import collections.abc
import functools
import inspect
import itertools
import operator
import time
import types
import warnings

import pkg_resources.extern.more_itertools


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 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, cache_wrapper=functools.lru_cache()):
    """
    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, *args, **kwargs):
        # it's the first call, replace the method with a cached, bound method
        bound_method = types.MethodType(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

    return _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 None

    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


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, owner=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


method_caller = first_invoke(
    lambda: warnings.warn(
        '`jaraco.functools.method_caller` is deprecated, '
        'use `operator.methodcaller` instead',
        DeprecationWarning,
        stacklevel=3,
    ),
    operator.methodcaller,
)


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 _ 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 None

    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


def identity(x):
    """
    Return the argument.

    >>> o = object()
    >>> identity(o) is o
    True
    """
    return x


def bypass_when(check, *, _op=identity):
    """
    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
    """

    def decorate(func):
        @functools.wraps(func)
        def wrapper(param, /):
            return param if _op(check) else func(param)

        return wrapper

    return decorate


def bypass_unless(check):
    """
    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
    """
    return bypass_when(check, _op=operator.not_)


@functools.singledispatch
def _splat_inner(args, func):
    """Splat args to func."""
    return func(*args)


@_splat_inner.register
def _(args: collections.abc.Mapping, func):
    """Splat kargs to func as kwargs."""
    return func(**args)


def splat(func):
    """
    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)]
    >>> pkg_resources.extern.more_itertools.consume(itertools.starmap(print, pairs))
    -1 1
    0 2
    >>> pkg_resources.extern.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}
    """
    return functools.wraps(func)(functools.partial(_splat_inner, func=func))
