# 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 os
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name, _test_eager_guard
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle


class TestDygraphLayerNormv2(unittest.TestCase):

    def test_dygraph(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
            places.append(fluid.CUDAPlace(0))
        for p in places:
            shape = [4, 10, 4, 4]

            def compute_v1(x):
                with fluid.dygraph.guard(p):
                    ln = fluid.dygraph.LayerNorm(shape[1:])
                    y = ln(paddle.to_tensor(x))
                return y.numpy()

            def compute_v2(x):
                with fluid.dygraph.guard(p):
                    ln = paddle.nn.LayerNorm(shape[1:])
                    y = ln(paddle.to_tensor(x))
                return y.numpy()

            x = np.random.randn(*shape).astype("float32")
            y1 = compute_v1(x)
            y2 = compute_v2(x)
            self.assertTrue(np.allclose(y1, y2))

    def test_eager(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
            places.append(fluid.CUDAPlace(0))
        for p in places:
            shape = [4, 10, 4, 4]

            def compute_v1(x):
                with fluid.dygraph.guard(p):
                    ln = fluid.dygraph.LayerNorm(shape[1:])
                    x1 = paddle.to_tensor(x)
                    x1.stop_gradient = False
                    y = ln(x1)
                    y.backward()
                    return y.numpy(), x1.gradient()

            def compute_v2(x):
                with fluid.dygraph.guard(p):
                    with _test_eager_guard():
                        ln = paddle.nn.LayerNorm(shape[1:])
                        x1 = paddle.to_tensor(x)
                        x1.stop_gradient = False
                        y = ln(x1)
                        y.backward()
                    return y.numpy(), x1.gradient()

            x = np.random.randn(*shape).astype("float32")
            y1, g1 = compute_v1(x)
            y2, g2 = compute_v2(x)
            self.assertTrue(np.allclose(y1, y2))
            self.assertTrue(np.allclose(g1, g2))

    def test_static(self):
        paddle.enable_static()
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
            places.append(fluid.CUDAPlace(0))
        for p in places:
            exe = fluid.Executor(p)
            shape = [4, 10, 16, 16]

            def compute_v1(x_np):
                with program_guard(Program(), Program()):
                    ln = fluid.dygraph.LayerNorm(shape[1:])
                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
                    y = ln(x)
                    exe.run(fluid.default_startup_program())
                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
                return r

            def compute_v2(x_np):
                with program_guard(Program(), Program()):
                    ln = paddle.nn.LayerNorm(shape[1:])
                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
                    y = ln(x)
                    exe.run(fluid.default_startup_program())
                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
                return r

            x = np.random.randn(*shape).astype("float32")
            y1 = compute_v1(x)
            y2 = compute_v2(x)
            self.assertTrue(np.allclose(y1, y2))


class TestLayerNormFunction(unittest.TestCase):

    def test_dygraph(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
            places.append(fluid.CUDAPlace(0))
        for p in places:
            shape = [4, 10, 4, 4]

            def compute_v0(x):
                with fluid.dygraph.guard(p):
                    ln = fluid.dygraph.LayerNorm(shape[1:])
                    y = ln(paddle.to_tensor(x))
                return y.numpy()

            def compute_v1(x):
                with fluid.dygraph.guard(p):
                    x = paddle.to_tensor(x)
                    y = paddle.nn.functional.layer_norm(x, shape[1:])
                return y.numpy()

            def compute_v2(x):
                with fluid.dygraph.guard(p):
                    x = paddle.to_tensor(x)
                    y = paddle.nn.functional.layer_norm(x, tuple(shape[1:]))
                return y.numpy()

            def compute_v3(x):
                with fluid.dygraph.guard(p):
                    ln = fluid.dygraph.LayerNorm(shape[-1])
                    y = ln(paddle.to_tensor(x))
                return y.numpy()

            def compute_v4(x):
                with fluid.dygraph.guard(p):
                    x = paddle.to_tensor(x)
                    y = paddle.nn.functional.layer_norm(x, shape[-1])
                return y.numpy()

            x = np.random.randn(*shape).astype("float32")
            y0 = compute_v0(x)
            y1 = compute_v1(x)
            y2 = compute_v2(x)
            self.assertTrue(np.allclose(y0, y1))
            self.assertTrue(np.allclose(y0, y2))
            y3 = compute_v3(x)
            y4 = compute_v4(x)
            self.assertTrue(np.allclose(y3, y4))

            self.assertRaises(ValueError,
                              paddle.nn.functional.layer_norm,
                              x=x,
                              normalized_shape=1.0)


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