from __future__ import absolute_import, print_function, division
import time

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

import theano
from theano.gof.opt import check_stack_trace
from theano.tensor.nnet.conv3d2d import conv3d, get_diagonal_subtensor_view, DiagonalSubtensor, IncDiagonalSubtensor
import theano.tests.unittest_tools as utt


def test_get_diagonal_subtensor_view(wrap=lambda a: a):
    x = np.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 np.all(np.asarray(xv01) == [[12, 9, 6, 3], [16, 13, 10, 7]])

    x = np.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 np.all(np.asarray(xv01) == [
        [[12, 13], [8, 9], [4, 5]],
        [[18, 19], [14, 15], [10, 11]]])

    assert np.all(np.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 np.all(xvi == get_diagonal_subtensor_view(xi, 0, 1))


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

    # if border_mode is not 'valid', the signals need zero-padding
    if border_mode == 'full':
        Tpad = Tf - 1
        Hpad = Hf - 1
        Wpad = Wf - 1
    elif border_mode == 'half':
        Tpad = Tf // 2
        Hpad = Hf // 2
        Wpad = Wf // 2
    else:
        Tpad = 0
        Hpad = 0
        Wpad = 0

    if Tpad > 0 or Hpad > 0 or Wpad > 0:
        # zero-pad signals
        signals_padded = np.zeros((Ns, Ts + 2 * Tpad, C,
                                   Hs + 2 * Hpad, Ws + 2 * Wpad), 'float32')
        signals_padded[:, Tpad:(Ts + Tpad), :, Hpad:(Hs + Hpad),
                       Wpad:(Ws + Wpad)] = signals
        Ns, Ts, C, Hs, Ws = signals_padded.shape
        signals = signals_padded

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

    rval = np.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 check_diagonal_subtensor_view_traces(fn):
    assert check_stack_trace(
        fn, ops_to_check=(DiagonalSubtensor, IncDiagonalSubtensor))


@parameterized.expand(('valid', 'full', 'half'), utt.custom_name_func)
def test_conv3d(border_mode):
    if ndimage is None or not theano.config.cxx:
        raise SkipTest("conv3d2d tests need SciPy and a c++ compiler")

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

    shared = theano.tensor._shared

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

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

    t0 = time.time()
    pyres = pyconv3d(signals, filters, border_mode)
    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,
                 border_mode=border_mode)

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

    check_diagonal_subtensor_view_traces(newconv3d)
    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')
    check_diagonal_subtensor_view_traces(gnewconv3d)

    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 = np.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = np.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
    utt.verify_grad(lambda s, f: conv3d(s, f, border_mode=border_mode),
                    [signals, filters], eps=1e-1, mode=mode)

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

    t0 = time.time()
    pyres = pyconv3d(signals, filters, border_mode)
    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,
                 border_mode=border_mode)

    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 = np.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
    filters = np.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
    utt.verify_grad(lambda s, f: conv3d(s, f, border_mode=border_mode),
                    [signals, filters], eps=1e-1, mode=mode)
