# 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 it should be easy to adapt to other kinds of progress bars.

    from tqdm import tqdm

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

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

## Parsing a Language with Significant Indentation

If your grammar needs to support significant indentation (e.g. Python, YAML), you will need to use the `Indenter` class. Take a look at the indented tree example as well as the Python grammar for inspiration.