#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import unittest
import numpy as np
import sys

sys.path.append("..")
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test import OpTest

from test_conv2d_op import conv2d_forward_naive

paddle.enable_static()


def create_test_channel_last_class(parent):
    class TestChannelLastCase(parent):
        def init_data_format(self):
            self.data_format = "NHWC"

        def init_test_case_2(self):
            N, C, H, W = self.input_size
            self.input_size = [N, H, W, C]

    cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast")
    TestChannelLastCase.__name__ = cls_name
    globals()[cls_name] = TestChannelLastCase


def create_test_padding_SAME_class(parent):
    class TestPaddingSMAECase(parent):
        def init_paddings(self):
            self.pad = [0, 0]
            self.padding_algorithm = "SAME"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
    TestPaddingSMAECase.__name__ = cls_name
    globals()[cls_name] = TestPaddingSMAECase


def create_test_padding_VALID_class(parent):
    class TestPaddingVALIDCase(parent):
        def init_paddings(self):
            self.pad = [1, 1]
            self.padding_algorithm = "VALID"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
    TestPaddingVALIDCase.__name__ = cls_name
    globals()[cls_name] = TestPaddingVALIDCase


def create_test_fp16_class(parent):
    class TestFp16Case(parent):
        def init_dtype(self):
            self.dtype = np.float16

    cls_name = "{0}_{1}".format(parent.__name__, "Fp16")
    TestFp16Case.__name__ = cls_name
    globals()[cls_name] = TestFp16Case


class TestConv2DOp(OpTest):
    def set_mlu(self):
        self.__class__.use_mlu = True
        self.place = paddle.device.MLUPlace(0)

    def init_dtype(self):
        self.dtype = np.float32

    def init_data_format(self):
        self.data_format = "NCHW"

    def setUp(self):
        self.set_mlu()
        self.op_type = "conv2d"
        self.init_data_format()
        self.init_dtype()
        self.init_group()
        self.init_dilation()
        self.init_test_case()

        conv2d_param = {
            'stride': self.stride,
            'pad': self.pad,
            'dilation': self.dilations,
        }

        input = np.random.random(self.input_size).astype(self.dtype)
        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)

        output, _, _, _, _ = conv2d_forward_naive(
            input,
            filter,
            self.groups,
            conv2d_param,
            data_format=self.data_format,
        )
        output = output.astype(self.dtype)

        self.inputs = {
            'Input': OpTest.np_dtype_to_fluid_dtype(input),
            'Filter': OpTest.np_dtype_to_fluid_dtype(filter),
        }
        self.attrs = {
            'strides': self.stride,
            'paddings': self.pad,
            'groups': self.groups,
            'dilations': self.dilations,
            'data_format': self.data_format,
        }
        self.outputs = {'Output': output}

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-2)

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            {'Input', 'Filter'},
            'Output',
            max_relative_error=0.03,
            numeric_place=paddle.CPUPlace(),
        )

    def test_check_grad_no_filter(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            ['Input'],
            'Output',
            max_relative_error=0.03,
            no_grad_set=set(['Filter']),
            numeric_place=paddle.CPUPlace(),
        )

    def test_check_grad_no_input(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            ['Filter'],
            'Output',
            max_relative_error=0.03,
            no_grad_set=set(['Input']),
            numeric_place=paddle.CPUPlace(),
        )

    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3]

    def init_dilation(self):
        self.dilations = [1, 1]

    def init_group(self):
        self.groups = 1


class TestWithPad(TestConv2DOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3]


class TestWithStride(TestConv2DOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [2, 2]
        self.input_size = [2, 3, 6, 6]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3]


class TestWithGroup(TestConv2DOp):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        self.group = 3
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [18, f_c, 3, 3]


class TestWith1x1(TestConv2DOp):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1]

    def init_group(self):
        # FIXME: Supporting group = 3 in this case.
        # NOTE(wangran16): There is an unknown error (acl error code is : 507015)
        # when group = 3, which needs to be fixed.
        self.groups = 1


class TestWithDepthWise5x5(TestConv2DOp):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [2, 4, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [8, f_c, 5, 5]

    def init_group(self):
        self.groups = 4


class TestWithDepthWise7x7(TestConv2DOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [2, 2]
        self.input_size = [2, 8, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [16, f_c, 7, 7]

    def init_group(self):
        self.groups = 8


class TestWithDilation(TestConv2DOp):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [2, 3, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [12, f_c, 3, 3]

    def init_dilation(self):
        self.dilations = [2, 2]

    # TODO(MLU): Depthwise opration does not support dilation yet
    # it will throw an error of CNNL_STATUS_NOT_SUPPORTED.
    # def init_group(self):
    #     self.groups = 3


class TestWithInput1x1Filter1x1(TestConv2DOp):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 1]
        self.input_size = [100, 1, 1, 1]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1]

    def init_group(self):
        self.groups = 1


class TestConv2DOp_v2(OpTest):
    def set_mlu(self):
        self.__class__.use_mlu = True
        self.place = paddle.device.MLUPlace(0)

    def setUp(self):
        self.set_mlu()
        self.op_type = "conv2d"
        self.dtype = np.float32
        self.init_kernel_type()
        self.init_group()
        self.init_dilation()
        self.init_data_format()
        self.init_test_case()
        self.init_paddings()
        self.init_test_case_2()

        conv2d_param = {
            'stride': self.stride,
            'pad': self.pad,
            'dilation': self.dilations,
        }

        input = np.random.random(self.input_size).astype(self.dtype)
        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
        output, _, _, _, _ = conv2d_forward_naive(
            input,
            filter,
            self.groups,
            conv2d_param,
            self.padding_algorithm,
            self.data_format,
        )
        output = output.astype(self.dtype)

        self.inputs = {
            'Input': OpTest.np_dtype_to_fluid_dtype(input),
            'Filter': OpTest.np_dtype_to_fluid_dtype(filter),
        }
        self.attrs = {
            'strides': self.stride,
            'paddings': self.pad,
            'padding_algorithm': self.padding_algorithm,
            'groups': self.groups,
            'dilations': self.dilations,
            'data_format': self.data_format,
        }
        self.outputs = {'Output': output}

    def test_check_output(self):
        self.check_output_with_place(self.place, atol=1e-2)

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            {'Input', 'Filter'},
            'Output',
            max_relative_error=0.02,
            numeric_place=paddle.CPUPlace(),
        )

    def test_check_grad_no_filter(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            ['Input'],
            'Output',
            max_relative_error=0.02,
            no_grad_set=set(['Filter']),
            numeric_place=paddle.CPUPlace(),
        )

    def test_check_grad_no_input(self):
        if self.dtype == np.float16:
            return
        self.check_grad_with_place(
            self.place,
            ['Filter'],
            'Output',
            no_grad_set=set(['Input']),
            numeric_place=paddle.CPUPlace(),
        )

    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 2]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 4, 3]

    def init_dilation(self):
        self.dilations = [1, 1]

    def init_group(self):
        self.groups = 1

    def init_kernel_type(self):
        pass

    def init_paddings(self):
        self.pad = [0, 0]
        self.padding_algorithm = "EXPLICIT"

    def init_data_format(self):
        self.data_format = "NCHW"

    def init_test_case_2(self):
        pass


class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
    def init_paddings(self):
        self.pad = [0, 0, 1, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithPad_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3]

    def init_paddings(self):
        self.pad = [2, 1, 3, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithStride_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [2, 2]
        self.input_size = [2, 3, 6, 6]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3]

    def init_paddings(self):
        self.pad = [2, 1, 3, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithGroup_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.pad = [0, 0]
        self.stride = [1, 2]
        self.input_size = [2, 3, 5, 5]  # NCHW
        self.group = 3
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [24, f_c, 4, 3]


class TestWith1x1_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [2, 3, 5, 5]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1]

    def init_group(self):
        self.groups = 1

    def init_paddings(self):
        self.pad = [2, 2, 4, 0]
        self.padding_algorithm = "EXPLICIT"


class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [3, 4, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [16, f_c, 3, 3]

    # TODO(MLU): Depthwise opration does not support dilation yet
    # it will throw an error of CNNL_STATUS_NOT_SUPPORTED.
    # def init_dilation(self):
    #     self.dilations = [2, 2]

    def init_group(self):
        self.groups = 4

    def init_paddings(self):
        self.pad = [1, 3, 2, 1]
        self.padding_algorithm = "EXPLICIT"


class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [2, 4, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [8, f_c, 5, 5]

    def init_group(self):
        self.groups = 4

    def init_paddings(self):
        self.pad = [0, 1, 1, 0]
        self.padding_algorithm = "EXPLICIT"


class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [2, 2]
        self.input_size = [2, 8, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [16, f_c, 7, 7]

    def init_group(self):
        self.groups = 8

    def init_paddings(self):
        self.pad = [1, 3, 4, 1]
        self.padding_algorithm = "EXPLICIT"


class TestWithDilation_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [2, 3, 10, 10]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [24, f_c, 3, 3]

    def init_dilation(self):
        self.dilations = [2, 2]

    # TODO(MLU): Depthwise opration does not support dilation yet
    # it will throw an error of CNNL_STATUS_NOT_SUPPORTED.
    # def init_group(self):
    #     self.groups = 3

    def init_paddings(self):
        self.pad = [0, 1, 3, 0]
        self.padding_algorithm = "EXPLICIT"


class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2):
    def init_test_case(self):
        self.stride = [1, 1]
        self.input_size = [100, 1, 1, 1]  # NCHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1]

    def init_group(self):
        self.groups = 1

    def init_paddings(self):
        self.pad = [0, 3, 4, 0]
        self.padding_algorithm = "EXPLICIT"


create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
create_test_padding_SAME_class(TestWithPad_AsyPadding)
create_test_padding_SAME_class(TestWithStride_AsyPadding)
create_test_padding_SAME_class(TestWithGroup_AsyPadding)
create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)

create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
create_test_padding_VALID_class(TestWithPad_AsyPadding)
create_test_padding_VALID_class(TestWithStride_AsyPadding)
create_test_padding_VALID_class(TestWithGroup_AsyPadding)
create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)

create_test_channel_last_class(TestConv2DOp_AsyPadding)
create_test_channel_last_class(TestWithPad_AsyPadding)
create_test_channel_last_class(TestWithGroup_AsyPadding)
create_test_channel_last_class(TestWith1x1_AsyPadding)
create_test_channel_last_class(TestWithInput1x1Filter1x1_AsyPadding)

create_test_fp16_class(TestConv2DOp_AsyPadding)
create_test_fp16_class(TestWithPad_AsyPadding)
create_test_fp16_class(TestWithStride_AsyPadding)
create_test_fp16_class(TestWithGroup_AsyPadding)
create_test_fp16_class(TestWithInput1x1Filter1x1_AsyPadding)

if __name__ == "__main__":
    unittest.main()
