# Copyright (c) Microsoft Corporation. 
# Licensed under the MIT license.
## Finetuning AMOS for question-answering on SQuAD.
## The script is largely adapted from the huggingface transformers library.

from __future__ import absolute_import, division, print_function

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

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler

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

from tqdm import tqdm, trange

from transformers import WEIGHTS_NAME

from transformers import AdamW, get_linear_schedule_with_warmup

from utils_for_squad import (read_squad_examples, convert_examples_to_features,
                                          RawResult, write_predictions,
                                          RawResultExtended, write_predictions_extended)

# The follwing import is the official SQuAD evaluation script (2.0).
# You can remove it from the dependencies if you are using this script outside of the library
from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad
from amos.tokenization_amos import AMOSTokenizer
from amos.configuration_amos import AMOSConfig
from amos.modeling_amos import AMOSForQuestionAnswering

logger = logging.getLogger(__name__)

MODEL_CLASSES = {
    'amos': (AMOSConfig, AMOSForQuestionAnswering, AMOSTokenizer),
}


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 to_list(tensor):
    return tensor.detach().cpu().tolist()


def get_optimizer_grouped_parameters(
        model, weight_decay, learning_rate, layer_decay, n_layers, layer_wise_weight_decay):
    assert isinstance(model, torch.nn.Module)
    groups = {}
    num_max_layer = 0
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    for para_name, para_var in model.named_parameters():
        if any(nd in para_name for nd in no_decay):
            weight_decay_in_this_group = 0.0
        else:
            weight_decay_in_this_group = weight_decay
        if para_name.startswith('amos.embedding') or para_name == 'amos.rel_pos_bias.weight':
            depth = 0
        elif para_name.startswith('amos.encoder.layer'):
            depth = int(para_name.split('.')[3]) + 1
            num_max_layer = max(num_max_layer, depth)
        elif para_name.startswith('task_layer'):
            depth = n_layers + 2
        else:
            if layer_decay < 1.0:
                raise NotImplementedError()
            depth = 0

        if layer_decay < 1.0 and layer_wise_weight_decay:
            weight_decay_in_this_group *= (layer_decay ** (n_layers + 2 - depth))
        if layer_decay < 1.0:
            group_name = "layer{}_decay{}".format(depth, weight_decay_in_this_group)
        else:
            group_name = "weight_decay{}".format(weight_decay_in_this_group)
        if group_name not in groups:
            group = {
                "params": [para_var],
                "weight_decay": weight_decay_in_this_group,
            }
            if layer_decay < 1.0:
                group["lr"] = learning_rate * (layer_decay ** (n_layers + 2 - depth))
            groups[group_name] = group
        else:
            group = groups[group_name]
            group["params"].append(para_var)

    assert num_max_layer == n_layers

    return list(groups.values())


def train(args, train_dataset, model, tokenizer):
    """ Train the model """
    if args.local_rank in [-1, 0] and args.log_dir:
        tb_writer = SummaryWriter(log_dir=args.log_dir)
    else:
        tb_writer = None

    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, num_workers=1)

    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)
    optimizer_grouped_parameters = get_optimizer_grouped_parameters(
        model=model, weight_decay=args.weight_decay, learning_rate=args.learning_rate,
        layer_decay=args.layer_decay, n_layers=model.config.num_hidden_layers,
        layer_wise_weight_decay=args.layer_wise_weight_decay,
    )
    
    warmup_steps = t_total * args.warmup_ratio
    correct_bias = not args.disable_bias_correct

    logger.info("*********** Optimizer setting: ***********")
    logger.info("Learning rate = %.10f" % args.learning_rate)
    logger.info("Adam epsilon = %.10f" % args.adam_epsilon)
    logger.info("Adam_betas = (%.4f, %.4f)" % (float(args.adam_betas[0]), float(args.adam_betas[1])))
    logger.info("Correct_bias = %s" % str(correct_bias))
    optimizer = AdamW(
        optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon,
        betas=(float(args.adam_betas[0]), float(args.adam_betas[1])),
        correct_bias=correct_bias,
    )
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total)
    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)
        amp_state_dict = amp.state_dict()
        amp_state_dict['loss_scaler0']['loss_scale'] = args.fp16_init_loss_scale
        logger.info("Set fp16_init_loss_scale to %.1f" % args.fp16_init_loss_scale)
        amp.load_state_dict(amp_state_dict)

    # 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)

    metric_for_best = args.metric_for_choose_best_checkpoint
    best_performance = None
    best_epoch = None
    global_step = 1
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
    for _ in train_iterator:
        if args.disable_tqdm:
            epoch_iterator = train_dataloader
        else:
            epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])

        for step, batch in enumerate(epoch_iterator):
            model.train()
            batch = tuple(t.to(args.device) for t in batch)
            inputs = {'input_ids':       batch[0],
                      'attention_mask':  batch[1],
                      'token_type_ids':  batch[2],
                      'start_positions': batch[3],
                      'end_positions':   batch[4]}
            
            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 (not distributed) 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.max_grad_norm > 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:
                    # Log metrics
                    logs = {}
                    logs['learning_rate'] = scheduler.get_lr()[0]
                    logs['loss'] = (tr_loss - logging_loss) / args.logging_steps
                    if tb_writer is not None:
                        tb_writer.add_scalar('lr', max(scheduler.get_lr()), global_step)
                        tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                    logger.info(json.dumps({**logs, **{'step': global_step}}))
                    logging_loss = tr_loss

            if args.max_steps > 0 and global_step > args.max_steps:
                if not args.disable_tqdm:
                    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]:
            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='epoch-{}'.format(_ + 1))

                if metric_for_best is None:
                    metric_for_best = list(results.items())[0][0]
                if best_epoch is None:
                    best_epoch = _ + 1
                    best_performance = results
                else:
                    if best_performance[metric_for_best] < results[metric_for_best]:
                        best_performance = results
                        best_epoch = _ + 1

            # Save model checkpoint
            output_dir = os.path.join(args.output_dir, 'epoch-{}'.format(_ + 1))
            if not os.path.exists(output_dir):
                os.makedirs(output_dir)
            if not args.do_not_save:
                model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                model_to_save.save_pretrained(output_dir)
                logger.info("Saving model checkpoint to %s", output_dir)
            torch.save(args, os.path.join(output_dir, 'training_args.bin'))

    if args.local_rank in [-1, 0] and tb_writer is not None:
        tb_writer.close()
    if best_epoch is not None:
        logger.info(" ***************** Best checkpoint: {}, chosen by {} *****************".format(
            best_epoch, metric_for_best))
        logger.info("Best performance = %s" % json.dumps(best_performance))
        save_best_result(best_epoch, best_performance, args.output_dir)

    return global_step, tr_loss / global_step


def save_best_result(best_epoch, best_performance, output_dir):
    best_performance["checkpoint"] = best_epoch
    with open(os.path.join(output_dir, "best_performance.json"), mode="w") as writer:
        writer.write(json.dumps(best_performance, indent=2))


def evaluate(args, model, tokenizer, prefix=""):
    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)

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

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(dataset)
    eval_dataloader = DataLoader(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 {} *****".format(prefix))
    logger.info("  Num examples = %d", len(dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    all_results = []
    start_time = timeit.default_timer()
    if args.disable_tqdm:
        epoch_iterator = eval_dataloader
    else:
        epoch_iterator = tqdm(eval_dataloader, desc="Evaluating")
    for batch in epoch_iterator:
        model.eval()
        batch = tuple(t.to(args.device) for t in batch)
        with torch.no_grad():
            inputs = {'input_ids':      batch[0],
                      'attention_mask': batch[1],
                      'token_type_ids': batch[2]
                      }
            
            example_indices = batch[3]
            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)
            result = RawResult(unique_id    = unique_id,
                                start_logits = to_list(outputs[0][0][i]),
                                end_logits   = to_list(outputs[0][1][i]))
            all_results.append(result)

    evalTime = timeit.default_timer() - start_time
    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))

    # Compute predictions
    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
    if args.version_2_with_negative:
        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
    else:
        output_null_log_odds_file = None
    
    tokens_to_text = None
    if hasattr(tokenizer, 'convert_tokens_to_string'):
        tokens_to_text = tokenizer.convert_tokens_to_string
    write_predictions(examples, features, all_results, args.n_best_size,
                    args.max_answer_length, args.do_lower_case, output_prediction_file,
                    output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                    args.version_2_with_negative, args.null_score_diff_threshold, tokens_to_text=tokens_to_text)

    # Evaluate with the official SQuAD script
    evaluate_options = EVAL_OPTS(data_file=args.predict_file,
                                 pred_file=output_prediction_file,
                                 na_prob_file=output_null_log_odds_file)
    results = evaluate_on_squad(evaluate_options)

    eval_output_dir = os.path.join(args.output_dir, prefix)
    if not os.path.exists(eval_output_dir):
        os.makedirs(eval_output_dir)
    output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
    with open(output_eval_file, "w") as writer:
        logger.info("***** Eval results {} *****".format(prefix))
        writer.write(json.dumps(results, indent=2))
        logger.info("Results = %s" % json.dumps(results, indent=2))
    return results


def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=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

    # Load data features from cache or dataset file
    input_file = args.predict_file if evaluate else args.train_file
    cached_features_file = args.cached_train_file if not evaluate else args.cached_dev_file
    if cached_features_file is None:
        if args.disable_auto_cache and args.local_rank != -1:
            logger.warning("Please cache the features in DDP mode !")
            raise RuntimeError()
        if not args.disable_auto_cache:
            cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
                'dev' if evaluate else 'train',
                list(filter(None, args.model_name_or_path.split('/'))).pop(),
                str(args.max_seq_length)))
    if cached_features_file is not None and os.path.exists(cached_features_file) and not args.overwrite_cache:
        examples = read_squad_examples(input_file=input_file,
                                                is_training=not evaluate,
                                                version_2_with_negative=args.version_2_with_negative)
        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", input_file)
        examples = read_squad_examples(input_file=input_file,
                                                is_training=not evaluate,
                                                version_2_with_negative=args.version_2_with_negative)
        features = convert_examples_to_features(examples=examples,
                                                tokenizer=tokenizer,
                                                max_seq_length=args.max_seq_length,
                                                doc_stride=args.doc_stride,
                                                max_query_length=args.max_query_length,
                                                is_training=not evaluate,
                                                cls_token=tokenizer.cls_token,
                                                sep_token=tokenizer.sep_token,
                                                pad_token_id=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                                                cls_token_segment_id=0,
                                                pad_token_segment_id=0,
                                                cls_token_at_end=False,
                                                sequence_a_is_doc=False,
                                                add_two_separators=True if args.model_type in ['amos'] else False)
        if args.local_rank in [-1, 0] and cached_features_file is not None:
            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_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_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
    if evaluate:
        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_example_index, all_cls_index, all_p_mask)
    else:
        all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
        all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
        all_is_impossible = torch.tensor([1 if f.is_impossible else 0 for f in features], dtype=torch.long)
        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
                                all_start_positions, all_end_positions,
                                all_cls_index, all_p_mask, all_is_impossible)

    if output_examples:
        return dataset, examples, features
    return dataset


def eval_str_list(x, type=float):
    if x is None:
        return None
    if isinstance(x, str):
        x = eval(x)
    try:
        return list(map(type, x))
    except TypeError:
        return [type(x)]


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

    ## Required parameters
    parser.add_argument("--train_file", default=None, type=str, required=True,
                        help="SQuAD json for training. E.g., train-v1.1.json")
    parser.add_argument("--predict_file", default=None, type=str, required=True,
                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
    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")
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model checkpoints and predictions will be written.")

    parser.add_argument("--do_not_save", action='store_true',
                        help="Disable save models after each epoch. ")
    parser.add_argument("--log_dir", default=None, type=str,
                        help="The output directory where the log will be written.")

    parser.add_argument("--cached_train_file", default=None, type=str,
                        help="Path to cache the train set features. ")
    parser.add_argument("--cached_dev_file", default=None, type=str,
                        help="Path to cache the dev set features. ")
    parser.add_argument('--disable_auto_cache', action='store_true',
                        help='Disable the function for automatic cache the training/dev features.')
    parser.add_argument('--disable_tqdm', action='store_true',
                        help='Disable the tqdm bar. ')

    ## 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_or_path", 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('--version_2_with_negative', action='store_true',
                        help='If true, the SQuAD examples contain some that do not have an answer.')
    parser.add_argument('--null_score_diff_threshold', type=float, default=0.0,
                        help="If null_score - best_non_null is greater than the threshold predict null.")

    parser.add_argument("--max_seq_length", default=384, type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
                             "longer than this will be truncated, and sequences shorter than this will be padded.")
    parser.add_argument("--doc_stride", default=128, type=int,
                        help="When splitting up a long document into chunks, how much stride to take between chunks.")
    parser.add_argument("--max_query_length", default=64, type=int,
                        help="The maximum number of tokens for the question. Questions longer than this will "
                             "be truncated to this length.")
    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("--evaluate_during_training", action='store_true',
                        help="Rul evaluation during training at each logging step.")
    parser.add_argument("--layer_wise_weight_decay", action='store_true',
                        help="Apply the weight decay for each layer with a decayed rate (--layer_decay). ")
    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("--layer_decay", default=1.0, type=float,
                        help="Layer decay rate for the layer-wise learning rate. ")

    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('--adam_betas', '--adam_beta', default='0.9,0.999', type=eval_str_list, metavar='B',
                        help='betas for Adam optimizer')
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--disable_bias_correct", action='store_true',
                        help="Disable the bias correction items. ")
    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_ratio", default=0.1, type=float,
                        help="Linear warmup over warmup_ratio.")
    parser.add_argument("--n_best_size", default=20, type=int,
                        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.")
    parser.add_argument("--beam_size", default=20, type=int,
                        help="beam size when doing joint predictions. ")
    parser.add_argument("--max_answer_length", default=30, type=int,
                        help="The maximum length of an answer that can be generated. This is needed because the start "
                             "and end predictions are not conditioned on one another.")
    parser.add_argument("--verbose_logging", action='store_true',
                        help="If true, all of the warnings related to data processing will be printed. "
                             "A number of warnings are expected for a normal SQuAD evaluation.")

    parser.add_argument('--logging_steps', type=int, default=50,
                        help="Log 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="Whether not to use 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('--metric_for_choose_best_checkpoint', type=str, default=None,
                        help="Set the metric to choose the best checkpoint")

    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('--fp16_init_loss_scale', type=float, default=128.0,
                        help="For fp16: initial value for loss scale.")
    parser.add_argument("--local_rank", type=int, default=-1,
                        help="local_rank for distributed training on gpus")
    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")

    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

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    with open(os.path.join(args.output_dir, 'training_args.json'), mode='w', encoding="utf-8") as writer:
        writer.write(json.dumps(args.__dict__, indent=2, sort_keys=True))

    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)

    # 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,
                                          cache_dir=args.cache_dir if args.cache_dir else None)
    tokenizer_name_or_path = args.tokenizer_name_or_path if args.tokenizer_name_or_path else args.model_name_or_path
    tokenizer = tokenizer_class.from_pretrained(tokenizer_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),
                                        model_type=args.model_type,
                                        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)

    logger.info("Model = %s" % str(model))

    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
    # Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
    # remove the need for this code, but it is still valid.
    if args.fp16:
        try:
            import apex
            apex.amp.register_half_function(torch, 'einsum')
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")

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


    # Save the trained model and the tokenizer
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # Create output directory if needed
        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(args.output_dir)

        if not args.do_not_save:
            logger.info("Saving model checkpoint to %s", args.output_dir)
            # Save a trained model and configuration 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(args.output_dir)

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

        # Load a trained model that you have fine-tuned
        # model = model_class.from_pretrained(args.output_dir, force_download=True)
        # model.to(args.device)


    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
    results = []
    if args.do_eval and args.local_rank in [-1, 0]:
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs

        logger.info("Evaluate the following checkpoints: %s", checkpoints)

        for checkpoint in checkpoints:
            # Reload the model
            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
            model = model_class.from_pretrained(checkpoint, force_download=True)
            model.to(args.device)

            # Evaluate
            result = evaluate(args, model, tokenizer, prefix=global_step)

            result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
            result["checkpoint"] = checkpoint
            results.append(result)

    # logger.info("Results: %s" % (json.dumps(results, indent=2)))
    result_file = os.path.join(args.output_dir, "results.json")
    with open(result_file, mode="w", encoding="utf-8") as writer:
        writer.write(json.dumps(results, indent=2))

    return results


if __name__ == "__main__":
    main()
