""" A set of descriptors that document intended types for attributes on
classes and implement convenience behaviors like default values, etc.
"""
from __future__ import absolute_import, print_function

import re
import types
import difflib
import datetime
import dateutil.parser
from importlib import import_module
from copy import copy
import inspect
import logging
logger = logging.getLogger(__name__)

from six import integer_types, string_types, add_metaclass, iteritems
import numpy as np

from . import enums, colors
from .utils import nice_join

bokeh_integer_types = (np.int8, np.int16, np.int32, np.int64) + integer_types

# used to indicate properties that are not set (vs null, None, etc)
class _NotSet(object):
    pass

class DeserializationError(Exception):
    pass

class Property(object):
    """ Base class for all type properties. """

    def __init__(self, default=None, help=None):
        """ This is how the descriptor is created in the class declaration """
        if isinstance(default, types.FunctionType): # aka. lazy value
            self.validate(default())
        else:
            self.validate(default)

        self._default = default
        self.help = help
        self.alternatives = []

        # This gets set by the class decorator at class creation time
        self.name = "unnamed"

    def __str__(self):
        return self.__class__.__name__

    @property
    def _name(self):
        return "_" + self.name

    @property
    def default(self):
        if not isinstance(self._default, types.FunctionType):
            return copy(self._default)
        else:
            value = self._default()
            self.validate(value)
            return value

    @classmethod
    def autocreate(cls, name=None):
        """ Called by the metaclass to create a
        new instance of this descriptor
        if the user just assigned it to a property without trailing
        parentheses.
        """
        return cls()

    def matches(self, new, old):
        # XXX: originally this code warned about not being able to compare values, but that
        # doesn't make sense, because most comparisons involving numpy arrays will fail with
        # ValueError exception, thus warning about inevitable.
        try:
            if new is None or old is None:
                return new is old           # XXX: silence FutureWarning from NumPy
            else:
                return new == old
        except (KeyboardInterrupt, SystemExit):
            raise
        except Exception as e:
            logger.debug("could not compare %s and %s for property %s (Reason: %s)", new, old, self.name, e)
        return False

    def from_json(self, json, models=None):
        return json

    def transform(self, value):
        return value

    def validate(self, value):
        pass

    def is_valid(self, value):
        try:
            self.validate(value)
        except ValueError:
            return False
        else:
            return True

    def _get(self, obj):
        if not hasattr(obj, self._name):
            setattr(obj, self._name, self.default)
        return getattr(obj, self._name)

    def __get__(self, obj, owner=None):
        if obj is not None:
            return self._get(obj)
        elif owner is not None:
            return self
        else:
            raise ValueError("both 'obj' and 'owner' are None, don't know what to do")

    def __set__(self, obj, value):
        try:
            self.validate(value)
        except ValueError as e:
            for tp, converter in self.alternatives:
                if tp.is_valid(value):
                    value = converter(value)
                    break
            else:
                raise e
        else:
            value = self.transform(value)

        old = self.__get__(obj)
        obj._changed_vars.add(self.name)
        if self._name in obj.__dict__ and self.matches(value, old):
            return
        setattr(obj, self._name, value)
        obj._dirty = True
        if hasattr(obj, '_trigger'):
            if hasattr(obj, '_block_callbacks') and obj._block_callbacks:
                obj._callback_queue.append((self.name, old, value))
            else:
                obj._trigger(self.name, old, value)

    def __delete__(self, obj):
        if hasattr(obj, self._name):
            delattr(obj, self._name)

    @property
    def has_ref(self):
        return False

    def accepts(self, tp, converter):
        tp = ParameterizedProperty._validate_type_param(tp)
        self.alternatives.append((tp, converter))
        return self

    def __or__(self, other):
        return Either(self, other)

class DataSpec(Property):
    """ Because the BokehJS glyphs support a fixed value or a named
    field for most data fields, we capture that in this descriptor.
    Fields can have a fixed value, or be a name that is looked up
    on the datasource (usually as a column or record array field).
    Numerical data can also have units of screen or data space.

    We mirror the JS convention in this Python descriptor.  For details,
    see renderers/properties.coffee in BokehJS, and specifically the
    select() function.

    There are multiple ways to set a DataSpec, illustrated below with comments
    and example code.

    Setting DataSpecs


    Simple example::

        class Foo(HasProps):
            x = DataSpec("x", units="data")

        f = Foo()
        f.x = "fieldname"  # Use the datasource field named "fieldname"
        f.x = 12           # A fixed value of 12

    Can provide a dict with the fields explicitly named::

        f.width = {"name": "foo"}
        f.size = {"name": "foo", "units": "screen"}

    Reading DataSpecs


    In the cases when the dataspec is set to just a field name or a
    fixed value, then those are returned.  If the no values have
    been set, then the value of to_dict() is returned.

    In all cases, to determine the full dict that will be used to
    represent this dataspec, use the to_dict() method.

    Implementation


    The DataSpec instance is stored in the class dict, and acts as a
    descriptor.  Thus, it is shared between all instances of the class.
    Instance-specific data is stored in the instance dict, in a private
    variable named _[attrname].  This stores the actual value that the
    user last set (and does not exist if the user has not yet set the
    value).

    """

    def __init__(self, field=None, units="data", min_value=None, default=_NotSet, help=None):
        """
        Parameters
        ==========
        **field** is the string name of a data column to look up.
        **units** is either "data" or "screen"
        """
        # Don't use .name because the HasProps metaclass uses that to
        # store the attribute name on this descriptor.
        self.field = field
        self.units = units
        self._default = default
        self.min_value = min_value
        self.help = help

    @classmethod
    def autocreate(cls, name=None):
        # In this case, use the name the user assigned this DataSpec to
        # as the default field name.
        d = cls(field=name)
        return d

    def _get(self, obj):
        """ Try to implement a "natural" interface: if the user just set
        simple values or field names, the getter just returns those.
        However, if the user has also overridden the "units" or "default"
        settings, then a dictionary is returned.
        """
        if hasattr(obj, self._name):
            setval = getattr(obj, self._name)
            if isinstance(setval, string_types):
                # A string representing the field
                return setval
            elif not isinstance(setval, dict):
                # Typically a number presenting the fixed value
                return setval
            else:
                return self.to_dict(obj)
        else:
            # If the user hasn't set anything
            if self.field is not None:
                return self.field
            if self.default != _NotSet:
                return self.default
            # XXX: implicit `return None` or unreachable?

    def to_dict(self, obj):
        # Build the complete dict
        setval = getattr(obj, self._name, None)
        if isinstance(setval, string_types):
            d = {"field": setval, "units": self.units}
        elif isinstance(setval, dict):
            d = {"units": self.units}
            d.update(setval)
        elif setval is not None:
            # a fixed value of some sort; no need to store the default value
            d = {"value": setval, "units": self.units}
        else:
            # If the user never set a value
            if self.field is not None:
                d = {"field": self.field, "units": self.units}
            elif self.default != _NotSet:
                d = {"value": self.default, "units": self.units}
            else:
                d = {}

        if "value" in d and self.min_value is not None:
            if d["value"] < self.min_value:
                raise ValueError("value must be greater than %s" % str(self.min_value))
        return d

    def __repr__(self):
        return "DataSpec(field=%r, units=%r)" % (self.field, self.units)


class ColorSpec(DataSpec):
    """ Subclass of DataSpec for specifying colors.

    Although this serves the same role as a DataSpec, its usage is somewhat
    different because:

    * Specifying a fixed value is much more common
    * Strings can be both field identifiers or refer to one of the SVG
      Named Colors (or be a hex value starting with "#")
    * There are no units

    For colors, because we support named colors and hex values prefaced
    with a "#", when we are handed a string value, there is a little
    interpretation: if the value is one of the 147 SVG named colors or
    it starts with a "#", then it is interpreted as a value.  Otherwise,
    it is treated as a field name.

    If a 3-tuple is provided, then it is treated as an RGB (0..255).
    If a 4-tuple is provided, then it is treated as an RGBa (0..255), with
    alpha as a float between 0 and 1.  (This follows the HTML5 Canvas API.)

    Unlike DataSpec, ColorSpecs do not have a "units" property.

    When reading out a ColorSpec, it returns a tuple, hex value, or
    field name

    There are two common use cases for ColorSpec: setting a constant value,
    and indicating a field name to look for on the datasource:

    >>> class Bar(HasProps):
    ...     col = ColorSpec("green")
    ...     col2 = ColorSpec("colorfield")

    >>> b = Bar()
    >>> b.col = "red"  # sets a fixed value of red
    >>> b.col
    "red"
    >>> b.col = "myfield"  # Use the datasource field named "myfield"
    >>> b.col
    "myfield"

    For more examples, see tests/test_glyphs.py
    """

    NAMEDCOLORS = set(colors.__colors__)

    def __init__(self, field_or_value=None, field=None, value=None, default=_NotSet, help=None):
        # The fancy footwork below is so we auto-interpret the first positional
        # parameter as either a field or a fixed value.  If either "field" or
        # "value" are then supplied as keyword arguments, then those will
        # override the inferred value from the positional argument.

        self.field = field
        self._default = default
        self.value = value
        self.help = help

        if field_or_value is not None:
            if self.isconst(field_or_value):
                self.value = field_or_value
            else:
                self.field = field_or_value

        # We need to distinguish if the user ever explicitly sets the attribute; if
        # they explicitly set it to None, we should pass on None in the dict.
        self._isset = False

    @classmethod
    def isconst(cls, arg):
        """ Returns True if the argument is a literal color.  Check for a
        well-formed hexadecimal color value.
        """
        return isinstance(arg, string_types) and \
               ((len(arg) == 7 and arg[0] == "#") or arg in cls.NAMEDCOLORS)

    def _formattuple(self, colortuple):
        if isinstance(colortuple, tuple):
            if len(colortuple) == 3:
                return "rgb%r" % (colortuple,)
            else:
                return "rgba%r" % (colortuple,)
        else:
            return colortuple

    def _get(self, obj):
        # One key difference in ColorSpec.__get__ from the base class is
        # that we do not call self.to_dict() in any circumstance, because
        # this could lead to formatting color tuples as "rgb(R,G,B)" instead
        # of keeping them as tuples.
        if hasattr(obj, self._name):
            setval = getattr(obj, self._name)
            if self.isconst(setval) or isinstance(setval, tuple):
                # Fixed color value
                return setval
            elif isinstance(setval, string_types):
                return setval
            elif setval is None:
                return None
            else:
                # setval should be a dict at this point
                assert(isinstance(setval, dict))
                return setval
        else:
            if self.value is not None:
                return self.value
            if self.default != _NotSet:
                return self.default
            else:
                return self.field

    def __set__(self, obj, arg):
        self._isset = True
        if isinstance(arg, tuple):
            if len(arg) in (3, 4):
                # RGB or RGBa
                pass
            else:
                raise RuntimeError("Invalid tuple being assigned to ColorSpec; must be length 2, 3, or 4.")
        elif isinstance(arg, colors.Color):
            arg = arg.to_css()
        super(ColorSpec, self).__set__(obj, arg)

    def to_dict(self, obj):
        setval = getattr(obj, self._name, None)
        if self.default != _NotSet and not self._isset:
            setval = self.default
        if setval is not None:
            if self.isconst(setval):
                # Hexadecimal or named color
                return {"value": setval}
            elif isinstance(setval, tuple):
                # RGB or RGBa
                # TODO: Should we validate that alpha is between 0..1?
                return {"value": self._formattuple(setval)}
            elif isinstance(setval, string_types):
                return {"field": setval}
            elif isinstance(setval, dict):
                # this is considerably simpler than the DataSpec case because
                # there are no units involved, and we've handled all of the
                # value cases above.
                return setval.copy()
        else:
            if self._isset:
                if self.value is None:
                    return {"value": None}
                else:
                    return {"value": getattr(obj, self._name, self.value)}
            else:
                if self.value:
                    return {"value": self.value}
                return {"field": self.field}

    def __repr__(self):
        return "ColorSpec(field=%r)" % self.field

class Include(object):
    ''' Include other properties from mixin Models, with a given prefix. '''

    def __init__(self, delegate):
        if not (isinstance(delegate, type) and issubclass(delegate, HasProps)):
            raise ValueError("expected a subclass of HasProps, got %r" % delegate)

        self.delegate = delegate

class MetaHasProps(type):
    def __new__(cls, class_name, bases, class_dict):
        names = set()
        names_with_refs = set()
        container_names = set()

        # First pre-process to handle all the Includes
        includes = {}
        removes = set()
        for name, prop in class_dict.items():
            if not isinstance(prop, Include):
                continue

            delegate = prop.delegate
            prefix = re.sub("_props$", "", name) + "_"

            for subpropname in delegate.class_properties(withbases=False):
                fullpropname = prefix + subpropname
                subprop = delegate.lookup(subpropname)
                if isinstance(subprop, Property):
                    # If it's an actual instance, then we need to make a copy
                    # so two properties don't write to the same hidden variable
                    # inside the instance.
                    subprop = copy(subprop)
                includes[fullpropname] = subprop
            # Remove the name of the Include attribute itself
            removes.add(name)

        # Update the class dictionary, taking care not to overwrite values
        # from the delegates that the subclass may have explicitly defined
        for key, val in includes.items():
            if key not in class_dict:
                class_dict[key] = val
        for tmp in removes:
            del class_dict[tmp]

        dataspecs = {}
        for name, prop in class_dict.items():
            if isinstance(prop, Property):
                prop.name = name
                if prop.has_ref:
                    names_with_refs.add(name)
                elif isinstance(prop, ContainerProperty):
                    container_names.add(name)
                names.add(name)
                if isinstance(prop, DataSpec):
                    dataspecs[name] = prop

            elif isinstance(prop, type) and issubclass(prop, Property):
                # Support the user adding a property without using parens,
                # i.e. using just the Property subclass instead of an
                # instance of the subclass
                newprop = prop.autocreate(name=name)
                class_dict[name] = newprop
                newprop.name = name
                names.add(name)

                # Process dataspecs
                if issubclass(prop, DataSpec):
                    dataspecs[name] = newprop

        class_dict["__properties__"] = names
        class_dict["__properties_with_refs__"] = names_with_refs
        class_dict["__container_props__"] = container_names
        if dataspecs:
            class_dict["_dataspecs"] = dataspecs
        return type.__new__(cls, class_name, bases, class_dict)

def accumulate_from_subclasses(cls, propname):
    s = set()
    for c in inspect.getmro(cls):
        if issubclass(c, HasProps):
            s.update(getattr(c, propname))
    return s

@add_metaclass(MetaHasProps)
class HasProps(object):

    def __init__(self, **properties):
        super(HasProps, self).__init__()
        self._changed_vars = set()

        for name, value in properties.items():
            setattr(self, name, value)

    def __setattr__(self, name, value):
        props = sorted(self.properties())

        if name.startswith("_") or name in props:
            super(HasProps, self).__setattr__(name, value)
        else:
            matches, text = difflib.get_close_matches(name.lower(), props), "similar"

            if not matches:
                matches, text = props, "possible"

            raise AttributeError("unexpected attribute '%s' to %s, %s attributes are %s" %
                (name, self.__class__.__name__, text, nice_join(matches)))

    def clone(self):
        """ Returns a duplicate of this object with all its properties
        set appropriately.  Values which are containers are shallow-copied.
        """
        return self.__class__(**self.changed_properties_with_values())

    @classmethod
    def lookup(cls, name):
        return getattr(cls, name)

    @classmethod
    def properties_with_refs(cls):
        """ Returns a set of the names of this object's properties that
        have references. We traverse the class hierarchy and
        pull together the full list of properties.
        """
        if not hasattr(cls, "__cached_allprops_with_refs"):
            s = accumulate_from_subclasses(cls, "__properties_with_refs__")
            cls.__cached_allprops_with_refs = s
        return cls.__cached_allprops_with_refs

    @classmethod
    def properties_containers(cls):
        """ Returns a list of properties that are containers
        """
        if not hasattr(cls, "__cached_allprops_containers"):
            s = accumulate_from_subclasses(cls, "__container_props__")
            cls.__cached_allprops_containers = s
        return cls.__cached_allprops_containers

    @classmethod
    def properties(cls):
        """ Returns a set of the names of this object's properties. We
        traverse the class hierarchy and pull together the full
        list of properties.
        """
        if not hasattr(cls, "__cached_allprops"):
            s = cls.class_properties()
            cls.__cached_allprops = s
        return cls.__cached_allprops

    @classmethod
    def dataspecs(cls):
        """ Returns a set of the names of this object's dataspecs (and
        dataspec subclasses).  Traverses the class hierarchy.
        """
        if not hasattr(cls, "__cached_dataspecs"):
            dataspecs = set()
            for c in reversed(inspect.getmro(cls)):
                if hasattr(c, "_dataspecs"):
                    dataspecs.update(c._dataspecs.keys())
            cls.__cached_dataspecs = dataspecs
        return cls.__cached_dataspecs

    @classmethod
    def dataspecs_with_refs(cls):
        dataspecs = {}
        for c in reversed(inspect.getmro(cls)):
            if hasattr(c, "_dataspecs"):
                dataspecs.update(c._dataspecs)
        return dataspecs

    def changed_vars(self):
        """ Returns which variables changed since the creation of the object,
        or the last called to reset_changed_vars().
        """
        return set.union(self._changed_vars, self.properties_with_refs(),
                         self.properties_containers())

    def reset_changed_vars(self):
        self._changed_vars = set()

    def properties_with_values(self):
        return dict([ (attr, getattr(self, attr)) for attr in self.properties() ])

    def changed_properties(self):
        return self.changed_vars()

    def changed_properties_with_values(self):
        return dict([ (attr, getattr(self, attr)) for attr in self.changed_properties() ])

    @classmethod
    def class_properties(cls, withbases=True):
        if withbases:
            return accumulate_from_subclasses(cls, "__properties__")
        else:
            return set(cls.__properties__)

    def set(self, **kwargs):
        """ Sets a number of properties at once """
        for kw in kwargs:
            setattr(self, kw, kwargs[kw])

    def pprint_props(self, indent=0):
        """ Prints the properties of this object, nicely formatted """
        for key, value in self.properties_with_values().items():
            print("%s%s: %r" % ("  "*indent, key, value))

class PrimitiveProperty(Property):

    _underlying_type = None

    def validate(self, value):
        super(PrimitiveProperty, self).validate(value)

        if not (value is None or isinstance(value, self._underlying_type)):
            raise ValueError("expected a value of type %s, got %s of type %s" %
                (nice_join([ cls.__name__ for cls in self._underlying_type ]), value, type(value).__name__))

    def from_json(self, json, models=None):
        if json is None or isinstance(json, self._underlying_type):
            return json
        else:
            expected = nice_join([ cls.__name__ for cls in self._underlying_type ])
            raise DeserializationError("%s expected %s, got %s" % (self, expected, json))

class Bool(PrimitiveProperty):
    ''' Boolean type property. '''
    _underlying_type = (bool,)

class Int(PrimitiveProperty):
    ''' Signed integer type property. '''
    _underlying_type = bokeh_integer_types

class Float(PrimitiveProperty):
    ''' Floating point type property. '''
    _underlying_type = (float, ) + bokeh_integer_types

class Complex(PrimitiveProperty):
    ''' Complex floating point type property. '''
    _underlying_type = (complex, float) + bokeh_integer_types

class String(PrimitiveProperty):
    ''' String type property. '''
    _underlying_type = string_types

class Regex(String):
    ''' Regex type property validates that text values match the
    given regular expression.
    '''
    def __init__(self, regex, default=None, help=None):
        self.regex = re.compile(regex)
        super(Regex, self).__init__(default=default, help=help)

    def validate(self, value):
        super(Regex, self).validate(value)

        if not (value is None or self.regex.match(value) is not None):
            raise ValueError("expected a string matching %r pattern, got %r" % (self.regex.pattern, value))

    def __str__(self):
        return "%s(%r)" % (self.__class__.__name__, self.regex.pattern)

class ParameterizedProperty(Property):
    """ Base class for Properties that have type parameters, e.g. `List(String)`. """

    @staticmethod
    def _validate_type_param(type_param):
        if isinstance(type_param, type):
            if issubclass(type_param, Property):
                return type_param()
            else:
                type_param = type_param.__name__
        elif isinstance(type_param, Property):
            return type_param

        raise ValueError("expected a property as type parameter, got %s" % type_param)

    @property
    def type_params(self):
        raise NotImplementedError("abstract method")

    @property
    def has_ref(self):
        return any(type_param.has_ref for type_param in self.type_params)

class ContainerProperty(ParameterizedProperty):
    ''' Base class for Container-like type properties. '''
    # Base class for container-like things; this helps the auto-serialization
    # and attribute change detection code
    pass

class List(ContainerProperty):
    """ If a default value is passed in, then a shallow copy of it will be
    used for each new use of this property.

    People will also frequently pass in some other kind of property or a
    class (to indicate a list of instances).  In those cases, we want to
    just create an empty list
    """

    def __init__(self, item_type, default=[], help=None):
        self.item_type = self._validate_type_param(item_type)
        super(List, self).__init__(default=default, help=help)

    @property
    def type_params(self):
        return [self.item_type]

    def validate(self, value):
        super(List, self).validate(value)

        if value is not None:
            if not (isinstance(value, list) and \
                    all(self.item_type.is_valid(item) for item in value)):
                raise ValueError("expected an element of %s, got %r" % (self, value))

    def __str__(self):
        return "%s(%s)" % (self.__class__.__name__, self.item_type)

    def from_json(self, json, models=None):
        if json is None:
            return None
        elif isinstance(json, list):
            return [ self.item_type.from_json(item, models) for item in json ]
        else:
            raise DeserializationError("%s expected a list or None, got %s" % (self, json))

class Dict(ContainerProperty):
    """ If a default value is passed in, then a shallow copy of it will be
    used for each new use of this property.
    """

    def __init__(self, keys_type, values_type, default={}, help=None):
        self.keys_type = self._validate_type_param(keys_type)
        self.values_type = self._validate_type_param(values_type)
        super(Dict, self).__init__(default=default, help=help)

    @property
    def type_params(self):
        return [self.keys_type, self.values_type]

    def validate(self, value):
        super(Dict, self).validate(value)

        if value is not None:
            if not (isinstance(value, dict) and \
                    all(self.keys_type.is_valid(key) and self.values_type.is_valid(val) for key, val in iteritems(value))):
                raise ValueError("expected an element of %s, got %r" % (self, value))

    def __str__(self):
        return "%s(%s, %s)" % (self.__class__.__name__, self.keys_type, self.values_type)

    def from_json(self, json, models=None):
        if json is None:
            return None
        elif isinstance(json, dict):
            return { self.keys_type.from_json(key, models): self.values_type.from_json(value, models) for key, value in iteritems(json) }
        else:
            raise DeserializationError("%s expected a dict or None, got %s" % (self, json))

class Tuple(ContainerProperty):
    ''' Tuple type property. '''
    def __init__(self, tp1, tp2, *type_params, **kwargs):
        self._type_params = list(map(self._validate_type_param, (tp1, tp2) + type_params))
        super(Tuple, self).__init__(default=kwargs.get("default"), help=kwargs.get("help"))

    @property
    def type_params(self):
        return self._type_params

    def validate(self, value):
        super(Tuple, self).validate(value)

        if value is not None:
            if not (isinstance(value, (tuple, list)) and len(self.type_params) == len(value) and \
                    all(type_param.is_valid(item) for type_param, item in zip(self.type_params, value))):
                raise ValueError("expected an element of %s, got %r" % (self, value))

    def __str__(self):
        return "%s(%s)" % (self.__class__.__name__, ", ".join(map(str, self.type_params)))

    def from_json(self, json, models=None):
        if json is None:
            return None
        elif isinstance(json, list):
            return tuple(type_param.from_json(item, models) for type_param, item in zip(self.type_params, json))
        else:
            raise DeserializationError("%s expected a list or None, got %s" % (self, json))

class Array(ContainerProperty):
    """ Whatever object is passed in as a default value, np.asarray() is
    called on it to create a copy for the default value for each use of
    this property.
    """

    def __init__(self, item_type, default=None, help=None):
        self.item_type = self._validate_type_param(item_type)
        super(Array, self).__init__(default=default, help=help)

    @property
    def type_params(self):
        return [self.item_type]

    def validate(self, value):
        super(Array, self).validate(value)
        # TODO: implement validation of np.array, etc.

class Instance(Property):
    ''' Instance type property for referneces to other Models in the object
    graph.

    '''
    def __init__(self, instance_type, default=None, help=None):
        if not isinstance(instance_type, (type,) + string_types):
            raise ValueError("expected a type or string, got %s" % instance_type)

        if isinstance(instance_type, type) and not issubclass(instance_type, HasProps):
            raise ValueError("expected a subclass of HasProps, got %s" % instance_type)

        self._instance_type = instance_type

        super(Instance, self).__init__(default=default, help=help)

    @property
    def instance_type(self):
        if isinstance(self._instance_type, str):
            module, name = self._instance_type.rsplit(".", 1)
            self._instance_type = getattr(import_module(module, "bokeh"), name)

        return self._instance_type

    @property
    def has_ref(self):
        return True

    def validate(self, value):
        super(Instance, self).validate(value)

        if value is not None:
            if not isinstance(value, self.instance_type):
                raise ValueError("expected an instance of type %s, got %s of type %s" %
                    (self.instance_type.__name__, value, type(value).__name__))

    def __str__(self):
        return "%s(%s)" % (self.__class__.__name__, self.instance_type.__name__)

    def from_json(self, json, models=None):
        if json is None:
            return None
        elif isinstance(json, dict):
            from .plot_object import PlotObject
            if issubclass(self.instance_type, PlotObject):
                if models is None:
                    raise DeserializationError("%s can't deserialize without models" % self)
                else:
                    model = models.get(json["id"])

                    if model is not None:
                        return model
                    else:
                        raise DeserializationError("%s failed to deserilize reference to %s" % (self, json))
            else:
                attrs = {}

                for name, value in iteritems(json):
                    prop = self.instance_type.lookup(name)
                    attrs[name] = prop.from_json(value, models)

                # XXX: this doesn't work when Instance(Superclass) := Subclass()
                # Serialization dict must carry type information to resolve this.
                return self.instance_type(**attrs)
        else:
            raise DeserializationError("%s expected a dict or None, got %s" % (self, json))

class This(Property):
    """ A reference to an instance of the class being defined. """
    pass

# Fake types, ABCs
class Any(Property):
    ''' Any type property accepts any values. '''
    pass

class Function(Property):
    ''' Function type property. '''
    pass

class Event(Property):
    ''' Event type property. '''
    pass

class Range(ParameterizedProperty):
    ''' Range type property ensures values are between a range. '''
    def __init__(self, range_type, start, end, default=None, help=None):
        self.range_type = self._validate_type_param(range_type)
        self.range_type.validate(start)
        self.range_type.validate(end)
        self.start = start
        self.end = end
        super(Range, self).__init__(default=default, help=help)

    @property
    def type_params(self):
        return [self.range_type]

    def validate(self, value):
        super(Range, self).validate(value)

        if not (value is None or self.range_type.is_valid(value) and value >= self.start and value <= self.end):
            raise ValueError("expected a value of type %s in range [%s, %s], got %r" % (self.range_type, self.start, self.end, value))

    def __str__(self):
        return "%s(%s, %r, %r)" % (self.__class__.__name__, self.range_type, self.start, self.end)

class Byte(Range):
    ''' Byte type property. '''
    def __init__(self, default=0, help=None):
        super(Byte, self).__init__(Int, 0, 255, default=default, help=help)

class Either(ParameterizedProperty):
    """ Takes a list of valid properties and validates against them in succession. """

    def __init__(self, tp1, tp2, *type_params, **kwargs):
        self._type_params = list(map(self._validate_type_param, (tp1, tp2) + type_params))
        default = kwargs.get("default", self._type_params[0].default)
        help = kwargs.get("help")
        super(Either, self).__init__(default=default, help=help)

    @property
    def type_params(self):
        return self._type_params

    def validate(self, value):
        super(Either, self).validate(value)

        if not (value is None or any(param.is_valid(value) for param in self.type_params)):
            raise ValueError("expected an element of either %s, got %r" % (nice_join(self.type_params), value))

    def transform(self, value):
        for param in self.type_params:
            try:
                return param.transform(value)
            except ValueError:
                pass

        raise ValueError("Could not transform %r" % value)

    def from_json(self, json, models=None):
        for tp in self.type_params:
            try:
                return tp.from_json(json, models)
            except DeserializationError:
                pass
        else:
            raise DeserializationError("%s couldn't deserialize %s" % (self, json))

    def __str__(self):
        return "%s(%s)" % (self.__class__.__name__, ", ".join(map(str, self.type_params)))

    def __or__(self, other):
        return self.__class__(*(self.type_params + [other]), default=self._default, help=self.help)

class Enum(Property):
    """ An Enum with a list of allowed values. The first value in the list is
    the default value, unless a default is provided with the "default" keyword
    argument.
    """
    def __init__(self, enum, *values, **kwargs):
        if not (not values and isinstance(enum, enums.Enumeration)):
            enum = enums.enumeration(enum, *values)

        self.allowed_values = enum._values

        default = kwargs.get("default", enum._default)
        help = kwargs.get("help")
        super(Enum, self).__init__(default=default, help=help)

    def validate(self, value):
        super(Enum, self).validate(value)

        if not (value is None or value in self.allowed_values):
            raise ValueError("invalid value for %s: %r; allowed values are %s" % (self.name, value, nice_join(self.allowed_values)))

    def __str__(self):
        return "%s(%s)" % (self.__class__.__name__, ", ".join(map(repr, self.allowed_values)))

class Auto(Enum):

    def __init__(self):
        super(Auto, self).__init__("auto")

    def __str__(self):
        return self.__class__.__name__

# Properties useful for defining visual attributes
class Color(Either):
    """ Accepts color definition in a variety of ways, and produces an
    appropriate serialization of its value for whatever backend.

    For colors, because we support named colors and hex values prefaced
    with a "#", when we are handed a string value, there is a little
    interpretation: if the value is one of the 147 SVG named colors or
    it starts with a "#", then it is interpreted as a value.

    If a 3-tuple is provided, then it is treated as an RGB (0..255).
    If a 4-tuple is provided, then it is treated as an RGBa (0..255), with
    alpha as a float between 0 and 1.  (This follows the HTML5 Canvas API.)
    """

    def __init__(self, default=None, help=None):
        types = (Enum(enums.NamedColor),
                 Regex("^#[0-9a-fA-F]{6}$"),
                 Tuple(Byte, Byte, Byte),
                 Tuple(Byte, Byte, Byte, Percent))
        super(Color, self).__init__(*types, default=default, help=help)

    def __str__(self):
        return self.__class__.__name__

class Align(Property):
    pass

class DashPattern(Either):
    """
    This is a property that expresses line dashes.  It can be specified in
    a variety of forms:

    * "solid", "dashed", "dotted", "dotdash", "dashdot"
    * A tuple or list of integers in the HTML5 Canvas dash specification
      style: http://www.w3.org/html/wg/drafts/2dcontext/html5_canvas/#dash-list
      Note that if the list of integers has an odd number of elements, then
      it is duplicated, and that duplicated list becomes the new dash list.

    If dash is turned off, then the dash pattern is the empty list [].
    """

    _dash_patterns = {
        "solid": [],
        "dashed": [6],
        "dotted": [2,4],
        "dotdash": [2,4,6,4],
        "dashdot": [6,4,2,4],
    }

    def __init__(self, default=[], help=None):
        types = Enum(enums.DashPattern), Regex(r"^(\d+(\s+\d+)*)?$"), List(Int)
        super(DashPattern, self).__init__(*types, default=default, help=help)

    def transform(self, value):
        value = super(DashPattern, self).transform(value)

        if isinstance(value, string_types):
            try:
                return self._dash_patterns[value]
            except KeyError:
                return [int(x) for x in  value.split()]
        else:
            return value

    def __str__(self):
        return self.__class__.__name__

class Size(Float):
    """ Equivalent to an unsigned int """
    def validate(self, value):
        super(Size, self).validate(value)

        if not (value is None or 0.0 <= value):
            raise ValueError("expected a non-negative number, got %r" % value)

class Percent(Float):
    """ Percent is useful for alphas and coverage and extents; more
    semantically meaningful than Float(0..1)
    """
    def validate(self, value):
        super(Percent, self).validate(value)

        if not (value is None or 0.0 <= value <= 1.0):
            raise ValueError("expected a value in range [0, 1], got %r" % value)

class Angle(Float):
    ''' Angle type property. '''
    pass

class Date(Property):
    ''' Date (not datetime) type property. '''
    def __init__(self, default=datetime.date.today(), help=None):
        super(Date, self).__init__(default=default, help=help)

    def validate(self, value):
        super(Date, self).validate(value)

        if not (value is None or isinstance(value, (datetime.date,) + string_types + (float,) + bokeh_integer_types)):
            raise ValueError("expected a date, string or timestamp, got %r" % value)

    def transform(self, value):
        value = super(Date, self).transform(value)

        if isinstance(value, (float,) + bokeh_integer_types):
            try:
                value = datetime.date.fromtimestamp(value)
            except ValueError:
                value = datetime.date.fromtimestamp(value/1000)
        elif isinstance(value, string_types):
            value = dateutil.parser.parse(value).date()

        return value

class Datetime(Property):
    ''' Datetime type property. '''
    def __init__(self, default=datetime.date.today(), help=None):
        super(Datetime, self).__init__(default=default, help=help)

    def validate(self, value):
        super(Datetime, self).validate(value)

        if (isinstance(value, (datetime.datetime, datetime.date, np.datetime64))):
            return
        try:
            import pandas
            if isinstance(value, (pandas.Timestamp)):
                return
        except ImportError:
            pass

        raise ValueError("Expected a datetime instance, got %r" % value)

    def transform(self, value):
        value = super(Datetime, self).transform(value)
        return value
        # Handled by serialization in protocol.py for now


class RelativeDelta(Dict):
    ''' RelativeDelta type property for time deltas. '''
    def __init__(self, default={}, help=None):
        keys = Enum("years", "months", "days", "hours", "minutes", "seconds", "microseconds")
        values = Int
        super(RelativeDelta, self).__init__(keys, values, default=default, help=help)

    def __str__(self):
        return self.__class__.__name__
