LARK(7)

NAME
lark - Lark Documentation

PHILOSOPHY

Parsers are innately complicated and confusing. They're difficult to understand, difficult to write, and difficult to use. Even experts on the subject can become baffled by the nuances of these complicated state-machines.

Lark's mission is to make the process of writing them as simple and abstract as possible, by following these design principles:

  Design Principles

    •   Readability matters

    •   Keep the grammar clean and simple

    •   Don't force the user to decide on things that the parser can figure out on its own

    •   Usability is more important than performance

    •   Performance is still very important

    •   Follow the Zen of Python, whenever possible and applicable

In accordance with these principles, I arrived at the following design choices:

----

  Design Choices

  1. Separation of code and grammar

Grammars are the de-facto reference for your language, and for the structure of your parse-tree. For any non-trivial language, the conflation of code and grammar always turns out convoluted and difficult to read.

The grammars in Lark are EBNF-inspired, so they are especially easy to read & work with.

  2. Always build a parse-tree (unless told not to)

Trees are always simpler to work with than state-machines.

    •   Trees allow you to see the "state-machine" visually

    •   Trees allow your computation to be aware of previous and future states

    •   Trees allow you to process the parse in steps, instead of forcing you to do it all at once.

And anyway, every parse-tree can be replayed as a state-machine, so there is no loss of information.

See this answer in more detail here <https://github.com/erezsh/lark/issues/4>.

To improve performance, you can skip building the tree for LALR(1), by providing Lark with a transformer (see the JSON example <https://github.com/erezsh/lark/blob/master/examples/json_parser.py>).

  3. Earley is the default

The Earley algorithm can accept any context-free grammar you throw at it (i.e. any grammar you can write in EBNF, it can parse). That makes it extremely friendly to beginners, who are not aware of the strange and arbitrary restrictions that LALR(1) places on its grammars.

As the users grow to understand the structure of their grammar, the scope of their target language, and their performance requirements, they may choose to switch over to LALR(1) to gain a huge performance boost, possibly at the cost of some language features.

Both Earley and LALR(1) can use the same grammar, as long as all constraints are satisfied.

In short, "Premature optimization is the root of all evil."

  Other design features

    •   Automatically resolve terminal collisions whenever possible

    •   Automatically keep track of line & column numbers

FEATURES

  Main Features

    •   Earley parser, capable of parsing any context-free grammar

        •   Implements SPPF, for efficient parsing and storing of ambiguous grammars.

    •   LALR(1) parser, limited in power of expression, but very efficient in space and performance (O(n)).

        •   Implements a parse-aware lexer that provides a better power of expression than traditional LALR implementations (such as ply).

    •   EBNF-inspired grammar, with extra features (See: Grammar Reference <>)

    •   Builds a parse-tree (AST) automagically based on the grammar

    •   Stand-alone parser generator - create a small independent parser to embed in your project. (read more <tools.html#stand-alone-parser>)

    •   Flexible error handling by using an interactive parser interface (LALR only)

    •   Automatic line & column tracking (for both tokens and matched rules)

    •   Automatic terminal collision resolution

    •   Warns on regex collisions using the optional interegular library. (read more <how_to_use.html#regex-collisions>)

    •   Grammar composition - Import terminals and rules from other grammars (see example <https://github.com/lark-parser/lark/tree/master/examples/composition>).

    •   Standard library of terminals (strings, numbers, names, etc.)

    •   Unicode fully supported

    •   Extensive test suite

    •   Type annotations (MyPy support)

    •   Pure-Python implementation

Read more about the parsers <>

  Extra features

    •   Support for external regex module (see here <classes.html#using-unicode-character-classes-with-regex>)

    •   Import grammars from Nearley.js (read more <tools.html#importing-grammars-from-nearleyjs>)

    •   CYK parser

    •   Visualize your parse trees as dot or png files (see_example <https://github.com/lark-parser/lark/blob/master/examples/fruitflies.py>)

    •   Automatic reconstruction of input from parse-tree (see example <https://github.com/lark-parser/lark/blob/master/examples/advanced/reconstruct_json.py> and another example <https://github.com/lark-parser/lark/blob/master/examples/advanced/reconstruct_python.py>)

    •   Use Lark grammars in Julia <https://github.com/jamesrhester/Lerche.jl> and Javascript <https://github.com/lark-parser/Lark.js>.

PARSERS

Lark implements the following parsing algorithms: Earley, LALR(1), and CYK

  Earley

An Earley Parser <https://www.wikiwand.com/en/Earley_parser> is a chart parser capable of parsing any context-free grammar at O(n^3), and O(n^2) when the grammar is unambiguous. It can parse most LR grammars at O(n). Most programming languages are LR, and can be parsed at a linear time.

Lark's Earley implementation runs on top of a skipping chart parser, which allows it to use regular expressions, instead of matching characters one-by-one. This is a huge improvement to Earley that is unique to Lark. This feature is used by default, but can also be requested explicitly using lexer='dynamic'.

It's possible to bypass the dynamic lexing, and use the regular Earley parser with a basic lexer, that tokenizes as an independent first step. Doing so will provide a speed benefit, but will tokenize without using Earley's ambiguity-resolution ability. So choose this only if you know why! Activate with lexer='basic'

SPPF & Ambiguity resolution

Lark implements the Shared Packed Parse Forest data-structure for the Earley parser, in order to reduce the space and computation required to handle ambiguous grammars.

You can read more about SPPF here <https://web.archive.org/web/20191229100607/www.bramvandersanden.com/post/2014/06/shared-packed-parse-forest>

As a result, Lark can efficiently parse and store every ambiguity in the grammar, when using Earley.

Lark provides the following options to combat ambiguity:

    •   Lark will choose the best derivation for you (default). Users can choose between different disambiguation strategies, and can prioritize (or demote) individual rules over others, using the rule-priority syntax.

    •   Users may choose to receive the set of all possible parse-trees (using ambiguity='explicit'), and choose the best derivation themselves. While simple and flexible, it comes at the cost of space and performance, and so it isn't recommended for highly ambiguous grammars, or very long inputs.

    •   As an advanced feature, users may use specialized visitors to iterate the SPPF themselves. There is also a 3rd party utility for iterating over the SPPF <https://github.com/chanicpanic/lark-ambig-tools>.

lexer="dynamic_complete"

Earley's "dynamic" lexer uses regular expressions in order to tokenize the text. It tries every possible combination of terminals, but it matches each terminal exactly once, returning the longest possible match.

That means, for example, that when lexer="dynamic" (which is the default), the terminal /a+/, when given the text "aa", will return one result, aa, even though a would also be correct.

This behavior was chosen because it is much faster, and it is usually what you would expect.

Setting lexer="dynamic_complete" instructs the lexer to consider every possible regexp match. This ensures that the parser will consider and resolve every ambiguity, even inside the terminals themselves. This lexer provides the same capabilities as scannerless Earley, but with different performance tradeoffs.

Warning: This lexer can be much slower, especially for open-ended terminals such as /.*/

  LALR(1)

LALR(1) <https://www.wikiwand.com/en/LALR_parser> is a very efficient, true-and-tested parsing algorithm. It's incredibly fast and requires very little memory. It can parse most programming languages (For example: Python and Java).

LALR(1) stands for:

    •   Left-to-right parsing order

    •   Rightmost derivation, bottom-up

    •   Lookahead of 1 token

Lark comes with an efficient implementation that outperforms every other parsing library for Python (including PLY)

Lark extends the traditional YACC-based architecture with a contextual lexer, which processes feedback from the parser, making the LALR(1) algorithm stronger than ever.

The contextual lexer communicates with the parser, and uses the parser's lookahead prediction to narrow its choice of terminals. So at each point, the lexer only matches the subgroup of terminals that are legal at that parser state, instead of all of the terminals. It’s surprisingly effective at resolving common terminal collisions, and allows one to parse languages that LALR(1) was previously incapable of parsing.

(If you're familiar with YACC, you can think of it as automatic lexer-states)

This is an improvement to LALR(1) that is unique to Lark.

  Grammar constraints in LALR(1)

Due to having only a lookahead of one token, LALR is limited in its ability to choose between rules, when they both match the input.

Tips for writing a conforming grammar:

    •   Try to avoid writing different rules that can match the same sequence of characters.

    •   For the best performance, prefer left-recursion over right-recursion.

    •   Consider setting terminal priority only as a last resort.

For a better understanding of these constraints, it's recommended to learn how a SLR parser works. SLR is very similar to LALR but much simpler.

  CYK Parser

A CYK parser <https://www.wikiwand.com/en/CYK_algorithm> can parse any context-free grammar at O(n^3*|G|).

Its too slow to be practical for simple grammars, but it offers good performance for highly ambiguous grammars.

JSON PARSER - TUTORIAL

Lark is a parser - a program that accepts a grammar and text, and produces a structured tree that represents that text. In this tutorial we will write a JSON parser in Lark, and explore Lark's various features in the process.

It has 5 parts.

    •   Writing the grammar

    •   Creating the parser

    •   Shaping the tree

    •   Evaluating the tree

    •   Optimizing

Knowledge assumed:

    •   Using Python

    •   A basic understanding of how to use regular expressions

  Part 1 - The Grammar

Lark accepts its grammars in a format called EBNF <https://www.wikiwand.com/en/Extended_Backus%E2%80%93Naur_form>. It basically looks like this:

            rule_name : list of rules and TERMINALS to match
                      | another possible list of items
                      | etc.

            TERMINAL: "some text to match"

(a terminal is a string or a regular expression)

The parser will try to match each rule (left-part) by matching its items (right-part) sequentially, trying each alternative (In practice, the parser is predictive so we don't have to try every alternative).

How to structure those rules is beyond the scope of this tutorial, but often it's enough to follow one's intuition.

In the case of JSON, the structure is simple: A json document is either a list, or a dictionary, or a string/number/etc.

The dictionaries and lists are recursive, and contain other json documents (or "values").

Let's write this structure in EBNF form:

                value: dict
                     | list
                     | STRING
                     | NUMBER
                     | "true" | "false" | "null"

                list : "[" [value ("," value)*] "]"

                dict : "{" [pair ("," pair)*] "}"
                pair : STRING ":" value

A quick explanation of the syntax:

    •   Parenthesis let us group rules together.

    •   rule* means any amount. That means, zero or more instances of that rule.

    •   [rule] means optional. That means zero or one instance of that rule.

Lark also supports the rule+ operator, meaning one or more instances. It also supports the rule? operator which is another way to say optional.

Of course, we still haven't defined "STRING" and "NUMBER". Luckily, both these literals are already defined in Lark's common library:

                %import common.ESCAPED_STRING   -> STRING
                %import common.SIGNED_NUMBER    -> NUMBER

The arrow (->) renames the terminals. But that only adds obscurity in this case, so going forward we'll just use their original names.

We'll also take care of the white-space, which is part of the text, by simply matching and then throwing it away.

                %import common.WS
                %ignore WS

We tell our parser to ignore whitespace. Otherwise, we'd have to fill our grammar with WS terminals.

By the way, if you're curious what these terminals signify, they are roughly equivalent to this:

                NUMBER : /-?\d+(\.\d+)?([eE][+-]?\d+)?/
                STRING : /".*?(?<!\\)"/
                %ignore /[ \t\n\f\r]+/

Lark will accept this way of writing too, if you really want to complicate your life :)

You can find the original definitions in common.lark <https://github.com/lark-parser/lark/blob/master/lark/grammars/common.lark>. They don't strictly adhere to json.org <https://json.org/> - but our purpose here is to accept json, not validate it.

Notice that terminals are written in UPPER-CASE, while rules are written in lower-case. I'll touch more on the differences between rules and terminals later.

  Part 2 - Creating the Parser

Once we have our grammar, creating the parser is very simple.

We simply instantiate Lark, and tell it to accept a "value":

            from lark import Lark
            json_parser = Lark(r"""
                value: dict
                     | list
                     | ESCAPED_STRING
                     | SIGNED_NUMBER
                     | "true" | "false" | "null"

                list : "[" [value ("," value)*] "]"

                dict : "{" [pair ("," pair)*] "}"
                pair : ESCAPED_STRING ":" value

                %import common.ESCAPED_STRING
                %import common.SIGNED_NUMBER
                %import common.WS
                %ignore WS

                """, start='value')

It's that simple! Let's test it out:

            >>> text = '{"key": ["item0", "item1", 3.14]}'
            >>> json_parser.parse(text)
            Tree(value, [Tree(dict, [Tree(pair, [Token(STRING, "key"), Tree(value, [Tree(list, [Tree(value, [Token(STRING, "item0")]), Tree(value, [Token(STRING, "item1")]), Tree(value, [Token(NUMBER, 3.14)])])])])])])
            >>> print( _.pretty() )
            value
              dict
                pair
                  "key"
                  value
                    list
                      value	"item0"
                      value	"item1"
                      value	3.14

As promised, Lark automagically creates a tree that represents the parsed text.

But something is suspiciously missing from the tree. Where are the curly braces, the commas and all the other punctuation literals?

Lark automatically filters out literals from the tree, based on the following criteria:

    •   Filter out string literals without a name, or with a name that starts with an underscore.

    •   Keep regexps, even unnamed ones, unless their name starts with an underscore.

Unfortunately, this means that it will also filter out literals like "true" and "false", and we will lose that information. The next section, "Shaping the tree" deals with this issue, and others.

  Part 3 - Shaping the Tree

We now have a parser that can create a parse tree (or: AST), but the tree has some issues:

    •   "true", "false" and "null" are filtered out (test it out yourself!)

    •   Is has useless branches, like value, that clutter-up our view.

I'll present the solution, and then explain it:

                ?value: dict
                      | list
                      | string
                      | SIGNED_NUMBER      -> number
                      | "true"             -> true
                      | "false"            -> false
                      | "null"             -> null

                ...

                string : ESCAPED_STRING

    •   Those little arrows signify aliases. An alias is a name for a specific part of the rule. In this case, we will name the true/false/null matches, and this way we won't lose the information. We also alias SIGNED_NUMBER to mark it for later processing.

    •   The question-mark prefixing value ("?value") tells the tree-builder to inline this branch if it has only one member. In this case, value will always have only one member, and will always be inlined.

    •   We turned the ESCAPED_STRING terminal into a rule. This way it will appear in the tree as a branch. This is equivalent to aliasing (like we did for the number), but now string can also be used elsewhere in the grammar (namely, in the pair rule).

Here is the new grammar:

            from lark import Lark
            json_parser = Lark(r"""
                ?value: dict
                      | list
                      | string
                      | SIGNED_NUMBER      -> number
                      | "true"             -> true
                      | "false"            -> false
                      | "null"             -> null

                list : "[" [value ("," value)*] "]"

                dict : "{" [pair ("," pair)*] "}"
                pair : string ":" value

                string : ESCAPED_STRING

                %import common.ESCAPED_STRING
                %import common.SIGNED_NUMBER
                %import common.WS
                %ignore WS

                """, start='value')

And let's test it out:

            >>> text = '{"key": ["item0", "item1", 3.14, true]}'
            >>> print( json_parser.parse(text).pretty() )
            dict
              pair
                string	"key"
                list
                  string	"item0"
                  string	"item1"
                  number	3.14
                  true

Ah! That is much much nicer.

  Part 4 - Evaluating the tree

It's nice to have a tree, but what we really want is a JSON object.

The way to do it is to evaluate the tree, using a Transformer.

A transformer is a class with methods corresponding to branch names. For each branch, the appropriate method will be called with the children of the branch as its argument, and its return value will replace the branch in the tree.

So let's write a partial transformer, that handles lists and dictionaries:

            from lark import Transformer

            class MyTransformer(Transformer):
                def list(self, items):
                    return list(items)
                def pair(self, key_value):
                    k, v = key_value
                    return k, v
                def dict(self, items):
                    return dict(items)

And when we run it, we get this:

            >>> tree = json_parser.parse(text)
            >>> MyTransformer().transform(tree)
            {Tree(string, [Token(ANONRE_1, "key")]): [Tree(string, [Token(ANONRE_1, "item0")]), Tree(string, [Token(ANONRE_1, "item1")]), Tree(number, [Token(ANONRE_0, 3.14)]), Tree(true, [])]}

This is pretty close. Let's write a full transformer that can handle the terminals too.

Also, our definitions of list and dict are a bit verbose. We can do better:

            from lark import Transformer

            class TreeToJson(Transformer):
                def string(self, s):
                    (s,) = s
                    return s[1:-1]
                def number(self, n):
                    (n,) = n
                    return float(n)

                list = list
                pair = tuple
                dict = dict

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

And when we run it:

            >>> tree = json_parser.parse(text)
            >>> TreeToJson().transform(tree)
            {u'key': [u'item0', u'item1', 3.14, True]}

Magic!

  Part 5 - Optimizing

  Step 1 - Benchmark

By now, we have a fully working JSON parser, that can accept a string of JSON, and return its Pythonic representation.

But how fast is it?

Now, of course there are JSON libraries for Python written in C, and we can never compete with them. But since this is applicable to any parser you would write in Lark, let's see how far we can take this.

The first step for optimizing is to have a benchmark. For this benchmark I'm going to take data from json-generator.com/ <http://www.json-generator.com/>. I took their default suggestion and changed it to 5000 objects. The result is a 6.6MB sparse JSON file.

Our first program is going to be just a concatenation of everything we've done so far:

            import sys
            from lark import Lark, Transformer

            json_grammar = r"""
                ?value: dict
                      | list
                      | string
                      | SIGNED_NUMBER      -> number
                      | "true"             -> true
                      | "false"            -> false
                      | "null"             -> null

                list : "[" [value ("," value)*] "]"

                dict : "{" [pair ("," pair)*] "}"
                pair : string ":" value

                string : ESCAPED_STRING

                %import common.ESCAPED_STRING
                %import common.SIGNED_NUMBER
                %import common.WS
                %ignore WS
                """

            class TreeToJson(Transformer):
                def string(self, s):
                    (s,) = s
                    return s[1:-1]
                def number(self, n):
                    (n,) = n
                    return float(n)

                list = list
                pair = tuple
                dict = dict

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

            json_parser = Lark(json_grammar, start='value', lexer='basic')

            if __name__ == '__main__':
                with open(sys.argv[1]) as f:
                    tree = json_parser.parse(f.read())
                    print(TreeToJson().transform(tree))

We run it and get this:

            $ time python tutorial_json.py json_data > /dev/null

            real	0m36.257s
            user	0m34.735s
            sys         0m1.361s

That's unsatisfactory time for a 6MB file. Maybe if we were parsing configuration or a small DSL, but we're trying to handle large amount of data here.

Well, turns out there's quite a bit we can do about it!

  Step 2 - LALR(1)

So far we've been using the Earley algorithm, which is the default in Lark. Earley is powerful but slow. But it just so happens that our grammar is LR-compatible, and specifically LALR(1) compatible.

So let's switch to LALR(1) and see what happens:

            json_parser = Lark(json_grammar, start='value', parser='lalr')

            $ time python tutorial_json.py json_data > /dev/null

            real        0m7.554s
            user        0m7.352s
            sys         0m0.148s

Ah, that's much better. The resulting JSON is of course exactly the same. You can run it for yourself and see.

It's important to note that not all grammars are LR-compatible, and so you can't always switch to LALR(1). But there's no harm in trying! If Lark lets you build the grammar, it means you're good to go.

  Step 3 - Tree-less LALR(1)

So far, we've built a full parse tree for our JSON, and then transformed it. It's a convenient method, but it's not the most efficient in terms of speed and memory. Luckily, Lark lets us avoid building the tree when parsing with LALR(1).

Here's the way to do it:

            json_parser = Lark(json_grammar, start='value', parser='lalr', transformer=TreeToJson())

            if __name__ == '__main__':
                with open(sys.argv[1]) as f:
                    print( json_parser.parse(f.read()) )

We've used the transformer we've already written, but this time we plug it straight into the parser. Now it can avoid building the parse tree, and just send the data straight into our transformer. The parse() method now returns the transformed JSON, instead of a tree.

Let's benchmark it:

            real	0m4.866s
            user	0m4.722s
            sys 	0m0.121s

That's a measurable improvement! Also, this way is more memory efficient. Check out the benchmark table at the end to see just how much.

As a general practice, it's recommended to work with parse trees, and only skip the tree-builder when your transformer is already working.

  Step 4 - PyPy

PyPy is a JIT engine for running Python, and it's designed to be a drop-in replacement.

Lark is written purely in Python, which makes it very suitable for PyPy.

Let's get some free performance:

            $ time pypy tutorial_json.py json_data > /dev/null

            real	0m1.397s
            user	0m1.296s
            sys 	0m0.083s

PyPy is awesome!

  Conclusion

We've brought the run-time down from 36 seconds to 1.1 seconds, in a series of small and simple steps.

Now let's compare the benchmarks in a nicely organized table.

I measured memory consumption using a little script called memusg <https://gist.github.com/netj/526585>

I added a few other parsers for comparison. PyParsing and funcparselib fair pretty well in their memory usage (they don't build a tree), but they can't compete with the run-time speed of LALR(1).

These benchmarks are for Lark's alpha version. I already have several optimizations planned that will significantly improve run-time speed.

Once again, shout-out to PyPy for being so effective.

  Afterword

This is the end of the tutorial. I hoped you liked it and learned a little about Lark.

To see what else you can do with Lark, check out the examples </examples>.

Read the documentation here: https://lark-parser.readthedocs.io/en/latest/

HOW TO USE LARK - GUIDE

  Work process

This is the recommended process for working with Lark:

    •   Collect or create input samples, that demonstrate key features or behaviors in the language you're trying to parse.

    •   Write a grammar. Try to aim for a structure that is intuitive, and in a way that imitates how you would explain your language to a fellow human.

    •   Try your grammar in Lark against each input sample. Make sure the resulting parse-trees make sense.

    •   Use Lark's grammar features to shape the tree <>: Get rid of superfluous rules by inlining them, and use aliases when specific cases need clarification.

        You can perform steps 1-4 repeatedly, gradually growing your grammar to include more sentences.

    •   Create a transformer to evaluate the parse-tree into a structure you'll be comfortable to work with. This may include evaluating literals, merging branches, or even converting the entire tree into your own set of AST classes.

Of course, some specific use-cases may deviate from this process. Feel free to suggest these cases, and I'll add them to this page.

  Getting started

Browse the Examples <https://github.com/lark-parser/lark/tree/master/examples> to find a template that suits your purposes.

Read the tutorials to get a better understanding of how everything works. (links in the main page <>)

Use the Cheatsheet (PDF) <https://lark-parser.readthedocs.io/en/latest/_static/lark_cheatsheet.pdf> for quick reference.

Use the reference pages for more in-depth explanations. (links in the main page <>)

  Debug

Grammars may contain non-obvious bugs, usually caused by rules or terminals interfering with each other in subtle ways.

When trying to debug a misbehaving grammar, the following methodology is recommended:

    •   Create a copy of the grammar, so you can change the parser/grammar without any worries

    •   Find the minimal input that creates the error

    •   Slowly remove rules from the grammar, while making sure the error still occurs.

Usually, by the time you get to a minimal grammar, the problem becomes clear.

But if it doesn't, feel free to ask us on gitter, or even open an issue. Post a reproducing code, with the minimal grammar and input, and we'll do our best to help.

  Regex collisions

A likely source of bugs occurs when two regexes in a grammar can match the same input. If both terminals have the same priority, most lexers would arbitrarily choose the first one that matches, which isn't always the desired one. (a notable exception is the dynamic_complete lexer, which always tries all variations. But its users pay for that with performance.)

These collisions can be hard to notice, and their effects can be difficult to debug, as they are subtle and sometimes hard to reproduce.

To help with these situations, Lark can utilize a new external library called interegular. If it is installed, Lark uses it to check for collisions, and warn about any conflicts that it can find:

            import logging
            from lark import Lark, logger

            logger.setLevel(logging.WARN)

            collision_grammar = '''
            start: A | B
            A: /a+/
            B: /[ab]+/
            '''
            p = Lark(collision_grammar, parser='lalr')

            # Output:
            # Collision between Terminals B and A. The lexer will choose between them arbitrarily
            # Example Collision: a

You can install interegular for Lark using pip install 'lark[interegular]'.

Note 1: Interegular currently only runs when the lexer is basic or contextual.

Note 2: Some advanced regex features, such as lookahead and lookbehind, may prevent interegular from detecting existing collisions.

  Shift/Reduce collisions

By default Lark automatically resolves Shift/Reduce conflicts as Shift. It produces notifications as debug messages.

when users pass debug=True, those notifications are written as warnings.

Either way, to get the messages printed you have to configure the logger beforehand. For example:

            import logging
            from lark import Lark, logger

            logger.setLevel(logging.DEBUG)

            collision_grammar = '''
            start: as as
            as: a*
            a: "a"
            '''
            p = Lark(collision_grammar, parser='lalr', debug=True)
            # Shift/Reduce conflict for terminal A: (resolving as shift)
            #  * <as : >
            # Shift/Reduce conflict for terminal A: (resolving as shift)
            #  * <as : __as_star_0>

  Strict-Mode

Lark, by default, accepts grammars with unresolved Shift/Reduce collisions (which it always resolves to shift), and regex collisions.

Strict-mode allows users to validate that their grammars don't contain these collisions.

When Lark is initialized with strict=True, it raises an exception on any Shift/Reduce or regex collision.

If interegular isn't installed, an exception is thrown.

When using strict-mode, users will be expected to resolve their collisions manually:

    •   To resolve Shift/Reduce collisions, adjust the priority weights of the rules involved, until there are no more collisions.

    •   To resolve regex collisions, change the involved regexes so that they can no longer both match the same input (Lark provides an example).

Strict-mode only applies to LALR for now.

            from lark import Lark

            collision_grammar = '''
            start: as as
            as: a*
            a: "a"
            '''
            p = Lark(collision_grammar, parser='lalr', strict=True)

            # Traceback (most recent call last):
            #   ...
            # lark.exceptions.GrammarError: Shift/Reduce conflict for terminal A. [strict-mode]

  Tools

  Stand-alone parser

Lark can generate a stand-alone LALR(1) parser from a grammar.

The resulting module provides the same interface as Lark, but with a fixed grammar, and reduced functionality.

Run using:

            python -m lark.tools.standalone

For a play-by-play, read the tutorial <http://blog.erezsh.com/create-a-stand-alone-lalr1-parser-in-python/>

  Import Nearley.js grammars

It is possible to import Nearley grammars into Lark. The Javascript code is translated using Js2Py.

See the tools page <> for more information.

HOW TO DEVELOP LARK - GUIDE

There are many ways you can help the project:

    •   Help solve issues

    •   Improve the documentation

    •   Write new grammars for Lark's library

    •   Write a blog post introducing Lark to your audience

    •   Port Lark to another language

    •   Help with code development

If you're interested in taking one of these on, contact us on Gitter <https://gitter.im/lark-parser/Lobby> or Github Discussion <https://github.com/lark-parser/lark/discussions>, and we will provide more details and assist you in the process.

  Code Style

Lark does not follow a predefined code style. We accept any code style that makes sense, as long as it's Pythonic and easy to read.

  Unit Tests

Lark comes with an extensive set of tests. Many of the tests will run several times, once for each parser configuration.

To run the tests, just go to the lark project root, and run the command:

            python -m tests

or

            pypy -m tests

For a list of supported interpreters, you can consult the tox.ini file.

You can also run a single unittest using its class and method name, for example:

            ##   test_package test_class_name.test_function_name
            python -m tests TestLalrBasic.test_keep_all_tokens

  tox

To run all Unit Tests with tox, install tox and Python 2.7 up to the latest python interpreter supported (consult the file tox.ini). Then, run the command tox on the root of this project (where the main setup.py file is on).

And, for example, if you would like to only run the Unit Tests for Python version 2.7, you can run the command tox -e py27

  pytest

You can also run the tests using pytest:

            pytest tests

  Using setup.py

Another way to run the tests is using setup.py:

            python setup.py test

  Building the Documentation

To build the documentation:

            cd docs/
            pip install -r requirements.txt
            make html

To review the result, open the built HTML files under _build/html/ in your browser.

RECIPES

A collection of recipes to use Lark and its various features

  Use a transformer to parse integer tokens

Transformers are the common interface for processing matched rules and tokens.

They can be used during parsing for better performance.

            from lark import Lark, Transformer

            class T(Transformer):
                def INT(self, tok):
                    "Convert the value of `tok` from string to int, while maintaining line number & column."
                    return tok.update(value=int(tok))

            parser = Lark("""
            start: INT*
            %import common.INT
            %ignore " "
            """, parser="lalr", transformer=T())

            print(parser.parse('3 14 159'))

Prints out:

            Tree(start, [Token(INT, 3), Token(INT, 14), Token(INT, 159)])

  Collect all comments with lexer_callbacks

lexer_callbacks can be used to interface with the lexer as it generates tokens.

It accepts a dictionary of the form

            {TOKEN_TYPE: callback}

Where callback is of type f(Token) -> Token

It only works with the basic and contextual lexers.

This has the same effect of using a transformer, but can also process ignored tokens.

            from lark import Lark

            comments = []

            parser = Lark("""
                start: INT*

                COMMENT: /#.*/

                %import common (INT, WS)
                %ignore COMMENT
                %ignore WS
            """, parser="lalr", lexer_callbacks={'COMMENT': comments.append})

            parser.parse("""
            1 2 3  # hello
            # world
            4 5 6
            """)

            print(comments)

Prints out:

            [Token(COMMENT, '# hello'), Token(COMMENT, '# world')]

Note: We don't have to return a token, because comments are ignored

  CollapseAmbiguities

Parsing ambiguous texts with earley and ambiguity='explicit' produces a single tree with _ambig nodes to mark where the ambiguity occurred.

However, it's sometimes more convenient instead to work with a list of all possible unambiguous trees.

Lark provides a utility transformer for that purpose:

            from lark import Lark, Tree, Transformer
            from lark.visitors import CollapseAmbiguities

            grammar = """
                !start: x y

                !x: "a" "b"
                  | "ab"
                  | "abc"

                !y: "c" "d"
                  | "cd"
                  | "d"

            """
            parser = Lark(grammar, ambiguity='explicit')

            t = parser.parse('abcd')
            for x in CollapseAmbiguities().transform(t):
                print(x.pretty())

This prints out:

            start
            x
                a
                b
            y
                c
                d

            start
            x     ab
            y     cd

            start
            x     abc
            y     d

While convenient, this should be used carefully, as highly ambiguous trees will soon create an exponential explosion of such unambiguous derivations.

  Keeping track of parents when visiting

The following visitor assigns a parent attribute for every node in the tree.

If your tree nodes aren't unique (if there is a shared Tree instance), the assert will fail.

            class Parent(Visitor):
                def __default__(self, tree):
                    for subtree in tree.children:
                        if isinstance(subtree, Tree):
                            assert not hasattr(subtree, 'parent')
                            subtree.parent = proxy(tree)

  Unwinding VisitError after a transformer/visitor exception

Errors that happen inside visitors and transformers get wrapped inside a VisitError exception.

This can often be inconvenient, if you wish the actual error to propagate upwards, or if you want to catch it.

But, it's easy to unwrap it at the point of calling the transformer, by catching it and raising the VisitError.orig_exc attribute.

For example:

            from lark import Lark, Transformer
            from lark.visitors import VisitError

            tree = Lark('start: "a"').parse('a')

            class T(Transformer):
                def start(self, x):
                    raise KeyError("Original Exception")

            t = T()
            try:
                print( t.transform(tree))
            except VisitError as e:
                raise e.orig_exc

  Adding a Progress Bar to Parsing with tqdm

Parsing large files can take a long time, even with the parser='lalr' option. To make this process more user-friendly, it's useful to add a progress bar. One way to achieve this is to use the InteractiveParser to display each token as it is processed. In this example, we use tqdm <https://github.com/tqdm/tqdm>, but it should be easy to adapt to other kinds of progress bars.

            from tqdm import tqdm

            def parse_with_progress(parser: Lark, text: str, start=None):
                last = 0
                progress = tqdm(total=len(text))
                pi = parser.parse_interactive(text, start=start)
                for token in pi.iter_parse():
                    if token.end_pos is not None:
                        progress.update(token.end_pos - last)
                        last = token.end_pos
                return pi.resume_parse()    # Finish up and get the result

Keep in mind that this implementation relies on the InteractiveParser and, therefore, only works with the LALR(1) parser, and not Earley.

  Parsing a Language with Significant Indentation

If your grammar needs to support significant indentation (e.g. Python, YAML), you will need to use the Indenter class. Take a look at the indented tree example <examples/indented_tree.html> as well as the Python grammar <https://github.com/lark-parser/lark/blob/master/lark/grammars/python.lark> for inspiration.

EXAMPLES FOR LARK

How to run the examples:

After cloning the repo, open the terminal into the root directory of the project, and run the following:

            [lark]$ python -m examples.<name_of_example>

For example, the following will parse all the Python files in the standard library of your local installation:

            [lark]$ python -m examples.advanced.python_parser

  Beginner Examples

  Parsing Indentation

A demonstration of parsing indentation (“whitespace significant” language) and the usage of the Indenter class.

Since indentation is context-sensitive, a postlex stage is introduced to manufacture INDENT/DEDENT tokens.

It is crucial for the indenter that the NL_type matches the spaces (and tabs) after the newline.

If your whitespace-significant grammar supports comments, then NL_type must match those comments too. Otherwise, comments that appear in the middle of a line will confuse Lark <https://github.com/lark-parser/lark/issues/863>.

            from lark import Lark
            from lark.indenter import Indenter

            tree_grammar = r"""
                %import common.CNAME -> NAME
                %import common.WS_INLINE
                %import common.SH_COMMENT
                %ignore WS_INLINE
                %ignore SH_COMMENT
                %declare _INDENT _DEDENT

                ?start: _NL* tree
                tree: NAME _NL [_INDENT tree+ _DEDENT]
                _NL: (/\r?\n[\t ]*/ | SH_COMMENT)+
            """

            class TreeIndenter(Indenter):
                NL_type = '_NL'
                OPEN_PAREN_types = []
                CLOSE_PAREN_types = []
                INDENT_type = '_INDENT'
                DEDENT_type = '_DEDENT'
                tab_len = 8

            parser = Lark(tree_grammar, parser='lalr', postlex=TreeIndenter())

            test_tree = """
            a
                # check this comment out
                b
                c
                    d
                    e
                f
                    g
            """

            def test():
                print(parser.parse(test_tree).pretty())

            if __name__ == '__main__':
                test()

  Lark Grammar

A reference implementation of the Lark grammar (using LALR(1))

            import lark
            from pathlib import Path

            examples_path = Path(__file__).parent
            lark_path = Path(lark.__file__).parent

            parser = lark.Lark.open(lark_path / 'grammars/lark.lark', rel_to=__file__, parser="lalr")

            grammar_files = [
                examples_path / 'advanced/python2.lark',
                examples_path / 'relative-imports/multiples.lark',
                examples_path / 'relative-imports/multiple2.lark',
                examples_path / 'relative-imports/multiple3.lark',
                examples_path / 'tests/no_newline_at_end.lark',
                examples_path / 'tests/negative_priority.lark',
                examples_path / 'standalone/json.lark',
                lark_path / 'grammars/common.lark',
                lark_path / 'grammars/lark.lark',
                lark_path / 'grammars/unicode.lark',
                lark_path / 'grammars/python.lark',
            ]

            def test():
                for grammar_file in grammar_files:
                    tree = parser.parse(open(grammar_file).read())
                print("All grammars parsed successfully")

            if __name__ == '__main__':
                test()

  Handling Ambiguity

A demonstration of ambiguity

This example shows how to use get explicit ambiguity from Lark's Earley parser.

            import sys
            from lark import Lark, tree

            grammar = """
                sentence: noun verb noun        -> simple
                        | noun verb "like" noun -> comparative

                noun: adj? NOUN
                verb: VERB
                adj: ADJ

                NOUN: "flies" | "bananas" | "fruit"
                VERB: "like" | "flies"
                ADJ: "fruit"

                %import common.WS
                %ignore WS
            """

            parser = Lark(grammar, start='sentence', ambiguity='explicit')

            sentence = 'fruit flies like bananas'

            def make_png(filename):
                tree.pydot__tree_to_png( parser.parse(sentence), filename)

            def make_dot(filename):
                tree.pydot__tree_to_dot( parser.parse(sentence), filename)

            if __name__ == '__main__':
                print(parser.parse(sentence).pretty())
                # make_png(sys.argv[1])
                # make_dot(sys.argv[1])

            # Output:
            #
            # _ambig
            #   comparative
            #     noun  fruit
            #     verb  flies
            #     noun  bananas
            #   simple
            #     noun
            #       fruit
            #       flies
            #     verb  like
            #     noun  bananas
            #
            # (or view a nicer version at "./fruitflies.png")

  Basic calculator

A simple example of a REPL calculator

This example shows how to write a basic calculator with variables.

            from lark import Lark, Transformer, v_args

            try:
                input = raw_input   # For Python2 compatibility
            except NameError:
                pass

            calc_grammar = """
                ?start: sum
                      | NAME "=" sum    -> assign_var

                ?sum: product
                    | sum "+" product   -> add
                    | sum "-" product   -> sub

                ?product: atom
                    | product "*" atom  -> mul
                    | product "/" atom  -> div

                ?atom: NUMBER           -> number
                     | "-" atom         -> neg
                     | NAME             -> var
                     | "(" sum ")"

                %import common.CNAME -> NAME
                %import common.NUMBER
                %import common.WS_INLINE

                %ignore WS_INLINE
            """

            @v_args(inline=True)    # Affects the signatures of the methods
            class CalculateTree(Transformer):
                from operator import add, sub, mul, truediv as div, neg
                number = float

                def __init__(self):
                    self.vars = {}

                def assign_var(self, name, value):
                    self.vars[name] = value
                    return value

                def var(self, name):
                    try:
                        return self.vars[name]
                    except KeyError:
                        raise Exception("Variable not found: %s" % name)

            calc_parser = Lark(calc_grammar, parser='lalr', transformer=CalculateTree())
            calc = calc_parser.parse

            def main():
                while True:
                    try:
                        s = input('> ')
                    except EOFError:
                        break
                    print(calc(s))

            def test():
                print(calc("a = 1+2"))
                print(calc("1+a*-3"))

            if __name__ == '__main__':
                # test()
                main()

  Turtle DSL

Implements a LOGO-like toy language for Python’s turtle, with interpreter.

            try:
                input = raw_input   # For Python2 compatibility
            except NameError:
                pass

            import turtle

            from lark import Lark

            turtle_grammar = """
                start: instruction+

                instruction: MOVEMENT NUMBER            -> movement
                           | "c" COLOR [COLOR]          -> change_color
                           | "fill" code_block          -> fill
                           | "repeat" NUMBER code_block -> repeat

                code_block: "{" instruction+ "}"

                MOVEMENT: "f"|"b"|"l"|"r"
                COLOR: LETTER+

                %import common.LETTER
                %import common.INT -> NUMBER
                %import common.WS
                %ignore WS
            """

            parser = Lark(turtle_grammar)

            def run_instruction(t):
                if t.data == 'change_color':
                    turtle.color(*t.children)   # We just pass the color names as-is

                elif t.data == 'movement':
                    name, number = t.children
                    { 'f': turtle.fd,
                      'b': turtle.bk,
                      'l': turtle.lt,
                      'r': turtle.rt, }[name](int(number))

                elif t.data == 'repeat':
                    count, block = t.children
                    for i in range(int(count)):
                        run_instruction(block)

                elif t.data == 'fill':
                    turtle.begin_fill()
                    run_instruction(t.children[0])
                    turtle.end_fill()

                elif t.data == 'code_block':
                    for cmd in t.children:
                        run_instruction(cmd)
                else:
                    raise SyntaxError('Unknown instruction: %s' % t.data)

            def run_turtle(program):
                parse_tree = parser.parse(program)
                for inst in parse_tree.children:
                    run_instruction(inst)

            def main():
                while True:
                    code = input('> ')
                    try:
                        run_turtle(code)
                    except Exception as e:
                        print(e)

            def test():
                text = """
                    c red yellow
                    fill { repeat 36 {
                        f200 l170
                    }}
                """
                run_turtle(text)

            if __name__ == '__main__':
                # test()
                main()

  Simple JSON Parser

The code is short and clear, and outperforms every other parser (that's written in Python). For an explanation, check out the JSON parser tutorial at /docs/json_tutorial.md

            import sys

            from lark import Lark, Transformer, v_args

            json_grammar = r"""
                ?start: value

                ?value: object
                      | array
                      | string
                      | SIGNED_NUMBER      -> number
                      | "true"             -> true
                      | "false"            -> false
                      | "null"             -> null

                array  : "[" [value ("," value)*] "]"
                object : "{" [pair ("," pair)*] "}"
                pair   : string ":" value

                string : ESCAPED_STRING

                %import common.ESCAPED_STRING
                %import common.SIGNED_NUMBER
                %import common.WS

                %ignore WS
            """

            class TreeToJson(Transformer):
                @v_args(inline=True)
                def string(self, s):
                    return s[1:-1].replace('\\"', '"')

                array = list
                pair = tuple
                object = dict
                number = v_args(inline=True)(float)

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

            ### Create the JSON parser with Lark, using the Earley algorithm
            # json_parser = Lark(json_grammar, parser='earley', lexer='basic')
            # def parse(x):
            #     return TreeToJson().transform(json_parser.parse(x))

            ### Create the JSON parser with Lark, using the LALR algorithm
            json_parser = Lark(json_grammar, parser='lalr',
                               # Using the basic lexer isn't required, and isn't usually recommended.
                               # But, it's good enough for JSON, and it's slightly faster.
                               lexer='basic',
                               # Disabling propagate_positions and placeholders slightly improves speed
                               propagate_positions=False,
                               maybe_placeholders=False,
                               # Using an internal transformer is faster and more memory efficient
                               transformer=TreeToJson())
            parse = json_parser.parse

            def test():
                test_json = '''
                    {
                        "empty_object" : {},
                        "empty_array"  : [],
                        "booleans"     : { "YES" : true, "NO" : false },
                        "numbers"      : [ 0, 1, -2, 3.3, 4.4e5, 6.6e-7 ],
                        "strings"      : [ "This", [ "And" , "That", "And a \\"b" ] ],
                        "nothing"      : null
                    }
                '''

                j = parse(test_json)
                print(j)
                import json
                assert j == json.loads(test_json)

            if __name__ == '__main__':
                # test()
                with open(sys.argv[1]) as f:
                    print(parse(f.read()))

  Advanced Examples

GRAMMAR COMPOSITION

This example shows how to do grammar composition in Lark, by creating a new file format that allows both CSV and JSON to co-exist.

We show how, by using namespaces, Lark grammars and their transformers can be fully reused - they don't need to care if their grammar is used directly, or being imported, or who is doing the importing.

See main.py <https://github.com/lark-parser/lark/blob/master/examples/composition/main.py> for more details.

EXAMPLE GRAMMARS

This directory is a collection of lark grammars, taken from real world projects.

    •   Verilog <https://github.com/lark-parser/lark/blob/master/examples/grammars/verilog.lark> - Taken from <https://github.com/circuitgraph/circuitgraph/blob/main/circuitgraph/parsing/verilog.lark>

STANDALONE EXAMPLE

To initialize, cd to this folder, and run:

            ./create_standalone.sh

Or:

            python -m lark.tools.standalone json.lark > json_parser.py

Then run using:

            python json_parser_main.py <path-to.json>

  Advanced Examples

  LALR’s contextual lexer

This example demonstrates the power of LALR's contextual lexer, by parsing a toy configuration language.

The terminals NAME and VALUE overlap. They can match the same input. A basic lexer would arbitrarily choose one over the other, based on priority, which would lead to a (confusing) parse error. However, due to the unambiguous structure of the grammar, Lark's LALR(1) algorithm knows which one of them to expect at each point during the parse. The lexer then only matches the tokens that the parser expects. The result is a correct parse, something that is impossible with a regular lexer.

Another approach is to use the Earley algorithm. It will handle more cases than the contextual lexer, but at the cost of performance. See examples/conf_earley.py for an example of that approach.

            from lark import Lark

            parser = Lark(r"""
                    start: _NL? section+
                    section: "[" NAME "]" _NL item+
                    item: NAME "=" VALUE? _NL

                    NAME: /\w/+
                    VALUE: /./+

                    %import common.NEWLINE -> _NL
                    %import common.WS_INLINE
                    %ignore WS_INLINE
                """, parser="lalr")

            sample_conf = """
            [bla]
            a=Hello
            this="that",4
            empty=
            """

            print(parser.parse(sample_conf).pretty())

  Templates

This example shows how to use Lark's templates to achieve cleaner grammars

            from lark import Lark

            grammar = r"""
            start: list | dict

            list: "[" _seperated{atom, ","} "]"
            dict: "{" _seperated{key_value, ","} "}"
            key_value: atom ":" atom

            _seperated{x, sep}: x (sep x)*  // Define a sequence of 'x sep x sep x ...'

            atom: NUMBER | ESCAPED_STRING

            %import common (NUMBER, ESCAPED_STRING, WS)
            %ignore WS
            """

            parser = Lark(grammar)

            print(parser.parse('[1, "a", 2]'))
            print(parser.parse('{"a": 2, "b": 6}'))

  Earley’s dynamic lexer

Demonstrates the power of Earley’s dynamic lexer on a toy configuration language

Using a lexer for configuration files is tricky, because values don't have to be surrounded by delimiters. Using a basic lexer for this just won't work.

In this example we use a dynamic lexer and let the Earley parser resolve the ambiguity.

Another approach is to use the contextual lexer with LALR. It is less powerful than Earley, but it can handle some ambiguity when lexing and it's much faster. See examples/conf_lalr.py for an example of that approach.

            from lark import Lark

            parser = Lark(r"""
                    start: _NL? section+
                    section: "[" NAME "]" _NL item+
                    item: NAME "=" VALUE? _NL

                    NAME: /\w/+
                    VALUE: /./+

                    %import common.NEWLINE -> _NL
                    %import common.WS_INLINE
                    %ignore WS_INLINE
                """, parser="earley")

            def test():
                sample_conf = """
            [bla]

            a=Hello
            this="that",4
            empty=
            """

                r = parser.parse(sample_conf)
                print (r.pretty())

            if __name__ == '__main__':
                test()

  Error handling using an interactive parser

This example demonstrates error handling using an interactive parser in LALR

When the parser encounters an UnexpectedToken exception, it creates a an interactive parser with the current parse-state, and lets you control how to proceed step-by-step. When you've achieved the correct parse-state, you can resume the run by returning True.

            from lark import Token

            from _json_parser import json_parser

            def ignore_errors(e):
                if e.token.type == 'COMMA':
                    # Skip comma
                    return True
                elif e.token.type == 'SIGNED_NUMBER':
                    # Try to feed a comma and retry the number
                    e.interactive_parser.feed_token(Token('COMMA', ','))
                    e.interactive_parser.feed_token(e.token)
                    return True

                # Unhandled error. Will stop parse and raise exception
                return False

            def main():
                s = "[0 1, 2,, 3,,, 4, 5 6 ]"
                res = json_parser.parse(s, on_error=ignore_errors)
                print(res)      # prints [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]

            main()

  Reconstruct a JSON

Demonstrates the experimental text-reconstruction feature

The Reconstructor takes a parse tree (already filtered from punctuation, of course), and reconstructs it into correct text, that can be parsed correctly. It can be useful for creating "hooks" to alter data before handing it to other parsers. You can also use it to generate samples from scratch.

            import json

            from lark import Lark
            from lark.reconstruct import Reconstructor

            from _json_parser import json_grammar

            test_json = '''
                {
                    "empty_object" : {},
                    "empty_array"  : [],
                    "booleans"     : { "YES" : true, "NO" : false },
                    "numbers"      : [ 0, 1, -2, 3.3, 4.4e5, 6.6e-7 ],
                    "strings"      : [ "This", [ "And" , "That", "And a \\"b" ] ],
                    "nothing"      : null
                }
            '''

            def test_earley():

                json_parser = Lark(json_grammar, maybe_placeholders=False)
                tree = json_parser.parse(test_json)

                new_json = Reconstructor(json_parser).reconstruct(tree)
                print (new_json)
                print (json.loads(new_json) == json.loads(test_json))

            def test_lalr():

                json_parser = Lark(json_grammar, parser='lalr', maybe_placeholders=False)
                tree = json_parser.parse(test_json)

                new_json = Reconstructor(json_parser).reconstruct(tree)
                print (new_json)
                print (json.loads(new_json) == json.loads(test_json))

            test_earley()
            test_lalr()

  Custom lexer

Demonstrates using a custom lexer to parse a non-textual stream of data

You can use a custom lexer to tokenize text when the lexers offered by Lark are too slow, or not flexible enough.

You can also use it (as shown in this example) to tokenize streams of objects.

            from lark import Lark, Transformer, v_args
            from lark.lexer import Lexer, Token

            class TypeLexer(Lexer):
                def __init__(self, lexer_conf):
                    pass

                def lex(self, data):
                    for obj in data:
                        if isinstance(obj, int):
                            yield Token('INT', obj)
                        elif isinstance(obj, (type(''), type(u''))):
                            yield Token('STR', obj)
                        else:
                            raise TypeError(obj)

            parser = Lark("""
                    start: data_item+
                    data_item: STR INT*

                    %declare STR INT
                    """, parser='lalr', lexer=TypeLexer)

            class ParseToDict(Transformer):
                @v_args(inline=True)
                def data_item(self, name, *numbers):
                    return name.value, [n.value for n in numbers]

                start = dict

            def test():
                data = ['alice', 1, 27, 3, 'bob', 4, 'carrie', 'dan', 8, 6]

                print(data)

                tree = parser.parse(data)
                res = ParseToDict().transform(tree)

                print('-->')
                print(res) # prints {'alice': [1, 27, 3], 'bob': [4], 'carrie': [], 'dan': [8, 6]}

            if __name__ == '__main__':
                test()

  Transform a Forest

This example demonstrates how to subclass TreeForestTransformer to directly transform a SPPF.

            from lark import Lark
            from lark.parsers.earley_forest import TreeForestTransformer, handles_ambiguity, Discard

            class CustomTransformer(TreeForestTransformer):

                @handles_ambiguity
                def sentence(self, trees):
                    return next(tree for tree in trees if tree.data == 'simple')

                def simple(self, children):
                    children.append('.')
                    return self.tree_class('simple', children)

                def adj(self, children):
                    return Discard

                def __default_token__(self, token):
                    return token.capitalize()

            grammar = """
                sentence: noun verb noun        -> simple
                        | noun verb "like" noun -> comparative

                noun: adj? NOUN
                verb: VERB
                adj: ADJ

                NOUN: "flies" | "bananas" | "fruit"
                VERB: "like" | "flies"
                ADJ: "fruit"

                %import common.WS
                %ignore WS
            """

            parser = Lark(grammar, start='sentence', ambiguity='forest')
            sentence = 'fruit flies like bananas'
            forest = parser.parse(sentence)

            tree = CustomTransformer(resolve_ambiguity=False).transform(forest)
            print(tree.pretty())

            # Output:
            #
            # simple
            #   noun  Flies
            #   verb  Like
            #   noun  Bananas
            #   .
            #

  Simple JSON Parser

The code is short and clear, and outperforms every other parser (that's written in Python). For an explanation, check out the JSON parser tutorial at /docs/json_tutorial.md

(this is here for use by the other examples)

            from lark import Lark, Transformer, v_args

            json_grammar = r"""
                ?start: value

                ?value: object
                      | array
                      | string
                      | SIGNED_NUMBER      -> number
                      | "true"             -> true
                      | "false"            -> false
                      | "null"             -> null

                array  : "[" [value ("," value)*] "]"
                object : "{" [pair ("," pair)*] "}"
                pair   : string ":" value

                string : ESCAPED_STRING

                %import common.ESCAPED_STRING
                %import common.SIGNED_NUMBER
                %import common.WS

                %ignore WS
            """

            class TreeToJson(Transformer):
                @v_args(inline=True)
                def string(self, s):
                    return s[1:-1].replace('\\"', '"')

                array = list
                pair = tuple
                object = dict
                number = v_args(inline=True)(float)

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

            ### Create the JSON parser with Lark, using the LALR algorithm
            json_parser = Lark(json_grammar, parser='lalr',
                               # Using the basic lexer isn't required, and isn't usually recommended.
                               # But, it's good enough for JSON, and it's slightly faster.
                               lexer='basic',
                               # Disabling propagate_positions and placeholders slightly improves speed
                               propagate_positions=False,
                               maybe_placeholders=False,
                               # Using an internal transformer is faster and more memory efficient
                               transformer=TreeToJson())

  Custom SPPF Prioritizer

This example demonstrates how to subclass ForestVisitor to make a custom SPPF node prioritizer to be used in conjunction with TreeForestTransformer.

Our prioritizer will count the number of descendants of a node that are tokens. By negating this count, our prioritizer will prefer nodes with fewer token descendants. Thus, we choose the more specific parse.

            from lark import Lark
            from lark.parsers.earley_forest import ForestVisitor, TreeForestTransformer

            class TokenPrioritizer(ForestVisitor):

                def visit_symbol_node_in(self, node):
                    # visit the entire forest by returning node.children
                    return node.children

                def visit_packed_node_in(self, node):
                    return node.children

                def visit_symbol_node_out(self, node):
                    priority = 0
                    for child in node.children:
                        # Tokens do not have a priority attribute
                        # count them as -1
                        priority += getattr(child, 'priority', -1)
                    node.priority = priority

                def visit_packed_node_out(self, node):
                    priority = 0
                    for child in node.children:
                        priority += getattr(child, 'priority', -1)
                    node.priority = priority

                def on_cycle(self, node, path):
                    raise Exception("Oops, we encountered a cycle.")

            grammar = """
            start: hello " " world | hello_world
            hello: "Hello"
            world: "World"
            hello_world: "Hello World"
            """

            parser = Lark(grammar, parser='earley', ambiguity='forest')
            forest = parser.parse("Hello World")

            print("Default prioritizer:")
            tree = TreeForestTransformer(resolve_ambiguity=True).transform(forest)
            print(tree.pretty())

            forest = parser.parse("Hello World")

            print("Custom prioritizer:")
            tree = TreeForestTransformer(resolve_ambiguity=True, prioritizer=TokenPrioritizer()).transform(forest)
            print(tree.pretty())

            # Output:
            #
            # Default prioritizer:
            # start
            #   hello Hello
            #
            #   world World
            #
            # Custom prioritizer:
            # start
            #   hello_world   Hello World

  Python 3 to Python 2 converter (tree templates)

This example demonstrates how to translate between two trees using tree templates. It parses Python 3, translates it to a Python 2 AST, and then outputs the result as Python 2 code.

Uses reconstruct_python.py for generating the final Python 2 code.

            from lark import Lark
            from lark.tree_templates import TemplateConf, TemplateTranslator

            from lark.indenter import PythonIndenter
            from reconstruct_python import PythonReconstructor

            #
            # 1. Define a Python parser that also accepts template vars in the code (in the form of $var)
            #
            TEMPLATED_PYTHON = r"""
            %import python (single_input, file_input, eval_input, atom, var, stmt, expr, testlist_star_expr, _NEWLINE, _INDENT, _DEDENT, COMMENT, NAME)

            %extend atom: TEMPLATE_NAME -> var

            TEMPLATE_NAME: "$" NAME

            ?template_start: (stmt | testlist_star_expr _NEWLINE)

            %ignore /[\t \f]+/          // WS
            %ignore /\\[\t \f]*\r?\n/   // LINE_CONT
            %ignore COMMENT
            """

            parser = Lark(TEMPLATED_PYTHON, parser='lalr', start=['single_input', 'file_input', 'eval_input', 'template_start'], postlex=PythonIndenter(), maybe_placeholders=False)

            def parse_template(s):
                return parser.parse(s + '\n', start='template_start')

            def parse_code(s):
                return parser.parse(s + '\n', start='file_input')

            #
            # 2. Define translations using templates (each template code is parsed to a template tree)
            #

            pytemplate = TemplateConf(parse=parse_template)

            translations_3to2 = {
                'yield from $a':
                    'for _tmp in $a: yield _tmp',

                'raise $e from $x':
                        'raise $e',

                '$a / $b':
                    'float($a) / $b',
            }
            translations_3to2 = {pytemplate(k): pytemplate(v) for k, v in translations_3to2.items()}

            #
            # 3. Translate and reconstruct Python 3 code into valid Python 2 code
            #

            python_reconstruct = PythonReconstructor(parser)

            def translate_py3to2(code):
                tree = parse_code(code)
                tree = TemplateTranslator(translations_3to2).translate(tree)
                return python_reconstruct.reconstruct(tree)

            #
            # Test Code
            #

            _TEST_CODE = '''
            if a / 2 > 1:
                yield from [1,2,3]
            else:
                raise ValueError(a) from e

            '''

            def test():
                print(_TEST_CODE)
                print('   ----->    ')
                print(translate_py3to2(_TEST_CODE))

            if __name__ == '__main__':
                test()

  Grammar-complete Python Parser

A fully-working Python 2 & 3 parser (but not production ready yet!)

This example demonstrates usage of the included Python grammars

            import sys
            import os, os.path
            from io import open
            import glob, time

            from lark import Lark
            from lark.indenter import PythonIndenter

            kwargs = dict(postlex=PythonIndenter(), start='file_input')

            # Official Python grammar by Lark
            python_parser3 = Lark.open_from_package('lark', 'python.lark', ['grammars'], parser='lalr', **kwargs)

            # Local Python2 grammar
            python_parser2 = Lark.open('python2.lark', rel_to=__file__, parser='lalr', **kwargs)
            python_parser2_earley = Lark.open('python2.lark', rel_to=__file__, parser='earley', lexer='basic', **kwargs)

            try:
                xrange
            except NameError:
                chosen_parser = python_parser3
            else:
                chosen_parser = python_parser2

            def _read(fn, *args):
                kwargs = {'encoding': 'iso-8859-1'}
                with open(fn, *args, **kwargs) as f:
                    return f.read()

            def _get_lib_path():
                if os.name == 'nt':
                    if 'PyPy' in sys.version:
                        return os.path.join(sys.base_prefix, 'lib-python', sys.winver)
                    else:
                        return os.path.join(sys.base_prefix, 'Lib')
                else:
                    return [x for x in sys.path if x.endswith('%s.%s' % sys.version_info[:2])][0]

            def test_python_lib():
                path = _get_lib_path()

                start = time.time()
                files = glob.glob(path+'/*.py')
                total_kb = 0
                for f in files:
                    r = _read(os.path.join(path, f))
                    kb = len(r) / 1024
                    print( '%s -\t%.1f kb' % (f, kb))
                    chosen_parser.parse(r + '\n')
                    total_kb += kb

                end = time.time()
                print( "test_python_lib (%d files, %.1f kb), time: %.2f secs"%(len(files), total_kb, end-start) )

            def test_earley_equals_lalr():
                path = _get_lib_path()

                files = glob.glob(path+'/*.py')
                for f in files:
                    print( f )
                    tree1 = python_parser2.parse(_read(os.path.join(path, f)) + '\n')
                    tree2 = python_parser2_earley.parse(_read(os.path.join(path, f)) + '\n')
                    assert tree1 == tree2

            if __name__ == '__main__':
                test_python_lib()
                # test_earley_equals_lalr()
                # python_parser3.parse(_read(sys.argv[1]) + '\n')

  Creating an AST from the parse tree
        This example demonstrates how to transform a parse-tree into an AST using lark.ast_utils.

        create_transformer() collects every subclass of Ast subclass from the module, and creates a Lark transformer that builds the AST with no extra code.

        This example only works with Python 3.

            import sys
            from typing import List
            from dataclasses import dataclass

            from lark import Lark, ast_utils, Transformer, v_args
            from lark.tree import Meta

            this_module = sys.modules[__name__]

            #
            #   Define AST
            #
            class _Ast(ast_utils.Ast):
                # This will be skipped by create_transformer(), because it starts with an underscore
                pass

            class _Statement(_Ast):
                # This will be skipped by create_transformer(), because it starts with an underscore
                pass

            @dataclass
            class Value(_Ast, ast_utils.WithMeta):
                "Uses WithMeta to include line-number metadata in the meta attribute"
                meta: Meta
                value: object

            @dataclass
            class Name(_Ast):
                name: str

            @dataclass
            class CodeBlock(_Ast, ast_utils.AsList):
                # Corresponds to code_block in the grammar
                statements: List[_Statement]

            @dataclass
            class If(_Statement):
                cond: Value
                then: CodeBlock

            @dataclass
            class SetVar(_Statement):
                # Corresponds to set_var in the grammar
                name: str
                value: Value

            @dataclass
            class Print(_Statement):
                value: Value

            class ToAst(Transformer):
                # Define extra transformation functions, for rules that don't correspond to an AST class.

                def STRING(self, s):
                    # Remove quotation marks
                    return s[1:-1]

                def DEC_NUMBER(self, n):
                    return int(n)

                @v_args(inline=True)
                def start(self, x):
                    return x

            #
            #   Define Parser
            #

            parser = Lark("""
                start: code_block

                code_block: statement+

                ?statement: if | set_var | print

                if: "if" value "{" code_block "}"
                set_var: NAME "=" value ";"
                print: "print" value ";"

                value: name | STRING | DEC_NUMBER
                name: NAME

                %import python (NAME, STRING, DEC_NUMBER)
                %import common.WS
                %ignore WS
                """,
                parser="lalr",
            )

            transformer = ast_utils.create_transformer(this_module, ToAst())

            def parse(text):
                tree = parser.parse(text)
                return transformer.transform(tree)

            #
            #   Test
            #

            if __name__ == '__main__':
                print(parse("""
                    a = 1;
                    if a {
                        print "a is 1";
                        a = 2;
                    }
                """))

  Example-Driven Error Reporting

A demonstration of example-driven error reporting with the Earley parser (See also: error_reporting_lalr.py)

            from lark import Lark, UnexpectedInput

            from _json_parser import json_grammar   # Using the grammar from the json_parser example

            json_parser = Lark(json_grammar)

            class JsonSyntaxError(SyntaxError):
                def __str__(self):
                    context, line, column = self.args
                    return '%s at line %s, column %s.\n\n%s' % (self.label, line, column, context)

            class JsonMissingValue(JsonSyntaxError):
                label = 'Missing Value'

            class JsonMissingOpening(JsonSyntaxError):
                label = 'Missing Opening'

            class JsonMissingClosing(JsonSyntaxError):
                label = 'Missing Closing'

            class JsonMissingComma(JsonSyntaxError):
                label = 'Missing Comma'

            class JsonTrailingComma(JsonSyntaxError):
                label = 'Trailing Comma'

            def parse(json_text):
                try:
                    j = json_parser.parse(json_text)
                except UnexpectedInput as u:
                    exc_class = u.match_examples(json_parser.parse, {
                        JsonMissingOpening: ['{"foo": ]}',
                                             '{"foor": }}',
                                             '{"foo": }'],
                        JsonMissingClosing: ['{"foo": [}',
                                             '{',
                                             '{"a": 1',
                                             '[1'],
                        JsonMissingComma: ['[1 2]',
                                           '[false 1]',
                                           '["b" 1]',
                                           '{"a":true 1:4}',
                                           '{"a":1 1:4}',
                                           '{"a":"b" 1:4}'],
                        JsonTrailingComma: ['[,]',
                                            '[1,]',
                                            '[1,2,]',
                                            '{"foo":1,}',
                                            '{"foo":false,"bar":true,}']
                    }, use_accepts=True)
                    if not exc_class:
                        raise
                    raise exc_class(u.get_context(json_text), u.line, u.column)

            def test():
                try:
                    parse('{"example1": "value"')
                except JsonMissingClosing as e:
                    print(e)

                try:
                    parse('{"example2": ] ')
                except JsonMissingOpening as e:
                    print(e)

            if __name__ == '__main__':
                test()

  Example-Driven Error Reporting

A demonstration of example-driven error reporting with the LALR parser (See also: error_reporting_earley.py)

            from lark import Lark, UnexpectedInput

            from _json_parser import json_grammar   # Using the grammar from the json_parser example

            json_parser = Lark(json_grammar, parser='lalr')

            class JsonSyntaxError(SyntaxError):
                def __str__(self):
                    context, line, column = self.args
                    return '%s at line %s, column %s.\n\n%s' % (self.label, line, column, context)

            class JsonMissingValue(JsonSyntaxError):
                label = 'Missing Value'

            class JsonMissingOpening(JsonSyntaxError):
                label = 'Missing Opening'

            class JsonMissingClosing(JsonSyntaxError):
                label = 'Missing Closing'

            class JsonMissingComma(JsonSyntaxError):
                label = 'Missing Comma'

            class JsonTrailingComma(JsonSyntaxError):
                label = 'Trailing Comma'

            def parse(json_text):
                try:
                    j = json_parser.parse(json_text)
                except UnexpectedInput as u:
                    exc_class = u.match_examples(json_parser.parse, {
                        JsonMissingOpening: ['{"foo": ]}',
                                             '{"foor": }}',
                                             '{"foo": }'],
                        JsonMissingClosing: ['{"foo": [}',
                                             '{',
                                             '{"a": 1',
                                             '[1'],
                        JsonMissingComma: ['[1 2]',
                                           '[false 1]',
                                           '["b" 1]',
                                           '{"a":true 1:4}',
                                           '{"a":1 1:4}',
                                           '{"a":"b" 1:4}'],
                        JsonTrailingComma: ['[,]',
                                            '[1,]',
                                            '[1,2,]',
                                            '{"foo":1,}',
                                            '{"foo":false,"bar":true,}']
                    }, use_accepts=True)
                    if not exc_class:
                        raise
                    raise exc_class(u.get_context(json_text), u.line, u.column)

            def test():
                try:
                    parse('{"example1": "value"')
                except JsonMissingClosing as e:
                    print(e)

                try:
                    parse('{"example2": ] ')
                except JsonMissingOpening as e:
                    print(e)

            if __name__ == '__main__':
                test()

  Reconstruct Python

Demonstrates how Lark's experimental text-reconstruction feature can recreate functional Python code from its parse-tree, using just the correct grammar and a small formatter.

            from lark import Token, Lark
            from lark.reconstruct import Reconstructor
            from lark.indenter import PythonIndenter

            # Official Python grammar by Lark
            python_parser3 = Lark.open_from_package('lark', 'python.lark', ['grammars'],
                                                    parser='lalr', postlex=PythonIndenter(), start='file_input',
                                                    maybe_placeholders=False    # Necessary for reconstructor
                                                    )

            SPACE_AFTER = set(',+-*/~@<>="|:')
            SPACE_BEFORE = (SPACE_AFTER - set(',:')) | set('\'')

            def special(sym):
                return Token('SPECIAL', sym.name)

            def postproc(items):
                stack = ['\n']
                actions = []
                last_was_whitespace = True
                for item in items:
                    if isinstance(item, Token) and item.type == 'SPECIAL':
                        actions.append(item.value)
                    else:
                        if actions:
                            assert actions[0] == '_NEWLINE' and '_NEWLINE' not in actions[1:], actions

                            for a in actions[1:]:
                                if a == '_INDENT':
                                    stack.append(stack[-1] + ' ' * 4)
                                else:
                                    assert a == '_DEDENT'
                                    stack.pop()
                            actions.clear()
                            yield stack[-1]
                            last_was_whitespace = True
                        if not last_was_whitespace:
                            if item[0] in SPACE_BEFORE:
                                yield ' '
                        yield item
                        last_was_whitespace = item[-1].isspace()
                        if not last_was_whitespace:
                            if item[-1] in SPACE_AFTER:
                                yield ' '
                                last_was_whitespace = True
                yield "\n"

            class PythonReconstructor:
                def __init__(self, parser):
                    self._recons = Reconstructor(parser, {'_NEWLINE': special, '_DEDENT': special, '_INDENT': special})

                def reconstruct(self, tree):
                    return self._recons.reconstruct(tree, postproc)

            def test():
                python_reconstructor = PythonReconstructor(python_parser3)

                self_contents = open(__file__).read()

                tree = python_parser3.parse(self_contents+'\n')
                output = python_reconstructor.reconstruct(tree)

                tree_new = python_parser3.parse(output)
                print(tree.pretty())
                print(tree_new.pretty())
                # assert tree.pretty() == tree_new.pretty()
                assert tree == tree_new

                print(output)

            if __name__ == '__main__':
                test()

  Using lexer dynamic_complete

Demonstrates how to use lexer='dynamic_complete' and ambiguity='explicit'

Sometimes you have data that is highly ambiguous or 'broken' in some sense. When using parser='earley' and lexer='dynamic_complete', Lark will be able parse just about anything as long as there is a valid way to generate it from the Grammar, including looking 'into' the Regexes.

This examples shows how to parse a json input where the quotes have been replaced by underscores: {_foo_:{}, _bar_: [], _baz_: __} Notice that underscores might still appear inside strings, so a potentially valid reading of the above is: {"foo_:{}, _bar": [], "baz": ""}

            from pprint import pprint

            from lark import Lark, Tree, Transformer, v_args
            from lark.visitors import Transformer_InPlace

            GRAMMAR = r"""
            %import common.SIGNED_NUMBER
            %import common.WS_INLINE
            %import common.NEWLINE
            %ignore WS_INLINE

            ?start: value

            ?value: object
                  | array
                  | string
                  | SIGNED_NUMBER      -> number
                  | "true"             -> true
                  | "false"            -> false
                  | "null"             -> null

            array  : "[" (value ("," value)*)? "]"
            object : "{" (pair ("," pair)*)? "}"
            pair   : string ":" value

            string: STRING
            STRING : ESCAPED_STRING

            ESCAPED_STRING: QUOTE_CHAR _STRING_ESC_INNER QUOTE_CHAR
            QUOTE_CHAR: "_"

            _STRING_INNER: /.*/
            _STRING_ESC_INNER: _STRING_INNER /(?<!\\)(\\\\)*?/

            """

            def score(tree: Tree):
                """
                Scores an option by how many children (and grand-children, and
                grand-grand-children, ...) it has.
                This means that the option with fewer large terminals gets selected

                Between
                    object
                      pair
                        string  _foo_
                        object
                      pair
                        string  _bar_: [], _baz_
                        string  __

                and

                    object
                      pair
                        string  _foo_
                        object
                      pair
                        string  _bar_
                        array
                      pair
                        string  _baz_
                        string  __

                this will give the second a higher score. (9 vs 13)
                """
                return sum(len(t.children) for t in tree.iter_subtrees())

            class RemoveAmbiguities(Transformer_InPlace):
                """
                Selects an option to resolve an ambiguity using the score function above.
                Scores each option and selects the one with the higher score, e.g. the one
                with more nodes.

                If there is a performance problem with the Tree having to many _ambig and
                being slow and to large, this can instead be written as a ForestVisitor.
                Look at the 'Custom SPPF Prioritizer' example.
                """
                def _ambig(self, options):
                    return max(options, key=score)

            class TreeToJson(Transformer):
                """
                This is the same Transformer as the json_parser example.
                """
                @v_args(inline=True)
                def string(self, s):
                    return s[1:-1].replace('\\"', '"')

                array = list
                pair = tuple
                object = dict
                number = v_args(inline=True)(float)

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

            parser = Lark(GRAMMAR, parser='earley', ambiguity="explicit", lexer='dynamic_complete')

            EXAMPLES = [
                r'{_array_:[1,2,3]}',

                r'{_abc_: _array must be of the following format [_1_, _2_, _3_]_}',

                r'{_foo_:{}, _bar_: [], _baz_: __}',

                r'{_error_:_invalid_client_, _error_description_:_AADSTS7000215: Invalid '
                r'client secret is provided.\r\nTrace ID: '
                r'a0a0aaaa-a0a0-0a00-000a-00a00aaa0a00\r\nCorrelation ID: '
                r'aa0aaa00-0aaa-0000-00a0-00000aaaa0aa\r\nTimestamp: 1997-10-10 00:00:00Z_, '
                r'_error_codes_:[7000215], _timestamp_:_1997-10-10 00:00:00Z_, '
                r'_trace_id_:_a0a0aaaa-a0a0-0a00-000a-00a00aaa0a00_, '
                r'_correlation_id_:_aa0aaa00-0aaa-0000-00a0-00000aaaa0aa_, '
                r'_error_uri_:_https://example.com_}',

            ]
            for example in EXAMPLES:
                tree = parser.parse(example)
                tree = RemoveAmbiguities().transform(tree)
                result = TreeToJson().transform(tree)
                pprint(result)

  Syntax Highlighting

This example shows how to write a syntax-highlighted editor with Qt and Lark

Requirements:
        PyQt5==5.15.8 QScintilla==2.13.4

            import sys
            import textwrap

            from PyQt5.QtWidgets import QApplication
            from PyQt5.QtGui import QColor, QFont, QFontMetrics

            from PyQt5.Qsci import QsciScintilla
            from PyQt5.Qsci import QsciLexerCustom

            from lark import Lark

            class LexerJson(QsciLexerCustom):

                def __init__(self, parent=None):
                    super().__init__(parent)
                    self.create_parser()
                    self.create_styles()

                def create_styles(self):
                    deeppink = QColor(249, 38, 114)
                    khaki = QColor(230, 219, 116)
                    mediumpurple = QColor(174, 129, 255)
                    mediumturquoise = QColor(81, 217, 205)
                    yellowgreen = QColor(166, 226, 46)
                    lightcyan = QColor(213, 248, 232)
                    darkslategrey = QColor(39, 40, 34)

                    styles = {
                        0: mediumturquoise,
                        1: mediumpurple,
                        2: yellowgreen,
                        3: deeppink,
                        4: khaki,
                        5: lightcyan
                    }

                    for style, color in styles.items():
                        self.setColor(color, style)
                        self.setPaper(darkslategrey, style)
                        self.setFont(self.parent().font(), style)

                    self.token_styles = {
                        "COLON": 5,
                        "COMMA": 5,
                        "LBRACE": 5,
                        "LSQB": 5,
                        "RBRACE": 5,
                        "RSQB": 5,
                        "FALSE": 0,
                        "NULL": 0,
                        "TRUE": 0,
                        "STRING": 4,
                        "NUMBER": 1,
                    }

                def create_parser(self):
                    grammar = '''
                        anons: ":" "{" "}" "," "[" "]"
                        TRUE: "true"
                        FALSE: "false"
                        NULL: "NULL"
                        %import common.ESCAPED_STRING -> STRING
                        %import common.SIGNED_NUMBER  -> NUMBER
                        %import common.WS
                        %ignore WS
                    '''

                    self.lark = Lark(grammar, parser=None, lexer='basic')
                    # All tokens: print([t.name for t in self.lark.parser.lexer.tokens])

                def defaultPaper(self, style):
                    return QColor(39, 40, 34)

                def language(self):
                    return "Json"

                def description(self, style):
                    return {v: k for k, v in self.token_styles.items()}.get(style, "")

                def styleText(self, start, end):
                    self.startStyling(start)
                    text = self.parent().text()[start:end]
                    last_pos = 0

                    try:
                        for token in self.lark.lex(text):
                            ws_len = token.start_pos - last_pos
                            if ws_len:
                                self.setStyling(ws_len, 0)    # whitespace

                            token_len = len(bytearray(token, "utf-8"))
                            self.setStyling(
                                token_len, self.token_styles.get(token.type, 0))

                            last_pos = token.start_pos + token_len
                    except Exception as e:
                        print(e)

            class EditorAll(QsciScintilla):

                def __init__(self, parent=None):
                    super().__init__(parent)

                    # Set font defaults
                    font = QFont()
                    font.setFamily('Consolas')
                    font.setFixedPitch(True)
                    font.setPointSize(8)
                    font.setBold(True)
                    self.setFont(font)

                    # Set margin defaults
                    fontmetrics = QFontMetrics(font)
                    self.setMarginsFont(font)
                    self.setMarginWidth(0, fontmetrics.width("000") + 6)
                    self.setMarginLineNumbers(0, True)
                    self.setMarginsForegroundColor(QColor(128, 128, 128))
                    self.setMarginsBackgroundColor(QColor(39, 40, 34))
                    self.setMarginType(1, self.SymbolMargin)
                    self.setMarginWidth(1, 12)

                    # Set indentation defaults
                    self.setIndentationsUseTabs(False)
                    self.setIndentationWidth(4)
                    self.setBackspaceUnindents(True)
                    self.setIndentationGuides(True)

                    # self.setFolding(QsciScintilla.CircledFoldStyle)

                    # Set caret defaults
                    self.setCaretForegroundColor(QColor(247, 247, 241))
                    self.setCaretWidth(2)

                    # Set selection color defaults
                    self.setSelectionBackgroundColor(QColor(61, 61, 52))
                    self.resetSelectionForegroundColor()

                    # Set multiselection defaults
                    self.SendScintilla(QsciScintilla.SCI_SETMULTIPLESELECTION, True)
                    self.SendScintilla(QsciScintilla.SCI_SETMULTIPASTE, 1)
                    self.SendScintilla(
                        QsciScintilla.SCI_SETADDITIONALSELECTIONTYPING, True)

                    lexer = LexerJson(self)
                    self.setLexer(lexer)

            EXAMPLE_TEXT = textwrap.dedent("""\
                    {
                        "_id": "5b05ffcbcf8e597939b3f5ca",
                        "about": "Excepteur consequat commodo esse voluptate aute aliquip ad sint deserunt commodo eiusmod irure. Sint aliquip sit magna duis eu est culpa aliqua excepteur ut tempor nulla. Aliqua ex pariatur id labore sit. Quis sit ex aliqua veniam exercitation laboris anim adipisicing. Lorem nisi reprehenderit ullamco labore qui sit ut aliqua tempor consequat pariatur proident.",
                        "address": "665 Malbone Street, Thornport, Louisiana, 243",
                        "age": 23,
                        "balance": "$3,216.91",
                        "company": "BULLJUICE",
                        "email": "elisekelley@bulljuice.com",
                        "eyeColor": "brown",
                        "gender": "female",
                        "guid": "d3a6d865-0f64-4042-8a78-4f53de9b0707",
                        "index": 0,
                        "isActive": false,
                        "isActive2": true,
                        "latitude": -18.660714,
                        "longitude": -85.378048,
                        "name": "Elise Kelley",
                        "phone": "+1 (808) 543-3966",
                        "picture": "http://placehold.it/32x32",
                        "registered": "2017-09-30T03:47:40 -02:00",
                        "tags": [
                            "et",
                            "nostrud",
                            "in",
                            "fugiat",
                            "incididunt",
                            "labore",
                            "nostrud"
                        ]
                    }\
                """)

            def main():
                app = QApplication(sys.argv)
                ex = EditorAll()
                ex.setWindowTitle(__file__)
                ex.setText(EXAMPLE_TEXT)
                ex.resize(800, 600)
                ex.show()
                sys.exit(app.exec_())

            if __name__ == "__main__":
                main()

  Grammar Composition

This example shows how to do grammar composition in Lark, by creating a new file format that allows both CSV and JSON to co-exist.

We show how, by using namespaces, Lark grammars and their transformers can be fully reused - they don't need to care if their grammar is used directly, or being imported, or who is doing the importing.

See main.py <https://github.com/lark-parser/lark/blob/master/examples/composition/main.py> for more details. Transformer for evaluating json.lark

            from lark import Transformer, v_args

            class JsonTreeToJson(Transformer):
                @v_args(inline=True)
                def string(self, s):
                    return s[1:-1].replace('\\"', '"')

                array = list
                pair = tuple
                object = dict
                number = v_args(inline=True)(float)

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False
Transformer for evaluating csv.lark

            from lark import Transformer

            class CsvTreeToPandasDict(Transformer):
                INT = int
                FLOAT = float
                SIGNED_FLOAT = float
                WORD = str
                NON_SEPARATOR_STRING = str

                def row(self, children):
                    return children

                def start(self, children):
                    data = {}

                    header = children[0].children
                    for heading in header:
                        data[heading] = []

                    for row in children[1:]:
                        for i, element in enumerate(row):
                            data[header[i]].append(element)

                    return data

  Grammar Composition

This example shows how to do grammar composition in Lark, by creating a new file format that allows both CSV and JSON to co-exist.

    1.  We define storage.lark, which imports both csv.lark and json.lark,
        and allows them to be used one after the other.

        In the generated tree, each imported rule/terminal is automatically prefixed (with json__ or
        ``

        csv__
        ), which creates an implicit namespace and allows them to coexist without collisions.

    2.  We merge their respective transformers (unaware of each other) into a new base transformer. The resulting transformer can evaluate both JSON and CSV in the parse tree.
        The methods of each transformer are renamed into their appropriate namespace, using the given prefix. This approach allows full re-use: the transformers don't need to care if their grammar is used directly, or being imported, or who is doing the importing.

            from pathlib import Path
            from lark import Lark
            from json import dumps
            from lark.visitors import Transformer, merge_transformers

            from eval_csv import CsvTreeToPandasDict
            from eval_json import JsonTreeToJson

            __dir__ = Path(__file__).parent

            class Storage(Transformer):
                def start(self, children):
                    return children

            storage_transformer = merge_transformers(Storage(), csv=CsvTreeToPandasDict(), json=JsonTreeToJson())

            parser = Lark.open("storage.lark", rel_to=__file__)

            def main():
                json_tree = parser.parse(dumps({"test": "a", "dict": { "list": [1, 1.2] }}))
                res = storage_transformer.transform(json_tree)
                print("Just JSON: ", res)

                csv_json_tree = parser.parse(open(__dir__ / 'combined_csv_and_json.txt').read())
                res = storage_transformer.transform(csv_json_tree)
                print("JSON + CSV: ", dumps(res, indent=2))

            if __name__ == "__main__":
                main()

  Example Grammars

This directory is a collection of lark grammars, taken from real world projects.

    •   Verilog <https://github.com/lark-parser/lark/blob/master/examples/grammars/verilog.lark> - Taken from <https://github.com/circuitgraph/circuitgraph/blob/main/circuitgraph/parsing/verilog.lark>

  Standalone example

To initialize, cd to this folder, and run:

            ./create_standalone.sh

Or:

            python -m lark.tools.standalone json.lark > json_parser.py

Then run using:

            python json_parser_main.py <path-to.json>

  Standalone Parser
        This example demonstrates how to generate and use the standalone parser, using the JSON example.

        See README.md for more details.

            import sys

            from json_parser import Lark_StandAlone, Transformer, v_args

            inline_args = v_args(inline=True)

            class TreeToJson(Transformer):
                @inline_args
                def string(self, s):
                    return s[1:-1].replace('\\"', '"')

                array = list
                pair = tuple
                object = dict
                number = inline_args(float)

                null = lambda self, _: None
                true = lambda self, _: True
                false = lambda self, _: False

            parser = Lark_StandAlone(transformer=TreeToJson())

            if __name__ == '__main__':
                with open(sys.argv[1]) as f:
                    print(parser.parse(f.read()))

GRAMMAR REFERENCE

  Definitions

A grammar is a list of rules and terminals, that together define a language.

Terminals define the alphabet of the language, while rules define its structure.

In Lark, a terminal may be a string, a regular expression, or a concatenation of these and other terminals.

Each rule is a list of terminals and rules, whose location and nesting define the structure of the resulting parse-tree.

A parsing algorithm is an algorithm that takes a grammar definition and a sequence of symbols (members of the alphabet), and matches the entirety of the sequence by searching for a structure that is allowed by the grammar.

  General Syntax and notes

Grammars in Lark are based on EBNF <https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form> syntax, with several enhancements.

EBNF is basically a short-hand for common BNF patterns.

Optionals are expanded:

              a b? c    ->    (a c | a b c)

Repetition is extracted into a recursion:

              a: b*    ->    a: _b_tag
                             _b_tag: (_b_tag b)?

And so on.

Lark grammars are composed of a list of definitions and directives, each on its own line. A definition is either a named rule, or a named terminal, with the following syntax, respectively:

              rule: <EBNF-EXPRESSION>
                  | etc.

              TERM: <EBNF-EXPRESSION>   // Rules aren't allowed

Comments start with either // (C++ style) or # (Python style, since version 1.1.6) and last to the end of the line.

Lark begins the parse with the rule 'start', unless specified otherwise in the options.

Names of rules are always in lowercase, while names of terminals are always in uppercase. This distinction has practical effects, for the shape of the generated parse-tree, and the automatic construction of the lexer (aka tokenizer, or scanner).

  Terminals

Terminals are used to match text into symbols. They can be defined as a combination of literals and other terminals.

Syntax:

            <NAME> [. <priority>] : <literals-and-or-terminals>

Terminal names must be uppercase.

Literals can be one of:

    •   "string"

    •   /regular expression+/

    •   "case-insensitive string"i

    •   /re with flags/imulx

    •   Literal range: "a".."z", "1".."9", etc.

Terminals also support grammar operators, such as |, +, * and ?.

Terminals are a linear construct, and therefore may not contain themselves (recursion isn't allowed).

  Templates

Templates are expanded when preprocessing the grammar.

Definition syntax:

              my_template{param1, param2, ...}: <EBNF EXPRESSION>

Use syntax:

            some_rule: my_template{arg1, arg2, ...}

Example:

            _separated{x, sep}: x (sep x)*  // Define a sequence of 'x sep x sep x ...'

            num_list: "[" _separated{NUMBER, ","} "]"   // Will match "[1, 2, 3]" etc.

  Priority

Terminals can be assigned a priority to influence lexing. Terminal priorities are signed integers with a default value of 0.

When using a lexer, the highest priority terminals are always matched first.

When using Earley's dynamic lexing, terminal priorities are used to prefer certain lexings and resolve ambiguity.

  Regexp Flags

You can use flags on regexps and strings. For example:

            SELECT: "select"i     //# Will ignore case, and match SELECT or Select, etc.
            MULTILINE_TEXT: /.+/s
            SIGNED_INTEGER: /
                [+-]?  # the sign
                (0|[1-9][0-9]*)  # the digits
             /x

Supported flags are one of: imslux. See Python's regex documentation for more details on each one.

Regexps/strings of different flags can only be concatenated in Python 3.6+

  Notes for when using a lexer:

When using a lexer (basic or contextual), it is the grammar-author's responsibility to make sure the literals don't collide, or that if they do, they are matched in the desired order. Literals are matched according to the following precedence:

    •   Highest priority first (priority is specified as: TERM.number: ...)

    •   Length of match (for regexps, the longest theoretical match is used)

    •   Length of literal / pattern definition

    •   Name

Examples:

            IF: "if"
            INTEGER : /[0-9]+/
            INTEGER2 : ("0".."9")+          //# Same as INTEGER
            DECIMAL.2: INTEGER? "." INTEGER  //# Will be matched before INTEGER
            WHITESPACE: (" " | /\t/ )+
            SQL_SELECT: "select"i

  Regular expressions & Ambiguity

Each terminal is eventually compiled to a regular expression. All the operators and references inside it are mapped to their respective expressions.

For example, in the following grammar, A1 and A2, are equivalent:

            A1: "a" | "b"
            A2: /a|b/

This means that inside terminals, Lark cannot detect or resolve ambiguity, even when using Earley.

For example, for this grammar:

            start           : (A | B)+
            A               : "a" | "ab"
            B               : "b"

We get only one possible derivation, instead of two:

            >>> p = Lark(g, ambiguity="explicit")
            >>> p.parse("ab")
            Tree('start', [Token('A', 'ab')])

This is happening because Python's regex engine always returns the best matching option. There is no way to access the alternatives.

If you find yourself in this situation, the recommended solution is to either use the "dynamic_complete" lexer, or use rules instead.

Example using rules:

            >>> p = Lark("""start: (a | b)+
            ...             !a: "a" | "ab"
            ...             !b: "b"
            ...             """, ambiguity="explicit")
            >>> print(p.parse("ab").pretty())
            _ambig
              start
                a   ab
              start
                a   a
                b   b

Example using dynamic-complete:

            >>> g = """
            ...     start: (A | B)+
            ...     A    : "a" | "ab"
            ...     B    : "b"
            ... """
            >>> p = Lark(g, ambiguity="explicit", lexer="dynamic_complete")
            >>> rich.print(p.parse("ab"))
            _ambig
            ├── start
            │   └── ab
            └── start
                ├── a
                └── b

(note: the dynamic-complete lexer can significantly affect the performance of the parser)

  Rules

Syntax:

            <name> : <items-to-match>  [-> <alias> ]
                   | ...

Names of rules and aliases are always in lowercase.

Rule definitions can be extended to the next line by using the OR operator (signified by a pipe: | ).

An alias is a name for the specific rule alternative. It affects tree construction.

Each item is one of:

    •   rule

    •   TERMINAL

    •   "string literal" or /regexp literal/

    •   (item item ..) - Group items

    •   [item item ..] - Maybe. Same as (item item ..)?, but when maybe_placeholders=True, generates None if there is no match.

    •   item? - Zero or one instances of item ("maybe")

    •   item* - Zero or more instances of item

    •   item+ - One or more instances of item

    •   item ~ n - Exactly n instances of item

    •   item ~ n..m - Between n to m instances of item (not recommended for wide ranges, due to performance issues)

Note that all operators, including ~ n, apply directly to the item on their left. For instance a ~ 2 b ~ 3 is parsed as (a ~ 2) (b ~ 3).

Examples:

            hello_world: "hello" "world"
            mul: (mul "*")? number     //# Left-recursion is allowed and encouraged!
            expr: expr operator expr
                | value               //# Multi-line, belongs to expr

            four_words: word ~ 4

  Priority

Like terminals, rules can be assigned a priority. Rule priorities are signed integers with a default value of 0.

When using LALR, the highest priority rules are used to resolve collision errors.

When using Earley, rule priorities are used to resolve ambiguity.

  Directives

  %ignore

All occurrences of the terminal will be ignored, and won't be part of the parse.

Using the %ignore directive results in a cleaner grammar.

It's especially important for the LALR(1) algorithm, because adding whitespace (or comments, or other extraneous elements) explicitly in the grammar, harms its predictive abilities, which are based on a lookahead of 1.

Syntax:

            %ignore <TERMINAL>

Examples:

            %ignore " "

            COMMENT: "#" /[^\n]/*
            %ignore COMMENT

  %import

Allows one to import terminals and rules from lark grammars.

When importing rules, all their dependencies will be imported into a namespace, to avoid collisions. To override any of their dependencies (e.g. like you would override methods when inheriting a class), use the %override directive.

Syntax:

            %import <module>.<TERMINAL>
            %import <module>.<rule>
            %import <module>.<TERMINAL> -> <NEWTERMINAL>
            %import <module>.<rule> -> <newrule>
            %import <module> (<TERM1>, <TERM2>, <rule1>, <rule2>)

If the module path is absolute, Lark will attempt to load it from the built-in directory (which currently contains common.lark, lark.lark, python.lark, and unicode.lark).

If the module path is relative, such as .path.to.file, Lark will attempt to load it from the current working directory. Grammars must have the .lark extension.

The rule or terminal can be imported under another name (an alias) with the -> syntax.

Example:

            %import common.NUMBER

            %import .terminals_file (A, B, C)

            %import .rules_file.rule_a -> rule_b

Note that %ignore directives cannot be imported. Imported rules will abide by the %ignore directives declared in the main grammar.

  %declare

Declare a terminal without defining it. Useful for plugins.

  %override

Override a rule or terminals, affecting all references to it, even in imported grammars.

Useful for implementing an inheritance pattern when importing grammars.

Example:

            %import my_grammar (start, number, NUMBER)

            // Add hex support to my_grammar
            %override number: NUMBER | /0x\w+/

  %extend

Extend the definition of a rule or terminal, e.g. add a new option on what it can match, like when separated with |.

Useful for splitting up a definition of a complex rule with many different options over multiple files.

Can also be used to implement a plugin system where a core grammar is extended by others.

Example:

            %import my_grammar (start, NUMBER)

            // Add hex support to my_grammar
            %extend NUMBER: /0x\w+/

For both %extend and %override, there is not requirement for a rule/terminal to come from another file, but that is probably the most common use-case.

TREE CONSTRUCTION REFERENCE

Lark builds a tree automatically based on the structure of the grammar, where each rule that is matched becomes a branch (node) in the tree, and its children are its matches, in the order of matching.

For example, the rule node: child1 child2 will create a tree node with two children. If it is matched as part of another rule (i.e. if it isn't the root), the new rule's tree node will become its parent.

Using item+ or item* will result in a list of items, equivalent to writing item item item ...

Using item? will return the item if it matched, or nothing.

If maybe_placeholders=True (the default), then using [item] will return the item if it matched, or the value None, if it didn't.

If maybe_placeholders=False, then [] behaves like ()?.

  Terminals

Terminals are always values in the tree, never branches.

Lark filters out certain types of terminals by default, considering them punctuation:

    •   Terminals that won't appear in the tree are:

        •   Unnamed literals (like "keyword" or "+")

        •   Terminals whose name starts with an underscore (like _DIGIT)

    •   Terminals that will appear in the tree are:

        •   Unnamed regular expressions (like /[0-9]/)

        •   Named terminals whose name starts with a letter (like DIGIT)

Note: Terminals composed of literals and other terminals always include the entire match without filtering any part.

Example:

            start:  PNAME pname

            PNAME:  "(" NAME ")"
            pname:  "(" NAME ")"

            NAME:   /\w+/
            %ignore /\s+/

Lark will parse "(Hello) (World)" as:

            start
                (Hello)
                pname World

Rules prefixed with ! will retain all their literals regardless.

Example:

                expr: "(" expr ")"
                    | NAME+

                NAME: /\w+/

                %ignore " "

Lark will parse "((hello world))" as:

            expr
                expr
                    expr
                        "hello"
                        "world"

The brackets do not appear in the tree by design. The words appear because they are matched by a named terminal.

  Shaping the tree

Users can alter the automatic construction of the tree using a collection of grammar features.

  Inlining rules with _

Rules whose name begins with an underscore will be inlined into their containing rule.

Example:

                start: "(" _greet ")"
                _greet: /\w+/ /\w+/

Lark will parse "(hello world)" as:

            start
                "hello"
                "world"

  Conditionally inlining rules with ?

Rules that receive a question mark (?) at the beginning of their definition, will be inlined if they have a single child, after filtering.

Example:

                start: greet greet
                ?greet: "(" /\w+/ ")"
                      | /\w+/ /\w+/

Lark will parse "hello world (planet)" as:

            start
                greet
                    "hello"
                    "world"
                "planet"

  Pinning rule terminals with !

Rules that begin with an exclamation mark will keep all their terminals (they won't get filtered).

                !expr: "(" expr ")"
                     | NAME+
                NAME: /\w+/
                %ignore " "

Will parse "((hello world))" as:

            expr
              (
              expr
                (
                expr
                  hello
                  world
                )
              )

Using the ! prefix is usually a "code smell", and may point to a flaw in your grammar design.

  Aliasing rules

Aliases - options in a rule can receive an alias. It will be then used as the branch name for the option, instead of the rule name.

Example:

                start: greet greet
                greet: "hello"
                     | "world" -> planet

Lark will parse "hello world" as:

            start
                greet
                planet

API REFERENCE

  Lark

    class lark.Lark(grammar: Grammar | str | IO[str], **options)
        Main interface for the library.

        It's mostly a thin wrapper for the many different parsers, and for the tree constructor.

        Parameters

            •   grammar -- a string or file-object containing the grammar spec (using Lark's ebnf syntax)

            •   options -- a dictionary controlling various aspects of Lark.

    Example

        >>> Lark(r'''start: "foo" ''')
        Lark(...)

    === General Options ===

        start
            The start symbol. Either a string, or a list of strings for multiple possible starts (Default: "start")

        debug
            Display debug information and extra warnings. Use only when debugging (Default: False) When used with Earley, it generates a forest graph as "sppf.png", if 'dot' is installed.

        strict
            Throw an exception on any potential ambiguity, including shift/reduce conflicts, and regex collisions.

        transformer
            Applies the transformer to every parse tree (equivalent to applying it after the parse, but faster)

        propagate_positions
            Propagates positional attributes into the 'meta' attribute of all tree branches. Sets attributes: (line, column, end_line, end_column, start_pos, end_pos,
                container_line, container_column, container_end_line, container_end_column)

        Accepts False, True, or a callable, which will filter which nodes to ignore when propagating.

        maybe_placeholders
            When True, the [] operator returns None when not matched. When False, [] behaves like the ? operator, and returns no value at all. (default= True)

        cache
            Cache the results of the Lark grammar analysis, for x2 to x3 faster loading. LALR only for now.

            •   When False, does nothing (default)

            •   When True, caches to a temporary file in the local directory

            •   When given a string, caches to the path pointed by the string

        cache_grammar
            For use with cache option. When True, the unanalyzed grammar is also included in the cache. Useful for classes that require the Lark.grammar to be present (e.g. Reconstructor). (default= False)

        regex
            When True, uses the regex module instead of the stdlib re.

        g_regex_flags
            Flags that are applied to all terminals (both regex and strings)

        keep_all_tokens
            Prevent the tree builder from automagically removing "punctuation" tokens (Default: False)

        tree_class
            Lark will produce trees comprised of instances of this class instead of the default lark.Tree.

    === Algorithm Options ===

        parser
            Decides which parser engine to use. Accepts "earley" or "lalr". (Default: "earley"). (there is also a "cyk" option for legacy)

        lexer
            Decides whether or not to use a lexer stage

            •   "auto" (default): Choose for me based on the parser

            •   "basic": Use a basic lexer

            •   "contextual": Stronger lexer (only works with parser="lalr")

            •   "dynamic": Flexible and powerful (only with parser="earley")

            •   "dynamic_complete": Same as dynamic, but tries every variation of tokenizing possible.

        ambiguity
            Decides how to handle ambiguity in the parse. Only relevant if parser="earley"

            •   "resolve": The parser will automatically choose the simplest derivation (it chooses consistently: greedy for tokens, non-greedy for rules)

            •   "explicit": The parser will return all derivations wrapped in "_ambig" tree nodes (i.e. a forest).

            •   "forest": The parser will return the root of the shared packed parse forest.

    === Misc. / Domain Specific Options ===

        postlex
            Lexer post-processing (Default: None) Only works with the basic and contextual lexers.

        priority
            How priorities should be evaluated - "auto", None, "normal", "invert" (Default: "auto")

        lexer_callbacks
            Dictionary of callbacks for the lexer. May alter tokens during lexing. Use with caution.

        use_bytes
            Accept an input of type bytes instead of str.

        ordered_sets
            Should Earley use ordered-sets to achieve stable output (~10% slower than regular sets. Default: True)

        edit_terminals
            A callback for editing the terminals before parse.

        import_paths
            A List of either paths or loader functions to specify from where grammars are imported

        source_path
            Override the source of from where the grammar was loaded. Useful for relative imports and unconventional grammar loading

    === End of Options ===

        save(f, exclude_options: Collection[str] = ()) -> None
            Saves the instance into the given file object

            Useful for caching and multiprocessing.

        classmethod load(f) -> _T
            Loads an instance from the given file object

            Useful for caching and multiprocessing.

        classmethod open(grammar_filename: str, rel_to: str | None = None, **options) -> _T
            Create an instance of Lark with the grammar given by its filename

            If rel_to is provided, the function will find the grammar filename in relation to it.

            Example

                >>> Lark.open("grammar_file.lark", rel_to=__file__, parser="lalr")
                Lark(...)

        classmethod open_from_package(package: str, grammar_path: str, search_paths: Sequence[str] = [''], **options) -> _T
            Create an instance of Lark with the grammar loaded from within the package package. This allows grammar loading from zipapps.

            Imports in the grammar will use the package and search_paths provided, through FromPackageLoader

            Example

            Lark.open_from_package(__name__, "example.lark", ("grammars",), parser=...)

        lex(text: AnyStr | TextSlice <#lark.utils.TextSlice>[AnyStr], dont_ignore: bool = False) -> Iterator[Token <#lark.Token>]
            Only lex (and postlex) the text, without parsing it. Only relevant when lexer='basic'

            When dont_ignore=True, the lexer will return all tokens, even those marked for %ignore.

            Raises
                UnexpectedCharacters <#lark.exceptions.UnexpectedCharacters> -- In case the lexer cannot find a suitable match.

        get_terminal(name: str) -> TerminalDef
            Get information about a terminal

        parse_interactive(text: AnyStr | TextSlice <#lark.utils.TextSlice> | Any | None = None, start: str | None = None) -> InteractiveParser <#lark.parsers.lalr_interactive_parser.InteractiveParser>
            Start an interactive parsing session. Only works when parser='lalr'.

            Parameters

                •   text (LarkInput, optional) -- Text to be parsed. Required for resume_parse().

                •   start (str, optional) -- Start symbol

            Returns
                A new InteractiveParser instance.

        See Also: Lark.parse()

        parse(text: AnyStr | TextSlice <#lark.utils.TextSlice> | Any, start: str | None = None, on_error: Callable[[UnexpectedInput <#lark.exceptions.UnexpectedInput>], bool] | None = None) -> ParseTree
            Parse the given text, according to the options provided.

            Parameters

                •   text (LarkInput) -- Text to be parsed, as str or bytes. TextSlice may also be used, but only when lexer='basic' or 'contextual'. If Lark was created with a custom lexer, this may be an object of any type.

                •   start (str, optional) -- Required if Lark was given multiple possible start symbols (using the start option).

                •   on_error (function, optional) -- if provided, will be called on UnexpectedInput error, with the exception as its argument. Return true to resume parsing, or false to raise the exception. LALR only. See examples/advanced/error_handling.py for an example of how to use on_error.

            Returns
                If a transformer is supplied to __init__, returns whatever is the result of the transformation. Otherwise, returns a Tree instance.

            Raises
                UnexpectedInput <#lark.exceptions.UnexpectedInput> -- On a parse error, one of these sub-exceptions will rise: UnexpectedCharacters, UnexpectedToken, or UnexpectedEOF. For convenience, these sub-exceptions also inherit from ParserError and LexerError.

  Using Unicode character classes with regex

Python's builtin re module has a few persistent known bugs and also won't parse advanced regex features such as character classes. With pip install lark[regex], the regex module will be installed alongside lark and can act as a drop-in replacement to re.

Any instance of Lark instantiated with regex=True will use the regex module instead of re.

For example, we can use character classes to match PEP-3131 compliant Python identifiers:

            from lark import Lark
            >>> g = Lark(r"""
                                ?start: NAME
                                NAME: ID_START ID_CONTINUE*
                                ID_START: /[\p{Lu}\p{Ll}\p{Lt}\p{Lm}\p{Lo}\p{Nl}_]+/
                                ID_CONTINUE: ID_START | /[\p{Mn}\p{Mc}\p{Nd}\p{Pc}·]+/
                            """, regex=True)

            >>> g.parse('வணக்கம்')
            'வணக்கம்'

  Tree

    class lark.Tree(data: str, children: List[_Leaf_T | Tree <#lark.Tree>[_Leaf_T]], meta: Meta | None = None)
        The main tree class.

        Creates a new tree, and stores "data" and "children" in attributes of the same name. Trees can be hashed and compared.

        Parameters

            •   data -- The name of the rule or alias

            •   children -- List of matched sub-rules and terminals

            •   meta --

                Line & Column numbers (if propagate_positions is enabled). meta attributes: (line, column, end_line, end_column, start_pos, end_pos,
                    container_line, container_column, container_end_line, container_end_column)

            container_* attributes consider all symbols, including those that have been inlined in the tree. For example, in the rule 'a: _A B _C', the regular attributes will mark the start and end of B, but the container_* attributes will also include _A and _C in the range. However, rules that contain 'a' will consider it in full, including _A and _C for all attributes.

        pretty(indent_str: str = ' ') -> str
            Returns an indented string representation of the tree.

            Great for debugging.

        __rich__(parent: rich.tree.Tree | None = None) -> rich.tree.Tree
            Returns a tree widget for the 'rich' library.

            Example

            ::  from rich import print from lark import Tree

                tree = Tree('root', ['node1', 'node2']) print(tree)

        iter_subtrees() -> Iterator[Tree <#lark.Tree>[_Leaf_T]]
            Depth-first iteration.

            Iterates over all the subtrees, never returning to the same node twice (Lark's parse-tree is actually a DAG).

        iter_subtrees_topdown()
            Breadth-first iteration.

            Iterates over all the subtrees, return nodes in order like pretty() does.

        find_pred(pred: Callable[[Tree <#lark.Tree>[_Leaf_T]], bool]) -> Iterator[Tree <#lark.Tree>[_Leaf_T]]
            Returns all nodes of the tree that evaluate pred(node) as true.

        find_data(data: str) -> Iterator[Tree <#lark.Tree>[_Leaf_T]]
            Returns all nodes of the tree whose data equals the given data.

        scan_values(pred: Callable[[_Leaf_T | Tree <#lark.Tree>[_Leaf_T]], bool]) -> Iterator[_Leaf_T]
            Return all values in the tree that evaluate pred(value) as true.

            This can be used to find all the tokens in the tree.

            Example

                >>> all_tokens = tree.scan_values(lambda v: isinstance(v, Token))

  Token

    class lark.Token(type: str, value: Any, start_pos: int | None = None, line: int | None = None, column: int | None = None, end_line: int | None = None, end_column: int | None = None, end_pos: int | None = None)

    class lark.Token(type_: str, value: Any, start_pos: int | None = None, line: int | None = None, column: int | None = None, end_line: int | None = None, end_column: int | None = None, end_pos: int | None = None)
        A string with meta-information, that is produced by the lexer.

        When parsing text, the resulting chunks of the input that haven't been discarded, will end up in the tree as Token instances. The Token class inherits from Python's str, so normal string comparisons and operations will work as expected.

        type
            Name of the token (as specified in grammar)

            Type
                str

        value
            Value of the token (redundant, as token.value == token will always be true)

            Type
                Any

        start_pos
            The index of the token in the text

            Type
                int | None

        line
            The line of the token in the text (starting with 1)

            Type
                int | None

        column
            The column of the token in the text (starting with 1)

            Type
                int | None

        end_line
            The line where the token ends

            Type
                int | None

        end_column
            The next column after the end of the token. For example, if the token is a single character with a column value of 4, end_column will be 5.

            Type
                int | None

        end_pos
            the index where the token ends (basically start_pos + len(token))

            Type
                int | None

  Transformer, Visitor & Interpreter

See Transformers & Visitors <>.

  ForestVisitor, ForestTransformer, & TreeForestTransformer

See Working with the SPPF <>.

  UnexpectedInput

    class lark.exceptions.UnexpectedInput
        UnexpectedInput Error.

        Used as a base class for the following exceptions:

        •   UnexpectedCharacters: The lexer encountered an unexpected string

        •   UnexpectedToken: The parser received an unexpected token

        •   UnexpectedEOF: The parser expected a token, but the input ended

    After catching one of these exceptions, you may call the following helper methods to create a nicer error message.

        get_context(text: str, span: int = 40) -> str
            Returns a pretty string pinpointing the error in the text, with span amount of context characters around it.

            Note:
                The parser doesn't hold a copy of the text it has to parse, so you have to provide it again

        match_examples(parse_fn: Callable[[str], Tree <#lark.Tree>], examples: Mapping[T, Iterable[str]] | Iterable[Tuple[T, Iterable[str]]], token_type_match_fallback: bool = False, use_accepts: bool = True) -> T | None
            Allows you to detect what's wrong in the input text by matching against example errors.

            Given a parser instance and a dictionary mapping some label with some malformed syntax examples, it'll return the label for the example that bests matches the current error. The function will iterate the dictionary until it finds a matching error, and return the corresponding value.

            For an example usage, see examples/error_reporting_lalr.py

            Parameters

                •   parse_fn -- parse function (usually lark_instance.parse)

                •   examples -- dictionary of {'example_string': value}.

                •   use_accepts -- Recommended to keep this as use_accepts=True.

    class lark.exceptions.UnexpectedToken(token, expected, considered_rules=None, state=None, interactive_parser=None, terminals_by_name=None, token_history=None)
        An exception that is raised by the parser, when the token it received doesn't match any valid step forward.

        Parameters

            •   token -- The mismatched token

            •   expected -- The set of expected tokens

            •   considered_rules -- Which rules were considered, to deduce the expected tokens

            •   state -- A value representing the parser state. Do not rely on its value or type.

            •   interactive_parser -- An instance of InteractiveParser, that is initialized to the point of failure, and can be used for debugging and error handling.

    Note: These parameters are available as attributes of the instance.

    class lark.exceptions.UnexpectedCharacters(seq, lex_pos, line, column, allowed=None, considered_tokens=None, state=None, token_history=None, terminals_by_name=None, considered_rules=None)
        An exception that is raised by the lexer, when it cannot match the next string of characters to any of its terminals.

    class lark.exceptions.UnexpectedEOF(expected, state=None, terminals_by_name=None)
        An exception that is raised by the parser, when the input ends while it still expects a token.

  InteractiveParser

    class lark.parsers.lalr_interactive_parser.InteractiveParser(parser, parser_state: ParserState, lexer_thread: LexerThread)
        InteractiveParser gives you advanced control over parsing and error handling when parsing with LALR.

        For a simpler interface, see the on_error argument to Lark.parse().

        feed_token(token: Token <#lark.Token>)
            Feed the parser with a token, and advance it to the next state, as if it received it from the lexer.

            Note that token has to be an instance of Token.

        exhaust_lexer() -> List[Token <#lark.Token>]
            Try to feed the rest of the lexer state into the interactive parser.

            Note that this modifies the instance in place and does not feed an '$END' Token

        as_immutable()
            Convert to an ImmutableInteractiveParser.

        pretty()
            Print the output of choices() in a way that's easier to read.

        choices()
            Returns a dictionary of token types, matched to their action in the parser.

            Only returns token types that are accepted by the current state.

            Updated by feed_token().

        accepts()
            Returns the set of possible tokens that will advance the parser into a new valid state.

        resume_parse()
            Resume automated parsing from the current state.

    class lark.parsers.lalr_interactive_parser.ImmutableInteractiveParser(parser, parser_state: ParserState, lexer_thread: LexerThread)
        Same as InteractiveParser, but operations create a new instance instead of changing it in-place.

        feed_token(token)
            Feed the parser with a token, and advance it to the next state, as if it received it from the lexer.

            Note that token has to be an instance of Token.

        exhaust_lexer()
            Try to feed the rest of the lexer state into the parser.

            Note that this returns a new ImmutableInteractiveParser and does not feed an '$END' Token

        as_mutable()
            Convert to an InteractiveParser.

        accepts()
            Returns the set of possible tokens that will advance the parser into a new valid state.

        choices()
            Returns a dictionary of token types, matched to their action in the parser.

            Only returns token types that are accepted by the current state.

            Updated by feed_token().

        pretty()
            Print the output of choices() in a way that's easier to read.

        resume_parse()
            Resume automated parsing from the current state.

  ast_utils

For an example of using ast_utils, see /examples/advanced/create_ast.py <examples/advanced/create_ast.html>

    class lark.ast_utils.Ast
        Abstract class

        Subclasses will be collected by create_transformer()

    class lark.ast_utils.AsList
        Abstract class

        Subclasses will be instantiated with the parse results as a single list, instead of as arguments.

    lark.ast_utils.create_transformer(ast_module: ModuleType, transformer: Transformer <#lark.visitors.Transformer> | None = None, decorator_factory: Callable = <function v_args>) -> Transformer <#lark.visitors.Transformer>
        Collects Ast subclasses from the given module, and creates a Lark transformer that builds the AST.

        For each class, we create a corresponding rule in the transformer, with a matching name. CamelCase names will be converted into snake_case. Example: "CodeBlock" -> "code_block".

        Classes starting with an underscore (_) will be skipped.

        Parameters

            •   ast_module -- A Python module containing all the subclasses of ast_utils.Ast

            •   transformer (Optional[Transformer <#lark.visitors.Transformer>]) -- An initial transformer. Its attributes may be overwritten.

            •   decorator_factory (Callable) -- An optional callable accepting two booleans, inline, and meta, and returning a decorator for the methods of transformer. (default: v_args).

  Indenter

    class lark.indenter.Indenter
        This is a postlexer that "injects" indent/dedent tokens based on indentation.

        It keeps track of the current indentation, as well as the current level of parentheses. Inside parentheses, the indentation is ignored, and no indent/dedent tokens get generated.

        Note: This is an abstract class. To use it, inherit and implement all its abstract methods:

            •   tab_len

            •   NL_type

            •   OPEN_PAREN_types, CLOSE_PAREN_types

            •   INDENT_type, DEDENT_type

    See also: the postlex option in Lark.

    class lark.indenter.PythonIndenter
        A postlexer that "injects" _INDENT/_DEDENT tokens based on indentation, according to the Python syntax.

        See also: the postlex option in Lark.

  TextSlice

    class lark.utils.TextSlice(text: AnyStr, start: int, end: int)
        A view of a string or bytes object, between the start and end indices.

        Never creates a copy.

        Lark accepts instances of TextSlice as input (instead of a string), when the lexer is 'basic' or 'contextual'.

        Parameters

            •   text (str or bytes) -- The text to slice.

            •   start (int) -- The start index. Negative indices are supported.

            •   end (int) -- The end index. Negative indices are supported.

        Raises

            •   TypeError -- If text is not a str or bytes.

            •   AssertionError -- If start or end are out of bounds.

    Examples

        >>> TextSlice("Hello, World!", 7, -1)
        TextSlice(text='Hello, World!', start=7, end=12)

        >>> TextSlice("Hello, World!", 7, None).count("o")
        1

TRANSFORMERS & VISITORS

Transformers & Visitors provide a convenient interface to process the parse-trees that Lark returns.

They are used by inheriting from the correct class (visitor or transformer), and implementing methods corresponding to the rule you wish to process. Each method accepts the children as an argument. That can be modified using the v_args decorator, which allows one to inline the arguments (akin to *args), or add the tree meta property as an argument.

See: visitors.py <https://github.com/lark-parser/lark/blob/master/lark/visitors.py>

  Visitor

Visitors visit each node of the tree, and run the appropriate method on it according to the node's data.

They work bottom-up, starting with the leaves and ending at the root of the tree.

There are two classes that implement the visitor interface:

    •   Visitor: Visit every node (without recursion)

    •   Visitor_Recursive: Visit every node using recursion. Slightly faster.

    Example:

                class IncreaseAllNumbers(Visitor):
                    def number(self, tree):
                        assert tree.data == "number"
                        tree.children[0] += 1

                IncreaseAllNumbers().visit(parse_tree)

    class lark.visitors.Visitor
        Tree visitor, non-recursive (can handle huge trees).

        Visiting a node calls its methods (provided by the user via inheritance) according to tree.data

        visit(tree: Tree <#lark.Tree>[_Leaf_T]) -> Tree <#lark.Tree>[_Leaf_T]
            Visits the tree, starting with the leaves and finally the root (bottom-up)

        visit_topdown(tree: Tree <#lark.Tree>[_Leaf_T]) -> Tree <#lark.Tree>[_Leaf_T]
            Visit the tree, starting at the root, and ending at the leaves (top-down)

        __default__(tree)
            Default function that is called if there is no attribute matching tree.data

            Can be overridden. Defaults to doing nothing.

    class lark.visitors.Visitor_Recursive
        Bottom-up visitor, recursive.

        Visiting a node calls its methods (provided by the user via inheritance) according to tree.data

        Slightly faster than the non-recursive version.

        visit(tree: Tree <#lark.Tree>[_Leaf_T]) -> Tree <#lark.Tree>[_Leaf_T]
            Visits the tree, starting with the leaves and finally the root (bottom-up)

        visit_topdown(tree: Tree <#lark.Tree>[_Leaf_T]) -> Tree <#lark.Tree>[_Leaf_T]
            Visit the tree, starting at the root, and ending at the leaves (top-down)

        __default__(tree)
            Default function that is called if there is no attribute matching tree.data

            Can be overridden. Defaults to doing nothing.

  Interpreter

    class lark.visitors.Interpreter
        Interpreter walks the tree starting at the root.

        Visits the tree, starting with the root and finally the leaves (top-down)

        For each tree node, it calls its methods (provided by user via inheritance) according to tree.data.

        Unlike Transformer and Visitor, the Interpreter doesn't automatically visit its sub-branches. The user has to explicitly call visit, visit_children, or use the @visit_children_decor. This allows the user to implement branching and loops.

    Example:

                class IncreaseSomeOfTheNumbers(Interpreter):
                    def number(self, tree):
                        tree.children[0] += 1

                    def skip(self, tree):
                        # skip this subtree. don't change any number node inside it.
                        pass

                    IncreaseSomeOfTheNumbers().visit(parse_tree)

  Transformer

    class lark.visitors.Transformer(visit_tokens: bool = True)
        Transformers work bottom-up (or depth-first), starting with visiting the leaves and working their way up until ending at the root of the tree.

        For each node visited, the transformer will call the appropriate method (callbacks), according to the node's data, and use the returned value to replace the node, thereby creating a new tree structure.

        Transformers can be used to implement map & reduce patterns. Because nodes are reduced from leaf to root, at any point the callbacks may assume the children have already been transformed (if applicable).

        If the transformer cannot find a method with the right name, it will instead call __default__, which by default creates a copy of the node.

        To discard a node, return Discard (lark.visitors.Discard).

        Transformer can do anything Visitor can do, but because it reconstructs the tree, it is slightly less efficient.

        A transformer without methods essentially performs a non-memoized partial deepcopy.

        All these classes implement the transformer interface:

        •   Transformer - Recursively transforms the tree. This is the one you probably want.

        •   Transformer_InPlace - Non-recursive. Changes the tree in-place instead of returning new instances

        •   Transformer_InPlaceRecursive - Recursive. Changes the tree in-place instead of returning new instances

        Parameters
            visit_tokens (bool, optional) -- Should the transformer visit tokens in addition to rules. Setting this to False is slightly faster. Defaults to True. (For processing ignored tokens, use the lexer_callbacks options)

        transform(tree: Tree <#lark.Tree>[_Leaf_T]) -> _Return_T
            Transform the given tree, and return the final result

        __mul__(other: Transformer <#lark.visitors.Transformer> | TransformerChain[_Leaf_U, _Return_V]) -> TransformerChain[_Leaf_T, _Return_V]
            Chain two transformers together, returning a new transformer.

        __default__(data, children, meta)
            Default function that is called if there is no attribute matching data

            Can be overridden. Defaults to creating a new copy of the tree node (i.e. return Tree(data, children, meta))

        __default_token__(token)
            Default function that is called if there is no attribute matching token.type

            Can be overridden. Defaults to returning the token as-is.

    Example:

                from lark import Tree, Transformer

                class EvalExpressions(Transformer):
                    def expr(self, args):
                            return eval(args[0])

                t = Tree('a', [Tree('expr', ['1+2'])])
                print(EvalExpressions().transform( t ))

                # Prints: Tree(a, [3])

    Example:

                class T(Transformer):
                    INT = int
                    NUMBER = float
                    def NAME(self, name):
                        return lookup_dict.get(name, name)

                T(visit_tokens=True).transform(tree)

    class lark.visitors.Transformer_NonRecursive(visit_tokens: bool = True)
        Same as Transformer but non-recursive.

        Like Transformer, it doesn't change the original tree.

        Useful for huge trees.

    class lark.visitors.Transformer_InPlace(visit_tokens: bool = True)
        Same as Transformer, but non-recursive, and changes the tree in-place instead of returning new instances

        Useful for huge trees. Conservative in memory.

    class lark.visitors.Transformer_InPlaceRecursive(visit_tokens: bool = True)
        Same as Transformer, recursive, but changes the tree in-place instead of returning new instances

  v_args

    lark.visitors.v_args(inline: bool = False, meta: bool = False, tree: bool = False, wrapper: Callable | None = None) -> Callable[[Callable[[...], _Return_T] | type <#lark.Token.type>], Callable[[...], _Return_T] | type <#lark.Token.type>]
        A convenience decorator factory for modifying the behavior of user-supplied callback methods of Transformer classes.

        By default, transformer callback methods accept one argument - a list of the node's children.

        v_args can modify this behavior. When used on a Transformer class definition, it applies to all the callback methods inside it.

        v_args can be applied to a single method, or to an entire class. When applied to both, the options given to the method take precedence.

        Parameters

            •   inline (bool, optional) -- Children are provided as *args instead of a list argument (not recommended for very long lists).

            •   meta (bool, optional) -- Provides two arguments: meta and children (instead of just the latter); meta isn't available for transformers supplied to Lark using the transformer parameter (aka internal transformers).

            •   tree (bool, optional) -- Provides the entire tree as the argument, instead of the children.

            •   wrapper (function, optional) -- Provide a function to decorate all methods.

    Example

                @v_args(inline=True)
                class SolveArith(Transformer):
                    def add(self, left, right):
                        return left + right

                    @v_args(meta=True)
                    def mul(self, meta, children):
                        logger.info(f'mul at line {meta.line}')
                        left, right = children
                        return left * right

                class ReverseNotation(Transformer_InPlace):
                    @v_args(tree=True)
                    def tree_node(self, tree):
                        tree.children = tree.children[::-1]

  merge_transformers

    lark.visitors.merge_transformers(base_transformer=None, **transformers_to_merge)
        Merge a collection of transformers into the base_transformer, each into its own 'namespace'.

        When called, it will collect the methods from each transformer, and assign them to base_transformer, with their name prefixed with the given keyword, as prefix__methodname.

        This function is especially useful for processing grammars that import other grammars, thereby creating some of their rules in a 'namespace'. (i.e with a consistent name prefix). In this case, the key for the transformer should match the name of the imported grammar.

        Parameters

            •   base_transformer (Transformer <#lark.visitors.Transformer>, optional) -- The transformer that all other transformers will be added to.

            •   **transformers_to_merge -- Keyword arguments, in the form of name_prefix = transformer.

        Raises
            AttributeError -- In case of a name collision in the merged methods

    Example

                class TBase(Transformer):
                    def start(self, children):
                        return children[0] + 'bar'

                class TImportedGrammar(Transformer):
                    def foo(self, children):
                        return "foo"

                composed_transformer = merge_transformers(TBase(), imported=TImportedGrammar())

                t = Tree('start', [ Tree('imported__foo', []) ])

                assert composed_transformer.transform(t) == 'foobar'

  Discard

Discard is the singleton instance of _DiscardType.

    class lark.visitors._DiscardType
        When the Discard value is returned from a transformer callback, that node is discarded and won't appear in the parent.

        Note:
            This feature is disabled when the transformer is provided to Lark using the transformer keyword (aka Tree-less LALR mode).

    Example

                class T(Transformer):
                    def ignore_tree(self, children):
                        return Discard

                    def IGNORE_TOKEN(self, token):
                        return Discard

  VisitError

    class lark.exceptions.VisitError(rule, obj, orig_exc)
        VisitError is raised when visitors are interrupted by an exception

        It provides the following attributes for inspection:

        Parameters

            •   rule -- the name of the visit rule that failed

            •   obj -- the tree-node or token that was being processed

            •   orig_exc -- the exception that cause it to fail

    Note: These parameters are available as attributes

WORKING WITH THE SPPF

When parsing with Earley, Lark provides the ambiguity='forest' option to obtain the shared packed parse forest (SPPF) produced by the parser as an alternative to it being automatically converted to a tree.

Lark provides a few tools to facilitate working with the SPPF. Here are some things to consider when deciding whether or not to use the SPPF.

Pros

    •   Efficient storage of highly ambiguous parses

    •   Precise handling of ambiguities

    •   Custom rule prioritizers

    •   Ability to handle infinite ambiguities

    •   Directly transform forest -> object instead of forest -> tree -> object

Cons

    •   More complex than working with a tree

    •   SPPF may contain nodes corresponding to rules generated internally

    •   Loss of Lark grammar features:

        •   Rules starting with '_' are not inlined in the SPPF

        •   Rules starting with '?' are never inlined in the SPPF

        •   All tokens will appear in the SPPF

  SymbolNode

    class lark.parsers.earley_forest.SymbolNode(s, start, end)
        A Symbol Node represents a symbol (or Intermediate LR0).

        Symbol nodes are keyed by the symbol (s). For intermediate nodes s will be an LR0, stored as a tuple of (rule, ptr). For completed symbol nodes, s will be a string representing the non-terminal origin (i.e. the left hand side of the rule).

        The children of a Symbol or Intermediate Node will always be Packed Nodes; with each Packed Node child representing a single derivation of a production.

        Hence a Symbol Node with a single child is unambiguous.

        Parameters

            •   s -- A Symbol, or a tuple of (rule, ptr) for an intermediate node.

            •   start -- For dynamic lexers, the index of the start of the substring matched by this symbol (inclusive).

            •   end -- For dynamic lexers, the index of the end of the substring matched by this symbol (exclusive).

        Properties:
            is_intermediate: True if this node is an intermediate node. priority: The priority of the node's symbol.

        property is_ambiguous
            Returns True if this node is ambiguous.

        property children
            Returns a list of this node's children sorted from greatest to least priority.

  PackedNode

    class lark.parsers.earley_forest.PackedNode(parent, s, rule, start, left, right)
        A Packed Node represents a single derivation in a symbol node.

        Parameters

            •   rule -- The rule associated with this node.

            •   parent -- The parent of this node.

            •   left -- The left child of this node. None if one does not exist.

            •   right -- The right child of this node. None if one does not exist.

            •   priority -- The priority of this node.

        property children
            Returns a list of this node's children.

  ForestVisitor

    class lark.parsers.earley_forest.ForestVisitor(single_visit=False)
        An abstract base class for building forest visitors.

        This class performs a controllable depth-first walk of an SPPF. The visitor will not enter cycles and will backtrack if one is encountered. Subclasses are notified of cycles through the on_cycle method.

        Behavior for visit events is defined by overriding the visit*node* functions.

        The walk is controlled by the return values of the visit*node_in methods. Returning a node(s) will schedule them to be visited. The visitor will begin to backtrack if no nodes are returned.

        Parameters
            single_visit -- If True, non-Token nodes will only be visited once.

        visit_token_node(node)
            Called when a Token is visited. Token nodes are always leaves.

        visit_symbol_node_in(node)
            Called when a symbol node is visited. Nodes that are returned will be scheduled to be visited. If visit_intermediate_node_in is not implemented, this function will be called for intermediate nodes as well.

        visit_symbol_node_out(node)
            Called after all nodes returned from a corresponding visit_symbol_node_in call have been visited. If visit_intermediate_node_out is not implemented, this function will be called for intermediate nodes as well.

        visit_packed_node_in(node)
            Called when a packed node is visited. Nodes that are returned will be scheduled to be visited.

        visit_packed_node_out(node)
            Called after all nodes returned from a corresponding visit_packed_node_in call have been visited.

        on_cycle(node, path)
            Called when a cycle is encountered.

            Parameters

                •   node -- The node that causes a cycle.

                •   path -- The list of nodes being visited: nodes that have been entered but not exited. The first element is the root in a forest visit, and the last element is the node visited most recently. path should be treated as read-only.

        get_cycle_in_path(node, path)
            A utility function for use in on_cycle to obtain a slice of path that only contains the nodes that make up the cycle.

  ForestTransformer

    class lark.parsers.earley_forest.ForestTransformer
        The base class for a bottom-up forest transformation. Most users will want to use TreeForestTransformer instead as it has a friendlier interface and covers most use cases.

        Transformations are applied via inheritance and overriding of the transform*node methods.

        transform_token_node receives a Token as an argument. All other methods receive the node that is being transformed and a list of the results of the transformations of that node's children. The return value of these methods are the resulting transformations.

        If Discard is raised in a node's transformation, no data from that node will be passed to its parent's transformation.

        transform(root)
            Perform a transformation on an SPPF.

        transform_symbol_node(node, data)
            Transform a symbol node.

        transform_intermediate_node(node, data)
            Transform an intermediate node.

        transform_packed_node(node, data)
            Transform a packed node.

        transform_token_node(node)
            Transform a Token.

  TreeForestTransformer

    class lark.parsers.earley_forest.TreeForestTransformer(tree_class=<class 'lark.tree.Tree'>, prioritizer=<lark.parsers.earley_forest.ForestSumVisitor object>, resolve_ambiguity=True, use_cache=False)
        A ForestTransformer with a tree Transformer-like interface. By default, it will construct a tree.

        Methods provided via inheritance are called based on the rule/symbol names of nodes in the forest.

        Methods that act on rules will receive a list of the results of the transformations of the rule's children. By default, trees and tokens.

        Methods that act on tokens will receive a token.

        Alternatively, methods that act on rules may be annotated with handles_ambiguity. In this case, the function will receive a list of all the transformations of all the derivations of the rule. By default, a list of trees where each tree.data is equal to the rule name or one of its aliases.

        Non-tree transformations are made possible by override of __default__, __default_token__, and __default_ambig__.

        Note:
            Tree shaping features such as inlined rules and token filtering are not built into the transformation. Positions are also not propagated.

        Parameters

            •   tree_class -- The tree class to use for construction

            •   prioritizer -- A ForestVisitor that manipulates the priorities of nodes in the SPPF.

            •   resolve_ambiguity -- If True, ambiguities will be resolved based on priorities.

            •   use_cache (bool) -- If True, caches the results of some transformations, potentially improving performance when resolve_ambiguity==False. Only use if you know what you are doing: i.e. All transformation functions are pure and referentially transparent.

        __default__(name, data)
            Default operation on tree (for override).

            Returns a tree with name with data as children.

        __default_ambig__(name, data)
            Default operation on ambiguous rule (for override).

            Wraps data in an '_ambig_' node if it contains more than one element.

        __default_token__(node)
            Default operation on Token (for override).

            Returns node.

  handles_ambiguity

    lark.parsers.earley_forest.handles_ambiguity(func)
        Decorator for methods of subclasses of TreeForestTransformer. Denotes that the method should receive a list of transformed derivations.

TOOLS (STAND-ALONE, NEARLEY)

  Stand-alone parser

Lark can generate a stand-alone LALR(1) parser from a grammar.

The resulting module provides the same interface as Lark, but with a fixed grammar, and reduced functionality.

Run using:

            python -m lark.tools.standalone

For a play-by-play, read the tutorial <http://blog.erezsh.com/create-a-stand-alone-lalr1-parser-in-python/>

  Importing grammars from Nearley.js

Lark comes with a tool to convert grammars from Nearley <https://github.com/Hardmath123/nearley>, a popular Earley library for Javascript. It uses Js2Py <https://github.com/PiotrDabkowski/Js2Py> to convert and run the Javascript postprocessing code segments.

  Requirements

    •   Install Lark with the nearley component:

            pip install lark[nearley]

    •   Acquire a copy of the Nearley codebase. This can be done using:

            git clone https://github.com/Hardmath123/nearley

  Usage

The tool can be run using:

            python -m lark.tools.nearley <grammar.ne> <start_rule> <path_to_nearley_repo>

Here's an example of how to import nearley's calculator example into Lark:

            git clone https://github.com/Hardmath123/nearley
            python -m lark.tools.nearley nearley/examples/calculator/arithmetic.ne main ./nearley > ncalc.py

You can use the output as a regular python module:

            >>> import ncalc
            >>> ncalc.parse('sin(pi/4) ^ e')
            0.38981434460254655

The Nearley converter also supports an experimental converter for newer JavaScript (ES6+), using the --es6 flag:

            git clone https://github.com/Hardmath123/nearley
            python -m lark.tools.nearley nearley/examples/calculator/arithmetic.ne main nearley --es6 > ncalc.py

  Notes

    •   Lark currently cannot import templates from Nearley

    •   Lark currently cannot export grammars to Nearley

These might get added in the future, if enough users ask for them.

Lark is a modern parsing library for Python. Lark can parse any context-free grammar.

Lark provides:

    •   Advanced grammar language, based on EBNF

    •   Three parsing algorithms to choose from: Earley, LALR(1) and CYK

    •   Automatic tree construction, inferred from your grammar

    •   Fast unicode lexer with regexp support, and automatic line-counting

INSTALL LARK

            $ pip install lark

SYNTAX HIGHLIGHTING

    •   Sublime Text & TextMate <https://github.com/lark-parser/lark_syntax>

    •   Visual Studio Code <https://github.com/lark-parser/vscode-lark> (Or install through the vscode plugin system)

    •   Intellij & PyCharm <https://github.com/lark-parser/intellij-syntax-highlighting>

    •   Vim <https://github.com/lark-parser/vim-lark-syntax>

    •   Atom <https://github.com/Alhadis/language-grammars>

RESOURCES

    •   Philosophy <>

    •   Features <>

    •   Examples <https://github.com/lark-parser/lark/tree/master/examples>

    •   Third-party examples <https://github.com/ligurio/lark-grammars>

    •   Online IDE <https://lark-parser.org/ide>

    •   Tutorials

        •   How to write a DSL <https://eshsoft.com/blog/write-dsl-in-python-with-lark> - Implements a toy LOGO-like language with an interpreter

        •   JSON parser - Tutorial <> - Teaches you how to use Lark

        •   Unofficial

            •   Program Synthesis is Possible <https://www.cs.cornell.edu/~asampson/blog/minisynth.html> - Creates a DSL for Z3

            •   Using Lark to Parse Text - Robin Reynolds-Haertle (PyCascades 2023) <https://www.youtube.com/watch?v=CeOtqlh0UuQ> (video presentation)

    •   Guides

        •   How To Use Lark - Guide <>

        •   How to develop Lark - Guide <>

    •   Reference

        •   Grammar Reference <>

        •   Tree Construction Reference <>

        •   Transformers & Visitors <>

        •   Working with the SPPF <>

        •   API Reference <>

        •   Tools (Stand-alone, Nearley) <>

        •   Cheatsheet (PDF) <_static/lark_cheatsheet.pdf>

    •   Discussion

        •   Gitter <https://gitter.im/lark-parser/Lobby>

        •   Forum (Google Groups) <https://groups.google.com/forum/#!forum/lark-parser>

Author
Erez Shinan

Copyright
2020, Erez Shinan