code: # 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). """
  
  
  import argparse
  import glob
  import logging
  import os
  import random
  
  import numpy as np
  import torch
  from seqeval.metrics import f1_score, precision_score, recall_score
  from torch.nn import CrossEntropyLoss
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  import pickle
  # from pudb import set_trace
  # set_trace()
  
  from transformers import (
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      WEIGHTS_NAME,
      AdamW,
      AutoConfig,
      AutoModelForTokenClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
  )
  from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
  
  
  try:
      from torch.utils.tensorboard import SummaryWriter
  except ImportError:
      from tensorboardX import SummaryWriter
  
  
  logger = logging.getLogger(__name__)
  
  MODEL_CONFIG_CLASSES = list(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys())
  MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
  
  ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), ())
  
  TOKENIZER_ARGS = ["do_lower_case", "strip_accents", "keep_accents", "use_fast"]
  
  
  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, labels, pad_token_label_id):
      """ Train the model """
      if args.local_rank in [-1, 0]:
          tb_writer = SummaryWriter()
  
      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
  
      if args.warmup_ratio > 0:
          args.warmup_steps = int(t_total * args.warmup_ratio)
      # 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
          )
  
      # 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)
  
      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
          try:
              global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
          except ValueError:
              global_step = 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
      for _ in train_iterator:
          epoch_iterator = tqdm(train_dataloader, desc="Iter(loss=X.XXX, lr=X.XXXXXXXX)", disable=args.local_rank not in [-1, 0])
          for step, batch in enumerate(epoch_iterator):
  
              # Skip past any already trained steps if resuming training
              if steps_trained_in_current_epoch > 0:
                  steps_trained_in_current_epoch -= 1
                  continue
  
              model.train()
              batch = tuple(t.to(args.device) for t in batch)
              inputs = {"input_ids": batch[0], "attention_mask": batch[1], "soft_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)
              loss = outputs[0]  # model outputs are always tuple in pytorch-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 (step + 1) % args.gradient_accumulation_steps == 0:
                  epoch_iterator.set_description('Iter (loss=%5.3f) lr=%9.7f' % (loss.item(), scheduler.get_lr()[0]))
                  if args.fp16:
                      torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                  else:
                      torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
  
                  optimizer.step()
                  scheduler.step()  # Update learning rate schedule
                  model.zero_grad()
                  global_step += 1
  
                  if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                      # Log metrics
                      if (
                          args.local_rank == -1 and args.evaluate_during_training
                      ):  # Only evaluate when single GPU otherwise metrics may not average well
                          results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev")
                          for key, value in results.items():
                              tb_writer.add_scalar("eval_{}".format(key), value, global_step)
                      tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
                      tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
                      logging_loss = tr_loss
  
                  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))
                      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
          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, 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]])
  
      print("preds:", preds_list[0])
      print("labels:", out_label_list[0])
      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
     
      
      logger.info("Creating features from dataset file at %s", args.data_dir)
      examples = read_examples_from_file(args.eval_file, 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.pad_token_id,
          pad_token_segment_id=tokenizer.pad_token_type_id,
          pad_token_label_id=pad_token_label_id,
      )
     
  
      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)
  
      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,
          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_TYPES),
      )
      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(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
  
      # 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_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(
          "--keep_accents", action="store_const", const=True, help="Set this flag if model is trained with accents."
      )
      parser.add_argument(
          "--strip_accents", action="store_const", const=True, help="Set this flag if model is trained without accents."
      )
      parser.add_argument("--use_fast", action="store_const", const=True, help="Set this flag to use fast tokenization.")
      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("--logits_file", type=str, default="")
      parser.add_argument("--eval_file", type=str, default="")
      parser.add_argument("--warmup_ratio", type=float, default=0.1)
  
      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 distant debugging if needed
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
  
          print("Waiting for debugger attach")
          ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
          ptvsd.wait_for_attach()
  
      # Setup CUDA, GPU & distributed training
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
          args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          torch.distributed.init_process_group(backend="nccl")
          args.n_gpu = 1
      args.device = device
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
      )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
          args.local_rank,
          device,
          args.n_gpu,
          bool(args.local_rank != -1),
          args.fp16,
      )
  
      # Set seed
      set_seed(args)
  
      # 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 = AutoConfig.from_pretrained(
          args.config_name if args.config_name else args.model_name_or_path,
          num_labels=num_labels,
          id2label={str(i): label for i, label in enumerate(labels)},
          label2id={label: i for i, label in enumerate(labels)},
          cache_dir=args.cache_dir if args.cache_dir else None,
      )
      tokenizer_args = {k: v for k, v in vars(args).items() if v is not None and k in TOKENIZER_ARGS}
      logger.info("Tokenizer arguments: %s", tokenizer_args)
      tokenizer = AutoTokenizer.from_pretrained(
          args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
          cache_dir=args.cache_dir if args.cache_dir else None,
          **tokenizer_args,
      )
      model = AutoModelForTokenClassification.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)
  
      logger.info("Training/evaluation parameters %s", args)
  
      # Training
      if args.do_train:
          # train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train")
          with open(args.logits_file, "rb") as f:
              datasets = pickle.load(f)
          
          all_input_ids = torch.tensor(datasets[0], dtype=torch.long)
          all_input_mask = torch.tensor(datasets[1], dtype=torch.long)
          all_segment_ids = torch.tensor(datasets[2], dtype=torch.long)
          all_ner_logits = torch.tensor(datasets[3], dtype=torch.float)
  
          train_dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ner_logits)
  
          global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id)
          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 = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
          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 = AutoModelForTokenClassification.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 = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args)
          model = AutoModelForTokenClassification.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()
  
apis: torch.utils.data.DataLoader torch.cuda.manual_seed_all torch.no_grad numpy.random.seed torch.cuda.is_available torch.distributed.init_process_group torch.cuda.device_count torch.nn.DataParallel torch.utils.data.RandomSampler torch.device torch.cuda.set_device torch.distributed.get_world_size torch.distributed.get_rank torch.manual_seed torch.utils.data.SequentialSampler torch.tensor numpy.argmax torch.distributed.barrier torch.nn.parallel.DistributedDataParallel torch.utils.data.TensorDataset torch.utils.data.distributed.DistributedSampler torch.nn.CrossEntropyLoss