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 numpy as np
  import torch
  from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  import json
  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 = multiprocessing.cpu_count()
  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)
                            
  from transformers import (
      CONFIG_MAPPING,
      MODEL_MAPPING,
      AdamW,
      AutoConfig,
      AutoModelForMaskedLM,
      AutoTokenizer,
      DataCollatorForLanguageModeling,
      SchedulerType,
      get_scheduler,
      AdapterConfig,
      set_seed,
  )
  import transformers.adapters.composition as ac
  from transformers.trainer_utils import get_last_checkpoint
  
  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,
                   index,
                   label,
  
      ):
          self.input_tokens = input_tokens
          self.input_ids = input_ids
          self.index=index
          self.label=label
  
          
  def convert_examples_to_features(js,tokenizer,args):
      #source
      code=' '.join(js['code'].split())
      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,js['index'],int(js['label']))
  
  class TextDataset(Dataset):
      def __init__(self, tokenizer, args, file_path=None):
          self.examples = []
          data=[]
          with open(file_path) as f:
              for line in f:
                  line=line.strip()
                  js=json.loads(line)
                  data.append(js)
          for js in data:
              self.examples.append(convert_examples_to_features(js,tokenizer,args))
          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))))
          self.label_examples={}
          for e in self.examples:
              if e.label not in self.label_examples:
                  self.label_examples[e.label]=[]
              self.label_examples[e.label].append(e)
          
      def __len__(self):
          return len(self.examples)
  
      def __getitem__(self, i):   
          label=self.examples[i].label
          index=self.examples[i].index
          labels=list(self.label_examples)
          labels.remove(label)
          while True:
              shuffle_example=random.sample(self.label_examples[label],1)[0]
              if shuffle_example.index!=index:
                  p_example=shuffle_example
                  break
          n_example=random.sample(self.label_examples[random.sample(labels,1)[0]],1)[0]
          
          return (torch.tensor(self.examples[i].input_ids),torch.tensor(p_example.input_ids),
                  torch.tensor(n_example.input_ids),torch.tensor(label))
              
  
  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):
      """ 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,num_workers=4,pin_memory=True)
      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.max_steps*0.1,
                                                  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_acc=0.0
      # model.resize_token_embeddings(len(tokenizer))
      model.zero_grad()
      for idx in range(args.start_epoch, int(args.num_train_epochs)): 
          bar = train_dataloader
          tr_num=0
          train_loss=0
          for step, batch in enumerate(bar):
              inputs = batch[0].to(args.device)    
              p_inputs = batch[1].to(args.device)
              n_inputs = batch[2].to(args.device)
              labels = batch[3].to(args.device)
              model.train()
              loss,vec = model(inputs,p_inputs,n_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)
              if (step+1)% 100==0:
                  logger.info("epoch {} step {} loss {}".format(idx,step+1,avg_loss))
              #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,eval_when_training=True)
                          for key, value in results.items():
                              logger.info("  %s = %s", key, round(value,4))                    
                          # Save model checkpoint
                          tr_num=0
                          train_loss=0
   
                      if results['eval_map']>best_acc:
                          best_acc=results['eval_map']
                          logger.info("  "+"*"*20)  
                          logger.info("  Best map:%s",round(best_acc,4))
                          logger.info("  "+"*"*20)                          
                          
                                                
                          if not os.path.exists(args.output_dir):
                              os.makedirs(args.output_dir)    
                          if args.save_task_adap:
                              model.encoder.save_adapter(args.output_dir, args.task_adap_name)
                          else:
                              model.encoder.save_pretrained(args.output_dir)      
                          
                          logger.info("Saving task adapter checkpoint to %s", args.output_dir)
                          
                          checkpoint_prefix = 'checkpoint-best-map'
                          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)
  
  
  eval_dataset=None
  def evaluate(args, model, tokenizer,eval_when_training=False):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_output_dir = args.output_dir
      global eval_dataset
      if eval_dataset is None:
          eval_dataset = TextDataset(tokenizer, args,args.eval_data_file)
  
      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 *****")
      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()
      vecs=[] 
      labels=[]
      for batch in eval_dataloader:
          inputs = batch[0].to(args.device)    
          p_inputs = batch[1].to(args.device)
          n_inputs = batch[2].to(args.device)
          label = batch[3].to(args.device)
          with torch.no_grad():
              lm_loss,vec = model(inputs,p_inputs,n_inputs,label)
              eval_loss += lm_loss.mean().item()
              vecs.append(vec.cpu().numpy())
              labels.append(label.cpu().numpy())
          nb_eval_steps += 1
      vecs=np.concatenate(vecs,0)
      labels=np.concatenate(labels,0)
      eval_loss = eval_loss / nb_eval_steps
      perplexity = torch.tensor(eval_loss)
  
      scores=np.matmul(vecs,vecs.T)
      dic={}
      for i in range(scores.shape[0]):
          scores[i,i]=-1000000
          if int(labels[i]) not in dic:
              dic[int(labels[i])]=-1
          dic[int(labels[i])]+=1
      sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
      MAP=[]
      for i in range(scores.shape[0]):
          cont=0
          label=int(labels[i])
          Avep = []
          for j in range(dic[label]):
              index=sort_ids[i,j]
              if int(labels[index])==label:
                  Avep.append((len(Avep)+1)/(j+1))
          MAP.append(sum(Avep)/dic[label])
            
      result = {
          "eval_loss": float(perplexity),
          "eval_map":float(np.mean(MAP))
      }
  
  
      return result
  
  def test(args, model, tokenizer):
      # Loop to handle MNLI double evaluation (matched, mis-matched)
      eval_dataset = TextDataset(tokenizer, args,args.test_data_file)
  
  
      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 Test *****")
      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()
      vecs=[] 
      labels=[]
      for batch in eval_dataloader:
          inputs = batch[0].to(args.device)    
          p_inputs = batch[1].to(args.device)
          n_inputs = batch[2].to(args.device)
          label = batch[3].to(args.device)
          with torch.no_grad():
              lm_loss,vec = model(inputs,p_inputs,n_inputs,label)
              eval_loss += lm_loss.mean().item()
              vecs.append(vec.cpu().numpy())
              labels.append(label.cpu().numpy())
          nb_eval_steps += 1
      vecs=np.concatenate(vecs,0)
      labels=np.concatenate(labels,0)
      eval_loss = eval_loss / nb_eval_steps
      perplexity = torch.tensor(eval_loss)
  
      scores=np.matmul(vecs,vecs.T)
      for i in range(scores.shape[0]):
          scores[i,i]=-1000000
      sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
      indexs=[]
      for example in eval_dataset.examples:
          indexs.append(example.index)
      with open(os.path.join(args.output_dir,"predictions.jsonl"),'w') as f:
          for index,sort_id in zip(indexs,sort_ids):
              js={}
              js['index']=index
              js['answers']=[]
              for idx in sort_id[:129]:
                  js['answers'].append(indexs[int(idx)])
              f.write(json.dumps(js)+'\n')
          scores=np.matmul(vecs,vecs.T)
      dic={}
      for i in range(scores.shape[0]):
          scores[i,i]=-1000000
          if int(labels[i]) not in dic:
              dic[int(labels[i])]=-1
          dic[int(labels[i])]+=1
      sort_ids=np.argsort(scores, axis=-1, kind='quicksort', order=None)[:,::-1]
      MAP=[]
      for i in range(scores.shape[0]):
          cont=0
          label=int(labels[i])
          Avep = []
          for j in range(dic[label]):
              index=sort_ids[i,j]
              if int(labels[index])==label:
                  Avep.append((len(Avep)+1)/(j+1))
          MAP.append(sum(Avep)/dic[label])
            
      result = {
          "test_loss": float(perplexity),
          "test_map":float(np.mean(MAP))
      }
  
  
      return result
                          
                          
  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("--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("--freeze_ptlm", action='store_true',
                          help="Freeze weights of PTLM.")
      parser.add_argument("--train_adapter", action='store_true',
                          help="Whether to train the adapters")
      parser.add_argument("--save_task_adap", action='store_true',
                          help="Whether to save the task adapters separately")
      parser.add_argument(
          "--lang_adap_name",
          type=str,
          default="lang",
          help="Pass the name of the language adapter to be included",
      )
      parser.add_argument(
          "--task_adap_name",
          type=str,
          default="lang",
          help="Pass the name of the task adapter to be trained",
      )
      parser.add_argument(
          "--lang_adapter_config",
          type=str,
          default="pfeiffer",
          help="Choose the lang_adapter config. Choices include [houlsby|pfeiffer|either+inv].",
      )
      parser.add_argument(
          "--task_adapter_config",
          type=str,
          default="pfeiffer",
          help="Choose the task_adapter config. Choices include [houlsby|pfeiffer|either+inv].",
      )
      parser.add_argument(
          "--drop_layers",
          nargs='+',
          default=None,
          help="A list of layers for which the adapters should be dropped.",
      )
      parser.add_argument(
          "--lang_adapter_reduction_factor",
          type=int,
          default=2,
          help="Reduction of bottleneck layer. One of [2|16|32].",
      )
      parser.add_argument(
          "--task_adapter_reduction_factor",
          type=int,
          default=16,
          help="Reduction of bottleneck layer. One of [2|16|32].",
      )
      parser.add_argument(
          "--adapter_non_linearity",
          type=str,
          default="relu",
          help="Non-linear activation function for adapters. Choose between [relu|gelu]",
      )
      parser.add_argument(
          "--load_adapter",
          type=str,
          default=None,
          help="Path to pretrained adapter or adapter identifier from adapterhub.ml.",
      )
      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()
  
  
      # 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=1
      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)
      
      if args.freeze_ptlm:
          # Freeze tranformer layers.
          for param in model.parameters():
              param.requires_grad = False
              
      model.resize_token_embeddings(len(tokenizer))
      
      if args.train_adapter:
          lang_set = args.lang_adap_name or "mlm"
          # check if adapter already exists, otherwise add it
          if lang_set not in model.config.adapters:
              # resolve the adapter config
              adapter_config = AdapterConfig.load(
                  args.lang_adapter_config,
                  non_linearity=args.adapter_non_linearity,
                  reduction_factor=args.lang_adapter_reduction_factor,
              )
              drop_layers = None
              if args.drop_layers is not None:
                  drop_layers = [int(x) for x in args.drop_layers]
              # load a pre-trained from Hub if specified
              if args.load_adapter is not None:
                  model.load_adapter(
                      args.load_adapter,
                      config=adapter_config,
                      load_as=lang_set,
                      source=None,
                      leave_out=drop_layers,
                  )
              # otherwise, add a fresh adapter
              else:
                  model.add_adapter(lang_set, config=adapter_config)
          
          # check for task based fine-tuning request
          if args.task_adap_name is not None:
              task = args.task_adap_name
              # resolve adapter configuration
              adapter_config = AdapterConfig.load(
                  args.task_adapter_config,
                  non_linearity=args.adapter_non_linearity,
                  reduction_factor=args.task_adapter_reduction_factor,
              )
              
              # add the fresh task adapter
              model.add_adapter(task, config=adapter_config)
              
              # Stack adapters in MAD-X fashion
              model.active_adapters = ac.Stack(lang_set, task)
              # specify the adapters to be trained
              model.train_adapter([task])
          else:
              model.set_active_adapters([lang_set])
      else:
          if args.load_adapter is not None: # or adapter_args.load_lang_adapter:
              raise ValueError(
                  "Adapters can only be loaded in adapters training mode."
                  "Use --train_adapter to enable adapter training"
              )
              
  
      model=Model(model,config,tokenizer,args)
      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()
  
          train(args, train_dataset, model, tokenizer)
  
      # Evaluation
      results = {}
      if args.do_eval and args.local_rank in [-1, 0]:
          checkpoint_prefix = 'checkpoint-best-map/model.bin'
          output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
          model.load_state_dict(torch.load(output_dir),strict=False)      
          model.to(args.device)
          result=evaluate(args, model, tokenizer)
          logger.info("***** Eval results *****")
          for key in sorted(result.keys()):
              logger.info("  %s = %s", key, str(round(result[key],4)))
              
      if args.do_test and args.local_rank in [-1, 0]:
          checkpoint_prefix = 'checkpoint-best-map/model.bin'
          output_dir = os.path.join(args.output_dir, '{}'.format(checkpoint_prefix))  
          model.load_state_dict(torch.load(output_dir),strict=False)                  
          model.to(args.device)
          result = test(args, model, tokenizer)
          logger.info("***** Test results *****")
          for key in sorted(result.keys()):
              logger.info("  %s = %s", key, str(round(result[key],4)))
      return results
  
  
  if __name__ == "__main__":
      main()
  
  
  
  