
# Cache implementaion with a Least Recently Used (LRU) replacement policy and
# a basic dictionary interface.

# Copyright (c) 2006-2026 Jay Hutchinson
# SPDX-License-Identifier: MIT

# The cache is implemented using a combination of a python dictionary (hash
# table) and a circular doubly linked list. Items in the cache are stored in
# nodes. These nodes make up the linked list. The list is used to efficiently
# maintain the order that the items have been used in. The front or head of
# the list contains the most recently used item, the tail of the list
# contains the least recently used item. When an item is used it can easily
# (in a constant amount of time) be moved to the front of the list, thus
# updating its position in the ordering. These nodes are also placed in the
# hash table under their associated key. The hash table allows efficient
# lookup of values by key.

import sys
if sys.version_info < (3, 3):
    from collections import Mapping
else:
    from collections.abc import Mapping

# Class for the node objects.
class _dlnode(object):
    __slots__ = ('empty', 'next', 'prev', 'key', 'value')

    def __init__(self):
        self.empty = True


class lrucache(object):
    def __init__(self, size, callback=None):
        self.callback = callback

        self.table = {}

        # Initialize the doubly linked list with one empty node. This is an
        # invariant. The cache size must always be greater than zero. Each
        # node has a 'prev' and 'next' variable to hold the node that comes
        # before it and after it respectively. Initially the two variables
        # each point to the head node itself, creating a circular doubly
        # linked list of size one.
        self.head = _dlnode()
        self.head.next = self.head
        self.head.prev = self.head

        self.listSize = 1

        # Now that the invariant mentioned above is met, we can call size()
        # to adjust the list to the desired size.
        self.size(size)

    def __len__(self):
        return len(self.table)

    def clear(self):
        for node in self.dli():
            node.empty = True
            node.key = None
            node.value = None

        self.table.clear()

    def __contains__(self, key):
        return key in self.table

    # Looks up a value in the cache without affecting the cache's order.
    def peek(self, key):
        node = self.table[key]
        return node.value

    def __getitem__(self, key):
        node = self.table[key]

        # Update the list ordering. Move this node so that it directly
        # proceeds the head node. Then set the 'head' variable to it. This
        # makes it the new head of the list.
        self.mtf(node)
        self.head = node

        return node.value

    def get(self, key, default=None):
        if key not in self.table:
            return default
        
        return self[key]

    def __setitem__(self, key, value):
        # If any value is stored under 'key' in the cache already, then replace
        # that value with the new one.
        if key in self.table:
            node = self.table[key]

            # Replace the value.
            node.value = value

            # Update the list ordering.
            self.mtf(node)
            self.head = node

            return

        # No value is currently stored under 'key' in the cache. We need
        # to choose a node to place the new item in. There are two cases. If
        # the cache is full, some item will have to be pushed out of the
        # cache. We want to choose the node with the least recently used
        # item. This is the node at the tail of the list. If the cache is not
        # full, we want to choose a node that is empty. Because of the way the
        # list is managed, the empty nodes are always together at the tail
        # end of the list. Thus, in either case, by chooseing the node at the
        # tail of the list our conditions are satisfied.

        # Since the list is circular, the tail node directly preceeds the
        # 'head' node.
        node = self.head.prev

        # If the node already contains something we need to remove the old
        # key from the dictionary.
        if not node.empty:
            if self.callback is not None:
                self.callback(node.key, node.value)
            del self.table[node.key]

        # Place the new key and value in the node
        node.empty = False
        node.key = key
        node.value = value

        # Add the node to the dictionary under the new key.
        self.table[key] = node

        # We need to move the node to the head of the list. The node is the
        # tail node, so it directly preceeds the head node due to the list
        # being circular. Therefore, the ordering is already correct, we just
        # need to adjust the 'head' variable.
        self.head = node

    def __delitem__(self, key):
        # Lookup the node, remove it from the hash table, and mark it as empty.
        node = self.table[key]
        del self.table[key]
        node.empty = True

        # Not strictly necessary.
        node.key = None
        node.value = None

        # Because this node is now empty we want to reuse it before any
        # non-empty node. To do that we want to move it to the tail of the
        # list. We move it so that it directly preceeds the 'head' node. This
        # makes it the tail node. The 'head' is then adjusted. This
        # adjustment ensures correctness even for the case where the 'node'
        # is the 'head' node.
        self.mtf(node)
        self.head = node.next

    def update(self, *args, **kwargs):
        if len(args) > 0:
            other = args[0]
            if isinstance(other, Mapping):
                for key in other:
                    self[key] = other[key]
            elif hasattr(other, "keys"):
                for key in other.keys():
                    self[key] = other[key]
            else:
                for key, value in other:
                    self[key] = value

        for key, value in kwargs.items():
            self[key] = value

    __defaultObj = object()
    def pop(self, key, default=__defaultObj):
        if key in self.table:
            value = self.peek(key)
            del self[key]
            return value

        if default is self.__defaultObj:
            raise KeyError

        return default

    def popitem(self):
        # Make sure the cache isn't empty.
        if len(self) < 1:
            raise KeyError

        node = self.head

        # Save the key and value so that we can return them.
        key = node.key
        value = node.value

        # Remove the key from the hash table and mark the node as empty.
        del self.table[key]
        node.empty = True

        # Not strictly necessary.
        node.key = None
        node.value = None

        # Because this node is now empty we want to reuse it before any
        # non-empty node. To do that we want to move it to the tail of the
        # list. This node is the head node. Due to the list being circular,
        # the ordering is already correct, we just need to adjust the 'head'
        # variable.
        self.head = node.next

        return key, value

    def setdefault(self, key, default=None):
        if key in self.table:
            return self[key]

        self[key] = default
        return default

    def __iter__(self):
        # Return an iterator that returns the keys in the cache in order from
        # the most recently to least recently used. Does not modify the cache's
        # order.
        for node in self.dli():
            yield node.key

    def items(self):
        # Return an iterator that returns the (key, value) pairs in the cache
        # in order from the most recently to least recently used. Does not
        # modify the cache's order.
        for node in self.dli():
            yield (node.key, node.value)

    def keys(self):
        # Return an iterator that returns the keys in the cache in order from
        # the most recently to least recently used. Does not modify the cache's
        # order.
        for node in self.dli():
            yield node.key

    def values(self):
        # Return an iterator that returns the values in the cache in order
        # from the most recently to least recently used. Does not modify the
        # cache's order.
        for node in self.dli():
            yield node.value

    def size(self, size=None):
        if size is not None:
            assert size > 0
            if size > self.listSize:
                self.addTailNode(size - self.listSize)
            elif size < self.listSize:
                self.removeTailNode(self.listSize - size)

        return self.listSize

    # Increases the size of the cache by inserting n empty nodes at the tail
    # of the list.
    def addTailNode(self, n):
        for i in range(n):
            node = _dlnode()
            node.next = self.head
            node.prev = self.head.prev

            self.head.prev.next = node
            self.head.prev = node

        self.listSize += n

    # Decreases the size of the cache by removing n nodes from the tail of the
    # list.
    def removeTailNode(self, n):
        assert self.listSize > n
        for i in range(n):
            node = self.head.prev
            if not node.empty:
                if self.callback is not None:
                    self.callback(node.key, node.value)
                del self.table[node.key]

            # Splice the tail node out of the list
            self.head.prev = node.prev
            node.prev.next = self.head

            # The next four lines are not strictly necessary.
            node.prev = None
            node.next = None
            node.key = None
            node.value = None

        self.listSize -= n

    # This method adjusts the ordering of the doubly linked list so that
    # 'node' directly precedes the 'head' node. Because of the order of
    # operations, if 'node' already directly precedes the 'head' node, or if
    # 'node' is the 'head' node, the order of the list will be unchanged.
    def mtf(self, node):
        node.prev.next = node.next
        node.next.prev = node.prev

        node.prev = self.head.prev
        node.next = self.head.prev.next

        node.next.prev = node
        node.prev.next = node

    # This method returns an iterator that iterates over the non-empty nodes
    # in the doubly linked list in order from the most recently to the least
    # recently used.
    def dli(self):
        node = self.head
        for i in range(len(self.table)):
            yield node
            node = node.next

    # The methods __getstate__() and __setstate__() are used to correctly
    # support the copy and pickle modules from the standard library. In
    # particular, the doubly linked list trips up the introspection machinery
    # used by copy/pickle.
    def __getstate__(self):
        # Copy the instance attributes.
        d = self.__dict__.copy()

        # Remove those that we need to do by hand.
        del d['table']
        del d['head']

        # Package up the key/value pairs from the doubly linked list into a
        # normal list that can be copied/pickled correctly. We put the
        # key/value pairs into the list in order, as returned by dli(), from
        # most recently to least recently used, so that the copy can be
        # restored with the same ordering.
        elements = [(node.key, node.value) for node in self.dli()]
        return (d, elements)

    def __setstate__(self, state):
        d = state[0]
        elements = state[1]

        # Restore the instance attributes, except for the table and head.
        self.__dict__.update(d)

        # Rebuild the table and doubly linked list from the simple list of
        # key/value pairs in 'elements'.

        # The listSize is the size of the original cache. We want this cache
        # to have the same size, but we need to reset it temporarily to set up
        # table and head correctly, so save a copy of the size.
        size = self.listSize

        # Setup a table and double linked list. This is identical to the way
        # __init__() does it.
        self.table = {}

        self.head = _dlnode()
        self.head.next = self.head
        self.head.prev = self.head

        self.listSize = 1

        # Now adjust the list to the desired size.
        self.size(size)

        # Fill the cache with the keys/values. Because inserted items are
        # moved to the top of the doubly linked list, we insert the key/value
        # pairs in reverse order. This ensures that the order of the doubly
        # linked list is identical to the original cache.
        for key, value in reversed(elements):
            self[key] = value


class WriteThroughCacheManager(object):
    def __init__(self, store, size):
        self.store = store
        self.cache = lrucache(size)

    def __len__(self):
        return len(self.store)

    # Returns/sets the size of the managed cache.
    def size(self, size=None):
        return self.cache.size(size)

    def clear(self):
        self.cache.clear()
        self.store.clear()

    def __contains__(self, key):
        # Check the cache first. If it is there we can return quickly.
        if key in self.cache:
            return True

        # Not in the cache. Might be in the underlying store.
        if key in self.store:
            return True

        return False

    def __getitem__(self, key):
        # Try the cache first. If successful we can just return the value.
        if key in self.cache:
            return self.cache[key]

        # It wasn't in the cache. Look it up in the store, add the entry to
        # the cache, and return the value.
        value = self.store[key]
        self.cache[key] = value
        return value

    def get(self, key, default=None):
        try:
            return self[key]
        except KeyError:
            return default

    def __setitem__(self, key, value):
        # Add the key/value pair to the cache and store.
        self.cache[key] = value
        self.store[key] = value

    def __delitem__(self, key):
        # With write-through behavior the cache and store should be consistent.
        # Delete it from the store.
        del self.store[key]

        # It might also be in the cache, try to delete it. If it is not, we
        # will catch KeyError and ignore it.
        try:
            del self.cache[key]
        except KeyError:
            pass

    def __iter__(self):
        return self.keys()

    def keys(self):
        return self.store.keys()

    def values(self):
        return self.store.values()

    def items(self):
        return self.store.items()


class WriteBackCacheManager(object):
    def __init__(self, store, size):
        self.store = store

        # Create a set to hold the dirty keys.
        self.dirty = set()

        # Define a callback function to be called by the cache when a
        # key/value pair is about to be ejected. This callback will check to
        # see if the key is in the dirty set. If so, then it will update the
        # store object and remove the key from the dirty set.
        def callback(key, value):
            if key in self.dirty:
                self.store[key] = value
                self.dirty.remove(key)

        # Create a cache and give it the callback function.
        self.cache = lrucache(size, callback)

    # Returns/sets the size of the managed cache.
    def size(self, size=None):
        return self.cache.size(size)

    def len(self):
        self.sync()
        return len(self.store)

    def clear(self):
        self.cache.clear()
        self.dirty.clear()
        self.store.clear()

    def __contains__(self, key):
        # Check the cache first, since if it is there we can return quickly.
        if key in self.cache:
            return True

        # Not in the cache. Might be in the underlying store.
        if key in self.store:
            return True

        return False

    def __getitem__(self, key):
        # Try the cache first. If successful we can just return the value.
        if key in self.cache:
            return self.cache[key]

        # It wasn't in the cache. Look it up in the store, add the entry to
        # the cache, and return the value.
        value = self.store[key]
        self.cache[key] = value
        return value

    def get(self, key, default=None):
        try:
            return self[key]
        except KeyError:
            return default

    def __setitem__(self, key, value):
        # Add the key/value pair to the cache.
        self.cache[key] = value
        self.dirty.add(key)

    def __delitem__(self, key):
        found = False
        try:
            del self.cache[key]
            found = True
            self.dirty.remove(key)
        except KeyError:
            pass

        try:
            del self.store[key]
            found = True
        except KeyError:
            pass

        if not found:  # If not found in cache or store, raise error.
            raise KeyError

    def __iter__(self):
        return self.keys()

    def keys(self):
        for key in self.store.keys():
            if key not in self.dirty:
                yield key

        for key in self.dirty:
            yield key

    def values(self):
        for key, value in self.items():
            yield value

    def items(self):
        for key, value in self.store.items():
            if key not in self.dirty:
                yield (key, value)

        for key in self.dirty:
            value = self.cache.peek(key)
            yield (key, value)

    def sync(self):
        # For each dirty key, peek at its value in the cache and update the
        # store. Doesn't change the cache's order.
        for key in self.dirty:
            self.store[key] = self.cache.peek(key)
        # There are no dirty keys now.
        self.dirty.clear()

    def flush(self):
        self.sync()
        self.cache.clear()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.sync()
        return False


class FunctionCacheManager(object):
    def __init__(self, func, size, callback=None):
        self.func = func
        self.cache = lrucache(size, callback)

    def size(self, size=None):
        return self.cache.size(size)

    def clear(self):
        self.cache.clear()

    def __call__(self, *args, **kwargs):
        kwtuple = tuple((key, kwargs[key]) for key in sorted(kwargs.keys()))
        key = (args, kwtuple)
        try:
            return self.cache[key]
        except KeyError:
            pass

        value = self.func(*args, **kwargs)
        self.cache[key] = value
        return value


def lruwrap(store, size, writeback=False):
    if writeback:
        return WriteBackCacheManager(store, size)
    else:
        return WriteThroughCacheManager(store, size)

import functools

class lrudecorator(object):
    # Ben doesn't like the MIT License, but he agreed to it anyway. Thanks Ben!
    def __init__(self, size, callback=None):
        self.cache = lrucache(size, callback)

    def __call__(self, func):
        def wrapper(*args, **kwargs):
            kwtuple = tuple((key, kwargs[key]) for key in sorted(kwargs.keys()))
            key = (args, kwtuple)
            try:
                return self.cache[key]
            except KeyError:
                pass

            value = func(*args, **kwargs)
            self.cache[key] = value
            return value

        wrapper.cache = self.cache
        wrapper.size = self.cache.size
        wrapper.clear = self.cache.clear
        return functools.update_wrapper(wrapper, func)
