import numpy as np
import pytest

import aesara
import aesara.tensor as at
from aesara.compile.mode import Mode
from aesara.configdefaults import config
from aesara.graph.opt import check_stack_trace
from aesara.tensor.nnet import abstract_conv as conv
from aesara.tensor.nnet import conv2d_transpose, corr, corr3d
from aesara.tensor.nnet.abstract_conv import (
    AbstractConv2d,
    AbstractConv2d_gradInputs,
    AbstractConv2d_gradWeights,
    assert_conv_shape,
    assert_shape,
    bilinear_kernel_1D,
    bilinear_kernel_2D,
    bilinear_upsampling,
    causal_conv1d,
    check_conv_gradinputs_shape,
    get_conv_gradinputs_shape,
    get_conv_gradweights_shape,
    get_conv_output_shape,
    separable_conv2d,
    separable_conv3d,
)
from aesara.tensor.nnet.corr import CorrMM, CorrMM_gradInputs, CorrMM_gradWeights
from aesara.tensor.nnet.corr3d import Corr3dMM, Corr3dMMGradInputs, Corr3dMMGradWeights
from aesara.tensor.type import (
    TensorType,
    ftensor4,
    iscalar,
    lvector,
    tensor3,
    tensor4,
    tensor5,
)
from tests import unittest_tools as utt


def conv2d_corr(
    inputs,
    filters,
    border_mode="valid",
    subsample=(1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1),
):
    if conv_mode == "conv":
        filters = filters[:, :, ::-1, ::-1]
    return corr.CorrMM(border_mode, subsample, filter_dilation)(inputs, filters)


def conv2d_corr_gw(
    inputs,
    topgrad,
    filters_shape,
    border_mode="valid",
    subsample=(1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1),
):
    rval = corr.CorrMM_gradWeights(border_mode, subsample, filter_dilation)(
        inputs, topgrad, filters_shape[2:]
    )
    if conv_mode == "conv":
        rval = rval[:, :, ::-1, ::-1]
    return rval


def conv2d_corr_gi(
    filters,
    topgrad,
    inputs_shape,
    border_mode="valid",
    subsample=(1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1),
):
    if conv_mode == "conv":
        filters = filters[:, :, ::-1, ::-1]
    return corr.CorrMM_gradInputs(border_mode, subsample, filter_dilation)(
        filters, topgrad, inputs_shape[2:]
    )


def conv3d_corr(
    inputs,
    filters,
    border_mode="valid",
    subsample=(1, 1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1, 1),
):
    if conv_mode == "conv":
        filters = filters[:, :, ::-1, ::-1, ::-1]
    return corr3d.Corr3dMM(border_mode, subsample, filter_dilation)(inputs, filters)


def conv3d_corr_gw(
    inputs,
    topgrad,
    filters_shape,
    border_mode="valid",
    subsample=(1, 1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1, 1),
):
    rval = corr3d.Corr3dMMGradWeights(border_mode, subsample, filter_dilation)(
        inputs, topgrad, filters_shape[2:]
    )
    if conv_mode == "conv":
        rval = rval[:, :, ::-1, ::-1, ::-1]
    return rval


def conv3d_corr_gi(
    filters,
    topgrad,
    inputs_shape,
    border_mode="valid",
    subsample=(1, 1, 1),
    conv_mode="conv",
    filter_dilation=(1, 1, 1),
):
    if conv_mode == "conv":
        filters = filters[:, :, ::-1, ::-1, ::-1]
    return corr3d.Corr3dMMGradInputs(border_mode, subsample, filter_dilation)(
        filters, topgrad, inputs_shape[2:]
    )


class TestGetConvOutShape:
    def test_basic(self):
        image_shape, kernel_shape = (3, 2, 12, 9), (4, 2, 5, 6)
        sub_sample = (1, 2)
        filter_dilation = (2, 1)
        test1_params = get_conv_output_shape(
            image_shape, kernel_shape, "valid", sub_sample, filter_dilation
        )
        test2_params = get_conv_output_shape(
            image_shape, kernel_shape, "half", sub_sample, filter_dilation
        )
        test3_params = get_conv_output_shape(
            image_shape, kernel_shape, "full", sub_sample, filter_dilation
        )
        test4_params = get_conv_output_shape(
            image_shape, kernel_shape, (1, 2), sub_sample, filter_dilation
        )

        assert test1_params == (3, 4, 4, 2)
        assert test2_params == (3, 4, 12, 5)
        assert test3_params == (3, 4, 20, 7)
        assert test4_params == (3, 4, 6, 4)

    def test_basic_3d(self):
        image_shape, kernel_shape = (3, 2, 12, 9, 7), (4, 2, 5, 6, 4)
        sub_sample = (1, 2, 1)
        filter_dilation = (2, 1, 1)
        test1_params = get_conv_output_shape(
            image_shape, kernel_shape, "valid", sub_sample, filter_dilation
        )
        test2_params = get_conv_output_shape(
            image_shape, kernel_shape, "half", sub_sample, filter_dilation
        )
        test3_params = get_conv_output_shape(
            image_shape, kernel_shape, "full", sub_sample, filter_dilation
        )
        test4_params = get_conv_output_shape(
            image_shape, kernel_shape, (1, 2, 3), sub_sample, filter_dilation
        )

        assert test1_params == (3, 4, 4, 2, 4)
        assert test2_params == (3, 4, 12, 5, 8)
        assert test3_params == (3, 4, 20, 7, 10)
        assert test4_params == (3, 4, 6, 4, 10)


class TestConvGradInputsShape:
    def test_check_shape(self):
        for i in range(1, 20):
            for k in range(1, 10):
                for b in ("valid", "half", "full", (0, 2)):
                    for s in (1, 2, 3):
                        for d in (1, 2, 3):
                            image_shape = (59, 61, i, i)
                            kernel_shape = (67, 61, k, k)

                            # compute the output that these inputs and parameters would produce
                            computed_shape = get_conv_output_shape(
                                image_shape, kernel_shape, b, (s, s), (d, d)
                            )
                            # this should be accepted
                            assert check_conv_gradinputs_shape(
                                image_shape,
                                kernel_shape,
                                computed_shape,
                                b,
                                (s, s),
                                (d, d),
                            )

                            # one or more None should also be accepted
                            trial_shape = (None, None, computed_shape[2], None)
                            assert check_conv_gradinputs_shape(
                                image_shape,
                                kernel_shape,
                                trial_shape,
                                b,
                                (s, s),
                                (d, d),
                            )

                            # the batch size and number of filters are important
                            trial_shape = (1, 1, computed_shape[2], computed_shape[3])
                            assert not check_conv_gradinputs_shape(
                                image_shape,
                                kernel_shape,
                                trial_shape,
                                b,
                                (s, s),
                                (d, d),
                            )

                            # outputs that are too large or too small should be rejected
                            for o in (-3, -2, -1, 1, 2, 3):
                                trial_shape = (
                                    computed_shape[0],
                                    computed_shape[1],
                                    computed_shape[2] + o,
                                    computed_shape[3] + o,
                                )
                                assert not check_conv_gradinputs_shape(
                                    image_shape,
                                    kernel_shape,
                                    trial_shape,
                                    b,
                                    (s, s),
                                    (d, d),
                                )

    def test_get_shape(self):
        for i in range(1, 20):
            for k in range(1, 10):
                for b in ("valid", "half", "full", (0, 2)):
                    for d in (1, 2, 3):
                        image_shape = (59, 61, i, i)
                        kernel_shape = (67, 61, k, k)

                        # compute the output that these inputs and parameters would produce
                        output_shape = get_conv_output_shape(
                            image_shape, kernel_shape, b, (1, 1), (d, d)
                        )

                        # compute the image_shape given this output_shape
                        computed_image_shape = get_conv_gradinputs_shape(
                            kernel_shape, output_shape, b, (1, 1), (d, d)
                        )
                        assert computed_image_shape == image_shape

                        # if subsample > 1, the shape should be None
                        computed_image_shape = get_conv_gradinputs_shape(
                            kernel_shape, output_shape, b, (2, 3), (d, d)
                        )
                        image_shape_with_None = image_shape[:2] + (None, None)
                        assert computed_image_shape == image_shape_with_None

                        # compute the kernel_shape given this output_shape
                        computed_kernel_shape = get_conv_gradweights_shape(
                            image_shape, output_shape, b, (1, 1), (d, d)
                        )

                        # if border_mode == 'half', the shape should be None
                        if b == "half":
                            kernel_shape_with_None = kernel_shape[:2] + (None, None)
                            assert computed_kernel_shape == kernel_shape_with_None
                        else:
                            assert computed_kernel_shape == kernel_shape

                        # if subsample > 1, the shape should be None
                        computed_kernel_shape = get_conv_gradweights_shape(
                            kernel_shape, output_shape, b, (2, 3), (d, d)
                        )
                        kernel_shape_with_None = kernel_shape[:2] + (None, None)
                        assert computed_kernel_shape == kernel_shape_with_None


class TestAssertConvShape:
    def test_basic(self):
        shape = tuple(iscalar() for i in range(4))
        f = aesara.function(shape, assert_conv_shape(shape))

        assert [1, 2, 3, 4] == f(1, 2, 3, 4)
        assert [0, 0, 1, 1] == f(0, 0, 1, 1)
        with pytest.raises(AssertionError):
            f(3, 3, 3, 0)
        with pytest.raises(AssertionError):
            f(3, 3, 0, 3)
        with pytest.raises(AssertionError):
            f(3, 3, -1, 3)
        with pytest.raises(AssertionError):
            f(3, -1, 3, 3)
        with pytest.raises(AssertionError):
            f(-1, 3, 3, 3)


class TestAssertShape:
    @config.change_flags(conv__assert_shape=True)
    def test_basic(self):
        x = tensor4()
        s1 = iscalar()
        s2 = iscalar()
        expected_shape = [None, s1, s2, None]
        f = aesara.function([x, s1, s2], assert_shape(x, expected_shape))

        v = np.zeros((3, 5, 7, 11), dtype="float32")
        assert 0 == np.sum(f(v, 5, 7))

        with pytest.raises(AssertionError):
            f(v, 5, 0)

        with pytest.raises(AssertionError):
            f(v, 5, 9)

        with pytest.raises(AssertionError):
            f(v, 0, 7)

        with pytest.raises(AssertionError):
            f(v, 7, 7)

    @config.change_flags(conv__assert_shape=True)
    def test_shape_check_conv2d(self):
        input = tensor4()
        filters = tensor4()

        out = conv.abstract_conv2d(
            input, filters, input_shape=(3, 5, 7, 11), filter_shape=(7, 5, 3, 3)
        )
        f = aesara.function([input, filters], out)
        # mismatched input_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 5, 9, 11), dtype="float32"),
                np.zeros((7, 5, 3, 3), dtype="float32"),
            )
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 5, 7, 11), dtype="float32"),
                np.zeros((7, 5, 2, 2), dtype="float32"),
            )

    @config.change_flags(conv__assert_shape=True)
    @pytest.mark.skipif(config.cxx == "", reason="test needs cxx")
    def test_shape_check_conv3d(self):
        input = tensor5()
        filters = tensor5()

        out = conv.conv3d(
            input, filters, input_shape=(3, 5, 7, 11, 13), filter_shape=(7, 5, 3, 3, 3)
        )
        f = aesara.function([input, filters], out)
        # mismatched input_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 5, 9, 11, 13), dtype="float32"),
                np.zeros((7, 5, 3, 3, 3), dtype="float32"),
            )
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 5, 7, 11, 13), dtype="float32"),
                np.zeros((7, 5, 2, 2, 2), dtype="float32"),
            )

    @config.change_flags(conv__assert_shape=True)
    def test_shape_check_conv2d_grad_wrt_inputs(self):
        output_grad = tensor4()
        filters = tensor4()

        out = conv.conv2d_grad_wrt_inputs(
            output_grad,
            filters,
            input_shape=(None, None, 7, 11),
            filter_shape=(7, 5, 3, 3),
        )
        f = aesara.function([output_grad, filters], out)
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 6, 5, 9), dtype="float32"),
                np.zeros((7, 6, 3, 3), dtype="float32"),
            )

    @config.change_flags(conv__assert_shape=True)
    @pytest.mark.skipif(config.cxx == "", reason="test needs cxx")
    def test_shape_check_conv3d_grad_wrt_inputs(self):
        output_grad = tensor5()
        filters = tensor5()

        out = conv.conv3d_grad_wrt_inputs(
            output_grad,
            filters,
            input_shape=(None, None, 7, 11, 13),
            filter_shape=(7, 5, 3, 3, 3),
        )
        f = aesara.function([output_grad, filters], out)
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 6, 5, 9, 11), dtype="float32"),
                np.zeros((7, 6, 3, 3, 3), dtype="float32"),
            )

    @config.change_flags(conv__assert_shape=True)
    def test_shape_check_conv2d_grad_wrt_weights(self):
        input = tensor4()
        output_grad = tensor4()

        out = conv.conv2d_grad_wrt_weights(
            input,
            output_grad,
            filter_shape=(None, None, 3, 3),
            input_shape=(3, 5, 7, 11),
        )
        f = aesara.function([input, output_grad], out)
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 6, 7, 11), dtype="float32"),
                np.zeros((3, 7, 5, 9), dtype="float32"),
            )

    @config.change_flags(conv__assert_shape=True)
    @pytest.mark.skipif(config.cxx == "", reason="test needs cxx")
    def test_shape_check_conv3d_grad_wrt_weights(self):
        input = tensor5()
        output_grad = tensor5()

        out = conv.conv3d_grad_wrt_weights(
            input,
            output_grad,
            filter_shape=(None, None, 3, 3, 3),
            input_shape=(3, 5, 7, 11, 13),
        )
        f = aesara.function([input, output_grad], out)
        # mismatched filter_shape
        with pytest.raises(AssertionError):
            f(
                np.zeros((3, 6, 7, 11, 13), dtype="float32"),
                np.zeros((3, 7, 5, 9, 11), dtype="float32"),
            )


class BaseTestConv:
    def get_output_shape(
        self, inputs_shape, filters_shape, subsample, border_mode, filter_dilation
    ):
        dil_filters = tuple(
            (s - 1) * d + 1 for s, d in zip(filters_shape[2:], filter_dilation)
        )
        if border_mode == "valid":
            border_mode = (0,) * (len(inputs_shape) - 2)
        if border_mode == "half":
            border_mode = tuple(d // 2 for d in dil_filters)
        if border_mode == "full":
            border_mode = tuple(d - 1 for d in dil_filters)
        batch_size = inputs_shape[0]
        num_filters = filters_shape[0]
        return (batch_size, num_filters,) + tuple(
            None
            if i is None or k is None
            else ((i + 2 * pad - ((k - 1) * fd + 1)) // d + 1)
            for i, k, d, pad, fd in zip(
                inputs_shape[2:],
                filters_shape[2:],
                subsample,
                border_mode,
                filter_dilation,
            )
        )

    def run_fwd(
        self,
        inputs_shape,
        filters_shape,
        conv_fn,
        conv_op,
        ref,
        subsample=None,
        verify_grad=True,
        mode=None,
        border_mode="valid",
        filter_flip=True,
        provide_shape=False,
        target_op=None,
        check_trace=False,
        filter_dilation=None,
    ):
        if subsample is None:
            subsample = (1,) * (len(inputs_shape) - 2)
        if filter_dilation is None:
            filter_dilation = (1,) * (len(inputs_shape) - 2)

        inputs_val = np.random.random(inputs_shape).astype("float32")
        filters_val = np.random.random(filters_shape).astype("float32")

        # scale down values to prevent rounding errors
        inputs_val /= 10
        filters_val /= 10

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

        if provide_shape:
            imshp = inputs_shape
            kshp = filters_shape
        else:
            imshp = None
            kshp = None
        if filter_flip:
            conv_mode = "conv"
        else:
            conv_mode = "cross"

        c_ref = ref(
            inputs,
            filters,
            border_mode=border_mode,
            subsample=subsample,
            conv_mode=conv_mode,
            filter_dilation=filter_dilation,
        )
        c = conv_fn(
            inputs,
            filters,
            border_mode=border_mode,
            subsample=subsample,
            filter_flip=filter_flip,
            input_shape=imshp,
            filter_shape=kshp,
            filter_dilation=filter_dilation,
        )

        f_ref = aesara.function([], c_ref, mode="FAST_RUN")
        f = aesara.function([], c, mode=mode)

        if target_op is not None:
            assert any(isinstance(n.op, target_op) for n in f.maker.fgraph.toposort())
            if check_trace:
                assert check_stack_trace(f, ops_to_check=target_op)

        res_ref = np.array(f_ref())
        res = np.array(f())
        utt.assert_allclose(res_ref, res)
        if (
            verify_grad
            and inputs_val.size > 0
            and filters_val.size > 0
            and res.size > 0
        ):
            utt.verify_grad(
                conv_op(
                    border_mode=border_mode,
                    imshp=imshp,
                    kshp=kshp,
                    subsample=subsample,
                    filter_dilation=filter_dilation,
                ),
                [inputs_val, filters_val],
                mode=mode,
            )

    def run_gradweight(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradWeights_fn,
        ref,
        subsample=None,
        filter_flip=True,
        verify_grad=True,
        mode=None,
        border_mode="valid",
        provide_shape=False,
        target_op=None,
        check_trace=False,
        filter_dilation=None,
    ):
        if subsample is None:
            subsample = (1,) * (len(inputs_shape) - 2)
        if filter_dilation is None:
            filter_dilation = (1,) * (len(inputs_shape) - 2)

        inputs_val = np.random.random(inputs_shape).astype("float32")
        output_val = np.random.random(output_shape).astype("float32")

        inputs = self.shared(inputs_val)
        output = self.shared(output_val)

        if provide_shape:
            imshp = inputs_shape
            kshp = filters_shape
        else:
            imshp = None
            kshp = None
        if filter_flip:
            conv_mode = "conv"
        else:
            conv_mode = "cross"
        c = gradWeights_fn(
            border_mode=border_mode,
            filter_flip=filter_flip,
            subsample=subsample,
            imshp=imshp,
            kshp=kshp,
            filter_dilation=filter_dilation,
        )
        c = c(inputs, output, filters_shape[2:])
        c_ref = ref(
            inputs,
            output,
            filters_shape,
            border_mode=border_mode,
            subsample=subsample,
            conv_mode=conv_mode,
            filter_dilation=filter_dilation,
        )
        f = aesara.function([], c, mode=mode)
        f_ref = aesara.function([], c_ref, mode="FAST_RUN")

        if target_op is not None:
            assert any(isinstance(n.op, target_op) for n in f.maker.fgraph.toposort())
            if check_trace:
                assert check_stack_trace(f, ops_to_check=target_op)

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

        def abstract_conv_gradweight(inputs_val, output_val):
            conv_op = gradWeights_fn(
                border_mode=border_mode,
                subsample=subsample,
                filter_dilation=filter_dilation,
            )
            return conv_op(inputs_val, output_val, filters_shape[2:])

        if verify_grad and inputs_val.size > 0 and output_val.size > 0 and res.size > 0:
            utt.verify_grad(
                abstract_conv_gradweight, [inputs_val, output_val], mode=mode, eps=1
            )

    def run_gradinput(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradInputs_fn,
        ref,
        subsample=None,
        filter_flip=True,
        verify_grad=True,
        mode=None,
        border_mode="valid",
        provide_shape=False,
        target_op=None,
        check_trace=False,
        filter_dilation=None,
    ):
        if subsample is None:
            subsample = (1,) * (len(inputs_shape) - 2)
        if filter_dilation is None:
            filter_dilation = (1,) * (len(inputs_shape) - 2)

        output_val = np.random.random(output_shape).astype("float32")
        filters_val = np.random.random(filters_shape).astype("float32")
        output = self.shared(output_val)
        filters = self.shared(filters_val)

        if provide_shape:
            imshp = inputs_shape
            kshp = filters_shape
        else:
            imshp = None
            kshp = None
        if filter_flip:
            conv_mode = "conv"
        else:
            conv_mode = "cross"
        c = gradInputs_fn(
            border_mode=border_mode,
            subsample=subsample,
            filter_flip=filter_flip,
            imshp=imshp,
            kshp=kshp,
            filter_dilation=filter_dilation,
        )
        c = c(filters, output, inputs_shape[2:])
        f = aesara.function([], c, mode=mode)

        # ref is set to None for the inconsistent-shape tests.
        # The reference function also raises an exception, which would
        # mask the exception generated by the target implementation.
        if ref is not None:
            c_ref = ref(
                filters,
                output,
                inputs_shape,
                border_mode=border_mode,
                subsample=subsample,
                conv_mode=conv_mode,
                filter_dilation=filter_dilation,
            )
            f_ref = aesara.function([], c_ref, mode="FAST_RUN")

        if target_op is not None:
            assert any(isinstance(n.op, target_op) for n in f.maker.fgraph.toposort())
            if check_trace:
                assert check_stack_trace(f, ops_to_check=target_op)

        res = np.array(f())

        if ref is not None:
            res_ref = np.array(f_ref())
            utt.assert_allclose(res_ref, res)

        def abstract_conv_gradinputs(filters_val, output_val):
            conv_op = gradInputs_fn(
                border_mode=border_mode,
                subsample=subsample,
                filter_dilation=filter_dilation,
            )
            return conv_op(filters_val, output_val, inputs_shape[2:])

        if (
            verify_grad
            and filters_val.size > 0
            and output_val.size > 0
            and res.size > 0
        ):
            utt.verify_grad(
                abstract_conv_gradinputs, [filters_val, output_val], mode=mode, eps=1
            )

    def run_test_case(self, *args, **kargs):
        raise NotImplementedError()

    def test_all(self):
        ds = self.default_subsamples
        db = self.default_border_mode
        dflip = self.default_filter_flip
        dprovide_shape = self.default_provide_shape
        for (i, f) in zip(self.inputs_shapes, self.filters_shapes):
            for provide_shape in self.provide_shape:
                self.run_test_case(i, f, ds, db, dflip, provide_shape)
            if min(i) > 0 and min(f) > 0:
                for fd in self.filters_dilations:
                    for s in self.subsamples:
                        for b in self.border_modes:
                            self.run_test_case(i, f, s, b, dflip, dprovide_shape, fd)
                for flip in self.filter_flip:
                    self.run_test_case(i, f, ds, db, flip, dprovide_shape)


class BaseTestConv2d(BaseTestConv):
    @classmethod
    def setup_class(cls):
        # This tests can run even when config.blas__ldflags is empty.
        cls.inputs_shapes = [
            (8, 1, 6, 6),
            (8, 1, 8, 8),
            (2, 1, 7, 7),
            (6, 1, 10, 11),
            (2, 1, 6, 5),
            (1, 5, 9, 9),
            (0, 1, 6, 6),
            (1, 0, 6, 6),
            (1, 1, 6, 6),
        ]
        cls.filters_shapes = [
            (5, 1, 2, 2),
            (4, 1, 3, 3),
            (2, 1, 3, 3),
            (1, 1, 2, 3),
            (4, 1, 1, 3),
            (4, 5, 3, 2),
            (1, 1, 2, 2),
            (1, 0, 2, 2),
            (0, 1, 2, 2),
        ]
        cls.subsamples = [(1, 1), (2, 2), (2, 4)]
        cls.default_subsamples = (1, 1)
        cls.filters_dilations = [(1, 1), (1, 2), (2, 1)]
        cls.default_filters_dilations = (1, 1)
        cls.border_modes = ["valid", "half", "full", (0, 0), (1, 1), (5, 5), (5, 2)]
        cls.default_border_mode = (0, 0)
        cls.filter_flip = [True, False]
        cls.default_filter_flip = True
        cls.provide_shape = [True, False]
        cls.default_provide_shape = True
        cls.shared = staticmethod(aesara.compile.shared)

    def run_test_case_gi(self, *args, **kwargs):
        raise NotImplementedError()

    def test_gradinput_arbitrary_output_shapes(self):
        # this computes the grad wrt inputs for an output shape
        # that the forward convolution would not produce
        input_shape = (2, 1, 7, 7)
        filter_shape = (2, 1, 3, 3)
        for output_shape in [(2, 2, 8, 8), (2, 2, 9, 9), (2, 2, 12, 12)]:
            for border_mode in ["valid", "half", "full"]:
                computed_shape = get_conv_output_shape(
                    input_shape,
                    filter_shape,
                    border_mode,
                    self.default_subsamples,
                    self.default_filters_dilations,
                )
                # is this a valid combination?
                if tuple(computed_shape) == output_shape:
                    self.run_test_case_gi(
                        input_shape,
                        filter_shape,
                        output_shape,
                        self.default_subsamples,
                        border_mode,
                        True,
                        True,
                        self.default_filters_dilations,
                        False,
                    )
                else:
                    # expect an error
                    self.run_test_case_gi(
                        input_shape,
                        filter_shape,
                        output_shape,
                        self.default_subsamples,
                        border_mode,
                        True,
                        True,
                        self.default_filters_dilations,
                        True,
                    )

    def test_gradinput_impossible_output_shapes(self):
        def run_for_output_offsets(image_shape, kernel_shape, s, border_mode, d):
            # outputs that are too large or too small should be rejected
            for o in (-3, -1, 1, 2):
                output_shape = (1, 1, computed_shape[2] + o, computed_shape[3] + o)
                # expect an error
                self.run_test_case_gi(
                    image_shape,
                    kernel_shape,
                    output_shape,
                    (s, s),
                    border_mode,
                    True,
                    True,
                    (d, d),
                    True,
                )

        for (i, k) in ((1, 1), (1, 2), (2, 1), (4, 2), (4, 3), (7, 3), (9, 5)):
            for border_mode in ("valid", "half", "full", (0, 2)):
                for (s, d) in ((1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (1, 3)):
                    image_shape = (1, 1, i, i)
                    kernel_shape = (1, 1, k, k)

                    # compute the output that these inputs and parameters would produce
                    computed_shape = get_conv_output_shape(
                        image_shape, kernel_shape, border_mode, (s, s), (d, d)
                    )

                    run_for_output_offsets(
                        image_shape,
                        kernel_shape,
                        s,
                        border_mode,
                        d,
                    )

    def run_fwd(
        self,
        inputs_shape,
        filters_shape,
        conv_fn=conv.abstract_conv2d,
        conv_op=conv.AbstractConv2d,
        ref=conv2d_corr,
        **kwargs,
    ):
        super().run_fwd(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            conv_fn=conv_fn,
            conv_op=conv_op,
            ref=ref,
            **kwargs,
        )

    def run_gradweight(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradWeights_fn=conv.AbstractConv2d_gradWeights,
        ref=conv2d_corr_gw,
        **kwargs,
    ):
        super().run_gradweight(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            output_shape=output_shape,
            gradWeights_fn=gradWeights_fn,
            ref=ref,
            **kwargs,
        )

    def run_gradinput(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradInputs_fn=conv.AbstractConv2d_gradInputs,
        ref=conv2d_corr_gi,
        **kwargs,
    ):
        super().run_gradinput(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            output_shape=output_shape,
            gradInputs_fn=gradInputs_fn,
            ref=ref,
            **kwargs,
        )


@pytest.mark.skipif(
    not config.cxx or config.mode == "FAST_COMPILE",
    reason="Need blas to test conv2d",
)
class TestCorrConv2d(BaseTestConv2d):
    @classmethod
    def setup_class(cls):
        # This tests can run even when config.blas__ldflags is empty.
        super().setup_class()

    def run_test_case(self, i, f, s, b, flip, provide_shape, fd=(1, 1)):
        o = self.get_output_shape(i, f, s, b, fd)
        self.run_fwd(
            inputs_shape=i,
            filters_shape=f,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=CorrMM,
            check_trace=True,
            filter_dilation=fd,
        )
        self.run_gradweight(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=CorrMM_gradWeights,
            check_trace=True,
            filter_dilation=fd,
        )
        self.run_gradinput(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=CorrMM_gradInputs,
            check_trace=True,
            filter_dilation=fd,
        )

    def run_test_case_gi(
        self, i, f, o, s, b, flip, provide_shape, fd=(1, 1), expect_error=False
    ):
        if not expect_error:
            self.run_gradinput(
                inputs_shape=i,
                filters_shape=f,
                output_shape=o,
                subsample=s,
                verify_grad=True,
                provide_shape=provide_shape,
                border_mode=b,
                filter_flip=flip,
                target_op=CorrMM_gradInputs,
                check_trace=True,
                filter_dilation=fd,
            )
        else:
            with pytest.raises(ValueError):
                self.run_gradinput(
                    inputs_shape=i,
                    filters_shape=f,
                    output_shape=o,
                    subsample=s,
                    verify_grad=False,
                    provide_shape=provide_shape,
                    border_mode=b,
                    filter_flip=flip,
                    target_op=CorrMM_gradInputs,
                    ref=None,
                    check_trace=True,
                    filter_dilation=fd,
                )

    @pytest.mark.slow
    def test_all(self):
        super().test_all()


@pytest.mark.skipif(
    config.cxx == "",
    reason="SciPy and cxx needed",
)
class TestAbstractConvNoOptim(BaseTestConv2d):
    @classmethod
    def setup_class(cls):
        # This tests can run even when config.blas__ldflags is empty.
        super().setup_class()
        cls.inputs_shapes = [(8, 1, 6, 6)]
        cls.filters_shapes = [(5, 1, 2, 2)]
        cls.subsamples = [(1, 1), (2, 2)]
        cls.filters_dilations = [(1, 1), (1, 2), (2, 1)]
        cls.border_modes = ["valid", "half", "full"]
        cls.filter_flip = [True]
        cls.provide_shape = [False]

    def run_test_case(self, i, f, s, b, flip, provide_shape, fd=(1, 1)):
        o = self.get_output_shape(i, f, s, b, fd)
        mode = Mode(optimizer=None)
        self.run_fwd(
            inputs_shape=i,
            filters_shape=f,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=None,
            check_trace=True,
            filter_dilation=fd,
            mode=mode,
        )
        self.run_gradweight(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=None,
            check_trace=True,
            filter_dilation=fd,
            mode=mode,
        )
        self.run_gradinput(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=None,
            check_trace=True,
            filter_dilation=fd,
            mode=mode,
        )

    def run_test_case_gi(
        self, i, f, o, s, b, flip, provide_shape, fd=(1, 1), expect_error=False
    ):
        mode = Mode(optimizer=None)
        if not expect_error:
            self.run_gradinput(
                inputs_shape=i,
                filters_shape=f,
                output_shape=o,
                subsample=s,
                verify_grad=True,
                provide_shape=provide_shape,
                border_mode=b,
                filter_flip=flip,
                target_op=None,
                check_trace=True,
                filter_dilation=fd,
                mode=mode,
            )
        else:
            with pytest.raises(ValueError):
                self.run_gradinput(
                    inputs_shape=i,
                    filters_shape=f,
                    output_shape=o,
                    subsample=s,
                    verify_grad=False,
                    provide_shape=provide_shape,
                    border_mode=b,
                    filter_flip=flip,
                    target_op=None,
                    check_trace=True,
                    filter_dilation=fd,
                    ref=None,
                    mode=mode,
                )


class BaseTestConv3d(BaseTestConv):
    @classmethod
    def setup_class(cls):
        # This tests can run even when config.blas__ldflags is empty.
        cls.inputs_shapes = [
            (2, 1, 5, 5, 5),
            (1, 2, 7, 5, 6),
            (0, 1, 5, 5, 5),
            (1, 0, 5, 5, 5),
            (1, 1, 5, 5, 5),
        ]
        cls.filters_shapes = [
            (2, 1, 2, 2, 2),
            (1, 2, 2, 1, 3),
            (1, 1, 2, 2, 2),
            (1, 0, 2, 2, 2),
            (0, 1, 2, 2, 2),
        ]
        cls.subsamples = [(1, 1, 1), (2, 2, 2), (1, 2, 3)]
        cls.default_subsamples = (1, 1, 1)
        cls.filters_dilations = [(1, 1, 1), (1, 2, 1), (2, 1, 2)]
        cls.default_filters_dilations = (1, 1, 1)
        cls.border_modes = ["valid", "half", "full", (0, 0, 0), (2, 2, 3)]
        cls.default_border_mode = (0, 0, 0)
        cls.filter_flip = [True, False]
        cls.default_filter_flip = True
        cls.provide_shape = [True, False]
        cls.default_provide_shape = True
        cls.shared = staticmethod(aesara.compile.shared)

    def test_gradinput_arbitrary_output_shapes(self):
        # this computes the grad wrt inputs for an output shape
        # that the forward convolution would not produce
        input_shape = (2, 1, 7, 7, 7)
        filter_shape = (1, 1, 3, 3, 3)
        for output_shape in [(2, 1, 8, 8, 8), (2, 1, 9, 9, 9), (2, 1, 12, 12, 12)]:
            for border_mode in ["valid", "half", "full"]:
                # compute the output that these inputs and parameters would produce
                computed_shape = get_conv_output_shape(
                    input_shape,
                    filter_shape,
                    border_mode,
                    self.default_subsamples,
                    self.default_filters_dilations,
                )
                # is this a valid combination?
                if tuple(computed_shape) == output_shape:
                    self.run_test_case_gi(
                        input_shape,
                        filter_shape,
                        output_shape,
                        self.default_subsamples,
                        border_mode,
                        True,
                        True,
                        self.default_filters_dilations,
                        False,
                    )
                else:
                    # expect an error
                    self.run_test_case_gi(
                        input_shape,
                        filter_shape,
                        output_shape,
                        self.default_subsamples,
                        border_mode,
                        True,
                        True,
                        self.default_filters_dilations,
                        True,
                    )

    def test_gradinput_impossible_output_shapes(self):
        def run_for_output_offsets(image_shape, kernel_shape, s, border_mode, d):
            # outputs that are too large or too small should be rejected
            for o in (-3, -1, 1, 2):
                output_shape = (
                    1,
                    1,
                    computed_shape[2] + o,
                    computed_shape[3] + o,
                    computed_shape[4] + o,
                )
                # expect an error
                self.run_test_case_gi(
                    image_shape,
                    kernel_shape,
                    output_shape,
                    (s, s),
                    border_mode,
                    True,
                    True,
                    (d, d),
                    True,
                )

        for (i, k) in ((1, 1), (1, 2), (2, 1), (4, 2), (4, 3), (7, 3), (9, 5)):
            for border_mode in ("valid", "half", "full", (0, 2, 1)):
                for (s, d) in ((1, 1), (1, 2), (2, 1), (2, 2), (3, 1), (1, 3)):
                    image_shape = (1, 1, i, i, i)
                    kernel_shape = (1, 1, k, k, k)

                    # compute the output that these inputs and parameters would produce
                    computed_shape = get_conv_output_shape(
                        image_shape, kernel_shape, border_mode, (s, s, s), (d, d, d)
                    )

                    run_for_output_offsets(
                        image_shape,
                        kernel_shape,
                        s,
                        border_mode,
                        d,
                    )

    def run_fwd(
        self,
        inputs_shape,
        filters_shape,
        conv_fn=conv.conv3d,
        conv_op=conv.AbstractConv3d,
        ref=conv3d_corr,
        **kwargs,
    ):
        super().run_fwd(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            conv_fn=conv_fn,
            conv_op=conv_op,
            ref=ref,
            **kwargs,
        )

    def run_gradweight(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradWeights_fn=conv.AbstractConv3d_gradWeights,
        ref=conv3d_corr_gw,
        **kwargs,
    ):
        super().run_gradweight(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            output_shape=output_shape,
            gradWeights_fn=gradWeights_fn,
            ref=ref,
            **kwargs,
        )

    def run_gradinput(
        self,
        inputs_shape,
        filters_shape,
        output_shape,
        gradInputs_fn=conv.AbstractConv3d_gradInputs,
        ref=conv3d_corr_gi,
        **kwargs,
    ):
        super().run_gradinput(
            inputs_shape=inputs_shape,
            filters_shape=filters_shape,
            output_shape=output_shape,
            gradInputs_fn=gradInputs_fn,
            ref=ref,
            **kwargs,
        )


@pytest.mark.skipif(
    not config.cxx or config.mode == "FAST_COMPILE",
    reason="Need blas to test conv3d",
)
class TestCorrConv3d(BaseTestConv3d):
    @classmethod
    def setup_class(cls):
        # This tests can run even when config.blas__ldflags is empty.
        super().setup_class()

    def run_test_case(self, i, f, s, b, flip, provide_shape, fd=(1, 1, 1)):
        o = self.get_output_shape(i, f, s, b, fd)
        # This test can run even when config.blas__ldflags is empty.
        self.run_fwd(
            inputs_shape=i,
            filters_shape=f,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=Corr3dMM,
            check_trace=True,
            filter_dilation=fd,
        )
        self.run_gradweight(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=Corr3dMMGradWeights,
            check_trace=True,
            filter_dilation=fd,
        )
        self.run_gradinput(
            inputs_shape=i,
            filters_shape=f,
            output_shape=o,
            subsample=s,
            verify_grad=True,
            provide_shape=provide_shape,
            border_mode=b,
            filter_flip=flip,
            target_op=Corr3dMMGradInputs,
            check_trace=True,
            filter_dilation=fd,
        )

    def run_test_case_gi(
        self, i, f, o, s, b, flip, provide_shape, fd=(1, 1, 1), expect_error=False
    ):
        # This test can run even when config.blas__ldflags is empty.
        if not expect_error:
            self.run_gradinput(
                inputs_shape=i,
                filters_shape=f,
                output_shape=o,
                subsample=s,
                verify_grad=True,
                provide_shape=provide_shape,
                border_mode=b,
                filter_flip=flip,
                target_op=Corr3dMMGradInputs,
                check_trace=True,
                filter_dilation=fd,
            )
        else:
            with pytest.raises(ValueError):
                self.run_gradinput(
                    inputs_shape=i,
                    filters_shape=f,
                    output_shape=o,
                    subsample=s,
                    verify_grad=False,
                    provide_shape=provide_shape,
                    border_mode=b,
                    filter_flip=flip,
                    target_op=Corr3dMMGradInputs,
                    ref=None,
                    check_trace=True,
                    filter_dilation=fd,
                )


def test_constant_shapes():
    # Check that the `imshp` and `kshp` parameters of the AbstractConv Ops
    # are rejected if not constant or None
    dummy_t4 = ftensor4()
    alloc_dummy_t4 = at.zeros((3, 5, 7, 11), dtype="float32")

    dummy_shape = lvector()
    dummy_one_shape = at.ones(4, dtype="int64")
    constant_vec_shape = at.constant([3, 5, 7, 11])

    tuple_shape = (3, 5, 7, 11)
    list_shape = list(tuple_shape)
    constant_list_shape = [at.constant(i, dtype="int64") for i in tuple_shape]
    constant_tuple_shape = tuple(constant_list_shape)

    bad_shapes = (
        dummy_shape,
        dummy_one_shape,
        dummy_t4.shape,
        alloc_dummy_t4.shape,
        constant_vec_shape,
    )

    good_shapes = (constant_list_shape, constant_tuple_shape, tuple_shape, list_shape)

    ops_to_test = (
        AbstractConv2d,
        AbstractConv2d_gradInputs,
        AbstractConv2d_gradWeights,
    )

    for op in ops_to_test:
        for shp in bad_shapes:
            with pytest.raises(ValueError):
                op(imshp=shp)
            with pytest.raises(ValueError):
                op(kshp=shp)

        for shp in good_shapes:
            op(imshp=shp)
            op(kshp=shp)


class TestConvTypes:
    def setup_method(self):
        self.input = ftensor4()
        self.filters = ftensor4()
        self.topgrad = ftensor4()

        self.constant_tensor = np.zeros((3, 5, 7, 11), dtype="float32")

    def test_grad_types(self):
        # This function simply tests the behaviour of the AbstractConv
        # Ops, not their optimizations
        input = self.input
        filters = self.filters
        topgrad = self.topgrad

        out_shape = lvector()

        output = conv.abstract_conv2d(input, filters)
        grad_input, grad_filters = aesara.grad(output.sum(), wrt=(input, filters))
        assert grad_input.type == input.type, (
            grad_input,
            grad_input.type,
            input,
            input.type,
        )
        assert grad_filters.type == filters.type, (
            grad_filters,
            grad_filters.type,
            filters,
            filters.type,
        )

        grad_filters = conv.AbstractConv2d_gradWeights()(input, topgrad, out_shape)
        grad_input, grad_topgrad = aesara.grad(grad_filters.sum(), wrt=(input, topgrad))

        assert grad_input.type == input.type, (
            grad_input,
            grad_input.type,
            input,
            input.type,
        )
        assert grad_topgrad.type == topgrad.type, (
            grad_topgrad,
            grad_topgrad.type,
            topgrad,
            topgrad.type,
        )

        grad_input = conv.AbstractConv2d_gradInputs()(filters, topgrad, out_shape)
        grad_filters, grad_topgrad = aesara.grad(
            grad_input.sum(), wrt=(filters, topgrad)
        )

        assert grad_filters.type == filters.type, (
            grad_filters,
            grad_filters.type,
            filters,
            filters.type,
        )
        assert grad_topgrad.type == topgrad.type, (
            grad_topgrad,
            grad_topgrad.type,
            topgrad,
            topgrad.type,
        )

    def test_constant_input(self):
        # Check the AbstractConv Ops for constant inputs
        input = self.input
        filters = self.filters
        topgrad = self.topgrad
        constant_tensor = self.constant_tensor
        out_shape = lvector()

        # Check the forward Op
        output = conv.abstract_conv2d(constant_tensor, filters)
        grad_filters = aesara.grad(output.sum(), wrt=filters)
        assert filters.type.is_super(grad_filters.type), (
            grad_filters,
            grad_filters.type,
            filters,
            filters.type,
        )

        output = conv.abstract_conv2d(input, constant_tensor)
        grad_input = aesara.grad(output.sum(), wrt=input)
        assert input.type.is_super(grad_input.type), (
            grad_input,
            grad_input.type,
            input,
            input.type,
        )

        # Check grad wrt weights
        grad_filters = conv.AbstractConv2d_gradWeights()(
            constant_tensor, topgrad, out_shape
        )
        grad_topgrad = aesara.grad(grad_filters.sum(), wrt=topgrad)
        assert topgrad.type.is_super(grad_topgrad.type), (
            grad_topgrad,
            grad_topgrad.type,
            topgrad,
            topgrad.type,
        )

        grad_filters = conv.AbstractConv2d_gradWeights()(
            input, constant_tensor, out_shape
        )
        grad_input = aesara.grad(grad_filters.sum(), wrt=input)
        assert grad_input.type == input.type, (
            grad_input,
            grad_input.type,
            input,
            input.type,
        )

        # Check grad wrt inputs
        grad_input = conv.AbstractConv2d_gradInputs()(
            constant_tensor, topgrad, out_shape
        )
        grad_topgrad = aesara.grad(grad_input.sum(), wrt=topgrad)
        assert topgrad.type.is_super(grad_topgrad.type), (
            grad_topgrad,
            grad_topgrad.type,
            topgrad,
            topgrad.type,
        )

        grad_input = conv.AbstractConv2d_gradInputs()(
            filters, constant_tensor, out_shape
        )
        grad_filters = aesara.grad(grad_input.sum(), wrt=filters)
        assert grad_filters.type == filters.type, (
            grad_filters,
            grad_filters.type,
            filters,
            filters.type,
        )


class TestBilinearUpsampling:
    # If config.blas__ldflags is empty, Aesara will use
    # a NumPy C implementation of [sd]gemm_.
    compile_mode = aesara.compile.mode.get_default_mode()
    if config.mode == "FAST_COMPILE":
        compile_mode = compile_mode.excluding("conv_gemm")
        compile_mode = compile_mode.excluding("AbstractConvCheck")
    elif not config.cxx:
        compile_mode = compile_mode.excluding("AbstractConvCheck")

    def numerical_kernel_1D(self, ratio):
        """
        Gets numerical 1D kernel for bilinear upsampling
        """
        return np.array(list(range(1, ratio + 1)) + list(range(ratio - 1, 0, -1)))

    def numerical_kernel_2D(self, ratio):
        """
        Gets numerical 2D kernel for bilinear upsampling
        """
        return np.array(
            [
                i * j
                for i in self.numerical_kernel_1D(ratio)
                for j in self.numerical_kernel_1D(ratio)
            ]
        ).reshape(2 * ratio - 1, 2 * ratio - 1)

    def test_bilinear_kernel_2D(self):
        # Test 2D kernels used in bilinear upsampling
        #
        # This method tests the correctness of the
        # 2D kernel values used in bilinear upsampling
        # for some upsampling ratios.

        for ratio in [2, 3, 4, 5, 6, 7, 8, 9]:
            # getting the un-normalized kernel
            kernel = bilinear_kernel_2D(ratio=ratio, normalize=False)
            f = aesara.function([], kernel)
            kernel_2D = self.numerical_kernel_2D(ratio)
            utt.assert_allclose(kernel_2D, f())

            # getting the normalized kernel
            kernel = bilinear_kernel_2D(ratio=ratio, normalize=True)
            f = aesara.function([], kernel)
            kernel_2D = kernel_2D / float(ratio ** 2)
            utt.assert_allclose(kernel_2D, f())

    def test_bilinear_kernel_1D(self):
        # Test 1D kernels used in bilinear upsampling
        #
        # This method tests the correctness of the
        # 1D kernel values used in bilinear upsampling
        # for some upsampling ratios.

        rat = iscalar()
        kernel_ten = bilinear_kernel_1D(ratio=rat, normalize=False)
        f_ten = aesara.function([rat], kernel_ten)

        kernel_ten_norm = bilinear_kernel_1D(ratio=rat, normalize=True)
        f_ten_norm = aesara.function([rat], kernel_ten_norm)

        for ratio in [2, 3, 4, 5, 6, 7, 8, 9]:
            # getting the un-normalized kernel
            kernel = bilinear_kernel_1D(ratio=ratio, normalize=False)
            f = aesara.function([], kernel)
            kernel_1D = self.numerical_kernel_1D(ratio)
            utt.assert_allclose(kernel_1D, f())
            utt.assert_allclose(kernel_1D, f_ten(ratio))

            # getting the normalized kernel
            kernel = bilinear_kernel_1D(ratio=ratio, normalize=True)
            f = aesara.function([], kernel)
            kernel_1D = kernel_1D / float(ratio)
            utt.assert_allclose(kernel_1D, f())
            utt.assert_allclose(kernel_1D, f_ten_norm(ratio))

    def numerical_upsampling_multiplier(self, ratio):
        """
        Compute upsampling multiplier

        This method computes the multipliers of an array
        that will be upsampled using bilinear interpolation.

        Parameters
        ----------
        ratio: int
            the ratio by which the array will be upsampled.

        Returns
        -------
        1D numpy array
            The multipliers that can be used in bilinear interpolation
            to upsample an array.

        int
            The size of the multipliers array
        """
        kern = np.arange(ratio + 1)
        return kern, kern.shape[0]

    def get_upsampled_twobytwo_mat(self, two_by_two, ratio):
        """
        Upsample 4D array with two rows and two columns

        This method gets a 4D numpy array with two rows and two columns
        and computes its upsampled array by using bilinear interpolation

        Parameters
        ----------
        two_by_two: numpy 4D array
            The array that will be upsampled by bilinear interpolation.
            Array is of shape (batch size, num channels, 2, 2)

        ratio: int
            The ratio by which two_by_two's last
            two dimensions (row and col) will be upsampled.

        Returns
        -------
        4D numpy array
            The array upsampled by using bilinear interpolation. Array
            is of shape (batch size, num channels, 2*ratio, 2*ratio).
        """
        kern, shp = self.numerical_upsampling_multiplier(ratio)
        up_1D = two_by_two[:, :, :, :1] * kern[::-1] + two_by_two[:, :, :, 1:] * kern
        up_2D = (
            up_1D[:, :, :1, :] * kern[::-1][:, np.newaxis]
            + up_1D[:, :, 1:, :] * kern[:, np.newaxis]
        )
        num_concat = (ratio - 1) // 2
        for i in range(num_concat):
            up_2D = np.concatenate([up_2D[:, :, :1, :], up_2D], axis=2)
            up_2D = np.concatenate([up_2D, up_2D[:, :, -1:, :]], axis=2)
            up_2D = np.concatenate([up_2D[:, :, :, :1], up_2D], axis=3)
            up_2D = np.concatenate([up_2D, up_2D[:, :, :, -1:]], axis=3)
        if ratio % 2 == 0:
            up_2D = np.concatenate([up_2D, up_2D[:, :, -1:, :]], axis=2)
            up_2D = np.concatenate([up_2D, up_2D[:, :, :, -1:]], axis=3)
        return up_2D / float(ratio) ** 2

    def test_bilinear_upsampling_1D(self):
        # Test bilinear upsampling using 1D kernels
        #
        # This method tests the bilinear_upsampling method
        # when using 1D kernels for some upsampling ratios.

        # upsampling for a ratio of two
        input_x = np.array([[[[1, 2], [3, 4]]]], dtype=config.floatX)

        for ratio in [2, 3, 4, 5, 6, 7, 8, 9]:
            bilin_mat = bilinear_upsampling(
                input=input_x,
                ratio=ratio,
                batch_size=1,
                num_input_channels=1,
                use_1D_kernel=True,
            )
            f = aesara.function([], bilin_mat, mode=self.compile_mode)
            up_mat_2d = self.get_upsampled_twobytwo_mat(input_x, ratio)
            utt.assert_allclose(f(), up_mat_2d, rtol=1e-06)

    def test_bilinear_upsampling_reshaping(self):
        # Test bilinear upsampling without giving shape information
        #
        # This method tests the bilinear_upsampling method
        # without giving batch_size and num_input_channels

        # upsampling for a ratio of two
        input_x = np.array([[[[1, 2], [3, 4]]]], dtype=config.floatX)

        for ratio in [2, 3]:
            for use_1D_kernel in [True, False]:
                bilin_mat = bilinear_upsampling(
                    input=input_x,
                    ratio=ratio,
                    batch_size=None,
                    num_input_channels=None,
                    use_1D_kernel=use_1D_kernel,
                )
                f = aesara.function([], bilin_mat, mode=self.compile_mode)
                up_mat_2d = self.get_upsampled_twobytwo_mat(input_x, ratio)
                utt.assert_allclose(f(), up_mat_2d, rtol=1e-06)

    def test_compare_1D_and_2D_upsampling_values(self):
        # Compare 1D and 2D upsampling
        #
        # This method verifies the bilinear upsampling done by using
        # 1D and 2D kernels will generate the same result.

        # checking upsampling with ratio 5
        input_x = np.random.rand(5, 4, 6, 7).astype(config.floatX)
        mat_1D = bilinear_upsampling(
            input=input_x,
            ratio=5,
            batch_size=5,
            num_input_channels=4,
            use_1D_kernel=True,
        )
        mat_2D = bilinear_upsampling(
            input=input_x,
            ratio=5,
            batch_size=5,
            num_input_channels=4,
            use_1D_kernel=False,
        )
        f_1D = aesara.function([], mat_1D, mode=self.compile_mode)
        f_2D = aesara.function([], mat_2D, mode=self.compile_mode)
        utt.assert_allclose(f_1D(), f_2D(), rtol=1e-06)

        # checking upsampling with ratio 8
        input_x = np.random.rand(12, 11, 10, 7).astype(config.floatX)
        mat_1D = bilinear_upsampling(
            input=input_x,
            ratio=8,
            batch_size=12,
            num_input_channels=11,
            use_1D_kernel=True,
        )
        mat_2D = bilinear_upsampling(
            input=input_x,
            ratio=8,
            batch_size=12,
            num_input_channels=11,
            use_1D_kernel=False,
        )
        f_1D = aesara.function([], mat_1D, mode=self.compile_mode)
        f_2D = aesara.function([], mat_2D, mode=self.compile_mode)
        utt.assert_allclose(f_1D(), f_2D(), rtol=1e-06)

    def test_fractional_bilinear_upsampling(self):
        """Test bilinear upsampling with nonsimilar fractional
        row and col ratios
        """
        input_x = np.array(
            [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]], ndmin=4
        ).astype(config.floatX)
        up_x = bilinear_upsampling(
            input=input_x, frac_ratio=((7, 4), (5, 3)), use_1D_kernel=False
        )
        num_up_x = np.array(
            [
                [
                    [
                        [1.0, 1.2, 1.8, 2.0],
                        [1.28571429, 1.48571429, 2.08571429, 2.28571429],
                        [2.42857143, 2.62857143, 3.22857143, 3.42857143],
                        [3.0, 3.2, 3.8, 4.0],
                    ],
                    [
                        [5.0, 5.2, 5.8, 6.0],
                        [5.28571429, 5.48571429, 6.08571429, 6.28571429],
                        [6.42857143, 6.62857143, 7.22857143, 7.42857143],
                        [7.0, 7.2, 7.8, 8.0],
                    ],
                    [
                        [9.0, 9.2, 9.8, 10.0],
                        [9.28571429, 9.48571429, 10.08571429, 10.28571429],
                        [10.42857143, 10.62857143, 11.22857143, 11.42857143],
                        [11.0, 11.2, 11.8, 12.0],
                    ],
                ]
            ]
        ).astype(config.floatX)
        f_up_x = aesara.function([], up_x, mode=self.compile_mode)
        utt.assert_allclose(f_up_x(), num_up_x, rtol=1e-6)

    def test_fractional_bilinear_upsampling_shape(self):
        x = np.random.rand(1, 1, 200, 200).astype(config.floatX)
        resize = (24, 20)
        z = bilinear_upsampling(
            at.as_tensor_variable(x), frac_ratio=resize, use_1D_kernel=False
        )
        out = aesara.function([], z.shape, mode="FAST_RUN")()
        utt.assert_allclose(out, (1, 1, 240, 240))


class TestConv2dTranspose:
    mode = None

    @pytest.mark.skipif(config.cxx == "", reason="Test needs cxx")
    def test_interface(self):
        # Test conv2d_transpose wrapper.
        #
        # This method tests that the order of the filter's
        # axes expected by the function produces the correct
        # output shape.
        mode = self.mode
        if config.mode == "FAST_COMPILE":
            mode = (
                aesara.compile.get_mode(mode)
                .excluding("conv_gemm")
                .excluding("AbstractConvCheck")
            )

        output = aesara.function(
            inputs=[],
            outputs=conv2d_transpose(
                input=at.ones((2, 2, 4, 4)),
                filters=at.ones((2, 1, 4, 4)),
                output_shape=(2, 1, 10, 10),
                input_dilation=(2, 2),
            ),
            mode=mode,
        )()
        expected_output = np.array(
            [
                [
                    [
                        [2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
                        [2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [4, 4, 8, 8, 8, 8, 8, 8, 4, 4],
                        [2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
                        [2, 2, 4, 4, 4, 4, 4, 4, 2, 2],
                    ]
                ]
            ]
            * 2
        )
        np.testing.assert_equal(output, expected_output)


@pytest.mark.skipif(
    not config.cxx or config.mode == "FAST_COMPILE",
    reason="Need blas to test conv3d",
)
class TestConv2dGrads:
    def setup_method(self):
        self.random_stream = np.random.default_rng(utt.fetch_seed())

        self.inputs_shapes = [(8, 1, 12, 12), (1, 1, 5, 5), (1, 1, 5, 6), (1, 1, 6, 6)]
        self.filters_shapes = [(5, 1, 2, 2), (1, 1, 3, 3)]

        self.subsamples = [(1, 1), (2, 2)]
        self.border_modes = ["valid", "full"]
        self.filter_flip = [True, False]

        self.output_grad = tensor4()
        self.output_grad_wrt = tensor4()

        self.x = tensor4("x", config.floatX)  # inputs
        self.w = tensor4("w", config.floatX)  # filter weights

    def test_conv2d_grad_wrt_inputs(self):
        # Compares calculated abstract grads wrt inputs with the fwd grads
        # This method checks the outputs of `conv2_grad_wrt_inputs` against
        # the outputs of `aesara.tensor.nnet.conv` forward grads to make sure the
        # results are the same.

        for (in_shape, fltr_shape) in zip(self.inputs_shapes, self.filters_shapes):
            for bm in self.border_modes:
                for ss in self.subsamples:
                    for ff in self.filter_flip:
                        input_val = self.random_stream.random(in_shape).astype(
                            config.floatX
                        )
                        filter_val = self.random_stream.random(fltr_shape).astype(
                            config.floatX
                        )
                        out_grad_shape = (
                            aesara.tensor.nnet.abstract_conv.get_conv_output_shape(
                                image_shape=in_shape,
                                kernel_shape=fltr_shape,
                                border_mode=bm,
                                subsample=ss,
                            )
                        )
                        out_grad_val = self.random_stream.random(out_grad_shape).astype(
                            config.floatX
                        )
                        conv_out = aesara.tensor.nnet.conv2d(
                            self.x,
                            filters=self.w,
                            border_mode=bm,
                            subsample=ss,
                            input_shape=in_shape,
                            filter_shape=fltr_shape,
                            filter_flip=ff,
                        )
                        conv_grad = aesara.grad(
                            conv_out.sum(),
                            wrt=self.x,
                            known_grads={conv_out: self.output_grad},
                        )
                        f_old = aesara.function(
                            [self.x, self.w, self.output_grad], conv_grad
                        )

                        conv_wrt_i_out = (
                            aesara.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
                                output_grad=self.output_grad_wrt,
                                filters=self.w,
                                border_mode=bm,
                                subsample=ss,
                                input_shape=in_shape,
                                filter_shape=fltr_shape,
                                filter_flip=ff,
                            )
                        )
                        f_new = aesara.function(
                            [self.w, self.output_grad_wrt], conv_wrt_i_out
                        )

                        # check that they're equal
                        utt.assert_allclose(
                            f_new(filter_val, out_grad_val),
                            f_old(input_val, filter_val, out_grad_val),
                        )

    def test_conv2d_grad_wrt_weights(self):
        # Compares calculated abstract grads wrt weights with the fwd grads
        # This method checks the outputs of `conv2_grad_wrt_weights` against
        # the outputs of `aesara.tensor.nnet.conv` forward grads to make sure the
        # results are the same.

        for (in_shape, fltr_shape) in zip(self.inputs_shapes, self.filters_shapes):
            for bm in self.border_modes:
                for ss in self.subsamples:
                    for ff in self.filter_flip:
                        input_val = self.random_stream.random(in_shape).astype(
                            config.floatX
                        )
                        filter_val = self.random_stream.random(fltr_shape).astype(
                            config.floatX
                        )
                        out_grad_shape = (
                            aesara.tensor.nnet.abstract_conv.get_conv_output_shape(
                                image_shape=in_shape,
                                kernel_shape=fltr_shape,
                                border_mode=bm,
                                subsample=ss,
                            )
                        )
                        out_grad_val = self.random_stream.random(out_grad_shape).astype(
                            config.floatX
                        )
                        conv_out = aesara.tensor.nnet.conv2d(
                            self.x,
                            filters=self.w,
                            border_mode=bm,
                            subsample=ss,
                            input_shape=in_shape,
                            filter_shape=fltr_shape,
                            filter_flip=ff,
                        )
                        conv_grad = aesara.grad(
                            conv_out.sum(),
                            wrt=self.w,
                            known_grads={conv_out: self.output_grad},
                        )
                        f_old = aesara.function(
                            [self.x, self.w, self.output_grad], conv_grad
                        )

                        conv_wrt_w_out = (
                            aesara.tensor.nnet.abstract_conv.conv2d_grad_wrt_weights(
                                self.x,
                                output_grad=self.output_grad_wrt,
                                border_mode=bm,
                                subsample=ss,
                                input_shape=in_shape,
                                filter_shape=fltr_shape,
                                filter_flip=ff,
                            )
                        )
                        f_new = aesara.function(
                            [self.x, self.output_grad_wrt], conv_wrt_w_out
                        )
                        utt.assert_allclose(
                            f_new(input_val, out_grad_val),
                            f_old(input_val, filter_val, out_grad_val),
                        )


@pytest.mark.skipif(
    config.cxx == "",
    reason="SciPy and cxx needed",
)
class TestGroupedConvNoOptim:
    conv = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv_gradw = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv_gradi = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
    conv_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv_gradw_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv_gradi_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
    mode = Mode(optimizer=None)
    is_dnn = False

    def setup_method(self):
        self.num_groups = [3, 2, 4, 4]
        self.border_mode = "valid"
        self.subsample = (1, 1)
        self.img_shape = [(5, 6, 5, 5), (4, 4, 7, 5), (3, 8, 5, 3), (2, 4, 7, 7)]
        self.kern_shape = [(6, 2, 3, 3), (6, 2, 5, 3), (4, 2, 3, 3), (4, 1, 3, 5)]
        self.top_shape = [(5, 6, 3, 3), (4, 6, 3, 3), (3, 4, 3, 1), (2, 4, 5, 3)]
        self.filter_dilation = (1, 1)
        self.ref_mode = "FAST_RUN"
        self.convdim = 2
        self.corr_fwd = conv2d_corr
        self.corr_gradw = conv2d_corr_gw
        self.corr_gradi = conv2d_corr_gi

    def test_fwd(self):
        if self.convdim == 2:
            img_sym = tensor4("img")
            kern_sym = tensor4("kern")
        else:
            img_sym = tensor5("img")
            kern_sym = tensor5("kern")

        for imshp, kshp, groups in zip(
            self.img_shape, self.kern_shape, self.num_groups
        ):
            img = np.random.random(imshp).astype(config.floatX)
            kern = np.random.random(kshp).astype(config.floatX)
            split_imgs = np.split(img, groups, axis=1)
            split_kern = np.split(kern, groups, axis=0)

            grouped_conv_op = self.conv(
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
            )
            grouped_conv_output = grouped_conv_op(img_sym, kern_sym)

            grouped_func = aesara.function(
                [img_sym, kern_sym], grouped_conv_output, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv_op)
                    for node in grouped_func.maker.fgraph.toposort()
                ]
            )
            grouped_output = grouped_func(img, kern)

            ref_conv_op = self.corr_fwd(
                img_sym,
                kern_sym,
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
            )
            ref_func = aesara.function(
                [img_sym, kern_sym], ref_conv_op, mode=self.ref_mode
            )
            ref_concat_output = [
                ref_func(img_arr, kern_arr)
                for img_arr, kern_arr in zip(split_imgs, split_kern)
            ]
            ref_concat_output = np.concatenate(ref_concat_output, axis=1)

            utt.assert_allclose(grouped_output, ref_concat_output)

            utt.verify_grad(grouped_conv_op, [img, kern], mode=self.mode, eps=1)

    def test_gradweights(self):
        if self.convdim == 2:
            img_sym = tensor4("img")
            top_sym = tensor4("kern")
        else:
            img_sym = tensor5("img")
            top_sym = tensor5("kern")
        for imshp, kshp, tshp, groups in zip(
            self.img_shape, self.kern_shape, self.top_shape, self.num_groups
        ):
            img = np.random.random(imshp).astype(config.floatX)
            top = np.random.random(tshp).astype(config.floatX)
            split_imgs = np.split(img, groups, axis=1)
            split_top = np.split(top, groups, axis=1)

            grouped_convgrad_op = self.conv_gradw(
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
            )
            grouped_conv_output = grouped_convgrad_op(
                img_sym, top_sym, at.as_tensor_variable(kshp[-self.convdim :])
            )
            grouped_func = aesara.function(
                [img_sym, top_sym], grouped_conv_output, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv_gradw_op)
                    for node in grouped_func.maker.fgraph.toposort()
                ]
            )
            grouped_output = grouped_func(img, top)

            ref_conv_op = self.corr_gradw(
                img_sym,
                top_sym,
                kshp,
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
            )
            ref_func = aesara.function(
                [img_sym, top_sym], ref_conv_op, mode=self.ref_mode
            )
            ref_concat_output = [
                ref_func(img_arr, top_arr)
                for img_arr, top_arr in zip(split_imgs, split_top)
            ]
            ref_concat_output = np.concatenate(ref_concat_output, axis=0)

            utt.assert_allclose(grouped_output, ref_concat_output)

            def conv_gradweight(inputs_val, output_val):
                return grouped_convgrad_op(
                    inputs_val,
                    output_val,
                    at.as_tensor_variable(kshp[-self.convdim :]),
                )

            utt.verify_grad(conv_gradweight, [img, top], mode=self.mode, eps=1)

    def test_gradinputs(self):
        if self.convdim == 2:
            kern_sym = tensor4("kern")
            top_sym = tensor4("top")
        else:
            kern_sym = tensor5("kern")
            top_sym = tensor5("top")
        for imshp, kshp, tshp, groups in zip(
            self.img_shape, self.kern_shape, self.top_shape, self.num_groups
        ):
            kern = np.random.random(kshp).astype(config.floatX)
            top = np.random.random(tshp).astype(config.floatX)
            split_kerns = np.split(kern, groups, axis=0)
            split_top = np.split(top, groups, axis=1)

            grouped_convgrad_op = self.conv_gradi(
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
            )
            grouped_conv_output = grouped_convgrad_op(
                kern_sym, top_sym, at.as_tensor_variable(imshp[-self.convdim :])
            )
            grouped_func = aesara.function(
                [kern_sym, top_sym], grouped_conv_output, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv_gradi_op)
                    for node in grouped_func.maker.fgraph.toposort()
                ]
            )
            grouped_output = grouped_func(kern, top)

            ref_conv_op = self.corr_gradi(
                kern_sym,
                top_sym,
                imshp,
                border_mode=self.border_mode,
                subsample=self.subsample,
                filter_dilation=self.filter_dilation,
            )
            ref_func = aesara.function(
                [kern_sym, top_sym], ref_conv_op, mode=self.ref_mode
            )
            ref_concat_output = [
                ref_func(kern_arr, top_arr)
                for kern_arr, top_arr in zip(split_kerns, split_top)
            ]
            ref_concat_output = np.concatenate(ref_concat_output, axis=1)

            utt.assert_allclose(grouped_output, ref_concat_output)

            def conv_gradinputs(filters_val, output_val):
                return grouped_convgrad_op(
                    filters_val,
                    output_val,
                    at.as_tensor_variable(imshp[-self.convdim :]),
                )

            utt.verify_grad(conv_gradinputs, [kern, top], mode=self.mode, eps=1)


@pytest.mark.skipif(
    config.cxx == "",
    reason="SciPy and cxx needed",
)
class TestGroupedConv3dNoOptim(TestGroupedConvNoOptim):
    conv = aesara.tensor.nnet.abstract_conv.AbstractConv3d
    conv_gradw = aesara.tensor.nnet.abstract_conv.AbstractConv3d_gradWeights
    conv_gradi = aesara.tensor.nnet.abstract_conv.AbstractConv3d_gradInputs
    conv_op = aesara.tensor.nnet.abstract_conv.AbstractConv3d
    conv_gradw_op = aesara.tensor.nnet.abstract_conv.AbstractConv3d_gradWeights
    conv_gradi_op = aesara.tensor.nnet.abstract_conv.AbstractConv3d_gradInputs
    mode = Mode(optimizer=None)

    def setup_method(self):
        self.num_groups = [3, 2, 4, 4]
        self.border_mode = "valid"
        self.subsample = (1, 1, 1)
        self.img_shape = [
            (2, 6, 5, 5, 5),
            (1, 4, 7, 5, 7),
            (1, 8, 5, 3, 5),
            (2, 4, 7, 7, 7),
        ]
        self.kern_shape = [
            (3, 2, 3, 3, 3),
            (6, 2, 5, 3, 5),
            (4, 2, 3, 3, 3),
            (4, 1, 3, 5, 3),
        ]
        self.top_shape = [
            (2, 3, 3, 3, 3),
            (1, 6, 3, 3, 3),
            (1, 4, 3, 1, 3),
            (2, 4, 5, 3, 5),
        ]
        self.filter_dilation = (1, 1, 1)
        self.ref_mode = "FAST_RUN"
        self.convdim = 3
        self.corr_fwd = conv3d_corr
        self.corr_gradw = conv3d_corr_gw
        self.corr_gradi = conv3d_corr_gi


class TestSeparableConv:
    def setup_method(self):
        self.x = np.array(
            [
                [
                    [
                        [1, 2, 3, 4, 5],
                        [3, 2, 1, 4, 5],
                        [3, 3, 1, 3, 6],
                        [5, 3, 2, 1, 1],
                        [4, 7, 1, 2, 1],
                    ],
                    [
                        [3, 3, 1, 2, 6],
                        [6, 5, 4, 3, 1],
                        [3, 4, 5, 2, 3],
                        [6, 4, 1, 3, 4],
                        [2, 3, 4, 2, 5],
                    ],
                ]
            ]
        ).astype(config.floatX)

        self.depthwise_filter = np.array(
            [
                [[[3, 2, 1], [5, 3, 2], [6, 4, 2]]],
                [[[5, 5, 2], [3, 7, 4], [3, 5, 4]]],
                [[[7, 4, 7], [5, 3, 3], [1, 3, 1]]],
                [[[4, 4, 4], [2, 4, 6], [0, 0, 7]]],
            ]
        ).astype(config.floatX)

        self.pointwise_filter = np.array(
            [[[[4]], [[1]], [[3]], [[5]]], [[[2]], [[1]], [[2]], [[8]]]]
        ).astype(config.floatX)

        self.precomp_output_valid = np.array(
            [
                [
                    [[1385, 1333, 1339], [1382, 1243, 1291], [1303, 1120, 1228]],
                    [[1532, 1410, 1259], [1522, 1346, 1314], [1379, 1192, 1286]],
                ]
            ]
        ).astype(config.floatX)

        self.precomp_output_full = np.array(
            [
                [
                    [
                        [140, 266, 343, 206, 59],
                        [395, 697, 979, 585, 245],
                        [429, 863, 1385, 919, 453],
                        [243, 499, 864, 627, 371],
                        [90, 183, 291, 254, 202],
                    ],
                    [
                        [149, 289, 359, 213, 58],
                        [400, 750, 1076, 662, 266],
                        [387, 854, 1532, 1091, 540],
                        [174, 411, 971, 786, 518],
                        [51, 110, 286, 299, 298],
                    ],
                ]
            ]
        ).astype(config.floatX)

    @pytest.mark.skipif(config.cxx == "", reason="test needs cxx")
    def test_interface2d(self):
        x_sym = tensor4("x")
        dfilter_sym = tensor4("d")
        pfilter_sym = tensor4("p")

        sep_op = separable_conv2d(x_sym, dfilter_sym, pfilter_sym, self.x.shape[1])
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )

        # test for square matrix
        top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
        utt.assert_allclose(top, self.precomp_output_valid)

        # test for non-square matrix
        top = fun(self.x[:, :, :3, :], self.depthwise_filter, self.pointwise_filter)
        utt.assert_allclose(top, self.precomp_output_valid[:, :, :1, :])

        # test if it infers shape
        sep_op = separable_conv2d(
            x_sym,
            dfilter_sym,
            pfilter_sym,
            self.x.shape[1],
            input_shape=self.x.shape,
            depthwise_filter_shape=self.depthwise_filter.shape,
            pointwise_filter_shape=self.pointwise_filter.shape,
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
        utt.assert_allclose(top, self.precomp_output_valid)

        # test non-default subsample
        sep_op = separable_conv2d(
            x_sym, dfilter_sym, pfilter_sym, self.x.shape[1], subsample=(2, 2)
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(self.x, self.depthwise_filter, self.pointwise_filter)
        utt.assert_allclose(
            top, np.delete(np.delete(self.precomp_output_valid, 1, axis=3), 1, axis=2)
        )

        # test non-default border_mode
        sep_op = separable_conv2d(
            x_sym, dfilter_sym, pfilter_sym, self.x.shape[1], border_mode="full"
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(self.x[:, :, :3, :3], self.depthwise_filter, self.pointwise_filter)
        utt.assert_allclose(top, self.precomp_output_full)

    @pytest.mark.skipif(config.cxx == "", reason="test needs cxx")
    def test_interface3d(self):
        # Expand the filter along the depth
        x = np.tile(np.expand_dims(self.x, axis=2), (1, 1, 5, 1, 1))
        depthwise_filter = np.tile(
            np.expand_dims(self.depthwise_filter, axis=2), (1, 1, 3, 1, 1)
        )
        pointwise_filter = np.expand_dims(self.pointwise_filter, axis=2)
        precomp_output = (
            np.tile(np.expand_dims(self.precomp_output_valid, axis=2), (1, 1, 3, 1, 1))
            * 3
        )

        x_sym = tensor5("x")
        dfilter_sym = tensor5("d")
        pfilter_sym = tensor5("p")

        sep_op = separable_conv3d(x_sym, dfilter_sym, pfilter_sym, x.shape[1])
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )

        # test for square matrix
        top = fun(x, depthwise_filter, pointwise_filter)
        utt.assert_allclose(top, precomp_output)
        # test for non-square matrix
        top = fun(x[:, :, :3, :, :3], depthwise_filter, pointwise_filter)
        utt.assert_allclose(top, precomp_output[:, :, :1, :, :1])
        # test if it infers shape
        sep_op = separable_conv3d(
            x_sym,
            dfilter_sym,
            pfilter_sym,
            x.shape[1],
            input_shape=x.shape,
            depthwise_filter_shape=depthwise_filter.shape,
            pointwise_filter_shape=pointwise_filter.shape,
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(x, depthwise_filter, pointwise_filter)
        utt.assert_allclose(top, precomp_output)

        # test non-default subsample
        sep_op = separable_conv3d(
            x_sym, dfilter_sym, pfilter_sym, x.shape[1], subsample=(2, 2, 2)
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(x, depthwise_filter, pointwise_filter)
        utt.assert_allclose(
            top,
            np.delete(
                np.delete(np.delete(precomp_output, 1, axis=4), 1, axis=3), 1, axis=2
            ),
        )
        # test non-default border_mode
        precomp_output = np.tile(
            np.expand_dims(self.precomp_output_full, axis=2), (1, 1, 5, 1, 1)
        ) * np.array([[[[[1]], [[2]], [[3]], [[2]], [[1]]]]])

        sep_op = separable_conv3d(
            x_sym, dfilter_sym, pfilter_sym, x.shape[1], border_mode="full"
        )
        fun = aesara.function(
            [x_sym, dfilter_sym, pfilter_sym], sep_op, mode="FAST_RUN"
        )
        top = fun(x[:, :, :3, :3, :3], depthwise_filter, pointwise_filter)
        utt.assert_allclose(top, precomp_output)


@pytest.mark.skipif(
    config.cxx == "",
    reason="SciPy and cxx needed",
)
class TestUnsharedConv:
    conv2d = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv2d_gradw = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv2d_gradi = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
    conv2d_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv2d_gradw_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv2d_gradi_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs

    mode = Mode(optimizer="None")

    def setup_method(self):
        self.img_shape = [(2, 2, 4, 4), (3, 2, 4, 2), (3, 3, 5, 3), (3, 4, 4, 4)]
        self.kern_shape = [
            (2, 2, 2, 2, 3, 3),
            (2, 4, 2, 2, 4, 2),
            (3, 2, 1, 1, 3, 3),
            (4, 3, 3, 2, 4, 2),
        ]
        self.topgrad_shape = [(2, 2, 2, 2), (3, 2, 4, 2), (3, 3, 2, 1), (3, 4, 3, 3)]
        self.border_mode = ["valid", "full", "valid", "full"]
        self.subsample = [(1, 1), (2, 2), (2, 1), (3, 2)]
        self.filter_dilation = (1, 1)
        self.num_groups = [1, 1, 3, 2]

        # self.verify_flags = np.random.choice([True, False], 4, [0.5, 0.5])
        # Above line can be used instead if speed is a concern
        self.verify_flags = [True] * 4

        self.ref_mode = "FAST_RUN"

    def test_fwd(self):
        tensor6 = TensorType(config.floatX, (False,) * 6)
        img_sym = tensor4("img")
        kern_sym = tensor6("kern")
        ref_kern_sym = tensor4("ref_kern")

        for imshp, kshp, mode, sub, groups, verify in zip(
            self.img_shape,
            self.kern_shape,
            self.border_mode,
            self.subsample,
            self.num_groups,
            self.verify_flags,
        ):
            img = np.random.random(imshp).astype(config.floatX)
            kern = np.random.random(kshp).astype(config.floatX)

            unshared_conv_op = self.conv2d(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=True,
            )
            unshared_out_sym = unshared_conv_op(img_sym, kern_sym)
            unshared_func = aesara.function(
                [img_sym, kern_sym], unshared_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_op)
                    for node in unshared_func.maker.fgraph.toposort()
                ]
            )
            unshared_output = unshared_func(img, kern)

            single_kshp = kshp[:1] + kshp[3:]

            ref_conv_op = self.conv2d(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=False,
            )
            ref_out_sym = ref_conv_op(img_sym, ref_kern_sym)
            ref_func = aesara.function(
                [img_sym, ref_kern_sym], ref_out_sym, mode=self.mode
            )

            for i in range(0, kshp[1]):
                for j in range(0, kshp[2]):
                    single_kern = kern[:, i, j, ...].reshape(single_kshp)
                    ref_val = ref_func(img, single_kern)
                    utt.assert_allclose(
                        ref_val[:, :, i, j], unshared_output[:, :, i, j]
                    )

            if verify:
                utt.verify_grad(unshared_conv_op, [img, kern], mode=self.mode, eps=1)

    def test_gradweight(self):
        img_sym = tensor4("img")
        top_sym = tensor4("top")

        for imshp, kshp, topshp, mode, sub, groups, verify in zip(
            self.img_shape,
            self.kern_shape,
            self.topgrad_shape,
            self.border_mode,
            self.subsample,
            self.num_groups,
            self.verify_flags,
        ):
            img = np.random.random(imshp).astype(config.floatX)
            top = np.random.random(topshp).astype(config.floatX)

            unshared_conv_op = self.conv2d_gradw(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=True,
            )
            unshared_out_sym = unshared_conv_op(
                img_sym, top_sym, at.as_tensor_variable(kshp[-2:])
            )
            unshared_func = aesara.function(
                [img_sym, top_sym], unshared_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_gradw_op)
                    for node in unshared_func.maker.fgraph.toposort()
                ]
            )
            unshared_output = unshared_func(img, top)

            single_kshp = kshp[:1] + kshp[3:]

            ref_conv_op = self.conv2d_gradw(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=False,
            )
            ref_out_sym = ref_conv_op(
                img_sym, top_sym, at.as_tensor_variable(single_kshp[-2:])
            )
            ref_func = aesara.function([img_sym, top_sym], ref_out_sym, mode=self.mode)

            for i in range(0, topshp[2]):
                for j in range(0, topshp[3]):
                    top_single = np.zeros_like(top)
                    top_single[:, :, i, j] = top[:, :, i, j]
                    ref_output = ref_func(img, top_single)
                    utt.assert_allclose(unshared_output[:, i, j, ...], ref_output)

            def conv_gradweight(inputs_val, output_val):
                return unshared_conv_op(
                    inputs_val, output_val, at.as_tensor_variable(kshp[-2:])
                )

            if verify:
                utt.verify_grad(conv_gradweight, [img, top], mode=self.mode, eps=1)

    def test_gradinput(self):
        tensor6 = TensorType(config.floatX, (False,) * 6)
        kern_sym = tensor6("kern")
        top_sym = tensor4("top")
        ref_kern_sym = tensor4("ref_kern")

        for imshp, kshp, topshp, mode, sub, groups, verify in zip(
            self.img_shape,
            self.kern_shape,
            self.topgrad_shape,
            self.border_mode,
            self.subsample,
            self.num_groups,
            self.verify_flags,
        ):
            single_kshp = kshp[:1] + kshp[3:]

            kern = np.random.random(kshp).astype(config.floatX)
            top = np.random.random(topshp).astype(config.floatX)

            unshared_conv_op = self.conv2d_gradi(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=True,
            )
            unshared_out_sym = unshared_conv_op(
                kern_sym, top_sym, at.as_tensor_variable(imshp[-2:])
            )
            unshared_func = aesara.function(
                [kern_sym, top_sym], unshared_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_gradi_op)
                    for node in unshared_func.maker.fgraph.toposort()
                ]
            )
            unshared_output = unshared_func(kern, top)

            ref_conv_op = self.conv2d_gradi(
                border_mode=mode,
                subsample=sub,
                filter_dilation=self.filter_dilation,
                num_groups=groups,
                unshared=False,
            )
            ref_out_sym = ref_conv_op(
                ref_kern_sym, top_sym, at.as_tensor_variable(imshp[-2:])
            )
            ref_func = aesara.function(
                [ref_kern_sym, top_sym], ref_out_sym, mode=self.mode
            )

            ref_output = np.zeros(imshp)

            for i in range(0, topshp[2]):
                for j in range(0, topshp[3]):
                    single_kern = kern[:, i, j, ...].reshape(single_kshp)
                    top_single = np.zeros_like(top)
                    top_single[:, :, i, j] = top[:, :, i, j]
                    ref_output += ref_func(single_kern, top_single)

            utt.assert_allclose(ref_output, unshared_output)

            def conv_gradinputs(filters_val, output_val):
                return unshared_conv_op(
                    filters_val, output_val, at.as_tensor_variable(imshp[-2:])
                )

            if verify:
                utt.verify_grad(conv_gradinputs, [kern, top], mode=self.mode, eps=1)


class TestAsymmetricPadding:
    conv2d = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv2d_gradw = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv2d_gradi = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs
    conv2d_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d
    conv2d_gradw_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradWeights
    conv2d_gradi_op = aesara.tensor.nnet.abstract_conv.AbstractConv2d_gradInputs

    mode = Mode(optimizer="None")

    img_shape = [(2, 2, 4, 4), (3, 2, 4, 2), (3, 3, 5, 3)]
    kern_shape = [(4, 2, 2, 2), (2, 2, 4, 2), (2, 3, 3, 3)]
    topgrad_shape = [(2, 4, 6, 6), (3, 2, 3, 4), (3, 2, 6, 1)]
    border_mode = [((1, 2), (2, 1)), ((1, 1), (0, 3)), ((2, 1), (0, 0))]

    @pytest.mark.skipif(
        config.cxx == "",
        reason="SciPy and cxx needed",
    )
    def test_fwd(self):
        img_sym = tensor4("img")
        kern_sym = tensor4("kern")

        for imshp, kshp, pad in zip(self.img_shape, self.kern_shape, self.border_mode):
            img = np.random.random(imshp).astype(config.floatX)
            kern = np.random.random(kshp).astype(config.floatX)

            asymmetric_conv_op = self.conv2d(
                border_mode=pad, subsample=(1, 1), filter_dilation=(1, 1)
            )
            asymmetric_out_sym = asymmetric_conv_op(img_sym, kern_sym)
            asymmetric_func = aesara.function(
                [img_sym, kern_sym], asymmetric_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_op)
                    for node in asymmetric_func.maker.fgraph.toposort()
                ]
            )
            asymmetric_output = asymmetric_func(img, kern)

            ref_conv_op = self.conv2d(
                border_mode="valid", subsample=(1, 1), filter_dilation=(1, 1)
            )
            ref_out_sym = ref_conv_op(img_sym, kern_sym)
            ref_func = aesara.function([img_sym, kern_sym], ref_out_sym, mode=self.mode)

            exp_imshp = (
                imshp[0],
                imshp[1],
                imshp[2] + pad[0][0] + pad[0][1],
                imshp[3] + pad[1][0] + pad[1][1],
            )

            exp_img = np.zeros(exp_imshp, dtype=config.floatX)
            exp_img[
                :, :, pad[0][0] : imshp[2] + pad[0][0], pad[1][0] : imshp[3] + pad[1][0]
            ] = img
            ref_output = ref_func(exp_img, kern)

            utt.assert_allclose(asymmetric_output, ref_output)

            utt.verify_grad(asymmetric_conv_op, [img, kern], mode=self.mode, eps=1)

    @pytest.mark.skipif(
        config.cxx == "",
        reason="SciPy and cxx needed",
    )
    def test_gradweight(self):
        img_sym = tensor4("img")
        top_sym = tensor4("top")

        for imshp, kshp, topshp, pad in zip(
            self.img_shape, self.kern_shape, self.topgrad_shape, self.border_mode
        ):
            img = np.random.random(imshp).astype(config.floatX)
            top = np.random.random(topshp).astype(config.floatX)

            asymmetric_conv_op = self.conv2d_gradw(
                border_mode=pad, subsample=(1, 1), filter_dilation=(1, 1)
            )
            asymmetric_out_sym = asymmetric_conv_op(img_sym, top_sym, kshp[-2:])
            asymmetric_func = aesara.function(
                [img_sym, top_sym], asymmetric_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_gradw_op)
                    for node in asymmetric_func.maker.fgraph.toposort()
                ]
            )
            asymmetric_output = asymmetric_func(img, top)

            ref_conv_op = self.conv2d_gradw(
                border_mode="valid", subsample=(1, 1), filter_dilation=(1, 1)
            )
            ref_out_sym = ref_conv_op(img_sym, top_sym, kshp[-2:])
            ref_func = aesara.function([img_sym, top_sym], ref_out_sym, mode=self.mode)

            exp_imshp = (
                imshp[0],
                imshp[1],
                imshp[2] + pad[0][0] + pad[0][1],
                imshp[3] + pad[1][0] + pad[1][1],
            )

            exp_img = np.zeros(exp_imshp, dtype=config.floatX)
            exp_img[
                :, :, pad[0][0] : imshp[2] + pad[0][0], pad[1][0] : imshp[3] + pad[1][0]
            ] = img
            ref_output = ref_func(exp_img, top)

            utt.assert_allclose(asymmetric_output, ref_output)

            def conv_gradweight(inputs_val, output_val):
                return asymmetric_conv_op(
                    inputs_val, output_val, at.as_tensor_variable(kshp[-2:])
                )

            utt.verify_grad(conv_gradweight, [img, top], mode=self.mode, eps=1)

    @pytest.mark.skipif(
        config.cxx == "",
        reason="SciPy and cxx needed",
    )
    def test_gradinput(self):
        kern_sym = tensor4("kern")
        top_sym = tensor4("top")

        for imshp, kshp, topshp, pad in zip(
            self.img_shape, self.kern_shape, self.topgrad_shape, self.border_mode
        ):
            kern = np.random.random(kshp).astype(config.floatX)
            top = np.random.random(topshp).astype(config.floatX)

            asymmetric_conv_op = self.conv2d_gradi(
                border_mode=pad, subsample=(1, 1), filter_dilation=(1, 1)
            )
            asymmetric_out_sym = asymmetric_conv_op(kern_sym, top_sym, imshp[-2:])
            asymmetric_func = aesara.function(
                [kern_sym, top_sym], asymmetric_out_sym, mode=self.mode
            )
            assert any(
                [
                    isinstance(node.op, self.conv2d_gradi_op)
                    for node in asymmetric_func.maker.fgraph.toposort()
                ]
            )
            asymmetric_output = asymmetric_func(kern, top)

            ref_conv_op = self.conv2d_gradi(
                border_mode="valid", subsample=(1, 1), filter_dilation=(1, 1)
            )
            exp_imshp = [
                imshp[2] + pad[0][0] + pad[0][1],
                imshp[3] + pad[1][0] + pad[1][1],
            ]
            ref_out_sym = ref_conv_op(kern_sym, top_sym, exp_imshp)
            ref_func = aesara.function([kern_sym, top_sym], ref_out_sym, mode=self.mode)

            ref_output = ref_func(kern, top)

            ref_output = ref_output[
                :, :, pad[0][0] : imshp[2] + pad[0][0], pad[1][0] : imshp[3] + pad[1][0]
            ]

            utt.assert_allclose(asymmetric_output, ref_output)

            def conv_gradinputs(filters_val, output_val):
                return asymmetric_conv_op(
                    filters_val, output_val, at.as_tensor_variable(imshp[-2:])
                )

            utt.verify_grad(conv_gradinputs, [kern, top], mode=self.mode, eps=1)


class TestCausalConv:
    mode = Mode(optimizer="None")

    img = np.array(
        [
            [[2, 4, 9, 5, 8], [0, 0, 4, 0, 5]],
            [[2, 5, 8, 5, 5], [1, 3, 0, 7, 9]],
            [[7, 0, 7, 1, 0], [0, 1, 4, 7, 2]],
        ]
    ).astype(config.floatX)
    kern = np.array([[[5, 3, 1], [3, 1, 0]], [[6, 4, 9], [2, 2, 7]]]).astype(
        config.floatX
    )
    dilation = 2
    precomp_top = np.array(
        [
            [[10, 20, 63, 37, 88], [12, 24, 70, 46, 120]],
            [[13, 34, 47, 64, 78], [14, 36, 58, 70, 105]],
            [[35, 3, 68, 27, 38], [42, 2, 78, 22, 103]],
        ]
    ).astype(config.floatX)

    @pytest.mark.skipif(
        config.cxx == "",
        reason="SciPy and cxx needed",
    )
    def test_interface(self):
        img_sym = tensor3("img")
        kern_sym = tensor3("kern")
        sym_out = causal_conv1d(
            img_sym, kern_sym, self.kern.shape, filter_dilation=self.dilation
        )

        causal_func = aesara.function([img_sym, kern_sym], sym_out, mode=self.mode)

        output = causal_func(self.img, self.kern)

        utt.assert_allclose(output, self.precomp_top)

        def causal_conv_fn(inputs_val, filters_val):
            return causal_conv1d(
                inputs_val, filters_val, self.kern.shape, filter_dilation=1
            )

        utt.verify_grad(causal_conv_fn, [self.img, self.kern], mode=self.mode, eps=1)
