#   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.
"""ERNIE pretraining."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import

import os
import time
import multiprocessing
import logging

import numpy as np
import paddle.fluid as fluid

from reader.pretraining import ErnieDataReader
from model.ernie_v1 import ErnieModel, ErnieConfig
from optimization import optimization
from utils.args import print_arguments, check_cuda, prepare_logger
from utils.init import init_checkpoint, init_pretraining_params

from pretrain_args import parser

log = logging.getLogger()
args = parser.parse_args()

# yapf: enable.


def create_model(pyreader_name, ernie_config):
    src_ids = fluid.layers.data(name='1', shape=[-1, args.max_seq_len, 1], dtype='int64')
    pos_ids = fluid.layers.data(name='2', shape=[-1, args.max_seq_len, 1], dtype='int64')
    sent_ids= fluid.layers.data(name='3', shape=[-1, args.max_seq_len, 1], dtype='int64')
    input_mask = fluid.layers.data(name='4', shape=[-1, args.max_seq_len, 1], dtype='float32')
    mask_label = fluid.layers.data(name='5', shape=[-1, 1], dtype='int64')
    mask_pos = fluid.layers.data(name='6', shape=[-1, 1], dtype='int64')
    labels = fluid.layers.data(name='r', shape=[-1, 1], dtype='int64')

    pyreader = fluid.io.DataLoader.from_generator(feed_list=[
        src_ids, pos_ids, sent_ids, input_mask, mask_label, mask_pos, labels
        ], capacity=70, iterable=False)

    ernie = ErnieModel(
        src_ids=src_ids,
        position_ids=pos_ids,
        sentence_ids=sent_ids,
        input_mask=input_mask,
        config=ernie_config,
        weight_sharing=args.weight_sharing,
        use_fp16=args.use_fp16)

    next_sent_acc, mask_lm_loss, total_loss = ernie.get_pretraining_output(
        mask_label, mask_pos, labels)

    return pyreader, next_sent_acc, mask_lm_loss, total_loss


def predict_wrapper(args,
                    exe,
                    ernie_config,
                    test_prog=None,
                    pyreader=None,
                    fetch_list=None):
    # Context to do validation.
    filelist = args.test_filelist if args.do_test else args.valid_filelist
    data_reader = ErnieDataReader(
        filelist,
        vocab_path=args.vocab_path,
        batch_size=args.batch_size,
        voc_size=ernie_config['vocab_size'],
        shuffle_files=False,
        epoch=1,
        max_seq_len=args.max_seq_len,
        is_test=True)

    if args.do_test:
        assert args.init_checkpoint is not None, "[FATAL] Please use --init_checkpoint '/path/to/checkpoints' \
                                                  to specify you pretrained model checkpoints"

        init_pretraining_params(exe, args.init_checkpoint, test_prog)

    def predict(exe=exe, pyreader=pyreader):

        pyreader.set_batch_generator(data_reader.data_generator())
        pyreader.start()

        cost = 0
        lm_cost = 0
        acc = 0
        steps = 0
        time_begin = time.time()
        while True:
            try:
                each_next_acc, each_mask_lm_cost, each_total_cost = exe.run(
                    fetch_list=fetch_list, program=test_prog)
                acc += each_next_acc
                lm_cost += each_mask_lm_cost
                cost += each_total_cost
                steps += 1
                if args.do_test and steps % args.skip_steps == 0:
                    log.info("[test_set] steps: %d" % steps)

            except fluid.core.EOFException:
                pyreader.reset()
                break

        used_time = time.time() - time_begin
        return cost, lm_cost, acc, steps, (args.skip_steps / used_time)

    return predict


def test(args):
    ernie_config = ErnieConfig(args.ernie_config_path)
    ernie_config.print_config()

    test_prog = fluid.Program()
    test_startup = fluid.Program()
    with fluid.program_guard(test_prog, test_startup):
        with fluid.unique_name.guard():
            test_pyreader, next_sent_acc, mask_lm_loss, total_loss = create_model(
                pyreader_name='test_reader', ernie_config=ernie_config)

    test_prog = test_prog.clone(for_test=True)

    place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(test_startup)

    predict = predict_wrapper(
        args,
        exe,
        ernie_config,
        test_prog=test_prog,
        pyreader=test_pyreader,
        fetch_list=[next_sent_acc.name, mask_lm_loss.name, total_loss.name])

    log.info("test begin")
    loss, lm_loss, acc, steps, speed = predict()
    log.info(
        "[test_set] loss: %f, global ppl: %f, next_sent_acc: %f, speed: %f steps/s"
        % (np.mean(np.array(loss) / steps),
           np.exp(np.mean(np.array(lm_loss) / steps)),
           np.mean(np.array(acc) / steps), speed))


def train(args):
    log.info("pretraining start")
    ernie_config = ErnieConfig(args.ernie_config_path)
    ernie_config.print_config()

    train_program = fluid.Program()
    startup_prog = fluid.Program()
    with fluid.program_guard(train_program, startup_prog):
        with fluid.unique_name.guard():
            train_pyreader, next_sent_acc, mask_lm_loss, total_loss = create_model(
                pyreader_name='train_reader', ernie_config=ernie_config)
            scheduled_lr, _ = optimization(
                loss=total_loss,
                warmup_steps=args.warmup_steps,
                num_train_steps=args.num_train_steps,
                learning_rate=args.learning_rate,
                train_program=train_program,
                startup_prog=startup_prog,
                weight_decay=args.weight_decay,
                scheduler=args.lr_scheduler,
                use_fp16=args.use_fp16,
                use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
                init_loss_scaling=args.init_loss_scaling,
                incr_every_n_steps=args.incr_every_n_steps,
                decr_every_n_nan_or_inf=args.decr_every_n_nan_or_inf,
                incr_ratio=args.incr_ratio,
                decr_ratio=args.decr_ratio)


    test_prog = fluid.Program()
    with fluid.program_guard(test_prog, startup_prog):
        with fluid.unique_name.guard():
            test_pyreader, next_sent_acc, mask_lm_loss, total_loss = create_model(
                pyreader_name='test_reader', ernie_config=ernie_config)

    test_prog = test_prog.clone(for_test=True)

    if len(fluid.cuda_places()) == 0:
        raise RuntimeError('not cuda device cound, check ur env setting')

    if args.use_cuda:
        place = fluid.cuda_places()[0]
        dev_count = fluid.core.get_cuda_device_count()
    else:
        place = fluid.CPUPlace()
        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))

    log.info("Device count %d" % dev_count)
    log.info("theoretical memory usage: ")
    log.info(fluid.contrib.memory_usage(
        program=train_program, batch_size=args.batch_size // args.max_seq_len))

    nccl2_num_trainers = 1
    nccl2_trainer_id = 0
    log.info("args.is_distributed: %s" % args.is_distributed)
    if args.is_distributed:
        worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
        worker_endpoints = worker_endpoints_env.split(",")
        trainers_num = len(worker_endpoints)
        current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
        trainer_id = worker_endpoints.index(current_endpoint)
        if trainer_id == 0:
            log.info("train_id == 0, sleep 60s")
            time.sleep(60)
        log.info("worker_endpoints:{} trainers_num:{} current_endpoint:{} \
              trainer_id:{}".format(worker_endpoints, trainers_num,
                                    current_endpoint, trainer_id))

        # prepare nccl2 env.
        config = fluid.DistributeTranspilerConfig()
        config.mode = "nccl2"
        t = fluid.DistributeTranspiler(config=config)
        t.transpile(
            trainer_id,
            trainers=worker_endpoints_env,
            current_endpoint=current_endpoint,
            program=train_program,
            startup_program=startup_prog)
        nccl2_num_trainers = trainers_num
        nccl2_trainer_id = trainer_id

    exe = fluid.Executor(place)
    exe.run(startup_prog)

    if args.init_checkpoint and args.init_checkpoint != "":
        init_checkpoint(exe, args.init_checkpoint, train_program, args.use_fp16)

    data_reader = ErnieDataReader(
        filelist=args.train_filelist,
        batch_size=args.batch_size,
        vocab_path=args.vocab_path,
        voc_size=ernie_config['vocab_size'],
        epoch=args.epoch,
        max_seq_len=args.max_seq_len,
        generate_neg_sample=args.generate_neg_sample)

    exec_strategy = fluid.ExecutionStrategy()
    if args.use_fast_executor:
        exec_strategy.use_experimental_executor = True
    exec_strategy.num_threads = dev_count
    exec_strategy.num_iteration_per_drop_scope = min(10, args.skip_steps)

    build_strategy = fluid.BuildStrategy()
    build_strategy.remove_unnecessary_lock = False

    train_exe = fluid.ParallelExecutor(
        use_cuda=args.use_cuda,
        loss_name=total_loss.name,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy,
        main_program=train_program,
        num_trainers=nccl2_num_trainers,
        trainer_id=nccl2_trainer_id)

    if args.valid_filelist and args.valid_filelist != "":
        predict = predict_wrapper(
            args,
            exe,
            ernie_config,
            test_prog=test_prog,
            pyreader=test_pyreader,
            fetch_list=[
                next_sent_acc.name, mask_lm_loss.name, total_loss.name
            ])

    train_pyreader.set_batch_generator(data_reader.data_generator())
    train_pyreader.start()
    steps = 0
    cost = []
    lm_cost = []
    acc = []
    time_begin = time.time()
    while steps < args.num_train_steps:
        try:
            steps += nccl2_num_trainers
            skip_steps = args.skip_steps * nccl2_num_trainers

            if nccl2_trainer_id != 0:
                train_exe.run(fetch_list=[])
                continue

            if steps % skip_steps != 0:
                train_exe.run(fetch_list=[])
            else:
                each_next_acc, each_mask_lm_cost, each_total_cost, np_lr = train_exe.run(
                    fetch_list=[
                        next_sent_acc.name, mask_lm_loss.name, total_loss.name,
                        scheduled_lr.name
                    ])
                acc.extend(each_next_acc)
                lm_cost.extend(each_mask_lm_cost)
                cost.extend(each_total_cost)

                log.info("feed_queue size %d" % train_pyreader.queue.size())
                time_end = time.time()
                used_time = time_end - time_begin
                epoch, current_file_index, total_file, current_file, mask_type = data_reader.get_progress(
                )
                log.info("current learning_rate:%f" % np_lr[0])
                log.info(
                    "epoch: %d, progress: %d/%d, step: %d, loss: %f, "
                    "ppl: %f, next_sent_acc: %f, speed: %f steps/s, file: %s, mask_type: %s"
                    % (epoch, current_file_index, total_file, steps,
                       np.mean(np.array(cost)),
                       np.mean(np.exp(np.array(lm_cost))),
                       np.mean(np.array(acc)), skip_steps / used_time,
                       current_file, mask_type))
                cost = []
                lm_cost = []
                acc = []
                time_begin = time.time()

            if steps % args.save_steps == 0:
                save_path = os.path.join(args.checkpoints, "step_" + str(steps))
                fluid.io.save_persistables(exe, save_path, train_program)

            if args.valid_filelist and steps % args.validation_steps == 0:
                vali_cost, vali_lm_cost, vali_acc, vali_steps, vali_speed = predict(
                )
                log.info("[validation_set] epoch: %d, step: %d, "
                      "loss: %f, global ppl: %f, batch-averged ppl: %f, "
                      "next_sent_acc: %f, speed: %f steps/s" %
                      (epoch, steps, np.mean(np.array(vali_cost) / vali_steps),
                       np.exp(np.mean(np.array(vali_lm_cost) / vali_steps)),
                       np.mean(np.exp(np.array(vali_lm_cost) / vali_steps)),
                       np.mean(np.array(vali_acc) / vali_steps), vali_speed))

        except fluid.core.EOFException:
            train_pyreader.reset()
            break


if __name__ == '__main__':
    prepare_logger(log)
    print_arguments(args)
    check_cuda(args.use_cuda)
    if args.do_test:
        test(args)
    else:
        train(args)
