text: # coding=utf-8
  # Copyright 2018 The Google AI Team Authors.
  #
  # 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.
  """Utility functions for GLUE classification tasks."""
  
  from __future__ import absolute_import
  from __future__ import division
  # from __future__ import google_type_annotations
  from __future__ import print_function
  import collections
  import csv
  import os
  from albert import fine_tuning_utils
  from albert import modeling
  from albert import optimization
  from albert import tokenization
  import tensorflow.compat.v1 as tf
  from tensorflow.contrib import data as contrib_data
  from tensorflow.contrib import metrics as contrib_metrics
  from tensorflow.contrib import tpu as contrib_tpu
  
  
  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 PaddingInputExample(object):
    """Fake example so the num input examples is a multiple of the batch size.
  
    When running eval/predict on the TPU, we need to pad the number of examples
    to be a multiple of the batch size, because the TPU requires a fixed batch
    size. The alternative is to drop the last batch, which is bad because it means
    the entire output data won't be generated.
  
    We use this class instead of `None` because treating `None` as padding
    battches could cause silent errors.
    """
  
  
  class InputFeatures(object):
    """A single set of features of data."""
  
    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_id,
                 guid=None,
                 example_id=None,
                 is_real_example=True):
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.label_id = label_id
      self.example_id = example_id
      self.guid = guid
      self.is_real_example = is_real_example
  
  
  class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""
  
    def __init__(self, use_spm, do_lower_case):
      super(DataProcessor, self).__init__()
      self.use_spm = use_spm
      self.do_lower_case = do_lower_case
  
    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 prediction."""
      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 tf.gfile.Open(input_file, "r") as f:
        reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
        lines = []
        for line in reader:
          lines.append(line)
        return lines
  
    def process_text(self, text):
      if self.use_spm:
        return tokenization.preprocess_text(text, lower=self.do_lower_case)
      else:
        return tokenization.convert_to_unicode(text)
  
  
  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, "MNLI", "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, "MNLI", "dev_matched.tsv")),
          "dev_matched")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "MNLI", "test_matched.tsv")),
          "test")
  
    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
        # Note(mingdachen): We will rely on this guid for GLUE submission.
        guid = self.process_text(line[0])
        text_a = self.process_text(line[8])
        text_b = self.process_text(line[9])
        if set_type == "test":
          label = "contradiction"
        else:
          label = self.process_text(line[-1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class MisMnliProcessor(MnliProcessor):
    """Processor for the Mismatched MultiNLI 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, "MNLI", "dev_mismatched.tsv")),
          "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "MNLI", "test_mismatched.tsv")),
          "test")
  
  
  class MrpcProcessor(DataProcessor):
    """Processor for the MRPC 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, "MRPC", "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, "MRPC", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "MRPC", "test.tsv")), "test")
  
    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 = self.process_text(line[3])
        text_b = self.process_text(line[4])
        if set_type == "test":
          guid = line[0]
          label = "0"
        else:
          label = self.process_text(line[0])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  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, "CoLA", "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, "CoLA", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "CoLA", "test.tsv")), "test")
  
    def get_labels(self):
      """See base class."""
      return ["0", "1","2"]
  
    def _create_examples(self, lines, set_type):
      """Creates examples for the training and dev sets."""
      examples = []
      for (i, line) in enumerate(lines):
        # Only the test set has a header
        if set_type == "test" and i == 0:
          continue
        guid = "%s-%s" % (set_type, i)
        if set_type == "test":
          guid = line[0]
          text_a = self.process_text(line[1])
          label = "0"
        else:
          text_a = self.process_text(line[3])
          label = self.process_text(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, "SST-2", "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, "SST-2", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "SST-2", "test.tsv")), "test")
  
    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
        if set_type != "test":
          guid = "%s-%s" % (set_type, i)
          text_a = self.process_text(line[0])
          label = self.process_text(line[1])
        else:
          guid = self.process_text(line[0])
          # guid = "%s-%s" % (set_type, line[0])
          text_a = self.process_text(line[1])
          label = "0"
        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, "STS-B", "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, "STS-B", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "STS-B", "test.tsv")), "test")
  
    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 = self.process_text(line[0])
        # guid = "%s-%s" % (set_type, line[0])
        text_a = self.process_text(line[7])
        text_b = self.process_text(line[8])
        if set_type != "test":
          label = float(line[-1])
        else:
          label = 0
        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, "QQP", "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, "QQP", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "QQP", "test.tsv")), "test")
  
    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 = line[0]
        # guid = "%s-%s" % (set_type, line[0])
        if set_type != "test":
          try:
            text_a = self.process_text(line[3])
            text_b = self.process_text(line[4])
            label = self.process_text(line[5])
          except IndexError:
            continue
        else:
          text_a = self.process_text(line[1])
          text_b = self.process_text(line[2])
          label = "0"
        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, "QNLI", "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, "QNLI", "dev.tsv")),
          "dev_matched")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "QNLI", "test.tsv")),
          "test_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 = self.process_text(line[0])
        # guid = "%s-%s" % (set_type, line[0])
        text_a = self.process_text(line[1])
        text_b = self.process_text(line[2])
        if set_type == "test_matched":
          label = "entailment"
        else:
          label = self.process_text(line[-1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class RteProcessor(DataProcessor):
    """Processor for the RTE 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, "RTE", "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, "RTE", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "RTE", "test.tsv")), "test")
  
    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 = self.process_text(line[0])
        # guid = "%s-%s" % (set_type, line[0])
        text_a = self.process_text(line[1])
        text_b = self.process_text(line[2])
        if set_type == "test":
          label = "entailment"
        else:
          label = self.process_text(line[-1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class WnliProcessor(DataProcessor):
    """Processor for the WNLI 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, "WNLI", "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, "WNLI", "dev.tsv")), "dev")
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "WNLI", "test.tsv")), "test")
  
    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 = self.process_text(line[0])
        # guid = "%s-%s" % (set_type, line[0])
        text_a = self.process_text(line[1])
        text_b = self.process_text(line[2])
        if set_type != "test":
          label = self.process_text(line[-1])
        else:
          label = "0"
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class AXProcessor(DataProcessor):
    """Processor for the AX data set (GLUE version)."""
  
    def get_test_examples(self, data_dir):
      """See base class."""
      return self._create_examples(
          self._read_tsv(os.path.join(data_dir, "diagnostic", "diagnostic.tsv")),
          "test")
  
    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
        # Note(mingdachen): We will rely on this guid for GLUE submission.
        guid = self.process_text(line[0])
        text_a = self.process_text(line[1])
        text_b = self.process_text(line[2])
        if set_type == "test":
          label = "contradiction"
        else:
          label = self.process_text(line[-1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  def convert_single_example(ex_index, example, label_list, max_seq_length,
                             tokenizer, task_name):
    """Converts a single `InputExample` into a single `InputFeatures`."""
  
    if isinstance(example, PaddingInputExample):
      return InputFeatures(
          input_ids=[0] * max_seq_length,
          input_mask=[0] * max_seq_length,
          segment_ids=[0] * max_seq_length,
          label_id=0,
          is_real_example=False)
  
    if task_name != "sts-b":
      label_map = {}
      for (i, label) in enumerate(label_list):
        label_map[label] = i
  
    tokens_a = tokenizer.tokenize(example.text_a)
    tokens_b = None
    if example.text_b:
      tokens_b = tokenizer.tokenize(example.text_b)
  
    if tokens_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[0:(max_seq_length - 2)]
  
    # The convention in ALBERT 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
    # 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 the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
      tokens.append(token)
      segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)
  
    if tokens_b:
      for token in tokens_b:
        tokens.append(token)
        segment_ids.append(1)
      tokens.append("[SEP]")
      segment_ids.append(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.
    while len(input_ids) < max_seq_length:
      input_ids.append(0)
      input_mask.append(0)
      segment_ids.append(0)
  
    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
  
    if task_name != "sts-b":
      label_id = label_map[example.label]
    else:
      label_id = example.label
  
    if ex_index < 5:
      tf.logging.info("*** Example ***")
      tf.logging.info("guid: %s" % (example.guid))
      tf.logging.info("tokens: %s" % " ".join(
          [tokenization.printable_text(x) for x in tokens]))
      tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
      tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
      tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
      tf.logging.info("label: %s (id = %d)" % (example.label, label_id))
  
    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_id,
        is_real_example=True)
    return feature
  
  
  def file_based_convert_examples_to_features(
      examples, label_list, max_seq_length, tokenizer, output_file, task_name):
    """Convert a set of `InputExample`s to a TFRecord file."""
  
    writer = tf.python_io.TFRecordWriter(output_file)
  
    for (ex_index, example) in enumerate(examples):
      if ex_index % 10000 == 0:
        tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
  
      feature = convert_single_example(ex_index, example, label_list,
                                       max_seq_length, tokenizer, task_name)
  
      def create_int_feature(values):
        f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
        return f
  
      def create_float_feature(values):
        f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
        return f
  
      features = collections.OrderedDict()
      features["input_ids"] = create_int_feature(feature.input_ids)
      features["input_mask"] = create_int_feature(feature.input_mask)
      features["segment_ids"] = create_int_feature(feature.segment_ids)
      features["label_ids"] = create_float_feature([feature.label_id])\
          if task_name == "sts-b" else create_int_feature([feature.label_id])
      features["is_real_example"] = create_int_feature(
          [int(feature.is_real_example)])
  
      tf_example = tf.train.Example(features=tf.train.Features(feature=features))
      writer.write(tf_example.SerializeToString())
    writer.close()
  
  
  def file_based_input_fn_builder(input_file, seq_length, is_training,
                                  drop_remainder, task_name, use_tpu, bsz,
                                  multiple=1):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
    labeltype = tf.float32 if task_name == "sts-b" else tf.int64
  
    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length * multiple], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
        "label_ids": tf.FixedLenFeature([], labeltype),
        "is_real_example": tf.FixedLenFeature([], tf.int64),
    }
  
    def _decode_record(record, name_to_features):
      """Decodes a record to a TensorFlow example."""
      example = tf.parse_single_example(record, name_to_features)
  
      # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
      # So cast all int64 to int32.
      for name in list(example.keys()):
        t = example[name]
        if t.dtype == tf.int64:
          t = tf.to_int32(t)
        example[name] = t
  
      return example
  
    def input_fn(params):
      """The actual input function."""
      if use_tpu:
        batch_size = params["batch_size"]
      else:
        batch_size = bsz
  
      # For training, we want a lot of parallel reading and shuffling.
      # For eval, we want no shuffling and parallel reading doesn't matter.
      d = tf.data.TFRecordDataset(input_file)
      if is_training:
        d = d.repeat()
        d = d.shuffle(buffer_size=100)
  
      d = d.apply(
          contrib_data.map_and_batch(
              lambda record: _decode_record(record, name_to_features),
              batch_size=batch_size,
              drop_remainder=drop_remainder))
  
      return d
  
    return input_fn
  
  
  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 create_model(albert_config, is_training, input_ids, input_mask, segment_ids,
                   labels, num_labels, use_one_hot_embeddings, task_name,
                   hub_module):
    """Creates a classification model."""
    (output_layer, _) = fine_tuning_utils.create_albert(
        albert_config=albert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings,
        use_einsum=True,
        hub_module=hub_module)
  
    hidden_size = output_layer.shape[-1].value
  
    output_weights = tf.get_variable(
        "output_weights", [num_labels, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))
  
    output_bias = tf.get_variable(
        "output_bias", [num_labels], initializer=tf.zeros_initializer())
  
    with tf.variable_scope("loss"):
      if is_training:
        # I.e., 0.1 dropout
        output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
  
      logits = tf.matmul(output_layer, output_weights, transpose_b=True)
      logits = tf.nn.bias_add(logits, output_bias)
      if task_name != "sts-b":
        probabilities = tf.nn.softmax(logits, axis=-1)
        predictions = tf.argmax(probabilities, axis=-1, output_type=tf.int32)
        log_probs = tf.nn.log_softmax(logits, axis=-1)
        one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
  
        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
      else:
        probabilities = logits
        logits = tf.squeeze(logits, [-1])
        predictions = logits
        per_example_loss = tf.square(logits - labels)
      loss = tf.reduce_mean(per_example_loss)
  
      return (loss, per_example_loss, probabilities, logits, predictions)
  
  
  def model_fn_builder(albert_config, num_labels, init_checkpoint, learning_rate,
                       num_train_steps, num_warmup_steps, use_tpu,
                       use_one_hot_embeddings, task_name, hub_module=None,
                       optimizer="adamw"):
    """Returns `model_fn` closure for TPUEstimator."""
  
    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
      """The `model_fn` for TPUEstimator."""
  
      tf.logging.info("*** Features ***")
      for name in sorted(features.keys()):
        tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
  
      input_ids = features["input_ids"]
      input_mask = features["input_mask"]
      segment_ids = features["segment_ids"]
      label_ids = features["label_ids"]
      is_real_example = None
      if "is_real_example" in features:
        is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
      else:
        is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
  
      is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
      (total_loss, per_example_loss, probabilities, logits, predictions) = \
          create_model(albert_config, is_training, input_ids, input_mask,
                       segment_ids, label_ids, num_labels, use_one_hot_embeddings,
                       task_name, hub_module)
  
      tvars = tf.trainable_variables()
      initialized_variable_names = {}
      scaffold_fn = None
      if init_checkpoint:
        (assignment_map, initialized_variable_names
        ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
        if use_tpu:
  
          def tpu_scaffold():
            tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
            return tf.train.Scaffold()
  
          scaffold_fn = tpu_scaffold
        else:
          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
  
      tf.logging.info("**** Trainable Variables ****")
      for var in tvars:
        init_string = ""
        if var.name in initialized_variable_names:
          init_string = ", *INIT_FROM_CKPT*"
        tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                        init_string)
  
      output_spec = None
      if mode == tf.estimator.ModeKeys.TRAIN:
  
        train_op = optimization.create_optimizer(
            total_loss, learning_rate, num_train_steps, num_warmup_steps,
            use_tpu, optimizer)
  
        output_spec = contrib_tpu.TPUEstimatorSpec(
            mode=mode,
            loss=total_loss,
            train_op=train_op,
            scaffold_fn=scaffold_fn)
      elif mode == tf.estimator.ModeKeys.EVAL:
        if task_name not in ["sts-b", "cola"]:
          def metric_fn(per_example_loss, label_ids, logits, is_real_example):
            predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
            accuracy = tf.metrics.accuracy(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
            loss = tf.metrics.mean(
                values=per_example_loss, weights=is_real_example)
            return {
                "eval_accuracy": accuracy,
                "eval_loss": loss,
            }
        elif task_name == "sts-b":
          def metric_fn(per_example_loss, label_ids, logits, is_real_example):
            """Compute Pearson correlations for STS-B."""
            # Display labels and predictions
            concat1 = contrib_metrics.streaming_concat(logits)
            concat2 = contrib_metrics.streaming_concat(label_ids)
  
            # Compute Pearson correlation
            pearson = contrib_metrics.streaming_pearson_correlation(
                logits, label_ids, weights=is_real_example)
  
            # Compute MSE
            # mse = tf.metrics.mean(per_example_loss)
            mse = tf.metrics.mean_squared_error(
                label_ids, logits, weights=is_real_example)
  
            loss = tf.metrics.mean(
                values=per_example_loss,
                weights=is_real_example)
  
            return {"pred": concat1, "label_ids": concat2, "pearson": pearson,
                    "MSE": mse, "eval_loss": loss,}
        elif task_name == "cola":
          def metric_fn(per_example_loss, label_ids, logits, is_real_example):
            """Compute Matthew's correlations for COLA."""
            predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
            # https://en.wikipedia.org/wiki/Matthews_correlation_coefficient
            tp, tp_op = tf.metrics.true_positives(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
            tn, tn_op = tf.metrics.true_negatives(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
            fp, fp_op = tf.metrics.false_positives(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
            fn, fn_op = tf.metrics.false_negatives(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
  
            # Compute Matthew's correlation
            mcc = tf.div_no_nan(
                tp * tn - fp * fn,
                tf.pow((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn), 0.5))
  
            # Compute accuracy
            accuracy = tf.metrics.accuracy(
                labels=label_ids, predictions=predictions,
                weights=is_real_example)
  
            loss = tf.metrics.mean(
                values=per_example_loss,
                weights=is_real_example)
  
            return {"matthew_corr": (mcc, tf.group(tp_op, tn_op, fp_op, fn_op)),
                    "eval_accuracy": accuracy, "eval_loss": loss,}
  
        eval_metrics = (metric_fn,
                        [per_example_loss, label_ids, logits, is_real_example])
        output_spec = contrib_tpu.TPUEstimatorSpec(
            mode=mode,
            loss=total_loss,
            eval_metrics=eval_metrics,
            scaffold_fn=scaffold_fn)
      else:
        output_spec = contrib_tpu.TPUEstimatorSpec(
            mode=mode,
            predictions={
                "probabilities": probabilities,
                "predictions": predictions
            },
            scaffold_fn=scaffold_fn)
      return output_spec
  
    return model_fn
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  def input_fn_builder(features, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []
  
    for feature in features:
      all_input_ids.append(feature.input_ids)
      all_input_mask.append(feature.input_mask)
      all_segment_ids.append(feature.segment_ids)
      all_label_ids.append(feature.label_id)
  
    def input_fn(params):
      """The actual input function."""
      batch_size = params["batch_size"]
  
      num_examples = len(features)
  
      # This is for demo purposes and does NOT scale to large data sets. We do
      # not use Dataset.from_generator() because that uses tf.py_func which is
      # not TPU compatible. The right way to load data is with TFRecordReader.
      d = tf.data.Dataset.from_tensor_slices({
          "input_ids":
              tf.constant(
                  all_input_ids, shape=[num_examples, seq_length],
                  dtype=tf.int32),
          "input_mask":
              tf.constant(
                  all_input_mask,
                  shape=[num_examples, seq_length],
                  dtype=tf.int32),
          "segment_ids":
              tf.constant(
                  all_segment_ids,
                  shape=[num_examples, seq_length],
                  dtype=tf.int32),
          "label_ids":
              tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
      })
  
      if is_training:
        d = d.repeat()
        d = d.shuffle(buffer_size=100)
  
      d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
      return d
  
    return input_fn
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  def convert_examples_to_features(examples, label_list, max_seq_length,
                                   tokenizer, task_name):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""
  
    features = []
    for (ex_index, example) in enumerate(examples):
      if ex_index % 10000 == 0:
        tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
  
      feature = convert_single_example(ex_index, example, label_list,
                                       max_seq_length, tokenizer, task_name)
  
      features.append(feature)
    return features
  