image-match/python/py/Lib/site-packages/lark/parse_tree_builder.py

"""Provides functions for the automatic building and shaping of the parse-tree."""

from typing import List

from .exceptions import GrammarError, ConfigurationError
from .lexer import Token
from .tree import Tree
from .visitors import Transformer_InPlace
from .visitors import _vargs_meta, _vargs_meta_inline

###{standalone
from functools import partial, wraps
from itertools import product


class ExpandSingleChild:
    def __init__(self, node_builder):
        self.node_builder = node_builder

    def __call__(self, children):
        if len(children) == 1:
            return children[0]
        else:
            return self.node_builder(children)



class PropagatePositions:
    def __init__(self, node_builder, node_filter=None):
        self.node_builder = node_builder
        self.node_filter = node_filter

    def __call__(self, children):
        res = self.node_builder(children)

        if isinstance(res, Tree):
            # Calculate positions while the tree is streaming, according to the rule:
            # - nodes start at the start of their first child's container,
            #   and end at the end of their last child's container.
            # Containers are nodes that take up space in text, but have been inlined in the tree.

            res_meta = res.meta

            first_meta = self._pp_get_meta(children)
            if first_meta is not None:
                if not hasattr(res_meta, 'line'):
                    # meta was already set, probably because the rule has been inlined (e.g. `?rule`)
                    res_meta.line = getattr(first_meta, 'container_line', first_meta.line)
                    res_meta.column = getattr(first_meta, 'container_column', first_meta.column)
                    res_meta.start_pos = getattr(first_meta, 'container_start_pos', first_meta.start_pos)
                    res_meta.empty = False

                res_meta.container_line = getattr(first_meta, 'container_line', first_meta.line)
                res_meta.container_column = getattr(first_meta, 'container_column', first_meta.column)
                res_meta.container_start_pos = getattr(first_meta, 'container_start_pos', first_meta.start_pos)

            last_meta = self._pp_get_meta(reversed(children))
            if last_meta is not None:
                if not hasattr(res_meta, 'end_line'):
                    res_meta.end_line = getattr(last_meta, 'container_end_line', last_meta.end_line)
                    res_meta.end_column = getattr(last_meta, 'container_end_column', last_meta.end_column)
                    res_meta.end_pos = getattr(last_meta, 'container_end_pos', last_meta.end_pos)
                    res_meta.empty = False

                res_meta.container_end_line = getattr(last_meta, 'container_end_line', last_meta.end_line)
                res_meta.container_end_column = getattr(last_meta, 'container_end_column', last_meta.end_column)
                res_meta.container_end_pos = getattr(last_meta, 'container_end_pos', last_meta.end_pos)

        return res

    def _pp_get_meta(self, children):
        for c in children:
            if self.node_filter is not None and not self.node_filter(c):
                continue
            if isinstance(c, Tree):
                if not c.meta.empty:
                    return c.meta
            elif isinstance(c, Token):
                return c
            elif hasattr(c, '__lark_meta__'):
                return c.__lark_meta__()

def make_propagate_positions(option):
    if callable(option):
        return partial(PropagatePositions, node_filter=option)
    elif option is True:
        return PropagatePositions
    elif option is False:
        return None

    raise ConfigurationError('Invalid option for propagate_positions: %r' % option)


class ChildFilter:
    def __init__(self, to_include, append_none, node_builder):
        self.node_builder = node_builder
        self.to_include = to_include
        self.append_none = append_none

    def __call__(self, children):
        filtered = []

        for i, to_expand, add_none in self.to_include:
            if add_none:
                filtered += [None] * add_none
            if to_expand:
                filtered += children[i].children
            else:
                filtered.append(children[i])

        if self.append_none:
            filtered += [None] * self.append_none

        return self.node_builder(filtered)


class ChildFilterLALR(ChildFilter):
    """Optimized childfilter for LALR (assumes no duplication in parse tree, so it's safe to change it)"""

    def __call__(self, children):
        filtered = []
        for i, to_expand, add_none in self.to_include:
            if add_none:
                filtered += [None] * add_none
            if to_expand:
                if filtered:
                    filtered += children[i].children
                else:   # Optimize for left-recursion
                    filtered = children[i].children
            else:
                filtered.append(children[i])

        if self.append_none:
            filtered += [None] * self.append_none

        return self.node_builder(filtered)


class ChildFilterLALR_NoPlaceholders(ChildFilter):
    "Optimized childfilter for LALR (assumes no duplication in parse tree, so it's safe to change it)"
    def __init__(self, to_include, node_builder):
        self.node_builder = node_builder
        self.to_include = to_include

    def __call__(self, children):
        filtered = []
        for i, to_expand in self.to_include:
            if to_expand:
                if filtered:
                    filtered += children[i].children
                else:   # Optimize for left-recursion
                    filtered = children[i].children
            else:
                filtered.append(children[i])
        return self.node_builder(filtered)


def _should_expand(sym):
    return not sym.is_term and sym.name.startswith('_')


def maybe_create_child_filter(expansion, keep_all_tokens, ambiguous, _empty_indices: List[bool]):
    # Prepare empty_indices as: How many Nones to insert at each index?
    if _empty_indices:
        assert _empty_indices.count(False) == len(expansion)
        s = ''.join(str(int(b)) for b in _empty_indices)
        empty_indices = [len(ones) for ones in s.split('0')]
        assert len(empty_indices) == len(expansion)+1, (empty_indices, len(expansion))
    else:
        empty_indices = [0] * (len(expansion)+1)

    to_include = []
    nones_to_add = 0
    for i, sym in enumerate(expansion):
        nones_to_add += empty_indices[i]
        if keep_all_tokens or not (sym.is_term and sym.filter_out):
            to_include.append((i, _should_expand(sym), nones_to_add))
            nones_to_add = 0

    nones_to_add += empty_indices[len(expansion)]

    if _empty_indices or len(to_include) < len(expansion) or any(to_expand for i, to_expand,_ in to_include):
        if _empty_indices or ambiguous:
            return partial(ChildFilter if ambiguous else ChildFilterLALR, to_include, nones_to_add)
        else:
            # LALR without placeholders
            return partial(ChildFilterLALR_NoPlaceholders, [(i, x) for i,x,_ in to_include])


class AmbiguousExpander:
    """Deal with the case where we're expanding children ('_rule') into a parent but the children
       are ambiguous. i.e. (parent->_ambig->_expand_this_rule). In this case, make the parent itself
       ambiguous with as many copies as there are ambiguous children, and then copy the ambiguous children
       into the right parents in the right places, essentially shifting the ambiguity up the tree."""
    def __init__(self, to_expand, tree_class, node_builder):
        self.node_builder = node_builder
        self.tree_class = tree_class
        self.to_expand = to_expand

    def __call__(self, children):
        def _is_ambig_tree(t):
            return hasattr(t, 'data') and t.data == '_ambig'

        # -- When we're repeatedly expanding ambiguities we can end up with nested ambiguities.
        #    All children of an _ambig node should be a derivation of that ambig node, hence
        #    it is safe to assume that if we see an _ambig node nested within an ambig node
        #    it is safe to simply expand it into the parent _ambig node as an alternative derivation.
        ambiguous = []
        for i, child in enumerate(children):
            if _is_ambig_tree(child):
                if i in self.to_expand:
                    ambiguous.append(i)

                child.expand_kids_by_data('_ambig')

        if not ambiguous:
            return self.node_builder(children)

        expand = [child.children if i in ambiguous else (child,) for i, child in enumerate(children)]
        return self.tree_class('_ambig', [self.node_builder(list(f)) for f in product(*expand)])


def maybe_create_ambiguous_expander(tree_class, expansion, keep_all_tokens):
    to_expand = [i for i, sym in enumerate(expansion)
                 if keep_all_tokens or ((not (sym.is_term and sym.filter_out)) and _should_expand(sym))]
    if to_expand:
        return partial(AmbiguousExpander, to_expand, tree_class)


class AmbiguousIntermediateExpander:
    """
    Propagate ambiguous intermediate nodes and their derivations up to the
    current rule.

    In general, converts

    rule
      _iambig
        _inter
          someChildren1
          ...
        _inter
          someChildren2
          ...
      someChildren3
      ...

    to

    _ambig
      rule
        someChildren1
        ...
        someChildren3
        ...
      rule
        someChildren2
        ...
        someChildren3
        ...
      rule
        childrenFromNestedIambigs
        ...
        someChildren3
        ...
      ...

    propagating up any nested '_iambig' nodes along the way.
    """

    def __init__(self, tree_class, node_builder):
        self.node_builder = node_builder
        self.tree_class = tree_class

    def __call__(self, children):
        def _is_iambig_tree(child):
            return hasattr(child, 'data') and child.data == '_iambig'

        def _collapse_iambig(children):
            """
            Recursively flatten the derivations of the parent of an '_iambig'
            node. Returns a list of '_inter' nodes guaranteed not
            to contain any nested '_iambig' nodes, or None if children does
            not contain an '_iambig' node.
            """

            # Due to the structure of the SPPF,
            # an '_iambig' node can only appear as the first child
            if children and _is_iambig_tree(children[0]):
                iambig_node = children[0]
                result = []
                for grandchild in iambig_node.children:
                    collapsed = _collapse_iambig(grandchild.children)
                    if collapsed:
                        for child in collapsed:
                            child.children += children[1:]
                        result += collapsed
                    else:
                        new_tree = self.tree_class('_inter', grandchild.children + children[1:])
                        result.append(new_tree)
                return result

        collapsed = _collapse_iambig(children)
        if collapsed:
            processed_nodes = [self.node_builder(c.children) for c in collapsed]
            return self.tree_class('_ambig', processed_nodes)

        return self.node_builder(children)



def inplace_transformer(func):
    @wraps(func)
    def f(children):
        # function name in a Transformer is a rule name.
        tree = Tree(func.__name__, children)
        return func(tree)
    return f


def apply_visit_wrapper(func, name, wrapper):
    if wrapper is _vargs_meta or wrapper is _vargs_meta_inline:
        raise NotImplementedError("Meta args not supported for internal transformer; use YourTransformer().transform(parser.parse()) instead")

    @wraps(func)
    def f(children):
        return wrapper(func, name, children, None)
    return f


class ParseTreeBuilder:
    def __init__(self, rules, tree_class, propagate_positions=False, ambiguous=False, maybe_placeholders=False):
        self.tree_class = tree_class
        self.propagate_positions = propagate_positions
        self.ambiguous = ambiguous
        self.maybe_placeholders = maybe_placeholders

        self.rule_builders = list(self._init_builders(rules))

    def _init_builders(self, rules):
        propagate_positions = make_propagate_positions(self.propagate_positions)

        for rule in rules:
            options = rule.options
            keep_all_tokens = options.keep_all_tokens
            expand_single_child = options.expand1

            wrapper_chain = list(filter(None, [
                (expand_single_child and not rule.alias) and ExpandSingleChild,
                maybe_create_child_filter(rule.expansion, keep_all_tokens, self.ambiguous, options.empty_indices if self.maybe_placeholders else None),
                propagate_positions,
                self.ambiguous and maybe_create_ambiguous_expander(self.tree_class, rule.expansion, keep_all_tokens),
                self.ambiguous and partial(AmbiguousIntermediateExpander, self.tree_class)
            ]))

            yield rule, wrapper_chain

    def create_callback(self, transformer=None):
        callbacks = {}

        default_handler = getattr(transformer, '__default__', None)
        if default_handler:
            def default_callback(data, children):
                return default_handler(data, children, None)
        else:
            default_callback = self.tree_class

        for rule, wrapper_chain in self.rule_builders:

            user_callback_name = rule.alias or rule.options.template_source or rule.origin.name
            try:
                f = getattr(transformer, user_callback_name)
                wrapper = getattr(f, 'visit_wrapper', None)
                if wrapper is not None:
                    f = apply_visit_wrapper(f, user_callback_name, wrapper)
                elif isinstance(transformer, Transformer_InPlace):
                    f = inplace_transformer(f)
            except AttributeError:
                f = partial(default_callback, user_callback_name)

            for w in wrapper_chain:
                f = w(f)

            if rule in callbacks:
                raise GrammarError("Rule '%s' already exists" % (rule,))

            callbacks[rule] = f

        return callbacks

###}