<reponame>SunLemuria/JointBERT-Tensorflow1<filename>bert/run_intent_slot.py
# coding=utf-8
# Copyright 2018 The Google AI Language 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.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import os
import sys
import json
import random
import modeling
import optimization
import tokenization
import tensorflow as tf

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_bool("save_labels", False, "Save Labels and exit.")

flags.DEFINE_bool("saved_model", False, "Create Saved Model.")

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool(
    "do_predict", False,
    "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


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

  def __init__(self, guid, text_a, text_b=None, intent_label=None, slot_labels=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.intent_label = intent_label
    self.slot_labels = slot_labels


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

  def __init__(self,
               input_ids,
               input_mask,
               segment_ids,
               intent_label_id,
               slot_label_ids,
               slot_label_mask,
               is_real_example=True):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.intent_label_id = intent_label_id
    self.slot_label_ids = slot_label_ids
    self.slot_label_mask = slot_label_mask
    self.is_real_example = is_real_example

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

  def __init__(self, data_dir):
    self._data_dir = data_dir

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

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

  def get_test_examples(self):
    """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()

  def save_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


class IntentSlotProcessor(DataProcessor):
  """Processor for the Intent data set."""

  def __init__(self, data_dir):
    super(IntentSlotProcessor, self).__init__(data_dir)

    self._intent_label_list = []
    self._slot_label_list = []

    lines = self._read_tsv(os.path.join(data_dir, "train.tsv"))
    for line in lines:
      intent_label = tokenization.convert_to_unicode(line[1])
      if intent_label not in self._intent_label_list:
        self._intent_label_list.append(intent_label)
      for slot in line[2].split():
        slot_label = tokenization.convert_to_unicode(slot)
        if slot_label not in self._slot_label_list:
          self._slot_label_list.append(slot_label)

  def get_train_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "train.tsv")), "train")

  def get_dev_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "dev.tsv")), "dev")

  def get_test_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return (self._intent_label_list, self._slot_label_list)

  def save_labels(self):
    """See base class."""
    with open(os.path.join(self._data_dir, "intents.txt"), 'w') as out_file:
      for l in self._intent_label_list:
        out_file.write(l + os.linesep)

    with open(os.path.join(self._data_dir, "slots.txt"), 'w') as out_file:
      for l in self._slot_label_list:
        out_file.write(l + os.linesep)

  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)
      if set_type == "test":
        text_a = tokenization.convert_to_unicode(line[0])
        intent_label = self._intent_label_list[0]
        slot_labels = [self._slot_label_list[0]]
      else:
        text_a = tokenization.convert_to_unicode(line[0])
        intent_label = tokenization.convert_to_unicode(line[1])
        slot_labels = [tokenization.convert_to_unicode(l) for l in line[2].split()]
      examples.append(
          IntentSlotExample(guid=guid, text_a=text_a, text_b=None, intent_label=intent_label, slot_labels=slot_labels))
    return examples

def convert_single_example(ex_index, example, intent_label_list, slot_label_list, max_seq_length,
                           tokenizer):
  """Converts a single `InputExample` into a single `InputFeatures`."""

  #max_se_length-2: spot length does not include [CLS] + [SEP]
  if isinstance(example, PaddingInputExample):
    return IntentSlotFeatures(
        input_ids=[0] * max_seq_length,
        input_mask=[0] * max_seq_length,
        segment_ids=[0] * max_seq_length,
        intent_label_id=0,
        slot_label_ids=[0] * (max_seq_length-2),
        slot_label_mask=[0] * (max_seq_length-2),
        is_real_example=False)

  intent_label_map = {}
  for (i, intent_label) in enumerate(intent_label_list):
    intent_label_map[intent_label] = i

  slot_label_map = {}
  for (i, slot_label) in enumerate(slot_label_list):
    slot_label_map[slot_label] = i

  #Slot_label_mask indicates starting word piece token which correspond to slot labels
  tokens_a = []
  slot_label_mask = []
  slot_label_to_token_map = []
  for token in tokenizer.basic_tokenizer.tokenize(example.text_a):
    count = 0
    slot_label_to_token_map.append(len(tokens_a))
    for sub_token in tokenizer.wordpiece_tokenizer.tokenize(token):
      tokens_a.append(sub_token)
      count = count + 1
    slot_label_mask.append(1)
    if count > 1:
      slot_label_mask.extend([0] * (count-1))

  tokens_b = None
  # Account for [CLS] and [SEP] with "- 2"
  if len(tokens_a) > max_seq_length - 2:
    tokens_a = tokens_a[0:(max_seq_length - 2)]
    slot_label_mask = slot_label_mask[0:(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 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)

  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

  intent_label_id = intent_label_map[example.intent_label]
  #put labels according to map resulted from word piece tokenization
  slot_label_ids = [0] * (max_seq_length - 2)
  for i, slot_label in enumerate(example.slot_labels):
    slot_label_ids[slot_label_to_token_map[i]] = slot_label_map[slot_label]

  # Zero-pad slot label mask.
  while len(slot_label_mask) < max_seq_length - 2:
    slot_label_mask.append(0)

  assert len(slot_label_mask) == max_seq_length - 2

  if ex_index < 5:
    tf.logging.info("*** Example ***")
    tf.logging.info("guid: %s" % (example.guid))
    tf.logging.info("text_a: %s" % (example.text_a))
    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("intent label: %s (id = %d)" % (example.intent_label, intent_label_id))
    tf.logging.info("slot label ids: %s" % " ".join([str(x) for x in slot_label_ids]))
    tf.logging.info("slot labels: %s" % " ".join([x for x in example.slot_labels]))
    tf.logging.info("slot label mask: %s" % " ".join([str(x) for x in slot_label_mask]))
    tf.logging.info("slot label token map: %s" % " ".join([str(x) for x in slot_label_to_token_map]))

  feature = IntentSlotFeatures(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids,
      intent_label_id=intent_label_id,
      slot_label_ids=slot_label_ids,
      slot_label_mask=slot_label_mask,
      is_real_example=True)
  return feature


def file_based_convert_examples_to_features(
    examples, intent_label_list, slot_label_list, max_seq_length, tokenizer, output_file):
  """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, intent_label_list, slot_label_list,
                                     max_seq_length, tokenizer)

    def create_int_feature(values):
      f = tf.train.Feature(int64_list=tf.train.Int64List(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["intent_label_ids"] = create_int_feature([feature.intent_label_id])
    features["slot_label_ids"] = create_int_feature(feature.slot_label_ids)
    features["slot_label_mask"] = create_int_feature(feature.slot_label_mask)
    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):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""

  name_to_features = {
      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "intent_label_ids": tf.FixedLenFeature([], tf.int64),
      "slot_label_ids": tf.FixedLenFeature([seq_length-2], tf.int64),
      "slot_label_mask": tf.FixedLenFeature([seq_length-2], tf.int64),
      "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."""
    batch_size = params["batch_size"]

    # 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(
        tf.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(bert_config, is_training, input_ids, input_mask, segment_ids,
                 intent_labels, slot_labels, slot_label_mask, num_intent_labels, num_slot_labels, use_one_hot_embeddings):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  # In the demo, we are doing a simple classification task on the entire
  # segment.
  #
  # If you want to use the token-level output, use model.get_sequence_output()
  # instead.

  final_hidden = model.get_sequence_output()

  final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
  batch_size = final_hidden_shape[0]
  seq_length = final_hidden_shape[1]
  hidden_size = final_hidden_shape[2]

  with tf.variable_scope("loss"):

    #(b, h)
    intent_output = tf.squeeze(final_hidden[:, 0:1, :], axis=1)

    intent_weights = tf.get_variable(
      "intent_weights", [num_intent_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

    intent_bias = tf.get_variable(
      "intent_bias", [num_intent_labels], initializer=tf.zeros_initializer())
    #(b, l)
    intent_logits = tf.matmul(intent_output, intent_weights, transpose_b=True)
    intent_logits = tf.nn.bias_add(intent_logits, intent_bias)
    intent_probabilities = tf.nn.softmax(intent_logits, axis=-1)
    intent_log_probs = tf.nn.log_softmax(intent_logits, axis=-1)

    #(b, l)
    one_hot_intent_labels = tf.one_hot(intent_labels, depth=num_intent_labels, dtype=tf.float32)

    #(b)
    per_example_intent_loss = -tf.reduce_sum(one_hot_intent_labels * intent_log_probs, axis=-1)

    #(b, s, h)
    slot_output = final_hidden[:, 1:seq_length-1, :]

    slot_weights = tf.get_variable(
      "slot_weights", [num_slot_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

    slot_bias = tf.get_variable(
      "spot_bias", [num_slot_labels], initializer=tf.zeros_initializer())

    #if is_training:
      # I.e., 0.1 dropout
    #  intent_output = tf.nn.dropout(output_layer, keep_prob=0.9)
    #(bs, h)
    slot_matrix = tf.reshape(slot_output, [batch_size * (seq_length-2), hidden_size])

    #(bs, l)
    slot_logits = tf.matmul(slot_matrix, slot_weights, transpose_b=True)
    slot_logits = tf.nn.bias_add(slot_logits, slot_bias)

    #(b, s, l)
    slot_logits = tf.reshape(slot_logits, [batch_size, seq_length-2, num_slot_labels])

    slot_probabilities = tf.nn.softmax(slot_logits, axis=-1)

    #remove ##s and tail from slot_probabilities and pad it back to seq_length-2 with zeros
    slot_mask_bool = tf.math.equal(slot_label_mask, 1)
    slot_probabilities_masked = tf.ragged.boolean_mask(slot_probabilities, slot_mask_bool)
    paddings_size = (seq_length-2) - slot_probabilities_masked.bounding_shape(out_type=tf.dtypes.int64)[1]
    slot_probabilities_dense = slot_probabilities_masked.to_tensor(default_value=0.0)
    #paddings_size = (seq_length-2) - slot_probabilities.get_shape()[1] 
    paddings = tf.Variable(initial_value=lambda: tf.zeros((3, 2), dtype=tf.dtypes.int64), trainable=False)
    paddings[1, 1].assign(paddings_size)

    slot_probabilities_padded = tf.pad(slot_probabilities_dense, paddings, "CONSTANT", constant_values=0.0)

    paddings_size_value = tf.fill([batch_size], paddings_size)
    #paddings_size_value = tf.fill([batch_size], slot_probabilities.get_shape()[1])

    
    slot_log_probs = tf.nn.log_softmax(slot_logits, axis=-1)

    #(b,s)-->(b,s,l)
    one_hot_slot_labels = tf.one_hot(slot_labels, depth=num_slot_labels, dtype=tf.float32)

    #reduce_sum (b,s,l)*(b,s,l) = (b,s) Comment: individually per slot
    per_slot_loss = -tf.reduce_sum(one_hot_slot_labels * slot_log_probs, axis=-1)

    # Similar to run_pretraining: `slots` might be zero-padded (if the sequence is too
    # short to have the maximum number of predictions). The `input_mask`
    # tensor has a value of 1.0 for every real prediction and 0.0 for the
    # padding predictions. This way, we will count for 'loss' in real examples only, 
    # also, excluding begining [CLS] and ending [SEP]
    slot_mask = input_mask[:, 1:-1]
    slot_mask = tf.dtypes.cast(slot_mask, tf.float32)
    per_slot_loss = slot_mask * per_slot_loss

    #Slot_label_mask indicates starting word piece token which correspond to slot labels
    #from loss perspectivei, losses for non starting tokens are ignored
    per_slot_loss = tf.dtypes.cast(slot_label_mask, tf.float32) * per_slot_loss

    #(b) Comment: sum of all slots
    per_example_slot_loss = tf.reduce_sum(per_slot_loss, axis=-1)

    #(b) Comment: both intent and slot, per example
    per_example_loss = per_example_intent_loss + per_example_slot_loss

    #loss per batch: mean scalar value
    loss = tf.reduce_mean(per_example_loss, axis=-1)

  return (loss, per_example_intent_loss, per_example_slot_loss, per_slot_loss,
    intent_logits, intent_probabilities, intent_log_probs, slot_logits, slot_probabilities_padded, slot_log_probs, paddings_size_value)


def model_fn_builder(bert_config, num_intent_labels, num_slot_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
  """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"]
    intent_label_ids = features["intent_label_ids"]
    slot_label_ids = features["slot_label_ids"]
    slot_label_mask = features["slot_label_mask"]
    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(intent_label_ids), dtype=tf.float32)

    is_training = (mode == tf.estimator.ModeKeys.TRAIN)

    (total_loss, per_example_intent_loss, per_example_slot_loss, per_slot_loss,
      intent_logits, intent_probabilities, intent_log_probs, 
      slot_logits, slot_probabilities_padded, slot_log_probs, paddings_size_value) = create_model(
      bert_config, is_training, input_ids, input_mask, segment_ids, intent_label_ids, slot_label_ids, slot_label_mask,
      num_intent_labels, num_slot_labels, use_one_hot_embeddings)

    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)

      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op,
          scaffold_fn=scaffold_fn)
    elif mode == tf.estimator.ModeKeys.EVAL:

      def metric_fn(intent_example_loss, intent_log_probs, intent_label_ids,
                    slot_example_loss, slot_loss, slot_log_probs, slot_label_ids, slot_label_mask):
        """Computes the loss and accuracy of the model."""
        #(b, l)
        intent_log_probs = tf.reshape(intent_log_probs, [-1, intent_log_probs.shape[-1]])
        #(b)
        intent_predictions = tf.argmax(intent_log_probs, axis=-1, output_type=tf.int32)
        #(b)
        intent_label_ids = tf.reshape(intent_label_ids, [-1])

        intent_accuracy = tf.metrics.accuracy(labels=intent_label_ids, predictions=intent_predictions)

        intent_mean_loss = tf.metrics.mean(values=intent_example_loss)

        #(bs, l)
        slot_log_probs = tf.reshape(slot_log_probs, [-1, slot_log_probs.shape[-1]])
        #(bs)
        slot_predictions = tf.argmax(slot_log_probs, axis=-1, output_type=tf.int32)
        #(bs)
        slot_loss = tf.reshape(slot_loss, [-1])
        #(bs)
        slot_label_ids = tf.reshape(slot_label_ids, [-1])
        #(bs)
        slot_label_mask = tf.reshape(slot_label_mask, [-1])

        slot_accuracy = tf.metrics.accuracy(
            labels=slot_label_ids,
            predictions=slot_predictions,
            weights=slot_label_mask)

        slot_mean_loss = tf.metrics.mean(values=slot_loss, weights=slot_label_mask)

        return {
            "intent_accuracy": intent_accuracy,
            "intent_loss": intent_mean_loss,
            "slot_accuracy": slot_accuracy,
            "slot_lm_loss": slot_mean_loss,
        }

      eval_metrics = (metric_fn, [
          per_example_intent_loss, intent_log_probs, intent_label_ids,
          per_example_slot_loss, per_slot_loss, slot_log_probs, slot_label_ids, slot_label_mask
      ])
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics,
          scaffold_fn=scaffold_fn)
    else:
      predictions = {
        "intent_probabilities": intent_probabilities,
        "slot_probabilities_padded": slot_probabilities_padded,
        "paddings_size_value": paddings_size_value
      }
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
        mode=mode, 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_intent_label_ids = []
  all_slot_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_intent_label_ids.append(feature.intent_label_id)
    all_slot_label_ids.append(feature.slot_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),
        "intent_label_ids":
            tf.constant(all_intent_label_ids, shape=[num_examples], dtype=tf.int32),
        "slot_label_ids":
            tf.constant(all_slot_label_ids, shape=[num_examples], dtype=tf.int32),
    })

    if is_training:
      d = d.repeat()
      #d = d.shuffle(buffer_size=100)
      d = d.shuffle(buffer_size=20000)

    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, intent_label_list, slot_label_list, max_seq_length,
                                 tokenizer):
  """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, intent_label_list, slot_label_list,
                                     max_seq_length, tokenizer)

    features.append(feature)
  return features


def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)

  processors = {
      "intent_slot": IntentSlotProcessor
  }

  tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                FLAGS.init_checkpoint)

  if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict and not FLAGS.saved_model and not FLAGS.save_labels:
    raise ValueError(
        "At least one of `do_train`, `do_eval` or `do_predict' must be True.")

  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

  if FLAGS.max_seq_length > bert_config.max_position_embeddings:
    raise ValueError(
        "Cannot use sequence length %d because the BERT model "
        "was only trained up to sequence length %d" %
        (FLAGS.max_seq_length, bert_config.max_position_embeddings))

  tf.gfile.MakeDirs(FLAGS.output_dir)

  task_name = FLAGS.task_name.lower()

  if task_name not in processors:
    raise ValueError("Task not found: %s" % (task_name))

  processor = processors[task_name](FLAGS.data_dir)

  if FLAGS.save_labels:
    processor.save_labels()

  (intent_label_list, slot_label_list) = processor.get_labels()

  tokenizer = tokenization.FullTokenizer(
      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

  tpu_cluster_resolver = None
  if FLAGS.use_tpu and FLAGS.tpu_name:
    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
  run_config = tf.contrib.tpu.RunConfig(
      cluster=tpu_cluster_resolver,
      master=FLAGS.master,
      model_dir=FLAGS.output_dir,
      save_checkpoints_steps=FLAGS.save_checkpoints_steps,
      tpu_config=tf.contrib.tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_tpu_cores,
          per_host_input_for_training=is_per_host))

  train_examples = None
  num_train_steps = None
  num_warmup_steps = None
  if FLAGS.do_train:
    train_examples = processor.get_train_examples()
    num_train_steps = int(
        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    # Pre-shuffle the input to avoid having to make a very large shuffle
    # buffer in in the `input_fn`.
    rng = random.Random(12345)
    rng.shuffle(train_examples)

  model_fn = model_fn_builder(
      bert_config=bert_config,
      num_intent_labels=len(intent_label_list),
      num_slot_labels=len(slot_label_list),
      init_checkpoint=FLAGS.init_checkpoint,
      learning_rate=FLAGS.learning_rate,
      num_train_steps=num_train_steps,
      num_warmup_steps=num_warmup_steps,
      use_tpu=FLAGS.use_tpu,
      use_one_hot_embeddings=FLAGS.use_tpu)

  # If TPU is not available, this will fall back to normal Estimator on CPU
  # or GPU.
  estimator = tf.contrib.tpu.TPUEstimator(
      use_tpu=FLAGS.use_tpu,
      model_fn=model_fn,
      config=run_config,
      train_batch_size=FLAGS.train_batch_size,
      eval_batch_size=FLAGS.eval_batch_size,
      predict_batch_size=FLAGS.predict_batch_size)

  if FLAGS.do_train:
    train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
    file_based_convert_examples_to_features(
        train_examples, intent_label_list, slot_label_list, FLAGS.max_seq_length, tokenizer, train_file)
    tf.logging.info("***** Running training *****")
    tf.logging.info("  Num examples = %d", len(train_examples))
    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
    tf.logging.info("  Num steps = %d", num_train_steps)
    train_input_fn = file_based_input_fn_builder(
        input_file=train_file,
        seq_length=FLAGS.max_seq_length,
        is_training=True,
        drop_remainder=True)
    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

  if FLAGS.do_eval:
    eval_examples = processor.get_dev_examples()
    num_actual_eval_examples = len(eval_examples)
    if FLAGS.use_tpu:
      # TPU requires a fixed batch size for all batches, therefore the number
      # of examples must be a multiple of the batch size, or else examples
      # will get dropped. So we pad with fake examples which are ignored
      # later on. These do NOT count towards the metric (all tf.metrics
      # support a per-instance weight, and these get a weight of 0.0).
      while len(eval_examples) % FLAGS.eval_batch_size != 0:
        eval_examples.append(PaddingInputExample())

    eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
    file_based_convert_examples_to_features(
        eval_examples, intent_label_list, slot_label_list, FLAGS.max_seq_length, tokenizer, eval_file)

    tf.logging.info("***** Running evaluation *****")
    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                    len(eval_examples), num_actual_eval_examples,
                    len(eval_examples) - num_actual_eval_examples)
    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

    # This tells the estimator to run through the entire set.
    eval_steps = None
    # However, if running eval on the TPU, you will need to specify the
    # number of steps.
    if FLAGS.use_tpu:
      assert len(eval_examples) % FLAGS.eval_batch_size == 0
      eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)

    eval_drop_remainder = True if FLAGS.use_tpu else False
    eval_input_fn = file_based_input_fn_builder(
        input_file=eval_file,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=eval_drop_remainder)

    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
    with tf.gfile.GFile(output_eval_file, "w") as writer:
      tf.logging.info("***** Eval results *****")
      for key in sorted(result.keys()):
        tf.logging.info("  %s = %s", key, str(result[key]))
        writer.write("%s = %s\n" % (key, str(result[key])))

  if FLAGS.do_predict:
    predict_examples = processor.get_test_examples()
    num_actual_predict_examples = len(predict_examples)
    if FLAGS.use_tpu:
      # TPU requires a fixed batch size for all batches, therefore the number
      # of examples must be a multiple of the batch size, or else examples
      # will get dropped. So we pad with fake examples which are ignored
      # later on.
      while len(predict_examples) % FLAGS.predict_batch_size != 0:
        predict_examples.append(PaddingInputExample())

    predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples, intent_label_list, slot_label_list,
                                            FLAGS.max_seq_length, tokenizer, predict_file)

    tf.logging.info("***** Running prediction*****")
    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                    len(predict_examples), num_actual_predict_examples,
                    len(predict_examples) - num_actual_predict_examples)
    tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

    predict_drop_remainder = True if FLAGS.use_tpu else False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=predict_drop_remainder)

    result = estimator.predict(input_fn=predict_input_fn)
    
    #output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
    output_predict_file = os.path.join(FLAGS.output_dir, "test_results.json")
    with tf.gfile.GFile(output_predict_file, "w") as writer:
      num_written_lines = 0
      tf.logging.info("***** Predict results *****")
      predictions = []
      for (i, prediction) in enumerate(result):
        predictions.append({
          'intent_probabilities': prediction["intent_probabilities"].tolist(),
          'slot_probabilities': prediction["slot_probabilities_padded"].tolist(),
          'paddings_size_value': prediction["paddings_size_value"].tolist()
        })
        if i >= num_actual_predict_examples:
          break
    #    output_line = "\t".join(str(class_probability) for class_probability in intent_probabilities) + "\n"
    #    writer.write(output_line)
        num_written_lines += 1
      output = json.dumps(predictions)
      writer.write(output)
    #  writer.write(output_line)
    assert num_written_lines == num_actual_predict_examples

  if FLAGS.saved_model:

    feature_placeholders = {
        "input_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="input_ids"),
        "input_mask" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="input_mask"),
        "segment_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="segment_ids"),
        "intent_label_ids" : tf.placeholder(tf.int64, [None, 1], name="intent_label_ids"),
        "slot_label_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length-2], name="slot_label_ids"),
        "slot_label_mask" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length-2], name="slot_label_mask")
    }

    serving_input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feature_placeholders)

    estimator.export_saved_model(
        FLAGS.output_dir,
        serving_input_fn,
        assets_extra=None,
        as_text=False,
        checkpoint_path=FLAGS.init_checkpoint,
        experimental_mode=tf.compat.v1.estimator.ModeKeys.PREDICT
    )

if __name__ == "__main__":
  flags.mark_flag_as_required("data_dir")
  flags.mark_flag_as_required("task_name")
  flags.mark_flag_as_required("vocab_file")
  flags.mark_flag_as_required("bert_config_file")
  flags.mark_flag_as_required("output_dir")
  tf.app.run()

# coding=utf-8
# Copyright 2018 The Google AI Language 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.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import os
import sys
import json
import random
import modeling
import optimization
import tokenization
import tensorflow as tf

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_bool("save_labels", False, "Save Labels and exit.")

flags.DEFINE_bool("saved_model", False, "Create Saved Model.")

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool(
    "do_predict", False,
    "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


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

  def __init__(self, guid, text_a, text_b=None, intent_label=None, slot_labels=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.intent_label = intent_label
    self.slot_labels = slot_labels


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

  def __init__(self,
               input_ids,
               input_mask,
               segment_ids,
               intent_label_id,
               slot_label_ids,
               slot_label_mask,
               is_real_example=True):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.intent_label_id = intent_label_id
    self.slot_label_ids = slot_label_ids
    self.slot_label_mask = slot_label_mask
    self.is_real_example = is_real_example

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

  def __init__(self, data_dir):
    self._data_dir = data_dir

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

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

  def get_test_examples(self):
    """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()

  def save_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


class IntentSlotProcessor(DataProcessor):
  """Processor for the Intent data set."""

  def __init__(self, data_dir):
    super(IntentSlotProcessor, self).__init__(data_dir)

    self._intent_label_list = []
    self._slot_label_list = []

    lines = self._read_tsv(os.path.join(data_dir, "train.tsv"))
    for line in lines:
      intent_label = tokenization.convert_to_unicode(line[1])
      if intent_label not in self._intent_label_list:
        self._intent_label_list.append(intent_label)
      for slot in line[2].split():
        slot_label = tokenization.convert_to_unicode(slot)
        if slot_label not in self._slot_label_list:
          self._slot_label_list.append(slot_label)

  def get_train_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "train.tsv")), "train")

  def get_dev_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "dev.tsv")), "dev")

  def get_test_examples(self):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(self._data_dir, "test.tsv")), "test")

  def get_labels(self):
    """See base class."""
    return (self._intent_label_list, self._slot_label_list)

  def save_labels(self):
    """See base class."""
    with open(os.path.join(self._data_dir, "intents.txt"), 'w') as out_file:
      for l in self._intent_label_list:
        out_file.write(l + os.linesep)

    with open(os.path.join(self._data_dir, "slots.txt"), 'w') as out_file:
      for l in self._slot_label_list:
        out_file.write(l + os.linesep)

  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)
      if set_type == "test":
        text_a = tokenization.convert_to_unicode(line[0])
        intent_label = self._intent_label_list[0]
        slot_labels = [self._slot_label_list[0]]
      else:
        text_a = tokenization.convert_to_unicode(line[0])
        intent_label = tokenization.convert_to_unicode(line[1])
        slot_labels = [tokenization.convert_to_unicode(l) for l in line[2].split()]
      examples.append(
          IntentSlotExample(guid=guid, text_a=text_a, text_b=None, intent_label=intent_label, slot_labels=slot_labels))
    return examples

def convert_single_example(ex_index, example, intent_label_list, slot_label_list, max_seq_length,
                           tokenizer):
  """Converts a single `InputExample` into a single `InputFeatures`."""

  #max_se_length-2: spot length does not include [CLS] + [SEP]
  if isinstance(example, PaddingInputExample):
    return IntentSlotFeatures(
        input_ids=[0] * max_seq_length,
        input_mask=[0] * max_seq_length,
        segment_ids=[0] * max_seq_length,
        intent_label_id=0,
        slot_label_ids=[0] * (max_seq_length-2),
        slot_label_mask=[0] * (max_seq_length-2),
        is_real_example=False)

  intent_label_map = {}
  for (i, intent_label) in enumerate(intent_label_list):
    intent_label_map[intent_label] = i

  slot_label_map = {}
  for (i, slot_label) in enumerate(slot_label_list):
    slot_label_map[slot_label] = i

  #Slot_label_mask indicates starting word piece token which correspond to slot labels
  tokens_a = []
  slot_label_mask = []
  slot_label_to_token_map = []
  for token in tokenizer.basic_tokenizer.tokenize(example.text_a):
    count = 0
    slot_label_to_token_map.append(len(tokens_a))
    for sub_token in tokenizer.wordpiece_tokenizer.tokenize(token):
      tokens_a.append(sub_token)
      count = count + 1
    slot_label_mask.append(1)
    if count > 1:
      slot_label_mask.extend([0] * (count-1))

  tokens_b = None
  # Account for [CLS] and [SEP] with "- 2"
  if len(tokens_a) > max_seq_length - 2:
    tokens_a = tokens_a[0:(max_seq_length - 2)]
    slot_label_mask = slot_label_mask[0:(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 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)

  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

  intent_label_id = intent_label_map[example.intent_label]
  #put labels according to map resulted from word piece tokenization
  slot_label_ids = [0] * (max_seq_length - 2)
  for i, slot_label in enumerate(example.slot_labels):
    slot_label_ids[slot_label_to_token_map[i]] = slot_label_map[slot_label]

  # Zero-pad slot label mask.
  while len(slot_label_mask) < max_seq_length - 2:
    slot_label_mask.append(0)

  assert len(slot_label_mask) == max_seq_length - 2

  if ex_index < 5:
    tf.logging.info("*** Example ***")
    tf.logging.info("guid: %s" % (example.guid))
    tf.logging.info("text_a: %s" % (example.text_a))
    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("intent label: %s (id = %d)" % (example.intent_label, intent_label_id))
    tf.logging.info("slot label ids: %s" % " ".join([str(x) for x in slot_label_ids]))
    tf.logging.info("slot labels: %s" % " ".join([x for x in example.slot_labels]))
    tf.logging.info("slot label mask: %s" % " ".join([str(x) for x in slot_label_mask]))
    tf.logging.info("slot label token map: %s" % " ".join([str(x) for x in slot_label_to_token_map]))

  feature = IntentSlotFeatures(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids,
      intent_label_id=intent_label_id,
      slot_label_ids=slot_label_ids,
      slot_label_mask=slot_label_mask,
      is_real_example=True)
  return feature


def file_based_convert_examples_to_features(
    examples, intent_label_list, slot_label_list, max_seq_length, tokenizer, output_file):
  """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, intent_label_list, slot_label_list,
                                     max_seq_length, tokenizer)

    def create_int_feature(values):
      f = tf.train.Feature(int64_list=tf.train.Int64List(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["intent_label_ids"] = create_int_feature([feature.intent_label_id])
    features["slot_label_ids"] = create_int_feature(feature.slot_label_ids)
    features["slot_label_mask"] = create_int_feature(feature.slot_label_mask)
    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):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""

  name_to_features = {
      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "intent_label_ids": tf.FixedLenFeature([], tf.int64),
      "slot_label_ids": tf.FixedLenFeature([seq_length-2], tf.int64),
      "slot_label_mask": tf.FixedLenFeature([seq_length-2], tf.int64),
      "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."""
    batch_size = params["batch_size"]

    # 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(
        tf.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(bert_config, is_training, input_ids, input_mask, segment_ids,
                 intent_labels, slot_labels, slot_label_mask, num_intent_labels, num_slot_labels, use_one_hot_embeddings):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  # In the demo, we are doing a simple classification task on the entire
  # segment.
  #
  # If you want to use the token-level output, use model.get_sequence_output()
  # instead.

  final_hidden = model.get_sequence_output()

  final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
  batch_size = final_hidden_shape[0]
  seq_length = final_hidden_shape[1]
  hidden_size = final_hidden_shape[2]

  with tf.variable_scope("loss"):

    #(b, h)
    intent_output = tf.squeeze(final_hidden[:, 0:1, :], axis=1)

    intent_weights = tf.get_variable(
      "intent_weights", [num_intent_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

    intent_bias = tf.get_variable(
      "intent_bias", [num_intent_labels], initializer=tf.zeros_initializer())
    #(b, l)
    intent_logits = tf.matmul(intent_output, intent_weights, transpose_b=True)
    intent_logits = tf.nn.bias_add(intent_logits, intent_bias)
    intent_probabilities = tf.nn.softmax(intent_logits, axis=-1)
    intent_log_probs = tf.nn.log_softmax(intent_logits, axis=-1)

    #(b, l)
    one_hot_intent_labels = tf.one_hot(intent_labels, depth=num_intent_labels, dtype=tf.float32)

    #(b)
    per_example_intent_loss = -tf.reduce_sum(one_hot_intent_labels * intent_log_probs, axis=-1)

    #(b, s, h)
    slot_output = final_hidden[:, 1:seq_length-1, :]

    slot_weights = tf.get_variable(
      "slot_weights", [num_slot_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

    slot_bias = tf.get_variable(
      "spot_bias", [num_slot_labels], initializer=tf.zeros_initializer())

    #if is_training:
      # I.e., 0.1 dropout
    #  intent_output = tf.nn.dropout(output_layer, keep_prob=0.9)
    #(bs, h)
    slot_matrix = tf.reshape(slot_output, [batch_size * (seq_length-2), hidden_size])

    #(bs, l)
    slot_logits = tf.matmul(slot_matrix, slot_weights, transpose_b=True)
    slot_logits = tf.nn.bias_add(slot_logits, slot_bias)

    #(b, s, l)
    slot_logits = tf.reshape(slot_logits, [batch_size, seq_length-2, num_slot_labels])

    slot_probabilities = tf.nn.softmax(slot_logits, axis=-1)

    #remove ##s and tail from slot_probabilities and pad it back to seq_length-2 with zeros
    slot_mask_bool = tf.math.equal(slot_label_mask, 1)
    slot_probabilities_masked = tf.ragged.boolean_mask(slot_probabilities, slot_mask_bool)
    paddings_size = (seq_length-2) - slot_probabilities_masked.bounding_shape(out_type=tf.dtypes.int64)[1]
    slot_probabilities_dense = slot_probabilities_masked.to_tensor(default_value=0.0)
    #paddings_size = (seq_length-2) - slot_probabilities.get_shape()[1] 
    paddings = tf.Variable(initial_value=lambda: tf.zeros((3, 2), dtype=tf.dtypes.int64), trainable=False)
    paddings[1, 1].assign(paddings_size)

    slot_probabilities_padded = tf.pad(slot_probabilities_dense, paddings, "CONSTANT", constant_values=0.0)

    paddings_size_value = tf.fill([batch_size], paddings_size)
    #paddings_size_value = tf.fill([batch_size], slot_probabilities.get_shape()[1])

    
    slot_log_probs = tf.nn.log_softmax(slot_logits, axis=-1)

    #(b,s)-->(b,s,l)
    one_hot_slot_labels = tf.one_hot(slot_labels, depth=num_slot_labels, dtype=tf.float32)

    #reduce_sum (b,s,l)*(b,s,l) = (b,s) Comment: individually per slot
    per_slot_loss = -tf.reduce_sum(one_hot_slot_labels * slot_log_probs, axis=-1)

    # Similar to run_pretraining: `slots` might be zero-padded (if the sequence is too
    # short to have the maximum number of predictions). The `input_mask`
    # tensor has a value of 1.0 for every real prediction and 0.0 for the
    # padding predictions. This way, we will count for 'loss' in real examples only, 
    # also, excluding begining [CLS] and ending [SEP]
    slot_mask = input_mask[:, 1:-1]
    slot_mask = tf.dtypes.cast(slot_mask, tf.float32)
    per_slot_loss = slot_mask * per_slot_loss

    #Slot_label_mask indicates starting word piece token which correspond to slot labels
    #from loss perspectivei, losses for non starting tokens are ignored
    per_slot_loss = tf.dtypes.cast(slot_label_mask, tf.float32) * per_slot_loss

    #(b) Comment: sum of all slots
    per_example_slot_loss = tf.reduce_sum(per_slot_loss, axis=-1)

    #(b) Comment: both intent and slot, per example
    per_example_loss = per_example_intent_loss + per_example_slot_loss

    #loss per batch: mean scalar value
    loss = tf.reduce_mean(per_example_loss, axis=-1)

  return (loss, per_example_intent_loss, per_example_slot_loss, per_slot_loss,
    intent_logits, intent_probabilities, intent_log_probs, slot_logits, slot_probabilities_padded, slot_log_probs, paddings_size_value)


def model_fn_builder(bert_config, num_intent_labels, num_slot_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
  """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"]
    intent_label_ids = features["intent_label_ids"]
    slot_label_ids = features["slot_label_ids"]
    slot_label_mask = features["slot_label_mask"]
    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(intent_label_ids), dtype=tf.float32)

    is_training = (mode == tf.estimator.ModeKeys.TRAIN)

    (total_loss, per_example_intent_loss, per_example_slot_loss, per_slot_loss,
      intent_logits, intent_probabilities, intent_log_probs, 
      slot_logits, slot_probabilities_padded, slot_log_probs, paddings_size_value) = create_model(
      bert_config, is_training, input_ids, input_mask, segment_ids, intent_label_ids, slot_label_ids, slot_label_mask,
      num_intent_labels, num_slot_labels, use_one_hot_embeddings)

    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)

      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op,
          scaffold_fn=scaffold_fn)
    elif mode == tf.estimator.ModeKeys.EVAL:

      def metric_fn(intent_example_loss, intent_log_probs, intent_label_ids,
                    slot_example_loss, slot_loss, slot_log_probs, slot_label_ids, slot_label_mask):
        """Computes the loss and accuracy of the model."""
        #(b, l)
        intent_log_probs = tf.reshape(intent_log_probs, [-1, intent_log_probs.shape[-1]])
        #(b)
        intent_predictions = tf.argmax(intent_log_probs, axis=-1, output_type=tf.int32)
        #(b)
        intent_label_ids = tf.reshape(intent_label_ids, [-1])

        intent_accuracy = tf.metrics.accuracy(labels=intent_label_ids, predictions=intent_predictions)

        intent_mean_loss = tf.metrics.mean(values=intent_example_loss)

        #(bs, l)
        slot_log_probs = tf.reshape(slot_log_probs, [-1, slot_log_probs.shape[-1]])
        #(bs)
        slot_predictions = tf.argmax(slot_log_probs, axis=-1, output_type=tf.int32)
        #(bs)
        slot_loss = tf.reshape(slot_loss, [-1])
        #(bs)
        slot_label_ids = tf.reshape(slot_label_ids, [-1])
        #(bs)
        slot_label_mask = tf.reshape(slot_label_mask, [-1])

        slot_accuracy = tf.metrics.accuracy(
            labels=slot_label_ids,
            predictions=slot_predictions,
            weights=slot_label_mask)

        slot_mean_loss = tf.metrics.mean(values=slot_loss, weights=slot_label_mask)

        return {
            "intent_accuracy": intent_accuracy,
            "intent_loss": intent_mean_loss,
            "slot_accuracy": slot_accuracy,
            "slot_lm_loss": slot_mean_loss,
        }

      eval_metrics = (metric_fn, [
          per_example_intent_loss, intent_log_probs, intent_label_ids,
          per_example_slot_loss, per_slot_loss, slot_log_probs, slot_label_ids, slot_label_mask
      ])
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics,
          scaffold_fn=scaffold_fn)
    else:
      predictions = {
        "intent_probabilities": intent_probabilities,
        "slot_probabilities_padded": slot_probabilities_padded,
        "paddings_size_value": paddings_size_value
      }
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
        mode=mode, 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_intent_label_ids = []
  all_slot_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_intent_label_ids.append(feature.intent_label_id)
    all_slot_label_ids.append(feature.slot_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),
        "intent_label_ids":
            tf.constant(all_intent_label_ids, shape=[num_examples], dtype=tf.int32),
        "slot_label_ids":
            tf.constant(all_slot_label_ids, shape=[num_examples], dtype=tf.int32),
    })

    if is_training:
      d = d.repeat()
      #d = d.shuffle(buffer_size=100)
      d = d.shuffle(buffer_size=20000)

    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, intent_label_list, slot_label_list, max_seq_length,
                                 tokenizer):
  """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, intent_label_list, slot_label_list,
                                     max_seq_length, tokenizer)

    features.append(feature)
  return features


def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)

  processors = {
      "intent_slot": IntentSlotProcessor
  }

  tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                FLAGS.init_checkpoint)

  if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict and not FLAGS.saved_model and not FLAGS.save_labels:
    raise ValueError(
        "At least one of `do_train`, `do_eval` or `do_predict' must be True.")

  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

  if FLAGS.max_seq_length > bert_config.max_position_embeddings:
    raise ValueError(
        "Cannot use sequence length %d because the BERT model "
        "was only trained up to sequence length %d" %
        (FLAGS.max_seq_length, bert_config.max_position_embeddings))

  tf.gfile.MakeDirs(FLAGS.output_dir)

  task_name = FLAGS.task_name.lower()

  if task_name not in processors:
    raise ValueError("Task not found: %s" % (task_name))

  processor = processors[task_name](FLAGS.data_dir)

  if FLAGS.save_labels:
    processor.save_labels()

  (intent_label_list, slot_label_list) = processor.get_labels()

  tokenizer = tokenization.FullTokenizer(
      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

  tpu_cluster_resolver = None
  if FLAGS.use_tpu and FLAGS.tpu_name:
    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
  run_config = tf.contrib.tpu.RunConfig(
      cluster=tpu_cluster_resolver,
      master=FLAGS.master,
      model_dir=FLAGS.output_dir,
      save_checkpoints_steps=FLAGS.save_checkpoints_steps,
      tpu_config=tf.contrib.tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_tpu_cores,
          per_host_input_for_training=is_per_host))

  train_examples = None
  num_train_steps = None
  num_warmup_steps = None
  if FLAGS.do_train:
    train_examples = processor.get_train_examples()
    num_train_steps = int(
        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    # Pre-shuffle the input to avoid having to make a very large shuffle
    # buffer in in the `input_fn`.
    rng = random.Random(12345)
    rng.shuffle(train_examples)

  model_fn = model_fn_builder(
      bert_config=bert_config,
      num_intent_labels=len(intent_label_list),
      num_slot_labels=len(slot_label_list),
      init_checkpoint=FLAGS.init_checkpoint,
      learning_rate=FLAGS.learning_rate,
      num_train_steps=num_train_steps,
      num_warmup_steps=num_warmup_steps,
      use_tpu=FLAGS.use_tpu,
      use_one_hot_embeddings=FLAGS.use_tpu)

  # If TPU is not available, this will fall back to normal Estimator on CPU
  # or GPU.
  estimator = tf.contrib.tpu.TPUEstimator(
      use_tpu=FLAGS.use_tpu,
      model_fn=model_fn,
      config=run_config,
      train_batch_size=FLAGS.train_batch_size,
      eval_batch_size=FLAGS.eval_batch_size,
      predict_batch_size=FLAGS.predict_batch_size)

  if FLAGS.do_train:
    train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
    file_based_convert_examples_to_features(
        train_examples, intent_label_list, slot_label_list, FLAGS.max_seq_length, tokenizer, train_file)
    tf.logging.info("***** Running training *****")
    tf.logging.info("  Num examples = %d", len(train_examples))
    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
    tf.logging.info("  Num steps = %d", num_train_steps)
    train_input_fn = file_based_input_fn_builder(
        input_file=train_file,
        seq_length=FLAGS.max_seq_length,
        is_training=True,
        drop_remainder=True)
    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

  if FLAGS.do_eval:
    eval_examples = processor.get_dev_examples()
    num_actual_eval_examples = len(eval_examples)
    if FLAGS.use_tpu:
      # TPU requires a fixed batch size for all batches, therefore the number
      # of examples must be a multiple of the batch size, or else examples
      # will get dropped. So we pad with fake examples which are ignored
      # later on. These do NOT count towards the metric (all tf.metrics
      # support a per-instance weight, and these get a weight of 0.0).
      while len(eval_examples) % FLAGS.eval_batch_size != 0:
        eval_examples.append(PaddingInputExample())

    eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
    file_based_convert_examples_to_features(
        eval_examples, intent_label_list, slot_label_list, FLAGS.max_seq_length, tokenizer, eval_file)

    tf.logging.info("***** Running evaluation *****")
    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                    len(eval_examples), num_actual_eval_examples,
                    len(eval_examples) - num_actual_eval_examples)
    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

    # This tells the estimator to run through the entire set.
    eval_steps = None
    # However, if running eval on the TPU, you will need to specify the
    # number of steps.
    if FLAGS.use_tpu:
      assert len(eval_examples) % FLAGS.eval_batch_size == 0
      eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)

    eval_drop_remainder = True if FLAGS.use_tpu else False
    eval_input_fn = file_based_input_fn_builder(
        input_file=eval_file,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=eval_drop_remainder)

    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
    with tf.gfile.GFile(output_eval_file, "w") as writer:
      tf.logging.info("***** Eval results *****")
      for key in sorted(result.keys()):
        tf.logging.info("  %s = %s", key, str(result[key]))
        writer.write("%s = %s\n" % (key, str(result[key])))

  if FLAGS.do_predict:
    predict_examples = processor.get_test_examples()
    num_actual_predict_examples = len(predict_examples)
    if FLAGS.use_tpu:
      # TPU requires a fixed batch size for all batches, therefore the number
      # of examples must be a multiple of the batch size, or else examples
      # will get dropped. So we pad with fake examples which are ignored
      # later on.
      while len(predict_examples) % FLAGS.predict_batch_size != 0:
        predict_examples.append(PaddingInputExample())

    predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples, intent_label_list, slot_label_list,
                                            FLAGS.max_seq_length, tokenizer, predict_file)

    tf.logging.info("***** Running prediction*****")
    tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                    len(predict_examples), num_actual_predict_examples,
                    len(predict_examples) - num_actual_predict_examples)
    tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)

    predict_drop_remainder = True if FLAGS.use_tpu else False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=predict_drop_remainder)

    result = estimator.predict(input_fn=predict_input_fn)
    
    #output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
    output_predict_file = os.path.join(FLAGS.output_dir, "test_results.json")
    with tf.gfile.GFile(output_predict_file, "w") as writer:
      num_written_lines = 0
      tf.logging.info("***** Predict results *****")
      predictions = []
      for (i, prediction) in enumerate(result):
        predictions.append({
          'intent_probabilities': prediction["intent_probabilities"].tolist(),
          'slot_probabilities': prediction["slot_probabilities_padded"].tolist(),
          'paddings_size_value': prediction["paddings_size_value"].tolist()
        })
        if i >= num_actual_predict_examples:
          break
    #    output_line = "\t".join(str(class_probability) for class_probability in intent_probabilities) + "\n"
    #    writer.write(output_line)
        num_written_lines += 1
      output = json.dumps(predictions)
      writer.write(output)
    #  writer.write(output_line)
    assert num_written_lines == num_actual_predict_examples

  if FLAGS.saved_model:

    feature_placeholders = {
        "input_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="input_ids"),
        "input_mask" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="input_mask"),
        "segment_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length], name="segment_ids"),
        "intent_label_ids" : tf.placeholder(tf.int64, [None, 1], name="intent_label_ids"),
        "slot_label_ids" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length-2], name="slot_label_ids"),
        "slot_label_mask" : tf.placeholder(tf.int64, [None, FLAGS.max_seq_length-2], name="slot_label_mask")
    }

    serving_input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feature_placeholders)

    estimator.export_saved_model(
        FLAGS.output_dir,
        serving_input_fn,
        assets_extra=None,
        as_text=False,
        checkpoint_path=FLAGS.init_checkpoint,
        experimental_mode=tf.compat.v1.estimator.ModeKeys.PREDICT
    )

if __name__ == "__main__":
  flags.mark_flag_as_required("data_dir")
  flags.mark_flag_as_required("task_name")
  flags.mark_flag_as_required("vocab_file")
  flags.mark_flag_as_required("bert_config_file")
  flags.mark_flag_as_required("output_dir")
  tf.app.run()
# coding=utf-8 # Copyright 2018 The Google AI Language 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. BERT finetuning runner. ## Required parameters ## Other parameters A single training/test example for simple sequence classification. 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. 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. A single set of features of data. Base class for data converters for sequence classification data sets. Gets a collection of `InputExample`s for the train set. Gets a collection of `InputExample`s for the dev set. Gets a collection of `InputExample`s for prediction. Gets the list of labels for this data set. Gets the list of labels for this data set. Reads a tab separated value file. Processor for the Intent data set. See base class. See base class. See base class. See base class. See base class. Creates examples for the training and dev sets. Converts a single `InputExample` into a single `InputFeatures`. #max_se_length-2: spot length does not include [CLS] + [SEP] #Slot_label_mask indicates starting word piece token which correspond to slot labels # Account for [CLS] and [SEP] with "- 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 the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # Zero-pad up to the sequence length. #put labels according to map resulted from word piece tokenization # Zero-pad slot label mask. Convert a set of `InputExample`s to a TFRecord file. Creates an `input_fn` closure to be passed to TPUEstimator. Decodes a record to a TensorFlow example. # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. The actual input function. # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. 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. Creates a classification model. # In the demo, we are doing a simple classification task on the entire # segment. # # If you want to use the token-level output, use model.get_sequence_output() # instead. #(b, h) #(b, l) #(b, l) #(b) #(b, s, h) #if is_training: # I.e., 0.1 dropout #  intent_output = tf.nn.dropout(output_layer, keep_prob=0.9) #(bs, h) #(bs, l) #(b, s, l) #remove ##s and tail from slot_probabilities and pad it back to seq_length-2 with zeros #paddings_size = (seq_length-2) - slot_probabilities.get_shape()[1] #paddings_size_value = tf.fill([batch_size], slot_probabilities.get_shape()[1]) #(b,s)-->(b,s,l) #reduce_sum (b,s,l)*(b,s,l) = (b,s) Comment: individually per slot # Similar to run_pretraining: `slots` might be zero-padded (if the sequence is too # short to have the maximum number of predictions). The `input_mask` # tensor has a value of 1.0 for every real prediction and 0.0 for the # padding predictions. This way, we will count for 'loss' in real examples only, # also, excluding begining [CLS] and ending [SEP] #Slot_label_mask indicates starting word piece token which correspond to slot labels #from loss perspectivei, losses for non starting tokens are ignored #(b) Comment: sum of all slots #(b) Comment: both intent and slot, per example #loss per batch: mean scalar value Returns `model_fn` closure for TPUEstimator. # pylint: disable=unused-argument The `model_fn` for TPUEstimator. Computes the loss and accuracy of the model. #(b, l) #(b) #(b) #(bs, l) #(bs) #(bs) #(bs) #(bs) # This function is not used by this file but is still used by the Colab and # people who depend on it. Creates an `input_fn` closure to be passed to TPUEstimator. The actual input function. # 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 = d.shuffle(buffer_size=100) # This function is not used by this file but is still used by the Colab and # people who depend on it. Convert a set of `InputExample`s to a list of `InputFeatures`. # Pre-shuffle the input to avoid having to make a very large shuffle # buffer in in the `input_fn`. # If TPU is not available, this will fall back to normal Estimator on CPU # or GPU. # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. These do NOT count towards the metric (all tf.metrics # support a per-instance weight, and these get a weight of 0.0). # This tells the estimator to run through the entire set. # However, if running eval on the TPU, you will need to specify the # number of steps. # TPU requires a fixed batch size for all batches, therefore the number # of examples must be a multiple of the batch size, or else examples # will get dropped. So we pad with fake examples which are ignored # later on. #output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") #    output_line = "\t".join(str(class_probability) for class_probability in intent_probabilities) + "\n" #    writer.write(output_line) #  writer.write(output_line)