/
strategies.py
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/
strategies.py
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# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis-python
#
# Most of this work is copyright (C) 2013-2016 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 http://mozilla.org/MPL/2.0/.
#
# END HEADER
from __future__ import division, print_function, absolute_import
import math
from decimal import Decimal
from numbers import Rational
from fractions import Fraction
from hypothesis.errors import InvalidArgument
from hypothesis.control import assume
from hypothesis.searchstrategy import SearchStrategy
from hypothesis.internal.compat import ArgSpec, text_type, getargspec, \
integer_types, float_to_decimal
from hypothesis.internal.floats import is_negative, float_to_int, \
int_to_float, count_between_floats
from hypothesis.utils.conventions import not_set
from hypothesis.internal.reflection import proxies
from hypothesis.searchstrategy.reprwrapper import ReprWrapperStrategy
__all__ = [
'nothing',
'just', 'one_of',
'none',
'choices', 'streaming',
'booleans', 'integers', 'floats', 'complex_numbers', 'fractions',
'decimals',
'characters', 'text', 'binary',
'tuples', 'lists', 'sets', 'frozensets',
'dictionaries', 'fixed_dictionaries',
'sampled_from', 'permutations',
'builds',
'randoms', 'random_module',
'recursive', 'composite',
'shared', 'runner',
]
_strategies = set()
class FloatKey(object):
def __init__(self, f):
self.value = float_to_int(f)
def __eq__(self, other):
return isinstance(other, FloatKey) and (
other.value == self.value
)
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return hash(self.value)
def convert_value(v):
if isinstance(v, float):
return FloatKey(v)
return (type(v), v)
def cacheable(fn):
cache = {}
@proxies(fn)
def cached_strategy(*args, **kwargs):
kwargs_cache_key = set()
try:
for k, v in kwargs.items():
kwargs_cache_key.add((k, convert_value(v)))
except TypeError:
return fn(*args, **kwargs)
cache_key = (
tuple(map(convert_value, args)), frozenset(kwargs_cache_key))
try:
return cache[cache_key]
except TypeError:
return fn(*args, **kwargs)
except KeyError:
result = fn(*args, **kwargs)
cache[cache_key] = result
return result
return cached_strategy
def defines_strategy(strategy_definition):
from hypothesis.searchstrategy.deferred import DeferredStrategy
_strategies.add(strategy_definition.__name__)
@proxies(strategy_definition)
def accept(*args, **kwargs):
return DeferredStrategy(strategy_definition, args, kwargs)
return accept
class Nothing(SearchStrategy):
is_empty = True
def do_draw(self, data):
data.mark_invalid()
def __repr__(self):
return 'nothing()'
def map(self, f):
return self
def filter(self, f):
return self
def flatmap(self, f):
return self
NOTHING = Nothing()
@cacheable
def nothing():
"""This strategy never successfully draws a value and will always reject on
an attempt to draw."""
return NOTHING
def just(value):
"""Return a strategy which only generates value.
Note: value is not copied. Be wary of using mutable values.
"""
from hypothesis.searchstrategy.misc import JustStrategy
def calc_repr():
return 'just(%s)' % (repr(value),)
return ReprWrapperStrategy(JustStrategy(value), calc_repr)
@defines_strategy
def none():
"""Return a strategy which only generates None."""
return just(None)
def one_of(*args):
"""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.
"""
if len(args) == 1 and not isinstance(args[0], SearchStrategy):
try:
args = tuple(args[0])
except TypeError:
pass
for arg in args:
check_strategy(arg)
args = [a for a in args if not a.is_empty]
if not args:
return nothing()
if len(args) == 1:
return args[0]
from hypothesis.searchstrategy.strategies import OneOfStrategy
return OneOfStrategy(args)
@cacheable
@defines_strategy
def integers(min_value=None, max_value=None):
"""Returns a strategy which generates integers (in Python 2 these may be
ints or longs).
If min_value is not None then all values will be >= min_value. If
max_value is not None then all values will be <= max_value
"""
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
from hypothesis.searchstrategy.numbers import IntegersFromStrategy, \
BoundedIntStrategy, WideRangeIntStrategy
min_int_value = None
if min_value is not None:
min_int_value = int(min_value)
if min_int_value != min_value and min_value > 0:
min_int_value += 1
max_int_value = None
if max_value is not None:
max_int_value = int(max_value)
if max_int_value != max_value and max_value < 0:
max_int_value -= 1
if min_int_value is None:
if max_int_value is None:
return (
WideRangeIntStrategy()
)
else:
return IntegersFromStrategy(0).map(lambda x: max_int_value - x)
else:
if max_int_value is None:
return IntegersFromStrategy(min_int_value)
else:
assert min_int_value <= max_int_value
if min_int_value == max_int_value:
return just(min_int_value)
elif min_int_value >= 0:
return BoundedIntStrategy(min_int_value, max_int_value)
elif max_int_value <= 0:
return BoundedIntStrategy(
-max_int_value, -min_int_value
).map(lambda t: -t)
else:
return integers(min_value=0, max_value=max_int_value) | \
integers(min_value=min_int_value, max_value=0)
@cacheable
@defines_strategy
def booleans():
"""Returns a strategy which generates instances of bool."""
from hypothesis.searchstrategy.misc import BoolStrategy
return BoolStrategy()
@cacheable
@defines_strategy
def floats(
min_value=None, max_value=None, allow_nan=None, allow_infinity=None
):
"""Returns a strategy which generates floats.
- If min_value is not None, all values will be >= min_value.
- If max_value is not None, all values will be <= max_value.
- If min_value or max_value is not None, it is an error to enable
allow_nan.
- If both min_value and max_value are not None, it is an error to enable
allow_infinity.
Where not explicitly ruled out by the bounds, all of infinity, -infinity
and NaN are possible values generated by this strategy.
"""
if allow_nan is None:
allow_nan = bool(min_value is None and max_value is None)
elif allow_nan:
if min_value is not None or max_value is not None:
raise InvalidArgument(
'Cannot have allow_nan=%r, with min_value or max_value' % (
allow_nan
))
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
if min_value is not None:
min_value = float(min_value)
if max_value is not None:
max_value = float(max_value)
if min_value == float(u'-inf'):
min_value = None
if max_value == float(u'inf'):
max_value = None
if allow_infinity is None:
allow_infinity = bool(min_value is None or max_value is None)
elif allow_infinity:
if min_value is not None and max_value is not None:
raise InvalidArgument(
'Cannot have allow_infinity=%r, with both min_value and '
'max_value' % (
allow_infinity
))
from hypothesis.searchstrategy.numbers import FloatStrategy, \
FixedBoundedFloatStrategy
if min_value is None and max_value is None:
return FloatStrategy(
allow_infinity=allow_infinity, allow_nan=allow_nan,
)
elif min_value is not None and max_value is not None:
if min_value == max_value:
return just(min_value)
elif math.isinf(max_value - min_value):
assert min_value < 0 and max_value > 0
return floats(min_value=0, max_value=max_value) | floats(
min_value=min_value, max_value=0
)
elif count_between_floats(min_value, max_value) > 1000:
return FixedBoundedFloatStrategy(
lower_bound=min_value, upper_bound=max_value
)
elif is_negative(max_value):
assert is_negative(min_value)
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert ub_int <= lb_int
return integers(min_value=ub_int, max_value=lb_int).map(
int_to_float
)
elif is_negative(min_value):
return floats(min_value=min_value, max_value=-0.0) | floats(
min_value=0, max_value=max_value
)
else:
ub_int = float_to_int(max_value)
lb_int = float_to_int(min_value)
assert lb_int <= ub_int
return integers(min_value=lb_int, max_value=ub_int).map(
int_to_float
)
elif min_value is not None:
if min_value < 0:
result = floats(
min_value=0.0
) | floats(min_value=min_value, max_value=0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and min_value + abs(x)
)
)
if min_value == 0 and not is_negative(min_value):
result = result.filter(lambda x: math.copysign(1.0, x) == 1)
return result
else:
assert max_value is not None
if max_value > 0:
result = floats(
min_value=0.0,
max_value=max_value,
) | floats(max_value=0.0)
else:
result = (
floats(allow_infinity=allow_infinity, allow_nan=False).map(
lambda x: assume(not math.isnan(x)) and max_value - abs(x)
)
)
if max_value == 0 and is_negative(max_value):
result = result.filter(is_negative)
return result
@cacheable
@defines_strategy
def complex_numbers():
"""Returns a strategy that generates complex numbers."""
from hypothesis.searchstrategy.numbers import ComplexStrategy
return ComplexStrategy(
tuples(floats(), floats())
)
@cacheable
@defines_strategy
def tuples(*args):
"""Return a strategy which generates a tuple of the same length as args by
generating the value at index i from args[i].
e.g. tuples(integers(), integers()) would generate a tuple of length
two with both values an integer.
"""
for arg in args:
check_strategy(arg)
for arg in args:
if arg.is_empty:
return nothing()
from hypothesis.searchstrategy.collections import TupleStrategy
return TupleStrategy(args, tuple)
@defines_strategy
def sampled_from(elements):
"""Returns a strategy which generates any value present in the iterable
elements.
Note that as with just, values will not be copied and thus you
should be careful of using mutable data.
"""
from hypothesis.searchstrategy.misc import SampledFromStrategy, \
JustStrategy
elements = tuple(iter(elements))
if not elements:
return nothing()
if len(elements) == 1:
return JustStrategy(elements[0])
else:
return SampledFromStrategy(elements)
@cacheable
@defines_strategy
def lists(
elements=None, min_size=None, average_size=None, max_size=None,
unique_by=None, unique=False,
):
"""Returns a list containing values drawn from elements length in the
interval [min_size, max_size] (no bounds in that direction if these are
None). If max_size is 0 then elements may be None and only the empty list
will be drawn.
average_size may be used as a size hint to roughly control the size
of list but it may not be the actual average of sizes you get, due
to a variety of factors.
If unique is True (or something that evaluates to True), we compare direct
object equality, as if unique_by was `lambda x: x`. This comparison only
works for hashable types.
if unique_by is not None it must be a function returning a hashable type
when given a value drawn from elements. The resulting list will satisfy the
condition that for i != j, unique_by(result[i]) != unique_by(result[j]).
"""
check_valid_sizes(min_size, average_size, max_size)
if elements is None or (max_size is not None and max_size <= 0):
if max_size is None or max_size > 0:
raise InvalidArgument(
u'Cannot create non-empty lists without an element type'
)
else:
return builds(list)
check_strategy(elements)
if elements.is_empty:
if (min_size or 0) > 0:
raise InvalidArgument((
'Cannot create non-empty lists with elements drawn from '
'strategy %r because it has no values.') % (elements,))
else:
return builds(list)
if unique:
if unique_by is not None:
raise InvalidArgument((
'cannot specify both unique and unique_by (you probably only '
'want to set unique_by)'
))
else:
unique_by = lambda x: x
if unique_by is not None:
from hypothesis.searchstrategy.collections import UniqueListStrategy
check_strategy(elements)
min_size = min_size or 0
max_size = max_size or float(u'inf')
if average_size is None:
if max_size < float(u'inf'):
if max_size <= 5:
average_size = min_size + 0.75 * (max_size - min_size)
else:
average_size = (max_size + min_size) / 2
else:
average_size = max(
_AVERAGE_LIST_LENGTH,
min_size * 2
)
check_valid_sizes(min_size, average_size, max_size)
result = UniqueListStrategy(
elements=elements,
average_size=average_size,
max_size=max_size,
min_size=min_size,
key=unique_by
)
return result
check_valid_sizes(min_size, average_size, max_size)
from hypothesis.searchstrategy.collections import ListStrategy
if min_size is None:
min_size = 0
if average_size is None:
if max_size is None:
average_size = _AVERAGE_LIST_LENGTH
else:
average_size = (min_size + max_size) * 0.5
check_strategy(elements)
return ListStrategy(
(elements,), average_length=average_size,
min_size=min_size, max_size=max_size,
)
@cacheable
@defines_strategy
def sets(elements=None, min_size=None, average_size=None, max_size=None):
"""This has the same behaviour as lists, but returns sets instead.
Note that Hypothesis cannot tell if values are drawn from elements
are hashable until running the test, so you can define a strategy
for sets of an unhashable type but it will fail at test time.
"""
return lists(
elements=elements, min_size=min_size, average_size=average_size,
max_size=max_size, unique=True
).map(set)
@cacheable
@defines_strategy
def frozensets(elements=None, min_size=None, average_size=None, max_size=None):
"""This is identical to the sets function but instead returns
frozensets."""
return lists(
elements=elements, min_size=min_size, average_size=average_size,
max_size=max_size, unique=True
).map(frozenset)
@defines_strategy
def fixed_dictionaries(mapping):
"""Generate a dictionary of the same type as mapping with a fixed set of
keys mapping to strategies. mapping must be a dict subclass.
Generated values have all keys present in mapping, with the
corresponding values drawn from mapping[key]. If mapping is an
instance of OrderedDict the keys will also be in the same order,
otherwise the order is arbitrary.
"""
from hypothesis.searchstrategy.collections import FixedKeysDictStrategy
check_type(dict, mapping)
for v in mapping.values():
check_strategy(v)
for v in mapping.values():
if v.is_empty:
return nothing()
return FixedKeysDictStrategy(mapping)
@cacheable
@defines_strategy
def dictionaries(
keys, values, dict_class=dict,
min_size=None, average_size=None, max_size=None
):
"""Generates dictionaries of type dict_class with keys drawn from the keys
argument and values drawn from the values argument.
The size parameters have the same interpretation as for lists.
"""
check_valid_sizes(min_size, average_size, max_size)
if max_size == 0:
return fixed_dictionaries(dict_class())
check_strategy(keys)
check_strategy(values)
return lists(
tuples(keys, values),
min_size=min_size, average_size=average_size, max_size=max_size,
unique_by=lambda x: x[0]
).map(dict_class)
@cacheable
@defines_strategy
def streaming(elements):
"""Generates an infinite stream of values where each value is drawn from
elements.
The result is iterable (the iterator will never terminate) and
indexable.
"""
check_strategy(elements)
from hypothesis.searchstrategy.streams import StreamStrategy
return StreamStrategy(elements)
@cacheable
@defines_strategy
def characters(whitelist_categories=None, blacklist_categories=None,
blacklist_characters=None, min_codepoint=None,
max_codepoint=None):
"""Generates unicode text type (unicode on python 2, str on python 3)
characters following specified filtering rules.
This strategy accepts lists of Unicode categories, characters of which
should (`whitelist_categories`) or should not (`blacklist_categories`)
be produced.
Also there could be applied limitation by minimal and maximal produced
code point of the characters.
If you know what exactly characters you don't want to be produced,
pass them with `blacklist_characters` argument.
"""
if (
min_codepoint is not None and max_codepoint is not None and
min_codepoint > max_codepoint
):
raise InvalidArgument(
'Cannot have min_codepoint=%d > max_codepoint=%d ' % (
min_codepoint, max_codepoint
)
)
from hypothesis.searchstrategy.strings import OneCharStringStrategy
return OneCharStringStrategy(whitelist_categories=whitelist_categories,
blacklist_categories=blacklist_categories,
blacklist_characters=blacklist_characters,
min_codepoint=min_codepoint,
max_codepoint=max_codepoint)
@cacheable
@defines_strategy
def text(
alphabet=None,
min_size=None, average_size=None, max_size=None
):
"""Generates values of a unicode text type (unicode on python 2, str on
python 3) with values drawn from alphabet, which should be an iterable of
length one strings or a strategy generating such. If it is None it will
default to generating the full unicode range. If it is an empty collection
this will only generate empty strings.
min_size, max_size and average_size have the usual interpretations.
"""
from hypothesis.searchstrategy.strings import StringStrategy
if alphabet is None:
char_strategy = characters(blacklist_categories=('Cs',))
elif not alphabet:
if (min_size or 0) > 0:
raise InvalidArgument(
'Invalid min_size %r > 0 for empty alphabet' % (
min_size,
)
)
return just(u'')
elif isinstance(alphabet, SearchStrategy):
char_strategy = alphabet
else:
char_strategy = sampled_from(list(map(text_type, alphabet)))
return StringStrategy(lists(
char_strategy, average_size=average_size, min_size=min_size,
max_size=max_size
))
@cacheable
@defines_strategy
def binary(
min_size=None, average_size=None, max_size=None
):
"""Generates the appropriate binary type (str in python 2, bytes in python
3).
min_size, average_size and max_size have the usual interpretations.
"""
from hypothesis.searchstrategy.strings import BinaryStringStrategy, \
FixedSizeBytes
check_valid_sizes(min_size, average_size, max_size)
if min_size == max_size is not None:
return FixedSizeBytes(min_size)
return BinaryStringStrategy(
lists(
integers(min_value=0, max_value=255),
average_size=average_size, min_size=min_size, max_size=max_size
)
)
@cacheable
@defines_strategy
def randoms():
"""Generates instances of Random (actually a Hypothesis specific
RandomWithSeed class which displays what it was initially seeded with)"""
from hypothesis.searchstrategy.misc import RandomStrategy
return RandomStrategy(integers())
class RandomSeeder(object):
def __init__(self, seed):
self.seed = seed
def __repr__(self):
return 'random.seed(%r)' % (self.seed,)
@cacheable
@defines_strategy
def random_module():
"""If your code depends on the global random module then you need to use
this.
It will explicitly seed the random module at the start of your test
so that tests are reproducible. The value it passes you is an opaque
object whose only useful feature is that its repr displays the
random seed. It is not itself a random number generator. If you want
a random number generator you should use the randoms() strategy
which will give you one.
"""
from hypothesis.control import cleanup
import random
def seed_random(seed):
state = random.getstate()
random.seed(seed)
cleanup(lambda: random.setstate(state))
return RandomSeeder(seed)
return shared(
integers().map(seed_random),
'hypothesis.strategies.random_module()',
)
@cacheable
@defines_strategy
def builds(target, *args, **kwargs):
"""Generates values by drawing from args and kwargs and passing them to
target in the appropriate argument position.
e.g. builds(target, integers(), flag=booleans()) would draw an
integer i and a boolean b and call target(i, flag=b).
"""
return tuples(tuples(*args), fixed_dictionaries(kwargs)).map(
lambda value: target(*value[0], **value[1])
)
@cacheable
@defines_strategy
def fractions(min_value=None, max_value=None, max_denominator=None):
"""Returns a strategy which generates Fractions.
If min_value is not None then all generated values are no less than
min_value.
If max_value is not None then all generated values are no greater than
max_value.
If max_denominator is not None then the absolute value of the denominator
of any generated values is no greater than max_denominator. Note that
max_denominator must be at least 1.
"""
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
check_valid_integer(max_denominator)
if max_denominator is not None and max_denominator < 1:
raise InvalidArgument(
u'Invalid denominator bound %s' % max_denominator
)
denominator_strategy = integers(min_value=1, max_value=max_denominator)
def dm_func(denom):
max_num = max_value * denom if max_value is not None else None
min_num = min_value * denom if min_value is not None else None
return builds(
Fraction,
integers(min_value=min_num, max_value=max_num),
just(denom)
)
return denominator_strategy.flatmap(dm_func)
@cacheable
@defines_strategy
def decimals(min_value=None, max_value=None):
"""Generates instances of decimals.Decimal.
If min_value is not None then all generated values are no less than
min_value.
If max_value is not None then all generated values are no greater than
max_value.
"""
check_valid_bound(min_value, 'min_value')
check_valid_bound(max_value, 'max_value')
check_valid_interval(min_value, max_value, 'min_value', 'max_value')
floats_strategy = floats(min_value=min_value, max_value=max_value)
fractions_strategy = fractions(min_value=min_value, max_value=max_value)
return (
floats_strategy.map(float_to_decimal) |
fractions_strategy.map(lambda f: Decimal(f.numerator) / f.denominator)
)
def recursive(base, extend, max_leaves=100):
"""base: A strategy to start from.
extend: A function which takes a strategy and returns a new strategy.
max_leaves: The maximum number of elements to be drawn from base on a given
run.
This returns a strategy S such that S = extend(base | S). That is, values
maybe drawn from base, or from any strategy reachable by mixing
applications of | and extend.
An example may clarify: recursive(booleans(), lists) would return a
strategy that may return arbitrarily nested and mixed lists of booleans.
So e.g. False, [True], [False, []], [[[[True]]]], are all valid values to
be drawn from that strategy.
"""
from hypothesis.searchstrategy.recursive import RecursiveStrategy
return RecursiveStrategy(base, extend, max_leaves)
@defines_strategy
def permutations(values):
"""Return a strategy which returns permutations of the collection
"values"."""
values = list(values)
if not values:
return just(()).map(lambda _: [])
def build_permutation(swaps):
initial = list(values)
for i, j in swaps:
initial[i], initial[j] = initial[j], initial[i]
return initial
n = len(values)
index = integers(0, n - 1)
return lists(tuples(index, index), max_size=n ** 2).map(build_permutation)
@cacheable
def composite(f):
"""Defines a strategy that is built out of potentially arbitrarily many
other strategies.
This is intended to be used as a decorator. See the full
documentation for more details about how to use this function.
"""
from hypothesis.internal.reflection import copy_argspec
argspec = getargspec(f)
if (
argspec.defaults is not None and
len(argspec.defaults) == len(argspec.args)
):
raise InvalidArgument(
'A default value for initial argument will never be used')
if len(argspec.args) == 0 and not argspec.varargs:
raise InvalidArgument(
'Functions wrapped with composite must take at least one '
'positional argument.'
)
new_argspec = ArgSpec(
args=argspec.args[1:], varargs=argspec.varargs,
keywords=argspec.keywords, defaults=argspec.defaults
)
@defines_strategy
@copy_argspec(f.__name__, new_argspec)
def accept(*args, **kwargs):
class CompositeStrategy(SearchStrategy):
def do_draw(self, data):
return f(data.draw, *args, **kwargs)
return CompositeStrategy()
return accept
def shared(base, key=None):
"""Returns a strategy that draws a single shared value per run, drawn from
base. Any two shared instances with the same key will share the same value,
otherwise the identity of this strategy will be used. That is:
>>> x = shared(s)
>>> y = shared(s)
In the above x and y may draw different (or potentially the same) values.
In the following they will always draw the same:
>>> x = shared(s, key="hi")
>>> y = shared(s, key="hi")
"""
from hypothesis.searchstrategy.shared import SharedStrategy
return SharedStrategy(base, key)
@cacheable
def choices():
"""Strategy that generates a function that behaves like random.choice.
Will note choices made for reproducibility.
"""
from hypothesis.control import note, current_build_context
from hypothesis.internal.conjecture.utils import choice
class Chooser(object):
def __init__(self, build_context, data):
self.build_context = build_context
self.data = data
self.choice_count = 0
def __call__(self, values):
if not values:
raise IndexError('Cannot choose from empty sequence')
result = choice(self.data, values)
with self.build_context.local():
self.choice_count += 1
note('Choice #%d: %r' % (self.choice_count, result))
return result
def __repr__(self):
return 'choice'
class ChoiceStrategy(SearchStrategy):
supports_find = False
def do_draw(self, data):
return Chooser(current_build_context(), data)
return ReprWrapperStrategy(
shared(
ChoiceStrategy(),
key='hypothesis.strategies.chooser.choice_function'
), 'choices()')
@cacheable
def uuids():
"""Returns a strategy that generates UUIDs.
All returned values from this will be unique, so e.g. if you do
lists(uuids()) the resulting list will never contain duplicates.
"""
from uuid import UUID
return ReprWrapperStrategy(
shared(randoms(), key='hypothesis.strategies.uuids.generator').map(
lambda r: UUID(int=r.getrandbits(128))
), 'uuids()')
@defines_strategy
def runner(default=not_set):
"""A strategy for getting "the current test runner", whatever that may be.
The exact meaning depends on the entry point, but it will usually be the
associated 'self' value for it.
If there is no current test runner and a default is provided, return
that default. If no default is provided, raises InvalidArgument.
"""
class RunnerStrategy(SearchStrategy):
def do_draw(self, data):
runner = getattr(data, 'hypothesis_runner', not_set)
if runner is not_set:
if default is not_set:
raise InvalidArgument(