# 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 paddle
import os

import numpy as np
import paddle
import paddle.static as static
import paddle.distributed.fleet as fleet
import paddle.nn as nn
import paddle.nn.functional as F

paddle.enable_static()


class RNNEncoder(nn.Layer):
    def __init__(self,
                 input_size,
                 hidden_size,
                 num_layers=1,
                 direction="forward",
                 dropout=0.0,
                 pooling_type=None,
                 **kwargs):
        super().__init__()
        self._input_size = input_size
        self._hidden_size = hidden_size
        self._direction = direction
        self._pooling_type = pooling_type

        self.rnn_layer = nn.SimpleRNN(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
            direction=direction,
            dropout=dropout,
            **kwargs)

    def get_input_dim(self):
        return self._input_size

    def get_output_dim(self):
        if self._direction == "bidirect":
            return self._hidden_size * 2
        else:
            return self._hidden_size

    def forward(self, inputs, sequence_length):
        encoded_text, last_hidden = self.rnn_layer(
            inputs, sequence_length=sequence_length)
        output = paddle.max(encoded_text, axis=1)
        return output


class RNNModel(nn.Layer):
    def __init__(self,
                 vocab_size,
                 num_classes,
                 emb_dim=128,
                 padding_idx=0,
                 rnn_hidden_size=198,
                 direction='forward',
                 rnn_layers=1,
                 dropout_rate=0.0,
                 pooling_type=None,
                 fc_hidden_size=96):
        super().__init__()
        self.embedder = nn.Embedding(
            num_embeddings=vocab_size,
            embedding_dim=emb_dim,
            padding_idx=padding_idx)
        self.rnn_encoder = RNNEncoder(
            emb_dim,
            rnn_hidden_size,
            num_layers=rnn_layers,
            direction=direction,
            dropout=dropout_rate,
            pooling_type=pooling_type)
        self.fc = nn.Linear(self.rnn_encoder.get_output_dim(), fc_hidden_size)
        self.output_layer = nn.Linear(fc_hidden_size, num_classes)

    def forward(self, text, seq_len):
        embedded_text = self.embedder(text)
        text_repr = self.rnn_encoder(embedded_text, sequence_length=seq_len)
        fc_out = paddle.tanh(self.fc(text_repr))
        logits = self.output_layer(fc_out)
        return logits


def rnn_pretrain_forward(train_program, start_program, topo=None):
    with static.program_guard(train_program,
                              start_program), paddle.utils.unique_name.guard():
        batch_size = 1
        tokens = static.data(
            name="tokens", shape=[batch_size, -1], dtype="int64")
        seq_len = static.data(name="ids", shape=[batch_size], dtype="int64")
        labels = static.data(name="labels", shape=[batch_size], dtype="int64")
        data_holders = [tokens, seq_len, labels]
        vocab_size = 10
        num_classes = 2
        pad_token_id = 0
        model = RNNModel(
            vocab_size,
            num_classes,
            direction='forward',
            padding_idx=pad_token_id,
            pooling_type='max')

        optimizer = paddle.optimizer.Adam(
            parameters=model.parameters(), learning_rate=0.001)
        criterion = paddle.nn.CrossEntropyLoss()
        preds = model(tokens, seq_len)
        loss = criterion(preds, labels)

    return train_program, start_program, loss, optimizer, data_holders


class TestFleetMetaOptimizer(unittest.TestCase):
    def setUp(self):
        os.environ["PADDLE_TRAINER_ID"] = "1"
        os.environ[
            "PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001,127.0.0.1:36002"

    def test_rnn_raw_optimizer(self):
        import paddle.distributed.fleet as fleet
        import paddle.distributed.fleet.base.role_maker as role_maker
        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
        fleet.init(role)
        train_program = static.Program()
        start_program = static.Program()
        train_program, start_program, loss, optimizer, data_holders = \
            rnn_pretrain_forward(train_program, start_program)
        with paddle.static.program_guard(
                train_program, start_program), paddle.utils.unique_name.guard():
            strategy = fleet.DistributedStrategy()
            strategy.without_graph_optimization = True
            strategy.fuse_all_reduce_ops = True
            fleet.init(is_collective=True, strategy=strategy)
            optimizer = fleet.distributed_optimizer(optimizer)
            optimizer.minimize(loss)


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