from __future__ import print_function
import time

from nose.plugins.skip import SkipTest
import numpy
try:
    from scipy import ndimage
except ImportError:
    ndimage = None
from six.moves import xrange

import theano
from theano.tensor.nnet.conv3d2d import *
import theano.tests.unittest_tools as utt


if theano.config.mode == 'FAST_COMPILE':
    mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
else:
    mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')


def test_get_diagonal_subtensor_view(wrap=lambda a: a):
    x = numpy.arange(20).reshape(5, 4).astype('float32')
    x = wrap(x)
    xv01 = get_diagonal_subtensor_view(x, 0, 1)

    # test that it works in 2d
    assert numpy.all(numpy.asarray(xv01) == [[12, 9, 6, 3], [16, 13, 10, 7]])

    x = numpy.arange(24).reshape(4, 3, 2)
    xv01 = get_diagonal_subtensor_view(x, 0, 1)
    xv02 = get_diagonal_subtensor_view(x, 0, 2)
    xv12 = get_diagonal_subtensor_view(x, 1, 2)

    # print 'x', x
    # print 'xv01', xv01
    # print 'xv02', xv02
    assert numpy.all(numpy.asarray(xv01) == [
        [[12, 13], [8, 9], [4, 5]],
        [[18, 19], [14, 15], [10, 11]]])

    assert numpy.all(numpy.asarray(xv02) == [
        [[6, 1], [8, 3], [10, 5]],
        [[12, 7], [14, 9], [16, 11]],
        [[18, 13], [20, 15], [22, 17]],
        ])

    # diagonal views of each leading matrix is the same
    # as the slices out of the diagonal view of the entire 3d tensor
    for xi, xvi in zip(x, xv12):
        assert numpy.all(xvi == get_diagonal_subtensor_view(xi, 0, 1))


def pyconv3d(signals, filters):
    Ns, Ts, C, Hs, Ws = signals.shape
    Nf, Tf, C, Hf, Wf = filters.shape

    Tf2 = Tf//2
    Hf2 = Hf//2
    Wf2 = Wf//2

    rval = numpy.zeros((Ns, Ts-Tf+1, Nf, Hs-Hf+1, Ws-Wf+1))
    for ns in xrange(Ns):
        for nf in xrange(Nf):
            for c in xrange(C):
                s_i = signals[ns, :, c, :, :]
                f_i = filters[nf, :, c, :, :]
                r_i = rval[ns, :, nf, :, :]
                o_i = ndimage.convolve(s_i, f_i, mode='constant', cval=1)
                o_i_sh0 = o_i.shape[0]
                # print s_i.shape, f_i.shape, r_i.shape, o_i.shape
                r_i += o_i[Tf2:o_i_sh0-Tf2, Hf2:-Hf2, Wf2:-Wf2]
    return rval


def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
    if ndimage is None:
        raise SkipTest("conv3d2d tests need SciPy")

    Ns, Ts, C, Hs, Ws = 3, 10, 3, 32, 32
    Nf, Tf, C, Hf, Wf = 32, 5 , 3, 5 , 5

    signals = numpy.arange(Ns*Ts*C*Hs*Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = numpy.arange(Nf*Tf*C*Hf*Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')

    t0 = time.time()
    pyres = pyconv3d(signals, filters)
    print(time.time() - t0)

    s_signals = shared(signals)
    s_filters = shared(filters)
    s_output = shared(signals*0)

    out = conv3d(s_signals, s_filters,
                 signals_shape=signals.shape,
                 filters_shape=filters.shape)

    newconv3d = theano.function([], [],
                                updates={s_output: out},
                                mode=mode)

    t0 = time.time()
    newconv3d()
    print(time.time() - t0)
    utt.assert_allclose(pyres, s_output.get_value(borrow=True))
    gsignals, gfilters = theano.grad(out.sum(), [s_signals, s_filters])
    gnewconv3d = theano.function([], [],
                                 updates=[(s_filters, gfilters),
                                          (s_signals, gsignals)],
                                 mode=mode,
                                 name='grad')

    t0 = time.time()
    gnewconv3d()
    print('grad', time.time() - t0)

    Ns, Ts, C, Hs, Ws = 3, 3, 3, 5, 5
    Nf, Tf, C, Hf, Wf = 4, 2, 3, 2, 2

    signals = numpy.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
    utt.verify_grad(conv3d, [signals, filters], eps=1e-1)

    ### Additional Test that covers the case of patched implementation for filter with Tf=1
    Ns, Ts, C, Hs, Ws = 3, 10, 3, 32, 32
    Nf, Tf, C, Hf, Wf = 32, 1 , 3, 5 , 5

    signals = numpy.arange(Ns*Ts*C*Hs*Ws).reshape(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = numpy.arange(Nf*Tf*C*Hf*Wf).reshape(Nf, Tf, C, Hf, Wf).astype('float32')

    t0 = time.time()
    pyres = pyconv3d(signals, filters)
    print(time.time() - t0)

    s_signals = shared(signals)
    s_filters = shared(filters)
    s_output = shared(signals*0)

    out = conv3d(s_signals, s_filters,
                 signals_shape=signals.shape,
                 filters_shape=filters.shape)

    newconv3d = theano.function([], [],
                                updates={s_output: out},
                                mode=mode)

    t0 = time.time()
    newconv3d()
    print(time.time() - t0)
    utt.assert_allclose(pyres, s_output.get_value(borrow=True))
    gsignals, gfilters = theano.grad(out.sum(), [s_signals, s_filters])
    gnewconv3d = theano.function([], [],
                                 updates=[(s_filters, gfilters),
                                          (s_signals, gsignals)],
                                 mode=mode,
                                 name='grad')

    t0 = time.time()
    gnewconv3d()
    print('grad', time.time() - t0)

    Ns, Ts, C, Hs, Ws = 3, 3, 3, 5, 5
    Nf, Tf, C, Hf, Wf = 4, 1, 3, 2, 2

    signals = numpy.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
    utt.verify_grad(conv3d, [signals, filters], eps=1e-1)
