transform.py

# transform.py - transformation functions for converting the
#                concrete into the abstract syntax tree
#
# Copyright 2016  by Eckhart Arnold (arnold@badw.de)
#                 Bavarian Academy of Sciences an Humanities (badw.de)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.  See the License for the specific language governing
# permissions and limitations under the License.


"""
Module ``transform`` contains the functions for transforming the
concrete syntax tree (CST) into an abstract syntax tree (AST).

As these functions are very generic, they can in principle be
used for any kind of tree transformations, not necessarily only
for CST -> AST transformations.
"""


import collections.abc
from functools import partial, singledispatch, reduce
import inspect
import operator
from typing import AbstractSet, Any, ByteString, Callable, cast, Container, Dict, \
    Tuple, List, Sequence, Union, Text

from DHParser.error import ErrorCode, AST_TRANSFORM_CRASH, ERROR
from DHParser.syntaxtree import Node, WHITESPACE_PTYPE, TOKEN_PTYPE, LEAF_PTYPES, PLACEHOLDER, \
    RootNode, parse_sxpr, flatten_sxpr, TreeContext
from DHParser.toolkit import issubtype, isgenerictype, expand_table, smart_list, re, cython


__all__ = ('TransformationDict',
           'TransformationProc',
           'TransformationFunc',
           'ConditionFunc',
           'KeyFunc',
           'transformation_factory',
           'key_tag_name',
           'Filter',
           'BLOCK_LEAVES',
           'BLOCK_ANONYMOUS_LEAVES',
           'BLOCK_CHILDREN',
           'traverse',
           'always',
           'never',
           'neg',
           'any_of',
           'all_of',
           'is_named',
           'update_attr',
           'swap_attributes',
           'replace_by_single_child',
           'replace_by_children',
           'reduce_single_child',
           'replace_or_reduce',
           'change_tag_name',
           'replace_tag_names',
           'collapse',
           'collapse_children_if',
           'transform_content',
           'replace_content_with',
           'add_attributes',
           'normalize_whitespace',
           'merge_adjacent',
           'merge_leaves',
           'merge_connected',
           'merge_results',
           'move_adjacent',
           'left_associative',
           'lean_left',
           'apply_if',
           'apply_unless',
           'apply_ifelse',
           'traverse_locally',
           'is_anonymous',
           'is_anonymous_leaf',
           'contains_only_whitespace',
           # 'is_any_kind_of_whitespace',
           'is_empty',
           'is_token',
           'is_one_of',
           'not_one_of',
           'matches_re',
           'has_attr',
           'attr_equals',
           'has_content',
           'has_ancestor',
           'has_parent',
           'has_descendant',
           'has_child',
           'has_children',
           'has_sibling',
           'lstrip',
           'rstrip',
           'strip',
           'keep_children',
           'keep_children_if',
           'keep_tokens',
           'keep_nodes',
           'keep_content',
           'remove_children_if',
           'remove_children',
           'remove_content',
           # 'remove_first',
           # 'remove_last',
           'remove_whitespace',
           'remove_empty',
           'remove_anonymous_empty',
           'remove_anonymous_tokens',
           'remove_brackets',
           'remove_infix_operator',
           'remove_tokens',
           'remove_if',
           'flatten',
           'forbid',
           'require',
           'assert_content',
           'AT_THE_END',
           'node_maker',
           'delimit_children',
           'positions_of',
           'PositionType',
           'normalize_position_representation',
           'insert',
           'add_error',
           'error_on',
           'assert_has_children',
           'peek')


class Filter:
    def __call__(self, children: Tuple[Node]) -> Tuple[Node]:
        raise NotImplementedError


TransformationProc = Callable[[TreeContext], None]
TransformationDict = Dict[str, Union[Callable, Sequence[Callable]]]
TransformationCache = Dict[str, Tuple[Sequence[Filter], Sequence[Callable]]]
TransformationFunc = Union[Callable[[TreeContext], Any], partial]
ProcessingTableType = Dict[str, Union[Sequence[Callable], TransformationDict]]
ConditionFunc = Callable  # Callable[[TreeContext], bool]
KeyFunc = Callable[[Node], str]
CriteriaType = Union[int, str, Callable]


def transformation_factory(t1=None, t2=None, t3=None, t4=None, t5=None):
    """
    Creates factory functions from transformation-functions that
    dispatch on the first parameter after the context parameter.

    Decorating a transformation-function that has more than merely the
    ``context``-parameter with ``transformation_factory`` creates a
    function with the same name, which returns a partial-function that
    takes just the context-parameter.

    Additionally, there is some syntactic sugar for
    transformation-functions that receive a collection as their second
    parameter and do not have any further parameters. In this case a
    list of parameters passed to the factory function will be converted
    into a single collection-parameter.

    The primary benefit is the readability of the transformation-tables.

    Usage::

        @transformation_factory(AbstractSet[str])
        def remove_tokens(context, tokens):
            ...

    or, alternatively::

        @transformation_factory
        def remove_tokens(context, tokens: AbstractSet[str]):
            ...

    Example::

        trans_table = { 'expression': remove_tokens('+', '-') }

    instead of::

        trans_table = { 'expression': partial(remove_tokens, tokens={'+', '-'}) }

    Parameters:
        t1:  type of the second argument of the transformation function,
            only necessary if the transformation functions' parameter list
            does not have type annotations.
    """

    def type_guard(t):
        """Raises an error if type `t` is a generic type or could be mistaken
        for the type of the canonical first parameter "TreeContext of
        transformation functions. Returns `t`."""
        # if isinstance(t, GenericMeta):
        #     raise TypeError("Generic Type %s not permitted\n in transformation_factory "
        #                     "decorator. Use the equivalent non-generic type instead!"
        #                     % str(t))
        if isinstance(t, str):          # ensure compatibility with python versions
            t = eval(t.replace('unicode', 'str'))  # with alternative type handling.
        if isgenerictype(t):
            raise TypeError("Generic Type %s not permitted\n in transformation_factory "
                            "decorator. Use the equivalent non-generic type instead!"
                            % str(t))
        if issubtype(TreeContext, t):
            raise TypeError("Sequence type %s not permitted\nin transformation_factory "
                            "decorator, because it could be mistaken for a base class "
                            "of TreeContext\nwhich is the type of the canonical first "
                            "argument of transformation functions. Try 'tuple' instead!"
                            % str(t))
        return t

    def decorator(f):
        nonlocal t1
        sig = inspect.signature(f)
        params = list(sig.parameters.values())[1:]
        if len(params) == 0:
            return f  # '@transformer' not needed w/o free parameters
        assert t1 or params[0].annotation != params[0].empty, \
            "No type information on second parameter found! Please, use type " \
            "annotation or provide the type information via transformer-decorator."
        f = singledispatch(f)
        p1type = params[0].annotation
        if t1 is None:
            t1 = type_guard(p1type)
        elif issubtype(p1type, type_guard(t1)):
            try:
                if len(params) == 1 and issubtype(p1type, Container) \
                        and not (issubtype(p1type, Text) or issubtype(p1type, ByteString)):
                    def gen_special(*args):
                        c = set(args) if issubtype(p1type, AbstractSet) else \
                            tuple(args) if issubtype(p1type, Sequence) else args
                        d = {params[0].name: c}
                        return partial(f, **d)
                    assert p1type.__args__ is not None, "Please use more specific container " \
                        "type, e.g. %s[Callable] instead of %s, in signature of function " \
                        "%s !" % (str(p1type), str(p1type), f.__name__)
                    for alt_t in p1type.__args__:
                        f.register(type_guard(alt_t), gen_special)
                    # f.register(type_guard(p1type.__args__[0]), gen_special)
            except AttributeError:
                pass  # Union Type does not allow subclassing, but is not needed here
        else:
            raise TypeError("Annotated type %s is not a subclass of decorated type %s !"
                            % (str(p1type), str(t1)))

        def gen_partial(*args, **kwargs):
            d = {p.name: arg for p, arg in zip(params, args)}
            d.update(kwargs)
            return partial(f, **d)

        for t in (t1, t2, t3, t4, t5):
            if t:
                f.register(type_guard(t), gen_partial)
            else:
                break
        return f

    if isinstance(t1, type(lambda: 1)):
        # Provide for the case that transformation_factory has been
        # written as plain decorator and not as a function call that
        # returns the decorator proper.
        func = t1
        t1 = None
        return decorator(func)
    else:
        return decorator


def key_tag_name(node: Node) -> str:
    """
    Returns the tag name of the node as key for selecting transformations
    from the transformation table in function `traverse`.
    """
    return node.tag_name


class BlockChildren(Filter):
    def __call__(self, children: Tuple[Node]) -> Tuple[Node]:
        return ()

class BlockLeaves(Filter):
    def __call__(self, children: Tuple[Node]) -> Tuple[Node]:
        return tuple(child for child in children if child._children)


class BlockAnonymousLeaves(Filter):
    def __call__(self, children: Tuple[Node]) -> Tuple[Node]:
        try:
            return tuple(child for child in children
                         if child._children or not child.tag_name[0] == ':')
        except IndexError:
            return tuple(child for child in children
                         if child._children or not child.anonymous)


BLOCK_CHILDREN = BlockChildren()
BLOCK_LEAVES = BlockLeaves()
BLOCK_ANONYMOUS_LEAVES = BlockAnonymousLeaves()


def traverse(root_node: Node,
             processing_table: ProcessingTableType,
             key_func: KeyFunc = key_tag_name) -> None:
    """
    Traverses the syntax tree starting with the given ``node`` depth
    first and applies the sequences of callback-functions registered
    in the ``processing_table``-dictionary.

    The most important use case is the transformation of a concrete
    syntax tree into an abstract tree (AST). But it is also imaginable
    to employ tree-traversal for the semantic analysis of the AST.

    In order to assign sequences of callback-functions to nodes, a
    dictionary ("processing table") is used. The keys usually represent
    tag names, but any other key function is possible. There exist
    three special keys:

    - '<': always called (before any other processing function)
    - '*': called for those nodes for which no (other) processing
      function appears in the table
    - '>': always called (after any other processing function)

    Args:
        root_node (Node): The root-node of the syntax tree to be traversed
        processing_table (dict): node key -> sequence of functions that
            will be applied to matching nodes in order. This dictionary
            is interpreted as a ``compact_table``. See
            :func:`expand_table` or :func:`EBNFCompiler.EBNFTransTable`
        key_func (function): A mapping key_func(node) -> keystr. The default
            key_func yields node.tag_name.

    Example::

        table = { "term": [replace_by_single_child, flatten],
                  "factor, flowmarker, retrieveop": replace_by_single_child }
        traverse(node, table)

    """

    # Is this optimazation really needed?
    if '__cache__' in processing_table:
        # assume that processing table has already been expanded
        table = processing_table               # type: ProcessingTableType
        cache = cast(TransformationDict, processing_table['__cache__'])  # type: TransformationDict
    else:
        # normalize processing_table entries by turning single values
        # into lists with a single value
        table = {name: cast(Sequence[Callable], smart_list(call))
                 for name, call in list(processing_table.items())}
        table = expand_table(table)
        # substitute key for insiginificant whitespace
        assert '+' not in table, 'Symbol "+" in processing table is obsolete, use "<" instead'
        if '~' in table:
            if ':Whitespace' in table:
                raise AssertionError(
                    '"~" is a synonym for ":Whitespace" in the processing table. '
                    'To avoid confusion, choose either of the two, but do not use '
                    'both at the same time!')
            whitespace_transformation = table['~']
            del table['~']
            table[':Whitespace'] = whitespace_transformation
        # cache expanded table
        cache = cast(TransformationDict,
                     table.setdefault('__cache__', cast(TransformationDict, dict())))
        # change processing table in place, so its already expanded and cache filled next time
        processing_table.clear()
        processing_table.update(table)

    def split_filter(callables: Sequence[Callable]) -> Tuple[List[Filter], List[Callable]]:
        i = 0
        filter = []
        for callable in callables:
            if isinstance(callable, Filter):
                filter.append(callable)
                i += 1
            else:  break
        callables = list(callables[i:])
        assert not any(isinstance(callable, Filter) for callable in callables)
        return filter, callables

    def traverse_recursive(context):
        nonlocal cache
        node = context[-1]

        key = key_func(node)
        try:
            filters, sequence = cache[key]
        except KeyError:
            filters, pre = split_filter(table.get('<', []))
            assert BLOCK_CHILDREN not in filters
            more_filters, main = split_filter(table.get(key, table.get('*', [])))
            post = table.get('>', [])
            assert not any(isinstance(callable, Filter) for callable in post)
            sequence = pre + main + post
            all_filters = filters + more_filters
            if BLOCK_CHILDREN in all_filters:
                all_filters = [BLOCK_CHILDREN]
            cache[key] = (filters + more_filters, sequence)

        children = node._children
        for filter in filters:
            children = filter(children)
        if children:
            context.append(PLACEHOLDER)
            for child in children:
                context[-1] = child
                traverse_recursive(context)  # depth first
            context.pop()

        for call in sequence:
            try:
                call(context)
            except Exception as ae:
                raise AssertionError('An exception occurred when transforming "%s" with %s:\n%s'
                                     % (key, str(call), ae.__class__.__name__ + ': ' + str(ae)))

    traverse_recursive([root_node])
    # assert processing_table['__cache__']


#######################################################################
#
# specialized full tree transformations
#
#######################################################################

def merge_treetops(node: Node):
    """Recursively traverses the tree and "merges" nodes that contain
    only anonymous child nodes that are leaves. "mergeing" here means the
    node's result will be replaced by the merged content of the children.
    """
    if node._children:
        crunch = True
        for child in node._children:
            if child._children:
                merge_treetops(child)
                crunch = False
            elif not child.anonymous:
                crunch = False
        if crunch:
            node.result = node.content


#######################################################################
#
# meta transformations, i.e. transformations that call other
# transformations
#
#######################################################################


@transformation_factory(dict)
def traverse_locally(context: TreeContext,
                     processing_table: Dict,              # actually: ProcessingTableType
                     key_func: Callable = key_tag_name):  # actually: KeyFunc
    """
    Transforms the syntax tree starting from the last node in the context
    according to the given processing table. The purpose of this function is
    to apply certain transformations locally, i.e. only for those nodes that
    have the last node in the context as their parent node.
    """
    traverse(context[-1], processing_table, key_func)


def transformation_guard(value) -> None:
    if value is not None:
        raise AssertionError('Transformation a value instead of None!')


def condition_guard(value) -> bool:
    if value is None:
        raise AssertionError('Condition returned None instead of a bool!')
    return value


def apply_transformations(context: TreeContext, transformation: Union[Callable, Sequence[Callable]]):
    """Applies a single or a sequence of transformations to a context."""
    if callable(transformation):
        transformation_guard(transformation(context))
    else:
        assert isinstance(transformation, tuple)
        for trans in cast(tuple, transformation):
            transformation_guard(trans(context))


@transformation_factory(collections.abc.Callable, tuple)
def apply_if(context: TreeContext, transformation: Union[Callable, Tuple[Callable]], condition: Callable):
    """Applies a transformation only if a certain condition is met."""
    if condition_guard(condition(context)):
        apply_transformations(context, transformation)


@transformation_factory(collections.abc.Callable, tuple)
def apply_ifelse(context: TreeContext,
                 if_transformation: Union[Callable, Tuple[Callable]],
                 else_transformation: Union[Callable, Tuple[Callable]],
                 condition: Callable):
    """Applies a one particular transformation if a certain condition is met
    and another transformation otherwise."""
    if condition_guard(condition(context)):
        apply_transformations(context, if_transformation)
    else:
        apply_transformations(context, else_transformation)


@transformation_factory(collections.abc.Callable, tuple)
def apply_unless(context: TreeContext, transformation: Union[Callable, Tuple[Callable]], condition: Callable):
    """Applies a transformation if a certain condition is *not* met."""
    if not condition_guard(condition(context)):
        apply_transformations(context, transformation)


## boolean operators


def always(context: TreeContext) -> bool:
    """Always returns True, no matter what the state of the context is."""
    return True


def never(context: TreeContext) -> bool:
    """Always returns True, no matter what the state of the context is."""
    return False

@transformation_factory(collections.abc.Callable)
def neg(context: TreeContext, bool_func: collections.abc.Callable) -> bool:
    """Returns the inverted boolean result of `bool_func(context)`"""
    return not bool_func(context)


@transformation_factory(collections.abc.Set)
def any_of(context: TreeContext, bool_func_set: AbstractSet[collections.abc.Callable]) -> bool:
    """Returns True, if any of the bool functions in `bool_func_set` evaluate to True
    for the given context."""
    return any(bf(context) for bf in bool_func_set)


@transformation_factory(collections.abc.Set)
def all_of(context: TreeContext, bool_func_set: AbstractSet[collections.abc.Callable]) -> bool:
    """Returns True, if all of the bool functions in `bool_func_set` evaluate to True
    for the given context."""
    return all(bf(context) for bf in bool_func_set)


#######################################################################
#
# conditionals that determine whether the context (or the last node in
# the context for that matter) fulfill a specific condition.
# ---------------------------------------------------------------------
#
# The context of a node is understood as a list of all parent nodes
# leading up to and including the node itself. If represented as list,
# the last element of the list is the node itself.
#
#######################################################################


def is_single_child(context: TreeContext) -> bool:
    """Returns ``True`` if the current node does not have any siblings."""
    return len(context[-2]._children) == 1


# TODO: ambiguous: named, tagging...
def is_named(context: TreeContext) -> bool:
    """Returns ``True`` if the current node's parser is a named parser."""
    # return not context[-1].anonymous
    tn = context[-1].tag_name
    return tn and tn[0] != ':'


def is_anonymous(context: TreeContext) -> bool:
    """Returns ``True`` if the current node is anonymous."""
    # return context[-1].anonymous
    tn = context[-1].tag_name
    return not tn or tn[0] == ':'


def is_anonymous_leaf(context: TreeContext) -> bool:
    """Returns `True` if context ends in an anonymous leaf-node"""
    # return not context[-1].children and context[-1].anonymous
    node = context[-1]
    if node._children:
        return False
    tn = node.tag_name
    return not tn or tn[0] == ':'


RX_WHITESPACE = re.compile(r'\s*$')


def contains_only_whitespace(context: TreeContext) -> bool:
    r"""Returns ``True`` for nodes that contain only whitespace regardless
    of the tag_name, i.e. nodes the content of which matches the regular
    expression /\s*/, including empty nodes. Note, that this is not true
    for anonymous whitespace nodes that contain comments."""
    return bool(RX_WHITESPACE.match(context[-1].content))


def is_empty(context: TreeContext) -> bool:
    """Returns ``True`` if the current node's content is empty."""
    return not context[-1].result


@transformation_factory(collections.abc.Set)
def is_token(context: TreeContext, tokens: AbstractSet[str] = frozenset()) -> bool:
    """
    Checks whether the last node in the context has the tag_name ":Text"
    and it's content matches one of the given tokens. Leading and trailing
    whitespace-tokens will be ignored. In case an empty set of tokens is passed,
    any token is a match.
    """
    node = context[-1]
    return node.tag_name == TOKEN_PTYPE and (not tokens or node.content in tokens)


@transformation_factory(collections.abc.Set)
def is_one_of(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:
    """Returns true, if the node's tag_name is one of the given tag names."""
    return context[-1].tag_name in tag_name_set


@transformation_factory(collections.abc.Set)
def not_one_of(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:
    """Returns true, if the node's tag_name is not one of the given tag names."""
    return context[-1].tag_name not in tag_name_set


@transformation_factory(collections.abc.Set)
def matches_re(context: TreeContext, patterns: AbstractSet[str]) -> bool:
    """
    Returns true, if the node's tag_name matches one of the regular
    expressions in `patterns`. For example, ':.*' matches all anonymous nodes.
    """
    tn = context[-1].tag_name
    for pattern in patterns:
        if re.match(pattern, tn):
            return True
    return False


@transformation_factory(str)
def has_attr(context: TreeContext, attr: str) -> bool:
    """
    Returns true, if the node has the attribute `attr`, no matter
    what its value is.
    """
    node = context[-1]
    return node.has_attr(attr)


@transformation_factory(str)
def attr_equals(context: TreeContext, attr: str, value: str) -> bool:
    """
    Returns true, if the node has the attribute `attr` and its value equals
    `value`.
    """
    node = context[-1]
    return node.has_attr(attr) and node.attr[attr] == value


@transformation_factory
def has_content(context: TreeContext, regexp: str) -> bool:
    """
    Checks a node's content against a regular expression.

    In contrast to ``re.match`` the regular expression must match the complete
    string and not just the beginning of the string to succeed!
    """
    if not regexp.endswith('$'):
        regexp += "$"
    return bool(re.match(regexp, context[-1].content))


@transformation_factory(collections.abc.Set)
def has_ancestor(context: TreeContext,
                 tag_name_set: AbstractSet[str],
                 generations: int =-1) -> bool:
    """
    Checks whether a node with one of the given tag names appears somewhere
    in the context before the last node in the context.

    :param generations: determines how deep `has_ancestor` should dive into
        the ancestry. "1" means only the immediate parents wil be considered,
        "2" means also the grandparents, ans so on.
        A value smaller or equal zero means all ancestors will be considered.
    """
    if generations <= 0:
        return any(nd.tag_name in tag_name_set for nd in context)
    for i in range(2, min(generations + 2, len(context) + 1)):
        if context[-i].tag_name in tag_name_set:
            return True
    return False


@transformation_factory(collections.abc.Set)
def has_parent(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:
    """Checks whether the immediate predecessor in the context has one of the
    given tags."""
    return has_ancestor(context, tag_name_set, 1)


def has_children(context: TreeContext) -> bool:
    """Checks whether last node in context has children."""
    return bool(context[-1]._children)


@transformation_factory(collections.abc.Set)
def has_descendant(context: TreeContext, tag_name_set: AbstractSet[str],
                   generations: int = -1) -> bool:
    assert generations != 0
    for child in context[-1]._children:
        if child.tag_name in tag_name_set:
            return True
        if (generations < 0 or generations > 1) \
                and has_descendant(context + [child], tag_name_set, generations - 1):
            return True
    return False


@transformation_factory(collections.abc.Set)
def has_child(context: TreeContext, tag_name_set: AbstractSet[str]) -> bool:
    """Checks whether at least one child (i.e. immediate descendant) has one of
    the given tags."""
    return has_descendant(context, tag_name_set, 1)


@transformation_factory(collections.abc.Set)
def has_sibling(context: TreeContext, tag_name_set: AbstractSet[str]):
    if len(context) >= 2:
        node = context[-1]
        for child in context[-2]._children:
            if child != node and child.tag_name in tag_name_set:
                return True
    return False


#######################################################################
#
# utility functions (private)
#
#######################################################################


def update_attr(dest: Node, src: Union[Node, Tuple[Node, ...]], root: RootNode):
    """
    Adds all attributes from `src` to `dest` and transfers all errors
    from `src` to `dest`. This is needed, in order to keep the attributes
    if the child node is going to be eliminated.
    """
    if isinstance(src, Node):
        src = (Node,)
    for s in src:
        # update attributes
        if s != dest and hasattr(s, '_xml_attr'):
            for k, v in s.attr.items():
                if k in dest.attr and v != dest.attr[k]:
                    raise ValueError('Conflicting attribute values %s and %s for key %s '
                                     'when reducing %s to %s ! Tree transformation stopped.'
                                     % (v, dest.attr[k], k, str(src), str(dest)))
                dest.attr[k] = v
        # transfer errors
        try:
            root.transfer_errors(s, dest)
            # ids = id(s)
            # if ids in root.error_nodes:
            #     root.error_nodes.setdefault(id(dest), []).extend(root.error_nodes[ids])
            #     del root.error_nodes[ids]
        except AttributeError:
            # root was not of type RootNode, probably a doc-test
            pass


def swap_attributes(node: Node, other: Node):
    """
    Exchanges the attributes between node and other. This might be
    needed when rearangeing trees.
    """
    NA = node.has_attr()
    OA = other.has_attr()
    if NA or OA:
        save = node._xml_attr if NA else None
        if OA:
            node._xml_attr = other._xml_attr
        elif NA:
            node._xml_attr = None
        if NA:
            other._xml_attr = save
        elif OA:
            other._xml_attr = None


def _replace_by(node: Node, child: Node, root: RootNode):
    """
    Replaces node's contents by child's content including the tag name.
    """
    # if node.anonymous or not child.anonymous:
    #     node.tag_name = child.tag_name
    nd_tn = node.tag_name
    ch_tn = child.tag_name
    if not nd_tn or nd_tn[0] == ':' or (ch_tn and ch_tn[0] != ':'):
        node.tag_name = ch_tn
        # name, ptype = (node.tag_name.split(':') + [''])[:2]
        # child.parser = MockParser(name, ptype)
        # parser names must not be overwritten, else: child.parser.name = node.parser.name
    node._set_result(child._result)
    update_attr(node, (child,), root)


def _reduce_child(node: Node, child: Node, root: RootNode):
    """
    Sets node's results to the child's result, keeping node's tag_name.
    """
    node._set_result(child._result)
    update_attr(node, (child,), root)
    # update_attr(child, (node,), root)
    # if child.has_attr():
    #     node._xml_attr = child._xml_attr


#######################################################################
#
# rearranging transformations
#
# - tree may be rearranged (e.g.flattened)
# - nodes that are not leaves may be dropped
# - order is preserved
# - leave content is preserved (though not necessarily the leaves
#   themselves)
#
#######################################################################


def replace_by_single_child(context: TreeContext):
    """
    Removes single branch node, replacing it by its immediate descendant.
    Replacement only takes place, if the last node in the context has
    exactly one child. Attributes will be merged. In case one and the same
    attribute is defined for the child as well as the parent, the child's
    attribute value take precedence.
    """
    node = context[-1]
    if len(node._children) == 1:
        _replace_by(node, node._children[0], cast(RootNode, context[0]))


def replace_by_children(context: TreeContext):
    """
    Eliminates the last node in the context by replacing it with its children.
    The attributes of this node will be dropped. In case the last node is
    the root-note (i.e. len(context) == 1), it will only be eliminated, if
    there is but one child.

    WARNING: This should never be followed by move_adjacent() in the transformation list!!!
    """
    try:
        parent = context[-2]
    except IndexError:
        replace_by_single_child(context)
        return
    node = context[-1]
    if node._children:
        result = parent._children  # type: Tuple[Node, ...]
        i = result.index(node)
        parent._set_result(result[:i] + node._children + result[i + 1:])


def reduce_single_child(context: TreeContext):
    """
    Reduces a single branch node by transferring the result of its
    immediate descendant to this node, but keeping this node's parser entry.
    Reduction only takes place if the last node in the context has
    exactly one child. Attributes will be merged. In case one and the same
    attribute is defined for the child as well as the parent, the parent's
    attribute value take precedence.
    """
    node = context[-1]
    if len(node._children) == 1:
        _reduce_child(node, node._children[0], cast(RootNode, context[0]))


@transformation_factory(collections.abc.Callable)
def replace_or_reduce(context: TreeContext, condition: Callable = is_named):
    """
    Replaces node by a single child, if condition is met on child,
    otherwise (i.e. if the child is anonymous) reduces the child.
    """
    node = context[-1]
    if len(node._children) == 1:
        child = node._children[0]