# 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

import paddle
import paddle.fluid as fluid
from paddle import _C_ops, zeros_like
from paddle.fluid import Program, core, program_guard
from paddle.fluid.framework import _test_eager_guard, convert_np_dtype_to_dtype_


class TestZerosLikeAPIError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            x = paddle.fluid.data('x', [3, 4])
            self.assertRaises(TypeError, zeros_like, x, 'int8')

    def test_eager(self):
        with _test_eager_guard():
            self.test_errors()


class TestZerosLikeAPI(unittest.TestCase):
    def test_api(self):
        shape = [3, 4]
        startup_program = Program()
        train_program = Program()
        with program_guard(train_program, startup_program):
            x = paddle.fluid.data('X', shape)
            out1 = zeros_like(x)
            out2 = zeros_like(x, np.bool_)
            out3 = zeros_like(x, 'float64')
            out4 = zeros_like(x, 'int32')
            out5 = zeros_like(x, 'int64')
        place = (
            fluid.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )
        exe = fluid.Executor(place)
        outs = exe.run(
            train_program,
            feed={'X': np.ones(shape).astype('float32')},
            fetch_list=[out1, out2, out3, out4, out5],
        )
        for (i, dtype) in enumerate(
            [np.float32, np.bool_, np.float64, np.int32, np.int64]
        ):
            self.assertEqual(outs[i].dtype, dtype)
            self.assertEqual((outs[i] == np.zeros(shape, dtype)).all(), True)

    def test_eager(self):
        with _test_eager_guard():
            self.test_api()


class TestZerosLikeImpeartive(unittest.TestCase):
    def test_out(self):
        shape = [3, 4]
        place = (
            fluid.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )
        paddle.disable_static(place)
        x = paddle.to_tensor(np.ones(shape))
        for dtype in [np.bool_, np.float32, np.float64, np.int32, np.int64]:
            out = zeros_like(x, dtype)
            self.assertEqual(
                (out.numpy() == np.zeros(shape, dtype)).all(), True
            )
        out = paddle.tensor.zeros_like(x)
        self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(), True)
        out = paddle.tensor.creation.zeros_like(x)
        self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(), True)
        paddle.enable_static()

    def test_eager(self):
        with _test_eager_guard():
            self.test_out()


class TestZerosAPI(unittest.TestCase):
    def test_api(self):
        shape = [3, 4]
        place = (
            fluid.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else fluid.CPUPlace()
        )
        paddle.disable_static(place)

        for dtype in [np.float32, np.float64, np.int32, np.int64]:
            out = _C_ops.zeros(shape, convert_np_dtype_to_dtype_(dtype), place)
            self.assertEqual(
                (out.numpy() == np.zeros(shape, dtype)).all(), True
            )

        paddle.enable_static()


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