import cPickle
from copy import copy
from itertools import imap
import time
import unittest

import numpy
from nose.plugins.skip import SkipTest
from numpy.testing import dec

import theano
from theano.gof.python25 import all, any
from theano.gof import Variable, Op
from theano import gof, scalar, config

from theano import tensor
from theano.tensor import TensorType
from theano.compile.mode import get_default_mode
from theano.tensor.elemwise import (CAReduce, Elemwise, DimShuffle,
                                    Prod, ProdWithoutZeros)
from theano.tests import unittest_tools


def FunctionGraph(i, o):
    e = gof.FunctionGraph(i, o)
    return e


class test_DimShuffle(unittest_tools.InferShapeTester):

    def with_linker(self, linker):
        for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
                                  ((1, 2, 3), (1, 2), (2, 3)),
                                  ((1, 2, 1, 3), (1, 3), (2, 3)),
                                  ((2, 3, 4), (2, 1, 0), (4, 3, 2)),
                                  ((2, 3, 4), ('x', 2, 1, 0, 'x'),
                                   (1, 4, 3, 2, 1)),
                                  ((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
                                  ((1, 1, 4), (1, 2), (1, 4)),
                                  ((1, 1, 1), (), ()),
                                  ((1,), ('x', 'x'), (1, 1))]:
            ib = [(entry == 1) for entry in xsh]
            x = TensorType('float64', ib)('x')
            e = DimShuffle(ib, shuffle)(x)
            f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
            assert f(numpy.ones(xsh)).shape == zsh
            #test that DimShuffle.infer_shape work correctly
            x = TensorType('float64', ib)('x')
            e = DimShuffle(ib, shuffle)(x)
            f = copy(linker).accept(FunctionGraph([x], [e.
                shape])).make_function()
            assert all(f(numpy.ones(xsh))) == all(zsh)

        # Test when we drop a axis that is not broadcastable
        ib = [False, True, False]
        x = TensorType('float64', ib)('x')
        self.assertRaises(ValueError, DimShuffle, ib, shuffle)

        # Test when we drop a axis that don't have shape 1
        ib = [True, True, False]
        x = TensorType('float64', ib)('x')
        e = DimShuffle(ib, (1, 2))(x)
        f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
        self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))

        # Test that we can't take a dimensions multiple time
        xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
        ib = [False, True, False]
        x = TensorType('float64', ib)('x')
        self.assertRaises(ValueError, DimShuffle, ib, shuffle)

    def test_perform(self):
        self.with_linker(gof.PerformLinker())

    def test_c_or_py(self):
        # Shape op don't have C code.
        # But This will test DimShuffle c code
        self.with_linker(gof.OpWiseCLinker())

    def test_infer_shape(self):

        for xsh, shuffle in [((2, 3), (1, 'x', 0)),
                             ((1, 2, 3), (1, 2)),
                             ((1, 2, 1, 3), (1, 3)),
                             ((2, 3, 4), (2, 1, 0)),
                             ((2, 3, 4), ('x', 2, 1, 0, 'x')),
                             ((1, 4, 3, 2, 1), (3, 2, 1)),
                             ((1, 1, 4), (1, 2)),
                             ((1, 1, 1), ()),
                             ((1,), ('x', 'x'))]:
            ib = [(entry == 1) for entry in xsh]
            adtens = TensorType('float64', ib)('x')
            adtens_val = numpy.ones(xsh)
            self._compile_and_check([adtens],
                            [DimShuffle(ib, shuffle)(adtens)],
                            [adtens_val], DimShuffle)

    def test_too_big_rank(self):
        x = tensor.dscalar()
        y = x.dimshuffle(('x',) * (numpy.MAXDIMS + 1))
        self.assertRaises(ValueError, y.eval, {x: 0})
                          
class test_Broadcast(unittest.TestCase):
    def setUp(self):
        unittest_tools.seed_rng()

    def with_linker(self, linker):
        for xsh, ysh in [((3, 5), (3, 5)),
                         ((3, 5), (1, 5)),
                         ((3, 5), (3, 1)),
                         ((1, 5), (5, 1)),
                         ((1, 1), (1, 1)),
                         ((2, 3, 4, 5), (2, 3, 4, 5)),
                         ((2, 3, 4, 5), (1, 3, 1, 5)),
                         ((2, 3, 4, 5), (1, 1, 1, 1)),
                         ((), ())]:
            x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
            y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
            e = Elemwise(scalar.add)(x, y)
            f = copy(linker).accept(FunctionGraph([x, y], [e])).make_function()
            xv = numpy.asarray(numpy.random.rand(*xsh))
            yv = numpy.asarray(numpy.random.rand(*ysh))
            zv = xv + yv

            self.assertTrue((f(xv, yv) == zv).all())

            #test Elemwise.infer_shape
            #the Shape op don't implement c_code!
            if isinstance(linker, gof.PerformLinker):
                x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
                y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
                e = Elemwise(scalar.add)(x, y)
                f = copy(linker).accept(FunctionGraph([x,
                     y], [e.shape])).make_function()
                assert tuple(f(xv, yv)) == tuple(zv.shape)

    def with_linker_inplace(self, linker):
        for xsh, ysh in [((5, 5), (5, 5)),
                         ((5, 5), (1, 5)),
                         ((5, 5), (5, 1)),
                         ((1, 1), (1, 1)),
                         ((2, 3, 4, 5), (2, 3, 4, 5)),
                         ((2, 3, 4, 5), (1, 3, 1, 5)),
                         ((2, 3, 4, 5), (1, 1, 1, 1)),
                         ((), ())]:
            x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
            y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
            e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0: 0})(x, y)
            f = copy(linker).accept(FunctionGraph([x, y], [e])).make_function()
            xv = numpy.asarray(numpy.random.rand(*xsh))
            yv = numpy.asarray(numpy.random.rand(*ysh))
            zv = xv + yv

            f(xv, yv)

            self.assertTrue((xv == zv).all())
            #test Elemwise.infer_shape
            #the Shape op don't implement c_code!
            if isinstance(linker, gof.PerformLinker):
                x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
                y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
                e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0: 0})(x, y)
                f = copy(linker).accept(FunctionGraph([x,
                     y], [e.shape])).make_function()
                xv = numpy.asarray(numpy.random.rand(*xsh))
                yv = numpy.asarray(numpy.random.rand(*ysh))
                zv = xv + yv

                f(xv, yv)

                assert xv.shape == zv.shape

    def test_perform(self):
        self.with_linker(gof.PerformLinker())

    def test_c(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        self.with_linker(gof.CLinker())

    def test_perform_inplace(self):
        self.with_linker_inplace(gof.PerformLinker())

    def test_c_inplace(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        self.with_linker_inplace(gof.CLinker())

    def test_fill(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        x = TensorType('float64', [0, 0])('x')
        y = TensorType('float64', [1, 1])('y')
        e = Elemwise(scalar.Second(scalar.transfer_type(0)), {0: 0})(x, y)
        f = gof.CLinker().accept(FunctionGraph([x, y], [e])).make_function()
        xv = numpy.ones((5, 5))
        yv = numpy.random.rand(1, 1)
        f(xv, yv)
        assert (xv == yv).all()

    def test_weird_strides(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        x = TensorType('float64', [0, 0, 0, 0, 0])('x')
        y = TensorType('float64', [0, 0, 0, 0, 0])('y')
        e = Elemwise(scalar.add)(x, y)
        f = gof.CLinker().accept(FunctionGraph([x, y], [e])).make_function()
        xv = numpy.random.rand(2, 2, 2, 2, 2)
        yv = numpy.random.rand(2, 2, 2, 2, 2).transpose(4, 0, 3, 1, 2)
        zv = xv + yv
        assert (f(xv, yv) == zv).all()

    def test_same_inputs(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        x = TensorType('float64', [0, 0])('x')
        e = Elemwise(scalar.add)(x, x)
        f = gof.CLinker().accept(FunctionGraph([x], [e])).make_function()
        xv = numpy.random.rand(2, 2)
        zv = xv + xv
        assert (f(xv) == zv).all()


class test_CAReduce(unittest_tools.InferShapeTester):

    def with_linker(self, linker, scalar_op=scalar.add, dtype="floatX",
                    test_nan=False, tensor_op=None):
        for xsh, tosum in [((5, 6), None),
                           ((5, 6), (0, 1)),
                           ((5, 6), (0, )),
                           ((5, 6), (1, )),
                           ((5, 6), (-1, )),
                           ((5, 6), (-2, )),
                           ((5, 6), ()),
                           ((2, 3, 4, 5), (0, 1, 3)),
                           ((2, 3, 4, 5), (-2, -3)),
                           ((5, 0), None),
                           ((5, 0), (0, )),
                           ((5, 0), (1, )),
                           ((5, 0), ()),
                           ((), None),
                           ((), ())]:
            if dtype == "floatX":
                dtype = theano.config.floatX
            x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
            if tensor_op is None:
                e = CAReduce(scalar_op, axis=tosum)(x)
            else:
                e = tensor_op(x, axis=tosum)

            if tosum is None:
                tosum = range(len(xsh))

            f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
            xv = numpy.asarray(numpy.random.rand(*xsh))

            if not "int" in dtype:
                xv = numpy.asarray(xv, dtype=dtype)
            else:
                xv = numpy.asarray(xv < 0.5, dtype=dtype)

            if test_nan and xv.size > 0:
                if len(xsh) > 0:
                    xv = xv.flatten()
                    xv[0] = numpy.nan
                    xv = xv.reshape(*xsh)
                else:
                    xv = numpy.asarray(numpy.nan, dtype=dtype)
            zv = xv
            numpy_raised = False
            if len(tosum) > 1 and any([a < 0 for a in tosum]):
                #In that case, we need to use the good order of axis
                #in the reduction.
                axis2 = []
                for a in tosum:
                    if a < 0:
                        axis2.append(a + len(xsh))
                    else:
                        axis2.append(a)
                assert len(axis2) == len(tosum)
                tosum = tuple(axis2)
            if tensor_op == tensor.all:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.all(zv, axis)
                if len(tosum) == 0:
                    zv = zv != 0
            elif tensor_op == tensor.any:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.any(zv, axis)
                if len(tosum) == 0:
                    zv = zv != 0
            elif scalar_op == scalar.add:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.add.reduce(zv, axis)
            elif scalar_op == scalar.mul:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.multiply.reduce(zv, axis)
            elif scalar_op == scalar.maximum:
                try:
                    for axis in reversed(sorted(tosum)):
                        zv = numpy.maximum.reduce(zv, axis)
                except ValueError:
                    numpy_raised = True
            elif scalar_op == scalar.minimum:
                try:
                    for axis in reversed(sorted(tosum)):
                        zv = numpy.minimum.reduce(zv, axis)
                except ValueError:
                    numpy_raised = True
            elif scalar_op == scalar.or_:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.bitwise_or.reduce(zv, axis)
            elif scalar_op == scalar.and_:
                for axis in reversed(sorted(tosum)):
                    zv = numpy.bitwise_and.reduce(zv, axis)
            elif scalar_op == scalar.xor:
                # There is no identity value for the xor function
                # So we can't support shape of dimensions 0.
                if numpy.prod(zv.shape) == 0:
                    continue
                for axis in reversed(sorted(tosum)):
                    zv = numpy.bitwise_xor.reduce(zv, axis)
            else:
                raise Exception(
                    "Test for CAReduce with scalar_op %s not implemented" %
                    str(scalar_op))
            if scalar_op in [scalar.maximum, scalar.minimum] and numpy_raised:
                try:
                    out = f(xv)
                    assert out.dtype == dtype
                except ValueError:
                    pass
                else:
                    self.fail()
            else:
                # numpy.{all,any} return bool type,
                # but theano ops return an int8 array instead
                if scalar_op in [scalar.and_, scalar.or_]:
                    zv = numpy.asarray(zv, dtype='int8')
                if test_nan:
                    self.assertTrue(theano.tensor.TensorType.values_eq(f(xv),
                                                                       zv),
                                    (f(xv), zv))
                else:
                    f_xv = f(xv)
                    self.assertTrue((f_xv.shape == zv.shape), (f_xv, zv))
                    self.assertTrue(numpy.allclose(f_xv, zv), (f_xv, zv))

            #test CAReduce.infer_shape
            #the Shape op don't implement c_code!
            if isinstance(linker, gof.PerformLinker):
                x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
                if tensor_op is None:
                    e = CAReduce(scalar_op, axis=tosum)(x)
                else:
                    e = tensor_op(x, axis=tosum)
                if tosum is None:
                    tosum = range(len(xsh))
                f = copy(linker).accept(FunctionGraph([x],
                     [e.shape])).make_function()
                if not(scalar_op in [scalar.maximum, scalar.minimum] and
                       ((xsh == () or numpy.prod(xsh) == 0))):
                    assert all(f(xv) == zv.shape)

    def test_perform(self):
        for dtype in ["floatX", "complex64", "complex128", "int8", "uint8"]:
            self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.mul, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.maximum, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.minimum, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
                             tensor_op=tensor.all)
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype,
                             tensor_op=tensor.any)
        for dtype in ["int8", "uint8"]:
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.xor, dtype=dtype)

    def test_perform_nan(self):
        for dtype in ["floatX", "complex64", "complex128"]:
            self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.mul, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.maximum, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.minimum, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype,
                             test_nan=True, tensor_op=tensor.any)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
                             test_nan=True, tensor_op=tensor.all)

    def test_c(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")

        for dtype in ["floatX", "complex64", "complex128", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.add, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.mul, dtype=dtype)
        for dtype in ["floatX", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.minimum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.maximum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype,
                             tensor_op=tensor.all)
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype,
                             tensor_op=tensor.any)
        for dtype in ["int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.xor, dtype=dtype)

    def test_c_nan(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")
        for dtype in ["floatX", "complex64", "complex128"]:
            self.with_linker(gof.CLinker(), scalar.add, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.CLinker(), scalar.mul, dtype=dtype,
                             test_nan=True)
        for dtype in ["floatX"]:
            self.with_linker(gof.CLinker(), scalar.minimum, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.CLinker(), scalar.maximum, dtype=dtype,
                             test_nan=True)

    def test_infer_shape(self):
        for xsh, tosum in [((5, 6), None),
                           ((5, 6), (0, 1)),
                           ((5, 6), (0, )),
                           ((5, 6), (1, )),
                           ((5, 6), (-1, )),
                           ((5, 6), (-2, )),
                           ((2, 3, 4, 5), (0, 1, 3)),
                           ((2, 3, 4, 5), (-2, -3)),
                           ((5, 0), None),
                           ((5, 0), (0, )),
                           ((5, 0), (1, )),
                           ((5, 6), ()),
                           ((5, 0), ()),
                           ((), None),
                           ((), ())]:
            dtype = theano.config.floatX
            x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
            if tosum is None:
                tosum = range(len(xsh))
            xv = numpy.asarray(numpy.random.rand(*xsh), dtype=dtype)
            self._compile_and_check([x],
                            [CAReduce(scalar.add, axis=tosum)(x)],
                            [xv], CAReduce, ["local_cut_useless_reduce"])


class test_Prod(unittest.TestCase):
    def setUp(self):
        unittest_tools.seed_rng()

        # we want to allow nans in the matrices, so we disable this
        # DEBUG_MODE check
        mode = theano.compile.mode.get_default_mode()
        mode = copy(mode)
        mode.check_isfinite = False

        self.mode = mode

    def test_verify_grad(self):

        # including zeros, as the case with zeros is important
        # (and special cases: 1 zero in the row, more than 1 zero in the row)
        x_val = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
             dtype='float32')
        x = theano.tensor.dmatrix()
        # now with verify_grad
        unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)

        # second time, with some added complexity
        # verify_grad takes the sum of the matrices anyway
        def fn(x2):
            return theano.tensor.sqr(Prod(axis=1)(x2))

        unittest_tools.verify_grad(fn, [x_val], mode=self.mode)

    def test_verify_grad_with_zeros(self):
        # including zeros, as the case with zeros is important
        # (and special cases: 1 zero in the row, more than 1 zero in the row)
        x_val = numpy.asarray([[1., 2., 3.], [0., 5., 6.], [0., 0., 9.]],
             dtype='float32')
        x = theano.tensor.dmatrix()

        # sanity check
        x2 = theano.tensor.dmatrix()
        p = Prod(axis=1)(x)
        p2 = Prod(axis=1)(x2)
        fn = theano.function([x, x2], [p - p2], mode=self.mode)
        #print "hand computed diff for each row"
        x2_val = numpy.asarray([[1., 2., 3.003], [0.003, 5., 6], [
            0., 0., 9.01]])
        #print fn(x_val, x2_val)
        fn2 = theano.function([x], [theano.tensor.grad(p.sum(), x)],
             mode=self.mode)
        #print "real grad"
        #print fn2(x_val)
        fn3 = theano.function([x], [p], mode=self.mode)
        assert numpy.allclose(fn3(x_val), [6., 0., 0.])

        # now with verify_grad
        unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)

        # second time, with some added complexity
        # verify_grad takes the sum of the matrices anyway
        #def fn5(x5):
        #    return theano.tensor.sqr(Prod(axis=1)(x5))

        #x4 = theano.tensor.dmatrix()
        #p4 = theano.tensor.sqr(Prod(axis=1)(x4))
        #fn4 = theano.function([x4], p4)
        #print "with sqr"
        #print fn4(x_val)
        #print fn4(x2_val)

        #unittest_tools.verify_grad(fn5, [x_val])

    def test_prod_without_zeros(self):
        x = theano.tensor.dmatrix()
        x_val = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]], dtype='float32')
        pwz = ProdWithoutZeros(axis=1)(x)
        fn = theano.function([x], pwz, mode=self.mode)
        assert numpy.allclose(fn(x_val), [6, 30, 9])

        pwz_a0 = ProdWithoutZeros(axis=0)(x)
        fn_a0 = theano.function([x], pwz_a0, mode=self.mode)
        assert numpy.allclose(fn_a0(x_val), [1, 10, 162])

    def test_other_grad_tests(self):
        x = theano.tensor.dmatrix()
        x_val1 = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]],
             dtype='float32')
        x_val2 = numpy.array([[1, 2, 0], [0, 5, 6], [7, 8, 9], [9, 10, 0]],
             dtype='float32')
        rng = rng = numpy.random.RandomState(43)

        p = Prod(axis=1)
        grad_p = theano.tensor.grad(p(x).sum(), x)
        grad_fn = theano.function([x], grad_p, mode=self.mode)
        assert numpy.allclose(grad_fn(x_val1), [[6., 3., 2.], [30., 0.,
            0.], [0., 0., 0.]])
        assert numpy.allclose(grad_fn(x_val2), [[0., 0., 2.], [30.,
             0., 0.], [72., 63., 56.], [0., 0., 90.]])

        p_axis0 = Prod(axis=0)
        grad_p_axis0 = theano.tensor.grad(p_axis0(x).sum(), x)
        grad_fn_axis0 = theano.function([x], grad_p_axis0, mode=self.mode)
        assert numpy.allclose(grad_fn_axis0(x_val2), [[0., 400.,
             0.], [63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])

        tensor.verify_grad(p, [x_val1], rng=rng, mode=self.mode)

    def test_mul_without_zeros_zeros(self):
        a = numpy.zeros((3, 3))

        x = theano.tensor.dmatrix()

        mul1 = ProdWithoutZeros(axis=0)(x)

        fn_debug = theano.function([x], mul1, mode=self.mode)

        fn_debug(a)

    def test_pickle_bug(self):
        # Regression test for bug fixed in 24d4fd291054.
        o = Prod()
        s = cPickle.dumps(o, protocol=-1)
        o = cPickle.loads(s)
        cPickle.dumps(o)


class test_IsInf_IsNan(unittest.TestCase):

    def setUp(self):
        self.test_vals = [numpy.array(x, dtype=config.floatX) for x in [
            0,
            1,
            numpy.nan,
            numpy.inf,
            -numpy.inf,
            [numpy.nan, numpy.inf, -numpy.inf, 0, 1, -1],
            ]]
        self.scalar = tensor.scalar()
        self.vector = tensor.vector()
        self.mode = get_default_mode()
        if isinstance(self.mode, theano.compile.debugmode.DebugMode):
            # Disable the check preventing usage of NaN / Inf values.
            self.mode = copy(self.mode)
            self.mode.check_isfinite = False

    def run_isfunc(self, isfunc):
        for input in (self.scalar, self.vector):
            theano_isfunc = theano.function([input],
                                            getattr(tensor, isfunc)(input),
                                            mode=self.mode)
            numpy_isfunc = getattr(numpy, isfunc)
            for x in self.test_vals:
                if ((x.ndim == 0 and input is not self.scalar) or
                    (x.ndim == 1 and input is not self.vector)):
                    # We only test with the appropriate input type.
                    continue
                assert (theano_isfunc(x) == numpy_isfunc(x)).all()

    def test_isinf(self):
        return self.run_isfunc('isinf')

    def test_isnan(self):
        return self.run_isfunc('isnan')


class T_sum_dtype(unittest.TestCase):
    def test_sum_default_dtype(self):
        """
        Test the default dtype of a sum().
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
            axis = axes[idx % len(axes)]
            x = tensor.matrix(dtype=dtype).sum(axis=axis)
            assert x.dtype == dict(
                    int8='int64',
                    int16='int64',
                    int32='int64',
                    uint8='uint64',
                    uint16='uint64',
                    uint32='uint64',
                    ).get(dtype, dtype)

    def test_sum_custom_dtype(self):
        """
        Test the ability to provide your own output dtype for a sum.
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        idx = 0
        for input_dtype in imap(str, theano.scalar.all_types):
            x = tensor.matrix(dtype=input_dtype)
            for output_dtype in imap(str, theano.scalar.all_types):
                # If the output is a complex, the gradient of the sum will
                # cast the complex to the input dtype. We can't call the normal
                # cast on a complex to a not complex as this is ambiguous.
                if (not input_dtype.startswith('complex') and
                    output_dtype.startswith('complex')):
                    continue

                axis = axes[idx % len(axes)]
                # If output_dtype would force a downcast, we expect a TypeError
                # We always allow int/uint inputs with float/complex outputs.
                upcasted_dtype = scalar.upcast(input_dtype, output_dtype)
                if (output_dtype == upcasted_dtype or
                        (input_dtype in tensor.discrete_dtypes and
                            output_dtype in tensor.continuous_dtypes)
                        ):
                    sum_var = x.sum(dtype=output_dtype, axis=axis)
                    assert sum_var.dtype == output_dtype

                    if "complex" in input_dtype:
                        continue
                    # Check that we can take the gradient
                    grad_var = tensor.grad(sum_var.sum(), x,
                            disconnected_inputs='ignore')
                else:
                    self.assertRaises(TypeError,
                            x.sum, dtype=output_dtype, axis=axis)

                idx += 1


class T_mean_dtype(unittest.TestCase):
    def test_mean_default_dtype(self):
        """
        Test the default dtype of a mean().
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
            axis = axes[idx % len(axes)]
            x = tensor.matrix(dtype=dtype).mean(axis=axis)
            if dtype in tensor.discrete_dtypes:
                assert x.dtype == 'float64'
            else:
                assert x.dtype == dtype, (x, x.dtype, dtype)

    def test_mean_custom_dtype(self):

        """
        Test the ability to provide your own output dtype for a mean.
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        idx = 0
        for input_dtype in imap(str, theano.scalar.all_types):
            x = tensor.matrix(dtype=input_dtype)
            for sum_dtype in imap(str, theano.scalar.all_types):
                axis = axes[idx % len(axes)]
                # If the inner sum cannot be created, it will raise a TypeError.
                try:
                    mean_var = x.mean(dtype=sum_dtype, axis=axis)
                except TypeError:
                    pass
                else:
                    # Executed if no TypeError was raised
                    if sum_dtype in tensor.discrete_dtypes:
                        assert mean_var.dtype == 'float64', (mean_var.dtype, sum_dtype)
                    else:
                        assert mean_var.dtype == sum_dtype, (mean_var.dtype, sum_dtype)

                    # Check that we can take the gradient, when implemented
                    if "complex" in mean_var.dtype:
                        continue
                    try:
                        grad_var = tensor.grad(mean_var.sum(), x,
                                disconnected_inputs='ignore')
                    except NotImplementedError:
                        # TrueDiv does not seem to have a gradient when
                        # the numerator is complex.
                        if mean_var.dtype in tensor.complex_dtypes:
                            pass
                        else:
                            raise

                idx += 1


class T_prod_dtype(unittest.TestCase):
    def test_prod_default_dtype(self):
        """
        Test the default dtype of a prod().
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
            axis = axes[idx % len(axes)]
            x = tensor.matrix(dtype=dtype).prod(axis=axis)
            assert x.dtype == dict(
                    int8='int64',
                    int16='int64',
                    int32='int64',
                    uint8='uint64',
                    uint16='uint64',
                    uint32='uint64',
                    ).get(dtype, dtype)

    def test_prod_custom_dtype(self):
        """
        Test the ability to provide your own output dtype for a prod.
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        idx = 0
        for input_dtype in imap(str, theano.scalar.all_types):
            x = tensor.matrix(dtype=input_dtype)
            for output_dtype in imap(str, theano.scalar.all_types):
                axis = axes[idx % len(axes)]
                # If output_dtype would force a downcast, we expect a TypeError
                # We always allow int/uint inputs with float/complex outputs.
                upcasted_dtype = scalar.upcast(input_dtype, output_dtype)
                if (output_dtype == upcasted_dtype or
                        (input_dtype in tensor.discrete_dtypes and
                            output_dtype in tensor.continuous_dtypes)
                        ):
                    prod_var = x.prod(dtype=output_dtype, axis=axis)
                    assert prod_var.dtype == output_dtype

                    if "complex" in output_dtype:
                        continue
                    # Check that we can take the gradient
                    grad_var = tensor.grad(prod_var.sum(), x,
                            disconnected_inputs='ignore')
                else:
                    self.assertRaises(TypeError,
                            x.prod, dtype=output_dtype, axis=axis)

                idx += 1


class T_prod_without_zeros_dtype(unittest.TestCase):
    def test_prod_without_zeros_default_dtype(self):
        """
        Test the default dtype of a ProdWithoutZeros().
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        for idx, dtype in enumerate(imap(str, theano.scalar.all_types)):
            axis = axes[idx % len(axes)]
            x = ProdWithoutZeros(axis=axis)(tensor.matrix(dtype=dtype))
            assert x.dtype == dict(
                    int8='int64',
                    int16='int64',
                    int32='int64',
                    uint8='uint64',
                    uint16='uint64',
                    uint32='uint64',
                    ).get(dtype, dtype)

    def test_prod_without_zeros_custom_dtype(self):
        """
        Test the ability to provide your own output dtype for a ProdWithoutZeros().
        """
        # We try multiple axis combinations even though axis should not matter.
        axes = [None, 0, 1, [0], [1], [0, 1]]
        idx = 0
        for input_dtype in imap(str, theano.scalar.all_types):
            x = tensor.matrix(dtype=input_dtype)
            for output_dtype in imap(str, theano.scalar.all_types):
                axis = axes[idx % len(axes)]
                # If output_dtype would force a downcast, we expect a TypeError
                # We always allow int/uint inputs with float/complex outputs.
                upcasted_dtype = scalar.upcast(input_dtype, output_dtype)
                if (output_dtype == upcasted_dtype or
                        (input_dtype in tensor.discrete_dtypes and
                            output_dtype in tensor.continuous_dtypes)
                        ):
                    prod_woz_var = ProdWithoutZeros(
                            axis=axis, dtype=output_dtype)(x)
                    assert prod_woz_var.dtype == output_dtype
                else:
                    self.assertRaises(TypeError,
                            ProdWithoutZeros(axis=axis, dtype=output_dtype),
                            x)

                idx += 1


class TestElemwise(unittest_tools.InferShapeTester):

    def test_infer_shape(self):

        for s_left, s_right in [((5, 6), (5, 6)),
                           ((5, 6), (5, 1)),
                           ((5, 6), (1, 6)),
                           ((5, 1), (5, 6)),
                           ((1, 6), (5, 6)),
                           ((2, 3, 4, 5), (2, 3, 4, 5)),
                           ((2, 3, 4, 5), (2, 3, 1, 5)),
                            ((2, 3, 4, 5), (1, 3, 4, 5)),
                            ((2, 1, 4, 5), (2, 3, 4, 5)),
                            ((2, 3, 4, 1), (2, 3, 4, 5))]:
            dtype = theano.config.floatX
            t_left = TensorType(dtype, [(entry == 1) for entry in s_left])()
            t_right = TensorType(dtype, [(entry == 1) for entry in s_right])()
            t_left_val = numpy.zeros(s_left, dtype=dtype)
            t_right_val = numpy.zeros(s_right, dtype=dtype)
            self._compile_and_check([t_left, t_right],
                            [Elemwise(scalar.add)(t_left, t_right)],
                            [t_left_val, t_right_val], Elemwise)


def test_gt_grad():
    """A user test that failed.

    Something about it made Elemwise.grad return something that was
    too complicated for get_constant_value to recognize as being 0, so
    gradient.grad reported that it was not a valid gradient of an
    integer.

    """
    floatX = config.floatX
    T = theano.tensor

    input_ = T.vector(dtype=floatX)
    random_values = numpy.random.RandomState(1234).uniform(low=-1, high=1, size=(2,2))
    W_values = numpy.asarray(random_values, dtype=floatX)
    W = theano.shared(value=W_values, name='weights')
    correct_score = T.dot(input_, W)
    wrong_input = T.vector(dtype=floatX)
    wrong_score = theano.clone(correct_score, {input_: wrong_input})
    # Hinge loss

    scores = T.ones_like(correct_score) - correct_score + wrong_score
    cost = (scores * (scores > 0)).sum()
    T.grad(cost, input_)

"""
if __name__ == '__main__':
    #unittest.main()
    suite = unittest.TestSuite([test_Prod('test_mul_without_zeros_zeros')])
    #suite.addTest(test_Prod('test_verify_grad_with_zeros'))
    #suite.addTest(test_Prod('test_prod_without_zeros'))
    #suite.addTest(test_Prod('test_other_grad_tests'))
    unittest.TextTestRunner().run(suite)
"""

def test_clip_grad():

    # test the gradient of clip
    def func(x,y,z):
        return theano.tensor.clip(x,y,z)
    # use an x value less than y, an x value between y and z, and an x value
    # greater than z
    unittest_tools.verify_grad(func,
            [ numpy.asarray([-1.,0.5,2.]), 0., 1.])

def test_clip_grad_int():

    # test that integers don't crash clip gradient
    x = tensor.iscalar()
    y = tensor.iscalar()
    z = tensor.iscalar()
    c = tensor.clip(x,y,z)
    tensor.grad(c, [x, y, z])


if __name__ == '__main__':

    t = TestElemwise('setUp')
    t.setUp()
    t.test_infer_shape()
