📄 librand_core-e10882de99121156.rlib
/home/palash/git/iron_learn/target/release/deps/librand_core-e10882de99121156.rlib
Language: rlib • Lines: 3669
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�{��{������#  Random number generation traits�����LI This crate is mainly of interest to crates publishing implementations of���MJ [`RngCore`]. Other users are encouraged to use the [`rand`] crate instead���52 which re-exports the main traits and error types.�����QN [`RngCore`] is the core trait implemented by algorithmic pseudo-random number���2/ generators and external random-number sources.�����OL [`SeedableRng`] is an extension trait for construction from fixed seeds and���#  other random number generators.�����PM The [`impls`] and [`le`] sub-modules include a few small functions to assist���" implementation of [`RngCore`].�����" [`rand`]: https://docs.rs/rand�����
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<Gfi|����������r�s�r�[��j��i�z�h�{�x�{����������l��l���74 The `BlockRngCore` trait and implementation helpers�����MJ The [`BlockRngCore`] trait exists to assist in the implementation of RNGs���LI which generate a block of data in a cache instead of returning generated��� values directly.�����A> Usage of this trait is optional, but provides two advantages:���JG implementations only need to concern themselves with generation of the���PM block, not the various [`RngCore`] methods (especially [`fill_bytes`], where���A> the optimal implementations are not trivial), and this allows���NK `ReseedingRng` (see [`rand`](https://docs.rs/rand) crate) perform periodic���%" reseeding with very low overhead.���l�
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���[�q��4fhWYIK:<L�/����NK A trait for RNGs which do not generate random numbers individually, but in���EB blocks (typically `[u32; N]`). This technique is commonly used by���.+ cryptographic RNGs to improve performance.�����=: See the [module][crate::block] documentation for details.�d��������ӫӫ����T���������B���
�������ɬ�����<���B��2Ӭ$�L���%" Results element type, e.g. `u32`.�$���A��HE Results type. This is the 'block' an RNG implementing `BlockRngCore`���?< generates, which will usually be an array like `[u32; 16]`.�<���4��$! Generate a new block of results.�D����������6�
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p$�d��&r��(��B? Extract the raw OS error code (if this error came from the OS)�����MJ This method is identical to [`std::io::Error::raw_os_error()`][1], except���JG that it works in `no_std` contexts. If this method returns `None`, the���HE error value can still be formatted via the `Display` implementation.�����OL [1]: https://doc.rust-lang.org/std/io/struct.Error.html#method.raw_os_error�d��&��qq
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EB This trait encapsulates the low-level functionality common to all���GD generators, and is the "back end", to be implemented by generators.���96 End users should normally use the [`rand::Rng`] trait���MJ which is automatically implemented for every type implementing `RngCore`.�����MJ Three different methods for generating random data are provided since the���OL optimal implementation of each is dependent on the type of generator. There���EB is no required relationship between the output of each; e.g. many���NK implementations of [`fill_bytes`] consume a whole number of `u32` or `u64`���LI values and drop any remaining unused bytes. The same can happen with the���KH [`next_u32`] and [`next_u64`] methods, implementations may discard some��� random bits for efficiency.�����NK Implementers should produce bits uniformly. Pathological RNGs (e.g. always���PM returning the same value, or never setting certain bits) can break rejection���IF sampling used by random distributions, and also break other RNGs when���/, seeding them via [`SeedableRng::from_rng`].�����LI Algorithmic generators implementing [`SeedableRng`] should normally have���OL *portable, reproducible* output, i.e. fix Endianness when converting values���LI to avoid platform differences, and avoid making any changes which affect���KH output (except by communicating that the release has breaking changes).�����EB Typically an RNG will implement only one of the methods available���B? in this trait directly, then use the helper functions from the���41 [`impls`] module to implement the other methods.�����FC Note that implementors of [`RngCore`] also automatically implement���C@ the [`TryRngCore`] trait with the `Error` associated type being��� equal to [`Infallible`].�����:7 It is recommended that implementations also implement:�����NK - `Debug` with a custom implementation which *does not* print any internal���KH   state (at least, [`CryptoRng`]s should not risk leaking state through�|�   `Debug`).���IF - `Serialize` and `Deserialize` (from Serde), preferably making Serde���74   support optional at the crate level in PRNG libs.��� - `Clone`, if possible.���MJ - *never* implement `Copy` (accidental copies may cause repeated values).���KH - *do not* implement `Default` for pseudorandom generators, but instead���GD   implement [`SeedableRng`], to guide users towards proper seeding.���A>   External / hardware RNGs can choose to implement `Default`.��� MJ - `Eq` and `PartialEq` could be implemented, but are probably not useful.��!�l�!���!���!DA A simple example, obviously not generating very *random* output:��!�<�!���! #![allow(dead_code)]���"$! use rand_core::{RngCore, impls};��"��" struct CountingRng(u64);��"���"" impl RngCore for CountingRng {���"'$     fn next_u32(&mut self) -> u32 {���#"         self.next_u64() as u32�L�#���#���#'$     fn next_u64(&mut self) -> u64 {���#         self.0 += 1;���$         self.0�L�$���$���$2/     fn fill_bytes(&mut self, dst: &mut [u8]) {���$1.         impls::fill_bytes_via_next(self, dst)�L�%��,�%��<�%��%���%B? [`rand::Rng`]: https://docs.rs/rand/latest/rand/trait.Rng.html���%'¦��&#  [`next_u32`]: RngCore::next_u32���&#  [`next_u64`]: RngCore::next_u64���&-* [`Infallible`]: core::convert::Infallible�<�'||ӫ��'�	|ӫӫ}~}~}~��)��'! Return the next random `u32`.��'���'HE RNGs must implement at least one method from this trait directly. In���(KH the case this method is not implemented directly, it can be implemented���(GD using `self.next_u64() as u32` or via [`impls::next_u32_via_fill`].�D�)�����%|�)|
}��$�)��+��)! Return the next random `u64`.��*���*HƧ��*K����+HE via [`impls::next_u64_via_u32`] or via [`impls::next_u64_via_fill`].�D�,�����&|�,|
~��$�,��0)��,! Fill `dest` with random data.��,���,HƧ��-K����-'$ via [`impls::fill_bytes_via_next`].��.���.?< This method should guarantee that `dest` is entirely filled���.63 with new data, and may panic if this is impossible���/B? (e.g. reading past the end of a file that is being used as the���/ source of randomness).�T�0�������h�6|�0|
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��'$�2Ю4��?��3C@ A marker trait over [`RngCore`] for securely unpredictable RNGs��3���3LI This marker trait indicates that the implementing generator is intended,���4KH when correctly seeded and protected from side-channel attacks such as a���4OL leaking of state, to be a cryptographically secure generator. This trait is���5KH provided as a tool to aid review of cryptographic code, but does not by���6@= itself guarantee suitability for cryptographic applications.��6���6LI Implementors of `CryptoRng` automatically implement the [`TryCryptoRng`]�T�7 trait.��7���7HE Implementors of `CryptoRng` should only implement [`Default`] if the���8NK `default()` instances are themselves secure generators: for example if the���8OL implementing type is a stateless interface over a secure external generator���9NK (like [`OsRng`]) or if the `default()` instance uses a strong, fresh seed.��9���9PM Formally, a CSPRNG (Cryptographically Secure Pseudo-Random Number Generator)���:NK should satisfy an additional property over other generators: assuming that���;KH the generator has been appropriately seeded and has unknown state, then���;52 given the first *k* bits of an algorithm's output���<KH sequence, it should not be possible using polynomial-time algorithms to���<IF predict the next bit with probability significantly greater than 50%.��=���=OL An optional property of CSPRNGs is backtracking resistance: if the CSPRNG's���>MJ state is revealed, it will not be computationally-feasible to reconstruct���>FC prior output values. This property is not required by `CryptoRng`.�L�?��ӫ��?|��<�?�ӫ(��ӫ4��*��?<������?��D�?���L�?�'!��D��?1. A potentially fallible variant of [`RngCore`]��@���@IF This trait is a generalization of [`RngCore`] to support potentially-���@30 fallible IO-based generators such as [`OsRng`].��A���ANK All implementations of [`RngCore`] automatically support this `TryRngCore`���BLI trait, using [`Infallible`][core::convert::Infallible] as the associated���B `Error` type.��B���BNK An implementation of this trait may be made compatible with code requiring���CMJ an [`RngCore`] through [`TryRngCore::unwrap_err`]. The resulting RNG will���D>; panic in case the underlying fallible RNG yields an error.�T�D��ӫ��D��ӫӫ ���������������������E&�_��T�E�_��d�E��)������������E%��D2/ The type returned in the event of a RNG error.�,�E����E7��E!��d�E������������������������q�aX�%����E�
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��$$�PЮ����X"��QFC A marker trait over [`TryRngCore`] for securely unpredictable RNGs��Q���RFC This trait is like [`CryptoRng`] but for the trait [`TryRngCore`].��R���RL̳��SK����SO����TK͵��U@���U���UKH Implementors of `TryCryptoRng` should only implement [`Default`] if the���VN����VO����WN�d�X��ӫ��X%���T�X�ӫ)��ӫ5��*��X.�
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������e��)d�f�*��k��f<9 A random number generator that can be explicitly seeded.��f���fE���gGD pseudo-random number generators (PRNGs, or algorithmic generators).��h���hMJ A generator implementing `SeedableRng` will usually be deterministic, but���hNK beware that portability and reproducibility of results **is not implied**.���iOL Refer to documentation of the generator, noting that generators named after���iIF a specific algorithm are usually tested for reproducibility against a���jKH reference vector, while `SmallRng` and `StdRng` specifically opt out of���k reproducibility guarantees.��k���k"ד\�k����,�k�������������������������x7���,�x���<�x����h\�x����h\�x��O��D��:��-�� ��������x6��kKH Seed type, which is restricted to types mutably-dereferenceable as `u8`���l1. arrays (we recommend `[u8; N]` for some `N`).��m���mKH It is recommended to seed PRNGs with a seed of at least circa 100 bits,���mLI which means an array of `[u8; 12]` or greater to avoid picking RNGs with���n" partially overlapping periods.��n���nJG For cryptographic RNG's a seed of 256 bits is recommended, `[u8; 32]`.��o��o���o:7 # Implementing `SeedableRng` for RNGs with large seeds��o���oLI Note that [`Default`] is not implemented for large arrays `[u8; N]` with���pJG `N` > 32. To be able to implement the traits required by `SeedableRng`���qDA for RNGs with such large seeds, the newtype pattern can be used:��q�<�q���q use rand_core::SeedableRng;��r�Ĩr const N: usize = 64;���r #[derive(Clone)]���r&# pub struct MyRngSeed(pub [u8; N]);�̉s # #[allow(dead_code)]���s  pub struct MyRng(MyRngSeed);��s���s  impl Default for MyRngSeed {���s#      fn default() -> MyRngSeed {��t         MyRngSeed([0; N])�L�t��,�t���t���t$! impl AsRef<[u8]> for MyRngSeed {���u#      fn as_ref(&self) -> &[u8] {���u         &self.0�L�u��,�u���u���u$! impl AsMut<[u8]> for MyRngSeed {���v+(     fn as_mut(&mut self) -> &mut [u8] {���v         &mut self.0�L�v��,�v���v���w  impl SeedableRng for MyRng {���w     type Seed = MyRngSeed;��w���w0-     fn from_seed(seed: MyRngSeed) -> MyRng {���x         MyRng(seed)�L�x��,�x��<�x�$�x�����'��y+( Create a new PRNG using the given seed.��y���yHE PRNG implementations are allowed to assume that bits in the seed are���zHE well distributed. That means usually that the number of one and zero���zMJ bits are roughly equal, and values like 0, 1 and (size - 1) are unlikely.���{HE Note that many non-cryptographic PRNGs will show poor quality output���{KH if this is not adhered to. If you wish to seed from simple numbers, use���| `seed_from_u64` instead.��|���|KH All PRNG implementations should be reproducible unless otherwise noted:���}HE given a fixed `seed`, the same sequence of output should be produced���~LI on all runs, library versions and architectures (e.g. check endianness).���~KH Any "value-breaking" changes to the generator should require bumping at���<9 least the minor version and documentation of the change.�����C@ It is not required that this function yield the same state as a����LI reference implementation of the PRNG given equivalent seed; if necessary����>; another constructor replicating behaviour from a reference��Ё  implementation can be added.�������JG PRNG implementations should make sure `from_seed` never panics. In the��̂HE case that some special values (like an all zero seed) are not viable����IF seeds it is preferable to map these to alternative constant value(s),���KH for example `0xBAD5EEDu32` or `0x0DDB1A5E5BAD5EEDu64` ("odd biases? bad����LI seed"). This is assuming only a small number of values must be rejected.�L��������י�p$���‹(���)& Create a new PRNG using a `u64` seed.�����JG This is a convenience-wrapper around `from_seed` to allow construction����LI of any `SeedableRng` from a simple `u64` value. It is designed such that����HE low Hamming Weight numbers like 0 and 1 can be used and should still��؇DA result in good, independent seeds to the PRNG which is returned.�������LI This **is not suitable for cryptography**, as should be clear given that����#  the input size is only 64 bits.�������HE Implementations for PRNGs *may* provide their own implementations of����KH this function, but the default implementation should be good enough for��NJJG all purposes. *Changing* the implementation of this function should be����'$ considered a value-breaking change.�lŋ�&����
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�������/t����&t�����&��+���63 Create a new PRNG seeded from an infallible `Rng`.�������LI This may be useful when needing to rapidly seed many PRNGs from a master��ѕLI PRNG, and to allow forking of PRNGs. It may be considered deterministic.�������JG The master PRNG should be at least as high quality as the child PRNGs.����DA When seeding non-cryptographic child PRNGs, we recommend using a��—GD different algorithm for the master PRNG (ideally a CSPRNG) to avoid����GD correlations between the child PRNGs. If this is not possible (e.g.��ژJG forking using small non-crypto PRNGs) ensure that your PRNG has a good����IF mixing function on the output or consider use of a hash function with����
 `from_seed`.�������JG Note that seeding `XorShiftRng` from another `XorShiftRng` provides an���FC extreme example of what can go wrong: the new PRNG will be a clone���� of the parent.�ś��͛JG PRNG implementations are allowed to assume that a good RNG is provided����JG for seeding, and that it is cryptographically secure when appropriate.���GD As of `rand` 0.7 / `rand_core` 0.5, implementations overriding this����EB method should ensure the implementation satisfies reproducibility����.+ (in prior versions this was not required).�������"דD����s�����������d����<��
����s�s<�!#���E���?< Create a new PRNG seeded from a potentially fallible `Rng`.�Ӡ��۠FC See [`from_rng`][SeedableRng::from_rng] docs for more information.�d�����)����
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����)�s��!#Ĭ����?< Creates a new instance of the RNG seeded via [`getrandom`].�������KH This method is the recommended way to construct non-deterministic PRNGs����&# since it is convenient and secure.�����ĤQN Note that this method may panic on (extremely unlikely) [`getrandom`] errors.����FC If it's not desirable, use the [`try_from_os_rng`] method instead.������JG In case the overhead of using [`getrandom`] to seed *many* PRNGs is an����96 issue, one may prefer to seed from a local PRNG, e.g.����%" `from_rng(rand::rng()).unwrap()`.����d��������ŧPM If [`getrandom`] is unable to provide secure entropy this method will panic.�������,) [`getrandom`]: https://docs.rs/getrandom��Ө52 [`try_from_os_rng`]: SeedableRng::try_from_os_rng�\������
������6�ߪQN Creates a new instance of the RNG seeded via [`getrandom`] without unwrapping����#  potential [`getrandom`] errors.�ݫ���J�����9���*' `from_rng(&mut rand::rng()).unwrap()`.�������,ݤ|�����������5�˯ZW Adapter that enables reading through a [`io::Read`](std::io::Read) from a [`RngCore`].����t�� # Examples����l�����˰ # use std::{io, io::Read};��� # use std::fs::File;����)& # use rand_core::{OsRng, TryRngCore};�������li io::copy(&mut OsRng.read_adapter().take(100), &mut File::create("/tmp/random.bytes").unwrap()).unwrap();�<���tɲ���
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�8�)$��͔��&$��FC A wrapper type implementing [`RngCore`] for some type implementing���PM [`BlockRngCore`] with `u32` array buffer; i.e. this can be used to implement���/, a full RNG from just a `generate` function.�����MJ The `core` field may be accessed directly but the results buffer may not.���41 PRNG implementations can simply use a type alias���GD (`pub type MyRng = BlockRng<MyRngCore>;`) but might prefer to use a���LI wrapper type (`pub struct MyRng(BlockRng<MyRngCore>);`); the latter must���JG re-implement `RngCore` but hides the implementation details and allows���0- extra functionality to be defined on the RNG���41 (e.g. `impl MyRng { fn set_stream(...){...} }`).�����OL `BlockRng` has heavily optimized implementations of the [`RngCore`] methods���63 reading values from the results buffer, as well as���HE calling [`BlockRngCore::generate`] directly on the output array when���HE [`fill_bytes`] is called on a large array. These methods also handle���>; the bookkeeping of when to generate a new batch of values.�����OL No whole generated `u32` values are thrown away and all values are consumed���GD in-order. [`next_u32`] simply takes the next available `u32` value.���DA [`next_u64`] is implemented by combining two `u32` values, least��� MJ significant first. [`fill_bytes`] consume a whole number of `u32` values,��� WT converting each `u32` to a byte slice in little-endian order. If the requested byte���!@= length is not a multiple of 4, some bytes will be discarded.��!���"NK See also [`BlockRng64`] which uses `u64` array buffers. Currently there is���"-* no direct support for other buffer types.��"���#GD For easy initialization `BlockRng` also implements [`SeedableRng`].��#���##����##إ��$'¦D�&�
����&��d�&�$�!���t���(*��&<�&���.d�&,�&���/\�'��'EB The *core* part of the RNG, implementing the `generate` function.�$�'���$FF�
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��$,�$��O/��FF���FPM [`BlockRngCore`] with `u64` array buffer; i.e. this can be used to implement���G/���G���GPM This is similar to [`BlockRng`], but specialized for algorithms that operate���H on `u64` values.��H���HOL No whole generated `u64` values are thrown away and all values are consumed���IGD in-order. [`next_u64`] simply takes the next available `u64` value.���IOL [`next_u32`] is however a bit special: half of a `u64` is consumed, leaving���JMJ the other half in the buffer. If the next function called is [`next_u32`]���KGD then the other half is then consumed, however both [`next_u64`] and���KLI [`fill_bytes`] discard the rest of any half-consumed `u64`s when called.��L���LSP [`fill_bytes`] consumes a whole number of `u64` values. If the requested length���M96 is not a multiple of 8, some bytes will be discarded.��M���M#����M#إ��N'¦T�O�
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��$,�N����?< An interface over the operating-system's random data source�����PM This is a zero-sized struct. It can be freely constructed with just `OsRng`.�����EB The implementation is provided by the [getrandom] crate. Refer to���*' [getrandom] documentation for details.�����NK This struct is available as `rand_core::OsRng` and as `rand::rngs::OsRng`.���DA In both cases, this requires the crate feature `os_rng` or `std`���:7 (enabled by default in `rand` but not in `rand_core`).�����! # Blocking and error handling�����MJ It is possible that when used during early boot the first call to `OsRng`���IF will block until the system's RNG is initialised. It is also possible���HE (though highly unlikely) for `OsRng` to fail on some platforms, most���	+( likely due to system mis-configuration.��	���	KH After the first successful call, it is highly unlikely that failures or���
MJ significant delays will occur (although performance should be expected to���$! be much slower than a user-space���_\ [PRNG](https://rust-random.github.io/book/guide-gen.html#pseudo-random-number-generators)).����� # Usage example�<����'$ use rand_core::{TryRngCore, OsRng};����� let mut key = [0u8; 16];���,) OsRng.try_fill_bytes(&mut key).unwrap();���
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