from theano.gof.graph import list_of_nodes
from theano.compat.python2x import any, defaultdict
from theano.compat import cmp

## {{{ http://code.activestate.com/recipes/578231/ (r1)
# Copyright (c) Oren Tirosh 2012
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.


def memodict(f):
    """ Memoization decorator for a function taking a single argument """
    class memodict(defaultdict):
        def __missing__(self, key):
            ret = self[key] = f(key)
            return ret
    return memodict().__getitem__
## end of http://code.activestate.com/recipes/578231/ }}}


def make_depends():
    @memodict
    def depends((a, b)):
        """ Returns True if a depends on b """
        return (any(bout in a.inputs for bout in b.outputs)
                or any(depends((ainp.owner, b)) for ainp in a.inputs
                       if ainp.owner))
    return depends


def make_dependence_cmp():
    """ Create a comparator to represent the dependence of nodes in a graph """

    depends = make_depends()

    def dependence(a, b):
        """ A cmp function for nodes in a graph - does a depend on b?

        Returns positive number if a depends on b
        Returns negative number if b depends on a
        Returns 0 otherwise
        """
        if depends((a, b)):
            return 1
        if depends((b, a)):
            return -1
        return 0

    return dependence


def reverse_dict(d):
    """Reverses direction of dependence dict

    >>> d = {'a': (1, 2), 'b': (2, 3), 'c':()}
    >>> reverse_dict(d)
    {1: ('a',), 2: ('a', 'b'), 3: ('b',)}

    :note: dict order are not deterministic. As we iterate on the
        input dict, it make the output of this function depend on the
        dict order. So this function output order should be considered
        as undeterministic.

    """
    result = {}
    for key in d:
        for val in d[key]:
            result[val] = result.get(val, tuple()) + (key, )
    return result


def _toposort(edges):
    """ Topological sort algorithm by Kahn [1] - O(nodes + vertices)

    inputs:
        edges - a dict of the form {a: {b, c}} where b and c depend on a
    outputs:
        L - an ordered list of nodes that satisfy the dependencies of edges

    >>> _toposort({1: {2, 3}, 2: (3, )})
    [1, 2, 3]

    Closely follows the wikipedia page [2]

    [1] Kahn, Arthur B. (1962), "Topological sorting of large networks",
    Communications of the ACM
    [2] http://en.wikipedia.org/wiki/Toposort#Algorithms
    """
    incoming_edges = reverse_dict(edges)
    incoming_edges = dict((k, set(val)) for k, val in incoming_edges.items())
    S = set((v for v in edges if v not in incoming_edges))
    L = []

    while S:
        n = S.pop()
        L.append(n)
        for m in edges.get(n, ()):
            assert n in incoming_edges[m]
            incoming_edges[m].remove(n)
            if not incoming_edges[m]:
                S.add(m)
    if any(incoming_edges.get(v, None) for v in edges):
        raise ValueError("Input has cycles")
    return L


def posort(l, *cmps):
    """ Partially ordered sort with multiple comparators

    Given a list of comparators order the elements in l so that the comparators
    are satisfied as much as possible giving precedence to earlier comparators.

    inputs:
        l - an iterable of nodes in a graph
        cmps - a sequence of comparator functions that describe which nodes
               should come before which others

    outputs:
        a list of nodes which satisfy the comparators as much as possible.

    >>> lower_tens = lambda a, b: a/10 - b/10 # prefer lower numbers div 10
    >>> prefer evens = lambda a, b: a%2 - b%2 # prefer even numbers
    >>> posort(range(20), lower_tens, prefer_evens)
    [0, 8, 2, 4, 6, 1, 3, 5, 7, 9, 16, 18, 10, 12, 14, 17, 19, 11, 13, 15]

    implemented with _toposort """
    comes_before = dict((a, set()) for a in l)
    comes_after = dict((a, set()) for a in l)

    def add_links(a, b):  # b depends on a
        comes_after[a].add(b)
        comes_after[a].update(comes_after[b])
        for c in comes_before[a]:
            comes_after[c].update(comes_after[a])
        comes_before[b].add(a)
        comes_before[b].update(comes_before[a])
        for c in comes_after[b]:
            comes_before[c].update(comes_before[b])

    def check():
        """ Tests for cycles in manufactured edges """
        for a in l:
            for b in l:
                assert not(b in comes_after[a] and a in comes_after[b])

    for cmp in cmps:
        for a in l:
            for b in l:
                if cmp(a, b) < 0:  # a wants to come before b
                    # if this wouldn't cause a cycle and isn't already known
                    if not b in comes_before[a] and not b in comes_after[a]:
                        add_links(a, b)
    # check() # debug code

    return _toposort(comes_after)


def sort_apply_nodes(inputs, outputs, cmps):
    """ Order a graph of apply nodes according to a list of comparators

    The following example sorts first by dependence of nodes (this is a
    topological sort) and then by lexicographical ordering (nodes that start
    with 'E' come before nodes that start with 'I' if there is no dependence.

    >>> from theano.gof.graph import sort_apply_nodes, dependence
    >>> from theano.tensor import matrix, dot
    >>> x = matrix('x')
    >>> y = dot(x*2, x+1)
    >>> str_cmp = lambda a, b: cmp(str(a), str(b)) # lexicographical sort
    >>> sort_apply_nodes([x], [y], cmps=[dependence, str_cmp])
    [Elemwise{add,no_inplace}(x, InplaceDimShuffle{x,x}.0),
     InplaceDimShuffle{x,x}(TensorConstant{2}),
     Elemwise{mul,no_inplace}(x, InplaceDimShuffle{x,x}.0),
     InplaceDimShuffle{x,x}(TensorConstant{1}),
     dot(Elemwise{mul,no_inplace}.0, Elemwise{add,no_inplace}.0)]
    """

    return posort(list_of_nodes(inputs, outputs), *cmps)


def sort_schedule_fn(*cmps):
    """ Make a schedule function from comparators

    See also:
        sort_apply_nodes
    """
    dependence = make_dependence_cmp()
    cmps = (dependence,) + cmps

    def schedule(fgraph):
        """ Order nodes in a FunctionGraph """
        return sort_apply_nodes(fgraph.inputs, fgraph.outputs, cmps)
    return schedule


def key_to_cmp(key):
    def key_cmp(a, b):
        return cmp(key(a), key(b))
    return key_cmp
