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(*args, **kwds)

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(*args, **kwds)

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(*args, **kwds)

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: Union[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: Optional[Callable] = None) → Callable[[Union[Callable[[...], _Return_T], type]], Union[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