Log message:
py-hypothesis: updated to 6.100.1

6.100.1

This patch improve a rare error message for flaky tests.

6.100.0

The from_dtype() function no longer generates NaT (“not-a-time”) values for \ 
the datetime64 or timedelta64 dtypes if passed allow_nan=False.

6.99.13

This patch includes the backend setting in the how_generated field of our \ 
observability output.

6.99.12

If you were running Python 3.13 (currently in alpha) with pytest-xdist and then \ 
attempted to pretty-print a lambda functions which was created using the eval() \ 
builtin, it would have raised an AssertionError. Now you’ll get "lambda \ 
...: <unknown>", as expected.

6.99.11

This release improves an internal invariant.

6.99.10

This patch fixes Hypothesis sometimes raising a Flaky error when generating \ 
collections of unique floats containing nan.

Log message:
py-hypothesis: updated to 6.98.6

6.98.6

This patch clarifies the documentation on stateful testing.

6.98.5

This patch improves argument-to-json conversion for observability output. \ 
Checking for a .to_json() method on the object before a few other options like \ 
dataclass support allows better user control of the process.

6.98.4

This patch updates our vendored list of top-level domains, which is used by the \ 
provisional domains() strategy.

Log message:
py-hypothesis: updated to 6.98.3

6.98.3

This patch fixes an error when generating observability reports involving large \ 
(n > 1e308) integers.

6.98.2

This patch refactors some internals. There is no user-visible change.

6.98.1

This release improves our distribution of generated values for all strategies, \ 
by doing a better job of tracking which values we have generated before and \ 
avoiding generating them again.

For example, st.lists(st.integers()) previously generated ~5 each of [] [0] in \ 
100 examples. In this release, each of [] and [0] are generated ~1-2 times each.

6.98.0

This release deprecates use of the global random number generator while drawing \ 
from a strategy, because this makes test cases less diverse and prevents us from \ 
reporting minimal counterexamples.

If you see this new warning, you can get a quick fix by using randoms(); or use \ 
more idiomatic strategies sampled_from(), floats(), integers(), and so on.

Note that the same problem applies to e.g. numpy.random, but for performance \ 
reasons we only check the stdlib random module - ignoring even other sources \ 
passed to register_random().

6.97.6

This patch updates our vendored list of top-level domains, which is used by the \ 
provisional domains() strategy.

6.97.5

This patch adds some observability information about how many times predicates \ 
in assume() or precondition() were satisfied, so that downstream tools can warn \ 
you if some were never satisfied by any test case.

6.97.4

This patch improves formatting and adds some cross-references to our docs.

Log message:
py-hypothesis: updated to 6.97.3

6.97.3

Internal test refactoring.

6.97.2

This patch slightly changes how we replay examples from the database: if the \ 
behavior of the saved example has changed, we now keep running the test case \ 
instead of aborting at the size of the saved example. While we know it’s not \ 
the same example, we might as well continue running the test!

Because we now finish running a few more examples for affected tests, this might \ 
be a slight slowdown - but correspondingly more likely to find a bug.

We’ve also applied similar tricks to the target phase, where they are a pure \ 
performance improvement for affected tests.

6.97.1

Improves the performance of the arrays() strategy when generating unique values.

Log message:
py-hypothesis: updated to 6.97.0

6.97.0

Changes the distribution of sampled_from() when sampling from a Flag. \ 
Previously, no-flags-set values would never be generated, and all-flags-set \ 
values would be unlikely for large enums. With this change, the distribution is \ 
more uniform in the number of flags set.

6.96.4

This patch slightly refactors some internals. There is no user-visible change.

6.96.3

This patch fixes a spurious warning about slow imports when \ 
HYPOTHESIS_EXPERIMENTAL_OBSERVABILITY was set.

6.96.2

This patch refactors some more internals, continuing our work on supporting \ 
alternative backends (issue 3086). There is no user-visible change.

6.96.1

Fix a spurious warning seen when running pytest’s test suite, caused by never \ 
realizing we got out of initialization due to imbalanced hook calls.

6.96.0

Warns when constructing a repr that is overly long. This can happen by accident \ 
if stringifying arbitrary strategies, and is expensive in time and memory. The \ 
associated deferring of these long strings in sampled_from() should also lead to \ 
improved performance.

Log message:
py-hypothesis: updated to 6.93.2

6.93.2

This patch improves observability reports by moving timing information from \ 
metadata to a new timing key, and supporting conversion of additional argument \ 
types to json rather than string reprs via a .to_json() method (including e.g. \ 
Pandas dataframes).

Additionally, the too_slow health check will now report which strategies were \ 
slow, e.g. for strategies a, b, c, …:

    count | fraction |    slowest draws (seconds)
a |    3  |     65%  |      --      --      --   0.357,  2.000
b |    8  |     16%  |   0.100,  0.100,  0.100,  0.111,  0.123
c |    3  |      8%  |      --      --   0.030,  0.050,  0.200
(skipped 2 rows of fast draws)
6.93.1

This patch refactors some internals, continuing our work on supporting \ 
alternative backends. There is no user-visible change.

6.93.0

The from_lark() strategy now accepts an alphabet= argument, which is passed \ 
through to from_regex(), so that you can e.g. constrain the generated strings to \ 
a particular codec.

In support of this feature, from_regex() will avoid generating optional parts \ 
which do not fit the alphabet. For example, from_regex(r"abc|def", \ 
alphabet="abcd") was previously an error, and will now generate only \ 
'abc'. Cases where there are no valid strings remain an error.

6.92.9

This patch refactors some internals, continuing our work on supporting \ 
alternative backends. There is no user-visible change.

6.92.8

This patch adds a test statistics event when a generated example is rejected via \ 
assume.

This may also help with distinguishing gave_up examples in observability.

6.92.7

This introduces the rewriting of length filters on some collection strategies.

Thanks to Reagan Lee for implementing this feature!

6.92.6

If a test uses sampled_from() on a sequence of strategies, and raises a \ 
TypeError, we now add a note asking whether you meant to use one_of().

Log message:
py-hypothesis: updated to 6.92.5

6.92.5

This patch registers explicit strategies for a handful of builtin types, \ 
motivated by improved introspection in PyPy 7.3.14 triggering existing internal \ 
warnings. Thanks to Carl Friedrich Bolz-Tereick for helping us work out what \ 
changed!

6.92.4

This patch fixes an error when writing observability reports without a \ 
pre-existing .hypothesis directory.

6.92.3

This patch adds a new environment variable \ 
HYPOTHESIS_EXPERIMENTAL_OBSERVABILITY_NOCOVER, which turns on observability data \ 
collection without collecting code coverage data, which may be faster on Python \ 
3.11 and earlier.

6.92.2

This patch updates our vendored list of top-level domains, which is used by the \ 
provisional domains() strategy.

6.92.1

This patch fixes a bug introduced in version 6.92.0, where using the data() \ 
strategy would fail to draw a dataclass() with a defaultdict field. This was due \ 
to a bug in the standard library which was fixed in 3.12, so we’ve vendored \ 
the fix.

Log message:
py-hypothesis: add TOOL_DEPENDS

Log message:
py-hypothesis: updated to 6.92.0

6.92.0

This release adds an experimental observability mode. You can read the docs \ 
about it here.

6.91.2

This patch refactors some more internals, continuing our work on supporting \ 
alternative backends. There is no user-visible change.

6.91.1

This patch fixes an issue where builds() could not be used with attrs objects \ 
that defined private attributes (i.e. attributes with a leading underscore).

This patch also adds support more generally for using builds() with attrs’ \ 
alias parameter, which was previously unsupported.

This patch increases the minimum required version of attrs to 22.2.0.

6.91.0

This release adds an optional payload argument to hypothesis.event(), so that \ 
you can clearly express the difference between the label and the value of an \ 
observation. Test statistics will still summarize it as a string, but future \ 
observability options can preserve the distinction.

6.90.1

This patch supports assigning settings = settings(...) as a class attribute on a \ 
subclass of a .TestCase attribute of a RuleBasedStateMachine. Previously, this \ 
did nothing at all.

Thanks to Joey Tran for reporting these settings-related edge cases in stateful \ 
testing.

6.90.0

This release makes it an error to assign settings = settings(...) as a class \ 
attribute on a RuleBasedStateMachine. This has never had any effect, and it \ 
should be used as a decorator instead:

Log message:
py-hypothesis: updated to 6.88.1

6.88.1

This patch improves register_type_strategy() when used with tuple subclasses, by \ 
preventing them from being interpreted as generic and provided to strategies \ 
like st.from_type(Sequence[int]).

6.88.0

This release allows strategy-generating functions registered with \ 
register_type_strategy() to conditionally not return a strategy, by returning \ 
NotImplemented.

6.87.4

When randoms() was called with use_true_randoms=False, calling sample on it with \ 
an empty sequence and 0 elements would result in an error, when it should have \ 
returned an empty sequence to agree with the normal behaviour of random.Random. \ 
This fixes that discrepancy.

6.87.3

This patch ensures that the hypothesis codemod CLI will print a warning instead \ 
of stopping with an internal error if one of your files contains invalid syntax.

6.87.2

This patch makes some small changes to our NumPy integration to ensure forward \ 
compatibility.