## PENDING REWRITE OF tensor_opt.py


import time
import numpy

import theano
from theano import gof
from theano.tensor.opt import *
from theano import tensor  #do not use, there is  an import * below that hides it
from theano import tensor as TT  #ugly but works for now...
from theano.tensor import TensorType, inplace
from theano.gof import Env
from theano.tensor.elemwise import DimShuffle
from theano import pprint, shared
from theano.tests import unittest_tools as utt

from theano import function, compile
from nose.plugins.skip import SkipTest
import unittest, copy
from copy import copy as cp

def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)):
    x = TensorType(broadcastable = xbc, dtype = 'float64')('x')
    y = TensorType(broadcastable = ybc, dtype = 'float64')('y')
    z = TensorType(broadcastable = zbc, dtype = 'float64')('z')
    return x, y, z


ds = lambda x, y: DimShuffle(x.type.broadcastable, y)(x)
dimshuffle_lift = out2in(local_dimshuffle_lift)

class test_dimshuffle_lift(unittest.TestCase):

    def test_double_transpose(self):
        x, y, z = inputs()
        e = ds(ds(x, (1, 0)), (1, 0))
        g = Env([x], [e])
        self.failUnless(str(g) == "[DimShuffle{1,0}(DimShuffle{1,0}(x))]")
        dimshuffle_lift.optimize(g)
        self.failUnless(str(g) == "[x]")

    def test_merge2(self):
        x, y, z = inputs()
        e = ds(ds(x, (1, 'x', 0)), (2, 0, 'x', 1))
        g = Env([x], [e])
        self.failUnless(str(g) == "[DimShuffle{2,0,x,1}(DimShuffle{1,x,0}(x))]", str(g))
        dimshuffle_lift.optimize(g)
        self.failUnless(str(g) == "[DimShuffle{0,1,x,x}(x)]", str(g))

    def test_elim3(self):
        x, y, z = inputs()
        e = ds(ds(ds(x, (0, 'x', 1)), (2, 0, 'x', 1)), (1, 0))
        g = Env([x], [e])
        self.failUnless(str(g) == "[DimShuffle{1,0}(DimShuffle{2,0,x,1}(DimShuffle{0,x,1}(x)))]", str(g))
        dimshuffle_lift.optimize(g)
        self.failUnless(str(g) == "[x]", str(g))

    def test_lift(self):
        x, y, z = inputs([False]*1, [False]*2, [False]*3)
        e = x + y + z
        g = Env([x, y, z], [e])
        self.failUnless(str(g) == ("[Elemwise{add,no_inplace}("
            "InplaceDimShuffle{x,0,1}(Elemwise{add,no_inplace}"
            "(InplaceDimShuffle{x,0}(x), y)), z)]"), str(g))
        dimshuffle_lift.optimize(g)
        self.failUnless(str(g) == ("[Elemwise{add,no_inplace}(Elemwise"
            "{add,no_inplace}(InplaceDimShuffle{x,x,0}(x), InplaceDimShuffle"
            "{x,0,1}(y)), z)]"), str(g))


def test_add_canonizer_problem0():
    #observed in a real graph

    n_segments = 10
    label = lscalar('label')
    segment_labels = label + theano._asarray([0] * n_segments, dtype='int64')

    r = segment_labels * 5
    f = function([label], r)

from theano.tensor import *
# Why is there TWO 'import *' in this file???

class test_greedy_distribute(unittest.TestCase):
    def test_main(self):
        a, b, c, d, x, y, z = matrices('abcdxyz')
        e = (a/z + b/x) * x * z
        g = Env([a,b,c,d,x,y,z], [e])
        ##print pprint(g.outputs[0])
        mul_canonizer.optimize(g)
        gof.TopoOptimizer(gof.LocalOptGroup(local_greedy_distributor), order = 'out_to_in').optimize(g)
        ##print pprint(g.outputs[0])
    
    def test_kording_bug(self):
        x, y = vectors('xy')
        eps = scalar('eps')
        s = scalar('s')

        #r = theano.tensor.mul(theano.tensor.fill(x, 2.*a), x/a , (y+z) , a)
        #r = theano.tensor.mul((x/a+y) , a, z)
        r = mul(
                s - 1
                , eps + x/s
                , eps + y/s
                , s)

        f = function([s, eps, x,y], r**2)

        r0 = f(4,1.e-6, [1.5,2], [2.3,3.1])
        r1 = f(4,1.e-6, [1.5,2], [2.3,3.1])
        r2 = f(4,1.e-6, [1.5,2], [2.3,3.1])

        assert numpy.all(r0 == r1)
        assert numpy.all(r0 == r2)



class test_canonize(unittest.TestCase):

    def test_muldiv(self):
        x, y, z = matrices('xyz')
        a, b, c, d = matrices('abcd')
#        e = (2.0 * x) / (2.0 * y)
#        e = (2.0 * x) / (4.0 * y)
#        e = x / (y / z)
#        e = (x * y) / x
#        e = (x / y) * (y / z) * (z / x)
#        e = (a / b) * (b / c) * (c / d)
#        e = (a * b) / (b * c) / (c * d)
#        e = 2 * x / 2
#        e = x / y / x
#        e = (x / x) * (y / y)
        e = (-1 * x) / y / (-2 * z)
        g = Env([x, y, z, a, b, c, d], [e])
        print pprint(g.outputs[0])
        mul_canonizer.optimize(g)
        print pprint(g.outputs[0])

    def test_elemwise_multiple_inputs_optimisation(self):
        """
        verify that the Canonizer merge sequential Elemwise({mul,add}) part 1
        This part are that case that is done, but don't include case that are not implemented but are suposed to be.
        Test with and without DimShuffle
        """
        
        shp=(5,5)
        fx, fy, fz = fmatrices('xyz')
        dx, dy, dz = dmatrices('xyz')
        fv = fvector('r').dimshuffle('x',0)
        dv = dvector('s').dimshuffle('x',0)
        fxv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fyv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fzv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float32').reshape(1,shp[0])
        dxv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dyv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dzv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float64').reshape(1,shp[0])
        cases = [
            (fx+fy,(fx,fy),(fxv,fyv),1,'float32'),
            (fx*fy,(fx,fy),(fxv,fyv),1,'float32'),
#            (fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
#            (dx+dy+dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
#            (fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
#            (dx*dy*dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
#            (fx*fy*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
#            (dx*dy*(dx+dy+dz),(dx,dy,dz),(dxv,dyv,dzv),2,'float64'),
#            (fx*fy*(fx+fy+dz),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type add
#            (dz*fy*(fx+fy),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type mul
            #check with dimshuffle of constant
            (fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
#            (2+fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
#            (2*fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2+fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2*fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
#            (fx*fy*2*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
#            (fx*fy*(2+fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
            (fx*fy*2*(fx+fy+fz+2),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),

            #check with broadcast of row
#            (fx+fy+fz+fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
#            (fx*fy*fz*fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
#            (fv+fx+fy+fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
#            (fv*fx*fy*fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
#            (fx*fy*fv*(fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
#            (fx*fy*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
#            (fx*fy*fv*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
#            (dx+dy+dz+dv,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
#            (dx*dy*dz*dv,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
#            (dv+dx+dy+dz,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
#            (dv*dx*dy*dz,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
#            (dx*dy*dv*(dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),
#            (dx*dy*(dv+dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),
#            (dx*dy*dv*(dv+dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),
            ]#[10:11]
#        print cases


        #We must be sure that the Canonizer is working, but that we don't have other
        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
        mode=compile.mode.get_default_mode()
        old_optimizer = mode._optimizer
        try:
            mode._optimizer=gof.Query(["canonicalize"])
            mode._optimizer=mode._optimizer.excluding('local_elemwise_fusion')
            for id, [g, sym_inputs, val_inputs, nb_elemwise, out_dtype] in enumerate(cases):
                f = compile.function(list(sym_inputs), g,
                                     #we need the optimisation enabled, debug do this.
                                     mode=mode)
                
                out = f(*val_inputs)
                assert(len(f.maker.env.toposort())==nb_elemwise)
                assert(out_dtype==out.dtype)
        finally:
            mode._optimizer = old_optimizer

    def test_elemwise_multiple_inputs_optimisation2(self):
        """
        verify that the Canonizer merge sequential Elemwise({mul,add}) part 2.
        This part are that case that should have been done, but that are not implemented.
        Test with and without DimShuffle
        """
        raise SkipTest("Current implementation of Canonizer don't implement all case. Skip the corresponding test")
    
        shp=(5,5)
        fx, fy, fz = fmatrices('xyz')
        dx, dy, dz = dmatrices('xyz')
        fv = fvector('r').dimshuffle('x',0)
        dv = dvector('s').dimshuffle('x',0)
        fxv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fyv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fzv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float32').reshape(1,shp[0])
        dxv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dyv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dzv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float64').reshape(1,shp[0])
        cases = [
            (fx+fy,(fx,fy),(fxv,fyv),1,'float32'),
            (fx*fy,(fx,fy),(fxv,fyv),1,'float32'),
            (fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (dx+dy+dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
            (fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (dx*dy*dz,(dx,dy,dz),(dxv,dyv,dzv),1,'float64'),
            (fx*fy*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
            (dx*dy*(dx+dy+dz),(dx,dy,dz),(dxv,dyv,dzv),2,'float64'),
            (fx*fy*(fx+fy+dz),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type add
            (dz*fy*(fx+fy),(fx,fy,dz),(dxv,dyv,dzv),2,'float64'),#check mixed type mul
            #check with dimshuffle of constant
            (fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2+fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2*fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2+fx+fy+fz+2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (2*fx*fy*fz*2,(fx,fy,fz),(fxv,fyv,fzv),1,'float32'),
            (fx*fy*2*(fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
            (fx*fy*(2+fx+fy+fz),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),
            (fx*fy*2*(fx+fy+fz+2),(fx,fy,fz),(fxv,fyv,fzv),2,'float32'),

            #check with broadcast of row
            (fx+fy+fz+fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
            (fx*fy*fz*fv,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
            (fv+fx+fy+fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
            (fv*fx*fy*fz,(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),1,'float32'),
            (fx*fy*fv*(fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
            (fx*fy*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
            (fx*fy*fv*(fv+fx+fy+fz),(fx,fy,fz,fv),(fxv,fyv,fzv,fvv),2,'float32'),
            (dx+dy+dz+dv,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
            (dx*dy*dz*dv,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
            (dv+dx+dy+dz,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
            (dv*dx*dy*dz,(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),1,'float64'),
            (dx*dy*dv*(dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),
            (dx*dy*(dv+dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),
            (dx*dy*dv*(dv+dx+dy+dz),(dx,dy,dz,dv),(dxv,dyv,dzv,dvv),2,'float64'),

            ]#[10:11]
#        print cases

        #We must be sure that the Canonizer is working, but that we don't have other
        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
        mode=compile.mode.get_default_mode()
        mode._optimizer=gof.Query(["canonicalize"])
        mode._optimizer=mode._optimizer.excluding('local_elemwise_fusion')
        for id, [g, sym_inputs, val_inputs, nb_elemwise, out_dtype] in enumerate(cases):
            f = compile.function(list(sym_inputs), g,
                                 #we need the optimisation enabled, debug do this.
                                 mode=mode)
            
            out = f(*val_inputs)
            assert(len(f.maker.env.toposort())==nb_elemwise)
            assert(out_dtype==out.dtype)
            
    def test_multiple_case(self):
        """ test those case take from the comment in Canonizer
        x / x -> 1
        (x * y) / x -> y
        x / y / x -> 1 / y
        x / y / z -> x / (y * z)
        x / (y / z) -> (x * z) / y
        (a / b) * (b / c) * (c / d) -> a / d
        (2.0 * x) / (4.0 * y) -> (0.5 * x) / y
        2 * x / 2 -> x
        with and without DimShuffle
        TODO: with DimShuffle
        """
        import theano.tensor, theano.compile

        shp=(3,3)
        fx, fy, fz, fw = fmatrices('xyzw')
        dx, dy, dz, dw = dmatrices('xyzw')
        fv = fvector('r').dimshuffle('x',0)
        dv = dvector('s').dimshuffle('x',0)
        fxv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fyv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fzv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fwv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float32').reshape(1,shp[0])
        dxv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dyv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dzv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dwv = theano._asarray(numpy.random.rand(*shp),dtype='float64')
        dvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float64').reshape(1,shp[0])

        #We must be sure that the Canonizer is working, but that we don't have other
        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
        mode=compile.mode.get_default_mode()
        old_optimizer = mode._optimizer
        try:
            mode._optimizer=gof.Query(["canonicalize"])
            mode._optimizer=mode._optimizer.including('ShapeOpt')
            mode._optimizer=mode._optimizer.excluding('local_elemwise_fusion')

            #test x / x -> 1
            for id, (g, sym_inputs, val_inputs, out_dtype) in enumerate([(fx/fx,[fx],[fxv],'float32'),
                                                           (dx/dx,[dx],[dxv],'float64'),
                                                           (fv/fv,[fv],[fvv],'float32'),
                                                           (dv/dv,[dv],[dvv],'float64'),
                                                           ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert (out==numpy.ones(shp, dtype=out_dtype)).all()
                topo=f.maker.env.toposort()
                if sym_inputs[0].broadcastable[0]:
                    assert len(topo)==2
                    assert isinstance(topo[0].op, Shape_i)
                    assert isinstance(topo[1].op, TT.Alloc)
                else:
                    assert len(topo)==3
                    assert isinstance(topo[0].op, Shape_i)
                    assert isinstance(topo[1].op, Shape_i)
                    assert isinstance(topo[2].op, TT.Alloc)
                assert(out_dtype==out.dtype)

            #test (x * y) / x -> y
            for id,(g, sym_inputs, val_inputs, nb_elemwise, out_dtype) in enumerate([
                                                           ((dx*dy)/dx,[dx,dy],[dxv,dyv],0,'float64'),
                                                           ((fx*fy)/fx,[fx,fy],[fxv,fyv],0,'float32'),
                                                           ((dv*dy)/dv,[dv,dy],[dvv,dyv],0,'float64'),
                                                           ((fv*fy)/fv,[fv,fy],[fvv,fyv],0,'float32'),
                #must broadcast as their is a dimshuffle in the computation
                                                           ((dx*dv)/dx,[dx,dv],[dxv,dvv],1,'float64'),
                #topo: [Elemwise{second,no_inplace}(x, <TensorType(float64, row)>)]
                                                           ((fx*fv)/fx,[fx,fv],[fxv,fvv],1,'float32')
                #topo: [Elemwise{second,no_inplace}(x, <TensorType(float32, row)>)]
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert(out_dtype==out.dtype)
                assert numpy.allclose(out,val_inputs[1])
                topo=f.maker.env.toposort()
                print "ID TOPO", id, topo, sym_inputs
                for r,t in f.maker.env.shape_feature.shape_of.items():
                    print '  ', r, t
                if topo:
                    for node in topo[:-1]:
                        assert isinstance(node.op, Shape_i)
                    assert isinstance(topo[-1].op, TT.Alloc)

            #test x / y / x -> 1 / y
            for id,(g, sym_inputs, val_inputs, nb_elemwise, out_dtype) in enumerate([
                                                           ((dx/dy)/dx,[dx,dy],[dxv,dyv],1,'float64'),
                                                           ((fx/fy)/fx,[fx,fy],[fxv,fyv],1,'float32'),
                                                           ((dv/dy)/dv,[dv,dy],[dvv,dyv],1,'float64'),
                                                           ((fv/fy)/fv,[fv,fy],[fvv,fyv],1,'float32'),
                            #must broadcast as their is a dimshuffle in the computation

                                                           ((dx/dv)/dx,[dx,dv],[dxv,dvv],1,'float64'),
    #topo:            [Shape_i, Shape_i, Elemwise{inv,no_inplace}(<TensorType(float64, row)>), Alloc]
                                                           ((fx/fv)/fx,[fx,fv],[fxv,fvv],1,'float32'),
                #topo:[Shape_i, Shape_i, Elemwise{inv,no_inplace}(<TensorType(float32, row)>), Alloc]
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,(1/val_inputs[1]))
                topo=f.maker.env.toposort()
                print topo
                elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
                assert len(elem)==nb_elemwise
                assert isinstance(elem[0].op,(T.Elemwise,))
                assert isinstance(elem[0].op.scalar_op,(theano.scalar.basic.Inv, theano.scalar.basic.TrueDiv))
                assert(out_dtype==out.dtype)

            #test (a / b) * (b / c) * (c / d) -> a / d
            for id,(g, sym_inputs, val_inputs, out_dtype) in enumerate([
                                                           ((dx / dy) * (dy / dz) * (dz / dw),[dx,dy,dz,dw],[dxv,dyv,dzv,dwv],'float64'),
                                                           ((fx / fy) * (fy / fz) * (fz / fw),[fx,fy,fz,fw],[fxv,fyv,fzv,fwv],'float32'),
                                                           ((dv / dy) * (dy / dz) * (dz / dw),[dv,dy,dz,dw],[dvv,dyv,dzv,dwv],'float64'),
                                                           ((fv / fy) * (fy / fz) * (fz / fw),[fv,fy,fz,fw],[fvv,fyv,fzv,fwv],'float32'),
                                                           ((dx / dv) * (dv / dz) * (dz / dw),[dx,dv,dz,dw],[dxv,dvv,dzv,dwv],'float64'),
                                                           ((fx / fv) * (fv / fz) * (fz / fw),[fx,fv,fz,fw],[fxv,fvv,fzv,fwv],'float32'),
                                                           ((dx / dy) * (dy / dv) * (dv / dw),[dx,dy,dv,dw],[dxv,dyv,dvv,dwv],'float64'),
                                                           ((fx / fy) * (fy / fv) * (fv / fw),[fx,fy,fv,fw],[fxv,fyv,fvv,fwv],'float32'),
                                                           ((dx / dy) * (dy / dz) * (dz / dv),[dx,dy,dz,dv],[dxv,dyv,dzv,dvv],'float64'),
                                                           ((fx / fy) * (fy / fz) * (fz / fv),[fx,fy,fz,fv],[fxv,fyv,fzv,fvv],'float32'),
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,(val_inputs[0]/val_inputs[3]))
                topo=f.maker.env.toposort()
                assert len(topo)==1
                assert isinstance(topo[0].op,(T.Elemwise,))
                assert isinstance(topo[0].op.scalar_op,theano.scalar.basic.TrueDiv)
                assert len(topo[0].inputs)==2
                assert(out_dtype==out.dtype)

            #test (2.0 * x) / (4.0 * y) -> (0.5 * x) / y
            for id,(g, sym_inputs, val_inputs, out_dtype) in enumerate([
                                                           (((2.0*dx)/(4.0*dy)),[dx,dy],[dxv,dyv],'float64'),
                                                           (((2.0*fx)/(4.0*fy)),[fx,fy],[fxv,fyv],'float32'),
                                                           (((2.0*dv)/(4.0*dy)),[dv,dy],[dvv,dyv],'float64'),
                                                           (((2.0*fv)/(4.0*fy)),[fv,fy],[fvv,fyv],'float32'),
                                                           (((2.0*dx)/(4.0*dv)),[dx,dv],[dxv,dvv],'float64'),
                                                           (((2.0*fx)/(4.0*fv)),[fx,fv],[fxv,fvv],'float32'),
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,(0.5*val_inputs[0]/val_inputs[1]))
                topo=f.maker.env.toposort()
                assert len(topo)==2
                assert isinstance(topo[0].op,(T.Elemwise,))
                assert isinstance(topo[0].op.scalar_op,theano.scalar.basic.Mul)
                assert len(topo[0].inputs)==2
                assert isinstance(topo[1].op,(T.Elemwise,))
                assert isinstance(topo[1].op.scalar_op,theano.scalar.basic.TrueDiv)
                assert len(topo[1].inputs)==2
                assert(out_dtype==out.dtype)
                
            #test 2 * x / 2 -> x
            for id,(g, sym_inputs, val_inputs, out_dtype) in enumerate([
                                                           ((2*dx)/2,[dx],[dxv],'float64'),
                                                           ((2*fx)/2,[fx],[fxv],'float32'),
                                                           ((2*dv)/2,[dv],[dvv],'float64'),
                                                           ((2*fv)/2,[fv],[fvv],'float32'),
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,val_inputs[0])
                topo=f.maker.env.toposort()
                assert len(topo)==0
                assert(out_dtype==out.dtype)
                
            #test x / abs(x) -> sign(x)
            for id,(g, sym_inputs, val_inputs, out_dtype) in enumerate([
                                                           (dx/abs(dx),[dx],[0.5-dxv],'float64'),
                                                           (fx/abs(fx),[fx],[0.5-fxv],'float32'),
                                                           (dx/abs(dx),[dx],[0.0*dxv],'float64'),
                                                           (fx/abs(fx),[fx],[0.0*fxv],'float32'),
                                                           (dv/abs(dv),[dv],[0.5-dvv],'float64'),
                                                           (fv/abs(fv),[fv],[0.5-fvv],'float32'),
                ]):
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.all(numpy.isfinite(out))
                assert numpy.allclose(out,numpy.sign(val_inputs[0]))
                assert(out_dtype==out.dtype)
        finally:
            mode._optimizer = old_optimizer


    def test_multiple_case_that_fail(self):
        import theano.tensor, theano.compile
        raise SkipTest("Current implementation of Canonizer don't implement all case. Skip the corresponding test")

        shp=(4,4)
        fx, fy, fz = fmatrices('xyz')
        dx, dy, dz = dmatrices('xyz')
        fxv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fyv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fzv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        dxv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        dyv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        dzv = theano._asarray(numpy.random.rand(*shp),dtype='float32')
        fvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float32').reshape(1,shp[0])
        #We must be sure that the Canonizer is working, but that we don't have other
        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
        mode=compile.mode.get_default_mode()
        old_optimizer = mode._optimizer
        try:
            mode._optimizer=gof.Query(["canonicalize"])
            mode._optimizer=mode._optimizer.excluding('local_elemwise_fusion')

    #test fail!
            #test x / y / z -> x / (y * z)
            for (g, sym_inputs, val_inputs, out_dtype) in [
                                                           ((dx/dy)/dz,[dx,dy,dz],[dxv,dyv,dzv],'float64'),
                                                           ((fx/fy)/fz,[fx,fy,fz],[fxv,fyv,fzv],'float32')
                ]:
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,val_inputs[0]/val_inputs[1]/val_inputs[2])
                topo=f.maker.env.toposort()
                print topo
                assert len(topo)==2
                assert isinstance(topo[0].op,(T.Elemwise,))
                assert isinstance(topo[0].op.scalar_op,theano.scalar.basic.Inv)
                assert len(topo[0].inputs)==1
                assert(out_dtype==out.dtype)

            #test x / (y / z) -> (x * z) / y
            for (g, sym_inputs, val_inputs, out_dtype) in [
                                                           (dx/(dy/dz),[dx,dy,dz],[dxv,dyv,dzv],'float64'),
                                                           (fx/(fy/fz),[fx,fy,fz],[fxv,fyv,fzv],'float32')
                ]:
                f = compile.function(list(sym_inputs), g,
                                     mode=mode)
                out = f(*val_inputs)
                assert numpy.allclose(out,val_inputs[0]/(val_inputs[1]/val_inputs[2]))
                topo=f.maker.env.toposort()
                print topo
                assert len(topo)==2
                assert isinstance(topo[0].op,(T.Elemwise,))
                assert isinstance(topo[0].op.scalar_op,theano.scalar.basic.Inv)
                assert len(topo[0].inputs)==1
                assert(out_dtype==out.dtype)

        finally:
            mode._optimizer = old_optimizer

    def test_dont_merge_if_multiple_client(self):
        """ test those case take from the comment in Canonizer
        """
        raise SkipTest("Not implemented")

def test_mixeddiv():
    """Test that int division is preserved"""
    i = iscalar()
    d = dscalar()
    assert 0 == function([i,d], d*(i/(i+1)))(3, 1.0)

def test_const_type_in_mul_canonizer():
    input = dmatrix()
    w = dmatrix()
    visb = dvector()
    hidb = dvector()
    betas = dvector()
    a = dvector()

    def sigm(x): return 1./(1+exp(-x))

    hid = sigm( (dot(w,input) + hidb) * betas )

    vis_gauss1 = (dot(w.T, hid) + visb) * betas / (2 * a * a)
    vis_gauss2 = (dot(w.T, hid) + visb) * betas / (2. * a * a)

    f1 = function([input,w,visb,hidb,betas,a],vis_gauss1)
    f2 = function([input,w,visb,hidb,betas,a],vis_gauss2)

    ival = numpy.random.rand(5,5)
    wval = numpy.random.rand(5,5)
    visbval = numpy.random.rand(5)
    hidbval = numpy.random.rand(5)
    betaval = numpy.random.rand(5)
    aval = numpy.random.rand(5)

    assert numpy.allclose(
        f2(ival, wval, visbval, hidbval, betaval, aval),
        f1(ival, wval, visbval, hidbval, betaval, aval))

class test_fusion(unittest.TestCase):

    def do(self, mode, shared_fn, shp, gpu=False, nb_repeat=1, assert_len_topo=True, slice=None):
        """
        param shared_fn: if None, will use compile.function
        verify that the elemwise fusion work
        Test with and without DimShuffle
        """
        #TODO: disable the canonizer?
        def my_init(shp, dtype='float64', num=0):
            #ret = theano._asarray(numpy.random.rand(*shp),dtype=dtype)
            ret = numpy.zeros(shp, dtype=dtype)+num
            return ret
        fw, fx, fy, fz = fmatrices('wxyz')
        dw, dx, dy, dz = dmatrices('wxyz')
        ix, iy, iz = imatrices('xyz')
        fv = fvector('r')
        fwv = my_init(shp,'float32',1)
        fxv = my_init(shp,'float32',2)
        fyv = my_init(shp,'float32',3)
        fzv = my_init(shp,'float32',4)
        fvv = theano._asarray(numpy.random.rand(shp[0]),dtype='float32')
        dwv = my_init(shp,'float64',5)
        ixv = theano._asarray(my_init(shp,num=60),dtype='int32')
        iyv = theano._asarray(my_init(shp,num=70),dtype='int32')
        izv = theano._asarray(my_init(shp,num=70),dtype='int32')
        fwx=fw+fx
        cases = [
            (fx+fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+fzv,'float32'),#1
            (fx*fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv*fyv*fzv,'float32'),
            (fx+fy*fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv*fzv,'float32'),
            (fx*fy+fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv*fyv+fzv,'float32'),
            (fw+fx+fy+fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),#5
            ((fw+fx)+(fy+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),
            (((fw+fx)+fy)+fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),
            ((fw+(fx+fy))+fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),
            ((fw+(fx+fy)+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),
            (fw+(fx+(fy+fz)),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),#10
            ((fw+fx)+(fy+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv,'float32'),
            (fw*fx*fy*fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv*fxv*fyv*fzv,'float32'),
            (fw+fx*fy*fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv*fyv*fzv,'float32'),
            (fx+fy*fz*fx,(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv*fzv*fxv,'float32'),
            (fx*fy+fz+fy,(fx,fy,fz),(fxv,fyv,fzv),1,fxv*fyv+fzv+fyv,'float32'),#15
            (fx*fy*fz*fw+fx+fy+fz+fw,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),2,fxv*fyv*fzv*fwv+fxv+fyv+fzv+fwv,'float32'),#expect 2 as their is limit to the fusion on the gpu.
            #test with constant
            ((fw+fx)+(fy+fz)+2,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),
            (((fw+fx)+2+fy)+fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),
            ((fw+(fx+2+fy))+fz,(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),
            ((fw+(fx+fy)+2+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),#20
            (fw+(fx+(fy+fz)+2),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),
            (2+(fw+fx)+(fy+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),1,fwv+fxv+fyv+fzv+2,'float32'),
            #mix float32 and float64
            (2+(dw+fx)+(fy+fz),(dw,fx,fy,fz),(dwv,fxv,fyv,fzv),1,dwv+fxv+fyv+fzv+2,'float64'),
            (2+(fw+dw)+(fy+fz),(fw,dw,fy,fz),(fwv,dwv,fyv,fzv),1,fwv+dwv+fyv+fzv+2,'float64'),
            (2+(fw+fx)+(dw+fz),(fw,fx,dw,fz),(fwv,fxv,dwv,fzv),1,fwv+fxv+dwv+fzv+2,'float64'),#25
            (2+(fw+fx)+(fy+dw),(fw,fx,fy,dw),(fwv,fxv,fyv,dwv),1,fwv+fxv+fyv+dwv+2,'float64'),
            #test when their is other op then elemwise.
            #the good output for the next test.
#            (Pdb) p f.maker.env.toposort()
#[Elemwise{add,no_inplace}(w, x), Sum(Elemwise{add,no_inplace}.0), InplaceDimShuffle{x,x}(Sum.0), Elemwise{Composite{_impls=[<function <lambda> at 0x2c5c8c0>], nin=4, _c_code={
#npy_float32 V%(id)s_tmp1;
#V%(id)s_tmp1 = %(i2)s + %(i3)s;
#npy_float32 V%(id)s_tmp2;
#V%(id)s_tmp2 = %(i0)s + %(i1)s;
#%(o0)s = V%(id)s_tmp2 + V%(id)s_tmp1;
#}
#, nout=1, env=[add(add(<float32>, <float32>), add(<float32>, <float32>))]}}(InplaceDimShuffle{x,x}.0, Elemwise{add,no_inplace}.0, y, z)]
            ((fwx.sum())+(fwx)+(fy+fz),(fw,fx,fy,fz),(fwv,fxv,fyv,fzv),4,(fwv+fxv).sum()+fwv+fxv+fyv+fzv,'float32'),
            #test other elemwise op
            (fx+fy+cos(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cos(fzv),'float32'),
            (fx+fy+cosh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.cosh(fzv),'float32'),
            (fx+fy+abs(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.absolute(fzv),'float32'),#30
            (ix+iy+abs(iz),(ix,iy,iz),(ixv,iyv,izv),1,ixv+iyv+numpy.absolute(izv),'int32'),
            (fx+fy+theano.tensor.log(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.log(fzv),'float32'),
            (fx+fy+theano.tensor.log2(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.log2(fzv),'float32'),
            (fx+fy+theano.tensor.log10(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.log10(fzv),'float32'),
            (fx+fy**fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv**fzv,'float32'),#pow #35
            (fx+fy+theano.tensor.exp(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv+fyv+numpy.exp(fzv),'float32'),
            (fx-fy-fz,(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv-fzv,'float32'),
            (fx-(fy/fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv/fzv),'float32'),
            (fx-theano.tensor.true_div(fy,2),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv/2),'float32'),
            (fx-theano.tensor.true_div(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv/fzv),'float32'),#40
            (fx-theano.tensor.int_div(ix*100,iy*1000),(fx,ix,iy),(fxv,ixv,iyv),4,fxv-((ixv*100)//(iyv*1000)),'float64'),#int32 - float32 = float64 #No c_code for int_div
            (fx-(fy/2),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv/2),'float32'),
            (fx-(fy%fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv%fzv),'float32'),
            (fx-(fy>fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv>fzv),'float32'),
            (fx-(fy>=fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv>=fzv),'float32'),#45
            (fx-(fy<fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv<fzv),'float32'),
            (fx-(fy<=fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv<=fzv),'float32'),#TODO: bugged on the gpu
            (fx-(fy==fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv==fzv),'float32'),#TODO: bugged
            (fx-(fy!=fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fyv!=fzv),'float32'),
            (fx-fy+tan(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tan(fzv),'float32'),#50
            (fx-fy+tanh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.tanh(fzv),'float32'),
            (fx-fy+sin(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sin(fzv),'float32'),
            (fx-fy+sinh(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sinh(fzv),'float32'),
            (fx-fy+theano.tensor.sqr(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+(fzv*fzv),'float32'),
            (fx-fy+theano.tensor.sqrt(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.sqrt(fzv),'float32'),#55
            (fx-fy+theano.tensor.inv(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+(1/fzv),'float32'),
            (fx-fy+theano.tensor.neg(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+(-fzv),'float32'),
#            (fx-fy+theano.tensor.iround(fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-fyv+numpy.round(fzv),'float32'),#TODO: trouble with the output type. To my understanding, numpy and c round fct return the same type as the input. Why we don't do this?

            #TODO: BIT OP only with ints, xor, or, and, invert, cast
#            (fx-theano.tensor.or_(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fy|fz),'float32'),
#            (fx-theano.tensor.xor(fy,fz),(fx,fy,fz),(fxv,fyv,fzv),1,fxv-(fy^fz),'float32'),
            (theano.tensor.pow(fx*fy+fz,fx*fy),(fx,fy,fz),(fxv,fyv,fzv),2,numpy.power(fxv*fyv+fzv,fxv*fyv),'float32'),
            (fv+fy**fz,(fv,fy,fz),(fvv,fyv,fzv),2,fvv+fyv**fzv,'float32'),#fused with a dimshuffle
            (fv-fy+tanh(fz),(fv,fy,fz),(fvv,fyv,fzv),2,fvv-fyv+numpy.tanh(fzv),'float32'),#fused with a dimshuffle
            ]
        if slice:
            cases = cases[slice]
        times=numpy.zeros(len(cases))
        fail1=[]
        fail2=[]
        fail3=[]
        fail4=[]
        for id, [g, sym_inputs, val_inputs, nb_elemwise, answer, out_dtype] in enumerate(cases):
            if gpu and out_dtype!='float32':
                print "Skip test %d as the gpu code currently support only float32" % id
                continue
            print "new cases", id

            if shared_fn == None:
                assert gpu==False
                f = compile.function(list(sym_inputs), g,mode=mode)
                for x in range(nb_repeat):
                    out=f(*val_inputs)
                t1=time.time()
            else:
                out=shared_fn(numpy.zeros(shp, dtype=out_dtype),'out')
                f = function(sym_inputs,[],updates=[(out,out+g)],mode=mode)
                t0=time.time()
                for x in range(nb_repeat):
                    f(*val_inputs)
                t1=time.time()
                out=out.value

            times[id]=t1-t0
            atol=1e-8
            if out_dtype=='float32':atol=1e-6
            if not numpy.allclose(out,answer*nb_repeat,atol=atol):
                fail1.append(id)
            topo=f.maker.env.toposort()
            if gpu:
                import theano_cuda_ndarray as tcn

                topo_ = [x for x in topo if not isinstance(x.op,tcn.basic_ops.GpuFromHost)]
                gpu_ = [x for x in topo if isinstance(x.op,tcn.basic_ops.GpuFromHost)]
                if not len(gpu_)==len(sym_inputs):
                    fail2.append((id,gpu_,sym_inputs))
            else: topo_=topo
            if assert_len_topo:
                if not len(topo_)==nb_elemwise:
                    fail3.append((id,topo_,nb_elemwise))
            if not out_dtype==out.dtype:
                fail4.append((id,out_dtype,out.dtype))

#            cases[id]=None #to remove g, that link to out that link to the ndarray!
            #g.owner.inputs[0] is out... make owner a weakref?
            
        failed=len(fail1+fail2+fail3+fail4)
        print "Executed",len(cases),"cases", "failed", failed
        if failed>0:
            raise Exception("Failed %d cases"%failed, fail1, fail2, fail3, fail4)
        
        return times
    
    def test_elemwise_fusion(self):
        shp=(5,5)
        mode=cp(compile.mode.get_default_mode())
        #we need the optimisation enabled and the canonicalize.
        #the canonicalize is needed to merge multiplication/addition by constant.
        mode._optimizer=mode._optimizer.including('local_elemwise_fusion','canonicalize')
        self.do(mode, shared, shp)

    def gpu_fusion(self):
        shp=(5,5)
        #we need the optimisation enabled, debug do this.
        mode=compile.mode.predefined_modes['FAST_COMPILE']
        mode=compile.mode.predefined_modes['FAST_RUN']
        mode=compile.mode.predefined_modes['DEBUG_MODE']
        import theano.sandbox.cuda as tcn
        self.do(mode, tcn.shared_constructor, shp, gpu=True)

    def speed_fusion(self, shared_fn = shared, gpu = False, s=None):
        """
        param type s: a slice object
        param s: a slice to apply to the case to execute. If None, exec all case.
        """
        
        shp=(3000,3000)
        shp=(1000,1000)
        nb_repeat=50
#        linker=gof.CLinker
#        linker=gof.OpWiseCLinker
        
        mode1=cp(compile.get_default_mode())
        mode1._optimizer=mode1._optimizer.including('local_elemwise_fusion')
        #TODO:clinker is much faster... but use to much memory
        #Possible cause: as their is do deletion of intermediate value when we don't keep the fct.
        #More plausible cause: we keep a link to the output data?
        #Follow up. Clinker do the same... second cause?
        mode2=cp(compile.get_default_mode())
        mode2._optimizer=mode2._optimizer.excluding('local_elemwise_fusion')
        print "test with linker", str(mode1.linker)
        times1=self.do(mode1, shared_fn, shp, gpu=gpu, nb_repeat=nb_repeat, assert_len_topo=False,slice=s)
        times2=self.do(mode2, shared_fn, shp, gpu=gpu, nb_repeat=nb_repeat, assert_len_topo=False,slice=s)
        print "times1 with local_elemwise_fusion"
        print times1, times1.min(), times1.max(), times1.sum()
        print "times2 without local_elemwise_fusion"
        print times2, times2.min(), times2.max(), times2.sum()
        d=times2/times1
        
        print "times2/times1"
        print d
        print "min", d.min(), "argmin", d.argmin(), "max", d.max(), "mean", d.mean(), "std", d.std()

    def speed_fusion_gpu(self):
        import theano_cuda_ndarray as tcn
        self.speed_fusion(shared_fn=tcn.shared_constructor, gpu=True, s=slice(0,15))
        
    def speed_log_exp(self):
        s=slice(31,36)
#        linker=gof.CLinker
        linker=gof.OpWiseCLinker
        mode=compile.Mode(linker(), cp(compile.mode.OPT_FAST_RUN))
        mode=compile.ProfileMode()
        print "time", self.do(mode, shared, shp=(1000,1000),gpu=False, assert_len_topo=False,slice=s, nb_repeat=100)


    def tes_memory_leak(self, mode=compile.mode.Mode('c', 'merge'), shared_fn=shared, shp=(3000,3000), gpu=False, nb_repeat=30, assert_len_topo=True, slice=None):
        """
        param shared_fn: if None, will use compile.function
        verify that the elemwise fusion work
        Test with and without DimShuffle
        """
        #TODO: disable the canonizer?
        fx = fmatrices('x')
        fy = fmatrices('y')
        fxv = numpy.zeros(shp, dtype='float32')+ 2
        cases = [
            (fx,(fx),(fxv),'float32'),#1
            ]
        import gc, pdb, objgraph, weakref
        d={}
        dl=[]
        v1=None
        mode=compile.mode.Mode('c', 'merge')
        #TODO: if mode is Mode('py','merge') then their is no memory leak!
        from theano.compile.function_module import orig_function
        for id, [g, sym_inputs, val_inputs, out_dtype] in enumerate(cases):
            for zzzz in range(nb_repeat):
                v=numpy.zeros(shp, dtype=out_dtype)
                gc.collect();gc.collect();gc.collect()
#                print 'v1',v1
                v1=weakref.ref(v)
                pdb.set_trace()
                #f = orig_function([compile.In(fx),compile.In(variable=fy, value=None)],
                #            [fy+fx],mode=mode)#no memory leak
                f = orig_function([compile.In(fx),compile.In(variable=fy, value=v)],
                            [fy+fx],mode=mode)#memory leak
                del v
                gc.collect();gc.collect();gc.collect()
                pdb.set_trace()

                if False:
                    gc.collect();gc.collect();gc.collect()
                    nd=objgraph.typestats()
                    print 'key, old val, new val, diff'
                    for key in set(d.keys()+nd.keys()):
                        if d.has_key(key) and nd.has_key(key) and nd[key]!=d[key]:
                            print key, d.get(key),nd.get(key),
                            if d.has_key(key) and nd.has_key(key): print nd[key]-d[key]
                            else: print None
                    gc.collect();gc.collect();gc.collect()
                    d=nd
            
#                pdb.set_trace()
                if False:
                    gc.collect();gc.collect();gc.collect()
                    ndl=objgraph.by_type('list')
                    ll=[]
                    if len(dl)>0:
                        nb=0
                        for x in ndl:
                            cmp = not isinstance(x, list)
                            if not cmp and x:
                                cmp=x[0].__class__.__name__!='array_converter'
                                if cmp:
                                    cmp=x[0]!='Option'
                                if cmp:
                                    cmp=x[0]!=270
                                cmp=False
                            if cmp and x in dl:
                                nb+=1
                                ll.append(x)
#                                pdb.set_trace()
                                pass
                        pdb.set_trace()
                    dl=ndl

                gc.collect();gc.collect();gc.collect()
#                objgraph.show_most_common_types(limit=40)
#                f(*val_inputs)
                gc.collect();gc.collect();gc.collect()

#            cases[id]=None #to remove g, that link to out that link to the ndarray!
            #g.owner.inputs[0] is out... make owner a weakref?
            
def test_log1p():
    m = theano.config.mode
    if m == 'FAST_COMPILE':
        m = 'FAST_RUN'
    m = compile.mode.get_mode(m)
    m = m.excluding('fusion')
    # check some basic cases
    x = dvector()
    f = function([x], T.log(1+(x)), mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.log1p]
    f = function([x], T.log(1+(-x)), mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.neg, inplace.log1p_inplace]
    f = function([x], -T.log(1+(-x)), mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.neg, inplace.log1p_inplace, inplace.neg_inplace]


    # check trickier cases (and use different dtype)
    y = fmatrix()
    f = function([x,y], T.log(fill(y,1)+(x)), mode=m)
    print f.maker.env.toposort()
    # the first three ops are Shape_i, Shape_i, and Dimshuffle
    theano.printing.debugprint(f)
    assert [node.op for node in f.maker.env.toposort()][3:] \
            == [T.log1p, alloc]
    f = function([x,y], T.log(0+(x) + fill(y,1.0)), mode=m)
    theano.printing.debugprint(f)
    assert [node.op for node in f.maker.env.toposort()][3:] \
            == [T.log1p, alloc]
    f = function([x,y], T.log(2+(x) - fill(y,1.0)), mode=m)
    theano.printing.debugprint(f)
    assert [node.op for node in f.maker.env.toposort()][3:] \
            == [T.log1p, alloc]

    f([1e-7, 10], [[0, 0], [0, 0]]) #debugmode will verify values 
        
    if 0:
        # at one point this worked, but it has been broken since
        # the constant up-casting made 1 -> 1.0+0.0j
        # I was never sure if this optimization should work on complex numbers or not.
        z = zmatrix()
        f = function([z], T.log(1+(z)), mode=m)
        theano.printing.debugprint(f)
        assert [node.op for node in f.maker.env.toposort()] == [T.log1p]

    if 1:
        # should work for int
        z = imatrix()
        f = function([z], T.log(1+(z)), mode=m)
        theano.printing.debugprint(f)
        assert [node.op for node in f.maker.env.toposort()] == [T.log1p]

def test_log_add():
    m = theano.config.mode
    if m == 'FAST_COMPILE':
        m = 'FAST_RUN'
    m = compile.mode.get_mode(m)
    m = m.excluding('fusion')
    # check some basic cases
    x = dvector()
    y = dvector()
    f = function([x,y], T.log(T.exp(x) + T.exp(y)), mode=m)

    theano.printing.debugprint( f)
    print f([10000], [10000])  # causes overflow if handled incorrectly
    assert numpy.allclose(f([10000], [10000]), 10000+numpy.log1p(1))


    # test that it also works with more than two args, (this currently fails)
    x = dvector()
    y = dvector()
    f = function([x,y], T.log(T.exp(x) + T.exp(y) + T.exp(x-y) + T.exp(x+y)), mode=m)
    theano.printing.debugprint( f)


    print f([10000], [10000])  # causes overflow if handled incorrectly
    assert numpy.allclose(f([10000], [10000]), 20000)

    #TODO: test that the optimization works in the presence of broadcasting.

    #TODO: (write and) test that the optimization works with Sum in addition to working with Add.

class test_local_subtensor_unary(unittest.TestCase):

    def test0(self):
        # basic test that the Op works
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        x = TT.matrix()
        f = function([x], TT.exp(x)[0], mode=mode)

        prog=f.maker.env.toposort()
        assert isinstance(prog[0].op, TT.Subtensor) #first subtensor
        assert prog[1].op == TT.exp

        f([[0,1],[2,3]]) # let debugmode test something

    def test1(self):
        # basic test that the optimization doesn't work with broadcasting
        # ... It *could* be extended to,
        # ... but right now it doesn't, so it shouldn't try.
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        x = TT.matrix()
        y = TT.vector()
        f = function([x,y], TT.exp(x+y)[0], mode=mode)
        prog=f.maker.env.toposort()
        # the optimization works through exp() but not add()
        print prog
        assert isinstance(prog[0].op, TT.DimShuffle)
        assert prog[1].op == TT.add
        assert isinstance(prog[2].op, TT.Subtensor) #first subtensor
        assert prog[3].op == inplace.exp_inplace

        f([[0,1],[2,3]], [4,5]) # let debugmode test something

def test_local_fill_useless():
    m = theano.config.mode
    if m == 'FAST_COMPILE':
        m = 'FAST_RUN'

    x = dvector()
    y = dvector()
    z = lvector()

    # basic case
    f = function([x], T.fill(x,x)*2, mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.mul]

    # basic case
    f = function([x,y], T.second(y,x)*2, mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.mul]

    # now with different type
    f = function([x,z], T.fill(z,x)*2, mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.mul]

    # now cutting out the input ??
    f = function([x,y], T.fill(x,y)*2, mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.mul]

    # now filll is serving as a cast
    f = function([x,y], T.fill(x,y)*2, mode=m)
    assert [node.op for node in f.maker.env.toposort()] == [T.mul]

class test_shapeoptimizer(unittest.TestCase):
    def setUp(self):
        utt.seed_rng()

    def test0(self):
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        v = T.vector()
        m = T.matrix()
        f = function([v,m], (v+m).shape, mode=mode)
        for node in f.maker.env.toposort():
            assert node.op != T.add

    def test_constant(self):
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'

        v = T.vector()
        m = T.matrix()
        f = function([v,m], v.dimshuffle('x','x',0).shape[1], mode=mode)
        print f.maker.env.toposort()
        assert [] == f.maker.env.toposort()

    def test_local_track_shape_i(self):
        class IdentityNoShape(Op):
            '''Op that does not infer the output shape from the input one'''
            def make_node(self, x):
                x = as_tensor_variable(x)
                return Apply(self, [x], [x.type()])
            def perform(self, node, (x,), (out,)):
                out[0] = x.copy()
            #def infer_shape(self, node, (xshp,)):
                #return [tuple([self.shape_i(i)(r) for i in xrange(r.ndim)])]
        identity_noshape = IdentityNoShape()

        class IdentityShape(Op):
            '''Op that does infer the output shape from the input one'''
            def make_node(self, x):
                x = as_tensor_variable(x)
                return Apply(self, [x], [x.type()])
            def perform(self, node, (x,), (out,)):
                out[0] = x.copy()
            def infer_shape(self, node, (xshp,)):
                return (xshp,)
        identity_shape = IdentityShape()

        @gof.local_optimizer([IdentityNoShape])
        def local_identity_noshape_to_identity_shape(node):
            '''Optimization transforming the first Op into the second'''
            if isinstance(node.op, IdentityNoShape):
                return [identity_shape(node.inputs[0])]

        mode = theano.compile.get_default_mode().including('ShapeOpt', 'specialize')
        rng = numpy.random.RandomState(utt.fetch_seed())
        x = T.tensor3('x')
        ins_x = identity_noshape(x)

        # Without the optimization
        f = theano.function([x], ins_x.shape, mode=mode)
        assert numpy.all(f(rng.randn(3,4,7)) == [3,4,7])
        f_ops = [node.op for node in f.maker.env.toposort()]
        assert len(f_ops) == 5
        assert identity_noshape in f_ops
        assert identity_shape not in f_ops

        # Register the optimization
        register_specialize(local_identity_noshape_to_identity_shape)

        # With the optimization
        # The identity_shape op is should not be needed anymore to compute
        # the shape
        g = theano.function([x], ins_x.shape, mode=mode)
        assert numpy.all(g(rng.randn(6,1,2)) == [6,1,2])
        g_ops = [node.op for node in g.maker.env.toposort()]
        assert len(g_ops) == 4
        assert identity_noshape not in g_ops
        assert identity_shape not in g_ops


        ###test multiple level of op without infer_shape
        ins_x3 = identity_noshape(identity_noshape(identity_noshape(x)))
        h = theano.function([x], ins_x3.shape, mode=mode)
        assert numpy.all(h(rng.randn(6,1,2)) == [6,1,2])
        h_ops = [node.op for node in h.maker.env.toposort()]
        assert len(h_ops) == 4
        assert identity_noshape not in h_ops
        assert identity_shape not in h_ops

class test_assert(unittest.TestCase):
    def test0(self):
        x=T.scalar()
        y=T.scalar()
        f = theano.function([x,y],theano.tensor.opt.assert_(x,T.eq(x,y)))
        f(1,1)
        self.failUnlessRaises(AssertionError, f, 1,0)

    def test1(self):
        #remove assert that are always true
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        mode = compile.mode.get_mode(mode)

        x=T.scalar()
        f = theano.function([x],theano.tensor.opt.assert_(x,1),mode=mode)
        assert f(1)==1
        assert f(5)==5
        topo=f.maker.env.toposort()
        assert len(topo)==0

    def test2(self):
        #remove assert condition that are always true
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        mode = compile.mode.get_mode(mode)

        x=T.scalar()
        y=T.scalar()
        f = theano.function([x,y],theano.tensor.opt.assert_(x,y,1),mode=mode)
        assert f(1,1)==1
        assert f(5,1)==5
        topo=f.maker.env.toposort()
        assert len(topo)==1
        assert len(topo[0].inputs)==2

    def test3(self):
        #don't remove assert condition that are always false
        mode = theano.config.mode
        if mode == 'FAST_COMPILE':
            mode = 'FAST_RUN'
        mode = compile.mode.get_mode(mode)

        x=T.scalar()
        y=T.scalar()
        f = theano.function([x,y],theano.tensor.opt.assert_(x,y,0),mode=mode)
        self.failUnlessRaises(AssertionError, f, 1,0)
        topo=f.maker.env.toposort()
        assert len(topo)==1
        assert len(topo[0].inputs)==3

def test_local_mul_specialize():

    # test a few cases to make sure that the basics are covered
    # 

    mode = theano.config.mode
    if mode == 'FAST_COMPILE':
        mode = 'FAST_RUN'
    mode = compile.mode.get_mode(mode)
    mode = mode.excluding('fusion')

    v = T.vector()
    m = T.vector()

    f = function([v,m], v*1, mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    assert nodes == []

    f = function([v,m], v*0, mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    assert nodes == [Shape_i(0), T.alloc]

    f = function([v,m], v*(-1), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    assert nodes == [T.neg]

    f = function([v,m], v*1*(-m), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    theano.printing.debugprint(f)
    assert nodes == [T.mul, inplace.neg_inplace]

    f = function([v,m], v*0*(-m), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    theano.printing.debugprint(f)
    assert nodes == [Shape_i(0), T.alloc]

    f = function([v,m], v*(-1)*(-m), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    print nodes
    theano.printing.debugprint(f)
    assert nodes == [T.mul]


def speed_local_pow_specialize_range():
    val = numpy.random.rand(1e7)
    v = T.vector()
    mode = compile.mode.get_default_mode()
    mode_without_pow_opt = mode.excluding('local_pow_specialize')
    for i in range(500,513):
        f1 = function([v], v**i, mode=mode)
        f2 = function([v], v**i, mode=mode_without_pow_opt)
        assert len(f1.maker.env.toposort())==1
        t1=time.time()
        f1(val)
        t2=time.time()
        f2(val)
        t3=time.time()
        print i,t2-t1,t3-t2,t2-t1<t3-t2
        if not t2-t1<t3-t2:
            print "WARNING WE ARE SLOWER"
    for i in range(-3,-1500,-1):
        f1 = function([v], v**i, mode=mode)
        f2 = function([v], v**i, mode=mode_without_pow_opt)
        assert len(f1.maker.env.toposort())==1
        t1=time.time()
        f1(val)
        t2=time.time()
        f2(val)
        t3=time.time()
        print i,t2-t1,t3-t2,t2-t1<t3-t2
        if not t2-t1<t3-t2:
            print "WARNING WE ARE SLOWER"

def test_local_pow_specialize():

    # test a few cases to make sure that the basics are covered
    # 

    mode = theano.config.mode
    if mode == 'FAST_COMPILE':
       mode = 'FAST_RUN'
    mode = compile.mode.get_mode(mode)
    mode = mode.excluding('fusion')

    v = T.vector()
    val = numpy.arange(10,dtype=theano.config.floatX)
    val_no0 = numpy.arange(1,10,dtype=theano.config.floatX)

    f = function([v], v**0, mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert nodes == [Shape_i(0), T.alloc]
    assert numpy.allclose(f(val),val**0)

    f = function([v], v**1, mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert nodes == []
    assert numpy.allclose(f(val),val**1)

    f = function([v], v**(-1), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert nodes == [T.inv]
    assert numpy.allclose(f(val_no0),val_no0**(-1))

    f = function([v], v**2, mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert nodes == [T.sqr]
    assert numpy.allclose(f(val),val**2)

    f = function([v], v**(-2), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes)==2
    assert nodes[0] == T.sqr
    assert isinstance(nodes[1].scalar_op,theano.scalar.basic.Inv)
#    assert nodes == [T.sqr,T.inv]#Why this don't work?
    assert numpy.allclose(f(val_no0),val_no0**(-2))

    f = function([v], v**(.5), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert nodes == [T.sqrt]
    assert numpy.allclose(f(val),val**(.5))

    f = function([v], v**(-.5), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes)==2
    assert nodes[0] == T.sqrt
    assert isinstance(nodes[1].scalar_op,theano.scalar.basic.Inv)
#    assert nodes == [T.sqrt,T.inv]#Why this don't work?
    assert numpy.allclose(f(val_no0),val_no0**(-.5))

def test_local_pow_specialize_device():

    # test that on cpu we use more agressive optimization

    mode = theano.config.mode
    if mode == 'FAST_COMPILE':
       mode = 'FAST_RUN'
    mode = compile.mode.get_mode(mode)
    mode = mode.excluding('fusion').excluding('gpu')

    v = T.vector()
    val = numpy.arange(10,dtype=theano.config.floatX)
    val_no0 = numpy.arange(1,10,dtype=theano.config.floatX)
    f = function([v], v**(15), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes)==1
    assert isinstance(nodes[0].scalar_op,theano.scalar.Composite)
    assert numpy.allclose(f(val),val**15)
    
    f = function([v], v**(-15), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes)==2
    assert isinstance(nodes[0].scalar_op,theano.scalar.Composite)
    assert isinstance(nodes[-1].scalar_op,theano.scalar.basic.Inv)
    assert numpy.allclose(f(val_no0),val_no0**(-15))
    
    f = function([v], v**(16), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes) == 1
    assert isinstance(nodes[0].scalar_op,theano.scalar.Composite)
    assert numpy.allclose(f(val),val**16)
    
    f = function([v], v**(-16), mode=mode)
    nodes = [node.op for node in f.maker.env.toposort()]
    assert len(nodes) == 2
    assert isinstance(nodes[0].scalar_op,theano.scalar.Composite)
    assert isinstance(nodes[-1].scalar_op,theano.scalar.basic.Inv)
    assert numpy.allclose(f(val_no0),val_no0**(-16))
    
class T_Rebroadcast(unittest.TestCase):

    def test_local_useless_rebroadcast(self):
        mode = theano.compile.get_default_mode().including('canonicalize')
        v1 = T.vector()
        v2 = T.vector()
        j = T.join(0, v1, v2)
        f = theano.function([v1, v2], j, mode=mode)
        f([1,2], [3,4,5])
        e = f.maker.env.toposort()
        assert len([n for n in e if isinstance(n.op, T.Rebroadcast)]) == 0

    def test_rebroadcast_rebroadcast(self):
        mode = theano.compile.get_default_mode().including('canonicalize')
        m = T.matrix()
        s = T.addbroadcast(m, 0, 1)
        v = T.unbroadcast(s, 1)
        f = theano.function([m], v, mode=mode)
        f([[76]])
        e = f.maker.env.toposort()
        rebroadcast_nodes = [n for n in e if isinstance(n.op, T.Rebroadcast)]
        assert len(rebroadcast_nodes) == 1
        assert rebroadcast_nodes[0].op.axis == {0: True}

def test_local_useless_eq():
    mode = theano.compile.get_default_mode().including('canonicalize')
    x=T.dmatrix()
    y=T.dmatrix()
    f=theano.function([x,y],T.eq(x,y), mode=mode)
    vx=numpy.random.rand(5,4)
    vy=numpy.random.rand(5,4)
    f(vx,vy)
    topo = f.maker.env.toposort()
    assert len(topo)==1
    assert isinstance(topo[0].op,T.Elemwise)
    assert isinstance(topo[0].op.scalar_op,theano.scalar.EQ)
    f2=theano.function([x],T.eq(x,x), mode=mode)
    assert numpy.all(f2(vx)==numpy.ones((5,4)))
    topo2 = f2.maker.env.toposort()
    print topo2
    #Shape_i{1}(<TensorType(float64, matrix)>), Shape_i{0}(<TensorType(float64, matrix)>), Alloc([[1]], Shape_i{0}.0, Shape_i{1}.0
    assert len(topo2)==3
    assert isinstance(topo2[-1].op,T.Alloc)

def test_local_useless_neq():
    mode = theano.compile.get_default_mode().including('canonicalize')
    x=T.dmatrix()
    y=T.dmatrix()
    f=theano.function([x,y],T.neq(x,y), mode=mode)
    vx=numpy.random.rand(5,4)
    vy=numpy.random.rand(5,4)
    f(vx,vy)
    topo = f.maker.env.toposort()
    assert len(topo)==1
    assert isinstance(topo[0].op,T.Elemwise)
    assert isinstance(topo[0].op.scalar_op,theano.scalar.NEQ)
    f2=theano.function([x],T.neq(x,x), mode=mode)
    assert numpy.all(f2(vx)==numpy.zeros((5,4)))
    topo2 = f2.maker.env.toposort()
    print topo2
    assert len(topo2)==3
    assert isinstance(topo2[-1].op,T.Alloc)


if __name__ == '__main__':
#    unittest.main()
    test_fusion().tes_memory_leak()




