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.
  """
  Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
  GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
  using a masked language modeling (MLM) loss.
  """
  
  from __future__ import absolute_import, division, print_function
  
  import argparse
  import glob
  import logging
  import os
  import pickle
  import random
  import re
  import shutil
  import json
  import numpy as np
  import torch
  from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  
  try:
      from torch.utils.tensorboard import SummaryWriter
  except:
      from tensorboardX import SummaryWriter
  
  from tqdm import tqdm, trange
  import multiprocessing
  from model import Model
  
  cpu_cont = 16
  from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
                            BertConfig, BertForMaskedLM, BertTokenizer,
                            GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
                            OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
                            RobertaConfig, RobertaModel, RobertaTokenizer,
                            DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
  
  logger = logging.getLogger(__name__)
  
  MODEL_CLASSES = {
      'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer),
      'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
      'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
      'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer),
      'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
  }
  
  
  
  class InputFeatures(object):
      """A single training/test features for a example."""
      def __init__(self,
                   input_tokens,
                   input_ids,
                   idx,
                   label,
  
      ):
          self.input_tokens = input_tokens
          self.input_ids = input_ids
          self.idx=str(idx)
          self.label=label
          
  def convert_examples_to_features(code, label, tokenizer,args):
      #source
      code_tokens=tokenizer.tokenize(code)[:args.block_size-2]
      source_tokens =[tokenizer.cls_token]+code_tokens+[tokenizer.sep_token]
      source_ids =  tokenizer.convert_tokens_to_ids(source_tokens)
      padding_length = args.block_size - len(source_ids)
      source_ids+=[tokenizer.pad_token_id]*padding_length
      return InputFeatures(source_tokens,source_ids,0,label)
  
  class TextDataset(Dataset):
      def __init__(self, tokenizer, args, file_path=None):
          self.examples = []
          # To-Do: 这里需要根据code authorship的数据集重新做.
          file_type = file_path.split('/')[-1].split('.')[0]
          folder = '/'.join(file_path.split('/')[:-1]) # 得到文件目录
  
          cache_file_path = os.path.join(folder, 'cached_{}'.format(
                                      file_type))
          code_pairs_file_path = os.path.join(folder, 'cached_{}.pkl'.format(
                                      file_type))
  
          print('\n cached_features_file: ',cache_file_path)
          try:
              self.examples = torch.load(cache_file_path)
              with open(code_pairs_file_path, 'rb') as f:
                  code_files = pickle.load(f)
              
              logger.info("Loading features from cached file %s", cache_file_path)
          
          except:
              logger.info("Creating features from dataset file at %s", file_path)
              code_files = []
              with open(file_path) as f:
                  for line in f:
                      code = line.split(" <CODESPLIT> ")[0]
                      code = code.replace("\\n", "\n").replace('\"','"')
                      label = line.split(" <CODESPLIT> ")[1]
                      # 将这俩内容转化成input.
                      self.examples.append(convert_examples_to_features(code, int(label), tokenizer,args))
                      code_files.append(code)
                      # 这里每次都是重新读取并处理数据集，能否cache然后load
              assert(len(self.examples) == len(code_files))
              with open(code_pairs_file_path, 'wb') as f:
                  pickle.dump(code_files, f)
              logger.info("Saving features into cached file %s", cache_file_path)
              torch.save(self.examples, cache_file_path)
  
          if 'train' in file_path:
              for idx, example in enumerate(self.examples[:3]):
                      logger.info("*** Example ***")
                      logger.info("idx: {}".format(idx))
                      logger.info("label: {}".format(example.label))
                      logger.info("input_tokens: {}".format([x.replace('\u0120','_') for x in example.input_tokens]))
                      logger.info("input_ids: {}".format(' '.join(map(str, example.input_ids))))
  
  
  
      def __len__(self):
          return len(self.examples)
  
      def __getitem__(self, item):
          
          return torch.tensor(self.examples[item].input_ids),torch.tensor(self.examples[item].label)
  
  
  def load_and_cache_examples(args, tokenizer, evaluate=False,test=False,pool=None):
      # 问题是，我寻思你们也没cache啊....
      dataset = TextDataset(tokenizer, args, file_path=args.test_data_file if test else (args.eval_data_file if evaluate else args.train_data_file),block_size=args.block_size,pool=pool)
      return dataset
  
  def set_seed(seed=42):
      random.seed(seed)
      os.environ['PYHTONHASHSEED'] = str(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      torch.cuda.manual_seed(seed)
      torch.backends.cudnn.deterministic = True
  
  def train(args, train_dataset, model, tokenizer,pool):
      """ 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)
      args.max_steps=args.epoch*len( train_dataloader)
      args.save_steps=len( train_dataloader)
      args.warmup_steps=len( train_dataloader)
      args.logging_steps=len( train_dataloader)
      args.num_train_epochs=args.epoch
      model.to(args.device)
      # 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=args.max_steps)
      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)
  
      checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
      scheduler_last = os.path.join(checkpoint_last, 'scheduler.pt')
      optimizer_last = os.path.join(checkpoint_last, 'optimizer.pt')
      if os.path.exists(scheduler_last):
          scheduler.load_state_dict(torch.load(scheduler_last))
      if os.path.exists(optimizer_last):
          optimizer.load_state_dict(torch.load(optimizer_last))
      # 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", args.max_steps)
      
      global_step = args.start_step
      tr_loss, logging_loss,avg_loss,tr_nb,tr_num,train_loss = 0.0, 0.0,0.0,0,0,0
      best_mrr=0.0
      best_f1=0
      # model.resize_token_embeddings(len(tokenizer))
      model.zero_grad()
      set_seed(args.seed)  # Added here for reproducibility (even between python 2 and 3)
   
      for idx in range(args.start_epoch, int(args.num_train_epochs)): 
          bar = tqdm(train_dataloader,total=len(train_dataloader))
          tr_num=0
          train_loss=0
          for step, batch in enumerate(bar):
              inputs = batch[0].to(args.device)        
              labels=batch[1].to(args.device) 
              model.train()
              loss,logits = model(inputs,labels)
  
  
              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()
                  torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
              else:
                  loss.backward()
                  torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
  
              tr_loss += loss.item()
              tr_num+=1
              train_loss+=loss.item()
              if avg_loss==0:
                  avg_loss=tr_loss
              avg_loss=round(train_loss/tr_num,5)
              bar.set_description("epoch {} loss {}".format(idx,avg_loss))
  
                  
              if (step + 1) % args.gradient_accumulation_steps == 0:
                  optimizer.step()
                  optimizer.zero_grad()
                  scheduler.step()  
                  global_step += 1
                  output_flag=True
                  avg_loss=round(np.exp((tr_loss - logging_loss) /(global_step- tr_nb)),4)
                  if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
                      logging_loss = tr_loss
                      tr_nb=global_step
  
                  if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                      
                      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,pool=pool,eval_when_training=True)                 
                          # Save model checkpoint
  
                      if results['eval_f1']>best_f1:
                          best_f1=results['eval_f1']
                          logger.info("  "+"*"*20)  
                          logger.info("  Best f1:%s",round(best_f1,4))
                          logger.info("  "+"*"*20)                          
                          
                          checkpoint_prefix = 'checkpoint-best-f1'
                          output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))                        
                          if not os.path.exists(output_dir):
                              os.makedirs(output_dir)                        
                          model_to_save = model.module if hasattr(model,'module') else model
                          output_dir = os.path.join(output_dir, '{}'.format('model.bin')) 
                          torch.save(model_to_save.state_dict(), output_dir)
                          logger.info("Saving model checkpoint to %s", output_dir)
                          
          if args.max_steps > 0 and global_step > args.max_steps:
              train_iterator.close()
              break
      return global_step, tr_loss / global_step
  
  
  def evaluate(args, model, tokenizer, prefix="",pool=None,eval_when_training=False):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_output_dir = args.output_dir
      eval_dataset = TextDataset(tokenizer, args,args.eval_data_file)
      # 得到数据集.
      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,num_workers=4,pin_memory=True)
  
      # multi-gpu evaluate
      if args.n_gpu > 1 and eval_when_training is False:
          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
      model.eval()
      logits=[]  
      y_trues=[]
      for batch in tqdm(eval_dataloader):
          inputs = batch[0].to(args.device)        
          labels = batch[1].to(args.device) 
          with torch.no_grad():
              lm_loss,logit = model(inputs,labels)
              eval_loss += lm_loss.mean().item()
              logits.append(logit.cpu().numpy())
              y_trues.append(labels.cpu().numpy())
              # ground truth
          
          nb_eval_steps += 1
      logits=np.concatenate(logits,0)
      y_trues=np.concatenate(y_trues,0)
      best_threshold=0
      best_f1=0
      
      y_preds = []
      for logit in logits:
          y_preds.append(np.argmax(logit))
      print(y_trues)
      print(y_preds)
      from sklearn.metrics import recall_score
      recall=recall_score(y_trues, y_preds, average='macro')
      from sklearn.metrics import precision_score
      precision=precision_score(y_trues, y_preds, average='macro')   
      from sklearn.metrics import f1_score
      f1=f1_score(y_trues, y_preds, average='macro') 
  
      result = {
          "eval_recall": float(recall),
          "eval_precision": float(precision),
          "eval_f1": float(f1),
          "eval_threshold":best_threshold,
          
      }
  
      logger.info("***** Eval results {} *****".format(prefix))
      for key in sorted(result.keys()):
          logger.info("  %s = %s", key, str(round(result[key],4)))
  
      return result
  
  def test(args, model, tokenizer, prefix="",pool=None,best_threshold=0):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_dataset = load_and_cache_examples(args, tokenizer, test=True,pool=pool)
  
      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,num_workers=4,pin_memory=True)
  
      # multi-gpu evaluate
      if args.n_gpu > 1:
          model = torch.nn.DataParallel(model)
  
      # Eval!
      logger.info("***** Running Test {} *****".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
      model.eval()
      logits=[]  
      y_trues=[]
      for batch in tqdm(eval_dataloader):
          inputs = batch[0].to(args.device)        
          labels=batch[1].to(args.device) 
          with torch.no_grad():
              lm_loss,logit = model(inputs,labels)
              eval_loss += lm_loss.mean().item()
              logits.append(logit.cpu().numpy())
              y_trues.append(labels.cpu().numpy())
          nb_eval_steps += 1
      logits=np.concatenate(logits,0)
      y_preds=logits[:,1]>best_threshold
      with open(os.path.join(args.output_dir,"predictions.txt"),'w') as f:
          for example,pred in zip(eval_dataset.examples,y_preds):
              if pred:
                  f.write(example.url1+'\t'+example.url2+'\t'+'1'+'\n')
              else:
                  f.write(example.url1+'\t'+example.url2+'\t'+'0'+'\n')
                                                  
  def main():
      parser = argparse.ArgumentParser()
  
      ## Required parameters
      parser.add_argument("--train_data_file", default=None, type=str, required=True,
                          help="The input training data file (a text file).")
      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("--eval_data_file", default=None, type=str,
                          help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
      parser.add_argument("--test_data_file", default=None, type=str,
                          help="An optional input evaluation data file to evaluate the perplexity on (a text file).")
                      
      parser.add_argument("--model_type", default="bert", type=str,
                          help="The model architecture to be fine-tuned.")
      parser.add_argument("--model_name_or_path", default=None, type=str,
                          help="The model checkpoint for weights initialization.")
      parser.add_argument("--number_labels", type=int,
                          help="The model checkpoint for weights initialization.")
  
      parser.add_argument("--mlm", action='store_true',
                          help="Train with masked-language modeling loss instead of language modeling.")
      parser.add_argument("--mlm_probability", type=float, default=0.15,
                          help="Ratio of tokens to mask for masked language modeling loss")
  
      parser.add_argument("--config_name", default="", type=str,
                          help="Optional pretrained config name or path if not the same as model_name_or_path")
      parser.add_argument("--tokenizer_name", default="", type=str,
                          help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
      parser.add_argument("--cache_dir", default="", type=str,
                          help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)")
      parser.add_argument("--block_size", default=-1, type=int,
                          help="Optional input sequence length after tokenization."
                               "The training dataset will be truncated in block of this size for training."
                               "Default to the model max input length for single sentence inputs (take into account special tokens).")
      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_test", action='store_true',
                          help="Whether to run eval on the dev set.")    
      parser.add_argument("--evaluate_during_training", action='store_true',
                          help="Run evaluation during training at each logging step.")
      parser.add_argument("--do_lower_case", action='store_true',
                          help="Set this flag if you are using an uncased model.")
  
      parser.add_argument("--train_batch_size", default=4, type=int,
                          help="Batch size per GPU/CPU for training.")
      parser.add_argument("--eval_batch_size", default=4, 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 deay 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=1.0, 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('--save_total_limit', type=int, default=None,
                          help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default')
      parser.add_argument("--eval_all_checkpoints", action='store_true',
                          help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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('--epoch', 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.")
  
      
      pool = multiprocessing.Pool(cpu_cont)
      args = parser.parse_args()
  
      # Setup distant debugging if needed
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
          print("Waiting for debugger attach")
          ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
          ptvsd.wait_for_attach()
  
      # Setup CUDA, GPU & distributed training
      if args.local_rank == -1 or args.no_cuda:
          device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
          args.n_gpu = torch.cuda.device_count()
      else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
          torch.cuda.set_device(args.local_rank)
          device = torch.device("cuda", args.local_rank)
          torch.distributed.init_process_group(backend='nccl')
          args.n_gpu = 1
      args.device = device
      args.per_gpu_train_batch_size=args.train_batch_size//args.n_gpu
      args.per_gpu_eval_batch_size=args.eval_batch_size//args.n_gpu
      # 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.seed)
  
      # Load pretrained model and tokenizer
      if args.local_rank not in [-1, 0]:
          torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training download model & vocab
  
      args.start_epoch = 0
      args.start_step = 0
      checkpoint_last = os.path.join(args.output_dir, 'checkpoint-last')
      if os.path.exists(checkpoint_last) and os.listdir(checkpoint_last):
          args.model_name_or_path = os.path.join(checkpoint_last, 'pytorch_model.bin')
          args.config_name = os.path.join(checkpoint_last, 'config.json')
          idx_file = os.path.join(checkpoint_last, 'idx_file.txt')
          with open(idx_file, encoding='utf-8') as idxf:
              args.start_epoch = int(idxf.readlines()[0].strip()) + 1
  
          step_file = os.path.join(checkpoint_last, 'step_file.txt')
          if os.path.exists(step_file):
              with open(step_file, encoding='utf-8') as stepf:
                  args.start_step = int(stepf.readlines()[0].strip())
  
          logger.info("reload model from {}, resume from {} epoch".format(checkpoint_last, args.start_epoch))
  
      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,
                                            cache_dir=args.cache_dir if args.cache_dir else None)
      config.num_labels=args.number_labels
      tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name,
                                                  do_lower_case=args.do_lower_case,
                                                  cache_dir=args.cache_dir if args.cache_dir else None)
      if args.block_size <= 0:
          args.block_size = tokenizer.max_len_single_sentence  # Our input block size will be the max possible for the model
      args.block_size = min(args.block_size, tokenizer.max_len_single_sentence)
      if args.model_name_or_path:
          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)    
      else:
          model = model_class(config)
  
      model=Model(model,config,tokenizer,args)
      # load 模型.
      if args.local_rank == 0:
          torch.distributed.barrier()  # End of barrier to make sure only the first process in distributed training download model & vocab
  
      logger.info("Training/evaluation parameters %s", args)
  
      # Training
      if args.do_train:
          if args.local_rank not in [-1, 0]:
              torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
  
          train_dataset = TextDataset(tokenizer, args,args.train_data_file)
  
          if args.local_rank == 0:
              torch.distributed.barrier()
  
          global_step, tr_loss = train(args, train_dataset, model, tokenizer,pool)
  
  
      # Evaluation
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          checkpoint_prefix = 'checkpoint-best-f1/model.bin'
          output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
          model.load_state_dict(torch.load(output_dir))
          model.to(args.device)
          result=evaluate(args, model, tokenizer,pool=pool)
          
      if args.do_test and args.local_rank in [-1, 0]:
          checkpoint_prefix = 'checkpoint-best-f1/model.bin'
          output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
          model.load_state_dict(torch.load(output_dir))
          model.to(args.device)
          test(args, model, tokenizer,pool=pool,best_threshold=0.5)
  
      return results
  
  
  if __name__ == "__main__":
      main()
  
  