# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Finetuning the library models for sequence classification on SemEval-2020-Task5 (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""

import argparse
import glob
import json
import logging
import os
import random

import pandas as pd
import numpy as np
from scipy.special import softmax
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange

from transformers import (
    WEIGHTS_NAME,
    AdamW,
    AlbertConfig,
    AlbertForSequenceClassification,
    AlbertTokenizer,
    BertConfig,
    BertForSequenceClassification,
    BertTokenizer,
    BartConfig,
    BartForSequenceClassification,
    BartTokenizer,
    DistilBertConfig,
    DistilBertForSequenceClassification,
    DistilBertTokenizer,
    RobertaConfig,
    RobertaForSequenceClassification,
    RobertaTokenizer,
    XLMConfig,
    XLMForSequenceClassification,
    XLMRobertaConfig,
    XLMRobertaForSequenceClassification,
    XLMRobertaTokenizer,
    XLMTokenizer,
    XLNetConfig,
    XLNetForSequenceClassification,
    XLNetTokenizer,
    get_linear_schedule_with_warmup,
)
from models.modeling_custombert import CustomBertForSequenceClassification
from models.modeling_customroberta import CustomRobertaForSequenceClassification
from configs.configuration_custombert import CustomBertConfig
from configs.configuration_customroberta import CustomRobertaConfig
from utils.metrics import compute_metrics
from utils.processors import convert_examples_to_features
from utils.processors import output_modes
from utils.processors import processors

try:
    from torch.utils.tensorboard import SummaryWriter
except ImportError:
    from tensorboardX import SummaryWriter


logger = logging.getLogger(__name__)

ALL_MODELS = sum(
    (
        tuple(conf.pretrained_config_archive_map.keys())
        for conf in (
            BartConfig,
            BertConfig,
            XLNetConfig,
            XLMConfig,
            RobertaConfig,
            DistilBertConfig,
            AlbertConfig,
            XLMRobertaConfig,
        )
    ),
    (),
)

MODEL_CLASSES = {
    "bart": (BartConfig, BartForSequenceClassification, BartTokenizer),
    "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
    "custombert": (CustomBertConfig, CustomBertForSequenceClassification, BertTokenizer),
    "xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
    "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
    "roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
    "customroberta": (CustomRobertaConfig, CustomRobertaForSequenceClassification, RobertaTokenizer),
    "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
    "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
    "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
}


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)
    torch.backends.cudnn.deterministic = True


def get_eval_folder_name(args):
    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
    return f'seq_{args.max_seq_length}_batch_{args.train_batch_size}_epochs_{args.num_train_epochs}_lr_{args.learning_rate}'


def train(args, train_dataset, model, tokenizer):
    """ Train the model """
    if args.local_rank in [-1, 0]:
        tb_writer = SummaryWriter()

    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args.weight_decay,
        },
        {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
    ]

    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )

    # Check if saved optimizer or scheduler states exist
    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
        os.path.join(args.model_name_or_path, "scheduler.pt")
    ):
        # Load in optimizer and scheduler states
        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))

    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)

    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True,
        )

    # Train!
    logger.info("***** Running training *****")
    if args.cross_validation:
        logger.info("  Cross validation fold = %d", args.fold_number)
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
    logger.info(
        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
        args.train_batch_size
        * args.gradient_accumulation_steps
        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
    )
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    epochs_trained = 0
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path):
        try:
            # set global_step to gobal_step of last saved checkpoint from model path
            checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
            global_step = int(checkpoint_suffix)
            epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
            steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info("  Continuing training from epoch %d", epochs_trained)
            logger.info("  Continuing training from global step %d", global_step)
            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
        except ValueError:
            logger.info("  Starting fine-tuning.")

    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(
        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
    )
    set_seed(args)  # Added here for reproductibility
    for _ in train_iterator:
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
        for step, batch in enumerate(epoch_iterator):

            # Skip past any already trained steps if resuming training
            if steps_trained_in_current_epoch > 0:
                steps_trained_in_current_epoch -= 1
                continue

            model.train()
            batch = tuple(t.to(args.device) for t in batch)
            inputs = {"input_ids": batch[1], "attention_mask": batch[2], "labels": batch[4]}
            if args.model_type != "distilbert" and args.model_type != "bart":
                inputs["token_type_ids"] = (
                    batch[3] if args.model_type in ["bert", "xlnet", "albert"] else None
                )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

            if args.n_gpu > 1:
                loss = loss.mean()  # mean() to average on multi-gpu parallel training
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                loss.backward()

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

                optimizer.step()
                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    logs = {}
                    if (
                        args.local_rank == -1 and args.evaluate_during_training
                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(args, model, tokenizer, prefix="")
                        for key, value in results.items():
                            eval_key = "eval_{}".format(key)
                            logs[eval_key] = value

                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
                    learning_rate_scalar = scheduler.get_lr()[0]
                    logs["learning_rate"] = learning_rate_scalar
                    logs["loss"] = loss_scalar
                    logging_loss = tr_loss

                    for key, value in logs.items():
                        tb_writer.add_scalar(key, value, global_step)
                    print(json.dumps({**logs, **{"step": global_step}}))

                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Change output_dir with hyper parameters.
                    hyper_param_str = get_eval_folder_name(args)
                    output_dir =  os.path.join(args.output_dir, hyper_param_str)

                    if not os.path.exists(output_dir) and args.local_rank in [-1, 0]:
                        os.makedirs(output_dir)

                    if args.cross_validation:
                        logger.info('Cross validation is used, so no model is saved.')
                    else:
                        # Save model checkpoint
                        output_dir = os.path.join(output_dir, "checkpoint-{}".format(global_step))
                        if not os.path.exists(output_dir):
                            os.makedirs(output_dir)
                        model_to_save = (
                            model.module if hasattr(model, "module") else model
                        )  # Take care of distributed/parallel training
                        model_to_save.save_pretrained(output_dir)
                        tokenizer.save_pretrained(output_dir)

                        torch.save(args, os.path.join(output_dir, "training_args.bin"))
                        logger.info("Saving model checkpoint to %s", output_dir)

                        torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                        torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                        logger.info("Saving optimizer and scheduler states to %s", output_dir)

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

    if args.local_rank in [-1, 0]:
        tb_writer.close()

    return global_step, tr_loss / global_step


def evaluate(args, model, tokenizer, prefix=""):
    eval_task = args.task_name
    results = {}

    # Change output_dir with hyper parameters.
    hyper_param_str = get_eval_folder_name(args)
    eval_output_dir =  os.path.join(args.output_dir, hyper_param_str)
    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(eval_output_dir)

    eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) 
    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu eval
    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running evaluation {} *****".format(prefix))
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    all_guids = None
    all_probs = None
    preds = None
    out_label_ids = None
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)
        guids = batch[0]
        with torch.no_grad():
            inputs = {"input_ids": batch[1], "attention_mask": batch[2], "labels": batch[4]}
            if args.model_type != "distilbert" and args.model_type != "bart":
                inputs["token_type_ids"] = (
                    batch[3] if args.model_type in ["bert", "xlnet", "albert"] else None
                )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
            outputs = model(**inputs)
            tmp_eval_loss, logits = outputs[:2]

            eval_loss += tmp_eval_loss.mean().item()
        nb_eval_steps += 1
        if preds is None:
            all_guids = guids.detach().cpu().numpy()
            preds = logits.detach().cpu().numpy()
            out_label_ids = inputs["labels"].detach().cpu().numpy()
        else:
            all_guids = np.append(all_guids, guids.detach().cpu().numpy(), axis=0)
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
            out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)

    all_probs = softmax(preds, axis=1)[:,1]
    eval_loss = eval_loss / nb_eval_steps
    if args.output_mode == "classification":
        preds = np.argmax(preds, axis=1)
    elif args.output_mode == "regression":
        preds = np.squeeze(preds)

    result = compute_metrics(eval_task, preds, out_label_ids)
    results.update(result)

    if args.cross_validation:
        cross_validation_results_dir = os.path.join(eval_output_dir, "cross_validation_results") 
        if not os.path.exists(cross_validation_results_dir) and args.local_rank in [-1, 0]:
            os.makedirs(cross_validation_results_dir)
        output_eval_file = os.path.join(cross_validation_results_dir, "eval_results_fold_" + str(args.fold_number) +".txt")
        output_pred_file = os.path.join(cross_validation_results_dir, "submission_fold_" + str(args.fold_number) + ".csv")

        all_guids = all_guids.tolist()
        all_probs = all_probs.tolist()
        submission = pd.DataFrame()
        submission['sentenceID'] = all_guids
        submission['pred_prob'] = all_probs
        submission.to_csv(output_pred_file, index=False)
    else:
        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
        output_pred_file = os.path.join(eval_output_dir, prefix, "submission.csv")

        all_guids = all_guids.tolist()
        all_probs = all_probs.tolist()
        submission = pd.DataFrame()
        submission['sentenceID'] = all_guids
        submission['pred_prob'] = all_probs
        submission.to_csv(output_pred_file, index=False)
        # submission = pd.read_csv("submissions/sample_submission.csv")
        # submission = pd.read_csv("submissions/train_sample_submission.csv")
        # submission['pred_label'] = preds.reshape(len(preds), 1)
        # submission.to_csv(output_pred_file, index=False)
        
    with open(output_eval_file, "w") as writer:
        logger.info("***** Eval results {} *****".format(prefix))
        for key in sorted(result.keys()):
            logger.info("  %s = %s", key, str(result[key]))
            writer.write("%s = %s\n" % (key, str(result[key])))

    return results


def test(args, model, tokenizer, prefix=""):
    eval_task = args.task_name
    results = {}
    eval_output_dir =  args.output_dir

    eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True) 
    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu eval
    if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running evaluation {} *****".format(prefix))
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    all_guids = None
    all_probs = None
    preds = None
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)
        guids = batch[0]
        with torch.no_grad():
            inputs = {"input_ids": batch[1], "attention_mask": batch[2]}
            if args.model_type != "distilbert" and args.model_type != "bart":
                inputs["token_type_ids"] = (
                    batch[3] if args.model_type in ["bert", "xlnet", "albert"] else None
                )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
            outputs = model(**inputs)
            logits = outputs[0]

        nb_eval_steps += 1
        if preds is None:
            all_guids = guids.detach().cpu().numpy()
            preds = logits.detach().cpu().numpy()
        else:
            all_guids = np.append(all_guids, guids.detach().cpu().numpy(), axis=0)
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)

    all_probs = softmax(preds, axis=1)[:,1]
    if args.output_mode == "classification":
        preds = np.argmax(preds, axis=1)
    elif args.output_mode == "regression":
        preds = np.squeeze(preds)

    output_pred_file = os.path.join(eval_output_dir, prefix, "submission_stacking.csv")
    # output_pred_file = os.path.join(eval_output_dir, prefix, "submission_stacking_trial_test.csv")

    all_guids = all_guids.tolist()
    all_probs = all_probs.tolist()
    submission = pd.DataFrame()
    submission['sentenceID'] = all_guids
    submission['pred_prob'] = all_probs
    submission.to_csv(output_pred_file, index=False)
    
    return results


def load_and_cache_examples(args, task, tokenizer, evaluate=False):
    if args.local_rank not in [-1, 0] and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
    processor = processors[task](args)
    if args.cross_validation:
        # Load data features from cache or dataset file
        cached_features_file = os.path.join(
            args.data_dir + "cache/features",
            "cached_{}_{}_{}_{}_{}".format(
                "dev" if evaluate else "train",
                "fold-" + str(args.fold_number),
                list(filter(None, args.model_name_or_path.split("/"))).pop(),
                str(args.max_seq_length),
                str(task),
            ),
        )
    else:
        # Load data features from cache or dataset file
        cached_features_file = os.path.join(
            args.data_dir + "cache/features",
            "cached_{}_{}_{}_{}".format(
                "dev" if evaluate else "train",
                list(filter(None, args.model_name_or_path.split("/"))).pop(),
                str(args.max_seq_length),
                str(task),
            ),
        )
    output_mode = output_modes[task]
    
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        label_list = processor.get_labels()
        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
            # HACK(label indices are swapped in RoBERTa pretrained model)
            label_list[1], label_list[2] = label_list[2], label_list[1]
        if args.do_test:
            examples = (
                processor.get_test_examples(args.data_dir)
            )
        else:
            if args.cross_validation:
                examples = (
                    processor.get_dev_fold_examples() if evaluate else processor.get_train_fold_examples()
                )
            else:
                examples = (
                    processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
                )
        features = convert_examples_to_features(
            examples,
            tokenizer,
            label_list=label_list,
            max_length=args.max_seq_length,
            output_mode=output_mode,
            pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
            pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
            pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
        )
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)

    if args.local_rank == 0 and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Convert to Tensors and build dataset
    all_guids = torch.tensor([int(f.guid) for f in features], dtype=torch.long)
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
    
    if not args.do_test:
        if output_mode == "classification":
            all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
        elif output_mode == "regression":
            all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
        dataset = TensorDataset(all_guids, all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
    else:
        dataset = TensorDataset(all_guids, all_input_ids, all_attention_mask, all_token_type_ids)
    return dataset


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

    # Required parameters
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        required=True,
        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
    )
    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(ALL_MODELS),
    )
    parser.add_argument(
        "--task_name",
        default=None,
        type=str,
        required=True,
        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )

    # Other parameters
    parser.add_argument(
        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        default="",
        type=str,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--cache_dir",
        default="data/SemEval-2020-Task5/cache",
        type=str,
        help="Where do you want to store the pre-trained models downloaded from s3",
    )
    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("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
    parser.add_argument("--do_test", action="store_true", help="Whether to run test.")
    parser.add_argument(
        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.",
    )
    parser.add_argument(
        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.",
    )
    parser.add_argument(
        "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.",
    )
    parser.add_argument(
        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    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=3.0, type=float, 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(
        "--eval_all_checkpoints",
        action="store_true",
        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
    )
    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
    parser.add_argument(
        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory",
    )
    parser.add_argument(
        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets",
    )
    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
    parser.add_argument(
        "--fp16",
        action="store_true",
        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
    )
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
        default="O1",
        help="For fp16: Apex AMP optimization lfevel selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
    parser.add_argument("--local_rank", type=int, default=0, help="For distributed training: local_rank")
    parser.add_argument(
        '--train_with_pseudo_labels',
        action="store_true",
        help="Whether to use pseudo_labels for training."
    )
    parser.add_argument(
        '--cross_validation',
        action="store_true",
        help="Whether to use K-fold cross validation."
    )
    parser.add_argument(
        '--num_folds',
        type=int, 
        default=5,
        help="If use cross validation, this tells the total number of folds."
    )
    parser.add_argument(
        '--fold_number',
        type=int, 
        default=None,
        help="If use cross validation, this tells the model which fold to use."
    )
    parser.add_argument(
        '--experiment91',
        action="store_true",
        help="A flag indicates doing experiment91."
    )
    args = parser.parse_args()

    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
        and args.do_train
        and not args.overwrite_output_dir
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
                args.output_dir
            )
        )

    # Setup CUDA, GPU & distributed training
    if args.local_rank == -1 or args.no_cuda:
        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
        args.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend="nccl")
        args.n_gpu = 1
    args.device = device

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        args.local_rank,
        device,
        args.n_gpu,
        bool(args.local_rank != -1),
        args.fp16,
    )

    # Set seed
    set_seed(args)

    # Prepare GLUE task
    args.task_name = args.task_name.lower()
    if args.task_name not in processors:
        raise ValueError("Task not found: %s" % (args.task_name))
    processor = processors[args.task_name](args)
    args.output_mode = output_modes[args.task_name]
    label_list = processor.get_labels()
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

    args.model_type = args.model_type.lower()
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    config = config_class.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=args.task_name,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    tokenizer = tokenizer_class.from_pretrained(
        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
        do_lower_case=args.do_lower_case,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    model = model_class.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )

    if args.local_rank == 0:
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

    # Training
    if args.do_train:
        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # Create output directory if needed
        if args.cross_validation:
            logger.info('Cross validation is used, so no model is saved.')
        else:
            if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
                os.makedirs(args.output_dir)

            # Change output_dir with hyper parameters.
            hyper_param_str = get_eval_folder_name(args)
            output_dir =  os.path.join(args.output_dir, hyper_param_str)

            if not os.path.exists(output_dir) and args.local_rank in [-1, 0]:
                os.makedirs(output_dir)

            logger.info("Saving model checkpoint to %s", output_dir)
            # Save a trained model, configuration and tokenizer using `save_pretrained()`.
            # They can then be reloaded using `from_pretrained()`
            model_to_save = (
                model.module if hasattr(model, "module") else model
            )  # Take care of distributed/parallel training
            model_to_save.save_pretrained(output_dir)
            tokenizer.save_pretrained(output_dir)

            # Good practice: save your training arguments together with the trained model
            torch.save(args, os.path.join(output_dir, "training_args.bin"))

            # Load a trained model and vocabulary that you have fine-tuned
            model = model_class.from_pretrained(output_dir)
            tokenizer = tokenizer_class.from_pretrained(output_dir)
            model.to(args.device)

    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        if args.cross_validation:
            model.to(args.device)
            prefix = ""
            result = evaluate(args, model, tokenizer, prefix=prefix)
            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
            results.update(result)
        else:
            # Change output_dir with hyper parameters.
            hyper_param_str = get_eval_folder_name(args)
            output_dir =  os.path.join(args.output_dir, hyper_param_str)

            tokenizer = tokenizer_class.from_pretrained(output_dir, do_lower_case=args.do_lower_case)
            checkpoints = [output_dir]
            if args.eval_all_checkpoints:
                checkpoints = list(
                    os.path.dirname(c) for c in sorted(glob.glob(output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
                )
                logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
            logger.info("Evaluate the following checkpoints: %s", checkpoints)
            for checkpoint in checkpoints:
                global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
                prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""

                model = model_class.from_pretrained(checkpoint)
                model.to(args.device)
                result = evaluate(args, model, tokenizer, prefix=prefix)
                result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
                results.update(result)

    # Test
    results = {}
    if args.do_test and args.local_rank in [-1, 0]:
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        checkpoint = args.output_dir
        
        prefix = ""
        model = model_class.from_pretrained(checkpoint)
        model.to(args.device)
        result = test(args, model, tokenizer, prefix=prefix)
        logger.info("Test finished")

    return results


if __name__ == "__main__":
    main()