Source code for hypothesis.strategies

# 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) 2013-2018 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 enum
import math
import random
import string
import datetime as dt
import operator
from uuid import UUID
from decimal import Context, Decimal, localcontext
from inspect import isclass, isfunction
from fractions import Fraction
from functools import reduce

from hypothesis.errors import InvalidArgument, ResolutionFailed
from hypothesis.control import note, assume, cleanup, current_build_context
from hypothesis._settings import note_deprecation
from hypothesis.internal.cache import LRUReusedCache
from hypothesis.searchstrategy import SearchStrategy, check_strategy
from hypothesis.internal.compat import gcd, ceil, floor, hrange, \
    text_type, get_type_hints, getfullargspec, implements_iterator
from hypothesis.internal.floats import next_up, next_down, is_negative, \
    float_to_int, int_to_float, count_between_floats
from hypothesis.internal.charmap import as_general_categories
from hypothesis.internal.cathetus import cathetus
from hypothesis.internal.renaming import renamed_arguments
from hypothesis.utils.conventions import infer, not_set
from hypothesis.internal.reflection import proxies, required_args, \
    define_function_signature
from hypothesis.internal.validation import check_type, try_convert, \
    check_valid_size, check_valid_bound, check_valid_sizes, \
    check_valid_integer, check_valid_interval, check_valid_magnitude
from hypothesis.searchstrategy.lazy import LazyStrategy
from hypothesis.searchstrategy.misc import BoolStrategy, JustStrategy, \
    RandomStrategy, SampledFromStrategy
from hypothesis.searchstrategy.shared import SharedStrategy
from hypothesis.searchstrategy.numbers import FloatStrategy, \
    BoundedIntStrategy, IntegersFromStrategy, WideRangeIntStrategy, \
    FixedBoundedFloatStrategy
from hypothesis.searchstrategy.streams import StreamStrategy
from hypothesis.searchstrategy.strings import FixedSizeBytes, \
    StringStrategy, BinaryStringStrategy, OneCharStringStrategy
from hypothesis.searchstrategy.datetime import DateStrategy, \
    DatetimeStrategy, TimedeltaStrategy
from hypothesis.searchstrategy.deferred import DeferredStrategy
from hypothesis.searchstrategy.recursive import RecursiveStrategy
from hypothesis.internal.conjecture.utils import choice, check_sample, \
    integer_range, calc_label_from_cls
from hypothesis.searchstrategy.strategies import OneOfStrategy
from hypothesis.searchstrategy.collections import ListStrategy, \
    TupleStrategy, UniqueListStrategy, FixedKeysDictStrategy

typing = None   # type: Union[None, ModuleType]

try:
    import typing as typing_module
    typing = typing_module
except ImportError:
    pass

if False:
    from types import ModuleType  # noqa
    from random import Random  # noqa
    from typing import Any, Dict, Union, Sequence, Callable, Pattern  # noqa
    from typing import TypeVar, Tuple, List, Set, FrozenSet, overload  # noqa
    from typing import Type, Mapping, Text, AnyStr, Optional  # noqa

    from hypothesis.utils.conventions import InferType  # noqa
    from hypothesis.searchstrategy.strategies import T, Ex  # noqa
    K, V = TypeVar['K'], TypeVar['V']
    # See https://github.com/python/mypy/issues/3186 - numbers.Real is wrong!
    Real = Union[int, float, Fraction, Decimal]
    ExtendFunc = Callable[[SearchStrategy[Union[T, Ex]]], SearchStrategy[T]]
else:
    def overload(f):
        return f

__all__ = [
    'nothing',
    'just', 'one_of',
    'none',
    'choices', 'streaming',
    'booleans', 'integers', 'floats', 'complex_numbers', 'fractions',
    'decimals',
    'characters', 'text', 'from_regex', 'binary', 'uuids',
    'tuples', 'lists', 'sets', 'frozensets', 'iterables',
    'dictionaries', 'fixed_dictionaries',
    'sampled_from', 'permutations',
    'datetimes', 'dates', 'times', 'timedeltas',
    'builds',
    'randoms', 'random_module',
    'recursive', 'composite',
    'shared', 'runner', 'data',
    'deferred',
    'from_type', 'register_type_strategy', 'emails',
]

_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)


STRATEGY_CACHE = LRUReusedCache(1024)


def cacheable(fn):
    # type: (T) -> T
    @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 = (
            fn,
            tuple(map(convert_value, args)), frozenset(kwargs_cache_key))
        try:
            return STRATEGY_CACHE[cache_key]
        except TypeError:
            return fn(*args, **kwargs)
        except KeyError:
            result = fn(*args, **kwargs)
            if not isinstance(result, SearchStrategy) or result.is_cacheable:
                STRATEGY_CACHE[cache_key] = result
            return result
    cached_strategy.__clear_cache = STRATEGY_CACHE.clear
    return cached_strategy


def base_defines_strategy(force_reusable):
    # type: (bool) -> Callable[[T], T]
    """Returns a decorator for strategy functions.

    If force_reusable is True, the generated values are assumed to be
    reusable, i.e. immutable and safe to cache, across multiple test
    invocations.
    """
    def decorator(strategy_definition):
        """A decorator that registers the function as a strategy and makes it
        lazily evaluated."""
        _strategies.add(strategy_definition.__name__)

        @proxies(strategy_definition)
        def accept(*args, **kwargs):
            result = LazyStrategy(strategy_definition, args, kwargs)
            if force_reusable:
                result.force_has_reusable_values = True
                assert result.has_reusable_values
            return result
        return accept
    return decorator


defines_strategy = base_defines_strategy(False)
defines_strategy_with_reusable_values = base_defines_strategy(True)


class Nothing(SearchStrategy):
    def calc_is_empty(self, recur):
        return True

    def do_draw(self, data):
        # This method should never be called because draw() will mark the
        # data as invalid immediately because is_empty is True.
        assert False  # pragma: no cover

    def calc_has_reusable_values(self, recur):
        return True

    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()


[docs]@cacheable def nothing(): # type: () -> SearchStrategy """This strategy never successfully draws a value and will always reject on an attempt to draw. Examples from this strategy do not shrink (because there are none). """ return NOTHING
[docs]def just(value): # type: (T) -> SearchStrategy[T] """Return a strategy which only generates ``value``. Note: ``value`` is not copied. Be wary of using mutable values. If ``value`` is the result of a callable, you can use :func:`builds(callable) <hypothesis.strategies.builds>` instead of ``just(callable())`` to get a fresh value each time. Examples from this strategy do not shrink (because there is only one). """ return JustStrategy(value)
[docs]@defines_strategy_with_reusable_values def none(): # type: () -> SearchStrategy[None] """Return a strategy which only generates None. Examples from this strategy do not shrink (because there is only one). """ return just(None)
[docs]def one_of( *args # type: Union[SearchStrategy[Ex], Sequence[SearchStrategy[Ex]]] ): # type: (...) -> SearchStrategy[Ex] """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 return OneOfStrategy(args)
[docs]@cacheable @defines_strategy_with_reusable_values def integers(min_value=None, max_value=None): # type: (Real, Real) -> SearchStrategy[int] """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 Examples from this strategy will shrink towards zero, and negative values will also shrink towards positive (i.e. -n may be replaced by +n). """ 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') min_int_value = None if min_value is None else ceil(min_value) max_int_value = None if max_value is None else floor(max_value) if min_int_value is not None and max_int_value is not None and \ min_int_value > max_int_value: raise InvalidArgument('No integers between min_value=%r and ' 'max_value=%r' % (min_value, max_value)) 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)
[docs]@cacheable @defines_strategy def booleans(): # type: () -> SearchStrategy[bool] """Returns a strategy which generates instances of bool. Examples from this strategy will shrink towards False (i.e. shrinking will try to replace True with False where possible). """ return BoolStrategy()
[docs]@cacheable @defines_strategy_with_reusable_values 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. Examples from this strategy have a complicated and hard to explain shrinking behaviour, but it tries to improve "human readability". Finite numbers will be preferred to infinity and infinity will be preferred to NaN. """ 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') min_arg, max_arg = 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) check_valid_interval(min_value, max_value, 'min_value', 'max_value') if min_value == float(u'-inf'): min_value = None if max_value == float(u'inf'): max_value = None if min_value is not None and min_value < min_arg: min_value = next_up(min_value) assert min_value > min_arg if max_value is not None and max_value > max_arg: max_value = next_down(max_value) assert max_value < max_arg if None not in (min_value, max_value) and min_value > max_value: raise InvalidArgument( 'There are no floating-point values between min_value=%r and ' 'max_value=%r' % (min_arg, max_arg)) 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 )) 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 is_negative(min_value): if is_negative(max_value): return floats(min_value=-max_value, max_value=-min_value).map( operator.neg ) else: return floats(min_value=0.0, max_value=max_value) | floats( min_value=0.0, max_value=-min_value).map(operator.neg) elif count_between_floats(min_value, max_value) > 1000: return FixedBoundedFloatStrategy( lower_bound=min_value, upper_bound=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
[docs]@cacheable @defines_strategy def tuples(*args): # type: (*SearchStrategy) -> SearchStrategy[tuple] """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. Examples from this strategy shrink by shrinking their component parts. """ for arg in args: check_strategy(arg) return TupleStrategy(args)
@overload def sampled_from(elements): # type: (Sequence[T]) -> SearchStrategy[T] pass # pragma: no cover @overload def sampled_from(elements): # type: (Type[enum.Enum]) -> SearchStrategy[Any] # `SearchStrategy[Enum]` is unreliable due to mataclass issues. pass # pragma: no cover
[docs]@defines_strategy def sampled_from(elements): """Returns a strategy which generates any value present in ``elements``. Note that as with :func:`~hypotheses.strategies.just`, values will not be copied and thus you should be careful of using mutable data. ``sampled_from`` supports ordered collections, as well as :class:`~python:enum.Enum` objects. :class:`~python:enum.Flag` objects may also generate any combination of their members. Examples from this strategy shrink by replacing them with values earlier in the list. So e.g. sampled_from((10, 1)) will shrink by trying to replace 1 values with 10, and sampled_from((1, 10)) will shrink by trying to replace 10 values with 1. """ values = check_sample(elements, 'sampled_from') if not values: return nothing() if len(values) == 1: return just(values[0]) if hasattr(enum, 'Flag') and isclass(elements) and \ issubclass(elements, enum.Flag): # Combinations of enum.Flag members are also members. We generate # these dynamically, because static allocation takes O(2^n) memory. return sets(sampled_from(values), min_size=1).map( lambda s: reduce(operator.or_, s)) return SampledFromStrategy(values)
[docs]@cacheable @defines_strategy def lists( elements=None, # type: SearchStrategy[Ex] min_size=None, # type: int average_size=None, # type: int max_size=None, # type: int unique_by=None, # type: Callable[..., Any] unique=False, # type: bool ): # type: (...) -> SearchStrategy[List[Ex]] """Returns a list containing values drawn from elements with 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. The average_size argument is deprecated. Internal upgrades since Hypothesis 1.x mean we no longer needed this hint to generate useful data. 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]). Examples from this strategy shrink by trying to remove elements from the list, and by shrinking each individual element of the list. """ check_valid_sizes(min_size, average_size, max_size) if elements is None: note_deprecation( 'Passing a strategy for `elements` of the list will be required ' 'in a future version of Hypothesis. To create lists that are ' 'always empty, use `builds(list)` or `lists(nothing())`.' ) if min_size or average_size or max_size: # Checked internally for lists with an elements strategy, but # we're about to skip that and return builds(list) instead... raise InvalidArgument( 'Cannot create a non-empty collection (min_size=%r, ' 'average_size=%r, max_size=%r) without elements.' % (min_size, average_size, max_size) ) return builds(list) if max_size == 0: return builds(list) check_strategy(elements, 'elements') 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: def unique_by(x): return x if unique_by is not None: return UniqueListStrategy( elements=elements, max_size=max_size, min_size=min_size, key=unique_by ) return ListStrategy(elements, min_size=min_size, max_size=max_size)
[docs]@cacheable @defines_strategy def sets( elements=None, # type: SearchStrategy[Ex] min_size=None, # type: int average_size=None, # type: int max_size=None, # type: int ): # type: (...) -> SearchStrategy[Set[Ex]] """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. Examples from this strategy shrink by trying to remove elements from the set, and by shrinking each individual element of the set. """ if elements is None: note_deprecation( 'Passing a strategy for `elements` of the set will be required ' 'in a future version of Hypothesis. To create sets that are ' 'always empty, use `builds(set)` or `sets(nothing())`.' ) return lists( elements=elements, min_size=min_size, average_size=average_size, max_size=max_size, unique=True ).map(set)
[docs]@cacheable @defines_strategy def frozensets( elements=None, # type: SearchStrategy[Ex] min_size=None, # type: int average_size=None, # type: int max_size=None, # type: int ): # type: (...) -> SearchStrategy[FrozenSet[Ex]] """This is identical to the sets function but instead returns frozensets.""" if elements is None: note_deprecation( 'Passing a strategy for `elements` of the frozenset will be ' 'required in a future version of Hypothesis. To create ' 'frozensets that are always empty, use `builds(frozenset)` ' 'or `frozensets(nothing())`.' ) return lists( elements=elements, min_size=min_size, average_size=average_size, max_size=max_size, unique=True ).map(frozenset)
@implements_iterator class PrettyIter(object): def __init__(self, values): self._values = values self._iter = iter(self._values) def __iter__(self): return self._iter def __next__(self): return next(self._iter) def __repr__(self): return 'iter({!r})'.format(self._values)
[docs]@defines_strategy def iterables(elements=None, min_size=None, average_size=None, max_size=None, unique_by=None, unique=False): """This has the same behaviour as lists, but returns iterables instead. Some iterables cannot be indexed (e.g. sets) and some do not have a fixed length (e.g. generators). This strategy produces iterators, which cannot be indexed and do not have a fixed length. This ensures that you do not accidentally depend on sequence behaviour. """ if elements is None: note_deprecation( 'Passing a strategy for `elements` of the iterable will be ' 'required in a future version of Hypothesis. To create ' 'iterables that are always empty, use `iterables(nothing())`.' ) return lists( elements=elements, min_size=min_size, average_size=average_size, max_size=max_size, unique_by=unique_by, unique=unique ).map(PrettyIter)
[docs]@defines_strategy def fixed_dictionaries( mapping # type: Mapping[T, SearchStrategy[Ex]] ): # type: (...) -> SearchStrategy[Dict[T, Ex]] """Generates 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. Examples from this strategy shrink by shrinking each individual value in the generated dictionary. """ check_type(dict, mapping, 'mapping') for v in mapping.values(): check_strategy(v) return FixedKeysDictStrategy(mapping)
[docs]@cacheable @defines_strategy def dictionaries( keys, # type: SearchStrategy[Ex] values, # type: SearchStrategy[T] dict_class=dict, # type: Type[Mapping] min_size=None, # type: int average_size=None, # type: int max_size=None, # type: int ): # type: (...) -> SearchStrategy[Mapping[Ex, T]] """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. Examples from this strategy shrink by trying to remove keys from the generated dictionary, and by shrinking each generated key and value. """ 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, max_size=max_size, unique_by=lambda x: x[0] ).map(dict_class)
[docs]@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. Examples from this strategy shrink by trying to shrink each value drawn. .. deprecated:: 3.15.0 Use :func:`data() <hypothesis.strategies.data>` instead. """ note_deprecation( 'streaming() has been deprecated. Use the data() strategy instead and ' 'replace stream iteration with data.draw() calls.' ) check_strategy(elements) return StreamStrategy(elements)
[docs]@cacheable @defines_strategy_with_reusable_values def characters( whitelist_categories=None, # type: Sequence[Text] blacklist_categories=None, # type: Sequence[Text] blacklist_characters=None, # type: Sequence[Text] min_codepoint=None, # type: int max_codepoint=None, # type: int whitelist_characters=None, # type: Sequence[Text] ): # type: (...) -> SearchStrategy[Text] """Generates unicode text type (unicode on python 2, str on python 3) characters following specified filtering rules. - When no filtering rules are specifed, any character can be produced. - If ``min_codepoint`` or ``max_codepoint`` is specifed, then only characters having a codepoint in that range will be produced. - If ``whitelist_categories`` is specified, then only characters from those Unicode categories will be produced. This is a further restriction, characters must also satisfy ``min_codepoint`` and ``max_codepoint``. - If ``blacklist_categories`` is specified, then any character from those categories will not be produced. Any overlap between ``whitelist_categories`` and ``blacklist_categories`` will raise an exception, as each character can only belong to a single class. - If ``whitelist_characters`` is specified, then any additional characters in that list will also be produced. - If ``blacklist_characters`` is specified, then any characters in that list will be not be produced. Any overlap between \ ``whitelist_characters`` and ``blacklist_characters`` will raise an exception. The ``_codepoint`` arguments must be integers between zero and :obj:`python:sys.max_unicode`. The ``_characters`` arguments must be collections of length-one unicode strings, such as a unicode string. The ``_categories`` arguments must be used to specify either the one-letter Unicode major category or the two-letter Unicode `general category`_. For example, ``('Nd', 'Lu')`` signifies "Number, decimal digit" and "Letter, uppercase". A single letter ('major category') can be given to match all corresponding categories, for example ``'P'`` for characters in any punctuation category. .. _general category: https://wikipedia.org/wiki/Unicode_character_property Examples from this strategy shrink towards the codepoint for ``'0'``, or the first allowable codepoint after it if ``'0'`` is excluded. """ check_valid_size(min_codepoint, 'min_codepoint') check_valid_size(max_codepoint, 'max_codepoint') check_valid_interval(min_codepoint, max_codepoint, 'min_codepoint', 'max_codepoint') if all((whitelist_characters is not None, min_codepoint is None, max_codepoint is None, whitelist_categories is None, blacklist_categories is None, )): raise InvalidArgument( 'Passing only whitelist_characters=%r would have no effect. ' 'Perhaps you want sampled_from() ?' % (whitelist_characters,)) blacklist_characters = blacklist_characters or '' whitelist_characters = whitelist_characters or '' overlap = set(blacklist_characters).intersection(whitelist_characters) if overlap: raise InvalidArgument( 'Characters %r are present in both whitelist_characters=%r, and ' 'blacklist_characters=%r' % ( sorted(overlap), whitelist_characters, blacklist_characters)) blacklist_categories = as_general_categories( blacklist_categories, 'blacklist_categories') if whitelist_categories is not None and not whitelist_categories and \ not whitelist_characters: raise InvalidArgument( 'When whitelist_categories is an empty collection and there are ' 'no characters specified in whitelist_characters, nothing can ' 'be generated by the characters() strategy.') whitelist_categories = as_general_categories( whitelist_categories, 'whitelist_categories') both_cats = set( blacklist_categories or ()).intersection(whitelist_categories or ()) if both_cats: raise InvalidArgument( 'Categories %r are present in both whitelist_categories=%r, and ' 'blacklist_categories=%r' % ( sorted(both_cats), whitelist_categories, blacklist_categories)) return OneCharStringStrategy(whitelist_categories=whitelist_categories, blacklist_categories=blacklist_categories, blacklist_characters=blacklist_characters, min_codepoint=min_codepoint, max_codepoint=max_codepoint, whitelist_characters=whitelist_characters)
[docs]@cacheable @defines_strategy_with_reusable_values def text( alphabet=None, # type: Union[Text, SearchStrategy[Text]] min_size=None, # type: int average_size=None, # type: int max_size=None # type: int ): # type: (...) -> SearchStrategy[Text] """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 (excluding surrogate characters). If it is an empty collection this will only generate empty strings. min_size and max_size have the usual interpretations. The average_size argument is deprecated. Internal upgrades since Hypothesis 1.x mean we no longer needed this hint to generate useful data. Examples from this strategy shrink towards shorter strings, and with the characters in the text shrinking as per the alphabet strategy. """ check_valid_sizes(min_size, average_size, max_size) 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, min_size=min_size, max_size=max_size ))
[docs]@cacheable @defines_strategy def from_regex(regex): # type: (Union[AnyStr, Pattern[AnyStr]]) -> SearchStrategy[AnyStr] """Generates strings that contain a match for the given regex (i.e. ones for which :func:`re.search` will return a non-None result). ``regex`` may be a pattern or :func:`compiled regex <python:re.compile>`. Both byte-strings and unicode strings are supported, and will generate examples of the same type. You can use regex flags such as :const:`re.IGNORECASE`, :const:`re.DOTALL` or :const:`re.UNICODE` to control generation. Flags can be passed either in compiled regex or inside the pattern with a ``(?iLmsux)`` group. Some regular expressions are only partly supported - the underlying strategy checks local matching and relies on filtering to resolve context-dependent expressions. Using too many of these constructs may cause health-check errors as too many examples are filtered out. This mainly includes (positive or negative) lookahead and lookbehind groups. If you want the generated string to match the whole regex you should use boundary markers. So e.g. ``r"\\A.\\Z"`` will return a single character string, while ``"."`` will return any string, and ``r"\\A.$"`` will return a single character optionally followed by a ``"\\n"``. Examples from this strategy shrink towards shorter strings and lower character values. """ # TODO: We would like to move this to the top level, but pending some major # refactoring it's hard to do without creating circular imports. from hypothesis.searchstrategy.regex import regex_strategy return regex_strategy(regex)
[docs]@cacheable @defines_strategy_with_reusable_values def binary( min_size=None, average_size=None, max_size=None ): # type: (int, int, int) -> SearchStrategy[bytes] """Generates the appropriate binary type (str in python 2, bytes in python 3). min_size and max_size have the usual interpretations. The average_size argument is deprecated. Internal upgrades since Hypothesis 1.x mean we no longer needed this hint to generate useful data. Examples from this strategy shrink towards smaller strings and lower byte values. """ 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), min_size=min_size, max_size=max_size ) )
[docs]@cacheable @defines_strategy def randoms(): # type: () -> SearchStrategy[Random] """Generates instances of Random (actually a Hypothesis specific RandomWithSeed class which displays what it was initially seeded with) Examples from this strategy shrink to seeds closer to zero. """ return RandomStrategy(integers())
class RandomSeeder(object): def __init__(self, seed): self.seed = seed def __repr__(self): return 'random.seed(%r)' % (self.seed,) class RandomModule(SearchStrategy): def do_draw(self, data): data.can_reproduce_example_from_repr = False seed = data.draw(integers()) state = random.getstate() random.seed(seed) cleanup(lambda: random.setstate(state)) return RandomSeeder(seed)
[docs]@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. Examples from these strategy shrink to seeds closer to zero. """ return shared(RandomModule(), 'hypothesis.strategies.random_module()')
[docs]@cacheable @defines_strategy def builds( *callable_and_args, # type: Any **kwargs # type: Union[SearchStrategy[Any], InferType] ): # type: (...) -> SearchStrategy[Any] """Generates values by drawing from ``args`` and ``kwargs`` and passing them to the callable (provided as the first positional argument) 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)``. If the callable has type annotations, they will be used to infer a strategy for required arguments that were not passed to builds. You can also tell builds to infer a strategy for an optional argument by passing the special value :const:`hypothesis.infer` as a keyword argument to builds, instead of a strategy for that argument to the callable. Examples from this strategy shrink by shrinking the argument values to the callable. """ if callable_and_args: target, args = callable_and_args[0], callable_and_args[1:] if not callable(target): raise InvalidArgument( 'The first positional argument to builds() must be a callable ' 'target to construct.') elif 'target' in kwargs and callable(kwargs['target']): args = () note_deprecation( 'Specifying the target as a keyword argument to builds() is ' 'deprecated. Provide it as the first positional argument instead.') target = kwargs.pop('target') else: raise InvalidArgument( 'builds() must be passed a callable as the first positional ' 'argument, but no positional arguments were given.') if infer in args: # Avoid an implementation nightmare juggling tuples and worse things raise InvalidArgument('infer was passed as a positional argument to ' 'builds(), but is only allowed as a keyword arg') hints = get_type_hints(target.__init__ if isclass(target) else target) for kw in [k for k, v in kwargs.items() if v is infer]: if kw not in hints: raise InvalidArgument( 'passed %s=infer for %s, but %s has no type annotation' % (kw, target.__name__, kw)) kwargs[kw] = from_type(hints[kw]) required = required_args(target, args, kwargs) for ms in set(hints) & (required or set()): kwargs[ms] = from_type(hints[ms]) # Mypy doesn't realise that `infer` is gone from kwargs now kwarg_strat = fixed_dictionaries(kwargs) # type: ignore return tuples(tuples(*args), kwarg_strat).map( lambda value: target(*value[0], **value[1]) )
def _defer_from_type(func): # type: (T) -> T """Decorator to make from_type lazy to support recursive definitions.""" @proxies(func) def inner(*args, **kwargs): return deferred(lambda: func(*args, **kwargs)) return inner
[docs]@cacheable @_defer_from_type def from_type(thing): # type: (Type[Ex]) -> SearchStrategy[Ex] """Looks up the appropriate search strategy for the given type. ``from_type`` is used internally to fill in missing arguments to :func:`~hypothesis.strategies.builds` and can be used interactively to explore what strategies are available or to debug type resolution. You can use :func:`~hypothesis.strategies.register_type_strategy` to handle your custom types, or to globally redefine certain strategies - for example excluding NaN from floats, or use timezone-aware instead of naive time and datetime strategies. The resolution logic may be changed in a future version, but currently tries these four options: 1. If ``thing`` is in the default lookup mapping or user-registered lookup, return the corresponding strategy. The default lookup covers all types with Hypothesis strategies, including extras where possible. 2. If ``thing`` is from the :mod:`python:typing` module, return the corresponding strategy (special logic). 3. If ``thing`` has one or more subtypes in the merged lookup, return the union of the strategies for those types that are not subtypes of other elements in the lookup. 4. Finally, if ``thing`` has type annotations for all required arguments, it is resolved via :func:`~hypothesis.strategies.builds`. """ # TODO: We would like to move this to the top level, but pending some major # refactoring it's hard to do without creating circular imports. from hypothesis.searchstrategy import types if typing is not None: # pragma: no branch fr = typing._ForwardRef if not isinstance(thing, type): # At runtime, `typing.NewType` returns an identity function rather # than an actual type, but we can check that for a possible match # and then read the magic attribute to unwrap it. if all([ hasattr(thing, '__supertype__'), hasattr(typing, 'NewType'), isfunction(thing), getattr(thing, '__module__', 0) == 'typing' ]): return from_type(thing.__supertype__) # Under Python 3.6, Unions are not instances of `type` - but we # still want to resolve them! if getattr(thing, '__origin__', None) is typing.Union: args = sorted(thing.__args__, key=types.type_sorting_key) return one_of([from_type(t) for t in args]) # We can't resolve forward references, and under Python 3.5 (only) # a forward reference is an instance of type. Hence, explicit check: elif type(thing) == fr: # pragma: no cover raise ResolutionFailed( 'thing=%s cannot be resolved. Upgrading to python>=3.6 may ' 'fix this problem via improvements to the typing module.' % (thing,)) if not isinstance(thing, type): raise InvalidArgument('thing=%s must be a type' % (thing,)) # Now that we know `thing` is a type, the first step is to check for an # explicitly registered strategy. This is the best (and hopefully most # common) way to resolve a type to a strategy. Note that the value in the # lookup may be a strategy or a function from type -> strategy; and we # convert empty results into an explicit error. if thing in types._global_type_lookup: strategy = types._global_type_lookup[thing] if not isinstance(strategy, SearchStrategy): strategy = strategy(thing) if strategy.is_empty: raise ResolutionFailed( 'Error: %r resolved to an empty strategy' % (thing,)) return strategy # If there's no explicitly registered strategy, maybe a subtype of thing # is registered - if so, we can resolve it to the subclass strategy. # We'll start by checking if thing is from from the typing module, # because there are several special cases that don't play well with # subclass and instance checks. if typing is not None: # pragma: no branch if isinstance(thing, typing.TypingMeta): return types.from_typing_type(thing) # If it's not from the typing module, we get all registered types that are # a subclass of `thing` and are not themselves a subtype of any other such # type. For example, `Number -> integers() | floats()`, but bools() is # not included because bool is a subclass of int as well as Number. strategies = [ v if isinstance(v, SearchStrategy) else v(thing) for k, v in types._global_type_lookup.items() if issubclass(k, thing) and sum( types.try_issubclass(k, typ) for typ in types._global_type_lookup ) == 1 ] empty = ', '.join(repr(s) for s in strategies if s.is_empty) if empty: raise ResolutionFailed( 'Could not resolve %s to a strategy; consider using ' 'register_type_strategy' % empty) elif strategies: return one_of(strategies) # If we don't have a strategy registered for this type or any subtype, we # may be able to fall back on type annotations. # Types created via typing.NamedTuple use a custom attribute instead - # but we can still use builds(), if we work out the right kwargs. if issubclass(thing, tuple) and hasattr(thing, '_fields') \ and hasattr(thing, '_field_types'): kwargs = {k: from_type(thing._field_types[k]) for k in thing._fields} return builds(thing, **kwargs) if issubclass(thing, enum.Enum): assert len(thing), repr(thing) + ' has no members to sample' return sampled_from(thing) # If the constructor has an annotation for every required argument, # we can (and do) use builds() without supplying additional arguments. required = required_args(thing) if not required or required.issubset(get_type_hints(thing.__init__)): return builds(thing) # We have utterly failed, and might as well say so now. raise ResolutionFailed('Could not resolve %r to a strategy; consider ' 'using register_type_strategy' % (thing,))
[docs]@cacheable @defines_strategy_with_reusable_values def fractions( min_value=None, # type: Union[Real, AnyStr] max_value=None, # type: Union[Real, AnyStr] max_denominator=None, # type: int ): # type: (...) -> SearchStrategy[Fraction] """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. min_value and max_value may be anything accepted by the :class:`~fractions.Fraction` constructor. If max_denominator is not None then the denominator of any generated values is no greater than max_denominator. Note that max_denominator must be None or a positive integer. Examples from this strategy shrink towards smaller denominators, then closer to zero. """ min_value = try_convert(Fraction, min_value, 'min_value') max_value = try_convert(Fraction, max_value, 'max_value') if (min_value is not None and not isinstance(min_value, Fraction) or max_value is not None and not isinstance(max_value, Fraction)): assert False, 'Unreachable for Mypy' # pragma: no cover check_valid_interval(min_value, max_value, 'min_value', 'max_value') check_valid_integer(max_denominator) if max_denominator is not None: if max_denominator < 1: raise InvalidArgument( 'max_denominator=%r must be >= 1' % max_denominator) def fraction_bounds(value): # type: (Fraction) -> Tuple[Fraction, Fraction] """Find the best lower and upper approximation for value.""" # Adapted from CPython's Fraction.limit_denominator here: # https://github.com/python/cpython/blob/3.6/Lib/fractions.py#L219 assert max_denominator is not None if value is None or value.denominator <= max_denominator: return value, value p0, q0, p1, q1 = 0, 1, 1, 0 n, d = value.numerator, value.denominator while True: a = n // d q2 = q0 + a * q1 if q2 > max_denominator: break p0, q0, p1, q1 = p1, q1, p0 + a * p1, q2 n, d = d, n - a * d k = (max_denominator - q0) // q1 low, high = Fraction(p1, q1), Fraction(p0 + k * p1, q0 + k * q1) assert low < value < high return low, high # Take the high approximation for min_value and low for max_value bounds = (max_denominator, min_value, max_value) if min_value is not None: _, min_value = fraction_bounds(min_value) if max_value is not None: max_value, _ = fraction_bounds(max_value) if min_value is not None and max_value is not None and \ min_value > max_value: raise InvalidArgument( 'There are no fractions with a denominator <= %r between ' 'min_value=%r and max_value=%r' % bounds) if min_value is not None and min_value == max_value: return just(min_value) def dm_func(denom): """Take denom, construct numerator strategy, and build fraction.""" # Four cases of algebra to get integer bounds and scale factor. min_num, max_num = None, None if max_value is None and min_value is None: pass elif min_value is None: max_num = denom * max_value.numerator denom *= max_value.denominator elif max_value is None: min_num = denom * min_value.numerator denom *= min_value.denominator else: low = min_value.numerator * max_value.denominator high = max_value.numerator * min_value.denominator scale = min_value.denominator * max_value.denominator # After calculating our integer bounds and scale factor, we remove # the gcd to avoid drawing more bytes for the example than needed. # Note that `div` can be at most equal to `scale`. div = gcd(scale, gcd(low, high)) min_num = denom * low // div max_num = denom * high // div denom *= scale // div return builds( Fraction, integers(min_value=min_num, max_value=max_num), just(denom) ) if max_denominator is None: return integers(min_value=1).flatmap(dm_func) return integers(1, max_denominator).flatmap(dm_func).map( lambda f: f.limit_denominator(max_denominator))
def _as_finite_decimal( value, # type: Union[Real, AnyStr, None] name, # type: str allow_infinity, # type: Optional[bool] ): # type: (...) -> Optional[Decimal] """Convert decimal bounds to decimals, carefully.""" assert name in ('min_value', 'max_value') if value is None: return None if not isinstance(value, Decimal): with localcontext(Context()): # ensure that default traps are enabled value = try_convert(Decimal, value, name) assert isinstance(value, Decimal) if value.is_finite(): return value if value.is_infinite() and (value < 0 if 'min' in name else value > 0): if allow_infinity or allow_infinity is None: return None raise InvalidArgument('allow_infinity=%r, but %s=%r' % (allow_infinity, name, value)) # This could be infinity, quiet NaN, or signalling NaN raise InvalidArgument(u'Invalid %s=%r' % (name, value))
[docs]@cacheable @defines_strategy_with_reusable_values def decimals( min_value=None, # type: Union[Real, AnyStr] max_value=None, # type: Union[Real, AnyStr] allow_nan=None, # type: bool allow_infinity=None, # type: bool places=None, # type: int ): # type: (...) -> SearchStrategy[Decimal] """Generates instances of :class:`decimals.Decimal`, which may be: - A finite rational number, between ``min_value`` and ``max_value``. - Not a Number, if ``allow_nan`` is True. None means "allow NaN, unless ``min_value`` and ``max_value`` are not None". - Positive or negative infinity, if ``max_value`` and ``min_value`` respectively are None, and ``allow_infinity`` is not False. None means "allow infinity, unless excluded by the min and max values". Note that where floats have one ``NaN`` value, Decimals have four: signed, and either *quiet* or *signalling*. See `the decimal module docs <https://docs.python.org/3/library/decimal.html#special-values>`_ for more information on special values. If ``places`` is not None, all finite values drawn from the strategy will have that number of digits after the decimal place. Examples from this strategy do not have a well defined shrink order but try to maximize human readability when shrinking. """ # Convert min_value and max_value to Decimal values, and validate args check_valid_integer(places) if places is not None and places < 0: raise InvalidArgument('places=%r may not be negative' % places) min_value = _as_finite_decimal(min_value, 'min_value', allow_infinity) max_value = _as_finite_decimal(max_value, 'max_value', allow_infinity) check_valid_interval(min_value, max_value, 'min_value', 'max_value') if allow_infinity and (None not in (min_value, max_value)): raise InvalidArgument('Cannot allow infinity between finite bounds') # Set up a strategy for finite decimals. Note that both floating and # fixed-point decimals require careful handling to remain isolated from # any external precision context - in short, we always work out the # required precision for lossless operation and use context methods. if places is not None: # Fixed-point decimals are basically integers with a scale factor def ctx(val): """Return a context in which this value is lossless.""" precision = ceil(math.log10(abs(val) or 1)) + places + 1 return Context(prec=max([precision, 1])) def int_to_decimal(val): context = ctx(val) return context.quantize(context.multiply(val, factor), factor) factor = Decimal(10) ** -places min_num, max_num = None, None if min_value is not None: min_num = ceil(ctx(min_value).divide(min_value, factor)) if max_value is not None: max_num = floor(ctx(max_value).divide(max_value, factor)) if min_num is not None and max_num is not None and min_num > max_num: raise InvalidArgument( 'There are no decimals with %d places between min_value=%r ' 'and max_value=%r ' % (places, min_value, max_value)) strat = integers(min_num, max_num).map(int_to_decimal) else: # Otherwise, they're like fractions featuring a power of ten def fraction_to_decimal(val): precision = ceil(math.log10(abs(val.numerator) or 1) + math.log10(val.denominator)) + 1 return Context(prec=precision or 1).divide( Decimal(val.numerator), val.denominator) strat = fractions(min_value, max_value).map(fraction_to_decimal) # Compose with sampled_from for infinities and NaNs as appropriate special = [] # type: List[Decimal] if allow_nan or (allow_nan is None and (None in (min_value, max_value))): special.extend(map(Decimal, ('NaN', '-NaN', 'sNaN', '-sNaN'))) if allow_infinity or (allow_infinity is max_value is None): special.append(Decimal('Infinity')) if allow_infinity or (allow_infinity is min_value is None): special.append(Decimal('-Infinity')) return strat | sampled_from(special)
[docs]def recursive( base, # type: SearchStrategy[Ex] extend, # type: ExtendFunc max_leaves=100, # type: int ): # type: (...) -> SearchStrategy[Union[T, Ex]] """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 may be 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, []]``, and ``[[[[True]]]]`` are all valid values to be drawn from that strategy. Examples from this strategy shrink by trying to reduce the amount of recursion and by shrinking according to the shrinking behaviour of base and the result of extend. """ return RecursiveStrategy(base, extend, max_leaves)
class PermutationStrategy(SearchStrategy): def __init__(self, values): self.values = values def do_draw(self, data): # Reversed Fisher-Yates shuffle. Reverse order so that it shrinks # propertly: This way we prefer things that are lexicographically # closer to the identity. result = list(self.values) for i in hrange(len(result)): j = integer_range(data, i, len(result) - 1) result[i], result[j] = result[j], result[i] return result
[docs]@defines_strategy def permutations(values): # type: (Sequence[T]) -> SearchStrategy[List[T]] """Return a strategy which returns permutations of the ordered collection ``values``. Examples from this strategy shrink by trying to become closer to the original order of values. """ values = check_sample(values, 'permutations') if not values: return builds(list) return PermutationStrategy(values)
[docs]@defines_strategy_with_reusable_values @renamed_arguments( min_datetime='min_value', max_datetime='max_value', ) def datetimes( min_value=dt.datetime.min, # type: dt.datetime max_value=dt.datetime.max, # type: dt.datetime timezones=none(), # type: SearchStrategy[Optional[dt.tzinfo]] min_datetime=None, # type: dt.datetime max_datetime=None, # type: dt.datetime ): # type: (...) -> SearchStrategy[dt.datetime] """A strategy for generating datetimes, which may be timezone-aware. This strategy works by drawing a naive datetime between ``min_datetime`` and ``max_datetime``, which must both be naive (have no timezone). ``timezones`` must be a strategy that generates :class:`~python:datetime.tzinfo` objects (or None, which is valid for naive datetimes). A value drawn from this strategy will be added to a naive datetime, and the resulting tz-aware datetime returned. .. note:: tz-aware datetimes from this strategy may be ambiguous or non-existent due to daylight savings, leap seconds, timezone and calendar adjustments, etc. This is intentional, as malformed timestamps are a common source of bugs. :py:func:`hypothesis.extra.pytz.timezones` requires the :pypi:`pytz` package, but provides all timezones in the Olsen database. If you want to allow naive datetimes, combine strategies like ``none() | timezones()``. :py:func:`hypothesis.extra.dateutil.timezones` requires the :pypi:`python-dateutil` package, and similarly provides all timezones there. Alternatively, you can create a list of the timezones you wish to allow (e.g. from the standard library, ``datetutil``, or ``pytz``) and use :py:func:`sampled_from`. Ensure that simple values such as None or UTC are at the beginning of the list for proper minimisation. Examples from this strategy shrink towards midnight on January 1st 2000. """ # Why must bounds be naive? In principle, we could also write a strategy # that took aware bounds, but the API and validation is much harder. # If you want to generate datetimes between two particular momements in # time I suggest (a) just filtering out-of-bounds values; (b) if bounds # are very close, draw a value and subtract it's UTC offset, handling # overflows and nonexistent times; or (c) do something customised to # handle datetimes in e.g. a four-microsecond span which is not # representable in UTC. Handling (d), all of the above, leads to a much # more complex API for all users and a useful feature for very few. check_type(dt.datetime, min_value, 'min_value') check_type(dt.datetime, max_value, 'max_value') if min_value.tzinfo is not None: raise InvalidArgument('min_value=%r must not have tzinfo' % (min_value,)) if max_value.tzinfo is not None: raise InvalidArgument('max_value=%r must not have tzinfo' % (max_value,)) check_valid_interval(min_value, max_value, 'min_value', 'max_value') if not isinstance(timezones, SearchStrategy): raise InvalidArgument( 'timezones=%r must be a SearchStrategy that can provide tzinfo ' 'for datetimes (either None or dt.tzinfo objects)' % (timezones,)) return DatetimeStrategy(min_value, max_value, timezones)
[docs]@defines_strategy_with_reusable_values @renamed_arguments( min_date='min_value', max_date='max_value', ) def dates( min_value=dt.date.min, max_value=dt.date.max, min_date=None, max_date=None, ): # type: (dt.date, dt.date, dt.date, dt.date) -> SearchStrategy[dt.date] """A strategy for dates between ``min_date`` and ``max_date``. Examples from this strategy shrink towards January 1st 2000. """ check_type(dt.date, min_value, 'min_value') check_type(dt.date, max_value, 'max_value') check_valid_interval(min_value, max_value, 'min_value', 'max_value') if min_value == max_value: return just(min_value) return DateStrategy(min_value, max_value)
[docs]@defines_strategy_with_reusable_values @renamed_arguments( min_time='min_value', max_time='max_value', ) def times( min_value=dt.time.min, # type: dt.time max_value=dt.time.max, # type: dt.time timezones=none(), # type: SearchStrategy min_time=None, # type: dt.time max_time=None, # type: dt.time ): # type: (...) -> SearchStrategy[dt.time] """A strategy for times between ``min_time`` and ``max_time``. The ``timezones`` argument is handled as for :py:func:`datetimes`. Examples from this strategy shrink towards midnight, with the timezone component shrinking as for the strategy that provided it. """ check_type(dt.time, min_value, 'min_value') check_type(dt.time, max_value, 'max_value') if min_value.tzinfo is not None: raise InvalidArgument('min_value=%r must not have tzinfo' % min_value) if max_value.tzinfo is not None: raise InvalidArgument('max_value=%r must not have tzinfo' % max_value) check_valid_interval(min_value, max_value, 'min_value', 'max_value') day = dt.date(2000, 1, 1) return datetimes(min_value=dt.datetime.combine(day, min_value), max_value=dt.datetime.combine(day, max_value), timezones=timezones).map(lambda t: t.timetz())
[docs]@defines_strategy_with_reusable_values @renamed_arguments( min_delta='min_value', max_delta='max_value', ) def timedeltas( min_value=dt.timedelta.min, # type: dt.timedelta max_value=dt.timedelta.max, # type: dt.timedelta min_delta=None, # type: dt.timedelta max_delta=None, # type: dt.timedelta ): # type: (...) -> SearchStrategy[dt.timedelta] """A strategy for timedeltas between ``min_value`` and ``max_value``. Examples from this strategy shrink towards zero. """ check_type(dt.timedelta, min_value, 'min_value') check_type(dt.timedelta, max_value, 'max_value') check_valid_interval(min_value, max_value, 'min_value', 'max_value') if min_value == max_value: return just(min_value) return TimedeltaStrategy(min_value=min_value, max_value=max_value)
class CompositeStrategy(SearchStrategy): def __init__(self, definition, label, args, kwargs): self.definition = definition self.__label = label self.args = args self.kwargs = kwargs def do_draw(self, data): first_draw = [True] def draw(strategy): first_draw[0] = False return data.draw(strategy) return self.definition(draw, *self.args, **self.kwargs) def calc_label(self): return self.__label
[docs]@cacheable def composite(f): # type: (Callable[..., Ex]) -> Callable[..., SearchStrategy[Ex]] """Defines a strategy that is built out of potentially arbitrarily many other strategies. This is intended to be used as a decorator. See :ref:`the full documentation for more details <composite-strategies>` about how to use this function. Examples from this strategy shrink by shrinking the output of each draw call. """ argspec = getfullargspec(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.' ) annots = {k: v for k, v in argspec.annotations.items() if k in (argspec.args + argspec.kwonlyargs + ['return'])} new_argspec = argspec._replace(args=argspec.args[1:], annotations=annots) label = calc_label_from_cls(f) @defines_strategy @define_function_signature(f.__name__, f.__doc__, new_argspec) def accept(*args, **kwargs): return CompositeStrategy(f, label, args, kwargs) accept.__module__ = f.__module__ return accept
[docs]@defines_strategy_with_reusable_values @cacheable def complex_numbers(min_magnitude=0, max_magnitude=None, allow_infinity=None, allow_nan=None): # type: (Optional[Real], Real, bool, bool) -> SearchStrategy[complex] """Returns a strategy that generates complex numbers. This strategy draws complex numbers with constrained magnitudes. The ``min_magnitude`` and ``max_magnitude`` parameters should be non-negative :class:`~python:numbers.Real` numbers; values of ``None`` correspond to zero and infinite values respectively. If ``min_magnitude`` is positive or ``max_magnitude`` is finite, it is an error to enable ``allow_nan``. If ``max_magnitude`` is finite, it is an error to enable ``allow_infinity``. The magnitude contraints are respected up to a relative error of (around) floating-point epsilon, due to implementation via the system ``sqrt`` function. Examples from this strategy shrink by shrinking their real and imaginary parts, as :func:`~hypothesis.strategies.floats`. If you need to generate complex numbers with particular real and imaginary parts or relationships between parts, consider using `builds(complex, ...) <hypothesis.strategies.builds>` or `@composite <hypothesis.strategies.composite>` respectively. """ check_valid_magnitude(min_magnitude, 'min_magnitude') check_valid_magnitude(max_magnitude, 'max_magnitude') check_valid_interval(min_magnitude, max_magnitude, 'min_magnitude', 'max_magnitude') if max_magnitude == float('inf'): max_magnitude = None if min_magnitude == 0: min_magnitude = None if allow_infinity is None: allow_infinity = bool(max_magnitude is None) elif allow_infinity and max_magnitude is not None: raise InvalidArgument( 'Cannot have allow_infinity=%r with max_magnitude=%r' % (allow_infinity, max_magnitude) ) if allow_nan is None: allow_nan = bool(min_magnitude is None and max_magnitude is None) elif allow_nan and not (min_magnitude is None and max_magnitude is None): raise InvalidArgument( 'Cannot have allow_nan=%r, min_magnitude=%r max_magnitude=%r' % (allow_nan, min_magnitude, max_magnitude) ) allow_kw = dict(allow_nan=allow_nan, allow_infinity=allow_infinity) if min_magnitude is None and max_magnitude is None: # In this simple but common case, there are no constraints on the # magnitude and therefore no relationship between the real and # imaginary parts. return builds(complex, floats(**allow_kw), floats(**allow_kw)) @composite def constrained_complex(draw): # Draw the imaginary part, and determine the maximum real part given # this and the max_magnitude if max_magnitude is None: zi = draw(floats(**allow_kw)) rmax = float('inf') else: zi = draw(floats(-max_magnitude, max_magnitude, **allow_kw)) rmax = cathetus(max_magnitude, zi) # Draw the real part from the allowed range given the imaginary part if min_magnitude is None or math.fabs(zi) >= min_magnitude: zr = draw(floats(-rmax, rmax, **allow_kw)) else: zr = draw(floats(cathetus(min_magnitude, zi), rmax, **allow_kw)) # Order of conditions carefully tuned so that for a given pair of # magnitude arguments, we always either draw or do not draw the bool # (crucial for good shrinking behaviour) but only invert when needed. if min_magnitude is not None and draw(booleans()) and \ math.fabs(zi) <= min_magnitude: zr = -zr return complex(zr, zi) return constrained_complex()
[docs]def shared(base, key=None): # type: (SearchStrategy[Ex], Any) -> SearchStrategy[Ex] """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: >>> s = integers() # or any other strategy >>> 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") Examples from this strategy shrink as per their base strategy. """ return SharedStrategy(base, key)
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, check_sample(values, 'choices')) 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): data.can_reproduce_example_from_repr = False return Chooser(current_build_context(), data)
[docs]@defines_strategy def choices(): """Strategy that generates a function that behaves like random.choice. Will note choices made for reproducibility. .. deprecated:: 3.15.0 Use :func:`data() <hypothesis.strategies.data>` with :func:`sampled_from() <hypothesis.strategies.sampled_from>` instead. Examples from this strategy shrink by making each choice function return an earlier value in the sequence passed to it. """ note_deprecation( 'choices() has been deprecated. Use the data() strategy instead and ' 'replace its usage with data.draw(sampled_from(elements))) calls.' ) return shared( ChoiceStrategy(), key='hypothesis.strategies.chooser.choice_function' )
[docs]@cacheable @defines_strategy_with_reusable_values def uuids(version=None): # type: (int) -> SearchStrategy[UUID] """Returns a strategy that generates :class:`UUIDs <uuid.UUID>`. If the optional version argument is given, value is passed through to :class:`~python:uuid.UUID` and only UUIDs of that version will be generated. All returned values from this will be unique, so e.g. if you do ``lists(uuids())`` the resulting list will never contain duplicates. Examples from this strategy don't have any meaningful shrink order. """ if version not in (None, 1, 2, 3, 4, 5): raise InvalidArgument(( 'version=%r, but version must be in (None, 1, 2, 3, 4, 5) ' 'to pass to the uuid.UUID constructor.') % (version, ) ) return shared(randoms(), key='hypothesis.strategies.uuids.generator').map( lambda r: UUID(version=version, int=r.getrandbits(128)) )
class RunnerStrategy(SearchStrategy): def __init__(self, default): self.default = default def do_draw(self, data): runner = getattr(data, 'hypothesis_runner', not_set) if runner is not_set: if self.default is not_set: raise InvalidArgument( 'Cannot use runner() strategy with no ' 'associated runner or explicit default.' ) else: return self.default else: return runner
[docs]@defines_strategy_with_reusable_values 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. Examples from this strategy do not shrink (because there is only one). """ return RunnerStrategy(default)
class DataObject(object): def __init__(self, data): self.count = 0 self.data = data def __repr__(self): return 'data(...)' def draw(self, strategy, label=None): result = self.data.draw(strategy) self.count += 1 if label is not None: note('Draw %d (%s): %r' % (self.count, label, result)) else: note('Draw %d: %r' % (self.count, result)) return result class DataStrategy(SearchStrategy): supports_find = False def do_draw(self, data): data.can_reproduce_example_from_repr = False if not hasattr(data, 'hypothesis_shared_data_strategy'): data.hypothesis_shared_data_strategy = DataObject(data) return data.hypothesis_shared_data_strategy def __repr__(self): return 'data()' def map(self, f): self.__not_a_first_class_strategy('map') def filter(self, f): self.__not_a_first_class_strategy('filter') def flatmap(self, f): self.__not_a_first_class_strategy('flatmap') def example(self): self.__not_a_first_class_strategy('example') def __not_a_first_class_strategy(self, name): raise InvalidArgument(( 'Cannot call %s on a DataStrategy. You should probably be ' "using @composite for whatever it is you're trying to do." ) % (name,))
[docs]@cacheable def data(): # type: () -> SearchStrategy[Any] """This isn't really a normal strategy, but instead gives you an object which can be used to draw data interactively from other strategies. It can only be used within :func:`@given <hypothesis.given>`, not :func:`find() <hypothesis.find>`. This is because the lifetime of the object cannot outlast the test body. See :ref:`the rest of the documentation <interactive-draw>` for more complete information. Examples from this strategy do not shrink (because there is only one), but the result of calls to each draw() call shrink as they normally would. """ return DataStrategy()
[docs]def register_type_strategy(custom_type, strategy): # type: (type, Union[type, Callable[[type], SearchStrategy]]) -> None """Add an entry to the global type-to-strategy lookup. This lookup is used in :func:`~hypothesis.strategies.builds` and :func:`@given <hypothesis.given>`. :func:`~hypothesis.strategies.builds` will be used automatically for classes with type annotations on ``__init__`` , so you only need to register a strategy if one or more arguments need to be more tightly defined than their type-based default, or if you want to supply a strategy for an argument with a default value. ``strategy`` may be a search strategy, or a function that takes a type and returns a strategy (useful for generic types). """ # TODO: We would like to move this to the top level, but pending some major # refactoring it's hard to do without creating circular imports. from hypothesis.searchstrategy import types if not isinstance(custom_type, type): raise InvalidArgument('custom_type=%r must be a type') elif not (isinstance(strategy, SearchStrategy) or callable(strategy)): raise InvalidArgument( 'strategy=%r must be a SearchStrategy, or a function that takes ' 'a generic type and returns a specific SearchStrategy') elif isinstance(strategy, SearchStrategy) and strategy.is_empty: raise InvalidArgument('strategy=%r must not be empty') types._global_type_lookup[custom_type] = strategy from_type.__clear_cache() # type: ignore
[docs]@cacheable def deferred(definition): # type: (Callable[[], SearchStrategy[Ex]]) -> SearchStrategy[Ex] """A deferred strategy allows you to write a strategy that references other strategies that have not yet been defined. This allows for the easy definition of recursive and mutually recursive strategies. The definition argument should be a zero-argument function that returns a strategy. It will be evaluated the first time the strategy is used to produce an example. Example usage: >>> import hypothesis.strategies as st >>> x = st.deferred(lambda: st.booleans() | st.tuples(x, x)) >>> x.example() (((False, (True, True)), (False, True)), (True, True)) >>> x.example() True Mutual recursion also works fine: >>> a = st.deferred(lambda: st.booleans() | b) >>> b = st.deferred(lambda: st.tuples(a, a)) >>> a.example() True >>> b.example() (False, (False, ((False, True), False))) Examples from this strategy shrink as they normally would from the strategy returned by the definition. """ return DeferredStrategy(definition)
[docs]@defines_strategy_with_reusable_values def emails(): """A strategy for generating email addresses as unicode strings. The address format is specific in :rfc:`5322#section-3.4.1`. Values shrink towards shorter local-parts and host domains. This strategy is useful for generating "user data" for tests, as mishandling of email addresses is a common source of bugs. Future updates will generate more complicated addresses allowed by the RFC. """ from hypothesis.provisional import domains local_chars = string.ascii_letters + string.digits + "!#$%&'*+-/=^_`{|}~" local_part = text(local_chars, min_size=1, max_size=64) # TODO: include dot-atoms, quoted strings, escaped chars, etc in local part return builds(u'{}@{}'.format, local_part, domains()).filter( lambda addr: len(addr) <= 255)
assert _strategies.issubset(set(__all__)), _strategies - set(__all__)