# Source code for hypothesis._strategies

    # coding=utf-8
    #
    # This file is part of Hypothesis, which may be found at
    # https://github.com/HypothesisWorks/hypothesis/
    #
    # Most of this work is copyright (C) 2013-2019 David R. MacIver
    # (david@drmaciver.com), but it contains contributions by others. See
    # CONTRIBUTING.rst for a full list of people who may hold copyright, and
    # consult the git log if you need to determine who owns an individual
    # contribution.
    #
    # This Source Code Form is subject to the terms of the Mozilla Public License,
    # v. 2.0. If a copy of the MPL was not distributed with this file, You can
    # obtain one at https://mozilla.org/MPL/2.0/.
    #
    # END HEADER

    from __future__ import absolute_import, division, print_function

    import datetime as dt
    import enum
    import math
    import operator
    import string
    import sys
    from decimal import Context, Decimal, localcontext
    from fractions import Fraction
    from functools import reduce
    from inspect import isabstract, isclass
    from uuid import UUID

    import attr

    from hypothesis._settings import note_deprecation
    from hypothesis.control import cleanup, note, reject
    from hypothesis.errors import InvalidArgument, ResolutionFailed
    from hypothesis.internal.cache import LRUReusedCache
    from hypothesis.internal.cathetus import cathetus
    from hypothesis.internal.charmap import as_general_categories
    from hypothesis.internal.compat import (
        ceil,
        floor,
        gcd,
        get_type_hints,
        getfullargspec,
        hrange,
        implements_iterator,
        string_types,
        typing_root_type,
    )
    from hypothesis.internal.conjecture.utils import (
        calc_label_from_cls,
        check_sample,
        integer_range,
    )
    from hypothesis.internal.entropy import get_seeder_and_restorer
    from hypothesis.internal.floats import (
        count_between_floats,
        float_of,
        float_to_int,
        int_to_float,
        is_negative,
        next_down,
        next_up,
    )
    from hypothesis.internal.reflection import (
        define_function_signature,
        is_typed_named_tuple,
        nicerepr,
        proxies,
        required_args,
    )
    from hypothesis.internal.validation import (
        check_type,
        check_valid_bound,
        check_valid_integer,
        check_valid_interval,
        check_valid_magnitude,
        check_valid_size,
        check_valid_sizes,
        try_convert,
    )
    from hypothesis.searchstrategy import SearchStrategy, check_strategy
    from hypothesis.searchstrategy.collections import (
        FixedKeysDictStrategy,
        ListStrategy,
        TupleStrategy,
        UniqueListStrategy,
        UniqueSampledListStrategy,
    )
    from hypothesis.searchstrategy.datetime import (
        DateStrategy,
        DatetimeStrategy,
        TimedeltaStrategy,
    )
    from hypothesis.searchstrategy.deferred import DeferredStrategy
    from hypothesis.searchstrategy.functions import FunctionStrategy
    from hypothesis.searchstrategy.lazy import LazyStrategy
    from hypothesis.searchstrategy.misc import (
        BoolStrategy,
        JustStrategy,
        SampledFromStrategy,
    )
    from hypothesis.searchstrategy.numbers import (
        BoundedIntStrategy,
        FixedBoundedFloatStrategy,
        FloatStrategy,
        WideRangeIntStrategy,
    )
    from hypothesis.searchstrategy.recursive import RecursiveStrategy
    from hypothesis.searchstrategy.shared import SharedStrategy
    from hypothesis.searchstrategy.strategies import OneOfStrategy
    from hypothesis.searchstrategy.strings import (
        BinaryStringStrategy,
        FixedSizeBytes,
        OneCharStringStrategy,
        StringStrategy,
    )
    from hypothesis.types import RandomWithSeed
    from hypothesis.utils.conventions import infer, not_set

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

    try:
        import typing as typing_module

        typing = typing_module
    except ImportError:
        pass

    try:
        import numpy
    except ImportError:
        numpy = None

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

        from hypothesis.utils.conventions import InferType  # noqa
        from hypothesis.searchstrategy.strategies import T, Ex  # noqa

        K, V = TypeVar["K"], TypeVar["V"]
        # See https://github.com/python/mypy/issues/3186 - numbers.Real is wrong!
        Real = Union[int, float, Fraction, Decimal]
    else:

        def overload(f):
            return f

    _strategies = set()

    class FloatKey(object):
        def __init__(self, f):
            self.value = float_to_int(f)

        def __eq__(self, other):
            return isinstance(other, FloatKey) and (other.value == self.value)

        def __ne__(self, other):
            return not self.__eq__(other)

        def __hash__(self):
            return hash(self.value)

    def convert_value(v):
        if isinstance(v, float):
            return FloatKey(v)
        return (type(v), v)

    STRATEGY_CACHE = LRUReusedCache(1024)

    def cacheable(fn):
        # type: (T) -> T
        @proxies(fn)
        def cached_strategy(*args, **kwargs):
            try:
                kwargs_cache_key = {(k, convert_value(v)) for k, v in kwargs.items()}
            except TypeError:
                return fn(*args, **kwargs)
            cache_key = (fn, tuple(map(convert_value, args)), frozenset(kwargs_cache_key))
            try:
                if cache_key in STRATEGY_CACHE:
                    return STRATEGY_CACHE[cache_key]
            except TypeError:
                return fn(*args, **kwargs)
            else:
                result = fn(*args, **kwargs)
                if not isinstance(result, SearchStrategy) or result.is_cacheable:
                    STRATEGY_CACHE[cache_key] = result
                return result

        cached_strategy.__clear_cache = STRATEGY_CACHE.clear
        return cached_strategy

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

        If force_reusable is True, the generated values are assumed to be
        reusable, i.e. immutable and safe to cache, across multiple test
        invocations.
        """

        def decorator(strategy_definition):
            """A decorator that registers the function as a strategy and makes it
            lazily evaluated."""
            _strategies.add(strategy_definition.__name__)

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

            accept.is_hypothesis_strategy_function = True
            return accept

        return decorator

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

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

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

        def calc_has_reusable_values(self, recur):
            return True

        def __repr__(self):
            return "nothing()"

        def map(self, f):
            return self

        def filter(self, f):
            return self

        def flatmap(self, f):
            return self

    NOTHING = Nothing()

    @cacheable
    def nothing():
        # type: () -> SearchStrategy
        """This strategy never successfully draws a value and will always reject on
        an attempt to draw.

        Examples from this strategy do not shrink (because there are none).
        """
        return NOTHING

    def just(value):
        # type: (T) -> SearchStrategy[T]
        """Return a strategy which only generates ``value``.

        Note: ``value`` is not copied. Be wary of using mutable values.

        If ``value`` is the result of a callable, you can use
        :func:`builds(callable) <hypothesis.strategies.builds>` instead
        of ``just(callable())`` to get a fresh value each time.

        Examples from this strategy do not shrink (because there is only one).
        """
        return JustStrategy(value)

    @defines_strategy_with_reusable_values
    def none():
        # type: () -> SearchStrategy[None]
        """Return a strategy which only generates None.

        Examples from this strategy do not shrink (because there is only
        one).
        """
        return just(None)

    @overload
    def one_of(args):
        # type: (Sequence[SearchStrategy[Any]]) -> SearchStrategy[Any]
        pass  # pragma: no cover

    @overload
    def one_of(*args):
        # type: (SearchStrategy[Any]) -> SearchStrategy[Any]
        pass  # pragma: no cover

    def one_of(*args):
        # Mypy workaround alert:  Any is too loose above; the return paramater
        # should be the union of the input parameters.  Unfortunately, Mypy <=0.600
        # raises errors due to incompatible inputs instead.  See #1270 for links.
        # v0.610 doesn't error; it gets inference wrong for 2+ arguments instead.
        """Return a strategy which generates values from any of the argument
        strategies.

        This may be called with one iterable argument instead of multiple
        strategy arguments, in which case ``one_of(x)`` and ``one_of(*x)`` are
        equivalent.

        Examples from this strategy will generally shrink to ones that come from
        strategies earlier in the list, then shrink according to behaviour of the
        strategy that produced them. In order to get good shrinking behaviour,
        try to put simpler strategies first. e.g. ``one_of(none(), text())`` is
        better than ``one_of(text(), none())``.

        This is especially important when using recursive strategies. e.g.
        ``x = st.deferred(lambda: st.none() | st.tuples(x, x))`` will shrink well,
        but ``x = st.deferred(lambda: st.tuples(x, x) | st.none())`` will shrink
        very badly indeed.
        """
        if len(args) == 1 and not isinstance(args[0], SearchStrategy):
            try:
                args = tuple(args[0])
            except TypeError:
                pass
        return OneOfStrategy(args)

    @cacheable
    @defines_strategy_with_reusable_values
    def integers(min_value=None, max_value=None):
        # type: (Real, Real) -> SearchStrategy[int]
        """Returns a strategy which generates integers; in Python 2 these may be
        ints or longs.

        If min_value is not None then all values will be >= min_value. If
        max_value is not None then all values will be <= max_value

        Examples from this strategy will shrink towards zero, and negative values
        will also shrink towards positive (i.e. -n may be replaced by +n).
        """

        check_valid_bound(min_value, "min_value")
        check_valid_bound(max_value, "max_value")
        check_valid_interval(min_value, max_value, "min_value", "max_value")

        min_int_value = None if min_value is None else ceil(min_value)
        max_int_value = None if max_value is None else floor(max_value)

        if min_value != min_int_value:
            note_deprecation(
                "min_value=%r of type %r cannot be exactly represented as an "
                "integer, which will be an error in a future version.  "
                "Use %r instead." % (min_value, type(min_value), min_int_value),
                since="2018-10-10",
            )
        if max_value != max_int_value:
            note_deprecation(
                "max_value=%r of type %r cannot be exactly represented as an "
                "integer, which will be an error in a future version.  "
                "Use %r instead." % (max_value, type(max_value), max_int_value),
                since="2018-10-10",
            )

        if (
            min_int_value is not None
            and max_int_value is not None
            and min_int_value > max_int_value
        ):
            raise InvalidArgument(
                "No integers between min_value=%r and "
                "max_value=%r" % (min_value, max_value)
            )

        if min_int_value is None:
            if max_int_value is None:
                return WideRangeIntStrategy()
            else:
                if max_int_value > 0:
                    return WideRangeIntStrategy().filter(lambda x: x <= max_int_value)
                return WideRangeIntStrategy().map(lambda x: max_int_value - abs(x))
        else:
            if max_int_value is None:
                if min_int_value < 0:
                    return WideRangeIntStrategy().filter(lambda x: x >= min_int_value)
                return WideRangeIntStrategy().map(lambda x: min_int_value + abs(x))
            else:
                assert min_int_value <= max_int_value
                if min_int_value == max_int_value:
                    return just(min_int_value)
                elif min_int_value >= 0:
                    return BoundedIntStrategy(min_int_value, max_int_value)
                elif max_int_value <= 0:
                    return BoundedIntStrategy(-max_int_value, -min_int_value).map(
                        lambda t: -t
                    )
                else:
                    return integers(min_value=0, max_value=max_int_value) | integers(
                        min_value=min_int_value, max_value=0
                    )

    @cacheable
    @defines_strategy
    def booleans():
        # type: () -> SearchStrategy[bool]
        """Returns a strategy which generates instances of :class:`python:bool`.

        Examples from this strategy will shrink towards False (i.e.
        shrinking will try to replace True with False where possible).
        """
        return BoolStrategy()

    @cacheable
    @defines_strategy_with_reusable_values
    def floats(
        min_value=None,  # type: Real
        max_value=None,  # type: Real
        allow_nan=None,  # type: bool
        allow_infinity=None,  # type: bool
        width=64,  # type: int
        exclude_min=False,  # type: bool
        exclude_max=False,  # type: bool
    ):
        # type: (...) -> SearchStrategy[float]
        """Returns a strategy which generates floats.

        - If min_value is not None, all values will be >= min_value.
        - If max_value is not None, all values will be <= max_value.
        - If min_value or max_value is not None, it is an error to enable
          allow_nan.
        - If both min_value and max_value are not None, it is an error to enable
          allow_infinity.

        Where not explicitly ruled out by the bounds, all of infinity, -infinity
        and NaN are possible values generated by this strategy.

        The width argument specifies the maximum number of bits of precision
        required to represent the generated float. Valid values are 16, 32, or 64.
        Passing ``width=32`` will still use the builtin 64-bit ``float`` class,
        but always for values which can be exactly represented as a 32-bit float.
        Half-precision floats (``width=16``) are only supported on Python 3.6, or
        if :pypi:`Numpy` is installed.

        The exclude_min and exclude_max argument can be used to generate numbers
        from open or half-open intervals, by excluding the respective endpoints.
        Attempting to exclude an endpoint which is None will raise an error;
        use ``allow_infinity=False`` to generate finite floats.  You can however
        use e.g. ``min_value=float("-inf"), exclude_min=True`` to exclude only
        one infinite endpoint.

        Examples from this strategy have a complicated and hard to explain
        shrinking behaviour, but it tries to improve "human readability". Finite
        numbers will be preferred to infinity and infinity will be preferred to
        NaN.
        """
        check_type(bool, exclude_min, "exclude_min")
        check_type(bool, exclude_max, "exclude_max")

        if allow_nan is None:
            allow_nan = bool(min_value is None and max_value is None)
        elif allow_nan:
            if min_value is not None or max_value is not None:
                raise InvalidArgument(
                    "Cannot have allow_nan=%r, with min_value or max_value" % (allow_nan)
                )

        if width not in (16, 32, 64):
            raise InvalidArgument(
                "Got width=%r, but the only valid values are the integers 16, "
                "32, and 64." % (width,)
            )
        if width == 16 and sys.version_info[:2] < (3, 6) and numpy is None:
            raise InvalidArgument(  # pragma: no cover
                "width=16 requires either Numpy, or Python >= 3.6"
            )

        check_valid_bound(min_value, "min_value")
        check_valid_bound(max_value, "max_value")

        min_arg, max_arg = min_value, max_value
        if min_value is not None:
            min_value = float_of(min_value, width)
            assert isinstance(min_value, float)
        if max_value is not None:
            max_value = float_of(max_value, width)
            assert isinstance(max_value, float)

        if min_value != min_arg:
            note_deprecation(
                "min_value=%r cannot be exactly represented as a float of width "
                "%d, which will be an error in a future version. Use min_value=%r "
                "instead." % (min_arg, width, min_value),
                since="2018-10-10",
            )
        if max_value != max_arg:
            note_deprecation(
                "max_value=%r cannot be exactly represented as a float of width "
                "%d, which will be an error in a future version. Use max_value=%r "
                "instead" % (max_arg, width, max_value),
                since="2018-10-10",
            )

        if exclude_min and (min_value is None or min_value == float("inf")):
            raise InvalidArgument("Cannot exclude min_value=%r" % (min_value,))
        if exclude_max and (max_value is None or max_value == float("-inf")):
            raise InvalidArgument("Cannot exclude max_value=%r" % (max_value,))

        if min_value is not None and (
            exclude_min or (min_arg is not None and min_value < min_arg)
        ):
            min_value = next_up(min_value, width)
            assert min_value > min_arg or min_value == min_arg == 0  # type: ignore
        if max_value is not None and (
            exclude_max or (max_arg is not None and max_value > max_arg)
        ):
            max_value = next_down(max_value, width)
            assert max_value < max_arg or max_value == max_arg == 0  # type: ignore

        if min_value == float(u"-inf"):
            min_value = None
        if max_value == float(u"inf"):
            max_value = None

        bad_zero_bounds = (
            min_value == max_value == 0
            and is_negative(max_value)
            and not is_negative(min_value)
        )
        if (
            min_value is not None
            and max_value is not None
            and (min_value > max_value or bad_zero_bounds)
        ):
            # This is a custom alternative to check_valid_interval, because we want
            # to include the bit-width and exclusion information in the message.
            msg = (
                "There are no %s-bit floating-point values between min_value=%r "
                "and max_value=%r" % (width, min_arg, max_arg)
            )
            if exclude_min or exclude_max:
                msg += ", exclude_min=%r and exclude_max=%r" % (exclude_min, exclude_max)
            if bad_zero_bounds:
                note_deprecation(msg, since="2019-03-19")
            else:
                raise InvalidArgument(msg)

        if allow_infinity is None:
            allow_infinity = bool(min_value is None or max_value is None)
        elif allow_infinity:
            if min_value is not None and max_value is not None:
                raise InvalidArgument(
                    "Cannot have allow_infinity=%r, with both min_value and "
                    "max_value" % (allow_infinity)
                )
        elif min_value == float("inf"):
            raise InvalidArgument("allow_infinity=False excludes min_value=inf")
        elif max_value == float("-inf"):
            raise InvalidArgument("allow_infinity=False excludes max_value=-inf")

        if min_value is None and max_value is None:
            result = FloatStrategy(
                allow_infinity=allow_infinity, allow_nan=allow_nan
            )  # type: SearchStrategy[float]
        elif min_value is not None and max_value is not None:
            if min_value == max_value:
                assert isinstance(min_value, float)
                result = just(min_value)
            elif is_negative(min_value):
                if is_negative(max_value):
                    result = floats(min_value=-max_value, max_value=-min_value).map(
                        operator.neg
                    )
                else:
                    result = floats(min_value=0.0, max_value=max_value) | floats(
                        min_value=0.0, max_value=-min_value
                    ).map(operator.neg)
            elif count_between_floats(min_value, max_value) > 1000:
                result = FixedBoundedFloatStrategy(
                    lower_bound=min_value, upper_bound=max_value
                )
            else:
                ub_int = float_to_int(max_value, width)
                lb_int = float_to_int(min_value, width)
                assert lb_int <= ub_int
                result = integers(min_value=lb_int, max_value=ub_int).map(
                    lambda x: int_to_float(x, width)
                )
        elif min_value is not None:
            assert isinstance(min_value, float)
            if min_value < 0:
                result = floats(min_value=0.0, allow_infinity=allow_infinity) | floats(
                    min_value=min_value, max_value=-0.0
                )
            else:
                result = floats(allow_infinity=allow_infinity, allow_nan=False).map(
                    lambda x: min_value + abs(x)  # type: ignore
                )
                if not allow_infinity:
                    result = result.filter(lambda x: not math.isinf(x))
            if min_value == 0 and not is_negative(min_value):
                result = result.filter(lambda x: math.copysign(1.0, x) == 1)
        else:
            assert isinstance(max_value, float)
            if max_value > 0:
                result = floats(min_value=0.0, max_value=max_value) | floats(
                    max_value=-0.0, allow_infinity=allow_infinity
                )
            else:
                result = floats(allow_infinity=allow_infinity, allow_nan=False).map(
                    lambda x: max_value - abs(x)  # type: ignore
                )
                if not allow_infinity:
                    result = result.filter(lambda x: not math.isinf(x))
            if max_value == 0 and is_negative(max_value):
                result = result.filter(is_negative)

        if width < 64:

            def downcast(x):
                try:
                    return float_of(x, width)
                except OverflowError:
                    reject()

            return result.map(downcast)
        return result

    @cacheable
    @defines_strategy
    def tuples(*args):
        # type: (*SearchStrategy) -> SearchStrategy[tuple]
        """Return a strategy which generates a tuple of the same length as args by
        generating the value at index i from args[i].

        e.g. tuples(integers(), integers()) would generate a tuple of length
        two with both values an integer.

        Examples from this strategy shrink by shrinking their component parts.
        """
        for arg in args:
            check_strategy(arg)

        return TupleStrategy(args)

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

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

    @defines_strategy
    def sampled_from(elements):
        """Returns a strategy which generates any value present in ``elements``.

        Note that as with :func:`~hypothesis.strategies.just`, values will not be
        copied and thus you should be careful of using mutable data.

        ``sampled_from`` supports ordered collections, as well as
        :class:`~python:enum.Enum` objects.  :class:`~python:enum.Flag` objects
        may also generate any combination of their members.

        Examples from this strategy shrink by replacing them with values earlier in
        the list. So e.g. sampled_from((10, 1)) will shrink by trying to replace
        1 values with 10, and sampled_from((1, 10)) will shrink by trying to
        replace 10 values with 1.
        """
        values = check_sample(elements, "sampled_from")
        if not values:
            note_deprecation(
                "sampled_from() with nothing to sample is deprecated and will be an "
                "error in a future version.  It currently returns `st.nothing()`, "
                "which if unexpected can make parts of a strategy silently vanish.",
                since="2019-03-12",
            )
            return nothing()
        if len(values) == 1:
            return just(values[0])
        if hasattr(enum, "Flag") and isclass(elements) and issubclass(elements, enum.Flag):
            # Combinations of enum.Flag members are also members.  We generate
            # these dynamically, because static allocation takes O(2^n) memory.
            return sets(sampled_from(values), min_size=1).map(
                lambda s: reduce(operator.or_, s)
            )
        return SampledFromStrategy(values)

    @cacheable
    @defines_strategy
    def lists(
        elements,  # type: SearchStrategy[Ex]
        min_size=0,  # type: int
        max_size=None,  # type: int
        unique_by=None,  # type: Union[Callable, Tuple[Callable, ...]]
        unique=False,  # type: bool
    ):
        # type: (...) -> SearchStrategy[List[Ex]]
        """Returns a list containing values drawn from elements with length in the
        interval [min_size, max_size] (no bounds in that direction if these are
        None). If max_size is 0, 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:

                def unique_by(x):
                    return x

        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(
                    "unique_by=%r is not a callable or tuple of callables" % (unique_by)
                )
            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("unique_by[%i]=%r is not a callable" % (i, f))
            # Note that lazy strategies automatically unwrap when passed to a defines_strategy
            # function.
            if isinstance(elements, SampledFromStrategy):
                element_count = len(elements.elements)
                if min_size > element_count:
                    raise InvalidArgument(
                        "Cannot create a collection of min_size=%r unique elements with "
                        "values drawn from only %d distinct elements"
                        % (min_size, element_count)
                    )

                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
                )

            return UniqueListStrategy(
                elements=elements, max_size=max_size, min_size=min_size, keys=unique_by
            )
        return ListStrategy(elements, min_size=min_size, max_size=max_size)

    @cacheable
    @defines_strategy
    def sets(
        elements,  # type: SearchStrategy[Ex]
        min_size=0,  # type: int
        max_size=None,  # type: int
    ):
        # type: (...) -> SearchStrategy[Set[Ex]]
        """This has the same behaviour as lists, but returns sets instead.

        Note that Hypothesis cannot tell if values are drawn from elements
        are hashable until running the test, so you can define a strategy
        for sets of an unhashable type but it will fail at test time.

        Examples from this strategy shrink by trying to remove elements from the
        set, and by shrinking each individual element of the set.
        """
        return lists(
            elements=elements, min_size=min_size, max_size=max_size, unique=True
        ).map(set)

    @cacheable
    @defines_strategy
    def frozensets(
        elements,  # type: SearchStrategy[Ex]
        min_size=0,  # type: int
        max_size=None,  # type: int
    ):
        # type: (...) -> 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)

    @implements_iterator
    class PrettyIter(object):
        def __init__(self, values):
            self._values = values
            self._iter = iter(self._values)

        def __iter__(self):
            return self._iter

        def __next__(self):
            return next(self._iter)

        def __repr__(self):
            return "iter({!r})".format(self._values)

    @defines_strategy
    def iterables(elements, min_size=0, max_size=None, unique_by=None, unique=False):
        """This has the same behaviour as lists, but returns iterables instead.

        Some iterables cannot be indexed (e.g. sets) and some do not have a
        fixed length (e.g. generators). This strategy produces iterators,
        which cannot be indexed and do not have a fixed length. This ensures
        that you do not accidentally depend on sequence behaviour.
        """
        return lists(
            elements=elements,
            min_size=min_size,
            max_size=max_size,
            unique_by=unique_by,
            unique=unique,
        ).map(PrettyIter)

    @defines_strategy
    def fixed_dictionaries(mapping):
        # type: (Dict[T, SearchStrategy[Ex]]) -> SearchStrategy[Dict[T, Ex]]
        """Generates a dictionary of the same type as mapping with a fixed set of
        keys mapping to strategies. mapping must be a dict subclass.

        Generated values have all keys present in mapping, with the
        corresponding values drawn from mapping[key]. If mapping is an
        instance of OrderedDict the keys will also be in the same order,
        otherwise the order is arbitrary.

        Examples from this strategy shrink by shrinking each individual value in
        the generated dictionary.
        """
        check_type(dict, mapping, "mapping")
        for v in mapping.values():
            check_strategy(v)
        return FixedKeysDictStrategy(mapping)

    @cacheable
    @defines_strategy
    def dictionaries(
        keys,  # type: SearchStrategy[Ex]
        values,  # type: SearchStrategy[T]
        dict_class=dict,  # type: type
        min_size=0,  # type: int
        max_size=None,  # type: int
    ):
        # type: (...) -> 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)
        check_strategy(values)

        return lists(
            tuples(keys, values),
            min_size=min_size,
            max_size=max_size,
            unique_by=lambda x: x[0],
        ).map(dict_class)

    @cacheable
    @defines_strategy_with_reusable_values
    def characters(
        whitelist_categories=None,  # type: Sequence[Text]
        blacklist_categories=None,  # type: Sequence[Text]
        blacklist_characters=None,  # type: Sequence[Text]
        min_codepoint=None,  # type: int
        max_codepoint=None,  # type: int
        whitelist_characters=None,  # type: Sequence[Text]
    ):
        # type: (...) -> SearchStrategy[Text]
        """Generates unicode text type (unicode on python 2, str on python 3)
        characters following specified filtering rules.

        - When no filtering rules are specifed, any character can be produced.
        - If ``min_codepoint`` or ``max_codepoint`` is specifed, then only
          characters having a codepoint in that range will be produced.
        - If ``whitelist_categories`` is specified, then only characters from those
          Unicode categories will be produced. This is a further restriction,
          characters must also satisfy ``min_codepoint`` and ``max_codepoint``.
        - If ``blacklist_categories`` is specified, then any character from those
          categories will not be produced.  Any overlap between
          ``whitelist_categories`` and ``blacklist_categories`` will raise an
          exception, as each character can only belong to a single class.
        - If ``whitelist_characters`` is specified, then any additional characters
          in that list will also be produced.
        - If ``blacklist_characters`` is specified, then any characters in
          that list will be not be produced. Any overlap between
          ``whitelist_characters`` and ``blacklist_characters`` will raise an
          exception.

        The ``_codepoint`` arguments must be integers between zero and
        :obj:`python:sys.maxunicode`.  The ``_characters`` arguments must be
        collections of length-one unicode strings, such as a unicode string.

        The ``_categories`` arguments must be used to specify either the
        one-letter Unicode major category or the two-letter Unicode
        `general category`_.  For example, ``('Nd', 'Lu')`` signifies "Number,
        decimal digit" and "Letter, uppercase".  A single letter ('major category')
        can be given to match all corresponding categories, for example ``'P'``
        for characters in any punctuation category.

        .. _general category: https://wikipedia.org/wiki/Unicode_character_property

        Examples from this strategy shrink towards the codepoint for ``'0'``,
        or the first allowable codepoint after it if ``'0'`` is excluded.
        """
        check_valid_size(min_codepoint, "min_codepoint")
        check_valid_size(max_codepoint, "max_codepoint")
        check_valid_interval(min_codepoint, max_codepoint, "min_codepoint", "max_codepoint")
        if (
            min_codepoint is None
            and max_codepoint is None
            and whitelist_categories is None
            and blacklist_categories is None
            and whitelist_characters is not None
        ):
            raise InvalidArgument(
                "Nothing is excluded by other arguments, so passing only "
                "whitelist_characters=%(chars)r would have no effect.  Also pass "
                "whitelist_categories=(), or use sampled_from(%(chars)r) instead."
                % dict(chars=whitelist_characters)
            )
        blacklist_characters = blacklist_characters or ""
        whitelist_characters = whitelist_characters or ""
        overlap = set(blacklist_characters).intersection(whitelist_characters)
        if overlap:
            raise InvalidArgument(
                "Characters %r are present in both whitelist_characters=%r, and "
                "blacklist_characters=%r"
                % (sorted(overlap), whitelist_characters, blacklist_characters)
            )
        blacklist_categories = as_general_categories(
            blacklist_categories, "blacklist_categories"
        )
        if (
            whitelist_categories is not None
            and not whitelist_categories
            and not whitelist_characters
        ):
            raise InvalidArgument(
                "When whitelist_categories is an empty collection and there are "
                "no characters specified in whitelist_characters, nothing can "
                "be generated by the characters() strategy."
            )
        whitelist_categories = as_general_categories(
            whitelist_categories, "whitelist_categories"
        )
        both_cats = set(blacklist_categories or ()).intersection(whitelist_categories or ())
        if both_cats:
            raise InvalidArgument(
                "Categories %r are present in both whitelist_categories=%r, and "
                "blacklist_categories=%r"
                % (sorted(both_cats), whitelist_categories, blacklist_categories)
            )

        return OneCharStringStrategy(
            whitelist_categories=whitelist_categories,
            blacklist_categories=blacklist_categories,
            blacklist_characters=blacklist_characters,
            min_codepoint=min_codepoint,
            max_codepoint=max_codepoint,
            whitelist_characters=whitelist_characters,
        )

    @cacheable
    @defines_strategy_with_reusable_values
    def text(
        alphabet=characters(
            blacklist_categories=("Cs",)
        ),  # type: Union[Sequence[Text], SearchStrategy[Text]]
        min_size=0,  # type: int
        max_size=None,  # type: int
    ):
        # type: (...) -> SearchStrategy[Text]
        """Generates values of a unicode text type (unicode on python 2, str on
        python 3) with values drawn from ``alphabet``, which should be an iterable of
        length one strings or a strategy generating such 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 alphabet is None:
            note_deprecation(
                "alphabet=None is deprecated; just omit the argument", since="2018-10-05"
            )
            char_strategy = characters(blacklist_categories=("Cs",))
        elif isinstance(alphabet, SearchStrategy):
            char_strategy = alphabet
        else:
            non_string = [c for c in alphabet if not isinstance(c, string_types)]
            if non_string:
                raise InvalidArgument(
                    "The following elements in alphabet are not unicode "
                    "strings:  %r" % (non_string,)
                )
            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, which leads to violation of size constraints:  %r"
                    % (not_one_char,)
                )
            char_strategy = (
                characters(whitelist_categories=(), whitelist_characters=alphabet)
                if alphabet
                else nothing()
            )
        if (max_size == 0 or char_strategy.is_empty) and not min_size:
            return just(u"")
        return StringStrategy(lists(char_strategy, min_size=min_size, max_size=max_size))

    @cacheable
    @defines_strategy
    def from_regex(regex, fullmatch=False):
        # type: (Union[AnyStr, Pattern[AnyStr]], bool) -> 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.

        Examples from this strategy shrink towards shorter strings and lower
        character values, with exact behaviour that may depend on the pattern.
        """
        check_type(bool, fullmatch, "fullmatch")
        # TODO: We would like to move this to the top level, but pending some major
        # refactoring it's hard to do without creating circular imports.
        from hypothesis.searchstrategy.regex import regex_strategy

        return regex_strategy(regex, fullmatch)

    @cacheable
    @defines_strategy_with_reusable_values
    def binary(min_size=0, max_size=None):
        # type: (int, int) -> SearchStrategy[bytes]
        """Generates the appropriate binary type (str in python 2, bytes in python
        3).

        min_size and max_size have the usual interpretations.

        Examples from this strategy shrink towards smaller strings and lower byte
        values.
        """
        check_valid_sizes(min_size, max_size)
        if min_size == max_size is not None:
            return FixedSizeBytes(min_size)
        return BinaryStringStrategy(
            lists(
                integers(min_value=0, max_value=255), min_size=min_size, max_size=max_size
            )
        )

    @cacheable
    @defines_strategy
    def randoms():
        # type: () -> SearchStrategy[random.Random]
        """Generates instances of ``random.Random``, tweaked to show the seed value
        in the repr for reproducibility.

        Examples from this strategy shrink to seeds closer to zero.
        """
        return integers().map(RandomWithSeed)

    class RandomSeeder(object):
        def __init__(self, seed):
            self.seed = seed

        def __repr__(self):
            return "RandomSeeder(%r)" % (self.seed,)

    class RandomModule(SearchStrategy):
        def do_draw(self, data):
            data.can_reproduce_example_from_repr = False
            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():
        # type: () -> SearchStrategy[RandomSeeder]
        """The Hypothesis engine handles PRNG state for the stdlib and Numpy random
        modules internally, always seeding them to zero and restoring the previous
        state after the test.

        If having a fixed seed would unacceptably weaken your tests, and you
        cannot use a ``random.Random`` instance provided by
        :func:`~hypothesis.strategies.randoms`, this strategy calls
        :func:`python:random.seed` with an arbitrary integer and passes you
        an opaque object whose repr displays the seed value for debugging.
        If ``numpy.random`` is available, that state is also managed.

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

    @cacheable
    @defines_strategy
    def builds(
        *callable_and_args,  # type: Any
        **kwargs  # type: Union[SearchStrategy[Any], InferType]
    ):
        # type: (...) -> SearchStrategy[Any]
        """Generates values by drawing from ``args`` and ``kwargs`` and passing
        them to the callable (provided as the first positional argument) in the
        appropriate argument position.

        e.g. ``builds(target, integers(), flag=booleans())`` would draw an
        integer ``i`` and a boolean ``b`` and call ``target(i, flag=b)``.

        If the callable has type annotations, they will be used to infer a strategy
        for required arguments that were not passed to builds.  You can also tell
        builds to infer a strategy for an optional argument by passing the special
        value :const:`hypothesis.infer` as a keyword argument to
        builds, instead of a strategy for that argument to the callable.

        If the callable is a class defined with :pypi:`attrs`, missing required
        arguments will be inferred from the attribute on a best-effort basis,
        e.g. by checking :ref:`attrs standard validators <attrs:api_validators>`.
        Dataclasses are handled natively by the inference from type hints.

        Examples from this strategy shrink by shrinking the argument values to
        the callable.
        """
        if not callable_and_args:
            raise InvalidArgument(
                "builds() must be passed a callable as the first positional "
                "argument, but no positional arguments were given."
            )
        target, args = callable_and_args[0], callable_and_args[1:]
        if not callable(target):
            raise InvalidArgument(
                "The first positional argument to builds() must be a callable "
                "target to construct."
            )

        if infer in args:
            # Avoid an implementation nightmare juggling tuples and worse things
            raise InvalidArgument(
                "infer was passed as a positional argument to "
                "builds(), but is only allowed as a keyword arg"
            )
        required = required_args(target, args, kwargs) or set()
        to_infer = {k for k, v in kwargs.items() if v is infer}
        if required or to_infer:
            if isclass(target) and attr.has(target):
                # Use our custom introspection for attrs classes
                from hypothesis.searchstrategy.attrs import from_attrs

                return from_attrs(target, args, kwargs, required | to_infer)
            # Otherwise, try using type hints
            if isclass(target):
                if is_typed_named_tuple(target):
                    # Special handling for typing.NamedTuple
                    hints = target._field_types
                else:
                    hints = get_type_hints(target.__init__)
            else:
                hints = get_type_hints(target)
            if to_infer - set(hints):
                raise InvalidArgument(
                    "passed infer for %s, but there is no type annotation"
                    % (", ".join(sorted(to_infer - set(hints))))
                )
            for kw in set(hints) & (required | to_infer):
                kwargs[kw] = from_type(hints[kw])
        # Mypy doesn't realise that `infer` is gone from kwargs now
        kwarg_strat = fixed_dictionaries(kwargs)  # type: ignore
        return tuples(tuples(*args), kwarg_strat).map(
            lambda value: target(*value[0], **value[1])
        )

    def _defer_from_type(func):
        # type: (T) -> T
        """Decorator to make from_type lazy to support recursive definitions."""

        @proxies(func)
        def inner(*args, **kwargs):
            return deferred(lambda: func(*args, **kwargs))

        return inner

    @cacheable
    @_defer_from_type
    def from_type(thing):
        # type: (Type[Ex]) -> SearchStrategy[Ex]
        """Looks up the appropriate search strategy for the given type.

        ``from_type`` is used internally to fill in missing arguments to
        :func:`~hypothesis.strategies.builds` and can be used interactively
        to explore what strategies are available or to debug type resolution.

        You can use :func:`~hypothesis.strategies.register_type_strategy` to
        handle your custom types, or to globally redefine certain strategies -
        for example excluding NaN from floats, or use timezone-aware instead of
        naive time and datetime strategies.

        The resolution logic may be changed in a future version, but currently
        tries these five options:

        1. If ``thing`` is in the default lookup mapping or user-registered lookup,
           return the corresponding strategy.  The default lookup covers all types
           with Hypothesis strategies, including extras where possible.
        2. If ``thing`` is from the :mod:`python:typing` module, return the
           corresponding strategy (special logic).
        3. If ``thing`` has one or more subtypes in the merged lookup, return
           the union of the strategies for those types that are not subtypes of
           other elements in the lookup.
        4. Finally, if ``thing`` has type annotations for all required arguments,
           and is not an abstract class, it is resolved via
           :func:`~hypothesis.strategies.builds`.
        5. Because :mod:`abstract types <python:abc>` cannot be instantiated,
           we treat abstract types as the union of their concrete subclasses.
           Note that this lookup works via inheritance but not via
           :obj:`~python:abc.ABCMeta.register`, so you may still need to use
           :func:`~hypothesis.strategies.register_type_strategy`.

        There is a valuable recipe for leveraging ``from_type()`` to generate
        "everything except" values from a specified type. I.e.

        .. code-block:: python

            def everything_except(excluded_types):
                return (
                    from_type(type).flatmap(from_type)
                    .filter(lambda x: not isinstance(x, excluded_types))
                )

        For example, ``everything_except(int)`` returns a strategy that can
        generate anything that ``from_type()`` can ever generate, except for
        instances of :class:`python:int`, and excluding instances of types
        added via :func:`~hypothesis.strategies.register_type_strategy`.

        This is useful when writing tests which check that invalid input is
        rejected in a certain way.
        """
        # TODO: We would like to move this to the top level, but pending some major
        # refactoring it's hard to do without creating circular imports.
        from hypothesis.searchstrategy import types

        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 not isinstance(strategy, SearchStrategy):
                raise ResolutionFailed(
                    "Error: %s was registered for %r, but returned non-strategy %r"
                    % (thing, nicerepr(strat_or_callable), strategy)
                )
            if strategy.is_empty:
                raise ResolutionFailed("Error: %r resolved to an empty strategy" % (thing,))
            return strategy

        if typing is not None:  # pragma: no branch
            if not isinstance(thing, type):
                if types.is_a_new_type(thing):
                    # Check if we have an explicitly registered strategy for this thing,
                    # resolve it so, and otherwise resolve as for the base type.
                    if thing in types._global_type_lookup:
                        return as_strategy(types._global_type_lookup[thing], thing)
                    return from_type(thing.__supertype__)
                # Under Python 3.6, Unions are not instances of `type` - but we
                # still want to resolve them!
                if getattr(thing, "__origin__", None) is typing.Union:
                    args = sorted(thing.__args__, key=types.type_sorting_key)
                    return one_of([from_type(t) for t in args])
            # We can't resolve forward references, and under Python 3.5 (only)
            # a forward reference is an instance of type.  Hence, explicit check:
            elif type(thing) == getattr(typing, "_ForwardRef", None):  # pragma: no cover
                raise ResolutionFailed(
                    "thing=%s cannot be resolved.  Upgrading to python>=3.6 may "
                    "fix this problem via improvements to the typing module." % (thing,)
                )
        if not types.is_a_type(thing):
            raise InvalidArgument("thing=%s must be a type" % (thing,))
        # Now that we know `thing` is a type, the first step is to check for an
        # explicitly registered strategy.  This is the best (and hopefully most
        # common) way to resolve a type to a strategy.  Note that the value in the
        # lookup may be a strategy or a function from type -> strategy; and we
        # convert empty results into an explicit error.
        if thing in types._global_type_lookup:
            return as_strategy(types._global_type_lookup[thing], thing)
        # If there's no explicitly registered strategy, maybe a subtype of thing
        # is registered - if so, we can resolve it to the subclass strategy.
        # We'll start by checking if thing is from from the typing module,
        # because there are several special cases that don't play well with
        # subclass and instance checks.
        if typing is not None:  # pragma: no branch
            if isinstance(thing, typing_root_type):
                return types.from_typing_type(thing)
        # If it's not from the typing module, we get all registered types that are
        # a subclass of `thing` and are not themselves a subtype of any other such
        # type.  For example, `Number -> integers() | floats()`, but bools() is
        # not included because bool is a subclass of int as well as Number.
        strategies = [
            as_strategy(v, thing)
            for k, v in types._global_type_lookup.items()
            if isinstance(k, type)
            and issubclass(k, thing)
            and sum(types.try_issubclass(k, typ) for typ in types._global_type_lookup) == 1
        ]
        if 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)
        # If we know that builds(thing) will fail, give a better error message
        required = required_args(thing)
        if required and not any(
            [
                required.issubset(get_type_hints(thing.__init__)),
                attr.has(thing),
                # NamedTuples are weird enough that we need a specific check for them.
                is_typed_named_tuple(thing),
            ]
        ):
            raise ResolutionFailed(
                "Could not resolve %r to a strategy; consider "
                "using register_type_strategy" % (thing,)
            )
        # Finally, try to build an instance by calling the type object
        if not isabstract(thing):
            return builds(thing)
        subclasses = thing.__subclasses__()
        if not subclasses:
            raise ResolutionFailed(
                "Could not resolve %r to a strategy, because it is an abstract type "
                "without any subclasses. Consider using register_type_strategy" % (thing,)
            )
        return sampled_from(subclasses).flatmap(from_type)

    @cacheable
    @defines_strategy_with_reusable_values
    def fractions(
        min_value=None,  # type: Union[Real, AnyStr]
        max_value=None,  # type: Union[Real, AnyStr]
        max_denominator=None,  # type: int
    ):
        # type: (...) -> SearchStrategy[Fraction]
        """Returns a strategy which generates Fractions.

        If ``min_value`` is not None then all generated values are no less than
        ``min_value``.  If ``max_value`` is not None then all generated values are no
        greater than ``max_value``.  ``min_value`` and ``max_value`` may be anything accepted
        by the :class:`~fractions.Fraction` constructor.

        If ``max_denominator`` is not None then the denominator of any generated
        values is no greater than ``max_denominator``. Note that ``max_denominator`` must
        be None or a positive integer.

        Examples from this strategy shrink towards smaller denominators, then
        closer to zero.
        """
        min_value = try_convert(Fraction, min_value, "min_value")
        max_value = try_convert(Fraction, max_value, "max_value")

        if (
            min_value is not None
            and not isinstance(min_value, Fraction)
            or max_value is not None
            and not isinstance(max_value, Fraction)
        ):
            assert False, "Unreachable for Mypy"  # pragma: no cover

        check_valid_interval(min_value, max_value, "min_value", "max_value")
        check_valid_integer(max_denominator)

        if max_denominator is not None:
            if max_denominator < 1:
                raise InvalidArgument("max_denominator=%r must be >= 1" % max_denominator)

            def fraction_bounds(value):
                # type: (Fraction) -> Tuple[Fraction, Fraction]
                """Find the best lower and upper approximation for value."""
                # Adapted from CPython's Fraction.limit_denominator here:
                # https://github.com/python/cpython/blob/3.6/Lib/fractions.py#L219
                assert max_denominator is not None
                if value is None or value.denominator <= max_denominator:
                    return value, value

                p0, q0, p1, q1 = 0, 1, 1, 0
                n, d = value.numerator, value.denominator
                while True:
                    a = n // d
                    q2 = q0 + a * q1
                    if q2 > max_denominator:
                        break
                    p0, q0, p1, q1 = p1, q1, p0 + a * p1, q2
                    n, d = d, n - a * d
                k = (max_denominator - q0) // q1
                low, high = Fraction(p1, q1), Fraction(p0 + k * p1, q0 + k * q1)
                assert low < value < high
                return low, high

            # Take the high approximation for min_value and low for max_value
            bounds = (max_denominator, min_value, max_value)
            if min_value is not None:
                if min_value.denominator > max_denominator:
                    note_deprecation(
                        "The min_value=%r has a denominator greater than the "
                        "max_denominator=%r, which will be an error in a future "
                        "version." % (min_value, max_denominator),
                        since="2018-10-12",
                    )
                _, min_value = fraction_bounds(min_value)
            if max_value is not None:
                if max_value.denominator > max_denominator:
                    note_deprecation(
                        "The max_value=%r has a denominator greater than the "
                        "max_denominator=%r, which will be an error in a future "
                        "version." % (max_value, max_denominator),
                        since="2018-10-12",
                    )
                max_value, _ = fraction_bounds(max_value)

            if min_value is not None and max_value is not None and min_value > max_value:
                raise InvalidArgument(
                    "There are no fractions with a denominator <= %r between "
                    "min_value=%r and max_value=%r" % bounds
                )

        if min_value is not None and min_value == max_value:
            return just(min_value)

        def dm_func(denom):
            """Take denom, construct numerator strategy, and build fraction."""
            # Four cases of algebra to get integer bounds and scale factor.
            min_num, max_num = None, None
            if max_value is None and min_value is None:
                pass
            elif min_value is None:
                max_num = denom * max_value.numerator
                denom *= max_value.denominator
            elif max_value is None:
                min_num = denom * min_value.numerator
                denom *= min_value.denominator
            else:
                low = min_value.numerator * max_value.denominator
                high = max_value.numerator * min_value.denominator
                scale = min_value.denominator * max_value.denominator
                # After calculating our integer bounds and scale factor, we remove
                # the gcd to avoid drawing more bytes for the example than needed.
                # Note that `div` can be at most equal to `scale`.
                div = gcd(scale, gcd(low, high))
                min_num = denom * low // div
                max_num = denom * high // div
                denom *= scale // div

            return builds(
                Fraction, integers(min_value=min_num, max_value=max_num), just(denom)
            )

        if max_denominator is None:
            return integers(min_value=1).flatmap(dm_func)

        return (
            integers(1, max_denominator)
            .flatmap(dm_func)
            .map(lambda f: f.limit_denominator(max_denominator))
        )

    def _as_finite_decimal(
        value,  # type: Union[Real, AnyStr, None]
        name,  # type: str
        allow_infinity,  # type: Optional[bool]
    ):
        # type: (...) -> Optional[Decimal]
        """Convert decimal bounds to decimals, carefully."""
        assert name in ("min_value", "max_value")
        if value is None:
            return None
        if not isinstance(value, Decimal):
            with localcontext(Context()):  # ensure that default traps are enabled
                value = try_convert(Decimal, value, name)
        assert isinstance(value, Decimal)
        if value.is_finite():
            return value
        if value.is_infinite() and (value < 0 if "min" in name else value > 0):
            if allow_infinity or allow_infinity is None:
                return None
            raise InvalidArgument(
                "allow_infinity=%r, but %s=%r" % (allow_infinity, name, value)
            )
        # This could be infinity, quiet NaN, or signalling NaN
        raise InvalidArgument(u"Invalid %s=%r" % (name, value))

    @cacheable
    @defines_strategy_with_reusable_values
    def decimals(
        min_value=None,  # type: Union[Real, AnyStr]
        max_value=None,  # type: Union[Real, AnyStr]
        allow_nan=None,  # type: bool
        allow_infinity=None,  # type: bool
        places=None,  # type: int
    ):
        # type: (...) -> SearchStrategy[Decimal]
        """Generates instances of :class:`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)
        if places is not None and places < 0:
            raise InvalidArgument("places=%r may not be negative" % places)
        min_value = _as_finite_decimal(min_value, "min_value", allow_infinity)
        max_value = _as_finite_decimal(max_value, "max_value", allow_infinity)
        check_valid_interval(min_value, max_value, "min_value", "max_value")
        if allow_infinity and (None not in (min_value, max_value)):
            raise InvalidArgument("Cannot allow infinity between finite bounds")
        # Set up a strategy for finite decimals.  Note that both floating and
        # fixed-point decimals require careful handling to remain isolated from
        # any external precision context - in short, we always work out the
        # required precision for lossless operation and use context methods.
        if places is not None:
            # Fixed-point decimals are basically integers with a scale factor
            def ctx(val):
                """Return a context in which this value is lossless."""
                precision = ceil(math.log10(abs(val) or 1)) + places + 1
                return Context(prec=max([precision, 1]))

            def int_to_decimal(val):
                context = ctx(val)
                return context.quantize(context.multiply(val, factor), factor)

            factor = Decimal(10) ** -places
            min_num, max_num = None, None
            if min_value is not None:
                min_num = ceil(ctx(min_value).divide(min_value, factor))
            if max_value is not None:
                max_num = floor(ctx(max_value).divide(max_value, factor))
            if min_num is not None and max_num is not None and min_num > max_num:
                raise InvalidArgument(
                    "There are no decimals with %d places between min_value=%r "
                    "and max_value=%r " % (places, min_value, max_value)
                )
            strat = integers(min_num, max_num).map(int_to_decimal)
        else:
            # Otherwise, they're like fractions featuring a power of ten
            def fraction_to_decimal(val):
                precision = (
                    ceil(math.log10(abs(val.numerator) or 1) + math.log10(val.denominator))
                    + 1
                )
                return Context(prec=precision or 1).divide(
                    Decimal(val.numerator), val.denominator
                )

            strat = fractions(min_value, max_value).map(fraction_to_decimal)
        # Compose with sampled_from for infinities and NaNs as appropriate
        special = []  # type: List[Decimal]
        if allow_nan or (allow_nan is None and (None in (min_value, max_value))):
            special.extend(map(Decimal, ("NaN", "-NaN", "sNaN", "-sNaN")))
        if allow_infinity or (allow_infinity is max_value is None):
            special.append(Decimal("Infinity"))
        if allow_infinity or (allow_infinity is min_value is None):
            special.append(Decimal("-Infinity"))
        return strat | (sampled_from(special) if special else nothing())

    def recursive(
        base,  # type: SearchStrategy[Ex]
        extend,  # type: Callable[[SearchStrategy[Any]], SearchStrategy[T]]
        max_leaves=100,  # type: int
    ):
        # type: (...) -> SearchStrategy[Union[T, Ex]]
        """base: A strategy to start from.

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

        max_leaves: The maximum number of elements to be drawn from base on a given
        run.

        This returns a strategy ``S`` such that ``S = extend(base | S)``. That is,
        values may be drawn from base, or from any strategy reachable by mixing
        applications of | and extend.

        An example may clarify: ``recursive(booleans(), lists)`` would return a
        strategy that may return arbitrarily nested and mixed lists of booleans.
        So e.g. ``False``, ``[True]``, ``[False, []]``, and ``[[[[True]]]]`` are
        all valid values to be drawn from that strategy.

        Examples from this strategy shrink by trying to reduce the amount of
        recursion and by shrinking according to the shrinking behaviour of base
        and the result of extend.

        """

        return RecursiveStrategy(base, extend, max_leaves)

    class PermutationStrategy(SearchStrategy):
        def __init__(self, values):
            self.values = values

        def do_draw(self, data):
            # Reversed Fisher-Yates shuffle: 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 hrange(len(result) - 1):
                j = integer_range(data, i, len(result) - 1)
                result[i], result[j] = result[j], result[i]
            return result

    @defines_strategy
    def permutations(values):
        # type: (Sequence[T]) -> SearchStrategy[List[T]]
        """Return a strategy which returns permutations of the ordered collection
        ``values``.

        Examples from this strategy shrink by trying to become closer to the
        original order of values.
        """
        values = check_sample(values, "permutations")
        if not values:
            return builds(list)

        return PermutationStrategy(values)

    @defines_strategy_with_reusable_values
    def datetimes(
        min_value=dt.datetime.min,  # type: dt.datetime
        max_value=dt.datetime.max,  # type: dt.datetime
        timezones=none(),  # type: SearchStrategy[Optional[dt.tzinfo]]
    ):
        # type: (...) -> SearchStrategy[dt.datetime]
        """A strategy for generating datetimes, which may be timezone-aware.

        This strategy works by drawing a naive datetime between ``min_value``
        and ``max_value``, which must both be naive (have no timezone).

        ``timezones`` must be a strategy that generates
        :class:`~python:datetime.tzinfo` objects (or None,
        which is valid for naive datetimes).  A value drawn from this strategy
        will be added to a naive datetime, and the resulting tz-aware datetime
        returned.

        .. note::
            tz-aware datetimes from this strategy may be ambiguous or non-existent
            due to daylight savings, leap seconds, timezone and calendar
            adjustments, etc.  This is intentional, as malformed timestamps are a
            common source of bugs.

        :py:func:`hypothesis.extra.pytz.timezones` requires the :pypi:`pytz`
        package, but provides all timezones in the Olsen database.  If you want to
        allow naive datetimes, combine strategies like ``none() | timezones()``.

        :py:func:`hypothesis.extra.dateutil.timezones` requires the
        :pypi:`python-dateutil` package, and similarly provides all timezones
        there.

        Alternatively, you can create a list of the timezones you wish to allow
        (e.g. from the standard library, ``datetutil``, or ``pytz``) and use
        :py:func:`sampled_from`.  Ensure that simple values such as None or UTC
        are at the beginning of the list for proper minimisation.

        Examples from this strategy shrink towards midnight on January 1st 2000.
        """
        # Why must bounds be naive?  In principle, we could also write a strategy
        # that took aware bounds, but the API and validation is much harder.
        # If you want to generate datetimes between two particular momements in
        # time I suggest (a) just filtering out-of-bounds values; (b) if bounds
        # are very close, draw a value and subtract its UTC offset, handling
        # overflows and nonexistent times; or (c) do something customised to
        # handle datetimes in e.g. a four-microsecond span which is not
        # representable in UTC.  Handling (d), all of the above, leads to a much
        # more complex API for all users and a useful feature for very few.
        check_type(dt.datetime, min_value, "min_value")
        check_type(dt.datetime, max_value, "max_value")
        if min_value.tzinfo is not None:
            raise InvalidArgument("min_value=%r must not have tzinfo" % (min_value,))
        if max_value.tzinfo is not None:
            raise InvalidArgument("max_value=%r must not have tzinfo" % (max_value,))
        check_valid_interval(min_value, max_value, "min_value", "max_value")
        if not isinstance(timezones, SearchStrategy):
            raise InvalidArgument(
                "timezones=%r must be a SearchStrategy that can provide tzinfo "
                "for datetimes (either None or dt.tzinfo objects)" % (timezones,)
            )
        return DatetimeStrategy(min_value, max_value, timezones)

    @defines_strategy_with_reusable_values
    def dates(min_value=dt.date.min, max_value=dt.date.max):
        # type: (dt.date, dt.date) -> SearchStrategy[dt.date]
        """A strategy for dates between ``min_value`` and ``max_value``.

        Examples from this strategy shrink towards January 1st 2000.
        """
        check_type(dt.date, min_value, "min_value")
        check_type(dt.date, max_value, "max_value")
        check_valid_interval(min_value, max_value, "min_value", "max_value")
        if min_value == max_value:
            return just(min_value)
        return DateStrategy(min_value, max_value)

    @defines_strategy_with_reusable_values
    def times(min_value=dt.time.min, max_value=dt.time.max, timezones=none()):
        # type: (dt.time, dt.time, SearchStrategy) -> SearchStrategy[dt.time]
        """A strategy for times between ``min_value`` and ``max_value``.

        The ``timezones`` argument is handled as for :py:func:`datetimes`.

        Examples from this strategy shrink towards midnight, with the timezone
        component shrinking as for the strategy that provided it.
        """
        check_type(dt.time, min_value, "min_value")
        check_type(dt.time, max_value, "max_value")
        if min_value.tzinfo is not None:
            raise InvalidArgument("min_value=%r must not have tzinfo" % min_value)
        if max_value.tzinfo is not None:
            raise InvalidArgument("max_value=%r must not have tzinfo" % max_value)
        check_valid_interval(min_value, max_value, "min_value", "max_value")
        day = dt.date(2000, 1, 1)
        return datetimes(
            min_value=dt.datetime.combine(day, min_value),
            max_value=dt.datetime.combine(day, max_value),
            timezones=timezones,
        ).map(lambda t: t.timetz())

    @defines_strategy_with_reusable_values
    def timedeltas(min_value=dt.timedelta.min, max_value=dt.timedelta.max):
        # type: (dt.timedelta, dt.timedelta) -> SearchStrategy[dt.timedelta]
        """A strategy for timedeltas between ``min_value`` and ``max_value``.

        Examples from this strategy shrink towards zero.
        """
        check_type(dt.timedelta, min_value, "min_value")
        check_type(dt.timedelta, max_value, "max_value")
        check_valid_interval(min_value, max_value, "min_value", "max_value")
        if min_value == max_value:
            return just(min_value)
        return TimedeltaStrategy(min_value=min_value, max_value=max_value)

    class CompositeStrategy(SearchStrategy):
        def __init__(self, definition, label, args, kwargs):
            self.definition = definition
            self.__label = label
            self.args = args
            self.kwargs = kwargs

        def do_draw(self, data):
            return self.definition(data.draw, *self.args, **self.kwargs)

        def calc_label(self):
            return self.__label

    @cacheable
    def composite(f):
        # type: (Callable[..., Ex]) -> Callable[..., SearchStrategy[Ex]]
        """Defines a strategy that is built out of potentially arbitrarily many
        other strategies.

        This is intended to be used as a decorator. See
        :ref:`the full documentation for more details <composite-strategies>`
        about how to use this function.

        Examples from this strategy shrink by shrinking the output of each draw
        call.
        """
        argspec = getfullargspec(f)

        if argspec.defaults is not None and len(argspec.defaults) == len(argspec.args):
            raise InvalidArgument("A default value for initial argument will never be used")
        if len(argspec.args) == 0 and not argspec.varargs:
            raise InvalidArgument(
                "Functions wrapped with composite must take at least one "
                "positional argument."
            )

        annots = {
            k: v
            for k, v in argspec.annotations.items()
            if k in (argspec.args + argspec.kwonlyargs + ["return"])
        }
        new_argspec = argspec._replace(args=argspec.args[1:], annotations=annots)

        label = calc_label_from_cls(f)

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

        accept.__module__ = f.__module__
        return accept

    @defines_strategy_with_reusable_values
    @cacheable
    def complex_numbers(
        min_magnitude=0, max_magnitude=None, allow_infinity=None, allow_nan=None
    ):
        # type: (Optional[Real], Real, bool, bool) -> SearchStrategy[complex]
        """Returns a strategy that generates complex numbers.

        This strategy draws complex numbers with constrained magnitudes.
        The ``min_magnitude`` and ``max_magnitude`` parameters should be
        non-negative :class:`~python:numbers.Real` numbers; values
        of ``None`` correspond to zero and infinite values respectively.

        If ``min_magnitude`` is positive or ``max_magnitude`` is finite, it
        is an error to enable ``allow_nan``.  If ``max_magnitude`` is finite,
        it is an error to enable ``allow_infinity``.

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

        Examples from this strategy shrink by shrinking their real and
        imaginary parts, as :func:`~hypothesis.strategies.floats`.

        If you need to generate complex numbers with particular real and
        imaginary parts or relationships between parts, consider using
        :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 == float("inf"):
            max_magnitude = None
        if min_magnitude == 0:
            min_magnitude = None

        if allow_infinity is None:
            allow_infinity = bool(max_magnitude is None)
        elif allow_infinity and max_magnitude is not None:
            raise InvalidArgument(
                "Cannot have allow_infinity=%r with max_magnitude=%r"
                % (allow_infinity, max_magnitude)
            )
        if allow_nan is None:
            allow_nan = bool(min_magnitude is None and max_magnitude is None)
        elif allow_nan and not (min_magnitude is None and max_magnitude is None):
            raise InvalidArgument(
                "Cannot have allow_nan=%r, min_magnitude=%r max_magnitude=%r"
                % (allow_nan, min_magnitude, max_magnitude)
            )
        allow_kw = dict(allow_nan=allow_nan, allow_infinity=allow_infinity)

        if min_magnitude is None and max_magnitude is None:
            # In this simple but common case, there are no constraints on the
            # magnitude and therefore no relationship between the real and
            # imaginary parts.
            return builds(complex, floats(**allow_kw), floats(**allow_kw))

        @composite
        def constrained_complex(draw):
            # Draw the imaginary part, and determine the maximum real part given
            # this and the max_magnitude
            if max_magnitude is None:
                zi = draw(floats(**allow_kw))
                rmax = None
            else:
                zi = draw(floats(-max_magnitude, max_magnitude, **allow_kw))
                rmax = cathetus(max_magnitude, zi)
            # Draw the real part from the allowed range given the imaginary part
            if min_magnitude is None or math.fabs(zi) >= min_magnitude:
                zr = draw(floats(None if rmax is None else -rmax, rmax, **allow_kw))
            else:
                zr = draw(floats(cathetus(min_magnitude, zi), rmax, **allow_kw))
            # Order of conditions carefully tuned so that for a given pair of
            # magnitude arguments, we always either draw or do not draw the bool
            # (crucial for good shrinking behaviour) but only invert when needed.
            if (
                min_magnitude is not None
                and draw(booleans())
                and math.fabs(zi) <= min_magnitude
            ):
                zr = -zr
            return complex(zr, zi)

        return constrained_complex()

    def shared(base, key=None):
        # type: (SearchStrategy[Ex], Any) -> SearchStrategy[Ex]
        """Returns a strategy that draws a single shared value per run, drawn from
        base. Any two shared instances with the same key will share the same value,
        otherwise the identity of this strategy will be used. That is:

        >>> s = integers()  # or any other strategy
        >>> x = shared(s)
        >>> y = shared(s)

        In the above x and y may draw different (or potentially the same) values.
        In the following they will always draw the same:

        >>> x = shared(s, key="hi")
        >>> y = shared(s, key="hi")

        Examples from this strategy shrink as per their base strategy.
        """
        return SharedStrategy(base, key)

    @cacheable
    @defines_strategy_with_reusable_values
    def uuids(version=None):
        # type: (int) -> SearchStrategy[UUID]
        """Returns a strategy that generates :class:`UUIDs <uuid.UUID>`.

        If the optional version argument is given, value is passed through
        to :class:`~python:uuid.UUID` and only UUIDs of that version will
        be generated.

        All returned values from this will be unique, so e.g. if you do
        ``lists(uuids())`` the resulting list will never contain duplicates.

        Examples from this strategy don't have any meaningful shrink order.
        """
        if version not in (None, 1, 2, 3, 4, 5):
            raise InvalidArgument(
                (
                    "version=%r, but version must be in (None, 1, 2, 3, 4, 5) "
                    "to pass to the uuid.UUID constructor."
                )
                % (version,)
            )
        return shared(randoms(), key="hypothesis.strategies.uuids.generator").map(
            lambda r: UUID(version=version, int=r.getrandbits(128))
        )

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

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

    @defines_strategy_with_reusable_values
    def runner(default=not_set):
        """A strategy for getting "the current test runner", whatever that may be.
        The exact meaning depends on the entry point, but it will usually be the
        associated 'self' value for it.

        If there is no current test runner and a default is provided, return
        that default. If no default is provided, raises InvalidArgument.

        Examples from this strategy do not shrink (because there is only one).
        """
        return RunnerStrategy(default)

    class DataObject(object):
        """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):
            self.count = 0
            self.conjecture_data = data

        def __repr__(self):
            return "data(...)"

        def draw(self, strategy, label=None):
            # type: (SearchStrategy[Ex], Any) -> Ex
            result = self.conjecture_data.draw(strategy)
            self.count += 1
            if label is not None:
                note("Draw %d (%s): %r" % (self.count, label, result))
            else:
                note("Draw %d: %r" % (self.count, result))
            return result

    class DataStrategy(SearchStrategy):
        supports_find = False

        def do_draw(self, data):
            data.can_reproduce_example_from_repr = False

            if not hasattr(data, "hypothesis_shared_data_strategy"):
                data.hypothesis_shared_data_strategy = DataObject(data)
            return data.hypothesis_shared_data_strategy

        def __repr__(self):
            return "data()"

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

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

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

        def example(self):
            self.__not_a_first_class_strategy("example")

        def __not_a_first_class_strategy(self, name):
            raise InvalidArgument(
                "Cannot call %s on a DataStrategy. You should probably be using "
                "@composite for whatever it is you're trying to do." % (name,)
            )

    @cacheable
    def data():
        # type: () -> SearchStrategy[DataObject]
        """This isn't really a normal strategy, but instead gives you an object
        which can be used to draw data interactively from other strategies.

        See :ref:`the rest of the documentation <interactive-draw>` for more
        complete information.

        Examples from this strategy do not shrink (because there is only one),
        but the result of calls to each draw() call shrink as they normally would.
        """
        return DataStrategy()

    def register_type_strategy(
        custom_type,  # type: type
        strategy,  # type: Union[SearchStrategy, Callable[[type], SearchStrategy]]
    ):
        # type: (...) -> None
        """Add an entry to the global type-to-strategy lookup.

        This lookup is used in :func:`~hypothesis.strategies.builds` and
        :func:`@given <hypothesis.given>`.

        :func:`~hypothesis.strategies.builds` will be used automatically for
        classes with type annotations on ``__init__`` , so you only need to
        register a strategy if one or more arguments need to be more tightly
        defined than their type-based default, or if you want to supply a strategy
        for an argument with a default value.

        ``strategy`` may be a search strategy, or a function that takes a type and
        returns a strategy (useful for generic types).
        """
        # TODO: We would like to move this to the top level, but pending some major
        # refactoring it's hard to do without creating circular imports.
        from hypothesis.searchstrategy import types

        if not types.is_a_type(custom_type):
            raise InvalidArgument("custom_type=%r must be a type")
        elif not (isinstance(strategy, SearchStrategy) or callable(strategy)):
            raise InvalidArgument(
                "strategy=%r must be a SearchStrategy, or a function that takes "
                "a generic type and returns a specific SearchStrategy"
            )
        elif isinstance(strategy, SearchStrategy) and strategy.is_empty:
            raise InvalidArgument("strategy=%r must not be empty")
        types._global_type_lookup[custom_type] = strategy
        from_type.__clear_cache()  # type: ignore

    @cacheable
    def deferred(definition):
        # type: (Callable[[], SearchStrategy[Ex]]) -> SearchStrategy[Ex]
        """A deferred strategy allows you to write a strategy that references other
        strategies that have not yet been defined. This allows for the easy
        definition of recursive and mutually recursive strategies.

        The definition argument should be a zero-argument function that returns a
        strategy. It will be evaluated the first time the strategy is used to
        produce an example.

        Example usage:

        >>> import hypothesis.strategies as st
        >>> x = st.deferred(lambda: st.booleans() | st.tuples(x, x))
        >>> x.example()
        (((False, (True, True)), (False, True)), (True, True))
        >>> x.example()
        True

        Mutual recursion also works fine:

        >>> a = st.deferred(lambda: st.booleans() | b)
        >>> b = st.deferred(lambda: st.tuples(a, a))
        >>> a.example()
        True
        >>> b.example()
        (False, (False, ((False, True), False)))

        Examples from this strategy shrink as they normally would from the strategy
        returned by the definition.
        """
        return DeferredStrategy(definition)

    @defines_strategy_with_reusable_values
    def emails():
        """A strategy for generating email addresses as unicode strings. The
        address format is specified in :rfc:`5322#section-3.4.1`. Values shrink
        towards shorter local-parts and host domains.

        This strategy is useful for generating "user data" for tests, as
        mishandling of email addresses is a common source of bugs.
        """
        from hypothesis.provisional import domains

        local_chars = string.ascii_letters + string.digits + "!#$%&'*+-/=^_`{|}~"
        local_part = text(local_chars, min_size=1, max_size=64)
        # TODO: include dot-atoms, quoted strings, escaped chars, etc in local part
        return builds(u"{}@{}".format, local_part, domains()).filter(
            lambda addr: len(addr) <= 254
        )

    # Mypy can't yet handle default values with generic types or typevars, but the
    # @overload workaround from https://github.com/python/mypy/issues/3737 doesn't
    # work with @composite functions - Mypy can't see that the function implements
    # `(Any, Callable[..., T], SearchStrategy[T]) -> Callable[..., T]`

    @defines_strategy
    def functions(like=lambda: None, returns=none()):
        """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.

        Note that the generated functions do not validate their arguments, and
        may return a different value if called again with the same arguments.

        Generated functions can only be called within the scope of the ``@given``
        which created them.  This strategy does not support ``.example()``.
        """
        check_type(SearchStrategy, returns)
        if not callable(like):
            raise InvalidArgument(
                "The first argument to functions() must be a callable to imitate, "
                "but got non-callable like=%r" % (nicerepr(like),)
            )
        return FunctionStrategy(like, returns)

    @composite
    def slices(draw, size):
        """Generates slices that will select indices up to the supplied size

        Generated slices will have start and stop indices that range from 0 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_integer(size)
        if size is None or size < 1:
            raise InvalidArgument("size=%r must be at least one" % size)

        min_start = min_stop = 0
        max_start = max_stop = size
        min_step = 1
        # For slices start is inclusive and stop is exclusive
        start = draw(integers(min_start, max_start) | none())
        stop = draw(integers(min_stop, max_stop) | 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(min_step, max_step or 1))

        if (stop or 0) < (start or 0):
            step *= -1

        return slice(start, stop, step)