# ******************************************************************************
# Copyright 2017-2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
# flake8: noqa
import numpy as np

import ngraph as ng
from ngraph.impl import AxisSet, Function, Shape, Type
from ngraph.impl.op import Constant, Parameter
from tests.runtime import get_runtime
from tests import xfail_issue_34323


def binary_op(op_str, a, b):

    if op_str == "+":
        return a + b
    elif op_str == "Add":
        return ng.add(a, b)
    elif op_str == "-":
        return a - b
    elif op_str == "Sub":
        return ng.subtract(a, b)
    elif op_str == "*":
        return a * b
    elif op_str == "Mul":
        return ng.multiply(a, b)
    elif op_str == "/":
        return a / b
    elif op_str == "Div":
        return ng.divide(a, b)
    elif op_str == "Equal":
        return ng.equal(a, b)
    elif op_str == "Greater":
        return ng.greater(a, b)
    elif op_str == "GreaterEq":
        return ng.greater_equal(a, b)
    elif op_str == "Less":
        return ng.less(a, b)
    elif op_str == "LessEq":
        return ng.less_equal(a, b)
    elif op_str == "Maximum":
        return ng.maximum(a, b)
    elif op_str == "Minimum":
        return ng.minimum(a, b)
    elif op_str == "NotEqual":
        return ng.not_equal(a, b)
    elif op_str == "Power":
        return ng.power(a, b)


def binary_op_ref(op_str, a, b):

    if op_str == "+" or op_str == "Add":
        return a + b
    elif op_str == "-" or op_str == "Sub":
        return a - b
    elif op_str == "*" or op_str == "Mul":
        return a * b
    elif op_str == "/" or op_str == "Div":
        return a / b
    elif op_str == "Dot":
        return np.dot(a, b)
    elif op_str == "Equal":
        return np.equal(a, b)
    elif op_str == "Greater":
        return np.greater(a, b)
    elif op_str == "GreaterEq":
        return np.greater_equal(a, b)
    elif op_str == "Less":
        return np.less(a, b)
    elif op_str == "LessEq":
        return np.less_equal(a, b)
    elif op_str == "Maximum":
        return np.maximum(a, b)
    elif op_str == "Minimum":
        return np.minimum(a, b)
    elif op_str == "NotEqual":
        return np.not_equal(a, b)
    elif op_str == "Power":
        return np.power(a, b)


def binary_op_exec(op_str):

    element_type = Type.f32
    shape = Shape([2, 2])
    A = Parameter(element_type, shape)
    B = Parameter(element_type, shape)
    parameter_list = [A, B]
    function = Function([binary_op(op_str, A, B)], parameter_list, "test")

    a_arr = np.array([[1, 6], [7, 4]], dtype=np.float32)
    b_arr = np.array([[5, 2], [3, 8]], dtype=np.float32)

    runtime = get_runtime()
    computation = runtime.computation(function, A, B)
    result = computation(a_arr, b_arr)[0]

    expected = binary_op_ref(op_str, a_arr, b_arr)
    assert np.allclose(result, expected)


def binary_op_comparison(op_str):

    element_type = Type.f32
    shape = Shape([2, 2])
    A = Parameter(element_type, shape)
    B = Parameter(element_type, shape)
    parameter_list = [A, B]
    function = Function([binary_op(op_str, A, B)], parameter_list, "test")
    a_arr = np.array([[1, 5], [3, 2]], dtype=np.float32)
    b_arr = np.array([[2, 4], [3, 1]], dtype=np.float32)

    runtime = get_runtime()
    computation = runtime.computation(function, A, B)
    result = computation(a_arr, b_arr)[0]

    expected = binary_op_ref(op_str, a_arr, b_arr)
    assert np.allclose(result, expected)


def test_add():
    binary_op_exec("+")


def test_add_op():
    binary_op_exec("Add")


def test_sub():
    binary_op_exec("-")


def test_sub_op():
    binary_op_exec("Sub")


def test_mul():
    binary_op_exec("*")


def test_mul_op():
    binary_op_exec("Mul")


def test_div():
    binary_op_exec("/")


def test_div_op():
    binary_op_exec("Div")


def test_maximum():
    binary_op_exec("Maximum")


def test_minimum():
    binary_op_exec("Minimum")


def test_power():
    binary_op_exec("Power")


def test_greater():
    binary_op_comparison("Greater")


def test_greater_eq():
    binary_op_comparison("GreaterEq")


def test_less():
    binary_op_comparison("Less")


def test_less_eq():
    binary_op_comparison("LessEq")


def test_not_equal():
    binary_op_comparison("NotEqual")


def test_add_with_mul():

    element_type = Type.f32
    shape = Shape([4])
    A = Parameter(element_type, shape)
    B = Parameter(element_type, shape)
    C = Parameter(element_type, shape)
    parameter_list = [A, B, C]
    function = Function([ng.multiply(ng.add(A, B), C)], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, A, B, C)
    result = computation(
        np.array([1, 2, 3, 4], dtype=np.float32),
        np.array([5, 6, 7, 8], dtype=np.float32),
        np.array([9, 10, 11, 12], dtype=np.float32),
    )[0]

    a_arr = np.array([1, 2, 3, 4], dtype=np.float32)
    b_arr = np.array([5, 6, 7, 8], dtype=np.float32)
    c_arr = np.array([9, 10, 11, 12], dtype=np.float32)
    result_arr_ref = (a_arr + b_arr) * c_arr

    assert np.allclose(result, result_arr_ref)


def unary_op(op_str, a):
    if op_str == "Abs":
        return ng.abs(a)
    elif op_str == "Acos":
        return ng.acos(a)
    elif op_str == "Acosh":
        return ng.acosh(a)
    elif op_str == "Asin":
        return ng.asin(a)
    elif op_str == "Asinh":
        return ng.asinh(a)
    elif op_str == "Atan":
        return ng.atan(a)
    elif op_str == "Atanh":
        return ng.atanh(a)
    elif op_str == "Ceiling":
        return ng.ceiling(a)
    elif op_str == "Cos":
        return ng.cos(a)
    elif op_str == "Cosh":
        return ng.cosh(a)
    elif op_str == "Floor":
        return ng.floor(a)
    elif op_str == "log":
        return ng.log(a)
    elif op_str == "exp":
        return ng.exp(a)
    elif op_str == "negative":
        return ng.negative(a)
    elif op_str == "Sign":
        return ng.sign(a)
    elif op_str == "Sin":
        return ng.sin(a)
    elif op_str == "Sinh":
        return ng.sinh(a)
    elif op_str == "Sqrt":
        return ng.sqrt(a)
    elif op_str == "Tan":
        return ng.tan(a)
    elif op_str == "Tanh":
        return ng.tanh(a)


def unary_op_ref(op_str, a):
    if op_str == "Abs":
        return np.abs(a)
    elif op_str == "Acos":
        return np.arccos(a)
    elif op_str == "Acosh":
        return np.arccosh(a)
    elif op_str == "Asin":
        return np.arcsin(a)
    elif op_str == "Asinh":
        return np.arcsinh(a)
    elif op_str == "Atan":
        return np.arctan(a)
    elif op_str == "Atanh":
        return np.arctanh(a)
    elif op_str == "Ceiling":
        return np.ceil(a)
    elif op_str == "Cos":
        return np.cos(a)
    elif op_str == "Cosh":
        return np.cosh(a)
    elif op_str == "Floor":
        return np.floor(a)
    elif op_str == "log":
        return np.log(a)
    elif op_str == "exp":
        return np.exp(a)
    elif op_str == "negative":
        return np.negative(a)
    elif op_str == "Reverse":
        return np.fliplr(a)
    elif op_str == "Sign":
        return np.sign(a)
    elif op_str == "Sin":
        return np.sin(a)
    elif op_str == "Sinh":
        return np.sinh(a)
    elif op_str == "Sqrt":
        return np.sqrt(a)
    elif op_str == "Tan":
        return np.tan(a)
    elif op_str == "Tanh":
        return np.tanh(a)


def unary_op_exec(op_str, input_list):
    """
    input_list needs to have deep length of 4
    """
    element_type = Type.f32
    shape = Shape(np.array(input_list).shape)
    A = Parameter(element_type, shape)
    parameter_list = [A]
    function = Function([unary_op(op_str, A)], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(np.array(input_list, dtype=np.float32))[0]

    expected = unary_op_ref(op_str, np.array(input_list, dtype=np.float32))
    assert np.allclose(result, expected)


def test_abs():
    input_list = [-1, 0, 1, 2]
    op_str = "Abs"
    unary_op_exec(op_str, input_list)


def test_acos():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Acos"
    unary_op_exec(op_str, input_list)


def test_acosh():
    input_list = [2., 3., 1.5, 1.0]
    op_str = "Acosh"
    unary_op_exec(op_str, input_list)


def test_asin():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Asin"
    unary_op_exec(op_str, input_list)


def test_asinh():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Asinh"
    unary_op_exec(op_str, input_list)


def test_atan():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Atan"
    unary_op_exec(op_str, input_list)


def test_atanh():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Atanh"
    unary_op_exec(op_str, input_list)


def test_ceiling():
    input_list = [0.5, 0, 0.4, 0.5]
    op_str = "Ceiling"
    unary_op_exec(op_str, input_list)


def test_cos():
    input_list = [0, 0.7, 1.7, 3.4]
    op_str = "Cos"
    unary_op_exec(op_str, input_list)


def test_cosh():
    input_list = [-1, 0.0, 0.5, 1]
    op_str = "Cosh"
    unary_op_exec(op_str, input_list)


def test_floor():
    input_list = [-0.5, 0, 0.4, 0.5]
    op_str = "Floor"
    unary_op_exec(op_str, input_list)


def test_log():
    input_list = [1, 2, 3, 4]
    op_str = "log"
    unary_op_exec(op_str, input_list)


def test_exp():
    input_list = [-1, 0, 1, 2]
    op_str = "exp"
    unary_op_exec(op_str, input_list)


def test_negative():
    input_list = [-1, 0, 1, 2]
    op_str = "negative"
    unary_op_exec(op_str, input_list)


def test_sign():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Sign"
    unary_op_exec(op_str, input_list)


def test_sin():
    input_list = [0, 0.7, 1.7, 3.4]
    op_str = "Sin"
    unary_op_exec(op_str, input_list)


def test_sinh():
    input_list = [-1, 0.0, 0.5, 1]
    op_str = "Sinh"
    unary_op_exec(op_str, input_list)


def test_sqrt():
    input_list = [0.0, 0.5, 1, 2]
    op_str = "Sqrt"
    unary_op_exec(op_str, input_list)


def test_tan():
    input_list = [-np.pi / 4, 0, np.pi / 8, np.pi / 8]
    op_str = "Tan"
    unary_op_exec(op_str, input_list)


def test_tanh():
    input_list = [-1, 0, 0.5, 1]
    op_str = "Tanh"
    unary_op_exec(op_str, input_list)


def test_reshape():

    element_type = Type.f32
    shape = Shape([2, 3])
    A = Parameter(element_type, shape)
    parameter_list = [A]
    function = Function([ng.reshape(A, Shape([3, 2]), special_zero=False)], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(np.array(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32), dtype=np.float32))[0]

    expected = np.reshape(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32), (3, 2))
    assert np.allclose(result, expected)


def test_broadcast():

    element_type = Type.f32
    A = Parameter(element_type, Shape([3]))
    parameter_list = [A]
    function = Function([ng.broadcast(A, [3, 3])], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(np.array([1, 2, 3], dtype=np.float32))[0]

    a_arr = np.array([[0], [0], [0]], dtype=np.float32)
    b_arr = np.array([[1, 2, 3]], dtype=np.float32)
    expected = np.add(a_arr, b_arr)
    assert np.allclose(result, expected)


@xfail_issue_34323
def test_constant():
    element_type = Type.f32
    parameter_list = []
    function = Function([Constant(element_type, Shape([3, 3]), list(range(9)))], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation()[0]

    expected = np.arange(9).reshape(3, 3)
    assert np.allclose(result, expected)


def test_concat():

    element_type = Type.f32
    A = Parameter(element_type, Shape([1, 2]))
    B = Parameter(element_type, Shape([1, 2]))
    C = Parameter(element_type, Shape([1, 2]))
    parameter_list = [A, B, C]
    axis = 0
    function = Function([ng.concat([A, B, C], axis)], parameter_list, "test")

    a_arr = np.array([[1, 2]], dtype=np.float32)
    b_arr = np.array([[5, 6]], dtype=np.float32)
    c_arr = np.array([[7, 8]], dtype=np.float32)

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(a_arr, b_arr, c_arr)[0]

    expected = np.concatenate((a_arr, b_arr, c_arr), axis)
    assert np.allclose(result, expected)


def test_axisset():

    set_axisset = AxisSet({1, 2, 3})
    list_axisset = AxisSet([1, 2, 3])
    tuple_axisset = AxisSet((1, 2, 3))

    assert len(set_axisset) == 3
    assert set(set_axisset) == {1, 2, 3}

    assert len(list_axisset) == 3
    assert set(list_axisset) == set(set_axisset)

    assert len(tuple_axisset) == 3
    assert set(tuple_axisset) == set(set_axisset)


def test_select():
    element_type = Type.f32
    A = Parameter(Type.boolean, Shape([1, 2]))
    B = Parameter(element_type, Shape([1, 2]))
    C = Parameter(element_type, Shape([1, 2]))
    parameter_list = [A, B, C]

    function = Function([ng.select(A, B, C)], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(
        np.array([[True, False]], dtype=np.bool),
        np.array([[5, 6]], dtype=np.float32),
        np.array([[7, 8]], dtype=np.float32),
    )[0]

    expected = np.array([[5, 8]])
    assert np.allclose(result, expected)


def test_max_pool():
    # test 1d
    element_type = Type.f32
    shape = Shape([1, 1, 10])
    A = Parameter(element_type, shape)
    parameter_list = [A]

    input_arr = np.arange(10, dtype=np.float32).reshape([1, 1, 10])
    window_shape = [3]

    strides = [1] * len(window_shape)
    pads_begin = [0] * len(window_shape)
    pads_end = [0] * len(window_shape)

    model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(input_arr)[0]

    expected = (np.arange(8) + 2).reshape(1, 1, 8)
    assert np.allclose(result, expected)

    # test 1d with strides
    strides = [2]
    pads_begin = [0] * len(window_shape)
    pads_end = [0] * len(window_shape)

    model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
    function = Function([model], parameter_list, "test")

    size = 4
    computation = runtime.computation(function, *parameter_list)
    result = computation(input_arr)[0]

    expected = ((np.arange(size) + 1) * 2).reshape(1, 1, size)
    assert np.allclose(result, expected)

    # test 2d
    element_type = Type.f32
    shape = Shape([1, 1, 10, 10])
    A = Parameter(element_type, shape)
    parameter_list = [A]

    input_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
    window_shape = [3, 3]

    strides = [1, 1]
    pads_begin = [0, 0]
    pads_end = [0, 0]

    model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
    function = Function([model], parameter_list, "test")

    computation = runtime.computation(function, *parameter_list)
    result = computation(input_arr)[0]

    expected = ((np.arange(100).reshape(10, 10))[2:, 2:]).reshape(1, 1, 8, 8)
    assert np.allclose(result, expected)

    # test 2d with strides
    strides = [2, 2]
    pads_begin = [0, 0]
    pads_end = [0, 0]

    model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
    function = Function([model], parameter_list, "test")
    computation = runtime.computation(function, *parameter_list)
    result = computation(input_arr)[0]

    size = 4
    expected = ((np.arange(100).reshape(10, 10))[2::2, 2::2]).reshape(1, 1, size, size)
    assert np.allclose(result, expected)


def convolution2d(
    image,
    filterit,
    strides=(1, 1),
    dilation=(1, 1),
    padding_below=(0, 0),
    padding_above=(0, 0),
    data_dilation=(1, 1),
):
    def dilate(arr, dil=(1, 1)):
        m, n = arr.shape
        new_m, new_n = (m - 1) * dil[0] + 1, (n - 1) * dil[1] + 1
        new_arr = np.zeros(new_m * new_n, dtype=np.float32).reshape(new_m, new_n)
        for i in range(m):
            for j in range(n):
                new_arr[dil[0] * i][dil[1] * j] = arr[i][j]
        return new_arr

    i_m, i_n = image.shape
    new_image = np.zeros(
        (i_m + padding_below[0] + padding_above[0]) * (i_n + padding_below[1] + padding_above[1]),
        dtype=np.float32,
    ).reshape(i_m + padding_below[0] + padding_above[0], i_n + padding_below[1] + padding_above[1])
    new_image[padding_below[0] : padding_below[0] + i_m, padding_below[1] : padding_below[1] + i_n] = image
    image = new_image
    image = image if data_dilation[0] == data_dilation[1] == 1 else dilate(image, data_dilation)
    i_m, i_n = image.shape

    filterit = filterit if dilation[0] == dilation[1] == 1 else dilate(filterit, dilation)
    f_m, f_n = filterit.shape

    # result_shape
    r_m = i_m - f_m + 1
    r_n = i_n - f_n + 1
    r_m //= strides[0]
    r_n //= strides[1]

    result = np.zeros(r_m * r_n, dtype=np.float32).reshape(r_m, r_n)

    for i in range(r_m):
        for j in range(r_n):
            sub_m = image[i * strides[0] : i * strides[0] + f_m, j * strides[1] : j * strides[1] + f_n]
            result[i][j] = np.sum(sub_m * filterit)
    return result


def test_convolution_simple():

    element_type = Type.f32
    image_shape = Shape([1, 1, 16, 16])
    filter_shape = Shape([1, 1, 3, 3])
    data = Parameter(element_type, image_shape)
    filters = Parameter(element_type, filter_shape)
    parameter_list = [data, filters]

    image_arr = np.arange(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
    filter_arr = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
    filter_arr[0][0][0][0] = -1
    filter_arr[0][0][1][1] = -1
    filter_arr[0][0][2][2] = -1
    filter_arr[0][0][0][2] = -1
    filter_arr[0][0][2][0] = -1

    strides = [1, 1]
    pads_begin = [0, 0]
    pads_end = [0, 0]
    dilations = [1, 1]

    model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(image_arr, filter_arr)[0]

    expected = convolution2d(image_arr[0][0], filter_arr[0][0]).reshape(1, 1, 14, 14)
    assert np.allclose(result, expected)


def test_convolution_with_strides():

    element_type = Type.f32
    image_shape = Shape([1, 1, 10, 10])
    filter_shape = Shape([1, 1, 3, 3])
    data = Parameter(element_type, image_shape)
    filters = Parameter(element_type, filter_shape)
    parameter_list = [data, filters]

    image_arr = np.arange(100, dtype=np.float32).reshape([1, 1, 10, 10])
    filter_arr = np.zeros(9, dtype=np.float32).reshape([1, 1, 3, 3])
    filter_arr[0][0][1][1] = 1
    strides = [2, 2]
    pads_begin = [0, 0]
    pads_end = [0, 0]
    dilations = [1, 1]

    model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(image_arr, filter_arr)[0]

    expected = convolution2d(image_arr[0][0], filter_arr[0][0], strides).reshape(1, 1, 4, 4)
    assert np.allclose(result, expected)


def test_convolution_with_filter_dilation():

    element_type = Type.f32
    image_shape = Shape([1, 1, 10, 10])
    filter_shape = Shape([1, 1, 3, 3])
    data = Parameter(element_type, image_shape)
    filters = Parameter(element_type, filter_shape)
    parameter_list = [data, filters]

    image_arr = np.arange(100, dtype=np.float32).reshape([1, 1, 10, 10])
    filter_arr = np.ones(9, dtype=np.float32).reshape([1, 1, 3, 3])
    strides = [1, 1]
    pads_begin = [0, 0]
    pads_end = [0, 0]
    dilations = [2, 2]

    model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(image_arr, filter_arr)[0]

    expected = convolution2d(image_arr[0][0], filter_arr[0][0], strides, dilations).reshape([1, 1, 6, 6])
    assert np.allclose(result, expected)


def test_convolution_with_padding():

    element_type = Type.f32
    image_shape = Shape([1, 1, 10, 10])
    filter_shape = Shape([1, 1, 3, 3])
    data = Parameter(element_type, image_shape)
    filters = Parameter(element_type, filter_shape)
    parameter_list = [data, filters]

    image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
    filter_arr = np.zeros(9, dtype=np.float32).reshape(1, 1, 3, 3)
    filter_arr[0][0][1][1] = 1
    strides = [1, 1]
    dilations = [2, 2]
    pads_begin = [0, 0]
    pads_end = [0, 0]

    model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(image_arr, filter_arr)[0]

    expected = convolution2d(
        image_arr[0][0], filter_arr[0][0], strides, dilations, pads_begin, pads_end
    ).reshape([1, 1, 6, 6])
    assert np.allclose(result, expected)


def test_convolution_with_non_zero_padding():
    element_type = Type.f32
    image_shape = Shape([1, 1, 10, 10])
    filter_shape = Shape([1, 1, 3, 3])
    data = Parameter(element_type, image_shape)
    filters = Parameter(element_type, filter_shape)
    parameter_list = [data, filters]

    image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
    filter_arr = (np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)) * -1
    filter_arr[0][0][1][1] = 1
    strides = [1, 1]
    dilations = [2, 2]
    pads_begin = [2, 1]
    pads_end = [1, 2]

    model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
    function = Function([model], parameter_list, "test")

    runtime = get_runtime()
    computation = runtime.computation(function, *parameter_list)
    result = computation(image_arr, filter_arr)[0]

    expected = convolution2d(
        image_arr[0][0], filter_arr[0][0], strides, dilations, pads_begin, pads_end
    ).reshape([1, 1, 9, 9])
    assert np.allclose(result, expected)