def cast_bound_arguments_to_real(bound_arguments: BoundArguments,
signature: Signature):
"""Cast the requested parameters to simple types.
Args:
bound_arguments: Incoming parameters.
signature: Signature of method.
"""
# loop all arguments
for key, value in bound_arguments.arguments.items():
# get type of parameter
annotation = signature.parameters[key].annotation
# special cases
if value is None or annotation == Parameter.empty or annotation == Any:
# if value is None or no annotation is given, just copy it
bound_arguments.arguments[key] = value
elif annotation == Enum:
# cast to enum
bound_arguments.arguments[
key] = value if annotation == Parameter.empty else annotation(
value)
elif annotation == str:
# unescape strings
bound_arguments.arguments[key] = xml.sax.saxutils.unescape(value)
else:
# cast to type
bound_arguments.arguments[key] = annotation(value)
def signature_key(bound_args: inspect.BoundArguments,
prefix: str,
only: Optional[Iterable] = None,
exclude: Optional[Iterable] = None,
**generate_kwargs) -> str:
"""Generate a cache key based on function arguments for use with
Redis of the form:
'<prefix><sep><hashed_args>'
:param bound_args: function signature bounded arguments
:param prefix: unhashed prefix of the cache key
:param only: optional whitelist of arguments to include in cache key
:param exclude: optional blacklist of arguments to exclude in cache key
:param generate_kwargs: keyword arguments passed to ``generate_key``
"""
only = None if only is None else set(only)
exclude = None if exclude is None else set(exclude)
bound_args.apply_defaults()
objects = (obj for argname, value in bound_args.arguments.items()
for obj in [argname, value]
if (only is None or argname in only) and (
exclude is None or argname not in exclude))
return generate_key(prefix, objects, **generate_kwargs)
def cast_bound_arguments_to_simple(bound_arguments: BoundArguments):
"""Cast the requested parameters, which are of simple types, to the types required by the method.
Args:
bound_arguments: Incoming parameters.
"""
# loop all arguments
for key, value in bound_arguments.arguments.items():
# special cases
if isinstance(value, str):
# escape strings
bound_arguments.arguments[key] = xml.sax.saxutils.escape(value)
elif isinstance(value, Enum):
# get value of enum
bound_arguments.arguments[key] = value.value
def compile_model(
context: keras.TFKerasContext,
compile_args: inspect.BoundArguments,
env: det.EnvContext,
hvd_config: horovod.HorovodContext,
) -> None:
context.model = keras._get_multi_gpu_model_if_using_native_parallel(
pre_compiled_model=context.model,
env=env,
hvd_config=hvd_config,
)
if "optimizer" in compile_args.arguments:
# For backwards compatibility we check if an optimizer is passed as part
# of the compile call. If `wrap_optimizer()` is used, we will ignore this
# this optimizer.
compile_args.arguments[
"optimizer"] = context._process_optimizer_from_compile(
compile_args.arguments["optimizer"])
if hvd_config.use and version.parse("2.0.0") <= version.parse(
tf.__version__) < version.parse("2.2.0"):
logging.info(
"Calling `model.compile(...)` with `experimental_run_tf_function=False` to ensure "
"TensorFlow calls `optimizer.get_gradients()` to compute gradients."
)
context.model.compile(*compile_args.args,
**compile_args.kwargs,
experimental_run_tf_function=False)
else:
context.model.compile(*compile_args.args, **compile_args.kwargs)
def compile_model(
context: keras.TFKerasContext,
compile_args: inspect.BoundArguments,
env: det.EnvContext,
hvd_config: horovod.HorovodContext,
) -> None:
if "optimizer" in compile_args.arguments:
# For backwards compatibility we check if an optimizer is passed as part
# of the compile call. If `wrap_optimizer()` is used, we will ignore this
# this optimizer.
compile_args.arguments[
"optimizer"] = context._process_optimizer_from_compile(
compile_args.arguments["optimizer"])
# context.model is Optional[Model]. This assert signals to mypy it can't
# be none because we check that in `from_trial`.
assert context.model is not None
if hvd_config.use and version.parse("2.0.0") <= version.parse(
tf.__version__) < version.parse("2.2.0"):
logging.info(
"Calling `model.compile(...)` with `experimental_run_tf_function=False` to ensure "
"TensorFlow calls `optimizer.get_gradients()` to compute gradients."
)
context.model.compile(*compile_args.args,
**compile_args.kwargs,
experimental_run_tf_function=False)
else:
context.model.compile(*compile_args.args, **compile_args.kwargs)
def recreate_system(
ba: inspect.BoundArguments,
particles: Particles,
converter: (nbody_system.nbody_to_si, None) = None,
):
"""Reinitialize a System made with `initialize_system`.
Parameters
----------
ba : `inspect.BoundArguments`
particles : `Particles`
should have everything, including for evolution
converter : `generic_unit_converter`
needed if 'converter' not in `ba.kwargs`
if provided, will overwrite built-in, if exists
Notes
-----
A good way to do this is to have `particles` be the set with all the
up-to-date information. Then there is no need to pass `evolution` or
`gravity`.
"""
# change number_of_particles arg to the `particles` set
# this will then initialize the system off the existing particles
# skipping the IMF and distribution function steps
ba = copy.copy(ba)
ba.arguments["number_of_particles"] = particles
if converter is not None:
ba.arguments["converter"] = converter
# make the system
system = initialize_system(*ba.args, store_inputs=False, **ba.kwargs)
return system
def _coerce_arguments(
func: Callable[..., Any],
ba: inspect.BoundArguments) -> inspect.BoundArguments:
args = OrderedDict()
annotations = {}
if hasattr(func, "__annotations__"):
annotations = func.__annotations__
for key, val in ba.arguments.items():
annotation = annotations.get(key, None)
if annotation is not None and annotation == Millisatoshi:
args[key] = Millisatoshi(val)
else:
args[key] = val
ba.arguments = args
return ba
def _bind(thesignature, args, kwargs, *, partial):
"""Private method. Don't use directly."""
arguments = OrderedDict()
parameters = iter(thesignature.parameters.values())
parameters_ex = ()
arg_vals = iter(args)
# These are added for `unpythonic`.
unbound_parameters = []
extra_args = []
extra_kwargs = OrderedDict()
kwargs = copy.copy(kwargs) # the caller might need the original later
while True:
# Let's iterate through the positional arguments and corresponding
# parameters
try:
arg_val = next(arg_vals)
except StopIteration:
# No more positional arguments
try:
param = next(parameters)
except StopIteration:
# No more parameters. That's it. Just need to check that
# we have no `kwargs` after this while loop
break
else:
if param.kind == Parameter.VAR_POSITIONAL:
# That's OK, just empty *args. Let's start parsing
# kwargs
break
elif param.name in kwargs:
if param.kind == Parameter.POSITIONAL_ONLY:
msg = '{arg!r} parameter is positional only, ' \
'but was passed as a keyword'
msg = msg.format(arg=param.name)
raise TypeError(msg) from None
parameters_ex = (param, )
break
elif (param.kind == Parameter.VAR_KEYWORD
or param.default is not _empty):
# That's fine too - we have a default value for this
# parameter. So, lets start parsing `kwargs`, starting
# with the current parameter
parameters_ex = (param, )
break
else:
# No default, not VAR_KEYWORD, not VAR_POSITIONAL,
# not in `kwargs`
if partial:
parameters_ex = (param, )
break
else:
# msg = 'missing a required argument: {arg!r}'
# msg = msg.format(arg=param.name)
# raise TypeError(msg) from None
unbound_parameters.append(param)
else:
# We have a positional argument to process
try:
param = next(parameters)
except StopIteration:
# raise TypeError('too many positional arguments') from None
extra_args.append(arg_val)
else:
if param.kind in (Parameter.VAR_KEYWORD,
Parameter.KEYWORD_ONLY):
# Looks like we have no parameter for this positional
# argument
# raise TypeError(
# 'too many positional arguments') from None
extra_args.append(arg_val)
if param.kind == Parameter.VAR_POSITIONAL:
# We have an '*args'-like argument, let's fill it with
# all positional arguments we have left and move on to
# the next phase
values = [arg_val]
values.extend(arg_vals)
arguments[param.name] = tuple(values)
break
if param.name in kwargs and param.kind != Parameter.POSITIONAL_ONLY:
raise TypeError(
'multiple values for argument {arg!r}'.format(
arg=param.name)) from None
arguments[param.name] = arg_val
# Now, we iterate through the remaining parameters to process
# keyword arguments
kwargs_param = None
for param in itertools.chain(parameters_ex, parameters):
if param.kind == Parameter.VAR_KEYWORD:
# Memorize that we have a '**kwargs'-like parameter
kwargs_param = param
continue
if param.kind == Parameter.VAR_POSITIONAL:
# Named arguments don't refer to '*args'-like parameters.
# We only arrive here if the positional arguments ended
# before reaching the last parameter before *args.
continue
param_name = param.name
try:
arg_val = kwargs.pop(param_name)
except KeyError:
# We have no value for this parameter. It's fine though,
# if it has a default value, or it is an '*args'-like
# parameter, left alone by the processing of positional
# arguments.
if (not partial and param.kind != Parameter.VAR_POSITIONAL
and param.default is _empty):
# raise TypeError('missing a required argument: {arg!r}'.
# format(arg=param_name)) from None
unbound_parameters.append(param)
else:
if param.kind == Parameter.POSITIONAL_ONLY:
# This should never happen in case of a properly built
# Signature object (but let's have this check here
# to ensure correct behaviour just in case)
raise TypeError(
'{arg!r} parameter is positional only, '
'but was passed as a keyword'.format(arg=param.name))
arguments[param_name] = arg_val
if kwargs:
if kwargs_param is not None:
# Process our '**kwargs'-like parameter
arguments[kwargs_param.name] = kwargs
else:
# raise TypeError(
# 'got an unexpected keyword argument {arg!r}'.format(
# arg=next(iter(kwargs))))
extra_kwargs.update(kwargs)
return (BoundArguments(thesignature, arguments), tuple(unbound_parameters),
(tuple(extra_args), extra_kwargs))