# 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.

import unittest
from unittest import TestCase

import numpy as np
from op_test import get_places

import paddle
import paddle.base.dygraph as dg
import paddle.nn.functional as F
from paddle import base


class TestFunctionalConv2DError(TestCase):
    batch_size = 4
    spatial_shape = (16, 16)
    dtype = "float32"

    def setUp(self):
        self.in_channels = 3
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = "not_valid"
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.data_format = "NHWC"

    def test_exception(self):
        self.prepare()
        with self.assertRaises(ValueError):
            self.static_graph_case()

    def prepare(self):
        if isinstance(self.filter_shape, int):
            filter_shape = (self.filter_shape,) * 2
        else:
            filter_shape = tuple(self.filter_shape)
        self.weight_shape = (
            self.out_channels,
            self.in_channels // self.groups,
            *filter_shape,
        )
        self.bias_shape = (self.out_channels,)

    def static_graph_case(self):
        main = base.Program()
        start = base.Program()
        with (
            base.unique_name.guard(),
            base.program_guard(main, start),
        ):
            self.channel_last = self.data_format == "NHWC"
            if self.channel_last:
                x = x = paddle.static.data(
                    "input",
                    (-1, -1, -1, self.in_channels),
                    dtype=self.dtype,
                )
            else:
                x = paddle.static.data(
                    "input",
                    (-1, self.in_channels, -1, -1),
                    dtype=self.dtype,
                )
            weight = paddle.static.data(
                "weight", self.weight_shape, dtype=self.dtype
            )
            if not self.no_bias:
                bias = paddle.static.data(
                    "bias", self.bias_shape, dtype=self.dtype
                )
            y = F.conv2d(
                x,
                weight,
                None if self.no_bias else bias,
                padding=self.padding,
                stride=self.stride,
                dilation=self.dilation,
                groups=self.groups,
                data_format=self.data_format,
            )


class TestFunctionalConv2DErrorCase2(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = [[0, 0], [1, 2], [3, 4], [5, 6]]
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "NCHW"


class TestFunctionalConv2DErrorCase3(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 4
        self.filter_shape = 3
        self.padding = "same"
        self.stride = 1
        self.dilation = 1
        self.groups = 2
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "not_valid"


class TestFunctionalConv2DErrorCase4(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 4
        self.out_channels = 3
        self.filter_shape = 3
        self.padding = "same"
        self.stride = 1
        self.dilation = 1
        self.groups = 2
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "NCHW"


class TestFunctionalConv2DErrorCase7(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = "same"
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = True
        self.data_format = "not_valid"


class TestFunctionalConv2DErrorCase8(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = [1, 2, 1, 2, 1]
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = True
        self.data_format = "NCHW"


class TestFunctionalConv2DErrorCase9(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = -5
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = [[0, 0], [0, 0], [3, 2], [1, 2]]
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "NCHW"


class TestFunctionalConv2DErrorCase10(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 4
        self.filter_shape = 3
        self.padding = "same"
        self.stride = 1
        self.dilation = 1
        self.groups = 2
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "NHWC"


class TestFunctionalConv2DErrorCase11(TestFunctionalConv2DError):
    def setUp(self):
        self.in_channels = 3
        self.out_channels = 5
        self.filter_shape = 3
        self.padding = 0
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.no_bias = False
        self.act = "sigmoid"
        self.use_cudnn = False
        self.data_format = "NHCW"


class TestFunctionalConv2DErrorCase12(TestCase):
    def setUp(self):
        self.input = np.random.randn(1, 3, 3, 3)
        self.filter = np.random.randn(3, 3, 1, 1)
        self.num_filters = 3
        self.filter_size = 1
        self.bias = None
        self.padding = 0
        self.stride = 1
        self.dilation = 1
        self.groups = 0
        self.data_format = "NCHW"

    def dygraph_case(self):
        with dg.guard():
            x = paddle.to_tensor(self.input, dtype=paddle.float32)
            w = paddle.to_tensor(self.filter, dtype=paddle.float32)
            b = (
                None
                if self.bias is None
                else paddle.to_tensor(self.bias, dtype=paddle.float32)
            )
            y = F.conv2d(
                x,
                w,
                b,
                padding=self.padding,
                stride=self.stride,
                dilation=self.dilation,
                groups=self.groups,
                data_format=self.data_format,
            )

    def test_dygraph_exception(self):
        with self.assertRaises(ValueError):
            self.dygraph_case()


class TestFunctionalConv2D_ZeroSize(TestCase):
    def init_data(self):
        self.input = np.random.random([0, 3, 4, 4])
        self.filter = np.random.random([2, 3, 3, 3])
        self.np_out = np.zeros([0, 2, 2, 2])

    def setUp(self):
        self.init_data()
        self.bias = None
        self.padding = 0
        self.stride = 1
        self.dilation = 1
        self.groups = 1
        self.data_format = "NCHW"
        self.places = get_places()

    def test_dygraph(self):
        for place in self.places:
            with dg.guard(place):
                input = paddle.to_tensor(self.input)
                input.stop_gradient = False
                filter = paddle.to_tensor(self.filter)
                y = F.conv2d(
                    input,
                    filter,
                    self.bias,
                    padding=self.padding,
                    stride=self.stride,
                    dilation=self.dilation,
                    groups=self.groups,
                    data_format=self.data_format,
                )
                np.testing.assert_allclose(y.numpy(), self.np_out)
                loss = y.sum()
                loss.backward()
                np.testing.assert_allclose(input.grad.shape, input.shape)


class TestFunctionalConv2D_ZeroSize2(TestFunctionalConv2D_ZeroSize):
    def init_data(self):
        self.input = np.random.random([0, 0, 4, 4])
        self.filter = np.random.random([2, 0, 3, 3])
        self.np_out = np.zeros([0, 0, 2, 2])


class TestFunctionalConv2D_ZeroKernelError(TestCase):
    """kernel_size=0 in any spatial dim should raise InvalidArgument."""

    def _assert_raises(self, x_shape, w_shape, **kwargs):
        places = get_places()
        for place in places:
            with dg.guard(place):
                x = paddle.randn(x_shape)
                w = paddle.to_tensor(
                    np.random.randn(*w_shape).astype('float32')
                )
                with self.assertRaises(ValueError):
                    F.conv2d(x, w, **kwargs)

    def test_depthwise_zero_kH(self):
        # depthwise, kernel_height=0
        self._assert_raises(
            [16, 3, 260, 260],
            [3, 1, 0, 5],
            groups=3,
        )

    def test_depthwise_zero_kW(self):
        # depthwise, kernel_width=0
        self._assert_raises(
            [16, 3, 260, 260],
            [3, 1, 5, 0],
            groups=3,
        )

    def test_depthwise_large_zero_kH(self):
        self._assert_raises(
            [16, 3, 268, 268],
            [3, 1, 0, 13],
            groups=3,
        )

    def test_depthwise_large_zero_kW(self):
        self._assert_raises(
            [16, 3, 268, 268],
            [3, 1, 13, 0],
            groups=3,
        )

    def test_regular_conv_zero_kH(self):
        # regular conv2d (groups=1), kernel_height=0
        self._assert_raises(
            [2, 3, 8, 8],
            [6, 3, 0, 3],
            groups=1,
        )

    def test_regular_conv_zero_kW(self):
        # regular conv2d (groups=1), kernel_width=0
        self._assert_raises(
            [2, 3, 8, 8],
            [6, 3, 3, 0],
            groups=1,
        )


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