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/*-------------------------------------------------------------------------*\
** ScalaCheck **
** Copyright (c) 2007-2021 Rickard Nilsson. All rights reserved. **
** http://www.scalacheck.org **
** **
** This software is released under the terms of the Revised BSD License. **
** There is NO WARRANTY. See the file LICENSE for the full text. **
\*------------------------------------------------------------------------ */
package org.scalacheck
import language.higherKinds
import language.implicitConversions
import rng.Seed
import util.Buildable
import util.SerializableCanBuildFroms._
import ScalaVersionSpecific._
import scala.annotation.tailrec
import scala.collection.immutable.TreeMap
import scala.collection.mutable.ArrayBuffer
import scala.concurrent.duration.{Duration, FiniteDuration}
import java.util.{ Calendar, UUID }
import java.math.{BigInteger, BigDecimal => JavaDecimal}
sealed abstract class Gen[+T] extends Serializable { self =>
//// Private interface ////
import Gen.{R, gen}
// This is no longer used but preserved here for binary compatibility.
private[scalacheck] def sieveCopy(x: Any): Boolean = true
// If you implement new Gen[_] directly (instead of using
// combinators), make sure to use p.initialSeed or p.useInitialSeed
// in the implementation, instead of using seed directly.
private[scalacheck] def doApply(p: Gen.Parameters, seed: Seed): R[T]
//// Public interface ////
/** A class supporting filtered operations. */
final class WithFilter(p: T => Boolean) {
def map[U](f: T => U): Gen[U] = self.suchThat(p).map(f)
def flatMap[U](f: T => Gen[U]): Gen[U] = self.suchThat(p).flatMap(f)
def withFilter(q: T => Boolean): WithFilter = self.withFilter(x => p(x) && q(x))
}
/** Evaluate this generator with the given parameters */
def apply(p: Gen.Parameters, seed: Seed): Option[T] =
doApply(p, seed).retrieve
def doPureApply(p: Gen.Parameters, seed: Seed, retries: Int = 100): Gen.R[T] = {
@tailrec def loop(r: Gen.R[T], i: Int): Gen.R[T] =
if (r.retrieve.isDefined) r
else if (i > 0) loop(doApply(p, r.seed), i - 1)
else throw new Gen.RetrievalError()
loop(doApply(p, seed), retries)
}
/**
* Evaluate this generator with the given parameters.
*
* The generator will attempt to generate a valid `T` value. If a
* valid value is not produced it may retry several times,
* determined by the `retries` parameter (which defaults to 100).
*
* If all the retries fail it will throw a `Gen.RetrievalError`
* exception.
*/
def pureApply(p: Gen.Parameters, seed: Seed, retries: Int = 100): T =
doPureApply(p, seed, retries).retrieve.get
/** Create a new generator by mapping the result of this generator */
def map[U](f: T => U): Gen[U] = gen { (p, seed) => doApply(p, seed).map(f) }
/** Create a new generator by flat-mapping the result of this generator */
def flatMap[U](f: T => Gen[U]): Gen[U] = gen { (p, seed) =>
val rt = doApply(p, seed)
rt.flatMap(t => f(t).doApply(p, rt.seed))
}
/** Create a new generator that uses this generator to produce a value
* that fulfills the given condition. If the condition is not fulfilled,
* the generator fails (returns None). Also, make sure that the provided
* test property is side-effect free, e.g. it should not use external vars. */
def filter(p: T => Boolean): Gen[T] = suchThat(p)
/** Create a new generator that uses this generator to produce a value
* that doesn't fulfill the given condition. If the condition is fulfilled,
* the generator fails (returns None). Also, make sure that the provided
* test property is side-effect free, e.g. it should not use external vars. */
def filterNot(p: T => Boolean): Gen[T] = suchThat(x => !p(x))
/** Creates a non-strict filtered version of this generator. */
def withFilter(p: T => Boolean): WithFilter = new WithFilter(p)
/** Create a new generator that uses this generator to produce a value
* that fulfills the given condition. If the condition is not fulfilled,
* the generator fails (returns None). Also, make sure that the provided
* test property is side-effect free, e.g. it should not use external vars.
* This method is identical to [Gen.filter]. */
def suchThat(f: T => Boolean): Gen[T] =
new Gen[T] {
def doApply(p: Gen.Parameters, seed: Seed): Gen.R[T] =
p.useInitialSeed(seed) { (p0, s0) =>
val r = self.doApply(p0, s0)
r.copy(r = r.retrieve.filter(f))
}
}
case class RetryUntilException(n: Int) extends RuntimeException(s"retryUntil failed after $n attempts")
/**
* Create a generator that calls this generator repeatedly until the
* given condition is fulfilled. The generated value is then
* returned. Make sure that the provided test property is
* side-effect free (it should not use external vars).
*
* If the generator fails more than maxTries, a RetryUntilException
* will be thrown.
*/
def retryUntil(p: T => Boolean, maxTries: Int): Gen[T] = {
require(maxTries > 0)
def loop(params: Gen.Parameters, seed: Seed, tries: Int): R[T] =
if (tries > maxTries) throw RetryUntilException(tries) else {
val r = self.doApply(params, seed)
if (r.retrieve.exists(p)) r else loop(params, r.seed, tries + 1)
}
Gen.gen((params, seed) => loop(params, seed, 1))
}
/**
* Create a generator that calls this generator repeatedly until the
* given condition is fulfilled. The generated value is then
* returned. Make sure that the provided test property is
* side-effect free (it should not use external vars).
*
*
* If the generator fails more than 10000 times, a
* RetryUntilException will be thrown. You can call `retryUntil`
* with a second parameter to change this number.
*/
def retryUntil(p: T => Boolean): Gen[T] =
retryUntil(p, 10000)
def sample: Option[T] =
doApply(Gen.Parameters.default, Seed.random()).retrieve
/** Returns a new property that holds if and only if both this
* and the given generator generates the same result, or both
* generators generate no result. */
def ==[U](g: Gen[U]): Prop = Prop { prms =>
// test equality using a random seed
val seed = Seed.random()
val lhs = doApply(prms, seed).retrieve
val rhs = g.doApply(prms, seed).retrieve
if (lhs == rhs) Prop.proved(prms) else Prop.falsified(prms)
}
def !=[U](g: Gen[U]): Prop =
Prop.forAll(this)(r => Prop.forAll(g)(_ != r))
def !==[U](g: Gen[U]): Prop = Prop { prms =>
// test inequality using a random seed
val seed = Seed.random()
val lhs = doApply(prms, seed).retrieve
val rhs = g.doApply(prms, seed).retrieve
if (lhs != rhs) Prop.proved(prms) else Prop.falsified(prms)
}
/** Put a label on the generator to make test reports clearer */
def label(l: String): Gen[T] = new Gen[T] {
def doApply(p: Gen.Parameters, seed: Seed) =
p.useInitialSeed(seed) { (p0, s0) =>
val r = self.doApply(p0, s0)
r.copy(l = r.labels + l)
}
}
/** Put a label on the generator to make test reports clearer */
def :|(l: String): Gen[T] = label(l)
/** Put a label on the generator to make test reports clearer */
def |:(l: String): Gen[T] = label(l)
/** Put a label on the generator to make test reports clearer */
def :|(l: Symbol): Gen[T] = label(l.name)
/** Put a label on the generator to make test reports clearer */
def |:(l: Symbol): Gen[T] = label(l.name)
/** Perform some RNG perturbation before generating */
def withPerturb(f: Seed => Seed): Gen[T] =
Gen.gen((p, seed) => doApply(p, f(seed)))
}
object Gen extends GenArities with GenVersionSpecific {
//// Private interface ////
import Arbitrary.arbitrary
/** Just an alias */
private type P = Parameters
class RetrievalError extends RuntimeException("couldn't generate value")
private[scalacheck] trait R[+T] {
def labels: Set[String] = Set()
// sieve is no longer used but preserved for binary compatibility
final def sieve[U >: T]: U => Boolean = (_: U) => true
protected def result: Option[T]
def seed: Seed
def retrieve: Option[T] = result
def copy[U >: T](
l: Set[String] = this.labels,
// s is no longer used but preserved for binary compatibility
s: U => Boolean = this.sieve,
r: Option[U] = this.result,
sd: Seed = this.seed
): R[U] = new R[U] {
override val labels = l
val seed = sd
val result = r
}
def map[U](f: T => U): R[U] =
r(retrieve.map(f), seed).copy(l = labels)
def flatMap[U](f: T => R[U]): R[U] =
retrieve match {
case None =>
r(None, seed).copy(l = labels)
case Some(t) =>
val r = f(t)
r.copy(l = labels | r.labels)
}
}
private[scalacheck] def r[T](r: Option[T], sd: Seed): R[T] = new R[T] {
val result = r
val seed = sd
}
/** Generator factory method */
private[scalacheck] def gen[T](f: (P, Seed) => R[T]): Gen[T] = new Gen[T] {
def doApply(p: P, seed: Seed): R[T] = p.useInitialSeed(seed)(f)
}
//// Public interface ////
/** Generator parameters, used by [[org.scalacheck.Gen.apply]] */
sealed abstract class Parameters extends Serializable { outer =>
override def toString: String = {
val sb = new StringBuilder
sb.append("Parameters(")
sb.append(s"size=$size, ")
sb.append(s"initialSeed=$initialSeed, ")
sb.append(s"useLegacyShrinking=$useLegacyShrinking)")
sb.toString
}
/**
* The size of the generated value. Generator implementations are
* allowed to freely interpret (or ignore) this value. During test
* execution, the value of this parameter is controlled by
* [[Test.Parameters.minSize]] and [[Test.Parameters.maxSize]].
*/
val size: Int
private[this] def cpy(
size0: Int = outer.size,
initialSeed0: Option[Seed] = outer.initialSeed,
useLegacyShrinking0: Boolean = outer.useLegacyShrinking
): Parameters =
new Parameters {
val size: Int = size0
val initialSeed: Option[Seed] = initialSeed0
override val useLegacyShrinking: Boolean = useLegacyShrinking0
}
/**
* Create a copy of this [[Gen.Parameters]] instance with
* [[Gen.Parameters.size]] set to the specified value.
*/
def withSize(size: Int): Parameters =
cpy(size0 = size)
/**
*
*/
val initialSeed: Option[Seed]
def withInitialSeed(o: Option[Seed]): Parameters =
cpy(initialSeed0 = o)
def withInitialSeed(seed: Seed): Parameters =
cpy(initialSeed0 = Some(seed))
def withInitialSeed(n: Long): Parameters =
cpy(initialSeed0 = Some(Seed(n)))
def withNoInitialSeed: Parameters =
cpy(initialSeed0 = None)
def useInitialSeed[A](seed: Seed)(f: (Parameters, Seed) => A): A =
initialSeed match {
case Some(s) => f(this.withNoInitialSeed, s)
case None => f(this, seed)
}
val useLegacyShrinking: Boolean = true
def disableLegacyShrinking: Parameters =
withLegacyShrinking(false)
def enableLegacyShrinking: Parameters =
withLegacyShrinking(true)
def withLegacyShrinking(b: Boolean): Parameters =
cpy(useLegacyShrinking0 = b)
// no longer used, but preserved for binary compatibility
@deprecated("cp is deprecated. use cpy.", "1.14.1")
private case class cp(size: Int = size, initialSeed: Option[Seed] = None) extends Parameters
}
/** Provides methods for creating [[org.scalacheck.Gen.Parameters]] values */
object Parameters {
/** Default generator parameters instance. */
val default: Parameters = new Parameters {
val size: Int = 100
val initialSeed: Option[Seed] = None
}
}
/** A wrapper type for range types */
trait Choose[T] extends Serializable {
/** Creates a generator that returns a value in the given inclusive range */
def choose(min: T, max: T): Gen[T]
}
/** Provides implicit [[org.scalacheck.Gen.Choose]] instances */
object Choose extends time.JavaTimeChoose {
class IllegalBoundsError[A](low: A, high: A)
extends IllegalArgumentException(s"invalid bounds: low=$low, high=$high")
/**
* This method gets a ton of use -- so we want it to be as fast as
* possible for many of our common cases.
*/
private def chLng(l: Long, h: Long)(p: P, seed: Seed): R[Long] = {
if (h < l) {
throw new IllegalBoundsError(l, h)
} else if (h == l) {
const(l).doApply(p, seed)
} else if (l == Long.MinValue && h == Long.MaxValue) {
val (n, s) = seed.long
r(Some(n), s)
} else if (l == Int.MinValue && h == Int.MaxValue) {
val (n, s) = seed.long
r(Some(n.toInt.toLong), s)
} else if (l == Short.MinValue && h == Short.MaxValue) {
val (n, s) = seed.long
r(Some(n.toShort.toLong), s)
} else if (l == 0L && h == Char.MaxValue) {
val (n, s) = seed.long
r(Some(n.toChar.toLong), s)
} else if (l == Byte.MinValue && h == Byte.MaxValue) {
val (n, s) = seed.long
r(Some(n.toByte.toLong), s)
} else {
val d = h - l + 1
if (d <= 0) {
var tpl = seed.long
var n = tpl._1
var s = tpl._2
while (n < l || n > h) {
tpl = s.long
n = tpl._1
s = tpl._2
}
r(Some(n), s)
} else {
val (n, s) = seed.long
r(Some(l + (n & 0x7fffffffffffffffL) % d), s)
}
}
}
private def chDbl(l: Double, h: Double)(p: P, seed: Seed): R[Double] = {
val d = h - l
if (d < 0) {
throw new IllegalBoundsError(l, h)
} else if (d > Double.MaxValue) {
val (x, seed2) = seed.long
if (x < 0) chDbl(l, 0d)(p, seed2) else chDbl(0d, h)(p, seed2)
} else if (d == 0) {
r(Some(l), seed)
} else {
val (n, s) = seed.double
r(Some(n * (h-l) + l), s)
}
}
/**
* Generate a random BigInt within [lower, lower + span).
*
* Note that unlike the choose method, whose bounds are inclusive,
* this method's upper bound is exclusive. We determine how many
* random bits we need (bitLen), and then round up to the nearest
* number of bytes (byteLen). We generate the bytes, possibly
* truncating the most significant byte (bytes(0)) if bitLen is
* not evenly-divisible by 8.
*
* Finally, we check to see if the BigInt we ended up with is in
* our range. If it is not, we restart this method. The likelihood
* of needing to restart depends on span. In the worst case we
* have almost a 50% chance of this (which occurs when span is a
* power of 2 + 1) and in the best case we never restart (which
* occurs when span is a power of 2).
*/
private def chBigInteger(lower: BigInteger, span: BigInteger, seed0: Seed): R[BigInteger] = {
val bitLen = span.bitLength
val byteLen = (bitLen + 7) / 8
val bytes = new Array[Byte](byteLen)
var seed = seed0
var i = 0
while (i < bytes.length) {
// generate a random long value (i.e. 8 random bytes)
val (x0, seed1) = seed.long
var x = x0
seed = seed1
// extract each byte in turn and add them to our byte array
var j = 0
while (j < 8 && i < bytes.length) {
val b = (x & 0xff).toByte
bytes(i) = b
x = x >>> 8
i += 1
j += 1
}
}
// we may not need all 8 bits of our most significant byte. if
// not, mask off any unneeded upper bits.
val bitRem = bitLen & 7
if (bitRem != 0) {
val mask = 0xff >>> (8 - bitRem)
bytes(0) = (bytes(0) & mask).toByte
}
// construct a BigInteger and see if its valid. if so, we're
// done. otherwise, we need to restart using our new seed.
val big = new BigInteger(1, bytes)
if (big.compareTo(span) < 0) {
r(Some(big.add(lower)), seed)
} else {
chBigInteger(lower, span, seed)
}
}
implicit val chooseLong: Choose[Long] =
new Choose[Long] {
def choose(low: Long, high: Long): Gen[Long] =
if (low > high) throw new IllegalBoundsError(low, high)
else gen(chLng(low,high))
}
implicit val chooseInt: Choose[Int] =
Choose.xmap[Long, Int](_.toInt, _.toLong)
implicit val chooseShort: Choose[Short] =
Choose.xmap[Long, Short](_.toShort, _.toLong)
implicit val chooseChar: Choose[Char] =
Choose.xmap[Long, Char](_.toChar, _.toLong)
implicit val chooseByte: Choose[Byte] =
Choose.xmap[Long, Byte](_.toByte, _.toLong)
implicit val chooseDouble: Choose[Double] =
new Choose[Double] {
def choose(low: Double, high: Double) =
if (low > high) throw new IllegalBoundsError(low, high)
else if (low == Double.NegativeInfinity)
frequency(1 -> const(Double.NegativeInfinity),
9 -> choose(Double.MinValue, high))
else if (high == Double.PositiveInfinity)
frequency(1 -> const(Double.PositiveInfinity),
9 -> choose(low, Double.MaxValue))
else gen(chDbl(low,high))
}
implicit val chooseFloat: Choose[Float] =
Choose.xmap[Double, Float](_.toFloat, _.toDouble)
implicit val chooseFiniteDuration: Choose[FiniteDuration] =
Choose.xmap[Long, FiniteDuration](Duration.fromNanos, _.toNanos)
implicit object chooseBigInt extends Choose[BigInt] {
def choose(low: BigInt, high: BigInt): Gen[BigInt] =
chooseBigInteger
.choose(low.bigInteger, high.bigInteger)
.map(BigInt(_))
}
implicit object chooseBigInteger extends Choose[BigInteger] {
def choose(low: BigInteger, high: BigInteger): Gen[BigInteger] =
(low compareTo high) match {
case n if n > 0 => throw new IllegalBoundsError(low, high)
case 0 => Gen.const(low)
case _ => /* n < 0 */
val span = high.subtract(low).add(BigInteger.ONE)
gen((_, seed) => chBigInteger(low, span, seed))
}
}
/**
* Choose a BigDecimal number between two given numbers.
*
* The minimum scale used will be 34. That means that the
* fractional part will have at least 34 digits (more if one of
* the given numbers has a scale larger than 34).
*
* The minimum scale was chosen based on Scala's default scale for
* expanding infinite fractions:
*
* BigDecimal(1) / 3 // 0.3333333333333333333333333333333333
*
* See chooseBigDecimalScale for more information about scale.
*/
implicit val chooseBigDecimal: Choose[BigDecimal] =
chooseBigDecimalScale(minScale = 34)
/**
* The "scale" of a decimal number refers to the number of digits
* in the fractional part. For example, 3.0000 has a scale of 4.
*
* We can generate an arbitrary number of digits in the decimal
* expansion of a number, so if a user calls choose(0, 1) we need
* to decide "how much" work to do. The minScale ensures that we
* do "enough" work to generate interesting numbers.
*
* The implicit instance fixes this value, but since users may
* want to use other scales we expose this method as well.
*/
private[this] def chooseBigDecimalScale(minScale: Int): Choose[BigDecimal] =
new Choose[BigDecimal] {
private val c = chooseJavaBigDecimalScale(minScale)
def choose(low: BigDecimal, high: BigDecimal): Gen[BigDecimal] =
c.choose(low.bigDecimal, high.bigDecimal).map(BigDecimal(_))
}
/**
* Choose a java.math.BigDecimal number between two given numbers.
*
* See chooseBigDecimal and chooseBigDecimalScale for more comments.
*/
implicit val chooseJavaBigDecimal: Choose[JavaDecimal] =
chooseJavaBigDecimalScale(minScale = 34)
/**
* See chooseBigDecimalScale for comments.
*/
private[this] def chooseJavaBigDecimalScale(minScale: Int): Choose[JavaDecimal] =
new Choose[JavaDecimal] {
def choose(low: JavaDecimal, high: JavaDecimal): Gen[JavaDecimal] =
(low compareTo high) match {
case n if n > 0 => throw new IllegalBoundsError(low, high)
case 0 => Gen.const(low)
case _ => /* n < 0 */
val s = (low.scale max high.scale) max minScale
val x = if (low.scale < s) low.setScale(s) else low
val y = if (high.scale < s) high.setScale(s) else high
chooseBigInteger
.choose(x.unscaledValue, y.unscaledValue)
.map(n => new JavaDecimal(n, s))
}
}
/** Transform a Choose[T] to a Choose[U] where T and U are two isomorphic
* types whose relationship is described by the provided transformation
* functions. (exponential functor map) */
def xmap[T, U](from: T => U, to: U => T)(implicit c: Choose[T]): Choose[U] =
new Choose[U] {
def choose(low: U, high: U): Gen[U] =
c.choose(to(low), to(high)).map(from)
}
}
//// Various Generator Combinators ////
/** A generator that always generates the given value */
implicit def const[T](x: T): Gen[T] = gen((p, seed) => r(Some(x), seed))
/** A generator that never generates a value */
def fail[T]: Gen[T] = gen((p, seed) => failed[T](seed))
/**
* A fixed point generator. This is useful for making recusive structures
* e.g.
*
* Gen.recursive[List[Int]] { recurse =>
* Gen.choose(0, 10).flatMap { idx =>
* if (idx < 5) recurse.map(idx :: _)
* else Gen.const(idx :: Nil)
* }
* }
*/
def recursive[A](fn: Gen[A] => Gen[A]): Gen[A] = {
lazy val result: Gen[A] = lzy(fn(result))
result
}
/** A result that never contains a value */
private[scalacheck] def failed[T](seed0: Seed): R[T] =
new R[T] {
val result: Option[T] = None
val seed = seed0
}
/** A generator that generates a random value in the given (inclusive)
* range. If the range is invalid, an IllegalBoundsError exception will be
* thrown. */
def choose[T](min: T, max: T)(implicit c: Choose[T]): Gen[T] =
c.choose(min, max)
/** Sequences generators. If any of the given generators fails, the
* resulting generator will also fail. */
def sequence[C,T](gs: Traversable[Gen[T]])(implicit b: Buildable[T, C]): Gen[C] = {
val g = gen { (p, seed) =>
gs.foldLeft(r(Some(Vector.empty[T]), seed)) {
case (rs,g) =>
val rt = g.doApply(p, rs.seed)
rt.flatMap(t => rs.map(_ :+ t)).copy(sd = rt.seed)
}
}
g.map(b.fromIterable)
}
/** Monadic recursion on Gen
* This is a stack-safe loop that is the same as:
*
* {{{
*
* fn(a).flatMap {
* case Left(a) => tailRec(a)(fn)
* case Right(b) => Gen.const(b)
* }
*
* }}}
*
* which is useful for doing monadic loops without blowing up the
* stack
*/
def tailRecM[A, B](a0: A)(fn: A => Gen[Either[A, B]]): Gen[B] = {
@tailrec
def tailRecMR(a: A, seed: Seed, labs: Set[String])(fn: (A, Seed) => R[Either[A, B]]): R[B] = {
val re = fn(a, seed)
val nextLabs = labs | re.labels
re.retrieve match {
case None => r(None, re.seed).copy(l = nextLabs)
case Some(Right(b)) => r(Some(b), re.seed).copy(l = nextLabs)
case Some(Left(a)) => tailRecMR(a, re.seed, nextLabs)(fn)
}
}
// This is the "Reader-style" approach to making a stack-safe loop:
// we put one outer closure around an explicitly tailrec loop
gen[B] { (p: P, seed: Seed) =>
tailRecMR(a0, seed, Set.empty) { (a, seed) => fn(a).doApply(p, seed) }
}
}
/** Wraps a generator lazily. The given parameter is only evaluated once,
* and not until the wrapper generator is evaluated. */
def lzy[T](g: => Gen[T]): Gen[T] = {
lazy val h = g
gen { (p, seed) => h.doApply(p, seed) }
}
/** Wraps a generator for later evaluation. The given parameter is
* evaluated each time the wrapper generator is evaluated. */
def delay[T](g: => Gen[T]): Gen[T] =
gen { (p, seed) => g.doApply(p, seed) }
/** Creates a generator that can access its generation parameters */
def parameterized[T](f: Parameters => Gen[T]): Gen[T] =
gen { (p, seed) => f(p).doApply(p, seed) }
/** Creates a generator that can access its generation size */
def sized[T](f: Int => Gen[T]): Gen[T] =
gen { (p, seed) => f(p.size).doApply(p, seed) }
/** A generator that returns the current generation size */
lazy val size: Gen[Int] = sized { sz => sz }
/** Creates a resized version of a generator */
def resize[T](s: Int, g: Gen[T]) = gen((p, seed) => g.doApply(p.withSize(s), seed))
/** Picks a random value from a list. */
def oneOf[T](xs: Iterable[T]): Gen[T] =
if (xs.isEmpty) {
throw new IllegalArgumentException("oneOf called on empty collection")
} else {
val vector = xs.toVector
choose(0, vector.size - 1).map(vector(_))
}
/** Picks a random value from a list.
* @todo Remove this overloaded method in the next major release. See #438.
*/
def oneOf[T](xs: Seq[T]): Gen[T] =
oneOf(xs: Iterable[T])
/** Picks a random value from a list */
def oneOf[T](t0: T, t1: T, tn: T*): Gen[T] = oneOf(t0 +: t1 +: tn)
/** Picks a random generator from a list */
def oneOf[T](g0: Gen[T], g1: Gen[T], gn: Gen[T]*): Gen[T] = {
val gs = g0 +: g1 +: gn
choose(0, gs.size - 1).flatMap(i => gs(i))
}
/** Makes a generator result optional. Either `Some(T)` or `None` will be provided. */
def option[T](g: Gen[T]): Gen[Option[T]] =
frequency(1 -> const(None), 9 -> some(g))
/** A generator that returns `Some(T)` */
def some[T](g: Gen[T]): Gen[Option[T]] =
g.map(Some.apply)
/** Generates a `Left` of `T` or a `Right` of `U` with equal probability. */
def either[T, U](gt: Gen[T], gu: Gen[U]): Gen[Either[T, U]] =
oneOf(gt.map(Left(_)), gu.map(Right(_)))
/** Chooses one of the given generators with a weighted random distribution */
def frequency[T](gs: (Int, Gen[T])*): Gen[T] = {
val filtered = gs.iterator.filter(_._1 > 0).toVector
if (filtered.isEmpty) {
throw new IllegalArgumentException("no items with positive weights")
} else {
var total = 0L
val builder = TreeMap.newBuilder[Long, Gen[T]]
filtered.foreach { case (weight, value) =>
total += weight
builder += ((total, value))
}
val tree = builder.result
choose(1L, total).flatMap(r => tree.rangeFrom(r).head._2)
}
}
/** Implicit convenience method for using the `frequency` method
* like this:
* {{{
* frequency((1, "foo"), (3, "bar"))
* }}}
*/
implicit def freqTuple[T](t: (Int,T)): (Int,Gen[T]) = (t._1, const(t._2))
//// List Generators ////
/** Generates a container of any Traversable type for which there exists an
* implicit [[org.scalacheck.util.Buildable]] instance. The elements in the
* container will be generated by the given generator. The size of the
* generated container is limited by `n`. Depending on what kind of container
* that is generated, the resulting container may contain fewer elements than
* `n`, but not more. If the given generator fails generating a value, the
* complete container generator will also fail. */
def buildableOfN[C,T](n: Int, g: Gen[T])(implicit
evb: Buildable[T,C], evt: C => Traversable[T]
): Gen[C] = {
require(n >= 0, s"invalid size given: $n")
new Gen[C] {
def doApply(p: P, seed0: Seed): R[C] = {
var seed: Seed = p.initialSeed.getOrElse(seed0)
val bldr = evb.builder
val allowedFailures = Gen.collectionRetries(n)
var failures = 0
var count = 0
while (count < n) {
val res = g.doApply(p, seed)
res.retrieve match {
case Some(t) =>
bldr += t
count += 1
case None =>
failures += 1
if (failures >= allowedFailures) return r(None, res.seed)
}
seed = res.seed
}
r(Some(bldr.result), seed)
}
}
}
/** Generates a container of any Traversable type for which there exists an
* implicit [[org.scalacheck.util.Buildable]] instance. The elements in the
* container will be generated by the given generator. The size of the
* container is bounded by the size parameter used when generating values. */
def buildableOf[C,T](g: Gen[T])(implicit
evb: Buildable[T,C], evt: C => Traversable[T]
): Gen[C] =
sized(s => choose(0, Integer.max(s, 0)))
.flatMap(n => buildableOfN(n, g)(evb, evt))
/** Generates a non-empty container of any Traversable type for which there
* exists an implicit [[org.scalacheck.util.Buildable]] instance. The
* elements in the container will be generated by the given generator. The
* size of the container is bounded by the size parameter used when
* generating values. */
def nonEmptyBuildableOf[C,T](g: Gen[T])(implicit
evb: Buildable[T,C], evt: C => Traversable[T]
): Gen[C] =
sized(s => choose(1, Integer.max(s, 1)))
.flatMap(n => buildableOfN(n, g)(evb, evt))
/** A convenience method for calling `buildableOfN[C[T],T](n,g)`. */
def containerOfN[C[_],T](n: Int, g: Gen[T])(implicit
evb: Buildable[T,C[T]], evt: C[T] => Traversable[T]
): Gen[C[T]] = buildableOfN[C[T], T](n, g)(evb, evt)
/** A convenience method for calling `buildableOf[C[T],T](g)`. */
def containerOf[C[_],T](g: Gen[T])(implicit
evb: Buildable[T,C[T]], evt: C[T] => Traversable[T]
): Gen[C[T]] = buildableOf[C[T], T](g)(evb, evt)
/** A convenience method for calling `nonEmptyBuildableOf[C[T],T](g)`. */
def nonEmptyContainerOf[C[_],T](g: Gen[T])(implicit
evb: Buildable[T,C[T]], evt: C[T] => Traversable[T]
): Gen[C[T]] = nonEmptyBuildableOf[C[T], T](g)(evb, evt)
/** Generates a list of random length. The maximum length depends on the
* size parameter. This method is equal to calling
* `containerOf[List,T](g)`. */
def listOf[T](g: => Gen[T]) = buildableOf[List[T], T](g)
/** Generates a non-empty list of random length. The maximum length depends
* on the size parameter. This method is equal to calling
* `nonEmptyContainerOf[List,T](g)`. */
def nonEmptyListOf[T](g: => Gen[T]) = nonEmptyBuildableOf[List[T], T](g)
/** Generates a list with at most the given number of elements. This method
* is equal to calling `containerOfN[List,T](n,g)`. */
def listOfN[T](n: Int, g: Gen[T]) = buildableOfN[List[T], T](n, g)
/** Generates a map of random length. The maximum length depends on the
* size parameter. This method is equal to calling
* containerOf[Map,(T,U)](g). */
def mapOf[T, U](g: => Gen[(T, U)]) = buildableOf[Map[T, U], (T, U)](g)
/** Generates a non-empty map of random length. The maximum length depends
* on the size parameter. This method is equal to calling
* nonEmptyContainerOf[Map,(T,U)](g). */
def nonEmptyMap[T,U](g: => Gen[(T,U)]) = nonEmptyBuildableOf[Map[T, U],(T, U)](g)
/** Generates a map with at most the given number of elements. This method
* is equal to calling containerOfN[Map,(T,U)](n,g). */
def mapOfN[T,U](n: Int, g: Gen[(T, U)]) = buildableOfN[Map[T, U],(T, U)](n, g)
/**
* Generates an infinite stream.
*
* Failures in the underlying generator may terminate the stream.
* Otherwise it will continue forever.
*/
def infiniteStream[T](g: => Gen[T]): Gen[Stream[T]] = {
val attemptsPerItem = 10
def unfold(p: P, seed: Seed, attemptsLeft: Int): Stream[T] =
if (attemptsLeft <= 0) {
Stream.empty
} else {
val r = g.doPureApply(p, seed)
r.retrieve match {
case Some(t) => t #:: unfold(p, r.seed, attemptsPerItem)
case None => unfold(p, r.seed, attemptsLeft - 1)
}
}
gen { (p, seed0) =>
val stream = unfold(p, seed0, attemptsPerItem)
r(Some(stream), seed0.slide)
}
}
/** A generator that picks a random number of elements from a list */
def someOf[T](l: Iterable[T]) =
choose(0, l.size).flatMap(pick(_,l))
/** A generator that picks a random number of elements from a list */
def someOf[T](g1: Gen[T], g2: Gen[T], gs: Gen[T]*) =
choose(0, gs.length+2).flatMap(pick(_, g1, g2, gs: _*))
/** A generator that picks at least one element from a list */
def atLeastOne[T](l: Iterable[T]) = {
require(l.size > 0, "There has to be at least one option to choose from")
choose(1,l.size).flatMap(pick(_,l))
}
/** A generator that picks at least one element from a list */
def atLeastOne[T](g1: Gen[T], g2: Gen[T], gs: Gen[T]*) =
choose(1, gs.length+2).flatMap(pick(_, g1, g2, gs: _*))
/** A generator that randomly picks a given number of elements from a list
*
* The elements are not guaranteed to be permuted in random order.
*/
def pick[T](n: Int, l: Iterable[T]): Gen[collection.Seq[T]] = {
if (n > l.size || n < 0) throw new IllegalArgumentException(s"invalid choice: $n")
else if (n == 0) Gen.const(Nil)
else gen { (p, seed0) =>
val buf = ArrayBuffer.empty[T]
val it = l.iterator
var seed = seed0
var count = 0
while (it.hasNext) {
val t = it.next
count += 1
if (count <= n) {
buf += t
} else {
val (x, s) = seed.long
val i = (x & Long.MaxValue % count).toInt
if (i < n) buf(i) = t
seed = s
}
}
r(Some(buf), seed)
}
}
/** A generator that randomly picks a given number of elements from a list
*
* The elements are not guaranteed to be permuted in random order.
*/
def pick[T](n: Int, g1: Gen[T], g2: Gen[T], gn: Gen[T]*): Gen[Seq[T]] =
pick(n, g1 +: g2 +: gn).flatMap(sequence[Seq[T], T](_))
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T,R0](f: T => R0)(implicit a: Arbitrary[T]): Gen[R0] =
arbitrary[T] map f
/** Creates a Function0 generator. */
def function0[A](g: Gen[A]): Gen[() => A] =
g.map(a => () => a)
//// Character Generators ////
private def charSample(cs: Array[Char]): Gen[Char] =
new Gen[Char] {
def doApply(p: P, seed0: Seed): Gen.R[Char] = {
val seed1 = p.initialSeed.getOrElse(seed0)
val (x, seed2) = seed1.long
val i = ((x & Long.MaxValue) % cs.length).toInt
r(Some(cs(i)), seed2)
}
}
/** Generates a numerical character */
val numChar: Gen[Char] =
charSample(('0' to '9').toArray)
/** Generates an upper-case alpha character */
val alphaUpperChar: Gen[Char] =
charSample(('A' to 'Z').toArray)
/** Generates a lower-case alpha character */
val alphaLowerChar: Gen[Char] =
charSample(('a' to 'z').toArray)
/** Generates an alpha character */
val alphaChar: Gen[Char] =
charSample((('A' to 'Z') ++ ('a' to 'z')).toArray)
/** Generates an alphanumerical character */
val alphaNumChar: Gen[Char] =
charSample((('0' to '9') ++ ('A' to 'Z') ++ ('a' to 'z')).toArray)
/** Generates a ASCII character, with extra weighting for printable characters */
val asciiChar: Gen[Char] =
charSample((0.toChar to 127.toChar).toArray)
/** Generates a ASCII printable character */
val asciiPrintableChar: Gen[Char] =
charSample((32.toChar to 126.toChar).toArray)
/** Generates a character that can represent a valid hexadecimal digit. This
* includes both upper and lower case values.
*/
val hexChar: Gen[Char] =
charSample("0123456789abcdef0123456789ABCDEF".toArray)
//// String Generators ////
private def mkString(n: Int, sb: StringBuilder, gc: Gen[Char], p: P, seed0: Seed): R[String] = {
var seed: Seed = seed0
val allowedFailures = Gen.collectionRetries(n)
var failures = 0
var count = 0
while (count < n) {
val res = gc.doApply(p, seed)
res.retrieve match {
case Some(c) =>
sb += c
count += 1
case None =>
failures += 1
if (failures >= allowedFailures) return r(None, res.seed)
}
seed = res.seed
}
r(Some(sb.toString), seed)
}
def stringOfN(n: Int, gc: Gen[Char]): Gen[String] =
gen { (p, seed) =>
mkString(n, new StringBuilder, gc, p, seed)
}
def stringOf(gc: Gen[Char]): Gen[String] =
gen { (p, seed0) =>
val (n, seed1) = Gen.mkSize(p, seed0)
mkString(n, new StringBuilder, gc, p, seed1)
}
/** Generates a string that starts with a lower-case alpha character,
* and only contains alphanumerical characters */
val identifier: Gen[String] =
gen { (p, seed0) =>
val (n, seed1) = Gen.mkSize(p, seed0)
val sb = new StringBuilder
val res1 = alphaLowerChar.doApply(p, seed1)
sb += res1.retrieve.get
mkString(n - 1, sb, alphaNumChar, p, res1.seed)
}
/** Generates a string of digits */
val numStr: Gen[String] =
stringOf(numChar)
/** Generates a string of upper-case alpha characters */
val alphaUpperStr: Gen[String] =
stringOf(alphaUpperChar)
/** Generates a string of lower-case alpha characters */
val alphaLowerStr: Gen[String] =
stringOf(alphaLowerChar)
/** Generates a string of alpha characters */
val alphaStr: Gen[String] =
stringOf(alphaChar)
/** Generates a string of alphanumerical characters */
val alphaNumStr: Gen[String] =
stringOf(alphaNumChar)
/** Generates a string of ASCII characters, with extra weighting for printable characters */
val asciiStr: Gen[String] =
stringOf(asciiChar)
/** Generates a string of ASCII printable characters */
val asciiPrintableStr: Gen[String] =
stringOf(asciiPrintableChar)
/** Generates a string that can represent a valid hexadecimal digit. This
* includes both upper and lower case values.
*/
val hexStr: Gen[String] =
stringOf(hexChar)
//// Number Generators ////
/**
* Generate a uniformly-distributed Long.
*
* This method has an equally likely method of generating every
* possible Long value.
*/
val long: Gen[Long] =
gen { (_, s0) =>
val (n, s1) = s0.long
r(Some(n), s1)
}
/**
* Generate a Double uniformly-distributed in [0, 1).
*
* This method will generate one of 2^53 distinct Double values in
* the unit interval.
*/
val double: Gen[Double] =
gen { (_, s0) =>
val (x, s1) = s0.double
r(Some(x), s1)
}
/**
* Generates a Boolean which has the given chance to be true.
*
* - prob(1.0) is always true
* - prob(0.5) is true 50% of the time
* - prob(0.1) is true 10% of the time
* - prob(0.0) is never true
*/
def prob(chance: Double): Gen[Boolean] =
if (chance <= 0.0) Gen.const(false)
else if (chance >= 1.0) Gen.const(true)
else gen { (_, s0) =>
val (x, s1) = s0.double
r(Some(x < chance), s1)
}
/**
* Generates Double values according to the given gaussian
* distribution, specified by its mean and standard deviation.
*
* Gaussian distributions are also called normal distributions.
*
* The range of values is theoretically (-∞, ∞) but 99.7% of all
* values will be contained within (mean ± 3 * stdDev).
*/
def gaussian(mean: Double, stdDev: Double): Gen[Double] = {
def loop(s0: Seed): R[Double] = {
val (x0, s1) = s0.double
val (y0, s2) = s1.double
val x = x0 * 2.0 - 1.0
val y = y0 * 2.0 - 1.0
val s = x * x + y * y
if (s >= 1.0 || s == 0.0) {
loop(s2)
} else {
val scale = stdDev * Math.sqrt(-2.0 * Math.log(s) / s)
val res = x * scale + mean // dropping y * scale + mean
r(Some(res), s2)
}
}
gen((_, seed) => loop(seed))
}
/**
* Generates Double values according to the given exponential
* distribution, specified by its rate parameter.
*
* The mean and standard deviation are both equal to 1/rate.
*
* The range of values is [0, ∞).
*/
def exponential(rate: Double): Gen[Double] = {
require(rate > 0.0, s"rate must be positive (got: $rate)")
val mean = 1.0 / rate
gen { (_, s0) =>
val (x, s1) = s0.double
r(Some(-Math.log(x) * mean), s1)
}
}
/**
* Generates Int values according to the given geometric
* distribution, specified by its mean.
*
* This distribution represents the expected number of failures
* before a successful test, where the probability of a successful
* test is p = 1 / (mean + 1).
*
* The ideal range of values is [0, ∞), although the largest value
* that can be produced here is 2147483647 (Int.MaxValue).
*/
def geometric(mean: Double): Gen[Int] = {
require(mean > 0.0, s"mean must be positive (got: $mean)")
val p = 1.0 / (mean + 1.0)
val lognp = Math.log1p(-p) // log(1 - p)
gen { (_, s0) =>
val (u, s1) = s0.double
r(Some(Math.floor(Math.log(u) / lognp).toInt), s1)
}
}
/**
* Generates Int values according to the given Poisson distribution,
* specified by its rate parameters.
*
* The mean equals the rate; the standard deviation is sqrt(rate).
*
* In principle any positive value is a valid rate parameter.
* However, our method of generating values cannot handle large
* rates, so we require rate <= 745.
*/
def poisson(rate: Double): Gen[Int] = {
require(0 < rate && rate <= 745.0, s"rate must be between 0 and 745 (got $rate)")
val L = Math.exp(-rate)
def loop(s0: Seed, k: Int, p: Double): R[Int] =
if (p <= L) {
r(Some(k - 1), s0)
} else {
val (x, s1) = s0.double
loop(s1, k + 1, p * x)
}
gen((_, s) => loop(s, 0, 1.0))
}
/**
* Generates Int values according to the given binomial
* distribution, specified by the number of trials to conduct, and
* the probability of a true test.
*
* This distribution counts the number of trials which were
* successful according to a given test probability.
*
* The range of values is [0, trials].
*/
def binomial(test: Gen[Boolean], trials: Int): Gen[Int] = {
def loop(ps: Gen.Parameters, s: Seed, i: Int, n: Int): R[Int] =
if (i >= trials) {
r(Some(n), s)
} else {
val r = test.doPureApply(ps, s)
val success = r.retrieve.get
loop(ps, r.seed, i + 1, if (success) n + 1 else n)
}
gen((ps, s) => loop(ps, s, 0, 0))
}
/** Generates positive numbers of uniform distribution, with an
* upper bound of the generation size parameter. */
def posNum[T](implicit num: Numeric[T], c: Choose[T]): Gen[T] = {
import num._
num match {
case _: Fractional[_] => sized(n => c.choose(zero, max(fromInt(n), one)).suchThat(_ != zero))
case _ => sized(n => c.choose(one, max(fromInt(n), one)))
}
}
/** Generates negative numbers of uniform distribution, with an
* lower bound of the negated generation size parameter. */
def negNum[T](implicit num: Numeric[T], c: Choose[T]): Gen[T] = posNum.map(num.negate _)
/** Generates numbers within the given inclusive range, with
* extra weight on zero, +/- unity, both extremities, and any special
* numbers provided. The special numbers must lie within the given range,
* otherwise they won't be included. */
def chooseNum[T](minT: T, maxT: T, specials: T*)(
implicit num: Numeric[T], c: Choose[T]
): Gen[T] = {
import num._
val basics = List(minT, maxT, zero, one, -one)
val basicsAndSpecials = for {
t <- specials ++ basics if t >= minT && t <= maxT
} yield (1, const(t))
val other = (basicsAndSpecials.length, c.choose(minT, maxT))
val allGens = basicsAndSpecials :+ other
frequency(allGens: _*)
}
//// Misc Generators ////
/** Generates a version 4 (random) UUID. */
lazy val uuid: Gen[UUID] = for { // FIXME: Remove lazy
l1 <- Gen.choose(Long.MinValue, Long.MaxValue)
l2 <- Gen.choose(Long.MinValue, Long.MaxValue)
y <- Gen.oneOf('8', '9', 'a', 'b')
} yield UUID.fromString(
new UUID(l1,l2).toString.updated(14, '4').updated(19, y)
)
lazy val calendar: Gen[Calendar] = { // FIXME: Remove lazy
import Calendar._
def adjust(c: Calendar)(f: Calendar => Unit): Calendar = { f(c); c }
// We want to be sure we always initialize the calendar's time. By
// default, Calendar.getInstance uses the system time. We always
// overwrite it with a determinisitcally-generated time to be sure
// that calendar generation is also deterministic.
//
// We limit the time (in milliseconds) because extreme values will
// cause Calendar.getTime calls to fail. This range is relatively
// large but safe:
//
// -62135751600000 is 1 CE
// 64087186649116 is 4000 CE
val calendar: Gen[Calendar] =
Gen.chooseNum(-62135751600000L, 64087186649116L).map { t =>
adjust(Calendar.getInstance)(_.setTimeInMillis(t))
}
def yearGen(c: Calendar): Gen[Int] =
Gen.chooseNum(c.getGreatestMinimum(YEAR), c.getLeastMaximum(YEAR))
def moveToNearestLeapDate(c: Calendar, year: Int): Calendar = {
@tailrec def loop(y: Int): Calendar = {
c.set(YEAR, y)
if (c.getActualMaximum(DAY_OF_YEAR) > 365) c else loop(y + 1)
}
loop(if (year + 4 > c.getLeastMaximum(YEAR)) year - 5 else year)
}
val beginningOfDayGen: Gen[Calendar] =
calendar.map(c => adjust(c) { c =>
c.set(HOUR_OF_DAY, 0)
c.set(MINUTE, 0)
c.set(SECOND, 0)
c.set(MILLISECOND, 0)
})
val endOfDayGen: Gen[Calendar] =
calendar.map(c => adjust(c) { c =>
c.set(HOUR_OF_DAY, 23)
c.set(MINUTE, 59)
c.set(SECOND, 59)
c.set(MILLISECOND, 59)
})
val firstDayOfYearGen: Gen[Calendar] =
for { c <- calendar; y <- yearGen(c) } yield adjust(c)(_.set(y, JANUARY, 1))
val lastDayOfYearGen: Gen[Calendar] =
for { c <- calendar; y <- yearGen(c) } yield adjust(c)(_.set(y, DECEMBER, 31))
val closestLeapDateGen: Gen[Calendar] =
for { c <- calendar; y <- yearGen(c) } yield moveToNearestLeapDate(c, y)
val lastDayOfMonthGen: Gen[Calendar] =
calendar.map(c => adjust(c)(_.set(DAY_OF_MONTH, c.getActualMaximum(DAY_OF_MONTH))))
val firstDayOfMonthGen: Gen[Calendar] =
calendar.map(c => adjust(c)(_.set(DAY_OF_MONTH, 1)))
Gen.frequency(
(1, firstDayOfYearGen),
(1, lastDayOfYearGen),
(1, closestLeapDateGen),
(1, beginningOfDayGen),
(1, endOfDayGen),
(1, firstDayOfMonthGen),
(1, lastDayOfMonthGen),
(7, calendar))
}
val finiteDuration: Gen[FiniteDuration] =
// Duration.fromNanos doesn't allow Long.MinValue since it would create a
// duration that cannot be negated.
chooseNum(Long.MinValue + 1, Long.MaxValue).map(Duration.fromNanos)
/**
* Generates instance of Duration.
*
* In addition to `FiniteDuration` values, this can generate `Duration.Inf`,
* `Duration.MinusInf`, and `Duration.Undefined`.
*/
val duration: Gen[Duration] = frequency(
1 -> const(Duration.Inf),
1 -> const(Duration.MinusInf),
1 -> const(Duration.Undefined),
1 -> const(Duration.Zero),
6 -> finiteDuration)
// used to compute a uniformly-distributed size
private def mkSize(p: Gen.Parameters, seed0: Seed): (Int, Seed) = {
val maxSize = Integer.max(p.size + 1, 1)
val (x, seed1) = seed0.long
(((x & Long.MaxValue) % maxSize).toInt, seed1)
}
// used to calculate how many per-item retries we should allow.
private def collectionRetries(n: Int): Int =
Integer.max(10, n / 10)
}
