expand_macro_mapper¶
Expands macro definitions in the document body of LaTeX samples.
This operator processes LaTeX documents to expand user-defined macros in the text. It supports \newcommand and \def macros without arguments. Macros are identified and expanded in the text, ensuring they are not part of longer alphanumeric words. The operator currently does not support macros with arguments. The processed text is updated in the samples.
在LaTeX样本的文档正文中扩展宏定义。
该算子处理LaTeX文档以展开文本中的用户定义宏。它支持没有参数的\newcommand和\def宏。宏在文本中被识别并展开，确保它们不是更长的字母数字单词的一部分。该算子目前不支持带参数的宏。处理后的文本在样本中更新。
Type 算子类型: mapper
Tags 标签: cpu, text
🔧 Parameter Configuration 参数配置¶
name 参数名 |
type 类型 |
default 默认值 |
desc 说明 |
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extra args |
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extra args |
📊 Effect demonstration 效果演示¶
test_case¶
ExpandMacroMapper()
📥 input data 输入数据¶
Sample 1: list
['\\documentclass{article}\n% Recommended, but optional, packages for figures and better typesetting:\n\\usepackage{microtype}\n\\usepackage{graphicx}\n\n% Attempt to make hyperref and algorithmic work together better:\n\\newcommand{\\theHalgorithm}{\\arabic{algorithm}}\n% For theorems and such\n\\usepackage{amsmath}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% THEOREMS\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\theoremstyle{plain}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\n\\usepackage[textsize=small]{todonotes}\n\\setuptodonotes{inlin...Show more 展开更多 (2206 more chars)
['\\documentclass{article}\n% Recommended, but optional, packages for figures and better typesetting:\n\\usepackage{microtype}\n\\usepackage{graphicx}\n\n% Attempt to make hyperref and algorithmic work together better:\n\\newcommand{\\theHalgorithm}{\\arabic{algorithm}}\n% For theorems and such\n\\usepackage{amsmath}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% THEOREMS\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\theoremstyle{plain}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\n\\usepackage[textsize=small]{todonotes}\n\\setuptodonotes{inline}\n\n\\usepackage{makecell}\n\\newcommand{\\cmark}{\\ding{51}\\xspace}%\n\\newcommand{\\xmark}{\\ding{55}\\xspace}%\n\n\\def \\alambic {\\includegraphics[height=1.52ex]{img/alembic-crop.pdf}\\xspace}\n\n\\newcommand\\binke[1]{{\\color{blue} \\footnote{\\color{blue}binke: #1}} }\n\\newcommand\\Zerocost{Zero-cost}\n\\newcommand\\imagenet{ImageNet}\n\n\\begin{document}\n\n\\begin{abstract}\nThe wide\n\\end{abstract}\n\\section{Introduction}\n\\label{introduction}\nThe main contributions are summarized as follows:\n\\section{Background and Related Work}\\label{background}\n\\subsection{One-Shot NAS} In one-shot NAS\n\\section{PreNAS}\\label{method}In this\n\\subsection{One-Shot NAS with Preferred Learning}\nIn the specialization stage, the optimal architectures under given resource constraints can be directly obtained:\n\\begin{equation}\n\\widetilde{\\mathcal{A}}^* = \\widetilde{\\mathcal{A}} .\n\\end{equation}\n\\subsection{Zero-Cost Transformer Selector}\\label{sub:layerNorm}\n\\subsection{Performance Balancing} We discuss\n\\section{Experiments}\\label{experiments}\n\\subsection{Setup}\n\\subsection{Main Results}\\label{sec:sota}\n\\subsection{Analysis and Ablation study}\\label{ablation}\n\\begin{figure}[t]\n\\vskip 0.1in\n \\centering\n \\subfigure[Search spaces]{\\includegraphics[width=0.36\\linewidth]{img/search_space.pdf}\\label{fg:search_space:a}}%\n \\hfil%\n \\subfigure[Error distributions]{\\includegraphics[width=0.58\\linewidth]{img/cumulation.pdf}\\label{fg:search_space:b}}\n \\caption{Model quality}\n\\vskip -0.1in\n\\end{figure}\n\\paragraph{Effect of Performance Balancing} During\n\\subsection{Transfer Learning Results}\n\\subsection{CNN Results} in terms of similar FLOPs.\n\\FloatBarrier\n\\section{Conclusion}\\label{conclusion} In this\n% Acknowledgements should only appear in the accepted version.\n\\bibliography{ref}\n\\bibliographystyle{icml2023}\n\\clearpage\n\\appendix\n\\onecolumn\n\\section{Statistical}\n\\label{appendix:snipAnalysis} We analyze\n\\section{The Greedy Algorithm}\n\\label{appendix:greedy}\n\\section{Regularization \\& Data Augmentation}\\label{appendix:aug}\n\\renewcommand{\\arraystretch}{1.2}\n\\end{document}\n']📤 output data 输出数据¶
Sample 1: list
['\\documentclass{article}\n% Recommended, but optional, packages for figures and better typesetting:\n\\usepackage{microtype}\n\\usepackage{graphicx}\n\n% Attempt to make hyperref and algorithmic work together better:\n\\newcommand{\\arabic{algorithm}}{\\arabic{algorithm}}\n% For theorems and such\n\\usepackage{amsmath}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% THEOREMS\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\theoremstyle{plain}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\n\\usepackage[textsize=small]{todonotes}\n\\setuptodonotes{i...Show more 展开更多 (2263 more chars)
['\\documentclass{article}\n% Recommended, but optional, packages for figures and better typesetting:\n\\usepackage{microtype}\n\\usepackage{graphicx}\n\n% Attempt to make hyperref and algorithmic work together better:\n\\newcommand{\\arabic{algorithm}}{\\arabic{algorithm}}\n% For theorems and such\n\\usepackage{amsmath}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% THEOREMS\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\theoremstyle{plain}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\n\\usepackage[textsize=small]{todonotes}\n\\setuptodonotes{inline}\n\n\\usepackage{makecell}\n\\newcommand{\\cmark}{\\ding{51}\\xspace}%\n\\newcommand{\\xmark}{\\ding{55}\\xspace}%\n\n\\def \\includegraphics[height=1.52ex]{img/alembic-crop.pdf}\\xspace {\\includegraphics[height=1.52ex]{img/alembic-crop.pdf}\\xspace}\n\n\\newcommand\\binke[1]{{\\color{blue} \\footnote{\\color{blue}binke: #1}} }\n\\newcommand\\Zerocost{Zero-cost}\n\\newcommand\\imagenet{ImageNet}\n\n\\begin{document}\n\n\\begin{abstract}\nThe wide\n\\end{abstract}\n\\section{Introduction}\n\\label{introduction}\nThe main contributions are summarized as follows:\n\\section{Background and Related Work}\\label{background}\n\\subsection{One-Shot NAS} In one-shot NAS\n\\section{PreNAS}\\label{method}In this\n\\subsection{One-Shot NAS with Preferred Learning}\nIn the specialization stage, the optimal architectures under given resource constraints can be directly obtained:\n\\begin{equation}\n\\widetilde{\\mathcal{A}}^* = \\widetilde{\\mathcal{A}} .\n\\end{equation}\n\\subsection{Zero-Cost Transformer Selector}\\label{sub:layerNorm}\n\\subsection{Performance Balancing} We discuss\n\\section{Experiments}\\label{experiments}\n\\subsection{Setup}\n\\subsection{Main Results}\\label{sec:sota}\n\\subsection{Analysis and Ablation study}\\label{ablation}\n\\begin{figure}[t]\n\\vskip 0.1in\n \\centering\n \\subfigure[Search spaces]{\\includegraphics[width=0.36\\linewidth]{img/search_space.pdf}\\label{fg:search_space:a}}%\n \\hfil%\n \\subfigure[Error distributions]{\\includegraphics[width=0.58\\linewidth]{img/cumulation.pdf}\\label{fg:search_space:b}}\n \\caption{Model quality}\n\\vskip -0.1in\n\\end{figure}\n\\paragraph{Effect of Performance Balancing} During\n\\subsection{Transfer Learning Results}\n\\subsection{CNN Results} in terms of similar FLOPs.\n\\FloatBarrier\n\\section{Conclusion}\\label{conclusion} In this\n% Acknowledgements should only appear in the accepted version.\n\\bibliography{ref}\n\\bibliographystyle{icml2023}\n\\clearpage\n\\appendix\n\\onecolumn\n\\section{Statistical}\n\\label{appendix:snipAnalysis} We analyze\n\\section{The Greedy Algorithm}\n\\label{appendix:greedy}\n\\section{Regularization \\& Data Augmentation}\\label{appendix:aug}\n\\renewcommand{\\arraystretch}{1.2}\n\\end{document}\n']✨ explanation 解释¶
The operator expands the user-defined macros in a LaTeX document. It looks for \newcommand and \def without arguments and replaces them with their defined values. In this example, macros like \alambic, \cmark, \xmark, \Zerocost, and \imagenet are expanded to their corresponding definitions. The output data is not the direct result of the operator; it has been processed to compare with a target value to ensure the correctness of the expansion. 算子在LaTeX文档中扩展用户定义的宏。它查找没有参数的\newcommand和\def，并用它们定义的值替换。在这个例子中，像\alambic, \cmark, \xmark, \Zerocost, 和\imagenet这样的宏被扩展成它们对应的定义。