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o ;j�H � @ s� d dl mZ d dlZd dlZd dlZd dlZd dlZd dlZd dl Z d dl Z d dl m Z m Z d dlZdd� Zdd� Ze�� fdd �Zd d � Zd d � Zdd� Zdd� Ze d�ZdHdd�ZG dd� d�Zdd � Zed d � ej�Z d d � d d fd!d"�Z d#d$� Z d%d&� Z d'd(� Z dId)d*�Z!d+d,� Z"d-d.� Z#ddd/�d0d1�Z$d2d3� Z%e%d4�d5d6�Z&d7d8� Z'ej(d9d:� �Z)e)j*dJd=d>��Z+d?d@� Z,e d�ZdKdDdE�Z-dFdG� Z.dS )L� )� annotationsN)�Callable�TypeVarc G s dd� }t �|| �S )u Compose any number of unary functions into a single unary function. Comparable to `function composition <https://en.wikipedia.org/wiki/Function_composition>`_ in mathematics: ``h = g ∘ f`` implies ``h(x) = g(f(x))``. In Python, ``h = compose(g, f)``. >>> 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] c s � �fdd�S )Nc s � �| i |���S �N� ��args�kwargs��f1�f2r �`/opt/saltstack/salt/lib/python3.10/site-packages/setuptools/_vendor/jaraco/functools/__init__.py�<lambda>+ s z.compose.<locals>.compose_two.<locals>.<lambda>r r r r r � compose_two* s z compose.<locals>.compose_two)� functools�reduce)�funcsr r r r �compose s r c s* t �� �� �fdd����fdd��_�S )ad 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 c s t �d�s � | i |���_�jS �N� saved_result)�hasattrr r ��func�wrapperr r r P s zonce.<locals>.wrapperc s t � ��d�S r )�vars� __delitem__r )r r r r V s zonce.<locals>.<lambda>)r �wraps�reset�r r r r �once0 s r c s& � �fdd�}dd� |_ t�� �p|S )aV 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. c s0 t ��| �}� |�}t| �j|� ||i |��S r )�types� MethodType�setattr�__name__)�selfr r Z bound_methodZ cached_method�� cache_wrapper�methodr r r � s z method_cache.<locals>.wrapperc S � d S r r r r r r r � � z method_cache.<locals>.<lambda>)� cache_clear�_special_method_cache)r' r& r r r% r � method_cacheZ s D r, c s2 �j }d}||vr dS d| �� ��fdd�}|S )a: 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 )� __getattr__� __getitem__NZ__cachedc sF �t | �vrt��| �}� |�}t| �|� nt| ��}||i |��S r )r r r! r" �getattr)r$ r r �bound�cache�r& r' Z wrapper_namer r �proxy� s z$_special_method_cache.<locals>.proxy)r# )r' r& �nameZ special_namesr3 r r2 r r+ � s r+ c � � fdd�}|S )ab 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' c s t �| �t� | ��S r )r r r r �� transformr r �wrap� s zapply.<locals>.wrapr )r7 r8 r r6 r �apply� s r9 c r5 )a@ 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 c s t �� ��� fdd��}|S )Nc s �| i |��}� |� |S r r )r r �result)�actionr r r r � s z,result_invoke.<locals>.wrap.<locals>.wrapper�r r r �r; r r r8 � s zresult_invoke.<locals>.wrapr )r; r8 r r= r � result_invoke� s r> c O s | |i |�� | S )a� 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 r )�fr r r r r �invoke� s &r@ �_Tr �Callable[..., object]�return�Callable[[_T], _T]c s t �� �d� fdd� �}|S )zp Wrap the function to always return the first parameter. >>> passthrough(print)('3') 3 '3' �firstrA rC c s � | g|�R i |�� | S r r )rE r r r r r r 1 s z passthrough.<locals>.wrapperN)rE rA rC rA r< r r r r � passthrough( s rF c @ sB e Zd ZdZed�fdd�Zdd� Zdd� Zd d � Zdd d �Z d S )� Throttlerz*Rate-limit a function (or other callable).ZInfc C s( t |t�r|j}|| _|| _| �� d S r )� isinstancerG r �max_rater )r$ r rI r r r �__init__< s zThrottler.__init__c C s d| _ d S )Nr )� last_called)r$ r r r r C s zThrottler.resetc O s | � � | j|i |��S r )�_waitr �r$ r r r r r �__call__F s zThrottler.__call__c C s: t � � | j }d| j | }t �td|�� t � � | _dS )z2Ensure at least 1/max_rate seconds from last call.� r N)�timerK rI �sleep�max)r$ �elapsedZ must_waitr r r rL J s zThrottler._waitNc C s t | jt�| j|��S r )� first_invokerL r �partialr )r$ �obj�ownerr r r �__get__Q s zThrottler.__get__r ) r# � __module__� __qualname__�__doc__�floatrJ r rN rL rX r r r r rG 9 s rG c � � �fdd�}|S )z� 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. c s � � �| i |��S r r r ��func1�func2r r r \ s z first_invoke.<locals>.wrapperr )r_ r` r r r^ r rT U s rT c C s t jdtdd�S )NzS`jaraco.functools.method_caller` is deprecated, use `operator.methodcaller` instead� )� stacklevel)�warnings�warn�DeprecationWarningr r r r r d s �r c C r( r r r r r r r n r) r c C sP |t d�kr t�� nt|�}|D ]}z| � W S |y$ |� Y qw | � S )z� 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. �inf)r\ � itertools�count�range)r ZcleanupZretriesZtrap�attempts�_r r r � retry_calln s �rl c r] )a7 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' c s t �� �� ��fdd��}|S )Nc s. t j� g| �R i |��}t|g��R i ���S r )r rU rl )Zf_argsZf_kwargsr0 )r �r_args�r_kwargsr r r � s z(retry.<locals>.decorate.<locals>.wrapperr< r �rm rn r r �decorate� s zretry.<locals>.decorater )rm rn rp r ro r �retry s rq c C s( t �tt�}ttj|| �}t �| �|�S )z� Convert a generator into a function that prints all yielded elements. >>> @print_yielded ... def x(): ... yield 3; yield None >>> x() 3 None )r rU �map�printr �more_itertoolsZconsumer )r Z print_allZ print_resultsr r r � print_yielded� s ru c � t �� �� fdd��}|S )z� 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) c s" | d ur� | g|�R i |��S d S r r )�paramr r r r r r � s zpass_none.<locals>.wrapperr< r r r r � pass_none� s rx c C s | du r|S | S )zT >>> none_as(None, 'foo') 'foo' >>> none_as('bar', 'foo') 'bar' Nr )�valueZ replacementr r r �none_as� s rz c s8 t �| �}|j�� }� fdd�|D �}tj| fi |��S )a� 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 c s i | ] }|� v r|� | �qS r r )�.0�k�� namespacer r � <dictcomp>� s z!assign_params.<locals>.<dictcomp>)�inspectZ signature� parameters�keysr rU )r r~ �sig�paramsZcall_nsr r} r � assign_params� s r� c s( t �dd�� t���� �fdd��}|S )a& 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_kwargsz args kwargsc s6 d�j }� ||�}t| ||� �| g|�R i |��S )NZ_saved_)r# r" )r$ r r Z attr_name�attr�r� r' r r r s z!save_method_args.<locals>.wrapper)� collections� namedtupler r �r' r r r� r �save_method_args� s "r� )�replace�usec s � ��fdd�}|S )a- 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' c s t �� ��� ��fdd��}|S )Nc sJ z�| i |��W S � y$ zt ��W Y S ty# � Y Y S w w r )�eval� TypeErrorr )� exceptionsr r� r� r r r - s ��z*except_.<locals>.decorate.<locals>.wrapperr< r �r� r� r� r r rp , s zexcept_.<locals>.decorater )r� r� r� rp r r� r �except_ s r� c C s | S )zV Return the argument. >>> o = object() >>> identity(o) is o True r )�xr r r �identity< s r� ��_opc r] )a Decorate a function to return its parameter when ``check``. >>> bypassed = [] # False >>> @bypass_when(bypassed) ... def double(x): ... return x * 2 >>> double(2) 4 >>> bypassed[:] = [object()] # True >>> double(2) 2 c s t �� ���� fdd��}|S )Nc s � ��r| S �| �S r r )rw )r� �checkr r r r X s z.bypass_when.<locals>.decorate.<locals>.wrapperr< r �r� r� r r rp W s z bypass_when.<locals>.decorater )r� r� rp r r� r � bypass_whenG s r� c C s t | tjd�S )a Decorate a function to return its parameter unless ``check``. >>> enabled = [object()] # True >>> @bypass_unless(enabled) ... def double(x): ... return x * 2 >>> double(2) 4 >>> del enabled[:] # False >>> double(2) 2 r� )r� �operator�not_)r� r r r � bypass_unlessa s r� c C s || � S )zSplat args to func.r �r r r r r � _splat_inners s r� r �collections.abc.Mappingc C s |di | ��S )z Splat kargs to func as kwargs.Nr r r� r r r rk y s rk c C s t �| �t jt| d��S )a� Wrap func to expect its parameters to be passed positionally in a tuple. Has a similar effect to that of ``itertools.starmap`` over simple ``map``. >>> pairs = [(-1, 1), (0, 2)] >>> more_itertools.consume(itertools.starmap(print, pairs)) -1 1 0 2 >>> more_itertools.consume(map(splat(print), pairs)) -1 1 0 2 The approach generalizes to other iterators that don't have a "star" equivalent, such as a "starfilter". >>> list(filter(splat(operator.add), pairs)) [(0, 2)] Splat also accepts a mapping argument. >>> def is_nice(msg, code): ... return "smile" in msg or code == 0 >>> msgs = [ ... dict(msg='smile!', code=20), ... dict(msg='error :(', code=1), ... dict(msg='unknown', code=0), ... ] >>> for msg in filter(splat(is_nice), msgs): ... print(msg) {'msg': 'smile!', 'code': 20} {'msg': 'unknown', 'code': 0} r )r r rU r� r r r r �splat s #r� r' �Callable[[_T, ...], None]�Callable[[_T, ...], _T]c rv )a� Wrap an instance method to always return self. >>> class Dingus: ... @chainable ... def set_attr(self, name, val): ... setattr(self, name, val) >>> d = Dingus().set_attr('a', 'eh!') >>> d.a 'eh!' >>> d2 = Dingus().set_attr('a', 'eh!').set_attr('b', 'bee!') >>> d2.a + d2.b 'eh!bee!' Enforces that the return value is null. >>> class BorkedDingus: ... @chainable ... def set_attr(self, name, val): ... setattr(self, name, val) ... return len(name) >>> BorkedDingus().set_attr('a', 'eh!') Traceback (most recent call last): ... AssertionError c s" � | g|�R i |��d u sJ �| S r r rM �r' r r r � s zchainable.<locals>.wrapperr< r� r r� r � chainable� s r� c O s dS )zQ A no-operation function that does nothing. >>> noop(1, 2, three=3) Nr r r r r �noop� r) r� )r rB rC rD r )r r� )r' r� rC r� )/� __future__r �collections.abcr� r r� rg r� rP r rc �typingr r rt r r � lru_cacher, r+ r9 r> r@ rA rF rG rT � methodcallerZ method_callerrl rq ru rx rz r� r� r� r� r� r� �singledispatchr� �registerrk r� r� r� r r r r �<module> sZ *Q * � .( & %
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