# 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 paddle
paddle.set_default_dtype("float64")
from paddle.fluid.layers import sequence_mask

import numpy as np
import unittest

from convert import convert_params_for_net
from rnn_numpy import SimpleRNN, LSTM, GRU


class TestSimpleRNN(unittest.TestCase):
    def __init__(self, time_major=True, direction="forward", place="cpu"):
        super(TestSimpleRNN, self).__init__("runTest")
        self.time_major = time_major
        self.direction = direction
        self.num_directions = 2 if direction == "bidirectional" else 1
        self.place = place

    def setUp(self):
        # Since `set_device` is global, set `set_device` in `setUp` rather than
        # `__init__` to avoid using an error device set by another test case.
        place = paddle.set_device(self.place)
        paddle.disable_static(place)
        rnn1 = SimpleRNN(
            16, 32, 2, time_major=self.time_major, direction=self.direction)
        rnn2 = paddle.nn.SimpleRNN(
            16, 32, 2, time_major=self.time_major, direction=self.direction)
        convert_params_for_net(rnn1, rnn2)

        self.rnn1 = rnn1
        self.rnn2 = rnn2

    def test_with_initial_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        prev_h = np.random.randn(2 * self.num_directions, 4, 32)

        y1, h1 = rnn1(x, prev_h)
        y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])

        y1, h1 = rnn1(x)
        y2, h2 = rnn2(paddle.to_tensor(x))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_input_lengths(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)

        y1, h1 = rnn1(x, sequence_length=sequence_length)

        seq_len = paddle.to_tensor(sequence_length)
        mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
        if self.time_major:
            mask = paddle.transpose(mask, [1, 0])
        y2, h2 = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
        y2 = paddle.multiply(y2, mask, axis=0)

        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()


class TestGRU(unittest.TestCase):
    def __init__(self, time_major=True, direction="forward", place="cpu"):
        super(TestGRU, self).__init__("runTest")
        self.time_major = time_major
        self.direction = direction
        self.num_directions = 2 if direction == "bidirectional" else 1
        self.place = place

    def setUp(self):
        # Since `set_device` is global, set `set_device` in `setUp` rather than
        # `__init__` to avoid using an error device set by another test case.
        place = paddle.set_device(self.place)
        paddle.disable_static(place)
        rnn1 = GRU(16,
                   32,
                   2,
                   time_major=self.time_major,
                   direction=self.direction)
        rnn2 = paddle.nn.GRU(16,
                             32,
                             2,
                             time_major=self.time_major,
                             direction=self.direction)
        convert_params_for_net(rnn1, rnn2)

        self.rnn1 = rnn1
        self.rnn2 = rnn2

    def test_with_initial_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        prev_h = np.random.randn(2 * self.num_directions, 4, 32)

        y1, h1 = rnn1(x, prev_h)
        y2, h2 = rnn2(paddle.to_tensor(x), paddle.to_tensor(prev_h))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])

        y1, h1 = rnn1(x)
        y2, h2 = rnn2(paddle.to_tensor(x))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_input_lengths(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)

        y1, h1 = rnn1(x, sequence_length=sequence_length)

        seq_len = paddle.to_tensor(sequence_length)
        mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
        if self.time_major:
            mask = paddle.transpose(mask, [1, 0])
        y2, h2 = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
        y2 = paddle.multiply(y2, mask, axis=0)

        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()


class TestLSTM(unittest.TestCase):
    def __init__(self, time_major=True, direction="forward", place="cpu"):
        super(TestLSTM, self).__init__("runTest")
        self.time_major = time_major
        self.direction = direction
        self.num_directions = 2 if direction == "bidirectional" else 1
        self.place = place

    def setUp(self):
        # Since `set_device` is global, set `set_device` in `setUp` rather than
        # `__init__` to avoid using an error device set by another test case.
        place = paddle.set_device(self.place)
        paddle.disable_static(place)
        rnn1 = LSTM(
            16, 32, 2, time_major=self.time_major, direction=self.direction)
        rnn2 = paddle.nn.LSTM(
            16, 32, 2, time_major=self.time_major, direction=self.direction)
        convert_params_for_net(rnn1, rnn2)

        self.rnn1 = rnn1
        self.rnn2 = rnn2

    def test_with_initial_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        prev_h = np.random.randn(2 * self.num_directions, 4, 32)
        prev_c = np.random.randn(2 * self.num_directions, 4, 32)

        y1, (h1, c1) = rnn1(x, (prev_h, prev_c))
        y2, (h2, c2) = rnn2(
            paddle.to_tensor(x),
            (paddle.to_tensor(prev_h), paddle.to_tensor(prev_c)))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_zero_state(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])

        y1, (h1, c1) = rnn1(x)
        y2, (h2, c2) = rnn2(paddle.to_tensor(x))
        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)

    def test_with_input_lengths(self):
        rnn1 = self.rnn1
        rnn2 = self.rnn2

        x = np.random.randn(12, 4, 16)
        if not self.time_major:
            x = np.transpose(x, [1, 0, 2])
        sequence_length = np.array([12, 10, 9, 8], dtype=np.int64)

        y1, (h1, c1) = rnn1(x, sequence_length=sequence_length)

        seq_len = paddle.to_tensor(sequence_length)
        mask = sequence_mask(seq_len, dtype=paddle.get_default_dtype())
        if self.time_major:
            mask = paddle.transpose(mask, [1, 0])
        y2, (h2, c2) = rnn2(paddle.to_tensor(x), sequence_length=seq_len)
        y2 = paddle.multiply(y2, mask, axis=0)

        np.testing.assert_allclose(y1, y2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(h1, h2.numpy(), atol=1e-8, rtol=1e-5)
        np.testing.assert_allclose(c1, c2.numpy(), atol=1e-8, rtol=1e-5)

    def test_predict(self):
        place = paddle.set_device(self.place)
        paddle.manual_seed(123)
        np.random.seed(123)

        class Net(paddle.nn.Layer):
            def __init__(self):
                super(Net, self).__init__()
                self.rnn1 = paddle.nn.LSTM(
                    16, 32, 2, direction="bidirectional", dropout=0.1)

            def forward(self, input):
                return self.rnn1(input)

        x = paddle.randn((4, 10, 16))
        x.stop_gradient = False
        seq_len = paddle.to_tensor(np.array([10, 6, 8, 5]))
        mask = sequence_mask(seq_len, maxlen=10, dtype=x.dtype)
        mask = paddle.unsqueeze(mask, [2])
        rnn = Net()
        y, (h, c) = rnn(x)
        y = y * mask
        loss = paddle.mean(y)
        loss.backward()
        optimizer = paddle.optimizer.Adam(
            learning_rate=0.1, parameters=rnn.parameters())
        optimizer.step()
        rnn.eval()
        y, (h, c) = rnn(x)
        # `jit.to_static` would include a train_program, eval mode might cause
        # some errors currently, such as dropout grad op gets `is_test == True`.
        rnn.train()

        rnn = paddle.jit.to_static(
            rnn,
            [paddle.static.InputSpec(
                shape=[None, None, 16], dtype=x.dtype)])
        paddle.jit.save(rnn, "./inference/lstm_infer")

        paddle.enable_static()

        new_scope = paddle.static.Scope()
        with paddle.static.scope_guard(new_scope):
            exe = paddle.static.Executor(place)
            [inference_program, feed_target_names,
             fetch_targets] = paddle.static.load_inference_model(
                 dirname="./inference",
                 executor=exe,
                 model_filename="lstm_infer.pdmodel",
                 params_filename="lstm_infer.pdiparams")
            results = exe.run(inference_program,
                              feed={feed_target_names[0]: x.numpy()},
                              fetch_list=fetch_targets)
            np.testing.assert_equal(
                y.numpy(), results[0])  # eval results equal predict results
        paddle.disable_static()

    def runTest(self):
        self.test_with_initial_state()
        self.test_with_zero_state()
        self.test_with_input_lengths()
        self.test_predict()


def load_tests(loader, tests, pattern):
    suite = unittest.TestSuite()
    devices = ["cpu", "gpu"] if paddle.fluid.is_compiled_with_cuda() \
        else ["cpu"]
    for direction in ["forward", "backward", "bidirectional"]:
        for time_major in [True, False]:
            for device in devices:
                for test_class in [TestSimpleRNN, TestLSTM, TestGRU]:
                    suite.addTest(test_class(time_major, direction, device))
    return suite