import argparse
import ast
import copy
import logging
import multiprocessing
import os
import six
import sys
import time

import numpy as np
import paddle.fluid as fluid

import reader
from config import *
from model import transformer, position_encoding_init


def parse_args():
    parser = argparse.ArgumentParser("Training for Transformer.")
    parser.add_argument(
        "--src_vocab_fpath",
        type=str,
        required=True,
        help="The path of vocabulary file of source language.")
    parser.add_argument(
        "--trg_vocab_fpath",
        type=str,
        required=True,
        help="The path of vocabulary file of target language.")
    parser.add_argument(
        "--train_file_pattern",
        type=str,
        required=True,
        help="The pattern to match training data files.")
    parser.add_argument(
        "--val_file_pattern",
        type=str,
        help="The pattern to match validation data files.")
    parser.add_argument(
        "--use_token_batch",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to "
        "produce batch data according to token number.")
    parser.add_argument(
        "--batch_size",
        type=int,
        default=4096,
        help="The number of sequences contained in a mini-batch, or the maximum "
        "number of tokens (include paddings) contained in a mini-batch. Note "
        "that this represents the number on single device and the actual batch "
        "size for multi-devices will multiply the device number.")
    parser.add_argument(
        "--pool_size",
        type=int,
        default=200000,
        help="The buffer size to pool data.")
    parser.add_argument(
        "--sort_type",
        default="pool",
        choices=("global", "pool", "none"),
        help="The grain to sort by length: global for all instances; pool for "
        "instances in pool; none for no sort.")
    parser.add_argument(
        "--shuffle",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to shuffle instances in each pass.")
    parser.add_argument(
        "--shuffle_batch",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to shuffle the data batches.")
    parser.add_argument(
        "--special_token",
        type=str,
        default=["<s>", "<e>", "<unk>"],
        nargs=3,
        help="The <bos>, <eos> and <unk> tokens in the dictionary.")
    parser.add_argument(
        "--token_delimiter",
        type=lambda x: str(x.encode().decode("unicode-escape")),
        default=" ",
        help="The delimiter used to split tokens in source or target sentences. "
        "For EN-DE BPE data we provided, use spaces as token delimiter. ")
    parser.add_argument(
        'opts',
        help='See config.py for all options',
        default=None,
        nargs=argparse.REMAINDER)
    parser.add_argument(
        '--local',
        type=ast.literal_eval,
        default=True,
        help='Whether to run as local mode.')
    parser.add_argument(
        '--device',
        type=str,
        default='GPU',
        choices=['CPU', 'GPU'],
        help="The device type.")
    parser.add_argument(
        '--update_method',
        choices=("pserver", "nccl2"),
        default="pserver",
        help='Update method.')
    parser.add_argument(
        '--sync', type=ast.literal_eval, default=True, help="sync mode.")
    parser.add_argument(
        "--enable_ce",
        type=ast.literal_eval,
        default=False,
        help="The flag indicating whether to run the task "
        "for continuous evaluation.")
    parser.add_argument(
        "--use_mem_opt",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to use memory optimization.")
    parser.add_argument(
        "--use_py_reader",
        type=ast.literal_eval,
        default=True,
        help="The flag indicating whether to use py_reader.")
    parser.add_argument(
        "--fetch_steps",
        type=int,
        default=100,
        help="The frequency to fetch and print output.")

    args = parser.parse_args()
    # Append args related to dict
    src_dict = reader.DataReader.load_dict(args.src_vocab_fpath)
    trg_dict = reader.DataReader.load_dict(args.trg_vocab_fpath)
    dict_args = [
        "src_vocab_size", str(len(src_dict)), "trg_vocab_size",
        str(len(trg_dict)), "bos_idx", str(src_dict[args.special_token[0]]),
        "eos_idx", str(src_dict[args.special_token[1]]), "unk_idx",
        str(src_dict[args.special_token[2]])
    ]
    merge_cfg_from_list(args.opts + dict_args,
                        [TrainTaskConfig, ModelHyperParams])
    return args


def append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
                         current_endpoint):
    assert (trainer_id >= 0 and len(worker_endpoints) > 1 and
            current_endpoint in worker_endpoints)
    eps = copy.deepcopy(worker_endpoints)
    eps.remove(current_endpoint)
    nccl_id_var = startup_prog.global_block().create_var(
        name="NCCLID", persistable=True, type=fluid.core.VarDesc.VarType.RAW)
    startup_prog.global_block().append_op(
        type="gen_nccl_id",
        inputs={},
        outputs={"NCCLID": nccl_id_var},
        attrs={
            "endpoint": current_endpoint,
            "endpoint_list": eps,
            "trainer_id": trainer_id
        })
    return nccl_id_var


def pad_batch_data(insts,
                   pad_idx,
                   n_head,
                   is_target=False,
                   is_label=False,
                   return_attn_bias=True,
                   return_max_len=True,
                   return_num_token=False):
    """
    Pad the instances to the max sequence length in batch, and generate the
    corresponding position data and attention bias.
    """
    return_list = []
    max_len = max(len(inst) for inst in insts)
    # Any token included in dict can be used to pad, since the paddings' loss
    # will be masked out by weights and make no effect on parameter gradients.
    inst_data = np.array(
        [inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
    return_list += [inst_data.astype("int64").reshape([-1, 1])]
    if is_label:  # label weight
        inst_weight = np.array(
            [[1.] * len(inst) + [0.] * (max_len - len(inst)) for inst in insts])
        return_list += [inst_weight.astype("float32").reshape([-1, 1])]
    else:  # position data
        inst_pos = np.array([
            list(range(0, len(inst))) + [0] * (max_len - len(inst))
            for inst in insts
        ])
        return_list += [inst_pos.astype("int64").reshape([-1, 1])]
    if return_attn_bias:
        if is_target:
            # This is used to avoid attention on paddings and subsequent
            # words.
            slf_attn_bias_data = np.ones((inst_data.shape[0], max_len, max_len))
            slf_attn_bias_data = np.triu(slf_attn_bias_data,
                                         1).reshape([-1, 1, max_len, max_len])
            slf_attn_bias_data = np.tile(slf_attn_bias_data,
                                         [1, n_head, 1, 1]) * [-1e9]
        else:
            # This is used to avoid attention on paddings.
            slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
                                           (max_len - len(inst))
                                           for inst in insts])
            slf_attn_bias_data = np.tile(
                slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
                [1, n_head, max_len, 1])
        return_list += [slf_attn_bias_data.astype("float32")]
    if return_max_len:
        return_list += [max_len]
    if return_num_token:
        num_token = 0
        for inst in insts:
            num_token += len(inst)
        return_list += [num_token]
    return return_list if len(return_list) > 1 else return_list[0]


def prepare_batch_input(insts, data_input_names, src_pad_idx, trg_pad_idx,
                        n_head, d_model):
    """
    Put all padded data needed by training into a dict.
    """
    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
    src_word = src_word.reshape(-1, src_max_len, 1)
    src_pos = src_pos.reshape(-1, src_max_len, 1)
    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
    trg_word = trg_word.reshape(-1, trg_max_len, 1)
    trg_pos = trg_pos.reshape(-1, trg_max_len, 1)

    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
                                [1, 1, trg_max_len, 1]).astype("float32")

    lbl_word, lbl_weight, num_token = pad_batch_data(
        [inst[2] for inst in insts],
        trg_pad_idx,
        n_head,
        is_target=False,
        is_label=True,
        return_attn_bias=False,
        return_max_len=False,
        return_num_token=True)

    data_input_dict = dict(
        zip(data_input_names, [
            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
            trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
        ]))

    return data_input_dict, np.asarray([num_token], dtype="float32")


def prepare_data_generator(args,
                           is_test,
                           count,
                           pyreader,
                           py_reader_provider_wrapper,
                           place=None):
    """
    Data generator wrapper for DataReader. If use py_reader, set the data
    provider for py_reader
    """
    data_reader = reader.DataReader(
        fpattern=args.val_file_pattern if is_test else args.train_file_pattern,
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        token_delimiter=args.token_delimiter,
        use_token_batch=args.use_token_batch,
        batch_size=args.batch_size * (1 if args.use_token_batch else count),
        pool_size=args.pool_size,
        sort_type=args.sort_type,
        shuffle=args.shuffle,
        shuffle_batch=args.shuffle_batch,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        # count start and end tokens out
        max_length=ModelHyperParams.max_length - 2,
        clip_last_batch=False).batch_generator

    def stack(data_reader, count, clip_last=True):
        def __impl__():
            res = []
            for item in data_reader():
                res.append(item)
                if len(res) == count:
                    yield res
                    res = []
            if len(res) == count:
                yield res
            elif not clip_last:
                data = []
                for item in res:
                    data += item
                if len(data) > count:
                    inst_num_per_part = len(data) // count
                    yield [
                        data[inst_num_per_part * i:inst_num_per_part * (i + 1)]
                        for i in range(count)
                    ]

        return __impl__

    def split(data_reader, count):
        def __impl__():
            for item in data_reader():
                inst_num_per_part = len(item) // count
                for i in range(count):
                    yield item[inst_num_per_part * i:inst_num_per_part * (i + 1
                                                                          )]

        return __impl__

    if not args.use_token_batch:
        # to make data on each device have similar token number
        data_reader = split(data_reader, count)
    if args.use_py_reader:
        pyreader.decorate_tensor_provider(
            py_reader_provider_wrapper(data_reader, place))
        data_reader = None
    else:  # Data generator for multi-devices
        data_reader = stack(data_reader, count)
    return data_reader


def prepare_feed_dict_list(data_generator, init_flag, count):
    """
    Prepare the list of feed dict for multi-devices.
    """
    feed_dict_list = []
    if data_generator is not None:  # use_py_reader == False
        data_input_names = encoder_data_input_fields + \
                    decoder_data_input_fields[:-1] + label_data_input_fields
        data = next(data_generator)
        for idx, data_buffer in enumerate(data):
            data_input_dict, num_token = prepare_batch_input(
                data_buffer, data_input_names, ModelHyperParams.eos_idx,
                ModelHyperParams.eos_idx, ModelHyperParams.n_head,
                ModelHyperParams.d_model)
            feed_dict_list.append(data_input_dict)
    if init_flag:
        for idx in range(count):
            pos_enc_tables = dict()
            for pos_enc_param_name in pos_enc_param_names:
                pos_enc_tables[pos_enc_param_name] = position_encoding_init(
                    ModelHyperParams.max_length + 1, ModelHyperParams.d_model)
            if len(feed_dict_list) <= idx:
                feed_dict_list.append(pos_enc_tables)
            else:
                feed_dict_list[idx] = dict(
                    list(pos_enc_tables.items()) + list(feed_dict_list[idx]
                                                        .items()))

    return feed_dict_list if len(feed_dict_list) == count else None


def py_reader_provider_wrapper(data_reader, place):
    """
    Data provider needed by fluid.layers.py_reader.
    """

    def py_reader_provider():
        data_input_names = encoder_data_input_fields + \
                    decoder_data_input_fields[:-1] + label_data_input_fields
        for batch_id, data in enumerate(data_reader()):
            data_input_dict, num_token = prepare_batch_input(
                data, data_input_names, ModelHyperParams.eos_idx,
                ModelHyperParams.eos_idx, ModelHyperParams.n_head,
                ModelHyperParams.d_model)
            yield [data_input_dict[item] for item in data_input_names]

    return py_reader_provider


def test_context(exe, train_exe, dev_count):
    # Context to do validation.
    test_prog = fluid.Program()
    startup_prog = fluid.Program()
    if args.enable_ce:
        test_prog.random_seed = 1000
        startup_prog.random_seed = 1000
    with fluid.program_guard(test_prog, startup_prog):
        with fluid.unique_name.guard():
            sum_cost, avg_cost, predict, token_num, pyreader = transformer(
                ModelHyperParams.src_vocab_size,
                ModelHyperParams.trg_vocab_size,
                ModelHyperParams.max_length + 1,
                ModelHyperParams.n_layer,
                ModelHyperParams.n_head,
                ModelHyperParams.d_key,
                ModelHyperParams.d_value,
                ModelHyperParams.d_model,
                ModelHyperParams.d_inner_hid,
                ModelHyperParams.prepostprocess_dropout,
                ModelHyperParams.attention_dropout,
                ModelHyperParams.relu_dropout,
                ModelHyperParams.preprocess_cmd,
                ModelHyperParams.postprocess_cmd,
                ModelHyperParams.weight_sharing,
                TrainTaskConfig.label_smooth_eps,
                use_py_reader=args.use_py_reader,
                is_test=True)
    test_prog = test_prog.clone(for_test=True)
    test_data = prepare_data_generator(
        args,
        is_test=True,
        count=dev_count,
        pyreader=pyreader,
        py_reader_provider_wrapper=py_reader_provider_wrapper)

    exe.run(startup_prog)  # to init pyreader for testing
    if TrainTaskConfig.ckpt_path:
        fluid.io.load_persistables(
            exe, TrainTaskConfig.ckpt_path, main_program=test_prog)

    exec_strategy = fluid.ExecutionStrategy()
    exec_strategy.use_experimental_executor = True
    build_strategy = fluid.BuildStrategy()
    test_exe = fluid.ParallelExecutor(
        use_cuda=TrainTaskConfig.use_gpu,
        main_program=test_prog,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy,
        share_vars_from=train_exe)

    def test(exe=test_exe, pyreader=pyreader):
        test_total_cost = 0
        test_total_token = 0

        if args.use_py_reader:
            pyreader.start()
            data_generator = None
        else:
            data_generator = test_data()
        while True:
            try:
                feed_dict_list = prepare_feed_dict_list(data_generator, False,
                                                        dev_count)
                outs = test_exe.run(fetch_list=[sum_cost.name, token_num.name],
                                    feed=feed_dict_list)
            except (StopIteration, fluid.core.EOFException):
                # The current pass is over.
                if args.use_py_reader:
                    pyreader.reset()
                break
            sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
            test_total_cost += sum_cost_val.sum()
            test_total_token += token_num_val.sum()
        test_avg_cost = test_total_cost / test_total_token
        test_ppl = np.exp([min(test_avg_cost, 100)])
        return test_avg_cost, test_ppl

    return test


def train_loop(exe,
               train_prog,
               startup_prog,
               dev_count,
               sum_cost,
               avg_cost,
               token_num,
               predict,
               pyreader,
               nccl2_num_trainers=1,
               nccl2_trainer_id=0):
    # Initialize the parameters.
    if TrainTaskConfig.ckpt_path:
        exe.run(startup_prog)  # to init pyreader for training
        logging.info("load checkpoint from {}".format(
            TrainTaskConfig.ckpt_path))
        fluid.io.load_persistables(
            exe, TrainTaskConfig.ckpt_path, main_program=train_prog)
    else:
        logging.info("init fluid.framework.default_startup_program")
        exe.run(startup_prog)

    logging.info("begin reader")
    train_data = prepare_data_generator(
        args,
        is_test=False,
        count=dev_count,
        pyreader=pyreader,
        py_reader_provider_wrapper=py_reader_provider_wrapper)

    # For faster executor
    exec_strategy = fluid.ExecutionStrategy()
    exec_strategy.use_experimental_executor = True
    exec_strategy.num_iteration_per_drop_scope = int(args.fetch_steps)
    build_strategy = fluid.BuildStrategy()
    # Since the token number differs among devices, customize gradient scale to
    # use token average cost among multi-devices. and the gradient scale is
    # `1 / token_number` for average cost.
    # build_strategy.gradient_scale_strategy = fluid.BuildStrategy.GradientScaleStrategy.Customized

    logging.info("begin executor")
    train_exe = fluid.ParallelExecutor(
        use_cuda=TrainTaskConfig.use_gpu,
        loss_name=avg_cost.name,
        main_program=train_prog,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy,
        num_trainers=nccl2_num_trainers,
        trainer_id=nccl2_trainer_id)

    if args.val_file_pattern is not None:
        test = test_context(exe, train_exe, dev_count)

    # the best cross-entropy value with label smoothing
    loss_normalizer = -((1. - TrainTaskConfig.label_smooth_eps) * np.log(
        (1. - TrainTaskConfig.label_smooth_eps
         )) + TrainTaskConfig.label_smooth_eps *
                        np.log(TrainTaskConfig.label_smooth_eps / (
                            ModelHyperParams.trg_vocab_size - 1) + 1e-20))

    step_idx = 0
    init_flag = True

    logging.info("begin train")
    for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
        pass_start_time = time.time()

        if args.use_py_reader:
            pyreader.start()
            data_generator = None
        else:
            data_generator = train_data()

        batch_id = 0
        while True:
            try:
                feed_dict_list = prepare_feed_dict_list(data_generator,
                                                        init_flag, dev_count)
                outs = train_exe.run(
                    fetch_list=[sum_cost.name, token_num.name]
                    if step_idx % args.fetch_steps == 0 else [],
                    feed=feed_dict_list)

                if step_idx % args.fetch_steps == 0:
                    sum_cost_val, token_num_val = np.array(outs[0]), np.array(
                        outs[1])
                    # sum the cost from multi-devices
                    total_sum_cost = sum_cost_val.sum()
                    total_token_num = token_num_val.sum()
                    total_avg_cost = total_sum_cost / total_token_num

                    if step_idx == 0:
                        logging.info(
                            "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
                            "normalized loss: %f, ppl: %f" %
                            (step_idx, pass_id, batch_id, total_avg_cost,
                             total_avg_cost - loss_normalizer,
                             np.exp([min(total_avg_cost, 100)])))
                        avg_batch_time = time.time()
                    else:
                        logging.info(
                            "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
                            "normalized loss: %f, ppl: %f, speed: %.2f step/s" %
                            (step_idx, pass_id, batch_id, total_avg_cost,
                             total_avg_cost - loss_normalizer,
                             np.exp([min(total_avg_cost, 100)]),
                             args.fetch_steps / (time.time() - avg_batch_time)))
                        avg_batch_time = time.time()

                if step_idx % TrainTaskConfig.save_freq == 0 and step_idx > 0:
                    fluid.io.save_persistables(
                        exe,
                        os.path.join(TrainTaskConfig.ckpt_dir,
                                     "latest.checkpoint"), train_prog)
                    fluid.io.save_params(
                        exe,
                        os.path.join(TrainTaskConfig.model_dir,
                                     "iter_" + str(step_idx) + ".infer.model"),
                        train_prog)

                init_flag = False
                batch_id += 1
                step_idx += 1
            except (StopIteration, fluid.core.EOFException):
                # The current pass is over.
                if args.use_py_reader:
                    pyreader.reset()
                break

        time_consumed = time.time() - pass_start_time
        # Validate and save the persistable.
        if args.val_file_pattern is not None:
            val_avg_cost, val_ppl = test()
            logging.info(
                "epoch: %d, val avg loss: %f, val normalized loss: %f, val ppl: %f,"
                " consumed %fs" % (pass_id, val_avg_cost,
                                   val_avg_cost - loss_normalizer, val_ppl,
                                   time_consumed))
        else:
            logging.info("epoch: %d, consumed %fs" % (pass_id, time_consumed))
        if not args.enable_ce:
            fluid.io.save_persistables(
                exe,
                os.path.join(TrainTaskConfig.ckpt_dir,
                             "pass_" + str(pass_id) + ".checkpoint"),
                train_prog)

    if args.enable_ce:  # For CE
        print("kpis\ttrain_cost_card%d\t%f" % (dev_count, total_avg_cost))
        if args.val_file_pattern is not None:
            print("kpis\ttest_cost_card%d\t%f" % (dev_count, val_avg_cost))
        print("kpis\ttrain_duration_card%d\t%f" % (dev_count, time_consumed))


def train(args):
    # priority: ENV > args > config
    is_local = os.getenv("PADDLE_IS_LOCAL", "1")
    if is_local == '0':
        args.local = False
    logging.info(args)

    if args.device == 'CPU':
        TrainTaskConfig.use_gpu = False

    training_role = os.getenv("TRAINING_ROLE", "TRAINER")

    if training_role == "PSERVER" or (not TrainTaskConfig.use_gpu):
        place = fluid.CPUPlace()
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
    else:
        place = fluid.CUDAPlace(0)
        dev_count = fluid.core.get_cuda_device_count()

    exe = fluid.Executor(place)

    train_prog = fluid.Program()
    startup_prog = fluid.Program()

    if args.enable_ce:
        train_prog.random_seed = 1000
        startup_prog.random_seed = 1000

    with fluid.program_guard(train_prog, startup_prog):
        with fluid.unique_name.guard():
            sum_cost, avg_cost, predict, token_num, pyreader = transformer(
                ModelHyperParams.src_vocab_size,
                ModelHyperParams.trg_vocab_size,
                ModelHyperParams.max_length + 1,
                ModelHyperParams.n_layer,
                ModelHyperParams.n_head,
                ModelHyperParams.d_key,
                ModelHyperParams.d_value,
                ModelHyperParams.d_model,
                ModelHyperParams.d_inner_hid,
                ModelHyperParams.prepostprocess_dropout,
                ModelHyperParams.attention_dropout,
                ModelHyperParams.relu_dropout,
                ModelHyperParams.preprocess_cmd,
                ModelHyperParams.postprocess_cmd,
                ModelHyperParams.weight_sharing,
                TrainTaskConfig.label_smooth_eps,
                use_py_reader=args.use_py_reader,
                is_test=False)

            optimizer = None
            if args.sync:
                lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
                    ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
                logging.info("before adam")

                with fluid.default_main_program()._lr_schedule_guard():
                    learning_rate = lr_decay * TrainTaskConfig.learning_rate

                optimizer = fluid.optimizer.Adam(
                    learning_rate=learning_rate,
                    beta1=TrainTaskConfig.beta1,
                    beta2=TrainTaskConfig.beta2,
                    epsilon=TrainTaskConfig.eps)
            else:
                optimizer = fluid.optimizer.SGD(0.003)
            optimizer.minimize(avg_cost)

    if args.use_mem_opt:
        fluid.memory_optimize(train_prog)

    if args.local:
        logging.info("local start_up:")
        train_loop(exe, train_prog, startup_prog, dev_count, sum_cost, avg_cost,
                   token_num, predict, pyreader)
    else:
        if args.update_method == "nccl2":
            trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
            port = os.getenv("PADDLE_PORT")
            worker_ips = os.getenv("PADDLE_TRAINERS")
            worker_endpoints = []
            for ip in worker_ips.split(","):
                worker_endpoints.append(':'.join([ip, port]))
            trainers_num = len(worker_endpoints)
            current_endpoint = os.getenv("POD_IP") + ":" + port
            if trainer_id == 0:
                logging.info("train_id == 0, sleep 60s")
                time.sleep(60)
            logging.info("trainers_num:{}".format(trainers_num))
            logging.info("worker_endpoints:{}".format(worker_endpoints))
            logging.info("current_endpoint:{}".format(current_endpoint))
            append_nccl2_prepare(startup_prog, trainer_id, worker_endpoints,
                                 current_endpoint)
            train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
                       avg_cost, token_num, predict, pyreader, trainers_num,
                       trainer_id)
            return

        port = os.getenv("PADDLE_PORT", "6174")
        pserver_ips = os.getenv("PADDLE_PSERVERS")  # 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_NUM", "0"))
        current_endpoint = os.getenv("POD_IP") + ":" + port
        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))

        logging.info("pserver_endpoints:{}".format(pserver_endpoints))
        logging.info("current_endpoint:{}".format(current_endpoint))
        logging.info("trainer_id:{}".format(trainer_id))
        logging.info("pserver_ips:{}".format(pserver_ips))
        logging.info("port:{}".format(port))

        t = fluid.DistributeTranspiler()
        t.transpile(
            trainer_id,
            pservers=pserver_endpoints,
            trainers=trainers,
            program=train_prog,
            startup_program=startup_prog)

        if training_role == "PSERVER":
            logging.info("distributed: pserver started")
            current_endpoint = os.getenv("POD_IP") + ":" + os.getenv(
                "PADDLE_PORT")
            if not current_endpoint:
                logging.critical("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":
            logging.info("distributed: trainer started")
            trainer_prog = t.get_trainer_program()

            train_loop(exe, train_prog, startup_prog, dev_count, sum_cost,
                       avg_cost, token_num, predict, pyreader)
        else:
            logging.critical(
                "environment var TRAINER_ROLE should be TRAINER os PSERVER")
            exit(1)


if __name__ == "__main__":
    LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
    logging.basicConfig(
        stream=sys.stdout, level=logging.DEBUG, format=LOG_FORMAT)
    logging.getLogger().setLevel(logging.INFO)

    args = parse_args()
    train(args)
