#   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.

from __future__ import print_function
import contextlib

import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.framework as framework
import paddle.fluid.layers as pd
from paddle.fluid.executor import Executor
import unittest
import os

dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
hidden_dim = 32
word_dim = 16
batch_size = 2
max_length = 8
topk_size = 50
trg_dic_size = 10000
beam_size = 2

decoder_size = hidden_dim


def encoder(is_sparse):
    # encoder
    src_word_id = pd.data(
        name="src_word_id", shape=[1], dtype='int64', lod_level=1)
    src_embedding = pd.embedding(
        input=src_word_id,
        size=[dict_size, word_dim],
        dtype='float32',
        is_sparse=is_sparse,
        param_attr=fluid.ParamAttr(name='vemb'))

    fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
    lstm_hidden0, lstm_0 = pd.dynamic_lstm(input=fc1, size=hidden_dim * 4)
    encoder_out = pd.sequence_last_step(input=lstm_hidden0)
    return encoder_out


def decoder_train(context, is_sparse):
    # decoder
    trg_language_word = pd.data(
        name="target_language_word", shape=[1], dtype='int64', lod_level=1)
    trg_embedding = pd.embedding(
        input=trg_language_word,
        size=[dict_size, word_dim],
        dtype='float32',
        is_sparse=is_sparse,
        param_attr=fluid.ParamAttr(name='vemb'))

    rnn = pd.DynamicRNN()
    with rnn.block():
        current_word = rnn.step_input(trg_embedding)
        pre_state = rnn.memory(init=context)
        current_state = pd.fc(input=[current_word, pre_state],
                              size=decoder_size,
                              act='tanh')

        current_score = pd.fc(input=current_state,
                              size=target_dict_dim,
                              act='softmax')
        rnn.update_memory(pre_state, current_state)
        rnn.output(current_score)

    return rnn()


def decoder_decode(context, is_sparse):
    init_state = context
    array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
    counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)

    # fill the first element with init_state
    state_array = pd.create_array('float32')
    pd.array_write(init_state, array=state_array, i=counter)

    # ids, scores as memory
    ids_array = pd.create_array('int64')
    scores_array = pd.create_array('float32')

    init_ids = pd.data(name="init_ids", shape=[1], dtype="int64", lod_level=2)
    init_scores = pd.data(
        name="init_scores", shape=[1], dtype="float32", lod_level=2)

    pd.array_write(init_ids, array=ids_array, i=counter)
    pd.array_write(init_scores, array=scores_array, i=counter)

    cond = pd.less_than(x=counter, y=array_len)

    while_op = pd.While(cond=cond)
    with while_op.block():
        pre_ids = pd.array_read(array=ids_array, i=counter)
        pre_state = pd.array_read(array=state_array, i=counter)
        pre_score = pd.array_read(array=scores_array, i=counter)

        # expand the recursive_sequence_lengths of pre_state to be the same with pre_score
        pre_state_expanded = pd.sequence_expand(pre_state, pre_score)

        pre_ids_emb = pd.embedding(
            input=pre_ids,
            size=[dict_size, word_dim],
            dtype='float32',
            is_sparse=is_sparse)

        # use rnn unit to update rnn
        current_state = pd.fc(input=[pre_state_expanded, pre_ids_emb],
                              size=decoder_size,
                              act='tanh')
        current_state_with_lod = pd.lod_reset(x=current_state, y=pre_score)
        # use score to do beam search
        current_score = pd.fc(input=current_state_with_lod,
                              size=target_dict_dim,
                              act='softmax')
        topk_scores, topk_indices = pd.topk(current_score, k=beam_size)
        # calculate accumulated scores after topk to reduce computation cost
        accu_scores = pd.elementwise_add(
            x=pd.log(topk_scores), y=pd.reshape(
                pre_score, shape=[-1]), axis=0)
        selected_ids, selected_scores = pd.beam_search(
            pre_ids,
            pre_score,
            topk_indices,
            accu_scores,
            beam_size,
            end_id=10,
            level=0)

        pd.increment(x=counter, value=1, in_place=True)

        # update the memories
        pd.array_write(current_state, array=state_array, i=counter)
        pd.array_write(selected_ids, array=ids_array, i=counter)
        pd.array_write(selected_scores, array=scores_array, i=counter)

        # update the break condition: up to the max length or all candidates of
        # source sentences have ended.
        length_cond = pd.less_than(x=counter, y=array_len)
        finish_cond = pd.logical_not(pd.is_empty(x=selected_ids))
        pd.logical_and(x=length_cond, y=finish_cond, out=cond)

    translation_ids, translation_scores = pd.beam_search_decode(
        ids=ids_array, scores=scores_array, beam_size=beam_size, end_id=10)

    # return init_ids, init_scores

    return translation_ids, translation_scores


def train_main(use_cuda, is_sparse, is_local=True):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

    context = encoder(is_sparse)
    rnn_out = decoder_train(context, is_sparse)
    label = pd.data(
        name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
    cost = pd.cross_entropy(input=rnn_out, label=label)
    avg_cost = pd.mean(cost)

    optimizer = fluid.optimizer.Adagrad(
        learning_rate=1e-4,
        regularization=fluid.regularizer.L2DecayRegularizer(
            regularization_coeff=0.1))
    optimizer.minimize(avg_cost)

    train_data = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
        batch_size=batch_size)

    feed_order = [
        'src_word_id', 'target_language_word', 'target_language_next_word'
    ]

    exe = Executor(place)

    def train_loop(main_program):
        exe.run(framework.default_startup_program())

        feed_list = [
            main_program.global_block().var(var_name) for var_name in feed_order
        ]
        feeder = fluid.DataFeeder(feed_list, place)

        batch_id = 0
        for pass_id in range(1):
            for data in train_data():
                outs = exe.run(main_program,
                               feed=feeder.feed(data),
                               fetch_list=[avg_cost])
                avg_cost_val = np.array(outs[0])
                print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
                      " avg_cost=" + str(avg_cost_val))
                if batch_id > 3:
                    break
                batch_id += 1

    if is_local:
        train_loop(framework.default_main_program())
    else:
        port = os.getenv("PADDLE_PSERVER_PORT", "6174")
        pserver_ips = os.getenv("PADDLE_PSERVER_IPS")  # ip,ip...
        eplist = []
        for ip in pserver_ips.split(","):
            eplist.append(':'.join([ip, port]))
        pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
        trainers = int(os.getenv("PADDLE_TRAINERS"))
        current_endpoint = os.getenv("POD_IP") + ":" + port
        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
        training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
        t = fluid.DistributeTranspiler()
        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
        if training_role == "PSERVER":
            pserver_prog = t.get_pserver_program(current_endpoint)
            pserver_startup = t.get_startup_program(current_endpoint,
                                                    pserver_prog)
            exe.run(pserver_startup)
            exe.run(pserver_prog)
        elif training_role == "TRAINER":
            train_loop(t.get_trainer_program())


def decode_main(use_cuda, is_sparse):
    if use_cuda and not fluid.core.is_compiled_with_cuda():
        return
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

    context = encoder(is_sparse)
    translation_ids, translation_scores = decoder_decode(context, is_sparse)

    exe = Executor(place)
    exe.run(framework.default_startup_program())

    init_ids_data = np.array([1 for _ in range(batch_size)], dtype='int64')
    init_scores_data = np.array(
        [1. for _ in range(batch_size)], dtype='float32')
    init_ids_data = init_ids_data.reshape((batch_size, 1))
    init_scores_data = init_scores_data.reshape((batch_size, 1))
    init_recursive_seq_lens = [1] * batch_size
    init_recursive_seq_lens = [init_recursive_seq_lens, init_recursive_seq_lens]

    init_ids = fluid.create_lod_tensor(init_ids_data, init_recursive_seq_lens,
                                       place)
    init_scores = fluid.create_lod_tensor(init_scores_data,
                                          init_recursive_seq_lens, place)

    train_data = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
        batch_size=batch_size)

    feed_order = ['src_word_id']
    feed_list = [
        framework.default_main_program().global_block().var(var_name)
        for var_name in feed_order
    ]
    feeder = fluid.DataFeeder(feed_list, place)

    for data in train_data():
        feed_dict = feeder.feed([[x[0]] for x in data])
        feed_dict['init_ids'] = init_ids
        feed_dict['init_scores'] = init_scores

        result_ids, result_scores = exe.run(
            framework.default_main_program(),
            feed=feed_dict,
            fetch_list=[translation_ids, translation_scores],
            return_numpy=False)
        print(result_ids.recursive_sequence_lengths())
        break


class TestMachineTranslation(unittest.TestCase):
    pass


@contextlib.contextmanager
def scope_prog_guard():
    prog = fluid.Program()
    startup_prog = fluid.Program()
    scope = fluid.core.Scope()
    with fluid.scope_guard(scope):
        with fluid.program_guard(prog, startup_prog):
            yield


def inject_test_train(use_cuda, is_sparse):
    f_name = 'test_{0}_{1}_train'.format('cuda' if use_cuda else 'cpu', 'sparse'
                                         if is_sparse else 'dense')

    def f(*args):
        with scope_prog_guard():
            train_main(use_cuda, is_sparse)

    setattr(TestMachineTranslation, f_name, f)


def inject_test_decode(use_cuda, is_sparse, decorator=None):
    f_name = 'test_{0}_{1}_decode'.format('cuda'
                                          if use_cuda else 'cpu', 'sparse'
                                          if is_sparse else 'dense')

    def f(*args):
        with scope_prog_guard():
            decode_main(use_cuda, is_sparse)

    if decorator is not None:
        f = decorator(f)

    setattr(TestMachineTranslation, f_name, f)


for _use_cuda_ in (False, True):
    for _is_sparse_ in (False, True):
        inject_test_train(_use_cuda_, _is_sparse_)

for _use_cuda_ in (False, True):
    for _is_sparse_ in (False, True):

        _decorator_ = None
        if _use_cuda_:
            _decorator_ = unittest.skip(
                reason='Beam Search does not support CUDA!')

        inject_test_decode(
            is_sparse=_is_sparse_, use_cuda=_use_cuda_, decorator=_decorator_)

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