def _binary_quadratic_model_sampler(f, *args, **kw):
# convert into a sequence if necessary
if isinstance(which_args, int):
iter_args = (which_args,)
else:
iter_args = iter(which_args)
# check each sampler for the correct methods
new_args = [arg for arg in args]
for idx in iter_args:
sampler = args[idx]
# if no sampler is provided, get the default sampler if it has
# been set
if sampler is None:
# this sampler has already been vetted
default_sampler = dnx.get_default_sampler()
if default_sampler is None:
raise dnx.DWaveNetworkXMissingSampler('no default sampler set')
new_args[idx] = default_sampler
continue
if not hasattr(sampler, "sample_qubo") or not callable(sampler.sample_qubo):
raise TypeError("expected sampler to have a 'sample_qubo' method")
if not hasattr(sampler, "sample_ising") or not callable(sampler.sample_ising):
raise TypeError("expected sampler to have a 'sample_ising' method")
# now run the function and return the results
return f(*new_args, **kw)
def func(*args, **kwargs):
bound_arguments = inspect.signature(f).bind(*args, **kwargs)
bound_arguments.apply_defaults()
args = bound_arguments.args
kw = bound_arguments.kwargs
if isinstance(which_args, int):
iter_args = (which_args, )
else:
iter_args = iter(which_args)
# check each sampler for the correct methods
new_args = [arg for arg in args]
for idx in iter_args:
sampler = args[idx]
# if no sampler is provided, get the default sampler if it has
# been set
if sampler is None:
# this sampler has already been vetted
default_sampler = dnx.get_default_sampler()
if default_sampler is None:
raise dnx.DWaveNetworkXMissingSampler(
'no default sampler set')
new_args[idx] = default_sampler
continue
if not hasattr(sampler, "sample_qubo") or not callable(
sampler.sample_qubo):
raise TypeError(
"expected sampler to have a 'sample_qubo' method")
if not hasattr(sampler, "sample_ising") or not callable(
sampler.sample_ising):
raise TypeError(
"expected sampler to have a 'sample_ising' method")
# now run the function and return the results
return f(*new_args, **kw)