Lru cache python solution. def lru_cache(maxsize=128, typed=False): .

Lru cache python solution LRU Cache; 147. js and browsers. In Python syntax, this means adding @lru_cache() to the line preceding the function’s def statement. While the LRU cache is a powerful caching mechanism, it's not the only caching solution available in Python. You switched accounts on another tab or window. The class should support the following operations. When the cache reached its capacity, it should The C# solution is easier than NeetCode's Python solution because C# is relying on a built-in linked list class. lru_cache, the result is not functools. Write a class LRUCache(n) that accepts a size limit n. LRU = Least recently used cache. This isn't specific to Python either. Using inspect to normalize arg & kwargs Here’s example of solution 2: I understand why 1 might not best solution, but why isn’t 2 implemented? Is there any reason or could this be an You signed in with another tab or window. LRU Cache Description Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. Implement the LRUCache class: LRUCache(int capacity) Initialize the LRU cache with positive size capacity. Evaluate Reverse @ functools. If the key does not exist in the cache, it should return -1. LRU Cache is a type of high-speed memory, that is used to quicken the retrieval speed of frequently used data. Problem Description. lru_cache decorator: If you need to support older versions of Python, functools. . cache or functools. void put(int key, int value The core concept of the LRU algorithm is to evict the oldest data from the cache to accommodate more data. For example, if the cache size needs to be adjusted based on the function's usage patterns, you can create a custom decorator: The solution should include data structures and algorithms to efficiently manage the cache, track access frequencies, and evict items based on the LRU policy. My solution is reinstall from apt. In addition, the older entries get flushed-out and freed as the LRU cache gets newer entries. It's simple enough to write a cache yourself. def LRUcache (strArr): cache = [] for element in strArr: if element in cache: cache. [python -m] pip uninstall backports. int LeetCode 146: LRU Cache Solution in Python Explained. If the thread_clear option is specified, a background thread will clean it up every thread_clear_min_check seconds. It’s like having a secret stash of goodies that saves you time and effort LRUCache(int capacity) Initialize the LRU cache with positive size capacity. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company The cache_info() is showing that the cache container keeps a reference to the instance until it gets cleared. How can I use functools. . 实现 LRUCache 类： * LRUCache(int capacity) 以 正整数 作为容量 capacity 初始化 LRU 缓存 * int get(int key) 如果关键字 key 存在于缓存中，则返回关键字的值，否则返回 -1 。 * void put(int key, int value) 如果关键字 key 已经存在，则变更其数据值 value ；如果不存在，则向缓存中 Note, however, that cachier's in-memory core is simple, and has no monitoring or cap on cache size, and can thus lead to memory errors on large return values - it is mainly intended to be used with future multi-core functionality. To scope and define a problem, I’ll use Leetcode’s problem definition. Sample Solution: Python Code : I got you! Problem: Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. By Martin McBride, 2020-02-12 2^1000 elements, which is a vast number. It is implemented with the help of Queue and Hash data structures. It turns out that the solution to the problem can be decomposed into smaller subtasks. Here goes the algorithm for LRU cache. LRU Cache | Python solution and explanation Statement. It's a Least Recently Used cache, so there is no expiration time for the items in it, but as a fast hack it's very useful. A caching solution for asyncio. By default, it only caches the 128 most recently used calls, but you can set the maxsize to None to indicate that the cache should never expire: @functools. I have a use case where every time stored reference TTLs I need to make sure I close the reference explicitly [Do not want to rely on GC to do it]. You can serialize dictionary parameter to string and unserialize in the function to the dictionary back. Python lru_cache implementation. def lru_cache(maxsize=128, typed=False): Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. Designing an LRU (Least Recently Used) cache might feel like managing a busy library with limited shelf space, and LeetCode 146: LRU Cache is a medium-level challenge that makes it exciting! You need to implement an LRUCache class with get and put operations, maintaining a fixed capacity and evicting the This repository provides a systematic guide to solving Coderbyte coding problems by breaking down the process into steps, algorithm design, optimized code writing, test case generation in Python. The priority of the data Design and implement a data structure for Least Recently Used (LRU) cache. Advanced Usage: Parameterizing lru_cache. lru_cache; Python provides a built-in caching mechanism called lru_cache from the functools module. Декоратор @lru_cache() модуля functools в Python; functools. Otherwise, add the key To use LRU caching in Python, you just need to add two lines – import and declaration of the @lru_cache decorator. For example: from functools import with run_py_lru as plain @lru_cache, run_hashable_lru as the previous JSON-solution and run_pickled_lru as adapted version with pickle instead of json. int get(int key) Return the value of the key if the key exists, otherwise return -1 . By default, the size of the lru_cache is 128 but if I had applied lru_cache(maxsize=None), that would’ve kept the line 4 - The @timed_lru_cache decorator will support the lifetime of the entries in the cache (in seconds) and the maximum size of the cache. remove (element) cache. The most important To achieve the O(1) time complexity for both `get` and `put` operations, we make use of a hash map (dictionary in Python) to store the keys and their associated nodes, since access in a hash map has an average time complexity of O(1). - LRU cache. lru_cache inside classes without leaking memory?. get(key)- Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1. from functools import lru_cache @lru_cache(maxsize=256) def f(x): return x*x for x in range(20): print f(x) for x in range(20): print f(x) # Design and implement a data structure for Least Recently Used (LRU) cache. In this blog, we will focus on the LRU Cache strategy. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit Design a data structure that works like a LRU(Least Recently Used) Cache. As Grace Hopper, LRU Cache. Let’s build our own LRUCache class with the following methods:. class Node: def __init__ (self, key = None, val = None): Actually, I see no other solution. ; line 8 - the code wraps the decorated function with the lru_cache decorator. Sort List; 149. # It should support the following operations: get and put. takeuforward. The design uses a circular doubly-linked list of entries (arranged oldest-to-newest) and a hash table to locate individual links. The cache can getContent(key) - Get the value of the key if the key exists in the cache. When fibonacci(3) is called subsequently, fibonacci(3) is retrieved from the cache since it was one of the seven most recently computed values, Real-world use cases of LRU Cache in Python for memory management and garbage collection. If If you're allowed to not reinvent the wheel, you could also just use functools. This is all very well, but it is adding extra code to the fibonacci function. This is because decorators in python (with the @ notation) are special functions which are evaluated and called when the interpreter is importing the module. The Least Recently Used (LRU) is one of those algorithms. Note: For more information, refer to Python – LRU Cache How can one interact wit I'm using @functools. e. An LRU cache is a type of cache mechanism that discards the least recently used items Design a data structure for LRU Cache. Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations:getandset. * get (key) Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. This decorator caches function calls and removes the least recently LeetCode 146. The LRU cache is no more special in this regard than a dictionary, list, or set. lru_cache are thread safe only on reading, not writing. This method is referred to as cache_put(returned_value, *args, **kwargs) --> None in this proposal. functools lru_cache. Insertion Sort List; 148. The problem is the python based cache libraries including functools. find (key); if (it == keyToIterator. Implement the LRUCache class:. 7. Understanding LRU cache problem. run_pickled_lru and run_hashable_lru are a magnitude (if not more) slower than plain @lru_cache. In my Python solution, I only had to add a two lines of code and fix a few others to get this performance increase. Design Solution. But there are of course many available implementations on the internet. What is LRU Cache Implementation? LRU, or Least Recently Used, The LRU is the Least Recently Used cache. Enterprises Small and medium teams Startups Nonprofits By use case. " Same as chosen answer, just different import: from fastcache import lru_cache @lru_cache(maxsize=128, typed=False) def f(a, b): pass Also, it comes installed in Anaconda, unlike functools which needs to be installed. I am unable to rewrite this function in such way that @lru_cache() decorator can be applied and rpc_server will be passed as an argument (i. lru_cache decorator, as well as all the other approaches discussed here, store LRU Cache. Today, we're looking at the LRU Cache Problem solved by the built-in P You have to at least call lru_cache without args: @lru_cache() def f(): #content of the function This way, lru_cache is initialized with default parameters. The functools. When the cache reached its capacity, it should invalidate the least ["LRUCache","put","put","get","put","get","put","get","get","get"] To improve the performance, you want to build a simple key-value store to cache this data in memory, but you also want to limit the amount of memory used. Implementing LRU cache is one of the most famous interview questions. LRU Cache finds its place in scenarios where caching computed results can lead to performance gains, such as memoization, web server optimization, and database query caching. LRUCache is written in Python 3. put(key, value) – Inserts or updates the key-value pair in the cache. The reason I put up the LRU cache challenge up, was that I couldn’t think of a good solution to the problem without using linked lists. LRU Cache Constraints: The LRU cache has a fixed capacity, which limits the number of key-value pairs it can store. Cache hits use the hash table to find the relevant link and move it If various threads need to notify one another that they should refrain from processing for a certain duration, an in-memory cache with a Time To Live (TTL) is an effective solution. Blame. Join the elements in the cache list into a string separated by a hyphen. LRU (Least Recently Used) Cache is a technique used for caching where the Every container (except for the weakref containers) keeps their references alive. Get(key): Returns the value of the key, or − 1-1 − 1 if the key does not exist. put Operation: The put operation is used to update the value associated with 文章浏览阅读6. lru_cache装饰器是Python标准库中的一种缓存工具，它使用LRU策略来存储函数的输出结果。这意味着最近使用的函数调用结果将被保留在缓存中，而较长时间未被使用的结果将被清除，以释放内存。 Here, the fibonacci function is decorated with @lru_cache(maxsize=7), specifying that it should cache up to 7 most recent results. To define the different paths to the fourth step, we can add four ways to reach the third A third solution is to store just the cache as a property of the instance. LRUCache (Capacity c): Initialize LRU cache with positive size capacity c. put(key, value) - Set or insert the value if the key is not already present. lru_cache works in Python 3. At that point, we are going to have something like self. LRUCache(int capacity) Initialize the LRU cache of size capacity. In this article, we will dive deep into the implementation of an LRU (Least Recently Used) cache in Python. e adding a new item to the circular queue, remove the item from the circular queue happens. 2+. Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. org/In case you are thinking to buy courses, please check below: Link to get 20% additional Discount at Coding Ni What is LRU Cache? Cache replacement algorithms are efficiently designed to replace the cache when the space is full. Here, we will explore some robust methods for creating such a cache in Python, allowing you to set expiry times for your cached data. You decide to build a caching system that only keeps the N most recently used items—also known as a least recently used (LRU) cache. A quick test shows that it is working as intended — the name, annotations, docstring, and module are Check our Website: https://www. I am looking at utilizing a caching solution to store reference to a memory mapped file. big = BigClass() @lru_cache(maxsize=16) def Design a data structure that works like an LRU (Least Recently Used) Cache. insert (0, To implement a Least Recently Used (LRU) Cache in Python, we can use an OrderedDict from the collections module. lru_cache(maxsize=None) def calculate_double(num): # etc Let's talk about the nuances of relying on Python's ease of use in a coding interview. The lru_cache uses parameters as the identifier for caching, so in the case of the dictionary, lru_cache doesn't know how to interpret it. Our problem statement is to design and implement a data structure for Least Recently Used (LRU) cache. get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1. El módulo functools de Python viene con el decorador @lru_cache, que le brinda la The solution might be much simpler. There is no LRU cache in the standard library, and as far as I know, no way to dynamically wrap functions. When I manually cleared the cache and reassigned the variable slow_adder to None, only then did the garbage collector remove the instance. Inside the wrapper, the logic of adding item to the cache, LRU logic i. Learn about the LRU Cache solution in Python with a detailed explanation, code examples, and real-world analogies. 65. void put(int key, int value) Update the value of the key if the key exists. Note: For more information, refer to Python – LRU Cache How can one interact wit LeetCode 146: LRU Cache Solution in Python Explained. December 17, 2023. As a rule, Python's built-in From Python 3. python-lru-cache; Share. The LRU Cache problem, LeetCode 146, is a medium-difficulty challenge where you need to design a Least Recently Used (LRU) cache data structure. 2. The lru_cache decorator can be configured dynamically to suit different needs. It should support the following operations: get and pu t. Otherwise, add the key The LRU is the Least Recently Used cache. functools. Otherwise, return -1. lru_cache的使 Hay muchas formas de lograr aplicaciones rápidas y con capacidad de respuesta. The contents of the cache at any given time is maintained by a dictionary which stores Key, Node pair where Node = (key, value). Solutions By company size. I can think of 2 solution, disallow passing arg in typing interface (not runtime tho). 3 has O(1) insertion, deletion, and search. Caching in Python can be implemented using various strategies such as LRU Cache, MRU Cache, etc. DevSecOps DevOps CI/CD View all use cases By industry. It’s even featured on Cracking the Coding Interview classic interviews book. On subsequent calls with the same URL, the cached result is returned. As the name suggests when the cache memory is full, LRU picks the data that is least recently used and removes it in order to make space for the new data. void put(int key, LeetCode 146: LRU Cache - All Solutions Explained Problem Statement . int get(int key) Return the value of the key if the key exists, otherwise return -1. This allows you to use the cache functionality already provided by lru_cache. ; It has maxsize argument to set a limit to this works fine assuming you don't want to cache multiple results for different arguments to the function. set(key, value)- Set or insert the value if the key is not already present. The cache is implemented by a Doubly Linked List data structure. If the number of keys has reached the cache capacity, evict the least recently used Implementing LRU Cache in Python. cache (user_function) ¶ Simple lightweight unbounded function cache. Let's explore how to combine the LRU cache ["LRUCache","put","put","get","put","get","put","get","get","get"] There is fastcache, which is "C implementation of Python 3 functools. Reading Time: 9 mins read and the queue hashing approach with solution. If you want to refresh your memory of what LRU cache means, there’s a great article on Wikipedia. [Radomir Dopieralski] > So please consider this patch abandoned. So we can add in the one, one, we can also do that for two, two. LRU caches are often used to implement caches which you do not want to grow indefinitely. LRU Cache Solution in Python. LRUCache(int capacity) Initialize the LRU cache with positive size capacity. from functools import lru_cache class BigClass: pass class Foo: def __init__(self): self. Magic Magic. This issue is now closed. And when we put in one, one, length of self. Relying on Python's built-ins can make it easy too. Proposed Feature This is a proposal to support a new method for functions with the lru_cache annotation in order to allow users to add data to the cache without invoking the underlying function. Sometimes called “memoize”. get(key): Returns the value associated with the key if it exists, otherwise -1. The Least Recently Used (LRU) cache is a popular caching strategy that discards the least recently used items first to make room for new Python’s lru_cache, a decorator from the functools module, offers a powerful solution by caching the results of function calls to speed up execution. The LRU is the Least Recently Used cache. cap is the capacity of the cache and siz is the current size of the cache. Copy path. Latest commit I noticed based on the constructed arg/kwargs, the key used in lru_cache might not work perfectly. py. Java and Python Solution: Login to Access Content Instructor: A powerful caching library for Python, with TTL support and multiple algorithm options. put(key, value): if the key is already Integration with Other Python Caching Solutions. A doubly linked list helps in maintaining the eviction order and a hashmap helps with O(1) lookup of cached keys. Max Points on a Line; 150. com/problems/lru-cache/description/00:00 Recursion and the lru_cache in Python. cache函数，用于缓存函数结果以提高效率，特别适合重复计算。通过示例展示了如何使用cache和自定义缓存机制，并提到了线程安全、内存消耗和缓存清除。同时，还对比了lru_cache函数，讨论了其自动清除策略和线程安全问题。 By default, this cache will only expire items whenever you poke it - all methods on this class will result in a cleanup. Updated Nov 21, 2024; Typed LRU TTL cache for Node. functools_lru_cache [python -m] pip install backports. The second time, there is no print statement, because lru_cache finds the previous run we just did and uses its stored result. If you try to add more items than it can hold, it will kick out the Solved LRU Cache for the 4th time today after 3 months and loved the fact that I remembered it xD Reply reply techknowfile • Did you implement with a python dict? Dict and doubly Linked list? Reply reply I just don’t think anyone would come up with the optimal solution without ever seeing the problem before. Posted on 2008-01-13 by lorg 4 Comments. cache is going to be one, one and two, two and we try to get one. Python; Written by @ColeB2. If the cache reaches capacity, remove the least recently used item before adding the new item. 3. insertContent(key, value) - Set or insert the value if the key is not already present. FWIW the caches invoke update_wrapper(wrapper, user_function) to copy the metadata from the original function. So if you read from S3 then cache something using multiple instances you will still run into this problem. The cache should have the following operations: GET x: Return the value associated with key x if it exists in the cache. Imagine you have a magical backpack that can only hold a limited number of items. This caching system can enhance performance by storing frequently accessed data in memory, reducing the need to fetch it from slower storage mediums. It should support the following operations: get and put. python async asynchronous cache lru coroutines python3 asyncio ttl lru-cache coroutine ttl-cache ttl-cache-implementation async-cache. I hate problems like that in Implement an LRU Cache. ; Get method: If the key is not present in the cache, return -1. ; If key is present in the cache, move The LRU cache in Python3. C++ Solution Java Solution. Created on 2017-11-10 10:20 by ataraxy, last changed 2022-04-11 14:58 by admin. Implement an LRU cache class with the following functions: Init(capacity): Initializes an LRU cache with the capacity size. There are generally two terms use with LRU Cache, let’s see them – struct Node {int key; int value;}; class LRUCache {public: LRUCache (int capacity): capacity (capacity) {} int get (int key) {const auto it = keyToIterator. get Operation: When using the get operation, the cache should return the value associated with the provided key. turning that decorator into an instance of a class with a __call__ implementation, so that way the statefulness of the cache is hidden inside a Avoid using lru_cache for functions where the results may change without a change in input arguments. This was more involved with my C++ solution. line 10 and 12 - These two lines instrument the decorated function with Welcome to Subscribe On Youtube 146. The cache should support two operations: get(key) to retrieve a value (returning -1 if the key doesn’t exist) and put(key, value) to insert or update a In this example, the first time get_html is called, it fetches the data from the URL and caches it. When the cache reached its capacity, it should Difficulty: Hard, Asked-in: Amazon, Microsoft, Adobe, Google. 39. when a function is wrapped with functools. Python lru_cache false-negatives. Provides speedup of 10-30x over standard library. Integrating LRU cache with other caching solutions can enhance performance and provide more flexibility to handle different caching scenarios. If the key is already present, update its value. After a little bit of thinking, Specifically speaking about Python, it provides OrderedDict OrderedDict data structure that helps to implement LRU cache in a much more concise way. wraps’d with the given user function. Healthcare Financial services Manufacturing LRU Cache. Using Python’s functools. Set(key, value): Adds a new key-value pair or updates an existing key with a new value. 9+, earlier use lru_cache class Solution(object): def minCostClimbingStairs(self, cost): """ :type cost: List[int] :rtype: int """ def get_val(index): ' get value from cost array (handling index limit) ' try: return cost[index] except IndexError: return 0 Traditional lru_cache from functools import lru_cache from time import sleep @lru_cache def heavy_computation_function (*args): sleep(25) # to mimic heavy computation computed_value = 12345 return computed_value Limitation. But if the problem can be solved with a simple loop, that is probably the best solution. To implement an LRU cache we use two data structures: a hashmap and a doubly linked list. Let’s take a look:. 2 you can use the decorator @lru_cache from the functools library. When fibonacci(5) is called, the results for fibonacci(4), fibonacci(3), and fibonacci(2) are cached. Use functools' Python’s standard library has a functools module with a function decorator named @lru_cache() that automatically memoizes the function it decorates. The OrderedDict maintains the order in which keys The LRU Cache problem, LeetCode 146, is a medium-difficulty challenge where you need to design a Least Recently Used (LRU) cache data structure. Reload to refresh your session. So think twice before you need a cache that can deal with lists and dictionaries. El almacenamiento en caché es un enfoque que, cuando se utiliza correctamente, hace que las cosas sean mucho más rápidas y al mismo tiempo reduce la carga de los recursos informáticos. The cache has a max size, and when a new key is inserted that would make it grow larger than the max size, the key which has not been accessed for the longest time is removed to make space. I would like to save the cache to a file, in order to restore it when the program will be restarted. pip uninstall matplotlib sudo apt-get autoremove python-matplotlib sudo apt-get install python-matplotlib Share. 7k次，点赞6次，收藏15次。文章介绍了Python的functools. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item. put(key, value LRU cache solution: a case for linked lists in Python. That is how Python works. 557 2 2 gold badges 6 6 silver ["LRUCache","put","put","get","put","get","put","get","get","get"] Channel Discord Community: https://discord. Implement the LRUCache class: LRUCache(int capacity) Initialize the LRU cache with positive Implement the Least Recently Used (LRU) cache class LRUCache. lru_cache in Python 3. This is similar to the previous solution, but just the cache is set as the instance attribute. from functools import cache # cache only available in Python 3. Would highly recommend 1. The cache should support two Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. lru_cache, which adds memoization to any function through the magic of decorators: from functools import lru_cache @lru_cache def fibonacci(n): if n in {0, 1}: return n return fibonacci(n-1) + fibonacci(n-2) 本篇博客将结合python官方文档和源码详细讲述lru_cache缓存方法是怎么实现， 它与redis缓存的区别是什么， 在使用时碰上functiontools. cache is zero, or, yeah, and zero is definitely less than two. Depending on the workload, this might be a simple solution. Designing an LRU (Least Recently Used) cache might feel like managing a busy library with limited shelf space, and LeetCode The LRU caching scheme is to remove the least recently used frame when the cache is full and a new page is referenced which is not there in the cache. We find to find a way to combine hashmaps and linked lists such that we meet our LRU cache requirements. # get(key) - Get the value (will always be positive) of the key if the key exists in the cache, LeetCode The Hard Way 📚 Tutorials 📖 Solutions LRUCache(int capacity) Initialize the LRU cache with positive size capacity. get(key) – Returns the value of the given key if it exists in the cache; otherwise, returns -1. In the following minimal example the foo instance won't be released although going out of scope and having no referrer (other than the lru_cache). A map to store {key, value} and another map to store {key, address of the node in the list}. It should support the following operations: get and put. Mutability and hash-ability are a pervasive issues with caching in Python. turning this into a decorator to wrap around a function doing the computation and 2. lru_cache you can use as a decorator to cache the return value from a function. ; PUT x y: Set the value of key x to y. Key takeaway: an excellent algorithm to learn data structure design and problem-solving using hash tables and doubly-linked lists. A bottom up recursive solution using cache. The performance impact can Implementation using a doubly linked list. 1 which will avoid installing arrow merely to downgrade functools_lru_cache. Python3 pass lists to So we create a new LRU cache with max capacity of two. wrap装饰器时会发生怎样的变化，以及了解它给我们提供了哪些功能然后在其基础上实现我们自制的缓存方法my_cache。1. To implement the LRU cache, we need a doubly linked list to store the {key, value}. If you really want to save the cache you have to use a different solution that gives you access to the cache. com/invite/s8JX2ARnSgProblem Link: https://leetcode. Use Cases Avoid Expensive Calls When Data Is Already Present in the Декоратор lru_cache() модуля functools оборачивает функцию с переданными в нее аргументами и запоминает возвращаемый результат соответствующий этим аргументам. functools_lru_cache==1. Share. LRU Cache decorator checks for some base cases and then wraps the user function with the wrapper _lru_cache_wrapper. I am doubling 1 2 2 I am doubling 2 4 I am doubling 3 6 I am doubling 1 2 The first time we say to double, lru_cache checks its (empty) dictionary mapping inputs to outputs, sees it doesn’t have anything under an input of x=1, so it hits the function. The LRUCache class has two methods get and put which are defined as follows. lru_cache. get(key): returns the value of the key if it already exists in the cache otherwise returns -1. We show with examples how and why to use it. This LRU Cache Implementation (C++, Java, Python) by Jatin Chanana. You signed out in another tab or window. Follow asked May 8, 2024 at 0:09. ebbtnv xkh lglbb moksmo ldlc glfcz iimw ohyjd zenqlc wwozig rbng xxmc eluby mdrg xbe