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)."""
  
  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 torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  
  from hans_processors import glue_output_modes as output_modes
  from hans_processors import glue_processors as processors
  from hans_processors import hans_convert_examples_to_features as convert_examples_to_features
  from transformers import (
      WEIGHTS_NAME,
      AdamW,
      AlbertConfig,
      AlbertForSequenceClassification,
      AlbertTokenizer,
      BertConfig,
      BertForSequenceClassification,
      BertTokenizer,
      DistilBertConfig,
      DistilBertForSequenceClassification,
      DistilBertTokenizer,
      RobertaConfig,
      RobertaForSequenceClassification,
      RobertaTokenizer,
      XLMConfig,
      XLMForSequenceClassification,
      XLMTokenizer,
      XLNetConfig,
      XLNetForSequenceClassification,
      XLNetTokenizer,
      get_linear_schedule_with_warmup,
  )
  
  
  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)
      ),
      (),
  )
  
  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),
  }
  
  
  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.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
      )
      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
      tr_loss, logging_loss = 0.0, 0.0
      model.zero_grad()
      train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
      set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
      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):
              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"] else None
                  )  # XLM, DistilBERT and 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 (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
                          results = evaluate(args, model, tokenizer)
                          for key, value in results.items():
                              eval_key = "eval_{}".format(key)
                              logs[eval_key] = value
  
                      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))
                      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"))
                      logger.info("Saving model checkpoint to %s", output_dir)
  
              if args.max_steps > 0 and global_step > args.max_steps:
                  epoch_iterator.close()
                  break
          if args.max_steps > 0 and global_step > args.max_steps:
              train_iterator.close()
              break
  
      if args.local_rank in [-1, 0]:
          tb_writer.close()
  
      return global_step, tr_loss / global_step
  
  
  def evaluate(args, model, tokenizer, prefix=""):
      # 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, label_list = 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)
          # 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:
              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"] else None
                      )  # XLM, DistilBERT and 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()
                  pair_ids = batch[4].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)
                  pair_ids = np.append(pair_ids, batch[4].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)
  
          output_eval_file = os.path.join(eval_output_dir, "hans_predictions.txt")
          with open(output_eval_file, "w") as writer:
              writer.write("pairID,gld_label\n")
              for pid, pred in zip(pair_ids, preds):
                  writer.write("ex" + str(pid) + "," + label_list[int(pred)] + "\n")
  
      return results
  
  
  def load_and_cache_examples(args, task, tokenizer, evaluate=False):
      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),
          ),
      )
  
      label_list = processor.get_labels()
  
      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)
          if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta"]:
              # 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.pad_token_id,
              pad_token_segment_id=tokenizer.pad_token_type_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_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)
      all_pair_ids = torch.tensor([int(f.pairID) for f in features], dtype=torch.long)
  
      dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels, all_pair_ids)
      return dataset, label_list
  
  
  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.",
      )
  
      # 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="Rul 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=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 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 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
  
      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)
          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()
  