def process_arguments_to_given(wrapped_test, arguments, kwargs,
generator_arguments, generator_kwargs, argspec,
test, settings):
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]
# Ensure that we don't mistake mocks for self here.
# This can cause the mock to be used as the test runner.
if is_mock(selfy):
selfy = None
test_runner = new_style_executor(selfy)
arguments = tuple(arguments)
search_strategy = st.tuples(
st.just(arguments),
st.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)))
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
return arguments, kwargs, test_runner, search_strategy
def process_arguments_to_given(wrapped_test, arguments, kwargs,
generator_arguments, generator_kwargs, argspec,
test, settings):
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)
arguments = tuple(arguments)
given_specifier = sd.tuples(
sd.just(arguments),
sd.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)))
search_strategy = given_specifier
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
return arguments, kwargs, test_runner, search_strategy
def process_arguments_to_given(
wrapped_test,
arguments,
kwargs,
generator_arguments,
generator_kwargs,
argspec,
test,
settings,
):
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]
# Ensure that we don't mistake mocks for self here.
# This can cause the mock to be used as the test runner.
if is_mock(selfy):
selfy = None
test_runner = new_style_executor(selfy)
arguments = tuple(arguments)
# We use TupleStrategy over tuples() here to avoid polluting
# st.STRATEGY_CACHE with references (see #493), and because this is
# trivial anyway if the fixed_dictionaries strategy is cacheable.
search_strategy = TupleStrategy(
(
st.just(arguments),
st.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)
),
)
)
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
return arguments, kwargs, test_runner, search_strategy
def process_arguments_to_given(
wrapped_test,
arguments,
kwargs,
generator_arguments,
generator_kwargs,
argspec,
test,
settings,
):
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]
# Ensure that we don't mistake mocks for self here.
# This can cause the mock to be used as the test runner.
if is_mock(selfy):
selfy = None
test_runner = new_style_executor(selfy)
arguments = tuple(arguments)
# We use TupleStrategy over tuples() here to avoid polluting
# st.STRATEGY_CACHE with references (see #493), and because this is
# trivial anyway if the fixed_dictionaries strategy is cacheable.
search_strategy = TupleStrategy(
(
st.just(arguments),
st.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)
),
)
)
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
return arguments, kwargs, test_runner, search_strategy
def process_arguments_to_given(
wrapped_test, arguments, kwargs, generator_arguments, generator_kwargs,
argspec, test, settings
):
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]
# Ensure that we don't mistake mocks for self here.
# This can cause the mock to be used as the test runner.
if is_mock(selfy):
selfy = None
test_runner = new_style_executor(selfy)
arguments = tuple(arguments)
search_strategy = st.tuples(
st.just(arguments),
st.fixed_dictionaries(generator_kwargs).map(
lambda args: dict(args, **kwargs)
)
)
if selfy is not None:
search_strategy = WithRunner(search_strategy, selfy)
search_strategy.validate()
return arguments, kwargs, test_runner, search_strategy
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() as b:
test_runner(None, lambda data: test(*arguments, **example_kwargs))
except BaseException:
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 TestData, 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 = TestData(
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 = TestData(
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 TestRunner
falsifying_example = None
database_key = str_to_bytes(fully_qualified_name(test))
start_time = time.time()
runner = TestRunner(evaluate_test_data, settings=settings, random=random, database_key=database_key)
runner.run()
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(
TestData.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(
TestData.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 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
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(
None,
lambda data: test(*arguments, **example_kwargs)
)
except BaseException:
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):
message += (
'\nSee http://hypothesis.readthedocs.org/en/latest/health'
'checks.html for more information about this.'
)
raise FailedHealthCheck(message)
search_strategy = given_specifier
search_strategy.validate()
perform_health_check = settings.perform_health_check
perform_health_check &= Settings.default.perform_health_check
from hypothesis.internal.conjecture.data import TestData, Status, \
StopTest
if perform_health_check:
initial_state = getglobalrandomstate()
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 = TestData(
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 = TestData(
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 Exception:
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 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 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
))
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
))
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))
if getglobalrandomstate() != initial_state:
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]
performed_random_check = [False]
def evaluate_test_data(data):
if perform_health_check and not performed_random_check[0]:
initial_state = getglobalrandomstate()
performed_random_check[0] = True
else:
initial_state = None
try:
result = test_runner(data, reify_and_execute(
search_strategy, test,
))
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 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()
finally:
if (
initial_state is not None and
getglobalrandomstate() != initial_state
):
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.')
from hypothesis.internal.conjecture.engine import TestRunner
falsifying_example = None
database_key = str_to_bytes(fully_qualified_name(test))
start_time = time.time()
runner = TestRunner(
evaluate_test_data,
settings=settings, random=random,
database_key=database_key,
)
runner.run()
run_time = time.time() - start_time
timed_out = (
settings.timeout > 0 and
run_time >= settings.timeout
)
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(
TestData.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(
TestData.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 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)