# 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 op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)

import paddle
from paddle.base 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.prim_op_type = "comp"
        self.public_python_api = paddle.lerp
        self.init_dtype()
        self.init_shape()
        self.init_xyshape()
        self.init_wshape()
        if 0 in self.shape:
            x = np.random.rand(*self.xshape).astype(self.dtype)
            y = np.random.rand(*self.yshape).astype(self.dtype)
        else:
            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(check_pir=True, check_prim_pir=True)

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


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 TestLerp_ZeroSize(TestLerp):
    def init_shape(self):
        self.shape = [2, 0]


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)

    def test_static_api(self):
        paddle.enable_static()
        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(paddle.CPUPlace())
            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)

    def test_dygraph_api(self):
        paddle.disable_static()
        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()

    def test_inplace_api(self):
        paddle.disable_static()
        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()

    def test_inplace_api_exception(self):
        paddle.disable_static()
        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()

    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()

    def test_dygraph_compatibility(self):
        """Test parameter aliases, out parameter, and various calling patterns."""
        paddle.disable_static()
        x = paddle.to_tensor(self.x)
        y = paddle.to_tensor(self.y)
        w = paddle.to_tensor(np.full(4, 0.75).astype(self.dtype))
        paddle_dygraph_out = []

        # Position args
        out1 = paddle.lerp(x, y, 0.75)
        paddle_dygraph_out.append(out1)

        # Paddle keyword args
        out2 = paddle.lerp(x=x, y=y, weight=0.75)
        paddle_dygraph_out.append(out2)

        # Parameter aliases (input for x, end for y)
        out3 = paddle.lerp(input=x, end=y, weight=0.75)
        paddle_dygraph_out.append(out3)

        # Partial alias: input for x, y uses original name
        out4 = paddle.lerp(input=x, y=y, weight=0.75)
        paddle_dygraph_out.append(out4)

        # Partial alias: x uses original name, end for y
        out5 = paddle.lerp(x=x, end=y, weight=0.75)
        paddle_dygraph_out.append(out5)

        # Test out parameter
        out6 = paddle.empty([4], dtype=self.dtype)
        result6 = paddle.lerp(x, y, 0.75, out=out6)
        paddle_dygraph_out.append(out6)
        paddle_dygraph_out.append(result6)

        # Test out parameter with tensor weight
        out7 = paddle.empty([4], dtype=self.dtype)
        paddle.lerp(x, y, w, out=out7)
        paddle_dygraph_out.append(out7)

        # Test parameter aliases with out parameter
        out8 = paddle.empty([4], dtype=self.dtype)
        result8 = paddle.lerp(input=x, end=y, weight=0.75, out=out8)
        paddle_dygraph_out.append(out8)
        paddle_dygraph_out.append(result8)

        # Test out=None (default)
        out9 = paddle.lerp(x, y, 0.75, out=None)
        paddle_dygraph_out.append(out9)

        # Verify all outputs
        for out in paddle_dygraph_out:
            np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05)
        paddle.enable_static()

    def test_out_parameter_broadcast(self):
        """Test out parameter with broadcasting."""
        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])
        res_ref = x + 0.5 * (y - x)
        # Create output tensor with broadcast shape
        out_tensor = paddle.empty(res_ref.shape, dtype=self.dtype)
        result = paddle.lerp(
            paddle.to_tensor(x), paddle.to_tensor(y), 0.5, out=out_tensor
        )
        np.testing.assert_allclose(res_ref, out_tensor.numpy(), rtol=1e-05)
        np.testing.assert_allclose(
            result.numpy(), out_tensor.numpy(), rtol=1e-05
        )
        paddle.enable_static()


@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 and not support the bfloat16",
)
class TestLerpBF16(TestLerp):
    def setUp(self):
        self.op_type = "lerp"
        self.python_api = paddle.lerp
        self.prim_op_type = "comp"
        self.public_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 = get_device_place()
        self.check_output_with_place(place, check_pir=True, check_prim_pir=True)

    def test_check_grad(self):
        place = get_device_place()
        self.check_grad_with_place(
            place,
            ['X', 'Y'],
            'Out',
            user_defined_grads=[self.x_grad, self.y_grad],
            check_pir=True,
            check_prim_pir=True,
        )


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