text: """ Named entity recognition fine-tuning: utilities to work with CLUENER task. """
  import torch
  import logging
  import os
  import copy
  import json
  from .utils_ner import DataProcessor
  logger = logging.getLogger(__name__)
  
  LABEL_NAME = 'labels.txt'
  
  class InputExample(object):
      """A single training/test example for token classification."""
      def __init__(self, guid, text_a, labels):
          """Constructs a InputExample.
          Args:
              guid: Unique id for the example.
              text_a: list. The words of the sequence.
              labels: (Optional) list. The labels for each word of the sequence. This should be
              specified for train and dev examples, but not for test examples.
          """
          self.guid = guid
          self.text_a = text_a
          self.labels = labels
  
      def __repr__(self):
          return str(self.to_json_string())
      def to_dict(self):
          """Serializes this instance to a Python dictionary."""
          output = copy.deepcopy(self.__dict__)
          return output
      def to_json_string(self):
          """Serializes this instance to a JSON string."""
          return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
  class InputFeatures(object):
      """A single set of features of data."""
      def __init__(self, input_ids, input_mask, input_len,segment_ids, label_ids):
          self.input_ids = input_ids
          self.input_mask = input_mask
          self.segment_ids = segment_ids
          self.label_ids = label_ids
          self.input_len = input_len
  
      def __repr__(self):
          return str(self.to_json_string())
  
      def to_dict(self):
          """Serializes this instance to a Python dictionary."""
          output = copy.deepcopy(self.__dict__)
          return output
  
      def to_json_string(self):
          """Serializes this instance to a JSON string."""
          return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
  def collate_fn(batch):
      """
      batch should be a list of (sequence, target, length) tuples...
      Returns a padded tensor of sequences sorted from longest to shortest,
      """
      all_input_ids, all_attention_mask, all_token_type_ids, all_lens, all_labels = map(torch.stack, zip(*batch))
      max_len = max(all_lens).item()
      all_input_ids = all_input_ids[:, :max_len]
      all_attention_mask = all_attention_mask[:, :max_len]
      all_token_type_ids = all_token_type_ids[:, :max_len]
      all_labels = all_labels[:,:max_len]
      return all_input_ids, all_attention_mask, all_token_type_ids, all_labels,all_lens
  
  def convert_examples_to_features(examples,label_list,max_seq_length,tokenizer,
                                   cls_token_at_end=False,cls_token="[CLS]",cls_token_segment_id=1,
                                   sep_token="[SEP]",pad_on_left=False,pad_token=0,pad_token_segment_id=0,
                                   sequence_a_segment_id=0,mask_padding_with_zero=True,):
      """ Loads a data file into a list of `InputBatch`s
          `cls_token_at_end` define the location of the CLS token:
              - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
              - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
          `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
      """
      label_map = {label: i for i, label in enumerate(label_list)}
      features = []
      for (ex_index, example) in enumerate(examples):
          if ex_index % 10000 == 0:
              logger.info("Writing example %d of %d", ex_index, len(examples))
          tokens = tokenizer.tokenize(example.text_a)
          label_ids = [label_map[x] for x in example.labels]
          # Account for [CLS] and [SEP] with "- 2".
          special_tokens_count = 2
          if len(tokens) > max_seq_length - special_tokens_count:
              tokens = tokens[: (max_seq_length - special_tokens_count)]
              label_ids = label_ids[: (max_seq_length - special_tokens_count)]
  
          # 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 unambiguously 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 += [sep_token]
          label_ids += [label_map['O']]
          segment_ids = [sequence_a_segment_id] * len(tokens)
  
          if cls_token_at_end:
              tokens += [cls_token]
              label_ids += [label_map['O']]
              segment_ids += [cls_token_segment_id]
          else:
              tokens = [cls_token] + tokens
              label_ids = [label_map['O']] + label_ids
              segment_ids = [cls_token_segment_id] + segment_ids
  
          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 if mask_padding_with_zero else 0] * len(input_ids)
          input_len = len(label_ids)
          # Zero-pad up to the sequence length.
          padding_length = max_seq_length - len(input_ids)
          if pad_on_left:
              input_ids = ([pad_token] * padding_length) + input_ids
              input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
              segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
              label_ids = ([pad_token] * padding_length) + label_ids
          else:
              input_ids += [pad_token] * padding_length
              input_mask += [0 if mask_padding_with_zero else 1] * padding_length
              segment_ids += [pad_token_segment_id] * padding_length
              label_ids += [pad_token] * padding_length
  
          assert len(input_ids) == max_seq_length
          assert len(input_mask) == max_seq_length
          assert len(segment_ids) == max_seq_length
          assert len(label_ids) == max_seq_length
          if ex_index < 5:
              logger.info("*** Example ***")
              logger.info("guid: %s", example.guid)
              logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
              logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
              logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
              logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
              logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
  
          features.append(InputFeatures(input_ids=input_ids, input_mask=input_mask,input_len = input_len,
                                        segment_ids=segment_ids, label_ids=label_ids))
      return features
  
  
  class CnerProcessor(DataProcessor):
      """Processor for the chinese ner data set."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_text(os.path.join(data_dir, "train.char.bmes")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_text(os.path.join(data_dir, "dev.char.bmes")), "dev")
  
      def get_test_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_text(os.path.join(data_dir, "test.char.bmes")), "test")
  
      def get_single_example(self, texts:list):
          return self._create_examples(self.format_lines(texts),'pred')
  
      def format_lines(self, texts:list):
          res = []
          for text in texts:
              words = list(text)
              labels = ['O'] * len(words)
              res.append({"words": words, "labels": labels})
          return res
  
      def get_labels(self, data_dir, markup='bios'):
          """See base class."""
          b, i, s = [], [], []
          with open(os.path.join(data_dir, LABEL_NAME), 'r') as f:
              for j in f.readlines():
                  j = j.strip()
                  if not j:
                      continue
                  b.append("B-" + j)
                  i.append('I-' + j)
                  s.append("S-" + j)
          if markup == 'bio':
              labels = ["X"] + b + i + ["O", "[START]", "[END]"]
          else:
              labels = ["X"] + b + i + s + ["O", "[START]", "[END]"]
          return labels
  
      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
              guid = "%s-%s" % (set_type, i)
              text_a= line['words']
              # BIOS
              labels = []
              for x in line['labels']:
                  if 'M-' in x:
                      labels.append(x.replace('M-','I-'))
                  elif 'E-' in x:
                      labels.append(x.replace('E-', 'I-'))
                  else:
                      labels.append(x)
              examples.append(InputExample(guid=guid, text_a=text_a, labels=labels))
          return examples
  
  class CluenerProcessor(DataProcessor):
      """Processor for the chinese ner data set."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_json(os.path.join(data_dir, "train.json")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_json(os.path.join(data_dir, "dev.json")), "dev")
  
      def get_test_examples(self, data_dir):
          """See base class."""
          return self._create_examples(self._read_json(os.path.join(data_dir, "test.json")), "test")
  
      def get_single_example(self, texts:list):
          return self._create_examples(self.format_lines(texts),'pred')
  
      def format_lines(self, texts:list):
          res = []
          for text in texts:
              words = list(text)
              labels = ['O'] * len(words)
              res.append({"words": words, "labels": labels})
          return res
  
      def get_labels(self, data_dir, markup='bios'):
          """See base class."""
          b, i, s = [], [] ,[]
          with open(os.path.join(data_dir, LABEL_NAME), 'r') as f:
              for j in f.readlines():
                  j = j.strip()
                  if not j:
                      continue
                  b.append("B-"+j)
                  i.append('I-'+j)
                  s.append("S-"+j)
          if markup == 'bio':
              labels = ["X"] + b + i + ["O", "[START]", "[END]"]
          else:
              labels = ["X"] + b + i + s + ["O", "[START]", "[END]"]
          return labels
  
      def _create_examples(self, lines, set_type):
          """Creates examples for the training and dev sets."""
          examples = []
          for (i, line) in enumerate(lines):
              guid = "%s-%s" % (set_type, i)
              text_a= line['words']
              # BIOS
              labels = line['labels']
              examples.append(InputExample(guid=guid, text_a=text_a, labels=labels))
          return examples
  
  ner_processors = {
      "cner": CnerProcessor,
      'cluener':CluenerProcessor
  }
  