# Source code for hypothesis.strategies._internal.strategies

```
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Most of this work is copyright (C) 2013-2021 David R. MacIver
# (david@drmaciver.com), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
#
# 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/.
#
# END HEADER
import sys
import warnings
from collections import defaultdict
from random import choice as random_choice
from typing import (
Any,
Callable,
Dict,
Generic,
List,
Sequence,
TypeVar,
Union,
overload,
)
from hypothesis._settings import HealthCheck, Phase, Verbosity, settings
from hypothesis.control import _current_build_context, assume
from hypothesis.errors import (
HypothesisException,
InvalidArgument,
NonInteractiveExampleWarning,
UnsatisfiedAssumption,
)
from hypothesis.internal.conjecture import utils as cu
from hypothesis.internal.conjecture.data import ConjectureData
from hypothesis.internal.conjecture.utils import (
calc_label_from_cls,
calc_label_from_name,
combine_labels,
)
from hypothesis.internal.coverage import check_function
from hypothesis.internal.lazyformat import lazyformat
from hypothesis.internal.reflection import get_pretty_function_description
from hypothesis.utils.conventions import UniqueIdentifier
Ex = TypeVar("Ex", covariant=True)
T = TypeVar("T")
T3 = TypeVar("T3")
T4 = TypeVar("T4")
T5 = TypeVar("T5")
calculating = UniqueIdentifier("calculating")
MAPPED_SEARCH_STRATEGY_DO_DRAW_LABEL = calc_label_from_name(
"another attempted draw in MappedSearchStrategy"
)
FILTERED_SEARCH_STRATEGY_DO_DRAW_LABEL = calc_label_from_name(
"single loop iteration in FilteredStrategy"
)
def recursive_property(name, default):
"""Handle properties which may be mutually recursive among a set of
strategies.
These are essentially lazily cached properties, with the ability to set
an override: If the property has not been explicitly set, we calculate
it on first access and memoize the result for later.
The problem is that for properties that depend on each other, a naive
calculation strategy may hit infinite recursion. Consider for example
the property is_empty. A strategy defined as x = st.deferred(lambda: x)
is certainly empty (in order to draw a value from x we would have to
draw a value from x, for which we would have to draw a value from x,
...), but in order to calculate it the naive approach would end up
calling x.is_empty in order to calculate x.is_empty in order to etc.
The solution is one of fixed point calculation. We start with a default
value that is the value of the property in the absence of evidence to
the contrary, and then update the values of the property for all
dependent strategies until we reach a fixed point.
The approach taken roughly follows that in section 4.2 of Adams,
Michael D., Celeste Hollenbeck, and Matthew Might. "On the complexity
and performance of parsing with derivatives." ACM SIGPLAN Notices 51.6
(2016): 224-236.
"""
cache_key = "cached_" + name
calculation = "calc_" + name
force_key = "force_" + name
def forced_value(target):
try:
return getattr(target, force_key)
except AttributeError:
return getattr(target, cache_key)
def accept(self):
try:
return forced_value(self)
except AttributeError:
pass
mapping = {}
sentinel = object()
hit_recursion = [False]
# For a first pass we do a direct recursive calculation of the
# property, but we block recursively visiting a value in the
# computation of its property: When that happens, we simply
# note that it happened and return the default value.
def recur(strat):
try:
return forced_value(strat)
except AttributeError:
pass
result = mapping.get(strat, sentinel)
if result is calculating:
hit_recursion[0] = True
return default
elif result is sentinel:
mapping[strat] = calculating
mapping[strat] = getattr(strat, calculation)(recur)
return mapping[strat]
return result
recur(self)
# If we hit self-recursion in the computation of any strategy
# value, our mapping at the end is imprecise - it may or may
# not have the right values in it. We now need to proceed with
# a more careful fixed point calculation to get the exact
# values. Hopefully our mapping is still pretty good and it
# won't take a large number of updates to reach a fixed point.
if hit_recursion[0]:
needs_update = set(mapping)
# We track which strategies use which in the course of
# calculating their property value. If A ever uses B in
# the course of calculating its value, then whenever the
# value of B changes we might need to update the value of
# A.
listeners = defaultdict(set)
else:
needs_update = None
def recur2(strat):
def recur_inner(other):
try:
return forced_value(other)
except AttributeError:
pass
listeners[other].add(strat)
result = mapping.get(other, sentinel)
if result is sentinel:
needs_update.add(other)
mapping[other] = default
return default
return result
return recur_inner
count = 0
seen = set()
while needs_update:
count += 1
# If we seem to be taking a really long time to stabilize we
# start tracking seen values to attempt to detect an infinite
# loop. This should be impossible, and most code will never
# hit the count, but having an assertion for it means that
# testing is easier to debug and we don't just have a hung
# test.
# Note: This is actually covered, by test_very_deep_deferral
# in tests/cover/test_deferred_strategies.py. Unfortunately it
# runs into a coverage bug. See
# https://bitbucket.org/ned/coveragepy/issues/605/
# for details.
if count > 50: # pragma: no cover
key = frozenset(mapping.items())
assert key not in seen, (key, name)
seen.add(key)
to_update = needs_update
needs_update = set()
for strat in to_update:
new_value = getattr(strat, calculation)(recur2(strat))
if new_value != mapping[strat]:
needs_update.update(listeners[strat])
mapping[strat] = new_value
# We now have a complete and accurate calculation of the
# property values for everything we have seen in the course of
# running this calculation. We simultaneously update all of
# them (not just the strategy we started out with).
for k, v in mapping.items():
setattr(k, cache_key, v)
return getattr(self, cache_key)
accept.__name__ = name
return property(accept)
class SearchStrategy(Generic[Ex]):
"""A SearchStrategy is an object that knows how to explore data of a given
type.
Except where noted otherwise, methods on this class are not part of
the public API and their behaviour may change significantly between
minor version releases. They will generally be stable between patch
releases.
"""
supports_find = True
validate_called = False
__label = None
__module__ = "hypothesis.strategies"
def available(self, data):
"""Returns whether this strategy can *currently* draw any
values. This typically useful for stateful testing where ``Bundle``
grows over time a list of value to choose from.
Unlike ``empty`` property, this method's return value may change
over time.
Note: ``data`` parameter will only be used for introspection and no
value drawn from it.
"""
return not self.is_empty
# Returns True if this strategy can never draw a value and will always
# result in the data being marked invalid.
# The fact that this returns False does not guarantee that a valid value
# can be drawn - this is not intended to be perfect, and is primarily
# intended to be an optimisation for some cases.
is_empty = recursive_property("is_empty", True)
# Returns True if values from this strategy can safely be reused without
# this causing unexpected behaviour.
has_reusable_values = recursive_property("has_reusable_values", True)
# Whether this strategy is suitable for holding onto in a cache.
is_cacheable = recursive_property("is_cacheable", True)
def calc_is_cacheable(self, recur):
return True
def calc_is_empty(self, recur):
# Note: It is correct and significant that the default return value
# from calc_is_empty is False despite the default value for is_empty
# being true. The reason for this is that strategies should be treated
# as empty absent evidence to the contrary, but most basic strategies
# are trivially non-empty and it would be annoying to have to override
# this method to show that.
return False
def calc_has_reusable_values(self, recur):
return False
def example(self) -> Ex:
"""Provide an example of the sort of value that this strategy
generates. This is biased to be slightly simpler than is typical for
values from this strategy, for clarity purposes.
This method shouldn't be taken too seriously. It's here for interactive
exploration of the API, not for any sort of real testing.
This method is part of the public API.
"""
if getattr(sys, "ps1", None) is None: # pragma: no branch
# The other branch *is* covered in cover/test_examples.py; but as that
# uses `pexpect` for an interactive session `coverage` doesn't see it.
warnings.warn(
"The `.example()` method is good for exploring strategies, but should "
"only be used interactively. We recommend using `@given` for tests - "
"it performs better, saves and replays failures to avoid flakiness, "
"and reports minimal examples. (strategy: %r)" % (self,),
NonInteractiveExampleWarning,
)
context = _current_build_context.value
if context is not None:
if context.data is not None and context.data.depth > 0:
raise HypothesisException(
"Using example() inside a strategy definition is a bad "
"idea. Instead consider using hypothesis.strategies.builds() "
"or @hypothesis.strategies.composite to define your strategy."
" See https://hypothesis.readthedocs.io/en/latest/data.html"
"#hypothesis.strategies.builds or "
"https://hypothesis.readthedocs.io/en/latest/data.html"
"#composite-strategies for more details."
)
else:
raise HypothesisException(
"Using example() inside a test function is a bad "
"idea. Instead consider using hypothesis.strategies.data() "
"to draw more examples during testing. See "
"https://hypothesis.readthedocs.io/en/latest/data.html"
"#drawing-interactively-in-tests for more details."
)
from hypothesis.core import given
# Note: this function has a weird name because it might appear in
# tracebacks, and we want users to know that they can ignore it.
@given(self)
@settings(
database=None,
max_examples=10,
deadline=None,
verbosity=Verbosity.quiet,
phases=(Phase.generate,),
suppress_health_check=HealthCheck.all(),
)
def example_generating_inner_function(ex):
examples.append(ex)
examples: List[Ex] = []
example_generating_inner_function()
return random_choice(examples)
def map(self, pack: Callable[[Ex], T]) -> "SearchStrategy[T]":
"""Returns a new strategy that generates values by generating a value
from this strategy and then calling pack() on the result, giving that.
This method is part of the public API.
"""
return MappedSearchStrategy(pack=pack, strategy=self)
def flatmap(
self, expand: Callable[[Ex], "SearchStrategy[T]"]
) -> "SearchStrategy[T]":
"""Returns a new strategy that generates values by generating a value
from this strategy, say x, then generating a value from
strategy(expand(x))
This method is part of the public API.
"""
from hypothesis.strategies._internal.flatmapped import FlatMapStrategy
return FlatMapStrategy(expand=expand, strategy=self)
def filter(self, condition: Callable[[Ex], Any]) -> "SearchStrategy[Ex]":
"""Returns a new strategy that generates values from this strategy
which satisfy the provided condition. Note that if the condition is too
hard to satisfy this might result in your tests failing with
Unsatisfiable.
This method is part of the public API.
"""
return FilteredStrategy(conditions=(condition,), strategy=self)
def do_filtered_draw(self, data, filter_strategy):
# Hook for strategies that want to override the behaviour of
# FilteredStrategy. Most strategies don't, so by default we delegate
# straight back to the default filtered-draw implementation.
return filter_strategy.default_do_filtered_draw(data)
@property
def branches(self) -> List["SearchStrategy[Ex]"]:
return [self]
def __or__(self, other: "SearchStrategy[T]") -> "SearchStrategy[Union[Ex, T]]":
"""Return a strategy which produces values by randomly drawing from one
of this strategy or the other strategy.
This method is part of the public API.
"""
if not isinstance(other, SearchStrategy):
raise ValueError(f"Cannot | a SearchStrategy with {other!r}")
return OneOfStrategy((self, other))
def validate(self) -> None:
"""Throw an exception if the strategy is not valid.
This can happen due to lazy construction
"""
if self.validate_called:
return
try:
self.validate_called = True
self.do_validate()
self.is_empty
self.has_reusable_values
except Exception:
self.validate_called = False
raise
LABELS: Dict[type, int] = {}
@property
def class_label(self):
cls = self.__class__
try:
return cls.LABELS[cls]
except KeyError:
pass
result = calc_label_from_cls(cls)
cls.LABELS[cls] = result
return result
@property
def label(self):
if self.__label is calculating:
return 0
if self.__label is None:
self.__label = calculating
self.__label = self.calc_label()
return self.__label
def calc_label(self):
return self.class_label
def do_validate(self):
pass
def do_draw(self, data: ConjectureData) -> Ex:
raise NotImplementedError(f"{type(self).__name__}.do_draw")
def __init__(self):
pass
def is_simple_data(value):
try:
hash(value)
return True
except TypeError:
return False
class SampledFromStrategy(SearchStrategy):
"""A strategy which samples from a set of elements. This is essentially
equivalent to using a OneOfStrategy over Just strategies but may be more
efficient and convenient.
The conditional distribution chooses uniformly at random from some
non-empty subset of the elements.
"""
def __init__(self, elements, repr_=None, transformations=()):
SearchStrategy.__init__(self)
self.elements = cu.check_sample(elements, "sampled_from")
assert self.elements
self.repr_ = repr_
self._transformations = transformations
def map(self, pack):
return type(self)(
self.elements,
repr_=self.repr_,
transformations=self._transformations + (("map", pack),),
)
def filter(self, condition):
return type(self)(
self.elements,
repr_=self.repr_,
transformations=self._transformations + (("filter", condition),),
)
def __repr__(self):
return (
self.repr_
or "sampled_from(["
+ ", ".join(map(get_pretty_function_description, self.elements))
+ "])"
) + "".join(
f".{name}({get_pretty_function_description(f)})"
for name, f in self._transformations
)
def calc_has_reusable_values(self, recur):
return True
def calc_is_cacheable(self, recur):
return is_simple_data(self.elements)
def _transform(self, element, conditions=()):
# Used in UniqueSampledListStrategy
for name, f in self._transformations + tuple(("filter", c) for c in conditions):
if name == "map":
element = f(element)
else:
assert name == "filter"
if not f(element):
return filter_not_satisfied
return element
def do_draw(self, data):
result = self.do_filtered_draw(data, self)
if result is filter_not_satisfied:
data.note_event(f"Aborted test because unable to satisfy {self!r}")
data.mark_invalid()
return result
def get_element(self, i, conditions=()):
return self._transform(self.elements[i], conditions=conditions)
def do_filtered_draw(self, data, filter_strategy):
# Set of indices that have been tried so far, so that we never test
# the same element twice during a draw.
known_bad_indices = set()
# If we're being called via FilteredStrategy, the filter_strategy argument
# might have additional conditions we have to fulfill.
if isinstance(filter_strategy, FilteredStrategy):
conditions = filter_strategy.flat_conditions
else:
conditions = ()
# Start with ordinary rejection sampling. It's fast if it works, and
# if it doesn't work then it was only a small amount of overhead.
for _ in range(3):
i = cu.integer_range(data, 0, len(self.elements) - 1)
if i not in known_bad_indices:
element = self.get_element(i, conditions=conditions)
if element is not filter_not_satisfied:
return element
if not known_bad_indices:
FilteredStrategy.note_retried(self, data)
known_bad_indices.add(i)
# If we've tried all the possible elements, give up now.
max_good_indices = len(self.elements) - len(known_bad_indices)
if not max_good_indices:
return filter_not_satisfied
# Figure out the bit-length of the index that we will write back after
# choosing an allowed element.
write_length = len(self.elements).bit_length()
# Impose an arbitrary cutoff to prevent us from wasting too much time
# on very large element lists.
cutoff = 10000
max_good_indices = min(max_good_indices, cutoff)
# Before building the list of allowed indices, speculatively choose
# one of them. We don't yet know how many allowed indices there will be,
# so this choice might be out-of-bounds, but that's OK.
speculative_index = cu.integer_range(data, 0, max_good_indices - 1)
# Calculate the indices of allowed values, so that we can choose one
# of them at random. But if we encounter the speculatively-chosen one,
# just use that and return immediately. Note that we also track the
# allowed elements, in case of .map(some_stateful_function)
allowed = []
for i in range(min(len(self.elements), cutoff)):
if i not in known_bad_indices:
element = self.get_element(i, conditions=conditions)
if element is not filter_not_satisfied:
allowed.append((i, element))
if len(allowed) > speculative_index:
# Early-exit case: We reached the speculative index, so
# we just return the corresponding element.
data.draw_bits(write_length, forced=i)
return element
# The speculative index didn't work out, but at this point we've built
# and can choose from the complete list of allowed indices and elements.
if allowed:
i, element = cu.choice(data, allowed)
data.draw_bits(write_length, forced=i)
return element
# If there are no allowed indices, the filter couldn't be satisfied.
return filter_not_satisfied
class OneOfStrategy(SearchStrategy):
"""Implements a union of strategies. Given a number of strategies this
generates values which could have come from any of them.
The conditional distribution draws uniformly at random from some
non-empty subset of these strategies and then draws from the
conditional distribution of that strategy.
"""
def __init__(self, strategies):
SearchStrategy.__init__(self)
strategies = tuple(strategies)
self.original_strategies = list(strategies)
self.__element_strategies = None
self.__in_branches = False
def calc_is_empty(self, recur):
return all(recur(e) for e in self.original_strategies)
def calc_has_reusable_values(self, recur):
return all(recur(e) for e in self.original_strategies)
def calc_is_cacheable(self, recur):
return all(recur(e) for e in self.original_strategies)
@property
def element_strategies(self):
if self.__element_strategies is None:
# While strategies are hashable, they use object.__hash__ and are
# therefore distinguished only by identity.
#
# In principle we could "just" define a __hash__ method
# (and __eq__, but that's easy in terms of type() and hash())
# to make this more powerful, but this is harder than it sounds:
#
# 1. Strategies are often distinguished by non-hashable attributes,
# or by attributes that have the same hash value ("^.+" / b"^.+").
# 2. LazyStrategy: can't reify the wrapped strategy without breaking
# laziness, so there's a hash each for the lazy and the nonlazy.
#
# Having made several attempts, the minor benefits of making strategies
# hashable are simply not worth the engineering effort it would take.
# See also issues #2291 and #2327.
seen = {self}
strategies = []
for arg in self.original_strategies:
check_strategy(arg)
if not arg.is_empty:
for s in arg.branches:
if s not in seen and not s.is_empty:
seen.add(s)
strategies.append(s)
self.__element_strategies = strategies
return self.__element_strategies
def calc_label(self):
return combine_labels(
self.class_label, *[p.label for p in self.original_strategies]
)
def do_draw(self, data: ConjectureData) -> Ex:
strategy = data.draw(
SampledFromStrategy(self.element_strategies).filter(
lambda s: s.available(data)
)
)
return data.draw(strategy)
def __repr__(self):
return "one_of(%s)" % ", ".join(map(repr, self.original_strategies))
def do_validate(self):
for e in self.element_strategies:
e.validate()
@property
def branches(self):
if not self.__in_branches:
try:
self.__in_branches = True
return self.element_strategies
finally:
self.__in_branches = False
else:
return [self]
def filter(self, condition):
return FilteredStrategy(
OneOfStrategy([s.filter(condition) for s in self.original_strategies]),
conditions=(),
)
@overload
def one_of(args: Sequence[SearchStrategy[Any]]) -> SearchStrategy[Any]:
raise NotImplementedError
@overload # noqa: F811
def one_of(a1: SearchStrategy[Ex]) -> SearchStrategy[Ex]:
raise NotImplementedError
@overload # noqa: F811
def one_of(
a1: SearchStrategy[Ex], a2: SearchStrategy[T]
) -> SearchStrategy[Union[Ex, T]]:
raise NotImplementedError
@overload # noqa: F811
def one_of(
a1: SearchStrategy[Ex], a2: SearchStrategy[T], a3: SearchStrategy[T3]
) -> SearchStrategy[Union[Ex, T, T3]]:
raise NotImplementedError
@overload # noqa: F811
def one_of(
a1: SearchStrategy[Ex],
a2: SearchStrategy[T],
a3: SearchStrategy[T3],
a4: SearchStrategy[T4],
) -> SearchStrategy[Union[Ex, T, T3, T4]]:
raise NotImplementedError
@overload # noqa: F811
def one_of(
a1: SearchStrategy[Ex],
a2: SearchStrategy[T],
a3: SearchStrategy[T3],
a4: SearchStrategy[T4],
a5: SearchStrategy[T5],
) -> SearchStrategy[Union[Ex, T, T3, T4, T5]]:
raise NotImplementedError
@overload # noqa: F811
def one_of(*args: SearchStrategy[Any]) -> SearchStrategy[Any]:
raise NotImplementedError
[docs]def one_of(*args): # noqa: F811
# Mypy workaround alert: Any is too loose above; the return parameter
# should be the union of the input parameters. Unfortunately, Mypy <=0.600
# raises errors due to incompatible inputs instead. See #1270 for links.
# v0.610 doesn't error; it gets inference wrong for 2+ arguments instead.
"""Return a strategy which generates values from any of the argument
strategies.
This may be called with one iterable argument instead of multiple
strategy arguments, in which case ``one_of(x)`` and ``one_of(*x)`` are
equivalent.
Examples from this strategy will generally shrink to ones that come from
strategies earlier in the list, then shrink according to behaviour of the
strategy that produced them. In order to get good shrinking behaviour,
try to put simpler strategies first. e.g. ``one_of(none(), text())`` is
better than ``one_of(text(), none())``.
This is especially important when using recursive strategies. e.g.
``x = st.deferred(lambda: st.none() | st.tuples(x, x))`` will shrink well,
but ``x = st.deferred(lambda: st.tuples(x, x) | st.none())`` will shrink
very badly indeed.
"""
if len(args) == 1 and not isinstance(args[0], SearchStrategy):
try:
args = tuple(args[0])
except TypeError:
pass
if len(args) == 1 and isinstance(args[0], SearchStrategy):
# This special-case means that we can one_of over lists of any size
# without incurring any performance overhead when there is only one
# strategy, and keeps our reprs simple.
return args[0]
if args and not any(isinstance(a, SearchStrategy) for a in args):
# And this special case is to give a more-specific error message if it
# seems that the user has confused `one_of()` for `sampled_from()`;
# the remaining validation is left to OneOfStrategy. See PR #2627.
raise InvalidArgument(
f"Did you mean st.sampled_from({list(args)!r})? st.one_of() is used "
"to combine strategies, but all of the arguments were of other types."
)
return OneOfStrategy(args)
class MappedSearchStrategy(SearchStrategy):
"""A strategy which is defined purely by conversion to and from another
strategy.
Its parameter and distribution come from that other strategy.
"""
def __init__(self, strategy, pack=None):
SearchStrategy.__init__(self)
self.mapped_strategy = strategy
if pack is not None:
self.pack = pack
def calc_is_empty(self, recur):
return recur(self.mapped_strategy)
def calc_is_cacheable(self, recur):
return recur(self.mapped_strategy)
def __repr__(self):
if not hasattr(self, "_cached_repr"):
self._cached_repr = "{!r}.map({})".format(
self.mapped_strategy,
get_pretty_function_description(self.pack),
)
return self._cached_repr
def do_validate(self):
self.mapped_strategy.validate()
def pack(self, x):
"""Take a value produced by the underlying mapped_strategy and turn it
into a value suitable for outputting from this strategy."""
raise NotImplementedError(f"{self.__class__.__name__}.pack()")
def do_draw(self, data: ConjectureData) -> Ex:
for _ in range(3):
i = data.index
try:
data.start_example(MAPPED_SEARCH_STRATEGY_DO_DRAW_LABEL)
result = self.pack(data.draw(self.mapped_strategy))
data.stop_example()
return result
except UnsatisfiedAssumption:
data.stop_example(discard=True)
if data.index == i:
raise
raise UnsatisfiedAssumption()
@property
def branches(self) -> List[SearchStrategy[Ex]]:
return [
MappedSearchStrategy(pack=self.pack, strategy=strategy)
for strategy in self.mapped_strategy.branches
]
filter_not_satisfied = UniqueIdentifier("filter not satisfied")
class FilteredStrategy(SearchStrategy):
def __init__(self, strategy, conditions):
super().__init__()
if isinstance(strategy, FilteredStrategy):
# Flatten chained filters into a single filter with multiple conditions.
self.flat_conditions = strategy.flat_conditions + conditions
self.filtered_strategy = strategy.filtered_strategy
else:
self.flat_conditions = conditions
self.filtered_strategy = strategy
assert isinstance(self.flat_conditions, tuple)
assert not isinstance(self.filtered_strategy, FilteredStrategy)
self.__condition = None
def calc_is_empty(self, recur):
return recur(self.filtered_strategy)
def calc_is_cacheable(self, recur):
return recur(self.filtered_strategy)
def __repr__(self):
if not hasattr(self, "_cached_repr"):
self._cached_repr = "{!r}{}".format(
self.filtered_strategy,
"".join(
".filter(%s)" % get_pretty_function_description(cond)
for cond in self.flat_conditions
),
)
return self._cached_repr
def do_validate(self):
# Start by validating our inner filtered_strategy. If this was a LazyStrategy,
# validation also reifies it so that subsequent calls to e.g. `.filter()` will
# be passed through.
self.filtered_strategy.validate()
# So now we have a reified inner strategy, we'll replay all our saved
# predicates in case some or all of them can be rewritten. Note that this
# replaces the `fresh` strategy too!
fresh = self.filtered_strategy
for cond in self.flat_conditions:
fresh = fresh.filter(cond)
if isinstance(fresh, FilteredStrategy):
# In this case we have at least some non-rewritten filter predicates,
# so we just re-initialize the strategy.
FilteredStrategy.__init__(
self, fresh.filtered_strategy, fresh.flat_conditions
)
else:
# But if *all* the predicates were rewritten... well, do_validate() is
# an in-place method so we still just re-initialize the strategy!
FilteredStrategy.__init__(self, fresh, ())
def filter(self, condition):
# If we can, it's more efficient to rewrite our strategy to satisfy the
# condition. We therefore exploit the fact that the order of predicates
# doesn't matter (`f(x) and g(x) == g(x) and f(x)`) by attempting to apply
# condition directly to our filtered strategy as the inner-most filter.
out = self.filtered_strategy.filter(condition)
# If it couldn't be rewritten, we'll get a new FilteredStrategy - and then
# combine the conditions of each in our expected newest=last order.
if isinstance(out, FilteredStrategy):
return FilteredStrategy(
out.filtered_strategy, self.flat_conditions + out.flat_conditions
)
# But if it *could* be rewritten, we can return the more efficient form!
return FilteredStrategy(out, self.flat_conditions)
@property
def condition(self):
if self.__condition is None:
if len(self.flat_conditions) == 1:
# Avoid an extra indirection in the common case of only one condition.
self.__condition = self.flat_conditions[0]
elif len(self.flat_conditions) == 0:
# Possible, if unlikely, due to filter predicate rewriting
self.__condition = lambda _: True
else:
self.__condition = lambda x: all(
cond(x) for cond in self.flat_conditions
)
return self.__condition
def do_draw(self, data: ConjectureData) -> Ex:
result = self.filtered_strategy.do_filtered_draw(
data=data, filter_strategy=self
)
if result is not filter_not_satisfied:
return result
data.note_event(f"Aborted test because unable to satisfy {self!r}")
data.mark_invalid()
raise NotImplementedError("Unreachable, for Mypy")
def note_retried(self, data):
data.note_event(lazyformat("Retried draw from %r to satisfy filter", self))
def default_do_filtered_draw(self, data):
for i in range(3):
start_index = data.index
data.start_example(FILTERED_SEARCH_STRATEGY_DO_DRAW_LABEL)
value = data.draw(self.filtered_strategy)
if self.condition(value):
data.stop_example()
return value
else:
data.stop_example(discard=True)
if i == 0:
self.note_retried(data)
# This is to guard against the case where we consume no data.
# As long as we consume data, we'll eventually pass or raise.
# But if we don't this could be an infinite loop.
assume(data.index > start_index)
return filter_not_satisfied
@property
def branches(self) -> List[SearchStrategy[Ex]]:
return [
FilteredStrategy(strategy=strategy, conditions=self.flat_conditions)
for strategy in self.filtered_strategy.branches
]
@check_function
def check_strategy(arg, name=""):
if not isinstance(arg, SearchStrategy):
hint = ""
if isinstance(arg, (list, tuple)):
hint = ", such as st.sampled_from({}),".format(name or "...")
if name:
name += "="
raise InvalidArgument(
"Expected a SearchStrategy%s but got %s%r (type=%s)"
% (hint, name, arg, type(arg).__name__)
)
```