# coding=utf-8
# 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.
""" Fine-pruning Masked BERT for question-answering on SQuAD."""


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

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange

from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
from transformers import (
    WEIGHTS_NAME,
    AdamW,
    BertConfig,
    BertForQuestionAnswering,
    BertTokenizer,
    get_linear_schedule_with_warmup,
    squad_convert_examples_to_features,
)
from transformers.data.metrics.squad_metrics import (
    compute_predictions_log_probs,
    compute_predictions_logits,
    squad_evaluate,
)
from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor


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


logger = logging.getLogger(__name__)

MODEL_CLASSES = {
    "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
    "masked_bert": (MaskedBertConfig, MaskedBertForQuestionAnswering, BertTokenizer),
}


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 schedule_threshold(
    step: int,
    total_step: int,
    warmup_steps: int,
    initial_threshold: float,
    final_threshold: float,
    initial_warmup: int,
    final_warmup: int,
    final_lambda: float,
):
    if step <= initial_warmup * warmup_steps:
        threshold = initial_threshold
    elif step > (total_step - final_warmup * warmup_steps):
        threshold = final_threshold
    else:
        spars_warmup_steps = initial_warmup * warmup_steps
        spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps
        mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps)
        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff ** 3)
    regu_lambda = final_lambda * threshold / final_threshold
    return threshold, regu_lambda


def regularization(model: nn.Module, mode: str):
    regu, counter = 0, 0
    for name, param in model.named_parameters():
        if "mask_scores" in name:
            if mode == "l1":
                regu += torch.norm(torch.sigmoid(param), p=1) / param.numel()
            elif mode == "l0":
                regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel()
            else:
                ValueError("Don't know this mode.")
            counter += 1
    return regu / counter


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


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

    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 "mask_score" in n and p.requires_grad],
            "lr": args.mask_scores_learning_rate,
        },
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay)
            ],
            "lr": args.learning_rate,
            "weight_decay": args.weight_decay,
        },
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay)
            ],
            "lr": args.learning_rate,
            "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)
    # Distillation
    if teacher is not None:
        logger.info("  Training with distillation")

    global_step = 1
    # Global TopK
    if args.global_topk:
        threshold_mem = None
    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 global_step of last saved checkpoint from model path
        try:
            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]
    )
    # Added here for reproductibility
    set_seed(args)

    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)
            threshold, regu_lambda = schedule_threshold(
                step=global_step,
                total_step=t_total,
                warmup_steps=args.warmup_steps,
                final_threshold=args.final_threshold,
                initial_threshold=args.initial_threshold,
                final_warmup=args.final_warmup,
                initial_warmup=args.initial_warmup,
                final_lambda=args.final_lambda,
            )
            # Global TopK
            if args.global_topk:
                if threshold == 1.0:
                    threshold = -1e2  # Or an indefinitely low quantity
                else:
                    if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0):
                        # Sort all the values to get the global topK
                        concat = torch.cat(
                            [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
                        )
                        n = concat.numel()
                        kth = max(n - (int(n * threshold) + 1), 1)
                        threshold_mem = concat.kthvalue(kth).values.item()
                        threshold = threshold_mem
                    else:
                        threshold = threshold_mem
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
                "token_type_ids": batch[2],
                "start_positions": batch[3],
                "end_positions": batch[4],
            }

            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                del inputs["token_type_ids"]

            if args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": batch[5], "p_mask": batch[6]})
                if args.version_2_with_negative:
                    inputs.update({"is_impossible": batch[7]})
                if hasattr(model, "config") and hasattr(model.config, "lang2id"):
                    inputs.update(
                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
                    )

            if "masked" in args.model_type:
                inputs["threshold"] = threshold

            outputs = model(**inputs)
            # model outputs are always tuple in transformers (see doc)
            loss, start_logits_stu, end_logits_stu = outputs

            # Distillation loss
            if teacher is not None:
                with torch.no_grad():
                    start_logits_tea, end_logits_tea = teacher(
                        input_ids=inputs["input_ids"],
                        token_type_ids=inputs["token_type_ids"],
                        attention_mask=inputs["attention_mask"],
                    )

                loss_start = F.kl_div(
                    input=F.log_softmax(start_logits_stu / args.temperature, dim=-1),
                    target=F.softmax(start_logits_tea / args.temperature, dim=-1),
                    reduction="batchmean",
                ) * (args.temperature ** 2)
                loss_end = F.kl_div(
                    input=F.log_softmax(end_logits_stu / args.temperature, dim=-1),
                    target=F.softmax(end_logits_tea / args.temperature, dim=-1),
                    reduction="batchmean",
                ) * (args.temperature ** 2)
                loss_logits = (loss_start + loss_end) / 2.0

                loss = args.alpha_distil * loss_logits + args.alpha_ce * loss

            # Regularization
            if args.regularization is not None:
                regu_ = regularization(model=model, mode=args.regularization)
                loss = loss + regu_lambda * regu_

            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)

                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    tb_writer.add_scalar("threshold", threshold, global_step)
                    for name, param in model.named_parameters():
                        if not param.requires_grad:
                            continue
                        tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step)
                        tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step)
                        tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step)
                        tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step)
                        if "pooler" in name:
                            continue
                        tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step)
                        tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step)
                        if args.regularization is not None and "mask_scores" in name:
                            if args.regularization == "l1":
                                perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel()
                            elif args.regularization == "l0":
                                perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel()
                            tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step)

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

                # Log metrics
                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Only evaluate when single GPU otherwise metrics may not average well
                    if args.local_rank == -1 and args.evaluate_during_training:
                        results = evaluate(args, model, tokenizer)
                        for key, value in results.items():
                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
                    learning_rate_scalar = scheduler.get_lr()
                    tb_writer.add_scalar("lr", learning_rate_scalar[0], global_step)
                    if len(learning_rate_scalar) > 1:
                        for idx, lr in enumerate(learning_rate_scalar[1:]):
                            tb_writer.add_scalar(f"lr/{idx+1}", lr, global_step)
                    tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                    if teacher is not None:
                        tb_writer.add_scalar("loss/distil", loss_logits.item(), global_step)
                    if args.regularization is not None:
                        tb_writer.add_scalar("loss/regularization", regu_.item(), global_step)
                    if (teacher is not None) or (args.regularization is not None):
                        if (teacher is not None) and (args.regularization is not None):
                            tb_writer.add_scalar(
                                "loss/instant_ce",
                                (loss.item() - regu_lambda * regu_.item() - args.alpha_distil * loss_logits.item())
                                / args.alpha_ce,
                                global_step,
                            )
                        elif teacher is not None:
                            tb_writer.add_scalar(
                                "loss/instant_ce",
                                (loss.item() - args.alpha_distil * loss_logits.item()) / args.alpha_ce,
                                global_step,
                            )
                        else:
                            tb_writer.add_scalar(
                                "loss/instant_ce", loss.item() - regu_lambda * regu_.item(), global_step
                            )
                    logging_loss = tr_loss

                # Save model checkpoint
                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    # Take care of distributed/parallel training
                    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)

            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=""):
    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 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(dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)

    all_results = []
    start_time = timeit.default_timer()
    # Global TopK
    if args.global_topk:
        threshold_mem = None

    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        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],
            }

            if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                del inputs["token_type_ids"]

            example_indices = batch[3]

            # XLNet and XLM use more arguments for their predictions
            if args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
                # for lang_id-sensitive xlm models
                if hasattr(model, "config") and hasattr(model.config, "lang2id"):
                    inputs.update(
                        {"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
                    )
            if "masked" in args.model_type:
                inputs["threshold"] = args.final_threshold
                if args.global_topk:
                    if threshold_mem is None:
                        concat = torch.cat(
                            [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
                        )
                        n = concat.numel()
                        kth = max(n - (int(n * args.final_threshold) + 1), 1)
                        threshold_mem = concat.kthvalue(kth).values.item()
                    inputs["threshold"] = threshold_mem
            outputs = model(**inputs)

        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)

            output = [to_list(output[i]) for output in outputs]

            # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
            # models only use two.
            if len(output) >= 5:
                start_logits = output[0]
                start_top_index = output[1]
                end_logits = output[2]
                end_top_index = output[3]
                cls_logits = output[4]

                result = SquadResult(
                    unique_id,
                    start_logits,
                    end_logits,
                    start_top_index=start_top_index,
                    end_top_index=end_top_index,
                    cls_logits=cls_logits,
                )

            else:
                start_logits, end_logits = output
                result = SquadResult(unique_id, start_logits, end_logits)

            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

    # XLNet and XLM use a more complex post-processing procedure
    if args.model_type in ["xlnet", "xlm"]:
        start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
        end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top

        predictions = compute_predictions_log_probs(
            examples,
            features,
            all_results,
            args.n_best_size,
            args.max_answer_length,
            output_prediction_file,
            output_nbest_file,
            output_null_log_odds_file,
            start_n_top,
            end_n_top,
            args.version_2_with_negative,
            tokenizer,
            args.verbose_logging,
        )
    else:
        predictions = compute_predictions_logits(
            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,
            tokenizer,
        )

    # Compute the F1 and exact scores.
    results = squad_evaluate(examples, predictions)
    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:
        # Make sure only the first process in distributed training process the dataset, and the others will use the cache
        torch.distributed.barrier()

    # Load data features from cache or dataset file
    input_dir = args.data_dir if args.data_dir else "."
    cached_features_file = os.path.join(
        input_dir,
        "cached_{}_{}_{}_{}".format(
            "dev" if evaluate else "train",
            args.tokenizer_name
            if args.tokenizer_name
            else list(filter(None, args.model_name_or_path.split("/"))).pop(),
            str(args.max_seq_length),
            list(filter(None, args.predict_file.split("/"))).pop()
            if evaluate
            else list(filter(None, args.train_file.split("/"))).pop(),
        ),
    )

    # Init features and dataset from cache if it exists
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features_and_dataset = torch.load(cached_features_file)
        features, dataset, examples = (
            features_and_dataset["features"],
            features_and_dataset["dataset"],
            features_and_dataset["examples"],
        )
    else:
        logger.info("Creating features from dataset file at %s", input_dir)

        if not args.data_dir and ((evaluate and not args.predict_file) or (not evaluate and not args.train_file)):
            try:
                import tensorflow_datasets as tfds
            except ImportError:
                raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")

            if args.version_2_with_negative:
                logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.")

            tfds_examples = tfds.load("squad")
            examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
        else:
            processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
            if evaluate:
                examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
            else:
                examples = processor.get_train_examples(args.data_dir, filename=args.train_file)

        features, dataset = squad_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,
            return_dataset="pt",
            threads=args.threads,
        )

        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file)

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

    if output_examples:
        return dataset, examples, features
    return dataset


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

    # Required parameters
    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 pretrained model or model identifier from huggingface.co/models",
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model checkpoints and predictions will be written.",
    )

    # Other parameters
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        help="The input data dir. Should contain the .json files for the task."
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    parser.add_argument(
        "--train_file",
        default=None,
        type=str,
        help="The input training file. If a data dir is specified, will look for the file there"
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    parser.add_argument(
        "--predict_file",
        default=None,
        type=str,
        help="The input evaluation file. If a data dir is specified, will look for the file there"
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    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(
        "--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="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("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")

    # Pruning parameters
    parser.add_argument(
        "--mask_scores_learning_rate",
        default=1e-2,
        type=float,
        help="The Adam initial learning rate of the mask scores.",
    )
    parser.add_argument(
        "--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)."
    )
    parser.add_argument(
        "--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)."
    )
    parser.add_argument(
        "--initial_warmup",
        default=1,
        type=int,
        help="Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays"
        "at its `initial_threshold` value (sparsity schedule).",
    )
    parser.add_argument(
        "--final_warmup",
        default=2,
        type=int,
        help="Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays"
        "at its final_threshold value (sparsity schedule).",
    )

    parser.add_argument(
        "--pruning_method",
        default="topK",
        type=str,
        help="Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning, sigmoied_threshold = Soft movement pruning).",
    )
    parser.add_argument(
        "--mask_init",
        default="constant",
        type=str,
        help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.",
    )
    parser.add_argument(
        "--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method."
    )

    parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.")
    parser.add_argument(
        "--final_lambda",
        default=0.0,
        type=float,
        help="Regularization intensity (used in conjunction with `regulariation`.",
    )

    parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.")
    parser.add_argument(
        "--global_topk_frequency_compute",
        default=25,
        type=int,
        help="Frequency at which we compute the TopK global threshold.",
    )

    # Distillation parameters (optional)
    parser.add_argument(
        "--teacher_type",
        default=None,
        type=str,
        help="Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for distillation.",
    )
    parser.add_argument(
        "--teacher_name_or_path",
        default=None,
        type=str,
        help="Path to the already SQuAD fine-tuned teacher model. Only for distillation.",
    )
    parser.add_argument(
        "--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation."
    )
    parser.add_argument(
        "--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation."
    )
    parser.add_argument(
        "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation."
    )

    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("--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(
        "--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(
        "--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(
        "--lang_id",
        default=0,
        type=int,
        help="language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)",
    )

    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="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("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
    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("--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.")

    parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features")
    args = parser.parse_args()

    # Regularization
    if args.regularization == "null":
        args.regularization = None

    if args.doc_stride >= args.max_seq_length - args.max_query_length:
        logger.warning(
            "WARNING - You've set a doc stride which may be superior to the document length in some "
            "examples. This could result in errors when building features from the examples. Please reduce the doc "
            "stride or increase the maximum length to ensure the features are correctly built."
        )

    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 = 0 if args.no_cuda else 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)

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

    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,
        pruning_method=args.pruning_method,
        mask_init=args.mask_init,
        mask_scale=args.mask_scale,
    )
    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.teacher_type is not None:
        assert args.teacher_name_or_path is not None
        assert args.alpha_distil > 0.0
        assert args.alpha_distil + args.alpha_ce > 0.0
        teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type]
        teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path)
        teacher = teacher_model_class.from_pretrained(
            args.teacher_name_or_path,
            from_tf=False,
            config=teacher_config,
            cache_dir=args.cache_dir if args.cache_dir else None,
        )
        teacher.to(args.device)
    else:
        teacher = None

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

    model.to(args.device)

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

    # 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, teacher=teacher)
        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)

        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        # Take care of distributed/parallel training
        model_to_save = model.module if hasattr(model, "module") else model
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.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 and vocabulary that you have fine-tuned
        model = model_class.from_pretrained(args.output_dir)  # , force_download=True)
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        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]:
        if args.do_train:
            logger.info("Loading checkpoints saved during training for evaluation")
            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
        else:
            logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path)
            checkpoints = [args.model_name_or_path]

        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())
            results.update(result)

    logger.info("Results: {}".format(results))
    predict_file = list(filter(None, args.predict_file.split("/"))).pop()
    if not os.path.exists(os.path.join(args.output_dir, predict_file)):
        os.makedirs(os.path.join(args.output_dir, predict_file))
    output_eval_file = os.path.join(args.output_dir, predict_file, "eval_results.txt")
    with open(output_eval_file, "w") as writer:
        for key in sorted(results.keys()):
            writer.write("%s = %s\n" % (key, str(results[key])))

    return results


if __name__ == "__main__":
    main()
