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

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

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+regex-automata
+==============
+A low level regular expression library that uses deterministic finite automata.
+It supports a rich syntax with Unicode support, has extensive options for
+configuring the best space vs time trade off for your use case and provides
+support for cheap deserialization of automata for use in `no_std` environments.
+
+[![Build status](https://github.com/BurntSushi/regex-automata/workflows/ci/badge.svg)](https://github.com/BurntSushi/regex-automata/actions)
+[![on crates.io](https://meritbadge.herokuapp.com/regex-automata)](https://crates.io/crates/regex-automata)
+![Minimum Supported Rust Version 1.41](https://img.shields.io/badge/rustc-1.41-green)
+
+Dual-licensed under MIT or the [UNLICENSE](https://unlicense.org/).
+
+
+### Documentation
+
+https://docs.rs/regex-automata
+
+
+### Usage
+
+Add this to your `Cargo.toml`:
+
+```toml
+[dependencies]
+regex-automata = "0.1"
+```
+
+and this to your crate root (if you're using Rust 2015):
+
+```rust
+extern crate regex_automata;
+```
+
+
+### Example: basic regex searching
+
+This example shows how to compile a regex using the default configuration
+and then use it to find matches in a byte string:
+
+```rust
+use regex_automata::Regex;
+
+let re = Regex::new(r"[0-9]{4}-[0-9]{2}-[0-9]{2}").unwrap();
+let text = b"2018-12-24 2016-10-08";
+let matches: Vec<(usize, usize)> = re.find_iter(text).collect();
+assert_eq!(matches, vec![(0, 10), (11, 21)]);
+```
+
+For more examples and information about the various knobs that can be turned,
+please see the [docs](https://docs.rs/regex-automata).
+
+
+### Support for `no_std`
+
+This crate comes with a `std` feature that is enabled by default. When the
+`std` feature is enabled, the API of this crate will include the facilities
+necessary for compiling, serializing, deserializing and searching with regular
+expressions. When the `std` feature is disabled, the API of this crate will
+shrink such that it only includes the facilities necessary for deserializing
+and searching with regular expressions.
+
+The intended workflow for `no_std` environments is thus as follows:
+
+* Write a program with the `std` feature that compiles and serializes a
+  regular expression. Serialization should only happen after first converting
+  the DFAs to use a fixed size state identifier instead of the default `usize`.
+  You may also need to serialize both little and big endian versions of each
+  DFA. (So that's 4 DFAs in total for each regex.)
+* In your `no_std` environment, follow the examples above for deserializing
+  your previously serialized DFAs into regexes. You can then search with them
+  as you would any regex.
+
+Deserialization can happen anywhere. For example, with bytes embedded into a
+binary or with a file memory mapped at runtime.
+
+Note that the
+[`ucd-generate`](https://github.com/BurntSushi/ucd-generate)
+tool will do the first step for you with its `dfa` or `regex` sub-commands.
+
+
+### Cargo features
+
+* `std` - **Enabled** by default. This enables the ability to compile finite
+  automata. This requires the `regex-syntax` dependency. Without this feature
+  enabled, finite automata can only be used for searching (using the approach
+  described above).
+* `transducer` - **Disabled** by default. This provides implementations of the
+  `Automaton` trait found in the `fst` crate. This permits using finite
+  automata generated by this crate to search finite state transducers. This
+  requires the `fst` dependency.
+
+
+### Differences with the regex crate
+
+The main goal of the [`regex`](https://docs.rs/regex) crate is to serve as a
+general purpose regular expression engine. It aims to automatically balance low
+compile times, fast search times and low memory usage, while also providing
+a convenient API for users. In contrast, this crate provides a lower level
+regular expression interface that is a bit less convenient while providing more
+explicit control over memory usage and search times.
+
+Here are some specific negative differences:
+
+* **Compilation can take an exponential amount of time and space** in the size
+  of the regex pattern. While most patterns do not exhibit worst case
+  exponential time, such patterns do exist. For example, `[01]*1[01]{N}` will
+  build a DFA with `2^(N+1)` states. For this reason, untrusted patterns should
+  not be compiled with this library. (In the future, the API may expose an
+  option to return an error if the DFA gets too big.)
+* This crate does not support sub-match extraction, which can be achieved with
+  the regex crate's "captures" API. This may be added in the future, but is
+  unlikely.
+* While the regex crate doesn't necessarily sport fast compilation times, the
+  regexes in this crate are almost universally slow to compile, especially when
+  they contain large Unicode character classes. For example, on my system,
+  compiling `\w{3}` with byte classes enabled takes just over 1 second and
+  almost 5MB of memory! (Compiling a sparse regex takes about the same time
+  but only uses about 500KB of memory.) Conversly, compiling the same regex
+  without Unicode support, e.g., `(?-u)\w{3}`, takes under 1 millisecond and
+  less than 5KB of memory. For this reason, you should only use Unicode
+  character classes if you absolutely need them!
+* This crate does not support regex sets.
+* This crate does not support zero-width assertions such as `^`, `$`, `\b` or
+  `\B`.
+* As a lower level crate, this library does not do literal optimizations. In
+  exchange, you get predictable performance regardless of input. The
+  philosophy here is that literal optimizations should be applied at a higher
+  level, although there is no easy support for this in the ecosystem yet.
+* There is no `&str` API like in the regex crate. In this crate, all APIs
+  operate on `&[u8]`. By default, match indices are guaranteed to fall on
+  UTF-8 boundaries, unless `RegexBuilder::allow_invalid_utf8` is enabled.
+
+With some of the downsides out of the way, here are some positive differences:
+
+* Both dense and sparse DFAs can be serialized to raw bytes, and then cheaply
+  deserialized. Deserialization always takes constant time since searching can
+  be performed directly on the raw serialized bytes of a DFA.
+* This crate was specifically designed so that the searching phase of a DFA has
+  minimal runtime requirements, and can therefore be used in `no_std`
+  environments. While `no_std` environments cannot compile regexes, they can
+  deserialize pre-compiled regexes.
+* Since this crate builds DFAs ahead of time, it will generally out-perform
+  the `regex` crate on equivalent tasks. The performance difference is likely
+  not large. However, because of a complex set of optimizations in the regex
+  crate (like literal optimizations), an accurate performance comparison may be
+  difficult to do.
+* Sparse DFAs provide a way to build a DFA ahead of time that sacrifices search
+  performance a small amount, but uses much less storage space. Potentially
+  even less than what the regex crate uses.
+* This crate exposes DFAs directly, such as `DenseDFA` and `SparseDFA`,
+  which enables one to do less work in some cases. For example, if you only
+  need the end of a match and not the start of a match, then you can use a DFA
+  directly without building a `Regex`, which always requires a second DFA to
+  find the start of a match.
+* Aside from choosing between dense and sparse DFAs, there are several options
+  for configuring the space usage vs search time trade off. These include
+  things like choosing a smaller state identifier representation, to
+  premultiplying state identifiers and splitting a DFA's alphabet into
+  equivalence classes. Finally, DFA minimization is also provided, but can
+  increase compilation times dramatically.
+
+
+### Future work
+
+* Look into being smarter about generating NFA states for large Unicode
+  character classes. These can create a lot of additional work for both the
+  determinizer and the minimizer, and I suspect this is the key thing we'll
+  want to improve if we want to make DFA compile times faster. I *believe*
+  it's possible to potentially build minimal or nearly minimal NFAs for the
+  special case of Unicode character classes by leveraging Daciuk's algorithms
+  for building minimal automata in linear time for sets of strings. See
+  https://blog.burntsushi.net/transducers/#construction for more details. The
+  key adaptation I think we need to make is to modify the algorithm to operate
+  on byte ranges instead of enumerating every codepoint in the set. Otherwise,
+  it might not be worth doing.
+* Add support for regex sets. It should be possible to do this by "simply"
+  introducing more match states. I think we can also report the positions at
+  each match, similar to how Aho-Corasick works. I think the long pole in the
+  tent here is probably the API design work and arranging it so that we don't
+  introduce extra overhead into the non-regex-set case without duplicating a
+  lot of code. It seems doable.
+* Stretch goal: support capturing groups by implementing "tagged" DFA
+  (transducers). Laurikari's paper is the usual reference here, but Trofimovich
+  has a much more thorough treatment here:
+  https://re2c.org/2017_trofimovich_tagged_deterministic_finite_automata_with_lookahead.pdf
+  I've only read the paper once. I suspect it will require at least a few more
+  read throughs before I understand it.
+  See also: https://re2c.org
+* Possibly less ambitious goal: can we select a portion of Trofimovich's work
+  to make small fixed length look-around work? It would be really nice to
+  support ^, $ and \b, especially the Unicode variant of \b and CRLF aware $.
+* Experiment with code generating Rust code. There is an early experiment in
+  src/codegen.rs that is thoroughly bit-rotted. At the time, I was
+  experimenting with whether or not codegen would significant decrease the size
+  of a DFA, since if you squint hard enough, it's kind of like a sparse
+  representation. However, it didn't shrink as much as I thought it would, so
+  I gave up. The other problem is that Rust doesn't support gotos, so I don't
+  even know whether the "match on each state" in a loop thing will be fast
+  enough. Either way, it's probably a good option to have. For one thing, it
+  would be endian independent where as the serialization format of the DFAs in
+  this crate are endian dependent (so you need two versions of every DFA, but
+  you only need to compile one of them for any given arch).
+* Experiment with unrolling the match loops and fill out the benchmarks.
+* Add some kind of streaming API. I believe users of the library can already
+  implement something for this outside of the crate, but it would be good to
+  provide an official API. The key thing here is figuring out the API. I
+  suspect we might want to support several variants.
+* Make a decision on whether or not there is room for literal optimizations
+  in this crate. My original intent was to not let this crate sink down into
+  that very very very deep rabbit hole. But instead, we might want to provide
+  some way for literal optimizations to hook into the match routines. The right
+  path forward here is to probably build something outside of the crate and
+  then see about integrating it. After all, users can implement their own
+  match routines just as efficiently as what the crate provides.
+* A key downside of DFAs is that they can take up a lot of memory and can be
+  quite costly to build. Their worst case compilation time is O(2^n), where
+  n is the number of NFA states. A paper by Yang and Prasanna (2011) actually
+  seems to provide a way to character state blow up such that it is detectable.
+  If we could know whether a regex will exhibit state explosion or not, then
+  we could make an intelligent decision about whether to ahead-of-time compile
+  a DFA.
+  See: https://www.researchgate.net/profile/Xu-Shutu/publication/229032602_Characterization_of_a_global_germplasm_collection_and_its_potential_utilization_for_analysis_of_complex_quantitative_traits_in_maize/links/02bfe50f914d04c837000000/Characterization-of-a-global-germplasm-collection-and-its-potential-utilization-for-analysis-of-complex-quantitative-traits-in-maize.pdf