# Source code for asdf.yamlutil

    import warnings
    from collections import OrderedDict
    from types import GeneratorType

    import numpy as np

    import yaml

    from . import schema
    from . import tagged
    from . import treeutil
    from . import util
    from .constants import YAML_TAG_PREFIX, STSCI_SCHEMA_TAG_BASE
    from .versioning import split_tag_version
    from .exceptions import AsdfConversionWarning
    from .tags.core import AsdfObject

    __all__ = ['custom_tree_to_tagged_tree', 'tagged_tree_to_custom_tree']

    if getattr(yaml, '__with_libyaml__', None):  # pragma: no cover
        _yaml_base_dumper = yaml.CSafeDumper
        _yaml_base_loader = yaml.CSafeLoader
    else:  # pragma: no cover
        _yaml_base_dumper = yaml.SafeDumper
        _yaml_base_loader = yaml.SafeLoader

    YAML_OMAP_TAG = YAML_TAG_PREFIX + 'omap'

    # ----------------------------------------------------------------------
    # Custom loader/dumpers

    class AsdfDumper(_yaml_base_dumper):
        """
        A specialized YAML dumper that understands "tagged basic Python
        data types" as implemented in the `tagged` module.
        """

        def __init__(self, *args, **kwargs):
            kwargs['default_flow_style'] = None
            super().__init__(*args, **kwargs)

        def represent_data(self, data):
            node = super(AsdfDumper, self).represent_data(data)

            tag_name = getattr(data, '_tag', None)
            if tag_name is not None:
                node.tag = tag_name

            return node

    _flow_style_map = {
        'flow': True,
        'block': False
    }

    def represent_sequence(dumper, sequence):
        flow_style = _flow_style_map.get(sequence.flow_style, None)
        sequence = sequence.data
        return super(AsdfDumper, dumper).represent_sequence(
            None, sequence, flow_style)

    def represent_mapping(dumper, mapping):
        flow_style = _flow_style_map.get(mapping.flow_style, None)
        node = super(AsdfDumper, dumper).represent_mapping(
            None, mapping.data, flow_style)

        if mapping.property_order:
            values = node.value
            new_mapping = {}
            for key, val in values:
                new_mapping[key.value] = (key, val)

            new_values = []
            for key in mapping.property_order:
                if key in mapping:
                    new_values.append(new_mapping[key])

            property_order = set(mapping.property_order)
            for key, val in values:
                if key.value not in property_order:
                    new_values.append((key, val))

            node.value = new_values

        return node

    _style_map = {
        'inline': '"',
        'folded': '>',
        'literal': '|'
    }

    def represent_scalar(dumper, value):
        style = _style_map.get(value.style, None)
        return super(AsdfDumper, dumper).represent_scalar(
            None, value.data, style)

    def represent_ordered_mapping(dumper, tag, data):
        # TODO: Again, adjust for preferred flow style, and other stylistic details
        # NOTE: For block style this uses the compact omap notation, but for flow style
        # it does not.

        # TODO: Need to see if I can figure out a mechanism so that classes that
        # use this representer can specify which values should use flow style
        values = []
        node = yaml.SequenceNode(tag, values,
                                 flow_style=dumper.default_flow_style)
        if dumper.alias_key is not None:
            dumper.represented_objects[dumper.alias_key] = node
        for key, value in data.items():
            key_item = dumper.represent_data(key)
            value_item = dumper.represent_data(value)
            node_item = yaml.MappingNode(YAML_OMAP_TAG,
                                         [(key_item, value_item)],
                                         flow_style=False)
            values.append(node_item)
        return node

    def represent_ordereddict(dumper, data):
        return represent_ordered_mapping(dumper, YAML_OMAP_TAG, data)

    AsdfDumper.add_representer(tagged.TaggedList, represent_sequence)
    AsdfDumper.add_representer(tagged.TaggedDict, represent_mapping)
    AsdfDumper.add_representer(tagged.TaggedString, represent_scalar)
    AsdfDumper.add_representer(OrderedDict, represent_ordereddict)

    # ----------------------------------------------------------------------
    # Handle numpy scalars

    for scalar_type in util.iter_subclasses(np.floating):
        AsdfDumper.add_representer(scalar_type, AsdfDumper.represent_float)

    for scalar_type in util.iter_subclasses(np.integer):
        AsdfDumper.add_representer(scalar_type, AsdfDumper.represent_int)

    def represent_numpy_str(dumper, data):
        # The CSafeDumper implementation will raise an error if it
        # doesn't recognize data as a string.  The Python SafeDumper
        # has no problem with np.str_.
        return dumper.represent_str(str(data))

    AsdfDumper.add_representer(np.str_, represent_numpy_str)
    AsdfDumper.add_representer(np.bytes_, AsdfDumper.represent_binary)

    class AsdfLoader(_yaml_base_loader):
        """
        A specialized YAML loader that can construct "tagged basic Python
        data types" as implemented in the `tagged` module.
        """

        def construct_undefined(self, node):
            if isinstance(node, yaml.MappingNode):
                return self._construct_tagged_mapping(node)
            elif isinstance(node, yaml.SequenceNode):
                return self._construct_tagged_sequence(node)
            elif isinstance(node, yaml.ScalarNode):
                return self._construct_tagged_scalar(node)
            else:
                return super().construct_undefined(node)

        def _construct_tagged_mapping(self, node):
            data = tagged.tag_object(node.tag, {})
            yield data
            data.update(self.construct_mapping(node))

        def _construct_tagged_sequence(self, node):
            data = tagged.tag_object(node.tag, [])
            yield data
            data.extend(self.construct_sequence(node))

        def _construct_tagged_scalar(self, node):
            return tagged.tag_object(node.tag, self.construct_scalar(node))

        # Custom omap deserializer that builds an OrderedDict instead
        # of a list of tuples.  Code is mostly identical to pyyaml's SafeConstructor.
        def construct_yaml_omap(self, node):
            omap = OrderedDict()
            yield omap
            if not isinstance(node, yaml.SequenceNode):
                raise yaml.ConstructorError("while constructing an ordered map", node.start_mark,
                        "expected a sequence, but found %s" % node.id, node.start_mark)
            for subnode in node.value:
                if not isinstance(subnode, yaml.MappingNode):
                    raise yaml.ConstructorError("while constructing an ordered map", node.start_mark,
                            "expected a mapping of length 1, but found %s" % subnode.id,
                            subnode.start_mark)
                if len(subnode.value) != 1:
                    raise yaml.ConstructorError("while constructing an ordered map", node.start_mark,
                            "expected a single mapping item, but found %d items" % len(subnode.value),
                            subnode.start_mark)
                key_node, value_node = subnode.value[0]
                key = self.construct_object(key_node)
                value = self.construct_object(value_node)
                omap[key] = value

    # pyyaml will invoke the constructor associated with None when a node's
    # tag is not explicitly handled by another constructor.
    AsdfLoader.add_constructor(None, AsdfLoader.construct_undefined)
    AsdfLoader.add_constructor(YAML_TAG_PREFIX + "omap", AsdfLoader.construct_yaml_omap)

    def custom_tree_to_tagged_tree(tree, ctx, _serialization_context=None):
        """
        Convert a tree, possibly containing custom data types that aren't
        directly representable in YAML, to a tree of basic data types,
        annotated with tags.
        """
        if _serialization_context is None:
            _serialization_context = ctx._create_serialization_context()

        extension_manager = _serialization_context.extension_manager

        def _convert_obj(obj):
            converter = extension_manager.get_converter_for_type(type(obj))
            tag = converter.select_tag(obj, _serialization_context)
            node = converter.to_yaml_tree(obj, tag, _serialization_context)

            if isinstance(node, GeneratorType):
                generator = node
                node = next(generator)
            else:
                generator = None

            if isinstance(node, dict):
                tagged_node = tagged.TaggedDict(node, tag)
            elif isinstance(node, list):
                tagged_node = tagged.TaggedList(node, tag)
            elif isinstance(node, str):
                tagged_node = tagged.TaggedString(node)
                tagged_node._tag = tag
            else:
                raise TypeError(
                    "Converter returned illegal node type: {}".format(util.get_class_name(node))
                )

            _serialization_context._mark_extension_used(converter.extension)

            yield tagged_node
            if generator is not None:
                yield from generator

        def _walker(obj):
            if extension_manager.handles_type(type(obj)):
                return _convert_obj(obj)
            else:
                tag = ctx.type_index.from_custom_type(
                    type(obj),
                    ctx.version_string,
                    _serialization_context=_serialization_context
                )

                if tag is not None:
                    return tag.to_tree_tagged(obj, ctx)
                return obj

        return treeutil.walk_and_modify(
            tree,
            _walker,
            ignore_implicit_conversion=ctx._ignore_implicit_conversion,
            # Walk the tree in preorder, so that extensions can return
            # container nodes with unserialized children.
            postorder=False,
            _context=ctx._tree_modification_context,
        )

    def tagged_tree_to_custom_tree(tree, ctx, force_raw_types=False, _serialization_context=None):
        """
        Convert a tree containing only basic data types, annotated with
        tags, to a tree containing custom data types.
        """
        if _serialization_context is None:
            _serialization_context = ctx._create_serialization_context()

        extension_manager = _serialization_context.extension_manager

        def _walker(node):
            if force_raw_types:
                return node

            tag = getattr(node, '_tag', None)
            if tag is None:
                return node

            if extension_manager.handles_tag(tag):
                converter = extension_manager.get_converter_for_tag(tag)
                obj = converter.from_yaml_tree(node.data, tag, _serialization_context)
                _serialization_context._mark_extension_used(converter.extension)
                return obj

            tag_type = ctx.type_index.from_yaml_tag(ctx, tag, _serialization_context=_serialization_context)
            # This means the tag did not correspond to any type in our type index.
            if tag_type is None:
                if not ctx._ignore_unrecognized_tag:
                    warnings.warn("{} is not recognized, converting to raw Python "
                        "data structure".format(tag), AsdfConversionWarning)
                return node

            tag_name, tag_version = split_tag_version(tag)
            # This means that there is an explicit description of versions that are
            # compatible with the associated tag class implementation, but the
            # version we found does not fit that description.
            if tag_type.incompatible_version(tag_version):
                warnings.warn("Version {} of {} is not compatible with any "
                    "existing tag implementations".format(
                        tag_version, tag_name),
                    AsdfConversionWarning)
                return node

            # If a tag class does not explicitly list compatible versions, then all
            # versions of the corresponding schema are assumed to be compatible.
            # Therefore we need to check to make sure whether the conversion is
            # actually successful, and just return a raw Python data type if it is
            # not.
            try:
                return tag_type.from_tree_tagged(node, ctx)
            except TypeError as err:
                warnings.warn("Failed to convert {} to custom type (detail: {}). "
                    "Using raw Python data structure instead".format(tag, err),
                    AsdfConversionWarning)

            return node

        return treeutil.walk_and_modify(
            tree,
            _walker,
            ignore_implicit_conversion=ctx._ignore_implicit_conversion,
            # Walk the tree in postorder, so that extensions receive
            # container nodes with children already deserialized.
            postorder=True,
            _context=ctx._tree_modification_context,
        )

    def load_tree(stream):
        """
        Load YAML, returning a tree of objects.

        Parameters
        ----------
        stream : readable file-like object
            Stream containing the raw YAML content.
        """
        # The following call to yaml.load is safe because we're
        # using a loader that inherits from pyyaml's SafeLoader.
        return yaml.load(stream, Loader=AsdfLoader) # nosec

    def dump_tree(tree, fd, ctx, tree_finalizer=None, _serialization_context=None):
        """
        Dump a tree of objects, possibly containing custom types, to YAML.

        Parameters
        ----------
        tree : object
            Tree of objects, possibly containing custom data types.

        fd : asdf.generic_io.GenericFile
            A file object to dump the serialized YAML to.

        ctx : Context
            The writing context.

        tree_finalizer : callable, optional
            Callback that receives the tagged tree before it is validated
            and defaults are removed.  `asdf.AsdfFile` uses this to update
            extension metadata on the tagged tree before it is fully
            serialized to YAML.
        """
        # The _serialization_context parameter allows AsdfFile to track
        # what extensions were used when converting the tree's custom
        # types.  In 3.0, it will be passed as the `ctx` instead of the
        # AsdfFile itself.
        if type(tree) is not AsdfObject:
            raise TypeError("Root node of ASDF tree must be of type AsdfObject")

        tags = {'!': STSCI_SCHEMA_TAG_BASE + '/'}
        tree = custom_tree_to_tagged_tree(tree, ctx, _serialization_context=_serialization_context)
        if tree_finalizer is not None:
            tree_finalizer(tree)
        schema.validate(tree, ctx)

        yaml_version = tuple(
            int(x) for x in ctx.version_map['YAML_VERSION'].split('.'))

        # add yaml %TAG definitions from extensions
        if _serialization_context:
            for ext in _serialization_context._extensions_used:
                for key, val in ext.yaml_tag_handles.items():
                    if key not in tags:
                        tags[key] = val

        yaml.dump_all(
            [tree], stream=fd, Dumper=AsdfDumper,
            explicit_start=True, explicit_end=True,
            version=yaml_version,
            allow_unicode=True, encoding='utf-8',
            default_flow_style=None,
            tags=tags)