Source code for hypothesis.strategies._internal.core

# This file is part of Hypothesis, which may be found at
# Most of this work is copyright (C) 2013-2021 David R. MacIver
# (, 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

import enum
import math
import operator
import random
import re
import string
import sys
import typing
from decimal import Context, Decimal, localcontext
from fractions import Fraction
from functools import reduce
from inspect import Parameter, Signature, getfullargspec, isabstract, isclass, signature
from types import FunctionType
from typing import (
from uuid import UUID

import attr

from hypothesis.control import cleanup, note
from hypothesis.errors import InvalidArgument, ResolutionFailed
from hypothesis.internal.cathetus import cathetus
from hypothesis.internal.charmap import as_general_categories
from hypothesis.internal.compat import (
from hypothesis.internal.conjecture.utils import (
from hypothesis.internal.entropy import get_seeder_and_restorer
from hypothesis.internal.reflection import (
from hypothesis.internal.validation import (
from hypothesis.strategies._internal import SearchStrategy, check_strategy
from hypothesis.strategies._internal.collections import (
from hypothesis.strategies._internal.deferred import DeferredStrategy
from hypothesis.strategies._internal.functions import FunctionStrategy
from hypothesis.strategies._internal.lazy import LazyStrategy
from hypothesis.strategies._internal.misc import just, none, nothing
from hypothesis.strategies._internal.numbers import (
from hypothesis.strategies._internal.recursive import RecursiveStrategy
from hypothesis.strategies._internal.shared import SharedStrategy
from hypothesis.strategies._internal.strategies import (
from hypothesis.strategies._internal.strings import (
from hypothesis.strategies._internal.utils import cacheable, defines_strategy
from hypothesis.utils.conventions import InferType, infer, not_set

    from typing import Protocol
except ImportError:  # < py3.8
    Protocol = object  # type: ignore[assignment]

UniqueBy = Union[Callable[[Ex], Hashable], Tuple[Callable[[Ex], Hashable], ...]]

[docs]@cacheable @defines_strategy() def booleans() -> SearchStrategy[bool]: """Returns a strategy which generates instances of :class:`python:bool`. Examples from this strategy will shrink towards ``False`` (i.e. shrinking will replace ``True`` with ``False`` where possible). """ return SampledFromStrategy([False, True], repr_="booleans()")
@overload def sampled_from(elements: Sequence[T]) -> SearchStrategy[T]: raise NotImplementedError @overload # noqa: F811 def sampled_from(elements: Type[enum.Enum]) -> SearchStrategy[Any]: # `SearchStrategy[Enum]` is unreliable due to metaclass issues. raise NotImplementedError
[docs]@defines_strategy(try_non_lazy=True) # noqa: F811 def sampled_from(elements): """Returns a strategy which generates any value present in ``elements``. Note that as with :func:`~hypothesis.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. It is an error to sample from an empty sequence, because returning :func:`nothing` makes it too easy to silently drop parts of compound strategies. If you need that behaviour, use ``sampled_from(seq) if seq else nothing()``. """ values = check_sample(elements, "sampled_from") if not values: if ( isinstance(elements, type) and issubclass(elements, enum.Enum) and vars(elements).get("__annotations__") ): # See raise InvalidArgument( f"Cannot sample from {elements.__module__}.{elements.__name__} " "because it contains no elements. It does however have annotations, " "so maybe you tried to write an enum as if it was a dataclass?" ) raise InvalidArgument("Cannot sample from a length-zero sequence.") if len(values) == 1: return just(values[0]) if isinstance(elements, type) and issubclass(elements, enum.Enum): repr_ = f"sampled_from({elements.__module__}.{elements.__name__})" else: repr_ = f"sampled_from({elements!r})" if 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. # LazyStrategy is used for the ease of force_repr. inner = sets(sampled_from(list(values)), min_size=1).map( lambda s: reduce(operator.or_, s) ) return LazyStrategy(lambda: inner, args=[], kwargs={}, force_repr=repr_) return SampledFromStrategy(values, repr_)
def identity(x): return x
[docs]@cacheable @defines_strategy() def lists( elements: SearchStrategy[Ex], *, min_size: int = 0, max_size: Optional[int] = None, unique_by: Optional[UniqueBy] = None, unique: bool = False, ) -> 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, only the empty list will be drawn. 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 callable or tuple of callables 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])``. If ``unique_by`` is a tuple of callables the uniqueness will be respective to each callable. For example, the following will produce two columns of integers with both columns being unique respectively. .. code-block:: pycon >>> twoints = st.tuples(st.integers(), st.integers()) >>> st.lists(twoints, unique_by=(lambda x: x[0], lambda x: x[1])) 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, max_size) 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: unique_by = identity if max_size == 0: return builds(list) if unique_by is not None: if not (callable(unique_by) or isinstance(unique_by, tuple)): raise InvalidArgument( f"unique_by={unique_by!r} is not a callable or tuple of callables" ) if callable(unique_by): unique_by = (unique_by,) if len(unique_by) == 0: raise InvalidArgument("unique_by is empty") for i, f in enumerate(unique_by): if not callable(f): raise InvalidArgument(f"unique_by[{i}]={f!r} is not a callable") # Note that lazy strategies automatically unwrap when passed to a defines_strategy # function. tuple_suffixes = None if ( # We're generating a list of tuples unique by the first element, perhaps # via st.dictionaries(), and this will be more efficient if we rearrange # our strategy somewhat to draw the first element then draw add the rest. isinstance(elements, TupleStrategy) and len(elements.element_strategies) >= 1 and len(unique_by) == 1 and ( # Introspection for either `itemgetter(0)`, or `lambda x: x[0]` isinstance(unique_by[0], operator.itemgetter) and repr(unique_by[0]) == "operator.itemgetter(0)" or isinstance(unique_by[0], FunctionType) and re.fullmatch( get_pretty_function_description(unique_by[0]), r"lambda ([a-z]+): \1\[0\]", ) ) ): unique_by = (identity,) tuple_suffixes = TupleStrategy(elements.element_strategies[1:]) elements = elements.element_strategies[0] # UniqueSampledListStrategy offers a substantial performance improvement for # unique arrays with few possible elements, e.g. of eight-bit integer types. if ( isinstance(elements, IntegersStrategy) and None not in (elements.start, elements.end) and (elements.end - elements.start) <= 255 ): elements = SampledFromStrategy( sorted(range(elements.start, elements.end + 1), key=abs) if elements.end < 0 or elements.start > 0 else list(range(0, elements.end + 1)) + list(range(-1, elements.start - 1, -1)) ) if isinstance(elements, SampledFromStrategy): element_count = len(elements.elements) if min_size > element_count: raise InvalidArgument( f"Cannot create a collection of min_size={min_size!r} unique " f"elements with values drawn from only {element_count} distinct " "elements" ) if max_size is not None: max_size = min(max_size, element_count) else: max_size = element_count return UniqueSampledListStrategy( elements=elements, max_size=max_size, min_size=min_size, keys=unique_by, tuple_suffixes=tuple_suffixes, ) return UniqueListStrategy( elements=elements, max_size=max_size, min_size=min_size, keys=unique_by, tuple_suffixes=tuple_suffixes, ) return ListStrategy(elements, min_size=min_size, max_size=max_size)
[docs]@cacheable @defines_strategy() def sets( elements: SearchStrategy[Ex], *, min_size: int = 0, max_size: Optional[int] = None, ) -> 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. """ return lists( elements=elements, min_size=min_size, max_size=max_size, unique=True ).map(set)
[docs]@cacheable @defines_strategy() def frozensets( elements: SearchStrategy[Ex], *, min_size: int = 0, max_size: Optional[int] = None, ) -> SearchStrategy[FrozenSet[Ex]]: """This is identical to the sets function but instead returns frozensets.""" return lists( elements=elements, min_size=min_size, max_size=max_size, unique=True ).map(frozenset)
class PrettyIter: 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 f"iter({self._values!r})"
[docs]@defines_strategy() def iterables( elements: SearchStrategy[Ex], *, min_size: int = 0, max_size: Optional[int] = None, unique_by: Optional[UniqueBy] = None, unique: bool = False, ) -> SearchStrategy[Iterable[Ex]]: """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. """ return lists( elements=elements, min_size=min_size, max_size=max_size, unique_by=unique_by, unique=unique, ).map(PrettyIter)
[docs]@defines_strategy() def fixed_dictionaries( mapping: Dict[T, SearchStrategy[Ex]], *, optional: Optional[Dict[T, SearchStrategy[Ex]]] = None, ) -> 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, in iteration order, with the corresponding values drawn from mapping[key]. If ``optional`` is passed, the generated value *may or may not* contain each key from ``optional`` and a value drawn from the corresponding strategy. Generated values may contain optional keys in an arbitrary order. Examples from this strategy shrink by shrinking each individual value in the generated dictionary, and omitting optional key-value pairs. """ check_type(dict, mapping, "mapping") for k, v in mapping.items(): check_strategy(v, f"mapping[{k!r}]") if optional is not None: check_type(dict, optional, "optional") for k, v in optional.items(): check_strategy(v, f"optional[{k!r}]") if type(mapping) != type(optional): raise InvalidArgument( "Got arguments of different types: mapping=%s, optional=%s" % (nicerepr(type(mapping)), nicerepr(type(optional))) ) if set(mapping) & set(optional): raise InvalidArgument( "The following keys were in both mapping and optional, " f"which is invalid: {set(mapping) & set(optional)!r}" ) return FixedAndOptionalKeysDictStrategy(mapping, optional) return FixedKeysDictStrategy(mapping)
[docs]@cacheable @defines_strategy() def dictionaries( keys: SearchStrategy[Ex], values: SearchStrategy[T], *, dict_class: type = dict, min_size: int = 0, max_size: Optional[int] = None, ) -> SearchStrategy[Dict[Ex, T]]: # Describing the exact dict_class to Mypy drops the key and value types, # so we report Dict[K, V] instead of Mapping[Any, Any] for now. Sorry! """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 :func:`~hypothesis.strategies.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, max_size) if max_size == 0: return fixed_dictionaries(dict_class()) check_strategy(keys, "keys") check_strategy(values, "values") return lists( tuples(keys, values), min_size=min_size, max_size=max_size, unique_by=operator.itemgetter(0), ).map(dict_class)
[docs]@cacheable @defines_strategy(force_reusable_values=True) def characters( *, whitelist_categories: Optional[Sequence[str]] = None, blacklist_categories: Optional[Sequence[str]] = None, blacklist_characters: Optional[Sequence[str]] = None, min_codepoint: Optional[int] = None, max_codepoint: Optional[int] = None, whitelist_characters: Optional[Sequence[str]] = None, ) -> SearchStrategy[str]: r"""Generates characters, length-one :class:`python:str`\ ings, following specified filtering rules. - When no filtering rules are specified, any character can be produced. - If ``min_codepoint`` or ``max_codepoint`` is specified, 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.maxunicode`. 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: 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 ( min_codepoint is None and max_codepoint is None and whitelist_categories is None and blacklist_categories is None and whitelist_characters is not None ): raise InvalidArgument( "Nothing is excluded by other arguments, so passing only " f"whitelist_characters={whitelist_characters!r} would have no effect. " "Also pass whitelist_categories=(), or use " f"sampled_from({whitelist_characters!r}) instead." ) blacklist_characters = blacklist_characters or "" whitelist_characters = whitelist_characters or "" overlap = set(blacklist_characters).intersection(whitelist_characters) if overlap: raise InvalidArgument( f"Characters {sorted(overlap)!r} are present in both " f"whitelist_characters={whitelist_characters!r}, and " f"blacklist_characters={blacklist_characters!r}" ) 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( f"Categories {sorted(both_cats)!r} are present in both " f"whitelist_categories={whitelist_categories!r}, and " f"blacklist_categories={blacklist_categories!r}" ) 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(force_reusable_values=True) def text( alphabet: Union[Sequence[str], SearchStrategy[str]] = characters( blacklist_categories=("Cs",) ), *, min_size: int = 0, max_size: Optional[int] = None, ) -> SearchStrategy[str]: """Generates strings with characters drawn from ``alphabet``, which should be a collection of length one strings or a strategy generating such strings. The default alphabet strategy can generate the full unicode range but excludes surrogate characters because they are invalid in the UTF-8 encoding. You can use :func:`~hypothesis.strategies.characters` without arguments to find surrogate-related bugs such as :bpo:`34454`. ``min_size`` and ``max_size`` have the usual interpretations. Note that Python measures string length by counting codepoints: U+00C5 ``Å`` is a single character, while U+0041 U+030A ``Å`` is two - the ``A``, and a combining ring above. Examples from this strategy shrink towards shorter strings, and with the characters in the text shrinking as per the alphabet strategy. This strategy does not :func:`~python:unicodedata.normalize` examples, so generated strings may be in any or none of the 'normal forms'. """ check_valid_sizes(min_size, max_size) if isinstance(alphabet, SearchStrategy): char_strategy = alphabet else: non_string = [c for c in alphabet if not isinstance(c, str)] if non_string: raise InvalidArgument( "The following elements in alphabet are not unicode " f"strings: {non_string!r}" ) not_one_char = [c for c in alphabet if len(c) != 1] if not_one_char: raise InvalidArgument( "The following elements in alphabet are not of length one, " f"which leads to violation of size constraints: {not_one_char!r}" ) char_strategy = ( characters(whitelist_categories=(), whitelist_characters=alphabet) if alphabet else nothing() ) if (max_size == 0 or char_strategy.is_empty) and not min_size: return just("") return lists(char_strategy, min_size=min_size, max_size=max_size).map("".join)
[docs]@cacheable @defines_strategy() def from_regex( regex: Union[AnyStr, Pattern[AnyStr]], *, fullmatch: bool = False ) -> SearchStrategy[AnyStr]: r"""Generates strings that contain a match for the given regex (i.e. ones for which :func:`` 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 :obj:`python:re.IGNORECASE` or :obj:`python:re.DOTALL` 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"``. Alternatively, passing ``fullmatch=True`` will ensure that the whole string is a match, as if you had used the ``\A`` and ``\Z`` markers. Examples from this strategy shrink towards shorter strings and lower character values, with exact behaviour that may depend on the pattern. """ check_type(bool, fullmatch, "fullmatch") # 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.strategies._internal.regex import regex_strategy return regex_strategy(regex, fullmatch)
[docs]@cacheable @defines_strategy(force_reusable_values=True) def binary( *, min_size: int = 0, max_size: Optional[int] = None, ) -> SearchStrategy[bytes]: """Generates :class:`python:bytes`. The generated :class:`python:bytes` will have a length of at least ``min_size`` and at most ``max_size``. If ``max_size`` is None there is no upper limit. Examples from this strategy shrink towards smaller strings and lower byte values. """ check_valid_sizes(min_size, max_size) if min_size == max_size: return FixedSizeBytes(min_size) return lists( integers(min_value=0, max_value=255), min_size=min_size, max_size=max_size ).map(bytes)
[docs]@cacheable @defines_strategy() def randoms( *, note_method_calls: bool = False, use_true_random: bool = False, ) -> SearchStrategy[random.Random]: """Generates instances of ``random.Random``. The generated Random instances are of a special HypothesisRandom subclass. - If ``note_method_calls`` is set to ``True``, Hypothesis will print the randomly drawn values in any falsifying test case. This can be helpful for debugging the behaviour of randomized algorithms. - If ``use_true_random`` is set to ``True`` then values will be drawn from their usual distribution, otherwise they will actually be Hypothesis generated values (and will be shrunk accordingly for any failing test case). Setting ``use_true_random=False`` will tend to expose bugs that would occur with very low probability when it is set to True, and this flag should only be set to True when your code relies on the distribution of values for correctness. """ check_type(bool, note_method_calls, "note_method_calls") check_type(bool, use_true_random, "use_true_random") from hypothesis.strategies._internal.random import RandomStrategy return RandomStrategy( use_true_random=use_true_random, note_method_calls=note_method_calls )
class RandomSeeder: def __init__(self, seed): self.seed = seed def __repr__(self): return f"RandomSeeder({self.seed!r})" class RandomModule(SearchStrategy): def do_draw(self, data): seed = data.draw(integers(0, 2 ** 32 - 1)) seed_all, restore_all = get_seeder_and_restorer(seed) seed_all() cleanup(restore_all) return RandomSeeder(seed)
[docs]@cacheable @defines_strategy() def random_module() -> SearchStrategy[RandomSeeder]: """The Hypothesis engine handles PRNG state for the stdlib and Numpy random modules internally, always seeding them to zero and restoring the previous state after the test. If having a fixed seed would unacceptably weaken your tests, and you cannot use a ``random.Random`` instance provided by :func:`~hypothesis.strategies.randoms`, this strategy calls :func:`python:random.seed` with an arbitrary integer and passes you an opaque object whose repr displays the seed value for debugging. If ``numpy.random`` is available, that state is also managed. Examples from these strategy shrink to seeds closer to zero. """ return shared(RandomModule(), key="hypothesis.strategies.random_module()")
class BuildsStrategy(SearchStrategy): def __init__(self, target, args, kwargs): = target self.args = args self.kwargs = kwargs def do_draw(self, data): try: return *(data.draw(a) for a in self.args), **{k: data.draw(v) for k, v in self.kwargs.items()}, ) except TypeError as err: if ( isinstance(, type) and issubclass(, enum.Enum) and not (self.args or self.kwargs) ): name = + "." + raise InvalidArgument( f"Calling {name} with no arguments raised an error - " f"try using sampled_from({name}) instead of builds({name})" ) from err if not (self.args or self.kwargs): from .types import is_a_new_type, is_generic_type if is_a_new_type( or is_generic_type( raise InvalidArgument( f"Calling {!r} with no arguments raised an " f"error - try using from_type({!r}) instead " f"of builds({!r})" ) from err raise def validate(self): tuples(*self.args).validate() fixed_dictionaries(self.kwargs).validate() def __repr__(self): bits = [get_pretty_function_description(] bits.extend(map(repr, self.args)) bits.extend(f"{k}={v!r}" for k, v in self.kwargs.items()) return f"builds({', '.join(bits)})" # The ideal signature builds(target, /, *args, **kwargs) is unfortunately a # SyntaxError before Python 3.8 so we emulate it with manual argument unpacking. # Note that for the benefit of documentation and introspection tools, we set the # __signature__ attribute to show the semantic rather than actual signature.
[docs]@cacheable @defines_strategy() def builds( *callable_and_args: Union[Callable[..., Ex], SearchStrategy[Any]], **kwargs: Union[SearchStrategy[Any], InferType], ) -> SearchStrategy[Ex]: """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. If the callable is a class defined with :pypi:`attrs`, missing required arguments will be inferred from the attribute on a best-effort basis, e.g. by checking :ref:`attrs standard validators <attrs:api_validators>`. Dataclasses are handled natively by the inference from type hints. Examples from this strategy shrink by shrinking the argument values to the callable. """ if not callable_and_args: raise InvalidArgument( "builds() must be passed a callable as the first positional " "argument, but no positional arguments were given." ) 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." ) 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" ) required = required_args(target, args, kwargs) to_infer = {k for k, v in kwargs.items() if v is infer} if required or to_infer: if isinstance(target, type) and attr.has(target): # Use our custom introspection for attrs classes from hypothesis.strategies._internal.attrs import from_attrs return from_attrs(target, args, kwargs, required | to_infer) # Otherwise, try using type hints hints = get_type_hints(target) if to_infer - set(hints): badargs = ", ".join(sorted(to_infer - set(hints))) raise InvalidArgument( f"passed infer for {badargs}, but there is no type annotation" ) for kw in set(hints) & (required | to_infer): kwargs[kw] = from_type(hints[kw]) return BuildsStrategy(target, args, kwargs)
if sys.version_info[:2] >= (3, 8): # pragma: no branch # See notes above definition - this signature is compatible and better # matches the semantics of the function. Great for documentation! sig = signature(builds) args, kwargs = sig.parameters.values() builds.__signature__ = sig.replace( parameters=[ Parameter( name="target", kind=Parameter.POSITIONAL_ONLY, annotation=Callable[..., Ex], ), args.replace(name="args", annotation=SearchStrategy[Any]), kwargs, ] )
[docs]@cacheable @defines_strategy(never_lazy=True) def from_type(thing: 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 five 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, and is not an abstract class, it is resolved via :func:`~hypothesis.strategies.builds`. 5. Because :mod:`abstract types <python:abc>` cannot be instantiated, we treat abstract types as the union of their concrete subclasses. Note that this lookup works via inheritance but not via :obj:`~python:abc.ABCMeta.register`, so you may still need to use :func:`~hypothesis.strategies.register_type_strategy`. There is a valuable recipe for leveraging ``from_type()`` to generate "everything except" values from a specified type. I.e. .. code-block:: python def everything_except(excluded_types): return ( from_type(type) .flatmap(from_type) .filter(lambda x: not isinstance(x, excluded_types)) ) For example, ``everything_except(int)`` returns a strategy that can generate anything that ``from_type()`` can ever generate, except for instances of :class:`python:int`, and excluding instances of types added via :func:`~hypothesis.strategies.register_type_strategy`. This is useful when writing tests which check that invalid input is rejected in a certain way. """ # This tricky little dance is because we want to show the repr of the actual # underlying strategy wherever possible, as a form of user education, but # would prefer to fall back to the default "from_type(...)" repr instead of # "deferred(...)" for recursive types or invalid arguments. try: return _from_type(thing) except Exception: return LazyStrategy( lambda thing: deferred(lambda: _from_type(thing)), (thing,), {}, force_repr=f"from_type({thing!r})", )
def _from_type(thing: Type[Ex]) -> SearchStrategy[Ex]: # 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.strategies._internal import types def as_strategy(strat_or_callable, thing, final=True): # User-provided strategies need some validation, and callables even more # of it. We do this in three places, hence the helper function if not isinstance(strat_or_callable, SearchStrategy): assert callable(strat_or_callable) # Validated in register_type_strategy try: # On Python 3.6, typing.Hashable is just an alias for abc.Hashable, # and the resolver function for Type throws an AttributeError because # Hashable has no __args__. We discard such errors when attempting # to resolve subclasses, because the function was passed a weird arg. strategy = strat_or_callable(thing) except Exception: # pragma: no cover if not final: return nothing() raise else: strategy = strat_or_callable if not isinstance(strategy, SearchStrategy): raise ResolutionFailed( f"Error: {thing} was registered for {nicerepr(strat_or_callable)}, " f"but returned non-strategy {strategy!r}" ) if strategy.is_empty: raise ResolutionFailed(f"Error: {thing!r} resolved to an empty strategy") return strategy if not isinstance(thing, type): if types.is_a_new_type(thing): # Check if we have an explicitly registered strategy for this thing, # resolve it so, and otherwise resolve as for the base type. if thing in types._global_type_lookup: return as_strategy(types._global_type_lookup[thing], thing) 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]) if not types.is_a_type(thing): # The implementation of typing_extensions.Literal under Python 3.6 is # *very strange*. Notably, `type(Literal[x]) != Literal` so we have to # use the first form directly, and because it uses __values__ instead of # __args__ we inline the relevant logic here until the end of life date. if types.is_typing_literal(thing): # pragma: no cover assert sys.version_info[:2] == (3, 6) args_dfs_stack = list(thing.__values__) # type: ignore literals = [] while args_dfs_stack: arg = args_dfs_stack.pop() if types.is_typing_literal(arg): args_dfs_stack.extend(reversed(arg.__values__)) else: literals.append(arg) return sampled_from(literals) if isinstance(thing, str): # See raise InvalidArgument( f"Got {thing!r} as a type annotation, but the forward-reference " "could not be resolved from a string to a type. Consider using " "`from __future__ import annotations` instead of forward-reference " "strings." ) raise InvalidArgument(f"thing={thing!r} must be a type") # pragma: no cover # 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. try: if thing in types._global_type_lookup: return as_strategy(types._global_type_lookup[thing], thing) except TypeError: # pragma: no cover # This is due to a bizarre divergence in behaviour under Python 3.9.0: # typing.Callable[[], foo] has __args__ = (foo,) but # has __args__ = ([], foo); and as a result is non-hashable. pass if ( hasattr(typing, "_TypedDictMeta") and type(thing) is typing._TypedDictMeta # type: ignore or hasattr(types.typing_extensions, "_TypedDictMeta") # type: ignore and type(thing) is types.typing_extensions._TypedDictMeta # type: ignore ): # pragma: no cover # The __optional_keys__ attribute may or may not be present, but if there's no # way to tell and we just have to assume that everything is required. # See for details. optional = getattr(thing, "__optional_keys__", ()) anns = {k: from_type(v) for k, v in thing.__annotations__.items()} return fixed_dictionaries( # type: ignore mapping={k: v for k, v in anns.items() if k not in optional}, optional={k: v for k, v in anns.items() if k in optional}, ) # We also have a special case for TypeVars. # They are represented as instances like `~T` when they come here. # We need to work with their type instead. if isinstance(thing, TypeVar) and type(thing) in types._global_type_lookup: return as_strategy(types._global_type_lookup[type(thing)], thing) # 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 isinstance(thing, typing_root_type) or ( sys.version_info[:2] >= (3, 9) and isinstance(getattr(thing, "__origin__", None), type) and getattr(thing, "__args__", None) ): 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 = [ as_strategy(v, thing, final=False) for k, v in sorted(types._global_type_lookup.items(), key=repr) if isinstance(k, type) and issubclass(k, thing) and sum(types.try_issubclass(k, typ) for typ in types._global_type_lookup) == 1 ] if any(not s.is_empty for s in 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. if issubclass(thing, enum.Enum): return sampled_from(thing) # Finally, try to build an instance by calling the type object. Unlike builds(), # this block *does* try to infer strategies for arguments with default values. # That's because of the semantic different; builds() -> "call this with ..." # so we only infer when *not* doing so would be an error; from_type() -> "give # me arbitrary instances" so the greater variety is acceptable. # And if it's *too* varied, express your opinions with register_type_strategy() if not isabstract(thing): # If we know that builds(thing) will fail, give a better error message required = required_args(thing) if required and not ( required.issubset(get_type_hints(thing)) or attr.has(thing) or is_typed_named_tuple(thing) # weird enough that we have a specific check ): raise ResolutionFailed( f"Could not resolve {thing!r} to a strategy; consider " "using register_type_strategy" ) try: hints = get_type_hints(thing) params = signature(thing).parameters except Exception: return builds(thing) kwargs = {} for k, p in params.items(): if ( k in hints and k != "return" and p.default is not Parameter.empty and p.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY) ): kwargs[k] = just(p.default) | _from_type(hints[k]) return builds(thing, **kwargs) # And if it's an abstract type, we'll resolve to a union of subclasses instead. subclasses = thing.__subclasses__() if not subclasses: raise ResolutionFailed( f"Could not resolve {thing!r} to a strategy, because it is an abstract " "type without any subclasses. Consider using register_type_strategy" ) subclass_strategies = nothing() for sc in subclasses: try: subclass_strategies |= _from_type(sc) except Exception: pass if subclass_strategies.is_empty: # We're unable to resolve subclasses now, but we might be able to later - # so we'll just go back to the mixed distribution. return sampled_from(subclasses).flatmap(from_type) return subclass_strategies
[docs]@cacheable @defines_strategy(force_reusable_values=True) def fractions( min_value: Optional[Union[Real, str]] = None, max_value: Optional[Union[Real, str]] = None, *, max_denominator: Optional[int] = None, ) -> 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") # These assertions tell Mypy what happened in try_convert assert min_value is None or isinstance(min_value, Fraction) assert max_value is None or isinstance(max_value, Fraction) check_valid_interval(min_value, max_value, "min_value", "max_value") check_valid_integer(max_denominator, "max_denominator") if max_denominator is not None: if max_denominator < 1: raise InvalidArgument(f"max_denominator={max_denominator!r} must be >= 1") if min_value is not None and min_value.denominator > max_denominator: raise InvalidArgument( f"The min_value={min_value!r} has a denominator greater than the " f"max_denominator={max_denominator!r}" ) if max_value is not None and max_value.denominator > max_denominator: raise InvalidArgument( f"The max_value={max_value!r} has a denominator greater than the " f"max_denominator={max_denominator!r}" ) 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 = math.gcd(scale, math.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: Union[Real, str, None], name: str, allow_infinity: Optional[bool] ) -> 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( f"allow_infinity={allow_infinity!r}, but {name}={value!r}" ) # This could be infinity, quiet NaN, or signalling NaN raise InvalidArgument(f"Invalid {name}={value!r}")
[docs]@cacheable @defines_strategy(force_reusable_values=True) def decimals( min_value: Optional[Union[Real, str]] = None, max_value: Optional[Union[Real, str]] = None, *, allow_nan: Optional[bool] = None, allow_infinity: Optional[bool] = None, places: Optional[int] = None, ) -> SearchStrategy[Decimal]: """Generates instances of :class:`python:decimal.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 <>`_ 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, "places") if places is not None and places < 0: raise InvalidArgument(f"places={places!r} may not be negative") 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( f"There are no decimals with {places} places between " f"min_value={min_value!r} and max_value={max_value!r}" ) 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: 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) if special else nothing())
[docs]@defines_strategy(never_lazy=True) def recursive( base: SearchStrategy[Ex], extend: Callable[[SearchStrategy[Any]], SearchStrategy[T]], *, max_leaves: int = 100, ) -> 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: swap each element with itself or with # a later element. This shrinks i==j for each element, i.e. to no # change. We don't consider the last element as it's always a no-op. result = list(self.values) for i in range(len(result) - 1): j = integer_range(data, i, len(result) - 1) result[i], result[j] = result[j], result[i] return result
[docs]@defines_strategy() def permutations(values: 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)
class CompositeStrategy(SearchStrategy): def __init__(self, definition, args, kwargs): self.definition = definition self.args = args self.kwargs = kwargs def do_draw(self, data): return self.definition(data.draw, *self.args, **self.kwargs) def calc_label(self): return calc_label_from_cls(self.definition)
[docs]class DrawFn(Protocol): """This type only exists so that you can write type hints for functions decorated with :func:`@composite <hypothesis.strategies.composite>`. .. code-block:: python @composite def list_and_index(draw: DrawFn) -> Tuple[int, str]: i = draw(integers()) # type inferred as 'int' s = draw(text()) # type inferred as 'str' """ def __init__(self): raise TypeError("Protocols cannot be instantiated") # pragma: no cover # On Python 3.8+, Protocol overrides our signature for __init__, # so we override it right back to make the docs look nice. __signature__: Signature = Signature(parameters=[]) # We define this as a callback protocol because a simple typing.Callable is # insufficient to fully represent the interface, due to the optional `label` # parameter. def __call__(self, strategy: SearchStrategy[Ex], label: object = None) -> Ex: raise NotImplementedError
[docs]@cacheable def composite(f: 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. """ if isinstance(f, (classmethod, staticmethod)): special_method = type(f) f = f.__func__ else: special_method = None 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) @defines_strategy() @define_function_signature(f.__name__, f.__doc__, new_argspec) def accept(*args, **kwargs): return CompositeStrategy(f, args, kwargs) accept.__module__ = f.__module__ if special_method is not None: return special_method(accept) return accept
[docs]@defines_strategy(force_reusable_values=True) @cacheable def complex_numbers( *, min_magnitude: Real = 0, max_magnitude: Optional[Real] = None, allow_infinity: Optional[bool] = None, allow_nan: Optional[bool] = None, ) -> 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; a value of ``None`` corresponds an infinite upper bound. If ``min_magnitude`` is nonzero 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 constraints 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 :func:`builds(complex, ...) <hypothesis.strategies.builds>` or :func:`@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 == math.inf: max_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( f"Cannot have allow_infinity={allow_infinity!r} with " f"max_magnitude={max_magnitude!r}" ) if allow_nan is None: allow_nan = bool(min_magnitude == 0 and max_magnitude is None) elif allow_nan and not (min_magnitude == 0 and max_magnitude is None): raise InvalidArgument( f"Cannot have allow_nan={allow_nan!r}, min_magnitude={min_magnitude!r} " f"max_magnitude={max_magnitude!r}" ) allow_kw = {"allow_nan": allow_nan, "allow_infinity": allow_infinity} if min_magnitude == 0 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 = None 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 == 0 or math.fabs(zi) >= min_magnitude: zr = draw(floats(None if rmax is None else -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 > 0 and draw(booleans()) and math.fabs(zi) <= min_magnitude: zr = -zr return complex(zr, zi) return constrained_complex()
[docs]@defines_strategy(never_lazy=True) def shared( base: SearchStrategy[Ex], *, key: Optional[Hashable] = None, ) -> 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)
[docs]@cacheable @defines_strategy(force_reusable_values=True) def uuids(*, version: Optional[int] = None) -> 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( f"version={version!r}, but version must be in " "(None, 1, 2, 3, 4, 5) to pass to the uuid.UUID constructor." ) return shared( randoms(use_true_random=True), 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(force_reusable_values=True) def runner(*, default: Any = not_set) -> SearchStrategy[Any]: """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)
[docs]class DataObject: """This type only exists so that you can write type hints for tests using the :func:`` strategy. Do not use it directly! """ # Note that "only exists" here really means "is only exported to users", # but we want to treat it as "semi-stable", not document it as "public API". def __init__(self, data): self.count = 0 self.conjecture_data = data def __repr__(self): return "data(...)" def draw(self, strategy: SearchStrategy[Ex], label: Any = None) -> Ex: check_strategy(strategy, "strategy") result = self.conjecture_data.draw(strategy) self.count += 1 if label is not None: note(f"Draw {self.count} ({label}): {result!r}") else: note(f"Draw {self.count}: {result!r}") return result
class DataStrategy(SearchStrategy): supports_find = False def do_draw(self, data): 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( f"Cannot call {name} on a DataStrategy. You should probably " "be using @composite for whatever it is you're trying to do." )
[docs]@cacheable @defines_strategy(never_lazy=True) def data() -> SearchStrategy[DataObject]: """This isn't really a normal strategy, but instead gives you an object which can be used to draw data interactively from other strategies. 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: Type[Ex], strategy: Union[SearchStrategy[Ex], Callable[[Type[Ex]], SearchStrategy[Ex]]], ) -> 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). Note that you may not register a parametrised generic type (such as ``MyCollection[int]``) directly, because the resolution logic does not handle this case correctly. Instead, you may register a *function* for ``MyCollection`` and `inspect the type parameters within that function <>`__. """ # 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.strategies._internal import types if not types.is_a_type(custom_type): raise InvalidArgument(f"custom_type={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") elif types.has_type_arguments(custom_type): origin = getattr(custom_type, "__origin__", None) raise InvalidArgument( f"Cannot register generic type {custom_type!r}, because it has type " "arguments which would not be handled. Instead, register a function " f"for {origin!r} which can inspect specific type objects and return a " "strategy." ) types._global_type_lookup[custom_type] = strategy from_type.__clear_cache() # type: ignore
[docs]@cacheable @defines_strategy(never_lazy=True) def deferred(definition: 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(force_reusable_values=True) def emails() -> SearchStrategy[str]: """A strategy for generating email addresses as unicode strings. The address format is specified 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. """ 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("{}@{}".format, local_part, domains()).filter( lambda addr: len(addr) <= 254 )
[docs]@defines_strategy() def functions( *, like: Callable[..., Any] = lambda: None, returns: Optional[SearchStrategy[Any]] = None, pure: bool = False, ) -> SearchStrategy[Callable[..., Any]]: # The proper type signature of `functions()` would have T instead of Any, but mypy # disallows default args for generics: """functions(*, like=lambda: None, returns=none(), pure=False) A strategy for functions, which can be used in callbacks. The generated functions will mimic the interface of ``like``, which must be a callable (including a class, method, or function). The return value for the function is drawn from the ``returns`` argument, which must be a strategy. If ``pure=True``, all arguments passed to the generated function must be hashable, and if passed identical arguments the original return value will be returned again - *not* regenerated, so beware mutable values. If ``pure=False``, generated functions do not validate their arguments, and may return a different value if called again with the same arguments. Generated functions can only be called within the scope of the ``@given`` which created them. This strategy does not support ``.example()``. """ check_type(bool, pure, "pure") if not callable(like): raise InvalidArgument( "The first argument to functions() must be a callable to imitate, " f"but got non-callable like={nicerepr(like)!r}" ) if returns is None: hints = get_type_hints(like) returns = from_type(hints.get("return", type(None))) check_strategy(returns, "returns") return FunctionStrategy(like, returns, pure)
[docs]@composite def slices(draw: Any, size: int) -> slice: """Generates slices that will select indices up to the supplied size Generated slices will have start and stop indices that range from -size to size - 1 and will step in the appropriate direction. Slices should only produce an empty selection if the start and end are the same. Examples from this strategy shrink toward 0 and smaller values """ check_valid_size(size, "size") if size == 0: step = draw(none() | integers().filter(bool)) return slice(None, None, step) # For slices start is inclusive and stop is exclusive start = draw(integers(0, size - 1) | none()) stop = draw(integers(0, size) | none()) # Limit step size to be reasonable if start is None and stop is None: max_step = size elif start is None: max_step = stop elif stop is None: max_step = start else: max_step = abs(start - stop) step = draw(integers(1, max_step or 1)) if (draw(booleans()) and start == stop) or (stop or 0) < (start or 0): step *= -1 if draw(booleans()) and start is not None: start -= size if draw(booleans()) and stop is not None: stop -= size if (not draw(booleans())) and step == 1: step = None return slice(start, stop, step)