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
// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT
// file at the top-level directory of this distribution and at
// https://rust-lang.org/COPYRIGHT.
//
// 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.

//! Utilities for random number generation
//!
//! Rand provides utilities to generate random numbers, to convert them to
//! useful types and distributions, and some randomness-related algorithms.
//!
//! # Basic usage
//!
//! To get you started quickly, the easiest and highest-level way to get
//! a random value is to use [`random()`].
//!
//! ```
//! let x: u8 = rand::random();
//! println!("{}", x);
//!
//! let y = rand::random::<f64>();
//! println!("{}", y);
//!
//! if rand::random() { // generates a boolean
//!     println!("Heads!");
//! }
//! ```
//!
//! This supports generating most common types but is not very flexible, thus
//! you probably want to learn a bit more about the Rand library.
//!
//!
//! # The two-step process to get a random value
//!
//! Generating random values is typically a two-step process:
//!
//! - get some *random data* (an integer or bit/byte sequence) from a random
//!   number generator (RNG);
//! - use some function to transform that *data* into the type of value you want
//!   (this function is an implementation of some *distribution* describing the
//!   kind of value produced).
//!
//! Rand represents the first step with the [`RngCore`] trait and the second
//! step via a combination of the [`Rng`] extension trait and the
//! [`distributions` module].
//! In practice you probably won't use [`RngCore`] directly unless you are
//! implementing a random number generator (RNG).
//!
//! There are many kinds of RNGs, with different trade-offs. You can read more
//! about them in the [`rngs` module] and even more in the [`prng` module],
//! however, often you can just use [`thread_rng()`]. This function
//! automatically initializes an RNG in thread-local memory, then returns a
//! reference to it. It is fast, good quality, and secure (unpredictable).
//!
//! To turn the output of the RNG into something usable, you usually want to use
//! the methods from the [`Rng`] trait. Some of the most useful methods are:
//!
//! - [`gen`] generates a random value appropriate for the type (just like
//!   [`random()`]). For integers this is normally the full representable range
//!   (e.g. from `0u32` to `std::u32::MAX`), for floats this is between 0 and 1,
//!   and some other types are supported, including arrays and tuples. See the
//!   [`Standard`] distribution which provides the implementations.
//! - [`gen_range`] samples from a specific range of values; this is like
//!   [`gen`] but with specific upper and lower bounds.
//! - [`sample`] samples directly from some distribution.
//!
//! [`random()`] is defined using just the above: `thread_rng().gen()`.
//!
//! ## Distributions
//!
//! What are distributions, you ask? Specifying only the type and range of
//! values (known as the *sample space*) is not enough; samples must also have
//! a *probability distribution*, describing the relative probability of
//! sampling each value in that space.
//!
//! In many cases a *uniform* distribution is used, meaning roughly that each
//! value is equally likely (or for "continuous" types like floats, that each
//! equal-sized sub-range has the same probability of containing a sample).
//! [`gen`] and [`gen_range`] both use statistically uniform distributions.
//!
//! The [`distributions` module] provides implementations
//! of some other distributions, including Normal, Log-Normal and Exponential.
//! 
//! It is worth noting that the functionality already mentioned is implemented
//! with distributions: [`gen`] samples values using the [`Standard`]
//! distribution, while [`gen_range`] uses [`Uniform`].
//!
//! ## Importing (prelude)
//!
//! The most convenient way to import items from Rand is to use the [prelude].
//! This includes the most important parts of Rand, but only those unlikely to
//! cause name conflicts.
//!
//! Note that Rand 0.5 has significantly changed the module organization and
//! contents relative to previous versions. Where possible old names have been
//! kept (but are hidden in the documentation), however these will be removed
//! in the future. We therefore recommend migrating to use the prelude or the
//! new module organization in your imports.
//!
//!
//! ## Examples
//!
//! ```
//! use rand::prelude::*;
//!
//! // thread_rng is often the most convenient source of randomness:
//! let mut rng = thread_rng();
//! 
//! if rng.gen() { // random bool
//!     let x: f64 = rng.gen(); // random number in range [0, 1)
//!     println!("x is: {}", x);
//!     let ch = rng.gen::<char>(); // using type annotation
//!     println!("char is: {}", ch);
//!     println!("Number from 0 to 9: {}", rng.gen_range(0, 10));
//! }
//! ```
//!
//!
//! # More functionality
//!
//! The [`Rng`] trait includes a few more methods not mentioned above:
//!
//! - [`Rng::sample_iter`] allows iterating over values from a chosen
//!   distribution.
//! - [`Rng::gen_bool`] generates boolean "events" with a given probability.
//! - [`Rng::fill`] and [`Rng::try_fill`] are fast alternatives to fill a slice
//!   of integers.
//! - [`Rng::shuffle`] randomly shuffles elements in a slice.
//! - [`Rng::choose`] picks one element at random from a slice.
//!
//! For more slice/sequence related functionality, look in the [`seq` module].
//!
//! There is also [`distributions::WeightedChoice`], which can be used to pick
//! elements at random with some probability. But it does not work well at the
//! moment and is going through a redesign.
//!
//!
//! # Error handling
//!
//! Error handling in Rand is a compromise between simplicity and necessity.
//! Most RNGs and sampling functions will never produce errors, and making these
//! able to handle errors would add significant overhead (to code complexity
//! and ergonomics of usage at least, and potentially also performance,
//! depending on the approach).
//! However, external RNGs can fail, and being able to handle this is important.
//!
//! It has therefore been decided that *most* methods should not return a
//! `Result` type, with as exceptions [`Rng::try_fill`],
//! [`RngCore::try_fill_bytes`], and [`SeedableRng::from_rng`].
//!
//! Note that it is the RNG that panics when it fails but is not used through a
//! method that can report errors. Currently Rand contains only three RNGs that
//! can return an error (and thus may panic), and documents this property:
//! [`OsRng`], [`EntropyRng`] and [`ReadRng`]. Other RNGs, like [`ThreadRng`]
//! and [`StdRng`], can be used with all methods without concern.
//!
//! One further problem is that if Rand is unable to get any external randomness
//! when initializing an RNG with [`EntropyRng`], it will panic in
//! [`FromEntropy::from_entropy`], and notably in [`thread_rng()`]. Except by
//! compromising security, this problem is as unsolvable as running out of
//! memory.
//!
//!
//! # Distinction between Rand and `rand_core`
//!
//! The [`rand_core`] crate provides the necessary traits and functionality for
//! implementing RNGs; this includes the [`RngCore`] and [`SeedableRng`] traits
//! and the [`Error`] type.
//! Crates implementing RNGs should depend on [`rand_core`].
//!
//! Applications and libraries consuming random values are encouraged to use the
//! Rand crate, which re-exports the common parts of [`rand_core`].
//!
//!
//! # More examples
//!
//! For some inspiration, see the examples:
//!
//! - [Monte Carlo estimation of π](
//!   https://github.com/rust-lang-nursery/rand/blob/master/examples/monte-carlo.rs)
//! - [Monty Hall Problem](
//!    https://github.com/rust-lang-nursery/rand/blob/master/examples/monty-hall.rs)
//!
//!
//! [`distributions` module]: distributions/index.html
//! [`distributions::WeightedChoice`]: distributions/struct.WeightedChoice.html
//! [`FromEntropy::from_entropy`]: trait.FromEntropy.html#tymethod.from_entropy
//! [`EntropyRng`]: rngs/struct.EntropyRng.html
//! [`Error`]: struct.Error.html
//! [`gen_range`]: trait.Rng.html#method.gen_range
//! [`gen`]: trait.Rng.html#method.gen
//! [`OsRng`]: rngs/struct.OsRng.html
//! [prelude]: prelude/index.html
//! [`rand_core`]: https://crates.io/crates/rand_core
//! [`random()`]: fn.random.html
//! [`ReadRng`]: rngs/adapter/struct.ReadRng.html
//! [`Rng::choose`]: trait.Rng.html#method.choose
//! [`Rng::fill`]: trait.Rng.html#method.fill
//! [`Rng::gen_bool`]: trait.Rng.html#method.gen_bool
//! [`Rng::gen`]: trait.Rng.html#method.gen
//! [`Rng::sample_iter`]: trait.Rng.html#method.sample_iter
//! [`Rng::shuffle`]: trait.Rng.html#method.shuffle
//! [`RngCore`]: trait.RngCore.html
//! [`RngCore::try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes
//! [`rngs` module]: rngs/index.html
//! [`prng` module]: prng/index.html
//! [`Rng`]: trait.Rng.html
//! [`Rng::try_fill`]: trait.Rng.html#method.try_fill
//! [`sample`]: trait.Rng.html#method.sample
//! [`SeedableRng`]: trait.SeedableRng.html
//! [`SeedableRng::from_rng`]: trait.SeedableRng.html#method.from_rng
//! [`seq` module]: seq/index.html
//! [`SmallRng`]: rngs/struct.SmallRng.html
//! [`StdRng`]: rngs/struct.StdRng.html
//! [`thread_rng()`]: fn.thread_rng.html
//! [`ThreadRng`]: rngs/struct.ThreadRng.html
//! [`Standard`]: distributions/struct.Standard.html
//! [`Uniform`]: distributions/struct.Uniform.html


#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png",
       html_favicon_url = "https://www.rust-lang.org/favicon.ico",
       html_root_url = "https://docs.rs/rand/0.5.5")]

#![deny(missing_docs)]
#![deny(missing_debug_implementations)]
#![doc(test(attr(allow(unused_variables), deny(warnings))))]

#![cfg_attr(not(feature="std"), no_std)]
#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))]
#![cfg_attr(all(feature="i128_support", feature="nightly"), allow(stable_features))] // stable since 2018-03-27
#![cfg_attr(all(feature="i128_support", feature="nightly"), feature(i128_type, i128))]
#![cfg_attr(feature = "stdweb", recursion_limit="128")]

#[cfg(feature="std")] extern crate std as core;
#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc;

#[cfg(test)] #[cfg(feature="serde1")] extern crate bincode;
#[cfg(feature="serde1")] extern crate serde;
#[cfg(feature="serde1")] #[macro_use] extern crate serde_derive;

#[cfg(all(target_arch="wasm32", not(target_os="emscripten"), feature="stdweb"))]
#[macro_use]
extern crate stdweb;

extern crate rand_core;

#[cfg(feature = "log")] #[macro_use] extern crate log;
#[allow(unused)]
#[cfg(not(feature = "log"))] macro_rules! trace { ($($x:tt)*) => () }
#[allow(unused)]
#[cfg(not(feature = "log"))] macro_rules! debug { ($($x:tt)*) => () }
#[allow(unused)]
#[cfg(not(feature = "log"))] macro_rules! info { ($($x:tt)*) => () }
#[allow(unused)]
#[cfg(not(feature = "log"))] macro_rules! warn { ($($x:tt)*) => () }
#[allow(unused)]
#[cfg(not(feature = "log"))] macro_rules! error { ($($x:tt)*) => () }


// Re-exports from rand_core
pub use rand_core::{RngCore, CryptoRng, SeedableRng};
pub use rand_core::{ErrorKind, Error};

// Public exports
#[cfg(feature="std")] pub use rngs::thread::thread_rng;

// Public modules
pub mod distributions;
pub mod prelude;
pub mod prng;
pub mod rngs;
#[cfg(feature = "alloc")] pub mod seq;

////////////////////////////////////////////////////////////////////////////////
// Compatibility re-exports. Documentation is hidden; will be removed eventually.

#[cfg(feature="std")] #[doc(hidden)] pub use rngs::adapter::read;
#[doc(hidden)] pub use rngs::adapter::ReseedingRng;

#[allow(deprecated)]
#[cfg(feature="std")] #[doc(hidden)] pub use rngs::EntropyRng;

#[allow(deprecated)]
#[cfg(all(feature="std",
          any(target_os = "linux", target_os = "android",
              target_os = "netbsd",
              target_os = "dragonfly",
              target_os = "haiku",
              target_os = "emscripten",
              target_os = "solaris",
              target_os = "cloudabi",
              target_os = "macos", target_os = "ios",
              target_os = "freebsd",
              target_os = "openbsd", target_os = "bitrig",
              target_os = "redox",
              target_os = "fuchsia",
              windows,
              all(target_arch = "wasm32", feature = "stdweb")
)))]
#[doc(hidden)]
pub use rngs::OsRng;

#[doc(hidden)] pub use prng::{ChaChaRng, IsaacRng, Isaac64Rng, XorShiftRng};
#[doc(hidden)] pub use rngs::StdRng;


#[allow(deprecated)]
#[doc(hidden)]
pub mod jitter {
    pub use rngs::{JitterRng, TimerError};
}
#[allow(deprecated)]
#[cfg(all(feature="std",
          any(target_os = "linux", target_os = "android",
              target_os = "netbsd",
              target_os = "dragonfly",
              target_os = "haiku",
              target_os = "emscripten",
              target_os = "solaris",
              target_os = "cloudabi",
              target_os = "macos", target_os = "ios",
              target_os = "freebsd",
              target_os = "openbsd", target_os = "bitrig",
              target_os = "redox",
              target_os = "fuchsia",
              windows,
              all(target_arch = "wasm32", feature = "stdweb")
)))]
#[doc(hidden)]
pub mod os {
    pub use rngs::OsRng;
}
#[allow(deprecated)]
#[doc(hidden)]
pub mod chacha {
    //! The ChaCha random number generator.
    pub use prng::ChaChaRng;
}
#[doc(hidden)]
pub mod isaac {
    //! The ISAAC random number generator.
    pub use prng::{IsaacRng, Isaac64Rng};
}

#[cfg(feature="std")] #[doc(hidden)] pub use rngs::ThreadRng;

////////////////////////////////////////////////////////////////////////////////


use core::{marker, mem, slice};
use distributions::{Distribution, Standard};
use distributions::uniform::{SampleUniform, UniformSampler};


/// A type that can be randomly generated using an [`Rng`].
/// 
/// This is merely an adapter around the [`Standard`] distribution for
/// convenience and backwards-compatibility.
/// 
/// [`Rng`]: trait.Rng.html
/// [`Standard`]: distributions/struct.Standard.html
#[deprecated(since="0.5.0", note="replaced by distributions::Standard")]
pub trait Rand : Sized {
    /// Generates a random instance of this type using the specified source of
    /// randomness.
    fn rand<R: Rng>(rng: &mut R) -> Self;
}

/// An automatically-implemented extension trait on [`RngCore`] providing high-level
/// generic methods for sampling values and other convenience methods.
/// 
/// This is the primary trait to use when generating random values.
/// 
/// # Generic usage
/// 
/// The basic pattern is `fn foo<R: Rng + ?Sized>(rng: &mut R)`. Some
/// things are worth noting here:
/// 
/// - Since `Rng: RngCore` and every `RngCore` implements `Rng`, it makes no
///   difference whether we use `R: Rng` or `R: RngCore`.
/// - The `+ ?Sized` un-bounding allows functions to be called directly on
///   type-erased references; i.e. `foo(r)` where `r: &mut RngCore`. Without
///   this it would be necessary to write `foo(&mut r)`.
/// 
/// An alternative pattern is possible: `fn foo<R: Rng>(rng: R)`. This has some
/// trade-offs. It allows the argument to be consumed directly without a `&mut`
/// (which is how `from_rng(thread_rng())` works); also it still works directly
/// on references (including type-erased references). Unfortunately within the
/// function `foo` it is not known whether `rng` is a reference type or not,
/// hence many uses of `rng` require an extra reference, either explicitly
/// (`distr.sample(&mut rng)`) or implicitly (`rng.gen()`); one may hope the
/// optimiser can remove redundant references later.
/// 
/// Example:
/// 
/// ```
/// # use rand::thread_rng;
/// use rand::Rng;
/// 
/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
///     rng.gen()
/// }
///
/// # let v = foo(&mut thread_rng());
/// ```
/// 
/// [`RngCore`]: trait.RngCore.html
pub trait Rng: RngCore {
    /// Return a random value supporting the [`Standard`] distribution.
    ///
    /// [`Standard`]: distributions/struct.Standard.html
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let x: u32 = rng.gen();
    /// println!("{}", x);
    /// println!("{:?}", rng.gen::<(f64, bool)>());
    /// ```
    #[inline]
    fn gen<T>(&mut self) -> T where Standard: Distribution<T> {
        Standard.sample(self)
    }

    /// Generate a random value in the range [`low`, `high`), i.e. inclusive of
    /// `low` and exclusive of `high`.
    ///
    /// This function is optimised for the case that only a single sample is
    /// made from the given range. See also the [`Uniform`] distribution
    /// type which may be faster if sampling from the same range repeatedly.
    ///
    /// # Panics
    ///
    /// Panics if `low >= high`.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let n: u32 = rng.gen_range(0, 10);
    /// println!("{}", n);
    /// let m: f64 = rng.gen_range(-40.0f64, 1.3e5f64);
    /// println!("{}", m);
    /// ```
    ///
    /// [`Uniform`]: distributions/uniform/struct.Uniform.html
    fn gen_range<T: PartialOrd + SampleUniform>(&mut self, low: T, high: T) -> T {
        T::Sampler::sample_single(low, high, self)
    }

    /// Sample a new value, using the given distribution.
    ///
    /// ### Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    /// use rand::distributions::Uniform;
    ///
    /// let mut rng = thread_rng();
    /// let x = rng.sample(Uniform::new(10u32, 15));
    /// // Type annotation requires two types, the type and distribution; the
    /// // distribution can be inferred.
    /// let y = rng.sample::<u16, _>(Uniform::new(10, 15));
    /// ```
    fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
        distr.sample(self)
    }

    /// Create an iterator that generates values using the given distribution.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    /// use rand::distributions::{Alphanumeric, Uniform, Standard};
    ///
    /// let mut rng = thread_rng();
    ///
    /// // Vec of 16 x f32:
    /// let v: Vec<f32> = thread_rng().sample_iter(&Standard).take(16).collect();
    ///
    /// // String:
    /// let s: String = rng.sample_iter(&Alphanumeric).take(7).collect();
    ///
    /// // Combined values
    /// println!("{:?}", thread_rng().sample_iter(&Standard).take(5)
    ///                              .collect::<Vec<(f64, bool)>>());
    ///
    /// // Dice-rolling:
    /// let die_range = Uniform::new_inclusive(1, 6);
    /// let mut roll_die = rng.sample_iter(&die_range);
    /// while roll_die.next().unwrap() != 6 {
    ///     println!("Not a 6; rolling again!");
    /// }
    /// ```
    fn sample_iter<'a, T, D: Distribution<T>>(&'a mut self, distr: &'a D)
        -> distributions::DistIter<'a, D, Self, T> where Self: Sized
    {
        distr.sample_iter(self)
    }

    /// Fill `dest` entirely with random bytes (uniform value distribution),
    /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
    /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
    ///
    /// On big-endian platforms this performs byte-swapping to ensure
    /// portability of results from reproducible generators.
    ///
    /// This uses [`fill_bytes`] internally which may handle some RNG errors
    /// implicitly (e.g. waiting if the OS generator is not ready), but panics
    /// on other errors. See also [`try_fill`] which returns errors.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut arr = [0i8; 20];
    /// thread_rng().fill(&mut arr[..]);
    /// ```
    ///
    /// [`fill_bytes`]: trait.RngCore.html#method.fill_bytes
    /// [`try_fill`]: trait.Rng.html#method.try_fill
    /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html
    fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) {
        self.fill_bytes(dest.as_byte_slice_mut());
        dest.to_le();
    }

    /// Fill `dest` entirely with random bytes (uniform value distribution),
    /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
    /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
    ///
    /// On big-endian platforms this performs byte-swapping to ensure
    /// portability of results from reproducible generators.
    ///
    /// This uses [`try_fill_bytes`] internally and forwards all RNG errors. In
    /// some cases errors may be resolvable; see [`ErrorKind`] and
    /// documentation for the RNG in use. If you do not plan to handle these
    /// errors you may prefer to use [`fill`].
    ///
    /// # Example
    ///
    /// ```
    /// # use rand::Error;
    /// use rand::{thread_rng, Rng};
    ///
    /// # fn try_inner() -> Result<(), Error> {
    /// let mut arr = [0u64; 4];
    /// thread_rng().try_fill(&mut arr[..])?;
    /// # Ok(())
    /// # }
    ///
    /// # try_inner().unwrap()
    /// ```
    ///
    /// [`ErrorKind`]: enum.ErrorKind.html
    /// [`try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes
    /// [`fill`]: trait.Rng.html#method.fill
    /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html
    fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
        self.try_fill_bytes(dest.as_byte_slice_mut())?;
        dest.to_le();
        Ok(())
    }

    /// Return a bool with a probability `p` of being true.
    ///
    /// This is a wrapper around [`distributions::Bernoulli`].
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// println!("{}", rng.gen_bool(1.0 / 3.0));
    /// ```
    ///
    /// # Panics
    ///
    /// If `p` < 0 or `p` > 1.
    ///
    /// [`distributions::Bernoulli`]: distributions/bernoulli/struct.Bernoulli.html
    #[inline]
    fn gen_bool(&mut self, p: f64) -> bool {
        let d = distributions::Bernoulli::new(p);
        self.sample(d)
    }

    /// Return a random element from `values`.
    ///
    /// Return `None` if `values` is empty.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let choices = [1, 2, 4, 8, 16, 32];
    /// let mut rng = thread_rng();
    /// println!("{:?}", rng.choose(&choices));
    /// assert_eq!(rng.choose(&choices[..0]), None);
    /// ```
    fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
        if values.is_empty() {
            None
        } else {
            Some(&values[self.gen_range(0, values.len())])
        }
    }

    /// Return a mutable pointer to a random element from `values`.
    ///
    /// Return `None` if `values` is empty.
    fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> {
        if values.is_empty() {
            None
        } else {
            let len = values.len();
            Some(&mut values[self.gen_range(0, len)])
        }
    }

    /// Shuffle a mutable slice in place.
    ///
    /// This applies Durstenfeld's algorithm for the [Fisher–Yates shuffle](
    /// https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
    /// which produces an unbiased permutation.
    ///
    /// # Example
    ///
    /// ```
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let mut y = [1, 2, 3];
    /// rng.shuffle(&mut y);
    /// println!("{:?}", y);
    /// rng.shuffle(&mut y);
    /// println!("{:?}", y);
    /// ```
    fn shuffle<T>(&mut self, values: &mut [T]) {
        let mut i = values.len();
        while i >= 2 {
            // invariant: elements with index >= i have been locked in place.
            i -= 1;
            // lock element i in place.
            values.swap(i, self.gen_range(0, i + 1));
        }
    }

    /// Return an iterator that will yield an infinite number of randomly
    /// generated items.
    ///
    /// # Example
    ///
    /// ```
    /// # #![allow(deprecated)]
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>();
    /// println!("{:?}", x);
    /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5)
    ///                     .collect::<Vec<(f64, bool)>>());
    /// ```
    #[allow(deprecated)]
    #[deprecated(since="0.5.0", note="use Rng::sample_iter(&Standard) instead")]
    fn gen_iter<T>(&mut self) -> Generator<T, &mut Self> where Standard: Distribution<T> {
        Generator { rng: self, _marker: marker::PhantomData }
    }

    /// Return a bool with a 1 in n chance of true
    ///
    /// # Example
    ///
    /// ```
    /// # #![allow(deprecated)]
    /// use rand::{thread_rng, Rng};
    ///
    /// let mut rng = thread_rng();
    /// assert_eq!(rng.gen_weighted_bool(0), true);
    /// assert_eq!(rng.gen_weighted_bool(1), true);
    /// // Just like `rng.gen::<bool>()` a 50-50% chance, but using a slower
    /// // method with different results.
    /// println!("{}", rng.gen_weighted_bool(2));
    /// // First meaningful use of `gen_weighted_bool`.
    /// println!("{}", rng.gen_weighted_bool(3));
    /// ```
    #[deprecated(since="0.5.0", note="use gen_bool instead")]
    fn gen_weighted_bool(&mut self, n: u32) -> bool {
        // Short-circuit after `n <= 1` to avoid panic in `gen_range`
        n <= 1 || self.gen_range(0, n) == 0
    }

    /// Return an iterator of random characters from the set A-Z,a-z,0-9.
    ///
    /// # Example
    ///
    /// ```
    /// # #![allow(deprecated)]
    /// use rand::{thread_rng, Rng};
    ///
    /// let s: String = thread_rng().gen_ascii_chars().take(10).collect();
    /// println!("{}", s);
    /// ```
    #[allow(deprecated)]
    #[deprecated(since="0.5.0", note="use sample_iter(&Alphanumeric) instead")]
    fn gen_ascii_chars(&mut self) -> AsciiGenerator<&mut Self> {
        AsciiGenerator { rng: self }
    }
}

impl<R: RngCore + ?Sized> Rng for R {}

/// Trait for casting types to byte slices
/// 
/// This is used by the [`fill`] and [`try_fill`] methods.
/// 
/// [`fill`]: trait.Rng.html#method.fill
/// [`try_fill`]: trait.Rng.html#method.try_fill
pub trait AsByteSliceMut {
    /// Return a mutable reference to self as a byte slice
    fn as_byte_slice_mut(&mut self) -> &mut [u8];
    
    /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms).
    fn to_le(&mut self);
}

impl AsByteSliceMut for [u8] {
    fn as_byte_slice_mut(&mut self) -> &mut [u8] {
        self
    }
    
    fn to_le(&mut self) {}
}

macro_rules! impl_as_byte_slice {
    ($t:ty) => {
        impl AsByteSliceMut for [$t] {
            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
                if self.len() == 0 {
                    unsafe {
                        // must not use null pointer
                        slice::from_raw_parts_mut(0x1 as *mut u8, 0)
                    }
                } else {
                    unsafe {
                        slice::from_raw_parts_mut(&mut self[0]
                            as *mut $t
                            as *mut u8,
                            self.len() * mem::size_of::<$t>()
                        )
                    }
                }
            }
            
            fn to_le(&mut self) {
                for x in self {
                    *x = x.to_le();
                }
            }
        }
    }
}

impl_as_byte_slice!(u16);
impl_as_byte_slice!(u32);
impl_as_byte_slice!(u64);
#[cfg(feature="i128_support")] impl_as_byte_slice!(u128);
impl_as_byte_slice!(usize);
impl_as_byte_slice!(i8);
impl_as_byte_slice!(i16);
impl_as_byte_slice!(i32);
impl_as_byte_slice!(i64);
#[cfg(feature="i128_support")] impl_as_byte_slice!(i128);
impl_as_byte_slice!(isize);

macro_rules! impl_as_byte_slice_arrays {
    ($n:expr,) => {};
    ($n:expr, $N:ident, $($NN:ident,)*) => {
        impl_as_byte_slice_arrays!($n - 1, $($NN,)*);
        
        impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
                self[..].as_byte_slice_mut()
            }

            fn to_le(&mut self) {
                self[..].to_le()
            }
        }
    };
    (!div $n:expr,) => {};
    (!div $n:expr, $N:ident, $($NN:ident,)*) => {
        impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*);

        impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
                self[..].as_byte_slice_mut()
            }
            
            fn to_le(&mut self) {
                self[..].to_le()
            }
        }
    };
}
impl_as_byte_slice_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,);
impl_as_byte_slice_arrays!(!div 4096, N,N,N,N,N,N,N,);

/// Iterator which will generate a stream of random items.
///
/// This iterator is created via the [`gen_iter`] method on [`Rng`].
///
/// [`gen_iter`]: trait.Rng.html#method.gen_iter
/// [`Rng`]: trait.Rng.html
#[derive(Debug)]
#[allow(deprecated)]
#[deprecated(since="0.5.0", note="use Rng::sample_iter instead")]
pub struct Generator<T, R: RngCore> {
    rng: R,
    _marker: marker::PhantomData<fn() -> T>,
}

#[allow(deprecated)]
impl<T, R: RngCore> Iterator for Generator<T, R> where Standard: Distribution<T> {
    type Item = T;

    fn next(&mut self) -> Option<T> {
        Some(self.rng.gen())
    }
}

/// Iterator which will continuously generate random ascii characters.
///
/// This iterator is created via the [`gen_ascii_chars`] method on [`Rng`].
///
/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars
/// [`Rng`]: trait.Rng.html
#[derive(Debug)]
#[allow(deprecated)]
#[deprecated(since="0.5.0", note="use distributions::Alphanumeric instead")]
pub struct AsciiGenerator<R: RngCore> {
    rng: R,
}

#[allow(deprecated)]
impl<R: RngCore> Iterator for AsciiGenerator<R> {
    type Item = char;

    fn next(&mut self) -> Option<char> {
        const GEN_ASCII_STR_CHARSET: &[u8] =
            b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
              abcdefghijklmnopqrstuvwxyz\
              0123456789";
        Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
    }
}


/// A convenience extension to [`SeedableRng`] allowing construction from fresh
/// entropy. This trait is automatically implemented for any PRNG implementing
/// [`SeedableRng`] and is not intended to be implemented by users.
///
/// This is equivalent to using `SeedableRng::from_rng(EntropyRng::new())` then
/// unwrapping the result.
///
/// Since this is convenient and secure, it is the recommended way to create
/// PRNGs, though two alternatives may be considered:
///
/// *   Deterministic creation using [`SeedableRng::from_seed`] with a fixed seed
/// *   Seeding from `thread_rng`: `SeedableRng::from_rng(thread_rng())?`;
///     this will usually be faster and should also be secure, but requires
///     trusting one extra component.
///
/// ## Example
///
/// ```
/// use rand::{Rng, FromEntropy};
/// use rand::rngs::StdRng;
///
/// let mut rng = StdRng::from_entropy();
/// println!("Random die roll: {}", rng.gen_range(1, 7));
/// ```
///
/// [`EntropyRng`]: rngs/struct.EntropyRng.html
/// [`SeedableRng`]: trait.SeedableRng.html
/// [`SeedableRng::from_seed`]: trait.SeedableRng.html#tymethod.from_seed
#[cfg(feature="std")]
pub trait FromEntropy: SeedableRng {
    /// Creates a new instance, automatically seeded with fresh entropy.
    ///
    /// Normally this will use `OsRng`, but if that fails `JitterRng` will be
    /// used instead. Both should be suitable for cryptography. It is possible
    /// that both entropy sources will fail though unlikely; failures would
    /// almost certainly be platform limitations or build issues, i.e. most
    /// applications targetting PC/mobile platforms should not need to worry
    /// about this failing.
    ///
    /// # Panics
    ///
    /// If all entropy sources fail this will panic. If you need to handle
    /// errors, use the following code, equivalent aside from error handling:
    ///
    /// ```
    /// # use rand::Error;
    /// use rand::prelude::*;
    /// use rand::rngs::EntropyRng;
    ///
    /// # fn try_inner() -> Result<(), Error> {
    /// // This uses StdRng, but is valid for any R: SeedableRng
    /// let mut rng = StdRng::from_rng(EntropyRng::new())?;
    ///
    /// println!("random number: {}", rng.gen_range(1, 10));
    /// # Ok(())
    /// # }
    ///
    /// # try_inner().unwrap()
    /// ```
    fn from_entropy() -> Self;
}

#[cfg(feature="std")]
impl<R: SeedableRng> FromEntropy for R {
    fn from_entropy() -> R {
        R::from_rng(EntropyRng::new()).unwrap_or_else(|err|
            panic!("FromEntropy::from_entropy() failed: {}", err))
    }
}


/// DEPRECATED: use [`SmallRng`] instead.
///
/// Create a weak random number generator with a default algorithm and seed.
///
/// It returns the fastest `Rng` algorithm currently available in Rust without
/// consideration for cryptography or security. If you require a specifically
/// seeded `Rng` for consistency over time you should pick one algorithm and
/// create the `Rng` yourself.
///
/// This will seed the generator with randomness from `thread_rng`.
///
/// [`SmallRng`]: rngs/struct.SmallRng.html
#[deprecated(since="0.5.0", note="removed in favor of SmallRng")]
#[cfg(feature="std")]
pub fn weak_rng() -> XorShiftRng {
    XorShiftRng::from_rng(thread_rng()).unwrap_or_else(|err|
        panic!("weak_rng failed: {:?}", err))
}

/// Generates a random value using the thread-local random number generator.
///
/// This is simply a shortcut for `thread_rng().gen()`. See [`thread_rng`] for
/// documentation of the entropy source and [`Standard`] for documentation of
/// distributions and type-specific generation.
///
/// # Examples
///
/// ```
/// let x = rand::random::<u8>();
/// println!("{}", x);
///
/// let y = rand::random::<f64>();
/// println!("{}", y);
///
/// if rand::random() { // generates a boolean
///     println!("Better lucky than good!");
/// }
/// ```
///
/// If you're calling `random()` in a loop, caching the generator as in the
/// following example can increase performance.
///
/// ```
/// # #![allow(deprecated)]
/// use rand::Rng;
///
/// let mut v = vec![1, 2, 3];
///
/// for x in v.iter_mut() {
///     *x = rand::random()
/// }
///
/// // can be made faster by caching thread_rng
///
/// let mut rng = rand::thread_rng();
///
/// for x in v.iter_mut() {
///     *x = rng.gen();
/// }
/// ```
///
/// [`thread_rng`]: fn.thread_rng.html
/// [`Standard`]: distributions/struct.Standard.html
#[cfg(feature="std")]
#[inline]
pub fn random<T>() -> T where Standard: Distribution<T> {
    thread_rng().gen()
}

/// DEPRECATED: use `seq::sample_iter` instead.
///
/// Randomly sample up to `amount` elements from a finite iterator.
/// The order of elements in the sample is not random.
///
/// # Example
///
/// ```
/// # #![allow(deprecated)]
/// use rand::{thread_rng, sample};
///
/// let mut rng = thread_rng();
/// let sample = sample(&mut rng, 1..100, 5);
/// println!("{:?}", sample);
/// ```
#[cfg(feature="std")]
#[inline]
#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")]
pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T>
    where I: IntoIterator<Item=T>,
          R: Rng,
{
    // the legacy sample didn't care whether amount was met
    seq::sample_iter(rng, iterable, amount)
        .unwrap_or_else(|e| e)
}

#[cfg(test)]
mod test {
    use rngs::mock::StepRng;
    use super::*;
    #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::boxed::Box;

    pub struct TestRng<R> { inner: R }

    impl<R: RngCore> RngCore for TestRng<R> {
        fn next_u32(&mut self) -> u32 {
            self.inner.next_u32()
        }
        fn next_u64(&mut self) -> u64 {
            self.inner.next_u64()
        }
        fn fill_bytes(&mut self, dest: &mut [u8]) {
            self.inner.fill_bytes(dest)
        }
        fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
            self.inner.try_fill_bytes(dest)
        }
    }

    pub fn rng(seed: u64) -> TestRng<StdRng> {
        // TODO: use from_hashable
        let mut state = seed;
        let mut seed = <StdRng as SeedableRng>::Seed::default();
        for x in seed.iter_mut() {
            // PCG algorithm
            const MUL: u64 = 6364136223846793005;
            const INC: u64 = 11634580027462260723;
            let oldstate = state;
            state = oldstate.wrapping_mul(MUL).wrapping_add(INC);

            let xorshifted = (((oldstate >> 18) ^ oldstate) >> 27) as u32;
            let rot = (oldstate >> 59) as u32;
            *x = xorshifted.rotate_right(rot) as u8;
        }
        TestRng { inner: StdRng::from_seed(seed) }
    }

    #[test]
    fn test_fill_bytes_default() {
        let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);

        // check every remainder mod 8, both in small and big vectors.
        let lengths = [0, 1, 2, 3, 4, 5, 6, 7,
                       80, 81, 82, 83, 84, 85, 86, 87];
        for &n in lengths.iter() {
            let mut buffer = [0u8; 87];
            let v = &mut buffer[0..n];
            r.fill_bytes(v);

            // use this to get nicer error messages.
            for (i, &byte) in v.iter().enumerate() {
                if byte == 0 {
                    panic!("byte {} of {} is zero", i, n)
                }
            }
        }
    }
    
    #[test]
    fn test_fill() {
        let x = 9041086907909331047;    // a random u64
        let mut rng = StepRng::new(x, 0);
        
        // Convert to byte sequence and back to u64; byte-swap twice if BE.
        let mut array = [0u64; 2];
        rng.fill(&mut array[..]);
        assert_eq!(array, [x, x]);
        assert_eq!(rng.next_u64(), x);
        
        // Convert to bytes then u32 in LE order
        let mut array = [0u32; 2];
        rng.fill(&mut array[..]);
        assert_eq!(array, [x as u32, (x >> 32) as u32]);
        assert_eq!(rng.next_u32(), x as u32);
    }
    
    #[test]
    fn test_fill_empty() {
        let mut array = [0u32; 0];
        let mut rng = StepRng::new(0, 1);
        rng.fill(&mut array);
        rng.fill(&mut array[..]);
    }

    #[test]
    fn test_gen_range() {
        let mut r = rng(101);
        for _ in 0..1000 {
            let a = r.gen_range(-3, 42);
            assert!(a >= -3 && a < 42);
            assert_eq!(r.gen_range(0, 1), 0);
            assert_eq!(r.gen_range(-12, -11), -12);
        }

        for _ in 0..1000 {
            let a = r.gen_range(10, 42);
            assert!(a >= 10 && a < 42);
            assert_eq!(r.gen_range(0, 1), 0);
            assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000);
        }

    }

    #[test]
    #[should_panic]
    fn test_gen_range_panic_int() {
        let mut r = rng(102);
        r.gen_range(5, -2);
    }

    #[test]
    #[should_panic]
    fn test_gen_range_panic_usize() {
        let mut r = rng(103);
        r.gen_range(5, 2);
    }

    #[test]
    #[allow(deprecated)]
    fn test_gen_weighted_bool() {
        let mut r = rng(104);
        assert_eq!(r.gen_weighted_bool(0), true);
        assert_eq!(r.gen_weighted_bool(1), true);
    }

    #[test]
    fn test_gen_bool() {
        let mut r = rng(105);
        for _ in 0..5 {
            assert_eq!(r.gen_bool(0.0), false);
            assert_eq!(r.gen_bool(1.0), true);
        }
    }

    #[test]
    fn test_choose() {
        let mut r = rng(107);
        assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1));

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

    #[test]
    fn test_shuffle() {
        let mut r = rng(108);
        let empty: &mut [isize] = &mut [];
        r.shuffle(empty);
        let mut one = [1];
        r.shuffle(&mut one);
        let b: &[_] = &[1];
        assert_eq!(one, b);

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

        let mut x = [1, 1, 1];
        r.shuffle(&mut x);
        let b: &[_] = &[1, 1, 1];
        assert_eq!(x, b);
    }

    #[test]
    fn test_rng_trait_object() {
        use distributions::{Distribution, Standard};
        let mut rng = rng(109);
        let mut r = &mut rng as &mut RngCore;
        r.next_u32();
        r.gen::<i32>();
        let mut v = [1, 1, 1];
        r.shuffle(&mut v);
        let b: &[_] = &[1, 1, 1];
        assert_eq!(v, b);
        assert_eq!(r.gen_range(0, 1), 0);
        let _c: u8 = Standard.sample(&mut r);
    }

    #[test]
    #[cfg(feature="alloc")]
    fn test_rng_boxed_trait() {
        use distributions::{Distribution, Standard};
        let rng = rng(110);
        let mut r = Box::new(rng) as Box<RngCore>;
        r.next_u32();
        r.gen::<i32>();
        let mut v = [1, 1, 1];
        r.shuffle(&mut v);
        let b: &[_] = &[1, 1, 1];
        assert_eq!(v, b);
        assert_eq!(r.gen_range(0, 1), 0);
        let _c: u8 = Standard.sample(&mut r);
    }
    
    #[test]
    #[cfg(feature="std")]
    fn test_random() {
        // not sure how to test this aside from just getting some values
        let _n : usize = random();
        let _f : f32 = random();
        let _o : Option<Option<i8>> = random();
        let _many : ((),
                     (usize,
                      isize,
                      Option<(u32, (bool,))>),
                     (u8, i8, u16, i16, u32, i32, u64, i64),
                     (f32, (f64, (f64,)))) = random();
    }
}