Python sqlalchemy.orm.dependency.set() Examples

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def __new__(cls, arg):
        values = set([
                    c for c
                    in re.split('\s*,\s*', arg or "")
                    if c
                ])

        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                    "Invalid cascade option(s): %s" %
                    ", ".join([repr(x) for x in
                        sorted(
                            values.difference(cls._allowed_cascades)
                    )])
            )

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                        "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet

    topological.set = (
        unitofwork.set
    ) = session.set = mapper.set = dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s"
                % ", ".join(
                    [
                        repr(x)
                        for x in sorted(
                            values.difference(cls._allowed_cascades)
                        )
                    ]
                )
            )

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard("all")

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = "save-update" in values
        self.delete = "delete" in values
        self.refresh_expire = "refresh-expire" in values
        self.merge = "merge" in values
        self.expunge = "expunge" in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn(
                "The 'delete-orphan' cascade " "option requires 'delete'."
            )
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def __new__(cls, arg):
        values = set([
            c for c
            in re.split('\s*,\s*', arg or "")
            if c
        ])

        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(
                    values.difference(cls._allowed_cascades)
                )])
            )

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the fundamental
    mapping and operation that's failing, while still reproducing the issue
    on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
            dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def __new__(cls, value_list):
        if isinstance(value_list, util.string_types) or value_list is None:
            return cls.from_string(value_list)
        values = set(value_list)
        if values.difference(cls._allowed_cascades):
            raise sa_exc.ArgumentError(
                "Invalid cascade option(s): %s" %
                ", ".join([repr(x) for x in
                           sorted(values.difference(cls._allowed_cascades))]))

        if "all" in values:
            values.update(cls._add_w_all_cascades)
        if "none" in values:
            values.clear()
        values.discard('all')

        self = frozenset.__new__(CascadeOptions, values)
        self.save_update = 'save-update' in values
        self.delete = 'delete' in values
        self.refresh_expire = 'refresh-expire' in values
        self.merge = 'merge' in values
        self.expunge = 'expunge' in values
        self.delete_orphan = "delete-orphan" in values

        if self.delete_orphan and not self.delete:
            util.warn("The 'delete-orphan' cascade "
                      "option requires 'delete'.")
        return self 

def randomize_unitofwork():
    """Use random-ordering sets within the unit of work in order
    to detect unit of work sorting issues.

    This is a utility function that can be used to help reproduce
    inconsistent unit of work sorting issues.   For example,
    if two kinds of objects A and B are being inserted, and
    B has a foreign key reference to A - the A must be inserted first.
    However, if there is no relationship between A and B, the unit of work
    won't know to perform this sorting, and an operation may or may not
    fail, depending on how the ordering works out.   Since Python sets
    and dictionaries have non-deterministic ordering, such an issue may
    occur on some runs and not on others, and in practice it tends to
    have a great dependence on the state of the interpreter.  This leads
    to so-called "heisenbugs" where changing entirely irrelevant aspects
    of the test program still cause the failure behavior to change.

    By calling ``randomize_unitofwork()`` when a script first runs, the
    ordering of a key series of sets within the unit of work implementation
    are randomized, so that the script can be minimized down to the
    fundamental mapping and operation that's failing, while still reproducing
    the issue on at least some runs.

    This utility is also available when running the test suite via the
    ``--reversetop`` flag.

    .. versionadded:: 0.8.1 created a standalone version of the
       ``--reversetop`` feature.

    """
    from sqlalchemy.orm import unitofwork, session, mapper, dependency
    from sqlalchemy.util import topological
    from sqlalchemy.testing.util import RandomSet
    topological.set = unitofwork.set = session.set = mapper.set = \
        dependency.set = RandomSet 

def _validator_events(
        desc, key, validator, include_removes, include_backrefs):
    """Runs a validation method on an attribute value to be set or
    appended.
    """

    if not include_backrefs:
        def detect_is_backref(state, initiator):
            impl = state.manager[key].impl
            return initiator.impl is not impl

    if include_removes:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def remove(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                validator(state.obj(), key, value, True)

    else:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

    event.listen(desc, 'append', append, raw=True, retval=True)
    event.listen(desc, 'set', set_, raw=True, retval=True)
    if include_removes:
        event.listen(desc, "remove", remove, raw=True, retval=True) 

def polymorphic_union(table_map, typecolname,
                        aliasname='p_union', cast_nulls=True):
    """Create a ``UNION`` statement used by a polymorphic mapper.

    See  :ref:`concrete_inheritance` for an example of how
    this is used.

    :param table_map: mapping of polymorphic identities to
     :class:`.Table` objects.
    :param typecolname: string name of a "discriminator" column, which will be
     derived from the query, producing the polymorphic identity for
     each row.  If ``None``, no polymorphic discriminator is generated.
    :param aliasname: name of the :func:`~sqlalchemy.sql.expression.alias()`
     construct generated.
    :param cast_nulls: if True, non-existent columns, which are represented
     as labeled NULLs, will be passed into CAST.   This is a legacy behavior
     that is problematic on some backends such as Oracle - in which case it
     can be set to False.

    """

    colnames = util.OrderedSet()
    colnamemaps = {}
    types = {}
    for key in table_map:
        table = table_map[key]

        # mysql doesnt like selecting from a select;
        # make it an alias of the select
        if isinstance(table, sql.Select):
            table = table.alias()
            table_map[key] = table

        m = {}
        for c in table.c:
            colnames.add(c.key)
            m[c.key] = c
            types[c.key] = c.type
        colnamemaps[table] = m

    def col(name, table):
        try:
            return colnamemaps[table][name]
        except KeyError:
            if cast_nulls:
                return sql.cast(sql.null(), types[name]).label(name)
            else:
                return sql.type_coerce(sql.null(), types[name]).label(name)

    result = []
    for type, table in table_map.items():
        if typecolname is not None:
            result.append(
                    sql.select([col(name, table) for name in colnames] +
                    [sql.literal_column(sql_util._quote_ddl_expr(type)).
                            label(typecolname)],
                             from_obj=[table]))
        else:
            result.append(sql.select([col(name, table) for name in colnames],
                                     from_obj=[table]))
    return sql.union_all(*result).alias(aliasname) 

def _validator_events(desc, key, validator, include_removes, include_backrefs):
    """Runs a validation method on an attribute value to be set or appended."""

    if not include_backrefs:
        def detect_is_backref(state, initiator):
            impl = state.manager[key].impl
            return initiator.impl is not impl

    if include_removes:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def remove(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                validator(state.obj(), key, value, True)

    else:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

    event.listen(desc, 'append', append, raw=True, retval=True)
    event.listen(desc, 'set', set_, raw=True, retval=True)
    if include_removes:
        event.listen(desc, "remove", remove, raw=True, retval=True) 

def _validator_events(
        desc, key, validator, include_removes, include_backrefs):
    """Runs a validation method on an attribute value to be set or
    appended.
    """

    if not include_backrefs:
        def detect_is_backref(state, initiator):
            impl = state.manager[key].impl
            return initiator.impl is not impl

    if include_removes:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def remove(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                validator(state.obj(), key, value, True)

    else:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

    event.listen(desc, 'append', append, raw=True, retval=True)
    event.listen(desc, 'set', set_, raw=True, retval=True)
    if include_removes:
        event.listen(desc, "remove", remove, raw=True, retval=True) 

def _validator_events(
        desc, key, validator, include_removes, include_backrefs):
    """Runs a validation method on an attribute value to be set or
    appended.
    """

    if not include_backrefs:
        def detect_is_backref(state, initiator):
            impl = state.manager[key].impl
            return initiator.impl is not impl

    if include_removes:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def remove(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                validator(state.obj(), key, value, True)

    else:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

    event.listen(desc, 'append', append, raw=True, retval=True)
    event.listen(desc, 'set', set_, raw=True, retval=True)
    if include_removes:
        event.listen(desc, "remove", remove, raw=True, retval=True) 

def _validator_events(
        desc, key, validator, include_removes, include_backrefs):
    """Runs a validation method on an attribute value to be set or
    appended.
    """

    if not include_backrefs:
        def detect_is_backref(state, initiator):
            impl = state.manager[key].impl
            return initiator.impl is not impl

    if include_removes:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value, False)
            else:
                return value

        def remove(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                validator(state.obj(), key, value, True)

    else:
        def append(state, value, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

        def set_(state, value, oldvalue, initiator):
            if include_backrefs or not detect_is_backref(state, initiator):
                return validator(state.obj(), key, value)
            else:
                return value

    event.listen(desc, 'append', append, raw=True, retval=True)
    event.listen(desc, 'set', set_, raw=True, retval=True)
    if include_removes:
        event.listen(desc, "remove", remove, raw=True, retval=True)