# Copyright (c) 2022 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 import fluid
from paddle.fluid import Program, core, program_guard
from paddle.fluid.backward import append_backward


class TestWhereOp(OpTest):
    def setUp(self):
        self.op_type = 'where'
        self.python_api = paddle.where
        self.init_config()
        self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y}
        self.outputs = {'Out': np.where(self.cond, self.x, self.y)}

    def test_check_output(self):
        self.check_output()

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

    def init_config(self):
        self.x = np.random.uniform((-3), 5, 100).astype('float64')
        self.y = np.random.uniform((-3), 5, 100).astype('float64')
        self.cond = np.zeros(100).astype('bool')


class TestWhereOp2(TestWhereOp):
    def init_config(self):
        self.x = np.random.uniform((-5), 5, (60, 2)).astype('float64')
        self.y = np.random.uniform((-5), 5, (60, 2)).astype('float64')
        self.cond = np.ones((60, 2)).astype('bool')


class TestWhereFP16OP(TestWhereOp):
    def init_config(self):
        self.dtype = np.float16
        self.x = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
        self.y = np.random.uniform((-5), 5, (60, 2)).astype(self.dtype)
        self.cond = np.ones((60, 2)).astype('bool')


@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 TestWhereBF16OP(OpTest):
    def setUp(self):
        self.op_type = 'where'
        self.dtype = np.uint16
        self.python_api = paddle.where
        self.init_config()
        self.inputs = {
            'Condition': self.cond,
            'X': convert_float_to_uint16(self.x),
            'Y': convert_float_to_uint16(self.y),
        }
        self.outputs = {
            'Out': convert_float_to_uint16(np.where(self.cond, self.x, self.y))
        }

    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', numeric_grad_delta=0.05
        )

    def init_config(self):
        self.x = np.random.uniform((-5), 5, (60, 2)).astype(np.float32)
        self.y = np.random.uniform((-5), 5, (60, 2)).astype(np.float32)
        self.cond = np.random.randint(2, size=(60, 2)).astype('bool')


class TestWhereOp3(TestWhereOp):
    def init_config(self):
        self.x = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
        self.y = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
        self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool)


class TestWhereAPI(unittest.TestCase):
    def setUp(self):
        self.init_data()

    def init_data(self):
        self.shape = [10, 15]
        self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool)
        self.x = np.random.uniform((-2), 3, self.shape).astype(np.float32)
        self.y = np.random.uniform((-2), 3, self.shape).astype(np.float32)
        self.out = np.where(self.cond, self.x, self.y)

    def ref_x_backward(self, dout):
        return np.where(self.cond, dout, 0)

    def ref_y_backward(self, dout):
        return np.where(~self.cond, dout, 0)

    def test_api(self, use_cuda=False):
        for x_stop_gradient in [False, True]:
            for y_stop_gradient in [False, True]:
                with fluid.program_guard(Program(), Program()):
                    cond = paddle.static.data(
                        name='cond', shape=[-1] + self.shape, dtype='bool'
                    )
                    cond.desc.set_need_check_feed(False)
                    x = paddle.static.data(
                        name='x', shape=[-1] + self.shape, dtype='float32'
                    )
                    x.desc.set_need_check_feed(False)
                    y = paddle.static.data(
                        name='y', shape=[-1] + self.shape, dtype='float32'
                    )
                    y.desc.set_need_check_feed(False)
                    x.stop_gradient = x_stop_gradient
                    x.desc.set_need_check_feed(False)
                    y.stop_gradient = y_stop_gradient
                    y.desc.set_need_check_feed(False)
                    result = paddle.where(cond, x, y)
                    result.stop_gradient = False
                    append_backward(paddle.mean(result))
                    for use_cuda in [False, True]:
                        if use_cuda and (
                            not fluid.core.is_compiled_with_cuda()
                        ):
                            break
                        place = (
                            fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
                        )
                        exe = fluid.Executor(place)
                        fetch_list = [result, result.grad_name]
                        if x_stop_gradient is False:
                            fetch_list.append(x.grad_name)
                        if y_stop_gradient is False:
                            fetch_list.append(y.grad_name)
                        out = exe.run(
                            fluid.default_main_program(),
                            feed={'cond': self.cond, 'x': self.x, 'y': self.y},
                            fetch_list=fetch_list,
                        )
                        assert np.array_equal(out[0], self.out)
                        if x_stop_gradient is False:
                            assert np.array_equal(
                                out[2], self.ref_x_backward(out[1])
                            )
                            if y.stop_gradient is False:
                                assert np.array_equal(
                                    out[3], self.ref_y_backward(out[1])
                                )
                        elif y.stop_gradient is False:
                            assert np.array_equal(
                                out[2], self.ref_y_backward(out[1])
                            )

    def test_api_broadcast(self, use_cuda=False):
        main_program = Program()
        with fluid.program_guard(main_program):
            x = paddle.static.data(name='x', shape=[-1, 4, 1], dtype='float32')
            x.desc.set_need_check_feed(False)
            y = paddle.static.data(name='y', shape=[-1, 4, 2], dtype='float32')
            y.desc.set_need_check_feed(False)
            x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype('float32')
            y_i = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]).astype(
                'float32'
            )
            result = paddle.where((x > 1), x=x, y=y)
            for use_cuda in [False, True]:
                if use_cuda and (not fluid.core.is_compiled_with_cuda()):
                    return
                place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
                exe = fluid.Executor(place)
                out = exe.run(
                    fluid.default_main_program(),
                    feed={'x': x_i, 'y': y_i},
                    fetch_list=[result],
                )
                assert np.array_equal(out[0], np.where((x_i > 1), x_i, y_i))

    def test_scalar(self):
        paddle.enable_static()
        main_program = Program()
        with fluid.program_guard(main_program):
            cond_shape = [2, 4]
            cond = paddle.static.data(
                name='cond', shape=[-1] + cond_shape, dtype='bool'
            )
            cond.desc.set_need_check_feed(False)
            x_data = 1.0
            y_data = 2.0
            cond_data = np.array([False, False, True, True]).astype('bool')
            result = paddle.where(condition=cond, x=x_data, y=y_data)
            for use_cuda in [False, True]:
                if use_cuda and (not fluid.core.is_compiled_with_cuda()):
                    return
                place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
                exe = fluid.Executor(place)
                out = exe.run(
                    fluid.default_main_program(),
                    feed={'cond': cond_data},
                    fetch_list=[result],
                )
                expect = np.where(cond_data, x_data, y_data)
                assert np.array_equal(out[0], expect)

    def __test_where_with_broadcast_static(self, cond_shape, x_shape, y_shape):
        paddle.enable_static()
        main_program = Program()
        with fluid.program_guard(main_program):
            cond = paddle.static.data(
                name='cond', shape=[-1] + cond_shape, dtype='bool'
            )
            x = paddle.static.data(
                name='x', shape=[-1] + x_shape, dtype='float32'
            )
            y = paddle.static.data(
                name='y', shape=[-1] + y_shape, dtype='float32'
            )
            x.desc.set_need_check_feed(False)
            y.desc.set_need_check_feed(False)
            cond.desc.set_need_check_feed(False)
            cond_data_tmp = np.random.random(size=cond_shape).astype('float32')
            cond_data = cond_data_tmp < 0.3
            x_data = np.random.random(size=x_shape).astype('float32')
            y_data = np.random.random(size=y_shape).astype('float32')
            result = paddle.where(condition=cond, x=x, y=y)
            for use_cuda in [False, True]:
                if use_cuda and (not fluid.core.is_compiled_with_cuda()):
                    return
                place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
                exe = fluid.Executor(place)
                out = exe.run(
                    fluid.default_main_program(),
                    feed={'cond': cond_data, 'x': x_data, 'y': y_data},
                    fetch_list=[result],
                )
                expect = np.where(cond_data, x_data, y_data)
                assert np.array_equal(out[0], expect)

    def test_static_api_broadcast_1(self):
        cond_shape = [2, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_2(self):
        cond_shape = [2, 1]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_3(self):
        cond_shape = [2, 2, 1]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_4(self):
        cond_shape = [2, 1, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_5(self):
        cond_shape = [3, 2, 2, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_6(self):
        cond_shape = [2, 2, 4]
        a_shape = [2, 2, 1]
        b_shape = [2, 2, 1]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_7(self):
        cond_shape = [2, 2, 4]
        a_shape = [2, 1, 4]
        b_shape = [2, 1, 4]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)

    def test_static_api_broadcast_8(self):
        cond_shape = [3, 2, 2, 4]
        a_shape = [2, 2, 1]
        b_shape = [2, 2, 1]
        self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)


class TestWhereDygraphAPI(unittest.TestCase):
    def test_api(self):
        with fluid.dygraph.guard():
            x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
            y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
            cond_i = np.array([False, False, True, True]).astype('bool')
            x = fluid.dygraph.to_variable(x_i)
            y = fluid.dygraph.to_variable(y_i)
            cond = fluid.dygraph.to_variable(cond_i)
            out = paddle.where(cond, x, y)
            assert np.array_equal(out.numpy(), np.where(cond_i, x_i, y_i))

    def test_scalar(self):
        with fluid.dygraph.guard():
            cond_i = np.array([False, False, True, True]).astype('bool')
            x = 1.0
            y = 2.0
            cond = fluid.dygraph.to_variable(cond_i)
            out = paddle.where(cond, x, y)
            assert np.array_equal(out.numpy(), np.where(cond_i, x, y))

    def __test_where_with_broadcast_dygraph(self, cond_shape, a_shape, b_shape):
        with fluid.dygraph.guard():
            cond_tmp = paddle.rand(cond_shape)
            cond = cond_tmp < 0.3
            a = paddle.rand(a_shape)
            b = paddle.rand(b_shape)
            result = paddle.where(cond, a, b)
            result = result.numpy()
            expect = np.where(cond, a, b)
            np.testing.assert_array_equal(expect, result)

    def test_dygraph_api_broadcast_1(self):
        cond_shape = [2, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_2(self):
        cond_shape = [2, 1]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_3(self):
        cond_shape = [2, 2, 1]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_4(self):
        cond_shape = [2, 1, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_5(self):
        cond_shape = [3, 2, 2, 4]
        a_shape = [2, 2, 4]
        b_shape = [2, 2, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_6(self):
        cond_shape = [2, 2, 4]
        a_shape = [2, 2, 1]
        b_shape = [2, 2, 1]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_7(self):
        cond_shape = [2, 2, 4]
        a_shape = [2, 1, 4]
        b_shape = [2, 1, 4]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_dygraph_api_broadcast_8(self):
        cond_shape = [3, 2, 2, 4]
        a_shape = [2, 2, 1]
        b_shape = [2, 2, 1]
        self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)

    def test_where_condition(self):
        data = np.array([[True, False], [False, True]])
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[(-1), 2], dtype='float32')
            x.desc.set_need_check_feed(False)
            y = paddle.where(x)
            self.assertEqual(type(y), tuple)
            self.assertEqual(len(y), 2)
            z = paddle.concat(list(y), axis=1)
            exe = fluid.Executor(fluid.CPUPlace())
            (res,) = exe.run(
                feed={'x': data}, fetch_list=[z.name], return_numpy=False
            )
        expect_out = np.array([[0, 0], [1, 1]])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
        data = np.array([True, True, False])
        with program_guard(Program(), Program()):
            x = paddle.static.data(name='x', shape=[(-1)], dtype='float32')
            x.desc.set_need_check_feed(False)
            y = paddle.where(x)
            self.assertEqual(type(y), tuple)
            self.assertEqual(len(y), 1)
            z = paddle.concat(list(y), axis=1)
            exe = fluid.Executor(fluid.CPUPlace())
            (res,) = exe.run(
                feed={'x': data}, fetch_list=[z.name], return_numpy=False
            )
        expect_out = np.array([[0], [1]])
        np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)


class TestWhereOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
            y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
            cond_i = np.array([False, False, True, True]).astype('bool')

            def test_Variable():
                paddle.where(cond_i, x_i, y_i)

            self.assertRaises(TypeError, test_Variable)

            def test_type():
                x = paddle.static.data(name='x', shape=[-1, 4], dtype='bool')
                x.desc.set_need_check_feed(False)
                y = paddle.static.data(name='y', shape=[-1, 4], dtype='float16')
                y.desc.set_need_check_feed(False)
                cond = paddle.static.data(
                    name='cond', shape=[-1, 4], dtype='int32'
                )
                cond.desc.set_need_check_feed(False)
                paddle.where(cond, x, y)

            self.assertRaises(TypeError, test_type)

    def test_value_error(self):
        with fluid.dygraph.guard():
            cond_shape = [2, 2, 4]
            cond_tmp = paddle.rand(cond_shape)
            cond = cond_tmp < 0.3
            a = paddle.rand(cond_shape)
            self.assertRaises(ValueError, paddle.where, cond, a)


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