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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 pytest
from hypothesis import Phase, given, seed, settings, strategies as st, target
from tests.common.utils import Why, xfail_on_crosshair
pytest_plugins = "pytester"
TESTSUITE = """
from hypothesis import given, strategies as st, target
@given(st.integers(min_value=0))
def test_threshold_problem(x):
target(float(x))
{0}target(float(x * 2), label="double")
{0}assert x <= 100000
assert x <= 100
"""
@pytest.mark.parametrize("multiple", [False, True])
def test_reports_target_results(testdir, multiple):
script = testdir.makepyfile(TESTSUITE.format("" if multiple else "# "))
result = testdir.runpytest(script, "--tb=native", "-rN")
out = "\n".join(result.stdout.lines)
assert "Falsifying example" in out
assert "x=101" in out, out
assert out.count("Highest target score") == 1
assert result.ret != 0
def test_targeting_increases_max_length():
strat = st.lists(st.booleans())
@settings(database=None, max_examples=200, phases=[Phase.generate, Phase.target])
@given(strat)
def test_with_targeting(ls):
target(float(len(ls)))
assert len(ls) <= 80
with pytest.raises(AssertionError):
test_with_targeting()
@given(st.integers(), st.integers())
def test_target_returns_value(a, b):
difference = target(abs(a - b))
assert difference == abs(a - b)
assert isinstance(difference, int)
@xfail_on_crosshair(Why.symbolic_outside_context)
def test_targeting_can_be_disabled():
strat = st.lists(st.integers(0, 255))
def score(enabled):
result = 0
phases = [Phase.generate]
if enabled:
phases.append(Phase.target)
@seed(0)
@settings(database=None, max_examples=100, phases=phases)
@given(strat)
def test(ls):
nonlocal result
# cap the score to avoid long test times by unbounded driving of list
# length upwards
score = min(sum(ls), 10_000)
result = max(result, score)
target(score)
test()
return result
assert score(enabled=True) > score(enabled=False)
@pytest.mark.skipif(
settings._current_profile == "crosshair",
reason="takes ~15 minutes, mostly just unrolling the rejection sampling loop",
)
def test_issue_2395_regression():
@given(d=st.floats().filter(lambda x: abs(x) < 1000))
@settings(max_examples=1000, database=None)
@seed(93962505385993024185959759429298090872)
def test_targeting_square_loss(d):
target(-((d - 42.5) ** 2.0))
test_targeting_square_loss()
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