testbed/lark-parser__lark/docs/recipes.md

# 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.

```python
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:

```python
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 standard and contextual lexers.

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

```python
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:

```python
[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 occured.

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:

```python
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.

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