#   Copyright (c) 2020 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 get_device_place, is_custom_device
from utils import dygraph_guard

import paddle
from paddle.static import Program, program_guard


class TestRandnOp(unittest.TestCase):
    def test_api(self):
        shape = [1000, 784]
        train_program = Program()
        startup_program = Program()
        with program_guard(train_program, startup_program):
            x1 = paddle.randn(shape, 'float32')
            x2 = paddle.randn(shape, 'float64')

            dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
            dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
            x3 = paddle.randn([dim_1, dim_2, 784])

            var_shape = paddle.static.data('X', [2], 'int32')
            x4 = paddle.randn(var_shape)

        place = get_device_place()
        exe = paddle.static.Executor(place)
        res = exe.run(
            train_program,
            feed={'X': np.array(shape, dtype='int32')},
            fetch_list=[x1, x2, x3, x4],
        )

        for out in res:
            self.assertAlmostEqual(np.mean(out), 0.0, delta=0.1)
            self.assertAlmostEqual(np.std(out), 1.0, delta=0.1)


class TestRandnOpForDygraph(unittest.TestCase):
    def test_api(self):
        shape = [1000, 784]
        place = get_device_place()
        paddle.disable_static(place)
        x1 = paddle.randn(shape, 'float32')
        x2 = paddle.randn(shape, 'float64')

        dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
        dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
        x3 = paddle.randn(shape=[dim_1, dim_2, 784])

        var_shape = paddle.to_tensor(np.array(shape))
        x4 = paddle.randn(var_shape)

        for out in [x1, x2, x3, x4]:
            self.assertAlmostEqual(np.mean(out.numpy()), 0.0, delta=0.1)
            self.assertAlmostEqual(np.std(out.numpy()), 1.0, delta=0.1)
        paddle.enable_static()


class TestRandnOpError(unittest.TestCase):
    def test_error(self):
        with program_guard(Program(), Program()):
            # The argument dtype of randn_op should be float32 or float64.
            self.assertRaises(TypeError, paddle.randn, [1, 2], 'int32')


class TestRandnOpCompatibility(unittest.TestCase):
    def setUp(self):
        self.places = [paddle.CPUPlace()]
        if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
            self.places.append(get_device_place())
        self.expected_shape = [2, 3]
        self.dtype = paddle.float32

    def test_gather_with_param_aliases(self):
        with dygraph_guard():
            for place in self.places:
                paddle.device.set_device(place)
                for param_name in ['shape', 'size']:
                    tensor = paddle.randn(
                        **{param_name: self.expected_shape}, dtype=self.dtype
                    )
                    self.assertEqual(tensor.shape, self.expected_shape)
                    self.assertEqual(tensor.dtype, self.dtype)

                    shape_tensor = paddle.to_tensor(
                        self.expected_shape, dtype='int32'
                    )
                    tensor = paddle.randn(
                        **{param_name: shape_tensor}, dtype=self.dtype
                    )
                    self.assertEqual(tensor.shape, self.expected_shape)
                    self.assertEqual(tensor.dtype, self.dtype)

                tensor = paddle.randn(*self.expected_shape, dtype=self.dtype)
                self.assertEqual(tensor.shape, self.expected_shape)
                self.assertEqual(tensor.dtype, self.dtype)


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