# 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/.
"""The settings module configures runtime options for Hypothesis.
Either an explicit settings object can be used or the default object on
this module can be modified.
"""
import contextlib
import datetime
import inspect
import os
from collections.abc import Collection, Generator, Sequence
from enum import Enum, EnumMeta, unique
from functools import total_ordering
from typing import (
TYPE_CHECKING,
Any,
ClassVar,
Optional,
TypeVar,
)
from hypothesis.errors import (
InvalidArgument,
)
from hypothesis.internal.conjecture.providers import AVAILABLE_PROVIDERS
from hypothesis.internal.reflection import get_pretty_function_description
from hypothesis.internal.validation import check_type, try_convert
from hypothesis.utils.conventions import not_set
from hypothesis.utils.deprecation import note_deprecation
from hypothesis.utils.dynamicvariables import DynamicVariable
if TYPE_CHECKING:
from hypothesis.database import ExampleDatabase
__all__ = ["settings"]
T = TypeVar("T")
all_settings: list[str] = [
"max_examples",
"derandomize",
"database",
"verbosity",
"phases",
"stateful_step_count",
"report_multiple_bugs",
"suppress_health_check",
"deadline",
"print_blob",
"backend",
]
[docs]
@unique
@total_ordering
class Verbosity(Enum):
"""Options for the |settings.verbosity| argument to |@settings|."""
quiet = "quiet"
"""
Hypothesis will not print any output, not even the final falsifying example.
"""
normal = "normal"
"""
Standard verbosity. Hypothesis will print the falsifying example, alongside
any notes made with |note| (only for the falsfying example).
"""
verbose = "verbose"
"""
Increased verbosity. In addition to everything in |Verbosity.normal|, Hypothesis
will:
* Print each test case as it tries it
* Print any notes made with |note| for each test case
* Print each shrinking attempt
* Print all explicit failing examples when using |@example|, instead of only
the simplest one
"""
debug = "debug"
"""
Even more verbosity. Useful for debugging Hypothesis internals. You probably
don't want this.
"""
@classmethod
def _missing_(cls, value):
# deprecation pathway for integer values. Can be removed in Hypothesis 7.
if isinstance(value, int) and not isinstance(value, bool):
int_to_name = {0: "quiet", 1: "normal", 2: "verbose", 3: "debug"}
if value in int_to_name:
note_deprecation(
f"Passing Verbosity({value}) as an integer is deprecated. "
"Hypothesis now treats Verbosity values as strings, not integers. "
f"Use Verbosity.{int_to_name[value]} instead.",
since="2025-11-05",
has_codemod=False,
stacklevel=2,
)
return cls(int_to_name[value])
return None
def __repr__(self) -> str:
return f"Verbosity.{self.name}"
@staticmethod
def _int_value(value: "Verbosity") -> int:
# we would just map Verbosity keys, except it's not hashable
mapping = {
Verbosity.quiet.name: 0,
Verbosity.normal.name: 1,
Verbosity.verbose.name: 2,
Verbosity.debug.name: 3,
}
# make sure we don't forget any new verbosity members
assert list(mapping.keys()) == [verbosity.name for verbosity in Verbosity]
return mapping[value.name]
def __eq__(self, other: Any) -> bool:
if isinstance(other, Verbosity):
return super().__eq__(other)
return Verbosity._int_value(self) == other
def __gt__(self, other: Any) -> bool:
value1 = Verbosity._int_value(self)
value2 = Verbosity._int_value(other) if isinstance(other, Verbosity) else other
return value1 > value2
[docs]
@unique
class Phase(Enum):
"""Options for the |settings.phases| argument to |@settings|."""
explicit = "explicit"
"""
Controls whether explicit examples are run.
"""
reuse = "reuse"
"""
Controls whether previous examples will be reused.
"""
generate = "generate"
"""
Controls whether new examples will be generated.
"""
target = "target"
"""
Controls whether examples will be mutated for targeting.
"""
shrink = "shrink"
"""
Controls whether examples will be shrunk.
"""
explain = "explain"
"""
Controls whether Hypothesis attempts to explain test failures.
The explain phase has two parts, each of which is best-effort - if Hypothesis
can't find a useful explanation, we'll just print the minimal failing example.
"""
@classmethod
def _missing_(cls, value):
# deprecation pathway for integer values. Can be removed in Hypothesis 7.
if isinstance(value, int) and not isinstance(value, bool):
int_to_name = {
0: "explicit",
1: "reuse",
2: "generate",
3: "target",
4: "shrink",
5: "explain",
}
if value in int_to_name:
note_deprecation(
f"Passing Phase({value}) as an integer is deprecated. "
"Hypothesis now treats Phase values as strings, not integers. "
f"Use Phase.{int_to_name[value]} instead.",
since="2025-11-05",
has_codemod=False,
stacklevel=2,
)
return cls(int_to_name[value])
return None
def __repr__(self) -> str:
return f"Phase.{self.name}"
class HealthCheckMeta(EnumMeta):
def __iter__(self):
deprecated = (HealthCheck.return_value, HealthCheck.not_a_test_method)
return iter(x for x in super().__iter__() if x not in deprecated)
[docs]
@unique
class HealthCheck(Enum, metaclass=HealthCheckMeta):
"""
A |HealthCheck| is proactively raised by Hypothesis when Hypothesis detects
that your test has performance problems, which may result in less rigorous
testing than you expect. For example, if your test takes a long time to generate
inputs, or filters away too many inputs using |assume| or |filter|, Hypothesis
will raise a corresponding health check.
A health check is a proactive warning, not an error. We encourage suppressing
health checks where you have evaluated they will not pose a problem, or where
you have evaluated that fixing the underlying issue is not worthwhile.
With the exception of |HealthCheck.function_scoped_fixture| and
|HealthCheck.differing_executors|, all health checks warn about performance
problems, not correctness errors.
Disabling health checks
-----------------------
Health checks can be disabled by |settings.suppress_health_check|. To suppress
all health checks, you can pass ``suppress_health_check=list(HealthCheck)``.
.. seealso::
See also the :doc:`/how-to/suppress-healthchecks` how-to.
Correctness health checks
-------------------------
Some health checks report potential correctness errors, rather than performance
problems.
* |HealthCheck.function_scoped_fixture| indicates that a function-scoped
pytest fixture is used by an |@given| test. Many Hypothesis users expect
function-scoped fixtures to reset once per input, but they actually reset once
per test. We proactively raise |HealthCheck.function_scoped_fixture| to
ensure you have considered this case.
* |HealthCheck.differing_executors| indicates that the same |@given| test has
been executed multiple times with multiple distinct executors.
We recommend treating these particular health checks with more care, as
suppressing them may result in an unsound test.
"""
@classmethod
def _missing_(cls, value):
# deprecation pathway for integer values. Can be removed in Hypothesis 7.
if isinstance(value, int) and not isinstance(value, bool):
int_to_name = {
1: "data_too_large",
2: "filter_too_much",
3: "too_slow",
5: "return_value",
7: "large_base_example",
8: "not_a_test_method",
9: "function_scoped_fixture",
10: "differing_executors",
11: "nested_given",
}
if value in int_to_name:
note_deprecation(
f"Passing HealthCheck({value}) as an integer is deprecated. "
"Hypothesis now treats HealthCheck values as strings, not integers. "
f"Use HealthCheck.{int_to_name[value]} instead.",
since="2025-11-05",
has_codemod=False,
stacklevel=2,
)
return cls(int_to_name[value])
return None
def __repr__(self) -> str:
return f"{self.__class__.__name__}.{self.name}"
@classmethod
def all(cls) -> list["HealthCheck"]:
# Skipping of deprecated attributes is handled in HealthCheckMeta.__iter__
note_deprecation(
"`HealthCheck.all()` is deprecated; use `list(HealthCheck)` instead.",
since="2023-04-16",
has_codemod=True,
stacklevel=1,
)
return list(HealthCheck)
data_too_large = "data_too_large"
"""Checks if too many examples are aborted for being too large.
This is measured by the number of random choices that Hypothesis makes
in order to generate something, not the size of the generated object.
For example, choosing a 100MB object from a predefined list would take
only a few bits, while generating 10KB of JSON from scratch might trigger
this health check.
"""
filter_too_much = "filter_too_much"
"""Check for when the test is filtering out too many examples, either
through use of |assume| or |.filter|, or occasionally for Hypothesis
internal reasons."""
too_slow = "too_slow"
"""
Check for when input generation is very slow. Since Hypothesis generates 100
(by default) inputs per test execution, a slowdown in generating each input
can result in very slow tests overall.
"""
return_value = "return_value"
"""Deprecated; we always error if a test returns a non-None value."""
large_base_example = "large_base_example"
"""
Checks if the smallest natural input to your test is very large. This makes
it difficult for Hypothesis to generate good inputs, especially when trying to
shrink failing inputs.
"""
not_a_test_method = "not_a_test_method"
"""Deprecated; we always error if |@given| is applied
to a method defined by :class:`python:unittest.TestCase` (i.e. not a test)."""
function_scoped_fixture = "function_scoped_fixture"
"""Checks if |@given| has been applied to a test
with a pytest function-scoped fixture. Function-scoped fixtures run once
for the whole function, not once per example, and this is usually not what
you want.
Because of this limitation, tests that need to set up or reset
state for every example need to do so manually within the test itself,
typically using an appropriate context manager.
Suppress this health check only in the rare case that you are using a
function-scoped fixture that does not need to be reset between individual
examples, but for some reason you cannot use a wider fixture scope
(e.g. session scope, module scope, class scope).
This check requires the :ref:`Hypothesis pytest plugin<pytest-plugin>`,
which is enabled by default when running Hypothesis inside pytest."""
differing_executors = "differing_executors"
"""Checks if |@given| has been applied to a test
which is executed by different :ref:`executors<custom-function-execution>`.
If your test function is defined as a method on a class, that class will be
your executor, and subclasses executing an inherited test is a common way
for things to go wrong.
The correct fix is often to bring the executor instance under the control
of hypothesis by explicit parametrization over, or sampling from,
subclasses, or to refactor so that |@given| is
specified on leaf subclasses."""
nested_given = "nested_given"
"""Checks if |@given| is used inside another
|@given|. This results in quadratic generation and
shrinking behavior, and can usually be expressed more cleanly by using
:func:`~hypothesis.strategies.data` to replace the inner
|@given|.
Nesting @given can be appropriate if you set appropriate limits for the
quadratic behavior and cannot easily reexpress the inner function with
:func:`~hypothesis.strategies.data`. To suppress this health check, set
``suppress_health_check=[HealthCheck.nested_given]`` on the outer
|@given|. Setting it on the inner
|@given| has no effect. If you have more than one
level of nesting, add a suppression for this health check to every
|@given| except the innermost one.
"""
class duration(datetime.timedelta):
"""A timedelta specifically measured in milliseconds."""
def __repr__(self) -> str:
ms = self.total_seconds() * 1000
return f"timedelta(milliseconds={int(ms) if ms == int(ms) else ms!r})"
# see https://adamj.eu/tech/2020/03/09/detect-if-your-tests-are-running-on-ci
# initially from https://github.com/tox-dev/tox/blob/e911788a/src/tox/util/ci.py
_CI_VARS = {
"CI": None, # various, including GitHub Actions, Travis CI, and AppVeyor
# see https://github.com/tox-dev/tox/issues/3442
"__TOX_ENVIRONMENT_VARIABLE_ORIGINAL_CI": None,
"TF_BUILD": "true", # Azure Pipelines
"bamboo.buildKey": None, # Bamboo
"BUILDKITE": "true", # Buildkite
"CIRCLECI": "true", # Circle CI
"CIRRUS_CI": "true", # Cirrus CI
"CODEBUILD_BUILD_ID": None, # CodeBuild
"GITHUB_ACTIONS": "true", # GitHub Actions
"GITLAB_CI": None, # GitLab CI
"HEROKU_TEST_RUN_ID": None, # Heroku CI
"TEAMCITY_VERSION": None, # TeamCity
}
def is_in_ci() -> bool:
return any(
key in os.environ and (value is None or os.environ[key] == value)
for key, value in _CI_VARS.items()
)
default_variable = DynamicVariable[Optional["settings"]](None)
def _validate_choices(name: str, value: T, *, choices: Sequence[object]) -> T:
if value not in choices:
msg = f"Invalid {name}, {value!r}. Valid choices: {choices!r}"
raise InvalidArgument(msg)
return value
def _validate_enum_value(cls: Any, value: object, *, name: str) -> Any:
try:
return cls(value)
except ValueError:
raise InvalidArgument(
f"{name}={value} is not a valid value. The options "
f"are: {', '.join(repr(m.name) for m in cls)}"
) from None
def _validate_max_examples(max_examples: int) -> int:
check_type(int, max_examples, name="max_examples")
if max_examples < 1:
raise InvalidArgument(
f"max_examples={max_examples!r} must be at least one. If you want "
"to disable generation entirely, use phases=[Phase.explicit] instead."
)
return max_examples
def _validate_database(
database: Optional["ExampleDatabase"],
) -> Optional["ExampleDatabase"]:
from hypothesis.database import ExampleDatabase
if database is None or isinstance(database, ExampleDatabase):
return database
raise InvalidArgument(
"Arguments to the database setting must be None or an instance of "
"ExampleDatabase. Use one of the database classes in "
"hypothesis.database"
)
def _validate_phases(phases: Collection[Phase]) -> Sequence[Phase]:
phases = try_convert(tuple, phases, "phases")
phases = tuple(
_validate_enum_value(Phase, phase, name="phases") for phase in phases
)
# sort by definition order
return tuple(phase for phase in list(Phase) if phase in phases)
def _validate_stateful_step_count(stateful_step_count: int) -> int:
check_type(int, stateful_step_count, name="stateful_step_count")
if stateful_step_count < 1:
raise InvalidArgument(
f"stateful_step_count={stateful_step_count!r} must be at least one."
)
return stateful_step_count
def _validate_suppress_health_check(suppressions: object) -> tuple[HealthCheck, ...]:
suppressions = try_convert(tuple, suppressions, "suppress_health_check")
for health_check in suppressions:
if health_check in (HealthCheck.return_value, HealthCheck.not_a_test_method):
note_deprecation(
f"The {health_check.name} health check is deprecated, because this is always an error.",
since="2023-03-15",
has_codemod=False,
stacklevel=2,
)
return tuple(
_validate_enum_value(HealthCheck, health_check, name="suppress_health_check")
for health_check in suppressions
)
def _validate_deadline(
deadline: int | float | datetime.timedelta | None,
) -> duration | None:
if deadline is None:
return deadline
invalid_deadline_error = InvalidArgument(
f"deadline={deadline!r} (type {type(deadline).__name__}) must be a timedelta object, "
"an integer or float number of milliseconds, or None to disable the "
"per-test-case deadline."
)
if isinstance(deadline, (int, float)):
if isinstance(deadline, bool):
raise invalid_deadline_error
try:
deadline = duration(milliseconds=deadline)
except OverflowError:
raise InvalidArgument(
f"deadline={deadline!r} is invalid, because it is too large to represent "
"as a timedelta. Use deadline=None to disable deadlines."
) from None
if isinstance(deadline, datetime.timedelta):
if deadline <= datetime.timedelta(0):
raise InvalidArgument(
f"deadline={deadline!r} is invalid, because it is impossible to meet a "
"deadline <= 0. Use deadline=None to disable deadlines."
)
return duration(seconds=deadline.total_seconds())
raise invalid_deadline_error
def _validate_backend(backend: str) -> str:
if backend not in AVAILABLE_PROVIDERS:
if backend == "crosshair": # pragma: no cover
install = '`pip install "hypothesis[crosshair]"` and try again.'
raise InvalidArgument(f"backend={backend!r} is not available. {install}")
raise InvalidArgument(
f"backend={backend!r} is not available - maybe you need to install a plugin?"
f"\n Installed backends: {sorted(AVAILABLE_PROVIDERS)!r}"
)
return backend
class settingsMeta(type):
def __init__(cls, *args, **kwargs):
super().__init__(*args, **kwargs)
@property
def default(cls) -> Optional["settings"]:
v = default_variable.value
if v is not None:
return v
if getattr(settings, "_current_profile", None) is not None:
assert settings._current_profile is not None
settings.load_profile(settings._current_profile)
assert default_variable.value is not None
return default_variable.value
def __setattr__(cls, name: str, value: object) -> None:
if name == "default":
raise AttributeError(
"Cannot assign to the property settings.default - "
"consider using settings.load_profile instead."
)
elif not name.startswith("_"):
raise AttributeError(
f"Cannot assign hypothesis.settings.{name}={value!r} - the settings "
"class is immutable. You can change the global default "
"settings with settings.load_profile, or use @settings(...) "
"to decorate your test instead."
)
super().__setattr__(name, value)
def __repr__(cls):
return "hypothesis.settings"
[docs]
class settings(metaclass=settingsMeta):
"""
A settings object controls the following aspects of test behavior:
|~settings.max_examples|, |~settings.derandomize|, |~settings.database|,
|~settings.verbosity|, |~settings.phases|, |~settings.stateful_step_count|,
|~settings.report_multiple_bugs|, |~settings.suppress_health_check|,
|~settings.deadline|, |~settings.print_blob|, and |~settings.backend|.
A settings object can be applied as a decorator to a test function, in which
case that test function will use those settings. A test may only have one
settings object applied to it. A settings object can also be passed to
|settings.register_profile| or as a parent to another |settings|.
Attribute inheritance
---------------------
Settings objects are immutable once created. When a settings object is created,
it uses the value specified for each attribute. Any attribute which is
not specified will inherit from its value in the ``parent`` settings object.
If ``parent`` is not passed, any attributes which are not specified will inherit
from the current settings profile instead.
For instance, ``settings(max_examples=10)`` will have a ``max_examples`` of ``10``,
and the value of all other attributes will be equal to its value in the
current settings profile.
Changes made from activating a new settings profile with |settings.load_profile|
will be reflected in settings objects created after the profile was loaded,
but not in existing settings objects.
.. _builtin-profiles:
Built-in profiles
-----------------
While you can register additional profiles with |settings.register_profile|,
Hypothesis comes with two built-in profiles: ``default`` and ``ci``.
By default, the ``default`` profile is active. If the ``CI`` environment
variable is set to any value, the ``ci`` profile is active by default. Hypothesis
also automatically detects various vendor-specific CI environment variables.
The attributes of the currently active settings profile can be retrieved with
``settings()`` (so ``settings().max_examples`` is the currently active default
for |settings.max_examples|).
The settings attributes for the built-in profiles are as follows:
.. code-block:: python
default = settings.register_profile(
"default",
max_examples=100,
derandomize=False,
database=not_set, # see settings.database for default behavior
verbosity=Verbosity.normal,
phases=tuple(Phase),
stateful_step_count=50,
report_multiple_bugs=True,
suppress_health_check=(),
deadline=duration(milliseconds=200),
print_blob=False,
backend="hypothesis",
)
ci = settings.register_profile(
"ci",
parent=default,
derandomize=True,
deadline=None,
database=None,
print_blob=True,
suppress_health_check=[HealthCheck.too_slow],
)
You can replace either of the built-in profiles with |settings.register_profile|:
.. code-block:: python
# run more examples in CI
settings.register_profile(
"ci",
settings.get_profile("ci"),
max_examples=1000,
)
"""
_profiles: ClassVar[dict[str, "settings"]] = {}
_current_profile: ClassVar[str | None] = None
def __init__(
self,
parent: Optional["settings"] = None,
*,
# This looks pretty strange, but there's good reason: we want Mypy to detect
# bad calls downstream, but not to freak out about the `= not_set` part even
# though it's not semantically valid to pass that as an argument value.
# The intended use is "like **kwargs, but more tractable for tooling".
max_examples: int = not_set, # type: ignore
derandomize: bool = not_set, # type: ignore
database: Optional["ExampleDatabase"] = not_set, # type: ignore
verbosity: "Verbosity" = not_set, # type: ignore
phases: Collection["Phase"] = not_set, # type: ignore
stateful_step_count: int = not_set, # type: ignore
report_multiple_bugs: bool = not_set, # type: ignore
suppress_health_check: Collection["HealthCheck"] = not_set, # type: ignore
deadline: int | float | datetime.timedelta | None = not_set, # type: ignore
print_blob: bool = not_set, # type: ignore
backend: str = not_set, # type: ignore
) -> None:
self._in_definition = True
if parent is not None:
check_type(settings, parent, "parent")
if derandomize not in (not_set, False):
if database not in (not_set, None): # type: ignore
raise InvalidArgument(
"derandomize=True implies database=None, so passing "
f"{database=} too is invalid."
)
database = None
# fallback is None if we're creating the default settings object, and
# the parent (or default settings object) otherwise
self._fallback = parent or settings.default
self._max_examples = (
self._fallback.max_examples # type: ignore
if max_examples is not_set # type: ignore
else _validate_max_examples(max_examples)
)
self._derandomize = (
self._fallback.derandomize # type: ignore
if derandomize is not_set # type: ignore
else _validate_choices("derandomize", derandomize, choices=[True, False])
)
if database is not not_set: # type: ignore
database = _validate_database(database)
self._database = database
self._cached_database = None
self._verbosity = (
self._fallback.verbosity # type: ignore
if verbosity is not_set # type: ignore
else _validate_enum_value(Verbosity, verbosity, name="verbosity")
)
self._phases = (
self._fallback.phases # type: ignore
if phases is not_set # type: ignore
else _validate_phases(phases)
)
self._stateful_step_count = (
self._fallback.stateful_step_count # type: ignore
if stateful_step_count is not_set # type: ignore
else _validate_stateful_step_count(stateful_step_count)
)
self._report_multiple_bugs = (
self._fallback.report_multiple_bugs # type: ignore
if report_multiple_bugs is not_set # type: ignore
else _validate_choices(
"report_multiple_bugs", report_multiple_bugs, choices=[True, False]
)
)
self._suppress_health_check = (
self._fallback.suppress_health_check # type: ignore
if suppress_health_check is not_set # type: ignore
else _validate_suppress_health_check(suppress_health_check)
)
self._deadline = (
self._fallback.deadline # type: ignore
if deadline is not_set # type: ignore
else _validate_deadline(deadline)
)
self._print_blob = (
self._fallback.print_blob # type: ignore
if print_blob is not_set # type: ignore
else _validate_choices("print_blob", print_blob, choices=[True, False])
)
self._backend = (
self._fallback.backend # type: ignore
if backend is not_set # type: ignore
else _validate_backend(backend)
)
self._in_definition = False
@property
def max_examples(self):
"""
Once this many satisfying examples have been considered without finding any
counter-example, Hypothesis will stop looking.
Note that we might call your test function fewer times if we find a bug early
or can tell that we've exhausted the search space; or more if we discard some
examples due to use of .filter(), assume(), or a few other things that can
prevent the test case from completing successfully.
The default value is chosen to suit a workflow where the test will be part of
a suite that is regularly executed locally or on a CI server, balancing total
running time against the chance of missing a bug.
If you are writing one-off tests, running tens of thousands of examples is
quite reasonable as Hypothesis may miss uncommon bugs with default settings.
For very complex code, we have observed Hypothesis finding novel bugs after
*several million* examples while testing :pypi:`SymPy <sympy>`.
If you are running more than 100k examples for a test, consider using our
:ref:`integration for coverage-guided fuzzing <fuzz_one_input>` - it really
shines when given minutes or hours to run.
The default max examples is ``100``.
"""
return self._max_examples
@property
def derandomize(self):
"""
If True, seed Hypothesis' random number generator using a hash of the test
function, so that every run will test the same set of examples until you
update Hypothesis, Python, or the test function.
This allows you to `check for regressions and look for bugs
<https://blog.nelhage.com/post/two-kinds-of-testing/>`__ using separate
settings profiles - for example running
quick deterministic tests on every commit, and a longer non-deterministic
nightly testing run.
The default is ``False``. If running on CI, the default is ``True`` instead.
"""
return self._derandomize
@property
def database(self):
"""
An instance of |ExampleDatabase| that will be used to save examples to
and load previous examples from.
If not set, a |DirectoryBasedExampleDatabase| is created in the current
working directory under ``.hypothesis/examples``. If this location is
unusable, e.g. due to the lack of read or write permissions, Hypothesis
will emit a warning and fall back to an |InMemoryExampleDatabase|.
If ``None``, no storage will be used.
See the :ref:`database documentation <database>` for a list of database
classes, and how to define custom database classes.
"""
from hypothesis.database import _db_for_path
# settings.database has two conflicting requirements:
# * The default settings should respect changes to set_hypothesis_home_dir
# in-between accesses
# * `s.database is s.database` should be true, except for the default settings
#
# We therefore cache s.database for everything except the default settings,
# which always recomputes dynamically.
if self._fallback is None:
# if self._fallback is None, we are the default settings, at which point
# we should recompute the database dynamically
assert self._database is not_set
return _db_for_path(not_set)
# otherwise, we cache the database
if self._cached_database is None:
self._cached_database = (
self._fallback.database if self._database is not_set else self._database
)
return self._cached_database
@property
def verbosity(self):
"""
Control the verbosity level of Hypothesis messages.
To see what's going on while Hypothesis runs your tests, you can turn
up the verbosity setting.
.. code-block:: pycon
>>> from hypothesis import settings, Verbosity
>>> from hypothesis.strategies import lists, integers
>>> @given(lists(integers()))
... @settings(verbosity=Verbosity.verbose)
... def f(x):
... assert not any(x)
... f()
Trying example: []
Falsifying example: [-1198601713, -67, 116, -29578]
Shrunk example to [-1198601713]
Shrunk example to [-128]
Shrunk example to [32]
Shrunk example to [1]
[1]
The four levels are |Verbosity.quiet|, |Verbosity.normal|,
|Verbosity.verbose|, and |Verbosity.debug|. |Verbosity.normal| is the
default. For |Verbosity.quiet|, Hypothesis will not print anything out,
not even the final falsifying example. |Verbosity.debug| is basically
|Verbosity.verbose| but a bit more so. You probably don't want it.
Verbosity can be passed either as a |Verbosity| enum value, or as the
corresponding string value, or as the corresponding integer value. For
example:
.. code-block:: python
# these three are equivalent
settings(verbosity=Verbosity.verbose)
settings(verbosity="verbose")
If you are using :pypi:`pytest`, you may also need to :doc:`disable
output capturing for passing tests <pytest:how-to/capture-stdout-stderr>`
to see verbose output as tests run.
"""
return self._verbosity
@property
def phases(self):
"""
Control which phases should be run.
Hypothesis divides tests into logically distinct phases.
- |Phase.explicit|: Running explicit examples from |@example|.
- |Phase.reuse|: Running examples from the database which previously failed.
- |Phase.generate|: Generating new random examples.
- |Phase.target|: Mutating examples for :ref:`targeted property-based
testing <targeted>`. Requires |Phase.generate|.
- |Phase.shrink|: Shrinking failing examples.
- |Phase.explain|: Attempting to explain why a failure occurred.
Requires |Phase.shrink|.
The phases argument accepts a collection with any subset of these. E.g.
``settings(phases=[Phase.generate, Phase.shrink])`` will generate new examples
and shrink them, but will not run explicit examples or reuse previous failures,
while ``settings(phases=[Phase.explicit])`` will only run explicit examples
from |@example|.
Phases can be passed either as a |Phase| enum value, or as the corresponding
string value. For example:
.. code-block:: python
# these two are equivalent
settings(phases=[Phase.explicit])
settings(phases=["explicit"])
Following the first failure, Hypothesis will (usually, depending on
which |Phase| is enabled) track which lines of code are always run on
failing but never on passing inputs. On 3.12+, this uses
:mod:`sys.monitoring`, while 3.11 and earlier uses :func:`python:sys.settrace`.
For python 3.11 and earlier, we therefore automatically disable the explain
phase on PyPy, or if you are using :pypi:`coverage` or a debugger. If
there are no clearly suspicious lines of code, :pep:`we refuse the
temptation to guess <20>`.
After shrinking to a minimal failing example, Hypothesis will try to find
parts of the example -- e.g. separate args to |@given|
-- which can vary freely without changing the result
of that minimal failing example. If the automated experiments run without
finding a passing variation, we leave a comment in the final report:
.. code-block:: python
test_x_divided_by_y(
x=0, # or any other generated value
y=0,
)
Just remember that the *lack* of an explanation sometimes just means that
Hypothesis couldn't efficiently find one, not that no explanation (or
simpler failing example) exists.
"""
return self._phases
@property
def stateful_step_count(self):
"""
The maximum number of times to call an additional |@rule| method in
:ref:`stateful testing <stateful>` before we give up on finding a bug.
Note that this setting is effectively multiplicative with max_examples,
as each example will run for a maximum of ``stateful_step_count`` steps.
The default stateful step count is ``50``.
"""
return self._stateful_step_count
@property
def report_multiple_bugs(self):
"""
Because Hypothesis runs the test many times, it can sometimes find multiple
bugs in a single run. Reporting all of them at once is usually very useful,
but replacing the exceptions can occasionally clash with debuggers.
If disabled, only the exception with the smallest minimal example is raised.
The default value is ``True``.
"""
return self._report_multiple_bugs
@property
def suppress_health_check(self):
"""
Suppress the given |HealthCheck| exceptions. Those health checks will not
be raised by Hypothesis. To suppress all health checks, you can pass
``suppress_health_check=list(HealthCheck)``.
Health checks can be passed either as a |HealthCheck| enum value, or as
the corresponding string value. For example:
.. code-block:: python
# these two are equivalent
settings(suppress_health_check=[HealthCheck.filter_too_much])
settings(suppress_health_check=["filter_too_much"])
Health checks are proactive warnings, not correctness errors, so we
encourage suppressing health checks where you have evaluated they will
not pose a problem, or where you have evaluated that fixing the underlying
issue is not worthwhile.
.. seealso::
See also the :doc:`/how-to/suppress-healthchecks` how-to.
"""
return self._suppress_health_check
@property
def deadline(self):
"""
The maximum allowed duration of an individual test case, in milliseconds.
You can pass an integer, float, or timedelta. If ``None``, the deadline
is disabled entirely.
We treat the deadline as a soft limit in some cases, where that would
avoid flakiness due to timing variability.
The default deadline is 200 milliseconds. If running on CI, the default is
``None`` instead.
"""
return self._deadline
@property
def print_blob(self):
"""
If set to ``True``, Hypothesis will print code for failing examples that
can be used with |@reproduce_failure| to reproduce the failing example.
The default value is ``False``. If running on CI, the default is ``True`` instead.
"""
return self._print_blob
@property
def backend(self):
"""
.. warning::
EXPERIMENTAL AND UNSTABLE - see :ref:`alternative-backends`.
The importable name of a backend which Hypothesis should use to generate
primitive types. We support heuristic-random, solver-based, and fuzzing-based
backends.
"""
return self._backend
def __call__(self, test: T) -> T:
"""Make the settings object (self) an attribute of the test.
The settings are later discovered by looking them up on the test itself.
"""
# Aliasing as Any avoids mypy errors (attr-defined) when accessing and
# setting custom attributes on the decorated function or class.
_test: Any = test
# Using the alias here avoids a mypy error (return-value) later when
# ``test`` is returned, because this check results in type refinement.
if not callable(_test):
raise InvalidArgument(
"settings objects can be called as a decorator with @given, "
f"but decorated {test=} is not callable."
)
if inspect.isclass(test):
from hypothesis.stateful import RuleBasedStateMachine
if issubclass(_test, RuleBasedStateMachine):
attr_name = "_hypothesis_internal_settings_applied"
if getattr(test, attr_name, False):
raise InvalidArgument(
"Applying the @settings decorator twice would "
"overwrite the first version; merge their arguments "
"instead."
)
setattr(test, attr_name, True)
_test.TestCase.settings = self
return test
else:
raise InvalidArgument(
"@settings(...) can only be used as a decorator on "
"functions, or on subclasses of RuleBasedStateMachine."
)
if hasattr(_test, "_hypothesis_internal_settings_applied"):
# Can't use _hypothesis_internal_use_settings as an indicator that
# @settings was applied, because @given also assigns that attribute.
descr = get_pretty_function_description(test)
raise InvalidArgument(
f"{descr} has already been decorated with a settings object.\n"
f" Previous: {_test._hypothesis_internal_use_settings!r}\n"
f" This: {self!r}"
)
_test._hypothesis_internal_use_settings = self
_test._hypothesis_internal_settings_applied = True
return test
def __setattr__(self, name: str, value: object) -> None:
if not name.startswith("_") and not self._in_definition:
raise AttributeError("settings objects are immutable")
return super().__setattr__(name, value)
def __repr__(self) -> str:
bits = sorted(
f"{name}={getattr(self, name)!r}"
for name in all_settings
if (name != "backend" or len(AVAILABLE_PROVIDERS) > 1) # experimental
)
return "settings({})".format(", ".join(bits))
def show_changed(self) -> str:
bits = []
for name in all_settings:
value = getattr(self, name)
if value != getattr(default, name):
bits.append(f"{name}={value!r}")
return ", ".join(sorted(bits, key=len))
[docs]
@staticmethod
def register_profile(
name: str,
parent: Optional["settings"] = None,
**kwargs: Any,
) -> None:
"""
Register a settings object as a settings profile, under the name ``name``.
The ``parent`` and ``kwargs`` arguments to this method are as for
|settings|.
If a settings profile already exists under ``name``, it will be overwritten.
Registering a profile with the same name as the currently active profile
will cause those changes to take effect in the active profile immediately,
and do not require reloading the profile.
Registered settings profiles can be retrieved later by name with
|settings.get_profile|.
"""
check_type(str, name, "name")
if (
default_variable.value
and settings._current_profile
and default_variable.value != settings._profiles[settings._current_profile]
):
note_deprecation(
"Cannot register a settings profile when the current settings differ "
"from the current profile (usually due to an @settings decorator). "
"Register profiles at module level instead.",
since="2025-11-15",
has_codemod=False,
)
# if we just pass the parent and no kwargs, like
# settings.register_profile(settings(max_examples=10))
# then optimize out the pointless intermediate settings object which
# would just forward everything to the parent.
settings._profiles[name] = (
parent
if parent is not None and not kwargs
else settings(parent=parent, **kwargs)
)
if settings._current_profile == name:
settings.load_profile(name)
[docs]
@staticmethod
def get_profile(name: str) -> "settings":
"""
Returns the settings profile registered under ``name``. If no settings
profile is registered under ``name``, raises |InvalidArgument|.
"""
check_type(str, name, "name")
try:
return settings._profiles[name]
except KeyError:
raise InvalidArgument(f"Profile {name!r} is not registered") from None
[docs]
@staticmethod
def load_profile(name: str) -> None:
"""
Makes the settings profile registered under ``name`` the active profile.
If no settings profile is registered under ``name``, raises |InvalidArgument|.
"""
check_type(str, name, "name")
settings._current_profile = name
default_variable.value = settings.get_profile(name)
[docs]
@staticmethod
def get_current_profile_name() -> str:
"""
The name of the current settings profile. For example:
.. code-block:: python
>>> settings.load_profile("myprofile")
>>> settings.get_current_profile_name()
'myprofile'
"""
assert settings._current_profile is not None
return settings._current_profile
@contextlib.contextmanager
def local_settings(s: settings) -> Generator[settings, None, None]:
with default_variable.with_value(s):
yield s
default = settings(
max_examples=100,
derandomize=False,
database=not_set, # type: ignore
verbosity=Verbosity.normal,
phases=tuple(Phase),
stateful_step_count=50,
report_multiple_bugs=True,
suppress_health_check=(),
deadline=duration(milliseconds=200),
print_blob=False,
backend="hypothesis",
)
settings.register_profile("default", default)
settings.load_profile("default")
assert settings.default is not None
CI = settings(
derandomize=True,
deadline=None,
database=None,
print_blob=True,
suppress_health_check=[HealthCheck.too_slow],
)
settings.register_profile("ci", CI)
if is_in_ci(): # pragma: no cover # covered in ci, but not locally
settings.load_profile("ci")
assert settings.default is not None
# Check that the kwonly args to settings.__init__ is the same as the set of
# defined settings - in case we've added or remove something from one but
# not the other.
assert set(all_settings) == {
p.name
for p in inspect.signature(settings.__init__).parameters.values()
if p.kind == inspect.Parameter.KEYWORD_ONLY
}