#   Copyright (c) 2018 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
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest, convert_uint16_to_float
from paddle.fluid.framework import _test_eager_guard
import paddle


class TestGaussianRandomOp(OpTest):
    def setUp(self):
        self.op_type = "gaussian_random"
        self.python_api = paddle.normal
        self.set_attrs()
        self.inputs = {}
        self.use_mkldnn = False
        self.attrs = {
            "shape": [123, 92],
            "mean": self.mean,
            "std": self.std,
            "seed": 10,
            "use_mkldnn": self.use_mkldnn,
        }
        paddle.seed(10)

        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}

    def set_attrs(self):
        self.mean = 1.0
        self.std = 2.0

    def test_check_output(self):
        self.check_output_customized(self.verify_output)

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

    def verify_output(self, outs):
        self.assertEqual(outs[0].shape, (123, 92))
        hist, _ = np.histogram(outs[0], range=(-3, 5))
        hist = hist.astype("float32")
        hist /= float(outs[0].size)
        data = np.random.normal(size=(123, 92), loc=1, scale=2)
        hist2, _ = np.histogram(data, range=(-3, 5))
        hist2 = hist2.astype("float32")
        hist2 /= float(outs[0].size)
        np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.01)


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestGaussianRandomBF16Op(OpTest):
    def setUp(self):
        self.op_type = "gaussian_random"
        self.python_api = paddle.normal
        self.set_attrs()
        self.inputs = {}
        self.use_mkldnn = False
        self.attrs = {
            "shape": [123, 92],
            "mean": self.mean,
            "std": self.std,
            "seed": 10,
            "dtype": paddle.fluid.core.VarDesc.VarType.BF16,
            "use_mkldnn": self.use_mkldnn,
        }
        paddle.seed(10)

        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}

    def set_attrs(self):
        self.mean = 1.0
        self.std = 2.0

    def test_check_output(self):
        self.check_output_with_place_customized(
            self.verify_output, place=core.CUDAPlace(0)
        )

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

    def verify_output(self, outs):
        outs = convert_uint16_to_float(outs)
        self.assertEqual(outs[0].shape, (123, 92))
        hist, _ = np.histogram(outs[0], range=(-3, 5))
        hist = hist.astype("float32")
        hist /= float(outs[0].size)
        data = np.random.normal(size=(123, 92), loc=1, scale=2)
        hist2, _ = np.histogram(data, range=(-3, 5))
        hist2 = hist2.astype("float32")
        hist2 /= float(outs[0].size)
        np.testing.assert_allclose(hist, hist2, rtol=0, atol=0.05)


class TestMeanStdAreInt(TestGaussianRandomOp):
    def set_attrs(self):
        self.mean = 1
        self.std = 2


# Situation 2: Attr(shape) is a list(with tensor)
class TestGaussianRandomOp_ShapeTensorList(TestGaussianRandomOp):
    def setUp(self):
        '''Test gaussian_random op with specified value'''
        self.op_type = "gaussian_random"
        self.init_data()
        shape_tensor_list = []
        for index, ele in enumerate(self.shape):
            shape_tensor_list.append(
                ("x" + str(index), np.ones((1)).astype('int32') * ele)
            )

        self.attrs = {
            'shape': self.infer_shape,
            'mean': self.mean,
            'std': self.std,
            'seed': self.seed,
            'use_mkldnn': self.use_mkldnn,
        }

        self.inputs = {"ShapeTensorList": shape_tensor_list}
        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}

    def init_data(self):
        self.shape = [123, 92]
        self.infer_shape = [-1, 92]
        self.use_mkldnn = False
        self.mean = 1.0
        self.std = 2.0
        self.seed = 10

    def test_check_output(self):
        self.check_output_customized(self.verify_output)


class TestGaussianRandomOp2_ShapeTensorList(
    TestGaussianRandomOp_ShapeTensorList
):
    def init_data(self):
        self.shape = [123, 92]
        self.infer_shape = [-1, -1]
        self.use_mkldnn = False
        self.mean = 1.0
        self.std = 2.0
        self.seed = 10


class TestGaussianRandomOp3_ShapeTensorList(
    TestGaussianRandomOp_ShapeTensorList
):
    def init_data(self):
        self.shape = [123, 92]
        self.infer_shape = [123, -1]
        self.use_mkldnn = True
        self.mean = 1.0
        self.std = 2.0
        self.seed = 10


class TestGaussianRandomOp4_ShapeTensorList(
    TestGaussianRandomOp_ShapeTensorList
):
    def init_data(self):
        self.shape = [123, 92]
        self.infer_shape = [123, -1]
        self.use_mkldnn = False
        self.mean = 1.0
        self.std = 2.0
        self.seed = 10


# Situation 3: shape is a tensor
class TestGaussianRandomOp1_ShapeTensor(TestGaussianRandomOp):
    def setUp(self):
        '''Test gaussian_random op with specified value'''
        self.op_type = "gaussian_random"
        self.init_data()
        self.use_mkldnn = False

        self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")}
        self.attrs = {
            'mean': self.mean,
            'std': self.std,
            'seed': self.seed,
            'use_mkldnn': self.use_mkldnn,
        }
        self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}

    def init_data(self):
        self.shape = [123, 92]
        self.use_mkldnn = False
        self.mean = 1.0
        self.std = 2.0
        self.seed = 10


# Test python API
class TestGaussianRandomAPI(unittest.TestCase):
    def test_api(self):
        positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2000)

        positive_2_int64 = fluid.layers.fill_constant([1], "int64", 500)
        shape_tensor_int32 = fluid.data(
            name="shape_tensor_int32", shape=[2], dtype="int32"
        )

        shape_tensor_int64 = fluid.data(
            name="shape_tensor_int64", shape=[2], dtype="int64"
        )

        out_1 = fluid.layers.gaussian_random(
            shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10
        )

        out_2 = fluid.layers.gaussian_random(
            shape=[2000, positive_2_int32],
            dtype="float32",
            mean=0.0,
            std=1.0,
            seed=10,
        )

        out_3 = fluid.layers.gaussian_random(
            shape=[2000, positive_2_int64],
            dtype="float32",
            mean=0.0,
            std=1.0,
            seed=10,
        )

        out_4 = fluid.layers.gaussian_random(
            shape=shape_tensor_int32,
            dtype="float32",
            mean=0.0,
            std=1.0,
            seed=10,
        )

        out_5 = fluid.layers.gaussian_random(
            shape=shape_tensor_int64,
            dtype="float32",
            mean=0.0,
            std=1.0,
            seed=10,
        )

        out_6 = fluid.layers.gaussian_random(
            shape=shape_tensor_int64,
            dtype=np.float32,
            mean=0.0,
            std=1.0,
            seed=10,
        )

        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3, res_4, res_5, res_6 = exe.run(
            fluid.default_main_program(),
            feed={
                "shape_tensor_int32": np.array([2000, 500]).astype("int32"),
                "shape_tensor_int64": np.array([2000, 500]).astype("int64"),
            },
            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6],
        )

        self.assertAlmostEqual(np.mean(res_1), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_1), 1.0, delta=0.1)
        self.assertAlmostEqual(np.mean(res_2), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_2), 1.0, delta=0.1)
        self.assertAlmostEqual(np.mean(res_3), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_3), 1.0, delta=0.1)
        self.assertAlmostEqual(np.mean(res_4), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1)
        self.assertAlmostEqual(np.mean(res_5), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_5), 1.0, delta=0.1)
        self.assertAlmostEqual(np.mean(res_6), 0.0, delta=0.1)
        self.assertAlmostEqual(np.std(res_6), 1.0, delta=0.1)

    def test_default_dtype(self):
        paddle.disable_static()

        def test_default_fp16():
            paddle.framework.set_default_dtype('float16')
            paddle.tensor.random.gaussian([2, 3])

        self.assertRaises(TypeError, test_default_fp16)

        def test_default_fp32():
            paddle.framework.set_default_dtype('float32')
            out = paddle.tensor.random.gaussian([2, 3])
            self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)

        def test_default_fp64():
            paddle.framework.set_default_dtype('float64')
            out = paddle.tensor.random.gaussian([2, 3])
            self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)

        test_default_fp64()
        test_default_fp32()

        paddle.enable_static()


class TestStandardNormalDtype(unittest.TestCase):
    def test_default_dtype(self):
        paddle.disable_static()

        def test_default_fp16():
            paddle.framework.set_default_dtype('float16')
            paddle.tensor.random.standard_normal([2, 3])

        self.assertRaises(TypeError, test_default_fp16)

        def test_default_fp32():
            paddle.framework.set_default_dtype('float32')
            out = paddle.tensor.random.standard_normal([2, 3])
            self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)

        def test_default_fp64():
            paddle.framework.set_default_dtype('float64')
            out = paddle.tensor.random.standard_normal([2, 3])
            self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)

        test_default_fp64()
        test_default_fp32()

        paddle.enable_static()


class TestRandomValue(unittest.TestCase):
    def test_fixed_random_number(self):
        # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'
        if not paddle.is_compiled_with_cuda():
            return

        # Different GPU generatte different random value. Only test V100 here.
        if "V100" not in paddle.device.cuda.get_device_name():
            return

        def _check_random_value(dtype, expect, expect_mean, expect_std):
            x = paddle.randn([32, 3, 1024, 1024], dtype=dtype)
            actual = x.numpy()
            np.testing.assert_allclose(
                actual[2, 1, 512, 1000:1010], expect, rtol=1e-05
            )
            self.assertTrue(np.mean(actual), expect_mean)
            self.assertTrue(np.std(actual), expect_std)

        print("Test Fixed Random number on V100 GPU------>")
        paddle.disable_static()
        paddle.set_device('gpu')
        paddle.seed(2021)
        expect = [
            -0.79037829,
            -0.54411126,
            -0.32266671,
            0.35791815,
            1.44169267,
            -0.87785644,
            -1.23909874,
            -2.18194139,
            0.49489656,
            0.40703062,
        ]
        expect_mean = (
            -0.0000053026194133403266873214888799115129813799285329878330230713
        )
        expect_std = 0.99999191058126390974081232343451119959354400634765625
        _check_random_value(
            core.VarDesc.VarType.FP64, expect, expect_mean, expect_std
        )

        expect = [
            -0.7988942,
            1.8644791,
            0.02782744,
            1.3692524,
            0.6419724,
            0.12436751,
            0.12058455,
            -1.9984808,
            1.5635862,
            0.18506318,
        ]
        expect_mean = -0.00004762359094456769526004791259765625
        expect_std = 0.999975681304931640625
        _check_random_value(
            core.VarDesc.VarType.FP32, expect, expect_mean, expect_std
        )
        paddle.enable_static()


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