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# Source code for asdf.schema

    import copy
    import datetime
    import json
    import warnings
    from collections import OrderedDict
    from collections.abc import Mapping
    from functools import lru_cache
    from numbers import Integral
    from operator import methodcaller

    import numpy as np
    import yaml

    from asdf._jsonschema import validators as mvalidators
    from asdf._jsonschema.exceptions import RefResolutionError, ValidationError

    from . import constants, generic_io, reference, tagged, treeutil, util, versioning, yamlutil
    from .config import get_config
    from .exceptions import AsdfWarning
    from .util import _patched_urllib_parse

    YAML_SCHEMA_METASCHEMA_ID = "http://stsci.edu/schemas/yaml-schema/draft-01"

    __all__ = ["check_schema", "fill_defaults", "load_schema", "remove_defaults", "validate"]

    PYTHON_TYPE_TO_YAML_TAG = {
        None: "null",
        str: "str",
        bytes: "str",
        bool: "bool",
        int: "int",
        float: "float",
        list: "seq",
        dict: "map",
        set: "set",
        OrderedDict: "omap",
    }

    # Prepend full YAML tag prefix
    for k, v in PYTHON_TYPE_TO_YAML_TAG.items():
        PYTHON_TYPE_TO_YAML_TAG[k] = constants.YAML_TAG_PREFIX + v

    def _type_to_tag(type_):
        for base in type_.mro():
            if base in PYTHON_TYPE_TO_YAML_TAG:
                return PYTHON_TYPE_TO_YAML_TAG[base]
        return None

    def validate_tag(validator, tag_pattern, instance, schema):
        """
        Implements the tag validation directive, which checks the
        tag against a pattern that may include wildcards.  See
        `asdf.util.uri_match` for details on the matching behavior.
        """
        instance_tag = instance._tag if hasattr(instance, "_tag") else _type_to_tag(type(instance))

        if instance_tag is None:
            yield ValidationError(
                f"mismatched tags, wanted '{tag_pattern}', got unhandled object type '{util.get_class_name(instance)}'",
            )

        if not util.uri_match(tag_pattern, instance_tag):
            yield ValidationError(f"mismatched tags, wanted '{tag_pattern}', got '{instance_tag}'")

    def validate_propertyOrder(validator, order, instance, schema):
        """
        Stores a value on the `tagged.TaggedDict` instance so that
        properties can be written out in the preferred order.  In that
        sense this isn't really a "validator", but using the `jsonschema`
        library's extensible validation system is the easiest way to get
        this property assigned.
        """
        if not validator.is_type(instance, "object"):
            return

        if not order:
            # propertyOrder may be an empty list
            return

        instance.property_order = order

    def validate_flowStyle(validator, flow_style, instance, schema):
        """
        Sets a flag on the `tagged.TaggedList` or `tagged.TaggedDict`
        object so that the YAML generator knows which style to use to
        write the element.  In that sense this isn't really a "validator",
        but using the `jsonschema` library's extensible validation system
        is the easiest way to get this property assigned.
        """
        if not (validator.is_type(instance, "object") or validator.is_type(instance, "array")):
            return

        instance.flow_style = flow_style

    def validate_style(validator, style, instance, schema):
        """
        Sets a flag on the `tagged.TaggedString` object so that the YAML
        generator knows which style to use to write the string.  In that
        sense this isn't really a "validator", but using the `jsonschema`
        library's extensible validation system is the easiest way to get
        this property assigned.
        """
        if not validator.is_type(instance, "string"):
            return

        instance.style = style

    def validate_type(validator, types, instance, schema):
        """
        PyYAML returns strings that look like dates as datetime objects.
        However, as far as JSON is concerned, this is type==string and
        format==date-time.  That detects for that case and doesn't raise
        an error, otherwise falling back to the default type checker.
        """
        if isinstance(instance, datetime.datetime) and schema.get("format") == "date-time" and "string" in types:
            return None

        return mvalidators.Draft4Validator.VALIDATORS["type"](validator, types, instance, schema)

    def validate_enum(validator, enums, instance, schema):
        """
        `asdf.tagged.Tagged` objects will fail in the default enum validator
        """

        if isinstance(instance, tagged.Tagged):
            instance = instance.base

        yield from mvalidators.Draft4Validator.VALIDATORS["enum"](validator, enums, instance, schema)

    YAML_VALIDATORS = util.HashableDict(mvalidators.Draft4Validator.VALIDATORS.copy())
    YAML_VALIDATORS.update(
        {
            "tag": validate_tag,
            "propertyOrder": validate_propertyOrder,
            "flowStyle": validate_flowStyle,
            "style": validate_style,
            "type": validate_type,
            "enum": validate_enum,
        },
    )

    def validate_fill_default(validator, properties, instance, schema):
        if not validator.is_type(instance, "object"):
            return

        for property_, subschema in properties.items():
            if "default" in subschema:
                instance.setdefault(property_, subschema["default"])

        yield from mvalidators.Draft4Validator.VALIDATORS["properties"](validator, properties, instance, schema)

    FILL_DEFAULTS = util.HashableDict()
    for key in ("allOf", "items"):
        FILL_DEFAULTS[key] = mvalidators.Draft4Validator.VALIDATORS[key]
    FILL_DEFAULTS["properties"] = validate_fill_default

    def validate_remove_default(validator, properties, instance, schema):
        if not validator.is_type(instance, "object"):
            return

        for property_, subschema in properties.items():
            if subschema.get("default", None) is not None and instance.get(property_, None) == subschema["default"]:
                del instance[property_]

        yield from mvalidators.Draft4Validator.VALIDATORS["properties"](validator, properties, instance, schema)

    REMOVE_DEFAULTS = util.HashableDict()
    for key in ("allOf", "items"):
        REMOVE_DEFAULTS[key] = mvalidators.Draft4Validator.VALIDATORS[key]
    REMOVE_DEFAULTS["properties"] = validate_remove_default

    class _ValidationContext:
        """
        Context that tracks (tree node, schema fragment) pairs that have
        already been validated.

        Instances of this class are context managers that track
        how many times they have been entered, and only reset themselves
        when exiting the outermost context.
        """

        __slots__ = ["_depth", "_seen"]

        def __init__(self):
            self._depth = 0
            self._seen = set()

        def add(self, instance, schema):
            """
            Inform the context that an instance has
            been validated against a schema fragment.
            """
            self._seen.add(self._make_seen_key(instance, schema))

        def seen(self, instance, schema):
            """
            Return True if an instance has already been
            validated against a schema fragment.
            """
            return self._make_seen_key(instance, schema) in self._seen

        def __enter__(self):
            self._depth += 1
            return self

        def __exit__(self, exc_type, exc_value, traceback):
            self._depth -= 1

            if self._depth == 0:
                self._seen = set()

        def _make_seen_key(self, instance, schema):
            return (id(instance), id(schema))

    @lru_cache
    def _create_validator(validators=YAML_VALIDATORS, visit_repeat_nodes=False):
        meta_schema = _load_schema_cached(YAML_SCHEMA_METASCHEMA_ID, False)

        type_checker = mvalidators.Draft4Validator.TYPE_CHECKER.redefine_many(
            {
                "array": lambda checker, instance: isinstance(instance, (list, tuple)),
                "integer": lambda checker, instance: not isinstance(instance, bool) and isinstance(instance, Integral),
                "string": lambda checker, instance: isinstance(instance, (str, np.str_)),
            },
        )
        id_of = mvalidators.Draft4Validator.ID_OF
        ASDFvalidator = mvalidators.create(
            meta_schema=meta_schema,
            validators=validators,
            type_checker=type_checker,
            id_of=id_of,
        )

        def _patch_init(cls):
            original_init = cls.__init__

            def init(self, *args, **kwargs):
                self.ctx = kwargs.pop("ctx", None)
                self.serialization_context = kwargs.pop("serialization_context", None)

                original_init(self, *args, **kwargs)

            cls.__init__ = init

        def _patch_iter_errors(cls):
            original_iter_errors = cls.iter_errors

            cls._context = _ValidationContext()

            def iter_errors(self, instance, *args, **kwargs):
                # We can't validate anything that looks like an external reference,
                # since we don't have the actual content, so we just have to defer
                # it for now.  If the user cares about complete validation, they
                # can call `AsdfFile.resolve_references`.
                with self._context:
                    if self._context.seen(instance, self.schema):
                        # We've already validated this instance against this schema,
                        # no need to do it again.
                        return

                    if not visit_repeat_nodes:
                        self._context.add(instance, self.schema)

                    if (isinstance(instance, dict) and "$ref" in instance) or isinstance(instance, reference.Reference):
                        return

                    if not self.schema:
                        tag = tagged.get_tag(instance)
                        if tag is not None and self.serialization_context.extension_manager.handles_tag_definition(tag):
                            tag_def = self.serialization_context.extension_manager.get_tag_definition(tag)
                            schema_uris = tag_def.schema_uris

                            # Must validate against all schema_uris
                            for schema_uri in schema_uris:
                                try:
                                    with self.resolver.resolving(schema_uri) as resolved:
                                        yield from self.descend(instance, resolved)
                                except RefResolutionError:
                                    warnings.warn(f"Unable to locate schema file for '{tag}': '{schema_uri}'", AsdfWarning)

                        if isinstance(instance, dict):
                            for val in instance.values():
                                yield from self.iter_errors(val)

                        elif isinstance(instance, list):
                            for val in instance:
                                yield from self.iter_errors(val)
                    else:
                        yield from original_iter_errors(self, instance)

            cls.iter_errors = iter_errors

        _patch_init(ASDFvalidator)
        _patch_iter_errors(ASDFvalidator)

        return ASDFvalidator

    @lru_cache
    def _load_schema(url):
        # handle case where the provided url is a non-str (Path)
        url = str(url)

        if url.startswith("http://") or url.startswith("https://") or url.startswith("asdf://"):
            msg = f"Unable to fetch schema from non-file URL: {url}"
            raise FileNotFoundError(msg)

        with generic_io.get_file(url) as fd:
            if isinstance(url, str) and url.endswith("json"):
                json_data = fd.read().decode("utf-8")
                result = json.loads(json_data, object_pairs_hook=OrderedDict)
            else:
                # The following call to yaml.load is safe because we're
                # using a loader that inherits from pyyaml's SafeLoader.
                result = yaml.load(fd, Loader=yamlutil.AsdfLoader)  # noqa: S506
        return result, fd.uri

    def _make_schema_loader():
        def load_schema(url):
            # Check if this is a URI provided by the new
            # Mapping API:
            resource_manager = get_config().resource_manager

            if url in resource_manager:
                content = resource_manager[url]
                # The jsonschema metaschemas are JSON, but pyyaml
                # doesn't mind.
                # The following call to yaml.load is safe because we're
                # using a loader that inherits from pyyaml's SafeLoader.
                result = yaml.load(content, Loader=yamlutil.AsdfLoader)  # noqa: S506
                return result, url

            # If not, this must be a URL (or missing).  Fall back to fetching
            # the schema the old way:
            return _load_schema(url)

        return load_schema

    def _make_jsonschema_refresolver():
        handlers = {}
        schema_loader = _make_schema_loader()

        def get_schema(url):
            return schema_loader(url)[0]

        for x in ["http", "https", "file", "tag", "asdf"]:
            handlers[x] = get_schema

        # Supplying our own implementation of urljoin_cache
        # allows asdf:// URIs to be resolved correctly.
        urljoin_cache = lru_cache(1024)(_patched_urllib_parse.urljoin)

        # We set cache_remote=False here because we do the caching of
        # remote schemas here in `load_schema`, so we don't need
        # jsonschema to do it on our behalf.  Setting it to `True`
        # counterintuitively makes things slower.
        return mvalidators.RefResolver(
            "",
            {},
            cache_remote=False,
            handlers=handlers,
            urljoin_cache=urljoin_cache,
        )

    def load_schema(url, resolve_references=False):
        """
        Load a schema from the given URL.

        Parameters
        ----------
        url : str
            The path to the schema

        resolve_references : bool, optional
            If ``True``, resolve all ``$ref`` references.

        """
        # We want to cache the work that went into constructing the schema, but returning
        # the same object is treacherous, because users who mutate the result will not
        # expect that they're changing the schema everywhere.
        return copy.deepcopy(_load_schema_cached(url, resolve_references))

    def _safe_resolve(json_id, uri):
        """
        This function handles the tricky task of resolving a schema URI
        in the presence of both new and legacy extensions.

        There are two senses of "resolve" here: one is to resolve the URI
        to a file:// URL using the legacy extension resolver object. The other
        is to resolve relative URIs against the id of the current schema document,
        which is what generic_io.resolve_uri does.

        For URIs associated with new-style extensions, we want to resolve with
        generic_io.resolve_uri, but not with the resolver object, otherwise we risk
        mangling URIs that share a prefix with a resolver mapping.
        """
        # We can't use urllib.parse here because tag: URIs don't
        # parse correctly.
        parts = uri.split("#")
        base = parts[0]
        fragment = parts[1] if len(parts) > 1 else ""

        # Resolve relative URIs (e.g., #foo/bar, ../foo/bar) against
        # the current schema id.
        base = generic_io.resolve_uri(json_id, base)

        return base, fragment

    @lru_cache
    def _load_schema_cached(url, resolve_references):
        loader = _make_schema_loader()
        schema, url = loader(url)

        if resolve_references:

            def resolve_refs(node, json_id):
                if json_id is None:
                    json_id = url

                if isinstance(node, dict) and "$ref" in node:
                    suburl_base, suburl_fragment = _safe_resolve(json_id, node["$ref"])

                    if suburl_base == url or suburl_base == schema.get("id"):
                        # This is a local ref, which we'll resolve in both cases.
                        subschema = schema
                    else:
                        subschema = load_schema(suburl_base, True)

                    return reference.resolve_fragment(subschema, suburl_fragment)

                return node

            schema = treeutil.walk_and_modify(schema, resolve_refs)

        return schema

    def get_validator(
        schema=None,
        ctx=None,
        validators=None,
        *args,
        _visit_repeat_nodes=False,
        _serialization_context=None,
        **kwargs,
    ):
        """
        Get a JSON schema validator object for the given schema.

        The additional *args and **kwargs are passed to the
        constructor of the returned ``Validator``.

        Parameters
        ----------
        schema : schema, optional
            Explicit schema to use.  If not provided, the schema to use
            is determined by the tag on instance (or subinstance).

        ctx : AsdfFile context
            Used to resolve tags and urls

        validators : dict, optional
            A dictionary mapping properties to validators to use (instead
            of the built-in ones and ones provided by extension types).

        _visit_repeat_nodes : bool, optional
            Force the validator to visit nodes that it has already
            seen.  This flag is a temporary hack to support a specific
            project that uses a custom validator to update a .fits file.
            Setting `True` is discouraged and will lead to RecursionError
            in trees containing reference cycles.

        Returns
        -------
        validator : jsonschema.Validator
        """
        if ctx is None:
            from ._asdf import AsdfFile

            ctx = AsdfFile()

        if _serialization_context is None:
            _serialization_context = ctx._create_serialization_context()

        if validators is None:
            validators = util.HashableDict(YAML_VALIDATORS.copy())
            validators.update(ctx._extension_manager.validator_manager.get_jsonschema_validators())

        kwargs["resolver"] = _make_jsonschema_refresolver()

        # We don't just call validators.validate() directly here, because
        # that validates the schema itself, wasting a lot of time (at the
        # time of this writing, it was half of the runtime of the unit
        # test suite!!!).  Instead, we assume that the schemas are valid
        # through the running of the unit tests, not at run time.
        cls = _create_validator(validators=validators, visit_repeat_nodes=_visit_repeat_nodes)
        return cls({} if schema is None else schema, *args, ctx=ctx, serialization_context=_serialization_context, **kwargs)

    def _validate_large_literals(instance, reading):
        """
        Validate that the tree has no large numeric literals.
        """

        def _validate(value):
            if value <= constants.MAX_NUMBER and value >= constants.MIN_NUMBER:
                return

            if reading:
                warnings.warn(
                    f"Invalid integer literal value {value} detected while reading file. "
                    "The value has been read safely, but the file should be "
                    "fixed.",
                    AsdfWarning,
                )
            else:
                msg = f"Integer value {value} is too large to safely represent as a literal in ASDF"
                raise ValidationError(msg)

        if isinstance(instance, Integral):
            _validate(instance)
        elif isinstance(instance, Mapping):
            for key in instance:
                if isinstance(key, Integral):
                    _validate(key)

    def _validate_mapping_keys(instance, reading):
        """
        Validate that mappings do not contain illegal key types
        (as of ASDF core schemas 1.6.0, only str, int, and bool are
        permitted).
        """
        if not isinstance(instance, Mapping):
            return

        for key in instance:
            if isinstance(key, tagged.Tagged) or not isinstance(key, (str, int, bool)):
                if reading:
                    warnings.warn(
                        f"Invalid mapping key {key} detected while reading file. "
                        "The value has been read safely, but the file should be "
                        "fixed.",
                        AsdfWarning,
                    )
                else:
                    msg = f"Mapping key {key} is not permitted.  Valid types: str, int, bool."
                    raise ValidationError(msg)

    def validate(instance, ctx=None, schema=None, validators=None, reading=False, *args, **kwargs):
        """
        Validate the given instance (which must be a tagged tree) against
        the appropriate schema.  The schema itself is located using the
        tag on the instance.

        The additional ``*args`` and ``**kwargs`` are passed along to
        `~jsonschema.protocols.Validator.validate`.

        Parameters
        ----------
        instance : tagged tree

        ctx : AsdfFile context
            Used to resolve tags and urls

        schema : schema, optional
            Explicit schema to use.  If not provided, the schema to use
            is determined by the tag on instance (or subinstance) and
            any custom schema provided to ``ctx``.

        validators : dict, optional
            A dictionary mapping properties to validators to use (instead
            of the built-in ones and ones provided by extension types).

        reading: bool, optional
            Indicates whether validation is being performed when the file is being
            read. This is useful to allow for different validation behavior when
            reading vs writing files.
        """
        if ctx is None:
            from ._asdf import AsdfFile

            ctx = AsdfFile()

        if schema is None and ctx._custom_schema:
            schema = ctx._custom_schema
        validator = get_validator({} if schema is None else schema, ctx, validators, *args, **kwargs)
        validator.validate(instance)

        additional_validators = [_validate_large_literals]
        if ctx.version >= versioning.RESTRICTED_KEYS_MIN_VERSION:
            additional_validators.append(_validate_mapping_keys)

        def _callback(instance):
            for validator in additional_validators:
                validator(instance, reading)

        treeutil.walk(instance, _callback)

    def fill_defaults(instance, ctx, reading=False):
        """
        For any default values in the schema, add them to the tree if they
        don't exist.

        Parameters
        ----------
        instance : tagged tree

        ctx : AsdfFile context
            Used to resolve tags and urls

        reading: bool, optional
            Indicates whether the ASDF file is being read (in contrast to being
            written).
        """
        validate(instance, ctx, validators=FILL_DEFAULTS, reading=reading)

    def remove_defaults(instance, ctx):
        """
        For any values in the tree that are the same as the default values
        specified in the schema, remove them from the tree.

        Parameters
        ----------
        instance : tagged tree

        ctx : AsdfFile context
            Used to resolve tags and urls
        """
        validate(instance, ctx, validators=REMOVE_DEFAULTS)

    def check_schema(schema, validate_default=True):
        """
        Check a given schema to make sure it is valid YAML schema.

        Parameters
        ----------
        schema : dict
            The schema object, as returned by ``load_schema``.
        validate_default : bool, optional
            Set to `True` to validate the content of the default
            field against the schema.
        """
        validators = util.HashableDict(mvalidators.Draft4Validator.VALIDATORS.copy())

        if validate_default:
            # The jsonschema library doesn't validate defaults
            # on its own.
            instance_validator = get_validator(schema)
            instance_scope = schema.get("id", "")

            def _validate_default(validator, default, instance, schema):
                if not validator.is_type(instance, "object"):
                    return

                if "default" in instance:
                    instance_validator.resolver.push_scope(instance_scope)
                    try:
                        yield from instance_validator.descend(instance["default"], instance)
                    finally:
                        instance_validator.resolver.pop_scope()

            validators.update({"default": _validate_default})

            def applicable_validators(schema):
                items = list(schema.items())
                items.append(("default", ""))
                return items

        else:
            applicable_validators = methodcaller("items")

        meta_schema_id = schema.get("$schema", YAML_SCHEMA_METASCHEMA_ID)
        meta_schema = _load_schema_cached(meta_schema_id, False)

        resolver = _make_jsonschema_refresolver()

        cls = mvalidators.create(
            meta_schema=meta_schema,
            validators=validators,
            type_checker=mvalidators.Draft4Validator.TYPE_CHECKER,
            id_of=mvalidators.Draft4Validator.ID_OF,
            applicable_validators=applicable_validators,
        )
        validator = cls(meta_schema, resolver=resolver)
        validator.validate(schema)