"""
Data Object Restrictions.
:date_created: 2019-05-13
"""

import copy
import sys
from abc import ABCMeta, abstractmethod, abstractproperty

from builtins import object
from datetime import date, datetime
from future.moves import builtins
from future.types import newint, newlist, newstr
from future.utils import with_metaclass
from past.builtins import long, unicode

from ..abc import ABCRestrictionMeta
from ..exceptions import RestrictionError
from ..utils import classproperty


class AbstractRestriction(tuple):
    """
    Manage restriction syntax. Pull allowed and default values for the tuple syntax.

    E.g.
    For R.INT, allowed = [int] and default = None

    Syntax:
    Restrictions are typically declared as tuples. The first item in the restriction is allowed. The second item is
    the default value. See specific restriction for more details.

    allowed:
    Data restrictions are captured as allowed. See specific restriction for more details.

    default:
    Every restriction provides a default value. If not explicitly specified, it is implicitly set to None.
    """
    _default = None
    _allowed = None

    def __init__(self, *args, **kwargs):
        """
        default and allowed are provided as tuple.
        """
        if len(self) > 1:
            self._default = self[1]

        self._allowed = self[0]

    def __call__(self, data, **kwargs):
        """
        Execute data validation. See specific restriction for more details.
        """

    def with_default(self, default):
        """
        w: short for `with_default`
        Add/update the default of an existing restriction.
        :param default: The new default for this cls.
        :return: cls.__class__
        """
        return self.__class__(self._allowed, default)

    @property
    def allowed(self):
        return self._allowed

    @property
    def default(self):
        return self._default

    # NOTE:
    # Opportunity 1: ES restriction building capability can be triggered from es_ables.
    # Opportunity 2: The encoder system can be abstracted to support multiple encoding needs (including custom).
    # Possibility to share restrictions between python and JS.
    # Techs similar to ES may use different encoding systems.
    #
    # E.g. default json package in python supports custom encoding.
    #   json.loads(x, cls=CustomEncoder)
    #
    # See https://docs.python.org/2/library/json.html for more details on this example. ComplexEncoder is custom
    # encoder.
    #
    # import json
    # >>> class ComplexEncoder(json.JSONEncoder):
    # ...     def default(self, obj):
    # ...         if isinstance(obj, complex):
    # ...             return [obj.real, obj.imag]
    # ...         # Let the base class default method raise the TypeError
    # ...         return json.JSONEncoder.default(self, obj)
    # ...
    # >>> json.dumps(2 + 1j, cls=ComplexEncoder)
    # '[2.0, 1.0]'
    # >>> ComplexEncoder().encode(2 + 1j)
    # '[2.0, 1.0]'
    # >>> list(ComplexEncoder().iterencode(2 + 1j))
    # ['[', '2.0', ', ', '1.0', ']']
    @property
    def es_restrictions(self):
        raise NotImplementedError('ES restrictions not defined.')

    def __copy__(self):
        return tuple(self)

    def __deepcopy__(self, memodict={}):
        """
        NOTE: `deepcopy` uses memoization to store a previously copied instance.
        """
        _r = copy.deepcopy(self.__copy__())
        instance = self.__class__(_r[0], default=_r[1])
        memodict[id(self)] = instance
        return instance


def is_immutable(obj):
    return any(isinstance(obj, x) for x in [bool, int, float, tuple, str, frozenset, type(None)])


class SingletonRestriction(AbstractRestriction):
    """
    This is an interface for Restriction type to use singleton structure. The objective is to use pre-defined
    restrictions and reduce the memory footprint of DataObject declarations.
    """
    _cache = {}

    def __new__(cls, restriction_tuple):
        """
        This will hash and stash this restriction to allow a singleton instance to be used throughout the entire system
        per system instance.
        :param restriction_tuple: (allowed, default)
        :type restriction_tuple: tuple
        :rtype: SingletonRestriction
        """
        try:
            rt1 = None
            if is_immutable(restriction_tuple[1]):
                rt1 = restriction_tuple[1]
            elif type(restriction_tuple[1]) in [datetime, date]:
                # date and datetime instances are mutable. They are not iterable, hence not hashable.
                rt1 = restriction_tuple[1].isoformat()
            else:
                rt1 = frozenset(restriction_tuple[1])

            # cls.__name__ supports restriction inheritance, i.e. _NullableDataObjectRestriction
            if type(restriction_tuple[0]) is ABCRestrictionMeta:
                hashable = (cls.__name__, restriction_tuple[0])
            else:
                hashable = (cls.__name__, frozenset(restriction_tuple[0]), rt1)
        except TypeError:
            raise RestrictionError.from_unhashable(restriction_tuple[0], restriction_tuple[1])

        if hashable in cls._cache:
            return cls._cache[hashable]
        else:
            cls._cache[hashable] = super(SingletonRestriction, cls).__new__(cls, restriction_tuple)
            return cls._cache[hashable]

    @property
    def es_restrictions(self):
        raise NotImplementedError('ES restrictions not defined.')


class _ListTypeRestriction(SingletonRestriction):
    """
    Manage restriction of syntax ([type], None)

    Syntax:
    This uses standard tuple syntax.

    allowed:
    allowed is declared as a list of types.

    default:
    None if not specified.

    Validation:
    The type of supplied data must be an item in the allowed list.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R.INT
            }

    Here, x can be numbers like 1, 1.0, -3, etc.
    Good for some data like pulsechecks sent (x >= 0).
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListTypeRestriction, cls).__new__(cls, (allowed, default))

    def __init__(self, *args, **kwargs):
        super(_ListTypeRestriction, self).__init__()
        if len([e for e in self._allowed if type(e) is ABCRestrictionMeta]) != 0:
            raise RestrictionError.from_dataobj_in_rstr_list(self._allowed)

        if not all([isinstance(r, type) for r in self._allowed]):
            raise RestrictionError.from_mixed_value_and_type(self._allowed)

    def __call__(self, data, **kwargs):
        if type(data) not in self._allowed:
            raise RestrictionError.bad_data(type(data), self._allowed)
        return data

    @property
    def es_restrictions(self):
        _x = set()
        for e in self.allowed:
            if e not in [None, type(None)]:
                for i in ESEncoder.default(e).items():
                    _x.add(i)
        if len(_x) == 1:
            return dict([_x.pop()])
        raise RestrictionError('Ambiguous ES restricitons.')


class _ListValueRestriction(SingletonRestriction):
    """
    Manage restriction of syntax ([values], None)

    Syntax:
    This uses standard tuple syntax.

    allowed:
    allowed is declared as a list of values.

    default:
    None if not specified.

    Validation:
    Supplied data must be an item in the allowed list.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R.INT
            }

    Here, x can be numbers like 1, 2 and 3. However, numbers like 1.0, -3, etc are not allowed. Typically, this is
    more restrictive than _ListTypeRestriction.

    Good choice for some data such as Review ratings (1 <= ratings <= 5).
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListValueRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, **kwargs):
        if data not in self._allowed:
            raise RestrictionError.bad_data(data, self._allowed)
        return data

    @property
    def es_restrictions(self):
        return ESEncoder.default('keyword')


class _ListNoRestriction(SingletonRestriction):
    """
    Manage restriction of syntax R()

    Syntax:
    Empty list for allowed.

    allowed:
    allowed is declared as an empty list.

    default:
    None if not specified.

    Validation:
    Any data is allowed. This is the most flexible restriction.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R()
            }

    Here, x can be primitive data types like numbers, strings, boolean, etc.
    It can also be more complex data such as dict, list, set, classes, instances, etc.

    Good choice for data where no validation is necessary.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListNoRestriction, cls).__new__(cls, (allowed, default))

    def __init__(self, *args, **kwargs):
        super(_ListNoRestriction, self).__init__()
        assert isinstance(self._allowed, list) and len(self._allowed) == 0, 'Complain'

    def __call__(self, data, **kwargs):
        return data

    @property
    def es_restrictions(self):
        return None


class _MgdRestRestriction(AbstractRestriction):
    """
    Manages restriction of type ManagedRestrictions.

    Syntax:
    Specified as an instance of ManagedRestrictions class. See example below.

    allowed:
    allowed is an instance of ManagedRestrictions class.

    default:
    None by default. User can take-over default management in their implementation of ManagedRestrictions.

    Validation:
    Validation is defined by user in ManagedRestrictions class, more specifically, manage method.

    E.g.:

    class ManagedX(ManagedRestrictions):
        _restriction = R.INT

        def manage(self):
            assert self.data % 2, 'Even numbers not allowed'

    class A(DataObject):
        _restrictions = {
            'x': ManagedX()
            }

    In this example, we allow odd numbers for x. Even numbers are not allowed.
    Good choice for custom data validation.

    For more information on ManagedRestrictions, see ManagedRestrictions class.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_MgdRestRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, **kwargs):
        return self._allowed(data)

    @property
    def es_restrictions(self):
        # NOTE: _allowed is of ManagedRestrictions type. _restriction is of AbstractRestriction type
        return self._allowed._restriction.es_restrictions


class _NullableDataObjectRestriction(SingletonRestriction):
    """
    Manage restriction of type [DataObject, type(None)].

    Syntax:
    Specified as a list containing a class reference to a DO and type(None). See example below.

    allowed:
    allowed is a list containing a class reference to a DO and type(None).

    default:
    None by default.

    Validation:
    Validation is performed by the DataObject. None is allowed.

    E.g.:

    class B(DataObject):
        _restrictions = {
            'x': R.INT
            }

    class A(DataObject):
        _restrictions = {
            'b': B
            }

    In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.
    A value of None is also allowed for 'b'.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_NullableDataObjectRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, strict=True, **kwargs):
        if data is None:
            return data
        elif strict and not isinstance(data, dict):
            raise RestrictionError.bad_data(data, self._allowed)
        return self._allowed(data=data, strict=strict)

    @property
    def dataobj(self):
        return self.allowed

    @property
    def es_restrictions(self):
        if hasattr(self.dataobj, 'es_restrictions'):
            return self.dataobj.es_restrictions
        else:
            return {
                'properties': {
                    k: v.es_restrictions for k, v in self.dataobj._restrictions.items()
                    }
                }


class _DataObjectRestriction(_NullableDataObjectRestriction):
    """
    Manages restriction of type DataObject.

    Syntax:
    Specified as a class refence to a DO. See example below.

    allowed:
    allowed is a class reference to a DO.

    default:
    None by default.

    Validation:
    Validation is performed by the DataObject.

    E.g.:

    class B(DataObject):
        _restrictions = {
            'x': R.INT
            }

    class A(DataObject):
        _restrictions = {
            'b': B
            }

    In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.
    Complex data requires data nesting. This typically requires nested DOs, which is supported by this restriction.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        default = allowed(strict=False)
        return super(_DataObjectRestriction, cls).__new__(cls, allowed, default=default)

    def __call__(self, data, strict=True, **kwargs):
        if type(data) is self._allowed:
            return data
        return self._allowed(data=data, strict=strict)


class Restriction(object):
    """
    Restriction factory which manages restriction delegation.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        """
        :param allowed: Allowed per restrictions.
        :type allowed: list or ManagedRestrictions or ABCRestrictionMeta
        :param default: Default value.
        """
        if type(default) is type:
            raise RestrictionError.from_invalid_default_value(default)
        if isinstance(allowed, ManagedRestrictions):
            return _MgdRestRestriction(allowed, default=allowed.default, **kwargs)
        elif type(allowed) in [list, newlist]:
            if len(allowed) == 0:
                return _ListNoRestriction(allowed, default=default, **kwargs)
            elif len(allowed) == 2 and ABCRestrictionMeta in [type(e) for e in allowed]:
                if type(None) not in allowed:
                    raise RestrictionError.from_unsupported_dataobj_in_rstr_list(allowed)
                return _NullableDataObjectRestriction(allowed[1 - allowed.index(type(None))], default=default, **kwargs)
            elif any([isinstance(r, type) for r in allowed]):
                return _ListTypeRestriction(allowed, default=default, **kwargs)
            else:
                return _ListValueRestriction(allowed, default=default, **kwargs)
        elif type(allowed) is ABCRestrictionMeta:
            return _DataObjectRestriction(allowed, default=default, **kwargs)
        else:
            raise RestrictionError.from_unsupported(allowed)

    def __init__(cls, allowed, default=None, **kwargs):
        """
        Dummy init to support classmethod(s)
        """

    @classmethod
    def legacy(cls, declaration):
        """
        Support of legacy tuple syntax.

        Legacy syntax:
        In current usage of DataObjects, restrictions declarations do not use the specific restriction classes. E.g.,
        ([int, float], None)

        Restriction class syntax:
        The example above can be also be declared using restriction class, i.e.,
        _ListTypeRestriction([int, float], default=None)

        Vision:
        2019-06-06:
        As of this day, the plan is to expose the restrictions classes using simplied syntax. Example,

            R(int, default=1)

        Also, there is a plan to collect the most common restrictions into a STL (standard template library). Example,

            In STL,

            R_int = R([int, float])

            In user code,

            class A(DataObject):
                _restrictions = {
                    'x': R.INT
                    }

        :param declaration: Legacy tuple syntax
        :type declaration: tuple or list or ManagedRestrictions or ABCRestrictionMeta
        """
        # Python 3 format
        if isinstance(declaration, AbstractRestriction):
            # NOTE: This is already a restriction instance, so nothing to do here.
            return declaration
        elif isinstance(declaration, ABCRestrictionMeta) or isinstance(declaration, ManagedRestrictions):
            return cls(declaration)
        elif type(declaration) is list:
            if any([isinstance(r, type) for r in declaration]):
                raise RestrictionError.from_unsupported(declaration)
            return cls(declaration)
        elif type(declaration) is tuple:
            # Legacy format
            raise RestrictionError.from_unsupported(declaration)
        else:
            return cls(declaration)


class ManagedRestrictions(object, with_metaclass(ABCMeta)):
    """
    Useful for managing complex data validations for restrictions in DataObject. E.g.
    1. Regular expression validation such as review URL matches expected template.
    2. Phone number validation and standardization such that number is in ISO format and is SMS enabled.

    DataObject understands that restrictions can be mapped to classes inherited from ManagedRestrictions. When
    DataObject encounters a ManagedRestriction, a call to manage method is made. Managed restriction value is stored in
    data attribute.

    Example:
    A toy example to demonstrate usage. 'name' is a key for Contact and it is managed by Name class. Functionally, the
    Name class ensures that key 'name' is always in title case.

    class Name(ManagedRestrictions):
        _restriction = R.STR

        def manage(self):
            # validation
            assert self.data, 'Invalid name'
            # standardization
            self.data = self.data.title()

    class Contact(DataObject):
        _restrictions: {
            'name': Name(),
            }

    :attribute manage: users must implement validation/standardization logic in manage
    """
    data = None

    def __new__(cls, *args, **kwargs):
        cls._restriction = Restriction.legacy(cls._restriction)
        return super(ManagedRestrictions, cls).__new__(cls)

    @abstractproperty
    def _restriction(self):
        """
        Specify restriction for the key this class manages. Follow the same format as _restrictions in DataObject.
        """

    @abstractmethod
    def manage(self):
        """
        Data management logic is handled in this method. This can be used to standardize and/or validate data. Users of
        this class must implement this method. The value to manage can be found in self.data. It is best practice to
        raise exception if data fails validation.
        """

    @property
    def default(self):
        """
        Expose restriction default.
        """
        return self._restriction.default

    def __call__(self, value, strict=True):
        """
        Entry point for data management. DataObject will trigger a call to __call__ when it encounters a managed
        restriction.
        :param value: Data value that needs to be managed
        :param strict: Validation strictness.
        :return: Standardized data
        """
        data_copy = copy.deepcopy(self.data)
        try:
            self.data = value
            if strict:
                self.manage()
        except Exception:
            self.data = data_copy
            raise

        return self.data

    def __eq__(self, other):
        return self._restriction == other._restriction


# NOTE: ESR should be refactored to common.py, but OOS for now due to circular imports with ESEncoder
class ESR(object):
    """
    Constants class housing the different types of ES restrictions.
    """
    INT = {'type': 'integer'}
    FLOAT = {'type': 'float'}
    DATE = {'type': 'date'}
    BOOL = {'type': 'boolean'}
    STR = {'type': 'text'}
    KEYWORD = {'type': 'keyword'}

    @staticmethod
    def OBJECT(nested_data_object):
        """
        Object-type ElasticSearch restrictions. This restriction defines a nested object on ES mapping.
        :type nested_data_object: DataObject
        :rtype: dict
        """
        if not hasattr(nested_data_object, 'es_restrictions'):
            raise RestrictionError.bad_data(nested_data_object, 'DataObject')
        return {
            'type': 'object',
            'properties': nested_data_object.es_restrictions
            }

    @staticmethod
    def NESTED(nested_data_object):
        """
        Nested-type ElasticSearh restriction. This restriction defines a list of nested objects on ES mapping.
        :type nested_data_object: DataObject
        :rtype: dict
        """
        if not hasattr(nested_data_object, 'es_restrictions'):
            raise RestrictionError.bad_data(nested_data_object, 'DataObject')
        return {
            'type': 'nested',
            'properties': nested_data_object.es_restrictions
            }


class ESEncoder(object):
    """
    Convert restrictions into ES Document Property Map
    Ref: https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html
    """

    @classproperty
    def encoding(cls):
        """
        :rtype: dict
        """
        encoding = {
            int: ESR.INT,
            long: ESR.INT,
            builtins.int: ESR.INT,
            float: ESR.FLOAT,
            builtins.float: ESR.FLOAT,
            datetime: ESR.DATE,
            date: ESR.DATE,
            bool: ESR.BOOL,
            str: ESR.STR,
            builtins.str: ESR.STR,
            unicode: ESR.STR,
            'keyword': ESR.KEYWORD,
            # list: {},
            }
        if sys.version_info.major == 3:
            encoding.update({
                newint.newint: ESR.INT,
                newstr.newstr: ESR.STR
                })
        return encoding

    @classmethod
    def default(cls, obj):
        try:
            return cls.encoding[obj]
        except KeyError:
            raise RestrictionError('%s cannot be encoded!' % obj)

"""
Data Object Restrictions.
:date_created: 2019-05-13
"""

import copy
import sys
from abc import ABCMeta, abstractmethod, abstractproperty

from builtins import object
from datetime import date, datetime
from future.moves import builtins
from future.types import newint, newlist, newstr
from future.utils import with_metaclass
from past.builtins import long, unicode

from ..abc import ABCRestrictionMeta
from ..exceptions import RestrictionError
from ..utils import classproperty


class AbstractRestriction(tuple):
    """
    Manage restriction syntax. Pull allowed and default values for the tuple syntax.

    E.g.
    For R.INT, allowed = [int] and default = None

    Syntax:
    Restrictions are typically declared as tuples. The first item in the restriction is allowed. The second item is
    the default value. See specific restriction for more details.

    allowed:
    Data restrictions are captured as allowed. See specific restriction for more details.

    default:
    Every restriction provides a default value. If not explicitly specified, it is implicitly set to None.
    """
    _default = None
    _allowed = None

    def __init__(self, *args, **kwargs):
        """
        default and allowed are provided as tuple.
        """
        if len(self) > 1:
            self._default = self[1]

        self._allowed = self[0]

    def __call__(self, data, **kwargs):
        """
        Execute data validation. See specific restriction for more details.
        """

    def with_default(self, default):
        """
        w: short for `with_default`
        Add/update the default of an existing restriction.
        :param default: The new default for this cls.
        :return: cls.__class__
        """
        return self.__class__(self._allowed, default)

    @property
    def allowed(self):
        return self._allowed

    @property
    def default(self):
        return self._default

    # NOTE:
    # Opportunity 1: ES restriction building capability can be triggered from es_ables.
    # Opportunity 2: The encoder system can be abstracted to support multiple encoding needs (including custom).
    # Possibility to share restrictions between python and JS.
    # Techs similar to ES may use different encoding systems.
    #
    # E.g. default json package in python supports custom encoding.
    #   json.loads(x, cls=CustomEncoder)
    #
    # See https://docs.python.org/2/library/json.html for more details on this example. ComplexEncoder is custom
    # encoder.
    #
    # import json
    # >>> class ComplexEncoder(json.JSONEncoder):
    # ...     def default(self, obj):
    # ...         if isinstance(obj, complex):
    # ...             return [obj.real, obj.imag]
    # ...         # Let the base class default method raise the TypeError
    # ...         return json.JSONEncoder.default(self, obj)
    # ...
    # >>> json.dumps(2 + 1j, cls=ComplexEncoder)
    # '[2.0, 1.0]'
    # >>> ComplexEncoder().encode(2 + 1j)
    # '[2.0, 1.0]'
    # >>> list(ComplexEncoder().iterencode(2 + 1j))
    # ['[', '2.0', ', ', '1.0', ']']
    @property
    def es_restrictions(self):
        raise NotImplementedError('ES restrictions not defined.')

    def __copy__(self):
        return tuple(self)

    def __deepcopy__(self, memodict={}):
        """
        NOTE: `deepcopy` uses memoization to store a previously copied instance.
        """
        _r = copy.deepcopy(self.__copy__())
        instance = self.__class__(_r[0], default=_r[1])
        memodict[id(self)] = instance
        return instance


def is_immutable(obj):
    return any(isinstance(obj, x) for x in [bool, int, float, tuple, str, frozenset, type(None)])


class SingletonRestriction(AbstractRestriction):
    """
    This is an interface for Restriction type to use singleton structure. The objective is to use pre-defined
    restrictions and reduce the memory footprint of DataObject declarations.
    """
    _cache = {}

    def __new__(cls, restriction_tuple):
        """
        This will hash and stash this restriction to allow a singleton instance to be used throughout the entire system
        per system instance.
        :param restriction_tuple: (allowed, default)
        :type restriction_tuple: tuple
        :rtype: SingletonRestriction
        """
        try:
            rt1 = None
            if is_immutable(restriction_tuple[1]):
                rt1 = restriction_tuple[1]
            elif type(restriction_tuple[1]) in [datetime, date]:
                # date and datetime instances are mutable. They are not iterable, hence not hashable.
                rt1 = restriction_tuple[1].isoformat()
            else:
                rt1 = frozenset(restriction_tuple[1])

            # cls.__name__ supports restriction inheritance, i.e. _NullableDataObjectRestriction
            if type(restriction_tuple[0]) is ABCRestrictionMeta:
                hashable = (cls.__name__, restriction_tuple[0])
            else:
                hashable = (cls.__name__, frozenset(restriction_tuple[0]), rt1)
        except TypeError:
            raise RestrictionError.from_unhashable(restriction_tuple[0], restriction_tuple[1])

        if hashable in cls._cache:
            return cls._cache[hashable]
        else:
            cls._cache[hashable] = super(SingletonRestriction, cls).__new__(cls, restriction_tuple)
            return cls._cache[hashable]

    @property
    def es_restrictions(self):
        raise NotImplementedError('ES restrictions not defined.')


class _ListTypeRestriction(SingletonRestriction):
    """
    Manage restriction of syntax ([type], None)

    Syntax:
    This uses standard tuple syntax.

    allowed:
    allowed is declared as a list of types.

    default:
    None if not specified.

    Validation:
    The type of supplied data must be an item in the allowed list.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R.INT
            }

    Here, x can be numbers like 1, 1.0, -3, etc.
    Good for some data like pulsechecks sent (x >= 0).
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListTypeRestriction, cls).__new__(cls, (allowed, default))

    def __init__(self, *args, **kwargs):
        super(_ListTypeRestriction, self).__init__()
        if len([e for e in self._allowed if type(e) is ABCRestrictionMeta]) != 0:
            raise RestrictionError.from_dataobj_in_rstr_list(self._allowed)

        if not all([isinstance(r, type) for r in self._allowed]):
            raise RestrictionError.from_mixed_value_and_type(self._allowed)

    def __call__(self, data, **kwargs):
        if type(data) not in self._allowed:
            raise RestrictionError.bad_data(type(data), self._allowed)
        return data

    @property
    def es_restrictions(self):
        _x = set()
        for e in self.allowed:
            if e not in [None, type(None)]:
                for i in ESEncoder.default(e).items():
                    _x.add(i)
        if len(_x) == 1:
            return dict([_x.pop()])
        raise RestrictionError('Ambiguous ES restricitons.')


class _ListValueRestriction(SingletonRestriction):
    """
    Manage restriction of syntax ([values], None)

    Syntax:
    This uses standard tuple syntax.

    allowed:
    allowed is declared as a list of values.

    default:
    None if not specified.

    Validation:
    Supplied data must be an item in the allowed list.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R.INT
            }

    Here, x can be numbers like 1, 2 and 3. However, numbers like 1.0, -3, etc are not allowed. Typically, this is
    more restrictive than _ListTypeRestriction.

    Good choice for some data such as Review ratings (1 <= ratings <= 5).
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListValueRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, **kwargs):
        if data not in self._allowed:
            raise RestrictionError.bad_data(data, self._allowed)
        return data

    @property
    def es_restrictions(self):
        return ESEncoder.default('keyword')


class _ListNoRestriction(SingletonRestriction):
    """
    Manage restriction of syntax R()

    Syntax:
    Empty list for allowed.

    allowed:
    allowed is declared as an empty list.

    default:
    None if not specified.

    Validation:
    Any data is allowed. This is the most flexible restriction.

    E.g.:

    class A(DataObject):
        _restrictions = {
            'x': R()
            }

    Here, x can be primitive data types like numbers, strings, boolean, etc.
    It can also be more complex data such as dict, list, set, classes, instances, etc.

    Good choice for data where no validation is necessary.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_ListNoRestriction, cls).__new__(cls, (allowed, default))

    def __init__(self, *args, **kwargs):
        super(_ListNoRestriction, self).__init__()
        assert isinstance(self._allowed, list) and len(self._allowed) == 0, 'Complain'

    def __call__(self, data, **kwargs):
        return data

    @property
    def es_restrictions(self):
        return None


class _MgdRestRestriction(AbstractRestriction):
    """
    Manages restriction of type ManagedRestrictions.

    Syntax:
    Specified as an instance of ManagedRestrictions class. See example below.

    allowed:
    allowed is an instance of ManagedRestrictions class.

    default:
    None by default. User can take-over default management in their implementation of ManagedRestrictions.

    Validation:
    Validation is defined by user in ManagedRestrictions class, more specifically, manage method.

    E.g.:

    class ManagedX(ManagedRestrictions):
        _restriction = R.INT

        def manage(self):
            assert self.data % 2, 'Even numbers not allowed'

    class A(DataObject):
        _restrictions = {
            'x': ManagedX()
            }

    In this example, we allow odd numbers for x. Even numbers are not allowed.
    Good choice for custom data validation.

    For more information on ManagedRestrictions, see ManagedRestrictions class.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_MgdRestRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, **kwargs):
        return self._allowed(data)

    @property
    def es_restrictions(self):
        # NOTE: _allowed is of ManagedRestrictions type. _restriction is of AbstractRestriction type
        return self._allowed._restriction.es_restrictions


class _NullableDataObjectRestriction(SingletonRestriction):
    """
    Manage restriction of type [DataObject, type(None)].

    Syntax:
    Specified as a list containing a class reference to a DO and type(None). See example below.

    allowed:
    allowed is a list containing a class reference to a DO and type(None).

    default:
    None by default.

    Validation:
    Validation is performed by the DataObject. None is allowed.

    E.g.:

    class B(DataObject):
        _restrictions = {
            'x': R.INT
            }

    class A(DataObject):
        _restrictions = {
            'b': B
            }

    In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.
    A value of None is also allowed for 'b'.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        return super(_NullableDataObjectRestriction, cls).__new__(cls, (allowed, default))

    def __call__(self, data, strict=True, **kwargs):
        if data is None:
            return data
        elif strict and not isinstance(data, dict):
            raise RestrictionError.bad_data(data, self._allowed)
        return self._allowed(data=data, strict=strict)

    @property
    def dataobj(self):
        return self.allowed

    @property
    def es_restrictions(self):
        if hasattr(self.dataobj, 'es_restrictions'):
            return self.dataobj.es_restrictions
        else:
            return {
                'properties': {
                    k: v.es_restrictions for k, v in self.dataobj._restrictions.items()
                    }
                }


class _DataObjectRestriction(_NullableDataObjectRestriction):
    """
    Manages restriction of type DataObject.

    Syntax:
    Specified as a class refence to a DO. See example below.

    allowed:
    allowed is a class reference to a DO.

    default:
    None by default.

    Validation:
    Validation is performed by the DataObject.

    E.g.:

    class B(DataObject):
        _restrictions = {
            'x': R.INT
            }

    class A(DataObject):
        _restrictions = {
            'b': B
            }

    In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.
    Complex data requires data nesting. This typically requires nested DOs, which is supported by this restriction.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        default = allowed(strict=False)
        return super(_DataObjectRestriction, cls).__new__(cls, allowed, default=default)

    def __call__(self, data, strict=True, **kwargs):
        if type(data) is self._allowed:
            return data
        return self._allowed(data=data, strict=strict)


class Restriction(object):
    """
    Restriction factory which manages restriction delegation.
    """

    def __new__(cls, allowed, default=None, **kwargs):
        """
        :param allowed: Allowed per restrictions.
        :type allowed: list or ManagedRestrictions or ABCRestrictionMeta
        :param default: Default value.
        """
        if type(default) is type:
            raise RestrictionError.from_invalid_default_value(default)
        if isinstance(allowed, ManagedRestrictions):
            return _MgdRestRestriction(allowed, default=allowed.default, **kwargs)
        elif type(allowed) in [list, newlist]:
            if len(allowed) == 0:
                return _ListNoRestriction(allowed, default=default, **kwargs)
            elif len(allowed) == 2 and ABCRestrictionMeta in [type(e) for e in allowed]:
                if type(None) not in allowed:
                    raise RestrictionError.from_unsupported_dataobj_in_rstr_list(allowed)
                return _NullableDataObjectRestriction(allowed[1 - allowed.index(type(None))], default=default, **kwargs)
            elif any([isinstance(r, type) for r in allowed]):
                return _ListTypeRestriction(allowed, default=default, **kwargs)
            else:
                return _ListValueRestriction(allowed, default=default, **kwargs)
        elif type(allowed) is ABCRestrictionMeta:
            return _DataObjectRestriction(allowed, default=default, **kwargs)
        else:
            raise RestrictionError.from_unsupported(allowed)

    def __init__(cls, allowed, default=None, **kwargs):
        """
        Dummy init to support classmethod(s)
        """

    @classmethod
    def legacy(cls, declaration):
        """
        Support of legacy tuple syntax.

        Legacy syntax:
        In current usage of DataObjects, restrictions declarations do not use the specific restriction classes. E.g.,
        ([int, float], None)

        Restriction class syntax:
        The example above can be also be declared using restriction class, i.e.,
        _ListTypeRestriction([int, float], default=None)

        Vision:
        2019-06-06:
        As of this day, the plan is to expose the restrictions classes using simplied syntax. Example,

            R(int, default=1)

        Also, there is a plan to collect the most common restrictions into a STL (standard template library). Example,

            In STL,

            R_int = R([int, float])

            In user code,

            class A(DataObject):
                _restrictions = {
                    'x': R.INT
                    }

        :param declaration: Legacy tuple syntax
        :type declaration: tuple or list or ManagedRestrictions or ABCRestrictionMeta
        """
        # Python 3 format
        if isinstance(declaration, AbstractRestriction):
            # NOTE: This is already a restriction instance, so nothing to do here.
            return declaration
        elif isinstance(declaration, ABCRestrictionMeta) or isinstance(declaration, ManagedRestrictions):
            return cls(declaration)
        elif type(declaration) is list:
            if any([isinstance(r, type) for r in declaration]):
                raise RestrictionError.from_unsupported(declaration)
            return cls(declaration)
        elif type(declaration) is tuple:
            # Legacy format
            raise RestrictionError.from_unsupported(declaration)
        else:
            return cls(declaration)


class ManagedRestrictions(object, with_metaclass(ABCMeta)):
    """
    Useful for managing complex data validations for restrictions in DataObject. E.g.
    1. Regular expression validation such as review URL matches expected template.
    2. Phone number validation and standardization such that number is in ISO format and is SMS enabled.

    DataObject understands that restrictions can be mapped to classes inherited from ManagedRestrictions. When
    DataObject encounters a ManagedRestriction, a call to manage method is made. Managed restriction value is stored in
    data attribute.

    Example:
    A toy example to demonstrate usage. 'name' is a key for Contact and it is managed by Name class. Functionally, the
    Name class ensures that key 'name' is always in title case.

    class Name(ManagedRestrictions):
        _restriction = R.STR

        def manage(self):
            # validation
            assert self.data, 'Invalid name'
            # standardization
            self.data = self.data.title()

    class Contact(DataObject):
        _restrictions: {
            'name': Name(),
            }

    :attribute manage: users must implement validation/standardization logic in manage
    """
    data = None

    def __new__(cls, *args, **kwargs):
        cls._restriction = Restriction.legacy(cls._restriction)
        return super(ManagedRestrictions, cls).__new__(cls)

    @abstractproperty
    def _restriction(self):
        """
        Specify restriction for the key this class manages. Follow the same format as _restrictions in DataObject.
        """

    @abstractmethod
    def manage(self):
        """
        Data management logic is handled in this method. This can be used to standardize and/or validate data. Users of
        this class must implement this method. The value to manage can be found in self.data. It is best practice to
        raise exception if data fails validation.
        """

    @property
    def default(self):
        """
        Expose restriction default.
        """
        return self._restriction.default

    def __call__(self, value, strict=True):
        """
        Entry point for data management. DataObject will trigger a call to __call__ when it encounters a managed
        restriction.
        :param value: Data value that needs to be managed
        :param strict: Validation strictness.
        :return: Standardized data
        """
        data_copy = copy.deepcopy(self.data)
        try:
            self.data = value
            if strict:
                self.manage()
        except Exception:
            self.data = data_copy
            raise

        return self.data

    def __eq__(self, other):
        return self._restriction == other._restriction


# NOTE: ESR should be refactored to common.py, but OOS for now due to circular imports with ESEncoder
class ESR(object):
    """
    Constants class housing the different types of ES restrictions.
    """
    INT = {'type': 'integer'}
    FLOAT = {'type': 'float'}
    DATE = {'type': 'date'}
    BOOL = {'type': 'boolean'}
    STR = {'type': 'text'}
    KEYWORD = {'type': 'keyword'}

    @staticmethod
    def OBJECT(nested_data_object):
        """
        Object-type ElasticSearch restrictions. This restriction defines a nested object on ES mapping.
        :type nested_data_object: DataObject
        :rtype: dict
        """
        if not hasattr(nested_data_object, 'es_restrictions'):
            raise RestrictionError.bad_data(nested_data_object, 'DataObject')
        return {
            'type': 'object',
            'properties': nested_data_object.es_restrictions
            }

    @staticmethod
    def NESTED(nested_data_object):
        """
        Nested-type ElasticSearh restriction. This restriction defines a list of nested objects on ES mapping.
        :type nested_data_object: DataObject
        :rtype: dict
        """
        if not hasattr(nested_data_object, 'es_restrictions'):
            raise RestrictionError.bad_data(nested_data_object, 'DataObject')
        return {
            'type': 'nested',
            'properties': nested_data_object.es_restrictions
            }


class ESEncoder(object):
    """
    Convert restrictions into ES Document Property Map
    Ref: https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html
    """

    @classproperty
    def encoding(cls):
        """
        :rtype: dict
        """
        encoding = {
            int: ESR.INT,
            long: ESR.INT,
            builtins.int: ESR.INT,
            float: ESR.FLOAT,
            builtins.float: ESR.FLOAT,
            datetime: ESR.DATE,
            date: ESR.DATE,
            bool: ESR.BOOL,
            str: ESR.STR,
            builtins.str: ESR.STR,
            unicode: ESR.STR,
            'keyword': ESR.KEYWORD,
            # list: {},
            }
        if sys.version_info.major == 3:
            encoding.update({
                newint.newint: ESR.INT,
                newstr.newstr: ESR.STR
                })
        return encoding

    @classmethod
    def default(cls, obj):
        try:
            return cls.encoding[obj]
        except KeyError:
            raise RestrictionError('%s cannot be encoded!' % obj)

Data Object Restrictions. :date_created: 2019-05-13 Manage restriction syntax. Pull allowed and default values for the tuple syntax.      E.g.     For R.INT, allowed = [int] and default = None      Syntax:     Restrictions are typically declared as tuples. The first item in the restriction is allowed. The second item is     the default value. See specific restriction for more details.      allowed:     Data restrictions are captured as allowed. See specific restriction for more details.      default:     Every restriction provides a default value. If not explicitly specified, it is implicitly set to None. default and allowed are provided as tuple. Execute data validation. See specific restriction for more details. w: short for `with_default`         Add/update the default of an existing restriction.         :param default: The new default for this cls.         :return: cls.__class__ # NOTE: # Opportunity 1: ES restriction building capability can be triggered from es_ables. # Opportunity 2: The encoder system can be abstracted to support multiple encoding needs (including custom). # Possibility to share restrictions between python and JS. # Techs similar to ES may use different encoding systems. # # E.g. default json package in python supports custom encoding. #   json.loads(x, cls=CustomEncoder) # # See https://docs.python.org/2/library/json.html for more details on this example. ComplexEncoder is custom # encoder. # # import json # >>> class ComplexEncoder(json.JSONEncoder): # ...     def default(self, obj): # ...         if isinstance(obj, complex): # ...             return [obj.real, obj.imag] # ...         # Let the base class default method raise the TypeError # ...         return json.JSONEncoder.default(self, obj) # ... # >>> json.dumps(2 + 1j, cls=ComplexEncoder) # '[2.0, 1.0]' # >>> ComplexEncoder().encode(2 + 1j) # '[2.0, 1.0]' # >>> list(ComplexEncoder().iterencode(2 + 1j)) # ['[', '2.0', ', ', '1.0', ']'] NOTE: `deepcopy` uses memoization to store a previously copied instance. This is an interface for Restriction type to use singleton structure. The objective is to use pre-defined     restrictions and reduce the memory footprint of DataObject declarations. This will hash and stash this restriction to allow a singleton instance to be used throughout the entire system         per system instance.         :param restriction_tuple: (allowed, default)         :type restriction_tuple: tuple         :rtype: SingletonRestriction # date and datetime instances are mutable. They are not iterable, hence not hashable. # cls.__name__ supports restriction inheritance, i.e. _NullableDataObjectRestriction Manage restriction of syntax ([type], None)      Syntax:     This uses standard tuple syntax.      allowed:     allowed is declared as a list of types.      default:     None if not specified.      Validation:     The type of supplied data must be an item in the allowed list.      E.g.:      class A(DataObject):         _restrictions = {             'x': R.INT             }      Here, x can be numbers like 1, 1.0, -3, etc.     Good for some data like pulsechecks sent (x >= 0). Manage restriction of syntax ([values], None)      Syntax:     This uses standard tuple syntax.      allowed:     allowed is declared as a list of values.      default:     None if not specified.      Validation:     Supplied data must be an item in the allowed list.      E.g.:      class A(DataObject):         _restrictions = {             'x': R.INT             }      Here, x can be numbers like 1, 2 and 3. However, numbers like 1.0, -3, etc are not allowed. Typically, this is     more restrictive than _ListTypeRestriction.      Good choice for some data such as Review ratings (1 <= ratings <= 5). Manage restriction of syntax R()      Syntax:     Empty list for allowed.      allowed:     allowed is declared as an empty list.      default:     None if not specified.      Validation:     Any data is allowed. This is the most flexible restriction.      E.g.:      class A(DataObject):         _restrictions = {             'x': R()             }      Here, x can be primitive data types like numbers, strings, boolean, etc.     It can also be more complex data such as dict, list, set, classes, instances, etc.      Good choice for data where no validation is necessary. Manages restriction of type ManagedRestrictions.      Syntax:     Specified as an instance of ManagedRestrictions class. See example below.      allowed:     allowed is an instance of ManagedRestrictions class.      default:     None by default. User can take-over default management in their implementation of ManagedRestrictions.      Validation:     Validation is defined by user in ManagedRestrictions class, more specifically, manage method.      E.g.:      class ManagedX(ManagedRestrictions):         _restriction = R.INT          def manage(self):             assert self.data % 2, 'Even numbers not allowed'      class A(DataObject):         _restrictions = {             'x': ManagedX()             }      In this example, we allow odd numbers for x. Even numbers are not allowed.     Good choice for custom data validation.      For more information on ManagedRestrictions, see ManagedRestrictions class. # NOTE: _allowed is of ManagedRestrictions type. _restriction is of AbstractRestriction type Manage restriction of type [DataObject, type(None)].      Syntax:     Specified as a list containing a class reference to a DO and type(None). See example below.      allowed:     allowed is a list containing a class reference to a DO and type(None).      default:     None by default.      Validation:     Validation is performed by the DataObject. None is allowed.      E.g.:      class B(DataObject):         _restrictions = {             'x': R.INT             }      class A(DataObject):         _restrictions = {             'b': B             }      In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.     A value of None is also allowed for 'b'. Manages restriction of type DataObject.      Syntax:     Specified as a class refence to a DO. See example below.      allowed:     allowed is a class reference to a DO.      default:     None by default.      Validation:     Validation is performed by the DataObject.      E.g.:      class B(DataObject):         _restrictions = {             'x': R.INT             }      class A(DataObject):         _restrictions = {             'b': B             }      In this example, the value of key 'b' in A._restrictions will be validated by class B, which is also a DataObject.     Complex data requires data nesting. This typically requires nested DOs, which is supported by this restriction. Restriction factory which manages restriction delegation. :param allowed: Allowed per restrictions.         :type allowed: list or ManagedRestrictions or ABCRestrictionMeta         :param default: Default value. Dummy init to support classmethod(s) Support of legacy tuple syntax.          Legacy syntax:         In current usage of DataObjects, restrictions declarations do not use the specific restriction classes. E.g.,         ([int, float], None)          Restriction class syntax:         The example above can be also be declared using restriction class, i.e.,         _ListTypeRestriction([int, float], default=None)          Vision:         2019-06-06:         As of this day, the plan is to expose the restrictions classes using simplied syntax. Example,              R(int, default=1)          Also, there is a plan to collect the most common restrictions into a STL (standard template library). Example,              In STL,              R_int = R([int, float])              In user code,              class A(DataObject):                 _restrictions = {                     'x': R.INT                     }          :param declaration: Legacy tuple syntax         :type declaration: tuple or list or ManagedRestrictions or ABCRestrictionMeta # Python 3 format # NOTE: This is already a restriction instance, so nothing to do here. # Legacy format Useful for managing complex data validations for restrictions in DataObject. E.g.     1. Regular expression validation such as review URL matches expected template.     2. Phone number validation and standardization such that number is in ISO format and is SMS enabled.      DataObject understands that restrictions can be mapped to classes inherited from ManagedRestrictions. When     DataObject encounters a ManagedRestriction, a call to manage method is made. Managed restriction value is stored in     data attribute.      Example:     A toy example to demonstrate usage. 'name' is a key for Contact and it is managed by Name class. Functionally, the     Name class ensures that key 'name' is always in title case.      class Name(ManagedRestrictions):         _restriction = R.STR          def manage(self):             # validation             assert self.data, 'Invalid name'             # standardization             self.data = self.data.title()      class Contact(DataObject):         _restrictions: {             'name': Name(),             }      :attribute manage: users must implement validation/standardization logic in manage Specify restriction for the key this class manages. Follow the same format as _restrictions in DataObject. Data management logic is handled in this method. This can be used to standardize and/or validate data. Users of         this class must implement this method. The value to manage can be found in self.data. It is best practice to         raise exception if data fails validation. Expose restriction default. Entry point for data management. DataObject will trigger a call to __call__ when it encounters a managed         restriction.         :param value: Data value that needs to be managed         :param strict: Validation strictness.         :return: Standardized data # NOTE: ESR should be refactored to common.py, but OOS for now due to circular imports with ESEncoder Constants class housing the different types of ES restrictions. Object-type ElasticSearch restrictions. This restriction defines a nested object on ES mapping.         :type nested_data_object: DataObject         :rtype: dict Nested-type ElasticSearh restriction. This restriction defines a list of nested objects on ES mapping.         :type nested_data_object: DataObject         :rtype: dict Convert restrictions into ES Document Property Map     Ref: https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html :rtype: dict # list: {},