# Copyright (c) 2022 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 argparse
import os
import sys
import time
from pprint import pprint

import numpy as np
import paddle
import paddle.distributed as dist
import yaml
from attrdict import AttrDict
from modeling import CrossEntropyCriterion, TransformerModel

from paddlenlp.utils.log import logger

sys.path.append(
    os.path.abspath(
        os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, "examples", "machine_translation", "transformer")
    )
)
import reader  # noqa: E402
from tls.record import AverageStatistical  # noqa: E402

paddle.set_default_dtype("float64")


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--config", default="./configs/transformer.big.yaml", type=str, help="Path of the config file. "
    )
    parser.add_argument(
        "--benchmark",
        action="store_true",
        help="Whether to print logs on each cards and use benchmark vocab. Normally, not necessary to set --benchmark. ",
    )
    parser.add_argument("--max_iter", default=None, type=int, help="The maximum iteration for training. ")
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        help="The dir of train, dev and test datasets. If data_dir is given, train_file and dev_file and test_file will be replaced by data_dir/[train|dev|test].\{src_lang\}-\{trg_lang\}.[\{src_lang\}|\{trg_lang\}]. ",
    )
    parser.add_argument(
        "--train_file",
        nargs="+",
        default=None,
        type=str,
        help="The files for training, including [source language file, target language file]. If it's None, the default WMT14 en-de dataset will be used. ",
    )
    parser.add_argument(
        "--dev_file",
        nargs="+",
        default=None,
        type=str,
        help="The files for validation, including [source language file, target language file]. If it's None, the default WMT14 en-de dataset will be used. ",
    )
    parser.add_argument(
        "--vocab_file",
        default=None,
        type=str,
        help="The vocab file. Normally, it shouldn't be set and in this case, the default WMT14 dataset will be used.",
    )
    parser.add_argument(
        "--src_vocab",
        default=None,
        type=str,
        help="The vocab file for source language. If --vocab_file is given, the --vocab_file will be used. ",
    )
    parser.add_argument(
        "--trg_vocab",
        default=None,
        type=str,
        help="The vocab file for target language. If --vocab_file is given, the --vocab_file will be used. ",
    )
    parser.add_argument("-s", "--src_lang", default=None, type=str, help="Source language. ")
    parser.add_argument("-t", "--trg_lang", default=None, type=str, help="Target language. ")
    parser.add_argument(
        "--unk_token",
        default=None,
        type=str,
        help="The unknown token. It should be provided when use custom vocab_file. ",
    )
    parser.add_argument(
        "--bos_token", default=None, type=str, help="The bos token. It should be provided when use custom vocab_file. "
    )
    parser.add_argument(
        "--eos_token", default=None, type=str, help="The eos token. It should be provided when use custom vocab_file. "
    )
    parser.add_argument(
        "--pad_token",
        default=None,
        type=str,
        help="The pad token. It should be provided when use custom vocab_file. And if it's None, bos_token will be used. ",
    )
    args = parser.parse_args()
    return args


def transfer_param(state_dict):
    for item in state_dict:
        state_dict[item] = paddle.cast(state_dict[item], dtype="float32")
    return state_dict


def do_train(args):
    if args.device == "gpu":
        rank = dist.get_rank()
        trainer_count = dist.get_world_size()
    else:
        rank = 0
        trainer_count = 1
        paddle.set_device("cpu")

    if trainer_count > 1:
        dist.init_parallel_env()

    # Set seed for CE
    random_seed = eval(str(args.random_seed))
    if random_seed is not None:
        paddle.seed(random_seed)

    # Define data loader
    (train_loader), (eval_loader) = reader.create_data_loader(args)

    # Define model
    transformer = TransformerModel(
        src_vocab_size=args.src_vocab_size,
        trg_vocab_size=args.trg_vocab_size,
        max_length=args.max_length + 1,
        num_encoder_layers=args.n_layer,
        num_decoder_layers=args.n_layer,
        n_head=args.n_head,
        d_model=args.d_model,
        d_inner_hid=args.d_inner_hid,
        dropout=args.dropout,
        weight_sharing=args.weight_sharing,
        bos_id=args.bos_idx,
        eos_id=args.eos_idx,
    )

    # Define loss
    criterion = CrossEntropyCriterion(args.label_smooth_eps, args.bos_idx)

    scheduler = paddle.optimizer.lr.NoamDecay(args.d_model, args.warmup_steps, args.learning_rate, last_epoch=0)

    # Define optimizer
    optimizer = paddle.optimizer.Adam(
        learning_rate=scheduler,
        beta1=args.beta1,
        beta2=args.beta2,
        epsilon=float(args.eps),
        parameters=transformer.parameters(),
    )

    # Init from some checkpoint, to resume the previous training
    if args.init_from_checkpoint:
        model_dict = paddle.load(os.path.join(args.init_from_checkpoint, "transformer.pdparams"))
        opt_dict = paddle.load(os.path.join(args.init_from_checkpoint, "transformer.pdopt"))
        transformer.set_state_dict(model_dict)
        optimizer.set_state_dict(opt_dict)
        print("loaded from checkpoint.")
    # Init from some pretrain models, to better solve the current task
    if args.init_from_pretrain_model:
        model_dict = paddle.load(os.path.join(args.init_from_pretrain_model, "transformer.pdparams"))
        transformer.set_state_dict(model_dict)
        print("loaded from pre-trained model.")

    if trainer_count > 1:
        transformer = paddle.DataParallel(transformer)

    # The best cross-entropy value with label smoothing
    loss_normalizer = -(
        (1.0 - args.label_smooth_eps) * np.log((1.0 - args.label_smooth_eps))
        + args.label_smooth_eps * np.log(args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20)
    )

    step_idx = 0

    # For benchmark
    reader_cost_avg = AverageStatistical()
    batch_cost_avg = AverageStatistical()
    batch_ips_avg = AverageStatistical()

    # Train loop
    for pass_id in range(args.epoch):
        epoch_start = time.time()

        batch_id = 0
        batch_start = time.time()
        for input_data in train_loader:
            train_reader_cost = time.time() - batch_start
            (src_word, trg_word, lbl_word) = input_data

            if args.use_amp:
                scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
                with paddle.amp.auto_cast():
                    logits = transformer(src_word=src_word, trg_word=trg_word)
                    sum_cost, avg_cost, token_num = criterion(logits, lbl_word)

                scaled = scaler.scale(avg_cost)  # scale the loss
                scaled.backward()  # do backward

                scaler.minimize(optimizer, scaled)  # update parameters
                optimizer.clear_grad()
            else:
                logits = transformer(src_word=src_word, trg_word=trg_word)
                sum_cost, avg_cost, token_num = criterion(logits, lbl_word)

                avg_cost.backward()

                optimizer.step()
                optimizer.clear_grad()

            tokens_per_cards = token_num.numpy()

            train_batch_cost = time.time() - batch_start
            reader_cost_avg.record(train_reader_cost)
            batch_cost_avg.record(train_batch_cost)
            batch_ips_avg.record(train_batch_cost, tokens_per_cards)

            # NOTE: For benchmark, loss infomation on all cards will be printed.
            if step_idx % args.print_step == 0 and (args.benchmark or rank == 0):
                total_avg_cost = avg_cost.numpy()

                if step_idx == 0:
                    logger.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)]),
                        )
                    )
                else:
                    train_avg_batch_cost = args.print_step / batch_cost_avg.get_total_time()
                    logger.info(
                        "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
                        "normalized loss: %f, ppl: %f, avg_speed: %.2f step/sec, "
                        "batch_cost: %.5f sec, reader_cost: %.5f sec, tokens: %d, "
                        "ips: %.5f words/sec"
                        % (
                            step_idx,
                            pass_id,
                            batch_id,
                            total_avg_cost,
                            total_avg_cost - loss_normalizer,
                            np.exp([min(total_avg_cost, 100)]),
                            train_avg_batch_cost,
                            batch_cost_avg.get_average(),
                            reader_cost_avg.get_average(),
                            batch_ips_avg.get_total_cnt(),
                            batch_ips_avg.get_average_per_sec(),
                        )
                    )
                reader_cost_avg.reset()
                batch_cost_avg.reset()
                batch_ips_avg.reset()

            if step_idx % args.save_step == 0 and step_idx != 0:
                # Validation
                transformer.eval()
                total_sum_cost = 0
                total_token_num = 0
                with paddle.no_grad():
                    for input_data in eval_loader:
                        (src_word, trg_word, lbl_word) = input_data
                        logits = transformer(src_word=src_word, trg_word=trg_word)
                        sum_cost, avg_cost, token_num = criterion(logits, lbl_word)
                        total_sum_cost += sum_cost.numpy()
                        total_token_num += token_num.numpy()
                        total_avg_cost = total_sum_cost / total_token_num
                    logger.info(
                        "validation, step_idx: %d, avg loss: %f, "
                        "normalized loss: %f, ppl: %f"
                        % (
                            step_idx,
                            total_avg_cost,
                            total_avg_cost - loss_normalizer,
                            np.exp([min(total_avg_cost, 100)]),
                        )
                    )
                transformer.train()

                if args.save_model and rank == 0:
                    model_dir = os.path.join(args.save_model, "step_" + str(step_idx))
                    if not os.path.exists(model_dir):
                        os.makedirs(model_dir)
                    paddle.save(transformer.state_dict(), os.path.join(model_dir, "transformer.pdparams"))
                    paddle.save(optimizer.state_dict(), os.path.join(model_dir, "transformer.pdopt"))

            # NOTE: Used for benchmark and use None as default.
            if args.max_iter and step_idx == args.max_iter:
                break
            batch_id += 1
            step_idx += 1
            scheduler.step()
            batch_start = time.time()

        # NOTE: Used for benchmark and use None as default.
        if args.max_iter and step_idx == args.max_iter:
            break

        train_epoch_cost = time.time() - epoch_start
        logger.info("train epoch: %d, epoch_cost: %.5f s" % (pass_id, train_epoch_cost))

    if args.save_model and rank == 0:
        model_dir = os.path.join(args.save_model, "step_final")
        if not os.path.exists(model_dir):
            os.makedirs(model_dir)
        # Transform dtype from float64 to float32,
        # since some pass during inference doesn't
        # support float64 kernel.
        param_sd = transfer_param(transformer.state_dict())
        paddle.save(param_sd, os.path.join(model_dir, "transformer.pdparams"))

        optim_sd = transfer_param(transformer.state_dict())
        paddle.save(optim_sd, os.path.join(model_dir, "transformer.pdopt"))


if __name__ == "__main__":
    ARGS = parse_args()
    yaml_file = ARGS.config
    with open(yaml_file, "rt") as f:
        args = AttrDict(yaml.safe_load(f))
    args.benchmark = ARGS.benchmark
    if ARGS.max_iter:
        args.max_iter = ARGS.max_iter
    args.data_dir = ARGS.data_dir
    args.train_file = ARGS.train_file
    args.dev_file = ARGS.dev_file

    if ARGS.vocab_file is not None:
        args.src_vocab = ARGS.vocab_file
        args.trg_vocab = ARGS.vocab_file
        args.joined_dictionary = True
    elif ARGS.src_vocab is not None and ARGS.trg_vocab is None:
        args.vocab_file = args.trg_vocab = args.src_vocab = ARGS.src_vocab
        args.joined_dictionary = True
    elif ARGS.src_vocab is None and ARGS.trg_vocab is not None:
        args.vocab_file = args.trg_vocab = args.src_vocab = ARGS.trg_vocab
        args.joined_dictionary = True
    else:
        args.src_vocab = ARGS.src_vocab
        args.trg_vocab = ARGS.trg_vocab
        args.joined_dictionary = not (
            args.src_vocab is not None and args.trg_vocab is not None and args.src_vocab != args.trg_vocab
        )
    if args.weight_sharing != args.joined_dictionary:
        if args.weight_sharing:
            raise ValueError("The src_vocab and trg_vocab must be consistency when weight_sharing is True. ")
        else:
            raise ValueError(
                "The src_vocab and trg_vocab must be specified respectively when weight sharing is False. "
            )

    if ARGS.src_lang is not None:
        args.src_lang = ARGS.src_lang
    if ARGS.trg_lang is not None:
        args.trg_lang = ARGS.trg_lang

    args.unk_token = ARGS.unk_token
    args.bos_token = ARGS.bos_token
    args.eos_token = ARGS.eos_token
    args.pad_token = ARGS.pad_token
    pprint(args)

    do_train(args)
