import unittest
import numpy

import theano
from theano.tests import unittest_tools as utt

# Skip tests if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if not cuda_ndarray.cuda_available:
    raise SkipTest('Optional package cuda not available')
from theano.sandbox.cuda import float32_shared_constructor as shared
from  theano.sandbox.cuda.blas import GpuCorr3dMM, GpuCorr3dMM_gradWeights, GpuCorr3dMM_gradInputs
from theano.sandbox.cuda.basic_ops import gpu_contiguous

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


class TestCorr3DMM(unittest.TestCase):

    def run_conv_valid(self, inputs_shape, filters_shape,
                       subsample=(1, 1, 1)):
        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')

        inputs = shared(inputs_val)
        filters = shared(filters_val)
        bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
        conv_ref = theano.tensor.nnet.conv3D(V=inputs, W=filters,
                                             b=bias, d=subsample)
        conv = GpuCorr3dMM(border_mode = "valid",
                           subsample=subsample)(inputs.dimshuffle(0, 4, 1, 2, 3),
                                                filters.dimshuffle(0, 4, 1, 2, 3))
        conv = conv.dimshuffle(0, 2, 3, 4, 1)

        f_ref = theano.function([], conv_ref)
        f = theano.function([], conv, mode=mode_with_gpu)

        res_ref = f_ref()
        res = f()
        utt.assert_allclose(res_ref, res)

    def test_valid(self):
        self.run_conv_valid(inputs_shape=(16, 20, 12, 16, 1),
                            filters_shape=(10, 6, 12, 4, 1))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(2, 2, 2))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(2, 2, 2))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(3, 3, 3))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(3, 3, 3))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(3, 2, 1))
        self.run_conv_valid(inputs_shape=(16, 20, 12, 15, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            subsample=(1, 2, 3))

    def run_gradweight(self, inputs_shape, filters_shape, dCdH_shape,
                        subsample=(1, 1, 1)):
        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        dCdH_val = numpy.random.random(dCdH_shape).astype('float32')
        inputs = shared(inputs_val)
        dCdH = shared(dCdH_val)

        conv = theano.tensor.nnet.convGrad3D(V=inputs, dCdH=dCdH,
                                              WShape=filters_shape,
                                              d=subsample)
        img = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
        topgrad = gpu_contiguous(dCdH.dimshuffle(0, 4, 1, 2, 3))
        if (subsample == (1, 1, 1)):
            conv_gemm = GpuCorr3dMM_gradWeights(subsample=subsample)(img,
                                                                     topgrad)
        else:
            conv_gemm = GpuCorr3dMM_gradWeights(subsample=subsample)(img,
                                                                     topgrad,
                                                                     shape=filters_shape[1:4])
        conv_gemm = conv_gemm.dimshuffle(0, 2, 3, 4, 1)
        f_ref = theano.function([], conv)
        f = theano.function([], conv_gemm)

        res_ref = f_ref()
        res = f()
        utt.assert_allclose(res_ref, res)

    def test_gradweight(self):
        self.run_gradweight(inputs_shape=(16, 10, 12, 16, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            dCdH_shape=(16, 5, 1, 13, 10),
                            subsample=(1, 1, 1))
        self.run_gradweight(inputs_shape=(16, 20, 10, 16, 1),
                            filters_shape=(10, 6, 4, 4, 1),
                            dCdH_shape=(16, 8, 4, 7, 10),
                            subsample=(2, 2, 2))
        self.run_gradweight(inputs_shape=(16, 20, 10, 16, 1),
                            filters_shape=(10, 6, 3, 4, 1),
                            dCdH_shape=(16, 5, 3, 5, 10),
                            subsample=(3, 3, 3))
        self.run_gradweight(inputs_shape=(16, 20, 12, 16, 1),
                            filters_shape=(10, 6, 12, 4, 1),
                            dCdH_shape=(16, 8, 1, 5, 10),
                            subsample=(2, 1, 3))

    def run_gradinput(self, inputs_shape, filters_shape,
                      subsample=(1, 1, 1)):

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')

        inputs = shared(inputs_val)
        filters = shared(filters_val)
        bias = shared(numpy.zeros(filters_shape[4]).astype('float32'))
        conv = theano.tensor.nnet.convTransp3D(W=filters, b=bias, d=subsample,
                                               H=inputs)
        f_ref = theano.function([], conv)
        res_ref = f_ref()

        ### Get bottom shape using convTransp3D
        bottom_shape = res_ref.shape
        bottom_val = numpy.random.random(bottom_shape).astype('float32')
        bottom = shared(bottom_val)

        weight = gpu_contiguous(filters.dimshuffle(0, 4, 1, 2, 3))
        top = gpu_contiguous(inputs.dimshuffle(0, 4, 1, 2, 3))
        if (subsample == (1, 1, 1)):
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(kern=weight, topgrad=top)
        else:
            conv_gemm = GpuCorr3dMM_gradInputs(subsample=subsample)(kern=weight, topgrad=top,
                                                                    shape=bottom.shape[1:4])
        conv_gemm = conv_gemm.dimshuffle(0, 2, 3, 4, 1)
        f = theano.function([], conv_gemm)

        res = f()
        utt.assert_allclose(res_ref, res)

    def test_gradinput(self):
        self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
                           filters_shape=(10, 6, 12, 4, 1))
        self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
                           filters_shape=(10, 6, 12, 4, 1),
                           subsample=(2,2,2))
        self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
                           filters_shape=(10, 6, 12, 4, 1),
                           subsample=(3,3,3))
        self.run_gradinput(inputs_shape=(16, 15, 12, 12, 10),
                           filters_shape=(10, 6, 12, 4, 1),
                           subsample=(3,1,2))


    def test_opt_conv3d_gemm(self):
        inputs_shape = (16, 20, 32, 16, 1)
        filters_shape = (10, 6, 12, 4, 1)

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')

        inputs = shared(inputs_val)
        filters = shared(filters_val)
        bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))

        conv = theano.tensor.nnet.conv3D(V=inputs, W=filters,
                                         b=bias, d=(1,1,1))
        mode = mode_with_gpu.including('conv3d_gemm')

        f_ref = theano.function([], conv)
        f_gemm = theano.function([], conv, mode=mode)

        # make sure we inserted the gemm trickery
        topo = f_gemm.maker.fgraph.toposort()
        assert sum(isinstance(n.op, GpuCorr3dMM) for n in topo) > 0

        res_ref = f_ref()
        res_gemm = f_gemm()
        utt.assert_allclose(res_ref, res_gemm)

    def test_opt_convgrad3d_gemm(self):
        inputs_shape = (16, 10, 12, 16, 1)
        filters_shape = (10, 6, 12, 4, 1)
        dCdH_shape = (16, 5, 1, 13, 10)

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        dCdH_val = numpy.random.random(dCdH_shape).astype('float32')

        inputs = shared(inputs_val)
        dCdH = shared(dCdH_val)

        conv = theano.tensor.nnet.convGrad3D(V=inputs, dCdH=dCdH,
                                             WShape=filters_shape,
                                             d=(1,1,1))
        mode = mode_with_gpu.including('convgrad3d_gemm')

        f_ref = theano.function([], conv)
        f_gemm = theano.function([], conv, mode=mode)

        # make sure we inserted the gemm trickery
        topo = f_gemm.maker.fgraph.toposort()
        assert sum(isinstance(n.op, GpuCorr3dMM_gradWeights) for n in topo) > 0

        res_ref = f_ref()
        res_gemm = f_gemm()
        utt.assert_allclose(res_ref, res_gemm)


    def test_opt_convtransp3d_gemm(self):
        inputs_shape = (16, 15, 12, 12, 10)
        filters_shape = (10, 6, 12, 4, 1)

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')
        bias = shared(numpy.zeros(filters_shape[4]).astype('float32'))

        inputs = shared(inputs_val)
        filters = shared(filters_val)

        conv = theano.tensor.nnet.convTransp3D(W=filters, b=bias, d=(1,1,1),
                                               H=inputs)
        mode = mode_with_gpu.including('convtransp3d_gemm')

        f_ref = theano.function([], conv)
        f_gemm = theano.function([], conv, mode=mode)

        # make sure we inserted the gemm trickery
        topo = f_gemm.maker.fgraph.toposort()
        assert sum(isinstance(n.op, GpuCorr3dMM_gradInputs) for n in topo) > 0

        res_ref = f_ref()
        res_gemm = f_gemm()
        utt.assert_allclose(res_ref, res_gemm)

