# coding=utf-8

# SPDX-FileCopyrightText: Copyright (c) 2025 The torch-harmonics Authors. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#

import functools
from copy import deepcopy

# copying LRU cache decorator a la:
# https://stackoverflow.com/questions/54909357/how-to-get-functools-lru-cache-to-return-new-instances
def lru_cache(maxsize=20, typed=False, copy=False):
    """
    Least Recently Used (LRU) cache decorator with optional deep copying.
    
    This is a wrapper around functools.lru_cache that adds the ability to return
    deep copies of cached results to prevent unintended modifications to cached objects.
    
    Parameters
    -----------
    maxsize : int, optional
        Maximum number of items to cache, by default 20
    typed : bool, optional
        Whether to cache different types separately, by default False
    copy : bool, optional
        Whether to return deep copies of cached results, by default False
        
    Returns
    -------
    function
        Decorated function with LRU caching
        
    Example
    -------
    >>> @lru_cache(maxsize=10, copy=True)
    ... def expensive_function(x):
    ...     return [x, x*2, x*3]
    """
    def decorator(f):
        cached_func = functools.lru_cache(maxsize=maxsize, typed=typed)(f)
        def wrapper(*args, **kwargs):
            res = cached_func(*args, **kwargs)
            if copy:
                return deepcopy(res)
            else:
                return res
                
        return wrapper
    return decorator