import numpy

import theano
import theano.misc.pycuda_init

if not theano.misc.pycuda_init.pycuda_available:
    from nose.plugins.skip import SkipTest
    raise SkipTest("Pycuda not installed. Skip test of theano op with pycuda code.")

import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
    from nose.plugins.skip import SkipTest
    raise SkipTest('Optional package cuda disabled')

import theano
import theano.tensor as T
from theano.misc.pycuda_example import PycudaElemwiseSourceModuleOp, PycudaElemwiseKernelOp

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 test_pycuda_elemwise_source_module():
    for shape in [(5,5), (10,49), (50,49),(500,501),(5000,5001)]:
        for op in [theano.scalar.basic.mul, theano.scalar.basic.add]:
            x=T.fmatrix('x')
            y=T.fmatrix('y')
            pycuda_op = PycudaElemwiseSourceModuleOp(op)
            elemwise_op = theano.tensor.Elemwise(op)
            f=theano.function([x,y], elemwise_op(x,y), mode=mode_with_gpu)
            f2 = theano.function([x,y], theano.sandbox.cuda.host_from_gpu(pycuda_op(x,y)))
            f3 = theano.function([x,y], elemwise_op(x,y),
                                 mode=mode_with_gpu.including("local_pycuda_gpu_elemwise"))

            assert any([ isinstance(node.op, theano.sandbox.cuda.GpuElemwise) for node in f.maker.env.toposort()])
            assert any([ isinstance(node.op, PycudaElemwiseSourceModuleOp) for node in f2.maker.env.toposort()])
            assert any([ isinstance(node.op, PycudaElemwiseSourceModuleOp) for node in f3.maker.env.toposort()])

            val1 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
            val2 = numpy.asarray(numpy.random.rand(*shape), dtype='float32')
            assert (f(val1,val2) == f2(val1,val2)).all()
            assert (f(val1,val2) == f3(val1,val2)).all()
            #print f(val1,val2)
            #print f2(val1,val2)

def test_pycuda_elemwise_kernel():
    x=T.fmatrix('x')
    y=T.fmatrix('y')
    f=theano.function([x,y],x+y, mode=mode_with_gpu)
    print f.maker.env.toposort()
    f2 = theano.function([x,y],x+y, mode=mode_with_gpu.including("local_pycuda_gpu_elemwise_kernel"))
    print f2.maker.env.toposort()

    assert any([ isinstance(node.op, theano.sandbox.cuda.GpuElemwise) for node in f.maker.env.toposort()])
    assert any([ isinstance(node.op, PycudaElemwiseKernelOp) for node in f2.maker.env.toposort()])

    val1 = numpy.asarray(numpy.random.rand(5,5), dtype='float32')
    val2 = numpy.asarray(numpy.random.rand(5,5), dtype='float32')
    #val1 = numpy.ones((5,5))
    #val2 = numpy.arange(25).reshape(5,5)
    assert (f(val1,val2) == f2(val1,val2)).all()
    print f(val1,val2)
    print f2(val1,val2)


    x3=T.ftensor3('x')
    y3=T.ftensor3('y')
    z3=T.ftensor3('y')

    f4 = theano.function([x3,y3,z3],x3*y3+z3, mode=mode_with_gpu.including("local_pycuda_gpu_elemwise_kernel"))
    print f4.maker.env.toposort()
    assert any([ isinstance(node.op, PycudaElemwiseKernelOp) for node in f4.maker.env.toposort()])

    val1 = numpy.random.rand(2,2,2)
    print val1
    print f4(val1,val1,val1)
    assert numpy.allclose(f4(val1,val1,val1),val1*val1+val1)
