from itertools import izip
import logging
import sys
import unittest

from nose.plugins.skip import SkipTest
import numpy

import theano
from theano.compat import exc_message
from theano.compat.six import StringIO
from theano.gof.python25 import any
from theano.compile import DeepCopyOp
from theano import config
from theano import gof
import theano.scalar as scal
import theano.tensor as tensor
from theano.tests import unittest_tools as utt
from theano.tensor.subtensor import (inc_subtensor, set_subtensor,
                                     Subtensor, IncSubtensor,
                                     AdvancedSubtensor1, AdvancedSubtensor,
                                     advanced_subtensor1, inplace_increment,
                                     AdvancedIncSubtensor1,
                                     AdvancedIncSubtensor,
                                     get_canonical_form_slice)
from theano.tensor import (as_tensor_variable, _shared,
                           NotScalarConstantError,
                           fscalar, iscalar, dscalar, cscalar,
                           vector, dvector, fvector, lvector,
                           fmatrix, dmatrix, lmatrix, matrix,
                           ctensor3, dtensor4)
from theano.tensor.tests.test_basic import rand, randint_ranged, inplace_func


class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
    """
    This is build in a way that allow to reuse it to test the
    equivalent gpu op.
    """
    def __init__(self, name, shared=tensor._shared,
                 sub=tensor.Subtensor,
                 inc_sub=tensor.IncSubtensor,
                 adv_sub1=tensor.AdvancedSubtensor1,
                 adv_incsub1=tensor.AdvancedIncSubtensor1,
                 mode=None,
                 dtype=theano.config.floatX,
                 ignore_topo=DeepCopyOp):
        self.shared = shared
        self.sub = sub
        self.inc_sub = inc_sub
        self.adv_sub1 = adv_sub1
        self.adv_incsub1 = adv_incsub1
        if mode is None:
            mode = theano.compile.mode.get_default_mode()
        self.mode = mode
        self.dtype = dtype
        self.ignore_topo = ignore_topo
        self.fast_compile = theano.config.mode == 'FAST_COMPILE'
        self.ops = (sub, inc_sub, adv_sub1, adv_incsub1)
        return super(T_subtensor, self).__init__(name)

    def function(self, inputs, outputs, accept_inplace=False,
                 op=None, mode=None, N=1, N_fast=None):
        """ wrapper around theano.function that also check the output

        :param N: the number of op expected in the toposort
                  if tuple of length 2, (expected if fast_compile,
                                         if not fast_compile)
        """
        if self.fast_compile and N_fast is not None:
            N = N_fast
        if mode is None:
            mode = self.mode
        if op is None:
            op = self.sub

        f = theano.function(inputs, outputs, mode=mode,
                            accept_inplace=accept_inplace)
        self.assertFunctionContainsClassN(f, op, N)
        return f

    def setUp(self):
        Subtensor.debug = False
        utt.seed_rng()

    def eval_output_and_check(self, t, list=False):
        f = inplace_func([], t, mode=self.mode)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        assert len(topo_) == 1
        if not list:
            assert isinstance(topo_[0].op, self.sub)
        else:
            assert isinstance(topo_[0].op, self.adv_sub1)
        tval = f()
        return tval

    def test0_err_invalid(self):
        #it is impossible to retrieve a view of a 0-d tensor
        n = self.shared(numpy.ones((), dtype=self.dtype))
        try:
            t = n[0]
        except ValueError, e:
            self.assertTrue(hasattr(e, 'subtensor_invalid'))
            return
        self.fail()

    def test1_err_bounds(self):
        n = self.shared(numpy.ones(3, dtype=self.dtype))
        ctv_backup = config.compute_test_value
        config.compute_test_value = 'off'
        try:
            t = n[7]
        finally:
            config.compute_test_value = ctv_backup
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        # Silence expected error messages
        _logger = logging.getLogger('theano.gof.opt')
        oldlevel = _logger.level
        _logger.setLevel(logging.CRITICAL)
        try:
            try:
                self.eval_output_and_check(t)
                assert 0
            except Exception, e:
                if 'out of bounds' not in exc_message(e):
                    raise
        finally:
            _logger.setLevel(oldlevel)

    def test1_err_subslice(self):
        n = self.shared(numpy.ones(3, dtype=self.dtype))
        try:
            t = n[slice(0, slice(1, 2, None), None)]
        except Exception, e:
            ### Relax constraint on the type of Exception,
            ### since this might be handled by AvancedSubtensor
            #if e[0] != Subtensor.e_indextype:
            #    raise
            return
        self.fail()

    def test1_ok_range_finite(self):
        n = self.shared(numpy.arange(3, dtype=self.dtype))
        t = n[0:2]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue((tval == [0, 1]).all())

    def test2_ok_range_finite(self):
        n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((3, 4)))
        # Also check negative index
        for idx in [(slice(0, 2), 3), ((slice(0, 2), -1)), (slice(0, 2), -4)]:
            t = n[idx]  # l]#0:2,3]
            self.assertTrue(isinstance(t.owner.op, Subtensor))
            tval = self.eval_output_and_check(t)
            self.assertTrue(tval.shape == (2,))
            self.assertTrue(numpy.allclose(tval, n.get_value()[idx]))

    def test1_0_dims(self):
        n = self.shared(numpy.ones((), dtype=self.dtype))
        t = theano.tensor.Subtensor([])(n)
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        mode = self.mode
        self.mode = mode.excluding("local_useless_subtensor")
        try:
            self.eval_output_and_check(t)
        finally:
            self.mode = mode

    def test1_err_invalid(self):
        n = self.shared(numpy.ones(1, dtype=self.dtype))
        try:
            t = n[0, 0]
        except ValueError, e:
            self.assertTrue(hasattr(e, 'subtensor_invalid'))
            return
        self.fail()

    def test1_ok_elem(self):
        n = self.shared(numpy.ones(1, dtype=self.dtype) * 5)
        t = n[0]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == ())
        self.assertTrue(tval == 5.0)

    def test1_ok_range_infinite(self):
        #Subtensor.debug = True
        n = self.shared(numpy.arange(3, dtype=self.dtype))
        t = n[1:]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue((tval == [1.0, 2.0]).all())

    def test1_ok_strided(self):
        n = self.shared(numpy.arange(5, dtype=self.dtype))
        t = n[1::2]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue((tval == [1.0, 3.0]).all())

        t = n[0:-1:2]  # 0 to 1 from the end stepping by 2
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue((tval == [0.0, 2.0]).all())

    def test2_err_bounds0(self):
        n = self.shared(numpy.ones((2, 3), dtype=self.dtype) * 5)
        ctv_backup = config.compute_test_value
        config.compute_test_value = 'off'
        try:
            for idx in [(0, 4), (0, -4)]:
                t = n[idx]
                self.assertTrue(isinstance(t.owner.op, Subtensor))
                # Silence expected warnings
                _logger = logging.getLogger('theano.gof.opt')
                oldlevel = _logger.level
                _logger.setLevel(logging.CRITICAL)
                try:
                    self.assertRaises(IndexError,
                                      self.eval_output_and_check, [t])
                finally:
                    _logger.setLevel(oldlevel)
        finally:
            config.compute_test_value = ctv_backup

    def test2_err_bounds1(self):
        n = self.shared((numpy.ones((2, 3), dtype=self.dtype) * 5))
        t = n[4:5, 3]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        old_stderr = sys.stderr
        sys.stderr = StringIO()
        try:
            self.assertRaises(IndexError,
                              self.eval_output_and_check, [t])
        finally:
            sys.stderr = old_stderr

    def test2_ok_elem(self):
        n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
        t = n[0, 2]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == ())
        self.assertTrue(numpy.all(tval == 2))

    def test2_ok_row(self):
        n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
        t = n[1]
        self.assertFalse(any(n.type.broadcastable))
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (3,))
        self.assertTrue(numpy.all(tval == [3, 4, 5]))

    def test2_ok_col(self):
        n = self.shared(numpy.arange(6, dtype=self.dtype).reshape((2, 3)))
        t = n[:, 0]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        self.assertFalse(any(n.type.broadcastable))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue(numpy.all(tval == [0, 3]))

    def test2_ok_rows_finite(self):
        n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
        t = n[1:3, 0]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue(numpy.all(tval == [3, 6]))

    def test2_ok_cols_infinite(self):
        n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
        t = n[1, 2:]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (1,))
        self.assertTrue(numpy.all(tval == 5))

    def test2_ok_strided(self):
        n = self.shared(numpy.arange(20, dtype=self.dtype).reshape((4, 5)))
        t = n[1:4:2, 1:5:2]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2, 2))
        self.assertTrue(numpy.all(tval == [[6, 8], [16, 18]]))

    def test3_ok_mat(self):
        n = self.shared(numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
        t = n[0, 0, 0]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == ())
        self.assertTrue(numpy.all(tval == 0))

    def test_long(self):
        n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
        t = n[1L:4L:2L, 1L]
        self.assertTrue(isinstance(t.owner.op, Subtensor))
        tval = self.eval_output_and_check(t)
        self.assertTrue(tval.shape == (2,))
        self.assertTrue(numpy.all(tval == [4, 10]))

    def test_long_too_big(self):
        # Currently, we cast Python longs to int64 when used for indexing.
        # This test checks that using a long that does not fit raises an error.
        n = self.shared(numpy.arange(12, dtype=self.dtype).reshape((4, 3)))
        self.assertRaises(Exception, lambda: n[:(2L ** 63)])

    def test_newaxis(self):
        """
        newaxis support comes from logic in the __getitem__ of TensorType
        Variables, which currently inserts dimshuffle to get the right number
        of dimensions, and adjusts the slice tuple accordingly.

        So testing is done via square-bracket notation rather than direct
        interaction with the Subtensor Op (which has no support of its own for
        newaxis).
        """
        newaxis = numpy.newaxis

        n = self.shared(numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4)))
        assert n.ndim == 3

        n4 = n[newaxis, :, :, :]
        assert n4.broadcastable == (True, False, False, False), n4

        n4 = n[:, newaxis, :, :]
        assert n4.broadcastable == (False, True, False, False), n4

        n4 = n[:, :, newaxis, :]
        assert n4.broadcastable == (False, False, True, False), n4

        n4 = n[:, :, :, newaxis]
        assert n4.broadcastable == (False, False, False, True), n4

        n3 = n.flatten()[newaxis, :, newaxis]
        assert n3.broadcastable == (True, False, True), n3

        s = cscalar()
        s1 = s[newaxis]
        assert s1.broadcastable == (True,), s1

        vs1, vn3, vn4 = theano.function([s], [s1, n3, n4])(-2.0)

        assert numpy.all(vs1 == [-2.0])
        assert numpy.all(vn3
                == numpy.arange(24)[newaxis, :, newaxis])
        assert numpy.all(vn4
                == numpy.arange(24).reshape((2, 3, 4))[:, :, :, newaxis])

    def test_grad_1d(self):
        subi = 0
        data = numpy.asarray(rand(2, 3), dtype=self.dtype)
        n = self.shared(data)
        z = scal.constant(subi)
        t = n[z:, z]
        gn = theano.tensor.grad(theano.tensor.sum(theano.tensor.exp(t)), n)

        f = inplace_func([], gn, mode=self.mode)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        if not self.fast_compile:
            assert len(topo_) == 6
        assert numpy.sum([isinstance(node.op, self.inc_sub)
             for node in topo_]) == 1
        assert numpy.sum([isinstance(node.op, self.sub)
             for node in topo_]) == 1
        gval = f()

        good = numpy.zeros_like(data)
        good[subi:, subi] = numpy.exp(data[subi:, subi])
        self.assertTrue(numpy.allclose(gval, good), (gval, good))

    def test_grad_0d(self):
        data = numpy.asarray(rand(2, 3), dtype=self.dtype)
        n = self.shared(data)
        t = n[1, 0]
        gn = theano.tensor.grad(theano.tensor.sum(theano.tensor.exp(t)), n)
        f = self.function([], gn)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        if not self.fast_compile:
            assert len(topo_) == 6
        assert numpy.sum([isinstance(node.op, self.inc_sub)
             for node in topo_]) == 1
        assert numpy.sum([isinstance(node.op, self.sub)
             for node in topo_]) == 1

        gval = f()
        good = numpy.zeros_like(data)
        good[1, 0] = numpy.exp(data[1, 0])
        self.assertTrue(numpy.allclose(gval, good), (gval, good))

    def test_ok_list(self):
        for data, idx in [(rand(4), [1, 0]),
                          (rand(4, 5), [2, 3]),
                          (rand(4, 2, 3), [0, 3]),
                          (rand(4, 2, 3), [3, 3, 1, 1, 2, 2, 0, 0]),
                          (rand(4, 2, 3), [3, 3, 1, 1, 2, 2, 0, 0,
                                           -1, -2, -3, -4]),
                          # Test 4 dims as gpu code use another algo
                          # in that case This new algo is not as much
                          # optimized for that case.
                          (rand(4, 4, 2, 3), [3,
                               3, 1, 1, 2, 2, 0, 0, -1, -2, -3, -4]),
                          # Test with TensorConstant index.
                          (rand(4, 2, 3),
                           theano.tensor.constant([3, 3, 1, 1, 2, 2, 0, 0])),
                          ]:
            data = numpy.asarray(data, dtype=self.dtype)
            n = self.shared(data)
            t = n[idx]

            # We test again AdvancedSubtensor1 as we transfer data to the cpu.
            self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))

            val = self.eval_output_and_check(t, list=True)
            if isinstance(idx, list):
                good = data[idx]
            else:
                good = data[idx.data]
            self.assertTrue(val.ndim == data.ndim)
            self.assertTrue(numpy.allclose(val, good), (val, good))

            # Test reuse of output memory
            if isinstance(self.adv_sub1, tensor.AdvancedSubtensor1):
                op = self.adv_sub1()
                # When idx is a TensorConstant.
                if hasattr(idx, "data"):
                    idx = idx.data
                test_out = [[None]]
                op.perform(None, [data, idx], test_out)
                out1 = test_out[0][0]
                op.perform(None, [data, idx], test_out)
                out2 = test_out[0][0]
                assert out1 is out2

    def test_err_invalid_list(self):
        n = self.shared(numpy.asarray(5, dtype=self.dtype))
        self.assertRaises(TypeError, n.__getitem__, [0, 0])

    def test_err_invalid_2list_dtype(self):
        n = self.shared(numpy.ones((3, 3), dtype=self.dtype) * 5)
        self.assertRaises(TypeError, n.__getitem__, ([0., 0], [1, 1]))

    def test_err_bound_list(self):
        n = self.shared(numpy.ones((2, 3), dtype=self.dtype) * 5)
        l = lvector()
        t = n[l]
        # We test again AdvancedSubtensor1 as we transfer data to the cpu.
        self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))

        f = self.function([l], t, op=self.adv_sub1)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        assert len(topo_) == 1
        self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
        for shp in [[0, 4], [0, -3], [-10]]:
            self.assertRaises(IndexError, f, shp)

    def test_adv_sub1_broadcast(self):
        ones = numpy.ones((1, 3), dtype=self.dtype)
        n = self.shared(ones * 5, broadcastable=(True, False))
        idx = tensor.lvector()
        t = n[idx]
        self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))

        f = self.function([idx], t, op=self.adv_sub1)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        assert len(topo_) == 1
        self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
        self.assertTrue(numpy.allclose(f([0]), ones[0] * 5))
        self.assertRaises(IndexError, f, [0, 1])

    def test_adv_sub1_idx_broadcast(self):
        # The idx can be a broadcastable vector.
        ones = numpy.ones((4, 3), dtype=self.dtype)
        n = self.shared(ones * 5)
        idx = tensor.TensorType(dtype='int64', broadcastable=(True,))()
        assert idx.type.broadcastable == (True,)
        t = n[idx]
        self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))

        f = self.function([idx], t, op=self.adv_sub1)
        topo = f.maker.fgraph.toposort()
        topo_ = [node for node in topo if not isinstance(node.op,
             self.ignore_topo)]
        assert len(topo_) == 1
        self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
        self.assertTrue(numpy.allclose(f([0]), ones[0] * 5))

    def test_shape_i_const(self):
        # Each axis is treated independently by shape_i/shape operators

        mode_opt = self.mode.including("fast_run")
        data = self.shared(numpy.array(numpy.arange(5), dtype=self.dtype))
        for start in [None] + [-8, -5, -1, 0, 1, 5, 8]:
            outs = []
            shapes = []
            for stop in [None] + [-8, -5, -1, 0, 1, 5, 8]:
                for step in [None] + [-3, -1, 2]:
                    outs += [data[start:stop:step].shape]
                    shapes += [data.get_value(
                        borrow=True)[start:stop:step].shape]
            f = self.function([], outs, mode=mode_opt,
                              op=self.ops, N=0)
            t_shapes = f()
            for t_shape, shape in zip(t_shapes, shapes):
                assert numpy.all(t_shape == shape)
            assert tensor.Subtensor not in [x.op for x in
                                           f.maker.fgraph.toposort()]

    def test_shape_i_scalar(self):
        # Each axis is treated independently by shape_i/shape operators

        mode_opt = self.mode.including("fast_run")

        v_data = numpy.array(numpy.arange(5), dtype=self.dtype)
        t_data = self.shared(v_data)
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        f = self.function([start, stop, step],
                          t_data[start:stop:step].shape,
                          mode=mode_opt,
                          op=self.ops,
                          N=0)
        assert tensor.Subtensor not in [x.op for x in f.maker.
            fgraph.toposort()]
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                for step in [-3, -1, 2, 5]:
                    assert numpy.all(f(start, stop, step) ==
                                     v_data[start:stop:step].shape)

    def test_slice_canonical_form_0(self):
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, stop, step), length)
        f = self.function([start, stop, step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                for step in [-6, -3, -1, 2, 5]:
                    out = f(start, stop, step, length)
                    t_out = a[out[0]:out[1]:out[2]][::out[3]]
                    v_out = a[start:stop:step]
                    assert numpy.all(t_out == v_out)
                    assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_1(self):
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, stop, step), length)
        f = self.function([stop, step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for step in [-6, -3, -1, 2, 5]:
                out = f(stop, step, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[:stop:step]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_2(self):
        start = tensor.iscalar('b')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, None, step), length)
        f = self.function([start, step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for step in [-6, -3, -1, 2, 5]:
                out = f(start, step, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[start:None:step]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_3(self):
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, stop, None), length)
        f = self.function([start, stop, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                out = f(start, stop, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[start:stop:None]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_4(self):
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, None, step), length)
        f = self.function([step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for step in [-6, -3, -1, 2, 5]:
            out = f(step, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[None:None:step]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_5(self):
        start = tensor.iscalar('b')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, None, None), length)
        f = self.function([start, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            out = f(start, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[start:None:None]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape)

    def test_slice_canonical_form_6(self):
        stop = tensor.iscalar('e')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, stop, None), length)
        f = self.function([stop, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            out = f(stop, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[None:stop:None]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape)

    def grad_list_(self, idxs, data):
        n = self.shared(data)

        for idx in idxs:
            # Should stay on the cpu.
            idx_ = _shared(numpy.asarray(idx))
            t = n[idx_]
            gn = theano.tensor.grad(theano.tensor.sum(theano.tensor.exp(t)), n)
            f = self.function([], [gn, gn.shape], op=self.adv_incsub1)
            topo = f.maker.fgraph.toposort()
            if not self.fast_compile:
                assert any([isinstance(node.op, self.
                    adv_incsub1) and node.op.inplace for node in topo])
            else:
                assert any([isinstance(node.op, self.
                    adv_incsub1) for node in topo])
            assert any([isinstance(node.op, self.adv_sub1) for node in topo])
            gval, gshape = f()
            good = numpy.zeros_like(data)
            # don't work when the same index is used many time
            # good[idx] += numpy.exp(data[idx])
            for i in idx:
                good[i] += numpy.exp(data[i])
            self.assertTrue(gval.ndim == data.ndim)
            self.assertTrue(numpy.allclose(gval, good), (gval, good))
            self.assertTrue(numpy.allclose(gshape, data.shape))

            def fct(t):
                return theano.tensor.sum(t[idx_])
            utt.verify_grad(fct, [data])

            # Test the grad of the grad (e.i. AdvancedIncSubtensor1.grad)
            def fct2(t):
                return theano.tensor.grad(theano.tensor.sum(t[idx_]), t)
            utt.verify_grad(fct2, [data])

            # Test shape of AdvancedIncSubtensor1 and AdvancedSubtensor1
            if not self.fast_compile:
                ops = (self.adv_incsub1, self.adv_sub1)
            else:
                ops = self.ops
            if idx is idxs[0]:
                f = self.function([], [gn.shape, n[idx_].shape],
                                  op=ops,
                                  N=0, N_fast=2)
                f()

    def test_wrong_exception_regression(self):
        a = fscalar()
        b = fscalar()
        c = vector()
        try:
            c[a:b]
        except NotImplementedError:
            self.fail()
        except TypeError:
            pass
        try:
            c[a:]
        except NotImplementedError:
            self.fail()
        except TypeError:
            pass
        try:
            c[:b]
        except NotImplementedError:
            self.fail()
        except TypeError:
            pass

    def test_grad_list(self):
        data = rand(4)
        data = numpy.asarray(data, dtype=self.dtype)
        idxs = [[i] for i in range(data.shape[0])]
        for i in range(data.shape[0]):
            for j in range(0, data.shape[0], 2):
                idxs.append([i, j, (i + 1) % data.shape[0]])
        self.grad_list_(idxs, data)

        data = rand(4, 3)
        data = numpy.asarray(data, dtype=self.dtype)
        self.grad_list_(idxs, data)

        data = rand(4, 3, 2)
        data = numpy.asarray(data, dtype=self.dtype)
        self.grad_list_(idxs, data)

    def test_shape_list(self):
        #TODO for all type of subtensor shape
        for data, idx in [(rand(4), [1, 0]),
                          (rand(4, 2), [2, 3]),
                          (rand(4, 2, 3), [0, 3]),
                          (rand(4, 2, 3), [3, 3, 1, 2, 2, ]),
                          ]:
            data = numpy.asarray(data, dtype=self.dtype)
            n = self.shared(data)
            t = n[idx]
            f = self.function([], t.shape, op=self.ops, N=0, N_fast=1)
            val = f()
            self.assertTrue(numpy.allclose(val, data[idx].shape))

    def test_grad_advanced_inc_subtensor(self):
        def inc_slice(*s):
            def just_numeric_args(a, b):
                cost = (a[s] + b).sum()
                cost_wrt_a = theano.tensor.grad(cost, a)
                cost_wrt_b = theano.tensor.grad(cost, b)
                grads = cost_wrt_a.sum() + cost_wrt_b.sum()
                return grads
            return just_numeric_args

        # vector
        utt.verify_grad(
            inc_slice(slice(2, 4, None)),
            (numpy.asarray([0, 1, 2, 3, 4, 5.]), numpy.asarray([9, 9.]),))

        # matrix
        utt.verify_grad(
            inc_slice(slice(1, 2, None), slice(None, None, None)),
            (numpy.asarray([[0, 1], [2, 3], [4, 5.]]),
             numpy.asarray([[9, 9.]]),))

        #single element
        utt.verify_grad(
            inc_slice(2, 1),
            (numpy.asarray([[0, 1], [2, 3], [4, 5.]]), numpy.asarray(9.),))

    def test_advanced_inc_and_set(self):
        """
        Test advanced increment and set.
        """
        rng = numpy.random.RandomState(seed=utt.fetch_seed())
        all_inputs_var = []
        all_inputs_num = []
        all_outputs_var = []
        all_outputs_num = []
        for set_instead_of_inc in (False, True):
            for inplace in (False, True):
                for data_shape in ((10,), (4, 5), (1, 2, 3), (4, 5, 6, 7)):
                    data_n_dims = len(data_shape)
                    data_size = numpy.product(data_shape)
                    # Corresponding numeric variable.
                    data_num_init = numpy.arange(data_size, dtype=self.dtype)
                    data_num_init = data_num_init.reshape(data_shape)
                    inc_shapes = [data_shape[i:]
                                  for i in xrange(0, len(data_shape) + 1)]
                    for inc_shape in inc_shapes:
                        inc_n_dims = len(inc_shape)
                        # We copy the numeric value to be 100% sure there is no
                        # risk of accidentally sharing it.
                        data_num = data_num_init.copy()
                        # Symbolic variable to be incremented.
                        # We create a new one every time in order not to
                        # have duplicated variables in the function's inputs
                        data_var = tensor.tensor(
                                broadcastable=[False] * data_n_dims,
                                dtype=self.dtype)
                        # Symbolic variable with rows to be incremented.
                        idx_var = theano.tensor.vector(dtype='int64')
                        n_to_inc = rng.randint(data_shape[0])
                        # Corresponding numeric variable.
                        idx_num = rng.randint(0, data_shape[0], n_to_inc)
                        idx_num = idx_num.astype('int64')
                        # Symbolic variable with increment value.
                        inc_var = tensor.tensor(
                                broadcastable=[False] * inc_n_dims,
                                dtype=self.dtype)
                        # Trick for the case where `inc_shape` is the same as
                        # `data_shape`: what we actually want is the first
                        # shape element to be equal to the number of rows to
                        # increment.
                        if len(inc_shape) == len(data_shape):
                            inc_shape = (n_to_inc,) + inc_shape[1:]
                        inc_size = numpy.product(inc_shape)
                        # Corresponding numeric variable.
                        inc_num = rng.uniform(size=inc_size).astype(self.dtype)
                        inc_num = inc_num.reshape(inc_shape)
                        # Result of the incrementation.
                        # (i) Theano
                        if set_instead_of_inc:
                            op = set_subtensor
                        else:
                            op = inc_subtensor
                        output = op(data_var[idx_var], inc_var,
                                    inplace=inplace)
                        # (ii) Numpy (note that Numpy increments only once
                        # duplicated indices, so we cannot directly use +=).
                        data_copy = data_num.copy()
                        for j, idx in enumerate(idx_num):
                            if len(inc_shape) == len(data_shape):
                                # Special case where there is no broadcasting.
                                if set_instead_of_inc:
                                    data_copy[idx] = inc_num[j]
                                else:
                                    data_copy[idx] += inc_num[j]
                            else:
                                if set_instead_of_inc:
                                    data_copy[idx] = inc_num
                                else:
                                    data_copy[idx] += inc_num
                        data_var = theano.In(data_var, mutable=True)

                        # Remember data for the Theano function (see below).
                        all_inputs_var += [data_var, idx_var, inc_var]
                        all_inputs_num += [data_num, idx_num, inc_num]
                        all_outputs_var.append(output)
                        all_outputs_num.append(data_copy)
                        if False:  # Enable for debugging purpose.
                            f = self.function([data_var, idx_var, inc_var],
                                              output, accept_inplace=inplace,
                                              op=self.adv_incsub1)
                            if inplace:
                                # Ensure calling `f` will not alter `data_num`.
                                data_num = data_num.copy()
                            f_out = f(data_num.copy(), idx_num, inc_num)
                            assert numpy.allclose(f_out, data_copy)
                            if not inplace:
                                # Sanity check: `data_num` should be intact.
                                assert (data_num == data_num_init).all()

        # Actual test (we compile a single Theano function to make it faster).
        orig_warn = theano.config.warn.gpu_set_subtensor1
        try:
            theano.config.warn.gpu_set_subtensor1 = False
            f = self.function(all_inputs_var, all_outputs_var,
                              accept_inplace=True,
                              op=self.adv_incsub1,
                              N=len(all_outputs_var))
        finally:
            theano.config.warn.gpu_set_subtensor1 = orig_warn

        f_outs = f(*all_inputs_num)
        assert len(f_outs) == len(all_outputs_num)
        for f_out, output_num in izip(f_outs, all_outputs_num):
            # NB: if this assert fails, it will probably be easier to debug if
            # you enable the debug code above.
            assert numpy.allclose(f_out, output_num)

    def test_adv_constant_arg(self):
        # Test case provided (and bug detected, gh-607) by John Salvatier
        m = matrix('m')
        gv = numpy.array([0, 1, 3])
        g = theano.tensor.constant(gv)
        i = theano.tensor.lvector('i')

        # s1 used to fail
        s1 = m[gv, i]
        s2 = m[g, i]

        assert gof.graph.is_same_graph(s1, s2)

    def test_adv1_inc_sub_notlastdim(self):
        # Test that taking 1-dimensional advanced indexing
        # over a dimension that's not the first (outer-most) works.
        m = matrix('m')
        i = lvector('i')

        m1 = set_subtensor(m[:, i], 0)
        m2 = inc_subtensor(m[:, i], 1)
        f = theano.function([m, i], [m1, m2])

        m_val = rand(3, 5)
        i_val = randint_ranged(min=0, max=4, shape=(4,))
        m1_ref = m_val.copy()
        m2_ref = m_val.copy()

        m1_val, m2_val = f(m_val, i_val)
        for idx in i_val:
            m1_ref[:, idx] = 0
            m2_ref[:, idx] += 1

        assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
        assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)

    def test_adv1_inc_sub_notlastdim_2didx(self):
        # Test that taking 1-dimensional advanced indexing
        # over a dimension that's not the first (outer-most) works,
        # if the index is a matrix.
        m = matrix('m')
        i = lmatrix('i')

        m1 = set_subtensor(m[:, i], 0)
        m2 = inc_subtensor(m[:, i], 1)
        f = theano.function([m, i], [m1, m2])

        m_val = rand(5, 7)
        i_val = randint_ranged(min=0, max=6, shape=(4, 2))
        m1_ref = m_val.copy()
        m2_ref = m_val.copy()

        m1_val, m2_val = f(m_val, i_val)
        for idx in i_val.ravel():
            m1_ref[:, idx] = 0
            m2_ref[:, idx] += 1

        assert numpy.allclose(m1_val, m1_ref), (m1_val, m1_ref)
        assert numpy.allclose(m2_val, m2_ref), (m2_val, m2_ref)


class TestIncSubtensor1(unittest.TestCase):
    # test inc_subtensor
    # also tests set_subtensor

    def setUp(self):
        self.s = tensor.iscalar()
        self.v = tensor.fvector()
        self.m = tensor.dmatrix()
        self.t = tensor.ctensor3()

        self.adv1q = tensor.lvector()  # advanced 1d query

    def test_cant_adv_idx_into_scalar(self):
        self.assertRaises(TypeError, lambda: self.s[self.adv1q])

    def test_index_into_vec_w_vec(self):
        a = self.v[self.adv1q]
        assert a.type == self.v.type

    def test_1d_set_adv_selection(self):
        a = set_subtensor(self.v[self.adv1q], self.v[self.adv1q])

        assert a.type == self.v.type

        #TODO: compile a function and verify that the subtensor is removed
        #      completely, because the whole expression is redundant.

        f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
        aval = f([.4, .9, .1], [1, 2])
        assert numpy.allclose(aval, [.4, 0.9, 0.1])

    def test_1d_inc_adv_selection(self):
        a = inc_subtensor(self.v[self.adv1q], self.v[self.adv1q])

        assert a.type == self.v.type
        f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
        aval = f([.4, .9, .1], [1, 2])
        assert numpy.allclose(aval, [.4, 1.8, 0.2])

    def test_1d_inc_adv_selection_w_broadcasting(self):
        a = inc_subtensor(self.v[self.adv1q], 3.0)

        assert a.type == self.v.type
        f = theano.function([self.v, self.adv1q], a, allow_input_downcast=True)
        aval = f([.4, .9, .1], [1, 2])
        assert numpy.allclose(aval, [.4, 3.9, 3.1])

    def test_assigning_matrix_to_vector_selection(self):
        self.assertRaises(TypeError,
                          lambda: inc_subtensor(self.v[self.adv1q], fmatrix()))


inplace_increment_missing = SkipTest(
    "inc_subtensor with advanced indexing not enabled. "
    "Installing NumPy 1.8 or the latest development version "
    "should make that feature available.")


class TestAdvancedSubtensor(unittest.TestCase):
    # test inc_subtensor
    # also tests set_subtensor

    def setUp(self):
        self.s = iscalar()
        self.v = fvector()
        self.m = dmatrix()
        self.t = ctensor3()

        self.ix1 = lvector()  # advanced 1d query
        self.ix12 = lvector()
        self.ix2 = lmatrix()

    def test_cant_adv_idx_into_scalar(self):
        self.assertRaises(TypeError, lambda: self.s[self.ix1])

    def test_index_into_vec_w_vec(self):
        a = self.v[self.ix1]
        assert a.type == self.v.type, (a.type, self.v.type)

    def test_index_into_vec_w_matrix(self):
        a = self.v[self.ix2]
        assert a.dtype == self.v.dtype, (a.dtype, self.v.dtype)
        assert a.broadcastable == self.ix2.broadcastable, (
                a.broadcastable, self.ix2.broadcastable)

    def test_inc_adv_subtensor_w_matrix(self):
        subt = self.v[self.ix2]
        a = inc_subtensor(subt, subt)

        assert a.type == self.v.type, (a.type, self.v.type)
        f = theano.function([self.v, self.ix2], a, allow_input_downcast=True)
        aval = f([.4, .9, .1], [[1, 2],
                                [1, 2]])
        assert numpy.allclose(aval, [.4, .9 * 3, .1 * 3])

    def test_inc_adv_subtensor_w_2vec(self):
        if inplace_increment is None:
            raise inplace_increment_missing

        subt = self.m[self.ix1, self.ix12]
        a = inc_subtensor(subt, subt)

        typ = TensorType(self.m.type.dtype, self.ix2.type.broadcastable)
        assert a.type == typ, (a.type, typ)
        f = theano.function([self.m, self.ix1, self.ix12], a,
                            allow_input_downcast=True)
        aval = f([[.4, .9, .1],
                  [5, 6, 7],
                  [.5, .3, .15]],
                 [1, 2, 1],
                 [0, 1, 0])
        assert numpy.allclose(aval,
                [[.4, .9, .1],
                  [5 * 3, 6, 7],
                  [.5, .3 * 2, .15]]), aval

    def test_inc_adv_subtensor_with_broadcasting(self):
        if inplace_increment is None:
            raise inplace_increment_missing

        a = inc_subtensor(self.m[self.ix1, self.ix12], 2.1)

        assert a.type == self.m.type, (a.type, self.m.type)
        f = theano.function([self.m, self.ix1, self.ix12], a,
                            allow_input_downcast=True)
        aval = f([[.4, .9, .1],
                  [5, 6, 7],
                  [.5, .3, .15]],
                 [1, 2, 1],
                 [0, 1, 0])
        assert numpy.allclose(aval,
                [[.4, .9, .1],
                  [5 + 2.1 * 2, 6, 7],
                  [.5, .3 + 2.1, .15]]), aval

    def test_inc_adv_subtensor_with_index_broadcasting(self):
        if inplace_increment is None:
            raise inplace_increment_missing

        a = inc_subtensor(self.m[self.ix1, self.ix2], 2.1)

        assert a.type == self.m.type, (a.type, self.m.type)
        f = theano.function([self.m, self.ix1, self.ix2], a,
                            allow_input_downcast=True)
        aval = f([[.4, .9, .1],
                  [5, 6, 7],
                  [.5, .3, .15]],
                 [0, 2, 0],
                 [[0, 1, 0],
                  [2, 2, 2]])
        assert numpy.allclose(aval,
                [[.4 + 2 * 2.1, .9, .1 + 2 * 2.1],
                  [5, 6, 7],
                  [.5, .3 + 2.1, .15 + 2.1]]), aval


class TestInferShape(utt.InferShapeTester):
    def test_infer_shape(self):
        # IncSubtensor
        admat = dmatrix()
        bdmat = dmatrix()
        advec = dvector()
        adscal = dscalar()
        admat_val = rand(5, 4)
        self._compile_and_check([admat, bdmat],
                            [inc_subtensor(admat[2:4], bdmat)],
                            [admat_val, [[1, 2, 3, 4]]], IncSubtensor)

        self._compile_and_check([admat, advec],
                            [inc_subtensor(admat[2], advec)],
                            [admat_val, [1, 2, 3, 4]], IncSubtensor)

        self._compile_and_check([admat, adscal],
                            [inc_subtensor(admat[2, 3], adscal)],
                            [admat_val, 1], IncSubtensor)

        self._compile_and_check([admat, adscal],
                            [inc_subtensor(admat[1:3, 2], adscal)],
                            [admat_val, 1], IncSubtensor)

        self._compile_and_check([admat, bdmat],
                            [set_subtensor(admat[2:4], bdmat)],
                            [admat_val, [[1, 2, 3, 4]]], IncSubtensor)

        self._compile_and_check([admat, advec],
                            [set_subtensor(admat[2], advec)],
                            [admat_val, [1, 2, 3, 4]], IncSubtensor)

        self._compile_and_check([admat, adscal],
                            [set_subtensor(admat[2, 3], adscal)],
                            [admat_val, 1], IncSubtensor)

        self._compile_and_check([admat, adscal],
                            [set_subtensor(admat[1:3, 2], adscal)],
                            [admat_val, 1], IncSubtensor)

        adtens4 = dtensor4()
        bdtens4 = dtensor4()
        adtens4_val = rand(3, 4, 2, 5)
        self._compile_and_check([adtens4, bdtens4],
                            [inc_subtensor(adtens4[::, 2:4, ::, ::], bdtens4)],
                            [adtens4_val, [[[[1, 2, 3, 4, 5]]]]], IncSubtensor,
                            warn=False)
        self._compile_and_check([adtens4, bdmat],
                            [inc_subtensor(adtens4[2, 2:4, 1, ::], bdmat)],
                            [adtens4_val, [[1, 2, 3, 4, 5]]], IncSubtensor)

        self._compile_and_check([adtens4, advec],
                            [inc_subtensor(adtens4[0, 1, ::, 4], advec)],
                            [adtens4_val, [1, 2]], IncSubtensor)

        self._compile_and_check([adtens4, adscal],
                            [inc_subtensor(adtens4[1:3, 1, ::, 2:4], adscal)],
                            [adtens4_val, 1], IncSubtensor)

        self._compile_and_check([adtens4, bdtens4],
                            [set_subtensor(adtens4[::, 2:4, ::, ::], bdtens4)],
                            [adtens4_val, [[[[1, 2, 3, 4, 5]]]]], IncSubtensor,
                            warn=False)

        self._compile_and_check([adtens4, bdmat],
                            [set_subtensor(adtens4[2, 2:4, 1, ::], bdmat)],
                            [adtens4_val, [[1, 2, 3, 4, 5]]], IncSubtensor)

        self._compile_and_check([adtens4, advec],
                            [set_subtensor(adtens4[0, 1, ::, 4], advec)],
                            [adtens4_val, [1, 2]], IncSubtensor)

        self._compile_and_check([adtens4, adscal],
                            [set_subtensor(adtens4[1:3, 1, ::, 2:4], adscal)],
                            [adtens4_val, 1], IncSubtensor)

        # AdvancedIncSubtensor1
        admat = dmatrix()
        bdmat = dmatrix()
        advec = dvector()
        adscal = dscalar()
        admat_val = rand(5, 4)
        aivec_val = [2, 3]
        self._compile_and_check([admat, bdmat],
                            [set_subtensor(admat[aivec_val], bdmat)],
                            [admat_val, [[1, 2, 3, 4]]], AdvancedIncSubtensor1)

        aivec_val = [1, 3, 2]
        self._compile_and_check([admat, advec],
                            [set_subtensor(admat[aivec_val], advec)],
                            [admat_val, [1, 2, 3, 4]], AdvancedIncSubtensor1)

        aivec_val = [0, 3, 0]
        self._compile_and_check([admat, adscal],
                            [set_subtensor(admat[aivec_val], adscal)],
                            [admat_val, 1], AdvancedIncSubtensor1)

        bdtens4 = dtensor4()
        adtens4_val = rand(4, 3, 2, 5)
        aivec_val = [2, 3]
        self._compile_and_check([adtens4, bdtens4],
                            [set_subtensor(adtens4[aivec_val], bdtens4)],
                            [adtens4_val, [[[[1, 2, 3, 4, 5]]]]],
                            AdvancedIncSubtensor1,
                            warn=False)

        aivec_val = [1, 3, 2]
        self._compile_and_check([adtens4, advec],
                            [set_subtensor(adtens4[aivec_val], advec)],
                            [adtens4_val, [1, 2, 3, 4, 5]],
                            AdvancedIncSubtensor1)

        aivec_val = [0, 3, 0]
        self._compile_and_check([adtens4, adscal],
                            [set_subtensor(adtens4[aivec_val], adscal)],
                            [adtens4_val, 1],
                            AdvancedIncSubtensor1)

        aivec_val = [2, 3]
        self._compile_and_check([admat, bdmat],
                                [inc_subtensor(admat[aivec_val], bdmat)],
                                [admat_val, [[1, 2, 3, 4], [5, 6, 7, 8]]],
                                AdvancedIncSubtensor1)

        aivec_val = [1, 3, 2]
        self._compile_and_check([admat, advec],
                            [inc_subtensor(admat[aivec_val], advec)],
                            [admat_val, [1, 2, 3, 4]], AdvancedIncSubtensor1)

        aivec_val = [0, 3, 0]
        self._compile_and_check([admat, adscal],
                            [inc_subtensor(admat[aivec_val], adscal)],
                            [admat_val, 1], AdvancedIncSubtensor1)

        bdtens4 = dtensor4()
        adtens4_val = rand(4, 3, 2, 5)
        aivec_val = [2, 3]
        self._compile_and_check([adtens4, bdtens4],
                            [inc_subtensor(adtens4[aivec_val], bdtens4)],
                            [adtens4_val, [[[[1, 2, 3, 4, 5]]],
                                           [[[6, 7, 8, 9, 10]]]]],
                            AdvancedIncSubtensor1,
                            warn=False)

        aivec_val = [1, 2, 1]
        self._compile_and_check([adtens4, advec],
                            [inc_subtensor(adtens4[aivec_val], advec)],
                            [adtens4_val, [1, 2, 3, 4, 5]],
                            AdvancedIncSubtensor1)

        aivec_val = [0, 3, 0]
        self._compile_and_check([adtens4, adscal],
                            [inc_subtensor(adtens4[aivec_val], adscal)],
                            [adtens4_val, 2],
                            AdvancedIncSubtensor1)

        # AdvancedIncSubtensor
        aivec_val = [1, 3, 2]
        bivec_val = [0, 3, 3]
        advec_val = [23, 24, 25]
        self._compile_and_check([admat, advec],
                    [set_subtensor(admat[aivec_val, bivec_val], advec)],
                    [admat_val, advec_val], AdvancedIncSubtensor)
