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.
  """ BERT classification fine-tuning: utilities to work with GLUE tasks """
  
  from __future__ import absolute_import, division, print_function
  
  import csv
  import logging
  import os
  import sys
  from io import open
  
  from seq_utils import *
  
  logger = logging.getLogger(__name__)
  
  SMALL_POSITIVE_CONST = 1e-4
  
  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 SeqInputFeatures(object):
      """A single set of features of data for the ABSA task"""
      def __init__(self, input_ids, input_mask, segment_ids, label_ids, evaluate_label_ids):
          self.input_ids = input_ids
          self.input_mask = input_mask
          self.segment_ids = segment_ids
          self.label_ids = label_ids
          # mapping between word index and head token index
          self.evaluate_label_ids = evaluate_label_ids
  
  
  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_test_examples(self, data_dir):
          """Gets a collection of `InputExample`s for the test 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", encoding="utf-8-sig") as f:
              reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
              lines = []
              for line in reader:
                  if sys.version_info[0] == 2:
                      line = list(cell for cell in line)
                  lines.append(line)
              return lines
  
  
  class ABSAProcessor(DataProcessor):
      """Processor for the ABSA datasets"""
      def get_train_examples(self, data_dir, tagging_schema):
          return self._create_examples(data_dir=data_dir, set_type='train', tagging_schema=tagging_schema)
  
      def get_dev_examples(self, data_dir, tagging_schema):
          return self._create_examples(data_dir=data_dir, set_type='dev', tagging_schema=tagging_schema)
  
      def get_test_examples(self, data_dir, tagging_schema):
          return self._create_examples(data_dir=data_dir, set_type='test', tagging_schema=tagging_schema)
  
      def get_labels(self, tagging_schema):
          if tagging_schema == 'OT':
              return []
          elif tagging_schema == 'BIO':
              return ['O', 'EQ', 'B-POS', 'I-POS', 'B-NEG', 'I-NEG', 'B-NEU', 'I-NEU']
          elif tagging_schema == 'BIEOS':
              return ['O', 'EQ', 'B-POS', 'I-POS', 'E-POS', 'S-POS',
              'B-NEG', 'I-NEG', 'E-NEG', 'S-NEG',
              'B-NEU', 'I-NEU', 'E-NEU', 'S-NEU']
          else:
              raise Exception("Invalid tagging schema %s..." % tagging_schema)
  
      def _create_examples(self, data_dir, set_type, tagging_schema):
          examples = []
          file = os.path.join(data_dir, "%s.txt" % set_type)
          class_count = np.zeros(3)
          with open(file, 'r', encoding='UTF-8') as fp:
              sample_id = 0
              for line in fp:
                  sent_string, tag_string = line.strip().split('####')
                  words = []
                  tags = []
                  for tag_item in tag_string.split(' '):
                      eles = tag_item.split('=')
                      if len(eles) == 1:
                          raise Exception("Invalid samples %s..." % tag_string)
                      elif len(eles) == 2:
                          word, tag = eles
                      else:
                          word = ''.join((len(eles) - 2) * ['='])
                          tag = eles[-1]
                      words.append(word)
                      tags.append(tag)
                  # convert from ot to bieos
                  if tagging_schema == 'BIEOS':
                      tags = ot2bieos_ts(tags)
                  elif tagging_schema == 'BIO':
                      tags = ot2bio_ts(tags)
                  else:
                      # original tags follow the OT tagging schema, do nothing
                      pass
                  guid = "%s-%s" % (set_type, sample_id)
                  text_a = ' '.join(words)
                  #label = [absa_label_vocab[tag] for tag in tags]
                  gold_ts = tag2ts(ts_tag_sequence=tags)
                  for (b, e, s) in gold_ts:
                      if s == 'POS':
                          class_count[0] += 1
                      if s == 'NEG':
                          class_count[1] += 1
                      if s == 'NEU':
                          class_count[2] += 1
                  examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=tags))
                  sample_id += 1
          print("%s class count: %s" % (set_type, class_count))
          return examples
  
  
  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()
          else:
              tokens_b.pop()
  
  
  def convert_examples_to_seq_features(examples, label_list, tokenizer,
                                       cls_token_at_end=False, pad_on_left=False, cls_token='[CLS]',
                                       sep_token='[SEP]', pad_token=0, sequence_a_segment_id=0,
                                       sequence_b_segment_id=1, cls_token_segment_id=1, pad_token_segment_id=0,
                                       mask_padding_with_zero=True):
      # feature extraction for sequence labeling
      label_map = {label: i for i, label in enumerate(label_list)}
      features = []
      max_seq_length = -1
      examples_tokenized = []
      for (ex_index, example) in enumerate(examples):
          tokens_a = []
          labels_a = []
          evaluate_label_ids = []
          words = example.text_a.split(' ')
          wid, tid = 0, 0
          for word, label in zip(words, example.label):
              subwords = tokenizer.tokenize(word)
              tokens_a.extend(subwords)
              if label != 'O':
                  labels_a.extend([label] + ['EQ'] * (len(subwords) - 1))
              else:
                  labels_a.extend(['O'] * len(subwords))
              evaluate_label_ids.append(tid)
              wid += 1
              # move the token pointer
              tid += len(subwords)
          #print(evaluate_label_ids)
          assert tid == len(tokens_a)
          evaluate_label_ids = np.array(evaluate_label_ids, dtype=np.int32)
          examples_tokenized.append((tokens_a, labels_a, evaluate_label_ids))
          if len(tokens_a) > max_seq_length:
              max_seq_length = len(tokens_a)
      # count on the [CLS] and [SEP]
      max_seq_length += 2
      #max_seq_length = 128
      for ex_index, (tokens_a, labels_a, evaluate_label_ids) in enumerate(examples_tokenized):
          #tokens_a = tokenizer.tokenize(example.text_a)
  
          # Account for [CLS] and [SEP] with "- 2"
          # for sequence labeling, better not truncate the sequence
          #if len(tokens_a) > max_seq_length - 2:
          #    tokens_a = tokens_a[:(max_seq_length - 2)]
          #    labels_a = labels_a
          tokens = tokens_a + [sep_token]
          segment_ids = [sequence_a_segment_id] * len(tokens)
          labels = labels_a + ['O']
          if cls_token_at_end:
              # evaluate label ids not change
              tokens = tokens + [cls_token]
              segment_ids = segment_ids + [cls_token_segment_id]
              labels = labels + ['O']
          else:
              # right shift 1 for evaluate label ids
              tokens = [cls_token] + tokens
              segment_ids = [cls_token_segment_id] + segment_ids
              labels = ['O'] + labels
              evaluate_label_ids += 1
          input_ids = tokenizer.convert_tokens_to_ids(tokens)
          input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
          # Zero-pad up to the sequence length.
          padding_length = max_seq_length - len(input_ids)
          #print("Current labels:", labels)
          label_ids = [label_map[label] for label in labels]
  
          # pad the input sequence and the mask sequence
          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
              # pad sequence tag 'O'
              label_ids = ([0] * padding_length) + label_ids
              # right shift padding_length for evaluate_label_ids
              evaluate_label_ids += padding_length
          else:
              # evaluate ids not change
              input_ids = input_ids + ([pad_token] * padding_length)
              input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
              segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
              # pad sequence tag 'O'
              label_ids = label_ids + ([0] * 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("labels: %s " % ' '.join([str(x) for x in label_ids]))
              logger.info("evaluate label ids: %s" % evaluate_label_ids)
  
          features.append(
              SeqInputFeatures(input_ids=input_ids,
                               input_mask=input_mask,
                               segment_ids=segment_ids,
                               label_ids=label_ids,
                               evaluate_label_ids=evaluate_label_ids))
      print("maximal sequence length is", max_seq_length)
      return features
  
  
  def convert_examples_to_features(examples, label_list, max_seq_length,
                                   tokenizer, output_mode,
                                   cls_token_at_end=False, pad_on_left=False,
                                   cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                   sequence_a_segment_id=0, sequence_b_segment_id=1,
                                   cls_token_segment_id=1, pad_token_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_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 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 = tokens_a + [sep_token]
          segment_ids = [sequence_a_segment_id] * len(tokens)
  
          if tokens_b:
              tokens += tokens_b + [sep_token]
              segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)
  
          if cls_token_at_end:
              tokens = tokens + [cls_token]
              segment_ids = segment_ids + [cls_token_segment_id]
          else:
              tokens = [cls_token] + tokens
              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)
  
          # 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
          else:
              input_ids = input_ids + ([pad_token] * padding_length)
              input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
              segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
  
          assert len(input_ids) == max_seq_length
          assert len(input_mask) == max_seq_length
          assert len(segment_ids) == max_seq_length
  
          if output_mode == "classification":
              label_id = label_map[example.label]
          elif output_mode == "regression":
              label_id = float(example.label)
          else:
              raise KeyError(output_mode)
  
          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: %s (id = %d)" % (example.label, label_id))
  
          features.append(
                  InputFeatures(input_ids=input_ids,
                                input_mask=input_mask,
                                segment_ids=segment_ids,
                                label_id=label_id))
      return features
  
  
  def match_ts(gold_ts_sequence, pred_ts_sequence):
      """
      calculate the number of correctly predicted targeted sentiment
      :param gold_ts_sequence: gold standard targeted sentiment sequence
      :param pred_ts_sequence: predicted targeted sentiment sequence
      :return:
      """
      # positive, negative and neutral
      tag2tagid = {'POS': 0, 'NEG': 1, 'NEU': 2}
      hit_count, gold_count, pred_count = np.zeros(3), np.zeros(3), np.zeros(3)
      for t in gold_ts_sequence:
          #print(t)
          ts_tag = t[2]
          tid = tag2tagid[ts_tag]
          gold_count[tid] += 1
      for t in pred_ts_sequence:
          ts_tag = t[2]
          tid = tag2tagid[ts_tag]
          if t in gold_ts_sequence:
              hit_count[tid] += 1
          pred_count[tid] += 1
      return hit_count, gold_count, pred_count
  
  
  def compute_metrics_absa(preds, labels, all_evaluate_label_ids, tagging_schema):
      if tagging_schema == 'BIEOS':
          absa_label_vocab = {'O': 0, 'EQ': 1, 'B-POS': 2, 'I-POS': 3, 'E-POS': 4, 'S-POS': 5,
                          'B-NEG': 6, 'I-NEG': 7, 'E-NEG': 8, 'S-NEG': 9,
                          'B-NEU': 10, 'I-NEU': 11, 'E-NEU': 12, 'S-NEU': 13}
      elif tagging_schema == 'BIO':
          absa_label_vocab = {'O': 0, 'EQ': 1, 'B-POS': 2, 'I-POS': 3, 
          'B-NEG': 4, 'I-NEG': 5, 'B-NEU': 6, 'I-NEU': 7}
      elif tagging_schema == 'OT':
          absa_label_vocab = {'O': 0, 'EQ': 1, 'T-POS': 2, 'T-NEG': 3, 'T-NEU': 4}
      else:
          raise Exception("Invalid tagging schema %s..." % tagging_schema)
      absa_id2tag = {}
      for k in absa_label_vocab:
          v = absa_label_vocab[k]
          absa_id2tag[v] = k
      # number of true postive, gold standard, predicted targeted sentiment
      n_tp_ts, n_gold_ts, n_pred_ts = np.zeros(3), np.zeros(3), np.zeros(3)
      # precision, recall and f1 for aspect-based sentiment analysis
      ts_precision, ts_recall, ts_f1 = np.zeros(3), np.zeros(3), np.zeros(3)
      n_samples = len(all_evaluate_label_ids)
      pred_y, gold_y = [], []
      class_count = np.zeros(3)
      for i in range(n_samples):
          evaluate_label_ids = all_evaluate_label_ids[i]
          pred_labels = preds[i][evaluate_label_ids]
          gold_labels = labels[i][evaluate_label_ids]
          assert len(pred_labels) == len(gold_labels)
          # here, no EQ tag will be induced
          pred_tags = [absa_id2tag[label] for label in pred_labels]
          gold_tags = [absa_id2tag[label] for label in gold_labels]
  
          if tagging_schema == 'OT':
              gold_tags = ot2bieos_ts(gold_tags)
              pred_tags = ot2bieos_ts(pred_tags)
          elif tagging_schema == 'BIO':
              gold_tags = ot2bieos_ts(bio2ot_ts(gold_tags))
              pred_tags = ot2bieos_ts(bio2ot_ts(pred_tags))
          else:
              # current tagging schema is BIEOS, do nothing
              pass
          g_ts_sequence, p_ts_sequence = tag2ts(ts_tag_sequence=gold_tags), tag2ts(ts_tag_sequence=pred_tags)
  
          hit_ts_count, gold_ts_count, pred_ts_count = match_ts(gold_ts_sequence=g_ts_sequence,
                                                                pred_ts_sequence=p_ts_sequence)
          n_tp_ts += hit_ts_count
          n_gold_ts += gold_ts_count
          n_pred_ts += pred_ts_count
          for (b, e, s) in g_ts_sequence:
              if s == 'POS':
                  class_count[0] += 1
              if s == 'NEG':
                  class_count[1] += 1
              if s == 'NEU':
                  class_count[2] += 1
      for i in range(3):
          n_ts = n_tp_ts[i]
          n_g_ts = n_gold_ts[i]
          n_p_ts = n_pred_ts[i]
          ts_precision[i] = float(n_ts) / float(n_p_ts + SMALL_POSITIVE_CONST)
          ts_recall[i] = float(n_ts) / float(n_g_ts + SMALL_POSITIVE_CONST)
          ts_f1[i] = 2 * ts_precision[i] * ts_recall[i] / (ts_precision[i] + ts_recall[i] + SMALL_POSITIVE_CONST)
  
      macro_f1 = ts_f1.mean()
  
      # calculate micro-average scores for ts task
      # TP
      n_tp_total = sum(n_tp_ts)
      # TP + FN
      n_g_total = sum(n_gold_ts)
      print("class_count:", class_count)
  
      # TP + FP
      n_p_total = sum(n_pred_ts)
      micro_p = float(n_tp_total) / (n_p_total + SMALL_POSITIVE_CONST)
      micro_r = float(n_tp_total) / (n_g_total + SMALL_POSITIVE_CONST)
      micro_f1 = 2 * micro_p * micro_r / (micro_p + micro_r + SMALL_POSITIVE_CONST)
      scores = {'macro-f1': macro_f1, 'precision': micro_p, "recall": micro_r, "micro-f1": micro_f1}
      return scores
  
  
  processors = {
      "laptop14": ABSAProcessor,
      "rest_total": ABSAProcessor,
      "rest_total_revised": ABSAProcessor,
      "rest14": ABSAProcessor,
      "rest15": ABSAProcessor,
      "rest16": ABSAProcessor,
  }
  
  output_modes = {
      "cola": "classification",
      "mnli": "classification",
      "mnli-mm": "classification",
      "mrpc": "classification",
      "sst-2": "classification",
      "sts-b": "regression",
      "qqp": "classification",
      "qnli": "classification",
      "rte": "classification",
      "wnli": "classification",
      "laptop14": "classification",
      "rest_total": "classification",
      "rest14": "classification",
      "rest15": "classification",
      "rest16": "classification",
      "rest_total_revised": "classification",
  }
  