1])\n\t\t\t));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'editInsert') {\n\t\t\tawait editor.edit(e => e.insert(new vscode.Position(action.position[0], action.position[1]), action.text));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalShow') {\n\t\t\tterm.show();\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalSendText') {\n\t\t\tterm.sendText(action.text);\n\t\t\tcontinue;\n\t\t}\n\t}\n}\n\n// -------------------- UI State & Helpers --------------------\nconst UI_CONTEXT_KEY = 'crowdPilot.uiVisible';\nlet previewVisible = false;\nlet previewQuickPick: vscode.QuickPick<(vscode.QuickPickItem & { index: number })> | undefined;\n\nfunction showPreviewUI(plan: PlannedAction[]): void {\n\tconst items: (vscode.QuickPickItem & { index: number })[] = plan.map((action, index) => {\n\t\tswitch (action.kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\treturn { index, label: '$(file) Focus active text document' };\n\t\t\tcase 'setSelections':\n\t\t\t\t{\n\t\t\t\t\tconst cursors = action.selections.map(s => `(${s.start[0]}, ${s.start[1]})`).join(', ');\n\t\t\t\t\treturn { index, label: `$(cursor) Move cursor to ${cursors}` };\n\t\t\t\t}\n\t\t\tcase 'editInsert':\n\t\t\t\treturn { index, label: `$(pencil) Insert ""${action.text.replace(/\n/g, '\\n')}"" at (${action.position[0]}, ${action.position[1]})` };\n\t\t\tcase 'terminalShow':\n\t\t\t\treturn { index, label: '$(terminal) Focus terminal' };\n\t\t\tcase 'terminalSendText':\n\t\t\t\treturn { index, label: `$(terminal) Run ""${action.text}"" in terminal` };\n\t\t}\n\t});\n    if (!previewQuickPick) {\n        previewQuickPick = vscode.window.createQuickPick<(vscode.QuickPickItem & { index: number })>();\n\t\tpreviewQuickPick.title = 'crowd-pilot: preview';\n\t\tpreviewQuickPick.matchOnDetail = true;\n\t\tpreviewQuickPick.ignoreFocusOut = true;\n\t\tpreviewQuickPick.canSelectMany = false;\n        previewQuickPick.onDidAccept(async () => {\n            const qp = previewQuickPick!;\n            const selected = qp.selectedItems?.[0];\n            qp.hide();\n            if (selected) {\n                await executePlan([plan[selected.index]]);\n                vscode.window.showInformationMessage('Action executed');\n            }\n        });\n\t\tpreviewQuickPick.onDidHide(() => {\n\t\t\tpreviewVisible = false;\n\t\t\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n\t\t\ttry { previewQuickPick?.dispose(); } catch {}\n\t\t\tpreviewQuickPick = undefined;\n\t\t});\n\t}\n\tpreviewQuickPick.items = items;\n\tpreviewQuickPick.placeholder = 'Press Tab to run all, Enter for selected, or Esc to hide';\n\tpreviewQuickPick.show();\n\tpreviewVisible = true;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, true);\n\tcurrentPlan = plan;\n}\n\nfunction hidePreviewUI(): void {\n\tif (previewQuickPick) {\n\t\ttry { previewQuickPick.hide(); } catch {}\n\t\treturn;\n\t}\n\tpreviewVisible = false;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n}\n",typescript,tab
+142,714329,"src/extension.ts",34,6665,"import * as http from 'http';\nimport { Buffer } from 'buffer';\n\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('[crowd-pilot] Extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t(async () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get<string[]>('commandsToSkipShell', []);\n\t\tlet updated = false;\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (!commandsToSkipShell.includes('crowd-pilot.hideUi')) {\n\t\t\tcommandsToSkipShell.push('crowd-pilot.hideUi');\n\t\t\tupdated = true;\n\t\t}\n\t\tif (updated) {\n\t\t\tawait config.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\t}\n\t\t// Prime terminal subsystem after intercept is enabled (NOTE: this is a workaround)\n\t\tawait primeTerminalSubsystem();\n\t})().catch((err) => console.error('[crowd-pilot] Startup initialization error:', err));\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\tconst doc = editor.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst plan = buildTestRunPlan(editor, doc, term);\n\n\t\tif (!previewVisible) {\n\t\t\tshowPreviewUI(plan);\n\t\t\treturn;\n\t\t}\n\n\t\tconst runPlan = currentPlan ?? plan;\n\t\thidePreviewUI();\n\n\t\tawait executePlan(runPlan);\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t  });\n\n\tconst hideUi = vscode.commands.registerCommand('crowd-pilot.hideUi', () => {\n\t\thidePreviewUI();\n\t});\n\n\tconst modelRun = vscode.commands.registerCommand('crowd-pilot.modelRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tif (!editor) {\n\t\t\treturn;\n\t\t}\n\t\ttry {\n\t\t\tconst portInput = await vscode.window.showInputBox({\n\t\t\t\tprompt: 'Enter SGLang server port',\n\t\t\t\tvalue: '30000'\n\t\t\t});\n\t\t\tif (!portInput) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tconst port = Number(portInput);\n\t\t\tif (!Number.isFinite(port) || port <= 0) {\n\t\t\t\tvscode.window.showErrorMessage('Invalid port');\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tconst plan = await requestModelActions(port, editor);\n\n\t\t\tif (!previewVisible) {\n\t\t\t\tshowPreviewUI(plan);\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tconst runPlan = currentPlan ?? plan;\n\t\t\thidePreviewUI();\n\t\t\tawait executePlan(runPlan);\n\t\t\tvscode.window.showInformationMessage('All actions emitted');\n\t\t} catch (err) {\n\t\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\t\tvscode.window.showErrorMessage(`Model run failed: ${errorMessage}`);\n\t\t}\n\t});\n\n\tconst sglangTest = vscode.commands.registerCommand('crowd-pilot.sglangTest', async () => {\n\t\ttry {\n\t\t\tconst portInput = await vscode.window.showInputBox({\n\t\t\t\tprompt: 'Enter SGLang server port',\n\t\t\t\tvalue: '30000'\n\t\t\t});\n\t\t\tif (!portInput) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tconst port = Number(portInput);\n\t\t\tif (!Number.isFinite(port) || port <= 0) {\n\t\t\t\tvscode.window.showErrorMessage('Invalid port');\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tawait callSGLangChat(port);\n\t\t} catch (err) {\n\t\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\t\tvscode.window.showErrorMessage(`SGLang test failed: ${errorMessage}`);\n\t\t}\n\t});\n\n\tcontext.subscriptions.push(testRun, hideUi, sglangTest, modelRun);\n}\n\nexport function deactivate() {}\n\nasync function primeTerminalSubsystem(): Promise<void> {\n\ttry {\n\t\tif (vscode.window.terminals.length > 0) {\n\t\t\treturn;\n\t\t}\n\t\tconst opened = new Promise<void>((resolve) => {\n\t\t\tconst d = vscode.window.onDidOpenTerminal(() => {\n\t\t\t\ttry { d.dispose(); } catch {}\n\t\t\t\tresolve();\n\t\t\t});\n\t\t});\n\t\tconst t = vscode.window.createTerminal('crowd-pilot prime');\n\t\tawait Promise.race([\n\t\t\topened,\n\t\t\tnew Promise<void>(r => setTimeout(r, 150))\n\t\t]);\n\t\ttry { t.dispose(); } catch {}\n\t\tawait new Promise<void>(r => setTimeout(r, 50));\n\t\tconsole.log('[crowd-pilot] Primed terminal subsystem');\n\t} catch (err) {\n\t\tconsole.error('[crowd-pilot] Failed to prime terminal subsystem:', err);\n\t}\n}\n\n// -------------------- Plan Types & Execution --------------------\ntype PlannedAction =\n| { kind: 'showTextDocument' }\n| { kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }\n| { kind: 'editInsert', position: [number, number], text: string }\n| { kind: 'terminalShow' }\n| { kind: 'terminalSendText', text: string };\n\nlet currentPlan: PlannedAction[] | undefined;\n\nfunction buildTestRunPlan(_editor: vscode.TextEditor, _doc: vscode.TextDocument, _term: vscode.Terminal): PlannedAction[] {\n\tconst plan: PlannedAction[] = [];\n\tplan.push({ kind: 'showTextDocument' });\n\tplan.push({ kind: 'setSelections', selections: [{ start: [0, 0], end: [0, 0] }] });\n\tplan.push({ kind: 'editInsert', position: [0, 0], text: 'hello world\n' });\n\tplan.push({ kind: 'terminalShow' });\n\tplan.push({ kind: 'terminalSendText', text: 'echo VSCode test' });\n\treturn plan;\n}\n\nasync function executePlan(plan: PlannedAction[]): Promise<void> {\n\tconst editor = vscode.window.activeTextEditor;\n\tif (!editor) { return; }\n\tconst doc = editor.document;\n\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\tfor (const action of plan) {\n\t\tif (action.kind === 'showTextDocument') {\n\t\t\tawait vscode.window.showTextDocument(doc);\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'setSelections') {\n\t\t\teditor.selections = action.selections.map(s => new vscode.Selection(\n\t\t\t\tnew vscode.Position(s.start[0], s.start[1]),\n\t\t\t\tnew vscode.Position(s.end[0], s.end[1])\n\t\t\t));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'editInsert') {\n\t\t\tawait editor.edit((e: vscode.TextEditorEdit) => e.insert(new vscode.Position(action.position[0], action.position[1]), action.text));\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalShow') {\n\t\t\tterm.show();\n\t\t\tcontinue;\n\t\t}\n\t\tif (action.kind === 'terminalSendText') {\n\t\t\tterm.sendText(action.text);\n\t\t\tcontinue;\n\t\t}\n\t}\n}\n\n// -------------------- UI State & Helpers --------------------\nconst UI_CONTEXT_KEY = 'crowdPilot.uiVisible';\nlet previewVisible = false;\nlet previewQuickPick: vscode.QuickPick<(vscode.QuickPickItem & { index: number })> | undefined;\n\nfunction showPreviewUI(plan: PlannedAction[]): void {\n\tconst items: (vscode.QuickPickItem & { index: number })[] = plan.map((action, index) => {\n\t\tswitch (action.kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\treturn { index, label: '$(file) Focus active text document' };\n\t\t\tcase 'setSelections':\n\t\t\t\t{\n\t\t\t\t\tconst cursors = action.selections.map(s => `(${s.start[0]}, ${s.start[1]})`).join(', ');\n\t\t\t\t\treturn { index, label: `$(cursor) Move cursor to ${cursors}` };\n\t\t\t\t}\n\t\t\tcase 'editInsert':\n\t\t\t\treturn { index, label: `$(pencil) Insert ""${action.text.replace(/\n/g, '\\n')}"" at (${action.position[0]}, ${action.position[1]})` };\n\t\t\tcase 'terminalShow':\n\t\t\t\treturn { index, label: '$(terminal) Focus terminal' };\n\t\t\tcase 'terminalSendText':\n\t\t\t\treturn { index, label: `$(terminal) Run ""${action.text}"" in terminal` };\n\t\t}\n\t});\n    if (!previewQuickPick) {\n        previewQuickPick = vscode.window.createQuickPick<(vscode.QuickPickItem & { index: number })>();\n\t\tpreviewQuickPick.title = 'crowd-pilot: preview';\n\t\tpreviewQuickPick.matchOnDetail = true;\n\t\tpreviewQuickPick.ignoreFocusOut = true;\n\t\tpreviewQuickPick.canSelectMany = false;\n        previewQuickPick.onDidAccept(async () => {\n            const qp = previewQuickPick!;\n            const selected = qp.selectedItems?.[0];\n            qp.hide();\n            if (selected) {\n                await executePlan([plan[selected.index]]);\n                vscode.window.showInformationMessage('Action executed');\n            }\n        });\n\t\tpreviewQuickPick.onDidHide(() => {\n\t\t\tpreviewVisible = false;\n\t\t\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n\t\t\ttry { previewQuickPick?.dispose(); } catch {}\n\t\t\tpreviewQuickPick = undefined;\n\t\t});\n\t}\n\tpreviewQuickPick.items = items;\n\tpreviewQuickPick.placeholder = 'Press Tab to run all, Enter for selected, or Esc to hide';\n\tpreviewQuickPick.show();\n\tpreviewVisible = true;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, true);\n\tcurrentPlan = plan;\n}\n\nfunction hidePreviewUI(): void {\n\tif (previewQuickPick) {\n\t\ttry { previewQuickPick.hide(); } catch {}\n\t\treturn;\n\t}\n\tpreviewVisible = false;\n\tvscode.commands.executeCommand('setContext', UI_CONTEXT_KEY, false);\n}\n\n// -------------------- SGLang Client (simple test) --------------------\nasync function callSGLangChat(port: number): Promise<void> {\n\tconst requestBody = {\n\t\tmodel: 'qwen/qwen2.5-0.5b-instruct',\n\t\tmessages: [\n\t\t\t{ role: 'user', content: 'What is the capital of France?' }\n\t\t]\n\t};\n\tconst postData = JSON.stringify(requestBody);\n\n\tconst options = {\n\t\thostname: 'hai001',\n\t\tport: port,\n\t\tpath: '/v1/chat/completions',\n\t\tmethod: 'POST',\n\t\theaders: {\n\t\t\t'Content-Type': 'application/json',\n\t\t\t'Content-Length': Buffer.byteLength(postData)\n\t\t}\n\t};\n\n\ttry {\n\t\tconst json = await new Promise<any>((resolve, reject) => {\n\t\t\tconst req = http.request(options, (res: http.IncomingMessage) => {\n\t\t\t\tlet data = '';\n\t\t\t\tres.on('data', (chunk: Buffer) => {\n\t\t\t\t\tdata += chunk.toString();\n\t\t\t\t});\n\t\t\t\tres.on('end', () => {\n\t\t\t\t\ttry {\n\t\t\t\t\t\tresolve(JSON.parse(data));\n\t\t\t\t\t} catch (err) {\n\t\t\t\t\t\treject(new Error(`Failed to parse response: ${err instanceof Error ? err.message : String(err)}`));\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t});\n\n\t\t\treq.on('error', (err: Error) => {\n\t\t\t\treject(err);\n\t\t\t});\n\n\t\t\treq.write(postData);\n\t\t\treq.end();\n\t\t});\n\n\t\tvscode.window.showInformationMessage(`SGLang response: ${JSON.stringify(json, null, 2)}`);\n\t} catch (err) {\n\t\tconst errorMessage = err instanceof Error ? err.message : String(err);\n\t\tvscode.window.showErrorMessage(`SGLang request failed: ${errorMessage}`);\n\t}\n}\n\n// -------------------- Model-planned Actions --------------------\nasync function requestModelActions(port: number, editor: vscode.TextEditor): Promise<PlannedAction[]> {\n\tconst schemaDescription = [\n\t\t'Output ONLY a JSON array. No prose, no code fences.',\n\t\t'Allowed actions (TypeScript-like schema):',\n\t\t""{ kind: 'showTextDocument' }"",\n\t\t""{ kind: 'setSelections', selections: Array<{ start: [number, number], end: [number, number] }> }"",\n\t\t""{ kind: 'editInsert', position: [number, number], text: string }"",\n\t\t""{ kind: 'terminalShow' }"",\n\t\t""{ kind: 'terminalSendText', text: string }"",\n\t\t'Coordinates are zero-based [line, column].'\n\t].join('\n');\n\n\tconst demoGoal = [\n\t\t'Create a concise demo plan that:',\n\t\t'- focuses the active text document',\n\t\t'- moves the cursor to (0, 0)',\n\t\t""- inserts the line \""hello from model\\n\"" at (0, 0)"",\n\t\t'- focuses the terminal',\n\t\t'- runs the command ""echo model run""'\n\t].join('\n');\n\n\tconst requestBody = {\n\t\tmodel: 'qwen/qwen2.5-0.5b-instruct',\n\t\tmessages: [\n\t\t\t{ role: 'system', content: schemaDescription },\n\t\t\t{ role: 'user', content: demoGoal }\n\t\t]\n\t};\n\n\tconst postData = JSON.stringify(requestBody);\n\tconst options = {\n\t\thostname: 'hai001',\n\t\tport: port,\n\t\tpath: '/v1/chat/completions',\n\t\tmethod: 'POST',\n\t\theaders: {\n\t\t\t'Content-Type': 'application/json',\n\t\t\t'Content-Length': Buffer.byteLength(postData)\n\t\t}\n\t};\n\n\tconst json = await new Promise<any>((resolve, reject) => {\n\t\tconst req = http.request(options, (res: http.IncomingMessage) => {\n\t\t\tlet data = '';\n\t\t\tres.on('data', (chunk: Buffer) => { data += chunk.toString(); });\n\t\t\tres.on('end', () => {\n\t\t\t\ttry {\n\t\t\t\t\tresolve(JSON.parse(data));\n\t\t\t\t} catch (err) {\n\t\t\t\t\treject(new Error(`Failed to parse response: ${err instanceof Error ? err.message : String(err)}`));\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t\treq.on('error', (err: Error) => reject(err));\n\t\treq.write(postData);\n\t\treq.end();\n\t});\n\n\tconst content = extractChatContent(json);\n\tif (typeof content !== 'string' || content.trim().length === 0) {\n\t\tthrow new Error('Empty model content');\n\t}\n\tconst actions = parsePlannedActions(content);\n\tif (actions.length === 0) {\n\t\tthrow new Error('No valid actions parsed from model output');\n\t}\n\treturn actions;\n}\n\nfunction extractChatContent(json: any): string | undefined {\n\ttry {\n\t\tif (json && Array.isArray(json.choices) && json.choices[0]) {\n\t\t\tconst choice = json.choices[0];\n\t\t\tif (choice.message && typeof choice.message.content === 'string') {\n\t\t\t\treturn choice.message.content;\n\t\t\t}\n\t\t\tif (typeof choice.text === 'string') {\n\t\t\t\treturn choice.text;\n\t\t\t}\n\t\t}\n\t\treturn undefined;\n\t} catch {\n\t\treturn undefined;\n\t}\n}\n\nfunction parsePlannedActions(raw: string): PlannedAction[] {\n\tlet text = raw.trim();\n\ttext = text.replace(/^```(?:json)?\s*/i, '').replace(/```\s*$/i, '').trim();\n\tconst arrayMatch = text.match(/\[[\s\S]*\]/);\n\tconst jsonText = arrayMatch ? arrayMatch[0] : text;\n\tlet parsed: unknown;\n\ttry {\n\t\tparsed = JSON.parse(jsonText);\n\t} catch (err) {\n\t\treturn [];\n\t}\n\tif (!Array.isArray(parsed)) { return []; }\n\tconst result: PlannedAction[] = [];\n\tfor (const item of parsed) {\n\t\tif (!item || typeof item !== 'object' || typeof (item as any).kind !== 'string') { continue; }\n\t\tswitch ((item as any).kind) {\n\t\t\tcase 'showTextDocument':\n\t\t\t\tresult.push({ kind: 'showTextDocument' });\n\t\t\t\tbreak;\n\t\t\tcase 'setSelections': {\n\t\t\t\tconst selections = Array.isArray((item as any).selections) ? (item as any).selections : [];\n\t\t\t\tconst norm = selections.map((s: any) => ({\n\t\t\t\t\tstart: Array.isArray(s?.start) && s.start.length === 2 ? [Number(s.start[0]) || 0, Number(s.start[1]) || 0] as [number, number] : [0, 0] as [number, number],\n\t\t\t\t\tend: Array.isArray(s?.end) && s.end.length === 2 ? [Number(s.end[0]) || 0, Number(s.end[1]) || 0] as [number, number] : [0, 0] as [number, number]\n\t\t\t\t}));\n\t\t\t\tresult.push({ kind: 'setSelections', selections: norm });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 'editInsert': {\n\t\t\t\tconst pos = Array.isArray((item as any).position) && (item as any).position.length === 2 ? [Number((item as any).position[0]) || 0, Number((item as any).position[1]) || 0] as [number, number] : [0, 0] as [number, number];\n\t\t\t\tconst text = typeof (item as any).text === 'string' ? (item as any).text : '';\n\t\t\t\tresult.push({ kind: 'editInsert', position: pos, text });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 'terminalShow':\n\t\t\t\tresult.push({ kind: 'terminalShow' });\n\t\t\t\tbreak;\n\t\t\tcase 'terminalSendText': {\n\t\t\t\tconst text = typeof (item as any).text === 'string' ? (item as any).text : '';\n\t\t\t\tresult.push({ kind: 'terminalSendText', text });\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tdefault:\n\t\t\t\tbreak;\n\t\t}\n\t}\n\treturn result;\n",typescript,content
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+152,778065,"TERMINAL",0,0,"\r\n> crowd-pilot@0.0.1 watch\r\n> tsc -watch -p ./\r\n\r\n",,terminal_output
+153,778422,"TERMINAL",0,0,"[5:08:13 PM] Starting compilation in watch mode...\r\n\r\n",,terminal_output
+154,779998,"TERMINAL",0,0,"[5:08:15 PM] Found 0 errors. Watching for file changes.\r\n\r\n",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-51002999-75fd-453b-98e2-6003b6e5c8e61755511610416-2025_08_18-12.06.53.598/source.csv

@@ -0,0 +1,19 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"megatron/core/optimizer/optimizer_config.py",0,0,"# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.\n\nfrom dataclasses import dataclass\nfrom typing import Callable, Optional\n\nimport torch\n\nfrom ..utils import is_te_min_version\n\n\n@dataclass\nclass OptimizerConfig:\n    """"""Configuration for optimizer.""""""\n\n    ##############\n    # General\n    ##############\n    optimizer: str = 'adam'\n    """"""Optimizer to use (one of Adam or SGD).""""""\n\n    lr: Optional[float] = None\n    """"""Initial learning rate. Depending on decay style and initial warmup, the learning rate at each\n       iteration would be different.\n    """"""\n\n    min_lr: Optional[float] = None\n    """"""Minumum value for learning rate. The scheduler clip values below this threshold.""""""\n\n    decoupled_lr: Optional[float] = None\n    """"""Separate learning rate for the input and output layer.""""""\n\n    decoupled_min_lr: Optional[float] = None\n    """"""Minimum value for learning rate for the input and output layer. The scheduler clip values\n       below this threshold.\n    """"""\n\n    weight_decay: float = 0.01\n    """"""Weight decay coefficient for L2 regularization.""""""\n\n    ##############\n    # Precision\n    ##############\n    fp8_recipe: Optional[str] = None\n    """"""The type of fp8 recipe will affect the processing logic inside distributed optimizer.""""""\n\n    fp16: bool = False\n    """"""If true, train with fp16 mixed precision training. Defaults to False.""""""\n\n    bf16: bool = False\n    """"""If true, train with bf16 mixed precision training. Defaults to False.""""""\n\n    reuse_grad_buf_for_mxfp8_param_ag: bool = False\n    """"""If true, reuse the grad buffer for param AG when using mxfp8 recipe. Should be \n       set to True only when fp8_recipe is mxfp8 and fp8_param_gather is True.""""""\n\n    params_dtype: torch.dtype = torch.float32\n    """"""dtype used when intializing the weights. Defaults to torch.float32.""""""\n\n    use_precision_aware_optimizer: bool = False\n    """"""If true, allows optimizer-related tensors (master_param, gradients and optimizer states)\n    to be set to lower precision. Defaults to False.\n    """"""\n\n    store_param_remainders: bool = True\n    """"""If true, store the 16-bit FP32 parameter remainders in the optimizer state, excluding the\n        16 bits shared with the BF16 parameters. This lowers GPU memory usage. Defaults to True.\n    """"""\n\n    main_grads_dtype: torch.dtype = torch.float32\n    """"""dtype of main grads when enabling precision-aware-optimizer""""""\n\n    main_params_dtype: torch.dtype = torch.float32\n    """"""dtype of main params when enabling precision-aware-optimizer""""""\n\n    exp_avg_dtype: torch.dtype = torch.float32\n    """"""dtype of exp_avg when enabling precision-aware-optimizer""""""\n\n    exp_avg_sq_dtype: torch.dtype = torch.float32\n    """"""dtype of exp_avg_sq when enabling precision-aware-optimizer""""""\n\n    ###############\n    # Loss scaling\n    ###############\n    loss_scale: Optional[float] = None\n    """"""Static loss scaling, positive power of 2 values can improve fp16 convergence. If None,\n       dynamic loss scaling is used.\n    """"""\n\n    initial_loss_scale: float = 2**32\n    """"""Initial loss-scale for dynamic loss scaling.""""""\n\n    min_loss_scale: float = 1.0\n    """"""Minimum loss scale for dynamic loss scaling.""""""\n\n    loss_scale_window: float = 1000\n    """"""Window over which to raise/lower dynamic scale.""""""\n\n    hysteresis: int = 2\n    """"""Hysteresis for dynamic loss scaling.""""""\n\n    ##############\n    # Optimizer\n    ##############\n    # Adam\n    adam_beta1: float = 0.9\n    """"""First coefficient for computing running averages of gradient and its square in Adam\n    optimizer.\n    """"""\n\n    adam_beta2: float = 0.999\n    """"""Second coefficient for computing running averages of gradient and its square in Adam\n    optimizer.\n    """"""\n\n    adam_eps: float = 1e-08\n    """"""Term added to the denominator to improve numerical stability in Adam optimizer.""""""\n\n    # SGD.\n    sgd_momentum: float = 0.9\n    """"""Momentum factor for SGD optimizer.""""""\n\n    #######################\n    # Distributed optimizer\n    #######################\n    use_distributed_optimizer: bool = False\n    """"""Distribute optimizer state over data-parallel replicas.""""""\n\n    overlap_param_gather: bool = False\n    """"""If true, overlap param all-gather with forward compute. \n        This argument is intended to have the same value as the ""overlap_param_gather"" argument \n        in the ""distributed_data_parallel_config.py"" file. In the optimizer, this argument is \n        only used when ""reuse_grad_buf_for_mxfp8_param_ag=True & fp8_param_gather=True"".\n    """"""\n\n    overlap_param_gather_with_optimizer_step: bool = False\n    """"""If true, overlap param all-gather of first bucket with optimizer step.""""""\n\n    #######################\n    # Optimizer Offload\n    #######################\n\n    optimizer_cpu_offload: bool = False\n    """"""If True, offload optimizer states tensor and compute to CPU.""""""\n\n    optimizer_offload_fraction: float = 0.0\n    """"""Specifies the fraction of optimizer states to offload from GPU memory to CPU.""""""\n\n    use_torch_optimizer_for_cpu_offload: bool = False\n    """"""If True, use torch.optim.Optimizer for CPU offload.""""""\n\n    overlap_cpu_optimizer_d2h_h2d: bool = False\n    """"""\n    When set to `True`, this flag enables overlapping of the CPU optimizer\n    update process with the data transfer operations. This can help improve\n    overall training efficiency by reducing idle time during data movement,\n    allowing the optimizer to perform updates while gradients and parameters\n    are being transferred between devices.\n    """"""\n\n    pin_cpu_grads: bool = True\n    """"""If True, pin the optimizer gradients to CPU memory.""""""\n\n    pin_cpu_params: bool = True\n    """"""If True, pin the optimizer parameters to CPU memory.""""""\n\n    ################\n    # Miscellaneous\n    ################\n    clip_grad: float = 1.0\n    """"""Gradient clipping based on global L2 norm.""""""\n\n    log_num_zeros_in_grad: bool = False\n    """"""If true, calculate and log the number of zeros in gradient.""""""\n\n    barrier_with_L1_time: bool = False\n    """"""If true, use barrier with level 1 time measurements.""""""\n\n    timers: Optional[Callable] = None\n    """"""Function to get timers.""""""\n\n    config_logger_dir: str = """"\n    """"""When non-empty, dumps entry-point configs to config_logger_dir""""""\n\n    def __post_init__(self):\n        """"""Check the validity of the config.""""""\n\n        # The following condition is used to avoid repetition in distrib_optimizer.py.\n        # This is because in distrib_optimizer.py, the process to handle parameters are\n        # different for different training precision settings. FP8 cases require different\n        # handling while FP8 delayed scaling is an exception because the Adam optimizer in\n        # TransformerEngine supports it in the kernel computation.\n        # This is also the flag to determine the usage of param.grad or param.decoupled_grad\n        self.use_precision_aware_optimizer_no_fp8_or_ds_fp8 = (\n            self.use_precision_aware_optimizer\n            and (\n                self.main_params_dtype != torch.float32\n                or (self.fp8_recipe is None or self.fp8_recipe == ""delayed"")\n                or self.optimizer_cpu_offload\n            )\n        )\n\n        if self.fp8_recipe == ""mxfp8"":\n            if not self.reuse_grad_buf_for_mxfp8_param_ag:\n                import warnings\n\n                warnings.warn(\n                    ""mxfp8 without using reuse_grad_buf_for_mxfp8_param_ag and fp8_param_gather""\n                    ""will use significant amount additional GPU memory.""\n                    ""Setting --reuse-grad-buf-for-mxfp8-param-ag and --fp8-param-gather is ""\n                    ""recommended for mxfp8 training.""\n                )\n\n        if self.use_precision_aware_optimizer:\n            assert (\n                self.optimizer == 'adam'\n            ), '--use-precision-aware-optimizer only supported with adam'\n            assert (\n                self.use_distributed_optimizer\n            ), '--use-precision-aware-optimizer only supported with distributed optimizer'\n\n            if not is_te_min_version(""2.1.0""):\n                self.store_param_remainders = False\n\n            # Only the FusedAdam in TE and HybridDeviceOptimizer supports\n            # --use-precision-aware-optimizer.\n            # TODO: Remove this check when apex's FusedAdam is no longer used.\n            if self.optimizer_cpu_offload:\n                return\n            try:\n                import inspect\n\n                from transformer_engine.pytorch.optimizers import FusedAdam as Adam\n\n                adam_args = inspect.signature(Adam).parameters\n                arg_names = [\n                    'master_weight_dtype',\n                    'exp_avg_dtype',\n                    'exp_avg_sq_dtype',\n                    'use_decoupled_grad',\n                ]\n                for name in arg_names:\n                    assert name in adam_args, (\n                        ""Current FusedAdam of TE doesn't support --use-precision-aware-optimizer, ""\n                        ""please update TE version.""\n                    )\n            except ImportError:\n                raise RuntimeError(\n                    '--use-precision-aware-optimizer requires FusedAdam from TransformerEngine, '\n                    'but not found.'\n                )\n        else:\n            assert (\n                self.main_grads_dtype == torch.float32\n            ), ""main_grads_dtype can only be fp32 when not using precision-aware optimizer""\n            assert (\n                self.main_params_dtype == torch.float32\n            ), ""main_params_dtype can only be fp32 when not using precision-aware optimizer""\n            assert (\n                self.exp_avg_dtype == torch.float32\n            ), ""exp_avg_dtype can only be fp32 when not using precision-aware optimizer""\n            assert (\n                self.exp_avg_sq_dtype == torch.float32\n            ), ""exp_avg_sq_dtype can only be fp32 when not using precision-aware optimizer""\n",python,tab
+2,65,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:06:53 PM [info] Activating crowd-code\n12:06:53 PM [info] Recording started\n12:06:53 PM [info] Initializing git provider using file system watchers...\n12:06:53 PM [info] Git repository found\n12:06:53 PM [info] Git provider initialized successfully\n",Log,tab
+3,187,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"12:06:53 PM [info] Initial git state: [object Object]\n",Log,content
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+6,5466765,"megatron/core/transformer/module.py",0,0,"# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.\n\n""""""Megatron Module.""""""\nfrom typing import Optional, Tuple\n\nimport torch\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\n\nfrom megatron.core import parallel_state\nfrom megatron.core.dist_checkpointing.mapping import ShardedStateDict\nfrom megatron.core.transformer.transformer_config import TransformerConfig\nfrom megatron.core.transformer.utils import (\n    make_sharded_tensors_for_checkpoint,\n    sharded_state_dict_default,\n)\n\n_FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)\n_HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)\n_BF16_TYPES = (torch.BFloat16Tensor, torch.cuda.BFloat16Tensor)\n\n\ndef param_is_not_shared(param):  # pylint: disable=missing-function-docstring\n    return not hasattr(param, 'shared') or not param.shared\n\n\nclass MegatronModule(torch.nn.Module):\n    """"""Base Megatron module inhertied by all Models.\n\n    Megatron specific extensions of torch Module with support\n    for pipelining\n\n    Args:\n        config (TransformerConfig): Transformer config\n    """"""\n\n    # def __init__(self, config: TransformerConfig, share_word_embeddings=True):\n    def __init__(self, config: TransformerConfig):\n        super().__init__()\n        self.config = config\n\n    def state_dict_for_save_checkpoint(self, prefix: str = '', keep_vars: bool = False):\n        """"""Override state dict for saving checkpoints Use this function to override the\n        state dict for saving checkpoints.\n\n        Args:\n            prefix (str, optional): _description_. Defaults to ''.\n            keep_vars (bool, optional): _description_. Defaults to False.\n\n        Returns:\n            _type_: _description_\n        """"""\n\n        return self.state_dict(prefix=prefix, keep_vars=keep_vars)\n\n    def sharded_state_dict(\n        self,\n        prefix: str = '',\n        sharded_offsets: Tuple[Tuple[int, int, int]] = (),\n        metadata: Optional[dict] = None,\n    ) -> ShardedStateDict:\n        """"""Default implementation for sharded state dict for distributed checkpointing.\n\n        General definition of sharded_state_dict simply calls `sharded_state_dict_default`\n        (which call sharded_state_dict method if possible or a default implementation otherwise)\n        recursively on all submodules.\n\n        Args:\n            prefix (str): prefix for the state dict keys\n            sharded_offsets (Tuple[Tuple[int, int, int]], optional): sharding already\n                applied (e.g. PP related) by sup-modules. Passed along to ShardedTensor\n            metadata (dict, optional): metadata passed recursively to sharded_state_dict methods\n\n        Returns:\n            dict: dictionary of state dict keys mapped to ShardedTensors\n        """"""\n        sharded_state_dict = {}\n        # Save parameters\n        self._save_to_state_dict(sharded_state_dict, '', keep_vars=True)\n        sharded_state_dict = make_sharded_tensors_for_checkpoint(\n            sharded_state_dict, prefix, sharded_offsets=sharded_offsets\n        )\n        # Recurse into submodules\n        for name, module in self.named_children():\n            sharded_state_dict.update(\n                sharded_state_dict_default(module, f'{prefix}{name}.', sharded_offsets, metadata)\n            )\n        return sharded_state_dict\n\n    def set_is_first_microbatch(self):\n        """"""Sets the is_first_microbatch flag if it exists and config.fp8==True.\n        When this flag is set, TE modules will update their fp8 parameter cache.\n        If kitchen is being used, kitchen controls quantization level.\n        """"""\n        if self.config.fp8 is not None or getattr(self.config, 'use_kitchen', False):\n            if not hasattr(self, ""modules_with_is_first_microbatch""):\n                self.modules_with_is_first_microbatch = []\n                for m in self.modules():\n                    if hasattr(m, ""is_first_microbatch""):\n                        self.modules_with_is_first_microbatch.append(m)\n            for m in self.modules_with_is_first_microbatch:\n                m.is_first_microbatch = True\n\n    def set_symmetric_ar(self, set_to: Optional[str] = None) -> None:\n        """"""\n        Set symmetric all-reduce functionality across all eligible modules.\n\n        This method traverses the model's module hierarchy to find all modules\n        with the 'symmetric_ar_type' attribute, caches them, and then sets their\n        '_symmetric_ar_cache' attribute to the specified value to enable or\n        disable symmetric all-reduce operations.\n\n        Args:\n            set_to (Any, optional): Value to set for the 'symmetric_ar_type' to.\n            Allowed choices ['two_shot', ""one_shot"", ""multimem_all_reduce"", None]\n        """"""\n        assert set_to in ['two_shot', ""one_shot"", ""multimem_all_reduce"", None]\n\n        # Recursive function to find all modules with our target attributes\n        def create_ar_cache(module):\n            # Check if this module has any of our target attributes\n            if hasattr(module, ""symmetric_ar_type""):\n                self._symmetric_ar_cache.append(module)\n\n            # Check all children modules recursively\n            for child in module._modules.values():\n                if child is not None:\n                    create_ar_cache(child)\n\n        if not hasattr(self, ""_symmetric_ar_cache""):\n            self._symmetric_ar_cache = []\n            create_ar_cache(self)\n\n        for module in self._symmetric_ar_cache:\n            module._symmetric_ar_cache = set_to\n\n\ndef conversion_helper(val, conversion):\n    """"""Recursively applies a conversion function to values in nested data structures.\n\n    Args:\n        val: A single value or a nested structure (tuple/list) of values to convert\n        conversion (callable): A function that performs the desired conversion on a single value\n\n    Returns:\n        The converted value, maintaining the same nested structure as the input.\n        If input is a single value, returns the converted value.\n        If input is a tuple/list, returns a tuple/list with all elements converted.\n    """"""\n    if not isinstance(val, (tuple, list)):\n        return conversion(val)\n    rtn = [conversion_helper(v, conversion) for v in val]\n    if isinstance(val, tuple):\n        rtn = tuple(rtn)\n    return rtn\n\n\ndef fp32_to_float16(val, float16_convertor):\n    """"""Converts floating-point values from fp32 to fp16.\n\n    Args:\n        val: The value to convert. Can be a single number, a tuple, or a list.\n        float16_convertor: A function that converts a single fp32 value to fp16\n    """"""\n\n    def half_conversion(val):\n        val_typecheck = val\n        if isinstance(val_typecheck, (Parameter, Variable)):\n            val_typecheck = val.data\n        if isinstance(val_typecheck, _FLOAT_TYPES):\n            val = float16_convertor(val)\n        return val\n\n    return conversion_helper(val, half_conversion)\n\n\ndef float16_to_fp32(val):\n    """"""Converts floating-point values from fp16 to fp32.\n\n    Args:\n        val: The value to convert. Can be a single number, a tuple, or a list.\n    """"""\n\n    def float_conversion(val):\n        val_typecheck = val\n        if isinstance(val_typecheck, (Parameter, Variable)):\n            val_typecheck = val.data\n        if isinstance(val_typecheck, (_BF16_TYPES, _HALF_TYPES)):\n            val = val.float()\n        return val\n\n    return conversion_helper(val, float_conversion)\n\n\nclass Float16Module(MegatronModule):\n    """"""Float 16 Module.\n\n    Attributes:\n        config (TransformerConfig): Transformer config\n        fp16 (bool) : Specifies if the model runs in fp16 mode\n        bf16 (bool) : Specifies if the model runs in bf16 mode\n\n    Args:\n        config (TransformerConfig): The transformer config used to initalize the model\n    """"""\n\n    def __init__(self, config: TransformerConfig, module: torch.nn.Module):\n        super(Float16Module, self).__init__(config)\n        self.config = config\n        self.fp16 = config.fp16\n        self.bf16 = config.bf16\n        self.vp_stage = getattr(module, 'vp_stage', None)\n\n        if self.fp16:\n            self.add_module('module', module.half())\n\n            def float16_convertor(val):\n                return val.half()\n\n        elif self.bf16:\n            self.add_module('module', module.bfloat16())\n\n            def float16_convertor(val):\n                return val.bfloat16()\n\n        else:\n            raise Exception('Either config.fp16 or config.bf16 should be True.')\n\n        self.float16_convertor = float16_convertor\n\n    def set_input_tensor(self, input_tensor):  # pylint: disable=missing-function-docstring\n        return self.module.set_input_tensor(input_tensor)\n\n    def forward(self, *inputs, **kwargs):  # pylint: disable=missing-function-docstring\n        if parallel_state.is_pipeline_first_stage(ignore_virtual=False, vp_stage=self.vp_stage):\n            inputs = fp32_to_float16(inputs, self.float16_convertor)\n        outputs = self.module(*inputs, **kwargs)\n        if parallel_state.is_pipeline_last_stage(ignore_virtual=False, vp_stage=self.vp_stage):\n            outputs = float16_to_fp32(outputs)\n        return outputs\n\n    def state_dict(\n        self, destination=None, prefix='', keep_vars=False\n    ):  # pylint: disable=missing-function-docstring\n        return self.module.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)\n\n    def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):\n        """"""Retrieve state_dict from the module being wrapped.""""""\n        return self.module.state_dict_for_save_checkpoint(prefix=prefix, keep_vars=keep_vars)\n\n    def sharded_state_dict(self, prefix='', *args, **kwargs):\n        """"""Retrieve sharded_state_dict from the module being wrapped.""""""\n        return self.module.sharded_state_dict(prefix, *args, **kwargs)\n\n    def load_state_dict(\n        self, state_dict, strict=True\n    ):  # pylint: disable=missing-function-docstring\n        self.module.load_state_dict(state_dict, strict=strict)\n",python,tab
+7,5466770,"megatron/core/transformer/module.py",8635,445,"    def forward(self, *inputs, **kwargs):  # pylint: disable=missing-function-docstring\n        if parallel_state.is_pipeline_first_stage(ignore_virtual=False, vp_stage=self.vp_stage):\n            inputs = fp32_to_float16(inputs, self.float16_convertor)\n        outputs = self.module(*inputs, **kwargs)\n        if parallel_state.is_pipeline_last_stage(ignore_virtual=False, vp_stage=self.vp_stage):\n            outputs = float16_to_fp32(outputs)",python,selection_command
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-78105cc3-5fe0-4c79-a145-2a2fd28c2d411758789399345-2025_09_25-10.36.42.235/source.csv

@@ -0,0 +1,244 @@
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+1,3,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"#include QMK_KEYBOARD_H\n#if __has_include(""keymap.h"")\n#    include ""keymap.h""\n#endif\n\n\n/* THIS FILE WAS GENERATED!\n *\n * This file was generated by qmk json2c. You may or may not want to\n * edit it directly.\n */\n\nconst uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n    [0] = LAYOUT_60_ansi(KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC, KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS, KC_RCTL, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT, KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP, KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT),\n    [1] = LAYOUT_60_ansi(KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [2] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, KC_CAPS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [3] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_BTN1, MS_BTN2, MS_BTN3, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [4] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_LPRN, KC_RPRN, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LCBR, KC_AMPR, KC_ASTR, KC_NO, KC_RCBR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_COLN, KC_DLR, KC_PERC, KC_CIRC, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_TILD, KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [5] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO)\n};\n\n\n\n#ifdef OTHER_KEYMAP_C\n#    include OTHER_KEYMAP_C\n#endif // OTHER_KEYMAP_C\n",c,tab
+2,59,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:36:42 AM [info] Activating crowd-code\n10:36:42 AM [info] Recording started\n10:36:42 AM [info] Initializing git provider using file system watchers...\n10:36:42 AM [info] Git repository found\n10:36:42 AM [info] Git provider initialized successfully\n10:36:42 AM [info] Initial git state: [object Object]\n",Log,tab
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+17,59143,"keyboards/annepro2/keymaps/calmar_one/keymap.c",829,488,"    [1] = LAYOUT_60_ansi(KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),",c,selection_command
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+23,65249,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2741,452,"    [5] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO)",c,selection_command
+24,205421,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2741,452,"    [5] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_NO, KC_0, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO)",c,content
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+444,2862196,"README.md",0,0,"<h1 align=""center"">🧞‍♀️ Jasmine: A simple, performant and scalable JAX-based world modeling codebase 🧞‍♀️</h1>\n\n<p align=""center"">\n    <a href= ""https://github.com/FLAIROx/jafar/blob/main/LICENSE"">\n        <img src=""https://img.shields.io/badge/license-Apache2.0-blue.svg"" /></a>\n    <a href= ""https://github.com/psf/black"">\n        <img src=""https://img.shields.io/badge/code%20style-black-000000.svg"" /></a>\n</p>\n\nJasmine is a production-ready JAX-based world modeling codebase. It currently implements the high-level architecture of [Genie: Generative Interactive Environments](https://arxiv.org/abs/2402.15391) (Bruce et al., 2024) with [MaskGIT](https://arxiv.org/abs/2202.04200) (Chang et al., 2022), as well as an autoregressive (causal) baseline. A diffusion baseline is coming soon.\n\nJasmine scales from single hosts to hundreds of xPUs thanks to XLA and strives to be an easily hackable, batteries-included foundation for world modeling research.\n\n<h2 name=""overview"" id=""overview"">Overview</h2>\n\n- Asynchronous & distributed checkpointing thanks to [orbax.checkpoint](https://github.com/google/orbax)\n    - Jasmine also supports mixing and matching hardware topologies (e.g. train on four nodes, load the checkpoint on a single node)\n- Optimized dataloading thanks to [Grain](https://github.com/google/grain)\n    - Dataloading scales with the number of processes (i.e. nodes/xPUs)\n- Checkpointing of model weights, optimizer and dataloader states\n- Full reproducibility with **identical** training curves (thanks to seeded dataloading and training, and [JAX' approach to pseudo random numbers](https://docs.jax.dev/en/latest/random-numbers.html))\n- Automatic checkpoint deletion/retention according to specified retention policy thanks to `orbax.checkpoint.CheckpointManager`\n- Mixed precision training using `bfloat16`\n    - `int8` training is on the roadmap via [aqt](https://github.com/google/aqt)\n- FlashAttention thanks to [cuDNN SDPA](https://github.com/jax-ml/jax/blob/a155c5a9997924170e0067d552351a9833c12c11/jax/_src/cudnn/fused_attention_stablehlo.py#L842)\n- Frame-level KV cache resets for accelerated spatiotemporal attention in causal baseline (still in PR)\n- Activation checkpointing (even onto host memory if desired)\n- DDP (changing to FSDP requires changing **a single line of code**)\n- WSD learning rate schedule\n    -  No need to retrain from scratch if you want to train for longer\n- Index-shuffling during dataloading\n- Google-native stack\n    - https://github.com/google/orbax for checkpointing\n    - https://github.com/google/grain for dataloading\n    - https://github.com/google-deepmind/dm_pix for image manipulation\n    - https://github.com/google/array_record as the data format\n- Easy model inspection thanks to [treescope](https://github.com/google-deepmind/treescope)\n- Modularized training script for easy inspection using notebooks ([demo notebook](https://colab.research.google.com/drive/1zHkciFIZxXloJgue9F5LtFlA0m00rJIf?usp=sharing))\n- Easy model surgery thanks to the new [flax.nnx](https://flax.readthedocs.io/en/latest/migrating/linen_to_nnx.html) API\n- [Shape suffixes](https://medium.com/@NoamShazeer/shape-suffixes-good-coding-style-f836e72e24fd) throughout the repository\n\n<h2 name=""start"" id=""start"">Setup 🧗</h2>\n\nJasmine requires `python 3.10`, `jax 0.6.2`, and `flax 0.10.7`. To install the requirements, run:\n\n```bash\npip install -r requirements.txt\npre-commit install\n```\n\n---\n\n<h2 name=""dataset"" id=""dataset"">Dataset 📂</h2>\n\nYou can either download our preprocessed dataset from [Hugging Face](https://huggingface.co/datasets/p-doom/open_ai_minecraft_arrayrecords_chunked) or preprocess [OpenAI's VPT dataset](https://github.com/openai/Video-Pre-Training) manually.\n\n### Option 1: Use Preprocessed Dataset (Recommended)\n\nThe easiest way to get started is to download our preprocessed dataset from Hugging Face. This script will handle downloading and extracting it:\n\n```bash\nbash input_pipeline/download/huggingface/download_openai_array_records.sh\n```\n\n---\n\n### Option 2: Manual Download & Preprocessing of OpenAI's VPT Dataset\n\nIf you prefer to use the raw VPT dataset from OpenAI and preprocess it yourself, follow these steps:\n\n1. **Download index files:**\n   This will download the initial index file:\n\n   ```bash\n   bash input_pipeline/download/openai/download_index_files.sh\n   ```\n\n2. **Download from all index files:**\n   This may take a long time depending on your bandwidth:\n\n   ```bash\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_7xx_Apr_6.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_8xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_9xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_10xx_Jun_29.json\n   ```\n\n3. **Preprocess videos into ArrayRecords:**\n   For efficient distributed training, convert the raw videos into the arrayrecord format (make sure to have [ffmpeg](https://github.com/FFmpeg/FFmpeg) installed on your machine):\n\n   ```bash\n   python input_pipeline/preprocess/video_to_array_records.py\n   ```\n\n> **Note:** This is a large dataset and may take considerable time and storage to download and process.\n\n\n<h2 name=""train"" id=""train"">Quick Start 🚀 </h2>\n\nGenie has three components: a [video tokenizer](models/tokenizer.py), a [latent action model](models/lam.py), and a [dynamics model](models/dynamics.py). Each of these components are trained separately, however, the dynamics model requires a pre-trained video tokenizer (and latent action model).\n\nTo train the video tokenizer, run:\n\n```bash\npython train_tokenizer.py --ckpt_dir <path>\n```\n\nTo train the latent action model, run:\n\n```bash\npython train_lam.py --ckpt_dir <path>\n```\n\nOnce the tokenizer and LAM are trained, the dynamics model can be trained with:\n\n```bash\npython train_dynamics.py --tokenizer_checkpoint <path> --lam_checkpoint <path>\n```\n\nLogging with `wandb` is supported. To enable logging, set the `WANDB_API_KEY` environment variable or run:\n\n```bash\nwandb login\n```\n\nTraining can then be logged by setting the `--log` flag:\n\n```bash\npython train_tokenizer.py --log --entity <wandb-entity> --project <wandb-project>\n```\n\n<h2 name=""cite"" id=""cite"">Citing 📜 </h2>\n\nJasmine was built by [Mihir Mahajan](https://maharajamihir.github.io/), [Alfred Nguyen](https://avocadoali.github.io/) and [Franz Srambical](https://srambical.fr/), but started as a fork of [Jafar](https://github.com/flairox/jafar), built by [Matthew Jackson](https://matthewtjackson.com) and [Timon Willi](https://www.timonwilli.com).\n\nIf you use Jasmine in your work, please cite us, Jafar, and the original Genie paper as follows:\n\n```\n@article{\n    mahajan2025jasmine,\n    title={Jasmine: A simple, performant and scalable JAX-based world modeling codebase},\n    author={Mihir Mahajan and Alfred Nguyen and Franz Srambical and Stefan Bauer},\n    journal = {p(doom) blog},\n    year={2025},\n    url={https://pdoom.org/jasmine.html},\n    note = {https://pdoom.org/blog.html}\n}\n```\n```\n@inproceedings{\n    willi2024jafar,\n    title={Jafar: An Open-Source Genie Reimplemention in Jax},\n    author={Timon Willi and Matthew Thomas Jackson and Jakob Nicolaus Foerster},\n    booktitle={First Workshop on Controllable Video Generation @ ICML 2024},\n    year={2024},\n    url={https://openreview.net/forum?id=ZZGaQHs9Jb}\n}\n```\n```\n@inproceedings{\n    bruce2024genie,\n    title={Genie: Generative Interactive Environments},\n    author={Jake Bruce and Michael D Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Maria Elisabeth Bechtle and Feryal Behbahani and Stephanie C.Y. Chan and Nicolas Heess and Lucy Gonzalez and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Zolna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rockt{\""a}schel},\n    booktitle={Forty-first International Conference on Machine Learning},\n    year={2024},\n    url={https://openreview.net/forum?id=bJbSbJskOS}\n}\n```\n",markdown,tab
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+667,4780376,"pyproject.toml",0,0,"[project]\nname = ""jafar""\nversion = ""0.1.0""\nrequires-python = "">=3.10""\ndependencies = []\n\n[dependency-groups]\ncore = [\n  ""dm-pix>=0.4.3"",\n  ""einops>=0.8.0"",\n  ""flax>=0.10.7"",\n  ""jax[cuda12]>=0.6.2"",\n  ""optax>=0.2.3"",\n  ""tyro>=0.8.5"",\n  ""wandb>=0.17.4"",\n  ""grain>=0.2.10"",\n  ""array-record>=0.7.2"",\n  ""pre-commit>=4.2.0"",\n]\ncoinrun = [\n  ""procgen>=0.10.7"",\n]\nminecraft = [\n  ""hf-transfer==0.1.9"",\n  ""huggingface-hub[cli]>=0.34.3"",\n  ""ffmpeg-python==0.2.0"",\n  ""tqdm>=4.67.1"",\n]\n\n[build-system]\nrequires = [""uv-build>=0.8.21,<0.9.0""]\nbuild-backend = ""uv_build""\n\n\n",plaintext,tab
+668,4783725,"pyproject.toml",0,557,"",plaintext,content
+669,4783758,"pyproject.toml",0,1," ",plaintext,content
+670,4783998,"README.md",0,0,"",markdown,tab
+671,4869408,"data/coinrun/generate_coinrun_dataset.py",0,0,"""""""\nGenerates a dataset of random-action CoinRun episodes.\nEpisodes are saved individually as memory-mapped files for efficient loading.\n""""""\n\nfrom dataclasses import dataclass\n\nfrom gym3 import types_np\nimport numpy as np\nfrom procgen import ProcgenGym3Env\nimport tyro\nimport json\nimport os\nfrom utils import save_chunks\n\n\n@dataclass\nclass Args:\n    num_episodes_train: int = 10000\n    num_episodes_val: int = 500\n    num_episodes_test: int = 500\n    output_dir: str = ""data/coinrun_episodes""\n    min_episode_length: int = 1000\n    max_episode_length: int = 1000\n    chunk_size: int = 160\n    chunks_per_file: int = 100\n    seed: int = 0\n\n\nargs = tyro.cli(Args)\nassert (\n    args.max_episode_length >= args.min_episode_length\n), ""Maximum episode length must be greater than or equal to minimum episode length.""\n\nif args.min_episode_length < args.chunk_size:\n    print(\n        ""Warning: Minimum episode length is smaller than chunk size. Note that episodes shorter than the chunk size will be discarded.""\n    )\n\n\n# --- Generate episodes ---\ndef generate_episodes(num_episodes, split):\n    episode_idx = 0\n    episode_metadata = []\n    obs_chunks = []\n    act_chunks = []\n    file_idx = 0\n    output_dir_split = os.path.join(args.output_dir, split)\n    while episode_idx < num_episodes:\n        seed = np.random.randint(0, 10000)\n        env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=seed)\n\n        observations_seq = []\n        actions_seq = []\n        episode_obs_chunks = []\n        episode_act_chunks = []\n\n        # --- Run episode ---\n        step_t = 0\n        first_obs = True\n        for step_t in range(args.max_episode_length):\n            _, obs, first = env.observe()\n            action = types_np.sample(env.ac_space, bshape=(env.num,))\n            env.act(action)\n            observations_seq.append(obs[""rgb""])\n            actions_seq.append(action)\n            if len(observations_seq) == args.chunk_size:\n                episode_obs_chunks.append(observations_seq)\n                episode_act_chunks.append(actions_seq)\n                observations_seq = []\n                actions_seq = []\n            if first and not first_obs:\n                break\n            first_obs = False\n\n        # --- Save episode ---\n        if step_t + 1 >= args.min_episode_length:\n            if observations_seq:\n                if len(observations_seq) < args.chunk_size:\n                    print(\n                        f""Warning: Inconsistent chunk_sizes. Episode has {len(observations_seq)} frames, ""\n                        f""which is smaller than the requested chunk_size: {args.chunk_size}. ""\n                        ""This might lead to performance degradation during training.""\n                    )\n                episode_obs_chunks.append(observations_seq)\n                episode_act_chunks.append(actions_seq)\n\n            obs_chunks_data = [\n                np.concatenate(seq, axis=0).astype(np.uint8)\n                for seq in episode_obs_chunks\n            ]\n            act_chunks_data = [\n                np.concatenate(act, axis=0) for act in episode_act_chunks\n            ]\n            obs_chunks.extend(obs_chunks_data)\n            act_chunks.extend(act_chunks_data)\n\n            ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\n                file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\n            )\n            episode_metadata.extend(ep_metadata)\n\n            print(f""Episode {episode_idx} completed, length: {step_t + 1}."")\n            episode_idx += 1\n        else:\n            print(f""Episode too short ({step_t + 1}), resampling..."")\n\n    if len(obs_chunks) > 0:\n        print(\n            f""Warning: Dropping {len(obs_chunks)} chunks for consistent number of chunks per file."",\n            ""Consider changing the chunk_size and chunks_per_file parameters to prevent data-loss."",\n        )\n\n    print(f""Done generating {split} split"")\n    return episode_metadata\n\n\ndef get_action_space():\n    env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=0)\n    return env.ac_space.eltype.n\n\n\ndef main():\n    # Set random seed and create dataset directories\n    np.random.seed(args.seed)\n    # --- Generate episodes ---\n    train_episode_metadata = generate_episodes(args.num_episodes_train, ""train"")\n    val_episode_metadata = generate_episodes(args.num_episodes_val, ""val"")\n    test_episode_metadata = generate_episodes(args.num_episodes_test, ""test"")\n\n    # --- Save metadata ---\n    metadata = {\n        ""env"": ""coinrun"",\n        ""num_actions"": get_action_space(),\n        ""num_episodes_train"": args.num_episodes_train,\n        ""num_episodes_val"": args.num_episodes_val,\n        ""num_episodes_test"": args.num_episodes_test,\n        ""avg_episode_len_train"": np.mean(\n            [ep[""avg_seq_len""] for ep in train_episode_metadata]\n        ),\n        ""avg_episode_len_val"": np.mean(\n            [ep[""avg_seq_len""] for ep in val_episode_metadata]\n        ),\n        ""avg_episode_len_test"": np.mean(\n            [ep[""avg_seq_len""] for ep in test_episode_metadata]\n        ),\n        ""episode_metadata_train"": train_episode_metadata,\n        ""episode_metadata_val"": val_episode_metadata,\n        ""episode_metadata_test"": test_episode_metadata,\n    }\n    with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n        json.dump(metadata, f)\n\n    print(f""Done generating dataset."")\n\n\nif __name__ == ""__main__"":\n    main()\n",python,tab
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+703,4905982,"README.md",0,0,"",markdown,tab
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+706,4917140,"data/minecraft/huggingface/download_openai_array_records.sh",0,0,"#!/bin/bash\n\n# Download and extract array records from Hugging Face\n# \n# This script performs a two-step process:\n# 1. Downloads compressed array records from a Hugging Face dataset repository\n# 2. Extracts the compressed tar files in parallel for better performance\n#\n# Usage:\n#   ./download_openai_array_records.sh [hf_download_dir] [final_dataset_dir]\n#\n# Arguments:\n#   hf_download_dir    - Directory to store compressed downloads (default: data/minecraft_arrayrecords_compressed)\n#   final_dataset_dir  - Directory for extracted array records (default: data/minecraft_arrayrecords)\n\n# Set default directories if not provided as arguments\nhf_download_dir=""${1:-data/minecraft_arrayrecords_compressed}"" \nfinal_dataset_dir=""${2:-data/minecraft_arrayrecords}""          \n\nmkdir -p $hf_download_dir\nmkdir -p $final_dataset_dir\n\n# Step 1: Download compressed dataset from Hugging Face\necho ""Starting download from Hugging Face...""\nrepo_id=p-doom/open_ai_minecraft_arrayrecords_chunked\nstart_time_hf_download=$(date +%s)\n\nHF_HUB_ENABLE_HF_TRANSFER=1 HF_HUB_DISABLE_SYMLINKS=1 \\nhuggingface-cli download --repo-type dataset $repo_id --local-dir $hf_download_dir\n\nend_time_hf_download=$(date +%s)\necho ""Download completed. Time taken: $((end_time_hf_download - start_time_hf_download)) seconds""\n\n# Step 2: Extract compressed array records in parallel\necho ""Starting parallel extraction of tar files...""\nnum_workers=64  # Number of parallel extraction processes\nstart_time_uncompress=$(date +%s)\n\n# Find all shard tar files and extract them in parallel:\nxargs -0 -P $num_workers -I {} bash -c 'echo ""Extracting {}""; tar -xf ""{}"" -C ""'$final_dataset_dir'""'\n\nend_time_uncompress=$(date +%s)\n\n# Display timing summary\necho ""================================""\necho ""Extraction completed successfully!""\necho ""Uncompress time: $((end_time_uncompress - start_time_uncompress)) seconds""\necho ""Download time: $((end_time_hf_download - start_time_hf_download)) seconds""\necho ""Total time: $((end_time_uncompress - start_time_hf_download)) seconds""\necho ""Final dataset location: $final_dataset_dir""\n",shellscript,tab
+707,4920228,"README.md",0,0,"",markdown,tab
+708,4924670,"data/minecraft/openai/download_actions_files.py",0,0,"import subprocess\nimport json\nimport tyro\nfrom dataclasses import dataclass\nimport os\nfrom multiprocessing import Pool, cpu_count\nfrom tqdm import tqdm\n\n\n@dataclass\nclass Args:\n    index_file: str = ""data/open_ai_index_files/all_6xx_Jun_29.json""\n    output_dir: str = ""data/open_ai_minecraft_actions_files""\n    num_workers: int = -1  # -1 means use all available cores\n\n\ndef flatten_path(relpath):\n    """"""Convert nested path to flattened filename with subdirectory as prefix\n    e.g. data/6.10/filename.mp4 -> 6.10_filename.mp4\n    """"""\n\n    parts = relpath.split(""/"")\n\n    if len(parts) >= 3:\n        subdir = parts[1]\n        filename = parts[2]\n        return f""{subdir}_{filename}""\n    else:\n        return relpath.replace(""/"", ""_"")\n\n\ndef download_file(args):\n    try:\n        url, base_dir, output_dir = args\n        jsonl_url = url.rsplit(""."", 1)[0] + "".jsonl""\n        filename = flatten_path(jsonl_url)\n        output_file = os.path.join(output_dir, filename)\n        subprocess.run(\n            [""wget"", ""-q"", base_dir + jsonl_url, ""-O"", output_file], check=True\n        )\n        return {""file"": jsonl_url, ""success"": True}\n    except subprocess.CalledProcessError as e:\n        # delete file if it exists\n        if os.path.exists(output_file):\n            os.remove(output_file)\n        return {""file"": jsonl_url, ""success"": False, ""error"": str(e)}\n\n\ndef download_actions_files(index_file: str, output_dir: str, num_workers: int):\n    # load json file\n    with open(index_file, ""r"") as f:\n        data = json.load(f)\n\n    base_dir = data[""basedir""]\n    urls = data[""relpaths""]\n\n    # Prepare arguments for each process\n    args_list = [(url, base_dir, output_dir) for url in urls]\n\n    results = []\n    with tqdm(total=len(args_list), desc=""Downloading actions files"") as pbar:\n        with Pool(processes=num_workers) as pool:\n            for result in pool.imap_unordered(download_file, args_list):\n                results.append(result)\n                pbar.update(1)\n\n    # save results to json\n    meta_data_file_name = index_file.split(""/"")[-1].split(""."")[0] + ""_metadata.json""\n    with open(os.path.join(output_dir, meta_data_file_name), ""w"") as f:\n        json.dump(results, f)\n\n    # print number of failed downloads\n    failed_downloads = [result for result in results if not result[""success""]]\n    print(f""Number of failed downloads: {len(failed_downloads)}"")\n\n    # print number of successful downloads\n    successful_downloads = [result for result in results if result[""success""]]\n    print(f""Number of successful downloads: {len(successful_downloads)}"")\n\n\nif __name__ == ""__main__"":\n    args = tyro.cli(Args)\n    if not os.path.exists(args.output_dir):\n        os.makedirs(args.output_dir)\n\n    if args.num_workers == -1:\n        args.num_workers = cpu_count()\n\n    print(f""Index file: {args.index_file}"")\n    print(f""Output directory: {args.output_dir}"")\n    print(f""Number of workers: {args.num_workers}"")\n\n    download_actions_files(args.index_file, args.output_dir, args.num_workers)\n",python,tab
+709,4930223,"README.md",0,0,"",markdown,tab
+710,4933542,"data/minecraft/openai/download_videos.py",0,0,"import json\nimport requests\nimport os\nimport tyro\nimport logging\nfrom urllib.parse import urljoin\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom multiprocessing import Pool, cpu_count\nimport time\n\n\n@dataclass\nclass DownloadVideos:\n    index_file_path: str = ""data/open_ai_index_files/all_6xx_Jun_29.json""\n    num_workers: int = -1  # -1 means use all available cores\n    output_dir: str = ""data/minecraft_videos/""\n\n\ndef download_single_file(args):\n    """"""Download a single file - designed to be used with multiprocessing""""""\n    relpath, url, output_path = args\n\n    if os.path.exists(output_path):\n        return f""Skipped {relpath} (already exists)""\n\n    # No need to create parent directories since we're flattening the structure\n    try:\n        response = requests.get(url, stream=True, timeout=30)\n        if response.status_code == 200:\n            file_size = 0\n            with open(output_path, ""wb"") as f:\n                for chunk in response.iter_content(chunk_size=8192):\n                    if chunk:\n                        f.write(chunk)\n                        file_size += len(chunk)\n\n            # Convert to MB for logging\n            file_size_mb = file_size / (1024 * 1024)\n            return f""Downloaded {relpath} ({file_size_mb:.2f} MB)""\n        else:\n            return f""Failed to download {relpath}: HTTP {response.status_code}""\n    except requests.exceptions.RequestException as e:\n        return f""Request failed for {relpath}: {e}""\n    except Exception as e:\n        return f""Unexpected error downloading {relpath}: {e}""\n\n\ndef flatten_path(relpath):\n    """"""Convert nested path to flattened filename with subdirectory as prefix\n    e.g. data/6.10/filename.mp4 -> 6.10_filename.mp4\n    """"""\n\n    parts = relpath.split(""/"")\n\n    if len(parts) >= 3:\n        subdir = parts[1]\n        filename = parts[2]\n        return f""{subdir}_{filename}""\n    else:\n        return relpath.replace(""/"", ""_"")\n\n\ndef download_dataset(index_file_path, output_dir, num_workers=64):\n    # Load the index file\n    with open(index_file_path, ""r"") as f:\n        index_data = json.load(f)\n\n    basedir = index_data[""basedir""]\n    relpaths = index_data[""relpaths""]\n\n    # Filter for mp4 files only and flatten the path structure\n    mp4_files = []\n    for relpath in relpaths:\n        if relpath.endswith("".mp4""):\n            url = urljoin(basedir, relpath)\n            flattened_filename = flatten_path(relpath)\n            output_path = os.path.join(output_dir, flattened_filename)\n            mp4_files.append((relpath, url, output_path))\n\n    print(f""Found {len(mp4_files)} MP4 files to download"")\n    print(f""Using {num_workers} workers for parallel downloads"")\n\n    start_time = time.time()\n\n    if num_workers > len(mp4_files):\n        num_workers = len(mp4_files)\n\n    with tqdm(\n        total=len(mp4_files), desc=""Overall Download Progress"", unit=""files""\n    ) as pbar:\n        with Pool(processes=num_workers) as pool:\n            results = []\n            for result in pool.imap_unordered(\n                download_single_file,\n                [\n                    (relpath, url, output_path)\n                    for relpath, url, output_path in mp4_files\n                ],\n            ):\n                results.append(result)\n                pbar.update(1)\n    # Print final results summary\n    successful_downloads = sum(1 for r in results if ""Downloaded"" in r)\n    skipped_files = sum(1 for r in results if ""Skipped"" in r)\n    failed_downloads = len(results) - successful_downloads - skipped_files\n\n    print(f""\nDownload Summary:"")\n    print(f""  Successful downloads: {successful_downloads}"")\n    print(f""  Skipped files: {skipped_files}"")\n    print(f""  Failed downloads: {failed_downloads}"")\n\n    end_time = time.time()\n    total_time = end_time - start_time\n    print(f""Download completed in {total_time:.2f} seconds"")\n\n\nif __name__ == ""__main__"":\n    args = tyro.cli(DownloadVideos)\n    os.makedirs(args.output_dir, exist_ok=True)\n\n    if args.num_workers == -1:\n        args.num_workers = cpu_count()\n\n    print(f""Index file path: {args.index_file_path}"")\n    print(f""Output directory: {args.output_dir}"")\n    print(f""Number of workers: {args.num_workers}"")\n\n    download_dataset(args.index_file_path, args.output_dir, args.num_workers)\n",python,tab
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+720,6988890,"data/pngs_to_array_records.py",0,0,"import os\nimport numpy as np\nfrom PIL import Image\nimport tyro\nfrom dataclasses import dataclass\nimport json\nimport multiprocessing as mp\nfrom utils import save_chunks\n\n\n@dataclass\nclass Args:\n    input_path: str\n    output_path: str\n    env_name: str\n    train_ratio: float = 0.8\n    val_ratio: float = 0.1\n    test_ratio: float = 0.1\n    multigame: bool = False\n    original_fps: int = 60\n    target_fps: int = 10\n    target_width: int = 64\n    chunk_size: int = 160\n    chunks_per_file: int = 100\n\n\ndef preprocess_pngs(input_dir, original_fps, target_fps, chunk_size, target_width):\n    print(f""Processing PNGs in {input_dir}"")\n    try:\n        png_files = sorted(\n            [f for f in os.listdir(input_dir) if f.lower().endswith("".png"")],\n            key=lambda x: int(os.path.splitext(x)[0]),\n        )\n\n        if not png_files:\n            print(f""No PNG files found in {input_dir}"")\n            return []\n\n        # Downsample indices\n        n_total = len(png_files)\n        if original_fps == target_fps:\n            selected_indices = np.arange(n_total)\n        else:\n            n_target = int(np.floor(n_total * target_fps / original_fps))\n            selected_indices = np.linspace(0, n_total - 1, n_target, dtype=int)\n\n        selected_files = [png_files[i] for i in selected_indices]\n\n        # Load images\n        chunks = []\n        frames = []\n        for fname in selected_files:\n            img = Image.open(os.path.join(input_dir, fname)).convert(""RGB"")\n            w, h = img.size  # PIL gives (width, height)\n            if w != target_width:\n                target_height = int(round(h * (target_width / float(w))))\n                resample_filter = Image.LANCZOS\n                img = img.resize(\n                    (target_width, target_height), resample=resample_filter\n                )\n            frames.append(np.array(img))\n            if len(frames) == chunk_size:\n                chunks.append(frames)\n                frames = []\n\n        if len(frames) < chunk_size:\n            print(\n                f""Warning: Inconsistent chunk_sizes. Episode has {len(frames)} frames, ""\n                f""which is smaller than the requested chunk_size: {chunk_size}. ""\n                ""This might lead to performance degradation during training.""\n            )\n        chunks.append(frames)\n        chunks = [np.stack(chunk, axis=0) for chunk in chunks]\n\n        return chunks\n    except Exception as e:\n        print(f""Error processing {input_dir}: {e}"")\n        return []\n\n\ndef save_split(pool_args, chunks_per_file, output_path):\n    num_processes = mp.cpu_count()\n    print(f""Number of processes: {num_processes}"")\n    chunks = []\n    file_idx = 0\n    results = []\n    for bucket_idx in range(0, len(pool_args), num_processes):\n        args_batch = pool_args[bucket_idx : bucket_idx + num_processes]\n        with mp.Pool(processes=num_processes) as pool:\n            for episode_chunks in pool.starmap(preprocess_pngs, args_batch):\n                chunks.extend(episode_chunks)\n        results_batch, file_idx, chunks, _ = save_chunks(\n            file_idx, chunks_per_file, output_path, chunks\n        )\n        results.extend(results_batch)\n\n    if len(chunks) > 0:\n        print(\n            f""Warning: Dropping {len(chunks)} chunks for consistent number of chunks per file."",\n            ""Consider changing the chunk_size and chunks_per_file parameters to prevent data-loss."",\n        )\n\n    print(f""Done processing files. Saved to {output_path}"")\n    return results\n\n\ndef main():\n    args = tyro.cli(Args)\n    print(f""Output path: {args.output_path}"")\n    total_ratio = args.train_ratio + args.val_ratio + args.test_ratio\n    assert np.isclose(total_ratio, 1.0), ""Ratios must sum to 1.0""\n\n    directories = [\n        os.path.join(args.input_path, d)\n        for d in os.listdir(args.input_path)\n        if os.path.isdir(os.path.join(args.input_path, d))\n    ]\n    if args.multigame:\n        episodes = [\n            os.path.join(game, d) for game in directories for d in os.listdir(game)\n        ]\n    else:\n        episodes = directories\n\n    n_total = sum([len(os.listdir(episode)) for episode in episodes])\n    n_train = round(n_total * args.train_ratio)\n    n_val = round(n_total * args.val_ratio)\n\n    pool_args_train = []\n    pool_args_val = []\n    pool_args_test = []\n\n    train_counter = 0\n    val_counter = 0\n    np.random.shuffle(episodes)\n    for episode in episodes:\n        pool_arg = (\n            episode,\n            args.original_fps,\n            args.target_fps,\n            args.chunk_size,\n            args.target_width,\n        )\n        n_frames = len(os.listdir(episode))\n        if train_counter < n_train:\n            pool_args_train.append(pool_arg)\n            train_counter += n_frames\n        elif val_counter < n_val:\n            pool_args_val.append(pool_arg)\n            val_counter += n_frames\n        else:\n            pool_args_test.append(pool_arg)\n\n    train_episode_metadata = save_split(\n        pool_args_train, args.chunks_per_file, os.path.join(args.output_path, ""train"")\n    )\n    val_episode_metadata = save_split(\n        pool_args_val, args.chunks_per_file, os.path.join(args.output_path, ""val"")\n    )\n    test_episode_metadata = save_split(\n        pool_args_test, args.chunks_per_file, os.path.join(args.output_path, ""test"")\n    )\n\n    # Calculate total number of chunks\n    total_chunks = sum(\n        ep[""num_chunks""]\n        for ep in train_episode_metadata + val_episode_metadata + test_episode_metadata\n    )\n\n    print(""Done converting png to array_record files"")\n\n    print(f""Total number of chunks: {total_chunks}"")\n\n    metadata = {\n        ""env"": args.env_name,\n        ""total_chunks"": total_chunks,\n        ""avg_episode_len_train"": np.mean(\n            [ep[""avg_seq_len""] for ep in train_episode_metadata]\n        ),\n        ""avg_episode_len_val"": np.mean(\n            [ep[""avg_seq_len""] for ep in val_episode_metadata]\n        ),\n        ""avg_episode_len_test"": np.mean(\n            [ep[""avg_seq_len""] for ep in test_episode_metadata]\n        ),\n        ""episode_metadata_train"": train_episode_metadata,\n        ""episode_metadata_val"": val_episode_metadata,\n        ""episode_metadata_test"": test_episode_metadata,\n    }\n\n    with open(os.path.join(args.output_path, ""metadata.json""), ""w"") as f:\n        json.dump(metadata, f)\n\n    print(""Done."")\n\n\nif __name__ == ""__main__"":\n    main()\n",python,tab
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@@ -0,0 +1,113 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,1,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"#include QMK_KEYBOARD_H\n#if __has_include(""keymap.h"")\n#    include ""keymap.h""\n#endif\n\n\n/* THIS FILE WAS GENERATED!\n *\n * This file was generated by qmk json2c. You may or may not want to\n * edit it directly.\n */\n\nconst uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n    [0] = LAYOUT_60_ansi(KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC, KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS, KC_RCTL, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT, KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP, KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT),\n    [1] = LAYOUT_60_ansi(KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [2] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, KC_CAPS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [3] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_BTN1, MS_BTN2, MS_BTN3, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [4] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_LPRN, KC_RPRN, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LCBR, KC_AMPR, KC_ASTR, KC_NO, KC_RCBR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_COLN, KC_DLR, KC_PERC, KC_CIRC, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_TILD, KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [5] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_0, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO)\n};\n\n\n\n#ifdef OTHER_KEYMAP_C\n#    include OTHER_KEYMAP_C\n#endif // OTHER_KEYMAP_C\n",c,tab
+2,86,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:57:42 PM [info] Activating crowd-code\n1:57:42 PM [info] Recording started\n1:57:42 PM [info] Initializing git provider using file system watchers...\n1:57:42 PM [info] Git repository found\n1:57:42 PM [info] Git provider initialized successfully\n",Log,tab
+3,150,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"1:57:42 PM [info] Initial git state: [object Object]\n",Log,content
+4,82551661,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab
+5,82554680,"keyboards/annepro2/keymaps/calmar_one/keymap.c",186,0,"",c,selection_mouse
+6,82554690,"keyboards/annepro2/keymaps/calmar_one/keymap.c",185,0,"",c,selection_command
+7,82555468,"keyboards/annepro2/keymaps/calmar_one/keymap.c",118,0,"",c,selection_command
+8,82556063,"keyboards/annepro2/keymaps/calmar_one/keymap.c",187,0,"",c,selection_command
+9,82556156,"keyboards/annepro2/keymaps/calmar_one/keymap.c",208,0,"",c,selection_command
+10,82556271,"keyboards/annepro2/keymaps/calmar_one/keymap.c",212,0,"",c,selection_command
+11,82556424,"keyboards/annepro2/keymaps/calmar_one/keymap.c",213,0,"",c,selection_command
+12,82556592,"keyboards/annepro2/keymaps/calmar_one/keymap.c",276,0,"",c,selection_command
+13,82558084,"keyboards/annepro2/keymaps/calmar_one/keymap.c",276,552,"    [0] = LAYOUT_60_ansi(KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC, KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS, KC_RCTL, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT, KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP, KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT),",c,selection_command
+14,82655499,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab
+15,82655530,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2741,0,"",c,selection_command
+16,82666840,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"#include QMK_KEYBOARD_H\n#if __has_include(""keymap.h"")\n#    include ""keymap.h""\n#endif\n\n\n/* THIS FILE WAS GENERATED!\n *\n * This file was generated by qmk json2c. You may or may not want to\n * edit it directly.\n */\n\nconst uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n    [0] = LAYOUT_60_ansi(KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC, KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS, KC_RCTL, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT, KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP, KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT),\n    [1] = LAYOUT_60_ansi(KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [2] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, KC_CAPS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [3] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_BTN1, MS_BTN2, MS_BTN3, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [4] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_LPRN, KC_RPRN, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LCBR, KC_AMPR, KC_ASTR, KC_NO, KC_RCBR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_COLN, KC_DLR, KC_PERC, KC_CIRC, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_TILD, KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO),\n    [5] = LAYOUT_60_ansi(KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_0, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO)\n};\n\n\n\n#ifdef OTHER_KEYMAP_C\n#    include OTHER_KEYMAP_C\n#endif // OTHER_KEYMAP_C\n",c,tab
+17,82666885,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2741,0,"",c,selection_command
+18,82715360,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab
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+20,82859754,"keyboards/annepro2/keymaps/calmar_one/keymap.c",276,552,"    [0] = LAYOUT_60_ansi(KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC, KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS, LCTL_T(KC_ESC), LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT, KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP, KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT),",c,content
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+84,86298034,"keyboards/annepro2/keymaps/calmar_one/keymap.c",746,0,"",c,selection_command
+85,86298067,"keyboards/annepro2/keymaps/calmar_one/keymap.c",755,0,"",c,selection_command
+86,86298100,"keyboards/annepro2/keymaps/calmar_one/keymap.c",762,0,"",c,selection_command
+87,86298134,"keyboards/annepro2/keymaps/calmar_one/keymap.c",771,0,"",c,selection_command
+88,86298169,"keyboards/annepro2/keymaps/calmar_one/keymap.c",780,0,"",c,selection_command
+89,86298202,"keyboards/annepro2/keymaps/calmar_one/keymap.c",789,0,"",c,selection_command
+90,86298235,"keyboards/annepro2/keymaps/calmar_one/keymap.c",797,0,"",c,selection_command
+91,86298269,"keyboards/annepro2/keymaps/calmar_one/keymap.c",806,0,"",c,selection_command
+92,86298303,"keyboards/annepro2/keymaps/calmar_one/keymap.c",815,0,"",c,selection_command
+93,86298336,"keyboards/annepro2/keymaps/calmar_one/keymap.c",824,0,"",c,selection_command
+94,86302456,"keyboards/annepro2/keymaps/calmar_one/keymap.c",834,0,"",c,selection_command
+95,86303493,"keyboards/annepro2/keymaps/calmar_one/keymap.c",276,0,"",c,selection_command
+96,86309748,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab
+97,86309776,"keyboards/annepro2/keymaps/calmar_one/keymap.c",276,0,"",c,selection_command
+98,86344280,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
+99,86346424,"TERMINAL",0,0,"",,terminal_focus
+100,86346429,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab
+101,86348626,"TERMINAL",0,0,"make annepro2/c18:calmar_one",,terminal_command
+102,86348674,"TERMINAL",0,0,"]633;C",,terminal_output
+103,86351629,"TERMINAL",0,0,"",,terminal_output
+104,86352156,"TERMINAL",0,0,"Making annepro2/c18 with keymap calmar_one\r\n\r\n",,terminal_output
+105,86353215,"TERMINAL",0,0,"arm-none-eabi-gcc (Arm GNU Toolchain 14.3.Rel1 (Build arm-14.174)) 14.3.1 20250623\r\nCopyright (C) 2024 Free Software Foundation, Inc.\r\nThis is free software; see the source for copying conditions.  There is NO\r\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\r\n\r\nSize before:\r\n   text\t   data\t    bss\t    dec\t    hex\tfilename\r\n      0\t  37712\t      0\t  37712\t   9350\tannepro2_c18_calmar_one.bin\r\n\r\nCompiling: quantum/keymap_introspection.c                                                          ",,terminal_output
+106,86353313,"TERMINAL",0,0," [OK]\r\nLinking: .build/annepro2_c18_calmar_one.elf                                                        ",,terminal_output
+107,86354231,"TERMINAL",0,0," [OK]\r\nCreating binary load file for flashing: .build/annepro2_c18_calmar_one.bin                          [OK]\r\nCreating load file for flashing: .build/annepro2_c18_calmar_one.hex                                 [OK]\r\n\r\nSize after:\r\n   text\t   data\t    bss\t    dec\t    hex\tfilename\r\n      0\t  37712\t      0\t  37712\t   9350\tannepro2_c18_calmar_one.bin\r\n\r\nCopying annepro2_c18_calmar_one.bin to qmk_firmware folder                                          [OK]\r\n%                                                                                                                                                                                   \r \r",,terminal_output
+108,86368108,"TERMINAL",0,0,"cd ../AnnePro2-Tools",,terminal_command
+109,86368109,"TERMINAL",0,0,"]633;C%                                                                                                                                                                                   \r \r",,terminal_output
+110,86399516,"TERMINAL",0,0,"./target/release/annepro2_tools ../qmk_firmware/annepro2_c18_calmar_one.bin",,terminal_command
+111,86399583,"TERMINAL",0,0,"]633;Cargs: ArgOpts {\r\n    base: 0x4000,\r\n    boot: false,\r\n    target: ""main"",\r\n    file: ""../qmk_firmware/annepro2_c18_calmar_one.bin"",\r\n}\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 0000:0000 (Headset)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 0000:0000 (BTM)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 04d9:8009 (USB-HID IAP)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 05ac:0000 (Keyboard Backlight)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 0000:0000 (Headset)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 0000:0000 (BTM)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 ()\r\nHID Dev: 04d9:8009 (USB-HID IAP)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 0000:0000 (Apple Internal Keyboard / Trackpad)\r\nHID Dev: 05ac:8104 ()\r\nHID Dev: 05ac:0000 (Keyboard Backlight)\r\ndevice is Some(""USB-HID IAP"")\r\n",,terminal_output
+112,86411402,"TERMINAL",0,0,"read back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 43 00 00  00 00 00 00   {......}.C......\r\n0010:   00 00 00 00  00 00 00 00  00 00 00 00  00 00 00 00   ................\r\n0020:   00 00 00 00  00 00 00 00  00 00 00 00  00 00 00 00   ................\r\n0030:   00 00 00 00  00 00 00 00  00 00 00 00  00 00 00 00   ................\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  00 00 39 9c   {......}.1.A..9.\r\n0010:   00 00 93 41  00 00 93 41  00 00 93 41  00 00 93 41   ...A...A...A...A\r\n0020:   00 00 93 41  00 00 93 41  00 00 93 41  00 00 93 41   ...A...A...A...A\r\n0030:   00 00 93 41  00 00 00 00  00 00 00 00  00 00 00 00   ...A............\r\n[INFO] Wrote 48 bytes, at 0x004000, total: 48 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  00 00 93 41   {......}.1.A...A\r\n0010:   00 00 6d 9e  00 00 93 41  00 00 93 41  00 00 93 41   ..m....A...A...A\r\n0020:   00 00 93 41  00 00 93 41  00 00 93 41  00 00 93 41   ...A...A...A...A\r\n0030:   00 00 93 41  00 00 00 00  00 00 00 00  00 00 00 00   ...A............\r\n[INFO] Wrote 48 bytes, at 0x004030, total: 96 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  00 00 93 41   {......}.1.A...A\r\n0010:   00 00 93 41  00 00 93 41  00 00 93 41  00 00 93 41   ...A...A...A...A\r\n0020:   00 00 93 41  00 00 93 41  00 00 93 41  00 00 93 41   ...A...A...A...A\r\n0030:   00 00 93 41  00 00 00 00  00 00 00 00  00 00 00 00   ...A............\r\n[INFO] Wrote 48 bytes, at 0x004060, total: 144 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  00 00 f5 9e   {......}.1.A....\r\n0010:   00 00 15 b4  00 00 2d b4  00 00 93 41  00 00 93 41   ......-....A...A\r\n0020:   00 00 93 41  00 00 93 41  00 00 f1 b0  00 00 93 41   ...A...A.......A\r\n0030:   00 00 93 41  00 00 00 00  00 00 00 00  00 00 00 00   ...A............\r\n[INFO] Wrote 48 bytes, at 0x004090, total: 192 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 f3  08 88 23 48   {......}.1....#H\r\n0010:   80 f3 09 88  02 20 80 f3  14 88 bf f3  6f 8f 04 f0   ..... ......o...\r\n0020:   e9 fd 04 f0  e8 fd 1e 48  1f 49 1b 4a  91 42 02 da   .......H.I.J.B..\r\n0030:   08 60 04 31  fa e7 00 00  00 00 00 00  00 00 00 00   .`.1............\r\n[INFO] Wrote 48 bytes, at 0x0040c0, total: 240 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 42  02 da 08 60   {......}.1.B...`\r\n0010:   04 31 fa e7  1a 49 1a 4a  1b 4b 9a 42  04 da 08 68   .1...I.J.K.B...h\r\n0020:   10 60 04 31  04 32 f8 e7  00 20 17 49  18 4a 91 42   .`.1.2... .I.J.B\r\n0030:   02 da 08 60  04 31 00 00  00 00 00 00  00 00 00 00   ...`.1..........\r\n[INFO] Wrote 48 bytes, at 0x0040f0, total: 288 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 fd  04 f0 c7 fd   {......}.1......\r\n0010:   14 4c 14 4d  ac 42 03 da  21 68 88 47  04 34 f9 e7   .L.M.B..!h.G.4..\r\n0020:   06 f0 ad f8  11 4c 11 4d  ac 42 03 da  21 68 88 47   .....L.M.B..!h.G\r\n0030:   04 34 f9 e7  0f 49 00 00  00 00 00 00  00 00 00 00   .4...I..........\r\n[INFO] Wrote 48 bytes, at 0x004120, total: 336 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 04  00 20 00 0c   {......}.1... ..\r\n0010:   00 20 55 55  55 55 00 00  00 20 00 04  00 20 a4 cf   . UUUU... ... ..\r\n0020:   00 00 00 0c  00 20 ac 0f  00 20 b0 0f  00 20 c4 1f   ..... ... ... ..\r\n0030:   00 20 c0 40  00 00 00 00  00 00 00 00  00 00 00 00   . .@............\r\n[INFO] Wrote 48 bytes, at 0x004150, total: 384 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 40  00 00 c0 40   {......}.1.@...@\r\n0010:   00 00 bb 8c  00 00 96 e7  00 f0 00 f8  fe e7 f0 b5   ................\r\n0020:   44 46 4d 46  56 46 5f 46  f0 b4 6b 46  cb 60 c3 68   DFMFVF_F..kF.`.h\r\n0030:   9d 46 f0 bc  a0 46 00 00  00 00 00 00  00 00 00 00   .F...F..........\r\n[INFO] Wrote 48 bytes, at 0x004180, total: 432 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 46  f0 bd 62 b6   {......}.1.F..b.\r\n0010:   28 1c a0 47  00 20 04 f0  c6 fd fe e7  04 f0 9b fd   (..G. ..........\r\n0020:   02 4a 02 4b  13 60 fe e7  c0 46 04 ed  00 e0 00 00   .J.K.`...F......\r\n0030:   00 80 02 b4  71 46 00 00  00 00 00 00  00 00 00 00   ....qF..........\r\n[INFO] Wrote 48 bytes, at 0x0041b0, total: 480 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 5c  49 00 8e 44   {......}.1.\I..D\r\n0010:   02 bc 70 47  c0 46 02 b4  71 46 49 08  49 00 09 56   ..pG.F..qFI.I..V\r\n0020:   49 00 8e 44  02 bc 70 47  c0 46 03 b4  71 46 49 08   I..D..pG.F..qFI.\r\n0030:   40 00 49 00  09 5a 00 00  00 00 00 00  00 00 00 00   @.I..Z..........\r\n[INFO] Wrote 48 bytes, at 0x0041e0, total: 528 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 bc  70 47 03 b4   {......}.1..pG..\r\n0010:   71 46 49 08  40 00 49 00  09 5e 49 00  8e 44 03 bc   qFI.@.I..^I..D..\r\n0020:   70 47 00 22  43 08 8b 42  74 d3 03 09  8b 42 5f d3   pG.""C..Bt....B_.\r\n0030:   03 0a 8b 42  44 d3 00 00  00 00 00 00  00 00 00 00   ...BD...........\r\n[INFO] Wrote 48 bytes, at 0x004210, total: 576 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d3  03 0c 8b 42   {......}.1.....B\r\n0010:   0d d3 ff 22  09 02 12 ba  03 0c 8b 42  02 d3 12 12   ..."".......B....\r\n0020:   09 02 65 d0  03 0b 8b 42  19 d3 00 e0  09 0a c3 0b   ..e....B........\r\n0030:   8b 42 01 d3  cb 03 00 00  00 00 00 00  00 00 00 00   .B..............\r\n[INFO] Wrote 48 bytes, at 0x004240, total: 624 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 0b  8b 42 01 d3   {......}.1...B..\r\n0010:   8b 03 c0 1a  52 41 43 0b  8b 42 01 d3  4b 03 c0 1a   ....RAC..B..K...\r\n0020:   52 41 03 0b  8b 42 01 d3  0b 03 c0 1a  52 41 c3 0a   RA...B......RA..\r\n0030:   8b 42 01 d3  cb 02 00 00  00 00 00 00  00 00 00 00   .B..............\r\n[INFO] Wrote 48 bytes, at 0x004270, total: 672 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 0a  8b 42 01 d3   {......}.1...B..\r\n0010:   8b 02 c0 1a  52 41 43 0a  8b 42 01 d3  4b 02 c0 1a   ....RAC..B..K...\r\n0020:   52 41 03 0a  8b 42 01 d3  0b 02 c0 1a  52 41 cd d2   RA...B......RA..\r\n0030:   c3 09 8b 42  01 d3 00 00  00 00 00 00  00 00 00 00   ...B............\r\n[INFO] Wrote 48 bytes, at 0x0042a0, total: 720 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  83 09 8b 42   {......}.1.A...B\r\n0010:   01 d3 8b 01  c0 1a 52 41  43 09 8b 42  01 d3 4b 01   ......RAC..B..K.\r\n0020:   c0 1a 52 41  03 09 8b 42  01 d3 0b 01  c0 1a 52 41   ..RA...B......RA\r\n0030:   c3 08 8b 42  01 d3 00 00  00 00 00 00  00 00 00 00   ...B............\r\n[INFO] Wrote 48 bytes, at 0x0042d0, total: 768 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  83 08 8b 42   {......}.1.A...B\r\n0010:   01 d3 8b 00  c0 1a 52 41  43 08 8b 42  01 d3 4b 00   ......RAC..B..K.\r\n0020:   c0 1a 52 41  41 1a 00 d2  01 46 52 41  10 46 70 47   ..RAA....FRA.FpG\r\n0030:   ff e7 01 b5  00 20 00 00  00 00 00 00  00 00 00 00   ..... ..........\r\n[INFO] Wrote 48 bytes, at 0x004300, total: 816 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 bd  c0 46 00 29   {......}.1...F.)\r\n0010:   f7 d0 76 e7  70 47 03 46  0b 43 7f d4  00 22 43 08   ..v.pG.F.C...""C.\r\n0020:   8b 42 74 d3  03 09 8b 42  5f d3 03 0a  8b 42 44 d3   .Bt....B_....BD.\r\n0030:   03 0b 8b 42  28 d3 00 00  00 00 00 00  00 00 00 00   ...B(...........\r\n[INFO] Wrote 48 bytes, at 0x004330, total: 864 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d3  ff 22 09 02   {......}.1...""..\r\n0010:   12 ba 03 0c  8b 42 02 d3  12 12 09 02  65 d0 03 0b   .....B......e...\r\n0020:   8b 42 19 d3  00 e0 09 0a  c3 0b 8b 42  01 d3 cb 03   .B.........B....\r\n0030:   c0 1a 52 41  83 0b 00 00  00 00 00 00  00 00 00 00   ..RA............\r\n[INFO] Wrote 48 bytes, at 0x004360, total: 912 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 03  c0 1a 52 41   {......}.1....RA\r\n0010:   43 0b 8b 42  01 d3 4b 03  c0 1a 52 41  03 0b 8b 42   C..B..K...RA...B\r\n0020:   01 d3 0b 03  c0 1a 52 41  c3 0a 8b 42  01 d3 cb 02   ......RA...B....\r\n0030:   c0 1a 52 41  83 0a 00 00  00 00 00 00  00 00 00 00   ..RA............\r\n[INFO] Wrote 48 bytes, at 0x004390, total: 960 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 02  c0 1a 52 41   {......}.1....RA\r\n0010:   43 0a 8b 42  01 d3 4b 02  c0 1a 52 41  03 0a 8b 42   C..B..K...RA...B\r\n0020:   01 d3 0b 02  c0 1a 52 41  cd d2 c3 09  8b 42 01 d3   ......RA.....B..\r\n0030:   cb 01 c0 1a  52 41 00 00  00 00 00 00  00 00 00 00   ....RA..........\r\n[INFO] Wrote 48 bytes, at 0x0043c0, total: 1008 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d3  8b 01 c0 1a   {......}.1......\r\n0010:   52 41 43 09  8b 42 01 d3  4b 01 c0 1a  52 41 03 09   RAC..B..K...RA..\r\n0020:   8b 42 01 d3  0b 01 c0 1a  52 41 c3 08  8b 42 01 d3   .B......RA...B..\r\n0030:   cb 00 c0 1a  52 41 00 00  00 00 00 00  00 00 00 00   ....RA..........\r\n[INFO] Wrote 48 bytes, at 0x0043f0, total: 1056 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d3  8b 00 c0 1a   {......}.1......\r\n0010:   52 41 43 08  8b 42 01 d3  4b 00 c0 1a  52 41 41 1a   RAC..B..K...RAA.\r\n0020:   00 d2 01 46  52 41 10 46  70 47 5d e0  ca 0f 00 d0   ...FRA.FpG].....\r\n0030:   49 42 03 10  00 d3 00 00  00 00 00 00  00 00 00 00   IB..............\r\n[INFO] Wrote 48 bytes, at 0x004420, total: 1104 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 22  9c 46 03 09   {......}.1."".F..\r\n0010:   8b 42 2d d3  03 0a 8b 42  12 d3 fc 22  89 01 12 ba   .B-....B...""....\r\n0020:   03 0a 8b 42  0c d3 89 01  92 11 8b 42  08 d3 89 01   ...B.......B....\r\n0030:   92 11 8b 42  04 d3 00 00  00 00 00 00  00 00 00 00   ...B............\r\n[INFO] Wrote 48 bytes, at 0x004450, total: 1152 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 11  00 e0 89 09   {......}.1......\r\n0010:   c3 09 8b 42  01 d3 cb 01  c0 1a 52 41  83 09 8b 42   ...B......RA...B\r\n0020:   01 d3 8b 01  c0 1a 52 41  43 09 8b 42  01 d3 4b 01   ......RAC..B..K.\r\n0030:   c0 1a 52 41  03 09 00 00  00 00 00 00  00 00 00 00   ..RA............\r\n[INFO] Wrote 48 bytes, at 0x004480, total: 1200 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 01  c0 1a 52 41   {......}.1....RA\r\n0010:   c3 08 8b 42  01 d3 cb 00  c0 1a 52 41  83 08 8b 42   ...B......RA...B\r\n0020:   01 d3 8b 00  c0 1a 52 41  d9 d2 43 08  8b 42 01 d3   ......RA..C..B..\r\n0030:   4b 00 c0 1a  52 41 00 00  00 00 00 00  00 00 00 00   K...RA..........\r\n[INFO] Wrote 48 bytes, at 0x0044b0, total: 1248 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 46  63 46 52 41   {......}.1.FcFRA\r\n0010:   5b 10 10 46  01 d3 40 42  00 2b 00 d5  49 42 70 47   [..F..@B.+..IBpG\r\n0020:   63 46 5b 10  00 d3 40 42  01 b5 00 20  00 f0 05 f8   cF[...@B... ....\r\n0030:   02 bd 00 29  f8 d0 00 00  00 00 00 00  00 00 00 00   ...)............\r\n[INFO] Wrote 48 bytes, at 0x0044e0, total: 1296 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 47  c0 46 00 20   {......}.1.G.F. \r\n0010:   70 47 70 47  00 00 03 78  41 2b 0c d1  c2 78 06 4b   pGpG...xA+...x.K\r\n0020:   1a 70 02 79  5a 70 42 79  9a 70 82 79  da 70 c2 79   .p.yZpBy.p.y.p.y\r\n0030:   1a 71 02 7a  5a 71 00 00  00 00 00 00  00 00 00 00   .q.zZq..........\r\n[INFO] Wrote 48 bytes, at 0x004510, total: 1344 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 1c  00 20 00 23   {......}.1... .#\r\n0010:   70 b5 07 4c  08 4d 00 22  18 19 81 5c  ff 29 01 d0   p..L.M.""...\.)..\r\n0020:   9e 18 6e 54  01 32 0e 2a  f7 d1 0e 33  46 2b f2 d1   ..nT.2.*...3F+..\r\n0030:   70 bd ae cd  00 00 00 00  00 00 00 00  00 00 00 00   p...............\r\n[INFO] Wrote 48 bytes, at 0x004540, total: 1392 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b5  05 4c 24 5c   {......}.1...L$\\r\n0010:   05 48 a4 00  03 55 00 19  42 70 ff 22  81 70 c2 70   .H...U..Bp."".p.p\r\n0020:   10 bd 20 1b  00 20 08 1a  00 20 f8 b5  05 00 0e 00   .. .. ... ......\r\n0030:   17 00 00 24  20 00 00 00  00 00 00 00  00 00 00 00   ...$ ...........\r\n[INFO] Wrote 48 bytes, at 0x004570, total: 1440 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  01 34 ff f7   {......}.1...4..\r\n0010:   e4 ff 3d 2c  f6 d1 f8 bd  00 00 bf f3  4f 8f 03 4b   ..=,........O..K\r\n0020:   04 4a da 60  bf f3 4f 8f  c0 46 fd e7  c0 46 00 ed   .J.`..O..F...F..\r\n0030:   00 e0 04 00  fa 05 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x0045a0, total: 1488 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 4b  1a 88 0a 43   {......}.1.K...C\r\n0010:   1a 80 70 47  c0 46 b8 19  00 20 01 21  81 40 02 4b   ..pG.F... .!.@.K\r\n0020:   1a 88 8a 43  1a 80 70 47  c0 46 b8 19  00 20 73 b5   ...C..pG.F... s.\r\n0030:   82 b2 01 ab  18 80 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x0045d0, total: 1536 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d8  1b 78 0d 2b   {......}.1...x.+\r\n0010:   19 d8 0e 20  01 26 42 43  9b 18 d8 08  0b 4a 80 00   ... .&BC.....J..\r\n0020:   80 18 07 22  35 00 13 40  9d 40 00 22  ed b2 0b 00   ...""5..@.@.""....\r\n0030:   d3 40 84 5c  33 40 00 00  00 00 00 00  00 00 00 00   .@.\3@..........\r\n[INFO] Wrote 48 bytes, at 0x004600, total: 1584 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 40  5c 40 84 54   {......}.1.@\@.T\r\n0010:   01 32 04 2a  f3 d1 73 bd  c0 46 78 19  00 20 37 b5   .2.*..s..Fx.. 7.\r\n0020:   82 b2 01 ab  18 80 12 0a  00 20 04 2a  16 d8 1b 78   ......... .*...x\r\n0030:   0d 2b 13 d8  0e 21 00 00  00 00 00 00  00 00 00 00   .+...!..........\r\n[INFO] Wrote 48 bytes, at 0x004630, total: 1632 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 25  01 00 9b 18   {......}.1.%....\r\n0010:   1c 40 06 4a  db 08 9b 00  9b 18 5a 5c  e2 40 2a 40   .@.J......Z\.@*@\r\n0020:   8a 40 01 31  10 43 c0 b2  04 29 f6 d1  3e bd 78 19   .@.1.C...)..>.x.\r\n0030:   00 20 07 21  05 4b 00 00  00 00 00 00  00 00 00 00   . .!.K..........\r\n[INFO] Wrote 48 bytes, at 0x004660, total: 1680 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 78  9a 42 00 d1   {......}.1.x.B..\r\n0010:   70 47 01 33  0b 40 f9 e7  c0 46 bd 19  00 20 bc 19   pG.3.@...F... ..\r\n0020:   00 20 70 b5  0b 4c 23 78  1a 00 82 43  10 00 22 70   . p..L#x...C..""p\r\n0030:   05 00 07 22  15 40 00 00  00 00 00 00  00 00 00 00   ..."".@..........\r\n[INFO] Wrote 48 bytes, at 0x004690, total: 1728 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 42  07 d0 05 4b   {......}.1.B...K\r\n0010:   5b 78 5b 07  03 d5 c0 08  ff f7 87 ff  25 70 70 bd   [x[.........%pp.\r\n0020:   c0 46 ba 19  00 20 c4 1c  00 20 02 4a  13 78 83 43   .F... ... .J.x.C\r\n0030:   13 70 70 47  c0 46 00 00  00 00 00 00  00 00 00 00   .ppG.F..........\r\n[INFO] Wrote 48 bytes, at 0x0046c0, total: 1776 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b5  8a b2 01 ac   {......}.1......\r\n0010:   03 00 21 80  00 20 12 0a  04 2a 0e d8  24 78 0d 2c   ..!.. ...*..$x.,\r\n0020:   0b d8 01 30  05 2b 08 d8  0d 30 50 43  46 22 53 43   ...0.+...0PCF""SC\r\n0030:   c0 18 02 49  00 19 00 00  00 00 00 00  00 00 00 00   ...I............\r\n[INFO] Wrote 48 bytes, at 0x0046f0, total: 1824 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 bd  c0 46 30 ca   {......}.1...F0.\r\n0010:   00 00 03 00  10 b5 e7 28  0a d8 df 28  0a d8 31 28   .......(...(..1(\r\n0020:   00 d1 73 e0  0f d8 29 28  66 d0 2a 28  00 d1 74 e0   ..s...)(f.*(..t.\r\n0030:   18 00 10 bd  e0 38 00 00  00 00 00 00  00 00 00 00   .....8..........\r\n[INFO] Wrote 48 bytes, at 0x004720, total: 1872 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 f7  4c fd 18 f9   {......}.1..L...\r\n0010:   27 30 3d f9  47 4c 39 28  08 d0 82 28  06 d0 35 28   '0=.GL9(...(..5(\r\n0020:   ee d1 37 4a  12 78 92 06  ea d5 06 e0  35 4a 11 78   ..7J.x......5J.x\r\n0030:   89 07 61 d1  52 78 00 00  00 00 00 00  00 00 00 00   ..a.Rx..........\r\n[INFO] Wrote 48 bytes, at 0x004750, total: 1920 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 23  e0 e7 30 48   {......}.1.#..0H\r\n0010:   01 21 02 78  14 00 0c 40  0a 42 45 d1  40 78 08 42   .!.x...@.BE.@x.B\r\n0020:   d6 d0 23 00  d2 06 d3 d4  e3 23 d1 e7  29 4a 12 78   ..#......#..)J.x\r\n0030:   51 07 cd d5  00 23 00 00  00 00 00 00  00 00 00 00   Q....#..........\r\n[INFO] Wrote 48 bytes, at 0x004780, total: 1968 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 33  c8 e7 24 49   {......}.1.3..$I\r\n0010:   e2 23 0a 78  50 07 c3 d4  48 78 01 21  04 00 02 3b   .#.xP...Hx.!...;\r\n0020:   0c 40 08 42  e5 d0 bb e7  1e 4a 51 78  89 07 b7 d5   .@.B.....JQx....\r\n0030:   12 78 00 23  d2 06 00 00  00 00 00 00  00 00 00 00   .x.#............\r\n[INFO] Wrote 48 bytes, at 0x0047b0, total: 2016 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 e7  19 4a 12 78   {......}.1...J.x\r\n0010:   11 07 f6 d4  ac e7 16 49  e6 23 0a 78  10 07 a7 d4   .......I.#.x....\r\n0020:   48 78 02 21  04 00 02 3b  0c 40 08 42  a0 d1 23 00   Hx.!...;.@.B..#.\r\n0030:   d2 06 9d d4  e7 23 00 00  00 00 00 00  00 00 00 00   .....#..........\r\n[INFO] Wrote 48 bytes, at 0x0047e0, total: 2064 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 78  89 06 15 d4   {......}.1.x....\r\n0010:   52 78 12 07  00 d4 93 e7  39 23 91 e7  09 4a 12 78   Rx......9#...J.x\r\n0020:   52 06 00 d4  8c e7 2a 23  8a e7 05 4a  12 78 52 06   R.....*#...J.xR.\r\n0030:   00 d4 85 e7  31 23 00 00  00 00 00 00  00 00 00 00   ....1#..........\r\n[INFO] Wrote 48 bytes, at 0x004810, total: 2112 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 e7  35 23 7f e7   {......}.1..5#..\r\n0010:   c0 46 c4 1c  00 20 30 b5  24 4c 21 78  4b 07 0d d5   .F... 0.$L!xK...\r\n0020:   14 22 02 40  04 3a 53 42  5a 41 18 23  03 40 08 3b   ."".@.:SBZA.#.@.;\r\n0030:   5d 42 6b 41  9a 42 00 00  00 00 00 00  00 00 00 00   ]BkA.B..........\r\n[INFO] Wrote 48 bytes, at 0x004840, total: 2160 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 40  0b 07 0d d5   {......}.1.@....\r\n0010:   14 22 02 40  14 3a 53 42  5a 41 18 23  03 40 18 3b   ."".@.:SBZA.#.@.;\r\n0020:   5d 42 6b 41  9a 42 01 d0  0c 23 58 40  64 78 e3 07   ]BkA.B...#X@dx..\r\n0030:   0d d5 11 22  02 40 00 00  00 00 00 00  00 00 00 00   ..."".@..........\r\n[INFO] Wrote 48 bytes, at 0x004870, total: 2208 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  18 23 03 40   {......}.1.A.#.@\r\n0010:   08 3b 5d 42  6b 41 9a 42  01 d0 09 23  58 40 a4 07   .;]BkA.B...#X@..\r\n0020:   0d d5 11 22  02 40 11 3a  53 42 5a 41  18 23 03 40   ..."".@.:SBZA.#.@\r\n0030:   18 3b 5c 42  63 41 00 00  00 00 00 00  00 00 00 00   .;\BcA..........\r\n[INFO] Wrote 48 bytes, at 0x0048a0, total: 2256 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 23  58 40 c9 06   {......}.1.#X@..\r\n0010:   01 d5 18 23  98 43 30 bd  c0 46 c4 1c  00 20 70 b5   ...#.C0..F... p.\r\n0020:   ff f7 1f ff  a4 23 04 00  db 01 98 42  4c d2 a0 23   .....#.....BL..#\r\n0030:   db 01 98 42  00 d3 00 00  00 00 00 00  00 00 00 00   ...B............\r\n[INFO] Wrote 48 bytes, at 0x0048d0, total: 2304 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 d8  cc 28 59 d8   {......}.1...(Y.\r\n0010:   a7 28 0a d8  a4 28 00 d9  79 e0 01 28  69 d0 03 1f   .(...(..y..(i...\r\n0020:   9b b2 a0 2b  65 d9 00 24  63 e0 03 00  a8 3b 9b b2   ...+e..$c....;..\r\n0030:   1a 2b f8 d8  58 34 00 00  00 00 00 00  00 00 00 00   .+..X4..........\r\n[INFO] Wrote 48 bytes, at 0x004900, total: 2352 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  e3 5a 88 24   {......}.1...Z.$\r\n0010:   e4 01 1c 43  55 e0 80 23  db 01 98 42  19 d2 80 26   ...CU..#...B...&\r\n0020:   b6 01 b0 42  00 d3 83 e0  e7 28 4a d9  f8 22 43 1e   ...B.....(J..""C.\r\n0030:   ff 3b 9b b2  52 01 00 00  00 00 00 00  00 00 00 00   .;..R...........\r\n[INFO] Wrote 48 bytes, at 0x004930, total: 2400 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 0a  c0 b2 ff f7   {......}.1......\r\n0010:   72 ff 05 00  e0 b2 ff f7  dc fe 2d 02  28 43 84 b2   r.........-.(C..\r\n0020:   37 e0 c0 b2  ff f7 d5 fe  f0 23 1b 01  23 40 3b 4c   7........#..#@;L\r\n0030:   18 43 04 43  a4 b2 00 00  00 00 00 00  00 00 00 00   .C.C............\r\n[INFO] Wrote 48 bytes, at 0x004960, total: 2448 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 01  98 42 1d d2   {......}.1...B..\r\n0010:   38 4b 98 42  3c d8 37 4b  98 42 0d d8  37 4b 98 42   8K.B<.7K.B..7K.B\r\n0020:   31 d8 03 22  c4 b2 a3 08  14 40 02 3a  a2 40 5b 01   1.."".....@.:.@[.\r\n0030:   33 4c 13 43  1c 43 00 00  00 00 00 00  00 00 00 00   3L.C.C..........\r\n[INFO] Wrote 48 bytes, at 0x004990, total: 2496 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 22  01 40 8a 40   {......}.1."".@.@\r\n0010:   c3 06 5b 0f  5b 01 2e 4c  13 43 f3 e7  2e 4b 98 42   ..[.[..L.C...K.B\r\n0020:   09 d8 1f 20  2d 4b 20 40  9c 42 23 d8  2c 4c 00 02   ... -K @.B#.,L..\r\n0030:   04 43 20 1c  70 bd 00 00  00 00 00 00  00 00 00 00   .C .p...........\r\n[INFO] Wrote 48 bytes, at 0x0049c0, total: 2544 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b2  1f 2b 00 d9   {......}.1...+..\r\n0010:   91 e7 f8 23  04 02 5b 01  1c 40 26 4b  99 e7 80 23   ...#..[..@&K...#\r\n0020:   24 3c db 01  d6 e7 1f 23  03 40 23 4c  1b 02 90 e7   $<.....#.@#L....\r\n0030:   03 21 01 22  01 40 00 00  00 00 00 00  00 00 00 00   .!."".@..........\r\n[INFO] Wrote 48 bytes, at 0x0049f0, total: 2592 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 0f  5b 01 1f 4c   {......}.1..[..L\r\n0010:   13 43 c7 e7  ff f7 0f ff  80 24 00 02  a4 01 a8 e7   .C.......$......\r\n0020:   1f 20 20 40  ff f7 07 ff  e3 05 1b 0f  1b 02 02 b2   .  @............\r\n0030:   c0 06 02 d5  ff 21 00 00  00 00 00 00  00 00 00 00   .....!..........\r\n[INFO] Wrote 48 bytes, at 0x004a20, total: 2640 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 4c  13 43 b1 e7   {......}.1.L.C..\r\n0010:   c0 04 c0 0e  ff f7 f7 fe  05 00 e0 b2  ff f7 61 fe   ..............a.\r\n0020:   2d 02 05 43  2e 43 b4 b2  bb e7 78 cd  00 00 00 a0   -..C.C....x.....\r\n0030:   ff ff 5f 52  00 00 00 00  00 00 00 00  00 00 00 00   .._R............\r\n[INFO] Wrote 48 bytes, at 0x004a50, total: 2688 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 52  00 00 00 8d   {......}.1.R....\r\n0010:   ff ff 00 8c  ff ff bf 52  00 00 9f 52  00 00 f4 a0   .......R...R....\r\n0020:   00 00 40 ad  ff ff f0 a0  00 00 f1 a0  00 00 00 8a   ..@.............\r\n0030:   ff ff 00 90  ff ff 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x004a80, total: 2736 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 4a  11 4b 12 88   {......}.1.J.K..\r\n0010:   1b 88 85 b0  01 af 1a 43  38 80 00 92  03 ae 00 9b   .......C8.......\r\n0020:   01 22 23 41  13 42 05 d1  01 3c f8 d2  00 25 28 00   .""#A.B...<...%(.\r\n0030:   05 b0 f0 bd  3b 88 00 00  00 00 00 00  00 00 00 00   ....;...........\r\n[INFO] Wrote 48 bytes, at 0x004ab0, total: 2784 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 68  28 00 ff f7   {......}.1.h(...\r\n0010:   04 fe ff f7  fc fe 80 b2  01 28 ed d0  ef e7 b8 19   .........(......\r\n0020:   00 20 b6 19  00 20 13 b5  43 79 00 88  01 aa 10 80   . ... ..Cy......\r\n0030:   01 98 00 2b  0d d0 00 00  00 00 00 00  00 00 00 00   ...+............\r\n[INFO] Wrote 48 bytes, at 0x004ae0, total: 2832 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 f7  cc ff 04 00   {......}.1......\r\n0010:   01 00 01 98  ff f7 6b fd  20 00 01 99  ff f7 e5 fd   ......k. .......\r\n0020:   16 bd ff f7  8c fd 04 00  f6 e7 1f b5  01 ac 20 80   .............. .\r\n0030:   01 98 ff f7  b8 ff 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x004b10, total: 2880 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 80  03 99 ff f7   {......}.1......\r\n0010:   d4 fd ff f7  cc fe 04 b0  10 bd 10 b5  00 24 03 79   .............$.y\r\n0020:   a3 42 1e d0  00 88 ff f7  e8 ff c2 b2  00 04 03 0f   .B..............\r\n0030:   06 2b 18 d0  0f 21 00 00  00 00 00 00  00 00 00 00   .+...!..........\r\n[INFO] Wrote 48 bytes, at 0x004b40, total: 2928 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 33  0b 40 01 2b   {......}.1.3.@.+\r\n0010:   0f d8 01 24  f0 2a 0c d0  07 d8 00 24  e7 23 93 42   ...$.*.....$.#.B\r\n0020:   64 41 e4 b2  05 e0 06 33  f0 e7 f4 3a  53 42 5a 41   dA.....3...:SBZA\r\n0030:   d4 b2 20 00  10 bd 00 00  00 00 00 00  00 00 00 00   .. .............\r\n[INFO] Wrote 48 bytes, at 0x004b70, total: 2976 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 41  f1 3a 53 42   {......}.1.A.:SB\r\n0010:   5a 41 14 43  ed e7 00 20  70 47 00 20  70 47 30 bf   ZA.C... pG. pG0.\r\n0020:   fd e7 04 4a  05 4b 02 20  d1 5c 00 29  01 d1 01 38   ...J.K. .\.)...8\r\n0030:   d0 54 70 47  c0 46 00 00  00 00 00 00  00 00 00 00   .TpG.F..........\r\n[INFO] Wrote 48 bytes, at 0x004ba0, total: 3024 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 04  00 00 04 4a   {......}.1.....J\r\n0010:   05 4b 02 20  d1 5c 00 29  02 d0 00 21  01 38 d1 54   .K. .\.)...!.8.T\r\n0020:   70 47 08 13  00 20 04 04  00 00 7f b5  2a 4a 03 0c   pG... ......*J..\r\n0030:   db b2 d4 5c  05 0a 00 00  00 00 00 00  00 00 00 00   ...\............\r\n[INFO] Wrote 48 bytes, at 0x004bd0, total: 3072 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  01 90 00 2d   {......}.1.....-\r\n0010:   2d d0 06 20  01 ab 1e 78  ff 21 70 43  ff f7 93 fb   -.. ...x.!pC....\r\n0020:   55 22 52 42  42 43 76 00  b2 18 03 26  56 43 ff 21   U""RBBCv....&VC.!\r\n0030:   f6 b2 32 00  8e 1b 00 00  00 00 00 00  00 00 00 00   ..2.............\r\n[INFO] Wrote 48 bytes, at 0x004c00, total: 3120 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 43  12 12 2d 12   {......}.1.C..-.\r\n0010:   8a 1a 49 1b  63 43 62 43  61 43 1b 12  12 12 09 12   ..I.cCbCaC......\r\n0020:   db b2 d2 b2  c9 b2 06 28  20 d8 ff f7  c2 fa 04 06   .......( .......\r\n0030:   13 16 1a 1f  04 00 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x004c30, total: 3168 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  14 00 ff 22   {......}.1.....""\r\n0010:   20 00 11 40  10 40 09 02  13 40 1b 04  08 43 18 43    ..@.@...@...C.C\r\n0020:   04 b0 70 bd  1a 00 0b 00  ef e7 11 00  1a 00 23 00   ..p...........#.\r\n0030:   ea e7 22 00  1c 00 00 00  00 00 00 00  00 00 00 00   .."".............\r\n[INFO] Wrote 48 bytes, at 0x004c60, total: 3216 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 e7  11 00 22 00   {......}.1...."".\r\n0010:   1c 00 f0 e7  c0 46 29 c8  00 00 01 00  30 b5 c0 b2   .....F).....0...\r\n0020:   01 29 16 d9  f7 23 ff 22  5b 01 99 42  02 d8 4a 09   .)...#.""[..B..J.\r\n0030:   08 32 d2 b2  01 24 00 00  00 00 00 00  00 00 00 00   .2...$..........\r\n[INFO] Wrote 48 bytes, at 0x004c90, total: 3264 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  45 43 ad b2   {......}.1..EC..\r\n0010:   c3 b2 a9 42  06 d2 01 3b  da b2 94 42  f3 d9 01 3c   ...B...;...B...<\r\n0020:   e0 b2 30 bd  ff 28 fc d0  01 33 dc b2  f5 e7 05 4a   ..0..(...3.....J\r\n0030:   06 4b 11 88  4b 43 00 00  00 00 00 00  00 00 00 00   .K..KC..........\r\n[INFO] Wrote 48 bytes, at 0x004cc0, total: 3312 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b2  18 0a c0 18   {......}.1......\r\n0010:   c0 b2 13 80  70 47 9c 0d  00 20 05 08  00 00 19 36   ....pG... .....6\r\n0020:   00 00 10 b5  04 00 ff f7  ea ff 60 43  00 12 c0 b2   ..........`C....\r\n0030:   10 bd 0f 21  43 06 00 00  00 00 00 00  00 00 00 00   ...!C...........\r\n[INFO] Wrote 48 bytes, at 0x004cf0, total: 3360 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 00  03 40 92 06   {......}.1...@..\r\n0010:   0b 49 92 0f  52 00 52 18  51 78 12 78  4b 43 1b 11   .I..R.R.Qx.xKC..\r\n0020:   9b 18 40 b2  db b2 00 28  01 da 5b 42  db b2 80 3b   ..@....(..[B...;\r\n0030:   d8 b2 70 47  c3 43 00 00  00 00 00 00  00 00 00 00   ..pG.C..........\r\n[INFO] Wrote 48 bytes, at 0x004d20, total: 3408 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 33  e7 e7 21 c8   {......}.1.3..!.\r\n0010:   00 00 10 b5  0b 00 04 1e  02 d1 c8 0f  c0 01 10 bd   ................\r\n0020:   c1 17 42 18  4a 40 12 b2  d1 18 00 2b  0b db 9b 1a   ..B.J@.....+....\r\n0030:   58 01 ff f7  e0 fa 00 00  00 00 00 00  00 00 00 00   X...............\r\n[INFO] Wrote 48 bytes, at 0x004d50, total: 3456 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b2  00 2c ee da   {......}.1...,..\r\n0010:   40 42 c0 b2  eb e7 48 01  d1 1a ff f7  d4 fa 60 23   @B....H.......`#\r\n0020:   f2 e7 1f b5  04 0c 01 90  50 08 ff f7  ba ff 02 00   ........P.......\r\n0030:   80 3a e4 b2  d3 b2 00 00  00 00 00 00  00 00 00 00   .:..............\r\n[INFO] Wrote 48 bytes, at 0x004d80, total: 3504 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 23  5b 42 1b 1a   {......}.1.#[B..\r\n0010:   db b2 01 aa  12 88 ff 21  10 00 5b 00  01 34 db b2   .......!..[..4..\r\n0020:   63 43 12 0a  1b 12 08 40  12 02 0b 40  1b 04 10 43   cC.....@...@...C\r\n0030:   18 43 04 b0  10 bd 00 00  00 00 00 00  00 00 00 00   .C..............\r\n[INFO] Wrote 48 bytes, at 0x004db0, total: 3552 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 90  20 31 d8 b2   {......}.1.. 1..\r\n0010:   0f 4b 49 00  5b 18 99 79  e5 23 4b 43  1b 12 1c 33   .KI.[..y.#KC...3\r\n0020:   9a 1a d3 17  d2 18 5a 40  d2 00 82 1a  01 ac 13 1c   ......Z@........\r\n0030:   12 04 00 d5  00 23 00 00  00 00 00 00  00 00 00 00   .....#..........\r\n[INFO] Wrote 48 bytes, at 0x004de0, total: 3600 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 43  00 12 a3 78   {......}.1.C...x\r\n0010:   60 70 20 88  1b 04 18 43  04 b0 10 bd  c0 46 ae cd   `p ....C.....F..\r\n0020:   00 00 03 0c  84 b0 01 90  20 31 d8 b2  11 4b 49 00   ........ 1...KI.\r\n0030:   5b 18 99 79  e5 23 00 00  00 00 00 00  00 00 00 00   [..y.#..........\r\n[INFO] Wrote 48 bytes, at 0x004e10, total: 3648 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 33  9a 1a d3 17   {......}.1.3....\r\n0010:   d2 18 5a 40  d2 00 82 1a  13 1c 12 04  00 d5 00 23   ..Z@...........#\r\n0020:   1a b2 43 1c  53 43 01 aa  12 88 ff 21  10 00 1b 12   ..C.SC.....!....\r\n0030:   12 0a 08 40  12 02 00 00  00 00 00 00  00 00 00 00   ...@............\r\n[INFO] Wrote 48 bytes, at 0x004e40, total: 3696 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 43  18 43 04 b0   {......}.1.C.C..\r\n0010:   70 47 ae cd  00 00 7f b5  04 0a 01 90  e4 b2 10 00   pG..............\r\n0020:   e5 1a ff f7  66 ff 83 00  c0 1a ed b2  2d 18 ed b2   ....f.......-...\r\n0030:   01 34 6c 43  01 ae 00 00  00 00 00 00  00 00 00 00   .4lC............\r\n[INFO] Wrote 48 bytes, at 0x004e70, total: 3744 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 70  30 88 1b 04   {......}.1.p0...\r\n0010:   18 43 04 b0  70 bd 7f b5  04 0c 05 1c  e4 b2 10 00   .C..p...........\r\n0020:   e6 1a ff f7  4e ff 83 00  c0 1a ff 23  f6 b2 36 18   ....N......#..6.\r\n0030:   18 00 f6 b2  01 34 00 00  00 00 00 00  00 00 00 00   .....4..........\r\n[INFO] Wrote 48 bytes, at 0x004ea0, total: 3792 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 0a  1d 40 24 12   {......}.1...@$.\r\n0010:   2d 02 1c 40  03 4b 28 43  24 04 18 40  20 43 04 b0   -..@.K(C$..@ C..\r\n0020:   70 bd ff ff  00 ff 7f b5  04 0a 01 90  10 00 08 aa   p...............\r\n0030:   15 78 e4 b2  5d 1b 00 00  00 00 00 00  00 00 00 00   .x..]...........\r\n[INFO] Wrote 48 bytes, at 0x004ed0, total: 3840 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 19  ed b2 2d 1a   {......}.1....-.\r\n0010:   ed b2 01 34  6c 43 01 ae  24 12 b3 78  74 70 30 88   ...4lC..$..xtp0.\r\n0020:   1b 04 18 43  04 b0 70 bd  00 00 7f b5  05 1c 04 0c   ...C..p.........\r\n0030:   10 00 08 aa  16 78 00 00  00 00 00 00  00 00 00 00   .....x..........\r\n[INFO] Wrote 48 bytes, at 0x004f00, total: 3888 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 f7  12 ff ff 23   {......}.1.....#\r\n0010:   a6 19 f6 b2  36 1a 18 00  f6 b2 01 34  74 43 28 40   ....6......4tC(@\r\n0020:   2d 0a 1d 40  24 12 2d 02  1c 40 03 4b  28 43 24 04   -..@$.-..@.K(C$.\r\n0030:   18 40 20 43  04 b0 00 00  00 00 00 00  00 00 00 00   .@ C............\r\n[INFO] Wrote 48 bytes, at 0x004f30, total: 3936 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 ff  00 ff 84 b0   {......}.1......\r\n0010:   01 a9 01 90  8b 78 0a 70  08 88 1b 04  18 43 04 b0   .....x.p.....C..\r\n0020:   70 47 1f b5  07 4b 20 31  49 00 5b 18  9b 79 01 ac   pG...K 1I.[..y..\r\n0030:   01 90 9b 1a  23 70 00 00  00 00 00 00  00 00 00 00   ....#p..........\r\n[INFO] Wrote 48 bytes, at 0x004f60, total: 3984 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 04  18 43 04 b0   {......}.1...C..\r\n0010:   10 bd ae cd  00 00 1f b5  07 4b 20 31  49 00 5b 18   .........K 1I.[.\r\n0020:   db 79 01 ac  01 90 9b 1a  23 70 a3 78  20 88 1b 04   .y......#p.x ...\r\n0030:   18 43 04 b0  10 bd 00 00  00 00 00 00  00 00 00 00   .C..............\r\n[INFO] Wrote 48 bytes, at 0x004f90, total: 4032 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b5  20 31 0e 4d   {......}.1.. 1.M\r\n0010:   4b 00 eb 18  df 79 16 00  3a 00 85 b0  20 3a 01 90   K....y..:... :..\r\n0020:   01 ac c0 b2  53 b2 12 06  02 d5 20 23  db 1b 5b b2   ....S..... #..[.\r\n0030:   49 00 69 18  8a 79 00 00  00 00 00 00  00 00 00 00   I.i..y..........\r\n[INFO] Wrote 48 bytes, at 0x004fc0, total: 4080 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 18  a3 78 20 70   {......}.1...x p\r\n0010:   20 88 1b 04  18 43 05 b0  f0 bd ae cd  00 00 84 b0    ....C..........\r\n0020:   04 aa 01 90  10 78 03 22  5a 43 52 10  01 a9 80 18   .....x.""ZCR.....\r\n0030:   8b 78 08 70  08 88 00 00  00 00 00 00  00 00 00 00   .x.p............\r\n[INFO] Wrote 48 bytes, at 0x004ff0, total: 4128 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b0  70 47 1f b5   {......}.1..pG..\r\n0010:   1c 00 01 90  4b b2 08 06  01 d5 49 42  4b b2 38 20   ....K.....IBK.8 \r\n0020:   c0 1a 80 b2  92 b2 52 43  40 43 80 18  80 b2 ff f7   ......RC@C......\r\n0030:   2c fe 03 23  43 43 00 00  00 00 00 00  00 00 00 00   ,..#CC..........\r\n[INFO] Wrote 48 bytes, at 0x005020, total: 4176 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 70  93 78 10 88   {......}.1.p.x..\r\n0010:   1b 04 18 43  04 b0 10 bd  30 b5 85 b0  01 90 10 00   ...C....0.......\r\n0020:   1d 00 ff f7  76 fe 01 ac  40 19 a3 78  20 70 20 88   ....v...@..x p .\r\n0030:   1b 04 18 43  05 b0 00 00  00 00 00 00  00 00 00 00   ...C............\r\n[INFO] Wrote 48 bytes, at 0x005050, total: 4224 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b0  01 90 10 00   {......}.1......\r\n0010:   08 aa 14 78  01 ad 1c 1b  ff f7 63 fe  e4 b2 24 1a   ...x......c...$.\r\n0020:   ab 78 2c 70  28 88 1b 04  18 43 05 b0  30 bd 30 b5   .x,p(....C..0.0.\r\n0030:   20 33 0c 4d  5b 00 00 00  00 00 00 00  00 00 00 00    3.M[...........\r\n[INFO] Wrote 48 bytes, at 0x005080, total: 4272 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 79  20 3d 70 3b   {......}.1.y =p;\r\n0010:   59 43 7f 23  6a 43 51 18  ca 17 1a 40  52 18 85 b0   YC.#jCQ....@R...\r\n0020:   d2 11 01 ac  01 90 12 18  a3 78 22 70  20 88 1b 04   .........x""p ...\r\n0030:   18 43 05 b0  30 bd 00 00  00 00 00 00  00 00 00 00   .C..0...........\r\n[INFO] Wrote 48 bytes, at 0x0050b0, total: 4320 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b5  20 33 0c 4d   {......}.1.. 3.M\r\n0010:   5b 00 eb 18  dd 79 9b 79  20 3d 70 3b  59 43 3f 23   [....y.y =p;YC?#\r\n0020:   6a 43 51 18  ca 17 1a 40  52 18 85 b0  92 11 01 ac   jCQ....@R.......\r\n0030:   01 90 12 18  a3 78 00 00  00 00 00 00  00 00 00 00   .....x..........\r\n[INFO] Wrote 48 bytes, at 0x0050e0, total: 4368 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 04  18 43 05 b0   {......}.1...C..\r\n0010:   30 bd ae cd  00 00 7f b5  13 4d 20 33  5b 00 eb 18   0........M 3[...\r\n0020:   dd 79 9e 79  20 3d 6a 43  35 00 70 3d  01 90 01 ac   .y.y =jC5.p=....\r\n0030:   c0 b2 6b b2  2d 06 00 00  00 00 00 00  00 00 00 00   ..k.-...........\r\n[INFO] Wrote 48 bytes, at 0x005110, total: 4416 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 1b  5b b2 38 25   {......}.1..[.8%\r\n0010:   eb 1a 59 43  03 23 8a 18  7f 21 53 43  da 17 0a 40   ..YC.#...!SC...@\r\n0020:   d2 18 d2 11  80 18 a3 78  20 70 20 88  1b 04 18 43   .......x p ....C\r\n0030:   04 b0 70 bd  c0 46 00 00  00 00 00 00  00 00 00 00   ..p..F..........\r\n[INFO] Wrote 48 bytes, at 0x005140, total: 4464 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b5  85 b0 01 90   {......}.1......\r\n0010:   c4 b2 50 08  ff f7 cd fd  02 00 80 3a  01 ad d3 b2   ..P........:....\r\n0020:   12 06 03 d5  80 23 5b 42  1b 1a db b2  0d 22 5b 00   .....#[B.....""[.\r\n0030:   db b2 53 43  1b 12 00 00  00 00 00 00  00 00 00 00   ..SC............\r\n[INFO] Wrote 48 bytes, at 0x005170, total: 4512 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 78  28 88 1b 04   {......}.1.x(...\r\n0010:   18 43 05 b0  30 bd 7f b5  0e 00 04 1c  01 90 10 00   .C..0...........\r\n0020:   ff f7 af fd  20 36 0e 4b  76 00 9b 19  9b 79 01 ad   .... 6.Kv....y..\r\n0030:   c0 18 c0 b2  02 00 00 00  00 00 00 00  00 00 00 00   ................\r\n[INFO] Wrote 48 bytes, at 0x0051a0, total: 4560 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b2  12 06 03 d5   {......}.1......\r\n0010:   80 23 5b 42  1b 1a db b2  0d 22 5b 00  db b2 53 43   .#[B.....""[...SC\r\n0020:   1b 12 e4 18  2c 70 ab 78  28 88 1b 04  18 43 04 b0   ....,p.x(....C..\r\n0030:   70 bd ae cd  00 00 00 00  00 00 00 00  00 00 00 00   p...............\r\n[INFO] Wrote 48 bytes, at 0x0051d0, total: 4608 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 31  49 00 5b 18   {......}.1.1I.[.\r\n0010:   9d 79 85 b0  ab 1a 01 90  01 ac c0 b2  d9 b2 1b 06   .y..............\r\n0020:   01 d5 52 1b  d1 b2 19 23  4b 43 1b 12  c0 18 20 70   ..R....#KC.... p\r\n0030:   a3 78 20 88  1b 04 00 00  00 00 00 00  00 00 00 00   .x .............\r\n[INFO] Wrote 48 bytes, at 0x005200, total: 4656 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 bd  c0 46 ae cd   {......}.1...F..\r\n0010:   00 00 1f b5  0b 1c 01 ac  01 90 ff 29  00 d9 ff 23   ...........)...#\r\n0020:   db 43 da b2  a3 78 ff 21  01 33 53 43  22 88 1b 12   .C...x.!.3SC""...\r\n0030:   10 00 12 0a  08 40 00 00  00 00 00 00  00 00 00 00   .....@..........\r\n[INFO] Wrote 48 bytes, at 0x005230, total: 4704 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 04  10 43 18 43   {......}.1...C.C\r\n0010:   04 b0 10 bd  1f b5 0b 1c  01 ac 01 90  ff 29 00 d9   .............)..\r\n0020:   ff 23 db 43  da b2 41 23  53 43 22 78  1b 12 9b 18   .#.C..A#SC""x....\r\n0030:   23 70 a3 78  20 88 00 00  00 00 00 00  00 00 00 00   #p.x ...........\r\n[INFO] Wrote 48 bytes, at 0x005260, total: 4752 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b0  10 bd 1f b5   {......}.1......\r\n0010:   01 90 05 20  43 43 06 aa  12 88 01 a9  d3 18 1a 1c   ... CC..........\r\n0020:   9b b2 ff 2b  00 d9 ff 22  d3 43 8a 78  db b2 9b 18   ...+..."".C.x....\r\n0030:   1c 1c ff 2b  00 d9 00 00  00 00 00 00  00 00 00 00   ...+............\r\n[INFO] Wrote 48 bytes, at 0x005290, total: 4800 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 23  10 00 12 0a   {......}.1.#....\r\n0010:   18 40 12 02  23 40 1b 04  10 43 18 43  04 b0 10 bd   .@..#@...C.C....\r\n0020:   30 b5 85 b0  01 90 08 a8  00 88 1b 18  c8 17 09 18   0...............\r\n0030:   41 40 d0 17  12 18 00 00  00 00 00 00  00 00 00 00   A@..............\r\n[INFO] Wrote 48 bytes, at 0x0052c0, total: 4848 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 b2  92 b2 09 01   {......}.1......\r\n0010:   ff 29 00 dd  ff 21 12 b2  12 01 ff 2a  00 dd ff 22   .)...!.....*...""\r\n0020:   0c 1c 15 b2  09 b2 01 a8  a9 42 00 dd  14 1c 1b 19   .........B......\r\n0030:   1a 1c 9b b2  ff 2b 00 00  00 00 00 00  00 00 00 00   .....+..........\r\n[INFO] Wrote 48 bytes, at 0x0052f0, total: 4896 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 43  82 78 db b2   {......}.1.C.x..\r\n0010:   9b 18 19 1c  ff 2b 00 d9  ff 21 02 88  ff 23 10 00   .....+...!...#..\r\n0020:   12 0a 18 40  12 02 0b 40  1b 04 10 43  18 43 05 b0   ...@...@...C.C..\r\n0030:   30 bd 7f b5  05 0c 00 00  00 00 00 00  00 00 00 00   0...............\r\n[INFO] Wrote 48 bytes, at 0x005320, total: 4944 bytes written\r\nread back: Length: 64 (0x40) bytes\r\n0000:   7b 10 13 10  03 00 00 7d  02 31 00 90  12 88 e8 b2   {......}.1......\r\n0010:   01\n[... 284470 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-a313d008-5546-415a-a27c-b4bbbd49fb041754912780018-2025_08_11-13.46.25.836/source.csv

@@ -0,0 +1,69 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,313,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:46:25 PM [info] Activating crowd-code\n1:46:25 PM [info] Recording started\n1:46:25 PM [info] Initializing git provider using file system watchers...\n1:46:25 PM [info] Git repository found\n1:46:25 PM [info] Git provider initialized successfully\n1:46:25 PM [info] Initial git state: [object Object]\n",Log,tab
+3,11854,"sz.py",0,0,"#!/usr/bin/env python3\nimport os, sys\nimport token\nimport tokenize\nimport itertools\nfrom tabulate import tabulate\n\nTOKEN_WHITELIST = [token.OP, token.NAME, token.NUMBER, token.STRING]\n\ndef is_docstring(t):\n  return t.type == token.STRING and t.string.startswith('""""""') and t.line.strip().startswith('""""""')\n\ndef is_js_token(s): return len(s) and not s.startswith('//')\n\ndef gen_stats(base_path="".""):\n  table = []\n  for path, _, files in os.walk(os.path.join(base_path, ""tinygrad"")):\n    for name in files:\n      if not (name.endswith("".py"") or name.endswith("".js"")): continue\n      if any(s in path.replace('\\', '/') for s in ['tinygrad/runtime/autogen', 'tinygrad/viz/assets']): continue\n      filepath = os.path.join(path, name)\n      relfilepath = os.path.relpath(filepath, base_path).replace('\\', '/')\n      if name.endswith("".js""):\n        with open(filepath) as file_: lines = [line.strip() for line in file_.readlines()]\n        token_count, line_count = sum(len(line.split()) for line in lines if is_js_token(line)), sum(1 for line in lines if is_js_token(line))\n      else:\n        with tokenize.open(filepath) as file_:\n          tokens = [t for t in tokenize.generate_tokens(file_.readline) if t.type in TOKEN_WHITELIST and not is_docstring(t)]\n          token_count, line_count = len(tokens), len(set([x for t in tokens for x in range(t.start[0], t.end[0]+1)]))\n      if line_count > 0: table.append([relfilepath, line_count, token_count/line_count])\n  return table\n\ndef gen_diff(table_old, table_new):\n  table = []\n  files_new = set([x[0] for x in table_new])\n  files_old = set([x[0] for x in table_old])\n  added, deleted, unchanged = files_new - files_old, files_old - files_new, files_new & files_old\n  if added:\n    for file in added:\n      file_stat = [stats for stats in table_new if file in stats]\n      table.append([file_stat[0][0], file_stat[0][1], file_stat[0][1]-0, file_stat[0][2], file_stat[0][2]-0])\n  if deleted:\n    for file in deleted:\n      file_stat = [stats for stats in table_old if file in stats]\n      table.append([file_stat[0][0], 0, 0 - file_stat[0][1], 0, 0-file_stat[0][2]])\n  if unchanged:\n    for file in unchanged:\n      file_stat_old = [stats for stats in table_old if file in stats]\n      file_stat_new = [stats for stats in table_new if file in stats]\n      if file_stat_new[0][1]-file_stat_old[0][1] != 0 or file_stat_new[0][2]-file_stat_old[0][2] != 0:\n        table.append([file_stat_new[0][0], file_stat_new[0][1], file_stat_new[0][1]-file_stat_old[0][1], file_stat_new[0][2],\n                      file_stat_new[0][2]-file_stat_old[0][2]])\n  return table\n\ndef display_diff(diff): return ""+""+str(diff) if diff > 0 else str(diff)\n\nif __name__ == ""__main__"":\n  if len(sys.argv) == 3:\n    headers = [""Name"", ""Lines"", ""Diff"", ""Tokens/Line"", ""Diff""]\n    table = gen_diff(gen_stats(sys.argv[1]), gen_stats(sys.argv[2]))\n  elif len(sys.argv) == 2:\n    headers = [""Name"", ""Lines"", ""Tokens/Line""]\n    table = gen_stats(sys.argv[1])\n  else:\n    headers = [""Name"", ""Lines"", ""Tokens/Line""]\n    table = gen_stats(""."")\n\n  if table:\n    if len(sys.argv) == 3:\n      print(""### Changes"")\n      print(""```"")\n      print(tabulate([headers] + sorted(table, key=lambda x: -x[1]), headers=""firstrow"", intfmt=(..., ""d"", ""+d""),\n                     floatfmt=(..., ..., ..., "".1f"", ""+.1f""))+""\n"")\n      print(f""\ntotal lines changes: {display_diff(sum([x[2] for x in table]))}"")\n      print(""```"")\n    else:\n      print(tabulate([headers] + sorted(table, key=lambda x: -x[1]), headers=""firstrow"", floatfmt="".1f"")+""\n"")\n      groups = sorted([('/'.join(x[0].rsplit(""/"", 1)[0].split(""/"")[0:2]), x[1], x[2]) for x in table])\n      for dir_name, group in itertools.groupby(groups, key=lambda x:x[0]):\n        print(f""{dir_name:30s} : {sum([x[1] for x in group]):6d}"")\n      total_lines = sum([x[1] for x in table])\n      print(f""\ntotal line count: {total_lines}"")\n      max_line_count = int(os.getenv(""MAX_LINE_COUNT"", ""-1""))\n      assert max_line_count == -1 or total_lines <= max_line_count, f""OVER {max_line_count} LINES""\n",python,tab
+4,17972,"test_driven_development.sh",0,0,"#!/bin/bash\npython3 test/external/process_replay/reset.py\nCAPTURE_PROCESS_REPLAY=1 pytest -n auto test/test_tiny.py test/test_uop_graph.py test/test_ops.py test/test_linearizer.py\nwhile true; do\n  if python3 test/test_tiny.py; then\n    PYTHONPATH=""."" python3 test/external/process_replay/process_replay.py\n  fi\ndone\n",shellscript,tab
+5,80622,"examples/stable_diffusion.py",0,0,"# https://arxiv.org/pdf/2112.10752.pdf\n# https://github.com/ekagra-ranjan/huggingface-blog/blob/main/stable_diffusion.md\nimport tempfile\nfrom pathlib import Path\nimport argparse\nfrom collections import namedtuple\nfrom typing import Dict, Any\n\nfrom PIL import Image\nimport numpy as np\nfrom tinygrad import Device, GlobalCounters, dtypes, Tensor, TinyJit\nfrom tinygrad.helpers import Timing, Context, getenv, fetch, colored, tqdm\nfrom tinygrad.nn import Conv2d, GroupNorm\nfrom tinygrad.nn.state import torch_load, load_state_dict, get_state_dict\nfrom extra.models.clip import Closed, Tokenizer\nfrom extra.models.unet import UNetModel\nfrom extra.bench_log import BenchEvent, WallTimeEvent\n\nclass AttnBlock:\n  def __init__(self, in_channels):\n    self.norm = GroupNorm(32, in_channels)\n    self.q = Conv2d(in_channels, in_channels, 1)\n    self.k = Conv2d(in_channels, in_channels, 1)\n    self.v = Conv2d(in_channels, in_channels, 1)\n    self.proj_out = Conv2d(in_channels, in_channels, 1)\n\n  # copied from AttnBlock in ldm repo\n  def __call__(self, x):\n    h_ = self.norm(x)\n    q,k,v = self.q(h_), self.k(h_), self.v(h_)\n\n    # compute attention\n    b,c,h,w = q.shape\n    q,k,v = [x.reshape(b,c,h*w).transpose(1,2) for x in (q,k,v)]\n    h_ = Tensor.scaled_dot_product_attention(q,k,v).transpose(1,2).reshape(b,c,h,w)\n    return x + self.proj_out(h_)\n\nclass ResnetBlock:\n  def __init__(self, in_channels, out_channels=None):\n    self.norm1 = GroupNorm(32, in_channels)\n    self.conv1 = Conv2d(in_channels, out_channels, 3, padding=1)\n    self.norm2 = GroupNorm(32, out_channels)\n    self.conv2 = Conv2d(out_channels, out_channels, 3, padding=1)\n    self.nin_shortcut = Conv2d(in_channels, out_channels, 1) if in_channels != out_channels else lambda x: x\n\n  def __call__(self, x):\n    h = self.conv1(self.norm1(x).swish())\n    h = self.conv2(self.norm2(h).swish())\n    return self.nin_shortcut(x) + h\n\nclass Mid:\n  def __init__(self, block_in):\n    self.block_1 = ResnetBlock(block_in, block_in)\n    self.attn_1 = AttnBlock(block_in)\n    self.block_2 = ResnetBlock(block_in, block_in)\n\n  def __call__(self, x):\n    return x.sequential([self.block_1, self.attn_1, self.block_2])\n\nclass Decoder:\n  def __init__(self):\n    sz = [(128, 256), (256, 512), (512, 512), (512, 512)]\n    self.conv_in = Conv2d(4,512,3, padding=1)\n    self.mid = Mid(512)\n\n    arr = []\n    for i,s in enumerate(sz):\n      arr.append({""block"":\n        [ResnetBlock(s[1], s[0]),\n         ResnetBlock(s[0], s[0]),\n         ResnetBlock(s[0], s[0])]})\n      if i != 0: arr[-1]['upsample'] = {""conv"": Conv2d(s[0], s[0], 3, padding=1)}\n    self.up = arr\n\n    self.norm_out = GroupNorm(32, 128)\n    self.conv_out = Conv2d(128, 3, 3, padding=1)\n\n  def __call__(self, x):\n    x = self.conv_in(x)\n    x = self.mid(x)\n\n    for l in self.up[::-1]:\n      print(""decode"", x.shape)\n      for b in l['block']: x = b(x)\n      if 'upsample' in l:\n        # https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html ?\n        bs,c,py,px = x.shape\n        x = x.reshape(bs, c, py, 1, px, 1).expand(bs, c, py, 2, px, 2).reshape(bs, c, py*2, px*2)\n        x = l['upsample']['conv'](x)\n      x.realize()\n\n    return self.conv_out(self.norm_out(x).swish())\n\nclass Encoder:\n  def __init__(self):\n    sz = [(128, 128), (128, 256), (256, 512), (512, 512)]\n    self.conv_in = Conv2d(3,128,3, padding=1)\n\n    arr = []\n    for i,s in enumerate(sz):\n      arr.append({""block"":\n        [ResnetBlock(s[0], s[1]),\n         ResnetBlock(s[1], s[1])]})\n      if i != 3: arr[-1]['downsample'] = {""conv"": Conv2d(s[1], s[1], 3, stride=2, padding=(0,1,0,1))}\n    self.down = arr\n\n    self.mid = Mid(512)\n    self.norm_out = GroupNorm(32, 512)\n    self.conv_out = Conv2d(512, 8, 3, padding=1)\n\n  def __call__(self, x):\n    x = self.conv_in(x)\n\n    for l in self.down:\n      print(""encode"", x.shape)\n      for b in l['block']: x = b(x)\n      if 'downsample' in l: x = l['downsample']['conv'](x)\n\n    x = self.mid(x)\n    return self.conv_out(self.norm_out(x).swish())\n\nclass AutoencoderKL:\n  def __init__(self):\n    self.encoder = Encoder()\n    self.decoder = Decoder()\n    self.quant_conv = Conv2d(8, 8, 1)\n    self.post_quant_conv = Conv2d(4, 4, 1)\n\n  def __call__(self, x):\n    latent = self.encoder(x)\n    latent = self.quant_conv(latent)\n    latent = latent[:, 0:4]  # only the means\n    print(""latent"", latent.shape)\n    latent = self.post_quant_conv(latent)\n    return self.decoder(latent)\n\ndef get_alphas_cumprod(beta_start=0.00085, beta_end=0.0120, n_training_steps=1000):\n  betas = np.linspace(beta_start ** 0.5, beta_end ** 0.5, n_training_steps, dtype=np.float32) ** 2\n  alphas = 1.0 - betas\n  alphas_cumprod = np.cumprod(alphas, axis=0)\n  return Tensor(alphas_cumprod)\n\nunet_params: Dict[str,Any] = {\n  ""adm_in_ch"": None,\n  ""in_ch"": 4,\n  ""out_ch"": 4,\n  ""model_ch"": 320,\n  ""attention_resolutions"": [4, 2, 1],\n  ""num_res_blocks"": 2,\n  ""channel_mult"": [1, 2, 4, 4],\n  ""n_heads"": 8,\n  ""transformer_depth"": [1, 1, 1, 1],\n  ""ctx_dim"": 768,\n  ""use_linear"": False,\n}\n\nclass StableDiffusion:\n  def __init__(self):\n    self.alphas_cumprod = get_alphas_cumprod()\n    self.model = namedtuple(""DiffusionModel"", [""diffusion_model""])(diffusion_model = UNetModel(**unet_params))\n    self.first_stage_model = AutoencoderKL()\n    self.cond_stage_model = namedtuple(""CondStageModel"", [""transformer""])(transformer = namedtuple(""Transformer"", [""text_model""])(text_model = Closed.ClipTextTransformer()))\n\n  def get_x_prev_and_pred_x0(self, x, e_t, a_t, a_prev):\n    temperature = 1\n    sigma_t = 0\n    sqrt_one_minus_at = (1-a_t).sqrt()\n    #print(a_t, a_prev, sigma_t, sqrt_one_minus_at)\n\n    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()\n\n    # direction pointing to x_t\n    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t\n\n    x_prev = a_prev.sqrt() * pred_x0 + dir_xt\n    return x_prev, pred_x0\n\n  def get_model_output(self, unconditional_context, context, latent, timestep, unconditional_guidance_scale):\n    # put into diffuser\n    latents = self.model.diffusion_model(latent.expand(2, *latent.shape[1:]), timestep, unconditional_context.cat(context, dim=0))\n    unconditional_latent, latent = latents[0:1], latents[1:2]\n\n    e_t = unconditional_latent + unconditional_guidance_scale * (latent - unconditional_latent)\n    return e_t\n\n  def decode(self, x):\n    x = self.first_stage_model.post_quant_conv(1/0.18215 * x)\n    x = self.first_stage_model.decoder(x)\n\n    # make image correct size and scale\n    x = (x + 1.0) / 2.0\n    x = x.reshape(3,512,512).permute(1,2,0).clip(0,1)*255\n    return x.cast(dtypes.uint8)\n\n  def __call__(self, unconditional_context, context, latent, timestep, alphas, alphas_prev, guidance):\n    e_t = self.get_model_output(unconditional_context, context, latent, timestep, guidance)\n    x_prev, _ = self.get_x_prev_and_pred_x0(latent, e_t, alphas, alphas_prev)\n    #e_t_next = get_model_output(x_prev)\n    #e_t_prime = (e_t + e_t_next) / 2\n    #x_prev, pred_x0 = get_x_prev_and_pred_x0(latent, e_t_prime, index)\n    return x_prev.realize()\n\n# ** ldm.models.autoencoder.AutoencoderKL (done!)\n# 3x512x512 <--> 4x64x64 (16384)\n# decode torch.Size([1, 4, 64, 64]) torch.Size([1, 3, 512, 512])\n# section 4.3 of paper\n# first_stage_model.encoder, first_stage_model.decoder\n\n# ** ldm.modules.diffusionmodules.openaimodel.UNetModel\n# this is what runs each time to sample. is this the LDM?\n# input:  4x64x64\n# output: 4x64x64\n# model.diffusion_model\n# it has attention?\n\n# ** ldm.modules.encoders.modules.FrozenCLIPEmbedder\n# cond_stage_model.transformer.text_model\n\nif __name__ == ""__main__"":\n  default_prompt = ""a horse sized cat eating a bagel""\n  parser = argparse.ArgumentParser(description='Run Stable Diffusion', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n  parser.add_argument('--steps', type=int, default=6, help=""Number of steps in diffusion"")\n  parser.add_argument('--prompt', type=str, default=default_prompt, help=""Phrase to render"")\n  parser.add_argument('--out', type=str, default=Path(tempfile.gettempdir()) / ""rendered.png"", help=""Output filename"")\n  parser.add_argument('--noshow', action='store_true', help=""Don't show the image"")\n  parser.add_argument('--fp16', action='store_true', help=""Cast the weights to float16"")\n  parser.add_argument('--timing', action='store_true', help=""Print timing per step"")\n  parser.add_argument('--seed', type=int, help=""Set the random latent seed"")\n  parser.add_argument('--guidance', type=float, default=7.5, help=""Prompt strength"")\n  args = parser.parse_args()\n\n  model = StableDiffusion()\n\n  # load in weights\n  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):\n    load_state_dict(model, torch_load(fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt'))['state_dict'], strict=False)\n\n    if args.fp16:\n      for k,v in get_state_dict(model).items():\n        if k.startswith(""model""):\n          v.replace(v.cast(dtypes.float16).realize())\n\n  # run through CLIP to get context\n  tokenizer = Tokenizer.ClipTokenizer()\n  prompt = Tensor([tokenizer.encode(args.prompt)])\n  context = model.cond_stage_model.transformer.text_model(prompt).realize()\n  print(""got CLIP context"", context.shape)\n\n  prompt = Tensor([tokenizer.encode("""")])\n  unconditional_context = model.cond_stage_model.transformer.text_model(prompt).realize()\n  print(""got unconditional CLIP context"", unconditional_context.shape)\n\n  # done with clip model\n  del model.cond_stage_model\n\n  timesteps = list(range(1, 1000, 1000//args.steps))\n  print(f""running for {timesteps} timesteps"")\n  alphas = model.alphas_cumprod[Tensor(timesteps)]\n  alphas_prev = Tensor([1.0]).cat(alphas[:-1])\n\n  # start with random noise\n  if args.seed is not None: Tensor.manual_seed(args.seed)\n  latent = Tensor.randn(1,4,64,64)\n\n  @TinyJit\n  def run(model, *x): return model(*x).realize()\n\n  # this is diffusion\n  with Context(BEAM=getenv(""LATEBEAM"")):\n    for index, timestep in (t:=tqdm(list(enumerate(timesteps))[::-1])):\n      GlobalCounters.reset()\n      t.set_description(""%3d %3d"" % (index, timestep))\n      with Timing(""step in "", enabled=args.timing, on_exit=lambda _: f"", using {GlobalCounters.mem_used/1e9:.2f} GB""):\n        with WallTimeEvent(BenchEvent.STEP):\n          tid = Tensor([index])\n          latent = run(model, unconditional_context, context, latent, Tensor([timestep]), alphas[tid], alphas_prev[tid], Tensor([args.guidance]))\n          if args.timing: Device[Device.DEFAULT].synchronize()\n    del run\n\n  # upsample latent space to image with autoencoder\n  x = model.decode(latent)\n  print(x.shape)\n\n  # save image\n  im = Image.fromarray(x.numpy())\n  print(f""saving {args.out}"")\n  im.save(args.out)\n  # Open image.\n  if not args.noshow: im.show()\n\n  # validation!\n  if args.prompt == default_prompt and args.steps == 6 and args.seed == 0 and args.guidance == 7.5:\n    ref_image = Tensor(np.array(Image.open(Path(__file__).parent / ""stable_diffusion_seed0.png"")))\n    distance = (((x.cast(dtypes.float) - ref_image.cast(dtypes.float)) / ref_image.max())**2).mean().item()\n    assert distance < 3e-3, colored(f""validation failed with {distance=}"", ""red"")  # higher distance with WINO\n    print(colored(f""output validated with {distance=}"", ""green""))\n",python,tab
+6,120509,"examples/gpt2.py",0,0,"#!/usr/bin/env python3\nimport os, argparse, contextlib\nfrom typing import Optional, Union\nwith contextlib.suppress(ImportError): import tiktoken\nfrom tinygrad import Tensor, TinyJit, Device, GlobalCounters, Variable, dtypes\nfrom tinygrad.uop.ops import UOp\nfrom tinygrad.helpers import Timing, DEBUG, JIT, getenv, fetch, colored, trange\nfrom tinygrad.nn import Embedding, Linear, LayerNorm\nfrom tinygrad.nn.state import gguf_load, torch_load, load_state_dict, get_state_dict\nfrom extra.bench_log import BenchEvent, WallTimeEvent\n\nMAX_CONTEXT = getenv(""MAX_CONTEXT"", 128)\nHALF = getenv(""HALF"")\n\nclass Attention:\n  def __init__(self, dim, n_heads):\n    self.c_attn = Linear(dim, 3*dim, bias=True)\n    self.c_proj = Linear(dim, dim, bias=True)\n    self.n_heads = n_heads\n    self.dim = dim\n    self.head_dim = dim // n_heads\n\n  def __call__(self, x:Tensor, start_pos:Variable, mask:Optional[Tensor]) -> Tensor:\n    if mask is not None or start_pos.val == 0:\n      # no symbolic shape qkv when consuming prompts\n      start_pos = start_pos.val\n\n    if HALF: x = x.half()\n    xqkv = self.c_attn(x)\n    xq, xk, xv = [xqkv.shrink((None, None, (i*self.dim, (i+1)*self.dim))).reshape(None, None, self.n_heads, self.head_dim) for i in range(3)]\n    bsz, seqlen, _, _ = xq.shape\n\n    # create kv cache\n    if not hasattr(self, ""cache_kv""):\n      self.cache_kv = Tensor.zeros(2, bsz, MAX_CONTEXT, self.n_heads, self.head_dim, dtype=x.dtype).contiguous().realize()\n\n    # update the cache\n    self.cache_kv.shrink((None, None,(start_pos,start_pos+seqlen),None,None)).assign(Tensor.stack(xk, xv)).realize()\n\n    if start_pos > 0:\n      keys = self.cache_kv[0].shrink((None, (0, start_pos+seqlen), None, None))\n      values = self.cache_kv[1].shrink((None, (0, start_pos+seqlen), None, None))\n    else:\n      keys = xk\n      values = xv\n\n    xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)\n    return self.c_proj(xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2).reshape(bsz, seqlen, self.dim))\n\nclass FeedForward:\n  def __init__(self, dim, hidden_dim):\n    self.c_fc = Linear(dim, hidden_dim, bias=True)\n    self.c_proj = Linear(hidden_dim, dim, bias=True)\n\n  def __call__(self, x:Tensor) -> Tensor:\n    return self.c_proj(self.c_fc(x).gelu())\n\nclass TransformerBlock:\n  def __init__(self, dim, n_heads, norm_eps):\n    self.attn = Attention(dim, n_heads)\n    self.mlp = FeedForward(dim, 4*dim)\n    self.ln_1 = LayerNorm(dim, norm_eps)\n    self.ln_2 = LayerNorm(dim, norm_eps)\n\n  def __call__(self, x:Tensor, start_pos:Variable, mask:Optional[Tensor]):\n    h = x + self.attn(self.ln_1(x), start_pos, mask).float()\n    return (h + self.mlp(self.ln_2(h)))\n\nclass Transformer:\n  def __init__(self, dim, n_heads, n_layers, norm_eps, vocab_size, max_seq_len=1024):\n    self.vocab_size = vocab_size\n    self.wte = Embedding(vocab_size, dim)\n    self.wpe = Embedding(max_seq_len, dim)\n    self.h = [TransformerBlock(dim, n_heads, norm_eps) for _ in range(n_layers)]\n    self.ln_f = LayerNorm(dim, norm_eps)\n    self.lm_head = Linear(dim, vocab_size, bias=False)\n    self.forward_jit = TinyJit(self.forward)\n\n  def forward(self, tokens:Union[Tensor,UOp], start_pos:Variable, temperature:float=0.0):\n    if not hasattr(self, 'allpos'): self.allpos = Tensor.arange(0, MAX_CONTEXT).reshape(1, -1).realize()\n    if isinstance(tokens, UOp):\n      seqlen = 1\n      tok_emb = self.wte.weight.shrink(((tokens, tokens+1), None))\n    else:\n      seqlen = tokens.shape[1]\n      tok_emb = self.wte(tokens)\n\n    # not symbolic when consuming the prompt\n    selected_pos = (0, seqlen) if start_pos.val == 0 else (start_pos, start_pos+1)\n    pos_emb = self.wpe(self.allpos.shrink((None, selected_pos)))\n\n    h = tok_emb + pos_emb\n\n    if HALF: h = h.half()\n\n    mask = Tensor.full((1, 1, seqlen, start_pos.val+seqlen), float(""-inf""), dtype=h.dtype).triu(start_pos.val+1) if seqlen > 1 else None\n\n    for hi in self.h: h = hi(h, start_pos, mask)\n\n    logits = self.lm_head(self.ln_f(h))\n\n    if logits.shape[1] == 0:\n      # special case for empty prompt\n      logits = Tensor.ones((logits.shape[0], self.vocab_size), dtype=logits.dtype, device=logits.device)\n    else:\n      logits = logits[:, -1, :]\n\n    if temperature < 1e-6:\n      ret = logits.argmax(-1)\n    else:\n      ret = (logits / temperature).softmax().multinomial()\n    return ret.flatten().realize()\n\n  def __call__(self, tokens:Union[Tensor,UOp], start_pos:Variable, temperature:float=0.0) -> Tensor:\n    forward = (self.forward_jit if JIT and (isinstance(tokens, UOp) or tokens.shape[1] == 1) else self.forward)\n    return forward(tokens, start_pos, temperature)\n\nVOCAB_SIZE = 50257\nMODEL_PARAMS = {\n  'gpt2':         dict(n_layers=12, n_heads=12, dim=768, norm_eps=1e-5, vocab_size=VOCAB_SIZE),   # 124M params\n  'gpt2-medium':  dict(n_layers=24, n_heads=16, dim=1024, norm_eps=1e-5, vocab_size=VOCAB_SIZE),  # 350M params\n  'gpt2-large':   dict(n_layers=36, n_heads=20, dim=1280, norm_eps=1e-5, vocab_size=VOCAB_SIZE),  # 774M params\n  'gpt2-xl':      dict(n_layers=48, n_heads=25, dim=1600, norm_eps=1e-5, vocab_size=VOCAB_SIZE),  # 1558M params\n}\n\nclass GPT2:\n  @staticmethod\n  def build(model_size=""gpt2""):\n    tokenizer = tiktoken.get_encoding(""gpt2"")\n\n    model = Transformer(**MODEL_PARAMS[model_size])\n    weights = torch_load(fetch(f'https://huggingface.co/{model_size}/resolve/main/pytorch_model.bin'))\n    # special treatment for the Conv1D weights we need to transpose\n    transposed = ('attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight')\n    for k in weights:\n      if k.endswith(transposed):\n        weights[k] = weights[k].T\n    # lm head and wte are tied\n    weights['lm_head.weight'] = weights['wte.weight']\n\n    with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):\n      load_state_dict(model, weights)\n\n      if HALF:\n        for l in get_state_dict(model).values():\n          l.replace(l.half().realize())\n\n    return GPT2(model, tokenizer)\n\n  @staticmethod\n  def build_gguf(model_size: str):\n    q_type = model_size[len(""gpt2_gguf_""):].upper()\n    fn = fetch(f""https://huggingface.co/PrunaAI/gpt2-GGUF-smashed/resolve/main/gpt2.{q_type}.gguf?download=true"")\n    gguf_tensor = Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f""disk:{fn}"").to(Device.DEFAULT)\n    kv_data, state_dict = gguf_load(gguf_tensor)\n\n    gpt2_params = {\n      ""dim"": kv_data[""gpt2.embedding_length""], ""n_heads"": kv_data[""gpt2.attention.head_count""],\n      ""n_layers"": kv_data[""gpt2.block_count""], ""norm_eps"": kv_data[""gpt2.attention.layer_norm_epsilon""],\n      ""vocab_size"": VOCAB_SIZE, ""max_seq_len"": kv_data[""gpt2.context_length""],\n    }\n    def _remap_gguf_key(key: str):\n      replaces = [\n        (""blk."", ""h.""), ("".attn_qkv.bias"", "".attn.c_attn.bias""), ("".attn_qkv.weight"", "".attn.c_attn.weight""),\n        ("".ffn_norm.bias"", "".ln_2.bias""), ("".ffn_norm.weight"", "".ln_2.weight""), ("".attn_norm.bias"", "".ln_1.bias""),\n        ("".attn_norm.weight"", "".ln_1.weight""), ("".attn_output.bias"", "".attn.c_proj.bias""), ("".attn_output.weight"", "".attn.c_proj.weight""),\n        ("".ffn_up.bias"", "".mlp.c_fc.bias""), ("".ffn_up.weight"", "".mlp.c_fc.weight""), ("".ffn_down.bias"", "".mlp.c_proj.bias""),\n        ("".ffn_down.weight"", "".mlp.c_proj.weight""), (""token_embd.weight"", ""wte.weight""), (""output.weight"", ""lm_head.weight""),\n        (""output_norm.bias"", ""ln_f.bias""), (""output_norm.weight"", ""ln_f.weight""), (""position_embd.weight"", ""wpe.weight""),\n      ]\n      for ostr, ns in replaces: key = key.replace(ostr, ns)\n      return key\n    state_dict = { _remap_gguf_key(k): v for k, v in state_dict.items() }\n    model = Transformer(**gpt2_params)\n    with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):\n      load_state_dict(model, state_dict)\n    return GPT2(model, tiktoken.get_encoding(""gpt2""))\n\n  def __init__(self, model, tokenizer):\n    self.model = model\n    self.tokenizer = tokenizer\n\n  def generate(self, prompt:str, max_length:int, temperature:float, timing:bool=False, batch_size:int=1):\n    prompt_tokens = self.tokenizer.encode(prompt, allowed_special={""<|endoftext|>""})\n    toks = [prompt_tokens[:] for _ in range(batch_size)]\n    start_pos = 0\n    for _ in trange(max_length, disable=(timing==True)):\n      GlobalCounters.reset()\n      if timing: print("""")\n      st = GlobalCounters.time_sum_s\n      with Timing(""ran model in "", on_exit=(lambda et: (f"", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU"" if DEBUG>=2 else """")+\n                  f"", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB""+\n                  (f"", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s"" if DEBUG>=2 else """")) if DEBUG else None, enabled=timing):\n        with WallTimeEvent(BenchEvent.STEP):\n          if batch_size == 1 and len(toks[0][start_pos:]) == 1:\n            tokens = Variable(""tokens"", 0, VOCAB_SIZE-1).bind(toks[0][start_pos])\n          else:\n            tokens = Tensor([x[start_pos:] for x in toks])\n          tok = self.model(tokens, Variable(""start_pos"", 1 if start_pos else 0, MAX_CONTEXT-1).bind(start_pos), temperature).tolist()\n      start_pos = len(toks[0])\n      for i,t in enumerate(tok): toks[i].append(t)\n    return [self.tokenizer.decode(x) for x in toks]\n\n# **** main code ****\n\nif __name__ == ""__main__"":\n  print(f""using {Device.DEFAULT} backend"")\n  default_prompt = ""What is the answer to life, the universe, and everything?""\n\n  parser = argparse.ArgumentParser(description='Run GPT2 in tinygrad', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n  parser.add_argument('--prompt', type=str, default=default_prompt, help=""Phrase to start with"")\n  parser.add_argument('--count', type=int, default=100, help=""Max number of tokens to generate"")\n  parser.add_argument('--temperature', type=float, default=0.8, help=""Temperature in the softmax"")\n  parser.add_argument('--model_size', type=str, default=""gpt2-medium"", help=""Size of model to use [gpt2, gpt2-medium, gpt2-large, gpt2-xl]"")\n  parser.add_argument('--timing', action='store_true', help=""Print timing per token"")\n  parser.add_argument('--seed', type=int, help=""Set the random seed"")\n  parser.add_argument('--batch_size', type=int, default=1, help=""Set the input batch size"")\n  parser.add_argument('--benchmark', type=int, default=-1, help=""Benchmark GPT with the given number of tokens"")\n  parser.add_argument('--noshow', action='store_true', help=""Don't show the output"")\n  args = parser.parse_args()\n\n  if args.seed is not None:\n    Tensor.manual_seed(args.seed)\n\n  print(f""using {args.model_size}"")\n  gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith(""gpt2_gguf_"") else GPT2.build(args.model_size)\n\n  if args.benchmark != -1:\n    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable(""a"", 0, MAX_CONTEXT).bind(0)).realize()\n  else:\n    texts = gpt2.generate(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)\n    if not args.noshow:\n      print('Generating text...')\n      if len(texts) == 1: print(texts[0])\n      else:\n        for i,text in enumerate(texts): print(colored(f""Response {i}:"", ""green""), text)\n\n    # validate output!\n    if args.temperature == 0 and args.model_size == ""gpt2-medium"" and args.count == 10:\n      expected = {\n        default_prompt: ""What is the answer to life, the universe, and everything?\n\nThe answer is that we are all one"",\n        ""Hello."": ""Hello. I'm a little late to the party, but"",\n      }\n      try:\n        assert texts[0] == expected[args.prompt]\n        print(colored(""output validated"", ""green""))\n      except KeyError:\n        pass\n",python,tab
+7,121560,"examples/gpt2.py",593,0,"",python,selection_mouse
+8,482010,"examples/gpt2.py",864,0,"",python,selection_command
+9,482406,"tinygrad/__init__.py",0,0,"import os\nif int(os.getenv(""TYPED"", ""0"")):\n  from typeguard import install_import_hook\n  install_import_hook(__name__)\nfrom tinygrad.tensor import Tensor                                    # noqa: F401\nfrom tinygrad.engine.jit import TinyJit                               # noqa: F401\nfrom tinygrad.uop.ops import UOp\nVariable = UOp.variable\nfrom tinygrad.dtype import dtypes                                     # noqa: F401\nfrom tinygrad.helpers import GlobalCounters, fetch, Context, getenv   # noqa: F401\nfrom tinygrad.device import Device                                    # noqa: F401\n",python,tab
+10,482416,"tinygrad/__init__.py",318,0,"",python,selection_command
+11,484052,"tinygrad/__init__.py",327,0,"",python,selection_command
+12,484164,"tinygrad/__init__.py",329,0,"",python,selection_command
+13,484524,"tinygrad/__init__.py",332,0,"",python,selection_command
+14,484722,"tinygrad/__init__.py",333,0,"",python,selection_command
+15,484999,"tinygrad/uop/ops.py",0,0,"from __future__ import annotations\nfrom typing import Any, Callable, cast, TYPE_CHECKING, Type, Sequence\nimport sys, time, functools, itertools, math, operator, hashlib, os, types, pickle, pathlib, inspect, weakref\nfrom dataclasses import dataclass, field\nfrom enum import Enum, auto\nfrom tinygrad.uop import Ops, GroupOp\nfrom tinygrad.uop.mathtraits import MathTrait\nfrom tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType\nfrom tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten\nfrom tinygrad.helpers import PICKLE_BUFFERS, PROFILE, dedup, cdiv, cmod, diskcache_put, to_function_name, cpu_profile, TracingKey\nif TYPE_CHECKING:\n  from tinygrad.shape.shapetracker import ShapeTracker\n  from tinygrad.device import Buffer, MultiBuffer\n\n# https://en.wikipedia.org/wiki/Identity_element\ndef identity_element(op:Ops, dt:DType) -> ConstType: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dtypes.min(dt)}[op], dt)\n\ndef can_pad(root:UOp, edges:dict[UOp, None]) -> bool:\n  return all(u.op not in GroupOp.UnsafePad for u in root.toposort(gate=lambda x:x not in edges))\n\n# With True as the default, this matches the old symbolic behavior\ndef resolve(x:UOp|bool, default:bool=True):\n  if isinstance(x, bool): return x\n  assert x.dtype == dtypes.bool, ""UOp in resolve must be bool""\n  # NOTE: generating the text for the exception is expensive, so we do this\n  return bool(sx.vmin) if (sx:=x.simplify()).vmin == sx.vmax else default\n\n# smax/smin are replacements for max/min that preserve symbolic\ndef _suop(lst, uop_fxn, python_fxn):\n  uops, nums = partition(lst, lambda x: isinstance(x, UOp))\n  return ssimplify(functools.reduce(uop_fxn, uops + ([python_fxn(nums)] if nums else [])))\ndef smax(*lst): return _suop(argfix(*lst), UOp.maximum, max)\ndef smin(*lst): return _suop(argfix(*lst), UOp.minimum, min)\ndef srender(x) -> str: return x.render() if isinstance(x, UOp) else str(x)\n\ndef ssimplify(uop): return uop.ssimplify() if isinstance(uop, UOp) else uop\ndef sym_infer(uop: UOp|int, var_vals: dict[UOp, int]) -> int: return uop.sym_infer(var_vals) if isinstance(uop, UOp) else uop\n\n# used for UOp and UPat\ndef pretty_print(x:Any, rep:Callable, srcfn=lambda x: x.src, cache=None, d=0)->str:\n  def dfs(x:Any, cache:dict):\n    for s in srcfn(x) or []:\n      cache.setdefault(s, [len(cache), 0, False])[1] += 1\n      if cache[s][1] == 1: dfs(s, cache)\n  if cache is None: dfs(x, cache:={})\n  if (cx:=cache.setdefault(x, [0,0,False]))[2]: return f""{' '*d} x{cx[0]}""\n  cx[2], srcs = True, ('None' if srcfn(x) is None else ''.join(f'\n{pretty_print(s, rep, srcfn, cache, d+2)},' for s in srcfn(x)))\n  return f""{' '*d}{f'x{cx[0]}:=' * (cx[1]>1)}{rep(x)}"" % srcs\n\nclass UOpMetaClass(type):\n  ucache:dict[tuple, weakref.ReferenceType[UOp]] = {}\n  def __call__(cls, op:Ops, dtype:DType=dtypes.void, src:tuple[UOp,...]=tuple(), arg:Any=None, tag:Any=None,\n               metadata:tuple[Metadata,...]|None=None, _buffer:Buffer|None=None):\n    if (wret:=UOpMetaClass.ucache.get(key:=(op, dtype, src, arg, tag), None)) is not None and (ret:=wret()) is not None: return ret\n    UOpMetaClass.ucache[key] = ref = weakref.ref(created:=super().__call__(*key))\n    for s in src: s.children.add(ref)\n    if metadata is not None: all_metadata[created] = metadata\n    # NOTE: this value is set by pickle when pickling a realized tensor\n    if _buffer is not None:\n      assert op is Ops.BUFFER, f""trying to set Buffer {_buffer} for {op}""\n      buffers[created] = _buffer\n    return created\n\n# some uops map to other stuff\nbuffers:weakref.WeakKeyDictionary[UOp, Buffer|MultiBuffer] = weakref.WeakKeyDictionary() # this maps BUFFER uops to their device Buffers\nall_metadata:weakref.WeakKeyDictionary[UOp, tuple[Metadata, ...]] = weakref.WeakKeyDictionary() # TODO: should this be here?\n\n# NOTE: this should be frozen, but frozen is slower\n@dataclass(eq=False, slots=True)\nclass UOp(MathTrait, metaclass=UOpMetaClass):\n  op:Ops\n  dtype:DType = dtypes.void\n  src:tuple[UOp, ...] = tuple()\n  arg:Any = None\n  tag:Any = None\n  children:set[weakref.ref[UOp]] = field(default_factory=set)\n  def __del__(self):\n    if Ops is not None and self.op is Ops.BUFFER and (buffer:=buffers.get(self)) is not None: buffer.ref(-1)\n    try:\n      if (ref:=UOpMetaClass.ucache.get(k:=(self.op, self.dtype, self.src, self.arg, self.tag))) is not None:\n        for s in self.src: s.children.discard(ref)\n        del UOpMetaClass.ucache[k]\n    except AttributeError: pass\n  def __reduce__(self):\n    args = [self.op, self.dtype, self.src, self.arg, self.tag, self.metadata]\n    if self.op is Ops.BUFFER and self.realized is not None and PICKLE_BUFFERS: args.append(self.realized)\n    return UOp, tuple(args)\n  def replace(self, **kwargs) -> UOp:\n    new_args = (kwargs.pop(""op"", self.op), kwargs.pop(""dtype"", self.dtype), kwargs.pop(""src"", self.src),\n                kwargs.pop(""arg"", self.arg), kwargs.pop(""tag"", self.tag))\n    assert len(kwargs) == 0, f""unused kwargs in replace {list(kwargs)}""\n    if (self.op, self.dtype, self.src, self.arg, self.tag) == new_args: return self\n    return UOp(*new_args)\n  def rtag(self, tag=True): return self.replace(tag=tag)\n  @functools.cached_property\n  def key(self) -> bytes:\n    return hashlib.sha256(str((self.op, self.dtype, self.arg)).encode() + b"""".join([s.key for s in self.src])).digest()\n  def __repr__(self): return pretty_print(self, lambda x: f""{type(self).__name__}({x.op}, {x.dtype}, arg={x.argstr()}{x.tagstr()}, src=(%s))"")\n  def argstr(self): return f'({"", "".join(map(str, self.arg))})' if self.op is Ops.REDUCE_AXIS else repr(self.arg)\n  def tagstr(self): return f"", tag={self.tag}"" if self.tag is not None else """"\n\n  @functools.cached_property\n  def parents(self:UOp) -> dict[UOp, None]:\n    ret = {s:None for s in self.src}\n    for s in self.src: ret.update(s.parents)\n    return ret\n  @property\n  def sparents(self:UOp) -> dict[UOp, None]: return {self:None, **self.parents}\n\n  def toposort(self, gate:Callable|None=None) -> dict[UOp, None]:\n    ret: dict[UOp, None] = {}\n    stack: list[tuple[UOp, bool]] = [(self, False)] # each stack entry is (node, visited_flag)\n    while stack:\n      node, visited = stack.pop()\n      if node in ret: continue\n      if not visited:\n        if gate is None or gate(node):\n          stack.append((node, True))  # push node back on stack to process after its parents\n          for parent in reversed(node.src): stack.append((parent, False)) # push parents on the stack\n      else: ret[node] = None # second time i'm seeing this node, add it to returned toposort\n    return ret\n\n  # returns map of UOps to their children in the graph rooted by self\n  def get_children_map(self) -> dict[UOp, dict[UOp, None]]:\n    ret: dict[UOp, dict[UOp, None]] = {}\n    for u in self.toposort():\n      ret[u] = {}\n      for s in u.src: ret[s][u] = None\n    return ret\n\n  @functools.cached_property\n  def tuplize(self:UOp) -> tuple:\n    return (self.op.value, self.arg, self.dtype,)+tuple([x.tuplize for x in self.src])\n\n  # *** uop shape stuff ***\n\n  @functools.cached_property\n  def st(self) -> ShapeTracker|None:\n    if self.op in GroupOp.Block or self.op is Ops.INDEX: return None\n    from tinygrad.shape.shapetracker import ShapeTracker\n    # VIEW and MovementOps define a new ShapeTracker from the arg\n    if self.op is Ops.VIEW: return self.arg\n    if self.op in GroupOp.Movement: return unwrap(self.src[0].st).mop(self.op, self.arg)\n    # CONST with a DEVICE has a shape of ()\n    if self.op is Ops.CONST and len(self.src) and self.src[0].op is Ops.DEVICE: return ShapeTracker.from_shape(())\n    # BufferOps and ASSIGN flow ShapeTracker from a direct edge\n    if self.op in {Ops.STORE, Ops.ASSIGN, Ops.LOAD}: return self.src[0].st\n    if self.op in GroupOp.Buffer: return views[0] if (views:=[x.st for x in self.src if x.op is Ops.VIEW]) else None\n\n    # BUFFER/BUFFER_VIEW and KERNEL only have a size\n    if self.op in {Ops.BUFFER, Ops.BUFFER_VIEW}: return ShapeTracker.from_shape((self.size,))\n    if self.op is Ops.KERNEL: return ShapeTracker.from_shape((self.arg.ast.size,))\n    if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:\n      sz = cast(PtrDType, self.dtype).size\n      return ShapeTracker.from_shape((sz,)) if sz > 0 else None\n\n    # hack for PTX, CASTing the ptr loses the shape\n    if self.op is Ops.CAST and self.src[0].op is Ops.DEFINE_GLOBAL: return None\n\n    # otherwise we get the shape from sources\n    if not (src_sts := [x.st for x in self.src if x.st is not None]): return None\n    assert all_same([x.shape for x in src_sts]), f""UOp sources must have the same shape {self} {[x.shape for x in src_sts]}""\n    match self.op:\n      case Ops.MULTI: shape = tuple(self.src[0].shape[a]*len(self.device) if a == self.axis else s for a,s in enumerate(self.src[0].shape))\n      case Ops.BITCAST:\n        shape = src_sts[0].shape\n        if self.dtype.itemsize != (input_sz:=self.src[0].dtype.itemsize): shape = shape[:-1]+((shape[-1]*input_sz) // self.dtype.itemsize,)\n      case Ops.REDUCE_AXIS | Ops.WMMA: shape = src_sts[0].reduce(self.axis_arg)\n      case _: shape = src_sts[0].shape\n    return ShapeTracker.from_shape(shape)\n\n  @functools.cached_property\n  def full_shape(self) -> tuple[sint, ...]:\n    if self.op is Ops.VIEW: return self.shape\n    # NOTE: if a parent doesn't have st its full_shape is empty\n    parent_shapes = [x.full_shape for x in self.src]\n    return tuple(smax(x) for x in itertools.zip_longest(*parent_shapes, fillvalue=1))\n  @property\n  def shape(self) -> tuple[sint, ...]:\n    assert self.st is not None, f""{self.op} doesn't have a shape""\n    return unwrap(self.st).shape\n  @property\n  def size(self) -> int: return self.arg[0] if self.op is Ops.BUFFER_VIEW else self.arg if self.op is Ops.BUFFER else unwrap(self.st).size\n\n  # *** uop evaluation ***\n\n  def simplify(self):\n    # late import!\n    from tinygrad.uop.symbolic import symbolic\n    with Context(TRACK_MATCH_STATS=0):\n      return graph_rewrite(self, symbolic)\n  def ssimplify(self) -> UOp|ConstType: return ret.arg if (ret:=self.simplify()).op is Ops.CONST else ret\n  def _eval(self, dtype, expected_type:Type[T]) -> T:\n    assert self.dtype in dtype, f""eval with wrong dtype {self}""\n    vmin, vmax = (simple_self:=self.simplify())._min_max\n    if vmin != vmax: raise ValueError(f""eval failed to be a single number, range is {vmin} to {vmax} in {simple_self.render()}"")\n    assert isinstance(vmin, expected_type), f""vmin is wrong dtype {type(vmin)} != {expected_type}""\n    return vmin\n  def __bool__(self): return self._eval((dtypes.bool,), bool)\n  def __int__(self): return self._eval(dtypes.ints, int)\n  def __float__(self): return self._eval(dtypes.floats, float)\n  def substitute(self, dvars:dict[UOp, UOp], name:str|None=None):\n    dvars = {k:v for k,v in dvars.items() if k is not v}\n    if len(dvars) == 0: return self\n    with Context(TRACK_MATCH_STATS=(0 if name is None else TRACK_MATCH_STATS.value)):\n      return graph_rewrite(self, _substitute, dvars, bottom_up=True, name=name)\n\n  # *** uop syntactic sugar ***\n\n  @property\n  def st_arg(self) -> ShapeTracker:\n    assert self.op in GroupOp.Buffer, f""st_arg called on {self.op}""\n    return unwrap(self.st)\n  @property\n  def axis_arg(self) -> tuple[int, ...]:\n    assert self.op in {Ops.REDUCE_AXIS, Ops.WMMA}, f""axis_arg called on {self.op}""\n    ret = self.arg[1] if self.op is Ops.REDUCE_AXIS else self.arg[7]\n    assert isinstance(ret, tuple) and all(isinstance(x, int) for x in ret), f""axis_arg trying to return {ret}""\n    return ret\n  def sink(self, *srcs:UOp|None, **kwargs): return UOp(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs)\n  def detach(self): return UOp(Ops.DETACH, self.dtype, (self,))\n  def index(self, idx:UOp, valid:UOp|None=None): return UOp(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))\n  def __getitem__(self, idx): return self.index(idx)\n  def const_like(self, b:ConstLike):\n    # constants can optionally have a DEVICE source\n    return UOp.const(self.dtype, b, device=self._device, shape=self.shape if self.st is not None else None)\n  def broadcast(self, count:int):\n    assert self.dtype.count == 1\n    if count == 1: return self\n    return UOp(Ops.VECTORIZE, self.dtype.vec(count), (self,)*count)\n  def cast(self, dtype:DType):\n    if self.dtype == dtype: return self\n    return UOp(Ops.CAST, dtype, (self,))\n  def cast_vec(self, dtype:DType): return UOp(Ops.CAST, dtype.vec(self.dtype.count), (self,))\n  def bitcast(self, dtype:DType): return UOp(Ops.BITCAST, dtype, (self,))\n  def gep(self, i:tuple[int, ...]|int):\n    if isinstance(i, tuple) and len(i) == 1: return self.gep(i[0])\n    if isinstance(i, int):\n      # NOTE: these are just shortcuts to not have to create and fold later\n      if self.op is Ops.VECTORIZE: return self.src[i]\n      if self.op is Ops.VCONST: return UOp.const(self.dtype.scalar(), self.arg[i])\n      if self.op is Ops.CONST: return UOp.const(self.dtype.scalar(), self.arg)\n      i = (i,)\n    return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)\n  def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop(""dtype"", self.dtype.base), src=(self,)+src, **kwargs)\n  def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, dtypes.void, (self,)+src, **kwargs)\n  def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))\n  def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)\n  def alu(self, op, *src:UOp, **kwargs):\n    out_dtype = (self, *src)[-1].dtype\n    if op in {Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool\n    return UOp(op, out_dtype, (self,)+src, **kwargs)\n  @staticmethod\n  def const(dtype:DType, b:ConstLike, device:str|tuple[str, ...]|None=None, shape:tuple[sint, ...]|None=None):\n    if isinstance(b, UOp): return b.unbind()[0] if b.op is Ops.BIND else b\n    if isinstance(b, tuple) and all_same(b): b = b[0]  # doesn't have to be a VCONST if they are all the same\n    ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype))\n    if shape is not None:\n      from tinygrad.shape.shapetracker import ShapeTracker\n      ret = ret.replace(src=(UOp(Ops.VIEW, dtypes.void, (), ShapeTracker.from_shape(shape, (0,)*len(shape))),))\n    if device is not None:\n      ret = ret.replace(src=(UOp(Ops.DEVICE, arg=device).view(unwrap(ret.st)),))\n    return ret\n  @staticmethod\n  def range(dtype:DType, end:sint, idx:int): return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=idx)\n  def r(self, op:Ops, axis:tuple[int, ...]):\n    axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))\n    if len(axis) == 0: return self\n    # move any non reduce axis before the first reduce axis\n    move_early, rest = partition(range(axis[0], len(self.shape)), lambda i: i not in axis and resolve(self.shape[i] != 1))\n    permaxis = tuple(range(axis[0])) + tuple(move_early) + tuple(rest)\n    ret = self.permute(permaxis)\n    new_axis = tuple([x for x in range(axis[0]+len(move_early), len(self.shape)) if resolve(ret.shape[x] != 1)])\n    assert len(axis) == len(new_axis)\n    ret = UOp(Ops.REDUCE_AXIS, self.dtype, (ret,), (op, new_axis))\n    return ret.reshape(tuple([x if i not in axis else 1 for i,x in enumerate(self.shape)]))\n  def reduce(self, *src:UOp, **kwargs): return UOp(Ops.REDUCE, kwargs.pop('dtype', self.dtype), src=(self,)+src, **kwargs)\n  def contiguous(self): return self.alu(Ops.CONTIGUOUS)\n  def contiguous_backward(self): return self.alu(Ops.CONTIGUOUS_BACKWARD)\n  def fuse(self): return self.alu(Ops.FUSE)\n  def allreduce(self, op, device:str|tuple[str, ...]|UOp):\n    assert isinstance(self.device, tuple), f""allreduce must be on tuple {self.device} isn't""\n    return UOp(Ops.ALLREDUCE, self.dtype, (self, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device), op)\n\n  # *** from MultiLazyBuffer ***\n\n  def multi(self, axis:int|None):\n    assert isinstance(self.device, tuple), f""multi device must be tuple, {self.device} isn't""\n    assert axis is not None, ""multi None is no longer supported""\n    return UOp(Ops.MULTI, self.dtype, (self,), axis)\n\n  @property\n  def bounds(self):\n    if self.axis is None: raise RuntimeError(""bounds is not defined when axis is None"")\n    return tuple(itertools.pairwise(itertools.accumulate([self.src[0].shape[self.axis] for _ in self.device], initial=0)))\n\n  @functools.cached_property\n  def axis(self) -> int|None:\n    if self.op is Ops.MULTI: return self.arg\n    # NOTE: they all have to share an axis, we always choose [-1]\n    if self.op in GroupOp.ALU: return axes[-1] if (axes := dedup([x.axis for x in self.src if x.axis is not None])) else None\n    if len(self.src) == 0: return None\n    src_axis = self.src[0].axis\n    if self.op is Ops.REDUCE_AXIS: return None if src_axis is not None and src_axis in self.arg[1] else src_axis\n    if self.op is Ops.RESHAPE:\n      if src_axis is None: return None\n      arg_acc:list[sint] = list(itertools.accumulate(self.arg, operator.mul, initial=1))\n      # new_axis is the last one that preserves prod(prior to new_axis) and must not move items between shards\n      # TODO: what to do about shrinking to self.shape[self.axis]==1 len(self.real_lbs)==1?\n      return len(arg_acc) - arg_acc[::-1].index(prod(self.src[0].shape[:src_axis])) - 1\n    if self.op is Ops.PERMUTE: return self.arg.index(src_axis) if src_axis is not None else None\n    return src_axis\n\n  def _unshard(self, axis:int) -> UOp:\n    bsz, dcount = self.shape[axis], len(self.device)\n    dnum = UOp.variable(""_device_num"", 0, dcount-1)\n    return self.pad(tuple((0,0) if a != axis else (bsz*dnum, bsz*(dcount-1) - bsz*dnum) for a in range(len(self.shape))))\n\n  def _shard(self, axis:int) -> UOp:\n    dcount = len(self.device)\n    dnum = UOp.variable(""_device_num"", 0, dcount-1)\n    if self.shape[axis] % dcount != 0: raise RuntimeError(f""multi axis uneven: {self.shape[axis]=} {axis=} {dcount=}"")\n    sz = self.shape[axis] // dcount\n    return self.shrink(tuple((0,s) if i != axis else (dnum*sz,dnum*sz+sz) for i,s in enumerate(self.shape)))\n  def shard(self, devices:tuple[str, ...], axis:int) -> UOp: return self.copy_to_device(devices)._shard(axis).multi(axis)\n\n  # *** from LazyBuffer ***\n\n  def copy_to_device(self, device:str|tuple[str, ...]|UOp, arg=None):\n    assert arg is None or isinstance(self.device, tuple)\n    inp = self if arg is None else UOp(Ops.MSELECT, self.dtype, src=(self,), arg=arg)\n    return UOp(Ops.COPY, self.dtype, (inp, UOp(Ops.DEVICE, arg=device) if not isinstance(device, UOp) else device))\n  def mselect(self, arg:int) -> UOp: return UOp(Ops.MSELECT, self.dtype, (self,), arg)\n  @property\n  def metadata(self) -> tuple[Metadata, ...]|None: return all_metadata.get(self, None)\n\n  # *** uop movement ops ***\n\n  @property\n  def base(self) -> UOp:\n    if (self.op is Ops.VIEW and len(self.src) != 0) or self.op in GroupOp.Movement: return self.src[0].base\n    if self.op is Ops.MULTI: return self.src[0].base  # MULTI is really a VIEW\n    return self\n  def view(self, new_st:ShapeTracker) -> UOp: return UOp(Ops.VIEW, self.dtype, (self,), new_st)\n\n  def _mop(self, op:Ops, arg) -> UOp:\n    ret = UOp(op, self.dtype, (self,), arg)\n    if self.st == ret.st: return self  # ignore NOOPs, also check ret.st\n    return ret\n\n  def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg)\n  def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg)\n  def expand(self, arg:tuple[sint, ...]): return self._mop(Ops.EXPAND, arg)\n  def permute(self, arg:tuple[sint, ...]): return self._mop(Ops.PERMUTE, arg)\n  def shrink(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.SHRINK, arg)\n  def flip(self, arg:tuple[bool, ...]): return self._mop(Ops.FLIP, arg)\n\n  # *** uop UNIQUE ***\n\n  # TODO: use this in Buffer\n  unique_num = itertools.count(0)\n  @staticmethod\n  def unique(): return UOp(Ops.UNIQUE, arg=next(UOp.unique_num))\n\n  # *** uop Buffer stuff ***\n\n  @staticmethod\n  def new_buffer(device:str|tuple[str, ...], size:int, dtype:DType): return UOp(Ops.BUFFER, dtype, (UOp.unique(), UOp(Ops.DEVICE, arg=device)), size)\n  @property\n  def device(self) -> str|tuple[str, ...]: return cast(str|tuple[str, ...], unwrap(self._device))\n  @functools.cached_property\n  def _device(self) -> str|tuple[str, ...]|None:\n    if self.op is Ops.DEVICE: return self.arg\n    if self.op is Ops.MSELECT:\n      assert isinstance(self.src[0].device, tuple), ""mselect must be on tuple device""\n      return self.src[0].device[self.arg]\n    if self.op is Ops.MSTACK: return tuple(cast(str, x.device) for x in self.src)\n    if self.op in {Ops.COPY, Ops.BUFFER, Ops.ALLREDUCE}: return self.src[1].device\n    return next((x._device for x in self.src if x._device is not None), None)\n  @property\n  def buf_uop(self) -> UOp:\n    if self.op is Ops.BUFFER: return self\n    if self.op is Ops.MSELECT: return self.src[0].buf_uop.mselect(self.arg)\n    if self.op is Ops.MSTACK: return UOp(Ops.MSTACK, self.dtype, src=tuple(x.buf_uop for x in self.src))\n    assert self.op is Ops.ASSIGN, f""must be ASSIGN {self.op}""\n    return self.src[0].base\n  @property\n  def buffer(self) -> Buffer|MultiBuffer:\n    from tinygrad.device import Buffer, MultiBuffer\n    if self is not self.base:\n      assert unwrap(self.st).contiguous, ""VIEW only works here if it's contiguous""\n      return self.src[0].buffer\n    if self.op is Ops.MSELECT:\n      ret = self.src[0].buffer\n      assert isinstance(ret, MultiBuffer)\n      return ret.bufs[self.arg]\n    if self.op is Ops.MSTACK:\n      ret = MultiBuffer.__new__(MultiBuffer)\n      ret.bufs = [cast(Buffer, x.buffer) for x in self.src]\n      assert all_same([x.size for x in ret.bufs]) and all_same([x.dtype for x in ret.bufs]), ""multibuffers mismatch buffers""\n      return ret\n    assert self.op is Ops.BUFFER, f""must be BUFFER {self.op}""\n    if (cret:=buffers.get(self)) is not None: return cret\n    rdtype = self.dtype if isinstance(self.dtype, ImageDType) else self.dtype.base\n    if isinstance(self.device, tuple): ret = MultiBuffer(self.device, self.size, rdtype).ref(1)\n    else: ret = Buffer(self.device, self.size, rdtype).ref(1)\n    buffers[self] = ret\n    return ret\n  @property\n  def realized(self) -> Buffer|MultiBuffer|None:\n    # NOTE: this is used by the JIT to determine which inputs we capture\n    return self.buffer if self.op in {Ops.BUFFER, Ops.MSTACK} and self.buffer.is_allocated() else None\n  @property\n  def is_realized(self) -> bool:\n    return all(x.base.realized is not None for x in self.base.src) if self.base.op is Ops.MULTI else self.base.realized is not None\n\n  # *** uop Variable stuff ***\n\n  @staticmethod\n  def variable(name:str, min_val:ConstType, max_val:ConstType, dtype:DType=dtypes.int) -> UOp:\n    assert not isinstance(min_val, UOp) and not isinstance(max_val, UOp), f""can't create Variable {name} with {min_val}/{max_val}""\n    return UOp(Ops.DEFINE_VAR, dtype, arg=(name, min_val, max_val))\n  @property\n  def expr(self):\n    assert self.op is Ops.DEFINE_VAR, f""op is {self.op}, need DEFINE_VAR""\n    return self.arg[0]\n  def bind(self, val:int|UOp):\n    assert self.op is Ops.DEFINE_VAR, f""op is {self.op}, need DEFINE_VAR""\n    uval = self.const_like(val) if isinstance(val, int) else val\n    assert self.arg[1] <= uval.vmin and uval.vmax <= self.arg[2], f""bind {val} not in range [{self.arg[1]}, {self.arg[2]}]""\n    return UOp(Ops.BIND, self.dtype, (self, uval))\n  def unbind(self) -> tuple[Variable, int]:\n    assert self.op is Ops.BIND and self.src[0].op is Ops.DEFINE_VAR and self.src[1].op is Ops.CONST, f""can't unbind {self}""\n    return self.src[0], self.src[1].arg\n  @property\n  def val(self) -> int: return self.unbind()[1]\n  def vars(self) -> set[UOp]:\n    bound_vars = set([x for x in self.toposort() if x.op is Ops.BIND and x.src[0].op is Ops.DEFINE_VAR])\n    bound_var_base = set(x.src[0] for x in bound_vars)\n    all_vars = set([x for x in self.toposort() if x.op is Ops.DEFINE_VAR])\n    return bound_vars.union(set([x for x in all_vars if x not in bound_var_base]))\n  def variables(self) -> list[Variable]:\n    st_vars: list[set[Variable]] = [x.arg.vars() for x in self.toposort() if x.op is Ops.VIEW]\n    return sorted(set.union(*st_vars, set([x.unbind()[0] if x.op is not Ops.DEFINE_VAR else x for x in self.vars()])), key=lambda v: v.arg)\n\n  # *** uop symbolic stuff ***\n\n  def is_increasing(self:UOp) -> bool:\n    # is f a monotonically increasing function regards its input\n    if self.op in GroupOp.Irreducible: return True\n    if self.op is Ops.ADD: return self.src[0].is_increasing() and self.src[1].is_increasing()\n    if self.op in (Ops.MUL, Ops.IDIV) and self.src[1].op is Ops.CONST and self.src[1].arg >= 0: return self.src[0].is_increasing()\n    return False  # False if not sure\n  def const_factor(self) -> int:\n    """"""largest known int that divides self""""""\n    # TODO: for negatives it's not the largest\n    if self.op is Ops.CONST: return self.arg\n    if self.op is Ops.VCONST: return math.gcd(*self.arg)\n    if self.op is Ops.ADD: return math.gcd(self.src[0].const_factor(), self.src[1].const_factor())\n    if self.op is Ops.MUL: return self.src[0].arg if self.src[0].op is Ops.CONST else self.src[1].arg if self.src[1].op is Ops.CONST else 1\n    return 1\n  def divides(self, v:int) -> UOp|None:\n    if v==1: return self\n    if self.op is Ops.CONST: return self.const_like(self.arg//v) if self.arg%v == 0 else None\n    if self.op is Ops.VCONST: return self.const_like(tuple(x//v for x in self.arg)) if all(x%v == 0 for x in self.arg) else None\n    if self.op is Ops.ADD: return d0+d1 if (d0:=self.src[0].divides(v)) is not None and (d1:=self.src[1].divides(v)) is not None else None\n    if self.op is Ops.MUL:\n      if (d0:=self.src[0].divides(v)) is not None: return d0 * self.src[1]\n      if (d1:=self.src[1].divides(v)) is not None: return self.src[0] * d1\n    return None # generic None if we aren't sure\n  @property\n  def vmin(self) -> ConstType: return self._min_max[0]\n  @property\n  def vmax(self) -> ConstType: return self._min_max[1]\n  @functools.cached_property\n  def _min_max(self) -> tuple[ConstType, ConstType]:\n    if self.op in GroupOp.Binary and not dtypes.is_float(self.dtype):\n      (s0_vmin, s0_vmax), (s1_vmin, s1_vmax) = self.src[0]._min_max, self.src[1]._min_max\n      if self.op is Ops.ADD: return s0_vmin+s1_vmin, s0_vmax+s1_vmax\n      if self.op is Ops.SUB: return s0_vmin-s1_vmax, s0_vmax-s1_vmin\n      if self.op is Ops.AND and s1_vmin == s1_vmax and s0_vmin >= 0 and s1_vmin >= 0: return min(0, s0_vmin), min(s0_vmax, s1_vmax)\n      if self.op is Ops.MUL: return min(vals:=(s0_vmin*s1_vmin, s0_vmin*s1_vmax, s0_vmax*s1_vmin, s0_vmax*s1_vmax)), max(vals)\n      # SHL/SHR on consts only\n      if self.op is Ops.SHL and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] << t[2], t[1] << t[2]\n      if self.op is Ops.SHR and s1_vmin == s1_vmax and all_int(t:=(s0_vmin, s0_vmax, s1_vmin)): return t[0] >> t[2], t[1] >> t[2]\n      if self.op is Ops.MOD:\n        if s1_vmin > 0: return (0, s1_vmax-1) if s0_vmin >= 0 else (-(s1_vmax-1), 0) if s0_vmax <= 0 else (-(s1_vmax-1), s1_vmax-1)\n        if s1_vmax < 0: return (0, -s1_vmin-1) if s0_vmin >= 0 else (-(-s1_vmin-1), 0) if s0_vmax <= 0 else (-(-s1_vmin-1), -s1_vmin-1)\n      if self.op is Ops.IDIV:\n        assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)\n        if (c:=s1_vmin) == s1_vmax:  # s1 is a const\n          if c > 0: return cdiv(s0_vmin, c), cdiv(s0_vmax, c)\n          if c < 0: return cdiv(s0_vmax, c), cdiv(s0_vmin, c)\n        if (s0_vmax <= 0 and s1_vmax < 0): return cdiv(s0_vmax, s1_vmin), cdiv(s0_vmin, s1_vmax)\n        if (s0_vmin >= 0 and s1_vmin > 0): return cdiv(s0_vmin, s1_vmax), cdiv(s0_vmax, s1_vmin)\n        if (s0_vmax <= 0 and s1_vmin > 0): return cdiv(s0_vmin, s1_vmin), cdiv(s0_vmax, s1_vmax)\n        if (s0_vmin >= 0 and s1_vmax < 0): return cdiv(s0_vmax, s1_vmax), cdiv(s0_vmin, s1_vmin)\n      if self.op is Ops.MAX: return max(s0_vmin, s1_vmin), max(s0_vmax, s1_vmax)\n      if self.op is Ops.CMPLT: return (s0_vmax<s1_vmin, s0_vmin<s1_vmax)\n      if self.op is Ops.CMPNE: return ((s0_vmax < s1_vmin) or (s1_vmax < s0_vmin), not (s0_vmin == s0_vmax == s1_vmin == s1_vmax))\n      if self.dtype == dtypes.bool:\n        if self.op is Ops.OR: return s0_vmin or s1_vmin, s0_vmax or s1_vmax\n        if self.op is Ops.AND: return s0_vmin and s1_vmin, s0_vmax and s1_vmax\n    # float has NAN issue and we use explicit NAN in transcendental\n    if self.op is Ops.WHERE and dtypes.is_int(self.dtype): return min(self.src[1].vmin, self.src[2].vmin), max(self.src[1].vmax, self.src[2].vmax)\n    # NOTE: returned UOp is assumed to be CONST\n    if self.op is Ops.DEFINE_VAR and self.arg: return self.arg[1], self.arg[2]\n    if self.op is Ops.RANGE: return 0, (self.src[0]-1).vmax\n    if self.op is Ops.BIND: return self.src[0]._min_max # ignore the bound value\n    if self.op in {Ops.UNROLL, Ops.VECTORIZE}: return min(x.vmin for x in self.src), max(x.vmax for x in self.src)\n    # TODO: Ops.SPECIAL is Ops.DEFINE_VAR\n    if self.op is Ops.SPECIAL: return 0, self.arg[1]-1 if isinstance(self.arg[1], int) else self.arg[1].vmax\n    if self.op is Ops.CONST: return self.arg, self.arg\n    if self.op is Ops.VCONST: return (min(self.arg), max(self.arg))\n    # TODO: CAST to bool/unsigned is not monotone, still some case can be simplified\n    if self.op is Ops.CAST and self.dtype in (dtypes.floats+dtypes.sints):\n      return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))\n    return dtypes.min(self.dtype), dtypes.max(self.dtype)\n\n  @functools.cached_property\n  def _sym_fxn(self):\n    sself = self.simplify()\n    varnames = tuple(x.arg[0] for x in sself.toposort() if x.op is Ops.DEFINE_VAR)\n    # TODO: sanitize varnames, or don't use naked eval while staying fast\n    return eval(""lambda ""+','.join(varnames)+"": ""+sself.render(pm=renderer_infer)), varnames  # pylint: disable=eval-used\n\n  def sym_infer(self, var_vals:dict[UOp, int]):\n    fxn, varnames = self._sym_fxn\n    return fxn(**{k.arg[0]:v for k,v in var_vals.items() if k.arg[0] in varnames})\n\n  def render(self, simplify=True, pm:PatternMatcher|None=None) -> str:\n    ret = graph_rewrite(self.simplify() if simplify else self, renderer if pm is None else pm)\n    return ret.arg if ret.op is Ops.NOOP else str(ret)\n\nclass AxisType(Enum):\n  GLOBAL = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto()  # noqa: E702\n\n@dataclass(frozen=True)\nclass KernelInfo:\n  name: str = ""test""            # name of the kernel\n  axis_types: tuple[AxisType, ...] = tuple()\n  dont_use_locals: bool = False # don't use local indexing\n  applied_opts: tuple = tuple()\n  opts_to_apply: tuple|None = None\n  @property\n  def function_name(self): return to_function_name(self.name)\n\n# ******** ops in python ********\n\ndef safe_exp2(x):\n  try: return 2 ** x\n  except OverflowError: return math.inf\n\ndef safe_pow(x, y):\n  try: return math.nan if isinstance(p:=pow(x, y), complex) else p\n  except ZeroDivisionError: return math.inf\n  except ValueError: return math.inf if x > 0 else -math.inf\n\npython_alu: dict[Ops, Callable]  = {\n  Ops.LOG2: lambda x: math.log2(x) if x > 0 else -math.inf if x == 0 else math.nan, Ops.EXP2: safe_exp2,\n  Ops.SQRT: lambda x: math.sqrt(x) if x >= 0 else math.nan, Ops.RECIP: lambda x: 1/x if x != 0 else math.copysign(math.inf, x),\n  Ops.SIN: lambda x: math.sin(x) if not math.isinf(x) else math.nan, Ops.POW: safe_pow,\n  Ops.NEG: operator.neg, Ops.ADD: operator.add, Ops.SUB: operator.sub, Ops.MUL: operator.mul, Ops.CMPNE: operator.ne, Ops.CMPLT: operator.lt,\n  Ops.XOR: operator.xor, Ops.OR: operator.or_, Ops.AND: operator.and_, Ops.SHR: operator.rshift, Ops.SHL: operator.lshift, Ops.MAX: max,\n  Ops.MOD: cmod, Ops.IDIV: cdiv, Ops.MULACC: lambda x,y,z: (x*y)+z, Ops.WHERE: lambda x,y,z: y if x else z, Ops.CMPEQ: operator.eq}\n\ndef exec_alu(op:Ops, dtype:DType, operands, truncate_output=True):\n  if dtype.count > 1:\n    return tuple([exec_alu(op, dtype.scalar(), [x[i] if isinstance(x, tuple) else x for x in operands]) for i in range(dtype.count)])\n  alu = python_alu[op](*operands)\n  return truncate.get(dtype, lambda x: x)(alu) if truncate_output else alu\n\n# ***** uop helpers *****\n\ndef print_uops(uops:list[UOp]):\n  for i,u in enumerate(uops):\n    formatted_parents = [(uops.index(x) if x.op is not Ops.CONST else f""{x.arg}"") if x in uops else ""--"" for x in u.src]\n    print(f""{i:4d} {str(u.op):20s}: {str(u.dtype):30s} "" f""{str(formatted_parents):32s} {u.arg}"")\n\n# ***** pattern matcher *****\n\ndef get_location() -> tuple[str, int]:\n  frm = sys._getframe(1)\n  # skip over ops.py/mathtraits.py (unless there's nothing but ops.py/mathtraits.py)\n  while pathlib.Path(frm.f_code.co_filename).name in (""ops.py"", ""mathtraits.py"") and frm.f_back is not None and \\n      not frm.f_back.f_code.co_filename.startswith(""<frozen""):\n    frm = frm.f_back\n  return frm.f_code.co_filename, frm.f_lineno\n\n@functools.cache\ndef lines(fn) -> list[str]:\n  with open(fn) as f: return f.readlines()\n\ndef printable(loc:tuple[str, int]) -> str:\n  try: return lines(loc[0])[loc[1]-1].strip()\n  except FileNotFoundError: return ""<missing>""\n\nclass UPat(MathTrait):\n  __slots__ = (""op"", ""dtype"", ""arg"", ""name"", ""src"")\n  def __init__(self, op:Ops|tuple[Ops, ...]|set[Ops]|None=None, dtype:DType|tuple[DType, ...]|None=None,\n               src:tuple[UPat, ...]|list[UPat]|UPat|None=None, arg:Any=None,\n               name:str|None=None, allow_any_len:bool=False, custom_early_reject:set[Ops]|None=None, location=None):\n    assert op is None or isinstance(op, (Ops, tuple, set)), ""op must be Ops or tuple of Ops""\n    self.op: tuple[Ops, ...]|None = (op,) if isinstance(op, Ops) else (tuple(op) if isinstance(op, set) else op)\n    self.dtype: tuple[DType, ...]|None = (dtype,) if isinstance(dtype, DType) else dtype\n    self.arg, self.name, self._in_src, self.custom_early_reject = arg, name, src, custom_early_reject\n    self.src: Any = None\n    assert self.name != ""ctx"", ""UPat can't be named ctx""\n    assert dtype is None or isinstance(dtype, DType) or all(isinstance(x, DType) for x in dtype), f""invalid dtype {dtype}""\n\n    # try all permutations if it's a list\n    if isinstance(src, list): self.src = list(itertools.permutations(src)) if not all_same(src) else [tuple(src)]\n    # only one if it's a tuple\n    elif isinstance(src, tuple): self.src = [src]\n    # repeat if it's a UPat\n    elif isinstance(src, UPat): self.src = [itertools.repeat(src)]\n\n    self.strict_length = not (allow_any_len or isinstance(src, UPat) or src is None)\n    self.required_len: int = 0 if isinstance(src, UPat) or src is None else len(src)\n    self.location = location or get_location()\n\n    if custom_early_reject is not None: self.early_reject = custom_early_reject\n    else:\n      upat_match = [src] if isinstance(src, UPat) else ([] if src is None else self.src[0])\n      self.early_reject = {pp.op[0] for pp in upat_match if pp.op is not None and len(pp.op) == 1}\n\n  def __reduce__(self):\n    return UPat, (self.op, self.dtype, self._in_src, self.arg, self.name, not self.strict_length, self.custom_early_reject, self.location)\n  def named(self, name:str): return UPat(self.op, self.dtype, self._in_src, self.arg, name, not self.strict_length, self.custom_early_reject)\n\n  @staticmethod\n  def any(*src): return UPatAny(src=src)\n  def or_casted(self, name:str|None=None): return UPat.any(self if name is None else self.named(name), UPat(Ops.CAST, name=name, src=(self,)))\n\n  @staticmethod\n  @functools.cache\n  def var(name:str|None=None, dtype:DType|tuple[DType, ...]|None=None): return UPat(dtype=dtype, name=name)\n  @staticmethod\n  @functools.cache\n  def cvar(name:str|None=None, dtype:DType|None=None, vec=True): return UPat((Ops.CONST,Ops.VCONST) if vec else Ops.CONST, dtype, name=name)\n  @staticmethod\n  def const(dtype:DType|tuple[DType, ...]|None, b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)\n\n  # copied from UOp\n  def sink(self, *srcs:UPat|None, **kwargs): return UPat(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs)\n  def index(self, idx:UPat, valid:UPat|None=None): return UPat(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))\n  def view(self, st=None, **kwargs): return UPat(Ops.VIEW, self.dtype, (self,), st, **kwargs)\n  def cast(self, dtype=None, **kwargs): return UPat(Ops.CAST, dtype, (self,), **kwargs)\n  def bitcast(self, dtype=None): return UPat(Ops.BITCAST, dtype, (self,))\n  def gep(self, i:int|None=None, **kwargs): return UPat(Ops.GEP, None, (self,), (i,) if i is not None else None, **kwargs)\n  def load(self, *src:UPat, **kwargs): return UPat(Ops.LOAD, src=(self,)+src, **kwargs)\n  def store(self, *src:UPat, **kwargs): return UPat(Ops.STORE, self.dtype, (self,)+src, **kwargs)\n  def assign(self, x:UPat, **kwargs): return UPat(Ops.ASSIGN, self.dtype, (self,x), **kwargs)\n  def reduce(self, *src:UPat, **kwargs): return UPat(Ops.REDUCE, self.dtype, src=(self,)+src, **kwargs)\n  def fuse(self): return self.alu(Ops.FUSE)\n  def or_broadcasted(self, **kwargs): return UPat.any(self, UPat(Ops.VECTORIZE, self.dtype, src=self, **kwargs))\n\n  def const_like(self, b:ConstLike): return UPat.const(self.dtype, cast(ConstType, b))\n  def alu(self, op:Ops, *src:UPat):\n    asrc = (self,)+src\n    return UPat(op, dtypes.bool if op in {Ops.CMPLT, Ops.CMPNE} else asrc[-1].dtype, list(asrc) if op in GroupOp.Commutative else asrc)\n\n  def __repr__(self):\n    def rep(x):\n      form = ""UPat(%s, %s, name=%s, dtype=%s, allow_any_len=%s, src=%s)""\n      return form % (None if x.op is None else ('(%s)'%', '.join(map(str, x.op))), x.arg, repr(x.name),\n        set(x.dtype) if x.dtype else None, not x.strict_length, ""[%s]"" if x.src and len(x.src)>1 else (""(%s)"" if x.src else ""%s""))\n    return pretty_print(self, rep, srcfn=lambda x:None if x.src is None else [next(x.src[0])] if isinstance(x.src[0], itertools.repeat) else x.src[0])\n\n  def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:\n    if (self.op is not None and uop.op not in self.op) or \\n       (self.name is not None and store.setdefault(self.name, uop) is not uop) or \\n       (self.dtype is not None and uop.dtype not in self.dtype and uop.dtype.scalar() not in self.dtype) or \\n       (self.arg is not None and self.arg != uop.arg) or \\n       (len(uop.src) < self.required_len) or \\n       (self.strict_length and len(uop.src) != self.required_len): return []\n    if self.src is None: return [store]\n    res: list[dict[str, UOp]] = []\n    for vp in self.src:\n      stores, new_stores = [store.copy()], []\n      for uu, vv in zip(uop.src, vp):\n        for s in stores: new_stores.extend(vv.match(uu, s))\n        stores, new_stores = new_stores, []\n      res.extend(stores)\n    return res\n\nclass UPatAny(UPat):\n  def match(self:UPat, uop:UOp, store:dict[str, UOp]) -> list[dict[str, UOp]]:\n    matches = [x.match(uop, store.copy()) for x in self.src[0]]\n    return flatten([x for x in matches if x is not None])\n\ndef deconstruct_function(fxn:Callable) -> tuple:\n  new_globals = {k:v for k,v in fxn.__globals__.items() if k in fxn.__code__.co_names}\n  for co in fxn.__code__.co_consts:\n    if isinstance(co, types.CodeType): new_globals.update({k:v for k,v in fxn.__globals__.items() if k in co.co_names})\n  # NOTE: optional round trip through pickle!\n  assert fxn.__closure__ is None, ""closures are not supported in pattern matchers""\n  ret = fxn.__code__, new_globals, fxn.__name__, fxn.__defaults__\n  return pickle.loads(pickle.dumps(ret)) if getenv(""TEST_PICKLE"") else ret\n\n@functools.cache\ndef upat_interpret(p:UPat, fxn:Callable) -> Callable:\n  real_fxn = types.FunctionType(*deconstruct_function(fxn))\n  if 'ctx' in inspect.signature(real_fxn).parameters:\n    def universal_match(uop, ctx):\n      for match in p.match(uop, {}):\n        if (ret:=real_fxn(ctx=ctx, **match)) is not None: return ret  # pylint: disable=not-callable\n      return None\n  else:\n    def universal_match(uop, _):\n      for match in p.match(uop, {}):\n        if (ret:=real_fxn(**match)) is not None: return ret  # pylint: disable=not-callable\n      return None\n  return universal_match\n\nclass PatternMatcher:\n  def __init__(self, patterns:Sequence[tuple[UPat, Callable|tuple]], compiled=bool(getenv(""UPAT_COMPILE"", 1))):\n    if compiled: from tinygrad.uop.upat import upat_compile\n    # if this comes from a pickle, we reconstruct the lambda functions here\n    self.patterns:list[tuple[UPat, Callable]] = [(p,types.FunctionType(*fxn) if isinstance(fxn, tuple) else fxn) for p,fxn in patterns]\n    # NOTE: use of DefaultDict here is very dangerous! all keys will live for the lifetime of the PatternMatcher!\n    self.pdict: dict[Ops, list[tuple[UPat, Callable, set]]] = {}\n    # uop is required, arg is optional\n    for p,fxn in self.patterns:\n      assert p.op is not None\n      if compiled and (match:=upat_compile(p, fxn)) is not None: pass # pylint: disable=E0606\n      else: match = upat_interpret(p, fxn)\n      for uop in p.op: self.pdict.setdefault(uop, []).append((p, match, p.early_reject))\n\n  def __reduce__(self): return PatternMatcher, ([(x,deconstruct_function(fxn) if fxn.__name__ == ""<lambda>"" else fxn) for x,fxn in self.patterns],)\n\n  @functools.cache  # pylint: disable=method-cache-max-size-none\n  def __add__(self, more:PatternMatcher): return PatternMatcher(self.patterns+more.patterns)\n\n  def rewrite(self, uop:UOp, ctx=None) -> UOp|None:\n    ler = {u.op for u in uop.src}\n    for _,match,early_reject in self.pdict.get(uop.op, []):\n      if not early_reject.issubset(ler): continue\n      if (ret:=match(uop, ctx)) is not None and ret is not uop: return ret\n    return None\n\n  def fixed_point_rewrite(self, uop:UOp, ctx=None) -> UOp:\n    # apply rewrite rules until a fixed point is reached. may return `uop` itself if PatternMatcher doesn't match\n    new_n: UOp|None = uop\n    seen = set()\n    while new_n is not None:\n      if new_n in seen: raise RuntimeError(""infinite loop in fixed_point_rewrite"")\n      seen.add(new_n)\n      last_n, new_n = new_n, self.rewrite(new_n, ctx)\n    return last_n\n\n# *** non-blocking UOp tracker ***\n\nucount = itertools.count()\nuop_number:weakref.WeakKeyDictionary[UOp, int] = weakref.WeakKeyDictionary()\nuop_fields:dict[int, tuple] = {}\ndef track_uop(u:UOp):\n  if (cret:=uop_number.get(u)) is not None: return cret\n  uop_number[u] = num = next(ucount)\n  # KERNEL also has a UOp in the arg\n  arg = type(u.arg)(track_uop(u.arg.ast), u.arg.metadata) if u.op is Ops.KERNEL else u.arg\n  uop_fields[num] = (u.op, u.dtype, tuple(track_uop(s) for s in u.src), arg, u.tag)\n  return num\n\n# *** tracking pattern matcher ***\n\nVIZ = ContextVar(""VIZ"", 0)\nTRACK_MATCH_STATS = ContextVar(""TRACK_MATCH_STATS"", 2 if VIZ else 0)\nmatch_stats:dict[UPat, list[int|float]] = dict()\n\n@dataclass(frozen=True)\nclass TrackedGraphRewrite:\n  loc:tuple[str, int]                    # location that called graph_rewrite\n  sink:int                               # the sink input to graph_rewrite\n  matches:list[tuple[int, int, tuple]]   # before/after UOp, UPat location\n  name:str|None                          # optional name of the rewrite\n  depth:int                              # depth if it's a subrewrite\n  bottom_up:bool\n\ntracked_keys:list[TracingKey] = []\ntracked_ctxs:list[list[TrackedGraphRewrite]] = []\n_name_cnt:dict[str, itertools.count] = {}\n\nif getenv(""CAPTURE_PROCESS_REPLAY""):\n  replay_capture: dict[str, bytes] = {}\n  import atexit\n  @atexit.register\n  def save_to_diskcache():\n    for k,v in replay_capture.items(): diskcache_put(""process_replay"", k, v, prepickled=True)\n\ndef track_rewrites(name:Callable[..., str|TracingKey]|bool=True):\n  def _decorator(func):\n    def __wrapper(*args, **kwargs):\n      fn = key = func.__name__\n      if TRACK_MATCH_STATS >= 2:\n        tracked_keys.append(key:=TracingKey(n:=f""{fn} n{next(_name_cnt.setdefault(fn, itertools.count(1)))}"", (n,), cat=fn))\n        tracked_ctxs.append([])\n      with cpu_profile(key, ""TINY"") as e:\n        ret = func(*args, **kwargs)\n      if TRACK_MATCH_STATS >= 2 and callable(name):\n        name_ret = name(*args, **kwargs, ret=ret)\n        assert isinstance(name_ret, (TracingKey, str)), f""name function returned {type(name_ret)}""\n        tracked_keys[-1] = k = TracingKey(n:=tracked_keys[-1].display_name.replace(fn, name_ret), (n,)) if isinstance(name_ret, str) else name_ret\n        e.name = TracingKey(k.display_name if isinstance(name_ret, str) else f""{fn} for {k.display_name}"", k.keys, cat=fn)\n      if getenv(""CAPTURE_PROCESS_REPLAY""):\n        # find the unittest frame we're capturing in\n        frm = sys._getframe(1)\n        while (f_back:=frm.f_back) is not None and ""unittest"" not in f_back.f_code.co_filename: frm = f_back\n        loc = f""{frm.f_code.co_filename.split('/')[-1]}:{frm.f_lineno} {frm.f_code.co_name}""\n        # capture global context vars and all the args passed in\n        with Context(PICKLE_BUFFERS=0):\n          inputs = (fn, args, kwargs, ContextVar._cache)\n          replay_capture[hashlib.sha256(pickle.dumps(inputs)).hexdigest()] = pickle.dumps(inputs+(loc, ret))\n      return ret\n    return __wrapper\n  return _decorator\n\nactive_rewrites:list[TrackedGraphRewrite] = []\ndef track_matches(func):\n  def _track_func(*args, **kwargs):\n    if tracking:=(TRACK_MATCH_STATS >= 2 and tracked_ctxs):\n      loc = ((frm:=sys._getframe(1)).f_code.co_filename, frm.f_lineno)\n      depth = len(active_rewrites)\n      tracked_ctxs[-1].append(ctx:=TrackedGraphRewrite(loc, track_uop(args[0]), [], kwargs.get(""name"", None), depth, kwargs.get(""bottom_up"", False)))\n      active_rewrites.append(ctx)\n    with cpu_profile(kwargs.get(""name"", ""<unnamed>""), ""TINY"", display=tracking):\n      ret = func(*args, **kwargs)\n    if tracking: active_rewrites.pop()\n    return ret\n  return _track_func\n\nclass TrackedPatternMatcher(PatternMatcher):\n  def rewrite(self, uop:UOp, ctx=None) -> UOp|None:\n    ret = None\n    ler = {u.op for u in uop.src}\n    for p,match,early_reject in self.pdict.get(uop.op, []):\n      if p not in match_stats: match_stats[p] = [0,0,0.0,0.0]\n      st = time.perf_counter()\n      if not early_reject.issubset(ler):\n        match_stats[p][2] += time.perf_counter()-st\n        continue\n      match_stats[p][1] += 1\n      if (ret:=match(uop, ctx)) is not None and ret is not uop:\n        match_stats[p][0] += 1\n        match_stats[p][3] += (et:=time.perf_counter()-st)\n        if TRACK_MATCH_STATS >= 3: print(f""{et*1e6:7.2f} us -- "", printable(p.location))\n        if TRACK_MATCH_STATS >= 2 and isinstance(ret, UOp) and active_rewrites:\n          active_rewrites[-1].matches.append((track_uop(uop), track_uop(ret), p.location))\n        return ret\n      match_stats[p][2] += time.perf_counter()-st\n    return None\n\nif TRACK_MATCH_STATS or PROFILE:\n  PatternMatcher = TrackedPatternMatcher  # type: ignore\n  import atexit\n  @atexit.register\n  def print_match_stats():\n    if TRACK_MATCH_STATS >= 2:\n      with open(fn:=temp(""rewrites.pkl"", append_user=True), ""wb"") as f:\n        print(f""rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}"")\n        pickle.dump((tracked_keys, tracked_ctxs, uop_fields), f)\n    if VIZ: launch_viz(VIZ, temp(""rewrites.pkl"", append_user=True))\n    if getenv(""PRINT_MATCH_STATS"", TRACK_MATCH_STATS.value):\n      ret = [0,0,0.0,0.0]\n      for k,v in sorted(list(match_stats.items()), key=lambda x: x[1][2]+x[1][3]):\n        loc_str = f""{k.location[0].split('/')[-1]}:{k.location[1]}""\n        if v[1] != 0: print(f""{v[0]:6d} / {v[1]:7d} -- {v[3]*1000.:9.2f} / {(v[2]+v[3])*1000.:9.2f} ms -- {loc_str:20s}"", printable(k.location))\n        ret = [x+y for x,y in zip(ret, v)]\n      print(f""{ret[0]:6d} / {ret[1]:7d} -- {ret[3]*1000.:9.2f} / {(ret[2]+ret[3])*1000.:9.2f} ms -- TOTAL"")\n      print(f""{len(match_stats)} rules, {sum(v[0] > 0 for v in match_stats.values())} matched once"")\n\n  def launch_viz(var:ContextVar, data:str):\n    os.environ[(env_str:=var.key)] = ""0""\n    os.environ[f""{env_str}_DATA""] = data\n    os.environ[f""{env_str}_VALUE""] = str(var.value)\n    if not int(os.getenv(""VIZ"", ""0"")) and not int(os.getenv(""PROFILE"", ""0"")):\n      args = ['--kernels', getenv(""VIZ_DATA"", """")] if getenv(""VIZ_DATA"", """") else []\n      args += ['--profile', getenv(""PROFILE_DATA"", """")] if getenv(""PROFILE_DATA"", """") else []\n      os.execv(sys.executable, [sys.executable] + [os.path.join(os.path.dirname(__file__), ""../"", ""viz"", ""serve.py"")] + args)\n\n# *** simple graph rewrite engine ***\n\nclass RewriteNotReady(Exception): pass\nclass RewriteContext:\n  def __init__(self, pm, bpm, ctx=None):\n    self.pm: PatternMatcher|None = pm\n    self.bpm: PatternMatcher|None = bpm\n    self.ctx = ctx\n    self.replace: dict[UOp, UOp] = {}\n\n  def unified_rewrite(self, root:UOp) -> UOp:\n    stack: list[tuple[UOp, int, UOp]] = [(root, 0, root)]\n    while stack:\n      if len(stack) >= 200000: raise RuntimeError(""infinite loop in graph_rewrite"")\n      n, stage, new_n = stack.pop()\n      if n in self.replace: continue  # skip any nodes we have seen\n      try:\n        if stage == 0:\n          # if bottom up, we rewrite this node early. in both cases, we add its parents to the stack\n          if self.bpm is not None: new_n = self.bpm.fixed_point_rewrite(new_n, self.ctx)\n          stack.append((n, 1, new_n))\n          for x in reversed(new_n.src): stack.append((x, 0, x))\n        elif stage == 1:\n          try: new_src = tuple([self.replace[x] for x in new_n.src])\n          except KeyError: raise RewriteNotReady  # pylint: disable=raise-missing-from\n          if new_src == new_n.src:\n            # if top down, do the rewrite. if no rewrite or bottom up, we are done rewriting this node so we add it to the dict\n            if self.pm is None or (new_src_n:=self.pm.rewrite(new_n, self.ctx)) is None:\n              self.replace[n] = new_n\n              continue\n          else:\n            # if srcs changed from rewrites, construct a new UOp with the new srcs\n            new_src_n = UOp(new_n.op, new_n.dtype, new_src, new_n.arg, new_n.tag)\n          # trigger a rewrite of new_src_n, then after that rewrite is done, link it back to n\n          stack.append((n, 2, new_src_n))\n          stack.append((new_src_n, 0, new_src_n))\n        else:\n          # in stage 2, we link the result of new_n to the result of n\n          try: self.replace[n] = self.replace[new_n]\n          except KeyError: raise RewriteNotReady  # pylint: disable=raise-missing-from\n      except RewriteNotReady:\n        # retry this later\n        stack.insert(0, (n, stage, new_n))\n    return self.replace[root]\n\n@track_matches\ndef graph_rewrite(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None) -> UOp:\n  rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx)\n  return rewrite_ctx.unified_rewrite(sink)\n\n@track_matches\ndef graph_rewrite_map(sink:UOp, pm:PatternMatcher, ctx=None, bottom_up=False, name=None, bpm=None,\n                      input_map:dict[UOp, UOp]|None=None, ) -> dict[UOp, UOp]:\n  rewrite_ctx = RewriteContext(pm if not bottom_up else None, pm if bottom_up else bpm, ctx)\n  new_map: dict[UOp, UOp] = {}\n  for k in sink.toposort():\n    new_map[k] = v = rewrite_ctx.unified_rewrite(k)\n    if k is not v and k.metadata is not None: all_metadata[v] = tuple(dedup(all_metadata.get(v, ())))+k.metadata\n  if input_map is not None:\n    for k,v in input_map.items(): new_map[k] = new_map.get(v,v)\n  return new_map\n\ndef sint_to_uop(x:sint, dtype:DType=dtypes.int) -> UOp: return UOp.const(dtype, x) if isinstance(x, int) else x\n\n_substitute = PatternMatcher([(UPat(tuple(Ops), name=""x""), lambda ctx,x: ctx.get(x,None))])\n\n# for debug\nsyms = { Ops.ADD: ""+"", Ops.SUB: ""-"", Ops.IDIV: ""//"", Ops.MOD: ""%"", Ops.SHL: ""<<"", Ops.SHR: "">>"",\n         Ops.MUL: ""*"", Ops.CMPLT: ""<"", Ops.CMPNE: ""!="", Ops.AND: ""&"", Ops.OR: ""|"", Ops.XOR: ""^""}\nrenderer = PatternMatcher([\n  (UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name=""x""), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),\n  (UPat(Ops.RANGE, name=""x""), lambda x: UOp(Ops.NOOP, arg=f""ridx{x.arg}"")),\n  (UPat((Ops.CONST, Ops.VCONST), name=""x""), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),\n  (UPat(Ops.UNROLL, name=""x""), lambda x: UOp(Ops.NOOP, arg=f""UNROLL({x.src[0].arg}, {x.arg})"")),\n  (UPat(Ops.CAST, name=""x""), lambda x: UOp(Ops.NOOP, arg=f""({str(x.dtype)[7:]})({x.src[0].arg})"")),\n  (UPat(Ops.LOAD), lambda: UOp(Ops.NOOP, arg=""load"")),\n  (UPat(Ops.BIND, src=UPat(Ops.NOOP), name=""x""), lambda x: x.src[0]),\n  #(UPat(Ops.BIND, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""{x.src[0].arg}[={x.src[1].arg}]"")),\n  (UPat(Ops.NEG, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""(-{x.src[0].arg})"")),\n  (UPat(Ops.RECIP, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""(1/{x.src[0].arg})"")),\n  (UPat(Ops.MAX, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""max({x.src[0].arg}, {x.src[1].arg})"")),\n  (UPat(Ops.MULACC, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})"")),\n  (UPat(Ops.WHERE, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})"")),\n  (UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""({x.src[0].arg}{syms[x.op]}{x.src[1].arg})"")),\n])\nrenderer_infer = PatternMatcher([\n  (UPat(Ops.MOD, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""cmod({x.src[0].arg}, {x.src[1].arg})"")),\n  (UPat(Ops.IDIV, src=UPat(Ops.NOOP), name=""x""), lambda x: UOp(Ops.NOOP, arg=f""cdiv({x.src[0].arg}, {x.src[1].arg})"")),\n  *renderer.patterns\n])\n\n# *** what was symbolic.py ***\n\nsint = int|UOp\nVariable = UOp\n\nConstLike = ConstType|Variable|tuple[ConstType, ...]\n",python,tab
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for compatibility with earlier versions\n\def\PYGZat{@}\n\def\PYGZlb{[}\n\def\PYGZrb{]}\n\makeatother\n\n\title{Jasmine: A Simple, Performant and Scalable JAX-based World Modeling Codebase}%\n\correspondingauthor{[mihir,alfred,franz]@pdoom.org}\n\n\keywords{World Modeling, JAX, Reinforcement Learning}\n\n\reportnumber{001} %\n\n\author{\n{\Authfont\nMihir Mahajan\textsuperscript{*1,2},\nAlfred Nguyen\textsuperscript{*1,2},\nFranz Srambical\textsuperscript{*1,2}}\n\leavevmode \protect\\[1em]\n{\Authfont\nStefan Bauer\textsuperscript{2}}\n\leavevmode \protect\\[1em]\n{\Affilfont\n\textsuperscript{*}Contributed equally\protect\\\n\textsuperscript{1}p(doom), \textsuperscript{2}TUM \n}}\n\n\begin{abstract}\n    While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastructure for world modeling remains nascent. We introduce Jasmine, a performant JAX-based world modeling codebase that scales from single hosts to hundreds of accelerators with minimal code changes. Jasmine achieves an order-of-magnitude faster reproduction of the CoinRun case study compared to prior open implementations, enabled by performance optimizations across data loading, training and checkpointing. The codebase guarantees fully reproducible training and supports diverse sharding configurations. By pairing Jasmine with curated large-scale datasets, we establish infrastructure for rigorous benchmarking pipelines across model families and architectural ablations.\n\end{abstract}\n\n\n\begin{document}\n\maketitle\n\n\section{Introduction}\n\nOver the past decades, the field of deep learning has increasingly been shaped by methods that leverage vast data troves \citep{jozefowicz2016exploring, radford2018improving, radford2019language, chowdhery2022palm, raffel2020exploring, JMLR:v23:21-0998, deng2009imagenet}, and paradigms that unlock new ones \citep{guo2025deepseek, christiano2017deep, radford2021learning, srambical2025crowd-sourcing, silver2016mastering, berner2019dota}. Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models to attain human-level performance \citep{LuongLockhart2025GeminiIMO, LinCheng2025GeminiICPC}, yet data is increasingly bottlenecking progress from spiky towards general intelligence. While some domains can leverage user feedback from deployed products for iterative model improvement \citep{cursor2025tab}, domains like robotics cannot afford such a privilege.\n\nOne paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models \citep{ha2018world}. World models can aid frontier model development in numerous ways, but one particularly ambitious goal of the community is to train a world model to act as a simulation of the real world \citep{bruce2024genie, parker2022evolving, deepmind2025genie3, agarwal2025cosmos}, in order to train agents in that simulation \citep{hafner2025training}, via an adaptive curriculum \citep{parker2022evolving}, or otherwise. This regime requires the compounding error of the world model to be orders of magnitude smaller than when solely used for short-term look-ahead. The feasibility of such a world model in its truest sense is entirely understudied.\nJasmine provides the foundational infrastructure for future empirical investigation of how compute and data requirements scale with environment complexity for downstream agent training.\n\n\begin{figure}\n  \centering\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_105.png}\n  \caption{Autoregressive sampling of Jafar \citep{willi2024jafar} (middle row) and Jasmine (bottom row) on the CoinRun case study with four conditioning frames (conditioning frames not shown). \n  The top row shows the ground-truth sequence. \n}\n\n\label{fig:sampling}\n\end{figure}\n\n\section{Jasmine}\n\label{sec:jasmine}\n\n\n\n\textbf{Our contributions} in this work are threefold: i) we introduce Jasmine, a highly optimized and scalable JAX-based codebase for world modeling, which we use to reproduce Genie's CoinRun case study \citep{bruce2024genie,cobbe2019leveraging} an order of magnitude faster than prior work \citep{willi2024jafar}. This speedup is the result of infrastructure optimizations, including a fully reproducible, scalable training and data pipeline built on the JAX \citep{jax2018github} ecosystem. ii) We find that a critical modification to the original Genie architecture, prepending latent actions instead of adding them to video embeddings, is required for the world model to yield generations that faithfully reproduce the CoinRun environment. iii) Finally, we openly release the Jasmine codebase, along with pretrained checkpoints, curated datasets, model inspection notebooks, and a dataset of dense IDE interactions captured during Jasmine's development, providing the first openly published dataset of months-long software engineering.\n\nJasmine implements the Genie \citep{bruce2024genie} architecture, enabling training of interactive environments from unlabeled videos. The architecture includes a video tokenizer, which encodes videos into tokens, a latent action model (LAM) that extracts latent actions between video frames, and a dynamics model that predicts the tokens of the next frame based on the previous tokens and corresponding latent actions. At sampling time, the LAM is discarded and replaced by input from the user. All modules use an ST-Transformer \citep{ho2019axial} backbone which approximates full attention by performing intra-frame (spatial) followed by inter-frame (temporal) attention, thus reducing the attention sequence length.\nThe tokenizer uses a VQ-VAE \citep{van2017neural} to encode image patches using reconstruction, vector-quantization, and commitment losses.\nTo train an action-conditioned video-generation model from unlabeled videos, Genie learns latent actions \citep{schmidt2023learning}. Like the tokenizer, the LAM uses a VQ-VAE, with its codebook representing the latent actions. The model learns to distill information from future frames into this bottlenecked codebook:\nFrames $x_{0:t}$ are encoded, producing latent actions $a_{0:t}$, which the decoder receives along with past frames $x_{0:t-1}$ to predict the next frame $x_{t}$. A temporal causal mask allows the entire sequence to be processed in a single forward pass.\nThe dynamics model is a decoder-only transformer that predicts future frames conditioned on past frames and corresponding latent actions. Genie uses MaskGIT \citep{chang2022maskgit}, which masks input-tokens at training time, similar in spirit to BERT \citep{devlin2019bert}. Unlike MaskGIT, Genie masks with probability $p \sim U(0.5, 1)$ (refer to Appendix \ref{sec:maskgit_to_videos} for details about extending MaskGIT to videos).\n\n\n\begin{figure}[htb]\n  \centering\n  \includegraphics[width=\textwidth]{fig/case-study-plots.pdf}\n  \caption{Validation metrics of the CoinRun case study (patch size 4). While the loss (left) is similar between the default Genie configuration and our minimal modification, rollout metrics (middle and right, refer to \Cref{sec:experiment-metrics}) differ substantially.}\n  \label{fig:case-study-plots}\n\end{figure}\n\n\n\nBuilding upon prior work \citep{willi2024jafar} that openly published a reimplementation of the Genie \citep{bruce2024genie} architecture, we release a highly optimized JAX-based world modeling codebase amenable to scale. Jasmine implements a range of baselines, including MaskGIT-based \citep{chang2022maskgit}, fully causal \citep{srambical2024going}, and diffusion-based approaches (Appendix \ref{sec:diffusion-baseline}). The codebase depends solely  on battle-tested libraries from the Google ecosystem (JAX, NNX, Grain \citep{grain2023github}, Orbax, Optax, Treescope \citep{johnson2024penzai}, ArrayRecord \citep{ArrayRecord}), and scales from single hosts to hundreds of accelerators using XLA.\nJasmine supports complex sharding configurations in a few lines of code through Shardy \citep{openxla-shardy}. It provides asynchronous distributed checkpointing with configurable policies, process-parallel dataloading, and checkpointing of model, optimizer, and data loader states. Training runs are bitwise deterministic, yielding identical loss curves under identical seeds (Appendix \ref{sec:bitwise_deterministic}).\nTo enable efficient large-scale experimentation, Jasmine integrates mixed-precision, FlashAttention via cuDNN SDPA \citep{NVIDIA_cuDNN_Attention}, activation checkpointing, host memory offloading, and index-shuffling during data loading. The codebase follows the shape suffix convention of \citet{shazeer2024shape}, aiming to provide a didactic implementation of modern world modeling architectures.\n\nWe run the reproducible case study described in \citet{bruce2024genie} by generating a dataset containing 50M transitions of CoinRun, an environment of the Procgen benchmark \citep{cobbe2019leveraging} (Appendix \ref{sec:coinrun-case-study}). In contrast to \citet{bruce2024genie} we find that strict adoption of the architecture and hyperparameters described in their case study leads to deteriorating autoregressive generations in both Jasmine and Jafar \citep{willi2024jafar} (Figure \ref{fig:sampling_prepend_vs_no_prepend}, middle row). However, a minimal modification of the case study setting, namely prepending latent actions instead of adding them to the video embeddings, yields autoregressive generations that faithfully simulate the CoinRun environment (Figure \ref{fig:sampling_prepend_vs_no_prepend}, bottom row and Figure \ref{fig:case-study-plots}). We hypothesize that this discrepancy between \citet{bruce2024genie} and our work stems from an ambiguity in extending MaskGIT to videos (refer to Appendix \ref{sec:maskgit_to_videos} for further discussion).\n\n\begin{figure}[htb]\n  \centering\n  \includegraphics[width=0.75\textwidth]{fig/1-wc-time-comparison.pdf}\n  \caption{An order of magnitude faster convergence in wall-clock time in Jasmine (blue) compared to Jafar \citep{willi2024jafar} (orange). We report the train loss since Jafar does not collect validation metrics. Refer to Appendix \ref{sec:arch-ablations} for Jasmine's validation metrics. Jasmine's lower variance stems from a subtle refinement in its batched masking logic (Appendix \ref{sec:jafar-batched-masking}).}\n  \label{fig:wc_time}\n\end{figure}\n\n\nBeyond openly publishing\footnote{\url{https://github.com/p-doom/jasmine}} the Jasmine codebase, we recorded every keystroke during Jasmine's development using \emph{crowd-code} \citep{srambical2025crowd-sourcing}, a VS Code/Cursor extension that enables crowd-sourcing dense IDE interaction data. To our knowledge, this represents one of the first open datasets capturing the full temporal scale of months-long software development, thus laying groundwork for future work in behaviour cloning, goal-conditioning, and verification signal mining.\n\n\section{Experiments}\n\nWe evaluate the performance of Jasmine and analyze the impact of its core components through rigorous ablations. Using Jasmine, we reproduce the CoinRun case study at a patch size of 16 in under nine hours on a single GPU, compared to over 100 hours reported in prior work \citep{willi2024jafar} under the same setting (Figure \ref{fig:wc_time}). We present ablations identifying the factors responsible for this speedup in Table \ref{tab:infra-ablations}.\n\Cref{sec:arch-ablations} further reports results from architectural modifications including replacing the latent action model with ground-truth actions, ablating co-training, adopting fully causal and diffusion baselines, and setting the feedforward expansion factor to four.\n\n\n\paragraph{Architectural optimizations}\n\label{sec:speed-arch-ablations}\nWe adapt Genie's architectural choices by integrating best practices from the language modeling community. Specifically, we use a feedforward expansion factor of four relative to the model dimension, following common practice in large-scale language modeling \citep{raffel2020exploring,radford2019language,brown2020language}. We simultaneously reduce network depth, resulting in lower overall parameter count than the Genie defaults, thus achieving higher throughput (Table \ref{tab:ffn-ablation}) while maintaing competitive performance (Figure \ref{fig:arch-ablations}). We employ the warmup-stable-decay (WSD) learning rate schedule \citep{zhai2022scaling, mahajan2018exploring}, which allows flexible training durations by resuming from a checkpoint prior to the decay phase. Unlike \citep{bruce2024genie}, we warm up from and decay the learning rate to zero, in line with established best practices \citep{zhai2022scaling}.\nWe further compare co-training LAM and dynamics model (as done in \citet{bruce2024genie}) with pre-training the LAM (as done in \citet{willi2024jafar}), embedding ground-truth actions instead of using the latent action model (Appendix \ref{sec:ablation-gt}), and replacing MaskGIT with fully causal and diffusion baselines. Co-training, pre-training the LAM, and using ground-truth actions are all competitive (Figure \ref{fig:arch-ablations}), while the fully causal baseline underperforms in the 200k steps training regime (\Cref{fig:causal-ablations}). However, our results indicate that the fully causal baseline in particular may benefit from longer training. Diffusion-forcing \citep{chen2024diffusion} outperforms MaskGIT, even when using identical per-frame sampling step counts and untuned hyperparameters (Figure \ref{fig:diffusion-plot}, Appendix \ref{sec:diffusion-baseline}).\n\n\begin{table}[htb]\n\begin{center}\n\begin{tabular}{lcc}\n\toprule\n & \textbf{Throughput (bs=36)} & \textbf{Throughput (bs=2048)} \\\n\midrule\nJasmine-base & 1.00x\t            & 1.00x \\\n1x feedforward expansion & 0.93x\t            & 0.79x \\\n\bottomrule\n\end{tabular}\n\caption{Training throughput with a feedforward expansion factor of one, relative to Jasmine-base. We double the number of layers compared to Jasmine-base to roughly match the parameter count (refer to Genie's default configuration in \ref{tab:hparams}).}\n\label{tab:ffn-ablation}\n\end{center}\n\end{table}\n\n\paragraph{Infrastructure optimizations}\n\label{sec:speed-infra-ablations}\nA substantial portion of our speedup compared to \citet{willi2024jafar} arises from our data loader design (Tables \ref{tab:chunking-formats-1} and \ref{tab:chunking-formats-2}). We use Grain for data loading with prefetching enabled and preprocess datasets into ArrayRecords \citep{ArrayRecord}, a file format optimized for random access indexing. The chosen chunking strategy significantly affects throughput, and we describe our configuration in Appendix \ref{sec:throughput-ablation}. Jasmine further leverages FlashAttention \citep{dao2022flashattention} via cuDNN SDPA \citep{NVIDIA_cuDNN_Attention} and mixed precision training with bfloat16. We report throughputs when ablating mixed precision, FlashAttention and Grain in Table \ref{tab:infra-ablations}.\n\n\section{Related Work and Discussion}\nAlthough research on world models with its inception decades ago \citep{sutton1991dyna} has matured over the years \citep{ha2018world,hafner2019learning,hafner2019dream,hafner2020mastering,hafner2023mastering,alonso2024diffusion}, they have only been scaled up recently \citep{hafner2025training,bruce2024genie,parkerholder2024genie2,deepmind2025genie3,valevski2024diffusion,agarwal2025cosmos,hu2023gaia,guo2025mineworld,decart2024oasis,lucid2024lucidv1,li2025hunyuan,pearce2024scaling}. While the open training ecosystem in language modeling provides mature solutions for large-scale language pretraining \citep{megatron-lm,maxtext-library}, open training infrastructure for world modeling is still nascent \citep{wayfarer_labs_owl_wms,Savov_2025_CVPR}. Closest to our work is \citet{willi2024jafar}, an open-source reproduction of Genie \citep{bruce2024genie}, which we build upon and significantly extend.\n\nWith Jasmine we make progress towards democratizing world modeling research. Alongside the codebase, we openly release checkpoints and datasets for CoinRun, Atari and Doom, as well as dense IDE interaction data collected over months of research engineering\footnote{Datasets published under the CC0 license at \url{https://huggingface.co/datasets/p-doom}}. While Jasmine greatly accelerates wall-clock convergence compared to prior work, it has yet to match throughput efficiencies of frontier language model implementations.\n\n\subsubsection*{Acknowledgments}\nWe thank Matthew T. Jackson, Andrea Dittadi and Diego Marti Monso for useful discussions as well as the Jafar authors for openly publishing their repository. \nThe authors gratefully acknowledge the computing time provided on the high-performance computer HoreKa by the National High-Performance Computing Center at KIT (NHR@KIT). This center is jointly supported by the Federal Ministry of Education and Research and the Ministry of Science, Research and the Arts of Baden-Württemberg, as part of the National High-Performance Computing (NHR) joint funding program (https://www.nhr-verein.de/en/our-partners). HoreKa is partly funded by the German Research Foundation (DFG).\n\n\bibliography{bibliography}\n\appendix\n\n\begin{figure}\n  \centering\n  \includegraphics[width=\textwidth]{fig/0-vanilla-action-prepend.png}\n  \caption{Autoregressive sampling of Jasmine when adding (middle row) and prepending actions (bottom row) on the CoinRun case study with four conditioning frames (conditioning frames not shown). \n  The top row shows the ground-truth sequence. \n}\n\label{fig:sampling_prepend_vs_no_prepend}\n\end{figure}\n\n\section{Coinrun Case Study}\n\label{sec:coinrun-case-study}\nFor the CoinRun case study, we strictly adhere to the setting of \citet{bruce2024genie} and train our models to unmask sequences of 16 frames with a resolution of 64x64 pixels per frame. To generate the dataset, we capture 50M observation frames and corresponding ground-truth actions during random agent rollouts and only use the ground-truth actions for a LAM ablation (\Cref{sec:ablation-gt}). Instead of sampling seeds from a fixed pool as described in \citet{bruce2024genie}, we initialize all episodes with a seed unique to the respective episode. Furthermore, we verify that our generated dataset contains no duplicate episodes and only find 7.46\% duplicate frames. \nWe confirm that the validation and test set are disjoint from the train set and publish our script for duplication detection along with the repository\footnote{\url{{https://github.com/p-doom/jasmine/blob/main/data/jasmine_data/detect_array_record_duplicates.py}}}.\nWhile train metrics are near-identical between Genie's configuration and our action-prepending modification, rollout quality differs significantly (\Cref{fig:case-study-plots}). We collect rollout metrics during training (\Cref{sec:experiment-metrics}) that capture this discrepancy.\n\n\n\n\n\n\n\section{Ablations}\nFor our ablations, we reuse the CoinRun setting and ablate from Jasmine's base configuration, depicted in \Cref{tab:hparams}.\n\label{sec:arch-ablations}\n\begin{figure}\n  \centering\n  \includegraphics[width=1.0\textwidth]{fig/3-arch-ablations.pdf}\n  \caption{Architectural ablations of Jasmine's base configuration (refer to \Cref{tab:hparams}) on CoinRun. We report loss (left) and rollout metrics (middle and right) of the dynamics model on a validation set.}\n  \label{fig:arch-ablations}\n\end{figure}\n\n\paragraph{Co-training LAM and dynamics model}\n\label{sec:ablation-co-training}\n\citet{bruce2024genie} co-train the LAM and the dynamics model. However, their implementation remains unclear as the LAM is supervised on frames while the dynamics model is supervised on tokens. One approach to co-training is a combined loss function including stop-gradients that prevent gradients from flowing from the dynamics model to the LAM. However, such a combined loss formulation remains unmentioned in \citet{bruce2024genie}. \n\citet{willi2024jafar} instead train LAM and dynamics model sequentially, thus reducing memory footprint at the cost of longer total training time. \nFor Jasmine's co-training implementation, we omit the LAM decoder entirely and allow gradients to flow from the dynamics model to the LAM.\n\n\paragraph{Training with ground-truth actions}\n\label{sec:ablation-gt}\nWe ablate the LAM (Figure \ref{fig:arch-ablations}) by training the dynamics model using ground-truth actions captured in the environment. We use an embedding layer to map action indices to action latents, which are then used as additional input to the dynamics model.\n\n\begin{figure}\n  \centering\n  \includegraphics[width=1.0\textwidth]{fig/2-causal.pdf}\n  \caption{Loss (left) and rollout metrics (middle and right) of the fully causal baseline. We depict the final performance of our MaskGIT implementation (Jasmine's default configuration) for the rollout metrics. The loss indicates that the causal baseline might benefit from longer training and separately tuned hyperparameters. The losses between the two architectures are not comparable, hence we omit the MaskGIT loss.}\n  \label{fig:causal-ablations}\n\end{figure}\n\n\paragraph{Throughput ablations}\n\label{sec:throughput-ablation}\n\nWe ablate core components of Jasmine's infrastructure in Table \ref{tab:infra-ablations}. Replacing Grain with the default data loader of \citet{willi2024jafar} reduces throughput by an order of magnitude. In Jasmine's base CoinRun configuration (sub-100M parameter model and a maximum attention sequence length of 16) the XLA compiler dispatches higher-throughput CUDA kernels than FlashAttention. While FlashAttention only outperforms XLA-compiled kernels at large model sizes and sequence lengths (\Cref{tab:big-model-flash-attn-ablation}), we enable it by default to reduce accelerator memory usage. At small batch sizes, the compiled train loop of our configuration achieves higher throughput using full precision. We attribute this to XLA largely operating based on heuristics, and posit that writing optimized Pallas kernels for key operations in the model forward pass will result in mixed precision outperforming full precision in throughput, even at small batch sizes. \n\nThe ArrayRecord file format allows storing a configurable amount of records per file. In our case, each record corresponds to a sequence of frames and actions. We find that the chosen format significantly affects throughput (Tables \ref{tab:chunking-formats-1} and \ref{tab:chunking-formats-2}). Based on preliminary experiments, we preprocess the dataset to have 100 records per ArrayRecord file with 160 frames per record.\n\n\begin{table}\n\begin{center}\n\begin{tabular}{lcc}\n\toprule\n & \textbf{Throughput (bs=36)} & \textbf{Throughput (bs=2048)} \\\n\midrule\nJasmine-base & 1.00x\t            & 1.00x \\\nw/o grain data loader & 0.25x     & 0.11x \\\nw/o flash attention    & 1.15x\t& 1.04x \\\nw/o mixed precision & 1.18x\t& 0.71x \\\n\bottomrule\n\end{tabular}\n\caption{Training throughput of infrastructure ablations, relative to Jasmine-base. We report the throughput at Genie's default batch size (36), as well as at the batch size resulting in the highest throughput (2048) on a single H100 with 80GB of accelerator memory.}\n\label{tab:infra-ablations}\n\end{center}\n\end{table}\n\n\begin{table}\n\begin{center}\n\begin{tabular}{lcc}\n\toprule\n & \textbf{Throughput (frames/sec)} & \textbf{Throughput (relative)} \\\n\midrule\nw/ flash attention & 36.15\t         &  1.00x \\\nw/o flash attention & 24.24 &  0.67x \\\n\bottomrule\n\end{tabular}\n\caption{Training throughput using a larger model (1B parameter) and spatial sequence length (1024). We decrease the patch size to two. In this regime, FlashAttention yields higher throughput than the XLA compiler.}\n\label{tab:big-model-flash-attn-ablation}\n\end{center}\n\end{table}\n\n\begin{table}\n\begin{center}\n\begin{tabular}{llcc}\n\toprule\n\textbf{\# frames per record} & \textbf{\# records per file} & \textbf{Throughput (frames/sec)} & \textbf{Throughput (relative)}   \\\n\midrule\n16          & 100 & 7,527.27 & 1.12x \\\n160 (Base)  & 100 & 6,709.09 & 1.00x \\\n1,600       & 100 & 3,752.73 & 0.56x \\\n16,000      & 100 & 3,720.00 & 0.55x \\\n160,000     & 100 & 3,785.45 & 0.56x \\\n\n\bottomrule\n\end{tabular}\n\caption{Training throughput at Genie's default batch size (36) with different number of frames per record. Throughput decreases as the number of frames per record increases. We opt for 160 frames per record to be able to vary the sequence length.} \n\label{tab:chunking-formats-1}\n\end{center}\n\end{table}\n\n\begin{table}\n\begin{center}\n\begin{tabular}{llcc}\n\toprule\n\textbf{\# frames per record} & \textbf{\# records per file} & \textbf{Throughput (frames/sec)} & \textbf{Throughput (relative)}\\\n\midrule\n160         & 1         & 6,098.18 & 0.91x \\\n160         & 10        & 6,643.64 & 0.99x \\\n160 (Base)  & 100       & 6,709.09 & 1.00x \\\n160         & 1,000     & 6,480.00 & 0.97x \\\n160         & 10,000    & 6,763.64 & 1.01x \\\n\bottomrule\n\end{tabular}\n\caption{Training throughput at Genie's default batch size (36) with different number of records per file.} \n\label{tab:chunking-formats-2}\n\end{center}\n\end{table}\n\n\section{Diffusion Baseline}\n\label{sec:diffusion-baseline}\nWe implement a diffusion baseline inspired by the Dreamer 4 architecture \citep{hafner2025training}, combining a masked autoencoder (MAE, \citet{he2022masked}) tokenizer  with an ST-DiT \citep{ho2019axial,peebles2023scalable} dynamics model trained under the diffusion-forcing objective \citep{chen2024diffusion}.\nWe leave implementing the shortcut objective \citep{frans2024one} to future work. \n\paragraph{Tokenizer} Following \citet{hafner2025training}, we use a MAE to compress raw video frames into continuous latents. Our autoencoder implementation uses an ST-Transformer backbone and a latent bottleneck. Before passing the latents to the decoder, we apply the tanh activation to constrain them to the range $(-1, 1)$ for downstream dynamics model training. We uniformly sample per-frame masking probabilities $p_i \sim U(0, 0.9)$. Unlike \citet{hafner2025training}, we omit the auxiliary LPIPS loss \citep{zhang2018unreasonable} and directly train on pixel-level reconstructions using mean-squared error. We find the tokenizer hyperparameters from \Cref{tab:hparams} to work well for MAE training as well.\n\n\paragraph{Dynamics model}\nWe implement diffusion forcing \citep{chen2024diffusion}, sampling an independent noise level per frame during training. Analogous to \citet{hafner2025training} and Jasmine-base, we prepend latent actions and the embedded denoising step to the patch latents. Following \citet{hafner2025training}, we use x-prediction\footnote{In the diffusion literature, x-prediction often refers to supervision in latent-space rather than pixel-space. We follow that nomenclature but believe that the term z-prediction is a more accurate description.} and employ a ramp loss. During inference, frame-wise latents are autoregressively generated with 25 denoising steps per frame, while past input latents are slightly corrupted using a noise level of 0.1. We adopt the hyperparameters of Jasmine-base, and only change the learning rate to 1e-4 following \citet{peebles2023scalable}.\n\n\begin{figure}\n  \centering\n  \includegraphics[width=1.0\textwidth]{fig/4-diffusion-maskgit-comparison.pdf}\n  \caption{Loss (left) and rollout metrics (middle and right) of the diffusion baseline. We omit the MaskGIT loss as the losses are not comparable between the two architectures.}\n  \label{fig:diffusion-plot}\n\end{figure}\n\n\section{Bitwise Determinism}\n\label{sec:bitwise_deterministic}\nOn TPUs, bitwise determinism is guaranteed by Jasmine via proper usage of JAX's implementation of parallel random number generation via Threefry counters \citep{salmon2011parallel}. On GPUs however, an additional XLA flag (\texttt{xla\_gpu\_deterministic\_ops=true}) is needed in certain cases to guarantee identical training curves.\n\n\section{Hyperparameter Configurations}\n\label{sec:default-config}\nWe mention four distinct training configurations. We present hyperparameters and training settings of each configuration in \Cref{tab:hparams} and briefly describe them:\n\n\begin{itemize}\n    \item \textbf{Genie}: The hyperparameter configuration of Appendix F of \citet{bruce2024genie}, but with a patch size of 16 (for easier comparison against Jafar). We use this configuration for the CoinRun case study (\Cref{fig:sampling_prepend_vs_no_prepend}, middle row; refer to \Cref{sec:coinrun-case-study}).\n    \item \textbf{Genie w/ prepend}: As detailed in \Cref{sec:jasmine}, we found a minimal modification to the Genie configuration to be necessary to yield generations faithful to the CoinRun environment (Figure \ref{fig:sampling}, bottom row and \Cref{fig:sampling_prepend_vs_no_prepend}, bottom row). This setting is identical to our Genie configuration, with the exception that we prepend latent actions to the video embeddings instead of adding them.\n    \item \textbf{Jafar}: The hyperparameters used by Jafar \citep{willi2024jafar} for their CoinRun case study. This setting is identical to \citet{bruce2024genie}, but \citet{willi2024jafar} use a patch size of 16 for faster training. Unlike \citet{bruce2024genie}, they pre-train the LAM as opposed to co-training the LAM with the dynamics model. We use this configuration for the Jafar baseline runs (Figure \ref{fig:sampling}, middle row and \Cref{fig:wc_time}). We solely run this configuration with the Jafar repository.\n    \item \textbf{Jasmine-base}: We define a base configuration for Jasmine that represents a trade-off between training speed, modeling quality and simplicity, integrating best practices from the language modeling literature. This is Jasmine's configuration in the wall-clock convergence comparison (\Cref{fig:wc_time}) between Jasmine and Jafar, as well as our architectural and infrastructure ablations (Figures \ref{fig:arch-ablations}, \ref{fig:sampling_multi} and Tables \ref{tab:ffn-ablation}, \ref{tab:infra-ablations}, \ref{tab:big-model-flash-attn-ablation}, \ref{tab:chunking-formats-1}, \ref{tab:chunking-formats-2}).\n\end{itemize}\n\n\begin{table}\n\begin{center}\n\begin{tabular}{ll| r|r|r|r}\n\toprule\n & \textbf{Parameter} & \textbf{Genie} & \textbf{Genie w/ prepend} & \textbf{Jafar} & \textbf{Jasmine-base} \\ \n\midrule\n\midrule\nTokenizer & \# blocks    & 8      & & & 4 \\\n          & \# heads     & 8      & & & \\\n          & model dim       & 512    & & & \\\n          & ffn dim       & 512 & & & 2048 \\\n          & \# codes & 1024 & & & \\\n          & latent dim & 32 & & & \\\n          & patch size & 16 & & & \\\n          & total train steps  & 300k  & & & \\\n          & learning rate   & $3 * 10^{-4}$  & & & \\\n          & lr decay end & $3 * 10^{-4}$ & & &  0 \\ \n          & batch size & 48 & & & \\\n\midrule\nLAM       & \# blocks    & 8      & & & 4 \\\n          & \# heads     & 8      & & & \\\n          & model dim       & 512    & & & \\\n          & ffn dim       & 512 & & & 2048 \\\n          & \# codes & 6 & & & \\\n          & latent dim & 32 & & & \\\n          & patch size & 16 & & & \\\n          & total train steps  & 200k  & & & \\\n          & learning rate   & $3 * 10^{-5}$ & & & \\\n          & lr decay end & $3 * 10^{-6}$ & & & 0 \\ \n          & batch size   & 48 & & & \\\n\midrule\nDynamics  & \# blocks    & 12      & & & 6 \\\n          & \# heads     & 8      & & & \\\n          & model dim       & 512    & & & \\\n          & ffn dim       & 512 & & & 2048 \\\n          & total train steps  & 200k  & & & \\\n          & learning rate   & $3 * 10^{-5}$  & & & \\\n          & lr decay end & $3 * 10^{-6}$ & & & 0 \\ \n          & batch size   & 36 & & & \\\n          & action conditioning & additive & prepend & & prepend \\\n          & baseline & MaskGIT & & & \\\n\midrule\n\midrule\nTraining & optimizer    & AdamW      & & & \\\n          & lr schedule & cos     & & & wsd \\\n          & warmup steps  & 1k & & & \\\n          & wsd decay steps & - & & & 10\% \\\n          & dataset size (frames) & 50M & & & \\\n          & co-training & yes & & no & \\\n\midrule\n\midrule\nInference & temperature & 1.0 & & & \\\n          & maskgit steps & 25 & & & \\\n\bottomrule\n\end{tabular}\n  \caption{Configurations used in our experiments. We only show the difference to our base Genie configuration.\n  Note that \citet{bruce2024genie} uses a tokenizer patch size of four, and that we use an expanded dataset with 50M frames for all of our runs (to ensure that no method performs worse due to overfitting).}\n  \label{tab:hparams}\n\end{center}\n\end{table}\n\n\section{Extending MaskGIT to Videos}\n\label{sec:maskgit_to_videos}\nMaskGIT \citep{chang2022maskgit} is defined on images and there are multiple ways to extend it to videos. We follow \citet{willi2024jafar} by randomly masking tokens in the entire sequence using the uniformly sampled probability $p \sim U(0.5,1)$. An alternative would be to sample a different masking probability per frame, similar to \citet{chen2024diffusion}, or leaving $k \sim U(0,T)$ frames unmasked to closely emulate inference.\n\n\section{Evaluation Metrics}\n\label{sec:experiment-metrics}\nIn early experiments, we found the Genie configuration to suffer from a discrepancy in performance between validation loss and autoregressive rollouts (Figure \ref{fig:case-study-plots}). Therefore, besides validation loss, Jasmine also tracks rollout metrics throughout training of the dynamics model. We generate a single frame using the sampling logic of the respective architecture and calculate SSIM and PSNR between the generated frame and the ground-truth. \nAlthough rollout metrics are calculated on a single frame, we find that they directly correlate with the model's performance in generating full rollouts. \nValidation and rollout metrics are calculated on a validation set. The rollouts in Figures \ref{fig:sampling}, \ref{fig:sampling_prepend_vs_no_prepend}, \ref{fig:sampling_multi} and \ref{fig:sampling_diffusion} are sampled from frames of a test set.\n\n\section{Jafar's Batched Masking Logic}\n\label{sec:jafar-batched-masking}\n\begin{figure*} \n    \centering \n    \begin{Verbatim}[commandchars=\\\{\}]\n\PYG{n}{mask\PYGZus{}prob} \PYG{o}{=} \PYG{n}{jax}\PYG{o}{.}\PYG{n}{random}\PYG{o}{.}\PYG{n}{uniform}\PYG{p}{(}\PYG{n}{rng1}\PYG{p}{,} \PYG{n}{minval}\PYG{o}{=}\PYG{n+nb}{self}\PYG{o}{.}\PYG{n}{mask\PYGZus{}limit}\PYG{p}{)}\n\PYG{n}{mask} \PYG{o}{=} \PYG{n}{jax}\PYG{o}{.}\PYG{n}{random}\PYG{o}{.}\PYG{n}{bernoulli}\PYG{p}{(}\PYG{n}{rng2}\PYG{p}{,} \PYG{n}{mask\PYGZus{}prob}\PYG{p}{,} \PYG{n}{vid\PYGZus{}embed}\PYG{o}{.}\PYG{n}{shape}\PYG{p}{[}\PYG{o}{:}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{1}\PYG{p}{]}\PYG{p}{)}\n\PYG{n}{mask} \PYG{o}{=} \PYG{n}{mask}\PYG{o}{.}\PYG{n}{at}\PYG{p}{[}\PYG{p}{:}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{]}\PYG{o}{.}\PYG{n}{set}\PYG{p}{(}\PYG{k+kc}{False}\PYG{p}{)}\n\PYG{n}{vid\PYGZus{}embed} \PYG{o}{=} \PYG{n}{jnp}\PYG{o}{.}\PYG{n}{where}\PYG{p}{(}\PYG{n}{jnp}\PYG{o}{.}\PYG{n}{expand\PYGZus{}dims}\PYG{p}{(}\PYG{n}{mask}\PYG{p}{,} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{1}\PYG{p}{),} \PYG{n+nb}{self}\PYG{o}{.}\PYG{n}{mask\PYGZus{}token}\PYG{p}{,} \PYG{n}{vid\PYGZus{}embed}\PYG{p}{)}\n\end{Verbatim} \n\caption{Code snippet from \citet{willi2024jafar} showing their batched masking logic.} \label{fig:jafar-batched-masking} \n\end{figure*}\n\n\begin{figure*} \n    \centering \n    \begin{Verbatim}[commandchars=\\\{\}]\n\PYG{n}{mask\PYGZus{}prob} \PYG{o}{=} \PYG{n}{jax}\PYG{o}{.}\PYG{n}{random}\PYG{o}{.}\PYG{n}{uniform}\PYG{p}{(}\n    \PYG{n}{\PYGZus{}rng\PYGZus{}prob}\PYG{p}{,} \PYG{n}{shape}\PYG{o}{=}\PYG{p}{(}\PYG{n}{batch\PYGZus{}size}\PYG{p}{,}\PYG{p}{),} \PYG{n}{minval}\PYG{o}{=}\PYG{n+nb}{self}\PYG{o}{.}\PYG{n}{mask\PYGZus{}limit}\n\PYG{p}{)}\n\PYG{n}{per\PYGZus{}sample\PYGZus{}shape} \PYG{o}{=} \PYG{n}{vid\PYGZus{}embed\PYGZus{}BTNM}\PYG{o}{.}\PYG{n}{shape}\PYG{p}{[}\PYG{l+m+mi}{1}\PYG{p}{:}\PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{1}\PYG{p}{]}\n\PYG{n}{mask} \PYG{o}{=} \PYG{n}{jax}\PYG{o}{.}\PYG{n}{vmap}\PYG{p}{(}\n    \PYG{k}{lambda} \PYG{n}{rng}\PYG{p}{,} \PYG{n}{prob}\PYG{p}{:} \PYG{n}{jax}\PYG{o}{.}\PYG{n}{random}\PYG{o}{.}\PYG{n}{bernoulli}\PYG{p}{(}\PYG{n}{rng}\PYG{p}{,} \PYG{n}{prob}\PYG{p}{,} \PYG{n}{per\PYGZus{}sample\PYGZus{}shape}\PYG{p}{),}\n    \PYG{n}{in\PYGZus{}axes}\PYG{o}{=}\PYG{p}{(}\PYG{l+m+mi}{0}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{),}\n\PYG{p}{)(}\PYG{n}{jnp}\PYG{o}{.}\PYG{n}{asarray}\PYG{p}{(}\PYG{n}{\PYGZus{}rngs\PYGZus{}mask}\PYG{p}{),} \PYG{n}{mask\PYGZus{}prob}\PYG{p}{)}\n\PYG{n}{mask} \PYG{o}{=} \PYG{n}{mask}\PYG{o}{.}\PYG{n}{at}\PYG{p}{[}\PYG{p}{:}\PYG{p}{,} \PYG{l+m+mi}{0}\PYG{p}{]}\PYG{o}{.}\PYG{n}{set}\PYG{p}{(}\PYG{k+kc}{False}\PYG{p}{)}\n\PYG{n}{vid\PYGZus{}embed\PYGZus{}BTNM} \PYG{o}{=} \PYG{n}{jnp}\PYG{o}{.}\PYG{n}{where}\PYG{p}{(}\n    \PYG{n}{jnp}\PYG{o}{.}\PYG{n}{expand\PYGZus{}dims}\PYG{p}{(}\PYG{n}{mask}\PYG{p}{,} \PYG{o}{\PYGZhy{}}\PYG{l+m+mi}{1}\PYG{p}{),} \PYG{n+nb}{self}\PYG{o}{.}\PYG{n}{mask\PYGZus{}token}\PYG{o}{.}\PYG{n}{value}\PYG{p}{,} \PYG{n}{vid\PYGZus{}embed\PYGZus{}BTNM}\n\PYG{p}{)}\n\end{Verbatim} \n\caption{Code snippet from Jasmine showing its batched masking logic.} \label{fig:jasmine-batched-masking}\n\end{figure*}\n\nWhereas \citet{willi2024jafar} sample a single masking probability and apply the same masking pattern to all samples in a batch (Figure \ref{fig:jafar-batched-masking}), Jasmine samples \texttt{batch\_size} many sampling probabilities and uses per-sequence masking patterns (Figure \ref{fig:jasmine-batched-masking}). This leads to significantly reduced loss variance (Figure \ref{fig:wc_time}), especially in highly distributed settings.\n\n\section{Model Inspection using Treescope}\n%TODO: switch out for camera-ready\nJasmine's training loop is highly modular and supports easy model surgery as well as model inspection using Treescope \citep{johnson2024penzai}. We provide a demo notebook\footnote{\url{https://colab.research.google.com/drive/1zHkciFIZxXloJgue9F5LtFlA0m00rJIf}} alongside our repository, which illustrates debugging a common training instability (\Cref{fig:demo-notebook}).\n%Jasmine's training loop is highly modular and supports easy model surgery as well as model inspection using Treescope \citep{johnson2024penzai}. We provide a demo notebook\footnote{\url{https://colab.research.google.com/drive/[RETRACTED]}} alongside our repository, which illustrates debugging a common training instability (\Cref{fig:demo-notebook}).\n\n\n\begin{figure}\n  \centering\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_100.png}\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_101.png}\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_102.png}\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_103.png}\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/samples/cropped/gt_jafar_prepend_104.png}\n  \caption{Autoregressive rollouts on five randomly selected trajectories from the CoinRun environment. Each set of three rows corresponds to one trajectory, showing ground-truth frames (top), Jafar samples (middle), and Jasmine samples (bottom). The four conditioning frames are omitted.}\n  \label{fig:sampling_multi}\n\end{figure}\n\n\begin{figure}\n  \centering\n  \includegraphics[width=\textwidth, trim=1 0 0 0, clip]{fig/diffusion_samples.png}\n  \caption{Autoregressive rollouts on five randomly selected trajectories from the CoinRun environment. Each set of three rows corresponds to one trajectory, showing ground-truth frames (top), samples using the diffusion baseline (middle), and samples using the MaskGIT baseline (bottom). The four conditioning frames are omitted.}\n  \label{fig:sampling_diffusion}\n\end{figure}\n\n\begin{figure}\n  \centering\n  \includegraphics[width=\textwidth]{fig/model-surgery.png}\n  \caption{Treescope visualization when performing model inspection. The notebook illustrates inspecting output logits at specific checkpoints.}\n  \label{fig:demo-notebook}\n\end{figure}\n\end{document}\n\n\end{document}\n",latex,tab
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+13,368302,"main.tex",7448,814,"While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastructure for world modeling remains nascent. We introduce Jasmine, a performant JAX-based world modeling codebase that scales from single hosts to hundreds of accelerators with minimal code changes. Jasmine achieves an order-of-magnitude faster reproduction of the CoinRun case study compared to prior open implementations, enabled by performance optimizations across data loading, training and checkpointing. The codebase guarantees fully reproducible training and supports diverse sharding configurations. By pairing Jasmine with curated large-scale datasets, we establish infrastructure for rigorous benchmarking pipelines across model families and architectural ablations.\n",latex,selection_command
+14,369548,"main.tex",7448,813,"While world models are increasingly positioned as a pathway to overcoming data scarcity in domains such as robotics, open training infrastructure for world modeling remains nascent. We introduce Jasmine, a performant JAX-based world modeling codebase that scales from single hosts to hundreds of accelerators with minimal code changes. Jasmine achieves an order-of-magnitude faster reproduction of the CoinRun case study compared to prior open implementations, enabled by performance optimizations across data loading, training and checkpointing. The codebase guarantees fully reproducible training and supports diverse sharding configurations. By pairing Jasmine with curated large-scale datasets, we establish infrastructure for rigorous benchmarking pipelines across model families and architectural ablations.",latex,selection_command
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-fbe873ed-df06-4c80-a2bd-1e121a51eee51758618195748-2025_09_23-11.03.22.40/source.csv

@@ -0,0 +1,10 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"cleanrl/ppo_atari_envpool.py",0,0,"# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy\nimport os\nimport random\nimport time\nfrom collections import deque\nfrom dataclasses import dataclass\n\nimport envpool\nimport gym\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport tyro\nfrom torch.distributions.categorical import Categorical\nfrom torch.utils.tensorboard.writer import SummaryWriter\nfrom typing import Optional\nfrom gym import spaces\nfrom .trajectory_saver import TrajectorySaver\n\n\n@dataclass\nclass Args:\n    exp_name: str = os.path.basename(__file__)[: -len("".py"")]\n    """"""the name of this experiment""""""\n    seed: int = 1\n    """"""seed of the experiment""""""\n    torch_deterministic: bool = True\n    """"""if toggled, `torch.backends.cudnn.deterministic=False`""""""\n    cuda: bool = True\n    """"""if toggled, cuda will be enabled by default""""""\n    track: bool = False\n    """"""if toggled, this experiment will be tracked with Weights and Biases""""""\n    wandb_project_name: str = ""cleanRL""\n    """"""the wandb's project name""""""\n    wandb_entity: Optional[str] = None\n    """"""the entity (team) of wandb's project""""""\n    capture_video: bool = False\n    """"""whether to capture videos of the agent performances (check out `videos` folder)""""""\n\n    # Trajectory capture\n    capture_trajectories: bool = False\n    """"""if toggled, save observation frames and actions during training""""""\n    trajectories_output_dir: str = ""data/atari_trajectories""\n    """"""directory to store trajectory chunks (.npz)""""""\n    trajectories_chunk_size: int = 160\n    """"""number of time steps per chunk before writing""""""\n    trajectories_chunks_per_file: int = 100\n    """"""number of chunks grouped per npz file""""""\n\n    # Algorithm specific arguments\n    env_id: str = ""Breakout-v5""\n    """"""the id of the environment""""""\n    total_timesteps: int = 10000000\n    """"""total timesteps of the experiments""""""\n    learning_rate: float = 2.5e-4\n    """"""the learning rate of the optimizer""""""\n    num_envs: int = 8\n    """"""the number of parallel game environments""""""\n    num_steps: int = 128\n    """"""the number of steps to run in each environment per policy rollout""""""\n    anneal_lr: bool = True\n    """"""Toggle learning rate annealing for policy and value networks""""""\n    gamma: float = 0.99\n    """"""the discount factor gamma""""""\n    gae_lambda: float = 0.95\n    """"""the lambda for the general advantage estimation""""""\n    num_minibatches: int = 4\n    """"""the number of mini-batches""""""\n    update_epochs: int = 4\n    """"""the K epochs to update the policy""""""\n    norm_adv: bool = True\n    """"""Toggles advantages normalization""""""\n    clip_coef: float = 0.1\n    """"""the surrogate clipping coefficient""""""\n    clip_vloss: bool = True\n    """"""Toggles whether or not to use a clipped loss for the value function, as per the paper.""""""\n    ent_coef: float = 0.01\n    """"""coefficient of the entropy""""""\n    vf_coef: float = 0.5\n    """"""coefficient of the value function""""""\n    max_grad_norm: float = 0.5\n    """"""the maximum norm for the gradient clipping""""""\n    target_kl: Optional[float] = None\n    """"""the target KL divergence threshold""""""\n\n    # to be filled in runtime\n    batch_size: int = 0\n    """"""the batch size (computed in runtime)""""""\n    minibatch_size: int = 0\n    """"""the mini-batch size (computed in runtime)""""""\n    num_iterations: int = 0\n    """"""the number of iterations (computed in runtime)""""""\n\n\nclass RecordEpisodeStatistics(gym.Wrapper):\n    def __init__(self, env, deque_size=100):\n        super().__init__(env)\n        self.num_envs = getattr(env, ""num_envs"", 1)\n        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)\n\n    def reset(self, **kwargs):\n        observations = super().reset(**kwargs)\n        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)\n        self.lives = np.zeros(self.num_envs, dtype=np.int32)\n        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)\n        return observations\n\n    def step(self, action):\n        observations, rewards, dones, infos = super().step(action)\n        self.episode_returns += infos[""reward""]\n        self.episode_lengths += 1\n        self.returned_episode_returns[:] = self.episode_returns\n        self.returned_episode_lengths[:] = self.episode_lengths\n        self.episode_returns *= 1 - infos[""terminated""]\n        self.episode_lengths *= 1 - infos[""terminated""]\n        infos[""r""] = self.returned_episode_returns\n        infos[""l""] = self.returned_episode_lengths\n        return (\n            observations,\n            rewards,\n            dones,\n            infos,\n        )\n\n\ndef layer_init(layer, std=np.sqrt(2), bias_const=0.0):\n    torch.nn.init.orthogonal_(layer.weight, std)\n    torch.nn.init.constant_(layer.bias, bias_const)\n    return layer\n\n\nclass Agent(nn.Module):\n    def __init__(self, envs):\n        super().__init__()\n        self.network = nn.Sequential(\n            layer_init(nn.Conv2d(4, 32, 8, stride=4)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.ReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(64 * 7 * 7, 512)),\n            nn.ReLU(),\n        )\n        self.actor = layer_init(nn.Linear(512, envs.single_action_space.n), std=0.01)\n        self.critic = layer_init(nn.Linear(512, 1), std=1)\n\n    def get_value(self, x):\n        return self.critic(self.network(x / 255.0))\n\n    def get_action_and_value(self, x, action=None):\n        hidden = self.network(x / 255.0)\n        logits = self.actor(hidden)\n        probs = Categorical(logits=logits)\n        if action is None:\n            action = probs.sample()\n        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden)\n\n\nif __name__ == ""__main__"":\n    args = tyro.cli(Args)\n    args.batch_size = int(args.num_envs * args.num_steps)\n    args.minibatch_size = int(args.batch_size // args.num_minibatches)\n    args.num_iterations = args.total_timesteps // args.batch_size\n    run_name = f""{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}""\n    if args.track:\n        import wandb\n\n        wandb.init(\n            project=args.wandb_project_name,\n            entity=args.wandb_entity,\n            sync_tensorboard=True,\n            config=vars(args),\n            name=run_name,\n            monitor_gym=True,\n            save_code=True,\n        )\n    writer = SummaryWriter(f""runs/{run_name}"")\n    writer.add_text(\n        ""hyperparameters"",\n        ""|param|value|\n|-|-|\n%s"" % (""\n"".join([f""|{key}|{value}|"" for key, value in vars(args).items()])),\n    )\n\n    # TRY NOT TO MODIFY: seeding\n    random.seed(args.seed)\n    np.random.seed(args.seed)\n    torch.manual_seed(args.seed)\n    torch.backends.cudnn.deterministic = args.torch_deterministic\n\n    device = torch.device(""cuda"" if torch.cuda.is_available() and args.cuda else ""cpu"")\n\n    # env setup\n    envs = envpool.make(\n        args.env_id,\n        env_type=""gym"",\n        num_envs=args.num_envs,\n        episodic_life=True,\n        reward_clip=True,\n        seed=args.seed,\n    )\n    envs.num_envs = args.num_envs\n    envs.single_action_space = envs.action_space\n    envs.single_observation_space = envs.observation_space\n    envs = RecordEpisodeStatistics(envs)\n    assert isinstance(envs.action_space, spaces.Discrete), ""only discrete action space is supported""\n\n    # Optional: trajectory saver\n    traj_saver = None\n    if args.capture_trajectories:\n        traj_saver = TrajectorySaver(\n            output_dir=args.trajectories_output_dir,\n            num_envs=args.num_envs,\n            chunk_size=args.trajectories_chunk_size,\n            chunks_per_file=args.trajectories_chunks_per_file,\n        )\n\n    agent = Agent(envs).to(device)\n    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)\n\n    # ALGO Logic: Storage setup\n    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)\n    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)\n    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    values = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    avg_returns = deque(maxlen=20)\n\n    # TRY NOT TO MODIFY: start the game\n    global_step = 0\n    start_time = time.time()\n    next_obs = torch.Tensor(envs.reset()).to(device)\n    next_done = torch.zeros(args.num_envs).to(device)\n\n    for iteration in range(1, args.num_iterations + 1):\n        # Annealing the rate if instructed to do so.\n        if args.anneal_lr:\n            frac = 1.0 - (iteration - 1.0) / args.num_iterations\n            lrnow = frac * args.learning_rate\n            optimizer.param_groups[0][""lr""] = lrnow\n\n        for step in range(0, args.num_steps):\n            global_step += args.num_envs\n            obs[step] = next_obs\n            dones[step] = next_done\n\n            # ALGO LOGIC: action logic\n            with torch.no_grad():\n                action, logprob, _, value = agent.get_action_and_value(next_obs)\n                values[step] = value.flatten()\n            actions[step] = action\n            logprobs[step] = logprob\n\n            # TRY NOT TO MODIFY: execute the game and log data.\n            # Capture current observation and action prior to stepping the envs\n            obs_to_save = None\n            actions_to_save = None\n            if traj_saver is not None:\n                obs_to_save = next_obs.detach().cpu().numpy().astype(np.uint8)\n                actions_to_save = action.detach().cpu().numpy()\n            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())\n            rewards[step] = torch.tensor(reward).to(device).view(-1)\n            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)\n\n            if traj_saver is not None and obs_to_save is not None and actions_to_save is not None:\n                # Use true termination flags when available\n                terminated = np.asarray(info.get(""terminated"", next_done.detach().cpu().numpy()), dtype=bool)\n                traj_saver.add_step(obs_to_save, actions_to_save, terminated)\n\n            for idx, d in enumerate(next_done):\n                if d and info[""lives""][idx] == 0:\n                    print(f""global_step={global_step}, episodic_return={info['r'][idx]}"")\n                    avg_returns.append(info[""r""][idx])\n                    writer.add_scalar(""charts/avg_episodic_return"", np.average(avg_returns), global_step)\n                    writer.add_scalar(""charts/episodic_return"", info[""r""][idx], global_step)\n                    writer.add_scalar(""charts/episodic_length"", info[""l""][idx], global_step)\n\n        # bootstrap value if not done\n        with torch.no_grad():\n            next_value = agent.get_value(next_obs).reshape(1, -1)\n            advantages = torch.zeros_like(rewards).to(device)\n            lastgaelam = 0\n            for t in reversed(range(args.num_steps)):\n                if t == args.num_steps - 1:\n                    nextnonterminal = 1.0 - next_done\n                    nextvalues = next_value\n                else:\n                    nextnonterminal = 1.0 - dones[t + 1]\n                    nextvalues = values[t + 1]\n                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]\n                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam\n            returns = advantages + values\n\n        # flatten the batch\n        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)\n        b_logprobs = logprobs.reshape(-1)\n        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)\n        b_advantages = advantages.reshape(-1)\n        b_returns = returns.reshape(-1)\n        b_values = values.reshape(-1)\n\n        # Optimizing the policy and value network\n        b_inds = np.arange(args.batch_size)\n        clipfracs = []\n        for epoch in range(args.update_epochs):\n            np.random.shuffle(b_inds)\n            for start in range(0, args.batch_size, args.minibatch_size):\n                end = start + args.minibatch_size\n                mb_inds = b_inds[start:end]\n\n                _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds])\n                logratio = newlogprob - b_logprobs[mb_inds]\n                ratio = logratio.exp()\n\n                with torch.no_grad():\n                    # calculate approx_kl http://joschu.net/blog/kl-approx.html\n                    old_approx_kl = (-logratio).mean()\n                    approx_kl = ((ratio - 1) - logratio).mean()\n                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]\n\n                mb_advantages = b_advantages[mb_inds]\n                if args.norm_adv:\n                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)\n\n                # Policy loss\n                pg_loss1 = -mb_advantages * ratio\n                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)\n                pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n\n                # Value loss\n                newvalue = newvalue.view(-1)\n                if args.clip_vloss:\n                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2\n                    v_clipped = b_values[mb_inds] + torch.clamp(\n                        newvalue - b_values[mb_inds],\n                        -args.clip_coef,\n                        args.clip_coef,\n                    )\n                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2\n                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)\n                    v_loss = 0.5 * v_loss_max.mean()\n                else:\n                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()\n\n                entropy_loss = entropy.mean()\n                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef\n\n                optimizer.zero_grad()\n                loss.backward()\n                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)\n                optimizer.step()\n\n            if args.target_kl is not None and approx_kl > args.target_kl:\n                break\n\n        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()\n        var_y = np.var(y_true)\n        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n\n        # TRY NOT TO MODIFY: record rewards for plotting purposes\n        writer.add_scalar(""charts/learning_rate"", optimizer.param_groups[0][""lr""], global_step)\n        writer.add_scalar(""losses/value_loss"", v_loss.item(), global_step)\n        writer.add_scalar(""losses/policy_loss"", pg_loss.item(), global_step)\n        writer.add_scalar(""losses/entropy"", entropy_loss.item(), global_step)\n        writer.add_scalar(""losses/old_approx_kl"", old_approx_kl.item(), global_step)\n        writer.add_scalar(""losses/approx_kl"", approx_kl.item(), global_step)\n        writer.add_scalar(""losses/clipfrac"", np.mean(clipfracs), global_step)\n        writer.add_scalar(""losses/explained_variance"", explained_var, global_step)\n        print(""SPS:"", int(global_step / (time.time() - start_time)))\n        writer.add_scalar(""charts/SPS"", int(global_step / (time.time() - start_time)), global_step)\n\n    envs.close()\n    if traj_saver is not None:\n        traj_saver.close()\n    writer.close()\n",python,tab
+2,74,"tasks",0,0,"",Log,tab
+3,76,"cleanrl/ppo_atari_envpool.py",0,0,"",python,tab
+4,120,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:03:22 AM [info] Activating crowd-code\n11:03:22 AM [info] Recording started\n11:03:22 AM [info] Initializing git provider using file system watchers...\n",Log,tab
+5,156,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"11:03:22 AM [info] Git repository found\n11:03:22 AM [info] Git provider initialized successfully\n11:03:22 AM [info] Initial git state: [object Object]\n",Log,content
+6,5286,"TERMINAL",0,0,"",,terminal_command
+7,12033,"TERMINAL",0,0,"",,terminal_command
+8,18094,"cleanrl/ppo_atari_envpool.py",0,0,"",python,tab
+9,120180,"TERMINAL",0,0,"",,terminal_command

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-068d329a-b3e1-4e75-8573-185857d88d961757006182441-2025_09_04-19.16.37.170/source.csv

@@ -0,0 +1,9 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,4,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/lam/%x_%j.log\n#SBATCH --job-name=train_lam_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og reproduction 10m_dataset lam repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/$job_name/$slurm_job_id""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n    --ckpt_dir $CHECKPOINT_DIR \\n    --log_image_interval=1000 \\n    --log \\n    --name=""${job_name}_${slurm_job_id}"" \\n    --tags $tags \\n    --entity instant-uv \\n    --project jafar \\n    --data_dir $npy_records_dir \\n    --wandb_id $slurm_job_id\n",shellscript,tab
+2,1056,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"7:16:37 PM [info] Activating crowd-code\n7:16:37 PM [info] Recording started\n7:16:37 PM [info] Initializing git provider using file system watchers...\n7:16:37 PM [info] Git repository found\n7:16:37 PM [info] Git provider initialized successfully\n7:16:37 PM [info] Initial git state: [object Object]\n",Log,tab
+3,5886,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",0,0,"",shellscript,tab
+4,5888,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",1214,0,"",shellscript,selection_mouse
+5,5889,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",1213,0,"",shellscript,selection_command
+6,7442,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",982,0,"",shellscript,selection_mouse
+7,8803,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\n\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nimport optax\nimport orbax\nfrom orbax.checkpoint import PyTreeCheckpointer\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n    # Experiment\n    num_steps: int = 300_000\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_resolution: int = 64\n    data_dir: str = ""data/coinrun_episodes""\n    checkpoint: str = """"\n    # Optimization\n    vq_beta: float = 0.25\n    batch_size: int = 48\n    min_lr: float = 3e-4\n    max_lr: float = 3e-4\n    warmup_steps: int = 10000\n    # Tokenizer\n    model_dim: int = 512\n    latent_dim: int = 32\n    num_latents: int = 1024\n    patch_size: int = 4\n    num_blocks: int = 8\n    num_heads: int = 8\n    dropout: float = 0.0\n    codebook_dropout: float = 0.01\n    # Logging\n    log: bool = False\n    entity: str = """"\n    project: str = """"\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 10000\n    log_gradients: bool = False\n    name: str = """"\n    tags: list[str] = field(default_factory=lambda: [""tokenizer""])\n    wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(params, state, inputs):\n    # --- Compute loss ---\n    outputs = state.apply_fn(\n        params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n    )\n    mse = jnp.square(inputs[""videos""] - outputs[""recon""]).mean()\n    q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()\n    commitment_loss = jnp.square(\n        outputs[""emb""] - jax.lax.stop_gradient(outputs[""z""])\n    ).mean()\n    loss = mse + q_loss + args.vq_beta * commitment_loss\n\n    # --- Compute validation metrics ---\n    gt = inputs[""videos""].clip(0, 1).reshape(-1, *inputs[""videos""].shape[2:])\n    recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n    psnr = pix.psnr(gt, recon).mean()\n    ssim = pix.ssim(gt, recon).mean()\n    _, index_counts = jnp.unique_counts(\n        jnp.ravel(outputs[""indices""]), size=args.num_latents, fill_value=0\n    )\n    codebook_usage = (index_counts != 0).mean()\n    metrics = dict(\n        loss=loss,\n        mse=mse,\n        q_loss=q_loss,\n        commitment_loss=commitment_loss,\n        psnr=psnr,\n        ssim=ssim,\n        codebook_usage=codebook_usage,\n    )\n    return loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    grad_fn = jax.value_and_grad(tokenizer_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""encoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""encoder""]\n        )\n        metrics[""vq_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""vq""]\n        )\n        metrics[""decoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""decoder""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    rng = jax.random.PRNGKey(args.seed)\n    if args.log:\n        wandb_init_kwargs = {\n            ""entity"": args.entity,\n            ""project"": args.project,\n            ""name"": args.name,\n            ""tags"": args.tags,\n            ""group"": ""debug"",\n            ""config"": args,\n        }\n        if args.wandb_id:\n            wandb_init_kwargs.update(\n                {\n                    ""id"": args.wandb_id,\n                    ""resume"": ""allow"",\n                }\n            )\n        wandb.init(**wandb_init_kwargs)\n\n    # --- Initialize model ---\n    tokenizer = TokenizerVQVAE(\n        in_dim=args.image_channels,\n        model_dim=args.model_dim,\n        latent_dim=args.latent_dim,\n        num_latents=args.num_latents,\n        patch_size=args.patch_size,\n        num_blocks=args.num_blocks,\n        num_heads=args.num_heads,\n        dropout=args.dropout,\n        codebook_dropout=args.codebook_dropout,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_resolution, args.image_resolution, args.image_channels)\n    inputs = dict(\n        videos=jnp.zeros(\n            (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n        ),\n    )\n    init_params = tokenizer.init(_rng, inputs)\n\n    # --- Load checkpoint ---\n    step = 0\n    if args.checkpoint:\n        init_params[""params""].update(\n            PyTreeCheckpointer().restore(args.checkpoint)[""model""][""params""][""params""]\n        )\n        # Assume checkpoint is of the form tokenizer_<timestamp>_<step>\n        step += int(args.checkpoint.split(""_"")[-1])\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=tokenizer.apply, params=init_params, tx=tx)\n\n    # --- TRAIN LOOP ---\n    dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- Train step ---\n            rng, _rng = jax.random.split(rng)\n            inputs = dict(videos=videos, rng=_rng)\n            train_state, loss, recon, metrics = train_step(train_state, inputs)\n            print(f""Step {step}, loss: {loss}"")\n            step += 1\n\n            # --- Logging ---\n            if args.log:\n                if step % args.log_interval == 0:\n                    wandb.log({""loss"": loss, ""step"": step, **metrics})\n                if step % args.log_image_interval == 0:\n                    gt_seq = inputs[""videos""][0]\n                    recon_seq = recon[0].clip(0, 1)\n                    comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n                    comparison_seq = einops.rearrange(\n                        comparison_seq * 255, ""t h w c -> h (t w) c""\n                    )\n                    log_images = dict(\n                        image=wandb.Image(np.asarray(gt_seq[0])),\n                        recon=wandb.Image(np.asarray(recon_seq[0])),\n                        true_vs_recon=wandb.Image(\n                            np.asarray(comparison_seq.astype(np.uint8))\n                        ),\n                    )\n                    wandb.log(log_images)\n                if step % args.log_checkpoint_interval == 0:\n                    ckpt = {""model"": train_state}\n                    orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n                    save_args = orbax_utils.save_args_from_target(ckpt)\n                    orbax_checkpointer.save(\n                        os.path.join(\n                            os.getcwd(), args.ckpt_dir, f""tokenizer_{ts}_{step}""\n                        ),\n                        ckpt,\n                        save_args=save_args,\n                    )\n            if step >= args.num_steps:\n                break\n",python,tab
+8,24445,"train_tokenizer.py",3307,0,"",python,selection_mouse

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-64e32f08-906b-4f34-b713-7cc9256fb7151757509655627-2025_09_10-15.07.50.47/source.csv

@@ -0,0 +1,10 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,5,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nimport optax\nimport orbax\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n    # Experiment\n    num_steps: int = 200_000\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_resolution: int = 64\n    data_dir: str = ""data/coinrun_episodes""\n    # Optimization\n    batch_size: int = 36\n    min_lr: float = 3e-6\n    max_lr: float = 3e-5\n    warmup_steps: int = 5000\n    # Tokenizer\n    tokenizer_dim: int = 512\n    latent_patch_dim: int = 32\n    num_patch_latents: int = 1024\n    patch_size: int = 4\n    tokenizer_num_blocks: int = 8\n    tokenizer_num_heads: int = 8\n    tokenizer_checkpoint: str = """"\n    # LAM\n    lam_dim: int = 512\n    latent_action_dim: int = 32\n    num_latent_actions: int = 6\n    lam_patch_size: int = 16\n    lam_num_blocks: int = 8\n    lam_num_heads: int = 8\n    lam_checkpoint: str = """"\n    # Dynamics\n    dyna_dim: int = 512\n    dyna_num_blocks: int = 12\n    dyna_num_heads: int = 8\n    dropout: float = 0.0\n    mask_limit: float = 0.5\n    # Logging\n    log: bool = False\n    entity: str = """"\n    project: str = """"\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 25000\n    log_gradients: bool = False\n    name: str = """"\n    tags: list[str] = field(default_factory=lambda: [""dynamics""])\n    wandb_id: str = """"\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n    """"""Compute masked dynamics loss""""""\n    outputs = state.apply_fn(\n        params, inputs, training=True, rngs={""dropout"": inputs[""dropout_rng""]}\n    )\n    mask = outputs[""mask""]\n    ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n        outputs[""token_logits""], outputs[""video_tokens""]\n    )\n    ce_loss = (mask * ce_loss).sum() / mask.sum()\n    acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n    acc = (mask * acc).sum() / mask.sum()\n    select_probs = jax.nn.softmax(outputs[""token_logits""])\n    metrics = dict(\n        cross_entropy_loss=ce_loss,\n        masked_token_accuracy=acc,\n        select_logit=outputs[""token_logits""].max(-1).mean(),\n        select_p=select_probs.max(-1).mean(),\n        entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n    )\n    return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    """"""Update state and compute metrics""""""\n    grad_fn = jax.value_and_grad(dynamics_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""dynamics""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    rng = jax.random.PRNGKey(args.seed)\n    if args.log:\n        wandb_init_kwargs = {\n            ""entity"": args.entity,\n            ""project"": args.project,\n            ""name"": args.name,\n            ""tags"": args.tags,\n            ""group"": ""debug"",\n            ""config"": args,\n        }\n        if args.wandb_id:\n            wandb_init_kwargs.update(\n                {\n                    ""id"": args.wandb_id,\n                    ""resume"": ""allow"",\n                }\n            )\n        wandb.init(**wandb_init_kwargs)\n\n    # --- Initialize model ---\n    genie = Genie(\n        # Tokenizer\n        in_dim=args.image_channels,\n        tokenizer_dim=args.tokenizer_dim,\n        latent_patch_dim=args.latent_patch_dim,\n        num_patch_latents=args.num_patch_latents,\n        patch_size=args.patch_size,\n        tokenizer_num_blocks=args.tokenizer_num_blocks,\n        tokenizer_num_heads=args.tokenizer_num_heads,\n        # LAM\n        lam_dim=args.lam_dim,\n        latent_action_dim=args.latent_action_dim,\n        num_latent_actions=args.num_latent_actions,\n        lam_patch_size=args.lam_patch_size,\n        lam_num_blocks=args.lam_num_blocks,\n        lam_num_heads=args.lam_num_heads,\n        # Dynamics\n        dyna_dim=args.dyna_dim,\n        dyna_num_blocks=args.dyna_num_blocks,\n        dyna_num_heads=args.dyna_num_heads,\n        dropout=args.dropout,\n        mask_limit=args.mask_limit,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_resolution, args.image_resolution, args.image_channels)\n    dummy_inputs = dict(\n        videos=jnp.zeros(\n            (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n        ),\n        mask_rng=_rng,\n    )\n    rng, _rng = jax.random.split(rng)\n    init_params = genie.init(_rng, dummy_inputs)\n    init_params = restore_genie_components(\n        init_params, args.tokenizer_checkpoint, args.lam_checkpoint\n    )\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\n    # --- TRAIN LOOP ---\n    dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n    step = 0\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- Train step ---\n            rng, _rng, _mask_rng = jax.random.split(rng, 3)\n            inputs = dict(\n                videos=videos,\n                action=jnp.zeros((args.batch_size, args.seq_len), dtype=jnp.float32),\n                dropout_rng=_rng,\n                mask_rng=_mask_rng,\n            )\n            train_state, loss, recon, metrics = train_step(train_state, inputs)\n            print(f""Step {step}, loss: {loss}"")\n            step += 1\n\n            # --- Logging ---\n            if args.log:\n                if step % args.log_interval == 0:\n                    wandb.log({""loss"": loss, ""step"": step, **metrics})\n                if step % args.log_image_interval == 0:\n                    gt_seq = inputs[""videos""][0]\n                    recon_seq = recon[0].clip(0, 1)\n                    comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n                    comparison_seq = einops.rearrange(\n                        comparison_seq * 255, ""t h w c -> h (t w) c""\n                    )\n                    log_images = dict(\n                        image=wandb.Image(np.asarray(gt_seq[15])),\n                        recon=wandb.Image(np.asarray(recon_seq[15])),\n                        true_vs_recon=wandb.Image(\n                            np.asarray(comparison_seq.astype(np.uint8))\n                        ),\n                    )\n                    wandb.log(log_images)\n                if step % args.log_checkpoint_interval == 0:\n                    ckpt = {""model"": train_state}\n                    orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n                    save_args = orbax_utils.save_args_from_target(ckpt)\n                    orbax_checkpointer.save(\n                        os.path.join(os.getcwd(), args.ckpt_dir, f""genie_{ts}_{step}""),\n                        ckpt,\n                        save_args=save_args,\n                    )\n            if step >= args.num_steps:\n                break\n",python,tab
+2,278,"tasks",0,0,"",Log,tab
+3,282,"train_dynamics.py",0,0,"",python,tab
+4,848,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"3:07:50 PM [info] Activating crowd-code\n3:07:50 PM [info] Recording started\n3:07:50 PM [info] Initializing git provider using file system watchers...\n",Log,tab
+5,1160,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"3:07:50 PM [info] Git repository found\n3:07:50 PM [info] Git provider initialized successfully\n3:07:50 PM [info] Initial git state: [object Object]\n",Log,content
+6,2658,"train_dynamics.py",0,0,"",python,tab
+7,2665,"TERMINAL",0,0,"",,terminal_focus
+8,5161,"TERMINAL",0,0,"source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate",,terminal_command
+9,5196,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1991:~/Projects/jafar",,terminal_output

927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-b69885bf-32d4-47c3-9c98-056c07697d2d1754060276680-2025_08_01-16.58.39.604/source.csv

@@ -0,0 +1,86 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,666,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"4:58:39 PM [info] Activating crowd-code\n4:58:39 PM [info] Recording started\n4:58:39 PM [info] Initializing git provider using file system watchers...\n4:58:39 PM [info] Git repository found\n4:58:39 PM [info] Git provider initialized successfully\n4:58:39 PM [info] Initial git state: [object Object]\n",Log,tab
+3,7740,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_causal_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n    echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n    # optional: trigger checkpoint saving here\n    # e.g., touch $checkpoint_dir/requeue_trigger\n    scontrol requeue $SLURM_JOB_ID\n    exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n    restore_ckpt_flag=""--no-restore-ckpt""\nelse\n    restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/causal/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# tokenizer with the new structure supporting larger ffn_dim\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n    --save_ckpt \\n    $restore_ckpt_flag \\n    --wandb_id $SLURM_JOB_ID \\n    --ckpt_dir $CHECKPOINT_DIR \\n    --batch_size=384 \\n    --init_lr=0 \\n    --max_lr=8e-5 \\n    --log_image_interval=1000 \\n    --log \\n    --log_checkpoint_interval=1000 \\n    --name=dynamics-causal-8-node-$slurm_job_id \\n    --tags dynamics causal 8-node \\n    --entity instant-uv \\n    --project jafar \\n    --dyna_dim=1024 \\n    --dyna_num_blocks=16 \\n    --dyna_num_heads=16 \\n    --dyna_ffn_dim=4096 \\n    --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n    --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab
+4,11774,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_command
+5,11810,"TERMINAL",0,0,"]633;E;2025-08-01 16:58:51 source .venv/bin/activate;adbf53fe-397b-40d3-9339-94ea79afad56]633;C]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output
+6,14501,"TERMINAL",0,0,"runner-3",,terminal_command
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+8,18415,"TERMINAL",0,0,"]633;E;2025-08-01 16:58:57 vim slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes.sbatch;adbf53fe-397b-40d3-9339-94ea79afad56]633;C[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25l""slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes.sbatch"" 83L, 2571B▽  Pzz\[0%m           [>c]10;?]11;?requeue_job() {\r\n    echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""    # optional: trigger checkpoint saving here# e.g., touch $checkpoint_dir/requeue_trigger\r\n    scontrol requeue $SLURM_JOB_ID\r\n    exit 0\r\n}\r\n\r\ntrap requeue_job sigusr1\r\n\r\n# set checkpoint flag based on restart count\r\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\r\n\r\nif [ $restart_count -eq 0 ]; then\r\n    restore_ckpt_flag=""--no-restore-ckpt""\r\nelse\r\n    restore_ckpt_flag=""--restore-ckpt""\r\nfi# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\n\r\njob_name=$SLURM_JOB_NAME\r\n# slurm_job_id=$SLURM_JOB_ID\r\nslurm_job_id=3388140\r\n\r\n# CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/causal/$job_name/$slurm_job_id\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/causal/train_dynamics_causal_8_node/33888140\r\nmkdir -p $CHECKPOINT_DIR48,2032%[?25h",,terminal_output
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