ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.

Overview

ANTLR v4

Java 7+ License

Build status

Github CI Build Status (MacOSX) AppVeyor CI Build Status (Windows) Circle CI Build Status (Linux)

ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface (or visitor) that makes it easy to respond to the recognition of phrases of interest.

Donate

Authors and major contributors

Useful information

You might also find the following pages useful, particularly if you want to mess around with the various target languages.

The Definitive ANTLR 4 Reference

Programmers run into parsing problems all the time. Whether it’s a data format like JSON, a network protocol like SMTP, a server configuration file for Apache, a PostScript/PDF file, or a simple spreadsheet macro language—ANTLR v4 and this book will demystify the process. ANTLR v4 has been rewritten from scratch to make it easier than ever to build parsers and the language applications built on top. This completely rewritten new edition of the bestselling Definitive ANTLR Reference shows you how to take advantage of these new features.

You can buy the book The Definitive ANTLR 4 Reference at amazon or an electronic version at the publisher's site.

You will find the Book source code useful.

Additional grammars

This repository is a collection of grammars without actions where the root directory name is the all-lowercase name of the language parsed by the grammar. For example, java, cpp, csharp, c, etc...

Comments
  • New extended Unicode escape \u{10ABCD} to support Unicode literals > U+FFFF

    New extended Unicode escape \u{10ABCD} to support Unicode literals > U+FFFF

    Fixes #276 .

    This used to be a WIP PR, but it's now ready for review.

    This PR introduces a new extended Unicode escape \u{10ABCD} in ANTLR4 grammars to support Unicode literal values > U+FFFF.

    The serialized ATN represents any atom or range with a Unicode value > U+FFFF as a set. Any such set is serialized in the ATN with 32-bit arguments.

    I bumped the UUID, since this changes the serialized ATN format.

    I included lots of tests and made sure everything is passing on Linux, Mac, and Windows.

    type:feature unicode 
    opened by bhamiltoncx 115
  • Terrible Golang performance

    Terrible Golang performance

    Stackoverflow: https://stackoverflow.com/questions/72266899/golang-performance-issues

    Google group: https://groups.google.com/g/antlr-discussion/c/OdhAIsy2GfI

    Example code: https://github.com/movelazar/perf-repro

    A simple rule such as:

    1 EQ 2 OR
    1 EQ 2 OR
    1 EQ 2 OR
    1 EQ 2 OR
    1 EQ 2
    

    takes exponentially longer to parse the more 1 EQ 2 OR clauses there are. This does not happen in python (by my testing) or CSharp, Dart, Java (by stackoverflow comment).

    On my machine, # of lines vs parse time:

    11: 0.5s
    12: 1.2s
    13: 3.2s
    14: 8.1s
    15: 21.9s
    16: 57.5s
    

    Given that Python doesn't face this issue I can't imagine I'm doing something terrible in my grammar.

    Issue goes away if I put parens on things but that's not a real solution.

    On 4.10.1, first noticed with 4.9.1.

    Any help is greatly appreciated. Surprised I can't find others with this issue.

    type:bug target:go comp:performance 
    opened by movelazar 101
  • splitting version numbers for targets

    splitting version numbers for targets

    Hiya: @pboyer, @mike-lischke, @janyou, @ewanmellor, @hanjoes, @ericvergnaud, @lingyv-li, @marcospassos

    Eric has raised the point that it would be nice to be able to make quick patches to the various runtimes; e.g., there is a stopping bug now in the JavaScript target. He proposes something along these lines:

    • any change in the tool or the runtime algorithm bumps the middle version #: 4.9 -> 4.10 -> 4.11
    • any bug fix in a runtime we bump the last digit of that runtime only: 4.9 -> 4.9.1 -> 4.9.2
    • if bumping the java runtime for bug fix we also bump the tool since it contains the runtime

    This is in optimal as people have criticized me in the past for bumping, say, 4.6 to 4.7 for some minor changes. It also has the problem that 4.9.x will not mean the same thing in two different targets possibly, as each target will now have their own version number.

    Rather than break up all of the targets into separate repositories or similar, can you guys think of a better solution? Any suggestions? The goal here is to allow more rapid target releases, and independent of me having to do a major release of the tool.

    type:question 
    opened by parrt 94
  • Improve memory usage and perf of CodePointCharStream: Use 8-bit, 16-bit, or 32-bit buffer

    Improve memory usage and perf of CodePointCharStream: Use 8-bit, 16-bit, or 32-bit buffer

    This greatly improves the memory usage and performance of CodePointCharStream by ensuring the internal storage uses either an 8-bit buffer (for Unicode code points <= U+00FF), 16-bit buffer (for Unicode code points <= U+FFFF), or a 32-bit buffer (Unicode code points > U+FFFF).

    I split out the internal storage into a class CodePointBuffer which has a CodePointBuffer.Builder class which has the logic to upgrade from 8-bit to 16-bit to 32-bit storage.

    I found the perf hotspot in CodePointCharStream on master was the virtual method calls from CharStream.LA(offset) into IntBuffer.

    Refactoring it into CodePointBuffer didn't help (in fact, it added another virtual method call).

    To fix the perf, I made CodePointCharStream an abstract class and made three concrete subclasses: CodePoint8BitCharStream, CodePoint16BitCharStream, and CodePoint32BitCharStream which directly access the array of underlying code points in the CodePointBuffer without virtual method calls.

    lexers target:java comp:performance 
    opened by bhamiltoncx 85
  • initial discussion to start integration of new targets

    initial discussion to start integration of new targets

    As promised, I am now ready to integrate the new ANTLR target languages you folks have been working on. This issue is meant to get everybody in sync, check status, and discuss the proper order of integration and resolve issues etc.

    There are two administrative details to get out of the way first:

    1. Please let me know if there is another github user that should be added to one of the categories. Or, of course, if you would like your user ID removed from this discussion.
    2. Nothing can be merged into antlr/antlr4 unless every single committer has added themselves to the contributors.txt file. It's onerous, particularly for simple commits, but it is requirement for anything merged into the master. Eclipse foundation lawyers tell me that we have one of the cleanest licenses out there and it contributes to ANTLR's widespread use because companies are not afraid to use the software. See the genesis of such heinous requirements in SCO v IBM. This means lead target authors have to go back through their committers list quickly and ask them to sign the contributors file with a new commit. Or, they can remove that commit and enter their own version of the functionality, being careful not to violate copyright on the previous.

    As we proceed, please keep in mind that I have a difficult role, balancing the needs of multiple targets and keeping discussions in the civil and practical zone. Decisions I make come from the perspective of over 25 years managing and leading this project. I look forward to incorporating your hard work into the main antlr repo.

    C++ current location

    • @mike-lischke
    • @DanMcLaughlin
    • @nburles
    • @davesisson

    Go current location, previous discussion

    • @pboyer

    Swift current location: unclear, previous discussion

    • @jeffreyguenther
    • @hanjoes
    • @janyou
    • @ewanmellor

    Likely interested/supporting humans (scraped from github issues):

    • @RYDB3RG
    • @wjkohnen
    • @willfaught
    • @parrt
    • @sharwell
    • @ericvergnaud
    type:improvement target:swift target:cpp target:go 
    opened by parrt 84
  • Add a new CharStream that converts the symbols to upper or lower case.

    Add a new CharStream that converts the symbols to upper or lower case.

    This is useful for many of the case insensitive grammars found at https://github.com/antlr/grammars-v4/ which assume the input would be all upper or lower case. Related discussion can be found at https://github.com/antlr/antlr4test-maven-plugin/issues/1

    It would be used like so:

    input, _ := antlr.NewFileStream("filename")
    
    in = antlr.NewCaseChangingStream(is, true) // true forces upper case symbols, false forces lower case.
    
    lexer := parser.NewAbnfLexer(in)
    

    While writing this, I found other people have written their own similar implementations (go, java). It makes sense to place this in the core, so everyone can use it.

    I would love for the grammar to have a option that says the lexer should upper/lower case all input, and then this code could be moved into the generated Lexer, and no user would need to explicitly use a CaseChangingStream (similar to what's discussed in #1002).

    lexers comp:runtime target:java target:javascript target:go 
    opened by bramp 69
  • Swift Target

    Swift Target

    I did a quick search and I didn't see anything written about this yet. What's the likelihood of a Swift target for ANTLR?

    There are C#, Javascript, and Python targets at the moment.

    What does it take to implement a target? Given that Swift is more Java-like, it seems like it should be possible. Maybe start with a code translator if there is one for (Java to Swift), and iterate towards a more idiomatic implementation.

    type:question 
    opened by jeffreyguenther 69
  • Clean up ATN serialization: rm UUID and shifting by value of 2

    Clean up ATN serialization: rm UUID and shifting by value of 2

    • I think we don't need the UUID in the serialization, since it has not changed in a decade. We can bump the version number and remove the UU ID
    • I did some tests and there seems to be no reason to shift the values in the serialized ATN by 2 for the purposes of improving the UTF-8 encoding for the Java target.

    If you guys agree, we can make this small change for cleanup purposes. I'm happy to do it if you guys don't want to. The second fix will require changes to each target but it's trivial to fix.

    atn-analysis type:cleanup 
    opened by parrt 68
  • Preparing for 4.9.3 release

    Preparing for 4.9.3 release

    It's that time of year again! @pboyer, @mike-lischke, @janyou, @ewanmellor, @hanjoes, @ericvergnaud, @lingyv-li, @marcospassos Shall we do a 4.9.3 release?

    I went through and marked all of the merged PRs and related issues with 4.9.3 and try to tag them according to their target. Would you guys like to go through the PRs to see if there's something that should be merged quickly?

    opened by parrt 68
  • [CSharp] #2021 fixes nuget packaging options to avoid missing dll exceptions

    [CSharp] #2021 fixes nuget packaging options to avoid missing dll exceptions

    @ericvergnaud Hi, I modified csproj options a bit, now I can get a working nuget package locally without the issue we described in #2021. I added .net 3.5 as a target to "main" csproj along with netstandard, since it's easier to keep track of requirements for both sets of api's when editing code and, ideally, both targets can be packed into a nuget package with a single command. Right now it's possible only on Windows via msbuild /t:pack or Visual Studio; unfortunately, due to https://github.com/Microsoft/msbuild/issues/1333, right now dotnet build pack does not work for .net 3.5 target the way it should, so I adjusted the existing script to create packages from .nuspec and different solutions for different targets.

    comp:build target:csharp 
    opened by listerenko 68
  • A few updates to the Unicode documentation.

    A few updates to the Unicode documentation.

    It should be made clear that the recommended use of CharStreams.fromPath() is a Java-only solution. The other targets just have their ANTLRInputStream class extended to support full Unicode.

    comp:doc 
    opened by mike-lischke 61
  • Assorted problems in calling the wrong wrapper for reachesIntoOuterContext

    Assorted problems in calling the wrong wrapper for reachesIntoOuterContext

    This is a serious bug in the CSharp runtime, ParserATNSimulator.cs.

    reachesIntoOuterContext is an integer that tracks the depth of how far we dip into the outer context. This field must be interpreted with the bit map mask SUPPRESS_PRECEDENCE_FILTER, and careful attention placed on whether to access the field raw or with the bit map mask.

    In Java, this code accesses the field raw.

    In CSharp, the field is accessed through a method that applies the bit map mask. This is a serious problem in that it causes ATN parser trace divergence.

    I did a cursory check on the other targets, and I am not confident the field access is done correctly across targets.

    The fix is the change ParserATNInterpreter.cs:

    diff --git a/runtime/CSharp/src/Atn/ParserATNSimulator.cs b/runtime/CSharp/src/Atn/ParserATNSimulator.cs
    index 4a7a6a3d5..8463bfcc3 100644
    --- a/runtime/CSharp/src/Atn/ParserATNSimulator.cs
    +++ b/runtime/CSharp/src/Atn/ParserATNSimulator.cs
    @@ -1579,7 +1579,7 @@ namespace Antlr4.Runtime.Atn
     						// This assignment also propagates the
     						// isPrecedenceFilterSuppressed() value to the new
     						// configuration.
    -						c.reachesIntoOuterContext = config.OuterContextDepth;
    +                                                c.reachesIntoOuterContext = config.reachesIntoOuterContext;
     						ClosureCheckingStopState(c, configSet, closureBusy, collectPredicates,
     												 fullCtx, depth - 1, treatEofAsEpsilon);
     					}
    

    (Note, the source code should really not be using tabs. Tab expansion is editor specific.)

    atn-analysis type:bug target:csharp 
    opened by kaby76 3
  • Python runtime test failure with 4.10 and later

    Python runtime test failure with 4.10 and later

    After upgrading the antlr4 python runtime past 4.9.1 (tested 4.10.1 and 4.11.1) in nixpkgs, we've been seeing its test suite fail with

    Traceback (most recent call last):
      File "/build/source/runtime/Python3/tests/ctest.py", line 10, in <module>
        from parser.cparser import CParser
      File "/build/source/runtime/Python3/tests/parser/cparser.py", line 631, in <module>
        class CParser ( Parser ):
      File "/build/source/runtime/Python3/tests/parser/cparser.py", line 635, in CParser
        atn = ATNDeserializer().deserialize(serializedATN())
      File "/nix/store/ch8i929c63av55h9nxkinifh61mazf1h-python3.10-antlr4-python3-runtime-4.11.1/lib/python3.10/site-packages/antlr4/atn/ATNDeserializer.py", line 28, in deserialize
        self.checkVersion()
      File "/nix/store/ch8i929c63av55h9nxkinifh61mazf1h-python3.10-antlr4-python3-runtime-4.11.1/lib/python3.10/site-packages/antlr4/atn/ATNDeserializer.py", line 50, in checkVersion
        raise Exception("Could not deserialize ATN with version " + str(version) + " (expected " + str(SERIALIZED_VERSION) + ").")
    Exception: Could not deserialize ATN with version  (expected 4).
    

    The version returned seems to be \x03, while the test suite expects it to be 4.

    While similar to #3997 and #3895 we are running the test suite, not our own code.

    We're running python ctest.py from the tests directory.

    opened by mweinelt 4
  • Suggestion for the Test Rig GUI tool

    Suggestion for the Test Rig GUI tool

    This is not a defect or reporting a problem. It is a suggestion that the GUI tool have a new button added between "OK" and "Export as PNG". That can be named "Reload".

    The idea being that I can edit/save a test source file and just click "reload" rather than doing what I do which is close the tool and go back to my command window and rerun the batch file that starts the GUI.

    Thoughts?

    opened by Korporal 0
  •  invalid syntax `self.from = None` in python3 generated parsers

    invalid syntax `self.from = None` in python3 generated parsers

    Please include the following information

    python3 antlr4 version is 4.9.3

    • smallest possible grammar and code that reproduces the behavior
    import sys
    import antlr4
    from PrestoSqlLexer import PrestoSqlLexer
    from PrestoSqlParser import PrestoSqlParser
    
    
    def main():
        input_stream = antlr4.CharStream('select 1')
        lexer = PrestoSqlLexer(input_stream)
        tokens = antlr4.CommonTokenStream(lexer)
        tokens.fill()
        print([token.text for token in tokens.tokens][:-1])
    
    
    if __name__ == '__main__':
        main()
    
    • description of the expected behavior and actual behavior Pointers to suspicious code regions are also very welcome.

    error:

    Traceback (most recent call last):
      File "/Users/clients/autocomplete/parseQuery.py", line 4, in <module>
        from PrestoSqlParser import PrestoSqlParser
      File "/Users/clients/autocomplete/PrestoSqlParser.py", line 1837
        self.from = None # QualifiedNameContext
             ^^^^
    SyntaxError: invalid syntax
    
    opened by chengchengpei 2
Releases(4.11.1)
Owner
Antlr Project
The Project organization for the ANTLR parser generator.
Antlr Project
Toy example of an applied ML pipeline for me to experiment with MLOps tools.

Toy Machine Learning Pipeline Table of Contents About Getting Started ML task description and evaluation procedure Dataset description Repository stru

Shreya Shankar 190 Dec 21, 2022
Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.

Introduction The goal of this analysis is to find a model that fits the observed cumulative cases of COVID-19 in the US, starting in Mid-July 2021 and

Alexander Keeney 1 Jan 05, 2022
A simple visual front end to the Maya UE4 RBF plugin delivered with MetaHumans

poseWrangler Overview PoseWrangler is a simple UI to create and edit pose-driven relationships in Maya using the MayaUE4RBF plugin. This plugin is dis

Christopher Evans 105 Dec 18, 2022
Nested Named Entity Recognition

Nested Named Entity Recognition Training Dataset: CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark url: https://tianchi.aliyun.

8 Dec 25, 2022
[ICLR 2021 Spotlight] Pytorch implementation for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."

RIDE: Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. by Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu and Stella X. Yu at UC

Xudong (Frank) Wang 205 Dec 16, 2022
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Sergio Burdisso 285 Jan 02, 2023
The PyTorch based implementation of continuous integrate-and-fire (CIF) module.

CIF-PyTorch This is a PyTorch based implementation of continuous integrate-and-fire (CIF) module for end-to-end (E2E) automatic speech recognition (AS

Minglun Han 24 Dec 29, 2022
Global Rhythm Style Transfer Without Text Transcriptions

Global Prosody Style Transfer Without Text Transcriptions This repository provides a PyTorch implementation of AutoPST, which enables unsupervised glo

Kaizhi Qian 193 Dec 30, 2022
Korean Simple Contrastive Learning of Sentence Embeddings using SKT KoBERT and kakaobrain KorNLU dataset

KoSimCSE Korean Simple Contrastive Learning of Sentence Embeddings implementation using pytorch SimCSE Installation git clone https://github.com/BM-K/

34 Nov 24, 2022
This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If

1.1k Dec 27, 2022
Translates basic English sentences into the Huna language (hoo-NAH)

huna-translator The Huna Language Translates basic English sentences into the Huna language (hoo-NAH). The Huna constructed language was developed in

Miles Smith 0 Jan 20, 2022
An assignment from my grad-level data mining course demonstrating some experience with NLP/neural networks/Pytorch

NLP-Pytorch-Assignment An assignment from my grad-level data mining course (before I started personal projects) demonstrating some experience with NLP

David Thorne 0 Feb 06, 2022
The implementation of Parameter Differentiation based Multilingual Neural Machine Translation

The implementation of Parameter Differentiation based Multilingual Neural Machine Translation .

Qian Wang 21 Dec 17, 2022
Chinese NewsTitle Generation Project by GPT2.带有超级详细注释的中文GPT2新闻标题生成项目。

GPT2-NewsTitle 带有超详细注释的GPT2新闻标题生成项目 UpDate 01.02.2021 从网上收集数据,将清华新闻数据、搜狗新闻数据等新闻数据集,以及开源的一些摘要数据进行整理清洗,构建一个较完善的中文摘要数据集。 数据集清洗时,仅进行了简单地规则清洗。

logCong 785 Dec 29, 2022
Text-Based zombie apocalyptic decision-making game in Python

Inspiration We shared university first year game coursework.[to gauge previous experience and start brainstorming] Adapted a particular nuclear fallou

Amin Sabbagh 2 Feb 17, 2022
BiQE: Code and dataset for the BiQE paper

BiQE: Bidirectional Query Embedding This repository includes code for BiQE and the datasets introduced in Answering Complex Queries in Knowledge Graph

Bhushan Kotnis 1 Oct 20, 2021
Fake news detector filters - Smart filter project allow to classify the quality of information and web pages

fake-news-detector-1.0 Lists, lists and more lists... Spam filter list, quality keyword list, stoplist list, top-domains urls list, news agencies webs

Memo Sim 1 Jan 04, 2022
Search with BERT vectors in Solr and Elasticsearch

Search with BERT vectors in Solr and Elasticsearch

Dmitry Kan 123 Dec 29, 2022
Code for the project carried out fulfilling the course requirements for Fall 2021 NLP at NYU

Introduction Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization,

Sai Himal Allu 1 Apr 25, 2022
Winner system (DAMO-NLP) of SemEval 2022 MultiCoNER shared task over 10 out of 13 tracks.

KB-NER: a Knowledge-based System for Multilingual Complex Named Entity Recognition The code is for the winner system (DAMO-NLP) of SemEval 2022 MultiC

116 Dec 27, 2022