"""
This file test tensor op that should also operate on CudaNdaray.
"""
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest

import numpy

import theano
from theano import tensor
import theano.tensor as T

# Skip test if cuda_ndarray is not available.
import theano.sandbox.cuda as cuda
from theano.tensor.nnet.tests import test_conv3d2d
if cuda.cuda_available is False:
    raise SkipTest('Optional package cuda disabled')


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_shape_i():
    x = cuda.ftensor3()
    v = cuda.CudaNdarray(numpy.zeros((3, 4, 5), dtype='float32'))
    f = theano.function([x], x.shape[1])
    topo = f.maker.fgraph.toposort()
    assert f(v) == 4
    if theano.config.mode != 'FAST_COMPILE':
        assert len(topo) == 1
        assert isinstance(topo[0].op, T.opt.Shape_i)


def test_shape():
    x = cuda.ftensor3()
    v = cuda.CudaNdarray(numpy.zeros((3, 4, 5), dtype='float32'))
    f = theano.function([x], x.shape)
    topo = f.maker.fgraph.toposort()
    assert numpy.all(f(v) == (3, 4, 5))
    if theano.config.mode != 'FAST_COMPILE':
        assert len(topo) == 4
        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.Shape_i)
        assert isinstance(topo[3].op, T.opt.MakeVector)


def test_softmax_optimizations():
    from theano.tensor.nnet.nnet import softmax, crossentropy_categorical_1hot
    x = tensor.fmatrix('x')
    one_of_n = tensor.lvector('one_of_n')
    op = crossentropy_categorical_1hot

    op(x, one_of_n)

    fgraph = theano.gof.FunctionGraph(
        [x, one_of_n],
        [op(softmax(x), one_of_n)])
    assert fgraph.outputs[0].owner.op == op

    mode_with_gpu.optimizer.optimize(fgraph)

    assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
    assert fgraph.outputs[0].owner.inputs[0].owner.op == cuda.host_from_gpu
    assert fgraph.outputs[0].owner.inputs[0].owner.inputs[0].owner.op == cuda.nnet.gpu_crossentropy_softmax_argmax_1hot_with_bias


def test_may_share_memory_cuda():
    from theano.misc.may_share_memory import may_share_memory
    a = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
    b = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))
    na = numpy.zeros((3, 4))
    nb = numpy.zeros((3, 4))
    va = a.view()
    vb = b.view()
    ra = a.reshape((4, 3))
    rb = b.reshape((4, 3))

    # can't test the transpose as ta._strides = is not implemented
    # manual transpose of a
    # ta = a.reshape((4,3))
    # ta._strides = (ta._strides[1],ta._strides[0])#not implemented
    # elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
    # ta.gpudata += ta.size*elem_size

    for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
                        (a, na, False), (b, nb, False),
                        (na, b, False), (nb, a, False),
                        (a, va, True), (b, vb, True),
                        (va, b, False), (a, vb, False),
                        (a, ra, True), (b, rb, True),
                        (ra, b, False), (a, rb, False), ]:
        assert may_share_memory(a_, b_) == rep
        assert may_share_memory(b_, a_) == rep

    # test that it raise error when needed.
    for a_, b_, rep in [(a, (0,), False), (a, 1, False), (a, None, False)]:
        assert may_share_memory(a_, b_, False) == rep
        assert may_share_memory(b_, a_, False) == rep
        try:
            may_share_memory(a_, b_)
            raise Exception("An error was expected")
        except TypeError:
            pass
        try:
            may_share_memory(b_, a_)
            raise Exception("An error was expected")
        except TypeError:
            pass


def test_deepcopy():
    a = cuda.fmatrix()
    a_v = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))

    # We force the c code to check that we generate c code
    mode = theano.Mode("c", mode_with_gpu.optimizer)
    f = theano.function([a], a, mode=mode)
    theano.printing.debugprint(f)
    out = f(a_v)
    assert out is not a_v
    assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out))

    # We force the python linker as the default code should work for this op
    mode = theano.Mode("py", mode_with_gpu.optimizer)
    f = theano.function([a], a, mode=mode)
    theano.printing.debugprint(f)
    out = f(a_v)
    assert out is not a_v
    assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out))


def test_get_diagonal_subtensor_view():
    test_conv3d2d.test_get_diagonal_subtensor_view(wrap=cuda.CudaNdarray)


def test_conv3d():
    test_conv3d2d.test_conv3d(mode=mode_with_gpu,
                              shared=cuda.shared_constructor)
