def expt_rules():
rules = collections.deque()
solver = z3.Solver()
solver.set('arith.nl', False)
solver.add(hyps)
def show(p):
report('trying to show(', p, '):')
report(' hypotheses = ', solver)
solver.push()
solver.add(z3.Not(p))
outcome = solver.check()
s1 = ' the negation is ' + str(outcome)
if (outcome == z3.unsat):
report(s1, "; therefore the original claim is valid")
elif (outcome == z3.sat):
report(s1, "\n here's a counter-example to ", p, "\n ",
solver.model())
elif (outcome == z3.unknown):
report(s1, "; therefore, the original claim is undecided")
else:
report(s1, "; how'd that happen?")
solver.pop()
return outcome == z3.unsat
def add_rule(p):
report('add_rule(', p, ')')
rules.append(p)
solver.add(p)
while (len(workQ) > 0):
v = workQ.pop()
x = v.children()[0]
n = v.children()[1]
report('rewriting expt(', x, ', ', n, ')')
# Many of the rules below should have guards to ensure that we don't
# accidentally say expt(x, n) is defined when x==0 and n < 0.
# Rather that figuring out # all of the corner cases, I first check to
# see if (x == 0) and (n < 0) is satisfiable. If so, this code just
# throws an exception. I could probably work out a better error message
# later.
# Now that we know that expt(x, n) is well-defined, we still need to be careful.
# Consider expt(x, n+m) where x==0, n==3, and m==(-2). In this case, expt(x, n+m)
# is well-defined, but we can't conclude:
# expt(x, n+m) == expt(x, n) * expt(x, m)
# Rather than working out lots of side conditions (and probably making a mistake),
# I just check to see if implies(hyps, x > 0), and then plunge ahead without fear.
# Of course, this means I don't generate all of the rules that I could, but I'll
# do that later if this simple version turns out to be useful.
def expt_rewrite_const(x2, n2):
if (n2 == 0): return z3.intVal(1)
elif ((0 < n2) and (n2 <= self.maxPowExpand)):
add_rule(v == prod(map(lambda _: x2, range(n2))))
elif ((-self.maxPowExpand <= n2) and (n2 < 0)):
add_rule(v * prod(map(lambda _: x2, range(-n2))) == 1)
if (not show(z3.Or(x != 0, n >= 0))):
raise ExptRewriteFailure(
'possible attempt to raise 0 to a negative power')
x_is_pos = show(x > 0)
x_is_nz = x_is_pos or show(x != 0)
x_is_z = (not x_is_nz) and show(x == 0)
n_is_pos = show(n > 0)
n_is_neg = (not n_is_pos) and show(n < 0)
n_is_z = (not n_is_pos) and (not n_is_neg) and show(n == 0)
if (n_is_z or x_is_z):
if (n_is_z): add_rule(v == 1)
elif (n_is_pos): add_rule(v == 0)
else: add_rule(v == z3.If(n == 0, 1, 0))
continue
elif (x_is_pos):
x_lt_1 = show(x < 1)
x_gt_1 = (not x_lt_1) and show(x > 1)
if ((not x_lt_1) and (not x_gt_1) and show(x == 1)):
add_rule(v == 1)
continue
add_rule(v > 0)
else:
add_rule(z3.Implies(x > 0, v > 0))
if (x_is_nz): add_rule(z != 0)
else: add_rule(z3.Implies(z3.Or(x != 0, n == 0), v != 0))
if ((x.decl().name() == '*')
and (len(x.children()) > 1)): # expt(x0*x1*..., n)
add_rule(v == prod(map(lambda y: xpt(y, n), x.children())))
elif ((n.decl().name() == '+')
and (len(n.children()) > 1)): # expt(x, n0+n1+...)
add_rule(v == prod(map(lambda m: xpt(x, m), n.children())))
elif (n.decl().name() == '-'):
nn = n.children()
if (len(nn) == 0): pass # a variable named '-'
elif (len(nn) == 1): # expt(x, -n)
add_rule(z3.Implies(x != 0, v * xpt(x, nn[0]) == 1))
elif (len(nn) == 2): # expt(x, n-m)
add_rule(
z3.Implies(x != 0,
v * xpt(x, nn[1]) == xpt(x, nn[0])))
else:
RewriteExptFailure(
"unexpected: '-' expression with more than two children"
)
elif (n.decl().name() == '*'): # expt(x, n0*n1*...)
# check to see if n0 is integer constants and not "too big".
# if so, replace it with repeated multiplication
nn = n.children()
if ((len(nn) > 0) and not (longVal(nn[0]) is None)):
if (len(nn) == 1): ex = x
else: ex = xpt(x, prod(nn[1:]))
expt_rewrite_const(ex, longVal(nn[0]))
elif (not (longVal(n) is None)):
expt_rewrite_const(x, longVal(n))
else: # we can't think of a way to simplify it
if (x_lt_1 or x_gt_1):
if (n_is_pos or n_is_neg): pass
else: add_rule(z3.Implies(n == 0, v == 1))
else:
if (n_is_pos or n_is_neg):
add_rule(z3.Implies(x == 1, v == 1))
else:
add_rule(z3.Implies(z3.Or(x == 1, n == 0), v == 1))
if (x_is_pos):
if (x_lt_1):
if (n_is_pos): add_rule(v <= x)
elif (n_is_neg): add_rule(v * x >= 1)
else:
add_rule(
z3.And(z3.Implies(n > 0, v <= x),
z3.Implies(n < 0, v * x >= 1)))
elif (x_gt_1):
if (n_is_pos): add_rule(v >= x)
elif (n_is_neg): add_rule(v * x <= 1)
else:
add_rule(
z3.And(z3.Implies(n > 0, v >= x),
z3.Implies(n < 0, v * x <= 1)))
else:
add_rule(
z3.And(
z3.Implies(z3.And(x < 1, n > 0), v <= x),
z3.Implies(z3.And(x < 1, n < 0),
v * x >= 1),
z3.Implies(z3.And(x > 1, n > 0), v >= x),
z3.Implies(z3.And(x > 1, n < 0),
v * x <= 1)))
return rules
def __init__(self):
self._s = z3.Solver()
import os
import z3
f = open("graph1.txt", "r")
n = int((f.readline()).split()[0])
rest = f.read()
lines = rest.splitlines()
edges = [tuple(map(int, line.split())) for line in lines]
colors = range(0, 3)
color_names = ["Blue", "Green", "Red"]
#number of nodes times the number of colors
nvars = n * len(colors)
vars = [None] * nvars
solver = z3.Solver()
def varnum(i, j):
assert (i in range(0, n) and j in colors)
return len(colors) * i + j
# define variables
for i in range(0, n):
for j in colors:
vars[varnum(i, j)] = z3.Bool("x" + str(varnum(i, j)))
def exactly_one_of(literals):
def clauses_sat(clauses1):
"""True if clauses1 imply clauses2.
"""
s = z3.Solver()
s.add(clauses_to_z3(clauses1))
return s.check() != z3.unsat
def mk(cls, using_nla, myseed=None):
return z3.Solver()
def solve(self, goal, lemmas=None):
"""
Primary function of the NPSolver class. Attempts to prove the goal with respect to given lemmas and the theory
defined by the AnnotatedContext in self.annctx.
:param goal: z3.BoolRef
:param lemmas: set of z3.BoolRef
:return: NPSolution
"""
z3.set_param('smt.random_seed', 0)
z3.set_param('sat.random_seed', 0)
# TODO: check that the given lemmas are legitimate bound formula instances with their formal parameters from
# the foreground sort.
options = self.options
# Make recursive definition unfoldings
recdefs = get_recursive_definition(None,
alldefs=True,
annctx=self.annctx)
recdef_unfoldings = make_recdef_unfoldings(recdefs)
# Sometimes we don't need the unfoldings indexed by recdefs
untagged_unfoldings = set(recdef_unfoldings.values())
# Add them to the set of axioms and lemmas to instantiate
axioms = get_all_axioms(self.annctx)
if lemmas is None:
lemmas = set()
else:
# Check that each bound parameter in all the lemmas are of the foreground sort
for lemma in lemmas:
bound_vars, lemma_body = lemma
if not all(
is_expr_fg_sort(bound_var, annctx=self.annctx)
for bound_var in bound_vars):
raise TypeError(
'Bound variables of lemma: {} must be of the foreground sort'
.format(lemma_body))
recdef_indexed_lemmas = _sort_by_trigger(
lemmas, list(recdef_unfoldings.keys()))
if options.instantiation_mode == proveroptions.lean_instantiation_with_lemmas:
# Recdefs and lemmas need to be treated separately using 'lean' instantiation
fo_abstractions = axioms
if options.instantiation_mode == proveroptions.lean_instantiation:
# Recdefs need to be treated separately using 'lean' instantiation
fo_abstractions = axioms | lemmas
else:
# If the instantiation isn't the 'lean' kind then all defs are going to be instantiated with all terms
fo_abstractions = axioms | untagged_unfoldings | lemmas
# All parameters have been set appropriately. Begin constructing instantiations
# Negate the goal
neg_goal = z3.Not(goal)
# Create a solver object and add the goal negation to it
z3solver = z3.Solver()
z3solver.add(neg_goal)
# Keep track of terms in the quantifier-free problem given to the solver
initial_terms = get_foreground_terms(neg_goal, annctx=self.annctx)
extraction_terms = initial_terms
recdef_application_terms = get_recdef_applications(neg_goal,
annctx=self.annctx)
instantiation_terms = set()
# Instantiate and check for provability according to options
# Handle manual instantiation mode first
if options.instantiation_mode == proveroptions.manual_instantiation:
terms_to_instantiate = options.terms_to_instantiate
instantiations = instantiate(fo_abstractions, terms_to_instantiate)
if instantiations != set():
instantiation_terms = terms_to_instantiate
extraction_terms = extraction_terms.union(
get_foreground_terms(instantiations, annctx=self.annctx))
z3solver.add(instantiations)
if_sat = _solver_check(z3solver)
model = z3solver.model() if if_sat else None
return NPSolution(if_sat=if_sat,
model=model,
extraction_terms=extraction_terms,
instantiation_terms=instantiation_terms,
options=options)
# Automatic instantiation modes
# stratified instantiation strategy
if options.instantiation_mode == proveroptions.depth_one_stratified_instantiation:
conservative_fo_abstractions = axioms | untagged_unfoldings
tracked_instantiations = instantiate(conservative_fo_abstractions,
initial_terms)
if tracked_instantiations != set():
instantiation_terms = initial_terms
tracked_terms = get_foreground_terms(tracked_instantiations,
annctx=self.annctx)
extraction_terms = extraction_terms.union(tracked_terms)
z3solver.add(tracked_instantiations)
untracked_instantiations = instantiate(lemmas, extraction_terms)
if untracked_instantiations != set():
instantiation_terms = instantiation_terms.union(
extraction_terms)
untracked_terms = get_foreground_terms(
untracked_instantiations, annctx=self.annctx)
extraction_terms = extraction_terms.union(untracked_terms)
other_instantiations = instantiate(conservative_fo_abstractions,
extraction_terms)
z3solver.add(untracked_instantiations)
z3solver.add(other_instantiations)
if_sat = _solver_check(z3solver)
model = z3solver.model() if if_sat else None
return NPSolution(if_sat=if_sat,
model=model,
extraction_terms=extraction_terms,
instantiation_terms=instantiation_terms,
options=options)
# Set up initial values of variables
depth_counter = 0
# Keep track of formulae produced by instantiation
instantiations = set()
# When the instantiation mode is infinite we realistically can't exceed 10^3 instantiations anyway
target_depth = 1000 if options.instantiation_mode == proveroptions.infinite_depth else options.depth
while depth_counter < target_depth:
# Try to prove with available instantiations
z3solver.add(instantiations)
# If the instantiation mode is fixed depth we can continue instantiating until we get to that depth
if options.instantiation_mode != proveroptions.fixed_depth:
# Otherwise check satisfiability with current state of instantiations
if_sat = _solver_check(z3solver)
# If unsat, stop and return NPSolution instance
if not if_sat:
return NPSolution(if_sat=if_sat,
model=None,
extraction_terms=extraction_terms,
instantiation_terms=instantiation_terms,
depth=depth_counter,
options=options)
# target depth not reached or unsat not reached
# Do another round of instantiations.
# TODO: optimise instantiations so repeated instantiation is not done. Currently all instantiations
# are done in every round. But optimisation is difficult in the presence of multiple arities.
instantiation_terms = extraction_terms
# Instantiate all basic abstractions
instantiations = instantiate(fo_abstractions, instantiation_terms)
# Instantiate other abstractions depending on instantiation mode. Typically recdefs and lemmas
if options.instantiation_mode in {
proveroptions.lean_instantiation,
proveroptions.lean_instantiation_with_lemmas
}:
# Add recursive definition instantiations to the set of all instantiations
for recdef, application_terms in recdef_application_terms.items(
):
lean_instantiations = instantiate(
recdef_unfoldings[recdef], application_terms)
instantiations.update(lean_instantiations)
if options.instantiation_mode == proveroptions.lean_instantiation_with_lemmas:
# Add lemma instantiations to the set of all instantiations
for recdef, application_terms in recdef_application_terms.items(
):
triggered_lemmas = recdef_indexed_lemmas.get(recdef, [])
triggered_instantiations = instantiate(
triggered_lemmas, application_terms)
instantiations.update(triggered_instantiations)
# If the set of instantiations is empty exit the loop
if instantiations == set():
instantiation_terms = set()
break
# Update the variables for the next round
depth_counter = depth_counter + 1
new_terms = get_foreground_terms(instantiations,
annctx=self.annctx)
recdef_application_terms = get_recdef_applications(
instantiations, annctx=self.annctx)
extraction_terms = extraction_terms.union(new_terms)
# Reach this case when depth_counter = target depth, either in fixed_depth or bounded_depth mode.
# Final attempt at proving goal
z3solver.add(instantiations)
if_sat = _solver_check(z3solver)
model = z3solver.model() if if_sat else None
return NPSolution(if_sat=if_sat,
model=model,
extraction_terms=extraction_terms,
instantiation_terms=instantiation_terms,
depth=depth_counter,
options=options)
def get_small_model(clauses,
sorts_to_minimize,
relations_to_minimize,
final_cond=None,
shrink=True):
"""
Return a HerbrandModel with a "small" model of clauses.
sorts_to_minimize is a list of sorts, and relations_to_minimize is
a list of relations,
The model minimization occurs in 2 ways:
First, minimize universe size lexicographically according to the order of
sorts_to_minimize.
Second, minimize the number of positive entries in the relations
according to the order of relations_to_minimize.
The parameter final_cond can be a list of objects have the following interface:
cond(): returns a final condition as a clauses object
start(): called before starting
sat(): called if sat, return True if should ignore result
unsat() : called if unsat
assume() : if returns true, assume rather than check
"""
s = z3.Solver()
s.add(clauses_to_z3(clauses))
# res = decide(s)
# if res == z3.unsat:
# return None
if final_cond is not None:
if isinstance(final_cond, list):
res = z3.unsat
for fc in final_cond:
fc.start()
if fc.assume():
s.add(clauses_to_z3(fc.cond()))
else:
s.push()
s.add(clauses_to_z3(fc.cond()))
res = decide(s)
if res != z3.unsat:
if fc.sat():
res = z3.unsat
else:
break
else:
fc.unsat()
s.pop()
else:
s.add(clauses_to_z3(final_cond))
res = decide(s)
else:
res = decide(s)
if res == z3.unsat:
return None
if shrink:
print "searching for a small model...",
sys.stdout.flush()
for x in chain(sorts_to_minimize, relations_to_minimize):
for n in itertools.count(1):
s.push()
sc = size_constraint(x, n)
s.add(formula_to_z3(sc))
res = decide(s)
if res == z3.sat:
break
else:
s.pop()
print "done"
m = get_model(s)
# print "model = {}".format(m)
# f = open("ivy.smt2","w")
# f.write(s.to_smt2())
# f.close()
h = HerbrandModel(s, m, used_symbols_clauses(clauses))
return h
def __init__(self,
spec: TyrellSpec,
loc=None,
sym_breaker=False,
break_sym_online=False):
self.z3_solver = z3.Solver()
self.z3_solver.set('random_seed', 7)
self.z3_solver.set('sat.random_seed', 7)
# productions that are leaves
self.leaf_productions = []
self.line_productions = []
# for learning
self.program2tree = {}
# z3 variables for each production node
self.variables = []
self.spec = spec
self.num_constraints = 0
self.num_variables = 0
self.sym_breaker = sym_breaker
self.break_sym_online = break_sym_online
if loc <= 0:
raise ValueError(f'LOC cannot be non-positive: {loc}')
self.start_time = time.time()
self.loc = loc
self.parentId = dict()
if self.sym_breaker:
if self.loc > 2:
root = Node(1)
root.h = 1
id, tree = self.create_clean_tree(1, root)
tree.insert(0, root)
self.cleanedTree = tree
self.symFinder = SymmetryFinder(self.loc)
self.lattices = dict()
if self.loc > 2 and not self.break_sym_online:
self.find_lattices()
self.cleanedModel = dict()
self.num_prods = self.spec.num_productions()
self.max_children = self.max_children()
self.find_types()
self.init_leaf_productions()
self.init_line_productions()
self.linesVars = []
self.typeVars = []
self.line_vars_by_line = defaultdict(list)
self.roots, self.leafs = self.build_trees()
self.model = None
# Times
self.symTime = 0
self.totalSymTime = 0
self.solverTime = 0
self.blockedModels = 0
self.totalBlockedModels = 0
self.modelConstraint = 0
self.create_output_constraints()
self.create_lines_constraints()
self.create_type_constraints()
self.create_children_constraints()
self._production_id_cache = defaultdict(set)
for p in self.spec.productions():
if p.is_enum():
self._production_id_cache[p._get_rhs()].add(p.id)
self.resolve_predicates(self.spec.predicates())
logger.info('Number of Nodes: {} '.format(len(self.roots +
self.leafs)))
logger.info('Number of Variables: {}'.format(
len(self.variables + self.typeVars + self.linesVars)))
logger.info('Number of Constraints: {}'.format(self.num_constraints))
logger.info('Time spent encoding: {}'.format(time.time() -
self.start_time))
res = self.z3_solver.check()
if res != z3.sat:
logger.warning(
f"There is no solution for current loc ({self.loc}).")
return
self.model = self.z3_solver.model()
self.get_model_constraint()
def __init__(self, eqns=None):
self.eqns = []
self.locs = []
self.solver = z3.Solver()
if eqns: self.add(eqns)
self._ctr = 0
def getString(tar):
v1 = z3.BitVec('v1', 32)
v2 = z3.BitVec('v2', 32)
v3 = z3.BitVec('v3', 32)
s = z3.Solver()
lol = "BCDGPTVZ"
tar0 = lol.index(tar[0])
tar1 = lol.index(tar[1])
tar2 = lol.index(tar[2])
tar3 = lol.index(tar[3])
tar4 = lol.index(tar[4])
tar5 = lol.index(tar[5])
tar6 = lol.index(tar[6])
tar7 = lol.index(tar[7])
tar8 = lol.index(tar[8])
tar9 = lol.index(tar[9])
tar10 = lol.index(tar[10])
tar11 = lol.index(tar[11])
tar12 = lol.index(tar[12])
tar13 = lol.index(tar[13])
tar14 = lol.index(tar[14])
tar15 = lol.index(tar[15])
tar16 = lol.index(tar[16])
tar17 = lol.index(tar[17])
tar18 = lol.index(tar[18])
tar19 = lol.index(tar[19])
tar20 = lol.index(tar[20])
tar21 = lol.index(tar[21])
tar22 = lol.index(tar[22])
tar23 = lol.index(tar[23])
tar24 = lol.index(tar[24])
tar25 = lol.index(tar[25])
tar26 = lol.index(tar[26])
tar27 = lol.index(tar[27])
tar28 = lol.index(tar[28])
tar29 = lol.index(tar[29])
tar30 = lol.index(tar[30])
tar31 = lol.index(tar[31])
s.add(v2 >> 0x1F & 0x1 | v3 >> 0x0 & 0x3 == tar0)
s.add(v1 >> 0x09 & 0x7 == tar1)
s.add(v3 >> 0x05 & 0x7 == tar2)
s.add(v3 >> 0x08 & 0x7 == tar3)
s.add(v1 >> 0x15 & 0x7 == tar4)
s.add(v1 >> 0x06 & 0x7 == tar5)
s.add(v3 >> 0x1D & 0x7 == tar6)
s.add(v1 >> 0x1B & 0x7 == tar7)
s.add(v2 >> 0x04 & 0x7 == tar8)
s.add(v2 >> 0x0D & 0x7 == tar9)
s.add(v2 >> 0x0A & 0x7 == tar10)
s.add(v3 >> 0x1A & 0x7 == tar11)
s.add(v2 >> 0x16 & 0x7 == tar12)
s.add(v3 >> 0x17 & 0x7 == tar13)
s.add(v2 >> 0x1C & 0x7 == tar14)
s.add(v3 >> 0x14 & 0x7 == tar15)
s.add(v2 >> 0x01 & 0x7 == tar16)
s.add(v3 >> 0x11 & 0x7 == tar17)
s.add(v1 >> 0x00 & 0x7 == tar18)
s.add(v2 >> 0x13 & 0x7 == tar19)
s.add(v1 >> 0x18 & 0x7 == tar20)
s.add(v3 >> 0x0B & 0x7 == tar21)
s.add(v2 >> 0x19 & 0x7 == tar22)
s.add(v2 >> 0x10 & 0x7 == tar23)
s.add(v1 >> 0x03 & 0x7 == tar24)
s.add(v1 >> 0x12 & 0x7 == tar25)
s.add(v1 >> 0x0F & 0x7 == tar26)
s.add(v3 >> 0x02 & 0x7 == tar27)
s.add(v1 >> 0x0C & 0x7 == tar28)
s.add(v2 >> 0x07 & 0x7 == tar29)
s.add(v3 >> 0x0E & 0x7 == tar30)
s.add(v1 >> 0x1E & 0x3 | v2 >> 0x00 & 0x1 == tar31)
## s.add(v1 < 1000)
## s.add(v2 < 1000)
## s.add(v3 < 1000)
#print s
s.check()
return [
s.model()[v1].as_long(),
s.model()[v2].as_long(),
s.model()[v3].as_long()
]
def initialize(_sketch, _holes, _hole_options, symmetries, differents,
_formulae, _mc_formulae, _mc_formulae_alt, _thresholds,
_accept_if_above, optimality_setting):
Family._sketch = _sketch
Family._holes = _holes
Family._hole_options = _hole_options
# map holes to their indices
Family.hole_list = list(Family._holes.keys())
Family._hole_indices = dict()
for hole_index, hole in enumerate(Family.hole_list):
Family._hole_indices[hole] = hole_index
# map hole options to their indices
Family._hole_option_indices = dict()
for hole, options in Family._hole_options.items():
indices = dict()
k = 0
for option in options:
indices[option] = k
k += 1
Family._hole_option_indices[hole] = indices
# initialize z3 solver
Family._solver = z3.Solver()
Family._solver_meta_vars = OrderedDict()
variables = dict()
for k, v in Family._hole_options.items():
# create integer variable
var = z3.Int(k)
# store the variable
Family._solver_meta_vars[var] = k
variables[k] = var
# add constraints for the hole assignments
Family._solver.add(var >= 0)
Family._solver.add(var < len(v))
# encode restrictions
if symmetries is not None:
for sym in symmetries:
for x, y in zip(sym, sym[1:]):
Family._solver.add(variables[x] < variables[y])
if differents is not None:
for diff in differents:
for idx, x in enumerate(diff):
for y in diff[idx + 1:]:
Family._solver.add(variables[x] != variables[y])
# store formulae info
Family._formulae = _formulae
Family._mc_formulae = _mc_formulae
Family._mc_formulae_alt = _mc_formulae_alt
Family._thresholds = _thresholds
Family._accept_if_above = _accept_if_above
Family._optimality_setting = optimality_setting
# build quotient MDP
quotient_builder = JaniQuotientBuilder(Family._sketch, Family._holes)
Family._quotient_container = quotient_builder.construct(
Family._hole_options, remember=set())
Family._quotient_container.prepare(Family._mc_formulae,
Family._mc_formulae_alt)
Family._quotient_mdp = Family._quotient_container.mdp_handling.full_mdp
Family._quotient_mdp_stats = (Family._quotient_mdp_stats[0] +
Family._quotient_mdp.nr_states,
Family._quotient_mdp_stats[1] + 1)
logger.debug(
f"Constructed MDP of size {Family._quotient_mdp.nr_states}.")
def __init__(self, worldmap=World(), curTime=0, tasks=[]):
self.tasks = tasks
self.world = worldmap
self.globalTime = curTime
self.solver = z3.Solver()
def __init__(self, y):
self.y = y
self.equations = z3.Solver()
self.sc = {'s1': z3.Real('s1'), 's2': z3.Real('s2'),
's3': z3.Real('s3'), 's4': z3.Real('s4')}
def solve(): f = z3.parse_smt2_file(OUTPUT_PATH) s = z3.Solver() s.add(f) return (s, str(s.check()))
def schedule(nurses, surgeries, monthTable, clientTable, monthInfo,
surgeryTimeTable):
solver = z3.Solver()
grid = dict()
result = dict()
for date in clientTable.keys():
# night
nightNurses = list()
for groupi in range(1, groupNumPerNight + 1):
for nuri in range(1, nurseNumPerGroup + 1):
value = z3.Int("night_%sgroup_%dnur_%d" % (date, groupi, nuri))
grid[(date, groupi, nuri)] = value
nightNurses.append(value)
# constraint: make sure the nurses in night are the same as monthTable
solver.add(value == int(monthTable[date][groupi - 1][nuri -
1]))
# surgery
for surgeryID in clientTable[date]:
nightAndDayNurses = list(nightNurses)
# nightAndDayNurses = [value for value in nightNurses]
for nurseType in nurseTypes:
value = z3.Int("day_%ssid_%stype_%s" %
(date, surgeryID, nurseType))
grid[(date, surgeryID, nurseType)] = value
# department of surgery
nurseIDs = clientTable[date][surgeryID]
surgery = surgeries[surgeryID]
orListForDepartment = list()
# constraint: make sure all of the nurses come from client
for nurseID in nurseIDs:
nurse = nurses[nurseID]
if matchDepartment(surgery, nurse):
# constraint: make sure the department of nurse matches with surgery
orListForDepartment.append(
z3.Or(value == int(nurseID)))
# constraint: make sure the department of nurse matches with surgery
# constraint: make sure all of the nurses come from client
solver.add(z3.Or(orListForDepartment))
nightAndDayNurses.append(value)
# constraint: make sure nurses working for day are not working for night
solver.add(z3.Distinct(nightAndDayNurses))
# solver.add(z3.Eqel)
# constraint: make sure the number of surgeries is limited by work hours
for nurseID in nurses.keys():
for date in clientTable.keys():
dayNursesConstraint = list()
for surgeryID in clientTable[date]:
for nurseType in nurseTypes:
value = grid[(date, surgeryID, nurseType)]
dayNursesConstraint.append((value == int(nurseID), 1))
#<type 'unicode'>
if nurses[nurseID].is_pregnant == "true":
solver.add(z3.PbLe(dayNursesConstraint, 1))
else:
solver.add(z3.PbLe(dayNursesConstraint, 1))
# # constraint: make sure nurses are different in the same time
# for surIDs in surgeryTimeTable :
# sameTimeNurses = list()
# for surID in surIDs :
# for nurseType in nurseTypes :
# value = grid[(surgeries[surID].date, surID, nurseType)]
# sameTimeNurses.append(value)
# # constraint: make sure nurses are different in the same time
# solver.add(z3.Distinct(sameTimeNurses))
# check sat
if solver.check() != z3.sat:
print "unsat"
return None
else:
print "sat"
# get the model
model = solver.model()
# get the result
for date in clientTable.keys():
dateTable = dict()
nightNurses = list()
for groupi in range(1, groupNumPerNight + 1):
for nuri in range(1, nurseNumPerGroup + 1):
nurseID = str(model[grid[(date, groupi, nuri)]].as_long())
nightNurses.append(nurseID)
# assert
assert nurseID == monthTable[date][groupi - 1][nuri - 1]
dateTable["night"] = nightNurses
dayTable = dict()
for surgeryID in clientTable[date]:
surgeryNurses = dict()
for nurseType in nurseTypes:
surgeryNurses[nurseType] = str(
model[grid[date, surgeryID, nurseType]].as_long())
# print model[grid[date, surgeryID, nurseType]].as_long()
# print nurses[str(surgeryNurses[nurseType])].priority
# constraint: make sure the qualification of roving nurse is higher than instrument nurse
if nurses[surgeryNurses["instrument"]].priority > nurses[
surgeryNurses["roving"]].priority:
temp = surgeryNurses["instrument"]
surgeryNurses["instrument"] = surgeryNurses["roving"]
surgeryNurses["roving"] = temp
assert nurses[surgeryNurses["instrument"]].priority <= nurses[
surgeryNurses["roving"]].priority
dayTable[surgeryID] = surgeryNurses
dateTable["day"] = dayTable
result[date] = dateTable
return result
pass
def __init__(self, context, network):
self.ctx = context
self.net = network
self.solver = z3.Solver()
self.constraints = list()
def execute(trial = False):
startTime = datetime.datetime.now()
# Set up the database connection.
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('debhe_shizhan0_wangdayu_xt', 'debhe_shizhan0_wangdayu_xt')
schoolDict = {}
subwayDict = {}
distanceHi = 0.002
distanceLo = 0.000003
# Loads the collection
schools = []
subwayStop = []
#schools = repo['debhe_shizhan0_wangdayu_xt.allSchool'].find()
schools = repo['debhe_shizhan0_wangdayu_xt.schoolSubwayDistance'].find()
subwayStop = repo['debhe_shizhan0_wangdayu_xt.subwayStop'].find()
#schools = repo['debhe_shizhan0_wangdayu_xt.schoolSubwayDistanceTrial'].find()
#subwayStop = repo['debhe_shizhan0_wangdayu_xt.subwayStopTrial'].find()
# Need to make the deepcopy of record for further use
schools_1 = copy.deepcopy(schools)
schools_2 = copy.deepcopy(schools)
schools_3 = copy.deepcopy(schools)
listSchool = []
totalDistance = 0
maxLength = 0
for i in schools_3:
temp = i['Distance']
totalDistance += temp
if(temp > maxLength):
maxLength = temp
distanceLo = totalDistance / schools.count()
distanceHi = maxLength + 0.000001
#print(distanceLo)
# Compute all the schools that need a bike hub
# For each school get the list of all the subway station that is in the certain range
# create the system for the z3 solver
for row_1 in schools_1:
if(row_1['Distance'] < distanceLo):
continue
else:
listSchool += [row_1['schoolName']]
schoolDict[row_1['schoolName']] = []
subwayStop_temp = copy.deepcopy(subwayStop)
for row_2 in subwayStop_temp:
if(row_2['X'] != '""' and row_2['Y'] != '""' and row_1['schoolX'] != '""' and row_1['schoolY'] != '""'):
s = (row_1['schoolName'], row_2['stopName'], (float(row_1['schoolX']) - float(row_2['X']))**2 + ( float(row_1['schoolY']) - float(row_2['Y']) )**2, row_2['id'])
if(s[2] < distanceHi and s[2] > distanceLo):
schoolDict[row_1['schoolName']] += [row_2['stopName']]
#schoolDict_1 = copy.deepcopy(schoolDict)
#print(len(schoolDict))
solver = z3.Solver()
# Create the variables for the z3 solver
allSchool = [[z3.Int(subStops) for subStops in schoolDict[s]] for s in listSchool]
allSchool_1 = copy.deepcopy(allSchool)
# Add the constraint (There must be at least one station that get assigned by a hub)
solver.add(sum([sum(school) for school in allSchool_1]) > 0)
# Add the constraint
# For each school, there must be at least one station that get assigned by a hub
for school in allSchool:
solver.add(sum(school) > 0)
for subStops in school:
solver.add(subStops < 2)
solver.add(subStops >= 0)
# Optimization
# We want to find the minimum number of stations that get assigned
'''
minPlacementPossible = 199
for i in range(199, -1, -1):
solver.push()
solver.add(sum([sum(school) for school in allSchool_1]) < 20)
if( str(solver.check()) == "sat"):
minPlacementPossible = i
solver.pop()
'''
minPlacementPossible = 0
for i in range(9, -1, -1):
solver.push()
solver.add(sum([sum(school) for school in allSchool_1]) < (2**i + minPlacementPossible) )
#print(2**i)
if( str(solver.check()) != 'unsat' ):
minPlacementPossible += 2**i
#print(minPlacementPossible)
solver.pop()
# Once we find the number, add it to the constraint
solver.add(sum([sum(school) for school in allSchool_1]) < minPlacementPossible)
#print(minPlacementPossible)
print(solver.check())
placementResult = solver.model()
#placementResult_1 = copy.deepcopy(placementResult)
print(placementResult)
#print(len(placementResult))
#print(placementResult[placementResult[0]])
# store the assignment to the database
finalResult = []
assignCount = 0
for i in range(len(placementResult)):
dic = {}
stopNameParsed = str(placementResult[i]).replace(".","")
assignment = str(placementResult[placementResult[i]])
dic[stopNameParsed] = assignment
if(assignment == "1"):
assignCount += 1
finalResult.append(dic)
print(assignCount)
# save the information to the database
repo.dropCollection("optimizeBikePlacement")
repo.createCollection("optimizeBikePlacement")
repo['debhe_shizhan0_wangdayu_xt.optimizeBikePlacement'].insert_many(finalResult)
repo['debhe_shizhan0_wangdayu_xt.optimizeBikePlacement'].metadata({'complete': True})
print("Saved optimizeBikePlacement", repo['debhe_shizhan0_wangdayu_xt.optimizeBikePlacement'].metadata())
repo.logout()
endTime = datetime.datetime.now()
return {"start":startTime, "end":endTime}
def isreachable_check(self, check, interval=None):
logger.debug(f"Calculating whether all of the checks are reachable")
solver = z3.Optimize()
check_ports = check.get_ports()
# build a mapping that shows the propagation of information to the guard (what influences the guard)
modifier_map = self.get_modifier_map(check_ports)
z3var_constraints, z3_vars = self.get_z3_vars(modifier_map)
solver.add(z3var_constraints)
if interval is None:
solver.add(z3_vars['dt'] >= 0)
logger.debug(f"Adding time start constraint: dt >= 0")
else:
interval = interval.resolve_infinitesimal()
solver.add(interval.start_operator(z3_vars['dt'], interval.start))
# logger.debug(f"Adding time start constraint: {interval.start_operator(z3_vars['dt'], interval.start)}")
if interval.end is not math.inf: # if it's infinity, just drop it...
end = interval.end
solver.add(interval.end_operator(z3_vars['dt'], end))
# logger.debug(f"Adding time end constraint: {interval.end_operator(z3_vars['dt'], end)}")
# create the constraints for updates and influences
for port, modifiers in modifier_map.items():
for modifier in modifiers:
constraints = self._get_constraints_from_modifier(
modifier, z3_vars)
solver.add(constraints)
conv = checklib.CheckZ3Converter(z3_vars)
check_constraints = conv.convert(check)
if logger.getEffectiveLevel(
) <= logging.DEBUG: # only log if the level is appropriate, since z3's string-conversion takes ages
logger.debug(f"Check constraints: {check_constraints}")
solver.add(check_constraints)
objective = solver.minimize(z3_vars['dt']) # find minimal value of dt
check = solver.check()
# logger.debug("satisfiability: %s", check)
if solver.check() == z3.sat:
inf_coeff, numeric_coeff, eps_coeff = objective.lower_values()
returnvalue = Epsilon(numeric_coeff, eps_coeff)
logger.info(
f"Minimum time to reach passing checks is {returnvalue}")
return returnvalue
elif check == z3.unknown:
logger.warning(
f"The calculation of the check reachability was UNKNOWN. This usually happening in the presence of non-linear constraints. Do we have any?"
)
std_solver = z3.Solver()
std_solver.add(solver.assertions())
std_solver_check = std_solver.check()
if std_solver_check == z3.sat:
min_dt = std_solver.model()[z3_vars['dt']]
as_python = to_python(min_dt)
logger.info(
f"We did get a solution using the standard solver though: {as_python} Assuming that this is the smallest solution. CAREFUL THIS MIGHT BE WRONG!!!"
)
return as_python
elif std_solver_check == z3.unknown:
logger.error(
f"The standard solver was also not able to decide if there is a solution or not. The constraints are too hard!!!"
)
return False
else:
logger.info(
"The standard solver says there is no solution to the constraints. This means we also couldn't minimize. Problem solved."
)
return False
else:
logger.debug(f"Constraint set to reach checks is unsatisfiable.")
return False
def main():
N = 9
frames = []
for p in range(N):
frames.append(z3.BitVec('cnt_%d' % p, 3))
def init(cnt):
return (cnt == 0)
def R(cnt, cntp):
return (cntp == cnt + 1)
def to_gray(cnt):
return (cnt ^ z3.LShR(cnt, 1))
def hd(n1, n2):
d = (n1 ^ n2)
d0 = z3.ZeroExt(1, z3.Extract(0, 0, d))
d1 = z3.ZeroExt(1, z3.Extract(1, 1, d))
d2 = z3.ZeroExt(1, z3.Extract(2, 2, d))
return (d0 + d1 + d2)
S = z3.Solver()
S.add(init(frames[0]))
checks = []
for p in range(1, N):
# get count in previous step
cnt_prev = frames[p - 1]
# get count in current step.
cnt_curr = frames[p]
# ADD constraint to solver.
S.add(R(cnt_prev, cnt_curr))
# get gray encoded version of count previous.
gray_cnt_prev = to_gray(cnt_prev)
# get gray encoded version of count current.
gray_cnt_curr = to_gray(cnt_curr)
# compute hamming distance b/w gray cnt prev and curr
dist = hd(gray_cnt_prev, gray_cnt_curr)
# check if this is not equal to one.
check_curr = (dist != 1)
# add to the list or checks.
checks.append(check_curr)
S.push()
S.add(z3.Or(*checks))
result = S.check()
if result == z3.unsat:
print("Property HOLDS.")
else:
print("Property does not HOLD.")
S.pop()
S.push()
# add a constraint stating that all the states must be distict
# check for satisfiability and fix the condition below
if True: # FIXME: change this condition.
print('Reachability diameter not reached.')
else:
print('Reachability diameter reached.')
def __init__(self):
self._ctx = z3.Context()
self._solver = z3.Solver(ctx=self._ctx)
def new_solver():
return z3.Solver()
def synthesize_tests(dataset: Dataset, assumptions: Dict[str, str],
combined_data: CombinedData):
reset_all_tests()
tea_logger = TeaLogger.get_logger()
construct_all_tests(combined_data)
global name
stat_var_map = {}
# Reorder variables so that y var is at the end
combined_data._update_vars()
# Compute unique statisical variable names from the combined data.
combined_data_vars = []
for v in combined_data.vars:
var = StatVar(v.metadata[name])
stat_var_map[v.metadata[name]] = var
combined_data_vars.append(var)
# Assume properties based on user assumptions and mode
solver = z3.Solver()
# s = Tactic('qflia').solver()
assumed_props = assume_properties(stat_var_map, assumptions, solver,
dataset, combined_data)
# import pdb; pdb.set_trace()
# Update the arity of test-level properties
for prop in test_props:
prop._update(len(combined_data.vars))
# Apply all tests to the variables we are considering now in combined_data
for test in all_tests():
test.apply(*combined_data_vars)
solver.push() # Create backtracking point
model = None # Store model
# For each test, add it to the solver as a constraint.
# Add the tests and their properties
for test in all_tests():
tea_logger.log_debug(f"\nCurrently considering {test.name}")
solver.add(test.__z3__ == z3.And(*test.query()))
solver.add(test.__z3__ == z3.BoolVal(True))
# Check the model
result = solver.check()
if result == z3.unsat:
tea_logger.log_debug("Test is unsat.\n")
solver.pop()
elif result == z3.unknown:
print("failed to solve")
try:
pass
except z3.Z3Exception:
return
else:
model = solver.model()
test_invalid = False
val = False
# Does the test apply?
# Would this ever be false??
if model and z3.is_true(model.evaluate(test.__z3__)):
# Verify the properties for that test
for prop in test._properties:
if is_assumed_prop(assumed_props, prop):
tea_logger.log_debug(
f"User asserted property: {prop._name}.")
val = True
solver.add(prop.__z3__ == z3.BoolVal(val))
else:
tea_logger.log_debug(
f"Testing assumption: {prop._name}.")
# Does this property need to hold for the test to be valid?
# If so, verify that the property does hold
if model and z3.is_true(model.evaluate(prop.__z3__)):
val = verify_prop(dataset, combined_data, prop)
if val:
tea_logger.log_debug(f"Property holds.")
else: # The property does not verify
assert (val == False)
tea_logger.log_debug(f"Property FAILS")
# if not test_invalid:
solver.pop() # remove the last test
test_invalid = True
model = None
# else: # test is already invalid. Going here just for completeness of logging
# tea_logger.log_debug(f"EVER GET HERE?")
solver.add(prop.__z3__ == z3.BoolVal(val))
solver.push() # Push latest state as backtracking point
solver.check()
model = solver.model() # final model
tests_to_conduct = []
# Could add all the test props first
# Then add all the tests
for test in all_tests():
if model and z3.is_true(model.evaluate(test.__z3__)):
tests_to_conduct.append(test.name)
elif not model: # No test applies
pass
# reset_all_tests()
return tests_to_conduct
def mk(self, using_nla=False, myseed=None):
return z3.Solver()
def generateTraces(self, maxNumberOfSolutions=3):
def stateToRangeOfExistence(state, propositions, solution):
try:
stateIds = self.labelsToIds[state]
except:
return []
coveredTimesteps = []
for id in stateIds:
coveredTimesteps += [
i for i in range(
int(solution[propositions[id][0]].as_string()),
int(solution[propositions[id][1]].as_string()) + 1)
]
return coveredTimesteps
allSolutions = []
# allEvents = [("message", i, j) for i in range(self.numMachines) for j in range(self.numMachines)]
# allEvents += [("nodeRestart", i) for i in range(self.numMachines)]
# allEvents = { allEvents[i] : i for i in range(len(allEvents)) }
propositions = { node.eventId : (z3.Int("var_"+repr(node.eventId)+"_"+repr(node.label)+"_start"), z3.Int("var_"+repr(node.eventId)+"_"+repr(node.label)+"_end"))\
for node in self.nodes}
sol = z3.Solver()
self.lengthOfTrace = self.lengthOfLongestChain + 4
for node in self.nodes:
propKey = node.eventId
sol.add(propositions[propKey][0] <= propositions[propKey][1])
sol.add(propositions[propKey][0] >= 0)
sol.add(propositions[propKey][1] <= self.lengthOfTrace)
for pred in node.preds:
if node.label[0] == self.idToNodes[pred].label[0]:
sol.add(propositions[pred][1] +
1 == propositions[node.eventId][0])
else:
sol.add((propositions[node.eventId][0] >
propositions[pred][0]))
if node.lastOnProcess == True:
sol.add(propositions[propKey][1] == self.lengthOfTrace - 1)
while len(allSolutions) < maxNumberOfSolutions and sol.check(
) == z3.sat:
model = sol.model()
allSolutions.append(model)
forbidExistingSolution = [d() != model[d] for d in model]
sol.add(z3.Or(forbidExistingSolution))
traces = []
allStates = [(machineId, stateKind, stateArg)
for stateKind in ["expect", "ready"]
for stateArg in range(self.numMachines)
for machineId in range(self.numMachines)]
allStates += [(machineId, ev) for machineId in range(self.numMachines)
for ev in ["crash", "end"]]
allStates = sorted(allStates)
print({idx: allStates[idx] for idx in range(len(allStates))})
for solution in allSolutions:
trace = [[0 for _ in range(len(allStates))]
for _ in range(self.lengthOfTrace)]
for idx, state in enumerate(allStates):
coveredRange = stateToRangeOfExistence(state, propositions,
solution)
for trueTimestep in coveredRange:
trace[trueTimestep][idx] = 1
traces.append(trace)
return traces
#!/usr/bin/env python2
## -*- coding: utf-8 -*-
##
## Triton
##
import sys
import z3
def sx(bits, value):
sign_bit = 1 << (bits - 1)
return (value & (sign_bit - 1)) - (value & sign_bit)
ctx = z3.Context()
s = z3.Solver()
SymVar_0 = z3.BitVec('SymVar_0', 64)
ref_693 = SymVar_0
ref_708 = ref_693 # MOV operation
ref_15247 = ref_708 # MOV operation
ref_15308 = ref_15247 # MOV operation
ref_15322 = ((0x0 + ((ref_15308 + ((0x217E6161 * 0x1) & 0xFFFFFFFFFFFFFFFF)) & 0xFFFFFFFFFFFFFFFF)) & 0xFFFFFFFFFFFFFFFF) # LEA operation
ref_15326 = ref_15247 # MOV operation
ref_15340 = (0x217E6161 & ref_15326) # AND operation
ref_15347 = (~(ref_15340) & 0xFFFFFFFFFFFFFFFF) # NOT operation
ref_15349 = ((ref_15347 + ref_15322) & 0xFFFFFFFFFFFFFFFF) # ADD operation
ref_15357 = ((ref_15349 + 0x1) & 0xFFFFFFFFFFFFFFFF) # ADD operation
ref_27405 = ref_15357 # MOV operation
ref_60810 = ref_27405 # MOV operation
ref_65532 = ref_60810 # MOV operation
ref_65540 = ref_65532 # MOV operation
if __name__ == "__main__":
v_1 = z3.Real('v_1')
l5 = z3.And(l2_T, v_1 == x)
if __name__ == "__main__":
v = z3.Real('v')
for s in [z3.And(l3, v == v_0), z3.And(l5, v == v_1)]:
z3.solve(x != 0, s)
if __name__ == "__main__":
v = z3.Real('v')
l6 = z3.Or(z3.And(l3, v == v_0), z3.And(l5, v == v_1))
z3.solve(l6)
if __name__ == "__main__":
s = z3.Solver()
s.add(l6)
for i in range(5):
if s.check() == z3.sat:
m = s.model()
x_val = m[x]
print(m)
else:
print('no solution')
break
s.add(z3.Not(x == x_val))
s
if __name__ == "__main__":
s.add(x < 0)
for i in range(5):
#!/usr/bin/env python3
import z3
import sys
ctx = z3.Context()
s = z3.Solver(ctx=ctx)
queries = z3.parse_smt2_file(sys.argv[1])
k = int(sys.argv[2])
outfile = sys.argv[3]
query = queries[k]
with open(outfile, "w") as fout:
fout.write("(assert\n" + query.sexpr() + "\n)\n")
def cons_sat(data_copy, zones, percent_max, percent_min, factor):
# ----------------- Each parameter will look like this (r1-1, r1-2, ..., rk-k) -----------------
# ----------------- For example, r2-4 represents a route where the rider mey exit/enter in zone 2 and exit/enter in zone 4 -----------------
parameter_names_z = []
for i in range(1, zones + 1):
for j in range(i, zones + 1):
val = "r{0:d}".format(i)
val += "-{0:d}".format(j)
parameter_names_z.append(val)
parameters_z = [z3.Real(n) for n in parameter_names_z]
S = z3.Solver()
projected_route_total = [0] * len(parameter_names_z)
real_route_total = 0
for item in data_copy:
for i in range(len(parameter_names_z)):
dest_1 = int(parameter_names_z[i][1])
dest_2 = int(parameter_names_z[i][-1])
# ----------------- Find daily revenue based on data -----------------
# ----------------- This number will be the sum of the revenue from all route combinations -----------------
if item['zone'] == dest_1 or item['zone'] == dest_2:
projected_route_total[i] += (float(item['population']) *
(item['trans_percent'] / 100))
real_route_total += float(
item['population']) * (item['trans_percent'] / 100) * 2.75
real_route_total = int(real_route_total)
val = 0
for i in range(len(parameters_z)):
val += (parameters_z[i] * projected_route_total[i])
# ----------------- Ensure that new fares for each route still == overall real revenue -----------------
S.add(val == real_route_total)
for i in range(len(parameters_z)):
val = (parameters_z[i] * projected_route_total[i]) / real_route_total
# ----------------- Enusre new fares dont make one route responsible for more than 20% of revenue or less than 1% revenue -----------------
S.add(val < percent_max, val > percent_min)
# ----------------- Ensure that routes between two zones with lower avg incomes pay less than routes between two zones with higher average income -----------------
for i in range(len(parameters_z)):
outer_1 = int(parameter_names_z[i][1])
outer_2 = int(parameter_names_z[i][-1])
for j in range(i, len(parameters_z)):
inner_1 = int(parameter_names_z[j][1])
inner_2 = int(parameter_names_z[j][-1])
# ----------------- E.g: r1-1 (1+1) < r3-4 (3+4); therefore r1-1 will have a higher fare than r3-4 -----------------
if (outer_1 + outer_2 < inner_1 + inner_2):
S.add(parameters_z[i] > parameters_z[j])
# ----------------- Opposite of above -----------------
elif (outer_1 + outer_2 > inner_1 + inner_2):
S.add(parameters_z[i] < parameters_z[j])
# ----------------- Ensure that the most expesnive route (r1-1) is at most 1.4 times the cheapest route (r5-5) -----------------
S.add(parameters_z[-1] * factor > parameters_z[0])
if str(S.check()) == 'unsat':
return 'unsat'
else:
sat = S.model()
new_fares = []
for i in range(len(sat)):
route = sat[i].name()
price = sat[sat[i]].as_decimal(2)
if price[-1] == '?':
price = price[:-1]
temp_dict = {}
temp_dict[route] = price
new_fares.append(temp_dict)
return new_fares
def synthesize(self, outputNames, cnsts, sim):
"""Main synthesizer."""
outs, y1s, y2s, y1cs, y2cs = self.createOutputExpressions(outputNames)
input_vars = self.createInputVars()
# let's create the initial instance.
S = z3.Solver()
# do we want unsat cores?
if self.unsat_core: S.set(unsat_core=True)
# constraints.
self.addConstraints(S, cnsts)
# let's now create the expression that states one of the outputs must be different.
ys = []
for i, (y1, y2, y1c,
y2c) in enumerate(itertools.izip(y1s, y2s, y1cs, y2cs)):
yi = z3.Bool('y%d' % i)
self.addExpr(S, yi == z3.Distinct(y1, y2), 'prob%d' % i)
ys.append(yi)
if y1c:
assert y2c
self.addExpr(S, y1c, 'cnst%d_1' % i)
self.addExpr(S, y2c, 'cnst%d_2' % i)
y = z3.Bool('y')
self.addExpr(S, y == z3.Or(*ys), 'prob')
# now for the actual solution loop.
iterations = 0
# FIXME
# y1mz3 = None
# y2mz3 = None
while S.check(y) == z3.sat:
iterations += 1
m = S.model()
if self.VERBOSITY >= 2:
self.log('\niteration #%d' % iterations)
self.dumpModel(m, y1s, y2s)
sim_inputs = self.extractInputsFromModel(m, input_vars)
sim_outputs = {}
# FIXME sim signature.
sim(sim_inputs, sim_outputs)
if self.DEBUG:
pass
if self.VERBOSITY >= 2:
self.log_dict('sim_inputs', sim_inputs)
self.log_dict('sim_outputs', sim_outputs)
def assertEquality(ocnst, name):
n1 = 'out_%s_1_%d' % (name, iterations)
n2 = 'out_%s_2_%d' % (name, iterations)
if self.unsat_core:
S.assert_and_track(ocnst == y1mz3, n1)
S.assert_and_track(ocnst == y2mz3, n2)
else:
S.add(ocnst == y1mz3)
S.add(ocnst == y2mz3)
if self.VERBOSITY >= 4:
self.log('out:' + repr(ocnst))
y1z3s, y2z3s = [], []
for name, out in itertools.izip(outputNames, outs):
out.clearCache()
y1mz3 = z3.simplify(
out.toZ3Constraints(Synthesizer.P1, sim_inputs))
out.clearCache()
y2mz3 = z3.simplify(
out.toZ3Constraints(Synthesizer.P2, sim_inputs))
y1mz3c = out.z3ClausesWithConstraints(Synthesizer.P1,
sim_inputs)
y2mz3c = out.z3ClausesWithConstraints(Synthesizer.P2,
sim_inputs)
if y1mz3c:
assert y2mz3c
S.add(z3.simplify(y1mz3c))
S.add(z3.simplify(y2mz3c))
y1z3s.append(y1mz3)
y2z3s.append(y2mz3)
if self.VERBOSITY >= 4:
self.log('#it:' + repr(iterations))
self.log('y1mz3:' + y1mz3.sexpr())
self.log('y2mz3:' + y2mz3.sexpr())
if self.outputTypes[name] == Synthesizer.BITVEC:
assertEquality(z3.BitVecVal(sim_outputs[name], out.width),
name)
elif self.outputTypes[name] == Synthesizer.BOOL:
assertEquality(z3.BoolVal(sim_outputs[name]), name)
else:
assert self.outputTypes[name] == Synthesizer.MEM
assertEquality(
ast.createConstantArray(out.awidth, out.dwidth,
sim_outputs[name]), name)
if iterations >= self.MAXITER:
raise RuntimeError, 'Too many (%d) iterations executed.' % iterations
if self.VERBOSITY >= 4 or self.DUMP_SMT2:
filename = 'model%d.smt2' % iterations
with open(filename, 'wt') as fileobj:
print >> fileobj, S.to_smt2()
# and finally we are done.
if self.VERBOSITY >= 1:
self.log('Finished after %d iteration(s).' % iterations)
# now we need to extract solution
result = S.check(z3.Not(y))
if result == z3.unsat and self.unsat_core:
self.log('UNSAT core: ' + repr(S.unsat_core()))
S.add(z3.Not(y))
assert result == z3.sat
m = S.model()
if self.VERBOSITY >= 3:
self.log('model:' + repr(m))
for out in outs:
out.clearCache()
rs = [
self.cleanup(cnsts, outputNames[i], out.synthesize(m))
for i, out in enumerate(outs)
]
for r in rs:
r.clearCache()
return rs
def check_bounds(lc,
pre,
rec,
ref,
bounds,
z3_vars,
bound2s,
z3_var2s,
v=0,
progbar=False):
all_z3_vars = z3_vars.copy()
all_z3_vars.update(z3_var2s)
ref2, _ = gen_rec_constraints(ref, z3_vars)
s = z3.Solver()
s2 = z3.Solver()
s.set("timeout", 5000)
s2.set("timeout", 5000)
res = z3.sat
# first check eq constraint won't cause timeouts:
s.push()
res = check_valid([], [], ref, ref2, pre, lc, rec, s)
s.pop()
if res == z3.unknown:
ref, ref2 = [], []
valid_bounds = []
valid_bound2s = []
for bound, bound2 in tqdm(list(zip(bounds, bound2s))):
s.push()
res = check_valid(valid_bounds + [bound], valid_bound2s + [bound2],
ref, ref2, pre, lc, rec, s)
if res == z3.unsat:
valid_bounds.append(bound)
valid_bound2s.append(bound2)
elif res == z3.unknown:
ref = []
ref2 = []
else:
# print('model',s.model())
pass
s.pop()
loop = range(len(bounds) + len(ref))
if progbar:
loop = tqdm(loop)
for i in loop:
s.push()
res1 = check_valid(bounds, bound2s, ref, ref2, pre, lc, rec, s)
if v:
print('check\n', s, res)
if res1 == z3.unsat:
if progbar:
loop.update(1)
break
if res1 == z3.unknown:
s.pop()
ref = []
ref2 = []
continue
m = s.model()
try:
model_constraint = [all_z3_vars[str(mi)] == m[mi] for mi in m]
except KeyError:
for mi in m:
all_z3_vars[str(mi)] = z3.Int(str(mi))
model_constraint = [all_z3_vars[str(mi)] == m[mi] for mi in m]
if v:
print('model\n', m)
s2.push()
s2.add(model_constraint)
if v:
print('bounds')
print(bounds)
bound_sats = []
for bound, bound2 in zip(bounds, bound2s):
s2.push()
s2.add(bound, bound2)
res = s2.check()
bound_sats.append(res)
if v:
print('\t', bound, res)
s2.pop()
pruned_bounds = []
pruned_bound2s = []
for bound, bound2, bound_sat in zip(bounds, bound2s, bound_sats):
if bound_sat == z3.sat or bound in valid_bounds:
pruned_bounds.append(bound)
pruned_bound2s.append(bound2)
if len(pruned_bounds) == len(bounds):
pruned_ref, pruned_ref2 = [], []
for eq_constr, eq_constr2 in zip(ref, ref2):
s2.push()
s2.add(eq_constr, eq_constr2)
res = s2.check()
if res == z3.sat:
pruned_ref.append(eq_constr)
pruned_ref2.append(eq_constr2)
ref = pruned_ref
ref2 = pruned_ref2
bounds = pruned_bounds
bound2s = pruned_bound2s
s.pop()
s2.pop()
if res1 != z3.unsat:
# could not verify soundness, do not return any bounds
bounds = valid_bounds
return bounds
