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test_ir.py
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test_ir.py
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# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
import math
import sys
from copy import deepcopy
import pytest
from hypothesis import HealthCheck, assume, example, given, settings, strategies as st
from hypothesis.errors import StopTest
from hypothesis.internal.conjecture.data import (
ConjectureData,
IRNode,
Status,
ir_value_equal,
ir_value_permitted,
)
from hypothesis.internal.conjecture.datatree import (
MAX_CHILDREN_EFFECTIVELY_INFINITE,
all_children,
compute_max_children,
)
from hypothesis.internal.floats import SMALLEST_SUBNORMAL, next_down, next_up
from hypothesis.internal.intervalsets import IntervalSet
from tests.common.debug import minimal
from tests.conjecture.common import fresh_data, ir_types_and_kwargs, kwargs_strategy
def draw_value(ir_type, kwargs):
data = fresh_data()
return getattr(data, f"draw_{ir_type}")(**kwargs)
# we max out at 128 bit integers in the *unbounded* case, but someone may
# specify a bound with a larger magnitude. Ensure we calculate max children for
# those cases correctly.
@example(("integer", {"min_value": None, "max_value": -(2**200), "weights": None}))
@example(("integer", {"min_value": 2**200, "max_value": None, "weights": None}))
@example(("integer", {"min_value": -(2**200), "max_value": 2**200, "weights": None}))
@given(ir_types_and_kwargs())
def test_compute_max_children_is_positive(ir_type_and_kwargs):
(ir_type, kwargs) = ir_type_and_kwargs
assert compute_max_children(ir_type, kwargs) >= 0
@pytest.mark.parametrize(
"ir_type, kwargs, count_children",
[
("integer", {"min_value": 1, "max_value": 2, "weights": [0, 1]}, 1),
("integer", {"min_value": 1, "max_value": 4, "weights": [0, 0.5, 0, 0.5]}, 2),
# only possibility is the empty string
(
"string",
{"min_size": 0, "max_size": 100, "intervals": IntervalSet.from_string("")},
1,
),
(
"string",
{"min_size": 0, "max_size": 0, "intervals": IntervalSet.from_string("abc")},
1,
),
# 3 possibilities for each character, 8 characters, 3 ** 8 possibilities.
(
"string",
{"min_size": 8, "max_size": 8, "intervals": IntervalSet.from_string("abc")},
3**8,
),
(
"string",
{
"min_size": 2,
"max_size": 8,
"intervals": IntervalSet.from_string("abcd"),
},
sum(4**k for k in range(2, 8 + 1)),
),
(
"string",
{
"min_size": 0,
"max_size": None,
"intervals": IntervalSet.from_string("a"),
},
MAX_CHILDREN_EFFECTIVELY_INFINITE,
),
(
"string",
{
"min_size": 0,
"max_size": 10_000,
"intervals": IntervalSet.from_string("abcdefg"),
},
MAX_CHILDREN_EFFECTIVELY_INFINITE,
),
("boolean", {"p": 0.0}, 1),
("boolean", {"p": 1.0}, 1),
("boolean", {"p": 0.5}, 2),
("boolean", {"p": 0.001}, 2),
("boolean", {"p": 0.999}, 2),
(
"float",
{
"min_value": 0.0,
"max_value": 0.0,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
1,
),
(
"float",
{
"min_value": -0.0,
"max_value": -0.0,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
1,
),
(
"float",
{
"min_value": -0.0,
"max_value": 0.0,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
2,
),
(
"float",
{
"min_value": next_down(-0.0),
"max_value": next_up(0.0),
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
4,
),
(
"float",
{
"min_value": next_down(next_down(-0.0)),
"max_value": next_up(next_up(0.0)),
"smallest_nonzero_magnitude": next_up(SMALLEST_SUBNORMAL),
},
4,
),
(
"float",
{
"min_value": -math.inf,
"max_value": math.inf,
"smallest_nonzero_magnitude": next_down(math.inf),
},
6,
),
(
"float",
{
"min_value": 1,
"max_value": 10,
"smallest_nonzero_magnitude": 11.0,
},
0,
),
(
"float",
{
"min_value": -3,
"max_value": -2,
"smallest_nonzero_magnitude": 4.0,
},
0,
),
],
)
def test_compute_max_children(ir_type, kwargs, count_children):
assert compute_max_children(ir_type, kwargs) == count_children
@given(st.text(min_size=1, max_size=1), st.integers(0, 100))
def test_draw_string_single_interval_with_equal_bounds(s, n):
data = fresh_data()
intervals = IntervalSet.from_string(s)
assert data.draw_string(intervals, min_size=n, max_size=n) == s * n
@example(("boolean", {"p": 2**-65}))
@example(("boolean", {"p": 1 - 2**-65}))
@example(
(
"string",
{"min_size": 0, "max_size": 0, "intervals": IntervalSet.from_string("abc")},
)
)
@example(
("string", {"min_size": 0, "max_size": 3, "intervals": IntervalSet.from_string("")})
)
@example(
(
"string",
{"min_size": 0, "max_size": 3, "intervals": IntervalSet.from_string("a")},
)
)
# all combinations of float signs
@example(
(
"float",
{
"min_value": next_down(-0.0),
"max_value": -0.0,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
)
)
@example(
(
"float",
{
"min_value": next_down(-0.0),
"max_value": next_up(0.0),
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
)
)
@example(
(
"float",
{
"min_value": 0.0,
"max_value": next_up(0.0),
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
)
)
# using a smallest_nonzero_magnitude which happens to filter out everything
@example(
("float", {"min_value": 1.0, "max_value": 2.0, "smallest_nonzero_magnitude": 3.0})
)
@example(
(
"integer",
{
"min_value": 1,
"max_value": 2,
"weights": [0, 1],
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
)
)
@given(ir_types_and_kwargs())
def test_compute_max_children_and_all_children_agree(ir_type_and_kwargs):
(ir_type, kwargs) = ir_type_and_kwargs
max_children = compute_max_children(ir_type, kwargs)
# avoid slowdowns / OOM when reifying extremely large all_children generators.
# We also hard cap at MAX_CHILDREN_EFFECTIVELY_INFINITE, because max_children
# returns approximations after this value and so will disagree with
# all_children.
cap = min(100_000, MAX_CHILDREN_EFFECTIVELY_INFINITE)
assume(max_children < cap)
assert len(list(all_children(ir_type, kwargs))) == max_children
@given(st.randoms())
def test_ir_nodes(random):
data = fresh_data(random=random)
data.draw_float(min_value=-10.0, max_value=10.0, forced=5.0)
data.draw_boolean(forced=True)
data.start_example(42)
data.draw_string(IntervalSet.from_string("abcd"), forced="abbcccdddd")
data.draw_bytes(8, forced=bytes(8))
data.stop_example()
data.draw_integer(0, 100, forced=50)
data.freeze()
expected_tree_nodes = [
IRNode(
ir_type="float",
value=5.0,
kwargs={
"min_value": -10.0,
"max_value": 10.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=True,
),
IRNode(
ir_type="boolean",
value=True,
kwargs={"p": 0.5},
was_forced=True,
),
IRNode(
ir_type="string",
value="abbcccdddd",
kwargs={
"intervals": IntervalSet.from_string("abcd"),
"min_size": 0,
"max_size": None,
},
was_forced=True,
),
IRNode(
ir_type="bytes",
value=bytes(8),
kwargs={"size": 8},
was_forced=True,
),
IRNode(
ir_type="integer",
value=50,
kwargs={
"min_value": 0,
"max_value": 100,
"weights": None,
"shrink_towards": 0,
},
was_forced=True,
),
]
assert data.examples.ir_tree_nodes == expected_tree_nodes
@st.composite
def ir_nodes(draw, *, was_forced=None):
(ir_type, kwargs) = draw(ir_types_and_kwargs())
value = draw_value(ir_type, kwargs)
was_forced = draw(st.booleans()) if was_forced is None else was_forced
return IRNode(ir_type=ir_type, value=value, kwargs=kwargs, was_forced=was_forced)
@given(ir_nodes())
def test_copy_ir_node(node):
assert node == node
assume(not node.was_forced)
new_value = draw_value(node.ir_type, node.kwargs)
# if we drew the same value as before, the node should still be equal
assert (node.copy(with_value=new_value) == node) is (
ir_value_equal(node.ir_type, new_value, node.value)
)
@given(ir_nodes())
def test_ir_node_equality(node):
assert node == node
# for coverage on our NotImplemented return, more than anything.
assert node != 42
def test_data_with_empty_ir_tree_is_overrun():
data = ConjectureData.for_ir_tree([])
with pytest.raises(StopTest):
data.draw_integer()
assert data.status is Status.OVERRUN
# root cause of too_slow is filtering too much via assume in kwargs strategies.
# exacerbated in this test because we draw kwargs twice.
# TODO revisit and improve the kwargs strategies at some point, once the ir
# is further along we can maybe remove e.g. a string assumption.
@given(st.data())
@settings(suppress_health_check=[HealthCheck.too_slow])
def test_node_with_different_ir_type_is_invalid(data):
node = data.draw(ir_nodes())
(ir_type, kwargs) = data.draw(ir_types_and_kwargs())
# drawing a node with a different ir type should cause a misalignment.
assume(ir_type != node.ir_type)
data = ConjectureData.for_ir_tree([node])
draw_func = getattr(data, f"draw_{ir_type}")
with pytest.raises(StopTest):
draw_func(**kwargs)
assert data.status is Status.INVALID
@given(st.data())
def test_node_with_same_ir_type_but_different_value_is_invalid(data):
node = data.draw(ir_nodes())
kwargs = data.draw(kwargs_strategy(node.ir_type))
# drawing a node with the same ir type, but a non-compatible value, should
# also cause a misalignment.
assume(not ir_value_permitted(node.value, node.ir_type, kwargs))
data = ConjectureData.for_ir_tree([node])
draw_func = getattr(data, f"draw_{node.ir_type}")
with pytest.raises(StopTest):
draw_func(**kwargs)
assert data.status is Status.INVALID
@given(st.data())
@settings(suppress_health_check=[HealthCheck.too_slow])
def test_data_with_changed_was_forced(data):
# we had a normal node and then tried to draw a different forced value from it.
# ir tree: v1 [was_forced=False]
# drawing: [forced=v2]
node = data.draw(ir_nodes(was_forced=False))
data = ConjectureData.for_ir_tree([node])
draw_func = getattr(data, f"draw_{node.ir_type}")
kwargs = deepcopy(node.kwargs)
kwargs["forced"] = draw_value(node.ir_type, node.kwargs)
assume(not ir_value_equal(node.ir_type, kwargs["forced"], node.value))
assert ir_value_equal(node.ir_type, draw_func(**kwargs), kwargs["forced"])
@given(ir_nodes(was_forced=True))
@settings(suppress_health_check=[HealthCheck.too_slow])
def test_data_with_changed_forced_value(node):
# we had a forced node and then tried to draw a different forced value from it.
# ir tree: v1 [was_forced=True]
# drawing: [forced=v2]
#
# This is actually fine; we'll just ignore the forced node (v1) and return
# what the draw expects (v2).
data = ConjectureData.for_ir_tree([node])
draw_func = getattr(data, f"draw_{node.ir_type}")
kwargs = deepcopy(node.kwargs)
kwargs["forced"] = draw_value(node.ir_type, node.kwargs)
assume(not ir_value_equal(node.ir_type, kwargs["forced"], node.value))
assert ir_value_equal(node.ir_type, draw_func(**kwargs), kwargs["forced"])
# ensure we hit bare-minimum coverage for all ir types.
@example(
IRNode(
ir_type="float",
value=0.0,
kwargs={
"min_value": -math.inf,
"max_value": math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=True,
)
)
@example(
IRNode(
ir_type="boolean",
value=False,
kwargs={"p": 0.5},
was_forced=True,
)
)
@example(
IRNode(
ir_type="integer",
value=50,
kwargs={
"min_value": 50,
"max_value": 100,
"weights": None,
"shrink_towards": 0,
},
was_forced=True,
)
)
@example(
IRNode(
ir_type="string",
value="aaaa",
kwargs={
"intervals": IntervalSet.from_string("bcda"),
"min_size": 4,
"max_size": None,
},
was_forced=True,
)
)
@example(
IRNode(
ir_type="bytes",
value=bytes(8),
kwargs={"size": 8},
was_forced=True,
)
)
@given(ir_nodes(was_forced=True))
def test_data_with_same_forced_value_is_valid(node):
# we had a forced node and then drew the same forced value. This is totally
# fine!
# ir tree: v1 [was_forced=True]
# drawing: [forced=v1]
data = ConjectureData.for_ir_tree([node])
draw_func = getattr(data, f"draw_{node.ir_type}")
kwargs = deepcopy(node.kwargs)
kwargs["forced"] = node.value
assert ir_value_equal(node.ir_type, draw_func(**kwargs), kwargs["forced"])
@given(ir_types_and_kwargs())
def test_all_children_are_permitted_values(ir_type_and_kwargs):
(ir_type, kwargs) = ir_type_and_kwargs
max_children = compute_max_children(ir_type, kwargs)
cap = min(100_000, MAX_CHILDREN_EFFECTIVELY_INFINITE)
assume(max_children < cap)
# test that all_children -> ir_value_permitted (but not necessarily the converse.)
for value in all_children(ir_type, kwargs):
assert ir_value_permitted(value, ir_type, kwargs), value
@pytest.mark.parametrize(
"value, ir_type, kwargs, permitted",
[
(0, "integer", {"min_value": 1, "max_value": 2}, False),
(2, "integer", {"min_value": 0, "max_value": 1}, False),
(10, "integer", {"min_value": 0, "max_value": 20}, True),
(
math.nan,
"float",
{"min_value": 0.0, "max_value": 0.0, "allow_nan": True},
True,
),
(
math.nan,
"float",
{"min_value": 0.0, "max_value": 0.0, "allow_nan": False},
False,
),
(
2.0,
"float",
{
"min_value": 1.0,
"max_value": 3.0,
"allow_nan": True,
"smallest_nonzero_magnitude": 2.5,
},
False,
),
(
-2.0,
"float",
{
"min_value": -3.0,
"max_value": -1.0,
"allow_nan": True,
"smallest_nonzero_magnitude": 2.5,
},
False,
),
(
1.0,
"float",
{
"min_value": 1.0,
"max_value": 1.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
True,
),
(
"abcd",
"string",
{
"min_size": 10,
"max_size": 20,
"intervals": IntervalSet.from_string("abcd"),
},
False,
),
(
"abcd",
"string",
{
"min_size": 1,
"max_size": 3,
"intervals": IntervalSet.from_string("abcd"),
},
False,
),
(
"abcd",
"string",
{"min_size": 1, "max_size": 10, "intervals": IntervalSet.from_string("e")},
False,
),
(
"e",
"string",
{"min_size": 1, "max_size": 10, "intervals": IntervalSet.from_string("e")},
True,
),
(b"a", "bytes", {"size": 2}, False),
(b"aa", "bytes", {"size": 2}, True),
(True, "boolean", {"p": 0}, False),
(False, "boolean", {"p": 0}, True),
(True, "boolean", {"p": 1}, True),
(False, "boolean", {"p": 1}, False),
(True, "boolean", {"p": 0.5}, True),
(False, "boolean", {"p": 0.5}, True),
],
)
def test_ir_value_permitted(value, ir_type, kwargs, permitted):
assert ir_value_permitted(value, ir_type, kwargs) == permitted
@given(ir_nodes(was_forced=True))
def test_forced_nodes_are_trivial(node):
assert node.trivial
@pytest.mark.parametrize(
"node",
[
IRNode(
ir_type="float",
value=5.0,
kwargs={
"min_value": 5.0,
"max_value": 10.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=0.0,
kwargs={
"min_value": -5.0,
"max_value": 5.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=0.0,
kwargs={
"min_value": -math.inf,
"max_value": math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="boolean",
value=False,
kwargs={"p": 0.5},
was_forced=False,
),
IRNode(
ir_type="string",
value="",
kwargs={
"intervals": IntervalSet.from_string("abcd"),
"min_size": 0,
"max_size": None,
},
was_forced=False,
),
IRNode(
ir_type="string",
value="aaaa",
kwargs={
"intervals": IntervalSet.from_string("bcda"),
"min_size": 4,
"max_size": None,
},
was_forced=False,
),
IRNode(
ir_type="bytes",
value=bytes(8),
kwargs={"size": 8},
was_forced=False,
),
IRNode(
ir_type="integer",
value=50,
kwargs={
"min_value": 50,
"max_value": 100,
"weights": None,
"shrink_towards": 0,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=0,
kwargs={
"min_value": -10,
"max_value": 10,
"weights": None,
"shrink_towards": 0,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=2,
kwargs={
"min_value": -10,
"max_value": 10,
"weights": None,
"shrink_towards": 2,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=-10,
kwargs={
"min_value": -10,
"max_value": 10,
"weights": None,
"shrink_towards": -12,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=10,
kwargs={
"min_value": -10,
"max_value": 10,
"weights": None,
"shrink_towards": 12,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=0,
kwargs={
"min_value": None,
"max_value": None,
"weights": None,
"shrink_towards": 0,
},
was_forced=False,
),
],
)
def test_trivial_nodes(node):
assert node.trivial
@st.composite
def values(draw):
data = draw(st.data()).conjecture_data
return getattr(data, f"draw_{node.ir_type}")(**node.kwargs)
# if we're trivial, then shrinking should produce the same value.
assert ir_value_equal(node.ir_type, minimal(values()), node.value)
@pytest.mark.parametrize(
"node",
[
IRNode(
ir_type="float",
value=6.0,
kwargs={
"min_value": 5.0,
"max_value": 10.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=-5.0,
kwargs={
"min_value": -5.0,
"max_value": 5.0,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=1.0,
kwargs={
"min_value": -math.inf,
"max_value": math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="boolean",
value=True,
kwargs={"p": 0.5},
was_forced=False,
),
IRNode(
ir_type="string",
value="d",
kwargs={
"intervals": IntervalSet.from_string("abcd"),
"min_size": 1,
"max_size": None,
},
was_forced=False,
),
IRNode(
ir_type="bytes",
value=b"\x01",
kwargs={"size": 1},
was_forced=False,
),
IRNode(
ir_type="integer",
value=-10,
kwargs={
"min_value": -10,
"max_value": 10,
"weights": None,
"shrink_towards": 0,
},
was_forced=False,
),
IRNode(
ir_type="integer",
value=42,
kwargs={
"min_value": None,
"max_value": None,
"weights": None,
"shrink_towards": 0,
},
was_forced=False,
),
],
)
def test_nontrivial_nodes(node):
assert not node.trivial
@st.composite
def values(draw):
data = draw(st.data()).conjecture_data
return getattr(data, f"draw_{node.ir_type}")(**node.kwargs)
# if we're nontrivial, then shrinking should produce something different.
assert not ir_value_equal(node.ir_type, minimal(values()), node.value)
@pytest.mark.parametrize(
"node",
[
IRNode(
ir_type="float",
value=1.5,
kwargs={
"min_value": 1.1,
"max_value": 1.6,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=math.floor(sys.float_info.max),
kwargs={
"min_value": sys.float_info.max - 1,
"max_value": math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=math.ceil(-sys.float_info.max),
kwargs={
"min_value": -math.inf,
"max_value": -sys.float_info.max + 1,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=math.inf,
kwargs={
"min_value": math.inf,
"max_value": math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
IRNode(
ir_type="float",
value=-math.inf,
kwargs={
"min_value": -math.inf,
"max_value": -math.inf,
"allow_nan": True,
"smallest_nonzero_magnitude": SMALLEST_SUBNORMAL,
},
was_forced=False,
),
],
)
def test_conservative_nontrivial_nodes(node):
# these nodes actually are trivial, but our analysis doesn't compute them
# as such. We'd like to improve this in the future!
assert not node.trivial
@st.composite
def values(draw):
data = draw(st.data()).conjecture_data
return getattr(data, f"draw_{node.ir_type}")(**node.kwargs)
assert ir_value_equal(node.ir_type, minimal(values()), node.value)