print("Boolector id " + b.GitId())
print()
print(b.Copyright())
# Try pushing a context without enabling incremental usage,
# raises Exception
try:
print("Expect exception to be raised (incremental usage not enabled).")
b.Push()
except BoolectorException as e:
print("Caught exception: " + str(e))
# Try popping a context without first having pushed a context,
# raises Exception
try:
b.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
print("Expect exception to be raised (no context pushed).")
b.Pop()
except BoolectorException as e:
print("Caught exception: " + str(e))
### Creating Boolector nodes
# Sorts
_boolsort = b.BoolSort()
# Try creating a bit-vector sort of size 0,
# raises Exception
try:
print("Expect exception to be raised (bit-vector size of 0).")
_bvsort = b.BitVecSort(0)
except BoolectorException as e:
def randomize(self, ri: RandInfo, bound_m: Dict[FieldModel,
VariableBoundModel]):
"""Randomize the variables and constraints in a RandInfo collection"""
# for rs in ri.randsets():
# print("RandSet")
# for f in rs.all_fields():
# print(" " + f.name + " " + str(bound_m[f].domain.range_l))
# for uf in ri.unconstrained():
# print("Unconstrained: " + uf.name)
rs_i = 0
start_rs_i = 0
max_fields = 20
while rs_i < len(ri.randsets()):
btor = Boolector()
self.btor = btor
btor.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
start_rs_i = rs_i
constraint_l = []
# Collect up to max_fields fields to randomize at a time
n_fields = 0
while rs_i < len(ri.randsets()):
rs = ri.randsets()[rs_i]
try:
for f in rs.all_fields():
f.build(btor)
n_fields += 1
except Exception as e:
for c in rs.constraints():
print("Constraint: " + self.pretty_printer.do_print(c))
raise e
constraint_l.extend(
list(
map(
lambda c: (c, c.build(btor),
isinstance(c, ConstraintSoftModel)),
rs.constraints())))
rs_i += 1
if n_fields > max_fields:
break
for c in constraint_l:
try:
btor.Assume(c[1])
except Exception as e:
from ..visitors.model_pretty_printer import ModelPrettyPrinter
print("Exception: " + ModelPrettyPrinter.print(c[0]))
raise e
soft_node_l = list(
map(lambda c: c[1], filter(lambda c: c[2], constraint_l)))
node_l = list(
map(lambda c: c[1], filter(lambda c: not c[2], constraint_l)))
# Perform an initial solve to establish correctness
if btor.Sat() != btor.SAT:
if len(soft_node_l) > 0:
# Try one more time before giving up
for i, f in enumerate(btor.Failed(*soft_node_l)):
if f:
soft_node_l[i] = None
# Add back the hard-constraint nodes and soft-constraints that
# didn't fail
for n in filter(lambda n: n is not None,
node_l + soft_node_l):
btor.Assume(n)
# If we fail again, then we truly have a problem
if btor.Sat() != btor.SAT:
# Ensure we clean up
x = start_rs_i
while x < rs_i:
rs = ri.randsets()[x]
for f in rs.all_fields():
f.dispose()
x += 1
raise Exception("solve failure")
else:
# Still need to convert assumptions to assertions
for n in filter(lambda n: n is not None,
node_l + soft_node_l):
btor.Assert(n)
else:
print("Failed constraints:")
i = 1
for c in constraint_l:
if btor.Failed(c[1]):
print("[" + str(i) + "]: " +
self.pretty_printer.do_print(c[0], False))
print("[" + str(i) + "]: " +
self.pretty_printer.do_print(c[0], True))
i += 1
# Ensure we clean up
for rs in ri.randsets():
for f in rs.all_fields():
f.dispose()
print("Solve failure")
raise Exception("solve failure")
else:
# Still need to convert assumptions to assertions
btor.Assert(*(node_l + soft_node_l))
self.swizzle_randvars(btor, ri, start_rs_i, rs_i, bound_m)
# Finalize the value of the field
x = start_rs_i
while x < rs_i:
rs = ri.randsets()[x]
for f in rs.all_fields():
f.post_randomize()
f.dispose(
) # Get rid of the solver var, since we're done with it
x += 1
uc_rand = list(filter(lambda f: f.is_used_rand, ri.unconstrained()))
for uf in uc_rand:
bounds = bound_m[uf]
range_l = bounds.domain.range_l
if len(range_l) == 1:
# Single (likely domain-based) range
uf.set_val(self.randint(range_l[0][0], range_l[0][1]))
else:
# Most likely an enumerated type
# TODO: are there any cases where these could be ranges?
idx = self.randint(0, len(range_l) - 1)
uf.set_val(range_l[idx][0])
def create_diagnostics(self, active_randsets) -> str:
ret = ""
btor = Boolector()
btor.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
diagnostic_constraint_l = []
diagnostic_field_l = []
# First, determine how many randsets are actually failing
i = 0
while i < len(active_randsets):
rs = active_randsets[i]
for f in rs.all_fields():
f.build(btor)
# Assume that we can omit all soft constraints, since they
# will have already been omitted (?)
constraint_l = list(map(lambda c:(c,c.build(btor)), filter(lambda c:not isinstance(c,ConstraintSoftModel), rs.constraints())))
for c in constraint_l:
btor.Assume(c[1])
if btor.Sat() != btor.SAT:
# Save fields and constraints if the randset doesn't
# solve on its own
diagnostic_constraint_l.extend(constraint_l)
diagnostic_field_l.extend(rs.fields())
i += 1
problem_constraints = []
solving_constraints = []
# Okay, now perform a series of solves to identify
# constraints that are actually a problem
for c in diagnostic_constraint_l:
btor.Assume(c[1])
if btor.Sat() != btor.SAT:
# This is a problematic constraint
# Save it for later
problem_constraints.append(c[0])
else:
# Not a problem. Assert it now
btor.Assert(c[1])
solving_constraints.append(c[0])
# problem_constraints.append(c[0])
if btor.Sat() != btor.SAT:
raise Exception("internal error: system should solve")
# Okay, we now have a constraint system that solves, and
# a list of constraints that are a problem. We want to
# resolve the value of all variables referenced by the
# solving constraints so and then display the non-solving
# constraints. This will (hopefully) help highlight the
# reason for the failure
for c in solving_constraints:
c.accept(RefFieldsPostRandVisitor())
ret += "Problem Constraints:\n"
for i,pc in enumerate(problem_constraints):
ret += "Constraint " + str(i+1) + ":\n"
ret += ModelPrettyPrinter.print(pc, print_values=True)
ret += ModelPrettyPrinter.print(pc, print_values=False)
for rs in active_randsets:
for f in rs.all_fields():
f.dispose()
return ret
def randomize(self, ri : RandInfo, bound_m : Dict[FieldModel,VariableBoundModel]):
"""Randomize the variables and constraints in a RandInfo collection"""
if self.debug > 0:
for rs in ri.randsets():
print("RandSet")
for f in rs.all_fields():
if f in bound_m.keys():
print(" Field: " + f.fullname + " " + str(bound_m[f].domain.range_l))
for c in rs.constraints():
print(" Constraint: " + self.pretty_printer.do_print(c, show_exp=True))
for uf in ri.unconstrained():
print("Unconstrained: " + uf.name)
# Assign values to the unconstrained fields first
uc_rand = list(filter(lambda f:f.is_used_rand, ri.unconstrained()))
for uf in uc_rand:
if self.debug > 0:
print("Randomizing unconstrained: " + uf.fullname)
bounds = bound_m[uf]
range_l = bounds.domain.range_l
if len(range_l) == 1:
# Single (likely domain-based) range
uf.set_val(
self.randint(range_l[0][0], range_l[0][1]))
else:
# Most likely an enumerated type
# TODO: are there any cases where these could be ranges?
idx = self.randint(0, len(range_l)-1)
uf.set_val(range_l[idx][0])
# Lock so we don't overwrite
uf.set_used_rand(False)
rs_i = 0
start_rs_i = 0
max_fields = 20
while rs_i < len(ri.randsets()):
btor = Boolector()
self.btor = btor
btor.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
start_rs_i = rs_i
constraint_l = []
# Collect up to max_fields fields to randomize at a time
n_fields = 0
while rs_i < len(ri.randsets()):
rs = ri.randsets()[rs_i]
rs_node_builder = RandSetNodeBuilder(btor)
all_fields = rs.all_fields()
if self.debug > 0:
print("Pre-Randomize: RandSet")
for f in all_fields:
if f in bound_m.keys():
print(" Field: " + f.fullname + " " + str(bound_m[f].domain.range_l))
for c in rs.constraints():
print(" Constraint: " + self.pretty_printer.do_print(c, show_exp=True, print_values=True))
rs_node_builder.build(rs)
n_fields += len(all_fields)
constraint_l.extend(list(map(lambda c:(c,c.build(btor),isinstance(c,ConstraintSoftModel)), rs.constraints())))
rs_i += 1
if n_fields > max_fields or rs.order != -1:
break
for c in constraint_l:
try:
btor.Assume(c[1])
except Exception as e:
from ..visitors.model_pretty_printer import ModelPrettyPrinter
print("Exception: " + ModelPrettyPrinter.print(c[0]))
raise e
soft_node_l = list(map(lambda c:c[1], filter(lambda c:c[2], constraint_l)))
node_l = list(map(lambda c:c[1], filter(lambda c:not c[2], constraint_l)))
# Perform an initial solve to establish correctness
if btor.Sat() != btor.SAT:
if len(soft_node_l) > 0:
# Try one more time before giving up
for i,f in enumerate(soft_node_l):
if btor.Failed(f):
soft_node_l[i] = None
# Add back the hard-constraint nodes and soft-constraints that
# didn't fail
# for n in filter(lambda n:n is not None, node_l+soft_node_l):
for n in filter(lambda n:n is not None, node_l):
btor.Assume(n)
for n in filter(lambda n:n is not None, soft_node_l):
btor.Assume(n)
# If we fail again, then we truly have a problem
if btor.Sat() != btor.SAT:
# Ensure we clean up
# active_randsets = []
# x=start_rs_i
# while x < rs_i:
# rs = ri.randsets()[x]
# active_randsets.append(rs)
# for f in rs.all_fields():
# f.dispose()
# x += 1
active_randsets = []
for rs in ri.randsets():
active_randsets.append(rs)
for f in rs.all_fields():
f.dispose()
raise SolveFailure(
"solve failure",
self.create_diagnostics(active_randsets))
# raise SolveFailure(
# "solve failure",
# self.create_diagnostics(active_randsets))
else:
# Still need to convert assumptions to assertions
for n in filter(lambda n:n is not None, node_l+soft_node_l):
btor.Assert(n)
else:
# print("Failed constraints:")
# i=1
# for c in constraint_l:
# if btor.Failed(c[1]):
# print("[" + str(i) + "]: " + self.pretty_printer.do_print(c[0], False))
# print("[" + str(i) + "]: " + self.pretty_printer.do_print(c[0], True))
# i+=1
# Ensure we clean up
active_randsets = []
for rs in ri.randsets():
active_randsets.append(rs)
for f in rs.all_fields():
f.dispose()
raise SolveFailure(
"solve failure",
self.create_diagnostics(active_randsets))
else:
# Still need to convert assumptions to assertions
btor.Assert(*(node_l+soft_node_l))
self.swizzle_randvars(btor, ri, start_rs_i, rs_i, bound_m)
# Finalize the value of the field
x = start_rs_i
reset_v = DynamicExprResetVisitor()
while x < rs_i:
rs = ri.randsets()[x]
for f in rs.all_fields():
f.post_randomize()
f.set_used_rand(False, 0)
f.dispose() # Get rid of the solver var, since we're done with it
f.accept(reset_v)
# for f in rs.nontarget_field_s:
# f.dispose()
for c in rs.constraints():
c.accept(reset_v)
RandSetDisposeVisitor().dispose(rs)
x += 1
end = int(round(time.time() * 1000))
def __init__(self, quantized_model, btor=None, verbose=None, config=None):
if btor is None:
btor = Boolector()
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, 2)
self.btor = btor
self._debug_list = []
self._verbose = verbose
# This setting should yield the best results on average
self.config = {
"add_bound_constraints": True,
"sat_engine_cadical": True,
"rebalance_sum": True,
"recursive_sum": True,
"sort_sum": None,
"propagate_bounds": True,
"relu_simplify": True,
"subsum_elimination":
False, # subsub elimination can hurt performance -> better not activate it
"min_bits": True,
"shift_elimination": True,
}
# Overwrite default config with argument
if not config is None:
for k, v in config.items():
self.config[k] = v
self._stats = {
"constant_neurons": 0,
"linear_neurons": 0,
"unstable_neurons": 0,
"reused_expressions": 0,
"partially_stable_neurons": 0,
"build_time": 0,
"sat_time": 0,
"total_time": 0,
}
self.dense_layers = []
self.quantized_model = quantized_model
self._last_layer_signed = quantized_model._last_layer_signed
self.quantization_config = quantized_model.quantization_config
current_bits = self.quantization_config["input_bits"]
for i, l in enumerate(quantized_model.dense_layers):
self.dense_layers.append(
LayerEncoding(
layer_size=l.units,
btor=self.btor,
bit_width=self.quantization_config["quantization_bits"],
frac_bits=self.quantization_config["quantization_bits"] -
self.quantization_config["int_bits_activation"],
quantization_config=self.quantization_config,
signed_output=l.signed_output,
))
# Create input vars
input_size = quantized_model._input_shape[-1]
self.input_layer = LayerEncoding(
layer_size=input_size,
btor=self.btor,
bit_width=self.quantization_config["input_bits"],
frac_bits=self.quantization_config["input_bits"] -
self.quantization_config["int_bits_input"],
quantization_config=self.quantization_config,
)
self.input_vars = self.input_layer.vars
self.output_vars = self.dense_layers[-1].vars
def randomize(self, ri: RandInfo, bound_m: Dict[FieldModel,
VariableBoundModel]):
"""Randomize the variables and constraints in a RandInfo collection"""
if self.solve_info is not None:
self.solve_info.n_randsets = len(ri.randsets())
if self.debug > 0:
rs_i = 0
while rs_i < len(ri.randsets()):
rs = ri.randsets()[rs_i]
print("RandSet[%d]" % rs_i)
for f in rs.all_fields():
if f in bound_m.keys():
print(" Field: %s is_rand=%s %s" %
(f.fullname, str(f.is_used_rand),
str(bound_m[f].domain.range_l)))
else:
print(" Field: %s is_rand=%s (unbounded)" %
(f.fullname, str(f.is_used_rand)))
for c in rs.constraints():
print(" Constraint: " +
self.pretty_printer.do_print(c, show_exp=True))
for c in rs.soft_constraints():
print(" SoftConstraint: " +
self.pretty_printer.do_print(c, show_exp=True))
rs_i += 1
for uf in ri.unconstrained():
print("Unconstrained: " + uf.fullname)
# Assign values to the unconstrained fields first
uc_rand = list(filter(lambda f: f.is_used_rand, ri.unconstrained()))
for uf in uc_rand:
if self.debug > 0:
print("Randomizing unconstrained: " + uf.fullname)
bounds = bound_m[uf]
range_l = bounds.domain.range_l
if len(range_l) == 1:
# Single (likely domain-based) range
uf.set_val(self.randstate.randint(range_l[0][0],
range_l[0][1]))
else:
# Most likely an enumerated type
# TODO: are there any cases where these could be ranges?
idx = self.randstate.randint(0, len(range_l) - 1)
uf.set_val(range_l[idx][0])
# Lock so we don't overwrite
uf.set_used_rand(False)
rs_i = 0
start_rs_i = 0
# max_fields = 20
max_fields = 0
while rs_i < len(ri.randsets()):
btor = Boolector()
self.btor = btor
btor.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
start_rs_i = rs_i
constraint_l = []
soft_constraint_l = []
# Collect up to max_fields fields to randomize at a time
n_fields = 0
while rs_i < len(ri.randsets()):
rs = ri.randsets()[rs_i]
rs_node_builder = RandSetNodeBuilder(btor)
all_fields = rs.all_fields()
if self.debug > 0:
print("Pre-Randomize: RandSet[%d]" % rs_i)
for f in all_fields:
if f in bound_m.keys():
print(" Field: %s is_rand=%s %s var=%s" %
(f.fullname, str(f.is_used_rand),
str(bound_m[f].domain.range_l), str(f.var)))
else:
print(" Field: %s is_rand=%s (unbounded)" %
(f.fullname, str(f.is_used_rand)))
for c in rs.constraints():
print(" Constraint: " + self.pretty_printer.do_print(
c, show_exp=True, print_values=True))
for c in rs.soft_constraints():
print(" SoftConstraint: " +
self.pretty_printer.do_print(
c, show_exp=True, print_values=True))
if self.solve_info is not None:
self.solve_info.n_cfields += len(all_fields)
rs_node_builder.build(rs)
n_fields += len(all_fields)
# constraint_l.extend(list(map(lambda c:(c,c.build(btor),isinstance(c,ConstraintSoftModel)), rs.constraints())))
constraint_l.extend(
list(map(lambda c: (c, c.build(btor)), rs.constraints())))
soft_constraint_l.extend(
list(
map(lambda c: (c, c.build(btor)),
rs.soft_constraints())))
# Sort the list in descending order so we know which constraints
# to prioritize
soft_constraint_l.sort(key=lambda c: c[0].priority,
reverse=True)
rs_i += 1
if n_fields > max_fields or rs.order != -1:
break
for c in constraint_l:
try:
btor.Assume(c[1])
except Exception as e:
print("Exception: " + self.pretty_printer.print(c[0]))
raise e
if self.solve_info is not None:
self.solve_info.n_sat_calls += 1
if btor.Sat() != btor.SAT:
# If the system doesn't solve with hard constraints added,
# then we may as well bail now
active_randsets = []
for rs in ri.randsets():
active_randsets.append(rs)
for f in rs.all_fields():
f.dispose()
if self.solve_fail_debug > 0:
raise SolveFailure(
"solve failure",
self.create_diagnostics(active_randsets))
else:
raise SolveFailure(
"solve failure",
"Solve failure: set 'solve_fail_debug=1' for more details"
)
else:
# Lock down the hard constraints that are confirmed
# to be valid
for c in constraint_l:
btor.Assert(c[1])
# If there are soft constraints, add these now
if len(soft_constraint_l) > 0:
for c in soft_constraint_l:
try:
btor.Assume(c[1])
except Exception as e:
from ..visitors.model_pretty_printer import ModelPrettyPrinter
print("Exception: " + ModelPrettyPrinter.print(c[0]))
raise e
if self.solve_info is not None:
self.solve_info.n_sat_calls += 1
if btor.Sat() != btor.SAT:
# All the soft constraints cannot be satisfied. We'll need to
# add them incrementally
if self.debug > 0:
print(
"Note: some of the %d soft constraints could not be satisfied"
% len(soft_constraint_l))
for c in soft_constraint_l:
btor.Assume(c[1])
if self.solve_info is not None:
self.solve_info.n_sat_calls += 1
if btor.Sat() == btor.SAT:
if self.debug > 0:
print("Note: soft constraint %s (%d) passed" %
(self.pretty_printer.print(
c[0]), c[0].priority))
btor.Assert(c[1])
else:
if self.debug > 0:
print("Note: soft constraint %s (%d) failed" %
(self.pretty_printer.print(
c[0]), c[0].priority))
else:
# All the soft constraints could be satisfied. Assert them now
if self.debug > 0:
print(
"Note: all %d soft constraints could be satisfied"
% len(soft_constraint_l))
for c in soft_constraint_l:
btor.Assert(c[1])
# btor.Sat()
x = start_rs_i
while x < rs_i:
self.swizzler.swizzle(btor, ri.randsets()[x], bound_m)
x += 1
# Finalize the value of the field
x = start_rs_i
reset_v = DynamicExprResetVisitor()
while x < rs_i:
rs = ri.randsets()[x]
for f in rs.all_fields():
f.post_randomize()
f.set_used_rand(False, 0)
f.dispose(
) # Get rid of the solver var, since we're done with it
f.accept(reset_v)
# for f in rs.nontarget_field_s:
# f.dispose()
for c in rs.constraints():
c.accept(reset_v)
RandSetDisposeVisitor().dispose(rs)
if self.debug > 0:
print("Post-Randomize: RandSet[%d]" % x)
for f in all_fields:
if f in bound_m.keys():
print(" Field: %s %s" %
(f.fullname, str(f.val.val)))
else:
print(" Field: %s (unbounded) %s" %
(f.fullname, str(f.val.val)))
for c in rs.constraints():
print(" Constraint: " + self.pretty_printer.do_print(
c, show_exp=True, print_values=True))
for c in rs.soft_constraints():
print(" SoftConstraint: " +
self.pretty_printer.do_print(
c, show_exp=True, print_values=True))
x += 1
end = int(round(time.time() * 1000))
def create_diagnostics(self, active_randsets) -> str:
btor = Boolector()
btor.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
model_valid = False
diagnostic_constraint_l = []
diagnostic_field_l = []
# First, determine how many randsets are actually failing
i = 0
while i < len(active_randsets):
rs = active_randsets[i]
for f in rs.all_fields():
f.build(btor)
# Assume that we can omit all soft constraints, since they
# will have already been omitted (?)
constraint_l = list(
map(
lambda c: (c, c.build(btor)),
filter(lambda c: not isinstance(c, ConstraintSoftModel),
rs.constraints())))
for c in constraint_l:
btor.Assume(c[1])
if btor.Sat() != btor.SAT:
# Save fields and constraints if the randset doesn't
# solve on its own
diagnostic_constraint_l.extend(constraint_l)
diagnostic_field_l.extend(rs.fields())
i += 1
problem_sets = []
degree = 1
while True:
init_size = len(diagnostic_constraint_l)
tmp_l = []
ret = self._collect_failing_constraints(btor,
diagnostic_constraint_l, 0,
degree, tmp_l,
problem_sets)
if len(diagnostic_constraint_l) == init_size and degree > 3:
break
else:
degree += 1
if Randomizer.EN_DEBUG > 0:
print("%d constraints remaining ; %d problem sets" %
(len(diagnostic_constraint_l), len(problem_sets)))
# Assert the remaining constraints
for c in diagnostic_constraint_l:
btor.Assert(c[1])
if btor.Sat() != btor.SAT:
raise Exception("internal error: system should solve")
# Okay, we now have a constraint system that solves, and
# a list of constraints that are a problem. We want to
# resolve the value of all variables referenced by the
# solving constraints so and then display the non-solving
# constraints. This will (hopefully) help highlight the
# reason for the failure
ret = ""
for ps in problem_sets:
ret += ("Problem Set: %d constraints\n" % len(ps))
for pc in ps:
ret += " %s:\n" % SourceInfo.toString(pc[0].srcinfo)
ret += " %s" % ModelPrettyPrinter.print(pc[0],
print_values=False)
pc = []
for c in ps:
pc.append(c[0])
lint_r = LintVisitor().lint([], pc)
if lint_r != "":
ret += "Lint Results:\n" + lint_r
for rs in active_randsets:
for f in rs.all_fields():
f.dispose()
return ret
print("Boolector id " + b.GitId())
print()
print(b.Copyright())
# Try pushing a context without enabling incremental usage,
# raises Exception
try:
print("Expect exception to be raised (incremental usage not enabled).")
b.Push()
except BoolectorException as e:
print("Caught exception: " + str(e))
# Try popping a context without first having pushed a context,
# raises Exception
try:
b.Set_opt(pyboolector.BTOR_OPT_INCREMENTAL, True)
print("Expect exception to be raised (no context pushed).")
b.Pop()
except BoolectorException as e:
print("Caught exception: " + str(e))
### Creating Boolector nodes
# Sorts
_boolsort = b.BoolSort()
# Try creating a bit-vector sort of size 0,
# raises Exception
try:
print("Expect exception to be raised (bit-vector size of 0).")
_bvsort = b.BitVecSort(0)
except BoolectorException as e:
import os
import pyboolector
from pyboolector import Boolector, BoolectorException
if __name__ == "__main__":
try:
# Create Boolector instance
btor = Boolector()
# Enable model generation
btor.Set_opt(pyboolector.BTOR_OPT_MODEL_GEN, True)
# Create bit-vector sort of size 8
bvsort8 = btor.BitVecSort(8)
# Create expressions
x = btor.Var(bvsort8, "x")
y = btor.Var(bvsort8, "y")
zero = btor.Const(0, 8)
hundred = btor.Const(100, 8)
# 0 < x
ult_x = btor.Ult(zero, x)
btor.Assert(ult_x)
# x <= 100
ulte_x = btor.Ulte(x, hundred)
btor.Assert(ulte_x)
# 0 < y
ult_y = btor.Ult(zero, y)
btor.Assert(ult_y)
# y <= 100
ulte_y = btor.Ulte(y, hundred)
btor.Assert(ulte_y)
# x * y
mul = btor.Mul(x, y)