# Copyright (c) 2021 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 eager_op_test import OpTest, convert_float_to_uint16

import paddle
from paddle.fluid import core

paddle.enable_static()
np.random.seed(0)


class TestLerp(OpTest):
    def setUp(self):
        self.op_type = "lerp"
        self.python_api = paddle.lerp
        self.init_dtype()
        self.init_shape()
        self.init_xyshape()
        self.init_wshape()
        x = np.arange(1.0, 101.0).astype(self.dtype).reshape(self.xshape)
        y = np.full(100, 10.0).astype(self.dtype).reshape(self.yshape)
        w = np.random.random(self.wshape).astype(self.dtype)
        self.inputs = {'X': x, 'Y': y, 'Weight': w}
        self.outputs = {'Out': x + w * (y - x)}

    def init_dtype(self):
        self.dtype = np.float64

    def init_shape(self):
        self.shape = [100]

    def init_xyshape(self):
        self.xshape = self.shape
        self.yshape = self.shape

    def init_wshape(self):
        self.wshape = [1]

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X', 'Y'], 'Out')


class TestLerpWithDim2(TestLerp):
    def init_shape(self):
        self.shape = [2, 50]


class TestLerpWithDim3(TestLerp):
    def init_shape(self):
        self.shape = [2, 2, 25]


class TestLerpWithDim4(TestLerp):
    def init_shape(self):
        self.shape = [2, 2, 5, 5]


class TestLerpWithDim5(TestLerp):
    def init_shape(self):
        self.shape = [2, 1, 2, 5, 5]


class TestLerpWithDim6(TestLerp):
    def init_shape(self):
        self.shape = [2, 1, 2, 5, 1, 5]


class TestLerpWithDim6Fp16(TestLerp):
    def init_shape(self):
        self.shape = [2, 1, 2, 5, 1, 5]

    def init_dtype(self):
        self.dtype = np.float16


class TestLerpWihFp16BroadXY(TestLerp):
    def init_xyshape(self):
        self.xshape = [2, 1, 2, 5, 5]
        self.yshape = [2, 2, 1, 5, 5]

    def init_dtype(self):
        self.dtype = np.float16


class TestLerpWithFp16BroadWToXY(TestLerp):
    def init_shape(self):
        self.shape = [2, 2, 5, 5]

    def init_wshape(self):
        self.wshape = [5]

    def init_dtype(self):
        self.dtype = np.float16


class TestLerpBroadXY(TestLerp):
    def init_xyshape(self):
        self.xshape = [2, 1, 2, 5, 5]
        self.yshape = [2, 2, 1, 5, 5]


class TestLerpBroadWToXY(TestLerp):
    def init_shape(self):
        self.shape = [2, 2, 5, 5]

    def init_wshape(self):
        self.wshape = [5]


class TestLerpAPI(unittest.TestCase):
    def init_dtype(self):
        self.dtype = 'float32'

    def setUp(self):
        self.init_dtype()
        self.x = np.arange(1.0, 5.0).astype(self.dtype)
        self.y = np.full(4, 10.0).astype(self.dtype)
        self.w = np.asarray([0.75]).astype(self.dtype)
        self.res_ref = self.x + self.w * (self.y - self.x)
        self.place = [paddle.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.place.append(paddle.CUDAPlace(0))

    def test_static_api(self):
        paddle.enable_static()

        def run(place):
            with paddle.static.program_guard(paddle.static.Program()):
                x = paddle.static.data('x', [1, 4], dtype=self.dtype)
                y = paddle.static.data('y', [1, 4], dtype=self.dtype)
                out = paddle.lerp(x, y, 0.5)
                exe = paddle.static.Executor(place)
                res = exe.run(
                    feed={
                        'x': self.x.reshape([1, 4]),
                        'y': self.y.reshape([1, 4]),
                    }
                )
            for r in res:
                np.testing.assert_allclose(self.res_ref, r, rtol=1e-05)

        for place in self.place:
            run(place)

    def test_dygraph_api(self):
        def run(place):
            paddle.disable_static(place)
            x = paddle.to_tensor(self.x)
            y = paddle.to_tensor(self.y)
            w = paddle.to_tensor(np.full(4, 0.75).astype(self.dtype))
            out = paddle.lerp(x, y, w)
            np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05)
            paddle.enable_static()

        for place in self.place:
            run(place)

    def test_inplace_api(self):
        def run(place):
            paddle.disable_static(place)
            x = paddle.to_tensor(self.x)
            y = paddle.to_tensor(self.y)
            x.lerp_(y, 0.75)
            np.testing.assert_allclose(self.res_ref, x.numpy(), rtol=1e-05)
            paddle.enable_static()

        for place in self.place:
            run(place)

    def test_inplace_api_exception(self):
        def run(place):
            paddle.disable_static(place)
            x = paddle.to_tensor(self.x)
            y = paddle.to_tensor(self.y)
            w = paddle.to_tensor([0.75, 0.75], dtype=self.dtype)
            with self.assertRaises(ValueError):
                x.lerp_(y, w)
            paddle.enable_static()

        for place in self.place:
            run(place)

    def test_x_broadcast_y(self):
        paddle.disable_static()
        x = np.arange(1.0, 21.0).astype(self.dtype).reshape([2, 2, 5])
        y = np.full(30, 10.0).astype(self.dtype).reshape([3, 2, 1, 5])
        out = paddle.lerp(paddle.to_tensor(x), paddle.to_tensor(y), 0.5)
        res_ref = x + 0.5 * (y - x)
        np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05)
        paddle.enable_static()

    def test_x_y_broadcast_w(self):
        paddle.disable_static()
        x = np.arange(11.0, 21.0).astype(self.dtype).reshape([2, 5])
        y = np.full(20, 7.5).astype(self.dtype).reshape([2, 2, 5])
        w = np.full(40, 0.225).astype(self.dtype).reshape([2, 2, 2, 5])
        out = paddle.lerp(
            paddle.to_tensor(x), paddle.to_tensor(y), paddle.to_tensor(w)
        )
        res_ref = x + w * (y - x)
        np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05)
        paddle.enable_static()


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not complied with CUDA and not support the bfloat16",
)
class TestLerpBF16(TestLerp):
    def setUp(self):
        self.op_type = "lerp"
        self.python_api = paddle.lerp
        self.dtype = np.uint16
        self.init_shape()
        self.init_xyshape()
        self.init_wshape()
        x = np.arange(1.0, 101.0).astype("float32").reshape(self.xshape)
        y = np.full(100, 10.0).astype("float32").reshape(self.yshape)
        w = np.random.random(self.wshape).astype("float32")
        self.init_grad(w)
        self.inputs = {
            'X': convert_float_to_uint16(x),
            'Y': convert_float_to_uint16(y),
            'Weight': convert_float_to_uint16(w),
        }
        self.outputs = {'Out': convert_float_to_uint16(x + w * (y - x))}

    def init_shape(self):
        self.shape = [100]

    def init_xyshape(self):
        self.xshape = self.shape
        self.yshape = self.shape

    def init_wshape(self):
        self.wshape = [1]

    def init_grad(self, w):
        self.x_grad = (
            np.ones(self.xshape)
            * (1 - w)
            / (np.prod(self.xshape) / np.prod(self.wshape))
        )
        self.y_grad = (
            np.ones(self.yshape)
            * w
            / (np.prod(self.yshape) / np.prod(self.wshape))
        )

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(place)

    def test_check_grad(self):
        place = core.CUDAPlace(0)
        self.check_grad_with_place(
            place,
            ['X', 'Y'],
            'Out',
            user_defined_grads=[self.x_grad, self.y_grad],
        )


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