content: # 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 """
  
  import csv, json
  import logging
  import os
  import sys
  from io import open
  
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import matthews_corrcoef, f1_score
  
  logger = logging.getLogger(__name__)
  
  
  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", encoding="utf-8-sig") as f:
              reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
              lines = []
              for line in reader:
                  lines.append(line)
              return lines
  
  
  class MrpcProcessor(DataProcessor):
      """Processor for the MRPC data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
  
      def get_labels(self):
          """See base class."""
          return ["0", "1"]
  
      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[3]
              text_b = line[4]
              label = line[0]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
          return examples
  
  
  class MnliProcessor(DataProcessor):
      """Processor for the MultiNLI data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
              "dev_matched")
  
      def get_labels(self):
          """See base class."""
          return ["contradiction", "entailment", "neutral"]
  
      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, line[0])
              text_a = line[8]
              text_b = line[9]
              label = line[-1]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
          return examples
  
  
  class MnliMismatchedProcessor(MnliProcessor):
      """Processor for the MultiNLI Mismatched data set (GLUE version)."""
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
              "dev_matched")
  
  
  class ColaProcessor(DataProcessor):
      """Processor for the CoLA data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
  
      def get_labels(self):
          """See base class."""
          return ["0", "1"]
  
      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[3]
              label = line[1]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
          return examples
  
  
  class Sst2Processor(DataProcessor):
      """Processor for the SST-2 data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
  
      def get_labels(self):
          """See base class."""
          return ["0", "1"]
  
      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[0]
              label = line[1]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
          return examples
  
  
  class StsbProcessor(DataProcessor):
      """Processor for the STS-B data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
  
      def get_labels(self):
          """See base class."""
          return [None]
  
      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, line[0])
              text_a = line[7]
              text_b = line[8]
              label = line[-1]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
          return examples
  
  
  class QqpProcessor(DataProcessor):
      """Processor for the QQP data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
  
      def get_labels(self):
          """See base class."""
          return ["0", "1"]
  
      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, line[0])
              try:
                  text_a = line[3]
                  text_b = line[4]
                  label = line[5]
              except IndexError:
                  continue
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
          return examples
  
  
  class QnliProcessor(DataProcessor):
      """Processor for the QNLI data set (GLUE version)."""
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
      def get_dev_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "dev.tsv")),
              "dev_matched")
  
      def get_labels(self):
          """See base class."""
          return ["entailment", "not_entailment"]
  
      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, line[0])
              text_a = line[1]
              text_b = line[2]
              label = line[-1]
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
          return examples
  
  
  
  from dataclasses import dataclass
  import dataclasses
  from typing import List,Optional,Union
  @dataclass(frozen=True)
  class Trans3InputFeatures:
      input_ids: List[int]
      attention_mask: Optional[List[int]] = None
      token_type_ids: Optional[List[int]] = None
      label: Optional[Union[int, float]] = None
  
      def to_json_string(self):
          return json.dumps(dataclasses.asdict(self)) + "\n"
  
  
  def trans3_convert_examples_to_features(examples, label_list, max_length,
                                   tokenizer, output_mode):
      if max_length is None:
          max_length = tokenizer.max_len
  
      label_map = {label: i for i, label in enumerate(label_list)}
  
      def label_from_example(example: InputExample):
          if example.label is None:
              return None
          if output_mode == "classification":
              return label_map[example.label]
          elif output_mode == "regression":
              return float(example.label)
          raise KeyError(output_mode)
  
      labels = [label_from_example(example) for example in examples]
  
      batch_encoding = tokenizer(
          [(example.text_a, example.text_b) for example in examples],
          max_length=max_length,
          padding="max_length",
          truncation=True,
          return_token_type_ids=True
      )
  
      features = []
      for i in range(len(examples)):
          inputs = {k: batch_encoding[k][i] for k in batch_encoding}
  
          feature = Trans3InputFeatures(**inputs, label=labels[i])
          features.append(feature)
  
      for i, example in enumerate(examples[:5]):
          logger.info("*** Example ***")
          logger.info("guid: %s" % (example.guid))
          logger.info("features: %s" % features[i])
  
      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 _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 simple_accuracy(preds, labels):
      return (preds == labels).mean()
  
  
  def acc_and_f1(preds, labels):
      acc = simple_accuracy(preds, labels)
      f1 = f1_score(y_true=labels, y_pred=preds)
      return {
          "acc": acc,
          "f1": f1,
          "acc_and_f1": (acc + f1) / 2,
      }
  
  
  def pearson_and_spearman(preds, labels):
      pearson_corr = pearsonr(preds, labels)[0]
      spearman_corr = spearmanr(preds, labels)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
  
  
  def compute_metrics(task_name, preds, labels):
      assert len(preds) == len(labels)
      if task_name == "cola":
          return {"mcc": matthews_corrcoef(labels, preds)}
      elif task_name == "sst-2":
          return {"acc": simple_accuracy(preds, labels)}
      elif task_name == "mrpc":
          return acc_and_f1(preds, labels)
      elif task_name == "sts-b":
          return pearson_and_spearman(preds, labels)
      elif task_name == "qqp":
          return acc_and_f1(preds, labels)
      elif task_name == "mnli":
          return {"acc": simple_accuracy(preds, labels)}
      elif task_name == "mnli-mm":
          return {"acc": simple_accuracy(preds, labels)}
      elif task_name == "qnli":
          return {"acc": simple_accuracy(preds, labels)}
      elif task_name == "rte":
          return {"acc": simple_accuracy(preds, labels)}
      elif task_name == "wnli":
          return {"acc": simple_accuracy(preds, labels)}
      else:
          raise KeyError(task_name)
  
  processors = {
      #"cola": ColaProcessor,
      "mnli": MnliProcessor,
      "mnli-mm": MnliMismatchedProcessor,
      #"mrpc": MrpcProcessor,
      #"sst-2": Sst2Processor,
      #"sts-b": StsbProcessor,
      #"qqp": QqpProcessor,
      #"qnli": QnliProcessor,
      #"rte": RteProcessor,
      #"wnli": WnliProcessor,
  }
  
  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",
  }
  
  GLUE_TASKS_NUM_LABELS = {
      "cola": 2,
      "mnli": 3,
      "mrpc": 2,
      "sst-2": 2,
      "sts-b": 1,
      "qqp": 2,
      "qnli": 2,
      "rte": 2,
      "wnli": 2,
  }
  