text: """
  SST-2 with Bidirectional Encoder Representations from Transformers
  =========================================================================================
  This example shows how to implement finetune a model with pre-trained BERT parameters for
  for The Stanford Sentiment Treebank 2 (SST-2) task.
  @article{devlin2018bert,
    title={BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
    author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
    journal={arXiv preprint arXiv:1810.04805},
    year={2018}
  }
  """
  
  # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you 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.
  # pylint:disable=redefined-outer-name,logging-format-interpolation
  
  from __future__ import absolute_import, division, print_function
  
  import os
  import time
  import logging
  import argparse
  
  import numpy as np
  import onnx
  import onnxruntime
  import torch
  import random
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from tqdm import tqdm
  from transformers import BertTokenizer
  from transformers import glue_compute_metrics as compute_metrics
  from transformers import glue_output_modes as output_modes
  from transformers import glue_processors as processors
  from transformers import glue_convert_examples_to_features as convert_examples_to_features
  from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
  
  from processors import glue_output_modes as output_modes
  from processors import glue_processors as processors
  from processors import glue_convert_examples_to_features as convert_examples_to_features
  from processors import collate_fn
  
  from callback.optimization.adamw import AdamW
  from callback.lr_scheduler import get_linear_schedule_with_warmup
  from callback.progressbar import ProgressBar
  
  
  logger = logging.getLogger(__name__)
  logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                      datefmt = '%m/%d/%Y %H:%M:%S',
                      level = logging.WARN)
  def get_argparse():
      parser = argparse.ArgumentParser(
      description='BERT fine-tune examples for classification/regression tasks.',
      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  
      ## 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: ")
      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")
      parser.add_argument("--task_name", default=None, type=str, required=True,
                          help="The name of the task to train selected in the list: ")
      #parser.add_argument("--output_dir", default=None, type=str, required=True,
      #                    help="The output directory where the model predictions and checkpoints will be written.")
      parser.add_argument("--output_model", default=None, type=str, required=True,
                          help="The output dierectory for the trained model.")
      ## 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=512, type=int,
                          help="The maximum total input sequence length after tokenization. Sequences longer "
                               "than this will be truncated, sequences shorter will be padded.")
      parser.add_argument("--do_train", action='store_true',
                          help="Whether to run training.")
      parser.add_argument("--do_eval", action='store_true',
                          help="Whether to run eval on the dev set.")
      parser.add_argument("--do_predict", action='store_true',
                          help="Whether to run the model in inference mode on the test set.")
      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=0.01, type=float,
                          help="Weight deay if we apply some.")
      parser.add_argument("--adam_epsilon", default=1e-6, 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_proportion", default=0.1, type=float,
                          help="Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training.")
      parser.add_argument('--logging_steps', type=int, default=10,
                          help="Log every X updates steps.")
      parser.add_argument('--save_steps', type=int, default=1000,
                          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("--local_rank", type=int, default=-1,
                          help="For distributed training: local_rank")
      parser.add_argument('--prune', action='store_true',
                          help="Prune the model with a certain sparsity ratio and acc")
      parser.add_argument("--config", default=None, help="tuning config")
      return parser.parse_args()
  
  def load_and_cache_examples(args, task, tokenizer, data_type='train'):
      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
      if not os.path.exists("./dataset_cached"):
          os.makedirs("./dataset_cached")
      cached_features_file = os.path.join("./dataset_cached", '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):
          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 'roberta' in args.model_type:
              # HACK(label indices are swapped in RoBERTa pretrained model)
              label_list[1], label_list[2] = label_list[2], label_list[1]
  
          if data_type == 'train':
              examples = processor.get_train_examples(args.data_dir)
          elif data_type == 'dev':
              examples = processor.get_dev_examples(args.data_dir)
          else:
              examples = processor.get_test_examples(args.data_dir)
  
          features = convert_examples_to_features(examples,
                                                  tokenizer,
                                                  label_list=label_list,
                                                  max_seq_length=args.max_seq_length,
                                                  output_mode=output_mode)
          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)
      all_lens = torch.tensor([f.input_len 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_lens, all_labels)
      return dataset
  
  def evaluate(args, model, tokenizer, 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):
      for eval_task in eval_task_names:
          eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, data_type='dev')
          #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)
          # Note that DistributedSampler samples randomly
          eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
          eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size,
                                       collate_fn=collate_fn)
  
          # 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
          pbar = ProgressBar(n_total=len(eval_dataloader), desc="Evaluating")
          for step, batch in enumerate(eval_dataloader):
              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]}
                  #inputs['token_type_ids'] = batch[2]
                  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)
              pbar(step)
          print(' ')
          if 'cuda' in str(args.device):
              torch.cuda.empty_cache()
          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)
          logger.info("***** Eval results {} *****".format(prefix))
          for key in sorted(result.keys()):
              logger.info("  %s = %s", key, str(result[key]))
      return results
  
  def take_eval_steps(args, model, tokenizer, prune, 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):
      for eval_task in eval_task_names:
          eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, data_type='dev')
          #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)
          # Note that DistributedSampler samples randomly
          eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
          eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size,
                                       collate_fn=collate_fn)
  
          # 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
          model.eval()
          pbar = ProgressBar(n_total=len(eval_dataloader), desc="Evaluating")
          for step, batch in enumerate(eval_dataloader):
              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]}
                  #inputs['token_type_ids'] = batch[2]
                  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)
              pbar(step)
          if 'cuda' in str(args.device):
              torch.cuda.empty_cache()
          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)
          logger.info("***** Eval results {} *****".format(prefix))
          for key in sorted(result.keys()):
              logger.info("  %s = %s", key, str(result[key]))
      return results
  
  def take_train_steps(args, model, tokenizer, train_dataloader, prune):
      if args.max_steps > 0:
          num_training_steps = args.max_steps
          args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
      else:
          num_training_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
      args.warmup_steps = int(num_training_steps * args.warmup_proportion)
      # 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(params=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=num_training_steps)
      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", num_training_steps)
  
      for epoch in range(int(args.num_train_epochs)):
          pbar = ProgressBar(n_total=len(train_dataloader), desc='Training')
          model.train()
          prune.on_epoch_begin(epoch)
          for step, batch in enumerate(train_dataloader):
              prune.on_batch_begin(step)
              batch = tuple(t.to(args.device) for t in batch)
              inputs = {'input_ids': batch[0],
                        'attention_mask': batch[1],
                        'labels': batch[3]}
              #inputs['token_type_ids'] = batch[2]
              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
  
              loss.backward()
              torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
  
  
              if (step + 1) % args.gradient_accumulation_steps == 0:
                  optimizer.step()
                  scheduler.step()  # Update learning rate schedule
                  model.zero_grad()
  
              prune.on_batch_end()
              if step >= 20:
                  break;
              if args.local_rank in [-1, 0] and args.logging_steps > 0 and step % args.logging_steps == 20:
                  # Log metrics
                  if args.local_rank == -1:  # Only evaluate when single GPU otherwise metrics may not average well
                      evaluate(args, model, tokenizer)
  
              #if args.local_rank in [-1, 0] and args.save_steps > 0 and step % args.save_steps  == 20:
              #    # Save model checkpoint
              #    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(step + epoch * args.save_steps))
              #    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)
              #    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
              #    torch.save(model, os.path.join(output_dir, 'model.bin'))
              #    logger.info("Saving model checkpoint to %s", output_dir)
              pbar(step, {'loss': loss.item()})
          prune.on_epoch_end()
          print(" ")
          if 'cuda' in str(args.device):
              torch.cuda.empty_cache()
  
  def train(args, train_dataset, model, tokenizer):
      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,
                                    collate_fn=collate_fn)
      def train_func(model):
          return take_train_steps(args, model, tokenizer, train_dataloader, prune)
  
      def eval_func(model):
          return take_eval_steps(args, model, tokenizer, prune)
  
      if args.prune:
          from neural_compressor.experimental import Pruning, common
          prune = Pruning(args.config)
          prune.model = common.Model(model)
          prune.train_dataloader = train_dataloader
          prune.pruning_func = train_func
          prune.eval_dataloader = train_dataloader
          prune.eval_func = eval_func
          model = prune.fit()
          torch.save(model, args.output_model)
  
  if __name__ == "__main__":
      args = get_argparse()
      args.task_name = args.task_name.lower()
      #if not os.path.exists(args.output_dir):
      #    os.mkdir(args.output_dir)
      #args.output_dir = args.output_dir + '{}'.format(args.model_type)
      #if not os.path.exists(args.output_dir):
      #    os.mkdir(args.output_dir)
      #if os.path.exists(args.output_dir) and os.listdir(
      #        args.output_dir) and args.do_train and not args.overwrite_output_dir:
      #    raise ValueError(
      #        "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
      #            args.output_dir))
     # Setup CUDA, GPU & distributed training
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
          args.n_gpu = torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          torch.distributed.init_process_group(backend='nccl')
          args.n_gpu = 1
      #args.device = device
      args.device = "cpu"
      # Set seed
      random.seed(args.seed)
      os.environ['PYTHONHASHSEED'] = str(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      torch.cuda.manual_seed(args.seed)
      torch.cuda.manual_seed_all(args.seed)
      # some cudnn methods can be random even after fixing the seed
      # unless you tell it to be deterministic
      torch.backends.cudnn.deterministic = True
      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()
      tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
      model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
      if args.local_rank == 0:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
      model.to(args.device)
      logger.info("Training/evaluation parameters %s", args)
  
      # Training
      if args.do_train:
          train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, data_type='train')
          train(args, train_dataset, model, tokenizer)
  
      # Save model
  