import sys, time

from theano.compile.pfunc import pfunc
from theano import tensor

import numpy
import theano
import theano.tensor as T

# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
    raise SkipTest('Optional package cuda disabled')

import theano.sandbox.cuda as tcn
import theano.sandbox.cuda as cuda
import theano.sandbox.cuda.basic_ops as B
from theano.tensor.basic import _allclose
from theano.tests import unittest_tools as utt

if theano.config.mode=='FAST_COMPILE':
    mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
    mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
else:
    mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
    mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')

def rand_cuda_ndarray(shape):
    return cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))

#intentionally disabled
def tes_use():
    tcn.use()

def test_sum():
    """
    test sum pattern 1, 11, 10, 01, 100, 110, 011, 001, 111, 0011, 0101, 0111, 1011, 1111

    test sum pattern implemented with reshape:
    1000, 0100, 0010, 0001, 11111

    others implemented by reshape that are not tested
    0011,0101,0110,1001,1010,1100
    1110,1101,1011
    
    TODO: test with broadcast
    """
    for shape, pattern in [((100,3,1300),[1]),
                           ((0,),[0]),((5,),[0]),
                           ((0,0),[0,1]),((1,0),[0,1]),((5,4),[0,1]),((33,31),[0,1]),((5,4),[1]),((5,4),[0]),#need something bigger then 32 for some opt test.
                           ((5,4,3),[0]),((5,4,3),[1]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[1,2]),((5,4,3),[0,1,2]),
                           ((0,0,0,0),[0,1,2,3]),
                           ((5,4,3,20),[2,3]), ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3]),((5,4,3,2),[1,2,3]),
                           ((5,4,3,10,11),[1,2]),
                           ((5,4,3,20),[2,3]), ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3]),((5,4,3,2),[1,2,3]),

                           #test shape bigger then 4096 on each dimension to make sure that we work correctly when we don't have enought thread/block in each dimensions
                           ((4100,3),[0]),((3,4101),[0]),#10

                           ((4100,3),[1]),((3,4101),[1]),#01
                           ((4100,3),[0,1]),((3,4101),[0,1]),#11

                           ((4100,4,3),[0]),((5,4100,3),[0]),((5,4,4100),[0]),#100
                           ((4100,4,3),[1]),((5,4100,3),[1]),((5,4,4100),[1]),#010
                           ((4100,4,3),[2]),((5,4100,3),[2]),((5,4,4100),[2]),#001
                           ((4100,4,3),[0,1]),((5,4100,3),[0,1]),((5,4,4100),[0,1]),#110
                           ((4100,4,3),[1,2]),((5,4100,3),[1,2]),((5,4,4100),[1,2]),#011
                           #((4100,4,3),[0,2]),((5,4100,3),[0,2]),((5,4,4100),[0,2]),#101 ##not implemented
                           ((4100,4,3),[0,1,2]),((5,4100,3),[0,1,2]),((5,4,4100),[0,1,2]),#111

                           ((4100,4,3,2),[2,3]),((4,4100,3,2),[2,3]),((4,3,4100,2),[2,3]),((4,3,2,4100),[2,3]),#0011
                           ((4100,4,3,2),[1,3]),((4,4100,3,2),[1,3]),((4,3,4100,2),[1,3]),((4,3,2,4100),[1,3]),#0101
                           ((4100,4,3,2),[0,2,3]),((4,4100,3,2),[0,2,3]),((4,3,4100,2),[0,2,3]),#((4,3,2,4100),[0,2,3]),#1011
                           ((4100,4,3,2),[1,2,3]),((4,4100,3,2),[1,2,3]),((4,3,4100,2),[1,2,3]),((4,3,2,4100),[1,2,3]),#0111
                           ((4100,2,3,4),[0,1,2,3]),((2,4100,3,4),[0,1,2,3]),((2,3,4100,4),[0,1,2,3]),((2,3,4,4100),[0,1,2,3]),#1111


                           #test pattern implemented by reshape
                           ((4100,4,3,2),[0]),((4,4100,3,2),[0]),((4,3,4100,2),[0]),((4,3,2,4100),[0]),#1000
                           ((4100,4,3,2),[1]),((4,4100,3,2),[1]),((4,3,4100,2),[1]),((4,3,2,4100),[1]),#0100
                           ((4100,4,3,2),[2]),((4,4100,3,2),[2]),((4,3,4100,2),[2]),((4,3,2,4100),[2]),#0010
                           ((4100,4,3,2),[3]),((4,4100,3,2),[3]),((4,3,4100,2),[3]),((4,3,2,4100),[3]),#0001
                           ((1100,2,3,4,5),[0,1,2,3,4]),((2,1100,3,4,5),[0,1,2,3,4]),((2,3,1100,4,5),[0,1,2,3,4]),((2,3,4,1100,5),[0,1,2,3,4]),((2,3,4,5,1100),[0,1,2,3,4]),#11111

                           ]:
        a = tensor.TensorType('float32',(False,)*len(shape))()
        b = T.Sum(pattern)(a)
        val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
#        val = numpy.ones(shape)
#        val = numpy.arange(numpy.prod(shape)).reshape(shape)
        val = theano._asarray(val,dtype='float32')
        f = theano.function([a],b, mode=mode_with_gpu)
        f2 = theano.function([a],b, mode=mode_without_gpu)
        assert tcn.GpuSum in [x.op.__class__ for x in f.maker.env.toposort()]
        assert T.Sum in [x.op.__class__ for x in f2.maker.env.toposort()]
        if val.size==0:
            assert f2(val)==f(val), ('shape', shape, 'pattern', pattern)
        else:
            assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
        

        #test with dimshuffle
        #we shuffle the 2 outer dims.
    for shape, pattern in [#((5,),[0]),
                           ((5,4),[0,1]),((5,4),[0]),
                           ((5,4,3),[0]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[0,1,2]),
                           ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3])]:
        a = tensor.TensorType('float32',(False,)*len(shape))()
        dim_pattern = range(len(shape))
        dim_pattern[0]=1
        dim_pattern[1]=0
        a = a.dimshuffle(dim_pattern)
        b = T.Sum(pattern)(a)
        val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
#        val = numpy.ones(shape)
#        val = numpy.arange(numpy.prod(shape)).reshape(shape)
        val = theano._asarray(val,dtype='float32')
        f = theano.function([a],b, mode=mode_with_gpu)
        f2 = theano.function([a],b, mode=mode_without_gpu)
        assert tcn.GpuSum in [x.op.__class__ for x in f.maker.env.toposort()]
        assert T.Sum in [x.op.__class__ for x in f2.maker.env.toposort()]
        assert _allclose(f2(val),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
        

        #test with broadcast
    for shape, pattern in [((5,),[0]),
                           ((5,4),[0,1]),((5,4),[0]),
                           ((5,4,3),[0]),((5,4,3),[0,1]),((5,4,3),[2]),((5,4,3),[0,1,2]),
                           ((5,4,3,2),[0,1,2,3]), ((5,4,3,2),[0,2,3])]:
        shape = numpy.asarray(shape)*2
        a = tensor.TensorType('float32',(False,)*len(shape))()
        a2 = tcn.CudaNdarrayType((False,)*len(shape))()
        b = T.Sum(pattern)(a)
        b2 = T.Sum(pattern)(a2)
        val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
#        val = numpy.ones(shape)
#        val = numpy.arange(numpy.prod(shape)).reshape(shape)
        val = theano._asarray(val,dtype='float32')
        val2 = cuda.CudaNdarray(val)
        if len(shape)==1:
            val = val[::2]
            val2 = val2[::2]
        elif len(shape)==2:
            val = val[::2,::2]
            val2 = val2[::2,::2]
        elif len(shape)==3:
            val = val[::2,::2,::2]
            val2 = val2[::2,::2,::2]
        elif len(shape)==4:
            val = val[::2,::2,::2,::2]
            val2 = val2[::2,::2,::2,::2]
        f = theano.function([a],b, mode=mode_without_gpu)
        f2 = theano.function([a2],b2, mode=mode_with_gpu)
        assert tcn.GpuSum in [x.op.__class__ for x in f2.maker.env.toposort()]
        assert T.Sum in [x.op.__class__ for x in f.maker.env.toposort()]
        assert _allclose(f2(val2),f(val)), ('shape', shape, 'pattern', pattern, sum([shape[i] for i in pattern]))
        
def test_flatten():
    x = cuda.fmatrix('x')
    f = theano.function([x], x.flatten())
    assert len(f( [[0.,0.],[0.,0.]] ).shape)==1

def test_reshape():

    a = tcn.CudaNdarrayType((False,))()
    b = tcn.CudaNdarrayType((False,False))()
    c = T.reshape(a, [2,3])

    #basic
    f = theano.function([a], c, mode=mode_without_gpu)
    fv = f(cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32')))
    assert numpy.all(fv == numpy.asarray([[0,1,2], [3,4,5]]))

    #test that it works without inplace operations
    a_val = cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32'))
    a_val_copy = cuda_ndarray.CudaNdarray(theano._asarray([0,1,2,3,4,5],dtype='float32'))
    b_val = cuda_ndarray.CudaNdarray(theano._asarray([[0,1,2],[3,4,5]],dtype='float32'))

    f_sub = theano.function([a,b], c-b, mode=mode_without_gpu)
    assert numpy.all(f_sub(a_val, b_val) == 0.0)
    assert numpy.all(numpy.asarray(a_val) == numpy.asarray(a_val_copy))

    #test that it works with inplace operations
    a_val = theano._asarray([0,1,2,3,4,5], dtype='float32')
    a_val_copy = theano._asarray([0,1,2,3,4,5], dtype='float32')
    b_val = theano._asarray([[0,1,2],[3,4,5]], dtype='float32')

    f_sub = theano.function([a,b], c-b, mode=mode_without_gpu)
    assert numpy.all(f_sub(a_val, b_val) == 0.0)
    assert numpy.all(numpy.asarray(a_val) == numpy.asarray(a_val_copy))

    # verify gradient
    def just_vals(v):
        return T.Reshape(2)(v, theano._asarray([2,3], dtype='int32'))
    utt.verify_grad(just_vals, [a_val])

def test_elemwise_empty():
    #test with 0 element
    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(0,0), dtype='float32'), 'a')

    b = tensor.fmatrix()

    f = pfunc([b], [], updates=[(a, a+b)], mode=mode_with_gpu)
    f2 = pfunc([b], [], updates=[(a, a+b)], mode=mode_without_gpu)

    a0 = a.value * 1.0
    f(numpy.ones((0,0), dtype='float32'))

    assert numpy.all(a0 + 1.0 == a.value)

def test_elemwise0():

    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(4,4), dtype='float32'), 'a')

    b = tensor.fmatrix()

    f = pfunc([b], [], updates=[(a, a+b)], mode=mode_with_gpu)

    #check that we work inplace.
    assert f.maker.env.toposort()[1].op.destroy_map.items()==[(0,[0])]

    a0 = a.value * 1.0
    print 'BEFORE ADD', a.value
    for i, node in enumerate(f.maker.env.toposort()):
        print i, node
    f(numpy.ones((4,4), dtype='float32'))
    print 'AFTER ADD', a.value

    assert numpy.all(a0 + 1.0 == a.value)

def test_elemwise_bad_broadcast():
    x = cuda.fmatrix('x')
    y = cuda.fmatrix('y')

    f = theano.function([x, y], x * y, mode=mode_with_gpu)
    print f.maker.env.toposort()
    assert len(f.maker.env.toposort())==2
    assert isinstance(f.maker.env.toposort()[0].op, cuda.GpuElemwise)
    assert f.maker.env.toposort()[1].op==cuda.host_from_gpu

    try:
        f(rand_cuda_ndarray((10, 3)), rand_cuda_ndarray((10, 1)))
    except ValueError:
        pass
    else:
        raise Exception("Theano should have raised an error")

def test_elemwise1():
    """ Several kinds of elemwise expressions with no broadcasting, non power-of-two shape """

    shape = (3,4)
    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32')+0.5, 'a')
    b = tensor.fmatrix()

    #let debugmode catch any mistakes
    print >> sys.stdout, "STARTING FUNCTION 1"
    f = pfunc([b], [], updates=[(a, b**a)], mode=mode_with_gpu)
    for i, node in enumerate(f.maker.env.toposort()):
        print i, node
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32')+0.3)

    print >> sys.stdout, "STARTING FUNCTION 2"
    #let debugmode catch any mistakes
    f = pfunc([b], [], updates=[(a, tensor.exp(b**a))], mode=mode_with_gpu)
    for i, node in enumerate(f.maker.env.toposort()):
        print i, node
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32')+0.3)

    print >> sys.stdout, "STARTING FUNCTION 3"
    #let debugmode catch any mistakes
    f = pfunc([b], [], updates=[(a, a+b * tensor.exp(b**a))], mode=mode_with_gpu)
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32')+0.3)

def test_elemwise2():
    """ Several kinds of elemwise expressions with dimension permutations """
    rng = numpy.random.RandomState(int(time.time()))
    print 'random?', rng.rand(3)
    shape = (3,5)
    for pattern in [(0,1), (1,0)]:
        a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),dtype='float32'), name=None)
        b = tensor.Tensor(dtype='float32', broadcastable=[0]*len(shape))()
        f = pfunc([b], [], updates=[(a, (a+b).dimshuffle(pattern))], mode=mode_with_gpu)
        has_elemwise = False
        for i, node in enumerate(f.maker.env.toposort()):
            print >> sys.stdout, i, node
            has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
        assert not has_elemwise
        #let debugmode catch errors
        print >> sys.stdout, 'pattern', pattern
        f(theano._asarray(rng.rand(*shape),dtype='float32')*.3)
    
    shape = (3,4,5,6)
    a = tcn.shared_constructor(theano._asarray(rng.rand(*shape),dtype='float32'), 'a')
    b = tensor.Tensor(dtype='float32', broadcastable=[0]*len(shape))()
    f = pfunc([b], [], updates=[(a, (a+b).dimshuffle([2,0,3,1]) *
        tensor.exp(b**a).dimshuffle([2,0,3,1]))], mode=mode_with_gpu)
    has_elemwise = False
    for i, node in enumerate(f.maker.env.toposort()):
        print i, node
        has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
    assert not has_elemwise
    #let debugmode catch errors
    f(theano._asarray(rng.rand(*shape),dtype='float32'))

def test_elemwise3():
    """ Several kinds of elemwise expressions with dimension permutations and broadcasting"""
    
    shape = (3,4,5,6)
    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32'), 'a')
    b = tensor.fvector()
    print b.type
    print tensor.constant(1).type
    print (1 + b).type
    print (1 + b**a).type
    print tensor.exp((1 + b**a)).type
    f = pfunc([b], [], updates=[(a, (a+b).dimshuffle([2,0,3,1]) * tensor.exp(1 +
        b**a).dimshuffle([2,0,3,1]))], mode=mode_with_gpu)
    has_elemwise = False
    for i, node in enumerate(f.maker.env.toposort()):
        print >> sys.stdout, i, node
        has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
    assert not has_elemwise
    #let debugmode catch errors
    f(theano._asarray(numpy.random.rand(6), dtype='float32'))

def test_elemwise4():
    """ Test that two vectors can be broadcast to form an outer product (by performing rank-1 matrix update"""
    
    shape = (3,4)
    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape), dtype='float32'), 'a')
    b = tensor.fvector()
    c = tensor.fvector()
    f = pfunc([b,c], [], updates=[(a, (a+b.dimshuffle('x', 0)*c.dimshuffle(0, 'x')))], mode=mode_with_gpu)
    has_elemwise = False
    for i, node in enumerate(f.maker.env.toposort()):
        print >> sys.stdout, i, node
        has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
    assert not has_elemwise
    #let debugmode catch errors
    f(theano._asarray(numpy.random.rand(4), dtype='float32'), theano._asarray(numpy.random.rand(3), dtype='float32'))


def speed_elemwise_collapse():
    """ used to time if the collapse of ccontiguous dims are usefull """
    
    shape = (30,40,50,600)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2[:,::2,:,:]
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = a3+b * tensor.exp(1 + b**a3)
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    v = v[:,::2,:,:]
    v=cuda_ndarray.CudaNdarray(v)
    for id,n in enumerate(f.maker.env.toposort()):
        print id, n
    t1=time.time()
    for i in range(100):
       #let debugmode catch errors
       f(v)
    t2=time.time()

def speed_elemwise_collapse2():
    """ used to test the speed up of the generalised collapse of ccontiguous dims"""
    
    shape = (30,40,50,600)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2[:,:,:,::2]
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = a3+b * tensor.exp(1 + b**a3)
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    v = v[:,:,:,::2]
    v=cuda_ndarray.CudaNdarray(v)
    for id,n in enumerate(f.maker.env.toposort()):
        print id, n
    t1=time.time()
    for i in range(100):
       #let debugmode catch errors
       f(v)
    t2=time.time()

def test_elemwise_collapse():
    """ Test when all inputs have one(and the same) broadcastable dimension """
    
    shape = (4,5,60)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle(0,'x',1,2)
    b = tcn.CudaNdarrayType((False, True, False, False))()
    c = a3+b
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(shape[0],1,*shape[1:]),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(shape[0],1,*shape[1:])+v)
    print "Expected collapse of all dimensions"

def test_elemwise_collapse2():
    """ Test when only one inputs have one broadcastable dimension """
    
    shape = (4,5,60)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle(0,'x',1,2)
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = a3+b
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(shape[0],5,*shape[1:]),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(shape[0],1,*shape[1:])+v)
    print "Expected collapse to 3 dimensions"

def test_elemwise_collapse3():
    """ Test when only one inputs have two broadcastable dimension at each ends """
    
    shape = (4,5)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle('x',0,1,'x')
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = (a3+b)
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(5,shape[0],shape[1],4),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(1,shape[0],shape[1],1)+v)
    print "Expected collapse to 3 dimensions"

def test_elemwise_collapse4():
    """ Test when only one inputs have two broadcastable dimension at each ends and we add a scalar"""
    
    shape = (4,5)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle('x',0,1,'x')
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = (a3+b+2)
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(5,shape[0],shape[1],4),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(1,shape[0],shape[1],1)+v+2)
    print "Expected collapse to 3 dimensions"

def test_elemwise_collapse5():
    """ Test when only one inputs have two broadcastable dimension at the beginning and we add a scalar"""
    
    shape = (4,5)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle('x','x',0,1)
    b = tcn.CudaNdarrayType((False, False, False, False))()
    c = (a3+b+2)
    f = pfunc([b], [c])


    v = theano._asarray(numpy.random.rand(5,4,shape[0],shape[1]),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(1,1,shape[0],shape[1])+v+2)
    print "Expected collapse to 2 dimensions"

def test_elemwise_collapse6():
    """ Test when all inputs have two broadcastable dimension at the beginning"""
    
    shape = (4,5)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a, 'a')
    a3 = a2.dimshuffle('x','x',0,1)
    b = tcn.CudaNdarrayType((True, True, False, False))()
    f = pfunc([b], [a3+b])

    v = theano._asarray(numpy.random.rand(1,1,shape[0],shape[1]),dtype='float32')
    v=cuda_ndarray.CudaNdarray(v)
    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f(v)[0]
    assert numpy.allclose(out,a.reshape(1,1,shape[0],shape[1])+v)
    print "Expected collapse to c contiguous"


def test_elemwise_collapse7(atol=1e-6):
    """ Test when one input have one broadcastable dimension and the other is a scalar"""
    
    shape = (5,4,1)
    a = cuda_ndarray.CudaNdarray(theano._asarray(numpy.random.rand(*shape),dtype='float32'))
    a = theano._asarray(numpy.random.rand(*shape),dtype='float32')
    a2 = tcn.shared_constructor(a.copy(), 'a')
    a3 = a2.dimshuffle(0, 'x', 1, 2)
    f = pfunc([], [a3+2])

    if False:
        for id,n in enumerate(f.maker.env.toposort()):
            print id, n
    #let debugmode catch errors
    out=f()[0]
    ans=(a+2).reshape(shape[0],1,shape[1],shape[2])
    assert numpy.allclose(out,ans, atol=atol)
    print "Expected collapse to c contiguous"


def test_hostfromgpu_shape_i():
    """
    Test that the shape is lifted over hostfromgpu
    """
    pass

    m = mode_with_gpu.including('local_dot_to_dot22','local_dot22_to_dot22scalar','specialize')
    a=T.fmatrix('a')
    ca=theano.sandbox.cuda.var.CudaNdarrayType((False,False))()

    av=numpy.asarray(numpy.random.rand(5,4),dtype='float32')
    cv=cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5,4),dtype='float32'))

    f = theano.function([a],cuda.basic_ops.gpu_from_host(a), mode=m)
    assert cuda.basic_ops.gpu_from_host in [x.op for x in f.maker.env.toposort()]
    f = theano.function([a],cuda.basic_ops.gpu_from_host(a).shape, mode=m)
    topo = f.maker.env.toposort()
    assert isinstance(topo[0].op,T.opt.Shape_i)
    assert isinstance(topo[1].op,T.opt.Shape_i)
    assert isinstance(topo[2].op,T.opt.MakeVector)
    assert tuple(f(av))==(5,4)



    f = theano.function([ca],cuda.basic_ops.host_from_gpu(ca), mode=m)
    assert cuda.basic_ops.host_from_gpu in [x.op for x in f.maker.env.toposort()]
    f = theano.function([ca],cuda.basic_ops.host_from_gpu(ca).shape, mode=m)
    topo = f.maker.env.toposort()
    assert isinstance(topo[0].op,T.opt.Shape_i)
    assert isinstance(topo[1].op,T.opt.Shape_i)
    assert isinstance(topo[2].op,T.opt.MakeVector)
    assert tuple(f(cv))==(5,4)

# -----------------------------------------------------------------------

import theano.sandbox.cuda as cuda_ndarray
from theano.sandbox.cuda.basic_ops import gpu_join, GpuDimShuffle

def test_gpujoin_twomatrices_joincolumns():
    _a = numpy.asarray([[1,2],[3,4]],dtype='float32')
    _b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
    a = tcn.shared_constructor(_a)
    b = tcn.shared_constructor(_b)

    c = gpu_join(1,a,b)

    f = theano.function([], c)

    assert numpy.all(f() == numpy.concatenate([_a,_b], axis=1))

def test_gpujoin_twomatrices_badshapes():
    _a = numpy.asarray([[1,2],[3,4]],dtype='float32')
    _b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
    a = tcn.shared_constructor(_a)
    b = tcn.shared_constructor(_b)

    # try to join on dimension 0 where they don't agree (2!=3)
    c = gpu_join(0,a,b)

    f = theano.function([], c)

    try:
        f()
        assert False
    except ValueError:
        assert True




def test_gpujoin_preserves_broadcasting():
    _a = numpy.asarray([[1,2],[3,4]],dtype='float32')
    _b = numpy.asarray([[5,6,7],[8,9,10]],dtype='float32')
    a = tcn.shared_constructor(_a)
    b = tcn.shared_constructor(_b)

    # [0,0] : the two original dims were non-broadcastable
    # [1,x,0]: new order and broadcastability
    gpu_dimshuffle = GpuDimShuffle([0,0], [1,'x',0])

    a_shuffled = gpu_dimshuffle(a)
    b_shuffled = gpu_dimshuffle(b)

    c = gpu_join(0,a_shuffled,b_shuffled)

    assert c.type.broadcastable == (False,True,False)

    f = theano.function([], c, mode=mode_with_gpu)
    
    res = f()

    a_reshaped = numpy.asarray([[[1,3]],[[2,4]]], dtype='float32')
    b_reshaped = numpy.asarray([[[5,8]],[[6,9]],[[7,10]]], dtype='float32')

    concat = numpy.concatenate([a_reshaped,b_reshaped], axis=0)

    assert numpy.all(res == concat)


def test_gpujoin_assert_cndas():
    # this will end up being an ndarray, as it's float64
    _a = numpy.asarray([[1,2],[3,4]],dtype='float64')
    a = theano.shared(_a)

    try:
        c = gpu_join(1,a)
        # can't "assert False" here, as we want the assertion 
        # error from gpu_join
    except AssertionError:
        assert True
        return

    assert False
    
def test_gpujoin_no_rebroadcast():
    _a = numpy.asarray([[1,2],[3,4]],dtype='float32')
    a = tcn.shared_constructor(_a)
    f = theano.function([],T.join(1,a))
    l = f.maker.env.toposort()
    assert not any([isinstance(x.op,T.Rebroadcast) for x in l])


def test_gpualloc_input_on_gpu():
    a_val = numpy.asarray(numpy.random.rand(4,5),dtype='float32')
    a = tcn.shared_constructor(a_val)

    b = T.fscalar()
    f = theano.function([b], T.ones_like(a)+b, mode=mode_without_gpu)
    f_gpu = theano.function([b], T.ones_like(a)+b, mode=mode_with_gpu)

    assert sum([node.op == T.alloc for node in f.maker.env.toposort()])==1
    assert sum([node.op == B.gpu_alloc for node in f_gpu.maker.env.toposort()])==1

    assert numpy.allclose(numpy.ones(a.value.shape)+9,f_gpu(9))
    assert numpy.allclose(f(5),f_gpu(5))

def test_gpujoin_gpualloc():
    a = T.fmatrix('a')
    a_val = numpy.asarray(numpy.random.rand(4,5),dtype='float32')
    b = T.fmatrix('b')
    b_val = numpy.asarray(numpy.random.rand(3,5),dtype='float32')

    f = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b))+4, mode=mode_without_gpu)
    f_gpu = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b)), mode=mode_with_gpu)
    f_gpu2 = theano.function([a,b], T.join(0,T.zeros_like(a),T.ones_like(b))+4, mode=mode_with_gpu)
    
    assert sum([node.op == T.alloc for node in f.maker.env.toposort()])==2
    assert sum([node.op == T.join for node in f.maker.env.toposort()])==1
    assert sum([node.op == B.gpu_alloc for node in f_gpu.maker.env.toposort()])==2
    assert sum([node.op == B.gpu_join for node in f_gpu.maker.env.toposort()])==1
    assert sum([node.op == B.gpu_alloc for node in f_gpu2.maker.env.toposort()])==2
    assert sum([node.op == B.gpu_join for node in f_gpu2.maker.env.toposort()])==1
    assert numpy.allclose(f(a_val,b_val),f_gpu2(a_val,b_val))

def test_gpualloc_output_to_gpu():
    a_val = numpy.asarray(numpy.random.rand(4,5),dtype='float32')
    a = tcn.shared_constructor(a_val)

    b = T.fscalar()
    f = theano.function([b], T.ones_like(a)+b, mode=mode_without_gpu)
    f_gpu = theano.function([b], B.gpu_from_host(T.ones_like(a))+b, mode=mode_with_gpu)

    print f.maker.env.toposort()
    print f_gpu.maker.env.toposort()
    print f(2)
    print f_gpu(2)

    assert sum([node.op == T.alloc for node in f.maker.env.toposort()])==1
    assert sum([node.op == B.gpu_alloc for node in f_gpu.maker.env.toposort()])==1

    assert numpy.allclose(numpy.ones(a.value.shape)+9,f_gpu(9))
    assert numpy.allclose(f(5),f_gpu(5))

def test_inc_subtensor():
    shared = cuda.shared_constructor
    #shared = tensor.shared
    x,y = T.fmatrices('x','y')
    xval = numpy.asarray([[1,2,3], [4,5,6], [7,8,9]],
                      dtype='float32')
    yval = numpy.asarray([[10,10,10], [10,10,10], [10,10,10]],
                      dtype='float32')
    expr = T.inc_subtensor(x[:,1:3], y[:,1:3])
    f=theano.function([x,y], expr, mode=mode_with_gpu)
    print f.maker.env.toposort()
    assert sum([isinstance(node.op,cuda.GpuSubtensor) for node in f.maker.env.toposort() ])==1
    assert sum([isinstance(node.op,cuda.GpuIncSubtensor) and node.op.set_instead_of_inc==False for node in f.maker.env.toposort() ])==1
    assert numpy.allclose(f(xval,yval),[[1.,12.,13.], [4.,15.,16.], [7.,18.,19.]])


def test_set_subtensor():
    shared = cuda.shared_constructor
    #shared = tensor.shared
    x,y = T.fmatrices('x','y')
    xval = numpy.asarray([[1,2,3], [4,5,6], [7,8,9]],
                      dtype='float32')
    yval = numpy.asarray([[10,10,10], [10,10,10], [10,10,10]],
                      dtype='float32')
    expr = T.set_subtensor(x[:,1:3], y[:,1:3])
    f=theano.function([x,y], expr, mode=mode_with_gpu)
    print f.maker.env.toposort()
    assert sum([isinstance(node.op,cuda.GpuSubtensor) for node in f.maker.env.toposort() ])==1
    assert sum([isinstance(node.op,cuda.GpuIncSubtensor) and node.op.set_instead_of_inc==True for node in f.maker.env.toposort() ])==1
    print f(xval,yval)


def test_many_arg_elemwise():
    """this test checks whether the + and * elemwise ops can handle extremely large numbers of
    arguments on gpu
    i.e., it is a test of the optimization theano/sandbox/cuda/opt.py:local_gpu_huge_add_or_mul """
    rng = numpy.random.RandomState( [1,2,3])

    for num_args in [25]:
        for op_to_test in [ theano.tensor.add, theano.tensor.mul ]:
            for nb_dim in [2,3,4,5]:
                shapes = [rng.randint(1,5) for i in range(nb_dim)]
                args = [ numpy.cast['float32'](rng.randn(*shapes)) for arg in xrange(0,num_args) ]
                
                symb_args = [ theano.tensor.TensorType('float32', (False,)*nb_dim)() for arg in xrange(0,num_args) ]            

            
                outputs = []
                for mode in [ mode_with_gpu, mode_without_gpu ]:
                    #test the optijmization local_gpu_elemwise_0
                    f = theano.function( symb_args, op_to_test(*symb_args), mode = mode.excluding("local_gpu_elemwise_1") )
                    outputs.append( f( * args) )
                    #assert that the test was done on the gpu.
                    if mode is mode_with_gpu:
                        assert any([isinstance(node.op, cuda.GpuElemwise) for node in f.maker.env.nodes])
                        
                    #test the optijmization local_gpu_elemwise_1
                    f = theano.function( symb_args, 
                                         cuda.gpu_from_host(op_to_test(*symb_args)), 
                                         mode = mode.excluding("local_gpu_elemwise_0") )
                    out = f( * args)
                    #assert that the test was done on the gpu.
                    if mode is mode_with_gpu:
                        assert any([isinstance(node.op, cuda.GpuElemwise) for node in f.maker.env.nodes])
                    assert numpy.allclose(out, outputs[-1])
            
                results_gpu, results_cpu = outputs

                assert numpy.allclose(results_gpu, results_cpu)

def test_duplicate_arg_elemwise():
    A = theano.tensor.fmatrix()
    B = A + A

    f = theano.function([A],B, mode = mode_with_gpu)

    Aval = numpy.random.RandomState([1,2,3]).randn(5,5).astype('float32')
    Bval = Aval + Aval

    assert numpy.allclose(Bval,f(Aval))

def test_shared_float32():
    '''Test use of cuda.shared_constructor through theano.shared'''
    # Register cuda.shared_constructor in theano.shared
    theano.shared.constructors.append(cuda.shared_constructor)

    a = theano.shared(numpy.ones((2,3), dtype='float32'))
    assert isinstance(a.type, tcn.CudaNdarrayType)

    # Unregister
    del theano.shared.constructors[-1]


import theano.tensor.tests.test_basic
test_shared_options = theano.tensor.tests.test_basic.makeSharedTester(
    tcn.shared_constructor, 'float32', False, False, False,
    cuda_ndarray.CudaNdarray,
    lambda a: isinstance(a,cuda_ndarray.CudaNdarray),
    theano.tensor.exp, numpy.exp,
    add_matrix_ = True)

if __name__ == '__main__':
    test_many_arg_elemwise()
    test_gpujoin_twomatrices_joincolumns()
    test_gpujoin_assert_cndas()
    test_gpujoin_preserves_broadcasting()
    test_gpujoin_twomatrices_badshapes()



