#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 math

import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.fluid as fluid
import time
import os

word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
label_dict_len = len(label_dict)
pred_len = len(verb_dict)

mark_dict_len = 2
word_dim = 32
mark_dim = 5
hidden_dim = 512
depth = 8
mix_hidden_lr = 1e-3

IS_SPARSE = True
PASS_NUM = 10
BATCH_SIZE = 20

embedding_name = 'emb'


def load_parameter(file_name, h, w):
    with open(file_name, 'rb') as f:
        f.read(16)  # skip header.
        return np.fromfile(f, dtype=np.float32).reshape(h, w)


def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
            **ignored):
    # 8 features
    predicate_embedding = fluid.layers.embedding(
        input=predicate,
        size=[pred_len, word_dim],
        dtype='float32',
        is_sparse=IS_SPARSE,
        param_attr='vemb')

    mark_embedding = fluid.layers.embedding(
        input=mark,
        size=[mark_dict_len, mark_dim],
        dtype='float32',
        is_sparse=IS_SPARSE)

    word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
    emb_layers = [
        fluid.layers.embedding(
            size=[word_dict_len, word_dim],
            input=x,
            param_attr=fluid.ParamAttr(
                name=embedding_name, trainable=False)) for x in word_input
    ]
    emb_layers.append(predicate_embedding)
    emb_layers.append(mark_embedding)

    hidden_0_layers = [
        fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
    ]

    hidden_0 = fluid.layers.sums(input=hidden_0_layers)

    lstm_0 = fluid.layers.dynamic_lstm(
        input=hidden_0,
        size=hidden_dim,
        candidate_activation='relu',
        gate_activation='sigmoid',
        cell_activation='sigmoid')

    # stack L-LSTM and R-LSTM with direct edges
    input_tmp = [hidden_0, lstm_0]

    for i in range(1, depth):
        mix_hidden = fluid.layers.sums(input=[
            fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
            fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
        ])

        lstm = fluid.layers.dynamic_lstm(
            input=mix_hidden,
            size=hidden_dim,
            candidate_activation='relu',
            gate_activation='sigmoid',
            cell_activation='sigmoid',
            is_reverse=((i % 2) == 1))

        input_tmp = [mix_hidden, lstm]

    feature_out = fluid.layers.sums(input=[
        fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
        fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
    ])

    return feature_out


def to_lodtensor(data, place):
    seq_lens = [len(seq) for seq in data]
    cur_len = 0
    lod = [cur_len]
    for l in seq_lens:
        cur_len += l
        lod.append(cur_len)
    flattened_data = np.concatenate(data, axis=0).astype("int64")
    flattened_data = flattened_data.reshape([len(flattened_data), 1])
    res = fluid.LoDTensor()
    res.set(flattened_data, place)
    res.set_lod([lod])
    return res


def main():
    # define network topology
    word = fluid.layers.data(
        name='word_data', shape=[1], dtype='int64', lod_level=1)
    predicate = fluid.layers.data(
        name='verb_data', shape=[1], dtype='int64', lod_level=1)
    ctx_n2 = fluid.layers.data(
        name='ctx_n2_data', shape=[1], dtype='int64', lod_level=1)
    ctx_n1 = fluid.layers.data(
        name='ctx_n1_data', shape=[1], dtype='int64', lod_level=1)
    ctx_0 = fluid.layers.data(
        name='ctx_0_data', shape=[1], dtype='int64', lod_level=1)
    ctx_p1 = fluid.layers.data(
        name='ctx_p1_data', shape=[1], dtype='int64', lod_level=1)
    ctx_p2 = fluid.layers.data(
        name='ctx_p2_data', shape=[1], dtype='int64', lod_level=1)
    mark = fluid.layers.data(
        name='mark_data', shape=[1], dtype='int64', lod_level=1)
    feature_out = db_lstm(**locals())
    target = fluid.layers.data(
        name='target', shape=[1], dtype='int64', lod_level=1)
    crf_cost = fluid.layers.linear_chain_crf(
        input=feature_out,
        label=target,
        param_attr=fluid.ParamAttr(
            name='crfw', learning_rate=mix_hidden_lr))
    avg_cost = fluid.layers.mean(x=crf_cost)

    # TODO(qiao)
    # check other optimizers and check why out will be NAN
    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
    optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)

    # TODO(qiao)
    # add dependency track and move this config before optimizer
    crf_decode = fluid.layers.crf_decoding(
        input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))

    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
        input=crf_decode,
        label=target,
        chunk_scheme="IOB",
        num_chunk_types=int(math.ceil((label_dict_len - 1) / 2.0)))

    train_data = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.conll05.test(), buf_size=8192),
        batch_size=BATCH_SIZE)
    place = fluid.CPUPlace()
    feeder = fluid.DataFeeder(
        feed_list=[
            word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
        ],
        place=place)
    exe = fluid.Executor(place)

    t = fluid.DistributeTranspiler()
    pserver_endpoints = os.getenv("PSERVERS")
    # server endpoint for current node
    current_endpoint = os.getenv("SERVER_ENDPOINT")
    # run as trainer or parameter server
    training_role = os.getenv(
        "TRAINING_ROLE", "TRAINER")  # get the training role: trainer/pserver
    t.transpile(
        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)

    if training_role == "PSERVER":
        if not current_endpoint:
            print("need env SERVER_ENDPOINT")
            exit(1)
        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":
        trainer_prog = t.get_trainer_program()
        start_time = time.time()
        batch_id = 0
        exe.run(fluid.default_startup_program())
        embedding_param = fluid.global_scope().find_var(
            embedding_name).get_tensor()
        embedding_param.set(
            load_parameter(conll05.get_embedding(), word_dict_len, word_dim),
            place)
        for pass_id in xrange(PASS_NUM):
            chunk_evaluator.reset(exe)
            for data in train_data():
                cost, precision, recall, f1_score = exe.run(
                    trainer_prog,
                    feed=feeder.feed(data),
                    fetch_list=[avg_cost] + chunk_evaluator.metrics)
                pass_precision, pass_recall, pass_f1_score = chunk_evaluator.eval(
                    exe)

                if batch_id % 10 == 0:
                    print("avg_cost:" + str(cost) + " precision:" + str(
                        precision) + " recall:" + str(recall) + " f1_score:" +
                          str(f1_score) + " pass_precision:" + str(
                              pass_precision) + " pass_recall:" + str(
                                  pass_recall) + " pass_f1_score:" + str(
                                      pass_f1_score))
                    if batch_id != 0:
                        print("second per batch: " + str((time.time(
                        ) - start_time) / batch_id))

                batch_id = batch_id + 1


if __name__ == '__main__':
    main()
