text: # 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.
  """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
  
  
  import argparse
  import copy
  import glob
  import json
  import logging
  import os
  import random
  
  import numpy as np
  from scipy.stats import truncnorm
  import torch
  import torch.nn as nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, Subset
  from torch.utils.data.sampler import SubsetRandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  
  from transformers import (
      WEIGHTS_NAME,
      AdamW,
      AlbertConfig,
      AlbertForSequenceClassification,
      AlbertTokenizer,
      BertConfig,
      BertForSequenceClassification,
      BertTokenizer,
      DistilBertConfig,
      DistilBertForSequenceClassification,
      DistilBertTokenizer,
      FlaubertConfig,
      FlaubertForSequenceClassification,
      FlaubertTokenizer,
      RobertaConfig,
      RobertaForSequenceClassification,
      RobertaTokenizer,
      XLMConfig,
      XLMForSequenceClassification,
      XLMRobertaConfig,
      XLMRobertaForSequenceClassification,
      XLMRobertaTokenizer,
      XLMTokenizer,
      XLNetConfig,
      XLNetForSequenceClassification,
      XLNetTokenizer,
      get_linear_schedule_with_warmup,
  )
  from transformers import glue_compute_metrics as compute_metrics
  from transformers import glue_convert_examples_to_features as convert_examples_to_features
  from transformers import glue_output_modes as output_modes
  from transformers import glue_processors as processors
  
  # early stopping
  from pytorchtools import EarlyStopping
  
  try:
      from torch.utils.tensorboard import SummaryWriter
  except ImportError:
      from tensorboardX import SummaryWriter
  
  
  logger = logging.getLogger(__name__)
  
  ALL_MODELS = sum(
      (
          tuple(conf.pretrained_config_archive_map.keys())
          for conf in (
              BertConfig,
              XLNetConfig,
              XLMConfig,
              RobertaConfig,
              DistilBertConfig,
              AlbertConfig,
              XLMRobertaConfig,
              FlaubertConfig,
          )
      ),
      (),
  )
  
  MODEL_CLASSES = {
      "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
      "xlnet": (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
      "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
      "roberta": (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
      "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
      "albert": (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer),
      "xlmroberta": (XLMRobertaConfig, XLMRobertaForSequenceClassification, XLMRobertaTokenizer),
      "flaubert": (FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer),
  }
  
  
  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, model, tokenizer):
      """ Train the model """
      if args.local_rank in [-1, 0]:
          tb_writer = SummaryWriter(args.log_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 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,
          )
          
      # EARLY STOPPING: split training data into training and validation
      # obtain training indices that will be used for validation
      if args.es:
          num_train = len(train_dataset)
          indices = list(range(num_train))
          np.random.shuffle(indices)
          split = int(np.floor(args.valid_size * num_train))
          train_idx, valid_idx = indices[split:], indices[:split]
  
          # define samplers for obtaining training and validation batches
          es_train_sampler = SubsetRandomSampler(train_idx)
          es_valid_sampler = SubsetRandomSampler(valid_idx)
  
          # load training data in batches
          train_dataloader = torch.utils.data.DataLoader(train_dataset,
                                                         batch_size=batch_size,
                                                         sampler=es_train_sampler,
                                                         num_workers=0)
  
          # load validation data in batches
          valid_dataloader = torch.utils.data.DataLoader(train_dataset,
                                                         batch_size=batch_size,
                                                         sampler=es_valid_sampler,
                                                         num_workers=0)
      # END EARLY STOPPING data loading -------------------------------
  
      # Train!
      logger.info("***** Running training *****")
      logger.info("  Num examples = %d", len(train_dataset) if args.es else int(np.ceil((1-args.valid_size) * len(train_dataset))))
      logger.info("  Num Epochs = %d", args.num_train_epochs)
      logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
      logger.info(
          "  Total train batch size (w. parallel, distributed & accumulation) = %d",
          args.train_batch_size
          * args.gradient_accumulation_steps
          * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
      )
      logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
      logger.info("  Total optimization steps = %d", t_total)
  
      global_step = 0
      epochs_trained = 0
      steps_trained_in_current_epoch = 0
      # Check if continuing training from a checkpoint
      if os.path.exists(args.model_name_or_path):
          # 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)
  
      tr_loss, logging_loss = 0.0, 0.0
      model.zero_grad()
      train_iterator = trange(
          epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
      )
      set_seed(args)  # Added here for reproductibility
      
      # EARLY STOPPING setup----------------------------------------------
      if args.es:
          # to track the training loss as the model trains
          train_losses = []
          # to track the validation loss as the model trains
          valid_losses = []
          # to track the average training loss per epoch as the model trains
          avg_train_losses = []
          # to track the average validation loss per epoch as the model trains
          avg_valid_losses = [] 
          # initialize the early_stopping object
          early_stopping = EarlyStopping(patience=patience, verbose=True)
      # END EARLY STOPPING setup------------------------------------------
      
      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
  
              logger.info("  Num examples = %d", len(train_dataset) if args.es else int(np.ceil((1-args.valid_size) * len(train_dataset))))
              logger.info("  Num Epochs = %d", args.num_train_epochs)
              logger.info("  Total optimization steps = %d", t_total)
              logger.info("  Epochs trained = %d", epochs_trained)
              logger.info("  Steps trained in current epoch = %d", steps_trained_in_current_epoch)
              model.train()
              batch = tuple(t.to(args.device) for t in batch)
              inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
              if args.model_type != "distilbert":
                  inputs["token_type_ids"] = (
                      batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
                  )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
              outputs = model(**inputs)
              loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
              
              if args.n_gpu > 1:
                  loss = loss.mean()  # mean() to average on multi-gpu parallel training
              if args.gradient_accumulation_steps > 1:
                  loss = loss / args.gradient_accumulation_steps
  
              if args.fp16:
                  with amp.scale_loss(loss, optimizer) as scaled_loss:
                      scaled_loss.backward()
              else:
                  loss.backward()
  
              tr_loss += loss.item()
              if args.es:
                  train_losses.append(loss.item())
              if (step + 1) % args.gradient_accumulation_steps == 0:
                  if args.fp16:
                      torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                  else:
                      torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
  
                  optimizer.step()
                  scheduler.step()  # Update learning rate schedule
                  model.zero_grad()
                  global_step += 1
  
                  if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                      logs = {}
                      if (
                          args.local_rank == -1 and args.evaluate_during_training
                      ):  # Only evaluate when single GPU otherwise metrics may not average well
                          if args.es:
                              results = evaluate(args, model, tokenizer, valid_dataloader)
                          else:
                              results = evaluate(args, model, tokenizer)
                          for key, value in results.items():
                              eval_key = "eval_{}".format(key)
                              logs[eval_key] = value
                          if args.es:
                              valid_losses.append(logs[eval_key])
  
                      loss_scalar = (tr_loss - logging_loss) / args.logging_steps
                      learning_rate_scalar = scheduler.get_lr()[0]
                      logs["learning_rate"] = learning_rate_scalar
                      logs["loss"] = loss_scalar
                      logging_loss = tr_loss
  
                      for key, value in logs.items():
                          tb_writer.add_scalar(key, value, global_step)
                      print(json.dumps({**logs, **{"step": global_step}}))
  
                  # if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                      # Save model checkpoint
                      # output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
                      # output_dir = os.path.join(args.output_dir, "checkpoint-last")
                      # 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
              # check for early stopping at end of epoch
          if args.es:
              # print training/validation statistics 
              # calculate average loss over an epoch
              train_loss = np.average(train_losses)
              valid_loss = np.average(valid_losses)
              avg_train_losses.append(train_loss)
              avg_valid_losses.append(valid_loss)
  
              epoch_len = args.num_train_epochs
  
              print_msg = (f'[{epoch:>{epoch_len}}/{n_epochs:>{epoch_len}}] ' +
                           f'train_loss: {train_loss:.5f} ' +
                           f'valid_loss: {valid_loss:.5f}')
  
              print(print_msg)
  
              # clear lists to track next epoch
              train_losses = []
              valid_losses = []
  
              # early_stopping needs the validation loss to check if it has decresed, 
              # and if it has, it will make a checkpoint of the current model
              early_stopping(valid_loss, model)
  
              if early_stopping.early_stop:
                  print("Early stopping")
                  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, es_dataloader=None, prefix=""):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
      eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
  
      results = {}
      for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
          eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
  
          if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
              os.makedirs(eval_output_dir)
  
          args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
          
          if es_dataloader:
              eval_dataloader = es_dataloader
          else:
              # Note that DistributedSampler samples randomly
              eval_sampler = SequentialSampler(eval_dataset)
              eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
  
          # multi-gpu eval
          if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
              model = torch.nn.DataParallel(model)
  
          # Eval!
  #        logger.info("***** Running evaluation {} *****".format(prefix))
  #        logger.info("  Num examples = %d", len(eval_dataset))
  #        logger.info("  Batch size = %d", args.eval_batch_size)
          eval_loss = 0.0
          nb_eval_steps = 0
          preds = None
          out_label_ids = None
          for batch in tqdm(eval_dataloader, desc="Evaluating"):
              model.eval()
              batch = tuple(t.to(args.device) for t in batch)
  
              with torch.no_grad():
                  inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
                  if args.model_type != "distilbert":
                      inputs["token_type_ids"] = (
                          batch[2] if args.model_type in ["bert", "xlnet", "albert"] else None
                      )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
                  outputs = model(**inputs)
                  tmp_eval_loss, logits = outputs[:2]
  
                  eval_loss += tmp_eval_loss.mean().item()
              nb_eval_steps += 1
              if preds is None:
                  preds = logits.detach().cpu().numpy()
                  out_label_ids = inputs["labels"].detach().cpu().numpy()
              else:
                  preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                  out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
  
          eval_loss = eval_loss / nb_eval_steps
          if args.output_mode == "classification":
              preds = np.argmax(preds, axis=1)
          elif args.output_mode == "regression":
              preds = np.squeeze(preds)
          result = compute_metrics(eval_task, preds, out_label_ids)
          results.update(result)
  
          output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
          with open(output_eval_file, "w") as writer:
              logger.info("***** Eval results {} *****".format(prefix))
              for key in sorted(result.keys()):
                  logger.info("  %s = %s", key, str(result[key]))
                  writer.write("%s = %s\n" % (key, str(result[key])))
  
      return results
  
  
  def load_and_cache_examples(args, task, tokenizer, evaluate=False, subset_perc=100):
      if args.local_rank not in [-1, 0] and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      processor = processors[task]()
      output_mode = output_modes[task]
      # Load data features from cache or dataset file
      cached_features_file = os.path.join(
          args.data_dir,
          "cached_{}_{}_{}_{}".format(
              "dev" if evaluate else "train",
              list(filter(None, args.model_name_or_path.split("/"))).pop(),
              str(args.max_seq_length),
              str(task),
          ),
      )
      if os.path.exists(cached_features_file) and not args.overwrite_cache:
          logger.info("Loading features from cached file %s", cached_features_file)
          features = torch.load(cached_features_file)
      else:
          logger.info("Creating features from dataset file at %s", args.data_dir)
          label_list = processor.get_labels()
          if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
              # HACK(label indices are swapped in RoBERTa pretrained model)
              label_list[1], label_list[2] = label_list[2], label_list[1]
          examples = (
              processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
          )
          features = convert_examples_to_features(
              examples,
              tokenizer,
              label_list=label_list,
              max_length=args.max_seq_length,
              output_mode=output_mode,
              pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
              pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
              pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
          )
          if args.local_rank in [-1, 0]:
              logger.info("Saving features into cached file %s", cached_features_file)
              torch.save(features, cached_features_file)
  
      if args.local_rank == 0 and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      # Convert to Tensors and build dataset
      all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
      all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
      all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
      if output_mode == "classification":
          all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
      elif output_mode == "regression":
          all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
      dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
      # subset the data if specified
      if subset_perc < 100:
          subset_idxs = torch.randperm(len(dataset))[:round(subset_perc*len(dataset)//100)]
          dataset = Subset(dataset, subset_idxs)
      return dataset
  
  
  def main():
      parser = argparse.ArgumentParser()
  
      # Required parameters
      parser.add_argument(
          "--data_dir",
          default=None,
          type=str,
          required=True,
          help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
      )
      parser.add_argument(
          "--model_type",
          default=None,
          type=str,
          required=True,
          help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
      )
      parser.add_argument(
          "--model_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
      )
      parser.add_argument(
          "--task_name",
          default=None,
          type=str,
          required=True,
          help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
      )
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      # layer initialization params
      parser.add_argument(
          "--start_layer", default=12, type=int, help="Start range for reinitializing bert layers"
      )
      parser.add_argument(
          "--end_layer", default=11, type=int, help="End range for reinitializing bert layers"
      )
      # Percentange of training data to subset
      parser.add_argument("--subset_perc", type=float, default=100, help="% of training data we want to keep")
      # Other parameters
      parser.add_argument(
          "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          default="",
          type=str,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--cache_dir",
          default="",
          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(
          "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.",
      )
      parser.add_argument(
          "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.",
      )
  
      parser.add_argument(
          "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.",
      )
      parser.add_argument(
          "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
      parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
      parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
      parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
      parser.add_argument(
          "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.",
      )
      parser.add_argument(
          "--max_steps",
          default=-1,
          type=int,
          help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
      )
      parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
  
      parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
      parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
      parser.add_argument(
          "--eval_all_checkpoints",
          action="store_true",
          help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
      )
      parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
      parser.add_argument(
          "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory",
      )
      parser.add_argument(
          "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
  
      parser.add_argument(
          "--fp16",
          action="store_true",
          help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
      )
      parser.add_argument(
          "--fp16_opt_level",
          type=str,
          default="O1",
          help="For fp16: Apex AMP optimization 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.")
      parser.add_argument("--log_dir", type=str, default="../logs", help="logging directory for tensorboard")
      # early stopping
      parser.add_argument("--es", action="store_true", help="Flag for whether to do early stopping")
      parser.add_argument("--patience", type=int, default=5, help="Number of epochs to to wait after last time validation loss improved")
      parser.add_argument("--valid_size", type=float, default=0.2, help="percentage of training set to use as validation")
      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)
  
      # Prepare GLUE task
      args.task_name = args.task_name.lower()
      if args.task_name not in processors:
          raise ValueError("Task not found: %s" % (args.task_name))
      processor = processors[args.task_name]()
      args.output_mode = output_modes[args.task_name]
      label_list = processor.get_labels()
      num_labels = len(label_list)
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      args.model_type = args.model_type.lower()
      config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
      config = config_class.from_pretrained(
          args.config_name if args.config_name else args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=args.task_name,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
      tokenizer = tokenizer_class.from_pretrained(
          args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
          do_lower_case=args.do_lower_case,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
      model = model_class.from_pretrained(
          args.model_name_or_path,
          from_tf=bool(".ckpt" in args.model_name_or_path),
          config=config,
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
  
      if args.local_rank == 0:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
      
      """
      START PROBING
      - Trim model to include only up to k layers
      - Freeze remaining layers
      """
      
      def deleteEncodingLayers(model, num_layers_to_keep): 
          oldModuleList = model.bert.encoder.layer
          newModuleList = nn.ModuleList()
          # Now iterate over all layers, only keepign only the relevant layers.
          for i in range(0, num_layers_to_keep):
              newModuleList.append(oldModuleList[i])
          # Create a copy of the model, modify it with the new list, and return
          copyOfModel = copy.deepcopy(model)
          copyOfModel.bert.encoder.layer = newModuleList
          return copyOfModel
  
      model = deleteEncodingLayers(model, args.end_layer)
      
      for name, param in model.named_parameters():
          if 'classifier' not in name:
              param.requires_grad = False
              
      # # DEBUG
      # print("-----------------------------------------------------")
      # print(list(model.bert.parameters())[0])
      # print("-----------------------------------------------------")
      
      # END PROBING
      
      model.to(args.device)
  
      logger.info("Training/evaluation parameters %s", args)
  
      # Training
      if args.do_train:
          train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False, subset_perc=args.subset_perc)
          global_step, tr_loss = train(args, train_dataset, model, tokenizer)
          logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
  
      # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
      if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
          # Create output directory if needed
          if 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()`
          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)
          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)
          tokenizer = tokenizer_class.from_pretrained(args.output_dir)
          model.to(args.device)
  
      # Evaluation
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
          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 logging
          logger.info("Evaluate the following checkpoints: %s", checkpoints)
          for checkpoint in checkpoints:
              global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
              prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
  
              model = model_class.from_pretrained(checkpoint)
              model.to(args.device)
              result = evaluate(args, model, tokenizer, prefix=prefix)
              result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
              results.update(result)
  
      return results
  
  
  if __name__ == "__main__":
      main()
  