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import random
import numpy as np
import torch
import os
import csv
import sys
import logging
from transformers import BertTokenizer, AutoTokenizer
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
from sentence_transformers import SentenceTransformer

class BERT_Loader:
    
    def __init__(self, args, base_attrs, logger_name = 'Discovery'):

        self.logger = logging.getLogger(logger_name)

        if args.method == 'SCCL' :
            self.tokenizer = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')[0].tokenizer
        else:
            self.tokenizer = BertTokenizer.from_pretrained(args.pretrained_bert_model, do_lower_case=True)    

        if args.setting == 'unsupervised':

            self.train_examples = get_examples(args, base_attrs, 'train')
            self.eval_examples = get_examples(args, base_attrs, 'eval')

            self.train_examples = self.train_examples + self.eval_examples
            self.train_outputs = get_loader(self.train_examples, args, base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
            self.logger.info("Number of train samples = %s", str(len(self.train_examples)))

            self.test_examples = get_examples(args, base_attrs, 'test')
            self.logger.info("Number of testing samples = %s", str(len(self.test_examples)))
            self.test_outputs = get_loader(self.test_examples, args, base_attrs['all_label_list'], 'test', self.tokenizer)
            
 
        elif args.setting == 'semi_supervised':

            self.train_examples, self.train_labeled_examples, self.train_unlabeled_examples  = get_examples(args, base_attrs, 'train')
            self.logger.info("Number of labeled training samples = %s", str(len(self.train_labeled_examples)))
            self.logger.info("Number of unlabeled training samples = %s", str(len(self.train_unlabeled_examples)))
            self.eval_examples = get_examples(args, base_attrs, 'eval')
            self.logger.info("Number of evaluation samples = %s", str(len(self.eval_examples)))
            self.test_examples = get_examples(args, base_attrs, 'test')
            self.logger.info("Number of testing samples = %s", str(len(self.test_examples)))
            
            self.train_labeled_outputs = get_loader(self.train_labeled_examples, args, base_attrs['known_label_list'], 'train_labeled', self.tokenizer)
            self.train_unlabeled_outputs = get_loader(self.train_unlabeled_examples, args, base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
            self.train_outputs = get_semi_loader(self.train_labeled_examples, self.train_unlabeled_examples, base_attrs, args, self.tokenizer)
            self.eval_outputs = get_loader(self.eval_examples, args, base_attrs['known_label_list'], 'eval', self.tokenizer)
            self.test_outputs = get_loader(self.test_examples, args, base_attrs['all_label_list'], 'test', self.tokenizer)
            if args.method == 'DTC_BERT':
                self.get_examples_dtc_predict(args ,base_attrs)

        self.num_train_examples = len(self.train_examples)
    def get_examples_dtc_predict(self, args ,base_attrs):

        num_val_cls = round(base_attrs['n_known_cls'] * 0.75 )
        self.num_val_cls = num_val_cls

        label_val = list(np.random.choice(np.array(base_attrs['known_label_list']), num_val_cls, replace=False))  #44  
        label_train = [label for label in base_attrs['known_label_list'] if label not in label_val]  

        ntrain = len(self.train_examples)
        train_labels = np.array([example.label for example in self.train_examples])

        train_base_attrs = {}
        train_base_attrs['known_label_list'] = label_train
        train_base_attrs['data_dir'] = base_attrs['data_dir']
        train_base_attrs['all_label_list'] = base_attrs['all_label_list']

        self.train_examples_dtc, self.train_labeled_examples_dtc, self.train_unlabeled_examples_dtc  = get_examples(args, train_base_attrs, 'train')
        self.logger.info("Number of labeled training samples = %s", str(len(self.train_labeled_examples_dtc)))
        self.logger.info("Number of unlabeled training samples = %s", str(len(self.train_unlabeled_examples_dtc)))
        self.eval_examples_dtc = get_examples(args, train_base_attrs, 'eval')
        self.logger.info("Number of evaluation samples = %s", str(len(self.eval_examples_dtc)))

        self.train_labeled_outputs_dtc = get_loader(self.train_labeled_examples_dtc, args, train_base_attrs['known_label_list'], 'train_labeled', self.tokenizer)
        self.train_unlabeled_outputs_dtc = get_loader(self.train_unlabeled_examples_dtc, args, train_base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
        self.eval_outputs_dtc = get_loader(self.eval_examples_dtc, args, train_base_attrs['known_label_list'], 'eval', self.tokenizer)
        
        val_base_attrs = {}
        val_base_attrs['known_label_list'] = label_val
        val_base_attrs['data_dir'] = base_attrs['data_dir']
        val_base_attrs['all_label_list'] = base_attrs['all_label_list']

        self.val_examples_dtc, self.val_labeled_examples_dtc, self.val_unlabeled_examples_dtc  = get_examples(args, val_base_attrs, 'train')
        self.val_labeled_outputs_dtc = get_loader(self.val_labeled_examples_dtc, args, val_base_attrs['known_label_list'], 'train_labeled', self.tokenizer)

def get_examples(args, base_attrs, mode):

    processor = DatasetProcessor()
    ori_examples = processor.get_examples(base_attrs['data_dir'], mode)
    
    if args.setting == 'unsupervised':
        
        return ori_examples

    elif args.setting == 'semi_supervised':

        if mode == 'train':
            
            train_labels = np.array([example.label for example in ori_examples])
            train_labeled_ids = []
            for label in base_attrs['known_label_list']:
                num = round(len(train_labels[train_labels == label]) * args.labeled_ratio)
                pos = list(np.where(train_labels == label)[0])
                train_labeled_ids.extend(random.sample(pos, num))

            labeled_examples, unlabeled_examples = [], []
            for idx, example in enumerate(ori_examples):
                if idx in train_labeled_ids:
                    labeled_examples.append(example)
                else:
                    unlabeled_examples.append(example)

            return ori_examples, labeled_examples, unlabeled_examples

        elif mode == 'eval':

            examples = []
            for example in ori_examples:
                if (example.label in base_attrs['known_label_list']):
                    examples.append(example)
            
            return examples
        
        elif mode == 'test':
            return ori_examples

def get_loader(examples, args, label_list, mode, tokenizer):
    
    features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer)
    input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)

    if mode == 'train_unlabeled':
        label_ids = torch.tensor([-1 for f in features], dtype=torch.long)
    else:
        label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)


    datatensor = TensorDataset(input_ids, input_mask, segment_ids, label_ids)

    if mode == 'train_labeled':  
        sampler = RandomSampler(datatensor)
        dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True)  #, num_workers = args.num_workers, pin_memory = True

    else:
        sampler = SequentialSampler(datatensor)

        if mode == 'train_unlabeled':
            dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True)    

        elif mode == 'eval':
            dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.eval_batch_size, num_workers = args.num_workers, pin_memory = True)
        
        elif mode == 'test':
            dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.test_batch_size, num_workers = args.num_workers, pin_memory = True)

    outputs = {
        'loader': dataloader,
        'input_ids': input_ids,
        'input_mask': input_mask,
        'segment_ids': segment_ids,
        'label_ids': label_ids,
        'data': datatensor
    }
    
    return outputs

def get_semi_loader(labeled_examples, unlabeled_examples, base_attrs, args, tokenizer):

    labeled_features = convert_examples_to_features(labeled_examples, base_attrs['known_label_list'], args.max_seq_length, tokenizer)
    unlabeled_features = convert_examples_to_features(unlabeled_examples, base_attrs['all_label_list'], args.max_seq_length, tokenizer)
    
    labeled_input_ids = torch.tensor([f.input_ids for f in labeled_features], dtype=torch.long)
    labeled_input_mask = torch.tensor([f.input_mask for f in labeled_features], dtype=torch.long)
    labeled_segment_ids = torch.tensor([f.segment_ids for f in labeled_features], dtype=torch.long)
    labeled_label_ids = torch.tensor([f.label_id for f in labeled_features], dtype=torch.long)      

    unlabeled_input_ids = torch.tensor([f.input_ids for f in unlabeled_features], dtype=torch.long)
    unlabeled_input_mask = torch.tensor([f.input_mask for f in unlabeled_features], dtype=torch.long)
    unlabeled_segment_ids = torch.tensor([f.segment_ids for f in unlabeled_features], dtype=torch.long)
    unlabeled_label_ids = torch.tensor([-1 for f in unlabeled_features], dtype=torch.long)       

    semi_input_ids = torch.cat([labeled_input_ids, unlabeled_input_ids])
    semi_input_mask = torch.cat([labeled_input_mask, unlabeled_input_mask])
    semi_segment_ids = torch.cat([labeled_segment_ids, unlabeled_segment_ids])
    semi_label_ids = torch.cat([labeled_label_ids, unlabeled_label_ids])

    semi_data = TensorDataset(semi_input_ids, semi_input_mask, semi_segment_ids, semi_label_ids)
    semi_sampler = SequentialSampler(semi_data)
    semi_dataloader = DataLoader(semi_data, sampler=semi_sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True)#args.train_batch_size)

    outputs = {
        'loader': semi_dataloader,
        'input_ids': semi_input_ids,
        'input_mask': semi_input_mask,
        'segment_ids': semi_segment_ids,
        'label_ids': semi_label_ids,
        'semi_data' : semi_data
    }
    return outputs

class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.
        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines

class DatasetProcessor(DataProcessor):

    def get_examples(self, data_dir, mode):
        if mode == 'train':
            return self._create_examples(
                self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
        elif mode == 'eval':
            return self._create_examples(
                self._read_tsv(os.path.join(data_dir, "dev.tsv")), "train")
        elif mode == 'test':
            return self._create_examples(
                self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            if len(line) != 2:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[0]
            label = line[1]

            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples

def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
    """Loads a data file into a list of `InputBatch`s."""
    label_map = {}
    for i, label in enumerate(label_list):
        label_map[label] = i
    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambigiously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding = [0] * (max_seq_length - len(input_ids))
        input_ids += padding
        input_mask += padding
        segment_ids += padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        label_id = label_map[example.label]

        features.append(
            InputFeatures(input_ids=input_ids,
                          input_mask=input_mask,
                          segment_ids=segment_ids,
                          label_id=label_id))
    return features

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""
    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop(0)  # For dialogue context
        else:
            tokens_b.pop()

	import random
	import numpy as np
	import torch
	import os
	import csv
	import sys
	import logging
	from transformers import BertTokenizer, AutoTokenizer
	from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
	from sentence_transformers import SentenceTransformer

	class BERT_Loader:

	def __init__(self, args, base_attrs, logger_name = 'Discovery'):

	self.logger = logging.getLogger(logger_name)

	if args.method == 'SCCL' :
	self.tokenizer = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens')[0].tokenizer
	else:
	self.tokenizer = BertTokenizer.from_pretrained(args.pretrained_bert_model, do_lower_case=True)

	if args.setting == 'unsupervised':

	self.train_examples = get_examples(args, base_attrs, 'train')
	self.eval_examples = get_examples(args, base_attrs, 'eval')

	self.train_examples = self.train_examples + self.eval_examples
	self.train_outputs = get_loader(self.train_examples, args, base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
	self.logger.info("Number of train samples = %s", str(len(self.train_examples)))

	self.test_examples = get_examples(args, base_attrs, 'test')
	self.logger.info("Number of testing samples = %s", str(len(self.test_examples)))
	self.test_outputs = get_loader(self.test_examples, args, base_attrs['all_label_list'], 'test', self.tokenizer)


	elif args.setting == 'semi_supervised':

	self.train_examples, self.train_labeled_examples, self.train_unlabeled_examples = get_examples(args, base_attrs, 'train')
	self.logger.info("Number of labeled training samples = %s", str(len(self.train_labeled_examples)))
	self.logger.info("Number of unlabeled training samples = %s", str(len(self.train_unlabeled_examples)))
	self.eval_examples = get_examples(args, base_attrs, 'eval')
	self.logger.info("Number of evaluation samples = %s", str(len(self.eval_examples)))
	self.test_examples = get_examples(args, base_attrs, 'test')
	self.logger.info("Number of testing samples = %s", str(len(self.test_examples)))

	self.train_labeled_outputs = get_loader(self.train_labeled_examples, args, base_attrs['known_label_list'], 'train_labeled', self.tokenizer)
	self.train_unlabeled_outputs = get_loader(self.train_unlabeled_examples, args, base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
	self.train_outputs = get_semi_loader(self.train_labeled_examples, self.train_unlabeled_examples, base_attrs, args, self.tokenizer)
	self.eval_outputs = get_loader(self.eval_examples, args, base_attrs['known_label_list'], 'eval', self.tokenizer)
	self.test_outputs = get_loader(self.test_examples, args, base_attrs['all_label_list'], 'test', self.tokenizer)
	if args.method == 'DTC_BERT':
	self.get_examples_dtc_predict(args ,base_attrs)

	self.num_train_examples = len(self.train_examples)
	def get_examples_dtc_predict(self, args ,base_attrs):

	num_val_cls = round(base_attrs['n_known_cls'] * 0.75 )
	self.num_val_cls = num_val_cls

	label_val = list(np.random.choice(np.array(base_attrs['known_label_list']), num_val_cls, replace=False)) #44
	label_train = [label for label in base_attrs['known_label_list'] if label not in label_val]

	ntrain = len(self.train_examples)
	train_labels = np.array([example.label for example in self.train_examples])

	train_base_attrs = {}
	train_base_attrs['known_label_list'] = label_train
	train_base_attrs['data_dir'] = base_attrs['data_dir']
	train_base_attrs['all_label_list'] = base_attrs['all_label_list']

	self.train_examples_dtc, self.train_labeled_examples_dtc, self.train_unlabeled_examples_dtc = get_examples(args, train_base_attrs, 'train')
	self.logger.info("Number of labeled training samples = %s", str(len(self.train_labeled_examples_dtc)))
	self.logger.info("Number of unlabeled training samples = %s", str(len(self.train_unlabeled_examples_dtc)))
	self.eval_examples_dtc = get_examples(args, train_base_attrs, 'eval')
	self.logger.info("Number of evaluation samples = %s", str(len(self.eval_examples_dtc)))

	self.train_labeled_outputs_dtc = get_loader(self.train_labeled_examples_dtc, args, train_base_attrs['known_label_list'], 'train_labeled', self.tokenizer)
	self.train_unlabeled_outputs_dtc = get_loader(self.train_unlabeled_examples_dtc, args, train_base_attrs['all_label_list'], 'train_unlabeled', self.tokenizer)
	self.eval_outputs_dtc = get_loader(self.eval_examples_dtc, args, train_base_attrs['known_label_list'], 'eval', self.tokenizer)

	val_base_attrs = {}
	val_base_attrs['known_label_list'] = label_val
	val_base_attrs['data_dir'] = base_attrs['data_dir']
	val_base_attrs['all_label_list'] = base_attrs['all_label_list']

	self.val_examples_dtc, self.val_labeled_examples_dtc, self.val_unlabeled_examples_dtc = get_examples(args, val_base_attrs, 'train')
	self.val_labeled_outputs_dtc = get_loader(self.val_labeled_examples_dtc, args, val_base_attrs['known_label_list'], 'train_labeled', self.tokenizer)

	def get_examples(args, base_attrs, mode):

	processor = DatasetProcessor()
	ori_examples = processor.get_examples(base_attrs['data_dir'], mode)

	if args.setting == 'unsupervised':

	return ori_examples

	elif args.setting == 'semi_supervised':

	if mode == 'train':

	train_labels = np.array([example.label for example in ori_examples])
	train_labeled_ids = []
	for label in base_attrs['known_label_list']:
	num = round(len(train_labels[train_labels == label]) * args.labeled_ratio)
	pos = list(np.where(train_labels == label)[0])
	train_labeled_ids.extend(random.sample(pos, num))

	labeled_examples, unlabeled_examples = [], []
	for idx, example in enumerate(ori_examples):
	if idx in train_labeled_ids:
	labeled_examples.append(example)
	else:
	unlabeled_examples.append(example)

	return ori_examples, labeled_examples, unlabeled_examples

	elif mode == 'eval':

	examples = []
	for example in ori_examples:
	if (example.label in base_attrs['known_label_list']):
	examples.append(example)

	return examples

	elif mode == 'test':
	return ori_examples

	def get_loader(examples, args, label_list, mode, tokenizer):

	features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer)
	input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
	input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
	segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)

	if mode == 'train_unlabeled':
	label_ids = torch.tensor([-1 for f in features], dtype=torch.long)
	else:
	label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)


	datatensor = TensorDataset(input_ids, input_mask, segment_ids, label_ids)

	if mode == 'train_labeled':
	sampler = RandomSampler(datatensor)
	dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True) #, num_workers = args.num_workers, pin_memory = True

	else:
	sampler = SequentialSampler(datatensor)

	if mode == 'train_unlabeled':
	dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True)

	elif mode == 'eval':
	dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.eval_batch_size, num_workers = args.num_workers, pin_memory = True)

	elif mode == 'test':
	dataloader = DataLoader(datatensor, sampler=sampler, batch_size = args.test_batch_size, num_workers = args.num_workers, pin_memory = True)

	outputs = {
	'loader': dataloader,
	'input_ids': input_ids,
	'input_mask': input_mask,
	'segment_ids': segment_ids,
	'label_ids': label_ids,
	'data': datatensor
	}

	return outputs

	def get_semi_loader(labeled_examples, unlabeled_examples, base_attrs, args, tokenizer):

	labeled_features = convert_examples_to_features(labeled_examples, base_attrs['known_label_list'], args.max_seq_length, tokenizer)
	unlabeled_features = convert_examples_to_features(unlabeled_examples, base_attrs['all_label_list'], args.max_seq_length, tokenizer)

	labeled_input_ids = torch.tensor([f.input_ids for f in labeled_features], dtype=torch.long)
	labeled_input_mask = torch.tensor([f.input_mask for f in labeled_features], dtype=torch.long)
	labeled_segment_ids = torch.tensor([f.segment_ids for f in labeled_features], dtype=torch.long)
	labeled_label_ids = torch.tensor([f.label_id for f in labeled_features], dtype=torch.long)

	unlabeled_input_ids = torch.tensor([f.input_ids for f in unlabeled_features], dtype=torch.long)
	unlabeled_input_mask = torch.tensor([f.input_mask for f in unlabeled_features], dtype=torch.long)
	unlabeled_segment_ids = torch.tensor([f.segment_ids for f in unlabeled_features], dtype=torch.long)
	unlabeled_label_ids = torch.tensor([-1 for f in unlabeled_features], dtype=torch.long)

	semi_input_ids = torch.cat([labeled_input_ids, unlabeled_input_ids])
	semi_input_mask = torch.cat([labeled_input_mask, unlabeled_input_mask])
	semi_segment_ids = torch.cat([labeled_segment_ids, unlabeled_segment_ids])
	semi_label_ids = torch.cat([labeled_label_ids, unlabeled_label_ids])

	semi_data = TensorDataset(semi_input_ids, semi_input_mask, semi_segment_ids, semi_label_ids)
	semi_sampler = SequentialSampler(semi_data)
	semi_dataloader = DataLoader(semi_data, sampler=semi_sampler, batch_size = args.train_batch_size, num_workers = args.num_workers, pin_memory = True)#args.train_batch_size)

	outputs = {
	'loader': semi_dataloader,
	'input_ids': semi_input_ids,
	'input_mask': semi_input_mask,
	'segment_ids': semi_segment_ids,
	'label_ids': semi_label_ids,
	'semi_data' : semi_data
	}
	return outputs

	class InputExample(object):
	"""A single training/test example for simple sequence classification."""

	def __init__(self, guid, text_a, text_b=None, label=None):
	"""Constructs a InputExample.

	Args:
	guid: Unique id for the example.
	text_a: string. The untokenized text of the first sequence. For single
	sequence tasks, only this sequence must be specified.
	text_b: (Optional) string. The untokenized text of the second sequence.
	Only must be specified for sequence pair tasks.
	label: (Optional) string. The label of the example. This should be
	specified for train and dev examples, but not for test examples.
	"""
	self.guid = guid
	self.text_a = text_a
	self.text_b = text_b
	self.label = label

	class InputFeatures(object):
	"""A single set of features of data."""

	def __init__(self, input_ids, input_mask, segment_ids, label_id):
	self.input_ids = input_ids
	self.input_mask = input_mask
	self.segment_ids = segment_ids
	self.label_id = label_id

	class DataProcessor(object):
	"""Base class for data converters for sequence classification data sets."""

	def get_train_examples(self, data_dir):
	"""Gets a collection of `InputExample`s for the train set."""
	raise NotImplementedError()

	def get_dev_examples(self, data_dir):
	"""Gets a collection of `InputExample`s for the dev set."""
	raise NotImplementedError()

	def get_labels(self):
	"""Gets the list of labels for this data set."""
	raise NotImplementedError()

	@classmethod
	def _read_tsv(cls, input_file, quotechar=None):
	"""Reads a tab separated value file."""
	with open(input_file, "r") as f:
	reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
	lines = []
	for line in reader:
	if sys.version_info[0] == 2:
	line = list(unicode(cell, 'utf-8') for cell in line)
	lines.append(line)
	return lines

	class DatasetProcessor(DataProcessor):

	def get_examples(self, data_dir, mode):
	if mode == 'train':
	return self._create_examples(
	self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
	elif mode == 'eval':
	return self._create_examples(
	self._read_tsv(os.path.join(data_dir, "dev.tsv")), "train")
	elif mode == 'test':
	return self._create_examples(
	self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")

	def _create_examples(self, lines, set_type):
	"""Creates examples for the training and dev sets."""
	examples = []
	for (i, line) in enumerate(lines):
	if i == 0:
	continue
	if len(line) != 2:
	continue
	guid = "%s-%s" % (set_type, i)
	text_a = line[0]
	label = line[1]

	examples.append(
	InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
	return examples

	def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
	"""Loads a data file into a list of `InputBatch`s."""
	label_map = {}
	for i, label in enumerate(label_list):
	label_map[label] = i
	features = []
	for (ex_index, example) in enumerate(examples):
	tokens_a = tokenizer.tokenize(example.text_a)

	tokens_b = None
	if example.text_b:
	tokens_b = tokenizer.tokenize(example.text_b)
	# Modifies `tokens_a` and `tokens_b` in place so that the total
	# length is less than the specified length.
	# Account for [CLS], [SEP], [SEP] with "- 3"
	_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
	else:
	# Account for [CLS] and [SEP] with "- 2"
	if len(tokens_a) > max_seq_length - 2:
	tokens_a = tokens_a[:(max_seq_length - 2)]

	# The convention in BERT is:
	# (a) For sequence pairs:
	# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
	# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
	# (b) For single sequences:
	# tokens: [CLS] the dog is hairy . [SEP]
	# type_ids: 0 0 0 0 0 0 0
	#
	# Where "type_ids" are used to indicate whether this is the first
	# sequence or the second sequence. The embedding vectors for `type=0` and
	# `type=1` were learned during pre-training and are added to the wordpiece
	# embedding vector (and position vector). This is not strictly necessary
	# since the [SEP] token unambigiously separates the sequences, but it makes
	# it easier for the model to learn the concept of sequences.
	#
	# For classification tasks, the first vector (corresponding to [CLS]) is
	# used as as the "sentence vector". Note that this only makes sense because
	# the entire model is fine-tuned.
	tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
	segment_ids = [0] * len(tokens)

	if tokens_b:
	tokens += tokens_b + ["[SEP]"]
	segment_ids += [1] * (len(tokens_b) + 1)

	input_ids = tokenizer.convert_tokens_to_ids(tokens)

	# The mask has 1 for real tokens and 0 for padding tokens. Only real
	# tokens are attended to.
	input_mask = [1] * len(input_ids)

	# Zero-pad up to the sequence length.
	padding = [0] * (max_seq_length - len(input_ids))
	input_ids += padding
	input_mask += padding
	segment_ids += padding

	assert len(input_ids) == max_seq_length
	assert len(input_mask) == max_seq_length
	assert len(segment_ids) == max_seq_length

	label_id = label_map[example.label]

	features.append(
	InputFeatures(input_ids=input_ids,
	input_mask=input_mask,
	segment_ids=segment_ids,
	label_id=label_id))
	return features

	def _truncate_seq_pair(tokens_a, tokens_b, max_length):
	"""Truncates a sequence pair in place to the maximum length."""
	# This is a simple heuristic which will always truncate the longer sequence
	# one token at a time. This makes more sense than truncating an equal percent
	# of tokens from each, since if one sequence is very short then each token
	# that's truncated likely contains more information than a longer sequence.
	while True:
	total_length = len(tokens_a) + len(tokens_b)
	if total_length <= max_length:
	break
	if len(tokens_a) > len(tokens_b):
	tokens_a.pop(0) # For dialogue context
	else:
	tokens_b.pop()