# 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

import numpy as np
from op_test import get_device_place, is_custom_device

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


class Conv1DTestCase(unittest.TestCase):
    def __init__(
        self,
        methodName='runTest',
        batch_size=4,
        spartial_shape=(16,),
        num_channels=6,
        num_filters=8,
        filter_size=3,
        padding=0,
        padding_mode="zeros",
        stride=1,
        dilation=1,
        groups=1,
        no_bias=False,
        dtype="float32",
        data_format="NCL",
    ):
        super().__init__(methodName)
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.num_filters = num_filters
        self.spartial_shape = spartial_shape
        self.filter_size = filter_size
        self.data_format = data_format
        self.channel_last = self.data_format == "NLC"

        self.padding = padding
        self.padding_mode = padding_mode
        self.stride = stride
        self.dilation = dilation
        self.groups = groups
        self.no_bias = no_bias
        self.dtype = dtype

    def setUp(self):
        input_shape = (
            (self.batch_size, self.num_channels, *self.spartial_shape)
            if not self.channel_last
            else (self.batch_size, *self.spartial_shape, self.num_channels)
        )
        self.input = np.random.randn(*input_shape).astype(self.dtype)

        if isinstance(self.filter_size, int):
            filter_size = [self.filter_size]
        else:
            filter_size = self.filter_size
        self.weight_shape = weight_shape = (
            self.num_filters,
            self.num_channels // self.groups,
            *filter_size,
        )
        self.weight = np.random.uniform(-1, 1, size=weight_shape).astype(
            self.dtype
        )
        if not self.no_bias:
            self.bias = np.random.uniform(
                -1, 1, size=(self.num_filters,)
            ).astype(self.dtype)
        else:
            self.bias = None

    def functional(self, place):
        main = base.Program()
        start = base.Program()
        with (
            base.unique_name.guard(),
            base.program_guard(main, start),
        ):
            input_shape = (
                (-1, self.num_channels, -1)
                if not self.channel_last
                else (-1, -1, self.num_channels)
            )
            x_var = paddle.static.data("input", input_shape, dtype=self.dtype)
            w_var = paddle.static.data(
                "weight", self.weight_shape, dtype=self.dtype
            )
            if not self.no_bias:
                b_var = paddle.static.data(
                    "bias", (self.num_filters,), dtype=self.dtype
                )
            else:
                b_var = None
            y_var = F.conv1d(
                x_var,
                w_var,
                b_var,
                padding=self.padding,
                stride=self.stride,
                dilation=self.dilation,
                groups=self.groups,
                data_format=self.data_format,
            )
        feed_dict = {"input": self.input, "weight": self.weight}
        if self.bias is not None:
            feed_dict["bias"] = self.bias
        exe = base.Executor(place)
        exe.run(start)
        (y_np,) = exe.run(main, feed=feed_dict, fetch_list=[y_var])
        return y_np

    def paddle_nn_layer(self):
        x_var = paddle.to_tensor(self.input)
        conv = nn.Conv1D(
            self.num_channels,
            self.num_filters,
            self.filter_size,
            padding=self.padding,
            padding_mode=self.padding_mode,
            stride=self.stride,
            dilation=self.dilation,
            groups=self.groups,
            data_format=self.data_format,
        )
        conv.weight.set_value(self.weight)
        if not self.no_bias:
            conv.bias.set_value(self.bias)
        y_var = conv(x_var)
        y_np = y_var.numpy()
        return y_np

    def _test_equivalence(self, place):
        result1 = self.functional(place)
        with dg.guard(place):
            result2 = self.paddle_nn_layer()
        np.testing.assert_array_almost_equal(result1, result2)

    def runTest(self):
        place = base.CPUPlace()
        self._test_equivalence(place)

        if base.core.is_compiled_with_cuda() or is_custom_device():
            place = get_device_place()
            self._test_equivalence(place)


class Conv1DErrorTestCase(Conv1DTestCase):
    def runTest(self):
        place = base.CPUPlace()
        with (
            dg.guard(place),
            self.assertRaises(ValueError),
        ):
            self.paddle_nn_layer()


class Conv1DTypeErrorTestCase(Conv1DTestCase):
    def runTest(self):
        place = base.CPUPlace()
        with (
            dg.guard(place),
            self.assertRaises(TypeError),
        ):
            self.paddle_nn_layer()


def add_cases(suite):
    suite.addTest(Conv1DTestCase(methodName='runTest'))
    suite.addTest(Conv1DTestCase(methodName='runTest', stride=[1], dilation=2))
    suite.addTest(Conv1DTestCase(methodName='runTest', stride=2, dilation=(1)))
    suite.addTest(
        Conv1DTestCase(methodName='runTest', padding="same", no_bias=True)
    )
    suite.addTest(
        Conv1DTestCase(methodName='runTest', filter_size=3, padding='valid')
    )
    suite.addTest(
        Conv1DTestCase(methodName='runTest', num_filters=512, padding='valid')
    )
    suite.addTest(
        Conv1DTestCase(methodName='runTest', num_filters=512, padding=[1, 2])
    )
    suite.addTest(
        Conv1DTestCase(methodName='runTest', padding=2, data_format='NLC')
    )
    suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1]))
    suite.addTest(Conv1DTestCase(methodName='runTest', padding=[1, 2]))
    suite.addTest(
        Conv1DTestCase(methodName='runTest', padding=[1, 2], data_format='NLC')
    )
    suite.addTest(Conv1DTestCase(methodName='runTest', padding=2))
    suite.addTest(Conv1DTestCase(methodName='runTest'))
    suite.addTest(
        Conv1DTestCase(methodName='runTest', groups=2, padding="valid")
    )
    suite.addTest(
        Conv1DTestCase(
            methodName='runTest',
            num_filters=6,
            num_channels=3,
            groups=3,
            padding="valid",
            data_format='NLC',
        )
    )


def add_error_cases(suite):
    suite.addTest(
        Conv1DTypeErrorTestCase(
            methodName='runTest', padding_mode="reflect", padding="valid"
        )
    )
    suite.addTest(
        Conv1DErrorTestCase(methodName='runTest', data_format="VALID")
    )
    suite.addTest(
        Conv1DErrorTestCase(methodName='runTest', padding_mode="VALID")
    )
    suite.addTest(
        Conv1DErrorTestCase(methodName='runTest', num_channels=5, groups=2)
    )
    suite.addTest(
        Conv1DErrorTestCase(
            methodName='runTest', num_filters=8, num_channels=15, groups=3
        )
    )
    suite.addTest(
        Conv1DErrorTestCase(methodName='runTest', padding=[1, 2, 3, 4, 5])
    )
    suite.addTest(
        Conv1DErrorTestCase(
            methodName='runTest', padding=[1, 2, 3, 4, 5], data_format='NLC'
        )
    )
    suite.addTest(
        Conv1DErrorTestCase(
            methodName='runTest', num_filters=512, padding=[1, 2, 3, 4, 5]
        )
    )
    suite.addTest(Conv1DErrorTestCase(methodName='runTest', dilation=-10))


def load_tests(loader, standard_tests, pattern):
    suite = unittest.TestSuite()
    add_cases(suite)
    add_error_cases(suite)
    return suite


def conv1d_forward_naive(
    input,
    filter,
    group,
    conv_param,
    padding_algorithm="EXPLICIT",
    data_format="NCL",
):
    if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
        raise ValueError(
            f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
            "It can only be 'SAME' or 'VALID'."
        )

    if data_format not in ["NCL", "NLC"]:
        raise ValueError(
            f"Unknown Attr(data_format): '{data_format}' ."
            "It can only be 'NCL' or 'NLC'."
        )

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

    in_n, in_c, in_l = input.shape
    f_n, f_c, f_l = 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]]  # 1D kernel size
    if padding_algorithm == "VALID":
        pad = [0, 0]
    elif padding_algorithm == "SAME":
        dilation = [1]
        input_data_shape = [input.shape[2]]  # 1D input shape
        pad = _get_padding_with_SAME(input_data_shape, ksize, stride)

    pad_l_0, pad_l_1 = pad[0], pad[0]
    if len(pad) == 2:
        pad_l_0, pad_l_1 = pad[0], pad[1]

    out_l = (
        1
        + (in_l + pad_l_0 + pad_l_1 - (dilation[0] * (f_l - 1) + 1))
        // stride[0]
    )
    out = np.zeros((out_n, out_c, out_l))

    d_block_l = dilation[0] * (f_l - 1) + 1

    input_pad = np.pad(
        input,
        ((0, 0), (0, 0), (pad_l_0, pad_l_1)),
        mode="constant",
        constant_values=0,
    )

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

    for i in range(out_l):
        for g in range(group):
            input_pad_masked = input_pad[
                :,
                g * f_c : (g + 1) * f_c,
                i * stride[0] : i * stride[0] + d_block_l,
            ]

            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] = np.sum(
                    input_pad_masked * f_sub[k, :, :], axis=(1, 2)
                )

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

    return out, in_n, out_l, out_c


def get_places():
    places = []
    if core.is_compiled_with_xpu():
        places.append(paddle.device.XPUPlace(0))
    elif core.is_compiled_with_cuda():
        places.append(paddle.CUDAPlace(0))
    places.append(paddle.CPUPlace())
    return places


class TestConv1dAPI_Compatibility(unittest.TestCase):
    def setUp(self):
        np.random.seed(2025)
        self.places = get_places()
        self.shape_x = [2, 3, 16]  # NCL
        self.shape_w = [6, 3, 3]  # Co, Cin, kL
        self.dtype = "float32"
        self.init_data()

    def init_data(self):
        self.np_x = np.random.rand(*self.shape_x).astype(self.dtype)
        self.np_w = np.random.rand(*self.shape_w).astype(self.dtype)
        conv_param = {"stride": [1], "pad": [0], "dilation": [1]}
        self.np_ref_out, _, _, _ = conv1d_forward_naive(
            self.np_x, self.np_w, 1, conv_param
        )

    def test_dygraph_Compatibility(self):
        for place in self.places:
            paddle.device.set_device(place)
            paddle.disable_static()
            x = paddle.to_tensor(self.np_x)
            w = paddle.to_tensor(self.np_w)

            paddle_dygraph_out = []
            # Position args (args)
            out1 = paddle.nn.functional.conv1d(x, w)
            paddle_dygraph_out.append(out1)
            # Keywords args (kwargs) for paddle
            out2 = paddle.nn.functional.conv1d(x=x, weight=w)
            paddle_dygraph_out.append(out2)
            # Keywords args for alias compatibility - testing x->input
            out3 = paddle.nn.functional.conv1d(input=x, weight=w)
            paddle_dygraph_out.append(out3)
            # Combined args and kwargs
            out4 = paddle.nn.functional.conv1d(x, weight=w)
            paddle_dygraph_out.append(out4)

            if isinstance(place, core.XPUPlace):
                rtol = 5e-3
                atol = 5e-3
            else:
                rtol = 1e-5
                atol = 0

            # Check all dygraph results against reference
            for out in paddle_dygraph_out:
                np.testing.assert_allclose(
                    self.np_ref_out, out.numpy(), rtol=rtol, atol=atol
                )
            paddle.enable_static()

    def test_static_Compatibility(self):
        paddle.enable_static()

        fetch_list = []
        main = paddle.static.Program()
        startup = paddle.static.Program()
        with base.program_guard(main, startup):
            x = paddle.static.data(
                name="x", shape=self.shape_x, dtype=self.dtype
            )
            w = paddle.static.data(
                name="w", shape=self.shape_w, dtype=self.dtype
            )

            # Position args (args)
            out1 = paddle.nn.functional.conv1d(x, w)
            fetch_list.append(out1)
            # Keywords args (kwargs) for paddle
            out2 = paddle.nn.functional.conv1d(x=x, weight=w)
            fetch_list.append(out2)
            # Keywords args for alias compatibility - testing x->input
            out3 = paddle.nn.functional.conv1d(input=x, weight=w)
            fetch_list.append(out3)
            # Combined args and kwargs
            out4 = paddle.nn.functional.conv1d(x, weight=w)
            fetch_list.append(out4)

            for place in self.places:
                if isinstance(place, core.XPUPlace):
                    rtol = 5e-3
                    atol = 5e-3
                else:
                    rtol = 1e-5
                    atol = 0

                exe = base.Executor(place)
                fetches = exe.run(
                    main,
                    feed={"x": self.np_x, "w": self.np_w},
                    fetch_list=fetch_list,
                )
                for out in fetches:
                    np.testing.assert_allclose(
                        out, self.np_ref_out, rtol=rtol, atol=atol
                    )


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