# Copyright (c) 2020 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 collections
import itertools
import os
import random
import time
from functools import partial

import numpy as np

import paddle
from paddle.io import DataLoader

from paddlenlp.datasets.dataset import *
from paddlenlp.datasets.glue import *
from paddlenlp.data import *
from paddlenlp.data.sampler import SamplerHelper
from paddlenlp.transformers.model_bert import *
from paddlenlp.transformers.tokenizer_bert import BertTokenizer
from paddlenlp.transformers import LinearDecayWithWarmup

TASK_CLASSES = {
    "qnli": (GlueQNLI, paddle.metric.Accuracy),  # (dataset, metric)
    "sst-2": (GlueSST2, paddle.metric.Accuracy),
}

MODEL_CLASSES = {"bert": (BertForSequenceClassification, BertTokenizer), }


def parse_args():
    parser = argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        "--task_name",
        default=None,
        type=str,
        required=True,
        help="The name of the task to train selected in the list: " +
        ", ".join(TASK_CLASSES.keys()), )
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        required=True,
        help="Model type selected in the list: " +
        ", ".join(MODEL_CLASSES.keys()), )
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pre-trained model or shortcut name selected in the list: "
        + ", ".join(
            sum([
                list(classes[-1].pretrained_init_configuration.keys())
                for classes in MODEL_CLASSES.values()
            ], [])), )

    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )

    parser.add_argument(
        "--max_seq_length",
        default=128,
        type=int,
        help="The maximum total input sequence length after tokenization. Sequences longer "
        "than this will be truncated, sequences shorter will be padded.", )

    parser.add_argument(
        "--batch_size",
        default=8,
        type=int,
        help="Batch size per GPU/CPU for training.", )
    parser.add_argument(
        "--learning_rate",
        default=5e-5,
        type=float,
        help="The initial learning rate for Adam.")
    parser.add_argument(
        "--weight_decay",
        default=0.0,
        type=float,
        help="Weight decay if we apply some.")
    parser.add_argument(
        "--adam_epsilon",
        default=1e-8,
        type=float,
        help="Epsilon for Adam optimizer.")
    parser.add_argument(
        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument(
        "--num_train_epochs",
        default=3,
        type=int,
        help="Total number of training epochs to perform.", )
    parser.add_argument(
        "--max_steps",
        default=-1,
        type=int,
        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
    )
    parser.add_argument(
        "--warmup_steps",
        default=0,
        type=int,
        help="Linear warmup over warmup_steps.")

    parser.add_argument(
        "--logging_steps",
        type=int,
        default=500,
        help="Log every X updates steps.")
    parser.add_argument(
        "--save_steps",
        type=int,
        default=500,
        help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--seed", type=int, default=42, help="random seed for initialization")
    parser.add_argument(
        "--eager_run", type=eval, default=True, help="Use dygraph mode.")
    parser.add_argument(
        "--n_gpu",
        type=int,
        default=1,
        help="number of gpus to use, 0 for cpu.")

    parser.add_argument(
        "--params_pd_path", type=str, default=None, help="params pd path")

    args = parser.parse_args()
    return args


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    paddle.seed(args.seed)


def evaluate(model, loss_fct, metric, data_loader):
    model.eval()
    metric.reset()
    # losses = []
    for batch in data_loader:
        input_ids, segment_ids, labels = batch
        logits = model(input_ids, segment_ids)
        loss = loss_fct(logits, labels)
        # losses.append(loss)
        correct = metric.compute(logits, labels)
        metric.update(correct)
        accu = metric.accumulate()
    print("eval loss: %f, accu: %f" % (loss.numpy(), accu))
    model.train()


def convert_example(example,
                    tokenizer,
                    label_list,
                    max_seq_length=512,
                    is_test=False):
    """convert a glue example into necessary features"""

    def _truncate_seqs(seqs, max_seq_length):
        if len(seqs) == 1:  # single sentence
            # Account for [CLS] and [SEP] with "- 2"
            seqs[0] = seqs[0][0:(max_seq_length - 2)]
        else:  # sentence pair
            # Account for [CLS], [SEP], [SEP] with "- 3"
            tokens_a, tokens_b = seqs
            max_seq_length -= 3
            while True:  # truncate with longest_first strategy
                total_length = len(tokens_a) + len(tokens_b)
                if total_length <= max_seq_length:
                    break
                if len(tokens_a) > len(tokens_b):
                    tokens_a.pop()
                else:
                    tokens_b.pop()
        return seqs

    def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1):
        concat = sum((seq + sep for sep, seq in zip(separators, seqs)), [])
        segment_ids = sum(
            ([i] * (len(seq) + len(sep))
             for i, (sep, seq) in enumerate(zip(separators, seqs))), [])
        if isinstance(seq_mask, int):
            seq_mask = [[seq_mask] * len(seq) for seq in seqs]
        if isinstance(separator_mask, int):
            separator_mask = [[separator_mask] * len(sep) for sep in separators]
        p_mask = sum((s_mask + mask
                      for sep, seq, s_mask, mask in zip(
                          separators, seqs, seq_mask, separator_mask)), [])
        return concat, segment_ids, p_mask

    if not is_test:
        # `label_list == None` is for regression task
        label_dtype = "int64" if label_list else "float32"
        # get the label
        label = example[-1]
        example = example[:-1]
        #create label maps if classification task
        if label_list:
            label_map = {}
            for (i, l) in enumerate(label_list):
                label_map[l] = i
            label = label_map[label]
        label = np.array([label], dtype=label_dtype)

    # tokenize raw text
    tokens_raw = [tokenizer(l) for l in example]
    # truncate to the truncate_length,
    tokens_trun = _truncate_seqs(tokens_raw, max_seq_length)
    # concate the sequences with special tokens
    tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0]
    tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] *
                                          len(tokens_trun))
    # convert the token to ids
    input_ids = tokenizer.convert_tokens_to_ids(tokens)
    valid_length = len(input_ids)
    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    # input_mask = [1] * len(input_ids)
    if not is_test:
        return input_ids, segment_ids, valid_length, label
    else:
        return input_ids, segment_ids, valid_length


def do_train(args):
    paddle.enable_static() if not args.eager_run else None
    paddle.set_device("gpu" if args.n_gpu else "cpu")
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    set_seed(args)

    args.task_name = args.task_name.lower()
    dataset_class, metric_class = TASK_CLASSES[args.task_name]
    args.model_type = args.model_type.lower()
    model_class, tokenizer_class = MODEL_CLASSES[args.model_type]

    train_dataset, dev_dataset = dataset_class.get_datasets(["train", "dev"])
    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)

    trans_func = partial(
        convert_example,
        tokenizer=tokenizer,
        label_list=train_dataset.get_labels(),
        max_seq_length=args.max_seq_length)
    train_dataset = train_dataset.apply(trans_func, lazy=True)
    # train_batch_sampler = SamplerHelper(train_dataset).shuffle().batch(
    #     batch_size=args.batch_size).shard()
    train_batch_sampler = paddle.io.DistributedBatchSampler(
        # train_dataset, batch_size=args.batch_size, shuffle=True)
        train_dataset,
        batch_size=args.batch_size,
        shuffle=False)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # input
        Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]),  # segment
        Stack(),  # length
        Stack(dtype="int64" if train_dataset.get_labels() else "float32")  # label
    ): [data for i, data in enumerate(fn(samples)) if i != 2]
    train_data_loader = DataLoader(
        dataset=train_dataset,
        batch_sampler=train_batch_sampler,
        collate_fn=batchify_fn,
        num_workers=0,
        return_list=True)
    dev_dataset = dev_dataset.apply(trans_func, lazy=True)
    # dev_batch_sampler = SamplerHelper(dev_dataset).batch(
    #     batch_size=args.batch_size)
    dev_batch_sampler = paddle.io.BatchSampler(
        dev_dataset, batch_size=args.batch_size, shuffle=False)
    dev_data_loader = DataLoader(
        dataset=dev_dataset,
        batch_sampler=dev_batch_sampler,
        collate_fn=batchify_fn,
        num_workers=0,
        return_list=True)

    # model = model_class.from_pretrained(
    #     args.model_name_or_path,) num_classes=len(train_dataset.get_labels()))
    model = BertForPreTraining(
        BertModel(**model_class.pretrained_init_configuration[
            args.model_name_or_path]))
    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    num_training_steps = args.max_steps if args.max_steps > 0 else len(
        train_data_loader) * args.num_train_epochs

    lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
                                         args.warmup_steps)

    optimizer = paddle.optimizer.AdamW(
        learning_rate=lr_scheduler,
        epsilon=args.adam_epsilon,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        apply_decay_param_fun=lambda x: x in [
            p.name for n, p in model.named_parameters()
            if not any(nd in n for nd in ["bias", "norm"])
        ])

    loss_fct = paddle.nn.loss.CrossEntropyLoss() if train_dataset.get_labels(
    ) else paddle.nn.loss.MSELoss()

    metric = metric_class()

    ### TODO: use hapi
    # trainer = paddle.hapi.Model(model)
    # trainer.prepare(optimizer, loss_fct, paddle.metric.Accuracy())
    # trainer.fit(train_data_loader,
    #             dev_data_loader,
    #             log_freq=args.logging_steps,
    #             epochs=args.num_train_epochs,
    #             save_dir=args.output_dir)

    model.eval()
    param_names = list(model.state_dict().keys())
    import pickle
    with open(args.params_pd_path, "rb") as f:
        np_params = pickle.load(f)
    model.set_state_dict(dict(zip(param_names, np_params)))
    paddle.save(model.state_dict(), "%s.pdparams" % args.model_name_or_path)
    for data in train_data_loader():
        print(model(*data[:-1]))
        exit(0)

    global_step = 0
    tic_train = time.time()
    for epoch in range(args.num_train_epochs):
        for step, batch in enumerate(train_data_loader):
            input_ids, segment_ids, labels = batch
            logits = model(input_ids, segment_ids)
            loss = loss_fct(logits, labels)
            if global_step % args.logging_steps == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss,
                       args.logging_steps / (time.time() - tic_train)))
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()
            if global_step % args.save_steps == 0:
                evaluate(model, loss_fct, metric, dev_data_loader)
                if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
                    paddle.save(model.state_dict(),
                                os.path.join(args.output_dir,
                                             "model_%d.pdparams" % global_step))
            global_step += 1


if __name__ == "__main__":
    args = parse_args()
    if args.n_gpu > 1:
        paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_gpu)
    else:
        do_train(args)