def test_transfer_cuda_gpu():
    import theano.sandbox.cuda as cuda_ndarray
    if cuda_ndarray.cuda_available == False:
        raise SkipTest("Can't test interaction with cuda if cuda not present")
    g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
    c = cuda_ndarray.CudaNdarrayType((False, False))('c')

    av = theano._asarray(rng.rand(5, 4), dtype='float32')
    gv = gpuarray.array(av)
    cv = cuda_ndarray.CudaNdarray(av)
    gvs = gv[:,::-2]
    cvs = cv[:,::-2]

    f = theano.function([c], gpu_from_cuda(c))
    fv = f(cv)
    assert GpuArrayType.values_eq_approx(fv, gv)

    fvs = f(cvs)
    assert GpuArrayType.values_eq_approx(fvs, gvs)

    f = theano.function([g], cuda_from_gpu(g))
    fv = f(gv)
    assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fv, cv)

    fvs = f(gvs)
    assert cuda_ndarray.CudaNdarrayType.values_eq_approx(fvs, cvs)

def tensor_to_gpu(x):
    if isinstance(x.type, tensor.TensorType):
        y = GpuArrayType(broadcastable=x.type.broadcastable, dtype=x.type.dtype)()
        if x.name:
            y.name = x.name + "[Gpua]"
        return y
    else:
        return x

def test_values_eq_approx():
    a = rand_gpuarray(20, dtype='float32')
    g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
    assert GpuArrayType.values_eq_approx(a, a)
    b = a.copy()
    b[0] = numpy.asarray(b[0]) + 1.
    assert not GpuArrayType.values_eq_approx(a, b)
    b = a.copy()
    b[0] = -numpy.asarray(b[0])
    assert not GpuArrayType.values_eq_approx(a, b)

def test_deep_copy():
    a = rand_gpuarray(20, dtype='float32')
    g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')

    f = theano.function([g], g)

    assert isinstance(f.maker.fgraph.toposort()[0].op, DeepCopyOp)

    res = f(a)

    assert GpuArrayType.values_eq(res, a)

def test_transfer_cpu_gpu():
    a = T.fmatrix('a')
    g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')
    
    av = numpy.asarray(rng.rand(5, 4), dtype='float32')
    gv = gpuarray.array(av)
    
    f = theano.function([a], gpu_from_host(a))
    fv = f(av)
    assert GpuArrayType.values_eq(fv, gv)

    f = theano.function([g], host_from_gpu(g))
    fv = f(gv)
    assert numpy.all(fv == av)

    def values_eq_approx(a, b):
        """This fct is needed to don't have DebugMode raise useless
        error due to ronding error.

        This happen as We reduce on the two last dimensions, so this
        can raise the absolute error if the number of element we
        reduce on is significant.

        """
        assert a.ndim == 4
        atol = None
        if a.shape[-1] * a.shape[-2] > 100:
            # For float32 the default atol is 1e-5
            atol = 3e-5
        return GpuArrayType.values_eq_approx(a, b, atol=atol)

def test_transfer_strided():
    # This is just to ensure that it works in theano
    # compyte has a much more comprehensive suit of tests to ensure correctness
    a = T.fmatrix('a')
    g = GpuArrayType(dtype='float32', broadcastable=(False, False))('g')

    av = numpy.asarray(rng.rand(5, 8), dtype='float32')
    gv = gpuarray.array(av)

    av = av[:,::2]
    gv = gv[:,::2]

    f = theano.function([a], gpu_from_host(a))
    fv = f(av)
    assert GpuArrayType.values_eq(fv, gv)

    f = theano.function([g], host_from_gpu(g))
    fv = f(gv)
    assert numpy.all(fv == av)

def makeTester(name, op, expected, good=None, bad_build=None, checks=None,
               bad_runtime=None, mode=None, skip=False, eps=1e-10):
    if good is None:
        good = {}
    if bad_build is None:
        bad_build = {}
    if bad_runtime is None:
        bad_runtime = {}
    if checks is None:
        checks = {}

    _op = op
    _expected = expected
    _good = good
    _bad_build = bad_build
    _bad_runtime = bad_runtime
    _skip = skip
    _checks = checks

    class Checker(unittest.TestCase):
        op = staticmethod(_op)
        expected = staticmethod(_expected)
        good = _good
        bad_build = _bad_build
        bad_runtime = _bad_runtime
        skip = _skip
        checks = _checks

        def setUp(self):
            eval(self.__class__.__module__ + '.' + self.__class__.__name__)

        def test_good(self):
            if skip:
                raise SkipTest(skip)

            for testname, inputs in good.items():
                inputs = [copy(input) for input in inputs]
                inputrs = [fake_shared(input) for input in inputs]

                try:
                    node = safe_make_node(self.op, *inputrs)
                except Exception, exc:
                    err_msg = ("Test %s::%s: Error occured while making "
                               "a node with inputs %s") % (self.op, testname,
                                                           inputs)
                    exc.args += (err_msg,)
                    raise

                try:
                    f = inplace_func([], node.outputs, mode=mode,
                                     name='test_good')
                except Exception, exc:
                    err_msg = ("Test %s::%s: Error occured while trying to "
                               "make a Function") % (self.op, testname)
                    exc.args += (err_msg,)
                    raise

                if isinstance(self.expected, dict) and \
                        testname in self.expected:
                    expecteds = self.expected[testname]
                else:
                    expecteds = self.expected(*inputs)

                if not isinstance(expecteds, (list, tuple)):
                    expecteds = (expecteds,)

                try:
                    variables = f()
                except Exception, exc:
                    err_msg = ("Test %s::%s: Error occured while calling "
                               "the Function on the inputs %s") % (self.op,
                                                                   testname,
                                                                   inputs)
                    exc.args += (err_msg,)
                    raise

                for i, (variable, expected) in \
                        enumerate(izip(variables, expecteds)):
                    if variable.dtype != expected.dtype or \
                            variable.shape != expected.shape or \
                            not GpuArrayType.values_eq_approx(variable,
                                                             expected):
                        self.fail(("Test %s::%s: Output %s gave the wrong "
                                   "value. With inputs %s, expected %s "
                                   "(dtype %s), got %s (dtype %s).") % (
                                self.op, testname, i, inputs, expected,
                                expected.dtype, variable, variable.dtype))

                for description, check in self.checks.items():
                    if not check(inputs, variables):
                        self.fail(("Test %s::%s: Failed check: %s "
                                   "(inputs were %s, ouputs were %s)") %
                                  (self.op, testname, description,
                                   inputs, variables))