127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
|
"""
|
||
|
|
- the popular ``_memoize_default`` works like a typical memoize and returns the
|
||
|
|
default otherwise.
|
||
|
|
- ``CachedMetaClass`` uses ``_memoize_default`` to do the same with classes.
|
||
|
|
"""
|
||
|
|
from functools import wraps
|
||
|
|
|
||
|
|
from jedi import debug
|
||
|
|
|
||
|
|
_NO_DEFAULT = object()
|
||
|
|
_RECURSION_SENTINEL = object()
|
||
|
|
|
||
|
|
|
||
|
|
def _memoize_default(default=_NO_DEFAULT, inference_state_is_first_arg=False,
|
||
|
|
second_arg_is_inference_state=False):
|
||
|
|
""" This is a typical memoization decorator, BUT there is one difference:
|
||
|
|
To prevent recursion it sets defaults.
|
||
|
|
|
||
|
|
Preventing recursion is in this case the much bigger use than speed. I
|
||
|
|
don't think, that there is a big speed difference, but there are many cases
|
||
|
|
where recursion could happen (think about a = b; b = a).
|
||
|
|
"""
|
||
|
|
def func(function):
|
||
|
|
def wrapper(obj, *args, **kwargs):
|
||
|
|
# TODO These checks are kind of ugly and slow.
|
||
|
|
if inference_state_is_first_arg:
|
||
|
|
cache = obj.memoize_cache
|
||
|
|
elif second_arg_is_inference_state:
|
||
|
|
cache = args[0].memoize_cache # needed for meta classes
|
||
|
|
else:
|
||
|
|
cache = obj.inference_state.memoize_cache
|
||
|
|
|
||
|
|
try:
|
||
|
|
memo = cache[function]
|
||
|
|
except KeyError:
|
||
|
|
cache[function] = memo = {}
|
||
|
|
|
||
|
|
key = (obj, args, frozenset(kwargs.items()))
|
||
|
|
if key in memo:
|
||
|
|
return memo[key]
|
||
|
|
else:
|
||
|
|
if default is not _NO_DEFAULT:
|
||
|
|
memo[key] = default
|
||
|
|
rv = function(obj, *args, **kwargs)
|
||
|
|
memo[key] = rv
|
||
|
|
return rv
|
||
|
|
return wrapper
|
||
|
|
|
||
|
|
return func
|
||
|
|
|
||
|
|
|
||
|
|
def inference_state_function_cache(default=_NO_DEFAULT):
|
||
|
|
def decorator(func):
|
||
|
|
return _memoize_default(default=default, inference_state_is_first_arg=True)(func)
|
||
|
|
|
||
|
|
return decorator
|
||
|
|
|
||
|
|
|
||
|
|
def inference_state_method_cache(default=_NO_DEFAULT):
|
||
|
|
def decorator(func):
|
||
|
|
return _memoize_default(default=default)(func)
|
||
|
|
|
||
|
|
return decorator
|
||
|
|
|
||
|
|
|
||
|
|
def inference_state_as_method_param_cache():
|
||
|
|
def decorator(call):
|
||
|
|
return _memoize_default(second_arg_is_inference_state=True)(call)
|
||
|
|
|
||
|
|
return decorator
|
||
|
|
|
||
|
|
|
||
|
|
class CachedMetaClass(type):
|
||
|
|
"""
|
||
|
|
This is basically almost the same than the decorator above, it just caches
|
||
|
|
class initializations. Either you do it this way or with decorators, but
|
||
|
|
with decorators you lose class access (isinstance, etc).
|
||
|
|
"""
|
||
|
|
@inference_state_as_method_param_cache()
|
||
|
|
def __call__(self, *args, **kwargs):
|
||
|
|
return super().__call__(*args, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def inference_state_method_generator_cache():
|
||
|
|
"""
|
||
|
|
This is a special memoizer. It memoizes generators and also checks for
|
||
|
|
recursion errors and returns no further iterator elemends in that case.
|
||
|
|
"""
|
||
|
|
def func(function):
|
||
|
|
@wraps(function)
|
||
|
|
def wrapper(obj, *args, **kwargs):
|
||
|
|
cache = obj.inference_state.memoize_cache
|
||
|
|
try:
|
||
|
|
memo = cache[function]
|
||
|
|
except KeyError:
|
||
|
|
cache[function] = memo = {}
|
||
|
|
|
||
|
|
key = (obj, args, frozenset(kwargs.items()))
|
||
|
|
|
||
|
|
if key in memo:
|
||
|
|
actual_generator, cached_lst = memo[key]
|
||
|
|
else:
|
||
|
|
actual_generator = function(obj, *args, **kwargs)
|
||
|
|
cached_lst = []
|
||
|
|
memo[key] = actual_generator, cached_lst
|
||
|
|
|
||
|
|
i = 0
|
||
|
|
while True:
|
||
|
|
try:
|
||
|
|
next_element = cached_lst[i]
|
||
|
|
if next_element is _RECURSION_SENTINEL:
|
||
|
|
debug.warning('Found a generator recursion for %s' % obj)
|
||
|
|
# This means we have hit a recursion.
|
||
|
|
return
|
||
|
|
except IndexError:
|
||
|
|
cached_lst.append(_RECURSION_SENTINEL)
|
||
|
|
next_element = next(actual_generator, None)
|
||
|
|
if next_element is None:
|
||
|
|
cached_lst.pop()
|
||
|
|
return
|
||
|
|
cached_lst[-1] = next_element
|
||
|
|
yield next_element
|
||
|
|
i += 1
|
||
|
|
return wrapper
|
||
|
|
|
||
|
|
return func
|