Lerche (German for Lark) is a partial port of the Lark grammar processor from Python to Julia. Lark grammars should work unchanged in Lerche.

Installation: at the Julia REPL, using Pkg; Pkg.add("Lerche")

Documentation:

See also 'Notes for Lark users' below.

Lerche reads Lark EBNF grammars to produce a parser. This parser, when provided with text conforming to the grammar, produces a parse tree. This tree can be visited and transformed using "rules". A rule is a function named after the production whose arguments it should be called on, and the first argument of a rule is an object which is a subtype of Visitor or Transformer.

Given an EBNF grammar, it can be used to parse text into your data structure as follows:

1. Define one or more subtypes of Transformer or Visitor instances of which will be passed as the first argument to the appropriate rule. The instance can also be used to hold information during transformation if you wish, in which case it must have a concrete type.
2. Define visit_tokens(t::MyNewType) = false if you will not be processing token values. This is about 25% faster than leaving the default true.
3. For every production in your grammar that you wish to process, write a rule with identical name to the production
4. The rule should be prefixed with macro @rule if the second argument is an array containing all of the arguments to the grammar production
5. The rule should be prefixed with macro @inline_rule if the second and following arguments refer to each argument in the grammar production
6. For every token which you wish to process, define an identically-named method as for rules, but precede it with a @terminal macro instead of @rule.

If your grammar is in String variable mygrammar, your text to be parsed and transformed is in String variable mytext, and your Transformer subtype is MyTransformer, the following commands will produce a data structure from the text:

using Lerche
p = Lark(mygrammar,parser="lalr",lexer="contextual") #create parser
t = Lerche.parse(p,mytext)     #Create parse tree
x = Lerche.transform(MyTransformer(),t)  #transform parse tree

For a real-world example of usage, see this file.

If you are publishing work where Lerche has been useful, please consider citing the Lerche paper.

Please raise any issues or problems with using Lerche in the Github issue tracker.

Contributions of all types are welcome. Examples include:

• Improvements to processing speed
• Improved documentation
• Links to projects using Lerche
• Commenting and triaging issues

The most straightforward way to make a contribution is to fork the repository, make your changes, and create a pull request.

Please read the Lark documentation. When converting from Lark programs written in Python to Lerche programs written in Julia, the changes outlined below are necessary.

1. All Transformer and Visitor classes become subtypes of Transformer/Visitor
2. All class method calls become Julia method calls with an instance of the type as the first argument (i.e. replacing self)
3. Transformation or visitor rules should be preceded by the @rule macro. Inline rules use the @inline_rule macro and token processing methods use @terminal.
4. The first argument of transformer and visitor rules is a variable of the desired transformer/visitor type.
5. Any grammars containing backslash-double quote sequences need to be fixed (see below).
6. Any grammars containing backslash-x to denote a byte value need to be fixed (see below).

1. Earley and CYK grammars are not implemented.
2. Dynamic lexer is not implemented.
3. All errors with messages attached must be at the bottom of the exception type hierarchy, as these are the only types that can have contents. Thus an UnexpectedInput exception must become e.g an UnexpectedCharacter exception if a message is included.
4. The PuppetParser invoked when there is a parse error is not yet functional
5. There may be issues with correctly interpreting import paths to find imported grammars: please raise an issue if this happens.
6. No choice of regex engine, Tree structure or byte/string choices are available as they make no sense for Julia.

Lerche is currently based off Lark 0.11.1. The priority has been on maintaining fidelity with Lark. For example, global regex flags which are integers in Lark are still integers in Lerche, which means you will need to look their values up. This may be changed to a more Julian approach in future.

The @rule and @inline_rule macros define methods of Lerche function transformer_func. Julia multiple dispatch is used to select the appropriate method at runtime. @terminal similarly defines methods of token_func.

Parsing a large (500K) file suggest Lerche is about 3 times faster than Lark with CPython for parsing. Parser generation is much slower as no optimisation techniques have been applied (yet). Calculating and storing your grammar in a Julia const variable at the top level of your package will allow it to be precompiled and thus avoid grammar re-analysis each time your package is loaded.