import numpy as np

import theano

from theano import config
from theano.tensor.nnet.corr import CorrMM, CorrMM_gradWeights, CorrMM_gradInputs
from theano.gpuarray.type import gpuarray_shared_constructor
from theano.gpuarray.blas import GpuCorrMM, GpuCorrMM_gradWeights, GpuCorrMM_gradInputs

from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu, ref_cast
from tests.tensor.nnet.test_abstract_conv import (
    TestGroupedConvNoOptim,
    TestUnsharedConv,
)
from tests.tensor.nnet.test_abstract_conv import (
    TestAsymmetricPadding,
    TestCausalConv,
)


class TestCorrMM:
    def run_conv_valid(
        self,
        inputs_shape,
        filters_shape,
        border_mode="valid",
        filter_dilation=(1, 1),
        subsample=(1, 1),
        unshared=False,
        verify_grad=False,
    ):
        inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
        if unshared:
            filters_shape = [filters_shape[i] for i in (0, 1, 2, 5, 3, 4)]
        else:
            filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]

        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
        filters_val = np.random.random(filters_shape).astype(config.floatX)

        inputs = gpuarray_shared_constructor(inputs_val)
        filters = gpuarray_shared_constructor(filters_val)

        conv_ref = CorrMM(
            border_mode=border_mode,
            filter_dilation=filter_dilation,
            subsample=subsample,
            unshared=unshared,
        )(ref_cast(inputs), ref_cast(filters))
        f_ref = theano.function([], conv_ref, mode=mode_without_gpu)

        conv = GpuCorrMM(
            border_mode=border_mode,
            filter_dilation=filter_dilation,
            subsample=subsample,
            unshared=unshared,
        )(inputs, filters)
        f = theano.function([], conv, mode=mode_with_gpu)

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

        if verify_grad:
            utt.verify_grad(
                GpuCorrMM(
                    border_mode=border_mode,
                    filter_dilation=filter_dilation,
                    subsample=subsample,
                    unshared=unshared,
                ),
                [inputs_val, filters_val],
                mode=mode_with_gpu,
            )

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

    def test_border_mode(self):
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode="valid",
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode="half",
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode="full",
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode=(0, 0),
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode=(1, 2),
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 6, 12, 1),
            border_mode=(3, 2),
        )

    def test_filter_dilation(self):
        inputs_shape = [16, 20, 12, 1]
        filters_shape = [10, 6, 5, 1]

        for filter_dilation in [(2, 1), (1, 2)]:
            for border_mode in ["valid", "half", "full"]:
                self.run_conv_valid(
                    inputs_shape=inputs_shape,
                    filters_shape=filters_shape,
                    filter_dilation=filter_dilation,
                    border_mode=border_mode,
                )

    def test_verify_gradients(self):
        # use a small example to check the gradients
        inputs_shape = [2, 7, 9, 1]
        filters_shape = [1, 3, 3, 1]

        for filter_dilation in [(2, 1), (1, 2)]:
            for border_mode in ["valid", "half", "full", (2, 1)]:
                self.run_conv_valid(
                    inputs_shape=inputs_shape,
                    filters_shape=filters_shape,
                    filter_dilation=filter_dilation,
                    border_mode=border_mode,
                    verify_grad=True,
                )

    def test_unshared(self):
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 15, 1, 6, 12, 1),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 8, 1, 6, 12, 1),
            subsample=(2, 2),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 5, 1, 6, 12, 1),
            subsample=(3, 3),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 5, 1, 6, 12, 1),
            subsample=(3, 2),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 15, 1, 6, 12, 1),
            subsample=(1, 2),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 15, 1, 6, 12, 1),
            border_mode="valid",
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 21, 13, 6, 12, 1),
            border_mode="half",
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 25, 23, 6, 12, 1),
            border_mode="full",
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 15, 1, 6, 12, 1),
            border_mode=(0, 0),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 17, 5, 6, 12, 1),
            border_mode=(1, 2),
            unshared=True,
        )
        self.run_conv_valid(
            inputs_shape=(16, 20, 12, 1),
            filters_shape=(10, 21, 5, 6, 12, 1),
            border_mode=(3, 2),
            unshared=True,
        )

    def run_gradweight(self, inputs_shape, filters_shape, dCdH_shape, subsample=(1, 1)):
        inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
        filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]
        dCdH_shape = [dCdH_shape[i] for i in (0, 3, 1, 2)]

        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
        dCdH_val = np.random.random(dCdH_shape).astype(config.floatX)
        inputs = gpuarray_shared_constructor(inputs_val)
        dCdH = gpuarray_shared_constructor(dCdH_val)
        shape = gpuarray_shared_constructor(np.array(filters_shape[2:]))

        if subsample == (1, 1):
            conv_ref = CorrMM_gradWeights(subsample=subsample)(
                ref_cast(inputs), ref_cast(dCdH)
            )
            conv_gemm = GpuCorrMM_gradWeights(subsample=subsample)(inputs, dCdH)
        else:
            conv_ref = CorrMM_gradWeights(subsample=subsample)(
                ref_cast(inputs), ref_cast(dCdH), shape=shape
            )
            conv_gemm = GpuCorrMM_gradWeights(subsample=subsample)(
                inputs, dCdH, shape=shape
            )

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

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

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

    def run_gradinput(self, inputs_shape, filters_shape, subsample=(1, 1)):
        inputs_shape = [inputs_shape[i] for i in (0, 3, 1, 2)]
        filters_shape = [filters_shape[i] for i in (0, 3, 1, 2)]

        inputs_val = np.random.random(inputs_shape).astype(config.floatX)
        filters_val = np.random.random(filters_shape).astype(config.floatX)
        inputs = gpuarray_shared_constructor(inputs_val)
        filters = gpuarray_shared_constructor(filters_val)

        bottom_height = (inputs_shape[2] - 1) * subsample[0] + filters_shape[2]
        bottom_width = (inputs_shape[3] - 1) * subsample[1] + filters_shape[3]
        bottom_shape = gpuarray_shared_constructor(
            np.array([bottom_height, bottom_width])
        )

        if subsample == (1, 1):
            conv_ref = CorrMM_gradInputs(subsample=subsample)(
                kern=ref_cast(filters), topgrad=ref_cast(inputs)
            )
            conv_gemm = GpuCorrMM_gradInputs(subsample=subsample)(
                kern=filters, topgrad=inputs
            )
        else:
            conv_ref = CorrMM_gradInputs(subsample=subsample)(
                kern=ref_cast(filters), topgrad=ref_cast(inputs), shape=bottom_shape
            )
            conv_gemm = GpuCorrMM_gradInputs(subsample=subsample)(
                kern=filters, topgrad=inputs, shape=bottom_shape
            )

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

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

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

    def test_large_input(self):
        # This tests the number-of-threads computation
        # by making (channels * height) > (max_threads_dim ** 2).
        # (See also issue #5165.)
        self.run_conv_valid(
            inputs_shape=(1, 1024, 3, 1024),
            filters_shape=(1, 1, 1, 1024),
            verify_grad=False,
        )
        self.run_gradinput(inputs_shape=(1, 1024, 3, 1), filters_shape=(1, 1, 1, 1024))


class TestGroupGpuCorr2d(TestGroupedConvNoOptim):
    mode = mode_with_gpu.excluding("cudnn")
    conv_op = GpuCorrMM
    conv_gradw_op = GpuCorrMM_gradWeights
    conv_gradi_op = GpuCorrMM_gradInputs
    flip_filter = True
    is_dnn = False


class TestUnsharedGpuCorr2d(TestUnsharedConv):
    mode = mode_with_gpu
    conv2d_op = GpuCorrMM
    conv2d_gradw_op = GpuCorrMM_gradWeights
    conv2d_gradi_op = GpuCorrMM_gradInputs


class TestAsymmetricGpu(TestAsymmetricPadding):
    mode = mode_with_gpu
    conv2d_op = GpuCorrMM
    conv2d_gradw_op = GpuCorrMM_gradWeights
    conv2d_gradi_op = GpuCorrMM_gradInputs


class TestCausalGpuCorr(TestCausalConv):
    mode = mode_with_gpu
