content: # 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-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """
  
  # from __future__ import absolute_import, division, print_function
  
  import argparse
  import glob
  import logging
  import os
  import random
  
  import numpy as np
  import torch
  from seqeval.metrics import precision_score, recall_score, f1_score
  from tensorboardX import SummaryWriter
  from torch.nn import CrossEntropyLoss
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, Dataset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
  
  from transformers import AdamW, get_linear_schedule_with_warmup
  from transformers import WEIGHTS_NAME, BertConfig, BertForTokenClassification, BertTokenizer
  
  class MyDataset(Dataset):
      def __init__(self,all_input_ids, all_input_mask, all_segment_ids, all_labels):
          super(MyDataset, self).__init__()
          self.all_input_ids = all_input_ids
          self.all_input_mask = all_input_mask
          self.all_segment_ids =all_segment_ids
          self.all_labels = all_labels
      def __getitem__(self, index):
          input_ids = self.all_input_ids[index]
          input_mask = self.all_input_mask[index]
          segment_ids = self.all_segment_ids[index]
          labels = self.all_labels[index]
          return {'input_ids':input_ids,
                  'attention_mask':input_mask,
                  'token_type_ids':segment_ids,
                  'labels':labels}
      def __len__(self):
          return len(self.all_labels)
  
  logger = logging.getLogger(__name__)
  
  ALL_MODELS = sum(
      (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig,)),
      ())
  
  MODEL_CLASSES = {
      "bert": (BertConfig, BertForTokenClassification, 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 train(args, train_dataset,model_T, model, tokenizer, labels, pad_token_label_id,predict_callback):
      """ Train the model """
      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_class = get_linear_schedule_with_warmup
      scheduler_args = {'num_warmup_steps':int(args.warmup_steps*t_total), 'num_training_steps':t_total}
      if args.fp16:
          try:
              from apex import amp
          except ImportError:
              raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
          model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
  
      # 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)
      if args.do_train and args.do_distill:
          from textbrewer import DistillationConfig,TrainingConfig,GeneralDistiller
          distill_config = DistillationConfig(
              temperature = 8,
                # intermediate_matches = [{'layer_T':10, 'layer_S':3, 'feature':'hidden','loss': 'hidden_mse', 'weight' : 1}]
              )
          train_config = TrainingConfig(device="cuda",log_dir=args.output_dir,output_dir=args.output_dir)
          def adaptor_T(batch,model_output):
              return {"logits":(model_output[1],),
                      'logits_mask':(batch['attention_mask'],)}
          def adaptor_S(batch,model_output):
              return {"logits":(model_output[1],),
                      'logits_mask':(batch['attention_mask'],)}
  
          distiller=GeneralDistiller(train_config,distill_config,model_T,model,adaptor_T,adaptor_S,)
          distiller.train(optimizer,train_dataloader,,args.num_train_epochs,
                          scheduler_class=scheduler_class, scheduler_args=scheduler_args,
                          max_grad_norm=1.0, callback=predict_callback)
          return
  
  
  def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""):
      eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode)
  
      args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
      # Note that DistributedSampler samples randomly
      eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
      eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
  
      # multi-gpu evaluate
      if args.n_gpu > 1:
          model = torch.nn.DataParallel(model)
  
      # Eval!
      logger.info("***** Running evaluation %s *****", 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()
      for batch in tqdm(eval_dataloader, desc="Evaluating"):
          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"] else None  # XLM and RoBERTa don"t use segment_ids
              outputs = model(**inputs)
              tmp_eval_loss, logits = outputs[:2]
  
              if args.n_gpu > 1:
                  tmp_eval_loss = tmp_eval_loss.mean()  # mean() to average on multi-gpu parallel evaluating
  
              eval_loss += tmp_eval_loss.item()
          nb_eval_steps += 1
          if preds is None:
              preds = logits.detach().cpu().numpy()
              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
      preds = np.argmax(preds, axis=2)
  
      label_map = {i: label for i, label in enumerate(labels)}
  
      out_label_list = [[] for _ in range(out_label_ids.shape[0])]
      preds_list = [[] for _ in range(out_label_ids.shape[0])]
  
      for i in range(out_label_ids.shape[0]):
          for j in range(out_label_ids.shape[1]):
              if out_label_ids[i, j] != pad_token_label_id:
                  out_label_list[i].append(label_map[out_label_ids[i][j]])
                  preds_list[i].append(label_map[preds[i][j]])
  
      results = {
          "loss": eval_loss,
          "precision": precision_score(out_label_list, preds_list),
          "recall": recall_score(out_label_list, preds_list),
          "f1": f1_score(out_label_list, preds_list)
      }
  
      logger.info("***** Eval results %s *****", prefix)
      for key in sorted(results.keys()):
          logger.info("  %s = %s", key, str(results[key]))
  
      return results, preds_list
  
  
  def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode):
      if args.local_rank not in [-1, 0] and not evaluate:
          torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
  
      # Load data features from cache or dataset file
      cached_features_file = os.path.join(args.data_dir, "cached_{}_{}_{}".format(mode,
          list(filter(None, args.model_name_or_path.split("/"))).pop(),
          str(args.max_seq_length)))
      if os.path.exists(cached_features_file) and not args.overwrite_cache:
          logger.info("Loading features from cached file %s", cached_features_file)
          features = torch.load(cached_features_file)
      else:
          logger.info("Creating features from dataset file at %s", args.data_dir)
          examples = read_examples_from_file(args.data_dir, mode)
          features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer,
                                                  cls_token_at_end=bool(args.model_type in ["xlnet"]),
                                                  # xlnet has a cls token at the end
                                                  cls_token=tokenizer.cls_token,
                                                  cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
                                                  sep_token=tokenizer.sep_token,
                                                  sep_token_extra=bool(args.model_type in ["roberta"]),
                                                  # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
                                                  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,
                                                  pad_token_label_id=pad_token_label_id
                                                  )
          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_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
      all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
      all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)
      if mode=="train":
          dataset = MyDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) #distill must input a dict
      else:
          dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
      return dataset
  
  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--data_dir", default=None, type=str, required=True,
                          help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.")
      parser.add_argument("--model_type", default=None, type=str, required=True,
                          help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
      parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                          help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
      parser.add_argument("--model_name_or_path_student", default=None, type=str, required=False,
                          help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
      parser.add_argument("--output_dir", default=None, type=str, required=True,
                          help="The output directory where the model predictions and checkpoints will be written.")
      parser.add_argument("--num_hidden_layers", default=3, type=int,
                          help="number of layers of student model")
  
      ## Other parameters
      parser.add_argument("--labels", default="", type=str,
                          help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.")
      parser.add_argument("--config_name", default="", type=str,
                          help="Pretrained config name or path if not the same as model_name")
      parser.add_argument("--tokenizer_name", default="", type=str,
                          help="Pretrained tokenizer name or path if not the same as model_name")
      parser.add_argument("--cache_dir", default="", type=str,
                          help="Where do you want to store the pre-trained models downloaded from s3")
      parser.add_argument("--max_seq_length", default=128, type=int,
                          help="The maximum total input sequence length after tokenization. Sequences longer "
                               "than this will be truncated, sequences shorter will be padded.")
      parser.add_argument("--do_train", action="store_true",
                          help="Whether to run training.")
      parser.add_argument("--do_distill", action="store_true",
                          help="Whether to run training.")
      parser.add_argument("--do_eval", action="store_true",
                          help="Whether to run eval on the dev set.")
      parser.add_argument("--do_predict", action="store_true",
                          help="Whether to run predictions on the test set.")
      parser.add_argument("--evaluate_during_training", action="store_true",
                          help="Whether to run evaluation during training at each logging step.")
      parser.add_argument("--do_lower_case", action="store_true",
                          help="Set this flag if you are using an uncased model.")
  
      parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
                          help="Batch size per GPU/CPU for training.")
      parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
                          help="Batch size per GPU/CPU for evaluation.")
      parser.add_argument("--gradient_accumulation_steps", type=int, default=1,
                          help="Number of updates steps to accumulate before performing a backward/update pass.")
      parser.add_argument("--learning_rate", default=5e-5, type=float,
                          help="The initial learning rate for Adam.")
      parser.add_argument("--weight_decay", default=0.0, type=float,
                          help="Weight decay if we apply some.")
      parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                          help="Epsilon for Adam optimizer.")
      parser.add_argument("--max_grad_norm", default=1.0, type=float,
                          help="Max gradient norm.")
      parser.add_argument("--num_train_epochs", default=3.0, type=float,
                          help="Total number of training epochs to perform.")
      parser.add_argument("--max_steps", default=-1, type=int,
                          help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
      parser.add_argument("--warmup_steps", default=0, type=float,
                          help="Linear warmup over warmup_steps.")
  
      parser.add_argument("--logging_steps", type=int, default=50,
                          help="Log every X updates steps.")
      parser.add_argument("--save_steps", type=int, default=50,
                          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.")
      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 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 CONLL-2003 task
      labels = get_labels(args.labels)
      num_labels = len(labels)
      # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
      pad_token_label_id = CrossEntropyLoss().ignore_index
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
  
      args.model_type = args.model_type.lower()
      config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
      config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                            num_labels=num_labels,
                                            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_T = 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.model_name_or_path_student != None:
          config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path_student,
                                                num_labels=num_labels,
                                                cache_dir=args.cache_dir if args.cache_dir else None)
          config.num_hidden_layers=args.num_hidden_layers
          model = model_class.from_pretrained(args.model_name_or_path_student,
                                          from_tf=bool(".ckpt" in args.model_name_or_path),
                                          config=config,
                                          cache_dir=args.cache_dir if args.cache_dir else None)
      else:
          config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                            num_labels=num_labels,
                                            cache_dir=args.cache_dir if args.cache_dir else None)
          config.num_hidden_layers=args.num_hidden_layers
          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
  
      model.to(args.device)
      model_T.to(args.device)
  
      logger.info("Training/evaluation parameters %s", args)
      def predict_callback(model,step):
          labels = get_labels(args.labels)
          config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
          # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
          pad_token_label_id = CrossEntropyLoss().ignore_index
          if args.do_predict and args.local_rank in [-1, 0]:
              tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path, do_lower_case=args.do_lower_case)
              evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test")
          model.train()
  
      # Training
      if args.do_train :
          train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train")
          # global_step, tr_loss = \
          train(args, train_dataset,model_T, model, tokenizer, labels, pad_token_label_id,predict_callback)
          # 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"))
  
      # 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("pytorch_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 ""
              model = model_class.from_pretrained(checkpoint)
              model.to(args.device)
              result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step)
              if global_step:
                  result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
              results.update(result)
          output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
          with open(output_eval_file, "w") as writer:
              for key in sorted(results.keys()):
                  writer.write("{} = {}\n".format(key, str(results[key])))
  
      if args.do_predict and args.local_rank in [-1, 0]:
          tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
          model = model_class.from_pretrained(args.output_dir)
          model.to(args.device)
          result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test")
          # Save results
          output_test_results_file = os.path.join(args.output_dir, "test_results.txt")
          with open(output_test_results_file, "w") as writer:
              for key in sorted(result.keys()):
                  writer.write("{} = {}\n".format(key, str(result[key])))
          # Save predictions
          output_test_predictions_file = os.path.join(args.output_dir, "test_predictions.txt")
          with open(output_test_predictions_file, "w") as writer:
              with open(os.path.join(args.data_dir, "test.txt"), "r") as f:
                  example_id = 0
                  for line in f:
                      if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                          writer.write(line)
                          if not predictions[example_id]:
                              example_id += 1
                      elif predictions[example_id]:
                          output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n"
                          writer.write(output_line)
                      else:
                          logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
  
      return results
  
  
  if __name__ == "__main__":
      main()
  
  