Adding upstream version 1.64.0+dfsg1.upstream/1.64.0+dfsg1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

51 files changed, 11811 insertions, 0 deletions

diff --git a/vendor/rand-0.7.3/.cargo-checksum.json b/vendor/rand-0.7.3/.cargo-checksum.json
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diff --git a/vendor/rand-0.7.3/CHANGELOG.md b/vendor/rand-0.7.3/CHANGELOG.md
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@@ -0,0 +1,598 @@
+# Changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+A [separate changelog is kept for rand_core](rand_core/CHANGELOG.md).
+
+You may also find the [Upgrade Guide](https://rust-random.github.io/book/update.html) useful.
+
+## [0.7.3] - 2020-01-10
+### Fixes
+- The `Bernoulli` distribution constructors now reports an error on NaN and on
+  `denominator == 0`. (#925)
+- Use `std::sync::Once` to register fork handler, avoiding possible atomicity violation (#928)
+- Fix documentation on the precision of generated floating-point values
+
+### Changes
+- Unix: make libc dependency optional; only use fork protection with std feature (#928)
+
+### Additions
+- Implement `std::error::Error` for `BernoulliError` (#919)
+
+## [0.7.2] - 2019-09-16
+### Fixes
+- Fix dependency on `rand_core` 0.5.1 (#890)
+
+### Additions
+- Unit tests for value stability of distributions added (#888)
+
+## [0.7.1] - 2019-09-13
+### Yanked
+This release was yanked since it depends on `rand_core::OsRng` added in 0.5.1
+but specifies a dependency on version 0.5.0 (#890), causing a broken builds
+when updating from `rand 0.7.0` without also updating `rand_core`.
+
+### Fixes
+- Fix `no_std` behaviour, appropriately enable c2-chacha's `std` feature (#844)
+- `alloc` feature in `no_std` is available since Rust 1.36 (#856)
+- Fix or squelch issues from Clippy lints (#840)
+
+### Additions
+- Add a `no_std` target to CI to continously evaluate `no_std` status (#844)
+- `WeightedIndex`: allow adjusting a sub-set of weights (#866)
+
+## [0.7.0] - 2019-06-28
+
+### Fixes
+- Fix incorrect pointer usages revealed by Miri testing (#780, #781)
+- Fix (tiny!) bias in `Uniform` for 8- and 16-bit ints (#809)
+
+### Crate
+- Bumped MSRV (min supported Rust version) to 1.32.0
+- Updated to Rust Edition 2018  (#823, #824)
+- Removed dependence on `rand_xorshift`, `rand_isaac`, `rand_jitter` crates (#759, #765)
+- Remove dependency on `winapi` (#724)
+- Removed all `build.rs` files (#824)
+- Removed code already deprecated in version 0.6 (#757)
+- Removed the serde1 feature (It's still available for backwards compatibility, but it does not do anything. #830)
+- Many documentation changes
+
+### rand_core
+- Updated to `rand_core` 0.5.0
+- `Error` type redesigned with new API (#800)
+- Move `from_entropy` method to `SeedableRng` and remove `FromEntropy` (#800)
+- `SeedableRng::from_rng` is now expected to be value-stable (#815)
+
+### Standard RNGs
+- OS interface moved from `rand_os` to new `getrandom` crate (#765, [getrandom](https://github.com/rust-random/getrandom))
+- Use ChaCha for `StdRng` and `ThreadRng` (#792)
+- Feature-gate `SmallRng` (#792)
+- `ThreadRng` now supports `Copy` (#758)
+- Deprecated `EntropyRng` (#765)
+- Enable fork protection of ReseedingRng without `std` (#724)
+
+### Distributions
+- Many distributions have been moved to `rand_distr` (#761)
+- `Bernoulli::new` constructor now returns a `Result` (#803)
+- `Distribution::sample_iter` adjusted for more flexibility (#758)
+- Added `distributions::weighted::alias_method::WeightedIndex` for `O(1)` sampling (#692)
+- Support sampling `NonZeroU*` types with the `Standard` distribution (#728)
+- Optimised `Binomial` distribution sampling (#735, #740, #752)
+- Optimised SIMD float sampling (#739)
+
+### Sequences
+- Make results portable across 32- and 64-bit by using `u32` samples for `usize` where possible (#809)
+
+## [0.6.5] - 2019-01-28
+### Crates
+- Update `rand_core` to 0.4 (#703)
+- Move `JitterRng` to its own crate (#685)
+- Add a wasm-bindgen test crate (#696)
+
+### Platforms
+- Fuchsia: Replaced fuchsia-zircon with fuchsia-cprng
+
+### Doc
+- Use RFC 1946 for doc links (#691)
+- Fix some doc links and notes (#711)
+
+## [0.6.4] - 2019-01-08
+### Fixes
+- Move wasm-bindgen shims to correct crate (#686)
+- Make `wasm32-unknown-unknown` compile but fail at run-time if missing bindingsg (#686)
+
+## [0.6.3] - 2019-01-04
+### Fixes
+- Make the `std` feature require the optional `rand_os` dependency (#675)
+- Re-export the optional WASM dependencies of `rand_os` from `rand` to avoid breakage (#674)
+
+## [0.6.2] - 2019-01-04
+### Additions
+- Add `Default` for `ThreadRng` (#657)
+- Move `rngs::OsRng` to `rand_os` sub-crate; clean up code; use as dependency (#643) ##BLOCKER##
+- Add `rand_xoshiro` sub-crate, plus benchmarks (#642, #668)
+
+### Fixes
+- Fix bias in `UniformInt::sample_single` (#662)
+- Use `autocfg` instead of `rustc_version` for rustc version detection (#664)
+- Disable `i128` and `u128` if the `target_os` is `emscripten` (#671: work-around Emscripten limitation)
+- CI fixes (#660, #671)
+
+### Optimisations
+- Optimise memory usage of `UnitCircle` and `UnitSphereSurface` distributions (no PR)
+
+## [0.6.1] - 2018-11-22
+- Support sampling `Duration` also for `no_std` (only since Rust 1.25) (#649)
+- Disable default features of `libc` (#647)
+
+## [0.6.0] - 2018-11-14
+
+### Project organisation
+- Rand has moved from [rust-lang-nursery](https://github.com/rust-lang-nursery/rand)
+  to [rust-random](https://github.com/rust-random/rand)! (#578)
+- Created [The Rust Random Book](https://rust-random.github.io/book/)
+  ([source](https://github.com/rust-random/book))
+- Update copyright and licence notices (#591, #611)
+- Migrate policy documentation from the wiki (#544)
+
+### Platforms
+- Add fork protection on Unix (#466)
+- Added support for wasm-bindgen. (#541, #559, #562, #600)
+- Enable `OsRng` for powerpc64, sparc and sparc64 (#609)
+- Use `syscall` from `libc` on Linux instead of redefining it (#629)
+
+### RNGs
+- Switch `SmallRng` to use PCG (#623)
+- Implement `Pcg32` and `Pcg64Mcg` generators (#632)
+- Move ISAAC RNGs to a dedicated crate (#551)
+- Move Xorshift RNG to its own crate (#557)
+- Move ChaCha and HC128 RNGs to dedicated crates (#607, #636)
+- Remove usage of `Rc` from `ThreadRng` (#615)
+
+### Sampling and distributions
+- Implement `Rng.gen_ratio()` and `Bernoulli::new_ratio()` (#491)
+- Make `Uniform` strictly respect `f32` / `f64` high/low bounds (#477)
+- Allow `gen_range` and `Uniform` to work on non-`Copy` types (#506)
+- `Uniform` supports inclusive ranges: `Uniform::from(a..=b)`. This is
+  automatically enabled for Rust >= 1.27. (#566)
+- Implement `TrustedLen` and `FusedIterator` for `DistIter` (#620)
+
+#### New distributions
+- Add the `Dirichlet` distribution (#485)
+- Added sampling from the unit sphere and circle. (#567)
+- Implement the triangular distribution (#575)
+- Implement the Weibull distribution (#576)
+- Implement the Beta distribution (#574)
+
+#### Optimisations
+
+- Optimise `Bernoulli::new` (#500)
+- Optimise `char` sampling (#519)
+- Optimise sampling of `std::time::Duration` (#583)
+
+### Sequences
+- Redesign the `seq` module (#483, #515)
+- Add `WeightedIndex` and `choose_weighted` (#518, #547)
+- Optimised and changed return type of the `sample_indices` function. (#479)
+- Use `Iterator::size_hint()` to speed up `IteratorRandom::choose` (#593)
+
+### SIMD
+- Support for generating SIMD types (#523, #542, #561, #630)
+
+### Other
+- Revise CI scripts (#632, #635)
+- Remove functionality already deprecated in 0.5 (#499)
+- Support for `i128` and `u128` is automatically enabled for Rust >= 1.26. This
+  renders the `i128_support` feature obsolete. It still exists for backwards
+  compatibility but does not have any effect. This breaks programs using Rand
+  with `i128_support` on nightlies older than Rust 1.26. (#571)
+
+
+## [0.5.5] - 2018-08-07
+### Documentation
+- Fix links in documentation (#582)
+
+
+## [0.5.4] - 2018-07-11
+### Platform support
+- Make `OsRng` work via WASM/stdweb for WebWorkers
+
+
+## [0.5.3] - 2018-06-26
+### Platform support
+- OpenBSD, Bitrig: fix compilation (broken in 0.5.1) (#530)
+
+
+## [0.5.2] - 2018-06-18
+### Platform support
+- Hide `OsRng` and `JitterRng` on unsupported platforms (#512; fixes #503).
+
+
+## [0.5.1] - 2018-06-08
+
+### New distributions
+- Added Cauchy distribution. (#474, #486)
+- Added Pareto distribution. (#495)
+
+### Platform support and `OsRng`
+- Remove blanket Unix implementation. (#484)
+- Remove Wasm unimplemented stub. (#484)
+- Dragonfly BSD: read from `/dev/random`. (#484)
+- Bitrig: use `getentropy` like OpenBSD. (#484)
+- Solaris: (untested) use `getrandom` if available, otherwise `/dev/random`. (#484)
+- Emscripten, `stdweb`: split the read up in chunks. (#484)
+- Emscripten, Haiku: don't do an extra blocking read from `/dev/random`. (#484)
+- Linux, NetBSD, Solaris: read in blocking mode on first use in `fill_bytes`. (#484)
+- Fuchsia, CloudABI: fix compilation (broken in Rand 0.5). (#484)
+
+
+## [0.5.0] - 2018-05-21
+
+### Crate features and organisation
+- Minimum Rust version update: 1.22.0. (#239)
+- Create a separate `rand_core` crate. (#288)
+- Deprecate `rand_derive`. (#256)
+- Add `prelude` (and module reorganisation). (#435)
+- Add `log` feature. Logging is now available in `JitterRng`, `OsRng`, `EntropyRng` and `ReseedingRng`. (#246)
+- Add `serde1` feature for some PRNGs. (#189)
+- `stdweb` feature for `OsRng` support on WASM via stdweb. (#272, #336)
+
+### `Rng` trait
+- Split `Rng` in `RngCore` and `Rng` extension trait.
+  `next_u32`, `next_u64` and `fill_bytes` are now part of `RngCore`. (#265)
+- Add `Rng::sample`. (#256)
+- Deprecate `Rng::gen_weighted_bool`. (#308)
+- Add `Rng::gen_bool`. (#308)
+- Remove `Rng::next_f32` and `Rng::next_f64`. (#273)
+- Add optimized `Rng::fill` and `Rng::try_fill` methods. (#247)
+- Deprecate `Rng::gen_iter`. (#286)
+- Deprecate `Rng::gen_ascii_chars`. (#279)
+
+### `rand_core` crate
+- `rand` now depends on new `rand_core` crate (#288)
+- `RngCore` and `SeedableRng` are now part of `rand_core`. (#288)
+- Add modules to help implementing RNGs `impl` and `le`. (#209, #228)
+- Add `Error` and `ErrorKind`. (#225)
+- Add `CryptoRng` marker trait. (#273)
+- Add `BlockRngCore` trait. (#281)
+- Add `BlockRng` and `BlockRng64` wrappers to help implementations. (#281, #325)
+- Revise the `SeedableRng` trait. (#233)
+- Remove default implementations for `RngCore::next_u64` and `RngCore::fill_bytes`. (#288)
+- Add `RngCore::try_fill_bytes`. (#225)
+
+### Other traits and types
+- Add `FromEntropy` trait. (#233, #375)
+- Add `SmallRng` wrapper. (#296)
+- Rewrite `ReseedingRng` to only work with `BlockRngCore` (substantial performance improvement). (#281)
+- Deprecate `weak_rng`. Use `SmallRng` instead. (#296)
+- Deprecate `AsciiGenerator`. (#279)
+
+### Random number generators
+- Switch `StdRng` and `thread_rng` to HC-128. (#277)
+- `StdRng` must now be created with `from_entropy` instead of `new`
+- Change `thread_rng` reseeding threshold to 32 MiB. (#277)
+- PRNGs no longer implement `Copy`. (#209)
+- `Debug` implementations no longer show internals. (#209)
+- Implement `Clone` for `ReseedingRng`, `JitterRng`, OsRng`. (#383, #384)
+- Implement serialization for `XorShiftRng`, `IsaacRng` and `Isaac64Rng` under the `serde1` feature. (#189)
+- Implement `BlockRngCore` for `ChaChaCore` and `Hc128Core`. (#281)
+- All PRNGs are now portable across big- and little-endian architectures. (#209)
+- `Isaac64Rng::next_u32` no longer throws away half the results. (#209)
+- Add `IsaacRng::new_from_u64` and `Isaac64Rng::new_from_u64`. (#209)
+- Add the HC-128 CSPRNG `Hc128Rng`. (#210)
+- Change ChaCha20 to have 64-bit counter and 64-bit stream. (#349)
+- Changes to `JitterRng` to get its size down from 2112 to 24 bytes. (#251)
+- Various performance improvements to all PRNGs.
+
+### Platform support and `OsRng`
+- Add support for CloudABI. (#224)
+- Remove support for NaCl. (#225)
+- WASM support for `OsRng` via stdweb, behind the `stdweb` feature. (#272, #336)
+- Use `getrandom` on more platforms for Linux, and on Android. (#338)
+- Use the `SecRandomCopyBytes` interface on macOS. (#322)
+- On systems that do not have a syscall interface, only keep a single file descriptor open for `OsRng`. (#239)
+- On Unix, first try a single read from `/dev/random`, then `/dev/urandom`. (#338)
+- Better error handling and reporting in `OsRng` (using new error type). (#225)
+- `OsRng` now uses non-blocking when available. (#225)
+- Add `EntropyRng`, which provides `OsRng`, but has `JitterRng` as a fallback. (#235)
+
+### Distributions
+- New `Distribution` trait. (#256)
+- Add `Distribution::sample_iter` and `Rng::::sample_iter`. (#361)
+- Deprecate `Rand`, `Sample` and `IndependentSample` traits. (#256)
+- Add a `Standard` distribution (replaces most `Rand` implementations). (#256)
+- Add `Binomial` and `Poisson` distributions. (#96)
+- Add `Bernoulli` dsitribution. (#411)
+- Add `Alphanumeric` distribution. (#279)
+- Remove `Closed01` distribution, add `OpenClosed01`. (#274, #420)
+- Rework `Range` type, making it possible to implement it for user types. (#274)
+- Rename `Range` to `Uniform`. (#395)
+- Add `Uniform::new_inclusive` for inclusive ranges. (#274)
+- Use widening multiply method for much faster integer range reduction. (#274)
+- `Standard` distribution for `char` uses `Uniform` internally. (#274)
+- `Standard` distribution for `bool` uses sign test. (#274)
+- Implement `Standard` distribution for `Wrapping<T>`. (#436)
+- Implement `Uniform` distribution for `Duration`. (#427)
+
+
+## [0.4.3] - 2018-08-16
+### Fixed
+- Use correct syscall number for PowerPC (#589)
+
+
+## [0.4.2] - 2018-01-06
+### Changed
+- Use `winapi` on Windows
+- Update for Fuchsia OS
+- Remove dev-dependency on `log`
+
+
+## [0.4.1] - 2017-12-17
+### Added
+- `no_std` support
+
+
+## [0.4.0-pre.0] - 2017-12-11
+### Added
+- `JitterRng` added as a high-quality alternative entropy source using the
+  system timer
+- new `seq` module with `sample_iter`, `sample_slice`, etc.
+- WASM support via dummy implementations (fail at run-time)
+- Additional benchmarks, covering generators and new seq code
+
+### Changed
+- `thread_rng` uses `JitterRng` if seeding from system time fails
+  (slower but more secure than previous method)
+
+### Deprecated
+  - `sample` function deprecated (replaced by `sample_iter`)
+
+
+## [0.3.20] - 2018-01-06
+### Changed
+- Remove dev-dependency on `log`
+- Update `fuchsia-zircon` dependency to 0.3.2
+
+
+## [0.3.19] - 2017-12-27
+### Changed
+- Require `log <= 0.3.8` for dev builds
+- Update `fuchsia-zircon` dependency to 0.3
+- Fix broken links in docs (to unblock compiler docs testing CI)
+
+
+## [0.3.18] - 2017-11-06
+### Changed
+- `thread_rng` is seeded from the system time if `OsRng` fails
+- `weak_rng` now uses `thread_rng` internally
+
+
+## [0.3.17] - 2017-10-07
+### Changed
+ - Fuchsia: Magenta was renamed Zircon
+
+## [0.3.16] - 2017-07-27
+### Added
+- Implement Debug for mote non-public types
+- implement `Rand` for (i|u)i128
+- Support for Fuchsia
+
+### Changed
+- Add inline attribute to SampleRange::construct_range.
+  This improves the benchmark for sample in 11% and for shuffle in 16%.
+- Use `RtlGenRandom` instead of `CryptGenRandom`
+
+
+## [0.3.15] - 2016-11-26
+### Added
+- Add `Rng` trait method `choose_mut`
+- Redox support
+
+### Changed
+- Use `arc4rand` for `OsRng` on FreeBSD.
+- Use `arc4random(3)` for `OsRng` on OpenBSD.
+
+### Fixed
+- Fix filling buffers 4 GiB or larger with `OsRng::fill_bytes` on Windows
+
+
+## [0.3.14] - 2016-02-13
+### Fixed
+- Inline definitions from winapi/advapi32, wich decreases build times
+
+
+## [0.3.13] - 2016-01-09
+### Fixed
+- Compatible with Rust 1.7.0-nightly (needed some extra type annotations)
+
+
+## [0.3.12] - 2015-11-09
+### Changed
+- Replaced the methods in `next_f32` and `next_f64` with the technique described
+  Saito & Matsumoto at MCQMC'08. The new method should exhibit a slightly more
+  uniform distribution.
+- Depend on libc 0.2
+
+### Fixed
+- Fix iterator protocol issue in `rand::sample`
+
+
+## [0.3.11] - 2015-08-31
+### Added
+- Implement `Rand` for arrays with n <= 32
+
+
+## [0.3.10] - 2015-08-17
+### Added
+- Support for NaCl platforms
+
+### Changed
+- Allow `Rng` to be `?Sized`, impl for `&mut R` and `Box<R>` where `R: ?Sized + Rng`
+
+
+## [0.3.9] - 2015-06-18
+### Changed
+- Use `winapi` for Windows API things
+
+### Fixed
+- Fixed test on stable/nightly
+- Fix `getrandom` syscall number for aarch64-unknown-linux-gnu
+
+
+## [0.3.8] - 2015-04-23
+### Changed
+- `log` is a dev dependency
+
+### Fixed
+- Fix race condition of atomics in `is_getrandom_available`
+
+
+## [0.3.7] - 2015-04-03
+### Fixed
+- Derive Copy/Clone changes
+
+
+## [0.3.6] - 2015-04-02
+### Changed
+- Move to stable Rust!
+
+
+## [0.3.5] - 2015-04-01
+### Fixed
+- Compatible with Rust master
+
+
+## [0.3.4] - 2015-03-31
+### Added
+- Implement Clone for `Weighted`
+
+### Fixed
+- Compatible with Rust master
+
+
+## [0.3.3] - 2015-03-26
+### Fixed
+- Fix compile on Windows
+
+
+## [0.3.2] - 2015-03-26
+
+
+## [0.3.1] - 2015-03-26
+### Fixed
+- Fix compile on Windows
+
+
+## [0.3.0] - 2015-03-25
+### Changed
+- Update to use log version 0.3.x
+
+
+## [0.2.1] - 2015-03-22
+### Fixed
+- Compatible with Rust master
+- Fixed iOS compilation
+
+
+## [0.2.0] - 2015-03-06
+### Fixed
+- Compatible with Rust master (move from `old_io` to `std::io`)
+
+
+## [0.1.4] - 2015-03-04
+### Fixed
+- Compatible with Rust master (use wrapping ops)
+
+
+## [0.1.3] - 2015-02-20
+### Fixed
+- Compatible with Rust master
+
+### Removed
+- Removed Copy implementations from RNGs
+
+
+## [0.1.2] - 2015-02-03
+### Added
+- Imported functionality from `std::rand`, including:
+  - `StdRng`, `SeedableRng`, `TreadRng`, `weak_rng()`
+  - `ReaderRng`: A wrapper around any Reader to treat it as an RNG.
+- Imported documentation from `std::rand`
+- Imported tests from `std::rand`
+
+
+## [0.1.1] - 2015-02-03
+### Added
+- Migrate to a cargo-compatible directory structure.
+
+### Fixed
+- Do not use entropy during `gen_weighted_bool(1)`
+
+
+## [Rust 0.12.0] - 2014-10-09
+### Added
+- Impl Rand for tuples of arity 11 and 12
+- Include ChaCha pseudorandom generator
+- Add `next_f64` and `next_f32` to Rng
+- Implement Clone for PRNGs
+
+### Changed
+- Rename `TaskRng` to `ThreadRng` and `task_rng` to `thread_rng` (since a
+  runtime is removed from Rust).
+
+### Fixed
+- Improved performance of ISAAC and ISAAC64 by 30% and 12 % respectively, by
+  informing the optimiser that indexing is never out-of-bounds.
+
+### Removed
+- Removed the Deprecated `choose_option`
+
+
+## [Rust 0.11.0] - 2014-07-02
+### Added
+- document when to use `OSRng` in cryptographic context, and explain why we use `/dev/urandom` instead of `/dev/random`
+- `Rng::gen_iter()` which will return an infinite stream of random values
+- `Rng::gen_ascii_chars()` which will return an infinite stream of random ascii characters
+
+### Changed
+- Now only depends on libcore!
+- Remove `Rng.choose()`, rename `Rng.choose_option()` to `.choose()`
+- Rename OSRng to OsRng
+- The WeightedChoice structure is no longer built with a `Vec<Weighted<T>>`,
+  but rather a `&mut [Weighted<T>]`. This means that the WeightedChoice
+  structure now has a lifetime associated with it.
+- The `sample` method on `Rng` has been moved to a top-level function in the
+  `rand` module due to its dependence on `Vec`.
+
+### Removed
+- `Rng::gen_vec()` was removed. Previous behavior can be regained with
+  `rng.gen_iter().take(n).collect()`
+- `Rng::gen_ascii_str()` was removed. Previous behavior can be regained with
+  `rng.gen_ascii_chars().take(n).collect()`
+- {IsaacRng, Isaac64Rng, XorShiftRng}::new() have all been removed. These all
+  relied on being able to use an OSRng for seeding, but this is no longer
+  available in librand (where these types are defined). To retain the same
+  functionality, these types now implement the `Rand` trait so they can be
+  generated with a random seed from another random number generator. This allows
+  the stdlib to use an OSRng to create seeded instances of these RNGs.
+- Rand implementations for `Box<T>` and `@T` were removed. These seemed to be
+  pretty rare in the codebase, and it allows for librand to not depend on
+  liballoc.  Additionally, other pointer types like Rc<T> and Arc<T> were not
+  supported.
+- Remove a slew of old deprecated functions
+
+
+## [Rust 0.10] - 2014-04-03
+### Changed
+- replace `Rng.shuffle's` functionality with `.shuffle_mut`
+- bubble up IO errors when creating an OSRng
+
+### Fixed
+- Use `fill()` instead of `read()`
+- Rewrite OsRng in Rust for windows
+
+## [0.10-pre] - 2014-03-02
+### Added
+- Seperate `rand` out of the standard library
diff --git a/vendor/rand-0.7.3/COPYRIGHT b/vendor/rand-0.7.3/COPYRIGHT
new file mode 100644
index 000000000..468d907ca
--- /dev/null
+++ b/vendor/rand-0.7.3/COPYRIGHT
@@ -0,0 +1,12 @@
+Copyrights in the Rand project are retained by their contributors. No
+copyright assignment is required to contribute to the Rand project.
+
+For full authorship information, see the version control history.
+
+Except as otherwise noted (below and/or in individual files), Rand is
+licensed under the Apache License, Version 2.0 <LICENSE-APACHE> or
+<http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+<LICENSE-MIT> or <http://opensource.org/licenses/MIT>, at your option.
+
+The Rand project includes code from the Rust project
+published under these same licenses.
diff --git a/vendor/rand-0.7.3/Cargo.lock b/vendor/rand-0.7.3/Cargo.lock
new file mode 100644
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diff --git a/vendor/rand-0.7.3/Cargo.toml b/vendor/rand-0.7.3/Cargo.toml
new file mode 100644
index 000000000..78a29b0e1
--- /dev/null
+++ b/vendor/rand-0.7.3/Cargo.toml
@@ -0,0 +1,80 @@
+# THIS FILE IS AUTOMATICALLY GENERATED BY CARGO
+#
+# When uploading crates to the registry Cargo will automatically
+# "normalize" Cargo.toml files for maximal compatibility
+# with all versions of Cargo and also rewrite `path` dependencies
+# to registry (e.g., crates.io) dependencies
+#
+# If you believe there's an error in this file please file an
+# issue against the rust-lang/cargo repository. If you're
+# editing this file be aware that the upstream Cargo.toml
+# will likely look very different (and much more reasonable)
+
+[package]
+edition = "2018"
+name = "rand"
+version = "0.7.3"
+authors = ["The Rand Project Developers", "The Rust Project Developers"]
+exclude = ["/utils/*", "/.travis.yml", "/appveyor.yml", ".gitignore"]
+autobenches = true
+description = "Random number generators and other randomness functionality.\n"
+homepage = "https://crates.io/crates/rand"
+documentation = "https://rust-random.github.io/rand/"
+readme = "README.md"
+keywords = ["random", "rng"]
+categories = ["algorithms", "no-std"]
+license = "MIT OR Apache-2.0"
+repository = "https://github.com/rust-random/rand"
+[package.metadata.docs.rs]
+all-features = true
+[dependencies.getrandom_package]
+version = "0.1.1"
+optional = true
+package = "getrandom"
+
+[dependencies.log]
+version = "0.4.4"
+optional = true
+
+[dependencies.packed_simd]
+version = "0.3"
+features = ["into_bits"]
+optional = true
+
+[dependencies.rand_core]
+version = "0.5.1"
+
+[dependencies.rand_pcg]
+version = "0.2"
+optional = true
+[dev-dependencies.rand_hc]
+version = "0.2"
+
+[dev-dependencies.rand_pcg]
+version = "0.2"
+
+[features]
+alloc = ["rand_core/alloc"]
+default = ["std"]
+getrandom = ["getrandom_package", "rand_core/getrandom"]
+nightly = ["simd_support"]
+serde1 = []
+simd_support = ["packed_simd"]
+small_rng = ["rand_pcg"]
+std = ["rand_core/std", "rand_chacha/std", "alloc", "getrandom", "libc"]
+stdweb = ["getrandom_package/stdweb"]
+wasm-bindgen = ["getrandom_package/wasm-bindgen"]
+[target."cfg(not(target_os = \"emscripten\"))".dependencies.rand_chacha]
+version = "0.2.1"
+default-features = false
+[target."cfg(target_os = \"emscripten\")".dependencies.rand_hc]
+version = "0.2"
+[target."cfg(unix)".dependencies.libc]
+version = "0.2.22"
+optional = true
+default-features = false
+[badges.appveyor]
+repository = "rust-random/rand"
+
+[badges.travis-ci]
+repository = "rust-random/rand"
diff --git a/vendor/rand-0.7.3/LICENSE-APACHE b/vendor/rand-0.7.3/LICENSE-APACHE
new file mode 100644
index 000000000..17d74680f
--- /dev/null
+++ b/vendor/rand-0.7.3/LICENSE-APACHE
@@ -0,0 +1,201 @@
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+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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diff --git a/vendor/rand-0.7.3/LICENSE-MIT b/vendor/rand-0.7.3/LICENSE-MIT
new file mode 100644
index 000000000..d93b5baf3
--- /dev/null
+++ b/vendor/rand-0.7.3/LICENSE-MIT
@@ -0,0 +1,26 @@
+Copyright 2018 Developers of the Rand project
+Copyright (c) 2014 The Rust Project Developers
+
+Permission is hereby granted, free of charge, to any
+person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the
+Software without restriction, including without
+limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software
+is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice
+shall be included in all copies or substantial portions
+of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
+ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
+SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
diff --git a/vendor/rand-0.7.3/README.md b/vendor/rand-0.7.3/README.md
new file mode 100644
index 000000000..c4bd676a9
--- /dev/null
+++ b/vendor/rand-0.7.3/README.md
@@ -0,0 +1,126 @@
+# Rand
+
+[![Build Status](https://travis-ci.org/rust-random/rand.svg?branch=master)](https://travis-ci.org/rust-random/rand)
+[![Build Status](https://ci.appveyor.com/api/projects/status/github/rust-random/rand?svg=true)](https://ci.appveyor.com/project/rust-random/rand)
+[![Crate](https://img.shields.io/crates/v/rand.svg)](https://crates.io/crates/rand)
+[![Book](https://img.shields.io/badge/book-master-yellow.svg)](https://rust-random.github.io/book/)
+[![API](https://img.shields.io/badge/api-master-yellow.svg)](https://rust-random.github.io/rand)
+[![API](https://docs.rs/rand/badge.svg)](https://docs.rs/rand)
+[![Minimum rustc version](https://img.shields.io/badge/rustc-1.32+-lightgray.svg)](https://github.com/rust-random/rand#rust-version-requirements)
+
+A Rust library for random number generation.
+
+Rand provides utilities to generate random numbers, to convert them to useful
+types and distributions, and some randomness-related algorithms.
+
+The core random number generation traits of Rand live in the [rand_core](
+https://crates.io/crates/rand_core) crate but are also exposed here; RNG
+implementations should prefer to use `rand_core` while most other users should
+depend on `rand`.
+
+Documentation:
+-   [The Rust Rand Book](https://rust-random.github.io/book)
+-   [API reference (master)](https://rust-random.github.io/rand)
+-   [API reference (docs.rs)](https://docs.rs/rand)
+
+
+## Usage
+
+Add this to your `Cargo.toml`:
+
+```toml
+[dependencies]
+rand = "0.7"
+```
+
+To get started using Rand, see [The Book](https://rust-random.github.io/book).
+
+
+## Versions
+
+Rand libs have inter-dependencies and make use of the
+[semver trick](https://github.com/dtolnay/semver-trick/) in order to make traits
+compatible across crate versions. (This is especially important for `RngCore`
+and `SeedableRng`.) A few crate releases are thus compatibility shims,
+depending on the *next* lib version (e.g. `rand_core` versions `0.2.2` and
+`0.3.1`). This means, for example, that `rand_core_0_4_0::SeedableRng` and
+`rand_core_0_3_0::SeedableRng` are distinct, incompatible traits, which can
+cause build errors. Usually, running `cargo update` is enough to fix any issues.
+
+The Rand lib is not yet stable, however we are careful to limit breaking changes
+and warn via deprecation wherever possible. Patch versions never introduce
+breaking changes. The following minor versions are supported:
+
+-   Version 0.7 was released in June 2019, moving most non-uniform distributions
+    to an external crate, moving `from_entropy` to `SeedableRng`, and many small
+    changes and fixes.
+-   Version 0.6 was released in November 2018, redesigning the `seq` module,
+    moving most PRNGs to external crates, and many small changes.
+-   Version 0.5 was released in May 2018, as a major reorganisation
+    (introducing `RngCore` and `rand_core`, and deprecating `Rand` and the
+    previous distribution traits).
+-   Version 0.4 was released in December 2017, but contained almost no breaking
+    changes from the 0.3 series.
+
+A detailed [changelog](CHANGELOG.md) is available.
+
+When upgrading to the next minor series (especially 0.4 → 0.5), we recommend
+reading the [Upgrade Guide](https://rust-random.github.io/book/update.html).
+
+### Yanked versions
+
+Some versions of Rand crates have been yanked ("unreleased"). Where this occurs,
+the crate's CHANGELOG *should* be updated with a rationale, and a search on the
+issue tracker with the keyword `yank` *should* uncover the motivation.
+
+### Rust version requirements
+
+Since version 0.7, Rand requires **Rustc version 1.32 or greater**.
+Rand 0.5 requires Rustc 1.22 or greater while versions
+0.4 and 0.3 (since approx. June 2017) require Rustc version 1.15 or
+greater. Subsets of the Rand code may work with older Rust versions, but this
+is not supported.
+
+Travis CI always has a build with a pinned version of Rustc matching the oldest
+supported Rust release. The current policy is that this can be updated in any
+Rand release if required, but the change must be noted in the changelog.
+
+## Crate Features
+
+Rand is built with these features enabled by default:
+
+-   `std` enables functionality dependent on the `std` lib
+-   `alloc` (implied by `std`) enables functionality requiring an allocator (when using this feature in `no_std`, Rand requires Rustc version 1.36 or greater)
+-   `getrandom` (implied by `std`) is an optional dependency providing the code
+    behind `rngs::OsRng`
+
+Optionally, the following dependencies can be enabled:
+
+-   `log` enables logging via the `log` crate
+-   `stdweb` implies `getrandom/stdweb` to enable
+    `getrandom` support on `wasm32-unknown-unknown`
+    (will be removed in rand 0.8; activate via `getrandom` crate instead)
+-   `wasm-bindgen` implies `getrandom/wasm-bindgen` to enable
+    `getrandom` support on `wasm32-unknown-unknown`
+    (will be removed in rand 0.8; activate via `getrandom` crate instead)
+
+Additionally, these features configure Rand:
+
+-   `small_rng` enables inclusion of the `SmallRng` PRNG
+-   `nightly` enables all experimental features
+-   `simd_support` (experimental) enables sampling of SIMD values
+    (uniformly random SIMD integers and floats)
+
+Rand supports limited functionality in `no_std` mode (enabled via
+`default-features = false`). In this case, `OsRng` and `from_entropy` are
+unavailable (unless `getrandom` is enabled), large parts of `seq` are
+unavailable (unless `alloc` is enabled), and `thread_rng` and `random` are
+unavailable.
+
+# License
+
+Rand is distributed under the terms of both the MIT license and the
+Apache License (Version 2.0).
+
+See [LICENSE-APACHE](LICENSE-APACHE) and [LICENSE-MIT](LICENSE-MIT), and
+[COPYRIGHT](COPYRIGHT) for details.
diff --git a/vendor/rand-0.7.3/SECURITY.md b/vendor/rand-0.7.3/SECURITY.md
new file mode 100644
index 000000000..daedb78f0
--- /dev/null
+++ b/vendor/rand-0.7.3/SECURITY.md
@@ -0,0 +1,69 @@
+# Security Policy
+
+## No guarantees
+
+Support is provided on a best-effort bases only.
+No binding guarantees can be provided.
+
+## Security premises
+
+Rand provides the trait `rand_core::CryptoRng` aka `rand::CryptoRng` as a marker
+trait. Generators implementating `RngCore` *and* `CryptoRng`, and given the
+additional constraints that:
+
+-   Instances of seedable RNGs (those implementing `SeedableRng`) are
+    constructed with cryptographically secure seed values
+-   The state (memory) of the RNG and its seed value are not be exposed
+
+are expected to provide the following:
+
+-   An attacker can gain no advantage over chance (50% for each bit) in
+    predicting the RNG output, even with full knowledge of all prior outputs.
+
+For some RNGs, notably `OsRng`, `ThreadRng` and those wrapped by `ReseedingRng`,
+we provide limited mitigations against side-channel attacks:
+
+-   After a process fork on Unix, there is an upper-bound on the number of bits
+    output by the RNG before the processes diverge, after which outputs from
+    each process's RNG are uncorrelated
+-   After the state (memory) of an RNG is leaked, there is an upper-bound on the
+    number of bits of output by the RNG before prediction of output by an
+    observer again becomes computationally-infeasible
+
+Additionally, derivations from such an RNG (including the `Rng` trait,
+implementations of the `Distribution` trait, and `seq` algorithms) should not
+introduce signficant bias other than that expected from the operation in
+question (e.g. bias from a weighted distribution).
+
+## Supported Versions
+
+We will attempt to uphold these premises in the following crate versions,
+provided that only the latest patch version is used, and with potential
+exceptions for theoretical issues without a known exploit:
+
+| Crate | Versions | Exceptions |
+| ----- | -------- | ---------- |
+| `rand` | 0.7 |  |
+| `rand` | 0.5, 0.6 | Jitter |
+| `rand` | 0.4 | Jitter, ISAAC |
+| `rand_core` | 0.2 - 0.5 | |
+| `rand_chacha` | 0.1 - 0.2 | |
+| `rand_hc` | 0.1 - 0.2 | |
+
+Explanation of exceptions:
+
+-   Jitter: `JitterRng` is used as an entropy source when the primary source
+    fails; this source may not be secure against side-channel attacks, see #699.
+-   ISAAC: the [ISAAC](https://burtleburtle.net/bob/rand/isaacafa.html) RNG used
+    to implement `thread_rng` is difficult to analyse and thus cannot provide
+    strong assertions of security.
+
+## Known issues
+
+In `rand` version 0.3 (0.3.18 and later), if `OsRng` fails, `thread_rng` is
+seeded from the system time in an insecure manner.
+
+## Reporting a Vulnerability
+
+To report a vulnerability, [open a new issue](https://github.com/rust-random/rand/issues/new).
+Once the issue is resolved, the vulnerability should be [reported to RustSec](https://github.com/RustSec/advisory-db/blob/master/CONTRIBUTING.md).
diff --git a/vendor/rand-0.7.3/benches/generators.rs b/vendor/rand-0.7.3/benches/generators.rs
new file mode 100644
index 000000000..3e264083d
--- /dev/null
+++ b/vendor/rand-0.7.3/benches/generators.rs
@@ -0,0 +1,165 @@
+// 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.
+
+#![feature(test)]
+#![allow(non_snake_case)]
+
+extern crate test;
+
+const RAND_BENCH_N: u64 = 1000;
+const BYTES_LEN: usize = 1024;
+
+use std::mem::size_of;
+use test::{black_box, Bencher};
+
+use rand::prelude::*;
+use rand::rngs::adapter::ReseedingRng;
+use rand::rngs::{mock::StepRng, OsRng};
+use rand_chacha::{ChaCha12Rng, ChaCha20Core, ChaCha20Rng, ChaCha8Rng};
+use rand_hc::Hc128Rng;
+use rand_pcg::{Pcg32, Pcg64, Pcg64Mcg};
+
+macro_rules! gen_bytes {
+    ($fnn:ident, $gen:expr) => {
+        #[bench]
+        fn $fnn(b: &mut Bencher) {
+            let mut rng = $gen;
+            let mut buf = [0u8; BYTES_LEN];
+            b.iter(|| {
+                for _ in 0..RAND_BENCH_N {
+                    rng.fill_bytes(&mut buf);
+                    black_box(buf);
+                }
+            });
+            b.bytes = BYTES_LEN as u64 * RAND_BENCH_N;
+        }
+    };
+}
+
+gen_bytes!(gen_bytes_step, StepRng::new(0, 1));
+gen_bytes!(gen_bytes_pcg32, Pcg32::from_entropy());
+gen_bytes!(gen_bytes_pcg64, Pcg64::from_entropy());
+gen_bytes!(gen_bytes_pcg64mcg, Pcg64Mcg::from_entropy());
+gen_bytes!(gen_bytes_chacha8, ChaCha8Rng::from_entropy());
+gen_bytes!(gen_bytes_chacha12, ChaCha12Rng::from_entropy());
+gen_bytes!(gen_bytes_chacha20, ChaCha20Rng::from_entropy());
+gen_bytes!(gen_bytes_hc128, Hc128Rng::from_entropy());
+gen_bytes!(gen_bytes_std, StdRng::from_entropy());
+#[cfg(feature = "small_rng")]
+gen_bytes!(gen_bytes_small, SmallRng::from_entropy());
+gen_bytes!(gen_bytes_os, OsRng);
+
+macro_rules! gen_uint {
+    ($fnn:ident, $ty:ty, $gen:expr) => {
+        #[bench]
+        fn $fnn(b: &mut Bencher) {
+            let mut rng = $gen;
+            b.iter(|| {
+                let mut accum: $ty = 0;
+                for _ in 0..RAND_BENCH_N {
+                    accum = accum.wrapping_add(rng.gen::<$ty>());
+                }
+                accum
+            });
+            b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N;
+        }
+    };
+}
+
+gen_uint!(gen_u32_step, u32, StepRng::new(0, 1));
+gen_uint!(gen_u32_pcg32, u32, Pcg32::from_entropy());
+gen_uint!(gen_u32_pcg64, u32, Pcg64::from_entropy());
+gen_uint!(gen_u32_pcg64mcg, u32, Pcg64Mcg::from_entropy());
+gen_uint!(gen_u32_chacha8, u32, ChaCha8Rng::from_entropy());
+gen_uint!(gen_u32_chacha12, u32, ChaCha12Rng::from_entropy());
+gen_uint!(gen_u32_chacha20, u32, ChaCha20Rng::from_entropy());
+gen_uint!(gen_u32_hc128, u32, Hc128Rng::from_entropy());
+gen_uint!(gen_u32_std, u32, StdRng::from_entropy());
+#[cfg(feature = "small_rng")]
+gen_uint!(gen_u32_small, u32, SmallRng::from_entropy());
+gen_uint!(gen_u32_os, u32, OsRng);
+
+gen_uint!(gen_u64_step, u64, StepRng::new(0, 1));
+gen_uint!(gen_u64_pcg32, u64, Pcg32::from_entropy());
+gen_uint!(gen_u64_pcg64, u64, Pcg64::from_entropy());
+gen_uint!(gen_u64_pcg64mcg, u64, Pcg64Mcg::from_entropy());
+gen_uint!(gen_u64_chacha8, u64, ChaCha8Rng::from_entropy());
+gen_uint!(gen_u64_chacha12, u64, ChaCha12Rng::from_entropy());
+gen_uint!(gen_u64_chacha20, u64, ChaCha20Rng::from_entropy());
+gen_uint!(gen_u64_hc128, u64, Hc128Rng::from_entropy());
+gen_uint!(gen_u64_std, u64, StdRng::from_entropy());
+#[cfg(feature = "small_rng")]
+gen_uint!(gen_u64_small, u64, SmallRng::from_entropy());
+gen_uint!(gen_u64_os, u64, OsRng);
+
+macro_rules! init_gen {
+    ($fnn:ident, $gen:ident) => {
+        #[bench]
+        fn $fnn(b: &mut Bencher) {
+            let mut rng = Pcg32::from_entropy();
+            b.iter(|| {
+                let r2 = $gen::from_rng(&mut rng).unwrap();
+                r2
+            });
+        }
+    };
+}
+
+init_gen!(init_pcg32, Pcg32);
+init_gen!(init_pcg64, Pcg64);
+init_gen!(init_pcg64mcg, Pcg64Mcg);
+init_gen!(init_hc128, Hc128Rng);
+init_gen!(init_chacha, ChaCha20Rng);
+
+const RESEEDING_BYTES_LEN: usize = 1024 * 1024;
+const RESEEDING_BENCH_N: u64 = 16;
+
+macro_rules! reseeding_bytes {
+    ($fnn:ident, $thresh:expr) => {
+        #[bench]
+        fn $fnn(b: &mut Bencher) {
+            let mut rng = ReseedingRng::new(ChaCha20Core::from_entropy(), $thresh * 1024, OsRng);
+            let mut buf = [0u8; RESEEDING_BYTES_LEN];
+            b.iter(|| {
+                for _ in 0..RESEEDING_BENCH_N {
+                    rng.fill_bytes(&mut buf);
+                    black_box(&buf);
+                }
+            });
+            b.bytes = RESEEDING_BYTES_LEN as u64 * RESEEDING_BENCH_N;
+        }
+    };
+}
+
+reseeding_bytes!(reseeding_chacha20_4k, 4);
+reseeding_bytes!(reseeding_chacha20_16k, 16);
+reseeding_bytes!(reseeding_chacha20_32k, 32);
+reseeding_bytes!(reseeding_chacha20_64k, 64);
+reseeding_bytes!(reseeding_chacha20_256k, 256);
+reseeding_bytes!(reseeding_chacha20_1M, 1024);
+
+
+macro_rules! threadrng_uint {
+    ($fnn:ident, $ty:ty) => {
+        #[bench]
+        fn $fnn(b: &mut Bencher) {
+            let mut rng = thread_rng();
+            b.iter(|| {
+                let mut accum: $ty = 0;
+                for _ in 0..RAND_BENCH_N {
+                    accum = accum.wrapping_add(rng.gen::<$ty>());
+                }
+                accum
+            });
+            b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N;
+        }
+    };
+}
+
+threadrng_uint!(thread_rng_u32, u32);
+threadrng_uint!(thread_rng_u64, u64);
diff --git a/vendor/rand-0.7.3/benches/misc.rs b/vendor/rand-0.7.3/benches/misc.rs
new file mode 100644
index 000000000..e46137f19
--- /dev/null
+++ b/vendor/rand-0.7.3/benches/misc.rs
@@ -0,0 +1,140 @@
+// 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.
+
+#![feature(test)]
+
+extern crate test;
+
+const RAND_BENCH_N: u64 = 1000;
+
+use test::Bencher;
+
+use rand::distributions::{Bernoulli, Distribution, Standard};
+use rand::prelude::*;
+use rand_pcg::{Pcg32, Pcg64Mcg};
+
+#[bench]
+fn misc_gen_bool_const(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        for _ in 0..crate::RAND_BENCH_N {
+            accum ^= rng.gen_bool(0.18);
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_gen_bool_var(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        let mut p = 0.18;
+        for _ in 0..crate::RAND_BENCH_N {
+            accum ^= rng.gen_bool(p);
+            p += 0.0001;
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_gen_ratio_const(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        for _ in 0..crate::RAND_BENCH_N {
+            accum ^= rng.gen_ratio(2, 3);
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_gen_ratio_var(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        for i in 2..(crate::RAND_BENCH_N as u32 + 2) {
+            accum ^= rng.gen_ratio(i, i + 1);
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_bernoulli_const(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let d = rand::distributions::Bernoulli::new(0.18).unwrap();
+        let mut accum = true;
+        for _ in 0..crate::RAND_BENCH_N {
+            accum ^= rng.sample(d);
+        }
+        accum
+    })
+}
+
+#[bench]
+fn misc_bernoulli_var(b: &mut Bencher) {
+    let mut rng = Pcg32::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let mut accum = true;
+        let mut p = 0.18;
+        for _ in 0..crate::RAND_BENCH_N {
+            let d = Bernoulli::new(p).unwrap();
+            accum ^= rng.sample(d);
+            p += 0.0001;
+        }
+        accum
+    })
+}
+
+#[bench]
+fn gen_1k_iter_repeat(b: &mut Bencher) {
+    use std::iter;
+    let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let v: Vec<u64> = iter::repeat(()).map(|()| rng.gen()).take(128).collect();
+        v
+    });
+    b.bytes = 1024;
+}
+
+#[bench]
+fn gen_1k_sample_iter(b: &mut Bencher) {
+    let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        let v: Vec<u64> = Standard.sample_iter(&mut rng).take(128).collect();
+        v
+    });
+    b.bytes = 1024;
+}
+
+#[bench]
+fn gen_1k_gen_array(b: &mut Bencher) {
+    let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
+    b.iter(|| {
+        // max supported array length is 32!
+        let v: [[u64; 32]; 4] = rng.gen();
+        v
+    });
+    b.bytes = 1024;
+}
+
+#[bench]
+fn gen_1k_fill(b: &mut Bencher) {
+    let mut rng = Pcg64Mcg::from_rng(&mut thread_rng()).unwrap();
+    let mut buf = [0u64; 128];
+    b.iter(|| {
+        rng.fill(&mut buf[..]);
+        buf
+    });
+    b.bytes = 1024;
+}
diff --git a/vendor/rand-0.7.3/benches/seq.rs b/vendor/rand-0.7.3/benches/seq.rs
new file mode 100644
index 000000000..7da2ff8a0
--- /dev/null
+++ b/vendor/rand-0.7.3/benches/seq.rs
@@ -0,0 +1,179 @@
+// 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.
+
+#![feature(test)]
+#![allow(non_snake_case)]
+
+extern crate test;
+
+use test::Bencher;
+
+use rand::prelude::*;
+use rand::seq::*;
+use std::mem::size_of;
+
+// We force use of 32-bit RNG since seq code is optimised for use with 32-bit
+// generators on all platforms.
+use rand_pcg::Pcg32 as SmallRng;
+
+const RAND_BENCH_N: u64 = 1000;
+
+#[bench]
+fn seq_shuffle_100(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &mut [usize] = &mut [1; 100];
+    b.iter(|| {
+        x.shuffle(&mut rng);
+        x[0]
+    })
+}
+
+#[bench]
+fn seq_slice_choose_1_of_1000(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &mut [usize] = &mut [1; 1000];
+    for i in 0..1000 {
+        x[i] = i;
+    }
+    b.iter(|| {
+        let mut s = 0;
+        for _ in 0..RAND_BENCH_N {
+            s += x.choose(&mut rng).unwrap();
+        }
+        s
+    });
+    b.bytes = size_of::<usize>() as u64 * crate::RAND_BENCH_N;
+}
+
+macro_rules! seq_slice_choose_multiple {
+    ($name:ident, $amount:expr, $length:expr) => {
+        #[bench]
+        fn $name(b: &mut Bencher) {
+            let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+            let x: &[i32] = &[$amount; $length];
+            let mut result = [0i32; $amount];
+            b.iter(|| {
+                // Collect full result to prevent unwanted shortcuts getting
+                // first element (in case sample_indices returns an iterator).
+                for (slot, sample) in result.iter_mut().zip(x.choose_multiple(&mut rng, $amount)) {
+                    *slot = *sample;
+                }
+                result[$amount - 1]
+            })
+        }
+    };
+}
+
+seq_slice_choose_multiple!(seq_slice_choose_multiple_1_of_1000, 1, 1000);
+seq_slice_choose_multiple!(seq_slice_choose_multiple_950_of_1000, 950, 1000);
+seq_slice_choose_multiple!(seq_slice_choose_multiple_10_of_100, 10, 100);
+seq_slice_choose_multiple!(seq_slice_choose_multiple_90_of_100, 90, 100);
+
+#[bench]
+fn seq_iter_choose_from_1000(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &mut [usize] = &mut [1; 1000];
+    for i in 0..1000 {
+        x[i] = i;
+    }
+    b.iter(|| {
+        let mut s = 0;
+        for _ in 0..RAND_BENCH_N {
+            s += x.iter().choose(&mut rng).unwrap();
+        }
+        s
+    });
+    b.bytes = size_of::<usize>() as u64 * crate::RAND_BENCH_N;
+}
+
+#[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 WindowHintedIterator<I: ExactSizeIterator + Iterator + Clone> {
+    iter: I,
+    window_size: usize,
+}
+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>) {
+        (std::cmp::min(self.iter.len(), self.window_size), None)
+    }
+}
+
+#[bench]
+fn seq_iter_unhinted_choose_from_1000(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &[usize] = &[1; 1000];
+    b.iter(|| {
+        UnhintedIterator { iter: x.iter() }
+            .choose(&mut rng)
+            .unwrap()
+    })
+}
+
+#[bench]
+fn seq_iter_window_hinted_choose_from_1000(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &[usize] = &[1; 1000];
+    b.iter(|| {
+        WindowHintedIterator {
+            iter: x.iter(),
+            window_size: 7,
+        }
+        .choose(&mut rng)
+    })
+}
+
+#[bench]
+fn seq_iter_choose_multiple_10_of_100(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &[usize] = &[1; 100];
+    b.iter(|| x.iter().cloned().choose_multiple(&mut rng, 10))
+}
+
+#[bench]
+fn seq_iter_choose_multiple_fill_10_of_100(b: &mut Bencher) {
+    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+    let x: &[usize] = &[1; 100];
+    let mut buf = [0; 10];
+    b.iter(|| x.iter().cloned().choose_multiple_fill(&mut rng, &mut buf))
+}
+
+macro_rules! sample_indices {
+    ($name:ident, $fn:ident, $amount:expr, $length:expr) => {
+        #[bench]
+        fn $name(b: &mut Bencher) {
+            let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
+            b.iter(|| index::$fn(&mut rng, $length, $amount))
+        }
+    };
+}
+
+sample_indices!(misc_sample_indices_1_of_1k, sample, 1, 1000);
+sample_indices!(misc_sample_indices_10_of_1k, sample, 10, 1000);
+sample_indices!(misc_sample_indices_100_of_1k, sample, 100, 1000);
+sample_indices!(misc_sample_indices_100_of_1M, sample, 100, 1000_000);
+sample_indices!(misc_sample_indices_100_of_1G, sample, 100, 1000_000_000);
+sample_indices!(misc_sample_indices_200_of_1G, sample, 200, 1000_000_000);
+sample_indices!(misc_sample_indices_400_of_1G, sample, 400, 1000_000_000);
+sample_indices!(misc_sample_indices_600_of_1G, sample, 600, 1000_000_000);
diff --git a/vendor/rand-0.7.3/benches/weighted.rs b/vendor/rand-0.7.3/benches/weighted.rs
new file mode 100644
index 000000000..68722908a
--- /dev/null
+++ b/vendor/rand-0.7.3/benches/weighted.rs
@@ -0,0 +1,36 @@
+// Copyright 2019 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.
+
+#![feature(test)]
+
+extern crate test;
+
+use rand::distributions::WeightedIndex;
+use rand::Rng;
+use test::Bencher;
+
+#[bench]
+fn weighted_index_creation(b: &mut Bencher) {
+    let mut rng = rand::thread_rng();
+    let weights = [1u32, 2, 4, 0, 5, 1, 7, 1, 2, 3, 4, 5, 6, 7];
+    b.iter(|| {
+        let distr = WeightedIndex::new(weights.to_vec()).unwrap();
+        rng.sample(distr)
+    })
+}
+
+#[bench]
+fn weighted_index_modification(b: &mut Bencher) {
+    let mut rng = rand::thread_rng();
+    let weights = [1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7];
+    let mut distr = WeightedIndex::new(weights.to_vec()).unwrap();
+    b.iter(|| {
+        distr.update_weights(&[(2, &4), (5, &1)]).unwrap();
+        rng.sample(&distr)
+    })
+}
diff --git a/vendor/rand-0.7.3/examples/monte-carlo.rs b/vendor/rand-0.7.3/examples/monte-carlo.rs
new file mode 100644
index 000000000..70560d0fa
--- /dev/null
+++ b/vendor/rand-0.7.3/examples/monte-carlo.rs
@@ -0,0 +1,51 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2018 The Rust Project Developers.
+//
+// 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.
+
+//! # Monte Carlo estimation of π
+//!
+//! Imagine that we have a square with sides of length 2 and a unit circle
+//! (radius = 1), both centered at the origin. The areas are:
+//!
+//! ```text
+//!     area of circle  = πr² = π * r * r = π
+//!     area of square  = 2² = 4
+//! ```
+//!
+//! The circle is entirely within the square, so if we sample many points
+//! randomly from the square, roughly π / 4 of them should be inside the circle.
+//!
+//! We can use the above fact to estimate the value of π: pick many points in
+//! the square at random, calculate the fraction that fall within the circle,
+//! and multiply this fraction by 4.
+
+#![cfg(feature = "std")]
+
+use rand::distributions::{Distribution, Uniform};
+
+fn main() {
+    let range = Uniform::new(-1.0f64, 1.0);
+    let mut rng = rand::thread_rng();
+
+    let total = 1_000_000;
+    let mut in_circle = 0;
+
+    for _ in 0..total {
+        let a = range.sample(&mut rng);
+        let b = range.sample(&mut rng);
+        if a * a + b * b <= 1.0 {
+            in_circle += 1;
+        }
+    }
+
+    // prints something close to 3.14159...
+    println!(
+        "π is approximately {}",
+        4. * (in_circle as f64) / (total as f64)
+    );
+}
diff --git a/vendor/rand-0.7.3/examples/monty-hall.rs b/vendor/rand-0.7.3/examples/monty-hall.rs
new file mode 100644
index 000000000..30e2f44d1
--- /dev/null
+++ b/vendor/rand-0.7.3/examples/monty-hall.rs
@@ -0,0 +1,123 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2018 The Rust Project Developers.
+//
+// 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.
+
+//! ## Monty Hall Problem
+//!
+//! This is a simulation of the [Monty Hall Problem][]:
+//!
+//! > Suppose you're on a game show, and you're given the choice of three doors:
+//! > Behind one door is a car; behind the others, goats. You pick a door, say
+//! > No. 1, and the host, who knows what's behind the doors, opens another
+//! > door, say No. 3, which has a goat. He then says to you, "Do you want to
+//! > pick door No. 2?" Is it to your advantage to switch your choice?
+//!
+//! The rather unintuitive answer is that you will have a 2/3 chance of winning
+//! if you switch and a 1/3 chance of winning if you don't, so it's better to
+//! switch.
+//!
+//! This program will simulate the game show and with large enough simulation
+//! steps it will indeed confirm that it is better to switch.
+//!
+//! [Monty Hall Problem]: https://en.wikipedia.org/wiki/Monty_Hall_problem
+
+#![cfg(feature = "std")]
+
+use rand::distributions::{Distribution, Uniform};
+use rand::Rng;
+
+struct SimulationResult {
+    win: bool,
+    switch: bool,
+}
+
+// Run a single simulation of the Monty Hall problem.
+fn simulate<R: Rng>(random_door: &Uniform<u32>, rng: &mut R) -> SimulationResult {
+    let car = random_door.sample(rng);
+
+    // This is our initial choice
+    let mut choice = random_door.sample(rng);
+
+    // The game host opens a door
+    let open = game_host_open(car, choice, rng);
+
+    // Shall we switch?
+    let switch = rng.gen();
+    if switch {
+        choice = switch_door(choice, open);
+    }
+
+    SimulationResult {
+        win: choice == car,
+        switch,
+    }
+}
+
+// Returns the door the game host opens given our choice and knowledge of
+// where the car is. The game host will never open the door with the car.
+fn game_host_open<R: Rng>(car: u32, choice: u32, rng: &mut R) -> u32 {
+    use rand::seq::SliceRandom;
+    *free_doors(&[car, choice]).choose(rng).unwrap()
+}
+
+// Returns the door we switch to, given our current choice and
+// the open door. There will only be one valid door.
+fn switch_door(choice: u32, open: u32) -> u32 {
+    free_doors(&[choice, open])[0]
+}
+
+fn free_doors(blocked: &[u32]) -> Vec<u32> {
+    (0..3).filter(|x| !blocked.contains(x)).collect()
+}
+
+fn main() {
+    // The estimation will be more accurate with more simulations
+    let num_simulations = 10000;
+
+    let mut rng = rand::thread_rng();
+    let random_door = Uniform::new(0u32, 3);
+
+    let (mut switch_wins, mut switch_losses) = (0, 0);
+    let (mut keep_wins, mut keep_losses) = (0, 0);
+
+    println!("Running {} simulations...", num_simulations);
+    for _ in 0..num_simulations {
+        let result = simulate(&random_door, &mut rng);
+
+        match (result.win, result.switch) {
+            (true, true) => switch_wins += 1,
+            (true, false) => keep_wins += 1,
+            (false, true) => switch_losses += 1,
+            (false, false) => keep_losses += 1,
+        }
+    }
+
+    let total_switches = switch_wins + switch_losses;
+    let total_keeps = keep_wins + keep_losses;
+
+    println!(
+        "Switched door {} times with {} wins and {} losses",
+        total_switches, switch_wins, switch_losses
+    );
+
+    println!(
+        "Kept our choice {} times with {} wins and {} losses",
+        total_keeps, keep_wins, keep_losses
+    );
+
+    // With a large number of simulations, the values should converge to
+    // 0.667 and 0.333 respectively.
+    println!(
+        "Estimated chance to win if we switch: {}",
+        switch_wins as f32 / total_switches as f32
+    );
+    println!(
+        "Estimated chance to win if we don't: {}",
+        keep_wins as f32 / total_keeps as f32
+    );
+}
diff --git a/vendor/rand-0.7.3/rustfmt.toml b/vendor/rand-0.7.3/rustfmt.toml
new file mode 100644
index 000000000..6a2d9d482
--- /dev/null
+++ b/vendor/rand-0.7.3/rustfmt.toml
@@ -0,0 +1,32 @@
+# This rustfmt file is added for configuration, but in practice much of our
+# code is hand-formatted, frequently with more readable results.
+
+# Comments:
+normalize_comments = true
+wrap_comments = false
+comment_width = 90      # small excess is okay but prefer 80
+
+# Arguments:
+use_small_heuristics = "Default"
+# TODO: single line functions only where short, please?
+# https://github.com/rust-lang/rustfmt/issues/3358
+fn_single_line = false
+fn_args_layout = "Compressed"
+overflow_delimited_expr = true
+where_single_line = true
+
+# enum_discrim_align_threshold = 20
+# struct_field_align_threshold = 20
+
+# Compatibility:
+edition = "2018"        # we require compatibility back to 1.32.0
+
+# Misc:
+inline_attribute_width = 80
+blank_lines_upper_bound = 2
+reorder_impl_items = true
+# report_todo = "Unnumbered"
+# report_fixme = "Unnumbered"
+
+# Ignored files:
+ignore = []
diff --git a/vendor/rand-0.7.3/src/distributions/bernoulli.rs b/vendor/rand-0.7.3/src/distributions/bernoulli.rs
new file mode 100644
index 000000000..a1fa86e14
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/bernoulli.rs
@@ -0,0 +1,199 @@
+// 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.
+
+//! The Bernoulli distribution.
+
+use crate::distributions::Distribution;
+use crate::Rng;
+use core::{fmt, u64};
+
+/// The Bernoulli distribution.
+///
+/// This is a special case of the Binomial distribution where `n = 1`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Bernoulli, Distribution};
+///
+/// let d = Bernoulli::new(0.3).unwrap();
+/// let v = d.sample(&mut rand::thread_rng());
+/// println!("{} is from a Bernoulli distribution", v);
+/// ```
+///
+/// # Precision
+///
+/// This `Bernoulli` distribution uses 64 bits from the RNG (a `u64`),
+/// so only probabilities that are multiples of 2<sup>-64</sup> can be
+/// represented.
+#[derive(Clone, Copy, Debug)]
+pub struct Bernoulli {
+    /// Probability of success, relative to the maximal integer.
+    p_int: u64,
+}
+
+// To sample from the Bernoulli distribution we use a method that compares a
+// random `u64` value `v < (p * 2^64)`.
+//
+// If `p == 1.0`, the integer `v` to compare against can not represented as a
+// `u64`. We manually set it to `u64::MAX` instead (2^64 - 1 instead of 2^64).
+// Note that  value of `p < 1.0` can never result in `u64::MAX`, because an
+// `f64` only has 53 bits of precision, and the next largest value of `p` will
+// result in `2^64 - 2048`.
+//
+// Also there is a 100% theoretical concern: if someone consistenly wants to
+// generate `true` using the Bernoulli distribution (i.e. by using a probability
+// of `1.0`), just using `u64::MAX` is not enough. On average it would return
+// false once every 2^64 iterations. Some people apparently care about this
+// case.
+//
+// That is why we special-case `u64::MAX` to always return `true`, without using
+// the RNG, and pay the performance price for all uses that *are* reasonable.
+// Luckily, if `new()` and `sample` are close, the compiler can optimize out the
+// extra check.
+const ALWAYS_TRUE: u64 = u64::MAX;
+
+// This is just `2.0.powi(64)`, but written this way because it is not available
+// in `no_std` mode.
+const SCALE: f64 = 2.0 * (1u64 << 63) as f64;
+
+/// Error type returned from `Bernoulli::new`.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum BernoulliError {
+    /// `p < 0` or `p > 1`.
+    InvalidProbability,
+}
+
+impl fmt::Display for BernoulliError {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        f.write_str(match self {
+            BernoulliError::InvalidProbability => "p is outside [0, 1] in Bernoulli distribution",
+        })
+    }
+}
+
+#[cfg(feature = "std")]
+impl ::std::error::Error for BernoulliError {}
+
+impl Bernoulli {
+    /// Construct a new `Bernoulli` with the given probability of success `p`.
+    ///
+    /// # Precision
+    ///
+    /// For `p = 1.0`, the resulting distribution will always generate true.
+    /// For `p = 0.0`, the resulting distribution will always generate false.
+    ///
+    /// This method is accurate for any input `p` in the range `[0, 1]` which is
+    /// a multiple of 2<sup>-64</sup>. (Note that not all multiples of
+    /// 2<sup>-64</sup> in `[0, 1]` can be represented as a `f64`.)
+    #[inline]
+    pub fn new(p: f64) -> Result<Bernoulli, BernoulliError> {
+        if !(p >= 0.0 && p < 1.0) {
+            if p == 1.0 {
+                return Ok(Bernoulli { p_int: ALWAYS_TRUE });
+            }
+            return Err(BernoulliError::InvalidProbability);
+        }
+        Ok(Bernoulli {
+            p_int: (p * SCALE) as u64,
+        })
+    }
+
+    /// Construct a new `Bernoulli` with the probability of success of
+    /// `numerator`-in-`denominator`. I.e. `new_ratio(2, 3)` will return
+    /// a `Bernoulli` with a 2-in-3 chance, or about 67%, of returning `true`.
+    ///
+    /// return `true`. If `numerator == 0` it will always return `false`.
+    /// For `numerator > denominator` and `denominator == 0`, this returns an
+    /// error. Otherwise, for `numerator == denominator`, samples are always
+    /// true; for `numerator == 0` samples are always false.
+    #[inline]
+    pub fn from_ratio(numerator: u32, denominator: u32) -> Result<Bernoulli, BernoulliError> {
+        if numerator > denominator || denominator == 0 {
+            return Err(BernoulliError::InvalidProbability);
+        }
+        if numerator == denominator {
+            return Ok(Bernoulli { p_int: ALWAYS_TRUE });
+        }
+        let p_int = ((f64::from(numerator) / f64::from(denominator)) * SCALE) as u64;
+        Ok(Bernoulli { p_int })
+    }
+}
+
+impl Distribution<bool> for Bernoulli {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
+        // Make sure to always return true for p = 1.0.
+        if self.p_int == ALWAYS_TRUE {
+            return true;
+        }
+        let v: u64 = rng.gen();
+        v < self.p_int
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Bernoulli;
+    use crate::distributions::Distribution;
+    use crate::Rng;
+
+    #[test]
+    fn test_trivial() {
+        let mut r = crate::test::rng(1);
+        let always_false = Bernoulli::new(0.0).unwrap();
+        let always_true = Bernoulli::new(1.0).unwrap();
+        for _ in 0..5 {
+            assert_eq!(r.sample::<bool, _>(&always_false), false);
+            assert_eq!(r.sample::<bool, _>(&always_true), true);
+            assert_eq!(Distribution::<bool>::sample(&always_false, &mut r), false);
+            assert_eq!(Distribution::<bool>::sample(&always_true, &mut r), true);
+        }
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_average() {
+        const P: f64 = 0.3;
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        let d1 = Bernoulli::new(P).unwrap();
+        let d2 = Bernoulli::from_ratio(NUM, DENOM).unwrap();
+        const N: u32 = 100_000;
+
+        let mut sum1: u32 = 0;
+        let mut sum2: u32 = 0;
+        let mut rng = crate::test::rng(2);
+        for _ in 0..N {
+            if d1.sample(&mut rng) {
+                sum1 += 1;
+            }
+            if d2.sample(&mut rng) {
+                sum2 += 1;
+            }
+        }
+        let avg1 = (sum1 as f64) / (N as f64);
+        assert!((avg1 - P).abs() < 5e-3);
+
+        let avg2 = (sum2 as f64) / (N as f64);
+        assert!((avg2 - (NUM as f64) / (DENOM as f64)).abs() < 5e-3);
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(3);
+        let distr = Bernoulli::new(0.4532).unwrap();
+        let mut buf = [false; 10];
+        for x in &mut buf {
+            *x = rng.sample(&distr);
+        }
+        assert_eq!(buf, [
+            true, false, false, true, false, false, true, true, true, true
+        ]);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/binomial.rs b/vendor/rand-0.7.3/src/distributions/binomial.rs
new file mode 100644
index 000000000..c096e4a86
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/binomial.rs
@@ -0,0 +1,321 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// 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.
+
+//! The binomial distribution.
+#![allow(deprecated)]
+#![allow(clippy::all)]
+
+use crate::distributions::{Distribution, Uniform};
+use crate::Rng;
+
+/// The binomial distribution `Binomial(n, p)`.
+///
+/// This distribution has density function:
+/// `f(k) = n!/(k! (n-k)!) p^k (1-p)^(n-k)` for `k >= 0`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Binomial {
+    /// Number of trials.
+    n: u64,
+    /// Probability of success.
+    p: f64,
+}
+
+impl Binomial {
+    /// Construct a new `Binomial` with the given shape parameters `n` (number
+    /// of trials) and `p` (probability of success).
+    ///
+    /// Panics if `p < 0` or `p > 1`.
+    pub fn new(n: u64, p: f64) -> Binomial {
+        assert!(p >= 0.0, "Binomial::new called with p < 0");
+        assert!(p <= 1.0, "Binomial::new called with p > 1");
+        Binomial { n, p }
+    }
+}
+
+/// Convert a `f64` to an `i64`, panicing on overflow.
+// In the future (Rust 1.34), this might be replaced with `TryFrom`.
+fn f64_to_i64(x: f64) -> i64 {
+    assert!(x < (::std::i64::MAX as f64));
+    x as i64
+}
+
+impl Distribution<u64> for Binomial {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        // Handle these values directly.
+        if self.p == 0.0 {
+            return 0;
+        } else if self.p == 1.0 {
+            return self.n;
+        }
+
+        // The binomial distribution is symmetrical with respect to p -> 1-p,
+        // k -> n-k switch p so that it is less than 0.5 - this allows for lower
+        // expected values we will just invert the result at the end
+        let p = if self.p <= 0.5 { self.p } else { 1.0 - self.p };
+
+        let result;
+        let q = 1. - p;
+
+        // For small n * min(p, 1 - p), the BINV algorithm based on the inverse
+        // transformation of the binomial distribution is efficient. Otherwise,
+        // the BTPE algorithm is used.
+        //
+        // Voratas Kachitvichyanukul and Bruce W. Schmeiser. 1988. Binomial
+        // random variate generation. Commun. ACM 31, 2 (February 1988),
+        // 216-222. http://dx.doi.org/10.1145/42372.42381
+
+        // Threshold for prefering the BINV algorithm. The paper suggests 10,
+        // Ranlib uses 30, and GSL uses 14.
+        const BINV_THRESHOLD: f64 = 10.;
+
+        if (self.n as f64) * p < BINV_THRESHOLD && self.n <= (::std::i32::MAX as u64) {
+            // Use the BINV algorithm.
+            let s = p / q;
+            let a = ((self.n + 1) as f64) * s;
+            let mut r = q.powi(self.n as i32);
+            let mut u: f64 = rng.gen();
+            let mut x = 0;
+            while u > r as f64 {
+                u -= r;
+                x += 1;
+                r *= a / (x as f64) - s;
+            }
+            result = x;
+        } else {
+            // Use the BTPE algorithm.
+
+            // Threshold for using the squeeze algorithm. This can be freely
+            // chosen based on performance. Ranlib and GSL use 20.
+            const SQUEEZE_THRESHOLD: i64 = 20;
+
+            // Step 0: Calculate constants as functions of `n` and `p`.
+            let n = self.n as f64;
+            let np = n * p;
+            let npq = np * q;
+            let f_m = np + p;
+            let m = f64_to_i64(f_m);
+            // radius of triangle region, since height=1 also area of region
+            let p1 = (2.195 * npq.sqrt() - 4.6 * q).floor() + 0.5;
+            // tip of triangle
+            let x_m = (m as f64) + 0.5;
+            // left edge of triangle
+            let x_l = x_m - p1;
+            // right edge of triangle
+            let x_r = x_m + p1;
+            let c = 0.134 + 20.5 / (15.3 + (m as f64));
+            // p1 + area of parallelogram region
+            let p2 = p1 * (1. + 2. * c);
+
+            fn lambda(a: f64) -> f64 {
+                a * (1. + 0.5 * a)
+            }
+
+            let lambda_l = lambda((f_m - x_l) / (f_m - x_l * p));
+            let lambda_r = lambda((x_r - f_m) / (x_r * q));
+            // p1 + area of left tail
+            let p3 = p2 + c / lambda_l;
+            // p1 + area of right tail
+            let p4 = p3 + c / lambda_r;
+
+            // return value
+            let mut y: i64;
+
+            let gen_u = Uniform::new(0., p4);
+            let gen_v = Uniform::new(0., 1.);
+
+            loop {
+                // Step 1: Generate `u` for selecting the region. If region 1 is
+                // selected, generate a triangularly distributed variate.
+                let u = gen_u.sample(rng);
+                let mut v = gen_v.sample(rng);
+                if !(u > p1) {
+                    y = f64_to_i64(x_m - p1 * v + u);
+                    break;
+                }
+
+                if !(u > p2) {
+                    // Step 2: Region 2, parallelograms. Check if region 2 is
+                    // used. If so, generate `y`.
+                    let x = x_l + (u - p1) / c;
+                    v = v * c + 1.0 - (x - x_m).abs() / p1;
+                    if v > 1. {
+                        continue;
+                    } else {
+                        y = f64_to_i64(x);
+                    }
+                } else if !(u > p3) {
+                    // Step 3: Region 3, left exponential tail.
+                    y = f64_to_i64(x_l + v.ln() / lambda_l);
+                    if y < 0 {
+                        continue;
+                    } else {
+                        v *= (u - p2) * lambda_l;
+                    }
+                } else {
+                    // Step 4: Region 4, right exponential tail.
+                    y = f64_to_i64(x_r - v.ln() / lambda_r);
+                    if y > 0 && (y as u64) > self.n {
+                        continue;
+                    } else {
+                        v *= (u - p3) * lambda_r;
+                    }
+                }
+
+                // Step 5: Acceptance/rejection comparison.
+
+                // Step 5.0: Test for appropriate method of evaluating f(y).
+                let k = (y - m).abs();
+                if !(k > SQUEEZE_THRESHOLD && (k as f64) < 0.5 * npq - 1.) {
+                    // Step 5.1: Evaluate f(y) via the recursive relationship. Start the
+                    // search from the mode.
+                    let s = p / q;
+                    let a = s * (n + 1.);
+                    let mut f = 1.0;
+                    if m < y {
+                        let mut i = m;
+                        loop {
+                            i += 1;
+                            f *= a / (i as f64) - s;
+                            if i == y {
+                                break;
+                            }
+                        }
+                    } else if m > y {
+                        let mut i = y;
+                        loop {
+                            i += 1;
+                            f /= a / (i as f64) - s;
+                            if i == m {
+                                break;
+                            }
+                        }
+                    }
+                    if v > f {
+                        continue;
+                    } else {
+                        break;
+                    }
+                }
+
+                // Step 5.2: Squeezing. Check the value of ln(v) againts upper and
+                // lower bound of ln(f(y)).
+                let k = k as f64;
+                let rho = (k / npq) * ((k * (k / 3. + 0.625) + 1. / 6.) / npq + 0.5);
+                let t = -0.5 * k * k / npq;
+                let alpha = v.ln();
+                if alpha < t - rho {
+                    break;
+                }
+                if alpha > t + rho {
+                    continue;
+                }
+
+                // Step 5.3: Final acceptance/rejection test.
+                let x1 = (y + 1) as f64;
+                let f1 = (m + 1) as f64;
+                let z = (f64_to_i64(n) + 1 - m) as f64;
+                let w = (f64_to_i64(n) - y + 1) as f64;
+
+                fn stirling(a: f64) -> f64 {
+                    let a2 = a * a;
+                    (13860. - (462. - (132. - (99. - 140. / a2) / a2) / a2) / a2) / a / 166320.
+                }
+
+                if alpha
+                        > x_m * (f1 / x1).ln()
+                        + (n - (m as f64) + 0.5) * (z / w).ln()
+                        + ((y - m) as f64) * (w * p / (x1 * q)).ln()
+                        // We use the signs from the GSL implementation, which are
+                        // different than the ones in the reference. According to
+                        // the GSL authors, the new signs were verified to be
+                        // correct by one of the original designers of the
+                        // algorithm.
+                        + stirling(f1)
+                        + stirling(z)
+                        - stirling(x1)
+                        - stirling(w)
+                {
+                    continue;
+                }
+
+                break;
+            }
+            assert!(y >= 0);
+            result = y as u64;
+        }
+
+        // Invert the result for p < 0.5.
+        if p != self.p {
+            self.n - result
+        } else {
+            result
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Binomial;
+    use crate::distributions::Distribution;
+    use crate::Rng;
+
+    fn test_binomial_mean_and_variance<R: Rng>(n: u64, p: f64, rng: &mut R) {
+        let binomial = Binomial::new(n, p);
+
+        let expected_mean = n as f64 * p;
+        let expected_variance = n as f64 * p * (1.0 - p);
+
+        let mut results = [0.0; 1000];
+        for i in results.iter_mut() {
+            *i = binomial.sample(rng) as f64;
+        }
+
+        let mean = results.iter().sum::<f64>() / results.len() as f64;
+        assert!(
+            (mean as f64 - expected_mean).abs() < expected_mean / 50.0,
+            "mean: {}, expected_mean: {}",
+            mean,
+            expected_mean
+        );
+
+        let variance =
+            results.iter().map(|x| (x - mean) * (x - mean)).sum::<f64>() / results.len() as f64;
+        assert!(
+            (variance - expected_variance).abs() < expected_variance / 10.0,
+            "variance: {}, expected_variance: {}",
+            variance,
+            expected_variance
+        );
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_binomial() {
+        let mut rng = crate::test::rng(351);
+        test_binomial_mean_and_variance(150, 0.1, &mut rng);
+        test_binomial_mean_and_variance(70, 0.6, &mut rng);
+        test_binomial_mean_and_variance(40, 0.5, &mut rng);
+        test_binomial_mean_and_variance(20, 0.7, &mut rng);
+        test_binomial_mean_and_variance(20, 0.5, &mut rng);
+    }
+
+    #[test]
+    fn test_binomial_end_points() {
+        let mut rng = crate::test::rng(352);
+        assert_eq!(rng.sample(Binomial::new(20, 0.0)), 0);
+        assert_eq!(rng.sample(Binomial::new(20, 1.0)), 20);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_binomial_invalid_lambda_neg() {
+        Binomial::new(20, -10.0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/cauchy.rs b/vendor/rand-0.7.3/src/distributions/cauchy.rs
new file mode 100644
index 000000000..dc54c98a3
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/cauchy.rs
@@ -0,0 +1,99 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// 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.
+
+//! The Cauchy distribution.
+#![allow(deprecated)]
+#![allow(clippy::all)]
+
+use crate::distributions::Distribution;
+use crate::Rng;
+use std::f64::consts::PI;
+
+/// The Cauchy distribution `Cauchy(median, scale)`.
+///
+/// This distribution has a density function:
+/// `f(x) = 1 / (pi * scale * (1 + ((x - median) / scale)^2))`
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Cauchy {
+    median: f64,
+    scale: f64,
+}
+
+impl Cauchy {
+    /// Construct a new `Cauchy` with the given shape parameters
+    /// `median` the peak location and `scale` the scale factor.
+    /// Panics if `scale <= 0`.
+    pub fn new(median: f64, scale: f64) -> Cauchy {
+        assert!(scale > 0.0, "Cauchy::new called with scale factor <= 0");
+        Cauchy { median, scale }
+    }
+}
+
+impl Distribution<f64> for Cauchy {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        // sample from [0, 1)
+        let x = rng.gen::<f64>();
+        // get standard cauchy random number
+        // note that π/2 is not exactly representable, even if x=0.5 the result is finite
+        let comp_dev = (PI * x).tan();
+        // shift and scale according to parameters
+        let result = self.median + self.scale * comp_dev;
+        result
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Cauchy;
+    use crate::distributions::Distribution;
+
+    fn median(mut numbers: &mut [f64]) -> f64 {
+        sort(&mut numbers);
+        let mid = numbers.len() / 2;
+        numbers[mid]
+    }
+
+    fn sort(numbers: &mut [f64]) {
+        numbers.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    }
+
+    #[test]
+    fn test_cauchy_averages() {
+        // NOTE: given that the variance and mean are undefined,
+        // this test does not have any rigorous statistical meaning.
+        let cauchy = Cauchy::new(10.0, 5.0);
+        let mut rng = crate::test::rng(123);
+        let mut numbers: [f64; 1000] = [0.0; 1000];
+        let mut sum = 0.0;
+        for i in 0..1000 {
+            numbers[i] = cauchy.sample(&mut rng);
+            sum += numbers[i];
+        }
+        let median = median(&mut numbers);
+        println!("Cauchy median: {}", median);
+        assert!((median - 10.0).abs() < 0.4); // not 100% certain, but probable enough
+        let mean = sum / 1000.0;
+        println!("Cauchy mean: {}", mean);
+        // for a Cauchy distribution the mean should not converge
+        assert!((mean - 10.0).abs() > 0.4); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_zero() {
+        Cauchy::new(0.0, 0.0);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_cauchy_invalid_scale_neg() {
+        Cauchy::new(0.0, -10.0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/dirichlet.rs b/vendor/rand-0.7.3/src/distributions/dirichlet.rs
new file mode 100644
index 000000000..a75678a85
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/dirichlet.rs
@@ -0,0 +1,126 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! The dirichlet distribution.
+#![allow(deprecated)]
+#![allow(clippy::all)]
+
+use crate::distributions::gamma::Gamma;
+use crate::distributions::Distribution;
+use crate::Rng;
+
+/// The dirichelet distribution `Dirichlet(alpha)`.
+///
+/// The Dirichlet distribution is a family of continuous multivariate
+/// probability distributions parameterized by a vector alpha of positive reals.
+/// It is a multivariate generalization of the beta distribution.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Debug)]
+pub struct Dirichlet {
+    /// Concentration parameters (alpha)
+    alpha: Vec<f64>,
+}
+
+impl Dirichlet {
+    /// Construct a new `Dirichlet` with the given alpha parameter `alpha`.
+    ///
+    /// # Panics
+    /// - if `alpha.len() < 2`
+    #[inline]
+    pub fn new<V: Into<Vec<f64>>>(alpha: V) -> Dirichlet {
+        let a = alpha.into();
+        assert!(a.len() > 1);
+        for i in 0..a.len() {
+            assert!(a[i] > 0.0);
+        }
+
+        Dirichlet { alpha: a }
+    }
+
+    /// Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.
+    ///
+    /// # Panics
+    /// - if `alpha <= 0.0`
+    /// - if `size < 2`
+    #[inline]
+    pub fn new_with_param(alpha: f64, size: usize) -> Dirichlet {
+        assert!(alpha > 0.0);
+        assert!(size > 1);
+        Dirichlet {
+            alpha: vec![alpha; size],
+        }
+    }
+}
+
+impl Distribution<Vec<f64>> for Dirichlet {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Vec<f64> {
+        let n = self.alpha.len();
+        let mut samples = vec![0.0f64; n];
+        let mut sum = 0.0f64;
+
+        for i in 0..n {
+            let g = Gamma::new(self.alpha[i], 1.0);
+            samples[i] = g.sample(rng);
+            sum += samples[i];
+        }
+        let invacc = 1.0 / sum;
+        for i in 0..n {
+            samples[i] *= invacc;
+        }
+        samples
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Dirichlet;
+    use crate::distributions::Distribution;
+
+    #[test]
+    fn test_dirichlet() {
+        let d = Dirichlet::new(vec![1.0, 2.0, 3.0]);
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    fn test_dirichlet_with_param() {
+        let alpha = 0.5f64;
+        let size = 2;
+        let d = Dirichlet::new_with_param(alpha, size);
+        let mut rng = crate::test::rng(221);
+        let samples = d.sample(&mut rng);
+        let _: Vec<f64> = samples
+            .into_iter()
+            .map(|x| {
+                assert!(x > 0.0);
+                x
+            })
+            .collect();
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_length() {
+        Dirichlet::new_with_param(0.5f64, 1);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_dirichlet_invalid_alpha() {
+        Dirichlet::new_with_param(0.0f64, 2);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/exponential.rs b/vendor/rand-0.7.3/src/distributions/exponential.rs
new file mode 100644
index 000000000..5fdf7aa74
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/exponential.rs
@@ -0,0 +1,114 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! The exponential distribution.
+#![allow(deprecated)]
+
+use crate::distributions::utils::ziggurat;
+use crate::distributions::{ziggurat_tables, Distribution};
+use crate::Rng;
+
+/// Samples floating-point numbers according to the exponential distribution,
+/// with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or
+/// sampling with `-rng.gen::<f64>().ln()`, but faster.
+///
+/// See `Exp` for the general exponential distribution.
+///
+/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method. The exact
+/// description in the paper was adjusted to use tables for the exponential
+/// distribution rather than normal.
+///
+/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
+///       Generate Normal Random Samples*](
+///       https://www.doornik.com/research/ziggurat.pdf).
+///       Nuffield College, Oxford
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Exp1;
+
+// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
+impl Distribution<f64> for Exp1 {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        #[inline]
+        fn pdf(x: f64) -> f64 {
+            (-x).exp()
+        }
+        #[inline]
+        fn zero_case<R: Rng + ?Sized>(rng: &mut R, _u: f64) -> f64 {
+            ziggurat_tables::ZIG_EXP_R - rng.gen::<f64>().ln()
+        }
+
+        ziggurat(
+            rng,
+            false,
+            &ziggurat_tables::ZIG_EXP_X,
+            &ziggurat_tables::ZIG_EXP_F,
+            pdf,
+            zero_case,
+        )
+    }
+}
+
+/// The exponential distribution `Exp(lambda)`.
+///
+/// This distribution has density function: `f(x) = lambda * exp(-lambda * x)`
+/// for `x > 0`.
+///
+/// Note that [`Exp1`](crate::distributions::Exp1) is an optimised implementation for `lambda = 1`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Exp {
+    /// `lambda` stored as `1/lambda`, since this is what we scale by.
+    lambda_inverse: f64,
+}
+
+impl Exp {
+    /// Construct a new `Exp` with the given shape parameter
+    /// `lambda`. Panics if `lambda <= 0`.
+    #[inline]
+    pub fn new(lambda: f64) -> Exp {
+        assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0");
+        Exp {
+            lambda_inverse: 1.0 / lambda,
+        }
+    }
+}
+
+impl Distribution<f64> for Exp {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let n: f64 = rng.sample(Exp1);
+        n * self.lambda_inverse
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Exp;
+    use crate::distributions::Distribution;
+
+    #[test]
+    fn test_exp() {
+        let exp = Exp::new(10.0);
+        let mut rng = crate::test::rng(221);
+        for _ in 0..1000 {
+            assert!(exp.sample(&mut rng) >= 0.0);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_exp_invalid_lambda_zero() {
+        Exp::new(0.0);
+    }
+    #[test]
+    #[should_panic]
+    fn test_exp_invalid_lambda_neg() {
+        Exp::new(-10.0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/float.rs b/vendor/rand-0.7.3/src/distributions/float.rs
new file mode 100644
index 000000000..0a45f3977
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/float.rs
@@ -0,0 +1,307 @@
+// 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.
+
+//! Basic floating-point number distributions
+
+use crate::distributions::utils::FloatSIMDUtils;
+use crate::distributions::{Distribution, Standard};
+use crate::Rng;
+use core::mem;
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+/// A distribution to sample floating point numbers uniformly in the half-open
+/// interval `(0, 1]`, i.e. including 1 but not 0.
+///
+/// All values that can be generated are of the form `n * ε/2`. For `f32`
+/// the 24 most significant random bits of a `u32` are used and for `f64` the
+/// 53 most significant bits of a `u64` are used. The conversion uses the
+/// multiplicative method.
+///
+/// See also: [`Standard`] which samples from `[0, 1)`, [`Open01`]
+/// which samples from `(0, 1)` and [`Uniform`] which samples from arbitrary
+/// ranges.
+///
+/// # Example
+/// ```
+/// use rand::{thread_rng, Rng};
+/// use rand::distributions::OpenClosed01;
+///
+/// let val: f32 = thread_rng().sample(OpenClosed01);
+/// println!("f32 from (0, 1): {}", val);
+/// ```
+///
+/// [`Standard`]: crate::distributions::Standard
+/// [`Open01`]: crate::distributions::Open01
+/// [`Uniform`]: crate::distributions::uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+pub struct OpenClosed01;
+
+/// A distribution to sample floating point numbers uniformly in the open
+/// interval `(0, 1)`, i.e. not including either endpoint.
+///
+/// All values that can be generated are of the form `n * ε + ε/2`. For `f32`
+/// the 23 most significant random bits of an `u32` are used, for `f64` 52 from
+/// an `u64`. The conversion uses a transmute-based method.
+///
+/// See also: [`Standard`] which samples from `[0, 1)`, [`OpenClosed01`]
+/// which samples from `(0, 1]` and [`Uniform`] which samples from arbitrary
+/// ranges.
+///
+/// # Example
+/// ```
+/// use rand::{thread_rng, Rng};
+/// use rand::distributions::Open01;
+///
+/// let val: f32 = thread_rng().sample(Open01);
+/// println!("f32 from (0, 1): {}", val);
+/// ```
+///
+/// [`Standard`]: crate::distributions::Standard
+/// [`OpenClosed01`]: crate::distributions::OpenClosed01
+/// [`Uniform`]: crate::distributions::uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+pub struct Open01;
+
+
+// This trait is needed by both this lib and rand_distr hence is a hidden export
+#[doc(hidden)]
+pub trait IntoFloat {
+    type F;
+
+    /// Helper method to combine the fraction and a contant exponent into a
+    /// float.
+    ///
+    /// Only the least significant bits of `self` may be set, 23 for `f32` and
+    /// 52 for `f64`.
+    /// The resulting value will fall in a range that depends on the exponent.
+    /// As an example the range with exponent 0 will be
+    /// [2<sup>0</sup>..2<sup>1</sup>), which is [1..2).
+    fn into_float_with_exponent(self, exponent: i32) -> Self::F;
+}
+
+macro_rules! float_impls {
+    ($ty:ident, $uty:ident, $f_scalar:ident, $u_scalar:ty,
+     $fraction_bits:expr, $exponent_bias:expr) => {
+        impl IntoFloat for $uty {
+            type F = $ty;
+            #[inline(always)]
+            fn into_float_with_exponent(self, exponent: i32) -> $ty {
+                // The exponent is encoded using an offset-binary representation
+                let exponent_bits: $u_scalar =
+                    (($exponent_bias + exponent) as $u_scalar) << $fraction_bits;
+                $ty::from_bits(self | exponent_bits)
+            }
+        }
+
+        impl Distribution<$ty> for Standard {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Multiply-based method; 24/53 random bits; [0, 1) interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+                let precision = $fraction_bits + 1;
+                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
+
+                let value: $uty = rng.gen();
+                let value = value >> (float_size - precision);
+                scale * $ty::cast_from_int(value)
+            }
+        }
+
+        impl Distribution<$ty> for OpenClosed01 {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Multiply-based method; 24/53 random bits; (0, 1] interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+                let precision = $fraction_bits + 1;
+                let scale = 1.0 / ((1 as $u_scalar << precision) as $f_scalar);
+
+                let value: $uty = rng.gen();
+                let value = value >> (float_size - precision);
+                // Add 1 to shift up; will not overflow because of right-shift:
+                scale * $ty::cast_from_int(value + 1)
+            }
+        }
+
+        impl Distribution<$ty> for Open01 {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                // Transmute-based method; 23/52 random bits; (0, 1) interval.
+                // We use the most significant bits because for simple RNGs
+                // those are usually more random.
+                use core::$f_scalar::EPSILON;
+                let float_size = mem::size_of::<$f_scalar>() as u32 * 8;
+
+                let value: $uty = rng.gen();
+                let fraction = value >> (float_size - $fraction_bits);
+                fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
+            }
+        }
+    }
+}
+
+float_impls! { f32, u32, f32, u32, 23, 127 }
+float_impls! { f64, u64, f64, u64, 52, 1023 }
+
+#[cfg(feature = "simd_support")]
+float_impls! { f32x2, u32x2, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x4, u32x4, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x8, u32x8, f32, u32, 23, 127 }
+#[cfg(feature = "simd_support")]
+float_impls! { f32x16, u32x16, f32, u32, 23, 127 }
+
+#[cfg(feature = "simd_support")]
+float_impls! { f64x2, u64x2, f64, u64, 52, 1023 }
+#[cfg(feature = "simd_support")]
+float_impls! { f64x4, u64x4, f64, u64, 52, 1023 }
+#[cfg(feature = "simd_support")]
+float_impls! { f64x8, u64x8, f64, u64, 52, 1023 }
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::rngs::mock::StepRng;
+
+    const EPSILON32: f32 = ::core::f32::EPSILON;
+    const EPSILON64: f64 = ::core::f64::EPSILON;
+
+    macro_rules! test_f32 {
+        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
+            #[test]
+            fn $fnn() {
+                // Standard
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
+                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
+
+                // OpenClosed01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 8 | 1 << (8 + 32), 0);
+                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
+
+                // Open01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 9 | 1 << (9 + 32), 0);
+                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
+            }
+        };
+    }
+    test_f32! { f32_edge_cases, f32, 0.0, EPSILON32 }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x2_edge_cases, f32x2, f32x2::splat(0.0), f32x2::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x4_edge_cases, f32x4, f32x4::splat(0.0), f32x4::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x8_edge_cases, f32x8, f32x8::splat(0.0), f32x8::splat(EPSILON32) }
+    #[cfg(feature = "simd_support")]
+    test_f32! { f32x16_edge_cases, f32x16, f32x16::splat(0.0), f32x16::splat(EPSILON32) }
+
+    macro_rules! test_f64 {
+        ($fnn:ident, $ty:ident, $ZERO:expr, $EPSILON:expr) => {
+            #[test]
+            fn $fnn() {
+                // Standard
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.gen::<$ty>(), $ZERO);
+                let mut one = StepRng::new(1 << 11, 0);
+                assert_eq!(one.gen::<$ty>(), $EPSILON / 2.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.gen::<$ty>(), 1.0 - $EPSILON / 2.0);
+
+                // OpenClosed01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(OpenClosed01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 11, 0);
+                assert_eq!(one.sample::<$ty, _>(OpenClosed01), $EPSILON);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(OpenClosed01), $ZERO + 1.0);
+
+                // Open01
+                let mut zeros = StepRng::new(0, 0);
+                assert_eq!(zeros.sample::<$ty, _>(Open01), 0.0 + $EPSILON / 2.0);
+                let mut one = StepRng::new(1 << 12, 0);
+                assert_eq!(one.sample::<$ty, _>(Open01), $EPSILON / 2.0 * 3.0);
+                let mut max = StepRng::new(!0, 0);
+                assert_eq!(max.sample::<$ty, _>(Open01), 1.0 - $EPSILON / 2.0);
+            }
+        };
+    }
+    test_f64! { f64_edge_cases, f64, 0.0, EPSILON64 }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x2_edge_cases, f64x2, f64x2::splat(0.0), f64x2::splat(EPSILON64) }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x4_edge_cases, f64x4, f64x4::splat(0.0), f64x4::splat(EPSILON64) }
+    #[cfg(feature = "simd_support")]
+    test_f64! { f64x8_edge_cases, f64x8, f64x8::splat(0.0), f64x8::splat(EPSILON64) }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<T: Copy + core::fmt::Debug + PartialEq, D: Distribution<T>>(
+            distr: &D, zero: T, expected: &[T],
+        ) {
+            let mut rng = crate::test::rng(0x6f44f5646c2a7334);
+            let mut buf = [zero; 3];
+            for x in &mut buf {
+                *x = rng.sample(&distr);
+            }
+            assert_eq!(&buf, expected);
+        }
+
+        test_samples(&Standard, 0f32, &[0.0035963655, 0.7346052, 0.09778172]);
+        test_samples(&Standard, 0f64, &[
+            0.7346051961657583,
+            0.20298547462974248,
+            0.8166436635290655,
+        ]);
+
+        test_samples(&OpenClosed01, 0f32, &[0.003596425, 0.73460525, 0.09778178]);
+        test_samples(&OpenClosed01, 0f64, &[
+            0.7346051961657584,
+            0.2029854746297426,
+            0.8166436635290656,
+        ]);
+
+        test_samples(&Open01, 0f32, &[0.0035963655, 0.73460525, 0.09778172]);
+        test_samples(&Open01, 0f64, &[
+            0.7346051961657584,
+            0.20298547462974248,
+            0.8166436635290656,
+        ]);
+
+        #[cfg(feature = "simd_support")]
+        {
+            // We only test a sub-set of types here. Values are identical to
+            // non-SIMD types; we assume this pattern continues across all
+            // SIMD types.
+
+            test_samples(&Standard, f32x2::new(0.0, 0.0), &[
+                f32x2::new(0.0035963655, 0.7346052),
+                f32x2::new(0.09778172, 0.20298547),
+                f32x2::new(0.34296435, 0.81664366),
+            ]);
+
+            test_samples(&Standard, f64x2::new(0.0, 0.0), &[
+                f64x2::new(0.7346051961657583, 0.20298547462974248),
+                f64x2::new(0.8166436635290655, 0.7423708925400552),
+                f64x2::new(0.16387782224016323, 0.9087068770169618),
+            ]);
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/gamma.rs b/vendor/rand-0.7.3/src/distributions/gamma.rs
new file mode 100644
index 000000000..f19738dbe
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/gamma.rs
@@ -0,0 +1,373 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! The Gamma and derived distributions.
+#![allow(deprecated)]
+
+use self::ChiSquaredRepr::*;
+use self::GammaRepr::*;
+
+use crate::distributions::normal::StandardNormal;
+use crate::distributions::{Distribution, Exp, Open01};
+use crate::Rng;
+
+/// The Gamma distribution `Gamma(shape, scale)` distribution.
+///
+/// The density function of this distribution is
+///
+/// ```text
+/// f(x) =  x^(k - 1) * exp(-x / θ) / (Γ(k) * θ^k)
+/// ```
+///
+/// where `Γ` is the Gamma function, `k` is the shape and `θ` is the
+/// scale and both `k` and `θ` are strictly positive.
+///
+/// The algorithm used is that described by Marsaglia & Tsang 2000[^1],
+/// falling back to directly sampling from an Exponential for `shape
+/// == 1`, and using the boosting technique described in that paper for
+/// `shape < 1`.
+///
+/// [^1]: George Marsaglia and Wai Wan Tsang. 2000. "A Simple Method for
+///       Generating Gamma Variables" *ACM Trans. Math. Softw.* 26, 3
+///       (September 2000), 363-372.
+///       DOI:[10.1145/358407.358414](https://doi.acm.org/10.1145/358407.358414)
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Gamma {
+    repr: GammaRepr,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum GammaRepr {
+    Large(GammaLargeShape),
+    One(Exp),
+    Small(GammaSmallShape),
+}
+
+// These two helpers could be made public, but saving the
+// match-on-Gamma-enum branch from using them directly (e.g. if one
+// knows that the shape is always > 1) doesn't appear to be much
+// faster.
+
+/// Gamma distribution where the shape parameter is less than 1.
+///
+/// Note, samples from this require a compulsory floating-point `pow`
+/// call, which makes it significantly slower than sampling from a
+/// gamma distribution where the shape parameter is greater than or
+/// equal to 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaSmallShape {
+    inv_shape: f64,
+    large_shape: GammaLargeShape,
+}
+
+/// Gamma distribution where the shape parameter is larger than 1.
+///
+/// See `Gamma` for sampling from a Gamma distribution with general
+/// shape parameters.
+#[derive(Clone, Copy, Debug)]
+struct GammaLargeShape {
+    scale: f64,
+    c: f64,
+    d: f64,
+}
+
+impl Gamma {
+    /// Construct an object representing the `Gamma(shape, scale)`
+    /// distribution.
+    ///
+    /// Panics if `shape <= 0` or `scale <= 0`.
+    #[inline]
+    pub fn new(shape: f64, scale: f64) -> Gamma {
+        assert!(shape > 0.0, "Gamma::new called with shape <= 0");
+        assert!(scale > 0.0, "Gamma::new called with scale <= 0");
+
+        let repr = if shape == 1.0 {
+            One(Exp::new(1.0 / scale))
+        } else if shape < 1.0 {
+            Small(GammaSmallShape::new_raw(shape, scale))
+        } else {
+            Large(GammaLargeShape::new_raw(shape, scale))
+        };
+        Gamma { repr }
+    }
+}
+
+impl GammaSmallShape {
+    fn new_raw(shape: f64, scale: f64) -> GammaSmallShape {
+        GammaSmallShape {
+            inv_shape: 1. / shape,
+            large_shape: GammaLargeShape::new_raw(shape + 1.0, scale),
+        }
+    }
+}
+
+impl GammaLargeShape {
+    fn new_raw(shape: f64, scale: f64) -> GammaLargeShape {
+        let d = shape - 1. / 3.;
+        GammaLargeShape {
+            scale,
+            c: 1. / (9. * d).sqrt(),
+            d,
+        }
+    }
+}
+
+impl Distribution<f64> for Gamma {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        match self.repr {
+            Small(ref g) => g.sample(rng),
+            One(ref g) => g.sample(rng),
+            Large(ref g) => g.sample(rng),
+        }
+    }
+}
+impl Distribution<f64> for GammaSmallShape {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let u: f64 = rng.sample(Open01);
+
+        self.large_shape.sample(rng) * u.powf(self.inv_shape)
+    }
+}
+impl Distribution<f64> for GammaLargeShape {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        loop {
+            let x = rng.sample(StandardNormal);
+            let v_cbrt = 1.0 + self.c * x;
+            if v_cbrt <= 0.0 {
+                // a^3 <= 0 iff a <= 0
+                continue;
+            }
+
+            let v = v_cbrt * v_cbrt * v_cbrt;
+            let u: f64 = rng.sample(Open01);
+
+            let x_sqr = x * x;
+            if u < 1.0 - 0.0331 * x_sqr * x_sqr
+                || u.ln() < 0.5 * x_sqr + self.d * (1.0 - v + v.ln())
+            {
+                return self.d * v * self.scale;
+            }
+        }
+    }
+}
+
+/// The chi-squared distribution `χ²(k)`, where `k` is the degrees of
+/// freedom.
+///
+/// For `k > 0` integral, this distribution is the sum of the squares
+/// of `k` independent standard normal random variables. For other
+/// `k`, this uses the equivalent characterisation
+/// `χ²(k) = Gamma(k/2, 2)`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct ChiSquared {
+    repr: ChiSquaredRepr,
+}
+
+#[derive(Clone, Copy, Debug)]
+enum ChiSquaredRepr {
+    // k == 1, Gamma(alpha, ..) is particularly slow for alpha < 1,
+    // e.g. when alpha = 1/2 as it would be for this case, so special-
+    // casing and using the definition of N(0,1)^2 is faster.
+    DoFExactlyOne,
+    DoFAnythingElse(Gamma),
+}
+
+impl ChiSquared {
+    /// Create a new chi-squared distribution with degrees-of-freedom
+    /// `k`. Panics if `k < 0`.
+    pub fn new(k: f64) -> ChiSquared {
+        let repr = if k == 1.0 {
+            DoFExactlyOne
+        } else {
+            assert!(k > 0.0, "ChiSquared::new called with `k` < 0");
+            DoFAnythingElse(Gamma::new(0.5 * k, 2.0))
+        };
+        ChiSquared { repr }
+    }
+}
+impl Distribution<f64> for ChiSquared {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        match self.repr {
+            DoFExactlyOne => {
+                // k == 1 => N(0,1)^2
+                let norm = rng.sample(StandardNormal);
+                norm * norm
+            }
+            DoFAnythingElse(ref g) => g.sample(rng),
+        }
+    }
+}
+
+/// The Fisher F distribution `F(m, n)`.
+///
+/// This distribution is equivalent to the ratio of two normalised
+/// chi-squared distributions, that is, `F(m,n) = (χ²(m)/m) /
+/// (χ²(n)/n)`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct FisherF {
+    numer: ChiSquared,
+    denom: ChiSquared,
+    // denom_dof / numer_dof so that this can just be a straight
+    // multiplication, rather than a division.
+    dof_ratio: f64,
+}
+
+impl FisherF {
+    /// Create a new `FisherF` distribution, with the given
+    /// parameter. Panics if either `m` or `n` are not positive.
+    pub fn new(m: f64, n: f64) -> FisherF {
+        assert!(m > 0.0, "FisherF::new called with `m < 0`");
+        assert!(n > 0.0, "FisherF::new called with `n < 0`");
+
+        FisherF {
+            numer: ChiSquared::new(m),
+            denom: ChiSquared::new(n),
+            dof_ratio: n / m,
+        }
+    }
+}
+impl Distribution<f64> for FisherF {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        self.numer.sample(rng) / self.denom.sample(rng) * self.dof_ratio
+    }
+}
+
+/// The Student t distribution, `t(nu)`, where `nu` is the degrees of
+/// freedom.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct StudentT {
+    chi: ChiSquared,
+    dof: f64,
+}
+
+impl StudentT {
+    /// Create a new Student t distribution with `n` degrees of
+    /// freedom. Panics if `n <= 0`.
+    pub fn new(n: f64) -> StudentT {
+        assert!(n > 0.0, "StudentT::new called with `n <= 0`");
+        StudentT {
+            chi: ChiSquared::new(n),
+            dof: n,
+        }
+    }
+}
+impl Distribution<f64> for StudentT {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let norm = rng.sample(StandardNormal);
+        norm * (self.dof / self.chi.sample(rng)).sqrt()
+    }
+}
+
+/// The Beta distribution with shape parameters `alpha` and `beta`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Beta {
+    gamma_a: Gamma,
+    gamma_b: Gamma,
+}
+
+impl Beta {
+    /// Construct an object representing the `Beta(alpha, beta)`
+    /// distribution.
+    ///
+    /// Panics if `shape <= 0` or `scale <= 0`.
+    pub fn new(alpha: f64, beta: f64) -> Beta {
+        assert!((alpha > 0.) & (beta > 0.));
+        Beta {
+            gamma_a: Gamma::new(alpha, 1.),
+            gamma_b: Gamma::new(beta, 1.),
+        }
+    }
+}
+
+impl Distribution<f64> for Beta {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let x = self.gamma_a.sample(rng);
+        let y = self.gamma_b.sample(rng);
+        x / (x + y)
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::{Beta, ChiSquared, FisherF, StudentT};
+    use crate::distributions::Distribution;
+
+    const N: u32 = 100;
+
+    #[test]
+    fn test_chi_squared_one() {
+        let chi = ChiSquared::new(1.0);
+        let mut rng = crate::test::rng(201);
+        for _ in 0..N {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    fn test_chi_squared_small() {
+        let chi = ChiSquared::new(0.5);
+        let mut rng = crate::test::rng(202);
+        for _ in 0..N {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    fn test_chi_squared_large() {
+        let chi = ChiSquared::new(30.0);
+        let mut rng = crate::test::rng(203);
+        for _ in 0..N {
+            chi.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_chi_squared_invalid_dof() {
+        ChiSquared::new(-1.0);
+    }
+
+    #[test]
+    fn test_f() {
+        let f = FisherF::new(2.0, 32.0);
+        let mut rng = crate::test::rng(204);
+        for _ in 0..N {
+            f.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    fn test_t() {
+        let t = StudentT::new(11.0);
+        let mut rng = crate::test::rng(205);
+        for _ in 0..N {
+            t.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    fn test_beta() {
+        let beta = Beta::new(1.0, 2.0);
+        let mut rng = crate::test::rng(201);
+        for _ in 0..N {
+            beta.sample(&mut rng);
+        }
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_beta_invalid_dof() {
+        Beta::new(0., 0.);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/integer.rs b/vendor/rand-0.7.3/src/distributions/integer.rs
new file mode 100644
index 000000000..f2db1f1c6
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/integer.rs
@@ -0,0 +1,279 @@
+// 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.
+
+//! The implementations of the `Standard` distribution for integer types.
+
+use crate::distributions::{Distribution, Standard};
+use crate::Rng;
+#[cfg(all(target_arch = "x86", feature = "nightly"))] use core::arch::x86::*;
+#[cfg(all(target_arch = "x86_64", feature = "nightly"))]
+use core::arch::x86_64::*;
+#[cfg(not(target_os = "emscripten"))] use core::num::NonZeroU128;
+use core::num::{NonZeroU16, NonZeroU32, NonZeroU64, NonZeroU8, NonZeroUsize};
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+impl Distribution<u8> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u8 {
+        rng.next_u32() as u8
+    }
+}
+
+impl Distribution<u16> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u16 {
+        rng.next_u32() as u16
+    }
+}
+
+impl Distribution<u32> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u32 {
+        rng.next_u32()
+    }
+}
+
+impl Distribution<u64> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        rng.next_u64()
+    }
+}
+
+#[cfg(not(target_os = "emscripten"))]
+impl Distribution<u128> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u128 {
+        // Use LE; we explicitly generate one value before the next.
+        let x = u128::from(rng.next_u64());
+        let y = u128::from(rng.next_u64());
+        (y << 64) | x
+    }
+}
+
+impl Distribution<usize> for Standard {
+    #[inline]
+    #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        rng.next_u32() as usize
+    }
+
+    #[inline]
+    #[cfg(target_pointer_width = "64")]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        rng.next_u64() as usize
+    }
+}
+
+macro_rules! impl_int_from_uint {
+    ($ty:ty, $uty:ty) => {
+        impl Distribution<$ty> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                rng.gen::<$uty>() as $ty
+            }
+        }
+    };
+}
+
+impl_int_from_uint! { i8, u8 }
+impl_int_from_uint! { i16, u16 }
+impl_int_from_uint! { i32, u32 }
+impl_int_from_uint! { i64, u64 }
+#[cfg(not(target_os = "emscripten"))]
+impl_int_from_uint! { i128, u128 }
+impl_int_from_uint! { isize, usize }
+
+macro_rules! impl_nzint {
+    ($ty:ty, $new:path) => {
+        impl Distribution<$ty> for Standard {
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                loop {
+                    if let Some(nz) = $new(rng.gen()) {
+                        break nz;
+                    }
+                }
+            }
+        }
+    };
+}
+
+impl_nzint!(NonZeroU8, NonZeroU8::new);
+impl_nzint!(NonZeroU16, NonZeroU16::new);
+impl_nzint!(NonZeroU32, NonZeroU32::new);
+impl_nzint!(NonZeroU64, NonZeroU64::new);
+#[cfg(not(target_os = "emscripten"))]
+impl_nzint!(NonZeroU128, NonZeroU128::new);
+impl_nzint!(NonZeroUsize, NonZeroUsize::new);
+
+#[cfg(feature = "simd_support")]
+macro_rules! simd_impl {
+    ($(($intrinsic:ident, $vec:ty),)+) => {$(
+        impl Distribution<$intrinsic> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $intrinsic {
+                $intrinsic::from_bits(rng.gen::<$vec>())
+            }
+        }
+    )+};
+
+    ($bits:expr,) => {};
+    ($bits:expr, $ty:ty, $($ty_more:ty,)*) => {
+        simd_impl!($bits, $($ty_more,)*);
+
+        impl Distribution<$ty> for Standard {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
+                let mut vec: $ty = Default::default();
+                unsafe {
+                    let ptr = &mut vec;
+                    let b_ptr = &mut *(ptr as *mut $ty as *mut [u8; $bits/8]);
+                    rng.fill_bytes(b_ptr);
+                }
+                vec.to_le()
+            }
+        }
+    };
+}
+
+#[cfg(feature = "simd_support")]
+simd_impl!(16, u8x2, i8x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(32, u8x4, i8x4, u16x2, i16x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(64, u8x8, i8x8, u16x4, i16x4, u32x2, i32x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(128, u8x16, i8x16, u16x8, i16x8, u32x4, i32x4, u64x2, i64x2,);
+#[cfg(feature = "simd_support")]
+simd_impl!(256, u8x32, i8x32, u16x16, i16x16, u32x8, i32x8, u64x4, i64x4,);
+#[cfg(feature = "simd_support")]
+simd_impl!(512, u8x64, i8x64, u16x32, i16x32, u32x16, i32x16, u64x8, i64x8,);
+#[cfg(all(
+    feature = "simd_support",
+    feature = "nightly",
+    any(target_arch = "x86", target_arch = "x86_64")
+))]
+simd_impl!((__m64, u8x8), (__m128i, u8x16), (__m256i, u8x32),);
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_integers() {
+        let mut rng = crate::test::rng(806);
+
+        rng.sample::<isize, _>(Standard);
+        rng.sample::<i8, _>(Standard);
+        rng.sample::<i16, _>(Standard);
+        rng.sample::<i32, _>(Standard);
+        rng.sample::<i64, _>(Standard);
+        #[cfg(not(target_os = "emscripten"))]
+        rng.sample::<i128, _>(Standard);
+
+        rng.sample::<usize, _>(Standard);
+        rng.sample::<u8, _>(Standard);
+        rng.sample::<u16, _>(Standard);
+        rng.sample::<u32, _>(Standard);
+        rng.sample::<u64, _>(Standard);
+        #[cfg(not(target_os = "emscripten"))]
+        rng.sample::<u128, _>(Standard);
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<T: Copy + core::fmt::Debug + PartialEq>(zero: T, expected: &[T])
+        where Standard: Distribution<T> {
+            let mut rng = crate::test::rng(807);
+            let mut buf = [zero; 3];
+            for x in &mut buf {
+                *x = rng.sample(Standard);
+            }
+            assert_eq!(&buf, expected);
+        }
+
+        test_samples(0u8, &[9, 247, 111]);
+        test_samples(0u16, &[32265, 42999, 38255]);
+        test_samples(0u32, &[2220326409, 2575017975, 2018088303]);
+        test_samples(0u64, &[
+            11059617991457472009,
+            16096616328739788143,
+            1487364411147516184,
+        ]);
+        test_samples(0u128, &[
+            296930161868957086625409848350820761097,
+            145644820879247630242265036535529306392,
+            111087889832015897993126088499035356354,
+        ]);
+        #[cfg(any(target_pointer_width = "32", target_pointer_width = "16"))]
+        test_samples(0usize, &[2220326409, 2575017975, 2018088303]);
+        #[cfg(target_pointer_width = "64")]
+        test_samples(0usize, &[
+            11059617991457472009,
+            16096616328739788143,
+            1487364411147516184,
+        ]);
+
+        test_samples(0i8, &[9, -9, 111]);
+        // Skip further i* types: they are simple reinterpretation of u* samples
+
+        #[cfg(feature = "simd_support")]
+        {
+            // We only test a sub-set of types here and make assumptions about the rest.
+
+            test_samples(u8x2::default(), &[
+                u8x2::new(9, 126),
+                u8x2::new(247, 167),
+                u8x2::new(111, 149),
+            ]);
+            test_samples(u8x4::default(), &[
+                u8x4::new(9, 126, 87, 132),
+                u8x4::new(247, 167, 123, 153),
+                u8x4::new(111, 149, 73, 120),
+            ]);
+            test_samples(u8x8::default(), &[
+                u8x8::new(9, 126, 87, 132, 247, 167, 123, 153),
+                u8x8::new(111, 149, 73, 120, 68, 171, 98, 223),
+                u8x8::new(24, 121, 1, 50, 13, 46, 164, 20),
+            ]);
+
+            test_samples(i64x8::default(), &[
+                i64x8::new(
+                    -7387126082252079607,
+                    -2350127744969763473,
+                    1487364411147516184,
+                    7895421560427121838,
+                    602190064936008898,
+                    6022086574635100741,
+                    -5080089175222015595,
+                    -4066367846667249123,
+                ),
+                i64x8::new(
+                    9180885022207963908,
+                    3095981199532211089,
+                    6586075293021332726,
+                    419343203796414657,
+                    3186951873057035255,
+                    5287129228749947252,
+                    444726432079249540,
+                    -1587028029513790706,
+                ),
+                i64x8::new(
+                    6075236523189346388,
+                    1351763722368165432,
+                    -6192309979959753740,
+                    -7697775502176768592,
+                    -4482022114172078123,
+                    7522501477800909500,
+                    -1837258847956201231,
+                    -586926753024886735,
+                ),
+            ]);
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/mod.rs b/vendor/rand-0.7.3/src/distributions/mod.rs
new file mode 100644
index 000000000..4e1b1a6e3
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/mod.rs
@@ -0,0 +1,406 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2017 The Rust Project Developers.
+//
+// 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.
+
+//! Generating random samples from probability distributions
+//!
+//! This module is the home of the [`Distribution`] trait and several of its
+//! implementations. It is the workhorse behind some of the convenient
+//! functionality of the [`Rng`] trait, e.g. [`Rng::gen`], [`Rng::gen_range`] and
+//! of course [`Rng::sample`].
+//!
+//! Abstractly, a [probability distribution] describes the probability of
+//! occurance of each value in its sample space.
+//!
+//! More concretely, an implementation of `Distribution<T>` for type `X` is an
+//! algorithm for choosing values from the sample space (a subset of `T`)
+//! according to the distribution `X` represents, using an external source of
+//! randomness (an RNG supplied to the `sample` function).
+//!
+//! A type `X` may implement `Distribution<T>` for multiple types `T`.
+//! Any type implementing [`Distribution`] is stateless (i.e. immutable),
+//! but it may have internal parameters set at construction time (for example,
+//! [`Uniform`] allows specification of its sample space as a range within `T`).
+//!
+//!
+//! # The `Standard` distribution
+//!
+//! The [`Standard`] distribution is important to mention. This is the
+//! distribution used by [`Rng::gen`] and represents the "default" way to
+//! produce a random value for many different types, including most primitive
+//! types, tuples, arrays, and a few derived types. See the documentation of
+//! [`Standard`] for more details.
+//!
+//! Implementing `Distribution<T>` for [`Standard`] for user types `T` makes it
+//! possible to generate type `T` with [`Rng::gen`], and by extension also
+//! with the [`random`] function.
+//!
+//! ## Random characters
+//!
+//! [`Alphanumeric`] is a simple distribution to sample random letters and
+//! numbers of the `char` type; in contrast [`Standard`] may sample any valid
+//! `char`.
+//!
+//!
+//! # Uniform numeric ranges
+//!
+//! The [`Uniform`] distribution is more flexible than [`Standard`], but also
+//! more specialised: it supports fewer target types, but allows the sample
+//! space to be specified as an arbitrary range within its target type `T`.
+//! Both [`Standard`] and [`Uniform`] are in some sense uniform distributions.
+//!
+//! Values may be sampled from this distribution using [`Rng::gen_range`] or
+//! by creating a distribution object with [`Uniform::new`],
+//! [`Uniform::new_inclusive`] or `From<Range>`. When the range limits are not
+//! known at compile time it is typically faster to reuse an existing
+//! distribution object than to call [`Rng::gen_range`].
+//!
+//! User types `T` may also implement `Distribution<T>` for [`Uniform`],
+//! although this is less straightforward than for [`Standard`] (see the
+//! documentation in the [`uniform`] module. Doing so enables generation of
+//! values of type `T` with  [`Rng::gen_range`].
+//!
+//! ## Open and half-open ranges
+//!
+//! There are surprisingly many ways to uniformly generate random floats. A
+//! range between 0 and 1 is standard, but the exact bounds (open vs closed)
+//! and accuracy differ. In addition to the [`Standard`] distribution Rand offers
+//! [`Open01`] and [`OpenClosed01`]. See "Floating point implementation" section of
+//! [`Standard`] documentation for more details.
+//!
+//! # Non-uniform sampling
+//!
+//! Sampling a simple true/false outcome with a given probability has a name:
+//! the [`Bernoulli`] distribution (this is used by [`Rng::gen_bool`]).
+//!
+//! For weighted sampling from a sequence of discrete values, use the
+//! [`weighted`] module.
+//!
+//! This crate no longer includes other non-uniform distributions; instead
+//! it is recommended that you use either [`rand_distr`] or [`statrs`].
+//!
+//!
+//! [probability distribution]: https://en.wikipedia.org/wiki/Probability_distribution
+//! [`rand_distr`]: https://crates.io/crates/rand_distr
+//! [`statrs`]: https://crates.io/crates/statrs
+
+//! [`random`]: crate::random
+//! [`rand_distr`]: https://crates.io/crates/rand_distr
+//! [`statrs`]: https://crates.io/crates/statrs
+
+use crate::Rng;
+use core::iter;
+
+pub use self::bernoulli::{Bernoulli, BernoulliError};
+pub use self::float::{Open01, OpenClosed01};
+pub use self::other::Alphanumeric;
+#[doc(inline)] pub use self::uniform::Uniform;
+#[cfg(feature = "alloc")]
+pub use self::weighted::{WeightedError, WeightedIndex};
+
+// The following are all deprecated after being moved to rand_distr
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::binomial::Binomial;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::cauchy::Cauchy;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::dirichlet::Dirichlet;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::exponential::{Exp, Exp1};
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::gamma::{Beta, ChiSquared, FisherF, Gamma, StudentT};
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::normal::{LogNormal, Normal, StandardNormal};
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::pareto::Pareto;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::poisson::Poisson;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::triangular::Triangular;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::unit_circle::UnitCircle;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::unit_sphere::UnitSphereSurface;
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::weibull::Weibull;
+
+mod bernoulli;
+#[cfg(feature = "std")] mod binomial;
+#[cfg(feature = "std")] mod cauchy;
+#[cfg(feature = "std")] mod dirichlet;
+#[cfg(feature = "std")] mod exponential;
+#[cfg(feature = "std")] mod gamma;
+#[cfg(feature = "std")] mod normal;
+#[cfg(feature = "std")] mod pareto;
+#[cfg(feature = "std")] mod poisson;
+#[cfg(feature = "std")] mod triangular;
+pub mod uniform;
+#[cfg(feature = "std")] mod unit_circle;
+#[cfg(feature = "std")] mod unit_sphere;
+#[cfg(feature = "std")] mod weibull;
+#[cfg(feature = "alloc")] pub mod weighted;
+
+mod float;
+#[doc(hidden)]
+pub mod hidden_export {
+    pub use super::float::IntoFloat; // used by rand_distr
+}
+mod integer;
+mod other;
+mod utils;
+#[cfg(feature = "std")] mod ziggurat_tables;
+
+/// Types (distributions) that can be used to create a random instance of `T`.
+///
+/// It is possible to sample from a distribution through both the
+/// `Distribution` and [`Rng`] traits, via `distr.sample(&mut rng)` and
+/// `rng.sample(distr)`. They also both offer the [`sample_iter`] method, which
+/// produces an iterator that samples from the distribution.
+///
+/// All implementations are expected to be immutable; this has the significant
+/// advantage of not needing to consider thread safety, and for most
+/// distributions efficient state-less sampling algorithms are available.
+///
+/// Implementations are typically expected to be portable with reproducible
+/// results when used with a PRNG with fixed seed; see the
+/// [portability chapter](https://rust-random.github.io/book/portability.html)
+/// of The Rust Rand Book. In some cases this does not apply, e.g. the `usize`
+/// type requires different sampling on 32-bit and 64-bit machines.
+///
+/// [`sample_iter`]: Distribution::method.sample_iter
+pub trait Distribution<T> {
+    /// Generate a random value of `T`, using `rng` as the source of randomness.
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;
+
+    /// Create an iterator that generates random values of `T`, using `rng` as
+    /// the source of randomness.
+    ///
+    /// Note that this function takes `self` by value. This works since
+    /// `Distribution<T>` is impl'd for `&D` where `D: Distribution<T>`,
+    /// however borrowing is not automatic hence `distr.sample_iter(...)` may
+    /// need to be replaced with `(&distr).sample_iter(...)` to borrow or
+    /// `(&*distr).sample_iter(...)` to reborrow an existing reference.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::thread_rng;
+    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
+    ///
+    /// let rng = thread_rng();
+    ///
+    /// // Vec of 16 x f32:
+    /// let v: Vec<f32> = Standard.sample_iter(rng).take(16).collect();
+    ///
+    /// // String:
+    /// let s: String = Alphanumeric.sample_iter(rng).take(7).collect();
+    ///
+    /// // Dice-rolling:
+    /// let die_range = Uniform::new_inclusive(1, 6);
+    /// let mut roll_die = die_range.sample_iter(rng);
+    /// while roll_die.next().unwrap() != 6 {
+    ///     println!("Not a 6; rolling again!");
+    /// }
+    /// ```
+    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
+    where
+        R: Rng,
+        Self: Sized,
+    {
+        DistIter {
+            distr: self,
+            rng,
+            phantom: ::core::marker::PhantomData,
+        }
+    }
+}
+
+impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
+        (*self).sample(rng)
+    }
+}
+
+
+/// An iterator that generates random values of `T` with distribution `D`,
+/// using `R` as the source of randomness.
+///
+/// This `struct` is created by the [`sample_iter`] method on [`Distribution`].
+/// See its documentation for more.
+///
+/// [`sample_iter`]: Distribution::sample_iter
+#[derive(Debug)]
+pub struct DistIter<D, R, T> {
+    distr: D,
+    rng: R,
+    phantom: ::core::marker::PhantomData<T>,
+}
+
+impl<D, R, T> Iterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+    type Item = T;
+
+    #[inline(always)]
+    fn next(&mut self) -> Option<T> {
+        // Here, self.rng may be a reference, but we must take &mut anyway.
+        // Even if sample could take an R: Rng by value, we would need to do this
+        // since Rng is not copyable and we cannot enforce that this is "reborrowable".
+        Some(self.distr.sample(&mut self.rng))
+    }
+
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        (usize::max_value(), None)
+    }
+}
+
+impl<D, R, T> iter::FusedIterator for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+#[cfg(features = "nightly")]
+impl<D, R, T> iter::TrustedLen for DistIter<D, R, T>
+where
+    D: Distribution<T>,
+    R: Rng,
+{
+}
+
+
+/// A generic random value distribution, implemented for many primitive types.
+/// Usually generates values with a numerically uniform distribution, and with a
+/// range appropriate to the type.
+///
+/// ## Provided implementations
+///
+/// Assuming the provided `Rng` is well-behaved, these implementations
+/// generate values with the following ranges and distributions:
+///
+/// * Integers (`i32`, `u32`, `isize`, `usize`, etc.): Uniformly distributed
+///   over all values of the type.
+/// * `char`: Uniformly distributed over all Unicode scalar values, i.e. all
+///   code points in the range `0...0x10_FFFF`, except for the range
+///   `0xD800...0xDFFF` (the surrogate code points). This includes
+///   unassigned/reserved code points.
+/// * `bool`: Generates `false` or `true`, each with probability 0.5.
+/// * Floating point types (`f32` and `f64`): Uniformly distributed in the
+///   half-open range `[0, 1)`. See notes below.
+/// * Wrapping integers (`Wrapping<T>`), besides the type identical to their
+///   normal integer variants.
+///
+/// The `Standard` distribution also supports generation of the following
+/// compound types where all component types are supported:
+///
+/// *   Tuples (up to 12 elements): each element is generated sequentially.
+/// *   Arrays (up to 32 elements): each element is generated sequentially;
+///     see also [`Rng::fill`] which supports arbitrary array length for integer
+///     types and tends to be faster for `u32` and smaller types.
+/// *   `Option<T>` first generates a `bool`, and if true generates and returns
+///     `Some(value)` where `value: T`, otherwise returning `None`.
+///
+/// ## Custom implementations
+///
+/// The [`Standard`] distribution may be implemented for user types as follows:
+///
+/// ```
+/// # #![allow(dead_code)]
+/// use rand::Rng;
+/// use rand::distributions::{Distribution, Standard};
+///
+/// struct MyF32 {
+///     x: f32,
+/// }
+///
+/// impl Distribution<MyF32> for Standard {
+///     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> MyF32 {
+///         MyF32 { x: rng.gen() }
+///     }
+/// }
+/// ```
+///
+/// ## Example usage
+/// ```
+/// use rand::prelude::*;
+/// use rand::distributions::Standard;
+///
+/// let val: f32 = StdRng::from_entropy().sample(Standard);
+/// println!("f32 from [0, 1): {}", val);
+/// ```
+///
+/// # Floating point implementation
+/// The floating point implementations for `Standard` generate a random value in
+/// the half-open interval `[0, 1)`, i.e. including 0 but not 1.
+///
+/// All values that can be generated are of the form `n * ε/2`. For `f32`
+/// the 24 most significant random bits of a `u32` are used and for `f64` the
+/// 53 most significant bits of a `u64` are used. The conversion uses the
+/// multiplicative method: `(rng.gen::<$uty>() >> N) as $ty * (ε/2)`.
+///
+/// See also: [`Open01`] which samples from `(0, 1)`, [`OpenClosed01`] which
+/// samples from `(0, 1]` and `Rng::gen_range(0, 1)` which also samples from
+/// `[0, 1)`. Note that `Open01` and `gen_range` (which uses [`Uniform`]) use
+/// transmute-based methods which yield 1 bit less precision but may perform
+/// faster on some architectures (on modern Intel CPUs all methods have
+/// approximately equal performance).
+///
+/// [`Uniform`]: uniform::Uniform
+#[derive(Clone, Copy, Debug)]
+pub struct Standard;
+
+
+#[cfg(all(test, feature = "std"))]
+mod tests {
+    use super::{Distribution, Uniform};
+    use crate::Rng;
+
+    #[test]
+    fn test_distributions_iter() {
+        use crate::distributions::Open01;
+        let mut rng = crate::test::rng(210);
+        let distr = Open01;
+        let results: Vec<f32> = distr.sample_iter(&mut rng).take(100).collect();
+        println!("{:?}", results);
+    }
+
+    #[test]
+    fn test_make_an_iter() {
+        fn ten_dice_rolls_other_than_five<'a, R: Rng>(
+            rng: &'a mut R,
+        ) -> impl Iterator<Item = i32> + 'a {
+            Uniform::new_inclusive(1, 6)
+                .sample_iter(rng)
+                .filter(|x| *x != 5)
+                .take(10)
+        }
+
+        let mut rng = crate::test::rng(211);
+        let mut count = 0;
+        for val in ten_dice_rolls_other_than_five(&mut rng) {
+            assert!(val >= 1 && val <= 6 && val != 5);
+            count += 1;
+        }
+        assert_eq!(count, 10);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/normal.rs b/vendor/rand-0.7.3/src/distributions/normal.rs
new file mode 100644
index 000000000..ec62fa9ab
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/normal.rs
@@ -0,0 +1,177 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! The normal and derived distributions.
+#![allow(deprecated)]
+
+use crate::distributions::utils::ziggurat;
+use crate::distributions::{ziggurat_tables, Distribution, Open01};
+use crate::Rng;
+
+/// Samples floating-point numbers according to the normal distribution
+/// `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to
+/// `Normal::new(0.0, 1.0)` but faster.
+///
+/// See `Normal` for the general normal distribution.
+///
+/// Implemented via the ZIGNOR variant[^1] of the Ziggurat method.
+///
+/// [^1]: Jurgen A. Doornik (2005). [*An Improved Ziggurat Method to
+///       Generate Normal Random Samples*](
+///       https://www.doornik.com/research/ziggurat.pdf).
+///       Nuffield College, Oxford
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct StandardNormal;
+
+impl Distribution<f64> for StandardNormal {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        #[inline]
+        fn pdf(x: f64) -> f64 {
+            (-x * x / 2.0).exp()
+        }
+        #[inline]
+        fn zero_case<R: Rng + ?Sized>(rng: &mut R, u: f64) -> f64 {
+            // compute a random number in the tail by hand
+
+            // strange initial conditions, because the loop is not
+            // do-while, so the condition should be true on the first
+            // run, they get overwritten anyway (0 < 1, so these are
+            // good).
+            let mut x = 1.0f64;
+            let mut y = 0.0f64;
+
+            while -2.0 * y < x * x {
+                let x_: f64 = rng.sample(Open01);
+                let y_: f64 = rng.sample(Open01);
+
+                x = x_.ln() / ziggurat_tables::ZIG_NORM_R;
+                y = y_.ln();
+            }
+
+            if u < 0.0 {
+                x - ziggurat_tables::ZIG_NORM_R
+            } else {
+                ziggurat_tables::ZIG_NORM_R - x
+            }
+        }
+
+        ziggurat(
+            rng,
+            true, // this is symmetric
+            &ziggurat_tables::ZIG_NORM_X,
+            &ziggurat_tables::ZIG_NORM_F,
+            pdf,
+            zero_case,
+        )
+    }
+}
+
+/// The normal distribution `N(mean, std_dev**2)`.
+///
+/// This uses the ZIGNOR variant of the Ziggurat method, see [`StandardNormal`]
+/// for more details.
+///
+/// Note that [`StandardNormal`] is an optimised implementation for mean 0, and
+/// standard deviation 1.
+///
+/// [`StandardNormal`]: crate::distributions::StandardNormal
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Normal {
+    mean: f64,
+    std_dev: f64,
+}
+
+impl Normal {
+    /// Construct a new `Normal` distribution with the given mean and
+    /// standard deviation.
+    ///
+    /// # Panics
+    ///
+    /// Panics if `std_dev < 0`.
+    #[inline]
+    pub fn new(mean: f64, std_dev: f64) -> Normal {
+        assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0");
+        Normal { mean, std_dev }
+    }
+}
+impl Distribution<f64> for Normal {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let n = rng.sample(StandardNormal);
+        self.mean + self.std_dev * n
+    }
+}
+
+
+/// The log-normal distribution `ln N(mean, std_dev**2)`.
+///
+/// If `X` is log-normal distributed, then `ln(X)` is `N(mean, std_dev**2)`
+/// distributed.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct LogNormal {
+    norm: Normal,
+}
+
+impl LogNormal {
+    /// Construct a new `LogNormal` distribution with the given mean
+    /// and standard deviation.
+    ///
+    /// # Panics
+    ///
+    /// Panics if `std_dev < 0`.
+    #[inline]
+    pub fn new(mean: f64, std_dev: f64) -> LogNormal {
+        assert!(std_dev >= 0.0, "LogNormal::new called with `std_dev` < 0");
+        LogNormal {
+            norm: Normal::new(mean, std_dev),
+        }
+    }
+}
+impl Distribution<f64> for LogNormal {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        self.norm.sample(rng).exp()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::{LogNormal, Normal};
+    use crate::distributions::Distribution;
+
+    #[test]
+    fn test_normal() {
+        let norm = Normal::new(10.0, 10.0);
+        let mut rng = crate::test::rng(210);
+        for _ in 0..1000 {
+            norm.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_normal_invalid_sd() {
+        Normal::new(10.0, -1.0);
+    }
+
+
+    #[test]
+    fn test_log_normal() {
+        let lnorm = LogNormal::new(10.0, 10.0);
+        let mut rng = crate::test::rng(211);
+        for _ in 0..1000 {
+            lnorm.sample(&mut rng);
+        }
+    }
+    #[test]
+    #[should_panic]
+    fn test_log_normal_invalid_sd() {
+        LogNormal::new(10.0, -1.0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/other.rs b/vendor/rand-0.7.3/src/distributions/other.rs
new file mode 100644
index 000000000..c95060e51
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/other.rs
@@ -0,0 +1,291 @@
+// 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.
+
+//! The implementations of the `Standard` distribution for other built-in types.
+
+use core::char;
+use core::num::Wrapping;
+
+use crate::distributions::{Distribution, Standard, Uniform};
+use crate::Rng;
+
+// ----- Sampling distributions -----
+
+/// Sample a `char`, uniformly distributed over ASCII letters and numbers:
+/// a-z, A-Z and 0-9.
+///
+/// # Example
+///
+/// ```
+/// use std::iter;
+/// use rand::{Rng, thread_rng};
+/// use rand::distributions::Alphanumeric;
+///
+/// let mut rng = thread_rng();
+/// let chars: String = iter::repeat(())
+///         .map(|()| rng.sample(Alphanumeric))
+///         .take(7)
+///         .collect();
+/// println!("Random chars: {}", chars);
+/// ```
+#[derive(Debug)]
+pub struct Alphanumeric;
+
+
+// ----- Implementations of distributions -----
+
+impl Distribution<char> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char {
+        // A valid `char` is either in the interval `[0, 0xD800)` or
+        // `(0xDFFF, 0x11_0000)`. All `char`s must therefore be in
+        // `[0, 0x11_0000)` but not in the "gap" `[0xD800, 0xDFFF]` which is
+        // reserved for surrogates. This is the size of that gap.
+        const GAP_SIZE: u32 = 0xDFFF - 0xD800 + 1;
+
+        // Uniform::new(0, 0x11_0000 - GAP_SIZE) can also be used but it
+        // seemed slower.
+        let range = Uniform::new(GAP_SIZE, 0x11_0000);
+
+        let mut n = range.sample(rng);
+        if n <= 0xDFFF {
+            n -= GAP_SIZE;
+        }
+        unsafe { char::from_u32_unchecked(n) }
+    }
+}
+
+impl Distribution<char> for Alphanumeric {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> char {
+        const RANGE: u32 = 26 + 26 + 10;
+        const GEN_ASCII_STR_CHARSET: &[u8] = b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
+                abcdefghijklmnopqrstuvwxyz\
+                0123456789";
+        // We can pick from 62 characters. This is so close to a power of 2, 64,
+        // that we can do better than `Uniform`. Use a simple bitshift and
+        // rejection sampling. We do not use a bitmask, because for small RNGs
+        // the most significant bits are usually of higher quality.
+        loop {
+            let var = rng.next_u32() >> (32 - 6);
+            if var < RANGE {
+                return GEN_ASCII_STR_CHARSET[var as usize] as char;
+            }
+        }
+    }
+}
+
+impl Distribution<bool> for Standard {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> bool {
+        // We can compare against an arbitrary bit of an u32 to get a bool.
+        // Because the least significant bits of a lower quality RNG can have
+        // simple patterns, we compare against the most significant bit. This is
+        // easiest done using a sign test.
+        (rng.next_u32() as i32) < 0
+    }
+}
+
+macro_rules! tuple_impl {
+    // use variables to indicate the arity of the tuple
+    ($($tyvar:ident),* ) => {
+        // the trailing commas are for the 1 tuple
+        impl< $( $tyvar ),* >
+            Distribution<( $( $tyvar ),* , )>
+            for Standard
+            where $( Standard: Distribution<$tyvar> ),*
+        {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> ( $( $tyvar ),* , ) {
+                (
+                    // use the $tyvar's to get the appropriate number of
+                    // repeats (they're not actually needed)
+                    $(
+                        _rng.gen::<$tyvar>()
+                    ),*
+                    ,
+                )
+            }
+        }
+    }
+}
+
+impl Distribution<()> for Standard {
+    #[allow(clippy::unused_unit)]
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, _: &mut R) -> () {
+        ()
+    }
+}
+tuple_impl! {A}
+tuple_impl! {A, B}
+tuple_impl! {A, B, C}
+tuple_impl! {A, B, C, D}
+tuple_impl! {A, B, C, D, E}
+tuple_impl! {A, B, C, D, E, F}
+tuple_impl! {A, B, C, D, E, F, G}
+tuple_impl! {A, B, C, D, E, F, G, H}
+tuple_impl! {A, B, C, D, E, F, G, H, I}
+tuple_impl! {A, B, C, D, E, F, G, H, I, J}
+tuple_impl! {A, B, C, D, E, F, G, H, I, J, K}
+tuple_impl! {A, B, C, D, E, F, G, H, I, J, K, L}
+
+macro_rules! array_impl {
+    // recursive, given at least one type parameter:
+    {$n:expr, $t:ident, $($ts:ident,)*} => {
+        array_impl!{($n - 1), $($ts,)*}
+
+        impl<T> Distribution<[T; $n]> for Standard where Standard: Distribution<T> {
+            #[inline]
+            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] {
+                [_rng.gen::<$t>(), $(_rng.gen::<$ts>()),*]
+            }
+        }
+    };
+    // empty case:
+    {$n:expr,} => {
+        impl<T> Distribution<[T; $n]> for Standard {
+            fn sample<R: Rng + ?Sized>(&self, _rng: &mut R) -> [T; $n] { [] }
+        }
+    };
+}
+
+array_impl! {32, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T, T,}
+
+impl<T> Distribution<Option<T>> for Standard
+where Standard: Distribution<T>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Option<T> {
+        // UFCS is needed here: https://github.com/rust-lang/rust/issues/24066
+        if rng.gen::<bool>() {
+            Some(rng.gen())
+        } else {
+            None
+        }
+    }
+}
+
+impl<T> Distribution<Wrapping<T>> for Standard
+where Standard: Distribution<T>
+{
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Wrapping<T> {
+        Wrapping(rng.gen())
+    }
+}
+
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::RngCore;
+    #[cfg(all(not(feature = "std"), feature = "alloc"))] use alloc::string::String;
+
+    #[test]
+    fn test_misc() {
+        let rng: &mut dyn RngCore = &mut crate::test::rng(820);
+
+        rng.sample::<char, _>(Standard);
+        rng.sample::<bool, _>(Standard);
+    }
+
+    #[cfg(feature = "alloc")]
+    #[test]
+    fn test_chars() {
+        use core::iter;
+        let mut rng = crate::test::rng(805);
+
+        // Test by generating a relatively large number of chars, so we also
+        // take the rejection sampling path.
+        let word: String = iter::repeat(())
+            .map(|()| rng.gen::<char>())
+            .take(1000)
+            .collect();
+        assert!(word.len() != 0);
+    }
+
+    #[test]
+    fn test_alphanumeric() {
+        let mut rng = crate::test::rng(806);
+
+        // Test by generating a relatively large number of chars, so we also
+        // take the rejection sampling path.
+        let mut incorrect = false;
+        for _ in 0..100 {
+            let c = rng.sample(Alphanumeric);
+            incorrect |= !((c >= '0' && c <= '9') ||
+                           (c >= 'A' && c <= 'Z') ||
+                           (c >= 'a' && c <= 'z') );
+        }
+        assert!(incorrect == false);
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<T: Copy + core::fmt::Debug + PartialEq, D: Distribution<T>>(
+            distr: &D, zero: T, expected: &[T],
+        ) {
+            let mut rng = crate::test::rng(807);
+            let mut buf = [zero; 5];
+            for x in &mut buf {
+                *x = rng.sample(&distr);
+            }
+            assert_eq!(&buf, expected);
+        }
+
+        test_samples(&Standard, 'a', &[
+            '\u{8cdac}',
+            '\u{a346a}',
+            '\u{80120}',
+            '\u{ed692}',
+            '\u{35888}',
+        ]);
+        test_samples(&Alphanumeric, 'a', &['h', 'm', 'e', '3', 'M']);
+        test_samples(&Standard, false, &[true, true, false, true, false]);
+        test_samples(&Standard, None as Option<bool>, &[
+            Some(true),
+            None,
+            Some(false),
+            None,
+            Some(false),
+        ]);
+        test_samples(&Standard, Wrapping(0i32), &[
+            Wrapping(-2074640887),
+            Wrapping(-1719949321),
+            Wrapping(2018088303),
+            Wrapping(-547181756),
+            Wrapping(838957336),
+        ]);
+
+        // We test only sub-sets of tuple and array impls
+        test_samples(&Standard, (), &[(), (), (), (), ()]);
+        test_samples(&Standard, (false,), &[
+            (true,),
+            (true,),
+            (false,),
+            (true,),
+            (false,),
+        ]);
+        test_samples(&Standard, (false, false), &[
+            (true, true),
+            (false, true),
+            (false, false),
+            (true, false),
+            (false, false),
+        ]);
+
+        test_samples(&Standard, [0u8; 0], &[[], [], [], [], []]);
+        test_samples(&Standard, [0u8; 3], &[
+            [9, 247, 111],
+            [68, 24, 13],
+            [174, 19, 194],
+            [172, 69, 213],
+            [149, 207, 29],
+        ]);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/pareto.rs b/vendor/rand-0.7.3/src/distributions/pareto.rs
new file mode 100644
index 000000000..ac5473b8c
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/pareto.rs
@@ -0,0 +1,70 @@
+// 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.
+
+//! The Pareto distribution.
+#![allow(deprecated)]
+
+use crate::distributions::{Distribution, OpenClosed01};
+use crate::Rng;
+
+/// Samples floating-point numbers according to the Pareto distribution
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Pareto {
+    scale: f64,
+    inv_neg_shape: f64,
+}
+
+impl Pareto {
+    /// Construct a new Pareto distribution with given `scale` and `shape`.
+    ///
+    /// In the literature, `scale` is commonly written as x<sub>m</sub> or k and
+    /// `shape` is often written as α.
+    ///
+    /// # Panics
+    ///
+    /// `scale` and `shape` have to be non-zero and positive.
+    pub fn new(scale: f64, shape: f64) -> Pareto {
+        assert!((scale > 0.) & (shape > 0.));
+        Pareto {
+            scale,
+            inv_neg_shape: -1.0 / shape,
+        }
+    }
+}
+
+impl Distribution<f64> for Pareto {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let u: f64 = rng.sample(OpenClosed01);
+        self.scale * u.powf(self.inv_neg_shape)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::Pareto;
+    use crate::distributions::Distribution;
+
+    #[test]
+    #[should_panic]
+    fn invalid() {
+        Pareto::new(0., 0.);
+    }
+
+    #[test]
+    fn sample() {
+        let scale = 1.0;
+        let shape = 2.0;
+        let d = Pareto::new(scale, shape);
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let r = d.sample(&mut rng);
+            assert!(r >= scale);
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/poisson.rs b/vendor/rand-0.7.3/src/distributions/poisson.rs
new file mode 100644
index 000000000..ce94d7542
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/poisson.rs
@@ -0,0 +1,151 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2016-2017 The Rust Project Developers.
+//
+// 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.
+
+//! The Poisson distribution.
+#![allow(deprecated)]
+
+use crate::distributions::utils::log_gamma;
+use crate::distributions::{Cauchy, Distribution};
+use crate::Rng;
+
+/// The Poisson distribution `Poisson(lambda)`.
+///
+/// This distribution has a density function:
+/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Poisson {
+    lambda: f64,
+    // precalculated values
+    exp_lambda: f64,
+    log_lambda: f64,
+    sqrt_2lambda: f64,
+    magic_val: f64,
+}
+
+impl Poisson {
+    /// Construct a new `Poisson` with the given shape parameter
+    /// `lambda`. Panics if `lambda <= 0`.
+    pub fn new(lambda: f64) -> Poisson {
+        assert!(lambda > 0.0, "Poisson::new called with lambda <= 0");
+        let log_lambda = lambda.ln();
+        Poisson {
+            lambda,
+            exp_lambda: (-lambda).exp(),
+            log_lambda,
+            sqrt_2lambda: (2.0 * lambda).sqrt(),
+            magic_val: lambda * log_lambda - log_gamma(1.0 + lambda),
+        }
+    }
+}
+
+impl Distribution<u64> for Poisson {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        // using the algorithm from Numerical Recipes in C
+
+        // for low expected values use the Knuth method
+        if self.lambda < 12.0 {
+            let mut result = 0;
+            let mut p = 1.0;
+            while p > self.exp_lambda {
+                p *= rng.gen::<f64>();
+                result += 1;
+            }
+            result - 1
+        }
+        // high expected values - rejection method
+        else {
+            let mut int_result: u64;
+
+            // we use the Cauchy distribution as the comparison distribution
+            // f(x) ~ 1/(1+x^2)
+            let cauchy = Cauchy::new(0.0, 1.0);
+
+            loop {
+                let mut result;
+                let mut comp_dev;
+
+                loop {
+                    // draw from the Cauchy distribution
+                    comp_dev = rng.sample(cauchy);
+                    // shift the peak of the comparison ditribution
+                    result = self.sqrt_2lambda * comp_dev + self.lambda;
+                    // repeat the drawing until we are in the range of possible values
+                    if result >= 0.0 {
+                        break;
+                    }
+                }
+                // now the result is a random variable greater than 0 with Cauchy distribution
+                // the result should be an integer value
+                result = result.floor();
+                int_result = result as u64;
+
+                // this is the ratio of the Poisson distribution to the comparison distribution
+                // the magic value scales the distribution function to a range of approximately 0-1
+                // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1
+                // this doesn't change the resulting distribution, only increases the rate of failed drawings
+                let check = 0.9
+                    * (1.0 + comp_dev * comp_dev)
+                    * (result * self.log_lambda - log_gamma(1.0 + result) - self.magic_val).exp();
+
+                // check with uniform random value - if below the threshold, we are within the target distribution
+                if rng.gen::<f64>() <= check {
+                    break;
+                }
+            }
+            int_result
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Poisson;
+    use crate::distributions::Distribution;
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_poisson_10() {
+        let poisson = Poisson::new(10.0);
+        let mut rng = crate::test::rng(123);
+        let mut sum = 0;
+        for _ in 0..1000 {
+            sum += poisson.sample(&mut rng);
+        }
+        let avg = (sum as f64) / 1000.0;
+        println!("Poisson average: {}", avg);
+        assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    fn test_poisson_15() {
+        // Take the 'high expected values' path
+        let poisson = Poisson::new(15.0);
+        let mut rng = crate::test::rng(123);
+        let mut sum = 0;
+        for _ in 0..1000 {
+            sum += poisson.sample(&mut rng);
+        }
+        let avg = (sum as f64) / 1000.0;
+        println!("Poisson average: {}", avg);
+        assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_poisson_invalid_lambda_zero() {
+        Poisson::new(0.0);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_poisson_invalid_lambda_neg() {
+        Poisson::new(-10.0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/triangular.rs b/vendor/rand-0.7.3/src/distributions/triangular.rs
new file mode 100644
index 000000000..37be19867
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/triangular.rs
@@ -0,0 +1,83 @@
+// 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.
+
+//! The triangular distribution.
+#![allow(deprecated)]
+
+use crate::distributions::{Distribution, Standard};
+use crate::Rng;
+
+/// The triangular distribution.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Triangular {
+    min: f64,
+    max: f64,
+    mode: f64,
+}
+
+impl Triangular {
+    /// Construct a new `Triangular` with minimum `min`, maximum `max` and mode
+    /// `mode`.
+    ///
+    /// # Panics
+    ///
+    /// If `max < mode`, `mode < max` or `max == min`.
+    #[inline]
+    pub fn new(min: f64, max: f64, mode: f64) -> Triangular {
+        assert!(max >= mode);
+        assert!(mode >= min);
+        assert!(max != min);
+        Triangular { min, max, mode }
+    }
+}
+
+impl Distribution<f64> for Triangular {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let f: f64 = rng.sample(Standard);
+        let diff_mode_min = self.mode - self.min;
+        let diff_max_min = self.max - self.min;
+        if f * diff_max_min < diff_mode_min {
+            self.min + (f * diff_max_min * diff_mode_min).sqrt()
+        } else {
+            self.max - ((1. - f) * diff_max_min * (self.max - self.mode)).sqrt()
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::Triangular;
+    use crate::distributions::Distribution;
+
+    #[test]
+    fn test_new() {
+        for &(min, max, mode) in &[
+            (-1., 1., 0.),
+            (1., 2., 1.),
+            (5., 25., 25.),
+            (1e-5, 1e5, 1e-3),
+            (0., 1., 0.9),
+            (-4., -0.5, -2.),
+            (-13.039, 8.41, 1.17),
+        ] {
+            println!("{} {} {}", min, max, mode);
+            let _ = Triangular::new(min, max, mode);
+        }
+    }
+
+    #[test]
+    fn test_sample() {
+        let norm = Triangular::new(0., 1., 0.5);
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            norm.sample(&mut rng);
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/uniform.rs b/vendor/rand-0.7.3/src/distributions/uniform.rs
new file mode 100644
index 000000000..8584152f0
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/uniform.rs
@@ -0,0 +1,1380 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2017 The Rust Project Developers.
+//
+// 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.
+
+//! A distribution uniformly sampling numbers within a given range.
+//!
+//! [`Uniform`] is the standard distribution to sample uniformly from a range;
+//! e.g. `Uniform::new_inclusive(1, 6)` can sample integers from 1 to 6, like a
+//! standard die. [`Rng::gen_range`] supports any type supported by
+//! [`Uniform`].
+//!
+//! This distribution is provided with support for several primitive types
+//! (all integer and floating-point types) as well as [`std::time::Duration`],
+//! and supports extension to user-defined types via a type-specific *back-end*
+//! implementation.
+//!
+//! The types [`UniformInt`], [`UniformFloat`] and [`UniformDuration`] are the
+//! back-ends supporting sampling from primitive integer and floating-point
+//! ranges as well as from [`std::time::Duration`]; these types do not normally
+//! need to be used directly (unless implementing a derived back-end).
+//!
+//! # Example usage
+//!
+//! ```
+//! use rand::{Rng, thread_rng};
+//! use rand::distributions::Uniform;
+//!
+//! let mut rng = thread_rng();
+//! let side = Uniform::new(-10.0, 10.0);
+//!
+//! // sample between 1 and 10 points
+//! for _ in 0..rng.gen_range(1, 11) {
+//!     // sample a point from the square with sides -10 - 10 in two dimensions
+//!     let (x, y) = (rng.sample(side), rng.sample(side));
+//!     println!("Point: {}, {}", x, y);
+//! }
+//! ```
+//!
+//! # Extending `Uniform` to support a custom type
+//!
+//! To extend [`Uniform`] to support your own types, write a back-end which
+//! implements the [`UniformSampler`] trait, then implement the [`SampleUniform`]
+//! helper trait to "register" your back-end. See the `MyF32` example below.
+//!
+//! At a minimum, the back-end needs to store any parameters needed for sampling
+//! (e.g. the target range) and implement `new`, `new_inclusive` and `sample`.
+//! Those methods should include an assert to check the range is valid (i.e.
+//! `low < high`). The example below merely wraps another back-end.
+//!
+//! The `new`, `new_inclusive` and `sample_single` functions use arguments of
+//! type SampleBorrow<X> in order to support passing in values by reference or
+//! by value. In the implementation of these functions, you can choose to
+//! simply use the reference returned by [`SampleBorrow::borrow`], or you can choose
+//! to copy or clone the value, whatever is appropriate for your type.
+//!
+//! ```
+//! use rand::prelude::*;
+//! use rand::distributions::uniform::{Uniform, SampleUniform,
+//!         UniformSampler, UniformFloat, SampleBorrow};
+//!
+//! struct MyF32(f32);
+//!
+//! #[derive(Clone, Copy, Debug)]
+//! struct UniformMyF32(UniformFloat<f32>);
+//!
+//! impl UniformSampler for UniformMyF32 {
+//!     type X = MyF32;
+//!     fn new<B1, B2>(low: B1, high: B2) -> Self
+//!         where B1: SampleBorrow<Self::X> + Sized,
+//!               B2: SampleBorrow<Self::X> + Sized
+//!     {
+//!         UniformMyF32(UniformFloat::<f32>::new(low.borrow().0, high.borrow().0))
+//!     }
+//!     fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
+//!         where B1: SampleBorrow<Self::X> + Sized,
+//!               B2: SampleBorrow<Self::X> + Sized
+//!     {
+//!         UniformSampler::new(low, high)
+//!     }
+//!     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
+//!         MyF32(self.0.sample(rng))
+//!     }
+//! }
+//!
+//! impl SampleUniform for MyF32 {
+//!     type Sampler = UniformMyF32;
+//! }
+//!
+//! let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
+//! let uniform = Uniform::new(low, high);
+//! let x = uniform.sample(&mut thread_rng());
+//! ```
+//!
+//! [`SampleUniform`]: crate::distributions::uniform::SampleUniform
+//! [`UniformSampler`]: crate::distributions::uniform::UniformSampler
+//! [`UniformInt`]: crate::distributions::uniform::UniformInt
+//! [`UniformFloat`]: crate::distributions::uniform::UniformFloat
+//! [`UniformDuration`]: crate::distributions::uniform::UniformDuration
+//! [`SampleBorrow::borrow`]: crate::distributions::uniform::SampleBorrow::borrow
+
+#[cfg(not(feature = "std"))] use core::time::Duration;
+#[cfg(feature = "std")] use std::time::Duration;
+
+use crate::distributions::float::IntoFloat;
+use crate::distributions::utils::{BoolAsSIMD, FloatAsSIMD, FloatSIMDUtils, WideningMultiply};
+use crate::distributions::Distribution;
+use crate::Rng;
+
+#[cfg(not(feature = "std"))]
+#[allow(unused_imports)] // rustc doesn't detect that this is actually used
+use crate::distributions::utils::Float;
+
+
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+/// Sample values uniformly between two bounds.
+///
+/// [`Uniform::new`] and [`Uniform::new_inclusive`] construct a uniform
+/// distribution sampling from the given range; these functions may do extra
+/// work up front to make sampling of multiple values faster.
+///
+/// When sampling from a constant range, many calculations can happen at
+/// compile-time and all methods should be fast; for floating-point ranges and
+/// the full range of integer types this should have comparable performance to
+/// the `Standard` distribution.
+///
+/// Steps are taken to avoid bias which might be present in naive
+/// implementations; for example `rng.gen::<u8>() % 170` samples from the range
+/// `[0, 169]` but is twice as likely to select numbers less than 85 than other
+/// values. Further, the implementations here give more weight to the high-bits
+/// generated by the RNG than the low bits, since with some RNGs the low-bits
+/// are of lower quality than the high bits.
+///
+/// Implementations must sample in `[low, high)` range for
+/// `Uniform::new(low, high)`, i.e., excluding `high`. In particular care must
+/// be taken to ensure that rounding never results values `< low` or `>= high`.
+///
+/// # Example
+///
+/// ```
+/// use rand::distributions::{Distribution, Uniform};
+///
+/// fn main() {
+///     let between = Uniform::from(10..10000);
+///     let mut rng = rand::thread_rng();
+///     let mut sum = 0;
+///     for _ in 0..1000 {
+///         sum += between.sample(&mut rng);
+///     }
+///     println!("{}", sum);
+/// }
+/// ```
+///
+/// [`new`]: Uniform::new
+/// [`new_inclusive`]: Uniform::new_inclusive
+#[derive(Clone, Copy, Debug)]
+pub struct Uniform<X: SampleUniform>(X::Sampler);
+
+impl<X: SampleUniform> Uniform<X> {
+    /// Create a new `Uniform` instance which samples uniformly from the half
+    /// open range `[low, high)` (excluding `high`). Panics if `low >= high`.
+    pub fn new<B1, B2>(low: B1, high: B2) -> Uniform<X>
+    where
+        B1: SampleBorrow<X> + Sized,
+        B2: SampleBorrow<X> + Sized,
+    {
+        Uniform(X::Sampler::new(low, high))
+    }
+
+    /// Create a new `Uniform` instance which samples uniformly from the closed
+    /// range `[low, high]` (inclusive). Panics if `low > high`.
+    pub fn new_inclusive<B1, B2>(low: B1, high: B2) -> Uniform<X>
+    where
+        B1: SampleBorrow<X> + Sized,
+        B2: SampleBorrow<X> + Sized,
+    {
+        Uniform(X::Sampler::new_inclusive(low, high))
+    }
+}
+
+impl<X: SampleUniform> Distribution<X> for Uniform<X> {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X {
+        self.0.sample(rng)
+    }
+}
+
+/// Helper trait for creating objects using the correct implementation of
+/// [`UniformSampler`] for the sampling type.
+///
+/// See the [module documentation] on how to implement [`Uniform`] range
+/// sampling for a custom type.
+///
+/// [module documentation]: crate::distributions::uniform
+pub trait SampleUniform: Sized {
+    /// The `UniformSampler` implementation supporting type `X`.
+    type Sampler: UniformSampler<X = Self>;
+}
+
+/// Helper trait handling actual uniform sampling.
+///
+/// See the [module documentation] on how to implement [`Uniform`] range
+/// sampling for a custom type.
+///
+/// Implementation of [`sample_single`] is optional, and is only useful when
+/// the implementation can be faster than `Self::new(low, high).sample(rng)`.
+///
+/// [module documentation]: crate::distributions::uniform
+/// [`sample_single`]: UniformSampler::sample_single
+pub trait UniformSampler: Sized {
+    /// The type sampled by this implementation.
+    type X;
+
+    /// Construct self, with inclusive lower bound and exclusive upper bound
+    /// `[low, high)`.
+    ///
+    /// Usually users should not call this directly but instead use
+    /// `Uniform::new`, which asserts that `low < high` before calling this.
+    fn new<B1, B2>(low: B1, high: B2) -> Self
+    where
+        B1: SampleBorrow<Self::X> + Sized,
+        B2: SampleBorrow<Self::X> + Sized;
+
+    /// Construct self, with inclusive bounds `[low, high]`.
+    ///
+    /// Usually users should not call this directly but instead use
+    /// `Uniform::new_inclusive`, which asserts that `low <= high` before
+    /// calling this.
+    fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
+    where
+        B1: SampleBorrow<Self::X> + Sized,
+        B2: SampleBorrow<Self::X> + Sized;
+
+    /// Sample a value.
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X;
+
+    /// Sample a single value uniformly from a range with inclusive lower bound
+    /// and exclusive upper bound `[low, high)`.
+    ///
+    /// By default this is implemented using
+    /// `UniformSampler::new(low, high).sample(rng)`. However, for some types
+    /// more optimal implementations for single usage may be provided via this
+    /// method (which is the case for integers and floats).
+    /// Results may not be identical.
+    ///
+    /// Note that to use this method in a generic context, the type needs to be
+    /// retrieved via `SampleUniform::Sampler` as follows:
+    /// ```
+    /// use rand::{thread_rng, distributions::uniform::{SampleUniform, UniformSampler}};
+    /// # #[allow(unused)]
+    /// fn sample_from_range<T: SampleUniform>(lb: T, ub: T) -> T {
+    ///     let mut rng = thread_rng();
+    ///     <T as SampleUniform>::Sampler::sample_single(lb, ub, &mut rng)
+    /// }
+    /// ```
+    fn sample_single<R: Rng + ?Sized, B1, B2>(low: B1, high: B2, rng: &mut R) -> Self::X
+    where
+        B1: SampleBorrow<Self::X> + Sized,
+        B2: SampleBorrow<Self::X> + Sized,
+    {
+        let uniform: Self = UniformSampler::new(low, high);
+        uniform.sample(rng)
+    }
+}
+
+impl<X: SampleUniform> From<::core::ops::Range<X>> for Uniform<X> {
+    fn from(r: ::core::ops::Range<X>) -> Uniform<X> {
+        Uniform::new(r.start, r.end)
+    }
+}
+
+impl<X: SampleUniform> From<::core::ops::RangeInclusive<X>> for Uniform<X> {
+    fn from(r: ::core::ops::RangeInclusive<X>) -> Uniform<X> {
+        Uniform::new_inclusive(r.start(), r.end())
+    }
+}
+
+/// Helper trait similar to [`Borrow`] but implemented
+/// only for SampleUniform and references to SampleUniform in
+/// order to resolve ambiguity issues.
+///
+/// [`Borrow`]: std::borrow::Borrow
+pub trait SampleBorrow<Borrowed> {
+    /// Immutably borrows from an owned value. See [`Borrow::borrow`]
+    ///
+    /// [`Borrow::borrow`]: std::borrow::Borrow::borrow
+    fn borrow(&self) -> &Borrowed;
+}
+impl<Borrowed> SampleBorrow<Borrowed> for Borrowed
+where Borrowed: SampleUniform
+{
+    #[inline(always)]
+    fn borrow(&self) -> &Borrowed {
+        self
+    }
+}
+impl<'a, Borrowed> SampleBorrow<Borrowed> for &'a Borrowed
+where Borrowed: SampleUniform
+{
+    #[inline(always)]
+    fn borrow(&self) -> &Borrowed {
+        *self
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// What follows are all back-ends.
+
+
+/// The back-end implementing [`UniformSampler`] for integer types.
+///
+/// Unless you are implementing [`UniformSampler`] for your own type, this type
+/// should not be used directly, use [`Uniform`] instead.
+///
+/// # Implementation notes
+///
+/// For simplicity, we use the same generic struct `UniformInt<X>` for all
+/// integer types `X`. This gives us only one field type, `X`; to store unsigned
+/// values of this size, we take use the fact that these conversions are no-ops.
+///
+/// For a closed range, the number of possible numbers we should generate is
+/// `range = (high - low + 1)`. To avoid bias, we must ensure that the size of
+/// our sample space, `zone`, is a multiple of `range`; other values must be
+/// rejected (by replacing with a new random sample).
+///
+/// As a special case, we use `range = 0` to represent the full range of the
+/// result type (i.e. for `new_inclusive($ty::MIN, $ty::MAX)`).
+///
+/// The optimum `zone` is the largest product of `range` which fits in our
+/// (unsigned) target type. We calculate this by calculating how many numbers we
+/// must reject: `reject = (MAX + 1) % range = (MAX - range + 1) % range`. Any (large)
+/// product of `range` will suffice, thus in `sample_single` we multiply by a
+/// power of 2 via bit-shifting (faster but may cause more rejections).
+///
+/// The smallest integer PRNGs generate is `u32`. For 8- and 16-bit outputs we
+/// use `u32` for our `zone` and samples (because it's not slower and because
+/// it reduces the chance of having to reject a sample). In this case we cannot
+/// store `zone` in the target type since it is too large, however we know
+/// `ints_to_reject < range <= $unsigned::MAX`.
+///
+/// An alternative to using a modulus is widening multiply: After a widening
+/// multiply by `range`, the result is in the high word. Then comparing the low
+/// word against `zone` makes sure our distribution is uniform.
+#[derive(Clone, Copy, Debug)]
+pub struct UniformInt<X> {
+    low: X,
+    range: X,
+    z: X, // either ints_to_reject or zone depending on implementation
+}
+
+macro_rules! uniform_int_impl {
+    ($ty:ty, $unsigned:ident, $u_large:ident) => {
+        impl SampleUniform for $ty {
+            type Sampler = UniformInt<$ty>;
+        }
+
+        impl UniformSampler for UniformInt<$ty> {
+            // We play free and fast with unsigned vs signed here
+            // (when $ty is signed), but that's fine, since the
+            // contract of this macro is for $ty and $unsigned to be
+            // "bit-equal", so casting between them is a no-op.
+
+            type X = $ty;
+
+            #[inline] // if the range is constant, this helps LLVM to do the
+                      // calculations at compile-time.
+            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(low < high, "Uniform::new called with `low >= high`");
+                UniformSampler::new_inclusive(low, high - 1)
+            }
+
+            #[inline] // if the range is constant, this helps LLVM to do the
+                      // calculations at compile-time.
+            fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(
+                    low <= high,
+                    "Uniform::new_inclusive called with `low > high`"
+                );
+                let unsigned_max = ::core::$u_large::MAX;
+
+                let range = high.wrapping_sub(low).wrapping_add(1) as $unsigned;
+                let ints_to_reject = if range > 0 {
+                    let range = $u_large::from(range);
+                    (unsigned_max - range + 1) % range
+                } else {
+                    0
+                };
+
+                UniformInt {
+                    low: low,
+                    // These are really $unsigned values, but store as $ty:
+                    range: range as $ty,
+                    z: ints_to_reject as $unsigned as $ty,
+                }
+            }
+
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
+                let range = self.range as $unsigned as $u_large;
+                if range > 0 {
+                    let unsigned_max = ::core::$u_large::MAX;
+                    let zone = unsigned_max - (self.z as $unsigned as $u_large);
+                    loop {
+                        let v: $u_large = rng.gen();
+                        let (hi, lo) = v.wmul(range);
+                        if lo <= zone {
+                            return self.low.wrapping_add(hi as $ty);
+                        }
+                    }
+                } else {
+                    // Sample from the entire integer range.
+                    rng.gen()
+                }
+            }
+
+            fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(low < high, "UniformSampler::sample_single: low >= high");
+                let range = high.wrapping_sub(low) as $unsigned as $u_large;
+                let zone = if ::core::$unsigned::MAX <= ::core::u16::MAX as $unsigned {
+                    // Using a modulus is faster than the approximation for
+                    // i8 and i16. I suppose we trade the cost of one
+                    // modulus for near-perfect branch prediction.
+                    let unsigned_max: $u_large = ::core::$u_large::MAX;
+                    let ints_to_reject = (unsigned_max - range + 1) % range;
+                    unsigned_max - ints_to_reject
+                } else {
+                    // conservative but fast approximation. `- 1` is necessary to allow the
+                    // same comparison without bias.
+                    (range << range.leading_zeros()).wrapping_sub(1)
+                };
+
+                loop {
+                    let v: $u_large = rng.gen();
+                    let (hi, lo) = v.wmul(range);
+                    if lo <= zone {
+                        return low.wrapping_add(hi as $ty);
+                    }
+                }
+            }
+        }
+    };
+}
+
+uniform_int_impl! { i8, u8, u32 }
+uniform_int_impl! { i16, u16, u32 }
+uniform_int_impl! { i32, u32, u32 }
+uniform_int_impl! { i64, u64, u64 }
+#[cfg(not(target_os = "emscripten"))]
+uniform_int_impl! { i128, u128, u128 }
+uniform_int_impl! { isize, usize, usize }
+uniform_int_impl! { u8, u8, u32 }
+uniform_int_impl! { u16, u16, u32 }
+uniform_int_impl! { u32, u32, u32 }
+uniform_int_impl! { u64, u64, u64 }
+uniform_int_impl! { usize, usize, usize }
+#[cfg(not(target_os = "emscripten"))]
+uniform_int_impl! { u128, u128, u128 }
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+macro_rules! uniform_simd_int_impl {
+    ($ty:ident, $unsigned:ident, $u_scalar:ident) => {
+        // The "pick the largest zone that can fit in an `u32`" optimization
+        // is less useful here. Multiple lanes complicate things, we don't
+        // know the PRNG's minimal output size, and casting to a larger vector
+        // is generally a bad idea for SIMD performance. The user can still
+        // implement it manually.
+
+        // TODO: look into `Uniform::<u32x4>::new(0u32, 100)` functionality
+        //       perhaps `impl SampleUniform for $u_scalar`?
+        impl SampleUniform for $ty {
+            type Sampler = UniformInt<$ty>;
+        }
+
+        impl UniformSampler for UniformInt<$ty> {
+            type X = $ty;
+
+            #[inline] // if the range is constant, this helps LLVM to do the
+                      // calculations at compile-time.
+            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
+                where B1: SampleBorrow<Self::X> + Sized,
+                      B2: SampleBorrow<Self::X> + Sized
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(low.lt(high).all(), "Uniform::new called with `low >= high`");
+                UniformSampler::new_inclusive(low, high - 1)
+            }
+
+            #[inline] // if the range is constant, this helps LLVM to do the
+                      // calculations at compile-time.
+            fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
+                where B1: SampleBorrow<Self::X> + Sized,
+                      B2: SampleBorrow<Self::X> + Sized
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(low.le(high).all(),
+                        "Uniform::new_inclusive called with `low > high`");
+                let unsigned_max = ::core::$u_scalar::MAX;
+
+                // NOTE: these may need to be replaced with explicitly
+                // wrapping operations if `packed_simd` changes
+                let range: $unsigned = ((high - low) + 1).cast();
+                // `% 0` will panic at runtime.
+                let not_full_range = range.gt($unsigned::splat(0));
+                // replacing 0 with `unsigned_max` allows a faster `select`
+                // with bitwise OR
+                let modulo = not_full_range.select(range, $unsigned::splat(unsigned_max));
+                // wrapping addition
+                let ints_to_reject = (unsigned_max - range + 1) % modulo;
+                // When `range` is 0, `lo` of `v.wmul(range)` will always be
+                // zero which means only one sample is needed.
+                let zone = unsigned_max - ints_to_reject;
+
+                UniformInt {
+                    low: low,
+                    // These are really $unsigned values, but store as $ty:
+                    range: range.cast(),
+                    z: zone.cast(),
+                }
+            }
+
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
+                let range: $unsigned = self.range.cast();
+                let zone: $unsigned = self.z.cast();
+
+                // This might seem very slow, generating a whole new
+                // SIMD vector for every sample rejection. For most uses
+                // though, the chance of rejection is small and provides good
+                // general performance. With multiple lanes, that chance is
+                // multiplied. To mitigate this, we replace only the lanes of
+                // the vector which fail, iteratively reducing the chance of
+                // rejection. The replacement method does however add a little
+                // overhead. Benchmarking or calculating probabilities might
+                // reveal contexts where this replacement method is slower.
+                let mut v: $unsigned = rng.gen();
+                loop {
+                    let (hi, lo) = v.wmul(range);
+                    let mask = lo.le(zone);
+                    if mask.all() {
+                        let hi: $ty = hi.cast();
+                        // wrapping addition
+                        let result = self.low + hi;
+                        // `select` here compiles to a blend operation
+                        // When `range.eq(0).none()` the compare and blend
+                        // operations are avoided.
+                        let v: $ty = v.cast();
+                        return range.gt($unsigned::splat(0)).select(result, v);
+                    }
+                    // Replace only the failing lanes
+                    v = mask.select(v, rng.gen());
+                }
+            }
+        }
+    };
+
+    // bulk implementation
+    ($(($unsigned:ident, $signed:ident),)+ $u_scalar:ident) => {
+        $(
+            uniform_simd_int_impl!($unsigned, $unsigned, $u_scalar);
+            uniform_simd_int_impl!($signed, $unsigned, $u_scalar);
+        )+
+    };
+}
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+uniform_simd_int_impl! {
+    (u64x2, i64x2),
+    (u64x4, i64x4),
+    (u64x8, i64x8),
+    u64
+}
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+uniform_simd_int_impl! {
+    (u32x2, i32x2),
+    (u32x4, i32x4),
+    (u32x8, i32x8),
+    (u32x16, i32x16),
+    u32
+}
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+uniform_simd_int_impl! {
+    (u16x2, i16x2),
+    (u16x4, i16x4),
+    (u16x8, i16x8),
+    (u16x16, i16x16),
+    (u16x32, i16x32),
+    u16
+}
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+uniform_simd_int_impl! {
+    (u8x2, i8x2),
+    (u8x4, i8x4),
+    (u8x8, i8x8),
+    (u8x16, i8x16),
+    (u8x32, i8x32),
+    (u8x64, i8x64),
+    u8
+}
+
+
+/// The back-end implementing [`UniformSampler`] for floating-point types.
+///
+/// Unless you are implementing [`UniformSampler`] for your own type, this type
+/// should not be used directly, use [`Uniform`] instead.
+///
+/// # Implementation notes
+///
+/// Instead of generating a float in the `[0, 1)` range using [`Standard`], the
+/// `UniformFloat` implementation converts the output of an PRNG itself. This
+/// way one or two steps can be optimized out.
+///
+/// The floats are first converted to a value in the `[1, 2)` interval using a
+/// transmute-based method, and then mapped to the expected range with a
+/// multiply and addition. Values produced this way have what equals 23 bits of
+/// random digits for an `f32`, and 52 for an `f64`.
+///
+/// [`new`]: UniformSampler::new
+/// [`new_inclusive`]: UniformSampler::new_inclusive
+/// [`Standard`]: crate::distributions::Standard
+#[derive(Clone, Copy, Debug)]
+pub struct UniformFloat<X> {
+    low: X,
+    scale: X,
+}
+
+macro_rules! uniform_float_impl {
+    ($ty:ty, $uty:ident, $f_scalar:ident, $u_scalar:ident, $bits_to_discard:expr) => {
+        impl SampleUniform for $ty {
+            type Sampler = UniformFloat<$ty>;
+        }
+
+        impl UniformSampler for UniformFloat<$ty> {
+            type X = $ty;
+
+            fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(low.all_lt(high), "Uniform::new called with `low >= high`");
+                assert!(
+                    low.all_finite() && high.all_finite(),
+                    "Uniform::new called with non-finite boundaries"
+                );
+                let max_rand = <$ty>::splat(
+                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
+                );
+
+                let mut scale = high - low;
+
+                loop {
+                    let mask = (scale * max_rand + low).ge_mask(high);
+                    if mask.none() {
+                        break;
+                    }
+                    scale = scale.decrease_masked(mask);
+                }
+
+                debug_assert!(<$ty>::splat(0.0).all_le(scale));
+
+                UniformFloat { low, scale }
+            }
+
+            fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(
+                    low.all_le(high),
+                    "Uniform::new_inclusive called with `low > high`"
+                );
+                assert!(
+                    low.all_finite() && high.all_finite(),
+                    "Uniform::new_inclusive called with non-finite boundaries"
+                );
+                let max_rand = <$ty>::splat(
+                    (::core::$u_scalar::MAX >> $bits_to_discard).into_float_with_exponent(0) - 1.0,
+                );
+
+                let mut scale = (high - low) / max_rand;
+
+                loop {
+                    let mask = (scale * max_rand + low).gt_mask(high);
+                    if mask.none() {
+                        break;
+                    }
+                    scale = scale.decrease_masked(mask);
+                }
+
+                debug_assert!(<$ty>::splat(0.0).all_le(scale));
+
+                UniformFloat { low, scale }
+            }
+
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
+                // Generate a value in the range [1, 2)
+                let value1_2 = (rng.gen::<$uty>() >> $bits_to_discard).into_float_with_exponent(0);
+
+                // Get a value in the range [0, 1) in order to avoid
+                // overflowing into infinity when multiplying with scale
+                let value0_1 = value1_2 - 1.0;
+
+                // We don't use `f64::mul_add`, because it is not available with
+                // `no_std`. Furthermore, it is slower for some targets (but
+                // faster for others). However, the order of multiplication and
+                // addition is important, because on some platforms (e.g. ARM)
+                // it will be optimized to a single (non-FMA) instruction.
+                value0_1 * self.scale + self.low
+            }
+
+            #[inline]
+            fn sample_single<R: Rng + ?Sized, B1, B2>(low_b: B1, high_b: B2, rng: &mut R) -> Self::X
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                let low = *low_b.borrow();
+                let high = *high_b.borrow();
+                assert!(
+                    low.all_lt(high),
+                    "UniformSampler::sample_single: low >= high"
+                );
+                let mut scale = high - low;
+
+                loop {
+                    // Generate a value in the range [1, 2)
+                    let value1_2 =
+                        (rng.gen::<$uty>() >> $bits_to_discard).into_float_with_exponent(0);
+
+                    // Get a value in the range [0, 1) in order to avoid
+                    // overflowing into infinity when multiplying with scale
+                    let value0_1 = value1_2 - 1.0;
+
+                    // Doing multiply before addition allows some architectures
+                    // to use a single instruction.
+                    let res = value0_1 * scale + low;
+
+                    debug_assert!(low.all_le(res) || !scale.all_finite());
+                    if res.all_lt(high) {
+                        return res;
+                    }
+
+                    // This handles a number of edge cases.
+                    // * `low` or `high` is NaN. In this case `scale` and
+                    //   `res` are going to end up as NaN.
+                    // * `low` is negative infinity and `high` is finite.
+                    //   `scale` is going to be infinite and `res` will be
+                    //   NaN.
+                    // * `high` is positive infinity and `low` is finite.
+                    //   `scale` is going to be infinite and `res` will
+                    //   be infinite or NaN (if value0_1 is 0).
+                    // * `low` is negative infinity and `high` is positive
+                    //   infinity. `scale` will be infinite and `res` will
+                    //   be NaN.
+                    // * `low` and `high` are finite, but `high - low`
+                    //   overflows to infinite. `scale` will be infinite
+                    //   and `res` will be infinite or NaN (if value0_1 is 0).
+                    // So if `high` or `low` are non-finite, we are guaranteed
+                    // to fail the `res < high` check above and end up here.
+                    //
+                    // While we technically should check for non-finite `low`
+                    // and `high` before entering the loop, by doing the checks
+                    // here instead, we allow the common case to avoid these
+                    // checks. But we are still guaranteed that if `low` or
+                    // `high` are non-finite we'll end up here and can do the
+                    // appropriate checks.
+                    //
+                    // Likewise `high - low` overflowing to infinity is also
+                    // rare, so handle it here after the common case.
+                    let mask = !scale.finite_mask();
+                    if mask.any() {
+                        assert!(
+                            low.all_finite() && high.all_finite(),
+                            "Uniform::sample_single: low and high must be finite"
+                        );
+                        scale = scale.decrease_masked(mask);
+                    }
+                }
+            }
+        }
+    };
+}
+
+uniform_float_impl! { f32, u32, f32, u32, 32 - 23 }
+uniform_float_impl! { f64, u64, f64, u64, 64 - 52 }
+
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f32x2, u32x2, f32, u32, 32 - 23 }
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f32x4, u32x4, f32, u32, 32 - 23 }
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f32x8, u32x8, f32, u32, 32 - 23 }
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f32x16, u32x16, f32, u32, 32 - 23 }
+
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f64x2, u64x2, f64, u64, 64 - 52 }
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f64x4, u64x4, f64, u64, 64 - 52 }
+#[cfg(feature = "simd_support")]
+uniform_float_impl! { f64x8, u64x8, f64, u64, 64 - 52 }
+
+
+/// The back-end implementing [`UniformSampler`] for `Duration`.
+///
+/// Unless you are implementing [`UniformSampler`] for your own types, this type
+/// should not be used directly, use [`Uniform`] instead.
+#[derive(Clone, Copy, Debug)]
+pub struct UniformDuration {
+    mode: UniformDurationMode,
+    offset: u32,
+}
+
+#[derive(Debug, Copy, Clone)]
+enum UniformDurationMode {
+    Small {
+        secs: u64,
+        nanos: Uniform<u32>,
+    },
+    Medium {
+        nanos: Uniform<u64>,
+    },
+    Large {
+        max_secs: u64,
+        max_nanos: u32,
+        secs: Uniform<u64>,
+    },
+}
+
+impl SampleUniform for Duration {
+    type Sampler = UniformDuration;
+}
+
+impl UniformSampler for UniformDuration {
+    type X = Duration;
+
+    #[inline]
+    fn new<B1, B2>(low_b: B1, high_b: B2) -> Self
+    where
+        B1: SampleBorrow<Self::X> + Sized,
+        B2: SampleBorrow<Self::X> + Sized,
+    {
+        let low = *low_b.borrow();
+        let high = *high_b.borrow();
+        assert!(low < high, "Uniform::new called with `low >= high`");
+        UniformDuration::new_inclusive(low, high - Duration::new(0, 1))
+    }
+
+    #[inline]
+    fn new_inclusive<B1, B2>(low_b: B1, high_b: B2) -> Self
+    where
+        B1: SampleBorrow<Self::X> + Sized,
+        B2: SampleBorrow<Self::X> + Sized,
+    {
+        let low = *low_b.borrow();
+        let high = *high_b.borrow();
+        assert!(
+            low <= high,
+            "Uniform::new_inclusive called with `low > high`"
+        );
+
+        let low_s = low.as_secs();
+        let low_n = low.subsec_nanos();
+        let mut high_s = high.as_secs();
+        let mut high_n = high.subsec_nanos();
+
+        if high_n < low_n {
+            high_s -= 1;
+            high_n += 1_000_000_000;
+        }
+
+        let mode = if low_s == high_s {
+            UniformDurationMode::Small {
+                secs: low_s,
+                nanos: Uniform::new_inclusive(low_n, high_n),
+            }
+        } else {
+            let max = high_s
+                .checked_mul(1_000_000_000)
+                .and_then(|n| n.checked_add(u64::from(high_n)));
+
+            if let Some(higher_bound) = max {
+                let lower_bound = low_s * 1_000_000_000 + u64::from(low_n);
+                UniformDurationMode::Medium {
+                    nanos: Uniform::new_inclusive(lower_bound, higher_bound),
+                }
+            } else {
+                // An offset is applied to simplify generation of nanoseconds
+                let max_nanos = high_n - low_n;
+                UniformDurationMode::Large {
+                    max_secs: high_s,
+                    max_nanos,
+                    secs: Uniform::new_inclusive(low_s, high_s),
+                }
+            }
+        };
+        UniformDuration {
+            mode,
+            offset: low_n,
+        }
+    }
+
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Duration {
+        match self.mode {
+            UniformDurationMode::Small { secs, nanos } => {
+                let n = nanos.sample(rng);
+                Duration::new(secs, n)
+            }
+            UniformDurationMode::Medium { nanos } => {
+                let nanos = nanos.sample(rng);
+                Duration::new(nanos / 1_000_000_000, (nanos % 1_000_000_000) as u32)
+            }
+            UniformDurationMode::Large {
+                max_secs,
+                max_nanos,
+                secs,
+            } => {
+                // constant folding means this is at least as fast as `gen_range`
+                let nano_range = Uniform::new(0, 1_000_000_000);
+                loop {
+                    let s = secs.sample(rng);
+                    let n = nano_range.sample(rng);
+                    if !(s == max_secs && n > max_nanos) {
+                        let sum = n + self.offset;
+                        break Duration::new(s, sum);
+                    }
+                }
+            }
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::rngs::mock::StepRng;
+
+    #[should_panic]
+    #[test]
+    fn test_uniform_bad_limits_equal_int() {
+        Uniform::new(10, 10);
+    }
+
+    #[test]
+    fn test_uniform_good_limits_equal_int() {
+        let mut rng = crate::test::rng(804);
+        let dist = Uniform::new_inclusive(10, 10);
+        for _ in 0..20 {
+            assert_eq!(rng.sample(dist), 10);
+        }
+    }
+
+    #[should_panic]
+    #[test]
+    fn test_uniform_bad_limits_flipped_int() {
+        Uniform::new(10, 5);
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_integers() {
+        #[cfg(not(target_os = "emscripten"))] use core::{i128, u128};
+        use core::{i16, i32, i64, i8, isize};
+        use core::{u16, u32, u64, u8, usize};
+
+        let mut rng = crate::test::rng(251);
+        macro_rules! t {
+            ($ty:ident, $v:expr, $le:expr, $lt:expr) => {{
+                for &(low, high) in $v.iter() {
+                    let my_uniform = Uniform::new(low, high);
+                    for _ in 0..1000 {
+                        let v: $ty = rng.sample(my_uniform);
+                        assert!($le(low, v) && $lt(v, high));
+                    }
+
+                    let my_uniform = Uniform::new_inclusive(low, high);
+                    for _ in 0..1000 {
+                        let v: $ty = rng.sample(my_uniform);
+                        assert!($le(low, v) && $le(v, high));
+                    }
+
+                    let my_uniform = Uniform::new(&low, high);
+                    for _ in 0..1000 {
+                        let v: $ty = rng.sample(my_uniform);
+                        assert!($le(low, v) && $lt(v, high));
+                    }
+
+                    let my_uniform = Uniform::new_inclusive(&low, &high);
+                    for _ in 0..1000 {
+                        let v: $ty = rng.sample(my_uniform);
+                        assert!($le(low, v) && $le(v, high));
+                    }
+
+                    for _ in 0..1000 {
+                        let v: $ty = rng.gen_range(low, high);
+                        assert!($le(low, v) && $lt(v, high));
+                    }
+                }
+            }};
+
+            // scalar bulk
+            ($($ty:ident),*) => {{
+                $(t!(
+                    $ty,
+                    [(0, 10), (10, 127), ($ty::MIN, $ty::MAX)],
+                    |x, y| x <= y,
+                    |x, y| x < y
+                );)*
+            }};
+
+            // simd bulk
+            ($($ty:ident),* => $scalar:ident) => {{
+                $(t!(
+                    $ty,
+                    [
+                        ($ty::splat(0), $ty::splat(10)),
+                        ($ty::splat(10), $ty::splat(127)),
+                        ($ty::splat($scalar::MIN), $ty::splat($scalar::MAX)),
+                    ],
+                    |x: $ty, y| x.le(y).all(),
+                    |x: $ty, y| x.lt(y).all()
+                );)*
+            }};
+        }
+        t!(i8, i16, i32, i64, isize, u8, u16, u32, u64, usize);
+        #[cfg(not(target_os = "emscripten"))]
+        t!(i128, u128);
+
+        #[cfg(all(feature = "simd_support", feature = "nightly"))]
+        {
+            t!(u8x2, u8x4, u8x8, u8x16, u8x32, u8x64 => u8);
+            t!(i8x2, i8x4, i8x8, i8x16, i8x32, i8x64 => i8);
+            t!(u16x2, u16x4, u16x8, u16x16, u16x32 => u16);
+            t!(i16x2, i16x4, i16x8, i16x16, i16x32 => i16);
+            t!(u32x2, u32x4, u32x8, u32x16 => u32);
+            t!(i32x2, i32x4, i32x8, i32x16 => i32);
+            t!(u64x2, u64x4, u64x8 => u64);
+            t!(i64x2, i64x4, i64x8 => i64);
+        }
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_floats() {
+        let mut rng = crate::test::rng(252);
+        let mut zero_rng = StepRng::new(0, 0);
+        let mut max_rng = StepRng::new(0xffff_ffff_ffff_ffff, 0);
+        macro_rules! t {
+            ($ty:ty, $f_scalar:ident, $bits_shifted:expr) => {{
+                let v: &[($f_scalar, $f_scalar)] = &[
+                    (0.0, 100.0),
+                    (-1e35, -1e25),
+                    (1e-35, 1e-25),
+                    (-1e35, 1e35),
+                    (<$f_scalar>::from_bits(0), <$f_scalar>::from_bits(3)),
+                    (-<$f_scalar>::from_bits(10), -<$f_scalar>::from_bits(1)),
+                    (-<$f_scalar>::from_bits(5), 0.0),
+                    (-<$f_scalar>::from_bits(7), -0.0),
+                    (10.0, ::core::$f_scalar::MAX),
+                    (-100.0, ::core::$f_scalar::MAX),
+                    (-::core::$f_scalar::MAX / 5.0, ::core::$f_scalar::MAX),
+                    (-::core::$f_scalar::MAX, ::core::$f_scalar::MAX / 5.0),
+                    (-::core::$f_scalar::MAX * 0.8, ::core::$f_scalar::MAX * 0.7),
+                    (-::core::$f_scalar::MAX, ::core::$f_scalar::MAX),
+                ];
+                for &(low_scalar, high_scalar) in v.iter() {
+                    for lane in 0..<$ty>::lanes() {
+                        let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar);
+                        let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar);
+                        let my_uniform = Uniform::new(low, high);
+                        let my_incl_uniform = Uniform::new_inclusive(low, high);
+                        for _ in 0..100 {
+                            let v = rng.sample(my_uniform).extract(lane);
+                            assert!(low_scalar <= v && v < high_scalar);
+                            let v = rng.sample(my_incl_uniform).extract(lane);
+                            assert!(low_scalar <= v && v <= high_scalar);
+                            let v = rng.gen_range(low, high).extract(lane);
+                            assert!(low_scalar <= v && v < high_scalar);
+                        }
+
+                        assert_eq!(
+                            rng.sample(Uniform::new_inclusive(low, low)).extract(lane),
+                            low_scalar
+                        );
+
+                        assert_eq!(zero_rng.sample(my_uniform).extract(lane), low_scalar);
+                        assert_eq!(zero_rng.sample(my_incl_uniform).extract(lane), low_scalar);
+                        assert_eq!(zero_rng.gen_range(low, high).extract(lane), low_scalar);
+                        assert!(max_rng.sample(my_uniform).extract(lane) < high_scalar);
+                        assert!(max_rng.sample(my_incl_uniform).extract(lane) <= high_scalar);
+
+                        // Don't run this test for really tiny differences between high and low
+                        // since for those rounding might result in selecting high for a very
+                        // long time.
+                        if (high_scalar - low_scalar) > 0.0001 {
+                            let mut lowering_max_rng = StepRng::new(
+                                0xffff_ffff_ffff_ffff,
+                                (-1i64 << $bits_shifted) as u64,
+                            );
+                            assert!(
+                                lowering_max_rng.gen_range(low, high).extract(lane) < high_scalar
+                            );
+                        }
+                    }
+                }
+
+                assert_eq!(
+                    rng.sample(Uniform::new_inclusive(
+                        ::core::$f_scalar::MAX,
+                        ::core::$f_scalar::MAX
+                    )),
+                    ::core::$f_scalar::MAX
+                );
+                assert_eq!(
+                    rng.sample(Uniform::new_inclusive(
+                        -::core::$f_scalar::MAX,
+                        -::core::$f_scalar::MAX
+                    )),
+                    -::core::$f_scalar::MAX
+                );
+            }};
+        }
+
+        t!(f32, f32, 32 - 23);
+        t!(f64, f64, 64 - 52);
+        #[cfg(feature = "simd_support")]
+        {
+            t!(f32x2, f32, 32 - 23);
+            t!(f32x4, f32, 32 - 23);
+            t!(f32x8, f32, 32 - 23);
+            t!(f32x16, f32, 32 - 23);
+            t!(f64x2, f64, 64 - 52);
+            t!(f64x4, f64, 64 - 52);
+            t!(f64x8, f64, 64 - 52);
+        }
+    }
+
+    #[test]
+    #[cfg(all(
+        feature = "std",
+        not(target_arch = "wasm32"),
+        not(target_arch = "asmjs")
+    ))]
+    fn test_float_assertions() {
+        use super::SampleUniform;
+        use std::panic::catch_unwind;
+        fn range<T: SampleUniform>(low: T, high: T) {
+            let mut rng = crate::test::rng(253);
+            rng.gen_range(low, high);
+        }
+
+        macro_rules! t {
+            ($ty:ident, $f_scalar:ident) => {{
+                let v: &[($f_scalar, $f_scalar)] = &[
+                    (::std::$f_scalar::NAN, 0.0),
+                    (1.0, ::std::$f_scalar::NAN),
+                    (::std::$f_scalar::NAN, ::std::$f_scalar::NAN),
+                    (1.0, 0.5),
+                    (::std::$f_scalar::MAX, -::std::$f_scalar::MAX),
+                    (::std::$f_scalar::INFINITY, ::std::$f_scalar::INFINITY),
+                    (
+                        ::std::$f_scalar::NEG_INFINITY,
+                        ::std::$f_scalar::NEG_INFINITY,
+                    ),
+                    (::std::$f_scalar::NEG_INFINITY, 5.0),
+                    (5.0, ::std::$f_scalar::INFINITY),
+                    (::std::$f_scalar::NAN, ::std::$f_scalar::INFINITY),
+                    (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::NAN),
+                    (::std::$f_scalar::NEG_INFINITY, ::std::$f_scalar::INFINITY),
+                ];
+                for &(low_scalar, high_scalar) in v.iter() {
+                    for lane in 0..<$ty>::lanes() {
+                        let low = <$ty>::splat(0.0 as $f_scalar).replace(lane, low_scalar);
+                        let high = <$ty>::splat(1.0 as $f_scalar).replace(lane, high_scalar);
+                        assert!(catch_unwind(|| range(low, high)).is_err());
+                        assert!(catch_unwind(|| Uniform::new(low, high)).is_err());
+                        assert!(catch_unwind(|| Uniform::new_inclusive(low, high)).is_err());
+                        assert!(catch_unwind(|| range(low, low)).is_err());
+                        assert!(catch_unwind(|| Uniform::new(low, low)).is_err());
+                    }
+                }
+            }};
+        }
+
+        t!(f32, f32);
+        t!(f64, f64);
+        #[cfg(feature = "simd_support")]
+        {
+            t!(f32x2, f32);
+            t!(f32x4, f32);
+            t!(f32x8, f32);
+            t!(f32x16, f32);
+            t!(f64x2, f64);
+            t!(f64x4, f64);
+            t!(f64x8, f64);
+        }
+    }
+
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_durations() {
+        #[cfg(not(feature = "std"))] use core::time::Duration;
+        #[cfg(feature = "std")] use std::time::Duration;
+
+        let mut rng = crate::test::rng(253);
+
+        let v = &[
+            (Duration::new(10, 50000), Duration::new(100, 1234)),
+            (Duration::new(0, 100), Duration::new(1, 50)),
+            (
+                Duration::new(0, 0),
+                Duration::new(u64::max_value(), 999_999_999),
+            ),
+        ];
+        for &(low, high) in v.iter() {
+            let my_uniform = Uniform::new(low, high);
+            for _ in 0..1000 {
+                let v = rng.sample(my_uniform);
+                assert!(low <= v && v < high);
+            }
+        }
+    }
+
+    #[test]
+    fn test_custom_uniform() {
+        use crate::distributions::uniform::{
+            SampleBorrow, SampleUniform, UniformFloat, UniformSampler,
+        };
+        #[derive(Clone, Copy, PartialEq, PartialOrd)]
+        struct MyF32 {
+            x: f32,
+        }
+        #[derive(Clone, Copy, Debug)]
+        struct UniformMyF32(UniformFloat<f32>);
+        impl UniformSampler for UniformMyF32 {
+            type X = MyF32;
+
+            fn new<B1, B2>(low: B1, high: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                UniformMyF32(UniformFloat::<f32>::new(low.borrow().x, high.borrow().x))
+            }
+
+            fn new_inclusive<B1, B2>(low: B1, high: B2) -> Self
+            where
+                B1: SampleBorrow<Self::X> + Sized,
+                B2: SampleBorrow<Self::X> + Sized,
+            {
+                UniformSampler::new(low, high)
+            }
+
+            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
+                MyF32 {
+                    x: self.0.sample(rng),
+                }
+            }
+        }
+        impl SampleUniform for MyF32 {
+            type Sampler = UniformMyF32;
+        }
+
+        let (low, high) = (MyF32 { x: 17.0f32 }, MyF32 { x: 22.0f32 });
+        let uniform = Uniform::new(low, high);
+        let mut rng = crate::test::rng(804);
+        for _ in 0..100 {
+            let x: MyF32 = rng.sample(uniform);
+            assert!(low <= x && x < high);
+        }
+    }
+
+    #[test]
+    fn test_uniform_from_std_range() {
+        let r = Uniform::from(2u32..7);
+        assert_eq!(r.0.low, 2);
+        assert_eq!(r.0.range, 5);
+        let r = Uniform::from(2.0f64..7.0);
+        assert_eq!(r.0.low, 2.0);
+        assert_eq!(r.0.scale, 5.0);
+    }
+
+    #[test]
+    fn test_uniform_from_std_range_inclusive() {
+        let r = Uniform::from(2u32..=6);
+        assert_eq!(r.0.low, 2);
+        assert_eq!(r.0.range, 5);
+        let r = Uniform::from(2.0f64..=7.0);
+        assert_eq!(r.0.low, 2.0);
+        assert!(r.0.scale > 5.0);
+        assert!(r.0.scale < 5.0 + 1e-14);
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<T: SampleUniform + Copy + core::fmt::Debug + PartialEq>(
+            lb: T, ub: T, expected_single: &[T], expected_multiple: &[T],
+        ) where Uniform<T>: Distribution<T> {
+            let mut rng = crate::test::rng(897);
+            let mut buf = [lb; 3];
+
+            for x in &mut buf {
+                *x = T::Sampler::sample_single(lb, ub, &mut rng);
+            }
+            assert_eq!(&buf, expected_single);
+
+            let distr = Uniform::new(lb, ub);
+            for x in &mut buf {
+                *x = rng.sample(&distr);
+            }
+            assert_eq!(&buf, expected_multiple);
+        }
+
+        // We test on a sub-set of types; possibly we should do more.
+        // TODO: SIMD types
+
+        test_samples(11u8, 219, &[17, 66, 214], &[181, 93, 165]);
+        test_samples(11u32, 219, &[17, 66, 214], &[181, 93, 165]);
+
+        test_samples(0f32, 1e-2f32, &[0.0003070104, 0.0026630748, 0.00979833], &[
+            0.008194133,
+            0.00398172,
+            0.007428536,
+        ]);
+        test_samples(
+            -1e10f64,
+            1e10f64,
+            &[-4673848682.871551, 6388267422.932352, 4857075081.198343],
+            &[1173375212.1808167, 1917642852.109581, 2365076174.3153973],
+        );
+
+        test_samples(
+            Duration::new(2, 0),
+            Duration::new(4, 0),
+            &[
+                Duration::new(2, 532615131),
+                Duration::new(3, 638826742),
+                Duration::new(3, 485707508),
+            ],
+            &[
+                Duration::new(3, 117337521),
+                Duration::new(3, 191764285),
+                Duration::new(3, 236507617),
+            ],
+        );
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/unit_circle.rs b/vendor/rand-0.7.3/src/distributions/unit_circle.rs
new file mode 100644
index 000000000..37885d8eb
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/unit_circle.rs
@@ -0,0 +1,102 @@
+// 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.
+
+#![allow(deprecated)]
+#![allow(clippy::all)]
+
+use crate::distributions::{Distribution, Uniform};
+use crate::Rng;
+
+/// Samples uniformly from the edge of the unit circle in two dimensions.
+///
+/// Implemented via a method by von Neumann[^1].
+///
+/// [^1]: von Neumann, J. (1951) [*Various Techniques Used in Connection with
+///       Random Digits.*](https://mcnp.lanl.gov/pdf_files/nbs_vonneumann.pdf)
+///       NBS Appl. Math. Ser., No. 12. Washington, DC: U.S. Government Printing
+///       Office, pp. 36-38.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct UnitCircle;
+
+impl UnitCircle {
+    /// Construct a new `UnitCircle` distribution.
+    #[inline]
+    pub fn new() -> UnitCircle {
+        UnitCircle
+    }
+}
+
+impl Distribution<[f64; 2]> for UnitCircle {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [f64; 2] {
+        let uniform = Uniform::new(-1., 1.);
+        let mut x1;
+        let mut x2;
+        let mut sum;
+        loop {
+            x1 = uniform.sample(rng);
+            x2 = uniform.sample(rng);
+            sum = x1 * x1 + x2 * x2;
+            if sum < 1. {
+                break;
+            }
+        }
+        let diff = x1 * x1 - x2 * x2;
+        [diff / sum, 2. * x1 * x2 / sum]
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::UnitCircle;
+    use crate::distributions::Distribution;
+
+    /// Assert that two numbers are almost equal to each other.
+    ///
+    /// On panic, this macro will print the values of the expressions with their
+    /// debug representations.
+    macro_rules! assert_almost_eq {
+        ($a:expr, $b:expr, $prec:expr) => {
+            let diff = ($a - $b).abs();
+            if diff > $prec {
+                panic!(format!(
+                    "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
+                     (left: `{}`, right: `{}`)",
+                    diff, $prec, $a, $b
+                ));
+            }
+        };
+    }
+
+    #[test]
+    fn norm() {
+        let mut rng = crate::test::rng(1);
+        let dist = UnitCircle::new();
+        for _ in 0..1000 {
+            let x = dist.sample(&mut rng);
+            assert_almost_eq!(x[0] * x[0] + x[1] * x[1], 1., 1e-15);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+            [-0.9965658683520504, -0.08280380447614634],
+            [-0.9790853270389644, -0.20345004884984505],
+            [-0.8449189758898707, 0.5348943112253227],
+        ];
+        let samples = [
+            UnitCircle.sample(&mut rng),
+            UnitCircle.sample(&mut rng),
+            UnitCircle.sample(&mut rng),
+        ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/unit_sphere.rs b/vendor/rand-0.7.3/src/distributions/unit_sphere.rs
new file mode 100644
index 000000000..5b8c8ad55
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/unit_sphere.rs
@@ -0,0 +1,97 @@
+// 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.
+
+#![allow(deprecated)]
+#![allow(clippy::all)]
+
+use crate::distributions::{Distribution, Uniform};
+use crate::Rng;
+
+/// Samples uniformly from the surface of the unit sphere in three dimensions.
+///
+/// Implemented via a method by Marsaglia[^1].
+///
+/// [^1]: Marsaglia, George (1972). [*Choosing a Point from the Surface of a
+///       Sphere.*](https://doi.org/10.1214/aoms/1177692644)
+///       Ann. Math. Statist. 43, no. 2, 645--646.
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct UnitSphereSurface;
+
+impl UnitSphereSurface {
+    /// Construct a new `UnitSphereSurface` distribution.
+    #[inline]
+    pub fn new() -> UnitSphereSurface {
+        UnitSphereSurface
+    }
+}
+
+impl Distribution<[f64; 3]> for UnitSphereSurface {
+    #[inline]
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> [f64; 3] {
+        let uniform = Uniform::new(-1., 1.);
+        loop {
+            let (x1, x2) = (uniform.sample(rng), uniform.sample(rng));
+            let sum = x1 * x1 + x2 * x2;
+            if sum >= 1. {
+                continue;
+            }
+            let factor = 2. * (1.0_f64 - sum).sqrt();
+            return [x1 * factor, x2 * factor, 1. - 2. * sum];
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::UnitSphereSurface;
+    use crate::distributions::Distribution;
+
+    /// Assert that two numbers are almost equal to each other.
+    ///
+    /// On panic, this macro will print the values of the expressions with their
+    /// debug representations.
+    macro_rules! assert_almost_eq {
+        ($a:expr, $b:expr, $prec:expr) => {
+            let diff = ($a - $b).abs();
+            if diff > $prec {
+                panic!(format!(
+                    "assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
+                     (left: `{}`, right: `{}`)",
+                    diff, $prec, $a, $b
+                ));
+            }
+        };
+    }
+
+    #[test]
+    fn norm() {
+        let mut rng = crate::test::rng(1);
+        let dist = UnitSphereSurface::new();
+        for _ in 0..1000 {
+            let x = dist.sample(&mut rng);
+            assert_almost_eq!(x[0] * x[0] + x[1] * x[1] + x[2] * x[2], 1., 1e-15);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        let mut rng = crate::test::rng(2);
+        let expected = [
+            [0.03247542860231647, -0.7830477442152738, 0.6211131755296027],
+            [-0.09978440840914075, 0.9706650829833128, -0.21875184231323952],
+            [0.2735582468624679, 0.9435374242279655, -0.1868234852870203],
+        ];
+        let samples = [
+            UnitSphereSurface.sample(&mut rng),
+            UnitSphereSurface.sample(&mut rng),
+            UnitSphereSurface.sample(&mut rng),
+        ];
+        assert_eq!(samples, expected);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/utils.rs b/vendor/rand-0.7.3/src/distributions/utils.rs
new file mode 100644
index 000000000..2d36b0226
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/utils.rs
@@ -0,0 +1,547 @@
+// Copyright 2018 Developers of the Rand project.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! Math helper functions
+
+#[cfg(feature = "std")] use crate::distributions::ziggurat_tables;
+#[cfg(feature = "std")] use crate::Rng;
+#[cfg(feature = "simd_support")] use packed_simd::*;
+
+
+pub trait WideningMultiply<RHS = Self> {
+    type Output;
+
+    fn wmul(self, x: RHS) -> Self::Output;
+}
+
+macro_rules! wmul_impl {
+    ($ty:ty, $wide:ty, $shift:expr) => {
+        impl WideningMultiply for $ty {
+            type Output = ($ty, $ty);
+
+            #[inline(always)]
+            fn wmul(self, x: $ty) -> Self::Output {
+                let tmp = (self as $wide) * (x as $wide);
+                ((tmp >> $shift) as $ty, tmp as $ty)
+            }
+        }
+    };
+
+    // simd bulk implementation
+    ($(($ty:ident, $wide:ident),)+, $shift:expr) => {
+        $(
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, x: $ty) -> Self::Output {
+                    // For supported vectors, this should compile to a couple
+                    // supported multiply & swizzle instructions (no actual
+                    // casting).
+                    // TODO: optimize
+                    let y: $wide = self.cast();
+                    let x: $wide = x.cast();
+                    let tmp = y * x;
+                    let hi: $ty = (tmp >> $shift).cast();
+                    let lo: $ty = tmp.cast();
+                    (hi, lo)
+                }
+            }
+        )+
+    };
+}
+wmul_impl! { u8, u16, 8 }
+wmul_impl! { u16, u32, 16 }
+wmul_impl! { u32, u64, 32 }
+#[cfg(not(target_os = "emscripten"))]
+wmul_impl! { u64, u128, 64 }
+
+// This code is a translation of the __mulddi3 function in LLVM's
+// compiler-rt. It is an optimised variant of the common method
+// `(a + b) * (c + d) = ac + ad + bc + bd`.
+//
+// For some reason LLVM can optimise the C version very well, but
+// keeps shuffling registers in this Rust translation.
+macro_rules! wmul_impl_large {
+    ($ty:ty, $half:expr) => {
+        impl WideningMultiply for $ty {
+            type Output = ($ty, $ty);
+
+            #[inline(always)]
+            fn wmul(self, b: $ty) -> Self::Output {
+                const LOWER_MASK: $ty = !0 >> $half;
+                let mut low = (self & LOWER_MASK).wrapping_mul(b & LOWER_MASK);
+                let mut t = low >> $half;
+                low &= LOWER_MASK;
+                t += (self >> $half).wrapping_mul(b & LOWER_MASK);
+                low += (t & LOWER_MASK) << $half;
+                let mut high = t >> $half;
+                t = low >> $half;
+                low &= LOWER_MASK;
+                t += (b >> $half).wrapping_mul(self & LOWER_MASK);
+                low += (t & LOWER_MASK) << $half;
+                high += t >> $half;
+                high += (self >> $half).wrapping_mul(b >> $half);
+
+                (high, low)
+            }
+        }
+    };
+
+    // simd bulk implementation
+    (($($ty:ty,)+) $scalar:ty, $half:expr) => {
+        $(
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, b: $ty) -> Self::Output {
+                    // needs wrapping multiplication
+                    const LOWER_MASK: $scalar = !0 >> $half;
+                    let mut low = (self & LOWER_MASK) * (b & LOWER_MASK);
+                    let mut t = low >> $half;
+                    low &= LOWER_MASK;
+                    t += (self >> $half) * (b & LOWER_MASK);
+                    low += (t & LOWER_MASK) << $half;
+                    let mut high = t >> $half;
+                    t = low >> $half;
+                    low &= LOWER_MASK;
+                    t += (b >> $half) * (self & LOWER_MASK);
+                    low += (t & LOWER_MASK) << $half;
+                    high += t >> $half;
+                    high += (self >> $half) * (b >> $half);
+
+                    (high, low)
+                }
+            }
+        )+
+    };
+}
+#[cfg(target_os = "emscripten")]
+wmul_impl_large! { u64, 32 }
+#[cfg(not(target_os = "emscripten"))]
+wmul_impl_large! { u128, 64 }
+
+macro_rules! wmul_impl_usize {
+    ($ty:ty) => {
+        impl WideningMultiply for usize {
+            type Output = (usize, usize);
+
+            #[inline(always)]
+            fn wmul(self, x: usize) -> Self::Output {
+                let (high, low) = (self as $ty).wmul(x as $ty);
+                (high as usize, low as usize)
+            }
+        }
+    };
+}
+#[cfg(target_pointer_width = "32")]
+wmul_impl_usize! { u32 }
+#[cfg(target_pointer_width = "64")]
+wmul_impl_usize! { u64 }
+
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+mod simd_wmul {
+    use super::*;
+    #[cfg(target_arch = "x86")] use core::arch::x86::*;
+    #[cfg(target_arch = "x86_64")] use core::arch::x86_64::*;
+
+    wmul_impl! {
+        (u8x2, u16x2),
+        (u8x4, u16x4),
+        (u8x8, u16x8),
+        (u8x16, u16x16),
+        (u8x32, u16x32),,
+        8
+    }
+
+    wmul_impl! { (u16x2, u32x2),, 16 }
+    #[cfg(not(target_feature = "sse2"))]
+    wmul_impl! { (u16x4, u32x4),, 16 }
+    #[cfg(not(target_feature = "sse4.2"))]
+    wmul_impl! { (u16x8, u32x8),, 16 }
+    #[cfg(not(target_feature = "avx2"))]
+    wmul_impl! { (u16x16, u32x16),, 16 }
+
+    // 16-bit lane widths allow use of the x86 `mulhi` instructions, which
+    // means `wmul` can be implemented with only two instructions.
+    #[allow(unused_macros)]
+    macro_rules! wmul_impl_16 {
+        ($ty:ident, $intrinsic:ident, $mulhi:ident, $mullo:ident) => {
+            impl WideningMultiply for $ty {
+                type Output = ($ty, $ty);
+
+                #[inline(always)]
+                fn wmul(self, x: $ty) -> Self::Output {
+                    let b = $intrinsic::from_bits(x);
+                    let a = $intrinsic::from_bits(self);
+                    let hi = $ty::from_bits(unsafe { $mulhi(a, b) });
+                    let lo = $ty::from_bits(unsafe { $mullo(a, b) });
+                    (hi, lo)
+                }
+            }
+        };
+    }
+
+    #[cfg(target_feature = "sse2")]
+    wmul_impl_16! { u16x4, __m64, _mm_mulhi_pu16, _mm_mullo_pi16 }
+    #[cfg(target_feature = "sse4.2")]
+    wmul_impl_16! { u16x8, __m128i, _mm_mulhi_epu16, _mm_mullo_epi16 }
+    #[cfg(target_feature = "avx2")]
+    wmul_impl_16! { u16x16, __m256i, _mm256_mulhi_epu16, _mm256_mullo_epi16 }
+    // FIXME: there are no `__m512i` types in stdsimd yet, so `wmul::<u16x32>`
+    // cannot use the same implementation.
+
+    wmul_impl! {
+        (u32x2, u64x2),
+        (u32x4, u64x4),
+        (u32x8, u64x8),,
+        32
+    }
+
+    // TODO: optimize, this seems to seriously slow things down
+    wmul_impl_large! { (u8x64,) u8, 4 }
+    wmul_impl_large! { (u16x32,) u16, 8 }
+    wmul_impl_large! { (u32x16,) u32, 16 }
+    wmul_impl_large! { (u64x2, u64x4, u64x8,) u64, 32 }
+}
+#[cfg(all(feature = "simd_support", feature = "nightly"))]
+pub use self::simd_wmul::*;
+
+
+/// Helper trait when dealing with scalar and SIMD floating point types.
+pub(crate) trait FloatSIMDUtils {
+    // `PartialOrd` for vectors compares lexicographically. We want to compare all
+    // the individual SIMD lanes instead, and get the combined result over all
+    // lanes. This is possible using something like `a.lt(b).all()`, but we
+    // implement it as a trait so we can write the same code for `f32` and `f64`.
+    // Only the comparison functions we need are implemented.
+    fn all_lt(self, other: Self) -> bool;
+    fn all_le(self, other: Self) -> bool;
+    fn all_finite(self) -> bool;
+
+    type Mask;
+    fn finite_mask(self) -> Self::Mask;
+    fn gt_mask(self, other: Self) -> Self::Mask;
+    fn ge_mask(self, other: Self) -> Self::Mask;
+
+    // Decrease all lanes where the mask is `true` to the next lower value
+    // representable by the floating-point type. At least one of the lanes
+    // must be set.
+    fn decrease_masked(self, mask: Self::Mask) -> Self;
+
+    // Convert from int value. Conversion is done while retaining the numerical
+    // value, not by retaining the binary representation.
+    type UInt;
+    fn cast_from_int(i: Self::UInt) -> Self;
+}
+
+/// Implement functions available in std builds but missing from core primitives
+#[cfg(not(std))]
+pub(crate) trait Float: Sized {
+    fn is_nan(self) -> bool;
+    fn is_infinite(self) -> bool;
+    fn is_finite(self) -> bool;
+}
+
+/// Implement functions on f32/f64 to give them APIs similar to SIMD types
+pub(crate) trait FloatAsSIMD: Sized {
+    #[inline(always)]
+    fn lanes() -> usize {
+        1
+    }
+    #[inline(always)]
+    fn splat(scalar: Self) -> Self {
+        scalar
+    }
+    #[inline(always)]
+    fn extract(self, index: usize) -> Self {
+        debug_assert_eq!(index, 0);
+        self
+    }
+    #[inline(always)]
+    fn replace(self, index: usize, new_value: Self) -> Self {
+        debug_assert_eq!(index, 0);
+        new_value
+    }
+}
+
+pub(crate) trait BoolAsSIMD: Sized {
+    fn any(self) -> bool;
+    fn all(self) -> bool;
+    fn none(self) -> bool;
+}
+
+impl BoolAsSIMD for bool {
+    #[inline(always)]
+    fn any(self) -> bool {
+        self
+    }
+
+    #[inline(always)]
+    fn all(self) -> bool {
+        self
+    }
+
+    #[inline(always)]
+    fn none(self) -> bool {
+        !self
+    }
+}
+
+macro_rules! scalar_float_impl {
+    ($ty:ident, $uty:ident) => {
+        #[cfg(not(std))]
+        impl Float for $ty {
+            #[inline]
+            fn is_nan(self) -> bool {
+                self != self
+            }
+
+            #[inline]
+            fn is_infinite(self) -> bool {
+                self == ::core::$ty::INFINITY || self == ::core::$ty::NEG_INFINITY
+            }
+
+            #[inline]
+            fn is_finite(self) -> bool {
+                !(self.is_nan() || self.is_infinite())
+            }
+        }
+
+        impl FloatSIMDUtils for $ty {
+            type Mask = bool;
+            type UInt = $uty;
+
+            #[inline(always)]
+            fn all_lt(self, other: Self) -> bool {
+                self < other
+            }
+
+            #[inline(always)]
+            fn all_le(self, other: Self) -> bool {
+                self <= other
+            }
+
+            #[inline(always)]
+            fn all_finite(self) -> bool {
+                self.is_finite()
+            }
+
+            #[inline(always)]
+            fn finite_mask(self) -> Self::Mask {
+                self.is_finite()
+            }
+
+            #[inline(always)]
+            fn gt_mask(self, other: Self) -> Self::Mask {
+                self > other
+            }
+
+            #[inline(always)]
+            fn ge_mask(self, other: Self) -> Self::Mask {
+                self >= other
+            }
+
+            #[inline(always)]
+            fn decrease_masked(self, mask: Self::Mask) -> Self {
+                debug_assert!(mask, "At least one lane must be set");
+                <$ty>::from_bits(self.to_bits() - 1)
+            }
+
+            #[inline]
+            fn cast_from_int(i: Self::UInt) -> Self {
+                i as $ty
+            }
+        }
+
+        impl FloatAsSIMD for $ty {}
+    };
+}
+
+scalar_float_impl!(f32, u32);
+scalar_float_impl!(f64, u64);
+
+
+#[cfg(feature = "simd_support")]
+macro_rules! simd_impl {
+    ($ty:ident, $f_scalar:ident, $mty:ident, $uty:ident) => {
+        impl FloatSIMDUtils for $ty {
+            type Mask = $mty;
+            type UInt = $uty;
+
+            #[inline(always)]
+            fn all_lt(self, other: Self) -> bool {
+                self.lt(other).all()
+            }
+
+            #[inline(always)]
+            fn all_le(self, other: Self) -> bool {
+                self.le(other).all()
+            }
+
+            #[inline(always)]
+            fn all_finite(self) -> bool {
+                self.finite_mask().all()
+            }
+
+            #[inline(always)]
+            fn finite_mask(self) -> Self::Mask {
+                // This can possibly be done faster by checking bit patterns
+                let neg_inf = $ty::splat(::core::$f_scalar::NEG_INFINITY);
+                let pos_inf = $ty::splat(::core::$f_scalar::INFINITY);
+                self.gt(neg_inf) & self.lt(pos_inf)
+            }
+
+            #[inline(always)]
+            fn gt_mask(self, other: Self) -> Self::Mask {
+                self.gt(other)
+            }
+
+            #[inline(always)]
+            fn ge_mask(self, other: Self) -> Self::Mask {
+                self.ge(other)
+            }
+
+            #[inline(always)]
+            fn decrease_masked(self, mask: Self::Mask) -> Self {
+                // Casting a mask into ints will produce all bits set for
+                // true, and 0 for false. Adding that to the binary
+                // representation of a float means subtracting one from
+                // the binary representation, resulting in the next lower
+                // value representable by $ty. This works even when the
+                // current value is infinity.
+                debug_assert!(mask.any(), "At least one lane must be set");
+                <$ty>::from_bits(<$uty>::from_bits(self) + <$uty>::from_bits(mask))
+            }
+
+            #[inline]
+            fn cast_from_int(i: Self::UInt) -> Self {
+                i.cast()
+            }
+        }
+    };
+}
+
+#[cfg(feature="simd_support")] simd_impl! { f32x2, f32, m32x2, u32x2 }
+#[cfg(feature="simd_support")] simd_impl! { f32x4, f32, m32x4, u32x4 }
+#[cfg(feature="simd_support")] simd_impl! { f32x8, f32, m32x8, u32x8 }
+#[cfg(feature="simd_support")] simd_impl! { f32x16, f32, m32x16, u32x16 }
+#[cfg(feature="simd_support")] simd_impl! { f64x2, f64, m64x2, u64x2 }
+#[cfg(feature="simd_support")] simd_impl! { f64x4, f64, m64x4, u64x4 }
+#[cfg(feature="simd_support")] simd_impl! { f64x8, f64, m64x8, u64x8 }
+
+/// Calculates ln(gamma(x)) (natural logarithm of the gamma
+/// function) using the Lanczos approximation.
+///
+/// The approximation expresses the gamma function as:
+/// `gamma(z+1) = sqrt(2*pi)*(z+g+0.5)^(z+0.5)*exp(-z-g-0.5)*Ag(z)`
+/// `g` is an arbitrary constant; we use the approximation with `g=5`.
+///
+/// Noting that `gamma(z+1) = z*gamma(z)` and applying `ln` to both sides:
+/// `ln(gamma(z)) = (z+0.5)*ln(z+g+0.5)-(z+g+0.5) + ln(sqrt(2*pi)*Ag(z)/z)`
+///
+/// `Ag(z)` is an infinite series with coefficients that can be calculated
+/// ahead of time - we use just the first 6 terms, which is good enough
+/// for most purposes.
+#[cfg(feature = "std")]
+pub fn log_gamma(x: f64) -> f64 {
+    // precalculated 6 coefficients for the first 6 terms of the series
+    let coefficients: [f64; 6] = [
+        76.18009172947146,
+        -86.50532032941677,
+        24.01409824083091,
+        -1.231739572450155,
+        0.1208650973866179e-2,
+        -0.5395239384953e-5,
+    ];
+
+    // (x+0.5)*ln(x+g+0.5)-(x+g+0.5)
+    let tmp = x + 5.5;
+    let log = (x + 0.5) * tmp.ln() - tmp;
+
+    // the first few terms of the series for Ag(x)
+    let mut a = 1.000000000190015;
+    let mut denom = x;
+    for coeff in &coefficients {
+        denom += 1.0;
+        a += coeff / denom;
+    }
+
+    // get everything together
+    // a is Ag(x)
+    // 2.5066... is sqrt(2pi)
+    log + (2.5066282746310005 * a / x).ln()
+}
+
+/// Sample a random number using the Ziggurat method (specifically the
+/// ZIGNOR variant from Doornik 2005). Most of the arguments are
+/// directly from the paper:
+///
+/// * `rng`: source of randomness
+/// * `symmetric`: whether this is a symmetric distribution, or one-sided with P(x < 0) = 0.
+/// * `X`: the $x_i$ abscissae.
+/// * `F`: precomputed values of the PDF at the $x_i$, (i.e. $f(x_i)$)
+/// * `F_DIFF`: precomputed values of $f(x_i) - f(x_{i+1})$
+/// * `pdf`: the probability density function
+/// * `zero_case`: manual sampling from the tail when we chose the
+///    bottom box (i.e. i == 0)
+
+// the perf improvement (25-50%) is definitely worth the extra code
+// size from force-inlining.
+#[cfg(feature = "std")]
+#[inline(always)]
+pub fn ziggurat<R: Rng + ?Sized, P, Z>(
+    rng: &mut R,
+    symmetric: bool,
+    x_tab: ziggurat_tables::ZigTable,
+    f_tab: ziggurat_tables::ZigTable,
+    mut pdf: P,
+    mut zero_case: Z
+) -> f64
+where
+    P: FnMut(f64) -> f64,
+    Z: FnMut(&mut R, f64) -> f64,
+{
+    use crate::distributions::float::IntoFloat;
+    loop {
+        // As an optimisation we re-implement the conversion to a f64.
+        // From the remaining 12 most significant bits we use 8 to construct `i`.
+        // This saves us generating a whole extra random number, while the added
+        // precision of using 64 bits for f64 does not buy us much.
+        let bits = rng.next_u64();
+        let i = bits as usize & 0xff;
+
+        let u = if symmetric {
+            // Convert to a value in the range [2,4) and substract to get [-1,1)
+            // We can't convert to an open range directly, that would require
+            // substracting `3.0 - EPSILON`, which is not representable.
+            // It is possible with an extra step, but an open range does not
+            // seem neccesary for the ziggurat algorithm anyway.
+            (bits >> 12).into_float_with_exponent(1) - 3.0
+        } else {
+            // Convert to a value in the range [1,2) and substract to get (0,1)
+            (bits >> 12).into_float_with_exponent(0) - (1.0 - ::core::f64::EPSILON / 2.0)
+        };
+        let x = u * x_tab[i];
+
+        let test_x = if symmetric { x.abs() } else { x };
+
+        // algebraically equivalent to |u| < x_tab[i+1]/x_tab[i] (or u < x_tab[i+1]/x_tab[i])
+        if test_x < x_tab[i + 1] {
+            return x;
+        }
+        if i == 0 {
+            return zero_case(rng, u);
+        }
+        // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
+        if f_tab[i + 1] + (f_tab[i] - f_tab[i + 1]) * rng.gen::<f64>() < pdf(x) {
+            return x;
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/weibull.rs b/vendor/rand-0.7.3/src/distributions/weibull.rs
new file mode 100644
index 000000000..ffbc93b01
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/weibull.rs
@@ -0,0 +1,67 @@
+// 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.
+
+//! The Weibull distribution.
+#![allow(deprecated)]
+
+use crate::distributions::{Distribution, OpenClosed01};
+use crate::Rng;
+
+/// Samples floating-point numbers according to the Weibull distribution
+#[deprecated(since = "0.7.0", note = "moved to rand_distr crate")]
+#[derive(Clone, Copy, Debug)]
+pub struct Weibull {
+    inv_shape: f64,
+    scale: f64,
+}
+
+impl Weibull {
+    /// Construct a new `Weibull` distribution with given `scale` and `shape`.
+    ///
+    /// # Panics
+    ///
+    /// `scale` and `shape` have to be non-zero and positive.
+    pub fn new(scale: f64, shape: f64) -> Weibull {
+        assert!((scale > 0.) & (shape > 0.));
+        Weibull {
+            inv_shape: 1. / shape,
+            scale,
+        }
+    }
+}
+
+impl Distribution<f64> for Weibull {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> f64 {
+        let x: f64 = rng.sample(OpenClosed01);
+        self.scale * (-x.ln()).powf(self.inv_shape)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::Weibull;
+    use crate::distributions::Distribution;
+
+    #[test]
+    #[should_panic]
+    fn invalid() {
+        Weibull::new(0., 0.);
+    }
+
+    #[test]
+    fn sample() {
+        let scale = 1.0;
+        let shape = 2.0;
+        let d = Weibull::new(scale, shape);
+        let mut rng = crate::test::rng(1);
+        for _ in 0..1000 {
+            let r = d.sample(&mut rng);
+            assert!(r >= 0.);
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/weighted/alias_method.rs b/vendor/rand-0.7.3/src/distributions/weighted/alias_method.rs
new file mode 100644
index 000000000..7d42a3526
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/weighted/alias_method.rs
@@ -0,0 +1,517 @@
+//! This module contains an implementation of alias method for sampling random
+//! indices with probabilities proportional to a collection of weights.
+
+use super::WeightedError;
+#[cfg(not(feature = "std"))] use crate::alloc::vec;
+#[cfg(not(feature = "std"))] use crate::alloc::vec::Vec;
+use crate::distributions::uniform::SampleUniform;
+use crate::distributions::Distribution;
+use crate::distributions::Uniform;
+use crate::Rng;
+use core::fmt;
+use core::iter::Sum;
+use core::ops::{Add, AddAssign, Div, DivAssign, Mul, MulAssign, Sub, SubAssign};
+
+/// A distribution using weighted sampling to pick a discretely selected item.
+///
+/// Sampling a [`WeightedIndex<W>`] distribution returns the index of a randomly
+/// selected element from the vector used to create the [`WeightedIndex<W>`].
+/// The chance of a given element being picked is proportional to the value of
+/// the element. The weights can have any type `W` for which a implementation of
+/// [`Weight`] exists.
+///
+/// # Performance
+///
+/// Given that `n` is the number of items in the vector used to create an
+/// [`WeightedIndex<W>`], [`WeightedIndex<W>`] will require `O(n)` amount of
+/// memory. More specifically it takes up some constant amount of memory plus
+/// the vector used to create it and a [`Vec<u32>`] with capacity `n`.
+///
+/// Time complexity for the creation of a [`WeightedIndex<W>`] is `O(n)`.
+/// Sampling is `O(1)`, it makes a call to [`Uniform<u32>::sample`] and a call
+/// to [`Uniform<W>::sample`].
+///
+/// # Example
+///
+/// ```
+/// use rand::distributions::weighted::alias_method::WeightedIndex;
+/// use rand::prelude::*;
+///
+/// let choices = vec!['a', 'b', 'c'];
+/// let weights = vec![2, 1, 1];
+/// let dist = WeightedIndex::new(weights).unwrap();
+/// let mut rng = thread_rng();
+/// for _ in 0..100 {
+///     // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
+///     println!("{}", choices[dist.sample(&mut rng)]);
+/// }
+///
+/// let items = [('a', 0), ('b', 3), ('c', 7)];
+/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1).collect()).unwrap();
+/// for _ in 0..100 {
+///     // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
+///     println!("{}", items[dist2.sample(&mut rng)].0);
+/// }
+/// ```
+///
+/// [`WeightedIndex<W>`]: crate::distributions::weighted::alias_method::WeightedIndex
+/// [`Weight`]: crate::distributions::weighted::alias_method::Weight
+/// [`Vec<u32>`]: Vec
+/// [`Uniform<u32>::sample`]: Distribution::sample
+/// [`Uniform<W>::sample`]: Distribution::sample
+pub struct WeightedIndex<W: Weight> {
+    aliases: Vec<u32>,
+    no_alias_odds: Vec<W>,
+    uniform_index: Uniform<u32>,
+    uniform_within_weight_sum: Uniform<W>,
+}
+
+impl<W: Weight> WeightedIndex<W> {
+    /// Creates a new [`WeightedIndex`].
+    ///
+    /// Returns an error if:
+    /// - The vector is empty.
+    /// - The vector is longer than `u32::MAX`.
+    /// - For any weight `w`: `w < 0` or `w > max` where `max = W::MAX /
+    ///   weights.len()`.
+    /// - The sum of weights is zero.
+    pub fn new(weights: Vec<W>) -> Result<Self, WeightedError> {
+        let n = weights.len();
+        if n == 0 {
+            return Err(WeightedError::NoItem);
+        } else if n > ::core::u32::MAX as usize {
+            return Err(WeightedError::TooMany);
+        }
+        let n = n as u32;
+
+        let max_weight_size = W::try_from_u32_lossy(n)
+            .map(|n| W::MAX / n)
+            .unwrap_or(W::ZERO);
+        if !weights
+            .iter()
+            .all(|&w| W::ZERO <= w && w <= max_weight_size)
+        {
+            return Err(WeightedError::InvalidWeight);
+        }
+
+        // The sum of weights will represent 100% of no alias odds.
+        let weight_sum = Weight::sum(weights.as_slice());
+        // Prevent floating point overflow due to rounding errors.
+        let weight_sum = if weight_sum > W::MAX {
+            W::MAX
+        } else {
+            weight_sum
+        };
+        if weight_sum == W::ZERO {
+            return Err(WeightedError::AllWeightsZero);
+        }
+
+        // `weight_sum` would have been zero if `try_from_lossy` causes an error here.
+        let n_converted = W::try_from_u32_lossy(n).unwrap();
+
+        let mut no_alias_odds = weights;
+        for odds in no_alias_odds.iter_mut() {
+            *odds *= n_converted;
+            // Prevent floating point overflow due to rounding errors.
+            *odds = if *odds > W::MAX { W::MAX } else { *odds };
+        }
+
+        /// This struct is designed to contain three data structures at once,
+        /// sharing the same memory. More precisely it contains two linked lists
+        /// and an alias map, which will be the output of this method. To keep
+        /// the three data structures from getting in each other's way, it must
+        /// be ensured that a single index is only ever in one of them at the
+        /// same time.
+        struct Aliases {
+            aliases: Vec<u32>,
+            smalls_head: u32,
+            bigs_head: u32,
+        }
+
+        impl Aliases {
+            fn new(size: u32) -> Self {
+                Aliases {
+                    aliases: vec![0; size as usize],
+                    smalls_head: ::core::u32::MAX,
+                    bigs_head: ::core::u32::MAX,
+                }
+            }
+
+            fn push_small(&mut self, idx: u32) {
+                self.aliases[idx as usize] = self.smalls_head;
+                self.smalls_head = idx;
+            }
+
+            fn push_big(&mut self, idx: u32) {
+                self.aliases[idx as usize] = self.bigs_head;
+                self.bigs_head = idx;
+            }
+
+            fn pop_small(&mut self) -> u32 {
+                let popped = self.smalls_head;
+                self.smalls_head = self.aliases[popped as usize];
+                popped
+            }
+
+            fn pop_big(&mut self) -> u32 {
+                let popped = self.bigs_head;
+                self.bigs_head = self.aliases[popped as usize];
+                popped
+            }
+
+            fn smalls_is_empty(&self) -> bool {
+                self.smalls_head == ::core::u32::MAX
+            }
+
+            fn bigs_is_empty(&self) -> bool {
+                self.bigs_head == ::core::u32::MAX
+            }
+
+            fn set_alias(&mut self, idx: u32, alias: u32) {
+                self.aliases[idx as usize] = alias;
+            }
+        }
+
+        let mut aliases = Aliases::new(n);
+
+        // Split indices into those with small weights and those with big weights.
+        for (index, &odds) in no_alias_odds.iter().enumerate() {
+            if odds < weight_sum {
+                aliases.push_small(index as u32);
+            } else {
+                aliases.push_big(index as u32);
+            }
+        }
+
+        // Build the alias map by finding an alias with big weight for each index with
+        // small weight.
+        while !aliases.smalls_is_empty() && !aliases.bigs_is_empty() {
+            let s = aliases.pop_small();
+            let b = aliases.pop_big();
+
+            aliases.set_alias(s, b);
+            no_alias_odds[b as usize] =
+                no_alias_odds[b as usize] - weight_sum + no_alias_odds[s as usize];
+
+            if no_alias_odds[b as usize] < weight_sum {
+                aliases.push_small(b);
+            } else {
+                aliases.push_big(b);
+            }
+        }
+
+        // The remaining indices should have no alias odds of about 100%. This is due to
+        // numeric accuracy. Otherwise they would be exactly 100%.
+        while !aliases.smalls_is_empty() {
+            no_alias_odds[aliases.pop_small() as usize] = weight_sum;
+        }
+        while !aliases.bigs_is_empty() {
+            no_alias_odds[aliases.pop_big() as usize] = weight_sum;
+        }
+
+        // Prepare distributions for sampling. Creating them beforehand improves
+        // sampling performance.
+        let uniform_index = Uniform::new(0, n);
+        let uniform_within_weight_sum = Uniform::new(W::ZERO, weight_sum);
+
+        Ok(Self {
+            aliases: aliases.aliases,
+            no_alias_odds,
+            uniform_index,
+            uniform_within_weight_sum,
+        })
+    }
+}
+
+impl<W: Weight> Distribution<usize> for WeightedIndex<W> {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        let candidate = rng.sample(self.uniform_index);
+        if rng.sample(&self.uniform_within_weight_sum) < self.no_alias_odds[candidate as usize] {
+            candidate as usize
+        } else {
+            self.aliases[candidate as usize] as usize
+        }
+    }
+}
+
+impl<W: Weight> fmt::Debug for WeightedIndex<W>
+where
+    W: fmt::Debug,
+    Uniform<W>: fmt::Debug,
+{
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        f.debug_struct("WeightedIndex")
+            .field("aliases", &self.aliases)
+            .field("no_alias_odds", &self.no_alias_odds)
+            .field("uniform_index", &self.uniform_index)
+            .field("uniform_within_weight_sum", &self.uniform_within_weight_sum)
+            .finish()
+    }
+}
+
+impl<W: Weight> Clone for WeightedIndex<W>
+where Uniform<W>: Clone
+{
+    fn clone(&self) -> Self {
+        Self {
+            aliases: self.aliases.clone(),
+            no_alias_odds: self.no_alias_odds.clone(),
+            uniform_index: self.uniform_index.clone(),
+            uniform_within_weight_sum: self.uniform_within_weight_sum.clone(),
+        }
+    }
+}
+
+/// Trait that must be implemented for weights, that are used with
+/// [`WeightedIndex`]. Currently no guarantees on the correctness of
+/// [`WeightedIndex`] are given for custom implementations of this trait.
+pub trait Weight:
+    Sized
+    + Copy
+    + SampleUniform
+    + PartialOrd
+    + Add<Output = Self>
+    + AddAssign
+    + Sub<Output = Self>
+    + SubAssign
+    + Mul<Output = Self>
+    + MulAssign
+    + Div<Output = Self>
+    + DivAssign
+    + Sum
+{
+    /// Maximum number representable by `Self`.
+    const MAX: Self;
+
+    /// Element of `Self` equivalent to 0.
+    const ZERO: Self;
+
+    /// Produce an instance of `Self` from a `u32` value, or return `None` if
+    /// out of range. Loss of precision (where `Self` is a floating point type)
+    /// is acceptable.
+    fn try_from_u32_lossy(n: u32) -> Option<Self>;
+
+    /// Sums all values in slice `values`.
+    fn sum(values: &[Self]) -> Self {
+        values.iter().map(|x| *x).sum()
+    }
+}
+
+macro_rules! impl_weight_for_float {
+    ($T: ident) => {
+        impl Weight for $T {
+            const MAX: Self = ::core::$T::MAX;
+            const ZERO: Self = 0.0;
+
+            fn try_from_u32_lossy(n: u32) -> Option<Self> {
+                Some(n as $T)
+            }
+
+            fn sum(values: &[Self]) -> Self {
+                pairwise_sum(values)
+            }
+        }
+    };
+}
+
+/// In comparison to naive accumulation, the pairwise sum algorithm reduces
+/// rounding errors when there are many floating point values.
+fn pairwise_sum<T: Weight>(values: &[T]) -> T {
+    if values.len() <= 32 {
+        values.iter().map(|x| *x).sum()
+    } else {
+        let mid = values.len() / 2;
+        let (a, b) = values.split_at(mid);
+        pairwise_sum(a) + pairwise_sum(b)
+    }
+}
+
+macro_rules! impl_weight_for_int {
+    ($T: ident) => {
+        impl Weight for $T {
+            const MAX: Self = ::core::$T::MAX;
+            const ZERO: Self = 0;
+
+            fn try_from_u32_lossy(n: u32) -> Option<Self> {
+                let n_converted = n as Self;
+                if n_converted >= Self::ZERO && n_converted as u32 == n {
+                    Some(n_converted)
+                } else {
+                    None
+                }
+            }
+        }
+    };
+}
+
+impl_weight_for_float!(f64);
+impl_weight_for_float!(f32);
+impl_weight_for_int!(usize);
+#[cfg(not(target_os = "emscripten"))]
+impl_weight_for_int!(u128);
+impl_weight_for_int!(u64);
+impl_weight_for_int!(u32);
+impl_weight_for_int!(u16);
+impl_weight_for_int!(u8);
+impl_weight_for_int!(isize);
+#[cfg(not(target_os = "emscripten"))]
+impl_weight_for_int!(i128);
+impl_weight_for_int!(i64);
+impl_weight_for_int!(i32);
+impl_weight_for_int!(i16);
+impl_weight_for_int!(i8);
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weighted_index_f32() {
+        test_weighted_index(f32::into);
+
+        // Floating point special cases
+        assert_eq!(
+            WeightedIndex::new(vec![::core::f32::INFINITY]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![-0_f32]).unwrap_err(),
+            WeightedError::AllWeightsZero
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![-1_f32]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![-::core::f32::INFINITY]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![::core::f32::NAN]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    #[cfg(not(target_os = "emscripten"))]
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weighted_index_u128() {
+        test_weighted_index(|x: u128| x as f64);
+    }
+
+    #[cfg(all(rustc_1_26, not(target_os = "emscripten")))]
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weighted_index_i128() {
+        test_weighted_index(|x: i128| x as f64);
+
+        // Signed integer special cases
+        assert_eq!(
+            WeightedIndex::new(vec![-1_i128]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![::core::i128::MIN]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weighted_index_u8() {
+        test_weighted_index(u8::into);
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weighted_index_i8() {
+        test_weighted_index(i8::into);
+
+        // Signed integer special cases
+        assert_eq!(
+            WeightedIndex::new(vec![-1_i8]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![::core::i8::MIN]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    fn test_weighted_index<W: Weight, F: Fn(W) -> f64>(w_to_f64: F)
+    where WeightedIndex<W>: fmt::Debug {
+        const NUM_WEIGHTS: u32 = 10;
+        const ZERO_WEIGHT_INDEX: u32 = 3;
+        const NUM_SAMPLES: u32 = 15000;
+        let mut rng = crate::test::rng(0x9c9fa0b0580a7031);
+
+        let weights = {
+            let mut weights = Vec::with_capacity(NUM_WEIGHTS as usize);
+            let random_weight_distribution = crate::distributions::Uniform::new_inclusive(
+                W::ZERO,
+                W::MAX / W::try_from_u32_lossy(NUM_WEIGHTS).unwrap(),
+            );
+            for _ in 0..NUM_WEIGHTS {
+                weights.push(rng.sample(&random_weight_distribution));
+            }
+            weights[ZERO_WEIGHT_INDEX as usize] = W::ZERO;
+            weights
+        };
+        let weight_sum = weights.iter().map(|w| *w).sum::<W>();
+        let expected_counts = weights
+            .iter()
+            .map(|&w| w_to_f64(w) / w_to_f64(weight_sum) * NUM_SAMPLES as f64)
+            .collect::<Vec<f64>>();
+        let weight_distribution = WeightedIndex::new(weights).unwrap();
+
+        let mut counts = vec![0; NUM_WEIGHTS as usize];
+        for _ in 0..NUM_SAMPLES {
+            counts[rng.sample(&weight_distribution)] += 1;
+        }
+
+        assert_eq!(counts[ZERO_WEIGHT_INDEX as usize], 0);
+        for (count, expected_count) in counts.into_iter().zip(expected_counts) {
+            let difference = (count as f64 - expected_count).abs();
+            let max_allowed_difference = NUM_SAMPLES as f64 / NUM_WEIGHTS as f64 * 0.1;
+            assert!(difference <= max_allowed_difference);
+        }
+
+        assert_eq!(
+            WeightedIndex::<W>::new(vec![]).unwrap_err(),
+            WeightedError::NoItem
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![W::ZERO]).unwrap_err(),
+            WeightedError::AllWeightsZero
+        );
+        assert_eq!(
+            WeightedIndex::new(vec![W::MAX, W::MAX]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<W: Weight>(weights: Vec<W>, buf: &mut [usize], expected: &[usize]) {
+            assert_eq!(buf.len(), expected.len());
+            let distr = WeightedIndex::new(weights).unwrap();
+            let mut rng = crate::test::rng(0x9c9fa0b0580a7031);
+            for r in buf.iter_mut() {
+                *r = rng.sample(&distr);
+            }
+            assert_eq!(buf, expected);
+        }
+
+        let mut buf = [0; 10];
+        test_samples(vec![1i32, 1, 1, 1, 1, 1, 1, 1, 1], &mut buf, &[
+            6, 5, 7, 5, 8, 7, 6, 2, 3, 7,
+        ]);
+        test_samples(vec![0.7f32, 0.1, 0.1, 0.1], &mut buf, &[
+            2, 0, 0, 0, 0, 0, 0, 0, 1, 3,
+        ]);
+        test_samples(vec![1.0f64, 0.999, 0.998, 0.997], &mut buf, &[
+            2, 1, 2, 3, 2, 1, 3, 2, 1, 1,
+        ]);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/weighted/mod.rs b/vendor/rand-0.7.3/src/distributions/weighted/mod.rs
new file mode 100644
index 000000000..357e3a9f0
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/weighted/mod.rs
@@ -0,0 +1,413 @@
+// 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.
+
+//! Weighted index sampling
+//!
+//! This module provides two implementations for sampling indices:
+//!
+//! *   [`WeightedIndex`] allows `O(log N)` sampling
+//! *   [`alias_method::WeightedIndex`] allows `O(1)` sampling, but with
+//!      much greater set-up cost
+//!      
+//! [`alias_method::WeightedIndex`]: alias_method/struct.WeightedIndex.html
+
+pub mod alias_method;
+
+use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler};
+use crate::distributions::Distribution;
+use crate::Rng;
+use core::cmp::PartialOrd;
+use core::fmt;
+
+// Note that this whole module is only imported if feature="alloc" is enabled.
+#[cfg(not(feature = "std"))] use crate::alloc::vec::Vec;
+
+/// A distribution using weighted sampling to pick a discretely selected
+/// item.
+///
+/// Sampling a `WeightedIndex` distribution returns the index of a randomly
+/// selected element from the iterator used when the `WeightedIndex` was
+/// created. The chance of a given element being picked is proportional to the
+/// value of the element. The weights can use any type `X` for which an
+/// implementation of [`Uniform<X>`] exists.
+///
+/// # Performance
+///
+/// A `WeightedIndex<X>` contains a `Vec<X>` and a [`Uniform<X>`] and so its
+/// size is the sum of the size of those objects, possibly plus some alignment.
+///
+/// Creating a `WeightedIndex<X>` will allocate enough space to hold `N - 1`
+/// weights of type `X`, where `N` is the number of weights. However, since
+/// `Vec` doesn't guarantee a particular growth strategy, additional memory
+/// might be allocated but not used. Since the `WeightedIndex` object also
+/// contains, this might cause additional allocations, though for primitive
+/// types, ['Uniform<X>`] doesn't allocate any memory.
+///
+/// Time complexity of sampling from `WeightedIndex` is `O(log N)` where
+/// `N` is the number of weights.
+///
+/// Sampling from `WeightedIndex` will result in a single call to
+/// `Uniform<X>::sample` (method of the [`Distribution`] trait), which typically
+/// will request a single value from the underlying [`RngCore`], though the
+/// exact number depends on the implementaiton of `Uniform<X>::sample`.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand::distributions::WeightedIndex;
+///
+/// let choices = ['a', 'b', 'c'];
+/// let weights = [2,   1,   1];
+/// let dist = WeightedIndex::new(&weights).unwrap();
+/// let mut rng = thread_rng();
+/// for _ in 0..100 {
+///     // 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
+///     println!("{}", choices[dist.sample(&mut rng)]);
+/// }
+///
+/// let items = [('a', 0), ('b', 3), ('c', 7)];
+/// let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).unwrap();
+/// for _ in 0..100 {
+///     // 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
+///     println!("{}", items[dist2.sample(&mut rng)].0);
+/// }
+/// ```
+///
+/// [`Uniform<X>`]: crate::distributions::uniform::Uniform
+/// [`RngCore`]: crate::RngCore
+#[derive(Debug, Clone)]
+pub struct WeightedIndex<X: SampleUniform + PartialOrd> {
+    cumulative_weights: Vec<X>,
+    total_weight: X,
+    weight_distribution: X::Sampler,
+}
+
+impl<X: SampleUniform + PartialOrd> WeightedIndex<X> {
+    /// Creates a new a `WeightedIndex` [`Distribution`] using the values
+    /// in `weights`. The weights can use any type `X` for which an
+    /// implementation of [`Uniform<X>`] exists.
+    ///
+    /// Returns an error if the iterator is empty, if any weight is `< 0`, or
+    /// if its total value is 0.
+    ///
+    /// [`Uniform<X>`]: crate::distributions::uniform::Uniform
+    pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>
+    where
+        I: IntoIterator,
+        I::Item: SampleBorrow<X>,
+        X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
+    {
+        let mut iter = weights.into_iter();
+        let mut total_weight: X = iter.next().ok_or(WeightedError::NoItem)?.borrow().clone();
+
+        let zero = <X as Default>::default();
+        if total_weight < zero {
+            return Err(WeightedError::InvalidWeight);
+        }
+
+        let mut weights = Vec::<X>::with_capacity(iter.size_hint().0);
+        for w in iter {
+            if *w.borrow() < zero {
+                return Err(WeightedError::InvalidWeight);
+            }
+            weights.push(total_weight.clone());
+            total_weight += w.borrow();
+        }
+
+        if total_weight == zero {
+            return Err(WeightedError::AllWeightsZero);
+        }
+        let distr = X::Sampler::new(zero, total_weight.clone());
+
+        Ok(WeightedIndex {
+            cumulative_weights: weights,
+            total_weight,
+            weight_distribution: distr,
+        })
+    }
+
+    /// Update a subset of weights, without changing the number of weights.
+    ///
+    /// `new_weights` must be sorted by the index.
+    ///
+    /// Using this method instead of `new` might be more efficient if only a small number of
+    /// weights is modified. No allocations are performed, unless the weight type `X` uses
+    /// allocation internally.
+    ///
+    /// In case of error, `self` is not modified.
+    pub fn update_weights(&mut self, new_weights: &[(usize, &X)]) -> Result<(), WeightedError>
+    where X: for<'a> ::core::ops::AddAssign<&'a X>
+            + for<'a> ::core::ops::SubAssign<&'a X>
+            + Clone
+            + Default {
+        if new_weights.is_empty() {
+            return Ok(());
+        }
+
+        let zero = <X as Default>::default();
+
+        let mut total_weight = self.total_weight.clone();
+
+        // Check for errors first, so we don't modify `self` in case something
+        // goes wrong.
+        let mut prev_i = None;
+        for &(i, w) in new_weights {
+            if let Some(old_i) = prev_i {
+                if old_i >= i {
+                    return Err(WeightedError::InvalidWeight);
+                }
+            }
+            if *w < zero {
+                return Err(WeightedError::InvalidWeight);
+            }
+            if i >= self.cumulative_weights.len() + 1 {
+                return Err(WeightedError::TooMany);
+            }
+
+            let mut old_w = if i < self.cumulative_weights.len() {
+                self.cumulative_weights[i].clone()
+            } else {
+                self.total_weight.clone()
+            };
+            if i > 0 {
+                old_w -= &self.cumulative_weights[i - 1];
+            }
+
+            total_weight -= &old_w;
+            total_weight += w;
+            prev_i = Some(i);
+        }
+        if total_weight == zero {
+            return Err(WeightedError::AllWeightsZero);
+        }
+
+        // Update the weights. Because we checked all the preconditions in the
+        // previous loop, this should never panic.
+        let mut iter = new_weights.iter();
+
+        let mut prev_weight = zero.clone();
+        let mut next_new_weight = iter.next();
+        let &(first_new_index, _) = next_new_weight.unwrap();
+        let mut cumulative_weight = if first_new_index > 0 {
+            self.cumulative_weights[first_new_index - 1].clone()
+        } else {
+            zero.clone()
+        };
+        for i in first_new_index..self.cumulative_weights.len() {
+            match next_new_weight {
+                Some(&(j, w)) if i == j => {
+                    cumulative_weight += w;
+                    next_new_weight = iter.next();
+                }
+                _ => {
+                    let mut tmp = self.cumulative_weights[i].clone();
+                    tmp -= &prev_weight; // We know this is positive.
+                    cumulative_weight += &tmp;
+                }
+            }
+            prev_weight = cumulative_weight.clone();
+            core::mem::swap(&mut prev_weight, &mut self.cumulative_weights[i]);
+        }
+
+        self.total_weight = total_weight;
+        self.weight_distribution = X::Sampler::new(zero, self.total_weight.clone());
+
+        Ok(())
+    }
+}
+
+impl<X> Distribution<usize> for WeightedIndex<X>
+where X: SampleUniform + PartialOrd
+{
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize {
+        use ::core::cmp::Ordering;
+        let chosen_weight = self.weight_distribution.sample(rng);
+        // Find the first item which has a weight *higher* than the chosen weight.
+        self.cumulative_weights
+            .binary_search_by(|w| {
+                if *w <= chosen_weight {
+                    Ordering::Less
+                } else {
+                    Ordering::Greater
+                }
+            })
+            .unwrap_err()
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_weightedindex() {
+        let mut r = crate::test::rng(700);
+        const N_REPS: u32 = 5000;
+        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);
+            }
+        };
+
+        // WeightedIndex from vec
+        let mut chosen = [0i32; 14];
+        let distr = WeightedIndex::new(weights.to_vec()).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        // WeightedIndex from slice
+        chosen = [0i32; 14];
+        let distr = WeightedIndex::new(&weights[..]).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        // WeightedIndex from iterator
+        chosen = [0i32; 14];
+        let distr = WeightedIndex::new(weights.iter()).unwrap();
+        for _ in 0..N_REPS {
+            chosen[distr.sample(&mut r)] += 1;
+        }
+        verify(chosen);
+
+        for _ in 0..5 {
+            assert_eq!(WeightedIndex::new(&[0, 1]).unwrap().sample(&mut r), 1);
+            assert_eq!(WeightedIndex::new(&[1, 0]).unwrap().sample(&mut r), 0);
+            assert_eq!(
+                WeightedIndex::new(&[0, 0, 0, 0, 10, 0])
+                    .unwrap()
+                    .sample(&mut r),
+                4
+            );
+        }
+
+        assert_eq!(
+            WeightedIndex::new(&[10][0..0]).unwrap_err(),
+            WeightedError::NoItem
+        );
+        assert_eq!(
+            WeightedIndex::new(&[0]).unwrap_err(),
+            WeightedError::AllWeightsZero
+        );
+        assert_eq!(
+            WeightedIndex::new(&[10, 20, -1, 30]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(&[-10, 20, 1, 30]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+        assert_eq!(
+            WeightedIndex::new(&[-10]).unwrap_err(),
+            WeightedError::InvalidWeight
+        );
+    }
+
+    #[test]
+    fn test_update_weights() {
+        let data = [
+            (
+                &[10u32, 2, 3, 4][..],
+                &[(1, &100), (2, &4)][..], // positive change
+                &[10, 100, 4, 4][..],
+            ),
+            (
+                &[1u32, 2, 3, 0, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7][..],
+                &[(2, &1), (5, &1), (13, &100)][..], // negative change and last element
+                &[1u32, 2, 1, 0, 5, 1, 7, 1, 2, 3, 4, 5, 6, 100][..],
+            ),
+        ];
+
+        for (weights, update, expected_weights) in data.iter() {
+            let total_weight = weights.iter().sum::<u32>();
+            let mut distr = WeightedIndex::new(weights.to_vec()).unwrap();
+            assert_eq!(distr.total_weight, total_weight);
+
+            distr.update_weights(update).unwrap();
+            let expected_total_weight = expected_weights.iter().sum::<u32>();
+            let expected_distr = WeightedIndex::new(expected_weights.to_vec()).unwrap();
+            assert_eq!(distr.total_weight, expected_total_weight);
+            assert_eq!(distr.total_weight, expected_distr.total_weight);
+            assert_eq!(distr.cumulative_weights, expected_distr.cumulative_weights);
+        }
+    }
+
+    #[test]
+    fn value_stability() {
+        fn test_samples<X: SampleUniform + PartialOrd, I>(
+            weights: I, buf: &mut [usize], expected: &[usize],
+        ) where
+            I: IntoIterator,
+            I::Item: SampleBorrow<X>,
+            X: for<'a> ::core::ops::AddAssign<&'a X> + Clone + Default,
+        {
+            assert_eq!(buf.len(), expected.len());
+            let distr = WeightedIndex::new(weights).unwrap();
+            let mut rng = crate::test::rng(701);
+            for r in buf.iter_mut() {
+                *r = rng.sample(&distr);
+            }
+            assert_eq!(buf, expected);
+        }
+
+        let mut buf = [0; 10];
+        test_samples(&[1i32, 1, 1, 1, 1, 1, 1, 1, 1], &mut buf, &[
+            0, 6, 2, 6, 3, 4, 7, 8, 2, 5,
+        ]);
+        test_samples(&[0.7f32, 0.1, 0.1, 0.1], &mut buf, &[
+            0, 0, 0, 1, 0, 0, 2, 3, 0, 0,
+        ]);
+        test_samples(&[1.0f64, 0.999, 0.998, 0.997], &mut buf, &[
+            2, 2, 1, 3, 2, 1, 3, 3, 2, 1,
+        ]);
+    }
+}
+
+/// Error type returned from `WeightedIndex::new`.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum WeightedError {
+    /// The provided weight collection contains no items.
+    NoItem,
+
+    /// A weight is either less than zero, greater than the supported maximum or
+    /// otherwise invalid.
+    InvalidWeight,
+
+    /// All items in the provided weight collection are zero.
+    AllWeightsZero,
+
+    /// Too many weights are provided (length greater than `u32::MAX`)
+    TooMany,
+}
+
+#[cfg(feature = "std")]
+impl ::std::error::Error for WeightedError {}
+
+impl fmt::Display for WeightedError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        match *self {
+            WeightedError::NoItem => write!(f, "No weights provided."),
+            WeightedError::InvalidWeight => write!(f, "A weight is invalid."),
+            WeightedError::AllWeightsZero => write!(f, "All weights are zero."),
+            WeightedError::TooMany => write!(f, "Too many weights (hit u32::MAX)"),
+        }
+    }
+}
diff --git a/vendor/rand-0.7.3/src/distributions/ziggurat_tables.rs b/vendor/rand-0.7.3/src/distributions/ziggurat_tables.rs
new file mode 100644
index 000000000..f830a601b
--- /dev/null
+++ b/vendor/rand-0.7.3/src/distributions/ziggurat_tables.rs
@@ -0,0 +1,283 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+// Tables for distributions which are sampled using the ziggurat
+// algorithm. Autogenerated by `ziggurat_tables.py`.
+
+pub type ZigTable = &'static [f64; 257];
+pub const ZIG_NORM_R: f64 = 3.654152885361008796;
+#[rustfmt::skip]
+pub static ZIG_NORM_X: [f64; 257] =
+    [3.910757959537090045, 3.654152885361008796, 3.449278298560964462, 3.320244733839166074,
+     3.224575052047029100, 3.147889289517149969, 3.083526132001233044, 3.027837791768635434,
+     2.978603279880844834, 2.934366867207854224, 2.894121053612348060, 2.857138730872132548,
+     2.822877396825325125, 2.790921174000785765, 2.760944005278822555, 2.732685359042827056,
+     2.705933656121858100, 2.680514643284522158, 2.656283037575502437, 2.633116393630324570,
+     2.610910518487548515, 2.589575986706995181, 2.569035452680536569, 2.549221550323460761,
+     2.530075232158516929, 2.511544441625342294, 2.493583041269680667, 2.476149939669143318,
+     2.459208374333311298, 2.442725318198956774, 2.426670984935725972, 2.411018413899685520,
+     2.395743119780480601, 2.380822795170626005, 2.366237056715818632, 2.351967227377659952,
+     2.337996148795031370, 2.324308018869623016, 2.310888250599850036, 2.297723348901329565,
+     2.284800802722946056, 2.272108990226823888, 2.259637095172217780, 2.247375032945807760,
+     2.235313384928327984, 2.223443340090905718, 2.211756642882544366, 2.200245546609647995,
+     2.188902771624720689, 2.177721467738641614, 2.166695180352645966, 2.155817819875063268,
+     2.145083634046203613, 2.134487182844320152, 2.124023315687815661, 2.113687150684933957,
+     2.103474055713146829, 2.093379631137050279, 2.083399693996551783, 2.073530263516978778,
+     2.063767547809956415, 2.054107931648864849, 2.044547965215732788, 2.035084353727808715,
+     2.025713947862032960, 2.016433734904371722, 2.007240830558684852, 1.998132471356564244,
+     1.989106007615571325, 1.980158896898598364, 1.971288697931769640, 1.962493064942461896,
+     1.953769742382734043, 1.945116560006753925, 1.936531428273758904, 1.928012334050718257,
+     1.919557336591228847, 1.911164563769282232, 1.902832208548446369, 1.894558525668710081,
+     1.886341828534776388, 1.878180486290977669, 1.870072921069236838, 1.862017605397632281,
+     1.854013059758148119, 1.846057850283119750, 1.838150586580728607, 1.830289919680666566,
+     1.822474540091783224, 1.814703175964167636, 1.806974591348693426, 1.799287584547580199,
+     1.791640986550010028, 1.784033659547276329, 1.776464495522344977, 1.768932414909077933,
+     1.761436365316706665, 1.753975320315455111, 1.746548278279492994, 1.739154261283669012,
+     1.731792314050707216, 1.724461502945775715, 1.717160915015540690, 1.709889657069006086,
+     1.702646854797613907, 1.695431651932238548, 1.688243209434858727, 1.681080704722823338,
+     1.673943330923760353, 1.666830296159286684, 1.659740822855789499, 1.652674147080648526,
+     1.645629517902360339, 1.638606196773111146, 1.631603456932422036, 1.624620582830568427,
+     1.617656869570534228, 1.610711622367333673, 1.603784156023583041, 1.596873794420261339,
+     1.589979870021648534, 1.583101723393471438, 1.576238702733332886, 1.569390163412534456,
+     1.562555467528439657, 1.555733983466554893, 1.548925085471535512, 1.542128153226347553,
+     1.535342571438843118, 1.528567729435024614, 1.521803020758293101, 1.515047842773992404,
+     1.508301596278571965, 1.501563685112706548, 1.494833515777718391, 1.488110497054654369,
+     1.481394039625375747, 1.474683555695025516, 1.467978458615230908, 1.461278162507407830,
+     1.454582081885523293, 1.447889631277669675, 1.441200224845798017, 1.434513276002946425,
+     1.427828197027290358, 1.421144398672323117, 1.414461289772464658, 1.407778276843371534,
+     1.401094763676202559, 1.394410150925071257, 1.387723835686884621, 1.381035211072741964,
+     1.374343665770030531, 1.367648583594317957, 1.360949343030101844, 1.354245316759430606,
+     1.347535871177359290, 1.340820365893152122, 1.334098153216083604, 1.327368577624624679,
+     1.320630975217730096, 1.313884673146868964, 1.307128989027353860, 1.300363230327433728,
+     1.293586693733517645, 1.286798664489786415, 1.279998415710333237, 1.273185207661843732,
+     1.266358287014688333, 1.259516886060144225, 1.252660221891297887, 1.245787495544997903,
+     1.238897891102027415, 1.231990574742445110, 1.225064693752808020, 1.218119375481726552,
+     1.211153726239911244, 1.204166830140560140, 1.197157747875585931, 1.190125515422801650,
+     1.183069142678760732, 1.175987612011489825, 1.168879876726833800, 1.161744859441574240,
+     1.154581450355851802, 1.147388505416733873, 1.140164844363995789, 1.132909248648336975,
+     1.125620459211294389, 1.118297174115062909, 1.110938046009249502, 1.103541679420268151,
+     1.096106627847603487, 1.088631390649514197, 1.081114409698889389, 1.073554065787871714,
+     1.065948674757506653, 1.058296483326006454, 1.050595664586207123, 1.042844313139370538,
+     1.035040439828605274, 1.027181966030751292, 1.019266717460529215, 1.011292417434978441,
+     1.003256679539591412, 0.995156999629943084, 0.986990747093846266, 0.978755155288937750,
+     0.970447311058864615, 0.962064143217605250, 0.953602409875572654, 0.945058684462571130,
+     0.936429340280896860, 0.927710533396234771, 0.918898183643734989, 0.909987953490768997,
+     0.900975224455174528, 0.891855070726792376, 0.882622229578910122, 0.873271068082494550,
+     0.863795545546826915, 0.854189171001560554, 0.844444954902423661, 0.834555354079518752,
+     0.824512208745288633, 0.814306670128064347, 0.803929116982664893, 0.793369058833152785,
+     0.782615023299588763, 0.771654424216739354, 0.760473406422083165, 0.749056662009581653,
+     0.737387211425838629, 0.725446140901303549, 0.713212285182022732, 0.700661841097584448,
+     0.687767892786257717, 0.674499822827436479, 0.660822574234205984, 0.646695714884388928,
+     0.632072236375024632, 0.616896989996235545, 0.601104617743940417, 0.584616766093722262,
+     0.567338257040473026, 0.549151702313026790, 0.529909720646495108, 0.509423329585933393,
+     0.487443966121754335, 0.463634336771763245, 0.437518402186662658, 0.408389134588000746,
+     0.375121332850465727, 0.335737519180459465, 0.286174591747260509, 0.215241895913273806,
+     0.000000000000000000];
+#[rustfmt::skip]
+pub static ZIG_NORM_F: [f64; 257] =
+    [0.000477467764586655, 0.001260285930498598, 0.002609072746106363, 0.004037972593371872,
+     0.005522403299264754, 0.007050875471392110, 0.008616582769422917, 0.010214971439731100,
+     0.011842757857943104, 0.013497450601780807, 0.015177088307982072, 0.016880083152595839,
+     0.018605121275783350, 0.020351096230109354, 0.022117062707379922, 0.023902203305873237,
+     0.025705804008632656, 0.027527235669693315, 0.029365939758230111, 0.031221417192023690,
+     0.033093219458688698, 0.034980941461833073, 0.036884215688691151, 0.038802707404656918,
+     0.040736110656078753, 0.042684144916619378, 0.044646552251446536, 0.046623094902089664,
+     0.048613553216035145, 0.050617723861121788, 0.052635418276973649, 0.054666461325077916,
+     0.056710690106399467, 0.058767952921137984, 0.060838108349751806, 0.062921024437977854,
+     0.065016577971470438, 0.067124653828023989, 0.069245144397250269, 0.071377949059141965,
+     0.073522973714240991, 0.075680130359194964, 0.077849336702372207, 0.080030515814947509,
+     0.082223595813495684, 0.084428509570654661, 0.086645194450867782, 0.088873592068594229,
+     0.091113648066700734, 0.093365311913026619, 0.095628536713353335, 0.097903279039215627,
+     0.100189498769172020, 0.102487158942306270, 0.104796225622867056, 0.107116667775072880,
+     0.109448457147210021, 0.111791568164245583, 0.114145977828255210, 0.116511665626037014,
+     0.118888613443345698, 0.121276805485235437, 0.123676228202051403, 0.126086870220650349,
+     0.128508722280473636, 0.130941777174128166, 0.133386029692162844, 0.135841476571757352,
+     0.138308116449064322, 0.140785949814968309, 0.143274978974047118, 0.145775208006537926,
+     0.148286642733128721, 0.150809290682410169, 0.153343161060837674, 0.155888264725064563,
+     0.158444614156520225, 0.161012223438117663, 0.163591108232982951, 0.166181285765110071,
+     0.168782774801850333, 0.171395595638155623, 0.174019770082499359, 0.176655321444406654,
+     0.179302274523530397, 0.181960655600216487, 0.184630492427504539, 0.187311814224516926,
+     0.190004651671193070, 0.192709036904328807, 0.195425003514885592, 0.198152586546538112,
+     0.200891822495431333, 0.203642749311121501, 0.206405406398679298, 0.209179834621935651,
+     0.211966076307852941, 0.214764175252008499, 0.217574176725178370, 0.220396127481011589,
+     0.223230075764789593, 0.226076071323264877, 0.228934165415577484, 0.231804410825248525,
+     0.234686861873252689, 0.237581574432173676, 0.240488605941449107, 0.243408015423711988,
+     0.246339863502238771, 0.249284212419516704, 0.252241126056943765, 0.255210669955677150,
+     0.258192911338648023, 0.261187919133763713, 0.264195763998317568, 0.267216518344631837,
+     0.270250256366959984, 0.273297054069675804, 0.276356989296781264, 0.279430141762765316,
+     0.282516593084849388, 0.285616426816658109, 0.288729728483353931, 0.291856585618280984,
+     0.294997087801162572, 0.298151326697901342, 0.301319396102034120, 0.304501391977896274,
+     0.307697412505553769, 0.310907558127563710, 0.314131931597630143, 0.317370638031222396,
+     0.320623784958230129, 0.323891482377732021, 0.327173842814958593, 0.330470981380537099,
+     0.333783015832108509, 0.337110066638412809, 0.340452257045945450, 0.343809713148291340,
+     0.347182563958251478, 0.350570941482881204, 0.353974980801569250, 0.357394820147290515,
+     0.360830600991175754, 0.364282468130549597, 0.367750569780596226, 0.371235057669821344,
+     0.374736087139491414, 0.378253817247238111, 0.381788410875031348, 0.385340034841733958,
+     0.388908860020464597, 0.392495061461010764, 0.396098818517547080, 0.399720314981931668,
+     0.403359739222868885, 0.407017284331247953, 0.410693148271983222, 0.414387534042706784,
+     0.418100649839684591, 0.421832709231353298, 0.425583931339900579, 0.429354541031341519,
+     0.433144769114574058, 0.436954852549929273, 0.440785034667769915, 0.444635565397727750,
+     0.448506701509214067, 0.452398706863882505, 0.456311852680773566, 0.460246417814923481,
+     0.464202689050278838, 0.468180961407822172, 0.472181538469883255, 0.476204732721683788,
+     0.480250865911249714, 0.484320269428911598, 0.488413284707712059, 0.492530263646148658,
+     0.496671569054796314, 0.500837575128482149, 0.505028667945828791, 0.509245245998136142,
+     0.513487720749743026, 0.517756517232200619, 0.522052074674794864, 0.526374847174186700,
+     0.530725304406193921, 0.535103932383019565, 0.539511234259544614, 0.543947731192649941,
+     0.548413963257921133, 0.552910490428519918, 0.557437893621486324, 0.561996775817277916,
+     0.566587763258951771, 0.571211506738074970, 0.575868682975210544, 0.580559996103683473,
+     0.585286179266300333, 0.590047996335791969, 0.594846243770991268, 0.599681752622167719,
+     0.604555390700549533, 0.609468064928895381, 0.614420723892076803, 0.619414360609039205,
+     0.624450015550274240, 0.629528779928128279, 0.634651799290960050, 0.639820277456438991,
+     0.645035480824251883, 0.650298743114294586, 0.655611470583224665, 0.660975147780241357,
+     0.666391343912380640, 0.671861719900766374, 0.677388036222513090, 0.682972161648791376,
+     0.688616083008527058, 0.694321916130032579, 0.700091918140490099, 0.705928501336797409,
+     0.711834248882358467, 0.717811932634901395, 0.723864533472881599, 0.729995264565802437,
+     0.736207598131266683, 0.742505296344636245, 0.748892447223726720, 0.755373506511754500,
+     0.761953346841546475, 0.768637315803334831, 0.775431304986138326, 0.782341832659861902,
+     0.789376143571198563, 0.796542330428254619, 0.803849483176389490, 0.811307874318219935,
+     0.818929191609414797, 0.826726833952094231, 0.834716292992930375, 0.842915653118441077,
+     0.851346258465123684, 0.860033621203008636, 0.869008688043793165, 0.878309655816146839,
+     0.887984660763399880, 0.898095921906304051, 0.908726440060562912, 0.919991505048360247,
+     0.932060075968990209, 0.945198953453078028, 0.959879091812415930, 0.977101701282731328,
+     1.000000000000000000];
+pub const ZIG_EXP_R: f64 = 7.697117470131050077;
+#[rustfmt::skip]
+pub static ZIG_EXP_X: [f64; 257] =
+    [8.697117470131052741, 7.697117470131050077, 6.941033629377212577, 6.478378493832569696,
+     6.144164665772472667, 5.882144315795399869, 5.666410167454033697, 5.482890627526062488,
+     5.323090505754398016, 5.181487281301500047, 5.054288489981304089, 4.938777085901250530,
+     4.832939741025112035, 4.735242996601741083, 4.644491885420085175, 4.559737061707351380,
+     4.480211746528421912, 4.405287693473573185, 4.334443680317273007, 4.267242480277365857,
+     4.203313713735184365, 4.142340865664051464, 4.084051310408297830, 4.028208544647936762,
+     3.974606066673788796, 3.923062500135489739, 3.873417670399509127, 3.825529418522336744,
+     3.779270992411667862, 3.734528894039797375, 3.691201090237418825, 3.649195515760853770,
+     3.608428813128909507, 3.568825265648337020, 3.530315889129343354, 3.492837654774059608,
+     3.456332821132760191, 3.420748357251119920, 3.386035442460300970, 3.352149030900109405,
+     3.319047470970748037, 3.286692171599068679, 3.255047308570449882, 3.224079565286264160,
+     3.193757903212240290, 3.164053358025972873, 3.134938858084440394, 3.106389062339824481,
+     3.078380215254090224, 3.050890016615455114, 3.023897504455676621, 2.997382949516130601,
+     2.971327759921089662, 2.945714394895045718, 2.920526286512740821, 2.895747768600141825,
+     2.871364012015536371, 2.847360965635188812, 2.823725302450035279, 2.800444370250737780,
+     2.777506146439756574, 2.754899196562344610, 2.732612636194700073, 2.710636095867928752,
+     2.688959688741803689, 2.667573980773266573, 2.646469963151809157, 2.625639026797788489,
+     2.605072938740835564, 2.584763820214140750, 2.564704126316905253, 2.544886627111869970,
+     2.525304390037828028, 2.505950763528594027, 2.486819361740209455, 2.467904050297364815,
+     2.449198932978249754, 2.430698339264419694, 2.412396812688870629, 2.394289099921457886,
+     2.376370140536140596, 2.358635057409337321, 2.341079147703034380, 2.323697874390196372,
+     2.306486858283579799, 2.289441870532269441, 2.272558825553154804, 2.255833774367219213,
+     2.239262898312909034, 2.222842503111036816, 2.206569013257663858, 2.190438966723220027,
+     2.174449009937774679, 2.158595893043885994, 2.142876465399842001, 2.127287671317368289,
+     2.111826546019042183, 2.096490211801715020, 2.081275874393225145, 2.066180819490575526,
+     2.051202409468584786, 2.036338080248769611, 2.021585338318926173, 2.006941757894518563,
+     1.992404978213576650, 1.977972700957360441, 1.963642687789548313, 1.949412758007184943,
+     1.935280786297051359, 1.921244700591528076, 1.907302480018387536, 1.893452152939308242,
+     1.879691795072211180, 1.866019527692827973, 1.852433515911175554, 1.838931967018879954,
+     1.825513128903519799, 1.812175288526390649, 1.798916770460290859, 1.785735935484126014,
+     1.772631179231305643, 1.759600930889074766, 1.746643651946074405, 1.733757834985571566,
+     1.720942002521935299, 1.708194705878057773, 1.695514524101537912, 1.682900062917553896,
+     1.670349953716452118, 1.657862852574172763, 1.645437439303723659, 1.633072416535991334,
+     1.620766508828257901, 1.608518461798858379, 1.596327041286483395, 1.584191032532688892,
+     1.572109239386229707, 1.560080483527888084, 1.548103603714513499, 1.536177455041032092,
+     1.524300908219226258, 1.512472848872117082, 1.500692176842816750, 1.488957805516746058,
+     1.477268661156133867, 1.465623682245745352, 1.454021818848793446, 1.442462031972012504,
+     1.430943292938879674, 1.419464582769983219, 1.408024891569535697, 1.396623217917042137,
+     1.385258568263121992, 1.373929956328490576, 1.362636402505086775, 1.351376933258335189,
+     1.340150580529504643, 1.328956381137116560, 1.317793376176324749, 1.306660610415174117,
+     1.295557131686601027, 1.284481990275012642, 1.273434238296241139, 1.262412929069615330,
+     1.251417116480852521, 1.240445854334406572, 1.229498195693849105, 1.218573192208790124,
+     1.207669893426761121, 1.196787346088403092, 1.185924593404202199, 1.175080674310911677,
+     1.164254622705678921, 1.153445466655774743, 1.142652227581672841, 1.131873919411078511,
+     1.121109547701330200, 1.110358108727411031, 1.099618588532597308, 1.088889961938546813,
+     1.078171191511372307, 1.067461226479967662, 1.056759001602551429, 1.046063435977044209,
+     1.035373431790528542, 1.024687873002617211, 1.014005623957096480, 1.003325527915696735,
+     0.992646405507275897, 0.981967053085062602, 0.971286240983903260, 0.960602711668666509,
+     0.949915177764075969, 0.939222319955262286, 0.928522784747210395, 0.917815182070044311,
+     0.907098082715690257, 0.896370015589889935, 0.885629464761751528, 0.874874866291025066,
+     0.864104604811004484, 0.853317009842373353, 0.842510351810368485, 0.831682837734273206,
+     0.820832606554411814, 0.809957724057418282, 0.799056177355487174, 0.788125868869492430,
+     0.777164609759129710, 0.766170112735434672, 0.755139984181982249, 0.744071715500508102,
+     0.732962673584365398, 0.721810090308756203, 0.710611050909655040, 0.699362481103231959,
+     0.688061132773747808, 0.676703568029522584, 0.665286141392677943, 0.653804979847664947,
+     0.642255960424536365, 0.630634684933490286, 0.618936451394876075, 0.607156221620300030,
+     0.595288584291502887, 0.583327712748769489, 0.571267316532588332, 0.559100585511540626,
+     0.546820125163310577, 0.534417881237165604, 0.521885051592135052, 0.509211982443654398,
+     0.496388045518671162, 0.483401491653461857, 0.470239275082169006, 0.456886840931420235,
+     0.443327866073552401, 0.429543940225410703, 0.415514169600356364, 0.401214678896277765,
+     0.386617977941119573, 0.371692145329917234, 0.356399760258393816, 0.340696481064849122,
+     0.324529117016909452, 0.307832954674932158, 0.290527955491230394, 0.272513185478464703,
+     0.253658363385912022, 0.233790483059674731, 0.212671510630966620, 0.189958689622431842,
+     0.165127622564187282, 0.137304980940012589, 0.104838507565818778, 0.063852163815001570,
+     0.000000000000000000];
+#[rustfmt::skip]
+pub static ZIG_EXP_F: [f64; 257] =
+    [0.000167066692307963, 0.000454134353841497, 0.000967269282327174, 0.001536299780301573,
+     0.002145967743718907, 0.002788798793574076, 0.003460264777836904, 0.004157295120833797,
+     0.004877655983542396, 0.005619642207205489, 0.006381905937319183, 0.007163353183634991,
+     0.007963077438017043, 0.008780314985808977, 0.009614413642502212, 0.010464810181029981,
+     0.011331013597834600, 0.012212592426255378, 0.013109164931254991, 0.014020391403181943,
+     0.014945968011691148, 0.015885621839973156, 0.016839106826039941, 0.017806200410911355,
+     0.018786700744696024, 0.019780424338009740, 0.020787204072578114, 0.021806887504283581,
+     0.022839335406385240, 0.023884420511558174, 0.024942026419731787, 0.026012046645134221,
+     0.027094383780955803, 0.028188948763978646, 0.029295660224637411, 0.030414443910466622,
+     0.031545232172893622, 0.032687963508959555, 0.033842582150874358, 0.035009037697397431,
+     0.036187284781931443, 0.037377282772959382, 0.038578995503074871, 0.039792391023374139,
+     0.041017441380414840, 0.042254122413316254, 0.043502413568888197, 0.044762297732943289,
+     0.046033761076175184, 0.047316792913181561, 0.048611385573379504, 0.049917534282706379,
+     0.051235237055126281, 0.052564494593071685, 0.053905310196046080, 0.055257689676697030,
+     0.056621641283742870, 0.057997175631200659, 0.059384305633420280, 0.060783046445479660,
+     0.062193415408541036, 0.063615431999807376, 0.065049117786753805, 0.066494496385339816,
+     0.067951593421936643, 0.069420436498728783, 0.070901055162371843, 0.072393480875708752,
+     0.073897746992364746, 0.075413888734058410, 0.076941943170480517, 0.078481949201606435,
+     0.080033947542319905, 0.081597980709237419, 0.083174093009632397, 0.084762330532368146,
+     0.086362741140756927, 0.087975374467270231, 0.089600281910032886, 0.091237516631040197,
+     0.092887133556043569, 0.094549189376055873, 0.096223742550432825, 0.097910853311492213,
+     0.099610583670637132, 0.101322997425953631, 0.103048160171257702, 0.104786139306570145,
+     0.106537004050001632, 0.108300825451033755, 0.110077676405185357, 0.111867631670056283,
+     0.113670767882744286, 0.115487163578633506, 0.117316899211555525, 0.119160057175327641,
+     0.121016721826674792, 0.122886979509545108, 0.124770918580830933, 0.126668629437510671,
+     0.128580204545228199, 0.130505738468330773, 0.132445327901387494, 0.134399071702213602,
+     0.136367070926428829, 0.138349428863580176, 0.140346251074862399, 0.142357645432472146,
+     0.144383722160634720, 0.146424593878344889, 0.148480375643866735, 0.150551185001039839,
+     0.152637142027442801, 0.154738369384468027, 0.156854992369365148, 0.158987138969314129,
+     0.161134939917591952, 0.163298528751901734, 0.165478041874935922, 0.167673618617250081,
+     0.169885401302527550, 0.172113535315319977, 0.174358169171353411, 0.176619454590494829,
+     0.178897546572478278, 0.181192603475496261, 0.183504787097767436, 0.185834262762197083,
+     0.188181199404254262, 0.190545769663195363, 0.192928149976771296, 0.195328520679563189,
+     0.197747066105098818, 0.200183974691911210, 0.202639439093708962, 0.205113656293837654,
+     0.207606827724221982, 0.210119159388988230, 0.212650861992978224, 0.215202151075378628,
+     0.217773247148700472, 0.220364375843359439, 0.222975768058120111, 0.225607660116683956,
+     0.228260293930716618, 0.230933917169627356, 0.233628783437433291, 0.236345152457059560,
+     0.239083290262449094, 0.241843469398877131, 0.244625969131892024, 0.247431075665327543,
+     0.250259082368862240, 0.253110290015629402, 0.255985007030415324, 0.258883549749016173,
+     0.261806242689362922, 0.264753418835062149, 0.267725419932044739, 0.270722596799059967,
+     0.273745309652802915, 0.276793928448517301, 0.279868833236972869, 0.282970414538780746,
+     0.286099073737076826, 0.289255223489677693, 0.292439288161892630, 0.295651704281261252,
+     0.298892921015581847, 0.302163400675693528, 0.305463619244590256, 0.308794066934560185,
+     0.312155248774179606, 0.315547685227128949, 0.318971912844957239, 0.322428484956089223,
+     0.325917972393556354, 0.329440964264136438, 0.332998068761809096, 0.336589914028677717,
+     0.340217149066780189, 0.343880444704502575, 0.347580494621637148, 0.351318016437483449,
+     0.355093752866787626, 0.358908472948750001, 0.362762973354817997, 0.366658079781514379,
+     0.370594648435146223, 0.374573567615902381, 0.378595759409581067, 0.382662181496010056,
+     0.386773829084137932, 0.390931736984797384, 0.395136981833290435, 0.399390684475231350,
+     0.403694012530530555, 0.408048183152032673, 0.412454465997161457, 0.416914186433003209,
+     0.421428728997616908, 0.425999541143034677, 0.430628137288459167, 0.435316103215636907,
+     0.440065100842354173, 0.444876873414548846, 0.449753251162755330, 0.454696157474615836,
+     0.459707615642138023, 0.464789756250426511, 0.469944825283960310, 0.475175193037377708,
+     0.480483363930454543, 0.485871987341885248, 0.491343869594032867, 0.496901987241549881,
+     0.502549501841348056, 0.508289776410643213, 0.514126393814748894, 0.520063177368233931,
+     0.526104213983620062, 0.532253880263043655, 0.538516872002862246, 0.544898237672440056,
+     0.551403416540641733, 0.558038282262587892, 0.564809192912400615, 0.571723048664826150,
+     0.578787358602845359, 0.586010318477268366, 0.593400901691733762, 0.600968966365232560,
+     0.608725382079622346, 0.616682180915207878, 0.624852738703666200, 0.633251994214366398,
+     0.641896716427266423, 0.650805833414571433, 0.660000841079000145, 0.669506316731925177,
+     0.679350572264765806, 0.689566496117078431, 0.700192655082788606, 0.711274760805076456,
+     0.722867659593572465, 0.735038092431424039, 0.747868621985195658, 0.761463388849896838,
+     0.775956852040116218, 0.791527636972496285, 0.808421651523009044, 0.826993296643051101,
+     0.847785500623990496, 0.871704332381204705, 0.900469929925747703, 0.938143680862176477,
+     1.000000000000000000];
diff --git a/vendor/rand-0.7.3/src/lib.rs b/vendor/rand-0.7.3/src/lib.rs
new file mode 100644
index 000000000..d42a79fb1
--- /dev/null
+++ b/vendor/rand-0.7.3/src/lib.rs
@@ -0,0 +1,723 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013-2017 The Rust Project Developers.
+//
+// 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.
+//!
+//! # Quick Start
+//!
+//! To get you started quickly, the easiest and highest-level way to get
+//! a random value is to use [`random()`]; alternatively you can use
+//! [`thread_rng()`]. The [`Rng`] trait provides a useful API on all RNGs, while
+//! the [`distributions`] and [`seq`] modules provide further
+//! functionality on top of RNGs.
+//!
+//! ```
+//! use rand::prelude::*;
+//!
+//! if rand::random() { // generates a boolean
+//!     // Try printing a random unicode code point (probably a bad idea)!
+//!     println!("char: {}", rand::random::<char>());
+//! }
+//!
+//! let mut rng = rand::thread_rng();
+//! let y: f64 = rng.gen(); // generates a float between 0 and 1
+//!
+//! let mut nums: Vec<i32> = (1..100).collect();
+//! nums.shuffle(&mut rng);
+//! ```
+//!
+//! # The Book
+//!
+//! For the user guide and futher documentation, please read
+//! [The Rust Rand Book](https://rust-random.github.io/book).
+
+#![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://rust-random.github.io/rand/"
+)]
+#![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 = "simd_support", feature = "nightly"), feature(stdsimd))]
+#![allow(
+    clippy::excessive_precision,
+    clippy::unreadable_literal,
+    clippy::float_cmp
+)]
+
+#[cfg(all(feature = "alloc", not(feature = "std")))] extern crate alloc;
+
+#[allow(unused)]
+macro_rules! trace { ($($x:tt)*) => (
+    #[cfg(feature = "log")] {
+        log::trace!($($x)*)
+    }
+) }
+#[allow(unused)]
+macro_rules! debug { ($($x:tt)*) => (
+    #[cfg(feature = "log")] {
+        log::debug!($($x)*)
+    }
+) }
+#[allow(unused)]
+macro_rules! info { ($($x:tt)*) => (
+    #[cfg(feature = "log")] {
+        log::info!($($x)*)
+    }
+) }
+#[allow(unused)]
+macro_rules! warn { ($($x:tt)*) => (
+    #[cfg(feature = "log")] {
+        log::warn!($($x)*)
+    }
+) }
+#[allow(unused)]
+macro_rules! error { ($($x:tt)*) => (
+    #[cfg(feature = "log")] {
+        log::error!($($x)*)
+    }
+) }
+
+// Re-exports from rand_core
+pub use rand_core::{CryptoRng, Error, RngCore, SeedableRng};
+
+// Public exports
+#[cfg(feature = "std")] pub use crate::rngs::thread::thread_rng;
+
+// Public modules
+pub mod distributions;
+pub mod prelude;
+pub mod rngs;
+pub mod seq;
+
+
+use crate::distributions::uniform::{SampleBorrow, SampleUniform, UniformSampler};
+use crate::distributions::{Distribution, Standard};
+use core::num::Wrapping;
+use core::{mem, slice};
+
+/// 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());
+/// ```
+pub trait Rng: RngCore {
+    /// Return a random value supporting the [`Standard`] distribution.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let x: u32 = rng.gen();
+    /// println!("{}", x);
+    /// println!("{:?}", rng.gen::<(f64, bool)>());
+    /// ```
+    ///
+    /// # Arrays and tuples
+    ///
+    /// The `rng.gen()` method is able to generate arrays (up to 32 elements)
+    /// and tuples (up to 12 elements), so long as all element types can be
+    /// generated.
+    ///
+    /// For arrays of integers, especially for those with small element types
+    /// (< 64 bit), it will likely be faster to instead use [`Rng::fill`].
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// let tuple: (u8, i32, char) = rng.gen(); // arbitrary tuple support
+    ///
+    /// let arr1: [f32; 32] = rng.gen();        // array construction
+    /// let mut arr2 = [0u8; 128];
+    /// rng.fill(&mut arr2);                    // array fill
+    /// ```
+    ///
+    /// [`Standard`]: distributions::Standard
+    #[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::Uniform
+    fn gen_range<T: SampleUniform, B1, B2>(&mut self, low: B1, high: B2) -> T
+    where
+        B1: SampleBorrow<T> + Sized,
+        B2: SampleBorrow<T> + Sized,
+    {
+        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.
+    ///
+    /// Note that this function takes its arguments by value. This works since
+    /// `(&mut R): Rng where R: Rng` and
+    /// `(&D): Distribution where D: Distribution`,
+    /// however borrowing is not automatic hence `rng.sample_iter(...)` may
+    /// need to be replaced with `(&mut rng).sample_iter(...)`.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    /// use rand::distributions::{Alphanumeric, Uniform, Standard};
+    ///
+    /// let rng = thread_rng();
+    ///
+    /// // Vec of 16 x f32:
+    /// let v: Vec<f32> = rng.sample_iter(Standard).take(16).collect();
+    ///
+    /// // String:
+    /// let s: String = rng.sample_iter(Alphanumeric).take(7).collect();
+    ///
+    /// // Combined values
+    /// println!("{:?}", 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<T, D>(self, distr: D) -> distributions::DistIter<D, Self, T>
+    where
+        D: Distribution<T>,
+        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`]: RngCore::fill_bytes
+    /// [`try_fill`]: Rng::try_fill
+    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 is identical to [`fill`] except that it uses [`try_fill_bytes`]
+    /// internally and forwards RNG errors.
+    ///
+    /// # 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()
+    /// ```
+    ///
+    /// [`try_fill_bytes`]: RngCore::try_fill_bytes
+    /// [`fill`]: Rng::fill
+    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.
+    ///
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same probability repeatedly.
+    ///
+    /// # 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`.
+    ///
+    /// [`Bernoulli`]: distributions::bernoulli::Bernoulli
+    #[inline]
+    fn gen_bool(&mut self, p: f64) -> bool {
+        let d = distributions::Bernoulli::new(p).unwrap();
+        self.sample(d)
+    }
+
+    /// Return a bool with a probability of `numerator/denominator` of being
+    /// true. I.e. `gen_ratio(2, 3)` has chance of 2 in 3, or about 67%, of
+    /// returning true. If `numerator == denominator`, then the returned value
+    /// is guaranteed to be `true`. If `numerator == 0`, then the returned
+    /// value is guaranteed to be `false`.
+    ///
+    /// See also the [`Bernoulli`] distribution, which may be faster if
+    /// sampling from the same `numerator` and `denominator` repeatedly.
+    ///
+    /// # Panics
+    ///
+    /// If `denominator == 0` or `numerator > denominator`.
+    ///
+    /// # Example
+    ///
+    /// ```
+    /// use rand::{thread_rng, Rng};
+    ///
+    /// let mut rng = thread_rng();
+    /// println!("{}", rng.gen_ratio(2, 3));
+    /// ```
+    ///
+    /// [`Bernoulli`]: distributions::bernoulli::Bernoulli
+    #[inline]
+    fn gen_ratio(&mut self, numerator: u32, denominator: u32) -> bool {
+        let d = distributions::Bernoulli::from_ratio(numerator, denominator).unwrap();
+        self.sample(d)
+    }
+}
+
+impl<R: RngCore + ?Sized> Rng for R {}
+
+/// Trait for casting types to byte slices
+///
+/// This is used by the [`Rng::fill`] and [`Rng::try_fill`] methods.
+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(self.as_mut_ptr()
+                            as *mut u8,
+                            self.len() * mem::size_of::<$t>()
+                        )
+                    }
+                }
+            }
+
+            fn to_le(&mut self) {
+                for x in self {
+                    *x = x.to_le();
+                }
+            }
+        }
+
+        impl AsByteSliceMut for [Wrapping<$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(self.as_mut_ptr()
+                            as *mut u8,
+                            self.len() * mem::size_of::<$t>()
+                        )
+                    }
+                }
+            }
+
+            fn to_le(&mut self) {
+                for x in self {
+                    *x = Wrapping(x.0.to_le());
+                }
+            }
+        }
+    };
+    ($t:ty, $($tt:ty,)*) => {
+        impl_as_byte_slice!($t);
+        // TODO: this could replace above impl once Rust #32463 is fixed
+        // impl_as_byte_slice!(Wrapping<$t>);
+        impl_as_byte_slice!($($tt,)*);
+    }
+}
+
+impl_as_byte_slice!(u16, u32, u64, usize,);
+#[cfg(not(target_os = "emscripten"))]
+impl_as_byte_slice!(u128);
+impl_as_byte_slice!(i8, i16, i32, i64, isize,);
+#[cfg(not(target_os = "emscripten"))]
+impl_as_byte_slice!(i128);
+
+macro_rules! impl_as_byte_slice_arrays {
+    ($n:expr,) => {};
+    ($n:expr, $N:ident) => {
+        impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
+            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
+                self[..].as_byte_slice_mut()
+            }
+
+            fn to_le(&mut self) {
+                self[..].to_le()
+            }
+        }
+    };
+    ($n:expr, $N:ident, $($NN:ident,)*) => {
+        impl_as_byte_slice_arrays!($n, $N);
+        impl_as_byte_slice_arrays!($n - 1, $($NN,)*);
+    };
+    (!div $n:expr,) => {};
+    (!div $n:expr, $N:ident, $($NN:ident,)*) => {
+        impl_as_byte_slice_arrays!($n, $N);
+        impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*);
+    };
+}
+#[rustfmt::skip]
+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,);
+
+/// 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.
+///
+/// ```
+/// 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();
+/// }
+/// ```
+///
+/// [`Standard`]: distributions::Standard
+#[cfg(feature = "std")]
+#[inline]
+pub fn random<T>() -> T
+where Standard: Distribution<T> {
+    thread_rng().gen()
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+    use crate::rngs::mock::StepRng;
+    #[cfg(all(not(feature = "std"), feature = "alloc"))] use alloc::boxed::Box;
+
+    /// Construct a deterministic RNG with the given seed
+    pub fn rng(seed: u64) -> impl RngCore {
+        // For tests, we want a statistically good, fast, reproducible RNG.
+        // PCG32 will do fine, and will be easy to embed if we ever need to.
+        const INC: u64 = 11634580027462260723;
+        rand_pcg::Pcg32::new(seed, INC)
+    }
+
+    #[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);
+
+        // Check equivalence using wrapped arrays
+        let mut warray = [Wrapping(0u32); 2];
+        rng.fill(&mut warray[..]);
+        assert_eq!(array[0], warray[0].0);
+        assert_eq!(array[1], warray[1].0);
+    }
+
+    #[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(-4711, 17);
+            assert!(a >= -4711 && a < 17);
+            let a = r.gen_range(-3i8, 42);
+            assert!(a >= -3i8 && a < 42i8);
+            let a = r.gen_range(&10u16, 99);
+            assert!(a >= 10u16 && a < 99u16);
+            let a = r.gen_range(-100i32, &2000);
+            assert!(a >= -100i32 && a < 2000i32);
+            let a = r.gen_range(&12u32, &24u32);
+            assert!(a >= 12u32 && a < 24u32);
+
+            assert_eq!(r.gen_range(0u32, 1), 0u32);
+            assert_eq!(r.gen_range(-12i64, -11), -12i64);
+            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]
+    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_rng_trait_object() {
+        use crate::distributions::{Distribution, Standard};
+        let mut rng = rng(109);
+        let mut r = &mut rng as &mut dyn RngCore;
+        r.next_u32();
+        r.gen::<i32>();
+        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 crate::distributions::{Distribution, Standard};
+        let rng = rng(110);
+        let mut r = Box::new(rng) as Box<dyn RngCore>;
+        r.next_u32();
+        r.gen::<i32>();
+        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();
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_gen_ratio_average() {
+        const NUM: u32 = 3;
+        const DENOM: u32 = 10;
+        const N: u32 = 100_000;
+
+        let mut sum: u32 = 0;
+        let mut rng = rng(111);
+        for _ in 0..N {
+            if rng.gen_ratio(NUM, DENOM) {
+                sum += 1;
+            }
+        }
+        // Have Binomial(N, NUM/DENOM) distribution
+        let expected = (NUM * N) / DENOM; // exact integer
+        assert!(((sum - expected) as i32).abs() < 500);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/prelude.rs b/vendor/rand-0.7.3/src/prelude.rs
new file mode 100644
index 000000000..98ae3bb43
--- /dev/null
+++ b/vendor/rand-0.7.3/src/prelude.rs
@@ -0,0 +1,33 @@
+// 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.
+
+//! Convenience re-export of common members
+//!
+//! Like the standard library's prelude, this module simplifies importing of
+//! common items. Unlike the standard prelude, the contents of this module must
+//! be imported manually:
+//!
+//! ```
+//! use rand::prelude::*;
+//! # let mut r = StdRng::from_rng(thread_rng()).unwrap();
+//! # let _: f32 = r.gen();
+//! ```
+
+#[doc(no_inline)] pub use crate::distributions::Distribution;
+#[cfg(feature = "small_rng")]
+#[doc(no_inline)]
+pub use crate::rngs::SmallRng;
+#[doc(no_inline)] pub use crate::rngs::StdRng;
+#[doc(no_inline)]
+#[cfg(feature = "std")]
+pub use crate::rngs::ThreadRng;
+#[doc(no_inline)] pub use crate::seq::{IteratorRandom, SliceRandom};
+#[doc(no_inline)]
+#[cfg(feature = "std")]
+pub use crate::{random, thread_rng};
+#[doc(no_inline)] pub use crate::{CryptoRng, Rng, RngCore, SeedableRng};
diff --git a/vendor/rand-0.7.3/src/rngs/adapter/mod.rs b/vendor/rand-0.7.3/src/rngs/adapter/mod.rs
new file mode 100644
index 000000000..45e56af72
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/adapter/mod.rs
@@ -0,0 +1,15 @@
+// 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.
+
+//! Wrappers / adapters forming RNGs
+
+#[cfg(feature = "std")] mod read;
+mod reseeding;
+
+#[cfg(feature = "std")] pub use self::read::{ReadError, ReadRng};
+pub use self::reseeding::ReseedingRng;
diff --git a/vendor/rand-0.7.3/src/rngs/adapter/read.rs b/vendor/rand-0.7.3/src/rngs/adapter/read.rs
new file mode 100644
index 000000000..9a4b55d4e
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/adapter/read.rs
@@ -0,0 +1,155 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! A wrapper around any Read to treat it as an RNG.
+
+use std::fmt;
+use std::io::Read;
+
+use rand_core::{impls, Error, RngCore};
+
+
+/// An RNG that reads random bytes straight from any type supporting
+/// [`std::io::Read`], for example files.
+///
+/// This will work best with an infinite reader, but that is not required.
+///
+/// This can be used with `/dev/urandom` on Unix but it is recommended to use
+/// [`OsRng`] instead.
+///
+/// # Panics
+///
+/// `ReadRng` uses [`std::io::Read::read_exact`], which retries on interrupts.
+/// All other errors from the underlying reader, including when it does not
+/// have enough data, will only be reported through [`try_fill_bytes`].
+/// The other [`RngCore`] methods will panic in case of an error.
+///
+/// # Example
+///
+/// ```
+/// use rand::Rng;
+/// use rand::rngs::adapter::ReadRng;
+///
+/// let data = vec![1, 2, 3, 4, 5, 6, 7, 8];
+/// let mut rng = ReadRng::new(&data[..]);
+/// println!("{:x}", rng.gen::<u32>());
+/// ```
+///
+/// [`OsRng`]: crate::rngs::OsRng
+/// [`try_fill_bytes`]: RngCore::try_fill_bytes
+#[derive(Debug)]
+pub struct ReadRng<R> {
+    reader: R,
+}
+
+impl<R: Read> ReadRng<R> {
+    /// Create a new `ReadRng` from a `Read`.
+    pub fn new(r: R) -> ReadRng<R> {
+        ReadRng { reader: r }
+    }
+}
+
+impl<R: Read> RngCore for ReadRng<R> {
+    fn next_u32(&mut self) -> u32 {
+        impls::next_u32_via_fill(self)
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        impls::next_u64_via_fill(self)
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.try_fill_bytes(dest).unwrap_or_else(|err| {
+            panic!(
+                "reading random bytes from Read implementation failed; error: {}",
+                err
+            )
+        });
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        if dest.is_empty() {
+            return Ok(());
+        }
+        // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`.
+        self.reader
+            .read_exact(dest)
+            .map_err(|e| Error::new(ReadError(e)))
+    }
+}
+
+/// `ReadRng` error type
+#[derive(Debug)]
+pub struct ReadError(std::io::Error);
+
+impl fmt::Display for ReadError {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        write!(f, "ReadError: {}", self.0)
+    }
+}
+
+impl std::error::Error for ReadError {
+    fn source(&self) -> Option<&(dyn std::error::Error + 'static)> {
+        Some(&self.0)
+    }
+}
+
+
+#[cfg(test)]
+mod test {
+    use super::ReadRng;
+    use crate::RngCore;
+
+    #[test]
+    fn test_reader_rng_u64() {
+        // transmute from the target to avoid endianness concerns.
+        #[rustfmt::skip]
+        let v = vec![0u8, 0, 0, 0, 0, 0, 0, 1,
+                     0  , 0, 0, 0, 0, 0, 0, 2,
+                     0,   0, 0, 0, 0, 0, 0, 3];
+        let mut rng = ReadRng::new(&v[..]);
+
+        assert_eq!(rng.next_u64(), 1_u64.to_be());
+        assert_eq!(rng.next_u64(), 2_u64.to_be());
+        assert_eq!(rng.next_u64(), 3_u64.to_be());
+    }
+
+    #[test]
+    fn test_reader_rng_u32() {
+        let v = vec![0u8, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 3];
+        let mut rng = ReadRng::new(&v[..]);
+
+        assert_eq!(rng.next_u32(), 1_u32.to_be());
+        assert_eq!(rng.next_u32(), 2_u32.to_be());
+        assert_eq!(rng.next_u32(), 3_u32.to_be());
+    }
+
+    #[test]
+    fn test_reader_rng_fill_bytes() {
+        let v = [1u8, 2, 3, 4, 5, 6, 7, 8];
+        let mut w = [0u8; 8];
+
+        let mut rng = ReadRng::new(&v[..]);
+        rng.fill_bytes(&mut w);
+
+        assert!(v == w);
+    }
+
+    #[test]
+    fn test_reader_rng_insufficient_bytes() {
+        let v = [1u8, 2, 3, 4, 5, 6, 7, 8];
+        let mut w = [0u8; 9];
+
+        let mut rng = ReadRng::new(&v[..]);
+
+        let result = rng.try_fill_bytes(&mut w);
+        assert!(result.is_err());
+        println!("Error: {}", result.unwrap_err());
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/adapter/reseeding.rs b/vendor/rand-0.7.3/src/rngs/adapter/reseeding.rs
new file mode 100644
index 000000000..5460e3431
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/adapter/reseeding.rs
@@ -0,0 +1,369 @@
+// Copyright 2018 Developers of the Rand project.
+// Copyright 2013 The Rust Project Developers.
+//
+// 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.
+
+//! A wrapper around another PRNG that reseeds it after it
+//! generates a certain number of random bytes.
+
+use core::mem::size_of;
+
+use rand_core::block::{BlockRng, BlockRngCore};
+use rand_core::{CryptoRng, Error, RngCore, SeedableRng};
+
+/// A wrapper around any PRNG that implements [`BlockRngCore`], that adds the
+/// ability to reseed it.
+///
+/// `ReseedingRng` reseeds the underlying PRNG in the following cases:
+///
+/// - On a manual call to [`reseed()`].
+/// - After `clone()`, the clone will be reseeded on first use.
+/// - After a process is forked, the RNG in the child process is reseeded within
+///   the next few generated values, depending on the block size of the
+///   underlying PRNG. For ChaCha and Hc128 this is a maximum of
+///   15 `u32` values before reseeding.
+/// - After the PRNG has generated a configurable number of random bytes.
+///
+/// # When should reseeding after a fixed number of generated bytes be used?
+///
+/// Reseeding after a fixed number of generated bytes is never strictly
+/// *necessary*. Cryptographic PRNGs don't have a limited number of bytes they
+/// can output, or at least not a limit reachable in any practical way. There is
+/// no such thing as 'running out of entropy'.
+///
+/// Occasionally reseeding can be seen as some form of 'security in depth'. Even
+/// if in the future a cryptographic weakness is found in the CSPRNG being used,
+/// or a flaw in the implementation, occasionally reseeding should make
+/// exploiting it much more difficult or even impossible.
+///
+/// Use [`ReseedingRng::new`] with a `threshold` of `0` to disable reseeding
+/// after a fixed number of generated bytes.
+///
+/// # Error handling
+///
+/// Although unlikely, reseeding the wrapped PRNG can fail. `ReseedingRng` will
+/// never panic but try to handle the error intelligently through some
+/// combination of retrying and delaying reseeding until later.
+/// If handling the source error fails `ReseedingRng` will continue generating
+/// data from the wrapped PRNG without reseeding.
+///
+/// Manually calling [`reseed()`] will not have this retry or delay logic, but
+/// reports the error.
+///
+/// # Example
+///
+/// ```
+/// use rand::prelude::*;
+/// use rand_chacha::ChaCha20Core; // Internal part of ChaChaRng that
+///                              // implements BlockRngCore
+/// use rand::rngs::OsRng;
+/// use rand::rngs::adapter::ReseedingRng;
+///
+/// let prng = ChaCha20Core::from_entropy();
+/// let mut reseeding_rng = ReseedingRng::new(prng, 0, OsRng);
+///
+/// println!("{}", reseeding_rng.gen::<u64>());
+///
+/// let mut cloned_rng = reseeding_rng.clone();
+/// assert!(reseeding_rng.gen::<u64>() != cloned_rng.gen::<u64>());
+/// ```
+///
+/// [`BlockRngCore`]: rand_core::block::BlockRngCore
+/// [`ReseedingRng::new`]: ReseedingRng::new
+/// [`reseed()`]: ReseedingRng::reseed
+#[derive(Debug)]
+pub struct ReseedingRng<R, Rsdr>(BlockRng<ReseedingCore<R, Rsdr>>)
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore;
+
+impl<R, Rsdr> ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    /// Create a new `ReseedingRng` from an existing PRNG, combined with a RNG
+    /// to use as reseeder.
+    ///
+    /// `threshold` sets the number of generated bytes after which to reseed the
+    /// PRNG. Set it to zero to never reseed based on the number of generated
+    /// values.
+    pub fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self {
+        ReseedingRng(BlockRng::new(ReseedingCore::new(rng, threshold, reseeder)))
+    }
+
+    /// Reseed the internal PRNG.
+    pub fn reseed(&mut self) -> Result<(), Error> {
+        self.0.core.reseed()
+    }
+}
+
+// TODO: this should be implemented for any type where the inner type
+// implements RngCore, but we can't specify that because ReseedingCore is private
+impl<R, Rsdr: RngCore> RngCore for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore<Item = u32> + SeedableRng,
+    <R as BlockRngCore>::Results: AsRef<[u32]> + AsMut<[u32]>,
+{
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest)
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl<R, Rsdr> Clone for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + Clone,
+    Rsdr: RngCore + Clone,
+{
+    fn clone(&self) -> ReseedingRng<R, Rsdr> {
+        // Recreating `BlockRng` seems easier than cloning it and resetting
+        // the index.
+        ReseedingRng(BlockRng::new(self.0.core.clone()))
+    }
+}
+
+impl<R, Rsdr> CryptoRng for ReseedingRng<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + CryptoRng,
+    Rsdr: RngCore + CryptoRng,
+{
+}
+
+#[derive(Debug)]
+struct ReseedingCore<R, Rsdr> {
+    inner: R,
+    reseeder: Rsdr,
+    threshold: i64,
+    bytes_until_reseed: i64,
+    fork_counter: usize,
+}
+
+impl<R, Rsdr> BlockRngCore for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    type Item = <R as BlockRngCore>::Item;
+    type Results = <R as BlockRngCore>::Results;
+
+    fn generate(&mut self, results: &mut Self::Results) {
+        let global_fork_counter = fork::get_fork_counter();
+        if self.bytes_until_reseed <= 0 || self.is_forked(global_fork_counter) {
+            // We get better performance by not calling only `reseed` here
+            // and continuing with the rest of the function, but by directly
+            // returning from a non-inlined function.
+            return self.reseed_and_generate(results, global_fork_counter);
+        }
+        let num_bytes = results.as_ref().len() * size_of::<Self::Item>();
+        self.bytes_until_reseed -= num_bytes as i64;
+        self.inner.generate(results);
+    }
+}
+
+impl<R, Rsdr> ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng,
+    Rsdr: RngCore,
+{
+    /// Create a new `ReseedingCore`.
+    fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self {
+        use ::core::i64::MAX;
+        fork::register_fork_handler();
+
+        // Because generating more values than `i64::MAX` takes centuries on
+        // current hardware, we just clamp to that value.
+        // Also we set a threshold of 0, which indicates no limit, to that
+        // value.
+        let threshold = if threshold == 0 {
+            MAX
+        } else if threshold <= MAX as u64 {
+            threshold as i64
+        } else {
+            MAX
+        };
+
+        ReseedingCore {
+            inner: rng,
+            reseeder,
+            threshold: threshold as i64,
+            bytes_until_reseed: threshold as i64,
+            fork_counter: 0,
+        }
+    }
+
+    /// Reseed the internal PRNG.
+    fn reseed(&mut self) -> Result<(), Error> {
+        R::from_rng(&mut self.reseeder).map(|result| {
+            self.bytes_until_reseed = self.threshold;
+            self.inner = result
+        })
+    }
+
+    fn is_forked(&self, global_fork_counter: usize) -> bool {
+        // In theory, on 32-bit platforms, it is possible for
+        // `global_fork_counter` to wrap around after ~4e9 forks.
+        //
+        // This check will detect a fork in the normal case where
+        // `fork_counter < global_fork_counter`, and also when the difference
+        // between both is greater than `isize::MAX` (wrapped around).
+        //
+        // It will still fail to detect a fork if there have been more than
+        // `isize::MAX` forks, without any reseed in between. Seems unlikely
+        // enough.
+        (self.fork_counter.wrapping_sub(global_fork_counter) as isize) < 0
+    }
+
+    #[inline(never)]
+    fn reseed_and_generate(
+        &mut self, results: &mut <Self as BlockRngCore>::Results, global_fork_counter: usize,
+    ) {
+        #![allow(clippy::if_same_then_else)] // false positive
+        if self.is_forked(global_fork_counter) {
+            info!("Fork detected, reseeding RNG");
+        } else {
+            trace!("Reseeding RNG (periodic reseed)");
+        }
+
+        let num_bytes = results.as_ref().len() * size_of::<<R as BlockRngCore>::Item>();
+
+        if let Err(e) = self.reseed() {
+            warn!("Reseeding RNG failed: {}", e);
+            let _ = e;
+        }
+        self.fork_counter = global_fork_counter;
+
+        self.bytes_until_reseed = self.threshold - num_bytes as i64;
+        self.inner.generate(results);
+    }
+}
+
+impl<R, Rsdr> Clone for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + Clone,
+    Rsdr: RngCore + Clone,
+{
+    fn clone(&self) -> ReseedingCore<R, Rsdr> {
+        ReseedingCore {
+            inner: self.inner.clone(),
+            reseeder: self.reseeder.clone(),
+            threshold: self.threshold,
+            bytes_until_reseed: 0, // reseed clone on first use
+            fork_counter: self.fork_counter,
+        }
+    }
+}
+
+impl<R, Rsdr> CryptoRng for ReseedingCore<R, Rsdr>
+where
+    R: BlockRngCore + SeedableRng + CryptoRng,
+    Rsdr: RngCore + CryptoRng,
+{
+}
+
+
+#[cfg(all(unix, feature = "std", not(target_os = "emscripten")))]
+mod fork {
+    use core::sync::atomic::{AtomicUsize, Ordering};
+    use std::sync::Once;
+
+    // Fork protection
+    //
+    // We implement fork protection on Unix using `pthread_atfork`.
+    // When the process is forked, we increment `RESEEDING_RNG_FORK_COUNTER`.
+    // Every `ReseedingRng` stores the last known value of the static in
+    // `fork_counter`. If the cached `fork_counter` is less than
+    // `RESEEDING_RNG_FORK_COUNTER`, it is time to reseed this RNG.
+    //
+    // If reseeding fails, we don't deal with this by setting a delay, but just
+    // don't update `fork_counter`, so a reseed is attempted as soon as
+    // possible.
+
+    static RESEEDING_RNG_FORK_COUNTER: AtomicUsize = AtomicUsize::new(0);
+
+    pub fn get_fork_counter() -> usize {
+        RESEEDING_RNG_FORK_COUNTER.load(Ordering::Relaxed)
+    }
+
+    extern "C" fn fork_handler() {
+        // Note: fetch_add is defined to wrap on overflow
+        // (which is what we want).
+        RESEEDING_RNG_FORK_COUNTER.fetch_add(1, Ordering::Relaxed);
+    }
+
+    pub fn register_fork_handler() {
+        static REGISTER: Once = Once::new();
+        REGISTER.call_once(|| unsafe {
+            libc::pthread_atfork(None, None, Some(fork_handler));
+        });
+    }
+}
+
+#[cfg(not(all(unix, feature = "std", not(target_os = "emscripten"))))]
+mod fork {
+    pub fn get_fork_counter() -> usize {
+        0
+    }
+    pub fn register_fork_handler() {}
+}
+
+
+#[cfg(test)]
+mod test {
+    use super::ReseedingRng;
+    use crate::rngs::mock::StepRng;
+    use crate::rngs::std::Core;
+    use crate::{Rng, SeedableRng};
+
+    #[test]
+    fn test_reseeding() {
+        let mut zero = StepRng::new(0, 0);
+        let rng = Core::from_rng(&mut zero).unwrap();
+        let thresh = 1; // reseed every time the buffer is exhausted
+        let mut reseeding = ReseedingRng::new(rng, thresh, zero);
+
+        // RNG buffer size is [u32; 64]
+        // Debug is only implemented up to length 32 so use two arrays
+        let mut buf = ([0u32; 32], [0u32; 32]);
+        reseeding.fill(&mut buf.0);
+        reseeding.fill(&mut buf.1);
+        let seq = buf;
+        for _ in 0..10 {
+            reseeding.fill(&mut buf.0);
+            reseeding.fill(&mut buf.1);
+            assert_eq!(buf, seq);
+        }
+    }
+
+    #[test]
+    fn test_clone_reseeding() {
+        let mut zero = StepRng::new(0, 0);
+        let rng = Core::from_rng(&mut zero).unwrap();
+        let mut rng1 = ReseedingRng::new(rng, 32 * 4, zero);
+
+        let first: u32 = rng1.gen();
+        for _ in 0..10 {
+            let _ = rng1.gen::<u32>();
+        }
+
+        let mut rng2 = rng1.clone();
+        assert_eq!(first, rng2.gen::<u32>());
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/entropy.rs b/vendor/rand-0.7.3/src/rngs/entropy.rs
new file mode 100644
index 000000000..9ad0d71e0
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/entropy.rs
@@ -0,0 +1,76 @@
+// 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.
+
+//! Entropy generator, or wrapper around external generators
+
+#![allow(deprecated)] // whole module is deprecated
+
+use crate::rngs::OsRng;
+use rand_core::{CryptoRng, Error, RngCore};
+
+/// An interface returning random data from external source(s), provided
+/// specifically for securely seeding algorithmic generators (PRNGs).
+///
+/// This is deprecated. It is suggested you use [`rngs::OsRng`] instead.
+///
+/// [`rngs::OsRng`]: crate::rngs::OsRng
+#[derive(Debug)]
+#[deprecated(since = "0.7.0", note = "use rngs::OsRng instead")]
+pub struct EntropyRng {
+    source: OsRng,
+}
+
+impl EntropyRng {
+    /// Create a new `EntropyRng`.
+    ///
+    /// This method will do no system calls or other initialization routines,
+    /// those are done on first use. This is done to make `new` infallible,
+    /// and `try_fill_bytes` the only place to report errors.
+    pub fn new() -> Self {
+        EntropyRng { source: OsRng }
+    }
+}
+
+impl Default for EntropyRng {
+    fn default() -> Self {
+        EntropyRng::new()
+    }
+}
+
+impl RngCore for EntropyRng {
+    fn next_u32(&mut self) -> u32 {
+        self.source.next_u32()
+    }
+
+    fn next_u64(&mut self) -> u64 {
+        self.source.next_u64()
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.source.fill_bytes(dest)
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.source.try_fill_bytes(dest)
+    }
+}
+
+impl CryptoRng for EntropyRng {}
+
+
+#[cfg(test)]
+mod test {
+    use super::*;
+
+    #[test]
+    fn test_entropy() {
+        let mut rng = EntropyRng::new();
+        let n = (rng.next_u32() ^ rng.next_u32()).count_ones();
+        assert!(n >= 2); // p(failure) approx 1e-7
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/mock.rs b/vendor/rand-0.7.3/src/rngs/mock.rs
new file mode 100644
index 000000000..9a47264a7
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/mock.rs
@@ -0,0 +1,67 @@
+// 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.
+
+//! Mock random number generator
+
+use rand_core::{impls, Error, RngCore};
+
+/// A simple implementation of `RngCore` for testing purposes.
+///
+/// This generates an arithmetic sequence (i.e. adds a constant each step)
+/// over a `u64` number, using wrapping arithmetic. If the increment is 0
+/// the generator yields a constant.
+///
+/// ```
+/// use rand::Rng;
+/// use rand::rngs::mock::StepRng;
+///
+/// let mut my_rng = StepRng::new(2, 1);
+/// let sample: [u64; 3] = my_rng.gen();
+/// assert_eq!(sample, [2, 3, 4]);
+/// ```
+#[derive(Debug, Clone)]
+pub struct StepRng {
+    v: u64,
+    a: u64,
+}
+
+impl StepRng {
+    /// Create a `StepRng`, yielding an arithmetic sequence starting with
+    /// `initial` and incremented by `increment` each time.
+    pub fn new(initial: u64, increment: u64) -> Self {
+        StepRng {
+            v: initial,
+            a: increment,
+        }
+    }
+}
+
+impl RngCore for StepRng {
+    #[inline]
+    fn next_u32(&mut self) -> u32 {
+        self.next_u64() as u32
+    }
+
+    #[inline]
+    fn next_u64(&mut self) -> u64 {
+        let result = self.v;
+        self.v = self.v.wrapping_add(self.a);
+        result
+    }
+
+    #[inline]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        impls::fill_bytes_via_next(self, dest);
+    }
+
+    #[inline]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.fill_bytes(dest);
+        Ok(())
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/mod.rs b/vendor/rand-0.7.3/src/rngs/mod.rs
new file mode 100644
index 000000000..111219602
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/mod.rs
@@ -0,0 +1,116 @@
+// 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.
+
+//! Random number generators and adapters
+//!
+//! ## Background: Random number generators (RNGs)
+//!
+//! Computers cannot produce random numbers from nowhere. We classify
+//! random number generators as follows:
+//!
+//! -   "True" random number generators (TRNGs) use hard-to-predict data sources
+//!     (e.g. the high-resolution parts of event timings and sensor jitter) to
+//!     harvest random bit-sequences, apply algorithms to remove bias and
+//!     estimate available entropy, then combine these bits into a byte-sequence
+//!     or an entropy pool. This job is usually done by the operating system or
+//!     a hardware generator (HRNG).
+//! -   "Pseudo"-random number generators (PRNGs) use algorithms to transform a
+//!     seed into a sequence of pseudo-random numbers. These generators can be
+//!     fast and produce well-distributed unpredictable random numbers (or not).
+//!     They are usually deterministic: given algorithm and seed, the output
+//!     sequence can be reproduced. They have finite period and eventually loop;
+//!     with many algorithms this period is fixed and can be proven sufficiently
+//!     long, while others are chaotic and the period depends on the seed.
+//! -   "Cryptographically secure" pseudo-random number generators (CSPRNGs)
+//!     are the sub-set of PRNGs which are secure. Security of the generator
+//!     relies both on hiding the internal state and using a strong algorithm.
+//!
+//! ## Traits and functionality
+//!
+//! All RNGs implement the [`RngCore`] trait, as a consequence of which the
+//! [`Rng`] extension trait is automatically implemented. Secure RNGs may
+//! additionally implement the [`CryptoRng`] trait.
+//!
+//! All PRNGs require a seed to produce their random number sequence. The
+//! [`SeedableRng`] trait provides three ways of constructing PRNGs:
+//!
+//! -   `from_seed` accepts a type specific to the PRNG
+//! -   `from_rng` allows a PRNG to be seeded from any other RNG
+//! -   `seed_from_u64` allows any PRNG to be seeded from a `u64` insecurely
+//! -   `from_entropy` securely seeds a PRNG from fresh entropy
+//!
+//! Use the [`rand_core`] crate when implementing your own RNGs.
+//!
+//! ## Our generators
+//!
+//! This crate provides several random number generators:
+//!
+//! -   [`OsRng`] is an interface to the operating system's random number
+//!     source. Typically the operating system uses a CSPRNG with entropy
+//!     provided by a TRNG and some type of on-going re-seeding.
+//! -   [`ThreadRng`], provided by the [`thread_rng`] function, is a handle to a
+//!     thread-local CSPRNG with periodic seeding from [`OsRng`]. Because this
+//!     is local, it is typically much faster than [`OsRng`]. It should be
+//!     secure, though the paranoid may prefer [`OsRng`].
+//! -   [`StdRng`] is a CSPRNG chosen for good performance and trust of security
+//!     (based on reviews, maturity and usage). The current algorithm is ChaCha20,
+//!     which is well established and rigorously analysed.
+//!     [`StdRng`] provides the algorithm used by [`ThreadRng`] but without
+//!     periodic reseeding.
+//! -   [`SmallRng`] is an **insecure** PRNG designed to be fast, simple, require
+//!     little memory, and have good output quality.
+//!
+//! The algorithms selected for [`StdRng`] and [`SmallRng`] may change in any
+//! release and may be platform-dependent, therefore they should be considered
+//! **not reproducible**.
+//!
+//! ## Additional generators
+//!
+//! **TRNGs**: The [`rdrand`] crate provides an interface to the RDRAND and
+//! RDSEED instructions available in modern Intel and AMD CPUs.
+//! The [`rand_jitter`] crate provides a user-space implementation of
+//! entropy harvesting from CPU timer jitter, but is very slow and has
+//! [security issues](https://github.com/rust-random/rand/issues/699).
+//!
+//! **PRNGs**: Several companion crates are available, providing individual or
+//! families of PRNG algorithms. These provide the implementations behind
+//! [`StdRng`] and [`SmallRng`] but can also be used directly, indeed *should*
+//! be used directly when **reproducibility** matters.
+//! Some suggestions are: [`rand_chacha`], [`rand_pcg`], [`rand_xoshiro`].
+//! A full list can be found by searching for crates with the [`rng` tag].
+//!
+//! [`Rng`]: crate::Rng
+//! [`RngCore`]: crate::RngCore
+//! [`CryptoRng`]: crate::CryptoRng
+//! [`SeedableRng`]: crate::SeedableRng
+//! [`thread_rng`]: crate::thread_rng
+//! [`rdrand`]: https://crates.io/crates/rdrand
+//! [`rand_jitter`]: https://crates.io/crates/rand_jitter
+//! [`rand_chacha`]: https://crates.io/crates/rand_chacha
+//! [`rand_pcg`]: https://crates.io/crates/rand_pcg
+//! [`rand_xoshiro`]: https://crates.io/crates/rand_xoshiro
+//! [`rng` tag]: https://crates.io/keywords/rng
+
+pub mod adapter;
+
+#[cfg(feature = "std")] mod entropy;
+pub mod mock; // Public so we don't export `StepRng` directly, making it a bit
+              // more clear it is intended for testing.
+#[cfg(feature = "small_rng")] mod small;
+mod std;
+#[cfg(feature = "std")] pub(crate) mod thread;
+
+#[allow(deprecated)]
+#[cfg(feature = "std")]
+pub use self::entropy::EntropyRng;
+
+#[cfg(feature = "small_rng")] pub use self::small::SmallRng;
+pub use self::std::StdRng;
+#[cfg(feature = "std")] pub use self::thread::ThreadRng;
+
+#[cfg(feature = "getrandom")] pub use rand_core::OsRng;
diff --git a/vendor/rand-0.7.3/src/rngs/small.rs b/vendor/rand-0.7.3/src/rngs/small.rs
new file mode 100644
index 000000000..d67689814
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/small.rs
@@ -0,0 +1,113 @@
+// 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.
+
+//! A small fast RNG
+
+use rand_core::{Error, RngCore, SeedableRng};
+
+#[cfg(all(not(target_os = "emscripten"), target_pointer_width = "64"))]
+type Rng = rand_pcg::Pcg64Mcg;
+#[cfg(not(all(not(target_os = "emscripten"), target_pointer_width = "64")))]
+type Rng = rand_pcg::Pcg32;
+
+/// A small-state, fast non-crypto PRNG
+///
+/// `SmallRng` may be a good choice when a PRNG with small state, cheap
+/// initialization, good statistical quality and good performance are required.
+/// It is **not** a good choice when security against prediction or
+/// reproducibility are important.
+///
+/// This PRNG is **feature-gated**: to use, you must enable the crate feature
+/// `small_rng`.
+///
+/// The algorithm is deterministic but should not be considered reproducible
+/// due to dependence on platform and possible replacement in future
+/// library versions. For a reproducible generator, use a named PRNG from an
+/// external crate, e.g. [rand_pcg] or [rand_chacha].
+/// Refer also to [The Book](https://rust-random.github.io/book/guide-rngs.html).
+///
+/// The PRNG algorithm in `SmallRng` is chosen to be
+/// efficient on the current platform, without consideration for cryptography
+/// or security. The size of its state is much smaller than [`StdRng`].
+/// The current algorithm is [`Pcg64Mcg`](rand_pcg::Pcg64Mcg) on 64-bit
+/// platforms and [`Pcg32`](rand_pcg::Pcg32) on 32-bit platforms. Both are
+/// implemented by the [rand_pcg] crate.
+///
+/// # Examples
+///
+/// Initializing `SmallRng` with a random seed can be done using [`SeedableRng::from_entropy`]:
+///
+/// ```
+/// use rand::{Rng, SeedableRng};
+/// use rand::rngs::SmallRng;
+///
+/// // Create small, cheap to initialize and fast RNG with a random seed.
+/// // The randomness is supplied by the operating system.
+/// let mut small_rng = SmallRng::from_entropy();
+/// # let v: u32 = small_rng.gen();
+/// ```
+///
+/// When initializing a lot of `SmallRng`'s, using [`thread_rng`] can be more
+/// efficient:
+///
+/// ```
+/// use rand::{SeedableRng, thread_rng};
+/// use rand::rngs::SmallRng;
+///
+/// // Create a big, expensive to initialize and slower, but unpredictable RNG.
+/// // This is cached and done only once per thread.
+/// let mut thread_rng = thread_rng();
+/// // Create small, cheap to initialize and fast RNGs with random seeds.
+/// // One can generally assume this won't fail.
+/// let rngs: Vec<SmallRng> = (0..10)
+///     .map(|_| SmallRng::from_rng(&mut thread_rng).unwrap())
+///     .collect();
+/// ```
+///
+/// [`StdRng`]: crate::rngs::StdRng
+/// [`thread_rng`]: crate::thread_rng
+/// [rand_chacha]: https://crates.io/crates/rand_chacha
+/// [rand_pcg]: https://crates.io/crates/rand_pcg
+#[derive(Clone, Debug)]
+pub struct SmallRng(Rng);
+
+impl RngCore for SmallRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    #[inline(always)]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest);
+    }
+
+    #[inline(always)]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl SeedableRng for SmallRng {
+    type Seed = <Rng as SeedableRng>::Seed;
+
+    #[inline(always)]
+    fn from_seed(seed: Self::Seed) -> Self {
+        SmallRng(Rng::from_seed(seed))
+    }
+
+    #[inline(always)]
+    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
+        Rng::from_rng(rng).map(SmallRng)
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/std.rs b/vendor/rand-0.7.3/src/rngs/std.rs
new file mode 100644
index 000000000..8b07081a0
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/std.rs
@@ -0,0 +1,103 @@
+// 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.
+
+//! The standard RNG
+
+use crate::{CryptoRng, Error, RngCore, SeedableRng};
+
+#[cfg(all(any(test, feature = "std"), not(target_os = "emscripten")))]
+pub(crate) use rand_chacha::ChaCha20Core as Core;
+#[cfg(all(any(test, feature = "std"), target_os = "emscripten"))]
+pub(crate) use rand_hc::Hc128Core as Core;
+
+#[cfg(not(target_os = "emscripten"))] use rand_chacha::ChaCha20Rng as Rng;
+#[cfg(target_os = "emscripten")] use rand_hc::Hc128Rng as Rng;
+
+/// The standard RNG. The PRNG algorithm in `StdRng` is chosen to be efficient
+/// on the current platform, to be statistically strong and unpredictable
+/// (meaning a cryptographically secure PRNG).
+///
+/// The current algorithm used is the ChaCha block cipher with 20 rounds.
+/// This may change as new evidence of cipher security and performance
+/// becomes available.
+///
+/// The algorithm is deterministic but should not be considered reproducible
+/// due to dependence on configuration and possible replacement in future
+/// library versions. For a secure reproducible generator, we recommend use of
+/// the [rand_chacha] crate directly.
+///
+/// [rand_chacha]: https://crates.io/crates/rand_chacha
+#[derive(Clone, Debug)]
+pub struct StdRng(Rng);
+
+impl RngCore for StdRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        self.0.next_u32()
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        self.0.next_u64()
+    }
+
+    #[inline(always)]
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        self.0.fill_bytes(dest);
+    }
+
+    #[inline(always)]
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        self.0.try_fill_bytes(dest)
+    }
+}
+
+impl SeedableRng for StdRng {
+    type Seed = <Rng as SeedableRng>::Seed;
+
+    #[inline(always)]
+    fn from_seed(seed: Self::Seed) -> Self {
+        StdRng(Rng::from_seed(seed))
+    }
+
+    #[inline(always)]
+    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
+        Rng::from_rng(rng).map(StdRng)
+    }
+}
+
+impl CryptoRng for StdRng {}
+
+
+#[cfg(test)]
+mod test {
+    use crate::rngs::StdRng;
+    use crate::{RngCore, SeedableRng};
+
+    #[test]
+    fn test_stdrng_construction() {
+        // Test value-stability of StdRng. This is expected to break any time
+        // the algorithm is changed.
+        #[rustfmt::skip]
+        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
+                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
+
+        #[cfg(any(feature = "stdrng_strong", not(feature = "stdrng_fast")))]
+        let target = [3950704604716924505, 5573172343717151650];
+        #[cfg(all(not(feature = "stdrng_strong"), feature = "stdrng_fast"))]
+        let target = [10719222850664546238, 14064965282130556830];
+
+        let mut rng0 = StdRng::from_seed(seed);
+        let x0 = rng0.next_u64();
+
+        let mut rng1 = StdRng::from_rng(rng0).unwrap();
+        let x1 = rng1.next_u64();
+
+        assert_eq!([x0, x1], target);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/rngs/thread.rs b/vendor/rand-0.7.3/src/rngs/thread.rs
new file mode 100644
index 000000000..91ed4c30a
--- /dev/null
+++ b/vendor/rand-0.7.3/src/rngs/thread.rs
@@ -0,0 +1,124 @@
+// 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.
+
+//! Thread-local random number generator
+
+use std::cell::UnsafeCell;
+use std::ptr::NonNull;
+
+use super::std::Core;
+use crate::rngs::adapter::ReseedingRng;
+use crate::rngs::OsRng;
+use crate::{CryptoRng, Error, RngCore, SeedableRng};
+
+// Rationale for using `UnsafeCell` in `ThreadRng`:
+//
+// Previously we used a `RefCell`, with an overhead of ~15%. There will only
+// ever be one mutable reference to the interior of the `UnsafeCell`, because
+// we only have such a reference inside `next_u32`, `next_u64`, etc. Within a
+// single thread (which is the definition of `ThreadRng`), there will only ever
+// be one of these methods active at a time.
+//
+// A possible scenario where there could be multiple mutable references is if
+// `ThreadRng` is used inside `next_u32` and co. But the implementation is
+// completely under our control. We just have to ensure none of them use
+// `ThreadRng` internally, which is nonsensical anyway. We should also never run
+// `ThreadRng` in destructors of its implementation, which is also nonsensical.
+
+
+// Number of generated bytes after which to reseed `ThreadRng`.
+// According to benchmarks, reseeding has a noticable impact with thresholds
+// of 32 kB and less. We choose 64 kB to avoid significant overhead.
+const THREAD_RNG_RESEED_THRESHOLD: u64 = 1024 * 64;
+
+/// The type returned by [`thread_rng`], essentially just a reference to the
+/// PRNG in thread-local memory.
+///
+/// `ThreadRng` uses the same PRNG as [`StdRng`] for security and performance.
+/// As hinted by the name, the generator is thread-local. `ThreadRng` is a
+/// handle to this generator and thus supports `Copy`, but not `Send` or `Sync`.
+///
+/// Unlike `StdRng`, `ThreadRng` uses the  [`ReseedingRng`] wrapper to reseed
+/// the PRNG from fresh entropy every 64 kiB of random data.
+/// [`OsRng`] is used to provide seed data.
+///
+/// Note that the reseeding is done as an extra precaution against side-channel
+/// attacks and mis-use (e.g. if somehow weak entropy were supplied initially).
+/// The PRNG algorithms used are assumed to be secure.
+///
+/// [`ReseedingRng`]: crate::rngs::adapter::ReseedingRng
+/// [`StdRng`]: crate::rngs::StdRng
+#[derive(Copy, Clone, Debug)]
+pub struct ThreadRng {
+    // inner raw pointer implies type is neither Send nor Sync
+    rng: NonNull<ReseedingRng<Core, OsRng>>,
+}
+
+thread_local!(
+    static THREAD_RNG_KEY: UnsafeCell<ReseedingRng<Core, OsRng>> = {
+        let r = Core::from_rng(OsRng).unwrap_or_else(|err|
+                panic!("could not initialize thread_rng: {}", err));
+        let rng = ReseedingRng::new(r,
+                                    THREAD_RNG_RESEED_THRESHOLD,
+                                    OsRng);
+        UnsafeCell::new(rng)
+    }
+);
+
+/// Retrieve the lazily-initialized thread-local random number generator,
+/// seeded by the system. Intended to be used in method chaining style,
+/// e.g. `thread_rng().gen::<i32>()`, or cached locally, e.g.
+/// `let mut rng = thread_rng();`.  Invoked by the `Default` trait, making
+/// `ThreadRng::default()` equivalent.
+///
+/// For more information see [`ThreadRng`].
+pub fn thread_rng() -> ThreadRng {
+    let raw = THREAD_RNG_KEY.with(|t| t.get());
+    let nn = NonNull::new(raw).unwrap();
+    ThreadRng { rng: nn }
+}
+
+impl Default for ThreadRng {
+    fn default() -> ThreadRng {
+        crate::prelude::thread_rng()
+    }
+}
+
+impl RngCore for ThreadRng {
+    #[inline(always)]
+    fn next_u32(&mut self) -> u32 {
+        unsafe { self.rng.as_mut().next_u32() }
+    }
+
+    #[inline(always)]
+    fn next_u64(&mut self) -> u64 {
+        unsafe { self.rng.as_mut().next_u64() }
+    }
+
+    fn fill_bytes(&mut self, dest: &mut [u8]) {
+        unsafe { self.rng.as_mut().fill_bytes(dest) }
+    }
+
+    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
+        unsafe { self.rng.as_mut().try_fill_bytes(dest) }
+    }
+}
+
+impl CryptoRng for ThreadRng {}
+
+
+#[cfg(test)]
+mod test {
+    #[test]
+    fn test_thread_rng() {
+        use crate::Rng;
+        let mut r = crate::thread_rng();
+        r.gen::<i32>();
+        assert_eq!(r.gen_range(0, 1), 0);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/seq/index.rs b/vendor/rand-0.7.3/src/seq/index.rs
new file mode 100644
index 000000000..551d409e7
--- /dev/null
+++ b/vendor/rand-0.7.3/src/seq/index.rs
@@ -0,0 +1,438 @@
+// 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.
+
+//! Low-level API for sampling indices
+
+#[cfg(feature = "alloc")] use core::slice;
+
+#[cfg(all(feature = "alloc", not(feature = "std")))]
+use crate::alloc::vec::{self, Vec};
+#[cfg(feature = "std")] use std::vec;
+// BTreeMap is not as fast in tests, but better than nothing.
+#[cfg(all(feature = "alloc", not(feature = "std")))]
+use crate::alloc::collections::BTreeSet;
+#[cfg(feature = "std")] use std::collections::HashSet;
+
+#[cfg(feature = "alloc")]
+use crate::distributions::{uniform::SampleUniform, Distribution, Uniform};
+use crate::Rng;
+
+/// A vector of indices.
+///
+/// Multiple internal representations are possible.
+#[derive(Clone, Debug)]
+pub enum IndexVec {
+    #[doc(hidden)]
+    U32(Vec<u32>),
+    #[doc(hidden)]
+    USize(Vec<usize>),
+}
+
+impl IndexVec {
+    /// Returns the number of indices
+    #[inline]
+    pub fn len(&self) -> usize {
+        match *self {
+            IndexVec::U32(ref v) => v.len(),
+            IndexVec::USize(ref v) => v.len(),
+        }
+    }
+
+    /// Returns `true` if the length is 0.
+    #[inline]
+    pub fn is_empty(&self) -> bool {
+        match *self {
+            IndexVec::U32(ref v) => v.is_empty(),
+            IndexVec::USize(ref v) => v.is_empty(),
+        }
+    }
+
+    /// Return the value at the given `index`.
+    ///
+    /// (Note: we cannot implement [`std::ops::Index`] because of lifetime
+    /// restrictions.)
+    #[inline]
+    pub fn index(&self, index: usize) -> usize {
+        match *self {
+            IndexVec::U32(ref v) => v[index] as usize,
+            IndexVec::USize(ref v) => v[index],
+        }
+    }
+
+    /// Return result as a `Vec<usize>`. Conversion may or may not be trivial.
+    #[inline]
+    pub fn into_vec(self) -> Vec<usize> {
+        match self {
+            IndexVec::U32(v) => v.into_iter().map(|i| i as usize).collect(),
+            IndexVec::USize(v) => v,
+        }
+    }
+
+    /// Iterate over the indices as a sequence of `usize` values
+    #[inline]
+    pub fn iter(&self) -> IndexVecIter<'_> {
+        match *self {
+            IndexVec::U32(ref v) => IndexVecIter::U32(v.iter()),
+            IndexVec::USize(ref v) => IndexVecIter::USize(v.iter()),
+        }
+    }
+
+    /// Convert into an iterator over the indices as a sequence of `usize` values
+    #[inline]
+    pub fn into_iter(self) -> IndexVecIntoIter {
+        match self {
+            IndexVec::U32(v) => IndexVecIntoIter::U32(v.into_iter()),
+            IndexVec::USize(v) => IndexVecIntoIter::USize(v.into_iter()),
+        }
+    }
+}
+
+impl PartialEq for IndexVec {
+    fn eq(&self, other: &IndexVec) -> bool {
+        use self::IndexVec::*;
+        match (self, other) {
+            (&U32(ref v1), &U32(ref v2)) => v1 == v2,
+            (&USize(ref v1), &USize(ref v2)) => v1 == v2,
+            (&U32(ref v1), &USize(ref v2)) => {
+                (v1.len() == v2.len()) && (v1.iter().zip(v2.iter()).all(|(x, y)| *x as usize == *y))
+            }
+            (&USize(ref v1), &U32(ref v2)) => {
+                (v1.len() == v2.len()) && (v1.iter().zip(v2.iter()).all(|(x, y)| *x == *y as usize))
+            }
+        }
+    }
+}
+
+impl From<Vec<u32>> for IndexVec {
+    #[inline]
+    fn from(v: Vec<u32>) -> Self {
+        IndexVec::U32(v)
+    }
+}
+
+impl From<Vec<usize>> for IndexVec {
+    #[inline]
+    fn from(v: Vec<usize>) -> Self {
+        IndexVec::USize(v)
+    }
+}
+
+/// Return type of `IndexVec::iter`.
+#[derive(Debug)]
+pub enum IndexVecIter<'a> {
+    #[doc(hidden)]
+    U32(slice::Iter<'a, u32>),
+    #[doc(hidden)]
+    USize(slice::Iter<'a, usize>),
+}
+
+impl<'a> Iterator for IndexVecIter<'a> {
+    type Item = usize;
+
+    #[inline]
+    fn next(&mut self) -> Option<usize> {
+        use self::IndexVecIter::*;
+        match *self {
+            U32(ref mut iter) => iter.next().map(|i| *i as usize),
+            USize(ref mut iter) => iter.next().cloned(),
+        }
+    }
+
+    #[inline]
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        match *self {
+            IndexVecIter::U32(ref v) => v.size_hint(),
+            IndexVecIter::USize(ref v) => v.size_hint(),
+        }
+    }
+}
+
+impl<'a> ExactSizeIterator for IndexVecIter<'a> {}
+
+/// Return type of `IndexVec::into_iter`.
+#[derive(Clone, Debug)]
+pub enum IndexVecIntoIter {
+    #[doc(hidden)]
+    U32(vec::IntoIter<u32>),
+    #[doc(hidden)]
+    USize(vec::IntoIter<usize>),
+}
+
+impl Iterator for IndexVecIntoIter {
+    type Item = usize;
+
+    #[inline]
+    fn next(&mut self) -> Option<Self::Item> {
+        use self::IndexVecIntoIter::*;
+        match *self {
+            U32(ref mut v) => v.next().map(|i| i as usize),
+            USize(ref mut v) => v.next(),
+        }
+    }
+
+    #[inline]
+    fn size_hint(&self) -> (usize, Option<usize>) {
+        use self::IndexVecIntoIter::*;
+        match *self {
+            U32(ref v) => v.size_hint(),
+            USize(ref v) => v.size_hint(),
+        }
+    }
+}
+
+impl ExactSizeIterator for IndexVecIntoIter {}
+
+
+/// Randomly sample exactly `amount` distinct indices from `0..length`, and
+/// return them in random order (fully shuffled).
+///
+/// This method is used internally by the slice sampling methods, but it can
+/// sometimes be useful to have the indices themselves so this is provided as
+/// an alternative.
+///
+/// The implementation used is not specified; we automatically select the
+/// fastest available algorithm for the `length` and `amount` parameters
+/// (based on detailed profiling on an Intel Haswell CPU). Roughly speaking,
+/// complexity is `O(amount)`, except that when `amount` is small, performance
+/// is closer to `O(amount^2)`, and when `length` is close to `amount` then
+/// `O(length)`.
+///
+/// Note that performance is significantly better over `u32` indices than over
+/// `u64` indices. Because of this we hide the underlying type behind an
+/// abstraction, `IndexVec`.
+///
+/// If an allocation-free `no_std` function is required, it is suggested
+/// to adapt the internal `sample_floyd` implementation.
+///
+/// Panics if `amount > length`.
+pub fn sample<R>(rng: &mut R, length: usize, amount: usize) -> IndexVec
+where R: Rng + ?Sized {
+    if amount > length {
+        panic!("`amount` of samples must be less than or equal to `length`");
+    }
+    if length > (::core::u32::MAX as usize) {
+        // We never want to use inplace here, but could use floyd's alg
+        // Lazy version: always use the cache alg.
+        return sample_rejection(rng, length, amount);
+    }
+    let amount = amount as u32;
+    let length = length as u32;
+
+    // Choice of algorithm here depends on both length and amount. See:
+    // https://github.com/rust-random/rand/pull/479
+    // We do some calculations with f32. Accuracy is not very important.
+
+    if amount < 163 {
+        const C: [[f32; 2]; 2] = [[1.6, 8.0 / 45.0], [10.0, 70.0 / 9.0]];
+        let j = if length < 500_000 { 0 } else { 1 };
+        let amount_fp = amount as f32;
+        let m4 = C[0][j] * amount_fp;
+        // Short-cut: when amount < 12, floyd's is always faster
+        if amount > 11 && (length as f32) < (C[1][j] + m4) * amount_fp {
+            sample_inplace(rng, length, amount)
+        } else {
+            sample_floyd(rng, length, amount)
+        }
+    } else {
+        const C: [f32; 2] = [270.0, 330.0 / 9.0];
+        let j = if length < 500_000 { 0 } else { 1 };
+        if (length as f32) < C[j] * (amount as f32) {
+            sample_inplace(rng, length, amount)
+        } else {
+            sample_rejection(rng, length, amount)
+        }
+    }
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using Floyd's
+/// combination algorithm.
+///
+/// The output values are fully shuffled. (Overhead is under 50%.)
+///
+/// This implementation uses `O(amount)` memory and `O(amount^2)` time.
+fn sample_floyd<R>(rng: &mut R, length: u32, amount: u32) -> IndexVec
+where R: Rng + ?Sized {
+    // For small amount we use Floyd's fully-shuffled variant. For larger
+    // amounts this is slow due to Vec::insert performance, so we shuffle
+    // afterwards. Benchmarks show little overhead from extra logic.
+    let floyd_shuffle = amount < 50;
+
+    debug_assert!(amount <= length);
+    let mut indices = Vec::with_capacity(amount as usize);
+    for j in length - amount..length {
+        let t = rng.gen_range(0, j + 1);
+        if floyd_shuffle {
+            if let Some(pos) = indices.iter().position(|&x| x == t) {
+                indices.insert(pos, j);
+                continue;
+            }
+        } else if indices.contains(&t) {
+            indices.push(j);
+            continue;
+        }
+        indices.push(t);
+    }
+    if !floyd_shuffle {
+        // Reimplement SliceRandom::shuffle with smaller indices
+        for i in (1..amount).rev() {
+            // invariant: elements with index > i have been locked in place.
+            indices.swap(i as usize, rng.gen_range(0, i + 1) as usize);
+        }
+    }
+    IndexVec::from(indices)
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using an inplace
+/// partial Fisher-Yates method.
+/// Sample an amount of indices using an inplace partial fisher yates method.
+///
+/// This allocates the entire `length` of indices and randomizes only the first `amount`.
+/// It then truncates to `amount` and returns.
+///
+/// This method is not appropriate for large `length` and potentially uses a lot
+/// of memory; because of this we only implement for `u32` index (which improves
+/// performance in all cases).
+///
+/// Set-up is `O(length)` time and memory and shuffling is `O(amount)` time.
+fn sample_inplace<R>(rng: &mut R, length: u32, amount: u32) -> IndexVec
+where R: Rng + ?Sized {
+    debug_assert!(amount <= length);
+    let mut indices: Vec<u32> = Vec::with_capacity(length as usize);
+    indices.extend(0..length);
+    for i in 0..amount {
+        let j: u32 = rng.gen_range(i, length);
+        indices.swap(i as usize, j as usize);
+    }
+    indices.truncate(amount as usize);
+    debug_assert_eq!(indices.len(), amount as usize);
+    IndexVec::from(indices)
+}
+
+trait UInt: Copy + PartialOrd + Ord + PartialEq + Eq + SampleUniform + core::hash::Hash {
+    fn zero() -> Self;
+    fn as_usize(self) -> usize;
+}
+impl UInt for u32 {
+    #[inline]
+    fn zero() -> Self {
+        0
+    }
+
+    #[inline]
+    fn as_usize(self) -> usize {
+        self as usize
+    }
+}
+impl UInt for usize {
+    #[inline]
+    fn zero() -> Self {
+        0
+    }
+
+    #[inline]
+    fn as_usize(self) -> usize {
+        self
+    }
+}
+
+/// Randomly sample exactly `amount` indices from `0..length`, using rejection
+/// sampling.
+///
+/// Since `amount <<< length` there is a low chance of a random sample in
+/// `0..length` being a duplicate. We test for duplicates and resample where
+/// necessary. The algorithm is `O(amount)` time and memory.
+///
+/// This function  is generic over X primarily so that results are value-stable
+/// over 32-bit and 64-bit platforms.
+fn sample_rejection<X: UInt, R>(rng: &mut R, length: X, amount: X) -> IndexVec
+where
+    R: Rng + ?Sized,
+    IndexVec: From<Vec<X>>,
+{
+    debug_assert!(amount < length);
+    #[cfg(feature = "std")]
+    let mut cache = HashSet::with_capacity(amount.as_usize());
+    #[cfg(not(feature = "std"))]
+    let mut cache = BTreeSet::new();
+    let distr = Uniform::new(X::zero(), length);
+    let mut indices = Vec::with_capacity(amount.as_usize());
+    for _ in 0..amount.as_usize() {
+        let mut pos = distr.sample(rng);
+        while !cache.insert(pos) {
+            pos = distr.sample(rng);
+        }
+        indices.push(pos);
+    }
+
+    debug_assert_eq!(indices.len(), amount.as_usize());
+    IndexVec::from(indices)
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+    #[cfg(all(feature = "alloc", not(feature = "std")))] use crate::alloc::vec;
+    #[cfg(feature = "std")] use std::vec;
+
+    #[test]
+    fn test_sample_boundaries() {
+        let mut r = crate::test::rng(404);
+
+        assert_eq!(sample_inplace(&mut r, 0, 0).len(), 0);
+        assert_eq!(sample_inplace(&mut r, 1, 0).len(), 0);
+        assert_eq!(sample_inplace(&mut r, 1, 1).into_vec(), vec![0]);
+
+        assert_eq!(sample_rejection(&mut r, 1u32, 0).len(), 0);
+
+        assert_eq!(sample_floyd(&mut r, 0, 0).len(), 0);
+        assert_eq!(sample_floyd(&mut r, 1, 0).len(), 0);
+        assert_eq!(sample_floyd(&mut r, 1, 1).into_vec(), vec![0]);
+
+        // These algorithms should be fast with big numbers. Test average.
+        let sum: usize = sample_rejection(&mut r, 1 << 25, 10u32).into_iter().sum();
+        assert!(1 << 25 < sum && sum < (1 << 25) * 25);
+
+        let sum: usize = sample_floyd(&mut r, 1 << 25, 10).into_iter().sum();
+        assert!(1 << 25 < sum && sum < (1 << 25) * 25);
+    }
+
+    #[test]
+    #[cfg_attr(miri, ignore)] // Miri is too slow
+    fn test_sample_alg() {
+        let seed_rng = crate::test::rng;
+
+        // We can't test which algorithm is used directly, but Floyd's alg
+        // should produce different results from the others. (Also, `inplace`
+        // and `cached` currently use different sizes thus produce different results.)
+
+        // A small length and relatively large amount should use inplace
+        let (length, amount): (usize, usize) = (100, 50);
+        let v1 = sample(&mut seed_rng(420), length, amount);
+        let v2 = sample_inplace(&mut seed_rng(420), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+
+        // Test Floyd's alg does produce different results
+        let v3 = sample_floyd(&mut seed_rng(420), length as u32, amount as u32);
+        assert!(v1 != v3);
+
+        // A large length and small amount should use Floyd
+        let (length, amount): (usize, usize) = (1 << 20, 50);
+        let v1 = sample(&mut seed_rng(421), length, amount);
+        let v2 = sample_floyd(&mut seed_rng(421), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+
+        // A large length and larger amount should use cache
+        let (length, amount): (usize, usize) = (1 << 20, 600);
+        let v1 = sample(&mut seed_rng(422), length, amount);
+        let v2 = sample_rejection(&mut seed_rng(422), length as u32, amount as u32);
+        assert!(v1.iter().all(|e| e < length));
+        assert_eq!(v1, v2);
+    }
+}
diff --git a/vendor/rand-0.7.3/src/seq/mod.rs b/vendor/rand-0.7.3/src/seq/mod.rs
new file mode 100644
index 000000000..dabf32927
--- /dev/null
+++ b/vendor/rand-0.7.3/src/seq/mod.rs
@@ -0,0 +1,850 @@
+// 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::weighted`] module which provides
+//!     implementations of 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")] pub mod index;
+
+#[cfg(feature = "alloc")] use core::ops::Index;
+
+#[cfg(all(feature = "alloc", not(feature = "std")))] use crate::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;
+///
+/// fn main() {
+///     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")]
+    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")]
+    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")]
+    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;
+
+    /// 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 sized iterators, providing methods for
+/// choosing one or more elements. You must `use` this trait:
+///
+/// ```
+/// use rand::seq::IteratorRandom;
+///
+/// fn main() {
+///     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.
+    ///
+    /// For slices, prefer [`SliceRandom::choose`] which guarantees `O(1)`
+    /// performance.
+    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;
+
+        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;
+                let denom = consumed as f64; // accurate to 2^53 elements
+                if rng.gen_bool(1.0 / denom) {
+                    result = elem;
+                }
+            }
+
+            let hint = self.size_hint();
+            lower = hint.0;
+            upper = hint.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")]
+    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)])
+    }
+
+    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")]
+#[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);
+    }
+
+    #[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_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)
+        );
+    }
+}