def define_set_strategy(specifier, settings):
if not specifier:
return st.sets(max_size=0)
else:
with settings:
return st.sets(st.one_of(
*[self(s, settings) for s in specifier]))
def type_to_strat(x, opts): # type: (type) -> SearchStrategy
'''
Given a type, return a strategy which yields a value of that type. Types maybe complex: Union, NamedTuple, etc.
For more information, see https://docs.python.org/3/library/typing.html
Usage:
>>> type_to_strat(Union[int,str]).exmample()
. . . 3
'''
recur = lambda y: type_to_strat(y, opts)
if x in primitives:
prim = primitives[x].filter(opts.get(x, lambda x: x))
return prim
elif hasattr(x, '_fields'):# NamedTuple isn't a type, it's a function
#elif isinstance(x, Callable): #this catches List[T] for some reason
name = x.__name__
fts = OrderedDict(x._field_types)
vals = map(recur, fts.values())
# `NamedTuple` is actually a ... `namedtuple` itself
toArgDict = lambda xs: dict(zip(fts.keys(), xs))
return st.tuples(*vals).map(lambda ys: x(**toArgDict(ys)))
elif issubclass(x, Dict):
return st.dictionaries(*map(recur, x.__parameters__))
elif issubclass(x, Tuple):
if x.__tuple_use_ellipsis__: # variable lenth tuple
element_type = x.__tuple_params__[0]
return recur(List[element_type]).map(tuple)
return st.tuples(*map(recur, x.__tuple_params__))
elif issubclass(x, Union):
return reduce(operator.ior, map(recur, x.__union_params__))
elif issubclass(x, Optional):
# Optional[X] is equivalent to Union[X, type(None)]. second param is always Nonetype.
value = x.__union_params__[0]
return (recur(value) | st.none()) # type: SearchStrategy
else:
element_type = recur(x.__parameters__[0])
if issubclass(x, list):
return st.lists(element_type)
elif issubclass(x, set):
return st.sets(element_type)
elif issubclass(x, Sequence):
anySizeTuple = recur(Tuple[element_type,...])
return st.sets(element_type) | st.lists(element_type) | anySizeTuple
elif issubclass(x, Generator):
toGen = lambda xs: (x for x in xs) # type: Callable[[Iterable[T]], Generator[T]]
return recur(List[element_type]).map(toGen)
# not sure how to create an Iterable (it doesn't have an `__next__` method)
elif issubclass(x, Iterator) or issubclass(x, Iteratable):
return recur(List[element_type]).map(iter)
else:
raise ValueError("Could not find strategy for type %s" % x)
def test_can_form_sets_of_recursive_data():
trees = st.sets(st.recursive(
st.booleans(), lambda x: st.lists(x).map(tuple), max_leaves=4))
xs = find(trees, lambda x: len(x) >= 10)
assert len(xs) == 10
assert False in xs
assert True in xs
def lists_pairs_nodup(draw, elements=IMMUTABLES, min_size=0, max_size=LISTS_MAX_SIZE):
"""Generate a list of pairs from the given elements with no duplication."""
n = draw(st.integers(min_value=min_size, max_value=max_size))
size_n_sets = st.sets(elements, min_size=n, max_size=n)
keys = draw(size_n_sets)
vals = draw(size_n_sets)
return list(izip(keys, vals))
def list_of_sets():
return st.lists(
st.sets(
st.integers(max_value=8),
max_size=8
),
max_size=16
)
def patches():
"""Returns a strategy which generates a Patch object.
At present, planex.patchqueue.Patchqueue only supports a single
guard on each patch.
"""
# If support for multiple guards is added, the max_size constraint
# can be increased.
return st.builds(Patch, files(), st.sets(guards(), min_size=0, max_size=1))
def test_sets_of_fixed_length(n):
x = find(
sets(integers(), min_size=n, max_size=n), lambda x: True)
if not n:
assert x == set()
else:
assert x == set(range(min(x), min(x) + n))
def test_can_draw_sets_of_hard_to_find_elements(rnd):
rarebool = floats(0, 1).map(lambda x: x <= 0.05)
find_any(
sets(rarebool, min_size=2),
lambda x: True,
random=rnd,
settings=settings(database=None),
)
def test_minimize_sets_of_sets():
elements = integers(1, 100)
size = 15
set_of_sets = minimal(sets(frozensets(elements)), lambda s: len(s) >= size)
assert frozenset() in set_of_sets
assert len(set_of_sets) == size
for s in set_of_sets:
if len(s) > 1:
assert any(s != t and t.issubset(s) for t in set_of_sets)
def test_can_form_sets_of_recursive_data():
size = 3
trees = st.sets(st.recursive(
st.booleans(),
lambda x: st.lists(x, min_size=size).map(tuple),
max_leaves=20))
xs = minimal(trees, lambda x: len(x) >= size, timeout_after=None)
assert len(xs) == size
def test_can_form_sets_of_recursive_data():
trees = st.sets(st.recursive(
st.booleans(),
lambda x: st.lists(x, min_size=5).map(tuple),
max_leaves=20))
xs = find(trees, lambda x: len(x) >= 10, settings=settings(
database=None, timeout=20, max_shrinks=1000, max_examples=1000
))
assert len(xs) == 10
def test_reified_templates_are_simpler(seed):
s = sets(integers())
t1 = s.draw_and_produce(Random(seed))
t2 = s.draw_and_produce(Random(seed))
assert t1 == t2
assert not s.strictly_simpler(t1, t2)
assume(s.reify(t1))
assert s.strictly_simpler(t1, t2)
assert not s.strictly_simpler(t2, t1)
def add_extra_searchwords(draw, result):
for lang in LANGUAGES:
words = draw(sets(text(SAFE_LETTERS + 'åäöÅÄÖ ',
min_size=1, max_size=25)))
if len(words) == 0:
event('extra searchwords length {}'.format(len(words)))
words = None
else:
event('extra searchwords length >0')
words = ', '.join((word.strip() for word in words))
result['extra_searchwords_{}'.format(lang)] = words
def test_can_form_sets_of_recursive_data():
trees = st.sets(st.recursive(
st.booleans(),
lambda x: st.lists(x, min_size=5).map(tuple),
max_leaves=10))
xs = find(trees, lambda x: len(x) >= 10, settings=settings(
database=None
))
print(xs)
assert len(xs) == 10
assert False in xs
assert True in xs
def options_string(cls):
"""
Strategy for generation of nfs options formatted for an exports line.
"""
def format_options(opts):
if not opts:
return ''
else:
return '({0:s})'.format(','.join(opts))
options = cls.options()
return builds(format_options, sets(options))
def _build_node(applications):
# All the manifestations in `applications`.
app_manifestations = set(app.volume.manifestation for app in applications if app.volume)
# A set that contains all of those, plus an arbitrary set of
# manifestations.
dataset_ids = frozenset(app.volume.manifestation.dataset_id for app in applications if app.volume)
manifestations = (
st.sets(MANIFESTATIONS.filter(lambda m: m.dataset_id not in dataset_ids))
.map(pset)
.map(lambda ms: ms.union(app_manifestations))
.map(lambda ms: dict((m.dataset.dataset_id, m) for m in ms))
)
return st.builds(Node, uuid=UUIDS, applications=st.just(applications), manifestations=manifestations)
def test_simplify_does_not_error_on_unreified_data():
s = sets(integers())
for i in range(100):
t1 = s.draw_and_produce(Random(i))
t2 = s.draw_and_produce(Random(i))
if t1.size > 1:
break
s.reify(t1)
simplifiers = list(s.simplifiers(Random(1), t1))
assert len(simplifiers) > 2
for s in simplifiers:
assert list(s(Random(1), t2)) == []
def export(cls):
"""
Strategy that generates :py:class:`scality_manila_utils.export.Export`.
"""
export_point = cls.path()
host = cls.host()
options = sets(cls.options(), average_size=3)
clients = dictionaries(
keys=host,
values=options,
min_size=1,
average_size=5
)
return builds(Export, export_point, clients)
def test_find_large_union_list():
def large_mostly_non_overlapping(xs):
assume(xs)
assume(all(xs))
union = reduce(operator.or_, xs)
return len(union) >= 30
result = minimal(lists(sets(integers())), large_mostly_non_overlapping, timeout_after=30)
union = reduce(operator.or_, result)
assert len(union) == 30
assert max(union) == min(union) + len(union) - 1
for x in result:
for y in result:
if x is not y:
assert not (x & y)
def objects(elements=None, *,
required_fields=None,
optional_fields=None,
min_size=None,
average_size=None,
max_size=None):
"""Returns a strategy that generates dicts of specified `elements`.
The `elements` must be valid Hypothesis strategy. The keys are ensured to
be string only as JSON requires.
While choice of `elements` left here for user side, it's not recommended to
use anything that produces non-serializable to JSON values.
Also possible to ensure that some fields with values generated by a certain
strategy are always exists (`required_fields`) or just optional
(`optional_fields`).
"""
def check_type(varname, var, expected):
if not isinstance(var, expected):
raise TypeError('{} must be {}, got {}'.format(
varname, expected, type(var)))
acc = []
if required_fields:
check_type('required_fields', required_fields, dict)
for key in required_fields:
check_type('required field name', key, str)
acc.append(st.fixed_dictionaries(required_fields))
if optional_fields:
check_type('optional_fields', optional_fields, dict)
for key in optional_fields:
check_type('optional field name', key, str)
acc.append(st.sets(st.sampled_from(optional_fields)).flatmap(
lambda keys: st.fixed_dictionaries({key: optional_fields[key]
for key in keys})))
if elements:
acc.append(st.dictionaries(strings(), elements,
min_size=min_size,
average_size=average_size,
max_size=max_size))
if not acc:
raise RuntimeError('object must have any strategy for fields')
return st.tuples(*reversed(acc)).map(
lambda s: reduce(lambda a, d: dict(a, **d), s))
def test_change(self, C, data):
"""
Changes work.
"""
# Take the first attribute, and change it.
assume(fields(C)) # Skip classes with no attributes.
field_names = [a.name for a in fields(C)]
original = C()
chosen_names = data.draw(st.sets(st.sampled_from(field_names)))
# We pay special attention to private attributes, they should behave
# like in `__init__`.
change_dict = {name.replace('_', ''): data.draw(st.integers())
for name in chosen_names}
changed = evolve(original, **change_dict)
for name in chosen_names:
assert getattr(changed, name) == change_dict[name.replace('_', '')]
def test_template_equality():
s = sets(integers())
t = s.draw_and_produce(Random(1))
assert t != 1
t2 = s.draw_and_produce(Random(1))
assert t is not t2
assert t == t2
s.reify(t2)
assert t == t2
assert hash(t) == hash(t2)
t3 = s.draw_and_produce(Random(1))
s.reify(t3)
for ts in s.full_simplify(Random(1), t3):
assert t3 != ts
def test_change(self, C, data):
"""
Changes work.
"""
# Take the first attribute, and change it.
assume(fields(C)) # Skip classes with no attributes.
field_names = [a.name for a in fields(C)]
original = C()
chosen_names = data.draw(st.sets(st.sampled_from(field_names)))
change_dict = {name: data.draw(st.integers())
for name in chosen_names}
with pytest.deprecated_call():
changed = assoc(original, **change_dict)
for k, v in change_dict.items():
assert getattr(changed, k) == v
def test_struct(data):
_elements = one_of(characters(), integers(), text())
_sets = sets(elements=_elements, min_size=1, average_size=5)
allowed_keys = data.draw(_sets)
keys = list(allowed_keys)
values = data.draw(lists(elements=_elements, min_size=len(keys), max_size=len(keys)))
s = py_tools.Struct(allowed_keys)
for key, value in zip(keys, values):
s[key] = value
assert s[key] == value
assert s == {k: v for k, v in zip(keys, values)}
prohibited_keys = {k for k in data.draw(_sets) if k not in allowed_keys}
for key in prohibited_keys:
with pytest.raises(KeyError):
s[key] = data.draw(_elements)
def test_template_equality(seed):
s = sets(integers())
t = s.draw_and_produce(Random(seed))
assert t != 1
t2 = s.draw_and_produce(Random(seed))
assert t is not t2
assert t == t2
s.reify(t2)
assert t == t2
assert hash(t) == hash(t2)
t3 = s.draw_and_produce(Random(seed))
s.reify(t3)
simplified = False
for ts in s.full_simplify(Random(seed), t3):
simplified = True
assert t3 != ts
assume(simplified)
def test_regression_issue_1230():
strategy = st.builds(
lambda x, y: dict((list(x.items()) + list(y.items()))),
st.fixed_dictionaries({'0': st.text()}),
st.builds(
lambda dictionary, keys: {key: dictionary[key] for key in keys},
st.fixed_dictionaries({
'1': st.lists(elements=st.sampled_from(['a']))
}),
st.sets(elements=st.sampled_from(['1']))
)
)
@seed(81581571036124932593143257836081491416)
@settings(database=None)
@given(strategy)
def test_false_is_false(params):
assume(params.get('0') not in ('', '\x00'))
raise ValueError()
with pytest.raises(ValueError):
test_false_is_false()
def test_can_draw_sets_of_hard_to_find_elements(rnd):
rarebool = floats(0, 1).map(lambda x: x <= 0.01)
find(
sets(rarebool, min_size=2), lambda x: True,
random=rnd, settings=settings(database=None))
assert p.state == states.INITIAL
assert p.state_history == [states.INITIAL]
assert p._stage_count == 0
assert p.current_stage == 0
assert type(p.lock) == type(threading.Lock())
assert type(p._completed_flag) == type(threading.Event())
assert p.completed == False
# ------------------------------------------------------------------------------
#
@given(t=st.text(),
l=st.lists(st.text()),
i=st.integers().filter(lambda x: type(x) == int),
b=st.booleans(),
se=st.sets(st.text()))
def test_pipeline_assignment_exceptions(t, l, i, b, se):
p = Pipeline()
data_type = [t, l, i, b, se]
for data in data_type:
if not isinstance(data, str):
with pytest.raises(TypeError):
p.name = data
with pytest.raises(TypeError):
p.stages = data
class DefinitionTests(TestCase):
def test_roundtrip_examples(self):
# tree has this structure:
#
# ┌--0--┐
# | |
# ┌--1--┌--2--┐
# | | |
# ┌-3-┐ ┌-4-┐ ┌-5-┐
# | | | | | |
# 6 7 8 9 10 11
edges = [
("0", "1"),
("0", "2"),
("1", "3"),
("1", "4"),
("2", "4"),
("2", "5"),
("3", "6"),
("3", "7"),
("4", "8"),
("4", "9"),
("5", "10"),
("5", "11"),
]
paths = edges_to_paths("0", edges)
tree = paths_to_tree(paths)
for codes, query in [
# the root element
({"0"}, ["0"]),
# an intermediate element
({"2"}, ["2"]),
# a leaf element
({"8"}, ["8"]),
# everything under intermediate element, inclusive
({"2", "4", "5", "8", "9", "10", "11"}, ["2<"]),
# everything under intermediate element, exclusive
({"4", "5", "8", "9", "10", "11"}, ["4<", "5<"]),
# everything under intermediate element, except another intermediate element
({"2", "5", "8", "9", "10", "11"}, ["2<", "~4"]),
# everything under intermediate element, except leaf element
({"2", "4", "5", "9", "10", "11"}, ["2<", "~8"]),
# everything under root element, except intermediate element and its children (i)
({"0", "1", "2", "3", "4", "6", "7", "8", "9"}, ["0", "1<", "2"]),
# everything under root element, except intermediate element and its children (ii)
(
{"0", "1", "2", "3", "5", "6", "7", "10", "11"},
["0", "1", "2", "3<", "5<"],
),
]:
with self.subTest(codes=codes, query=query):
defn1 = Definition.from_codes(codes, tree)
self.assertEqual(sorted(str(e) for e in defn1.elements),
sorted(query))
self.assertEqual(defn1.codes(tree), codes)
defn2 = Definition.from_query(query)
self.assertEqual(sorted(str(e) for e in defn2.elements),
sorted(query))
self.assertEqual(defn2.codes(tree), codes)
@settings(deadline=None)
@given(dags(24), st.sets(st.sampled_from(range(16))), st.floats(0.1, 0.5))
def test_roundtrip(self, dag, codes, r):
edges = [(parent, child) for parent, children in dag.items()
for child in children]
paths = edges_to_paths(0, edges)
tree = paths_to_tree(paths)
definition = Definition.from_codes(codes, tree, r)
self.assertEqual(definition.codes(tree), codes)
fragments = [e.fragment for e in definition.elements]
self.assertEqual(len(fragments), len(set(fragments)))
definition_codes = [e.code for e in definition.elements]
self.assertEqual(len(definition_codes), len(set(definition_codes)))
class FeaturesTestCase(TestCase):
def test_read(self):
self.assertTrue(isinstance(extractor.pos_tags, tuple))
self.assertIn('PROPN', extractor.pos_tags)
self.assertIn('CONJ', extractor.pos_tags)
self.assertTrue(isinstance(extractor.morph, dict))
self.assertIn('Case', extractor.morph)
self.assertIn('Definite', extractor.morph)
self.assertIn('Number', extractor.morph)
self.assertTrue(isinstance(extractor.lemmas, dict))
self.assertIn('_', extractor.lemmas)
self.assertIn('\xa0', extractor.lemmas)
self.assertIn('Atenas', extractor.lemmas)
self.assertIn('ordea', extractor.lemmas)
@given(sets(text()))
def test_featurise_lemma(self, lemmas):
assume(all([lemma not in ['_', '\xa0'] for lemma in lemmas]))
extractor = Extractor()
for lemma in lemmas:
extractor.lemmas[lemma]
self.assertEqual(extractor.get_vocab_sizes()['lemmas'],
len(lemmas) + 2)
self.assertEqual(extractor.featurise_lemma('_'), 0)
self.assertEqual(extractor.featurise_lemma('\xa0'), 1)
res = [extractor.featurise_lemma(lemma) for lemma in lemmas]
self.assertEqual(len(res), len(lemmas))
self.assertTrue(all([number not in [0, 1] for number in res]))
@given(sampled_from(extractor.pos_tags))
def test_featurise_pos_tag(self, pos_tag):
res = extractor.featurise_pos_tag(pos_tag)
self.assertTrue(isinstance(res, int))
self.assertTrue(res > 0)
self.assertTrue(res <= len(extractor.pos_tags))
@given(subdicts(extractor.morph))
def test_featurise_morph(self, subdict):
res = extractor.featurise_morph(subdict)
self.assertTrue(isinstance(res, np.ndarray))
self.assertEqual(
len(res), sum([len(value) for value in extractor.morph.values()]))
self.assertTrue(all([i == 0 or i == 1 for i in res]))
self.assertEqual(len([i for i in res if i == 1]),
sum([len(value) for value in subdict.values()]))
def test_model_files(self):
with tempfile.TemporaryDirectory() as temp_dir:
path = os.path.join(temp_dir, 'model')
extractor.write_to_model_file(path)
new_ext = Extractor.create_from_model_file(path)
self.assertTrue(isinstance(new_ext, Extractor))
self.assertEqual(new_ext.lemmas, extractor.lemmas)
self.assertEqual(new_ext.morph, extractor.morph)
self.assertEqual(new_ext.pos_tags, extractor.pos_tags)
self.assertFalse(new_ext.ignore_lemmas)
self.assertFalse(new_ext.ignore_morph)
def test_model_files_error(self):
with tempfile.TemporaryDirectory() as temp_dir:
path = os.path.join(temp_dir, 'model')
with self.assertRaises(OSError):
Extractor.create_from_model_file(path)
with open(path, 'w') as f:
f.write('hi')
with self.assertRaises(OSError):
Extractor.create_from_model_file(path)
assert not os.path.exists(temp_dir)
with self.assertRaises(OSError):
extractor.write_to_model_file(path)
# END HEADER
from __future__ import division, print_function, absolute_import, \
unicode_literals
import pytest
from hypothesis.strategies import just, sets, text, lists, binary, \
floats, one_of, tuples, randoms, booleans, integers, frozensets, \
sampled_from, complex_numbers, fixed_dictionaries
from hypothesis.searchstrategy.narytree import n_ary_tree
from hypothesis.searchstrategy.strategies import BadData, strategy
@pytest.mark.parametrize(('specifier', 'data'), [
(sets(text()), 0j),
(complex_numbers(), set('hi')),
(lists(sets(booleans())), 0),
(just(1), 'hi'),
(binary(), 0.0),
(binary(), frozenset()),
(fixed_dictionaries({True: sets(integers())}), []),
(randoms(), []),
(integers(), ''),
(integers(), [0, '']),
(text(), 'kittens'),
(tuples(integers(), integers(), integers()), (1, 2)),
(sampled_from((1, 2, 3)), 'fish'),
(sampled_from((1, 2, 3)), 5),
(sampled_from((1, 2, 3)), -2),
(one_of(integers(), floats()), 1),
class Foo(ForkingTestCase):
@given(sets(booleans()))
def runs_normally(self, x):
pass
def test_minimize_3_set():
assert minimal(sets(integers()),
lambda x: len(x) >= 3) in ({0, 1, 2}, {-1, 0, 1})
test_reusable_strategies_are_all_reusable
)
test_reusable_strategies_are_all_reusable = example(st.tuples(s))(
test_reusable_strategies_are_all_reusable
)
def test_composing_breaks_reusability():
s = st.integers()
assert s.has_reusable_values
assert not s.filter(lambda x: True).has_reusable_values
assert not s.map(lambda x: x).has_reusable_values
assert not s.flatmap(lambda x: st.just(x)).has_reusable_values
@pytest.mark.parametrize(
"strat",
[
st.lists(st.booleans()),
st.sets(st.booleans()),
st.dictionaries(st.booleans(), st.booleans()),
],
)
def test_mutable_collections_do_not_have_reusable_values(strat):
assert not strat.has_reusable_values
def test_recursion_does_not_break_reusability():
x = st.deferred(lambda: st.none() | st.tuples(x))
assert x.has_reusable_values
nothing,
sets,
text,
tuples,
)
from tests.common.debug import find_any, minimal
from tests.common.utils import flaky
@pytest.mark.parametrize(
("col", "strat"),
[
((), tuples()),
([], lists(none(), max_size=0)),
(set(), sets(none(), max_size=0)),
(frozenset(), frozensets(none(), max_size=0)),
({}, fixed_dictionaries({})),
({}, fixed_dictionaries({}, optional={})),
(OrderedDict(), fixed_dictionaries(OrderedDict(), optional=OrderedDict())),
({}, fixed_dictionaries({}, optional={1: booleans()})),
({0: False}, fixed_dictionaries({0: booleans()}, optional={1: booleans()})),
({}, fixed_dictionaries({}, optional={(): booleans(), 0: booleans()})),
([], lists(nothing())),
([], lists(nothing(), unique=True)),
],
)
def test_find_empty_collection_gives_empty(col, strat):
assert minimal(strat, lambda x: True) == col
test_sampled_from_large_number_can_mix = define_test(
lists(sampled_from(range(50))), 0.1, lambda x: len(set(x)) >= 25,
condition=lambda t: len(t) >= 50,
)
test_sampled_from_often_distorted = define_test(
lists(sampled_from(range(5))), 0.28, distorted_value,
condition=lambda x: len(x) >= 3,
)
test_non_empty_subset_of_two_is_usually_large = define_test(
sets(sampled_from((1, 2))), 0.15,
lambda t: len(t) == 2
)
test_subset_of_ten_is_sometimes_empty = define_test(
sets(integers(1, 10)), 0.05, lambda t: len(t) == 0
)
test_subset_of_ten_with_large_average_is_usually_full = define_test(
sets(integers(1, 10), average_size=9.5), 0.6, lambda t: len(t) == 10
)
test_mostly_sensible_floats = define_test(
floats(), 0.5,
lambda t: t + 1 > t
def testDistribution(self, dist_name, data):
if tf.executing_eagerly() != (FLAGS.tf_mode == 'eager'):
return
tf1.set_random_seed(
data.draw(
hpnp.arrays(dtype=np.int64, shape=[]).filter(lambda x: x != 0)))
dist, batch_shape = data.draw(
distributions(dist_name=dist_name, enable_vars=True))
batch_shape2 = data.draw(tfp_hps.broadcast_compatible_shape(batch_shape))
dist2, _ = data.draw(
distributions(
dist_name=dist_name,
batch_shape=batch_shape2,
event_dim=get_event_dim(dist),
enable_vars=True))
del batch_shape
logging.info(
'distribution: %s; parameters used: %s', dist,
[k for k, v in six.iteritems(dist.parameters) if v is not None])
self.evaluate([var.initializer for var in dist.variables])
for k, v in six.iteritems(dist.parameters):
if not tensor_util.is_mutable(v):
continue
try:
self.assertIs(getattr(dist, k), v)
except AssertionError as e:
raise AssertionError(
'No attr found for parameter {} of distribution {}: \n{}'.format(
k, dist_name, e))
for stat in data.draw(
hps.sets(
hps.one_of(
map(hps.just, [
'covariance', 'entropy', 'mean', 'mode', 'stddev',
'variance'
])),
min_size=3,
max_size=3)):
logging.info('%s.%s', dist_name, stat)
try:
with tfp_hps.assert_no_excessive_var_usage(
'statistic `{}` of `{}`'.format(stat, dist)):
getattr(dist, stat)()
except NotImplementedError:
pass
with tf.GradientTape() as tape:
with tfp_hps.assert_no_excessive_var_usage(
'method `sample` of `{}`'.format(dist)):
sample = dist.sample()
if dist.reparameterization_type == tfd.FULLY_REPARAMETERIZED:
grads = tape.gradient(sample, dist.variables)
for grad, var in zip(grads, dist.variables):
var_name = var.name.rstrip('_0123456789:')
if var_name in NO_SAMPLE_PARAM_GRADS.get(dist_name, ()):
continue
if grad is None:
raise AssertionError(
'Missing sample -> {} grad for distribution {}'.format(
var_name, dist_name))
# Turn off validations, since log_prob can choke on dist's own samples.
# Also, to relax conversion counts for KL (might do >2 w/ validate_args).
dist = dist.copy(validate_args=False)
dist2 = dist2.copy(validate_args=False)
try:
for d1, d2 in (dist, dist2), (dist2, dist):
with tf.GradientTape() as tape:
with tfp_hps.assert_no_excessive_var_usage(
'`kl_divergence` of (`{}` (vars {}), `{}` (vars {}))'.format(
d1, d1.variables, d2, d2.variables),
max_permissible=1): # No validation => 1 convert per var.
kl = d1.kl_divergence(d2)
wrt_vars = list(d1.variables) + list(d2.variables)
grads = tape.gradient(kl, wrt_vars)
for grad, var in zip(grads, wrt_vars):
if grad is None and dist_name not in NO_KL_PARAM_GRADS:
raise AssertionError('Missing KL({} || {}) -> {} grad:\n'
'{} vars: {}\n{} vars: {}'.format(
d1, d2, var, d1, d1.variables, d2,
d2.variables))
except NotImplementedError:
pass
if dist_name not in NO_LOG_PROB_PARAM_GRADS:
with tf.GradientTape() as tape:
lp = dist.log_prob(tf.stop_gradient(sample))
grads = tape.gradient(lp, dist.variables)
for grad, var in zip(grads, dist.variables):
if grad is None:
raise AssertionError(
'Missing log_prob -> {} grad for distribution {}'.format(
var, dist_name))
for evaluative in data.draw(
hps.sets(
hps.one_of(
map(hps.just, [
'log_prob', 'prob', 'log_cdf', 'cdf',
'log_survival_function', 'survival_function'
])),
min_size=3,
max_size=3)):
logging.info('%s.%s', dist_name, evaluative)
try:
with tfp_hps.assert_no_excessive_var_usage(
'evaluative `{}` of `{}`'.format(evaluative, dist),
max_permissible=1): # No validation => 1 convert
getattr(dist, evaluative)(sample)
except NotImplementedError:
pass
from hypothesis.errors import InvalidArgument
from hypothesis.strategies import sets, lists, floats, randoms, integers
def test_unique_lists_error_on_too_large_average_size():
with pytest.raises(InvalidArgument):
lists(integers(), unique=True, average_size=10, max_size=5).example()
@given(randoms())
@settings(max_examples=5)
def test_can_draw_sets_of_hard_to_find_elements(rnd):
rarebool = floats(0, 1).map(lambda x: x <= 0.01)
find(
sets(rarebool, min_size=2), lambda x: True,
random=rnd, settings=settings(database=None))
def test_sets_of_small_average_size():
assert len(sets(integers(), average_size=1.0).example()) <= 10
@given(sets(max_size=0))
def test_empty_sets(x):
assert x == set()
@given(sets(integers(), max_size=2))
def test_bounded_size_sets(x):
assert len(x) <= 2
def test_minimize_3_set():
assert minimal(sets(integers()), lambda x: len(x) >= 3) in (
set((0, 1, 2)),
set((-1, 0, 1)),
)
def test_minimize_list_of_sets():
assert find(
lists(sets(booleans())),
lambda x: len(list(filter(None, x))) >= 3) == (
[set((False,))] * 3
)
from hypothesis import given, strategies as st
from openff.toolkit.topology import Molecule as OFFMolecule
import numpy as np
import polymetrizer as pet
from polymetrizer.oligomer import (Oligomer, Monomer,
AtomWrapper, HYDROGEN,
create_hydrogen_caps)
from .data import PEGMA_R_SMILES, BMA_R_SMILES, SPLIT_MOL_R_SMILES, SPLIT_MOL_R_LINKAGES, FULL_MOL_SMARTS
CAPLIST = [(1, HYDROGEN), (2, HYDROGEN), (3, HYDROGEN)]
SUBSTITUENTS = {i: CAPLIST for i in range(1, 10)}
@given(r_numbers=st.sets(st.integers()))
def test_create_hydrogen_caps(r_numbers):
r_group_numbers = list(r_numbers)
caps = create_hydrogen_caps(r_group_numbers)
assert list(caps) == r_group_numbers
assert all(len(x) == 1 for x in caps.values())
assert all(x[0][0] == 1 for x in caps.values())
class TestMonomer:
@pytest.mark.parametrize(
"smiles, r_group_indices, central_indices",
[
("[1*:1][H]", {1: 0}, [1]),
("[R1][H]", {1: 0}, [1]),
("[*:3][H]", {3: 0}, [1]),
("[R3][H]", {3: 0}, [1]),
def test_can_draw_empty_set_from_unsatisfiable_strategy():
assert find_any(sets(integers().filter(lambda s: False))) == set()
class FormatMixin:
"""
Tests for the file format.
"""
def setUp(self):
fd, self.temp_file = tempfile.mkstemp(suffix=".kas",
prefix="kas_rt_test")
os.close(fd)
def tearDown(self):
os.unlink(self.temp_file)
def test_header_format(self):
for n in range(10):
kas.dump(
{str(j): np.zeros(1)
for j in range(n)},
self.temp_file,
engine=self.engine,
)
with open(self.temp_file, "rb") as f:
contents = f.read()
self.assertEqual(contents[0:8], store.MAGIC)
major, minor, num_items, size = struct.unpack(
"<HHIQ", contents[8:24])
self.assertEqual(major, store.VERSION_MAJOR)
self.assertEqual(minor, store.VERSION_MINOR)
self.assertEqual(num_items, n)
self.assertEqual(size, len(contents))
trailer = contents[24:store.HEADER_SIZE]
# The remainder should be zeros.
self.assertEqual(
trailer, bytearray(0 for _ in range(store.HEADER_SIZE - 24)))
def test_zero_items(self):
kas.dump({}, self.temp_file, engine=self.engine)
with open(self.temp_file, "rb") as f:
contents = f.read()
self.assertEqual(len(contents), 64)
def test_item_descriptor_format(self):
for n in range(10):
kas.dump(
{str(j): j * np.ones(j)
for j in range(n)},
self.temp_file,
engine=self.engine,
)
with open(self.temp_file, "rb") as f:
contents = f.read()
self.assertEqual(struct.unpack("<I", contents[12:16])[0], n)
offset = store.HEADER_SIZE
for _ in range(n):
descriptor = contents[offset:offset +
store.ITEM_DESCRIPTOR_SIZE]
offset += store.ITEM_DESCRIPTOR_SIZE
type_ = struct.unpack("<B", descriptor[0:1])[0]
key_start, key_len, array_start, array_len = struct.unpack(
"<QQQQ", descriptor[8:40])
trailer = descriptor[40:store.ITEM_DESCRIPTOR_SIZE]
# The remainder should be zeros.
self.assertEqual(
trailer,
bytearray(0
for _ in range(store.ITEM_DESCRIPTOR_SIZE - 40)),
)
self.assertEqual(descriptor[1:4], bytearray([0, 0, 0]))
self.assertEqual(type_, store.FLOAT64)
self.assertGreater(key_start, 0)
self.assertGreater(key_len, 0)
self.assertGreater(array_start, 0)
self.assertGreaterEqual(array_len, 0)
def validate_storage(self, data):
kas.dump(data, self.temp_file, engine=self.engine)
with open(self.temp_file, "rb") as f:
contents = f.read()
offset = store.HEADER_SIZE
descriptors = []
for _ in range(len(data)):
descriptor = store.ItemDescriptor.unpack(
contents[offset:offset + store.ItemDescriptor.size])
descriptors.append(descriptor)
offset += store.ItemDescriptor.size
# Keys must be sorted lexicographically.
sorted_keys = sorted(data.keys())
# Keys should be packed sequentially immediately after the descriptors.
offset = store.HEADER_SIZE + len(data) * store.ITEM_DESCRIPTOR_SIZE
for d, key in zip(descriptors, sorted_keys):
self.assertEqual(d.key_start, offset)
unpacked_key = contents[d.key_start:d.key_start + d.key_len]
self.assertEqual(key.encode("utf8"), unpacked_key)
offset += d.key_len
# Arrays should be packed sequentially immediately after the keys on
# 8 byte boundaries
for d, key in zip(descriptors, sorted_keys):
remainder = offset % 8
if remainder != 0:
offset += 8 - remainder
self.assertEqual(d.array_start, offset)
nbytes = d.array_len * store.type_size(d.type)
array = np.frombuffer(
contents[d.array_start:d.array_start + nbytes],
dtype=store.type_to_np_dtype_map[d.type],
)
np.testing.assert_equal(data[key], array)
offset += nbytes
def test_simple_key_storage(self):
for n in range(10):
self.validate_storage(
{"a" * (j + 1): np.ones(1)
for j in range(n)})
def test_simple_array_storage(self):
for n in range(10):
self.validate_storage({str(j): j * np.ones(j) for j in range(n)})
# Note that hypothesis seems to be leaking memory, so when we're running tests
# against the C API for memory leaks this must be commented out.
@hypothesis.given(keys=hst.sets(hst.text(alphabet=key_alphabet,
min_size=1),
min_size=1))
def test_many_keys(self, keys):
data = {
key: np.ones(j, dtype=np.int32) * j
for j, key in enumerate(keys)
}
self.validate_storage(data)
xs.remove(y)
except ValueError:
pass
assert y not in xs
@fails
@given(lists(integers(), average_size=25.0), integers())
def test_removing_an_element_from_a_non_unique_list(xs, y):
assume(y in xs)
xs.remove(y)
assert y not in xs
@given(sets(sampled_from(list(range(10)))))
def test_can_test_sets_sampled_from(xs):
assert all(isinstance(x, int) for x in xs)
assert all(0 <= x < 10 for x in xs)
mix = one_of(sampled_from([1, 2, 3]), text())
@fails
@given(mix, mix)
def test_can_mix_sampling_with_generating(x, y):
assert type(x) == type(y)
@fails
def test_minimize_list_of_sets():
assert minimal(lists(sets(booleans())),
lambda x: len(list(filter(None, x))) >= 3) == ([{False}] *
3)
test_sampled_from_large_number_can_mix = define_test(
lists(sampled_from(range(50)), min_size=50),
0.1,
lambda x: len(set(x)) >= 25,
)
test_sampled_from_often_distorted = define_test(
lists(sampled_from(range(5))),
0.28,
distorted_value,
condition=lambda x: len(x) >= 3,
)
test_non_empty_subset_of_two_is_usually_large = define_test(
sets(sampled_from((1, 2))), 0.1, lambda t: len(t) == 2)
test_subset_of_ten_is_sometimes_empty = define_test(sets(integers(1,
10)), 0.05,
lambda t: len(t) == 0)
test_mostly_sensible_floats = define_test(floats(), 0.5, lambda t: t + 1 > t)
test_mostly_largish_floats = define_test(
floats(),
0.5,
lambda t: t + 1 > 1,
condition=lambda x: x > 0,
)
test_ints_can_occasionally_be_really_large = define_test(
def test_minimize_3_set_of_tuples():
assert minimal(sets(tuples(integers())), lambda x: len(x) >= 2) == {(0, ),
(1, )}
s = st.sampled_from(range(n))
for i in range(n):
if i != target:
s = forbid(s, i)
return s
@given(rare_value_strategy(n=128, target=80))
def test_chained_filters_find_rare_value(x):
assert x == 80
@fails_with(InvalidArgument)
@given(st.sets(st.sampled_from(range(10)), min_size=11))
def test_unsat_sets_of_samples(x):
assert False
@given(st.sets(st.sampled_from(range(50)), min_size=50))
def test_efficient_sets_of_samples(x):
assert x == set(range(50))
class AnEnum(enum.Enum):
a = enum.auto()
b = enum.auto()
def test_enum_repr_uses_class_not_a_list():
result = runner.invoke(["sync"])
assert not result.exception
assert set(result.output.splitlines()) == {
"Syncing bambaz",
"Syncing foobar",
}
collections_strategy = st.sets(
st.text(
st.characters(
blacklist_characters=set(
"./\x00"), # Invalid chars on POSIX filesystems
# Surrogates can't be encoded to utf-8 in Python
blacklist_categories={"Cs"},
),
min_size=1,
max_size=50,
),
min_size=1,
)
# XXX: https://github.com/pimutils/vdirsyncer/issues/617
@pytest.mark.skipif(sys.platform == "darwin",
reason="This test inexplicably fails")
@pytest.mark.parametrize(
"collections",
[
("persönlich", ),
def resolve_Set(thing):
return st.sets(st.from_type(thing.__args__[0]))
def testDistribution(self, dist_name, data):
seed = test_util.test_seed()
# Explicitly draw event_dim here to avoid relying on _params_event_ndims
# later, so this test can support distributions that do not implement the
# slicing protocol.
event_dim = data.draw(hps.integers(min_value=2, max_value=6))
dist = data.draw(
dhps.distributions(dist_name=dist_name,
event_dim=event_dim,
enable_vars=True))
batch_shape = dist.batch_shape
batch_shape2 = data.draw(
tfp_hps.broadcast_compatible_shape(batch_shape))
dist2 = data.draw(
dhps.distributions(dist_name=dist_name,
batch_shape=batch_shape2,
event_dim=event_dim,
enable_vars=True))
self.evaluate([var.initializer for var in dist.variables])
# Check that the distribution passes Variables through to the accessor
# properties (without converting them to Tensor or anything like that).
for k, v in six.iteritems(dist.parameters):
if not tensor_util.is_ref(v):
continue
self.assertIs(getattr(dist, k), v)
# Check that standard statistics do not read distribution parameters more
# than twice (once in the stat itself and up to once in any validation
# assertions).
max_permissible = 2 + extra_tensor_conversions_allowed(dist)
for stat in sorted(
data.draw(
hps.sets(hps.one_of(
map(hps.just, [
'covariance', 'entropy', 'mean', 'mode', 'stddev',
'variance'
])),
min_size=3,
max_size=3))):
hp.note('Testing excessive var usage in {}.{}'.format(
dist_name, stat))
try:
with tfp_hps.assert_no_excessive_var_usage(
'statistic `{}` of `{}`'.format(stat, dist),
max_permissible=max_permissible):
getattr(dist, stat)()
except NotImplementedError:
pass
# Check that `sample` doesn't read distribution parameters more than twice,
# and that it produces non-None gradients (if the distribution is fully
# reparameterized).
with tf.GradientTape() as tape:
# TDs do bijector assertions twice (once by distribution.sample, and once
# by bijector.forward).
max_permissible = 2 + extra_tensor_conversions_allowed(dist)
with tfp_hps.assert_no_excessive_var_usage(
'method `sample` of `{}`'.format(dist),
max_permissible=max_permissible):
sample = dist.sample(seed=seed)
if dist.reparameterization_type == tfd.FULLY_REPARAMETERIZED:
grads = tape.gradient(sample, dist.variables)
for grad, var in zip(grads, dist.variables):
var_name = var.name.rstrip('_0123456789:')
if var_name in NO_SAMPLE_PARAM_GRADS.get(dist_name, ()):
continue
if grad is None:
raise AssertionError(
'Missing sample -> {} grad for distribution {}'.format(
var_name, dist_name))
# Turn off validations, since TODO(b/129271256) log_prob can choke on dist's
# own samples. Also, to relax conversion counts for KL (might do >2 w/
# validate_args).
dist = dist.copy(validate_args=False)
dist2 = dist2.copy(validate_args=False)
# Test that KL divergence reads distribution parameters at most once, and
# that is produces non-None gradients.
try:
for d1, d2 in (dist, dist2), (dist2, dist):
with tf.GradientTape() as tape:
with tfp_hps.assert_no_excessive_var_usage(
'`kl_divergence` of (`{}` (vars {}), `{}` (vars {}))'
.format(d1, d1.variables, d2, d2.variables),
max_permissible=1
): # No validation => 1 convert per var.
kl = d1.kl_divergence(d2)
wrt_vars = list(d1.variables) + list(d2.variables)
grads = tape.gradient(kl, wrt_vars)
for grad, var in zip(grads, wrt_vars):
if grad is None and dist_name not in NO_KL_PARAM_GRADS:
raise AssertionError(
'Missing KL({} || {}) -> {} grad:\n' # pylint: disable=duplicate-string-formatting-argument
'{} vars: {}\n{} vars: {}'.format(
d1, d2, var, d1, d1.variables, d2,
d2.variables))
except NotImplementedError:
pass
# Test that log_prob produces non-None gradients, except for distributions
# on the NO_LOG_PROB_PARAM_GRADS blacklist.
if dist_name not in NO_LOG_PROB_PARAM_GRADS:
with tf.GradientTape() as tape:
lp = dist.log_prob(tf.stop_gradient(sample))
grads = tape.gradient(lp, dist.variables)
for grad, var in zip(grads, dist.variables):
if grad is None:
raise AssertionError(
'Missing log_prob -> {} grad for distribution {}'.
format(var, dist_name))
# Test that all forms of probability evaluation avoid reading distribution
# parameters more than once.
for evaluative in sorted(
data.draw(
hps.sets(hps.one_of(
map(hps.just, [
'log_prob', 'prob', 'log_cdf', 'cdf',
'log_survival_function', 'survival_function'
])),
min_size=3,
max_size=3))):
hp.note('Testing excessive var usage in {}.{}'.format(
dist_name, evaluative))
try:
# No validation => 1 convert. But for TD we allow 2:
# dist.log_prob(bijector.inverse(samp)) + bijector.ildj(samp)
max_permissible = 2 + extra_tensor_conversions_allowed(dist)
with tfp_hps.assert_no_excessive_var_usage(
'evaluative `{}` of `{}`'.format(evaluative, dist),
max_permissible=max_permissible):
getattr(dist, evaluative)(sample)
except NotImplementedError:
pass
if v != w:
adjacencies[v].add(w)
adjacencies[w].add(v)
return adjacencies
def test_degeneracy_ordering_empty() -> None:
g = UndirectedGraph(adjacencies=[])
assert list(degeneracy_ordering(g)) == []
assert list(degeneracy_ordering(g, drop=1)) == []
@given(
builds(
symmetric_adjacencies,
integers(min_value=1,
max_value=99).flatmap(lambda order: lists(sets(
integers(min_value=0, max_value=order - 1)),
min_size=order,
max_size=order))))
def test_degeneracy_ordering_nonempty(adjacencies: List[Set[Vertex]]) -> None:
g = UndirectedGraph(adjacencies=adjacencies)
connected_vertices = g.connected_vertices()
ordering = list(degeneracy_ordering(g))
assert set(ordering) == connected_vertices
assert all(g.degree(ordering[0]) <= g.degree(v) for v in ordering[1:])
ordering_min1 = list(degeneracy_ordering(g, drop=1))
assert ordering_min1 == ordering[:-1]
CONSERVATIVE_REGEX.map(lambda s: s + u"+"),
CONSERVATIVE_REGEX.map(lambda s: s + u"?"),
CONSERVATIVE_REGEX.map(lambda s: s + u"*"),
st.lists(CONSERVATIVE_REGEX, min_size=1,
max_size=3).map(u"|".join),
st.lists(CONSERVATIVE_REGEX, min_size=1, max_size=3).map(u"".join),
))
assume(COMBINED_MATCHER.search(result) is None)
control = sum(result.count(c) for c in "?+*")
assume(control <= 3)
return result
CONSERVATIVE_REGEX = conservative_regex()
FLAGS = st.sets(st.sampled_from(
[getattr(re, "A", 0), re.I, re.M,
re.S])).map(lambda flag_set: reduce(int.__or__, flag_set, 0))
@given(st.data())
def test_conservative_regex_are_correct_by_construction(data):
pattern = re.compile(data.draw(CONSERVATIVE_REGEX), flags=data.draw(FLAGS))
result = data.draw(base_regex_strategy(pattern))
assert pattern.search(result) is not None
@given(st.data())
def test_fuzz_stuff(data):
pattern = data.draw(
st.text(min_size=1, max_size=5)
| st.binary(min_size=1, max_size=5)
])
result = runner.invoke(['sync'])
assert not result.exception
assert set(result.output.splitlines()) == set([
'Syncing bambaz',
'Syncing foobar',
])
@given(collections=st.sets(
st.text(
st.characters(
blacklist_characters=set(
u'./\x00' # Invalid chars on POSIX filesystems
),
# Surrogates can't be encoded to utf-8 in Python
blacklist_categories=set(['Cs'])),
min_size=1,
max_size=50),
min_size=1))
@example(collections=[u'persönlich'])
@example(collections=set('aA'))
def test_create_collections(subtest, collections):
@subtest
def test_inner(tmpdir, runner):
runner.write_with_general(
dedent('''
[pair foobar]
a = "foo"
b = "bar"
def metric(draw):
tags = draw(strategies.sets(tag))
return draw(metric_without_value).with_value(
value=draw(value), ).with_tags(*tags)
def test_sets_of_small_average_size():
assert len(sets(integers(), average_size=1.0).example()) <= 10
def test_can_draw_empty_set_from_unsatisfiable_strategy():
assert sets(integers().filter(lambda s: False)).example() == set()
from coverage.backward import byte_to_int
from coverage.numbits import (
nums_to_numbits,
numbits_to_nums,
numbits_union,
numbits_intersection,
numbits_any_intersection,
num_in_numbits,
register_sqlite_functions,
)
from tests.coveragetest import CoverageTest
# Hypothesis-generated line number data
line_numbers = integers(min_value=1, max_value=9999)
line_number_sets = sets(line_numbers)
# When coverage-testing ourselves, hypothesis complains about a test being
# flaky because the first run exceeds the deadline (and fails), and the second
# run succeeds. Disable the deadline if we are coverage-testing.
default_settings = settings()
if env.METACOV:
default_settings = settings(default_settings, deadline=None)
def good_numbits(numbits):
"""Assert that numbits is good."""
# It shouldn't end with a zero byte, that should have been trimmed off.
assert (not numbits) or (byte_to_int(numbits[-1]) != 0)
def test_minimize_3_set_of_tuples():
assert minimal(
sets(tuples(integers())),
lambda x: len(x) >= 2) == set(((0,), (1,)))
@given(lists(integers(), min_size=2))
def test_inits_heads(elements: List[int]) -> None:
"""inits(elements)[:1] == [[], [elements[0]]."""
assert sut.inits(elements)[0] == []
assert sut.inits(elements)[1] == [elements[0]]
@given(lists(integers()))
def test_inits_monotonic_length(elements: List[int]) -> None:
"""[len(x) for xs in inits(elements)] == range(len(elements) + 1)."""
lengths = [len(xs) for xs in sut.inits(elements)]
assert lengths == list(range(len(elements) + 1))
@given(sets(integers()), sets(integers()))
def test_venn_subsets(lefts: Set[int], rights: Set[int]) -> None:
"""All combinations of venn with subsets."""
r_lefts: List[int]
r_middles: List[int]
r_rights: List[int]
r_lefts, r_middles, r_rights = sut.venn(list(lefts), list(lefts | rights))
assert (set(r_lefts), set(r_middles), set(r_rights)) == (set(), lefts,
rights - lefts)
r_lefts, r_middles, r_rights = sut.venn(list(lefts | rights), list(rights))
assert (set(r_lefts), set(r_middles), set(r_rights)) == (lefts - rights,
rights, set())