# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
import copy
import math
from collections.abc import Callable, Iterable
from typing import Any, overload
from hypothesis import strategies as st
from hypothesis.control import current_build_context
from hypothesis.errors import InvalidArgument
from hypothesis.internal.conjecture import utils as cu
from hypothesis.internal.conjecture.data import ConjectureData
from hypothesis.internal.conjecture.engine import BUFFER_SIZE
from hypothesis.internal.conjecture.junkdrawer import LazySequenceCopy
from hypothesis.internal.conjecture.utils import combine_labels
from hypothesis.internal.filtering import get_integer_predicate_bounds
from hypothesis.internal.reflection import is_identity_function
from hypothesis.strategies._internal.strategies import (
T3,
T4,
T5,
Ex,
FilteredStrategy,
RecurT,
SampledFromStrategy,
SearchStrategy,
T,
check_strategy,
filter_not_satisfied,
)
from hypothesis.strategies._internal.utils import cacheable, defines_strategy
from hypothesis.utils.conventions import UniqueIdentifier
from hypothesis.vendor.pretty import (
ArgLabelsT,
IDKey,
_fixeddict_pprinter,
_tuple_pprinter,
)
class TupleStrategy(SearchStrategy[tuple[Ex, ...]]):
"""A strategy responsible for fixed length tuples based on heterogeneous
strategies for each of their elements."""
def __init__(self, strategies: Iterable[SearchStrategy[Any]]):
super().__init__()
self.element_strategies = tuple(strategies)
def do_validate(self) -> None:
for s in self.element_strategies:
s.validate()
def calc_label(self) -> int:
return combine_labels(
self.class_label, *(s.label for s in self.element_strategies)
)
def __repr__(self) -> str:
tuple_string = ", ".join(map(repr, self.element_strategies))
return f"TupleStrategy(({tuple_string}))"
def calc_has_reusable_values(self, recur: RecurT) -> bool:
return all(recur(e) for e in self.element_strategies)
def do_draw(self, data: ConjectureData) -> tuple[Ex, ...]:
context = current_build_context()
arg_labels: ArgLabelsT = {}
result = []
for i, strategy in enumerate(self.element_strategies):
with context.track_arg_label(f"arg[{i}]") as arg_label:
result.append(data.draw(strategy))
arg_labels |= arg_label
result = tuple(result)
if arg_labels:
context.known_object_printers[IDKey(result)].append(
_tuple_pprinter(arg_labels)
)
return result
def calc_is_empty(self, recur: RecurT) -> bool:
return any(recur(e) for e in self.element_strategies)
@overload
def tuples() -> SearchStrategy[tuple[()]]: # pragma: no cover
...
@overload
def tuples(__a1: SearchStrategy[Ex]) -> SearchStrategy[tuple[Ex]]: # pragma: no cover
...
@overload
def tuples(
__a1: SearchStrategy[Ex], __a2: SearchStrategy[T]
) -> SearchStrategy[tuple[Ex, T]]: # pragma: no cover
...
@overload
def tuples(
__a1: SearchStrategy[Ex], __a2: SearchStrategy[T], __a3: SearchStrategy[T3]
) -> SearchStrategy[tuple[Ex, T, T3]]: # pragma: no cover
...
@overload
def tuples(
__a1: SearchStrategy[Ex],
__a2: SearchStrategy[T],
__a3: SearchStrategy[T3],
__a4: SearchStrategy[T4],
) -> SearchStrategy[tuple[Ex, T, T3, T4]]: # pragma: no cover
...
@overload
def tuples(
__a1: SearchStrategy[Ex],
__a2: SearchStrategy[T],
__a3: SearchStrategy[T3],
__a4: SearchStrategy[T4],
__a5: SearchStrategy[T5],
) -> SearchStrategy[tuple[Ex, T, T3, T4, T5]]: # pragma: no cover
...
@overload
def tuples(
*args: SearchStrategy[Any],
) -> SearchStrategy[tuple[Any, ...]]: # pragma: no cover
...
[docs]
@cacheable
@defines_strategy()
def tuples(*args: SearchStrategy[Any]) -> SearchStrategy[tuple[Any, ...]]:
"""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)
class ListStrategy(SearchStrategy[list[Ex]]):
"""A strategy for lists which takes a strategy for its elements and the
allowed lengths, and generates lists with the correct size and contents."""
_nonempty_filters: tuple[Callable[[Any], Any], ...] = (bool, len, tuple, list)
def __init__(
self,
elements: SearchStrategy[Ex],
min_size: int = 0,
max_size: float | int | None = math.inf,
):
super().__init__()
self.min_size = min_size or 0
self.max_size = max_size if max_size is not None else math.inf
assert 0 <= self.min_size <= self.max_size
self.average_size = min(
max(self.min_size * 2, self.min_size + 5),
0.5 * (self.min_size + self.max_size),
)
self.element_strategy = elements
if min_size > BUFFER_SIZE:
raise InvalidArgument(
f"{self!r} can never generate an example, because min_size is larger "
"than Hypothesis supports. Including it is at best slowing down your "
"tests for no benefit; at worst making them fail (maybe flakily) with "
"a HealthCheck error."
)
def calc_label(self) -> int:
return combine_labels(self.class_label, self.element_strategy.label)
def do_validate(self) -> None:
self.element_strategy.validate()
if self.is_empty:
raise InvalidArgument(
"Cannot create non-empty lists with elements drawn from "
f"strategy {self.element_strategy!r} because it has no values."
)
if self.element_strategy.is_empty and 0 < self.max_size < float("inf"):
raise InvalidArgument(
f"Cannot create a collection of max_size={self.max_size!r}, "
"because no elements can be drawn from the element strategy "
f"{self.element_strategy!r}"
)
def calc_is_empty(self, recur: RecurT) -> bool:
if self.min_size == 0:
return False
return recur(self.element_strategy)
def do_draw(self, data: ConjectureData) -> list[Ex]:
if self.element_strategy.is_empty:
assert self.min_size == 0
return []
elements = cu.many(
data,
min_size=self.min_size,
max_size=self.max_size,
average_size=self.average_size,
)
result = []
while elements.more():
result.append(data.draw(self.element_strategy))
return result
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}({self.element_strategy!r}, "
f"min_size={self.min_size:_}, max_size={self.max_size:_})"
)
def filter(self, condition: Callable[[list[Ex]], Any]) -> SearchStrategy[list[Ex]]:
if condition in self._nonempty_filters or is_identity_function(condition):
assert self.max_size >= 1, "Always-empty is special cased in st.lists()"
if self.min_size >= 1:
return self
new = copy.copy(self)
new.min_size = 1
return new
constraints, pred = get_integer_predicate_bounds(condition)
if constraints.get("len") and (
"min_value" in constraints or "max_value" in constraints
):
new = copy.copy(self)
new.min_size = max(
self.min_size, constraints.get("min_value", self.min_size)
)
new.max_size = min(
self.max_size, constraints.get("max_value", self.max_size)
)
# Unsatisfiable filters are easiest to understand without rewriting.
if new.min_size > new.max_size:
return SearchStrategy.filter(self, condition)
# Recompute average size; this is cheaper than making it into a property.
new.average_size = min(
max(new.min_size * 2, new.min_size + 5),
0.5 * (new.min_size + new.max_size),
)
if pred is None:
return new
return SearchStrategy.filter(new, condition)
return SearchStrategy.filter(self, condition)
class UniqueListStrategy(ListStrategy[Ex]):
def __init__(
self,
elements: SearchStrategy[Ex],
min_size: int,
max_size: float | int | None,
# TODO: keys are guaranteed to be Hashable, not just Any, but this makes
# other things harder to type
keys: tuple[Callable[[Ex], Any], ...],
tuple_suffixes: SearchStrategy[tuple[Ex, ...]] | None,
):
super().__init__(elements, min_size, max_size)
self.keys = keys
self.tuple_suffixes = tuple_suffixes
def do_draw(self, data: ConjectureData) -> list[Ex]:
if self.element_strategy.is_empty:
assert self.min_size == 0
return []
elements = cu.many(
data,
min_size=self.min_size,
max_size=self.max_size,
average_size=self.average_size,
)
seen_sets: tuple[set[Ex], ...] = tuple(set() for _ in self.keys)
# actually list[Ex], but if self.tuple_suffixes is present then Ex is a
# tuple[T, ...] because self.element_strategy is a TuplesStrategy, and
# appending a concrete tuple to `result: list[Ex]` makes mypy unhappy
# without knowing that Ex = tuple.
result: list[Any] = []
# We construct a filtered strategy here rather than using a check-and-reject
# approach because some strategies have special logic for generation under a
# filter, and FilteredStrategy can consolidate multiple filters.
def not_yet_in_unique_list(val: Ex) -> bool: # type: ignore # covariant type param
return all(
key(val) not in seen
for key, seen in zip(self.keys, seen_sets, strict=True)
)
filtered = FilteredStrategy(
self.element_strategy, conditions=(not_yet_in_unique_list,)
)
while elements.more():
value = filtered.do_filtered_draw(data)
if value is filter_not_satisfied:
elements.reject(f"Aborted test because unable to satisfy {filtered!r}")
else:
assert not isinstance(value, UniqueIdentifier)
for key, seen in zip(self.keys, seen_sets, strict=True):
seen.add(key(value))
if self.tuple_suffixes is not None:
value = (value, *data.draw(self.tuple_suffixes)) # type: ignore
result.append(value)
assert self.max_size >= len(result) >= self.min_size
return result
class UniqueSampledListStrategy(UniqueListStrategy):
def do_draw(self, data: ConjectureData) -> list[Ex]:
assert isinstance(self.element_strategy, SampledFromStrategy)
should_draw = cu.many(
data,
min_size=self.min_size,
max_size=self.max_size,
average_size=self.average_size,
)
seen_sets: tuple[set[Ex], ...] = tuple(set() for _ in self.keys)
result: list[Any] = []
remaining = LazySequenceCopy(self.element_strategy.elements)
while remaining and should_draw.more():
j = data.draw_integer(0, len(remaining) - 1)
value = self.element_strategy._transform(remaining.pop(j))
if value is not filter_not_satisfied and all(
key(value) not in seen
for key, seen in zip(self.keys, seen_sets, strict=True)
):
for key, seen in zip(self.keys, seen_sets, strict=True):
seen.add(key(value))
if self.tuple_suffixes is not None:
value = (value, *data.draw(self.tuple_suffixes))
result.append(value)
else:
should_draw.reject(
"UniqueSampledListStrategy filter not satisfied or value already seen"
)
assert self.max_size >= len(result) >= self.min_size
return result
class FixedDictStrategy(SearchStrategy[dict[Any, Any]]):
"""A strategy which produces dicts with a fixed set of keys, given a
strategy for each of their equivalent values.
e.g. {'foo' : some_int_strategy} would generate dicts with the single
key 'foo' mapping to some integer.
"""
def __init__(
self,
mapping: dict[Any, SearchStrategy[Any]],
*,
optional: dict[Any, SearchStrategy[Any]] | None,
):
super().__init__()
dict_type = type(mapping)
self.mapping = mapping
keys = tuple(mapping.keys())
self.fixed = st.tuples(*[mapping[k] for k in keys]).map(
lambda value: dict_type(zip(keys, value, strict=True))
)
self.optional = optional
def do_draw(self, data: ConjectureData) -> dict[Any, Any]:
context = current_build_context()
arg_labels: ArgLabelsT = {}
value = type(self.mapping)()
for key, strategy in self.mapping.items():
with context.track_arg_label(str(key)) as arg_label:
value[key] = data.draw(strategy)
arg_labels |= arg_label
if self.optional is not None:
remaining = [k for k, v in self.optional.items() if not v.is_empty]
should_draw = cu.many(
data,
min_size=0,
max_size=len(remaining),
average_size=len(remaining) / 2,
)
while should_draw.more():
j = data.draw_integer(0, len(remaining) - 1)
remaining[-1], remaining[j] = remaining[j], remaining[-1]
key = remaining.pop()
with context.track_arg_label(str(key)) as arg_label:
value[key] = data.draw(self.optional[key])
arg_labels |= arg_label
if arg_labels:
context.known_object_printers[IDKey(value)].append(
_fixeddict_pprinter(arg_labels, self.mapping)
)
return value
def calc_is_empty(self, recur: RecurT) -> bool:
return recur(self.fixed)
def __repr__(self) -> str:
if self.optional is not None:
return f"fixed_dictionaries({self.mapping!r}, optional={self.optional!r})"
return f"fixed_dictionaries({self.mapping!r})"