LFU Cache
<p>Design and implement a data structure for a <a href="https://en.wikipedia.org/wiki/Least_frequently_used" target="_blank">Least Frequently Used (LFU)</a> cache.</p>

<p>Implement the <code>LFUCache</code> class:</p>

<ul>
	<li><code>LFUCache(int capacity)</code> Initializes the object with the <code>capacity</code> of the data structure.</li>
	<li><code>int get(int key)</code> Gets the value of the <code>key</code> if the <code>key</code> exists in the cache. Otherwise, returns <code>-1</code>.</li>
	<li><code>void put(int key, int value)</code> Update the value of the <code>key</code> if present, or inserts the <code>key</code> if not already present. When the cache reaches its <code>capacity</code>, it should invalidate and remove the <strong>least frequently used</strong> key before inserting a new item. For this problem, when there is a <strong>tie</strong> (i.e., two or more keys with the same frequency), the <strong>least recently used</strong> <code>key</code> would be invalidated.</li>
</ul>

<p>To determine the least frequently used key, a <strong>use counter</strong> is maintained for each key in the cache. The key with the smallest <strong>use counter</strong> is the least frequently used key.</p>

<p>When a key is first inserted into the cache, its <strong>use counter</strong> is set to <code>1</code> (due to the <code>put</code> operation). The <strong>use counter</strong> for a key in the cache is incremented either a <code>get</code> or <code>put</code> operation is called on it.</p>

<p>The functions&nbsp;<code data-stringify-type="code">get</code>&nbsp;and&nbsp;<code data-stringify-type="code">put</code>&nbsp;must each run in <code>O(1)</code> average time complexity.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<pre>
<strong>Input</strong>
[&quot;LFUCache&quot;, &quot;put&quot;, &quot;put&quot;, &quot;get&quot;, &quot;put&quot;, &quot;get&quot;, &quot;get&quot;, &quot;put&quot;, &quot;get&quot;, &quot;get&quot;, &quot;get&quot;]
[[2], [1, 1], [2, 2], [1], [3, 3], [2], [3], [4, 4], [1], [3], [4]]
<strong>Output</strong>
[null, null, null, 1, null, -1, 3, null, -1, 3, 4]

<strong>Explanation</strong>
// cnt(x) = the use counter for key x
// cache=[] will show the last used order for tiebreakers (leftmost element is  most recent)
LFUCache lfu = new LFUCache(2);
lfu.put(1, 1);   // cache=[1,_], cnt(1)=1
lfu.put(2, 2);   // cache=[2,1], cnt(2)=1, cnt(1)=1
lfu.get(1);      // return 1
                 // cache=[1,2], cnt(2)=1, cnt(1)=2
lfu.put(3, 3);   // 2 is the LFU key because cnt(2)=1 is the smallest, invalidate 2.
&nbsp;                // cache=[3,1], cnt(3)=1, cnt(1)=2
lfu.get(2);      // return -1 (not found)
lfu.get(3);      // return 3
                 // cache=[3,1], cnt(3)=2, cnt(1)=2
lfu.put(4, 4);   // Both 1 and 3 have the same cnt, but 1 is LRU, invalidate 1.
                 // cache=[4,3], cnt(4)=1, cnt(3)=2
lfu.get(1);      // return -1 (not found)
lfu.get(3);      // return 3
                 // cache=[3,4], cnt(4)=1, cnt(3)=3
lfu.get(4);      // return 4
                 // cache=[4,3], cnt(4)=2, cnt(3)=3
</pre>

<p>&nbsp;</p>
<p><strong>Constraints:</strong></p>

<ul>
	<li><code>1 &lt;= capacity&nbsp;&lt;= 10<sup>4</sup></code></li>
	<li><code>0 &lt;= key &lt;= 10<sup>5</sup></code></li>
	<li><code>0 &lt;= value &lt;= 10<sup>9</sup></code></li>
	<li>At most <code>2 * 10<sup>5</sup></code>&nbsp;calls will be made to <code>get</code> and <code>put</code>.</li>
</ul>

<p>&nbsp;</p>
<span style="display: none;">&nbsp;</span>
Design; Hash Table; Linked List; Doubly-Linked List
class Node:
    def __init__(self, key: int, value: int) -> None:
        self.key = key
        self.value = value
        self.freq = 1
        self.prev = None
        self.next = None


class DoublyLinkedList:
    def __init__(self) -> None:
        self.head = Node(-1, -1)
        self.tail = Node(-1, -1)
        self.head.next = self.tail
        self.tail.prev = self.head

    def add_first(self, node: Node) -> None:
        node.prev = self.head
        node.next = self.head.next
        self.head.next.prev = node
        self.head.next = node

    def remove(self, node: Node) -> Node:
        node.next.prev = node.prev
        node.prev.next = node.next
        node.next, node.prev = None, None
        return node

    def remove_last(self) -> Node:
        return self.remove(self.tail.prev)

    def is_empty(self) -> bool:
        return self.head.next == self.tail


class LFUCache:
    def __init__(self, capacity: int):
        self.capacity = capacity
        self.min_freq = 0
        self.map = defaultdict(Node)
        self.freq_map = defaultdict(DoublyLinkedList)

    def get(self, key: int) -> int:
        if self.capacity == 0 or key not in self.map:
            return -1
        node = self.map[key]
        self.incr_freq(node)
        return node.value

    def put(self, key: int, value: int) -> None:
        if self.capacity == 0:
            return
        if key in self.map:
            node = self.map[key]
            node.value = value
            self.incr_freq(node)
            return
        if len(self.map) == self.capacity:
            ls = self.freq_map[self.min_freq]
            node = ls.remove_last()
            self.map.pop(node.key)
        node = Node(key, value)
        self.add_node(node)
        self.map[key] = node
        self.min_freq = 1

    def incr_freq(self, node: Node) -> None:
        freq = node.freq
        ls = self.freq_map[freq]
        ls.remove(node)
        if ls.is_empty():
            self.freq_map.pop(freq)
            if freq == self.min_freq:
                self.min_freq += 1
        node.freq += 1
        self.add_node(node)

    def add_node(self, node: Node) -> None:
        freq = node.freq
        ls = self.freq_map[freq]
        ls.add_first(node)
        self.freq_map[freq] = ls


# Your LFUCache object will be instantiated and called as such:
# obj = LFUCache(capacity)
# param_1 = obj.get(key)
# obj.put(key,value)
