import theano
from theano import tensor, gof, scalar
import numpy

class ScalarSigmoid(scalar.UnaryScalarOp):
    def impl(self, x):
        return 1.0 / (1 + numpy.exp(-x))
    def grad(self, (x,), (gz,)):
        return gz * scalar_sigmoid(x) * (1.0 - scalar_sigmoid(x)),
    def c_foreach(self, (x,), (z,)): 
        return "%(z)s = 1.0 / (1 + exp(-%(x)s));" % locals()
scalar_sigmoid = gof.op.constructor(ScalarSigmoid)
Sigmoid, sigmoid, SigmoidInplace, sigmoid_inplace \
        = theano.tensor.broadcast(ScalarSigmoid, 'Sigmoid')



class CrossentropySoftmax1Hot(gof.op.Op):
    """A special compound Op for the output of neural-net classifiers.

    This Op has two outputs:
    - KL(softmax(x), y)
    - softmax(x)

    x[i] is assumed to be a dense vector
    softmax(x[i]) is the i'th distribution over len(x[i]) options
    y[i] is an integer index, encoding a 1-hot distribution

    """
    nin=2
    nout=2
    def __init__(self, x, y_idx,**kwargs):
        x = tensor._as_tensor(x)
        y_idx = tensor._as_tensor(y_idx)
#       TODO: Is this correct? It used to be y, not y_idx
        nll = tensor.Tensor(x.dtype, y_idx.broadcastable)
#        nll = Tensor(x.dtype, y.broadcastable)
        sm = tensor.Tensor(x.dtype, x.broadcastable)
        self.inputs = [x, y_idx]
        self.outputs = [nll,sm]
    def perform(self):
        x, y_idx = [i.data for i in self.inputs]
        sm = numpy.zeros_like(x) # softmax
        nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
        for i in xrange(sm.shape[0]):
            sm[i] = numpy.exp(x[i] - numpy.max(x[i])) #softmax
            sm[i] *= 1.0 / numpy.sum(sm[i]) #vector scale
            nll[i] = -numpy.log( sm[i, y_idx[i]]) #cross-entropy
        self.outputs[0].data = nll
        self.outputs[1].data = sm
    def grad(self, (x, y_idx), (g_nll, g_sm)):
        if g_sm is not None:
            raise NotImplementedError()
        nll, sm = cross_entropy_softmax_1hot(x, y_idx)
        dx = CrossentropySoftmax1Hot.Dx(g_nll, sm, y_idx).outputs[0]
        return dx, None

    class Dx (gof.op.Op):
        nin=3
        nout=1
        """Gradient wrt x of the CrossentropySoftmax1Hot Op"""
        def __init__(self, dy, sm, y_idx,**kwargs):
            dy = tensor._as_tensor(dy)
            sm = tensor._as_tensor(sm)
            y_idx = tensor._as_tensor(y_idx)
            self.inputs = [dy, sm, y_idx]
            self.outputs = [tensor.Tensor(sm.dtype, sm.broadcastable)]
        def perform(self):
            dy,sm,y_idx = [i.data for i in self.inputs]
            dx = numpy.zeros_like(sm)
            for i in xrange(sm.shape[0]):
                dx[i] = dy[i] * sm[i] #vector scale
                dx[i, y_idx[i]] -= dy[i] #scalar decrement
            self.outputs[0].data = dx
        def grad(self, *args):
            raise NotImplementedError()
cross_entropy_softmax_1hot = gof.op.constructor(CrossentropySoftmax1Hot)

#TODO: write a version of CrossentropySoftmax1Hot that accepts a bias for x, if
# this op needs to be faster.