def test_normal_vector(self):
        rng_R = random_state_type()
        avg = tensor.vector()
        std = tensor.vector()
        post_r, out = normal(rng_R, avg=avg, std=std)
        assert out.ndim == 1
        f = compile.function([rng_R, avg, std], [post_r, out], accept_inplace=True)

        def as_floatX(thing):
            return numpy.asarray(thing, dtype=theano.config.floatX)

        avg_val = [1, 2, 3]
        std_val = as_floatX([0.1, 0.2, 0.3])
        rng = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())

        # Arguments of size (3,)
        rng0, val0 = f(rng, avg_val, std_val)
        numpy_val0 = as_floatX(numpy_rng.normal(loc=as_floatX(avg_val), scale=as_floatX(std_val)))
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        rng1, val1 = f(rng0, avg_val[:-1], std_val[:-1])
        numpy_val1 = numpy.asarray(numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1]), dtype=theano.config.floatX)
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = compile.function([rng_R, avg, std], normal(rng_R, avg=avg, std=std, size=(3,)), accept_inplace=True)
        rng2, val2 = g(rng1, avg_val, std_val)
        numpy_val2 = numpy.asarray(numpy_rng.normal(loc=avg_val, scale=std_val, size=(3,)), dtype=theano.config.floatX)
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, rng2, avg_val[:-1], std_val[:-1])

    def test_multinomial_vector(self):
        rng_R = random_state_type()
        n = tensor.lvector()
        pvals = tensor.matrix()
        post_r, out = multinomial(rng_R, n=n, pvals=pvals)
        assert out.ndim == 2
        f = compile.function([rng_R, n, pvals], [post_r, out], accept_inplace=True)

        n_val = [1, 2, 3]
        pvals_val = [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]
        pvals_val = numpy.asarray(pvals_val, dtype=config.floatX)
        rng = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())

        # Arguments of size (3,)
        rng0, val0 = f(rng, n_val, pvals_val)
        numpy_val0 = numpy.asarray([numpy_rng.multinomial(n=nv, pvals=pv) for nv, pv in zip(n_val, pvals_val)])
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        rng1, val1 = f(rng0, n_val[:-1], pvals_val[:-1])
        numpy_val1 = numpy.asarray(
            [numpy_rng.multinomial(n=nv, pvals=pv) for nv, pv in zip(n_val[:-1], pvals_val[:-1])]
        )
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = compile.function([rng_R, n, pvals], multinomial(rng_R, n=n, pvals=pvals, size=(3,)), accept_inplace=True)
        rng2, val2 = g(rng1, n_val, pvals_val)
        numpy_val2 = numpy.asarray([numpy_rng.multinomial(n=nv, pvals=pv) for nv, pv in zip(n_val, pvals_val)])
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, rng2, n_val[:-1], pvals_val[:-1])

    def test_random_function_noshape_args(self):
        """Test if random_function helper works with args but without shape"""
        rng_R = random_state_type()

        # No shape, default args -> OK
        post_out, out = uniform(rng_R, size=None, ndim=2)
        f = compile.function(
            [compile.In(rng_R, value=numpy.random.RandomState(utt.fetch_seed()), update=post_out, mutable=True)],
            [out],
            accept_inplace=True,
        )
        o, = f()

        # No shape, args that have to be broadcasted -> OK
        low = tensor.TensorType(dtype="float64", broadcastable=(False, True, True))()
        high = tensor.TensorType(dtype="float64", broadcastable=(True, True, True, False))()
        post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
        self.assertEqual(out2.ndim, 4)
        self.assertEqual(out2.broadcastable, (True, False, True, False))

        g = compile.function(
            [
                low,
                high,
                compile.In(rng_R, value=numpy.random.RandomState(utt.fetch_seed()), update=post_out2, mutable=True),
            ],
            [out2],
            accept_inplace=True,
        )
        low_v = [[[3]], [[4]], [[-5]]]
        high_v = [[[[5, 8]]]]
        o2, = g(low_v, high_v)
        self.assertEqual(o2.shape, (1, 3, 1, 2))

    def test_uniform_vector(self):
        rng_R = random_state_type()
        low = tensor.vector()
        high = tensor.vector()
        post_r, out = uniform(rng_R, low=low, high=high)
        assert out.ndim == 1
        f = compile.function([rng_R, low, high], [post_r, out], accept_inplace=True)

        def as_floatX(thing):
            return numpy.asarray(thing, dtype=theano.config.floatX)

        low_val = as_floatX([0.1, 0.2, 0.3])
        high_val = as_floatX([1.1, 2.2, 3.3])
        rng = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())

        # Arguments of size (3,)
        rng0, val0 = f(rng, low_val, high_val)
        numpy_val0 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val))
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
        numpy_val1 = as_floatX(numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1]))
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = compile.function([rng_R, low, high], uniform(rng_R, low=low, high=high, size=(3,)), accept_inplace=True)
        rng2, val2 = g(rng1, low_val, high_val)
        numpy_val2 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val, size=(3,)))
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, rng2, low_val[:-1], high_val[:-1])

    def test_no_inplace(self):
        """Test that when not running inplace, the RandomState is
        not updated"""
        rf = RandomFunction("uniform", tensor.dvector)
        rng_R = random_state_type()

        post_r, out = rf(rng_R, (3,), 0.0, 1.0)
        f = compile.function([rng_R], [post_r, out])
        rng = numpy.random.RandomState(utt.fetch_seed())

        rng0, val0 = f(rng)
        rng_ = numpy.random.RandomState(utt.fetch_seed())
        # rng should still be in a fresh state
        self.assertTrue(rng_R.type.values_eq(rng, rng_))
        # rng0 should be in an updated state
        self.assertFalse(rng_R.type.values_eq(rng, rng0))

        f2 = compile.function([compile.In(rng_R, value=rng, update=post_r, mutable=False)], [post_r, out])
        rng2, val2 = f2()
        # rng should be in a fresh state
        self.assertTrue(rng_R.type.values_eq(rng, rng_))
        # rng2 should be in an updated state
        self.assertFalse(rng_R.type.values_eq(rng, rng2))
        # The updated state should be the same for both functions
        self.assertTrue(rng_R.type.values_eq(rng2, rng0))

        rng3, val3 = f2()
        # rng2 should not have changed
        self.assertTrue(rng_R.type.values_eq(rng2, rng0))
        # rng3 should be an updated again version of rng2
        self.assertFalse(rng_R.type.values_eq(rng3, rng2))
        self.assertFalse(rng_R.type.values_eq(rng3, rng))

    def test_binomial_vector(self):
        rng_R = random_state_type()
        n = tensor.lvector()
        prob = tensor.vector()
        post_r, out = binomial(rng_R, n=n, p=prob)
        assert out.ndim == 1
        f = compile.function([rng_R, n, prob], [post_r, out],
                             accept_inplace=True)

        n_val = [1, 2, 3]
        prob_val = numpy.asarray([.1, .2, .3], dtype=config.floatX)
        rng = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())

        # Arguments of size (3,)
        rng0, val0 = f(rng, n_val, prob_val)
        numpy_val0 = numpy_rng.binomial(n=n_val, p=prob_val)
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        rng1, val1 = f(rng0, n_val[:-1], prob_val[:-1])
        numpy_val1 = numpy_rng.binomial(n=n_val[:-1], p=prob_val[:-1])
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = compile.function([rng_R, n, prob],
                binomial(rng_R, n=n, p=prob, size=(3,)),
                accept_inplace=True)
        rng2, val2 = g(rng1, n_val, prob_val)
        numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3,))
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, rng2, n_val[:-1], prob_val[:-1])

    def test_random_integers_vector(self):
        rng_R = random_state_type()
        low = tensor.lvector()
        high = tensor.lvector()
        post_r, out = random_integers(rng_R, low=low, high=high)
        assert out.ndim == 1
        f = compile.function([rng_R, low, high], [post_r, out],
                             accept_inplace=True)

        low_val = [100, 200, 300]
        high_val = [110, 220, 330]
        rng = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())

        # Arguments of size (3,)
        rng0, val0 = f(rng, low_val, high_val)
        numpy_val0 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
            for lv, hv in zip(low_val, high_val)])
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
        numpy_val1 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
            for lv, hv in zip(low_val[:-1], high_val[:-1])])
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = compile.function([rng_R, low, high],
                random_integers(rng_R, low=low, high=high, size=(3,)),
                accept_inplace=True)
        rng2, val2 = g(rng1, low_val, high_val)
        numpy_val2 = numpy.asarray([numpy_rng.random_integers(low=lv, high=hv)
            for lv, hv in zip(low_val, high_val)])
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, rng2, low_val[:-1], high_val[:-1])

    def test_multiple_functions(self):
        a = T.scalar()  # the a is for 'anonymous' (un-named).
        x, s = T.scalars('xs')
        v = T.vector('v')

        # put in some inputs
        list_of_things = [s, x, v]

        # some derived thing, whose inputs aren't all in the list
        list_of_things.append(a * x + s )

        f1 = function([x, In(a, value=1.0, name='a'), In(s, value=0.0, update=s+a*x, mutable=True)], s+a*x)
        list_of_things.append(f1)

        # now put in a function sharing container with the previous one
        f2 = function([x, In(a, value=1.0, name='a'), In(s, value=f1.container[s], update=s+a*x, mutable=True)], s+a*x)
        list_of_things.append(f2)

        assert isinstance(f2.container[s].storage, list)
        assert f2.container[s].storage is f1.container[s].storage

        # now put in a function with non-scalar
        v_value = numpy.asarray([2, 3, 4.], dtype=config.floatX)
        f3 = function([x, In(v, value=v_value)], x+v)
        list_of_things.append(f3)

        # try to pickle the entire things
        try:
            saved_format = cPickle.dumps(list_of_things, protocol=-1)
            new_list_of_things = cPickle.loads(saved_format)
        except NotImplementedError, e:
            if e[0].startswith('DebugMode is not picklable'):
                return
            else:
                raise

 def function(inputs, output):
     if mode is None:
         f = compile.function(inputs, output, accept_inplace=True,
                 allow_input_downcast=True)
     else:
         f = compile.function(inputs, output, accept_inplace=True,
                 allow_input_downcast=True, mode=mode)
     return f

def test_empty_givens_updates():
    # Regression test for bug fixed in 8625e03.
    # Empty givens / updates dictionaries were not properly detected before,
    # triggering useless crashes at compile time.
    x = T.scalar()
    y = x * 2
    function([theano.In(x)], y, givens={})
    function([theano.In(x)], y, updates={})

 def function(inputs, output):
     if mode is None:
         f = compile.function(inputs, output, accept_inplace=True,
                 allow_input_downcast=True, on_unused_input='ignore')
     else:
         f = compile.function(inputs, output, accept_inplace=True,
                 allow_input_downcast=True, mode=mode,
                 on_unused_input='ignore')
     return f

    def test_permutation_helper(self):
        """Test that raw_random.permutation_helper generates the same
        results as numpy,
        and that the 'ndim_added' keyword behaves correctly."""
        # permutation_helper needs "ndim_added=1", because its output
        # is one dimension more than its "shape" argument (and there's
        # no way to determine that automatically).
        # Check the working case, over two calls to see if the random
        # state is correctly updated.
        rf = RandomFunction(permutation_helper, tensor.imatrix, 8,
                            ndim_added=1)
        rng_R = random_state_type()
        post_r, out = rf(rng_R, (7,), 8)

        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r, mutable=True)],
                [out], accept_inplace=True)

        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        val0 = f()
        val1 = f()
        # numpy_rng.permutation outputs one vector at a time,
        # so we call it iteratively to generate all the samples.
        numpy_val0 = numpy.asarray([numpy_rng.permutation(8)
                                    for i in range(7)])
        numpy_val1 = numpy.asarray([numpy_rng.permutation(8)
                                    for i in range(7)])
        print val0
        print numpy_val0
        print val1
        print numpy_val1
        self.assertTrue(numpy.all(val0 == numpy_val0))
        self.assertTrue(numpy.all(val1 == numpy_val1))

        # This call lacks "ndim_added=1", so ndim_added defaults to 0.
        # A ValueError should be raised.
        rf0 = RandomFunction(permutation_helper, tensor.imatrix, 8)
        post_r0, out0 = rf0(rng_R, (7,), 8)
        f0 = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r0, mutable=True)],
                [out0], accept_inplace=True)
        self.assertRaises(ValueError, f0)

        # Here, ndim_added is 2 instead of 1. A ValueError should be raised.
        rf2 = RandomFunction(permutation_helper, tensor.imatrix, 8,
                             ndim_added=2)
        post_r2, out2 = rf2(rng_R, (7,), 8)
        f2 = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r2, mutable=True)],
                [out2], accept_inplace=True)
        self.assertRaises(ValueError, f2)

    def test_shared_state0(self):
        a = T.scalar()  # the a is for 'anonymous' (un-named).
        x, s = T.scalars('xs')

        f = function([x, In(a, value=1.0, name='a'), In(s, value=0.0, update=s+a*x, mutable=True)], s+a*x)
        g = function([x, In(a, value=1.0, name='a'), In(s, value=f.container[s], update=s-a*x, mutable=True)], s+a*x)

        f(1, 2)
        self.assertTrue(f[s] == 2)
        self.assertTrue(g[s] == 2)
        g(1, 2)
        self.assertTrue(f[s] == 0)
        self.assertTrue(g[s] == 0)

    def __init__(self):
        a = T.scalar()  # the a is for 'anonymous' (un-named).
        x, s = T.scalars('xs')
        v = T.vector('v')

        self.s = s
        self.x = x
        self.v = v

        self.e = a * x + s

        self.f1 = function([x, In(a, value=1.0, name='a'), In(s, value=0.0, update=s+a*x, mutable=True)], s+a*x)

        self.f2 = function([x, In(a, value=1.0, name='a'), In(s, value=self.f1.container[s], update=s+a*x, mutable=True)], s+a*x)

    def test_vector_arguments(self):
        rng_R = random_state_type()
        low = tensor.vector()
        post_r, out = uniform(rng_R, low=low, high=1)
        assert out.ndim == 1
        f = compile.function([rng_R, low], [post_r, out], accept_inplace=True)

        def as_floatX(thing):
            return numpy.asarray(thing, dtype=theano.config.floatX)

        rng_state0 = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        post0, val0 = f(rng_state0, [-5, .5, 0, 1])
        post1, val1 = f(post0, as_floatX([.9]))
        numpy_val0 = as_floatX(numpy_rng.uniform(low=[-5, .5, 0, 1], high=1))
        numpy_val1 = as_floatX(numpy_rng.uniform(low=as_floatX([.9]), high=1))

        assert numpy.all(val0 == numpy_val0)
        assert numpy.all(val1 == numpy_val1)

        high = tensor.vector()
        post_rb, outb = uniform(rng_R, low=low, high=high)
        assert outb.ndim == 1
        fb = compile.function([rng_R, low, high], [post_rb, outb],
                              accept_inplace=True)

        post0b, val0b = fb(post1, [-4., -2], [-1, 0])
        post1b, val1b = fb(post0b, [-4.], [-1])
        numpy_val0b = as_floatX(numpy_rng.uniform(low=[-4., -2], high=[-1, 0]))
        numpy_val1b = as_floatX(numpy_rng.uniform(low=[-4.], high=[-1]))
        assert numpy.all(val0b == numpy_val0b)
        assert numpy.all(val1b == numpy_val1b)
        self.assertRaises(ValueError, fb, post1b, [-4., -2], [-1, 0, 1])
        #TODO: do we want that?
        #self.assertRaises(ValueError, fb, post1b, [-4., -2], [-1])

        size = tensor.lvector()
        post_rc, outc = uniform(rng_R, low=low, high=high, size=size, ndim=1)
        fc = compile.function([rng_R, low, high, size], [post_rc, outc],
                              accept_inplace=True)
        post0c, val0c = fc(post1b, [-4., -2], [-1, 0], [2])
        post1c, val1c = fc(post0c, [-4.], [-1], [1])
        numpy_val0c = as_floatX(numpy_rng.uniform(low=[-4., -2], high=[-1, 0]))
        numpy_val1c = as_floatX(numpy_rng.uniform(low=[-4.], high=[-1]))
        assert numpy.all(val0c == numpy_val0c)
        assert numpy.all(val1c == numpy_val1c)
        self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1])
        self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [1, 2])
        self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1, 0], [2, 1])
        self.assertRaises(ValueError, fc, post1c, [-4., -2], [-1], [1])

    def test_inplace_optimization(self):
        """Test that FAST_RUN includes the random_make_inplace optimization"""
        #inplace = False
        rf2 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector)
        rng_R = random_state_type()

        # If calling RandomFunction directly, all args have to be specified,
        # because shape will have to be moved to the end
        post_r2, out2 = rf2(rng_R, (4,), 0., 1.)

        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r2,
                    mutable=True)],
                out2,
                mode='FAST_RUN')  # DEBUG_MODE can't pass the id-based
                                  # test below

        # test that the RandomState object stays the same from function call to
        # function call, but that the values returned change from call to call.

        id0 = id(f[rng_R])
        val0 = f()
        assert id0 == id(f[rng_R])
        val1 = f()
        assert id0 == id(f[rng_R])

        assert not numpy.allclose(val0, val1)

 def test_none(self):
     fn = function([], None) #ok
     rval = fn()
     if rval == []:
         raise KnownFailureTest('See #254: Using None as function output leads to [] return value')
     else:
         assert rval is None

    def test_mixed_shape(self):
        # Test when the provided shape is a tuple of ints and scalar vars
        rng_R = random_state_type()
        shape0 = tensor.lscalar()
        shape = (shape0, 3)
        post_r, u = uniform(rng_R, size=shape, ndim=2)
        f = compile.function([rng_R, shape0], u)
        rng_state0 = numpy.random.RandomState(utt.fetch_seed())

        assert f(rng_state0, 2).shape == (2, 3)
        assert f(rng_state0, 8).shape == (8, 3)

        post_r, v = uniform(rng_R, size=shape)
        g = compile.function([rng_R, shape0], v)
        assert g(rng_state0, 2).shape == (2, 3)
        assert g(rng_state0, 8).shape == (8, 3)

    def test_multinomial(self):
        """Test that raw_random.multinomial generates the same
        results as numpy."""
        # Check over two calls to see if the random state is correctly updated.
        rng_R = random_state_type()
        post_r, out = multinomial(rng_R, (7, 3), 6, [0.2] * 5)

        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r, mutable=True)],
                [out], accept_inplace=True)

        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        val0, = f()
        val1, = f()
        numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
        numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
        print val0
        print numpy_val0
        print val1
        print numpy_val1
        self.assertTrue(numpy.all(val0 == numpy_val0))
        self.assertTrue(numpy.all(val1 == numpy_val1))

        self.assertTrue(val0.shape == (7, 3, 5))
        self.assertTrue(val1.shape == (7, 3, 5))

    def test_permutation(self):
        """Test that raw_random.permutation generates the same
        results as numpy."""
        rng_R = random_state_type()
        post_r, out = permutation(rng_R, size=(9,), n=6)
        print 'OUT NDIM', out.ndim
        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r, mutable=True)],
                [out], accept_inplace=True)

        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        # Check over two calls to see if the random state is correctly updated.
        # numpy_rng.permutation outputs one vector at a time,
        # so we call it iteratively to generate all the samples.
        val0 = f()
        val1 = f()
        numpy_val0 = numpy.asarray([numpy_rng.permutation(6)
                                    for i in range(9)])
        numpy_val1 = numpy.asarray([numpy_rng.permutation(6)
                                    for i in range(9)])
        print val0
        print numpy_val0
        print val1
        print numpy_val1
        self.assertTrue(numpy.all(val0 == numpy_val0))
        self.assertTrue(numpy.all(val1 == numpy_val1))