# Copyright (c) 2019 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
import numpy as np
from rnn.rnn_numpy import LSTMCell
from rnn.rnn_numpy import rnn as numpy_rnn

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers.utils as utils
from paddle.fluid import framework
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, program_guard
from paddle.nn.layer.rnn import rnn as dynamic_rnn

paddle.enable_static()


class TestRnnError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            batch_size = 4
            input_size = 16
            hidden_size = 16
            seq_len = 4
            inputs = fluid.data(
                name='inputs', shape=[None, input_size], dtype='float32'
            )
            pre_hidden = paddle.static.data(
                name='pre_hidden',
                shape=[None, hidden_size],
                dtype='float32',
            )
            inputs_basic_lstm = fluid.data(
                name='inputs_basic_lstm',
                shape=[None, None, input_size],
                dtype='float32',
            )
            sequence_length = fluid.data(
                name="sequence_length", shape=[None], dtype='int64'
            )

            inputs_dynamic_rnn = paddle.transpose(
                inputs_basic_lstm, perm=[1, 0, 2]
            )
            cell = paddle.nn.LSTMCell(
                input_size, hidden_size, name="LSTMCell_for_rnn"
            )
            np_inputs_dynamic_rnn = np.random.random(
                (seq_len, batch_size, input_size)
            ).astype("float32")

            def test_input_Variable():
                dynamic_rnn(
                    cell=cell,
                    inputs=np_inputs_dynamic_rnn,
                    sequence_length=sequence_length,
                    is_reverse=False,
                )

            self.assertRaises(TypeError, test_input_Variable)

            def test_input_list():
                dynamic_rnn(
                    cell=cell,
                    inputs=[np_inputs_dynamic_rnn],
                    sequence_length=sequence_length,
                    is_reverse=False,
                )

            self.assertRaises(TypeError, test_input_list)

            def test_initial_states_type():
                cell = paddle.nn.GRUCell(
                    input_size, hidden_size, name="GRUCell_for_rnn"
                )
                error_initial_states = np.random.random(
                    (batch_size, hidden_size)
                ).astype("float32")
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    initial_states=error_initial_states,
                    sequence_length=sequence_length,
                    is_reverse=False,
                )

            self.assertRaises(TypeError, test_initial_states_type)

            def test_initial_states_list():
                error_initial_states = [
                    np.random.random((batch_size, hidden_size)).astype(
                        "float32"
                    ),
                    np.random.random((batch_size, hidden_size)).astype(
                        "float32"
                    ),
                ]
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    initial_states=error_initial_states,
                    sequence_length=sequence_length,
                    is_reverse=False,
                )

            self.assertRaises(TypeError, test_initial_states_type)

            def test_sequence_length_type():
                np_sequence_length = np.random.random((batch_size)).astype(
                    "float32"
                )
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    sequence_length=np_sequence_length,
                    is_reverse=False,
                )

            self.assertRaises(TypeError, test_sequence_length_type)


class TestRnn(unittest.TestCase):
    def setUp(self):
        self.batch_size = 4
        self.input_size = 16
        self.hidden_size = 16
        self.seq_len = 4

    def test_run(self):

        numpy_cell = LSTMCell(self.input_size, self.hidden_size)
        dynamic_cell = paddle.nn.LSTMCell(self.input_size, self.hidden_size)

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = Executor(place)
        exe.run(framework.default_startup_program())

        state = numpy_cell.parameters
        for k, v in dynamic_cell.named_parameters():
            param = np.random.uniform(-0.1, 0.1, size=state[k].shape).astype(
                'float64'
            )
            setattr(numpy_cell, k, param)
            fluid.global_scope().find_var(v.name).get_tensor().set(param, place)

        sequence_length = fluid.data(
            name="sequence_length", shape=[None], dtype='int64'
        )
        inputs_rnn = fluid.data(
            name='inputs_rnn',
            shape=[None, None, self.input_size],
            dtype='float64',
        )
        pre_hidden = fluid.data(
            name='pre_hidden', shape=[None, self.hidden_size], dtype='float64'
        )
        pre_cell = fluid.data(
            name='pre_cell', shape=[None, self.hidden_size], dtype='float64'
        )

        dynamic_output, dynamic_final_state = dynamic_rnn(
            cell=dynamic_cell,
            inputs=inputs_rnn,
            sequence_length=sequence_length,
            initial_states=(pre_hidden, pre_cell),
            is_reverse=False,
        )

        inputs_rnn_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.seq_len, self.input_size)
        ).astype('float64')
        sequence_length_np = (
            np.ones(self.batch_size, dtype='int64') * self.seq_len
        )
        pre_hidden_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)
        ).astype('float64')
        pre_cell_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)
        ).astype('float64')

        o1, _ = numpy_rnn(
            cell=numpy_cell,
            inputs=inputs_rnn_np,
            initial_states=(pre_hidden_np, pre_cell_np),
            sequence_length=sequence_length_np,
            is_reverse=False,
        )

        o2 = exe.run(
            feed={
                'inputs_rnn': inputs_rnn_np,
                'sequence_length': sequence_length_np,
                'pre_hidden': pre_hidden_np,
                'pre_cell': pre_cell_np,
            },
            fetch_list=[dynamic_output],
        )[0]
        np.testing.assert_allclose(o1, o2, rtol=0.001)


class TestRnnUtil(unittest.TestCase):
    """
    Test cases for rnn apis' utility methods for coverage.
    """

    def test_case(self):
        inputs = {"key1": 1, "key2": 2}
        func = lambda x: x + 1
        outputs = utils.map_structure(func, inputs)
        utils.assert_same_structure(inputs, outputs)
        try:
            inputs["key3"] = 3
            utils.assert_same_structure(inputs, outputs)
        except ValueError as identifier:
            pass


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