Merging upstream version 1.68.2+dfsg1.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
author: Daniel Baumann <daniel.baumann@progress-linux.org> 2024-04-17 12:19:03 +0000
committer: Daniel Baumann <daniel.baumann@progress-linux.org> 2024-04-17 12:19:03 +0000
commit: 64d98f8ee037282c35007b64c2649055c56af1db (patch)
tree: 5492bcf97fce41ee1c0b1cc2add283f3e66cdab0 /vendor/rand-0.7.3
parent: Adding debian version 1.67.1+dfsg1-1. (diff)
download: rustc-64d98f8ee037282c35007b64c2649055c56af1db.tar.xz
rustc-64d98f8ee037282c35007b64c2649055c56af1db.zip
51 files changed, 0 insertions, 11811 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
deleted file mode 100644
index b519d4836..000000000
--- a/vendor/rand-0.7.3/CHANGELOG.md
+++ /dev/null
@@ -1,598 +0,0 @@
-# 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
deleted file mode 100644
index 468d907ca..000000000
--- a/vendor/rand-0.7.3/COPYRIGHT
+++ /dev/null
@@ -1,12 +0,0 @@
-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
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diff --git a/vendor/rand-0.7.3/Cargo.toml b/vendor/rand-0.7.3/Cargo.toml
deleted file mode 100644
index 78a29b0e1..000000000
--- a/vendor/rand-0.7.3/Cargo.toml
+++ /dev/null
@@ -1,80 +0,0 @@
-# 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
deleted file mode 100644
index 17d74680f..000000000
--- a/vendor/rand-0.7.3/LICENSE-APACHE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/vendor/rand-0.7.3/LICENSE-MIT b/vendor/rand-0.7.3/LICENSE-MIT
deleted file mode 100644
index d93b5baf3..000000000
--- a/vendor/rand-0.7.3/LICENSE-MIT
+++ /dev/null
@@ -1,26 +0,0 @@
-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:
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-The above copyright notice and this permission notice
-shall be included in all copies or substantial portions
-of the Software.
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-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
deleted file mode 100644
index c4bd676a9..000000000
--- a/vendor/rand-0.7.3/README.md
+++ /dev/null
@@ -1,126 +0,0 @@
-# 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
deleted file mode 100644
index daedb78f0..000000000
--- a/vendor/rand-0.7.3/SECURITY.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# 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
deleted file mode 100644
index 3e264083d..000000000
--- a/vendor/rand-0.7.3/benches/generators.rs
+++ /dev/null
@@ -1,165 +0,0 @@
-// 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
deleted file mode 100644
index e46137f19..000000000
--- a/vendor/rand-0.7.3/benches/misc.rs
+++ /dev/null
@@ -1,140 +0,0 @@
-// 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
deleted file mode 100644
index 7da2ff8a0..000000000
--- a/vendor/rand-0.7.3/benches/seq.rs
+++ /dev/null
@@ -1,179 +0,0 @@
-// 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
deleted file mode 100644
index 68722908a..000000000
--- a/vendor/rand-0.7.3/benches/weighted.rs
+++ /dev/null
@@ -1,36 +0,0 @@
-// 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
deleted file mode 100644
index 70560d0fa..000000000
--- a/vendor/rand-0.7.3/examples/monte-carlo.rs
+++ /dev/null
@@ -1,51 +0,0 @@
-// 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
deleted file mode 100644
index 30e2f44d1..000000000
--- a/vendor/rand-0.7.3/examples/monty-hall.rs
+++ /dev/null
@@ -1,123 +0,0 @@
-// 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
deleted file mode 100644
index 6a2d9d482..000000000
--- a/vendor/rand-0.7.3/rustfmt.toml
+++ /dev/null
@@ -1,32 +0,0 @@
-# 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
deleted file mode 100644
index a1fa86e14..000000000
--- a/vendor/rand-0.7.3/src/distributions/bernoulli.rs
+++ /dev/null
@@ -1,199 +0,0 @@
-// 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
deleted file mode 100644
index c096e4a86..000000000
--- a/vendor/rand-0.7.3/src/distributions/binomial.rs
+++ /dev/null
@@ -1,321 +0,0 @@
-// 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
deleted file mode 100644
index dc54c98a3..000000000
--- a/vendor/rand-0.7.3/src/distributions/cauchy.rs
+++ /dev/null
@@ -1,99 +0,0 @@
-// 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
deleted file mode 100644
index a75678a85..000000000
--- a/vendor/rand-0.7.3/src/distributions/dirichlet.rs
+++ /dev/null
@@ -1,126 +0,0 @@
-// 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
deleted file mode 100644
index 5fdf7aa74..000000000
--- a/vendor/rand-0.7.3/src/distributions/exponential.rs
+++ /dev/null
@@ -1,114 +0,0 @@
-// 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
deleted file mode 100644
index 0a45f3977..000000000
--- a/vendor/rand-0.7.3/src/distributions/float.rs
+++ /dev/null
@@ -1,307 +0,0 @@
-// 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
deleted file mode 100644
index f19738dbe..000000000
--- a/vendor/rand-0.7.3/src/distributions/gamma.rs
+++ /dev/null
@@ -1,373 +0,0 @@
-// 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
deleted file mode 100644
index f2db1f1c6..000000000
--- a/vendor/rand-0.7.3/src/distributions/integer.rs
+++ /dev/null
@@ -1,279 +0,0 @@
-// 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
deleted file mode 100644
index 4e1b1a6e3..000000000
--- a/vendor/rand-0.7.3/src/distributions/mod.rs
+++ /dev/null
@@ -1,406 +0,0 @@
-// 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
deleted file mode 100644
index ec62fa9ab..000000000
--- a/vendor/rand-0.7.3/src/distributions/normal.rs
+++ /dev/null
@@ -1,177 +0,0 @@
-// 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
deleted file mode 100644
index c95060e51..000000000
--- a/vendor/rand-0.7.3/src/distributions/other.rs
+++ /dev/null
@@ -1,291 +0,0 @@
-// 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
deleted file mode 100644
index ac5473b8c..000000000
--- a/vendor/rand-0.7.3/src/distributions/pareto.rs
+++ /dev/null
@@ -1,70 +0,0 @@
-// 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
deleted file mode 100644
index ce94d7542..000000000
--- a/vendor/rand-0.7.3/src/distributions/poisson.rs
+++ /dev/null
@@ -1,151 +0,0 @@
-// 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
deleted file mode 100644
index 37be19867..000000000
--- a/vendor/rand-0.7.3/src/distributions/triangular.rs
+++ /dev/null
@@ -1,83 +0,0 @@
-// 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
deleted file mode 100644
index 8584152f0..000000000
--- a/vendor/rand-0.7.3/src/distributions/uniform.rs
+++ /dev/null
@@ -1,1380 +0,0 @@
-// 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
deleted file mode 100644
index 37885d8eb..000000000
--- a/vendor/rand-0.7.3/src/distributions/unit_circle.rs
+++ /dev/null
@@ -1,102 +0,0 @@
-// 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
deleted file mode 100644
index 5b8c8ad55..000000000
--- a/vendor/rand-0.7.3/src/distributions/unit_sphere.rs
+++ /dev/null
@@ -1,97 +0,0 @@
-// 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, $b
-                ));
-            }
-        };
-    }
-
-    #[test]
-    fn norm() {
-        let mut rng = crate::test::rng(1);
-        let dist = UnitSphereSurface::new();
-        for _ in 0..1000 {
-            let x = dist.sample(&mut rng);
-            assert_almost_eq!(x[0] * x[0] + x[1] * x[1] + x[2] * x[2], 1., 1e-15);
-        }
-    }
-
-    #[test]
-    fn value_stability() {
-        let mut rng = crate::test::rng(2);
-        let expected = [
-            [0.03247542860231647, -0.7830477442152738, 0.6211131755296027],
-            [-0.09978440840914075, 0.9706650829833128, -0.21875184231323952],
-            [0.2735582468624679, 0.9435374242279655, -0.1868234852870203],
-        ];
-        let samples = [
-            UnitSphereSurface.sample(&mut rng),
-            UnitSphereSurface.sample(&mut rng),
-            UnitSphereSurface.sample(&mut rng),
-        ];
-        assert_eq!(samples, expected);
-    }
-}
diff --git a/vendor/rand-0.7.3/src/distributions/utils.rs b/vendor/rand-0.7.3/src/distributions/utils.rs
deleted file mode 100644
index 2d36b0226..000000000
--- a/vendor/rand-0.7.3/src/distributions/utils.rs
+++ /dev/null
@@ -1,547 +0,0 @@
-// Copyright 2018 Developers of the Rand project.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Math helper functions
-
-#[cfg(feature = "std")] use crate::distributions::ziggurat_tables;
-#[cfg(feature = "std")] use crate::Rng;
-#[cfg(feature = "simd_support")] use packed_simd::*;
-
-
-pub trait WideningMultiply<RHS = Self> {
-    type Output;
-
-    fn wmul(self, x: RHS) -> Self::Output;
-}
-
-macro_rules! wmul_impl {
-    ($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
deleted file mode 100644
index ffbc93b01..000000000
--- a/vendor/rand-0.7.3/src/distributions/weibull.rs
+++ /dev/null
@@ -1,67 +0,0 @@
-// 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
deleted file mode 100644
index 7d42a3526..000000000
--- a/vendor/rand-0.7.3/src/distributions/weighted/alias_method.rs
+++ /dev/null
@@ -1,517 +0,0 @@
-//! 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
deleted file mode 100644
index 357e3a9f0..000000000
--- a/vendor/rand-0.7.3/src/distributions/weighted/mod.rs
+++ /dev/null
@@ -1,413 +0,0 @@
-// 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
deleted file mode 100644
index f830a601b..000000000
--- a/vendor/rand-0.7.3/src/distributions/ziggurat_tables.rs
+++ /dev/null
@@ -1,283 +0,0 @@
-// 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
deleted file mode 100644
index d42a79fb1..000000000
--- a/vendor/rand-0.7.3/src/lib.rs
+++ /dev/null
@@ -1,723 +0,0 @@
-// 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
deleted file mode 100644
index 98ae3bb43..000000000
--- a/vendor/rand-0.7.3/src/prelude.rs
+++ /dev/null
@@ -1,33 +0,0 @@
-// 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
deleted file mode 100644
index 45e56af72..000000000
--- a/vendor/rand-0.7.3/src/rngs/adapter/mod.rs
+++ /dev/null
@@ -1,15 +0,0 @@
-// 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
deleted file mode 100644
index 9a4b55d4e..000000000
--- a/vendor/rand-0.7.3/src/rngs/adapter/read.rs
+++ /dev/null
@@ -1,155 +0,0 @@
-// 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
deleted file mode 100644
index 5460e3431..000000000
--- a/vendor/rand-0.7.3/src/rngs/adapter/reseeding.rs
+++ /dev/null
@@ -1,369 +0,0 @@
-// 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
deleted file mode 100644
index 9ad0d71e0..000000000
--- a/vendor/rand-0.7.3/src/rngs/entropy.rs
+++ /dev/null
@@ -1,76 +0,0 @@
-// 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
deleted file mode 100644
index 9a47264a7..000000000
--- a/vendor/rand-0.7.3/src/rngs/mock.rs
+++ /dev/null
@@ -1,67 +0,0 @@
-// 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
deleted file mode 100644
index 111219602..000000000
--- a/vendor/rand-0.7.3/src/rngs/mod.rs
+++ /dev/null
@@ -1,116 +0,0 @@
-// 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
deleted file mode 100644
index d67689814..000000000
--- a/vendor/rand-0.7.3/src/rngs/small.rs
+++ /dev/null
@@ -1,113 +0,0 @@
-// 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
deleted file mode 100644
index 8b07081a0..000000000
--- a/vendor/rand-0.7.3/src/rngs/std.rs
+++ /dev/null
@@ -1,103 +0,0 @@
-// 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
deleted file mode 100644
index 91ed4c30a..000000000
--- a/vendor/rand-0.7.3/src/rngs/thread.rs
+++ /dev/null
@@ -1,124 +0,0 @@
-// 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
deleted file mode 100644
index 551d409e7..000000000
--- a/vendor/rand-0.7.3/src/seq/index.rs
+++ /dev/null
@@ -1,438 +0,0 @@
-// 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
deleted file mode 100644
index dabf32927..000000000
--- a/vendor/rand-0.7.3/src/seq/mod.rs
+++ /dev/null
@@ -1,850 +0,0 @@
-// 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)
-        );
-    }
-}
author	Daniel Baumann <daniel.baumann@progress-linux.org>	2024-04-17 12:19:03 +0000
committer	Daniel Baumann <daniel.baumann@progress-linux.org>	2024-04-17 12:19:03 +0000
commit	64d98f8ee037282c35007b64c2649055c56af1db (patch)
tree	5492bcf97fce41ee1c0b1cc2add283f3e66cdab0 /vendor/rand-0.7.3
parent	Adding debian version 1.67.1+dfsg1-1. (diff)
download	rustc-64d98f8ee037282c35007b64c2649055c56af1db.tar.xz rustc-64d98f8ee037282c35007b64c2649055c56af1db.zip