# Copyright (c) 2021s 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 logging
import math
import random
import time
from functools import partial

import numpy as np
import paddle
import paddle.nn as nn
from paddle.io import DataLoader
from paddle.metric import Accuracy

from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import (
    BertForSequenceClassification,
    BertTokenizer,
    LinearDecayWithWarmup,
    TinyBertForSequenceClassification,
    TinyBertTokenizer,
)

FORMAT = "%(asctime)s-%(levelname)s: %(message)s"
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)

METRIC_CLASSES = {
    "afqmc": Accuracy,
    "tnews": Accuracy,
    "iflytek": Accuracy,
    "ocnli": Accuracy,
    "cmnli": Accuracy,
    "cluewsc2020": Accuracy,
    "csl": Accuracy,
}

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


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(METRIC_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(
        "--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("--learning_rate", default=1e-4, type=float, help="The initial learning rate for Adam.")
    parser.add_argument(
        "--num_train_epochs",
        default=3,
        type=int,
        help="Total number of training epochs to perform.",
    )
    parser.add_argument("--logging_steps", type=int, default=100, help="Log every X updates steps.")
    parser.add_argument("--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.")
    parser.add_argument(
        "--batch_size",
        default=32,
        type=int,
        help="Batch size per GPU/CPU for training.",
    )
    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
    parser.add_argument(
        "--warmup_steps",
        default=0,
        type=int,
        help="Linear warmup over warmup_steps. If > 0: Override warmup_proportion",
    )
    parser.add_argument(
        "--warmup_proportion", default=0.1, type=float, help="Linear warmup proportion over total steps."
    )
    parser.add_argument("--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
    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("--seed", default=42, type=int, help="random seed for initialization")
    parser.add_argument(
        "--device", default="gpu", type=str, help="The device to select to train the model, is must be cpu/gpu/xpu."
    )
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="The max value of grad norm.")
    args = parser.parse_args()
    return args


def set_seed(args):
    # Use the same data seed(for data shuffle) for all procs to guarantee data
    # consistency after sharding.
    random.seed(args.seed)
    np.random.seed(args.seed)
    # Maybe different op seeds(for dropout) for different procs is better. By:
    # `paddle.seed(args.seed + paddle.distributed.get_rank())`
    paddle.seed(args.seed)


@paddle.no_grad()
def evaluate(model, loss_fct, metric, data_loader):
    model.eval()
    metric.reset()
    for batch in data_loader:
        input_ids, segment_ids, labels = batch
        logits = model(input_ids, segment_ids)
        loss = loss_fct(logits, labels)
        correct = metric.compute(logits, labels)
        metric.update(correct)
    res = metric.accumulate()
    print("eval loss: %f, acc: %s, " % (loss.numpy(), res), end="")
    model.train()
    return res


def convert_example(example, tokenizer, label_list, max_seq_length=512, is_test=False):
    """convert a glue example into necessary features"""
    if not is_test:
        # `label_list == None` is for regression task
        label_dtype = "int64" if label_list else "float32"
        # Get the label
        label = example["label"]
        label = np.array([label], dtype=label_dtype)
    # Convert raw text to feature
    if "sentence" in example:
        example = tokenizer(example["sentence"], max_seq_len=max_seq_length)
    elif "sentence1" in example:
        example = tokenizer(example["sentence1"], text_pair=example["sentence2"], max_seq_len=max_seq_length)
    elif "keyword" in example:  # CSL
        sentence1 = " ".join(example["keyword"])
        example = tokenizer(sentence1, text_pair=example["abst"], max_seq_len=max_seq_length)
    elif "target" in example:  # wsc
        text, query, pronoun, query_idx, pronoun_idx = (
            example["text"],
            example["target"]["span1_text"],
            example["target"]["span2_text"],
            example["target"]["span1_index"],
            example["target"]["span2_index"],
        )
        text_list = list(text)
        # print(text)
        assert text[pronoun_idx : (pronoun_idx + len(pronoun))] == pronoun, "pronoun: {}".format(pronoun)
        assert text[query_idx : (query_idx + len(query))] == query, "query: {}".format(query)
        if pronoun_idx > query_idx:
            text_list.insert(query_idx, "_")
            text_list.insert(query_idx + len(query) + 1, "_")
            text_list.insert(pronoun_idx + 2, "[")
            text_list.insert(pronoun_idx + len(pronoun) + 2 + 1, "]")
        else:
            text_list.insert(pronoun_idx, "[")
            text_list.insert(pronoun_idx + len(pronoun) + 1, "]")
            text_list.insert(query_idx + 2, "_")
            text_list.insert(query_idx + len(query) + 2 + 1, "_")
        text = "".join(text_list)
        example = tokenizer(text, max_seq_len=max_seq_length)

    if not is_test:
        return example["input_ids"], example["token_type_ids"], label
    else:
        return example["input_ids"], example["token_type_ids"]


def do_train(args):
    paddle.set_device(args.device)
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    set_seed(args)

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

    train_ds = load_dataset("clue", args.task_name, splits="train")
    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)

    trans_func = partial(
        convert_example, tokenizer=tokenizer, label_list=train_ds.label_list, max_seq_length=args.max_seq_length
    )
    train_ds = train_ds.map(trans_func, lazy=True)
    train_batch_sampler = paddle.io.DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment
        Stack(dtype="int64" if train_ds.label_list else "float32"),  # label
    ): fn(samples)
    train_data_loader = DataLoader(
        dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True
    )

    dev_ds = load_dataset("clue", args.task_name, splits="dev")
    dev_ds = dev_ds.map(trans_func, lazy=True)
    dev_batch_sampler = paddle.io.BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)
    dev_data_loader = DataLoader(
        dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True
    )

    num_classes = 1 if train_ds.label_list is None else len(train_ds.label_list)
    model = model_class.from_pretrained(args.model_name_or_path, num_classes=num_classes)
    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    if args.max_steps > 0:
        num_training_steps = args.max_steps
        num_train_epochs = math.ceil(num_training_steps / len(train_data_loader))
    else:
        num_training_steps = len(train_data_loader) * args.num_train_epochs
        num_train_epochs = args.num_train_epochs

    warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion

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

    # Generate parameter names needed to perform weight decay.
    # All bias and LayerNorm parameters are excluded.
    decay_params = [p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"])]
    optimizer = paddle.optimizer.AdamW(
        learning_rate=lr_scheduler,
        beta1=0.9,
        beta2=0.999,
        epsilon=args.adam_epsilon,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        apply_decay_param_fun=lambda x: x in decay_params,
        grad_clip=nn.ClipGradByGlobalNorm(args.max_grad_norm),
    )

    loss_fct = paddle.nn.loss.CrossEntropyLoss() if train_ds.label_list else paddle.nn.loss.MSELoss()

    metric = metric_class()
    best_acc = 0.0
    global_step = 0
    tic_train = time.time()
    for epoch in range(num_train_epochs):
        for step, batch in enumerate(train_data_loader):
            global_step += 1
            input_ids, segment_ids, labels = batch
            logits = model(input_ids, segment_ids)
            loss = loss_fct(logits, labels)
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()
            if global_step % args.logging_steps == 0:
                print(
                    "global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
                    % (
                        global_step,
                        num_training_steps,
                        epoch,
                        step,
                        paddle.distributed.get_rank(),
                        loss,
                        optimizer.get_lr(),
                        args.logging_steps / (time.time() - tic_train),
                    )
                )
                tic_train = time.time()
            if global_step % args.save_steps == 0 or global_step == num_training_steps:
                tic_eval = time.time()
                acc = evaluate(model, loss_fct, metric, dev_data_loader)
                print("eval done total : %s s" % (time.time() - tic_eval))
                if acc > best_acc:
                    best_acc = acc
            if global_step >= num_training_steps:
                print("best_acc: ", best_acc)
                return
    print("best_acc: ", best_acc)


def print_arguments(args):
    """print arguments"""
    print("-----------  Configuration Arguments -----------")
    for arg, value in sorted(vars(args).items()):
        print("%s: %s" % (arg, value))
    print("------------------------------------------------")


if __name__ == "__main__":
    args = parse_args()
    print_arguments(args)
    do_train(args)
