1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
// Copyright 2018 Developers of the Rand project.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Sequence-related functionality
//!
//! This module provides:
//!
//! *   [`SliceRandom`] slice sampling and mutation
//! *   [`IteratorRandom`] iterator sampling
//! *   [`index::sample`] low-level API to choose multiple indices from
//!     `0..length`
//!
//! Also see:
//!
//! *   [`crate::distributions::WeightedIndex`] distribution which provides
//!     weighted index sampling.
//!
//! In order to make results reproducible across 32-64 bit architectures, all
//! `usize` indices are sampled as a `u32` where possible (also providing a
//! small performance boost in some cases).


#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
pub mod index;

#[cfg(feature = "alloc")] use core::ops::Index;

#[cfg(feature = "alloc")] use alloc::vec::Vec;

#[cfg(feature = "alloc")]
use crate::distributions::uniform::{SampleBorrow, SampleUniform};
#[cfg(feature = "alloc")] use crate::distributions::WeightedError;
use crate::Rng;

/// Extension trait on slices, providing random mutation and sampling methods.
///
/// This trait is implemented on all `[T]` slice types, providing several
/// methods for choosing and shuffling elements. You must `use` this trait:
///
/// ```
/// use rand::seq::SliceRandom;
///
/// let mut rng = rand::thread_rng();
/// let mut bytes = "Hello, random!".to_string().into_bytes();
/// bytes.shuffle(&mut rng);
/// let str = String::from_utf8(bytes).unwrap();
/// println!("{}", str);
/// ```
/// Example output (non-deterministic):
/// ```none
/// l,nmroHado !le
/// ```
pub trait SliceRandom {
    /// The element type.
    type Item;

    /// Returns a reference to one random element of the slice, or `None` if the
    /// slice is empty.
    ///
    /// For slices, complexity is `O(1)`.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::thread_rng;
    /// use rand::seq::SliceRandom;
    ///
    /// let choices = [1, 2, 4, 8, 16, 32];
    /// let mut rng = thread_rng();
    /// println!("{:?}", choices.choose(&mut rng));
    /// assert_eq!(choices[..0].choose(&mut rng), None);
    /// ```
    fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
    where R: Rng + ?Sized;

    /// Returns a mutable reference to one random element of the slice, or
    /// `None` if the slice is empty.
    ///
    /// For slices, complexity is `O(1)`.
    fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
    where R: Rng + ?Sized;

    /// Chooses `amount` elements from the slice at random, without repetition,
    /// and in random order. The returned iterator is appropriate both for
    /// collection into a `Vec` and filling an existing buffer (see example).
    ///
    /// In case this API is not sufficiently flexible, use [`index::sample`].
    ///
    /// For slices, complexity is the same as [`index::sample`].
    ///
    /// # Example
    /// ```
    /// use rand::seq::SliceRandom;
    ///
    /// let mut rng = &mut rand::thread_rng();
    /// let sample = "Hello, audience!".as_bytes();
    ///
    /// // collect the results into a vector:
    /// let v: Vec<u8> = sample.choose_multiple(&mut rng, 3).cloned().collect();
    ///
    /// // store in a buffer:
    /// let mut buf = [0u8; 5];
    /// for (b, slot) in sample.choose_multiple(&mut rng, buf.len()).zip(buf.iter_mut()) {
    ///     *slot = *b;
    /// }
    /// ```
    #[cfg(feature = "alloc")]
    #[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
    fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
    where R: Rng + ?Sized;

    /// Similar to [`choose`], but where the likelihood of each outcome may be
    /// specified.
    ///
    /// The specified function `weight` maps each item `x` to a relative
    /// likelihood `weight(x)`. The probability of each item being selected is
    /// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
    ///
    /// For slices of length `n`, complexity is `O(n)`.
    /// See also [`choose_weighted_mut`], [`distributions::weighted`].
    ///
    /// # Example
    ///
    /// ```
    /// use rand::prelude::*;
    ///
    /// let choices = [('a', 2), ('b', 1), ('c', 1)];
    /// let mut rng = thread_rng();
    /// // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
    /// println!("{:?}", choices.choose_weighted(&mut rng, |item| item.1).unwrap().0);
    /// ```
    /// [`choose`]: SliceRandom::choose
    /// [`choose_weighted_mut`]: SliceRandom::choose_weighted_mut
    /// [`distributions::weighted`]: crate::distributions::weighted
    #[cfg(feature = "alloc")]
    #[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
    fn choose_weighted<R, F, B, X>(
        &self, rng: &mut R, weight: F,
    ) -> Result<&Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform
            + for<'a> ::core::ops::AddAssign<&'a X>
            + ::core::cmp::PartialOrd<X>
            + Clone
            + Default;

    /// Similar to [`choose_mut`], but where the likelihood of each outcome may
    /// be specified.
    ///
    /// The specified function `weight` maps each item `x` to a relative
    /// likelihood `weight(x)`. The probability of each item being selected is
    /// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
    ///
    /// For slices of length `n`, complexity is `O(n)`.
    /// See also [`choose_weighted`], [`distributions::weighted`].
    ///
    /// [`choose_mut`]: SliceRandom::choose_mut
    /// [`choose_weighted`]: SliceRandom::choose_weighted
    /// [`distributions::weighted`]: crate::distributions::weighted
    #[cfg(feature = "alloc")]
    #[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
    fn choose_weighted_mut<R, F, B, X>(
        &mut self, rng: &mut R, weight: F,
    ) -> Result<&mut Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform
            + for<'a> ::core::ops::AddAssign<&'a X>
            + ::core::cmp::PartialOrd<X>
            + Clone
            + Default;

    /// Similar to [`choose_multiple`], but where the likelihood of each element's
    /// inclusion in the output may be specified. The elements are returned in an
    /// arbitrary, unspecified order.
    ///
    /// The specified function `weight` maps each item `x` to a relative
    /// likelihood `weight(x)`. The probability of each item being selected is
    /// therefore `weight(x) / s`, where `s` is the sum of all `weight(x)`.
    ///
    /// If all of the weights are equal, even if they are all zero, each element has
    /// an equal likelihood of being selected.
    ///
    /// The complexity of this method depends on the feature `partition_at_index`.
    /// If the feature is enabled, then for slices of length `n`, the complexity
    /// is `O(n)` space and `O(n)` time. Otherwise, the complexity is `O(n)` space and
    /// `O(n * log amount)` time.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::prelude::*;
    ///
    /// let choices = [('a', 2), ('b', 1), ('c', 1)];
    /// let mut rng = thread_rng();
    /// // First Draw * Second Draw = total odds
    /// // -----------------------
    /// // (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'b']` in some order.
    /// // (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'c']` in some order.
    /// // (25% * 33%) + (25% * 33%) = 16.6% chance that the output is `['b', 'c']` in some order.
    /// println!("{:?}", choices.choose_multiple_weighted(&mut rng, 2, |item| item.1).unwrap().collect::<Vec<_>>());
    /// ```
    /// [`choose_multiple`]: SliceRandom::choose_multiple
    //
    // Note: this is feature-gated on std due to usage of f64::powf.
    // If necessary, we may use alloc+libm as an alternative (see PR #1089).
    #[cfg(feature = "std")]
    #[cfg_attr(doc_cfg, doc(cfg(feature = "std")))]
    fn choose_multiple_weighted<R, F, X>(
        &self, rng: &mut R, amount: usize, weight: F,
    ) -> Result<SliceChooseIter<Self, Self::Item>, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> X,
        X: Into<f64>;

    /// Shuffle a mutable slice in place.
    ///
    /// For slices of length `n`, complexity is `O(n)`.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::seq::SliceRandom;
    /// use rand::thread_rng;
    ///
    /// let mut rng = thread_rng();
    /// let mut y = [1, 2, 3, 4, 5];
    /// println!("Unshuffled: {:?}", y);
    /// y.shuffle(&mut rng);
    /// println!("Shuffled:   {:?}", y);
    /// ```
    fn shuffle<R>(&mut self, rng: &mut R)
    where R: Rng + ?Sized;

    /// Shuffle a slice in place, but exit early.
    ///
    /// Returns two mutable slices from the source slice. The first contains
    /// `amount` elements randomly permuted. The second has the remaining
    /// elements that are not fully shuffled.
    ///
    /// This is an efficient method to select `amount` elements at random from
    /// the slice, provided the slice may be mutated.
    ///
    /// If you only need to choose elements randomly and `amount > self.len()/2`
    /// then you may improve performance by taking
    /// `amount = values.len() - amount` and using only the second slice.
    ///
    /// If `amount` is greater than the number of elements in the slice, this
    /// will perform a full shuffle.
    ///
    /// For slices, complexity is `O(m)` where `m = amount`.
    fn partial_shuffle<R>(
        &mut self, rng: &mut R, amount: usize,
    ) -> (&mut [Self::Item], &mut [Self::Item])
    where R: Rng + ?Sized;
}

/// Extension trait on iterators, providing random sampling methods.
///
/// This trait is implemented on all iterators `I` where `I: Iterator + Sized`
/// and provides methods for
/// choosing one or more elements. You must `use` this trait:
///
/// ```
/// use rand::seq::IteratorRandom;
///
/// let mut rng = rand::thread_rng();
///
/// let faces = "😀😎😐😕😠😢";
/// println!("I am {}!", faces.chars().choose(&mut rng).unwrap());
/// ```
/// Example output (non-deterministic):
/// ```none
/// I am 😀!
/// ```
pub trait IteratorRandom: Iterator + Sized {
    /// Choose one element at random from the iterator.
    ///
    /// Returns `None` if and only if the iterator is empty.
    ///
    /// This method uses [`Iterator::size_hint`] for optimisation. With an
    /// accurate hint and where [`Iterator::nth`] is a constant-time operation
    /// this method can offer `O(1)` performance. Where no size hint is
    /// available, complexity is `O(n)` where `n` is the iterator length.
    /// Partial hints (where `lower > 0`) also improve performance.
    ///
    /// Note that the output values and the number of RNG samples used
    /// depends on size hints. In particular, `Iterator` combinators that don't
    /// change the values yielded but change the size hints may result in
    /// `choose` returning different elements. If you want consistent results
    /// and RNG usage consider using [`IteratorRandom::choose_stable`].
    fn choose<R>(mut self, rng: &mut R) -> Option<Self::Item>
    where R: Rng + ?Sized {
        let (mut lower, mut upper) = self.size_hint();
        let mut consumed = 0;
        let mut result = None;

        // Handling for this condition outside the loop allows the optimizer to eliminate the loop
        // when the Iterator is an ExactSizeIterator. This has a large performance impact on e.g.
        // seq_iter_choose_from_1000.
        if upper == Some(lower) {
            return if lower == 0 {
                None
            } else {
                self.nth(gen_index(rng, lower))
            };
        }

        // Continue until the iterator is exhausted
        loop {
            if lower > 1 {
                let ix = gen_index(rng, lower + consumed);
                let skip = if ix < lower {
                    result = self.nth(ix);
                    lower - (ix + 1)
                } else {
                    lower
                };
                if upper == Some(lower) {
                    return result;
                }
                consumed += lower;
                if skip > 0 {
                    self.nth(skip - 1);
                }
            } else {
                let elem = self.next();
                if elem.is_none() {
                    return result;
                }
                consumed += 1;
                if gen_index(rng, consumed) == 0 {
                    result = elem;
                }
            }

            let hint = self.size_hint();
            lower = hint.0;
            upper = hint.1;
        }
    }

    /// Choose one element at random from the iterator.
    ///
    /// Returns `None` if and only if the iterator is empty.
    ///
    /// This method is very similar to [`choose`] except that the result
    /// only depends on the length of the iterator and the values produced by
    /// `rng`. Notably for any iterator of a given length this will make the
    /// same requests to `rng` and if the same sequence of values are produced
    /// the same index will be selected from `self`. This may be useful if you
    /// need consistent results no matter what type of iterator you are working
    /// with. If you do not need this stability prefer [`choose`].
    ///
    /// Note that this method still uses [`Iterator::size_hint`] to skip
    /// constructing elements where possible, however the selection and `rng`
    /// calls are the same in the face of this optimization. If you want to
    /// force every element to be created regardless call `.inspect(|e| ())`.
    ///
    /// [`choose`]: IteratorRandom::choose
    fn choose_stable<R>(mut self, rng: &mut R) -> Option<Self::Item>
    where R: Rng + ?Sized {
        let mut consumed = 0;
        let mut result = None;

        loop {
            // Currently the only way to skip elements is `nth()`. So we need to
            // store what index to access next here.
            // This should be replaced by `advance_by()` once it is stable:
            // https://github.com/rust-lang/rust/issues/77404
            let mut next = 0;

            let (lower, _) = self.size_hint();
            if lower >= 2 {
                let highest_selected = (0..lower)
                    .filter(|ix| gen_index(rng, consumed+ix+1) == 0)
                    .last();

                consumed += lower;
                next = lower;

                if let Some(ix) = highest_selected {
                    result = self.nth(ix);
                    next -= ix + 1;
                    debug_assert!(result.is_some(), "iterator shorter than size_hint().0");
                }
            }

            let elem = self.nth(next);
            if elem.is_none() {
                return result
            }

            if gen_index(rng, consumed+1) == 0 {
                result = elem;
            }
            consumed += 1;
        }
    }

    /// Collects values at random from the iterator into a supplied buffer
    /// until that buffer is filled.
    ///
    /// Although the elements are selected randomly, the order of elements in
    /// the buffer is neither stable nor fully random. If random ordering is
    /// desired, shuffle the result.
    ///
    /// Returns the number of elements added to the buffer. This equals the length
    /// of the buffer unless the iterator contains insufficient elements, in which
    /// case this equals the number of elements available.
    ///
    /// Complexity is `O(n)` where `n` is the length of the iterator.
    /// For slices, prefer [`SliceRandom::choose_multiple`].
    fn choose_multiple_fill<R>(mut self, rng: &mut R, buf: &mut [Self::Item]) -> usize
    where R: Rng + ?Sized {
        let amount = buf.len();
        let mut len = 0;
        while len < amount {
            if let Some(elem) = self.next() {
                buf[len] = elem;
                len += 1;
            } else {
                // Iterator exhausted; stop early
                return len;
            }
        }

        // Continue, since the iterator was not exhausted
        for (i, elem) in self.enumerate() {
            let k = gen_index(rng, i + 1 + amount);
            if let Some(slot) = buf.get_mut(k) {
                *slot = elem;
            }
        }
        len
    }

    /// Collects `amount` values at random from the iterator into a vector.
    ///
    /// This is equivalent to `choose_multiple_fill` except for the result type.
    ///
    /// Although the elements are selected randomly, the order of elements in
    /// the buffer is neither stable nor fully random. If random ordering is
    /// desired, shuffle the result.
    ///
    /// The length of the returned vector equals `amount` unless the iterator
    /// contains insufficient elements, in which case it equals the number of
    /// elements available.
    ///
    /// Complexity is `O(n)` where `n` is the length of the iterator.
    /// For slices, prefer [`SliceRandom::choose_multiple`].
    #[cfg(feature = "alloc")]
    #[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
    fn choose_multiple<R>(mut self, rng: &mut R, amount: usize) -> Vec<Self::Item>
    where R: Rng + ?Sized {
        let mut reservoir = Vec::with_capacity(amount);
        reservoir.extend(self.by_ref().take(amount));

        // Continue unless the iterator was exhausted
        //
        // note: this prevents iterators that "restart" from causing problems.
        // If the iterator stops once, then so do we.
        if reservoir.len() == amount {
            for (i, elem) in self.enumerate() {
                let k = gen_index(rng, i + 1 + amount);
                if let Some(slot) = reservoir.get_mut(k) {
                    *slot = elem;
                }
            }
        } else {
            // Don't hang onto extra memory. There is a corner case where
            // `amount` was much less than `self.len()`.
            reservoir.shrink_to_fit();
        }
        reservoir
    }
}


impl<T> SliceRandom for [T] {
    type Item = T;

    fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
    where R: Rng + ?Sized {
        if self.is_empty() {
            None
        } else {
            Some(&self[gen_index(rng, self.len())])
        }
    }

    fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
    where R: Rng + ?Sized {
        if self.is_empty() {
            None
        } else {
            let len = self.len();
            Some(&mut self[gen_index(rng, len)])
        }
    }

    #[cfg(feature = "alloc")]
    fn choose_multiple<R>(&self, rng: &mut R, amount: usize) -> SliceChooseIter<Self, Self::Item>
    where R: Rng + ?Sized {
        let amount = ::core::cmp::min(amount, self.len());
        SliceChooseIter {
            slice: self,
            _phantom: Default::default(),
            indices: index::sample(rng, self.len(), amount).into_iter(),
        }
    }

    #[cfg(feature = "alloc")]
    fn choose_weighted<R, F, B, X>(
        &self, rng: &mut R, weight: F,
    ) -> Result<&Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform
            + for<'a> ::core::ops::AddAssign<&'a X>
            + ::core::cmp::PartialOrd<X>
            + Clone
            + Default,
    {
        use crate::distributions::{Distribution, WeightedIndex};
        let distr = WeightedIndex::new(self.iter().map(weight))?;
        Ok(&self[distr.sample(rng)])
    }

    #[cfg(feature = "alloc")]
    fn choose_weighted_mut<R, F, B, X>(
        &mut self, rng: &mut R, weight: F,
    ) -> Result<&mut Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform
            + for<'a> ::core::ops::AddAssign<&'a X>
            + ::core::cmp::PartialOrd<X>
            + Clone
            + Default,
    {
        use crate::distributions::{Distribution, WeightedIndex};
        let distr = WeightedIndex::new(self.iter().map(weight))?;
        Ok(&mut self[distr.sample(rng)])
    }

    #[cfg(feature = "std")]
    fn choose_multiple_weighted<R, F, X>(
        &self, rng: &mut R, amount: usize, weight: F,
    ) -> Result<SliceChooseIter<Self, Self::Item>, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> X,
        X: Into<f64>,
    {
        let amount = ::core::cmp::min(amount, self.len());
        Ok(SliceChooseIter {
            slice: self,
            _phantom: Default::default(),
            indices: index::sample_weighted(
                rng,
                self.len(),
                |idx| weight(&self[idx]).into(),
                amount,
            )?
            .into_iter(),
        })
    }

    fn shuffle<R>(&mut self, rng: &mut R)
    where R: Rng + ?Sized {
        for i in (1..self.len()).rev() {
            // invariant: elements with index > i have been locked in place.
            self.swap(i, gen_index(rng, i + 1));
        }
    }

    fn partial_shuffle<R>(
        &mut self, rng: &mut R, amount: usize,
    ) -> (&mut [Self::Item], &mut [Self::Item])
    where R: Rng + ?Sized {
        // This applies Durstenfeld's algorithm for the
        // [Fisher–Yates shuffle](https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
        // for an unbiased permutation, but exits early after choosing `amount`
        // elements.

        let len = self.len();
        let end = if amount >= len { 0 } else { len - amount };

        for i in (end..len).rev() {
            // invariant: elements with index > i have been locked in place.
            self.swap(i, gen_index(rng, i + 1));
        }
        let r = self.split_at_mut(end);
        (r.1, r.0)
    }
}

impl<I> IteratorRandom for I where I: Iterator + Sized {}


/// An iterator over multiple slice elements.
///
/// This struct is created by
/// [`SliceRandom::choose_multiple`](trait.SliceRandom.html#tymethod.choose_multiple).
#[cfg(feature = "alloc")]
#[cfg_attr(doc_cfg, doc(cfg(feature = "alloc")))]
#[derive(Debug)]
pub struct SliceChooseIter<'a, S: ?Sized + 'a, T: 'a> {
    slice: &'a S,
    _phantom: ::core::marker::PhantomData<T>,
    indices: index::IndexVecIntoIter,
}

#[cfg(feature = "alloc")]
impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> Iterator for SliceChooseIter<'a, S, T> {
    type Item = &'a T;

    fn next(&mut self) -> Option<Self::Item> {
        // TODO: investigate using SliceIndex::get_unchecked when stable
        self.indices.next().map(|i| &self.slice[i as usize])
    }

    fn size_hint(&self) -> (usize, Option<usize>) {
        (self.indices.len(), Some(self.indices.len()))
    }
}

#[cfg(feature = "alloc")]
impl<'a, S: Index<usize, Output = T> + ?Sized + 'a, T: 'a> ExactSizeIterator
    for SliceChooseIter<'a, S, T>
{
    fn len(&self) -> usize {
        self.indices.len()
    }
}


// Sample a number uniformly between 0 and `ubound`. Uses 32-bit sampling where
// possible, primarily in order to produce the same output on 32-bit and 64-bit
// platforms.
#[inline]
fn gen_index<R: Rng + ?Sized>(rng: &mut R, ubound: usize) -> usize {
    if ubound <= (core::u32::MAX as usize) {
        rng.gen_range(0..ubound as u32) as usize
    } else {
        rng.gen_range(0..ubound)
    }
}


#[cfg(test)]
mod test {
    use super::*;
    #[cfg(feature = "alloc")] use crate::Rng;
    #[cfg(all(feature = "alloc", not(feature = "std")))] use alloc::vec::Vec;

    #[test]
    fn test_slice_choose() {
        let mut r = crate::test::rng(107);
        let chars = [
            'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
        ];
        let mut chosen = [0i32; 14];
        // The below all use a binomial distribution with n=1000, p=1/14.
        // binocdf(40, 1000, 1/14) ~= 2e-5; 1-binocdf(106, ..) ~= 2e-5
        for _ in 0..1000 {
            let picked = *chars.choose(&mut r).unwrap();
            chosen[(picked as usize) - ('a' as usize)] += 1;
        }
        for count in chosen.iter() {
            assert!(40 < *count && *count < 106);
        }

        chosen.iter_mut().for_each(|x| *x = 0);
        for _ in 0..1000 {
            *chosen.choose_mut(&mut r).unwrap() += 1;
        }
        for count in chosen.iter() {
            assert!(40 < *count && *count < 106);
        }

        let mut v: [isize; 0] = [];
        assert_eq!(v.choose(&mut r), None);
        assert_eq!(v.choose_mut(&mut r), None);
    }

    #[test]
    fn value_stability_slice() {
        let mut r = crate::test::rng(413);
        let chars = [
            'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
        ];
        let mut nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];

        assert_eq!(chars.choose(&mut r), Some(&'l'));
        assert_eq!(nums.choose_mut(&mut r), Some(&mut 10));

        #[cfg(feature = "alloc")]
        assert_eq!(
            &chars
                .choose_multiple(&mut r, 8)
                .cloned()
                .collect::<Vec<char>>(),
            &['d', 'm', 'b', 'n', 'c', 'k', 'h', 'e']
        );

        #[cfg(feature = "alloc")]
        assert_eq!(chars.choose_weighted(&mut r, |_| 1), Ok(&'f'));
        #[cfg(feature = "alloc")]
        assert_eq!(nums.choose_weighted_mut(&mut r, |_| 1), Ok(&mut 5));

        let mut r = crate::test::rng(414);
        nums.shuffle(&mut r);
        assert_eq!(nums, [9, 5, 3, 10, 7, 12, 8, 11, 6, 4, 0, 2, 1]);
        nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
        let res = nums.partial_shuffle(&mut r, 6);
        assert_eq!(res.0, &mut [7, 4, 8, 6, 9, 3]);
        assert_eq!(res.1, &mut [0, 1, 2, 12, 11, 5, 10]);
    }

    #[derive(Clone)]
    struct UnhintedIterator<I: Iterator + Clone> {
        iter: I,
    }
    impl<I: Iterator + Clone> Iterator for UnhintedIterator<I> {
        type Item = I::Item;

        fn next(&mut self) -> Option<Self::Item> {
            self.iter.next()
        }
    }

    #[derive(Clone)]
    struct ChunkHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
        iter: I,
        chunk_remaining: usize,
        chunk_size: usize,
        hint_total_size: bool,
    }
    impl<I: ExactSizeIterator + Iterator + Clone> Iterator for ChunkHintedIterator<I> {
        type Item = I::Item;

        fn next(&mut self) -> Option<Self::Item> {
            if self.chunk_remaining == 0 {
                self.chunk_remaining = ::core::cmp::min(self.chunk_size, self.iter.len());
            }
            self.chunk_remaining = self.chunk_remaining.saturating_sub(1);

            self.iter.next()
        }

        fn size_hint(&self) -> (usize, Option<usize>) {
            (
                self.chunk_remaining,
                if self.hint_total_size {
                    Some(self.iter.len())
                } else {
                    None
                },
            )
        }
    }

    #[derive(Clone)]
    struct WindowHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
        iter: I,
        window_size: usize,
        hint_total_size: bool,
    }
    impl<I: ExactSizeIterator + Iterator + Clone> Iterator for WindowHintedIterator<I> {
        type Item = I::Item;

        fn next(&mut self) -> Option<Self::Item> {
            self.iter.next()
        }

        fn size_hint(&self) -> (usize, Option<usize>) {
            (
                ::core::cmp::min(self.iter.len(), self.window_size),
                if self.hint_total_size {
                    Some(self.iter.len())
                } else {
                    None
                },
            )
        }
    }

    #[test]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_iterator_choose() {
        let r = &mut crate::test::rng(109);
        fn test_iter<R: Rng + ?Sized, Iter: Iterator<Item = usize> + Clone>(r: &mut R, iter: Iter) {
            let mut chosen = [0i32; 9];
            for _ in 0..1000 {
                let picked = iter.clone().choose(r).unwrap();
                chosen[picked] += 1;
            }
            for count in chosen.iter() {
                // Samples should follow Binomial(1000, 1/9)
                // Octave: binopdf(x, 1000, 1/9) gives the prob of *count == x
                // Note: have seen 153, which is unlikely but not impossible.
                assert!(
                    72 < *count && *count < 154,
                    "count not close to 1000/9: {}",
                    count
                );
            }
        }

        test_iter(r, 0..9);
        test_iter(r, [0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned());
        #[cfg(feature = "alloc")]
        test_iter(r, (0..9).collect::<Vec<_>>().into_iter());
        test_iter(r, UnhintedIterator { iter: 0..9 });
        test_iter(r, ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: false,
        });
        test_iter(r, ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: true,
        });
        test_iter(r, WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: false,
        });
        test_iter(r, WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: true,
        });

        assert_eq!((0..0).choose(r), None);
        assert_eq!(UnhintedIterator { iter: 0..0 }.choose(r), None);
    }

    #[test]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_iterator_choose_stable() {
        let r = &mut crate::test::rng(109);
        fn test_iter<R: Rng + ?Sized, Iter: Iterator<Item = usize> + Clone>(r: &mut R, iter: Iter) {
            let mut chosen = [0i32; 9];
            for _ in 0..1000 {
                let picked = iter.clone().choose_stable(r).unwrap();
                chosen[picked] += 1;
            }
            for count in chosen.iter() {
                // Samples should follow Binomial(1000, 1/9)
                // Octave: binopdf(x, 1000, 1/9) gives the prob of *count == x
                // Note: have seen 153, which is unlikely but not impossible.
                assert!(
                    72 < *count && *count < 154,
                    "count not close to 1000/9: {}",
                    count
                );
            }
        }

        test_iter(r, 0..9);
        test_iter(r, [0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned());
        #[cfg(feature = "alloc")]
        test_iter(r, (0..9).collect::<Vec<_>>().into_iter());
        test_iter(r, UnhintedIterator { iter: 0..9 });
        test_iter(r, ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: false,
        });
        test_iter(r, ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: true,
        });
        test_iter(r, WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: false,
        });
        test_iter(r, WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: true,
        });

        assert_eq!((0..0).choose(r), None);
        assert_eq!(UnhintedIterator { iter: 0..0 }.choose(r), None);
    }

    #[test]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_iterator_choose_stable_stability() {
        fn test_iter(iter: impl Iterator<Item = usize> + Clone) -> [i32; 9] {
            let r = &mut crate::test::rng(109);
            let mut chosen = [0i32; 9];
            for _ in 0..1000 {
                let picked = iter.clone().choose_stable(r).unwrap();
                chosen[picked] += 1;
            }
            chosen
        }

        let reference = test_iter(0..9);
        assert_eq!(test_iter([0, 1, 2, 3, 4, 5, 6, 7, 8].iter().cloned()), reference);

        #[cfg(feature = "alloc")]
        assert_eq!(test_iter((0..9).collect::<Vec<_>>().into_iter()), reference);
        assert_eq!(test_iter(UnhintedIterator { iter: 0..9 }), reference);
        assert_eq!(test_iter(ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: false,
        }), reference);
        assert_eq!(test_iter(ChunkHintedIterator {
            iter: 0..9,
            chunk_size: 4,
            chunk_remaining: 4,
            hint_total_size: true,
        }), reference);
        assert_eq!(test_iter(WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: false,
        }), reference);
        assert_eq!(test_iter(WindowHintedIterator {
            iter: 0..9,
            window_size: 2,
            hint_total_size: true,
        }), reference);
    }

    #[test]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_shuffle() {
        let mut r = crate::test::rng(108);
        let empty: &mut [isize] = &mut [];
        empty.shuffle(&mut r);
        let mut one = [1];
        one.shuffle(&mut r);
        let b: &[_] = &[1];
        assert_eq!(one, b);

        let mut two = [1, 2];
        two.shuffle(&mut r);
        assert!(two == [1, 2] || two == [2, 1]);

        fn move_last(slice: &mut [usize], pos: usize) {
            // use slice[pos..].rotate_left(1); once we can use that
            let last_val = slice[pos];
            for i in pos..slice.len() - 1 {
                slice[i] = slice[i + 1];
            }
            *slice.last_mut().unwrap() = last_val;
        }
        let mut counts = [0i32; 24];
        for _ in 0..10000 {
            let mut arr: [usize; 4] = [0, 1, 2, 3];
            arr.shuffle(&mut r);
            let mut permutation = 0usize;
            let mut pos_value = counts.len();
            for i in 0..4 {
                pos_value /= 4 - i;
                let pos = arr.iter().position(|&x| x == i).unwrap();
                assert!(pos < (4 - i));
                permutation += pos * pos_value;
                move_last(&mut arr, pos);
                assert_eq!(arr[3], i);
            }
            for i in 0..4 {
                assert_eq!(arr[i], i);
            }
            counts[permutation] += 1;
        }
        for count in counts.iter() {
            // Binomial(10000, 1/24) with average 416.667
            // Octave: binocdf(n, 10000, 1/24)
            // 99.9% chance samples lie within this range:
            assert!(352 <= *count && *count <= 483, "count: {}", count);
        }
    }

    #[test]
    fn test_partial_shuffle() {
        let mut r = crate::test::rng(118);

        let mut empty: [u32; 0] = [];
        let res = empty.partial_shuffle(&mut r, 10);
        assert_eq!((res.0.len(), res.1.len()), (0, 0));

        let mut v = [1, 2, 3, 4, 5];
        let res = v.partial_shuffle(&mut r, 2);
        assert_eq!((res.0.len(), res.1.len()), (2, 3));
        assert!(res.0[0] != res.0[1]);
        // First elements are only modified if selected, so at least one isn't modified:
        assert!(res.1[0] == 1 || res.1[1] == 2 || res.1[2] == 3);
    }

    #[test]
    #[cfg(feature = "alloc")]
    fn test_sample_iter() {
        let min_val = 1;
        let max_val = 100;

        let mut r = crate::test::rng(401);
        let vals = (min_val..max_val).collect::<Vec<i32>>();
        let small_sample = vals.iter().choose_multiple(&mut r, 5);
        let large_sample = vals.iter().choose_multiple(&mut r, vals.len() + 5);

        assert_eq!(small_sample.len(), 5);
        assert_eq!(large_sample.len(), vals.len());
        // no randomization happens when amount >= len
        assert_eq!(large_sample, vals.iter().collect::<Vec<_>>());

        assert!(small_sample
            .iter()
            .all(|e| { **e >= min_val && **e <= max_val }));
    }

    #[test]
    #[cfg(feature = "alloc")]
    #[cfg_attr(miri, ignore)] // Miri is too slow
    fn test_weighted() {
        let mut r = crate::test::rng(406);
        const N_REPS: u32 = 3000;
        let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
        let total_weight = weights.iter().sum::<u32>() as f32;

        let verify = |result: [i32; 14]| {
            for (i, count) in result.iter().enumerate() {
                let exp = (weights[i] * N_REPS) as f32 / total_weight;
                let mut err = (*count as f32 - exp).abs();
                if err != 0.0 {
                    err /= exp;
                }
                assert!(err <= 0.25);
            }
        };

        // choose_weighted
        fn get_weight<T>(item: &(u32, T)) -> u32 {
            item.0
        }
        let mut chosen = [0i32; 14];
        let mut items = [(0u32, 0usize); 14]; // (weight, index)
        for (i, item) in items.iter_mut().enumerate() {
            *item = (weights[i], i);
        }
        for _ in 0..N_REPS {
            let item = items.choose_weighted(&mut r, get_weight).unwrap();
            chosen[item.1] += 1;
        }
        verify(chosen);

        // choose_weighted_mut
        let mut items = [(0u32, 0i32); 14]; // (weight, count)
        for (i, item) in items.iter_mut().enumerate() {
            *item = (weights[i], 0);
        }
        for _ in 0..N_REPS {
            items.choose_weighted_mut(&mut r, get_weight).unwrap().1 += 1;
        }
        for (ch, item) in chosen.iter_mut().zip(items.iter()) {
            *ch = item.1;
        }
        verify(chosen);

        // Check error cases
        let empty_slice = &mut [10][0..0];
        assert_eq!(
            empty_slice.choose_weighted(&mut r, |_| 1),
            Err(WeightedError::NoItem)
        );
        assert_eq!(
            empty_slice.choose_weighted_mut(&mut r, |_| 1),
            Err(WeightedError::NoItem)
        );
        assert_eq!(
            ['x'].choose_weighted_mut(&mut r, |_| 0),
            Err(WeightedError::AllWeightsZero)
        );
        assert_eq!(
            [0, -1].choose_weighted_mut(&mut r, |x| *x),
            Err(WeightedError::InvalidWeight)
        );
        assert_eq!(
            [-1, 0].choose_weighted_mut(&mut r, |x| *x),
            Err(WeightedError::InvalidWeight)
        );
    }

    #[test]
    fn value_stability_choose() {
        fn choose<I: Iterator<Item = u32>>(iter: I) -> Option<u32> {
            let mut rng = crate::test::rng(411);
            iter.choose(&mut rng)
        }

        assert_eq!(choose([].iter().cloned()), None);
        assert_eq!(choose(0..100), Some(33));
        assert_eq!(choose(UnhintedIterator { iter: 0..100 }), Some(40));
        assert_eq!(
            choose(ChunkHintedIterator {
                iter: 0..100,
                chunk_size: 32,
                chunk_remaining: 32,
                hint_total_size: false,
            }),
            Some(39)
        );
        assert_eq!(
            choose(ChunkHintedIterator {
                iter: 0..100,
                chunk_size: 32,
                chunk_remaining: 32,
                hint_total_size: true,
            }),
            Some(39)
        );
        assert_eq!(
            choose(WindowHintedIterator {
                iter: 0..100,
                window_size: 32,
                hint_total_size: false,
            }),
            Some(90)
        );
        assert_eq!(
            choose(WindowHintedIterator {
                iter: 0..100,
                window_size: 32,
                hint_total_size: true,
            }),
            Some(90)
        );
    }

    #[test]
    fn value_stability_choose_stable() {
        fn choose<I: Iterator<Item = u32>>(iter: I) -> Option<u32> {
            let mut rng = crate::test::rng(411);
            iter.choose_stable(&mut rng)
        }

        assert_eq!(choose([].iter().cloned()), None);
        assert_eq!(choose(0..100), Some(40));
        assert_eq!(choose(UnhintedIterator { iter: 0..100 }), Some(40));
        assert_eq!(
            choose(ChunkHintedIterator {
                iter: 0..100,
                chunk_size: 32,
                chunk_remaining: 32,
                hint_total_size: false,
            }),
            Some(40)
        );
        assert_eq!(
            choose(ChunkHintedIterator {
                iter: 0..100,
                chunk_size: 32,
                chunk_remaining: 32,
                hint_total_size: true,
            }),
            Some(40)
        );
        assert_eq!(
            choose(WindowHintedIterator {
                iter: 0..100,
                window_size: 32,
                hint_total_size: false,
            }),
            Some(40)
        );
        assert_eq!(
            choose(WindowHintedIterator {
                iter: 0..100,
                window_size: 32,
                hint_total_size: true,
            }),
            Some(40)
        );
    }

    #[test]
    fn value_stability_choose_multiple() {
        fn do_test<I: Iterator<Item = u32>>(iter: I, v: &[u32]) {
            let mut rng = crate::test::rng(412);
            let mut buf = [0u32; 8];
            assert_eq!(iter.choose_multiple_fill(&mut rng, &mut buf), v.len());
            assert_eq!(&buf[0..v.len()], v);
        }

        do_test(0..4, &[0, 1, 2, 3]);
        do_test(0..8, &[0, 1, 2, 3, 4, 5, 6, 7]);
        do_test(0..100, &[58, 78, 80, 92, 43, 8, 96, 7]);

        #[cfg(feature = "alloc")]
        {
            fn do_test<I: Iterator<Item = u32>>(iter: I, v: &[u32]) {
                let mut rng = crate::test::rng(412);
                assert_eq!(iter.choose_multiple(&mut rng, v.len()), v);
            }

            do_test(0..4, &[0, 1, 2, 3]);
            do_test(0..8, &[0, 1, 2, 3, 4, 5, 6, 7]);
            do_test(0..100, &[58, 78, 80, 92, 43, 8, 96, 7]);
        }
    }

    #[test]
    #[cfg(feature = "std")]
    fn test_multiple_weighted_edge_cases() {
        use super::*;

        let mut rng = crate::test::rng(413);

        // Case 1: One of the weights is 0
        let choices = [('a', 2), ('b', 1), ('c', 0)];
        for _ in 0..100 {
            let result = choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap()
                .collect::<Vec<_>>();

            assert_eq!(result.len(), 2);
            assert!(!result.iter().any(|val| val.0 == 'c'));
        }

        // Case 2: All of the weights are 0
        let choices = [('a', 0), ('b', 0), ('c', 0)];
        let result = choices
            .choose_multiple_weighted(&mut rng, 2, |item| item.1)
            .unwrap()
            .collect::<Vec<_>>();
        assert_eq!(result.len(), 2);

        // Case 3: Negative weights
        let choices = [('a', -1), ('b', 1), ('c', 1)];
        assert_eq!(
            choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap_err(),
            WeightedError::InvalidWeight
        );

        // Case 4: Empty list
        let choices = [];
        let result = choices
            .choose_multiple_weighted(&mut rng, 0, |_: &()| 0)
            .unwrap()
            .collect::<Vec<_>>();
        assert_eq!(result.len(), 0);

        // Case 5: NaN weights
        let choices = [('a', core::f64::NAN), ('b', 1.0), ('c', 1.0)];
        assert_eq!(
            choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap_err(),
            WeightedError::InvalidWeight
        );

        // Case 6: +infinity weights
        let choices = [('a', core::f64::INFINITY), ('b', 1.0), ('c', 1.0)];
        for _ in 0..100 {
            let result = choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap()
                .collect::<Vec<_>>();
            assert_eq!(result.len(), 2);
            assert!(result.iter().any(|val| val.0 == 'a'));
        }

        // Case 7: -infinity weights
        let choices = [('a', core::f64::NEG_INFINITY), ('b', 1.0), ('c', 1.0)];
        assert_eq!(
            choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap_err(),
            WeightedError::InvalidWeight
        );

        // Case 8: -0 weights
        let choices = [('a', -0.0), ('b', 1.0), ('c', 1.0)];
        assert!(choices
            .choose_multiple_weighted(&mut rng, 2, |item| item.1)
            .is_ok());
    }

    #[test]
    #[cfg(feature = "std")]
    fn test_multiple_weighted_distributions() {
        use super::*;

        // The theoretical probabilities of the different outcomes are:
        // AB: 0.5  * 0.5  = 0.250
        // AC: 0.5  * 0.5  = 0.250
        // BA: 0.25 * 0.67 = 0.167
        // BC: 0.25 * 0.33 = 0.082
        // CA: 0.25 * 0.67 = 0.167
        // CB: 0.25 * 0.33 = 0.082
        let choices = [('a', 2), ('b', 1), ('c', 1)];
        let mut rng = crate::test::rng(414);

        let mut results = [0i32; 3];
        let expected_results = [4167, 4167, 1666];
        for _ in 0..10000 {
            let result = choices
                .choose_multiple_weighted(&mut rng, 2, |item| item.1)
                .unwrap()
                .collect::<Vec<_>>();

            assert_eq!(result.len(), 2);

            match (result[0].0, result[1].0) {
                ('a', 'b') | ('b', 'a') => {
                    results[0] += 1;
                }
                ('a', 'c') | ('c', 'a') => {
                    results[1] += 1;
                }
                ('b', 'c') | ('c', 'b') => {
                    results[2] += 1;
                }
                (_, _) => panic!("unexpected result"),
            }
        }

        let mut diffs = results
            .iter()
            .zip(&expected_results)
            .map(|(a, b)| (a - b).abs());
        assert!(!diffs.any(|deviation| deviation > 100));
    }
}