def apply_to_backend_request(self,
block: Block,
level: Tuple[Factor, Union[SimpleLevel, DerivedLevel]],
backend_request: BackendRequest
) -> None:
sublists = self._build_variable_sublists(block, level, self.k)
implications = []
# Handle the regular cases (1 => 2 ^ ... ^ n ^ ~n+1)
trim = len(sublists) if self.k > 1 else len(sublists) - 1
for idx, l in enumerate(sublists[:trim]):
if idx > 0:
p_list = [Not(sublists[idx-1][0]), l[0]]
p = And(p_list) if len(p_list) > 1 else p_list[0]
else:
p = l[0]
if idx < len(sublists) - 1:
q_list = cast(List[Any], l[1:]) + [Not(sublists[idx+1][-1])]
q = And(q_list) if len(q_list) > 1 else q_list[0]
else:
q = And(l[1:]) if len(l[1:]) > 1 else l[self.k - 1]
implications.append(If(p, q))
# Handle the tail
if len(sublists[-1]) > 1:
tail = sublists[-1]
tail.reverse()
for idx in range(len(tail) - 1):
implications.append(If(l[idx], l[idx + 1]))
(cnf, new_fresh) = block.cnf_fn(And(implications), backend_request.fresh)
backend_request.cnfs.append(cnf)
backend_request.fresh = new_fresh
def __apply_derivation(self, block: Block, backend_request: BackendRequest) -> None:
trial_size = block.variables_per_trial()
cross_size = block.trials_per_sample()
iffs = []
for n in range(cross_size):
or_clause = Or(list(And(list(map(lambda x: x + (n * trial_size) + 1, l))) for l in self.dependent_idxs))
iffs.append(Iff(self.derived_idx + (n * trial_size) + 1, or_clause))
(cnf, new_fresh) = block.cnf_fn(And(iffs), backend_request.fresh)
backend_request.cnfs.append(cnf)
backend_request.fresh = new_fresh
def apply_to_backend_request(self, block: Block, level: Tuple[Factor, Union[SimpleLevel, DerivedLevel]],
backend_request: BackendRequest) -> None:
# Request sublists for k+1 to allow us to determine the transition
sublists = self._build_variable_sublists(block, level, self.k + 1)
implications = []
if sublists:
# Starting corner case
implications.append(If(sublists[0][0], And(sublists[0][1:-1])))
for sublist in sublists:
implications.append(If(And([Not(sublist[0]), sublist[1]]), And(sublist[2:])))
# Ending corner case
implications.append(If(sublists[-1][-1], And(sublists[-1][1:-1])))
(cnf, new_fresh) = block.cnf_fn(And(implications), backend_request.fresh)
backend_request.cnfs.append(cnf)
backend_request.fresh = new_fresh
def __apply_derivation_with_complex_window(self, block: Block, backend_request: BackendRequest) -> None:
trial_size = block.variables_per_trial()
trial_count = block.trials_per_sample()
iffs = []
f = self.factor
window = f.levels[0].window
t = 0
for n in range(trial_count):
if not f.applies_to_trial(n + 1):
continue
num_levels = len(f.levels)
get_trial_size = lambda x: trial_size if x < block.grid_variables() else len(block.decode_variable(x+1)[0].levels)
or_clause = Or(list(And(list(map(lambda x: x + (t * window.stride * get_trial_size(x) + 1), l))) for l in self.dependent_idxs))
iffs.append(Iff(self.derived_idx + (t * num_levels) + 1, or_clause))
t += 1
(cnf, new_fresh) = block.cnf_fn(And(iffs), backend_request.fresh)
backend_request.cnfs.append(cnf)
backend_request.fresh = new_fresh