# Copyright (c) 2018 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 (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)

import paddle
from paddle import base
from paddle.base import core
from paddle.nn.functional import interpolate

paddle.enable_static()


def create_test_case0(self):
    self.interp_method = 'nearest'
    self.input_shape = [2, 3, 4, 5]
    self.out_h = 2
    self.out_w = 2
    self.scale = []
    self.out_size = np.array([3, 3]).astype("int32")
    self.align_corners = True


def create_test_case1(self):
    self.interp_method = 'nearest'
    self.input_shape = [4, 1, 1, 7, 8]
    self.out_d = 1
    self.out_h = 1
    self.out_w = 1
    self.scale = []
    self.align_corners = True


def create_test_case2(self):
    self.interp_method = 'nearest'
    self.input_shape = [3, 3, 9, 6]
    self.out_h = 12
    self.out_w = 12
    self.scale = []
    self.align_corners = True


def create_test_case3(self):
    self.interp_method = 'nearest'
    self.input_shape = [1, 1, 32, 64]
    self.out_h = 64
    self.out_w = 32
    self.scale = []
    self.align_corners = True


def create_test_case4(self):
    self.interp_method = 'nearest'
    self.input_shape = [4, 1, 7, 8]
    self.out_h = 1
    self.out_w = 1
    self.scale = []
    self.out_size = np.array([2, 2]).astype("int32")
    self.align_corners = True


def create_test_case5(self):
    self.interp_method = 'nearest'
    self.input_shape = [3, 3, 9, 6]
    self.out_h = 12
    self.out_w = 12
    self.scale = []
    self.out_size = np.array([11, 11]).astype("int32")
    self.align_corners = True


def create_test_case6(self):
    self.interp_method = 'nearest'
    self.input_shape = [1, 1, 32, 64]
    self.out_h = 64
    self.out_w = 32
    self.scale = []
    self.out_size = np.array([65, 129]).astype("int32")
    self.align_corners = True


def nearest_interp_test(
    x,
    OutSize=None,
    SizeTensor=None,
    Scale=None,
    data_layout='NCHW',
    out_d=-1,
    out_h=-1,
    out_w=-1,
    scale=[],
    interp_method='nearest',
    align_corners=True,
    align_mode=0,
):
    if isinstance(scale, (float, int)):
        scale_list = []
        for _ in range(len(x.shape) - 2):
            scale_list.append(scale)
        scale = list(map(float, scale_list))
    elif isinstance(scale, (list, tuple)):
        scale = list(map(float, scale))
    if SizeTensor is not None:
        if not isinstance(SizeTensor, list) and not isinstance(
            SizeTensor, tuple
        ):
            SizeTensor = [SizeTensor]
    return paddle._C_ops.nearest_interp(
        x,
        OutSize,
        SizeTensor,
        Scale,
        data_layout,
        out_d,
        out_h,
        out_w,
        scale,
        interp_method,
        align_corners,
        align_mode,
    )


def nearest_neighbor_interp_np(
    X,
    out_h,
    out_w,
    scale_h=0,
    scale_w=0,
    out_size=None,
    actual_shape=None,
    align_corners=True,
    data_layout='NCHW',
):
    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
    if data_layout == "NHWC":
        X = np.transpose(X, (0, 3, 1, 2))  # NHWC => NCHW
    if out_size is not None:
        out_h = out_size[0]
        out_w = out_size[1]
    if actual_shape is not None:
        out_h = actual_shape[0]
        out_w = actual_shape[1]
    n, c, in_h, in_w = X.shape

    ratio_h = ratio_w = 0.0
    if out_h > 1:
        if align_corners:
            ratio_h = (in_h - 1.0) / (out_h - 1.0)
        else:
            if scale_h > 0:
                ratio_h = 1.0 / scale_h
            else:
                ratio_h = 1.0 * in_h / out_h
    if out_w > 1:
        if align_corners:
            ratio_w = (in_w - 1.0) / (out_w - 1.0)
        else:
            if scale_w > 0:
                ratio_w = 1.0 / scale_w
            else:
                ratio_w = 1.0 * in_w / out_w
    out = np.zeros((n, c, out_h, out_w))

    if align_corners:
        for i in range(out_h):
            in_i = int(ratio_h * i + 0.5)
            for j in range(out_w):
                in_j = int(ratio_w * j + 0.5)
                out[:, :, i, j] = X[:, :, in_i, in_j]
    else:
        for i in range(out_h):
            in_i = int(ratio_h * i)
            for j in range(out_w):
                in_j = int(ratio_w * j)
                out[:, :, i, j] = X[:, :, in_i, in_j]

    if data_layout == "NHWC":
        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
    # out = np.expand_dims(out, 2)
    return out.astype(X.dtype)


def nearest_neighbor_interp3d_np(
    X,
    out_d,
    out_h,
    out_w,
    scale_d=0,
    scale_h=0,
    scale_w=0,
    out_size=None,
    actual_shape=None,
    align_corners=True,
    data_layout='NCHW',
):
    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
    if data_layout == "NHWC":
        X = np.transpose(X, (0, 4, 1, 2, 3))  # NDHWC => NCDHW
    if out_size is not None:
        out_d = out_size[0]
        out_h = out_size[1]
        out_w = out_size[2]
    if actual_shape is not None:
        out_d = actual_shape[0]
        out_h = actual_shape[1]
        out_w = actual_shape[2]
    n, c, in_d, in_h, in_w = X.shape

    ratio_d = ratio_h = ratio_w = 0.0
    if out_d > 1:
        if align_corners:
            ratio_d = (in_d - 1.0) / (out_d - 1.0)
        else:
            if scale_d > 0:
                ratio_d = 1.0 / scale_d
            else:
                ratio_d = 1.0 * in_d / out_d
    if out_h > 1:
        if align_corners:
            ratio_h = (in_h - 1.0) / (out_h - 1.0)
        else:
            if scale_h > 0:
                ratio_h = 1.0 / scale_h
            else:
                ratio_h = 1.0 * in_h / out_h
    if out_w > 1:
        if align_corners:
            ratio_w = (in_w - 1.0) / (out_w - 1.0)
        else:
            if scale_w > 0:
                ratio_w = 1.0 / scale_w
            else:
                ratio_w = 1.0 * in_w / out_w
    out = np.zeros((n, c, out_d, out_h, out_w))

    if align_corners:
        for d in range(out_d):
            in_d = int(ratio_d * d + 0.5)
            for i in range(out_h):
                in_i = int(ratio_h * i + 0.5)
                for j in range(out_w):
                    in_j = int(ratio_w * j + 0.5)
                    out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
    else:
        for d in range(out_d):
            in_d = int(ratio_d * d)
            for i in range(out_h):
                in_i = int(ratio_h * i)
                for j in range(out_w):
                    in_j = int(ratio_w * j)
                    out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]

    if data_layout == "NDHWC":
        out = np.transpose(out, (0, 2, 3, 4, 1))  # NCDHW => NDHWC
    return out.astype(X.dtype)


class TestNearestInterpOp(OpTest):
    def setUp(self):
        self.python_api = nearest_interp_test
        self.out_size = None
        self.actual_shape = None
        self.data_layout = 'NCHW'
        self.dtype = np.float64
        self.init_test_case()
        self.op_type = "nearest_interp_v2"
        input_np = np.random.random(self.input_shape).astype(self.dtype)

        if self.data_layout == "NCHW" and len(self.input_shape) == 4:
            in_d = 1
            in_h = self.input_shape[2]
            in_w = self.input_shape[3]
        else:
            in_d = 1
            in_h = self.input_shape[1]
            in_w = self.input_shape[2]

        if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
            in_d = self.input_shape[2]
            in_h = self.input_shape[3]
            in_w = self.input_shape[4]
        else:
            in_d = self.input_shape[1]
            in_h = self.input_shape[2]
            in_w = self.input_shape[3]
        scale_d = 0
        scale_h = 0
        scale_w = 0
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    scale_d = scale_h = scale_w = float(self.scale)
            if isinstance(self.scale, list) and len(self.scale) == 1:
                scale_d = scale_w = scale_h = self.scale[0]
            elif isinstance(self.scale, list) and len(self.scale) > 1:
                if len(self.scale) == 5:
                    scale_w = self.scale[2]
                    scale_h = self.scale[1]
                    scale_d = self.scale[0]
                else:
                    scale_w = self.scale[1]
                    scale_h = self.scale[0]

            out_h = int(in_h * scale_h)
            out_w = int(in_w * scale_w)
            out_d = int(in_d * scale_d)
        else:
            if len(self.input_shape) == 5:
                out_d = self.out_d
            out_h = self.out_h
            out_w = self.out_w

        if len(self.input_shape) == 4:
            output_np = nearest_neighbor_interp_np(
                input_np,
                out_h,
                out_w,
                scale_h,
                scale_w,
                self.out_size,
                self.actual_shape,
                self.align_corners,
                self.data_layout,
            )
        elif len(self.input_shape) == 5:
            output_np = nearest_neighbor_interp3d_np(
                input_np,
                out_d,
                out_h,
                out_w,
                scale_d,
                scale_h,
                scale_w,
                self.out_size,
                self.actual_shape,
                self.align_corners,
                self.data_layout,
            )
        self.inputs = {'X': input_np}
        if self.out_size is not None:
            self.inputs['OutSize'] = self.out_size
        if self.actual_shape is not None:
            self.inputs['OutSize'] = self.actual_shape
        if len(self.input_shape) == 5:
            self.attrs = {
                'out_d': self.out_d,
                'out_h': self.out_h,
                'out_w': self.out_w,
                'interp_method': self.interp_method,
                'align_corners': self.align_corners,
                'data_layout': self.data_layout,
            }
        else:
            self.attrs = {
                'out_h': self.out_h,
                'out_w': self.out_w,
                'interp_method': self.interp_method,
                'align_corners': self.align_corners,
                'data_layout': self.data_layout,
            }
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    self.scale = [self.scale]
            if isinstance(self.scale, list) and len(self.scale) == 1:
                self.scale = [self.scale[0], self.scale[0]]
            self.attrs['scale'] = self.scale
        self.outputs = {'Out': output_np}

    def test_check_output(self):
        self.check_output(
            check_pir=True, check_symbol_infer=(self.out_size is None)
        )

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', in_place=True, check_pir=True)

    def init_test_case(self):
        create_test_case0(self)


class TestNearestNeighborInterpCase1(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case1(self)


class TestNearestNeighborInterpCase2(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case2(self)


class TestNearestNeighborInterpCase3(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case3(self)


class TestNearestNeighborInterpCase4(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case4(self)


class TestNearestNeighborInterpCase5(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case5(self)


class TestNearestNeighborInterpCase6(TestNearestInterpOp):
    def init_test_case(self):
        create_test_case6(self)


class TestNearestNeighborInterpSame(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 3, 32, 64]
        self.out_h = 32
        self.out_w = 64
        self.scale = []
        self.align_corners = True


class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 32, 16]
        self.out_h = 64
        self.out_w = 32
        self.scale = []
        self.out_size = np.array([66, 40]).astype("int32")
        self.align_corners = True


class TestNearestInterpOpFP16(TestNearestInterpOp):
    def test_check_output(self):
        self.check_output(
            check_pir=True, check_symbol_infer=(self.out_size is None)
        )

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', in_place=True, check_pir=True)

    def init_test_case(self):
        create_test_case0(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase1FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case1(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase2FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case2(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase3FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case3(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase4FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case4(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase5FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case5(self)
        self.dtype = np.float16


class TestNearestNeighborInterpCase6FP16(TestNearestInterpOpFP16):
    def init_test_case(self):
        create_test_case6(self)
        self.dtype = np.float16


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestInterpOpBF16(OpTest):
    def setUp(self):
        self.python_api = nearest_interp_test
        self.out_size = None
        self.actual_shape = None
        self.data_layout = 'NCHW'
        self.init_test_case()
        self.op_type = "nearest_interp_v2"
        self.dtype = np.uint16
        input_np = np.random.random(self.input_shape).astype("float32")

        if self.data_layout == "NCHW" and len(self.input_shape) == 4:
            in_d = 1
            in_h = self.input_shape[2]
            in_w = self.input_shape[3]
        else:
            in_d = 1
            in_h = self.input_shape[1]
            in_w = self.input_shape[2]

        if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
            in_d = self.input_shape[2]
            in_h = self.input_shape[3]
            in_w = self.input_shape[4]
        else:
            in_d = self.input_shape[1]
            in_h = self.input_shape[2]
            in_w = self.input_shape[3]
        scale_d = 0
        scale_h = 0
        scale_w = 0
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    scale_d = scale_h = scale_w = float(self.scale)
            if isinstance(self.scale, list) and len(self.scale) == 1:
                scale_d = scale_w = scale_h = self.scale[0]
            elif isinstance(self.scale, list) and len(self.scale) > 1:
                if len(self.scale) == 5:
                    scale_w = self.scale[2]
                    scale_h = self.scale[1]
                    scale_d = self.scale[0]
                else:
                    scale_w = self.scale[1]
                    scale_h = self.scale[0]

            out_h = int(in_h * scale_h)
            out_w = int(in_w * scale_w)
            out_d = int(in_d * scale_d)
        else:
            if len(self.input_shape) == 5:
                out_d = self.out_d
            out_h = self.out_h
            out_w = self.out_w

        if len(self.input_shape) == 4:
            output_np = nearest_neighbor_interp_np(
                input_np,
                out_h,
                out_w,
                scale_h,
                scale_w,
                self.out_size,
                self.actual_shape,
                self.align_corners,
                self.data_layout,
            )
        elif len(self.input_shape) == 5:
            output_np = nearest_neighbor_interp3d_np(
                input_np,
                out_d,
                out_h,
                out_w,
                scale_d,
                scale_h,
                scale_w,
                self.out_size,
                self.actual_shape,
                self.align_corners,
                self.data_layout,
            )
        self.inputs = {'X': convert_float_to_uint16(input_np)}
        if self.out_size is not None:
            self.inputs['OutSize'] = self.out_size
        if self.actual_shape is not None:
            self.inputs['OutSize'] = self.actual_shape
        if len(self.input_shape) == 5:
            self.attrs = {
                'out_d': self.out_d,
                'out_h': self.out_h,
                'out_w': self.out_w,
                'interp_method': self.interp_method,
                'align_corners': self.align_corners,
                'data_layout': self.data_layout,
            }
        else:
            self.attrs = {
                'out_h': self.out_h,
                'out_w': self.out_w,
                'interp_method': self.interp_method,
                'align_corners': self.align_corners,
                'data_layout': self.data_layout,
            }
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    self.scale = [self.scale]
            if isinstance(self.scale, list) and len(self.scale) == 1:
                self.scale = [self.scale[0], self.scale[0]]
            self.attrs['scale'] = self.scale
        self.outputs = {'Out': convert_float_to_uint16(output_np)}

    def test_check_output(self):
        self.check_output(
            check_pir=True, check_symbol_infer=(self.out_size is None)
        )

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', in_place=True, check_pir=True)

    def init_test_case(self):
        create_test_case0(self)


class TestNearestNeighborInterpCase1BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case1(self)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase2BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case2(self)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase3BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case3(self)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase4BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case4(self)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase5BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case5(self)


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestNearestNeighborInterpCase6BF16(TestNearestInterpOpBF16):
    def init_test_case(self):
        create_test_case6(self)


class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 4, 4, 5]
        self.out_h = 2
        self.out_w = 2
        self.scale = []
        self.out_size = np.array([3, 8]).astype("int32")
        self.align_corners = True
        self.data_layout = "NHWC"


class TestNearestInterpOpUint8(OpTest):
    def setUp(self):
        self.python_api = nearest_interp_test
        self.out_size = None
        self.actual_shape = None
        self.init_test_case()
        self.op_type = "nearest_interp_v2"
        input_np = np.random.randint(
            low=0, high=256, size=self.input_shape
        ).astype("uint8")

        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    scale_h = scale_w = float(self.scale)
            if isinstance(self.scale, list) and len(self.scale) == 1:
                scale_w = scale_h = self.scale[0]
            elif isinstance(self.scale, list) and len(self.scale) > 1:
                scale_w = self.scale[1]
                scale_h = self.scale[0]
            out_h = int(self.input_shape[2] * scale_h)
            out_w = int(self.input_shape[3] * scale_w)
        else:
            out_h = self.out_h
            out_w = self.out_w

        output_np = nearest_neighbor_interp_np(
            input_np,
            out_h,
            out_w,
            0,
            0,
            self.out_size,
            self.actual_shape,
            self.align_corners,
        )
        self.inputs = {'X': input_np}
        if self.out_size is not None:
            self.inputs['OutSize'] = self.out_size
        self.attrs = {
            'out_h': self.out_h,
            'out_w': self.out_w,
            'interp_method': self.interp_method,
            'align_corners': self.align_corners,
        }
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    self.scale = [self.scale]
            if isinstance(self.scale, list) and len(self.scale) == 1:
                self.scale = [self.scale[0], self.scale[0]]
            self.attrs['scale'] = self.scale
        self.outputs = {'Out': output_np}

    def test_check_output(self):
        self.check_output_with_place(
            place=core.CPUPlace(),
            atol=1,
            check_pir=True,
            check_symbol_infer=(self.out_size is None),
        )

    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [1, 3, 9, 6]
        self.out_h = 10
        self.out_w = 9
        self.scale = []
        self.align_corners = True


class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 3, 32, 64]
        self.out_h = 80
        self.out_w = 40
        self.scale = []
        self.align_corners = True


class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [4, 1, 7, 8]
        self.out_h = 5
        self.out_w = 13
        self.scale = []
        self.out_size = np.array([6, 15]).astype("int32")
        self.align_corners = True


class TestNearestInterpWithoutCorners(TestNearestInterpOp):
    def set_align_corners(self):
        self.align_corners = False


class TestNearestNeighborInterpScale1(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 7, 5]
        self.out_h = 64
        self.out_w = 32
        self.scale = 2.0
        self.out_size = np.array([66, 40]).astype("int32")
        self.align_corners = True


class TestNearestNeighborInterpScale2(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 5, 7]
        self.out_h = 64
        self.out_w = 32
        self.scale = 1.5
        self.out_size = np.array([66, 40]).astype("int32")
        self.align_corners = True


class TestNearestNeighborInterpScale3(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 7, 5]
        self.out_h = 64
        self.out_w = 32
        self.scale = [2.0, 3.0]
        self.out_size = np.array([66, 40]).astype("int32")
        self.align_corners = True


class TestNearestNeighbor3DInterp(TestNearestInterpOp):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 4, 7, 5]
        self.out_d = 8
        self.out_h = 64
        self.out_w = 32
        self.scale = [4.0, 2.0, 3.0]
        self.out_size = np.array([8, 66, 40]).astype("int32")
        self.align_corners = True


class TestNearestInterpOp_attr_tensor(OpTest):
    def setUp(self):
        self.python_api = nearest_interp_test
        self.out_size = None
        self.actual_shape = None
        self.shape_by_1Dtensor = False
        self.scale_by_1Dtensor = False
        self.scale_by_2Dtensor = False
        self.init_test_case()
        self.op_type = "nearest_interp_v2"
        self.attrs = {
            'interp_method': self.interp_method,
            'align_corners': self.align_corners,
        }

        input_np = np.random.random(self.input_shape).astype("float64")
        self.inputs = {'X': input_np}

        if self.scale_by_1Dtensor:
            self.inputs['Scale'] = np.array([self.scale]).astype("float32")
            out_h = int(self.input_shape[2] * self.scale)
            out_w = int(self.input_shape[3] * self.scale)
        elif self.scale_by_2Dtensor:
            self.inputs['Scale'] = np.array(self.scale).astype("float32")
            out_h = int(self.input_shape[2] * self.scale[0])
            out_w = int(self.input_shape[3] * self.scale[1])
        elif self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    scale_h = scale_w = float(self.scale)
            if isinstance(self.scale, list) and len(self.scale) == 1:
                scale_w = scale_h = self.scale[0]
            elif isinstance(self.scale, list) and len(self.scale) > 1:
                scale_w = self.scale[1]
                scale_h = self.scale[0]
            out_h = int(self.input_shape[2] * scale_h)
            out_w = int(self.input_shape[3] * scale_w)
        else:
            out_h = self.out_h
            out_w = self.out_w

        if self.shape_by_1Dtensor:
            self.inputs['OutSize'] = self.out_size
        elif self.out_size is not None:
            size_tensor = []
            for index, ele in enumerate(self.out_size):
                size_tensor.append(
                    ("x" + str(index), np.ones(1).astype('int32') * ele)
                )
            self.inputs['SizeTensor'] = size_tensor

        self.attrs['out_h'] = self.out_h
        self.attrs['out_w'] = self.out_w
        if self.scale:
            if isinstance(self.scale, (float, int)):
                if self.scale > 0:
                    self.scale = [self.scale]
            if isinstance(self.scale, list) and len(self.scale) == 1:
                self.scale = [self.scale[0], self.scale[0]]
            self.attrs['scale'] = self.scale
        output_np = nearest_neighbor_interp_np(
            input_np,
            out_h,
            out_w,
            0,
            0,
            self.out_size,
            self.actual_shape,
            self.align_corners,
        )
        self.outputs = {'Out': output_np}

    def test_check_output(self):
        self.check_output(
            check_pir=True, check_symbol_infer=(self.out_size is None)
        )

    def test_check_grad(self):
        self.check_grad(['X'], 'Out', in_place=True, check_pir=True)

    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 5, 4, 4]
        self.out_h = 3
        self.out_w = 3
        self.scale = []
        self.out_size = [3, 3]
        self.align_corners = True


# out_size is a tensor list
class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 3, 9, 6]
        self.out_h = 12
        self.out_w = 12
        self.scale = []
        self.out_size = [8, 12]
        self.align_corners = True


# out_size is a 1-D tensor
class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 32, 16]
        self.out_h = 64
        self.out_w = 32
        self.scale = []
        self.out_size = np.array([66, 40]).astype("int32")
        self.align_corners = True
        self.shape_by_1Dtensor = True


# scale is a 1-D tensor
class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 32, 16]
        self.out_h = 64
        self.out_w = 32
        self.scale = 2.0
        self.out_size = None
        self.align_corners = True
        self.scale_by_1Dtensor = True


# scale is a 2-D tensor
class TestNearestInterp_attr_tensor_Case4(TestNearestInterpOp_attr_tensor):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [3, 2, 32, 16]
        self.out_h = 64
        self.out_w = 32
        self.scale = [2.0, 2.0]
        self.out_size = None
        self.align_corners = True
        self.scale_by_2Dtensor = True


class TestNearestInterpOpAPI_dy(unittest.TestCase):
    def test_case(self):
        import paddle

        if core.is_compiled_with_cuda() or is_custom_device():
            place = get_device_place()
        else:
            place = core.CPUPlace()
        with base.dygraph.guard(place):
            input_data = np.random.random((2, 3, 6, 6)).astype("int64")
            scale_np = np.array([2, 2]).astype("int64")
            input_x = paddle.to_tensor(input_data)
            scale = paddle.to_tensor(scale_np)
            expect_res = nearest_neighbor_interp_np(
                input_data, out_h=12, out_w=12, align_corners=False
            )
            out = interpolate(
                x=input_x,
                scale_factor=scale,
                mode="nearest",
                align_corners=False,
            )
            np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)


class TestNearestInterp3DOpAPI_dy(unittest.TestCase):
    def test_case(self):
        import paddle

        if core.is_compiled_with_cuda() or is_custom_device():
            place = get_device_place()
        else:
            place = core.CPUPlace()
        with base.dygraph.guard(place):
            input_data = np.random.random((2, 2, 6, 6, 6)).astype("int64")
            scale_np = np.array([2, 2, 2]).astype("int64")
            input_x = paddle.to_tensor(input_data)
            scale = paddle.to_tensor(scale_np)
            expect_res = nearest_neighbor_interp3d_np(
                input_data, out_d=12, out_h=12, out_w=12, align_corners=False
            )
            out = interpolate(
                x=input_x,
                scale_factor=scale,
                mode="nearest",
                align_corners=False,
                data_format="NCDHW",
            )
            np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)


@unittest.skipIf(
    not (base.core.is_compiled_with_cuda() or is_custom_device()),
    "core is not compiled with CUDA",
)
class TestNearestInterp3DOpForFloat16(unittest.TestCase):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 2, 6, 6, 6]
        self.scale = [2, 2, 2]
        self.align_corners = False
        self.data_layout = 'NCDHW'

    def check_main(self, x_np, dtype):
        paddle.disable_static()
        x_np = x_np.astype(dtype)
        x = paddle.to_tensor(x_np)
        x.stop_gradient = False
        y = interpolate(
            x,
            scale_factor=self.scale,
            mode=self.interp_method,
            align_corners=self.align_corners,
            data_format=self.data_layout,
        )
        x_g = paddle.grad(y, x)
        y_np = y[0].numpy().astype('float32')
        x_g_np = x_g[0].numpy().astype('float32')
        paddle.enable_static()
        return y_np, x_g_np

    def test_main(self):
        self.init_test_case()
        x_np = np.random.random(self.input_shape).astype("float16")

        y_np_1, x_g_np_1 = self.check_main(x_np, 'float16')
        y_np_2, x_g_np_2 = self.check_main(x_np, 'float32')
        # forward
        np.testing.assert_allclose(y_np_1, y_np_2, rtol=1e-03)
        # backward
        np.testing.assert_allclose(x_g_np_1, x_g_np_2)


@unittest.skipIf(
    not (base.core.is_compiled_with_cuda() or is_custom_device()),
    "core is not compiled with CUDA",
)
class TestNearestInterpOpForFloat16(unittest.TestCase):
    def init_test_case(self):
        self.interp_method = 'nearest'
        self.input_shape = [2, 2, 6, 6]
        self.scale = [2, 2]
        self.align_corners = False

    def check_main(self, x_np, dtype):
        paddle.disable_static()
        x_np = x_np.astype(dtype)
        x = paddle.to_tensor(x_np)
        x.stop_gradient = False
        y = interpolate(
            x,
            scale_factor=self.scale,
            mode=self.interp_method,
            align_corners=self.align_corners,
        )
        x_g = paddle.grad(y, x)
        y_np = y[0].numpy().astype('float32')
        x_g_np = x_g[0].numpy().astype('float32')
        paddle.enable_static()
        return y_np, x_g_np

    def test_main(self):
        self.init_test_case()
        x_np = np.random.random(self.input_shape).astype("float16")

        y_np_1, x_g_np_1 = self.check_main(x_np, 'float16')
        y_np_2, x_g_np_2 = self.check_main(x_np, 'float32')
        # forward
        np.testing.assert_allclose(y_np_1, y_np_2)
        # backward
        np.testing.assert_allclose(x_g_np_1, x_g_np_2)


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