Skip to content
Back to top

# Source code for hypothesis.strategies._internal.core

    # This file is part of Hypothesis, which may be found at
    # https://github.com/HypothesisWorks/hypothesis/
    #
    # Copyright the Hypothesis Authors.
    # Individual contributors are listed in AUTHORS.rst and the git log.
    #
    # This Source Code Form is subject to the terms of the Mozilla Public License,
    # v. 2.0. If a copy of the MPL was not distributed with this file, You can
    # obtain one at https://mozilla.org/MPL/2.0/.

    import codecs
    import enum
    import math
    import operator
    import random
    import re
    import string
    import sys
    import typing
    import warnings
    from collections.abc import Callable, Collection, Hashable, Iterable, Sequence
    from contextvars import ContextVar
    from decimal import Context, Decimal, localcontext
    from fractions import Fraction
    from functools import reduce
    from inspect import Parameter, Signature, isabstract, isclass
    from re import Pattern
    from types import EllipsisType, FunctionType, GenericAlias
    from typing import (
        Annotated,
        Any,
        AnyStr,
        Concatenate,
        Literal,
        NewType,
        NoReturn,
        ParamSpec,
        Protocol,
        TypeAlias,
        TypeVar,
        cast,
        get_args,
        get_origin,
        overload,
    )
    from uuid import UUID

    from hypothesis.control import (
        cleanup,
        current_build_context,
        deprecate_random_in_strategy,
        note,
        should_note,
    )
    from hypothesis.errors import (
        HypothesisSideeffectWarning,
        HypothesisWarning,
        InvalidArgument,
        ResolutionFailed,
        RewindRecursive,
        SmallSearchSpaceWarning,
    )
    from hypothesis.internal.cathetus import cathetus
    from hypothesis.internal.charmap import (
        Categories,
        CategoryName,
        as_general_categories,
        categories as all_categories,
    )
    from hypothesis.internal.compat import (
        bit_count,
        ceil,
        floor,
        get_type_hints,
        is_typed_named_tuple,
    )
    from hypothesis.internal.conjecture.data import ConjectureData
    from hypothesis.internal.conjecture.utils import (
        calc_label_from_callable,
        calc_label_from_name,
        check_sample,
        combine_labels,
        identity,
    )
    from hypothesis.internal.entropy import get_seeder_and_restorer
    from hypothesis.internal.floats import float_of
    from hypothesis.internal.reflection import (
        define_function_signature,
        get_pretty_function_description,
        get_signature,
        is_first_param_referenced_in_function,
        nicerepr,
        repr_call,
        required_args,
    )
    from hypothesis.internal.validation import (
        check_type,
        check_valid_integer,
        check_valid_interval,
        check_valid_magnitude,
        check_valid_size,
        check_valid_sizes,
        try_convert,
    )
    from hypothesis.strategies._internal import SearchStrategy, check_strategy
    from hypothesis.strategies._internal.collections import (
        FixedDictStrategy,
        ListStrategy,
        TupleStrategy,
        UniqueListStrategy,
        UniqueSampledListStrategy,
        tuples,
    )
    from hypothesis.strategies._internal.deferred import DeferredStrategy
    from hypothesis.strategies._internal.functions import FunctionStrategy
    from hypothesis.strategies._internal.lazy import LazyStrategy, unwrap_strategies
    from hypothesis.strategies._internal.misc import BooleansStrategy, just, none, nothing
    from hypothesis.strategies._internal.numbers import (
        IntegersStrategy,
        Real,
        floats,
        integers,
    )
    from hypothesis.strategies._internal.recursive import RecursiveStrategy
    from hypothesis.strategies._internal.shared import SharedStrategy
    from hypothesis.strategies._internal.strategies import (
        Ex,
        SampledFromStrategy,
        T,
        one_of,
    )
    from hypothesis.strategies._internal.strings import (
        BytesStrategy,
        OneCharStringStrategy,
        TextStrategy,
        _check_is_single_character,
    )
    from hypothesis.strategies._internal.utils import cacheable, defines_strategy
    from hypothesis.utils.conventions import not_set
    from hypothesis.utils.deprecation import note_deprecation
    from hypothesis.vendor.pretty import ArgLabelsT, RepresentationPrinter

    @cacheable
    @defines_strategy(force_reusable_values=True)
    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 BooleansStrategy()

    @overload
    def sampled_from(elements: Sequence[T]) -> SearchStrategy[T]:  # pragma: no cover
        ...

    @overload
    def sampled_from(elements: type[enum.Enum]) -> SearchStrategy[Any]:  # pragma: no cover
        # `SearchStrategy[Enum]` is unreliable due to metaclass issues.
        ...

    @overload
    def sampled_from(
        elements: type[enum.Enum] | Sequence[Any],
    ) -> SearchStrategy[Any]:  # pragma: no cover
        ...

    @defines_strategy(eager="try")
    def sampled_from(
        elements: type[enum.Enum] | Sequence[Any],
    ) -> SearchStrategy[Any]:
        """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")
        force_repr = None
        # check_sample converts to tuple unconditionally, but we want to preserve
        # square braces for list reprs.
        # This will not cover custom sequence implementations which return different
        # braces (or other, more unusual things) for their reprs, but this is a tradeoff
        # between repr accuracy and greedily-evaluating all sequence reprs (at great
        # cost for large sequences).
        force_repr_braces = ("[", "]") if isinstance(elements, list) else None
        if isinstance(elements, type) and issubclass(elements, enum.Enum):
            force_repr = f"sampled_from({elements.__module__}.{elements.__name__})"

        if isclass(elements) and issubclass(elements, enum.Flag):
            # Combinations of enum.Flag members (including empty) are also members.  We generate these
            # dynamically, because static allocation takes O(2^n) memory.  LazyStrategy is used for the
            # ease of force_repr.
            # Add all named values, both flag bits (== list(elements)) and aliases. The aliases are
            # necessary for full coverage for flags that would fail enum.NAMED_FLAGS check, and they
            # are also nice values to shrink to.
            flags = sorted(
                set(elements.__members__.values()),
                key=lambda v: (bit_count(v.value), v.value),
            )
            # Finally, try to construct the empty state if it is not named. It's placed at the
            # end so that we shrink to named values.
            flags_with_empty = flags
            if not flags or flags[0].value != 0:
                try:
                    flags_with_empty = [*flags, elements(0)]
                except TypeError:  # pragma: no cover
                    # Happens on some python versions (at least 3.12) when there are no named values
                    pass
            inner = [
                # Consider one or no named flags set, with shrink-to-named-flag behaviour.
                # Special cases (length zero or one) are handled by the inner sampled_from.
                sampled_from(flags_with_empty),
            ]
            if len(flags) > 1:
                inner += [
                    # Uniform distribution over number of named flags or combinations set. The overlap
                    # at r=1 is intentional, it may lead to oversampling but gives consistent shrinking
                    # behaviour.
                    integers(min_value=1, max_value=len(flags))
                    .flatmap(lambda r: sets(sampled_from(flags), min_size=r, max_size=r))
                    .map(lambda s: elements(reduce(operator.or_, s))),
                ]
            return LazyStrategy(one_of, args=inner, kwargs={}, force_repr=force_repr)
        if not values:

            def has_annotations(elements):
                if sys.version_info[:2] < (3, 14):
                    return vars(elements).get("__annotations__")
                else:  # pragma: no cover  # covered by 3.14 tests
                    import annotationlib

                    return bool(annotationlib.get_annotations(elements))

            if (
                isinstance(elements, type)
                and issubclass(elements, enum.Enum)
                and has_annotations(elements)
            ):
                # See https://github.com/HypothesisWorks/hypothesis/issues/2923
                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])
        return SampledFromStrategy(
            values, force_repr=force_repr, force_repr_braces=force_repr_braces
        )

    def _gets_first_item(fn: Callable) -> bool:
        # Introspection for either `itemgetter(0)`, or `lambda x: x[0]`
        if isinstance(fn, FunctionType):
            s = get_pretty_function_description(fn)
            return bool(re.fullmatch(s, r"lambda ([a-z]+): \1\[0\]"))
        return isinstance(fn, operator.itemgetter) and repr(fn) == "operator.itemgetter(0)"

    @cacheable
    @defines_strategy()
    def lists(
        elements: SearchStrategy[Ex],
        *,
        min_size: int = 0,
        max_size: int | None = None,
        unique_by: (
            None | Callable[[Ex], Hashable] | tuple[Callable[[Ex], Hashable], ...]
        ) = 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=} 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 all(_gets_first_item(fn) for fn in unique_by)
            ):
                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 elements.start is not None
                and elements.end is not None
                and (elements.end - elements.start) <= 255
            ):
                elements = SampledFromStrategy(
                    sorted(range(elements.start, elements.end + 1), key=abs)  # type: ignore
                    if elements.end < 0 or elements.start > 0
                    else (
                        list(range(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=} 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)

    @cacheable
    @defines_strategy()
    def sets(
        elements: SearchStrategy[Ex],
        *,
        min_size: int = 0,
        max_size: int | None = 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)

    @cacheable
    @defines_strategy()
    def frozensets(
        elements: SearchStrategy[Ex],
        *,
        min_size: int = 0,
        max_size: int | None = 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) -> str:
            return f"iter({self._values!r})"

        def _repr_pretty_(self, printer, cycle):
            if cycle:
                printer.text("iter(...)")
            else:
                printer.text("iter(")
                printer.pretty(self._values)
                printer.text(")")

    @defines_strategy()
    def iterables(
        elements: SearchStrategy[Ex],
        *,
        min_size: int = 0,
        max_size: int | None = None,
        unique_by: (
            None | Callable[[Ex], Hashable] | tuple[Callable[[Ex], Hashable], ...]
        ) = 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)

    # this type definition is imprecise, in multiple ways:
    # * mapping and optional can be of different types:
    #      s: dict[str | int, int] = st.fixed_dictionaries(
    #         {"a": st.integers()}, optional={1: st.integers()}
    #     )
    # * the values in either mapping or optional need not all be of the same type:
    #      s: dict[str, int | bool] = st.fixed_dictionaries(
    #         {"a": st.integers(), "b": st.booleans()}
    #     )
    # * the arguments may be of any dict-compatible type, in which case the return
    #  value will be of that type instead of dit
    #
    # Overloads may help here, but I doubt we'll be able to satisfy all these
    # constraints.
    #
    # Here's some platonic ideal test cases for revealed_types.py, with the understanding
    # that some may not be achievable:
    #
    #   ("fixed_dictionaries({'a': booleans()})", "dict[str, bool]"),
    #   ("fixed_dictionaries({'a': booleans(), 'b': integers()})", "dict[str, bool | int]"),
    #   ("fixed_dictionaries({}, optional={'a': booleans()})", "dict[str, bool]"),
    #   (
    #       "fixed_dictionaries({'a': booleans()}, optional={1: booleans()})",
    #       "dict[str | int, bool]",
    #   ),
    #   (
    #       "fixed_dictionaries({'a': booleans()}, optional={1: integers()})",
    #       "dict[str | int, bool | int]",
    #   ),

    @defines_strategy()
    def fixed_dictionaries(
        mapping: dict[T, SearchStrategy[Ex]],
        *,
        optional: dict[T, SearchStrategy[Ex]] | None = 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(
                    f"Got arguments of different types: "
                    f"mapping={nicerepr(type(mapping))}, "
                    f"optional={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 FixedDictStrategy(mapping, optional=optional)

    _get_first_item = operator.itemgetter(0)

    @cacheable
    @defines_strategy()
    def dictionaries(
        keys: SearchStrategy[Ex],
        values: SearchStrategy[T],
        *,
        dict_class: type = dict,
        min_size: int = 0,
        max_size: int | None = 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=_get_first_item,
        ).map(dict_class)

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def characters(
        *,
        codec: str | None = None,
        min_codepoint: int | None = None,
        max_codepoint: int | None = None,
        categories: Collection[CategoryName] | None = None,
        exclude_categories: Collection[CategoryName] | None = None,
        exclude_characters: Collection[str] | None = None,
        include_characters: Collection[str] | None = None,
        # Note: these arguments are deprecated aliases for backwards compatibility
        blacklist_categories: Collection[CategoryName] | None = None,
        whitelist_categories: Collection[CategoryName] | None = None,
        blacklist_characters: Collection[str] | None = None,
        whitelist_characters: Collection[str] | None = 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 ``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 ``exclude_categories`` is specified, then any character from those
          categories will not be produced.  You must not pass both ``categories``
          and ``exclude_categories``; these arguments are alternative ways to
          specify exactly the same thing.
        - If ``include_characters`` is specified, then any additional characters
          in that list will also be produced.
        - If ``exclude_characters`` is specified, then any characters in
          that list will be not be produced. Any overlap between
          ``include_characters`` and ``exclude_characters`` will raise an
          exception.
        - If ``codec`` is specified, only characters in the specified `codec encodings`_
          will be produced.

        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.

        We allow codecs from the :mod:`codecs` module and their aliases, platform
        specific and user-registered codecs if they are available, and
        `python-specific text encodings`_ (but not text or binary transforms).
        ``include_characters`` which cannot be encoded using this codec will
        raise an exception.  If non-encodable codepoints or categories are
        explicitly allowed, the ``codec`` argument will exclude them without
        raising an exception.

        .. _general category: https://en.wikipedia.org/wiki/Unicode_character_property
        .. _codec encodings: https://docs.python.org/3/library/codecs.html#encodings-and-unicode
        .. _python-specific text encodings: https://docs.python.org/3/library/codecs.html#python-specific-encodings

        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")
        categories = cast(Categories | None, categories)
        if categories is not None and exclude_categories is not None:
            raise InvalidArgument(
                f"Pass at most one of {categories=} and {exclude_categories=} - "
                "these arguments both specify which categories are allowed, so it "
                "doesn't make sense to use both in a single call."
            )

        # Handle deprecation of whitelist/blacklist arguments
        has_old_arg = any(v is not None for k, v in locals().items() if "list" in k)
        has_new_arg = any(v is not None for k, v in locals().items() if "lude" in k)
        if has_old_arg and has_new_arg:
            raise InvalidArgument(
                "The deprecated blacklist/whitelist arguments cannot be used in "
                "the same call as their replacement include/exclude arguments."
            )
        if blacklist_categories is not None:
            exclude_categories = blacklist_categories
        if whitelist_categories is not None:
            categories = whitelist_categories
        if blacklist_characters is not None:
            exclude_characters = blacklist_characters
        if whitelist_characters is not None:
            include_characters = whitelist_characters

        if (
            min_codepoint is None
            and max_codepoint is None
            and categories is None
            and exclude_categories is None
            and include_characters is not None
            and codec is None
        ):
            raise InvalidArgument(
                "Nothing is excluded by other arguments, so passing only "
                f"{include_characters=} would have no effect.  "
                "Also pass categories=(), or use "
                f"sampled_from({include_characters!r}) instead."
            )
        exclude_characters = exclude_characters or ""
        include_characters = include_characters or ""
        if not_one_char := [c for c in exclude_characters if len(c) != 1]:
            raise InvalidArgument(
                "Elements of exclude_characters are required to be a single character, "
                f"but {not_one_char!r} passed in {exclude_characters=} was not."
            )
        if not_one_char := [c for c in include_characters if len(c) != 1]:
            raise InvalidArgument(
                "Elements of include_characters are required to be a single character, "
                f"but {not_one_char!r} passed in {include_characters=} was not."
            )
        overlap = set(exclude_characters).intersection(include_characters)
        if overlap:
            raise InvalidArgument(
                f"Characters {sorted(overlap)!r} are present in both "
                f"{include_characters=} and {exclude_characters=}"
            )
        if categories is not None:
            categories = as_general_categories(categories, "categories")
        if exclude_categories is not None:
            exclude_categories = as_general_categories(
                exclude_categories, "exclude_categories"
            )
        if categories is not None and not categories and not include_characters:
            raise InvalidArgument(
                "When `categories` is an empty collection and there are "
                "no characters specified in include_characters, nothing can "
                "be generated by the characters() strategy."
            )
        both_cats = set(exclude_categories or ()).intersection(categories or ())
        if both_cats:
            # Note: we check that exactly one of `categories` or `exclude_categories` is
            # passed above, but retain this older check for the deprecated arguments.
            raise InvalidArgument(
                f"Categories {sorted(both_cats)!r} are present in both "
                f"{categories=} and {exclude_categories=}"
            )
        elif exclude_categories is not None:
            categories = set(all_categories()) - set(exclude_categories)
        del exclude_categories

        if codec is not None:
            try:
                codec = codecs.lookup(codec).name
                # Check this is not a str-to-str or bytes-to-bytes codec; see
                # https://docs.python.org/3/library/codecs.html#binary-transforms
                "".encode(codec)
            except LookupError:
                raise InvalidArgument(f"{codec=} is not valid on this system") from None
            except Exception:
                raise InvalidArgument(f"{codec=} is not a valid codec") from None

            for char in include_characters:
                try:
                    char.encode(encoding=codec, errors="strict")
                except UnicodeEncodeError:
                    raise InvalidArgument(
                        f"Character {char!r} in {include_characters=} "
                        f"cannot be encoded with {codec=}"
                    ) from None

            # ascii and utf-8 are sufficient common that we have faster special handling
            if codec == "ascii":
                if (max_codepoint is None) or (max_codepoint > 127):
                    max_codepoint = 127
                codec = None
            elif codec == "utf-8":
                if categories is None:
                    categories = all_categories()
                categories = tuple(c for c in categories if c != "Cs")

        return OneCharStringStrategy.from_characters_args(
            categories=categories,
            exclude_characters=exclude_characters,
            min_codepoint=min_codepoint,
            max_codepoint=max_codepoint,
            include_characters=include_characters,
            codec=codec,
        )

    # Hide the deprecated aliases from documentation and casual inspection
    characters.__signature__ = (__sig := get_signature(characters)).replace(  # type: ignore
        parameters=[p for p in __sig.parameters.values() if "list" not in p.name]
    )

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def text(
        alphabet: Collection[str] | SearchStrategy[str] = characters(codec="utf-8"),
        *,
        min_size: int = 0,
        max_size: int | None = 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 = unwrap_strategies(alphabet)
            if isinstance(char_strategy, SampledFromStrategy):
                # Check this via the up-front validation logic below, and incidentally
                # convert into a `characters()` strategy for standard text shrinking.
                return text(char_strategy.elements, min_size=min_size, max_size=max_size)
            elif not isinstance(char_strategy, OneCharStringStrategy):
                char_strategy = char_strategy.map(_check_is_single_character)
        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}"
                )
            if alphabet in ["ascii", "utf-8"]:
                warnings.warn(
                    f"st.text({alphabet!r}): it seems like you are trying to use the "
                    f"codec {alphabet!r}. st.text({alphabet!r}) instead generates "
                    f"strings using the literal characters {list(alphabet)!r}. To specify "
                    f"the {alphabet} codec, use st.text(st.characters(codec={alphabet!r})). "
                    "If you intended to use character literals, you can silence this "
                    "warning by reordering the characters.",
                    HypothesisWarning,
                    # this stacklevel is of course incorrect, but breaking out of the
                    # levels of LazyStrategy and validation isn't worthwhile.
                    stacklevel=1,
                )
            char_strategy = (
                characters(categories=(), include_characters=alphabet)
                if alphabet
                else nothing()
            )
        if (max_size == 0 or char_strategy.is_empty) and not min_size:
            return just("")
        # mypy is unhappy with ListStrategy(SearchStrategy[list[Ex]]) and then TextStrategy
        # setting Ex = str. Mypy is correct to complain because we have an LSP violation
        # here in the TextStrategy.do_draw override. Would need refactoring to resolve.
        return TextStrategy(char_strategy, min_size=min_size, max_size=max_size)  # type: ignore

    @overload
    def from_regex(
        regex: bytes | Pattern[bytes],
        *,
        fullmatch: bool = False,
    ) -> SearchStrategy[bytes]:  # pragma: no cover
        ...

    @overload
    def from_regex(
        regex: str | Pattern[str],
        *,
        fullmatch: bool = False,
        alphabet: str | SearchStrategy[str] | None = characters(codec="utf-8"),
    ) -> SearchStrategy[str]:  # pragma: no cover
        ...

    @cacheable
    @defines_strategy()
    def from_regex(
        regex: AnyStr | Pattern[AnyStr],
        *,
        fullmatch: bool = False,
        alphabet: str | SearchStrategy[str] | None = None,
    ) -> SearchStrategy[AnyStr]:
        r"""Generates strings that contain a match for the given regex (i.e. ones
        for which :func:`python: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 :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.

        The ``alphabet=`` argument constrains the characters in the generated
        string, as for :func:`text`, and is only supported for unicode strings.

        Examples from this strategy shrink towards shorter strings and lower
        character values, with exact behaviour that may depend on the pattern.
        """
        check_type((str, bytes, re.Pattern), regex, "regex")
        check_type(bool, fullmatch, "fullmatch")
        pattern = regex.pattern if isinstance(regex, re.Pattern) else regex
        if alphabet is not None:
            check_type((str, SearchStrategy), alphabet, "alphabet")
            if not isinstance(pattern, str):
                raise InvalidArgument("alphabet= is not supported for bytestrings")
            alphabet = OneCharStringStrategy.from_alphabet(alphabet)
        elif isinstance(pattern, str):
            alphabet = characters(codec="utf-8")

        # 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, alphabet=alphabet)

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def binary(
        *,
        min_size: int = 0,
        max_size: int | None = 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)
        return BytesStrategy(min_size, max_size)

    @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.

        For managing global state, see the :func:`~hypothesis.strategies.random_module`
        strategy and :func:`~hypothesis.register_random` function.
        """
        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: ConjectureData) -> RandomSeeder:
            # It would be unsafe to do run this method more than once per test case,
            # because cleanup() runs tasks in FIFO order (at time of writing!).
            # Fortunately, the random_module() strategy wraps us in shared(), so
            # it's cached for all but the first of any number of calls.
            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)

    @cacheable
    @defines_strategy()
    def random_module() -> SearchStrategy[RandomSeeder]:
        """Hypothesis always seeds global PRNGs before running a test, and restores the
        previous state afterwards.

        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, as is anything
        managed by :func:`hypothesis.register_random`.

        Examples from these strategy shrink to seeds closer to zero.
        """
        return shared(RandomModule(), key="hypothesis.strategies.random_module()")

    class BuildsStrategy(SearchStrategy[Ex]):
        def __init__(
            self,
            target: Callable[..., Ex],
            args: tuple[SearchStrategy[Any], ...],
            kwargs: dict[str, SearchStrategy[Any]],
        ):
            super().__init__()
            self.target = target
            self.args = args
            self.kwargs = kwargs

        def calc_label(self) -> int:
            return combine_labels(
                self.class_label,
                calc_label_from_callable(self.target),
                *[strat.label for strat in self.args],
                *[calc_label_from_name(k) for k in self.kwargs],
                *[strat.label for strat in self.kwargs.values()],
            )

        def do_draw(self, data: ConjectureData) -> Ex:
            context = current_build_context()
            arg_labels: ArgLabelsT = {}

            args = []
            for i, s in enumerate(self.args):
                with context.track_arg_label(f"arg[{i}]") as arg_label:
                    args.append(data.draw(s))
                arg_labels |= arg_label

            kwargs = {}
            for k, v in self.kwargs.items():
                with context.track_arg_label(k) as arg_label:
                    kwargs[k] = data.draw(v)
                arg_labels |= arg_label

            try:
                obj = self.target(*args, **kwargs)
            except TypeError as err:
                if (
                    isinstance(self.target, type)
                    and issubclass(self.target, enum.Enum)
                    and not (self.args or self.kwargs)
                ):
                    name = self.target.__module__ + "." + self.target.__qualname__
                    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_generic_type

                    if isinstance(self.target, NewType) or is_generic_type(self.target):
                        raise InvalidArgument(
                            f"Calling {self.target!r} with no arguments raised an "
                            f"error - try using from_type({self.target!r}) instead "
                            f"of builds({self.target!r})"
                        ) from err
                if getattr(self.target, "__no_type_check__", None) is True:
                    # Note: could use PEP-678 __notes__ here.  Migrate over once we're
                    # using an `exceptiongroup` backport with support for that.
                    raise TypeError(
                        "This might be because the @no_type_check decorator prevented "
                        "Hypothesis from inferring a strategy for some required arguments."
                    ) from err
                raise

            context.record_call(
                obj, self.target, args=args, kwargs=kwargs, arg_labels=arg_labels
            )
            return obj

        def do_validate(self) -> None:
            tuples(*self.args).validate()
            fixed_dictionaries(self.kwargs).validate()

        def __repr__(self) -> str:
            bits = [get_pretty_function_description(self.target)]
            bits.extend(map(repr, self.args))
            bits.extend(f"{k}={v!r}" for k, v in self.kwargs.items())
            return f"builds({', '.join(bits)})"

    @cacheable
    @defines_strategy()
    def builds(
        target: Callable[..., Ex],
        /,
        *args: SearchStrategy[Any],
        **kwargs: SearchStrategy[Any] | EllipsisType,
    ) -> 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 ``...``
        (:obj:`python:Ellipsis`) 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(target):
            from hypothesis.strategies._internal.types import is_a_union

            # before 3.14, unions were callable, so it got an error message in
            # BuildsStrategy.do_draw. In 3.14+, unions are not callable, so
            # we error earlier here instead.
            suggestion = (
                f" Try using from_type({target}) instead?" if is_a_union(target) else ""
            )
            raise InvalidArgument(
                "The first positional argument to builds() must be a callable "
                f"target to construct.{suggestion}"
            )

        if ... in args:  # type: ignore  # we only annotated the allowed types
            # Avoid an implementation nightmare juggling tuples and worse things
            raise InvalidArgument(
                "... 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 ...}
        if required or to_infer:
            if (
                isinstance(target, type)
                and (attr := sys.modules.get("attr")) is not None
                and attr.has(target)
            ):  # pragma: no cover  # covered by our attrs tests in check-niche
                # 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 ... for {badargs}, but we cannot infer a strategy "
                    "because these arguments have no type annotation"
                )
            infer_for = {k: v for k, v in hints.items() if k in (required | to_infer)}
            if infer_for:
                from hypothesis.strategies._internal.types import _global_type_lookup

                for kw, t in infer_for.items():
                    if t in _global_type_lookup:
                        kwargs[kw] = from_type(t)
                    else:
                        # We defer resolution of these type annotations so that the obvious
                        # approach to registering recursive types just works.  I.e.,
                        # if we're inside `register_type_strategy(cls, builds(cls, ...))`
                        # and `...` contains recursion on `cls`.  See
                        # https://github.com/HypothesisWorks/hypothesis/issues/3026
                        kwargs[kw] = deferred(lambda t=t: from_type(t))  # type: ignore

        # validated by handling all EllipsisType in the to_infer case
        kwargs = cast(dict[str, SearchStrategy], kwargs)
        return BuildsStrategy(target, args, kwargs)

    @cacheable
    @defines_strategy(eager=True)
    def from_type(thing: type[T]) -> SearchStrategy[T]:
        """Looks up the appropriate search strategy for the given type.

        |st.from_type| is used internally to fill in missing arguments to
        |st.builds| and can be used interactively
        to explore what strategies are available or to debug type resolution.

        You can use |st.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.

        |st.from_type| looks up a strategy in the following order:

        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
           |st.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
           |st.register_type_strategy|.

        There is a valuable recipe for leveraging |st.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 |st.from_type| can ever generate, except for
        instances of |int|, and excluding instances of types
        added via |st.register_type_strategy|.

        This is useful when writing tests which check that invalid input is
        rejected in a certain way.
        """
        try:
            with warnings.catch_warnings():
                warnings.simplefilter("error")
                return _from_type(thing)
        except Exception:
            return _from_type_deferred(thing)

    def _from_type_deferred(thing: type[Ex]) -> SearchStrategy[Ex]:
        # 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:
            thing_repr = nicerepr(thing)
            if hasattr(thing, "__module__"):
                module_prefix = f"{thing.__module__}."
                if not thing_repr.startswith(module_prefix):
                    thing_repr = module_prefix + thing_repr
            repr_ = f"from_type({thing_repr})"
        except Exception:  # pragma: no cover
            repr_ = None
        return LazyStrategy(
            lambda thing: deferred(lambda: _from_type(thing)),
            (thing,),
            {},
            force_repr=repr_,
        )

    _recurse_guard: ContextVar = ContextVar("recurse_guard")

    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):
            # 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
                strategy = strat_or_callable(thing)
            else:
                strategy = strat_or_callable
            if strategy is NotImplemented:
                return NotImplemented
            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

        def from_type_guarded(thing):
            """Returns the result of producer, or ... if recursion on thing is encountered"""
            try:
                recurse_guard = _recurse_guard.get()
            except LookupError:
                # We can't simply define the contextvar with default=[], as the
                # default object would be shared across contexts
                _recurse_guard.set(recurse_guard := [])
            if thing in recurse_guard:
                raise RewindRecursive(thing)
            recurse_guard.append(thing)
            try:
                return _from_type(thing)
            except RewindRecursive as rr:
                if rr.target != thing:
                    raise
                return ...  # defer resolution
            finally:
                recurse_guard.pop()

        # Let registered extra modules handle their own recognized types first, before
        # e.g. Unions are resolved
        try:
            known = thing in types._global_type_lookup
        except TypeError:
            # thing is not always hashable!
            pass
        else:
            if not known:
                for module, resolver in types._global_extra_lookup.items():
                    if module in sys.modules:
                        strat = resolver(thing)
                        if strat is not None:
                            return strat

        if isinstance(thing, NewType):
            # 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:
                strategy = as_strategy(types._global_type_lookup[thing], thing)
                if strategy is not NotImplemented:
                    return strategy
            return _from_type(thing.__supertype__)
        if types.is_a_type_alias_type(thing):  # pragma: no cover # covered by 3.12+ tests
            if thing in types._global_type_lookup:
                strategy = as_strategy(types._global_type_lookup[thing], thing)
                if strategy is not NotImplemented:
                    return strategy
            return _from_type(thing.__value__)  # type: ignore
        if types.is_a_type_alias_type(origin := get_origin(thing)):  # pragma: no cover
            # Handle parametrized type aliases like `type A[T] = list[T]; thing = A[int]`.
            # In this case, `thing` is a GenericAlias whose origin is a TypeAliasType.
            #
            # covered by 3.12+ tests.
            if origin in types._global_type_lookup:
                strategy = as_strategy(types._global_type_lookup[origin], thing)
                if strategy is not NotImplemented:
                    return strategy
            return _from_type(types.evaluate_type_alias_type(thing))
        if types.is_a_union(thing):
            args = sorted(thing.__args__, key=types.type_sorting_key)  # type: ignore
            return one_of([_from_type(t) for t in args])
        if thing in types.LiteralStringTypes:  # pragma: no cover
            # We can't really cover this because it needs either
            # typing-extensions or python3.11+ typing.
            # `LiteralString` from runtime's point of view is just a string.
            # Fallback to regular text.
            return text()  # type: ignore

        # 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:
            strategy = as_strategy(types._global_type_lookup[type(thing)], thing)
            if strategy is not NotImplemented:
                return strategy

        if not types.is_a_type(thing):
            if isinstance(thing, str):
                # See https://github.com/HypothesisWorks/hypothesis/issues/3016
                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=} must be a type")  # pragma: no cover

        if thing in types.NON_RUNTIME_TYPES:
            # Some code like `st.from_type(TypeAlias)` does not make sense.
            # Because there are types in python that do not exist in runtime.
            raise InvalidArgument(
                f"Could not resolve {thing!r} to a strategy, "
                f"because there is no such thing as a runtime instance of {thing!r}"
            )

        # 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:
                strategy = as_strategy(types._global_type_lookup[thing], thing)
                if strategy is not NotImplemented:
                    return strategy
            elif (
                isinstance(thing, GenericAlias)
                and (origin := get_origin(thing)) in types._global_type_lookup
            ):
                strategy = as_strategy(types._global_type_lookup[origin], thing)
                if strategy is not NotImplemented:
                    return strategy
        except TypeError:  # pragma: no cover
            # This was originally due to a bizarre divergence in behaviour on Python 3.9.0:
            # typing.Callable[[], foo] has __args__ = (foo,) but collections.abc.Callable
            # has __args__ = ([], foo); and as a result is non-hashable.
            # We've kept it because we turn out to have more type errors from... somewhere.
            # FIXME: investigate that, maybe it should be fixed more precisely?
            pass

        if (hasattr(typing, "_TypedDictMeta") and type(thing) is typing._TypedDictMeta) or (
            hasattr(types.typing_extensions, "_TypedDictMeta")  # type: ignore
            and type(thing) is types.typing_extensions._TypedDictMeta  # type: ignore
        ):  # pragma: no cover

            def _get_annotation_arg(key, annotation_type):
                try:
                    return get_args(annotation_type)[0]
                except IndexError:
                    raise InvalidArgument(
                        f"`{key}: {annotation_type.__name__}` is not a valid type annotation"
                    ) from None

            # Taken from `Lib/typing.py` and modified:
            def _get_typeddict_qualifiers(key, annotation_type):
                qualifiers = []
                annotations = []
                while True:
                    annotation_origin = types.extended_get_origin(annotation_type)
                    if annotation_origin is Annotated:
                        if annotation_args := get_args(annotation_type):
                            annotation_type = annotation_args[0]
                            annotations.extend(annotation_args[1:])
                        else:
                            break
                    elif annotation_origin in types.RequiredTypes:
                        qualifiers.append(types.RequiredTypes)
                        annotation_type = _get_annotation_arg(key, annotation_type)
                    elif annotation_origin in types.NotRequiredTypes:
                        qualifiers.append(types.NotRequiredTypes)
                        annotation_type = _get_annotation_arg(key, annotation_type)
                    elif annotation_origin in types.ReadOnlyTypes:
                        qualifiers.append(types.ReadOnlyTypes)
                        annotation_type = _get_annotation_arg(key, annotation_type)
                    else:
                        break
                if annotations:
                    annotation_type = Annotated[(annotation_type, *annotations)]
                return set(qualifiers), annotation_type

            # 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 https://github.com/python/cpython/pull/17214 for details.
            optional = set(getattr(thing, "__optional_keys__", ()))
            required = set(
                getattr(thing, "__required_keys__", get_type_hints(thing).keys())
            )
            anns = {}
            for k, v in get_type_hints(thing).items():
                qualifiers, v = _get_typeddict_qualifiers(k, v)
                # We ignore `ReadOnly` type for now, only unwrap it.
                if types.RequiredTypes in qualifiers:
                    optional.discard(k)
                    required.add(k)
                if types.NotRequiredTypes in qualifiers:
                    optional.add(k)
                    required.discard(k)

                anns[k] = from_type_guarded(v)
                if anns[k] is ...:
                    anns[k] = _from_type_deferred(v)

            if not required.isdisjoint(optional):  # pragma: no cover
                # It is impossible to cover, because `typing.py` or `typing-extensions`
                # won't allow creating incorrect TypedDicts,
                # this is just a sanity check from our side.
                raise InvalidArgument(
                    f"Required keys overlap with optional keys in a TypedDict:"
                    f" {required=}, {optional=}"
                )
            if (
                (not anns)
                and thing.__annotations__
                and ".<locals>." in getattr(thing, "__qualname__", "")
            ):
                raise InvalidArgument("Failed to retrieve type annotations for local type")
            return fixed_dictionaries(  # type: ignore
                mapping={k: v for k, v in anns.items() if k in required},
                optional={k: v for k, v in anns.items() if k in optional},
            )

        # 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 the typing module,
        # because there are several special cases that don't play well with
        # subclass and instance checks.
        if (
            isinstance(thing, types.typing_root_type)
            or (isinstance(get_origin(thing), type) and get_args(thing))
            or isinstance(thing, typing.ForwardRef)
        ):
            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 = [
            s
            for s in (
                as_strategy(v, thing)
                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 s is not NotImplemented
        ]
        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 := sys.modules.get("attr")) is not None and 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 = get_signature(thing).parameters
            except Exception:
                params = {}  # type: ignore

            posonly_args = []
            kwargs = {}
            for k, p in params.items():
                if (
                    p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD, p.KEYWORD_ONLY)
                    and k in hints
                    and k != "return"
                ):
                    ps = from_type_guarded(hints[k])
                    if p.default is not Parameter.empty and ps is not ...:
                        ps = just(p.default) | ps
                    if p.kind is Parameter.POSITIONAL_ONLY:
                        # builds() doesn't infer strategies for positional args, so:
                        if ps is ...:  # pragma: no cover  # rather fiddly to test
                            if p.default is Parameter.empty:
                                raise ResolutionFailed(
                                    f"Could not resolve {thing!r} to a strategy; "
                                    "consider using register_type_strategy"
                                )
                            ps = just(p.default)
                        posonly_args.append(ps)
                    else:
                        kwargs[k] = ps
            if (
                params
                and not (posonly_args or kwargs)
                and not issubclass(thing, BaseException)
            ):
                from_type_repr = repr_call(from_type, (thing,), {})
                builds_repr = repr_call(builds, (thing,), {})
                warnings.warn(
                    f"{from_type_repr} resolved to {builds_repr}, because we could not "
                    "find any (non-varargs) arguments. Use st.register_type_strategy() "
                    "to resolve to a strategy which can generate more than one value, "
                    "or to silence this warning.",
                    SmallSearchSpaceWarning,
                    stacklevel=2,
                )
            return builds(thing, *posonly_args, **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: SearchStrategy = 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

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def fractions(
        min_value: Real | str | None = None,
        max_value: Real | str | None = None,
        *,
        max_denominator: int | None = 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=} must be >= 1")
            if min_value is not None and min_value.denominator > max_denominator:
                raise InvalidArgument(
                    f"The {min_value=} has a denominator greater than the "
                    f"{max_denominator=}"
                )
            if max_value is not None and max_value.denominator > max_denominator:
                raise InvalidArgument(
                    f"The {max_value=} has a denominator greater than the "
                    f"{max_denominator=}"
                )

        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: Real | str | None, name: str, allow_infinity: bool | None, places: int | None
    ) -> Decimal | None:
        """Convert decimal bounds to decimals, carefully."""
        assert name in ("min_value", "max_value")
        if value is None:
            return None
        old = value
        if isinstance(value, Fraction):
            value = Context(prec=places).divide(value.numerator, value.denominator)
            if old != value:
                raise InvalidArgument(
                    f"{old!r} cannot be exactly represented as a decimal with {places=}"
                )
        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_nan():
            raise InvalidArgument(f"Invalid {name}={value!r}")

        # If you are reading this conditional, I am so sorry.  I did my best.
        finitude_old = value if isinstance(old, str) else old
        if math.isfinite(finitude_old) != math.isfinite(value) or (
            value.is_finite() and Fraction(str(old)) != Fraction(str(value))
        ):
            note_deprecation(
                f"{old!r} cannot be exactly represented as a decimal with {places=}",
                since="2025-11-02",
                has_codemod=False,
                stacklevel=1,
            )

        if value.is_finite():
            return value
        assert value.is_infinite()
        if (value < 0 if "min" in name else value > 0) and allow_infinity is not False:
            return None
        raise InvalidArgument(f"{allow_infinity=}, but {name}={value!r}")

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def decimals(
        min_value: Real | str | None = None,
        max_value: Real | str | None = None,
        *,
        allow_nan: bool | None = None,
        allow_infinity: bool | None = None,
        places: int | None = 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
        <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, "places")
        if places is not None and places < 0:
            raise InvalidArgument(f"{places=} may not be negative")
        min_value = _as_finite_decimal(min_value, "min_value", allow_infinity, places)
        max_value = _as_finite_decimal(max_value, "max_value", allow_infinity, places)
        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=} and {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: list[Decimal] = []
        if allow_infinity or (allow_infinity is None and max_value is None):
            special.append(Decimal("Infinity"))
        if allow_infinity or (allow_infinity is None and min_value is None):
            special.append(Decimal("-Infinity"))
        if allow_nan or (allow_nan is None and (None in (min_value, max_value))):
            special.extend(map(Decimal, ("NaN", "-NaN", "sNaN", "-sNaN")))
        return strat | (sampled_from(special) if special else nothing())

    @defines_strategy(eager=True)
    def recursive(
        base: SearchStrategy[Ex],
        extend: Callable[[SearchStrategy[Any]], SearchStrategy[T]],
        *,
        min_leaves: int | None = None,
        max_leaves: int = 100,
    ) -> SearchStrategy[T | Ex]:
        """base: A strategy to start from.

        extend: A function which takes a strategy and returns a new strategy.

        min_leaves: The minimum number of elements to be drawn from base on a given run.

        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, min_leaves, max_leaves)

    class PermutationStrategy(SearchStrategy):
        def __init__(self, values):
            super().__init__()
            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 = data.draw_integer(i, len(result) - 1)
                result[i], result[j] = result[j], result[i]
            return result

    @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):
            super().__init__()
            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) -> int:
            return combine_labels(
                self.class_label,
                calc_label_from_callable(self.definition),
            )

    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

            def draw(strategy: SearchStrategy[Ex], label: object = None) -> Ex: ...

            @composite
            def list_and_index(draw: DrawFn) -> tuple[int, str]:
                i = draw(integers())  # type of `i` inferred as 'int'
                s = draw(text())  # type of `s` inferred as 'str'
                return i, s
        """

        def __init__(self):
            raise TypeError("Protocols cannot be instantiated")  # pragma: no cover

        # 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

    def _composite(f):
        # Wrapped below, using ParamSpec if available
        if isinstance(f, (classmethod, staticmethod)):
            special_method = type(f)
            f = f.__func__
        else:
            special_method = None

        sig = get_signature(f)
        params = tuple(sig.parameters.values())

        if not (params and "POSITIONAL" in params[0].kind.name):
            raise InvalidArgument(
                "Functions wrapped with composite must take at least one "
                "positional argument."
            )
        if params[0].default is not sig.empty:
            raise InvalidArgument("A default value for initial argument will never be used")
        if not (f is typing._overload_dummy or is_first_param_referenced_in_function(f)):
            note_deprecation(
                "There is no reason to use @st.composite on a function which "
                "does not call the provided draw() function internally.",
                since="2022-07-17",
                has_codemod=False,
            )
        if get_origin(sig.return_annotation) is SearchStrategy:
            ret_repr = repr(sig.return_annotation).replace("hypothesis.strategies.", "st.")
            warnings.warn(
                f"Return-type annotation is `{ret_repr}`, but the decorated "
                "function should return a value (not a strategy)",
                HypothesisWarning,
                stacklevel=3,
            )
        if params[0].kind.name != "VAR_POSITIONAL":
            params = params[1:]
        newsig = sig.replace(
            parameters=params,
            return_annotation=(
                SearchStrategy
                if sig.return_annotation is sig.empty
                else SearchStrategy[sig.return_annotation]
            ),
        )

        @defines_strategy()
        @define_function_signature(f.__name__, f.__doc__, newsig)
        def accept(*args, **kwargs):
            return CompositeStrategy(f, args, kwargs)

        accept.__module__ = f.__module__
        accept.__signature__ = newsig
        if special_method is not None:
            return special_method(accept)
        return accept

    composite_doc = """
    Defines a strategy that is built out of potentially arbitrarily many other
    strategies.

    @composite provides a callable ``draw`` as the first parameter to the decorated
    function, which can be used to dynamically draw a value from any strategy. For
    example:

    .. code-block:: python

        from hypothesis import strategies as st, given

        @st.composite
        def values(draw):
            n1 = draw(st.integers())
            n2 = draw(st.integers(min_value=n1))
            return (n1, n2)

        @given(values())
        def f(value):
            (n1, n2) = value
            assert n1 <= n2

    @composite cannot mix test code and generation code. If you need that, use
    |st.data|.

    If :func:`@composite <hypothesis.strategies.composite>` is used to decorate a
    method or classmethod, the ``draw`` argument must come before ``self`` or
    ``cls``. While we therefore recommend writing strategies as standalone functions
    and using |st.register_type_strategy| to associate them with a class, methods
    are supported and the ``@composite`` decorator may be applied either before or
    after ``@classmethod`` or ``@staticmethod``. See :issue:`2578` and :pull:`2634`
    for more details.

    Examples from this strategy shrink by shrinking the output of each draw call.
    """
    if typing.TYPE_CHECKING or ParamSpec is not None:
        P = ParamSpec("P")

        def composite(
            f: Callable[Concatenate[DrawFn, P], Ex],
        ) -> Callable[P, SearchStrategy[Ex]]:
            return _composite(f)

    else:  # pragma: no cover

        @cacheable
        def composite(f: Callable[..., Ex]) -> Callable[..., SearchStrategy[Ex]]:
            return _composite(f)

    composite.__doc__ = composite_doc

    @defines_strategy(force_reusable_values=True)
    @cacheable
    def complex_numbers(
        *,
        min_magnitude: Real = 0,
        max_magnitude: Real | None = None,
        allow_infinity: bool | None = None,
        allow_nan: bool | None = None,
        allow_subnormal: bool = True,
        width: Literal[32, 64, 128] = 128,
    ) -> SearchStrategy[complex]:
        """Returns a strategy that generates :class:`~python: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``.

        ``allow_infinity``, ``allow_nan``, and ``allow_subnormal`` are
        applied to each part of the complex number separately, as for
        :func:`~hypothesis.strategies.floats`.

        The magnitude constraints are respected up to a relative error
        of (around) floating-point epsilon, due to implementation via
        the system ``sqrt`` function.

        The ``width`` argument specifies the maximum number of bits of precision
        required to represent the entire generated complex number.
        Valid values are 32, 64 or 128, which correspond to the real and imaginary
        components each having width 16, 32 or 64, respectively.
        Passing ``width=64`` will still use the builtin 128-bit
        :class:`~python:complex` class, but always for values which can be
        exactly represented as two 32-bit floats.

        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=} with {max_magnitude=}")
        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=}, {min_magnitude=}, {max_magnitude=}"
            )
        check_type(bool, allow_subnormal, "allow_subnormal")
        if width not in (32, 64, 128):
            raise InvalidArgument(
                f"{width=}, but must be 32, 64 or 128 (other complex dtypes "
                "such as complex192 or complex256 are not supported)"
                # For numpy, these types would be supported (but not by CPython):
                # https://numpy.org/doc/stable/reference/arrays.scalars.html#complex-floating-point-types
            )
        component_width = width // 2
        allow_kw = {
            "allow_nan": allow_nan,
            "allow_infinity": allow_infinity,
            # If we have a nonzero normal min_magnitude and draw a zero imaginary part,
            # then allow_subnormal=True would be an error with the min_value to the floats()
            # strategy for the real part.  We therefore replace True with None.
            "allow_subnormal": None if allow_subnormal else allow_subnormal,
            "width": component_width,
        }

        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))  # type: ignore

        @composite
        def constrained_complex(draw):
            # We downcast drawn floats to the desired (component) width so we
            # guarantee the resulting complex values are representable. Note
            # truncating the mantissa bits with float_of() cannot increase the
            # magnitude of a float, so we are guaranteed to stay within the allowed
            # range. See https://github.com/HypothesisWorks/hypothesis/issues/3573

            # 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(
                        -float_of(max_magnitude, component_width),
                        float_of(max_magnitude, component_width),
                        **allow_kw,
                    )
                )
                rmax = float_of(cathetus(max_magnitude, zi), component_width)
            # 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:
                rmin = float_of(cathetus(min_magnitude, zi), component_width)
                zr = draw(floats(rmin, 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()

    @defines_strategy(eager=True)
    def shared(
        base: SearchStrategy[Ex],
        *,
        key: Hashable | None = 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)

    @composite
    def _maybe_nil_uuids(draw, uuid):
        # Equivalent to `random_uuids | just(...)`, with a stronger bias to the former.
        if draw(data()).conjecture_data.draw_boolean(1 / 64):
            return UUID("00000000-0000-0000-0000-000000000000")
        return uuid

    @cacheable
    @defines_strategy(force_reusable_values=True)
    def uuids(
        *, version: Literal[1, 2, 3, 4, 5] | None = None, allow_nil: bool = False
    ) -> 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.

        If ``allow_nil`` is True, generate the nil UUID much more often.
        Otherwise, 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.
        """
        check_type(bool, allow_nil, "allow_nil")
        if version not in (None, 1, 2, 3, 4, 5):
            raise InvalidArgument(
                f"{version=}, but version must be in "
                "(None, 1, 2, 3, 4, 5) to pass to the uuid.UUID constructor."
            )
        random_uuids = shared(
            randoms(use_true_random=True), key="hypothesis.strategies.uuids.generator"
        ).map(lambda r: UUID(version=version, int=r.getrandbits(128)))

        if allow_nil:
            if version is not None:
                raise InvalidArgument("The nil UUID is not of any version")
            return random_uuids.flatmap(_maybe_nil_uuids)
        return random_uuids

    class RunnerStrategy(SearchStrategy):
        def __init__(self, default):
            super().__init__()
            self.default = default

        def do_draw(self, data):
            if data.hypothesis_runner is not_set:
                if self.default is not_set:
                    raise InvalidArgument(
                        "Cannot use runner() strategy with no "
                        "associated runner or explicit default."
                    )
                return self.default
            return data.hypothesis_runner

    @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 you are using this in a rule for stateful testing, this strategy
        will return the instance of the :class:`~hypothesis.stateful.RuleBasedStateMachine`
        that the rule is running for.

        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:
        """This type only exists so that you can write type hints for tests using
        the :func:`~hypothesis.strategies.data` 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: ConjectureData) -> None:
            self.count = 0
            self.conjecture_data = data

        __signature__ = Signature()  # hide internals from Sphinx introspection

        def __repr__(self) -> str:
            return "data(...)"

        def draw(self, strategy: SearchStrategy[Ex], label: Any = None) -> Ex:
            """Like :obj:`~hypothesis.strategies.DrawFn`."""
            check_strategy(strategy, "strategy")
            self.count += 1
            desc = f"Draw {self.count}{'' if label is None else f' ({label})'}"
            with deprecate_random_in_strategy("{}from {!r}", desc, strategy):
                result = self.conjecture_data.draw(strategy, observe_as=f"generate:{desc}")

            # optimization to avoid needless printer.pretty
            if should_note():
                printer = RepresentationPrinter(context=current_build_context())
                printer.text(f"{desc}: ")
                if self.conjecture_data.provider.avoid_realization:
                    printer.text("<symbolic>")
                else:
                    printer.pretty(result)
                note(printer.getvalue())
            return result

    class DataStrategy(SearchStrategy):
        def do_draw(self, data):
            if data._shared_data_strategy is None:
                data._shared_data_strategy = DataObject(data)
            return data._shared_data_strategy

        def __repr__(self) -> str:
            return "data()"

        def map(self, f):
            self.__not_a_first_class_strategy("map")

        def filter(self, condition: Callable[[Ex], Any]) -> NoReturn:
            self.__not_a_first_class_strategy("filter")

        def flatmap(self, f):
            self.__not_a_first_class_strategy("flatmap")

        def example(self) -> NoReturn:
            self.__not_a_first_class_strategy("example")

        def __not_a_first_class_strategy(self, name: str) -> NoReturn:
            raise InvalidArgument(
                f"Cannot call {name} on a DataStrategy. You should probably "
                "be using @composite for whatever it is you're trying to do."
            )

    @cacheable
    @defines_strategy(eager=True)
    def data() -> SearchStrategy[DataObject]:
        """
        Provides an object ``data`` with a ``data.draw`` function which acts like
        the ``draw`` callable provided by |st.composite|, in that it can be used
        to dynamically draw values from strategies. |st.data| is more powerful
        than |st.composite|, because it allows you to mix generation and test code.

        Here's an example of dynamically generating values using |st.data|:

        .. code-block:: python

            from hypothesis import strategies as st, given

            @given(st.data())
            def test_values(data):
                n1 = data.draw(st.integers())
                n2 = data.draw(st.integers(min_value=n1))
                assert n1 + 1 <= n2

        If the test fails, each draw will be printed with the falsifying example.
        e.g. the above is wrong (it has a boundary condition error), so will print:

        .. code-block:: pycon

            Falsifying example: test_values(data=data(...))
            Draw 1: 0
            Draw 2: 0

        Optionally, you can provide a label to identify values generated by each call
        to ``data.draw()``.  These labels can be used to identify values in the
        output of a falsifying example.

        For instance:

        .. code-block:: python

            @given(st.data())
            def test_draw_sequentially(data):
                x = data.draw(st.integers(), label="First number")
                y = data.draw(st.integers(min_value=x), label="Second number")
                assert x < y

        will produce:

        .. code-block:: pycon

            Falsifying example: test_draw_sequentially(data=data(...))
            Draw 1 (First number): 0
            Draw 2 (Second number): 0

        Examples from this strategy shrink by shrinking the output of each draw call.
        """
        return DataStrategy()

    if sys.version_info < (3, 12):
        # TypeAliasType is new in 3.12
        RegisterTypeT: TypeAlias = type[Ex]
    else:  # pragma: no cover  # covered by test_mypy.py
        from typing import TypeAliasType

        # see https://github.com/HypothesisWorks/hypothesis/issues/4410
        RegisterTypeT: TypeAlias = type[Ex] | TypeAliasType

    def register_type_strategy(
        custom_type: RegisterTypeT,
        strategy: 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
        |@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). The function may return
        :data:`NotImplemented` to conditionally not provide a strategy for the type
        (the type will still be resolved by other methods, if possible, as if the
        function was not registered).

        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
        <https://stackoverflow.com/q/48572831>`__.
        """
        # 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=} must be a type")
        if custom_type in types.NON_RUNTIME_TYPES:
            raise InvalidArgument(
                f"{custom_type=} is not allowed to be registered, "
                f"because there is no such thing as a runtime instance of {custom_type!r}"
            )
        if not (isinstance(strategy, SearchStrategy) or callable(strategy)):
            raise InvalidArgument(
                f"{strategy=} must be a SearchStrategy, or a function that takes "
                "a generic type and returns a specific SearchStrategy"
            )
        if isinstance(strategy, SearchStrategy):
            with warnings.catch_warnings():
                warnings.simplefilter("error", HypothesisSideeffectWarning)

                # Calling is_empty forces materialization of lazy strategies. If this is done at import
                # time, lazy strategies will warn about it; here, we force that warning to raise to
                # avoid the materialization. Ideally, we'd just check if the strategy is lazy, but the
                # lazy strategy may be wrapped underneath another strategy so that's complicated.
                try:
                    if strategy.is_empty:
                        raise InvalidArgument(f"{strategy=} must not be empty")
                except HypothesisSideeffectWarning:  # pragma: no cover
                    pass
        if types.has_type_arguments(custom_type):
            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 {get_origin(custom_type)!r} which can inspect specific type "
                "objects and return a strategy."
            )
        if (
            "pydantic.generics" in sys.modules
            and isinstance(custom_type, type)
            and issubclass(custom_type, sys.modules["pydantic.generics"].GenericModel)
            and not re.search(r"[A-Za-z_]+\[.+\]", repr(custom_type))
            and callable(strategy)
        ):  # pragma: no cover
            # See https://github.com/HypothesisWorks/hypothesis/issues/2940
            raise InvalidArgument(
                f"Cannot register a function for {custom_type!r}, because parametrized "
                "`pydantic.generics.GenericModel` subclasses aren't actually generic "
                "types at runtime.  In this case, you should register a strategy "
                "directly for each parametrized form that you anticipate using."
            )

        types._global_type_lookup[custom_type] = strategy
        from_type.__clear_cache()  # type: ignore

    @cacheable
    @defines_strategy(eager=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)

    def domains() -> SearchStrategy[str]:
        import hypothesis.provisional

        return hypothesis.provisional.domains()

    @defines_strategy(force_reusable_values=True)
    def emails(
        *, domains: SearchStrategy[str] = LazyStrategy(domains, (), {})
    ) -> 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.

        If ``domains`` is given then it must be a strategy that generates domain
        names for the emails, defaulting to :func:`~hypothesis.provisional.domains`.

        This strategy is useful for generating "user data" for tests, as
        mishandling of email addresses is a common source of bugs.
        """
        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
        )

    def _functions(*, like, returns, pure):
        # Wrapped up to use ParamSpec below
        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 in (None, ...):
            # Passing `None` has never been *documented* as working, but it still
            # did from May 2020 to Jan 2022 so we'll avoid breaking it without cause.
            hints = get_type_hints(like)
            returns = from_type(hints.get("return", type(None)))
        check_strategy(returns, "returns")
        return FunctionStrategy(like, returns, pure)

    if typing.TYPE_CHECKING or ParamSpec is not None:

        @overload
        def functions(
            *, pure: bool = ...
        ) -> SearchStrategy[Callable[[], None]]:  # pragma: no cover
            ...

        @overload
        def functions(
            *,
            like: Callable[P, T],
            pure: bool = ...,
        ) -> SearchStrategy[Callable[P, T]]:  # pragma: no cover
            ...

        @overload
        def functions(
            *,
            returns: SearchStrategy[T],
            pure: bool = ...,
        ) -> SearchStrategy[Callable[[], T]]:  # pragma: no cover
            ...

        @overload
        def functions(
            *,
            like: Callable[P, Any],
            returns: SearchStrategy[T],
            pure: bool = ...,
        ) -> SearchStrategy[Callable[P, T]]:  # pragma: no cover
            ...

        @defines_strategy()
        def functions(*, like=lambda: None, returns=..., pure=False):
            # We shouldn't need overloads here, but mypy disallows default args for
            # generics: https://github.com/python/mypy/issues/3737
            """functions(*, like=lambda: None, returns=..., 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 ``returns`` is not passed, we attempt to infer a strategy
            from the return-type annotation if present, falling back to :func:`~none`.

            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.
            """
            return _functions(like=like, returns=returns, pure=pure)

    else:  # pragma: no cover

        @defines_strategy()
        def functions(
            *,
            like: Callable[..., Any] = lambda: None,
            returns: SearchStrategy[Any] | EllipsisType = ...,
            pure: bool = False,
        ) -> SearchStrategy[Callable[..., Any]]:
            """functions(*, like=lambda: None, returns=..., 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 ``returns`` is not passed, we attempt to infer a strategy
            from the return-type annotation if present, falling back to :func:`~none`.

            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.
            """
            return _functions(like=like, returns=returns, pure=pure)

    @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)