import sys
import typing
from collections.abc import Hashable
from typing import Union, Any, Type, List
from opendp.mod import UnknownTypeException, Measurement, Transformation
from opendp._lib import ATOM_EQUIVALENCE_CLASSES
if sys.version_info >= (3, 7):
from typing import _GenericAlias
else:
from typing import GenericMeta as _GenericAlias
ELEMENTARY_TYPES = {
int: 'i32',
float: 'f64',
str: 'String',
bool: 'bool',
Measurement: 'AnyMeasurementPtr',
Transformation: 'AnyTransformationPtr'
}
try:
import numpy as np
# https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases
ELEMENTARY_TYPES.update({
# np.bytes_: '&[u8]', # np.string_ # not used in OpenDP
np.str_: 'String', # np.unicode_
np.bool8: 'bool', # np.bool_
np.int8: 'i8', # np.byte
np.int16: 'i16', # np.short
np.int32: 'i32', # np.intc
np.int64: 'i64', # np.int_
np.longlong: 'i128',
np.uint8: 'u8', # np.ubyte
np.uint16: 'u16', # np.ushort
np.uint32: 'u32', # np.uintc
np.uint64: 'u64',
np.ulonglong: 'u128',
# np.intp: 'isize', # not used in OpenDP
# np.uintp: 'usize', # an alias for one of np.uint* that would overwrite the respective key
# np.float16: 'f16', # not used in OpenDP
np.float32: 'f32',
np.float64: 'f64', # np.double, np.float_
})
except ImportError:
np = None
# all ways of providing type information
RuntimeTypeDescriptor = Union[
"RuntimeType", # as the normalized type -- ChangeOneDistance; RuntimeType.parse("i32")
_GenericAlias, # a python type hint from the std typing module -- List[int]
str, # plaintext string in terms of rust types -- "Vec<i32>"
Type[Union[typing.List, typing.Tuple, int, float, str, bool]], # using the python type class itself -- int, float
tuple, # shorthand for tuples -- (float, "f64"); (ChangeOneDistance, List[int])
]
[docs]
def set_default_int_type(T: RuntimeTypeDescriptor):
"""Set the default integer type throughout the library.
This function is particularly useful when building computation chains with constructors.
When you build a computation chain, any unspecified integer types default to this int type.
The default int type is i32.
:params T: must be one of [u8, u16, u32, u64, u128, usize, i8, i16, i32, i64, i128, isize]
"""
equivalence_class = ATOM_EQUIVALENCE_CLASSES[ELEMENTARY_TYPES[int]]
assert T in equivalence_class, f"T must be one of {equivalence_class}"
ATOM_EQUIVALENCE_CLASSES[T] = ATOM_EQUIVALENCE_CLASSES.pop(ELEMENTARY_TYPES[int])
ELEMENTARY_TYPES[int] = T
[docs]
def set_default_float_type(T: RuntimeTypeDescriptor):
"""Set the default float type throughout the library.
This function is particularly useful when building computation chains with constructors.
When you build a computation chain, any unspecified float types default to this float type.
The default float type is f64.
:params T: must be one of [f32, f64]
"""
equivalence_class = ATOM_EQUIVALENCE_CLASSES[ELEMENTARY_TYPES[float]]
assert T in equivalence_class, f"T must be a float type in {equivalence_class}"
ATOM_EQUIVALENCE_CLASSES[T] = ATOM_EQUIVALENCE_CLASSES.pop(ELEMENTARY_TYPES[float])
ELEMENTARY_TYPES[float] = T
[docs]
class RuntimeType(object):
"""Utility for validating, manipulating, inferring and parsing/normalizing type information.
"""
def __init__(self, origin, args=None):
if not isinstance(origin, str):
raise ValueError("origin must be a string", origin)
self.origin = origin
self.args = args
def __eq__(self, other):
if isinstance(other, str):
other = RuntimeType.parse(other)
if isinstance(other, str):
return False
return self.origin == other.origin and self.args == other.args
def __str__(self):
result = self.origin or ''
if result == 'Tuple':
return f'({", ".join(map(str, self.args))})'
if self.args:
result += f'<{", ".join(map(str, self.args))}>'
return result
[docs]
@classmethod
def parse(cls, type_name: RuntimeTypeDescriptor, generics: List[str] = None) -> Union["RuntimeType", str]:
"""Parse type descriptor into a normalized rust type.
Type descriptor may be expressed as:
- python type hints from std typing module
- plaintext rust type strings for setting specific bit depth
- python type class - one of {int, str, float, bool}
- tuple of type information - for example: (float, float)
:param type_name: type specifier
:param generics: For internal use. List of type names to consider generic when parsing.
:type: List[str]
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:raises UnknownTypeError: if `type_name` fails to parse
:examples:
>>> from opendp.typing import RuntimeType, L1Distance
>>> assert RuntimeType.parse(int) == "i32"
>>> assert RuntimeType.parse("i32") == "i32"
>>> assert RuntimeType.parse(L1Distance[int]) == "L1Distance<i32>"
>>> assert RuntimeType.parse(L1Distance["f32"]) == "L1Distance<f32>"
"""
generics = generics or []
if isinstance(type_name, RuntimeType):
return type_name
# parse type hints from the typing module
if isinstance(type_name, _GenericAlias):
if sys.version_info < (3, 8):
raise NotImplementedError("parsing type hint annotations are only supported in python 3.8 and above")
origin = typing.get_origin(type_name)
args = [RuntimeType.parse(v, generics=generics) for v in typing.get_args(type_name)] or None
if origin == tuple:
origin = 'Tuple'
if origin == list:
origin = 'Vec'
return RuntimeType(RuntimeType.parse(origin, generics=generics), args)
# parse a tuple of types-- (int, "f64"); (List[int], (int, bool))
if isinstance(type_name, tuple):
return RuntimeType('Tuple', list(cls.parse(v, generics=generics) for v in type_name))
# parse a string-- "Vec<f32>",
if isinstance(type_name, str):
type_name = type_name.strip()
if type_name in generics:
return GenericType(type_name)
if type_name.startswith('(') and type_name.endswith(')'):
return RuntimeType('Tuple', cls._parse_args(type_name[1:-1], generics=generics))
start, end = type_name.find('<'), type_name.rfind('>')
# attempt to upgrade strings to the metric/measure instance
origin = type_name[:start] if 0 < start else type_name
closeness = {
'ChangeOneDistance': ChangeOneDistance,
'SymmetricDistance': SymmetricDistance,
'AbsoluteDistance': AbsoluteDistance,
'L1Distance': L1Distance,
'L2Distance': L2Distance,
'MaxDivergence': MaxDivergence,
'SmoothedMaxDivergence': SmoothedMaxDivergence
}.get(origin)
if closeness is not None:
if isinstance(closeness, (SensitivityMetric, PrivacyMeasure)):
return closeness[cls._parse_args(type_name[start + 1: end], generics=generics)[0]]
return closeness
domain = {
'AllDomain': AllDomain,
'BoundedDomain': BoundedDomain,
'VectorDomain': VectorDomain,
'OptionNullDomain': OptionNullDomain,
'InherentNullDomain': InherentNullDomain,
'SizedDomain': SizedDomain
}.get(origin)
if domain is not None:
return domain[cls._parse_args(type_name[start + 1: end], generics=generics)[0]]
if 0 < start < end < len(type_name):
return RuntimeType(origin, args=cls._parse_args(type_name[start + 1: end], generics=generics))
if start == end < 0:
if type_name == "int":
return ELEMENTARY_TYPES[int]
if type_name == "float":
return ELEMENTARY_TYPES[float]
return type_name
if isinstance(type_name, Hashable) and type_name in ELEMENTARY_TYPES:
return ELEMENTARY_TYPES[type_name]
if type_name == tuple:
raise UnknownTypeException(f"non-parameterized argument")
raise UnknownTypeException(f"unable to parse type: {type_name}")
@classmethod
def _parse_args(cls, args, generics=None):
import re
return [cls.parse(v, generics=generics) for v in re.split(r",\s*(?![^()<>]*\))", args)]
[docs]
@classmethod
def infer(cls, public_example: Any) -> Union["RuntimeType", str]:
"""Infer the normalized type from a public example.
:param public_example: data used to infer the type
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:raises UnknownTypeException: if inference fails on `public_example`
:examples:
>>> from opendp.typing import RuntimeType, L1Distance
>>> assert RuntimeType.infer(23) == "i32"
>>> assert RuntimeType.infer(12.) == "f64"
>>> assert RuntimeType.infer(["A", "B"]) == "Vec<String>"
>>> assert RuntimeType.infer((12., True, "A")) == "(f64, bool,String)" # eq doesn't care about whitespace
"""
if type(public_example) in ELEMENTARY_TYPES:
return ELEMENTARY_TYPES[type(public_example)]
if isinstance(public_example, tuple):
return RuntimeType('Tuple', list(map(cls.infer, public_example)))
if isinstance(public_example, list):
if public_example:
inner_type = cls.infer(public_example[0])
for inner in public_example[1:]:
other_type = cls.infer(inner)
if other_type != inner_type:
raise TypeError(f"vectors must be homogeneously typed, found {inner_type} and {other_type}")
else:
inner_type = UnknownType("cannot infer atomic type of empty list")
return RuntimeType('Vec', [inner_type])
if np is not None and isinstance(public_example, np.ndarray):
if public_example.ndim == 0:
return cls.infer(public_example.item())
if public_example.ndim == 1:
inner_type = ELEMENTARY_TYPES.get(public_example.dtype.type)
if inner_type is None:
raise UnknownTypeException(f"Unknown numpy array dtype: {public_example.dtype.type}")
return RuntimeType('Vec', [inner_type])
raise UnknownTypeException("arrays with greater than one axis are not yet supported")
if isinstance(public_example, dict):
return RuntimeType('HashMap', [
cls.infer(next(iter(public_example.keys()))),
cls.infer(next(iter(public_example.values())))
])
if isinstance(public_example, Measurement):
return "AnyMeasurementPtr"
if isinstance(public_example, Transformation):
return "AnyTransformationPtr"
if public_example is None:
return RuntimeType('Option', [UnknownType("Constructed Option from a None variant")])
raise UnknownTypeException(type(public_example))
[docs]
@classmethod
def parse_or_infer(
cls,
type_name: RuntimeTypeDescriptor = None,
public_example: Any = None,
generics: List[str] = None
) -> Union["RuntimeType", str]:
"""If type_name is supplied, normalize it. Otherwise, infer the normalized type from a public example.
:param type_name: type specifier. See RuntimeType.parse for documentation on valid inputs
:param public_example: data used to infer the type
:return: Normalized type. If the type has subtypes, returns a RuntimeType, else a str.
:rtype: Union["RuntimeType", str]
:param generics: For internal use. List of type names to consider generic when parsing.
:type: List[str]
:raises ValueError: if `type_name` fails to parse
:raises UnknownTypeException: if inference fails on `public_example` or no args are supplied
"""
if type_name is not None:
return cls.parse(type_name, generics)
if public_example is not None:
return cls.infer(public_example)
raise UnknownTypeException("either type_name or public_example must be passed")
[docs]
@classmethod
def assert_is_similar(cls, expected, inferred):
"""Assert that `inferred` is a member of the same equivalence class as `parsed`.
:param expected: the type that the data will be converted to
:param inferred: the type inferred from data
:raises TypeError: if `expected` type differs significantly from `inferred` type
"""
ERROR_URL_298 = "https://github.com/opendp/opendp/discussions/298"
if isinstance(inferred, UnknownType):
return
if isinstance(expected, str) and isinstance(inferred, str):
if inferred in ATOM_EQUIVALENCE_CLASSES:
if expected not in ATOM_EQUIVALENCE_CLASSES[inferred]:
raise TypeError(f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}")
else:
if expected != inferred:
raise TypeError(f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}")
elif isinstance(expected, RuntimeType) and isinstance(inferred, RuntimeType):
# allow extra flexibility around options, as the inferred type of an Option::<T>::Some will just be T
if expected.origin == "Option" and inferred.origin != "Option":
expected = expected.args[0]
if expected.origin != inferred.origin:
raise TypeError(f"inferred type is {inferred.origin}, expected {expected.origin}. See {ERROR_URL_298}")
if len(expected.args) != len(inferred.args):
raise TypeError(f"inferred type has {len(inferred.args)} arg(s), expected {len(expected.args)} arg(s). See {ERROR_URL_298}")
for (arg_par, arg_inf) in zip(expected.args, inferred.args):
RuntimeType.assert_is_similar(arg_par, arg_inf)
else:
# inferred type differs in structure
raise TypeError(f"inferred type is {inferred}, expected {expected}. See {ERROR_URL_298}")
[docs]
def substitute(self, **kwargs):
if isinstance(self, GenericType):
return kwargs.get(self.origin, self)
if isinstance(self, RuntimeType):
return RuntimeType(self.origin, self.args and [RuntimeType.substitute(arg, **kwargs) for arg in self.args])
return self
[docs]
class GenericType(RuntimeType):
def __str__(self):
raise UnknownTypeException(f"attempted to create a type_name with an unknown generic: {self.origin}")
[docs]
class UnknownType(RuntimeType):
"""Indicator for a type that cannot be inferred. Typically the atomic type of an empty list.
RuntimeTypes containing UnknownType cannot be used in FFI, but still pass RuntimeType.assert_is_similar
"""
def __init__(self, reason):
self.origin = None
self.args = []
self.reason = reason
def __str__(self):
raise UnknownTypeException(f"attempted to create a type_name with an unknown type: {self.reason}")
[docs]
class DatasetMetric(RuntimeType):
"""All dataset metric RuntimeTypes inherit from DatasetMetric.
Provides static type checking in user-code for dataset metrics.
"""
pass
SymmetricDistance = DatasetMetric('SymmetricDistance')
InsertDeleteDistance = DatasetMetric('InsertDeleteDistance')
ChangeOneDistance = DatasetMetric('ChangeOneDistance')
HammingDistance = DatasetMetric('HammingDistance')
[docs]
class SensitivityMetric(RuntimeType):
"""All sensitivity RuntimeTypes inherit from SensitivityMetric.
Provides static type checking in user-code for sensitivity metrics and a getitem interface like stdlib typing.
"""
def __getitem__(self, associated_type):
return SensitivityMetric(self.origin, [self.parse(type_name=associated_type)])
AbsoluteDistance = SensitivityMetric('AbsoluteDistance')
L1Distance = SensitivityMetric('L1Distance')
L2Distance = SensitivityMetric('L2Distance')
[docs]
class PrivacyMeasure(RuntimeType):
"""All measure RuntimeTypes inherit from PrivacyMeasure.
Provides static type checking in user-code for privacy measures and a getitem interface like stdlib typing.
"""
def __getitem__(self, associated_type):
return PrivacyMeasure(self.origin, [self.parse(type_name=associated_type)])
MaxDivergence = PrivacyMeasure('MaxDivergence')
SmoothedMaxDivergence = PrivacyMeasure('SmoothedMaxDivergence')
ZeroConcentratedDivergence = PrivacyMeasure('ZeroConcentratedDivergence')
[docs]
class Carrier(RuntimeType):
def __getitem__(self, subdomains):
if not isinstance(subdomains, tuple):
subdomains = (subdomains,)
return Carrier(self.origin, [self.parse(type_name=subdomain) for subdomain in subdomains])
Vec = Carrier('Vec')
HashMap = Carrier('HashMap')
i8 = 'i8'
i16 = 'i16'
i32 = 'i32'
i64 = 'i64'
i128 = 'i128'
isize = 'isize'
u8 = 'u8'
u16 = 'u16'
u32 = 'u32'
u64 = 'u64'
u128 = 'u128'
usize = 'usize'
f32 = 'f32'
f64 = 'f64'
[docs]
class Domain(RuntimeType):
def __getitem__(self, subdomain):
return Domain(self.origin, [self.parse(type_name=subdomain)])
AllDomain = Domain('AllDomain')
BoundedDomain = Domain('BoundedDomain')
VectorDomain = Domain('VectorDomain')
OptionNullDomain = Domain('OptionNullDomain')
InherentNullDomain = Domain('InherentNullDomain')
SizedDomain = Domain('SizedDomain')
[docs]
def get_atom(type_name):
type_name = RuntimeType.parse(type_name)
while isinstance(type_name, RuntimeType):
if isinstance(type_name, (UnknownType, GenericType)):
return
type_name = type_name.args[0]
return type_name
[docs]
def get_atom_or_infer(type_name: RuntimeType, example):
return get_atom(type_name) or RuntimeType.infer(example)
[docs]
def get_first(value):
return value[0]
