#   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.

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
import paddle.nn as nn
from paddle.fluid import Program, program_guard


def conv2dtranspose_forward_naive(input_, filter_, attrs):
    padding_algorithm = attrs['padding_algorithm']
    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 attrs['data_format'] == 'NHWC':
        input_ = np.transpose(input_, [0, 3, 1, 2])
    in_n, in_c, in_h, in_w = input_.shape
    f_c, f_out_c, f_h, f_w = filter_.shape
    groups = attrs['groups']
    assert in_c == f_c
    out_c = f_out_c * groups
    sub_in_c = in_c // groups

    stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
        'dilations']

    # update pad and dilation
    def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(
                input_shape, kernel_size, kernel_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":
        dilations = [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]

    d_bolck_h = dilations[0] * (f_h - 1) + 1
    d_bolck_w = dilations[1] * (f_w - 1) + 1
    out_h = (in_h - 1) * stride[0] + d_bolck_h
    out_w = (in_w - 1) * stride[1] + d_bolck_w
    if 'output_size' in attrs:
        output_size = attrs['output_size']
        out_h = output_size[0] + pad_h_0 + pad_h_1
        out_w = output_size[1] + pad_w_0 + pad_w_1
    out_pad_h = 0
    out_pad_w = 0
    if 'output_padding' in attrs:
        out_pad_h = attrs['output_padding'][0]
        out_pad_w = attrs['output_padding'][1]
    out = np.zeros(
        (in_n, out_c, out_h + out_pad_h, out_w + out_pad_w), dtype=input_.dtype)

    for n in range(in_n):
        for i in range(in_h):
            for j in range(in_w):
                for g in range(groups):
                    input_masked = input_[n, g * sub_in_c:(g + 1) * sub_in_c, i,
                                          j]  # (c)
                    input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
                    input_masked = np.tile(input_masked, (1, f_h, f_w))

                    for k in range(f_out_c):
                        tmp_out = np.sum(
                            input_masked *
                            filter_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :],
                            axis=0)
                        i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
                        j1, j2 = j * stride[1], j * stride[1] + d_bolck_w
                        out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2:
                            dilations[1]] += tmp_out

    out = out[:, :, pad_h_0:out_h - pad_h_1 + out_pad_h, pad_w_0:out_w - pad_w_1
              + out_pad_w]
    if attrs['data_format'] == 'NHWC':
        out = np.transpose(out, [0, 2, 3, 1])
    return out


class TestConv2DTransposeOp(XPUOpTest):
    def setUp(self):
        # init as conv transpose
        self.dtype = np.float32
        self.need_check_grad = True
        self.is_test = False
        self.use_cudnn = False
        self.use_mkldnn = False
        self.output_size = None
        self.output_padding = []
        self.data_format = "NCHW"
        self.pad = [0, 0]
        self.padding_algorithm = "EXPLICIT"
        self.init_op_type()
        self.init_test_case()
        self.__class__.op_type = "conv2d_transpose"

        input_ = np.random.random(self.input_size).astype(self.dtype)
        filter_ = np.random.random(self.filter_size).astype(self.dtype)

        self.inputs = {'Input': input_, 'Filter': 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,
            'is_test': self.is_test,
            'use_mkldnn': self.use_mkldnn,
            'data_format': self.data_format
        }
        if self.output_size is not None:
            self.attrs['output_size'] = self.output_size

        if len(self.output_padding) > 0:
            self.attrs['output_padding'] = self.output_padding

        output = conv2dtranspose_forward_naive(input_, filter_,
                                               self.attrs).astype(self.dtype)

        self.outputs = {'Output': output}

    def test_check_output(self):
        if core.is_compiled_with_xpu():
            paddle.enable_static()
            place = paddle.XPUPlace(0)
            self.check_output_with_place(place)

    def test_check_grad_no_input(self):
        if self.need_check_grad:
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                place = paddle.XPUPlace(0)
                self.check_grad_with_place(
                    place, ['Filter'], 'Output', no_grad_set=set(['Input']))

    def test_check_grad_no_filter(self):
        if self.need_check_grad:
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                place = paddle.XPUPlace(0)
                self.check_grad_with_place(
                    place, ['Input'], 'Output', no_grad_set=set(['Filter']))

    def test_check_grad(self):
        if self.need_check_grad:
            if core.is_compiled_with_xpu():
                paddle.enable_static()
                place = paddle.XPUPlace(0)
                self.check_grad_with_place(place,
                                           set(['Input', 'Filter']), 'Output')

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

    def init_op_type(self):
        self.op_type = "conv2d_transpose"


class TestWithSymmetricPad(TestConv2DTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [1, 1]
        self.dilations = [1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3]


class TestWithAsymmetricPad(TestConv2DTransposeOp):
    def init_test_case(self):
        self.pad = [1, 0, 1, 2]
        self.stride = [1, 1]
        self.dilations = [1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3]


class TestWithSAMEPad(TestConv2DTransposeOp):
    def init_test_case(self):
        self.stride = [2, 1]
        self.dilations = [1, 2]
        self.groups = 1
        self.input_size = [2, 3, 6, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 4, 3]
        self.padding_algorithm = 'SAME'


class TestWithVALIDPad(TestConv2DTransposeOp):
    def init_test_case(self):
        self.stride = [1, 1]
        self.dilations = [1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3]
        self.padding_algorithm = 'VALID'


class TestWithGroups(TestConv2DTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [1, 1]
        self.dilations = [1, 1]
        self.groups = 2
        self.input_size = [2, 4, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 3, 3, 3]


class TestWithStride(TestConv2DTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1]
        self.stride = [2, 2]
        self.dilations = [1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3]


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