Provided by: python3-lark_1.2.2-1_all 
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.
To improve performance, you can skip building the tree for LALR(1), by providing Lark with a transformer
(see the JSON example).
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)
• 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)
• Grammar composition - Import terminals and rules from other grammars (see example).
• 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)
• Import grammars from Nearley.js (read more)
• CYK parser
• Visualize your parse trees as dot or png files (see_example)
• Automatic reconstruction of input from parse-tree (see example and another example)
• Use Lark grammars in Julia and Javascript.
PARSERS
Lark implements the following parsing algorithms: Earley, LALR(1), and CYK
Earley
An 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
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.
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) 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 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. 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. They don't strictly adhere to 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/. 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
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.
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 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) 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
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 or Github Discussion, 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
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, but a similar approach should
work with GUIs.
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.result
Note that we don't simply wrap the iterable because tqdm would not be able to determine the total.
Additionally, keep in mind that this implementation relies on the InteractiveParser and, therefore, only
works with the LALR(1) parser, not earley.
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.
from lark import Lark
from lark.indenter import Indenter
tree_grammar = r"""
?start: _NL* tree
tree: NAME _NL [_INDENT tree+ _DEDENT]
%import common.CNAME -> NAME
%import common.WS_INLINE
%declare _INDENT _DEDENT
%ignore WS_INLINE
_NL: /(\r?\n[\t ]*)+/
"""
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
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 for more details.
EXAMPLE GRAMMARS
This directory is a collection of lark grammars, taken from real world projects.
• Verilog - 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 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 - 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 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 use rules instead.
Example:
>>> 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
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)
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. It's
not possible to override their dependencies (e.g. like you would when inheriting a class).
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, 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 with the -> syntax.
Example:
%import common.NUMBER
%import .terminals_file (A, B, C)
%import .rules_file.rulea -> ruleb
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 usecase
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
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: str, dont_ignore: bool = False) -> Iterator[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 -- In case the lexer cannot find a suitable match.
get_terminal(name: str) -> TerminalDef
Get information about a terminal
parse_interactive(text: str | None = None, start: str | None = None) -> InteractiveParser
Start an interactive parsing session.
Parameters
• text (str, 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: str, start: str | None = None, on_error: Callable[[UnexpectedInput], bool] | None =
None) -> ParseTree
Parse the given text, according to the options provided.
Parameters
• text (str) -- Text to be parsed.
• 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 UnexpectedToken
error. Return true to resume parsing. 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 -- 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[_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[_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[_Leaf_T]], bool]) -> Iterator[Tree[_Leaf_T]]
Returns all nodes of the tree that evaluate pred(node) as true.
find_data(data: str) -> Iterator[Tree[_Leaf_T]]
Returns all nodes of the tree whose data equals the given data.
scan_values(pred: Callable[[_Leaf_T | 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], 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)
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]
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.
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.
accepts()
Returns the set of possible tokens that will advance the parser into a new valid state.
ast_utils
For an example of using ast_utils, see /examples/advanced/create_ast.py
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: ~types.ModuleType, transformer: ~lark.visitors.Transformer
| None = None, decorator_factory: ~typing.Callable = <function v_args>) -> 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]) -- 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.
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
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[_Leaf_T]) -> Tree[_Leaf_T]
Visits the tree, starting with the leaves and finally the root (bottom-up)
visit_topdown(tree: Tree[_Leaf_T]) -> 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[_Leaf_T]) -> Tree[_Leaf_T]
Visits the tree, starting with the leaves and finally the root (bottom-up)
visit_topdown(tree: Tree[_Leaf_T]) -> 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[_Leaf_T]) -> _Return_T
Transform the given tree, and return the final result
__mul__(other: 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], Callable[[...], _Return_T] | type]
A convenience decorator factory for modifying the behavior of user-supplied visitor methods.
By default, callback methods of transformers/visitors accept one argument - a list of the node's
children.
v_args can modify this behavior. When used on a transformer/visitor 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)
• 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, 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
Importing grammars from Nearley.js
Lark comes with a tool to convert grammars from Nearley, a popular Earley library for Javascript. It uses
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
• Visual Studio Code (Or install through the vscode plugin system)
• Intellij & PyCharm
• Vim
• Atom
RESOURCES
• Philosophy
• Features
• Examples
• Third-party examples
• Online IDE
• Tutorials
• How to write a DSL - Implements a toy LOGO-like language with an interpreter
• JSON parser - Tutorial - Teaches you how to use Lark
• Unofficial
• Program Synthesis is Possible - Creates a DSL for Z3
• Using Lark to Parse Text - Robin Reynolds-Haertle (PyCascades 2023) (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)
• Discussion
• Gitter
• Forum (Google Groups)
AUTHOR
Erez Shinan
COPYRIGHT
2024, Erez Shinan
Aug 14, 2024 LARK(7)