import theano
import numpy
from . import scan


def test_001():
    x0 = theano.tensor.fvector('x0')
    state = theano.tensor.unbroadcast(
        theano.tensor.shape_padleft(x0), 0)
    out, _ = scan.scan(lambda x: x+numpy.float32(1),
                           states=state,
                           n_steps=5)
    fn = theano.function([x0], out[0])
    val_x0 = numpy.float32([1, 2, 3])
    assert numpy.all(fn(val_x0) == val_x0 + 5)


def test_002():
    x0 = theano.tensor.fvector('x0')
    state = theano.tensor.alloc(
        theano.tensor.constant(numpy.float32(0)),
        6,
        x0.shape[0])
    state = theano.tensor.set_subtensor(state[0], x0)

    out, _ = scan.scan(lambda x: x+numpy.float32(1),
                           states=state,
                           n_steps=5)
    fn = theano.function([x0], out)
    val_x0 = numpy.float32([1, 2, 3])
    assert numpy.all(fn(val_x0)[-1] == val_x0 + 5)
    assert numpy.all(fn(val_x0)[0] == val_x0)


def test_003():
    x0 = theano.tensor.fvector('x0')
    sq = theano.tensor.fvector('sq')
    state = theano.tensor.alloc(
        theano.tensor.constant(numpy.float32(0)),
        6,
        x0.shape[0])
    state = theano.tensor.set_subtensor(state[0], x0)

    out, _ = scan.scan(lambda s, x: x+s,
                           sequences=sq,
                           states=state,
                           n_steps=5)
    fn = theano.function([sq, x0], out)
    val_x0 = numpy.float32([1, 2, 3])
    val_sq = numpy.float32([1, 2, 3, 4, 5])
    assert numpy.all(fn(val_sq, val_x0)[-1] == val_x0 + 15)
    assert numpy.all(fn(val_sq, val_x0)[0] == val_x0)


def test_004():
    sq = theano.tensor.fvector('sq')
    nst = theano.tensor.iscalar('nst')
    out, _ = scan.scan(lambda s: s+numpy.float32(1),
                           sequences=sq,
                           states=[],
                           n_steps=nst)
    fn = theano.function([sq, nst], out)
    val_sq = numpy.float32([1, 2, 3, 4, 5])
    assert numpy.all(fn(val_sq, 5) == val_sq + 1)


def test_005():
    sq = theano.tensor.fvector('sq')
    nst = theano.tensor.iscalar('nst')
    out, _ = scan.scan(lambda s: s+numpy.float32(1),
                       sequences=sq,
                       states=[None],
                       n_steps=nst)
    fn = theano.function([sq, nst], out)
    val_sq = numpy.float32([1, 2, 3, 4, 5])
    assert numpy.all(fn(val_sq, 5) == val_sq + 1)


if __name__ == '__main__':
    test_001()
    test_002()
    test_003()
    test_004()
    test_005()

