use "collections"
use "assert"
use "itertools"
use "debug"
type ValueAndShrink[T1] is (T1^, Iterator[T1^])
"""
Possible return type for
[`Generator.generate`](pony_check-Generator.md#generate).
Represents a generated value and an Iterator of shrunken values.
"""
type GenerateResult[T2] is (T2^ | ValueAndShrink[T2])
"""
Return type for
[`Generator.generate`](pony_check-Generator.md#generate).
Either a single value or a Tuple of a value and an Iterator
of shrunken values based upon this value.
"""
class CountdownIter[T: (Int & Integer[T] val) = USize] is Iterator[T]
var _cur: T
let _to: T
new create(from: T, to: T = T.min_value()) =>
"""
Create am `Iterator` that counts down according to the specified arguments.
`from` is exclusive, `to` is inclusive.
"""
_cur = from
_to = to
fun ref has_next(): Bool =>
_cur > _to
fun ref next(): T =>
let res = _cur - 1
_cur = res
res
trait box GenObj[T]
fun generate(rnd: Randomness): GenerateResult[T] ?
fun shrink(t: T): ValueAndShrink[T] =>
(consume t, Poperator[T].empty())
fun generate_value(rnd: Randomness): T^ ? =>
"""
Simply generate a value and ignore any possible
shrink values.
"""
let g = this
match g.generate(rnd)?
| let t: T => consume t
| (let t: T, _) => consume t
end
fun generate_and_shrink(rnd: Randomness): ValueAndShrink[T] ? =>
"""
Generate a value and also return a shrink result,
even if the generator does not return any when calling `generate`.
"""
let g = this
match g.generate(rnd)?
| let t: T => g.shrink(consume t)
| (let t: T, let shrinks: Iterator[T^])=> (consume t, shrinks)
end
fun iter(rnd: Randomness): Iterator[GenerateResult[T]]^ =>
let that: GenObj[T] = this
object is Iterator[GenerateResult[T]]
fun ref has_next(): Bool => true
fun ref next(): GenerateResult[T] ? => that.generate(rnd)?
end
fun value_iter(rnd: Randomness): Iterator[T^] =>
let that: GenObj[T] = this
object is Iterator[T^]
fun ref has_next(): Bool => true
fun ref next(): T^ ? =>
match that.generate(rnd)?
| let value_only: T => consume value_only
| (let v: T, _) => consume v
end
end
fun value_and_shrink_iter(rnd: Randomness): Iterator[ValueAndShrink[T]] =>
let that: GenObj[T] = this
object is Iterator[ValueAndShrink[T]]
fun ref has_next(): Bool => true
fun ref next(): ValueAndShrink[T] ? =>
match that.generate(rnd)?
| let value_only: T => that.shrink(consume value_only)
| (let v: T, let shrinks: Iterator[T^]) => (consume v, consume shrinks)
end
end
class box Generator[T] is GenObj[T]
"""
A Generator is capable of generating random values of a certain type `T`
given a source of `Randomness`
and knows how to shrink or simplify values of that type.
When testing a property against one or more given Generators,
those generators' `generate` methods are being called many times
to generate sample values that are then used to validate the property.
When a failing sample is found, the PonyCheck engine is trying to find a
smaller or more simple sample by shrinking it with `shrink`.
If the generator did not provide any shrinked samples
as a result of `generate`, its `shrink` method is called
to obtain simpler results. PonyCheck obtains more shrunken samples until
the property is not failing anymore.
The last failing sample, which is considered the most simple one,
is then reported to the user.
"""
let _gen: GenObj[T]
new create(gen: GenObj[T]) =>
_gen = gen
fun generate(rnd: Randomness): GenerateResult[T] ? =>
"""
Let this generator generate a value
given a source of `Randomness`.
Also allow for returning a value and pre-generated shrink results
as a `ValueAndShrink[T]` instance, a tuple of `(T^, Seq[T])`.
This helps propagating shrink results through all kinds of Generator
combinators like `filter`, `map` and `flat_map`.
If implementing a custom `Generator` based on another one,
with a Generator Combinator, you should use shrunken values
returned by `generate` to also return shrunken values based on them.
If generating an example value is costly, it might be more efficient
to simply return the generated value and only shrink in big steps or do no
shrinking at all.
If generating values is lightweight, shrunken values should also be
returned.
"""
_gen.generate(rnd)?
fun shrink(t: T): ValueAndShrink[T] =>
"""
Simplify the given value.
As the returned value can also be `iso`, it needs to be consumed and
returned.
It is preferred to already return a `ValueAndShrink` from `generate`.
"""
_gen.shrink(consume t)
fun generate_value(rnd: Randomness): T^ ? =>
_gen.generate_value(rnd)?
fun generate_and_shrink(rnd: Randomness): ValueAndShrink[T] ? =>
_gen.generate_and_shrink(rnd)?
fun filter(predicate: {(T): (T^, Bool)} box): Generator[T] =>
"""
Apply `predicate` to the values generated by this Generator
and only yields values for which `predicate` returns `true`.
Example:
```pony
let even_i32s =
Generators.i32()
.filter(
{(t) => (t, ((t % 2) == 0)) })
```
"""
Generator[T](
object is GenObj[T]
fun generate(rnd: Randomness): GenerateResult[T] ? =>
(let t: T, let shrunken: Iterator[T^]) = _gen.generate_and_shrink(rnd)?
(let t1, let matches) = predicate(consume t)
if not matches then
generate(rnd)? // recurse, this might recurse infinitely
else
// filter the shrunken examples
(consume t1, _filter_shrunken(shrunken))
end
fun shrink(t: T): ValueAndShrink[T] =>
"""
shrink `t` using the generator this one filters upon
and call the filter predicate on the shrunken values
"""
(let s, let shrunken: Iterator[T^]) = _gen.shrink(consume t)
(consume s, _filter_shrunken(shrunken))
fun _filter_shrunken(shrunken: Iterator[T^]): Iterator[T^] =>
Iter[T^](shrunken)
.filter_map[T^]({
(t: T): (T^| None) =>
match predicate(consume t)
| (let matching: T, true) => consume matching
end
})
end)
fun map[U](fn: {(T): U^} box)
: Generator[U]
=>
"""
Apply `fn` to each value of this iterator
and yield the results.
Example:
```pony
let single_code_point_string_gen =
Generators.u32()
.map[String]({(u) => String.from_utf32(u) })
```
"""
Generator[U](
object is GenObj[U]
fun generate(rnd: Randomness): GenerateResult[U] ? =>
(let generated: T, let shrunken: Iterator[T^]) =
_gen.generate_and_shrink(rnd)?
(fn(consume generated), _map_shrunken(shrunken))
fun shrink(u: U): ValueAndShrink[U] =>
"""
We can only shrink if T is a subtype of U.
This method should in general not be called on this generator
as it is always returning shrinks with the call to `generate`
and they should be used for executing the shrink, but in case
a strange hierarchy of generators is used, which does not make use of
the pre-generated shrink results, we keep this method here.
"""
match u
| let ut: T =>
(let uts: T, let shrunken: Iterator[T^]) = _gen.shrink(consume ut)
(fn(consume uts), _map_shrunken(shrunken))
else
(consume u, Poperator[U].empty())
end
fun _map_shrunken(shrunken: Iterator[T^]): Iterator[U^] =>
Iter[T^](shrunken)
.map[U^]({(t) => fn(consume t) })
end)
fun flat_map[U](fn: {(T): Generator[U]} box): Generator[U] =>
"""
For each value of this generator, create a generator that is then combined.
"""
// TODO: enable proper shrinking:
Generator[U](
object is GenObj[U]
fun generate(rnd: Randomness): GenerateResult[U] ? =>
let value: T = _gen.generate_value(rnd)?
fn(consume value).generate_and_shrink(rnd)?
end)
fun union[U](other: Generator[U]): Generator[(T | U)] =>
"""
Create a generator that produces the value of this generator or the other
with the same probability, returning a union type of this generator and
the other one.
"""
Generator[(T | U)](
object is GenObj[(T | U)]
fun generate(rnd: Randomness): GenerateResult[(T | U)] ? =>
if rnd.bool() then
_gen.generate_and_shrink(rnd)?
else
other.generate_and_shrink(rnd)?
end
fun shrink(t: (T | U)): ValueAndShrink[(T | U)] =>
match consume t
| let tt: T => _gen.shrink(consume tt)
| let tu: U => other.shrink(consume tu)
end
end
)
type WeightedGenerator[T] is (USize, Generator[T] box)
"""
A generator with an associated weight, used in Generators.frequency.
"""
primitive Generators
"""
Convenience combinators and factories for common types and kind of Generators.
"""
fun unit[T](t: T, do_shrink: Bool = false): Generator[box->T] =>
"""
Generate a reference to the same value over and over again.
This reference will be of type `box->T` and not just `T`
as this generator will need to keep a reference to the given value.
"""
Generator[box->T](
object is GenObj[box->T]
let _t: T = consume t
fun generate(rnd: Randomness): GenerateResult[box->T] =>
if do_shrink then
(_t, Iter[box->T].repeat_value(_t))
else
_t
end
end)
fun none[T: None](): Generator[(T | None)] => Generators.unit[(T | None)](None)
fun repeatedly[T](f: {(): T^ ?} box): Generator[T] =>
"""
Generate values by calling the lambda `f` repeatedly,
once for every invocation of `generate`.
`f` needs to return an ephemeral type `T^`, that means
in most cases it needs to consume its returned value.
Otherwise we would end up with
an alias for `T` which is `T!`.
(e.g. `String iso` would be returned as `String iso!`,
which aliases as a `String tag`).
Example:
```pony
Generators.repeatedly[Writer]({(): Writer^ =>
let writer = Writer.>write("consume me, please")
consume writer
})
```
"""
Generator[T](
object is GenObj[T]
fun generate(rnd: Randomness): GenerateResult[T] ? =>
f()?
end)
fun seq_of[T, S: Seq[T] ref](
gen: Generator[T],
min: USize = 0,
max: USize = 100)
: Generator[S]
=>
"""
Create a `Seq` from the values of the given Generator with an optional
minimum and maximum size.
Defaults are 0 and 100, respectively.
"""
Generator[S](
object is GenObj[S]
let _gen: GenObj[T] = gen
fun generate(rnd: Randomness): GenerateResult[S] =>
let size = rnd.usize(min, max)
let result: S =
Iter[T^](_gen.value_iter(rnd))
.take(size)
.collect[S](S.create(size))
// create shrink_iter with smaller seqs and elements generated from _gen.value_iter
let shrink_iter =
Iter[USize](CountdownIter(size, min)) //Range(size, min, -1))
// .skip(1)
.map_stateful[S^]({
(s: USize): S^ =>
Iter[T^](_gen.value_iter(rnd))
.take(s)
.collect[S](S.create(s))
})
(consume result, shrink_iter)
end)
fun iso_seq_of[T: Any #send, S: Seq[T] iso](
gen: Generator[T],
min: USize = 0,
max: USize = 100)
: Generator[S]
=>
"""
Generate a `Seq[T]` where `T` must be sendable (i.e. it must have a
reference capability of either `tag`, `val`, or `iso`).
The constraint of the elements being sendable stems from the fact that
there is no other way to populate the iso seq if the elements might be
non-sendable (i.e. ref), as then the seq would leak references via
its elements.
"""
Generator[S](
object is GenObj[S]
let _gen: GenObj[T] = gen
fun generate(rnd: Randomness): GenerateResult[S] =>
let size = rnd.usize(min, max)
let result: S = recover iso S.create(size) end
let iter = _gen.value_iter(rnd)
var i = USize(0)
for elem in iter do
if i >= size then break end
result.push(consume elem)
i = i + 1
end
// create shrink_iter with smaller seqs and elements generated from _gen.value_iter
let shrink_iter =
Iter[USize](CountdownIter(size, min)) //Range(size, min, -1))
// .skip(1)
.map_stateful[S^]({
(s: USize): S^ =>
let res = recover iso S.create(s) end
let s_iter = _gen.value_iter(rnd)
var j = USize(0)
for s_elem in s_iter do
if j >= s then break end
res.push(consume s_elem)
j = j + 1
end
consume res
})
(consume result, shrink_iter)
end
)
fun array_of[T](
gen: Generator[T],
min: USize = 0,
max: USize = 100)
: Generator[Array[T]]
=>
Generators.seq_of[T, Array[T]](gen, min, max)
fun shuffled_array_gen[T](
gen: Generator[Array[T]])
: Generator[Array[T]]
=>
Generator[Array[T]](
object is GenObj[Array[T]]
let _gen: GenObj[Array[T]] = gen
fun generate(rnd: Randomness): GenerateResult[Array[T]] ? =>
(let arr, let source_shrink_iter) = _gen.generate_and_shrink(rnd)?
rnd.shuffle[T](arr)
let shrink_iter =
Iter[Array[T]](source_shrink_iter)
.map_stateful[Array[T]^]({
(shrink_arr: Array[T]): Array[T]^ =>
rnd.shuffle[T](shrink_arr)
consume shrink_arr
})
(consume arr, shrink_iter)
end
)
fun shuffled_iter[T](array: Array[T]): Generator[Iterator[this->T!]] =>
Generator[Iterator[this->T!]](
object is GenObj[Iterator[this->T!]]
fun generate(rnd: Randomness): GenerateResult[Iterator[this->T!]] =>
let cloned = array.clone()
rnd.shuffle[this->T!](cloned)
cloned.values()
end
)
fun list_of[T](
gen: Generator[T],
min: USize = 0,
max: USize = 100)
: Generator[List[T]]
=>
Generators.seq_of[T, List[T]](gen, min, max)
fun set_of[T: (Hashable #read & Equatable[T] #read)](
gen: Generator[T],
max: USize = 100)
: Generator[Set[T]]
=>
"""
Create a generator for `Set` filled with values
of the given generator `gen`.
The returned sets will have a size up to `max`,
but tend to have fewer than `max`
depending on the source generator `gen`.
E.g. if the given generator is for `U8` values and `max` is set to 1024,
the set will only ever be of size 256 max.
Also for efficiency purposes and to not loop forever,
this generator will only try to add at most `max` values to the set.
If there are duplicates, the set won't grow.
"""
Generator[Set[T]](
object is GenObj[Set[T]]
let _gen: GenObj[T] = gen
fun generate(rnd: Randomness): GenerateResult[Set[T]] =>
let size = rnd.usize(0, max)
let result: Set[T] =
Set[T].create(size).>union(
Iter[T^](_gen.value_iter(rnd))
.take(size)
)
let shrink_iter: Iterator[Set[T]^] =
Iter[USize](CountdownIter(size, 0)) // Range(size, 0, -1))
//.skip(1)
.map_stateful[Set[T]^]({
(s: USize): Set[T]^ =>
Set[T].create(s).>union(
Iter[T^](_gen.value_iter(rnd)).take(s)
)
})
(consume result, shrink_iter)
end)
fun set_is_of[T](
gen: Generator[T],
max: USize = 100)
: Generator[SetIs[T]]
=>
"""
Create a generator for `SetIs` filled with values
of the given generator `gen`.
The returned `SetIs` will have a size up to `max`,
but tend to have fewer entries
depending on the source generator `gen`.
E.g. if the given generator is for `U8` values and `max` is set to 1024
the set will only ever be of size 256 max.
Also for efficiency purposes and to not loop forever,
this generator will only try to add at most `max` values to the set.
If there are duplicates, the set won't grow.
"""
// TODO: how to remove code duplications
Generator[SetIs[T]](
object is GenObj[SetIs[T]]
fun generate(rnd: Randomness): GenerateResult[SetIs[T]] =>
let size = rnd.usize(0, max)
let result: SetIs[T] =
SetIs[T].create(size).>union(
Iter[T^](gen.value_iter(rnd))
.take(size)
)
let shrink_iter: Iterator[SetIs[T]^] =
Iter[USize](CountdownIter(size, 0)) //Range(size, 0, -1))
//.skip(1)
.map_stateful[SetIs[T]^]({
(s: USize): SetIs[T]^ =>
SetIs[T].create(s).>union(
Iter[T^](gen.value_iter(rnd)).take(s)
)
})
(consume result, shrink_iter)
end)
fun map_of[K: (Hashable #read & Equatable[K] #read), V](
gen: Generator[(K, V)],
max: USize = 100)
: Generator[Map[K, V]]
=>
"""
Create a generator for `Map` from a generator of key-value tuples.
The generated maps will have a size up to `max`,
but tend to have fewer entries depending on the source generator `gen`.
If the generator generates key-value pairs with
duplicate keys (based on structural equality),
the pair that is generated later will overwrite earlier entries in the map.
"""
Generator[Map[K, V]](
object is GenObj[Map[K, V]]
fun generate(rnd: Randomness): GenerateResult[Map[K, V]] =>
let size = rnd.usize(0, max)
let result: Map[K, V] =
Map[K, V].create(size).>concat(
Iter[(K^, V^)](gen.value_iter(rnd))
.take(size)
)
let shrink_iter: Iterator[Map[K, V]^] =
Iter[USize](CountdownIter(size, 0)) // Range(size, 0, -1))
// .skip(1)
.map_stateful[Map[K, V]^]({
(s: USize): Map[K, V]^ =>
Map[K, V].create(s).>concat(
Iter[(K^, V^)](gen.value_iter(rnd)).take(s)
)
})
(consume result, shrink_iter)
end)
fun map_is_of[K, V](
gen: Generator[(K, V)],
max: USize = 100)
: Generator[MapIs[K, V]]
=>
"""
Create a generator for `MapIs` from a generator of key-value tuples.
The generated maps will have a size up to `max`,
but tend to have fewer entries depending on the source generator `gen`.
If the generator generates key-value pairs with
duplicate keys (based on identity),
the pair that is generated later will overwrite earlier entries in the map.
"""
Generator[MapIs[K, V]](
object is GenObj[MapIs[K, V]]
fun generate(rnd: Randomness): GenerateResult[MapIs[K, V]] =>
let size = rnd.usize(0, max)
let result: MapIs[K, V] =
MapIs[K, V].create(size).>concat(
Iter[(K^, V^)](gen.value_iter(rnd))
.take(size)
)
let shrink_iter: Iterator[MapIs[K, V]^] =
Iter[USize](CountdownIter(size, 0)) //Range(size, 0, -1))
// .skip(1)
.map_stateful[MapIs[K, V]^]({
(s: USize): MapIs[K, V]^ =>
MapIs[K, V].create(s).>concat(
Iter[(K^, V^)](gen.value_iter(rnd)).take(s)
)
})
(consume result, shrink_iter)
end)
fun one_of[T](xs: ReadSeq[T], do_shrink: Bool = false): Generator[box->T] =>
"""
Generate a random value from the given ReadSeq.
This generator will generate nothing if the given xs is empty.
Generators created with this method do not support shrinking.
If `do_shrink` is set to `true`, it will return the same value
for each shrink round. Otherwise it will return nothing.
"""
Generator[box->T](
object is GenObj[box->T]
fun generate(rnd: Randomness): GenerateResult[box->T] ? =>
let idx = rnd.usize(0, xs.size() - 1)
let res = xs(idx)?
if do_shrink then
(res, Iter[box->T].repeat_value(res))
else
res
end
end)
fun one_of_safe[T](xs: ReadSeq[T], do_shrink: Bool = false): Generator[box->T] ? =>
"""
Version of `one_of` that will error if `xs` is empty.
"""
Fact(xs.size() > 0, "cannot use one_of_safe on empty ReadSeq")?
Generators.one_of[T](xs, do_shrink)
fun frequency[T](
weighted_generators: ReadSeq[WeightedGenerator[T]])
: Generator[T]
=>
"""
Choose a value of one of the given Generators,
while controlling the distribution with the associated weights.
The weights are of type `USize` and control how likely a value is chosen.
The likelihood of a value `v` to be chosen
is `weight_v` / `weights_sum`.
If all `weighted_generators` have equal size the distribution
will be uniform.
Example of a generator to output odd `U8` values
twice as likely as even ones:
```pony
Generators.frequency[U8]([
(1, Generators.u8().filter({(u) => (u, (u % 2) == 0 }))
(2, Generators.u8().filter({(u) => (u, (u % 2) != 0 }))
])
```
"""
// nasty hack to avoid handling the theoretical error case where we have
// no generator and thus would have to change the type signature
Generator[T](
object is GenObj[T]
fun generate(rnd: Randomness): GenerateResult[T] ? =>
let weight_sum: USize =
Iter[WeightedGenerator[T]](weighted_generators.values())
.fold[USize](
0,
// segfaults when types are removed - TODO: investigate
{(acc: USize, weighted_gen: WeightedGenerator[T]): USize^ =>
weighted_gen._1 + acc
})
let desired_sum = rnd.usize(0, weight_sum)
var running_sum: USize = 0
var chosen: (Generator[T] | None) = None
for weighted_gen in weighted_generators.values() do
let new_sum = running_sum + weighted_gen._1
if ((desired_sum == 0) or ((running_sum < desired_sum) and (desired_sum <= new_sum))) then
// we just crossed or reached the desired sum
chosen = weighted_gen._2
break
else
// update running sum
running_sum = new_sum
end
end
match chosen
| let x: Generator[T] box => x.generate(rnd)?
| None =>
Debug("chosen is None, desired_sum: " + desired_sum.string() +
"running_sum: " + running_sum.string())
error
end
end)
fun frequency_safe[T](
weighted_generators: ReadSeq[WeightedGenerator[T]])
: Generator[T] ?
=>
"""
Version of `frequency` that errors if the given `weighted_generators` is
empty.
"""
Fact(weighted_generators.size() > 0,
"cannot use frequency_safe on empty ReadSeq[WeightedGenerator]")?
Generators.frequency[T](weighted_generators)
fun zip2[T1, T2](
gen1: Generator[T1],
gen2: Generator[T2])
: Generator[(T1, T2)]
=>
"""
Zip two generators into a generator of a 2-tuple
containing the values generated by both generators.
"""
Generator[(T1, T2)](
object is GenObj[(T1, T2)]
fun generate(rnd: Randomness): GenerateResult[(T1, T2)] ? =>
(let v1: T1, let shrinks1: Iterator[T1^]) =
gen1.generate_and_shrink(rnd)?
(let v2: T2, let shrinks2: Iterator[T2^]) =
gen2.generate_and_shrink(rnd)?
((consume v1, consume v2), Iter[T1^](shrinks1).zip[T2^](shrinks2))
fun shrink(t: (T1, T2)): ValueAndShrink[(T1, T2)] =>
(let t1, let t2) = consume t
(let t11, let t1_shrunken: Iterator[T1^]) = gen1.shrink(consume t1)
(let t21, let t2_shrunken: Iterator[T2^]) = gen2.shrink(consume t2)
let shrunken = Iter[T1^](t1_shrunken).zip[T2^](t2_shrunken)
((consume t11, consume t21), shrunken)
end)
fun zip3[T1, T2, T3](
gen1: Generator[T1],
gen2: Generator[T2],
gen3: Generator[T3])
: Generator[(T1, T2, T3)]
=>
"""
Zip three generators into a generator of a 3-tuple
containing the values generated by those three generators.
"""
Generator[(T1, T2, T3)](
object is GenObj[(T1, T2, T3)]
fun generate(rnd: Randomness): GenerateResult[(T1, T2, T3)] ? =>
(let v1: T1, let shrinks1: Iterator[T1^]) =
gen1.generate_and_shrink(rnd)?
(let v2: T2, let shrinks2: Iterator[T2^]) =
gen2.generate_and_shrink(rnd)?
(let v3: T3, let shrinks3: Iterator[T3^]) =
gen3.generate_and_shrink(rnd)?
((consume v1, consume v2, consume v3),
Iter[T1^](shrinks1).zip2[T2^, T3^](shrinks2, shrinks3))
fun shrink(t: (T1, T2, T3)): ValueAndShrink[(T1, T2, T3)] =>
(let t1, let t2, let t3) = consume t
(let t11, let t1_shrunken: Iterator[T1^]) = gen1.shrink(consume t1)
(let t21, let t2_shrunken: Iterator[T2^]) = gen2.shrink(consume t2)
(let t31, let t3_shrunken: Iterator[T3^]) = gen3.shrink(consume t3)
let shrunken = Iter[T1^](t1_shrunken).zip2[T2^, T3^](t2_shrunken, t3_shrunken)
((consume t11, consume t21, consume t31), shrunken)
end)
fun zip4[T1, T2, T3, T4](
gen1: Generator[T1],
gen2: Generator[T2],
gen3: Generator[T3],
gen4: Generator[T4])
: Generator[(T1, T2, T3, T4)]
=>
"""
Zip four generators into a generator of a 4-tuple
containing the values generated by those four generators.
"""
Generator[(T1, T2, T3, T4)](
object is GenObj[(T1, T2, T3, T4)]
fun generate(rnd: Randomness): GenerateResult[(T1, T2, T3, T4)] ? =>
(let v1: T1, let shrinks1: Iterator[T1^]) =
gen1.generate_and_shrink(rnd)?
(let v2: T2, let shrinks2: Iterator[T2^]) =
gen2.generate_and_shrink(rnd)?
(let v3: T3, let shrinks3: Iterator[T3^]) =
gen3.generate_and_shrink(rnd)?
(let v4: T4, let shrinks4: Iterator[T4^]) =
gen4.generate_and_shrink(rnd)?
((consume v1, consume v2, consume v3, consume v4),
Iter[T1^](shrinks1).zip3[T2^, T3^, T4^](shrinks2, shrinks3, shrinks4))
fun shrink(t: (T1, T2, T3, T4)): ValueAndShrink[(T1, T2, T3, T4)] =>
(let t1, let t2, let t3, let t4) = consume t
(let t11, let t1_shrunken) = gen1.shrink(consume t1)
(let t21, let t2_shrunken) = gen2.shrink(consume t2)
(let t31, let t3_shrunken) = gen3.shrink(consume t3)
(let t41, let t4_shrunken) = gen4.shrink(consume t4)
let shrunken = Iter[T1^](t1_shrunken)
.zip3[T2^, T3^, T4^](t2_shrunken, t3_shrunken, t4_shrunken)
((consume t11, consume t21, consume t31, consume t41), shrunken)
end)
fun map2[T1, T2, T3](
gen1: Generator[T1],
gen2: Generator[T2],
fn: {(T1, T2): T3^})
: Generator[T3]
=>
"""
Convenience combinator for mapping 2 generators into 1.
"""
Generators.zip2[T1, T2](gen1, gen2)
.map[T3]({(arg) =>
(let arg1, let arg2) = consume arg
fn(consume arg1, consume arg2)
})
fun map3[T1, T2, T3, T4](
gen1: Generator[T1],
gen2: Generator[T2],
gen3: Generator[T3],
fn: {(T1, T2, T3): T4^})
: Generator[T4]
=>
"""
Convenience combinator for mapping 3 generators into 1.
"""
Generators.zip3[T1, T2, T3](gen1, gen2, gen3)
.map[T4]({(arg) =>
(let arg1, let arg2, let arg3) = consume arg
fn(consume arg1, consume arg2, consume arg3)
})
fun map4[T1, T2, T3, T4, T5](
gen1: Generator[T1],
gen2: Generator[T2],
gen3: Generator[T3],
gen4: Generator[T4],
fn: {(T1, T2, T3, T4): T5^})
: Generator[T5]
=>
"""
Convenience combinator for mapping 4 generators into 1.
"""
Generators.zip4[T1, T2, T3, T4](gen1, gen2, gen3, gen4)
.map[T5]({(arg) =>
(let arg1, let arg2, let arg3, let arg4) = consume arg
fn(consume arg1, consume arg2, consume arg3, consume arg4)
})
fun bool(): Generator[Bool] =>
"""
Create a generator of bool values.
"""
Generator[Bool](
object is GenObj[Bool]
fun generate(rnd: Randomness): Bool =>
rnd.bool()
end)
fun _int_shrink[T: (Int & Integer[T] val)](t: T^, min: T): ValueAndShrink[T] =>
"""
"""
let relation = t.compare(min)
let t_copy: T = T.create(t)
//Debug(t.string() + " is " + relation.string() + " than min " + min.string())
let sub_iter =
object is Iterator[T^]
var _cur: T = t_copy
var _subtract: F64 = 1.0
var _overflow: Bool = false
fun ref _next_minuend(): T =>
// f(x) = x + (2^-5 * x^2)
T.from[F64](_subtract = _subtract + (0.03125 * _subtract * _subtract))
fun ref has_next(): Bool =>
match relation
| Less => (_cur < min) and not _overflow
| Equal => false
| Greater => (_cur > min) and not _overflow
end
fun ref next(): T^ ? =>
match relation
| Less =>
let minuend: T = _next_minuend()
let old = _cur
_cur = _cur + minuend
if old > _cur then
_overflow = true
end
old
| Equal => error
| Greater =>
let minuend: T = _next_minuend()
let old = _cur
_cur = _cur - minuend
if old < _cur then
_overflow = true
end
old
end
end
let min_iter =
match relation
| let _: (Less | Greater) => Poperator[T]([min])
| Equal => Poperator[T].empty()
end
let shrunken_iter = Iter[T].chain(
[
Iter[T^](sub_iter).skip(1)
min_iter
].values())
(consume t, shrunken_iter)
fun u8(
min: U8 = U8.min_value(),
max: U8 = U8.max_value())
: Generator[U8]
=>
"""
Create a generator for U8 values.
"""
let that = this
Generator[U8](
object is GenObj[U8]
fun generate(rnd: Randomness): U8^ =>
rnd.u8(min, max)
fun shrink(u: U8): ValueAndShrink[U8] =>
that._int_shrink[U8](consume u, min)
end)
fun u16(
min: U16 = U16.min_value(),
max: U16 = U16.max_value())
: Generator[U16]
=>
"""
create a generator for U16 values
"""
let that = this
Generator[U16](
object is GenObj[U16]
fun generate(rnd: Randomness): U16^ =>
rnd.u16(min, max)
fun shrink(u: U16): ValueAndShrink[U16] =>
that._int_shrink[U16](consume u, min)
end)
fun u32(
min: U32 = U32.min_value(),
max: U32 = U32.max_value())
: Generator[U32]
=>
"""
Create a generator for U32 values.
"""
let that = this
Generator[U32](
object is GenObj[U32]
fun generate(rnd: Randomness): U32^ =>
rnd.u32(min, max)
fun shrink(u: U32): ValueAndShrink[U32] =>
that._int_shrink[U32](consume u, min)
end)
fun u64(
min: U64 = U64.min_value(),
max: U64 = U64.max_value())
: Generator[U64]
=>
"""
Create a generator for U64 values.
"""
let that = this
Generator[U64](
object is GenObj[U64]
fun generate(rnd: Randomness): U64^ =>
rnd.u64(min, max)
fun shrink(u: U64): ValueAndShrink[U64] =>
that._int_shrink[U64](consume u, min)
end)
fun u128(
min: U128 = U128.min_value(),
max: U128 = U128.max_value())
: Generator[U128]
=>
"""
Create a generator for U128 values.
"""
let that = this
Generator[U128](
object is GenObj[U128]
fun generate(rnd: Randomness): U128^ =>
rnd.u128(min, max)
fun shrink(u: U128): ValueAndShrink[U128] =>
that._int_shrink[U128](consume u, min)
end)
fun usize(
min: USize = USize.min_value(),
max: USize = USize.max_value())
: Generator[USize]
=>
"""
Create a generator for USize values.
"""
let that = this
Generator[USize](
object is GenObj[USize]
fun generate(rnd: Randomness): GenerateResult[USize] =>
rnd.usize(min, max)
fun shrink(u: USize): ValueAndShrink[USize] =>
that._int_shrink[USize](consume u, min)
end)
fun ulong(
min: ULong = ULong.min_value(),
max: ULong = ULong.max_value())
: Generator[ULong]
=>
"""
Create a generator for ULong values.
"""
let that = this
Generator[ULong](
object is GenObj[ULong]
fun generate(rnd: Randomness): ULong^ =>
rnd.ulong(min, max)
fun shrink(u: ULong): ValueAndShrink[ULong] =>
that._int_shrink[ULong](consume u, min)
end)
fun i8(
min: I8 = I8.min_value(),
max: I8 = I8.max_value())
: Generator[I8]
=>
"""
Create a generator for I8 values.
"""
let that = this
Generator[I8](
object is GenObj[I8]
fun generate(rnd: Randomness): I8^ =>
rnd.i8(min, max)
fun shrink(i: I8): ValueAndShrink[I8] =>
that._int_shrink[I8](consume i, min)
end)
fun i16(
min: I16 = I16.min_value(),
max: I16 = I16.max_value())
: Generator[I16]
=>
"""
Create a generator for I16 values.
"""
let that = this
Generator[I16](
object is GenObj[I16]
fun generate(rnd: Randomness): I16^ =>
rnd.i16(min, max)
fun shrink(i: I16): ValueAndShrink[I16] =>
that._int_shrink[I16](consume i, min)
end)
fun i32(
min: I32 = I32.min_value(),
max: I32 = I32.max_value())
: Generator[I32]
=>
"""
Create a generator for I32 values.
"""
let that = this
Generator[I32](
object is GenObj[I32]
fun generate(rnd: Randomness): I32^ =>
rnd.i32(min, max)
fun shrink(i: I32): ValueAndShrink[I32] =>
that._int_shrink[I32](consume i, min)
end)
fun i64(
min: I64 = I64.min_value(),
max: I64 = I64.max_value())
: Generator[I64]
=>
"""
Create a generator for I64 values.
"""
let that = this
Generator[I64](
object is GenObj[I64]
fun generate(rnd: Randomness): I64^ =>
rnd.i64(min, max)
fun shrink(i: I64): ValueAndShrink[I64] =>
that._int_shrink[I64](consume i, min)
end)
fun i128(
min: I128 = I128.min_value(),
max: I128 = I128.max_value())
: Generator[I128]
=>
"""
Create a generator for I128 values.
"""
let that = this
Generator[I128](
object is GenObj[I128]
fun generate(rnd: Randomness): I128^ =>
rnd.i128(min, max)
fun shrink(i: I128): ValueAndShrink[I128] =>
that._int_shrink[I128](consume i, min)
end)
fun ilong(
min: ILong = ILong.min_value(),
max: ILong = ILong.max_value())
: Generator[ILong]
=>
"""
Create a generator for ILong values.
"""
let that = this
Generator[ILong](
object is GenObj[ILong]
fun generate(rnd: Randomness): ILong^ =>
rnd.ilong(min, max)
fun shrink(i: ILong): ValueAndShrink[ILong] =>
that._int_shrink[ILong](consume i, min)
end)
fun isize(
min: ISize = ISize.min_value(),
max: ISize = ISize.max_value())
: Generator[ISize]
=>
"""
Create a generator for ISize values.
"""
let that = this
Generator[ISize](
object is GenObj[ISize]
fun generate(rnd: Randomness): ISize^ =>
rnd.isize(min, max)
fun shrink(i: ISize): ValueAndShrink[ISize] =>
that._int_shrink[ISize](consume i, min)
end)
fun byte_string(
gen: Generator[U8],
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for strings
generated from the bytes returned by the generator `gen`,
with a minimum length of `min` (default: 0)
and a maximum length of `max` (default: 100).
"""
Generator[String](
object is GenObj[String]
fun generate(rnd: Randomness): GenerateResult[String] =>
let size = rnd.usize(min, max)
let gen_iter = Iter[U8^](gen.value_iter(rnd))
.take(size)
let arr: Array[U8] iso = recover Array[U8](size) end
for b in gen_iter do
arr.push(b)
end
String.from_iso_array(consume arr)
fun shrink(s: String): ValueAndShrink[String] =>
"""
shrink string until `min` length.
"""
var str: String = s.trim(0, s.size()-1)
let shorten_iter: Iterator[String^] =
object is Iterator[String^]
fun ref has_next(): Bool => str.size() > min
fun ref next(): String^ =>
str = str.trim(0, str.size()-1)
end
let min_iter =
if s.size() > min then
Poperator[String]([s.trim(0, min)])
else
Poperator[String].empty()
end
let shrink_iter =
Iter[String^].chain([
shorten_iter
min_iter
].values())
(consume s, shrink_iter)
end)
fun ascii(
min: USize = 0,
max: USize = 100,
range: ASCIIRange = ASCIIAll)
: Generator[String]
=>
"""
Create a generator for strings withing the given `range`,
with a minimum length of `min` (default: 0)
and a maximum length of `max` (default: 100).
"""
let range_bytes = range.apply()
let fallback = U8(0)
let range_bytes_gen = usize(0, range_bytes.size()-1)
.map[U8]({(size) =>
try
range_bytes(size)?
else
// should never happen
fallback
end })
byte_string(range_bytes_gen, min, max)
fun ascii_printable(
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for strings of printable ASCII characters,
with a minimum length of `min` (default: 0)
and a maximum length of `max` (default: 100).
"""
ascii(min, max, ASCIIPrintable)
fun ascii_numeric(
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for strings of numeric ASCII characters,
with a minimum length of `min` (default: 0)
and a maximum length of `max` (default: 100).
"""
ascii(min, max, ASCIIDigits)
fun ascii_letters(
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for strings of ASCII letters,
with a minimum length of `min` (default: 0)
and a maximum length of `max` (default: 100).
"""
ascii(min, max, ASCIILetters)
fun utf32_codepoint_string(
gen: Generator[U32],
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for strings
from a generator of unicode codepoints,
with a minimum length of `min` codepoints (default: 0)
and a maximum length of `max` codepoints (default: 100).
Note that the byte length of the generated string can be up to 4 times
the size in code points.
"""
Generator[String](
object is GenObj[String]
fun generate(rnd: Randomness): GenerateResult[String] =>
let size = rnd.usize(min, max)
let gen_iter = Iter[U32^](gen.value_iter(rnd))
.filter({(cp) =>
// excluding surrogate pairs
(cp <= 0xD7FF) or (cp >= 0xE000) })
.take(size)
let s: String iso = recover String(size) end
for code_point in gen_iter do
s.push_utf32(code_point)
end
s
fun shrink(s: String): ValueAndShrink[String] =>
"""
Strip off codepoints from the end, not just bytes, so we
maintain a valid utf8 string.
Only shrink until given `min` is hit.
"""
var shrink_base = s
let s_len = s.codepoints()
let shrink_iter: Iterator[String^] =
if s_len > min then
Iter[String^].repeat_value(consume shrink_base)
.map_stateful[String^](
object
var len: USize = s_len - 1
fun ref apply(str: String): String =>
Generators._trim_codepoints(str, len = len - 1)
end
).take(s_len - min)
// take_while is buggy in pony < 0.21.0
//.take_while({(t) => t.codepoints() > min})
else
Poperator[String].empty()
end
(consume s, shrink_iter)
end)
fun _trim_codepoints(s: String, trim_to: USize): String =>
recover val
Iter[U32](s.runes())
.take(trim_to)
.fold[String ref](
String.create(trim_to),
{(acc, cp) => acc.>push_utf32(cp) })
end
fun unicode(
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for unicode strings,
with a minimum length of `min` codepoints (default: 0)
and a maximum length of `max` codepoints (default: 100).
Note that the byte length of the generated string can be up to 4 times
the size in code points.
"""
let range_1 = u32(0x0, 0xD7FF)
let range_1_size: USize = 0xD7FF
// excluding surrogate pairs
// this might be duplicate work but increases efficiency
let range_2 = u32(0xE000, 0x10FFFF)
let range_2_size = U32(0x10FFFF - 0xE000).usize()
let code_point_gen =
frequency[U32]([
(range_1_size, range_1)
(range_2_size, range_2)
])
utf32_codepoint_string(code_point_gen, min, max)
fun unicode_bmp(
min: USize = 0,
max: USize = 100)
: Generator[String]
=>
"""
Create a generator for unicode strings
from the basic multilingual plane only,
with a minimum length of `min` codepoints (default: 0)
and a maximum length of `max` codepoints (default: 100).
Note that the byte length of the generated string can be up to 4 times
the size in code points.
"""
let range_1 = u32(0x0, 0xD7FF)
let range_1_size: USize = 0xD7FF
// excluding surrogate pairs
// this might be duplicate work but increases efficiency
let range_2 = u32(0xE000, 0xFFFF)
let range_2_size = U32(0xFFFF - 0xE000).usize()
let code_point_gen =
frequency[U32]([
(range_1_size, range_1)
(range_2_size, range_2)
])
utf32_codepoint_string(code_point_gen, min, max)