//! Collection types.
//!
//! Rust's standard collection library provides efficient implementations of the
//! most common general purpose programming data structures. By using the
//! standard implementations, it should be possible for two libraries to
//! communicate without significant data conversion.
//!
//! To get this out of the way: you should probably just use [`Vec`] or [`HashMap`].
//! These two collections cover most use cases for generic data storage and
//! processing. They are exceptionally good at doing what they do. All the other
//! collections in the standard library have specific use cases where they are
//! the optimal choice, but these cases are borderline *niche* in comparison.
//! Even when `Vec` and `HashMap` are technically suboptimal, they're probably a
//! good enough choice to get started.
//!
//! Rust's collections can be grouped into four major categories:
//!
//! * Sequences: [`Vec`], [`VecDeque`], [`LinkedList`]
//! * Maps: [`HashMap`], [`BTreeMap`]
//! * Sets: [`HashSet`], [`BTreeSet`]
//! * Misc: [`BinaryHeap`]
//!
//! # When Should You Use Which Collection?
//!
//! These are fairly high-level and quick break-downs of when each collection
//! should be considered. Detailed discussions of strengths and weaknesses of
//! individual collections can be found on their own documentation pages.
//!
//! ### Use a `Vec` when:
//! * You want to collect items up to be processed or sent elsewhere later, and
//!   don't care about any properties of the actual values being stored.
//! * You want a sequence of elements in a particular order, and will only be
//!   appending to (or near) the end.
//! * You want a stack.
//! * You want a resizable array.
//! * You want a heap-allocated array.
//!
//! ### Use a `VecDeque` when:
//! * You want a [`Vec`] that supports efficient insertion at both ends of the
//!   sequence.
//! * You want a queue.
//! * You want a double-ended queue (deque).
//!
//! ### Use a `LinkedList` when:
//! * You want a [`Vec`] or [`VecDeque`] of unknown size, and can't tolerate
//!   amortization.
//! * You want to efficiently split and append lists.
//! * You are *absolutely* certain you *really*, *truly*, want a doubly linked
//!   list.
//!
//! ### Use a `HashMap` when:
//! * You want to associate arbitrary keys with an arbitrary value.
//! * You want a cache.
//! * You want a map, with no extra functionality.
//!
//! ### Use a `BTreeMap` when:
//! * You want a map sorted by its keys.
//! * You want to be able to get a range of entries on-demand.
//! * You're interested in what the smallest or largest key-value pair is.
//! * You want to find the largest or smallest key that is smaller or larger
//!   than something.
//!
//! ### Use the `Set` variant of any of these `Map`s when:
//! * You just want to remember which keys you've seen.
//! * There is no meaningful value to associate with your keys.
//! * You just want a set.
//!
//! ### Use a `BinaryHeap` when:
//!
//! * You want to store a bunch of elements, but only ever want to process the
//!   "biggest" or "most important" one at any given time.
//! * You want a priority queue.
//!
//! # Performance
//!
//! Choosing the right collection for the job requires an understanding of what
//! each collection is good at. Here we briefly summarize the performance of
//! different collections for certain important operations. For further details,
//! see each type's documentation, and note that the names of actual methods may
//! differ from the tables below on certain collections.
//!
//! Throughout the documentation, we will adhere to the following conventions
//! for operation notation:
//!
//! * The collection's size is denoted by `n`.
//! * If a second collection is involved, its size is denoted by `m`.
//! * Item indices are denoted by `i`.
//! * Operations which have an *amortized* cost are suffixed with a `*`.
//! * Operations with an *expected* cost are suffixed with a `~`.
//!
//! Calling operations that add to a collection will occasionally require a
//! collection to be resized - an extra operation that takes *O*(*n*) time.
//!
//! *Amortized* costs are calculated to account for the time cost of such resize
//! operations *over a sufficiently large series of operations*. An individual
//! operation may be slower or faster due to the sporadic nature of collection
//! resizing, however the average cost per operation will approach the amortized
//! cost.
//!
//! Rust's collections never automatically shrink, so removal operations aren't
//! amortized.
//!
//! [`HashMap`] uses *expected* costs. It is theoretically possible, though very
//! unlikely, for [`HashMap`] to experience significantly worse performance than
//! the expected cost. This is due to the probabilistic nature of hashing - i.e.
//! it is possible to generate a duplicate hash given some input key that will
//! requires extra computation to correct.
//!
//! ## Cost of Collection Operations
//!
//!
//! |                | get(i)                 | insert(i)               | remove(i)              | append(Vec(m))    | split_off(i)           | range           | append       |
//! |----------------|------------------------|-------------------------|------------------------|-------------------|------------------------|-----------------|--------------|
//! | [`Vec`]        | *O*(1)                 | *O*(*n*-*i*)*           | *O*(*n*-*i*)           | *O*(*m*)*         | *O*(*n*-*i*)           | N/A             | N/A          |
//! | [`VecDeque`]   | *O*(1)                 | *O*(min(*i*, *n*-*i*))* | *O*(min(*i*, *n*-*i*)) | *O*(*m*)*         | *O*(min(*i*, *n*-*i*)) | N/A             | N/A          |
//! | [`LinkedList`] | *O*(min(*i*, *n*-*i*)) | *O*(min(*i*, *n*-*i*))  | *O*(min(*i*, *n*-*i*)) | *O*(1)            | *O*(min(*i*, *n*-*i*)) | N/A             | N/A          |
//! | [`HashMap`]    | *O*(1)~                | *O*(1)~*                | *O*(1)~                | N/A               | N/A                    | N/A             | N/A          |
//! | [`BTreeMap`]   | *O*(log(*n*))          | *O*(log(*n*))           | *O*(log(*n*))          | N/A               | N/A                    | *O*(log(*n*))   | *O*(*n*+*m*) |
//!
//! Note that where ties occur, [`Vec`] is generally going to be faster than
//! [`VecDeque`], and [`VecDeque`] is generally going to be faster than
//! [`LinkedList`].
//!
//! For Sets, all operations have the cost of the equivalent Map operation.
//!
//! # Correct and Efficient Usage of Collections
//!
//! Of course, knowing which collection is the right one for the job doesn't
//! instantly permit you to use it correctly. Here are some quick tips for
//! efficient and correct usage of the standard collections in general. If
//! you're interested in how to use a specific collection in particular, consult
//! its documentation for detailed discussion and code examples.
//!
//! ## Capacity Management
//!
//! Many collections provide several constructors and methods that refer to
//! "capacity". These collections are generally built on top of an array.
//! Optimally, this array would be exactly the right size to fit only the
//! elements stored in the collection, but for the collection to do this would
//! be very inefficient. If the backing array was exactly the right size at all
//! times, then every time an element is inserted, the collection would have to
//! grow the array to fit it. Due to the way memory is allocated and managed on
//! most computers, this would almost surely require allocating an entirely new
//! array and copying every single element from the old one into the new one.
//! Hopefully you can see that this wouldn't be very efficient to do on every
//! operation.
//!
//! Most collections therefore use an *amortized* allocation strategy. They
//! generally let themselves have a fair amount of unoccupied space so that they
//! only have to grow on occasion. When they do grow, they allocate a
//! substantially larger array to move the elements into so that it will take a
//! while for another grow to be required. While this strategy is great in
//! general, it would be even better if the collection *never* had to resize its
//! backing array. Unfortunately, the collection itself doesn't have enough
//! information to do this itself. Therefore, it is up to us programmers to give
//! it hints.
//!
//! Any `with_capacity` constructor will instruct the collection to allocate
//! enough space for the specified number of elements. Ideally this will be for
//! exactly that many elements, but some implementation details may prevent
//! this. See collection-specific documentation for details. In general, use
//! `with_capacity` when you know exactly how many elements will be inserted, or
//! at least have a reasonable upper-bound on that number.
//!
//! When anticipating a large influx of elements, the `reserve` family of
//! methods can be used to hint to the collection how much room it should make
//! for the coming items. As with `with_capacity`, the precise behavior of
//! these methods will be specific to the collection of interest.
//!
//! For optimal performance, collections will generally avoid shrinking
//! themselves. If you believe that a collection will not soon contain any more
//! elements, or just really need the memory, the `shrink_to_fit` method prompts
//! the collection to shrink the backing array to the minimum size capable of
//! holding its elements.
//!
//! Finally, if ever you're interested in what the actual capacity of the
//! collection is, most collections provide a `capacity` method to query this
//! information on demand. This can be useful for debugging purposes, or for
//! use with the `reserve` methods.
//!
//! ## Iterators
//!
//! [Iterators][crate::iter]
//! are a powerful and robust mechanism used throughout Rust's
//! standard libraries. Iterators provide a sequence of values in a generic,
//! safe, efficient and convenient way. The contents of an iterator are usually
//! *lazily* evaluated, so that only the values that are actually needed are
//! ever actually produced, and no allocation need be done to temporarily store
//! them. Iterators are primarily consumed using a `for` loop, although many
//! functions also take iterators where a collection or sequence of values is
//! desired.
//!
//! All of the standard collections provide several iterators for performing
//! bulk manipulation of their contents. The three primary iterators almost
//! every collection should provide are `iter`, `iter_mut`, and `into_iter`.
//! Some of these are not provided on collections where it would be unsound or
//! unreasonable to provide them.
//!
//! `iter` provides an iterator of immutable references to all the contents of a
//! collection in the most "natural" order. For sequence collections like [`Vec`],
//! this means the items will be yielded in increasing order of index starting
//! at 0. For ordered collections like [`BTreeMap`], this means that the items
//! will be yielded in sorted order. For unordered collections like [`HashMap`],
//! the items will be yielded in whatever order the internal representation made
//! most convenient. This is great for reading through all the contents of the
//! collection.
//!
//! ```
//! let vec = vec![1, 2, 3, 4];
//! for x in vec.iter() {
//!    println!("vec contained {x:?}");
//! }
//! ```
//!
//! `iter_mut` provides an iterator of *mutable* references in the same order as
//! `iter`. This is great for mutating all the contents of the collection.
//!
//! ```
//! let mut vec = vec![1, 2, 3, 4];
//! for x in vec.iter_mut() {
//!    *x += 1;
//! }
//! ```
//!
//! `into_iter` transforms the actual collection into an iterator over its
//! contents by-value. This is great when the collection itself is no longer
//! needed, and the values are needed elsewhere. Using `extend` with `into_iter`
//! is the main way that contents of one collection are moved into another.
//! `extend` automatically calls `into_iter`, and takes any <code>T: [IntoIterator]</code>.
//! Calling `collect` on an iterator itself is also a great way to convert one
//! collection into another. Both of these methods should internally use the
//! capacity management tools discussed in the previous section to do this as
//! efficiently as possible.
//!
//! ```
//! let mut vec1 = vec![1, 2, 3, 4];
//! let vec2 = vec![10, 20, 30, 40];
//! vec1.extend(vec2);
//! ```
//!
//! ```
//! use std::collections::VecDeque;
//!
//! let vec = [1, 2, 3, 4];
//! let buf: VecDeque<_> = vec.into_iter().collect();
//! ```
//!
//! Iterators also provide a series of *adapter* methods for performing common
//! threads to sequences. Among the adapters are functional favorites like `map`,
//! `fold`, `skip` and `take`. Of particular interest to collections is the
//! `rev` adapter, which reverses any iterator that supports this operation. Most
//! collections provide reversible iterators as the way to iterate over them in
//! reverse order.
//!
//! ```
//! let vec = vec![1, 2, 3, 4];
//! for x in vec.iter().rev() {
//!    println!("vec contained {x:?}");
//! }
//! ```
//!
//! Several other collection methods also return iterators to yield a sequence
//! of results but avoid allocating an entire collection to store the result in.
//! This provides maximum flexibility as
//! [`collect`][crate::iter::Iterator::collect] or
//! [`extend`][crate::iter::Extend::extend] can be called to
//! "pipe" the sequence into any collection if desired. Otherwise, the sequence
//! can be looped over with a `for` loop. The iterator can also be discarded
//! after partial use, preventing the computation of the unused items.
//!
//! ## Entries
//!
//! The `entry` API is intended to provide an efficient mechanism for
//! manipulating the contents of a map conditionally on the presence of a key or
//! not. The primary motivating use case for this is to provide efficient
//! accumulator maps. For instance, if one wishes to maintain a count of the
//! number of times each key has been seen, they will have to perform some
//! conditional logic on whether this is the first time the key has been seen or
//! not. Normally, this would require a `find` followed by an `insert`,
//! effectively duplicating the search effort on each insertion.
//!
//! When a user calls `map.entry(key)`, the map will search for the key and
//! then yield a variant of the `Entry` enum.
//!
//! If a `Vacant(entry)` is yielded, then the key *was not* found. In this case
//! the only valid operation is to `insert` a value into the entry. When this is
//! done, the vacant entry is consumed and converted into a mutable reference to
//! the value that was inserted. This allows for further manipulation of the
//! value beyond the lifetime of the search itself. This is useful if complex
//! logic needs to be performed on the value regardless of whether the value was
//! just inserted.
//!
//! If an `Occupied(entry)` is yielded, then the key *was* found. In this case,
//! the user has several options: they can `get`, `insert` or `remove` the
//! value of the occupied entry. Additionally, they can convert the occupied
//! entry into a mutable reference to its value, providing symmetry to the
//! vacant `insert` case.
//!
//! ### Examples
//!
//! Here are the two primary ways in which `entry` is used. First, a simple
//! example where the logic performed on the values is trivial.
//!
//! #### Counting the number of times each character in a string occurs
//!
//! ```
//! use std::collections::btree_map::BTreeMap;
//!
//! let mut count = BTreeMap::new();
//! let message = "she sells sea shells by the sea shore";
//!
//! for c in message.chars() {
//!     *count.entry(c).or_insert(0) += 1;
//! }
//!
//! assert_eq!(count.get(&'s'), Some(&8));
//!
//! println!("Number of occurrences of each character");
//! for (char, count) in &count {
//!     println!("{char}: {count}");
//! }
//! ```
//!
//! When the logic to be performed on the value is more complex, we may simply
//! use the `entry` API to ensure that the value is initialized and perform the
//! logic afterwards.
//!
//! #### Tracking the inebriation of customers at a bar
//!
//! ```
//! use std::collections::btree_map::BTreeMap;
//!
//! // A client of the bar. They have a blood alcohol level.
//! struct Person { blood_alcohol: f32 }
//!
//! // All the orders made to the bar, by client ID.
//! let orders = vec![1, 2, 1, 2, 3, 4, 1, 2, 2, 3, 4, 1, 1, 1];
//!
//! // Our clients.
//! let mut blood_alcohol = BTreeMap::new();
//!
//! for id in orders {
//!     // If this is the first time we've seen this customer, initialize them
//!     // with no blood alcohol. Otherwise, just retrieve them.
//!     let person = blood_alcohol.entry(id).or_insert(Person { blood_alcohol: 0.0 });
//!
//!     // Reduce their blood alcohol level. It takes time to order and drink a beer!
//!     person.blood_alcohol *= 0.9;
//!
//!     // Check if they're sober enough to have another beer.
//!     if person.blood_alcohol > 0.3 {
//!         // Too drunk... for now.
//!         println!("Sorry {id}, I have to cut you off");
//!     } else {
//!         // Have another!
//!         person.blood_alcohol += 0.1;
//!     }
//! }
//! ```
//!
//! # Insert and complex keys
//!
//! If we have a more complex key, calls to `insert` will
//! not update the value of the key. For example:
//!
//! ```
//! use std::cmp::Ordering;
//! use std::collections::BTreeMap;
//! use std::hash::{Hash, Hasher};
//!
//! #[derive(Debug)]
//! struct Foo {
//!     a: u32,
//!     b: &'static str,
//! }
//!
//! // we will compare `Foo`s by their `a` value only.
//! impl PartialEq for Foo {
//!     fn eq(&self, other: &Self) -> bool { self.a == other.a }
//! }
//!
//! impl Eq for Foo {}
//!
//! // we will hash `Foo`s by their `a` value only.
//! impl Hash for Foo {
//!     fn hash<H: Hasher>(&self, h: &mut H) { self.a.hash(h); }
//! }
//!
//! impl PartialOrd for Foo {
//!     fn partial_cmp(&self, other: &Self) -> Option<Ordering> { self.a.partial_cmp(&other.a) }
//! }
//!
//! impl Ord for Foo {
//!     fn cmp(&self, other: &Self) -> Ordering { self.a.cmp(&other.a) }
//! }
//!
//! let mut map = BTreeMap::new();
//! map.insert(Foo { a: 1, b: "baz" }, 99);
//!
//! // We already have a Foo with an a of 1, so this will be updating the value.
//! map.insert(Foo { a: 1, b: "xyz" }, 100);
//!
//! // The value has been updated...
//! assert_eq!(map.values().next().unwrap(), &100);
//!
//! // ...but the key hasn't changed. b is still "baz", not "xyz".
//! assert_eq!(map.keys().next().unwrap().b, "baz");
//! ```

#![stable(feature = "rust1", since = "1.0.0")]

#[stable(feature = "try_reserve", since = "1.57.0")]
pub use alloc_crate::collections::TryReserveError;
#[unstable(
    feature = "try_reserve_kind",
    reason = "Uncertain how much info should be exposed",
    issue = "48043"
)]
pub use alloc_crate::collections::TryReserveErrorKind;
#[stable(feature = "rust1", since = "1.0.0")]
pub use alloc_crate::collections::{binary_heap, btree_map, btree_set};
#[stable(feature = "rust1", since = "1.0.0")]
pub use alloc_crate::collections::{linked_list, vec_deque};
#[stable(feature = "rust1", since = "1.0.0")]
pub use alloc_crate::collections::{BTreeMap, BTreeSet, BinaryHeap};
#[stable(feature = "rust1", since = "1.0.0")]
pub use alloc_crate::collections::{LinkedList, VecDeque};

#[stable(feature = "rust1", since = "1.0.0")]
#[doc(inline)]
pub use self::hash_map::HashMap;
#[stable(feature = "rust1", since = "1.0.0")]
#[doc(inline)]
pub use self::hash_set::HashSet;
#[stable(feature = "rust1", since = "1.0.0")]
// FIXME(#82080) The deprecation here is only theoretical, and does not actually produce a warning.
#[deprecated(note = "moved to `std::ops::Bound`", since = "1.26.0")]
#[doc(hidden)]
pub use crate::ops::Bound;

mod hash;

#[stable(feature = "rust1", since = "1.0.0")]
pub mod hash_map {
    //! A hash map implemented with quadratic probing and SIMD lookup.
    #[stable(feature = "rust1", since = "1.0.0")]
    pub use super::hash::map::*;
    #[stable(feature = "hashmap_build_hasher", since = "1.7.0")]
    pub use crate::hash::random::DefaultHasher;
    #[stable(feature = "hashmap_build_hasher", since = "1.7.0")]
    pub use crate::hash::random::RandomState;
}

#[stable(feature = "rust1", since = "1.0.0")]
pub mod hash_set {
    //! A hash set implemented as a `HashMap` where the value is `()`.
    #[stable(feature = "rust1", since = "1.0.0")]
    pub use super::hash::set::*;
}