run_metaphor_detection.py

"""
Train & Evaluate RoBERTa-Based Metaphor Detection Model
"""

import argparse
import glob
import logging
import os
import random
import sys
import numpy as np
import torch
import pickle
from sklearn.metrics import precision_score, recall_score, f1_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from data_utils import InputExample, InputFeatures, read_pos_tags
from vua_data_helper import read_vua_examples_from_file, convert_vua_examples_to_features
from toefl_data_helper import read_toefl_examples_from_file, convert_toefl_examples_to_features
from modeling_roberta_metaphor import RobertaForMetaphorDetection


from transformers import (
    WEIGHTS_NAME,
    AdamW,
    BertConfig,
    BertForTokenClassification,
    BertTokenizer,
    CamembertConfig,
    CamembertForTokenClassification,
    CamembertTokenizer,
    DistilBertConfig,
    DistilBertForTokenClassification,
    DistilBertTokenizer,
    RobertaConfig,
    RobertaForTokenClassification,
    RobertaTokenizer,
    XLMRobertaConfig,
    XLMRobertaForTokenClassification,
    XLMRobertaTokenizer,
    get_linear_schedule_with_warmup,
)

logger = logging.getLogger(__name__)

ALL_MODELS = sum(
    (
        tuple(conf.pretrained_config_archive_map.keys())
        for conf in (BertConfig, RobertaConfig, DistilBertConfig, CamembertConfig, XLMRobertaConfig)
    ),
    (),
)

MODEL_CLASSES = {
    "bert": (BertConfig, BertForTokenClassification, BertTokenizer),
    "roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
    "distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
    "camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
    "xlmroberta": (XLMRobertaConfig, XLMRobertaForTokenClassification, 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)

def train(args, train_dataset, dev_dataset, model, class_weights,
          tokenizer, pad_token_label_id):
    """ 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", "classifier"]
    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 *****")
    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)
    logger.info(" Weighted cross-entropy class weights: {}".format(class_weights))
    
    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):
        # set global_step to gobal_step of last saved checkpoint from model path
        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
        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)
        
    weights = torch.FloatTensor(class_weights).to(args.device)
    best_score, tr_loss, logging_loss = 0.0, 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[0], "attention_mask": batch[1], "pos_ids": batch[3], \
                      "biasdown_vectors": batch[4], "biasup_vectors": batch[5], "biasupdown_vectors": batch[6], \
                      "corp_vectors": batch[7], "topic_vectors": batch[8], "verbnet_vectors": batch[9], \
                      "wordnet_vectors": batch[10], "labels": batch[11], "class_weights": weights}
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
                    batch[2] if args.model_type in ["bert", "xlnet"] else None
                )  # XLM and RoBERTa don"t use segment_ids

            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in pytorch-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)

                scheduler.step()  # Update learning rate schedule
                optimizer.step()
                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:
                    # Log metrics
                    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, dev_dataset, pad_token_label_id,
                                              class_weights, mode="dev")
                        if results['f1'] > best_score:
                            best_score = results['f1']
                            logger.info("Best dev f1 score: {}".format(best_score))
                            # remove previous model
                            try:
                                os.system("rm "+output_dir+"/*")
                                logger.info("Success to delete old model.")
                            except:
                                flag = False
                                logger.info("Fail to delete old model.")
                            """
                            # save threshold
                            with open(args.output_dir+"/dev_results.pkl", "wb") as handle:
                                pickle.dump(results, handle)
                            """
                            # save model
                            output_dir = args.output_dir
                            if not os.path.exists(output_dir):
                                os.makedirs(output_dir)
                            model_to_save = (
                                model.module if hasattr(model, "module") else model
                            )
                            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)
                        #for key, value in results.items():
                        #    tb_writer.add_scalar("eval_{}".format(key), value, global_step)
                    #tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
                    #tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                    logging_loss = tr_loss

                # Save model peoriodically
                if False and args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.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

    return global_step, tr_loss / global_step


def evaluate(args, model, eval_dataset, pad_token_label_id, class_weights, mode):
    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) if args.local_rank == -1 else DistributedSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)

    # multi-gpu evaluate
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Eval!
    logger.info("***** Running evaluation *****")
    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
    preds = None
    out_label_ids = None
    model.eval()
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        batch = tuple(t.to(args.device) for t in batch)
        with torch.no_grad():
            if mode == "test":
                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "pos_ids": batch[3], \
                          "biasdown_vectors": batch[4], "biasup_vectors": batch[5], "biasupdown_vectors": batch[6], \
                          "corp_vectors": batch[7], "topic_vectors": batch[8], "verbnet_vectors": batch[9], \
                          "wordnet_vectors": batch[10], "labels": None}
            else:
                weights = torch.FloatTensor(class_weights).to(args.device)
                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "pos_ids": batch[3], \
                          "biasdown_vectors": batch[4], "biasup_vectors": batch[5], "biasupdown_vectors": batch[6], \
                          "corp_vectors": batch[7], "topic_vectors": batch[8], "verbnet_vectors": batch[9], \
                          "wordnet_vectors": batch[10], "labels": batch[11], "class_weights": weights}
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
                    batch[2] if args.model_type in ["bert", "xlnet"] else None
                )  # XLM and RoBERTa don"t use segment_ids
            outputs = model(**inputs)
            if mode == "test":
                logits = outputs[0]
            else:
                tmp_eval_loss, logits = outputs[:2]

            #probs = torch.nn.functional.softmax(logits, dim=-1)

            if args.n_gpu > 1 and mode != "test" :
                tmp_eval_loss = tmp_eval_loss.mean()  # mean() to average on multi-gpu parallel evaluating
                eval_loss += tmp_eval_loss.item()
                
        nb_eval_steps += 1
        if preds is None:
            preds = logits.detach().cpu().numpy()
            #preds = probs.detach().cpu().numpy()
            if inputs["labels"] is not None:
                out_label_ids = inputs["labels"].detach().cpu().numpy()
            else:
                out_label_ids = batch[11].detach().cpu().numpy()
        else:
            #preds = np.append(preds, probs.detach().cpu().numpy(), axis=0)
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
            if inputs["labels"] is not None:
                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
            else:
                out_label_ids = np.append(out_label_ids, batch[11].detach().cpu().numpy(), axis=0)

    eval_loss = eval_loss / nb_eval_steps
    # pred_labels: (batch_size, max_seq_len)
    pred_labels = np.argmax(preds, axis=2)
    
    out_label_list = []
    flat_preds_list = []
    preds_list = [[] for _ in range(out_label_ids.shape[0])]
    for i in range(out_label_ids.shape[0]):
        for j in range(out_label_ids.shape[1]):
            if out_label_ids[i, j] != pad_token_label_id:
                # flat
                out_label_list.append(out_label_ids[i][j])
                flat_preds_list.append(pred_labels[i][j])
                # nested                
                preds_list[i].append(pred_labels[i][j])

    results = None
    if mode != "test":
        results = {
            "loss": eval_loss,
            "precision": precision_score(out_label_list, flat_preds_list, average="binary", pos_label=1),
            "recall": recall_score(out_label_list, flat_preds_list, average="binary", pos_label=1),
            "f1": f1_score(out_label_list, flat_preds_list, average="binary", pos_label=1)
            }
        logger.info("***** Eval results *****")
        for key in sorted(results.keys()):
            logger.info("  %s = %s", key, str(results[key]))
            
    return results, preds_list


def convert_features_to_dataset(features):
    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
    all_pos_ids = torch.tensor([f.pos_ids for f in features], dtype=torch.long)
    # external features
    all_biasdown_vectors = torch.FloatTensor([f.biasdown_vectors for f in features])
    all_biasup_vectors = torch.FloatTensor([f.biasup_vectors for f in features])
    all_biasupdown_vectors = torch.FloatTensor([f.biasupdown_vectors for f in features])
    all_corp_vectors = torch.FloatTensor([f.corp_vectors for f in features])
    all_topic_vectors = torch.FloatTensor([f.topic_vectors for f in features])
    all_verbnet_vectors = torch.FloatTensor([f.verbnet_vectors for f in features])
    all_wordnet_vectors = torch.FloatTensor([f.wordnet_vectors for f in features])
    all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)
    
    dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_pos_ids,
                            all_biasdown_vectors, all_biasup_vectors, all_biasupdown_vectors,
                            all_corp_vectors, all_topic_vectors, all_verbnet_vectors, all_wordnet_vectors,
                            all_label_ids)
    return dataset


def get_class_weights(features):
    class_sizes = [0.0, 0.0]
    for feature in features:
        labels = feature.label_ids
        for label in labels:
            if label in [0, 1]:
                class_sizes[label] += 1
    # weight = total_size/size
    total_size = np.sum(class_sizes)
    class_weights = [float(total_size) / (5.0 * size) for size in class_sizes]
    print("class 0: {}, class 1: {}".format(class_sizes[0], class_sizes[1]))
    print("weights: {}".format(class_weights))
    return class_weights
    

def load_and_cache_examples(args, tokenizer, pad_token_label_id, mode):
    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

    # Load data features from cache or dataset file
    cached_features_file = os.path.join(
        args.data_dir,
        "cached_{}_{}_{}".format(
            mode, list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length)
        ),
    )
    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)
        # create pos vocab
        pos_vocab = read_pos_tags(args.data_dir)
        if args.dataset.lower() == "vua":
            examples = read_vua_examples_from_file(args.data_dir, mode)
            features = convert_vua_examples_to_features(
                examples,
                args.max_seq_length,
                tokenizer,
                cls_token_at_end=bool(args.model_type in ["xlnet"]),
                # xlnet has a cls token at the end
                cls_token=tokenizer.cls_token,
                cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
                sep_token=tokenizer.sep_token,
                sep_token_extra=bool(args.model_type in ["roberta"]),
                # roberta uses an extra separator b/w pairs of sentences,
                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,
                pos_vocab=pos_vocab,
                pad_pos_id=0,
                pad_token_label_id=pad_token_label_id,
                pad_feature_val=0,
                mode=mode
            )           
        elif args.dataset.lower() == "toefl":
            examples = read_toefl_examples_from_file(args.data_dir, mode)
            features = convert_toefl_examples_to_features(
                examples,
                args.max_seq_length,
                tokenizer,
                cls_token_at_end=bool(args.model_type in ["xlnet"]),
                # xlnet has a cls token at the end
                cls_token=tokenizer.cls_token,
                cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
                sep_token=tokenizer.sep_token,
                sep_token_extra=bool(args.model_type in ["roberta"]),
                # roberta uses an extra separator b/w pairs of sentences,
                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,
                pos_vocab=pos_vocab,
                pad_pos_id=0,
                pad_token_label_id=pad_token_label_id,
                pad_feature_val=0,
                mode=mode
            )
        else:
            logger.info("Unrecognized dataset: {}".format(args.dataset))
        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

    # mode: train, split into train & dev
    if mode == "train":
        total_size = len(features)
        train_ratio = 0.8
        train_inds = np.random.choice(range(total_size), size=int(total_size*train_ratio), replace=False)
        train_features = [features[ind] for ind in train_inds]
        train_dataset = convert_features_to_dataset(train_features)
        dev_features = [features[ind] for ind in range(total_size) if ind not in set(train_inds)]
        dev_dataset = convert_features_to_dataset(dev_features)
        class_weights = get_class_weights(features)
        print("train size: {}, dev_size: {}".format(len(train_features), len(dev_features)))
        return train_dataset, dev_dataset, class_weights
    elif mode == "test":
        dataset = convert_features_to_dataset(features)
        return dataset
    else:
        print("INVALID mode: {}".format(mode))
        sys.exit(0)
    
    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 training files for the CoNLL-2003 NER 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(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--dataset",
        default=None,
        type=str,
        required=True,
        help="The dataset, VUA or TOEFL"
    )

    # Other parameters
    parser.add_argument(
        "--labels",
        default="",
        type=str,
        help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.",
    )
    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="",
        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_predict", action="store_true", help="Whether to run predictions on the test set.")
    parser.add_argument(
        "--evaluate_during_training",
        action="store_true",
        help="Whether to 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 level 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=-1, help="For distributed training: local_rank")
    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
    # feature parameters
    #parser.add_argument("--use_init_embed", action="store_true", help="classifier compares initial and contextualized embedding")
    parser.add_argument("--use_pos", action="store_true", help="classifier uses pos embedding")
    parser.add_argument("--pos_vocab_size", type=int, default=43, help="Num of POS tags")
    parser.add_argument("--pos_dim", type=int, default=8, help="Dimension of POS embedding")
    parser.add_argument("--use_features", action="store_true", help="classifier uses external features")
    parser.add_argument("--feature_dim", type=int, default=128, help="Dimension of external features")
    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 distant debugging if needed
    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach")
        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
        ptvsd.wait_for_attach()

    # 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)

    pad_token_label_id = CrossEntropyLoss().ignore_index
    
    # 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=2,
        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 = RobertaForMetaphorDetection.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,
        use_pos=args.use_pos,
        pos_vocab_size=args.pos_vocab_size,
        pos_dim=args.pos_dim,
        use_features=args.use_features,
        feature_dim=args.feature_dim)
    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:
        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(args.output_dir)
        train_dataset, dev_dataset, class_weights = load_and_cache_examples(args, tokenizer,
                                                                            pad_token_label_id, mode="train")
        global_step, tr_loss = train(args, train_dataset, dev_dataset, model,
                                     class_weights, tokenizer, pad_token_label_id)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Predict/Test (without ground truth labels)
    if args.do_predict and args.local_rank in [-1, 0]:
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        model = RobertaForMetaphorDetection.from_pretrained(args.output_dir,
                                                            #use_init_embed=args.use_init_embed,
                                                            use_pos=args.use_pos,
                                                            pos_vocab_size=args.pos_vocab_size,
                                                            pos_dim=args.pos_dim,
                                                            use_features=args.use_features,
                                                            feature_dim=args.feature_dim)
        model.to(args.device)

        test_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, mode="test")
        result, predictions = evaluate(args, model, test_dataset, pad_token_label_id,
                                       class_weights=None, mode="test")
        # Save predictions
        output_test_predictions_file = os.path.join(args.output_dir, "test_labels.txt")
        with open(output_test_predictions_file, "w") as writer:
            for eid in range(len(predictions)):
                line = " ".join([str(label) for label in predictions[eid]])
                writer.write(line+"\n")
        logger.info("Saving predictions to {}".format(output_test_predictions_file))


if __name__=="__main__":
    main()