def __call__(self, specifier, settings=None):
from hypothesis.searchstrategy.strategies import SearchStrategy
if isinstance(specifier, SearchStrategy):
return specifier
self.load_deprecated_api()
if settings is None:
settings = Settings()
try:
result = super(StrategyExtMethod, self).__call__(
specifier, settings)
except NotImplementedError:
quiet_raise(NotImplementedError((
'Expected a SearchStrategy but got %r of type %s. '
'Note: This is a NotImplementedError for legacy reasons and '
'will become an InvalidArgumentError in Hypothesis 2.0.'
) % (specifier, type(specifier).__name__)))
note_deprecation((
'Conversion of %r to strategy is deprecated '
'and will be removed in Hypothesis 2.0. Use %r instead.') % (
specifier, result
), settings)
assert isinstance(result, SearchStrategy)
return result
def basic(
basic=None,
generate_parameter=None, generate=None, simplify=None, copy=None
):
"""Provides a facility to write your own strategies with significantly less
work.
See documentation for more details.
"""
note_deprecation(
'basic() is deprecated as Hypothesis will be moving to a new '
'model that cannot support it. There is probably no one-size fits all '
'replacement solution here. If you can\'t figure out a good '
'solution, please ask.'
)
from hypothesis.searchstrategy.basic import basic_strategy, BasicStrategy
from copy import deepcopy
if basic is not None:
if isinstance(basic, type):
basic = basic()
check_type(BasicStrategy, basic)
generate_parameter = generate_parameter or basic.generate_parameter
generate = generate or basic.generate
simplify = simplify or basic.simplify
copy = copy or basic.copy
return basic_strategy(
parameter=generate_parameter,
generate=generate, simplify=simplify, copy=copy or deepcopy
)
def strategy(spec, settings=None):
from hypothesis.settings import note_deprecation
note_deprecation(
'The strategy function is deprecated and will be removed in Hypothesis'
' 2.0. Please use the hypothesis.strategies module to construct your '
'strategies', settings)
return _strategy(spec, settings)
def strategy(spec, settings=None):
from hypothesis.settings import note_deprecation
note_deprecation(
'The strategy function is deprecated and will be removed in Hypothesis'
' 2.0. Please use the hypothesis.strategies module to construct your '
'strategies', settings)
return _strategy(spec, settings)
def unicode_safe_repr(x):
try:
r = repr(x)
except UnicodeEncodeError:
r = type(x).__repr__(x)
from hypothesis.settings import note_deprecation
note_deprecation(
('Type %s has a broken repr implementation. Calling __repr__ '
'on it returned %r, which cannot be represented as ASCII '
'text. This is not permitted in Python 2. Hypothesis is '
'currently working around this, but will stop doing so in '
'Hypothesis 2.0. You should fix your code.') %
(type(x).__name__, r))
if isinstance(r, unicode):
return r
else:
return r.decode(a_good_encoding())
def __call__(self, specifier, settings=None):
if isinstance(specifier, SearchStrategy):
return specifier
if settings is None:
settings = Settings()
note_deprecation((
u'Calling strategy with non-strategy object %s is deprecated '
u'and will be removed in Hypothesis 2.0. Use the functions in '
u'hypothesis.strategies instead.') % (
repr(specifier),
), settings)
result = super(StrategyExtMethod, self).__call__(specifier, settings)
assert isinstance(result, SearchStrategy)
return result
def __call__(self, specifier, settings=None):
from hypothesis.searchstrategy.strategies import SearchStrategy
if isinstance(specifier, SearchStrategy):
return specifier
self.load_deprecated_api()
if settings is None:
settings = Settings()
result = super(StrategyExtMethod, self).__call__(specifier, settings)
note_deprecation(
('Conversion of %r to strategy is deprecated '
'and will be removed in Hypothesis 2.0. Use %r instead.') %
(specifier, result), settings)
assert isinstance(result, SearchStrategy)
return result
def accept(f):
parent_rule = getattr(f, RULE_MARKER, None)
if parent_rule is not None:
note_deprecation(
'Applying the rule decorator to a function that is already '
'decorated by rule is deprecated. Please assign the result '
'of the rule function to separate names in your class.',
Settings.default,
)
precondition = getattr(f, PRECONDITION_MARKER, None)
rule = Rule(targets=tuple(converted_targets), arguments=kwargs,
function=f, precondition=precondition,
parent_rule=parent_rule)
@proxies(f)
def rule_wrapper(*args, **kwargs):
return f(*args, **kwargs)
setattr(rule_wrapper, RULE_MARKER, rule)
return rule_wrapper
def __call__(self, specifier, settings=None):
from hypothesis.searchstrategy.strategies import SearchStrategy
if isinstance(specifier, SearchStrategy):
return specifier
self.load_deprecated_api()
if settings is None:
settings = Settings()
result = super(StrategyExtMethod, self).__call__(specifier, settings)
note_deprecation((
'Conversion of %r to strategy is deprecated '
'and will be removed in Hypothesis 2.0. Use %r instead.') % (
specifier, result
), settings)
assert isinstance(result, SearchStrategy)
return result
def unicode_safe_repr(x):
try:
r = repr(x)
except UnicodeEncodeError:
r = type(x).__repr__(x)
from hypothesis.settings import note_deprecation
note_deprecation(
(
"Type %s has a broken repr implementation. Calling __repr__ "
"on it returned %r, which cannot be represented as ASCII "
"text. This is not permitted in Python 2. Hypothesis is "
"currently working around this, but will stop doing so in "
"Hypothesis 2.0. You should fix your code."
)
% (type(x).__name__, r)
)
if isinstance(r, unicode):
return r
else:
return r.decode(a_good_encoding())
def load_entry_points(name=None):
for entry_point in pkg_resources.iter_entry_points(
group=u('hypothesis.extra'), name=name
):
if entry_point.name in (
u('hypothesisdatetime'), u('hypothesisdjango'),
u('hypothesisfakefactory'), u('hypothesisnumpy')
):
base_name = entry_point.name.replace(u('hypothesis'), u(''))
note_deprecation(
u('Ignoring obsolete extra package hypothesis-%s. This '
'functionality is now included in hypothesis core. You '
'should uninstall the extra package.') % (base_name,),
Settings.default
)
continue
elif entry_point.name == u('hypothesispytest'):
note_deprecation(
u('You have an obsolete version of the hypothesis-pytest '
'plugin installed. Please update to a more recent version.'),
Settings.default
)
continue
else:
note_deprecation(
u('The extra package mechanism is deprecated and will go away ')
"in Hypothesis 2.0. Just write a normal package and don't "
'have it in the Hypothesis namespace.'), Settings.default
)
package = entry_point.load() # pragma: no cover
if package not in loaded:
loaded.add(package)
__path__.extend(package.__path__)
package.load()
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:
note_deprecation((
'Tests run under @given should return None, but '
'%s returned %r instead.'
'In Hypothesis 2.0 this will become an error.'
) % (test.__name__, result), settings)
return False
except HypothesisDeprecationWarning:
raise
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
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.'
)
def accept(f):
parent_rule = getattr(f, RULE_MARKER, None)
if parent_rule is not None:
note_deprecation(
'Applying the rule decorator to a function that is already '
'decorated by rule is deprecated. Please assign the result '
'of the rule function to separate names in your class.',
Settings.default,
)
precondition = getattr(f, PRECONDITION_MARKER, None)
rule = Rule(targets=tuple(converted_targets),
arguments=kwargs,
function=f,
precondition=precondition,
parent_rule=parent_rule)
@proxies(f)
def rule_wrapper(*args, **kwargs):
return f(*args, **kwargs)
setattr(rule_wrapper, RULE_MARKER, rule)
return rule_wrapper
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:
note_deprecation(
('Tests run under @given should return None, but '
'%s returned %r instead.'
'In Hypothesis 2.0 this will become an error.') %
(test.__name__, result), settings)
return False
except HypothesisDeprecationWarning:
raise
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
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.')
def wrapped_test(*arguments, **kwargs):
if settings.derandomize:
random = Random(function_digest(test))
else:
random = provided_random or new_random()
import hypothesis.strategies as sd
from hypothesis.internal.strategymethod import strategy
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
for arg in hypothesis_owned_arguments:
try:
value = kwargs[arg]
except KeyError:
continue
if not isinstance(value, HypothesisProvided):
note_deprecation(
'Passing in explicit values to override Hypothesis '
'provided values is deprecated and will no longer '
'work in Hypothesis 2.0. If you need to do this, '
'extract a common function and call that from a '
'Hypothesis based test.', settings)
# Anything in unused_kwargs hasn't been injected through
# argspec.defaults, so we need to add them.
for k in unused_kwargs:
if k not in kwargs:
kwargs[k] = unused_kwargs[k]
# 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]
if isinstance(selfy, HypothesisProvided):
selfy = None
test_runner = executor(selfy)
for example in reversed(
getattr(wrapped_test, u'hypothesis_explicit_examples',
())):
if example.args:
example_kwargs = dict(
zip(argspec.args[-len(example.args):], example.args))
else:
example_kwargs = dict(example.kwargs)
for k, v in kwargs.items():
if not isinstance(v, HypothesisProvided):
example_kwargs[k] = v
# 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 = u'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
if not any(
isinstance(x, HypothesisProvided)
for xs in (arguments, kwargs.values()) for x in xs):
# All arguments have been satisfied without needing to invoke
# hypothesis
test_runner(lambda: test(*arguments, **kwargs))
return
def convert_to_specifier(v):
if isinstance(v, HypothesisProvided):
return strategy(v.value, settings)
else:
return sd.just(v)
given_specifier = sd.tuples(
sd.tuples(*map(convert_to_specifier, arguments)),
sd.fixed_dictionaries(
dict((k, convert_to_specifier(v))
for (k, v) in kwargs.items())))
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 = strategy(given_specifier, settings)
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 you'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 you'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:
note_deprecation(
('Tests run under @given should return None, but '
'%s returned %r instead.'
'In Hypothesis 2.0 this will become an error.') %
(test.__name__, result), settings)
return False
except HypothesisDeprecationWarning:
raise
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
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)
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
with settings:
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test,
print_example=True,
is_final=True))
report(
u'Failed to reproduce exception. Expected: \n' +
last_exception[0], )
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test_is_flaky(
test, repr_for_last_exception[0]),
print_example=True,
is_final=True))
def wrapped_test(*arguments, **kwargs):
if settings.derandomize:
random = Random(function_digest(test))
else:
random = provided_random or new_random()
import hypothesis.strategies as sd
from hypothesis.internal.strategymethod import strategy
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
for arg in hypothesis_owned_arguments:
try:
value = kwargs[arg]
except KeyError:
continue
if not isinstance(value, HypothesisProvided):
note_deprecation(
'Passing in explicit values to override Hypothesis '
'provided values is deprecated and will no longer '
'work in Hypothesis 2.0. If you need to do this, '
'extract a common function and call that from a '
'Hypothesis based test.', settings
)
# Anything in unused_kwargs hasn't been injected through
# argspec.defaults, so we need to add them.
for k in unused_kwargs:
if k not in kwargs:
kwargs[k] = unused_kwargs[k]
# 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]
if isinstance(selfy, HypothesisProvided):
selfy = None
test_runner = executor(selfy)
for example in reversed(getattr(
wrapped_test, u'hypothesis_explicit_examples', ()
)):
if example.args:
example_kwargs = dict(zip(
argspec.args[-len(example.args):], example.args
))
else:
example_kwargs = dict(example.kwargs)
for k, v in kwargs.items():
if not isinstance(v, HypothesisProvided):
example_kwargs[k] = v
# 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 = u'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
if not any(
isinstance(x, HypothesisProvided)
for xs in (arguments, kwargs.values())
for x in xs
):
# All arguments have been satisfied without needing to invoke
# hypothesis
test_runner(lambda: test(*arguments, **kwargs))
return
def convert_to_specifier(v):
if isinstance(v, HypothesisProvided):
return strategy(v.value, settings)
else:
return sd.just(v)
given_specifier = sd.tuples(
sd.tuples(*map(convert_to_specifier, arguments)),
sd.fixed_dictionaries(dict(
(k, convert_to_specifier(v)) for (k, v) in kwargs.items()))
)
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 = strategy(given_specifier, settings)
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 you'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 you'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:
note_deprecation((
'Tests run under @given should return None, but '
'%s returned %r instead.'
'In Hypothesis 2.0 this will become an error.'
) % (test.__name__, result), settings)
return False
except HypothesisDeprecationWarning:
raise
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
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)
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
with settings:
test_runner(reify_and_execute(
search_strategy, falsifying_template, test,
print_example=True, is_final=True
))
report(
u'Failed to reproduce exception. Expected: \n' +
last_exception[0],
)
test_runner(reify_and_execute(
search_strategy, falsifying_template,
test_is_flaky(test, repr_for_last_exception[0]),
print_example=True, is_final=True
))
def given(*generator_arguments, **generator_kwargs):
"""A decorator for turning a test function that accepts arguments into a
randomized test.
This is the main entry point to Hypothesis. See the full tutorial
for details of its behaviour.
"""
# Keyword only arguments but actually supported in the full range of
# pythons Hypothesis handles. pop so we don't later pick these up as
# if they were keyword specifiers for data to pass to the test.
provided_random = generator_kwargs.pop(u'random', None)
settings = generator_kwargs.pop(u'settings', None) or Settings.default
if (provided_random is not None) and settings.derandomize:
raise InvalidArgument(
u'Cannot both be derandomized and provide an explicit random')
if not (generator_arguments or generator_kwargs):
raise InvalidArgument(
u'given must be called with at least one argument')
if generator_arguments and generator_kwargs:
note_deprecation(
u'Mixing positional and keyword arguments in a call to given is '
u'deprecated. Use one or the other.', settings)
def run_test_with_generator(test):
if settings.derandomize:
assert provided_random is None
random = Random(function_digest(test))
else:
random = provided_random or Random()
original_argspec = getargspec(test)
if generator_arguments and original_argspec.varargs:
raise InvalidArgument(
u'varargs are not supported with positional arguments to '
u'@given')
extra_kwargs = [
k for k in generator_kwargs if k not in original_argspec.args
]
if extra_kwargs and not original_argspec.keywords:
raise InvalidArgument(
u'%s() got an unexpected keyword argument %r' %
(test.__name__, extra_kwargs[0]))
if (len(generator_arguments) > len(original_argspec.args)):
raise InvalidArgument(
(u'Too many positional arguments for %s() (got %d but'
u' expected at most %d') %
(test.__name__, len(generator_arguments),
len(original_argspec.args)))
arguments = original_argspec.args
specifiers = list(generator_arguments)
seen_kwarg = None
for a in arguments:
if isinstance(a, list): # pragma: no cover
raise InvalidArgument(
(u'Cannot decorate function %s() because it has '
u'destructuring arguments') % (test.__name__, ))
if a in generator_kwargs:
seen_kwarg = seen_kwarg or a
specifiers.append(generator_kwargs[a])
else:
if seen_kwarg is not None:
raise InvalidArgument(
(u'Argument %s comes after keyword %s which has been '
u'specified, but does not itself have a '
u'specification') % (a, seen_kwarg))
argspec = inspect.ArgSpec(args=arguments,
keywords=original_argspec.keywords,
varargs=original_argspec.varargs,
defaults=tuple(
map(HypothesisProvided, specifiers)))
unused_kwargs = {}
for k in extra_kwargs:
unused_kwargs[k] = HypothesisProvided(generator_kwargs[k])
@impersonate(test)
@copy_argspec(test.__name__, argspec)
def wrapped_test(*arguments, **kwargs):
import hypothesis.strategies as sd
from hypothesis.internal.strategymethod import strategy
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
# Anything in unused_kwargs hasn't been injected through
# argspec.defaults, so we need to add them.
for k in unused_kwargs:
if k not in kwargs:
kwargs[k] = unused_kwargs[k]
# 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]
if isinstance(selfy, HypothesisProvided):
selfy = None
test_runner = executor(selfy)
for example in getattr(wrapped_test,
u'hypothesis_explicit_examples', ()):
if example.args:
example_kwargs = dict(
zip(argspec.args[-len(example.args):], example.args))
else:
example_kwargs = dict(example.kwargs)
for k, v in kwargs.items():
if not isinstance(v, HypothesisProvided):
example_kwargs[k] = v
# 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 = u'Falsifying example: %s(%s)' % (
test.__name__, arg_string(test, arguments, example_kwargs))
try:
test_runner(lambda: test(*arguments, **example_kwargs))
except BaseException:
report(message_on_failure)
raise
if not any(
isinstance(x, HypothesisProvided)
for xs in (arguments, kwargs.values()) for x in xs):
# All arguments have been satisfied without needing to invoke
# hypothesis
test_runner(lambda: test(*arguments, **kwargs))
return
def convert_to_specifier(v):
if isinstance(v, HypothesisProvided):
return strategy(v.value, settings)
else:
return sd.just(v)
given_specifier = sd.tuples(
sd.tuples(*map(convert_to_specifier, arguments)),
sd.fixed_dictionaries(
dict((k, convert_to_specifier(v))
for (k, v) in kwargs.items())))
search_strategy = strategy(given_specifier, settings)
if settings.database:
storage = settings.database.storage(fully_qualified_name(test))
else:
storage = None
last_exception = [None]
repr_for_last_exception = [None]
def is_template_example(xs):
record_repr = [None]
try:
test_runner(
reify_and_execute(
search_strategy,
xs,
test,
always_print=settings.max_shrinks <= 0,
record_repr=record_repr,
))
return False
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
if settings.max_shrinks <= 0:
raise e
last_exception[0] = traceback.format_exc()
repr_for_last_exception[0] = record_repr[0]
verbose_report(last_exception[0])
return True
is_template_example.__name__ = test.__name__
is_template_example.__qualname__ = qualname(test)
falsifying_template = None
try:
falsifying_template = best_satisfying_template(
search_strategy, random, is_template_example, settings,
storage)
except NoSuchExample:
return
assert last_exception[0] is not None
with settings:
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test,
print_example=True,
is_final=True))
report(
u'Failed to reproduce exception. Expected: \n' +
last_exception[0], )
test_runner(
reify_and_execute(search_strategy,
falsifying_template,
test_is_flaky(
test, repr_for_last_exception[0]),
print_example=True,
is_final=True))
for attr in dir(test):
if attr[0] != '_' and not hasattr(wrapped_test, attr):
setattr(wrapped_test, attr, getattr(test, attr))
wrapped_test.is_hypothesis_test = True
return wrapped_test
return run_test_with_generator
def given(*generator_arguments, **generator_kwargs):
"""A decorator for turning a test function that accepts arguments into a
randomized test.
This is the main entry point to Hypothesis. See the full tutorial
for details of its behaviour.
"""
# Keyword only arguments but actually supported in the full range of
# pythons Hypothesis handles. pop so we don't later pick these up as
# if they were keyword specifiers for data to pass to the test.
provided_random = generator_kwargs.pop(u'random', None)
settings = generator_kwargs.pop(u'settings', None) or Settings.default
if (provided_random is not None) and settings.derandomize:
raise InvalidArgument(
u'Cannot both be derandomized and provide an explicit random')
if not (generator_arguments or generator_kwargs):
raise InvalidArgument(
u'given must be called with at least one argument')
if generator_arguments and generator_kwargs:
note_deprecation(
u'Mixing positional and keyword arguments in a call to given is '
u'deprecated. Use one or the other.', settings
)
def run_test_with_generator(test):
if settings.derandomize:
assert provided_random is None
random = Random(function_digest(test))
else:
random = provided_random or Random()
original_argspec = getargspec(test)
if generator_arguments and original_argspec.varargs:
raise InvalidArgument(
u'varargs are not supported with positional arguments to '
u'@given'
)
extra_kwargs = [
k for k in generator_kwargs if k not in original_argspec.args]
if extra_kwargs and not original_argspec.keywords:
raise InvalidArgument(
u'%s() got an unexpected keyword argument %r' % (
test.__name__,
extra_kwargs[0]
))
if (
len(generator_arguments) > len(original_argspec.args)
):
raise InvalidArgument((
u'Too many positional arguments for %s() (got %d but'
u' expected at most %d') % (
test.__name__, len(generator_arguments),
len(original_argspec.args)))
arguments = original_argspec.args
specifiers = list(generator_arguments)
seen_kwarg = None
for a in arguments:
if isinstance(a, list): # pragma: no cover
raise InvalidArgument((
u'Cannot decorate function %s() because it has '
u'destructuring arguments') % (
test.__name__,
))
if a in generator_kwargs:
seen_kwarg = seen_kwarg or a
specifiers.append(generator_kwargs[a])
else:
if seen_kwarg is not None:
raise InvalidArgument((
u'Argument %s comes after keyword %s which has been '
u'specified, but does not itself have a '
u'specification') % (
a, seen_kwarg
))
argspec = inspect.ArgSpec(
args=arguments,
keywords=original_argspec.keywords,
varargs=original_argspec.varargs,
defaults=tuple(map(HypothesisProvided, specifiers))
)
unused_kwargs = {}
for k in extra_kwargs:
unused_kwargs[k] = HypothesisProvided(generator_kwargs[k])
@impersonate(test)
@copy_argspec(
test.__name__, argspec
)
def wrapped_test(*arguments, **kwargs):
import hypothesis.strategies as sd
from hypothesis.internal.strategymethod import strategy
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
# Anything in unused_kwargs hasn't been injected through
# argspec.defaults, so we need to add them.
for k in unused_kwargs:
if k not in kwargs:
kwargs[k] = unused_kwargs[k]
# 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]
if isinstance(selfy, HypothesisProvided):
selfy = None
test_runner = executor(selfy)
for example in getattr(
wrapped_test, u'hypothesis_explicit_examples', ()
):
if example.args:
example_kwargs = dict(zip(
argspec.args[-len(example.args):], example.args
))
else:
example_kwargs = dict(example.kwargs)
for k, v in kwargs.items():
if not isinstance(v, HypothesisProvided):
example_kwargs[k] = v
# 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 = u'Falsifying example: %s(%s)' % (
test.__name__, arg_string(test, arguments, example_kwargs)
)
try:
test_runner(
lambda: test(*arguments, **example_kwargs)
)
except BaseException:
report(message_on_failure)
raise
if not any(
isinstance(x, HypothesisProvided)
for xs in (arguments, kwargs.values())
for x in xs
):
# All arguments have been satisfied without needing to invoke
# hypothesis
test_runner(lambda: test(*arguments, **kwargs))
return
def convert_to_specifier(v):
if isinstance(v, HypothesisProvided):
return strategy(v.value, settings)
else:
return sd.just(v)
given_specifier = sd.tuples(
sd.tuples(*map(convert_to_specifier, arguments)),
sd.fixed_dictionaries(dict(
(k, convert_to_specifier(v)) for (k, v) in kwargs.items()))
)
search_strategy = strategy(given_specifier, settings)
if settings.database:
storage = settings.database.storage(
fully_qualified_name(test))
else:
storage = None
last_exception = [None]
repr_for_last_exception = [None]
def is_template_example(xs):
record_repr = [None]
try:
test_runner(reify_and_execute(
search_strategy, xs, test,
always_print=settings.max_shrinks <= 0,
record_repr=record_repr,
))
return False
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
if settings.max_shrinks <= 0:
raise e
last_exception[0] = traceback.format_exc()
repr_for_last_exception[0] = record_repr[0]
verbose_report(last_exception[0])
return True
is_template_example.__name__ = test.__name__
is_template_example.__qualname__ = qualname(test)
falsifying_template = None
try:
falsifying_template = best_satisfying_template(
search_strategy, random, is_template_example,
settings, storage
)
except NoSuchExample:
return
assert last_exception[0] is not None
with settings:
test_runner(reify_and_execute(
search_strategy, falsifying_template, test,
print_example=True, is_final=True
))
report(
u'Failed to reproduce exception. Expected: \n' +
last_exception[0],
)
test_runner(reify_and_execute(
search_strategy, falsifying_template,
test_is_flaky(test, repr_for_last_exception[0]),
print_example=True, is_final=True
))
for attr in dir(test):
if attr[0] != '_' and not hasattr(wrapped_test, attr):
setattr(wrapped_test, attr, getattr(test, attr))
wrapped_test.is_hypothesis_test = True
return wrapped_test
return run_test_with_generator
def given(*generator_arguments, **generator_kwargs):
"""A decorator for turning a test function that accepts arguments into a
randomized test.
This is the main entry point to Hypothesis. See the full tutorial
for details of its behaviour.
"""
# Keyword only arguments but actually supported in the full range of
# pythons Hypothesis handles. pop so we don't later pick these up as
# if they were keyword specifiers for data to pass to the test.
provided_random = generator_kwargs.pop(u'random', None)
settings = generator_kwargs.pop(u'settings', None) or Settings.default
if generator_arguments and generator_kwargs:
note_deprecation(
u'Mixing positional and keyword arguments in a call to given is '
u'deprecated. Use one or the other.', settings
)
def run_test_with_generator(test):
original_argspec = getargspec(test)
def invalid(message):
def wrapped_test(*arguments, **kwargs):
raise InvalidArgument(message)
return wrapped_test
if (provided_random is not None) and settings.derandomize:
return invalid(
u'Cannot both be derandomized and provide an explicit random')
if not (generator_arguments or generator_kwargs):
return invalid(
u'given must be called with at least one argument')
if generator_arguments and original_argspec.varargs:
return invalid(
u'varargs are not supported with positional arguments to '
u'@given'
)
extra_kwargs = [
k for k in generator_kwargs if k not in original_argspec.args]
if extra_kwargs and not original_argspec.keywords:
return invalid(
u'%s() got an unexpected keyword argument %r' % (
test.__name__,
extra_kwargs[0]
))
if (
len(generator_arguments) > len(original_argspec.args)
):
return invalid((
u'Too many positional arguments for %s() (got %d but'
u' expected at most %d') % (
test.__name__, len(generator_arguments),
len(original_argspec.args)))
arguments = original_argspec.args
specifiers = list(generator_arguments)
seen_kwarg = None
for a in arguments:
if isinstance(a, list): # pragma: no cover
return invalid((
u'Cannot decorate function %s() because it has '
u'destructuring arguments') % (
test.__name__,
))
if a in generator_kwargs:
seen_kwarg = seen_kwarg or a
specifiers.append(generator_kwargs[a])
else:
if seen_kwarg is not None:
return invalid((
u'Argument %s comes after keyword %s which has been '
u'specified, but does not itself have a '
u'specification') % (
a, seen_kwarg
))
argspec = inspect.ArgSpec(
args=arguments,
keywords=original_argspec.keywords,
varargs=original_argspec.varargs,
defaults=tuple(map(HypothesisProvided, specifiers))
)
unused_kwargs = {}
for k in extra_kwargs:
unused_kwargs[k] = HypothesisProvided(generator_kwargs[k])
hypothesis_owned_arguments = [
argspec.args[-1 - i] for i in hrange(len(argspec.defaults))
] + list(unused_kwargs)
@impersonate(test)
@copy_argspec(
test.__name__, argspec
)
def wrapped_test(*arguments, **kwargs):
if settings.derandomize:
random = Random(function_digest(test))
else:
random = provided_random or new_random()
import hypothesis.strategies as sd
from hypothesis.internal.strategymethod import strategy
selfy = None
arguments, kwargs = convert_positional_arguments(
wrapped_test, arguments, kwargs)
for arg in hypothesis_owned_arguments:
try:
value = kwargs[arg]
except KeyError:
continue
if not isinstance(value, HypothesisProvided):
note_deprecation(
'Passing in explicit values to override Hypothesis '
'provided values is deprecated and will no longer '
'work in Hypothesis 2.0. If you need to do this, '
'extract a common function and call that from a '
'Hypothesis based test.', settings
)
# Anything in unused_kwargs hasn't been injected through
# argspec.defaults, so we need to add them.
for k in unused_kwargs:
if k not in kwargs:
kwargs[k] = unused_kwargs[k]
# 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]
if isinstance(selfy, HypothesisProvided):
selfy = None
test_runner = executor(selfy)
for example in reversed(getattr(
wrapped_test, u'hypothesis_explicit_examples', ()
)):
if example.args:
example_kwargs = dict(zip(
argspec.args[-len(example.args):], example.args
))
else:
example_kwargs = dict(example.kwargs)
for k, v in kwargs.items():
if not isinstance(v, HypothesisProvided):
example_kwargs[k] = v
# 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 = u'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
if not any(
isinstance(x, HypothesisProvided)
for xs in (arguments, kwargs.values())
for x in xs
):
# All arguments have been satisfied without needing to invoke
# hypothesis
test_runner(lambda: test(*arguments, **kwargs))
return
def convert_to_specifier(v):
if isinstance(v, HypothesisProvided):
return strategy(v.value, settings)
else:
return sd.just(v)
given_specifier = sd.tuples(
sd.tuples(*map(convert_to_specifier, arguments)),
sd.fixed_dictionaries(dict(
(k, convert_to_specifier(v)) for (k, v) in kwargs.items()))
)
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 = strategy(given_specifier, settings)
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 you'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 you'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:
note_deprecation((
'Tests run under @given should return None, but '
'%s returned %r instead.'
'In Hypothesis 2.0 this will become an error.'
) % (test.__name__, result), settings)
return False
except HypothesisDeprecationWarning:
raise
except UnsatisfiedAssumption as e:
raise e
except Exception as e:
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(
u'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.'
)
for attr in dir(test):
if attr[0] != '_' and not hasattr(wrapped_test, attr):
setattr(wrapped_test, attr, getattr(test, attr))
wrapped_test.is_hypothesis_test = True
return wrapped_test
return run_test_with_generator