#   Copyright (c) 2020 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.

from __future__ import print_function
import sys

sys.path.append("..")
import unittest
import numpy as np

import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test_xpu import XPUOpTest
import paddle
from paddle.fluid import Program, program_guard
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper


def conv2d_forward_naive(input,
                         filter,
                         group,
                         conv_param,
                         padding_algorithm='EXPLICIT',
                         data_format='NCHW'):
    if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
        raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                         "It can only be 'SAME' or 'VALID'." %
                         str(padding_algorithm))

    if data_format not in ["NCHW", "NHWC"]:
        raise ValueError("Unknown Attr(data_format): '%s' ."
                         "It can only be 'NCHW' or 'NHWC'." % str(data_format))

    channel_last = (data_format == "NHWC")
    if channel_last:
        input = np.transpose(input, [0, 3, 1, 2])

    in_n, in_c, in_h, in_w = input.shape
    f_n, f_c, f_h, f_w = filter.shape
    out_n = in_n
    out_c = f_n
    assert f_c * group == in_c
    assert np.mod(out_c, group) == 0
    sub_out_c = out_c // group
    sub_f_n = f_n // group

    stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
        'dilation']

    # update pad and dilation
    def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                        pool_stride):
            out_size = int((input_size + stride_size - 1) / stride_size)
            pad_sum = np.max(
                ((out_size - 1) * stride_size + filter_size - input_size, 0))
            pad_0 = int(pad_sum / 2)
            pad_1 = int(pad_sum - pad_0)
            padding.append(pad_0)
            padding.append(pad_1)
        return padding

    ksize = filter.shape[2:4]
    if padding_algorithm == "VALID":
        pad = [0, 0, 0, 0]
    elif padding_algorithm == "SAME":
        dilation = [1, 1]
        input_data_shape = input.shape[2:4]
        pad = _get_padding_with_SAME(input_data_shape, ksize, stride)

    pad_h_0, pad_h_1 = pad[0], pad[0]
    pad_w_0, pad_w_1 = pad[1], pad[1]
    if len(pad) == 4:
        pad_h_0, pad_h_1 = pad[0], pad[1]
        pad_w_0, pad_w_1 = pad[2], pad[3]
    out_h = 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[0] *
                                             (f_h - 1) + 1)) // stride[0]
    out_w = 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[1] *
                                             (f_w - 1) + 1)) // stride[1]
    out = np.zeros((out_n, out_c, out_h, out_w))

    d_bolck_h = (dilation[0] * (f_h - 1) + 1)
    d_bolck_w = (dilation[1] * (f_w - 1) + 1)

    input_pad = np.pad(input,
                       ((0, 0), (0, 0), (pad_h_0, pad_h_1), (pad_w_0, pad_w_1)),
                       mode='constant',
                       constant_values=0)

    filter_dilation = np.zeros((f_n, f_c, d_bolck_h, d_bolck_w))
    filter_dilation[:, :, 0:d_bolck_h:dilation[0],
                    0:d_bolck_w:dilation[1]] = filter

    for i in range(out_h):
        for j in range(out_w):
            for g in range(group):
                input_pad_masked = \
                    input_pad[:, g * f_c:(g + 1) * f_c,
                    i * stride[0]:i * stride[0] + d_bolck_h,
                    j * stride[1]:j * stride[1] + d_bolck_w]

                f_sub = filter_dilation[g * sub_f_n:(g + 1) * sub_f_n, :, :, :]
                # sub_f_n == sub_out_c
                for k in range(sub_out_c):
                    # Multiplication of Corresponding Elements, then sum all
                    out[:, g * sub_out_c + k, i, j] = \
                        np.sum(input_pad_masked * f_sub[k, :, :, :],
                               axis=(1, 2, 3))

    if channel_last:
        out = np.transpose(out, [0, 2, 3, 1])

    return out, in_n, out_h, out_w, out_c


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


class XPUTestConv2DOp(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = 'conv2d'
        self.use_dynamic_create_class = False

    class TestConv2DOp(XPUOpTest):

        def setUp(self):
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)
            self.op_type = "conv2d"
            self.use_cudnn = False
            self.exhaustive_search = False
            self.use_cuda = False
            self.use_mkldnn = False
            self.fuse_relu_before_depthwise_conv = False
            self.data_format = "AnyLayout"
            self.init_kernel_type()
            self.init_group()
            self.init_dilation()
            self.init_test_case()

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

            np.random.seed(100)
            input = np.random.random(self.input_size).astype(self.dtype)
            if not self.has_cuda():
                self.fuse_relu_before_depthwise_conv = False
            if self.fuse_relu_before_depthwise_conv:
                input = input - 0.5
                input -= (input < 0) * 0.1
                input += (input >= 0) * 0.1
                input2 = np.maximum(input, 0.0)
            else:
                input2 = input
            np.random.seed(1)
            filter = np.random.uniform(-1, 1,
                                       self.filter_size).astype(self.dtype)

            output, _, _, _, _ = conv2d_forward_naive(input2, filter,
                                                      self.groups, conv2d_param)
            output = output.astype(self.dtype)

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

        def has_cuda(self):
            return core.is_compiled_with_cuda() and (self.use_cudnn
                                                     or self.use_cuda)

        def test_check_output(self):
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_output_with_place(self.place)

        def test_check_grad(self):
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, {'Input', 'Filter'},
                                           'Output')

        def test_check_grad_no_filter(self):
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, ['Input'],
                                           'Output',
                                           no_grad_set=set(['Filter']))

        def test_check_grad_no_input(self):
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, ['Filter'],
                                           'Output',
                                           no_grad_set=set(['Input']))

        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_test_case_2(self):
            pass

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

        def init_group(self):
            self.groups = 1

        def init_kernel_type(self):
            pass

    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 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):
            self.groups = 1


# ---- test asymmetric padding ----
class XPUTestConv2DOp_v2(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = 'conv2d'
        self.use_dynamic_create_class = False

    class TestConv2DOp_v2(XPUOpTest):

        def setUp(self):
            self.dtype = self.in_type
            self.place = paddle.XPUPlace(0)
            self.op_type = "conv2d"
            self.use_cudnn = False
            self.exhaustive_search = False
            self.use_cuda = False
            self.use_mkldnn = False
            self.fuse_relu_before_depthwise_conv = False
            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
            }

            np.random.seed(100)
            input = np.random.random(self.input_size).astype(self.dtype)
            if not self.has_cuda():
                self.fuse_relu_before_depthwise_conv = False
            if self.fuse_relu_before_depthwise_conv:
                input = input - 0.5
                input -= (input < 0) * 0.1
                input += (input >= 0) * 0.1
                input2 = np.maximum(input, 0.0)
            else:
                input2 = input
            np.random.seed(8)
            filter = np.random.uniform(-1, 1,
                                       self.filter_size).astype(self.dtype)
            output, _, _, _, _ = conv2d_forward_naive(input2, filter,
                                                      self.groups, conv2d_param,
                                                      self.padding_algorithm,
                                                      self.data_format)
            output = output.astype(self.dtype)

            self.inputs = {
                'Input': XPUOpTest.np_dtype_to_fluid_dtype(input),
                'Filter': XPUOpTest.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,
                'use_cudnn': self.use_cudnn,
                'use_mkldnn': self.use_mkldnn,
                'data_format': self.data_format,
                'fuse_relu_before_depthwise_conv':
                self.fuse_relu_before_depthwise_conv,
                'exhaustive_search': self.exhaustive_search
            }
            self.outputs = {'Output': output}

        def has_cuda(self):
            return core.is_compiled_with_cuda() and (self.use_cudnn
                                                     or self.use_cuda)

        def test_check_output(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_output_with_place(place=self.place)

        def test_check_grad(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, {'Input', 'Filter'},
                                           'Output')

        def test_check_grad_no_filter(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, ['Input'],
                                           'Output',
                                           no_grad_set=set(['Filter']))

        def test_check_grad_no_input(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if (hasattr(self, "no_need_check_grad")
                    and self.no_need_check_grad == True):
                return
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                self.check_grad_with_place(self.place, ['Filter'],
                                           'Output',
                                           no_grad_set=set(['Input']))

        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, 0, 0]
            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 = [1, 1, 1, 1]
            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 = [1, 1, 1, 1]
            self.padding_algorithm = "EXPLICIT"


class XPUTestConv2DOp_NHWC(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = 'conv2d'
        self.use_dynamic_create_class = False

    class TestConv2DOp_AsyPadding_NHWC(
            XPUTestConv2DOp_v2.TestConv2DOp_AsyPadding):

        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]

    class TestWithPad_AsyPadding_NHWC(XPUTestConv2DOp_v2.TestWithPad_AsyPadding
                                      ):

        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]


support_types = get_xpu_op_support_types('conv2d')
for stype in ['float32']:
    create_test_class(globals(), XPUTestConv2DOp, stype)
    create_test_class(globals(), XPUTestConv2DOp_v2, stype)
    create_test_class(globals(),
                      XPUTestConv2DOp_NHWC,
                      stype,
                      ignore_deivce_version=[core.XPUVersion.XPU1])

#---------- test SAME VALID -----------
#create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
#create_test_padding_SAME_class(TestWithPad_AsyPadding)
#create_test_padding_SAME_class(TestWithStride_AsyPadding)

#create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
#create_test_padding_VALID_class(TestWithPad_AsyPadding)
#create_test_padding_VALID_class(TestWithStride_AsyPadding)

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