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# A program to batch-compute the number of prime numbers below each of the given numbers in the input set

# TODO: Extend get_primes() to utilize caching
# TODO: Modify code to use more appropriate data structures where possible
from functools import lru_cache, wraps
from timeit import repeat
from datetime import datetime, timedelta
# from functools import timed_lru_cache, lru_cache, wraps

# cache = TTLCache(maxsize=100, ttl=86400)

# @lru_cache(maxsize=2000)

def memoize(function):
    memo = {}
    @wraps(function)
    def wrapper(*args):
        try:
            return memo[args]
        except KeyError:
            rv = function(*args)
            memo[args] = rv
            return rv
    return wrapper

# @memoize
def is_prime(n):
    sieved = []
    factor = 2
    while factor * factor <= n + 1:
        if factor not in sieved:
            for i in range(factor*factor, n + 1, factor):
                sieved.append(i)
        factor += 1
    return n not in sieved

# @lru_cache(maxsize=8)
"""
def timed_lru_cache(seconds: int, maxsize: int = 128):
    def wrapper_cache(func):
        func = lru_cache(maxsize=maxsize)(func)
        func.lifetime = timedelta(seconds=seconds)
        func.expiration = datetime.utcnow() + func.lifetime

        @wraps(func)
        def wrapped_func(*args, **kwargs):
            if datetime.utcnow() >= func.expiration:
                func.cache_clear()
                func.expiration = datetime.utcnow() + func.lifetime

            return func(*args, **kwargs)

        return wrapped_func

    return wrapper_cache
"""

# @timed_lru_cache(10)

# @cached(cache)
# @timed_lru_cache(10)
# @timed_lru_cache(maxsize=16)
# @lru_cache(maxsize=2000)
@memoize
def get_primes(n):
    return [p for p in range(1, n) if is_prime(p)]

if __name__ == 'builtins' or __name__ == '__main__':
    input_data = [6843, 1028, 3324]
    for n in input_data:
        result = get_primes(n)
        print(f'Found {len(result)} primes below {n}')