Current File : //usr/lib/python3/dist-packages/setuptools/_vendor/jaraco/__pycache__/functools.cpython-312.pyc
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    Compose any number of unary functions into a single unary function.

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    >>> 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]
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    Return a function that will call a named method on the
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    Decorate func so it's only ever called the first time.

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    >>> 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.

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    ...     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/
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    Decorate a function with a transform function that is
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    >>> @apply(reversed)
    ... def get_numbers(start):
    ...     "doc for get_numbers"
    ...     return range(start, start+3)
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    [6, 5, 4]
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    'doc for get_numbers'
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    Call a function for its side effect after initialization.

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    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

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    Decorator wrapper for retry_call. Accepts arguments to retry_call
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    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:

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    ...         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
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    ()
    >>> 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]
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    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.

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    'five'

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