from __future__ import (
	absolute_import, unicode_literals, print_function, division,
)

import functools
import time
import warnings
import inspect
import collections
from itertools import count

__metaclass__ = type


try:
	from functools import lru_cache
except ImportError:
	try:
		from backports.functools_lru_cache import lru_cache
	except ImportError:
		try:
			from functools32 import lru_cache
		except ImportError:
			warnings.warn("No lru_cache available")


import more_itertools.recipes


def compose(*funcs):
	"""
	Compose any number of unary functions into a single unary function.

	>>> import textwrap
	>>> from six import text_type
	>>> stripped = text_type.strip(textwrap.dedent(compose.__doc__))
	>>> compose(text_type.strip, textwrap.dedent)(compose.__doc__) == stripped
	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, cache_wrapper=None):
	"""
	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()

	Another cache wrapper may be supplied:

	>>> cache = 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.
	"""
	cache_wrapper = cache_wrapper or lru_cache()

	def wrapper(self, *args, **kwargs):
		# it's the first call, replace the method with a cached, bound method
		bound_method = functools.partial(method, self)
		cached_method = cache_wrapper(bound_method)
		setattr(self, method.__name__, cached_method)
		return cached_method(*args, **kwargs)

	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

	wrapper_name = '__cached' + name

	def proxy(self, *args, **kwargs):
		if wrapper_name not in vars(self):
			bound = functools.partial(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):
	...     return range(start, start+3)
	>>> list(get_numbers(4))
	[6, 5, 4]
	"""
	def wrap(func):
		return 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
	"""
	def wrap(func):
		@functools.wraps(func)
		def wrapper(*args, **kwargs):
			result = func(*args, **kwargs)
			action(result)
			return result
		return wrapper
	return wrap


def call_aside(f, *args, **kwargs):
	"""
	Call a function for its side effect after initialization.

	>>> @call_aside
	... def func(): print("called")
	called
	>>> func()
	called

	Use functools.partial to pass parameters to the initial call

	>>> @functools.partial(call_aside, 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, 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 = 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.recipes.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
	"""
	try:
		sig = inspect.signature(func)
		params = sig.parameters.keys()
	except AttributeError:
		spec = inspect.getargspec(func)
		params = spec.args
	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