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[tor-commits] [tor-rust-dependencies/master] Remove manually vendored rand-8c5b0ac51d dependency.

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Subject: [tor-commits] [tor-rust-dependencies/master] Remove manually vendored rand-8c5b0ac51d dependency.
From: nickm@xxxxxxxxxxxxxx
Date: Fri, 4 May 2018 21:19:52 +0000 (UTC)
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Patch-author: Isis Lovecruft <isis@xxxxxxxxxxxxxx>
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commit c5994e0a528ddfb14496fbba504042df3fcf19f2
Author: Isis Lovecruft <isis@xxxxxxxxxxxxxx>
Date:   Fri May 4 20:19:56 2018 +0000

    Remove manually vendored rand-8c5b0ac51d dependency.
---
 vendor/rand-8c5b0ac51d/CHANGELOG.md                |  369 ------
 vendor/rand-8c5b0ac51d/Cargo.toml                  |   60 -
 vendor/rand-8c5b0ac51d/LICENSE-APACHE              |  201 ----
 vendor/rand-8c5b0ac51d/LICENSE-MIT                 |   25 -
 vendor/rand-8c5b0ac51d/README.md                   |  152 ---
 vendor/rand-8c5b0ac51d/UPDATING.md                 |  266 -----
 vendor/rand-8c5b0ac51d/appveyor.yml                |   39 -
 vendor/rand-8c5b0ac51d/benches/distributions.rs    |  152 ---
 vendor/rand-8c5b0ac51d/benches/generators.rs       |  224 ----
 vendor/rand-8c5b0ac51d/benches/misc.rs             |  134 ---
 vendor/rand-8c5b0ac51d/examples/monte-carlo.rs     |   52 -
 vendor/rand-8c5b0ac51d/examples/monty-hall.rs      |  117 --
 vendor/rand-8c5b0ac51d/master.zip                  |  Bin 168983 -> 0 bytes
 vendor/rand-8c5b0ac51d/rand_core/CHANGELOG.md      |   21 -
 vendor/rand-8c5b0ac51d/rand_core/Cargo.toml        |   29 -
 vendor/rand-8c5b0ac51d/rand_core/LICENSE-APACHE    |  201 ----
 vendor/rand-8c5b0ac51d/rand_core/LICENSE-MIT       |   25 -
 vendor/rand-8c5b0ac51d/rand_core/README.md         |   62 -
 vendor/rand-8c5b0ac51d/rand_core/src/error.rs      |  163 ---
 vendor/rand-8c5b0ac51d/rand_core/src/impls.rs      |  543 ---------
 vendor/rand-8c5b0ac51d/rand_core/src/le.rs         |   70 --
 vendor/rand-8c5b0ac51d/rand_core/src/lib.rs        |  438 -------
 .../rand-8c5b0ac51d/src/distributions/binomial.rs  |  172 ---
 .../src/distributions/exponential.rs               |  122 --
 vendor/rand-8c5b0ac51d/src/distributions/float.rs  |   89 --
 vendor/rand-8c5b0ac51d/src/distributions/gamma.rs  |  360 ------
 .../rand-8c5b0ac51d/src/distributions/integer.rs   |  138 ---
 .../rand-8c5b0ac51d/src/distributions/log_gamma.rs |   51 -
 vendor/rand-8c5b0ac51d/src/distributions/mod.rs    |  643 -----------
 vendor/rand-8c5b0ac51d/src/distributions/normal.rs |  192 ----
 vendor/rand-8c5b0ac51d/src/distributions/other.rs  |  207 ----
 .../rand-8c5b0ac51d/src/distributions/poisson.rs   |  157 ---
 .../rand-8c5b0ac51d/src/distributions/uniform.rs   |  650 -----------
 .../src/distributions/ziggurat_tables.rs           |  280 -----
 vendor/rand-8c5b0ac51d/src/entropy_rng.rs          |  167 ---
 vendor/rand-8c5b0ac51d/src/jitter.rs               |  875 --------------
 vendor/rand-8c5b0ac51d/src/lib.rs                  | 1206 --------------------
 vendor/rand-8c5b0ac51d/src/mock.rs                 |   61 -
 vendor/rand-8c5b0ac51d/src/os.rs                   |  833 --------------
 vendor/rand-8c5b0ac51d/src/prng/chacha.rs          |  463 --------
 vendor/rand-8c5b0ac51d/src/prng/hc128.rs           |  457 --------
 vendor/rand-8c5b0ac51d/src/prng/isaac.rs           |  482 --------
 vendor/rand-8c5b0ac51d/src/prng/isaac64.rs         |  474 --------
 vendor/rand-8c5b0ac51d/src/prng/isaac_array.rs     |  130 ---
 vendor/rand-8c5b0ac51d/src/prng/mod.rs             |   55 -
 vendor/rand-8c5b0ac51d/src/prng/xorshift.rs        |  226 ----
 vendor/rand-8c5b0ac51d/src/read.rs                 |  129 ---
 vendor/rand-8c5b0ac51d/src/reseeding.rs            |  260 -----
 vendor/rand-8c5b0ac51d/src/seq.rs                  |  335 ------
 vendor/rand-8c5b0ac51d/src/thread_rng.rs           |  206 ----
 vendor/rand-8c5b0ac51d/utils/ci/install.sh         |   49 -
 vendor/rand-8c5b0ac51d/utils/ci/script.sh          |   27 -
 vendor/rand-8c5b0ac51d/utils/ziggurat_tables.py    |  127 ---
 53 files changed, 12966 deletions(-)

diff --git a/vendor/rand-8c5b0ac51d/CHANGELOG.md b/vendor/rand-8c5b0ac51d/CHANGELOG.md
deleted file mode 100644
index c0544e5..0000000
--- a/vendor/rand-8c5b0ac51d/CHANGELOG.md
+++ /dev/null
@@ -1,369 +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 [Update Guide](UPDATING.md) useful.
-
-
-## [0.5.0] - Unreleased
-
-### Crate features and organisation
-- Minimum Rust version update: 1.22.0. (#239)
-- Create a seperate `rand_core` crate. (#288)
-- Deprecate `rand_derive`. (#256)
-- 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 `random`. (#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 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)
-- Add `ChaChaRng::set_rounds` method. (#243)
-- 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)
-- Deprecate `Rand`, `Sample` and `IndependentSample` traits. (#256)
-- Add a `Standard` distribution (replaces most `Rand` implementations). (#256)
-- Add `Binomial` and `Poisson` distributions. (#96)
-- Add `Alphanumeric` distribution. (#279)
-- Remove `Open01` and `Closed01` distributions, use `Standard` instead (open distribution). (#274)
-- Rework `Range` type, making it possible to implement it for user types. (#274)
-- Add `Range::new_inclusive` for inclusive ranges. (#274)
-- Add `Range::sample_single` to allow for optimized implementations. (#274)
-- Use widening multiply method for much faster integer range reduction. (#274)
-- `Standard` distributions for `bool` uses `Range`. (#274)
-- `Standard` distributions for `bool` uses sign test. (#274)
-
-
-## [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 inplementaions 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-8c5b0ac51d/Cargo.toml b/vendor/rand-8c5b0ac51d/Cargo.toml
deleted file mode 100644
index 8dc9c3c..0000000
--- a/vendor/rand-8c5b0ac51d/Cargo.toml
+++ /dev/null
@@ -1,60 +0,0 @@
-[package]
-name = "rand"
-version = "0.5.0-pre.0" # NB: When modifying, also modify html_root_url in lib.rs
-authors = ["The Rust Project Developers"]
-license = "MIT/Apache-2.0"
-readme = "README.md"
-repository = "https://github.com/rust-lang-nursery/rand";
-documentation = "https://docs.rs/rand";
-homepage = "https://crates.io/crates/rand";
-description = """
-Random number generators and other randomness functionality.
-"""
-keywords = ["random", "rng"]
-categories = ["algorithms", "no-std"]
-
-[badges]
-travis-ci = { repository = "rust-lang-nursery/rand" }
-appveyor = { repository = "alexcrichton/rand" }
-
-[features]
-default = ["std" ] # without "std" rand uses libcore
-nightly = ["i128_support"] # enables all features requiring nightly rust
-std = ["rand_core/std", "alloc", "libc", "winapi", "cloudabi", "fuchsia-zircon"]
-alloc = ["rand_core/alloc"]  # enables Vec and Box support (without std)
-i128_support = [] # enables i128 and u128 support
-serde1 = ["serde", "serde_derive", "rand_core/serde1"] # enables serialization for PRNGs
-
-[workspace]
-members = ["rand_core"]
-
-[dependencies]
-rand_core = { path = "rand_core", version = "0.1.0", default-features = false }
-log = { version = "0.4", optional = true }
-serde = { version = "1", optional = true }
-serde_derive = { version = "1", optional = true }
-
-[target.'cfg(unix)'.dependencies]
-libc = { version = "0.2", optional = true }
-
-[target.'cfg(windows)'.dependencies]
-winapi = { version = "0.3", features = ["minwindef", "ntsecapi", "profileapi", "winnt"], optional = true }
-
-[target.'cfg(target_os = "cloudabi")'.dependencies]
-cloudabi = { version = "0.0.3", optional = true }
-
-[target.'cfg(target_os = "fuchsia")'.dependencies]
-fuchsia-zircon = { version = "0.3.2", optional = true }
-
-[target.wasm32-unknown-unknown.dependencies]
-# use with `--target wasm32-unknown-unknown --features=stdweb`
-stdweb = { version = "0.4", optional = true }
-
-[dev-dependencies]
-# This is for testing serde, unfortunately we can't specify feature-gated dev
-# deps yet, see: https://github.com/rust-lang/cargo/issues/1596
-bincode = "1.0"
-
-[package.metadata.docs.rs]
-all-features = true
-rustdoc-args = [ "--all" ] # also document rand_core
diff --git a/vendor/rand-8c5b0ac51d/LICENSE-APACHE b/vendor/rand-8c5b0ac51d/LICENSE-APACHE
deleted file mode 100644
index 17d7468..0000000
--- a/vendor/rand-8c5b0ac51d/LICENSE-APACHE
+++ /dev/null
@@ -1,201 +0,0 @@
-                              Apache License
-                        Version 2.0, January 2004
-                     https://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-1. Definitions.
-
-   "License" shall mean the terms and conditions for use, reproduction,
-   and distribution as defined by Sections 1 through 9 of this document.
-
-   "Licensor" shall mean the copyright owner or entity authorized by
-   the copyright owner that is granting the License.
-
-   "Legal Entity" shall mean the union of the acting entity and all
-   other entities that control, are controlled by, or are under common
-   control with that entity. For the purposes of this definition,
-   "control" means (i) the power, direct or indirect, to cause the
-   direction or management of such entity, whether by contract or
-   otherwise, or (ii) ownership of fifty percent (50%) or more of the
-   outstanding shares, or (iii) beneficial ownership of such entity.
-
-   "You" (or "Your") shall mean an individual or Legal Entity
-   exercising permissions granted by this License.
-
-   "Source" form shall mean the preferred form for making modifications,
-   including but not limited to software source code, documentation
-   source, and configuration files.
-
-   "Object" form shall mean any form resulting from mechanical
-   transformation or translation of a Source form, including but
-   not limited to compiled object code, generated documentation,
-   and conversions to other media types.
-
-   "Work" shall mean the work of authorship, whether in Source or
-   Object form, made available under the License, as indicated by a
-   copyright notice that is included in or attached to the work
-   (an example is provided in the Appendix below).
-
-   "Derivative Works" shall mean any work, whether in Source or Object
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diff --git a/vendor/rand-8c5b0ac51d/LICENSE-MIT b/vendor/rand-8c5b0ac51d/LICENSE-MIT
deleted file mode 100644
index 39d4bdb..0000000
--- a/vendor/rand-8c5b0ac51d/LICENSE-MIT
+++ /dev/null
@@ -1,25 +0,0 @@
-Copyright (c) 2014 The Rust Project Developers
-
-Permission is hereby granted, free of charge, to any
-person obtaining a copy of this software and associated
-documentation files (the "Software"), to deal in the
-Software without restriction, including without
-limitation the rights to use, copy, modify, merge,
-publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software
-is furnished to do so, subject to the following
-conditions:
-
-The above copyright notice and this permission notice
-shall be included in all copies or substantial portions
-of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
-ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
-TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
-PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
-SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
-IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-DEALINGS IN THE SOFTWARE.
diff --git a/vendor/rand-8c5b0ac51d/README.md b/vendor/rand-8c5b0ac51d/README.md
deleted file mode 100644
index 1f75787..0000000
--- a/vendor/rand-8c5b0ac51d/README.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# Rand
-
-[![Build Status](https://travis-ci.org/rust-lang-nursery/rand.svg?branch=master)](https://travis-ci.org/rust-lang-nursery/rand)
-[![Build Status](https://ci.appveyor.com/api/projects/status/github/rust-lang-nursery/rand?svg=true)](https://ci.appveyor.com/project/alexcrichton/rand)
-[![Latest version](https://img.shields.io/crates/v/rand.svg)](https://crates.io/crates/rand)
-[![Documentation](https://docs.rs/rand/badge.svg)](https://docs.rs/rand)
-[![Minimum rustc version](https://img.shields.io/badge/rustc-1.22+-yellow.svg)](https://github.com/rust-lang-nursery/rand#rust-version-requirements)
-
-A Rust library for random number generators and other randomness functionality.
-
-See also:
-
-*   [rand_core](https://crates.io/crates/rand_core)
-
-Documentation:
-[master branch](https://rust-lang-nursery.github.io/rand/rand/index.html),
-[by release](https://docs.rs/rand)
-
-## Usage
-
-Add this to your `Cargo.toml`:
-
-```toml
-[dependencies]
-rand = "0.5.0-pre.0"
-```
-
-and this to your crate root:
-
-```rust
-extern crate rand;
-
-// example usage:
-use rand::{Rng, thread_rng};
-let x: f64 = thread_rng().gen();
-```
-
-## Versions
-
-Version 0.5 is available as a pre-release and contains many breaking changes.
-See [the Upgrade Guide](UPDATING.md) for guidance on updating from previous
-versions.
-
-Version 0.4 was released in December 2017. It contains almost no breaking
-changes since the 0.3 series.
-
-For more details, see the [changelog](CHANGELOG.md).
-
-### Compatibility shims
-
-**As of now there is no compatibility shim between Rand 0.4 and 0.5.**
-It is also not entirely obvious how to make one due to the large differences
-between the two versions, although it would be possible to implement the new
-`RngCore` for any implementation of the old `Rng` (or vice-versa; unfortunately
-not both as that would result in circular implementation). If we implement a
-compatibility shim it will be optional (opt-in via a feature).
-
-There is a compatibility shim from 0.3 to 0.4 forcibly upgrading all Rand 0.3
-users; this is largely due to the small differences between the two versions.
-
-### Rust version requirements
-
-The 0.5 release of Rand will require **Rustc version 1.22 or greater**.
-Rand 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.
-
-## Functionality
-
-The `rand_core` crate provides:
-
--   base random number generator traits
--   error-reporting types
--   functionality to aid implementation of RNGs
-
-The `rand` crate provides:
-
--   most content from `rand_core` (re-exported)
--   fresh entropy: `EntropyRng`, `OsRng`, `JitterRng`
--   pseudo-random number generators: `StdRng`, `SmallRng`, `prng` module
--   convenient, auto-seeded crypto-grade thread-local generator: `thread_rng`
--   `distributions` producing many different types of random values:
-    -   a `Standard` distribution for integers, floats,and derived types
-        including tuples, arrays and `Option`
-    -   unbiased sampling from specified `Range`s
-    -   sampling from exponential/normal/gamma distributions
-    -   sampling from binomial/poisson distributions
-    -   `gen_bool` aka Bernoulli distribution
--   `seq`-uence related functionality:
-    -   sampling a subset of elements
-    -   randomly shuffling a list
-
-## Crate Features
-
-By default, Rand is built with all stable features available. The following
-optional features are available:
-
--   `alloc` can be used instead of `std` to provide `Vec` and `Box`
--   `i128_support` enables support for generating `u128` and `i128` values
--   `log` enables some logging via the `log` crate
--   `nightly` enables all unstable features (`i128_support`)
--   `serde1` enables serialization for some types, via Serde version 1
--   `stdweb` enables support for `OsRng` on WASM via stdweb.
--   `std` enabled by default; by setting "default-features = false" `no_std`
-    mode is activated; this removes features depending on `std` functionality:
-    -   `OsRng` is entirely unavailable
-    -   `JitterRng` code is still present, but a nanosecond timer must be
-        provided via `JitterRng::new_with_timer`
-    -   Since no external entropy is available, it is not possible to create
-        generators with fresh seeds (user must provide entropy)
-    -   `thread_rng`, `weak_rng` and `random` are all disabled
-    -   exponential, normal and gamma type distributions are unavailable
-        since `exp` and `log` functions are not provided in `core`
-    -   any code requiring `Vec` or `Box`
-
-## Testing
-
-Unfortunately, `cargo test` does not test everything. The following tests are
-recommended:
-
-```
-# Basic tests for Rand and sub-crates
-cargo test --all
-
-# Test no_std support
-cargo test --tests --no-default-features
-# Test no_std+alloc support
-cargo test --tests --no-default-features --features alloc
-
-# Test log and serde support
-cargo test --features serde1,log
-
-# Test 128-bit support (requires nightly)
-cargo test --all --features nightly
-
-# Benchmarks (requires nightly)
-cargo bench
-# or just to test the benchmark code:
-cargo test --benches
-```
-
-
-# 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) for details.
diff --git a/vendor/rand-8c5b0ac51d/UPDATING.md b/vendor/rand-8c5b0ac51d/UPDATING.md
deleted file mode 100644
index 6e009b7..0000000
--- a/vendor/rand-8c5b0ac51d/UPDATING.md
+++ /dev/null
@@ -1,266 +0,0 @@
-# Update Guide
-
-This guide gives a few more details than the [changelog], in particular giving
-guidance on how to use new features and migrate away from old ones.
-
-[changelog]: CHANGELOG.md
-
-## Rand 0.5
-
-The 0.5 release has quite significant changes over the 0.4 release; as such,
-it may be worth reading through the following coverage of breaking changes.
-This release also contains many optimisations, which are not detailed below.
-
-### Crates
-
-We have a new crate: `rand_core`! This crate houses some important traits,
-`RngCore`, `BlockRngCore`, `SeedableRng` and `CryptoRng`, the error types, as
-well as two modules with helpers for implementations: `le` and `impls`. It is
-recommended that implementations of generators use the `rand_core` crate while
-other users use only the `rand` crate, which re-exports most parts of `rand_core`.
-
-The `rand_derive` crate has been deprecated due to very low usage and
-deprecation of `Rand`.
-
-### Features
-
-Several new Cargo feature flags have been added:
-
--   `alloc`, used without `std`, allows use of `Box` and `Vec`
--   `serde1` adds serialization support to some PRNGs
--   `log` adds logging in a few places (primarily to `OsRng` and `JitterRng`)
-
-### `Rng` and friends (core traits)
-
-`Rng` trait has been split into two traits, a "back end" `RngCore` (implemented
-by generators) and a "front end" `Rng` implementing all the convenient extension
-methods.
-
-Implementations of generators must `impl RngCore` instead. Usage of `rand_core`
-for implementations is encouraged; the `rand_core::{le, impls}` modules may
-prove useful.
-
-Users of `Rng` *who don't need to implement it* won't need to make so many
-changes; often users can forget about `RngCore` and only import `Rng`. Instead
-of `RngCore::next_u32()` / `next_u64()` users should prefer `Rng::gen()`, and
-instead of `RngCore::fill_bytes(dest)`, `Rng::fill(dest)` can be used.
-
-#### `Rng` / `RngCore` methods
-
-To allow error handling from fallible sources (e.g. `OsRng`), a new
-`RngCore::try_fill_bytes` method has been added; for example `EntropyRng` uses
-this mechanism to fall back to `JitterRng` if `OsRng` fails, and various
-handlers produce better error messages.
-As before, the other methods will panic on failure, but since these are usually
-used with algorithmic generators which are usually infallible, this is
-considered an appropriate compromise.
-
-A few methods from the old `Rng` have been removed or deprecated:
-
--   `next_f32` and `next_f64`; these are no longer implementable by generators;
-    use `gen` instead
--   `gen_iter`; users may instead use standard iterators with closures:
-    `::std::iter::repeat(()).map(|()| rng.gen())`
--   `gen_ascii_chars`; use `repeat` as above and `rng.sample(Alphanumeric)`
--   `gen_weighted_bool(n)`; use `gen_bool(1.0 / n)` instead
-
-`Rng` has a few new methods:
-
--   `sample(distr)` is a shortcut for `distr.sample(rng)` for any `Distribution`
--   `gen_bool(p)` generates a boolean with probability `p` of being true
--   `fill` and `try_fill`, corresponding to `fill_bytes` and `try_fill_bytes`
-    respectively (i.e. the only difference is error handling); these can fill
-    and integer slice / array directly, and provide better performance
-    than `gen()`
-
-#### Constructing PRNGs
-
-##### New randomly-initialised PRNGs
-
-A new trait has been added: `FromEntropy`. This is automatically implemented for
-any type supporting `SeedableRng`, and provides construction from fresh, strong
-entropy:
-
-```rust
-use rand::{ChaChaRng, FromEntropy};
-
-let mut rng = ChaChaRng::from_entropy();
-```
-
-##### Seeding PRNGs
-
-The `SeedableRng` trait has been modified to include the seed type via an
-associated type (`SeedableRng::Seed`) instead of a template parameter
-(`SeedableRng<Seed>`). Additionally, all PRNGs now seed from a byte-array
-(`[u8; N]` for some fixed N). This allows generic handling of PRNG seeding
-which was not previously possible.
-
-PRNGs are no longer constructed from other PRNGs via `Rand` support / `gen()`,
-but through `SeedableRng::from_rng`, which allows error handling and is
-intentionally explicit.
-
-`SeedableRng::reseed` has been removed since it has no utility over `from_seed`
-and its performance advantage is questionable.
-
-Implementations of `SeedableRng` may need to change their `Seed` type to a
-byte-array; this restriction has been made to ensure portable handling of
-Endianness. Helper functions are available in `rand_core::le` to read `u32` and
-`u64` values from byte arrays.
-
-#### Block-based PRNGs
-
-rand_core has a new helper trait, `BlockRngCore`, and implementation,
-`BlockRng`. These are for use by generators which generate a block of random
-data at a time instead of word-sized values. Using this trait and implementation
-has two advantages: optimised `RngCore` methods are provided, and the PRNG can
-be used with `ReseedingRng` with very low overhead.
-
-#### Cryptographic RNGs
-
-A new trait has been added: `CryptoRng`. This is purely a marker trait to
-indicate which generators should be suitable for cryptography, e.g.
-`fn foo<R: Rng + CryptoRng>(rng: &mut R)`. *Suitability for cryptographic
-use cannot be guaranteed.*
-
-### Error handling
-
-A new `Error` type has been added, designed explicitly for no-std compatibility,
-simplicity, and enough flexibility for our uses (carrying a `cause` when
-possible):
-```rust
-pub struct Error {
-    pub kind: ErrorKind,
-    pub msg: &'static str,
-    // some fields omitted
-}
-```
-The associated `ErrorKind` allows broad classification of errors into permanent,
-unexpected, transient and not-yet-ready kinds.
-
-The following use the new error type:
-
--   `RngCore::try_fill_bytes`
--   `Rng::try_fill`
--   `OsRng::new`
--   `JitterRng::new`
-
-### External generators
-
-We have a new generator, `EntropyRng`, which wraps `OsRng` and `JitterRng`
-(preferring to use the former, but falling back to the latter if necessary).
-This allows easy construction with fallback via `SeedableRng::from_rng`,
-e.g. `IsaacRng::from_rng(EntropyRng::new())?`. This is equivalent to using
-`FromEntropy` except for error handling.
-
-It is recommended to use `EntropyRng` over `OsRng` to avoid errors on platforms
-with broken system generator, but it should be noted that the `JitterRng`
-fallback is very slow.
-
-### PRNGs
-
-*Pseudo-Random Number Generators* (i.e. deterministic algorithmic generators)
-have had a few changes since 0.4, and are now housed in the `prng` module
-(old names remain temporarily available for compatibility; eventually these
-generators will likely be housed outside the `rand` crate).
-
-All PRNGs now do not implement `Copy` to prevent accidental copying of the
-generator's state (and thus repetitions of generated values). Explicit cloning
-via `Clone` is still available. All PRNGs now have a custom implementation of
-`Debug` which does not print any internal state; this helps avoid accidentally
-leaking cryptographic generator state in log files. External PRNG
-implementations are advised to follow this pattern (see also doc on `RngCore`).
-
-`SmallRng` has been added as a wrapper, currently around `XorShiftRng` (but
-likely another algorithm soon). This is for uses where small state and fast
-initialisation are important but cryptographic strength is not required.
-(Actual performance of generation varies by benchmark; dependending on usage
-this may or may not be the fastest algorithm, but will always be fast.)
-
-#### `ReseedingRng`
-
-The `ReseedingRng` wrapper has been signficantly altered to reduce overhead.
-Unfortunately the new `ReseedingRng` is not compatible with all RNGs, but only
-those using `BlockRngCore`.
-
-#### ISAAC PRNGs
-
-The `IsaacRng` and `Isaac64Rng` PRNGs now have an additional construction
-method: `new_from_u64(seed)`. 64 bits of state is insufficient for cryptography
-but may be of use in simulations and games. This will likely be superceeded by
-a method to construct any PRNG from any hashable object in the future.
-
-#### HC-128
-
-This is a new cryptographic generator, selected as one of the "stream ciphers
-suitable for widespread adoption" by eSTREAM. This is now the default
-cryptographic generator, used by `StdRng` and `thread_rng()`.
-
-### Helper functions/traits
-
-The `Rand` trait has been deprecated. Instead, users are encouraged to use
-`Standard` which is a real distribution and supports the same sampling as
- `Rand`.`Rng::gen()` now uses `Standard` and should work exactly as before.
-
-The `random()` function has been removed; users may simply use
-`thread_rng().gen()` instead or may choose to cache
-`let mut rng = thread_rng();` locally, or even use a different generator.
-
-`weak_rng()` has been deprecated; use `SmallRng::from_entropy()` instead.
-
-### Distributions
-
-The `Sample` and `IndependentSample` traits have been replaced by a single
-trait, `Distribution`. This is largely equivalent to `IndependentSample`, but
-with `ind_sample` replaced by just `sample`. Support for mutable distributions
-has been dropped; although it appears there may be a few genuine uses, these
-are not used widely enough to justify the existance of two independent traits
-or of having to provide mutable access to a distribution object. Both `Sample`
-and `IndependentSample` are still available, but deprecated; they will be
-removed in a future release.
-
-`Distribution::sample` (as well as several other functions) can now be called
-directly on type-erased (unsized) RNGs.
-
-`RandSample` has been removed (see `Rand` deprecation and new `Standard`
-distribution).
-
-The `Open01` and `Closed01` wrappers have been removed. `Rng::gen()` (via
-`Standard`) now yields samples from `(0, 1)` for floats; i.e. the same as the
-old `Open01`. This is considered sufficient for most uses.
-
-#### Uniform distributions
-
-Two new distributions are available:
-
--   `Standard` produces uniformly-distributed samples for many different types,
-    and acts as a replacement for `Rand`
--   `Alphanumeric` samples `char`s from the ranges `a-z A-Z 0-9`
-
-##### Ranges
-
-The `Range` distribution has been heavily adapted, while remaining largely
-backwards compatible:
-
--   `Range::new(low, high)` remains (half open `[low, high)`)
--   `Range::new_inclusive(low, high)` has been added, including `high` in the sample range
--   `Range::sample_single(low, high, rng)` is a faster variant for single usage sampling from `[low, high)`
-
-`Range` can now be implemented for user-defined types; see the `RangeImpl` type.
-`SampleRange` has been adapted to suit the new `Range` model.
-
-#### Non-uniform distributions
-
-Two distributions have been added:
-
--   Poisson, modelling the number of events expected from a constant-rate
-    source within a fixed time interval (e.g. nuclear decay)
--   Binomial, modelling the outcome of a fixed number of yes-no trials
-
-The sampling methods are based on those in "Numerical Recipes in C".
-
-##### Exponential and Normal distributions
-
-The main `Exp` and `Normal` distributions are unchanged, however the
-"standard" versions, `Exp1` and `StandardNormal` are no longer wrapper types,
-but full distributions. Instead of writing `let Exp1(x) = rng.gen();` you now
-write `let x = rng.sample(Exp1);`.
diff --git a/vendor/rand-8c5b0ac51d/appveyor.yml b/vendor/rand-8c5b0ac51d/appveyor.yml
deleted file mode 100644
index d9b613e..0000000
--- a/vendor/rand-8c5b0ac51d/appveyor.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-environment:
-
-  # At the time this was added AppVeyor was having troubles with checking
-  # revocation of SSL certificates of sites like static.rust-lang.org and what
-  # we think is crates.io. The libcurl HTTP client by default checks for
-  # revocation on Windows and according to a mailing list [1] this can be
-  # disabled.
-  #
-  # The `CARGO_HTTP_CHECK_REVOKE` env var here tells cargo to disable SSL
-  # revocation checking on Windows in libcurl. Note, though, that rustup, which
-  # we're using to download Rust here, also uses libcurl as the default backend.
-  # Unlike Cargo, however, rustup doesn't have a mechanism to disable revocation
-  # checking. To get rustup working we set `RUSTUP_USE_HYPER` which forces it to
-  # use the Hyper instead of libcurl backend. Both Hyper and libcurl use
-  # schannel on Windows but it appears that Hyper configures it slightly
-  # differently such that revocation checking isn't turned on by default.
-  #
-  # [1]: https://curl.haxx.se/mail/lib-2016-03/0202.html
-  RUSTUP_USE_HYPER: 1
-  CARGO_HTTP_CHECK_REVOKE: false
-
-  matrix:
-  - TARGET: x86_64-pc-windows-msvc
-  - TARGET: i686-pc-windows-msvc
-install:
-  - appveyor DownloadFile https://win.rustup.rs/ -FileName rustup-init.exe
-  - rustup-init.exe -y --default-host %TARGET% --default-toolchain nightly
-  - set PATH=%PATH%;C:\Users\appveyor\.cargo\bin
-  - rustc -V
-  - cargo -V
-
-build: false
-
-test_script:
-  - cargo test --benches
-  - cargo test --all
-  - cargo test --features serde1,log,nightly
-  - cargo test --all --tests --no-default-features --features=alloc
-  - cargo test --tests --no-default-features --features=serde1
diff --git a/vendor/rand-8c5b0ac51d/benches/distributions.rs b/vendor/rand-8c5b0ac51d/benches/distributions.rs
deleted file mode 100644
index 24e0e7f..0000000
--- a/vendor/rand-8c5b0ac51d/benches/distributions.rs
+++ /dev/null
@@ -1,152 +0,0 @@
-#![feature(test)]
-#![cfg_attr(feature = "i128_support", feature(i128_type, i128))]
-
-extern crate test;
-extern crate rand;
-
-const RAND_BENCH_N: u64 = 1000;
-
-use std::mem::size_of;
-use test::{black_box, Bencher};
-
-use rand::{Rng, FromEntropy, XorShiftRng};
-use rand::distributions::*;
-
-macro_rules! distr_int {
-    ($fnn:ident, $ty:ty, $distr:expr) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = XorShiftRng::from_entropy();
-            let distr = $distr;
-
-            b.iter(|| {
-                let mut accum = 0 as $ty;
-                for _ in 0..::RAND_BENCH_N {
-                    let x: $ty = distr.sample(&mut rng);
-                    accum = accum.wrapping_add(x);
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N;
-        }
-    }
-}
-
-macro_rules! distr_float {
-    ($fnn:ident, $ty:ty, $distr:expr) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = XorShiftRng::from_entropy();
-            let distr = $distr;
-
-            b.iter(|| {
-                let mut accum = 0.0;
-                for _ in 0..::RAND_BENCH_N {
-                    let x: $ty = distr.sample(&mut rng);
-                    accum += x;
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N;
-        }
-    }
-}
-
-macro_rules! distr {
-    ($fnn:ident, $ty:ty, $distr:expr) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = XorShiftRng::from_entropy();
-            let distr = $distr;
-
-            b.iter(|| {
-                for _ in 0..::RAND_BENCH_N {
-                    let x: $ty = distr.sample(&mut rng);
-                    black_box(x);
-                }
-            });
-            b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N;
-        }
-    }
-}
-
-// uniform
-distr_int!(distr_uniform_i8, i8, Uniform::new(20i8, 100));
-distr_int!(distr_uniform_i16, i16, Uniform::new(-500i16, 2000));
-distr_int!(distr_uniform_i32, i32, Uniform::new(-200_000_000i32, 800_000_000));
-distr_int!(distr_uniform_i64, i64, Uniform::new(3i64, 123_456_789_123));
-#[cfg(feature = "i128_support")]
-distr_int!(distr_uniform_i128, i128, Uniform::new(-123_456_789_123i128, 123_456_789_123_456_789));
-
-distr_float!(distr_uniform_f32, f32, Uniform::new(2.26f32, 2.319));
-distr_float!(distr_uniform_f64, f64, Uniform::new(2.26f64, 2.319));
-
-// standard
-distr_int!(distr_standard_i8, i8, Standard);
-distr_int!(distr_standard_i16, i16, Standard);
-distr_int!(distr_standard_i32, i32, Standard);
-distr_int!(distr_standard_i64, i64, Standard);
-#[cfg(feature = "i128_support")]
-distr_int!(distr_standard_i128, i128, Standard);
-
-distr!(distr_standard_bool, bool, Standard);
-distr!(distr_standard_alphanumeric, char, Alphanumeric);
-distr!(distr_standard_codepoint, char, Standard);
-
-distr_float!(distr_standard_f32, f32, Standard);
-distr_float!(distr_standard_f64, f64, Standard);
-
-// distributions
-distr_float!(distr_exp, f64, Exp::new(1.23 * 4.56));
-distr_float!(distr_normal, f64, Normal::new(-1.23, 4.56));
-distr_float!(distr_log_normal, f64, LogNormal::new(-1.23, 4.56));
-distr_float!(distr_gamma_large_shape, f64, Gamma::new(10., 1.0));
-distr_float!(distr_gamma_small_shape, f64, Gamma::new(0.1, 1.0));
-distr_int!(distr_binomial, u64, Binomial::new(20, 0.7));
-distr_int!(distr_poisson, u64, Poisson::new(4.0));
-
-
-// construct and sample from a range
-macro_rules! gen_range_int {
-    ($fnn:ident, $ty:ident, $low:expr, $high:expr) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = XorShiftRng::from_entropy();
-
-            b.iter(|| {
-                let mut high = $high;
-                let mut accum: $ty = 0;
-                for _ in 0..::RAND_BENCH_N {
-                    accum = accum.wrapping_add(rng.gen_range($low, high));
-                    // force recalculation of range each time
-                    high = high.wrapping_add(1) & std::$ty::MAX;
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<$ty>() as u64 * ::RAND_BENCH_N;
-        }
-    }
-}
-
-gen_range_int!(gen_range_i8, i8, -20i8, 100);
-gen_range_int!(gen_range_i16, i16, -500i16, 2000);
-gen_range_int!(gen_range_i32, i32, -200_000_000i32, 800_000_000);
-gen_range_int!(gen_range_i64, i64, 3i64, 123_456_789_123);
-#[cfg(feature = "i128_support")]
-gen_range_int!(gen_range_i128, i128, -12345678901234i128, 123_456_789_123_456_789);
-
-#[bench]
-fn dist_iter(b: &mut Bencher) {
-    let mut rng = XorShiftRng::from_entropy();
-    let distr = Normal::new(-2.71828, 3.14159);
-    let mut iter = distr.sample_iter(&mut rng);
-
-    b.iter(|| {
-        let mut accum = 0.0;
-        for _ in 0..::RAND_BENCH_N {
-            accum += iter.next().unwrap();
-        }
-        black_box(accum);
-    });
-    b.bytes = size_of::<f64>() as u64 * ::RAND_BENCH_N;
-}
diff --git a/vendor/rand-8c5b0ac51d/benches/generators.rs b/vendor/rand-8c5b0ac51d/benches/generators.rs
deleted file mode 100644
index a86798d..0000000
--- a/vendor/rand-8c5b0ac51d/benches/generators.rs
+++ /dev/null
@@ -1,224 +0,0 @@
-#![feature(test)]
-
-extern crate test;
-extern crate rand;
-
-const RAND_BENCH_N: u64 = 1000;
-const BYTES_LEN: usize = 1024;
-
-use std::mem::size_of;
-use test::{black_box, Bencher};
-
-use rand::{RngCore, Rng, SeedableRng, FromEntropy};
-use rand::{StdRng, SmallRng, OsRng, EntropyRng, ReseedingRng};
-use rand::prng::{XorShiftRng, Hc128Rng, IsaacRng, Isaac64Rng, ChaChaRng};
-use rand::prng::hc128::Hc128Core;
-use rand::jitter::JitterRng;
-use rand::thread_rng;
-
-macro_rules! gen_bytes {
-    ($fnn:ident, $gen:expr) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = $gen;
-            let mut buf = [0u8; BYTES_LEN];
-            b.iter(|| {
-                for _ in 0..RAND_BENCH_N {
-                    rng.fill_bytes(&mut buf);
-                    black_box(buf);
-                }
-            });
-            b.bytes = BYTES_LEN as u64 * RAND_BENCH_N;
-        }
-    }
-}
-
-gen_bytes!(gen_bytes_xorshift, XorShiftRng::from_entropy());
-gen_bytes!(gen_bytes_hc128, Hc128Rng::from_entropy());
-gen_bytes!(gen_bytes_isaac, IsaacRng::from_entropy());
-gen_bytes!(gen_bytes_isaac64, Isaac64Rng::from_entropy());
-gen_bytes!(gen_bytes_std, StdRng::from_entropy());
-gen_bytes!(gen_bytes_small, SmallRng::from_entropy());
-gen_bytes!(gen_bytes_os, OsRng::new().unwrap());
-
-macro_rules! gen_uint {
-    ($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>());
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N;
-        }
-    }
-}
-
-gen_uint!(gen_u32_xorshift, u32, XorShiftRng::from_entropy());
-gen_uint!(gen_u32_hc128, u32, Hc128Rng::from_entropy());
-gen_uint!(gen_u32_isaac, u32, IsaacRng::from_entropy());
-gen_uint!(gen_u32_isaac64, u32, Isaac64Rng::from_entropy());
-gen_uint!(gen_u32_std, u32, StdRng::from_entropy());
-gen_uint!(gen_u32_small, u32, SmallRng::from_entropy());
-gen_uint!(gen_u32_os, u32, OsRng::new().unwrap());
-
-gen_uint!(gen_u64_xorshift, u64, XorShiftRng::from_entropy());
-gen_uint!(gen_u64_hc128, u64, Hc128Rng::from_entropy());
-gen_uint!(gen_u64_isaac, u64, IsaacRng::from_entropy());
-gen_uint!(gen_u64_isaac64, u64, Isaac64Rng::from_entropy());
-gen_uint!(gen_u64_std, u64, StdRng::from_entropy());
-gen_uint!(gen_u64_small, u64, SmallRng::from_entropy());
-gen_uint!(gen_u64_os, u64, OsRng::new().unwrap());
-
-// Do not test JitterRng like the others by running it RAND_BENCH_N times per,
-// measurement, because it is way too slow. Only run it once.
-#[bench]
-fn gen_u64_jitter(b: &mut Bencher) {
-    let mut rng = JitterRng::new().unwrap();
-    b.iter(|| {
-        black_box(rng.gen::<u64>());
-    });
-    b.bytes = size_of::<u64>() as u64;
-}
-
-macro_rules! init_gen {
-    ($fnn:ident, $gen:ident) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = XorShiftRng::from_entropy();
-            b.iter(|| {
-                let r2 = $gen::from_rng(&mut rng).unwrap();
-                black_box(r2);
-            });
-        }
-    }
-}
-
-init_gen!(init_xorshift, XorShiftRng);
-init_gen!(init_hc128, Hc128Rng);
-init_gen!(init_isaac, IsaacRng);
-init_gen!(init_isaac64, Isaac64Rng);
-init_gen!(init_chacha, ChaChaRng);
-
-#[bench]
-fn init_jitter(b: &mut Bencher) {
-    b.iter(|| {
-        black_box(JitterRng::new().unwrap());
-    });
-}
-
-macro_rules! chacha_rounds {
-    ($fn1:ident, $fn2:ident, $fn3:ident, $rounds:expr) => {
-        #[bench]
-        fn $fn1(b: &mut Bencher) {
-            let mut rng = ChaChaRng::from_entropy();
-            rng.set_rounds($rounds);
-            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;
-        }
-
-        #[bench]
-        fn $fn2(b: &mut Bencher) {
-            let mut rng = ChaChaRng::from_entropy();
-            rng.set_rounds($rounds);
-            b.iter(|| {
-                let mut accum: u32 = 0;
-                for _ in 0..RAND_BENCH_N {
-                    accum = accum.wrapping_add(rng.gen::<u32>());
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<u32>() as u64 * RAND_BENCH_N;
-        }
-
-        #[bench]
-        fn $fn3(b: &mut Bencher) {
-            let mut rng = ChaChaRng::from_entropy();
-            rng.set_rounds($rounds);
-            b.iter(|| {
-                let mut accum: u64 = 0;
-                for _ in 0..RAND_BENCH_N {
-                    accum = accum.wrapping_add(rng.gen::<u64>());
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<u64>() as u64 * RAND_BENCH_N;
-        }
-    }
-}
-
-chacha_rounds!(gen_bytes_chacha8, gen_u32_chacha8, gen_u64_chacha8, 8);
-chacha_rounds!(gen_bytes_chacha12, gen_u32_chacha12, gen_u64_chacha12, 12);
-chacha_rounds!(gen_bytes_chacha20, gen_u32_chacha20, gen_u64_chacha20, 20);
-
-
-const RESEEDING_THRESHOLD: u64 = 1024*1024*1024; // something high enough to get
-                                                 // deterministic measurements
-
-#[bench]
-fn reseeding_hc128_bytes(b: &mut Bencher) {
-    let mut rng = ReseedingRng::new(Hc128Core::from_entropy(),
-                                    RESEEDING_THRESHOLD,
-                                    EntropyRng::new());
-    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;
-}
-
-macro_rules! reseeding_uint {
-    ($fnn:ident, $ty:ty) => {
-        #[bench]
-        fn $fnn(b: &mut Bencher) {
-            let mut rng = ReseedingRng::new(Hc128Core::from_entropy(),
-                                            RESEEDING_THRESHOLD,
-                                            EntropyRng::new());
-            b.iter(|| {
-                let mut accum: $ty = 0;
-                for _ in 0..RAND_BENCH_N {
-                    accum = accum.wrapping_add(rng.gen::<$ty>());
-                }
-                black_box(accum);
-            });
-            b.bytes = size_of::<$ty>() as u64 * RAND_BENCH_N;
-        }
-    }
-}
-
-reseeding_uint!(reseeding_hc128_u32, u32);
-reseeding_uint!(reseeding_hc128_u64, u64);
-
-
-macro_rules! threadrng_uint {
-    ($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>());
-                }
-                black_box(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-8c5b0ac51d/benches/misc.rs b/vendor/rand-8c5b0ac51d/benches/misc.rs
deleted file mode 100644
index 258f082..0000000
--- a/vendor/rand-8c5b0ac51d/benches/misc.rs
+++ /dev/null
@@ -1,134 +0,0 @@
-#![feature(test)]
-
-extern crate test;
-extern crate rand;
-
-const RAND_BENCH_N: u64 = 1000;
-
-use test::{black_box, Bencher};
-
-use rand::{SeedableRng, SmallRng, Rng, thread_rng};
-use rand::seq::*;
-
-#[bench]
-fn misc_gen_bool(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    b.iter(|| {
-        let mut accum = true;
-        for _ in 0..::RAND_BENCH_N {
-            accum ^= rng.gen_bool(0.18);
-        }
-        black_box(accum);
-    })
-}
-
-#[bench]
-fn misc_gen_bool_var(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    b.iter(|| {
-        let mut p = 0.18;
-        let mut accum = true;
-        for _ in 0..::RAND_BENCH_N {
-            accum ^= rng.gen_bool(p);
-            p += 0.0001;
-        }
-        black_box(accum);
-    })
-}
-
-#[bench]
-fn misc_shuffle_100(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
-    let x : &mut [usize] = &mut [1; 100];
-    b.iter(|| {
-        rng.shuffle(x);
-        black_box(&x);
-    })
-}
-
-#[bench]
-fn misc_sample_iter_10_of_100(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
-    let x : &[usize] = &[1; 100];
-    b.iter(|| {
-        black_box(sample_iter(&mut rng, x, 10).unwrap_or_else(|e| e));
-    })
-}
-
-#[bench]
-fn misc_sample_slice_10_of_100(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
-    let x : &[usize] = &[1; 100];
-    b.iter(|| {
-        black_box(sample_slice(&mut rng, x, 10));
-    })
-}
-
-#[bench]
-fn misc_sample_slice_ref_10_of_100(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
-    let x : &[usize] = &[1; 100];
-    b.iter(|| {
-        black_box(sample_slice_ref(&mut rng, x, 10));
-    })
-}
-
-macro_rules! sample_indices {
-    ($name:ident, $amount:expr, $length:expr) => {
-        #[bench]
-        fn $name(b: &mut Bencher) {
-            let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
-            b.iter(|| {
-                black_box(sample_indices(&mut rng, $length, $amount));
-            })
-        }
-    }
-}
-
-sample_indices!(misc_sample_indices_10_of_1k, 10, 1000);
-sample_indices!(misc_sample_indices_50_of_1k, 50, 1000);
-sample_indices!(misc_sample_indices_100_of_1k, 100, 1000);
-
-#[bench]
-fn gen_1k_iter_repeat(b: &mut Bencher) {
-    use std::iter;
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    b.iter(|| {
-        let v: Vec<u64> = iter::repeat(()).map(|()| rng.gen()).take(128).collect();
-        black_box(v);
-    });
-    b.bytes = 1024;
-}
-
-#[bench]
-#[allow(deprecated)]
-fn gen_1k_gen_iter(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    b.iter(|| {
-        let v: Vec<u64> = rng.gen_iter().take(128).collect();
-        black_box(v);
-    });
-    b.bytes = 1024;
-}
-
-#[bench]
-fn gen_1k_sample_iter(b: &mut Bencher) {
-    use rand::distributions::{Distribution, Standard};
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    b.iter(|| {
-        let v: Vec<u64> = Standard.sample_iter(&mut rng).take(128).collect();
-        black_box(v);
-    });
-    b.bytes = 1024;
-}
-
-#[bench]
-fn gen_1k_fill(b: &mut Bencher) {
-    let mut rng = SmallRng::from_rng(&mut thread_rng()).unwrap();
-    let mut buf = [0u64; 128];
-    b.iter(|| {
-        rng.fill(&mut buf[..]);
-        black_box(buf);
-    });
-    b.bytes = 1024;
-}
diff --git a/vendor/rand-8c5b0ac51d/examples/monte-carlo.rs b/vendor/rand-8c5b0ac51d/examples/monte-carlo.rs
deleted file mode 100644
index c18108a..0000000
--- a/vendor/rand-8c5b0ac51d/examples/monte-carlo.rs
+++ /dev/null
@@ -1,52 +0,0 @@
-// Copyright 2013-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! # Monte Carlo estimation of Ï?
-//!
-//! 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  = Ï?rÂ² = Ï? * r * r = Ï?
-//!     area of square  = 2Â² = 4
-//! ```
-//!
-//! The circle is entirely within the square, so if we sample many points
-//! randomly from the square, roughly Ï? / 4 of them should be inside the circle.
-//!
-//! We can use the above fact to estimate the value of Ï?: 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")]
-
-
-extern crate rand;
-
-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!("Ï? is approximately {}", 4. * (in_circle as f64) / (total as f64));
-}
diff --git a/vendor/rand-8c5b0ac51d/examples/monty-hall.rs b/vendor/rand-8c5b0ac51d/examples/monty-hall.rs
deleted file mode 100644
index 3750f8f..0000000
--- a/vendor/rand-8c5b0ac51d/examples/monty-hall.rs
+++ /dev/null
@@ -1,117 +0,0 @@
-// Copyright 2013-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! ## 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")]
-
-
-extern crate rand;
-
-use rand::Rng;
-use rand::distributions::{Distribution, Uniform};
-
-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 {
-    let choices = free_doors(&[car, choice]);
-    rand::seq::sample_slice(rng, &choices, 1)[0]
-}
-
-// 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-8c5b0ac51d/master.zip b/vendor/rand-8c5b0ac51d/master.zip
deleted file mode 100644
index 1bf77e4..0000000
Binary files a/vendor/rand-8c5b0ac51d/master.zip and /dev/null differ
diff --git a/vendor/rand-8c5b0ac51d/rand_core/CHANGELOG.md b/vendor/rand-8c5b0ac51d/rand_core/CHANGELOG.md
deleted file mode 100644
index 0358bdc..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/CHANGELOG.md
+++ /dev/null
@@ -1,21 +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).
-
-
-## [0.1.0] - TODO - date
-(Split out of the Rand crate, changes here are relative to rand 0.4.2)
-- `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)
-
-## [0.0.1] - 2017-09-14 (yanked)
-Experimental version as part of the rand crate refactor.
diff --git a/vendor/rand-8c5b0ac51d/rand_core/Cargo.toml b/vendor/rand-8c5b0ac51d/rand_core/Cargo.toml
deleted file mode 100644
index e307f4d..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/Cargo.toml
+++ /dev/null
@@ -1,29 +0,0 @@
-[package]
-name = "rand_core"
-version = "0.1.0" # NB: When modifying, also modify html_root_url in lib.rs
-authors = ["The Rust Project Developers"]
-license = "MIT/Apache-2.0"
-readme = "README.md"
-repository = "https://github.com/rust-lang-nursery/rand";
-documentation = "https://docs.rs/rand_core";
-homepage = "https://crates.io/crates/rand_core";
-description = """
-Core random number generator traits and tools for implementation.
-"""
-keywords = ["random", "rng"]
-categories = ["algorithms", "no-std"]
-
-[badges]
-travis-ci = { repository = "rust-lang-nursery/rand" }
-appveyor = { repository = "alexcrichton/rand" }
-
-[features]
-# Bug: https://github.com/rust-lang/cargo/issues/4361
-# default = ["std"]
-std = ["alloc"]    # use std library; should be default but for above bug
-alloc = []  # enables Vec and Box support without std
-serde1 = ["serde", "serde_derive"] # enables serde for BlockRng wrapper
-
-[dependencies]
-serde = { version = "1", optional = true }
-serde_derive = { version = "1", optional = true }
diff --git a/vendor/rand-8c5b0ac51d/rand_core/LICENSE-APACHE b/vendor/rand-8c5b0ac51d/rand_core/LICENSE-APACHE
deleted file mode 100644
index 17d7468..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/LICENSE-APACHE
+++ /dev/null
@@ -1,201 +0,0 @@
-                              Apache License
-                        Version 2.0, January 2004
-                     https://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-1. Definitions.
-
-   "License" shall mean the terms and conditions for use, reproduction,
-   and distribution as defined by Sections 1 through 9 of this document.
-
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-   the copyright owner that is granting the License.
-
-   "Legal Entity" shall mean the union of the acting entity and all
-   other entities that control, are controlled by, or are under common
-   control with that entity. For the purposes of this definition,
-   "control" means (i) the power, direct or indirect, to cause the
-   direction or management of such entity, whether by contract or
-   otherwise, or (ii) ownership of fifty percent (50%) or more of the
-   outstanding shares, or (iii) beneficial ownership of such entity.
-
-   "You" (or "Your") shall mean an individual or Legal Entity
-   exercising permissions granted by this License.
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-5. Submission of Contributions. Unless You explicitly state otherwise,
-   any Contribution intentionally submitted for inclusion in the Work
-   by You to the Licensor shall be under the terms and conditions of
-   this License, without any additional terms or conditions.
-   Notwithstanding the above, nothing herein shall supersede or modify
-   the terms of any separate license agreement you may have executed
-   with Licensor regarding such Contributions.
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-6. Trademarks. This License does not grant permission to use the trade
-   names, trademarks, service marks, or product names of the Licensor,
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-   origin of the Work and reproducing the content of the NOTICE file.
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-   boilerplate notice, with the fields enclosed by brackets "[]"
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-	https://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
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diff --git a/vendor/rand-8c5b0ac51d/rand_core/LICENSE-MIT b/vendor/rand-8c5b0ac51d/rand_core/LICENSE-MIT
deleted file mode 100644
index 39d4bdb..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/LICENSE-MIT
+++ /dev/null
@@ -1,25 +0,0 @@
-Copyright (c) 2014 The Rust Project Developers
-
-Permission is hereby granted, free of charge, to any
-person obtaining a copy of this software and associated
-documentation files (the "Software"), to deal in the
-Software without restriction, including without
-limitation the rights to use, copy, modify, merge,
-publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software
-is furnished to do so, subject to the following
-conditions:
-
-The above copyright notice and this permission notice
-shall be included in all copies or substantial portions
-of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
-ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
-TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
-PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
-SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
-IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-DEALINGS IN THE SOFTWARE.
diff --git a/vendor/rand-8c5b0ac51d/rand_core/README.md b/vendor/rand-8c5b0ac51d/rand_core/README.md
deleted file mode 100644
index 2949222..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/README.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# rand_core
-
-[![Build Status](https://travis-ci.org/rust-lang-nursery/rand.svg)](https://travis-ci.org/rust-lang-nursery/rand)
-[![Build Status](https://ci.appveyor.com/api/projects/status/github/rust-lang-nursery/rand?svg=true)](https://ci.appveyor.com/project/alexcrichton/rand)
-[![Latest version](https://img.shields.io/crates/v/rand_core.svg)](https://crates.io/crates/rand_core)
-[![Documentation](https://docs.rs/rand_core/badge.svg)](https://docs.rs/rand_core)
-[![Minimum rustc version](https://img.shields.io/badge/rustc-1.22+-yellow.svg)](https://github.com/rust-lang-nursery/rand#rust-version-requirements)
-
-Core traits and error types of the [rand] library, plus tools for implementing
-RNGs.
-
-This crate is intended for use when implementing the core trait, `RngCore`; it
-defines the core traits to be implemented as well as several small functions to
-aid in their implementation and types required for error handling.
-
-The main [rand] crate re-exports most items defined in this crate, along with
-tools to convert the integer samples generated by `RngCore` to many different
-applications (including sampling from restricted ranges, conversion to floating
-point, list permutations and secure initialisation of RNGs). Most users should
-prefer to use the main [rand] crate.
-
-Documentation:
-[master branch](https://rust-lang-nursery.github.io/rand/rand_core/index.html),
-[by release](https://docs.rs/rand_core)
-
-[Changelog](CHANGELOG.md)
-
-[rand]: https://crates.io/crates/rand
-
-
-## Functionality
-
-The `rand_core` crate provides:
-
--   base random number generator traits
--   error-reporting types
--   functionality to aid implementation of RNGs
-
-The traits and error types are also available via `rand`.
-
-## Crate Features
-
-`rand_core` supports `no_std` and `alloc`-only configurations, as well as full
-`std` functionality. The differences between `no_std` and full `std` are small,
-comprising `RngCore` support for `Box<R>` types where `R: RngCore`, as well as
-extensions to the `Error` type's functionality.
-
-Due to [rust-lang/cargo#1596](https://github.com/rust-lang/cargo/issues/1596),
-`rand_core` is built without `std` support by default. Since features are
-unioned across the whole dependency tree, any crate using `rand` with its
-default features will also enable `std` support in `rand_core`.
-
-The `serde1` feature can be used to derive `Serialize` and `Deserialize` for RNG
-implementations that use the `BlockRng` or `BlockRng64` wrappers.
-
-
-# License
-
-`rand_core` 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) for details.
diff --git a/vendor/rand-8c5b0ac51d/rand_core/src/error.rs b/vendor/rand-8c5b0ac51d/rand_core/src/error.rs
deleted file mode 100644
index 34cfbf8..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/src/error.rs
+++ /dev/null
@@ -1,163 +0,0 @@
-// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Error types
-
-use core::fmt;
-
-#[cfg(feature="std")]
-use std::error::Error as stdError;
-
-/// Error kind which can be matched over.
-#[derive(PartialEq, Eq, Debug, Copy, Clone)]
-pub enum ErrorKind {
-    /// Feature is not available; not recoverable.
-    /// 
-    /// This is the most permanent failure type and implies the error cannot be
-    /// resolved simply by retrying (e.g. the feature may not exist in this
-    /// build of the application or on the current platform).
-    Unavailable,
-    /// General failure; there may be a chance of recovery on retry.
-    /// 
-    /// This is the catch-all kind for errors from known and unknown sources
-    /// which do not have a more specific kind / handling method.
-    /// 
-    /// It is suggested to retry a couple of times or retry later when
-    /// handling; some error sources may be able to resolve themselves,
-    /// although this is not likely.
-    Unexpected,
-    /// A transient failure which likely can be resolved or worked around.
-    /// 
-    /// This error kind exists for a few specific cases where it is known that
-    /// the error likely can be resolved internally, but is reported anyway.
-    Transient,
-    /// Not ready yet: recommended to try again a little later.
-    /// 
-    /// This error kind implies the generator needs more time or needs some
-    /// other part of the application to do something else first before it is
-    /// ready for use; for example this may be used by external generators
-    /// which require time for initialization.
-    NotReady,
-    #[doc(hidden)]
-    __Nonexhaustive,
-}
-
-impl ErrorKind {
-    /// True if this kind of error may resolve itself on retry.
-    /// 
-    /// See also `should_wait()`.
-    pub fn should_retry(self) -> bool {
-        self != ErrorKind::Unavailable
-    }
-    
-    /// True if we should retry but wait before retrying
-    /// 
-    /// This implies `should_retry()` is true.
-    pub fn should_wait(self) -> bool {
-        self == ErrorKind::NotReady
-    }
-    
-    /// A description of this error kind
-    pub fn description(self) -> &'static str {
-        match self {
-            ErrorKind::Unavailable => "permanently unavailable",
-            ErrorKind::Unexpected => "unexpected failure",
-            ErrorKind::Transient => "transient failure",
-            ErrorKind::NotReady => "not ready yet",
-            ErrorKind::__Nonexhaustive => unreachable!(),
-        }
-    }
-}
-
-
-/// Error type of random number generators
-/// 
-/// This is a relatively simple error type, designed for compatibility with and
-/// without the Rust `std` library. It embeds a "kind" code, a message (static
-/// string only), and an optional chained cause (`std` only). The `kind` and
-/// `msg` fields can be accessed directly; cause can be accessed via
-/// `std::error::Error::cause` or `Error::take_cause`. Construction can only be
-/// done via `Error::new` or `Error::with_cause`.
-#[derive(Debug)]
-pub struct Error {
-    /// The error kind
-    pub kind: ErrorKind,
-    /// The error message
-    pub msg: &'static str,
-    #[cfg(feature="std")]
-    cause: Option<Box<stdError + Send + Sync>>,
-}
-
-impl Error {
-    /// Create a new instance, with specified kind and a message.
-    pub fn new(kind: ErrorKind, msg: &'static str) -> Self {
-        #[cfg(feature="std")] {
-            Error { kind, msg, cause: None }
-        }
-        #[cfg(not(feature="std"))] {
-            Error { kind, msg }
-        }
-    }
-    
-    /// Create a new instance, with specified kind, message, and a
-    /// chained cause.
-    /// 
-    /// Note: `stdError` is an alias for `std::error::Error`.
-    /// 
-    /// If not targetting `std` (i.e. `no_std`), this function is replaced by
-    /// another with the same prototype, except that there are no bounds on the
-    /// type `E` (because both `Box` and `stdError` are unavailable), and the
-    /// `cause` is ignored.
-    #[cfg(feature="std")]
-    pub fn with_cause<E>(kind: ErrorKind, msg: &'static str, cause: E) -> Self
-        where E: Into<Box<stdError + Send + Sync>>
-    {
-        Error { kind, msg, cause: Some(cause.into()) }
-    }
-    
-    /// Create a new instance, with specified kind, message, and a
-    /// chained cause.
-    /// 
-    /// In `no_std` mode the *cause* is ignored.
-    #[cfg(not(feature="std"))]
-    pub fn with_cause<E>(kind: ErrorKind, msg: &'static str, _cause: E) -> Self {
-        Error { kind, msg }
-    }
-    
-    /// Take the cause, if any. This allows the embedded cause to be extracted.
-    /// This uses `Option::take`, leaving `self` with no cause.
-    #[cfg(feature="std")]
-    pub fn take_cause(&mut self) -> Option<Box<stdError + Send + Sync>> {
-        self.cause.take()
-    }
-}
-
-impl fmt::Display for Error {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        #[cfg(feature="std")] {
-            if let Some(ref cause) = self.cause {
-                return write!(f, "{} ({}); cause: {}",
-                        self.msg, self.kind.description(), cause);
-            }
-        }
-        write!(f, "{} ({})", self.msg, self.kind.description())
-    }
-}
-
-#[cfg(feature="std")]
-impl stdError for Error {
-    fn description(&self) -> &str {
-        self.msg
-    }
-
-    fn cause(&self) -> Option<&stdError> {
-        self.cause.as_ref().map(|e| e.as_ref() as &stdError)
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/rand_core/src/impls.rs b/vendor/rand-8c5b0ac51d/rand_core/src/impls.rs
deleted file mode 100644
index 530a2ed..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/src/impls.rs
+++ /dev/null
@@ -1,543 +0,0 @@
-// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Helper functions for implementing `RngCore` functions.
-//!
-//! For cross-platform reproducibility, these functions all use Little Endian:
-//! least-significant part first. For example, `next_u64_via_u32` takes `u32`
-//! values `x, y`, then outputs `(y << 32) | x`. To implement `next_u32`
-//! from `next_u64` in little-endian order, one should use `next_u64() as u32`.
-//!
-//! Byte-swapping (like the std `to_le` functions) is only needed to convert
-//! to/from byte sequences, and since its purpose is reproducibility,
-//! non-reproducible sources (e.g. `OsRng`) need not bother with it.
-
-use core::convert::AsRef;
-use core::intrinsics::transmute;
-use core::ptr::copy_nonoverlapping;
-use core::{fmt, slice};
-use core::cmp::min;
-use core::mem::size_of;
-use {RngCore, BlockRngCore, CryptoRng, SeedableRng, Error};
-
-#[cfg(feature="serde1")] use serde::{Serialize, Deserialize};
-
-/// Implement `next_u64` via `next_u32`, little-endian order.
-pub fn next_u64_via_u32<R: RngCore + ?Sized>(rng: &mut R) -> u64 {
-    // Use LE; we explicitly generate one value before the next.
-    let x = u64::from(rng.next_u32());
-    let y = u64::from(rng.next_u32());
-    (y << 32) | x
-}
-
-/// Implement `fill_bytes` via `next_u64` and `next_u32`, little-endian order.
-///
-/// The fastest way to fill a slice is usually to work as long as possible with
-/// integers. That is why this method mostly uses `next_u64`, and only when
-/// there are 4 or less bytes remaining at the end of the slice it uses
-/// `next_u32` once.
-pub fn fill_bytes_via_next<R: RngCore + ?Sized>(rng: &mut R, dest: &mut [u8]) {
-    let mut left = dest;
-    while left.len() >= 8 {
-        let (l, r) = {left}.split_at_mut(8);
-        left = r;
-        let chunk: [u8; 8] = unsafe {
-            transmute(rng.next_u64().to_le())
-        };
-        l.copy_from_slice(&chunk);
-    }
-    let n = left.len();
-    if n > 4 {
-        let chunk: [u8; 8] = unsafe {
-            transmute(rng.next_u64().to_le())
-        };
-        left.copy_from_slice(&chunk[..n]);
-    } else if n > 0 {
-        let chunk: [u8; 4] = unsafe {
-            transmute(rng.next_u32().to_le())
-        };
-        left.copy_from_slice(&chunk[..n]);
-    }
-}
-
-macro_rules! impl_uint_from_fill {
-    ($rng:expr, $ty:ty, $N:expr) => ({
-        debug_assert!($N == size_of::<$ty>());
-
-        let mut int: $ty = 0;
-        unsafe {
-            let ptr = &mut int as *mut $ty as *mut u8;
-            let slice = slice::from_raw_parts_mut(ptr, $N);
-            $rng.fill_bytes(slice);
-        }
-        int
-    });
-}
-
-macro_rules! fill_via_chunks {
-    ($src:expr, $dst:expr, $ty:ty, $size:expr) => ({
-        let chunk_size_u8 = min($src.len() * $size, $dst.len());
-        let chunk_size = (chunk_size_u8 + $size - 1) / $size;
-        if cfg!(target_endian="little") {
-            unsafe {
-                copy_nonoverlapping(
-                    $src.as_ptr() as *const u8,
-                    $dst.as_mut_ptr(),
-                    chunk_size_u8);
-            }
-        } else {
-            for (&n, chunk) in $src.iter().zip($dst.chunks_mut($size)) {
-                let tmp = n.to_le();
-                let src_ptr = &tmp as *const $ty as *const u8;
-                unsafe {
-                    copy_nonoverlapping(src_ptr,
-                                        chunk.as_mut_ptr(),
-                                        chunk.len());
-                }
-            }
-        }
-
-        (chunk_size, chunk_size_u8)
-    });
-}
-
-/// Implement `fill_bytes` by reading chunks from the output buffer of a block
-/// based RNG.
-///
-/// The return values are `(consumed_u32, filled_u8)`.
-///
-/// `filled_u8` is the number of filled bytes in `dest`, which may be less than
-/// the length of `dest`.
-/// `consumed_u32` is the number of words consumed from `src`, which is the same
-/// as `filled_u8 / 4` rounded up.
-///
-/// # Example
-/// (from `IsaacRng`)
-///
-/// ```rust,ignore
-/// fn fill_bytes(&mut self, dest: &mut [u8]) {
-///     let mut read_len = 0;
-///     while read_len < dest.len() {
-///         if self.index >= self.rsl.len() {
-///             self.isaac();
-///         }
-///
-///         let (consumed_u32, filled_u8) =
-///             impls::fill_via_u32_chunks(&mut self.rsl[self.index..],
-///                                        &mut dest[read_len..]);
-///
-///         self.index += consumed_u32;
-///         read_len += filled_u8;
-///     }
-/// }
-/// ```
-pub fn fill_via_u32_chunks(src: &[u32], dest: &mut [u8]) -> (usize, usize) {
-    fill_via_chunks!(src, dest, u32, 4)
-}
-
-/// Implement `fill_bytes` by reading chunks from the output buffer of a block
-/// based RNG.
-///
-/// The return values are `(consumed_u64, filled_u8)`.
-/// `filled_u8` is the number of filled bytes in `dest`, which may be less than
-/// the length of `dest`.
-/// `consumed_u64` is the number of words consumed from `src`, which is the same
-/// as `filled_u8 / 8` rounded up.
-///
-/// See `fill_via_u32_chunks` for an example.
-pub fn fill_via_u64_chunks(src: &[u64], dest: &mut [u8]) -> (usize, usize) {
-    fill_via_chunks!(src, dest, u64, 8)
-}
-
-/// Implement `next_u32` via `fill_bytes`, little-endian order.
-pub fn next_u32_via_fill<R: RngCore + ?Sized>(rng: &mut R) -> u32 {
-    impl_uint_from_fill!(rng, u32, 4)
-}
-
-/// Implement `next_u64` via `fill_bytes`, little-endian order.
-pub fn next_u64_via_fill<R: RngCore + ?Sized>(rng: &mut R) -> u64 {
-    impl_uint_from_fill!(rng, u64, 8)
-}
-
-/// Wrapper around PRNGs that implement [`BlockRngCore`] to keep a results
-/// buffer and offer the methods from [`RngCore`].
-///
-/// `BlockRng` has heavily optimized implementations of the [`RngCore`] methods
-/// reading values from the results buffer, as well as
-/// calling `BlockRngCore::generate` directly on the output array when
-/// `fill_bytes` / `try_fill_bytes` is called on a large array. These methods
-/// also handle the bookkeeping of when to generate a new batch of values.
-/// No generated values are ever thown away.
-///
-/// Currently `BlockRng` only implements `RngCore` for buffers which are slices
-/// of `u32` elements; this may be extended to other types in the future.
-///
-/// For easy initialization `BlockRng` also implements [`SeedableRng`].
-///
-/// [`BlockRngCore`]: ../BlockRngCore.t.html
-/// [`RngCore`]: ../RngCore.t.html
-/// [`SeedableRng`]: ../SeedableRng.t.html
-#[derive(Clone)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct BlockRng<R: BlockRngCore + ?Sized> {
-    #[cfg_attr(feature="serde1", serde(bound(
-        serialize = "R::Results: Serialize",
-        deserialize = "R::Results: Deserialize<'de>")))]
-    results: R::Results,
-    index: usize,
-    core: R,
-}
-
-// Custom Debug implementation that does not expose the contents of `results`.
-impl<R: BlockRngCore + fmt::Debug> fmt::Debug for BlockRng<R> {
-    fn fmt(&self, fmt: &mut fmt::Formatter) -> fmt::Result {
-        fmt.debug_struct("BlockRng")
-           .field("core", &self.core)
-           .field("result_len", &self.results.as_ref().len())
-           .field("index", &self.index)
-           .finish()
-    }
-}
-
-impl<R: BlockRngCore> BlockRng<R> {
-    /// Create a new `BlockRng` from an existing RNG implementing
-    /// `BlockRngCore`. Results will be generated on first use.
-    pub fn new(core: R) -> BlockRng<R>{
-        let results_empty = R::Results::default();
-        BlockRng {
-            core,
-            index: results_empty.as_ref().len(),
-            results: results_empty,
-        }
-    }
-
-    /// Return a reference the wrapped `BlockRngCore`.
-    pub fn inner(&self) -> &R {
-        &self.core
-    }
-
-    /// Return a mutable reference the wrapped `BlockRngCore`.
-    pub fn inner_mut(&mut self) -> &mut R {
-        &mut self.core
-    }
-
-    // Reset the number of available results.
-    // This will force a new set of results to be generated on next use.
-    pub fn reset(&mut self) {
-        self.index = self.results.as_ref().len();
-    }
-}
-
-impl<R: BlockRngCore<Item=u32>> RngCore for BlockRng<R>
-where <R as BlockRngCore>::Results: AsRef<[u32]>
-{
-    #[inline(always)]
-    fn next_u32(&mut self) -> u32 {
-        if self.index >= self.results.as_ref().len() {
-            self.core.generate(&mut self.results);
-            self.index = 0;
-        }
-
-        let value = self.results.as_ref()[self.index];
-        self.index += 1;
-        value
-    }
-
-    #[inline(always)]
-    fn next_u64(&mut self) -> u64 {
-        let read_u64 = |results: &[u32], index| {
-            if cfg!(any(target_arch = "x86", target_arch = "x86_64")) {
-                // requires little-endian CPU supporting unaligned reads:
-                unsafe { *(&results[index] as *const u32 as *const u64) }
-            } else {
-                let x = u64::from(results[index]);
-                let y = u64::from(results[index + 1]);
-                (y << 32) | x
-            }
-        };
-
-        let len = self.results.as_ref().len();
-
-        let index = self.index;
-        if index < len-1 {
-            self.index += 2;
-            // Read an u64 from the current index
-            read_u64(self.results.as_ref(), index)
-        } else if index >= len {
-            self.core.generate(&mut self.results);
-            self.index = 2;
-            read_u64(self.results.as_ref(), 0)
-        } else {
-            let x = u64::from(self.results.as_ref()[len-1]);
-            self.core.generate(&mut self.results);
-            self.index = 1;
-            let y = u64::from(self.results.as_ref()[0]);
-            (y << 32) | x
-        }
-    }
-
-    // As an optimization we try to write directly into the output buffer.
-    // This is only enabled for little-endian platforms where unaligned writes
-    // are known to be safe and fast.
-    #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        let mut filled = 0;
-
-        // Continue filling from the current set of results
-        if self.index < self.results.as_ref().len() {
-            let (consumed_u32, filled_u8) =
-                fill_via_u32_chunks(&self.results.as_ref()[self.index..],
-                                    dest);
-
-            self.index += consumed_u32;
-            filled += filled_u8;
-        }
-
-        let len_remainder =
-            (dest.len() - filled) % (self.results.as_ref().len() * 4);
-        let end_direct = dest.len() - len_remainder;
-
-        while filled < end_direct {
-            let dest_u32: &mut R::Results = unsafe {
-                &mut *(dest[filled..].as_mut_ptr() as
-                *mut <R as BlockRngCore>::Results)
-            };
-            self.core.generate(dest_u32);
-            filled += self.results.as_ref().len() * 4;
-        }
-        self.index = self.results.as_ref().len();
-
-        if len_remainder > 0 {
-            self.core.generate(&mut self.results);
-            let (consumed_u32, _) =
-                fill_via_u32_chunks(self.results.as_ref(),
-                                    &mut dest[filled..]);
-
-            self.index = consumed_u32;
-        }
-    }
-
-    #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        let mut read_len = 0;
-        while read_len < dest.len() {
-            if self.index >= self.results.as_ref().len() {
-                self.core.generate(&mut self.results);
-                self.index = 0;
-            }
-            let (consumed_u32, filled_u8) =
-                fill_via_u32_chunks(&self.results.as_ref()[self.index..],
-                                    &mut dest[read_len..]);
-
-            self.index += consumed_u32;
-            read_len += filled_u8;
-        }
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        self.fill_bytes(dest);
-        Ok(())
-    }
-}
-
-impl<R: BlockRngCore + SeedableRng> SeedableRng for BlockRng<R> {
-    type Seed = R::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        Self::new(R::from_seed(seed))
-    }
-
-    fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> {
-        Ok(Self::new(R::from_rng(rng)?))
-    }
-}
-
-
-
-/// Wrapper around PRNGs that implement [`BlockRngCore`] to keep a results
-/// buffer and offer the methods from [`RngCore`].
-///
-/// This is similar to [`BlockRng`], but specialized for algorithms that operate
-/// on `u64` values.
-///
-/// [`BlockRngCore`]: ../BlockRngCore.t.html
-/// [`RngCore`]: ../RngCore.t.html
-/// [`BlockRng`]: struct.BlockRng.html
-#[derive(Clone)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct BlockRng64<R: BlockRngCore + ?Sized> {
-    #[cfg_attr(feature="serde1", serde(bound(
-        serialize = "R::Results: Serialize",
-        deserialize = "R::Results: Deserialize<'de>")))]
-    results: R::Results,
-    index: usize,
-    half_used: bool, // true if only half of the previous result is used
-    core: R,
-}
-
-// Custom Debug implementation that does not expose the contents of `results`.
-impl<R: BlockRngCore + fmt::Debug> fmt::Debug for BlockRng64<R> {
-    fn fmt(&self, fmt: &mut fmt::Formatter) -> fmt::Result {
-        fmt.debug_struct("BlockRng64")
-           .field("core", &self.core)
-           .field("result_len", &self.results.as_ref().len())
-           .field("index", &self.index)
-           .field("half_used", &self.half_used)
-           .finish()
-    }
-}
-
-impl<R: BlockRngCore> BlockRng64<R> {
-    /// Create a new `BlockRng` from an existing RNG implementing
-    /// `BlockRngCore`. Results will be generated on first use.
-    pub fn new(core: R) -> BlockRng64<R>{
-        let results_empty = R::Results::default();
-        BlockRng64 {
-            core,
-            index: results_empty.as_ref().len(),
-            half_used: false,
-            results: results_empty,
-        }
-    }
-
-    /// Return a mutable reference the wrapped `BlockRngCore`.
-    pub fn inner(&mut self) -> &mut R {
-        &mut self.core
-    }
-
-    // Reset the number of available results.
-    // This will force a new set of results to be generated on next use.
-    pub fn reset(&mut self) {
-        self.index = self.results.as_ref().len();
-    }
-}
-
-impl<R: BlockRngCore<Item=u64>> RngCore for BlockRng64<R>
-where <R as BlockRngCore>::Results: AsRef<[u64]>
-{
-    #[inline(always)]
-    fn next_u32(&mut self) -> u32 {
-        let mut index = self.index * 2 - self.half_used as usize;
-        if index >= self.results.as_ref().len() * 2 {
-            self.core.generate(&mut self.results);
-            self.index = 0;
-            // `self.half_used` is by definition `false`
-            self.half_used = false;
-            index = 0;
-        }
-
-        self.half_used = !self.half_used;
-        self.index += self.half_used as usize;
-
-        // Index as if this is a u32 slice.
-        unsafe {
-            let results =
-                &*(self.results.as_ref() as *const [u64] as *const [u32]);
-            if cfg!(target_endian = "little") {
-                *results.get_unchecked(index)
-            } else {
-                *results.get_unchecked(index ^ 1)
-            }
-        }
-    }
-
-    #[inline(always)]
-    fn next_u64(&mut self) -> u64 {
-        if self.index >= self.results.as_ref().len() {
-            self.core.generate(&mut self.results);
-            self.index = 0;
-        }
-
-        let value = self.results.as_ref()[self.index];
-        self.index += 1;
-        self.half_used = false;
-        value
-    }
-
-    // As an optimization we try to write directly into the output buffer.
-    // This is only enabled for little-endian platforms where unaligned writes
-    // are known to be safe and fast.
-    #[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        let mut filled = 0;
-        self.half_used = false;
-
-        // Continue filling from the current set of results
-        if self.index < self.results.as_ref().len() {
-            let (consumed_u64, filled_u8) =
-                fill_via_u64_chunks(&self.results.as_ref()[self.index..],
-                                    dest);
-
-            self.index += consumed_u64;
-            filled += filled_u8;
-        }
-
-        let len_remainder =
-            (dest.len() - filled) % (self.results.as_ref().len() * 8);
-        let end_direct = dest.len() - len_remainder;
-
-        while filled < end_direct {
-            let dest_u64: &mut R::Results = unsafe {
-                ::core::mem::transmute(dest[filled..].as_mut_ptr())
-            };
-            self.core.generate(dest_u64);
-            filled += self.results.as_ref().len() * 8;
-        }
-        self.index = self.results.as_ref().len();
-
-        if len_remainder > 0 {
-            self.core.generate(&mut self.results);
-            let (consumed_u64, _) =
-                fill_via_u64_chunks(&mut self.results.as_ref(),
-                                    &mut dest[filled..]);
-
-            self.index = consumed_u64;
-        }
-    }
-
-    #[cfg(not(any(target_arch = "x86", target_arch = "x86_64")))]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        let mut read_len = 0;
-        self.half_used = false;
-        while read_len < dest.len() {
-            if self.index as usize >= self.results.as_ref().len() {
-                self.core.generate(&mut self.results);
-                self.index = 0;
-            }
-
-            let (consumed_u64, filled_u8) =
-                fill_via_u64_chunks(&self.results.as_ref()[self.index as usize..],
-                                    &mut dest[read_len..]);
-
-            self.index += consumed_u64;
-            read_len += filled_u8;
-        }
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        Ok(self.fill_bytes(dest))
-    }
-}
-
-impl<R: BlockRngCore + SeedableRng> SeedableRng for BlockRng64<R> {
-    type Seed = R::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        Self::new(R::from_seed(seed))
-    }
-
-    fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> {
-        Ok(Self::new(R::from_rng(rng)?))
-    }
-}
-
-impl<R: BlockRngCore + CryptoRng> CryptoRng for BlockRng<R> {}
-
-// TODO: implement tests for the above
diff --git a/vendor/rand-8c5b0ac51d/rand_core/src/le.rs b/vendor/rand-8c5b0ac51d/rand_core/src/le.rs
deleted file mode 100644
index bcc560e..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/src/le.rs
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Little-Endian utilities
-//! 
-//! Little-Endian order has been chosen for internal usage; this makes some
-//! useful functions available.
-
-use core::ptr;
-
-macro_rules! read_slice {
-    ($src:expr, $dst:expr, $size:expr, $which:ident) => {{
-        assert_eq!($src.len(), $size * $dst.len());
-
-        unsafe {
-            ptr::copy_nonoverlapping(
-                $src.as_ptr(),
-                $dst.as_mut_ptr() as *mut u8,
-                $src.len());
-        }
-        for v in $dst.iter_mut() {
-            *v = v.$which();
-        }
-    }};
-}
-
-/// Reads unsigned 32 bit integers from `src` into `dst`.
-/// Borrowed from the `byteorder` crate.
-#[inline]
-pub fn read_u32_into(src: &[u8], dst: &mut [u32]) {
-    read_slice!(src, dst, 4, to_le);
-}
-
-/// Reads unsigned 64 bit integers from `src` into `dst`.
-/// Borrowed from the `byteorder` crate.
-#[inline]
-pub fn read_u64_into(src: &[u8], dst: &mut [u64]) {
-    read_slice!(src, dst, 8, to_le);
-}
-
-#[test]
-fn test_read() {
-    let bytes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
-    
-    let mut buf = [0u32; 4];
-    read_u32_into(&bytes, &mut buf);
-    assert_eq!(buf[0], 0x04030201);
-    assert_eq!(buf[3], 0x100F0E0D);
-    
-    let mut buf = [0u32; 3];
-    read_u32_into(&bytes[1..13], &mut buf);  // unaligned
-    assert_eq!(buf[0], 0x05040302);
-    assert_eq!(buf[2], 0x0D0C0B0A);
-    
-    let mut buf = [0u64; 2];
-    read_u64_into(&bytes, &mut buf);
-    assert_eq!(buf[0], 0x0807060504030201);
-    assert_eq!(buf[1], 0x100F0E0D0C0B0A09);
-    
-    let mut buf = [0u64; 1];
-    read_u64_into(&bytes[7..15], &mut buf); // unaligned
-    assert_eq!(buf[0], 0x0F0E0D0C0B0A0908);
-}
diff --git a/vendor/rand-8c5b0ac51d/rand_core/src/lib.rs b/vendor/rand-8c5b0ac51d/rand_core/src/lib.rs
deleted file mode 100644
index 74d4e59..0000000
--- a/vendor/rand-8c5b0ac51d/rand_core/src/lib.rs
+++ /dev/null
@@ -1,438 +0,0 @@
-// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Random number generation traits
-//! 
-//! This crate is mainly of interest to crates publishing implementations of
-//! [`RngCore`]. Other users are encouraged to use the [rand] crate instead
-//! which re-exports the main traits and error types.
-//!
-//! [`RngCore`] is the core trait implemented by algorithmic pseudo-random number
-//! generators and external random-number sources.
-//! 
-//! [`SeedableRng`] is an extension trait for construction from fixed seeds and
-//! other random number generators.
-//! 
-//! [`Error`] is provided for error-handling. It is safe to use in `no_std`
-//! environments.
-//! 
-//! The [`impls`] and [`le`] sub-modules include a few small functions to assist
-//! implementation of [`RngCore`].
-//! 
-//! [rand]: https://crates.io/crates/rand
-//! [`RngCore`]: trait.RngCore.html
-//! [`SeedableRng`]: trait.SeedableRng.html
-//! [`Error`]: struct.Error.html
-//! [`impls`]: impls/index.html
-//! [`le`]: le/index.html
-
-#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png";,
-       html_favicon_url = "https://www.rust-lang.org/favicon.ico";,
-       html_root_url = "https://docs.rs/rand_core/0.1.0";)]
-
-#![deny(missing_debug_implementations)]
-
-#![cfg_attr(not(feature="std"), no_std)]
-#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))]
-
-#[cfg(feature="std")] extern crate core;
-#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc;
-#[cfg(feature="serde1")] extern crate serde;
-#[cfg(feature="serde1")] #[macro_use] extern crate serde_derive;
-
-
-use core::default::Default;
-use core::convert::AsMut;
-
-#[cfg(all(feature="alloc", not(feature="std")))] use alloc::boxed::Box;
-
-pub use error::{ErrorKind, Error};
-
-
-mod error;
-pub mod impls;
-pub mod le;
-
-
-/// The core of a random number generator.
-/// 
-/// This trait encapsulates the low-level functionality common to all
-/// generators, and is the "back end", to be implemented by generators.
-/// End users should normally use [`Rng`] from the [rand] crate, which is
-/// automatically implemented for every type implementing `RngCore`.
-/// 
-/// Three different methods for generating random data are provided since the
-/// optimal implementation of each is dependent on the type of generator. There
-/// is no required relationship between the output of each; e.g. many
-/// implementations of [`fill_bytes`] consume a whole number of `u32` or `u64`
-/// values and drop any remaining unused bytes.
-/// 
-/// The [`try_fill_bytes`] method is a variant of [`fill_bytes`] allowing error
-/// handling; it is not deemed sufficiently useful to add equivalents for
-/// [`next_u32`] or [`next_u64`] since the latter methods are almost always used
-/// with algorithmic generators (PRNGs), which are normally infallible.
-/// 
-/// Algorithmic generators implementing [`SeedableRng`] should normally have
-/// *portable, reproducible* output, i.e. fix Endianness when converting values
-/// to avoid platform differences, and avoid making any changes which affect
-/// output (except by communicating that the release has breaking changes).
-/// 
-/// Typically implementators will implement only one of the methods available
-/// in this trait directly, then use the helper functions from the
-/// [`rand_core::impls`] module to implement the other methods.
-/// 
-/// It is recommended that implementations also implement:
-/// 
-/// - `Debug` with a custom implementation which *does not* print any internal
-///   state (at least, [`CryptoRng`]s should not risk leaking state through
-///   `Debug`).
-/// - `Serialize` and `Deserialize` (from Serde), preferably making Serde
-///   support optional at the crate level in PRNG libs.
-/// - `Clone`, if possible.
-/// - *never* implement `Copy` (accidental copies may cause repeated values).
-/// - *do not* implement `Default` for pseudorandom generators, but instead
-///   implement [`SeedableRng`], to guide users towards proper seeding.
-///   External / hardware RNGs can choose to implement `Default`.
-/// - `Eq` and `PartialEq` could be implemented, but are probably not useful.
-/// 
-/// # Example
-/// 
-/// A simple example, obviously not generating very *random* output:
-/// 
-/// ```rust
-/// use rand_core::{RngCore, Error, impls};
-/// 
-/// struct CountingRng(u64);
-/// 
-/// impl RngCore for CountingRng {
-///     fn next_u32(&mut self) -> u32 {
-///         self.next_u64() as u32
-///     }
-///     
-///     fn next_u64(&mut self) -> u64 {
-///         self.0 += 1;
-///         self.0
-///     }
-///     
-///     fn fill_bytes(&mut self, dest: &mut [u8]) {
-///         impls::fill_bytes_via_next(self, dest)
-///     }
-///     
-///     fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-///         Ok(self.fill_bytes(dest))
-///     }
-/// }
-/// ```
-/// 
-/// [rand]: https://crates.io/crates/rand
-/// [`Rng`]: ../rand/trait.Rng.html
-/// [`SeedableRng`]: trait.SeedableRng.html
-/// [`rand_core::impls`]: ../rand_core/impls/index.html
-/// [`try_fill_bytes`]: trait.RngCore.html#tymethod.try_fill_bytes
-/// [`fill_bytes`]: trait.RngCore.html#tymethod.fill_bytes
-/// [`next_u32`]: trait.RngCore.html#tymethod.next_u32
-/// [`next_u64`]: trait.RngCore.html#tymethod.next_u64
-/// [`CryptoRng`]: trait.CryptoRng.html
-pub trait RngCore {
-    /// Return the next random `u32`.
-    ///
-    /// RNGs must implement at least one method from this trait directly. In
-    /// the case this method is not implemented directly, it can be implemented
-    /// using `self.next_u64() as u32` or
-    /// [via `fill_bytes`](../rand_core/impls/fn.next_u32_via_fill.html).
-    fn next_u32(&mut self) -> u32;
-
-    /// Return the next random `u64`.
-    ///
-    /// RNGs must implement at least one method from this trait directly. In
-    /// the case this method is not implemented directly, it can be implemented
-    /// [via `next_u32`](../rand_core/impls/fn.next_u64_via_u32.html) or
-    /// [via `fill_bytes`](../rand_core/impls/fn.next_u64_via_fill.html).
-    fn next_u64(&mut self) -> u64;
-
-    /// Fill `dest` with random data.
-    ///
-    /// RNGs must implement at least one method from this trait directly. In
-    /// the case this method is not implemented directly, it can be implemented
-    /// [via `next_u*`](../rand_core/impls/fn.fill_bytes_via_next.html) or
-    /// via `try_fill_bytes`; if this generator can fail the implementation
-    /// must choose how best to handle errors here (e.g. panic with a
-    /// descriptive message or log a warning and retry a few times).
-    /// 
-    /// This method should guarantee that `dest` is entirely filled
-    /// with new data, and may panic if this is impossible
-    /// (e.g. reading past the end of a file that is being used as the
-    /// source of randomness).
-    fn fill_bytes(&mut self, dest: &mut [u8]);
-
-    /// Fill `dest` entirely with random data.
-    ///
-    /// This is the only method which allows an RNG to report errors while
-    /// generating random data thus making this the primary method implemented
-    /// by external (true) RNGs (e.g. `OsRng`) which can fail. It may be used
-    /// directly to generate keys and to seed (infallible) PRNGs.
-    /// 
-    /// Other than error handling, this method is identical to [`fill_bytes`];
-    /// thus this may be implemented using `Ok(self.fill_bytes(dest))` or
-    /// `fill_bytes` may be implemented with
-    /// `self.try_fill_bytes(dest).unwrap()` or more specific error handling.
-    /// 
-    /// [`fill_bytes`]: trait.RngCore.html#method.fill_bytes
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error>;
-}
-
-/// A trait for RNGs which do not generate random numbers individually, but in
-/// blocks (typically `[u32; N]`). This technique is commonly used by
-/// cryptographic RNGs to improve performance.
-/// 
-/// Usage of this trait is optional, but provides two advantages:
-/// implementations only need to concern themselves with generation of the
-/// block, not the various [`RngCore`] methods (especially [`fill_bytes`], where the
-/// optimal implementations are not trivial), and this allows `ReseedingRng` to
-/// perform periodic reseeding with very low overhead.
-/// 
-/// # Example
-/// 
-/// ```norun
-/// use rand_core::BlockRngCore;
-/// use rand_core::impls::BlockRng;
-/// 
-/// struct MyRngCore;
-/// 
-/// impl BlockRngCore for MyRngCore {
-///     type Results = [u32; 16];
-///     
-///     fn generate(&mut self, results: &mut Self::Results) {
-///         unimplemented!()
-///     }
-/// }
-/// 
-/// impl SeedableRng for MyRngCore {
-///     type Seed = unimplemented!();
-///     fn from_seed(seed: Self::Seed) -> Self {
-///         unimplemented!()
-///     }
-/// }
-/// 
-/// // optionally, also implement CryptoRng for MyRngCore
-/// 
-/// // Final RNG.
-/// type MyRng = BlockRng<u32, MyRngCore>;
-/// ```
-/// 
-/// [`RngCore`]: trait.RngCore.html
-/// [`fill_bytes`]: trait.RngCore.html#tymethod.fill_bytes
-pub trait BlockRngCore {
-    /// Results element type, e.g. `u32`.
-    type Item;
-    
-    /// Results type. This is the 'block' an RNG implementing `BlockRngCore`
-    /// generates, which will usually be an array like `[u32; 16]`.
-    type Results: AsRef<[Self::Item]> + Default;
-
-    /// Generate a new block of results.
-    fn generate(&mut self, results: &mut Self::Results);
-}
-
-/// A marker trait used to indicate that an [`RngCore`] or [`BlockRngCore`]
-/// implementation is supposed to be cryptographically secure.
-/// 
-/// *Cryptographically secure generators*, also known as *CSPRNGs*, should
-/// satisfy an additional properties over other generators: given the first
-/// *k* bits of an algorithm's output
-/// sequence, it should not be possible using polynomial-time algorithms to
-/// predict the next bit with probability significantly greater than 50%.
-/// 
-/// Some generators may satisfy an additional property, however this is not
-/// required by this trait: if the CSPRNG's state is revealed, it should not be
-/// computationally-feasible to reconstruct output prior to this. Some other
-/// generators allow backwards-computation and are consided *reversible*.
-/// 
-/// Note that this trait is provided for guidance only and cannot guarantee
-/// suitability for cryptographic applications. In general it should only be
-/// implemented for well-reviewed code implementing well-regarded algorithms.
-/// 
-/// Note also that use of a `CryptoRng` does not protect against other
-/// weaknesses such as seeding from a weak entropy source or leaking state.
-/// 
-/// [`RngCore`]: trait.RngCore.html
-/// [`BlockRngCore`]: trait.BlockRngCore.html
-pub trait CryptoRng {}
-
-/// A random number generator that can be explicitly seeded.
-///
-/// This trait encapsulates the low-level functionality common to all
-/// pseudo-random number generators (PRNGs, or algorithmic generators).
-/// 
-/// The [`rand::FromEntropy`] trait is automatically implemented for every type
-/// implementing `SeedableRng`, providing a convenient `from_entropy()`
-/// constructor.
-/// 
-/// [`rand::FromEntropy`]: ../rand/trait.FromEntropy.html
-pub trait SeedableRng: Sized {
-    /// Seed type, which is restricted to types mutably-dereferencable as `u8`
-    /// arrays (we recommend `[u8; N]` for some `N`).
-    ///
-    /// It is recommended to seed PRNGs with a seed of at least circa 100 bits,
-    /// which means an array of `[u8; 12]` or greater to avoid picking RNGs with
-    /// partially overlapping periods.
-    ///
-    /// For cryptographic RNG's a seed of 256 bits is recommended, `[u8; 32]`.
-    ///
-    ///
-    /// # Implementing `SeedableRng` for RNGs with large seeds
-    ///
-    /// Note that the required traits `core::default::Default` and
-    /// `core::convert::AsMut<u8>` are not implemented for large arrays
-    /// `[u8; N]` with `N` > 32. To be able to implement the traits required by
-    /// `SeedableRng` for RNGs with such large seeds, the newtype pattern can be
-    /// used:
-    ///
-    /// ```
-    /// use rand_core::SeedableRng;
-    ///
-    /// const N: usize = 64;
-    /// pub struct MyRngSeed(pub [u8; N]);
-    /// pub struct MyRng(MyRngSeed);
-    ///
-    /// impl Default for MyRngSeed {
-    ///     fn default() -> MyRngSeed {
-    ///         MyRngSeed([0; N])
-    ///     }
-    /// }
-    ///
-    /// impl AsMut<[u8]> for MyRngSeed {
-    ///     fn as_mut(&mut self) -> &mut [u8] {
-    ///         &mut self.0
-    ///     }
-    /// }
-    ///
-    /// impl SeedableRng for MyRng {
-    ///     type Seed = MyRngSeed;
-    ///
-    ///     fn from_seed(seed: MyRngSeed) -> MyRng {
-    ///         MyRng(seed)
-    ///     }
-    /// }
-    /// ```
-    type Seed: Sized + Default + AsMut<[u8]>;
-
-    /// Create a new PRNG using the given seed.
-    ///
-    /// PRNG implementations are allowed to assume that bits in the seed are
-    /// well distributed. That means usually that the number of one and zero
-    /// bits are about equal, and values like 0, 1 and (size - 1) are unlikely.
-    ///
-    /// PRNG implementations are recommended to be reproducible. A PRNG seeded
-    /// using this function with a fixed seed should produce the same sequence
-    /// of output in the future and on different architectures (with for example
-    /// different endianness).
-    ///
-    /// It is however not required that this function yield the same state as a
-    /// reference implementation of the PRNG given equivalent seed; if necessary
-    /// another constructor replicating behaviour from a reference
-    /// implementation can be added.
-    ///
-    /// PRNG implementations should make sure `from_seed` never panics. In the
-    /// case that some special values (like an all zero seed) are not viable
-    /// seeds it is preferable to map these to alternative constant value(s),
-    /// for example `0xBAD5EEDu32` or `0x0DDB1A5E5BAD5EEDu64` ("odd biases? bad
-    /// seed"). This is assuming only a small number of values must be rejected.
-    fn from_seed(seed: Self::Seed) -> Self;
-
-    /// Create a new PRNG seeded from another `Rng`.
-    ///
-    /// This is the recommended way to initialize PRNGs with fresh entropy. The
-    /// [`FromEntropy`] trait provides a convenient `from_entropy` method
-    /// based on `from_rng`.
-    /// 
-    /// Usage of this method is not recommended when reproducibility is required
-    /// since implementing PRNGs are not required to fix Endianness and are
-    /// allowed to modify implementations in new releases.
-    ///
-    /// It is important to use a good source of randomness to initialize the
-    /// PRNG. Cryptographic PRNG may be rendered insecure when seeded from a
-    /// non-cryptographic PRNG or with insufficient entropy.
-    /// Many non-cryptographic PRNGs will show statistical bias in their first
-    /// results if their seed numbers are small or if there is a simple pattern
-    /// between them.
-    ///
-    /// Prefer to seed from a strong external entropy source like [`OsRng`] or
-    /// from a cryptographic PRNG; if creating a new generator for cryptographic
-    /// uses you *must* seed from a strong source.
-    ///
-    /// Seeding a small PRNG from another small PRNG is possible, but
-    /// something to be careful with. An extreme example of how this can go
-    /// wrong is seeding an Xorshift RNG from another Xorshift RNG, which
-    /// will effectively clone the generator. In general seeding from a
-    /// generator which is hard to predict is probably okay.
-    ///
-    /// PRNG implementations are allowed to assume that a good RNG is provided
-    /// for seeding, and that it is cryptographically secure when appropriate.
-    /// 
-    /// [`FromEntropy`]: ../rand/trait.FromEntropy.html
-    /// [`OsRng`]: ../rand/os/struct.OsRng.html
-    fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> {
-        let mut seed = Self::Seed::default();
-        rng.try_fill_bytes(seed.as_mut())?;
-        Ok(Self::from_seed(seed))
-    }
-}
-
-// Implement `RngCore` for references to an `RngCore`.
-// Force inlining all functions, so that it is up to the `RngCore`
-// implementation and the optimizer to decide on inlining.
-impl<'a, R: RngCore + ?Sized> RngCore for &'a mut R {
-    #[inline(always)]
-    fn next_u32(&mut self) -> u32 {
-        (**self).next_u32()
-    }
-
-    #[inline(always)]
-    fn next_u64(&mut self) -> u64 {
-        (**self).next_u64()
-    }
-
-    #[inline(always)]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        (**self).fill_bytes(dest)
-    }
-
-    #[inline(always)]
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        (**self).try_fill_bytes(dest)
-    }
-}
-
-// Implement `RngCore` for boxed references to an `RngCore`.
-// Force inlining all functions, so that it is up to the `RngCore`
-// implementation and the optimizer to decide on inlining.
-#[cfg(feature="alloc")]
-impl<R: RngCore + ?Sized> RngCore for Box<R> {
-    #[inline(always)]
-    fn next_u32(&mut self) -> u32 {
-        (**self).next_u32()
-    }
-
-    #[inline(always)]
-    fn next_u64(&mut self) -> u64 {
-        (**self).next_u64()
-    }
-
-    #[inline(always)]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        (**self).fill_bytes(dest)
-    }
-
-    #[inline(always)]
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        (**self).try_fill_bytes(dest)
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/binomial.rs b/vendor/rand-8c5b0ac51d/src/distributions/binomial.rs
deleted file mode 100644
index 8a03e1d..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/binomial.rs
+++ /dev/null
@@ -1,172 +0,0 @@
-// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The binomial distribution.
-
-use Rng;
-use distributions::Distribution;
-use distributions::log_gamma::log_gamma;
-use std::f64::consts::PI;
-
-/// 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`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Binomial, Distribution};
-///
-/// let bin = Binomial::new(20, 0.3);
-/// let v = bin.sample(&mut rand::thread_rng());
-/// println!("{} is from a binomial distribution", v);
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Binomial {
-    n: u64, // number of trials
-    p: f64, // probability of success
-}
-
-impl Binomial {
-    /// Construct a new `Binomial` with the given shape parameters
-    /// `n`, `p`. 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 }
-    }
-}
-
-impl Distribution<u64> for Binomial {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
-        // 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
-        };
-
-        // expected value of the sample
-        let expected = self.n as f64 * p;
-
-        let result =
-            // for low expected values we just simulate n drawings
-            if expected < 25.0 {
-                let mut lresult = 0.0;
-                for _ in 0 .. self.n {
-                    if rng.gen_bool(p) {
-                        lresult += 1.0;
-                    }
-                }
-                lresult
-            }
-            // high expected value - do the rejection method
-            else {
-                // prepare some cached values
-                let float_n = self.n as f64;
-                let ln_fact_n = log_gamma(float_n + 1.0);
-                let pc = 1.0 - p;
-                let log_p = p.ln();
-                let log_pc = pc.ln();
-                let sq = (expected * (2.0 * pc)).sqrt();
-
-                let mut lresult;
-
-                loop {
-                    let mut comp_dev: f64;
-                    // we use the lorentzian distribution as the comparison distribution
-                    // f(x) ~ 1/(1+x/^2)
-                    loop {
-                        // draw from the lorentzian distribution
-                        comp_dev = (PI*rng.gen::<f64>()).tan();
-                        // shift the peak of the comparison ditribution
-                        lresult = expected + sq * comp_dev;
-                        // repeat the drawing until we are in the range of possible values
-                        if lresult >= 0.0 && lresult < float_n + 1.0 {
-                            break;
-                        }
-                    }
-
-                    // the result should be discrete
-                    lresult = lresult.floor();
-
-                    let log_binomial_dist = ln_fact_n - log_gamma(lresult+1.0) -
-                        log_gamma(float_n - lresult + 1.0) + lresult*log_p + (float_n - lresult)*log_pc;
-                    // this is the binomial probability divided by the comparison probability
-                    // we will generate a uniform random value and if it is larger than this,
-                    // we interpret it as a value falling out of the distribution and repeat
-                    let comparison_coeff = (log_binomial_dist.exp() * sq) * (1.2 * (1.0 + comp_dev*comp_dev));
-
-                    if comparison_coeff >= rng.gen() {
-                        break;
-                    }
-                }
-
-                lresult
-            };
-
-        // invert the result for p < 0.5
-        if p != self.p {
-            self.n - result as u64
-        } else {
-            result as u64
-        }
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use Rng;
-    use distributions::Distribution;
-    use super::Binomial;
-
-    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);
-
-        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);
-    }
-
-    #[test]
-    fn test_binomial() {
-        let mut rng = ::test::rng(123);
-        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]
-    #[should_panic]
-    fn test_binomial_invalid_lambda_zero() {
-        Binomial::new(20, 0.0);
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_binomial_invalid_lambda_neg() {
-        Binomial::new(20, -10.0);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/exponential.rs b/vendor/rand-8c5b0ac51d/src/distributions/exponential.rs
deleted file mode 100644
index 915e02a..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/exponential.rs
+++ /dev/null
@@ -1,122 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The exponential distribution.
-
-use {Rng};
-use distributions::{ziggurat, ziggurat_tables, Distribution};
-
-/// Samples floating-point numbers according to the exponential distribution,
-/// with rate parameter `Î» = 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
-///
-/// # Example
-/// ```rust
-/// use rand::{FromEntropy, SmallRng, Rng};
-/// use rand::distributions::Exp1;
-///
-/// let val: f64 = SmallRng::from_entropy().sample(Exp1);
-/// println!("{}", val);
-/// ```
-#[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`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Exp, Distribution};
-///
-/// let exp = Exp::new(2.0);
-/// let v = exp.sample(&mut rand::thread_rng());
-/// println!("{} is from a Exp(2) distribution", v);
-/// ```
-#[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 distributions::Distribution;
-    use super::Exp;
-
-    #[test]
-    fn test_exp() {
-        let exp = Exp::new(10.0);
-        let mut rng = ::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-8c5b0ac51d/src/distributions/float.rs b/vendor/rand-8c5b0ac51d/src/distributions/float.rs
deleted file mode 100644
index b1b7685..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/float.rs
+++ /dev/null
@@ -1,89 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Basic floating-point number distributions
-
-use core::mem;
-use Rng;
-use distributions::{Distribution, Standard};
-
-pub(crate) 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:ty, $uty:ty, $fraction_bits:expr, $exponent_bias:expr,
-     $next_u:ident) => {
-        impl IntoFloat for $uty {
-            type F = $ty;
-            #[inline(always)]
-            fn into_float_with_exponent(self, exponent: i32) -> $ty {
-                // The exponent is encoded using an offset-binary representation
-                let exponent_bits =
-                    (($exponent_bias + exponent) as $uty) << $fraction_bits;
-                unsafe { mem::transmute(self | exponent_bits) }
-            }
-        }
-
-        impl Distribution<$ty> for Standard {
-            /// Generate a floating point number in the open interval `(0, 1)`
-            /// (not including either endpoint) with a uniform distribution.
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> $ty {
-                const EPSILON: $ty = 1.0 / (1u64 << $fraction_bits) as $ty;
-                let float_size = mem::size_of::<$ty>() * 8;
-
-                let value = rng.$next_u();
-                let fraction = value >> (float_size - $fraction_bits);
-                fraction.into_float_with_exponent(0) - (1.0 - EPSILON / 2.0)
-            }
-        }
-    }
-}
-float_impls! { f32, u32, 23, 127, next_u32 }
-float_impls! { f64, u64, 52, 1023, next_u64 }
-
-
-#[cfg(test)]
-mod tests {
-    use Rng;
-    use mock::StepRng;
-
-    const EPSILON32: f32 = ::core::f32::EPSILON;
-    const EPSILON64: f64 = ::core::f64::EPSILON;
-
-    #[test]
-    fn floating_point_edge_cases() {
-        let mut zeros = StepRng::new(0, 0);
-        assert_eq!(zeros.gen::<f32>(), 0.0 + EPSILON32 / 2.0);
-        assert_eq!(zeros.gen::<f64>(), 0.0 + EPSILON64 / 2.0);
-
-        let mut one = StepRng::new(1 << 9, 0);
-        let one32 = one.gen::<f32>();
-        assert!(EPSILON32 < one32 && one32 < EPSILON32 * 2.0);
-
-        let mut one = StepRng::new(1 << 12, 0);
-        let one64 = one.gen::<f64>();
-        assert!(EPSILON64 < one64 && one64 < EPSILON64 * 2.0);
-
-        let mut max = StepRng::new(!0, 0);
-        assert_eq!(max.gen::<f32>(), 1.0 - EPSILON32 / 2.0);
-        assert_eq!(max.gen::<f64>(), 1.0 - EPSILON64 / 2.0);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/gamma.rs b/vendor/rand-8c5b0ac51d/src/distributions/gamma.rs
deleted file mode 100644
index 4d68e57..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/gamma.rs
+++ /dev/null
@@ -1,360 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The Gamma and derived distributions.
-
-use self::GammaRepr::*;
-use self::ChiSquaredRepr::*;
-
-use Rng;
-use distributions::normal::StandardNormal;
-use distributions::{Distribution, Exp};
-
-/// The Gamma distribution `Gamma(shape, scale)` distribution.
-///
-/// The density function of this distribution is
-///
-/// ```text
-/// f(x) =  x^(k - 1) * exp(-x / Î¸) / (Î?(k) * Î¸^k)
-/// ```
-///
-/// where `Î?` is the Gamma function, `k` is the shape and `Î¸` is the
-/// scale and both `k` and `Î¸` 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 [1] for
-/// `shape < 1`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Distribution, Gamma};
-///
-/// let gamma = Gamma::new(2.0, 5.0);
-/// let v = gamma.sample(&mut rand::thread_rng());
-/// println!("{} is from a Gamma(2, 5) distribution", v);
-/// ```
-///
-/// [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)
-#[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.gen();
-
-        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.gen();
-
-            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 `Ï?Â²(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
-/// `Ï?Â²(k) = Gamma(k/2, 2)`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{ChiSquared, Distribution};
-///
-/// let chi = ChiSquared::new(11.0);
-/// let v = chi.sample(&mut rand::thread_rng());
-/// println!("{} is from a Ï?Â²(11) distribution", v)
-/// ```
-#[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) = (Ï?Â²(m)/m) /
-/// (Ï?Â²(n)/n)`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{FisherF, Distribution};
-///
-/// let f = FisherF::new(2.0, 32.0);
-/// let v = f.sample(&mut rand::thread_rng());
-/// println!("{} is from an F(2, 32) distribution", v)
-/// ```
-#[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.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{StudentT, Distribution};
-///
-/// let t = StudentT::new(11.0);
-/// let v = t.sample(&mut rand::thread_rng());
-/// println!("{} is from a t(11) distribution", v)
-/// ```
-#[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()
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use distributions::Distribution;
-    use super::{ChiSquared, StudentT, FisherF};
-
-    #[test]
-    fn test_chi_squared_one() {
-        let chi = ChiSquared::new(1.0);
-        let mut rng = ::test::rng(201);
-        for _ in 0..1000 {
-            chi.sample(&mut rng);
-        }
-    }
-    #[test]
-    fn test_chi_squared_small() {
-        let chi = ChiSquared::new(0.5);
-        let mut rng = ::test::rng(202);
-        for _ in 0..1000 {
-            chi.sample(&mut rng);
-        }
-    }
-    #[test]
-    fn test_chi_squared_large() {
-        let chi = ChiSquared::new(30.0);
-        let mut rng = ::test::rng(203);
-        for _ in 0..1000 {
-            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 = ::test::rng(204);
-        for _ in 0..1000 {
-            f.sample(&mut rng);
-        }
-    }
-
-    #[test]
-    fn test_t() {
-        let t = StudentT::new(11.0);
-        let mut rng = ::test::rng(205);
-        for _ in 0..1000 {
-            t.sample(&mut rng);
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/integer.rs b/vendor/rand-8c5b0ac51d/src/distributions/integer.rs
deleted file mode 100644
index 04bf166..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/integer.rs
+++ /dev/null
@@ -1,138 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The implementations of the `Standard` distribution for integer types.
-
-use {Rng};
-use distributions::{Distribution, Standard};
-
-impl Distribution<isize> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> isize {
-        rng.gen::<usize>() as isize
-    }
-}
-
-impl Distribution<i8> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> i8 {
-        rng.next_u32() as i8
-    }
-}
-
-impl Distribution<i16> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> i16 {
-        rng.next_u32() as i16
-    }
-}
-
-impl Distribution<i32> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> i32 {
-        rng.next_u32() as i32
-    }
-}
-
-impl Distribution<i64> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> i64 {
-        rng.next_u64() as i64
-    }
-}
-
-#[cfg(feature = "i128_support")]
-impl Distribution<i128> for Standard {
-    #[inline]
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> i128 {
-        rng.gen::<u128>() as i128
-    }
-}
-
-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
-    }
-}
-
-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(feature = "i128_support")]
-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 = rng.next_u64() as u128;
-        let y = rng.next_u64() as u128;
-        (y << 64) | x
-    }
-}
-
-
-#[cfg(test)]
-mod tests {
-    use Rng;
-    use distributions::{Standard};
-    
-    #[test]
-    fn test_integers() {
-        let mut rng = ::test::rng(806);
-        
-        rng.sample::<isize, _>(Standard);
-        rng.sample::<i8, _>(Standard);
-        rng.sample::<i16, _>(Standard);
-        rng.sample::<i32, _>(Standard);
-        rng.sample::<i64, _>(Standard);
-        #[cfg(feature = "i128_support")]
-        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(feature = "i128_support")]
-        rng.sample::<u128, _>(Standard);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/log_gamma.rs b/vendor/rand-8c5b0ac51d/src/distributions/log_gamma.rs
deleted file mode 100644
index f1fa383..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/log_gamma.rs
+++ /dev/null
@@ -1,51 +0,0 @@
-// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-/// 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.
-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()
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/mod.rs b/vendor/rand-8c5b0ac51d/src/distributions/mod.rs
deleted file mode 100644
index e036229..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/mod.rs
+++ /dev/null
@@ -1,643 +0,0 @@
-// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Sampling from random distributions.
-//!
-//! Distributions are stateless (i.e. immutable) objects controlling the
-//! production of values of some type `T` from a presumed uniform randomness
-//! source. These objects may have internal parameters set at contruction time
-//! (e.g. [`Uniform`], which has configurable bounds) or may have no internal
-//! parameters (e.g. [`Standard`]).
-//! 
-//! All distributions support the [`Distribution`] trait, and support usage
-//! via `distr.sample(&mut rng)` as well as via `rng.sample(distr)`.
-//! 
-//! [`Distribution`]: trait.Distribution.html
-//! [`Uniform`]: uniform/struct.Uniform.html
-//! [`Standard`]: struct.Standard.html
-
-use Rng;
-
-pub use self::other::Alphanumeric;
-pub use self::uniform::Uniform;
-#[deprecated(since="0.5.0", note="use Uniform instead")]
-pub use self::uniform::Uniform as Range;
-#[cfg(feature="std")]
-pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT};
-#[cfg(feature="std")]
-pub use self::normal::{Normal, LogNormal, StandardNormal};
-#[cfg(feature="std")]
-pub use self::exponential::{Exp, Exp1};
-#[cfg(feature = "std")]
-pub use self::poisson::Poisson;
-#[cfg(feature = "std")]
-pub use self::binomial::Binomial;
-
-pub mod uniform;
-#[cfg(feature="std")]
-pub mod gamma;
-#[cfg(feature="std")]
-pub mod normal;
-#[cfg(feature="std")]
-pub mod exponential;
-#[cfg(feature = "std")]
-pub mod poisson;
-#[cfg(feature = "std")]
-pub mod binomial;
-
-mod float;
-mod integer;
-#[cfg(feature="std")]
-mod log_gamma;
-mod other;
-#[cfg(feature="std")]
-mod ziggurat_tables;
-#[cfg(feature="std")]
-use distributions::float::IntoFloat;
-
-/// Types that can be used to create a random instance of `Support`.
-#[deprecated(since="0.5.0", note="use Distribution instead")]
-pub trait Sample<Support> {
-    /// Generate a random value of `Support`, using `rng` as the
-    /// source of randomness.
-    fn sample<R: Rng>(&mut self, rng: &mut R) -> Support;
-}
-
-/// `Sample`s that do not require keeping track of state.
-///
-/// Since no state is recorded, each sample is (statistically)
-/// independent of all others, assuming the `Rng` used has this
-/// property.
-#[allow(deprecated)]
-#[deprecated(since="0.5.0", note="use Distribution instead")]
-pub trait IndependentSample<Support>: Sample<Support> {
-    /// Generate a random value.
-    fn ind_sample<R: Rng>(&self, &mut R) -> Support;
-}
-
-/// DEPRECATED: Use `distributions::uniform` instead.
-#[deprecated(since="0.5.0", note="use uniform instead")]
-pub mod range {
-    pub use distributions::uniform::Uniform as Range;
-    pub use distributions::uniform::SampleUniform as SampleRange;
-}
-
-#[allow(deprecated)]
-mod impls {
-    use Rng;
-    use distributions::{Distribution, Sample, IndependentSample,
-            WeightedChoice};
-    #[cfg(feature="std")]
-    use distributions::exponential::Exp;
-    #[cfg(feature="std")]
-    use distributions::gamma::{Gamma, ChiSquared, FisherF, StudentT};
-    #[cfg(feature="std")]
-    use distributions::normal::{Normal, LogNormal};
-    use distributions::range::{Range, SampleRange};
-    
-    impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T> {
-        fn sample<R: Rng>(&mut self, rng: &mut R) -> T {
-            Distribution::sample(self, rng)
-        }
-    }
-    impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T> {
-        fn ind_sample<R: Rng>(&self, rng: &mut R) -> T {
-            Distribution::sample(self, rng)
-        }
-    }
-    
-    impl<T: SampleRange> Sample<T> for Range<T> {
-        fn sample<R: Rng>(&mut self, rng: &mut R) -> T {
-            Distribution::sample(self, rng)
-        }
-    }
-    impl<T: SampleRange> IndependentSample<T> for Range<T> {
-        fn ind_sample<R: Rng>(&self, rng: &mut R) -> T {
-            Distribution::sample(self, rng)
-        }
-    }
-    
-    #[cfg(feature="std")]
-    macro_rules! impl_f64 {
-        ($($name: ident), *) => {
-            $(
-                impl Sample<f64> for $name {
-                    fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 {
-                        Distribution::sample(self, rng)
-                    }
-                }
-                impl IndependentSample<f64> for $name {
-                    fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
-                        Distribution::sample(self, rng)
-                    }
-                }
-            )*
-        }
-    }
-    #[cfg(feature="std")]
-    impl_f64!(Exp, Gamma, ChiSquared, FisherF, StudentT, Normal, LogNormal);
-}
-
-/// Types (distributions) that can be used to create a random instance of `T`.
-/// 
-/// 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.
-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.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::thread_rng;
-    /// use rand::distributions::{Distribution, Alphanumeric, Uniform, Standard};
-    ///
-    /// let mut rng = thread_rng();
-    ///
-    /// // Vec of 16 x f32:
-    /// let v: Vec<f32> = Standard.sample_iter(&mut rng).take(16).collect();
-    ///
-    /// // String:
-    /// let s: String = Alphanumeric.sample_iter(&mut rng).take(7).collect();
-    ///
-    /// // Dice-rolling:
-    /// let die_range = Uniform::new_inclusive(1, 6);
-    /// let mut roll_die = die_range.sample_iter(&mut rng);
-    /// while roll_die.next().unwrap() != 6 {
-    ///     println!("Not a 6; rolling again!");
-    /// }
-    /// ```
-    fn sample_iter<'a, R: Rng>(&'a self, rng: &'a mut R)
-        -> DistIter<'a, Self, R, T> where Self: Sized
-    {
-        DistIter {
-            distr: self,
-            rng: rng,
-            phantom: ::core::marker::PhantomData,
-        }
-    }
-}
-
-/// 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.
-///
-/// [`Distribution`]: trait.Distribution.html
-/// [`sample_iter`]: trait.Distribution.html#method.sample_iter
-#[derive(Debug)]
-pub struct DistIter<'a, D, R, T> where D: Distribution<T> + 'a, R: Rng + 'a {
-    distr: &'a D,
-    rng: &'a mut R,
-    phantom: ::core::marker::PhantomData<T>,
-}
-
-impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T>
-    where D: Distribution<T>, R: Rng + 'a
-{
-    type Item = T;
-
-    #[inline(always)]
-    fn next(&mut self) -> Option<T> {
-        Some(self.distr.sample(self.rng))
-    }
-}
-
-impl<'a, T, D: Distribution<T>> Distribution<T> for &'a D {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
-        (*self).sample(rng)
-    }
-}
-
-/// A generic random value distribution. Generates values for various types
-/// with numerically uniform distribution.
-/// 
-/// For floating-point numbers, this generates values from the open range
-/// `(0, 1)` (i.e. excluding 0.0 and 1.0).
-///
-/// ## Built-in Implementations
-///
-/// This crate implements the distribution `Standard` for various primitive
-/// types.  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
-///   open range `(0, 1)`.
-///
-/// The following aggregate types also implement the distribution `Standard` as
-/// long as their component types implement it:
-///
-/// * Tuples and arrays: Each element of the tuple or array is generated
-///   independently, using the `Standard` distribution recursively.
-/// * `Option<T>`: Returns `None` with probability 0.5; otherwise generates a
-///   random `T` and returns `Some(T)`.
-///
-/// # Example
-/// ```rust
-/// use rand::{FromEntropy, SmallRng, Rng};
-/// use rand::distributions::Standard;
-///
-/// let val: f32 = SmallRng::from_entropy().sample(Standard);
-/// println!("f32 from (0,1): {}", val);
-/// ```
-///
-/// With dynamic dispatch (type erasure of `Rng`):
-/// 
-/// ```rust
-/// use rand::{thread_rng, Rng, RngCore};
-/// use rand::distributions::Standard;
-///
-/// let mut rng = thread_rng();
-/// let erased_rng: &mut RngCore = &mut rng;
-/// let val: f32 = erased_rng.sample(Standard);
-/// println!("f32 from (0, 1): {}", val);
-/// ```
-///
-/// # Open interval for floats
-/// In theory it is possible to choose between an open interval `(0, 1)`, and
-/// the half-open intervals `[0, 1)` and `(0, 1]`. All can give a distribution
-/// with perfectly uniform intervals. Many libraries in other programming
-/// languages default to the closed-open interval `[0, 1)`. We choose here to go
-/// with *open*, with the arguments:
-///
-/// - The chance to generate a specific value, like exactly 0.0, is *tiny*. No
-///   (or almost no) sensible code relies on an exact floating-point value to be
-///   generated with a very small chance (1 in 2<sup>23</sup> (approx. 8
-///   million) for `f32`, and 1 in 2<sup>52</sup> for `f64`). What is relied on
-///   is having a uniform distribution and a mean of `0.5`.
-/// - Several common algorithms rely on never seeing the value `0.0` generated,
-///   i.e. they rely on an open interval. For example when the logarithm of the
-///   value is taken, or used as a devisor.
-///
-/// In other words, the guarantee some value *could* be generated is less useful
-/// than the guarantee some value (`0.0`) is never generated. That makes an open
-/// interval a nicer choice.
-///
-/// Consider using `Rng::gen_range` if you really need a half-open interval (as
-/// the ranges use a half-open interval). It has the same performance. Example:
-///
-/// ```
-/// use rand::{thread_rng, Rng};
-///
-/// let mut rng = thread_rng();
-/// let val = rng.gen_range(0.0f32, 1.0);
-/// println!("f32 from [0, 1): {}", val);
-/// ```
-///
-/// [`Exp1`]: struct.Exp1.html
-/// [`StandardNormal`]: struct.StandardNormal.html
-#[derive(Debug)]
-pub struct Standard;
-
-#[allow(deprecated)]
-impl<T> ::Rand for T where Standard: Distribution<T> {
-    fn rand<R: Rng>(rng: &mut R) -> Self {
-        Standard.sample(rng)
-    }
-}
-
-
-/// A value with a particular weight for use with `WeightedChoice`.
-#[derive(Copy, Clone, Debug)]
-pub struct Weighted<T> {
-    /// The numerical weight of this item
-    pub weight: u32,
-    /// The actual item which is being weighted
-    pub item: T,
-}
-
-/// A distribution that selects from a finite collection of weighted items.
-///
-/// Each item has an associated weight that influences how likely it
-/// is to be chosen: higher weight is more likely.
-///
-/// The `Clone` restriction is a limitation of the `Distribution` trait.
-/// Note that `&T` is (cheaply) `Clone` for all `T`, as is `u32`, so one can
-/// store references or indices into another vector.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Weighted, WeightedChoice, Distribution};
-///
-/// let mut items = vec!(Weighted { weight: 2, item: 'a' },
-///                      Weighted { weight: 4, item: 'b' },
-///                      Weighted { weight: 1, item: 'c' });
-/// let wc = WeightedChoice::new(&mut items);
-/// let mut rng = rand::thread_rng();
-/// for _ in 0..16 {
-///      // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
-///      println!("{}", wc.sample(&mut rng));
-/// }
-/// ```
-#[derive(Debug)]
-pub struct WeightedChoice<'a, T:'a> {
-    items: &'a mut [Weighted<T>],
-    weight_range: Uniform<u32>,
-}
-
-impl<'a, T: Clone> WeightedChoice<'a, T> {
-    /// Create a new `WeightedChoice`.
-    ///
-    /// Panics if:
-    ///
-    /// - `items` is empty
-    /// - the total weight is 0
-    /// - the total weight is larger than a `u32` can contain.
-    pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T> {
-        // strictly speaking, this is subsumed by the total weight == 0 case
-        assert!(!items.is_empty(), "WeightedChoice::new called with no items");
-
-        let mut running_total: u32 = 0;
-
-        // we convert the list from individual weights to cumulative
-        // weights so we can binary search. This *could* drop elements
-        // with weight == 0 as an optimisation.
-        for item in items.iter_mut() {
-            running_total = match running_total.checked_add(item.weight) {
-                Some(n) => n,
-                None => panic!("WeightedChoice::new called with a total weight \
-                               larger than a u32 can contain")
-            };
-
-            item.weight = running_total;
-        }
-        assert!(running_total != 0, "WeightedChoice::new called with a total weight of 0");
-
-        WeightedChoice {
-            items,
-            // we're likely to be generating numbers in this range
-            // relatively often, so might as well cache it
-            weight_range: Uniform::new(0, running_total)
-        }
-    }
-}
-
-impl<'a, T: Clone> Distribution<T> for WeightedChoice<'a, T> {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T {
-        // we want to find the first element that has cumulative
-        // weight > sample_weight, which we do by binary since the
-        // cumulative weights of self.items are sorted.
-
-        // choose a weight in [0, total_weight)
-        let sample_weight = self.weight_range.sample(rng);
-
-        // short circuit when it's the first item
-        if sample_weight < self.items[0].weight {
-            return self.items[0].item.clone();
-        }
-
-        let mut idx = 0;
-        let mut modifier = self.items.len();
-
-        // now we know that every possibility has an element to the
-        // left, so we can just search for the last element that has
-        // cumulative weight <= sample_weight, then the next one will
-        // be "it". (Note that this greatest element will never be the
-        // last element of the vector, since sample_weight is chosen
-        // in [0, total_weight) and the cumulative weight of the last
-        // one is exactly the total weight.)
-        while modifier > 1 {
-            let i = idx + modifier / 2;
-            if self.items[i].weight <= sample_weight {
-                // we're small, so look to the right, but allow this
-                // exact element still.
-                idx = i;
-                // we need the `/ 2` to round up otherwise we'll drop
-                // the trailing elements when `modifier` is odd.
-                modifier += 1;
-            } else {
-                // otherwise we're too big, so go left. (i.e. do
-                // nothing)
-            }
-            modifier /= 2;
-        }
-        self.items[idx + 1].item.clone()
-    }
-}
-
-/// 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)]
-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 {
-    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;
-        }
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use Rng;
-    use mock::StepRng;
-    use super::{WeightedChoice, Weighted, Distribution};
-
-    #[test]
-    fn test_weighted_choice() {
-        // this makes assumptions about the internal implementation of
-        // WeightedChoice. It may fail when the implementation in
-        // `distributions::uniform::UniformInt` changes.
-
-        macro_rules! t {
-            ($items:expr, $expected:expr) => {{
-                let mut items = $items;
-                let mut total_weight = 0;
-                for item in &items { total_weight += item.weight; }
-
-                let wc = WeightedChoice::new(&mut items);
-                let expected = $expected;
-
-                // Use extremely large steps between the random numbers, because
-                // we test with small ranges and `UniformInt` is designed to prefer
-                // the most significant bits.
-                let mut rng = StepRng::new(0, !0 / (total_weight as u64));
-
-                for &val in expected.iter() {
-                    assert_eq!(wc.sample(&mut rng), val)
-                }
-            }}
-        }
-
-        t!([Weighted { weight: 1, item: 10}], [10]);
-
-        // skip some
-        t!([Weighted { weight: 0, item: 20},
-            Weighted { weight: 2, item: 21},
-            Weighted { weight: 0, item: 22},
-            Weighted { weight: 1, item: 23}],
-           [21, 21, 23]);
-
-        // different weights
-        t!([Weighted { weight: 4, item: 30},
-            Weighted { weight: 3, item: 31}],
-           [30, 31, 30, 31, 30, 31, 30]);
-
-        // check that we're binary searching
-        // correctly with some vectors of odd
-        // length.
-        t!([Weighted { weight: 1, item: 40},
-            Weighted { weight: 1, item: 41},
-            Weighted { weight: 1, item: 42},
-            Weighted { weight: 1, item: 43},
-            Weighted { weight: 1, item: 44}],
-           [40, 41, 42, 43, 44]);
-        t!([Weighted { weight: 1, item: 50},
-            Weighted { weight: 1, item: 51},
-            Weighted { weight: 1, item: 52},
-            Weighted { weight: 1, item: 53},
-            Weighted { weight: 1, item: 54},
-            Weighted { weight: 1, item: 55},
-            Weighted { weight: 1, item: 56}],
-           [50, 54, 51, 55, 52, 56, 53]);
-    }
-
-    #[test]
-    fn test_weighted_clone_initialization() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let clone = initial.clone();
-        assert_eq!(initial.weight, clone.weight);
-        assert_eq!(initial.item, clone.item);
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_clone_change_weight() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let mut clone = initial.clone();
-        clone.weight = 5;
-        assert_eq!(initial.weight, clone.weight);
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_clone_change_item() {
-        let initial : Weighted<u32> = Weighted {weight: 1, item: 1};
-        let mut clone = initial.clone();
-        clone.item = 5;
-        assert_eq!(initial.item, clone.item);
-
-    }
-
-    #[test] #[should_panic]
-    fn test_weighted_choice_no_items() {
-        WeightedChoice::<isize>::new(&mut []);
-    }
-    #[test] #[should_panic]
-    fn test_weighted_choice_zero_weight() {
-        WeightedChoice::new(&mut [Weighted { weight: 0, item: 0},
-                                  Weighted { weight: 0, item: 1}]);
-    }
-    #[test] #[should_panic]
-    fn test_weighted_choice_weight_overflows() {
-        let x = ::core::u32::MAX / 2; // x + x + 2 is the overflow
-        WeightedChoice::new(&mut [Weighted { weight: x, item: 0 },
-                                  Weighted { weight: 1, item: 1 },
-                                  Weighted { weight: x, item: 2 },
-                                  Weighted { weight: 1, item: 3 }]);
-    }
-    
-    #[test] #[allow(deprecated)]
-    fn test_backwards_compat_sample() {
-        use distributions::{Sample, IndependentSample};
-        
-        struct Constant<T> { val: T }
-        impl<T: Copy> Sample<T> for Constant<T> {
-            fn sample<R: Rng>(&mut self, _: &mut R) -> T { self.val }
-        }
-        impl<T: Copy> IndependentSample<T> for Constant<T> {
-            fn ind_sample<R: Rng>(&self, _: &mut R) -> T { self.val }
-        }
-        
-        let mut sampler = Constant{ val: 293 };
-        assert_eq!(sampler.sample(&mut ::test::rng(233)), 293);
-        assert_eq!(sampler.ind_sample(&mut ::test::rng(234)), 293);
-    }
-    
-    #[cfg(feature="std")]
-    #[test] #[allow(deprecated)]
-    fn test_backwards_compat_exp() {
-        use distributions::{IndependentSample, Exp};
-        let sampler = Exp::new(1.0);
-        sampler.ind_sample(&mut ::test::rng(235));
-    }
-
-    #[cfg(feature="std")]
-    #[test]
-    fn test_distributions_iter() {
-        use distributions::Normal;
-        let mut rng = ::test::rng(210);
-        let distr = Normal::new(10.0, 10.0);
-        let results: Vec<_> = distr.sample_iter(&mut rng).take(100).collect();
-        println!("{:?}", results);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/normal.rs b/vendor/rand-8c5b0ac51d/src/distributions/normal.rs
deleted file mode 100644
index a1adafb..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/normal.rs
+++ /dev/null
@@ -1,192 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The normal and derived distributions.
-
-use Rng;
-use distributions::{ziggurat, ziggurat_tables, Distribution};
-
-/// 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
-///
-/// # Example
-/// ```rust
-/// use rand::{FromEntropy, SmallRng, Rng};
-/// use rand::distributions::StandardNormal;
-///
-/// let val: f64 = SmallRng::from_entropy().sample(StandardNormal);
-/// println!("{}", val);
-/// ```
-#[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.gen();
-                let y_: f64 = rng.gen();
-
-                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.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Normal, Distribution};
-///
-/// // mean 2, standard deviation 3
-/// let normal = Normal::new(2.0, 3.0);
-/// let v = normal.sample(&mut rand::thread_rng());
-/// println!("{} is from a N(2, 9) distribution", v)
-/// ```
-#[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.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{LogNormal, Distribution};
-///
-/// // mean 2, standard deviation 3
-/// let log_normal = LogNormal::new(2.0, 3.0);
-/// let v = log_normal.sample(&mut rand::thread_rng());
-/// println!("{} is from an ln N(2, 9) distribution", v)
-/// ```
-#[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 distributions::Distribution;
-    use super::{Normal, LogNormal};
-
-    #[test]
-    fn test_normal() {
-        let norm = Normal::new(10.0, 10.0);
-        let mut rng = ::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 = ::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-8c5b0ac51d/src/distributions/other.rs b/vendor/rand-8c5b0ac51d/src/distributions/other.rs
deleted file mode 100644
index 1f74341..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/other.rs
+++ /dev/null
@@ -1,207 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The implementations of the `Standard` distribution for other built-in types.
-
-use core::char;
-
-use {Rng};
-use distributions::{Distribution, Standard, Uniform};
-
-// ----- Sampling distributions -----
-
-/// Sample a `char`, uniformly distributed over ASCII letters and numbers:
-/// a-z, A-Z and 0-9.
-/// 
-/// # Example
-///
-/// ```rust
-/// 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 {
-        let range = Uniform::new(0u32, 0x11_0000);
-        loop {
-            match char::from_u32(range.sample(rng)) {
-                Some(c) => return c,
-                // About 0.2% of numbers in the range 0..0x110000 are invalid
-                // codepoints (surrogates).
-                None => {}
-            }
-        }
-    }
-}
-
-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 {
-    #[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
-        }
-    }
-}
-
-
-#[cfg(test)]
-mod tests {
-    use {Rng, RngCore, Standard};
-    use distributions::Alphanumeric;
-    #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::String;
-
-    #[test]
-    fn test_misc() {
-        let rng: &mut RngCore = &mut ::test::rng(820);
-        
-        rng.sample::<char, _>(Standard);
-        rng.sample::<bool, _>(Standard);
-    }
-    
-    #[cfg(feature="alloc")]
-    #[test]
-    fn test_chars() {
-        use core::iter;
-        let mut rng = ::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 = ::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);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/poisson.rs b/vendor/rand-8c5b0ac51d/src/distributions/poisson.rs
deleted file mode 100644
index d1fa901..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/poisson.rs
+++ /dev/null
@@ -1,157 +0,0 @@
-// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The Poisson distribution.
-
-use Rng;
-use distributions::Distribution;
-use distributions::log_gamma::log_gamma;
-use std::f64::consts::PI;
-
-/// The Poisson distribution `Poisson(lambda)`.
-///
-/// This distribution has a density function:
-/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::distributions::{Poisson, Distribution};
-///
-/// let poi = Poisson::new(2.0);
-/// let v = poi.sample(&mut rand::thread_rng());
-/// println!("{} is from a Poisson(2) distribution", v);
-/// ```
-#[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;
-
-            loop {
-                let mut result;
-                let mut comp_dev;
-
-                // we use the lorentzian distribution as the comparison distribution
-                // f(x) ~ 1/(1+x/^2)
-                loop {
-                    // draw from the lorentzian distribution
-                    comp_dev = (PI * rng.gen::<f64>()).tan();
-                    // 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 Lorentzian 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 distributions::Distribution;
-    use super::Poisson;
-
-    #[test]
-    fn test_poisson_10() {
-        let poisson = Poisson::new(10.0);
-        let mut rng = ::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 = ::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-8c5b0ac51d/src/distributions/uniform.rs b/vendor/rand-8c5b0ac51d/src/distributions/uniform.rs
deleted file mode 100644
index 50e7bfe..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/uniform.rs
+++ /dev/null
@@ -1,650 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! A distribution uniformly generating numbers within a given range.
-
-use Rng;
-use distributions::Distribution;
-use distributions::float::IntoFloat;
-
-/// Sample values uniformly between two bounds.
-///
-/// `Uniform::new` and `Uniform::new_inclusive` construct a `Uniform`
-/// distribution sampling from the closed-open and the closed (inclusive) range.
-/// Some preparations are performed up front to make sampling values faster.
-/// `Uniform::sample_single` is optimized for sampling values once or only a
-/// limited number of times from a range.
-///
-/// If you need to sample many values from a range, consider using `new` or
-/// `new_inclusive`. This is also the best choice if the range is constant,
-/// because then the preparations can be evaluated at compile-time.
-/// Otherwise `sample_single` may be the best choice.
-///
-/// Sampling uniformly from a range can be surprisingly complicated to be both
-/// generic and correct. Consider for example edge cases like `low = 0u8`,
-/// `high = 170u8`, for which a naive modulo operation would return numbers less
-/// than 85 with double the probability to those greater than 85.
-///
-/// Types should attempt to sample in `[low, high)` for `Uniform::new(low, high)`,
-/// i.e., excluding `high`, but this may be very difficult. All the primitive
-/// integer types satisfy this property, and the float types normally satisfy
-/// it, but rounding may mean `high` can occur.
-///
-/// # Example
-///
-/// ```rust
-/// 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);
-/// }
-/// ```
-#[derive(Clone, Copy, Debug)]
-pub struct Uniform<X: SampleUniform> {
-    inner: X::Impl,
-}
-
-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(low: X, high: X) -> Uniform<X> {
-        assert!(low < high, "Uniform::new called with `low >= high`");
-        Uniform { inner: X::Impl::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(low: X, high: X) -> Uniform<X> {
-        assert!(low <= high, "Uniform::new_inclusive called with `low > high`");
-        Uniform { inner: X::Impl::new_inclusive(low, high) }
-    }
-
-    /// Sample a single value uniformly from `[low, high)`.
-    /// Panics if `low >= high`.
-    pub fn sample_single<R: Rng + ?Sized>(low: X, high: X, rng: &mut R) -> X {
-        assert!(low < high, "Uniform::sample_single called with low >= high");
-        X::Impl::sample_single(low, high, rng)
-    }
-}
-
-impl<X: SampleUniform> Distribution<X> for Uniform<X> {
-    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> X {
-        self.inner.sample(rng)
-    }
-}
-
-/// Helper trait for creating objects using the correct implementation of
-/// `UniformImpl` for the sampling type; this enables `Uniform::new(a, b)` to work.
-pub trait SampleUniform: PartialOrd+Sized {
-    /// The `UniformImpl` implementation supporting type `X`.
-    type Impl: UniformImpl<X = Self>;
-}
-
-/// Helper trait handling actual uniform sampling.
-///
-/// If you want to implement `Uniform` sampling for your own type, then
-/// implement both this trait and `SampleUniform`:
-///
-/// ```rust
-/// use rand::{Rng, thread_rng};
-/// use rand::distributions::Distribution;
-/// use rand::distributions::uniform::{Uniform, SampleUniform, UniformImpl, UniformFloat};
-///
-/// #[derive(Clone, Copy, PartialEq, PartialOrd)]
-/// struct MyF32(f32);
-///
-/// #[derive(Clone, Copy, Debug)]
-/// struct UniformMyF32 {
-///     inner: UniformFloat<f32>,
-/// }
-/// impl UniformImpl for UniformMyF32 {
-///     type X = MyF32;
-///     fn new(low: Self::X, high: Self::X) -> Self {
-///         UniformMyF32 {
-///             inner: UniformFloat::<f32>::new(low.0, high.0),
-///         }
-///     }
-///     fn new_inclusive(low: Self::X, high: Self::X) -> Self {
-///         UniformImpl::new(low, high)
-///     }
-///     fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
-///         MyF32(self.inner.sample(rng))
-///     }
-/// }
-///
-/// impl SampleUniform for MyF32 {
-///     type Impl = UniformMyF32;
-/// }
-///
-/// let (low, high) = (MyF32(17.0f32), MyF32(22.0f32));
-/// let uniform = Uniform::new(low, high);
-/// let x = uniform.sample(&mut thread_rng());
-/// ```
-pub trait UniformImpl: Sized {
-    /// The type sampled by this implementation.
-    type X: PartialOrd;
-
-    /// 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(low: Self::X, high: Self::X) -> Self;
-
-    /// 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(low: Self::X, high: Self::X) -> Self;
-
-    /// 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)`.
-    ///
-    /// Usually users should not call this directly but instead use
-    /// `Uniform::sample_single`, which asserts that `low < high` before calling
-    /// this.
-    ///
-    /// Via this method, implementations can provide a method optimized for
-    /// sampling only a single value from the specified range. The default
-    /// implementation simply calls `UniformImpl::new` then `sample` on the
-    /// result.
-    fn sample_single<R: Rng + ?Sized>(low: Self::X, high: Self::X, rng: &mut R)
-        -> Self::X
-    {
-        let uniform: Self = UniformImpl::new(low, high);
-        uniform.sample(rng)
-    }
-}
-
-/// Implementation of `UniformImpl` for integer types.
-///
-/// Unless you are implementing `UniformImpl` for your own type, this type should
-/// not be used directly, use `Uniform` instead.
-#[derive(Clone, Copy, Debug)]
-pub struct UniformInt<X> {
-    low: X,
-    range: X,
-    zone: X,
-}
-
-macro_rules! uniform_int_impl {
-    ($ty:ty, $signed:ty, $unsigned:ident,
-     $i_large:ident, $u_large:ident) => {
-        impl SampleUniform for $ty {
-            type Impl = UniformInt<$ty>;
-        }
-
-        impl UniformImpl 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(low: Self::X, high: Self::X) -> Self {
-                UniformImpl::new_inclusive(low, high - 1)
-            }
-
-            #[inline] // if the range is constant, this helps LLVM to do the
-                      // calculations at compile-time.
-            fn new_inclusive(low: Self::X, high: Self::X) -> Self {
-                // For a closed range, the number of possible numbers we should
-                // generate is `range = (high - low + 1)`. It is not possible to
-                // end up with a uniform distribution if we map _all_ the random
-                // integers that can be generated to this range. We have to map
-                // integers from a `zone` that is a multiple of the range. The
-                // rest of the integers, that cause a bias, are rejected.
-                //
-                // The problem with `range` is that to cover the full range of
-                // the type, it has to store `unsigned_max + 1`, which can't be
-                // represented. But if the range covers the full range of the
-                // type, no modulus is needed. A range of size 0 can't exist, so
-                // we use that to represent this special case. Wrapping
-                // arithmetic even makes representing `unsigned_max + 1` as 0
-                // simple.
-                //
-                // We don't calculate `zone` directly, but first calculate the
-                // number of integers to reject. To handle `unsigned_max + 1`
-                // not fitting in the type, we use:
-                // ints_to_reject = (unsigned_max + 1) % range;
-                // ints_to_reject = (unsigned_max - range + 1) % range;
-                //
-                // The smallest integer prngs generate is u32. That is why for
-                // small integer sizes (i8/u8 and i16/u16) there is an
-                // optimisation: don't pick the largest zone that can fit in the
-                // small type, but pick the largest zone that can fit in an u32.
-                // This improves the chance to get a random integer that fits in
-                // the zone to 998 in 1000 in the worst case.
-                //
-                // There is a problem however: we can't store such a large range
-                // in `UniformInt`, that can only hold values of the size of $ty.
-                // `ints_to_reject` is always less than half the size of the
-                // small integer. For an u8 it only ever uses 7 bits. This means
-                // that all but the last 7 bits of `zone` are always 1's (or 15
-                // in the case of u16). So nothing is lost by trucating `zone`.
-                //
-                // 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.
-                let unsigned_max: $u_large = ::core::$u_large::MAX;
-
-                let range = (high as $u_large)
-                            .wrapping_sub(low as $u_large)
-                            .wrapping_add(1);
-                let ints_to_reject =
-                    if range > 0 {
-                        (unsigned_max - range + 1) % range
-                    } else {
-                        0
-                    };
-                let zone = unsigned_max - ints_to_reject;
-
-                UniformInt {
-                    low: low,
-                    // These are really $unsigned values, but store as $ty:
-                    range: range as $ty,
-                    zone: zone 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 {
-                    // Some casting to recover the trucated bits of `zone`:
-                    // First bit-cast to a signed int. Next sign-extend to the
-                    // larger type. Then bit-cast to unsigned.
-                    // For types that already have the right size, all the
-                    // casting is a no-op.
-                    let zone = self.zone as $signed as $i_large 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>(low: Self::X,
-                                              high: Self::X,
-                                              rng: &mut R) -> Self::X
-            {
-                let range = (high as $u_large)
-                            .wrapping_sub(low 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
-                       range << range.leading_zeros()
-                    };
-
-                loop {
-                    let v: $u_large = rng.gen();
-                    let (hi, lo) = v.wmul(range);
-                    if lo <= zone {
-                        return low.wrapping_add(hi as $ty);
-                    }
-                }
-            }
-        }
-    }
-}
-
-impl<X: SampleUniform> From<::core::ops::Range<X>> for Uniform<X> {
-    fn from(r: ::core::ops::Range<X>) -> Uniform<X> {
-        Uniform::new(r.start, r.end)
-    }
-}
-
-uniform_int_impl! { i8, i8, u8, i32, u32 }
-uniform_int_impl! { i16, i16, u16, i32, u32 }
-uniform_int_impl! { i32, i32, u32, i32, u32 }
-uniform_int_impl! { i64, i64, u64, i64, u64 }
-#[cfg(feature = "i128_support")]
-uniform_int_impl! { i128, i128, u128, u128, u128 }
-uniform_int_impl! { isize, isize, usize, isize, usize }
-uniform_int_impl! { u8, i8, u8, i32, u32 }
-uniform_int_impl! { u16, i16, u16, i32, u32 }
-uniform_int_impl! { u32, i32, u32, i32, u32 }
-uniform_int_impl! { u64, i64, u64, i64, u64 }
-uniform_int_impl! { usize, isize, usize, isize, usize }
-#[cfg(feature = "i128_support")]
-uniform_int_impl! { u128, u128, u128, i128, u128 }
-
-
-trait WideningMultiply<RHS = Self> {
-    type Output;
-
-    fn wmul(self, x: RHS) -> Self::Output;
-}
-
-macro_rules! wmul_impl {
-    ($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 $ty)
-            }
-        }
-    }
-}
-
-wmul_impl! { u8, u16, 8 }
-wmul_impl! { u16, u32, 16 }
-wmul_impl! { u32, u64, 32 }
-#[cfg(feature = "i128_support")]
-wmul_impl! { u64, u128, 64 }
-
-// This code is a translation of the __mulddi3 function in LLVM's
-// compiler-rt. It is an optimised variant of the common method
-// `(a + b) * (c + d) = ac + ad + bc + bd`.
-//
-// For some reason LLVM can optimise the C version very well, but
-// keeps shuffeling registers in this Rust translation.
-macro_rules! wmul_impl_large {
-    ($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 >> $half);
-
-                (high, low)
-            }
-        }
-    }
-}
-
-#[cfg(not(feature = "i128_support"))]
-wmul_impl_large! { u64, 32 }
-#[cfg(feature = "i128_support")]
-wmul_impl_large! { u128, 64 }
-
-
-macro_rules! wmul_impl_usize {
-    ($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 $ty);
-                (high as usize, low as usize)
-            }
-        }
-    }
-}
-
-#[cfg(target_pointer_width = "32")]
-wmul_impl_usize! { u32 }
-#[cfg(target_pointer_width = "64")]
-wmul_impl_usize! { u64 }
-
-
-
-/// Implementation of `UniformImpl` for float types.
-///
-/// Unless you are implementing `UniformImpl` for your own type, this type should
-/// not be used directly, use `Uniform` instead.
-#[derive(Clone, Copy, Debug)]
-pub struct UniformFloat<X> {
-    scale: X,
-    offset: X,
-}
-
-macro_rules! uniform_float_impl {
-    ($ty:ty, $bits_to_discard:expr, $next_u:ident) => {
-        impl SampleUniform for $ty {
-            type Impl = UniformFloat<$ty>;
-        }
-
-        impl UniformImpl for UniformFloat<$ty> {
-            type X = $ty;
-
-            fn new(low: Self::X, high: Self::X) -> Self {
-                let scale = high - low;
-                let offset = low - scale;
-                UniformFloat {
-                    scale: scale,
-                    offset: offset,
-                }
-            }
-
-            fn new_inclusive(low: Self::X, high: Self::X) -> Self {
-                // Same as `new`, because the boundaries of a floats range are
-                // (at least currently) not exact due to rounding errors.
-                UniformImpl::new(low, high)
-            }
-
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
-                // Generate a value in the range [1, 2)
-                let value1_2 = (rng.$next_u() >> $bits_to_discard)
-                               .into_float_with_exponent(0);
-                // We don't use `f64::mul_add`, because it is not available with
-                // `no_std`. Furthermore, it is slower for some targets (but
-                // faster for others). However, the order of multiplication and
-                // addition is important, because on some platforms (e.g. ARM)
-                // it will be optimized to a single (non-FMA) instruction.
-                value1_2 * self.scale + self.offset
-            }
-
-            fn sample_single<R: Rng + ?Sized>(low: Self::X,
-                                              high: Self::X,
-                                              rng: &mut R) -> Self::X {
-                let scale = high - low;
-                let offset = low - scale;
-                // Generate a value in the range [1, 2)
-                let value1_2 = (rng.$next_u() >> $bits_to_discard)
-                               .into_float_with_exponent(0);
-                // Doing multiply before addition allows some architectures to
-                // use a single instruction.
-                value1_2 * scale + offset
-            }
-        }
-    }
-}
-
-uniform_float_impl! { f32, 32 - 23, next_u32 }
-uniform_float_impl! { f64, 64 - 52, next_u64 }
-
-
-#[cfg(test)]
-mod tests {
-    use Rng;
-    use distributions::uniform::{Uniform, UniformImpl, UniformFloat, SampleUniform};
-
-    #[should_panic]
-    #[test]
-    fn test_uniform_bad_limits_equal_int() {
-        Uniform::new(10, 10);
-    }
-
-    #[should_panic]
-    #[test]
-    fn test_uniform_bad_limits_equal_float() {
-        Uniform::new(10., 10.);
-    }
-
-    #[test]
-    fn test_uniform_good_limits_equal_int() {
-        let mut rng = ::test::rng(804);
-        let dist = Uniform::new_inclusive(10, 10);
-        for _ in 0..20 {
-            assert_eq!(rng.sample(dist), 10);
-        }
-    }
-
-    #[test]
-    fn test_uniform_good_limits_equal_float() {
-        let mut rng = ::test::rng(805);
-        let dist = Uniform::new_inclusive(10., 10.);
-        for _ in 0..20 {
-            assert_eq!(rng.sample(dist), 10.);
-        }
-    }
-
-    #[should_panic]
-    #[test]
-    fn test_uniform_bad_limits_flipped_int() {
-        Uniform::new(10, 5);
-    }
-
-    #[should_panic]
-    #[test]
-    fn test_uniform_bad_limits_flipped_float() {
-        Uniform::new(10., 5.);
-    }
-
-    #[test]
-    fn test_integers() {
-        let mut rng = ::test::rng(251);
-        macro_rules! t {
-            ($($ty:ident),*) => {{
-                $(
-                   let v: &[($ty, $ty)] = &[(0, 10),
-                                            (10, 127),
-                                            (::core::$ty::MIN, ::core::$ty::MAX)];
-                   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!(low <= v && v < high);
-                        }
-
-                        let my_uniform = Uniform::new_inclusive(low, high);
-                        for _ in 0..1000 {
-                            let v: $ty = rng.sample(my_uniform);
-                            assert!(low <= v && v <= high);
-                        }
-
-                        for _ in 0..1000 {
-                            let v: $ty = Uniform::sample_single(low, high, &mut rng);
-                            assert!(low <= v && v < high);
-                        }
-                    }
-                 )*
-            }}
-        }
-        t!(i8, i16, i32, i64, isize,
-           u8, u16, u32, u64, usize);
-        #[cfg(feature = "i128_support")]
-        t!(i128, u128)
-    }
-
-    #[test]
-    fn test_floats() {
-        let mut rng = ::test::rng(252);
-        macro_rules! t {
-            ($($ty:ty),*) => {{
-                $(
-                   let v: &[($ty, $ty)] = &[(0.0, 100.0),
-                                            (-1e35, -1e25),
-                                            (1e-35, 1e-25),
-                                            (-1e35, 1e35)];
-                   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!(low <= v && v < high);
-                        }
-                    }
-                 )*
-            }}
-        }
-
-        t!(f32, f64)
-    }
-    #[test]
-    fn test_custom_uniform() {
-        #[derive(Clone, Copy, PartialEq, PartialOrd)]
-        struct MyF32 {
-            x: f32,
-        }
-        #[derive(Clone, Copy, Debug)]
-        struct UniformMyF32 {
-            inner: UniformFloat<f32>,
-        }
-        impl UniformImpl for UniformMyF32 {
-            type X = MyF32;
-            fn new(low: Self::X, high: Self::X) -> Self {
-                UniformMyF32 {
-                    inner: UniformFloat::<f32>::new(low.x, high.x),
-                }
-            }
-            fn new_inclusive(low: Self::X, high: Self::X) -> Self {
-                UniformImpl::new(low, high)
-            }
-            fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Self::X {
-                MyF32 { x: self.inner.sample(rng) }
-            }
-        }
-        impl SampleUniform for MyF32 {
-            type Impl = UniformMyF32;
-        }
-
-        let (low, high) = (MyF32{ x: 17.0f32 }, MyF32{ x: 22.0f32 });
-        let uniform = Uniform::new(low, high);
-        let mut rng = ::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.inner.low, 2);
-        assert_eq!(r.inner.range, 5);
-        let r = Uniform::from(2.0f64..7.0);
-        assert_eq!(r.inner.offset, -3.0);
-        assert_eq!(r.inner.scale, 5.0);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/distributions/ziggurat_tables.rs b/vendor/rand-8c5b0ac51d/src/distributions/ziggurat_tables.rs
deleted file mode 100644
index 11a2172..0000000
--- a/vendor/rand-8c5b0ac51d/src/distributions/ziggurat_tables.rs
+++ /dev/null
@@ -1,280 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-// 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;
-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];
-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;
-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];
-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-8c5b0ac51d/src/entropy_rng.rs b/vendor/rand-8c5b0ac51d/src/entropy_rng.rs
deleted file mode 100644
index 6b31fc6..0000000
--- a/vendor/rand-8c5b0ac51d/src/entropy_rng.rs
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright 2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Entropy generator, or wrapper around external generators
-
-use rand_core::{RngCore, CryptoRng, Error, impls};
-use os::OsRng;
-use jitter::JitterRng;
-
-/// An interface returning random data from external source(s), provided
-/// specifically for securely seeding algorithmic generators (PRNGs).
-/// 
-/// Where possible, `EntropyRng` retrieves random data from the operating
-/// system's interface for random numbers ([`OsRng`]); if that fails it will
-/// fall back to the [`JitterRng`] entropy collector. In the latter case it will
-/// still try to use [`OsRng`] on the next usage.
-/// 
-/// If no secure source of entropy is available `EntropyRng` will panic on use;
-/// i.e. it should never output predictable data.
-/// 
-/// This is either a little slow ([`OsRng`] requires a system call) or extremely
-/// slow ([`JitterRng`] must use significant CPU time to generate sufficient
-/// jitter); for better performance it is common to seed a local PRNG from
-/// external entropy then primarily use the local PRNG ([`thread_rng`] is
-/// provided as a convenient, local, automatically-seeded CSPRNG).
-///
-/// [`OsRng`]: os/struct.OsRng.html
-/// [`JitterRng`]: jitter/struct.JitterRng.html
-/// [`thread_rng`]: fn.thread_rng.html
-#[derive(Debug)]
-pub struct EntropyRng {
-    rng: EntropySource,
-}
-
-#[derive(Debug)]
-enum EntropySource {
-    Os(OsRng),
-    Jitter(JitterRng),
-    None,
-}
-
-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 { rng: EntropySource::None }
-    }
-}
-
-impl Default for EntropyRng {
-    fn default() -> Self {
-        EntropyRng::new()
-    }
-}
-
-impl RngCore for EntropyRng {
-    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!("all entropy sources failed; first error: {}", err))
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        fn try_os_new(dest: &mut [u8]) -> Result<OsRng, Error>
-        {
-            let mut rng = OsRng::new()?;
-            rng.try_fill_bytes(dest)?;
-            Ok(rng)
-        }
-
-        fn try_jitter_new(dest: &mut [u8]) -> Result<JitterRng, Error>
-        {
-            let mut rng = JitterRng::new()?;
-            rng.try_fill_bytes(dest)?;
-            Ok(rng)
-        }
-
-        let mut switch_rng = None;
-        match self.rng {
-            EntropySource::None => {
-                let os_rng_result = try_os_new(dest);
-                match os_rng_result {
-                    Ok(os_rng) => {
-                        debug!("EntropyRng: using OsRng");
-                        switch_rng = Some(EntropySource::Os(os_rng));
-                    }
-                    Err(os_rng_error) => {
-                        warn!("EntropyRng: OsRng failed [falling back to JitterRng]: {}",
-                              os_rng_error);
-                        match try_jitter_new(dest) {
-                            Ok(jitter_rng) => {
-                                debug!("EntropyRng: using JitterRng");
-                                switch_rng = Some(EntropySource::Jitter(jitter_rng));
-                            }
-                            Err(_jitter_error) => {
-                                warn!("EntropyRng: JitterRng failed: {}",
-                                      _jitter_error);
-                                return Err(os_rng_error);
-                            }
-                        }
-                    }
-                }
-            }
-            EntropySource::Os(ref mut rng) => {
-                let os_rng_result = rng.try_fill_bytes(dest);
-                if let Err(os_rng_error) = os_rng_result {
-                    warn!("EntropyRng: OsRng failed [falling back to JitterRng]: {}",
-                          os_rng_error);
-                    match try_jitter_new(dest) {
-                        Ok(jitter_rng) => {
-                            debug!("EntropyRng: using JitterRng");
-                            switch_rng = Some(EntropySource::Jitter(jitter_rng));
-                        }
-                        Err(_jitter_error) => {
-                            warn!("EntropyRng: JitterRng failed: {}",
-                                  _jitter_error);
-                            return Err(os_rng_error);
-                        }
-                    }
-                }
-            }
-            EntropySource::Jitter(ref mut rng) => {
-                if let Ok(os_rng) = try_os_new(dest) {
-                    debug!("EntropyRng: using OsRng");
-                    switch_rng = Some(EntropySource::Os(os_rng));
-                } else {
-                    return rng.try_fill_bytes(dest); // use JitterRng
-                }
-            }
-        }
-        if let Some(rng) = switch_rng {
-            self.rng = rng;
-        }
-        Ok(())
-    }
-}
-
-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-8c5b0ac51d/src/jitter.rs b/vendor/rand-8c5b0ac51d/src/jitter.rs
deleted file mode 100644
index e633800..0000000
--- a/vendor/rand-8c5b0ac51d/src/jitter.rs
+++ /dev/null
@@ -1,875 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-//
-// Based on jitterentropy-library, http://www.chronox.de/jent.html.
-// Copyright Stephan Mueller <smueller@xxxxxxxxxx>, 2014 - 2017.
-//
-// With permission from Stephan Mueller to relicense the Rust translation under
-// the MIT license.
-
-//! Non-physical true random number generator based on timing jitter.
-
-// Note: the C implementation of `Jitterentropy` relies on being compiled
-// without optimizations. This implementation goes through lengths to make the
-// compiler not optimise out what is technically dead code, but that does
-// influence timing jitter.
-
-use rand_core::{RngCore, CryptoRng, Error, ErrorKind, impls};
-
-use core::{fmt, mem, ptr};
-#[cfg(feature="std")]
-use std::sync::atomic::{AtomicUsize, ATOMIC_USIZE_INIT, Ordering};
-
-const MEMORY_BLOCKS: usize = 64;
-const MEMORY_BLOCKSIZE: usize = 32;
-const MEMORY_SIZE: usize = MEMORY_BLOCKS * MEMORY_BLOCKSIZE;
-
-/// A true random number generator based on jitter in the CPU execution time,
-/// and jitter in memory access time.
-///
-/// This is a true random number generator, as opposed to pseudo-random
-/// generators. Random numbers generated by `JitterRng` can be seen as fresh
-/// entropy. A consequence is that is orders of magnitude slower than [`OsRng`]
-/// and PRNGs (about 10<sup>3</sup>..10<sup>6</sup> slower).
-///
-/// There are very few situations where using this RNG is appropriate. Only very
-/// few applications require true entropy. A normal PRNG can be statistically
-/// indistinguishable, and a cryptographic PRNG should also be as impossible to
-/// predict.
-///
-/// Use of `JitterRng` is recommended for initializing cryptographic PRNGs when
-/// [`OsRng`] is not available.
-///
-/// This implementation is based on
-/// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0.
-///
-/// [`OsRng`]: ../os/struct.OsRng.html
-pub struct JitterRng {
-    data: u64, // Actual random number
-    // Number of rounds to run the entropy collector per 64 bits
-    rounds: u8,
-    // Timer used by `measure_jitter`
-    timer: fn() -> u64,
-    // Memory for the Memory Access noise source
-    mem_prev_index: u16,
-    // Make `next_u32` not waste 32 bits
-    data_half_used: bool,
-}
-
-// Note: `JitterRng` maintains a small 64-bit entropy pool. With every
-// `generate` 64 new bits should be integrated in the pool. If a round of
-// `generate` were to collect less than the expected 64 bit, then the returned
-// value, and the new state of the entropy pool, would be in some way related to
-// the initial state. It is therefore better if the initial state of the entropy
-// pool is different on each call to `generate`. This has a few implications:
-// - `generate` should be called once before using `JitterRng` to produce the
-//   first usable value (this is done by default in `new`);
-// - We do not zero the entropy pool after generating a result. The reference
-//   implementation also does not support zeroing, but recommends generating a
-//   new value without using it if you want to protect a previously generated
-//   'secret' value from someone inspecting the memory;
-// - Implementing `Clone` seems acceptable, as it would not cause the systematic
-//   bias a constant might cause. Only instead of one value that could be
-//   potentially related to the same initial state, there are now two.
-
-// Entropy collector state.
-// These values are not necessary to preserve across runs.
-struct EcState {
-    // Previous time stamp to determine the timer delta
-    prev_time: u64,
-    // Deltas used for the stuck test
-    last_delta: i32,
-    last_delta2: i32,
-    // Memory for the Memory Access noise source
-    mem: [u8; MEMORY_SIZE],
-}
-
-impl EcState {
-    // Stuck test by checking the:
-    // - 1st derivation of the jitter measurement (time delta)
-    // - 2nd derivation of the jitter measurement (delta of time deltas)
-    // - 3rd derivation of the jitter measurement (delta of delta of time
-    //   deltas)
-    //
-    // All values must always be non-zero.
-    // This test is a heuristic to see whether the last measurement holds
-    // entropy.
-    fn stuck(&mut self, current_delta: i32) -> bool {
-        let delta2 = self.last_delta - current_delta;
-        let delta3 = delta2 - self.last_delta2;
-
-        self.last_delta = current_delta;
-        self.last_delta2 = delta2;
-
-        current_delta == 0 || delta2 == 0 || delta3 == 0
-    }
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for JitterRng {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "JitterRng {{}}")
-    }
-}
-
-impl Clone for JitterRng {
-    fn clone(&self) -> JitterRng {
-        JitterRng {
-            data: self.data,
-            rounds: self.rounds,
-            timer: self.timer,
-            mem_prev_index: self.mem_prev_index,
-            // The 32 bits that may still be unused from the previous round are
-            // for the original to use, not for the clone.
-            data_half_used: false,
-        }
-    }
-}
-
-/// An error that can occur when [`JitterRng::test_timer`] fails.
-///
-/// [`JitterRng::test_timer`]: struct.JitterRng.html#method.test_timer
-#[derive(Debug, Clone, PartialEq, Eq)]
-pub enum TimerError {
-    /// No timer available.
-    NoTimer,
-    /// Timer too coarse to use as an entropy source.
-    CoarseTimer,
-    /// Timer is not monotonically increasing.
-    NotMonotonic,
-    /// Variations of deltas of time too small.
-    TinyVariantions,
-    /// Too many stuck results (indicating no added entropy).
-    TooManyStuck,
-    #[doc(hidden)]
-    __Nonexhaustive,
-}
-
-impl TimerError {
-    fn description(&self) -> &'static str {
-        match *self {
-            TimerError::NoTimer => "no timer available",
-            TimerError::CoarseTimer => "coarse timer",
-            TimerError::NotMonotonic => "timer not monotonic",
-            TimerError::TinyVariantions => "time delta variations too small",
-            TimerError::TooManyStuck => "too many stuck results",
-            TimerError::__Nonexhaustive => unreachable!(),
-        }
-    }
-}
-
-impl fmt::Display for TimerError {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "{}", self.description())
-    }
-}
-
-#[cfg(feature="std")]
-impl ::std::error::Error for TimerError {
-    fn description(&self) -> &str {
-        self.description()
-    }
-}
-
-impl From<TimerError> for Error {
-    fn from(err: TimerError) -> Error {
-        // Timer check is already quite permissive of failures so we don't
-        // expect false-positive failures, i.e. any error is irrecoverable.
-        Error::with_cause(ErrorKind::Unavailable,
-                              "timer jitter failed basic quality tests", err)
-    }
-}
-
-// Initialise to zero; must be positive
-#[cfg(feature="std")]
-static JITTER_ROUNDS: AtomicUsize = ATOMIC_USIZE_INIT;
-
-impl JitterRng {
-    /// Create a new `JitterRng`. Makes use of `std::time` for a timer, or a
-    /// platform-specific function with higher accuracy if necessary and
-    /// available.
-    ///
-    /// During initialization CPU execution timing jitter is measured a few
-    /// hundred times. If this does not pass basic quality tests, an error is
-    /// returned. The test result is cached to make subsequent calls faster.
-    #[cfg(feature="std")]
-    pub fn new() -> Result<JitterRng, TimerError> {
-        let mut state = JitterRng::new_with_timer(platform::get_nstime);
-        let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u8;
-        if rounds == 0 {
-            // No result yet: run test.
-            // This allows the timer test to run multiple times; we don't care.
-            rounds = state.test_timer()?;
-            JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed);
-            info!("JitterRng: using {} rounds per u64 output", rounds);
-        }
-        state.set_rounds(rounds);
-
-        // Fill `data` with a non-zero value.
-        state.gen_entropy();
-        Ok(state)
-    }
-
-    /// Create a new `JitterRng`.
-    /// A custom timer can be supplied, making it possible to use `JitterRng` in
-    /// `no_std` environments.
-    ///
-    /// The timer must have nanosecond precision.
-    ///
-    /// This method is more low-level than `new()`. It is the responsibility of
-    /// the caller to run [`test_timer`] before using any numbers generated with
-    /// `JitterRng`, and optionally call [`set_rounds`]. Also it is important to
-    /// consume at least one `u64` before using the first result to initialize
-    /// the entropy collection pool.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// # use rand::{Rng, Error};
-    /// use rand::jitter::JitterRng;
-    ///
-    /// # fn try_inner() -> Result<(), Error> {
-    /// fn get_nstime() -> u64 {
-    ///     use std::time::{SystemTime, UNIX_EPOCH};
-    ///
-    ///     let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
-    ///     // The correct way to calculate the current time is
-    ///     // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
-    ///     // But this is faster, and the difference in terms of entropy is
-    ///     // negligible (log2(10^9) == 29.9).
-    ///     dur.as_secs() << 30 | dur.subsec_nanos() as u64
-    /// }
-    ///
-    /// let mut rng = JitterRng::new_with_timer(get_nstime);
-    /// let rounds = rng.test_timer()?;
-    /// rng.set_rounds(rounds); // optional
-    /// let _ = rng.gen::<u64>();
-    ///
-    /// // Ready for use
-    /// let v: u64 = rng.gen();
-    /// # Ok(())
-    /// # }
-    ///
-    /// # let _ = try_inner();
-    /// ```
-    ///
-    /// [`test_timer`]: struct.JitterRng.html#method.test_timer
-    /// [`set_rounds`]: struct.JitterRng.html#method.set_rounds
-    pub fn new_with_timer(timer: fn() -> u64) -> JitterRng {
-        JitterRng {
-            data: 0,
-            rounds: 64,
-            timer,
-            mem_prev_index: 0,
-            data_half_used: false,
-        }
-    }
-
-    /// Configures how many rounds are used to generate each 64-bit value.
-    /// This must be greater than zero, and has a big impact on performance
-    /// and output quality.
-    ///
-    /// [`new_with_timer`] conservatively uses 64 rounds, but often less rounds
-    /// can be used. The `test_timer()` function returns the minimum number of
-    /// rounds required for full strength (platform dependent), so one may use
-    /// `rng.set_rounds(rng.test_timer()?);` or cache the value.
-    ///
-    /// [`new_with_timer`]: struct.JitterRng.html#method.new_with_timer
-    pub fn set_rounds(&mut self, rounds: u8) {
-        assert!(rounds > 0);
-        self.rounds = rounds;
-    }
-
-    // Calculate a random loop count used for the next round of an entropy
-    // collection, based on bits from a fresh value from the timer.
-    //
-    // The timer is folded to produce a number that contains at most `n_bits`
-    // bits.
-    //
-    // Note: A constant should be added to the resulting random loop count to
-    // prevent loops that run 0 times.
-    #[inline(never)]
-    fn random_loop_cnt(&mut self, n_bits: u32) -> u32 {
-        let mut rounds = 0;
-
-        let mut time = (self.timer)();
-        // Mix with the current state of the random number balance the random
-        // loop counter a bit more.
-        time ^= self.data;
-
-        // We fold the time value as much as possible to ensure that as many
-        // bits of the time stamp are included as possible.
-        let folds = (64 + n_bits - 1) / n_bits;
-        let mask = (1 << n_bits) - 1;
-        for _ in 0..folds {
-            rounds ^= time & mask;
-            time >>= n_bits;
-        }
-
-        rounds as u32
-    }
-
-    // CPU jitter noise source
-    // Noise source based on the CPU execution time jitter
-    //
-    // This function injects the individual bits of the time value into the
-    // entropy pool using an LFSR.
-    //
-    // The code is deliberately inefficient with respect to the bit shifting.
-    // This function not only acts as folding operation, but this function's
-    // execution is used to measure the CPU execution time jitter. Any change to
-    // the loop in this function implies that careful retesting must be done.
-    #[inline(never)]
-    fn lfsr_time(&mut self, time: u64, var_rounds: bool) {
-        fn lfsr(mut data: u64, time: u64) -> u64{
-            for i in 1..65 {
-                let mut tmp = time << (64 - i);
-                tmp >>= 64 - 1;
-
-                // Fibonacci LSFR with polynomial of
-                // x^64 + x^61 + x^56 + x^31 + x^28 + x^23 + 1 which is
-                // primitive according to
-                // http://poincare.matf.bg.ac.rs/~ezivkovm/publications/primpol1.pdf
-                // (the shift values are the polynomial values minus one
-                // due to counting bits from 0 to 63). As the current
-                // position is always the LSB, the polynomial only needs
-                // to shift data in from the left without wrap.
-                data ^= tmp;
-                data ^= (data >> 63) & 1;
-                data ^= (data >> 60) & 1;
-                data ^= (data >> 55) & 1;
-                data ^= (data >> 30) & 1;
-                data ^= (data >> 27) & 1;
-                data ^= (data >> 22) & 1;
-                data = data.rotate_left(1);
-            }
-            data
-        }
-
-        // Note: in the reference implementation only the last round effects
-        // `self.data`, all the other results are ignored. To make sure the
-        // other rounds are not optimised out, we first run all but the last
-        // round on a throw-away value instead of the real `self.data`.
-        let mut lfsr_loop_cnt = 0;
-        if var_rounds { lfsr_loop_cnt = self.random_loop_cnt(4) };
-
-        let mut throw_away: u64 = 0;
-        for _ in 0..lfsr_loop_cnt {
-            throw_away = lfsr(throw_away, time);
-        }
-        black_box(throw_away);
-
-        self.data = lfsr(self.data, time);
-    }
-
-    // Memory Access noise source
-    // This is a noise source based on variations in memory access times
-    //
-    // This function performs memory accesses which will add to the timing
-    // variations due to an unknown amount of CPU wait states that need to be
-    // added when accessing memory. The memory size should be larger than the L1
-    // caches as outlined in the documentation and the associated testing.
-    //
-    // The L1 cache has a very high bandwidth, albeit its access rate is usually
-    // slower than accessing CPU registers. Therefore, L1 accesses only add
-    // minimal variations as the CPU has hardly to wait. Starting with L2,
-    // significant variations are added because L2 typically does not belong to
-    // the CPU any more and therefore a wider range of CPU wait states is
-    // necessary for accesses. L3 and real memory accesses have even a wider
-    // range of wait states. However, to reliably access either L3 or memory,
-    // the `self.mem` memory must be quite large which is usually not desirable.
-    #[inline(never)]
-    fn memaccess(&mut self, mem: &mut [u8; MEMORY_SIZE], var_rounds: bool) {
-        let mut acc_loop_cnt = 128;
-        if var_rounds { acc_loop_cnt += self.random_loop_cnt(4) };
-
-        let mut index = self.mem_prev_index as usize;
-        for _ in 0..acc_loop_cnt {
-            // Addition of memblocksize - 1 to index with wrap around logic to
-            // ensure that every memory location is hit evenly.
-            // The modulus also allows the compiler to remove the indexing
-            // bounds check.
-            index = (index + MEMORY_BLOCKSIZE - 1) % MEMORY_SIZE;
-
-            // memory access: just add 1 to one byte
-            // memory access implies read from and write to memory location
-            mem[index] = mem[index].wrapping_add(1);
-        }
-        self.mem_prev_index = index as u16;
-    }
-
-    // This is the heart of the entropy generation: calculate time deltas and
-    // use the CPU jitter in the time deltas. The jitter is injected into the
-    // entropy pool.
-    //
-    // Ensure that `ec.prev_time` is primed before using the output of this
-    // function. This can be done by calling this function and not using its
-    // result.
-    fn measure_jitter(&mut self, ec: &mut EcState) -> Option<()> {
-        // Invoke one noise source before time measurement to add variations
-        self.memaccess(&mut ec.mem, true);
-
-        // Get time stamp and calculate time delta to previous
-        // invocation to measure the timing variations
-        let time = (self.timer)();
-        // Note: wrapping_sub combined with a cast to `i64` generates a correct
-        // delta, even in the unlikely case this is a timer that is not strictly
-        // monotonic.
-        let current_delta = time.wrapping_sub(ec.prev_time) as i64 as i32;
-        ec.prev_time = time;
-
-        // Call the next noise source which also injects the data
-        self.lfsr_time(current_delta as u64, true);
-
-        // Check whether we have a stuck measurement (i.e. does the last
-        // measurement holds entropy?).
-        if ec.stuck(current_delta) { return None };
-
-        // Rotate the data buffer by a prime number (any odd number would
-        // do) to ensure that every bit position of the input time stamp
-        // has an even chance of being merged with a bit position in the
-        // entropy pool. We do not use one here as the adjacent bits in
-        // successive time deltas may have some form of dependency. The
-        // chosen value of 7 implies that the low 7 bits of the next
-        // time delta value is concatenated with the current time delta.
-        self.data = self.data.rotate_left(7);
-
-        Some(())
-    }
-
-    // Shuffle the pool a bit by mixing some value with a bijective function
-    // (XOR) into the pool.
-    //
-    // The function generates a mixer value that depends on the bits set and
-    // the location of the set bits in the random number generated by the
-    // entropy source. Therefore, based on the generated random number, this
-    // mixer value can have 2^64 different values. That mixer value is
-    // initialized with the first two SHA-1 constants. After obtaining the
-    // mixer value, it is XORed into the random number.
-    //
-    // The mixer value is not assumed to contain any entropy. But due to the
-    // XOR operation, it can also not destroy any entropy present in the
-    // entropy pool.
-    #[inline(never)]
-    fn stir_pool(&mut self) {
-        // This constant is derived from the first two 32 bit initialization
-        // vectors of SHA-1 as defined in FIPS 180-4 section 5.3.1
-        // The order does not really matter as we do not rely on the specific
-        // numbers. We just pick the SHA-1 constants as they have a good mix of
-        // bit set and unset.
-        const CONSTANT: u64 = 0x67452301efcdab89;
-
-        // The start value of the mixer variable is derived from the third
-        // and fourth 32 bit initialization vector of SHA-1 as defined in
-        // FIPS 180-4 section 5.3.1
-        let mut mixer = 0x98badcfe10325476;
-
-        // This is a constant time function to prevent leaking timing
-        // information about the random number.
-        // The normal code is:
-        // ```
-        // for i in 0..64 {
-        //     if ((self.data >> i) & 1) == 1 { mixer ^= CONSTANT; }
-        // }
-        // ```
-        // This is a bit fragile, as LLVM really wants to use branches here, and
-        // we rely on it to not recognise the opportunity.
-        for i in 0..64 {
-            let apply = (self.data >> i) & 1;
-            let mask = !apply.wrapping_sub(1);
-            mixer ^= CONSTANT & mask;
-            mixer = mixer.rotate_left(1);
-        }
-
-        self.data ^= mixer;
-    }
-
-    fn gen_entropy(&mut self) -> u64 {
-        trace!("JitterRng: collecting entropy");
-
-        // Prime `ec.prev_time`, and run the noice sources to make sure the
-        // first loop round collects the expected entropy.
-        let mut ec = EcState {
-            prev_time: (self.timer)(),
-            last_delta: 0,
-            last_delta2: 0,
-            mem: [0; MEMORY_SIZE],
-        };
-        let _ = self.measure_jitter(&mut ec);
-
-        for _ in 0..self.rounds {
-            // If a stuck measurement is received, repeat measurement
-            // Note: we do not guard against an infinite loop, that would mean
-            // the timer suddenly became broken.
-            while self.measure_jitter(&mut ec).is_none() {}
-        }
-
-        // Do a single read from `self.mem` to make sure the Memory Access noise
-        // source is not optimised out.
-        black_box(ec.mem[0]);
-
-        self.stir_pool();
-        self.data
-    }
-    
-    /// Basic quality tests on the timer, by measuring CPU timing jitter a few
-    /// hundred times.
-    ///
-    /// If succesful, this will return the estimated number of rounds necessary
-    /// to collect 64 bits of entropy. Otherwise a [`TimerError`] with the cause
-    /// of the failure will be returned.
-    ///
-    /// [`TimerError`]: enum.TimerError.html
-    #[cfg(not(all(target_arch = "wasm32", not(target_os = "emscripten"))))]
-    pub fn test_timer(&mut self) -> Result<u8, TimerError> {
-        debug!("JitterRng: testing timer ...");
-        // We could add a check for system capabilities such as `clock_getres`
-        // or check for `CONFIG_X86_TSC`, but it does not make much sense as the
-        // following sanity checks verify that we have a high-resolution timer.
-
-        let mut delta_sum = 0;
-        let mut old_delta = 0;
-
-        let mut time_backwards = 0;
-        let mut count_mod = 0;
-        let mut count_stuck = 0;
-
-        let mut ec = EcState {
-            prev_time: (self.timer)(),
-            last_delta: 0,
-            last_delta2: 0,
-            mem: [0; MEMORY_SIZE],
-        };
-
-        // TESTLOOPCOUNT needs some loops to identify edge systems.
-        // 100 is definitely too little.
-        const TESTLOOPCOUNT: u64 = 300;
-        const CLEARCACHE: u64 = 100;
-
-        for i in 0..(CLEARCACHE + TESTLOOPCOUNT) {
-            // Measure time delta of core entropy collection logic
-            let time = (self.timer)();
-            self.memaccess(&mut ec.mem, true);
-            self.lfsr_time(time, true);
-            let time2 = (self.timer)();
-
-            // Test whether timer works
-            if time == 0 || time2 == 0 {
-                return Err(TimerError::NoTimer);
-            }
-            let delta = time2.wrapping_sub(time) as i64 as i32;
-
-            // Test whether timer is fine grained enough to provide delta even
-            // when called shortly after each other -- this implies that we also
-            // have a high resolution timer
-            if delta == 0 {
-                return Err(TimerError::CoarseTimer);
-            }
-
-            // Up to here we did not modify any variable that will be
-            // evaluated later, but we already performed some work. Thus we
-            // already have had an impact on the caches, branch prediction,
-            // etc. with the goal to clear it to get the worst case
-            // measurements.
-            if i < CLEARCACHE { continue; }
-
-            if ec.stuck(delta) { count_stuck += 1; }
-
-            // Test whether we have an increasing timer.
-            if !(time2 > time) { time_backwards += 1; }
-
-            // Count the number of times the counter increases in steps of 100ns
-            // or greater.
-            if (delta % 100) == 0 { count_mod += 1; }
-
-            // Ensure that we have a varying delta timer which is necessary for
-            // the calculation of entropy -- perform this check only after the
-            // first loop is executed as we need to prime the old_delta value
-            delta_sum += (delta - old_delta).abs() as u64;
-            old_delta = delta;
-        }
-
-        // Do a single read from `self.mem` to make sure the Memory Access noise
-        // source is not optimised out.
-        black_box(ec.mem[0]);
-
-        // We allow the time to run backwards for up to three times.
-        // This can happen if the clock is being adjusted by NTP operations.
-        // If such an operation just happens to interfere with our test, it
-        // should not fail. The value of 3 should cover the NTP case being
-        // performed during our test run.
-        if time_backwards > 3 {
-            return Err(TimerError::NotMonotonic);
-        }
-
-        // Test that the available amount of entropy per round does not get to
-        // low. We expect 1 bit of entropy per round as a reasonable minimum
-        // (although less is possible, it means the collector loop has to run
-        // much more often).
-        // `assert!(delta_average >= log2(1))`
-        // `assert!(delta_sum / TESTLOOPCOUNT >= 1)`
-        // `assert!(delta_sum >= TESTLOOPCOUNT)`
-        if delta_sum < TESTLOOPCOUNT {
-            return Err(TimerError::TinyVariantions);
-        }
-
-        // Ensure that we have variations in the time stamp below 100 for at
-        // least 10% of all checks -- on some platforms, the counter increments
-        // in multiples of 100, but not always
-        if count_mod > (TESTLOOPCOUNT * 9 / 10) {
-            return Err(TimerError::CoarseTimer);
-        }
-
-        // If we have more than 90% stuck results, then this Jitter RNG is
-        // likely to not work well.
-        if count_stuck > (TESTLOOPCOUNT * 9 / 10) {
-            return Err(TimerError::TooManyStuck);
-        }
-
-        // Estimate the number of `measure_jitter` rounds necessary for 64 bits
-        // of entropy.
-        //
-        // We don't try very hard to come up with a good estimate of the
-        // available bits of entropy per round here for two reasons:
-        // 1. Simple estimates of the available bits (like Shannon entropy) are
-        //    too optimistic.
-        // 2. Unless we want to waste a lot of time during intialization, there
-        //    only a small number of samples are available.
-        //
-        // Therefore we use a very simple and conservative estimate:
-        // `let bits_of_entropy = log2(delta_average) / 2`.
-        //
-        // The number of rounds `measure_jitter` should run to collect 64 bits
-        // of entropy is `64 / bits_of_entropy`.
-        let delta_average = delta_sum / TESTLOOPCOUNT;
-
-        if delta_average >= 16 {
-            let log2 = 64 - delta_average.leading_zeros();
-            // Do something similar to roundup(64/(log2/2)):
-            Ok( ((64u32 * 2 + log2 - 1) / log2) as u8)
-        } else {
-            // For values < 16 the rounding error becomes too large, use a
-            // lookup table.
-            // Values 0 and 1 are invalid, and filtered out by the
-            // `delta_sum < TESTLOOPCOUNT` test above.
-            let log2_lookup = [0,  0, 128, 81, 64, 56, 50, 46,
-                               43, 41, 39, 38, 36, 35, 34, 33];
-            Ok(log2_lookup[delta_average as usize])
-        }
-    }
-    #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
-    pub fn test_timer(&mut self) -> Result<u8, TimerError> {
-        return Err(TimerError::NoTimer);
-    }
-
-    /// Statistical test: return the timer delta of one normal run of the
-    /// `JitterEntropy` entropy collector.
-    ///
-    /// Setting `var_rounds` to `true` will execute the memory access and the
-    /// CPU jitter noice sources a variable amount of times (just like a real
-    /// `JitterEntropy` round).
-    ///
-    /// Setting `var_rounds` to `false` will execute the noice sources the
-    /// minimal number of times. This can be used to measure the minimum amount
-    /// of entropy one round of entropy collector can collect in the worst case.
-    ///
-    /// # Example
-    ///
-    /// Use `timer_stats` to run the [NIST SP 800-90B Entropy Estimation Suite](
-    /// https://github.com/usnistgov/SP800-90B_EntropyAssessment).
-    ///
-    /// This is the recommended way to test the quality of `JitterRng`. It
-    /// should be run before using the RNG on untested hardware, after changes
-    /// that could effect how the code is optimised, and after major compiler
-    /// compiler changes, like a new LLVM version.
-    ///
-    /// First generate two files `jitter_rng_var.bin` and `jitter_rng_var.min`.
-    ///
-    /// Execute `python noniid_main.py -v jitter_rng_var.bin 8`, and validate it
-    /// with `restart.py -v jitter_rng_var.bin 8 <min-entropy>`.
-    /// This number is the expected amount of entropy that is at least available
-    /// for each round of the entropy collector. This number should be greater
-    /// than the amount estimated with `64 / test_timer()`.
-    ///
-    /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
-    /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
-    /// This number is the expected amount of entropy that is available in the
-    /// last 4 bits of the timer delta after running noice sources. Note that
-    /// a value of 3.70 is the minimum estimated entropy for true randomness.
-    ///
-    /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
-    /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
-    /// This number is the expected amount of entropy that is available to the
-    /// entropy collecter if both noice sources only run their minimal number of
-    /// times. This measures the absolute worst-case, and gives a lower bound
-    /// for the available entropy.
-    ///
-    /// ```rust,no_run
-    /// use rand::jitter::JitterRng;
-    /// #
-    /// # use std::error::Error;
-    /// # use std::fs::File;
-    /// # use std::io::Write;
-    /// #
-    /// # fn try_main() -> Result<(), Box<Error>> {
-    /// let mut rng = JitterRng::new()?;
-    ///
-    /// // 1_000_000 results are required for the NIST SP 800-90B Entropy
-    /// // Estimation Suite
-    /// const ROUNDS: usize = 1_000_000;
-    /// let mut deltas_variable: Vec<u8> = Vec::with_capacity(ROUNDS);
-    /// let mut deltas_minimal: Vec<u8> = Vec::with_capacity(ROUNDS);
-    ///
-    /// for _ in 0..ROUNDS {
-    ///     deltas_variable.push(rng.timer_stats(true) as u8);
-    ///     deltas_minimal.push(rng.timer_stats(false) as u8);
-    /// }
-    ///
-    /// // Write out after the statistics collection loop, to not disturb the
-    /// // test results.
-    /// File::create("jitter_rng_var.bin")?.write(&deltas_variable)?;
-    /// File::create("jitter_rng_min.bin")?.write(&deltas_minimal)?;
-    /// #
-    /// # Ok(())
-    /// # }
-    /// #
-    /// # fn main() {
-    /// #     try_main().unwrap();
-    /// # }
-    /// ```
-    ///
-    #[cfg(feature="std")]
-    pub fn timer_stats(&mut self, var_rounds: bool) -> i64 {
-        let mut mem = [0; MEMORY_SIZE];
-
-        let time = platform::get_nstime();
-        self.memaccess(&mut mem, var_rounds);
-        self.lfsr_time(time, var_rounds);
-        let time2 = platform::get_nstime();
-        time2.wrapping_sub(time) as i64
-    }
-}
-
-#[cfg(feature="std")]
-mod platform {
-    #[cfg(not(any(target_os = "macos", target_os = "ios", target_os = "windows",
-                  all(target_arch = "wasm32", not(target_os = "emscripten")))))]
-    pub fn get_nstime() -> u64 {
-        use std::time::{SystemTime, UNIX_EPOCH};
-
-        let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
-        // The correct way to calculate the current time is
-        // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
-        // But this is faster, and the difference in terms of entropy is
-        // negligible (log2(10^9) == 29.9).
-        dur.as_secs() << 30 | dur.subsec_nanos() as u64
-    }
-
-    #[cfg(any(target_os = "macos", target_os = "ios"))]
-    pub fn get_nstime() -> u64 {
-        extern crate libc;
-        // On Mac OS and iOS std::time::SystemTime only has 1000ns resolution.
-        // We use `mach_absolute_time` instead. This provides a CPU dependent
-        // unit, to get real nanoseconds the result should by multiplied by
-        // numer/denom from `mach_timebase_info`.
-        // But we are not interested in the exact nanoseconds, just entropy. So
-        // we use the raw result.
-        unsafe { libc::mach_absolute_time() }
-    }
-
-    #[cfg(target_os = "windows")]
-    pub fn get_nstime() -> u64 {
-        extern crate winapi;
-        unsafe {
-            let mut t = super::mem::zeroed();
-            winapi::um::profileapi::QueryPerformanceCounter(&mut t);
-            *t.QuadPart() as u64
-        }
-    }
-
-    #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
-    pub fn get_nstime() -> u64 {
-        unreachable!()
-    }
-}
-
-// A function that is opaque to the optimizer to assist in avoiding dead-code
-// elimination. Taken from `bencher`.
-fn black_box<T>(dummy: T) -> T {
-    unsafe {
-        let ret = ptr::read_volatile(&dummy);
-        mem::forget(dummy);
-        ret
-    }
-}
-
-impl RngCore for JitterRng {
-    fn next_u32(&mut self) -> u32 {
-        // We want to use both parts of the generated entropy
-        if self.data_half_used {
-            self.data_half_used = false;
-            (self.data >> 32) as u32
-        } else {
-            self.data = self.next_u64();
-            self.data_half_used = true;
-            self.data as u32
-        }
-    }
-
-    fn next_u64(&mut self) -> u64 {
-       self.data_half_used = false;
-       self.gen_entropy()
-    }
-
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        // Fill using `next_u32`. This is faster for filling small slices (four
-        // bytes or less), while the overhead is negligible.
-        //
-        // This is done especially for wrappers that implement `next_u32`
-        // themselves via `fill_bytes`.
-        impls::fill_bytes_via_next(self, dest)
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        Ok(self.fill_bytes(dest))
-    }
-}
-
-impl CryptoRng for JitterRng {}
-
-#[cfg(test)]
-mod test_jitter_init {
-    use jitter::JitterRng;
-
-    #[cfg(feature="std")]
-    #[test]
-    fn test_jitter_init() {
-        use RngCore;
-        // Because this is a debug build, measurements here are not representive
-        // of the final release build.
-        // Don't fail this test if initializing `JitterRng` fails because of a
-        // bad timer (the timer from the standard library may not have enough
-        // accuracy on all platforms).
-        match JitterRng::new() {
-            Ok(ref mut rng) => {
-                // false positives are possible, but extremely unlikely
-                assert!(rng.next_u32() | rng.next_u32() != 0);
-            },
-            Err(_) => {},
-        }
-    }
-
-    #[test]
-    fn test_jitter_bad_timer() {
-        fn bad_timer() -> u64 { 0 }
-        let mut rng = JitterRng::new_with_timer(bad_timer);
-        assert!(rng.test_timer().is_err());
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/lib.rs b/vendor/rand-8c5b0ac51d/src/lib.rs
deleted file mode 100644
index 5f6fae2..0000000
--- a/vendor/rand-8c5b0ac51d/src/lib.rs
+++ /dev/null
@@ -1,1206 +0,0 @@
-// Copyright 2013-2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Utilities for random number generation
-//! 
-//! ## Example
-//! 
-//! ```rust
-//! // Rng is the main trait and needs to be imported:
-//! use rand::{Rng, thread_rng};
-//!
-//! // thread_rng is often the most convenient source of randomness:
-//! let mut rng = thread_rng();
-//! if rng.gen() { // random bool
-//!     let x: f64 = rng.gen(); // random number in range (0, 1)
-//!     println!("x is: {}", x);
-//!     println!("Number from 0 to 9: {}", rng.gen_range(0, 10));
-//! }
-//! ```
-//!
-//! The key function is [`Rng::gen()`]. It is polymorphic and so can be used to
-//! generate many types; the [`Standard`] distribution carries the
-//! implementations. In some cases type annotation is required, e.g.
-//! `rng.gen::<f64>()`.
-//!
-//! # Getting random values
-//!
-//! The most convenient source of randomness is likely [`thread_rng`], which
-//! automatically initialises a fast algorithmic generator on first use per
-//! thread with thread-local storage.
-//! 
-//! If one wants to obtain random data directly from an external source it is
-//! recommended to use [`EntropyRng`] which manages multiple available sources
-//! or [`OsRng`] which retrieves random data directly from the OS. It should be
-//! noted that this is significantly slower than using a local generator like
-//! [`thread_rng`] and potentially much slower if [`EntropyRng`] must fall back to
-//! [`JitterRng`] as a source.
-//! 
-//! It is also common to use an algorithmic generator in local memory; this may
-//! be faster than `thread_rng` and provides more control. In this case
-//! [`StdRng`] â?? the generator behind [`thread_rng`] â?? and [`SmallRng`] â?? a
-//! small, fast, weak generator â?? are good choices; more options can be found in
-//! the [`prng`] module as well as in other crates.
-//! 
-//! Local generators need to be seeded. It is recommended to use [`FromEntropy`] or
-//! to seed from a strong parent generator with [`from_rng`]:
-//! 
-//! ```
-//! # use rand::{Rng, Error};
-//! // seed with fresh entropy:
-//! use rand::{StdRng, FromEntropy};
-//! let mut rng = StdRng::from_entropy();
-//! # let v: u32 = rng.gen();
-//! 
-//! // seed from thread_rng:
-//! use rand::{SmallRng, SeedableRng, thread_rng};
-//!
-//! # fn try_inner() -> Result<(), Error> {
-//! let mut rng = SmallRng::from_rng(thread_rng())?;
-//! # let v: u32 = rng.gen();
-//! # Ok(())
-//! # }
-//! # try_inner().unwrap()
-//! ```
-//! 
-//! In case you specifically want to have a reproducible stream of "random"
-//! data (e.g. to procedurally generate a game world), select a named algorithm
-//! (i.e. not [`StdRng`]/[`SmallRng`] which may be adjusted in the future), and
-//! use [`SeedableRng::from_seed`] or a constructor specific to the generator
-//! (e.g. [`IsaacRng::new_from_u64`]).
-//! 
-//! ## Applying / converting random data
-//! 
-//! The [`RngCore`] trait allows generators to implement a common interface for
-//! retrieving random data, but how should you use this? Typically users should
-//! use the [`Rng`] trait not [`RngCore`]; this provides more flexible ways to
-//! access the same data (e.g. `gen()` can output many more types than
-//! `next_u32()` and `next_u64()`; Rust's optimiser should eliminate any
-//! overhead). It also provides several useful algorithms,
-//! e.g. `gen_bool(p)` to generate events with weighted probability and
-//! `shuffle(&mut v[..])` to randomly-order a vector.
-//! 
-//! The [`distributions`] module provides several more ways to convert random
-//! data to useful values, e.g. time of decay is often modelled with an
-//! exponential distribution, and the log-normal distribution provides a good
-//! model of many natural phenomona.
-//! 
-//! The [`seq`] module has a few tools applicable to sliceable or iterable data.
-//! 
-//! ## Cryptographic security
-//! 
-//! First, lets recap some terminology:
-//!
-//! - **PRNG:** *Pseudo-Random-Number-Generator* is another name for an
-//!   *algorithmic generator*
-//! - **CSPRNG:** a *Cryptographically Secure* PRNG
-//!
-//! Security analysis requires a threat model and expert review; we can provide
-//! neither, but we can provide a few hints. We assume that the goal is to
-//! produce secret apparently-random data. Therefore, we need:
-//! 
-//! - A good source of entropy. A known algorithm given known input data is
-//!   trivial to predict, and likewise if there's a non-negligable chance that
-//!   the input to a PRNG is guessable then there's a chance its output is too.
-//!   We recommend seeding CSPRNGs with [`EntropyRng`] or [`OsRng`] which
-//!   provide fresh "random" values from an external source.
-//!   One can also seed from another CSPRNG, e.g. `thread_rng`, which is faster,
-//!   but adds another component which must be trusted.
-//! - A strong algorithmic generator. It is possible to use a good entropy
-//!   source like `OsRng` directly, and in some cases this is the best option,
-//!   but for better performance (or if requiring reproducible values generated
-//!   from a fixed seed) it is common to use a local CSPRNG. The basic security
-//!   that CSPRNGs must provide is making it infeasible to predict future output
-//!   given a sample of past output. A further security that *some* CSPRNGs
-//!   provide is *forward secrecy*; this ensures that in the event that the
-//!   algorithm's state is revealed, it is infeasible to reconstruct past
-//!   output. See the [`CryptoRng`] trait and notes on individual algorithms.
-//! - To be careful not to leak secrets like keys and CSPRNG's internal state
-//!   and robust against "side channel attacks". This goes well beyond the scope
-//!   of random number generation, but this crate takes some precautions:
-//!   - to avoid printing CSPRNG state in log files, implementations have a
-//!     custom `Debug` implementation which omits all internal state
-//!   - `thread_rng` uses [`ReseedingRng`] to periodically refresh its state
-//!   - in the future we plan to add some protection against fork attacks
-//!     (where the process is forked and each clone generates the same "random"
-//!     numbers); this is not yet implemented (see issues #314, #370)
-//! 
-//! # Examples
-//!
-//! For some inspiration, see the examples:
-//! 
-//! *   [Monte Carlo estimation of Ï?](
-//!     https://github.com/rust-lang-nursery/rand/blob/master/examples/monte-carlo.rs)
-//! *   [Monty Hall Problem](
-//!     https://github.com/rust-lang-nursery/rand/blob/master/examples/monty-hall.rs)
-//!
-//! [`Rng`]: trait.Rng.html
-//! [`Rng::gen()`]: trait.Rng.html#method.gen
-//! [`RngCore`]: trait.RngCore.html
-//! [`FromEntropy`]: trait.FromEntropy.html
-//! [`SeedableRng::from_seed`]: trait.SeedableRng.html#tymethod.from_seed
-//! [`from_rng`]: trait.SeedableRng.html#method.from_rng
-//! [`CryptoRng`]: trait.CryptoRng.html
-//! [`thread_rng`]: fn.thread_rng.html
-//! [`EntropyRng`]: struct.EntropyRng.html
-//! [`OsRng`]: os/struct.OsRng.html
-//! [`JitterRng`]: jitter/struct.JitterRng.html
-//! [`StdRng`]: struct.StdRng.html
-//! [`SmallRng`]: struct.SmallRng.html
-//! [`ReseedingRng`]: reseeding/struct.ReseedingRng.html
-//! [`prng`]: prng/index.html
-//! [`IsaacRng::new_from_u64`]: prng/isaac/struct.IsaacRng.html#method.new_from_u64
-//! [`Hc128Rng`]: prng/hc128/struct.Hc128Rng.html
-//! [`ChaChaRng`]: prng/chacha/struct.ChaChaRng.html
-//! [`IsaacRng`]: prng/isaac/struct.IsaacRng.html
-//! [`Isaac64Rng`]: prng/isaac64/struct.Isaac64Rng.html
-//! [`seq`]: seq/index.html
-//! [`distributions`]: distributions/index.html
-//! [`Standard`]: distributions/struct.Standard.html
-
-#![doc(html_logo_url = "https://www.rust-lang.org/logos/rust-logo-128x128-blk.png";,
-       html_favicon_url = "https://www.rust-lang.org/favicon.ico";,
-       html_root_url = "https://docs.rs/rand/0.5";)]
-
-#![deny(missing_docs)]
-#![deny(missing_debug_implementations)]
-#![doc(test(attr(allow(unused_variables), deny(warnings))))]
-
-#![cfg_attr(not(feature="std"), no_std)]
-#![cfg_attr(all(feature="alloc", not(feature="std")), feature(alloc))]
-#![cfg_attr(all(feature="i128_support", feature="nightly"), allow(stable_features))] // stable since 2018-03-27
-#![cfg_attr(all(feature="i128_support", feature="nightly"), feature(i128_type, i128))]
-#![cfg_attr(feature = "stdweb", recursion_limit="128")]
-
-#[cfg(feature="std")] extern crate std as core;
-#[cfg(all(feature = "alloc", not(feature="std")))] extern crate alloc;
-
-#[cfg(test)] #[cfg(feature="serde1")] extern crate bincode;
-#[cfg(feature="serde1")] extern crate serde;
-#[cfg(feature="serde1")] #[macro_use] extern crate serde_derive;
-
-#[cfg(all(target_arch = "wasm32", feature = "stdweb"))]
-#[macro_use]
-extern crate stdweb;
-
-extern crate rand_core;
-
-#[cfg(feature = "log")] #[macro_use] extern crate log;
-#[cfg(not(feature = "log"))] macro_rules! trace { ($($x:tt)*) => () }
-#[cfg(not(feature = "log"))] macro_rules! debug { ($($x:tt)*) => () }
-#[cfg(all(feature="std", not(feature = "log")))] macro_rules! info { ($($x:tt)*) => () }
-#[cfg(not(feature = "log"))] macro_rules! warn { ($($x:tt)*) => () }
-#[cfg(all(feature="std", not(feature = "log")))] macro_rules! error { ($($x:tt)*) => () }
-
-
-// Re-exports from rand_core
-pub use rand_core::{RngCore, BlockRngCore, CryptoRng, SeedableRng};
-pub use rand_core::{ErrorKind, Error};
-
-// Public exports
-#[cfg(feature="std")] pub use entropy_rng::EntropyRng;
-#[cfg(feature="std")] pub use os::OsRng;
-pub use reseeding::ReseedingRng;
-#[cfg(feature="std")] pub use thread_rng::{ThreadRng, thread_rng};
-#[cfg(feature="std")] #[allow(deprecated)] pub use thread_rng::random;
-
-// Public modules
-pub mod distributions;
-pub mod jitter; // Public because of the error type.
-pub mod mock;   // Public so we don't export `StepRng` directly, making it a bit
-                // more clear it is intended for testing.
-pub mod prng;
-#[cfg(feature="std")] pub mod read;
-#[cfg(feature = "alloc")] pub mod seq;
-
-// These modules are public to avoid API breakage, probably only temporarily.
-// Hidden in the documentation.
-#[cfg(feature="std")] #[doc(hidden)] pub mod os;
-#[doc(hidden)] pub use prng::{ChaChaRng, IsaacRng, Isaac64Rng, XorShiftRng};
-#[doc(hidden)]
-pub mod chacha {
-    //! The ChaCha random number generator.
-    pub use prng::ChaChaRng;
-}
-#[doc(hidden)]
-pub mod isaac {
-    //! The ISAAC random number generator.
-    pub use prng::{IsaacRng, Isaac64Rng};
-}
-
-// private modules
-#[cfg(feature="std")] mod entropy_rng;
-mod reseeding;
-#[cfg(feature="std")] mod thread_rng;
-
-
-// Normal imports just for this file
-use core::{marker, mem, slice};
-use distributions::{Distribution, Standard, Uniform};
-use distributions::uniform::SampleUniform;
-use prng::hc128::Hc128Rng;
-
-
-/// A type that can be randomly generated using an [`Rng`].
-/// 
-/// This is merely an adaptor around the [`Standard`] distribution for
-/// convenience and backwards-compatibility.
-/// 
-/// [`Rng`]: trait.Rng.html
-/// [`Standard`]: distributions/struct.Standard.html
-#[deprecated(since="0.5.0", note="replaced by distributions::Standard")]
-pub trait Rand : Sized {
-    /// Generates a random instance of this type using the specified source of
-    /// randomness.
-    fn rand<R: Rng>(rng: &mut R) -> Self;
-}
-
-/// An automatically-implemented extension trait on [`RngCore`] providing high-level
-/// generic methods for sampling values and other convenience methods.
-/// 
-/// This is the primary trait to use when generating random values.
-/// 
-/// # Generic usage
-/// 
-/// The basic pattern is `fn foo<R: Rng +Â ?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:
-/// 
-/// ```rust
-/// # use rand::thread_rng;
-/// use rand::Rng;
-/// 
-/// fn foo<R: Rng + ?Sized>(rng: &mut R) -> f32 {
-///     rng.gen()
-/// }
-///
-/// # let v = foo(&mut thread_rng());
-/// ```
-/// 
-/// [`RngCore`]: trait.RngCore.html
-pub trait Rng: RngCore {
-    /// Return a random value supporting the [`Standard`] distribution.
-    ///
-    /// [`Standard`]: distributions/struct.Standard.html
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut rng = thread_rng();
-    /// let x: u32 = rng.gen();
-    /// println!("{}", x);
-    /// println!("{:?}", rng.gen::<(f64, bool)>());
-    /// ```
-    #[inline(always)]
-    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 is a convenience wrapper around
-    /// `distributions::Uniform::sample_single`. If this function will be called
-    /// repeatedly with the same arguments, it will likely be faster to
-    /// construct a `Uniform` distribution object and sample from that; this
-    /// allows amortization of the computations that allow for perfect
-    /// uniformity within the `Uniform::new` constructor.
-    ///
-    /// # Panics
-    ///
-    /// Panics if `low >= high`.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// 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);
-    /// ```
-    fn gen_range<T: PartialOrd + SampleUniform>(&mut self, low: T, high: T) -> T {
-        Uniform::sample_single(low, high, self)
-    }
-
-    /// Sample a new value, using the given distribution.
-    ///
-    /// ### Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    /// use rand::distributions::Uniform;
-    ///
-    /// let mut rng = thread_rng();
-    /// let x: i32 = rng.sample(Uniform::new(10, 15));
-    /// ```
-    fn sample<T, D: Distribution<T>>(&mut self, distr: D) -> T {
-        distr.sample(self)
-    }
-
-    /// Create an iterator that generates values using the given distribution.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    /// use rand::distributions::{Alphanumeric, Uniform, Standard};
-    ///
-    /// let mut rng = thread_rng();
-    ///
-    /// // Vec of 16 x f32:
-    /// let v: Vec<f32> = thread_rng().sample_iter(&Standard).take(16).collect();
-    ///
-    /// // String:
-    /// let s: String = rng.sample_iter(&Alphanumeric).take(7).collect();
-    ///
-    /// // Combined values
-    /// println!("{:?}", thread_rng().sample_iter(&Standard).take(5)
-    ///                              .collect::<Vec<(f64, bool)>>());
-    ///
-    /// // Dice-rolling:
-    /// let die_range = Uniform::new_inclusive(1, 6);
-    /// let mut roll_die = rng.sample_iter(&die_range);
-    /// while roll_die.next().unwrap() != 6 {
-    ///     println!("Not a 6; rolling again!");
-    /// }
-    /// ```
-    fn sample_iter<'a, T, D: Distribution<T>>(&'a mut self, distr: &'a D)
-        -> distributions::DistIter<'a, D, Self, T> where Self: Sized
-    {
-        distr.sample_iter(self)
-    }
-
-    /// Fill `dest` entirely with random bytes (uniform value distribution),
-    /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
-    /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
-    ///
-    /// On big-endian platforms this performs byte-swapping to ensure
-    /// portability of results from reproducible generators.
-    ///
-    /// This uses [`fill_bytes`] internally which may handle some RNG errors
-    /// implicitly (e.g. waiting if the OS generator is not ready), but panics
-    /// on other errors. See also [`try_fill`] which returns errors.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut arr = [0i8; 20];
-    /// thread_rng().fill(&mut arr[..]);
-    /// ```
-    ///
-    /// [`fill_bytes`]: trait.RngCore.html#method.fill_bytes
-    /// [`try_fill`]: trait.Rng.html#method.try_fill
-    /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html
-    fn fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) {
-        self.fill_bytes(dest.as_byte_slice_mut());
-        dest.to_le();
-    }
-
-    /// Fill `dest` entirely with random bytes (uniform value distribution),
-    /// where `dest` is any type supporting [`AsByteSliceMut`], namely slices
-    /// and arrays over primitive integer types (`i8`, `i16`, `u32`, etc.).
-    ///
-    /// On big-endian platforms this performs byte-swapping to ensure
-    /// portability of results from reproducible generators.
-    ///
-    /// This uses [`try_fill_bytes`] internally and forwards all RNG errors. In
-    /// some cases errors may be resolvable; see [`ErrorKind`] and
-    /// documentation for the RNG in use. If you do not plan to handle these
-    /// errors you may prefer to use [`fill`].
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// # use rand::Error;
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// # fn try_inner() -> Result<(), Error> {
-    /// let mut arr = [0u64; 4];
-    /// thread_rng().try_fill(&mut arr[..])?;
-    /// # Ok(())
-    /// # }
-    ///
-    /// # try_inner().unwrap()
-    /// ```
-    ///
-    /// [`ErrorKind`]: enum.ErrorKind.html
-    /// [`try_fill_bytes`]: trait.RngCore.html#method.try_fill_bytes
-    /// [`fill`]: trait.Rng.html#method.fill
-    /// [`AsByteSliceMut`]: trait.AsByteSliceMut.html
-    fn try_fill<T: AsByteSliceMut + ?Sized>(&mut self, dest: &mut T) -> Result<(), Error> {
-        self.try_fill_bytes(dest.as_byte_slice_mut())?;
-        dest.to_le();
-        Ok(())
-    }
-
-    /// Return a bool with a probability `p` of being true.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut rng = thread_rng();
-    /// println!("{}", rng.gen_bool(1.0 / 3.0));
-    /// ```
-    ///
-    /// # Accuracy note
-    ///
-    /// `gen_bool` uses 32 bits of the RNG, so if you use it to generate close
-    /// to or more than `2^32` results, a tiny bias may become noticable.
-    /// A notable consequence of the method used here is that the worst case is
-    /// `rng.gen_bool(0.0)`: it has a chance of 1 in `2^32` of being true, while
-    /// it should always be false. But using `gen_bool` to consume *many* values
-    /// from an RNG just to consistently generate `false` does not match with
-    /// the intent of this method.
-    fn gen_bool(&mut self, p: f64) -> bool {
-        assert!(p >= 0.0 && p <= 1.0);
-        // If `p` is constant, this will be evaluated at compile-time.
-        let p_int = (p * f64::from(core::u32::MAX)) as u32;
-        self.gen::<u32>() <= p_int
-    }
-
-    /// Return a random element from `values`.
-    ///
-    /// Return `None` if `values` is empty.
-    ///
-    /// # Example
-    ///
-    /// ```
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let choices = [1, 2, 4, 8, 16, 32];
-    /// let mut rng = thread_rng();
-    /// println!("{:?}", rng.choose(&choices));
-    /// assert_eq!(rng.choose(&choices[..0]), None);
-    /// ```
-    fn choose<'a, T>(&mut self, values: &'a [T]) -> Option<&'a T> {
-        if values.is_empty() {
-            None
-        } else {
-            Some(&values[self.gen_range(0, values.len())])
-        }
-    }
-
-    /// Return a mutable pointer to a random element from `values`.
-    ///
-    /// Return `None` if `values` is empty.
-    fn choose_mut<'a, T>(&mut self, values: &'a mut [T]) -> Option<&'a mut T> {
-        if values.is_empty() {
-            None
-        } else {
-            let len = values.len();
-            Some(&mut values[self.gen_range(0, len)])
-        }
-    }
-
-    /// Shuffle a mutable slice in place.
-    ///
-    /// This applies Durstenfeld's algorithm for the [Fisherâ??Yates shuffle](
-    /// https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm)
-    /// which produces an unbiased permutation.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut rng = thread_rng();
-    /// let mut y = [1, 2, 3];
-    /// rng.shuffle(&mut y);
-    /// println!("{:?}", y);
-    /// rng.shuffle(&mut y);
-    /// println!("{:?}", y);
-    /// ```
-    fn shuffle<T>(&mut self, values: &mut [T]) {
-        let mut i = values.len();
-        while i >= 2 {
-            // invariant: elements with index >= i have been locked in place.
-            i -= 1;
-            // lock element i in place.
-            values.swap(i, self.gen_range(0, i + 1));
-        }
-    }
-
-    /// Return an iterator that will yield an infinite number of randomly
-    /// generated items.
-    ///
-    /// # Example
-    ///
-    /// ```
-    /// # #![allow(deprecated)]
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut rng = thread_rng();
-    /// let x = rng.gen_iter::<u32>().take(10).collect::<Vec<u32>>();
-    /// println!("{:?}", x);
-    /// println!("{:?}", rng.gen_iter::<(f64, bool)>().take(5)
-    ///                     .collect::<Vec<(f64, bool)>>());
-    /// ```
-    #[allow(deprecated)]
-    #[deprecated(since="0.5.0", note="use Rng::sample_iter(&Standard) instead")]
-    fn gen_iter<T>(&mut self) -> Generator<T, &mut Self> where Standard: Distribution<T> {
-        Generator { rng: self, _marker: marker::PhantomData }
-    }
-
-    /// Return a bool with a 1 in n chance of true
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// # #![allow(deprecated)]
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let mut rng = thread_rng();
-    /// assert_eq!(rng.gen_weighted_bool(0), true);
-    /// assert_eq!(rng.gen_weighted_bool(1), true);
-    /// // Just like `rng.gen::<bool>()` a 50-50% chance, but using a slower
-    /// // method with different results.
-    /// println!("{}", rng.gen_weighted_bool(2));
-    /// // First meaningful use of `gen_weighted_bool`.
-    /// println!("{}", rng.gen_weighted_bool(3));
-    /// ```
-    #[deprecated(since="0.5.0", note="use gen_bool instead")]
-    fn gen_weighted_bool(&mut self, n: u32) -> bool {
-        // Short-circuit after `n <= 1` to avoid panic in `gen_range`
-        n <= 1 || self.gen_range(0, n) == 0
-    }
-
-    /// Return an iterator of random characters from the set A-Z,a-z,0-9.
-    ///
-    /// # Example
-    ///
-    /// ```rust
-    /// # #![allow(deprecated)]
-    /// use rand::{thread_rng, Rng};
-    ///
-    /// let s: String = thread_rng().gen_ascii_chars().take(10).collect();
-    /// println!("{}", s);
-    /// ```
-    #[allow(deprecated)]
-    #[deprecated(since="0.5.0", note="use sample_iter(&Alphanumeric) instead")]
-    fn gen_ascii_chars(&mut self) -> AsciiGenerator<&mut Self> {
-        AsciiGenerator { rng: self }
-    }
-}
-
-impl<R: RngCore + ?Sized> Rng for R {}
-
-/// Trait for casting types to byte slices
-/// 
-/// This is used by the [`fill`] and [`try_fill`] methods.
-/// 
-/// [`fill`]: trait.Rng.html#method.fill
-/// [`try_fill`]: trait.Rng.html#method.try_fill
-pub trait AsByteSliceMut {
-    /// Return a mutable reference to self as a byte slice
-    fn as_byte_slice_mut(&mut self) -> &mut [u8];
-    
-    /// Call `to_le` on each element (i.e. byte-swap on Big Endian platforms).
-    fn to_le(&mut self);
-}
-
-impl AsByteSliceMut for [u8] {
-    fn as_byte_slice_mut(&mut self) -> &mut [u8] {
-        self
-    }
-    
-    fn to_le(&mut self) {}
-}
-
-macro_rules! impl_as_byte_slice {
-    ($t:ty) => {
-        impl AsByteSliceMut for [$t] {
-            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
-                unsafe {
-                    slice::from_raw_parts_mut(&mut self[0]
-                        as *mut $t
-                        as *mut u8,
-                        self.len() * mem::size_of::<$t>()
-                    )
-                }
-            }
-            
-            fn to_le(&mut self) {
-                for x in self {
-                    *x = x.to_le();
-                }
-            }
-        }
-    }
-}
-
-impl_as_byte_slice!(u16);
-impl_as_byte_slice!(u32);
-impl_as_byte_slice!(u64);
-#[cfg(feature="i128_support")] impl_as_byte_slice!(u128);
-impl_as_byte_slice!(usize);
-impl_as_byte_slice!(i8);
-impl_as_byte_slice!(i16);
-impl_as_byte_slice!(i32);
-impl_as_byte_slice!(i64);
-#[cfg(feature="i128_support")] impl_as_byte_slice!(i128);
-impl_as_byte_slice!(isize);
-
-macro_rules! impl_as_byte_slice_arrays {
-    ($n:expr,) => {};
-    ($n:expr, $N:ident, $($NN:ident,)*) => {
-        impl_as_byte_slice_arrays!($n - 1, $($NN,)*);
-        
-        impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
-            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
-                self[..].as_byte_slice_mut()
-            }
-
-            fn to_le(&mut self) {
-                self[..].to_le()
-            }
-        }
-    };
-    (!div $n:expr,) => {};
-    (!div $n:expr, $N:ident, $($NN:ident,)*) => {
-        impl_as_byte_slice_arrays!(!div $n / 2, $($NN,)*);
-
-        impl<T> AsByteSliceMut for [T; $n] where [T]: AsByteSliceMut {
-            fn as_byte_slice_mut(&mut self) -> &mut [u8] {
-                self[..].as_byte_slice_mut()
-            }
-            
-            fn to_le(&mut self) {
-                self[..].to_le()
-            }
-        }
-    };
-}
-impl_as_byte_slice_arrays!(32, N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,);
-impl_as_byte_slice_arrays!(!div 4096, N,N,N,N,N,N,N,);
-
-/// Iterator which will generate a stream of random items.
-///
-/// This iterator is created via the [`gen_iter`] method on [`Rng`].
-///
-/// [`gen_iter`]: trait.Rng.html#method.gen_iter
-/// [`Rng`]: trait.Rng.html
-#[derive(Debug)]
-#[allow(deprecated)]
-#[deprecated(since="0.5.0", note="use Rng::sample_iter instead")]
-pub struct Generator<T, R: RngCore> {
-    rng: R,
-    _marker: marker::PhantomData<fn() -> T>,
-}
-
-#[allow(deprecated)]
-impl<T, R: RngCore> Iterator for Generator<T, R> where Standard: Distribution<T> {
-    type Item = T;
-
-    fn next(&mut self) -> Option<T> {
-        Some(self.rng.gen())
-    }
-}
-
-/// Iterator which will continuously generate random ascii characters.
-///
-/// This iterator is created via the [`gen_ascii_chars`] method on [`Rng`].
-///
-/// [`gen_ascii_chars`]: trait.Rng.html#method.gen_ascii_chars
-/// [`Rng`]: trait.Rng.html
-#[derive(Debug)]
-#[allow(deprecated)]
-#[deprecated(since="0.5.0", note="use distributions::Alphanumeric instead")]
-pub struct AsciiGenerator<R: RngCore> {
-    rng: R,
-}
-
-#[allow(deprecated)]
-impl<R: RngCore> Iterator for AsciiGenerator<R> {
-    type Item = char;
-
-    fn next(&mut self) -> Option<char> {
-        const GEN_ASCII_STR_CHARSET: &[u8] =
-            b"ABCDEFGHIJKLMNOPQRSTUVWXYZ\
-              abcdefghijklmnopqrstuvwxyz\
-              0123456789";
-        Some(*self.rng.choose(GEN_ASCII_STR_CHARSET).unwrap() as char)
-    }
-}
-
-
-/// A convenience extension to [`SeedableRng`] allowing construction from fresh
-/// entropy. This trait is automatically implemented for any PRNG implementing
-/// [`SeedableRng`] and is not intended to be implemented by users.
-///
-/// This is equivalent to using `SeedableRng::from_rng(EntropyRng::new())` then
-/// unwrapping the result.
-///
-/// Since this is convenient and secure, it is the recommended way to create
-/// PRNGs, though two alternatives may be considered:
-///
-/// *   Deterministic creation using [`SeedableRng::from_seed`] with a fixed seed
-/// *   Seeding from `thread_rng`: `SeedableRng::from_rng(thread_rng())?`;
-///     this will usually be faster and should also be secure, but requires
-///     trusting one extra component.
-///
-/// ## Example
-///
-/// ```
-/// use rand::{StdRng, Rng, FromEntropy};
-///
-/// let mut rng = StdRng::from_entropy();
-/// println!("Random die roll: {}", rng.gen_range(1, 7));
-/// ```
-///
-/// [`EntropyRng`]: struct.EntropyRng.html
-/// [`SeedableRng`]: trait.SeedableRng.html
-/// [`SeedableRng::from_seed`]: trait.SeedableRng.html#tymethod.from_seed
-#[cfg(feature="std")]
-pub trait FromEntropy: SeedableRng {
-    /// Creates a new instance, automatically seeded with fresh entropy.
-    ///
-    /// Normally this will use `OsRng`, but if that fails `JitterRng` will be
-    /// used instead. Both should be suitable for cryptography. It is possible
-    /// that both entropy sources will fail though unlikely; failures would
-    /// almost certainly be platform limitations or build issues, i.e. most
-    /// applications targetting PC/mobile platforms should not need to worry
-    /// about this failing.
-    ///
-    /// If all entropy sources fail this will panic. If you need to handle
-    /// errors, use the following code, equivalent aside from error handling:
-    ///
-    /// ```rust
-    /// # use rand::Error;
-    /// use rand::{Rng, StdRng, EntropyRng, SeedableRng};
-    ///
-    /// # fn try_inner() -> Result<(), Error> {
-    /// // This uses StdRng, but is valid for any R: SeedableRng
-    /// let mut rng = StdRng::from_rng(EntropyRng::new())?;
-    ///
-    /// println!("random number: {}", rng.gen_range(1, 10));
-    /// # Ok(())
-    /// # }
-    ///
-    /// # try_inner().unwrap()
-    /// ```
-    fn from_entropy() -> Self;
-}
-
-#[cfg(feature="std")]
-impl<R: SeedableRng> FromEntropy for R {
-    fn from_entropy() -> R {
-        R::from_rng(EntropyRng::new()).unwrap_or_else(|err|
-            panic!("FromEntropy::from_entropy() failed: {}", err))
-    }
-}
-
-/// 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 on all platforms is [HC-128].
-///
-/// Reproducibility of output from this generator is however not required, thus
-/// future library versions may use a different internal generator with
-/// different output. Further, this generator may not be portable and can
-/// produce different output depending on the architecture. If you require
-/// reproducible output, use a named RNG, for example [`ChaChaRng`].
-///
-/// [HC-128]: prng/hc128/struct.Hc128Rng.html
-/// [`ChaChaRng`]: prng/chacha/struct.ChaChaRng.html
-#[derive(Clone, Debug)]
-pub struct StdRng(Hc128Rng);
-
-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()
-    }
-
-    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 SeedableRng for StdRng {
-    type Seed = <Hc128Rng as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        StdRng(Hc128Rng::from_seed(seed))
-    }
-
-    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
-        Hc128Rng::from_rng(rng).map(StdRng)
-    }
-}
-
-impl CryptoRng for StdRng {}
-
-/// An RNG recommended when small state, cheap initialization and good
-/// performance are required. 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 for [`StdRng`].
-///
-/// Reproducibility of output from this generator is however not required, thus
-/// future library versions may use a different internal generator with
-/// different output. Further, this generator may not be portable and can
-/// produce different output depending on the architecture. If you require
-/// reproducible output, use a named RNG, for example [`XorShiftRng`].
-///
-/// The current algorithm used on all platforms is [Xorshift].
-///
-/// # Examples
-///
-/// Initializing `SmallRng` with a random seed can be done using [`FromEntropy`]:
-///
-/// ```
-/// # use rand::Rng;
-/// use rand::{FromEntropy, 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 std::iter;
-/// use rand::{SeedableRng, SmallRng, thread_rng};
-///
-/// // 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> = iter::repeat(())
-///     .map(|()| SmallRng::from_rng(&mut thread_rng).unwrap())
-///     .take(10)
-///     .collect();
-/// ```
-///
-/// [`FromEntropy`]: trait.FromEntropy.html
-/// [`StdRng`]: struct.StdRng.html
-/// [`thread_rng`]: fn.thread_rng.html
-/// [Xorshift]: prng/struct.XorShiftRng.html
-/// [`XorShiftRng`]: prng/struct.XorShiftRng.html
-#[derive(Clone, Debug)]
-pub struct SmallRng(XorShiftRng);
-
-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()
-    }
-
-    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 SeedableRng for SmallRng {
-    type Seed = <XorShiftRng as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        SmallRng(XorShiftRng::from_seed(seed))
-    }
-
-    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
-        XorShiftRng::from_rng(rng).map(SmallRng)
-    }
-}
-
-/// DEPRECATED: use [`SmallRng`] instead.
-///
-/// Create a weak random number generator with a default algorithm and seed.
-///
-/// It returns the fastest `Rng` algorithm currently available in Rust without
-/// consideration for cryptography or security. If you require a specifically
-/// seeded `Rng` for consistency over time you should pick one algorithm and
-/// create the `Rng` yourself.
-///
-/// This will seed the generator with randomness from `thread_rng`.
-///
-/// [`SmallRng`]: struct.SmallRng.html
-#[deprecated(since="0.5.0", note="removed in favor of SmallRng")]
-#[cfg(feature="std")]
-pub fn weak_rng() -> XorShiftRng {
-    XorShiftRng::from_rng(thread_rng()).unwrap_or_else(|err|
-        panic!("weak_rng failed: {:?}", err))
-}
-
-/// DEPRECATED: use `seq::sample_iter` instead.
-///
-/// Randomly sample up to `amount` elements from a finite iterator.
-/// The order of elements in the sample is not random.
-///
-/// # Example
-///
-/// ```rust
-/// # #![allow(deprecated)]
-/// use rand::{thread_rng, sample};
-///
-/// let mut rng = thread_rng();
-/// let sample = sample(&mut rng, 1..100, 5);
-/// println!("{:?}", sample);
-/// ```
-#[cfg(feature="std")]
-#[inline(always)]
-#[deprecated(since="0.4.0", note="renamed to seq::sample_iter")]
-pub fn sample<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Vec<T>
-    where I: IntoIterator<Item=T>,
-          R: Rng,
-{
-    // the legacy sample didn't care whether amount was met
-    seq::sample_iter(rng, iterable, amount)
-        .unwrap_or_else(|e| e)
-}
-
-#[cfg(test)]
-mod test {
-    use mock::StepRng;
-    use super::*;
-    #[cfg(all(not(feature="std"), feature="alloc"))] use alloc::boxed::Box;
-
-    pub struct TestRng<R> { inner: R }
-
-    impl<R: RngCore> RngCore for TestRng<R> {
-        fn next_u32(&mut self) -> u32 {
-            self.inner.next_u32()
-        }
-        fn next_u64(&mut self) -> u64 {
-            self.inner.next_u64()
-        }
-        fn fill_bytes(&mut self, dest: &mut [u8]) {
-            self.inner.fill_bytes(dest)
-        }
-        fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            self.inner.try_fill_bytes(dest)
-        }
-    }
-
-    pub fn rng(seed: u64) -> TestRng<StdRng> {
-        // TODO: use from_hashable
-        let mut state = seed;
-        let mut seed = <StdRng as SeedableRng>::Seed::default();
-        for x in seed.iter_mut() {
-            // PCG algorithm
-            const MUL: u64 = 6364136223846793005;
-            const INC: u64 = 11634580027462260723;
-            let oldstate = state;
-            state = oldstate.wrapping_mul(MUL).wrapping_add(INC);
-
-            let xorshifted = (((oldstate >> 18) ^ oldstate) >> 27) as u32;
-            let rot = (oldstate >> 59) as u32;
-            *x = xorshifted.rotate_right(rot) as u8;
-        }
-        TestRng { inner: StdRng::from_seed(seed) }
-    }
-
-    #[test]
-    fn test_fill_bytes_default() {
-        let mut r = StepRng::new(0x11_22_33_44_55_66_77_88, 0);
-
-        // check every remainder mod 8, both in small and big vectors.
-        let lengths = [0, 1, 2, 3, 4, 5, 6, 7,
-                       80, 81, 82, 83, 84, 85, 86, 87];
-        for &n in lengths.iter() {
-            let mut buffer = [0u8; 87];
-            let v = &mut buffer[0..n];
-            r.fill_bytes(v);
-
-            // use this to get nicer error messages.
-            for (i, &byte) in v.iter().enumerate() {
-                if byte == 0 {
-                    panic!("byte {} of {} is zero", i, n)
-                }
-            }
-        }
-    }
-    
-    #[test]
-    fn test_fill() {
-        let x = 9041086907909331047;    // a random u64
-        let mut rng = StepRng::new(x, 0);
-        
-        // Convert to byte sequence and back to u64; byte-swap twice if BE.
-        let mut array = [0u64; 2];
-        rng.fill(&mut array[..]);
-        assert_eq!(array, [x, x]);
-        assert_eq!(rng.next_u64(), x);
-        
-        // Convert to bytes then u32 in LE order
-        let mut array = [0u32; 2];
-        rng.fill(&mut array[..]);
-        assert_eq!(array, [x as u32, (x >> 32) as u32]);
-        assert_eq!(rng.next_u32(), x as u32);
-    }
-
-    #[test]
-    fn test_gen_range() {
-        let mut r = rng(101);
-        for _ in 0..1000 {
-            let a = r.gen_range(-3, 42);
-            assert!(a >= -3 && a < 42);
-            assert_eq!(r.gen_range(0, 1), 0);
-            assert_eq!(r.gen_range(-12, -11), -12);
-        }
-
-        for _ in 0..1000 {
-            let a = r.gen_range(10, 42);
-            assert!(a >= 10 && a < 42);
-            assert_eq!(r.gen_range(0, 1), 0);
-            assert_eq!(r.gen_range(3_000_000, 3_000_001), 3_000_000);
-        }
-
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_gen_range_panic_int() {
-        let mut r = rng(102);
-        r.gen_range(5, -2);
-    }
-
-    #[test]
-    #[should_panic]
-    fn test_gen_range_panic_usize() {
-        let mut r = rng(103);
-        r.gen_range(5, 2);
-    }
-
-    #[test]
-    #[allow(deprecated)]
-    fn test_gen_weighted_bool() {
-        let mut r = rng(104);
-        assert_eq!(r.gen_weighted_bool(0), true);
-        assert_eq!(r.gen_weighted_bool(1), true);
-    }
-
-    #[test]
-    fn test_gen_bool() {
-        let mut r = rng(105);
-        for _ in 0..5 {
-            assert_eq!(r.gen_bool(0.0), false);
-            assert_eq!(r.gen_bool(1.0), true);
-        }
-    }
-
-    #[test]
-    fn test_choose() {
-        let mut r = rng(107);
-        assert_eq!(r.choose(&[1, 1, 1]).map(|&x|x), Some(1));
-
-        let v: &[isize] = &[];
-        assert_eq!(r.choose(v), None);
-    }
-
-    #[test]
-    fn test_shuffle() {
-        let mut r = rng(108);
-        let empty: &mut [isize] = &mut [];
-        r.shuffle(empty);
-        let mut one = [1];
-        r.shuffle(&mut one);
-        let b: &[_] = &[1];
-        assert_eq!(one, b);
-
-        let mut two = [1, 2];
-        r.shuffle(&mut two);
-        assert!(two == [1, 2] || two == [2, 1]);
-
-        let mut x = [1, 1, 1];
-        r.shuffle(&mut x);
-        let b: &[_] = &[1, 1, 1];
-        assert_eq!(x, b);
-    }
-
-    #[test]
-    fn test_rng_trait_object() {
-        use distributions::{Distribution, Standard};
-        let mut rng = rng(109);
-        let mut r = &mut rng as &mut RngCore;
-        r.next_u32();
-        r.gen::<i32>();
-        let mut v = [1, 1, 1];
-        r.shuffle(&mut v);
-        let b: &[_] = &[1, 1, 1];
-        assert_eq!(v, b);
-        assert_eq!(r.gen_range(0, 1), 0);
-        let _c: u8 = Standard.sample(&mut r);
-    }
-
-    #[test]
-    #[cfg(feature="alloc")]
-    fn test_rng_boxed_trait() {
-        use distributions::{Distribution, Standard};
-        let rng = rng(110);
-        let mut r = Box::new(rng) as Box<RngCore>;
-        r.next_u32();
-        r.gen::<i32>();
-        let mut v = [1, 1, 1];
-        r.shuffle(&mut v);
-        let b: &[_] = &[1, 1, 1];
-        assert_eq!(v, b);
-        assert_eq!(r.gen_range(0, 1), 0);
-        let _c: u8 = Standard.sample(&mut r);
-    }
-
-    #[test]
-    fn test_stdrng_construction() {
-        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];
-        let mut rng1 = StdRng::from_seed(seed);
-        assert_eq!(rng1.next_u64(), 15759097995037006553);
-
-        let mut rng2 = StdRng::from_rng(rng1).unwrap();
-        assert_eq!(rng2.next_u64(), 6766915756997287454);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/mock.rs b/vendor/rand-8c5b0ac51d/src/mock.rs
deleted file mode 100644
index 090258e..0000000
--- a/vendor/rand-8c5b0ac51d/src/mock.rs
+++ /dev/null
@@ -1,61 +0,0 @@
-// Copyright 2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Mock random number generator
-
-use rand_core::{RngCore, Error, impls};
-
-/// 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.
-/// 
-/// ```rust
-/// use rand::Rng;
-/// use rand::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 {
-    fn next_u32(&mut self) -> u32 {
-        self.next_u64() as u32
-    }
-
-    fn next_u64(&mut self) -> u64 {
-        let result = self.v;
-        self.v = self.v.wrapping_add(self.a);
-        result
-    }
-
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        impls::fill_bytes_via_next(self, dest);
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        Ok(self.fill_bytes(dest))
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/os.rs b/vendor/rand-8c5b0ac51d/src/os.rs
deleted file mode 100644
index ef96e31..0000000
--- a/vendor/rand-8c5b0ac51d/src/os.rs
+++ /dev/null
@@ -1,833 +0,0 @@
-// Copyright 2013-2015 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Interfaces to the operating system provided random number
-//! generators.
-
-use std::fmt;
-use rand_core::{RngCore, Error, impls};
-
-/// A random number generator that retrieves randomness straight from the
-/// operating system.
-///
-/// This is the preferred external source of entropy for most
-/// applications. Commonly it is used to initialize a user-space RNG, which can
-/// then be used to generate random values with much less overhead than `OsRng`.
-///
-/// You may prefer to use [`EntropyRng`] instead of `OsRng`. Is is unlikely, but
-/// not entirely theoretical, for `OsRng` to fail. In such cases `EntropyRng`
-/// falls back on a good alternative entropy source.
-///
-/// `OsRng` usually does not block. On some systems, and notably virtual
-/// machines, it may block very early in the init process, when the OS CSPRNG
-/// has not yet been seeded.
-///
-/// `OsRng::new()` is guaranteed to be very cheap (after the first successful
-/// call), and will never consume more than one file handle per process.
-///
-/// ## Platform sources:
-///
-/// - Linux, Android: reads from the `getrandom(2)` system call if available,
-///   otherwise from `/dev/urandom`.
-/// - macOS, iOS: calls `SecRandomCopyBytes`.
-/// - Windows: calls `RtlGenRandom`.
-/// - WASM: calls `window.crypto.getRandomValues` in browsers,
-///   and in Node.js `require("crypto").randomBytes`.
-/// - OpenBSD: calls `getentropy(2)`.
-/// - FreeBSD: uses the `kern.arandom` `sysctl(2)` mib.
-/// - Fuchsia: calls `cprng_draw`.
-/// - Redox: reads from `rand:` device.
-/// - CloudABI: calls `random_get`.
-/// - Other Unix-like systems: reads directly from `/dev/urandom`.
-///   Note: many Unix systems provide `/dev/random` as well as `/dev/urandom`.
-///   On all modern systems these two interfaces offer identical quality, with
-///   the difference that on some systems `/dev/random` may block. This is a
-///   dated design, and `/dev/urandom` is preferred by cryptography experts. [1]
-///
-/// [1] See [Myths about urandom](https://www.2uo.de/myths-about-urandom/).
-///
-/// [`EntropyRng`]: struct.EntropyRng.html
-
-#[allow(unused)]    // not used by all targets
-#[derive(Clone)]
-pub struct OsRng(imp::OsRng);
-
-impl fmt::Debug for OsRng {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        self.0.fmt(f)
-    }
-}
-
-impl OsRng {
-    /// Create a new `OsRng`.
-    pub fn new() -> Result<OsRng, Error> {
-        imp::OsRng::new().map(OsRng)
-    }
-}
-
-impl RngCore for OsRng {
-    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]) {
-        use std::{time, thread};
-
-        // We cannot return Err(..), so we try to handle before panicking.
-        const MAX_RETRY_PERIOD: u32 = 10; // max 10s
-        const WAIT_DUR_MS: u32 = 100; // retry every 100ms
-        let wait_dur = time::Duration::from_millis(WAIT_DUR_MS as u64);
-        const RETRY_LIMIT: u32 = (MAX_RETRY_PERIOD * 1000) / WAIT_DUR_MS;
-        const TRANSIENT_RETRIES: u32 = 8;
-        let mut err_count = 0;
-        let mut error_logged = false;
-
-        loop {
-            if let Err(e) = self.try_fill_bytes(dest) {
-                if err_count >= RETRY_LIMIT {
-                    error!("OsRng failed too many times; last error: {}", e);
-                    panic!("OsRng failed too many times; last error: {}", e);
-                }
-
-                if e.kind.should_wait() {
-                    if !error_logged {
-                        warn!("OsRng failed; waiting up to {}s and retrying. Error: {}",
-                                MAX_RETRY_PERIOD, e);
-                        error_logged = true;
-                    }
-                    err_count += 1;
-                    thread::sleep(wait_dur);
-                    continue;
-                } else if e.kind.should_retry() {
-                    if !error_logged {
-                        warn!("OsRng failed; retrying up to {} times. Error: {}",
-                                TRANSIENT_RETRIES, e);
-                        error_logged = true;
-                    }
-                    err_count += (RETRY_LIMIT + TRANSIENT_RETRIES - 1)
-                            / TRANSIENT_RETRIES;    // round up
-                    continue;
-                } else {
-                    error!("OsRng failed: {}", e);
-                    panic!("OsRng fatal error: {}", e);
-                }
-            }
-
-            break;
-        }
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        self.0.try_fill_bytes(dest)
-    }
-}
-
-#[cfg(all(unix,
-          not(target_os = "cloudabi"),
-          not(target_os = "freebsd"),
-          not(target_os = "fuchsia"),
-          not(target_os = "ios"),
-          not(target_os = "macos"),
-          not(target_os = "openbsd"),
-          not(target_os = "redox")))]
-mod imp {
-    extern crate libc;
-    use {Error, ErrorKind};
-    use std::fs::{OpenOptions, File};
-    use std::os::unix::fs::OpenOptionsExt;
-    use std::io;
-    use std::io::Read;
-    use std::sync::{Once, Mutex, ONCE_INIT};
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng(OsRngMethod);
-
-    #[derive(Clone, Debug)]
-    enum OsRngMethod {
-        GetRandom,
-        RandomDevice,
-    }
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            if is_getrandom_available() {
-                return Ok(OsRng(OsRngMethod::GetRandom));
-            }
-
-            open_dev_random()?;
-            Ok(OsRng(OsRngMethod::RandomDevice))
-        }
-
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            match self.0 {
-                OsRngMethod::GetRandom => getrandom_try_fill(dest),
-                OsRngMethod::RandomDevice => dev_random_try_fill(dest),
-            }
-        }
-    }
-
-    #[cfg(all(any(target_os = "linux", target_os = "android"),
-              any(target_arch = "x86_64", target_arch = "x86",
-                  target_arch = "arm", target_arch = "aarch64",
-                  target_arch = "s390x", target_arch = "powerpc",
-                  target_arch = "mips", target_arch = "mips64")))]
-    fn getrandom(buf: &mut [u8]) -> libc::c_long {
-        extern "C" {
-            fn syscall(number: libc::c_long, ...) -> libc::c_long;
-        }
-
-        #[cfg(target_arch = "x86_64")]
-        const NR_GETRANDOM: libc::c_long = 318;
-        #[cfg(target_arch = "x86")]
-        const NR_GETRANDOM: libc::c_long = 355;
-        #[cfg(target_arch = "arm")]
-        const NR_GETRANDOM: libc::c_long = 384;
-        #[cfg(target_arch = "aarch64")]
-        const NR_GETRANDOM: libc::c_long = 278;
-         #[cfg(target_arch = "s390x")]
-        const NR_GETRANDOM: libc::c_long = 349;
-        #[cfg(target_arch = "powerpc")]
-        const NR_GETRANDOM: libc::c_long = 359;
-        #[cfg(target_arch = "mips")] // old ABI
-        const NR_GETRANDOM: libc::c_long = 4353;
-        #[cfg(target_arch = "mips64")]
-        const NR_GETRANDOM: libc::c_long = 5313;
-
-        const GRND_NONBLOCK: libc::c_uint = 0x0001;
-
-        unsafe {
-            syscall(NR_GETRANDOM, buf.as_mut_ptr(), buf.len(), GRND_NONBLOCK)
-        }
-    }
-
-    #[cfg(not(all(any(target_os = "linux", target_os = "android"),
-                  any(target_arch = "x86_64", target_arch = "x86",
-                      target_arch = "arm", target_arch = "aarch64",
-                      target_arch = "s390x", target_arch = "powerpc",
-                      target_arch = "mips", target_arch = "mips64"))))]
-    fn getrandom(_buf: &mut [u8]) -> libc::c_long { -1 }
-
-    fn getrandom_try_fill(dest: &mut [u8]) -> Result<(), Error> {
-        trace!("OsRng: reading {} bytes via getrandom", dest.len());
-        let mut read = 0;
-        let len = dest.len();
-        while read < len {
-            let result = getrandom(&mut dest[read..]);
-            if result == -1 {
-                let err = io::Error::last_os_error();
-                let kind = err.kind();
-                if kind == io::ErrorKind::Interrupted {
-                    continue;
-                } else if kind == io::ErrorKind::WouldBlock {
-                    // Potentially this would waste bytes, but since we use
-                    // /dev/urandom blocking only happens if not initialised.
-                    // Also, wasting the bytes in dest doesn't matter very much.
-                    return Err(Error::with_cause(
-                        ErrorKind::NotReady,
-                        "getrandom not ready",
-                        err,
-                    ));
-                } else {
-                    return Err(Error::with_cause(
-                        ErrorKind::Unavailable,
-                        "unexpected getrandom error",
-                        err,
-                    ));
-                }
-            } else {
-                read += result as usize;
-            }
-        }
-        Ok(())
-    }
-
-    #[cfg(all(any(target_os = "linux", target_os = "android"),
-              any(target_arch = "x86_64", target_arch = "x86",
-                  target_arch = "arm", target_arch = "aarch64",
-                  target_arch = "s390x", target_arch = "powerpc",
-                  target_arch = "mips", target_arch = "mips64")))]
-    fn is_getrandom_available() -> bool {
-        use std::sync::atomic::{AtomicBool, ATOMIC_BOOL_INIT, Ordering};
-        use std::sync::{Once, ONCE_INIT};
-
-        static CHECKER: Once = ONCE_INIT;
-        static AVAILABLE: AtomicBool = ATOMIC_BOOL_INIT;
-
-        CHECKER.call_once(|| {
-            debug!("OsRng: testing getrandom");
-            let mut buf: [u8; 0] = [];
-            let result = getrandom(&mut buf);
-            let available = if result == -1 {
-                let err = io::Error::last_os_error().raw_os_error();
-                err != Some(libc::ENOSYS)
-            } else {
-                true
-            };
-            AVAILABLE.store(available, Ordering::Relaxed);
-            info!("OsRng: using {}", if available { "getrandom" } else { "/dev/urandom" });
-        });
-
-        AVAILABLE.load(Ordering::Relaxed)
-    }
-
-    #[cfg(not(all(any(target_os = "linux", target_os = "android"),
-                  any(target_arch = "x86_64", target_arch = "x86",
-                      target_arch = "arm", target_arch = "aarch64",
-                      target_arch = "s390x", target_arch = "powerpc",
-                      target_arch = "mips", target_arch = "mips64"))))]
-    fn is_getrandom_available() -> bool { false }
-
-    // TODO: remove outer Option when `Mutex::new(None)` is a constant expression
-    static mut READ_RNG_FILE: Option<Mutex<Option<File>>> = None;
-    static READ_RNG_ONCE: Once = ONCE_INIT;
-
-    // Note: all instances use a single internal file handle, to prevent
-    // possible exhaustion of file descriptors.
-    //
-    // On some systems reading from `/dev/urandom` "may return data prior to the
-    // to the entropy pool being initialized". I.e., early in the boot process,
-    // and especially on virtual machines, `/dev/urandom` may return data that
-    // is less random.
-    //
-    // As a countermeasure we try to do a single read from `/dev/random` in
-    // non-blocking mode. If the OS RNG is not yet properly seeded, we will get
-    // an error. Because we keep `/dev/urandom` open when succesful, this is
-    // only a small one-time cost.
-    fn open_dev_random() -> Result<(), Error> {
-        fn map_err(err: io::Error) -> Error {
-            match err.kind() {
-                io::ErrorKind::Interrupted =>
-                        Error::new(ErrorKind::Transient, "interrupted"),
-                io::ErrorKind::WouldBlock =>
-                        Error::with_cause(ErrorKind::NotReady,
-                        "OS RNG not yet seeded", err),
-                _ => Error::with_cause(ErrorKind::Unavailable,
-                        "error while opening random device", err)
-            }
-        }
-
-        READ_RNG_ONCE.call_once(|| {
-            unsafe { READ_RNG_FILE = Some(Mutex::new(None)) }
-        });
-
-        // We try opening the file outside the `call_once` fn because we cannot
-        // clone the error, thus we must retry on failure.
-
-        let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() };
-        let mut guard = mutex.lock().unwrap();
-        if (*guard).is_none() {
-            {
-                info!("OsRng: opening random device /dev/random");
-                let mut file = OpenOptions::new()
-                    .read(true)
-                    .custom_flags(libc::O_NONBLOCK)
-                    .open("/dev/random")
-                    .map_err(map_err)?;
-                let mut buf = [0u8; 1];
-                file.read_exact(&mut buf).map_err(map_err)?;
-            }
-
-            info!("OsRng: opening random device /dev/urandom");
-            let file = File::open("/dev/urandom").map_err(map_err)?;
-            *guard = Some(file);
-        };
-        Ok(())
-    }
-
-    fn dev_random_try_fill(dest: &mut [u8]) -> Result<(), Error> {
-        if dest.len() == 0 { return Ok(()); }
-        trace!("OsRng: reading {} bytes from random device", dest.len());
-
-        // We expect this function only to be used after `open_dev_random` was
-        // succesful. Therefore we can assume that our memory was set with a
-        // valid object.
-        let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() };
-        let mut guard = mutex.lock().unwrap();
-        let file = (*guard).as_mut().unwrap();
-        // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`.
-        file.read_exact(dest).map_err(|err| {
-            match err.kind() {
-                ::std::io::ErrorKind::WouldBlock => Error::with_cause(
-                    ErrorKind::NotReady,
-                    "reading from random device would block", err),
-                _ => Error::with_cause(ErrorKind::Unavailable,
-                    "error reading random device", err)
-            }
-        })
-    }
-}
-
-#[cfg(target_os = "cloudabi")]
-mod imp {
-    extern crate cloudabi;
-
-    use {Error, ErrorKind};
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            trace!("OsRng: reading {} bytes via cloadabi::random_get", dest.len());
-            let errno = unsafe { cloudabi::random_get(dest) };
-            if errno == cloudabi::errno::SUCCESS {
-                Ok(())
-            } else {
-                // Cloudlibc provides its own `strerror` implementation so we
-                // can use `from_raw_os_error` here.
-                Err(Error::with_cause(
-                    ErrorKind::Unavailable,
-                    "random_get() system call failed",
-                    io::Error::from_raw_os_error(errno),
-                ))
-            }
-        }
-    }
-}
-
-#[cfg(any(target_os = "macos", target_os = "ios"))]
-mod imp {
-    extern crate libc;
-
-    use {Error, ErrorKind};
-    
-    use std::io;
-    use self::libc::{c_int, size_t};
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    enum SecRandom {}
-
-    #[allow(non_upper_case_globals)]
-    const kSecRandomDefault: *const SecRandom = 0 as *const SecRandom;
-
-    #[link(name = "Security", kind = "framework")]
-    extern {
-        fn SecRandomCopyBytes(rnd: *const SecRandom,
-                              count: size_t, bytes: *mut u8) -> c_int;
-    }
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            trace!("OsRng: reading {} bytes via SecRandomCopyBytes", dest.len());
-            let ret = unsafe {
-                SecRandomCopyBytes(kSecRandomDefault, dest.len() as size_t, dest.as_mut_ptr())
-            };
-            if ret == -1 {
-                Err(Error::with_cause(
-                    ErrorKind::Unavailable,
-                    "couldn't generate random bytes",
-                    io::Error::last_os_error()))
-            } else {
-                Ok(())
-            }
-        }
-    }
-}
-
-#[cfg(target_os = "freebsd")]
-mod imp {
-    extern crate libc;
-
-    use {Error, ErrorKind};
-    
-    use std::ptr;
-    use std::io;
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            let mib = [libc::CTL_KERN, libc::KERN_ARND];
-            trace!("OsRng: reading {} bytes via kern.arandom", dest.len());
-            // kern.arandom permits a maximum buffer size of 256 bytes
-            for s in dest.chunks_mut(256) {
-                let mut s_len = s.len();
-                let ret = unsafe {
-                    libc::sysctl(mib.as_ptr(), mib.len() as libc::c_uint,
-                                 s.as_mut_ptr() as *mut _, &mut s_len,
-                                 ptr::null(), 0)
-                };
-                if ret == -1 || s_len != s.len() {
-                    return Err(Error::with_cause(
-                        ErrorKind::Unavailable,
-                        "kern.arandom sysctl failed",
-                        io::Error::last_os_error()));
-                }
-            }
-            Ok(())
-        }
-    }
-}
-
-#[cfg(target_os = "openbsd")]
-mod imp {
-    extern crate libc;
-
-    use {Error, ErrorKind};
-    
-    use std::io;
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            // getentropy(2) permits a maximum buffer size of 256 bytes
-            for s in dest.chunks_mut(256) {
-                trace!("OsRng: reading {} bytes via getentropy", s.len());
-                let ret = unsafe {
-                    libc::getentropy(s.as_mut_ptr() as *mut libc::c_void, s.len())
-                };
-                if ret == -1 {
-                    return Err(Error::with_cause(
-                        ErrorKind::Unavailable,
-                        "getentropy failed",
-                        io::Error::last_os_error()));
-                }
-            }
-            Ok(())
-        }
-    }
-}
-
-#[cfg(target_os = "redox")]
-mod imp {
-    use {Error, ErrorKind};
-    use std::fs::File;
-    use std::io::Read;
-    use std::io::ErrorKind::*;
-    use std::sync::{Once, Mutex, ONCE_INIT};
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng();
-
-    // TODO: remove outer Option when `Mutex::new(None)` is a constant expression
-    static mut READ_RNG_FILE: Option<Mutex<Option<File>>> = None;
-    static READ_RNG_ONCE: Once = ONCE_INIT;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            READ_RNG_ONCE.call_once(|| {
-                unsafe { READ_RNG_FILE = Some(Mutex::new(None)) }
-            });
-
-            // We try opening the file outside the `call_once` fn because we cannot
-            // clone the error, thus we must retry on failure.
-
-            let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() };
-            let mut guard = mutex.lock().unwrap();
-            if (*guard).is_none() {
-                info!("OsRng: opening random device 'rand:'");
-                let file = File::open("rand:").map_err(|err| {
-                    match err.kind() {
-                        Interrupted => Error::new(ErrorKind::Transient, "interrupted"),
-                        WouldBlock => Error::with_cause(ErrorKind::NotReady,
-                                "opening random device would block", err),
-                        _ => Error::with_cause(ErrorKind::Unavailable,
-                                "error while opening random device", err)
-                    }
-                })?;
-                *guard = Some(file);
-            };
-            Ok(OsRng())
-        }
-
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            if dest.len() == 0 { return Ok(()); }
-            trace!("OsRng: reading {} bytes from random device", dest.len());
-
-            // Since we have an instance of Self, we can assume that our memory was
-            // set with a valid object.
-            let mutex = unsafe { READ_RNG_FILE.as_ref().unwrap() };
-            let mut guard = mutex.lock().unwrap();
-            let file = (*guard).as_mut().unwrap();
-            // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`.
-            file.read_exact(dest).map_err(|err| {
-                Error::with_cause(ErrorKind::Unavailable,
-                                  "error reading random device", err)
-            })
-        }
-    }
-}
-
-#[cfg(target_os = "fuchsia")]
-mod imp {
-    extern crate fuchsia_zircon;
-
-    use {Error, ErrorKind};
-    
-    use std::io;
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            for s in dest.chunks_mut(fuchsia_zircon::sys::ZX_CPRNG_DRAW_MAX_LEN) {
-                trace!("OsRng: reading {} bytes via cprng_draw", s.len());
-                let mut filled = 0;
-                while filled < s.len() {
-                    match fuchsia_zircon::cprng_draw(&mut s[filled..]) {
-                        Ok(actual) => filled += actual,
-                        Err(e) => {
-                            return Err(Error::with_cause(
-                                ErrorKind::Unavailable,
-                                "cprng_draw failed",
-                                e));
-                        }
-                    };
-                }
-            }
-            Ok(())
-        }
-    }
-}
-
-#[cfg(windows)]
-mod imp {
-    extern crate winapi;
-    
-    use {Error, ErrorKind};
-    
-    use std::io;
-
-    use self::winapi::shared::minwindef::ULONG;
-    use self::winapi::um::ntsecapi::RtlGenRandom;
-    use self::winapi::um::winnt::PVOID;
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Ok(OsRng)
-        }
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            // RtlGenRandom takes an ULONG (u32) for the length so we need to
-            // split up the buffer.
-            for slice in dest.chunks_mut(<ULONG>::max_value() as usize) {
-                trace!("OsRng: reading {} bytes via RtlGenRandom", slice.len());
-                let ret = unsafe {
-                    RtlGenRandom(slice.as_mut_ptr() as PVOID, slice.len() as ULONG)
-                };
-                if ret == 0 {
-                    return Err(Error::with_cause(
-                        ErrorKind::Unavailable,
-                        "couldn't generate random bytes",
-                        io::Error::last_os_error()));
-                }
-            }
-            Ok(())
-        }
-    }
-}
-
-#[cfg(all(target_arch = "wasm32",
-          not(target_os = "emscripten"),
-          not(feature = "stdweb")))]
-mod imp {
-    use {Error, ErrorKind};
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng;
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            Err(Error::new(ErrorKind::Unavailable,
-                           "not supported on WASM without stdweb"))
-        }
-
-        pub fn try_fill_bytes(&mut self, _v: &mut [u8]) -> Result<(), Error> {
-            Err(Error::new(ErrorKind::Unavailable,
-                           "not supported on WASM without stdweb"))
-        }
-    }
-}
-
-#[cfg(all(target_arch = "wasm32",
-          not(target_os = "emscripten"),
-          feature = "stdweb"))]
-mod imp {
-    use std::mem;
-    use stdweb::unstable::TryInto;
-    use stdweb::web::error::Error as WebError;
-    use {Error, ErrorKind};
-
-    #[derive(Clone, Debug)]
-    enum OsRngInner {
-        Browser,
-        Node
-    }
-
-    #[derive(Clone, Debug)]
-    pub struct OsRng(OsRngInner);
-
-    impl OsRng {
-        pub fn new() -> Result<OsRng, Error> {
-            let result = js! {
-                try {
-                    if (
-                        typeof window === "object" &&
-                        typeof window.crypto === "object" &&
-                        typeof window.crypto.getRandomValues === "function"
-                    ) {
-                        return { success: true, ty: 1 };
-                    }
-
-                    if (typeof require("crypto").randomBytes === "function") {
-                        return { success: true, ty: 2 };
-                    }
-
-                    return { success: false, error: new Error("not supported") };
-                } catch(err) {
-                    return { success: false, error: err };
-                }
-            };
-
-            if js!{ return @{ result.as_ref() }.success } == true {
-                let ty = js!{ return @{ result }.ty };
-
-                if ty == 1 { Ok(OsRng(OsRngInner::Browser)) }
-                else if ty == 2 { Ok(OsRng(OsRngInner::Node)) }
-                else { unreachable!() }
-            } else {
-                let err: WebError = js!{ return @{ result }.error }.try_into().unwrap();
-                Err(Error::with_cause(ErrorKind::Unavailable, "WASM Error", err))
-            }
-        }
-
-        pub fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-            assert_eq!(mem::size_of::<usize>(), 4);
-
-            let len = dest.len() as u32;
-            let ptr = dest.as_mut_ptr() as i32;
-
-            let result = match self.0 {
-                OsRngInner::Browser => js! {
-                    try {
-                        let array = new Uint8Array(@{ len });
-                        window.crypto.getRandomValues(array);
-                        HEAPU8.set(array, @{ ptr });
-
-                        return { success: true };
-                    } catch(err) {
-                        return { success: false, error: err };
-                    }
-                },
-                OsRngInner::Node => js! {
-                    try {
-                        let bytes = require("crypto").randomBytes(@{ len });
-                        HEAPU8.set(new Uint8Array(bytes), @{ ptr });
-
-                        return { success: true };
-                    } catch(err) {
-                        return { success: false, error: err };
-                    }
-                }
-            };
-
-            if js!{ return @{ result.as_ref() }.success } == true {
-                Ok(())
-            } else {
-                let err: WebError = js!{ return @{ result }.error }.try_into().unwrap();
-                Err(Error::with_cause(ErrorKind::Unexpected, "WASM Error", err))
-            }
-        }
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use RngCore;
-    use OsRng;
-
-    #[test]
-    fn test_os_rng() {
-        let mut r = OsRng::new().unwrap();
-
-        r.next_u32();
-        r.next_u64();
-
-        let mut v1 = [0u8; 1000];
-        r.fill_bytes(&mut v1);
-
-        let mut v2 = [0u8; 1000];
-        r.fill_bytes(&mut v2);
-
-        let mut n_diff_bits = 0;
-        for i in 0..v1.len() {
-            n_diff_bits += (v1[i] ^ v2[i]).count_ones();
-        }
-
-        // Check at least 1 bit per byte differs. p(failure) < 1e-1000 with random input.
-        assert!(n_diff_bits >= v1.len() as u32);
-    }
-
-    #[cfg(not(any(target_arch = "wasm32", target_arch = "asmjs")))]
-    #[test]
-    fn test_os_rng_tasks() {
-        use std::sync::mpsc::channel;
-        use std::thread;
-
-        let mut txs = vec!();
-        for _ in 0..20 {
-            let (tx, rx) = channel();
-            txs.push(tx);
-
-            thread::spawn(move|| {
-                // wait until all the tasks are ready to go.
-                rx.recv().unwrap();
-
-                // deschedule to attempt to interleave things as much
-                // as possible (XXX: is this a good test?)
-                let mut r = OsRng::new().unwrap();
-                thread::yield_now();
-                let mut v = [0u8; 1000];
-
-                for _ in 0..100 {
-                    r.next_u32();
-                    thread::yield_now();
-                    r.next_u64();
-                    thread::yield_now();
-                    r.fill_bytes(&mut v);
-                    thread::yield_now();
-                }
-            });
-        }
-
-        // start all the tasks
-        for tx in txs.iter() {
-            tx.send(()).unwrap();
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/chacha.rs b/vendor/rand-8c5b0ac51d/src/prng/chacha.rs
deleted file mode 100644
index 55af770..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/chacha.rs
+++ /dev/null
@@ -1,463 +0,0 @@
-// Copyright 2014 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://www.rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The ChaCha random number generator.
-
-use core::fmt;
-use rand_core::{BlockRngCore, CryptoRng, RngCore, SeedableRng, Error, le};
-use rand_core::impls::BlockRng;
-
-const SEED_WORDS: usize = 8; // 8 words for the 256-bit key
-const STATE_WORDS: usize = 16;
-
-/// A cryptographically secure random number generator that uses the ChaCha
-/// algorithm.
-///
-/// ChaCha is a stream cipher designed by Daniel J. Bernstein [1], that we use
-/// as an RNG. It is an improved variant of the Salsa20 cipher family, which was
-/// selected as one of the "stream ciphers suitable for widespread adoption" by
-/// eSTREAM [2].
-///
-/// ChaCha uses add-rotate-xor (ARX) operations as its basis. These are safe
-/// against timing attacks, although that is mostly a concern for ciphers and
-/// not for RNGs. Also it is very suitable for SIMD implementation.
-/// Here we do not provide a SIMD implementation yet, except for what is
-/// provided by auto-vectorisation.
-///
-/// With the ChaCha algorithm it is possible to choose the number of rounds the
-/// core algorithm should run. By default `ChaChaRng` is created as ChaCha20,
-/// which means 20 rounds. The number of rounds is a tradeoff between performance
-/// and security, 8 rounds are considered the minimum to be secure. A different
-/// number of rounds can be set using [`set_rounds`].
-///
-/// We deviate slightly from the ChaCha specification regarding the nonce and
-/// the counter. Instead of a 64-bit nonce and 64-bit counter (or a 96-bit nonce
-/// and 32-bit counter in the IETF variant [3]), we use a 128-bit counter. This
-/// is because a nonce does not give a meaningful advantage for ChaCha when used
-/// as an RNG. The modification is provably as strong as the original cipher,
-/// though, since any distinguishing attack on our variant also works against
-/// ChaCha with a chosen nonce.
-///
-/// The modified word layout is:
-///
-/// ```text
-/// constant constant constant constant
-/// key      key      key      key
-/// key      key      key      key
-/// counter  counter  counter  counter
-/// ```
-///
-/// [1]: D. J. Bernstein, [*ChaCha, a variant of Salsa20*](
-///      https://cr.yp.to/chacha.html)
-///
-/// [2]: [eSTREAM: the ECRYPT Stream Cipher Project](
-///      http://www.ecrypt.eu.org/stream/)
-///
-/// [3]: [ChaCha20 and Poly1305 for IETF Protocols](
-///       https://tools.ietf.org/html/rfc7539)
-///
-/// [`set_rounds`]: #method.set_counter
-#[derive(Clone, Debug)]
-pub struct ChaChaRng(BlockRng<ChaChaCore>);
-
-impl RngCore for ChaChaRng {
-    #[inline]
-    fn next_u32(&mut self) -> u32 {
-        self.0.next_u32()
-    }
-
-    #[inline]
-    fn next_u64(&mut self) -> u64 {
-        self.0.next_u64()
-    }
-
-    #[inline]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        self.0.fill_bytes(dest)
-    }
-
-    #[inline]
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        self.0.try_fill_bytes(dest)
-    }
-}
-
-impl SeedableRng for ChaChaRng {
-    type Seed = <ChaChaCore as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        ChaChaRng(BlockRng::<ChaChaCore>::from_seed(seed))
-    }
-
-    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
-        BlockRng::<ChaChaCore>::from_rng(rng).map(ChaChaRng)
-    }
-}
-
-impl CryptoRng for ChaChaRng {}
-
-impl ChaChaRng {
-    /// Create an ChaCha random number generator using the default
-    /// fixed key of 8 zero words.
-    ///
-    /// # Examples
-    ///
-    /// ```rust
-    /// # #![allow(deprecated)]
-    /// use rand::{RngCore, ChaChaRng};
-    ///
-    /// let mut ra = ChaChaRng::new_unseeded();
-    /// println!("{:?}", ra.next_u32());
-    /// println!("{:?}", ra.next_u32());
-    /// ```
-    ///
-    /// Since this equivalent to a RNG with a fixed seed, repeated executions
-    /// of an unseeded RNG will produce the same result. This code sample will
-    /// consistently produce:
-    ///
-    /// - 2917185654
-    /// - 2419978656
-    #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")]
-    pub fn new_unseeded() -> ChaChaRng {
-        ChaChaRng::from_seed([0; SEED_WORDS*4])
-    }
-
-    /// Sets the internal 128-bit ChaCha counter to a user-provided value. This
-    /// permits jumping arbitrarily ahead (or backwards) in the pseudorandom
-    /// stream.
-    ///
-    /// The 128 bits used for the counter overlap with the nonce and smaller
-    /// counter of ChaCha when used as a stream cipher. It is in theory possible
-    /// to use `set_counter` to obtain the conventional ChaCha pseudorandom
-    /// stream associated with a particular nonce. This is not a supported use
-    /// of the RNG, because a nonce set that way is not treated as a constant
-    /// value but still as part of the counter, besides endian issues.
-    ///
-    /// # Examples
-    ///
-    /// ```rust
-    /// use rand::{ChaChaRng, RngCore, SeedableRng};
-    ///
-    /// // Note: Use `FromEntropy` or `ChaChaRng::from_rng()` outside of testing.
-    /// let mut rng1 = ChaChaRng::from_seed([0; 32]);
-    /// let mut rng2 = rng1.clone();
-    ///
-    /// // Skip to round 20. Because every round generates 16 `u32` values, this
-    /// // actually means skipping 320 values.
-    /// for _ in 0..(20*16) { rng1.next_u32(); }
-    /// rng2.set_counter(20, 0);
-    /// assert_eq!(rng1.next_u32(), rng2.next_u32());
-    /// ```
-    pub fn set_counter(&mut self, counter_low: u64, counter_high: u64) {
-        self.0.inner_mut().set_counter(counter_low, counter_high);
-        self.0.reset(); // force recomputation on next use
-    }
-
-    /// Sets the number of rounds to run the ChaCha core algorithm per block to
-    /// generate.
-    ///
-    /// By default this is set to 20. Other recommended values are 12 and 8,
-    /// which trade security for performance. `rounds` only supports values
-    /// that are multiples of 4 and less than or equal to 20.
-    ///
-    /// # Examples
-    ///
-    /// ```rust
-    /// use rand::{ChaChaRng, RngCore, SeedableRng};
-    ///
-    /// // Note: Use `FromEntropy` or `ChaChaRng::from_rng()` outside of testing.
-    /// let mut rng = ChaChaRng::from_seed([0; 32]);
-    /// rng.set_rounds(8);
-    ///
-    /// assert_eq!(rng.next_u32(), 0x2fef003e);
-    /// ```
-    pub fn set_rounds(&mut self, rounds: usize) {
-        self.0.inner_mut().set_rounds(rounds);
-        self.0.reset(); // force recomputation on next use
-    }
-}
-
-/// The core of `ChaChaRng`, used with `BlockRng`.
-#[derive(Clone)]
-pub struct ChaChaCore {
-    state: [u32; STATE_WORDS],
-    rounds:  usize,
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for ChaChaCore {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "ChaChaCore {{}}")
-    }
-}
-
-macro_rules! quarter_round{
-    ($a: expr, $b: expr, $c: expr, $d: expr) => {{
-        $a = $a.wrapping_add($b); $d ^= $a; $d = $d.rotate_left(16);
-        $c = $c.wrapping_add($d); $b ^= $c; $b = $b.rotate_left(12);
-        $a = $a.wrapping_add($b); $d ^= $a; $d = $d.rotate_left( 8);
-        $c = $c.wrapping_add($d); $b ^= $c; $b = $b.rotate_left( 7);
-    }}
-}
-
-macro_rules! double_round{
-    ($x: expr) => {{
-        // Column round
-        quarter_round!($x[ 0], $x[ 4], $x[ 8], $x[12]);
-        quarter_round!($x[ 1], $x[ 5], $x[ 9], $x[13]);
-        quarter_round!($x[ 2], $x[ 6], $x[10], $x[14]);
-        quarter_round!($x[ 3], $x[ 7], $x[11], $x[15]);
-        // Diagonal round
-        quarter_round!($x[ 0], $x[ 5], $x[10], $x[15]);
-        quarter_round!($x[ 1], $x[ 6], $x[11], $x[12]);
-        quarter_round!($x[ 2], $x[ 7], $x[ 8], $x[13]);
-        quarter_round!($x[ 3], $x[ 4], $x[ 9], $x[14]);
-    }}
-}
-
-impl BlockRngCore for ChaChaCore {
-    type Item = u32;
-    type Results = [u32; STATE_WORDS];
-
-    fn generate(&mut self, results: &mut Self::Results) {
-        // For some reason extracting this part into a separate function
-        // improves performance by 50%.
-        fn core(results: &mut [u32; STATE_WORDS],
-                state: &[u32; STATE_WORDS],
-                rounds: usize)
-        {
-            let mut tmp = *state;
-            for _ in 0..rounds / 2 {
-                double_round!(tmp);
-            }
-            for i in 0..STATE_WORDS {
-                results[i] = tmp[i].wrapping_add(state[i]);
-            }
-        }
-
-        core(results, &self.state, self.rounds);
-
-        // update 128-bit counter
-        self.state[12] = self.state[12].wrapping_add(1);
-        if self.state[12] != 0 { return; };
-        self.state[13] = self.state[13].wrapping_add(1);
-        if self.state[13] != 0 { return; };
-        self.state[14] = self.state[14].wrapping_add(1);
-        if self.state[14] != 0 { return; };
-        self.state[15] = self.state[15].wrapping_add(1);
-    }
-}
-
-impl ChaChaCore {
-    /// Sets the internal 128-bit ChaCha counter to a user-provided value. This
-    /// permits jumping arbitrarily ahead (or backwards) in the pseudorandom
-    /// stream.
-    pub fn set_counter(&mut self, counter_low: u64, counter_high: u64) {
-        self.state[12] = counter_low as u32;
-        self.state[13] = (counter_low >> 32) as u32;
-        self.state[14] = counter_high as u32;
-        self.state[15] = (counter_high >> 32) as u32;
-    }
-
-    /// Sets the number of rounds to run the ChaCha core algorithm per block to
-    /// generate.
-    pub fn set_rounds(&mut self, rounds: usize) {
-        assert!([4usize, 8, 12, 16, 20].iter().any(|x| *x == rounds));
-        self.rounds = rounds;
-    }
-}
-
-impl SeedableRng for ChaChaCore {
-    type Seed = [u8; SEED_WORDS*4];
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        let mut seed_le = [0u32; SEED_WORDS];
-        le::read_u32_into(&seed, &mut seed_le);
-        Self {
-            state: [0x61707865, 0x3320646E, 0x79622D32, 0x6B206574, // constants
-                    seed_le[0], seed_le[1], seed_le[2], seed_le[3], // seed
-                    seed_le[4], seed_le[5], seed_le[6], seed_le[7], // seed
-                    0, 0, 0, 0], // counter
-            rounds: 20,
-         }
-    }
-}
-
-impl CryptoRng for ChaChaCore {}
-
-#[cfg(test)]
-mod test {
-    use {RngCore, SeedableRng};
-    use super::ChaChaRng;
-
-    #[test]
-    fn test_chacha_construction() {
-        let seed = [0,0,0,0,0,0,0,0,
-            1,0,0,0,0,0,0,0,
-            2,0,0,0,0,0,0,0,
-            3,0,0,0,0,0,0,0];
-        let mut rng1 = ChaChaRng::from_seed(seed);
-        assert_eq!(rng1.next_u32(), 137206642);
-
-        let mut rng2 = ChaChaRng::from_rng(rng1).unwrap();
-        assert_eq!(rng2.next_u32(), 1325750369);
-    }
-
-    #[test]
-    fn test_chacha_true_values_a() {
-        // Test vectors 1 and 2 from
-        // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04
-        let seed = [0u8; 32];
-        let mut rng = ChaChaRng::from_seed(seed);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0xade0b876, 0x903df1a0, 0xe56a5d40, 0x28bd8653,
-                        0xb819d2bd, 0x1aed8da0, 0xccef36a8, 0xc70d778b,
-                        0x7c5941da, 0x8d485751, 0x3fe02477, 0x374ad8b8,
-                        0xf4b8436a, 0x1ca11815, 0x69b687c3, 0x8665eeb2];
-        assert_eq!(results, expected);
-
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0xbee7079f, 0x7a385155, 0x7c97ba98, 0x0d082d73,
-                        0xa0290fcb, 0x6965e348, 0x3e53c612, 0xed7aee32,
-                        0x7621b729, 0x434ee69c, 0xb03371d5, 0xd539d874,
-                        0x281fed31, 0x45fb0a51, 0x1f0ae1ac, 0x6f4d794b];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_true_values_b() {
-        // Test vector 3 from
-        // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04
-        let seed = [0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 1];
-        let mut rng = ChaChaRng::from_seed(seed);
-
-        // Skip block 0
-        for _ in 0..16 { rng.next_u32(); }
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0x2452eb3a, 0x9249f8ec, 0x8d829d9b, 0xddd4ceb1,
-                        0xe8252083, 0x60818b01, 0xf38422b8, 0x5aaa49c9,
-                        0xbb00ca8e, 0xda3ba7b4, 0xc4b592d1, 0xfdf2732f,
-                        0x4436274e, 0x2561b3c8, 0xebdd4aa6, 0xa0136c00];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_true_values_c() {
-        // Test vector 4 from
-        // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04
-        let seed = [0, 0xff, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 0,
-                    0, 0, 0, 0, 0, 0, 0, 0];
-        let expected = [0xfb4dd572, 0x4bc42ef1, 0xdf922636, 0x327f1394,
-                        0xa78dea8f, 0x5e269039, 0xa1bebbc1, 0xcaf09aae,
-                        0xa25ab213, 0x48a6b46c, 0x1b9d9bcb, 0x092c5be6,
-                        0x546ca624, 0x1bec45d5, 0x87f47473, 0x96f0992e];
-        let mut results = [0u32; 16];
-
-        // Test block 2 by skipping block 0 and 1
-        let mut rng1 = ChaChaRng::from_seed(seed);
-        for _ in 0..32 { rng1.next_u32(); }
-        for i in results.iter_mut() { *i = rng1.next_u32(); }
-        assert_eq!(results, expected);
-
-        // Test block 2 by using `set_counter`
-        let mut rng2 = ChaChaRng::from_seed(seed);
-        rng2.set_counter(2, 0);
-        for i in results.iter_mut() { *i = rng2.next_u32(); }
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_multiple_blocks() {
-        let seed = [0,0,0,0, 1,0,0,0, 2,0,0,0, 3,0,0,0, 4,0,0,0, 5,0,0,0, 6,0,0,0, 7,0,0,0];
-        let mut rng = ChaChaRng::from_seed(seed);
-
-        // Store the 17*i-th 32-bit word,
-        // i.e., the i-th word of the i-th 16-word block
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() {
-            *i = rng.next_u32();
-            for _ in 0..16 {
-                rng.next_u32();
-            }
-        }
-        let expected = [0xf225c81a, 0x6ab1be57, 0x04d42951, 0x70858036,
-                        0x49884684, 0x64efec72, 0x4be2d186, 0x3615b384,
-                        0x11cfa18e, 0xd3c50049, 0x75c775f6, 0x434c6530,
-                        0x2c5bad8f, 0x898881dc, 0x5f1c86d9, 0xc1f8e7f4];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_true_bytes() {
-        let seed = [0u8; 32];
-        let mut rng = ChaChaRng::from_seed(seed);
-        let mut results = [0u8; 32];
-        rng.fill_bytes(&mut results);
-        let expected = [118, 184, 224, 173, 160, 241, 61, 144,
-                        64, 93, 106, 229, 83, 134, 189, 40,
-                        189, 210, 25, 184, 160, 141, 237, 26,
-                        168, 54, 239, 204, 139, 119, 13, 199];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_set_counter() {
-        // Test vector 5 from
-        // https://tools.ietf.org/html/draft-nir-cfrg-chacha20-poly1305-04
-        // Although we do not support setting a nonce, we try it here anyway so
-        // we can use this test vector.
-        let seed = [0u8; 32];
-        let mut rng = ChaChaRng::from_seed(seed);
-        rng.set_counter(0, 2u64 << 56);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0x374dc6c2, 0x3736d58c, 0xb904e24a, 0xcd3f93ef,
-                        0x88228b1a, 0x96a4dfb3, 0x5b76ab72, 0xc727ee54,
-                        0x0e0e978a, 0xf3145c95, 0x1b748ea8, 0xf786c297,
-                        0x99c28f5f, 0x628314e8, 0x398a19fa, 0x6ded1b53];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_set_rounds() {
-        let seed = [0u8; 32];
-        let mut rng = ChaChaRng::from_seed(seed);
-        rng.set_rounds(8);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-
-        let expected = [0x2fef003e, 0xd6405f89, 0xe8b85b7f, 0xa1a5091f,
-                        0xc30e842c, 0x3b7f9ace, 0x88e11b18, 0x1e1a71ef,
-                        0x72e14c98, 0x416f21b9, 0x6753449f, 0x19566d45,
-                        0xa3424a31, 0x01b086da, 0xb8fd7b38, 0x42fe0c0e];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_chacha_clone() {
-        let seed = [0,0,0,0, 1,0,0,0, 2,0,0,0, 3,0,0,0, 4,0,0,0, 5,0,0,0, 6,0,0,0, 7,0,0,0];
-        let mut rng = ChaChaRng::from_seed(seed);
-        let mut clone = rng.clone();
-        for _ in 0..16 {
-            assert_eq!(rng.next_u64(), clone.next_u64());
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/hc128.rs b/vendor/rand-8c5b0ac51d/src/prng/hc128.rs
deleted file mode 100644
index bd7fa46..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/hc128.rs
+++ /dev/null
@@ -1,457 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://www.rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The HC-128 random number generator.
-
-use core::fmt;
-use rand_core::{BlockRngCore, CryptoRng, RngCore, SeedableRng, Error, le};
-use rand_core::impls::BlockRng;
-
-const SEED_WORDS: usize = 8; // 128 bit key followed by 128 bit iv
-
-/// A cryptographically secure random number generator that uses the HC-128
-/// algorithm.
-///
-/// HC-128 is a stream cipher designed by Hongjun Wu [1], that we use as an RNG.
-/// It is selected as one of the "stream ciphers suitable for widespread
-/// adoption" by eSTREAM [2].
-///
-/// HC-128 is an array based RNG. In this it is similar to RC-4 and ISAAC before
-/// it, but those have never been proven cryptographically secure (or have even
-/// been significantly compromised, as in the case of RC-4 [5]).
-///
-/// Because HC-128 works with simple indexing into a large array and with a few
-/// operations that parallelize well, it has very good performance. The size of
-/// the array it needs, 4kb, can however be a disadvantage.
-///
-/// This implementation is not based on the version of HC-128 submitted to the
-/// eSTREAM contest, but on a later version by the author with a few small
-/// improvements from December 15, 2009 [3].
-///
-/// HC-128 has no known weaknesses that are easier to exploit than doing a
-/// brute-force search of 2<sup>128</sup>. A very comprehensive analysis of the
-/// current state of known attacks / weaknesses of HC-128 is given in [4].
-///
-/// The average cycle length is expected to be
-/// 2<sup>1024*32-1</sup> = 2<sup>32767</sup>.
-/// We support seeding with a 256-bit array, which matches the 128-bit key
-/// concatenated with a 128-bit IV from the stream cipher.
-///
-/// ## References
-/// [1]: Hongjun Wu (2008). ["The Stream Cipher HC-128"](
-///      http://www.ecrypt.eu.org/stream/p3ciphers/hc/hc128_p3.pdf).
-///      *The eSTREAM Finalists*, LNCS 4986, pp. 39--47, Springer-Verlag.
-///
-/// [2]: [eSTREAM: the ECRYPT Stream Cipher Project](
-///      http://www.ecrypt.eu.org/stream/)
-///
-/// [3]: Hongjun Wu, [Stream Ciphers HC-128 and HC-256](
-///      https://www.ntu.edu.sg/home/wuhj/research/hc/index.html)
-///
-/// [4]: Shashwat Raizada (January 2015),["Some Results On Analysis And
-///      Implementation Of HC-128 Stream Cipher"](
-///      http://library.isical.ac.in:8080/jspui/bitstream/123456789/6636/1/TH431.pdf).
-///
-/// [5]: Internet Engineering Task Force (Februari 2015),
-///      ["Prohibiting RC4 Cipher Suites"](https://tools.ietf.org/html/rfc7465).
-#[derive(Clone, Debug)]
-pub struct Hc128Rng(BlockRng<Hc128Core>);
-
-impl RngCore for Hc128Rng {
-    #[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 SeedableRng for Hc128Rng {
-    type Seed = <Hc128Core as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        Hc128Rng(BlockRng::<Hc128Core>::from_seed(seed))
-    }
-
-    fn from_rng<R: RngCore>(rng: R) -> Result<Self, Error> {
-        BlockRng::<Hc128Core>::from_rng(rng).map(Hc128Rng)
-    }
-}
-
-impl CryptoRng for Hc128Rng {}
-
-/// The core of `Hc128Rng`, used with `BlockRng`.
-#[derive(Clone)]
-pub struct Hc128Core {
-    t: [u32; 1024],
-    counter1024: usize,
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for Hc128Core {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "Hc128Core {{}}")
-    }
-}
-
-impl BlockRngCore for Hc128Core {
-    type Item = u32;
-    type Results = [u32; 16];
-
-    fn generate(&mut self, results: &mut Self::Results) {
-        assert!(self.counter1024 % 16 == 0);
-
-        let cc = self.counter1024 % 512;
-        let dd = (cc + 16) % 512;
-        let ee = cc.wrapping_sub(16) % 512;
-
-        if self.counter1024 & 512 == 0 {
-            // P block
-            results[0]  = self.step_p(cc+0,  cc+1,  ee+13, ee+6,  ee+4);
-            results[1]  = self.step_p(cc+1,  cc+2,  ee+14, ee+7,  ee+5);
-            results[2]  = self.step_p(cc+2,  cc+3,  ee+15, ee+8,  ee+6);
-            results[3]  = self.step_p(cc+3,  cc+4,  cc+0,  ee+9,  ee+7);
-            results[4]  = self.step_p(cc+4,  cc+5,  cc+1,  ee+10, ee+8);
-            results[5]  = self.step_p(cc+5,  cc+6,  cc+2,  ee+11, ee+9);
-            results[6]  = self.step_p(cc+6,  cc+7,  cc+3,  ee+12, ee+10);
-            results[7]  = self.step_p(cc+7,  cc+8,  cc+4,  ee+13, ee+11);
-            results[8]  = self.step_p(cc+8,  cc+9,  cc+5,  ee+14, ee+12);
-            results[9]  = self.step_p(cc+9,  cc+10, cc+6,  ee+15, ee+13);
-            results[10] = self.step_p(cc+10, cc+11, cc+7,  cc+0,  ee+14);
-            results[11] = self.step_p(cc+11, cc+12, cc+8,  cc+1,  ee+15);
-            results[12] = self.step_p(cc+12, cc+13, cc+9,  cc+2,  cc+0);
-            results[13] = self.step_p(cc+13, cc+14, cc+10, cc+3,  cc+1);
-            results[14] = self.step_p(cc+14, cc+15, cc+11, cc+4,  cc+2);
-            results[15] = self.step_p(cc+15, dd+0,  cc+12, cc+5,  cc+3);
-        } else {
-            // Q block
-            results[0]  = self.step_q(cc+0,  cc+1,  ee+13, ee+6,  ee+4);
-            results[1]  = self.step_q(cc+1,  cc+2,  ee+14, ee+7,  ee+5);
-            results[2]  = self.step_q(cc+2,  cc+3,  ee+15, ee+8,  ee+6);
-            results[3]  = self.step_q(cc+3,  cc+4,  cc+0,  ee+9,  ee+7);
-            results[4]  = self.step_q(cc+4,  cc+5,  cc+1,  ee+10, ee+8);
-            results[5]  = self.step_q(cc+5,  cc+6,  cc+2,  ee+11, ee+9);
-            results[6]  = self.step_q(cc+6,  cc+7,  cc+3,  ee+12, ee+10);
-            results[7]  = self.step_q(cc+7,  cc+8,  cc+4,  ee+13, ee+11);
-            results[8]  = self.step_q(cc+8,  cc+9,  cc+5,  ee+14, ee+12);
-            results[9]  = self.step_q(cc+9,  cc+10, cc+6,  ee+15, ee+13);
-            results[10] = self.step_q(cc+10, cc+11, cc+7,  cc+0,  ee+14);
-            results[11] = self.step_q(cc+11, cc+12, cc+8,  cc+1,  ee+15);
-            results[12] = self.step_q(cc+12, cc+13, cc+9,  cc+2,  cc+0);
-            results[13] = self.step_q(cc+13, cc+14, cc+10, cc+3,  cc+1);
-            results[14] = self.step_q(cc+14, cc+15, cc+11, cc+4,  cc+2);
-            results[15] = self.step_q(cc+15, dd+0,  cc+12, cc+5,  cc+3);
-        }
-        self.counter1024 = self.counter1024.wrapping_add(16);
-    }
-}
-
-impl Hc128Core {
-    // One step of HC-128, update P and generate 32 bits keystream
-    #[inline(always)]
-    fn step_p(&mut self, i: usize, i511: usize, i3: usize, i10: usize, i12: usize)
-         -> u32
-    {
-        let (p, q) = self.t.split_at_mut(512);
-        // FIXME: it would be great if we the bounds checks here could be
-        // optimized out, and we would not need unsafe.
-        // This improves performance by about 7%.
-        unsafe {
-            let temp0 = p.get_unchecked(i511).rotate_right(23);
-            let temp1 = p.get_unchecked(i3).rotate_right(10);
-            let temp2 = p.get_unchecked(i10).rotate_right(8);
-            *p.get_unchecked_mut(i) = p.get_unchecked(i)
-                                       .wrapping_add(temp2)
-                                       .wrapping_add(temp0 ^ temp1);
-            let temp3 = {
-                // The h1 function in HC-128
-                let a = *p.get_unchecked(i12) as u8;
-                let c = (p.get_unchecked(i12) >> 16) as u8;
-                q[a as usize].wrapping_add(q[256 + c as usize])
-            };
-            temp3 ^ p.get_unchecked(i)
-        }
-    }
-
-    // One step of HC-128, update Q and generate 32 bits keystream
-    // Similar to `step_p`, but `p` and `q` are swapped, and the rotates are to
-    // the left instead of to the right.
-    #[inline(always)]
-    fn step_q(&mut self, i: usize, i511: usize, i3: usize, i10: usize, i12: usize)
-         -> u32
-    {
-        let (p, q) = self.t.split_at_mut(512);
-        unsafe {
-            let temp0 = q.get_unchecked(i511).rotate_left(23);
-            let temp1 = q.get_unchecked(i3).rotate_left(10);
-            let temp2 = q.get_unchecked(i10).rotate_left(8);
-            *q.get_unchecked_mut(i) = q.get_unchecked(i)
-                                       .wrapping_add(temp2)
-                                       .wrapping_add(temp0 ^ temp1);
-            let temp3 = {
-                // The h2 function in HC-128
-                let a = *q.get_unchecked(i12) as u8;
-                let c = (q.get_unchecked(i12) >> 16) as u8;
-                p[a as usize].wrapping_add(p[256 + c as usize])
-            };
-            temp3 ^ q.get_unchecked(i)
-        }
-    }
-
-    fn sixteen_steps(&mut self) {
-        assert!(self.counter1024 % 16 == 0);
-
-        let cc = self.counter1024 % 512;
-        let dd = (cc + 16) % 512;
-        let ee = cc.wrapping_sub(16) % 512;
-
-        if self.counter1024 < 512 {
-            // P block
-            self.t[cc+0]  = self.step_p(cc+0,  cc+1,  ee+13, ee+6,  ee+4);
-            self.t[cc+1]  = self.step_p(cc+1,  cc+2,  ee+14, ee+7,  ee+5);
-            self.t[cc+2]  = self.step_p(cc+2,  cc+3,  ee+15, ee+8,  ee+6);
-            self.t[cc+3]  = self.step_p(cc+3,  cc+4,  cc+0,  ee+9,  ee+7);
-            self.t[cc+4]  = self.step_p(cc+4,  cc+5,  cc+1,  ee+10, ee+8);
-            self.t[cc+5]  = self.step_p(cc+5,  cc+6,  cc+2,  ee+11, ee+9);
-            self.t[cc+6]  = self.step_p(cc+6,  cc+7,  cc+3,  ee+12, ee+10);
-            self.t[cc+7]  = self.step_p(cc+7,  cc+8,  cc+4,  ee+13, ee+11);
-            self.t[cc+8]  = self.step_p(cc+8,  cc+9,  cc+5,  ee+14, ee+12);
-            self.t[cc+9]  = self.step_p(cc+9,  cc+10, cc+6,  ee+15, ee+13);
-            self.t[cc+10] = self.step_p(cc+10, cc+11, cc+7,  cc+0,  ee+14);
-            self.t[cc+11] = self.step_p(cc+11, cc+12, cc+8,  cc+1,  ee+15);
-            self.t[cc+12] = self.step_p(cc+12, cc+13, cc+9,  cc+2,  cc+0);
-            self.t[cc+13] = self.step_p(cc+13, cc+14, cc+10, cc+3,  cc+1);
-            self.t[cc+14] = self.step_p(cc+14, cc+15, cc+11, cc+4,  cc+2);
-            self.t[cc+15] = self.step_p(cc+15, dd+0,  cc+12, cc+5,  cc+3);
-        } else {
-            // Q block
-            self.t[cc+512+0]  = self.step_q(cc+0,  cc+1,  ee+13, ee+6,  ee+4);
-            self.t[cc+512+1]  = self.step_q(cc+1,  cc+2,  ee+14, ee+7,  ee+5);
-            self.t[cc+512+2]  = self.step_q(cc+2,  cc+3,  ee+15, ee+8,  ee+6);
-            self.t[cc+512+3]  = self.step_q(cc+3,  cc+4,  cc+0,  ee+9,  ee+7);
-            self.t[cc+512+4]  = self.step_q(cc+4,  cc+5,  cc+1,  ee+10, ee+8);
-            self.t[cc+512+5]  = self.step_q(cc+5,  cc+6,  cc+2,  ee+11, ee+9);
-            self.t[cc+512+6]  = self.step_q(cc+6,  cc+7,  cc+3,  ee+12, ee+10);
-            self.t[cc+512+7]  = self.step_q(cc+7,  cc+8,  cc+4,  ee+13, ee+11);
-            self.t[cc+512+8]  = self.step_q(cc+8,  cc+9,  cc+5,  ee+14, ee+12);
-            self.t[cc+512+9]  = self.step_q(cc+9,  cc+10, cc+6,  ee+15, ee+13);
-            self.t[cc+512+10] = self.step_q(cc+10, cc+11, cc+7,  cc+0,  ee+14);
-            self.t[cc+512+11] = self.step_q(cc+11, cc+12, cc+8,  cc+1,  ee+15);
-            self.t[cc+512+12] = self.step_q(cc+12, cc+13, cc+9,  cc+2,  cc+0);
-            self.t[cc+512+13] = self.step_q(cc+13, cc+14, cc+10, cc+3,  cc+1);
-            self.t[cc+512+14] = self.step_q(cc+14, cc+15, cc+11, cc+4,  cc+2);
-            self.t[cc+512+15] = self.step_q(cc+15, dd+0,  cc+12, cc+5,  cc+3);
-        }
-        self.counter1024 += 16;
-    }
-
-    // Initialize an HC-128 random number generator. The seed has to be
-    // 256 bits in length (`[u32; 8]`), matching the 128 bit `key` followed by
-    // 128 bit `iv` when HC-128 where to be used as a stream cipher.
-    fn init(seed: [u32; SEED_WORDS]) -> Self {
-        #[inline]
-        fn f1(x: u32) -> u32 {
-            x.rotate_right(7) ^ x.rotate_right(18) ^ (x >> 3)
-        }
-
-        #[inline]
-        fn f2(x: u32) -> u32 {
-            x.rotate_right(17) ^ x.rotate_right(19) ^ (x >> 10)
-        }
-
-        let mut t = [0u32; 1024];
-
-        // Expand the key and iv into P and Q
-        let (key, iv) = seed.split_at(4);
-        t[..4].copy_from_slice(key);
-        t[4..8].copy_from_slice(key);
-        t[8..12].copy_from_slice(iv);
-        t[12..16].copy_from_slice(iv);
-
-        // Generate the 256 intermediate values W[16] ... W[256+16-1], and
-        // copy the last 16 generated values to the start op P.
-        for i in 16..256+16 {
-            t[i] = f2(t[i-2]).wrapping_add(t[i-7]).wrapping_add(f1(t[i-15]))
-                   .wrapping_add(t[i-16]).wrapping_add(i as u32);
-        }
-        {
-            let (p1, p2) = t.split_at_mut(256);
-            p1[0..16].copy_from_slice(&p2[0..16]);
-        }
-
-        // Generate both the P and Q tables
-        for i in 16..1024 {
-            t[i] = f2(t[i-2]).wrapping_add(t[i-7]).wrapping_add(f1(t[i-15]))
-                   .wrapping_add(t[i-16]).wrapping_add(256 + i as u32);
-        }
-
-        let mut core = Self { t, counter1024: 0 };
-
-        // run the cipher 1024 steps
-        for _ in 0..64 { core.sixteen_steps() };
-        core.counter1024 = 0;
-        core
-    }
-}
-
-impl SeedableRng for Hc128Core {
-    type Seed = [u8; SEED_WORDS*4];
-
-    /// Create an HC-128 random number generator with a seed. The seed has to be
-    /// 256 bits in length, matching the 128 bit `key` followed by 128 bit `iv`
-    /// when HC-128 where to be used as a stream cipher.
-    fn from_seed(seed: Self::Seed) -> Self {
-        let mut seed_u32 = [0u32; SEED_WORDS];
-        le::read_u32_into(&seed, &mut seed_u32);
-        Self::init(seed_u32)
-    }
-}
-
-impl CryptoRng for Hc128Core {}
-
-#[cfg(test)]
-mod test {
-    use {RngCore, SeedableRng};
-    use super::Hc128Rng;
-
-    #[test]
-    // Test vector 1 from the paper "The Stream Cipher HC-128"
-    fn test_hc128_true_values_a() {
-        let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng = Hc128Rng::from_seed(seed);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0x73150082, 0x3bfd03a0, 0xfb2fd77f, 0xaa63af0e,
-                        0xde122fc6, 0xa7dc29b6, 0x62a68527, 0x8b75ec68,
-                        0x9036db1e, 0x81896005, 0x00ade078, 0x491fbf9a,
-                        0x1cdc3013, 0x6c3d6e24, 0x90f664b2, 0x9cd57102];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    // Test vector 2 from the paper "The Stream Cipher HC-128"
-    fn test_hc128_true_values_b() {
-        let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    1,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng = Hc128Rng::from_seed(seed);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0xc01893d5, 0xb7dbe958, 0x8f65ec98, 0x64176604,
-                        0x36fc6724, 0xc82c6eec, 0x1b1c38a7, 0xc9b42a95,
-                        0x323ef123, 0x0a6a908b, 0xce757b68, 0x9f14f7bb,
-                        0xe4cde011, 0xaeb5173f, 0x89608c94, 0xb5cf46ca];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    // Test vector 3 from the paper "The Stream Cipher HC-128"
-    fn test_hc128_true_values_c() {
-        let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng = Hc128Rng::from_seed(seed);
-
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected = [0x518251a4, 0x04b4930a, 0xb02af931, 0x0639f032,
-                        0xbcb4a47a, 0x5722480b, 0x2bf99f72, 0xcdc0e566,
-                        0x310f0c56, 0xd3cc83e8, 0x663db8ef, 0x62dfe07f,
-                        0x593e1790, 0xc5ceaa9c, 0xab03806f, 0xc9a6e5a0];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_hc128_true_values_u64() {
-        let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng = Hc128Rng::from_seed(seed);
-
-        let mut results = [0u64; 8];
-        for i in results.iter_mut() { *i = rng.next_u64(); }
-        let expected = [0x3bfd03a073150082, 0xaa63af0efb2fd77f,
-                        0xa7dc29b6de122fc6, 0x8b75ec6862a68527,
-                        0x818960059036db1e, 0x491fbf9a00ade078,
-                        0x6c3d6e241cdc3013, 0x9cd5710290f664b2];
-        assert_eq!(results, expected);
-
-        // The RNG operates in a P block of 512 results and next a Q block.
-        // After skipping 2*800 u32 results we end up somewhere in the Q block
-        // of the second round
-        for _ in 0..800 { rng.next_u64(); }
-
-        for i in results.iter_mut() { *i = rng.next_u64(); }
-        let expected = [0xd8c4d6ca84d0fc10, 0xf16a5d91dc66e8e7,
-                        0xd800de5bc37a8653, 0x7bae1f88c0dfbb4c,
-                        0x3bfe1f374e6d4d14, 0x424b55676be3fa06,
-                        0xe3a1e8758cbff579, 0x417f7198c5652bcd];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_hc128_true_values_bytes() {
-        let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng = Hc128Rng::from_seed(seed);
-        let expected = [0x31, 0xf9, 0x2a, 0xb0, 0x32, 0xf0, 0x39, 0x06,
-                 0x7a, 0xa4, 0xb4, 0xbc, 0x0b, 0x48, 0x22, 0x57,
-                 0x72, 0x9f, 0xf9, 0x2b, 0x66, 0xe5, 0xc0, 0xcd,
-                 0x56, 0x0c, 0x0f, 0x31, 0xe8, 0x83, 0xcc, 0xd3,
-                 0xef, 0xb8, 0x3d, 0x66, 0x7f, 0xe0, 0xdf, 0x62,
-                 0x90, 0x17, 0x3e, 0x59, 0x9c, 0xaa, 0xce, 0xc5,
-                 0x6f, 0x80, 0x03, 0xab, 0xa0, 0xe5, 0xa6, 0xc9,
-                 0x60, 0x95, 0x84, 0x7a, 0xa5, 0x68, 0x5a, 0x84,
-                 0xea, 0xd5, 0xf3, 0xea, 0x73, 0xa9, 0xad, 0x01,
-                 0x79, 0x7d, 0xbe, 0x9f, 0xea, 0xe3, 0xf9, 0x74,
-                 0x0e, 0xda, 0x2f, 0xa0, 0xe4, 0x7b, 0x4b, 0x1b,
-                 0xdd, 0x17, 0x69, 0x4a, 0xfe, 0x9f, 0x56, 0x95,
-                 0xad, 0x83, 0x6b, 0x9d, 0x60, 0xa1, 0x99, 0x96,
-                 0x90, 0x00, 0x66, 0x7f, 0xfa, 0x7e, 0x65, 0xe9,
-                 0xac, 0x8b, 0x92, 0x34, 0x77, 0xb4, 0x23, 0xd0,
-                 0xb9, 0xab, 0xb1, 0x47, 0x7d, 0x4a, 0x13, 0x0a];
-
-        // Pick a somewhat large buffer so we can test filling with the
-        // remainder from `state.results`, directly filling the buffer, and
-        // filling the remainder of the buffer.
-        let mut buffer = [0u8; 16*4*2];
-        // Consume a value so that we have a remainder.
-        assert!(rng.next_u64() == 0x04b4930a518251a4);
-        rng.fill_bytes(&mut buffer);
-
-        // [u8; 128] doesn't implement PartialEq
-        assert_eq!(buffer.len(), expected.len());
-        for (b, e) in buffer.iter().zip(expected.iter()) {
-            assert_eq!(b, e);
-        }
-    }
-
-    #[test]
-    fn test_hc128_clone() {
-        let seed = [0x55,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0, // key
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0]; // iv
-        let mut rng1 = Hc128Rng::from_seed(seed);
-        let mut rng2 = rng1.clone();
-        for _ in 0..16 {
-            assert_eq!(rng1.next_u32(), rng2.next_u32());
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/isaac.rs b/vendor/rand-8c5b0ac51d/src/prng/isaac.rs
deleted file mode 100644
index 5bf739d..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/isaac.rs
+++ /dev/null
@@ -1,482 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The ISAAC random number generator.
-
-use core::{fmt, slice};
-use core::num::Wrapping as w;
-use rand_core::{BlockRngCore, RngCore, SeedableRng, Error, le};
-use rand_core::impls::BlockRng;
-use prng::isaac_array::IsaacArray;
-
-#[allow(non_camel_case_types)]
-type w32 = w<u32>;
-
-const RAND_SIZE_LEN: usize = 8;
-const RAND_SIZE: usize = 1 << RAND_SIZE_LEN;
-
-/// A random number generator that uses the ISAAC algorithm.
-///
-/// ISAAC stands for "Indirection, Shift, Accumulate, Add, and Count" which are
-/// the principal bitwise operations employed. It is the most advanced of a
-/// series of array based random number generator designed by Robert Jenkins
-/// in 1996[1][2].
-///
-/// ISAAC is notably fast and produces excellent quality random numbers for
-/// non-cryptographic applications.
-///
-/// In spite of being designed with cryptographic security in mind, ISAAC hasn't
-/// been stringently cryptanalyzed and thus cryptographers do not not
-/// consensually trust it to be secure. When looking for a secure RNG, prefer
-/// [`Hc128Rng`] instead, which, like ISAAC, is an array-based RNG and one of
-/// the stream-ciphers selected the by eSTREAM contest.
-///
-/// In 2006 an improvement to ISAAC was suggested by Jean-Philippe Aumasson,
-/// named ISAAC+[3]. But because the specification is not complete, because
-/// there is no good implementation, and because the suggested bias may not
-/// exist, it is not implemented here.
-///
-/// ## Overview of the ISAAC algorithm:
-/// (in pseudo-code)
-///
-/// ```text
-/// Input: a, b, c, s[256] // state
-/// Output: r[256]         // results
-///
-/// mix(a,i) = a ^ a << 13   if i = 0 mod 4
-///            a ^ a >>  6   if i = 1 mod 4
-///            a ^ a <<  2   if i = 2 mod 4
-///            a ^ a >> 16   if i = 3 mod 4
-///
-/// c = c + 1
-/// b = b + c
-///
-/// for i in 0..256 {
-///     x = s_[i]
-///     a = f(a,i) + s[i+128 mod 256]
-///     y = a + b + s[x>>2 mod 256]
-///     s[i] = y
-///     b = x + s[y>>10 mod 256]
-///     r[i] = b
-/// }
-/// ```
-///
-/// Numbers are generated in blocks of 256. This means the function above only
-/// runs once every 256 times you ask for a next random number. In all other
-/// circumstances the last element of the results array is returned.
-///
-/// ISAAC therefore needs a lot of memory, relative to other non-vrypto RNGs.
-/// 2 * 256 * 4 = 2 kb to hold the state and results.
-///
-/// ## References
-/// [1]: Bob Jenkins, [*ISAAC: A fast cryptographic random number generator*](
-///      http://burtleburtle.net/bob/rand/isaacafa.html)
-///
-/// [2]: Bob Jenkins, [*ISAAC and RC4*](
-///      http://burtleburtle.net/bob/rand/isaac.html)
-///
-/// [3]: Jean-Philippe Aumasson, [*On the pseudo-random generator ISAAC*](
-///      https://eprint.iacr.org/2006/438)
-///
-/// [`Hc128Rng`]: ../hc128/struct.Hc128Rng.html
-#[derive(Clone, Debug)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct IsaacRng(BlockRng<IsaacCore>);
-
-impl RngCore for IsaacRng {
-    #[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 SeedableRng for IsaacRng {
-    type Seed = <IsaacCore as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        IsaacRng(BlockRng::<IsaacCore>::from_seed(seed))
-    }
-
-    fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> {
-        BlockRng::<IsaacCore>::from_rng(rng).map(|rng| IsaacRng(rng))
-    }
-}
-
-impl IsaacRng {
-    /// Create an ISAAC random number generator using the default
-    /// fixed seed.
-    ///
-    /// DEPRECATED. `IsaacRng::new_from_u64(0)` will produce identical results.
-    #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")]
-    pub fn new_unseeded() -> Self {
-        Self::new_from_u64(0)
-    }
-
-    /// Create an ISAAC random number generator using an `u64` as seed.
-    /// If `seed == 0` this will produce the same stream of random numbers as
-    /// the reference implementation when used unseeded.
-    pub fn new_from_u64(seed: u64) -> Self {
-        IsaacRng(BlockRng::new(IsaacCore::new_from_u64(seed)))
-    }
-}
-
-/// The core of `IsaacRng`, used with `BlockRng`.
-#[derive(Clone)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct IsaacCore {
-    #[cfg_attr(feature="serde1",serde(with="super::isaac_array::isaac_array_serde"))]
-    mem: [w32; RAND_SIZE],
-    a: w32,
-    b: w32,
-    c: w32,
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for IsaacCore {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "IsaacCore {{}}")
-    }
-}
-
-impl BlockRngCore for IsaacCore {
-    type Item = u32;
-    type Results = IsaacArray<Self::Item>;
-
-    /// Refills the output buffer, `results`. See also the pseudocode desciption
-    /// of the algorithm in the [`Isaac64Rng`] documentation.
-    ///
-    /// Optimisations used (similar to the reference implementation):
-    /// 
-    /// - The loop is unrolled 4 times, once for every constant of mix().
-    /// - The contents of the main loop are moved to a function `rngstep`, to
-    ///   reduce code duplication.
-    /// - We use local variables for a and b, which helps with optimisations.
-    /// - We split the main loop in two, one that operates over 0..128 and one
-    ///   over 128..256. This way we can optimise out the addition and modulus
-    ///   from `s[i+128 mod 256]`.
-    /// - We maintain one index `i` and add `m` or `m2` as base (m2 for the
-    ///   `s[i+128 mod 256]`), relying on the optimizer to turn it into pointer
-    ///   arithmetic.
-    /// - We fill `results` backwards. The reference implementation reads values
-    ///   from `results` in reverse. We read them in the normal direction, to
-    ///   make `fill_bytes` a memcopy. To maintain compatibility we fill in
-    ///   reverse.
-    /// 
-    /// [`IsaacRng`]: struct.IsaacRng.html
-    fn generate(&mut self, results: &mut IsaacArray<Self::Item>) {
-        self.c += w(1);
-        // abbreviations
-        let mut a = self.a;
-        let mut b = self.b + self.c;
-        const MIDPOINT: usize = RAND_SIZE / 2;
-
-        #[inline]
-        fn ind(mem:&[w32; RAND_SIZE], v: w32, amount: usize) -> w32 {
-            let index = (v >> amount).0 as usize % RAND_SIZE;
-            mem[index]
-        }
-
-        #[inline]
-        fn rngstep(mem: &mut [w32; RAND_SIZE],
-                   results: &mut [u32; RAND_SIZE],
-                   mix: w32,
-                   a: &mut w32,
-                   b: &mut w32,
-                   base: usize,
-                   m: usize,
-                   m2: usize) {
-            let x = mem[base + m];
-            *a = mix + mem[base + m2];
-            let y = *a + *b + ind(&mem, x, 2);
-            mem[base + m] = y;
-            *b = x + ind(&mem, y, 2 + RAND_SIZE_LEN);
-            results[RAND_SIZE - 1 - base - m] = (*b).0;
-        }
-
-        let mut m = 0;
-        let mut m2 = MIDPOINT;
-        for i in (0..MIDPOINT/4).map(|i| i * 4) {
-            rngstep(&mut self.mem, results, a ^ (a << 13), &mut a, &mut b, i + 0, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a >> 6 ),  &mut a, &mut b, i + 1, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a << 2 ),  &mut a, &mut b, i + 2, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a >> 16),  &mut a, &mut b, i + 3, m, m2);
-        }
-
-        m = MIDPOINT;
-        m2 = 0;
-        for i in (0..MIDPOINT/4).map(|i| i * 4) {
-            rngstep(&mut self.mem, results, a ^ (a << 13), &mut a, &mut b, i + 0, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a >> 6 ),  &mut a, &mut b, i + 1, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a << 2 ),  &mut a, &mut b, i + 2, m, m2);
-            rngstep(&mut self.mem, results, a ^ (a >> 16),  &mut a, &mut b, i + 3, m, m2);
-        }
-
-        self.a = a;
-        self.b = b;
-    }
-}
-
-impl IsaacCore {
-    /// Create a new ISAAC random number generator.
-    ///
-    /// The author Bob Jenkins describes how to best initialize ISAAC here:
-    /// <https://rt.cpan.org/Public/Bug/Display.html?id=64324>
-    /// The answer is included here just in case:
-    ///
-    /// "No, you don't need a full 8192 bits of seed data. Normal key sizes will
-    /// do fine, and they should have their expected strength (eg a 40-bit key
-    /// will take as much time to brute force as 40-bit keysÂ usually will). You
-    /// could fill the remainder with 0, but set the last array element to the
-    /// length of the key provided (to distinguish keys that differ only by
-    /// different amounts of 0 padding). You do still need to call randinit() to
-    /// make sure the initial state isn't uniform-looking."
-    /// "After publishing ISAAC, I wanted to limit the key to half the size of
-    /// r[], and repeat it twice. That would have made it hard to provide a key
-    /// that sets the whole internal state to anything convenient. But I'd
-    /// already published it."
-    ///
-    /// And his answer to the question "For my code, would repeating the key
-    /// over and over to fill 256 integers be a better solution than
-    /// zero-filling, or would they essentially be the same?":
-    /// "If the seed is under 32 bytes, they're essentially the same, otherwise
-    /// repeating the seed would be stronger. randinit() takes a chunk of 32
-    /// bytes, mixes it, and combines that with the next 32 bytes, et cetera.
-    /// Then loops over all the elements the same way a second time."
-    #[inline]
-    fn init(mut mem: [w32; RAND_SIZE], rounds: u32) -> Self {
-        fn mix(a: &mut w32, b: &mut w32, c: &mut w32, d: &mut w32,
-               e: &mut w32, f: &mut w32, g: &mut w32, h: &mut w32) {
-            *a ^= *b << 11; *d += *a; *b += *c;
-            *b ^= *c >> 2;  *e += *b; *c += *d;
-            *c ^= *d << 8;  *f += *c; *d += *e;
-            *d ^= *e >> 16; *g += *d; *e += *f;
-            *e ^= *f << 10; *h += *e; *f += *g;
-            *f ^= *g >> 4;  *a += *f; *g += *h;
-            *g ^= *h << 8;  *b += *g; *h += *a;
-            *h ^= *a >> 9;  *c += *h; *a += *b;
-        }
-
-        // These numbers are the result of initializing a...h with the
-        // fractional part of the golden ratio in binary (0x9e3779b9)
-        // and applying mix() 4 times.
-        let mut a = w(0x1367df5a);
-        let mut b = w(0x95d90059);
-        let mut c = w(0xc3163e4b);
-        let mut d = w(0x0f421ad8);
-        let mut e = w(0xd92a4a78);
-        let mut f = w(0xa51a3c49);
-        let mut g = w(0xc4efea1b);
-        let mut h = w(0x30609119);
-
-        // Normally this should do two passes, to make all of the seed effect
-        // all of `mem`
-        for _ in 0..rounds {
-            for i in (0..RAND_SIZE/8).map(|i| i * 8) {
-                a += mem[i  ]; b += mem[i+1];
-                c += mem[i+2]; d += mem[i+3];
-                e += mem[i+4]; f += mem[i+5];
-                g += mem[i+6]; h += mem[i+7];
-                mix(&mut a, &mut b, &mut c, &mut d,
-                    &mut e, &mut f, &mut g, &mut h);
-                mem[i  ] = a; mem[i+1] = b;
-                mem[i+2] = c; mem[i+3] = d;
-                mem[i+4] = e; mem[i+5] = f;
-                mem[i+6] = g; mem[i+7] = h;
-            }
-        }
-
-        Self { mem, a: w(0), b: w(0), c: w(0) }
-    }
-
-    /// Create an ISAAC random number generator using an `u64` as seed.
-    /// If `seed == 0` this will produce the same stream of random numbers as
-    /// the reference implementation when used unseeded.
-    fn new_from_u64(seed: u64) -> Self {
-        let mut key = [w(0); RAND_SIZE];
-        key[0] = w(seed as u32);
-        key[1] = w((seed >> 32) as u32);
-        // Initialize with only one pass.
-        // A second pass does not improve the quality here, because all of the
-        // seed was already available in the first round.
-        // Not doing the second pass has the small advantage that if
-        // `seed == 0` this method produces exactly the same state as the
-        // reference implementation when used unseeded.
-        Self::init(key, 1)
-    }
-}
-
-impl SeedableRng for IsaacCore {
-    type Seed = [u8; 32];
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        let mut seed_u32 = [0u32; 8];
-        le::read_u32_into(&seed, &mut seed_u32);
-        // Convert the seed to `Wrapping<u32>` and zero-extend to `RAND_SIZE`.
-        let mut seed_extended = [w(0); RAND_SIZE];
-        for (x, y) in seed_extended.iter_mut().zip(seed_u32.iter()) {
-            *x = w(*y);
-        }
-        Self::init(seed_extended, 2)
-    }
-
-    fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> {
-        // Custom `from_rng` implementation that fills a seed with the same size
-        // as the entire state.
-        let mut seed = [w(0u32); RAND_SIZE];
-        unsafe {
-            let ptr = seed.as_mut_ptr() as *mut u8;
-
-            let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE * 4);
-            rng.try_fill_bytes(slice)?;
-        }
-        for i in seed.iter_mut() {
-            *i = w(i.0.to_le());
-        }
-
-        Ok(Self::init(seed, 2))
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use {RngCore, SeedableRng};
-    use super::IsaacRng;
-
-    #[test]
-    fn test_isaac_construction() {
-        // Test that various construction techniques produce a working RNG.
-        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];
-        let mut rng1 = IsaacRng::from_seed(seed);
-        assert_eq!(rng1.next_u32(), 2869442790);
-
-        let mut rng2 = IsaacRng::from_rng(rng1).unwrap();
-        assert_eq!(rng2.next_u32(), 3094074039);
-    }
-
-    #[test]
-    fn test_isaac_true_values_32() {
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                     57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng1 = IsaacRng::from_seed(seed);
-        let mut results = [0u32; 10];
-        for i in results.iter_mut() { *i = rng1.next_u32(); }
-        let expected = [
-            2558573138, 873787463, 263499565, 2103644246, 3595684709,
-            4203127393, 264982119, 2765226902, 2737944514, 3900253796];
-        assert_eq!(results, expected);
-
-        let seed = [57,48,0,0, 50,9,1,0, 49,212,0,0, 148,38,0,0,
-                    0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng2 = IsaacRng::from_seed(seed);
-        // skip forward to the 10000th number
-        for _ in 0..10000 { rng2.next_u32(); }
-
-        for i in results.iter_mut() { *i = rng2.next_u32(); }
-        let expected = [
-            3676831399, 3183332890, 2834741178, 3854698763, 2717568474,
-            1576568959, 3507990155, 179069555, 141456972, 2478885421];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac_true_values_64() {
-        // As above, using little-endian versions of above values
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                    57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng = IsaacRng::from_seed(seed);
-        let mut results = [0u64; 5];
-        for i in results.iter_mut() { *i = rng.next_u64(); }
-        let expected = [
-            3752888579798383186, 9035083239252078381,18052294697452424037,
-            11876559110374379111, 16751462502657800130];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac_true_bytes() {
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                     57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng = IsaacRng::from_seed(seed);
-        let mut results = [0u8; 32];
-        rng.fill_bytes(&mut results);
-        // Same as first values in test_isaac_true_values as bytes in LE order
-        let expected = [82, 186, 128, 152, 71, 240, 20, 52,
-                        45, 175, 180, 15, 86, 16, 99, 125,
-                        101, 203, 81, 214, 97, 162, 134, 250,
-                        103, 78, 203, 15, 150, 3, 210, 164];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac_new_uninitialized() {
-        // Compare the results from initializing `IsaacRng` with
-        // `new_from_u64(0)`, to make sure it is the same as the reference
-        // implementation when used uninitialized.
-        // Note: We only test the first 16 integers, not the full 256 of the
-        // first block.
-        let mut rng = IsaacRng::new_from_u64(0);
-        let mut results = [0u32; 16];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected: [u32; 16] = [
-            0x71D71FD2, 0xB54ADAE7, 0xD4788559, 0xC36129FA,
-            0x21DC1EA9, 0x3CB879CA, 0xD83B237F, 0xFA3CE5BD,
-            0x8D048509, 0xD82E9489, 0xDB452848, 0xCA20E846,
-            0x500F972E, 0x0EEFF940, 0x00D6B993, 0xBC12C17F];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac_clone() {
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                     57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng1 = IsaacRng::from_seed(seed);
-        let mut rng2 = rng1.clone();
-        for _ in 0..16 {
-            assert_eq!(rng1.next_u32(), rng2.next_u32());
-        }
-    }
-
-    #[test]
-    #[cfg(all(feature="serde1", feature="std"))]
-    fn test_isaac_serde() {
-        use bincode;
-        use std::io::{BufWriter, BufReader};
-
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                     57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng = IsaacRng::from_seed(seed);
-
-        let buf: Vec<u8> = Vec::new();
-        let mut buf = BufWriter::new(buf);
-        bincode::serialize_into(&mut buf, &rng).expect("Could not serialize");
-
-        let buf = buf.into_inner().unwrap();
-        let mut read = BufReader::new(&buf[..]);
-        let mut deserialized: IsaacRng = bincode::deserialize_from(&mut read).expect("Could not deserialize");
-
-        for _ in 0..300 { // more than the 256 buffered results
-            assert_eq!(rng.next_u32(), deserialized.next_u32());
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/isaac64.rs b/vendor/rand-8c5b0ac51d/src/prng/isaac64.rs
deleted file mode 100644
index 35376fb..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/isaac64.rs
+++ /dev/null
@@ -1,474 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! The ISAAC-64 random number generator.
-
-use core::{fmt, slice};
-use core::num::Wrapping as w;
-use rand_core::{BlockRngCore, RngCore, SeedableRng, Error, le};
-use rand_core::impls::BlockRng64;
-use prng::isaac_array::IsaacArray;
-
-#[allow(non_camel_case_types)]
-type w64 = w<u64>;
-
-const RAND_SIZE_LEN: usize = 8;
-const RAND_SIZE: usize = 1 << RAND_SIZE_LEN;
-
-/// A random number generator that uses ISAAC-64, the 64-bit variant of the
-/// ISAAC algorithm.
-///
-/// ISAAC stands for "Indirection, Shift, Accumulate, Add, and Count" which are
-/// the principal bitwise operations employed. It is the most advanced of a
-/// series of array based random number generator designed by Robert Jenkins
-/// in 1996[1].
-///
-/// ISAAC-64 is mostly similar to ISAAC. Because it operates on 64-bit integers
-/// instead of 32-bit, it uses twice as much memory to hold its state and
-/// results. Also it uses different constants for shifts and indirect indexing,
-/// optimized to give good results for 64bit arithmetic.
-///
-/// ISAAC-64 is notably fast and produces excellent quality random numbers for
-/// non-cryptographic applications.
-///
-/// In spite of being designed with cryptographic security in mind, ISAAC hasn't
-/// been stringently cryptanalyzed and thus cryptographers do not not
-/// consensually trust it to be secure. When looking for a secure RNG, prefer
-/// [`Hc128Rng`] instead, which, like ISAAC, is an array-based RNG and one of
-/// the stream-ciphers selected the by eSTREAM contest.
-///
-/// ## Overview of the ISAAC-64 algorithm:
-/// (in pseudo-code)
-///
-/// ```text
-/// Input: a, b, c, s[256] // state
-/// Output: r[256] // results
-///
-/// mix(a,i) = !(a ^ a << 21)  if i = 0 mod 4
-///              a ^ a >>  5   if i = 1 mod 4
-///              a ^ a << 12   if i = 2 mod 4
-///              a ^ a >> 33   if i = 3 mod 4
-///
-/// c = c + 1
-/// b = b + c
-///
-/// for i in 0..256 {
-///     x = s_[i]
-///     a = mix(a,i) + s[i+128 mod 256]
-///     y = a + b + s[x>>3 mod 256]
-///     s[i] = y
-///     b = x + s[y>>11 mod 256]
-///     r[i] = b
-/// }
-/// ```
-///
-/// See for more information the documentation of [`IsaacRng`].
-///
-/// [1]: Bob Jenkins, [*ISAAC and RC4*](
-///      http://burtleburtle.net/bob/rand/isaac.html)
-///
-/// [`IsaacRng`]: ../isaac/struct.IsaacRng.html
-/// [`Hc128Rng`]: ../hc128/struct.Hc128Rng.html
-#[derive(Clone, Debug)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct Isaac64Rng(BlockRng64<Isaac64Core>);
-
-impl RngCore for Isaac64Rng {
-    #[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 SeedableRng for Isaac64Rng {
-    type Seed = <Isaac64Core as SeedableRng>::Seed;
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        Isaac64Rng(BlockRng64::<Isaac64Core>::from_seed(seed))
-    }
-
-    fn from_rng<S: RngCore>(rng: S) -> Result<Self, Error> {
-        BlockRng64::<Isaac64Core>::from_rng(rng).map(|rng| Isaac64Rng(rng))
-    }
-}
-
-impl Isaac64Rng {
-    /// Create a 64-bit ISAAC random number generator using the
-    /// default fixed seed.
-    ///
-    /// DEPRECATED. `Isaac64Rng::new_from_u64(0)` will produce identical results.
-    #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")]
-    pub fn new_unseeded() -> Self {
-        Self::new_from_u64(0)
-    }
-
-    /// Create an ISAAC-64 random number generator using an `u64` as seed.
-    /// If `seed == 0` this will produce the same stream of random numbers as
-    /// the reference implementation when used unseeded.
-    pub fn new_from_u64(seed: u64) -> Self {
-        Isaac64Rng(BlockRng64::new(Isaac64Core::new_from_u64(seed)))
-    }
-}
-
-/// The core of `Isaac64Rng`, used with `BlockRng`.
-#[derive(Clone)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct Isaac64Core {
-    #[cfg_attr(feature="serde1",serde(with="super::isaac_array::isaac_array_serde"))]
-    mem: [w64; RAND_SIZE],
-    a: w64,
-    b: w64,
-    c: w64,
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for Isaac64Core {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "Isaac64Core {{}}")
-    }
-}
-
-impl BlockRngCore for Isaac64Core {
-    type Item = u64;
-    type Results = IsaacArray<Self::Item>;
-
-    /// Refills the output buffer, `results`. See also the pseudocode desciption
-    /// of the algorithm in the [`Isaac64Rng`] documentation.
-    ///
-    /// Optimisations used (similar to the reference implementation):
-    /// 
-    /// - The loop is unrolled 4 times, once for every constant of mix().
-    /// - The contents of the main loop are moved to a function `rngstep`, to
-    ///   reduce code duplication.
-    /// - We use local variables for a and b, which helps with optimisations.
-    /// - We split the main loop in two, one that operates over 0..128 and one
-    ///   over 128..256. This way we can optimise out the addition and modulus
-    ///   from `s[i+128 mod 256]`.
-    /// - We maintain one index `i` and add `m` or `m2` as base (m2 for the
-    ///   `s[i+128 mod 256]`), relying on the optimizer to turn it into pointer
-    ///   arithmetic.
-    /// - We fill `results` backwards. The reference implementation reads values
-    ///   from `results` in reverse. We read them in the normal direction, to
-    ///   make `fill_bytes` a memcopy. To maintain compatibility we fill in
-    ///   reverse.
-    /// 
-    /// [`Isaac64Rng`]: struct.Isaac64Rng.html
-    fn generate(&mut self, results: &mut IsaacArray<Self::Item>) {
-        self.c += w(1);
-        // abbreviations
-        let mut a = self.a;
-        let mut b = self.b + self.c;
-        const MIDPOINT: usize = RAND_SIZE / 2;
-
-        #[inline]
-        fn ind(mem:&[w64; RAND_SIZE], v: w64, amount: usize) -> w64 {
-            let index = (v >> amount).0 as usize % RAND_SIZE;
-            mem[index]
-        }
-
-        #[inline]
-        fn rngstep(mem: &mut [w64; RAND_SIZE],
-                   results: &mut [u64; RAND_SIZE],
-                   mix: w64,
-                   a: &mut w64,
-                   b: &mut w64,
-                   base: usize,
-                   m: usize,
-                   m2: usize) {
-            let x = mem[base + m];
-            *a = mix + mem[base + m2];
-            let y = *a + *b + ind(&mem, x, 3);
-            mem[base + m] = y;
-            *b = x + ind(&mem, y, 3 + RAND_SIZE_LEN);
-            results[RAND_SIZE - 1 - base - m] = (*b).0;
-        }
-
-        let mut m = 0;
-        let mut m2 = MIDPOINT;
-        for i in (0..MIDPOINT/4).map(|i| i * 4) {
-            rngstep(&mut self.mem, results, !(a ^ (a << 21)), &mut a, &mut b, i + 0, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a >> 5 ),  &mut a, &mut b, i + 1, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a << 12),  &mut a, &mut b, i + 2, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a >> 33),  &mut a, &mut b, i + 3, m, m2);
-        }
-
-        m = MIDPOINT;
-        m2 = 0;
-        for i in (0..MIDPOINT/4).map(|i| i * 4) {
-            rngstep(&mut self.mem, results, !(a ^ (a << 21)), &mut a, &mut b, i + 0, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a >> 5 ),  &mut a, &mut b, i + 1, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a << 12),  &mut a, &mut b, i + 2, m, m2);
-            rngstep(&mut self.mem, results,   a ^ (a >> 33),  &mut a, &mut b, i + 3, m, m2);
-        }
-
-        self.a = a;
-        self.b = b;
-    }
-}
-
-impl Isaac64Core {
-    /// Create a new ISAAC-64 random number generator.
-    fn init(mut mem: [w64; RAND_SIZE], rounds: u32) -> Self {
-        fn mix(a: &mut w64, b: &mut w64, c: &mut w64, d: &mut w64,
-               e: &mut w64, f: &mut w64, g: &mut w64, h: &mut w64) {
-            *a -= *e; *f ^= *h >> 9;  *h += *a;
-            *b -= *f; *g ^= *a << 9;  *a += *b;
-            *c -= *g; *h ^= *b >> 23; *b += *c;
-            *d -= *h; *a ^= *c << 15; *c += *d;
-            *e -= *a; *b ^= *d >> 14; *d += *e;
-            *f -= *b; *c ^= *e << 20; *e += *f;
-            *g -= *c; *d ^= *f >> 17; *f += *g;
-            *h -= *d; *e ^= *g << 14; *g += *h;
-        }
-
-        // These numbers are the result of initializing a...h with the
-        // fractional part of the golden ratio in binary (0x9e3779b97f4a7c13)
-        // and applying mix() 4 times.
-        let mut a = w(0x647c4677a2884b7c);
-        let mut b = w(0xb9f8b322c73ac862);
-        let mut c = w(0x8c0ea5053d4712a0);
-        let mut d = w(0xb29b2e824a595524);
-        let mut e = w(0x82f053db8355e0ce);
-        let mut f = w(0x48fe4a0fa5a09315);
-        let mut g = w(0xae985bf2cbfc89ed);
-        let mut h = w(0x98f5704f6c44c0ab);
-
-        // Normally this should do two passes, to make all of the seed effect
-        // all of `mem`
-        for _ in 0..rounds {
-            for i in (0..RAND_SIZE/8).map(|i| i * 8) {
-                a += mem[i  ]; b += mem[i+1];
-                c += mem[i+2]; d += mem[i+3];
-                e += mem[i+4]; f += mem[i+5];
-                g += mem[i+6]; h += mem[i+7];
-                mix(&mut a, &mut b, &mut c, &mut d,
-                    &mut e, &mut f, &mut g, &mut h);
-                mem[i  ] = a; mem[i+1] = b;
-                mem[i+2] = c; mem[i+3] = d;
-                mem[i+4] = e; mem[i+5] = f;
-                mem[i+6] = g; mem[i+7] = h;
-            }
-        }
-
-        Self { mem, a: w(0), b: w(0), c: w(0) }
-    }
-
-    /// Create an ISAAC-64 random number generator using an `u64` as seed.
-    /// If `seed == 0` this will produce the same stream of random numbers as
-    /// the reference implementation when used unseeded.
-    pub fn new_from_u64(seed: u64) -> Self {
-        let mut key = [w(0); RAND_SIZE];
-        key[0] = w(seed);
-        // Initialize with only one pass.
-        // A second pass does not improve the quality here, because all of the
-        // seed was already available in the first round.
-        // Not doing the second pass has the small advantage that if
-        // `seed == 0` this method produces exactly the same state as the
-        // reference implementation when used unseeded.
-        Self::init(key, 1)
-    }
-}
-
-impl SeedableRng for Isaac64Core {
-    type Seed = [u8; 32];
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        let mut seed_u64 = [0u64; 4];
-        le::read_u64_into(&seed, &mut seed_u64);
-        // Convert the seed to `Wrapping<u64>` and zero-extend to `RAND_SIZE`.
-        let mut seed_extended = [w(0); RAND_SIZE];
-        for (x, y) in seed_extended.iter_mut().zip(seed_u64.iter()) {
-            *x = w(*y);
-        }
-        Self::init(seed_extended, 2)
-    }
-
-    fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> {
-        // Custom `from_rng` implementation that fills a seed with the same size
-        // as the entire state.
-        let mut seed = [w(0u64); RAND_SIZE];
-        unsafe {
-            let ptr = seed.as_mut_ptr() as *mut u8;
-            let slice = slice::from_raw_parts_mut(ptr, RAND_SIZE * 8);
-            rng.try_fill_bytes(slice)?;
-        }
-        for i in seed.iter_mut() {
-            *i = w(i.0.to_le());
-        }
-
-        Ok(Self::init(seed, 2))
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use {RngCore, SeedableRng};
-    use super::Isaac64Rng;
-
-    #[test]
-    fn test_isaac64_construction() {
-        // Test that various construction techniques produce a working RNG.
-        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];
-        let mut rng1 = Isaac64Rng::from_seed(seed);
-        assert_eq!(rng1.next_u64(), 14964555543728284049);
-
-        let mut rng2 = Isaac64Rng::from_rng(rng1).unwrap();
-        assert_eq!(rng2.next_u64(), 919595328260451758);
-    }
-
-    #[test]
-    fn test_isaac64_true_values_64() {
-        let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0,
-                    200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0];
-        let mut rng1 = Isaac64Rng::from_seed(seed);
-        let mut results = [0u64; 10];
-        for i in results.iter_mut() { *i = rng1.next_u64(); }
-        let expected = [
-                   15071495833797886820, 7720185633435529318,
-                   10836773366498097981, 5414053799617603544,
-                   12890513357046278984, 17001051845652595546,
-                   9240803642279356310, 12558996012687158051,
-                   14673053937227185542, 1677046725350116783];
-        assert_eq!(results, expected);
-
-        let seed = [57,48,0,0, 0,0,0,0, 50,9,1,0, 0,0,0,0,
-                    49,212,0,0, 0,0,0,0, 148,38,0,0, 0,0,0,0];
-        let mut rng2 = Isaac64Rng::from_seed(seed);
-        // skip forward to the 10000th number
-        for _ in 0..10000 { rng2.next_u64(); }
-
-        for i in results.iter_mut() { *i = rng2.next_u64(); }
-        let expected = [
-            18143823860592706164, 8491801882678285927, 2699425367717515619,
-            17196852593171130876, 2606123525235546165, 15790932315217671084,
-            596345674630742204, 9947027391921273664, 11788097613744130851,
-            10391409374914919106];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac64_true_values_32() {
-        let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0,
-                    200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0];
-        let mut rng = Isaac64Rng::from_seed(seed);
-        let mut results = [0u32; 12];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        // Subset of above values, as an LE u32 sequence
-        let expected = [
-                    3477963620, 3509106075,
-                    687845478, 1797495790,
-                    227048253, 2523132918,
-                    4044335064, 1260557630,
-                    4079741768, 3001306521,
-                    69157722, 3958365844];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac64_true_values_mixed() {
-        let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0,
-                    200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0];
-        let mut rng = Isaac64Rng::from_seed(seed);
-        // Test alternating between `next_u64` and `next_u32` works as expected.
-        // Values are the same as `test_isaac64_true_values` and
-        // `test_isaac64_true_values_32`.
-        assert_eq!(rng.next_u64(), 15071495833797886820);
-        assert_eq!(rng.next_u32(), 687845478);
-        assert_eq!(rng.next_u32(), 1797495790);
-        assert_eq!(rng.next_u64(), 10836773366498097981);
-        assert_eq!(rng.next_u32(), 4044335064);
-        // Skip one u32
-        assert_eq!(rng.next_u64(), 12890513357046278984);
-        assert_eq!(rng.next_u32(), 69157722);
-    }
-
-    #[test]
-    fn test_isaac64_true_bytes() {
-        let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0,
-                    200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0];
-        let mut rng = Isaac64Rng::from_seed(seed);
-        let mut results = [0u8; 32];
-        rng.fill_bytes(&mut results);
-        // Same as first values in test_isaac64_true_values as bytes in LE order
-        let expected = [100, 131, 77, 207, 155, 181, 40, 209,
-                        102, 176, 255, 40, 238, 155, 35, 107,
-                        61, 123, 136, 13, 246, 243, 99, 150,
-                        216, 167, 15, 241, 62, 149, 34, 75];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac64_new_uninitialized() {
-        // Compare the results from initializing `IsaacRng` with
-        // `new_from_u64(0)`, to make sure it is the same as the reference
-        // implementation when used uninitialized.
-        // Note: We only test the first 16 integers, not the full 256 of the
-        // first block.
-        let mut rng = Isaac64Rng::new_from_u64(0);
-        let mut results = [0u64; 16];
-        for i in results.iter_mut() { *i = rng.next_u64(); }
-        let expected: [u64; 16] = [
-            0xF67DFBA498E4937C, 0x84A5066A9204F380, 0xFEE34BD5F5514DBB,
-            0x4D1664739B8F80D6, 0x8607459AB52A14AA, 0x0E78BC5A98529E49,
-            0xFE5332822AD13777, 0x556C27525E33D01A, 0x08643CA615F3149F,
-            0xD0771FAF3CB04714, 0x30E86F68A37B008D, 0x3074EBC0488A3ADF,
-            0x270645EA7A2790BC, 0x5601A0A8D3763C6A, 0x2F83071F53F325DD,
-            0xB9090F3D42D2D2EA];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_isaac64_clone() {
-        let seed = [1,0,0,0, 0,0,0,0, 23,0,0,0, 0,0,0,0,
-                    200,1,0,0, 0,0,0,0, 210,30,0,0, 0,0,0,0];
-        let mut rng1 = Isaac64Rng::from_seed(seed);
-        let mut rng2 = rng1.clone();
-        for _ in 0..16 {
-            assert_eq!(rng1.next_u64(), rng2.next_u64());
-        }
-    }
-
-    #[test]
-    #[cfg(all(feature="serde1", feature="std"))]
-    fn test_isaac64_serde() {
-        use bincode;
-        use std::io::{BufWriter, BufReader};
-
-        let seed = [1,0,0,0, 23,0,0,0, 200,1,0,0, 210,30,0,0,
-                     57,48,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng = Isaac64Rng::from_seed(seed);
-
-        let buf: Vec<u8> = Vec::new();
-        let mut buf = BufWriter::new(buf);
-        bincode::serialize_into(&mut buf, &rng).expect("Could not serialize");
-
-        let buf = buf.into_inner().unwrap();
-        let mut read = BufReader::new(&buf[..]);
-        let mut deserialized: Isaac64Rng = bincode::deserialize_from(&mut read).expect("Could not deserialize");
-
-        for _ in 0..300 { // more than the 256 buffered results
-            assert_eq!(rng.next_u64(), deserialized.next_u64());
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/isaac_array.rs b/vendor/rand-8c5b0ac51d/src/prng/isaac_array.rs
deleted file mode 100644
index 327cfbf..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/isaac_array.rs
+++ /dev/null
@@ -1,130 +0,0 @@
-// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! ISAAC helper functions for 256-element arrays.
-
-// Terrible workaround because arrays with more than 32 elements do not
-// implement `AsRef`, `Default`, `Serialize`, `Deserialize`, or any other
-// traits for that matter.
-
-#[cfg(feature="serde1")] use serde::{Serialize, Deserialize};
-
-const RAND_SIZE_LEN: usize = 8;
-const RAND_SIZE: usize = 1 << RAND_SIZE_LEN;
-
-
-#[derive(Copy, Clone)]
-#[allow(missing_debug_implementations)]
-#[cfg_attr(feature="serde1", derive(Serialize, Deserialize))]
-pub struct IsaacArray<T> {
-    #[cfg_attr(feature="serde1",serde(with="isaac_array_serde"))]
-    #[cfg_attr(feature="serde1", serde(bound(
-        serialize = "T: Serialize",
-        deserialize = "T: Deserialize<'de> + Copy + Default")))]
-    inner: [T; RAND_SIZE]
-}
-
-impl<T> ::core::convert::AsRef<[T]> for IsaacArray<T> {
-    #[inline(always)]
-    fn as_ref(&self) -> &[T] {
-        &self.inner[..]
-    }
-}
-
-impl<T> ::core::ops::Deref for IsaacArray<T> {
-    type Target = [T; RAND_SIZE];
-    #[inline(always)]
-    fn deref(&self) -> &Self::Target {
-        &self.inner
-    }
-}
-
-impl<T> ::core::ops::DerefMut for IsaacArray<T> {
-    #[inline(always)]
-    fn deref_mut(&mut self) -> &mut [T; RAND_SIZE] {
-        &mut self.inner
-    }
-}
-
-impl<T> ::core::default::Default for IsaacArray<T> where T: Copy + Default {
-    fn default() -> IsaacArray<T> {
-        IsaacArray { inner: [T::default(); RAND_SIZE] }
-    }
-}
-
-
-#[cfg(feature="serde1")]
-pub(super) mod isaac_array_serde {
-    const RAND_SIZE_LEN: usize = 8;
-    const RAND_SIZE: usize = 1 << RAND_SIZE_LEN;
-
-    use serde::{Deserialize, Deserializer, Serialize, Serializer};
-    use serde::de::{Visitor,SeqAccess};
-    use serde::de;
-
-    use core::fmt;
-
-    pub fn serialize<T, S>(arr: &[T;RAND_SIZE], ser: S) -> Result<S::Ok, S::Error>
-    where
-        T: Serialize,
-        S: Serializer
-    {
-        use serde::ser::SerializeTuple;
-
-        let mut seq = ser.serialize_tuple(RAND_SIZE)?;
-
-        for e in arr.iter() {
-            seq.serialize_element(&e)?;
-        }
-
-        seq.end()
-    }
-
-    #[inline]
-    pub fn deserialize<'de, T, D>(de: D) -> Result<[T;RAND_SIZE], D::Error>
-    where
-        T: Deserialize<'de>+Default+Copy,
-        D: Deserializer<'de>,
-    {
-        use core::marker::PhantomData;
-        struct ArrayVisitor<T> {
-            _pd: PhantomData<T>,
-        };
-        impl<'de,T> Visitor<'de> for ArrayVisitor<T>
-        where
-            T: Deserialize<'de>+Default+Copy
-        {
-            type Value = [T; RAND_SIZE];
-
-            fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
-                formatter.write_str("Isaac state array")
-            }
-
-            #[inline]
-            fn visit_seq<A>(self, mut seq: A) -> Result<[T; RAND_SIZE], A::Error>
-            where
-                A: SeqAccess<'de>,
-            {
-                let mut out = [Default::default();RAND_SIZE];
-
-                for i in 0..RAND_SIZE {
-                    match seq.next_element()? {
-                        Some(val) => out[i] = val,
-                        None => return Err(de::Error::invalid_length(i, &self)),
-                    };
-                }
-
-                Ok(out)
-            }
-        }
-
-        de.deserialize_tuple(RAND_SIZE, ArrayVisitor{_pd: PhantomData})
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/prng/mod.rs b/vendor/rand-8c5b0ac51d/src/prng/mod.rs
deleted file mode 100644
index c4bd003..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/mod.rs
+++ /dev/null
@@ -1,55 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Pseudo random number generators are algorithms to produce *apparently
-//! random* numbers deterministically, and usually fairly quickly.
-//!
-//! So long as the algorithm is computationally secure, is initialised with
-//! sufficient entropy (i.e. unknown by an attacker), and its internal state is
-//! also protected (unknown to an attacker), the output will also be
-//! *computationally secure*. Computationally Secure Pseudo Random Number
-//! Generators (CSPRNGs) are thus suitable sources of random numbers for
-//! cryptography. There are a couple of gotchas here, however. First, the seed
-//! used for initialisation must be unknown. Usually this should be provided by
-//! the operating system and should usually be secure, however this may not
-//! always be the case (especially soon after startup). Second, user-space
-//! memory may be vulnerable, for example when written to swap space, and after
-//! forking a child process should reinitialise any user-space PRNGs. For this
-//! reason it may be preferable to source random numbers directly from the OS
-//! for cryptographic applications.
-//!
-//! PRNGs are also widely used for non-cryptographic uses: randomised
-//! algorithms, simulations, games. In these applications it is usually not
-//! important for numbers to be cryptographically *unguessable*, but even
-//! distribution and independence from other samples (from the point of view
-//! of someone unaware of the algorithm used, at least) may still be important.
-//! Good PRNGs should satisfy these properties, but do not take them for
-//! granted; Wikipedia's article on
-//! [Pseudorandom number generators](https://en.wikipedia.org/wiki/Pseudorandom_number_generator)
-//! provides some background on this topic.
-//!
-//! Care should be taken when seeding (initialising) PRNGs. Some PRNGs have
-//! short periods for some seeds. If one PRNG is seeded from another using the
-//! same algorithm, it is possible that both will yield the same sequence of
-//! values (with some lag).
-
-pub mod chacha;
-pub mod hc128;
-pub mod isaac;
-pub mod isaac64;
-mod xorshift;
-
-mod isaac_array;
-
-pub use self::chacha::ChaChaRng;
-pub use self::hc128::Hc128Rng;
-pub use self::isaac::IsaacRng;
-pub use self::isaac64::Isaac64Rng;
-pub use self::xorshift::XorShiftRng;
diff --git a/vendor/rand-8c5b0ac51d/src/prng/xorshift.rs b/vendor/rand-8c5b0ac51d/src/prng/xorshift.rs
deleted file mode 100644
index 5f96170..0000000
--- a/vendor/rand-8c5b0ac51d/src/prng/xorshift.rs
+++ /dev/null
@@ -1,226 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Xorshift generators
-
-use core::num::Wrapping as w;
-use core::{fmt, slice};
-use rand_core::{RngCore, SeedableRng, Error, impls, le};
-
-/// An Xorshift[1] random number
-/// generator.
-///
-/// The Xorshift algorithm is not suitable for cryptographic purposes
-/// but is very fast. If you do not know for sure that it fits your
-/// requirements, use a more secure one such as `IsaacRng` or `OsRng`.
-///
-/// [1]: Marsaglia, George (July 2003). ["Xorshift
-/// RNGs"](https://www.jstatsoft.org/v08/i14/paper). *Journal of
-/// Statistical Software*. Vol. 8 (Issue 14).
-#[derive(Clone)]
-#[cfg_attr(feature="serde1", derive(Serialize,Deserialize))]
-pub struct XorShiftRng {
-    x: w<u32>,
-    y: w<u32>,
-    z: w<u32>,
-    w: w<u32>,
-}
-
-// Custom Debug implementation that does not expose the internal state
-impl fmt::Debug for XorShiftRng {
-    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
-        write!(f, "XorShiftRng {{}}")
-    }
-}
-
-impl XorShiftRng {
-    /// Creates a new XorShiftRng instance which is not seeded.
-    ///
-    /// The initial values of this RNG are constants, so all generators created
-    /// by this function will yield the same stream of random numbers. It is
-    /// highly recommended that this is created through `SeedableRng` instead of
-    /// this function
-    #[deprecated(since="0.5.0", note="use the FromEntropy or SeedableRng trait")]
-    pub fn new_unseeded() -> XorShiftRng {
-        XorShiftRng {
-            x: w(0x193a6754),
-            y: w(0xa8a7d469),
-            z: w(0x97830e05),
-            w: w(0x113ba7bb),
-        }
-    }
-}
-
-impl RngCore for XorShiftRng {
-    #[inline]
-    fn next_u32(&mut self) -> u32 {
-        let x = self.x;
-        let t = x ^ (x << 11);
-        self.x = self.y;
-        self.y = self.z;
-        self.z = self.w;
-        let w_ = self.w;
-        self.w = w_ ^ (w_ >> 19) ^ (t ^ (t >> 8));
-        self.w.0
-    }
-
-    #[inline]
-    fn next_u64(&mut self) -> u64 {
-        impls::next_u64_via_u32(self)
-    }
-
-    #[inline]
-    fn fill_bytes(&mut self, dest: &mut [u8]) {
-        impls::fill_bytes_via_next(self, dest)
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        Ok(self.fill_bytes(dest))
-    }
-}
-
-impl SeedableRng for XorShiftRng {
-    type Seed = [u8; 16];
-
-    fn from_seed(seed: Self::Seed) -> Self {
-        let mut seed_u32 = [0u32; 4];
-        le::read_u32_into(&seed, &mut seed_u32);
-
-        // Xorshift cannot be seeded with 0 and we cannot return an Error, but
-        // also do not wish to panic (because a random seed can legitimately be
-        // 0); our only option is therefore to use a preset value.
-        if seed_u32.iter().all(|&x| x == 0) {
-            seed_u32 = [0xBAD_5EED, 0xBAD_5EED, 0xBAD_5EED, 0xBAD_5EED];
-        }
-
-        XorShiftRng {
-            x: w(seed_u32[0]),
-            y: w(seed_u32[1]),
-            z: w(seed_u32[2]),
-            w: w(seed_u32[3]),
-        }
-    }
-
-    fn from_rng<R: RngCore>(mut rng: R) -> Result<Self, Error> {
-        let mut seed_u32 = [0u32; 4];
-        loop {
-            unsafe {
-                let ptr = seed_u32.as_mut_ptr() as *mut u8;
-
-                let slice = slice::from_raw_parts_mut(ptr, 4 * 4);
-                rng.try_fill_bytes(slice)?;
-            }
-            if !seed_u32.iter().all(|&x| x == 0) { break; }
-        }
-
-        Ok(XorShiftRng {
-            x: w(seed_u32[0]),
-            y: w(seed_u32[1]),
-            z: w(seed_u32[2]),
-            w: w(seed_u32[3]),
-        })
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use {RngCore, SeedableRng};
-    use super::XorShiftRng;
-
-    #[test]
-    fn test_xorshift_construction() {
-        // Test that various construction techniques produce a working RNG.
-        let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16];
-        let mut rng1 = XorShiftRng::from_seed(seed);
-        assert_eq!(rng1.next_u64(), 4325440999699518727);
-
-        let _rng2 = XorShiftRng::from_rng(rng1).unwrap();
-        // Note: we cannot test the state of _rng2 because from_rng does not
-        // fix Endianness. This is allowed in the trait specification.
-    }
-
-    #[test]
-    fn test_xorshift_true_values() {
-        let seed = [16,15,14,13, 12,11,10,9, 8,7,6,5, 4,3,2,1];
-        let mut rng = XorShiftRng::from_seed(seed);
-
-        let mut results = [0u32; 9];
-        for i in results.iter_mut() { *i = rng.next_u32(); }
-        let expected: [u32; 9] = [
-            2081028795, 620940381, 269070770, 16943764, 854422573, 29242889,
-            1550291885, 1227154591, 271695242];
-        assert_eq!(results, expected);
-
-        let mut results = [0u64; 9];
-        for i in results.iter_mut() { *i = rng.next_u64(); }
-        let expected: [u64; 9] = [
-            9247529084182843387, 8321512596129439293, 14104136531997710878,
-            6848554330849612046, 343577296533772213, 17828467390962600268,
-            9847333257685787782, 7717352744383350108, 1133407547287910111];
-        assert_eq!(results, expected);
-
-        let mut results = [0u8; 32];
-        rng.fill_bytes(&mut results);
-        let expected = [102, 57, 212, 16, 233, 130, 49, 183,
-                        158, 187, 44, 203, 63, 149, 45, 17,
-                        117, 129, 131, 160, 70, 121, 158, 155,
-                        224, 209, 192, 53, 10, 62, 57, 72];
-        assert_eq!(results, expected);
-    }
-
-    #[test]
-    fn test_xorshift_zero_seed() {
-        // Xorshift does not work with an all zero seed.
-        // Assert it does not panic.
-        let seed = [0,0,0,0, 0,0,0,0, 0,0,0,0, 0,0,0,0];
-        let mut rng = XorShiftRng::from_seed(seed);
-        let a = rng.next_u64();
-        let b = rng.next_u64();
-        assert!(a != 0);
-        assert!(b != a);
-    }
-
-    #[test]
-    fn test_xorshift_clone() {
-        let seed = [1,2,3,4, 5,5,7,8, 8,7,6,5, 4,3,2,1];
-        let mut rng1 = XorShiftRng::from_seed(seed);
-        let mut rng2 = rng1.clone();
-        for _ in 0..16 {
-            assert_eq!(rng1.next_u64(), rng2.next_u64());
-        }
-    }
-
-    #[cfg(all(feature="serde1", feature="std"))]
-    #[test]
-    fn test_xorshift_serde() {
-        use bincode;
-        use std::io::{BufWriter, BufReader};
-
-        let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16];
-        let mut rng = XorShiftRng::from_seed(seed);
-
-        let buf: Vec<u8> = Vec::new();
-        let mut buf = BufWriter::new(buf);
-        bincode::serialize_into(&mut buf, &rng).expect("Could not serialize");
-
-        let buf = buf.into_inner().unwrap();
-        let mut read = BufReader::new(&buf[..]);
-        let mut deserialized: XorShiftRng = bincode::deserialize_from(&mut read).expect("Could not deserialize");
-
-        assert_eq!(rng.x, deserialized.x);
-        assert_eq!(rng.y, deserialized.y);
-        assert_eq!(rng.z, deserialized.z);
-        assert_eq!(rng.w, deserialized.w);
-
-        for _ in 0..16 {
-            assert_eq!(rng.next_u64(), deserialized.next_u64());
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/read.rs b/vendor/rand-8c5b0ac51d/src/read.rs
deleted file mode 100644
index a6ab6f5..0000000
--- a/vendor/rand-8c5b0ac51d/src/read.rs
+++ /dev/null
@@ -1,129 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! A wrapper around any Read to treat it as an RNG.
-
-use std::io::Read;
-
-use rand_core::{RngCore, Error, ErrorKind, impls};
-
-
-/// An RNG that reads random bytes straight from a `Read`.
-///
-/// This will work best with an infinite reader, but that is not required.
-///
-/// # Panics
-///
-/// `ReadRng` uses `std::io::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 error.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::{read, Rng};
-///
-/// let data = vec![1, 2, 3, 4, 5, 6, 7, 8];
-/// let mut rng = read::ReadRng::new(&data[..]);
-/// println!("{:x}", rng.gen::<u32>());
-/// ```
-#[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.len() == 0 { return Ok(()); }
-        // Use `std::io::read_exact`, which retries on `ErrorKind::Interrupted`.
-        self.reader.read_exact(dest).map_err(|err| {
-            match err.kind() {
-                ::std::io::ErrorKind::UnexpectedEof => Error::with_cause(
-                    ErrorKind::Unavailable,
-                    "not enough bytes available, reached end of source", err),
-                _ => Error::with_cause(ErrorKind::Unavailable,
-                    "error reading from Read source", err)
-            }
-        })
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use super::ReadRng;
-    use {RngCore, ErrorKind};
-
-    #[test]
-    fn test_reader_rng_u64() {
-        // transmute from the target to avoid endianness concerns.
-        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[..]);
-
-        assert!(rng.try_fill_bytes(&mut w).err().unwrap().kind == ErrorKind::Unavailable);
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/reseeding.rs b/vendor/rand-8c5b0ac51d/src/reseeding.rs
deleted file mode 100644
index 0f7f049..0000000
--- a/vendor/rand-8c5b0ac51d/src/reseeding.rs
+++ /dev/null
@@ -1,260 +0,0 @@
-// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! 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::{RngCore, BlockRngCore, CryptoRng, SeedableRng, Error, ErrorKind};
-use rand_core::impls::BlockRng;
-
-/// A wrapper around any PRNG which reseeds the underlying PRNG after it has
-/// generated a certain number of random bytes.
-///
-/// When the RNG gets cloned, the clone is reseeded on first use.
-///
-/// Reseeding 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'.
-///
-/// Some small non-cryptographic PRNGs can have very small periods, for
-/// example less than 2<sup>64</sup>. Would reseeding help to ensure that you do
-/// not wrap around at the end of the period? A period of 2<sup>64</sup> still
-/// takes several centuries of CPU-years on current hardware. Reseeding will
-/// actually make things worse, because the reseeded PRNG will just continue
-/// somewhere else *in the same period*, with a high chance of overlapping with
-/// previously used parts of it.
-///
-/// # When should you use `ReseedingRng`?
-///
-/// - 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,
-///   occasionally reseeding should make exploiting it much more difficult or
-///   even impossible.
-/// - It can be used as a poor man's cryptography (not recommended, just use a
-///   good CSPRNG). Previous implementations of `thread_rng` for example used
-///   `ReseedingRng` with the ISAAC RNG. That algorithm, although apparently
-///   strong and with no known attack, does not come with any proof of security
-///   and does not meet the current standards for a cryptographically secure
-///   PRNG. By reseeding it frequently (every 32 kiB) it seems safe to assume
-///   there is no attack that can operate on the tiny window between reseeds.
-///
-/// # Error handling
-///
-/// Although extremely 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.
-#[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` with the given parameters.
-    ///
-    /// # Arguments
-    ///
-    /// * `rng`: the random number generator to use.
-    /// * `threshold`: the number of generated bytes after which to reseed the RNG.
-    /// * `reseeder`: the RNG to use for reseeding.
-    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.inner_mut().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]>
-{
-    #[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.inner().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,
-}
-
-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) {
-        if self.bytes_until_reseed <= 0 {
-            // We get better performance by not calling only `auto_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);
-        }
-        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` with the given parameters.
-    ///
-    /// # Arguments
-    ///
-    /// * `rng`: the random number generator to use.
-    /// * `threshold`: the number of generated bytes after which to reseed the RNG.
-    /// * `reseeder`: the RNG to use for reseeding.
-    pub fn new(rng: R, threshold: u64, reseeder: Rsdr) -> Self {
-        assert!(threshold <= ::core::i64::MAX as u64);
-        ReseedingCore {
-            inner: rng,
-            reseeder,
-            threshold: threshold as i64,
-            bytes_until_reseed: threshold as i64,
-        }
-    }
-
-    /// 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
-        })
-    }
-
-    #[inline(never)]
-    fn reseed_and_generate(&mut self,
-                           results: &mut <Self as BlockRngCore>::Results)
-    {
-        trace!("Reseeding RNG after {} generated bytes",
-               self.threshold - self.bytes_until_reseed);
-        let threshold = if let Err(e) = self.reseed()  {
-            let delay = match e.kind {
-                ErrorKind::Transient => 0,
-                kind @ _ if kind.should_retry() => self.threshold >> 8,
-                _ => self.threshold,
-            };
-            warn!("Reseeding RNG delayed reseeding by {} bytes due to \
-                    error from source: {}", delay, e);
-            delay
-        } else {
-            self.threshold
-        };
-        
-        let num_bytes = results.as_ref().len() * size_of::<<R as BlockRngCore>::Item>();
-        self.bytes_until_reseed = 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
-        }
-    }
-}
-
-impl<R, Rsdr> CryptoRng for ReseedingCore<R, Rsdr>
-where R: BlockRngCore + SeedableRng + CryptoRng,
-      Rsdr: RngCore + CryptoRng {}
-
-#[cfg(test)]
-mod test {
-    use {Rng, SeedableRng};
-    use prng::chacha::ChaChaCore;
-    use mock::StepRng;
-    use super::ReseedingRng;
-
-    #[test]
-    fn test_reseeding() {
-        let mut zero = StepRng::new(0, 0);
-        let rng = ChaChaCore::from_rng(&mut zero).unwrap();
-        let mut reseeding = ReseedingRng::new(rng, 32*4, zero);
-
-        // Currently we only support for arrays up to length 32.
-        // TODO: cannot generate seq via Rng::gen because it uses different alg
-        let mut buf = [0u32; 32]; // Needs to be a multiple of the RNGs result
-                                  // size to test exactly.
-        reseeding.fill(&mut buf);
-        let seq = buf;
-        for _ in 0..10 {
-            reseeding.fill(&mut buf);
-            assert_eq!(buf, seq);
-        }
-    }
-
-    #[test]
-    fn test_clone_reseeding() {
-        let mut zero = StepRng::new(0, 0);
-        let rng = ChaChaCore::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-8c5b0ac51d/src/seq.rs b/vendor/rand-8c5b0ac51d/src/seq.rs
deleted file mode 100644
index 1a128ce..0000000
--- a/vendor/rand-8c5b0ac51d/src/seq.rs
+++ /dev/null
@@ -1,335 +0,0 @@
-// Copyright 2017 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Functions for randomly accessing and sampling sequences.
-
-use super::Rng;
-
-// This crate is only enabled when either std or alloc is available.
-// BTreeMap is not as fast in tests, but better than nothing.
-#[cfg(feature="std")] use std::collections::HashMap;
-#[cfg(not(feature="std"))] use alloc::btree_map::BTreeMap;
-
-#[cfg(not(feature="std"))] use alloc::Vec;
-
-/// Randomly sample `amount` elements from a finite iterator.
-///
-/// The following can be returned:
-///
-/// - `Ok`: `Vec` of `amount` non-repeating randomly sampled elements. The order is not random.
-/// - `Err`: `Vec` of all the elements from `iterable` in sequential order. This happens when the
-///   length of `iterable` was less than `amount`. This is considered an error since exactly
-///   `amount` elements is typically expected.
-///
-/// This implementation uses `O(len(iterable))` time and `O(amount)` memory.
-///
-/// # Example
-///
-/// ```rust
-/// use rand::{thread_rng, seq};
-///
-/// let mut rng = thread_rng();
-/// let sample = seq::sample_iter(&mut rng, 1..100, 5).unwrap();
-/// println!("{:?}", sample);
-/// ```
-pub fn sample_iter<T, I, R>(rng: &mut R, iterable: I, amount: usize) -> Result<Vec<T>, Vec<T>>
-    where I: IntoIterator<Item=T>,
-          R: Rng + ?Sized,
-{
-    let mut iter = iterable.into_iter();
-    let mut reservoir = Vec::with_capacity(amount);
-    reservoir.extend(iter.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 iter.enumerate() {
-            let k = rng.gen_range(0, i + 1 + amount);
-            if let Some(spot) = reservoir.get_mut(k) {
-                *spot = elem;
-            }
-        }
-        Ok(reservoir)
-    } else {
-        // Don't hang onto extra memory. There is a corner case where
-        // `amount` was much less than `len(iterable)`.
-        reservoir.shrink_to_fit();
-        Err(reservoir)
-    }
-}
-
-/// Randomly sample exactly `amount` values from `slice`.
-///
-/// The values are non-repeating and in random order.
-///
-/// This implementation uses `O(amount)` time and memory.
-///
-/// Panics if `amount > slice.len()`
-///
-/// # Example
-///
-/// ```rust
-/// use rand::{thread_rng, seq};
-///
-/// let mut rng = thread_rng();
-/// let values = vec![5, 6, 1, 3, 4, 6, 7];
-/// println!("{:?}", seq::sample_slice(&mut rng, &values, 3));
-/// ```
-pub fn sample_slice<R, T>(rng: &mut R, slice: &[T], amount: usize) -> Vec<T>
-    where R: Rng + ?Sized,
-          T: Clone
-{
-    let indices = sample_indices(rng, slice.len(), amount);
-
-    let mut out = Vec::with_capacity(amount);
-    out.extend(indices.iter().map(|i| slice[*i].clone()));
-    out
-}
-
-/// Randomly sample exactly `amount` references from `slice`.
-///
-/// The references are non-repeating and in random order.
-///
-/// This implementation uses `O(amount)` time and memory.
-///
-/// Panics if `amount > slice.len()`
-///
-/// # Example
-///
-/// ```rust
-/// use rand::{thread_rng, seq};
-///
-/// let mut rng = thread_rng();
-/// let values = vec![5, 6, 1, 3, 4, 6, 7];
-/// println!("{:?}", seq::sample_slice_ref(&mut rng, &values, 3));
-/// ```
-pub fn sample_slice_ref<'a, R, T>(rng: &mut R, slice: &'a [T], amount: usize) -> Vec<&'a T>
-    where R: Rng + ?Sized
-{
-    let indices = sample_indices(rng, slice.len(), amount);
-
-    let mut out = Vec::with_capacity(amount);
-    out.extend(indices.iter().map(|i| &slice[*i]));
-    out
-}
-
-/// Randomly sample exactly `amount` indices from `0..length`.
-///
-/// The values are non-repeating and in random order.
-///
-/// This implementation uses `O(amount)` time and memory.
-///
-/// 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.
-///
-/// Panics if `amount > length`
-pub fn sample_indices<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize>
-    where R: Rng + ?Sized,
-{
-    if amount > length {
-        panic!("`amount` must be less than or equal to `slice.len()`");
-    }
-
-    // We are going to have to allocate at least `amount` for the output no matter what. However,
-    // if we use the `cached` version we will have to allocate `amount` as a HashMap as well since
-    // it inserts an element for every loop.
-    //
-    // Therefore, if `amount >= length / 2` then inplace will be both faster and use less memory.
-    // In fact, benchmarks show the inplace version is faster for length up to about 20 times
-    // faster than amount.
-    //
-    // TODO: there is probably even more fine-tuning that can be done here since
-    // `HashMap::with_capacity(amount)` probably allocates more than `amount` in practice,
-    // and a trade off could probably be made between memory/cpu, since hashmap operations
-    // are slower than array index swapping.
-    if amount >= length / 20 {
-        sample_indices_inplace(rng, length, amount)
-    } else {
-        sample_indices_cache(rng, length, amount)
-    }
-}
-
-/// 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 is better than using a `HashMap` "cache" when `amount >= length / 2`
-/// since it does not require allocating an extra cache and is much faster.
-fn sample_indices_inplace<R>(rng: &mut R, length: usize, amount: usize) -> Vec<usize>
-    where R: Rng + ?Sized,
-{
-    debug_assert!(amount <= length);
-    let mut indices: Vec<usize> = Vec::with_capacity(length);
-    indices.extend(0..length);
-    for i in 0..amount {
-        let j: usize = rng.gen_range(i, length);
-        indices.swap(i, j);
-    }
-    indices.truncate(amount);
-    debug_assert_eq!(indices.len(), amount);
-    indices
-}
-
-
-/// This method performs a partial fisher-yates on a range of indices using a
-/// `HashMap` as a cache to record potential collisions.
-///
-/// The cache avoids allocating the entire `length` of values. This is especially useful when
-/// `amount <<< length`, i.e. select 3 non-repeating from `1_000_000`
-fn sample_indices_cache<R>(
-    rng: &mut R,
-    length: usize,
-    amount: usize,
-) -> Vec<usize>
-    where R: Rng + ?Sized,
-{
-    debug_assert!(amount <= length);
-    #[cfg(feature="std")] let mut cache = HashMap::with_capacity(amount);
-    #[cfg(not(feature="std"))] let mut cache = BTreeMap::new();
-    let mut out = Vec::with_capacity(amount);
-    for i in 0..amount {
-        let j: usize = rng.gen_range(i, length);
-
-        // equiv: let tmp = slice[i];
-        let tmp = match cache.get(&i) {
-            Some(e) => *e,
-            None => i,
-        };
-
-        // equiv: slice[i] = slice[j];
-        let x = match cache.get(&j) {
-            Some(x) => *x,
-            None => j,
-        };
-
-        // equiv: slice[j] = tmp;
-        cache.insert(j, tmp);
-
-        // note that in the inplace version, slice[i] is automatically "returned" value
-        out.push(x);
-    }
-    debug_assert_eq!(out.len(), amount);
-    out
-}
-
-#[cfg(test)]
-mod test {
-    use super::*;
-    use {XorShiftRng, Rng, SeedableRng};
-    #[cfg(not(feature="std"))]
-    use alloc::Vec;
-
-    #[test]
-    fn test_sample_iter() {
-        let min_val = 1;
-        let max_val = 100;
-
-        let mut r = ::test::rng(401);
-        let vals = (min_val..max_val).collect::<Vec<i32>>();
-        let small_sample = sample_iter(&mut r, vals.iter(), 5).unwrap();
-        let large_sample = sample_iter(&mut r, vals.iter(), vals.len() + 5).unwrap_err();
-
-        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]
-    fn test_sample_slice_boundaries() {
-        let empty: &[u8] = &[];
-
-        let mut r = ::test::rng(402);
-
-        // sample 0 items
-        assert_eq!(&sample_slice(&mut r, empty, 0)[..], [0u8; 0]);
-        assert_eq!(&sample_slice(&mut r, &[42, 2, 42], 0)[..], [0u8; 0]);
-
-        // sample 1 item
-        assert_eq!(&sample_slice(&mut r, &[42], 1)[..], [42]);
-        let v = sample_slice(&mut r, &[1, 42], 1)[0];
-        assert!(v == 1 || v == 42);
-
-        // sample "all" the items
-        let v = sample_slice(&mut r, &[42, 133], 2);
-        assert!(&v[..] == [42, 133] || v[..] == [133, 42]);
-
-        assert_eq!(&sample_indices_inplace(&mut r, 0, 0)[..], [0usize; 0]);
-        assert_eq!(&sample_indices_inplace(&mut r, 1, 0)[..], [0usize; 0]);
-        assert_eq!(&sample_indices_inplace(&mut r, 1, 1)[..], [0]);
-
-        assert_eq!(&sample_indices_cache(&mut r, 0, 0)[..], [0usize; 0]);
-        assert_eq!(&sample_indices_cache(&mut r, 1, 0)[..], [0usize; 0]);
-        assert_eq!(&sample_indices_cache(&mut r, 1, 1)[..], [0]);
-
-        // Make sure lucky 777's aren't lucky
-        let slice = &[42, 777];
-        let mut num_42 = 0;
-        let total = 1000;
-        for _ in 0..total {
-            let v = sample_slice(&mut r, slice, 1);
-            assert_eq!(v.len(), 1);
-            let v = v[0];
-            assert!(v == 42 || v == 777);
-            if v == 42 {
-                num_42 += 1;
-            }
-        }
-        let ratio_42 = num_42 as f64 / 1000 as f64;
-        assert!(0.4 <= ratio_42 || ratio_42 <= 0.6, "{}", ratio_42);
-    }
-
-    #[test]
-    fn test_sample_slice() {
-        let xor_rng = XorShiftRng::from_seed;
-
-        let max_range = 100;
-        let mut r = ::test::rng(403);
-
-        for length in 1usize..max_range {
-            let amount = r.gen_range(0, length);
-            let mut seed = [0u8; 16];
-            r.fill(&mut seed);
-
-            // assert that the two index methods give exactly the same result
-            let inplace = sample_indices_inplace(
-                &mut xor_rng(seed), length, amount);
-            let cache = sample_indices_cache(
-                &mut xor_rng(seed), length, amount);
-            assert_eq!(inplace, cache);
-
-            // assert the basics work
-            let regular = sample_indices(
-                &mut xor_rng(seed), length, amount);
-            assert_eq!(regular.len(), amount);
-            assert!(regular.iter().all(|e| *e < length));
-            assert_eq!(regular, inplace);
-
-            // also test that sampling the slice works
-            let vec: Vec<usize> = (0..length).collect();
-            {
-                let result = sample_slice(&mut xor_rng(seed), &vec, amount);
-                assert_eq!(result, regular);
-            }
-
-            {
-                let result = sample_slice_ref(&mut xor_rng(seed), &vec, amount);
-                let expected = regular.iter().map(|v| v).collect::<Vec<_>>();
-                assert_eq!(result, expected);
-            }
-        }
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/src/thread_rng.rs b/vendor/rand-8c5b0ac51d/src/thread_rng.rs
deleted file mode 100644
index 1b93a8c..0000000
--- a/vendor/rand-8c5b0ac51d/src/thread_rng.rs
+++ /dev/null
@@ -1,206 +0,0 @@
-// Copyright 2017-2018 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-//! Thread-local random number generator
-
-use std::cell::UnsafeCell;
-use std::rc::Rc;
-
-use {RngCore, CryptoRng, SeedableRng, EntropyRng};
-use prng::hc128::Hc128Core;
-use {Distribution, Standard, Rng, Error};
-use reseeding::ReseedingRng;
-
-// 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.
-//
-// The additional `Rc` is not strictly neccesary, and could be removed. For now
-// it ensures `ThreadRng` stays `!Send` and `!Sync`, and implements `Clone`.
-
-
-// Number of generated bytes after which to reseed `TreadRng`.
-//
-// The time it takes to reseed HC-128 is roughly equivalent to generating 7 KiB.
-// We pick a treshold here that is large enough to not reduce the average
-// performance too much, but also small enough to not make reseeding something
-// that basically never happens.
-const THREAD_RNG_RESEED_THRESHOLD: u64 = 32*1024*1024; // 32 MiB
-
-/// The type returned by [`thread_rng`], essentially just a reference to the
-/// PRNG in thread-local memory.
-///
-/// Cloning this handle just produces a new reference to the same thread-local
-/// generator.
-/// 
-/// [`thread_rng`]: fn.thread_rng.html
-#[derive(Clone, Debug)]
-pub struct ThreadRng {
-    rng: Rc<UnsafeCell<ReseedingRng<Hc128Core, EntropyRng>>>,
-}
-
-thread_local!(
-    static THREAD_RNG_KEY: Rc<UnsafeCell<ReseedingRng<Hc128Core, EntropyRng>>> = {
-        let mut entropy_source = EntropyRng::new();
-        let r = Hc128Core::from_rng(&mut entropy_source).unwrap_or_else(|err|
-                panic!("could not initialize thread_rng: {}", err));
-        let rng = ReseedingRng::new(r,
-                                    THREAD_RNG_RESEED_THRESHOLD,
-                                    entropy_source);
-        Rc::new(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();`.
-///
-/// `ThreadRng` uses [`ReseedingRng`] wrapping the same PRNG as [`StdRng`],
-/// which is reseeded after generating 32 MiB of random data. A single instance
-/// is cached per thread and the returned `ThreadRng` is a reference to this
-/// instance â?? hence `ThreadRng` is neither `Send` nor `Sync` but is safe to use
-/// within a single thread. This RNG is seeded and reseeded via [`EntropyRng`]
-/// as required.
-/// 
-/// Note that the reseeding is done as an extra precaution against entropy
-/// leaks and is in theory unnecessary â?? to predict `thread_rng`'s output, an
-/// attacker would have to either determine most of the RNG's seed or internal
-/// state, or crack the algorithm used.
-/// 
-/// Like [`StdRng`], `ThreadRng` is a cryptographically secure PRNG. The current
-/// algorithm used is [HC-128], which is an array-based PRNG that trades memory
-/// usage for better performance. This makes it similar to ISAAC, the algorithm
-/// used in `ThreadRng` before rand 0.5.
-///
-/// [`ReseedingRng`]: reseeding/struct.ReseedingRng.html
-/// [`StdRng`]: struct.StdRng.html
-/// [`EntropyRng`]: struct.EntropyRng.html
-/// [HC-128]: prng/hc128/struct.Hc128Rng.html
-pub fn thread_rng() -> ThreadRng {
-    ThreadRng { rng: THREAD_RNG_KEY.with(|t| t.clone()) }
-}
-
-impl RngCore for ThreadRng {
-    #[inline(always)]
-    fn next_u32(&mut self) -> u32 {
-        unsafe { (*self.rng.get()).next_u32() }
-    }
-
-    #[inline(always)]
-    fn next_u64(&mut self) -> u64 {
-        unsafe { (*self.rng.get()).next_u64() }
-    }
-
-    fn fill_bytes(&mut self, bytes: &mut [u8]) {
-        unsafe { (*self.rng.get()).fill_bytes(bytes) }
-    }
-
-    fn try_fill_bytes(&mut self, dest: &mut [u8]) -> Result<(), Error> {
-        unsafe { (*self.rng.get()).try_fill_bytes(dest) }
-    }
-}
-
-impl CryptoRng for ThreadRng {}
-
-/// DEPRECATED: use `thread_rng().gen()` instead.
-///
-/// 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 [`Rand`] for documentation of
-/// distributions and type-specific generation.
-///
-/// # Examples
-///
-/// ```
-/// # #![allow(deprecated)]
-/// let x = rand::random::<u8>();
-/// println!("{}", x);
-///
-/// let y = rand::random::<f64>();
-/// println!("{}", y);
-///
-/// if rand::random() { // generates a boolean
-///     println!("Better lucky than good!");
-/// }
-/// ```
-///
-/// If you're calling `random()` in a loop, caching the generator as in the
-/// following example can increase performance.
-///
-/// ```
-/// # #![allow(deprecated)]
-/// use rand::Rng;
-///
-/// let mut v = vec![1, 2, 3];
-///
-/// for x in v.iter_mut() {
-///     *x = rand::random()
-/// }
-///
-/// // can be made faster by caching thread_rng
-///
-/// let mut rng = rand::thread_rng();
-///
-/// for x in v.iter_mut() {
-///     *x = rng.gen();
-/// }
-/// ```
-///
-/// [`thread_rng`]: fn.thread_rng.html
-/// [`Rand`]: trait.Rand.html
-#[deprecated(since="0.5.0", note="removed in favor of thread_rng().gen()")]
-#[inline]
-pub fn random<T>() -> T where Standard: Distribution<T> {
-    thread_rng().gen()
-}
-
-#[cfg(test)]
-mod test {
-    #[test]
-    #[cfg(not(feature="stdweb"))]
-    fn test_thread_rng() {
-        use Rng;
-        let mut r = ::thread_rng();
-        r.gen::<i32>();
-        let mut v = [1, 1, 1];
-        r.shuffle(&mut v);
-        let b: &[_] = &[1, 1, 1];
-        assert_eq!(v, b);
-        assert_eq!(r.gen_range(0, 1), 0);
-    }
-
-    #[test]
-    #[allow(deprecated)]
-    fn test_random() {
-        use super::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();
-    }
-}
diff --git a/vendor/rand-8c5b0ac51d/utils/ci/install.sh b/vendor/rand-8c5b0ac51d/utils/ci/install.sh
deleted file mode 100644
index 8e636e1..0000000
--- a/vendor/rand-8c5b0ac51d/utils/ci/install.sh
+++ /dev/null
@@ -1,49 +0,0 @@
-# From https://github.com/japaric/trust
-
-set -ex
-
-main() {
-    local target=
-    if [ $TRAVIS_OS_NAME = linux ]; then
-        target=x86_64-unknown-linux-musl
-        sort=sort
-    else
-        target=x86_64-apple-darwin
-        sort=gsort  # for `sort --sort-version`, from brew's coreutils.
-    fi
-
-    # Builds for iOS are done on OSX, but require the specific target to be
-    # installed.
-    case $TARGET in
-        aarch64-apple-ios)
-            rustup target install aarch64-apple-ios
-            ;;
-        armv7-apple-ios)
-            rustup target install armv7-apple-ios
-            ;;
-        armv7s-apple-ios)
-            rustup target install armv7s-apple-ios
-            ;;
-        i386-apple-ios)
-            rustup target install i386-apple-ios
-            ;;
-        x86_64-apple-ios)
-            rustup target install x86_64-apple-ios
-            ;;
-    esac
-
-    # This fetches latest stable release
-    local tag=$(git ls-remote --tags --refs --exit-code https://github.com/japaric/cross \
-                       | cut -d/ -f3 \
-                       | grep -E '^v[0.1.0-9.]+$' \
-                       | $sort --version-sort \
-                       | tail -n1)
-    curl -LSfs https://japaric.github.io/trust/install.sh | \
-        sh -s -- \
-           --force \
-           --git japaric/cross \
-           --tag $tag \
-           --target $target
-}
-
-main
diff --git a/vendor/rand-8c5b0ac51d/utils/ci/script.sh b/vendor/rand-8c5b0ac51d/utils/ci/script.sh
deleted file mode 100644
index a34dc5f..0000000
--- a/vendor/rand-8c5b0ac51d/utils/ci/script.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-# Derived from https://github.com/japaric/trust
-
-set -ex
-
-main() {
-    if [ ! -z $DISABLE_TESTS ]; then
-        cross build --all --no-default-features --target $TARGET --release
-        if [ -z $DISABLE_STD ]; then
-            cross build --features log,serde1 --target $TARGET
-        fi
-        return
-    fi
-
-    if [ ! -z $NIGHTLY ]; then
-        cross test --all --tests --no-default-features --features alloc --target $TARGET
-        cross test --features serde1,log,nightly --target $TARGET
-        cross test --all --benches --target $TARGET
-    else
-        cross test --all --tests --no-default-features --target $TARGET
-        cross test --features serde1,log --target $TARGET
-    fi
-}
-
-# we don't run the "test phase" when doing deploys
-if [ -z $TRAVIS_TAG ]; then
-    main
-fi
diff --git a/vendor/rand-8c5b0ac51d/utils/ziggurat_tables.py b/vendor/rand-8c5b0ac51d/utils/ziggurat_tables.py
deleted file mode 100755
index 9973b83..0000000
--- a/vendor/rand-8c5b0ac51d/utils/ziggurat_tables.py
+++ /dev/null
@@ -1,127 +0,0 @@
-#!/usr/bin/env python
-#
-# Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-# file at the top-level directory of this distribution and at
-# https://rust-lang.org/COPYRIGHT.
-#
-# Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-# https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-# <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-# option. This file may not be copied, modified, or distributed
-# except according to those terms.
-
-# This creates the tables used for distributions implemented using the
-# ziggurat algorithm in `rand::distributions;`. They are
-# (basically) the tables as used in the ZIGNOR variant (Doornik 2005).
-# They are changed rarely, so the generated file should be checked in
-# to git.
-#
-# It creates 3 tables: X as in the paper, F which is f(x_i), and
-# F_DIFF which is f(x_i) - f(x_{i-1}). The latter two are just cached
-# values which is not done in that paper (but is done in other
-# variants). Note that the adZigR table is unnecessary because of
-# algebra.
-#
-# It is designed to be compatible with Python 2 and 3.
-
-from math import exp, sqrt, log, floor
-import random
-
-# The order should match the return value of `tables`
-TABLE_NAMES = ['X', 'F']
-
-# The actual length of the table is 1 more, to stop
-# index-out-of-bounds errors. This should match the bitwise operation
-# to find `i` in `zigurrat` in `libstd/rand/mod.rs`. Also the *_R and
-# *_V constants below depend on this value.
-TABLE_LEN = 256
-
-# equivalent to `zigNorInit` in Doornik2005, but generalised to any
-# distribution. r = dR, v = dV, f = probability density function,
-# f_inv = inverse of f
-def tables(r, v, f, f_inv):
-    # compute the x_i
-    xvec = [0]*(TABLE_LEN+1)
-
-    xvec[0] = v / f(r)
-    xvec[1] = r
-
-    for i in range(2, TABLE_LEN):
-        last = xvec[i-1]
-        xvec[i] = f_inv(v / last + f(last))
-
-    # cache the f's
-    fvec = [0]*(TABLE_LEN+1)
-    for i in range(TABLE_LEN+1):
-        fvec[i] = f(xvec[i])
-
-    return xvec, fvec
-
-# Distributions
-# N(0, 1)
-def norm_f(x):
-    return exp(-x*x/2.0)
-def norm_f_inv(y):
-    return sqrt(-2.0*log(y))
-
-NORM_R = 3.6541528853610088
-NORM_V = 0.00492867323399
-
-NORM = tables(NORM_R, NORM_V,
-              norm_f, norm_f_inv)
-
-# Exp(1)
-def exp_f(x):
-    return exp(-x)
-def exp_f_inv(y):
-    return -log(y)
-
-EXP_R = 7.69711747013104972
-EXP_V = 0.0039496598225815571993
-
-EXP = tables(EXP_R, EXP_V,
-             exp_f, exp_f_inv)
-
-
-# Output the tables/constants/types
-
-def render_static(name, type, value):
-    # no space or
-    return 'pub static %s: %s =%s;\n' % (name, type, value)
-
-# static `name`: [`type`, .. `len(values)`] =
-#     [values[0], ..., values[3],
-#      values[4], ..., values[7],
-#      ... ];
-def render_table(name, values):
-    rows = []
-    # 4 values on each row
-    for i in range(0, len(values), 4):
-        row = values[i:i+4]
-        rows.append(', '.join('%.18f' % f for f in row))
-
-    rendered = '\n    [%s]' % ',\n     '.join(rows)
-    return render_static(name, '[f64, .. %d]' % len(values), rendered)
-
-
-with open('ziggurat_tables.rs', 'w') as f:
-    f.write('''// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
-// file at the top-level directory of this distribution and at
-// https://rust-lang.org/COPYRIGHT.
-//
-// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
-// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
-// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
-// option. This file may not be copied, modified, or distributed
-// except according to those terms.
-
-// Tables for distributions which are sampled using the ziggurat
-// algorithm. Autogenerated by `ziggurat_tables.py`.
-
-pub type ZigTable = &\'static [f64, .. %d];
-'''  % (TABLE_LEN + 1))
-    for name, tables, r in [('NORM', NORM, NORM_R),
-                            ('EXP', EXP, EXP_R)]:
-        f.write(render_static('ZIG_%s_R' % name, 'f64', ' %.18f' % r))
-        for (tabname, table) in zip(TABLE_NAMES, tables):
-            f.write(render_table('ZIG_%s_%s' % (name, tabname), table))

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