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functools.py
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import functools import time import inspect import collections import types import itertools import warnings import setuptools.extern.more_itertools from typing import Callable, TypeVar CallableT = TypeVar("CallableT", bound=Callable[..., object]) def compose(*funcs): """ Compose any number of unary functions into a single unary function. >>> import textwrap >>> expected = str.strip(textwrap.dedent(compose.__doc__)) >>> strip_and_dedent = compose(str.strip, textwrap.dedent) >>> strip_and_dedent(compose.__doc__) == expected True Compose also allows the innermost function to take arbitrary arguments. >>> round_three = lambda x: round(x, ndigits=3) >>> f = compose(round_three, int.__truediv__) >>> [f(3*x, x+1) for x in range(1,10)] [1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7] """ def compose_two(f1, f2): return lambda *args, **kwargs: f1(f2(*args, **kwargs)) return functools.reduce(compose_two, funcs) def method_caller(method_name, *args, **kwargs): """ Return a function that will call a named method on the target object with optional positional and keyword arguments. >>> lower = method_caller('lower') >>> lower('MyString') 'mystring' """ def call_method(target): func = getattr(target, method_name) return func(*args, **kwargs) return call_method def once(func): """ Decorate func so it's only ever called the first time. This decorator can ensure that an expensive or non-idempotent function will not be expensive on subsequent calls and is idempotent. >>> add_three = once(lambda a: a+3) >>> add_three(3) 6 >>> add_three(9) 6 >>> add_three('12') 6 To reset the stored value, simply clear the property ``saved_result``. >>> del add_three.saved_result >>> add_three(9) 12 >>> add_three(8) 12 Or invoke 'reset()' on it. >>> add_three.reset() >>> add_three(-3) 0 >>> add_three(0) 0 """ @functools.wraps(func) def wrapper(*args, **kwargs): if not hasattr(wrapper, 'saved_result'): wrapper.saved_result = func(*args, **kwargs) return wrapper.saved_result wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result') return wrapper def method_cache( method: CallableT, cache_wrapper: Callable[ [CallableT], CallableT ] = functools.lru_cache(), # type: ignore[assignment] ) -> CallableT: """ Wrap lru_cache to support storing the cache data in the object instances. Abstracts the common paradigm where the method explicitly saves an underscore-prefixed protected property on first call and returns that subsequently. >>> class MyClass: ... calls = 0 ... ... @method_cache ... def method(self, value): ... self.calls += 1 ... return value >>> a = MyClass() >>> a.method(3) 3 >>> for x in range(75): ... res = a.method(x) >>> a.calls 75 Note that the apparent behavior will be exactly like that of lru_cache except that the cache is stored on each instance, so values in one instance will not flush values from another, and when an instance is deleted, so are the cached values for that instance. >>> b = MyClass() >>> for x in range(35): ... res = b.method(x) >>> b.calls 35 >>> a.method(0) 0 >>> a.calls 75 Note that if method had been decorated with ``functools.lru_cache()``, a.calls would have been 76 (due to the cached value of 0 having been flushed by the 'b' instance). Clear the cache with ``.cache_clear()`` >>> a.method.cache_clear() Same for a method that hasn't yet been called. >>> c = MyClass() >>> c.method.cache_clear() Another cache wrapper may be supplied: >>> cache = functools.lru_cache(maxsize=2) >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache) >>> a = MyClass() >>> a.method2() 3 Caution - do not subsequently wrap the method with another decorator, such as ``@property``, which changes the semantics of the function. See also http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/ for another implementation and additional justification. """ def wrapper(self: object, *args: object, **kwargs: object) -> object: # it's the first call, replace the method with a cached, bound method bound_method: CallableT = types.MethodType( # type: ignore[assignment] method, self ) cached_method = cache_wrapper(bound_method) setattr(self, method.__name__, cached_method) return cached_method(*args, **kwargs) # Support cache clear even before cache has been created. wrapper.cache_clear = lambda: None # type: ignore[attr-defined] return ( # type: ignore[return-value] _special_method_cache(method, cache_wrapper) or wrapper ) def _special_method_cache(method, cache_wrapper): """ Because Python treats special methods differently, it's not possible to use instance attributes to implement the cached methods. Instead, install the wrapper method under a different name and return a simple proxy to that wrapper. https://github.com/jaraco/jaraco.functools/issues/5 """ name = method.__name__ special_names = '__getattr__', '__getitem__' if name not in special_names: return wrapper_name = '__cached' + name def proxy(self, *args, **kwargs): if wrapper_name not in vars(self): bound = types.MethodType(method, self) cache = cache_wrapper(bound) setattr(self, wrapper_name, cache) else: cache = getattr(self, wrapper_name) return cache(*args, **kwargs) return proxy def apply(transform): """ Decorate a function with a transform function that is invoked on results returned from the decorated function. >>> @apply(reversed) ... def get_numbers(start): ... "doc for get_numbers" ... return range(start, start+3) >>> list(get_numbers(4)) [6, 5, 4] >>> get_numbers.__doc__ 'doc for get_numbers' """ def wrap(func): return functools.wraps(func)(compose(transform, func)) return wrap def result_invoke(action): r""" Decorate a function with an action function that is invoked on the results returned from the decorated function (for its side-effect), then return the original result. >>> @result_invoke(print) ... def add_two(a, b): ... return a + b >>> x = add_two(2, 3) 5 >>> x 5 """ def wrap(func): @functools.wraps(func) def wrapper(*args, **kwargs): result = func(*args, **kwargs) action(result) return result return wrapper return wrap def invoke(f, *args, **kwargs): """ Call a function for its side effect after initialization. The benefit of using the decorator instead of simply invoking a function after defining it is that it makes explicit the author's intent for the function to be called immediately. Whereas if one simply calls the function immediately, it's less obvious if that was intentional or incidental. It also avoids repeating the name - the two actions, defining the function and calling it immediately are modeled separately, but linked by the decorator construct. The benefit of having a function construct (opposed to just invoking some behavior inline) is to serve as a scope in which the behavior occurs. It avoids polluting the global namespace with local variables, provides an anchor on which to attach documentation (docstring), keeps the behavior logically separated (instead of conceptually separated or not separated at all), and provides potential to re-use the behavior for testing or other purposes. This function is named as a pithy way to communicate, "call this function primarily for its side effect", or "while defining this function, also take it aside and call it". It exists because there's no Python construct for "define and call" (nor should there be, as decorators serve this need just fine). The behavior happens immediately and synchronously. >>> @invoke ... def func(): print("called") called >>> func() called Use functools.partial to pass parameters to the initial call >>> @functools.partial(invoke, name='bingo') ... def func(name): print("called with", name) called with bingo """ f(*args, **kwargs) return f def call_aside(*args, **kwargs): """ Deprecated name for invoke. """ warnings.warn("call_aside is deprecated, use invoke", DeprecationWarning) return invoke(*args, **kwargs) class Throttler: """ Rate-limit a function (or other callable) """ def __init__(self, func, max_rate=float('Inf')): if isinstance(func, Throttler): func = func.func self.func = func self.max_rate = max_rate self.reset() def reset(self): self.last_called = 0 def __call__(self, *args, **kwargs): self._wait() return self.func(*args, **kwargs) def _wait(self): "ensure at least 1/max_rate seconds from last call" elapsed = time.time() - self.last_called must_wait = 1 / self.max_rate - elapsed time.sleep(max(0, must_wait)) self.last_called = time.time() def __get__(self, obj, type=None): return first_invoke(self._wait, functools.partial(self.func, obj)) def first_invoke(func1, func2): """ Return a function that when invoked will invoke func1 without any parameters (for its side-effect) and then invoke func2 with whatever parameters were passed, returning its result. """ def wrapper(*args, **kwargs): func1() return func2(*args, **kwargs) return wrapper def retry_call(func, cleanup=lambda: None, retries=0, trap=()): """ Given a callable func, trap the indicated exceptions for up to 'retries' times, invoking cleanup on the exception. On the final attempt, allow any exceptions to propagate. """ attempts = itertools.count() if retries == float('inf') else range(retries) for attempt in attempts: try: return func() except trap: cleanup() return func() def retry(*r_args, **r_kwargs): """ Decorator wrapper for retry_call. Accepts arguments to retry_call except func and then returns a decorator for the decorated function. Ex: >>> @retry(retries=3) ... def my_func(a, b): ... "this is my funk" ... print(a, b) >>> my_func.__doc__ 'this is my funk' """ def decorate(func): @functools.wraps(func) def wrapper(*f_args, **f_kwargs): bound = functools.partial(func, *f_args, **f_kwargs) return retry_call(bound, *r_args, **r_kwargs) return wrapper return decorate def print_yielded(func): """ Convert a generator into a function that prints all yielded elements >>> @print_yielded ... def x(): ... yield 3; yield None >>> x() 3 None """ print_all = functools.partial(map, print) print_results = compose(more_itertools.consume, print_all, func) return functools.wraps(func)(print_results) def pass_none(func): """ Wrap func so it's not called if its first param is None >>> print_text = pass_none(print) >>> print_text('text') text >>> print_text(None) """ @functools.wraps(func) def wrapper(param, *args, **kwargs): if param is not None: return func(param, *args, **kwargs) return wrapper def assign_params(func, namespace): """ Assign parameters from namespace where func solicits. >>> def func(x, y=3): ... print(x, y) >>> assigned = assign_params(func, dict(x=2, z=4)) >>> assigned() 2 3 The usual errors are raised if a function doesn't receive its required parameters: >>> assigned = assign_params(func, dict(y=3, z=4)) >>> assigned() Traceback (most recent call last): TypeError: func() ...argument... It even works on methods: >>> class Handler: ... def meth(self, arg): ... print(arg) >>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))() crystal """ sig = inspect.signature(func) params = sig.parameters.keys() call_ns = {k: namespace[k] for k in params if k in namespace} return functools.partial(func, **call_ns) def save_method_args(method): """ Wrap a method such that when it is called, the args and kwargs are saved on the method. >>> class MyClass: ... @save_method_args ... def method(self, a, b): ... print(a, b) >>> my_ob = MyClass() >>> my_ob.method(1, 2) 1 2 >>> my_ob._saved_method.args (1, 2) >>> my_ob._saved_method.kwargs {} >>> my_ob.method(a=3, b='foo') 3 foo >>> my_ob._saved_method.args () >>> my_ob._saved_method.kwargs == dict(a=3, b='foo') True The arguments are stored on the instance, allowing for different instance to save different args. >>> your_ob = MyClass() >>> your_ob.method({str('x'): 3}, b=[4]) {'x': 3} [4] >>> your_ob._saved_method.args ({'x': 3},) >>> my_ob._saved_method.args () """ args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs') @functools.wraps(method) def wrapper(self, *args, **kwargs): attr_name = '_saved_' + method.__name__ attr = args_and_kwargs(args, kwargs) setattr(self, attr_name, attr) return method(self, *args, **kwargs) return wrapper def except_(*exceptions, replace=None, use=None): """ Replace the indicated exceptions, if raised, with the indicated literal replacement or evaluated expression (if present). >>> safe_int = except_(ValueError)(int) >>> safe_int('five') >>> safe_int('5') 5 Specify a literal replacement with ``replace``. >>> safe_int_r = except_(ValueError, replace=0)(int) >>> safe_int_r('five') 0 Provide an expression to ``use`` to pass through particular parameters. >>> safe_int_pt = except_(ValueError, use='args[0]')(int) >>> safe_int_pt('five') 'five' """ def decorate(func): @functools.wraps(func) def wrapper(*args, **kwargs): try: return func(*args, **kwargs) except exceptions: try: return eval(use) except TypeError: return replace return wrapper return decorate