text: import sys
  import os
  import pandas as pd
  import torch
  import transformers
  from torch.utils.data import Dataset, DataLoader
  from transformers import BertModel, BertTokenizer
  import numpy as np
  import argparse
  from sklearn import metrics
  from data.dataset import TextResponseDataset
  from util import get_cv_split_assignments
  from absl import flags
  from absl import app
  
  from torch import cuda
  device = 'cuda' if cuda.is_available() else 'cpu'
  
  class ProcDataset(Dataset):
  	def __init__(self, dataframe, tokenizer, max_len):
  		self.len = len(dataframe)
  		self.data = dataframe
  		self.tokenizer = tokenizer
  		self.max_len = max_len
  		
  	def __getitem__(self, index):
  		text = str(self.data.text[index])
  		text=" ".join(text.split())
  		inputs = self.tokenizer.encode_plus(
  			text,
  			None,
  			truncation=True,
  			add_special_tokens=True,
  			max_length=self.max_len,
  			pad_to_max_length=True,
  			return_token_type_ids=True
  		)
  		ids = inputs['input_ids']
  		mask = inputs['attention_mask']
  
  		return {
  			'ids': torch.tensor(ids, dtype=torch.long),
  			'mask': torch.tensor(mask, dtype=torch.long),
  			'targets': torch.tensor(self.data.label[index], dtype=torch.float)
  		} 
  	
  	def __len__(self):
  		return self.len
  
  class BERTClass(torch.nn.Module):
  	def __init__(self):
  		super(BERTClass, self).__init__()
  		self.l1 = transformers.BertModel.from_pretrained('bert-base-uncased')
  		self.l2 = torch.nn.Linear(768, 1)
  	
  	def forward(self, ids, mask):
  		output_1 = self.l1(ids, mask)[0]
  		output = self.l2(output_1.mean(1))
  		return output
  
  
  def train(model, training_loader, epoch, lr=1e-5, label_is_bool=True):
  	if label_is_bool:
  		loss_function = torch.nn.BCEWithLogitsLoss()
  	else:
  		loss_function = torch.nn.MSELoss()
  	optimizer = torch.optim.Adam(params = model.parameters(), lr=lr)
  	model.train()
  	for _,data in enumerate(training_loader, 0):
  		ids = data['ids'].to(device, dtype = torch.long)
  		mask = data['mask'].to(device, dtype = torch.long)
  		targets = data['targets'].to(device, dtype = torch.float)
  
  		outputs = model(ids, mask).squeeze()
  		optimizer.zero_grad()
  		loss = loss_function(outputs, targets)
  		if _%5000==0:
  			print(f'Epoch: {epoch}, Loss:  {loss.item()}', flush=True)
  		
  		optimizer.zero_grad()
  		loss.backward()
  		optimizer.step()
  
  def valid(model, testing_loader, label_is_bool=False):
  	model.eval()
  	eval_targets = []
  	eval_outputs = []
  	with torch.no_grad():
  		for _, data in enumerate(testing_loader, 0):
  			ids = data['ids'].to(device, dtype = torch.long)
  			mask = data['mask'].to(device, dtype = torch.long)
  			targets = data['targets'].to(device, dtype = torch.float)
  			outputs = model(ids, mask).squeeze()
  			if label_is_bool:
  				outputs = torch.sigmoid(outputs)
  			eval_targets += targets.cpu().detach().numpy().tolist()
  			eval_outputs += outputs.cpu().detach().numpy().tolist()
  	return np.array(eval_outputs), np.array(eval_targets)
  
  def main(argv):
  	tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
  
  	df = pd.read_csv(FLAGS.in_file)
  
  	label_is_bool = False
  	if FLAGS.data in TextResponseDataset.CLASSIFICATION_SETTINGS:
  		label_is_bool=True
  
  	if FLAGS.train_test_mode:
  		train_dataset = df.iloc[:FLAGS.train_size].reset_index(drop=True)
  		test_dataset = df.iloc[FLAGS.train_size:].reset_index(drop=True)
  	else:
  		n_docs = df.shape[0]
  		df['split'] = get_cv_split_assignments(n_docs, num_splits=FLAGS.n_folds)
  		train_dataset = df[df.split!=FLAGS.split].reset_index(drop=True)
  		test_dataset = df[df.split==FLAGS.split].reset_index(drop=True)
  
  	training_set = ProcDataset(train_dataset, tokenizer, FLAGS.max_len)
  	testing_set = ProcDataset(test_dataset, tokenizer, FLAGS.max_len)
  
  	train_params = {'batch_size': FLAGS.batch_size,
  					'shuffle': True,
  					'num_workers': 0
  					}
  
  	test_params = {'batch_size': FLAGS.batch_size,
  					'shuffle': True,
  					'num_workers': 0
  					}
  
  	training_loader = DataLoader(training_set, **train_params)
  	testing_loader = DataLoader(testing_set, **test_params)
  
  	model = BERTClass()
  	model.to(device)
  
  	for epoch in range(FLAGS.epochs):
  		train(model, training_loader, epoch, lr=FLAGS.learning_rate, label_is_bool=label_is_bool)
  
  	print("testing..")
  	eval_outputs, eval_targets = valid(model, testing_loader, label_is_bool=label_is_bool)
  	log_loss = mse = accuracy = auc = 0.0
  
  	if label_is_bool:
  		auc = metrics.roc_auc_score(eval_targets, eval_outputs)
  		log_loss = metrics.log_loss(eval_targets, eval_outputs)
  		preds = (eval_outputs >= 0.5)
  		accuracy = metrics.accuracy_score(eval_targets, preds)
  
  	else:
  		mse = metrics.mean_squared_error(eval_targets, eval_outputs)
  
  	print("MSE:", mse, "Acc:", accuracy, "Log loss:", log_loss, "AUC:", auc)
  	os.makedirs(FLAGS.outdir, exist_ok=True)
  	np.save(os.path.join(FLAGS.outdir, 'bert.result.' + 'split'+str(FLAGS.split)), np.array([mse, auc, log_loss, accuracy]))
  
  if __name__ == '__main__':
  	FLAGS = flags.FLAGS
  	flags.DEFINE_string("in_file", "", "path to processed data file that contains pairs of (untokenized) text and label.")
  	flags.DEFINE_string("data", "amazon", "name of dataset")
  	flags.DEFINE_string("outdir", "../out/", "path to directory where output is saved.")
  
  	flags.DEFINE_integer("split", 0, "for cross validation, indicates which split is used as the test split.")
  	flags.DEFINE_integer("n_folds", 10, "for cross validation, number of splits (i.e., folds) to use.")
  	flags.DEFINE_integer("train_size", 10000, "number of samples to set aside for training split (only valid if train/test setting is used)")
  	flags.DEFINE_integer("max_len", 128, "max length of sequences -- longer sequences will be trimmed.")
  	flags.DEFINE_integer("batch_size", 32, "batch size for training and evaluation.")
  	flags.DEFINE_integer("epochs", 5, "number of full passes to do over dataset when training.")
  
  	flags.DEFINE_float("learning_rate", 1e-5, "optimization learning rate.")
  	
  	flags.DEFINE_boolean("train_test_mode", False, "flag to use to run a train/test experiment instead of cross validation (default).")
  	app.run(main)