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"""
This module provides a way to work with and enumerate implementation configurations.
"""
from dataclasses import dataclass
from enum import Enum
from itertools import product
from typing import get_type_hints, Union, get_origin, get_args, Generator, FrozenSet
from public import public
from .coordinates import CoordinateModel
from .formula import Formula
from .model import CurveModel
from .mult import ScalarMultiplier
@public
class EnumDefine(Enum):
def __str__(self):
return self.value
def __repr__(self):
return self.value
@classmethod
def names(cls):
return list(e.name for e in cls)
@classmethod
def values(cls):
return list(e.value for e in cls)
@public
class Multiplication(EnumDefine):
"""Base multiplication algorithm to use."""
TOOM_COOK = "MUL_TOOM_COOK"
KARATSUBA = "MUL_KARATSUBA"
COMBA = "MUL_COMBA"
BASE = "MUL_BASE"
@public
class Squaring(EnumDefine):
"""Base squaring algorithm to use."""
TOOM_COOK = "SQR_TOOM_COOK"
KARATSUBA = "SQR_KARATSUBA"
COMBA = "SQR_COMBA"
BASE = "SQR_BASE"
@public
class Reduction(EnumDefine):
"""Modular reduction method used."""
BARRETT = "RED_BARRETT"
MONTGOMERY = "RED_MONTGOMERY"
BASE = "RED_BASE"
@public
class Inversion(EnumDefine):
"""Inversion algorithm used."""
GCD = "INV_GCD"
EULER = "INV_EULER"
@public
class HashType(EnumDefine):
"""Hash algorithm used in ECDH and ECDSA."""
NONE = "HASH_NONE"
SHA1 = "HASH_SHA1"
SHA224 = "HASH_SHA224"
SHA256 = "HASH_SHA256"
SHA384 = "HASH_SHA384"
SHA512 = "HASH_SHA512"
@public
class RandomMod(EnumDefine):
"""Method of sampling a uniform integer modulo order."""
SAMPLE = "MOD_RAND_SAMPLE"
REDUCE = "MOD_RAND_REDUCE"
@public
@dataclass(frozen=True)
class Configuration(object):
"""An ECC implementation configuration."""
model: CurveModel
coords: CoordinateModel
formulas: FrozenSet[Formula]
scalarmult: ScalarMultiplier
hash_type: HashType
mod_rand: RandomMod
mult: Multiplication
sqr: Squaring
red: Reduction
inv: Inversion
@public
def all_configurations(**kwargs) -> Generator[Configuration, Configuration, None]:
"""
Get all implementation configurations that match the given `kwargs`.
The keys in `kwargs` should be some of the attributes in the :py:class:`Configuration`,
and the values limit the returned configurations to configuration matching them.
.. note::
The `formulas` attribute is unsupported and formulas should be provided using the `scalarmult`
attribute, which is either a subclass of the :py:class:`ScalarMultiplier` class or an instance
of it or a dictionary giving arguments to a constructor of some :py:class:`ScalarMultiplier`
subclass.
.. warning::
The returned number of configurations might be quite large and take up significant
memory space.
:param kwargs: The configuration parameters to match.
:return: A generator of the configurations
"""
def is_optional(arg_type):
return get_origin(arg_type) == Union and len(get_args(arg_type)) == 2 and \
get_args(arg_type)[1] == type(None) # noqa
def leaf_subclasses(cls):
subs = cls.__subclasses__()
result = []
for subclass in subs:
if subclass.__subclasses__():
result.extend(leaf_subclasses(subclass))
else:
result.append(subclass)
return result
def independents(kwargs):
options = {
"hash_type": HashType,
"mod_rand": RandomMod,
"mult": Multiplication,
"sqr": Squaring,
"red": Reduction,
"inv": Inversion
}
keys = list(filter(lambda key: key not in kwargs, options.keys()))
values = [options[key] for key in keys]
fixed_args = {key: kwargs[key] for key in kwargs if key in options}
for value_choice in product(*values):
yield dict(zip(keys, value_choice), **fixed_args)
def multipliers(mult_classes, coords_formulas, fixed_args=None):
for mult_cls in mult_classes:
if fixed_args is not None and "cls" in fixed_args and mult_cls != fixed_args["cls"]:
continue
arg_options = {}
for name, required_type in get_type_hints(mult_cls.__init__).items():
if fixed_args is not None and name in fixed_args:
arg_options[name] = [fixed_args[name]]
continue
if is_optional(required_type):
opt_type = get_args(required_type)[0]
if issubclass(opt_type, Formula):
options = [formula for formula in coords_formulas if
isinstance(formula, opt_type)] + [None]
else:
options = [None] # TODO: anything here?
elif get_origin(required_type) is None and issubclass(required_type, Formula):
options = [formula for formula in coords_formulas if
isinstance(formula, required_type)]
elif get_origin(required_type) is None and issubclass(required_type, bool):
options = [True, False]
elif get_origin(required_type) is None and issubclass(required_type,
int) and name == "width":
options = [3, 5]
else:
options = []
arg_options[name] = options
keys = arg_options.keys()
values = arg_options.values()
for combination in product(*values):
try:
mult = mult_cls(**dict(zip(keys, combination)))
except Exception:
continue
yield mult
for model_cls in leaf_subclasses(CurveModel):
model = model_cls()
if "model" in kwargs:
if model != kwargs["model"]:
continue
for coords_name, coords in model.coordinates.items():
if "coords" in kwargs:
if coords != kwargs["coords"]:
continue
coords_formulas = coords.formulas.values()
mult_classes = leaf_subclasses(ScalarMultiplier)
if "scalarmult" in kwargs:
if isinstance(kwargs["scalarmult"], ScalarMultiplier):
mults = [kwargs["scalarmult"]]
if not set(kwargs["scalarmult"].formulas.values()).issubset(coords_formulas):
continue
elif isinstance(kwargs["scalarmult"], type) and issubclass(kwargs["scalarmult"],
ScalarMultiplier):
mult_classes = list(
filter(lambda mult: issubclass(mult, kwargs["scalarmult"]),
mult_classes))
mults = multipliers(mult_classes, coords_formulas)
else:
mults = multipliers(mult_classes, coords_formulas, kwargs["scalarmult"])
else:
mults = multipliers(mult_classes, coords_formulas)
for mult in mults:
formulas = frozenset(mult.formulas.values())
for independent_args in independents(kwargs):
yield Configuration(model, coords, formulas, mult, **independent_args)
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