def test_or_flaky(*args, **kwargs):
text_repr = arg_string(test, args, kwargs)
raise Flaky(('Hypothesis %s(%s) produces unreliable results: Falsified'
' on the first call but did not on a subsequent one') % (
test.__name__,
text_repr,
))
def test_or_flaky(*args, **kwargs):
text_repr = arg_string(test, args, kwargs)
if text_repr == expected_repr:
raise Flaky(
(
'Hypothesis %s(%s) produces unreliable results: Falsified'
' on the first call but did not on a subsequent one'
) % (test.__name__, text_repr,))
else:
raise Flaky(
(
'Hypothesis %s produces unreliable results: Falsified'
' on the first call but did not on a subsequent one. This '
' is possibly due to unreliable values, which may be a bug'
' in the strategy.\nCall 1: %s\nCall 2: %s\n'
) % (test.__name__, expected_repr, text_repr,))
def conclude_test(self, status, interesting_origin):
"""Says that ``status`` occurred at node ``node``. This updates the
node if necessary and checks for consistency."""
if status == Status.OVERRUN:
return
i = self.__index_in_current_node
node = self.__current_node
if i < len(node.values) or isinstance(node.transition, Branch):
inconsistent_generation()
new_transition = Conclusion(status, interesting_origin)
if node.transition is not None and node.transition != new_transition:
# As an, I'm afraid, horrible bodge, we deliberately ignore flakiness
# where tests go from interesting to valid, because it's much easier
# to produce good error messages for these further up the stack.
if isinstance(node.transition, Conclusion) and (
node.transition.status != Status.INTERESTING
or new_transition.status != Status.VALID
):
raise Flaky(
"Inconsistent test results! Test case was %r on first run but %r on second"
% (node.transition, new_transition)
)
else:
node.transition = new_transition
assert node is self.__trail[-1]
node.check_exhausted()
assert len(node.values) > 0 or node.check_exhausted()
if not self.killed:
self.__update_exhausted()
def run_state_machine_as_test(state_machine_factory, settings=None):
"""Run a state machine definition as a test, either silently doing nothing
or printing a minimal breaking program and raising an exception.
state_machine_factory is anything which returns an instance of
GenericStateMachine when called with no arguments - it can be a class or a
function. settings will be used to control the execution of the test.
"""
try:
breaker = find_breaking_runner(state_machine_factory, settings)
except NoSuchExample:
return
breaker.run(state_machine_factory(), print_steps=True)
raise Flaky('Run failed initially by succeeded on a second try')
def conclude_test(self, status, interesting_origin):
"""Says that ``status`` occurred at node ``node``. This updates the
node if necessary and checks for consistency."""
if status == Status.OVERRUN:
return
i = self.__index_in_current_node
node = self.__current_node
if i < len(node.values) or isinstance(node.transition, Branch):
inconsistent_generation()
new_transition = conclusion(status, interesting_origin)
if node.transition is not None and node.transition != new_transition:
# As an, I'm afraid, horrible bodge, we deliberately ignore flakiness
# where tests go from interesting to valid, because it's much easier
# to produce good error messages for these further up the stack.
if (
node.transition.status != Status.INTERESTING
or new_transition.status != Status.VALID
):
raise Flaky(
"Inconsistent test results! Test case was %r on first run but %r on second"
% (node.transition, new_transition)
)
else:
node.transition = new_transition
assert node is self.__trail[-1]
node.check_exhausted()
assert len(node.values) > 0 or node.check_exhausted()
for t in reversed(self.__trail):
# Any node we've traversed might have now become exhausted.
# We check from the right. As soon as we hit a node that
# isn't exhausted, this automatically implies that all of
# its parents are not exhausted, so we stop.
if not t.check_exhausted():
break
def run(self):
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
if global_force_seed is None:
database_key = str_to_bytes(fully_qualified_name(self.test))
else:
database_key = None
self.start_time = time.time()
global in_given
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings,
random=self.random,
database_key=database_key,
)
if in_given or self.collector is None:
runner.run()
else: # pragma: no cover
in_given = True
original_trace = sys.gettrace()
try:
sys.settrace(None)
runner.run()
finally:
in_given = False
sys.settrace(original_trace)
self.used_examples_from_database = \
runner.used_examples_from_database
note_engine_for_statistics(runner)
run_time = time.time() - self.start_time
self.used_examples_from_database = runner.used_examples_from_database
if runner.used_examples_from_database:
if self.settings.derandomize:
note_deprecation(
'In future derandomize will imply database=None, but your '
'test is currently using examples from the database. To '
'get the future behaviour, update your settings to '
'include database=None.')
if self.__had_seed:
note_deprecation(
'In future use of @seed will imply database=None in your '
'settings, but your test is currently using examples from '
'the database. To get the future behaviour, update your '
'settings for this test to include database=None.')
timed_out = runner.exit_reason == ExitReason.timeout
if runner.call_count == 0:
return
if runner.interesting_examples:
self.falsifying_examples = sorted(
[d for d in runner.interesting_examples.values()],
key=lambda d: sort_key(d.buffer),
reverse=True)
else:
if timed_out:
note_deprecation((
'Your tests are hitting the settings timeout (%.2fs). '
'This functionality will go away in a future release '
'and you should not rely on it. Instead, try setting '
'max_examples to be some value lower than %d (the number '
'of examples your test successfully ran here). Or, if you '
'would prefer your tests to run to completion, regardless '
'of how long they take, you can set the timeout value to '
'hypothesis.unlimited.') % (self.settings.timeout,
runner.valid_examples),
self.settings)
if runner.valid_examples == 0:
if timed_out:
raise Timeout(
('Ran out of time before finding a satisfying '
'example for %s. Only found %d examples in %.2fs.') %
(get_pretty_function_description(
self.test), runner.valid_examples, run_time))
else:
raise Unsatisfiable(
'Unable to satisfy assumptions of hypothesis %s.' %
(get_pretty_function_description(self.test), ))
if not self.falsifying_examples:
return
self.failed_normally = True
flaky = 0
for falsifying_example in self.falsifying_examples:
ran_example = ConjectureData.for_buffer(falsifying_example.buffer)
self.__was_flaky = False
assert falsifying_example.__expected_exception is not None
try:
self.execute(ran_example,
print_example=True,
is_final=True,
expected_failure=(
falsifying_example.__expected_exception,
falsifying_example.__expected_traceback,
))
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
self.__flaky(
'Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.')
except BaseException:
if len(self.falsifying_examples) <= 1:
raise
report(traceback.format_exc())
finally: # pragma: no cover
# This section is in fact entirely covered by the tests in
# test_reproduce_failure, but it seems to trigger a lovely set
# of coverage bugs: The branches show up as uncovered (despite
# definitely being covered - you can add an assert False else
# branch to verify this and see it fail - and additionally the
# second branch still complains about lack of coverage even if
# you add a pragma: no cover to it!
# See https://bitbucket.org/ned/coveragepy/issues/623/
if self.settings.print_blob is not PrintSettings.NEVER:
failure_blob = encode_failure(falsifying_example.buffer)
# Have to use the example we actually ran, not the original
# falsifying example! Otherwise we won't catch problems
# where the repr of the generated example doesn't parse.
can_use_repr = ran_example.can_reproduce_example_from_repr
if (self.settings.print_blob is PrintSettings.ALWAYS or
(self.settings.print_blob is PrintSettings.INFER
and not can_use_repr and len(failure_blob) < 200)):
report((
'\n'
'You can reproduce this example by temporarily '
'adding @reproduce_failure(%r, %r) as a decorator '
'on your test case') % (
__version__,
failure_blob,
))
if self.__was_flaky:
flaky += 1
# If we only have one example then we should have raised an error or
# flaky prior to this point.
assert len(self.falsifying_examples) > 1
if flaky > 0:
raise Flaky(
('Hypothesis found %d distinct failures, but %d of them '
'exhibited some sort of flaky behaviour.') %
(len(self.falsifying_examples), flaky))
else:
raise MultipleFailures(('Hypothesis found %d distinct failures.') %
(len(self.falsifying_examples, )))
def run(self):
database_key = str_to_bytes(fully_qualified_name(self.test))
start_time = time.time()
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings,
random=self.random,
database_key=database_key,
)
runner.run()
note_engine_for_statistics(runner)
run_time = time.time() - start_time
timed_out = (self.settings.timeout > 0
and run_time >= self.settings.timeout)
if runner.last_data is None:
return
if runner.last_data.status == Status.INTERESTING:
self.falsifying_example = runner.last_data.buffer
if self.settings.database is not None:
self.settings.database.save(database_key,
self.falsifying_example)
else:
if runner.valid_examples < min(
self.settings.min_satisfying_examples,
self.settings.max_examples,
) and not (runner.exit_reason == ExitReason.finished
and self.at_least_one_success):
if timed_out:
raise Timeout(
('Ran out of time before finding a satisfying '
'example for '
'%s. Only found %d examples in ' + '%.2fs.') %
(get_pretty_function_description(
self.test), runner.valid_examples, run_time))
else:
raise Unsatisfiable(
('Unable to satisfy assumptions of hypothesis '
'%s. Only %d examples considered '
'satisfied assumptions') % (
get_pretty_function_description(self.test),
runner.valid_examples,
))
if self.falsifying_example is None:
return
assert self.last_exception is not None
try:
with self.settings:
self.test_runner(
ConjectureData.for_buffer(self.falsifying_example),
reify_and_execute(self.search_strategy,
self.test,
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
raise Flaky('Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.')
report(
'Failed to reproduce exception. Expected: \n' +
self.last_exception, )
filter_message = (
'Unreliable test data: Failed to reproduce a failure '
'and then when it came to recreating the example in '
'order to print the test data with a flaky result '
'the example was filtered out (by e.g. a '
'call to filter in your strategy) when we didn\'t '
'expect it to be.')
try:
self.test_runner(
ConjectureData.for_buffer(self.falsifying_example),
reify_and_execute(self.search_strategy,
test_is_flaky(self.test,
self.repr_for_last_exception),
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
raise Flaky(filter_message)
def run(self):
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
database_key = str_to_bytes(fully_qualified_name(self.test))
self.start_time = time.time()
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings,
random=self.random,
database_key=database_key,
)
runner.run()
note_engine_for_statistics(runner)
run_time = time.time() - self.start_time
timed_out = runner.exit_reason == ExitReason.timeout
if runner.last_data is None:
return
if runner.interesting_examples:
self.falsifying_examples = sorted(
[d for d in runner.interesting_examples.values()],
key=lambda d: sort_key(d.buffer),
reverse=True)
else:
if timed_out:
note_deprecation((
'Your tests are hitting the settings timeout (%.2fs). '
'This functionality will go away in a future release '
'and you should not rely on it. Instead, try setting '
'max_examples to be some value lower than %d (the number '
'of examples your test successfully ran here). Or, if you '
'would prefer your tests to run to completion, regardless '
'of how long they take, you can set the timeout value to '
'hypothesis.unlimited.') % (self.settings.timeout,
runner.valid_examples),
self.settings)
if runner.valid_examples < min(
self.settings.min_satisfying_examples,
self.settings.max_examples,
) and not (runner.exit_reason == ExitReason.finished
and self.at_least_one_success):
if timed_out:
raise Timeout(
('Ran out of time before finding a satisfying '
'example for '
'%s. Only found %d examples in ' + '%.2fs.') %
(get_pretty_function_description(
self.test), runner.valid_examples, run_time))
else:
raise Unsatisfiable(
('Unable to satisfy assumptions of hypothesis '
'%s. Only %d examples considered '
'satisfied assumptions') % (
get_pretty_function_description(self.test),
runner.valid_examples,
))
if not self.falsifying_examples:
return
flaky = 0
for falsifying_example in self.falsifying_examples:
self.__was_flaky = False
raised_exception = False
try:
with self.settings:
self.test_runner(
ConjectureData.for_buffer(falsifying_example.buffer),
reify_and_execute(self.search_strategy,
self.test,
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
self.__flaky(
'Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.')
except:
if len(self.falsifying_examples) <= 1:
raise
raised_exception = True
report(traceback.format_exc())
if not raised_exception:
report(
'Failed to reproduce exception. Expected: \n' +
falsifying_example.__expected_exception, )
filter_message = (
'Unreliable test data: Failed to reproduce a failure '
'and then when it came to recreating the example in '
'order to print the test data with a flaky result '
'the example was filtered out (by e.g. a '
'call to filter in your strategy) when we didn\'t '
'expect it to be.')
try:
self.test_runner(
ConjectureData.for_buffer(falsifying_example.buffer),
reify_and_execute(self.search_strategy,
test_is_flaky(
self.test,
self.repr_for_last_exception),
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
self.__flaky(filter_message)
except Flaky as e:
if len(self.falsifying_examples) > 1:
self.__flaky(e.args[0])
else:
raise
if self.__was_flaky:
flaky += 1
# If we only have one example then we should have raised an error or
# flaky prior to this point.
assert len(self.falsifying_examples) > 1
if flaky > 0:
raise Flaky(
('Hypothesis found %d distinct failures, but %d of them '
'exhibited some sort of flaky behaviour.') %
(len(self.falsifying_examples), flaky))
else:
raise MultipleFailures(('Hypothesis found %d distinct failures.') %
(len(self.falsifying_examples, )))
def __flaky(self, message):
if len(self.falsifying_examples) <= 1:
raise Flaky(message)
else:
self.__was_flaky = True
report("Flaky example! " + message)
def falsify(self, hypothesis, *argument_types, **kwargs): # pylint: disable=too-many-locals,too-many-branches
"""
Attempt to construct an example tuple x matching argument_types such
that hypothesis(*x) returns a falsey value
"""
teardown_example = kwargs.get('teardown_example') or (lambda x: None)
setup_example = kwargs.get('setup_example') or (lambda: None)
random = self.random
if random is None:
random = Random(function_digest(hypothesis))
build_context = BuildContext(random)
search_strategy = strategy(argument_types, self.settings)
storage = None
if self.database is not None:
storage = self.database.storage_for(argument_types)
def falsifies(args): # pylint: disable=missing-docstring
example = None
try:
try:
setup_example()
example = search_strategy.reify(args)
return not hypothesis(*example)
except UnsatisfiedAssumption:
return False
finally:
teardown_example(example)
track_seen = Tracker()
falsifying_examples = []
if storage:
for example in storage.fetch():
track_seen.track(example)
if falsifies(example):
falsifying_examples = [example]
break
satisfying_examples = 0
timed_out = False
max_examples = self.max_examples
min_satisfying_examples = self.min_satisfying_examples
parameter_source = ParameterSource(context=build_context,
strategy=search_strategy,
min_parameters=max(
2,
int(float(max_examples) / 10)))
start_time = time.time()
def time_to_call_it_a_day():
"""Have we exceeded our timeout?"""
if self.timeout <= 0:
return False
return time.time() >= start_time + self.timeout
for parameter in islice(parameter_source,
max_examples - len(track_seen)):
if len(track_seen) >= search_strategy.size_upper_bound:
break
if falsifying_examples:
break
if time_to_call_it_a_day():
break
args = search_strategy.produce_template(build_context, parameter)
if track_seen.track(args) > 1:
parameter_source.mark_bad()
continue
try:
setup_example()
a = None
try:
a = search_strategy.reify(args)
is_falsifying_example = not hypothesis(*a)
finally:
teardown_example(a)
except UnsatisfiedAssumption:
parameter_source.mark_bad()
continue
satisfying_examples += 1
if is_falsifying_example:
falsifying_examples.append(args)
run_time = time.time() - start_time
timed_out = self.timeout >= 0 and run_time >= self.timeout
if not falsifying_examples:
if (satisfying_examples
and len(track_seen) >= search_strategy.size_lower_bound):
raise Exhausted(hypothesis, satisfying_examples)
elif satisfying_examples < min_satisfying_examples:
if timed_out:
raise Timeout(hypothesis, satisfying_examples, run_time)
else:
raise Unsatisfiable(hypothesis, satisfying_examples,
run_time)
else:
raise Unfalsifiable(hypothesis)
for example in falsifying_examples:
if not falsifies(example):
raise Flaky(hypothesis, example)
best_example = falsifying_examples[0]
for simpler in search_strategy.simplify_such_that(
random,
best_example,
falsifies,
tracker=track_seen,
):
best_example = simpler
if time_to_call_it_a_day():
# We no cover in here because it's a bit sensitive to timing
# and tends to make tests flaky. There are tests that mean
# this is definitely covered most of the time.
break # pragma: no cover
if storage is not None:
storage.save(best_example)
setup_example()
return search_strategy.reify(best_example)
def run(self):
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
if global_force_seed is None:
database_key = str_to_bytes(fully_qualified_name(self.test))
else:
database_key = None
self.start_time = time.time()
global in_given
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings, random=self.random,
database_key=database_key,
)
if in_given or self.collector is None:
runner.run()
else: # pragma: no cover
in_given = True
original_trace = sys.gettrace()
try:
sys.settrace(None)
runner.run()
finally:
in_given = False
sys.settrace(original_trace)
note_engine_for_statistics(runner)
run_time = time.time() - self.start_time
self.used_examples_from_database = runner.used_examples_from_database
if runner.used_examples_from_database:
if self.settings.derandomize:
note_deprecation(
'In future derandomize will imply database=None, but your '
'test is currently using examples from the database. To '
'get the future behaviour, update your settings to '
'include database=None.'
)
if self.__had_seed:
note_deprecation(
'In future use of @seed will imply database=None in your '
'settings, but your test is currently using examples from '
'the database. To get the future behaviour, update your '
'settings for this test to include database=None.'
)
timed_out = runner.exit_reason == ExitReason.timeout
if runner.last_data is None:
return
if runner.interesting_examples:
self.falsifying_examples = sorted(
[d for d in runner.interesting_examples.values()],
key=lambda d: sort_key(d.buffer), reverse=True
)
else:
if timed_out:
note_deprecation((
'Your tests are hitting the settings timeout (%.2fs). '
'This functionality will go away in a future release '
'and you should not rely on it. Instead, try setting '
'max_examples to be some value lower than %d (the number '
'of examples your test successfully ran here). Or, if you '
'would prefer your tests to run to completion, regardless '
'of how long they take, you can set the timeout value to '
'hypothesis.unlimited.'
) % (
self.settings.timeout, runner.valid_examples),
self.settings)
if runner.valid_examples < min(
self.settings.min_satisfying_examples,
self.settings.max_examples,
) and not (
runner.exit_reason == ExitReason.finished and
self.at_least_one_success
):
if timed_out:
raise Timeout((
'Ran out of time before finding a satisfying '
'example for '
'%s. Only found %d examples in ' +
'%.2fs.'
) % (
get_pretty_function_description(self.test),
runner.valid_examples, run_time
))
else:
raise Unsatisfiable((
'Unable to satisfy assumptions of hypothesis '
'%s. Only %d examples considered '
'satisfied assumptions'
) % (
get_pretty_function_description(self.test),
runner.valid_examples,))
if not self.falsifying_examples:
return
flaky = 0
for falsifying_example in self.falsifying_examples:
self.__was_flaky = False
assert falsifying_example.__expected_exception is not None
try:
self.execute(
ConjectureData.for_buffer(falsifying_example.buffer),
print_example=True, is_final=True,
expected_failure=(
falsifying_example.__expected_exception,
falsifying_example.__expected_traceback,
)
)
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
self.__flaky(
'Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.'
)
except BaseException:
if len(self.falsifying_examples) <= 1:
raise
report(traceback.format_exc())
if self.__was_flaky:
flaky += 1
# If we only have one example then we should have raised an error or
# flaky prior to this point.
assert len(self.falsifying_examples) > 1
if flaky > 0:
raise Flaky((
'Hypothesis found %d distinct failures, but %d of them '
'exhibited some sort of flaky behaviour.') % (
len(self.falsifying_examples), flaky))
else:
raise MultipleFailures((
'Hypothesis found %d distinct failures.') % (
len(self.falsifying_examples,)))
def wrapped_test(*arguments, **kwargs):
settings = wrapped_test._hypothesis_internal_use_settings
if wrapped_test._hypothesis_internal_use_seed is not None:
random = Random(
wrapped_test._hypothesis_internal_use_seed)
elif settings.derandomize:
random = Random(function_digest(test))
else:
random = new_random()
import hypothesis.strategies as sd
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
# If the test function is a method of some kind, the bound object
# will be the first named argument if there are any, otherwise the
# first vararg (if any).
if argspec.args:
selfy = kwargs.get(argspec.args[0])
elif arguments:
selfy = arguments[0]
test_runner = new_style_executor(selfy)
for example in reversed(getattr(
wrapped_test, 'hypothesis_explicit_examples', ()
)):
if example.args:
if len(example.args) > len(original_argspec.args):
raise InvalidArgument(
'example has too many arguments for test. '
'Expected at most %d but got %d' % (
len(original_argspec.args), len(example.args)))
example_kwargs = dict(zip(
original_argspec.args[-len(example.args):],
example.args
))
else:
example_kwargs = example.kwargs
if Phase.explicit not in settings.phases:
continue
example_kwargs.update(kwargs)
# Note: Test may mutate arguments and we can't rerun explicit
# examples, so we have to calculate the failure message at this
# point rather than than later.
message_on_failure = 'Falsifying example: %s(%s)' % (
test.__name__, arg_string(test, arguments, example_kwargs)
)
try:
with BuildContext(None) as b:
test_runner(
None,
lambda data: test(*arguments, **example_kwargs)
)
except BaseException:
traceback.print_exc()
report(message_on_failure)
for n in b.notes:
report(n)
raise
if settings.max_examples <= 0:
return
arguments = tuple(arguments)
given_specifier = sd.tuples(
sd.just(arguments),
sd.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)
)
)
def fail_health_check(message, label):
if label in settings.suppress_health_check:
return
message += (
'\nSee https://hypothesis.readthedocs.io/en/latest/health'
'checks.html for more information about this. '
)
message += (
'If you want to disable just this health check, add %s '
'to the suppress_health_check settings for this test.'
) % (label,)
raise FailedHealthCheck(message)
search_strategy = given_specifier
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
perform_health_check = settings.perform_health_check
perform_health_check &= Settings.default.perform_health_check
from hypothesis.internal.conjecture.data import ConjectureData, \
Status, StopTest
if not (
Phase.reuse in settings.phases or
Phase.generate in settings.phases
):
return
if perform_health_check:
health_check_random = Random(random.getrandbits(128))
# We "pre warm" the health check with one draw to give it some
# time to calculate any cached data. This prevents the case
# where the first draw of the health check takes ages because
# of loading unicode data the first time.
data = ConjectureData(
max_length=settings.buffer_size,
draw_bytes=lambda data, n, distribution:
distribution(health_check_random, n)
)
with Settings(settings, verbosity=Verbosity.quiet):
try:
test_runner(data, reify_and_execute(
search_strategy,
lambda *args, **kwargs: None,
))
except BaseException:
pass
count = 0
overruns = 0
filtered_draws = 0
start = time.time()
while (
count < 10 and time.time() < start + 1 and
filtered_draws < 50 and overruns < 20
):
try:
data = ConjectureData(
max_length=settings.buffer_size,
draw_bytes=lambda data, n, distribution:
distribution(health_check_random, n)
)
with Settings(settings, verbosity=Verbosity.quiet):
test_runner(data, reify_and_execute(
search_strategy,
lambda *args, **kwargs: None,
))
count += 1
except UnsatisfiedAssumption:
filtered_draws += 1
except StopTest:
if data.status == Status.INVALID:
filtered_draws += 1
else:
assert data.status == Status.OVERRUN
overruns += 1
except InvalidArgument:
raise
except Exception:
if (
HealthCheck.exception_in_generation in
settings.suppress_health_check
):
raise
report(traceback.format_exc())
if test_runner is default_new_style_executor:
fail_health_check(
'An exception occurred during data '
'generation in initial health check. '
'This indicates a bug in the strategy. '
'This could either be a Hypothesis bug or '
"an error in a function you've passed to "
'it to construct your data.',
HealthCheck.exception_in_generation,
)
else:
fail_health_check(
'An exception occurred during data '
'generation in initial health check. '
'This indicates a bug in the strategy. '
'This could either be a Hypothesis bug or '
'an error in a function you\'ve passed to '
'it to construct your data. Additionally, '
'you have a custom executor, which means '
'that this could be your executor failing '
'to handle a function which returns None. ',
HealthCheck.exception_in_generation,
)
if overruns >= 20 or (
not count and overruns > 0
):
fail_health_check((
'Examples routinely exceeded the max allowable size. '
'(%d examples overran while generating %d valid ones)'
'. Generating examples this large will usually lead to'
' bad results. You should try setting average_size or '
'max_size parameters on your collections and turning '
'max_leaves down on recursive() calls.') % (
overruns, count
), HealthCheck.data_too_large)
if filtered_draws >= 50 or (
not count and filtered_draws > 0
):
fail_health_check((
'It looks like your strategy is filtering out a lot '
'of data. Health check found %d filtered examples but '
'only %d good ones. This will make your tests much '
'slower, and also will probably distort the data '
'generation quite a lot. You should adapt your '
'strategy to filter less. This can also be caused by '
'a low max_leaves parameter in recursive() calls') % (
filtered_draws, count
), HealthCheck.filter_too_much)
runtime = time.time() - start
if runtime > 1.0 or count < 10:
fail_health_check((
'Data generation is extremely slow: Only produced '
'%d valid examples in %.2f seconds (%d invalid ones '
'and %d exceeded maximum size). Try decreasing '
"size of the data you're generating (with e.g."
'average_size or max_leaves parameters).'
) % (count, runtime, filtered_draws, overruns),
HealthCheck.too_slow,
)
last_exception = [None]
repr_for_last_exception = [None]
def evaluate_test_data(data):
try:
result = test_runner(data, reify_and_execute(
search_strategy, test,
))
if result is not None and settings.perform_health_check:
fail_health_check((
'Tests run under @given should return None, but '
'%s returned %r instead.'
) % (test.__name__, result), HealthCheck.return_value)
return False
except UnsatisfiedAssumption:
data.mark_invalid()
except (
HypothesisDeprecationWarning, FailedHealthCheck,
StopTest,
):
raise
except Exception:
last_exception[0] = traceback.format_exc()
verbose_report(last_exception[0])
data.mark_interesting()
from hypothesis.internal.conjecture.engine import ConjectureRunner
falsifying_example = None
database_key = str_to_bytes(fully_qualified_name(test))
start_time = time.time()
runner = ConjectureRunner(
evaluate_test_data,
settings=settings, random=random,
database_key=database_key,
)
runner.run()
note_engine_for_statistics(runner)
run_time = time.time() - start_time
timed_out = (
settings.timeout > 0 and
run_time >= settings.timeout
)
if runner.last_data is None:
return
if runner.last_data.status == Status.INTERESTING:
falsifying_example = runner.last_data.buffer
if settings.database is not None:
settings.database.save(
database_key, falsifying_example
)
else:
if runner.valid_examples < min(
settings.min_satisfying_examples,
settings.max_examples,
):
if timed_out:
raise Timeout((
'Ran out of time before finding a satisfying '
'example for '
'%s. Only found %d examples in ' +
'%.2fs.'
) % (
get_pretty_function_description(test),
runner.valid_examples, run_time
))
else:
raise Unsatisfiable((
'Unable to satisfy assumptions of hypothesis '
'%s. Only %d examples considered '
'satisfied assumptions'
) % (
get_pretty_function_description(test),
runner.valid_examples,))
return
assert last_exception[0] is not None
try:
with settings:
test_runner(
ConjectureData.for_buffer(falsifying_example),
reify_and_execute(
search_strategy, test,
print_example=True, is_final=True
))
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
raise Flaky(
'Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.'
)
report(
'Failed to reproduce exception. Expected: \n' +
last_exception[0],
)
filter_message = (
'Unreliable test data: Failed to reproduce a failure '
'and then when it came to recreating the example in '
'order to print the test data with a flaky result '
'the example was filtered out (by e.g. a '
'call to filter in your strategy) when we didn\'t '
'expect it to be.'
)
try:
test_runner(
ConjectureData.for_buffer(falsifying_example),
reify_and_execute(
search_strategy,
test_is_flaky(test, repr_for_last_exception[0]),
print_example=True, is_final=True
))
except (UnsatisfiedAssumption, StopTest):
raise Flaky(filter_message)
def inconsistent_generation():
raise Flaky(
"Inconsistent data generation! Data generation behaved differently "
"between different runs. Is your data generation depending on external "
"state?"
)
def run(self):
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
if global_force_seed is None:
database_key = function_digest(self.test)
else:
database_key = None
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings,
random=self.random,
database_key=database_key,
)
try:
runner.run()
finally:
self.used_examples_from_database = runner.used_examples_from_database
note_engine_for_statistics(runner)
self.used_examples_from_database = runner.used_examples_from_database
if runner.call_count == 0:
return
if runner.interesting_examples:
self.falsifying_examples = sorted(
[d for d in runner.interesting_examples.values()],
key=lambda d: sort_key(d.buffer),
reverse=True,
)
else:
if runner.valid_examples == 0:
raise Unsatisfiable(
"Unable to satisfy assumptions of hypothesis %s." %
(get_pretty_function_description(self.test), ))
if not self.falsifying_examples:
return
self.failed_normally = True
flaky = 0
for falsifying_example in self.falsifying_examples:
ran_example = ConjectureData.for_buffer(falsifying_example.buffer)
self.__was_flaky = False
assert falsifying_example.__expected_exception is not None
try:
self.execute(
ran_example,
print_example=True,
is_final=True,
expected_failure=(
falsifying_example.__expected_exception,
falsifying_example.__expected_traceback,
),
)
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
self.__flaky(
"Unreliable assumption: An example which satisfied "
"assumptions on the first run now fails it.")
except BaseException as e:
if len(self.falsifying_examples) <= 1:
raise
tb = get_trimmed_traceback()
report("".join(traceback.format_exception(type(e), e, tb)))
finally: # pragma: no cover
# This section is in fact entirely covered by the tests in
# test_reproduce_failure, but it seems to trigger a lovely set
# of coverage bugs: The branches show up as uncovered (despite
# definitely being covered - you can add an assert False else
# branch to verify this and see it fail - and additionally the
# second branch still complains about lack of coverage even if
# you add a pragma: no cover to it!
# See https://bitbucket.org/ned/coveragepy/issues/623/
if self.settings.print_blob is not PrintSettings.NEVER:
failure_blob = encode_failure(falsifying_example.buffer)
# Have to use the example we actually ran, not the original
# falsifying example! Otherwise we won't catch problems
# where the repr of the generated example doesn't parse.
can_use_repr = ran_example.can_reproduce_example_from_repr
if self.settings.print_blob is PrintSettings.ALWAYS or (
self.settings.print_blob is PrintSettings.INFER
and self.settings.verbosity >= Verbosity.normal
and not can_use_repr and len(failure_blob) < 200):
report((
"\nYou can reproduce this example by temporarily "
"adding @reproduce_failure(%r, %r) as a decorator "
"on your test case") % (__version__, failure_blob))
if self.__was_flaky:
flaky += 1
# If we only have one example then we should have raised an error or
# flaky prior to this point.
assert len(self.falsifying_examples) > 1
if flaky > 0:
raise Flaky(
("Hypothesis found %d distinct failures, but %d of them "
"exhibited some sort of flaky behaviour.") %
(len(self.falsifying_examples), flaky))
else:
raise MultipleFailures(("Hypothesis found %d distinct failures.") %
(len(self.falsifying_examples)))
def wrapped_test(*arguments, **kwargs):
settings = wrapped_test._hypothesis_internal_use_settings
if wrapped_test._hypothesis_internal_use_seed is not None:
random = Random(wrapped_test._hypothesis_internal_use_seed)
elif settings.derandomize:
random = Random(function_digest(test))
else:
random = new_random()
import hypothesis.strategies as sd
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
# If the test function is a method of some kind, the bound object
# will be the first named argument if there are any, otherwise the
# first vararg (if any).
if argspec.args:
selfy = kwargs.get(argspec.args[0])
elif arguments:
selfy = arguments[0]
test_runner = executor(selfy)
for example in reversed(
getattr(wrapped_test, 'hypothesis_explicit_examples', ())):
if example.args:
example_kwargs = dict(
zip(original_argspec.args[-len(example.args):],
example.args))
else:
example_kwargs = example.kwargs
example_kwargs.update(kwargs)
# Note: Test may mutate arguments and we can't rerun explicit
# examples, so we have to calculate the failure message at this
# point rather than than later.
message_on_failure = 'Falsifying example: %s(%s)' % (
test.__name__, arg_string(test, arguments, example_kwargs))
try:
with BuildContext() as b:
test_runner(lambda: test(*arguments, **example_kwargs))
except BaseException:
report(message_on_failure)
for n in b.notes:
report(n)
raise
arguments = tuple(arguments)
given_specifier = sd.tuples(
sd.just(arguments),
sd.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)))
def fail_health_check(message):
message += (
'\nSee http://hypothesis.readthedocs.org/en/latest/health'
'checks.html for more information about this.')
if settings.strict:
raise FailedHealthCheck(message)
else:
warnings.warn(FailedHealthCheck(message))
search_strategy = given_specifier
search_strategy.validate()
if settings.database:
storage = settings.database.storage(fully_qualified_name(test))
else:
storage = None
start = time.time()
warned_random = [False]
perform_health_check = settings.perform_health_check
if Settings.default is not None:
perform_health_check &= Settings.default.perform_health_check
if perform_health_check:
initial_state = getglobalrandomstate()
health_check_random = Random(random.getrandbits(128))
count = 0
bad_draws = 0
filtered_draws = 0
errors = 0
while (count < 10 and time.time() < start + 1
and filtered_draws < 50 and bad_draws < 50):
try:
with Settings(settings, verbosity=Verbosity.quiet):
test_runner(
reify_and_execute(
search_strategy,
search_strategy.draw_template(
health_check_random,
search_strategy.draw_parameter(
health_check_random, )),
lambda *args, **kwargs: None,
))
count += 1
except BadTemplateDraw:
bad_draws += 1
except UnsatisfiedAssumption:
filtered_draws += 1
except Exception:
if errors == 0:
report(traceback.format_exc())
errors += 1
if test_runner is default_executor:
fail_health_check(
'An exception occurred during data '
'generation in initial health check. '
'This indicates a bug in the strategy. '
'This could either be a Hypothesis bug or '
"an error in a function yo've passed to "
'it to construct your data.')
else:
fail_health_check(
'An exception occurred during data '
'generation in initial health check. '
'This indicates a bug in the strategy. '
'This could either be a Hypothesis bug or '
'an error in a function you\'ve passed to '
'it to construct your data. Additionally, '
'you have a custom executor, which means '
'that this could be your executor failing '
'to handle a function which returns None. ')
if filtered_draws >= 50:
fail_health_check((
'It looks like your strategy is filtering out a lot '
'of data. Health check found %d filtered examples but '
'only %d good ones. This will make your tests much '
'slower, and also will probably distort the data '
'generation quite a lot. You should adapt your '
'strategy to filter less.') % (filtered_draws, count))
if bad_draws >= 50:
fail_health_check(
'Hypothesis is struggling to generate examples. '
'This is often a sign of a recursive strategy which '
'fans out too broadly. If you\'re using recursive, '
'try to reduce the size of the recursive step or '
'increase the maximum permitted number of leaves.')
runtime = time.time() - start
if runtime > 1.0 or count < 10:
fail_health_check(
('Data generation is extremely slow: Only produced '
'%d valid examples in %.2f seconds. Try decreasing '
"size of the data yo're generating (with e.g."
'average_size or max_leaves parameters).') %
(count, runtime))
if getglobalrandomstate() != initial_state:
warned_random[0] = True
fail_health_check(
'Data generation depends on global random module. '
'This makes results impossible to replay, which '
'prevents Hypothesis from working correctly. '
'If you want to use methods from random, use '
'randoms() from hypothesis.strategies to get an '
'instance of Random you can use. Alternatively, you '
'can use the random_module() strategy to explicitly '
'seed the random module.')
last_exception = [None]
repr_for_last_exception = [None]
def is_template_example(xs):
if perform_health_check and not warned_random[0]:
initial_state = getglobalrandomstate()
record_repr = [None]
try:
result = test_runner(
reify_and_execute(
search_strategy,
xs,
test,
record_repr=record_repr,
))
if result is not None and settings.perform_health_check:
raise FailedHealthCheck(
('Tests run under @given should return None, but '
'%s returned %r instead.') %
(test.__name__, result), settings)
return False
except (HypothesisDeprecationWarning, FailedHealthCheck,
UnsatisfiedAssumption):
raise
except Exception:
last_exception[0] = traceback.format_exc()
repr_for_last_exception[0] = record_repr[0]
verbose_report(last_exception[0])
return True
finally:
if (not warned_random[0] and perform_health_check
and getglobalrandomstate() != initial_state):
warned_random[0] = True
fail_health_check(
'Your test used the global random module. '
'This is unlikely to work correctly. You should '
'consider using the randoms() strategy from '
'hypothesis.strategies instead. Alternatively, '
'you can use the random_module() strategy to '
'explicitly seed the random module.')
is_template_example.__name__ = test.__name__
is_template_example.__qualname__ = qualname(test)
with settings:
falsifying_template = None
try:
falsifying_template = best_satisfying_template(
search_strategy,
random,
is_template_example,
settings,
storage,
start_time=start,
)
except NoSuchExample:
return
assert last_exception[0] is not None
try:
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test,
print_example=True,
is_final=True))
except UnsatisfiedAssumption:
report(traceback.format_exc())
raise Flaky(
'Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.')
report(
'Failed to reproduce exception. Expected: \n' +
last_exception[0], )
try:
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test_is_flaky(
test,
repr_for_last_exception[0]),
print_example=True,
is_final=True))
except UnsatisfiedAssumption:
raise Flaky(
'Unreliable test data: Failed to reproduce a failure '
'and then when it came to recreating the example in '
'order to print the test data with a flaky result '
'the example was filtered out (by e.g. a '
'call to filter in your strategy) when we didn\'t '
'expect it to be.')
def run_engine(self):
"""Run the test function many times, on database input and generated
input, using the Conjecture engine.
"""
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
try:
database_key = self.wrapped_test._hypothesis_internal_database_key
except AttributeError:
if global_force_seed is None:
database_key = function_digest(self.test)
else:
database_key = None
runner = ConjectureRunner(
self._execute_once_for_engine,
settings=self.settings,
random=self.random,
database_key=database_key,
)
# Use the Conjecture engine to run the test function many times
# on different inputs.
runner.run()
note_engine_for_statistics(runner)
if runner.call_count == 0:
return
if runner.interesting_examples:
self.falsifying_examples = sorted(
runner.interesting_examples.values(),
key=lambda d: sort_key(d.buffer),
reverse=True,
)
else:
if runner.valid_examples == 0:
raise Unsatisfiable(
"Unable to satisfy assumptions of hypothesis %s." %
(get_pretty_function_description(self.test), ))
if not self.falsifying_examples:
return
elif not self.settings.report_multiple_bugs:
# Pretend that we only found one failure, by discarding the others.
del self.falsifying_examples[:-1]
# The engine found one or more failures, so we need to reproduce and
# report them.
self.failed_normally = True
flaky = 0
for falsifying_example in self.falsifying_examples:
info = falsifying_example.extra_information
ran_example = ConjectureData.for_buffer(falsifying_example.buffer)
self.__was_flaky = False
assert info.__expected_exception is not None
try:
self.execute_once(
ran_example,
print_example=not self.is_find,
is_final=True,
expected_failure=(
info.__expected_exception,
info.__expected_traceback,
),
)
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
self.__flaky(
"Unreliable assumption: An example which satisfied "
"assumptions on the first run now fails it.")
except BaseException as e:
if len(self.falsifying_examples) <= 1:
# There is only one failure, so we can report it by raising
# it directly.
raise
# We are reporting multiple failures, so we need to manually
# print each exception's stack trace and information.
tb = get_trimmed_traceback()
report("".join(traceback.format_exception(type(e), e, tb)))
finally: # pragma: no cover
# Mostly useful for ``find`` and ensuring that objects that
# hold on to a reference to ``data`` know that it's now been
# finished and they shouldn't attempt to draw more data from
# it.
ran_example.freeze()
# This section is in fact entirely covered by the tests in
# test_reproduce_failure, but it seems to trigger a lovely set
# of coverage bugs: The branches show up as uncovered (despite
# definitely being covered - you can add an assert False else
# branch to verify this and see it fail - and additionally the
# second branch still complains about lack of coverage even if
# you add a pragma: no cover to it!
# See https://bitbucket.org/ned/coveragepy/issues/623/
if self.settings.print_blob:
report(("\nYou can reproduce this example by temporarily "
"adding @reproduce_failure(%r, %r) as a decorator "
"on your test case") %
(__version__,
encode_failure(falsifying_example.buffer)))
if self.__was_flaky:
flaky += 1
# If we only have one example then we should have raised an error or
# flaky prior to this point.
assert len(self.falsifying_examples) > 1
if flaky > 0:
raise Flaky(
("Hypothesis found %d distinct failures, but %d of them "
"exhibited some sort of flaky behaviour.") %
(len(self.falsifying_examples), flaky))
else:
raise MultipleFailures(("Hypothesis found %d distinct failures.") %
(len(self.falsifying_examples)))
def run(self):
# Tell pytest to omit the body of this function from tracebacks
__tracebackhide__ = True
database_key = str_to_bytes(fully_qualified_name(self.test))
self.start_time = time.time()
runner = ConjectureRunner(
self.evaluate_test_data,
settings=self.settings,
random=self.random,
database_key=database_key,
)
runner.run()
note_engine_for_statistics(runner)
run_time = time.time() - self.start_time
timed_out = runner.exit_reason == ExitReason.timeout
if runner.last_data is None:
return
if runner.last_data.status == Status.INTERESTING:
self.falsifying_example = runner.last_data.buffer
if self.settings.database is not None:
self.settings.database.save(database_key,
self.falsifying_example)
else:
if timed_out:
note_deprecation((
'Your tests are hitting the settings timeout (%.2fs). '
'This functionality will go away in a future release '
'and you should not rely on it. Instead, try setting '
'max_examples to be some value lower than %d (the number '
'of examples your test successfully ran here). Or, if you '
'would prefer your tests to run to completion, regardless '
'of how long they take, you can set the timeout value to '
'hypothesis.unlimited.') % (self.settings.timeout,
runner.valid_examples),
self.settings)
if runner.valid_examples < min(
self.settings.min_satisfying_examples,
self.settings.max_examples,
) and not (runner.exit_reason == ExitReason.finished
and self.at_least_one_success):
if timed_out:
raise Timeout(
('Ran out of time before finding a satisfying '
'example for '
'%s. Only found %d examples in ' + '%.2fs.') %
(get_pretty_function_description(
self.test), runner.valid_examples, run_time))
else:
raise Unsatisfiable(
('Unable to satisfy assumptions of hypothesis '
'%s. Only %d examples considered '
'satisfied assumptions') % (
get_pretty_function_description(self.test),
runner.valid_examples,
))
if self.falsifying_example is None:
return
assert self.last_exception is not None
try:
with self.settings:
self.test_runner(
ConjectureData.for_buffer(self.falsifying_example),
reify_and_execute(self.search_strategy,
self.test,
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
report(traceback.format_exc())
raise Flaky('Unreliable assumption: An example which satisfied '
'assumptions on the first run now fails it.')
report(
'Failed to reproduce exception. Expected: \n' +
self.last_exception, )
filter_message = (
'Unreliable test data: Failed to reproduce a failure '
'and then when it came to recreating the example in '
'order to print the test data with a flaky result '
'the example was filtered out (by e.g. a '
'call to filter in your strategy) when we didn\'t '
'expect it to be.')
try:
self.test_runner(
ConjectureData.for_buffer(self.falsifying_example),
reify_and_execute(self.search_strategy,
test_is_flaky(self.test,
self.repr_for_last_exception),
print_example=True,
is_final=True))
except (UnsatisfiedAssumption, StopTest):
raise Flaky(filter_message)
def test_or_flaky(*args, **kwargs):
raise Flaky(test, (args, kwargs))
def test_or_flaky(*args, **kwargs):
raise Flaky(
(
'Hypothesis %r produces unreliable results: %r falsified it on'
' the first call but did not on a subsequent one'
) % (get_pretty_function_description(test), example))
