structure saas with tools

.venv/lib/python3.10/site-packages/cachetools/func.py

@@ -0,0 +1,121 @@
+"""`functools.lru_cache` compatible memoizing function decorators."""
+
+__all__ = ("fifo_cache", "lfu_cache", "lru_cache", "mru_cache", "rr_cache", "ttl_cache")
+
+import math
+import random
+import time
+
+try:
+    from threading import RLock
+except ImportError:  # pragma: no cover
+    from dummy_threading import RLock
+
+from . import FIFOCache, LFUCache, LRUCache, MRUCache, RRCache, TTLCache
+from . import cached
+from . import keys
+
+
+class _UnboundTTLCache(TTLCache):
+    def __init__(self, ttl, timer):
+        TTLCache.__init__(self, math.inf, ttl, timer)
+
+    @property
+    def maxsize(self):
+        return None
+
+
+def _cache(cache, maxsize, typed):
+    def decorator(func):
+        key = keys.typedkey if typed else keys.hashkey
+        wrapper = cached(cache=cache, key=key, lock=RLock(), info=True)(func)
+        wrapper.cache_parameters = lambda: {"maxsize": maxsize, "typed": typed}
+        return wrapper
+
+    return decorator
+
+
+def fifo_cache(maxsize=128, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a First In First Out (FIFO)
+    algorithm.
+
+    """
+    if maxsize is None:
+        return _cache({}, None, typed)
+    elif callable(maxsize):
+        return _cache(FIFOCache(128), 128, typed)(maxsize)
+    else:
+        return _cache(FIFOCache(maxsize), maxsize, typed)
+
+
+def lfu_cache(maxsize=128, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a Least Frequently Used (LFU)
+    algorithm.
+
+    """
+    if maxsize is None:
+        return _cache({}, None, typed)
+    elif callable(maxsize):
+        return _cache(LFUCache(128), 128, typed)(maxsize)
+    else:
+        return _cache(LFUCache(maxsize), maxsize, typed)
+
+
+def lru_cache(maxsize=128, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a Least Recently Used (LRU)
+    algorithm.
+
+    """
+    if maxsize is None:
+        return _cache({}, None, typed)
+    elif callable(maxsize):
+        return _cache(LRUCache(128), 128, typed)(maxsize)
+    else:
+        return _cache(LRUCache(maxsize), maxsize, typed)
+
+
+def mru_cache(maxsize=128, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a Most Recently Used (MRU)
+    algorithm.
+    """
+    from warnings import warn
+
+    warn("@mru_cache is deprecated", DeprecationWarning, stacklevel=2)
+
+    if maxsize is None:
+        return _cache({}, None, typed)
+    elif callable(maxsize):
+        return _cache(MRUCache(128), 128, typed)(maxsize)
+    else:
+        return _cache(MRUCache(maxsize), maxsize, typed)
+
+
+def rr_cache(maxsize=128, choice=random.choice, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a Random Replacement (RR)
+    algorithm.
+
+    """
+    if maxsize is None:
+        return _cache({}, None, typed)
+    elif callable(maxsize):
+        return _cache(RRCache(128, choice), 128, typed)(maxsize)
+    else:
+        return _cache(RRCache(maxsize, choice), maxsize, typed)
+
+
+def ttl_cache(maxsize=128, ttl=600, timer=time.monotonic, typed=False):
+    """Decorator to wrap a function with a memoizing callable that saves
+    up to `maxsize` results based on a Least Recently Used (LRU)
+    algorithm with a per-item time-to-live (TTL) value.
+    """
+    if maxsize is None:
+        return _cache(_UnboundTTLCache(ttl, timer), None, typed)
+    elif callable(maxsize):
+        return _cache(TTLCache(128, ttl, timer), 128, typed)(maxsize)
+    else:
+        return _cache(TTLCache(maxsize, ttl, timer), maxsize, typed)